EX-ACT USER MANUAL Estimating and Targeting Greenhouse Gas Mitigation in Agriculture Abstract The Ex-Ante Carbon-balance Tool (EX-ACT) is an appraisal system developed by FAO providing ex-ante estimates of the impact of agriculture and forestry development projects, programs and policies on the carbon- balance. The carbon-balance is defined as the net balance from all GHGs expressed in CO2 equivalent that were emitted or sequestered due to project implementation as compared to a business-as-usual scenario. EX-ACT is a land-based accounting system, estimating C stock changes (i.e. emissions or sinks of CO2) as well as GHG emissions per unit of land, expressed in equivalent tons of CO2 per hectare and year. The tool helps project designers to estimate and prioritize project activities with high benefits in economic and climate change mitigation terms. The amount of GHG mitigation may also be used as part of economic analysis as well as for the application for funding additional project components. EX-ACT can be applied on a wide range of development projects from all AFOLU sub-sectors, including others projects on climate change mitigation, watershed development, production intensification, food security, livestock, forest management or land use change. Furthermore, it is cost effective, requires a relatively small amount of data, and has resources (tables, maps) which can help in finding the required information. While EX- ACT is mostly used at project level it may easily be scaled-up to the program/sector level and can also be used for policy analysis. This manual provides all central information on methodology, application and utilization of EX-ACT and prepares the reader to its independent use. A shorter Quick Guidance is also available. EX-ACT is based on Microsoft Excel (without macros) and freely available from the FAO website. ‐ EX-ACT Website: www.fao.org/tc/exact ‐ Free Tool Access: www.fao.org/tc/exact/carbon-balance-tool-ex-act ‐ EX-ACT User Manual & EX-ACT Quick Guidance: www.fao.org/tc/exact/user-guidelines Acknowledgments This user manual was prepared by the EX-ACT team comprising Louis Bockel, Uwe Grewer and Chlo¸ Fernandez from the Agriculture Development Economics Division (ESA) of FAO and Martial Bernoux from the Institut de Recherche pour le Développement (IRD). The document was initiated by the World Bank and profited from valuable comments and the overall guidance from Ademola Braimoh (World Bank) and Leslie Lipper (FAO). For their valuable review the EX-ACT team thanks Hernan Gonzalez (FAO), Marjoleine Hanegraaf (Nutrient Management Institute) and Monica Petri (FAO). This User Manual and the development of EX-ACT benefitted greatly from EX-ACT case studies and applications as well as other publications from Ademola Braimoh (World Bank), Vincent Colomb (ADEME), Marianne Tinlot, Pierre Sutter, Giacomo Branca (FAO), Ophélie Touchemoulin, Madeleine Jönsson, Patricia Gorin (HELVETAS) and Rocio Sanz Cortés, as well as Philippe Ambrosi (World Bank). Marjory-Anne Bromhead (World Bank), Tuukka Castren (World Bank), Raffaello Cervigni (World Bank), Ijeoma Emenanjo (World Bank), Armel Gentien, Maurizio Guadagni (World Bank), Johannes Heister (World Bank), Gabriella Izzi (World Bank), Patrick Labaste (World Bank), Katia Medeiros (FAO), Grant Milne (World Bank), Hermann Pfeiffer (FAO), Riikka Rajalahti (World Bank), Janie Rioux (FAO), Garry Smith (FAO), Dipti Thapa (World Bank) and Benoit Thierry (IFAD). We further gratefully acknowledge the authors of chapter 14 of the Climate Smart Agriculture Sourcebook (FAO, 2013), Leslie Lipper and Bjorn Conrad, that is reprinted in parts of the annex. Contents   Chapter 1: Introduction..................................................................................................................................... 2  A.  Manual structure .................................................................................................................................. 2  B.  Key concepts and terminology ............................................................................................................. 3  C.  Targeted users ..................................................................................................................................... 4  D.  Why targeting GHG mitigation in AFOLU investment planning?........................................................ 4  E.  Main emission sources and mitigation potentials of AFOLU ............................................................... 6  Chapter 2: Overview of EX-ACT ..................................................................................................................... 9  A.  The added-value of a carbon-balance appraisal .................................................................................... 9  B.  Selecting the right carbon-balance accounting tool ............................................................................... 9  C.  What is EX-ACT? Main structure and outputs................................................................................... 11  Chapter 3: Methodological Background ....................................................................................................... 17  D.  Dynamics of change in EX-ACT ....................................................................................................... 19  E.  Defining project boundaries .............................................................................................................. 19  F.  Ex-ante versus ex-post....................................................................................................................... 20  G.  The baseline scenario ......................................................................................................................... 20  Part B: Step-by-Step Guide to Use EX-ACT................................................................................................. 27  .......................................................................................................................................................................... 27  Chapter 4: Data Requirements and Data Collection .................................................................................... 28  A.  Choosing the valid EX-ACT modules................................................................................................ 28  B.  Overview of data needs ..................................................................................................................... 30  C.  Data collection methods and central data sources .............................................................................. 33  Chapter 5: Getting Started – The Description Module ................................................................................ 35  A.  Introduction to the graphical interface ............................................................................................... 35  B.  The description module ..................................................................................................................... 35  Chapter 6: Entering Land Use Changes ....................................................................................................... 40  A.  Deforestation..................................................................................................................................... 40  B.  Afforestation and reforestation (A/R) ............................................................................................... 41  C.  Non forest land use change ............................................................................................................... 42  D.  Results per component ...................................................................................................................... 43  E.  Tier 2 specifications in the land use change module ........................................................................... 43  Chapter 7: Entering Crop Production ........................................................................................................... 46  A.  Annual crops ..................................................................................................................................... 46  B.  Entering agro-forestry and perennial systems ..................................................................................... 48  C.  Entering flooded rice systems ............................................................................................................ 49  D.  Tier 2 specifications in the annual crop module ................................................................................. 51  Chapter 8: Entering Livestock and Grassland Management ....................................................................... 52  A.  Grassland management ...................................................................................................................... 52  B.  Livestock management ...................................................................................................................... 52  C.  Tier 2 specifications in the livestock sub-module ............................................................................... 53  Chapter 9: Entering Land Degradation ........................................................................................................ 54  A.  Entering forest degradation ............................................................................................................... 54  B.  Entering organic soil drainage and peat extraction ............................................................................. 54  C.  Tier 2 specifications in the land degradation module .......................................................................... 55  Chapter 10: Entering Inputs and Investments .............................................................................................. 57  A.  Entering inputs .................................................................................................................................. 57  B.  Entering energy consumption ............................................................................................................ 57  C.  Entering infrastructure construction .................................................................................................. 58  D.  How to account for energy produced by the project (biogas, biofuel, etc.) ......................................... 59  E.  Tier 2 in the energy and investments sub-module .............................................................................. 60  Part C: Making Effective Use of EX-ACT Results ....................................................................................... 61  Chapter 11: Analysis of Results ...................................................................................................................... 62  A.  Checking the land use table................................................................................................................ 62  B.  Analysing non-aggregated module results........................................................................................... 63  C.  Analysing total results: Gross result and carbon-balance .................................................................... 64  D.  Emission intensity per product unit and further indicators ................................................................. 67  E.  Additional indicators.......................................................................................................................... 69  F.  Comparing project options or impact scenarios: Simulation work ...................................................... 70  G.  Using EX-ACT to support monitoring schemes ................................................................................ 72  Chapter 12: Using the Carbon-Balance as Part of Economic Analyses and Fund Mobilisation .............. 74  A.  Main concepts for the economic analysis of mitigation benefits ......................................................... 74  B.  Typology of projects to be appraised ................................................................................................. 75  C.  Basic elements of economic analyses: NPV and IRR.......................................................................... 77  D.  From the carbon-balance to climate finance and public funding ........................................................ 77  E.  Using the carbon-balance to assess potential Payments for Environmental Services........................... 80  F.  Using a Marginal Abatement cost Curve (MACC) to compare low carbon options ............................ 81  ANNEX 1: Bibliography .................................................................................................................................. 5  ANNEX 2: Glossary and Acronyms ................................................................................................................ 9  ANNEX 3: List of Tables, Figures and Case Studies .................................................................................. 15  ANNEX 4: Use of Marginal Abatement Cost Curves (MACC) in appraising low carbon options .......... 17  ANNEX 5: Using EX-ACT to Assess the Product Carbon Footprint along the Value Chain .................. 24  ANNEX 6: EX-ACT Appraisal and Funding for Climate Smart Agriculture ........................................... 29  ANNEX 7: Review of GHG Tools for Mitigation in Agriculture ................................................................ 33  ANNEX 8: Use of EX-ACT in Assessing Natural Resources Stock Changes ........................................... 36  ANNEX 9: Detailed List of Data Possibilities in EX-ACT ......................................................................... 44  Part A: Introduction and Rationale Chapter 1: Introduction This manual explains how to use the EX-Ante Carbon-balance Tool known as “EX-ACT” to estimate the impact of agricultural and forestry investment projects and programs on the GHG-balance. EX-ACT enables investment planners to design program activities that target high return outcomes in terms of climate change mitigation, and is intended to complement conventional ex-ante economic analysis. A. Manual structure Content is divided into short practical steps that do not have to be read in sequential order. Users can quickly find sections that relate directly to what they are working on. The manual consists of three parts: - A. This Introduction explains the rationale for incorporating appraisals of GHG balances into the planning and design of investments and projects, the methodology used in developing the tool, and how the tool is used to establish the initial baseline condition into which the project is introduced. - B. Step-by-Step Guide to Using EX-ACT leads the reader step-by-step through the analysis process, specifying how to procure the necessary data and enter it into each topic module of EX-ACT. - C. Making Effective Use of EX-ACT Results shows how to interpret the EX-ACT results, comparing the multidimensional performance of different project options and using the results for economic analyses. Dividing the three parts into a more detailed chapter-by-chapter overview: Part A (chapters 1 to 3) Chapter 1 gives background information on climate change mitigation in general and specifically in the Agriculture, Forestry and Other Land Use (AFOLU) sector. It should be useful to readers who are interested in determining whether climate change mitigation is a significant element of the investments and interventions they are planning. It provides practical information on major emission sources within the AFOLU sector and identifies where the greatest potential for mitigation can be expected. Chapter 2 provides an overview of the EX-ACT tool and a number of other useful methods for estimating carbon balance. Chapter 3 presents the methodological background of EX-ACT and how it was developed based on the IPCC guidelines for national GHG inventories (NGGI-IPCC). It answers a number of common questions about boundary analysis and explains the differences between pure ex-ante analyses and monitoring, as well as evaluation approaches. The chapter also introduces the concept of baseline scenarios and their importance to the analytical process. Part B (Chapters 4 to 10) Part B is the core of the manual, and describes how to use EX-ACT. Chapter 4 presents the main data needs and guides the reader through the process of data procurement. Chapters 5 -10 present the different topic modules of EX-ACT that are centered around climatic and geographical information (Chapter 5), information on forestry and land use change (Chapter 6), specifications on annual and perennial crop production as well as irrigated rice (Chapter 7), livestock production and grassland management (Chapter 8), land degradation (Chapter 9), as well as inputs and further investments (Chapter 10). 2 Part C (Chapters 11 and 12) Part C explains how to interpret EX-ACT results and how to calculate projected impacts under alternative scenarios – in terms of both GHG balance and social and economic effects. Chapter 11 provides reference for designing projects that deliver multi-purpose outcomes. Chapter 12 describes how to use obtained results as part of economic analyses. Annexes While the main body of the User Manual focuses on the practical elements of using the tool, a number of additional related issues are discussed in the annexes. These include among other things, the use of Marginal Abatement Cost Curves (MACC), and accounting for carbon footprint per product unit. For more specific questions that do not directly concern the core understanding and mastering of the tool, it is furthermore frequently referred to the accompanying and free available material on our website www.fao.org/tc/exact. B. Key concepts and terminology Carbon-balance or GHG-balance The carbon-balance is defined as the net balance from all GHGs expressed in CO2 equivalents that were emitted or sequestered due to project implementation as compared to a business-as-usual scenario. It thus accounts for the emissions from all GHGs as well as all kind of carbon pools concerned by the AFOLU sector. The carbon-balance can thereby be realized at different scales: for an investment project, the resource impact of an organization, a region, a value chain, a country, the planet. Within a dynamic process, it is also possible to appraise the carbon-balance effect of a strategy or policy. The expressions carbon-balance and GHG-balance are used synonymously (Bockel, et al., 2011). Baseline scenario or without-project scenario The baseline scenario portrays the hypothetical development of land use and activity data as well as GHG emissions that would have occurred in absence of an implemented project, programme or policy intervention. It serves as the assumed counterfactual scenario to which a projects impact on GHGs can be compared in order to appraise its marginal impact. Global Warming Potential (GWP) The Global Warming Potential is the factor describing the radiative forcing impact (degree of harm to the atmosphere) of one unit of a given GHG relative to one unit of carbon dioxide (CO2) over a specific time period. It thus allows expressing all sources and sinks of GHGs in CO2 equivalents, which leads to the evaluation of the net climate impact of a project. Following the Clean Development Mechanism (CDM) the official values for methane (CH4) are set for instance to 21 (meaning that 1 kg of CH4 is as effective, in terms of radiative forcing, as 21 kg of CO2) and to 310 for nitrous oxide (N2O). EX-ACT allows users to choose either the GWP standards of the CDM or the last IPCC update. IPCC Tier levels (cf. (IPCC, 2006)) A Tier represents a level of methodological complexity to estimate greenhouse gas emissions following the definition in NGGI-IPCC-2006. EX-ACT can accommodate two of these precision levels: Tier 1 and Tier 2. Tier 1 methods are designed to be the simplest to use, for which equations and default parameter values (e.g., emission and stock change factors) are provided in NGGI-IPCC-2006. While users need to furnish project specific activity data, the IPCC based emission coefficients are mostly applicable globally or at regional level. 3 Tier 2 can use the same methodological approach as Tier 1 but applies emission and stock change factors that are based on country- or region-specific data. Country-defined emission factors are characterized by more specificity for the climatic regions, land-use systems and livestock categories in the given country. Higher temporal and spatial resolution and more disaggregated activity data are typically used in Tier 2 to correspond with country-defined coefficients for specific regions and specialized land-use or livestock categories. Tier 3 refers instead to the use of more complex methodologies, including GHG modelling techniques. They are tailored to address national circumstances and are driven by high-resolution activity data and disaggregated at sub-national level. Their strong data requirements make an application time and resource intensive. EX-ACT version 5 EX-ACT version 5 is released in the two editions “Standard” and “Tier One.” The “Standard” edition comprises the full functionality of EX-ACT and is the focus of this manual. It allows users to either use the Tier 1 or the Tier 2 level of complexity, following the IPCC definition. The edition “Tier One” is a simplified version that is reduced to the exclusive use of the Tier 1 level, not allowing for the specification of regional specific Tier 2 data. Since it is in all other aspects identical to the standard edition this User Manual may equally guide users of the “Tier One” edition. For a more comprehensive definition of terms please consult the glossary in Annex 2. C. Targeted users International financial institutions increasingly commit themselves to structurally consider the impact of projects and programs on the GHG-balance as one directly targeted objective of their investment decisions. The identification of investments that are climate smart while leading to equally high socio-economic outcomes, requires an accepted methodology and practical tools for project and programme level GHG accounting. This EX-ACT User Manual targets investment planners and project designers in international financial institutions and national planning institutions that aim at estimating the GHG-balance of any investment proposals in the agriculture, forestry and land use sector (AFOLU). The main target users should be involved during the project design stage and pursue the objective of aligning ex-ante program and project documents in accordance with the results obtained from the EX-ACT appraisal. D. Why targeting GHG mitigation in AFOLU investment planning? Climate change poses large-scale risks for economic development and the environment (Stern, 2007). Changing food production systems, altered landscapes, rising sea levels, increased risks of drought, fire, and floods, more violent weather events, and increased storm damage, more heat-related illness and disease, and biodiversity and species loss are among the primary direct impacts that climate change is likely to lead to. Its more indirect effects on human welfare include those relating to economic losses associated with changing resource availability and disruptions to production systems – and to agricultural production systems in particular (Aggarwal, et al., 2007); (Stern, 2007). GHG Mitigation as a Necessary Component of Investment Planning Evidence of the scale of magnitude and severity of the irreversible impacts climate change is likely to have has made business-as-usual paths of development planning less and less feasible, and has made accounting for GHG emissions an imperative of responsible planning among international development institutions. The joint commitment of nine such international financial institutions to account for the GHG emissions during project and program appraisal acknowledges this imperative, and hopefully establishes a precedent for other investors to follow. The global scale of climate change impacts makes coordination between different institutions and investors a priority. Agriculture, Forestry and Other Land Use: A Priority Area for Climate Change Mitigation Agriculture, forestry and other land use, abbreviated as “AFOLU” in this manual, is a key focus of the global climate change agenda. 4  The agriculture and land use change sector is the main source of anthropogenic GHG emissions globally.  Surpassing its share of current emissions, agriculture and land use change have tremendous mitigation potential, with larger and more cost effective technical options than other sector of the economy.  When adequately targeted, GHG mitigation in agriculture is closely linked to benefits for climate change adaptation and food security that are priority areas for sustainable development. AFOLU as a source of greenhouse gas emissions Agriculture is a major source of greenhouse gases. In 2005 it contributed directly to between 10 and 12 per cent, or between 5.1 and 6.1 billion tons (gigatons) of CO2 equivalent annually (GtCO2-e/yr) of total global anthropogenic emissions (Smith, et al., 2007). When combined with related changes in land use, including deforestation (of which agriculture is a major driver), agriculture’s contribution rises to more than one-third of total GHG emissions. Globally, agricultural production (crops and livestock) is responsible for about half of methane emissions (cattle, rice plantations, and wetlands) and the majority of nitrous oxide emissions (application of fertilizer) (Smith, et al., 2007). The scale of global emissions from agriculture and land use change is increasing as a result of population growth, growing consumption of meat and dairy products, and the rising use of nitrogen fertilizers. The mitigation potential of AFOLU The potential for mitigation in agriculture is high and 74 percent of this potential can be found in developing countries. The IPCC estimates the global technical mitigation potential of agriculture to be 5500-6,000 MtCO2- e/yr by 2030, which is comparable to the mitigation potential of the energy and industrial sectors and is larger than in the transport sector. How much of the technical potential will be realized depends on the financial incentives per tonne of CO2 equivalent. The IPCC (2007) estimates that the economic mitigation potential for various prices per tonne of CO2-e at 1500-1600 MtCO2-e/yr (US$20), 2500-2700 MtCO2-e/yr (50 US$), and 4000-4300 MtCO2-e/yr (US$100). This makes agricultural mitigation a cost effective mitigation strategy when compared with non-agriculture sectors such as energy. Within agriculture, abatement options in the crop and livestock subsectors were identified as the most cost effective areas (Smith, et al., 2007). Figure 1: Some facts about GHG emissions and agriculture 5 Synergies between mitigation, adaptation, and food security: the Paradigm of Climate Smart Agriculture Climate change is likely to result in significant decreases in both the efficiency and the resilience of agricultural production globally.1 At the same time, the sector is being confronted with increasing demand from a growing population. And more than any other sector, agricultural systems are directly linked to the livelihoods and food security of vulnerable people. Nelson, et al. (2009) estimate that by 2050, negative impacts of climate change on agricultural production will lead to a decrease in the daily per capita calorie availability in developing countries by 16 per cent as compared to an alternative scenario without climate change.2 Measures to mitigate climate change often have potential to generate co-benefits for climate change adaptation and food security as well, assuming they are purposefully targeted. Incorporating the three elements of mitigation, adaptation, and food security elements constitutes the paradigm of Climate Smart Agriculture (CSA) (FAO, 2013). CSA practices are proven, innovative, practical techniques that can increase productivity in an environmentally and socially sustainable way, while strengthening farmers’ resilience to climate change, and simultaneously reducing greenhouse gas emissions and increasing carbon storage. Two notable practical examples are the benefits to soil fertility and soil moisture of soil carbon sequestration, and reduced emissions and production costs from micro-applications of fertilizer. The CSA approach also accounts for potential trade-offs between mitigation and other objectives relating to social and environmental goals. In some cases, these potential trade-offs can make prioritization necessary. This larger perspective that CSA exemplifies enables planners to avoid targeting mitigation effects that unduly compromise productivity and food security related objectives. It also enables planners to identify opportunities to target 3 multiple, complementary objectives (sometimes referred to as “sweet spots,” and double and triple “wins.”) E. Main emission sources and mitigation potentials of AFOLU The largest source of agricultural GHG emissions are land conversion (5,900 MtCO2-e); (Andrasko, et al., 2007), nitrous oxide from fertilised soils (2,128 MtCO2-e), and methane emissions from enteric fermentation of cattle (1,792 MtCO2-e). Further agriculture-related emissions stem from biomass burning, rice production, and manure decomposition. Figure 2: GHG emissions from agricultural sector by practices (in Mt CO2-e) Adapted from (Bellarby, et al., 2008). 1 C.f. (Gornall, et al., 2010), (IPCC, 2007), (Beddington, et al., 2012), (HLPE, 2012), (Thornton & Cramer, 2012) 2 Assuming average daily per capita calorie availability at 2,886 kcal/day in the scenario without climate change, while at 2,410 kcal/day (NCAR model) and 2,432 kcal/day (CISRO model) under climate change and without carbon fertilization. 3 For more information on CSA and related funding possibilities refer to Annex 5 and the Climate Smart Agriculture Sourcebook (FAO, 2013). 6 The most promising options for mitigating greenhouse gas emissions relate to avoiding converting land use for human purposes. Agriculture is not the only purpose that land is converted for human use, but it is the largest – and all such conversions result in substantial greenhouse gas emissions. The sequestration and storage of carbon in agricultural soils is a major factor in the sector’s potential for greater mitigation effects. 89 percent of agriculture’s mitigation potential consists of reducing emissions from cultivated soils and increasing the volume of carbon that is sequestered in agricultural soils between now and 2030. 9 percent of agriculture’s mitigation potential consists of reduced methane emissions, and 2 percent of reduced nitrous oxide emissions (Smith, et al., 2008). Improved cropland management, which largely consists of improved tillage practices and more highly adapted water and soil nutrient management, is the principal element of achieving this potential, followed by improved management of grazing and pasture areas. The restoration of organic soils in cultivated areas and in degraded lands is also a major element of the technical potential for mitigation by 2030. Figure 3: Global technical mitigation potential in agriculture by 2030 (in CO2-e yr-1) Source: (Smith, et al., 2008, p. 802) Capitalizing on the mitigation potential of the forestry sector involves establishing sustainable forms of forest management that maintain or increase forest carbon stocks, while producing an annual sustained yield of timber, fiber, and energy. The direct practices thereby centre on the reduction of deforestation, changed forest management as well as increases in afforestation and agro-forestry (Andrasko, et al., 2007). Like the other elements of the greater AFOLU sector, this mitigation potential is substantial, and entails technical methods which are well understood, readily available, and immediately deployable. 7 Figure 4: Main mitigation options of the agriculture, forestry and other land use (AFOLU) sector Mitigation possible through changes in agricultural  technologies and management practices  CO2  CH4, N2O  Sequestering carbon conservation  farming   rate  of deforestation  and  improved animal production  practices,  improved forest  forest degradation,  and management  of  livestock  management  practices,   adoption  of  improved  waste,  afforestation and  cropland management  more efficient  management  of  reforestation,  agro‐forestry,  practices (reduced  tillage,  irrigation water  on rice  paddies,  improved grasslands  integrated nutrient and  improved nutrient  management,  restoration  of  water management) management degraded  land 1 ha of avoided deforestation ,  1 ha plantation,  1 ha grasslands from severely  1 ha  from degraded  from tropical rain forest to  degraded land to tropical rain  degraded to improved  land to annual crops degraded lands plantation grasslands ‐42,7 t eq‐CO2 /ha/year ‐18,8 t eq‐CO 2/ha/year ‐1,7 à ‐3,8 t eq‐CO2 /ha/year ‐1,2 t eq‐CO 2/ha/year carbon appraisal 3+17 years 8 Chapter 2: Overview of EX-ACT A. The added-value of a carbon-balance appraisal In addition to the global public goods that can emerge from purposefully improving the carbon balance of planned investment projects, carbon balance appraisals also yield a number of advantages to the operations themselves. Some of the most important of these advantages relate to enabling policy makers, financial institutions, and stakeholders to document how mitigation objectives are targeted during the design stage and how those objectives are achieved in the monitoring stage of the project. Quantified projections of the extent to which alternative project designs achieve mitigation objectives enable planners to compare and contrast different scenarios. The ability to document how well objectives are being achieved during monitoring can be instrumental in securing supplemental finance and sometimes support from new green funding facilities. Ex-ante carbon balance appraisals are the first essential step in bringing these developments to pass. The use of information generated by carbon balance appraisal and subsequent monitoring can provide important inputs into the formulation and conduct of environment and AFOLU related policies that are intended to address climate change mitigation and adaptation. The information is also a useful basis for capacity building initiatives within agriculture, rural development, and environment ministries, as well as a focus for more effective coordination between ministries and agencies whose work concerns climate change. Primary among these are agencies responsible for watershed management, forestry strategies, livelihood resilience, disaster management, sustainable intensification, and food security policies. B. Selecting the right carbon-balance accounting tool A variety of GHG accounting tools have been developed to target climate change mitigation in agricultural projects. These vary, according to agricultural subsector, geographical scope, features of the value chain covered by the project, and other criteria – all of which can have different data requirements. In general however, the tools can be categorized as follows.  Awareness raising tools are relatively simple instruments that use limited parameters to reveal the principal climate change hotspots without reliable emission quantifications.  National reporting tools allow accounting for the sum of emissions and sinks in a given territory. They need to be populated with national data on changes in agricultural practices and land use, leading to an approximate identification of major emission sources and sinks.  Project evaluation tools are used to monitor specific project data throughout implementation, using the information among other things to draw comparisons between the ongoing with-project and the alternative without-project baseline scenario. These tools are tailored to capture data from the specific agricultural subsector the project’s activities relate to, and can be adapted into similar instruments used to analyse policy impacts as well.  Product oriented tools are used to calculate the carbon footprint per production unit and cover the entire life span of the product from production to end use. They aim at identifying and remunerating efforts for emission reduction that have been carried out by actors from the private sector and thus serve purposes of product differentiation and environmental branding.  Modelling tools can be used for a variety of the purposes already described and require a more precise set of input data concerning daily climate variations, a wide set of detailed soil parameters, and selected management practices. Examples are e.g. Daycent/Century, DNDC or GEFSOC. A study by Colomb, et al. (2012) – on which further information is provided in Annex 7 – identified 18 GHG tools and characterized them as follows. 9 Table 1: GHG accounting tools classified according to their main objective and geographical zone TOOLS AND GEOGRAPHICAL ZONE OF OBJECTIVE OF THE USER APPLICATION Carbon Calculator for New Zealand Agriculture and Horticulture Raising awareness (NZ), Cplan v0 (UK); Farming Enterprise GHG Calculator(AUS); US cropland GHG calculator (USA). Landscape tools ALU (World); Climagri (FR), FullCam (AUS) Reporting Diaterre(FR); CALM (UK); CFF Carbon Calculator (UK);IFSC Farm tools (USA) Focus on carbon Farmgas (AUS), Carbon Farming tool (NZ);Forest tools : markets TARAM (world), CO2 fix (world) Project evaluation No focus on carbon EX-ACT (World); US AID FCC (Developing countries), CBP markets (World), Holos(CAN), CAR livestock tools(USA) Cool farm tool (World); Diaterre (FR), LCA tools and associated Product oriented tools database (SimaPro, ecoinvent, LCA food etc: mainly data for developed countries.) AUS: Australia; CAN: Canada; FR: France, NZ: New Zealand; UK: United Kingdom; USA: United States of America; (Colomb, et al., 2012) The tested calculators account for different GHG sources and emissions and share nearly all the identical methodological bases of the IPCC 2006 Guidelines for National Greenhouse Gas Inventories. The various GHG calculators differ in their primary objectives and an interactive website hosted by the FAO has been developed to guide users in locating the most suitable tool for their purposes. The resource, which the FAO developed in collaboration with IRD and ADEME, is based on Colomb et al. (2013) and considers the 18 GHG calculators listed above. It is accessible at: www.fao.org/tc/exact/review-of-available-ghg-tools-in-agriculture The website allows users to specify the main preferences for their analysis using the five categories:  Region of analysis  Aim of analysis  Speed and ease of use  Scope of the assessment o By activity o By emission source Based on these specifications the website then lists the set of best fitting tools on the bottom of the page. By clicking on each GHG tool, users are subsequently provided with detailed information and web links. 10 Figure 5: Exemplary screenshot of the online selection guide to GHG emission calculators C. What is EX-ACT? Main structure and outputs EX-ACT is a land-based accounting system used to measure GHG and carbon impacts per unit of land, expressed in tons of carbon equivalent (tCO2-e) per hectare, per year. Another function measures the carbon balance per unit of produce (c.f. Annex 5: Use of EX-ACT in assessing product carbon footprints along the value chain). The calculated ex-ante carbon balance is intended to complement conventional ex-ante analyses of economic results and environmental impacts which are undertaken while investment projects and development policies are being planned. A number of its outputs can moreover be used in financial and economic analysis. EX-ACT is also cost-effective and easy to use, requiring a comparatively small amount of data – with resources to assist the user in locating that data. Basic contents of EX-ACT: Module structure EX-ACT asks the user to specify a few geographical, climatic, and agro-ecological variables and a wider set of information regarding land-use change activities and agricultural management practices. More specifically the input data requirements in EX-ACT are composed of six linked Microsoft Excel spreadsheets known as Modules in which project designers can insert information concerning: 11 1. General description of the project (geographic area, climate and soil characteristics, duration of the project); 2. Land use change (deforestation, afforestation/reforestation, non-forest LUC) 3. Crop production and management (agronomic practices, tillage practices, water & nutrient management, manure application) 4. Grassland and livestock (grassland management practices, feeding practices) 5. Land degradation (forest degradation, drainage of organic soils, peat extraction) 6. Inputs and further investments (fertilizer and agro-chemical use, fuel consumption, electricity use) Figure 6 below shows the six topic modules of EX-ACT. Here depicted as they appear in Excel as a navigation bar on top of the window. Figure 6: Screenshot of EX-ACT The coverage of these six topic modules allows EX-ACT to analyse a range of agricultural and forestry development projects, including: - Livestock development - Crop production intensification - Rural development - Food security - Forest protection and management - Watershed development - Land rehabilitation - Bio-energy - Climate change mitigation 12 It is only necessary to provide data in those areas (modules) in which the project is expected to have impacts. This criterion of where the project will have effects is different from basing the modules on project type. However, the tool is also intended to capture potential indirect effects the project may have, and to bring these into consideration on the part of the planners. 13 Table 2: Checklist for choosing relevant modules in EX-ACT Project Carbon-balance Impact EX-ACT intervention Main Impact Area Module(s) to be filled YES NO A Reduced emissions of carbon dioxide A1 Reduction in rate of deforestation Land use change A2 Reduction in forest degradation Land degradation A3 Adoption of improved cropland management Crop production Introduction of renewable energy and energy-saving A4 Investments technologies B Reduced emissions of methane and nitrous oxide POSITIVE (SINK) B1 Improved animal production Livestock B2 Improved management of livestock waste Livestock B3 More efficient management of irrigation water in rice Crop production Crop production, B4 Improved nutrient management Livestock C Carbon sequestration C1 Conservation farming practices Crop production C2 Improved forest management practices Land use change C3 Afforestation and reforestation Land use change C4 Adoption of agro-forestry Crop production C5 Improved grassland management Grassland C6 Restoration of degraded land Land use change Increased emissions of methane, nitrous oxide and D carbon dioxide D1 Increased livestock production Livestock D2 Increased irrigated rice production Crop production D3 Increased fertilizer use and over-fertilization Inputs NEGATIVE (SOURCE) Production, transportation, storage and transfer of D4 Inputs agricultural chemicals D5 Increased electricity consumption Investments D6 Increased fuel consumption Investments D7 Installation of irrigation systems Investments D8 Building of infrastructure Investments E Decreased carbon stocks E1 Increased deforestation & timber logging Land use change Increased land degradation (forests, croplands, Land degradation, E2 grassland) Grassland E3 Cropland expansion Land use change E4 Residue burning, deep tillage, ... Crop production Basic contents of EX-ACT: Scenario building Ex-ante evaluations of an activity plan compare the impacts of a planned intervention to the business as usual scenario. It is thus in the basic logic of EX-ACT that the specified data is required for three points in time:  The initial situation at project start  The with-project scenario  The without-project scenario (business as usual/baseline scenario) 14 In Figure 5, xo gives the initial situation of land use and existing management practices prior to the introduction of a project. Changes that occur as the result of project activities lead to the with-project scenario represented by x2, in this case the intensification of crop management systems. Alternative changes are likely to occur if the project does not take place, and this without-project scenario is represented by x1. EX-ACT differentiates between two time periods. The first is the implementation phase, the period during which active project activities are carried out, which is represented by the area between t0 and t1. Thereby the period covered by the analysis does not necessarily end with the termination of the active project intervention. Even after the point that a new equilibrium in land use and practices is reached at t1, further changes may occur as the result of the preceding intervention, for instance changes soil carbon content or biomass. This period defines the capitalization phase which lasts from t1 until t2. Figure 5: Building of development scenarios for the use in EX-ACT The three different sets of input data from the situation at project start, the with- and without-project scenarios then lead, to the calculation of GHG emissions and carbon stock changes. Figure 6 illustrates the combined impact on GHG emissions and sinks of the with-project and the without-project scenario. The graph shows that both scenarios are net emission sources, while the without-project scenario leads to much higher GHG emissions than the with-project scenario. The final carbon-balance that compares the expected emissions and sinks under the with- and without-project scenario may then be derived as the blue area between the two emission scenarios. It is the marginal difference that is brought about by project implementation. 15 Figure 6: Comparing the GHG impact of different scenarios Basic contents of EX-ACT: Main output Taking the example of the FAO / World Bank “Accelerated Food Security Project” (AFSP) in Tanzania an exemplary overview of such main results of an EX-ACT application is presented here below. The AFSP introduced the combined package of increased farmers’ access to critical agricultural inputs – as mainly fertilizers and improved seeds – while at the same time promoting sustainable land management practices that e.g. do not use burning practices for residue management or land preparation. The total project size accounted for roughly 1 million hectares. Figure 7: Exemplary results of an EX-ACT appraisal 16 In Figure 7, the increases in fertilizers (“inputs”) and the intensification of flooded (“irrigated”) rice systems both lead to substantial increases in GHG emissions. The fertilizer use causes 5.3 Mt of CO2-e and the irrigated rice systems cause 3.2 Mt of CO2-e. Yet both are essential elements of the food security objectives of the program. Enhanced land and crop management practices are projected to partially offset these increased emissions and serve as carbon sinks -0.4 Mt of CO2-e. Although the project is projected to lead to a source of GHG emissions, comparing it to the baseline scenario – and assuming the continuation of prevailing agricultural practices (in the blue frame) – the with-project outcome has a better carbon balance that the without-project outcome. Over the 20 year period, the with-project scenario leads to a -5.6 Mt of CO2-e compared to the non-project alternative. This is equal to a carbon-balance of -0.27 MtCO2-e per hectare and year. This project analysis made use of three EX-ACT modules: Description, Crop Production, and Inputs. This example illustrates an important point about the tool. EX-ACT results contain no implicit assumption that projects that lead to increased emissions are intrinsically undesirable, nor that projects that lead to carbon sinks are inherently desirable. Certain types of projects tend to automatically increase emissions, others can readily capitalize on opportunities to create carbon sinks. What EX-ACT enables planners to do is minimize a project’s negative carbon balance or to maximize its positive one – both without compromising the project’s other development objectives. Chapter 3: Methodological Background EX-ACT is a land-based accounting system that relates activities in the Agriculture, Forestry and Other Land Use (AFOLU) sector to:  Estimated values on the five carbon pools above ground biomass, below ground biomass, deadwood, litter, and soil organic carbon and  Estimated coefficients on methane (CH4), nitrous oxide (N2O) carbon dioxide (CO2) greenhouse gas emissions. Based on activity data from the AFOLU sector, EX-ACT allows estimations of carbon stocks, stock changes and CH4, N2O and CO2 emissions which are the basis of the overall carbon-balance. EX-ACT enables users to utilize default values for carbon pools and emission factors, deriving a carbon-balance by specifying activity and land use change data. For the specified default values, and in accounting structure and logic, EX-ACT has been developed using IPCC 2006 Guidelines for National Greenhouse Gas Inventories (IPCC 2006) and Chapter 8 of the Fourth Assessment Report from working group III of the IPCC (Smith, et al., 2007) for specific mitigation options not covered by the Guidelines. Other required coefficients are from published reviews or international databases. For instance, embodied GHG emissions for farm operations, transportation of inputs, and irrigation systems implementation come from Lal (Lal, 2004) and electricity emission factors are based on data from the International Energy Agency (IEA). Using the given accounting structure, users can utilize more site specific values replacing default values for carbon pool and emission factors. Table 1 specifies the sources used by EX-ACT in order to assume default values for emission factors and carbon values. A main conceptual differentiation in EX-ACT is the distinction between land categories that remain in the same land use and land that is converted into another land-use category during the period of analysis. EX-ACT adopted the six broad land use categories (and their sub-categories) proposed by the IPCC, and distinguishes between the three land uses: forest land, cropland, and grassland. 17 Table 1: Specification of sources for carbon values and emission factors used in EX-ACT (Bernoux, et al., 2010) Complementing the information given in the table above, the following list gives a better understanding for the process by type of carbon pool and type of GHG: • Above ground biomass. Default values correspond to estimates provided by NGGI-IPCC-2006 and are expressed in t ha–1 of dry matter. The corresponding carbon stock (in tons of carbon) is calculated using the specific carbon content, e.g. 0.47 for above-ground forest biomass. • Below ground biomass. In most cases, the below-ground biomass is estimated using a ratio of below-ground biomass to above-ground biomass. EX-ACT uses the default values provided by NGGI-IPCC-2006, e.g. the ratio is 0.37 for all tropical rainforest and 0.27 for tropical mountain systems. In some cases the total above plus below ground biomass is used if it is not mandatory for calculation to have separate estimates. • Litter and dead-wood. It is assumed that litter and dead wood pools are zero in all non-forest categories (excluding tree crops and perennial systems) and therefore transitions between non-forest categories involve no C stock changes in these two pools. For other transition default values are provided. • Soil carbon. For the soil carbon estimates, the default values are based on references for soil organic carbon stocks for mineral soils down to a depth of 30 cm. When soil organic carbon changes occur over time (land use change or management change), a default time period of 20 years is assumed for transitions between equilibria. These values are found in both IPCC 1997 and 2006 Guidelines, compiled from a wide range of observations 18 and data from long-term monitoring. Some modules use carbon change rates instead of the soil carbon stock difference and therefore do not require information on absolute soil carbon stocks. In both approaches it is hypothesized that soil organic carbon stock changes during the transition to a new equilibrium occur with a linear pattern. Although soil carbon changes in response to management changes may often be best described by a non-linear function, the linear assumption greatly simplifies the methodology and provides a good approximation over a multi-year period. • CH4, N2O and some CO2 emissions not covered in AFOLU chapter. For CH4 and N2O emissions, the generic approach consists of multiplying an emission factor for a specific gas or source category with activity data related to the emission source (e.g. area, animal numbers or mass unit…). Emissions of N2O and CH4 are either associated with a specific land use category or subcategory (e.g. CH4 emissions from rice), or are estimated at project aggregated data (e.g. emissions from livestock and N2O emission from fertilizers). Further emissions of CO2 instead are associated with indirect emissions from production, transport and storage of artificial inputs or with direct burning of fossil fuels. CH4 and N2O emissions are converted into CO2-e emissions based on the global warming potential of each gas. The user has the ability to use either the official values under the Kyoto Protocol of the UNFCCC, or the last update provided by the IPCC (2007). D. Dynamics of change in EX-ACT Periods of land use change as well as changes in further variables (livestock herd sizes or new agricultural practices and management options) specify the so called implementation phase in EX-ACT. For the impact on GHG emissions and carbon sequestration the pace at which changes occur are highly relevant. EX-ACT offers three options of default (linear or s-curve), immediate, or exponential dynamics of change, as illustrated in Figure 8. Users specify the scope and pace of anticipated land use change, for instance whether deforestation on all the specified area takes place immediately during the start of the implementation phase, increases linearly throughout it, or occurs exponentially until at the end of the implementation phase the complete land has been deforested. Figure 8: Different dynamics of change available in EX-ACT Absolute Change “Immediate” (x1-x0) “Linear” “Exponential” Time (years) t0 t1 t0 t1 t0 t1 E. Defining project boundaries As a first step users should clearly define the location and delimitation and size of the project intervention area. They should also differentiate between the delimitation of activities and impacts, meaning: 1. The direct zone where activities of the project are implemented, targeting a certain number of farmers. 2. Non target zones in which clear spillovers or externalities from project implementation occur. Exemplarily an agricultural development project that increases access to extension services and strengthens farmers’ cooperatives that diffuse technology for sustainable intensification may not only lead to production changes on the targeted project area. Instead, it may lead at the same time to prevent or promote further expansion of agriculture on forested lands (externalities) or lead as well to changes in agricultural practices on existing agricultural cropland in proximity (spillovers). The EX-ACT analysis usually concentrates on the direct project implementation zone. Zones that are affected by project externalities should only be included into the EX-ACT analysis when there is clear empirical evidence 19 on the way they will be impacted. This step of documenting the intervention area may entail recording the specific coordinates of where the project is located. Besides, visual mapping tools may be very useful means for communicating central information during data collection and as part of later project analysis. F. Ex-ante versus ex-post Ex-ante appraisals aim at improving the quality of new or renewed programmes. They can be carried out as part of a planning process in parallel with or as a part of the program design, feeding results into the preparation of the proposal (ARD, World Bank, 2011). Examples where EX-ACT has been used as ex-ante methodology include World Bank projects in Russia (Forest Fire Response Project), China (China Integrated Modern Agriculture Development Project), India (India Rajasthan Agricultural Competitiveness Project) and Madagascar (Irrigation and Watershed Management Project) (FAO, 2013). Ex-post evaluations or impact assessments are instead conducted after project completion, or after completion of certain segments of a project, e.g. as part of a monitoring and evaluation plan. The purpose of an ex-post assessment is to judge the effectiveness and/or efficiency of completed projects to increase the accountability of project implementation and to lead to improvement of future interventions and institutional learning (Contreras & Gregersen, 1995). Examples where EX-ACT has been used as ex-post evaluation include Niger (Projet d'Action Communautaire pour la Résilience Climatique), Morocco (Plan Maroc Vert), Madagascar (all IFAD projects), India (Sodic Soil III project), Brazil (Santa Catarina Rural Competitiveness Project, Rio de Janeiro Sustainable Rural Development Project) and Nepal (IFAD, Leasehold Forestry livestock project). G. The baseline scenario The creation of a baseline scenario is a central component of an EX-ACT appraisal. It depicts the counterfactual outcome in terms of input variables as well as resulting GHG emissions and sinks that would most likely have occurred in absence of the project intervention. The baseline scenario is thus the instrumental step to estimate and prove the additionality of a project, meaning its capacity to lead to lower GHG emissions than without project. Because the EX-ACT GHG-balance of a project is given by the difference of the overall effects of project and baseline scenario, the final results of EX-ACT are determined as strongly by the project as the baseline scenario. This is why the baseline scenario is of central importance and one of the major steps of an EX-ACT analysis. It is important to recognize that setting a baseline can have political implications as well as technical, as the level of emissions that a country or project might claim as a right, is not necessarily the same as the most likely emissions growth scenario without the project. This is a highly contentious issue in the UNFCCC and as yet there is no agreed standard for setting agricultural mitigation baselines internationally. EX-ACT thereby proposes three main methods to develop a baseline scenario: Assuming that no changes to the status-quo situation will occur throughout the baseline time span, assuming changes using past trends, modelling changes using future trends (c.f. Figure 9). 20 Figure 9: Three approaches to developing baseline scenarios Small‐scale  Large‐scale  baseline baseline NO CHANGE  USE OF PAST TRENDS USE OF FUTURE  SCENARIO • Assume a change on the  TRENDS • no change in the land  basis of expert  • Model future land uses  use/practices with  consultation (small scale  and practices based on  respect to the initial  projects) quantitative data (for  situation • Extrapolate past trends  several or few selected  from secondary data  variables) (long term or short term  past trends) Possibility to combine these 3 scenarios a. No change scenario: The no change scenario assumes that in the absence of the project, no changes in land use or agricultural practices will occur. The status quo at project start is assumed to continue in absence of the project. This assumption represents the most simplistic scenario, requires no additional data and is largely used for projects of small scale. In countries and regions undergoing profound agricultural transformation processes, for instance those characterized by strong pressure for land conversion and incentives towards intensification users should use another baseline methodology. b. Use of past trends: This approach assumes that the changes in land use and agricultural practices will continue to evolve in the near future with the same dynamic as in the close past. The scenario therefore forecasts the future situation, extrapolating either long term (30 years) or short term (5-10years) past trends using secondary data. For most variables of land use change dynamics and agricultural practices, short term trends are preferred over long term trends, due to their dependency on dynamically fluctuating context variables (Bernoux, et al., 2011). c. Use of future trends: This approach requires advanced methodological tools to engage in actual modelling of the future development of land use and agricultural practices using a wide range of quantitative input data. Compared to the other approaches it requires the use of dedicated software, extensive data, and is more time intensive. It is also associated with uncertainties due to the initial assumptions of parameters and equations. The use of future trends might be especially useful when non-project related processes with strong impacts on land use and practices emerge, which are not reflected by past trends. This might e.g. be the case after introduction of input subsidy schemes or changes in ownership rights of state and communal land. Modelling techniques are especially helpful when regarding very large project areas as e.g. on national level and when focussing on longer time periods. The extent of complexity of the baseline scenario building depends thus largely on the chosen approach. Future projections are thereby always associated to high uncertainty levels. This also translates into the situation that the three different baseline methodologies can possibly lead to considerably different results. The graph below demonstrates such an example for the case of future deforestation: 21 Figure 10: Example of results for the three types of baseline scenario (source FAO Stat) Evolution of the forested area following three different  scenario approaches  800,000  750,000 Forest surface area in 1000 ha no change  700,000  650,000 use of  600,000 past trends  550,000 use of future  500,000 trends  450,000  400,000 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 Figure 10 thus shows that the green no change scenario would suggest that any deforestation activity would have stopped from the moment of project start and the forest area remains unchanged from 2010 onwards. If no strong context conditions suggest such a scenario, it is thus highly unlikely and should not be considered as a baseline. In the violet past trend scenario, the annual deforestation rate of 0.5 percent over the past 20 years is extrapolated in the future. It is thus assumed that the drivers of deforestation continue to take effect with the same strength as in the close past. In the blue future trends scenario a modelling software has been applied in order to come up with the future deforestation rate as determined by a wide set of input variables. It assumes that the increasing demand for wood and agricultural land will increase the deforestation rate to 2 percent per year. Beyond this short illustration it is thus imaginable that EX-ACT users may want to engage in a sensitivity analysis comparing two baselines with each other – specifically whenever two strongly opposing baselines are both plausibly imaginable. Choosing the most adapted baseline approach The choice between these three types of scenarios depends largely on the scale of the project intervention area, the availability of data, and the time frame of the analysis. It is also important to consider potential sources of financing and the requirements they may set for establishing the baseline. The no change scenario is often applied in the case of small-scale projects, for simple appraisals or when the intervention area is characterized by a markedly static situation. It is the simplest way of building the baseline scenario, as the current situation is a well-known entry point and does not demand any additional data needs. Also, emission results based on current conditions are often easier to communicate to third actors as when comparisons to assumed future changes are done. However it gives a biased view in contexts subject to strong dynamics. Another alternative is to combine different baseline methodologies in order to develop the entire project baseline. The table below indicates in such a way that the use of past trends is in EX-ACT the most common method for deriving baselines of the development of land use as well as adoption rates of fertilizer and irrigation technology use. Instead it is mostly common to assume a no change baseline for the adoption of sustainable land management practices and the use of improved varieties. 22 Table 2: Guidance on choosing an adapted baseline scenario Type of data No change Past trends Future trend Preferred choice if data Land use & land use change If no data Most common choice available Technology adoption: No Most common choice No Irrigation and fertilizers Technology adoption: SLM Preferred choice if data Most common choice No & improved varieties available The following virtual case study provides a concrete example of combining different baseline methodologies to derive an overall project baseline. 23 Case Study 1: Exemplary development of a baseline scenario Short Project Description: “Support the development of cattle rearing in the Eastern region of country A” Context:  20,000 cattle heads (20 per breeder)  Pastoral systems (50,000 ha)  Moderately degraded pasture  1,000 livestock breeders Project:  Improve 50% of pasture areas  Increase drought resilience with adapted water management techniques (pastoral wells)  Increase productivity per head of cattle by 30% (increase of weight per head) while no further expansion of herd sizes  Introduce a common pasture management preventing overgrazing Data Available:  The pasture area went through an in pace increasing degradation dynamic in the recent 10 years but did not experience any land use change;  The newly established mining industry and resulting population inflow in country A leads in the next years to higher demand and prices for livestock products. Baseline scenario: The below derived project baseline utilizes a purposeful combination of all three methodologies (duration: 10 years): No change:  No land use change / constant area of 50,000 ha of pasture Extrapolation of past trend:  Continuing, 1.5% of pasture becomes moderately degraded per annum Modelling of future trends:  A quantitative model derives a future annual increase in livestock herd size by 2%, based on input data considering e.g. increasing price incentives for livestock products and open access management of pasture Source Photos: SDC (2010) Process guidance for baseline development Available data as part of national statistics and main climate change policy documents may thus serve as the first entry point to central information. This is complemented by a consultation process with project implementing and other regional staff that leads to a first scenario building and clarification of major assumptions. After the detailed development of the actual baseline scenario, country stakeholders should be re-consulted and confronted with the results, leading eventually to an iterative correction of the initially taken assumptions. Throughout the entire process it is of central importance that all assumptions are clearly documented and 24 presented as part of the EX-ACT analysis. Focusing on the practical process of developing a baseline scenario, the graphic below describes the typical iterative approach. Figure 11: Overview of the process of building a baseline scenario for the use in EX-ACT Exchange with  Develop baseline  •National scenarios, counterparts •Based on the results  scenario •National  policies from the consultation  •National inventories •MoA, MoF, MoE, ... process with country •National  •Procure project data •No change / past trends  •Optimistic /  communications •Consult stakeholders  / future trends scenario pessimistic baseline  •NAPAs, NAMAs concerning initial  •Transparency and  scenario scenario and  conservatism •Sensitivty analysis assumptions Get data at  Decide baseline  country level setting 25 Part B: Step-by-Step Guide to Use EX-ACT Chapter 4: Data Requirements and Data Collection This chapter describes the data that needs to be collected in order to use EX-ACT and offers guidance on methods of data collection. Data collection designs may vary with differences in data quality and precision as well as time intensity and cost. The purpose here is to provide users with an overview of data collection methods, while leaving it to individual users to make informed decisions about which methods to use. A. Choosing the valid EX-ACT modules EX-ACT does not necessarily require a full inventory of all land-uses in the project area, but is mainly concerned with all land areas which are altered due to the analyzed project process. Data is needed on all those areas in which change is observed between project start and end of the capitalization phase as well as on those areas where such alterations would have been observed in case of the business-as-usual scenario. Thus for example, existing native forest land or grasslands in a state of equilibrium which are not under conversion and without any considerable withdrawals (grazing, firewood collection) or depositions (fertilization), are no source of GHG emissions or changes in carbon stocks and do not have to be object of data collection. This may hold true e.g. for areas kept as nature reserves. Given that for each specific project it will be only necessary to fill a limited subset of EX-ACT topic modules, the Table 3 lists typical development projects by type and identifies which are the most relevant EX-ACT modules that need to be filled in order to calculate the GHG-balance. The information provided here revisits the checklist (Table 2 above) and has a similar purpose. EX-ACT is a flexible tool based on project impacts rather than objectives, implying that one type of project can involve several modules. In the Table 3 the first question to answer to is: What type of project is taking place? According to the project’s nature, it can be directly decided which modules will necessarily be used, such as the crop production module in the case of agricultural intensification projects. The second question is Which other modules may be impacted by the project i.e. what other specific positive or negative effects are occurring with or without the project? 28 Table 3: Modules to be used according to project profile First question to answer: what is the type of project? Modules to be used IF... Sustainable intensification project Go to Crop Production & Inputs IF... Livestock project Grassland & Livestock Afforestation/ IF... Watershed project Reforestation IF... Forest Management / Forest conservation Land Use Change IF... Irrigation project Inputs & Crop production IF... Land use change project Land Use Change IF... other Multi-objective projects See next Second question: Which other modules are needed based on project actions? Modules to be used: Specific positive or negative effects occurring with or without project? IF deforestation/afforestation with or without project o For expanding agriculture area or pastures Land Use Change o Additional land areas planted with forest Go to IF degraded land transformed in annual crops or pasture land IF annual crops switched to perennials, pastures Land Use Change IF agriculture land transformed in other land IF Inputs used and energy consumed Inputs & Investment All project types IF Drainage of organic soils Land Degradation IF Investment in buildings, roads, storages Inputs & Investment IF Irrigated areas rehabilitated with improved systems Inputs & Investment IF Degradation or improvement of existing pastures Grassland & Livestock IF Increased annual crop areas with manure – compost use Crop Production & Inputs IF Degradation or improvement of forestry areas Land Degradation IF Improved techniques in annual crops Crop Production o stop burning residue, compost, terracing… IF Inputs consumed in value chain processing, transport Carbon Footprint functionality (see annex) 29 B. Overview of data needs In the following the reader is provided with concrete specifications of the data needs for EX-ACT. It is differentiated between such information that is largely compulsory when considering a specific EX-ACT module and such information that is optional and may lead to higher degrees of precision in the analysis. This reflects the difference between using the Tier 1 coefficients provided by the IPCC as opposed to providing regional specific values for e.g. biomass densities per hectare or carbon content in soil. The extent that a user attempts to procure location specific values may follow the level of precision chosen in other parts of the project analysis and should be aligned to the overall purpose of engaging in the carbon-balance appraisal. Description module EX-ACT users first need to fill in information in the description module. This encompasses the specification of the name of the project, the duration of its implementation and capitalization phase and the most important agro-ecological variables of: ‐ The continent (or sub-continent) of the project location ‐ The type of climate ‐ The moisture regime of the zone ‐ The dominant regional soil type Land use change module The land use change module focuses on deforestation, afforestation, reforestation and other land use change. According to the IPCC Report on Land Use, Land Use Change, and Forestry (IPCC, 2000), Land Use Change (LUC) refers to a change in the use or management of land by humans, which may lead to a change in land cover. Thereby the IPCC considers the six land covers: Forest land, Cropland, Grassland, Wetland, Settlements and Other land. As part of the module users need to procure information specifying which land use changes are taking place and how the conversion process is executed. Most explicitly the areas of land use that are transformed need to be clearly specified in size and whether the conversion involves burning, in which pace the conversion takes place and whether wood is exported before burning. Based on the categorization of the IPCC, EX-ACT differentiates between the land covers:  Annual Crop  Perennial / Tree crop ( < 5 years)  Perennial / Tree crop (6-10 years)  Perennial / Tree crop (> 10 years)  Flooded Rice  Degraded Land  Set Aside  Grassland  Other Land In order to refine the analysis, users can specify the above and below ground biomass of the respective forest type, as well as the carbon content of soil, litter, and deadwood. In the case of reforestation and afforestation, users may also specify the biomass growth rates, while in the case of forest burning, specific values of the combustion process can be specified. Crop production module The module on crop production is subdivided into annual systems, perennial systems, and irrigated rice. Users collect data on the area in which each crop is cultivated, the associated management practices, and all eventually occurring changes in both variables throughout the different scenarios (project vs. baseline). More specifically data needs to be procured specifying yields, the time dynamic of crop shifts, and the whether crop residue is burned. 30 This data can be further complemented by location specific rates of e.g. annual soil carbon sequestration, annual biomass growth for perennials or the specific amount of residues burnt throughout the year. Irrigated rice instead asks for a rough differentiation of the duration of flooding during cropping season and preseason as well as the forms of management of organic amendments. Grassland and livestock module For the grassland module users collect data on the size and state of degradation of grassland, the grass yield, practices of grassland burning and the time dynamic of changes in the degradation state of the respective grassland area. The livestock part of the module requires information on the type and number of livestock and the percentage of herds that receive improved feeding practices, dietary additives that reduce CH4 emissions (Ionophores, vaccines, bST, etc.) or are subject to improved breeding practices. Information on livestock emissions may be refined by specifying the mean annual temperature as well as regional specific values for the emissions of CH4 and N2O from manure management and the CH4 emissions from enteric fermentation. Land degradation module The land degradation module covers issues of degradation as well as rehabilitation and comprises the issues of forest degradation as well as degradation of organic soils, while the change in the state of degradation of grasslands is already dealt with in the grassland module. Most importantly, forest degradation requires information about the type and size of forest area, the extent of change in its state of degradation, the time dynamic of occurring changes as well as the frequency and intensity of forest fires. The analysis of forest degradation can again be refined by location specific values on above and below ground biomass, soil organic carbon content as well as the carbon stocks in litter and deadwood. The issue of degradation of organic soils requires the specification of size and vegetation type of the area concerned by drainage of organic soils as well as peat extraction. This can be further complemented with specific values on how much carbon is lost due to drainage of organic soils and on-site CO2 and N2O emissions from peat extraction. Inputs & Investments module The inputs and investments module focuses on aspects of agricultural inputs, energy consumption and building of infrastructure. Users collect data on the quantity of applied fertilizer, pesticides, and liming, and specify the quantity of nutrient content. This may be refined by context specific emission factors for direct emissions from application as well as indirect emissions from production, transport, and storage. Further information is needed on the quantity of electricity, liquid and gaseous fuel as well as wood consumed. Specific emission factors may refine the analysis. The type and size of irrigation infrastructure build and buildings constructed under the project is another data need. Table 4 provides a concise overview of the main data that can be collected, focusing on the information needed for a Tier 1 analysis. Step 1 data is mandatory, as the description Module will always need to be filled for EX- ACT. However, all additional information only needs to be collected in case that the respective EX-ACT module and activities are relevant for the analyzed project (c.f. section 4.A Choosing the relevant EX-ACT module). Complementing the table below, a list including all Tier 2 data that can be accommodated by the tool is provided in the annex. 31 Table 4: Overview of Tier 1 activity data that can be accommodated in EX-ACT Obliga- Description module - Sub-continent - Dominant regional soil type tory - Type of climate - Project duration - Moisture regime Land use change module  Deforestation Forest type and size Final land use after conversion Only if project related - - - Area deforested - Burning during conversion?  Afforestation & reforestation - Type of current land use - Burning during conversion? - Type of future forest  Other land use change - Type of current land use - Burning during conversion? - Type of future land use Crop production module  Annual systems Current and future planted crop area (by type Practices of residue burning? Only if project related - - of crop) - Crop management practices  Perennial systems - Current and future planted crop area (by type - Practices of residue burning? of crop)  Irrigated rice - Specifications of water management practices Grassland and livestock module  Grassland - Current and future grassland area by state of - Practices of grassland burning? Only if project related degradation  Livestock - Type and number of livestock - Feeding and breeding practices Land degradation module  Forest degradation Only if project - Dynamic of forest degradation/ - Occurrence of forest fires? rehabilitation by forest type and size related  Degradation of organic soils (peatland) - Vegetation type and size concerned by - Area affected by peat extraction drainage of organic soils Input and investment module  Agricultural inputs Only if project - Weight of agricultural inputs by type  related Energy consumption - Quantity of electricity, liquid and gaseous fuel, and wood consumed  Building of infrastructure - Size of area with newly established irrigation infrastructure or buildings (by type) 32 C. Data collection methods and central data sources While the previous section provided the overview of the type of data needed for an EX-ACT appraisal, this section specifies which different methods may be used for data procurement and which main data sources may be accessed. Following from the basic structure of the initial situation at project start, project scenario, and baseline scenario, EX-ACT seeks to specify one set of input data based on project start information and two sets of input data based on projections of results (project and baseline scenario). The project scenario should follow the information given in the project document, while its assumptions should be evaluated by and be based on the available empiric information. The baseline scenario can be constructed either largely depending on static statistical information or a combination of expert judgments and modelling approaches. While most explicitly for the situation at project start, all three situations depend to some extent on input or benchmarking with empirical data. The following section thus presents central methods of data procurement and also presents important data sources. Secondary data sources National statistics and other statistical data may be an important data source for several of the data needs. As part of the Excel sheets EX-ACT already provides global soil maps that offer the user a first orientation on soil types that prevail in various locations and also provides a specific list how to convert soil classes from the international classification World Reference Base for Soil Resources (IUSS, ISRIC, FAO, 2006) to the IPCC soil taxonomy. For more location specific information on local soils, readers should instead use available national information or international datasets, such as the Harmonized World Soil Database (FAO, IIASA, ISS-CAS, JRC, 2009). Secondary data may furthermore be valuable whenever Tier 1 emission factors or assumed values of carbon content in soil and biomass can be replaced by representative, location specific values. This concerns manure management and enteric fermentation, the biomass growth rate of trees as well as the carbon sequestration rate of soil under specific management practices and the concentration of nutrients in organic and inorganic fertilizers. Table 5 is an overview of data sources that can be consulted for the procurement of secondary data. Primary data collection Some information may be difficult to procure by secondary data, such as information about specific agricultural management practices, livestock feeding practices, or wood extraction rates from forests. In order to evaluate current practices as well as the likelihood of future changes in management practices, it may be relevant to collect a limited number of primary data by conducting surveys on a sub-sample of the targeted farms. Besides questions of agricultural management practices, the consumption of fossil fuels and construction of irrigation systems and infrastructure can be accessed by surveys. In addition to farmer surveys, soil surveys may be conducted in a small sample to estimate soil organic carbon content. Remote sensing is an important supplementary data source that can provide land cover and land use data. It is furthermore an especially useful method when monitoring and evaluating projects. Expert judgments and stakeholder discussions Complementing the previous information the discussion with regional agricultural experts as well as project implementing staff can be an essential complementary data source to assume missing data which could not be procured by other methods. It is also an important mean to identify problematic assumptions that were made earlier on. All assumptions made by the EX-ACT user should be clearly stated, acknowledging the consulted institutions and allowing a maximum of transparency to readers. 33 Table 5: Central data sources for information on land use, land use change and agricultural practices Type of data Database – Data source Numerous agricultural and  National Offices of Statistics forestry data at national level  Ministries of Agriculture, Forestry, Rural Development, Environment Land Use:  FAOSTAT Land Use Database I. Arable land http://faostat.fao.org II. Forest land  FAO country profile III. Irrigated land http://www.fao.org/countries IV. Permanent crops  World Bank country profiles V. Rice http://data.worldbank.org/data-catalog/country-profiles  Global Land Cover Facility http://glcf.umiacs.umd.edu/data/  Global Land Cover Network http://www.glcn.org/dat_0_en.jsp Forests:  Forest Resources Assessment (FRA) VI. Deforestation rate http://www.fao.org/forestry/fra/fra2010/en VII. C content in  GlobAllomeTree different pools http://www.globallometree.org VIII. Forest land  U.S. Geological Survey – Land Cover Institute http://landcover.usgs.gov/globallandcover.php  Global Land Cover Facility http://glcf.umiacs.umd.edu/data/  Global Land Cover Network http://www.glcn.org/dat_0_en.jsp Soil and climate characteristics  Harmonized World Soil Database http://www.fao.org/nr/land/soils/harmonized-world-soil-database  CGIAR-CSI (CGIAR Consortium for Spatial Information) http://csi.cgiar.org/WhtIsCGIAR_CSI.asp Climate change:  UNFCCC submissions (GHG inventory, National communications, IX. GHG assessment NAMA, NAPA) X. CC vulnerability http://unfccc.int/national_reports/items/1408.php XI. Policies/strategies of adaptation/mitigation 34 Chapter 5: Getting Started – The Description Module A. Introduction to the graphical interface EX-ACT navigation bar The navigation bar allows users to move between the different topic modules. It provides the main overview about the topic and activity areas of relevance to EX-ACT. By clicking on the EX-ACT logo on the top left, users navigate directly to the EX-ACT homepage where they can find additional information. EX-ACT Screenshot 1: EX-ACT module bar EX-ACT colour codes Every EX-ACT module is subdivided into its different components using boxes. It is clearly delimited by an outside frame from other module components. EX-ACT uses a repeating color code throughout all modules. Thus cells in “light blue” indicate where users have to specify information, while the background colour, as here e.g. brown, specifies the variables and units that have to be provided as well as resulting changes in GHG emissions and carbon stock changes. By clicking on the orange boxes used throughout EX-ACT, users may find additional information that facilitates filling in the relevant module components. The violet boxes indicating “Tier 2” instead allow users to specify location specific values for carbon pools (e.g. soil organic carbon content) and GHG emission factors. EX-ACT Screenshot 2: EX-ACT colour codes 2.1. Deforestation Available   AEZ? 1.Subtropical humid forest - 2.Subtropical dry forest - 3.Subtropical steppe - 4.Subtropical mountains systems Type of vegetation HWP Fire Use Final use after deforestation Forested area (ha) Deforested area (ha) that will be deforested (tDM/ha) (y/n) Start Without * With * Without With Forest Zone 1 0 NO Annual Crop 1000 200 D 0 D 800 1000 Plantation Zone 2 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Forest Zone 2 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Plantation Zone 2 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Forest Zone 3 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Forest Zone 4 0 NO Select Use after deforestation 0 0 D 0 D 0 0 * Note concerning dynamics of change : D correspond to "Default", "I" to Immediate and "E" to Exponential (Ple Tier 2 Total Deforestation B. The description module Users can download the EXCEL file containing EX-ACT at www.fao.org/tc/exact/carbon-balance-tool-ex-act. After leaving the start screen, the first module users have to fill in is the description module. It has to be filled in with central descriptive information on the project and regional agro-ecological conditions. The user should start by filling the description module because the rest of EX-ACT otherwise does not contain the necessary input information to proceed. Users should fill in the following information: 35 EX-ACT Screenshot 3: The description module The EX‐Ante  Carbon‐balance Tool (EX‐ACT) ‐ Tier ONE Edit Land Use  Agriculture Grassland   La Start Description Annual, Perennial  Change and Rice Livestocks degra 1 Project Name GWP (choose values) Official-CDM 2 Continent Africa CO2 1 CH4 21 Climate Cool Temperate N2O 310 3 Climate? 6 Moisture regime Dry 4 Dominant Regional Soil Type LAC Soils Soil ? Duration of the Project (Years) Implementation phase 10 5 Capitalisation phase 10 Duration of accounting 20 1 Project name: Provide the name of the project, programme or policy. Location: Select the continental region in which the project will take place from the provided drop down 2 list, which will preselect a set of default values for the later emission calculations. In such a way e.g. emissions from dairy cattle vary according to the location and the IPCC coefficients allow for an averaged differentiation between them. 3 The eleven continental regions are: Africa / Asia (Continental) / Asia (Indian subcontinent) / Asia (Insular) / Middle East / Western Europe / Eastern Europe / Oceania / North America / Central America/ South America. Climate: The climate is strongly influencing GHG emissions and carbon sequestration in agriculture. A 4 careful choice of the correct climate information is thus essential. The default options are thereby: Boreal / Cool Temperate / Warm Temperate / Tropical / Tropical Montane (see also help facility below). The default options for the dominant moisture regime are: Dry / Wet / Moist. 5 Dominant soil type: The user should indicate the main dominant soil type using the simplified IPCC classification. IPCC retains only 6 soil categories: High Activity Clay Soils (HAC) / Low Activity Clay Soils (LAC) / Sandy Soils / Spodic Soils / Volcanic Soils / Wetland Soils. 6 Project duration: Users specify the duration of the active project intervention, which defines the implementation phase in EX-ACT, as well as the duration that further impacts from project interventions on GHG occur before a new equilibrium is reached. The latter defines the implementation phase of EX- ACT and is especially important when land use change activities implemented in a short time frame impact changes in SOC over a longer time period. The combined period of implementation and capitalization should not be shorter than 20 years when relevant land use change takes place (Lal, 2004). This is the minimum period during which the most important impacts on carbon stocks are expected to take place. 7 Help for soil selection: By clicking on Soils? this section provides guidance on which IPCC soil category to use as dominant soil type. This provides a global map that gives a first orientation of the distribution of IPCC soil categories. 36 Figure 12: Representation of IPCC climate zones (IPCC NGGI 2006) for use in selecting climate Users may insert the Mean Annual Temperature (MAT) in degree Celsius and the Mean Annual Precipitation (MAP) in millimetre, in order to receive a guiding climate and moisture indication by EX-ACT (c,f, Figure 13).4 Figure 13: The climate help tool Climate Helper : Help to determine the Climate category with MAT and MAP MAT 24 Tropical Moist MAP 1800 or Tropical Montane if elevation >1000m 4 This tool is based on the classification scheme for default climate regions proposed in Figure 3A.5.2 (page 3.39 of NGGI-IPCC-2006). 37 Figure 14: Tentative map of the distribution of the dominant soil type using IPCC classification As a further reference than the World Reference Base for Soil Resources (WRB) and the USDA soil taxonomy are essential, since secondary data on available soil types often follow their categorization system. In the following it is firstly provided an easy to use decision-tree while the help-facility in EX-ACT offers in addition a more detailed list of how to translate soil categories from the WRB and the USDA taxonomy into IPCC soil categories. 38 Figure 15: Decision tree for translating soils according to USDA taxonomy (left) and WRB (right) into IPCC soil classes (NGGI-IPCC, 2006) If not considering the concise and intuitive overview given by the decision tree, more detailed information on IPCC soils is thereby consciously described by the following:  High Activity Clay Soils (HAC). These mineral soils are light to moderately weathered soils which are dominated by 2:1 silicate clay minerals. Following the World Reference Base for Soil Resources (WRB), they include Leptosols, Vertisols, Kastanozems, Chernozems, Phaeozems, Luvisols, Alisols, Albeluvisols, Solonetz, Calcisols, Gypsisols, Umbrisols, Cambisols, and Regosols. In accordance with the USDA soil taxonomy, HAC soils include Mollisols, Vertisols, high-base status Alfisols, Aridisols, Inceptisols. As exception Ferric and Plinthic Luvisol are categorized as LAC Soils.  Low Activity Clay Soils (LAC). LAC soils are highly weathered soils, dominated by a composition of 1:1 clay minerals and amorphous iron and aluminium oxides. In accordance with WRB this includes Acrisols, Lixisols, Nitisols, Ferralsols, Durisols, while in the case of the USDA classification it comprises Ultisols, Oxisols, acidic Alfisols.  Sandy Soils include (regardless of their taxonomic classification) all soils having > 70% sand and < 8% clay, based on standard textural analyses. Following WRB this includes Aerosols, in accordance with the USDA classification it includes Psamments.  Spodic Soils are soils exhibiting strong podzolization. Following World Reference Base, this includes Podzols; in the USDA classification it comprises Spodosols.  Volcanic Soils are derived from volcanic ash with allophanic mineralogy. In accordance with the WRB classification they comprise Andosols, following the USDA taxonomy they comprise Andisols.  Wetland Soils are defined by restricted drainage leading to periodic flooding and anaerobic conditions. Wetland soils are Gleysols following WRS, and soils in aquic suborders in the USDA classification. 39 Chapter 6: Entering Land Use Changes The land use change module comprises the elements deforesteation, afforestation and reforestation as well as other land use changes. A. Deforestation When using the deforestation sub module, the following types of information will be needed: Identifying the current forest type: 1 Based on the climatic information provided in the Description Module users are provided with up to four different types of agro-ecological forest categories. 2 EX-ACT differentiates in addition between naturally grown forest and plantation forest. Users choose from the drop down list of eight different potential forest types. EX-ACT has six rows for the definition of different forest areas. The distinction in natural grown and plantation forest is justified by the fact that main characteristics (e.g. the growth rate of trees and respective biomass quantities) depend on the management regime. Therefore a distinction should be made between intensively (e.g., plantation forestry) and extensively (naturally re- growing stands with reduced or minimum human intervention) managed forests. For each default vegetation, five carbon pools are quantified according to the earlier presented generic methodologies. Harvest wood products (HWP) & fire use: The carbon stored in above ground biomass such as wood is in most cases released during deforestation. EX-ACT considers whether wood is logged and exported in order to be 3 used in manufacturing. Users can specify the amount of wood harvested per hectare. They also specify whether fire is used for land conversion. Identifying the final land use after deforestation: Users specify the final land use after conversion, which determines 4 default carbon stocks in the year following the conversion. Available options are: Annual Crop / Perennial or Tree Crop / Paddy Rice / Set Aside / Grassland / Degraded / Other. 5 Surface deforested: In this step users then specify the plot size of the forested area for each forest type. Information is entered on the forest size at project start, at the end of the project, and at the end of the baseline scenario. This concerns area covered by forest, while the area subject to deforestation is automatically displayed by EX-ACT (not visible on the excerpt of the below screenshot). Dynamic of change: In case of any changes in forest sizes (ha) between the start and either the outcome of the 6 project or baseline scenario, the dynamic of this land use change can be specified by the user either as linear (default), immediate, or exponential. Help for selecting the correct agro-ecological zone: The deforestation module provides a help facility for the selection of the appropriate agro-ecological zone, based on observed climate and vegetation patterns. This first 7 orientation often indicates the existing agro-ecological conditions of the area. However, users should validate this indication with the available information from project documents and national statistics as needed. EX-ACT Screenshot 4: Deforestation (Land use change module) 2.1. Deforestation Available   AEZ? 7 1 1.Tropical rain forest - 2.Tropical moist deciduous forest - 3.Tropical dry forest - 4.Tropical shrubland Type of vegetation HWP Fire Use Final use after deforestation Forested area (ha) that will be deforested (tDM/ha) (y/n) Start Without * With * Forest Zone 1 0 NO Annual Crop 5000 1000 D 4500 D Plantation Zone 2 2 0 3 NO Degraded 4 5 1000 500 D 900 D Select the vegetation 0 NO Select Use after deforestation 0 0 D 0 D Select the vegetation 0 NO Select Use after deforestation 0 0 6 D 0 D Forest Zone 3 0 NO Select Use after deforestation 0 0 D 0 D Forest Zone 4 0 NO Select Use after deforestation 0 0 D 0 D * Note concerning dynamics of change : D correspond to "Default" , "I" to Immediate 40 B. Afforestation and reforestation (A/R) In this part of the spreadsheet users specify any ongoing activities involving afforestation and reforestation. They describe the type of initial vegetation concerned, the type of forest that is planted, and specify whether fire is used for land conversion. Type of forest planted: Users specify here the type of forest that is planted by choosing from the drop-down 1 list. To identify which type of forest is meant by “plantation zone 1” and the other specifications, users again consult the top row (here marked with 1a ). User can thereby either select in the case of afforestation one of the present forestry systems (forest 1-4) or choose between plantation forests in the case of reforestation (plantation 1-4). Previous land use: Users indicate the previous land use prior to the new establishment of trees. The available 2 options from the drop-down list are: Annual Crop / Perennial & Tree Crop (<5yrs; 6-10 yrs; >10 yrs) / Paddy Rice / Set Aside / Grassland / Degraded Land. According to the selected land uses, EX-ACT proposes the default changes in carbon stocks per hectare. 3 Fire use: Users then specify whether fire is used as a means of land conversions. If it is set to “yes” the corresponding emission factors associated with the vegetation are used to estimate the emissions from the amount of concerned biomass. Surface afforested & reforested: In this field user identify how many hectares are subject to afforestation and 4 reforestation for the with- and without-project scenario. Dynamic of change: In the small violet boxes users can again specify whether the changes between the start 5 situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. EX-ACT Screenshot 5: Afforestation & reforestation 2.2. Afforestation and Reforestation Available AEZ 1.Subtropical humid forest - 2.Subtropical dry forest - 3.Subtropical steppe - 4.Subtropical mountains systems 1 Type of vegetation Fire Use Previous land use Area that will be afforested/reforested that will be planted (y/n) Without * With * Plantation Zone 1 NO Annual Crop 100 D 0 D Plantation Zone 2 1 NO 3 Grassland 2 4 0 D 200 D 5 Select the vegetation NO Select previous use 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D 41 Figure 16: Global ecological zones based on observed climate and vegetation patterns Global ecological zones, based on observed climate and vegetation patterns (FAO, 2001). Data for geographic information systems available at http://www.fao.org. Back to "Deforestation" Module C. Non forest land use change This component describes land use changes that do not concern forest, for instance the change from grassland to annual cropping systems or from degraded land to agro-forestry systems. Users specify the following information: 1 Naming the specific land use change: As a first step users may fill in an own title that describes properly which land use change is taking place. 2 Specifying initial land use: Then users specify from a drop-down list the initial land use. 3 Specifying final land use: Equivalently, the final land use has to be specified. The user may choose between Annual Crop / Perennial and Tree Crop (<5yrs; 6-10 yrs; >10 yrs) / Paddy Rice / Set Aside / Grassland / Degraded Land / and Other Land. 4 Fire use: Specification whether fire is used as means of land conversion. 5 Surface of area under land use change: Here, users specify the area sizes of each land use change activity taking place. 6 Dynamic of change in land use: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. 42 EX-ACT Screenshot 6: Other land use change 2.3. Other Land use changes Fill with you description Initial land use Final land use Message Fire use Area transformed (y/n) Without * With * Cereal expansion Grassland Annual Crop YES 4000 D 2000 D Mango tree plantation 1 Degraded Land 2 Perennial/Tree Crop 3 4 NO 0 D 2000 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 5 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D All final land uses that are expected to emerge as in this case a new annual crop area of 2,000 ha and a new perennial crop on as well 2,000 ha, will automatically appear in the module on crop production and do not have to be entered separately there. D. Results per component Based on the areas indicated, the vegetation characteristics and the information on conversion practices, EX- ACT provides the GHG-balance in CO2-e for each component of the Land Use Change Module. It is depicted here below for the deforestation activities. In this simplified example the net emissions from deforestation occurring as a result of the project lead to net reductions in carbon stocks and CO2 emissions accounting for 464,000 tonnes of CO2-e. Because this scenario contains strongly reduced deforestation as compared to the baseline scenario, the GHG-balance of the project scenario account for -3,122,000 tCO2-e of preserved stocks and prevented emission sources. EX-ACT Screenshot 7: Aggregated EX-ACT results for deforestation 2.1. Deforestation Deforested area (ha) Total Emissions (tCO2-eq) Balance Without With Without With 4000 500 3,372,549 421,569 -2,950,980 500 100 213,845 42,769 -171,076 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Deforestation 3,586,393 464,338 -3,122,056 E. Tier 2 specifications in the land use change module The central functionality of EX-ACT is to provide the user with default coefficients for carbon stocks of various carbon pools and emission factors of specific practices. Refining the analysis, EX-ACT allows more location specific carbon values and emission coefficients to be calculated. In the Land Use Change Module this mainly concerns the specification of: 1. Above ground biomass (t dm ha-1) 2. Below ground biomass (t dm ha-1) 3. Soil carbon content (t C ha-1) 43 4. Carbon stocks in litter and deadwood (t C ha-1) 5. Average growth rates for above and below ground biomass before and after the first 20 years after planting (t C ha-1) More specifically the screenshots below present the variables that may be specified with location specific values. EX-ACT Screenshot 8: Tier 2 values for deforestation You have indicated that your are using the following types of vegetationForest Zone 1 Back Plantation Zone 2 1 Use this part only if you want to refine analysis with Tier 2 coefficients All values are in t of carbon per ha (tC/ha) (default values are provided for your information, each time you fill a specific values, EX-ACT will use this value in calculation) Type of vegetation (that will be deforested) Above-ground Below-ground Litter Dead wood Soil C Default Tier 2 Default Tier 2 Default Tier 2 Default Tier 2 Default Tier 2 Forest - Zone 1 145.7 163.0 53.9 55.3 3.7 3.2 0.0 65.0 67.3 Forest - Zone 2 122.2 29.3 3.7 0.0 65.0 Forest - Zone 3 56.4 15.8 3.7 0.0 65.0 Forest - Zone 4 32.9 2 13.2 3 3.7 4 0.0 5 65.0 6 Plantation - Zone 1 70.5 26.1 3.7 0.0 65.0 Plantation - Zone 2 56.4 53.2 11.3 9.6 3.7 3.2 0.0 65.0 58.6 Plantation - Zone 3 28.2 7.9 3.7 0.0 65.0 Plantation - Zone 4 14.1 5.6 3.7 0.0 65.0 1 Reviewing relevant types of vegetation: In the first row EX-ACT presents the vegetation types that were earlier specified as relevant under the analyzed project. Since in this case only “forest 1” (Tropical rainforest) and “plantation zone 2” (tropical moist deciduous forest) are land uses that are altered under project or baseline scenario, we are only concerned with these two respective rows. 2 Above-ground biomass: Users specify the location specific value for above-ground biomass in tonnes of Carbon per hectare. 3 Below-ground biomass: Users specify here the location specific value for below-ground biomass in tonnes of Carbon per hectare. 4 Litter: Users may specify here the amount of carbon present in the respective forest from litter. Deadwood: Equivalently, users specify here the amount of carbon present in the respective forest from 5 deadwood. 6 Soil carbon: Lastly, users can define location specific values of carbon stored in soil. This is thereby one of the most important variables for Tier 2 specifications. The Tier 2 specifications for the Afforestation and Reforestation component follow the identical logic, but solicit data on growth rates of biomass in the newly established forest instead of data on existing biomass. 44 Case Study 2: Irrigation and Watershed Management in Madagascar Decreasing the rate of deforestation. The analyzed project is implemented on 35,000 hectares of tropical shrubland. In the past, the project area experienced high rates of deforestation, leading to expanding degraded land and destabilization of watershed. Without the project intervention, forested area is expected to decrease by 6,000 hectares through deforestation. With the project, forested area is expected to decrease by 4,000 hectares through deforestation. EX-ACT accounts for these activities in the Deforestation sub-module. 2.1. Deforestation Available  AEZ 1.Tropical rain forest - 2.Tropical moist deciduous forest - 3.Tropical dry forest - 4.Tropical shrubland Type of vegetation HWP Fire Use Final use after deforestation Forested area (ha) Deforested area (ha) that will be deforested (tDM/ha) (y/n) Start Without * With * Without With Forest Zone 4 0 NO Degraded 35000 29000 D 31000 D 6000 4000 Forest Zone 1 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Forest Zone 2 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Plantation Zone 2 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Forest Zone 3 0 NO Select Use after deforestation 0 0 D 0 D 0 0 Forest Zone 4 0 NO Select Use after deforestation 0 0 D 0 D 0 0 * Note concerning dynamics of change : D correspond to "Default", "I" to Immediate and "E" to Exponential (Ple Tier 2 Total Deforestation Increase in forested area. The project is expected to convert 2,250 hectares of degraded land into tropical moist deciduous forest which otherwise (without project) would remain degraded. This activity is entered in the Afforestation and Reforestation sub-module. 2.2. Afforestation and Reforestation Available  AEZ 1.Tropical rain forest - 2.Tropical moist deciduous forest - 3.Tropical dry forest - 4.Tropical shrubland Type of vegetation Fire Use Previous land use Area that will be afforested/reforested that will be planted (y/n) Without * With * Plantation Zone 2 NO Degraded Land 0 D 2250 D Plantation Zone 2 NO Grassland 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D Select the vegetation NO Select previous use 0 D 0 D * Note concerning dynamics of change : D correspond to "Default", "I" to Immediate and "E" to E Developing agro-forestry. 1,500 hectares of degraded land will be converted into coffee plantations. Under these conditions, the establishment of coffee bushes will lead to increased soil carbon stocks, improved fertility, and additional biomass in the form of coffee plants as well as other vegetation that capitalizes on the rehabilitated soils. This activity is entered in the Other Land Use Changes sub-module. 2.3. Other Land use changes Fill with you descriptionInitial land use Final land use Message Fire use Area transformed (y/n) Without * With * Coffee Degraded Land Perennial/Tree Crop NO 0 D 1500 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D Select Initial Land Use Select Final Land Use Fill initial LU NO 0 D 0 D 45 Chapter 7: Entering Crop Production A. Annual crops Land management practices are important determinants of soil carbon release and sequestration. EX-ACT allows users to differentiate between agricultural management practices of major importance for soil carbon dynamics. Annual crops in particular are generally characterized by more intensive forms of land preparation. The EX-ACT sub-module For the directly below following EX-ACT sub-module on annual crops it is thus essential to differentiate between the following improved practices:  Improved agronomic practices comprise all practices that may increase yields and thus generate higher quantities of crop residues. Examples of such practices reported by Smith et al. (2007) are the use of improved crop varieties, extending crop rotations, and rotations with legume crops.  Improved nutrient management includes the application of fertilizer, manure or biosolids in a way that improves either the efficiency (adjusting application rate, improving timing and location) or diminishes the potential losses (forms of fertilizer with slow release rate or nitrification inhibitors).  Improved tillage and residue management comprises the adoption of tillage practices of less intensity ranging from minimum tillage to no-tillage. It may include or not include mulching of crop residues and thus also comprises a key element of conservation agriculture.  Enhanced water management consists of enhanced irrigation measures that can lead to an increase in productivity and hence augment the quantity of residues. These practices have direct and indirect impacts on N20 and CH4 emissions, e.g. by increases in artificial inputs as well as organic fertilizer. These impacts are nevertheless already accounted for in the separate topic modules on inputs and livestock. EX-ACT is only concerned with the changes in soil carbon. The combination of various improved practices is not expected to lead to the addition of the sequestration potential of each individual measure. Because there is only limited scientific evidence on the sequestration potential of combined improvements in management practices, EX-ACT only considers the sequestration potential of the improved practice with the highest impact. This is a matter of caution intended to avoid the overestimation of soil carbon sequestration. As part of the Tier 2 specifications users may also specify their own management practices and associated mitigation potential (in t C ha-1 yr -1). EX-ACT Screenshot 9: Overview annual crops 3.1. Annual systems (to be used also for pluri-annual systems such as cotton or sugarcane) 3.1.1. Annual systems from other LUC or converted to other LUC (Please fill step 2.LUC previously) Description Improved agro- Nutrient NoTill./residues Water Manure Residue/Biomass Yield Area (ha) -nomic practices management management management application Burning (t/ha/yr) Start Without With Annual after Deforestation ? ? ? ? ? YES 0 4000 500 Converted to A/R ? ? ? ? ? NO 100 0 100 Annual after non-forest LU ? ? ? ? ? NO 0 4000 2000 Converted to OLUC ? ? ? ? ? NO 0 0 0 3.1.2. Annual systems remaining annual systems (total area must remains contant) Fill with you description Improved agro- Nutrient NoTill./residues Water Manure Residue/Biomass Yield Area (ha) -nomic practices management management management application Burning (t/ha/yr) Start Without * With * Conventional Maize ? ? ? ? ? YES 1500 1400 D 0 D Improved Maize Yes ? ? Yes ? NO 0 0 D 1200 D Conventional Sugar Cane ? ? ? ? ? NO 100 200 D 0 D Improved Sugar Cane Yes ? ? ? Yes NO 0 0 D 400 D description 5 ? ? ? ? ? NO 0 0 D 0 D description 6 ? ? ? ? ? NO 0 0 D 0 D description 7 ? ? ? ? ? NO 0 0 D 0 D description 8 ? ? ? ? ? NO 0 0 D 0 D description 9 ? ? ? ? ? NO 0 0 D 0 D description 10 ? ? ? ? ? NO 0 0 D 0 D Total 1600 1600 1600 46 The screenshot above shows the entire sub-module on annual crops. It is divided into two parts. In the section on top EX-ACT automatically inserted the three annual crop areas that are affected by land use change as specified in the Land Use Change Module in EX-ACT. In the lower part users enter additional areas continuously managed as annual crop areas. For simplicity we present in the following only the lower part of the table. The upper part is filled equivalently. Detail of screenshot 9 3.1.2. Annual systems remaining annual systems (total area must remains contant) Fill with you description Improved agro- Nutrient NoTill./residues Water Manure Residue/Biomass -nomic practices management management management application Burning Conventional Maize ? ? ? ? ? YES Improved Maize Yes ? ? Yes ? 3 NO 1 Conventional Sugar Cane ? ? ? ? ? NO 2 Improved Sugar Cane Yes ? ? ? Yes NO description 5 ? ? ? ? ? NO description 6 ? ? ? ? ? NO description 7 ? ? ? ? ? NO description 8 ? ? ? ? ? NO description 9 ? ? ? ? ? NO description 10 ? ? ? ? ? NO To fill the annual crop sub-module users need to specify: Naming the specific cropping system: Users begin by specifying the cropping system. In this case we analyse four 1 different cropping systems: At project start conventional cropping practices of maize and sugar cane dominate, while under the project improved management practices are introduced for both crops. 2 Specifying management practices: Under this point users specify which improved management practices are applied in the respective annual systems. Users choose from a drop-down list between Yes/No. A question mark, which is the default value, counts as if the improved practice will not be applied. 3 Fire use: In addition users specify whether crop residues are burned after harvest. A default amount of 10 t of dry matter per ha is thereby assumed as default value but may be replaced by a user specific value. Detail of screenshot 9: Fill with you description Yield Area (ha) (t/ha/yr) Start Without * With * Conventional Maize 1.5 1500 1400 D 0 D 4 5 Improved Maize 2.5 0 0 D 1200 D Conventional Sugar Cane 48 100 200 D 0 D Improved Sugar Cane 57 0 0 D 400 D description 5 0 0 D 6 0 D description 6 0 0 D 0 D description 7 0 0 D 0 D description 8 0 0 D 0 D description 9 0 0 D 0 D description 10 0 0 D 0 D Total 1600 1600 1600 47 Specifying yield (optional): Users may then specify also the yield level of their crop. Yields do not need to be 4 specified necessarily, but only if users intent to derive results also inform of the carbon-balance per unit of produce later on. In our example both improved management systems lead to yield increases. 5 Surface of annual crops: Users need to specify the different surface sizes of the various annual cropping systems for the three situations of project start, end of project and outcome of the baseline scenario. In the example, the project leads to a relative decrease in conventional cropping practices and introduces cropping systems under improved management practices. 6 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. B. Entering agro-forestry and perennial systems The sub-module on perennial crops is also structured with an upper section in which perennial crop areas subject to land use change are depicted. Surface sizes subject to land use change are again automatically filled by EX-ACT, based on the specifications made in the Land Use Change Module. Equivalent to the earlier description, only the lower part will be described in more detail below. The upper elements are filled using the same logic. EX-ACT Screenshot 10: Perennial crops 3.2. Perrenial systems (Agroforestry, Orchards, Tree crops…) 3.2.1. Perrenial systems from other LUC or converted to other LUC (Please fill step 2.LUC previously) Description Residue/Biomass Yield Area (ha) Burning (t/ha/yr) Start Without With Perennial after Deforestation NO 0 0 0 Converted to A/R NO 0 0 0 Perennial after non-forest LU NO 7 0 0 2,000 Converted to OLUC NO 0 0 0 3.2.2. Perennial systems remaining perrenial systems (total area must remains contant) Fill with you description Residue/Biomass Yield Area (ha) Burning (t/ya/yr) Start Without * With * Mango NO 7 100 100 D 300 D 1 2 3 4 5 Oilpalm NO 3.5 400 400 D 200 D Enter description of your system 3 NO 0 0 D 0 D Enter description of your system 4 NO 0 0 D 0 D Enter description of your system 5 NO 0 0 D 0 D Total 500 500 500 To fill the perennial crop sub-module users need to specify: Naming the specific cropping system: Users define the cropping system in order to avoid any misunderstandings 1 in data entry or later data modification. In this case we analyse the two different cropping systems of oil palm plantation and mango orchards. 2 Fire use: Users specify whether crop residues are burned after harvest. 48 Specifying yield (optional): Users may specify the relevant yield. Once more we point out that yields levels do 3 not necessarily need to be specified, but are only of use if later a more complex analysis of the carbon- balance per unit of produce is carried out. 4 Surface of perennial crops: Here again surface sizes of the crops need to be specified. In the displayed example at project start oil palm trees are the dominant perennial crop. Under the project, half of them are converted to mango orchards. The project does not therefore introduce any improved management practices on the plots that continue to be managed as oil palm plantation. 6 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. C. Entering flooded rice systems While all other annual crops are dealt with in the above described sub-module, the production of flooded rice systems, be it under irrigation or rainfed but deepwater conditions, has special implications for CH4 emissions and is thus dealt with separately in this sub-module. Rice that is grown without any extended flooding period, as e.g. upland rice, is still entered as annual crop in the respective sub-module above. The GHGs concerned by this sub-module are (i) methane emissions produced from anaerobic decomposition of organic matter and (ii) non-CO2 GHG emissions (CH4 and N2O) from biomass burning. CO2 emissions from biomass burning do not have to be considered since the carbon release during combustion is assumed to be reabsorbed by the vegetation during the next growing season. The N2O emissions from N-fertilizer are again accounted for in the “Inputs Module.” For emissions from flooded rice systems it is of central importance to differentiate between the various water management regimes during the pre-season as well as throughout the growing season. EX-ACT follows the central differentiation by the NGGI-IPCC (2006). It is roughly differentiated between those cultivation systems with a non-flooded preseason of either less or more than 180 days and those cultivation systems with a flooded preseason of at least 30 days or longer. Figure 17: Different water management regimes for flooded rice (c.f. NGGI-IPCC, 2006) Secondly, it is differentiated between three water regimes during the growing period:  Irrigated – Continuously flooded: Fields have standing water throughout the entire growing season and only dry out for harvest (end-season drainage). 49  Irrigated – Intermittently flooded: Fields have at least one aeration period of more than three days during the cropping period; no difference is made here for single or multiple aeration.  Rainfed, deep water: Fields are flooded for a significant period of time, while the water availability depends solely on precipitation. It includes the following subcases: (i) regular rainfed (the water level may rise up to 50 cm during the cropping season), (ii) drought prone (drought periods occur during every cropping season), and (iii) deep water rice (floodwater rises to more than 50 cm for a significant period of time during the cropping season). In the top half of the screen, the sub-module on flooded rice refers to rice areas that are impacted by land use change. In the bottom it refers to those areas that are constantly cultivated as flooded rice. Since both parts are filled in the equivalent way, only the areas constantly cultivated with flooded rice are exemplarily depicted here. EX-ACT Screenshot 11: Flooded rice (Detail 1) 3.3.2. Irrigated rice systems remaining irrigated rice systems (total area must remains contant) Fill with you description Cultivation Water Regime Organic Amendment type (Straw or other period (days) During the cultivation Period Before the cultivation period Conventional flooded 150 Irrigated - Continuously flooded Flooded preseason (>30 days) Compost Improved flooded 150 Irrigated - Intermittently flooded Non flooded preseason >180 days Compost Rice 3 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment Rice 4 1 2 150 3 Please select w ater regime 4 Please select preseason water regime 5 Please select type of Organic Amendment Rice 5 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment Rice 6 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment Rice 7 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment Rice 8 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment Rice 9 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment Rice 10 150 Please select w ater regime Please select preseason water regime Please select type of Organic Amendment To fill the flooded rice sub-module users need to specify: 1 Naming the specific cropping system: Users specify the cropping system in order to avoid any misunderstandings in data entry or later data modification. In this case we analyse one longer and one shorter flooded rice system. 2 Cultivation period: Users specify the length of the cultivation period. 3 Water regime during the cultivation period: Users then chose whether the rice field is continuously or intermittently flooded. As another option it may also be managed as a deepwater, rainfed system. 4 Water regime during the pre-season: Then, users specify from a drop-down list, whether the preseason is flooded, or not flooded and whether the non-flooded preseason is shorter or longer than 180 days. 5 Organic amendment: The next step specifies how crop residues are managed and utilized. The different options are: Straw burnt / straw exported / straw incorporated short or long before cultivation / compost / farmyard manure / green manure. 50 EX-ACT Screenshot 12: Flooded rice (Detail 2) Fill with you description Yield Area (ha) (t/ya/yr) Start Without * With * Conventional flooded 2.5 250 250 D 0 D 6 7 8 Improved flooded 2.3 0 0 D 250 D Rice 3 0 0 D 0 D Rice 4 0 0 D 0 D Rice 5 0 0 D 0 D Rice 6 0 0 D 0 D Rice 7 0 0 D 0 D Rice 8 0 0 D 0 D Rice 9 0 0 D 0 D Rice 10 0 0 D 0 D Total 250 250 250 6 Specifying yield (optional): Also in the flooded rice sub-module users can specify the yield, if later analysis per produced unit is intended. 7 Surface of flooded rice: The next step is to specify the surface sizes of the different rice management systems at project start as well as under project and baseline scenario. In the here displayed example the analyzed area shifts from a more intense flooding system to a less flood dominated system under the project scenario. 8 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. D. Tier 2 specifications in the annual crop module In the annual crop module users may replace a wide range of default coefficients with location specific values for carbon pools and growth rates as well as emission coefficients. This concerns the variables:  Rates of soil carbon sequestration (t C/ha/yr)  Quantity of residues and biomass available for burning as well as periodicity of burning practice (t of dry matter per ha  Above and below ground biomass growth rate (t C/ha/yr) In all three sub-modules user can specify Tier 2 values when clicking on the violet Tier 2 button: 51 Chapter 8: Entering Livestock and Grassland Management A. Grassland management Grasslands are an important stock of soil carbon and may become a source for emissions as e.g. through degradation or periodic burning. Like the crop production module, the grassland sub-module is divided into an upper section in which grassland that is subject to land use change is entered. In the lower section instead, areas that permanently stay grassland are considered. For the sake of briefness, we only show screenshots from the latter section, since it provides the full information on how to effectuate data entry. EX-ACT Screenshot 13: Grassland 4.1.2. Grassland systems remaining grassland systems (total area must remains contant) Description Initial state Final state of the grassland Fire use to manage Area Without project With project Without With (y/n) (year) (y/n) (year) (ha) Cattle grazing area Moderately Degraded Severely Degraded Improved w ith inputs improvement YES 5 NO 5 2,500 Less frequented area Non degraded Non degraded Non degraded NO 5 NO 5 1,000 Select state Select state Select state NO 5 NO 5 0 1 2 Select state Select state 3 Select state NO 5 4 NO 5 5 0 Select state Select state Select state NO 5 NO 5 0 Select state Select state Select state NO 5 NO 5 0 Select state Select state Select state NO 5 NO 5 0 Select state Select state Select state NO 5 NO 5 0 Select state Select state Select state NO 5 NO 5 0 Select state Select state Select state NO 5 NO 5 0 To fill the grassland sub-module users need to specify: Naming the specific cropping system: Users denominate the grassland area. In this case we analyse two different 1 types of grassland, one is frequently grazed, while another area is remotely located and only in limited ways frequented for grazing or any other activities. Initial state of degradation: In this step users specify the initial state of degradation of the grassland area by 2 choosing from a drop-down list between the options: Non-degraded / moderately degraded / severely degraded / improved without inputs / improved with inputs. Final state of degradation: Users specify the final state of degradation for the without- and with-project 3 scenarios, selecting again from the same options as previously. 4 Fire use: In the following step users specify whether and how frequent grassland is burned. In the presented example fire is used every five years on the area used for cattle grazing under the baseline scenario, while there are no burning practices anymore under the project scenario. 5 Surface size of grassland: Users specify the size of the grassland area. B. Livestock management The livestock sub-module was developed based on NGGI-IPCC (2006). For specific technical mitigation options not covered in NGGI-IPCC-2006, information was taken from the Fourth Assessment Report from working group III of the IPCC (Smith, et al., 2007). 52 GHGs covered by the livestock sub-module are (i) methane emissions from enteric fermentation, (ii) methane emissions from manure management, (iii) nitrous oxide emissions from manure management as well as, (iv) some additional technical mitigation options for methane emissions from livestock. EX-ACT Screenshot 14: Livestock management 4.2. Livestocks 4 Livestock categories Head number (mean per year) Technical mitigation option (%) Start Without With Feeding practices* Specific Agents* Breeding* project * project * Start Without With Start Without With Start Without With Dairy cattle 0 0 D 0 D 0% 0% 0% 0% 0% 0% 0% 0% 0% Other cattle 300 400 D 300 D 0% 0% 70% 0% 0% 0% 0% 0% 100% Buffalo 80 120 D 80 D 0% 0% 70% 0% 0% 0% 0% 0% 100% Sheep 1 0 2 0 3 D 0 D 0% 0% 0% 0% 0% 0% 0% 0% 0% Swine (Market) 0 0 D 0 D Feeding practices: e;g. more Specific agents: specific agents Breeding: Increasing concentrates, adding certain oils and dietary additives to reduces productivity through Swine (Breeding) 0 0 D 0 D or oilseeds to the diet, improving CH4 emisisons (Ionophores, breeding and better Please select 0 0 D 0 D pasture quality,… vaccines, bST…) management practices Please select 0 0 D 0 D (reduction in the number of replacement heifers) Please select 0 0 D 0 D To fill the livestock sub-module users need to specify: 1 Choosing the adequate animal categories: First users choose in which row they can find the relevant animal categories concerned by the project. Therefore they either use the already inserted animal types or choose from further types from a drop-down list. 2 Livestock numbers: In the second step users then specify livestock numbers at project start as well as at the end of the baseline and project scenario. 3 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. 4 Technical mitigation options: In this part of the analysis users specify which percentage of the livestock herds is subject to (i) improved feeding practices, (ii) application of specific agents, or (iii) improved breeding practices. C. Tier 2 specifications in the livestock sub-module The livestock sub-module allows for the specification of regional specific variables. The variables that can be defined are:  Mean annual temperature (MAT) of the region (in °C)  Emission of N2O from manure management (kg N-N2O/kg N)  Emissions of CH4 from manure management (kg CH4 per head/yr)  Enteric fermentation (kg CH4 per head/yr) 53 Chapter 9: Entering Land Degradation A. Entering forest degradation Currently there are no international recognized methodologies to assess forest degradation. The different available states of degradation within EX-ACT correspond to an average level of degradation, also expressed in terms of percentage of degraded area (see explanation here below). EX-ACT Screenshot 15: Forest degradation 5.1. Forest degradation Available Available  AEZzone Ecological 1.Tropical rain forest - 2.Tropical moist deciduous forest - 3.Tropical dry forest - 4.Tropical shrubland Type of vegetation Degradation level of the vegetation Fire occurrence and severity 4 Area (ha) that will be degraded Initial state At the end Without With Start without project with project (y/n) Periodicity (% burnt) (y/n) Periodicity (% burnt) (t/ha) Forest Zone 1 Very low Moderate None YES 1 25% NO 500 Forest Zone 1 1 Low 2 Large 3 Very low NO NO 5 1000 Select the vegetation Select level Select level Select level NO NO 0 Select the vegetation Select level Select level Select level NO NO 0 Select the vegetation Select level Select level Select level NO NO 0 Select the vegetation Select level Select level Select level NO NO 0 To fill the forest degradation sub-module users need to specify: Choosing the adequate forest type: First users choose the respective forest type. Equivalent to the deforestation 1 sub-module they choose from a drop-down list the relevant forest type either being a naturally grown forest or a plantation forest and consulting the different agro-ecological zones presented on top of the screenshot. 2 Initial state of degradation: In the second step users then specify the initial state of degradation and can choose from the following options in the drop-down list: None (0%) / very low (10%) / low (20%) / moderate (40%) / large(60%) / extreme (80%). In such a way 100 ha of low degraded forest is characterized by the same carbon stock as 80 ha of non-degraded forest when considering all five carbon pools. 3 Final state of degradation: Users specify the final degree of degradation for the baseline and project scenario. In the above example the project leads to a slight rehabilitation on both forest areas. 4 Fire occurrence and severity: Afterwards it is identified with which annual periodicity fire occurs (1 = annual occurrence, 5 = fire occurrence every five years) and how much of the forest is burnt in one of the incidences. 5 Forest size: In the last step users specify the size of the forest that is subject to such changes in their state of degradation. B. Entering organic soil drainage and peat extraction Two additional changes in land use that are of high specificity and only occur in limited contexts, though at the same time being of strong relevance for GHG emissions, are drainage of organic soils (as wetlands) and peat extraction. Peatlands are drained to gain fertile organic soils for agricultural production, while peat may be extracted for various purposes including as energy source, for horticulture production, for waste water treatment and further purposes. When peatlands are transformed into aerobic conditions the initiating decomposition of soil organic matter leads to CO2 and N2O emissions, while CH4 emissions decrease, leading to an overall effect of net emission growth. 54 EX-ACT thereby allows for calculating the impact of the drainage of organic soils on four types of land uses: Managed forest, annual crops, perennial crops and grassland. EX-ACT Screenshot 16: Drainage of organic soils and peatland extraction 5.2 degradation of organic soils (peatlands) 5.2.1. Drainage of organic soils (peatlands) Vegetation type where Superficies of drained organic soils drainage is occuring (ha) 1 Start Without * With * Managed Forest 0 150 D 0 D Annual 0 0 3 D 200 D Perennial 0 0 D 0 D Grassland 0 0 D 0 D 5.2.2 Active peat extraction Type of peat Superficies of drained organic soils (ha) 2 Start Without * With * Nutrient‐poor peat 100 0 D 0 D Nutrient‐rich peat 0 0 D 0 D To fill the peatland drainage and extraction sub-module users need to specify: Surfaces concerned by organic soil drainage: Users first specify the area sizes concerned by organic soil drainage. In 1 the example above the baseline scenario would lead to drainage of 150ha of managed forest, while the project scenario would establish effective drainage on 200 ha of existing annual crop area. 2 Surfaces concerned by peat extraction: Secondly users specify on which surface size peat extraction is taking place. They can thereby choose between the categories of more nutrient rich or more nutrient poor peat. Nutrient-poor peat bogs predominate in boreal regions, while in temperate regions, nutrient-rich fens and mires are more common. In the above example active peatland extraction takes place at project start on 100 ha, while both the baseline as well as the project scenario would lead to the discontinuation of any peatland extraction. 3 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. C. Tier 2 specifications in the land degradation module The variables that can be specified by location specific coefficients in the land degradation module are the following:  Degradation level (in % of biomass lost)  Specification of forest: • Above ground biomass (t dm ha-1) • Below ground biomass (t dm ha-1) 55 • Soil carbon content (t C ha-1) • Carbon stocks in litter and deadwood (t C ha-1)  Emissions factor for loss of C associated with drainage of organic soils (t C/ha/yr)  On-site CO2 and N2O emissions from active peat extraction (t C/ha/yr, kg N2O-N/ha/yr) Case Study 3: Russia Forestry Fire Response Project Fire is a major naturally-occurring disturbance in Russian ecosystems. The large number of forests that are situated in regions with limited amounts of precipitation and/or frequent and extended periods of drought are particularly prone to severe fire events. The objective of the Forestry Emergency Response Project is to improve forest fire prevention through sustainable forest management. Using the Forest Degradation sub-module, assumptions about the rate of forest degradation and its link to fire occurrence are entered as follows. Initially, forest fires reduced on average 20 percent of forest carbon stocks annually, with a mean annual burnt forest area of 4,886,000 hectares during the last 13 years. In the with-project scenario, fire is projected to occur every five years rather than each year. This is equivalent to an 80 percent decrease in the area affected by fire annually. The total forest area accounts for 24,430,000 hectares. Use of tier 2 data. Average above-ground biomass is set to 36 tons carbon per hectare (Sohngen 2005). Soil carbon content is set to 64.8 tons carbon per hectare (Moiseev 2003). 56 Chapter 10: Entering Inputs and Investments A. Entering inputs The methodological background used to develop this sub module can be found in NGGI-IPCC (2006) and was complemented with Lal (2004) for embodied GHG emissions associated with the use of agricultural chemicals in farm operations. GHGs generated by the inputs are (i) carbon dioxide emissions from lime application, (ii) carbon dioxide emissions from urea application, (iii) nitrous oxide emissions from N application on managed soils (except for manure management aspects that were treated in the livestock sub-module) and also (iv) emissions (in CO2 equivalent) from production, transportation storage and transfer of agricultural chemicals. EX-ACT Screenshot 17: Agricultural inputs 6.1 Inputs (liming, fertilizers, pesticides, herbicides,…) Description and unit to report Amount applied per year Lime application Start Without * With * 1 2 Limestone (tonnes per year) 1,000 1,000 D 1,000 D Dolomite tonnes per year) 0 0 D 0 D not-specified (tonnes per year) 0 0 D 0 D Fertilizers Urea (tonnes of N per year - Urea has 46.7% of N) 0 10,000 D 50,000 D Other N-fertilizers (tonnes of N per year) 0 0 D 30,000 D N-fertilizer in irrigated rice (tonnes of N per year) 0 0 D 0 D Sewage (tonnes of N per year) 0 0 D 0 D Compost (tonnes of N per year) 0 0 D 0 D Phosphorus (tonnes of P2O5 per year) 0 0 D 0 D Potassium (tonnes of K2O per year) 0 0 D 0 D Pesticides Herbicides (tonnes of active ingredient per year) 100 100 D 0 D Insecticides (tonnes of active ingredient per year) 100 0 D 0 D Fungicides (tonnes of active ingredient per year) 0 0 D 0 D To fill the sub-module on agricultural inputs users need to specify: Quantities of agricultural inputs: Annual quantities for the various agro-chemical products used as well as 1 organic fertilizers have to be specified for project start, the baseline scenario as well as the project scenario. Specifications have thereby to be made in quantities of active components, as e.g. in tonnes of nitrogen. In the example, the application of limestone stays unchanged throughout all three project scenarios, while the implementation of the project leads to strong increases in the use of urea as well as organic N-fertilizer. 2 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. B. Entering energy consumption Material used to develop this module came from diverse sources according to the specificity of the sector covered: Energy related emissions can be found in NGGI-IPCC (2006), in the “Bilan Carbone” used by AFD and from the International Energy Agency. Default values associated with the installations of irrigation systems are from Lal (2004). 57 GHGs covered here are (i) GHG emissions associated with electricity consumption, (ii) GHG emissions associated with fuel consumption, (iii) GHG emissions associated with installation of irrigation systems and (iv) GHG emissions associated with building infrastructure. EX-ACT Screenshot 18: Fossil energy consumption 6.2 Energy consumption (electricity, fuel,…) Description and unit to report Quantity consumed per year Start Without With Electricity (MWh per year) * * Angola 1 2 1000 1000 D 5000 D 3 3 Liquide or gaseous (in m per year) Gasoil/Diesel 100 100 D 500 D Gasoline 50 50 D 100 D Gas (LPG/ natural) 0 0 D 0 D Butane 0 0 D 0 D Propane 0 0 D 0 D Ethanol 100 250 D 400 D Solid (in tonnes of dry matter per year) Wood 1000 1000 D 5000 D Peat 0 0 D 0 D To fill the sub-module on fossil energy consumption users need to specify: Country of electricity production: First users have to specify in which country the consumed electricity was 1 produced, since this determines to which extend it is related to GHG emissions. Thereby it has not necessarily to be the country of project implementation. 2 Consumed quantities of fossil energy: Then, users have to specify the quantity of annual consumed fossil energy for each energy source. In the presented example, the project scenario leads to strong increases in energy consumption, as especially of gasoil and ethanol. 3 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. C. Entering infrastructure construction Agricultural development projects may also lead to additional construction of irrigation systems, roads and buildings that cause GHG emissions during their construction. The sub-module on infrastructure allows accounting also for these emissions. 58 EX-ACT Screenshot 19: Infrastructure construction 6.3 Construction of new infrastructure for the project (irrigation systems, buildings, roads) Description and unit to report Surface concerned Without With Irrigation systems (total in ha) * * Permanent sprinkle 0 D 2000 D 1 Please select 0 D 0 D 2 3 4 Buildings and roads (total in m ) Agricultural Buildings (concrete) 0 D 500 D Road for medium trafic (concrete) 2 0 D 2500 D Please select 0 D 0 D Please select 0 D 0 D Please select 0 D 0 D Please select 0 D 0 D Please select 0 D 0 D To fill the sub-module on fossil energy consumption users need to specify: Type of irrigation system installed: Users can choose from a drop-down list the type of irrigation system that is 1 installed under the project. Available options are: Surface without or with IRSS / solid set sprinkler / permanent sprinkler / hand moved sprinkler / solid roll sprinkler / centre-pivot sprinkler. In the given example the project is responsible for the installation of an irrigation system using permanent sprinklers. 2 Type of buildings and roads constructed: Users specify the type of buildings and roads that were constructed under the project. As an example, it is assumed here that the project constructs agricultural buildings from concrete and medium sized roads from concrete. 3 Infrastructure size: In the next step users specify how many surfaces are newly broad under irrigation or is covered by roads and buildings. In the example at hand, the project leads to increases concerning all three elements. 4 Dynamic of change: In the small violet boxes users can again specify whether the changes between the start situation and the without- and with-project situation materialize linearly (default) over time, occur immediately or exponentially. D. How to account for energy produced by the project (biogas, biofuel, etc.) In case that a project leads to the production of renewable energy as e.g. in the case of biogas plants, there are three possible options of how this energy is consumed and how to account for it:  The produced energy is consumed by the project and is replacing alternative sources of energy (fuel consumption or electricity consumption)  The energy is added in the form of electricity into the national electricity grid, where it substitutes the equivalent of electricity (produced with specific emissions based on the national energy mix)  The produced energy is used outside the project, substituting a specific type of energy (e.g. fuelwood in a remote area) 59 In the first case, the contribution of the project activity to reducing GHGs can be accounted by reducing the actual and/or assumed consumption of the relevant fossil fuels by the project in the EX-ACT sub-module on energy consumption. In the second case the project leads to an effective substitution of electricity by the renewable energy produced as part of the project. In case that the project scenario has no own electricity demand that could be minimized in the EX-ACT module on energy consumption, the substituted quantity of electricity may be inserted as energy consumed in the without-project scenario. In the third case, we also can assume a hypothetical consumption of equivalent energy in the without-project scenario, while the specific energy type should be taken into account (e.g. fuelwood). E. Tier 2 in the energy and investments sub-module Also in the energy and investments sub-module users may replace selected variables with location specific values by clicking on the violet “Tier 2” button. The variables that may be specified as part of the energy and investment module are:  CO2 and N20 emission factors from direct applications and indirect emissions (various units)  Emission factors by type of energy source  Emission factors by type of construction works (t CO2/m2) 60 Part C: Making Effective Use of EX-ACT Results Chapter 11: Analysis of Results Part B showed users how to procure data and enter it into the tool. Part C shows how to first interpret and then utilize the results provided as the carbon-balance in tonnes of CO2 equivalents. A. Checking the land use table One important output from EX-ACT is the evolution of land use, comparing the initial situation, the baseline scenario and the project scenario. It allows a concise overview of the information entered in EX-ACT in all topic modules at a central place. It is thus a central functionality that allows users to verify the correctness of all entered data, be it concerning the size of specific land uses as well as the consideration of equal total surface sizes under all three points in time. The latter element is evidently a basic precondition of the correct application of EX-ACT since the tool otherwise compares the emission potential from different sized territories. The screenshot below displays this first concise overview. Thus it is visible that at all three points in time EX-ACT calculates with a total area of 20200 ha and thus does not engage in any major accounting mistake. Besides it is visible that under all scenarios the total forest area and total grassland area diminishes, but that the project scenario reduces the pace of this land use change pattern. EX-ACT Screenshot 20: Land use evolution in EX-ACT Surfaces evolutions by land use / category (hectares - ha) State at the beginning Without Project With Project Forest/Plantation 7500 3100 7100 Annual 1700 9600 4200 Cropland Perennial 500 500 2500 Rice 250 250 250 Grassland 7700 3700 5500 Other Land Degraded 2000 2500 100 Other 0 0 0 Organic soils 550 550 550 Total area = 20200 20200 20200 By clicking on the button for the detailed matrices of land use change, the same information is once more displayed in more detail: Instead of only showing the total land use at different points in time, it specifies precisely which type of land use remains the same and which type of land use is transformed into a specific other one. EX-ACT Screenshot 21: Detailed land use matrices Matrix of changes without project Mineral soils (ha) FINAL Forest/ Cropland Grassland Other Land Plantation Annual Perennial Rice Degraded Other Total Initial Forest/Plantation 3000 4000 0 0 0 500 0 7500 Annual 100 1600 0 0 0 0 0 1700 Cropland Perennial 0 0 500 0 0 0 0 500 Rice 0 0 0 250 0 0 0 250 INITIAL Grassland 0 4000 0 0 3700 0 0 7700 Degraded 0 0 0 0 0 2000 0 2000 Other Land Other 0 0 0 0 0 0 0 0 Total Final 3100 9600 500 250 3700 2500 0 19650 62 Matrix of changes with project Mineral soils (ha) FINAL Forest/ Cropland Grassland Other Land Plantation Annual Perennial Rice Degraded Other Total Initial Forest/Plantation 6900 500 0 0 0 100 0 7500 Annual 0 1700 0 0 0 0 0 1700 Cropland Perennial 0 0 500 0 0 0 0 500 Rice 0 0 0 250 0 0 0 250 INITIAL Grassland 200 2000 0 0 5500 0 0 7700 Degraded 0 0 2000 0 0 0 0 2000 Other Land Other 0 0 0 0 0 0 0 0 Total Final 7100 4200 2500 250 5500 100 0 19650 The screenshot above provides one matrix each for the without- and with-project scenario. They specify on the middle diagonal all those land uses that do not experience any land use change from project start until the end of the project or baseline scenario. Reading the table in rows, provides the reader with the entire initial land use: In the upper “Without Project” table there have been thus initially 7,500 ha of forest, from which 4,000 ha have been converted into annual cropland and 500 ha into degraded land by the end of the baseline scenario. Reading the table along columns provides the reader instead with the sum of the final land uses: E.g. in the lower table showing the situation “With Project” there are 2,500 ha of perennial cropland at the end of the project scenario, whereby 2,000 ha of these stem from rehabilitated degraded land. In such a way EX-ACT users may once more control the inserted information on land utilization and land use change at a central place and control total surface sizes for each land use. B. Analysing non-aggregated module results Before presenting the main carbon-balance, users will have already recognized that in each EX-ACT module non-aggregated results are directly displayed on the right. EX-ACT Screenshot 22: Sub-aggregated results annual crop sub-module 3.1.2. Annual systems remaining annual systems (total area must remains contant) Fill with you description Area (ha) Total Emissions (tCO2-eq) Balance 1 2 Start Without * With * Without With Conventional Maize 1500 1400 D 0 D 22,266 4,704 -17,562 Improved Maize 0 0 D 1200 D 0 -25,650 -25,650 In such a way the screenshot above presents an excerpt of the sub-aggregated results for the annual crop module, for which data entry was described earlier. Summarizing the project scenario induced a shift from conventional cropping practices in maize to improved practices with benefits for productivity as well as GHG emissions. Total net emissions: The first results indicate the net emissions from project start until the end of the baseline 1 and project scenario. In this way the cultivation of conventional maize over the full time period in the baseline scenario and its linear phasing out in the project scenario lead to accumulated net emissions of 22,266 tCO2-e or 4,704 tCO2-e respectively. The improved maize cultivation instead that is linearly increased under the project scenario to reach 1,200 ha at the end of the implementation phase is itself a sink of carbon and leads to accumulated carbon sequestration of -25,650 tCO2-e. 2 Carbon-balance: The second type of results provided by EX-ACT is the carbon-balance that is given by the difference between the net emissions of project and baseline scenario. In such a way the phasing out of conventional maize production as opposed to its continuation under the baseline scenario leads to a net carbon-balance over the whole period of -17,552 tCO2-e. 63 Both net emissions and carbon-balance may be summed up within and across the single modules and thus lead to overall results for the project and baseline scenario. C. Analysing total results: Gross result and carbon-balance In the following, the overall results of applying the EX-ACT tool and thus the most central output of the analysis will be presented. While this provides a complete overview of the entire results, the following two screenshots show the table for convenience reasons once more separated in bigger size. EX-ACT Screenshot 23: EX-ACT Gross Results and Carbon-balance Component of Gross fluxes Share per GHG of the Balance Results per year the project Without With Balance Result per GHG without with Balance All GHG in tCO2eq CO2 N2O CH4 Positive = source / negative = sink Biomass Soil Other Land Use Changes CO2-BiomassCO2-Soil CO2-Other N2O CH4 Deforestation 3,740,693 481,117 ‐3,259,576 ‐2,873,750 ‐385,826 0 0 187,035 24,056 ‐162,979 Afforestation ‐61,922 ‐59,994 1,928 ‐7,367 9,295 0 0 ‐3,096 ‐3,000 96 Other 398,762 ‐51,877 ‐450,640 ‐22,293 ‐425,425 ‐1,677 ‐1,244 19,938 ‐2,594 ‐22,532 Agriculture Annual 55,507 ‐27,852 ‐83,359 0 ‐37,260 ‐12,760 ‐33,340 2,775 ‐1,393 ‐4,168 Perennial ‐7,000 ‐304,467 ‐297,467 ‐276,467 ‐21,000 0 0 ‐350 ‐15,223 ‐14,873 Rice 44,898 17,973 ‐26,925 0 0 0 ‐26,925 2,245 899 ‐1,346 Grassland & Livestocks Grassland 121,601 ‐113,685 ‐235,286 0 ‐229,873 ‐3,108 ‐2,306 6,080 ‐5,684 ‐11,764 Livestock 12,563 9,699 ‐2,864 ‐1,034 ‐1,830 628 485 ‐143 Degradation 499,722 103,011 ‐396,711 ‐521,698 146,218 ‐6,427 ‐14,803 24,986 5,151 ‐19,836 Inputs & Investments 162,352 664,934 502,581 287,139 248,443 8,118 33,247 25,129 Total 4,967,178 718,860 -4,248,318 ‐3,701,575 ‐943,871 287,139 223,437 ‐80,447 248,359 35,943 ‐212,416 Per hectare 246 36 ‐210 ‐169.0 ‐46.7 11.1 ‐4.0 0.0 Per hectare per year 12.3 1.8 ‐10.5 ‐8.5 ‐2.3 0.6 ‐0.2 0.0 12.3 1.8 ‐10.5 Detail of Screenshot 23: Component of Gross fluxes the project Without With Balance All GHG in tCO2eq Positive = source / negative = sink Land Use Changes 3 Deforestation 3,740,693 481,117 ‐3,259,576 Afforestation ‐61,922 ‐59,994 1,928 Other 398,762 ‐51,877 ‐450,640 Agriculture Annual 55,507 ‐27,852 ‐83,359 Perennial ‐7,000 ‐304,467 ‐297,467 Rice 44,898 17,973 ‐26,925 Grassland & Livestocks Grassland 121,601 ‐113,685 ‐235,286 Livestock 12,563 9,699 ‐2,864 Degradation 499,722 103,011 ‐396,711 Inputs & Investments 162,352 664,934 502,581 1 2 Total 4,967,178 718,860 -4,248,318 Per hectare 246 36 ‐210 Per hectare per year 12.3 1.8 ‐10.5 64 The EX-ACT results may be interpreted in the following way: Overall gross results: Users may first of all see the overall gross emissions and sequestration results of the 1 without- (left) and with-project scenario (right). The indications are made in tonnes of CO2 equivalents as total over the entire period of analysis, but also per hectare and per hectare and year. In the here chosen example the without-project scenario leads to combined effects from GHG emissions and carbon sequestration that add up to 4,967,178 tCO2-e. This translates into 246 tCO2-e per hectare over the full analysis duration or into 12.3 tCO2-e per hectare and year. The hypothetical project scenario has a considerably lower impact on GHG emissions and carbon sequestration leading only to a total impact of 718,860 tCO2-e. 2 Overall carbon-balance: Comparing the gross results between the without- and with-project scenario gives the difference achieved through project implementation, which is also called the project’s carbon-balance. It accounts for a total of -4,248,318 tCO2-e of avoided emissions or increased carbon sequestration over the full analysis duration of 20 years. This is equivalent to a combination of -210 tCO2-e per hectare over the full duration or -10.5 tCO2-e per hectare annually. 3 Gross results and carbon-balance by module: The three columns in the middle allow the sub-differentiating of gross results and carbon-balance by module. This is an essential functionality to identify those practices and activities that are the strongest sources of emissions or most important sinks leading to carbon sequestration. Regarding the gross results of the with-project scenario, the central components leading to reduced emissions or carbon sequestrations are the establishment of perennial crop land (-304,467 tCO2-e) and the rehabilitation of degraded grassland (-113,685 tCO2-e). The leading causes of carbon losses and GHG emissions are instead the use of fertilizers and other inputs (664,934 tCO2-e) as well as the ongoing deforestation (481,117 tCO2-e). Considering gross losses from the with-project scenario is thereby strongly different than considering the carbon-balance, as the difference between both scenarios. The strongest element contributing to the positive carbon-balance of the with-project scenario is thereby the reduction in pace of deforestation (- 3,259,576 tCO2-e), which is alone responsible for more than 75% of the projects carbon-balance. The following most important activities contributing to a positive carbon-balance of the project are the non- forest land use change activities (-450,640 tCO2-e), and the rehabilitation of degraded land (-396,711 tCO2- e). Going in further detail the EX-ACT results section also offers other information. 65 Detail of Screenshot 23: Component of Share per GHG of the Balance Results per year the project 4 Result per GHG without with Balance CO2 N2O CH4 5 Biomass Soil Other Land Use Changes CO2-BiomassCO2-Soil CO2-Other N2O CH4 Deforestation ‐2,873,750 ‐385,826 0 0 187,035 24,056 ‐162,979 Afforestation ‐7,367 9,295 0 0 ‐3,096 ‐3,000 96 Other ‐22,293 ‐425,425 ‐1,677 ‐1,244 19,938 ‐2,594 ‐22,532 Agriculture Annual 0 ‐37,260 ‐12,760 ‐33,340 2,775 ‐1,393 ‐4,168 Perennial ‐276,467 ‐21,000 0 0 ‐350 ‐15,223 ‐14,873 Rice 0 0 0 ‐26,925 2,245 899 ‐1,346 Grassland & Livestocks Grassland 0 ‐229,873 ‐3,108 ‐2,306 6,080 ‐5,684 ‐11,764 Livestock ‐1,034 ‐1,830 628 485 ‐143 Degradation ‐521,698 146,218 ‐6,427 ‐14,803 24,986 5,151 ‐19,836 Inputs & Investments 287,139 248,443 8,118 33,247 25,129 Total ‐3,701,575 ‐943,871 287,139 223,437 ‐80,447 248,359 35,943 ‐212,416 Per hectare ‐169.0 ‐46.7 11.1 ‐4.0 0.0 Per hectare per year ‐8.5 ‐2.3 0.6 ‐0.2 0.0 12.3 1.8 ‐10.5 4 Carbon-balance per GHG: The carbon-balance may nevertheless not only be sub-differentiated by activity, but also by type of GHG and carbon pool. Block 4 provides the non-aggregated emissions from methane, nitrous oxide and shows as well the impact of carbon sequestration in soil and biomass. It is thus visible, that the strongest factor constituting that avoided deforestation effectively leads to a positive carbon- balance stems from the conservation of carbon stocks in biomass that account in our example for - 2,873,750 tCO2-e. 5 Annual results per module: The sub-differentiated results that were already presented under point 3 are here once more expressed in form of their annual impact. 66 EX-ACT Screenshot 24: Results graphs 1 2 3 4 The graphical results are once more presenting: Gross results of without- and with project scenario 1 2 Carbon-balance by module 3 Gross results per scenario and overall carbon-balance 4 Carbon-balance per GHG D. Emission intensity per product unit and further indicators Development interventions most often follow multiple objectives and lead to changes in emissions per hectare in addition to changes in productivity levels. An intervention may increase emissions per hectare by decreasing emissions per product unit. To detect the project’s impact on the carbon footprint per produce from agricultural production, EX-ACT needs to be filled for one target product only. This procedure is described in detail in the annex on product carbon footprints, which also may include a complete life cycle assessment of GHG emissions. EX-ACT also provides a first rough indication as part of the full project analysis that may be assessed by clicking on the value chain button in the results section: After having inserted all relevant information as indicated earlier in this user guide, EX- ACT automatically provides the aggregated carbon footprint per product type (annual crop, perennial crop, rice, grassland, livestock). Thereby they exclude emissions from artificial inputs, such as fertilizer, which only can be taken into account in the more detailed methodology specified in the annex. Because of the two characteristics of excluding emissions from inputs and aggregating along different crops within the same product category, this 67 facility can only be used as a first orientation and a detailed analysis has to follow the requirements specified in the annex. The screenshot below provides an overview of the product carbon footprint of three crop and livestock products covered by an exemplary project. EX-ACT Screenshot 25: Product carbon footprint from agricultural production (except inputs) Name of the project trial project Climate Tropical (Moist) D Continent Africa Soil HAC Soils Tot Component of Gross fluxes Production Gross emission Intensity the project Without With Balance t of product tCO2eq per t of product All GHG in tCO2eq Without With Without With All Land Use Changes 0 0 0 1 62,720 -70,000 -132,720 2 200,000 350,000 3 0.314 -0.200 Annual Perennial -28,000 -28,000 0 240,000 440,000 -0.117 -0.064 Rice 0 0 0 0 0 0.000 0.000 Grassland 0 0 0 0 0 0.000 0.000 Livestock 68,425 49,514 -18,911 36,000 36,000 1.901 1.375 Degradation 0 0 0 Inputs & Investments 0 0 0 The screenshot above is based upon a project that has each one annual crop, one perennial culture and one livestock product. Within the project rational it is proposed:  Annual crop: To shift from conventional to improved maize cultivation that leads to soil carbon sequestration and higher productivity.  Perennial crop: To shift from conventional to improved mango production that is characterized by the same amount of emissions, but higher productivity.  Livestock product: To shift to improved breeding and feeding practices leading to less emissions, while maintaining the same productivity level of cattle meat. Focusing in more detail e.g. on the results for conventional and improved maize production, the carbon footprint table provides: 1 Gross results and carbon-balance. First the results from the annual crop module are once more provided. In our case we see that the conventional maize practices, that included residue burning, were a net source of GHG emissions. Instead the improved maize, that is making use of no tillage and mulching, leads to carbon sequestration. 2 Total production level. Following the yield specifications in the annual crop module, yield levels can be increased strongly as part of the intervention. The additional effects from increases in fertilizer use are thereby not accounted here. For this analysis please follow the specifications in the value chain section in the annex. 3 Emission intensity per product unit. The third column then provides the product carbon footprint from the agricultural production stage. Instead of emitting 314 kg of CO2-e per tonne of maize under the conventional production system, the improved maize leads to sequestration of 200 kg for each tonne of maize. Since also the productivity had increased, the sequestration per product unit is thereby relatively low. These results give an important evidence of the direction of changes concerning the carbon footprints of products under the different scenarios. In case the estimation of product carbon footprints is at the center of the user’s interest, other methods should nevertheless be consulted and a refined analysis should be carried out as specified in the annex. 68 Case Study 4: Banana Carbon Footprint from Production to Retail This example illustrates the use of EX-ACT to estimate a product carbon footprint along a variable part of the value chain as explained in detail in the annex. The example uses production data from a banana farm that is in the process of converting from conventional to organic production, and analyses emissions from the production stage to the supermarket shelf. The introduction of the organic production system leads to a slight decrease in productivity while at the same time requiring less fertilizer and fewer pesticides - including the additional impact of their aerial application. Using data provided by the producer as well as coefficients found in existing literature, emissions per unit are given for each stage of the product's life. The production stage itself contributes relatively little to the overall carbon footprint of 0.99 tons CO2 in conventional production, and 0.86 tons CO2 in organic production per 1 ton of bananas. While packaging and transport procedures are about the same for conventional and organic bananas, emissions from production substantially decreased, from 110 kilograms CO2 per ton of conventionally grown banana to -10 kilograms per ton of organically grown banana. The analysis reveals that the combined effect of slightly lower productivity and substantially lower use of inputs leads to an overall effect of a reduced carbon footprint. E. Additional indicators Besides the on GHG emissions focused results, EX-ACT also summarizes how much of the total area is irrigated and how much of the total area is managed with burning of vegetation or crop residues. 69 EX-ACT Screenshot 26: Further indicators – irrigation and fire use Other Indicators Area Irrigated - ha State at the beginning Without Project With Project Irrigated Rice 250 250 250 Annual crop 0 0 1200 Total 250 250 1450 Cumulated areas burnt - ha State at the beginning Without Project With Project From deforestation 0 0 From degradation 10000 0 Plantation 0 0 Other LUC 4000 2000 Annual 1770000 300000 Perennial 0 0 Irrigated Rice 0 0 Grassland 10000 0 Total 1794000 302000 Integrating all previously presented results of the EX-Ante Carbon-balance Tool thus provides the essential means to compare different project scenarios with each other for their impact on climate change mitigation. Questions on how to utilize the presented EX-ACT results for further analysis will be analyzed in the following. F. Comparing project options or impact scenarios: Simulation work Within project identification and formulation processes, there are steps in which project designers may need to compare project options in terms of their differential impact. EX-ACT allows quantifying the differential impact of alternative project components on the carbon-balance. Figure 18: Using EX-ACT to compare different scenarios and refine project documents Project Design  EX‐ACT Results  EX‐ACT appraisal  Feedback  Once the initial EX-ACT appraisal is effectuated, the tool provides a very cost and time effective possibility to estimate ex-ante estimates for technical planning as well as for discussions with donors and decision makers. Some of the many typical emerging issues as part of project design are e.g. given by the questions:  What is the differential impact of targeting province A, versus province B?  What is the impact of specific land use changes: E.g. has it a negative carbon-balance to establish perennial crops on non-degraded grassland, or leads the expansion in annual cropland always to a negative carbon-balance?  What is the impact of specific management changes: What is the per product carbon-balance of considerable increases in fertilization when it leads to strongly higher yields? 70  What happens if we upscale a specific project component: Consider e.g. the project presented in the box below. While watershed conservation measures are only a small part of the project’s initial design an alternative scenario with stronger watershed conservation measures is analyzed for its impact on the carbon-balance.  What is the impact of an added agro-forestry component as part of a project aiming at the rehabilitation of degraded mountainous territory? The example below describes a comprehensive simulation scenario with direct effects both on budget costs and the carbon-balance. Case Study 5: Irrigation and Watershed Project (Madagascar) - Watershed Scenarios Funding limitations led planners to consider to downsize the original watershed project proposal to accomodate a budget of US$4.58 million. Two scenarios were entered into EX-ACT to identify the differential impacts of the original and downsized projects. In the original project concept, watershed conservation activities would be implemented on 65,000 hectares, including: 15,000 hectares afforestation, 6,000 hectares avoided deforestation, 34,000 hectares improved pasture, and establishment of 10,000 hectares agroforestry. The downsized project would entail activities on just 8,250 hectares. The differential costs of the two project scales are estimated at US$1,500 per hectare for reforested area, $300 per hectare for avoided deforestation, $400 per hectare for improved pasture, and $10,000 for agroforestry. The additional watershed components in the original project are therefore estimated to require $47.9 million. The total original project budget is 103 percent higher at $83 million. The effect of doubling the budget and allocating the incremental funds to watershed management activities increases the project's greenhouse gas emissions impact by a factor of six. The downsized project saves 2.4 million tonnes of CO2 equivalent compared to 12.4 million tonnes saved by the original project - over the 20 year life of both. While the previous example focused on the simulation of alternative project components that can actively be chosen by project designers, simulation is also an important element when high degrees of uncertainty are associated to central project assumptions: In such a way the case study below assumes differential adoption rates of improved agronomic practices by smallholder farmers in the context of a food security project. The intervention relies on changes in agronomic practices as an important project component. It is therefore a good practice to be cautious about assuming too high adoption rates, given the empirical evidence for barriers to adoption of e.g. sustainable land management practices. In addition it should be tested whether a project still has a positive impact when assuming a pessimistic scenario, which is one way of testing the validity of the project in mitigation terms. 71 Case Study 6: Accelerated Food Security Project in Tanzania (AFSP) – Adoption Scenarios This project aims to contribute to raise food productivity and increase production levels mainly by improving farmers' access to critical agricultural inputs. It is part of an overall strategy to prevent potential food crises that may result from fluctuating food and input prices. It targets 2.5 million farmers, focusing on 1 million hectares of maize growing area and 86,000 hectares of rice. The first activity involves a voucher scheme by which to provide packages of inputs to maize and rice farmers, including urea, phosphorous, and nitrogen fertilizers and high quality seeds. The project also promotes the adoption of improved practices, including extension to encourage crop rotations and precision application of fertilizers, as well as discouraging farmers from burning crop residues. The combination of improved nitrogen efficiency and reduced burning are projected to have positive effects on the carbon balance. Yet adoption rates cannot be projected with certainty, and EX-ACT was used to make alternative projections for alternative adoption rates. The more optimistic projection assumed that 73 percent of farmers would apply the entire package of improved practices and technologies. A less optimistic scenario assumed that 73 percent of farmers would apply some elements of the package. While both scenarios lead to an improved carbon balance, the more optimistic one had a mitigation effect three times larger. G. Using EX-ACT to support monitoring schemes EX-ACT as described in this manual may most directly be used for ex-ante appraisals or ex-post evaluations of projects, programs and policies. It may also inform the design of monitoring schemes. Climate Smart Agriculture projects implicitly integrate longer-term and larger-scale processes. They also involve a greater number of potential tradeoffs. Unlike many projects where monitoring and evaluation address areas, beneficiaries and stakeholders within the projects’ boundaries for a shorter subsequent period, CSA projects are more likely to require longer-term post-project monitoring of trends and additional comparison areas (FAO, 2013). Some expected outcomes and impacts may not be evaluated at the time of project monitoring and evaluation. This is particularly true for monitoring and evaluation of mitigation benefits. Increases in soil carbon content in response to improved practices cannot continue indefinitely. Eventually, soil carbon storage will approach a new equilibrium where carbon gains equal carbon losses. A default time period, usually 20 years, is assumed for this transition. On the other hand achieved soil carbon levels may also again be subject to reductions at a later point in time (see below). 72 Projects and other interventions that engage in regular data collection of their key variables thereby for cost reasons often make use of proxy indicators, such as:  Soil carbon content measurement: the chosen proxy is the effective application of improved Climate Smart Agriculture techniques in hectares by type.  Methane emission measurement: the chosen proxy is the evolution of the number of heads of livestock and there feeding and breeding practices.  Fuel consumption: the chosen proxy is a rough estimate of the type of vehicles with their annual distance achieved (kilometres).  Agro-forestry development: instead of assessing total number and characteristics of all trees planted, tree density and tree types are assessed and extrapolated from a limited set of areas. The issue of leakages and permanency is important for the monitoring and evaluation of climate change mitigation. Permanency refers to the principle that emission reductions, represented by an offset, should be maintained over time. In some cases, abandoning a CSA practice after only a few years will counterbalance the emissions previously avoided. A post-project monitoring and evaluation is useful to ensure that the improved practices are maintained. Such a post-project phase is also needed in case of payments for carbon services. Leakage refers to a situation where emissions abatement achieved in one location is offset by increased emissions in unobserved locations. In this regard, the difficulty lies in the choice of appropriate boundaries to conduct the appraisal and might imply that selected monitoring in neighboring non-target areas is necessary. Since the main set of data used in EX-ACT relates to the distribution of land uses and practices at project start the collection of adequate data concerning the starting situation is crucial for any monitoring scheme that wants to engage in comparisons to the situation throughout project implementation. A focus on incentives and effective adoption rates Since most Climate Smart Agriculture projects face the issue of barriers to adoption, projects need to be scrutinized in regards to the way they manage incentives and are able to monitor adoption rates. Adoption rates of improved techniques (% of areas with a specific improved technique) are monitored through household surveys and/or field visits. They should focus equally on monitoring positive practices (e.g. sustainable management practices) as well as from a mitigation perspective negative activities (e.g. residue burning). Essential practices thereby also include agro-forestry techniques, improved pasture management and the rehabilitation of degraded forestlands. A meso-analysis of energy and inputs consumed In term of input and energy use, consumption per household or per farm is monitored using representative samples (tonnes of fertilizer, gasoline, pesticides, MW of electricity per year). Such data can be cross-checked e.g. with surveys from local merchants of agricultural inputs. Summarizing, in such a way EX-ACT may be used to identify which data needs to be collected as part of a monitoring scheme. In case an ex-ante appraisal of EX-ACT has been carried out the main emission sources and sinks may especially be targeted by such data collection. The monitoring of changes in natural capital – as discussed in the annex – is thereby an important complementary activity that can be carried out with strong synergies. 73 Chapter 12: Using the Carbon-Balance as Part of Economic Analyses and Fund Mobilisation The previous chapters focused on the question of the estimation of probable technical mitigation potentials of a given project, policy or program to inform intervention choices and design. In many contexts the pure technical mitigation potential in tonnes of CO2 equivalents is not sufficient as decision criteria, but needs to be translated into a monetary value as part of an economic analysis. This serves the purposes of:  Estimating the economic value of a specific amount of GHGs mitigated, defined by the prevented economic costs to society through climate change impacts  Comparing the benefits from mitigated GHGs and the costs spend in form of public project funds, as e.g. in public policy planning or cost-benefit analyses of projects  Comparing the benefits from mitigated GHGs and the financial incentives needed for farmers to engage in the related practices, as e.g. in the voluntary carbon market and in schemes of Payments for Environmental Services (PES) In this chapter, relevant contexts for the economic analysis are introduced and a typology of projects is provided to clarify their differing main objectives. The project type thereby largely determines in which type of economic analysis project designers are interested and whether additional efforts in a specific economic analysis are indicated and useful. Subsequently, different elements of economic analyses are presented conceptually and illustrated with case studies and also the options of using a carbon-balance appraisal and its economic analysis in fund mobilization are illustrated. A. Main concepts for the economic analysis of mitigation benefits This paragraph will shortly introduce central concepts used as part of the economic analysis of mitigation benefits. It focuses on the definition and differences of public goods, positive externalities, co-benefits and public values. Public Goods: Many environmental services have in fact the characteristic of public goods as given by their characteristic that people cannot be excluded from the provided benefits, while in addition the use of the service by one person does not diminish the availability of that service to other users. Many environmental services, ranging from flood control to climate change mitigation, are characterized by this non-rivalry in consumption and non-excludability from benefits. Carbon sequestration and reduced GHG emissions have thereby the specificity that the boundary of these characteristics are global, making them a pure international public good. One tonne of carbon emissions mitigated e.g. in Scotland have thus the same benefits for any global citizen as the same amount of GHGs mitigated in any other location (if we for the moment only focus on the climate change benefits from carbon sequestration and not on its other benefits as e.g. soil fertility). Value of carbon sequestration. There is no agreed single social monetary value of carbon sequestration and GHG emission reductions. This is much due to the fact that climate change impact studies are associated to great uncertainties. Mitigation of climate change can thereby be considered a transfer of wealth from the present to future generations. Furthermore the carbon value is neither static over time and GHGs mitigated today or in the near future are worth more than those in the distant future (Bateman, et al., 2003). Two distinct options of deriving prices are thereby (i) carbon markets and (ii) social costs of carbon:  Carbon markets are not per se related in their pricing mechanism to the future costs induced by further emissions, but instead introduce and increase prices of emissions through regulatory approaches that set maximum emission levels and allow for trade of emission rights between users. Other segments of the carbon market also simply provide monetary compensations for reduced emissions without necessarily involving a third party that increases its emission rights. 74 Thereby public and voluntary carbon markets did not experience in the close past strong any intervention measures that would have been sufficient to overcome the current crises of oversupplied emission rights and low costs of emission permits. Nevertheless past, current and intended price levels on carbon markets may be used for simulation work and scenario building within EX-ACT and constitute a central point of departure.  The “social cost of carbon” (SCC) is an approach derived from studies trying to estimate the future costs of climate change impacts generated through today’s GHG emissions. In other words it is the estimated monetary damages associated with an incremental increase in carbon emissions in a given year. This includes besides others changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of decreased ecosystem services. While there are various studies that established strongly different values, whereby the problem of high uncertainty levels is greatly accepted, one widely used study has been carried out by the U.S. Interagency Working Group on Social Cost of Carbon that established a value of 21 USD per tonne of CO2-e (Interagency Working Group on Social Cost of Carbon, 2010). It increases over time to $24 in 2015 and $26 in 2020. An externality is a cost or benefit resulting from an economic transaction that is borne or received by parties not directly involved in the transaction. An externality occurs when the consumption or production of a good impacts on people other than the producers or consumers that are participating in the market for that good (Hebling, 2012). Externalities can be either negative (e.g. water pollution caused by industrial production) or positive (e.g. the role of agriculture in maintaining the countryside and rural communities). In this perspective an agricultural development project which is not targeted to reduce greenhouse gas emissions and which however allows such GHG reduction is generation such impacts as a positive externality with no additional cost. Co-benefits refer to the occurrence of multiple benefits that concern distinct dimensions resulting from one project, programme or policy. Co-benefits occur in interventions that are implemented for various reasons. Thus they are mainly differing from positive externalities by the fact that they are actively targeted at the same time. The most policies designed to address greenhouse gas mitigation also have other, often at least equally important, rationales (e.g. related to objectives of economic development, sustainability, or social outcomes). The term co-impact is thereby used in a more generic sense to cover both the positive and negative side of the benefits. Farmers can become important suppliers of climate change mitigation services, which may be to a different degree associated with such co-benefits or trade-offs with agricultural production (FAO, 2007). Because the agricultural sectors of developing countries have undergone years of declining investment and neglect, mitigation finance can be an important potential source for the needed investments in efficient, productive and sustainable production systems that are often out of reach due to high initial costs and only later occurring benefits (Bernoux, et al., 2010). B. Typology of projects to be appraised Making use of the earlier introduced terminology it is necessary to distinguish between different types of investment projects. As a main difference, benefits arising from a positive carbon-balance may be regarded just as project externality, while on the other side of the extreme it is a central and explicit project target from the start. In this perspective, we distinguish three types of projects:  Type 1: Development projects  Type 2: Multi-objective projects  Type 3: Mitigation projects The main goal of Type 1 projects (Development projects) is the enhancement of food security through agricultural productivity increases and improvement of the net returns to agricultural production. These projects are formulated without specific mitigation targets as their main objective is to support a broader notion of agricultural development. In this project framework any positive impact on climate change mitigation is only considered as a positive externality. 75 Table 3: Project typology from a climate change mitigation perspective Case studies Country Project type Geographic area Test Accelerated Food Security Tanzania Type 1 Africa Desk Project National Agricultural Program Eritrea Type 2 Africa Field Irrigation and Watershed Madagascar Type 1 Africa Desk Management The Santa Catarina Rural Brazil Type 1 Latin America Field project The Rio Rural project Brazil Type 1 Latin America Field Grassland Restoration and China Type 3 Asia Field Conservation Type 2 projects (Multi-objective projects) are designed with explicit multiple objectives, as in the case of many integrated rural development projects. Typical examples are projects that are aiming at socio-economic and environmental objectives, as projects promoting productivity increases and enhanced soil organic carbon levels at the same time. This might be the case of projects that target the rehabilitation of degraded land. In this case, mitigation should be considered as a co-benefit. If policy decisions in the future lead to a stronger integration of mitigation objectives into sector development plans, the importance of such multipurpose frameworks will strongly increase. Type 3 projects (Mitigation projects) are those where mitigation is the primary objective. They are most closely linked to carbon markets or specific mitigation project funds. The following graph illustrates the difference between the three project types, relating them to their cost structure and total mitigation potential. Figure 19: Financing options for agriculture development and mitigation projects Source: adapted from (FAO, 2009). 76 C. Basic elements of economic analyses: NPV and IRR Independently from the conceptualization of the carbon-balance as positive externality, co-benefit or main objective, the calculation of the Net Present Value (NPV) and Internal Rate of Return (IRR) may serve as main points of orientation for valuing the benefits from mitigation. Based on the projects closeness to carbon markets, one should thereby decide whether to adopt a unit price of emissions stemming from carbon markets (simulating different prices beyond the current low levels may thereby be essential) or from estimations of the social costs of emissions (as introduced earlier in this document). The calculation of NPV and IRR is not included in the standard worksheets of EX-ACT and should be managed separately making use of EX-ACT results. Thereby project appraisals usually assume a constant price per tonne of CO2-e as well as a constant discount rate to compute the NPV of mitigated emissions. Although certain project appraisal methodologies propose lower discount rates for environmental project components (3-5%), in the case of carbon markets it is recommended to use the same discount rate as in the rest of the economic project analysis. The carbon-balance results may then be multiplied by the NPV per tonne of CO2-e to derive the total NPV of the mitigation benefits. The case study below provides an example of such calculations, assuming a progressive evolution of emission prices on the voluntary market. Case Study 7: Economic Analysis of the Carbon-balance - Irrigation and Watershed Management Programme in Madagascar The considered irrigation and watershed management program supports improved water and residue management and includes sustainable intensification measures to be undertaken on croplands. It also contributes to diversification to capitalize on the respective competitive advantages of irrigated and rainfed systems, and includes reduced deforestation and afforestation activities. EX-ACT estimates that an overall net carbon balance of nearly 2.4 million tonnes CO2 equivalent is generated through these project activities. Economic Analysis at Different Carbon Prices Carbon Total public Project Net Internal price value Present Rate of constant million US$ Value Return US$/ton million US$ without price incentive 0 0 9.1 14.7% with carbon price at 2 4.7 10.5 15.3% with carbon price at 5 11.9 12.7 16.2% with carbon price at 3.5 8.3 11.6 15.8% carbon price increasing from 2 to 20 38.7 17.8 17.7% between 2010 and 2020 carbon price increasing from from 2 to 10 20.4 13.9 16.5% 2010 to 2020 The calculation of NPV and IRR gives simulations for different carbon price scenarios in addition to non- carbon related economic benefits. According to the analysis, NPV and IRR fluctuate considerably with changes in carbon price. Yet because the largest economic value that stems from the program stems from other activities, the actual changes are relatively small. Nevertheless, the example clearly demonstrates how environmental benefits can be integrated into economic analysis. D. From the carbon-balance to climate finance and public funding Available finance is a key element for available mitigation options to materialize. While the sections above introduced the main differentiation of project and policy funded versus market funded initiatives, the section at hand provides some practical examples of how the carbon-balance may be used as part of both efforts to attract and justify funding negotiations. Monitoring, Reporting and Verification (MRV) systems vary structurally with the associated different funding sources and take thereby also an important part of the policy planning process. In other words the cost per 77 hectare for MRV of carbon mitigation projects may vary strongly by MRV requirements, which should be considered actively as an overall cost element when carbon prices are estimated that shall sufficiently incentivize changes in agricultural practices. MRV costs are currently particularly high for carbon market schemes in agriculture and access to carbon markets remains thus complex for agriculture projects. Designing and developing a carbon project takes a long time, requires a lot of technical expertise and considerable financial resources for the initial set‐up. Currently well performing projects funded over the carbon market can thereby be characterized by four main similarities: (i) a clearly defined geographic delimitation, (ii) an aggregator that groups the various beneficiaries within an organizational structure and provides a functioning channel for providing incentives and carrying out MRV in a cost effective manner, (iii) a clearly quantified carbon reduction target based on a GHG calculator and (iv) access to clearly defined carbon funds. The case study below presents the Santa Catarina project, who’s carbon-balance appraisal has been used in funding and financing negotiations. Case Study 8: BRAZIL - The Carbon-balance of the Rural Competitiveness Project (Santa Catarina) Project objectives and components. The project seeks to increase the competitiveness of small scale rural family agriculture and the producer organizations they belong to in the Brazilian state of Santa Caterina. The approximately 3.6 million hectares covered represents 37 percent of the state's total area. The project provides financial capital, technical assistance, and incentives for technological innovation, diversification, and increased productivity. The project will also reinforce the provision of public services, including decentralized management of water and other environmental resources, as well as sanitation and legal services. By promoting and scaling up the adoption of SLWM practices, the project will also contribute to climate change mitigation. The project activities with the most important positive impacts on the carbon balance are as follows. Expansion of perennial crops and agroforestry, promotion of improved grasslands, annual crop management, rehabilitation of areas of permanent preservation, and conservation of legal reserves, protection of existing forests, and forest regeneration and rehabilitation. Overall, the project is estimated to sequester 15 MtCO2e, while emitting just 2 MtCO2e annually over its duration. The average mitigation impact per hectare accounts for 1 MtCO2e annually. 60 percent of its mitigation potential stems from the expansion of agroforestry and 25 percent from improved grassland management. These mitigation outcomes are co-benefits that occur in addition to the primary objectives of the project that related to intensification and income. The carbon sequestration which the project brings about can be estimated in terms of its carbon balance, which can be priced, valued, and incorporated into the project's economic analysis. That analysis will need to examine how the discounted measures of project worth such as NPV and IRR will change when carbon benefits are accounted for. This may very well make the project eligible for carbon finance. 78 Existing funding mechanisms have started to move towards a more integrated view of adaptation and mitigation. Accordingly, funding eligibility criteria are changing to more readily accommodate combinations of adaptation and mitigation financing. The increasingly cross-cutting perspective also extends to the combination of climate change with other related areas such as forest management, biodiversity or land degradation. This shift better reflects the reality of integrated policy planning as e.g. represented by the approach of Climate Smart Agriculture. Regarding the overall availability of resources, the Green Climate Fund (GCF) can be identified as a relevant upcoming initiative, which was created with the expectation to disburse US$100 billion annually by the year 2020. The GEF approach towards combining adaptation and mitigation activities also actively promotes the CSA approach. This significant shift has implications well beyond the GEF itself, as it serves arguably as the most important source of examples and experiences for the design of upcoming initiatives (FAO, 2013). National policy planning nevertheless is expected to stay an essential element to provide incentives for environmental and climate smart practices. As an example the Plan Maroc Vert follows an integrated approach, also providing public incentives for CSA. In its river basin management activities, the strategy takes account of the increasing challenges posed by water scarcity in the agricultural sector of Morocco. With its dual approach it provides in irrigated areas incentives for improving water management and conservation measures that allow for the further integrating and expansion of national value chains within international markets. In rain-fed areas, the Plan Maroc Vert increases access to social services and supports participatory natural resource management initiatives. It thereby focuses stronger on replacing arable crops with more drought tolerant olive trees and other tree crops. A more specific description is given in the box below (c.f (Sutter, 2012)). Case Study 9: Use of EX-ACT in National Policy Analysis: The Plan Maroc Vert EX-ACT was used to assess the scope for climate smart agriculture in the context of the Plan Maroc Vert, which is the main national agricultural strategy in Morocco. Launched in 2008, the Plan Maroc Vert seeks to double agricultural value added within a decade through an overall transformation of the sector. Climate change is seen as a serious challenge to this objective owing to its likely effects on crop yields and volatility generally. 85 percent of agricultural land in Morocco is not irrigated, leaving farmers exposed to erratic rainfall and frequent drought. A series of pilot activities were used for the appraisal exercise to compare options (i) planting rainfed and irrigated olive orchards by converting cereals systems on two different project sites of 8,000 and 1,600 hectares, (ii) improvements to cereals systems through improved varieties and conservation agriculture on 1,000 hectares. The results of the carbon appraisal show that the adoption of activities that focus on climate change adaptation could mitigate 63MtCO2e over 20 years. Most of this mitigation potential is linked to sustainable land management practices and annual crop systems. The pilot analysis provides an example of how EX-ACT can be used as a litmus test to determine whether to actively target carbon finance. The exercise led to a dialogue about carbon finance in agriculture, and the Ministry of Agriculture is planning an analysis of knowledge and capacity building needs to access carbon funds. The Ministry is also discussing the need for a national monitoring system for GHG emissions with the World Bank. 79 E. Using the carbon-balance to assess potential Payments for Environmental Services Payments for Environmental Services (PES) are regularized payments for clearly defined environmental services that are not provided under pure market conditions due to limited incentives. They most often focus on services that provide direct benefits to the general public. PES are thereby an environmental policy tool that is becoming increasingly important in developing and developed countries that addresses environmental problems through positive incentives to land managers. As poverty is a major cause of environmental degradation, rewarding poor producers to adopt more environmentally friendly systems of production, would result in both environmental benefits and poverty reduction. Such existing PES initiatives with strong co-benefits for GHG mitigation and located in developing countries thereby focus often on:  Restoring natural habitat or afforestation  Maintaining existing natural habitats and protecting them from alteration (forest, grassland conservation)  Improving existing land use (soil conservation, efficient inputs use, etc.) Case Study 10: From the Carbon-balance per farmer and hectare to a pre-assessment of the potentials of Payments for Environmental Services (PES) Assuming net payments of US$3.5 per tonne of CO2 equivalent, the actual annual payments to smallholder farmers under the Madagascar Irrigation and Watershed Project are very low - at about $14 annually. Aggregated to the village level however, these payments amount to $1,400 - equivalent to the wage income of three full time permanent village workers, or to a team of 12 workers over a three month period. At the watershed level, this can provide a regular stream of funds for a variety of environmental services, such as activities to control and reduce deforestation and afforestation. For every watershed considered, the carbon rent could fund the equivalent of 40,000 to 45,000 days of public work, employing 200 workers for 20 days per month for seven to eight months a year in each watershed. This illustrates the potential of payment schemes for environmental services to generate employment in the area of climate change mitigation. Targeting vulnerable people with these employment opportunities is a promising direction for PES. Landscapes and watersheds as well as value chains appear as relevant levels for aggregating producers in order to effectively include them in payment schemes, as opposed to systems that work directly with individual landowners. This seeks to overcome some of the existing challenges to the implementation of PES schemes, as mainly the high transaction costs, difficulties in ensuring conditionality and limited inclusiveness leading to 80 inequitable distribution of benefits. In this perspective, some simulations of PES scenarios have been added in selected cases to the usual EX-ACT appraisal, mainly focusing on watershed support programmes (e.g. Madagascar, Uganda Agriculture Technology and Agribusiness Advisory Services Project) and rural development projects with strong environmental components (e.g. Brazil Santa Catarina Project, Rio De Janeiro Sustainable Development Project) or even in national policy simulations (e.g. Nigeria Vision 2020 – Assessing Low-Carbon Development in Nigeria). F. Using a Marginal Abatement cost Curve (MACC) to compare low carbon options A marginal abatement cost curve provides a relation between the cost-effectiveness of different abatement options and their total GHG abatement potential. In such a way the figure below shows on the horizontal axis the total mitigation potential of various agricultural practices and on the vertical axis the associated costs per tonnes of CO2-e. Figure 20: Exemplary Marginal Abatement Cost curve of different policy options in the agricultural sector in Nigeria (Cervigni, et al., 2013, p. 46) Using this method various institutions analyzed the global GHG abatement cost curves for different sectors, including agriculture, as e.g. (McKinsey&Company, 2009). MACC was also used as part of various EX-ACT analyses. The basics of the Marginal Abatement Cost Curve (MACC) methodology is presented in annex 4. Further information may besides be found in the guidelines “Using Marginal Abatement Cost Curves to Realize the Economic Appraisal of Climate Smart Agriculture Policy Options” (Bockel, et al., 2012). 81 Annex ANNEX Contents Abstract .............................................................................................................................................................. 1  Acknowledgments ............................................................................................................................................. 2  Part A: Introduction and Rationale ................................................................................................................. 5  ............................................................................................................................................................................ 5  Chapter 1: Introduction ..................................................................................................................................... 2  A.  Manual structure .................................................................................................................................. 2  Part A (chapters 1 to 3) ................................................................................................................................. 2  Part B (Chapters 4 to 10) ............................................................................................................................... 2  Part C (Chapters 11 and 12) .......................................................................................................................... 3  Annexes ...................................................................................................................................................... 3  B.  Key concepts and terminology ............................................................................................................. 3  Carbon-balance or GHG-balance ..................................................................................................................... 3  Baseline scenario or without-project scenario ........................................................................................................ 3  Global Warming Potential (GWP) ................................................................................................................... 3  IPCC Tier levels (cf. (IPCC, 2006)) ............................................................................................................... 3  EX-ACT version 5 ..................................................................................................................................... 4  C.  Targeted users ..................................................................................................................................... 4  D.  Why targeting GHG mitigation in AFOLU investment planning?........................................................ 4  GHG Mitigation as a Necessary Component of Investment Planning ........................................................ 4  Agriculture, Forestry and Other Land Use: A Priority Area for Climate Change Mitigation ........................ 4  E.  Main emission sources and mitigation potentials of AFOLU ............................................................... 6  Chapter 2: Overview of EX-ACT ..................................................................................................................... 9  A.  The added-value of a carbon-balance appraisal .................................................................................... 9  B.  Selecting the right carbon-balance accounting tool ............................................................................... 9  C.  What is EX-ACT? Main structure and outputs................................................................................... 11  Basic contents of EX-ACT: Module structure .......................................................................................... 11  Basic contents of EX-ACT: Scenario building .......................................................................................... 14  Basic contents of EX-ACT: Main output ................................................................................................. 16  Chapter 3: Methodological Background ....................................................................................................... 17  D.  Dynamics of change in EX-ACT ....................................................................................................... 19  E.  Defining project boundaries .............................................................................................................. 19  F.  Ex-ante versus ex-post ....................................................................................................................... 20  G.  The baseline scenario ......................................................................................................................... 20  Choosing the most adapted baseline approach ......................................................................................... 22  Process guidance for baseline development .............................................................................................. 24  Part B: Step-by-Step Guide to Use EX-ACT ................................................................................................. 27  1 Annex .......................................................................................................................................................................... 27  Chapter 4: Data Requirements and Data Collection .................................................................................... 28  A.  Choosing the valid EX-ACT modules................................................................................................ 28  B.  Overview of data needs ..................................................................................................................... 30  Description module ................................................................................................................................. 30  Land use change module .......................................................................................................................... 30  Crop production module.......................................................................................................................... 30  Grassland and livestock module ............................................................................................................... 31  Land degradation module......................................................................................................................... 31  Inputs & Investments module .................................................................................................................. 31  C.  Data collection methods and central data sources .............................................................................. 33  Secondary data sources ............................................................................................................................ 33  Primary data collection ............................................................................................................................. 33  Expert judgments and stakeholder discussions ......................................................................................... 33  Chapter 5: Getting Started – The Description Module ................................................................................ 35  A.  Introduction to the graphical interface ............................................................................................... 35  EX-ACT navigation bar ........................................................................................................................... 35  EX-ACT colour codes ............................................................................................................................. 35  B.  The description module ..................................................................................................................... 35  Chapter 6: Entering Land Use Changes ....................................................................................................... 40  A.  Deforestation..................................................................................................................................... 40  B.  Afforestation and reforestation (A/R) ............................................................................................... 41  C.  Non forest land use change................................................................................................................ 42  D.  Results per component ...................................................................................................................... 43  E.  Tier 2 specifications in the land use change module ........................................................................... 43  Chapter 7: Entering Crop Production ........................................................................................................... 46  A.  Annual crops ..................................................................................................................................... 46  B.  Entering agro-forestry and perennial systems ..................................................................................... 48  C.  Entering flooded rice systems ............................................................................................................ 49  D.  Tier 2 specifications in the annual crop module ................................................................................. 51  Chapter 8: Entering Livestock and Grassland Management ....................................................................... 52  A.  Grassland management ...................................................................................................................... 52  B.  Livestock management ...................................................................................................................... 52  C.  Tier 2 specifications in the livestock sub-module ............................................................................... 53  Chapter 9: Entering Land Degradation......................................................................................................... 54  A.  Entering forest degradation ............................................................................................................... 54  B.  Entering organic soil drainage and peat extraction ............................................................................. 54  C.  Tier 2 specifications in the land degradation module .......................................................................... 55  Chapter 10: Entering Inputs and Investments .............................................................................................. 57  A.  Entering inputs .................................................................................................................................. 57  B.  Entering energy consumption ............................................................................................................ 57  C.  Entering infrastructure construction .................................................................................................. 58  2 Annex D.  How to account for energy produced by the project (biogas, biofuel, etc.) ......................................... 59  E.  Tier 2 in the energy and investments sub-module .............................................................................. 60  Part C: Making Effective Use of EX-ACT Results ....................................................................................... 61  Chapter 11: Analysis of Results ...................................................................................................................... 62  A.  Checking the land use table ................................................................................................................ 62  B.  Analysing non-aggregated module results........................................................................................... 63  C.  Analysing total results: Gross result and carbon-balance .................................................................... 64  D.  Emission intensity per product unit and further indicators ................................................................. 67  E.  Additional indicators.......................................................................................................................... 69  F.  Comparing project options or impact scenarios: Simulation work ...................................................... 70  G.  Using EX-ACT to support monitoring schemes ................................................................................ 72  A focus on incentives and effective adoption rates ................................................................................... 73  A meso-analysis of energy and inputs consumed ...................................................................................... 73  Chapter 12: Using the Carbon-Balance as Part of Economic Analyses and Fund Mobilisation .............. 74  A.  Main concepts for the economic analysis of mitigation benefits ......................................................... 74  B.  Typology of projects to be appraised ................................................................................................. 75  C.  Basic elements of economic analyses: NPV and IRR.......................................................................... 77  D.  From the carbon-balance to climate finance and public funding ........................................................ 77  E.  Using the carbon-balance to assess potential Payments for Environmental Services........................... 80  F.  Using a Marginal Abatement cost Curve (MACC) to compare low carbon options ............................ 81  ANNEX 1: Bibliography .................................................................................................................................. 5  ANNEX 2: Glossary and Acronyms ................................................................................................................ 9  ANNEX 3: List of Tables, Figures and Case Studies .................................................................................. 15  ANNEX 4: Use of Marginal Abatement Cost Curves (MACC) in appraising low carbon options .......... 17  Background of Marginal Abatement Cost Curves..................................................................................... 17  Methodology and limits of MACCs .......................................................................................................... 17  A)  Histogram: Global MAC curve ..................................................................................................... 18  B)  Curve ............................................................................................................................................ 19  Linking EX-ACT results with MACC ...................................................................................................... 19  Evaluation of the cost effectiveness and analysis of the MACC results ..................................................... 20  ANNEX 5: Using EX-ACT to Assess the Product Carbon Footprint along the Value Chain .................. 24  How to enter Value chain data in EX-ACT .............................................................................................. 25  ANNEX 6: EX-ACT Appraisal and Funding for Climate Smart Agriculture ........................................... 29  ANNEX 7: Review of GHG Tools for Mitigation in Agriculture ................................................................ 33  ANNEX 8: Use of EX-ACT in Assessing Natural Resources Stock Changes ........................................... 36  Accounting and valuation framework of natural resources ....................................................................... 36  Categorization of natural resources stocks................................................................................................ 37  Estimation and valuation of natural resource stocks and stock changes .................................................... 38  Case study: Nepal Leasehold Forestry Livestock Programme ................................................................... 41  ANNEX 9: Detailed List of Data Possibilities in EX-ACT ......................................................................... 44  3 Annex 4 Annex ANNEX 1: Bibliography Acharya, K. 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Efficient Electricity Use: Estimates of Maximum Energy Savings. Palo Alto, Calif: Electric Power Research Institute. Filipchuk, A. N. & Moiseev, B. N., 2003. “Vklad lesov Rossii v uglerodnyy balans planety. [Contribution of the Russian Forest to Carbon-balance of the Planet]”. Lesokhozyaystvennaya informatsiya, 2003, 1: 27–34. In Russian., Volume 1, pp. 27-34. Gornall, J. et al., 2010. Implications of climate change for agricultural productivity in the early twenty-first century. Phil. Trans. R. Soc. B, 365(1554), pp. 2973-2989. Grewer, U. & Bockel, L., 2012. Comparing the Carbon Footprint of Alternative Banana Value-Chain Arrangements, Rome: FAO. Hebling, T., 2012. Externalities: Prices Do Not Capture All Costs. [Online] Available at: http://www.imf.org/external/pubs/ft/fandd/basics/external.htm [Accessed 07 10 2013]. HLPE, 2012. Food security and climate change, Rome: Committee on World Food Security. Holm-Müller, K. & Pérez Domínguez, I., 2005. Economic Incentives and Technological Options to Global Warming Emission Abatement in European Agriculture. Parma: European Association of Agricultural Economists (EAAE). IEA, 2013. International Energy Agency. [Online] Available at: www.iea.org Interagency Working Group on Social Cost of Carbon, 2010. Social Cost of Carbon for Regulatory Impact Analysis. New York, United States Government. IPCC, 2000. Land Use, Land-Use Change and Forestry, Cambridge: Cambridge University Press. IPCC, 2003. ANNEX 3A.1 Biomass Default Tables for Section 3.2 Forest Land. In: Good Practice Guidance for Land Use, Land-Use Change and Forestry. Hayama: IGES. IPCC, 2006. Guidelines for National Greenhouse Gas Inventories, Hayama: IGES. IPCC, 2007. Climate change 2007: Impacts, Adaptation and Vulnerability. Cambridge: Cambridge University Press. IPCC, 2013. IPCC organization. [Online] Available at: http://www.ipcc.ch/organization/organization.shtml#.UlJ4qlNxg40 [Accessed 07 10 2013]. 6 Annex IUSS, ISRIC & FAO, 2006. World reference base for soil resources , Rome: FAO. Jackson, J. E., 1991. A User's Guide to Principal Components. New York: John Wiley & Sons, Inc.. JICA, 2010. Issues in Ex-ante and Ex-post Evaluations. [Online] Available at: http://www.jica.go.jp/english/our_work/evaluation/tech_and_grant/guides/pdf/guideline03-02.pdf Kanel, K. R., Regmi, M. R., Shrestha, K. & Tuladhar, A. R., 2012. A Study on The Demand and Supply of Wood Products in Different Regions of Nepal. Babarmahal, Kathmandu: Nepal Forestry Association. Lal, R., 2004. Carbon Emission from Farm Operations. Environment International, 30(7), pp. 981-990. Live Smart BC, 2013. LiveSmart BC Climate Glossary. [Online] Available at: http://www.livesmartbc.ca/learn/glossary.html [Accessed 07 10 2013]. MacCarl, S., 2003. Economic Potential of Biomass Based Fuels forGreenhouse Gas Emission Mitigation,. Environmental and Resource Economics , 24(4). McKinsey & Company, 2009. Charting Our Water Future - Economic frameworks to inform decision-making by Addams, L; Boccaletti, G; Kerlin, M; Stuchtey, M, New York: McKinsey & Company. McKinsey&Company, 2009. Pathways to a Low-Carbon Economy. Version 2 of the Global Green House Gas Abatement Cost Curve, s.l.: McKinsey&Company. Milne, E. et al., 2012. Methods for the quantification of emissions at the landscape level of developing countries in smallholder contexts. CCAFS Report No. 9., Copenhagen: CCAFS. Milne, E. et al., 2013. Methods for the quantification of GHG emissions at the landscape level of developping countries in smallholder contexts. Environmental Research Letters, Volume 8, pp. 1-9. MoF; FAO, 2009. Nepal forestry outlook study, Bangkok: Ministry of Forests and Soil Conservation Nepal (MoF), Food and Agriculture Organization (FAO). Nelson, G. C. et al., 2009. Climate Change Impact on Agriculture and Costs of Adaptation, Washington, D.C.: IFPRI. Ohio State University, 2004. Soil Carbon Sequestration Fundamentals. [Online] Available at: http://ohioline.osu.edu/aex-fact/0510.html [Accessed 07 10 2013]. Richards, M., Kanel, K., Maharjan, M. & Davies, J., 1999. Towards Participatory Economic Analysis by Forest User Groups in Nepal, London: ODI & Community Forestry Project. Salzman, J., 2010. Designing Payments for Ecosystem Services, Bozeman: Property and Environment Research Center (PERC). Schau, E. M. & Fet, A. M., 2008. LCA studies of food products as background for environmental product declarations. The International Journal of Life Cycle Assessment, 13(3), pp. 255-264. Shrestha, R., Sharma, S., Timilsina, G. & Kumar, S., 2005. Baseline methodologies for clean development mechanism projects, Roskilde: UNEP Risk Center. Silverman, B. G., 1985. Heuristics in an Air Pollution Control Cost Model: The "Aircost" Model of the Electric Utility Industry. Management Science, 31(8), pp. 1030-1052. Smith, P. et al., 2007. Agriculture.. In: B. Metz, et al. eds. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the. Cambridge and New York: IPCC, pp. 497-540. Smith, P. et al., 2008. Greenhouse gas mitigation in agriculture. Phil. Transf R Soc. B, 363 (1492), pp. 789-813. Sohngen, B. et al., 2005. Stocks and flows: Carbon inventory and mitigation potential of the Russian forest and land base. Washington: World Resources institute, , 62 pp. Stern, N., 2007. The Economics of Climate Change. The Stern Review, Cambridge: Cambridge University Press. Thornton, P. & Cramer, L., 2012. Impacts of climate change on the agricultural and aquatic systems and natural resources within the CGIAR’s mandate. CCAFS Working Paper No. 23, Copenhagen: CCAFS. UNEP, 2010. Assessing the environmental impacts of consumption and production: priority products and materials, Nairobi: UNEP. UNFCCC, 2007. Approved afforestation and reforestation baseline and monitoring methodology, Bonn: UNFCCC. UNFCCC, 2013. GHG data from UNFCCC. [Online] Available at: http://unfccc.int/ghg_data/ghg_data_unfccc/items/4146.php [Accessed 07 10 2013]. 7 Annex Upadhyay, T., Sankhayan, P. L. & Solberg, B., 2005. A review of carbon sequestration dynamics in the Himalayan region as a function of land-use change and forest/soil degradation with special reference to Nepal. Agriculture, Ecosystems & Environment, 105(3), p. 449–465. US-EPA, 2006. Global Mitigation of Non-CO2 Greenhouse Gases, Washington, D.C: Environmental Protection Agency, EPA 430-R-06-005. US-EPA, 2013. Glossary of Climate Change Terms. [Online] Available at: http://www.epa.gov/climatechange/glossary.html van Tilburg, X., Würtenberger, L. & Rivera Tinoco, R., 2010. Policy brief: Marginal Abatement Cost (MAC) Curve, Amsterdam: Energy research Centre of the Netherlands (ECN). Wander, M. & Nissen, T., 2003. Value of soil organic carbon in agricultural lands. Mitigation and Adaptation Strategies for Global Change, 9(4), pp. 417-431. Weiske, A., 2005. Survey of Technical and Management-Based Mitigation Measures, Leipzig: Institute for Energy and Environment (IE). Weiske, A. & Michel, J., 2007. Greenhouse gas emissions and mitigation costs of selected mitigation measures in agriculture production, Leipzig: Institute for Energy and Environment (IE). World Bank , 2012. International Financial Institution Framework for a Harmonised. [Online] Available at: http://www.worldbank.org/content/dam/Worldbank/document/IFI_Framework_for_Harmonized_Approach%20to_Gr eenhouse_Gas_Accounting.pdf World Bank, 2012. Carbon Finance - Glossary of Terms. [Online] Available at: http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/ENVIRONMENT/EXTCARBONFINANCE/0,,content MDK:21849022~menuPK:5232839~pagePK:64168445~piPK:64168309~theSitePK:4125853,00.html [Accessed 07 10 2013]. Wreford, A., Moran, D. & Adger, N., 2010. Climate Change and Agriculture: Impacts, Adaptation and Mitigation, Paris: OECD. 8 Annex ANNEX 2: Glossary and Acronyms Glossary Additionality: Additionality in the context of the UN Framework Convention on Climate Change (UNFCCC) refers to an effort that is supplemental to the business-as-usual (BAU) scenario. The additionality of a project for GHG emissions is given by its characteristic to achieve additional emission reductions that would otherwise not have occurred in the absence of the project under a business-as-usual scenario. Besides this for us most relevant meaning, additionality may also be used in the context of mitigation finance, where it refers to the additionality of financial contributions to mitigate climate change, as e.g. in the context of the Clean Development Mechanism. Afforestation: Afforestation refers to the process of establishing and growing forests on bare or cultivated land, which has not been forested in recent history (c.f. (World Bank, 2012)). Article 3.3 of the Kyoto Protocol limits afforestation to activities since 1990. The canopy cover should reach at least above a 19 percent threshold (FAO, 2013). Anaerobic digestion: Anaerobic Digestion is a natural process in which micro-organisms break down organic matter, in the absence of oxygen, into biogas (a mixture of carbon dioxide and methane) and digestate (a nitrogen-rich fertiliser) (FAO, 2013). Baseline scenario: A hypothetical scenario that reasonably represents the anthropogenic emissions removals or storage by sources of greenhouse gases (GHG) that would occur in the absence of the proposed project activity (CAALTD, 2013). Carbon-balance or GHG-balance: The carbon-balance for a specific project (or scenario of action) in comparison with a reference, is defined as the net balance from all GHGs expressed in CO2 equivalent that were emitted or sequestered due to project implementation as compared to a business-as-usual scenario. It thus accounts for the emissions from all GHGs as well as all kind of carbon pools concerned by the AFOLU sector. The expressions carbon-balance and GHG-balance are used synonymously. The Carbon-balance can be realized at different scales, for an investment project, the resource impact of an organization, or for a region, a value chain, a country, the planet (Bockel, et al., 2011). Carbon footprint: A carbon footprint measures the total greenhouse gas emissions caused directly and indirectly as a result of a clearly defined process or activity. Often it is thereby differentiated between the carbon footprint of a product, value chain or organization (c.f. (Carbon Trust, 2013):  Organizational carbon footprint Emissions from all the activities across an organization, including buildings, energy use, industrial processes and company vehicles.  Value chain carbon footprint Includes emissions which are outside an organization’s own operations (also known as Scope 3 emissions). This represents emissions from both suppliers and consumers, including all use and end of life emissions.  Product carbon footprint Emissions over the whole life of a product or service, from the extraction of raw materials and manufacturing right through to its use and final reuse, recycling or disposal Carbon sequestration: Carbon sequestration (storage) is the natural or artificial isolation of carbon dioxide from the earth's atmosphere by increasing its storage in another form of reservoir. It may refer to the natural process of removing carbon dioxide from the atmosphere through the activity of plants leading to carbon sequestration in soil and biomass or to artificial processes that capture CO2 either intentionally (e.g. carbon capture and storage) or as a by-product of industrial processes (e.g. petroleum refining). Carbon sink: Processes that remove more carbon dioxide from the atmosphere than they release, as part of the carbon cycle. For example, forests and oceans act as carbon sinks (Live Smart BC, 2013). Climate Smart Agriculture (CSA) (FAO, 2013): Climate-smart agriculture, forestry and fisheries (CSA), as defined and presented by FAO at the Hague Conference on Agriculture, Food Security and Climate Change in 9 Annex 2010, contributes to the achievement of sustainable development goals. It integrates the three dimensions of sustainable development (economic, social and environmental) by jointly addressing food security and climate challenges. It is composed of three main pillars:  sustainably increasing agricultural productivity and incomes;  adapting and building resilience to climate change;  reducing and/or removing greenhouse gases emissions, where possible. CO2 equivalent (CO2-e): CO2-e is the universal unit of measurement used to indicate the global warming potential of each of the six greenhouse gases. Carbon dioxide— a naturally occurring gas that is a byproduct of burning fossil fuels and biomass, land-use changes, and other industrial processes— is the reference gas against which the other greenhouse gases are measured. One unit of a gas with a CO2-e rating of 21, for example, would have the warming effect of 21 units of carbon dioxide emissions (over a time frame of 100 years) (World Bank, 2012). Emission factor: A factor allowing emissions to be estimated from a unit of available activity data (e.g. tonnes of fuel consumed, tonnes of product produced). Enteric fermentation: Enteric fermentation is a natural part of the digestive process for many ruminant animals where anaerobic microbes, called methanogens, decompose and ferment food present in the digestive tract producing compounds that are then absorbed by the host animal. A resulting byproduct of this process is methane (FAO, 2013). Ex-ante GHG assessment: Estimating expected future GHG effects of policies and actions before implementation. Global warming potential (GWP): The Global Warming Potential is the factor describing the radiative forcing impact (degree of harm to the atmosphere) of one unit of a given GHG relative to one unit of CO2 over a specific time period. It thus allows expressing all sources and sinks of GHGs in CO2 equivalents, which leads to the evaluation of the combined climate impact of a project. For instance the official values for Clean Development Mechanism of methane (CH4) are set to 21 (meaning that 1 kg of CH4 is as effective, in terms of radiative forcing, as 21 kg of CO2) and to 310 for nitrous oxide (N2O). EX-ACT allows users to choose either the GWP standards of the CDM or of the last IPCC update. Greenhouse gases (GHGs): GHGs refer to the six gases considered as main responsible for climate change as specified under the Kyoto protocol. They are carbon dioxide (CO2); methane (CH4); nitrous oxide (N2O); hydrofluorocarbons (HFCs); perfluorocarbons (PFCs); sulfur hexafluoride (SF6). They also may include the indirect GHGs such as SO2, NOx, CO and NMVOC (UNFCCC, 2013). Intergovernmental Panel on Climate Change (IPCC): The leading international body for the assessment of climate change. It was established by the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO) in 1988 to provide a clear scientific view on the current state of knowledge in climate change and its potential environmental and socio-economic impacts. In the same year, the UN General Assembly endorsed the action by WMO and UNEP in jointly establishing the IPCC. The IPCC is a scientific body under the auspices of the United Nations (UN). It reviews and assesses the most recent scientific, technical and socio-economic information produced worldwide relevant to the understanding of climate change. It does not conduct any research nor does it monitor climate related data or parameters (IPCC, 2013). Kyoto protocol: The Kyoto Protocol to the Framework Convention on Climate Change () was adopted at the Third Session of the Conference of the Parties (COP) in 1997 in Kyoto. It contains legally binding commitments, in addition to those included in the UNFCCC. Annex B countries agreed to reduce their anthropogenic GHG emissions (carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride) by at least 5 percent below 1990 levels in the commitment period 2008-2012. The Kyoto Protocol came into force on 16 February 2005 (FAO, 2013). Leakage: Leakage is defined as the net change of anthropogenic emissions by sources of greenhouse gases (GHGs) which occurs outside the project boundary, and which is measurable and attributable to the project activity (CAALTD, 2013). 10 Annex Marginal Abatement Cost Curve (MACC): A Marginal Abatement Cost Curve represents the relationship between the cost-effectiveness of different abatement options and the total amount of GHGs abated. It reflects the additional costs of reducing the last unit of carbon and is usually upward-sloping: i.e. marginal costs rise with the increase of the abatement effort. National Adaptation Programmes of Action (NAPAs): Documents prepared by least developed countries (LDCs) that identify the activities to address urgent and immediate needs for adapting to climate change (FAO, 2013). Nationally Appropriate Mitigation Actions (NAMAs): A set of government prioritized actions aimed at reducing or limiting greenhouse gas emissions (FAO, 2013). Net GHG emissions: Net GHG emissions refer to the aggregation of GHG emissions (positive emissions) and removals (negative emissions) from a specified activity or process. Payment for Environmental services (PES): An economic instrument designed to provide positive incentives to users of agricultural land and those involved in coastal or marine management. These incentives are expected to result in continued or improved provision of ecosystem services, which, in turn, will benefit society as a whole (FAO, 2013). Soil carbon sequestration: Soil carbon sequestration is the process of transferring carbon dioxide from the atmosphere into the soil through crop residues and other organic solids, and in a form that is not immediately reemitted. This transfer or “sequestering” of carbon helps off-set emissions from fossil fuel combustion and other carbon-emitting activities, while enhancing soil quality and long-term agronomic productivity (Ohio State University, 2004). Soil organic matter (SOM): Soil organic matter is any material produced originally by living organisms (plant or animal) that is returned to the soil and goes through the decomposition process. At any given time, it consists of a range of materials from the intact original tissues of plants and animals to the substantially decomposed mixture of materials known as humus (FAO, 2013). Technical and economic mitigation potential: The technical mitigation potential refers to the maximum amount of GHGs that can be mitigated under current technological conditions. The economic mitigation potential instead refers to the amount of GHGs that are expected to be mitigated given a specified incentive structure and socio-economic context (e.g. a specific carbon price). Tier level (Tier 1, Tier 2 and Tier 3): A Tier represents a level of methodological complexity to estimate greenhouse gas emissions following the definition in NGGI-IPCC-2006. EX-ACT can accommodate two of these precision levels: Tier 1 and Tier 2. Tier 1 methods are designed to be the simplest to use, for which equations and default parameter values (e.g., emission and stock change factors) are provided in NGGI-IPCC-2006. While users need to furnish project specific activity data, the IPCC based emission coefficients are mostly applicable globally or at regional level. Tier 2 can use the same methodological approach as Tier 1 but applies emission and stock change factors that are based on country- or region-specific data. Country-defined emission factors are used characterized by more specificity for the climatic regions, land-use systems and livestock categories in that country. Higher temporal and spatial resolution and more disaggregated activity data are typically used in Tier 2 to correspond with country-defined coefficients for specific regions and specialized land- use or livestock categories. Tier 3 refers instead to the use of more complex methodologies, including GHG modelling techniques. They are tailored to address national circumstances and are driven by high-resolution activity data and disaggregated at sub-national level. Their strong data requirements makes an application time and resource intensive. United Nations Framework Convention on Climate Change (UNFCCC): An international treaty, developed at the 1992 UN Conference on Environment and Development, which aims to combat climate change by reducing global greenhouse gas emissions. The original treaty was considered legally non-binding, but 11 Annex made provisions for future protocols, such as the Kyoto Protocol, to set mandatory emissions limits (CAALTD, 2013). Watershed: A topographically delineated area that is drained by a stream system, i.e. the total land area that drains to some point on a stream or river. The watershed is a hydrologic unit that has been described and used as a physical-biological unit and a socio-economic-political unit for planning and managing of natural resources (FAO, 2013). 12 Annex Acronyms A/R: Afforestation and Reforestation AFOLU: Agriculture, Forestry and Other Land Use BEF: Biomass Expansion Factor C: Carbon CO2: Carbon Dioxide CH4: Methane CC: Climate Change CSA: Climate Smart Agriculture EC: European Commission EU: European Union EX-ACT: Ex-Ante Carbon-balance Tool FAO: Food and Agriculture Organization of the United Nations FFRP: Forest Fire Response Project GCCA: Global Climate Change Alliance GCF: Green Climate Fund GEF: Global Environment Facility GHGs: Greenhouse Gases GIS: Geographic Information Systems Gt: Giga tonne GWP: Global Warming Potential Ha: Hectare HAC: High Activity Clay Soils IEA: International Energy Agency IFAD: International Fund for Agricultural Development IFI: International Financial Institutions IIASA: International Institute for Applied Systems Analysis IMAD: China Integrated Modern Agriculture Development Project IPCC: Intergovernmental Panel on Climate Change IRR: Internal Rate of Return IRD: Institut de Recherche pour le Développement ISRIC: World Data Center for Soils ISSCAS: Institute of Soil Science, Chinese Academy of Sciences IUSS: International Union of Soil Sciences JICA: Japan International Cooperation Agency JRC: Joint Research Centre of the European Commission LAC: Low Activity Clay Soils LCA: Life Cycle Assessment LDCF: Least Developed Countries Fund LFLP: Leasehold Forestry and Livestock Programme LUC: Land Use Change MACC: Marginal Abatement Cost Curve MAT: Mean Annual Temperature MDB: Multilateral Development Bank MICCA: Mitigation of Climate Change in Agriculture MRV: Monitoring, Reporting and Verification Mt: Mega tonne N2O: Nitrous Oxide NGGI-IPCC-2006: IPCC 2006 Guidelines for National Greenhouse Gas Inventories NPV: Net Present Value NTFP: Non-Timber Forest Products OECD: Organization for Economic Co-operation and Development PES: Payments for Environmental Services REDD: Reducing Emissions from Deforestation and Forest Degradation REED: Rural Energy Enterprise Development 13 Annex SCC: Social Costs of Carbon SCCF: Special Climate Change Fund SEEA: System of Environmental Economic Accounting SLWM: Sustainable Land and Water Management SOC: Soil Organic Carbon SOM: Soil Organic Matter UN: United Nations UNEP: United Nations Environment Programme UNFCCC: United Nations Framework Convention on Climate Change USD: United States Dollar USDA: U.S. Department of Agriculture WGS84: World Geodetic System 1984 WMO: World Meteorological Organization WRB: World Reference Base for Soil Resources WRI: World Resource Institute WWF: World Wildlife Fund 14 Annex ANNEX 3: List of Tables, Figures and Case Studies List of Figures Figure 1: Some facts about GHG emissions and agriculture .............................................................................................5 Figure 2: GHG emissions from agricultural sector by practices (in Mt CO2-e) .............................................................6 Figure 3: Global technical mitigation potential in agriculture by 2030 (in CO2-e yr-1) ..................................................7 Figure 4: Main mitigation options of the agriculture, forestry and other land use (AFOLU) sector ..........................8 Figure 5: Building of development scenarios for the use in EX-ACT .......................................................................... 15 Figure 6: Comparing the GHG impact of different scenarios........................................................................................ 16 Figure 7: Exemplary results of an EX-ACT appraisal...................................................................................................... 16 Figure 8: Some practical principles for easy use of EX-ACT .................................... 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Figure 9: Different dynamics of change available in EX-ACT ....................................................................................... 19 Figure 10: Three main approaches to develop baseline scenarios.................................................................................. 21 Figure 11: Example of results for the three types of baseline scenario (source FAO Stat) ....................................... 22 Figure 12: Overview of the process of building a baseline scenario for the use in EX-ACT ................................... 25 Figure 13: Representation of IPCC climate zones ............................................................................................................ 37 Figure 14: The climate help tool .......................................................................................................................................... 37 Figure 15: Tentative map of the distribution of the dominant soil type using IPCC classification .......................... 38 Figure 16: Decision tree for translating soils according to USDA taxonomy (left) and WRB (right) into IPCC soil classes (NGGI-IPCC, 2006) ................................................................................................................................................. 39 Figure 17: Global Ecological Zones based on observed climate and vegetation patterns. ........................................ 42 Figure 18: Different water management regimes for flooded rice (c.f. NGGI-IPCC, 2006) .................................... 49 Figure 19: Using EX-ACT to compare different scenarios and refine project documents ....................................... 70 Figure 20: Financing options for agriculture development and mitigation projects ................................................... 76 Figure 21: Exemplary Marginal Abatement Cost curve of different policy options in the agricultural sector in Nigeria (Cervigni, et al., 2013, p. 46) ................................................................................................................................... 81 Figure 22: How to manage the complexity ........................................................................................................................ 19 Figure 23: ... with MACC to synthesize information ........................................................................................................ 20 Figure 24: Comparison of the MACC results depending on the discount rate ........................................................... 21 Figure 25: The scope of a product carbon footprint ........................................................................................................ 24 Figure 26: Climate finance options under UNFCCC ....................................................................................................... 30 Figure 27: Main criteria for differentiating between GHG tools ................................................................................... 33 Figure 28: Time and skill requirements for GHG tools .................................................................................................. 34 Figure 29: Mean annual net GHG emissions for wheat sown on grassland in temperate conditions ..................... 34 List of Tables Table 1: Specification of sources for carbon values and emission factors used in EX-ACT .................................... 18 Table 2: Guidance on choosing an adapted baseline scenario ........................................................................................ 23 Table 3: Modules to be used according to project profile ............................................................................................... 29 Table 4: Overview of Tier 1 activity data that can be addomodated in EX-ACT ....................................................... 32 Table 5: Central data sources for information on land use, land use change and agricultural practices .................. 34 Table 6: The two presentations of MACC ......................................................................................................................... 18 Table 7: Categorization of natural capital stock changes ................................................................................................. 38 Table 8: Impact of the LFLP on natural resource stock changes .................................................................................. 42 Table 9: Detailed list of data that can be accommodated in EX-ACT .......................................................................... 44 List of Case Studies Case Study 1: Exemplary development of a baseline scenario ....................................................................................... 24 Case Study 2: Irrigation and Watershed Management in Madagascar ........................................................................... 45 Case Study 3: Russia Forestry Fire Response Project ...................................................................................................... 56 15 Annex Case Study 4: Banana Carbon Footprint from Production to Retail ............................................................................. 69 Case Study 5: Irrigation and Watershed Project (Madagascar) - Watershed Scenarios............................................... 71 Case Study 6: Accelerated Food Security Project in Tanzania (AFSP) – Adoption Scenarios .................................. 72 Case Study 7: Economic Analysis of the Carbon-balance - Irrigation and Watershed Management Programme in Madagascar .............................................................................................................................................................................. 77 Case Study 8: BRAZIL - The Carbon-balance of the Rural Competitiveness Project (Santa Catarina) .................. 78 Case Study 9: Use of EX-ACT in National Policy Analysis: The Plan Maroc Vert .................................................... 79 Case Study 10: From the Carbon-balance per farmer and hectare to a pre-assessment of the potentials of Payments for environmental services (PES) ...................................................................................................................... 80 List of Screenshots EX-ACT Screenshot 1: EX-ACT module bar .................................................................................................................. 35 EX-ACT Screenshot 2: EX-ACT colour codes ................................................................................................................ 35 EX-ACT Screenshot 3: The Description Module ............................................................................................................ 36 EX-ACT Screenshot 4: Deforestation (Land Use Change Module) ............................................................................. 40 EX-ACT Screenshot 5: Afforestation & Reforestation ................................................................................................... 41 EX-ACT Screenshot 6: Other Land Use Change ............................................................................................................. 43 EX-ACT Screenshot 7: Aggregated EX-ACT results for Deforestation ...................................................................... 43 EX-ACT Screenshot 8: Tier 2 values for Deforestation ................................................................................................. 44 EX-ACT Screenshot 9: Overview Annual Crops ............................................................................................................. 46 EX-ACT Screenshot 10: Perennial Crops.......................................................................................................................... 48 EX-ACT Screenshot 11: Flooded Rice (Detail 1)............................................................................................................. 50 EX-ACT Screenshot 12: Flooded Rice (Detail 2)............................................................................................................. 51 EX-ACT Screenshot 13: Grassland .................................................................................................................................... 52 EX-ACT Screenshot 14: Livestock Management ............................................................................................................. 53 EX-ACT Screenshot 15: Forest degradation ..................................................................................................................... 54 EX-ACT Screenshot 16: Drainage of organic soils and peatland extraction ............................................................... 55 EX-ACT Screenshot 17: Agricultural inputs ..................................................................................................................... 57 EX-ACT Screenshot 18: Fossil Energy Consumption .................................................................................................... 58 EX-ACT Screenshot 19: Infrastructure construction ...................................................................................................... 59 EX-ACT Screenshot 20: Land use evolution in EX-ACT .............................................................................................. 62 EX-ACT Screenshot 21: Detailed land use matrices........................................................................................................ 62 EX-ACT Screenshot 22: Sub-aggregated results annual crop sub-module .................................................................. 63 EX-ACT Screenshot 23: EX-ACT Gross Results and Carbon-balance ....................................................................... 64 EX-ACT Screenshot 24: Results graphs ............................................................................................................................ 67 EX-ACT Screenshot 25: Product carbon footprint from agricultural production (except inputs) .......................... 68 EX-ACT Screenshot 26: Further indicators – irrigation and fire use ............................................................................ 70 EX-ACT Screenshot 27: Annual and Input Module for Carbon Footprint Assessment .......................................... 25 EX-ACT Screenshot 28: Detailed carbon footprint from production (including emissions from input)............... 26 EX-ACT Screenshot 29: Carbon footprint from cradle to shelf ................................................................................... 27 EX-ACT Screenshot 30: Banana carbon footprint along the value chain .................................................................... 28 16 Annex ANNEX 4: Use of Marginal Abatement Cost Curves (MACC) in appraising low carbon options Background of Marginal Abatement Cost Curves As explained by Mc Leod (Barnes, et al., 2010), Marginal Abatement Cost Curves (MACC) were first developed after the two oil price shocks, in the 1970’s. They were aimed at reducing crude oil consumption, and later electricity consumption ((Farugui, et al., 1990), (Jackson, 1991)). The MACC was then used for different purposes: assessment of abatement potential and costs of air pollution ((Silverman, 1985), (Amann, et al., 1994)) or water availability (McKinsey & Company, 2009). MACC began to be used in the agricultural sector in the years 2000, using qualitative judgments by the European Union ((European Climate Change Programme, 2001), (Weiske, 2005)) and other stronger empirically oriented methods ((MacCarl, 2003), (US-EPA, 2006), (Weiske & Michel, 2007), (Holm-Müller & Pérez Domínguez, 2005)). In recent years, MACC has become very popular with policy makers, but also through the McKinsey report (McKinsey&Company, 2009), analysing the global GHG abatement cost curves for different sectors, including agriculture. In this light, policy-makers use MAC-curves in order to demonstrate how much abatement an economy can afford in the area of focus, with respect to policies, to achieve the emission reductions. Recently, the study “Climate Change in Agriculture – Impacts, adaptation and mitigation” (Wreford, et al., 2010) has identified the development of marginal abatement cost modelling as one of the five areas of research and policy advocacy relevant for the OECD in relation to advancing the stand of knowledge on the economics of climate change in agriculture. Methodology and limits of MACCs A Marginal Abatement Cost Curve represents the relationship between the cost-effectiveness of different abatement options and the total amount of GHGs abated (cf. table 10). It reflects the additional costs of reducing the last unit of carbon and is usually upward-sloping: i.e. marginal costs rise with the increase of the abatement effort. MACCs can be derived in different ways, either as a histogram or as a curve, as presented in the table below. 17 Annex Table 6: The two presentations of MACC A) Histogram: Global MAC curve A B Source: (van Tilburg, et al., 2010) The histogram assesses the costs and reduction potential of each single abatement measure. Each bar represents a single mitigation option.  The width of the bar represents the amount of abatement potential available from the action (in MtCO2- e).  The height of the bar represents the average unit costs of the action (cost per tonne of CO2-e saved).  The area (height * width) of the bar represents the total costs of the action, i.e. how much it would cost altogether in order to deliver all the CO2 savings from the action. The total width of the MACC shows the total CO2 savings available from all actions, and the sum of the areas of the total amount of bars represents the total costs of abatement for all actions. This type of MACC representation is easy to understand; the marginal cost and the mitigation potential can be unambiguously assigned to one option. 18 Annex B) Curve Source: FAO 2011 The curve indicates the costs (usually in $/t CO2-e) associated with the last unit of emission abatement (usually in million tonnes of CO2). The curve enables to analyze the cost of the last abated unit of CO2 for a defined abatement level (marginal costs), while the integral of the abatement cost curve (the area under the curve) gives us the total abatement costs. For example here, the point (q,p) represents the marginal cost, p, of abating an additional unit of carbon emissions at quantity q. The integral of the area under the curve (shaded area) represents the total abatement costs. In both cases, moving along the curve from left to right worsen the cost-effectiveness of low carbon options since usually each additional tonne of CO2-e mitigated is associated to increasing marginal costs. Different mitigation options will occupy different positions on the curve, some options being more cost efficient (A) than others (B). Linking EX-ACT results with MACC The low carbon options planned by project designers are occasionally crosscutting the EX-ACT modules. The modular approach prevents us from clearly seeing the carbon-balance of each adopted activity. Figure 21: How to manage the complexity... Project / Policy Carbon-balance Project / Policy Planning Option 1 - X USD Option 2 -2X USD Option 3 -3/4X USD Inputs Cropland Other Option 4 + X USD Afforestation Option 5 +2 X USD 19 Annex A MACC facilitates the management of these two dimensions of complexity. The curves, presented to businesses or public policy makers, can lead to result in the comparison of different investments in terms of carbon storage and benefits. Figure 22: ... with MACC to synthesize information Cost of a t CO2-e abated Less efficient options: cost to be implemented Cumulative t CO2-e abated in comparison to a without scenario More efficient options: pay for themselves For visibility purposes it is recommended to limit the number of activities included in one MACC graph. A specific agricultural practice may be split into several EX-ACT modules, e.g. annual crop and inputs. In such slightly more complex cases it is thus necessary not to simply represent the results from one EX-ACT module within the MAC curve, but e.g. first sum the mitigation potential from the annual crop and input modules that together constitute a specific agricultural management practice. Evaluation of the cost effectiveness and analysis of the MACC results The next step is to calculate the costs of each option as well as its benefits. Costs reflect the implementation of the land use change or agricultural practice, which might occur only once (tree plantation, certification of organic farms...) or recurrently (nutrient management, no-tillage, use of pesticides, better feeding practices...). Thereby it is an especially useful exercise to differentiate between public and private costs, establishing the notion of private and public MACC, that help to identify the different outcomes for private actors as well as the general public. In regards to these costs, different analyses could be done, such as e.g. using the cost for the government to help and encourage the adoption of the option (e.g. vouchers to buy concentrates for the animals, bearing the costs of certification for farmers who want to turn to organic agriculture, free distribution of improved seeds). In the case presented below, the available data allows us to study the private costs occurring to farmers, except for the afforestation option, which is occurring to the government. The benefits have to be known as well, in order to calculate the free cash flow and the NPV. An Internal Rate on Return (IRR) and a payback period can equally be calculated, to enrich the economic analysis. Most of the benefits directly concern the farmer, e.g. increase in yield, savings concerning fertilizer purchase, water use and fuel (no-till). Other benefits occur both to society and farmers like the benefits from prevented erosion. Different situations have been analyzed to take into account the limits of a MACC assessment, varying discount rates and the extent to which there are interaction between the considered options. Thus it is in some cases reasonable to assume that the per unit costs of one option – e.g. improved livestock feeding practices – change based on the adoption of another proposed activity – lets say the establishment of forage crops on strongly degraded grassland. In the case below we compare a scenario using two different discount rates of 3.5% and 10%. 20 Annex Figure 23: Comparison of the MACC results depending on the discount rate Scenario 1: The improved rice irrigation is the most cost-effective action with an average price of -15 $/t CO2-e (leading to actual private Public discount rate of 3.5% benefits). Each tonne of GHGs MACC for the Turkey National Climate Change  avoided due to crop $/t CO2e 50 Strategy improvement, afforestation and grassland improvement 40 represents an Livestock  almost zero improvement cost option 30 Organic and no‐ (prices tillage farming between -1 and -0.1 $/t). The activities 20 Grassland  planned improvement within the livestock 10 management Crops  option are the improvement Afforestation most 0 expensive, with an 0 ‐200,000 ‐400,000 ‐600,000 ‐800,000 ‐1,000,000 ‐1,200,000 average price of 45 $/t ‐10 mitigation potential Mt CO2e CO2-e. The Rice irrigation  afforestation ‐20 improvement provides most of the mitigation potential. Regarding the total cost of mitigation (mitigation potential * average cost per t CO2-e), the promotion of organic and no-tillage 21 Annex farming is the cheapest action, followed by rice irrigation. Even if rice irrigation has a more profitable cost per t of CO2-e, its limited mitigation potential explains why it is not the more profitable option globally. Also concerning total costs the livestock management activities are the most expensive. The public discount rate gives an optimistic view of the abatement potential that can be achieved at profits for society through improved rice irrigation and the development of organic farming. Scenario 2: Also with a changed discount rate the ranks of Private discount rate of 10% the different considered mitigation options stay the same. Thus improved rice 22 Annex irrigation still MACC for the Turkey National Climate Change  has the most favourable $/t CO2e 50 Strategy marginal costs -8$/t CO2-e while livestock management is 40 still the most Livestock  expensive improvement (24$/t). 30 Organic and no‐ The higher tillage farming private discount rate thus leads to 20 Grassland  less optimistic improvement values for the most cost 10 efficient Crops  options (rice) improvement Afforestation and to the 0 estimation of less cost 0 ‐200,000 ‐400,000 ‐600,000 ‐800,000 ‐1,000,000 ‐1,200,000 pessimistic values for the ‐10 mitigation potential Mt CO2e most cost intensive Rice irrigation  options ‐20 improvement (livestock). The choice of the discount rate will depend on the adopted point of view for the MACC analysis. If it is to evaluate the mitigation potential and the cost of a farm or an agricultural cooperative, the private discount rate is the most appropriate. If the MACC is done from the point of view of a government, it would be more accurate to use a discount rate that includes both public and private criteria. Indeed, the interaction between both actors, the government and the private sector, is an important element for the design of mitigation policies. 23 Annex ANNEX 5: Using EX-ACT to Assess the Product Carbon Footprint along the Value Chain The business mantra “you cannot manage it if you cannot measure it” applies as much to carbon emissions as to resources and costs. Industries in Annex 1 countries of the UNFCCC have to calculate and report their emissions. In parallel to this obligation, a new type of carbon measurement has been developed during the last 10 years: the carbon footprint of a product, which takes into account the emitted CO2-emissions across the supply chain, from cradle to grave. The lifecycle assessment of GHG emissions of a good presents several benefits that can be classified into three main advantages: i) reduction of GHG emissions, ii) support to decision making and supply chain management, iii) differentiation on the market and trade advantages. Definition of the product carbon footprint The carbon footprint of a product is the quantity of greenhouse gases (GHGs), expressed in carbon dioxide equivalent (CO2-e), emitted across the value chain for a single unit of that product. Though boundaries of the analysis may vary, reasonable approaches may be e.g. from production to retail (cradle to shelf) or over the full life cycle (cradle to grave). Within the declared analysis each step of the value chain should be taken into account. As shown in figure 23 this includes emissions from the production of raw materials, transportation and transformation, product use as well as waste disposal and recycling. A further main differentiation is whether all indirect emissions are accounted for e.g. on the respective emissions generated by the production of products that serve as inputs for the analyzed production system. Depending on the methodology used to calculate the carbon footprint (CFP), the GHGs taken into account could comprise either the six main gases highlighted in the Kyoto protocol (carbon dioxide CO2, methane CH4, nitrous oxide N2O, hydrofluorocarbons HFCs, perfluorocarbons PFCs, sulphur hexafluoride SF6) or only a limited number of them. These numerous options show that a clear specification of boundaries and scope of a carbon footprint analysis is an important precondition for a transparent and adequate approach. Figure 24: The scope of a product carbon footprint Source: http://reclay-group.com/?id=267&L=1 24 Annex In such a way the carbon footprint along the value chain allows for a comparison of emission intensity within different value chain stages. This should nevertheless be approached with caution as all those stages along the value chain, with little emission intensity, can also contribute significantly to mitigation potentials. The product carbon footprint is, as explained previously, usually expressed in t CO2-e per product unit. Though reasonable in order to compare different production processes with each other, measures per product unit do not allow comparisons between products. In this light, selected authors have tried to establish emission intensity per nutrient content, energy content, fat or protein content. An alternative is to consider the emission intensity per economic value of the product (Schau & Fet, 2008). While the existence of these approaches is acknowledged here, we focus in the following on a classical carbon footprint analysis by product unit. How to enter Value chain data in EX-ACT In the results section of this User Manual it was explained how to use EX-ACT as part of a regular project, programme or policy analysis in order to obtain a first estimation of the interventions impact on product carbon footprints in the agricultural production stage. In order to properly include the effects from artificial inputs, such as fertilizers, and have a refined analysis it is nevertheless advised to fill an empty EX-ACT sheet separately with only one crop of interest. In such a way, EX-ACT can be used to estimate the product carbon footprint of production for a single agricultural product, but also to compare different production practices. In the example below, EX-ACT is used to analyze the changes in product carbon footprint from converting conventional banana cultivation into organic production. Using exemplary data from a model farm in Central America (c.f. (Grewer & Bockel, 2012)) that documented its conversion process, and complementing missing data based on assumptions from regional data, the main changes are given by a decrease in productivity, changes in agricultural practices, changes in the fertilization regime and phasing out of synthetic pesticides. For the purpose of the analysis EX-ACT is filled exactly in the same way as described earlier in this User Manual, but information is only specified on the relevant area cropped with banana. Thereby the conventional production system is inserted as without-project scenario and the organic cultivation practices as a with-project scenario. The screenshot below shows how the annual module and the input module have been filled: EX-ACT Screenshot 27: Annual and Input Module for Carbon Footprint Assessment 3.1.2. Annual systems remaining annual systems (total area must remains contant) Fill with you description Impro ved agro - Nutrient No Till./residues Water M anure Residue Yield Area (ha) -no mic practice management management management applicatio n Burning (t/ha/yr) Start Without * With * Conventional banana ? ? ? ? ? NO 45 200 200 I 0 I Organic banana Yes ? ? ? Yes NO 35 0 0 I 200 I 25 Annex 6.1 Inputs (liming, fertilizers, pesticides, herbicides,…) Description and unit to report Amount applied per year Lime application Start Without * With * Limestone (tonnes per year) 2,100 2,100 D 2,100 D Dolomite tonnes per year) 0 0 D 0 D not-specified (tonnes per year) 0 0 D 0 D Fertilizers Urea (tonnes of N per year - Urea has 46.7% of N) 0 0 D 0 D Other N-fertilizers (tonnes of N per year) 5,089 5,089 I 3,375 I N-fertilizer in irrigated rice (tonnes of N per year) 0 0 D 0 D Sewage (tonnes of N per year) 0 0 D 0 D Compost (tonnes of N per year) 0 0 D 0 D Phosphorus (tonnes of P2O5 per year) 0 0 D 0 D Potassium (tonnes of K2O per year) 0 0 D 0 D Pesticides Herbicides (tonnes of active ingredient per year) 0 0 D 0 D Insecticides (tonnes of active ingredient per year) 0 0 D 0 D Fungicides (tonnes of active ingredient per year) 986 986 I 0 I After inputting the information on the stages of agricultural production, users switch to the Life Cycle Carbon Footprint section of the results section. Here the emissions per tonne of banana from production are already automatically are provided, including emissions from all agricultural inputs. Thus the conventional production system leads to annual emissions of 107kg of CO2-e per tonne of banana, while the organic system is estimated to sequester 6kg of CO2-e, it may thus be evaluated as carbon neutral. EX-ACT Screenshot 28: Detailed carbon footprint from production (including emissions from input) Life cycle carbon footprint Detailled Emissions in t CO2-eq for the different phases of the Value Chain Emissions (tCO2/t product) Without With PRODUCTION Level (corresponding emissions calculated as a percentage of total quantity used) Direct and indirect (induced LUC, degradation, Inputs & Investments) emissions 0.107 -0.006 Nevertheless, product carbon footprints are usually not only estimated at the production stage, but also for a longer sequence of the value chain. For this purpose it is specified here how to analyze the product carbon footprint of bananas from cradle to supermarket shelf. Thereby we further assume a production in Central America, with shipment to Europe and road transportation to Germany. The used data is exemplary secondary data that was not verified by FAO. It is very likely that the listed resource needs are incomplete and the realistic carbon footprint is expected considerably higher. We assume for this example that organic and conventional bananas do not differentiate in terms of their resource needs for packaging and transportation. The following screenshot shows the information inserted along the value chain. 26 Annex EX-ACT Screenshot 29: Carbon footprint from cradle to shelf Life cycle carbon footprint Detailled Emissions in t CO2-eq for the different phases of the Value Chain Emissions (tCO2/t product) Without With PRODUCTION Level (corresponding emissions calculated as a percentage of total quantity used) Direct and indirect (induced LUC, degradation, Inputs & Investments) emissions 0.107 -0.006 PROCESSING Level (list inputs or processes necessary) Name of input Emission per input Input per t product Without With Without With Electricity 0.6 0.6 0.014 0.014 0.01 0.01 Diesel 1 2 2.9 2.9 3 0.005 0.005 4 0.01 0.01 Corrugated board 1.2 1.2 0.072 0.071 0.08 0.08 Plastic (LLDPE) 1.7 1.7 0.001 0.001 0.00 0.00 Total Processing level 0.11 0.11 TRANSPORT level (list the differents inputs or steps necessary) Name of input Emission per input Input per t product Without With Without With Electricity 0.6 0.6 0.159 0.156 0.10 0.09 Diesel 2.9 2.9 0.015 0.014 Refrigerants* 1.0 1.0 0.022 0.021 Heavy fuel 3.4 3.4 0.202 0.198 0.68 0.67 Ethylene 1.7 1.7 0.000 0.000 0.00 0.00 Total of transport Level 0.78 0.76 * For refrigerants Luske (2010) only reports t CO2/ton of transported banana. Thus we cannot calculate with an emission factor here. Total Emissions in t CO2-eq for the different phases of the Value Chain Emissions (tCO2/t product) Without With PRODUCTION 0.11 -0.01 PROCESSING 0.11 0.11 TRANSPORT 0.78 0.76 USE 0.00 0.00 WASTE 0.00 0.00 5 TOTAL 0.99 0.86 The above displayed EX-ACT section on the Life cycle carbon footprint is structured along the value chain and focuses sequentially on production, processing, transport, use phase and waste disposal. The latter two stages are omitted here since we only focus on a cradle to shelf analysis. The needed specifications are as follows: Listing of all resource needs for processing, packaging and transportation. 1 2 Specification of the emission factor for the respective resource in tCO2-e per resource unit for both scenarios. 3 Quantification of the amount of inputs per tonne of final product (banana) for both scenarios. 4 Then users are provided with the respective disaggregated emissions per tonne of final product, stemming from the specified input. 5 Lastly, users are provided with the aggregated carbon footprint per value chain stage and as overall total. In our example, the conventional bananas have a product carbon footprint from cradle to shelf of 994 kg CO2-e per tonne of banana, while the performance of organic bananas is slightly more positive at 865 kg CO2-e per tonne of banana. 27 Annex Thus organic production systems reduce overall emissions on a per hectare basis in a strong enough manner to outplay the reduction in productivity. The overall analysis along the value chain shows that both overseas and road transportation are responsible for the biggest share in emissions. This is further visualized by the below graph. EX-ACT Screenshot 30: Banana carbon footprint along the value chain 0.9 0.8 0.7 0.6 Emissions (tCO2/t product) PRODUCTION 0.5 PROCESSING 0.4 TRANSPORT USE 0.3 WASTE 0.2 0.1 0.0 Conventional Organic ‐0.1 28 Annex ANNEX 6: EX-ACT Appraisal and Funding for Climate Smart Agriculture (Excerpts from (FAO, 2013)) EX-ACT is currently mostly used (i) to appraise climate smart AFOLU projects, (ii) to assess the climate smartness of projects, or (iii) to appraise low carbon CSA policy options. Common questions are thereby “how to finance such projects?” or "how far do EX-ACT appraisals allow to mobilize funds?”. While this has been selectively answered as part of this manual, the here included annex provides some additional background information on climate change finance taken from the Climate Smart Agriculture Sourcebook (FAO, 2013). The reform of agricultural sectors to incorporate climate change considerations ultimately relies on the restructuring of agricultural investments, public as well as private, at the national level. Nevertheless, international financing plays a crucial role in this transition. International climate finance can act as a catalyst for the broader adoption of CSA practices by demonstrating the feasibility of CSA approaches, facilitating climate change mainstreaming into national policy and legal frameworks, and promoting the creation and transfer of skills, knowledge and technologies. If used correctly, the leverage of relatively small amounts of international climate finance can help to transform public agriculture budgets and private investments into sources of CSA financing. For many countries, learning how to access and effectively use international financing options represents the first step in the long-term transition towards CSA. The landscape of CSA financing options is complex, featuring a multitude of funding channels with different objectives and eligibility criteria. Financing options, specifically targeting CSA, are still limited, necessitating a strategic use and combination of existing funding sources. The basis for any CSA activity should be the identification of a country’s opportunities and vulnerabilities, corresponding needs and preferred options for CSA activities. After national priorities have been defined, a strategic approach to sources of international finance, based on an understanding of available channels, will not only increase the chances for approval, but also enhance the fit between the finance option and the country’s overall approach to climate change in agriculture. Without making the futile attempt to cover all available sources of international climate finance, it is possible to identify six categories of important climate finance options provided by: 1. Financing mechanisms directly under the UNFCCC; 2. United Nations (UN) organizations or programmes; 3. Multilateral Development Banks (MDBs); 4. Bilateral public financing channels; 5. Compliance and voluntary carbon markets; and 6. Private sector actors and philanthropy The first category entails climate finance options for CSA directly connected to the UNFCCC (see Figure 24). The Global Environment Facility (GEF) serves as one of the “entities operating the financial mechanism” of the UNFCCC. Through the GEF Trust Fund, donor countries provide financing to cover the incremental cost developing countries incur when undertaking activities that create global environmental benefits. Climate change mitigation, as a particularly clear-cut case of global environmental benefits, represents one of the GEF’s largest focal areas. Climate change adaptation activities are not funded under the GEF Trust Fund, but receive financing through separate funds, the Least Developed Countries Fund (LDCF) and the Special Climate Change Fund (SCCF) described below. 29 Annex Figure 25: Climate finance options under UNFCCC UN Agencies and Programmes: UN Agencies and Programmes play a central role as implementing agencies for the activities financed through the funding channels under the UNFCCC described in the previous section. In addition, UN Agencies also provide climate financing directly, primarily through multi-donor trust funds financed by member states. The UN REDD programme and the Rural Energy Enterprise Development (REED) Programme are two prominent examples for this category of international climate finance. Multilateral Development Banks (MDBs) : The primary function of MDBs is to provide loans under conditions and objectives based on their overall principles as well as the specific agreements between a specific country and the respective development bank. The agricultural sector remains one of the primary target sectors of MDB loans, representing a share of the agricultural official development assistance. As main MDBs are increasingly incorporating environmental sustainability criteria into their agricultural lending practices and recently signed a consensus towards a systematic use of carbon-balance and GHG performance in project appraisal, they should progressively increase their role as a financing option for CSA projects. Bilateral Public Financing Channels: Bilateral instruments remain one of the primary sources of climate finance. Analysis provided by the Climate Policy Initiative estimates that total annual climate finance to developing countries through bilateral sources (ca. US$ 23 billion) is in fact higher than the amount channeled through multilateral instruments (ca. US$ 17 billion). This gap becomes even wider when looking at climate change adaptation activities separately with bilateral sources amounting to US $3.6 billion and multilateral channels disbursing less than US $0.5 billion. Bilateral Financial Institutions play a central role as intermediaries disbursing climate funding to developing countries. Spending on climate change by the French Development Agency, the German Development Bank and the Japan International Cooperation Agency amounted to US$ 11.4 billion in 2009, including both official development assistance and non- official development assistance finance (UNEP, 2010). In addition, levels of South-South bilateral climate finance are increasing. The Brazilian Development Bank, China Development Bank, the Indian Renewable Energy Development Agency and the 30 Annex Overseas Private Investment Corporation have provided approximately US$ 4 billion of climate finance in 2010 (Buchner, et al., 2011). As with other funding channels, most of the bilateral climate financing is concentrated in the industrial and energy sectors and therefore not available for CSA activities. The member states of the European Union have traditionally been the main source of climate change financing assisting developing countries, both through national level initiatives as well as climate finance activities coordinated at the European Union level. Recently, the Global Financial Crisis and the European Debt Crisis have had devastating effects on the European Union’s funding levels for climate change. The official development assistance numbers released by the Organisation for Economic Co-operation and Development (OECD) show that European Union contributions for climate change adaptation in developing countries has dropped by 55 percent from € 1.4 billion in 2010 to € 619 million in 201110. Nevertheless, the European Union continues to finance a number of major initiatives providing international climate finance. One important programme from a CSA perspective is the Global Climate Change Alliance (GCCA), launched in 2007 as a European Union initiative coordinated by the European Commission (EC). Carbon Markets: Despite all the difficulties with its implementation, the concept of putting a price on GHG emissions and installing a market-based price-setting mechanism through certificate trading provides a powerful instrument of climate finance. Carbon markets could possibly be a large source of international funding for CSA activities. However, the inclusion of carbon credits from agricultural GHG reductions in compliance with carbon markets has been a matter of continuous controversy for at least two decades. The scope of this annex does not allow for a full presentation of the complex debate on agricultural carbon credits. However, this is a list of some of the central concerns: a) challenge of MRV and related difficulties to ensure environmental integrity with respect to possible leakage, uncertain permanence and additionality of GHG reductions; b) high transaction costs, especially through the coordination of large numbers of smallholder farmers that would be required to make soil carbon Certified Emission Reductions profitable; c) high opportunity costs through the diversion from conventional climate change efforts towards the complex process of achieving carbon market readiness... These issues are usually embedded in a more general rejection of carbon markets as a tool for agricultural mitigation, highlighting the unstable situation of carbon markets overall and concerns about shifting the burden of emission reductions to developing countries Private Sector and Philanthropy : In the context of this section, private sector CSA investments do not mean the “transformed” agricultural investments by agribusiness or smallholders that follow CSA principles, but international private sector funding that contributes to catalysing this transition. Looking at the entire landscape of climate finance, the private sector is in fact the single largest source of financing (Buchner, et al., 2011). However, private sector funding in the form of market-rate loans or capital investments is almost exclusively targeted at climate change mitigation activities in the renewable energy sector and in industrial energy efficiency. In agriculture, there are a number of innovative private sector initiatives worth highlighting in this context. Usually, these are driven by a combination of three factors: a) protection of a company’s value chain from climate change impacts; b) opportunities for increased profits through environmental certification schemes; and c) corporate social responsibility linked to a company’s image and self-understanding. These motives particularly apply to large, multinational food-product corporations with strong interests in increasing climate resilience of agricultural production within their value chain. Private sector: promoting the advancement of sustainable sugarcane in Brazil In 2007, The Coca-Cola Company and World Wildlife Fund (WWF) confirmed a joint commitment to improve water efficiency, reduce carbon emissions, and help conserve seven of the world’s most important freshwater river basins. As a critical piece of this initiative, Coca-Cola affirmed the goal of advancing sustainable agriculture practices through promoting environmental stewardship and ensuring workplace rights. Among agricultural products, sustainability in the sugarcane supply chain (farm, mill, and refining processes) is a key priority for The Coca-Cola Company and a focal point of the WWF/Coca-Cola partnership. As such, they also worked with Brazilian Sugar Mill suppliers. Coca-Cola and WWF have identified Bonsucro certification as a means of ensuring increased sustainability, and believe the newly formed standard will provide a globally recognized, third-party certification for sustainably produced sugarcane. Developed through an independent, multi-stakeholder initiative, the Bonsucro certification provides a mechanism for achieving sustainable production from sugarcane in respect of economic, social and environmental dimensions. Coca-Cola, in partnership with WWF, has collaborated with key suppliers to initiate 31 Annex activities that assist sugar mills to understand and work towards certification. As Coca-Cola and WWF support mills to meet certification standards, sugarcane producers will continue to benefit, with global implications of aligning the industry towards responsible and sustainable environmental stewardship. Source: Sustainable Agriculture Initiative, 2010 Fragmentation of climate finance sources has been a particular challenge for concepts such as CSA that draw their comparative advantage from the utilization of cross-cutting synergies. With the ongoing shift in focus towards integrative approaches, exploring ways to sensibly and effectively combine thematically separated channels of funding, this barrier to accessing international funding for CSA projects is gradually diminishing. This conceptual change is reinforced by an overall increasing attention on agriculture in a climate change context, representing not only the arguably most important sector for climate change adaptation, but at the same time one of the world’s largest sources of GHG emissions. Especially in combination with forest degradation and competing land use, agriculture is increasingly recognized as one of the crucial parts of the global climate challenge. While underdeveloped financing channels, like private sector investments or carbon markets, are likely to provide only limited financing for specific niches (e.g. manure management or product certification) in the midterm, bilateral as well as multilateral public financing is starting to put more explicit emphasis on CSA activities. For example, the ongoing process of the GEF-6 replenishment is pointing in this direction. Perhaps most importantly in the mid-term future, the current design process of the Green Climate Fund might be influenced by this overall dynamic, which bodes well for the development of CSA financing. Assuming that the GCF will have a significant impact on the entire climate finance landscape, not only in structure but also in prioritization and principles, a clear focus on CSA embedded in the GCF design would make a difference for the way CSA approaches can be realized and scaled-up in the coming decades. For developing countries, this implies an opportunity as much as a challenge. In order to successfully access, but more importantly to effectively use increasing volumes of international CSA financing, developing countries will have to ensure that the necessary prerequisites are in place. While significant readiness activities have been ongoing in REDD+ for a long period of time, there are still more gaps to be filled in the agricultural sector to improve the basis for larger-scale CSA investments. Challenges include the usual suspects, such as the quality and quantity of available data, the effectiveness of monitoring systems to institutional and technical implementation capacity as well as the suitability of policy and legal frameworks. Existing knowledge and experiences on CSA as well as the wealth of climate change needs assessments and priority setting at the national level (e.g. through NAPAs, Nationally Appropriate Mitigation Actions, etc.) providing a solid basis for concrete and country-specific preparatory measures. In order to get a head-start on CSA, developing countries could consider putting the fundamentals in place now as to be ready to use new CSA opportunities as they emerge. 32 Annex ANNEX 7: Review of GHG Tools for Mitigation in Agriculture (c.f. Colomb, et al. (2012) and Colomb, et al. (2013)) As presented in Chapter 2.B a variety of GHG appraisal tools is available. This annex synthesizes central information from Colomb, et al. (2012) and Colomb, et al. (2013). The study from Colomb, et al. (2013) is intended to help users select the most appropriate calculator for a landscape-scale greenhouse gas assessment of activities for agriculture and forestry. Eighteen calculators were assessed, which were designed for different objectives. Colomb et al. (2013) propose to differentiate between the tools using the criteria: Aim, geographical parameter, activity parameter as well as time & skill requirements, as depicted in the following figure: Figure 26: Main criteria for differentiating between GHG tools Following this structure the two papers assessed the main focus of the eighteen calculators across the given criteria. Focussing first only on criteria number 4, the speed of the assessment and the ease of use, the table below gives a rough orientation of the different tools: 33 Annex Figure 27: Time and skill requirements for GHG tools (+ time and skill intense; ++++ fast and easy) Another essential criterion is the scope of the activity parameters: Thus it is of central interest whether a tool e.g. includes or excludes emissions from CH4 and is able to take count of a high variety of agricultural management practices. The figure below illustrates exactly this difference in tools in not accounting for exactly the same activities and emission/sequestration sources. Figure 28: Mean annual net GHG emissions for wheat sown on grassland in temperate conditions 34 Annex This illustration highlights that differences in emission estimates are often problems of scope and not of disagreement concerning the scale of emissions of a clearly defined practice. Thus the figure above illustrates that the different tools roughly estimate similar scales of emissions when considering the same emission and sequestration sources. The broad typology of GHG calculators illustrates thereby main challenges in landscape assessment. Up-scaling from farm scale to landscape assessment implies a change in data availability. At plot scale and farm scale, technical data are easily available and can be provided directly by farmers. At regional scale, data inventory often needs to be obtained from statistical data bases or expert knowledge, increasing uncertainties. Accounting for wider time periods is especially important to consider soil and biomass carbon pools, with large quantities of CO2 at stake. These pools are impacted by management and land use changes. In the future, assisting technologies such as e.g. Near Infra-Red Spectroscopy or remote sensing might enable for cheap direct measurement of the carbon stock changes. Further development of process based models and cheap direct measurement methods for GHG fluxes, linked with GHG calculators are required to improve assessments accuracy. When assessing different GHG tools also the link between emissions by area and production quantities needs to be kept in mind in order to avoid leakage, i.e. an increase of emissions outside of studied perimeters induced by changes of production in the studied area. Permanency issues also need to be kept in mind: some reductions/increases of emissions are temporary, while others are continuous due to change in production systems. A very important point is that environmental/sustainability assessments cannot be restricted to GHG assessment and improvements of the GHG-balance must not be done ignoring possible drawbacks on other criteria (e.g. increase of pesticide use, water scarcity, reduced biodiversity etc.). In highly productive systems, GHG assessments should focus on improving input efficiency per production. In low productive systems the focus should be stressed on agriculture resilience and food security, through improvement of agronomic practices. There are clear synergies between agronomic efficiency and a strengthened agro-ecology approach as related to Climate Smart Agriculture. An important finding of this review is that adapted tools for each situation are already available, whereby links with socio-economic parameters are still missing and should be strengthened for integrated assessments. Further methodological standardisation, similar to the case of Life Cycle Assessment (LCA) methodologies which follows international standards (e.g. ISO 14040) could also contribute to more clarity and transparent references. Summarizing, depending on the final aim of the user, each calculator tries to find the best compromise between user-friendliness, time consumption and result accuracy. As long as GHG assessments is mostly voluntary and limited economic return is expected (no CO2 tax, no labelling etc.), cost and skill requirements for using GHG calculators should be limited. If more restrictive policies should be implemented, then method standardization and improved accuracy are essential. 35 Annex ANNEX 8: Use of EX-ACT in Assessing Natural Resources Stock Changes Projects that rehabilitate soil on degraded land within a watershed, engage in reforestation of degraded mountainous areas, reconstitute the watershed capacity and regulate water stream flow or that simply reduce deforestation and land degradation, produce a wide set of benefits distinct from their climate change mitigation achievements. Environmental resources and non-degraded natural capital may provide an important source for food security and income. Within the growing emphasis of sustainable development towards a green economy, an applicable appraisal method to analyse project impacts on the state of natural capital becomes thus crucial, e.g. in the context of rural development, watershed and land use investment projects. This annex proposes such a method that makes use of the natural resource indicators that are automatically accounted for in the standard version of EX-ACT. It allows establishing project impacts on quantity and quality of a series of natural resources (Cubic meters of biomass, tons of Soil Organic Carbon, hectares of restored land...). This method is currently used at a pilot level and is still under development. Its special focus lies on watershed projects and sustainable land management projects. It will be further upgraded in the forthcoming period. Accounting and valuation framework of natural resources Accounting and valuing environmental resources is promoted by the World Bank as part of a its work on operationalizing the System for Environmental and Economic Accounting (SEEA). The SEEA is an extension of the System of National Accounts (SNA) to account for 1) increasing scarcities of natural resources and 2) the degradation of environmental quality (Bartelmus et al. 1991). In 2003 the first edition of the SEEA was issued; while a revision was published in 2012 (European Commission et al. 2012). Valuation frameworks of natural resources need to adequately fulfil three objectives: first they have to provide a clear categorization framework for natural resources, then resource stocks and stock changes have to be estimated by adequate methodologies, and finally these stocks have to be valued at adequate prices for a given context. These three steps of categorization, resource stock estimation and environmental valuation thus form the basis of the here below outlined approach and will each be discussed subsequently. The time reference of the analysis will thereby, equally to other EX-ACT analyses, be 20 years, which is seen as the minimal timeframe that needs to be covered in order to capture major impacts of an intervention on natural resources, as well as the approximation of a new state of equilibrium. It must be mentioned that such an environmental valuation of total natural resource stock changes is associated to the following problem: it is unclear whether the total amount of natural resources created will ever be valorised in their full amount and at current prices even though it is relevant to measure it e.g. how much additional timber has been created in total throughout the project, the valuation of the full amount of resources at current timber prices only allows for an indicative interpretation of its potential worth, while this amount will not translate into an equal income stream to project beneficiaries and resource users. Increased natural resource stocks are understood as a natural form of capital that provide at a given point in time a specific set of functional environmental services as well as opportunities for remuneration on markets. Directly occurring income benefits, due to sustainable agricultural intensification measures, are adequately captured as part of the classical financial project analysis. Environmental resources that are for a considerable timeframe neither processed nor transacted, but are intermediately conserved in their natural state; provide distinct, additional private and public values that need to be accounted for separately. Natural resources thereby provide benefits either a) by continuously being in their natural form (e.g. yield increases due to higher SOM content on rehabilitated land), b) by creating a single revenue stream in a 36 Annex considerable distant future (e.g. timber harvest from newly planted forest) or c) by providing public values that do not generate income streams (e.g. climate change mitigation or stream regulation function of watersheds). The multiple benefits of natural resources for the rural population can thus be structured into:  Direct private values This concerns the benefits from self-consumption or sale (in a considerable distant future) of additional timber, fuel wood and NTFP. It thus concerns a direct private benefit to the household, in the form of monetary revenue, increased household consumption or supply of inputs regarding yield benefits of higher SOM contents through soil conservation practices, soil rehabilitation measures, composting, or the greater availability of fodder for livestock.  Indirect private values This category subsumes functions of natural resources that are over a longer period or that benefit mainly annual and perennial cultures but also, any other entities that provide indirect private values. It thus concerns the indirect contribution to increases in monetary household revenue or in household consumption. It also regards the indirect benefits due to prevention of future erosion or drought stresses, through practices that limit the impact of erratic rainfall and dry periods on yields or measures that increase water availability and protect productive areas from flooding.  Public values The mitigation of GHG emissions and increases in carbon sequestration provide an important public value by minimizing further climate change and limiting resulting damages and abatement costs to society. Other public values that are provided by natural resources include biodiversity conservation and habitat provision, through protected and conserved natural areas as well as watershed functions (such as stream regulation and flood protection for settlements and infrastructure). In such a way evaluated project benefits that occur through investment in natural resources, will be put into relations with the direct financial project benefits as calculated by standard project documents. Categorization of natural resources stocks The here below given classification provides a structured framework to account for the changes in natural resource stocks. It was oriented in elements at the System of Environmental-Economic Accounting (EC, OECD, UN and WB, 2012) and, was later further adapted in order to capture the main natural resources of the pilot study conditions in Nepal. Currently only used as part of a first test study, the framework will still be revised in future applications and should not be regarded as a final product. 37 Annex Table 7: Categorization of natural capital stock changes Natural Capital Unit Direct private value A01 Incremental accumulated SOC on cultivated land (soil fertility) tC A02 Incremental stocks of non-timber biomass t dm Fuelwood and -material t dm Fodder t dm Compost t dm A03 Incremental stocks of NTFP in forestry and agro-forestry Indirect private value A04 Incremental area with erosion protection ha A05 Incremental area with increased drought resilience ha A06 Incremental water volume stored (dams, ponds, water harvesting) m3 A07 Incremental water volume saved by improved irrigation practices m3 A08 Incremental flood protected area ha Public value A09 Incremental timber stocks in forestry and agro-forestry t dm A10 GHG-balance (reduced emissions and C sequestration) t CO2-e A11 Incremental protected natural areas (forestry, peatland, wetland) (existence value) ha A12 Incremental new forest plantation (existence value) ha Estimation and valuation of natural resource stocks and stock changes Estimations and accounting of natural resource stocks can be done by different means, either by linking to survey based national statistical datasets or, for certain variables, to GIS data. In the case of the given study, we make use of the documentation produced by IFAD project implementation staff as part of their regular project activities in Nepal, as well as on estimations and assumptions of project coordinators, implementing staff and other interviewed stakeholders. In some cases, the thus procured data was used directly in the section below, while for selected variables it served as input data into EX-ACT, where changes in biomass or SOC were estimated using the established EX-ACT methodology. It should be clearly recognized that no statistically representative dataset could be procured as the basis of this study. Options to improve the data quality, as well as propositions to include data collection on relevant aspects of project monitoring in future projects are discussed in the last chapter of this document. Besides accounting for the natural resource stock changes in physical units, the objective was also to value the occurring benefits. While the categorization of natural resources provided above can also be used in similar country contexts, the monetary valuation is necessarily context specific. All specifications on resource valuation and selected precisions on estimations of resource stock quantities follow here below by resource category5. A01 Incremental accumulated SOC on cultivated land (soil fertility) The effect on soil fertility from increased Soil Organic Matter is one of the most central and direct co-benefits between climate change mitigation and agricultural productivity. The capacity of increased levels of soil organic matter to bind carbon (mitigation), to increase the capacity to store water (adaptation) and to increase soil fertility (food security) makes it a central founding element of the concept of Climate Smart Agriculture. Further benefits of increases in SOM content include “increased soil warming rates in temperate latitudes, reductions in energy required for tillage, enhanced soil tilth, ph buffering and, [possibly] disease suppression” (Wander & 5 Thereby in the following only those resource categories are listed and valued for which stock changes actually occured in at least one of the cases of the four projects. Other resource categories are left out. 38 Annex Nissen, 2003). Increased SOC levels are achieved by beneficial forms of land use change (e.g. involving land rehabilitation), the shift to adapted crop rotations as well as the use of reduced tillage and manure application. In contrast to the A2 indicator, here we do not account for increased SOC due to incorporation of biomass. Also we do not consider the benefits of avoiding future losses of SOC due to erosion, which is accounted for separately in A4. The considered projects mainly achieve higher levels of SOC by reversing land degradation processes and bringing already strongly eroded land with low SOC content again under cultivation. The valuation of these benefits of SOM is nevertheless difficult. Wander & Nissen (2003) propose to link the value generated by marginal increases in SOC either to reduced production costs or improved yields. For this purpose they propose to estimate the increase in nitrogen availability through mineralization per additional SOC. Thereby the strength of this relation depends on many context variables, such as the initial SOM content or the soil type and structure. Nevertheless, to allow for a rough estimation, Wander & Nissen calculate for a given set of context parameters that, per tonne of SOC, 0.8 kg of nitrogen is made annually available through mineralization (Wander & Nissen, 2003). In this light, household data from Nepal States, e.g. DAP fertilizer prices at 20.3 RS/kg (Agrifood, 2003, p. 141), translate into 113 RS (USD 1.486) /kg N. Thus we arrive to a value of $1.18 USD/t of SOC, where if we discount the 1.18 over 20 years, generates a total value of USD 11.37. A02 Incremental stocks of non-timber biomass (Fuelwood and –material, Fodder & Compost) An increased amount of biomass provides a set of various benefits for the territory in which it occurs. These benefits are composed of a) the additional amount of animal fodder from fodder trees, grasses and crop residues, b) the increased amount of organic biomass for compost production and incorporation into agricultural soil and c) the increased quantity of available fuelwood, crop residues and animal dung for heating purposes. In the below calculation it was thereby assumed that of the additional biomass created through the project, 10% could be valued for energy production, 10% used for compost generation and 20% as animal fodder. The price of fodder was estimated at 12.96 USD/t, it was done by executing barter games with rural peasants, surveying how many hours they would be willing to work in exchange for a fixed amount of tree fodder in different locations of Nepal (Kanel, et al., 2012). The price per t of biomass used for compost was estimated at 10.06 USD, converting the biomass into resulting SOC and valuing it with the method displayed under A01. For fuel wood, Bajgain & Shakya (2005) reported prices at $27 USD/t in the Hills and 20 USD/t in Terai. On another note, WECs (2006) shows that in Kathmandu the price lies considerably higher at around $115 USD/t. With data from Baland et al. (2007), it is instead possible to roughly estimate the benefits of fuel wood based on an average collection time of 5 hours per head load of firewood, which we assume at 25kg. With the reported average opportunity costs of labour of 11.55 NR/h (ibd.) this would result in a worth of $27.18 USD/t of fuel wood. Seeing this relation of benefits and opportunity costs of fuel wood collection, we will assume a conservative average price of $10 USD/t of fuel wood in order to avoid overestimations. For the other variables, we executed estimations of the associated costs for labour in order to arrive to a net worth of fuel wood, tree fodder and compost using the prices from Richards, et al. (1999). A03 Incremental stocks of NTFP in forestry and agro-forestry Medicinal and aromatic plants; lokta paper; pine resin; katha, and sabai grass are important Non-Timber Forest Products in Nepal (Asia-Pacific Forestry Sector Outlook Study, 1997). Since the current study has been undertaken without intensive data from field surveys, the marginal difference in availability and use of NTFP due to project activities cannot be evaluated and is thus not accounted for in this study. Nevertheless NTFP can account for a considerable share of additional benefits and constitutes an important part of rural livelihoods in Nepal. 6 Prices and exchange rate from 2002. 39 Annex A04 Incremental area with erosion protection Complementing A01, it also has to be accounted for the benefits from prevented future erosion. Due to the dominantly mountainous project areas with predominance of slopes, soil erosion is a major issue. As we have lower uncertainty that a further decrease in SOC content is indeed prevented and that it has not yet taken place to a huge extend, here is accounted for benefits from active anti-erosive measures on productive land. In this regard, it is only the area that in light of the project, switches to soil conservation practises, which is studied. For managed agricultural land, based on recent work (Upadhyay, et al., 2005), it would be correct to use an average soil erosion rate of 7.5 t/ha/y, though there can be strong variation observed due to slope, soil texture, rainfall intensity and timing of crop plantation. We will evaluate the benefits of preventing this average rate of soil erosion using a cost of USD1.32 per tonne of lost soil, as established by Acharya et al. (2010) in the context of community forests in Nepal. This leads to annual benefits of USD9.87 per hectare, which are equal to discounted benefits of USD 94.8 per ha over the full 20 years. A09 Incremental timber stocks in forestry and agro-forestry Estimating average timber quantity per hectare is associated with certain imprecision, given the considerable variation of dominant tree types and densities in Nepal’s 35 major forest types and 118 ecosystems (MoF; FAO, 2009). EX-ACT estimates the total increased biomass on forestry and agro forestry area, whereby the IPCC guidelines (IPCC, 2003) provide an estimate of the average Biomass Expansion Factor (BEF) for tropical broadleaf forest at 3.4, which allows to calculate for the average amount of timber per amount of biomass. Timber prices vary strongly by species and quality of the wood. While Kanel et al. (2012) provides the detailed government fixed prices by species, length, girth and state of processing of the wood (round timber, sawn timber), Bhushal (2011) states that prices lie at RS 800 (USD 11.5) per cubic feet for Sal, Rs 1,000 (USD 14) for Shisham and ranged between Rs 300 (USD 4) to 500 (USD 7) for other type of wood. Since marketable quantities at the above mentioned relatively high prices are nevertheless strongly limited, we used a lower timber price derived from international markets lying at USD87.727 per t of timber. Again, for timber extraction we subtracted estimated production costs in order to better approximate the net worth of timber. A10 GHG-balance (reduced emissions and C sequestration) The analyzed four IFAD projects have at this project stage no possibilities to receive funding from carbon markets. Also for similar projects that would be initiated nowadays, current market circumstances offer only very limited options of payments for carbon benefits. Mitigation of climate change can be considered a transfer of wealth from the present generation to future generations. Thus, not representing an actual realized value flowing to a private actor, we try to value in this section the public benefits of the GHG-balance or more precisely, the avoided public costs from additional emissions and reduced sequestration. Such social costs of carbon can be estimated by integrated assessment models such as DICE or FUND, and are associated to high uncertainty on “(1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical 7 Comparing the European price (78 US$/m3) and US price (US$ 33/ m3) in the end of 2012, a conservative price of US$ 50/ m3 was chosen. Transformed in equivalents per tonne using a timber density of 0.57, it is 87.72 US$/t.” 40 Annex and biological environment, and (4) the translation of these environmental impacts into economic damages” (Interagency Working Group on Social Cost of Carbon, 2010). Results of integrated assessment models vary strongly. For the purpose of this study we will use the value established by the U.S. Interagency Working Group on Social Cost of Carbon lying at 21 USD per avoided tonne of carbon (ibd.). A11, A12 Incremental protected natural areas and new forest plantation Additional conserved natural forest, the conservation of peatlands and wetlands as well as additional plantation forest are by many societal actors also perceived as having a pure existence value, beyond the instrumental benefits they provide. One example of such cultural values of natural resources is the importance of forests for religious beliefs and practices in Nepal. While this study did not value such existence values in any way, their general importance is here shortly acknowledged. Case study: Nepal Leasehold Forestry Livestock Programme In fragile mountain ecosystems specific to the Himalayan region, land-use changes, forest and soil degradation add to strengthen greenhouse gas emissions. The research community converges to consider Nepal as an interesting choice for analysing the dynamics of land-use changes, forest and soil degradation and C sequestration processes due to its highly fragile ecosystem, coupled with one of the most serious problems of forest and soil degradation in the world (Upadhyay, et al., 2005). The Leasehold Forestry and Livestock Programme (LFLP) covers the middle hills area, where a large percentage of the population is poor. It targets low income households in the 22 districts not covered by the ongoing IFAD Western Uplands Poverty Alleviation Project, with particular attention to those living in areas adjacent to degraded forest and to those facing strong difficulties to secure enough food for their families all year round. With a budget of 16 million US$ (2005-2013), it targeted 43, 000 households, and combined with the precedent leasehold project, the Hills Leasehold Forestry and Forage Development Project (HLFFDP, 1991-2003), it represents an aggregate of 50,678 household beneficiaries (aggregated budget of 31 million). The LFLP strongly targets to increase the benefits of households by improving the natural resource base on which their agricultural livelihoods are based upon. This section tries to estimate physical quantities of natural resource stock changes and provides as well a careful attempt of valuing them monetarily in order to provide a rough estimation of their importance. Table below applies the earlier presented natural resource estimation and valuation framework on the LFLP. 41 Annex Table 8: Impact of the LFLP on natural resource stock changes Project: LFLP 1 United States Dollar = 85.85 (02/05/2013) Nb farmers 50678 Units Quantity Economic Estimated  total Area 45145 (units) price  (US$) Value (US$) Duration: 21 Natural Capital Direct private value A01 Incremental accumulated SOC on cultivated land (soil fertility) tC 106859 $11.37 $1,214,991 A02 Incremental stocks of non-timber biomass t dm 653634 / $8,636,386 Fuelwood and -material t dm 163409 $7.00 $1,143,860 Fodder t dm 326817 $18.74 $6,124,796 Compost t dm 163409 $8.37 $1,367,730 A03 Incremental stocks of NTFP in forestry and agro-forestry $0 Sum direct  private value $9,851,377 Indirect private value A04 Incremental area with erosion protection ha 33360 $94.80 $3,162,515 A05 Incremental area with increased drought resilience ha 7095 11.7 $83,011 A06 Incremental water volume stored (dams, ponds, water harvesting) m3 / / A07 Incremental water volume saved by improved irrigation practices m3 / / A08 Incremental flood protected area ha / / Sum indirect private  value $3,245,525 Public value A09 Incremental timber stocks in forestry and agro-forestry t dm 230608 $87.72 $20,228,776 A10 GHG balance (reduced emissions and C sequestration) t CO2-e 4333801 $21.00 $91,009,814 A11 Incremental protected natural areas (forestry, peatland, wetland) (existance value) ha / / A12 Incremental new forest plantation (existance value) ha / / Sum public value $111,238,590 Total Natural capital $124,335,492.70 Valuing natural resources is associated to various difficulties and uncertainties, such as type, quantity and time frame of their use, but also on the differing benefits from their existence, consumption or sale. Environmental valuation in contexts of limited markets and limited information is thus necessarily an approximate endeavour. Since the above listed resources will neither be valorised at the indicated prices, nor in full quantities, one has to strictly specify that the indicated values may not be interpreted as income streams flowing to any agent. The value of the natural capital stock instead is an indication and illustration of the value of natural resources created by the project beyond purely physical accounting. In such a way, the LFLP caused over the full period of the analysis an estimated increase in SOC by 106,859 t. Measured with the value of annually provided Nitrogen by mineralization on cultivated area, this is equivalent to a fertilizer value of appr. USD 1,2 million. The incremental fuelwood stock is assumed at 163,409 t of dry matter, equivalent to a value of appr. USD 1.1 million. The additional animal fodder accounts for 326,817 t of dry matter with a value of $6.1 million. The project induced amount of compost and crop residues incorporated in topsoil (mulching) accounts for a total amount of 163,409 t of dry matter that increases SOC and thus N availability to an extend equal to a fertilizer value of USD 1,4 mio. The area brought under active anti-erosive measures accounts for 33,360 ha, equal to an additional value provision of USD 3,2 million. Similarly only a small area of 7,095 ha under the LFLP is concerned by a higher drought resilience (and concerns former annual cultures that were brought under agroforestry), equal to a value of USD 83,011. Besides this, the LFLP creates 230,608 t of dry matter of timber with a value of USD 20.2 million. As provided by the EX-ACT accounting further above, the LFLP’s benefits of 4.3 million tCO2-e can be valued at 21 USD as the estimated global Social Cost of Carbon. In such a way the LFLP provided additional public benefits of USD 91 million. Besides the physical number in CO2-e, this monetary evaluation further underlines the strong co- benefits of the project for GHG mitigation. Focusing on the overall impact of the LFLP on natural capital, it accounts for a created value of USD 124 million. This is equal to an increased value of USD 2,754 per hectare over all the analyzed period of 21 years, or an annual value of USD 131 per hectare. 42 Annex Considering the high values of both, the additional natural resources as well as the GHG benefits, and acknowledging the crucial need for further increases in private household incomes, a moderate payment for such environmental services is imaginable. It would comprehensively target the different objectives of increasing incentives to invest in the natural resource base, mitigating GHG emissions and establishing a cash transfer to rural households. 43 Annex ANNEX 9: Detailed List of Data Possibilities in EX-ACT Complementing the most central information in section Chapter 4, the table below provides a full list of data and parameters that can be accommodated by EX-ACT. Following the cited main chapter on data needs, also the table at hand differentiates between those variables that make use of the Tier 1 approach that relates activity data to corresponding default coefficients. This is opposed to Tier 2 specifications that provide themselves regional specific values for carbon pools and emission factors. Thereby EX-ACT allows for a combination of the Tier 1 and Tier 2 approch and most oftenly a dominant Tier 1 approach is complemented with as much Tier 2 data as available. Data is only required when the analyzed project impacts the topic area. Table 9: Detailed list of data that can be accommodated in EX-ACT Tier Project scenario & Baseline Project start level scenario 1) Land use change module Deforestation Area subject to deforestation & type of Current forest type (ha) land use after conversion (ha) Amount of wood exported before Tier 1 conversion (t DM ha-1) Burning as form of conversion (yes/no) Time dynamic of conversion (linear, immediate, exponential) Specification of forest:  Above ground biomass (t dm ha-1) Tier 2 (optional)  Below ground biomass (t dm ha-1)  Soil carbon content (t C ha-1)  Carbon stocks in litter and deadwood (t C ha-1)  Specifications on forest combustion (Percentage of dm burnt; emission factors for CH4 and N20) Afforestation and Reforestation Current land use (ha) Future forest type (ha) Tier 1 Burning as form of conversion (yes/no) Time dynamic of conversion (linear, immediate, exponential) 44 Annex Specification of newly established forest:  Average growth rates during first 20 (optional) years for above and below ground biomass (t C ha-1) Tier 2  Growth rates after first 20 years (t C ha-1)  Soil carbon content (t C ha-1) Carbon stocks in litter and deadwood (t C ha- 1 ) Other land use change Current land use (ha) Future land use (ha) Burning as form of conversion (yes/no) Tier1 Time dynamic of conversion (linear, immediate, exponential) 2) Crop production module Annual systems Current crop production (ha) Future crop production (ha) Existence of the specific crop management practices: Improved agronomic practices, nutrient Tier 1 = management, no till/residue management, water management, manure application (yes/no) Crop yield (t ha-1 yr-1) = Practicing of residue / biomass burning = Time dynamic of crop shift (if applicable; linear, immediate, exponential) (optional) Rates of soil C sequestration (t C ha-1 yr-1) = Tier 2 Residues/Biomass available for burning (t dm = ha-1) Perennial systems Current crop production (ha) Future crop production (ha) Crop yield (t ha-1 yr-1) = Practicing of residue / biomass burning = Tier 1 Time dynamic of crop shift (if applicable; linear, immediate, exponential) Above and below ground biomass growth rate onal) (opti Tier = 2 (t C ha-1 yr-1) 45 Annex Rates of soil C sequestration (t C ha-1 yr-1) = Burning (Quantity of residues and periodicity) = Irrigated rice Current crop production (ha) Future crop production (ha) Specification of management practices:  Intermittently flooded, continuously flooded, deepwater/rainfed  Flooded preseason, non-flooded = preseason length shorter or longer Tier1 than 180 days  Specify how organic amendments are managed (burning, incorporation, export) Crop yield (t ha-1 yr-1) = Time dynamic of crop shift (if applicable; linear, immediate, exponential) (optional) Rates of soil C sequestration (t C ha-1 yr-1) = Tier 2 Quantity of straw burnt (t dm ha-1) = 3) Grassland and livestock module Grassland systems Current grassland area (ha) Future grassland area (ha) State of grassland degradation (non- degraded, moderately degraded, severely = degraded, improved with or without inputs) Tier 1 Grass yield (t ha-1 yr-1) = Practicing and periodicity of grassland = burning Time dynamic of grassland alteration (if applicable; linear, immediate, exponential) Livestock systems Current type and number of livestock Future type and number of livestock Production quantity of most central = Tier 1 product (meat, milk etc.) Feeding practices: Percentage of herd size subject to improved feeding practices, use = of dietary additives/specific agents, 46 Annex improved breeding practices Time dynamic of shift in livestock numbers (if applicable; linear, immediate, exponential) Mean annual temperature (MAT) of the region (in °C) Emission of N2O from manure management (kg N-N2O/kg N) (optional) Tier 2 Emissions of CH4 from manure management (kg CH4 per head & yr) Enteric fermentation (kg CH4 per head & yr) % feed corresponding to pasture range and paddock systems 1) Land degradation module Forest degradation Forest type (ha) Type of forest by state of degradation (non, very low, low, moderate, large, = extreme) Tier 1 Occurrence, periodicity and impact of fire Time dynamic of shift in state of degradation Degradation level (in % of biomass lost) (optional) Specification of forest: Tier 2 • Above ground biomass (t dm ha-1) • Below ground biomass (t dm ha-1) • Soil carbon content (t C ha-1) • Carbon stocks in litter and deadwood (t C ha-1) Degradation of organic soils (peatlands) Vegetation type and area concerned by drainage of organic soils Area effected by peat extraction (nutrient rich Tier 1 / poor) Time dynamic of drainage or peat extraction Emissions factor for loss of C associated with onal) (opti Tier 2 drainage of organic soils (t C ha-1 yr-1) 47 Annex On-site CO2 and N2O emissions from active peat extraction (t C ha-1 yr-1, kg N2O-N ha-1 yr- 1 ) 2) Inputs & investments module Inputs Quantity of fertilizer, pesticides, liming applied by type (in total tonnenes of = Tier 1 nutrients per year) Time dynamic of change in quantities of agricultural inputs (optional) CO2 and N20 emission factors from direct applications and indirect emissions Tier 2 Energy consumption Quantity of electricity, liquid or gaseous Tier 1 = fuel, and wood consumed by type (optional) Tier 2 Emission factors by type of energy source Building of infrastructure Type and amount of irrigation infrastructure installed (ha) Type and size of building constructed Tier1 (m2) Time dynamic of construction of irrigation infrastructure and buildings (optional) Tier 2 Emission factor (t CO2/m2) 48