2011 69859 Brazil Low Carbon Case Study Technical Synthesis Report TRANSPORT Coordination Wagner Colombini Martins, LOGIT Paul Procee, The Work Bank Christophe de Gouvello, The World Bank Jennifer Meihuy Chang, The World Bank Technical Team Fuad Jorge Alves José (Principal contributor), WORLD BANK Wagner Colombini Martins, Fernando Howat Rodrigues, Arthur C. Szasz, and Sérgio H. Demarchi, LOGIT Ronaldo Ballassiano, COPPE-UFRJ 2011 Brazil Low Carbon Case Study Technical Synthesis Report TRANSPORT Coordination Wagner Colombini Martins, LOGIT Paul Procee, The Work Bank Christophe de Gouvello, The World Bank Jennifer Meihuy Chang, The World Bank Technical Team Fuad Jorge Alves José (Principal contributor), Wagner Colombini Martins, Fernando Howat Rodrigues, Arthur C. Szasz, and Sérgio H. Demarchi, LOGIT Ronaldo Ballassiano, COPPE-UFRJ © 2011 The International Bank for Reconstruction and Development/The World Bank 1818 H Street, NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org E-mail: feedback@worldbank.org All rights reserved This volume is a product of the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work and accepts no re- sponsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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For more information on the Low Carbon Growth Country Studies Program or about ESMAP’s cli- mate change work, please visit us at www.esmap.org or write to us at: Energy Sector Management Assistance Program The World Bank 1818 H Street, NW Washington, DC 20433 USA e-mail: esmap@worldbank.org web:www.esmap.org TECHNICAL SYNTHESIS REPORT BRAZIL LOW CARBON CASE STUDY Coordenação Wagner Colombini Martins, LOGIT Paul Procee, The World Bank Christophe de Gouvello, The World Bank Jennifer Meihuy Chang, The World Bank Equipe Técnica Fuad Jorge Alves José (Principal contribuidor), Wagner Colombini Martins, Fernando Howat Rodrigues, Arthur C. Szasz e Sérgio H. Demarchi, LOGIT Ronaldo Ballassiano, COPPE-UFRJ Table of Contents 1. INTRODUCTION ---------------------------------------------------------------------------------------------16 1.1. Historical overview of transport in Brazil ---------------------------------------------------19 6 1.2. Transport and the productive sectors --------------------------------------------------------22 1.3. Impacts generated by the transport sector --------------------------------------------------23 1.4. Transport and carbon emissions --------------------------------------------------------------25 1.5. General considerations for urban transport ------------------------------------------------28 1.6. General considerations for regional transport --------------------------------------------34 1.7. Institutional overview ---------------------------------------------------------------------------38 1.8. Regulatory overview ----------------------------------------------------------------------------40 1.9. Initial prospective analysis ---------------------------------------------------------------------40 2. METHODOLOGICAL APPROACH -------------------------------------------------------------------------42 2.1. Scenario building - general considerations -------------------------------------------------42 2.2. Future carbon dioxide (CO2) emission scenarios ------------------------------------------43 2.3. Regional transport assumptions --------------------------------------------------------------44 2.4.1. Urban-center categories ------------------------------------------------------------------ 46 2.4. Urban transport assumptions -----------------------------------------------------------------45 2.4.2. Assumptions for modeling urban mobility -------------------------------------------- 50 2.4.3. Investment assumptions for the Reference Scenario -------------------------------- 54 2.5.1. Transport planning and modeling ------------------------------------------------------ 56 2.5. Aspects of transport modeling -----------------------------------------------------------------55 2.5.2. The “Four-Stages� model ------------------------------------------------------------------ 57 2.5.3. Macroeconomic scenarios ---------------------------------------------------------------- 59 2.5.4. Emission modeling in the transport sector -------------------------------------------- 59 2.6.1. Parameters and general criteria --------------------------------------------------------- 61 2.6. Models for assessing financial results --------------------------------------------------------61 2.6.2. Net investments curve --------------------------------------------------------------------- 62 Technical Synthesis Report | TRANSPORT 2.6.3. Other indicators selected as analysis parameters ------------------------------------ 63 2.6.3.1. Net investment curve with “fuel� effect --------------------------------------- 63 2.6.3.2. Net investment curve with “Fuel� and “Operation� effects ---------------- 66 2.6.3.3. Final net investment curve ------------------------------------------------------ 68 2.6.4. Parameters and criteria for evaluating fuel economies ------------------------------ 71 2.6.5. Criteria and sources for urban passenger modeling --------------------------------- 72 2.6.5.1. Evaluation of operating gains in the “T5 Corridor� -------------------------- 73 2.6.5.2. Investment, operation and maintenance costs and operating gains based on the study of the “T5 Corridor� --------------------------------------- 76 2.6.5.3. Investments, operation and maintenance costs and operating gains for BRT in the present study ----------------------------------------------------- 77 2.6.5.4. Evaluation of social benefits in the “T5 Corridor� ----------------------------81 2.6.5.5. Evaluation of the social benefits of the BRT ----------------------------------83 2.6.5.6. Evaluation of direct and indirect social benefits of the BRT ---------------85 2.6.6. Criteria and sources for regional modeling --------------------------------------------88 2.6.6.1. Benefits for operators ------------------------------------------------------------89 2.6.6.2. Benefits for users -----------------------------------------------------------------90 7 2.7. Conclusions ----------------------------------------------------------------------------------------91 3. REFERENCE SCENARIO ----------------------------------------------------------------------------------- 93 3.1. Building the Reference Scenario -------------------------------------------------------------- 94 3.2. Reference Scenario projections --------------------------------------------------------------- 95 4. MITIGATION OPTIONS ------------------------------------------------------------------------------------ 100 4.1.1. Modal shift - freight-------------------------------------------------------------------------100 4.1. Mitigation options for regional transport -------------------------------------------------- 100 4.1.2. Modal shift - passengers -------------------------------------------------------------------108 4.1.3. Existing barriers ---------------------------------------------------------------------------112 4.1.4. Measures for overcoming barriers ------------------------------------------------------112 4.2.1. Use of high-occupancy public transport ------------------------------------------------113 4.2. Mitigation options for urban transport ----------------------------------------------------- 113 4.2.2. Description of policies for the BRT and Metro -----------------------------------------123 4.2.3. Travel demand management -------------------------------------------------------------124 4.2.3.1. Existing Policies -------------------------------------------------------------------130 4.2.3.2. Political economy scenario -----------------------------------------------------131 4.2.4. Implementation of bikeways -------------------------------------------------------------132 4.2.4.1. Existing policies ------------------------------------------------------------------136 4.2.4.2. Description of policies -----------------------------------------------------------137 4.2.4.3. Political economy scenario -----------------------------------------------------137 4.3. Low carbon scenario for ethanol - increasing proportion of ethanol 4.3.1. Parameters for the low-carbon scenario ---------------------------------------------------------139 Technical Synthesis Report | TRANSPORT consumption by “Flex-Fuel� vehicles --------------------------------------------------------- 137 4.3.1.1. Assessing the size of the “Flex-Fuel� fleet -------------------------------------139 4.3.1.2. Proportion of consumption of bio-ethanol/gasoline “Flex-Fuel� fleet -------140 4.3.2. Gains in terms of emissions reductions -------------------------------------------------141 4.3.3. “Investments required� abatement curve ----------------------------------------------144 4.3.4. Barriers and measures to overcome them ---------------------------------------------145 4.3.4.1. Establishment of a National Fuels Policy --------------------------------------146 4.4. Consolidated results --------------------------------------------------------------------------- 148 5. GENERAL CONCLUSIONS---------------------------------------------------------------------------------- 158 LIST OF TABLES Table 1: Vehicle Production 1999 – 2008 ------------------------------------------------------------------------20 Table 2: Daily travel by motorized transport in Brazilian cities / Metropolitan Regions (2005) -------------------------------------------------------------------------------------29 Table 3: Total number of trips in RJMR (per day) ---------------------------------------------------------------33 8 Table 4: Percentage of Population and Urban Location - Brazil and cities with over 20,000 inhabitants -----------------------------------------------------------------39 Table 5: Investment in Regional Transport Infrastructure ---------------------------------------------------45 Table 6: Major urban regions and municipalities by similarity cluster-------------------------------------47 Table 7: Selected socioeconomic and demographic indicators for urban similarity cluster -----------48 Table 8: Households by income (minimum wages) by urban cluster – 2007 ------------------------------49 Table 9: Urban Mobility Plans Available -------------------------------------------------------------------------50 Table 10: Trip-Generating Factors by Similarity Clusters ----------------------------------------------------52 Table 11: Investments in Public and Mass Transport Systems-----------------------------------------------55 Table 12: Entry Data: COPERT-------------------------------------------------------------------------------------60 Table 13: Fuel Production costs -----------------------------------------------------------------------------------72 Table 14: Economic Operating Costs of the Public Transport System in Rio de Janeiro, considered this study (US$ per km) ------------------------------------------------------------------------------74 Table 15: Investment, Operation and Maintenance Costs and Operating Gains calculated on the basis of the “T5 Corridor� study (US$ per km) ---------------------------------------------------------76 Table 16: Km of BRT to be Implemented in the Reference and Low Carbon Scenarios ------------------77 Table 17: Values of Investments and Costs of O & M Operations and Gains in the Reference and Low-Carbon Scenarios ---------------------------------------------------------------------------78 Table 18: Values of Investments and Costs of O & M Operations and Gains in the Reference and the Low Carbon Scenarios for BRT Implementation ---------------------------------------80 Technical Synthesis Report | TRANSPORT Table 19: Investment values, O & M Costs, Operating Gains and Social Benefits based on the study of the T5 Corridor Study (in US$ per km) --------------------------------------------------------83 Table 20: Values of Investments and O & M Costs, Operating Gains and Social Benefits in the Reference and Low-Carbon Scenarios -------------------------------------------------------------------84 Table 21: Calculation of the Values of Indirect Social Benefits ----------------------------------------------- 86 Table 22: Values of Total Social Benefits (Direct and Indirect) Calculated for BRT Deployment ------- 87 Table 23A: Projections of Consumption by Type of Fuel in the Reference Scenario ---------------------- 93 Table 23B: Estimates of Emissions in 2007 by Type of Fuel, According to Different Criteria ----------- 94 Table 24: Load and GHG Emissions for the Reference Scenario, 2007–30 --------------------------------- 96 Table 25: Comparison of Projected Emissions Reduction for Regional Transport in 2030: Modal Shift Scenario ------------------------------------------------------------------------------------- 101 Table 26: Avoided Emissions - New Modal Shift---------------------------------------------------------------- 102 Table 27: Regional Freight Transport: Comparison of Investments in the Reference and Low-carbon Scenarios, 2010–30 ------------------------------------------------------- 103 Table 28: Average cost of avoided CO 2 --------------------------------------------------------------------------- 107 Table 29: High Speed Train (TAV), Loads and Emissions: Baseline x Low Carbon Scenarios ----------- 108 Table 30: High Speed Train (TAV): Emissions Avoided-------------------------------------------------------- 109 Table 31: Average costs of CO 2 avoided ------------------------------------------------------------------------- 111 9 Table 32: Loading and emissions - BRT - Baseline x Low Carbon -------------------------------------------- 115 Table 33: Emissions avoided – BRT------------------------------------------------------------------------------- 115 Table 34: Passenger Loads and Emissions - Metro - Baseline Scenario x Low Carbon ------------------- 119 Table 35: Avoided Emissions – Metro ---------------------------------------------------------------------------- 119 Table 35A: Emissions avoided - Metro + BRT ------------------------------------------------------------------- 120 Table 36: Average costs of avoided CO 2 e – BRT ---------------------------------------------------------------- 123 Table 38: Gains from Demand Management in Brazil´s large cities ----------------------------------------- 126 Table 39A: Loads and emissions - Demand Management of Urban Transport - Baseline x Low Carbon Scenarios--------------------------------------------------------------------------------- 127 Table 39B: Emissions avoided - Demand Management of Urban Transport - Baseline x Low Carbon Scenarios--------------------------------------------------------------------------------- 127 Table 40: Average cost of avoided CO 2 and t -------------------------------------------------------------------- 129 Table 41 Bikeway Loads and Gains in Avoided Emissions, 2010–30 --------------------------------------- 133 Table 41A: Loads and Emissions - Implementation of Bikeways - Baseline x Low Carbon Scenarios--------------------------------------------------------------------------------- 134 Table 41B: Avoided Emissions - Implementation of Bikeways ---------------------------------------------- 134 Table 42: Average Cost of Tons of CO 2 Avoided ---------------------------------------------------------------- 136 Table 43 - Light Passenger Vehicle Fleet ( by Type of Fuel ) --------------------------------------------------- 139 Table 44 - States were Alcohol Prices were Competitive with Gasoline Prices (April 2009) ----------- 140 Table 45: Avoided Emissions - Low Carbon Ethanol----------------------------------------------------------- 142 Table 46: Charging and Emissions, Ethanol - Baseline x Low Carbon -------------------------------------- 143 Technical Synthesis Report | TRANSPORT Table 47: Investments and Costs of Avoided Tons of CO2 ----------------------------------------------------- 145 Table 48: Fuel Consumption Trends in the Reference and Low-Carbon Scenarios ---------------------- 148 Table 49: Evolution of Direct Emissions (in MtCO2) in the Reference and Low Carbon Scenarios ----- 149 Table 50: Transport-sector Load and GHG Emissions in the Reference and Low-carbon Scenario ---------------------------------------------------------------------------- 156 LIST OF FIGURES Figure 1: Evolution of the road and rail networks (1996-2006)--------------------------------------------- 19 Figure 2: Production and numbers of light vehicles in circulation ------------------------------------------ 20 Figure 3: Production and numbers of heavy vehicles in circulation ---------------------------------------- 20 Figure 4: Growth rate of the vehicle fleet ------------------------------------------------------------------------ 21 10 Figure 4A: Percentage Evolution of the Fleet in Circulation, GDP and Population in Brazil ------------- 21 Figure 5: Fossil-fuel Consumption, by Sector ------------------------------------------------------------------- 22 Figure 6: Relative contribution of air pollutant emissions (by source) in the São Paulo Metropolitan Region ---------------------------------------------------------------------------- 24 Figure 7: Liquid fuels consumption in Brazil, by sector ------------------------------------------------------- 26 Figure 8: Emissions in Brazil´s Transport Sector (2007) ----------------------------------------------------- 27 Figure 9: Evolution of the Urban and Rural Population in Brazil -------------------------------------------- 28 Figure 10: Daily Motorized Trips: Public V Individual Transport Modes ---------------------------------- 29 Figure 11: Percentage Changes in Trips made by Public and Individual Transport in the São Paulo Metropolitan Region -------------------------------------------------------------- 30 Figure 12: Daily Motorized Trips, by mode, in the São Paulo Metropolitan Region (1997 and 2007) ------------------------------------------------------ 31 Figure 13: Changes in Incomes in the São Paulo Metropolitan Region (November 2007 values) ---------------------------------------------------------------- 32 Figure 14: Modal Split of Regional Freight Transport in Brazil ---------------------------------------------- 34 Figure 15: Geo-referenced Multimodal Network -------------------------------------------------------------- 35 Figure 16: Investments in Regional Transport Infrastructure outlined in the PAC and PNLT ---------- 38 Figure 17: Emissions by the Transport System, by segment (2007) ---------------------------------------- 41 Figure 18: Households by income (minimum wages) by urban cluster (2007) -------------------------- 49 Figure 19: Public Transport and Individual Trip-Generation Factors, by Urban Cluster Similarity--- 53 Technical Synthesis Report | TRANSPORT Figure 20: Household Trip-Generation Factors according to Income Levels, by Urban Cluster Similarity ---------------------------------------------------------------------------------------- 53 Figure 21: Analytical model for transport planning ----------------------------------------------------------- 56 Figure 22: Sequencing of the “Four-Stage� Transport Model ------------------------------------------------ 57 Figure 23: The “Net Investments� Curve ------------------------------------------------------------------------ 62 Figure 24: The “Net Investments with Fuel Effect� Curve ---------------------------------------------------- 64 Figure 25: The “Net Investments with Fuel and Operation Effects Curve:� -------------------------------- 67 Figure 26: The “Final Net Investments� Curve------------------------------------------------------------------ 70 Figure 27: Linking Regional and Urban Transport to Fuel Consumption ---------------------------------- 92 Figure 28: Evolution of Transport Sector Emissions in the Reference Scenario -------------------------- 97 Figure 29: Fuel Consumption Trends (in TEP) by 2030, by type of vehicle in the Reference Scenario -------------------------------------------------------------------- 97 Figure 30: Comparison of the Evolution of Emissions by Vehicle Type in the Reference Scenario x Hypothetical Scenario involving gasoline and diesel ------------------------------- 98 Figure 31: Evolution of Regional Transport Emissions to 2030 (by vehicle type) in the Reference Scenario ------------------------------------------------------------------------------------------ 99 Figure 32: Evolution of Urban Transport Emissions to 2030 (by type of vehicle) 11 in the Reference Scenario ------------------------------------------------------------------------------------------ 99 Figure 33: Comparison of Modal Distribution of Freight Load: Reference v Low Carbon Scenario -------------------------------------------------------------------------------- 102 Figure 33A: Freight carried on Teles Pires Hidrovia x BR-163 - Reference Scenario --------------------- 104 Figure 33B: Freight carried on Teles Pires Hidrovia x BR-163 - Low-carbon Scenario ------------------ 104 Figure 33C: Soybean Freight Loads in Bahia - Reference Scenario ------------------------------------------ 105 Figure 33D: Soybean Freight Loads in Bahia - Reference Scenario ----------------------------------------- 105 Figure 34: Evolution of Emissions: Reference versus Low Carbon Scenario ------------------------------ 106 Figure 35: Curves of cost reduction (nominal) ----------------------------------------------------------------- 106 Figure 36: Abatement Cost Curves (present value) ----------------------------------------------------------- 107 Figure 37: High Speed Train (TAV): Modal Load Shift - Baseline x Low Carbon Scenario --------------- 109 Figure 38: Evolution of emissions: Reference x Low Carbon Scenario ------------------------------------- 110 Figure 39: Cost Reduction Curves (Nominal) ------------------------------------------------------------------- 110 Figure 40: Abatement Cost Curves (Present Value) ----------------------------------------------------------- 111 Figure 40A: Belo Horizonte: with and without Investments in BRT (2030 Reference and Low Carbon Scenarios) - Public Transport Passenger Loads --------------------- 116 Figure 40B: Belo Horizonte: with and without Investments in BRT (2030 Reference and Low Carbon Scenarios) - Private Vehicle Users -------------------------------------- 116 Figure 41: Modal Distribution of Passenger Load - BRT - Baseline Scenario x Low Carbon ------------ 117 Figure 42: Fuel Consumption Trends (TEP) up to 2030, by Vehicle Type - BRT- Technical Synthesis Report | TRANSPORT Baseline x Low Carbon Scenario ---------------------------------------------------------------------------------- 117 Figure 43: Evolution of Emissions: Baseline Scenario x Low Carbon --------------------------------------- 118 Figure 44: Modal Distribution of loads - BRT + Metro --------------------------------------------------------- 120 Figure 45: Fuel consumption (TEP) - BRT + Metro ------------------------------------------------------------ 121 Figure 46: Evolution of emissions: BRT + Metro --------------------------------------------------------------- 121 Figure 47: Cost Abatement Curves for BRT + Subway (nominalNominal) --------------------------------- 122 Figure 48: Cost Abatement Curves for BRT + Subway (present value) ------------------------------------- 123 Figure 49: Evolution of Emissions - Demand Management of Urban Transport: Baseline x Low Carbon Scenario ---------------------------------------------------------------------------------- 128 Figure 50: Cost Reduction Curves (nominal) ------------------------------------------------------------------- 128 Figure 51: Abatement Cost Curves (present value) ----------------------------------------------------------- 129 Figure 52: Cost per Ton Avoided X Investments Required by Urban Demand Management (per annum up to 2030) --------------------------------------------------------------------------- 130 Figure 53: Evolution of Emissions - Implementation of Bikeways: Baseline x Low Carbon Scenario ---------------------------------------------------------------------------------- 135 Figure 54: Cost Abatement Curves ( nominal)------------------------------------------------------------------ 135 Figure 55: Cost Abatement Curves (present value) ----------------------------------------------------------- 136 Figure 56 - Evolution of Light Vehicle Sales by Fuel Type (1979-2007) ------------------------------------ 138 12 Figure 57: Consumption Ethanol x Gasoline for Vehicle Fleet (Total and “Flex-Fuel�) --------------------------------------------------------------------------------------------- 141 Figure 58: Emissions: With and Without the Effects of the Ethanol Measure ----------------------------- 143 Figure 59: Loading With and Without the Effect of Low Carbon Ethanol Measure ----------------------- 144 Figure 60: Emission and Mitigation of Urban and Regional Transport 2010 through 2030 ------------ 150 Figure 61: Growth in Transport Fleet, 2007 to 2030 ---------------------------------------------------------- 151 Figure 62: Changes in Passenger Load --------------------------------------------------------------------------- 152 Figure 63: Comparison of Modal Distribution of Freight Load, 2008–30 ---------------------------------- 153 Figure 64: Comparison of Modal Distribution of Passenger Load, 2008–30 ------------------------------ 154 Figure 65: Emissions-reduction Potential in the Transport Sector, 2008–30 ----------------------------- 155 Figure 66: Comparison of Emissions in Reference, Low-carbon, and “Fossil-fuel� Scenarios, 2008–30 ------------------------------------------------------------------------------------------------- 157 Technical Synthesis Report | TRANSPORT Acronyms ABRACICLO: Associação Brasileira dos Fabricantes de Motocicletas, Ciclomotores, Motonetas, Bicicletas e Similares (Brazilian Association of Motorcycle, Moped, and Bi- cycle Manufacturers) ANAC: Agência Nacional de Aviação Civil (National Civil Aviation Agency) 13 ANP: Agência Nacional de Petróleo (National Petroleum Agency) ANTAQ: Agência Nacional de Transportes Aquaviários (National Agency for Water Transport) ANTP: Agência Nacional de Transportes Públicos (National Agency for Public Trans- portation) ANTT: Agência Nacional de Transportes Terrestres (National Land Transport Agen- cy) BRT: Bus Rapid Transit CDIAC : Carbon Dioxide Information Analysis Center CETESB: Companhia de Tecnologia de Saneamento Ambiental (Environmental Sanita- tion Technology Company) DENATRAN: Departamento Nacional de Transportes (Transportation National De- partment) DUTO: Pipelines EMBRAPA: Empresa Brasileira de Pesquisa Agropecuária (Brazilian Enterprise for Agricultural Research) EMME: modeling software for transport FERRO: Railroads FIFA: International Federation of Football Association. FIPE: Fundação e Instituto de Pesquisas Econômicas (Foundation and Institute for Economic Research) GDP: Gross Domestic Product GEIPOT: Empresa Brasileira de Planejamento de Transportes (Brazilian Company for Transportation Planning) GHG: Greenhouse Gases HIDRO: Waterways Technical Synthesis Report | TRANSPORT IBGE: Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics) INFRAERO: Empresa Brasileira de Infra-Estrutura Aeroportuária (Brazilian Company for Airport Infrastructure) IPEA: Instituto de Pesquisa Econômica Aplicada (Institute for Applied Economic Re- search) MANTRA: modeling software for transport O&M: operation and maintenance of infrastructure for transport PAC: Plano de Aceleração do Crescimento (Growth Acceleration Program) PDTU: Plano de Desenvolvimento do Transporte Urbano (Plan for Urban Transporta- tion Development) PNE: Plano Nacional de Energia (Energy National Plan) PNLT: Plano Nacional de Logística e Transporte (National Plan for Logistics and Trans- port) PNMC: Plano Nacional Sobre Mudança do Clima (National Plan for Climate Change) POP: Population RM: Metropolitan Regions RODO: Roadways SM: Minimum wage TransCAD: Geographic processing and modeling software for transport VLP: Light commercial vehicles VLP: Light passenger vehicles 14 VPC: Heavy truck vehicles VPO: Heavy bus vehicles Units MtCO2e: Millions tons of CO2 equivalent Passengers x Km: loading of passengers or volume of passengers transported, expresses the total passengers carried multiplied by the number of miles trav- eled. TEP’s: Tons of Equivalent Petroleum Tons x Km: loading cargo or cargo volume transported, expresses the total cargo carried multiplied by the number of miles traveled. Technical Synthesis Report | TRANSPORT ACKNOWLEDGEMENTS This report summarizes the results for the transportation sector from a larger study, the Low Carbon Study for Brazil, developed by the World Bank as part of its initiative to support the integrated efforts of Brazil to reduce global and national Greenhouse Gases emissions, while promoting long-term development. The study is based on an extensive consultation and research process to identify the best available knowledge, scientists, 15 consultants, and centers of excellence. It was prepared after consultations and discussions on the scope of the work, conducted with the Ministries of Foreign Affairs, Environment, and Science and Technology. Several seminars were organized, enabling consultation with representatives of Ministries of Finance, Planning, Agriculture, Transport, Mines and Energy, Industry and Commerce. Public agencies and research center also participated during the consultation sessions, including EMBRAPA, INT, EPE, CETESB, INPE, COPPE, UFMG, UNICAMP and USP. The study covers four key areas with potential low carbon options: (i) land use, land use change and forestry (LULUCF), including deforestation, (ii) transport systems, (iii) production and use of energy, particularly electricity, oil, gas and biofuels, and (iv) municipal waste, solids and liquids. This document has received support from more than 15 technical reports and four summary reports for the four main areas. Also, the study has received support from the World Bank, through resources provided by the Sustainable Development Network for activities related to climate change and regional support through the Energy Sector Management Assistance Program (ESMAP). This synthesis report was prepared by a team led by Wagner Colombini Martins, LOGIT, Christophe de Gouvello and Paul Procee, Bank World. The main contributors were Fuad Jorge Alves José, Wagner Colombini Martins, Fernando Howat Rodrigues, Arthur C. Szasz, and Sérgio H. Demarchi, LOGIT. The World Bank supervision team for the Low Carbon Study for Brazil was composed by Christophe de Gouvello, Jennifer Meihuy Chang, Govinda Timilsina, Paul Procee, Mark Lundell, Garo Batmanian, Adriana Moreira, Fowzia Hassan, Barbara Farinelli, Augusto Jucá, Rogério Pinto, Francisco Technical Synthesis Report | TRANSPORT Sucre, Benoit Bosquet, Alexandre Kossoy, Flávio Chaves, Mauro Lopes de Azevedo, Fernanda Pacheco, Sebastien Pascual, and Megan Hansen. The supervision team would like to thank also Helena Jansen and John Penney for their support in editing and translating the report, respectively. 1 INTRODUCTION This study aims to underpin Brazil’s efforts to explore methods for reducing total emissions of greenhouse gases (GHGs)1 arising from all areas of human activity. More specifically, this study seeks to highlight low-carbon alternatives for Brazil´s transport sector. These alternatives could contribute positively to the world’s climate, as well as benefit Brazil’s socio-economic development. The technical inputs for evaluating 16 potential carbon emissions reduction will be submitted to the Brazilian government to assist it in the design and deployment of joint planning strategies in key sectors, including transport. To ensure that the study targets the most important areas, it adopts an overarching approach. This means that it made full use of available specialist knowledge (thereby avoiding replication of effort) by undertaking a comprehensive survey of the literature and engaging in a wide-ranging consultation process with recognized Brazilian experts and government technical staff. This preparatory work illuminated the need to study the prospects for more efficient solutions and lower GHG emissions, especially carbon dioxide (CO2) for the transport system as a whole. CO2 results from the combustion of any material containing carbon, including fossil fuels such as oil, coal and natural gas, which consist of long chains of hydrocarbons and are widely used for electricity generation and transport purposes. Industrial activities such as metallurgy, steel and cement manufacturing also produce large amounts of CO2. Changes in land use caused by forest fires during or after the process of deforestation are also responsible for CO2 emissions, since the loss of forest cover releases some of the carbon stored in the soil and dead vegetation. Brazil’s contribution to global CO2 emissions is substantial due to forest burning, and halting biomass burning should certainly be a national priority. On the other hand, the country´s contribution to global GHGs by burning fossil fuels is very small, amounting to around 1.2% of total global emissions in 2006, according to data from the CDIAC (Carbon Dioxide Information Analysis Center), an agency of the US Department of Energy. With a population of approximately 186 million inhabitants, Brazil´s emission rate per capita in 2006 was 0.51Mt CO 2, well below the world average of 1.25 Technical Synthesis Report | TRANSPORT Mt per capita. However, this current situation could change over the years if nothing is done to reverse certain trends emerging against a backdrop of sustained economic growth. Particularly, the exploitation of new oil reserves in the ‘pre-sal’ deep-water fields off the Brazilian coast could result in increased fossil-fuel use. Furthermore, although the country has low rates of GHG emissions, this is not the case for local pollutants which are highly hazardous to human health. In large Brazilian cities air pollution levels are excessively high, with transport accounting for over 90% of emissions of gases such as CO, HC and NOx (CETESB, 2005). Since these pollutants are caused by burning fossil fuels in motor vehicles, reducing them can lead to an overall reduction of GHGs - an important indirect benefit of the policies presented throughout this study. 1 The Kyoto Protocol (Annex A) lists six GHGs - carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF 6), for which quantified emissions reduction targets were set. These were selected as the most important gases related to human life, with CO2e the most prominent. Following the general guidelines for the transport sector study, we decided to adopt a methodological approach based on four steps: • Establishment of a “Reference Scenario:� an assessment of the development of future GHG emissions, reflecting the current objectives and long-term economic policies of the Brazilian government; • Identification and quantification of the prospects and options for reducing GHG emissions or for mitigating their effects, in line with the Brazilian government´s 17 development goals; • Assessment of the costs associated with the proposed mitigation options, identifying the main obstacles to their adoption and suggesting possible measures for overcoming them; and • Establishment of a “Low-Carbon Scenario,� also consistent with the government´s goals and long term economic policies; and, finally, an analysis of the macroeconomic impact of shifting from the “Reference Scenario� to a “Low-Carbon Scenario,� highlighting the financial implications this would entail. To quantify and evaluate the potential for reducing carbon emissions in the transport sector, this study developed special models (consistent with the macroeconomic scenario adopted for the study) to determine the requirements for moving freight and passengers. It considered different infrastructure investment levels and profiles on an annual basis through the study’s horizon in 2030, in the two emission scenarios analyzed (reference and low-carbon). The modeling involved constructing separate simulations for urban and regional transport, based upon the traditional groupings used in transport sector studies: • Segmentation according to geographical location in which trips occur: o Urban Transport: travel in the urban-metropolitan area; and o Regional Transport: travel in rural areas, in the air and in places where highways, waterways, railways and pipelines pass through large urban centers. • Segmentation according to load-types: o Passengers Technical Synthesis Report | TRANSPORT o Freight Trips were classified by modes and expressed in units measurable in terms of GHG emissions. In this way, it was possible to evaluate the impacts of different types of vehicles and/or modes of travel. The transport simulation processes used the volume indicator known as “loading� (the values represented by demand for freight or passenger transport on each stretch of a route - urban, regional, overland, waterborne or air), as the “standard unit of measurement�, customarily applied to freight and passenger movements and convertible into “units of GHG emissions�. Normally expressed in volumes of passengers or tons of freight carried times kilometers, the passenger and freight demand indicators were converted into kilometers traveled by each mode of transport: cars, buses, trucks, trains, metro trains, barge convoys, aircraft etc. Each combination of modes using different means of propulsion resulted in different levels of carbon emission. The reference and low-carbon scenarios were structured on the basis of bibliographic research, consultations with specialists and analysis of metropolitan area master plans and government plans and programs. The probable emissions for the reference scenario were calculated and a set of options for mitigation measures that might feasibly be deployed by 2030 was selected. The details of these measures, together with deployment costs and the expected reductions in GHG emissions, are explained throughout this report. 18 The results for the transport sector, calculated using the methodological approach and criteria outlined in the introductory paragraphs, are presented in this report as follows: • Chapter 1 is a general overview of the transport situation in Brazil, its evolution and impacts, highlighting specific issues related to GHGs; • Chapter 2 describes the methodology employed in the study; • In Chapters 3 and 4 analyzes possible strategies that could be considered for reducing GHGs in the transport sector; • Finally, Chapter 5 presents the main conclusions, suggesting possible policies and strategies for Brazil to pursue. Technical Synthesis Report | TRANSPORT 1.1 Historical Overview of Transport in Brazil The history of the transport system in Brazil was significantly influenced by the British railway industry through the 1930s. Over 30,000 km of railways were constructed mainly to facilitate the exportation of raw materials. Following the 1929 crisis and the advent of WWII, the industrialization of Brazil 19 proceeded apace, leading to increased demand for goods and services in the domestic market. This brought about the need to build roads for distributing goods produced in the Southeast - mainly in São Paulo, which had fast become Brazil´s preeminent industrial hub (Shiffer, 1999). With the expansion of the automobile industry from the second half of the 1950s, roads became the primary means of transport. Between the 1940s and 70s the country’s road network (both paved and unpaved) expanded from 185,000 km to around 1.5 million km. At the same time, the railway network declined from 38,000 km to around 30,000 km, of which less than 10% was electrified (ANTT, 2005). More recently, as shown in Figure 1, the paved-road network has continued to expand (32% in 1996-2006), while the railway network has stagnated at around 30,000 km, notwithstanding the upgrading resulting from the gradual privatization of much of the network for freight operations. Figure 1: Evolution of the road and rail networks (1996-2006) Technical Synthesis Report | TRANSPORT The rapid growth of the motor vehicle fleet in Brazil over the last decade confirms Source: ANTT / GEIPOT / Logit the importance of the roads sector. The number of motor vehicles produced in Brazil over the last 10 years has doubled, with the truck fleet (the main consumer of diesel) virtually tripling. Table 1 shows the evolution of production by type of vehicle. Table 1: Vehicle Production 1999 – 2008 Cars 2,425,68 1,109,509 118 Vehicle Type 2008 1999 Growth (%) Light Commercial 426,874 176, 994 131 Trucks 163,681 55, 277 196 20 Buses 38,202 14, 934 155 Total 3,054,725 1,356,714 125 Figures 2 and 3 show the production and total fleet figures for 1977-2007 for cars, Source: ANFAVEA (2009) light-duty commercial vehicles, buses and trucks. Note that the categories of light vehicles increased overall by 300% and trucks by 200%. Figure 2: Production and numbers of light vehicles in circulation Technical Synthesis Report | TRANSPORT Source: DENATRAN / ANFAVEA / Logit Figure 3: Production and numbers of heavy vehicles in circulation Source: DENATRAN / ANFAVEA Processing / Logit It is worth noting the recent upsurge in the number of motor vehicles in circulation (i.e. increased motorization) between 2002 and 2007, as can be seen in Figure 4. Figure 4: Growth rate of the vehicle fleet 21 In the light vehicle category, the number of cars increased by 21.4% and while light- Source: DENATRAN / ANFAVEA / Logit duty commercial vehicles increased by 19.4% during this period. As for heavy-duty vehicles, the number of buses and trucks increased by 16.4% and 17.0% respectively. These growth rates give an idea of the extent of the motorization phenomenon in the country. This finding also confirms the institutional complexity involved in dealing with issues related to atmospheric emissions, including CO2, in the transport sector. The following figure illustrates the comparison between rates of increase of the total fleet in circulation, GDP and Brazil’s population over the past thirty years. Figure 4A: Percentage Evolution of the Fleet in Circulation, GDP and Population in Brazil Technical Synthesis Report | TRANSPORT Source: DENATRAN / ANFAVEA / Logit It is clear that the vehicle fleet has developed much faster than either GDP or population. This development, particularly noticeable over the past 10 years, highlights the continuing upsurge in current growth levels and points to the prospect of even higher future motorization rates. 22 1.2 Transport and the Productive Sectors The recent changes in the dynamics of the global economy are reflected in the performance of the different productive sectors in Brazil. The 1990s were marked by profound changes in many sectors, particularly transport. In the specific case of regional transport, the privatization and concessionary outsourcing of the country´s main transport systems (roads, railways, ports) was responsible for the emergence of new market mechanisms. These are still undergoing a process of adaptation, restructuring and evaluation. Competition with international operators, spurred by easier access to the Brazilian market, is a challenge now faced by practitioners in this sector. Meanwhile, the general transport infrastructure - a determining factor for promoting and generating economic and regional development - has been deteriorating for many years. Therefore, transport costs (and, in the final analysis, the costs of many goods and services), have risen steeply (PLANET, 2006). The so-called “Brazil Cost� is also a key factor preventing Brazilian exports from competing with other, particularly Asian, “emerging� countries. The transport sector is the biggest consumer of fossil fuels, accounting for 50.5% of Brazil´s total. Figure 5 indicates the consumption of fossil fuels by different sectors of the economy. Figure 5: Fossil-fuel Consumption, by Sector Technical Synthesis Report | TRANSPORT In the urban transport sector in Brazil some of services that have larger capacities, Source: BEN (2008) National Energy Balance such as trains and metros, have benefited from privatization and are attracting more customers as a result of increased availability. In addition, low-capacity vehicles (mini-buses and vans) have begun operating in many cities. Frequently operating with little or no supervision by the responsible regulatory bodies, these compete directly with larger- capacity systems such as buses. While the smaller passenger-carrying vehicles provide alternative transport they tend to worsen general traffic conditions (unregulated routes and informal stopping points), contributing to increased congestion and negative environmental impacts. This situation is particularly bad in the large metropolitan areas, which face acute problems of balancing supply and demand for public transport, particularly at peak rush-hour times, when the road and street systems are unable to cope with the excessive volume of vehicles. 23 1.3 Impacts Generated by the Transport Sector The transport sector is vital to economic and social development. While it can be expected that in the coming decades the demand for transport will continue to accelerate worldwide, growth in the developing and emerging countries will be especially rapid due to increasing prosperity. This may well be accompanied by serious environmental impacts, particularly degraded air quality in the urban areas where most of the motorized vehicle fleets operate (Gwillian et al., 2004). In addition to the problems caused by vehicle emissions, other impacts (less studied but no less important) are generated by transport systems, including noise pollution, accidents, increased traffic congestion, increased fuel and energy consumption, time lost in traffic jams, and higher operating costs. Urban transport in Brazil predominantly concerns road transport, given that in urban areas most trips are made by car or bus (e.g. around 55% in the city of Rio de Janeiro). In the case of regional transport, approximately 60% of all freight is transported by trucks, which contribute significantly to the volume of emissions (PNLT, 2007). The gases and particles emitted daily by millions of vehicles in the cities and on intercity highways tend to accumulate in the atmosphere at different concentrations. They are dispersed by wind, trapped by thermal inversion, diluted and washed away by rain, or they may react with one another or with naturally- occurring elements to form secondary pollutants. The more stable gases remain Technical Synthesis Report | TRANSPORT in the atmosphere for longer, sometimes for months or years2, reaching the higher layers of the atmosphere and causing problems of a global magnitude such as the greenhouse effect and depletion of the ozone layer. �lvares Jr. (2007), claims that in the Metropolitan Region of São Paulo (MRSP), motor vehicles are major sources of pollutants from emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx) and sulfur oxides (SOx ). The same author also argues that motor vehicles contribute to emissions of particulate matter (PM), responsible for increasing rates of respiratory disease, especially during colder periods when pollutant concentrations are highest. According to �lvares, the transport sector accounts for the largest percentage of carbon monoxide (CO) emitted in the MRSP: 97% (CETESB, 2005), the result of incomplete combustion when the supply of oxygen is not sufficient to fully oxidize carbon into carbon dioxide. CO restricts the ability of blood to carry oxygen, which 2 CO 2 remains in the atmosphere, on average, for 140 years means that exposure to high levels is more dangerous for those with cardiovascular problems. In unventilated areas exposure to CO can lead to asphyxiation and death (�lvares Jr., 2007). Hydrocarbons (HC) contain various pollutants known as volatile organic compounds (VOCs). Vehicles emit these gases both through the tail-pipe exhaust system and evaporation. The transport sector accounts for approximately 25% of anthropogenic emissions of HC, 35% of them in the industrialized world. In the MRSP 24 vehicles contribute over 97% of the total emissions of this pollutant (CETESB, 2005). HCs are precursors to the formation of tropospheric ozone, and some are toxic or carcinogenic. The transport sector accounts for 50-75% of the HC types in the world considered to be carcinogenic. They can also cause neurological and respiratory problems as well as limit reproduction and children’s growth. In the case of Nitrogen Oxides (NOx), the growing number of vehicles in industrialized countries has virtually cancelled out the gains created by more stringent emission controls. As a result, vehicle emissions of NOx have varied little over the past 20 years. In vehicles, NOx is mainly the result of a thermal process which intensifies as combustion temperatures rise. Unlike CO and CH, the formation of NOx results from poor mixtures and high compression rates, as in diesel engines. In the MRSP the transport sector is the largest source of NOx emissions, contributing to the majority of emissions, as shown in Figure 6 below. Figure 6: Relative contribution of air pollutant emissions (by source) in the São Paulo Metropolitan Region Technical Synthesis Report | TRANSPORT In the specific case of Nitric Oxide (NO2), damage can be caused to the respiratory Source: Report of Air Quality - 2005 CETESB system, and exposure to high concentrations may increase the incidence of respiratory diseases in children, in addition to causing damage to the ecosystems of lakes, estuaries and forests. Ozone (O3) is a secondary pollutant (formed by photochemical reactions in the atmosphere). In the presence of sunlight NOx and HC are the primary precursors of the formation of tropospheric O3. In the stratosphere, ozone occurs naturally, forming a protective layer against ultraviolet radiation. At ground level, however, ozone is a dangerous pollutant and the primary component of photochemical smog.3 Evidence exists linking respiratory diseases with high atmospheric concentrations of O3. Prolonged exposure causes permanent damage to the lungs. O3 also affects crops and causes damage to woodland and general vegetation (�lvares Jr, 2007). 25 Sulfur Oxides (SOx), released mainly in the form of sulfur dioxide (SO2), originate from the oxidation of sulfur in fuel during combustion. Over recent years regulations have forced a marked reduction in the sulfur content of fuels permitted for use in transport. In the United States, Europe and Japan, the levels of sulfur in vehicle fuels are currently very low, at around 10-15 ppm. In Brazil, these are 500 ppm in the metropolitan areas and 2,000 ppm in the interior. �lvares Jr. (2007) claimed that a reduction in the sulfur content of diesel exhaust in Brazil to 50 ppm in metropolitan areas and 500 ppm in the interior, was expected in 2009. In diesel exhaust gases, aerosol sulfate is a major particulate-forming agent. Furthermore, it is estimated that over 12% of the SO2 emitted in urban areas turns into sulfate MP when released into the atmosphere. Sulfur dioxide can increase the incidence of respiratory diseases. Sulfuric acid, together with HNO3 , causes major damage to the ecosystem. Particulate Matter (PM) is composed of solid or liquid particles suspended in the air. MP includes acids and heavy hydrocarbons, carbonaceous material with soluble fractions absorbed even by dust grains. PM10 includes all particles with a diameter of less than 10 micrometers (�lvares Jr., 2007). Due to rainfall, the life-cycle of coarse particles in the atmosphere is less than twenty four hours. MP2.5 includes all particles of a diameter of less than 2.5 micrometers. Particles of this size can be emitted as primary pollutants such as soot, or formed by incomplete combustion, or as acid particles in vehicle exhaust gases. Emission records indicate that engines and vehicles are major contributors of fine particles and intermediate particles (between 2.5 and 10 micrometers) in urban areas. In the MRSP, CETESB estimated in 2005 that about 30% of PM10 was released from diesel vehicle exhausts and 10% from Otto engines. The smaller particles are easily inhaled and can settle deep in the lungs, creating more serious health hazards than those caused by larger particles, which would most Technical Synthesis Report | TRANSPORT probably be expelled or retained in the body’s defenses in the head or throat. 1.4 Transport and Carbon Emissions The alarm regarding increased concentrations of CO2 in the earth’s atmosphere was first sounded by the American scientist Roger Revelle in the 1960s. Revelle began taking daily measurements of the atmosphere on the top of Mauna Loa, Hawaii’s tallest mountain (Gore, 2006). But it was not until the 1980s and 90s that this message was taken seriously around the world, especially following the Rio Conference on Climate Change in 1992 and the signing of the Kyoto Protocol, ratified in 1999. 3 Photochemical smog results from chemical reactions between hydrocarbons (HC) and other gases in the atmosphere, particularly ozone (O3) , and nitrogen oxides (NOx) when they combine in presence of sunlight. According to May (2003), climate change was incorporated into the global political agenda in the mid-1980s and began to assume a central role as concern grew about possible changes in the world´s climate system. May also argues that, in addition to the many uncertainties surrounding the issue, a political undertone hangs over the issue: given the speed of the economic growth process, the consequences of global warming could be negative for some countries and positive for others - resulting in an additional source of inequality between North and South. 26 The UN Framework Convention on Climate Change, adopted in May 1992 in New York and later signed by over 150 countries at Rio-92, divides countries into two groups: the major CO2 (‘Annex 1’) emitters which pledged to adopt policies to mitigate greenhouse gas emissions and the ‘remaining’ smaller emitters (‘non-Annex I’ countries). The fact that Brazil is not on the list of Annex I countries, and was therefore not presented with fixed targets for reducing CO2 emissions, does not excuse it from a commitment to reduce emissions, given that Article 3 of the Convention enshrines the ‘precautionary principle’. According to Brazil´s National Energy Plan, PNE (2007), the transport sector demand for liquid fuels is the highest among all the country´s productive sectors. Figure 7: Liquid fuels consumption in Brazil, by sector Technical Synthesis Report | TRANSPORT As can be seen in Figure 7, Brazil´s transport sector consumed in 2005 Source: POE 2030 / Logit approximately 52 MTOE (million tons of oil equivalent) of liquid fuels, amounting to around 75% of the total amount consumed in the country. Projections for 2030 estimate that this percentage will remain at the same level (just over 73%), indicating that if nothing is done, the transport sector will continue to be responsible for the majority of CO2 emissions from the combustion of liquid fuels. The increased absolute consumption level will produce a higher amount of CO2 emissions. A pressing need therefore exists to structure a set of strategies and mitigation options that could, over time, lead to reduced consumption in the sector, thereby helping to reduce the impacts of GHG emissions overall. In a study conducted recently for Mexico it was found that in the transport sector, 27 90% of GHG emissions originate from road transport (CTS, 2008). The transport sector as a whole accounted for the second largest source of emissions of greenhouse gases (18% of total GHG emissions). In Brazil the percentage of GHG emissions in the transport sector is not significantly different, as shown by Figure 8. Figure 8: Emissions in Brazil´s Transport Sector (2007) Technical Synthesis Report | TRANSPORT According to estimates made in the course of our study, CO 2 emissions in the Source: Logit / 2009 transport sector in Brazil in 2007 amounted to approximately 159 million tons (91% of the sector total), with approximately 58% of the emissions occurring in urban- metropolitan areas (urban transport) and 33% in rural areas and at places where roads crossed urban-metropolitan areas (regional transport). From the above it is clear that measures to mitigate greenhouse gas emissions need to concentrate on increasing the use of alternative modes to road transport at the regional level, and to promote the rational use of modes in the case of urban transport. Furthermore, developing and encouraging the use of vehicles of all types capable of burning cleaner fuels could have a large impact. 1.5 General Considerations on Urban Transport According to the Demographic Census 2000, some 80% of all Brazilians now live in cities (compared with 56% in 1970). This increase in the number of city-dwellers was 28 not accompanied by a proportional increase in investments in transport, education and housing (See Figure 9). Figure 9: Evolution of the Urban and Rural Population in Brazil The lack of investment resources and the slow response of government agencies Source: IBGE Census / Logit responsible for major development programs (e.g. health, education and transport) have contributed to burgeoning social problems in the cities. As a result of the precarious urban infrastructure, quality of life in Brazil’s major cities has gradually Technical Synthesis Report | TRANSPORT declined. Travel by public transport remains one of the main problems faced by the population. Population growth in the cities has generated a substantial increase in daily trips, especially those involving people going to and from work. This involves large numbers of vehicles vying for limited space and, given the chronically bad state of much of the urban infrastructure, serious congestion is commonplace. Traffic is particularly heavy at peak times (morning and late afternoon rush-hours). In addition to generating economic losses, bottlenecks and delays undermine the quality of life of the travelling public and city-dwellers in general, and increase emissions of GHG and other pollutants. According to ANTP (2007), an estimated 90 million motorized trips are made every day in Brazil´s cities with populations of over 60,000. City buses account for 44.1% of urban trips, while suburban commuter trains and the metro are key components of the urban transport system in the metropolitan regions, accounting for an estimated 18% of trips in urban areas with populations of over 1 million. Table 2: Daily travel by motorized transport in Brazilian cities / Metropolitan Regions (2005) Daily trips (thousands) Cities by population (thousands) Mode Total 29 > 1 million 500-1000 250 - 500 100-250l 60 - 100 Bus 23,394 5,483 5,139 3,986 1,958 39,961 44.1 Absolute % Rail 5,008 17 0 0 0 5,025 5.5 Car 22,053 7,256 5,911 4,667 1,842 41,728 46.1 Public transport 28,403 5,500 5,139 3,986 1,958 44,986 49.7 Motorcycle 1,208 447 761 903 564 3,883 4.3 Individual 23,261 7,703 6,672 5,569 2,406 45,611 50.3 Abs 51,664 13,203 11,811 9,556 4,364 90,597 100.0 Total % 57.0 14.6 13.0 10.5 4.8 100.0 - In cities with over 1 million inhabitants, 55% of daily trips are on public transport Source: ANTP (2007) and 45% using individual modes (ANTP, 2007). In the remaining cities individual modes of transport predominate, as illustrated in Figure 10. Figure 10: Daily Motorized Trips: Public V Individual Transport Modes Technical Synthesis Report | TRANSPORT In the Rio de Janeiro Metropolitan Region (RJMR), approximately 6.5 million daily Source: ANTP (2007) trips are made by regular bus (PDTU, 2005), representing 35% of total trips. In the São Paulo Metropolitan Region the percentage is around 33%, representing around 8.3 million daily trips (Metro-OD survey, 2007). The above figures do not include trips made by low-capacity vehicles (vans and mini-buses). In Rio de Janeiro these account for about 1.7 million trips daily or 8% of the total (PDTU, 2005), while in the SPMR the number is lower: 0.7 million trips, amounting to 2.8% of total motorized trips (Metro-OD survey, 2007). Figure 11 below illustrates the changes (in %) that have occurred in the SPMR in trips made by public and individual transport over the past 40 years. Figure 12 indicates the total numbers of trips, by mode, made in the same area in the ten-year period 1997-2007. This data was assembled from the Origin and Destination surveys carried out every ten years (and more recently every 5 years) by the São Paulo public transport authorities, coordinated by the São Paulo Metro Company. Figure 11 clearly shows the rapid growth of individual motorized trips in São Paulo compared with travel by public transport: 70% and 30% in 1967 and 51% and 49% in 1997 respectively. 30 Figure 11: Percentage Changes in Trips made by Public and Individual Transport in the São Paulo Metropolitan Region In 1997, for the first time in the last 40 years, individual motorized trips in the 39 Source: OD - Metro / Logit municipalities within the São Paulo Metropolitan Region surpassed the number of trips made by public transport, which declined from 52% to 48%. In 2002, however, individual trips fell back to 45%, after peaking in 2002. Figure 12 indicates a significant increase in the number of daily motorized trips from 1997 to 2007 – about 4.6 million trips, with 3.5 million on public transportation Technical Synthesis Report | TRANSPORT (75%) and 1.1 million by individual means (25%). In the public transport sector, the building and upgrading of a number of suburban train and metro lines during this period led to a significant increase in passenger loads: 3.5% on the Metro and 5.6% on the suburban commuter train system. At the same time, the absolute numbers of passengers carried by the regular bus services increased at a slightly higher rate: 1,022,000 by bus, compared to 996,000 by rail. Figure 12: Daily Motorized Trips, by mode, in the São Paulo Metropolitan Region (1997 and 2007) 31 Technical Synthesis Report | TRANSPORT The three-fold increase in the number of trips by public transport compared to Source: OD- Metro / Logit individual motorized trips in São Paulo is due mainly to the increased use of light-duty commercial vehicles for school transport: 1.06 million trips, which increased the proportion of this mode of transport vis-à-vis the total of daily motorized trips from 1.3% in 1997 to 5.2% in 2007. Growing public concern over crime and safety (especially concerning schoolchildren’s use of public transport) may be one of the justifications for this rise. The increase in trips by chartered bus (4.7% a year) most likely also relates to the security question, although factors such as commuter preferences to travel by public transport rather than by car, as the result of increased traffic congestion, also need to be considered. Among the “individual� modes of transport, travel by motorcycle has increased by over 10% per year, mainly due to the growing need for document and small package deliveries. Below-average growth in the number of trips by car and taxi reflects improvements in the public transport infrastructure (which is gradually adapting to 32 the needs of the population) and may also be the result of a dip in consumer purchasing power between 1997 and 2007 in the MRSP. Figure 13 shows that from 1997-2007, average monthly individual incomes in real terms (November 2007 values), decreased by 3.6% a year in the MRSP. Figure 13: Changes in Incomes in the São Paulo Metropolitan Region (November 2007 values) Source: OD-Metro / Logit The Rio de Janeiro Urban Transport Master Plan (PDTU), completed in 2005, provides a breakdown of daily motorized trips in the city, including trips made by regular bus service. Table 3 shows the modal split of these trips. Approximately 63% of trips were Technical Synthesis Report | TRANSPORT motorized, with bus trips accounting for over half. Table 3: Total number of trips in RJMR (per day) Mode Number of trips Percentage of Motorized and Bus 6.5 million 32.5 51.8 total Non-Motorized (%) Other public transport 2.5 million 12.5 19.9 Cars 3 million 15.0 23.9 33 Motorcycle 100, 000 0.5 0.8 Other motorized (*) 450, 000 2.2 3.6 On foot 6.8 million 34.0 91.3 Sub-Total Motorized 12,550,000 62.7 100.0 Cycles 650, 000 3.3 8.7 Sub-Total Non-Motorized 7.45 million 37.3 100.0 Total 20 million 100.0 - (*) Includes school buses, taxis, chartered buses and trucks. Interestingly, the total number of trips by public transport in Rio de Janeiro Source: PDTU (2005) / Logit represents around 72% of all motorized trips, which is well above the 55% estimated by the MRSP Metro Origin and Destination Survey (2007). This is most likely due to the fact that trip lengths in Rio (Brazil´s second most highly-populated urban area after São Paulo) are longer, due to the different geophysical characteristics of the area. Therefore, trips by individual modes are more expensive, and daily trips on public transport are thus cost-effective.The research undertaken on this subject, and confirmed by the numerical evidence, highlights the important role played by the regular urban bus systems in Brazil. Currently, virtually all Brazilian cities depend on buses for transporting their populations. The volume of bus-users on a day-to-day basis justifies the development of an economy based on the expansion of the road network, coupled with the relatively low cost of deployment, maintenance and operation of this mode. Notwithstanding the significant percentage increase in the usage of buses, it is important to emphasize that the lack of consistent and integrated planning of urban transport networks, such as Technical Synthesis Report | TRANSPORT buses, trains, and metro,, may have influenced the picture described above. Economic and budgetary constraints in the majority of Brazilian cities will continue to impact the expansion and improvement of bus services. Upgrading bus services requires careful consideration of the costs, quality of service, and especially the operational economic and financial feasibility. A regular and efficient bus system can contribute to the welfare of communities in terms of increased mobility and accessibility (e.g. to places with good employment opportunities), as well as to reducing public subsidies needed to operate them (i.e. through higher passenger loads). Depending on passenger demand and available financial resources, metro and suburban train services should also be considered as major components of the urban transport network. The advantages of rail are well-known: operational efficiency, larger passenger capacity and low impact on the local environment. 1.6 General Considerations on Regional Transport Brazil’s natural and geographical characteristics directly influence the pattern of the transport network. This is especially true in the case of surface transport, e.g. the railway network. Historically railway lines were laid across flat or undulating regions, avoiding hills, steep gradients, rivers and lakes. Sidestep such barriers was frequently sufficient reason for railway engineers to alter a route. 34 Road transport, on the other hand, evolved quickly during the 20 th century throughout Brazil, mainly benefiting the most economically-advanced metropolitan regions. In the North and Center-West, coverage was significantly less due to lower population densities and the type of economic activities (agriculture and livestock raising). The road network expanded rapidly from the second half of the last century, following the growth of the auto industry in Brazil, which was accompanied by heavy investments in road infrastructure. Given the naturally-navigable waterways, water transport was developed in the North and South of the country, making it possible to link the interior to the Atlantic Ocean. Water transport was also developed intensively in the Southeast region, although the overcoming natural barriers involved substantial investment. Commercial air transport developed mainly to satisfy the demand for long-distance trips and served inter alia to integrate isolated areas into the national territory. Major air transport hubs took root in the larger, more densely-populated, and wealthier urban centers in response to growing demand for domestic and international flights. Pipeline networks began developing in Brazil to transport oil, oil products and certain mineral ores. In the late 1990s, this network was extended with the construction of gas pipelines linking Brazil to Bolivia and Argentina. The percentages of freight moved by different modes of transport, calculated in terms of tons per kilometer, are illustrated in Figure 14. Technical Synthesis Report | TRANSPORT Figure 14: Modal Split of Regional Freight Transport in Brazil The share of the total volume of freight transported often fails to mirror the relative Source: Ministry of Transport (2000) economic importance of each of the transport modalities. Some modes, despite moving relatively small freight volumes, are responsible for transporting high-value products. This is the case, for example, of air transport which, according to the Ministry of Transport, represents only 0.3% of the Brazilian transport matrix, while at the same time contributin a greater proportion in terms of monetary value. This is due to the fact that the vast majority of merchandise transported by plane possesses a high value-to- weight ratio, including computer equipment, jewels, and precious stones. Road transport represents around 60% of the total Brazilian matrix. This mode 35 expanded due to the rapid growth of the vehicle fleet since the 1950s, the shortage of public investment over recent years in infrastructure improvements for other modes, and the greater operational autonomy of road transporters. Additionally, the ease of access afforded by road travel contributed significantly to growth. The usage of rail and waterway modes is currently increasing following massive investment by private operators in port and railway installations and operations. Pipeline transport has only a modest share in Brazil’s modal matrix (4.5%). This is due to the fact that this mode is largely confined to transporting natural gas and oil products in specific regions. Meanwhile, as mentioned above, air transport, only contributes to less than 1% of the total volume of freight carried. In short, currently Brazil possesses a freight transport network consisting of the five above-mentioned transport modes. These are illustrated in Figure 10 by a map geo- referenced in TransCAD software, which shows the major transport routes radiating from Brazil’s state capitals and large metropolitan regions. Figure 15: Geo-referenced Multimodal Network Technical Synthesis Report | TRANSPORT Source: Network 2009 Logit An understanding of the complex relationship between transport and economic and social development is essential for ensuring that investment policies and strategies for improving the regional transport infrastructure are capable of producing the desired socio-economic impacts. From the economic point of view, the transport sector can be considered the most dynamic of all the sectors given that it involves a wide variety of infrastructural components such as roads, highways, ports, airports, and railways. The impacts and implications that investments in transport infrastructure can have 36 on development, especially at national and regional levels, continue to be researched by planners and specialists on the subject. Agreement has been reached on a few issues but many uncertainties and questions remain unanswered. Investments in transport infrastructure certainly have short and long-term impacts on regional economies. However, the scale and sustainability of production growth and competitiveness are difficult to measure. Hence it is often difficult to accurately identify the potential benefits that would arise from the existence of a top-quality regional transport infrastructure based on modal integration, inter-modal transfer, lower operational costs, shorter trip times, and better access to producer regions (PLANET, 2006). The relationship between transport infrastructure and regional development is complex, particularly when the multiplicity of “problems� faced by the transport sector is considered. These include: Different modes of transport (roads, railways, air). Different levels of infrastructure investment (strategic, regional or local); • Different socio-economic impacts (employment levels, efficiency, output); • Different environmental impacts (deforestation, pollution, noise pollution); • Different spatial and economic development targets (international, national, • regional or local). • The temporal dimension of investments is also important. The impact varies Technical Synthesis Report | TRANSPORT according to the types of transport infrastructure project, but it is generally accepted that most investments in transport infrastructure, regardless of mode, are likely to produce effects only over the longer term. At the regional level, decisions to invest in existing or new transport infrastructure produce at least three types of development-related impacts: The most immediate impact is on the number of new jobs generated (mostly temporary), accompanied by a set of multiplier effects related to the various • production chains in the goods and services sectors; The second impact (apparent when the new transport infrastructure is in place or the old one extended or upgraded) is the opportunity to develop • new prospects and activities in the region; • The third concerns the influence and impact that infrastructure works can have on the country´s total modal supply involving, for example, the establishment of new alternative routes and modal combinations for transporting goods and people. The impact on the spatial and temporal levels can vary considerably. The final result of this process (also influenced by other factors) is that the impacts are rarely synchronized in time and space - an uneven phenomenon commonly known as the “development process�. 37 Judging by these findings, and despite the many analytical uncertainties and problems, evidence appears to show a structural link between transport infrastructure and regional development. The role of transport cannot be separated from development questions. No analysis of overall regional development would be complete without highlighting the fundamental role played by transport infrastructure in this process. the question of national or regional development is closely linked with decisions about transport infrastructure investments at countrywide or regional levels. It is vital therefore that mechanisms exist to aid governments and decision-makers to establish appropriate instruments and action priorities. The instruments often consist of models or programs used to identify and analyze the impacts and consequences of the action alternatives. The Federal Government, through its Growth Acceleration Plan (PAC), aims to undertake a set of interventions in the country’s infrastructure, including transport projects. These range from improving and upgrading part of the existing transport network and port terminals, to the construction of new, essential transport facilities. In addition to the PAC, the development and deployment of the National Logistics and Transport Plan (PNLT), revisited in 2007 by the Ministry of Transport, aims to pursue a number of transport infrastructure projects. Before 2001 the Brazilian Transport Planning Company (GEIPOT) developed a structure and methodology for implementing a process for the regional planning of freight and passenger transport. This was discontinued after the transport sector was restructured, triggered by a number of privatizations and concessions. The GEIPOT was disbanded and surface- transport regulatory agencies were created. Technical Synthesis Report | TRANSPORT The structure of the information bank on freight and passenger transport used in the existing PNLT was based on historical data from the Annual Transport Statistical Reports (AETs) published by GEIPOT. This information bank was updated with information obtained from national freight and passenger OD surveys conducted under the aegis of the PNLT in 2007. However, the most substantial contribution made to the above-mentioned information bank was the incorporation of a portfolio of transport infrastructure investment projects analyzed and ranked according to the results of logistics simulations and national socio-economic priorities. In this way, the PNLT is not only a Government logistics plan but also a major State Plan designed to ensure a permanent process of participatory planning (i) to reflect sustainable development assumptions and environmental concerns, and (ii) to be integrated with relevant government agencies, operators and other stakeholders in the transport area. Figure 16: Investments in Regional Transport Infrastructure outlined in the PAC and PNLT 38 Figure 16 shows that the total investment projected in the PNLT are distributed Source: CAP / PNLT (2007) / Logit more evenly between the three modes than is the case in the PAC. This is likely due to the fact that the PNLT is a specific government plan for transport infrastructure. The PNLT, in addition to including virtually all the projects included in the PAC, contains a set of clear goals, including environmental sustainability targets, to be achieved by introducing greater balance into the Brazilian transport matrix. In short, these two plans will be considered as baselines for projections of future emissions in the reference and low-carbon scenarios, to be presented in later chapters. 1.7 Institutional Overview In the specific case of Brazil, institutional complexity makes it difficult to deal with the transport sector. Regional transport matters are linked to the Ministry of Transport, the Ministry of Defense (Air Transport) and the Special Secretariat Technical Synthesis Report | TRANSPORT for Ports, while urban transport is governed by guidelines issued by the Ministry of Cities. A further complicating factor is that the Federal Constitution awards autonomy to each municipality to manage its own transport and traffic system, which makes it increasingly complicated to harmonize policies and plans in Brazil’s 5564 municipalities. As for the environmental component, and more specifically GHGs (especially CO2), institutional complexity is largely responsible for delaying urgent actions that need to be taken in the transport sector. The recent National Action Plan on Climate Change (NMCP), based on the PNLT, defines targets for modal division through the time horizon of 2030, with a goal of reducing CO 2 emissions. While the Plan refers specifically only to the PNLT, it nevertheless indicates that transport issues, especially in the metropolitan regions, merit close attention, given that densely-populated areas are responsible for high CO2 emissions from increased motor vehicle usage. In addition to the various ministries involved in the sector, the authorities of the metropolitan regions and municipalities need to work together to optimize and implement actions to achieve the low-carbon scenario. Coherent policies and strategic actions by all stakeholders will hopefully create the synergy needed to reach the stated goals for reducing environmental impacts. The City Statute provides the appropriate legal instruments for Brazilian city managers to transform the good intentions contained in their Master Plans into 39 concrete proposals for improved city administration. The constitutional provision that all cities over 20,000 inhabitants are enjoined to produce their own Master Plans will require a proper assessment of the complex relationship between transport and urban development (Ceneviva, 2007). While only 1,560 of Brazil´s 5,564 municipalities fall into the above category, together they accounted for 82% of the urban population in 2007 (Institute of Geography and Statistics – IBGE). Within this group of municipalities, approximately 55% of the population resided in predominantly urban areas (metropolitan and core expansion areas) covering 96% of the municipal territories in 2000. See Table 4. Table 4: Percentage of Population and Urban Location - Brazil and cities with over 20,000 inhabitants Urban Brazil Municipalities > 20 000 inhabs location Number of Population in 2007 % Number of Population in 2007 % % Brazil Municipalities Absolute % Urban Municipalities Absolute % Urban (popula- Total Area Total Area tion 2007) in in Outside 5048 84,555,446 46.0 69.9 1241 67,810,819 45.1 77.5 80.2 2000 2000 MR (*) In MR 516 99,431,845 54.0 95.2 319 82,660,109 54.9 95.9 83.1 (*) Total 5564 183, 987, 291 100.0 81.2 1560 150, 470, 928 100.0 87.5 81.8 Source: Census 2000 and 2007 Count - IBGE / Logit Technical Synthesis Report | TRANSPORT (*): Metropolitan Region including Nucleus or Core Expansion Areas: IBGE classifications for groups of In the municipalities with over 20,000 inhabitants, the problems of transport and heavily urbanized municipalities of different sizes and density levels . general mobility are complex. It is precisely in such areas that the impacts need to be mitigated in order to ensure better quality of life for the population. The City Statute (Federal Law 10.257 of 10 July 2001), regulates Articles 182 and 183 of the Federal Constitution. According to Ceneviva (2007) the Statute fails to explain the meaning of “adequate public transport� and only refers to the ‘interests and needs’ of the population in this respect. Nevertheless, it asserts that (i) urban transport is essential for the proper functioning of cities and is a major engine of development and (ii) access to transport is one of the rights to be enjoyed by present and future generations, together with the right to sustainable cities, housing, sanitation, urban infrastructure, public services, employment and leisure. The same author argues that people’s right to transportation must also take into account the need for long-term sustainability of the city and the environment to ensure that the present generation does not leave a negative legacy for the future. 40 1.8 Regulatory Overview Various official agencies have been established over recent years in an effort to regulate and harmonize transport sector operations in Brazil. The National Land Transport Agency (ANTT) regulates all types of surface transport at the regional level, mainly passengers or cargo transported between different regions of the country. Water transport is the responsibility of the National Agency for Water Transport (ANTAQ) charged with regulating the use and development of ports and inland waterways. The National Civil Aviation Agency (ANAC) is responsible for regulating safety and security matters related to civil aircraft, personnel licensing and airports. The Brazilian Company for Airport Infrastructure (INFRAERO) is responsible for day-to-day airport- related operations. In addition to regulation at the regional level the situation is further complicated by the existence of separate regulatory bodies in the municipalities with mandates applying local rules and regulations to freight and passenger transport. From the above it is clear that it will not be a simple task to deploy specific programs or actions aimed at reducing CO 2 emissions in the transport sector. Substantial effort will be required to coordinate actions, raise the awareness of the need for emission reduction within the different regulatory agencies and bodies, and overcome obstacles which will inevitably arise in this complex regulatory environment. Technical Synthesis Report | TRANSPORT 1.9 Initial Prospective Analysis Road transport at both the regional and urban level is the largest consumer of fossil fuels (50.5%). The predicted surge in vehicle ownership in emerging economies, including Brazil, makes it necessary to adopt measures to rationalize private car use and introduce modal transfer from roads to rail and waterways for regional freight transport. Measures to discourage private car usage, such as reducing the number of parking spaces in congested areas and/or increasing parking costs, may be necessary. Such measures could be accompanied by strategies aimed at upgrading public transport as a real alternative modal shift. Some authors consider that passenger and freight movement in more heavily- populated urban areas can be significantly improved by taking advantage of increased density. A study by Stone et al. (2009) on a group of American cities, estimates that if population density were to double in a medium-sized city, a reduction of about 30% of CO 2 per household could be achieved. This urban consolidation approach, known as “Transport Oriented Development� (TOD) maintains that in areas with denser and more diversified land use (services, commercial establishments, homes) the use of non-motorized transport, such as bicycles, to cover shorter distances is a practical option, producing a number of collateral benefits such as relieving pressure on the 41 public transport system (Cervero & Day, 2009; Cervero, 1998) With the aim of encouraging municipal authorities to design urban transport plans in accordance with City Statute guidelines, the Council of Cities issued Resolution ConCidades No. 34 (1 July 2005), specifying that each municipal integrated urban transport plan, known as the “Transport and Mobility Master Plan�, should (Ceneviva, 2007): ensure that the different modes of transport maintain with the characteristics of the city, prioritizing public transport over individual transport, and • encouraging non-motorized transport and walking; ensure that urban mobility management is incorporated into the Municipal Master Plan; • ensure control of urban expansion and universalization of access to the city, improving environmental quality and mitigating the impacts of spatial • occupation and traffic. Given that the main goal of this study is to analyze the scope for reductions in GHG emissions, each transport sector will be addressed separately, according to the different characteristics of vehicles/transport modes and trips. In 2007 (see Figure 17), approximately 58% of GHG emissions occurred in urban-metropolitan areas while the other 42% occurred in the air, rural areas, and near roads, waterways, railways and pipelines passing through large urban centers. Figure 17: Emissions by the Transport System, by segment (2007) Technical Synthesis Report | TRANSPORT Source: Low Carbon Stydy for Brazil – Logit (2009) 2 METHODOLOGICAL APPROACH Different transport modes exist for efficiently moving passengers and freight, and the success of each depends on the unique infrastructure characteristics. Trips are categorized as ‘urban-metropolitan’ or ‘inter-urban/regional’. The operating characteristics of each mode of transport, the variety of freight 42 carried and people’s different reasons for travel (work, study or leisure) all contribute to the complexities involved in addressing questions of efficiency in the transport sector. This situation is further complicated by the volumes and types of freight transported (perishable goods, high-value merchandise, products requiring special packaging, refrigerated conditions) and the expectations of users in terms of speed, comfort, safety, and cost. A study aimed at estimating future carbon emission levels in the transport sector, and identifying ways of mitigating such emissions, necessarily involves a varied and complex range of procedures. Each transport mode, user and freight load-type requires a specific analytical approach in view of the inherently different characteristics of each trip. Furthermore, it is important to note that the transport sector is directly linked to other sectors of the economy. This increases the difficulties involved in obtaining estimates of the impacts on the environment, especially of carbon emissions. The aim of this chapter is to describe the methodological approach adopted for estimating emissions in the reference and low-carbon scenarios for 2030. The energy sector reference scenario was developed by the Energy Planning Company (EPE) and forms part of the National Energy Plan (PNE 2030). The methodology we used for transport sector estimates was adjusted to ensure consistency with the PNE scenario. A series of meetings and contacts with other institutions and groups involved in our study ensured a unified approach to the underlying assumptions and indicators. Data compiled from different sources provided a sound basis for modeling emissions estimates in the transport sector. Technical Synthesis Report | TRANSPORT 2.1 Scenario Building - General Considerations The “scenario development� technique has been widely used over the years by companies and professionals involved in strategic planning. Scenarios, an auxiliary instrument employed in prospective analysis, are particularly useful for forecasting processes involving a large number of variables and increasing outcome uncertainty. The scenarios seek to incorporate qualitative elements into traditional trend and model analyses and are particularly useful for decision-makers when considering future developments and options. Transport planning frequently employs forecasting models which, although considered relatively efficient, produce results which are often difficult to apply in practice. Scenarios therefore continue to be used in transport planning in the hope of providing improved trend analysis and ensuring more effective interventions over the longer term (Balassiano, 1998). The different components of scenarios, each with a specific but unknown probability, make scenario-building an effective tool for prospective analysis. Scenarios can increase the chance that long-term decisions result in successful outcomes. In short, this technique seeks to address the possibility of different outcomes within an uncertain future. Consistent assumptions at the outset tend to lead to better structured scenarios, which in turn should enhance planners´ capacities to address future uncertainties. 43 In the specific case of future GHG emissions, regardless of the changes that can and should occur in the transport sector, it is essential that consistent policies are adopted, together with mechanisms for monitoring and fine-tuning future developments. The uncertain new dynamics and impacts on the global economy will require significant changes in the transport sector. The latter, in turn, needs to be in a position to respond flexibly to the demands from different sectors of the economy. 2.2 Future Carbon Dioxide Emissions Scenarios We studied two different scenarios related to future transport sector GHG emissions. The first (reference scenario) is based on the premise that the transport sector is unlikely to be subjected to significant structural changes. It is basically a trend scenario involving few technical or operational innovations. The second scenario studied was an alternative, low-carbon scenario, involving a set of mitigation options considered and assessed in terms of: Potential for deployment; Existing barriers to deployment; • Policies required to implement the options; • Cost estimates for each option, and • Needed additional government financing to ensure implementation. Technical Synthesis Report | TRANSPORT • Given its complexity, the transport sector was divided into four distinct groups to • facilitate prospective analysis in each of the scenarios. The first two groups relate to regional freight and passenger transport. All non-urban trips taken outside the urban limits of Brazil´s 5,564 Brazilian municipalities were counted as “regional trips�. The same concept was applied to the urban transport sector, where freight and passenger trips were considered separately. Emissions linked to the transport sector both in the reference and low-carbon scenarios were analyzed through 2030. The study used a bottom-up approach to estimate passenger and load movements, fuel consumption, number, length, and type of trip, and energy content of the fuels consumed, in order to determine the amount of CO2 emissions. Load values were calculated in terms of number of passengers times kilometers and tons of freight time kilometer in both the reference and low-carbon scenarios. The loads were estimated for each transport mode (road, waterways, rail and air) for each subsector - urban transport (passengers and freight) and inter-urban/regional transport (passengers and freight). The structuring of the reference and low emission scenarios for regional and urban passenger transport is explained below. 44 2.3 Regional Transport Assumptions To model regional freight transport, this study uses the basic matrices employed in the PNLT 2007 studies. These matrices were revised and updated (incorporating new modeling data as appropriate) in line with the reference scenario adopted by consensus with the other Project teams. For modeling and projecting regional passenger transport a methodology was developed based on several assumptions regarding the different transport modes (buses, cars and air): In the case of private car travel, data on passenger movements included in the PNLT 2007 matrices were used, together with recent statistics based on • volumetric counts undertaken at toll booths; For projections of bus passenger movements, we secured data from the ANTT and ANTP on passenger numbers, vehicle movements (origin and • destination); Information on passenger travel by air (origin, destination and trip numbers) was supplied by INFRAERO and ANAC; • The socioeconomic and demographic assumptions used to construct the trend or reference scenario drew upon regional demographic and economic Technical Synthesis Report | TRANSPORT • data contained in the PNLT 2007 and PNE 2030. This data was also used to model regional freight transport and urban freight/passenger transport. To ensure that the modeling assumptions for freight and passenger projections were consistent with those of the other sectors studied in the Project, the methodology for the transport sector estimates also adopted the macroeconomic scenario contained in the National Energy Plan (PNE 2030) prepared by the Energy Planning Company (EPE). The PNE 2030 projections are based on the values of infrastructure investment needed for maintaining or extending existing transport network capacity to meet the economic growth trends expected for Brazil. Based on suggestions from a series of technical meetings with transport industry experts and suggestions by Ministry of Transport technical staff, the study’s 2030 reference scenario includes investments planned under the government´s Growth Acceleration Plan (PAC). In the light of the current global economic crisis, and assuming that no other unforeseen political/institutional or other major event occurs, the study includes the adoption of the PAC projects in the 2030 reference scenario. The PNLT- 2007 includes a series of environmental and self-sufficiency targets, together with lower investment (50.5%) projected for highways when compared to the PAC (72.7%) (see Figure 16). The low-emissions scenario includes some of the projects detailed in the PNLT, as including all of them would reach a total estimated cost of around US$51 billion. 45 In current economic circumstances some uncertainty exists regarding the feasibility of fully deploying the PNLT within the specified period. Furthermore, owing to political/institutional pressure, the portfolio contained many projects with limited economic viability. This study therefore assumes that by 2030, only a portion of the projects outlined in the PNLT will in fact be implemented. In short, only projects with satisfactory cost-benefit profiles were selected, confirmed by the results of the specific regional freight transport model. The costs involved amount to approximately 57% of the total value of PNLT investments, as seen in Table 5 below. Table 5: Investment in Regional Transport Infrastructure Mode Reference Scenario Low Carbon Roads 15.11 76.97 125.6 13.27 45.4 51.6 US$ billion % Total % PAC US$ billion % Total % PNLT Rail + 4.52 23.03 100.0 15.98 54.6 63.3 Waterway + Pipeline Total 19.63 100.00 118.6 29.25 100.0 57.4 Based on this assumption, and given the values indicated in Table 5, the low-carbon Source: CAP / PNLT (2007) / Logit scenario for regional transport is based upon an analysis of the additional mitigation options contained (or not) in the PNLT. Technical Synthesis Report | TRANSPORT 2.4 Urban Transport Assumptions The institutional complexities arising from the plethora of government bodies involved in the transport sector, and the lack of clearly-defined responsibilities of each, have already been mentioned. Mention has also been made to the equally complex regulatory architecture, where different regulatory agencies have been created over the past few years to supervise the different transport modes and services. In addition to the overlapping management and regulatory systems at regional level, an equally confusing situation exists in cities, where passenger and freight movements are regulated by a variety of municipal government bodies. The bodies frequently act independently of one another and often work with divergent and sometimes contradictory mandates. In the metropolitan or similar large urban areas, many daily trips are undertaken across municipal boundaries. It is obvious that the transport requirements of thousands, if not millions, of citizens would be better met if a single centralized management authority were responsible for handling public transport as a unified whole. To be effective, this should involve the establishment of a clear set of guidelines and a single transport budget independent of the present multiplicity of political and institutional interests. 46 In this scenario an accurate assessment of the need for transport infrastructure- upgrading projects in Brazil’s cities is urgently called for. This should take the form of an up-to-date road and mobility inventory prepared by the transport managers of the 516 municipalities in the 36 most heavily-urbanized regions (IBGE, 2008). Given the impossibility of our undertaking this task due to the immense effort required, this report uses aggregate numbers for urban mobility to evaluate GHG emissions in our metropolitan areas and cities. 2.4.1 Urban Center Categories (Clusters) To evaluate GHG emissions, this study established different urban center categories (similarity clusters) by employing an unsupervised data classification method based on demographic and socio-economic indicators. Indicators for the municipalities in the 36 most highly-urbanized areas were aggregated. The clusters were formulated on the basis of social economic and demographic indicators derived from a wide variety of secondary sources, as follows: Number of municipalities (IBGE); Population and households by income in minimum wages (IBGE Censuses • and Population Counts/ Logit); • GDP and GDP per capita data (IBGE / IPEA / FIPE / PNLT-2007 projections); • Existing vehicle fleet in circulation (thousands of units): light-duty Technical Synthesis Report | TRANSPORT commercial vehicles (LCV), light passenger vehicles (LPV), heavy-duty trucks • (VPC), heavy-duty buses (VPO) and motorcycles (ANFAVEA/DENATRAN/ ABRACICLO / Logit); Fuel sales at urban gas stations (in million TOE): Bio-ethanol, diesel and gasoline (ANP / PNE 2030 projections); • Total and % urban area (EMBRAPA). Table 6 below presents the main municipalities and the main most densely- • occupied urban areas (IBGE classification) in the eight clusters. Table 6: Major urban regions and municipalities by similarity cluster Cluster Densely-Populated Urban Municipalities Selection of Municipalities 1 RM São Paulo and Rio de Janeiro MR - and Metropolitan Regions 2 MR Belo Horizonte, Federal District and sur- - rounding areas, Fortaleza MR, Curitiba MR, 47 MR Recife, Porto Alegre MR and MR Salvador 3 MR Belem, MR Santos, MR Goiania, Campinas - MR, MR Manaus MR and Vitoria MR 4 Aglomeração Urbana do Sul (RS), Urban Campo Grande-MS, Uberlandia, Minas Ge- Aglomeração Urbana do Nordeste (RS), RIDE rais, São Jose dos Campos-SP, Feira de Santa- – Petrolina/PE and Juazeiro/ BA, Aglomera- na-BA, Sorocaba-SP, Ribeirão Preto, and Juiz çao Cuiabá/Várzea Grande, MR Aracaju, RIDE de Fora-MG Greater Teresina, MR Greater São Luis, MR Florianópolis, MR Londrina, MR João Pessoa, MR Maringá, MR Maceió, MR North-Northe- ast Santa Catarina, MR Natal, MR Vale do Ita- jai, and MR Vale do Aço 5 Macapá MR, Foz do Rio Itajaí MR, and MR Campos dos Goytacazes, São Jose do Rio Carbonífera e Aglomeração Urbana do Lito- Preto, Porto Velho-RO, Campina Grande-PB, ral Norte –RS. Piracicaba-SP, Bauru, SP, Montes Claros-MG, Jundiaí-SP, Anapolis-GO, Foz do Iguaçu-PR , Franca, Brazil, Vitoria da Conquista, Bahia, Petrópolis-RJ, Ponta Grossa, Paraná, Rio Branco-AC, Caruaru-PE, Uberaba-MG, Casca- vel-PR, Santarém-PA, Limeira-SP, Taubaté-SP, Buena Vista-RR, Santa Maria-RS and Volta Redonda-RJ 6 - Maraba-PA, Araraquara-SP, Itapemirim-ES, Rio Claro-SP, Passo Fundo-RS, Dourados, MS, Araçatuba-SP, Palmas-TO, Nova Friburgo-RJ, Sobral-CE, Barra Mansa-RJ, Rondonopolis- MT, Macaé, Chapecó-SC, Guarapuava-PR, Cabo Frio-RJ, Lages-SC, Castanhal-PA, Tere- sópolis-RJ, Rio Verde-GO and Angra dos Reis- RJ 7 - Votorantim-SP, Ourinhos-SP, Araruama- Technical Synthesis Report | TRANSPORT RJ,Patos-PB, Açailândia-MA, PR Arapongas- PR, São Mateus-ES, Corumbá-MS, Bar-Piraí- RJ, Muriaé-MG, Itaguaí-RJ, Umuarama PR , Bacabal-MA, Breves-PA, Ubá-MG, Eunápo- lis- BA, Assis-SP,- Erechim-RS, Itaperuna-RJ Ituiutaba-MG and Iguatu-EC 8 - Vacaria-RS, Escada-PE, Itaberaba-BA, Len- çois Paulista-SP, São Felix do Xingu-PA, Pe- nedo-AL, Camocim-CE, Carazinho-RS, Santo Amaro-BA, São Gabriel RS, Araranguá –SC, Rio do Sul-SC, Penápolis-SP, Palmares-PE, Bezerros-PE, Euclides da Cunha-BA, Floria- no-PI, Cajazeiras-PB, Ponte Nova-MG, Limo- eiro-PE, Oriximiná-PA Sources: IBGE / Logit Table 7 below presents the average percentages and indicators of the socio- economic and demographic variables, in addition to the population bands examined during the process to determine the eight urban similarity clusters: Table 7: Selected socioeconomic and demographic indicators for urban similarity cluster 48 Variables Percentages of total Brazil and Indicators by similarity cluster Brazil 1 2 3 4 5 6 7 8 (Total / Type of urban area MR MR MR MR MR Munici- Munici- Munici- - Abs) and and palities palities palities munici- munic- pality ipality Total Municipalities 1.01% 2.98% 1.10% 3.27% 1.71% 1.44% 3.00% 85.50% 5564 Number of Inhabitants Over 6 Over 1.5 to 3 500 200 to 100 to 60 to Up to 60 - (Band) million 3 to 6 million 000 500 200 100 000 000 million to 1.5 000 000 million Population (2007) in 16.61% 14.60% 6.45% 9.66% 6.57% 5.76% 6.78% 33.57% 183 988 000s GDP (2006) in US$ 26.79% 17.64% 8.33% 9.46% 7.14% 5.94% 5.67% 19.03% 2458190 million GDP per capita in US$ 21.55 16.15 17.26 13.09 14.53 13.76 11.16 7.57 13.36 000s Current VLC 17.36% 15.62% 8.69% 11.75% 8.15% 6.88% 6.90% 24.67% 3349 Fleet VLP 23.70% 18.82% 8.54% 12.20% 7.90% 6.13% 5.55% 17.14% 18 377 (2007) VPC 12.15% 13.43% 7.34% 10.80% 8.15% 7.84% 8.25% 32.05% 980 in POV 20.36% 17.04% 9.03% 10.24% 6.28% 6.03% 6.09% 24.93% 240 thousand Motorcycle 8.99% 9.56% 7.31% 11.61% 9.72% 9.08% 10.47% 33.26% 9227 units Fuel Con- Ethanol 25.95% 9.79% 9.59% 10.76% 9.97% 7.02% 6.11% 20.82% 3166 Total 18.45% 15.65% 8.17% 11.93% 8.45% 7.11% 7.19% 23.06% 32 173 sumption Diesel 6.24% 10.83% 5.02% 10.14% 8.69% 7.77% 8.95% 42.35% 19 061 2007 (in Gasoline 18.68% 18.62% 8.30% 12.43% 8.50% 6.36% 6.41% 20.71% 17 765 Technical Synthesis Report | TRANSPORT million TOE) Total 13.32% 14.21% 6.84% 11.21% 8.71% 7.08% 7.60% 31.03% 39 992 Total Area 0.15% 1.26% 1.42% 1.44% 1.62% 2.71% 8.16% 83.24% 8,530,611 Urban Area 14.19% 16.02% 8.72% 12.39% 6.67% 5.77% 6.21% 30.05% 21 295 Urban �rea % 23.79% 3.18% 1.53% 2.15% 1.03% 0.53% 0.19% 0.09% 0.25% While the large metropolitan areas (Classes 1, 2 and 3) contain only 5% of Brazil’s Sources: IBGE / IPEA / ANFAVEA / DENATRAN / ABRACICLO / ANP / EMBRAPA / Logit municipalities and occupy less than 3% of the country’s territory, their demographic and socio-economic indicators are high: population (38%), GDP (53%), vehicle fleet (42%), fuel consumption (34%) and proportion of occupied urban area (39%). These substantial numbers indicate that the vast majority of urban trips are concentrated in these areas. Of the variables selected for determining cluster similarity, GDP was the main indicator for assessing trip-generation in view of the wealth produced in these areas. The resident populations, the main component in the quantification of such trips, presented the lowest standard deviation of all the component units of each cluster. The number of households by income band underpins the two other main indicators (population and GDP) for calculating similarity clusters. This key socio-economic and demographic indicator shows the amount and distribution of wealth in the various areas and is an appropriate parameter for estimating mobility. The trip-generation 49 factor per household, according to income expressed in minimum salaries, was thus used as the main parameter for urban-metropolitan transport modeling in this study. Table 8 presents the figures for households by income level in 2007 and the eight similarity clusters: Table 8: Households by income (minimum wages) by urban cluster – 2007 Clusters Household Incomes in Minimum Wages (thousand units) Up to 2 MWs 2 to 5 MWs 5 to 10 MWs 10 to 20 MWs Over 20 MWs Total 1 2,518 24.8 3,972 39.2 2,093 20.6 1,063 10.5 494 4.9 10, 140 Abs % Abs % Abs % Abs % Abs % 2 2,699 32.6 3,106 37.5 1,439 17.4 682 8.2 359 4.3 8,285 3 886 25.9 1,371 40.1 705 20.6 331 9.7 124 3.6 3,418 4 1,445 28.8 1,921 38.2 1,010 20.1 455 9.1 191 3.8 5,023 5 1,020 28.5 1,439 40.2 710 19.8 305 8.5 103 2.9 3,577 6 989 31.3 1,284 40.7 575 18.2 230 7.3 76 2.4 3,155 7 1,463 38.5 1,496 39.4 566 14.9 206 5.4 66 1.7 3,797 8 9,519 49.9 6,806 35.7 1,938 10.2 616 3.2 180 0.9 19, 060 Total 20, 540 36.4 21, 396 37.9 9,035 16.0 3,888 6.9 1,595 2.8 56, 454 Figure 18 illustrates the figures presented in the above table: Sources: Census Counts and Population - IBGE / Logit Technical Synthesis Report | TRANSPORT Figure 18: Households by income (minimum wages) by urban cluster (2007) Sources: Census Counts and Population - IBGE / Logit The middle range (households with 2-10 minimum wages) has roughly the same share - between 54% and 60% for all the similarity clusters, except for Class 8 which comprises the 4,757 Brazilian municipalities with under 60,000 inhabitants, whose share is in the region of 45%. The stark differences should be noted between the families with incomes at either end of the spectrum: while the share of the top band (households with incomes of over ten MWs) gradually decreases (see Table 7), the share of people in the lowest income band (households with up to two minimum wages) gradually increases. 50 This phenomenon can be observed in the metropolitan areas of São Paulo and Rio de Janeiro, which form cluster 1 (over 6 million inhabitants). In these cities, on average 1.6 households receive incomes of up to two minimum wages compared to households with an income of above ten times the minimum wage. By contrast, in the cluster 8 municipalities (up to 60,000 inhabitants), households with incomes of up to 2 MWs outnumber those with incomes above 10 MWs by a factor of 12 to 1. 2.4.2 Assumptions for Modeling Urban Mobility Mobility and emissions estimates in urban areas were based on origin-destination surveys and urban Master Plans for certain heavily-populated municipalities and dense urban areas in similarity clusters 1-5: Table 9: Urban Mobility Plans Available 1 MR São Paulo and MR Rio de Janeiro - Cluster Densely-Populated Urban Areas Municipalities 2 MR Belo Horizonte, MR Curitiba, MR Recife, - MR Porto Alegre 3 MR Santos and MR Vitória - 4 Aglomeraçao Cuiabá/Várzea Grande, MR Campo Grande-MS, Vitoria da Conquista-BA, Florianópolis, MR Londrina, MR Maringa Ribeirão Preto-SP and Juiz de Fora-MG and MR Maceio 5 - Petrópolis-RJ, Piracicaba-SP, Campina Gran- Technical Synthesis Report | TRANSPORT de-PB, Rio Branco-AC and Santa Maria-RS The nature and quality of information available on urban mobility plans listed in Source: Logit (2009) Table 9 varies substantially. In addition to presenting different approaches, the time series of this information was outdated and discontinuous. Therefore, the OD Survey, conducted under the supervision of the São Paulo Metro Company (2007), for the São Paulo metropolitan region, and the Urban Transport Master Plan - PDTU (2005) for the Rio de Janeiro region and the Belo Horizonte Mobility Plan (2009) for Belo Horizonte, were used as the main benchmarks for extrapolating the urban trip estimates for all the similarity clusters. This information (up-to-date, using similar methodological approaches and producing a trip-generation factor by household according to income in MWs) served as an ideal starting-point for calculations for each of the eight similarity clusters. The numbers were then individually fine-tuned to reflect the following: Socioeconomic and demographic indicators used in the clustering process: (i) GDP, population and households, by income bands in minimum • wages for calculating trip-generation intensity; o (ii) Total urban area and ‘dense’ urban area to adjust average trip lengths; and o (iii) Size of the vehicle fleet in circulation and fuel consumption used for adjusting the modal split. 51 o Indicators of urban mobility from other plans used in the study for adjusting upgraded trip-generation and modal split factors; and • Information obtained from ANTP (2005-2007 on urban mobility and the public transport infrastructure in large cities, by different-sized municipalities • (by categories), for overall modal split adjustments. After adjustments, the household trip-generation factors were defined by mode and income bands for 2007. Technical Synthesis Report | TRANSPORT Table 10: Trip-Generating Factors by Similarity Clusters Mode Household Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Brazil VPO + VLC Up to 2 MWs 1.680 1.626 1.402 1.086 0.912 0.937 0.899 0.959 1.155 Incomes 1 2 3 4 5 6 7 8 (bus + light 2 to 5 MWs 2.081 2.012 1.734 1.339 1.122 1.155 1.109 1.185 1.509 commercial) 5 to 10 MWs 2.025 1.957 1.685 1.299 1.088 1.121 1.076 1.152 1.529 52 10 to 20 MWs 1.144 1.103 0.949 0.729 0.608 0.629 0.604 0.648 0.892 Over 20 MWs 0.566 0.547 0.470 0.362 0.302 0.312 0.300 0.321 0.462 Metro + Up to 2 MWs 0.364 0.042 0.000 0.008 0.000 0.000 0.000 0.000 0.051 TOTAL 1.798 1.738 1.516 1.165 0.988 1.022 0.982 1.043 1.312 Train 2 to 5 MWs 0.587 0.067 0.000 0.013 0.000 0.000 0.000 0.000 0.120 5 to 10 MWs 0.594 0.068 0.000 0.013 0.000 0.000 0.000 0.000 0.150 10 to 20 MWs 0.513 0.059 0.000 0.012 0.000 0.000 0.000 0.000 0.152 Over 20 MWs 0.398 0.046 0.000 0.009 0.000 0.000 0.000 0.000 0.134 Total Trips Up to 2 MWs 2.044 1.668 1.402 1.094 0.912 0.937 0.899 0.959 1.206 TOTAL 0.516 0.057 0.000 0.012 0.000 0.000 0.000 0.000 0.102 on Public 2 to 5 MWs 2.668 2.080 1.734 1.352 1.122 1.155 1.109 1.185 1.629 Transport 5 to 10 MWs 2.620 2.025 1.685 1.312 1.088 1.121 1.076 1.152 1.679 10 to 20 MWs 1.657 1.162 0.949 0.740 0.608 0.629 0.604 0.648 1.044 Over 20 MWs 0.964 0.592 0.470 0.371 0.302 0.312 0.300 0.321 0.596 I n d iv i d u a l Up to 2 MWs 0.693 0.879 0.882 0.958 0.941 1.001 1.035 1.096 0.982 TOTAL 2.314 1.796 1.516 1.177 0.988 1.022 0.982 1.043 1.414 Trips - VLP + 2 to 5 MWs 0.703 0.892 0.898 0.976 0.961 1.024 1.060 1.123 0.963 Moto + (cars 5 to 10 MWs 1.881 2.347 2.263 2.431 2.324 2.418 2.420 2.553 2.293 + motorcy- 10 to 20 MWs 3.550 4.391 4.145 4.430 4.170 4.284 4.203 4.423 4.116 cles) Over 20 MWs 5.114 6.286 5.838 6.211 5.775 5.867 5.661 5.945 5.762 Total Up to 2 MWs 2.737 2.547 2.284 2.052 1.853 1.938 1.934 2.055 2.189 TOTAL 1.457 1.662 1.670 1.776 1.638 1.626 1.504 1.407 1.536 Technical Synthesis Report | TRANSPORT 2 to 5 MWs 3.371 2.972 2.632 2.328 2.083 2.179 2.169 2.309 2.592 5 to 10 MWs 4.501 4.372 3.948 3.744 3.412 3.540 3.496 3.705 3.972 10 to 20 MWs 5.206 5.553 5.094 5.170 4.779 4.912 4.807 5.072 5.160 Over 20 MWs 6.078 6.879 6.309 6.582 6.077 6.179 5.960 6.266 6.358 TOTAL 3.771 3.458 3.186 2.953 2.626 2.648 2.485 2.451 2.949 The numbers confirm that the parameters established for modeling urban Source: Logit (2009) passenger transport indicate that trip-generation factors are higher in the metropolitan areas and larger cities than in smaller towns. Moreover, according to ANTP estimates, trip-generation factors for public transport trips in the large cities are higher than for individual trips. Figure 19: Public Transport and Individual Trip-Generation Factors, by Urban Cluster Similarity 53 Similarly, also confirming traditional modeling parameters, the trip-generation Source: Logit (2009) factors calculated by household income bands are higher for higher-income households than for lower-income groups, as indicated in Figure 20. Figure 20: Household Trip-Generation Factors according to Income Levels, by Urban Cluster Similarity Technical Synthesis Report | TRANSPORT Figure 20 also illustrates that the trip-generation factors for households receiving Source: Logit (2009) over ten minimum wages are similar for all similarity clusters, while for households with incomes of under 10 MWs, the trip-generation factors are higher in the larger cities than in smaller and medium-sized ones. The factors generating household trips, classified by five income groups and eight modes (bus, light commercial vehicles, trains, metro, cars, motorcycles, bicycles and on foot) for the eight urban similarity clusters, formed the basis for our calculations of passenger and freight movement in urban-metropolitan areas throughout Brazil, using the methodology explained below and applicable to both the reference and low-carbon scenarios. The decision to divide household trip-generation factors into five income bands and eight modal types was mainly to allow greater flexibility for estimating emissions produced in the alternative scenarios. 54 2.4.3 Investment Assumptions for the Reference Scenario In the calculations of probable investments in urban transport infrastructure in the reference scenario, the fact that Brazil will host the FIFA World Cup in 2014 was taken into account, as this will call for upgrading the public transport network. Considering that the majority of the host cities for the World Cup are situated in metropolitan areas, it can be assumed that investments will be forthcoming to improve some of the metro systems by 2014 in order to meet the commitments established at the time Brazil was chosen. Many city authorities have publicized their expectations in this respect in the media and on the internet. Although the above aspirations (and others) were taken into account when building the reference scenario, it was concluded that Bus Rapid Transit (BRT) systems were the most feasible option, incurring lower infrastructure investments and less time to execute in urban-metropolitan areas. This was considered the best alternative for improving traffic circulation and encouraging people to travel by public transport rather than by car. On this basis, all the investment types and values were specifically modeled for urban passenger transport according to the eight categories of urban similarity. A list of the investment probabilities in public urban transport infrastructure, for the reference and low-carbon scenarios, can be seen in the table below. Technical Synthesis Report | TRANSPORT Table 11: Investments in Public and Mass Transport Systems No. km to be constructed Low- car- Category Densely populated urban municipalities and metropoli- System Reference bon sce- 55 BRT 180 1.263 (no.)* tan regions type scenario nario MR with Metro 30 405 investments (1) São Paulo and Rio de Janeiro BRT 289 670 RM with Belo Horizonte, Federal District and Environs (RIDE), Metro 25 280 investments (2) Fortaleza, Curitiba, Recife, Porto Alegre, and Salvador BRT 60 300 MR with probable Belém, Baixada Santista, Goiânia, Campinas, Metro 0 100 investments (3) Manaus, and Greater Vitória Cuiabá-Várzea Grande, Aracaju, Grande Teresina BRT 80 240 (RIDE), Grande São Luís, Florianópolis, Londrina, Metro 0 0 MR/municipali- João Pessoa, Maringá, Maceió, Natal, São José dos ties with probable Campos, Ribeirão Preto, and Juiz de Fora investments (4) São José do Rio Preto, Campina Grande, Piracicaba, BRT 40 120 Municipalities Bauru, Montes Claros, Jundiaí, Anápolis, Foz do Igua- Metro 0 0 with probable çu, Franca, Rio Branco, Uberaba, Cascavel, and Volta investments (5) Redonda * MR = metropolitan region; RM = regional municipality. While the majority of Brazil’s municipalities need some kind of interventions to Source: Logit (2009) improve the operations and services of their public transport systems, only the larger municipalities have been addressed in this prospective analysis for deploying high- capacity passenger transport systems. Building new bus systems obviously involves much higher relative costs compared with options focused solely on improving existing Technical Synthesis Report | TRANSPORT systems. In the case of the metropolitan regions some major transport corridors are already saturated and investments in these can be easily justified. 2.5 Aspects of Transport Modeling The study based its evaluation of the potential for reducing vehicle carbon emissions, and of the different levels and types of investments required. Additionally, assessments and projections of both urban and regional passenger and freight movements were considered. These movements, categorized by transport mode, were expressed in units that could be measured in terms of carbon emissions. In this way it was possible to assess the emission impacts of different types of vehicles which might, or might not, be capable of using alternative low-emission engine technology. This approach enabled us to model different scenarios to take account of the possible impacts arising from different transport modes using alternative technologies. Employing general transport modeling and planning concepts, we developed a set of steps to obtain a picture of both 56 urban-metropolitan and regional freight and passenger movements. 2.5.1 Transport Planning and Modeling Transport planning needs to take into account existing systems and, to identify problems and solutions in line with sustainable development goals, forecast and control possible future scenarios, and present the results of planning studies to relevant decision-makers. In this respect, any decisions on transport planning need to be based on wide-ranging analytical models capable of evaluating various sets of alternatives through multiple interactions, as illustrated in Figure 21. Figure 21: Analytical model for transport planning Technical Synthesis Report | TRANSPORT Given the complexity and amount of information and alternatives which constitute Source: Logit (2009) the transport planning process, analytical models serve as a feedback system to ensure constant re-evaluation of the established goals and objectives. Using the traditional “Four-Stages� transport model in the transport planning process allows physical, economic and social changes to be incorporated at the regional and urban-metropolitan levels. Procedures for evaluating the different alternatives through multiple interactions and calibrations are ensured with the use of transport planning models such as TransCAD, EMME and MANTRA. Figure 22 illustrates the basic inputs and procedures using the “Four-Stages� model for obtaining a volume indicator, which in transport simulation processes is known as “loading�. This is the “standard unit of measurement� applied to freight and passenger movements, convertible into “carbon emission units�. The sequence of the various procedures is detailed below. Figure 22: Sequencing of the “Four-Stage� Transport Model 57 Source: Logit (2009) 2.5.2 The “Four-Stages� Model Over the past 50 years a methodology has been developed and consolidated for modeling transport supply and demand. Conceptual improvements were introduced at various stages of the methodological process. However, the basic structure adopted for addressing the problem has been maintained, while improvements arising from transport research have been gradually introduced and used in practical applications. As illustrated in Figure 22, the traditional modeling process is usually handled in four distinct stages: Trip generation or demand; Technical Synthesis Report | TRANSPORT Trip distribution; • Modal choice; • Route assignment. • The first three steps above simulate transport demand behaviour. This is based on • socio-economic and demographic information or economic activities performed in the study area. In addition data on use, occupation or the productive capacity of land in a particular area is used. Matrices of demand by mode (or a combination of different modes) of transport, broken down into flow-types (products, trip motives etc) or periods, are the result. As for traditional route assignment (the final modeling stage), we studied the interaction between supply, represented by transport mode, and demand, summarized in the trip matrices converted into passenger movements or numbers of vehicles transporting people and/or freight. The results obtained were fed into a process for evaluating the alternatives. This procedure involved economic aspects and the met transport demand. The specific objective of each stage was to simulate demand behaviour by using a set of models. The link between the various stages (involving the possible removal of some, or their substitution by alternative procedures) aimed to produce the same results, depending 58 inter alia on the goals of each study, the methodology adopted, and the information available. In this study, some procedures are adapted in view of the global nature of its objectives and the need to consider the possibility of vehicles using different technologies for reducing carbon emissions. Trip generation and distribution: The trip demand or generation stage defines the total demand for transport, which is attributed to each traffic zone as a function of its potential as a producer or attractor for trips. Once the global demand levels for each type of flow (in the period selected for analysis) have been established, their distribution can be calculated. This produces an estimate of the degree of interchange existing between each pair of specific zones. In this way, the spatial pattern of transport demand for each type of flow analyzed is represented by a set of distribution-demand or trip matrices. The latter are square matrices equal in size to the number of traffic zones. The matrix cell corresponding to line i (zone of origin or production i) and column j (destination zone, attraction or consumption j) contains an estimate of transport demand between traffic zones i and j. Demand can be represented in terms of passenger trips, vehicles or tonnage over a given period (hour, day, year etc.). Mode choice: The next modeling step (mode choice or split) attributes to each transport mode the probable portion of demand that it will absorb. At this stage a distinction has to be made between the flows which are captive modes of transport and flows considered to be competitive, or in other words those involving a choice between alternative modes. Once the choice of modes simulation is done, the transport demand estimate is Technical Synthesis Report | TRANSPORT concluded. The resulting information can be represented in a set of demand or trip matrices for each mode, flow-type and period (s) considered. Route assignment: These matrices must then be loaded onto the transport supply networks. This step, also known as “network loading�, produces the demand figures on each stretch of the transport system, as well as performance levels based on the estimated loading. Loading, the result of simulations for urban and regional freight and passenger transport, can be used for evaluating the socio-economic importance of projects and for assessing the operational quality of the modes via a feedback process. In this study, it will serve as a basis for assessing the impacts of vehicle carbon emissions. 2.5.3 Macroeconomic Scenarios The stages employed in a model targeted at simulating transport demand behavior require an input of socio-economic and demographic data and the deployment of a procedure for determining the parameters of this set of information within the proposed time horizon. This procedure consists of elaborating reference scenarios to reflect the development of the global and local economies. 59 The projection of future demand is framed on the basis of a reference scenario comprising a set of assumptions regarding the behavior of macroeconomic aggregates, technological change, consumer preferences, demographic projections, changes in the international scenario and trend data on regional and sectoral investments. These assumptions form the basis for projecting economic variables that impact demand for transport directly in a specific timeframe. Based on a reference scenario, the trajectory of economic variables over a pre- defined time horizon can be evaluated. Thus, using estimates of future economic evolution generated by experts in the subject, it is possible to assess the effects of economic growth on the levels of sectoral activity in states and key micro- regions and on the spatial aggregations of results related to areas of interest to the municipalities. The demand scenarios are also constructed by forecasting the possibilities for expansion of the agricultural frontiers, increased productivity, and projections of product supply and demand. 2.5.4 Emissions Modeling in the Transport Sector After determining trip allocations to the transport infrastructure and to vehicle types (network loading), it is possible to calculate the associated GHG emissions. To do this, this study drew on the concepts contained in the COPERT model, used by member countries of the European Union. This highly-detailed model allows emission estimates to be calculated according to a bottom-up procedure which contributes significantly to the accuracy of the estimates. Copert 4 is a software designed to calculate transport sector emissions. Developed initially by the Thessaloniki Aristotle University in Greece for use in European countries, Technical Synthesis Report | TRANSPORT it is sufficiently flexible to be adapted by other countries. Its robust methodology enables it to be applied in large areas (countries, states and cities) as well as in smaller areas (a minimum of one km2) without loss of reliability. This model differentiates between “cold� emissions (estimated at the outset of a trip before a vehicle reaches its correct working-efficiency level and engine temperature) and “hot� emissions (calculated when the engine reaches its stability level). It also accounts for vehicle deterioration resulting from age or high mileage. Emissions calculated by Copert 4 include the main transport-generated pollutants: ozone precursors (CO, NOx, NMVOC), greenhouse gases (CO2, CH4, N2O), acidifiers (NH3, SO2 ), particulate matter, carcinogens, polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs), heavy metals and other toxic substances (dioxins, furans). The model also calculates fuel consumption based on operating conditions. The entry data are presented in Table 12. Table 12: Entry Data: COPERT Fleet categorized by class of vehicle-engine tech- Total mileage by class of vehicle-engine technol- Variable Description nology for each year of study (urban, regional and ogy for each year of study road) Average trip mileage of trips per year and class of Average speeds by class of vehicle-engine tech- vehicle-engine technology nology, by year and category (urban, regional and 60 road) Size of fuel tank, by class of vehicle-engine technol- Canister size of each class of vehicle-engine tech- ogy nology Percentage of fuel injection Percentage control of fuel evaporation of fuel per engine type and category (urban, regional and road) Maximum and minimum ambient temperature, by Atmospheric pressure recorded by month and month and year year and the Beta distribution parameter Chemical composition of each fuel type Record of improvements in emissions of each type of pollutant per year Annual fuel consumption Specification of fuel used by vehicle-engine tech- nology COPERT 4 software was adjusted to the Brazilian context to accommodate available Source: COPERT 4 Manuals / Logit (2009) data, fleet characteristics, operational conditions, and fleet-maintenance conditions. Emission parameters established for the São Paulo Metropolitan Region by CETESB, based on the results of various experiments to determine general vehicle pollutant emissions, were used to put the COPERT model to practical use. The emission curves based on vehicle speeds according to the COPERT model were initially adapted to the CETESB figures and later fine-tuned to bring the resulting parameters into line with the other subjects addressed by the Brazil Low Carbon Study, mainly Ethanol & Cogeneration. CETESB is a technical body mandated by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA) as the agency responsible for Brazil´s vehicle emissions certification program. IBAMA is also responsible for Technical Synthesis Report | TRANSPORT implementing PROCONVE (Program for Controlling Air Pollution Produced by Motor Vehicles). CETESB has adapted international methodologies to Brazilian requirements and developed the technical means to combat motor vehicle-generated pollution. These served as the basis for the establishment of the aforementioned program in 1986 by CONAMA (the National Environment Council). This program has led to a reduction in pollutants from new vehicles by around 97%, by progressively restricting emissions through the introduction of technologies such as electronic fuel injection, catalysers and fuel improvements. As already mentioned, eight urban similarity clusters were established, based on the application of an unsupervised data classification method using socio-economic and demographic indicators from the 5564 Brazilian municipalities. The similarity clusters of Brazil’s largest cities possess traffic and congestion levels that are significantly higher than the clusters of small and medium-size cities. In other words, the larger cities experience slower vehicle speeds, resulting in higher levels of emissions per kilometer travelled by motor vehicles. The concept of different emissions factors for different speed levels (higher fuel burning per kilometer), according to the COPERT model, was applied in the present study to the eight urban clusters. 2.6 Models for Evaluating Economic Results 61 The evaluation of the economic results of mitigation options proposed in a study designed to explore the prospects for reducing GHG emissions cannot be confined to an analysis parameter framed on indicators which merely assess the proportion of emissions savings produced by investments in emissions-saving measures. Mitigation measures, principally in the transport sector, can also trigger other social economic benefits which, in studies of this type, should be considered in the comparative analyses of the economic outcomes of the various alternatives. In the economic evaluations undertaken in our study, “other socio-economic benefits� were also quantified and used as a comparative benchmark. The following section presents the parameters and the general and specific criteria used in the economic evaluation models adopted for the transport sector. It also provides details on how each of the indicators selected as analysis parameters for each of the low-carbon scenario mitigation measures for the transport sector was obtained. 2.6.1 Parameters and General Criteria All the mitigation measures included in the final low-carbon scenario proposed will require new investments in transport infrastructure, or a variety of expenditures generated by institutional actions. In this study we will refer to these investments as “investment required�. The majority of measures involving investments in infrastructure will also require during their useful lifetimes outlays on operation and maintenance (O&M costs). Some measures possess special characteristics which call for different approaches Technical Synthesis Report | TRANSPORT to the infrastructure investments considered in the reference scenario. Some investments will be avoided, while in other cases required investment will increase. In order to balance the effects of different disbursement dates and the launch of infrastructure investments within the timeframe of the study (up to 2030), these values were broken down into expected “useful lifetimes� and annualized with a discount rate of 8% per annum. The approach adopted for the individual economic evaluation of each of the mitigation measures contained in the final low-carbon scenario initially considers a hypothetical financial result. The method compares financial flows at present and nominal values with the possible flows which, in the absence of the proposed mitigation measures, would occur in the reference scenario. 2.6.2 Net Investments Curve Not all the mitigation measures will require investments to be intensified or avoided with respect to the reference scenario, but in the approach to this hypothetical financial result (“Net Investment�), all the flows of “Investment Required�, “O&M costs� and “Avoided Investments� will be taken into consideration. These flows, accumulated over time, will constitute the “Net Investments Curve� as illustrated in Figure 23 below. 62 Figure 23: The “Net Investments� Curve Figure 23 presents a hypothetical example where, in 2030, “Investment Required� will amount to US$891 million, “O&M Costs� to US$264 million, and “Investment Avoided� to US$623 million, resulting in a total “Net Investment� of US$531 million, calculated according to the following formula: where: Technical Synthesis Report | TRANSPORT IL (tot) = Σ IR + Σ O & M - Σ IE IL (tot) = total “Net Investment�; IR = flows of “Investment Required� (in annualized values - PMT 8% per annum); O & M = flow of “O&M costs “ and IE = flows of “Avoided Investments “(in annualized values - PMT 8% per annum). The emissions avoided in the reference scenario, compared with the total “Net Investment� in face and present values, constitute the first indicator calculated for each measure - the “Financial Cost of Ton of CO2 avoided�, according to the following formula: where: CF Ton CO 2e Ev = Σ IL (ACn) / Σ Ton CO 2e Ev CF Ton CO 2e Ev = “Financial Cost of Ton of CO 2 equivalent avoided; Σ IL (ACn) = Sum of total “net investment� accumulated on a year-on-year basis up to year “n�; Ton CO 2e Ev = Sum of the value of year-on-year emission savings within the study period, with respect to the reference scenario, in tons of CO 2 equivalent avoided. From the hypothetical example in Figure 23, it can be seen that if the accumulated emissions avoided during the study period amount to 50 million tons of CO2e and the indicator of “Financial Cost of Ton of CO 2 equivalent avoided� is US$150.44 per ton of 63 CO 2e the result would be the following: The values of total “net investment� accumulated year-by-year by the end-year of the project are, in increasing order (US$ million), 30, 87, 138, 84, 226, 263, 297, 327, 355, 379, 401, 421, 439, 455, 470, 483, 494, 505, 514, 523 and 531. The sum of these values of accumulated “net investment� amounts to US$7,522 million. Divided by the hypothetical 50 million tons of CO2e avoided, the final result is US$150.44 per ton of CO2e. 2.6.3 Other Indicators Selected as Analysis Parameters In addition to the reduction of emissions and net investments intensified or avoided in this study of the transport sector, the economic analysis of all the mitigation measures also takes into account the aforementioned “other socio-economic benefits� arising from these. In our assessment, these benefits were introduced as marginal abatement costs, with their effects assessed separately and their results incorporated into the “Financial Cost per Ton of CO2e avoided�, under three headings: fuel economy, comparative transport system operating gains and social benefits. The direct effects of these benefits were calculated using the transport system as an efficiency parameter. Any compensatory mitigation measures arising from other sectors of the economy were considered in the economic assessments for the transport sector by applying a production or opportunity costs approach, in line with the parameters adopted in other areas of the study. 2.6.3.1 Net Investments Curve with “Fuel� effect Technical Synthesis Report | TRANSPORT The vast majority of mitigation measures concern the increased operational efficiency of the transport system as a whole, either by reducing the number and/or length of trips in the reference scenario, or by exchanging high emissions-producing transport modes for low-emission modes. Both will involve fuel savings either by a reduction in load factors (lower numbers and length of trips) or by efficient modal shift, i.e. involving the same load factors, but with a new modal shift leading to lower fuel emissions (which frequently involves improved fuel economies). The benefits of modal shift will be apparent in most of the mitigation measures. The benefits to be gained from reducing the number and size of trips will be particularly significant when institutional administrative measures are established for urban areas (e.g. “Urban Demand Management for Individual Transport�, “Urban Planning Focused on Transport�, etc). With respect to the specific mitigation measures for passenger transport, the benefits of fuel savings were measured by comparing them with the reference scenario, calculating the impacts of avoided financial outlays evaluated in terms of production costs. In this case, these fuel savings will benefit car and motorcycle users as well as public transport operators (conventional buses, BRT, metro, passenger trains and air travel). As for the measures referring to freight transport, financial impacts were calculated 64 with the same methodology as for passenger transport. The beneficiaries in this case were the freight transporters (trucks, freight trains, waterways, coastal shipping and pipelines). The financial impacts of the greater or lesser consumption of fuel in the reference scenario were adjusted in the transport sector economic evaluations to reflect the costs of production adopted for evaluating the total fuels production and consumption. The value of the financial impacts will be incorporated into the flows comprising the “net investments curve�. The flows accumulated over time constitute the “curve of net investments with fuel effect�, as shown in Figure 24. Figure 24: The “Net Investments with Fuel Effect� Curve Technical Synthesis Report | TRANSPORT Figure 24 above illustrates the continuity of the hypothetical example shown in Figure 23. Thus in 2030, the cumulative “Net Investment� amounts to US$ 531 million, from which was subtracted the US$164 million relating to fuel economies. The result, US$367 million, comprises the “net investment with fuel effect curve�, calculated according to the following formula: where: ILEfC (tot) = IL (tot) - Σ EcoComb ILEfC (tot) = total “net investment with fuel effect “; IL (tot) = total “net investment�, and EcoComb = fuel economy flows. The emissions avoided in the reference scenario, compared to total “net investment with fuel effect�, at present and nominal values, constitutes the second indicator calculated for each measure - the “Financial Cost of Ton of CO2e avoided with Fuel Effect�, according to the following formula: 65 where: CFEfC Ton CO 2e Ev = Σ ILEfC (ACn) / Σ Ton CO2e Ev CFEfC Ton CO 2e Ev = “Financial Cost of Ton of CO2e avoided with Fuel Effect� Σ ILEfC (ACn) = The sum of the “net investment with fuel effect,� accumulated on a year-by-year basis up to year “n�; Ton CO2e Ev = The sum of the value of emission reductions annually within the study period, expressed in tons of CO2 equivalent avoided compared to the reference scenario. From the hypothetical example illustrated in Figures 23 and 24, the indicator of the “Financial Cost of a Ton of CO2 equivalent Avoided with Fuel Effect� will amount to US$123.16 per ton of CO2e and therefore: The values of the total “net investment with fuel effect� accumulated year- by-year through the end year of the project are, in incremental order (US$ • million): 30, 86, 134, 177, 214, 246, 273, 296, 316, 332 , 345, 356, 364, 370, 374, 377, 377, 377, 375, 372 and 367. The sum of the values of total “net investment with fuel effect� amounts to US$6,158 million. Divided by the hypothetical 50 million tons of CO2e • avoided, the result is US$123.16 per ton of CO2e. The possibility of reducing expenditure for transport operators resulting from lower fuel consumption with the introduction of these mitigation measures has a positive impact on the entire sector. However, for the economy as a whole, it is necessary to Technical Synthesis Report | TRANSPORT verify the forecast production and consumption of fuels, and especially to determine the destination of fuel consumed at lower levels than originally anticipated. The effects of the mitigation measures presented in this study were evaluated incrementally and cumulatively. Fuel savings were calculated for each of the different options, using the production costs of fuels used in the Brazil Low Carbon Study as a parameter, but without considering other possible macroeconomic outcomes. The overall macroeconomic effect was evaluated on the basis of “Increased Ethanol Consumption by Flex-Fuel vehicles,� where the fuel consumption values were considered to be investment flows required as the result of higher ethanol consumption (calculated on the basis of bio-ethanol production costs) and avoided by lower gasoline consumption (calculated on the production costs of gasoline). The final amounts of fuel consumption in the reference scenario were aligned with the data on ethanol and gasoline production in the rest of the Brazil Low Carbon Project. 2.6.3.2 Curve of Net Investment with “fuel� and “operation� effects In general terms a transport system can be seen as a production process which consumes resources in order to generate useful products for society. Products of the transport system can generate both advantages and disadvantages. However, a key objective of an economic evaluation of transport investments is to examine all the advantages and disadvantages, with the ultimate aim of securing net benefits from the transport system, and to compare these benefits with the costs involved. 66 Early transport studies undertook results evaluations purely in engineering terms, concentrating on the safest and cheapest way of installing and running a transport system. Later studies established monetary values for all the factors considered relevant, in order to make it possible to calculate rates of return on capital costs or present net values as a means of comparing alternative projects. In addition to the different levels of investment and capacities required, alternative transport systems also involve different operating and management cost structures for infrastructure and vehicles. Thus the so-called “operational gains� derived from the planned interventions were quantified monetarily, and the study adopts an approach based on comparing the difference between scenarios with and without the deployment of interventions, reflecting the costs and benefits projections associated with the new developments. The methodology of economic evaluation follows the general concepts adopted by the World Bank for economic feasibility studies of transport projects, where the basic goal is to estimate the benefits generated from deploying and streamlining a system. The main benefits considered as “gains� in urban transport operations include: Reduced Operating Costs: operational efficiency measured in terms of the gains achieved by comparing operating costs with infrastructure and/or • vehicle maintenance costs with the alternatives considered (cost of rolling stock, maintenance-staff costs, operation of vehicles and infrastructure etc). Reduced Management Costs: managerial efficiency measured in terms of gains achieved, by comparing the administrative costs needed for operation Technical Synthesis Report | TRANSPORT • of the infrastructure and/or vehicles with the alternatives considered (personnel costs, equipment costs etc); Accident Reduction: measuring the gains by comparing the number of expected accidents and anticipated replacement costs with the alternatives • considered (replacement cost of rolling stock, cost of lost workdays by drivers etc). In the case of regional freight and passenger transport, the operational gains correspond to the net variation of the revenues of each operator arising from the deployment of the mitigation measure under consideration (reference scenario vs. low-carbon scenario). In line with the proposed methodology, the initial stage of the evaluation process consisted of identifying the production costs of the transport services on a “present costs� basis. The following stage consisted of incorporating the demand to be captured in each of the simulated alternatives for the time-horizons of the study. The methodology adopted used the measurements generated by the transport planning model as a basic reference for quantifying the benefits associated with each of the alternatives studied. The monetized “operational gains� were incorporated into the flows comprising the “net investment with fuel effect curve�, and the resulting accumulated flows over time formed the “net investments with fuel and operation effects curve�, as illustrated in the 67 following figure. Figure 25: The “Net Investments with Fuel and Operation Effects Curve:� The above figure shows a value of US$367 million accumulated by 2030 for the “net investment with fuel effect� curve, from which US$362 million relating to fuel economy was subtracted. The result, US$5 million, constitutes the curve of “net investment with fuel and operation effect�, calculated according to the following formula: Technical Synthesis Report | TRANSPORT where: ILEfCO (tot) = ILEfC (tot) - Σ GOper ILEfCO (tot) = total “Net investment with fuel and operation effects “ ; ILEfC (tot) = total “Net investment with effect fuel�, and GOper = operation revenue gains. The avoided emissions in the reference scenario, compared with the total “net investment with fuel and operation effect� at face and present values, constitute the third indicator calculated for each measure - the “Financial Cost of a Ton of CO2e Avoided with Fuel Effects and Operation�, according to the following formula: where: CFEfCO Ton CO 2e Ev = Σ ILEfCO (ACn) / Σ Ton CO 2e Ev CFEfCO Ton CO 2e Ev = “Financial Cost of a Ton of CO 2e avoided with the Fuel and Operation effects�. Σ ILEfCO (ACn) = The sum of “net investment with fuel and operation effects,� accumulated year-on-year up to year “n�; Ton CO 2e Ev = the value of emission reductions for each year within the study period compared to the reference scenario in tons of CO2e avoided. 68 In the hypothetical example illustrated in Figures 23, 24 and 25, the indicator of “Financial Cost of a Ton of CO2 avoided with the Fuel and Operation effects� amounts to US$62.50 per ton of CO2e. Therefore: The values of total “net investment with fuel and operation effects� accumulated on a year-by-year basis up to the project horizon year, will be • (in US$ million) in ascending order: 30, 83, 126, 160, 187, 206, 220, 227, 230, 227, 221, 211, 198, 182, 163, 141, 117, 92, 64, 35 and 5; The sum of these values of “net investment with fuel and operation effects� produces an accumulated total of US$3,125 million. Divided by the • hypothetical 50 million tons of avoided CO2e avoided, this results in US$62.50 per ton of CO2e. 2.6.3.3 Final Net Investment Curve In the methodological approach of the economic evaluation model adopted, the financial costs arising from deployment of the mitigation measures initially constituted the “net investments curve�. Subsequently, the effects of the “other socio-economic benefits� were quantified monetarily and introduced into the comparative analysis of the alternatives. First the effects of fuel economy on the “net investments with fuel effect� curve was evaluated, focusing on the financial gains and the production costs • for transport users and operators; Technical Synthesis Report | TRANSPORT The potential gains in operational efficiency and management, measured by comparing the costs of alternative systems, were then incorporated • into the “net investments with fuel and operation effects� curve. These gains have a positive impact on the decisions made by logistics operators. Positive evaluations indicate that the mitigation options analyzed could be successful; To complete the economic assessment of mitigation options leading to framing the 2030 low-carbon scenario for the transport sector, the effects of • the so-called “social benefits� (both direct and indirect) were incorporated. Some of these effects will directly benefit passenger-transport users and were quantified by measuring, in monetary terms, the reduction of trip time for both public transport and individual means. The interdependence of many of the variables in the transport systems means that actions focused on a small part of the system which can produce reduced trip times effectively trigger a chain reaction which can impact both users of public transport and those using individual modes. Thus, in addition to the direct effects on users of the transport systems, society as a whole will benefit indirectly from the “social benefits�. In order to quantify the benefits monetarily, we evaluated the reduced health costs flowing from the alternatives analyzed, separated into two groups: Lower costs of pollution (CO2 is harmless to humans in the lower atmosphere, 69 i.e. there is no damage to health and therefore no health expenditure resulting • from CO2); Reduced cost of accidents. The parameters needed to calculate the benefits arising from the “lower costs of • pollution� were obtained from the figures in the “Study of Reduction of Diseconomies through Improvement of Urban Public Transport�, coordinated by IPEA and ANTP (August 1998).4 The study, involving 10 Brazilian cities, quantified the annual losses in these cities arising from inefficient transport systems. For regional transport, the parameters were adapted according to the emission levels of each of the transport modes. The reductions in terms of vehicles times kilometers, multiplied by the unit costs of pollution related to each mode, made it possible to determine the annual benefit generated by reduced air pollution. Evaluating the benefits arising from the “lower costs of accidents� also involves examining the changes in trip lengths by transport users based upon situations with and without the deployment of the mitigation measures. The parameters of unit costs of accidents for transport systems derived from studies conducted by the World Bank in Brazil, particularly the CBTU programs for the decentralization of urban rail transport in Rio de Janeiro, São Paulo, Belo Horizonte and Recife. The monetized “social benefits� will be incorporated into the flows that formed the “net investments with fuel and operation effects� curve and the resulting accumulated flows over time will constitute the “final net investments curve�, as shown in the diagram below: Technical Synthesis Report | TRANSPORT 4 The values that appear in the ANTP study refer to the costs of local pollution and not to CO2 and GHGs Figure 26: The “Final Net Investments� Curve 70 Still employing the hypothetical example presented in this section, US$5 million was accumulated by 2030 in the “net investment with fuel and operation� curve. From this, US$400 million was subtracted referring to “social benefits�, thereby producing a negative result: US$(395) million. This indicates that in 2030 the direct and indirect accumulated benefits will be greater than the financial costs of the investments required for implementing the hypothetical measure. This amount, which will comprise the “final net investment curve,� was calculated according to the following formula: Technical Synthesis Report | TRANSPORT where: ILFinal (tot) = ILEfCO (tot) - Σ BenSoc ILFinal (tot) = total “Net investment with fuel and operation effects�; ILEfCO (tot) = total “Net investment with fuel effect�, and BenSoc = flows related to monetarily quantified “social benefits� The emissions avoided, compared to the reference scenario, and compared with total “final net investment� in nominal and present values, constitutes the fourth indicator calculated for each of the measures, the “Final Cost of tons of CO2e avoided�, expressed by the formula: where: CFFinal Ton CO 2 and Ev = Σ ILFinal (ACN) / Σ Ton CO 2 and Ev CFFinal Ton CO 2 and Ev = “Final Cost of some of Avoided CO 2�; Σ ILFinal (ACN) = Sum of “final net investment� accumulated year-by-year up to year “n�; Ton CO2 and Ev = the value of emission reductions for each year within the study period, compared to the reference scenario in tons of CO2 avoided. In the hypothetical example illustrated in Figures 23, 24, 25 and 26, the indicator of the “Final Cost of a Ton of CO2e Avoided� will be negative: US$ (4.28) per ton of CO2e. In other words, with the inclusion of all the direct and indirect economic and social 71 benefits in the evaluation, the required investments for the hypothetical mitigation measure will be fully compensated for, producing a credit of US$ 4.28 for each ton of CO2e avoided. The sum total of the preferred benefits will exceed the amount of the aforementioned investments required: The values of the total “Final Cost of tons of CO2 Avoided� accumulated on a year-by-year basis up to the horizon year of the project will be, in ascending • order (in US$ million) 30, 79, 116, 142, 157, 163, 161, 151 , 135, 112, 85, 52, 15, (26), (70), (118) (169) (222) (277) (335) and (395); The sum of these total accumulated values of “net investment with fuel and operation effects� amount to a negative US$ (214) million. Divided by the • hypothetical 50 million tons of CO2e avoided, this results in a “Final Cost of a Ton of CO2 avoided�, if negative US$(4.28) per ton of CO2e. In other words, a credit of US$4.28 for every ton of CO2e avoided. 2.6.4 Parameters and Criteria for Evaluating Fuel Economy The calculation of the benefits derived from fuel economies foreshadowed by all the mitigation measures was based on the results of the transport and GHG emissions modeling undertaken as part of this study. These results were evaluated incrementally and cumulatively, employing the production costs of fuel as a parameter for assessing the benefits resulting from fuel savings, calculated for each of the options. The reduced consumption of a particular type of fuel may have undesirable effects on the economy, since it could force the government to adopt unforeseen fuel supply Technical Synthesis Report | TRANSPORT shortage policies which could result in financial loss to society as a whole. This study applied different reducers to the production costs adopted in the Brazil Low Carbon Study, using them as a parameter for evaluating the resulting fuel economies. Table 13 below contains the parameters used in the evaluations of all the mitigation measures. Table 13: Fuel Production costs Fuel Type => Ethanol Petrol Diesel Aviation Fuel Elec-(MW) % Considered in economic evaluation 30% 30% 50% 85% 100% (m 3) (m 3) (m 3) (m3) Production costs in US$ 2009 276.46 281.76 188.68 281.76 56.89 (Project parameters) 2010 276.46 281.76 188.68 281.76 56.89 72 2011 273.96 314.15 189.62 314.15 56.89 2012 269.77 321.13 193.84 321.13 56.89 2013 264.54 328.11 198.05 328.11 56.89 2014 258.75 335.09 202.26 335.09 56.89 2015 252.44 342.08 206.48 342.08 56.89 2016 249.52 349.06 210.69 349.06 56.65 2017 245.48 349.06 210.69 349.06 56.65 2018 241.24 349.06 210.69 349.06 56.65 2019 236.50 349.06 210.69 349.06 56.65 2020 231.42 314.15 189.62 314.15 56.40 2021 226.84 314.15 189.62 314.15 56.40 2022 221.87 314.15 189.62 314.15 56.40 2023 216.72 314.15 189.62 314.15 56.40 2024 211.47 314.15 189.62 314.15 56.40 2025 206.19 314.15 189.62 314.15 56.17 2026 203.22 314.15 189.62 314.15 56.17 2027 200.23 314.15 189.62 314.15 56.17 2028 197.22 314.15 189.62 314.15 56.17 2029 194.19 314.15 189.62 314.15 56.17 2030 191.18 314.15 189.62 314.15 55.94 Technical Synthesis Report | TRANSPORT 2.6.5 Criteria and Sources for Urban Passenger Modeling To define the criteria for calculating the social benefits and operational gains flowing from all the mitigation measures for the urban areas, this study adopted as parameters the values used in the feasibility study for the implementation of the BRT in Rio de Janeiro´s “T5 Corridor� under the 2005 Urban Transport Master Plan (PDTU). This study also drew from the same study of BRT-related mitigation criteria for calculating the “investment required� costs, as well as the maintenance and operational costs of the system. The basic premise of the methodology was to estimate the benefits generated by deploying the T5 Corridor and streamlining public transport in the project’s area of influence. The approach adopted in the economic feasibility study of the T5 Corridor was based on a comparison between the scenarios ‘with and without’ implementation of the interventions. This was presented in a pro-forma chart on a year-on-year basis, containing projections of the costs and benefits associated with the venture over a 25- year time span. The first step in the evaluation process consisted of identifying the transport production costs based on the methodology adapted by the Rio de Janeiro City Hall, and developed using the approach proposed by GEIPOT, the former government body responsible for study and research in transport planning. 73 The methodology used in the study of the T5 Corridor employed measures generated by the transport plnning model as a baseline for quantifying the benefits associated with each of the alternatives studied. The principal measures used were the total passenger times kilometers, passenger times kilometer hours and vehicles times kilometers, relating to the basic and alternative situations for baseline and horizon years. Other measures for quantifying benefits were based on the estimated average operating speeds of the vehicle fleet. In the economic feasibility study data on transport movements during the morning rush hour was used. Using OD Survey peak factors, these numbers were expanded to include all-day traffic performance. 2.6.5.1 Evaluation of Operational Gains in the T5 Corridor The basic source for determining the economic costs of operating the transport Reducing Operating Costs system in the city of Rio de Janeiro was the tariff sheet for December 2004. This made it possible to analyze the individual components of the cost of transport services. It was particularly useful for estimating the operating costs of the T5 Corridor, including the real prices of transport inputs in Rio de Janeiro, and for appraising the specific characteristics of the proposed technology. To determine the economic costs of public and individual transport, the input costs were evaluated according to the specific nature of each operational cost component. Table 14 illustrates the unit costs for the separate items dealt with in the aforementioned study, as well as the fixed and variable components of the operating Technical Synthesis Report | TRANSPORT costs of the conventional and articulated bus systems: Table 14: Economic Operating Costs of the Public Transport System in Rio de Janeiro, considered this study (US$ per km) Cost Items Conventional Buses Articulated buses Lubricants 0.0494 0.0494 Running-in 0.0511 0.0769 74 Parts & Accessories 0.0547 0.1099 Variable Cost Considered 0.1552 0.2362 Depreciation 0.1044 0.4998 Remuneration 0.0727 0.4600 Administrative costs 0.1025 0.3190 Staff 0.7934 0.4911 Fixed Cost Considered 1.0730 1.7699 Economic Cost 1.2282 2.0061 The total operating costs were determined by using the methodology proposed Source: Studies for Implementation of T5 Corridor T5 / Logit by GEIPOT. This approach made it possible to determine the fixed and variable components of the costs in situations with and without the implementation of the new T5 Corridor, for each of the modeling horizons. Based on the prices of each input and the composition of the fleets in operation, the methodology used permitted quantifying the fixed and variable unit costs of the system with and without the deployment of the alternative considered i.e. conventional vs. articulated bus services (Table 14). Based on this data, the model allowed quantification of (i) the operating indicators for each alternative and (ii) the total operating costs of the system for each modeling horizon and alternative considered. The fleet’s average age was assumed to be that recorded for the year that the T5 Corridor studies were underway. Multiplying the number of vehicles by kilometers by the unit cost of operation Technical Synthesis Report | TRANSPORT and the number of days in the year produces the annual operating cost for each type of vehicle for each time-horizon, and for situations with and without the project. The annual gain can thus be determined as the result of reduced operating costs. The general mathematical formulation adopted in the calculation of this type of benefits is expressed as follows: RCO = (Vehicle x km SP - Vehicle km x CP) / FP - supply) x Cop) x Days / Year where: RCO = Reduced Operating Cost Vehc x km SP =Total number of vehicles during peak period for the situation without project Vehc x km CP = Total vehicle x km in the peak period for the situation with project FP - Supply Factor = Peak factor related to supply Cop = unit costs of bus system operation determined according to the methodology described previously Days / Year = Total number of days in year Although the unit costs of the articulated bus used in the BRT system are higher than those of conventional buses (R$2.00 per km x R$1.23 / km - see Table 14), their passenger load capacity per unit is at least double- an important point to consider when determining cost reductions. 75 The procedure adopted for quantifying the benefits generated from the reduction Reduction of Cost Management Systems in the cost of bus system management involved determining a percentage reduction in operating costs. Based on recent data on public passenger transport in several Brazilian cities, management costs amount to around 3-5% of the costs of the entire bus operation. Four percent was added to the figure calculated for reducing the operating costs of buses, to mirror the reduction of the bus fleet in circulation and the resulting decreased management costs. The data on unit costs of accidents in bus systems were obtained from studies Reducing the Cost of Accidents conducted by the World Bank in Brazil, particularly the programs for the CBTU decentralization of urban rail transport in Rio de Janeiro, São Paulo, Belo Horizonte and Recife. The calculation procedure adopted to measure this type of benefit, considered the costs of accidents for each mode of transport. To determine the yearly benefits, the costs were obtained by calculating the difference in the total number of passengers times kilometers in the situations with and without the project, for each transport mode and each modeling horizon. The general mathematical formulation for each mode of transport is given by: RCAcid = ((Pass x km SP - Pass x km CP) C Acid / FP - Supply) x Days / Year Technical Synthesis Report | TRANSPORT where: RCAcid = Reduced Cost of Accidents Pass x km SP = Total passengers x km at peak period, without project Pass x km CP = Total passengers at peak period, with project FP - Supply Factor = Peak Demand Factor Acid C = Unit costs of accidents (specifically bus-related) based on the World Bank studies Days / Year = Total number of days in the year 2.6.5.2 Investment, Operation and Maintenance Costs and Operating Gains calculated on the basis of the “ T5 Corridor� study The data from the Corridor T5 study provided a good basis for determining the values of the investments required, operation and maintenance costs and operating gains for the year of implementation and over the 25-year lifespan of the entire system. These values are presented in the following table. 76 Table 15: Investment, Operation and Maintenance Costs and Operating Gains calculated on the basis of the “T5 Corridor� study (US$ per km) 0 5,32,5361 0 0 Year of Operation Investment Operation and Maintenance Costs Operating Gain 1 1,266,511 53,403 513,519 2 0 54, 472 546, 608 3 55, 329 55,561 581, 032 4 0 56,672 613, 145 5 0 57,806 646, 272 6 0 58,962 680, 443 7 0 60,141 715, 685 8 97, 463 61,344 752, 029 9 0 62,571 773, 876 10 0 63,822 795, 563 11 0 65,099 817, 077 12 0 66,400 838, 408 13 1,112,300 67,729 859, 543 14 0 69, 083 881, 165 15 43, 802 70, 465 902, 687 16 0 71, 874 924, 105 Technical Synthesis Report | TRANSPORT 17 0 73, 312 945, 414 18 48, 731 74, 778 966, 608 19 0 76, 273 985, 332 20 77, 158 77,799 1,003,841 21 0 79, 355 1,022,126 22 0 80, 942 1,040,180 23 12, 183 82, 561 1,057,995 24 0 84, 212 1,080,037 25 -398, 058 85, 896 1,102,078 The values indicated above for operating gains served as a parameter for calculating the benefits of all the urban mitigation measures. 2.6.5.3 Evaluation of Investments, Operation and Maintenance Costs and Operating Gains for BRT For the year-by-year calculation of the mitigation figures used in the BRT deployment, the data presented in Table 15 were used to construct a 26-line matrix (year of deployment + 25 year lifespan) for each year within the project study period, with the expected entry into operation of the new BRT extended lines, in both the reference and low-carbon scenarios. 77 The following table presents a year-by-year forecast of the launching of new BRT lines or extensions contained in the mitigation measure evaluation for the two scenarios: Table 16: Km of BRT to be Implemented in the Reference and Low Carbon Scenarios 2010 123 49 Year of Commencement of Operations Low-carbon Scenario Reference Scenario 2011 123 49 2012 123 49 2013 120 46 2014 120 46 2015 105 33 2016 102 30 2017 120 30 2018 118 28 2019 115 26 2020 115 26 2021 115 26 2022 113 24 2023 113 24 2024 135 24 2025 135 24 Technical Synthesis Report | TRANSPORT 2026 134 23 2027 134 23 2028 143 23 2029 143 23 2030 143 23 As can be seen in the above table, the forecast applies to the two scenarios for the 21- years period addressed in this study. However, investment, costs and benefits should be considered up to year 2054. The following table shows year-by-year figures related to the total investment amounts (real disbursement values), total operation and maintenance costs and the operating profits calculated for deploying the BRT according to the two-scenario schedule shown in Table 16: Table 17: Values of Investments and Costs of O & M Operations and Gains in the Reference and Low-Carbon Scenarios Year Reference Scenario Low-carbon Scenario Investment Operation and Operating Investment Operation and Operating Maintenance Gains Maintenance Gains 78 2009 259 0 0 679 0 0 Costs Costs 2010 320 3 25 841 8 33 2011 320 5 52 841 17 71 2012 310 8 80 833 25 112 2013 307 11 108 829 34 157 2014 236 13 138 745 42 205 2015 206 15 163 710 50 254 2016 203 17 188 808 58 306 2017 194 19 214 828 67 367 2018 178 21 239 809 76 430 2019 175 23 263 807 85 496 2020 174 25 288 806 95 565 2021 168 26 312 799 104 637 2022 219 28 337 937 114 712 2023 219 30 362 1059 124 790 2024 221 32 387 1095 135 877 2025 211 34 413 1084 147 968 2026 210 36 438 1083 159 1062 2027 197 38 464 1122 171 1160 2028 194 40 490 1131 184 1264 2029 197 42 517 1161 198 1372 2030 71 44 544 365 211 1484 2031 39 45 559 176 215 1560 Technical Synthesis Report | TRANSPORT 2032 39 46 574 177 220 1635 2033 37 47 589 168 224 1710 2034 16 48 603 115 228 1784 2035 15 44 563 116 220 1736 2036 15 41 522 143 211 1683 2037 16 37 479 143 201 1626 2038 12 34 437 128 192 1566 2039 17 31 395 134 183 1503 2040 18 29 366 146 175 1452 2041 18 26 340 139 167 1400 2042 19 24 313 141 158 1328 2043 -6 22 288 -22 148 1255 2044 -6 21 265 -22 138 1181 2045 -7 19 242 -28 128 1105 2046 -6 17 218 -27 118 1025 2047 -6 15 195 -27 108 943 2048 -8 13 171 -43 98 858 2049 -8 11 147 -43 85 748 2050 -9 10 122 -54 72 635 79 2051 -9 8 99 -54 59 521 2052 -9 6 75 -57 45 403 2053 -9 4 50 -59 30 272 2054 -9 2 25 -59 15 138 Given that the last year considered within the study is 2030, and in order to balance out the effects of the different dates for disbursements and for commencing investment operations in accordance with the time-spreads indicated in the economic evaluations, the values were annualized by a discount rate of 8% per annum for a period of 25 years (useful lifetime adopted for the system). The following table shows the annualized flow for investments required and avoided, together with the final flows considered for operation and maintenance costs and operating gains in the economic evaluation of the mitigation measure associated with the BRT deployment throughout the country: Technical Synthesis Report | TRANSPORT Table 18: Values of Investments and Costs of O & M Operations and Gains in the Reference and the Low Carbon Scenarios for BRT Implementation Year Genesis of Values Considered in Economic Evaluation (in US$ million) Required Investment and Investment Avoided Operation and Maintenance Curve of “Operating Low-carbon Scenario values Reference Scenario values Costs Net Gains� Investment Investment Investment Investment Low Reference Final I nve s t - 80 Real Va- Annualized Required Real Annualized Avoided Carbon Scenario Values m e n t lue Cost (PMT (A) = Value Cost (PMT (B) = Scenario Values (E = (A-B + - lifetime = Accum. - lifetime = Accum. Va l u e s (D) C-D) E) 25 years) Value PMT 25 years) Value PMT (c) 2009 816.6 76.5 76.5 258.9 24.3 24.3 0.0 0.0 0.0 52.2 0.0 2010 1010.8 94.7 171.2 320.5 30.0 54.3 8.2 2.6 5.6 122.5 33.3 2011 1010.8 94.7 265.9 320.5 30.0 84.3 16.5 5.2 11.3 192.9 70.6 2012 1001.0 93.8 359.7 310.2 29.1 113.4 25.1 7.9 17.1 263.4 111.9 2013 996.7 93.4 453.0 307.1 28.8 142.1 33.6 10.6 23.0 333.9 156.6 2014 896.1 83.9 537.0 235.9 22.1 164.2 42.2 13.2 29.0 401.8 205.3 2015 853.7 80.0 617.0 205.9 19.3 183.5 50.1 15.3 34.8 468.3 254.1 2016 971.3 91.0 707.9 202.8 19.0 202.5 57.9 17.2 40.7 546.1 306.1 2017 995.9 93.3 801.2 193.9 18.2 220.7 67.1 19.2 47.9 628.5 366.9 2018 973.1 91.2 892.4 177.7 16.7 237.3 76.3 21.0 55.3 710.3 430.3 2019 970.0 90.9 983.3 174.7 16.4 253.7 85.5 22.8 62.7 792.2 496.2 2020 969.5 90.8 1074.1 174.3 16.3 270.0 94.9 24.6 70.2 874.3 565.2 2021 960.1 89.9 1164.0 167.7 15.7 285.7 104.4 26.5 77.9 956.2 637.2 2022 1126.7 105.5 1269.6 218.9 20.5 306.2 114.1 28.3 85.8 1049.1 711.9 2023 1272.6 119.2 1388.8 218.7 20.5 326.7 124.0 30.2 93.8 1155.8 789.8 2024 1316.2 123.3 1512.1 220.7 20.7 347.4 135.5 32.1 103.4 1268.1 876.9 2025 1303.0 122.1 1634.2 211.3 19.8 367.2 147.2 34.0 113.2 1380.2 967.8 2026 1302.0 122.0 1756.1 209.5 19.6 386.8 159.1 35.9 123.2 1492.5 1062.1 2027 1349.2 126.4 1882.5 196.9 18.4 405.3 171.3 37.9 133.4 1610.6 1160.1 2028 1359.8w 127.4 2009.9 194.2 18.2 423.5 184.3 39.9 144.4 1730.8 1264.1 Technical Synthesis Report | TRANSPORT 2029 1396.1 130.8 2140.7 197.2 18.5 441.9 197.5 41.9 155.6 1854.3 1371.9 2030 438.9 41.1 2181.8 71.3 6.7 448.6 211.0 44.0 167.0 1900.2 1483.5 Total 23290.1 - 23,878.8 4788.9 - 5689.7 2105.8 510.5 1595.3 19784.4 13321.8 In order to obtain the curve of “net investment values�, an initial calculation was made of the values of the “investments required “ and “avoided investments� by analyzing and aggregating the real values considered for the low carbon and reference scenarios respectively. Subsequently, the value for operating and maintenance costs was calculated and the reference scenario numbers were subtracted from those of the low-carbon scenario. To conclude calculation of the “net investment values curve�, the “avoided investments� value was subtracted from the “required investments� and the calculated values were added to the operating and maintenance costs. The “operating gains� shown at Table 18 served as a basis for calculating the “operating gains� of all the urban mitigation measures. 2.6.5.4 Evaluation of Social Benefits in the T5 Corridor Some of the “social benefits� directly affect all passenger transport users. Interpreted as “reduced trip-times�, these benefits were calculated by measuring the monetary savings involved in shorter trip times of public and individual transport. The interdependency of the many variables in the transport systems means that actions related to a small part of this particular system leading to reduced trip times triggers a chain reaction, which impacts users of public and private transport. 81 The methodology used for calculating this benefit considered the net annual result of the variation between total passengers per hour in the “with and without the project� situations, based upon the time-value calculated in the Declared Preference Survey conducted in Rio de Janeiro at the time of the T5 Corridor studies. The values obtained for “Value of User Travel Time� were R$1.08 per passenger-hour for travel on public transport, and R$12.07 per passenger-hour for individual transport users. The general mathematical formula adopted for the calculation is as follows: RTV = ((SP Pass x hour - Pass CP x hour) x VT / (FP - Dem)) x Days / Year where: RTV = Reduced Travel Time Pass x hour SP = Total passengers per hour during peak hours for the situation without project Pass x hour CP = Total passengers per hour during peak hours for the situation with project VT = Value of Time FP - Dem = Peak Demand Factor Days / Year = Total number of days in the year The model adopted in the economic evaluation, in the T5 Corridor studies, assessed the net benefit of reducing trip times across the system, incorporating the reductions and increases in total passengers-hours at each modeling horizon for all the transport modes considered. Technical Synthesis Report | TRANSPORT In addition to the direct effects on users, society as a whole will also indirectly receive similar “social benefits�. The monetary quantification of these benefits takes into account savings on healthcare spending arising from the alternatives analyzed, separated into two groups as follows: Reducing Pollution costs Reducing Costs of Accidents • As already mentioned, the parameters needed for calculating the benefits arising • from “lower costs of pollution� were obtained from the “Study of Urban Diseconomies Reduction through the Improvement of Public Transport�, coordinated by IPEA and ANTP. The reductions in terms of vehicles times kilometers, multiplied by the unit costs of pollution related to each mode, make it possible to determine the annual benefit generated by reduced air pollution. Evaluating the benefits arising from the “lower costs of accidents� also involves examining the changes in the trip lengths by transport users based upon situations with and without the deployment of the projects. The parameters of transport accident unit costs derived from studies conducted by the World Bank in Brazil, particularly the CBTU programs for the decentralization of urban rail transport in Rio de Janeiro, São 82 Paulo, Belo Horizonte and Recife. The T5 Corridor studies contained an evaluation of the direct and indirect social benefits over the 25-year useful lifetime of the system as a whole. These values, together with the other transport modeling data, are presented in the following table (in US$ per kilometer of operation): Technical Synthesis Report | TRANSPORT Table 19: Investment values, O & M Costs, Operating Gains and Social Benefits based on the study of the T5 Corridor Study (in US$ per km) Year of Investment Operation Operating Gains Social Benefits Operation and Mainte- 0 5,325,361 0 0 0 nance Costs 83 1 1,266,511 53, 403 513,519 228, 311 2 0 54, 472 546, 608 230, 320 3 55, 329 55, 561 581, 032 232,392 4 0 56, 672 613 145 237, 784 5 0 57, 806 646, 272 239, 968 6 0 58, 962 680, 443 259, 862 7 0 60, 141 715, 685 265, 427 8 97, 463 61, 344 752, 029 286, 864 9 0 62, 571 773, 876 293, 275 10 0 63, 822 795, 563 299, 059 11 0 65, 099 817, 077 303, 794 12 0 66,400 838, 408 307, 425 13 1,112,300 67, 729 859, 543 311, 609 14 0 69, 083 881, 165 315, 329 15 43, 802 70, 465 902, 687 340, 829 16 0 71, 874 924, 105 344, 261 17 0 73, 312 945, 414 349, 163 18 48, 731 74, 778 966, 608 352, 555 19 0 76, 273 985, 332 366, 730 20 77, 158 77, 799 1,003,841 370, 072 21 0 79, 355 1,022,126 373, 638 22 0 80, 942 1,040,180 378, 340 Technical Synthesis Report | TRANSPORT 23 12, 183 82, 561 1,057,995 383, 104 24 0 84, 212 1,080,037 385, 392 25 -398, 058 85, 896 1,102,078 387, 679 The values indicated in the “operating gains� and “ social benefits� columns above served as a parameter for calculating the benefits of all the urban mitigation measures. 2.6.5.5 Evaluation of the Social Benefits of BRT in the Present Study The figures presented in Table 19 above were used to construct a 25-line matrix (25 years of useful lifetime) on a year-on-year basis over the study period, assuming the foreshadowed launching of the new BRT lines in the reference and low-carbon scenarios (see section 2.6.5.3 for background on the year-on-year calculation of the values involved in mitigation through BRT deployment). The figures on deployment of the new BRT lines are the same as those presented in Table 16. In Table 20 below, the total numbers referring to the social benefits arising from BRT deployment are presented for the two scenarios, together with other transport modeling data. Table 20: Values of Investments and O & M Costs, Operating Gains and Social Benefits in the Reference and Low-Carbon Scenarios Year Reference Scenario Low-carbon Scenario Investment O & M Operating Social Investment O & M Operating Social 2009 259 0 0 0 679 0 0 0 84 Costs Gains Benefits Costs Gains Benefits 2010 320 3 25 9 841 8 33 35 2011 320 5 52 19 841 17 71 70 2012 310 8 80 28 833 25 112 106 2013 307 11 108 38 829 34 157 142 2014 236 13 138 47 745 42 205 178 2015 206 15 163 55 710 50 254 212 2016 203 17 188 62 808 58 306 247 2017 194 19 214 71 828 67 367 290 2018 178 21 239 78 809 76 430 334 2019 175 23 263 86 807 85 496 377 2020 174 25 288 94 806 95 565 421 2021 168 26 312 101 799 104 637 465 2022 219 28 337 108 937 114 712 509 2023 219 30 362 116 1059 124 790 554 2024 221 32 387 124 1095 135 877 609 2025 211 34 413 132 1084 147 968 665 2026 210 36 431 140 1083 159 1062 721 2027 197 38 464 147 1122 171 1160 777 2028 194 40 490 156 1131 184 1264 839 2029 197 42 517 164 1161 198 1372 901 2030 71 44 544 172 365 211 1484 963 Technical Synthesis Report | TRANSPORT 2031 39 45 559 176 176 215 1560 987 2032 39 46 574 180 177 220 1635 1010 2033 37 47 589 183 168 224 1710 1034 2034 16 48 603 187 115 228 1784 1058 2035 15 44 563 174 116 220 1736 1022 2036 15 41 522 161 143 211 1683 982 2037 16 37 479 148 143 201 1626 942 2038 12 34 437 135 128 192 1566 900 2039 17 31 395 122 134 183 1503 856 2040 18 29 366 113 146 175 1452 820 2041 18 26 340 105 139 167 1400 784 2042 19 24 313 96 141 158 1328 739 2043 -6 22 288 89 -22 148 1255 694 2044 -6 21 265 82 -22 138 1181 651 2045 -7 19 242 74 -28 128 1105 603 2046 -6 17 218 67 -27 118 1025 555 2047 -6 15 195 60 -27 108 943 506 2048 -8 13 171 52 -43 98 858 457 2049 -8 11 147 45 -43 85 748 395 2050 -9 10 122 37 -54 72 635 333 85 2051 -9 8 99 30 -54 59 521 270 2052 -9 6 75 22 -57 45 403 207 2053 -9 4 50 15 -59 30 272 138 2054 -9 2 25 7 -59 15 138 69 In order to fine-tune and balance out the effects of investments and disbursements occurring at the different intervals indicated in the economic valuations, these values were levelized throughout the study period at a discount rate 8% annum for a 25- year period (the useful lifetime adopted for the system). The portions relating to the annualized investment values outside the study period were excluded. The values relating to social benefits listed in Table 20 were not annualized, since these were already considered in the “useful lifetime� of the BRT. The values considered served as a basis for calculating the “social benefits� of all the urban mitigation measures. 2.6.5.6 Evaluation of Direct and Indirect Social Benefits of the BRT System Using the methodology for the economic evaluation of urban mitigation measures based on information from the T5 Corridor studies, the year-on-year “social� benefit data was calculated on a consolidated basis, taking into account both the direct and indirect benefits (see Table 20). The criteria and parameters used for calculating these benefits separately are described below. The “direct social benefits� referred to in this study have an impact on all passenger transport users. Regarded as “reduced trip times�, these were calculated by measuring the monetary savings involved in shorter trip times in public and individual transportTechnical Synthesis Report | TRANSPORT users. In addition to the direct effectson transport sytems’ users, society as a whole will also indirectly benefit from other kinds of social benefits. The monetary quantification of these “indirect social benefits� was based on the evaluation of healthcare savings arising from the alternatives analyzed, separated into two groups: Costs of Pollution: the T5 corridor models used the basic parameters adopted by the “ Study of Reduction of Diseconomies through the Improvement of • Urban Public Transport “ (note that this does not deal with CO2 but only with low atmosphere local pollutants); Costs of Accidents: the T5 Corridor models used parameters related to the unit costs of accidents in the transport systems obtained from studies • undertaken by the World Bank in Brazil (cited above). These indirect benefits are assessed on the basis of the number of kilometers covered in each of the alternative scenarios. The fewer kilometers travelled, the lower the possibility of accidents and GHGs and other emissions, regardless of the type of vehicle or fuel used. In order to assess the value of the indirect benefits we adopted as an intensity parameter the lower number of kilometers that would be covered in the low-carbon scenario advocated by the BRT as against the reference scenario. Based on the studies which formed the basis of the T5 Corridor models, we 86 constructed an indicator comprising figures relating to the cost of accidents plus public health costs arising from traffic pollution per kilometer. Table 21 below presents, on a year-on-year basis, the number of fewer kilometers covered in the low-carbon scenario as a result of BRT deployment and the values calculated for the indirect social benefits, using US $0.004 x km as a parameter. Table 21: Calculation of the Values of Indirect Social Benefits Year Number of miles (millions) Indirect Social Reference Scenario Low-carbon scenario Low-carbon Benefits 2009 333,441.2 333,441.2 0.0 0 scenario savings 2010 342,837.1 341,111.6 1725.5 6902086 2011 352,494.1 348,966.0 3528.1 14,112,375 2012 362,419.4 357,009.3 5410.1 21,640,276 2013 372,615.8 365,283.2 7332.6 29,330,209 2014 383,095.4 373,757.9 9337.5 37,349,857 2015 393,839.7 382,649.9 11189.8 44,759,399 2016 404,877.4 391,802.4 13075.0 52,299,884 2017 416,254.6 400,916.7 15338.0 61,351,874 2018 427,943.1 410,295.5 17647.6 70,590,474 2019 439,951.4 419,947.9 20003.6 80,014,319 2020 452,293.7 429,840.1 22453.6 89,814,480 Technical Synthesis Report | TRANSPORT 2021 464,979.5 439,979.0 25000.5 100 001 853 2022 478,015.7 450,394.0 27621.7 110 486 987 2023 491,415.1 461,070.7 30344.4 121 377 507 2024 505,234.3 471,642.8 33591.5 134 365 976 2025 519,439.6 482,475.4 36964.2 147 856 644 2026 534,039.2 493,600.7 40438.5 161 754 084 2027 549,047.4 505,003.0 44044.5 176 177 845 2028 564,497.0 516,520.4 47976.6 191 906 246 2029 580,380.0 528,323.9 52056.0 208 224 059 2030 596,709.0 540,422.1 56286.9 225 147 785 Total 9,965,819.8 9,444,453.7 521,366.1 2085464219 The “direct social benefits� were calculated by subtracting the “indirect social benefits� from total social benefits. Table 22 presents the social benefits’ total values social benefits, (direct and indirect) and indicators detailing the link between these and the transport modeling parameters used in the study. Table 22: Values of Total Social Benefits (Direct and Indirect) Calculated for BRT Deployment 87 Year Social Benefits Indirect Social Direct Social Increase in lo- Indirect Direct social Total (A) Benefits (B) Benefits ading in pass Social Be- benefits (C = AB) x million km nefit / loading in pass by BRT in the / loading x million km by low-carbon in pass BRT(C / D) scenario (D) million x 2009 0 0 0 0 - - km (B / D) 2010 35,010,428 6,902,086 28,108,342 10, 695 645.38 2628.25 2011 70,329,060 14,112,375 56,216,684 22, 018 640.96 2553.26 2012 105, 965, 302 21,640,276 84,325,027 33, 997 636.54 2480.40 2013 141, 643, 929 29,330,209 112,313,720 46, 399 632.13 2420.60 2014 177,652,270 37,349,857 140, 302, 413 59, 501 627.72 2357.99 2015 212, 392, 434 44,759,399 167, 633, 035 71, 810 623.30 2334.39 2016 247, 143, 893 52,299,884 194,844, 009 84, 506 618.89 2305.67 2017 290, 356, 954 61,351,874 229, 005, 080 99, 841 614.49 2293.69 2018 333, 636, 975 70,590,474 263, 046, 501 115, 702 610.10 2273.47 2019 376, 982, 592 80,014,319 296, 968, 273 132, 098 605.72 2248.08 2020 420, 704, 525 89,814,480 330, 890, 046 149, 358 601.34 2215.42 2021 464, 813, 671 100, 001, 853 364, 811, 818 167, 517 596.97 2177.76 2022 509, 160, 753 110, 486, 987 398, 673, 766 186, 444 592.60 2138.30 2023 553, 913, 221 121, 377, 507 432, 535, 714 206, 339 588.24 2096.23 2024 609, 103, 754 134, 365, 976 474,737,778 230, 115 583.91 2063.05 2025 664, 796, 486 147, 856, 644 516, 939, 842 255, 109 579.58 2026.35 2026 720, 836, 166 161, 754, 084 559, 082, 082 281, 182 575.26 1988.33 Technical Synthesis Report | TRANSPORT 2027 777, 402, 167 176, 177, 845 601, 224 ,321 308, 567 570.95 1948.44 2028 838, 747, 856 191, 906, 246 646, 841, 610 338, 662 566.66 1909.99 2029 900, 682, 957 208, 224, 059 692, 458, 898 370, 259 562.37 1870.20 2030 963, 223, 971 225, 147, 785 738, 076, 186 403, 419 558.10 1829.55 Total 9,414,499,364 2,085,464,219 7,329,035,145 3,573,540 583.58 2050.92 The figures indicating the link between the gains obtained from direct/indirect social benefits and the increased number of passengers x km by the BRT in the low- carbon scenario as compared with the reference scenario (Table 22) confirm that the two types of social benefits are consistent with the calculations made in the transport and emissions models used.. The parameters indicated for the BRT in Table 22 were used for calculating the direct and indirect social benefits arising from the other proposed urban mitigation measures. 2.6.6 Criteria and Sources for Regional Modeling To calculate the operational gains and social benefits of the specific mitigation measures for regional freight and passenger transport, “MANTRA� was used: economic evaluation methodology andemploying the same inputs and results of the regional transport modeling process of the present study: for modeling regional freight transport the basic matrices formulated by the 88 PNLT 2007 studies were used. These matrices were revised and brought up- • to-date to reflect the reference scenario adopted by consensus with the other Project work groups and also taking into account new relevant information to the modeling process; for regional passenger transport modeling projections specific methodologies were developed for the different transport modes, drawing on a number • of institutional sources: PNLT 2007 and data supplied by transport sector agencies - ANTT, ANTP, ANAC and INFRAERO. The economic evaluation procedure for regional transport enabled an assessment of the economic performance of each alternative based on comparisons with a baseline situation (or alternative). Any consistent set of changes proposed for a transport system must be considered as an alternative. In this way, complete overhauling of the rail network, the addition of a new stretch of a strategically important road or even the simple alteration of a particular tax on the regional operation of a transport system, are considered as alternatives which can be compared to a baseline situation. Scenarios generally evolve in ways that are not directly controllable by transport planners. It is therefore necessary to estimate their “possible� evolution. On the other hand, alternatives are defined by transport planners interested in assessing the impact in different scenarios. These alternatives are the two mitigation measures proposed for regional transport which comprise the low-carbon scenario: (i) one for the freight sector (modal shift - freight) which involves fewer emissions to the detriment of road investments and (ii) one for the passenger sector (modal shift -passengers) which presupposes the deployment of a high-speed train between Rio de Janeiro and São Technical Synthesis Report | TRANSPORT Paulo. The baseline scenario to be compared is referred to in this study as the “reference scenario� To undertake the economic evaluation of the mitigation measures proposed for regional transport using “MANTRA�, the distributive criteria was used to present concepts, applied to urban and regional modeling. (In this respect see e.g., Flowerdew, A. - Evaluation Models for City and Regional Planning, Proceedings of the Australian Road Research Board. In: 9th Conference (1978). This criterion makes it possible to evaluate separately the economic impacts of each transport alternative on the two main groups of stakeholders involved: Transport operators of the different transport modes; Passenger or freight transport users (categorized by flow-type). • The economic efficiency of each alternative related to each of the above groups can • be evaluated. The total net benefits for all the groups should indicate the global impact on the economy. Once the effects of each alternative have been calculated for each of the stakeholders involved, these values are classified and presented according to the methodology explained in sections 2.6.1, 2.6.2 and 2.6.3. 89 2.6.6.1 Benefits to Operators The benefits accruing to transport operators are the result of the net change in the revenues received by each operator due to the deployment of alternatives or the mitigation measures under consideration. This benefit arises from the operating profits flowing from the alternative, as explained in section 2.6.2. The benefits can be expressed by the following formula: Ben. (Operator) = Profit (Alt) -Profits (Baseline) The profit for a particular operator in the Baseline or Reference Scenario - Profit (Baseline) – and in the alternative or low-carbon scenario - Profit (Alt) – is the difference between revenue earned (RT-Operator) and the transport operating costs (CT Operator). The benefit for each operator can thus be calculated by: Ben. (Operator) = [RT (Alt / Oper) - CT (Alt / Oper)] - [RT (Baseline / Oper) - CT (Baseline / Oper)] Fro the group of operators the benefit is equal to the sum total of the individual benefits, and can be expressed by: Ben. (Operators) = [RT (Alt) - CT (Alt)] - [RT (Baseline)] where TT (Baseline): total (money and time) on transport for all flow-types between all pairs of zones in the baseline situation (reference scenario); TT (Alt): total costs (money and time) transport for all flow-types between all pairs of zones in the alternative or mitigation measure under analysis (low-carbon scenario). Note that the total monetary costs of transport users correspond to the total revenue accruing to all transport system operators. Thus the TT value can also be expressed as: Technical Synthesis Report | TRANSPORT TT = RT + GT where: RT(Alt): total monetary expenditure by users on transport, i.e. the revenue accrued by the totality of operators with the alternative or mitigation measure under analysis (low-carbon scenario); RT (Baseline): the total expenditure by transport users, i.e. the revenue accrued by the totality of operators in the baseline situation (reference scenario); CT (Alt): operating costs incurred by all the operators with the alternative or mitigation measure under analysis (low-carbon scenario); CT (Baseline): operating costs incurred by all the operators with the baseline situation (reference scenario). 2.6.6.2 Benefits for Users The benefits for users are calculated in terms of money and time for each type of flow. The model offers the possibility of using different time-values to check the sensitivity of the economic evaluation against these assumptions. To illustrate the calculation of user benefits, the deployment of an alternative or mitigation measure is needed that can reduce the overall cost of transport to the user 90 across a given pair of zones. The overall cost of transport to the user corresponds to the average tariff or the average ticket price, plus the time spent (in monetary terms) to carry the user or product from origin to destination by the various forms of transport available. Assuming that, due to the reduced overall cost of transport from T to T1 the demand increases between D and D1, the user benefit can be interpreted as the sum of two parts:(i) related to the reduction of costs as compared to those incurred by former users (D), and (ii) related to the benefit accruing to future users (D1-D), which in effect corresponds to the increased demand for transport between that pair of zones due to reduced transport costs. It can be assumed (for reasons of clarity) that in this interval the demand curve can be represented by a straight line. This portion of the benefit is called “consumer surplus�. The calculation below expresses the benefit for each user across each pair of origin-destination zones: User Benefit = D * (T-T ‘) + [(D’-D) * (T-T’) / 2] where: the first tranche - D * (T-T ‘) - corresponds to the benefit accruing to the original users, and the second to the “consumer surplus� to benefit potential users, due to the deployment of the alternative or mitigation measure under consideration. It is often argued that demand between pairs of zones is inelastic over the short term. This presupposes that changes (in time and costs) in transport supplied for a given period will only produce benefits in terms of demand distribution after a certain time has elapsed. This effectively voids the second tranche - [(D’-D) * (T-T ‘) / 2] - corresponding to the consumer surplus produced by demand variation. The economic Technical Synthesis Report | TRANSPORT benefit for all users can be seen from the following representation of user savings on public transport. User benefit = TT (Baseline) - TT (Alt) where: TT (Baseline): total (money and time) on transport for all flow-types between all pairs of zones in the baseline situation (reference scenario); TT (Alt): total costs (money and time) on transport for all flow-types between all pairs of zones, in the alternative or mitigation measure under analysis (low-carbon scenario). Note is that total user-spending on transport corresponds to the total revenue accruing to the totality of system operators. The value TT can also be written as: TT = RT + GT where: RT: user expenditure on transport; GT: time spent by users (or their products) on transport. Thus the benefit to users can finally be expressed by: User benefit = [RT (Base) + GT (Base)] - [RT (Alt) + GT (Alt)] 91 where: RT (Baseline): user-expenditure on transport for all flow-types between all pairs of zones in the baseline situation (reference scenario); RT (Alt): user-expenditure on transport for all flow-types between all pairs of areas in the alternative or mitigation measure in the analysis (low-carbon scenario). GT (Baseline): time spent by users (or their products) on transport, expressed in monetary values, for all flow-types between all pairs of zones, in the base situation (reference scenario); GT (Alt): time spent by users (or their products) on transport, converted into monetary values, for all flow-types between all pairs of zones, in the alternative or mitigation measure in the analysis (low-carbon scenario). User benefits, calculated using the “MANTRA� methodology, were considered in the economic evaluation of regional transport mitigation measures as being direct “social benefits�, as referred to in 2.6.3.3 above. 2.7 Conclusions The methodology used and consolidated throughout this study resulted from the team’s experience of preparing studies related to the transport sector. Additional information was also needed to facilitate building the scenarios. The work revealed two controversial aspects of the transport sector in Brazil: (i) the difficulty of obtaining consistent data and information; and (ii) the lack of coordination among the various stakeholders in the sector. Technical Synthesis Report | TRANSPORT Notwithstanding the above problems, the work team succeeded in producing robust and consistent estimates which matched those developed by other groups working on the Project. The results can help to formulate specific policies for the transport sector and hopefully contribute to reducing carbon emissions for the different transport modes. Meanwhile, it is obvious that the behavior patterns of transport-users throughout Brazil need to be modified in future. Streamlining of both freight and passenger transport regionally and in urban areas will introduce more balance into the energy matrix for the sector, leading to more sustainable transport patterns. Figure 26 shows the modeling structure used for linking regional and urban transport to fuel consumption, as well as the related parameters and information sources used in our work. It is important to highlight the diversity of factors considered as representing the transport system and energy consumption by the sector in Brazil. A number of features of the model are worth highlighting: different modes of regional freight transport are heavily dependent on diesel; • regional passenger transport, with the exception of the airline sector, is also dependent on diesel; • 92 in the cities, diesel continues to be the main fuel used for freight transport; diesel also plays a major role in passenger transport in the urban areas, • although vehicle energy sources are more diverse. • Figure 27: Linking Regional and Urban Transport to Fuel Consumption Technical Synthesis Report | TRANSPORT Finally, it is important to note that in the course of formulating the methodology Source: Logit for the study’s work, a number of partnerships were established in order to take best advantage of the expertise existing in ministries, public agencies and other bodies. The Ministries of Transport, Cities, Environment, together with CETESB - São Paulo, and FIPE were particularly helpful in this respect. 3 REFERENCE SCENARIO The Reference Scenario considered for the transport sector is the same as that designed for the PNE 2030 (ranked as the “most likely�). To estimate future consumption and emissions, the PNE adopted a type of top-down methodology. The main parameters for the PNE projections were (i) the volume of fuel sold in the country and (ii) government economic policies, existing plans and programs between 2005 and 93 2007. It is important to emphasize that the fuel consumption volumes resulting from the transport sector modeling were reconciled and adjusted in line with the trend estimates identified by the PNE, given that transport modeling, as already noted, was based on a bottom-up methodology. The projections considered in the 2030 PNE assume that the investments foreshadowed in the PAC for transport infrastructure work would be implemented by the end of the study’s timescale (2030). Table 16 shows perfect harmony between the fuel consumption projections estimated by the PNE 2030 using the top-down methodology with the bottom-up method considered in the transport modeling. Table 23A: Projections of Consumption by Type of Fuel in the Reference Scenario Gasoline 42,190 42,376 Fuel PNE 2030 (1000 m3) Transport Modeling Diesel 74,760 74,767 (1000 m3) Ethanol 53,304 52,611 It is also important to draw attention to the fact that in the specific case of Brazil, Source: NAP 2030 / Logit which uses bio-ethanol widely as a fuel for light vehicles, the reference scenario can already be regarded as a “low emissions� scenario, compared to other countries with the same problems caused by rapid increases in the number of motorized vehicles. Technical Synthesis Report | TRANSPORT In the light of the above, particularly in the case of urban transport, the future low- carbon scenario proposed in this study does not reveal significant reductions in CO2 emissions by private vehicles. Meanwhile, prioritizing the public transport system at the expense of private car use, produced relatively more significant effects given that this alternative targets reductions in the number of private cars in circulation. Table 23-A presents estimates of CO 2, emissions calculated for the Brazilian transport sector in 2007, classified by type of fuel used and three different criteria: Direct Emissions: CO2 emissions resulting from direct combustion by all types of vehicles used in transporting freight and passengers. This was the • criterion adopted in this project for calculating emissions from the transport sector in the two scenarios studied, • Total Emissions: direct emissions, plus emissions from the processes needed to produce the different types of fuels for use in vehicles; Total emissions without the addition of bio-fuels: total emissions considering gasoline with no alcohol added (G0), and diesel without the addition of • biodiesel (B0). 94 Table 23B: Estimates of Emissions in 2007 by Type of Fuel, According to Different Criteria Total Emissions - Gasoline E0 and Direct Emissions Total Emissions Diesel B0 - Fuel type (In MtCO 2 e) (in MtCO 2 e) (in MtCO 2 e) Ethanol 0.00 4.48 4.48 Gasoline 40.76 51.14 64.12 Diesel 94.86 115.76 119.34 Aviation Fuel 8.44 10.25 10.25 Electricity 0.00 0.13 0.13 Total 144.06 181.76 198.33 Table 23A indicates that if biofuels had not been added to diesel and gasoline, Source: Logit (2009) emissions for 2007 would have been around 39% higher: 144.06 MtCO 2 x 181.76 MtCO 2 e. Furthermore, in this hypothetical scenario, if ethanol had not been used as engine fuel in Brazil, at least another 35.0 MtCO 2 (caused by burning gasoline as an alternative to ethanol) would be added to the 181.76 MtCO 2 e , totaling 216.76MtCO2e. These figures make it easier to view the reference scenario for the transport sector in Brazil as an already low-carbon scenario. 3.1 Structuring the Reference Scenario In the National Logistics and Transport Plan (PNLT 2007), the reference scenario Technical Synthesis Report | TRANSPORT forecasts a probable situation for 110 sectors of the Brazilian economy (557 micro-regions), considering the constraints under which they operate and framing assumptions about some of their basic structural aspects. The “probable� scenario was based on these assumptions, evaluations of the existing constraints and experience of the relatively- recent development of the economy. Assuming a reference scenario for the period 2007-2031 (with 2007 as the baseline year), the results were generated from projections based on the projections of the EFES model developed by FIPE-USP, which provides inputs for a module containing sub-sector variables. Formulating these scenarios made it possible to project inputs for calculating the transport matrices for the future PNLT horizons. The assumptions of future scenarios needed for formulating the projections of relevance to this project were initially the same as those utilized in the 2007 PNLT. As our work developed, these assumptions were adjusted in order to reconcile and/or align them with possible alternative scenarios structured by other groups or study teams participating in the project. In this way they were totally integrated into the parameters and assumptions postulated in the PNE 2030. This approach sought to ensure that the assumptions used for the projections of freight and passenger movements were consistent with the other premises adopted in the modeling of other study areas. In addition to ensuring the consistency of assumptions, the work also the possibility of producing sensitivity analyses by using different macroeconomic scenarios with different configurations of sectoral and/or 95 regional growth, with direct impacts on the results of the projections. As mentioned in sections 1.6 and 2.3 of this report, in order to build a “most likely� scenario (provided that unforeseen political-institutional or other unpredictable events do not intervene), it was decided that for the purposes of this study the transport infrastructure projects and interventions planned by the PAC were to be implemented by 2030. These investments, also dealt with in the PNLT-2007, were regarded as the “reference scenario� investments, thereby enabling an evaluation of emissions in the context of the present study. 3.2 Reference Scenario Projections The present study is based on bottom-up methodology which estimates future consumption and emissions on the basis of freight volumes, numbers of passengers transported and the distances covered by each mode of transport. The fuel consumption assumptions’growth rates are the same as those adopted in the PNE 2030. The set of models enabled us to project the relevant variables on an annualized basis, taking into account the impact of PAC investments on regional transport infrastructure and the Urban Mobility Plans “balance sheets� for urban transport infrastructure. In this way it was possible to estimate loads and the overall effects of polluting emissions for all the regional and urban transport modes, in the reference scenario. Table 24 presents the projected values for loads and the resulting direct CO 2 emissions in the reference scenario for regional and urban transport. Emissions Technical Synthesis Report | TRANSPORT from the transport sector in this scenario would increase by 72% during the period between 2007 and 2030, from 144 MtCO 2 to 247 MtCO 2, representing approximately 4.8 billion tons of CO 2 between 2010 and 2030. The relative contribution of urban transport in terms of direct emissions declines marginally, from 52.2% in 2007 to 51.9% in 2030 (52.1% of total emissions in 2010- 2030). The impact of urban transport on the total direct transport sector emissions can be explained in part by the substantial growth projected for bio-ethanol consumption up to 2030. The introduction of flex-fuel vehicles forms part of Brazil’s energy policy to encourage the use of alcohol in motor transport. According to ANFAVEA, around 90% of all the light vehicles in circulation will be flex-fuel by 2030. If ethanol’s price is competitive with gasoline’s, there is little doubt that all the PNE 2030 projections will be fulfilled. In the case of private vehicles for regional and urban transport, the projected passenger loads for gasoline-powered cars between 2007 and 2030 will increase 1.2 times (from 356 to 444 billion passengers x km). On the other hand, passenger loads for ethanol- fueled cars will grow 4.6 times (from 118 to 541 billion passengers x km). Table 24: Load and GHG Emissions for the Reference Scenario, 2007–30 96 Load Global direct (Mt * km or pax * km/ CO2 emissions* year) (Mt CO2) Transport Vehicle Fuel 2007 2030 2007 2030 2010 - 2030 Road Truck Diesel 32,436 49,151 4.9 7.6 131.1 Load type mode type type Urban Freight Bus 730,799 32.9 43.1 827.3 Total urban freight 32,436 49,151 4.85 7.58 131.1 Diesel BRT 0 102,332 0.0 2.1 19.6 Road Car Ethanol 96,399 364,894 0.0 0.0 0.0 Car and 272,570 347,346 36.6 66.2 1,087.0 Urban Gasoline motorbike passenger Metro 28,412 55,385 0.021 0.039 0.63 Rail Electricity Train 35,370 50,699 0.022 0.029 0.55 Total urban passenger 864,078 1,651,46 69.5 111.4 1,933.9 Rail Train 321,240 552,364 4.4 6.6 114.8 GHG emissions from urban transport - - 74.4 119.0 2,065.0 Waterway Boat 26,984 81,349 0.2 0.5 8.1 Diesel Pipeline Pipeline 15,732 24,727 0.1 0.1 1.5 Regional Road Truck 689,057 1,274,440 48.0 77.3 1,323.5 freight Car Ethanol 21,905 176,485 0.0 0.0 0.0 Total regional freight 1,053,013 1,932,880 52.7 84.5 1,447.9 Technical Synthesis Report | TRANSPORT Car and Gasoline 83,166 97,031 4.2 5.2 94.8 Road motorbike Bus Diesel 154,845 276,915 4.5 7.3 124.0 Plane Aviation 45,259 127,569 10.5 28.7 400.8 Air Regional kerosene passenger High- Electricity - - 0.0 0.0 0.0 Rail speed train Total regional passenger 305,175 678,001 19.1 41.2 619.6 GHG emissions from regional transport - - 71.7 125.7 2,067.5 TOTAL TRANSPORT-SECTOR EMISSIONS 146.2 244.7 4,132.5 (*) in order to avoid double counting with emissions already accounted for in the agriculture and energy sectors, only direct emissions are included in this table. Source: Logit Modeling and Processing - 2009 The following figures illustrate the position of CO2 emissions and fuel consumption by TEPs (tons of oil equivalent) in the baseline scenario for all types of vehicles, regardless of geographical location (urban or regional) or type of transport (freight or passenger). Figure 28 shows that most emissions in the transport sector in Brazil are produced by cars, trucks and buses - around 3.6 billion tons of CO2 - 88% of the total in 2010-2030. 97 Figure 28: Evolution of Transport Sector Emissions in the Reference Scenario Figure 29 below confirms the results of Brazil’s biofuels policy. It is well-known Source: Logit (2009) that fuel consumption and emissions are directly correlated. However, increasing ethanol production in Brazil means that between 2007 and 2030 fuel consumption will increase faster than emissions - 3.4% a year for fuels, compared with 2.4% for emissions. Technical Synthesis Report | TRANSPORT Figure 29: Fuel Consumption Trends (in TEP) by 2030, by type of vehicle in the Reference Scenario Source: Logit (2009) The growing consumption of ethanol compared to gasoline in private vehicles (the main users) is clear from a comparison between the two graphs in Figure 30. Note also that the same situation exists, although with a lower impact, when comparing the diesel-powered fleet, which under the PNE baseline scenario, should consist of 12% biofuel consumption by 2030 (Biodiesel and Bio-H). Figure 30 below compares the growth of emissions in the baseline scenario with a hypothetical scenario without ethanol or biodiesel. The difference in this hypothetical 98 scenario without “clean fuels� is substantial: in 2030, an additional 165 MtCO 2 would be emitted between 2010 and 2030, amounting in absolute terms to around 2.3 billion tons of CO 2, representing an increase of 57% compared to the baseline scenario. Figure 30: Comparison of the Evolution of Emissions by Vehicle Type in the Reference Scenario x Hypothetical Scenario involving gasoline and diesel Technical Synthesis Report | TRANSPORT Figures 31 and 32 present seprate regional and urban emissions figures. At the Source: Logit (2009) regional level, trucks are the mainly cause for emissions, accounting for around 66% in 2010-2030, followed by aircrafts, which account for only 17.5%. Note however that the emissions curve for the aircraft sector grows steeply (4.6% a year, compared to 2.0% for trucks). Figure 31: Evolution of Regional Transport Emissions to 2030 (by vehicle type) in the Reference Scenario 99 Cars and motorcycles are responsible for around 51% of emissions in urban areas Source: Logit (2009) for the transport sector between 2010 and 2030, followed by conventional buses which account for approximately 41.5%. Up to 2030, both these modes of transport display a fast growing emissions curve: 1.8% a year for conventional buses and 2.6% for cars and motorbikes. Figure 32: Evolution of Urban Transport Emissions to 2030 (by type of vehicle) in the Reference Scenario Technical Synthesis Report | TRANSPORT Source: Logit (2009) 4 MITIGATION OPTIONS Following consultations with experts, bibliographic research,an analysis of the Master Plans for the metropolitan regions, and government plans and programs, a set of mitigation options was selected which could feasibly be deployed by year 2030 in the context of the low-carbon scenario. 100 Details of these options, together with their deployment costs and the expected reductions of CO2 emissions, are presented separately for regional transport and urban transport respectively. 4.1 Mitigation Options for Regional Transport The set of public policies for regional transport addresses the modal supply and focuses on the possibility of gradual change in the transport matrix in Brazil, where at the regional level road transport is identified as the main type of transport used for transporting merchandise (around 60% of the total volume) and passengers. A more energy-efficient solution for transporting large volumes of freight, either bulk solids (e.g. soybeans) or bulk liquids (oil products, ethanol, alcohol etc) would be by rail and water transport. The new matrix prioritizing this type of modal shift would contribute to a substantial reduction of CO2 emissions at the regional level. The majority of regional passenger trips are also made by road, either by bus or private cars (and a small number by air). The latter produces emission levels per passenger x kilometer which are higher than the other transport modes. One of the mitigation measures being considered in this respect is the possibility of a special “fast train� link between Brazil’s two major economic centers, Rio de Janeiro and São Paulo. An auction for this line is expected to be organized in 2010. This is expected to attract resources for undertaking the works. Given that the idea is to have the new service in full operation during the World Cup in 2014 the project has been included in the low carbon scenario. However, government authorities and experts do not expect vast Technical Synthesis Report | TRANSPORT counterpart sums to be made available for this project. Thus the Federal Government is hoping to introduce a high “percentage waiver� to help establish the fast train service to link the cities of Campinas, São Paulo, São José dos Campos and Rio de Janeiro. The government has indicated that its option to share in the financing of this specific project (“state counterpart�) may be restricted to a small portion of the resource requirements, basically to cover the expropriations needed prior to carrying out the works. Note that the technical, economic, financial and environmental feasibility study has not yet been completed. 4.1.1 Modal Shift - Freight This option considers the need for significant change in the freight transport matrix in Brazil. Both the PNLT and PNMC have highlighted the importance of reducing the volume of freight carried by trucks and a corresponding increase in carriage by more energy-efficient modes, which would result in lower carbon emissions (i.e. a gradual shift aimed at shifting freight away from road transport to railways, waterways and pipelines). Tables 25 and 26 present the main transport indicators, as well as the CO2 emissions, for each mode/segment in the low-carbon scenario and their respective “modal shift� related to the mitigation option for regional freight transport. While this measure is aimed at freight transport it could also have an impact on regional passenger 101 transport on the roads. Table 25: Comparison of Projected Emissions Reduction for Regional Transport in 2030: Modal Shift Scenario Load GHG Avoided emis- (Mt * km or pax * km/year) direct emissions* sions (Mt CO2e/year) 2010 – 2030 Trans- Low- Low- port Vehicle Fuel Reference carbon Reference carbon MtCO2e Rail Train 552,364 703,854 6.5 8.3 -25.4 Segment mode type type scenario scenario scenario scenario Water Ship 81,349 133,503 0.5 0.9 -4.5 Pipeline Pipe Diesel 24,727 26,621 0.08 0.09 -0.1 Freight Road Truck 1,274,440 1,113,926 77.3 65.5 115.1 Car Ethanol 176,490 165,460 0 0 0 Total freight (regional) 1,932.880 1,977.904 84.5 74.9 85.1 Car and Gasoline 5.2 4.9 4.5 motor- 97,030 90,970 Road bike Bus Diesel 276,915 276,915 7.3 7.1 -0.6 Air Plane Aviation 127,569 127,569 28.7 28.7 0 Passenger kerosene Total passengers (regional) 678,010 660,920 41.2 40.7 3.9 Technical Synthesis Report | TRANSPORT TOTAL EMISSIONS: load and passenger (regional) 125.7 115.6 88.9 (*) in order to avoid double counting with emissions already accounted for in the agriculture and energy sectors, only direct emissions are included in this table. The data in these tables confirms the importance of the strategy for promoting a Source: Logit (2009) modal shift, particularly in the freight transport area, with the aim of reducing total CO2 emissions in a low-carbon scenario. Table 26: Avoided Emissions - New Modal Shift Segment Mode Vehicle Fuel Gains as result of measure Accumulated gains in Type low-carbon scenario Absolute % Baseline Absolute % Reference Scenario 102 2030 2010- 2030 2010- 2030 2010- 2030 2010- Regional Road Trucks Diesel 9054 84 137 12.0 6.5 9054 84 137 12.0 6.5 2030 2030 2030 2030 Freight Rail Train -2033 -28 239 -31.6 -25.1 -2033 -28 239 -31.6 -25.1 Waterways Vessels -363 -4766 -69.5 -59.8 -363 -4766 -69.5 -59.8 Pipeline Pipelines -9 -145 -11.2 -10.1 -9 -145 -11.2 -10.1 Regional 6650 50 987 8.0 3.6 6650 50 987 8.0 3.6 Regional Road Cars Ethanol 0 0 0.0 0.0 0 0 0.0 0.0 Total Charges Passenger Cars + Gasoline 327 4499 6.2 4.7 327 4499 6.2 4.7 Motorcycles Bus Diesel 837 7174 11.1 5.7 837 7174 11.1 5.7 Regional Passenger Total 1164 11 673 3.2 2.1 1164 11 673 3.2 2.1 Avoided Emissions Totals for 7813 62 660 6.6 3.2 7813 62 660 6.6 3.2 Regional Transport Total Avoided Emissions for Transport Sector 7813 62 660 3.2 1.5 7813 62 660 3.2 1.5 In terms of absolute values, it can be seen that in the 2030 low-carbon scenario Source: Logit (2009) the total quantity of CO2 emitted is reduced by around 9% compared to the reference scenario. This value stems mainly from the reduction in the relative share of freight transport by road – from 66% to 56%. Figure 33: Comparison of Modal Distribution of Freight Load: Technical Synthesis Report | TRANSPORT Reference v Low Carbon Scenario Source: Logit (2009) It is worth noting that any intervention aimed at altering the transport matrix must also take account of the needs of the existing or potential international, national and regional market. Substantial potential exists, for example, for waterways (hidrovias) in the North and Center-West of Brazil, although at present the demand in these regions for water transport is confined to agricultural and mineral commodities. While a number of logistical solutions have already been deployed for transporting these 103 products by water, competition from other transport modes is severe. As mentioned previously, the projects involving switching from road investments to other transport modes which are potentially lower CO2 emitters are also those with the greatest cost-benefit potential. Table 27 below is a summary of the investments planned for the mitigation option which considers a modal shift for freight transport in Brazil in 2030. The projections for the reference and low-carbon scenario are presented. It is clear that the low-carbon scenario will involve substantial investments: US $ 42 billion. Table 27: Regional Freight Transport: Comparison of Investments in the Reference and Low-carbon Scenarios, 2010–30 Reference scenario (million US$) Low-carbon scenario (million US$) Year 2010 0.396 - 0.396 0.396 - 0.396 Rail and waterway Road Total Rail and waterway Road Total 2011 0.793 - 0.793 0.793 - 0.793 2012 1.189 - 1.189 1.189 - 1.189 2013 - - - - - 2014 0.356 2.788 3.144 0.356 2.548 2.905 2015 0.712 5.575 6.288 0.712 5.097 5.809 2016 1.069 8.363 9.432 1.069 7.645 8.714 2017 - - - - - - 2018 - 0.554 0.554 1.331 0.554 1.885 2019 - 1.108 1.108 2.661 1.108 3.769 2020 - 1.662 1.662 3.992 1.662 5.654 Technical Synthesis Report | TRANSPORT 2021 - - - - - - 2022 - - - 0.581 - 0.581 2023 - - - 1.162 - 1.162 2024 - - - 1.742 - 1.742 2025 - - - - - - 2026 - 1.251 1.251 - 1.185 1.185 2027 - 2.503 2.503 - 2.369 2.369 2028 - 3.754 3.754 - 3.554 3.554 2029 - - - - - - 2030 - - - - - - Total 4.516 27.559 32.074 15.984 25.722 41.707 Source: PAC / PNLT / Logit Among the investments selected in the low-carbon scenario and linked to the modal shift, Highway BR-163 will lose significant volumes of soybean cargo for the Teles Pires Hidrovia (waterway), as can be seen in Figures 33A and 33B: Figure 33A: Freight carried on Teles Pires Hidrovia x BR-163 - Reference Scenario 104 ] Source: Logit (2009) Figure 33B: Freight carried on Teles Pires Hidrovia x BR-163 - Low-carbon Scenario Technical Synthesis Report | TRANSPORT Source: Logit (2009) Of the 6,150,000 tons of soya transported on the BR-163 highway in the reference scenario, 4,500,000 tons would be transferred to the Teles Pires Hidrovia in the low- carbon scenario. A further example: the Bahia-West Railway, deployed in the low-carbon scenario, will absorb the majority of the agricultural and liquid bulk products at present carried on the roads in the baseline scenario, as can be seen in the figures below: 105 Figure 33C: Soybean Freight Loads in Bahia - Reference Scenario Source: Logit (2009) Figure 33D: Soybean Freight Loads in Bahia - Reference Scenario Technical Synthesis Report | TRANSPORT Source: Logit (2009) In 2030, approximately 3 million tons of grain for export, from Western Bahia will be transported by road in the baseline scenario. In the low-carbon scenario this will use the “West Bahia Railway�. The additional resources required in the PNLT low carbon scenario compared to the reference scenario (PAC) will amount to around US$10 billion –mainly consisting of investment in railways and waterways. This policy would generate saving reduction of approximately 63 MtCO 2 between 2010 and 2030 (see Figure 34): 106 Figure 34: Evolution of Emissions: Reference versus Low Carbon Scenario For the first group of policies to be implemented successfully, it is important to Source: Logit (2009) structure an appropriate and realistic allocation of resources. In order to ensure the success of the modal shift being proposed, it is also vital to facilitate high financing costs of the appropriate infrastructure. The investments required to implement this low carbon policy and the related abatement costs are presented in the graphs below (reference year and 2009 values, expressed in US$). Technical Synthesis Report | TRANSPORT Figure 35: Curves of cost reduction (nominal) Source: Logit (2009) Figure 36: Abatement Cost Curves (present value) 107 Source: Logit (2009) Table 28: Average cost of avoided CO 2 Abatement Curves US$ per tCO 2 e Low Carbon Investment 827.80 289.04 Nominal Present Value in 2009 Avoided Investment 157.13 45.22 Fuel Savings 111.31 29.01 Operating Gains -18.21 -9.72 Social Benefits -151.14 -49.09 The “additional investment required� curve was provided by the MANTRA model, Source: Logit (2009) which compared all the investments expected to emerge from government plans and Technical Synthesis Report | TRANSPORT programs for 2030 with the new investments projected for a low carbon scenario. The O & M costs of the projects in the event of modal shift were also determined, with priority given to the alternatives to road transport. The curve of “avoided infrastructure investments� (and O&M) was modeled, targeting the projects which will not be deployed in a low-carbon scenario. Energy- saving and emissions-reducing projects were awarded priority. The costs (including operation and maintenance) of the non-implemented projects were calculated, with the resulting values considered to be “gains� or “avoided� costs in the low-carbon scenario. The modeling also indicated the amount and costs of the fuel not used in these projects. These values were also calculated as “avoided� costs in the low-carbon scenario. Finally, in the building of the abatement cost curves, the benefits generated for the different groups of transport users were taken into account. 4.1.2 Modal Shift - Passengers In the case of passenger transport, the mitigation option considers that a change is necessary in the present passenger transport structure at the regional level. With the almost total extinction of regional trains following the privatization of the rail rate network, travel is done virtually 100% by road. A small higher-income segment of the population tends to use air travel over the same routes. The mitigation option aims to increase the number of passenger light trains, such as for example the bullet train (TVA) 108 being studied at present to provide a link between Rio and São Paulo. If this proves to be viable from a technical, environmental and financial point of view, it is to be hoped that other train links between large metropolitan regions could enter service by 2030. The main idea is to reduce the number of people currently using road transport. Tables 29 and 30 present the projections for loads and direct emissions, by type of vehicle, in the baseline and low-carbon scenarios, together with the inclusion of the mitigation measure which considers the introduction of the above-mentioned TVA. Table 29: High Speed Train (TAV), Loads and Emissions: Baseline x Low Carbon Scenarios Segment Mode Vehicle Fuel Loading in 2030 CO 2 Type (millions pax * km) (thousand tons) Baseline Low Baseline Low Carbon Carbon 2030 2010- 2030 2010- Regional Road Cars Ethanol 165, 457 162, 280 0 0 0 0 2030 2030 Passenger Cars & Gasoline 90, 968 89, 221 4901 90, 308 4807 89, 066 Motorcycles Bus Diesel 276, 915 266, 675 6704 118, 478 6425 114, 822 Rail TAV Electricity 0 21, 092 0 0 0 0 Air Airplanes Aviation 127, 569 121, 641 23 740 324, 010 23, 128 317, 259 fuel Regional Passenger Total 660, 909 660, 909 35, 344 532, 796 34, 359 521, 148 Technical Synthesis Report | TRANSPORT Total Emissions Regional Transport - - 111, 348 1,900,591 110, 363 1,888,943 Total Emissions from Transport Sector - - 239, 675 4,038,079 238, 690 402,6431 Source: Logit (2009) Table 30: High Speed Train (TAV): Emissions Avoided Segment Mode Vehicle Fuel Gains from measure Gains from deployment of Type low-carbon scenario % Baseline % Reference Absolute Absolute Scenario 109 2030 2010- 2030 2010- 2030 2010- 2030 2010- Regional Road Cars Ethanol 0 0 0.0 0.0 0 0 0.0 0.0 2030 2030 2030 2030 Passenger Cars & Gasoline 94 1,242 1.9 1.4 421 5,741 8.0 6.1 Motorcycles Bus Diesel 279 3,656 4.2 3.1 1,116 10, 829 14.8 8.6 Rail TAV Electricity 0 0 0.0 0.0 0 0 0.0 0.0 Airway Airplanes Av i a t i o n 612 6,751 2.6 2.1 612 6,751 2.6 2.1 fuel Regional Passenger Total 985 11, 648 2.8 2.2 2,149 23, 321 5.9 4.3 Avoided Emissions Totals 985 11, 648 0.9 0.6 8,798 74, 308 7.4 3.8 Regional Transport Total Avoided Emissions by Transport Sector 985 11, 648 0.4 0.3 8,798 74, 308 3.6 1.8 While the quantity of emissions avoided in 2030 is probably not particularly Source: Logit (2009) substantial (1.0 MtCO 2 , or around 2.8% lower than the reference scenario), nevertheless the reduction of 1.9 g CO 2 emissions per passenger x km (53.9g CO 2 at the baseline less 52.0 g of CO 2 in the low-carbon scenario) shows that the savings could increase and be sustained. The increasing passenger loads under the mitigation option, resulting from specific modeling and presented in the following figure, show that although the TAV would be restricted to the Rio-São Paulo corridor, the service has the capacity to attract a significant number of passengers from other modes of transport. Figure 37: High Speed Train (TAV): Modal Load Shift - Baseline x Low Carbon Scenario Technical Synthesis Report | TRANSPORT Source: Logit (2009) An overall reduction of 11.6 MtCO 2 in net emissions between 2014 and 2030 alone, may not justify the US$16 billion projected investments for the Rio de Janeiro-São Paulo high-speed rail link. Nevertheless, in addition to the possibility of increased gains, civil society’s expectation is quite significant. 110 Figure 38: Evolution of emissions: Reference x Low Carbon Scenario In the baseline scenario, emissions from aircraft used for regional passenger transport Source: Logit (2009) are significant and increasing. As can be seen in Figure 38 the evolution curve in the low-carbon scenario shows a slight shift from 2014 - the year planned for launching the Rio-SP high-speed train. The benefits and impacts (both positive and negative) and social/economic impacts (positive and negative) arising from this project could be considerable. There remains no doubt that the Brazilian civil aviation sector will need to radically change its modus operandi in the Rio-SP corridor. The investments required for implementing this particular emissions mitigation op- tion in regional transport and its respective abatement costs are shown in the follow- ing figures. Technical Synthesis Report | TRANSPORT Figure 39: Cost Reduction Curves (Nominal) Source: Logit (2009) Figure 40: Abatement Cost Curves (Present Value) 111 Source: Logit (2009) Table 31: Average costs of tCO 2 avoided US$ per tCO 2 e Abatement Curves Low Carbon Investment 2187.49 862.73 Nominal Present Value (2009) Avoided Investment 1093.75 431.37 Fuel Savings 1005.84 400.34 Operating Gains 807.91 327.63 Social Benefits 584.27 248.41 As in the case of freight transport, the required additional investments concern new Source: Logit (2009) projects in a low-carbon scenario. The additional operation and maintenance costs Technical Synthesis Report | TRANSPORT were also taken into account in the modeling for the TVA. The tax relief on investment through PPPs was considered to be “avoided� investments. The “avoided investments� curve is the result of the modeling based upon projects that will be substituted after the high-speed train is launched. The maintenance and operation costs of such projects are also calculated. The fuel economy curve (“fuel consumption avoided�) is based upon the volumes of jet fuel, diesel and gasoline that will be saved (i.e. not spent) on roads and air transport due to the modal shift of some of the trips from road and air to rail. In the “benefits generated for users� curve, such as the freight modal shift, the modeling considers the different transport network user: (i) passengers classified by flow-type (by road or air);(ii) transport operators in the different sectors; and (iii) the Government. 4.1.3 Existing Barriers The Ministry of Transport, using the National Plan for Transport Logistics (PNLT) currently promotes actions to give concrete expression to the policies indicated in this first group. The time-horizon for completing the projects contained in the PNLT is 2023, but in view of the present economic situation, uncertainties exist regarding the feasibility of fully implementing the plan during this time period. The study assumed that it would be more feasible to envisage the implementation of all(or most of)the 112 projects contained in the PNLT by 2030. It is worth noting that a significant number of projects contained in the PNLT also include Federal Government interventions under the Growth Acceleration Plan (PAC). The projections resulting from the modeling developed for regional transport assume that the PAC projects are completed by 2030 - forming a kind of “Legality Scenario� alternative, producing more modest results than those expected from the deployment of the PNLT projects. Barriers identified: High infrastructure investment costs ; Need for centralized coordination of the new Infrastructure implementation • program; • Need for more integration among transport concessionaires; Lack of interest in coastal shipping (cabotage) for transporting specific • cargoes; • Need for expansion of the hydroways’ network; Relatively lower volume of freight in the North and Northeast, where potential • exists to increase rail and waterways. • Technical Synthesis Report | TRANSPORT 4.1.4 Measures for Overcoming Barriers It is obvious that operators using alternatives to road transport, particularly for freight, stand to gain most from this set of policies. The current competition between road transport and other modes for transporting large amounts of freight contributes to increasing GHG emissions. Non-road operators generally have difficulties with regard to modal integration and using a transport infrastructure in need of modernization. It is possible that a significant reduction in the volume of freight transported by road in 2030 would generate negative impacts for independent operators faced with fewer options to transport freight. A possible strategy for reducing these future impacts would be to establish integrated transport routes where the distances would be shorter, but where frequency would be enhanced. An operational balance could thus be achieved with a more streamlined system. A further aspect which is seldom discussed but which could influence the low- carbon scenario would be the introduction of the high-speed train (TVA). It is entirely possible that many current road and air passengers would shift to the more efficient train service. If as a result of this switch, the number of road and air passengers were to be reduced, the economic viability of a number of air and bus companies could be undermined. A prospective analysis of the possible impacts on this particular market 113 is recommended in order to avoid possibly serious economic and social problems in these sectors. Alternatives for overcoming barriers: Financing model that facilitates the raising of resources for new transport infrastructure investment; • Ministry of Transport to coordinate the deployment of new infrastructure; Consistent and rigorous conservation program for the existing transport • network; • Incentives to increase the volume of cabotage; To monitor deployment of PAC and PNLT projects. • • 4.2 Mitigation Options for Urban Transport Urban transport is more complex than regional transport due to the greater concentration of vehicles operating in densely-populated areas, and the need to ensure pedestrian safety in such areas. The close interaction between various modes of transport and the links between transport, land use, local economic development, and spatial-growth policies add to the complexity of modeling the effects of transport in urban areas. Technical Synthesis Report | TRANSPORT Three groups of emissions mitigation options were considered in the urban transport subsector. The first concerns improving quality of life and extending public transport facilities in the metropolitan regions. The second focused on interventions in travel demand management, where the priority is to reduce trip length and demand and promote a shift from private cars to high-occupancy transport. The third focuses on developing zero-carbon, non-motorized transport. 4.2.1 Use of High -Occupancy Public Transport The first group of mitigation measures considered for urban transport is the expansion of high-capacity mass public transport systems for transporting passengers in metropolitan areas using modes such as the BRT (Bus Rapid Transit) and the Metro. Urban transport in Brazilian cities is primarily by bus, which at present accounts for around 85% of all trips. The carbon emissions of buses are a function of speed, since lower velocities imply lower fuel consumption. BRT systems can operate in higher average speeds, because they can travel in dedicated lanes. This more constant and higher average speed, when compared to the operation of conventional buses results in a fewer emissions In addition, compared to conventional bus systems, BRT can transport many more passengers, thereby reducing fuel consumption per passenger kilometer. BRT 114 vehicles require less frequent maintenance due to its more regular operation. Policies to increase express bus services must be encouraged. Brazil’s metropolitan regions account for the largest vehicle fleet in circulation and policies that can increase the supply of public transport services must be encouraged in order to reduce the number of vehicules on the road and the level of CO2e emissions. Despite the high costs of expanding existing subway (metro) systems, cities such as San Francisco are expanding their networks and building new lines. In Rio de Janeiro, plans are underway, for example, to expand Metro Line 1 to Ipanema. Within the next few years, connection of the downtown Rio area with the Barra da Tijuca neighborhood via a private concession is likely to become operational. Plans for further expansion include building lines in other cities within the 2030 timeframe of the low-carbon scenario. Brasilia, for example, already has extensions to its metro underway and Belo Horizonte´s Master Plan, issued in 2009, anticipates building metro lines which could be in operation by 2030. Loads and global emissions for all urban passenger transport modes were projected on a year-on-year basis to reflect the impact of investing in a high-capacity public transport infrastructure. This was done initially for a low-carbon scenario which considered BRT investments in isolation, and only later included the effects that metro investments could add to the low-carbon scenario. The BRT usesdiesel, both in the reference and low-carbon scenarios. Table 32 below presents the results projected for loads and direct emissions by type of vehicle in the baseline and low-carbon scenarios, including the deployment of diesel -fueled BRTs. Table 33 details the absolute and total emissions avoided. Note that the total value of overall emissions in the low-carbon scenario for 2030 is considerably Technical Synthesis Report | TRANSPORT less than those for the baseline: 112 MtCO 2 x 121 MtCO 2, reflecting a reduction of around 8%. This saving is due to the introduction of the BRTs for passenger transport in the low-carbon scenario, compared to the base scenario – which, in 2030, will be considerably higher: 30% versus 6%. In the reference scenario, of the new passengers using the BRTs, 69% would be previous users of conventional buses (from 44% to 27%) and 17% would be potential car users (private car use would decline from 43% to 38%. Table 32: Loading and emissions - BRT - Baseline x Low Carbon Loading in 2030 CO 2 emissions (millions pax x km) (thousand tons) Segment Vehicle Mode Fuel Baseline Low Carbon Type Low Baseline 2010- 2010- Carbon 2030 2030 115 Urban Road Bus Diesel 730, 799 453, 337 51 310 887, 697 39 398 732, 048 2030 2030 Passengers BRT 102, 332 505, 751 3360 32,370 13 349 155, 713 Cars Ethanol 364, 894 329, 657 0 0 0 0 Cars + Gasoline 347, 346 313, 804 66 160 1,087,014 59 227 1,016,901 motorcycles Metro Metro Electricity 55, 385 39, 256 0 0 0 0 + rail Trains 50, 699 33, 594 0 0 0 0 Total Urban Passenger 1,651,455 1,675,399 120 829 2,007,082 111 974 1,904,662 Total Urban Transport Emissions - - 128 327 2,137,488 119 472 2,035,068 Total Emissions from Transport Sector - - 238 690 4,026,431 229 835 3,924,011 Source: Logit (2009) Table 33: Emissions avoided – BRT Accumulated gains from Gains from measure deployment of low-carbon scenario Segment Vehicle % Reference Mode Fuel % Baseline Absolute Type Absolute Scenario 2010- 2010- 2010- 2010- 2030 2030 2030 2030 Urban Road Trucks Diesel 0 0 0.0 0.0 0 0 0.0 0.0 2030 2030 2030 2030 Freight Urban Road Bus Diesel 11, 911 155, 649 30.2 21.3 11, 911 155, 649 23.2 17.5 Total Urban Loads 0 0 0.0 0.0 0 0 0.0 0.0 Passengers BRT -9,989 -123, 342 -74.8 -79.2 -9,989 -123, 342 -297.3 -381.0 Technical Synthesis Report | TRANSPORT Cars Ethanol 0 0 0.0 0.0 0 0 0.0 0.0 Cars + Gasoline 6,933 70, 114 11.7 6.9 6,933 70, 114 10.5 6.5 motorcycles Metro-rail Metro trains Electricity 0 0 0.0 0.0 0 0 0.0 0.0 Trains 0 0 0.0 0.0 0 0 0.0 0.0 Total Urban Passenger 8855 102 420 7.9 5.4 8855 102 420 7.3 5.1 Total Urban Transport Emissions 8855 102 420 7.4 5.0 8855 102 420 6.9 4.8 The studies for the Belo Horizonte Urban Mobility Plan, completed in 2009, served Source: Logit (2009) as a basis for the simulations undertaken in the present paper. Figures 40 and 40A below illustrate the results in 2030, based on simulations made under the above mentioned plan, with respect to the locomotion of passengers in collective trips and private trips, respectively, by vehicule, compared a scenario without investments with a scenario with more than 100km of BRT investments. Figure 40A: Belo Horizonte: with and without Investments in BRT (2030 Reference and Low Carbon Scenarios) - Public Transport Passenger Loads 116 Source: Belo Horizonte Transport Plan / Logit (2009) Figure 40B: Belo Horizonte: with and without Investments in BRT (2030 Reference and Low Carbon Scenarios) - Private Vehicle Users Technical Synthesis Report | TRANSPORT In 2030, the Belo Horizonte BRT will absorb virtually all the passengers from Source: Belo Horizonte Transport Plan / Logit (2009) conventional buses in the reference scenario. Ordinary buses (in the BRT investment scenario) will continue to be used in parallel wherever necessary. Travel by light vehicles and motorbikes will diminish slightly by 2030 in the scenario with investments compared to the scenario without investments. The trends presented in the Belo Horizonte transport plan are confirmed by the results of the simulations for the low-carbon scenario proposed in the present paper. In the low-carbon scenario for diesel-fueled BRT, compared with the baseline scenario, much of the passenger loads of conventional buses and a small segment of passengers shifting from cars and motorbikes will be absorbed by the BRTs, as seen in Figure 41. Figure 41: Modal Distribution of Passenger Load - BRT - Baseline Scenario x Low Carbon 117 Due to operational efficiency, the BRT will use less diesel fuel than conventional Source: Logit (2009) buses. As a result the new modal distribution in the low-carbon scenario will mean that lower volumes of CO2 will be produced. The migration of car and motorbike users to the BRT will not be as substantial as passengers shifting from ordinary buses to BRT, but any shift, even by a minority of individuals, will contribute to emissions and fuel savings, as indicated in Figure 42: Figure 42: Fuel Consumption Trends (TEP) up to 2030, by Vehicle Type - BRT - Baseline x Low Carbon Scenario Technical Synthesis Report | TRANSPORT The investment required to build the 649km of BRT lanes considered in the Source: Logit (2009) reference scenario would total about US$6.5 billion. The modeling indicates that it would be possible to expand the BRT system to the 2,600 km stipulated for the low- carbon scenario, requiring an additional US$26 billion. This would require public- sector financing since investments in mass urban public transport are unattractive to the private sector (given the need to obtain operational efficiencies and the low profit margins involved). 118 Figure 43: Evolution of Emissions: Baseline Scenario x Low Carbon The investments in BRT in the low-carbon scenario proposed here should lead to Source: Logit (2009) a reduction in net emissions in the urban passenger transport sector of 102 MtCO 2 between 2010 and 2030. The total emissions in the low-carbon scenario for 2030 will reduce further with the inclusion of investment in the metro system. Table 34 below shows loads and emissions in the reference and low-carbon scenarios, with investments in high- occupancy public transport, the introduction of diesel-fueled BRTs and improvements to the metro networks. Technical Synthesis Report | TRANSPORT Table 34: Passenger Loads and Emissions - Metro - Baseline Scenario x Low Carbon Loading in 2030 CO 2 (millions pax x km) (thousand tons) Type Segment Mode Fuel Baseline Low Carbon Vehicle Baseline Low Carbon 2010- 2010- 2030 2030 119 Urban Road Bus Diesel 453, 337 346 281 39, 398 732, 048 29, 696 670, 044 2030 2030 Passengers BRT 505, 751 470, 621 13, 349 155, 713 12, 430 143, 544 Cars Ethanol 329, 657 320, 239 0 0 0 0 Cars + Gasoline 313, 804 304, 838 59, 227 1,016,901 57, 540 1,019,654 Motorcycles Metro- Metro Electricity 39, 256 212, 844 0 0 0 0 rail Trains 33, 594 26, 577 0 0 0 0 Total Urban Passenger 1,675,399 1,681,400 111, 974 1,904,662 99, 667 1,833,243 Total Urban Transport Emissions - - 119, 472 2,035,068 107, 164 1,963,649 Total Emissions from Transport Sector - - 229, 835 3,924,011 217, 527 3,852,592 Table 35 records the figures referring to emissions avoided by including the Source: Logit (2009) investments targeted at improving the metro system. Table 35: Avoided Emissions – Metro Accumulated gains from deployment Gains from measure of low-carbon scenario Segment Vehicle % Reference Mode Fuel % Baseline Absolute Type Absolute Scenario 2010- 2010- 2010- 2010- 2030 2030 2030 2030 Urban Road Bus Diesel 9,702 62, 004 24.6 8.5 21, 613 217, 652 42.1 24.5 2030 2030 2030 2030 Technical Synthesis Report | TRANSPORT Passengers BRT 919 12, 168 6.9 7.8 -9070 -111, 174 -270.0 -343.4 Cars Ethanol 0 0 0.0 0.0 0 0 0.0 0.0 Cars + Gasoline 1,686 -2754 2.8 -0.3 8619 67, 360 13.0 6.2 Motorcycles Metro- Metro Electricity 0 0 0.0 0.0 0 0 0.0 0.0 rail Trains 0 0 0.0 0.0 0 0 0.0 0.0 Total Urban Passenger 12, 308 71, 418 11.0 3.7 21, 163 173, 838 17.5 8.7 Total Urban Transport Emissions 12, 308 71, 418 10.3 3.5 21, 163 173, 838 16.5 8.1 Table 35A shows that joint introduction of BRT and metro would result in a total Source: Logit (2009) annual CO2 emissions reduction between 2010 and 2030 from about 173 MtCO 2 e to 102 MtCO 2e, for the BRT and 71 MtCO 2e for the metro. Table 35A: Emissions avoided - Metro + BRT Gains Metro Gains BRT Total Gains Type Segment Mode Fuel 2010- 2010- 2010- Vehicle 2030 2030 2030 Urban Road Bus Diesel 9,702 62, 004 21, 613 155, 649 31, 315 217, 652 2030 2030 2030 Passenger BRT 919 12, 168 -9,070 -123, 342 -8,151 -111, 174 120 Cars Ethanol 0 0 0 0 0 0 Cars + Gasoline 1,686 -2754 8,619 70, 114 10, 306 67, 360 Motorcycles Metro- Metro Electricity 0 0 0 0 0 0 rail Trains 0 0 0 0 0 0 Total Urban Passenger 12, 308 71, 418 21, 163 102, 420 33, 470 173, 838 Avoided Emissions Total Urban Transport 12, 308 71, 418 21, 163 102, 420 33, 470 173, 838 With the addition of the metro in the low-carbon scenario, the passenger load in Source: Logit (2009) conventional buses would decline further (from 44% to 21%), and the number of passengers using private motor vehicles would also decline (from 43% to 37%). The addition of BRTs alone would result in a marginally-reduced passenger load factor for this transport mode: 30% to 28%, as illustrated in Figure 44: Figure 44: Modal Distribution of loads - BRT + Metro Technical Synthesis Report | TRANSPORT The fuel economy resulting from the implementation of the BRT alone will be 28 Source: Logit (2009) million tons of oil equivalent by 2030, compared to the baseline scenario. This figure would be expected to double with the addition of the metro, as shown in Figure 45. Figure 45: Fuel consumption (TEP) - BRT + Metro 121 Figure 46 shows the evolution of net emissions up to 2030 for the two low-carbon Source: Logit (2009) scenario alternatives - investments in high-capacity public transport, with and without the metro. It is clear that the effect of incorporating the metro is substantial (representing a reduction of over 71 MtCO2e by 2030), resulting in increasing and sustainable emissions reduction. Figure 46: Evolution of emissions: BRT + Metro Technical Synthesis Report | TRANSPORT The net emissions reduction of 173 MtCO 2, between 2010 and 2030, was calculated Source: Logit (2009) on the basis of a “full� low-carbon scenario (GMT + Metro). The modeling indicates that it would be possible to build an additional 785 km of metro lines. This would require an expenditure of approximately US$80 billion, which could be partially co-financed by the private sector (possibly involving tax waivers of up to 25% on rolling stock), following a similar Private Public Partnership (PPP) model adopted for the “Yellow Line� in São Paulo. As in the case of the abatement cost curves for regional transport, the urban transport curves compare the additional investments needed for implementing a BRT/ Metro network in a low-carbon scenario. The additional investments also include the operation and maintenance costs of the new systems planned. The “avoided investments� take into account the costs that would be incurred in the implementation of other, less energy-efficient public transport systems (conventional buses), as well as their substantial O&M costs. As for “fuel consumption avoided�, we considered the amounts avoided by the non-implementation of conventional bus systems. Furthermore, we estimated the 122 avoided fuel consumption arising from the private vehicle fleet, assuming that with the implementation of new mass transit systems congestion would be reduced. Private per km. car consumption will also be reduced as the result of more free-flowing traffic and lower congestion. For the “user benefit curve�, we considered the avoided costs arising from reduced operating costs, reduced trip times, reduced bus management costs, reduced pollution and fewer accidents. As for pollution and accident reduction, the modeling was based on values similar to those considered in the BRT projects scheduled for deployment in Rio de Janeiro and Belo Horizonte. The abatement costs for the combined BRT and Metro are presented below. In the case of metro expansion, we considered a selection of metropolitan regions, bearing in mind the very high costs of implementing this particular option. The modeling also took account of the possibility of introducing BRTs in a larger number of cities. Figure 47: Cost Abatement Curves for BRT + Subway (nominalNominal) Technical Synthesis Report | TRANSPORT Source: Logit (2009) Figure 48: Cost Abatement Curves for BRT + Subway (present value) 123 Source: Logit (2009) Table 36: Average costs of avoided tCO 2 e – BRT Abatement Curves US$ per tCO 2 e Low Carbon Investment 426.54 141.32 Nominal Present Value in 2009 Avoided Investment 384.23 124.55 Fuel Economies 334.39 106.47 Operating Gains 155.23 54.81 Social Benefits 0.24 10.20 4.2.2 Description of Policies for the BRT and Metro Various transport-related interventions are underway in the metropolitan regions and municipalities. Since municipalities have autonomous control over their transport systems it is difficult to identify progress made by every policy and/or strategy. The National Mobility Plan (PlanMob), under the aegis of the Ministry of Cities, Technical Synthesis Report | TRANSPORT aims to encourage Brazilian municipal authorities to implement their Transport Master Plans and to help them adopt appropriate policies and strategies in line with their development programs, taking into account municipality size, social, economic, cultural and environmental characteristics, as well as resource availability. The Master Plans are considered mandatory for municipalities with over 500 inhabitants, and essential for those with over 100,000. Given that each municipality posseses autonomous control over the management of its own transport and road/street systems, the Ministry of Cities will need to find a way of coordinating these policies at the central level. Incentives are needed to encourage the adoption of the policies, particularly by municipalities which aim to expand their large- capacity passenger transport systems. The policies outlined here are of an incremental nature, aimed at increasing the supply of good-quality public transport in the municipalities, and especially in Brazil’s metropolitan regions, which contain the largest numbers of private vehicles in circulation. Policies designed to enhance the supply of quality public transport can lead to reduced private car use in the large cities. Consideration could be given to establishing the new BRT-type systems (similar to those operating in Curitiba) in certain cities as a viable option for improving traffic circulation and as an alternative to private car transport. A key advantage is that BRT 124 involves lower investment costs in infrastructure and quicker deployment times. Productivity gains in the transport system are also important. A system of efficient and good quality public transport increases the prospects for increased demand and modal shift (from private cars to rapid high-capacity public transport). The cities stand to benefit from improved and more manageable traffic flows and less-congested streets given the smaller number of private vehicles in operation. Traffic congestion in the metropolitan regions and the larger cities would be reduced, producing a direct environmental benefit. Congestion increases energy consumption and exhaust emissions. Shorter travel times would improve the quality of life of transport- users. An urgent need exists for actions to be coordinated between the Federal, State and Municipal governments. Given the very substantial investments required, coordination by the authorities is vital for securing access to financial resources (e.g. credit for rolling stock) etc. in the anticipation of successfully implementing new mass transit systems and/or improving existing ones. 4.2.3 Travel Demand Management The second group of mitigation measures proposed for the urban transport low- carbon scenario aims at discouraging the use of private cars, while at the same time encouraging the use of public transport systems. The main measures are to: Prioritize bus systems in high-demand corridors; Manage traffic mobility to avoid overlapping routes and ensure compliance • Technical Synthesis Report | TRANSPORT with timetables etc issued by operating bodies; • Design strategies to restrict the use of private cars; Integrate the various transport modes; • Introduce tolls for private vehicles where good quality public transport • exists; • Increase parking costs to reduce the number of cars in saturated areas; Integrate land-use and transport policies (to reduce trip-numbers and • lengths). • Travel demand management measures designed to ensure continuous and effective operation must be fully integrated with those that promote and enhance the quality of public and mass transport systems and the rational use of motor cars. The set of mitigation measures suggested will inevitably improve the traffic situation in general in the metropolitan regions where, according to the Belo Horizonte Urban Mobility Plan, traffic speeds in large urban centers are generally under 22 km/ hour. It is well-known that fuel consumption and CO2 emissions are greatly reduced at 125 normal average speeds. All in all, it is expected that the sum of the above measures will bring about substantial emissions reductions. As explained in section 4.2.1, introducing high-performance public transport leads to significant fuel consumption and emissions reductions. However, a better flow of vehicles also runs the risk of actually creating more traffic, resulting from repressed demand. Thus, demand management strategies must ensure a balance between the development of transport supply and demand in order to prevent emissions reverting to former levels. The set of mitigation strategies based on travel demand management must, in addition to improving public transport systems, restricting private car use and introducing specific parking policies in central areas, also consider questions of land use and occupation. Curitiba, Bogotá, and other Latin American cities illustrate the significant reductions in CO2 emissions that can result from integrated planning. But for such strategies to succeed over the longer term, appropriate institutional, financial, and regulatory structures, as well as marketing and public outreach policies, must be in place. Long-range forecasting makes it difficult to estimate the size of the resources needed for managing the policies recommended for urban transport. The resources needed will largely depend on where and how the policies will be implemented and the special characteristics of each municipality involved. It was considered that the policies should target only the densely-populated urban areas forming part of Brazil´s metropolitan regions (similarity clusters 1-5). To apply the measures described, we assumed that 0.5% of the administrative expenditures of the transport, housing and urban planning agencies of the municipalities considered would be a reasonable amount for deploying Technical Synthesis Report | TRANSPORT on policy management. For the period 2060-2008 this amounted to approximately US$50 million. We estimated emissions reductions and fuel economies by assuming that the number and length of trips projected in the reference scenario would gradually reduce year over year by about 3% up to 2030. Table 38 presents possible emissions reductions on a year by year basis up to 2030, as the result of urban transport demand management, assuming the implementation of the mitigation measure. These figures were calculated according to the parameters and criteria described in this section. It can be seen that the gains arising from shorter and less numerous trips are substantial (around 58 billion passengers x kilometer fewer, and avoided emissions of approximately 45.4 MtCO 2). Table 38: Gains from Demand Management in Brazil´s large cities Reference Scenario Savings Loads Emissions % decrease in Loads Emissions Year (million (thousand number of trips (million (thousand tons CO 2 e) 2010 940 337 70508.1 0.14 0.14 1706 141.4 141.4 126 pass x km) tons CO 2 e) Number Length pass x km) Year Accumulated 2011 967 350 72307.6 0.28 0.28 3498 288.9 430.3 2012 995 186 74254.9 0.42 0.42 5381 442.4 872.7 2013 1023878 76258.7 0.56 0.56 7354 602.4 1475.2 2014 1053642 77522.3 0.70 0.71 9400 773.2 2248.4 2015 1084212 79279.9 0.84 0.85 11 552 949.9 3198.2 2016 1115712 81142.6 0.98 0.99 13,805 1134.0 4332.2 2017 1148250 83005.1 1.12 1.13 16 158 1327.2 5659.4 2018 1181816 84864.3 1.26 1.27 18 616 1529.8 7189.2 2019 1216444 86712.4 1.41 1.42 21 181 1742.9 8932.1 2020 1252180 88536.0 1.55 1.56 23 859 1966.4 10898.5 2021 1289063 90153.5 1.69 1.70 26 654 2198.6 13097.1 2022 1327128 91716.9 1.84 1.85 29 570 2438.6 15535.7 2023 1366421 93213.6 1.98 1.99 32 614 2691.6 18227.3 2024 1407112 94620.9 2.12 2.13 35 784 2959.0 21186.3 2025 1449143 95925.1 2.27 2.28 39 094 3238.6 24424.9 2026 1492556 97090.7 2.41 2.42 42 552 3535.2 27960.2 2027 1537409 98111.1 2.56 2.57 46 169 3842.6 31802.7 2028 1583827 98913.4 2.71 2.71 49 953 4171.2 35974.0 2029 1631805 99439.3 2.85 2.86 53 925 4522.3 40496.3 2030 1681400 99666.5 3.00 3.00 58 107 4888.4 45384.6 Table 39A presents loads and direct emissions by type of vehicle in the baseline and Source: Logit (2009) Technical Synthesis Report | TRANSPORT the low-carbon scenarios, with the inclusion of the mitigation measure “investments in urban transport demand management�. Table 39A: Loads and emissions - Demand Management of Urban Transport - Baseline x Low Carbon Scenarios Loads in 2030 Emissions of CO 2 (millions pax x km) (thousand tons) Type Segment Mode Fuel Baseline Low Carbon Vehicle Baseline Low 127 2010- 2010- in 2030 Carbon 2030 2030 Urban Road Bus Diesel 346, 281 331, 054 29, 696 670, 044 28, 390 658, 407 2030 2030 Passenger BRT 470,621 468, 454 12, 430 143,544 12,373 143, 082 Cars Ethanol 320, 239 301, 232 0 0 0 0 Cars+ Gasoline 304, 838 286, 162 57, 540 1,019,654 54, 015 986, 369 Motorcycles Metro- Metro Electricity 212, 844 211, 262 0 0 0 0 rail Trains 26, 577 25, 129 0 0 0 0 Total Urban Passenger 1,681,400 1,623,293 99, 667 1,833,243 94, 778 178,7858 Total Urban Transport Emissions - - 107, 164 1,963,649 102, 276 1,918,264 Table 39B shows the values of absolute and total emissions avoided. Source: Logit (2009) Table 39B: Emissions avoided - Demand Management of Urban Transport - Baseline x Low Carbon Scenarios Accumulated gains from deployment of Gains from measure low-carbon scenario Segment Vehicle % Reference Mode Fuel % Baseline Absolute Type Absolute Scenario 2010- 2010- 2010- 2010- 2030 2030 2030 2030 Urban Road Bus Diesel 1,306 11, 637 4.4 1.7 22, 919 229, 290 44.7 25.8 2030 2030 2030 2030 Passengers BRT 57 462 0.5 0.3 -9,013 - 1 1 0 , -268.3 -342.0 Technical Synthesis Report | TRANSPORT 712 Cars 0 0 0.0 0.0 0 0 0.0 0.0 Ethanol Cars+ Gasoline 3,525 33, 286 6.1 3.3 12, 145 100, 646 18.4 9.3 Motorcycles Total Urban Passenger 4,888 45, 385 4.9 2.5 26, 051 219, 223 21.6 10.9 Avoided Emissions Total Urban Transport 4,888 45, 385 4.6 2.3 26, 051 219, 223 20.3 10.3 Total Avoided Emissions from Transport Sector 4,888 45, 385 2.2 1.2 34, 849 293, 531 14.1 7.2 The values established for the mitigation measures are incremental. Figure 49 Source: Logit (2009) shows the figures in graph form for aggregated emissions in the low-carbon scenario for urban transport with the inclusion of the mitigation measure to encourage implementation of the urban transport demand management measure, where the volume of emissions avoided between 2010 and 2030 will increase by 45.4 MtCO 2 e, representing an overall increase of 2.5% in comparison with the baseline scenario. Figure 49: Evolution of Emissions - Demand Management of Urban Transport: Baseline x Low Carbon Scenario 128 The following are the abatement cost curves relating to the demand management Source: Logit (2009) strategies considered in this mitigation option. From these it can be seen that, although the total volume of avoided emissions is not especially significant, the cost of achieving them is relatively low. Demand management has one of the best cost-benefit ratios all the mitigation options presented. Figure 50: Cost Reduction Curves (nominal) Technical Synthesis Report | TRANSPORT Source: Logit (2009) Figure 51: Abatement Cost Curves (present value) 129 Table 40 shows that the average cost per ton of CO2 indicators arising from this Source: Logit (2009) mitigation measure are among the lowest. The indicator “Financial Cost of Tons of CO2 Avoided�, which is based on the ratio between avoided emissions and the values of investments required by the low-carbon scenario, less the avoided investment as a result of the measure, is relatively low at US$23.14 per ton of CO2 avoided. Since no investment is avoided, the “Financial Cost of Tons of CO2 Avoided� indicator is linked directly to the curve of “investments required� by the low-carbon scenario. The indicator of the “Final Cost of Tons of CO 2 Avoided� linked to the Social Benefits abatement curve is negative, atUS$ (163.52) per ton of CO 2 e. This negative cost reflects the inclusion of the gains from fuel economies, from improved urban transport operational efficiency and from all the direct and indirect social benefits, the gains from fuel savings, the efficiency gains from the operation of urban transport system. As a result, the required investments are fully compensated - showing a credit of US$163.52 per ton of CO 2 avoided, since the sum of the aforementioned benefits surpasses the Technical Synthesis Report | TRANSPORT amount of investment required. Table 40: Average cost of avoided CO 2 and t Abatement Curves US$ per tCO 2 e Low Carbon Investment 23.14 11.04 Nominal Present Value in 2009 Fuel Economy -15.93 -1.85 Operating Gains -113.59 -34.06 Social Benefits -163.52 -50.53 Source: Logit (2009) The chart at Figure 52 shows the evolution on a year-on-year basis between the accumulated investment values in US$ and the accumulated cost per ton of CO 2 avoided as a result of the implementation of the mitigation measure. Note that the gains are likely to be continuous and sustainable. 130 Figure 52: Cost per Ton Avoided X Investments Required by Urban Demand Management (per annum up to 2030) For the curve describing user social benefits the avoided costs were considered Source: Logit (2009) with a reduction of operational costs, trip-times, pollution and accidents. For pollution and accident reduction, the values of passenger loads proportionate to the mitigation option were used in the modeling, involving the introduction of high-capacity public transport systems. Technical Synthesis Report | TRANSPORT 4.2.3.1 Existing Policies The operational and management streamlining of public transport should contribute to reducing congestion, fuel consumption and pollutant emissions. Given that each municipality has independent power to manage its own transport and related systems the Ministry of Cities will need to coordinate adoption of the strategies centrally. Incentives are required to encourage municipal authorities to adopt policies aimed at improving operational and managerial performance in the public transport sector in accordance with the PlanMob recommendations. The policies outlined here are incremental in nature and seek to expand and streamline the provision of quality public transport in cities, specifically in the metropolitan areas where the impacts of transport systems are more easily identified. Brazil’s metropolitan areas concentrate the largest numbers private cars in circulation. Policies that enhance the provision of good public transport should be encouraged and the use of private cars discouraged. As already explained in the Group 1 mitigation options, the actions planned in this second group are in line with the PlanMob. The measures suggested serve to underpin future actions that could be implemented in a coordinated manner by the municipalities with the largest motorized vehicle fleets. The metropolitan regions would need special treatment, since transport problems are most severe in these areas. Congestion is heavy during much of the day, causing 131 substantial waste of fossil fuels and increasing levels of local pollutants and GHGs. The policies presented here are also incremental in nature. Effective monitoring and supervision of their implementation is vital for achieving the hoped-for results within the specified timeframe. The interventions presented individually in Group 2 call for coordination actions which prioritize public transport and discourage the use of private motor vehicles. The introduction of BRTs and the Metro (Group 1) needs to be coordinated by programs designed to improve urban mobility and ensure a better quality of life for people living in densely-populated cities. Restrictions on the use of private transport, by increasing parking costs in downtown areas can also be considered. Establishing parking lots on the outskirts of cities, integrated into public transport corridors, is also recommended for reducing the number of private cars in the most congested areas. Money from the possible establishment of traffic charging mechanisms (tolls) could be used to finance some public transport improvements. 4.2.3.2 Political Economy Scenario The proposals presented in this group of policies, considered to be more of a structural nature, will depend on their acceptance by the community. Acceptance of the proposals should in due course benefit users by improving transport supply within the existing infrastructure. Similarly, operators of public transport systems will benefit from lower operating and maintenance costs generated by systems better able to satisfy present and Technical Synthesis Report | TRANSPORT potential customer demand. Most of the stakeholders involved will benefit. However if the main goal of reducing the number of vehicles in circulation in the cities, especially at peak times, is achieved, some possibility exists of sales of new and used cars dropping as a result. One possiblesolution would be for car manufacturers and their agents to encourage more rapid vehicle sales turnover. Thus, even if the use of private cars in terms of km operated per year is limited, more frequent exchange of vehicles (e.g. replacing a two-year old vehicle) can prevent negative spinoffs for the automotive industry. This strategy has been used in European Union countries, where similar demand management strategies have been deployed over the past twenty years. This policy group, where managing demand for travel is prioritized as a means of reducing GHG emissions, also requires a coordinated approach by the different institutional stakeholders involved. In general, government policies need to be incorporated into the action agendas of the state and city administrations. The need for greater public policy coordination at the different levels of government is of particular importance in the transport sector. The problems typical of this sub-sector (different transport modes, etc) must be fully addressed at all levels of government to ensure satisfactory results. At the same 132 time, it is vital to strengthen the appropriate administrative skills to ensure timely deployment of policies and actions. In smaller towns, the lack of qualified staff capable of assessing transport needs and scope for improvement is a potential hurdle to be overcome. Specific training of traffic-dedicated municipal teams and upscaling relevant technical capacities is highly recommended. In general, the need for massive financial resources for infrastructure works is lower in smaller towns, where the main concerns are to improve existing transport arrangements. 4.2.4 Implementation of Bikeways In an effort to promote the use of non-motorized transport the establishment of bikeways has been proposed. It is expected that trips by bicycle would generate fewer environmental impacts and ensure more rational use of public spaces. Encouraging bicycle use involves ensuring safe conditions. The construction of interlinked bikeways in busy areas, integrating them with motorized public transport facilities, can produce satisfactory results. The cost of establishing bikeways in Brazil varies from US$25,000 to US $50,000 per kilometer depending on the type of lane. In cases where special bikeways are simply demarcated by lines painted on the surface of existing asphalt (or with marker bollards to separate them from other traffic), the cost can be lower. A cost of US $35,000 per kilometer of construction was adopted, for bikeways and US$400 per year for maintenance. Technical Synthesis Report | TRANSPORT The concept of cycling and establishing bikeways has gained significant public appeal in terms of swift and economical transport, preserving health, reducing pollution etc. Commercial firms are increasingly interested in linking their image to this “politically correct� sector. We believe therefore that it would not be difficult to find sponsors to underwrite a greater part of the investments required for establishing a 8400 km of bikeways throughout Brazil. We calculate that 400 km of bikeways could be installed annually between 2010 and 2030. We estimated the gains from cycling based on the potential for shifting loads from motorized vehicles (cars, motorbikes, BRTs and conventional buses) to bicycles, based on studies on the implementation of 400 km of bikeways in the city of Porto Alegre. Table 41 shows the gains resulting from this measure, with the cost of an avoided ton of CO2 evaluated in terms of nominal accumulated values, considering the investments required and their respective operational and maintenance costs compared to potential fuel savings. While the number of passengers x km shifting from motor vehicles increases, the cost indicator of an avoided ton of CO2 reduces exponentially up to 2030, beginning with US $2,229.91 per ton of CO2 avoided in 2010, decreasing to zero in 2030 (minus US$0.11 per ton of CO2 avoided). This means that the investment required for establishing bikeways, plus the costs of operation and maintenance, would be totally compensated by the fuel consumption savings. Between 133 2010 and 2030 a credit of US$ 0.11 for each ton of CO2 avoided would result. Table 41 Bikeway Loads and Gains in Avoided Emissions, 2010–30 Cumulative value Costs of tons Load transfer Avoided emis- (thousands of US$) of cumula- to bicycle (mil- sions Year tive avoided lions of pass (thousand tons Investment + Fuel savings CO2e per year x km) CO2/ year) O&M 2010 88 6 14,160 136 2,229.91 (US$/tCO2e) 2011 273 26 28,480 555 1,085.45 2012 563 66 42,960 1,418 630.88 2013 968 135 57,600 2,899 405.50 2014 1.497 242 72,400 5,187 277.56 2015 2.162 398 87,360 8,486 198.29 2016 2.973 613 102,480 13,014 145.98 2017 3.942 900 117,760 19,006 109.73 2018 5.083 1,273 133,200 26,714 83.66 2019 6.408 1,748 148,800 36,408 64.31 2020 7.932 2,341 164,560 48,375 49.62 2021 9.670 3,071 180,480 62,924 38.28 2022 11.501 3,941 196,560 80,188 29.53 2023 13.431 4,964 212,800 100,304 22.66 2024 15.464 6,151 229,200 123,402 17.20 2025 17.605 7,510 245,760 149,625 12.80 Technical Synthesis Report | TRANSPORT 2026 19.857 9,059 262,480 179,119 9.20 2027 22.225 10,801 279,360 212,033 6.23 2028 24.715 12,756 296,400 248,513 3.75 2029 27.332 14,940 313,600 288,710 1.67 2030 30.080 17,360 330,960 332,784 -0.11 Note: This potential scenario includes the construction of 8,400 km of bikeways and related facilities. Table 41A below presents the results of the loads and direct emissions by type Source: Logit (2009) of vehicle in the baseline and in the low-carbon scenario with the inclusion of the mitigation measure for implementation of bikeways throughout Brazil. Table 41A: Loads and Emissions - Implementation of Bikeways - Baseline x Low Carbon Scenarios Loads in 2030 Emissions of CO 2 (millions pax x km) (thousand tons) Type Segment Mode Fuel Baseline Low Carbon 134 Vehicle Baseline Low 2010 – 2010 - in 2030 Carbon 2030 2030 Urban Road Bus Diesel 331, 054 308, 538 28, 390 658, 407 26, 460 644, 640 2030 2030 Passengers BRT 468, 454 465, 301 12, 373 143, 082 12, 289 142, 525 Cars Ethanol 301, 232 298, 973 0 0 0 0 Cars+ Gasoline 286, 162 284, 011 54, 015 986, 369 53, 609 983, 332 Motorcycles Total Urban Passenger 1,623,293 1,593,213 94, 778 1,787,858 92, 358 1,770,498 Total Urban Transport Emissions - - 102, 276 1,918,264 99, 856 1,900,904 Total Emissions from Transport Sector - - 212, 639 3,807,208 210, 218 3,789,847 Table 41B shows the values of absolute emissions avoided and their respective Source: Logit (2009) percentage variations for the measure. Table 41B: Avoided Emissions - Implementation of Bikeways Accumulated gains from deployment Gains from the Measure of low-carbon scenario Vehicle % reference Segment Mode Fuel % baseline Absolute type Absolute scenario 2010- 2010- 2010- 2010- 2030 2030 2030 2030 Urban Road Bus Diesel 1,931 13, 767 6.8 2.1 24, 850 243, 056 48.4 27.4 2030 2030 2030 2030 Passengers BRT 83 557 0.7 0.4 -8,929 -110, 155 -265.8 -340.3 Cars Ethanol 0 0 0.0 0.0 0 0 0.0 0.0 Technical Synthesis Report | TRANSPORT Cars+ Gasoline 406 3,036 0.8 0.3 12, 551 103, 682 19.0 9.5 Motorcycles Total Urban Passenger 2,420 1 7 , 2.6 1.0 28, 471 236, 584 23.6 11.8 360 Total Urban Transport Emissions 2,420 17,360 2.4 0.9 28, 471 236, 584 22.2 11.1 The values of the mitigation measures are incremental. Figure 53 illustrates in Source: Logit (2009) graphic form the figures for aggregated low-carbon scenario emissions for urban transport with the inclusion of the bikeway mitigation measure. The avoided emissions between 2010 and 2030 would increase by 17.4 MtCO 2 e, representing a 2.6% increase from the baseline. Figure 53: Evolution of Emissions - Implementation of Bikeways: Baseline x Low Carbon Scenario 135 The following are the abatement cost curves related to the implementation of Source: Logit (2009) bikeways. Note the exact parallel with the cost curves for the demand management abatement mitigation measure (Figures 50 and 51) . In this case it is clear that the total volume of avoided emissions is not particularly significant, but the associated costs of deployment are relatively low. This results in advantageous cost benefit ratios compared with the other mitigation options presented. Figure 54: Cost Abatement Curves ( nominal) Technical Synthesis Report | TRANSPORT Source: Logit (2009) Figure 55: Cost Abatement Curves (present value) 136 Source: Logit (2009) Table 42: Average Cost of Tons of CO 2 Avoided Abatement Curves US $ per tCO 2 e Low Carbon Investment 19.58 7.04 Nominal Present Value in 2009 Fuel Economy 0.41 1.21 Operating Gains -47.51 -13.36 Social Benefits -70.66 -20.30 Technical Synthesis Report | TRANSPORT Source: Logit (2009) 4.2.4.1 Existing Policies The Ministry of Cities, through Secretariat of Mobility (SeMob), has issued a reference guide as an introduction to the Bicycle Mobility Plan (‘Bicicleta Brasil’) which underscores the Ministry’s philosophy of “Sustainable Urban Mobility�. Although this policy is not mandatory, the SeMob guide contains information to be used by municipalities as a first attempt to encourage more frequent bicycle use. The general thrust of this document is that bicycles should be integrated with other forms of public transport to generate fewer environmental impacts and make more rational use of public spaces. In addition to the recommendations for bikeways, the PlanMob booklet contains a set of proposals for upgrading pedestrian safety in urban areas, emphasizing the need for local authorities to include pedestrian concerns in their transport and urban-planning agendas. 4.2.4.2 Description of Policies Encouraging bicycle use involves ensuring safe conditions. Integrating a safe and attractive walking infrastructure and expanded bikeway networks into public- transport policies and systems can enhance the overall urban landscape and avoid significant amounts of CO2 emissions. These policies can be applied incrementally. Brazil´s largest cities in general do 137 not possess a cycling culture. Some cities have however begun to establish bikeways without reference to the municipal Master Plans. Some are isolated initiatives which fail to exploit the maximum potential of this mode of transport. In some smaller cities (in general those with under 50,000 inhabitants), bicycle use is more frequent. Given that the distances travelled are normally less than 5 km, cycling is part of local culture and contributes to environmental conservation. 4.2.4.3 Political Economy Scenario This scenario is basically one of gains, since the use of non-motorized transport contributes to local sustainability and encourages revitalization of urban areas, in terms of the need, for example, for local authorities and others to install safe places for bicycles to be kept and, in the case of pedestrians, the existence of well-maintained sidewalks, street furniture (shelters, benches etc) and other amenities. All of these initiatives serve to encourage enhanced use of cycling and walking, as well as reducing atmospheric emissions. The increased use of non-motorized transport in urban areas requires a coordinated approach by the three government levels. A further point is that cycling in particular has won the support of private-sector firms which have identified an original way of promoting their corporate image in tune with environmental causes. A number of projects have been sponsored in Brazil similar to those in other countries, where bicycles can be hired at strategic points. By sponsoring these initiatives, investors have perceived good opportunities for pursuing effective marketing campaigns. Technical Synthesis Report | TRANSPORT 4.3 Low-carbon Scenario for Ethanol - Increasing Proportion of Ethanol Consumption by “Flex-Fuel� vehicles Energy issues are considered as strategic concerns for the majority of countries, particularly since the Industrial Revolution - when manufacturing, economic development and a good supply of energy (coal and wood) were crucially interlinked. Over the years, oil products and oil-powered machines have played an increasing role in technological development. The demand for oil and derivatives has grown exponentially in line with economic development and the world is now totally dependent on them, with oil production highly concentrated in the Middle East. Serious economic problems were caused in the main oil consuming countries by the so-called “first and second oil shocks�. These crises led Brazil to seek an alternative. With the experience gained from producing and using alcohol as an alternative fuel to lessen vulnerability to energy shortages, the Brazilian government established the National Alcohol Program (Proálcool) in 1975. Ethyl alcohol (ethanol) has gone on to acquire significant importance in the national energy matrix. However, since 1985, fluctuations in international oil prices tended to narrow price differentials between gasoline and alcohol in the domestic market. Fuel distribution problems led to a shift in Brazilian energy policy which created a 138 fall in ethanol production. One problem was that attractive prices for sugar in the international market meant that much of the sugarcane produced in Brazil began to be directed towards producing sugar for export, thus undermining local production of alcohol. With a shortage of alcohol in the domestic market, confidence in the product was undermined, followed by a substantial decline in the production of ethanol-fueled vehicles. However, in the 1980s exciting new prospects were created for ethanol with the introduction of “flex-fuel� vehicles. The dual-fuel engine developed with US technology works like a conventional petrol engine using a fixed ratio between the quantities of alcohol and gasoline (E85). Meanwhile in Brazil, the automotive industry chose to invest in the creation of engines which could use either hydrated alcohol ( E100) or gasoline (E22) or any combination of both, automatically adapting to either or both fuels with no need for driver adjustments. This innovation represented the flexibility the market needed for mitigating supply and price risks for customers. The “flex-fuel� car was launched in Brazil in 2003. Since then over 8 million vehicles using this system have been sold in Brazil. By June 2009 flex-fuel cars represented around 89% of total car sales. Figure 56 - Evolution of Light Vehicle Sales by Fuel Type (1979-2007) Technical Synthesis Report | TRANSPORT Source: ANFAVEA / Logit (2009) 4.3.1 Parameters for the Low-carbon Scenario The size of the flex-fuel fleet and the proportion of ethanol/gasoline consumption determines the level of pollutant emissions reductions in the low-carbon scenario for bio-ethanol. Two main parameters determine the substitution of gasoline by ethanol as a fuel for individual cars: (i) the share of flex-fuel vehicles in the national fleet; and (ii) 139 the relative price of ethanol compared to gasoline for the final customer. 4.3.1.1 Assessing the Size of the Flex- Fuel Fleet For both the reference and low-carbon scenarios, projections were made of the sales of light vehicles according to type of fuel used, based on ANFAVEA statistics, correlated with GDP and population growth (as estimated in the PNE-2030). We also applied the Winfrey-3 Curve (for phasing out older vehicles) for the current fleet in order to estimate fleet numbers on a year-on-year basis up to 2030. These figures point to a probable fleet in 2030 comprising 1% ethanol-fueled cars, 8% gasoline-fueled cars and 92% for flex-fuel cars, as can be seen in the following table. Table 43 - Light Passenger Vehicle Fleet ( by Type of Fuel ) Reference Year Percentage distribution of the fleet of light passenger vehicles 2010 29% 6% 65% Flex Ethanol Gasoline 2011 32% 6% 62% 2012 35% 6% 60% 2013 37% 5% 58% 2014 40% 5% 55% 2015 43% 5% 53% 2016 46% 4% 50% 2017 48% 4% 48% 2018 51% 4% 45% 2019 54% 4% 42% Technical Synthesis Report | TRANSPORT 2020 57% 3% 39% 2021 61% 3% 37% 2022 64% 3% 34% 2023 67% 2% 31% 2024 70% 2% 28% 2025 74% 2% 25% 2026 77% 2% 21% 2027 81% 1% 18% 2028 84% 1% 15% 2029 88% 1% 11% 2030 92% 1% 8% Source: ANFAVEA / PNE-2030 / Logit (2009) 4.3.1.2 Proportion of Consumption Ethanol / Gasoline by “Flex-fuel� Fleet According to the PNE 2030, the figures for the reference scenario indicate that consumption of bio-ethanol and gasoline in the transport sector (oil equivalent), which at present stands at approximately 37% for ethanol and 63% for gasoline, will be 53% for ethanol and 47% for gasoline by 2030. Given that gasoline will be gradually replaced by ethanol, substantial CO2 emissions savings will result even in the reference scenario. These savings can be considerably increased if the flex-fuel fleet were to use ethanol as 140 the main fuel. Consumer decisions to use gasoline or alcohol in flex-fuel cars is basically economic (i.e. the need to secure the best price on offer). According to Brazil’s National Petroleum, Natural Gas and Bio-fuels Agency (ANP), ethanol was more attractive (in 2009) than gasoline for customers in 17 states, less attractive in 5 states, and equivalent in another 5. The main advantage of gasoline over ethanol is the higher per liter energy content of gasoline (70%). Table 44 below lists the states where the advantage of ethanol was more significant, taking account of the fact that this fuel is more attractive to customers when its price is less than 70% of that of gasoline. Table 44 - States were Alcohol Prices were Competitive with Gasoline Prices (April 2009) State Ethanol price versus Gasoline price São Paulo 53.47% Mato Grosso 56.35% Paraná 56.81% Bahia 60.59% Espírito Santo 61.78% Mato Grosso do Sul 62.62% Alagoas 62.73% Technical Synthesis Report | TRANSPORT Gasoline prices were more attractive mainly in the states of Roraima (where the Source: ANP / Logit (2009) price of ethanol in April 2009 was 80.25% of that of gasoline), Pará (75.44%) and Piauí (75.2%). In Amazonas, Ceará, Paraíba, Rio Grande do Norte Rondônia prices of both fuels were equivalent. The distribution cost of alcohol is a key factor, given that large amounts of sugarcane are required for the process, and that alcohol-processing plants normally need to be installed near plantations for logistical reasons. Without doubt the price of ethanol for the final consumers in centers closer to processing areas is more competitive with the price of gasoline than in other areas of the country. The seven most “competitive� states (Table 44 above) account for 70% of all alcohol consumption in the country (ANP 2009), and for approximately 80% of sugarcane production for alcohol-processing (CONAB-2008 ). The figures show that the prices of ethanol for final consumers vary significantly from region to region, depending on a range of factors. In order to ensure that ethanol is competitive compared to gasoline, the price of which is also subject to many uncertainties, a sensible national fuel-pricing policy needs to be established, targeted at encouraging ethanol consumption to replace gasoline. In the models based on reference scenario figures, we adopted a parameter which envisaged that the ratio of alcohol to gasoline consumption for the flex-fuel fleet would be on average 60% - 40%. This parameter, which we believe is credible and observable 141 in practice, produces numbers which are consistent with the forecast contained in the PNE 2030. For the low-carbon scenario proposed here (the outcome of the above-mentioned “sensible national policy on fuel prices�), this fuel consumption ratio over the years will develop from the 2010 figure of 60% ethanol x 40% gasoline to gradually reach 79% ethanol x 21% gasoline by 2030 – resulting in considerable emissions reductions. Figure 57: Consumption Ethanol x Gasoline for Vehicle Fleet (Total and “Flex-Fuel�) Technical Synthesis Report | TRANSPORT In the low-carbon scenario the increased domestic consumption of ethanol will Source: Logit (2009) result in lower export volumes of this fuel. At the same time, the reduction in domestic gasoline consumption will be possible due to strong demand from first world markets for this fuel. We considered in our analysis of abatement costs (“avoided investments�) that the required investments for implementing the ethanol mitigation measure will be the sum cost of the ethanol not exported from Brazil and the cost of the gasoline that will be exported. 4.3.2 Gains in terms of emissions reductions Table 45 presents data on avoided emissions: total avoided emissions in the transport sector and the light passenger fleet (regional and urban), calculated for the mitigation measure aimed at increasing ethanol consumption at the expense of gasoline in “flex fuel� vehicles and based on the application of an adequate fuel prices policy. Note that the cumulative emissions avoided using the “Low Carbon Ethanol� measure in the 2010-2030 period over the total emissions avoided in the transport sector, will increase from 176 MtCO 2 to 487 MtCO 2 (approximately 36%). 142 Table 45: Avoided Emissions - Low Carbon Ethanol Transport / Gains from applying the measure Gains by applying low-carbon scenario Segment Vehicle Type Absolute % Baseline Absolute % Reference Scenario 2030 2010- 2030 2010- 2030 2010- 2030 2010- Cars and 26, 350 161, 718 49.2 16.4 38, 901 265, 400 58.8 24.4 2030 2030 2030 2030 motorcycles Total Urban 26, 350 161, 718 28.5 9.1 54, 821 398, 302 45.4 19.8 Passenger Total Urban 26, 350 161, 718 26.4 8.5 54, 821 398, 302 42.7 18.6 Cars and 2,363 14, 148 49.2 15.9 2,783 19, 888 53.2 21 Transport Motorcycles Regional 2363 14, 148 6.9 2.7 4,511 37, 469 12.4 6.9 Passenger Total Total Regional 2,363 14, 148 2.1 0.7 11, 161 88, 456 9.4 4.5 Transport Total Transport 28, 712 175, 866 13.7 4.6 65, 982 486, 757 26.7 11.9 Sector Figure 58 illustrates the evolution of emissions by vehicle-type in the reference Source: Logit (2009) scenario, the low-carbon scenario without the ethanol measure and the final low- carbon scenario for the transport sector, which incorporates the low-carbon ethanol measure. It is clear that the effect of the low carbon measure for the latter is fairly Technical Synthesis Report | TRANSPORT substantial and, regardless of the fact that it is included in the “cars and motorbikes� category, it promises the highest potential for emissions reduction of all the low-carbon measures proposed in the present study: a 13.7% gain in comparison with its baseline. Figure 58: Emissions: With and Without the Effects of the Ethanol Measure 143 The comparison between the load figures in 2030 for cars and motorcycles of this Source: Logit (2009) final low-carbon scenario and the reference scenario, shown in Table 46 below, gives a fair idea of the effects on the modal split (by fuel-type) that would arise from the pricing policy recommended for encouraging the use of ethanol: in urban areas, in the baseline scenario, the proportion of the ethanol passenger load will be 51% (299 billion passengers x km), against 49% (284 billion passengers x km) for gasoline. In the low-carbon scenario, this would be a 77% ethanol load (447 billion passengers x km) compared to a 23% gasoline load (136 billion passengers x km). In the regional transport sector, the proportion of the ethanol x gasoline loads in the reference scenario will be 65% for the ethanol loads (162 billion passengers x km), against a 35% gasoline load (89 billion passenger x km). In the low-carbon scenario the ratio will be 85% for ethanol loads (214 billion passengers x km), compared to 15% for gasoline fueled loads (38 billion passengers x km). Table 46: Charging and Emissions, Ethanol - Baseline x Low Carbon Technical Synthesis Report | TRANSPORT Loading million CO 2 passengers x km thousand tons Vehicle Segment Mode Fuel Baseline Low Carbon Type Low- Baseline Carbon 2010- 2010- 2030 2030 Urban Road Cars Ethanol 298, 973 446, 579 0 0 0 0 2030 2030 Passenger Cars + Gasoline 284, 011 136, 404 53, 609 983, 332 27, 259 821, 614 Motorcycles Total Urban 1,593,213 1,593,213 92 ,358 1,770,498 66, 008 1,608,780 Passenger Total Urban - - 99, 856 1,900,904 73, 506 1,739,186 Regional Road Cars Ethanol 162,280 213, 720 0 0 0 0 Transport Emissions Passenger Cars + Mo- Gasoline 89, 221 37, 781 4,807 89, 066 2,444 74, 919 torcycles Regional 660, 909 660, 909 34, 359 521, 148 31, 997 507, 000 Passenger Total Total Emissions Regional Transport - - 110, 363 1,888,943 108, 000 1,874,795 Total Emissions from Transport Sector - - 210, 218 3,789,847 181, 506 3,613,981 Source: Logit (2009) Figure 59 compares the evolution of regional and urban aggregated passenger loads with and without the ethanol low-carbon measure. Note the effects of this measure on the modal split between private vehicles by fuel-type. Figure 59: Loading With and Without the Effect of Low Carbon Ethanol Measure 144 Source: Logit (2009) 4.3.3 “Investments Required� Abatement Curve The required investments for implementing this measure will be the cost of the non- exported ethanol and the cost of gasoline for export. As mentioned above, these will be the “ avoided investments� in the abatement cost analysis. The production costs of ethanol and gasoline used for the economic were the same as those calculated in the sub-themes “Ethanol� and “Cogeneration�, where the values are similar in the baseline year, and where the cost of a barrel of ethanol (for equivalency purposes) will gradually fall until it reaches around 61% of the cost of a barrel of gasoline in 2030. Technical Synthesis Report | TRANSPORT Table 47 shows the export values generated by the measure, the values of “required and avoided� investments, and the values of tCO2 avoided. Note that the present value of the total cost of a tCO2 is US$7.00 - effectively one of the lowest among those calculated for the mitigation measures presented here. Table 47: Investments and Costs of Avoided Tons of CO2 000 barrels Investments in US$ millions Cost per ton Avoided of CO 2 avoided Required Avoided Net Ethanol Emissions (US $ / tCO 2 e) Year Gasoline not in tons Present Present Present Present exported exported of CO 2 Nominal value Nominal value Nominal value Nominal value 145 (2009) (2009) (2009) (2009) 2010 1,612 1,100 293 71 66 49 46 22 20 73.68 68.22 2011 2,789 1,903 507 121 165 95 124 26 41 52.16 81.23 2012 4,914 3,353 893 211 320 171 250 40 70 44.33 77.87 2013 7,324 4,997 1,331 308 523 261 424 47 99 35.58 74.53 2014 6,348 4,332 1,153 261 662 231 549 30 113 26.34 97.54 2015 8,138 5,553 1,479 327 819 302 699 25 120 16.65 80.94 2016 10, 825 7,387 1,967 429 1,009 410 886 20 122 9.92 62.13 2017 14, 149 9,654 2,571 552 1,232 536 1,110 16 122 6.39 47.47 2018 18, 028 12, 301 3,276 692 1,487 683 1,369 9 117 2.68 35.84 2019 22, 473 15, 334 4,083 845 1,768 851 1,662 -6 106 -1.47 25.94 2020 27, 655 18, 870 5,025 1,018 2,074 943 1,943 75 130 14.93 25.92 2021 33, 588 22, 919 6,103 1,211 2,401 1,145 2,254 67 147 10.93 24.10 2022 40, 427 27, 585 7,345 1,426 2,748 1,378 2,594 48 154 6.58 20.96 2023 48, 201 32, 890 8,758 1,661 3,110 1,643 2,961 18 149 2.07 16.98 2024 57, 391 39, 160 10, 428 1,930 3,488 1,956 3,358 -26 129 -2.53 12.41 2025 69, 848 47, 660 12, 691 2,290 3,898 2,381 3,804 -91 93 -7.15 7.35 2026 83, 764 57,155 15, 220 2,707 4,340 2,855 4,294 -148 46 -9.74 3.04 2027 99, 313 67, 765 18, 045 3,162 4,810 3,385 4,823 -223 -13 -12.36 -0.72 2028 116, 800 79, 697 21, 222 3,663 5303 3,981 5,388 -318 -86 -15.00 -4.04 2029 136, 299 93, 002 24, 765 4,208 5813 4,645 5,986 -437 -173 -17.65 -6.99 2030 158, 023 107, 825 28, 712 4,804 6336 5,386 6,612 -582 -276 -20.28 -9.61 Total 967, 908 660, 441 175, 866 31, 896 52, 368 33, 285 51, 137 -1,389 1,231 -7.90 7.00 4.3.4 Barriers and measures to overcome them Technical Synthesis Report | TRANSPORT Brazilian Government policies have traditionally focused on mitigating the effects of high oil prices. From 1970 the fuel market went through four different stages, which portray the various fuel-substitution processes: Stage 1 - Petrol versus diesel: in an attempt to control inflation after the first oil shock, the government readjusted the price of diesel downward (to less than gasoline). Between 1973 and 1977 the price of the latter increased 107% and that of diesel 34%, resulting in a shift to diesel. Stage 2 - Gasoline versus alcohol: after the second oil shock, priority was given to reducing dependence on oil and substituting it for alcohol - development of which had already commenced with the establishment of the government´s Pro-Alcohol program in 1975. This process expanded with the launching of ethanol-fueled vehicles. By 1985, 85% of all vehicles produced in Brazil were dependent on alcohol. However, when oil prices fell, ethanol lost its competitive appeal and producers’ profits declined steeply. In this scenario alcohol production failed to keep up with demand, resulting in a serious shortage by 1989/1990. Stage 3 - Alcohol versus gasoline: attractive prices in the international sugar market, plus the low alcohol prices at the pumps caused much of the cane producers to concentrate on sugar production, leading to a shortage of cane for alcohol production. With the decline in alcohol production, gasoline recovered its position in the fuels 146 market. Stage 4 - Gasoline versus alcohol versus GNV (natural gas for vehicles): in the period 1998-2004, the GNV fleet grew by 71% per annum (used in around 850,000 vehicles). Sales of GNV increased by 61% per annum, amounting by 2004 to 4.3 million m3/day The following scenario was the result of a series of tax policies: In the main markets for GNV in Brazil, the ICMS tax on GNV was lower than that on other fuels; • In Rio de Janeiro, ICMS (tax on merchandise and services) and IPVA (tax on vehicules) on GNV were less than for alcohol; • In São Paulo, ICMS and IPVA were identical (since 2003) for GNV and alcohol. • Given the current problems of natural gas supply in Brazil the GNV market has retracted, while the sales of flex-fuel vehicles has led to increased sales of hydrated alcohol. 4.3.4.1 Establishment of a Fuels Policy In order to ensure the success of the ethanol mitigation measure proposed here, a long-term official policy based on instruments compatible with a strong market economy needs to be established. It is important that this policy guarantees a stable environment for investment and provides assurance to fuel consumers. Technical Synthesis Report | TRANSPORT Experience in the USA, Sweden, France, Denmark and the United Kingdom has shown that four policy instruments can be used to sustain the attractiveness of ethanol for car end-users: Financial incentives: tax abatement and special loan conditions for vehicle purchase; • Regulatory standards: mandatory minimum level of renewable fuels to be used, energy-efficiency emission standards introduced; • Taxation: higher tax rate on polluting fossil fuels (e.g. gasoline); and R&D: incentives for developing more efficient use of alternative fuels. • This policy should be framed on the basis of the following requirements: • to avoid programs requiring either ad hoc or permanent subsidies; to provide customers with flexible choices; • to ensure program sustainability; • to provide support for R&D with a view to ensuring a competitive environment • for renewable fuels, to enhance the comparative advantages of Brazil and to 147 • create new export markets; to ensure transparency in the Derivatives Pricing Policy: e.g. to avoid artificially reducing fossil fuel prices; • to ensure integrated planning by creating a National Automotive Fuels Plan, with achievable targets and clear supply-and-demand fuel scenarios in order • to minimize incompatibilities and resource wastage. A National Fuels Policy should be based on the following principles (in line with Art. 1 of Petroleum Law 9.478/97): to preserve the national interest; to promote development, expand the labor market and enhance energy • resources; • to protect consumer interests with regard to prices, quality and availability of automotive fuels; • to protect the environment and promote energy conservation; to ensure supplies of petroleum products throughout the national territory; • to increase the use of natural gas on an economic basis; • to identify the most appropriate solutions for supplying automotive fuel in • Technical Synthesis Report | TRANSPORT the various regions of the country; • to use alternative energy sources by the economic use of available inputs and applicable technologies, • to promote free competition; to attract investment in fuels production ; • To expand Brazil’s competitiveness in international markets. • • 4.4 Consolidated Results Tables 48 and 49 contain the main emissions indicators for the reference and the low-carbon scenarios of the six mitigation measures for the transport sector: Two measures for regional transport: deployment of investments to promote a new modal distribution for freight transport, by increasing the share of 148 • lower-emission transport modes and introduce a high-speed train service in the Rio de Janeiro-São Paulo corridor ; Three measures for urban transport: deployment of high-capacity public transport systems, diesel-fueled BRTs and building/expansion of metro • systems, establishment of an urban transport demand management system and the establishment of bikeways; and One measure to impact regional and urban passenger transport: the promotion of ethanol to replace gasoline, with the use of “flex fuel� passenger • vehicles. Table 48: Fuel Consumption Trends in the Reference and Low-Carbon Scenarios Scenario 2010- 2016- 2021- 2026- Total Reference Ethanol (million m 3) 89.14 120.91 188.90 295.13 694.07 Fuel Type 2015 2020 2025 2030 Scenario Gasoline (million m ) 3 158.52 137.79 132.66 106.36 535.32 Diesel (million m 3) 245.45 228.83 249.21 270.25 993.74 Aviation fuel (million m ) 3 26.14 26.98 33.59 42.25 128.97 Electricity (GWh) 11.30 14.18 18.77 25.13 69.38 Low-carbon Ethanol l (million m 3) 88.97 114.54 166.62 232.79 602.92 scenario Gasoline (million m 3) 166.54 158.25 178.36 202.52 705.67 Diesel (million m 3) 249.43 240.31 273.52 312.24 1075.51 Technical Synthesis Report | TRANSPORT Aviation fuel (million m ) 3 26.29 27.59 34.46 43.37 131.71 Electricity (GWh) 9.45 8.78 9.71 10.75 38.70 % Change Scenario: Ethanol 0.18 5.56 13.37 26.78 15.12 Low Carbon x Gasoline -4.82 -12.93 -25.62 -47.48 -24.14 Reference Scenario Diesel -1.60 -4.78 -8.89 -13.45 -7.60 Aviation Fuel -0.56 -2.22 -2.52 -2.58 -2.08 Electricity 19.51 61.47 93.30 133.81 79.30 The consumption patterns of the various types of fuels in the low-carbon scenario Source: Logit (2009) compared to the reference scenario, illustrated in Table 48, display the effect of the mitigation measures proposed: the gradual increase in ethanol consumption, compared to a gradual reduction in gasoline consumption, reflects the mitigation measure aimed at • increasing the share of ethanol consumed by the flex-fuel fleet ; decreased diesel consumption due to the mitigation measures covering the modal shift recommended for regional freight transport and the • implementation of high-capacity transport systems in Brazilian cities; the reduction in aviation fuel consumption will be due to passengers transferring from planes to the high-speed train (TAV) in the Rio de Janeiro- 149 • São Paulo corridor; and the increase in electricity consumption reflects the direct effect of the modal shift for regional freight transport, the implementation of high-capacity • public transport systems in the cities and the introduction the TAV. Table 49: Evolution of Direct Emissions (in MtCO2) in the Reference and Low Carbon Scenarios Urban Transport Regional Transport Grand Scenario Period 2010-2015 496.64 0.00 496.6 367.76 28.02 1.56 0.37 64.68 462.4 Road Rail Total Road Rail Water Pipeline Air Total Total 2016-2020 481.07 0.00 481.1 345.55 25.79 1.74 0.33 67.87 441.3 959.0 Reference 2021-2025 546.11 0.00 546.1 380.89 27.87 2.18 0.35 84.77 496.1 922.4 Scenario 2026-2030 613.67 0.00 613.7 423.17 30.77 2.50 0.38 106.69 563.5 1,042.2 Total 2,137.5 0.0 2,137.5 1,517.4 112.5 8.0 1.4 324.0 1,963.3 1,177.2 2010-2015 472.89 0.00 472.9 364.44 31.32 2.05 0.39 64.32 462.5 4,100.7 2016-2020 428.89 0.00 428.9 328.37 31.73 2.71 0.36 66.36 429.5 935.4 Low-Carbon 2021-2025 438.12 0.00 438.1 343.29 36.85 3.72 0.39 82.64 466.9 858.4 Scenario 2026-2030 399.29 0.00 399.3 366.42 40.80 4.27 0.43 103.94 515.8 905.0 Total 1,739.2 0.0 1,739.2 1,402.5 140.7 12.7 1.6 317.3 1,874.8 915.1 2010-2015 23.75 0.00 23.7 3.32 -3.30 -0.50 -0.02 0.36 -0.1 3,614.0 2016-2020 52.18 0.00 52.2 17.18 -5.94 -0.97 -0.03 1.51 11.7 23.6 Technical Synthesis Report | TRANSPORT Avoided 2021-2025 107.99 0.00 108.0 37.61 -8.98 -1.53 -0.04 2.13 29.2 63.9 emissions 2026-2030 214.38 0.00 214.4 56.75 -10.02 -1.77 -0.05 2.75 47.7 137.2 Total 398.3 0.0 398.3 114.9 -28.2 -4.8 -0.1 6.8 88.5 262.0 486.8 Source: Logit (2009) Figure 60: Emission and Mitigation of Urban and Regional Transport 2010 through 2030. 150 Technical Synthesis Report | TRANSPORT Table 49 presents the emissions by mode and geographical situation in both the Reference and Low Carbon Scenarios. Figure 60 presents these same emission values by state. Figure 61: Growth in Transport Fleet, 2007 to 2030 151 Technical Synthesis Report | TRANSPORT Figure 62: Changes in Passenger Load 152 Technical Synthesis Report | TRANSPORT The low-carbon scenario for the transport sector is built by combining the mitigation options proposed for regional and urban transport. Emission reductions are achieved by shifting part of the freight load and passenger trips from carbon-intensive to low- or zero-carbon transport modes (Figures 61 and 62). The most significant modal shifts are from truck to rail (freight transport) and from use of private vehicles to BRT and Metro, along with measures for travel demand management (passenger transport). Figure 63: Comparison of Modal Distribution of Freight Load, 2008–30 153 Technical Synthesis Report | TRANSPORT Figure 64: Comparison of Modal Distribution of Passenger Load, 2008–30 154 These modal shifts reflect an important emissions reduction, totaling about 7.3 Technical Synthesis Report | TRANSPORT percent over the study period or 302 Mt CO2e. However, another significant mitigation potential of around 4.3 percent could be harvested over the same period by increasing the use of ethanol, and another 1.5 percent by managing the demand for trips (Figure 65). In this way, emissions would be reduced more than 13 percent. Figure 65: Emissions-reduction Potential in the Transport Sector, 2008–30 155 As result, the increase in sector emissions would be reduced from 60 percent in the reference scenario to only 18% in the low-carbon scenario. That is, from 247 in the reference scenario to 182 Mt CO2 per year in the low-carbon scenario in 2030, compared to 154 Mt CO2 in 2010, thereby avoiding a total of 487Mt CO2e, or 23 Mt CO2e per year on average (Table 49). The potential for emissions reduction appears limited, given that biofuels, which are low carbon, play a large role in the reference scenario. For this reason, the study simulated the sector emissions that would result if biofuels were substituted by fossil fuels (mainly gasoline). In that case, reference-scenario emissions would be inflated by 50 percent in 2030 (45 percent in cumulative terms over the 2010–30 period), growing from 143 Mt CO2 in 2008 to 371 Mt CO2 per year in 2030. By comparison, emissions in Technical Synthesis Report | TRANSPORT the low-carbon scenario would be 51 percent lower in 2030 than in the “fossil-fuel� scenario (26% lower versus the reference scenario) (Figure 66) and 39 percent in cumulative terms over the 2010-30 period, that is 1.65 Gt CO2e less over the study period. 156 Load (Mt CO2e) Technical Synthesis Report | TRANSPORT (Mt * km or pass * km/year) Reference Reference scenario Low- carbon Low- carbon Avoided emissions Load type Transport mode Vehicle type Fuel type scenario 2030 scenario 2030 scenario 2030 2010–2030 2030 Urban freight Road Truck Diesel 49,151 49,151 7.6 7.6 0.0 Total urban freight 49,151 49,151 7.6 7.6 0.0 Bus 730,799 308,538 43.1 17.5 215.5 Diesel BRT 102,332 465,301 2.1 9.6 -72.3 Road Car Ethanol 364,894 446,579 - - Urban 0 passengers Car and Gasoline 347,346 136,404 motorbike 66.16 27.26 265.4 Metro 55,385 211,262 0.0 0.0 0.0 Rail Electricity Train 50,699 25,129 0.0 0.0 0.0 Total urban passengers 1,651,455 1,593,213 111.42 54.59 408.6 GHG emissions from urban transport - 119.01 62.18 408.6 Rail Train Diesel 552,364 703,854 6.6 8.3 -25.4 Waterway Boat 81,349 133,503 0.5 0.9 Regional -4.5 and Low-carbon Scenario reight Pipeline Pipeline 24,727 26,621 0.1 0.1 -0.1 Road Truck 1,274,440 1,113,926 77.3 65.6 115.1 Total freight 1,932,880 1,977,904 84.5 74.9 85.1 Car Ethanol 176,485 213,72 0.0 0.0 0 Road Car and motorbike Gasoline 97,031 37,781 5.23 2.44 19.9 Regional passengers Bus Diesel 276,915 266,675 7.3 6.8 2.9 Table 50: Transport-sector Load and GHG Emissions in the Reference Aviation Air Plane 127,569 121,641 28.7 28.0 kerosene 8.3 Train TAV Eletricity - 21,092 0.0 0.0 0 Total regional passenger 678,001 660,909 41.23 37.25 31.0 GHG Emissions from regional transport - 125.76 112.12 116.0 TOTAL TRANSPORT-SECTOR EMISSIONS 244.77 174.29 524.6 Figure 66: Comparison of Emissions in Reference, Low-carbon, and “Fossil-fuel� Scenarios, 2008–30 157 Implementing the proposed low-carbon scenario triggers two main challenges: (i) coordination and (ii) mobilization of additional financing. Because of the broad spectrum of public and private actors involved, harmonization of the many diverse initiatives represented requires federal government coordination. Furthermore, existing funding mechanisms may need to be supplemented by additional sources of Technical Synthesis Report | TRANSPORT financing to leverage the large volume of investment required by such capital-intensive infrastructure. Improved coordination is needed for both urban and regional transport. For example, the Ministry of Cities could offer municipalities, which manage their own transport systems, incentives to adhere to broader mass-transport plans under the National Mobility Plan (PlanMob). For regional transport, the Ministry of Transport (MT), under the PNLT, could facilitate the integrated development of new infrastructure and transport-services concessions. 5 General Conclusions The complexity of the transport sector justifies the use of bottom-up estimates of freight and passengers, fuel consumption and emissions. This approach requires a set of data and information that is often unavailable. Although extrapolations are often necessary, they should not affect the reliability of 158 estimates when performed in a consistent manner. With regard to institutional issues, there is an urgent and real need for greater integration between Federal, State and Municipal agencies. Notwithstanding integration, the formal structure of the transport sector complicates coordinated decision-making between the practitioners responsible for the different transport modes. The approach to regional and urban transport needs to be differentiated, in view of their different operating and management characteristics. This implies examining in greater detail each mode of transport to avoid possible distortions in projections. The problems faced by urban and regional transport are also very different. The much larger volume of vehicles in cities generates substantially more localized impacts. This should not however detract from efforts to accurately explore and assess regional transport conditions. The need for modal integration in the regional freight sector is crucial. Measures to ensure adequate logistics, and operational and technological up-scaling will undoubtedly contribute to reducing energy consumption and emissions in this sector. The high infrastructure-building costs at both the urban and regional levels add another level of complexity. Required additional resources not earmarked in the Federal, State and Municipal budgets make it necessary to seek international and private-sector funding. In this respect, the introduction of PPPs and concessions awarded to private highway-operators have contributed to increasing the volume of investments in the sector. The difficulty of monitoring this complex sector, and the multiplicity of regulatory Technical Synthesis Report | TRANSPORT agencies, management agencies and operators involved, highlight the pressing need to rationalize resources and ensure their efficient and effective deployment. All of society could benefit if the technical design of policies and the deployment of projects are effectively coordinated, while taking into account the substantial political component inherent in this sector. A rationally-organized functioning transport system can contribute much to the economic and social development of the country, promoting citizenship and ensuring a better quality of life for all, whilte at the same time preserving and enhancing the regional and urban environment. Bibliography: Cervero, R. (1998) The Transit Metropolis. Island Press, Washington, D.C.. Cervero, R. (2005) Accessible Cities and Regions: A Framework for Sustainable Transport and Urbanism in the 21st Century. Working Paper UCB-ITS–VWP– 159 2005-3, UC Berkeley Center for Future Urban Transport. Cherry, C. (2005) China’s Urban Transportation System: Issues and Policies Facing Cities. Working Paper UCB-ITS–VWP– 2005-4, UC Berkeley Center for Future Urban Transport. IPCC (1996) Greenhouse Gas Inventory Reporting Instructions – IPCC Guidelines for National Greenhouse Gas Inventories. Vol 1, 2, 3. 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