URBAN GROWTH MODEL AND SUSTAINABLE URBAN EXPANSION FOR THE HASHEMITE KINGDOM OF JORDAN Final Report May, 2018 2 CAPSUS collaborators Carmen Valdez Carolina Altamirano César Castillo Daniela Evia Guillermo Velasco José Díaz Judá Jiménez Karla Hernando Miguel Luis Ricardo García Ricardo Ochoa Tania Guerrero Urban Planning Technology S.C. de C.V. World Bank team Ellen Hamilton Bjorn Philipp Lina Abdallah Yuan Xiao Myriam Ababsa Ghada Shaquour Jordanian Government Ministry of Planning and International Cooperation, Ministry of Municipal Affairs, Housing and Urban Development Corporation, Department of Statistics, Department of Land and Survey, Land and Transport Regulatory Commission, and Municipalities of Amman, Irbid, Mafraq, Russeifa, and Zarqa. This report was prepared for the World Bank and funded by the Korean Green Growth Trust Fund. Prado Sur 274 Lomas de Chapultepec Miguel Hidalgo Ciudad de México, 11000 ideas@capsus.mx www.capsus.mx T: (52 55) 4744 48 32 ii Contents Executive Summary 1 1 Introduction 5 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Methodology 9 2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Stage 1 - Identify urban concerns and possible solutions . . . . . . . . . . 12 2.2.1 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Stage 2 - Policy levers definition . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.1 Urban growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Solid waste management improvements . . . . . . . . . . . . . . . 20 2.3.3 Public transportation expansion . . . . . . . . . . . . . . . . . . . . . 21 2.3.4 Green building code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.5 Clean energy generation . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.6 Efficient public lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.7 Reduce hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.8 Public space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Stage 3 - Data gathering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5 Stage 4 - Methods development . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.6 Stage 5 - Scenario development . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.7 Stage 6 - Results dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.8 BAU Expansion models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.8.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.8.2 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.8.3 Definition of urban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.8.4 Expansion model results . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.9 Population settlement modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3 Adaptation to Jordan 47 3.1 Population projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 Data limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.3 Population and housing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 CONTENTS 3.4 Job density data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.5 Housing densities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4 Results 53 4.1 Urban growth scenarios results . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5 Discussion 61 5.1 Amman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.1.1 Land consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.1.2 Infrastructure costs and municipal service costs . . . . . . . . . . 63 5.1.3 Proximity to urban services and amenities . . . . . . . . . . . . . . 65 5.1.4 Proximity to public transport . . . . . . . . . . . . . . . . . . . . . . . 65 5.1.5 Water and energy consumption . . . . . . . . . . . . . . . . . . . . . 66 5.1.6 GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.2 Irbid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.2.1 Land consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.2.2 Infrastructure costs and municipal service costs . . . . . . . . . . 70 5.2.3 Proximity to urban services and amenities . . . . . . . . . . . . . . 71 5.2.4 Proximity to public transport . . . . . . . . . . . . . . . . . . . . . . . 72 5.2.5 Water and energy consumption . . . . . . . . . . . . . . . . . . . . . 72 5.2.6 GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.3 Mafraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.3.1 Land consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.3.2 Infrastructure costs and municipal service costs . . . . . . . . . . 76 5.3.3 Proximity to urban services and amenities . . . . . . . . . . . . . . 77 5.3.4 Water and energy consumption . . . . . . . . . . . . . . . . . . . . . 78 5.3.5 GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.4 Russeifa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.4.1 Land consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.4.2 Infrastructure costs and municipal service costs . . . . . . . . . . 82 5.4.3 Proximity to urban services and amenities . . . . . . . . . . . . . . 84 5.4.4 Proximity to Public transport . . . . . . . . . . . . . . . . . . . . . . 88 5.4.5 GHG emissions and water and energy consumption . . . . . . . . 88 5.5 Zarqa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.5.1 Land consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.5.2 Infrastructure costs and municipal service costs . . . . . . . . . . 92 5.5.3 Proximity to urban services and amenities . . . . . . . . . . . . . . 93 5.5.4 Proximity to public transport . . . . . . . . . . . . . . . . . . . . . . . 96 5.5.5 GHG emissions, water and energy consumption . . . . . . . . . . . 96 6 Conclusions 99 6.1 Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.2 Further work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 A Indicators methodology 103 A.0.1 Land consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 iv CONTENTS A.0.2 Population density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 A.0.3 GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 A.0.4 Energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A.0.5 Energy consumption for commuting . . . . . . . . . . . . . . . . . . 108 A.0.6 Energy consumption for water distribution . . . . . . . . . . . . . . 110 A.0.7 Energy consumption for public lighting . . . . . . . . . . . . . . . . 111 A.0.8 Energy consumption for solid waste collection . . . . . . . . . . . 112 A.0.9 Energy consumption for dwellings . . . . . . . . . . . . . . . . . . . 115 A.0.10 Infrastructure costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 A.0.11 Infrastructure costs for urban expansion . . . . . . . . . . . . . . . 117 A.0.12 Infrastructure costs for upgrading existing capacity . . . . . . . . 118 A.0.13 Water consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 A.0.14 Job proximity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 A.0.15 Proximity to public transport . . . . . . . . . . . . . . . . . . . . . . . 122 A.0.16 Services proximity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 A.0.17 Municipal service costs . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 A.0.18 Exposure to hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 B Policy levers 127 C Data and assumptions 131 Bibliography 146 v CONTENTS vi Executive Summary The Urban Growth Scenarios for the Hashemite Kingdom of Jordan is a project developed in coordination with the Ministry of Planning and International Cooperation and the Ministry of Municipal Affairs. The project outlines sustainable development paths for five Jordanian cities: Amman, Irbid, Mafraq, Russeifa, and Zarqa. It is funded by the Korean Green Growth Trust Fund through the World Bank. The project has two main objectives: 1) develop urban growth scenarios that visualize the impacts of different public policies on the environmental, social, and economic dimensions of Jordanian cities, and 2) assess the advantages and disadvantages of the various combinations of public policies, projects, and conditions to reach a consensus on the most beneficial development path for each city. Urban growth scenarios are planning tools that facilitate the comparison and understanding of the “possible futures” of a city through numerical information. Planners and decision-makers can use the outcomes to create consensus with stakeholders, request funding from cooperation agencies, test rough ideas, or disseminate the potential benefits of their projects. This methodology involves three main steps: identifying problems and solutions, estimating indicators, and disseminating the results. The first step involves working closely with the local governments to understand the urban concerns of each city and the solutions that they are currently exploring. More than seven public policies and projects were modeled for each Jordanian city for the year 2030, including those relating to land use, transportation, energy, and waste management. Their potential effects were evaluated through 17 indicators, which cover the environmental, social, and economic dimensions. Policies were organized in a set of three scenarios, which represent three levels of effort (and potential impact): a Business-as-usual scenario (BAU scenario) that follows the historical growth of the city; a Moderate scenario that adheres to the city’s Master Plan and assumes that projects planned for each city will take place; and a Vision scenario that couples compact urban growth policies with a more ambitious implementation of projects and policies. The methodology used to forecast the BAU scenario is based on machine learning algorithms. These algorithms learn from the urban characteristics of the past to Figure 1: Scenarios and the policy levers they include predict the areas that have the highest probability of becoming urban in the forecasted year. The urban footprint of the other scenarios is forecasted using programmatic algorithms, which allocate the new population according to the maximum housing densities indicated in the Master Plans and defined priority areas. Results reveal that compact growth policies yield the highest environmental, social, and economic benefits. The expansion of the urban footprint has a cross-sector impact, affecting greenhouse gas emissions and energy consumption from providing urban services due to the kilometers of streets to serve, achieving a reduction between 4 and 14% compared to the BAU scenario. The investment and running costs associated with such policies can be reduced by 12 to 49%. Additionally, the accessibility of the population to jobs, schools, parks, etcetera improves considerably. In order to achieve compact growth, policy levers include reducing the vacant housing rate and prioritizing housing in areas close to public transportation and high job density. Furthermore, the results indicate that adding mandatory water efficiency measures to the building codes can save up to 6% of the annual volume of water supplied to urban dwellings. Replacing public lighting with LED bulbs can potentially save around 5% of the municipal money spent on municipal services. The Vision scenario (the best path for development) can potentially increase the population that could walk to work by 38%, reduce the annual costs of providing municipal services by 20%, and prevent 1.1 MtCO2 eq per year in the five cities, compared to the BAU scenario. This results in a 4% reduction in annual greenhouse gas emissions in Jordan. Results were disseminated through meetings with decision–makers and technical staff at the municipalities and through two national workshops. All of the disseminated material, including a web-based results visualization tool, is available 2 on the project’s website: http://jordan.capitalsustentable.com.mx/ Figure 2: Main results by scenario and city 3 CONTENTS 4 1. Introduction 1.1 Background The Urban Growth Scenarios for the Hashemite Kingdom of Jordan (2015-2030) is a project developed in coordination with the Ministry of Planning and International Cooperation and the Ministry of Municipal Affairs. The project outlines sustainable development paths for five Jordanian cities: Amman, Irbid, Mafraq, Russeifa, and Zarqa. The aim of this work is to provide relevant and objective information on the advantages and disadvantages of various paths of urban development. The current project builds upon previous work developed for an urban growth scenario tool in Mexico, called Metropolitan Profile. The Metropolitan Profile tool analyzed the physical, economic, environmental, and demographic conditions of 59 metropolitan areas in Mexico to produce urban growth scenarios based on different land consumption patterns. Scenarios revealed the impact of urban form in terms of energy, greenhouse gas (GHG) emissions, and costs, providing numeric support to demonstrate the benefits of moving from urban expansion to compact development. The Ministry of Agriculture, Territory and Urban Development (SEDATU) used the study as a reference to create consensus and modify the General Law on Human Settlements to include the concept of a compact city as one of its principles, laying the foundation for sustainable growth patterns in Mexico. This study is part of a set of efforts carried out by the World Bank to support the Government of The Hashemite Kingdom of Jordan. Through scenario modelling, Jordanian cities can discuss the cross-sector effects of public policies, create consensus, and make informed decisions that contribute to the sustainable development objectives of the Kingdom. 1.2. OBJECTIVE 1.2 Objective The overall objective of the study is to compare the environmental, social, and economic impacts of different urban growth paths for five Jordanian cities to guide the identification, preparation, and implementation of sustainable urban investment projects. The cities included in the present study area Greater Amman Municipality, Greater Irbid Municipality, Greater Mafraq Municipality, Russeifa Municipality, and Zarqa Municipality. Through the completion of the Urban Growth Model and Sustainable Urban Expansion for The Hashemite Kingdom of Jordan, governments are expected to: • Create consensus with stakeholders. • Request funding from cooperation agencies. • Disseminate the potential benefits of their projects. • Test rough ideas and present solid proposals. • Convince others by providing numerical data. 1.3 Context Jordan encompasses 89,342 km2 along the East bank of the Jordan River in the heart of the Middle East. The country suffers from exceptional scarcity of natural resources, particularly water, energy, and arable land [1]. Jordan is considered one of the world´s five poorest countries in terms of water resources. The population in Jordan increased by 87% from 2004 to 2015, from 5,074,242 to 9,531,712 inhabitants [2], according to the Department of Statistics (DOS). This demographic growth is mainly due to an influx of refugees from Syria, and in previous years from Iraq; this has created enormous challenges for the government in integrating the refugees and fulfilling their basic needs. Thus, Jordanian cities experienced rapid urbanization, resulting in a loss of balance between the built and natural environment and a reduction of agricultural land due to random urban expansion. Jordan is considered one of the most urbanized countries, with 90% of the population living in urban areas; however, 16% of the urban population lives in informal settlements [3]. Poverty and unemployment (especially among young people) are two of the most important challenges the country is facing despite significant government efforts. Other unresolved challenges include the development gap between governorates and the lack or laxity of public policies regarding land use. The lack of public transport systems in Jordanian cities is one of the most significant challenges to be addressed [1]. The most common transportation modes in Jordanian cities has shifted to personal vehicles and shared taxi cabs, increasing the number of vehicles used for transportation. Between 1981 and 2006, the number 6 CHAPTER 1. INTRODUCTION of automobiles using gasoline experienced a sixfold increase, while the rise in the number of diesel automobiles was tenfold [4]. 7 1.3. CONTEXT 8 2. Methodology The methodology used in this study involves urban growth scenario modelling. This methodology was designed to provide key information for city managers and simplify their decision-making process. This is done by forecasting how stakeholders’ present decisions might impact the city’s future conditions. The scenarios provide a platform for understanding how integrated solutions across varying levels of government and different sectors could be successful. Additionally, they help various actors understand their interdependency, creating a consensus among a wide range of stakeholders. The performance of each scenario is measured on the basis of a variety of indicators, which are calculated from the characteristics of the city (input variables). For example, indicators such as proximity to public transport and job proximity depend on the population density, the public transport system, and the employment density across the city. Figure 2.1 depicts the conceptual framework of the scenario modelling. The current year, or the year with the latest available information, is referred to as the base year, and the forecasted year is called the horizon year. For this project, the base year is 2015 and the horizon year is 2030. The first step is to model a Business-as-usual (BAU) scenario. This scenario forecasts the future characteristics of the city if the past repeats itself, using machine-learning algorithms. Such algorithms utilize statistical spatial data to determine the drivers of urban expansion and predict which areas have a high probability of becoming urban in the horizon year [5]. The detailed methodology can be read in Section 2.8. Alternative scenarios can be created to assess what will probably happen if certain interventions take place. These interventions can range from investment projects like public transportation systems, to changes in housing policy, urban planning instruments, or building construction codes. These interventions are known as policy levers, as they trigger changes in the performance of the city. The main policy levers tested for Jordanian cities were those that can control land use and urban expansion. Programmatic algorithms were used to model how and where the population will settle in the horizon year according to the maximum housing densities allowed in the Master Plans and the policies that prioritize settling in specific areas or housing typologies. Other levers related to transportation and urban infrastructure, as well as efficiency and clean energy measures, were then combined with the land use patterns to create the main scenarios for analysis. All data processing and indicator calculations were carried out using Urban Performance, a tool developed as a part of this project by CAPSUS SC and Urban Planning Technology SA de CV. Urban Performance is based on open source software PostgreSQL, PostGIS, and Python. Urban growth scenario modelling for Jordan was developed in six main stages. The urban concerns of each city were discussed and analyzed in close coordination with local stakeholders to identify the indicators that best assess the performance of each city. Local stakeholders also provided information about interventions they were planning, which were modelled as policy levers to create different scenarios. A series of meetings and workshops with government officials and technical specialists was designed with this objective. Figure 2.2 indicates the distribution of these meetings throughout the six stages. Figure 2.1: Conceptual framework for the urban growth scenarios Key definitions and methods are described in the following sections. 10 CHAPTER 2. METHODOLOGY Figure 2.2: Project Stages 2.1 Definitions Urban concerns are the most significant problems that a city faces. These “challenges” can be derived from different sources of information, such as a literature review, benchmark comparison, and interviews with experts. Urban concerns are strongly based on the perspectives of the local stakeholders in each municipality. Possible solutions are urban development projects, instruments, or policies that local stakeholders envision as potential interventions to deal with urban concerns. Possible solutions can be exposed as a detailed plan or a conceptual idea. Indicators are numeric values that describe the conditions and issues of a city. Indicators simplify the evaluation, monitoring, and communication of the status of a city and are the key for integrated urban planning. Indicators can be used to assess (or model) how a city is (or will be) dealing with a specific urban concern. Scenarios are “possible future conditions” that can be projected using statistical models and spatial data. Developing scenarios helps forecast what a city will be like in the future. To do so, practitioners analyze historical data and identify the key factors that led the city to its present conditions. Base year is the present year, or the year with the latest available information. Defining a base year is essential in scenario modelling because it represents a starting point for the forecasting process. 11 2.2. STAGE 1 - IDENTIFY URBAN CONCERNS AND POSSIBLE SOLUTIONS Horizon year is a selected year in the future in which scenarios take place. Defining a horizon year is key to avoiding bias when comparing scenarios. The definition of a horizon year strongly depends on the availability of historical data. Normally, the range between the base year and the horizon year is equal or less than the range of the historical data. Urban growth scenarios are planning tools that facilitate the understanding of various possible outcomes related to specific urban policies. These policies might include transportation or infrastructure investment plans, land use changes, and housing policies, among others. Scenarios contribute to an efficient communication of urban initiatives. They rely on indicators, which provide a “common language” based on numerical data and represent a consistent, transparent, and systematic approach to urban concerns. Scenarios can serve as a dialog platform between stakeholders. They can be used to assess synergies between initiatives, develop integrated solutions, understand interdependency of possible solutions, or create a multi-level and multi-sectorial consensus, among others. Policy levers are abstractions of real world investment projects, instruments, or public policies that can be modelled to test their potential impacts in a range of indicators. Policy levers are the abstraction of the “possible solutions.” By activating (or deactivating) a lever, stakeholders can visualize the potential impact of implementing a possible solution in a simulation platform. Policy levers are commonly designed to model the effects of a decision that is made in the present on a range of indicators that describe the city in the future—i.e., in the horizon year. 2.2 Stage 1 - Identify urban concerns and possible solutions The objective of the first stage was to identify the main urban concerns for each city and the possible solutions to these problems in order to define the indicators that best reflect how the possible solutions tackle the urban concerns. A series of workshops with the city officials was developed to present the objectives, scope, and limitations of the project. The meetings concluded with working sessions in which local stakeholders completed and improved preliminary lists of urban concerns and indicators previously drafted by this team from a literature review. Based on the workshops, a final list was defined by selecting only elements that met the following criteria: 1. An urban concern can be abstracted into and measured by one or more indicators. 2. The effects of possible solutions can be abstracted into one or more policy levers. 3. All indicators and policy levers can be modelled because either: 12 CHAPTER 2. METHODOLOGY (a) data from local, regional or national sources is available, (b) data from international sources can be adapted, or (c) reasonable assumptions can be made. 2.2.1 Indicators Urban indicators are numeric values that describe the conditions and issues of a city. Indicators simplify the evaluation, monitoring, and communication of the status of a city and are key for integrated urban planning. The list of indicators used for this project was defined in terms of assessing the sustainability of each scenario and tailored to the urban concerns specific to each city. The urban concerns repeatedly suggested during the workshops were related to road congestion, solid waste management, energy consumption and generation, accessibility to parks and schools, and pollution sources like the phosphate piles in Russeifa and artisanal workshops in Mafraq. An initial list was drafted after the first set of workshops and crafted with the stakeholders during the second set of workshops and meetings. Indicators such as water consumption, energy consumption, and GHG emissions measure the environmental aspect of sustainability, and the social sphere is covered by indicators such as proximity to schools, jobs, health facilities, and other urban services. Local authorities expressed their interest in the location of parks and public open spaces in relation to the population; therefore, proximity to this type of urban amenities was added as an indicator. For this project, proximity to worship places was added, recognizing their importance in the daily life of most Jordanians. Furthermore, indicators such as population density and vacant housing rate have been integrated to reflect current problems of the housing market in Jordan. Artisanal industries such as stone cutters, and industrial waste such as the phosphate waste piles in Russeifa, were identified as sources of air pollution; thus, it was necessary to measure the percentage of the population exposed to these hazards. Bearing in mind that investment and maintenance costs can be an overwhelming burden for local governments, infrastructure costs and the cost of municipal services are also included as indicators. The full list of indicators used for this study can be found in Table 2.1, and a description of each is provided in the following paragraphs. Index cards for each indicator can be found in Appendix A. They contain a description of the indicator, the calculation methods and equations, units, and the information sources used in the present study. Appendix C lists the data used to calculate the indicators in each city. 13 2.2. STAGE 1 - IDENTIFY URBAN CONCERNS AND POSSIBLE SOLUTIONS Table 2.1: Indicators list Indicator Units 1 Land consumption [km2 ] 2 Population density [population/km2 ] 3 GHG emissions [kgCO2 eq/capita*annum] 4 Energy consumption [kWh/capita*annum] 5 Infrastructure costs [JD] 6 Municipal service costs [JD] 7 Water consumption [m3 /capita*annum] 8 Job proximity [%] 9 Public transport proximity [%] 10 School proximity [%] 11 Public space proximity [%] 12 Sports facility proximity [%] 13 Worship place proximity [%] 14 Health facility proximity [%] 15 Public building proximity [%] 16 Cultural space proximity [%] 17 Exposure to hazards [%] Land consumption. The amount of land predicted to change from natural habitats or agricultural uses into urban human settlements between the base year and the horizon year, measured in square kilometers. Population density. The number of inhabitants per built-up area, expressed as inhabitants per square kilometer. GHG emissions. The average GHG emissions released annually per capita related to energy consumed for public lighting, municipal water supply, solid waste collection, electricity in dwellings, and commuting (public transportation and private vehicles). Emissions are measured as the total kilograms of carbon dioxide equivalent emitted per person per year. Energy consumption. The total average amount of energy consumed per person per year for public lighting, municipal water supply, solid waste management, electricity in dwellings, and commuting by public transportation and private 14 CHAPTER 2. METHODOLOGY vehicles. Energy consumption is expressed as total kilowatt hours of energy consumed per person per year. The energy consumption indicator is the addition of 5 sub-indicators: • Energy consumption for commuting [kWh/capita*annum]: The total average amount of energy consumed per person per year for commuting within the city via public transportation or private vehicle. • Energy consumption for water distribution [kWh/capita*annum]: The per capita annual amount of energy required to supply the volume of water demanded by the city’s dwellings. The calculation considers the energy embedded in water treatment and distribution, as well as water losses due to municipal network leakages. • Energy consumption for public lighting [kWh/capita*annum]: The annual average amount of energy consumption for public lighting per person. • Energy consumption for solid waste collection [kWh/capita*annum]: The average per capita amount of energy consumed annually by the solid waste management system of the city, including collection, transportation, and energy consumed in the landfill and transfer stations. • Energy consumption for dwellings [kWh/capita*annum]: Average annual housing electricity consumption per capita. The calculation is not connected to household income; only the city’s average electricity consumption is taken into consideration. Infrastructure costs. The total investment required to build the roads, water network, sewer network, public lighting, and electricity grids for the square kilometers that the city will expand, and the investment required to increase the capacity of the existing networks where the urban population will have a twofold increase. It is measured as the total Jordanian dinars (JD) required to invest between the base year and the horizon year. The Net Present Value is not taken into consideration; all costs used in the calculation are from the base year. The infrastructure costs indicator is the addition of 2 sub-indicators: • Infrastructure costs for urban expansion (expansion) [JD]: The total cost to build the roads and water, sewage, public lighting, and electricity networks within the km2 that the city is estimated to grow. • Infrastructure costs for upgrading existing capacity (infill) [JD]: The total cost to upgrade the water, sewage, and electricity networks within the areas of the existing city that are estimated to experience a twofold increase in their population. 15 2.2. STAGE 1 - IDENTIFY URBAN CONCERNS AND POSSIBLE SOLUTIONS Water consumption. The total average volume of water consumed per capita in the city’s households in one year. The calculation is not connected to household income; only the city’s average water consumption is taken into consideration. Job proximity. The percentage of the population that lives within 1,000 m from areas in the city with high job density. Public transport proximity. The percentage of the population that lives within walking distance of a public transportation station. Walking distance is considered 800 m for structured transportation systems like a Bus Rapid Transit (BRT) or subway, and 300 m for buses and similar modes. School proximity. The percentage of the population that lives within a radius of 700 m from an elementary school. Public space proximity. The percentage of the population that lives within a radius of 700 m from a public space or park. Sports facility proximity. The percentage of the population that lives within a radius of 1,000 m from a sports facility. Worship place proximity. The percentage of the population that lives within a radius of 1,000m from a Mosque, Church, Synagogue, or other place of worship. 16 CHAPTER 2. METHODOLOGY Health facility proximity. The percentage of the population that lives within a radius of 1,500m from a hospital, clinic, or doctor. Public building proximity. The percentage of the population that lives within a radius of 2,000 m from the city’s town hall or a public service office. Cultural space proximity. The percentage of the population that lives within a radius of 1,000 m from a cultural facility, community center, library, social facility, or theatre. Exposure to hazards. The percentage of the population that is exposed to hazardous pollutants from living near human-made stationary sources of pollution. Pollutant concentrations are not measured, only proximity. All proximity indicators are calculated by creating a buffer of the corresponding radius around the buildings being analyzed, then adding up the population that lives within that radius. Due to technical constraints, distances are not measured using the street networks. Figure 2.3 is a graphical representation of the databases and information gathered to calculate the selected indicators. Indicators are shown on the right side of the image and the information needed to build them is on the left. 17 2.3. STAGE 2 - POLICY LEVERS DEFINITION Figure 2.3: Indicators and the data required to calculate them 2.3 Stage 2 - Policy levers definition Possible solutions to the urban concerns identified in stage 1, including projects and public policies suggested by the local actors, were analyzed and structured into policy levers that can change the city’s path of development. These policy levers were defined in coordination with the local stakeholders and complemented with international experience, creating new levers or enriching those identified with local counterparts. The advantages and disadvantages of each policy lever are assessed based on the previously discussed indicators. Each policy lever has at least two options: one for current conditions (marked as lever 0) and a second for the planned changes or proposals (lever 1, 2, and so on). This allows researchers to compare the effects of turning the lever on/off. 18 CHAPTER 2. METHODOLOGY The following paragraphs explain the urban development issues addressed by each policy lever and indicate which levers were evaluated in each Jordanian city. The two tables found in Appendix B summarize the policy levers used in the urban growth scenarios in Jordan. 2.3.1 Urban growth The following three policy levers define how the population will settle in the future, and hence, how the urban area will expand: 1. Settlement lever: Defines how the population that is expected to increase by 2030 will settle. It chooses between occupying the modelled urban expansion with no restrictions, settling in the areas planned in the Master Plan, or prioritizing infill in areas close to jobs and public transportation. 2. Vacant housing lever: Reduces the vacant housing rate in the city to 8% by assuming that new inhabitants will occupy the existing empty dwellings. 3. Master plan lever: Changes the land uses and restrictions in the maximum number of housing units allowed in each zone of the city. The five Jordanian cities were assessed using the settlement and vacant housing levers, but the master plan lever was prepared only for Amman. Since Amman is already building the first phase of the BRT, which will travel from east Amman to central areas of the city, the lever aims to analyze the possible effects of increasing the maximum number of housing units allowed along this first BRT corridor. Land use will remain the same, but residential areas will experience a twofold increase in the number of permitted dwellings. Table 2.2: Settlement policy levers City Policy lever level Lever name 0 Settlement in new urbanized areas, expanding the city Amman 1 Settlement near employment and public transportation 2 Settlement according to the Master Plan 0 Settlement in new urbanized areas, expanding the city Irbid 1 Settlement near employment and public transportation 2 Settlement according to the Master Plan 0 Settlement in new urbanized areas, expanding the city Mafraq 1 Settlement near employment and public transportation 2 Settlement according to the Master Plan 0 Settlement in new urbanized areas, expanding the city Russeifa 1 Settlement near employment and public transportation 2 Settlement according to the Master Plan 0 Settlement in new urbanized areas, expanding the city Zarqa 1 Settlement near employment and public transportation 2 Settlement according to the Master Plan 19 2.3. STAGE 2 - POLICY LEVERS DEFINITION Table 2.3: Vacant housing policy levers City Policy lever level Lever name 0 Keep existing vacant housing rate Amman 1 Reduce vacant housing rate to 8% 0 Keep existing vacant housing rate Irbid 1 Reduce vacant housing rate to 8% 0 Keep existing vacant housing rate Mafraq 1 Reduce vacant housing rate to 8% 0 Keep existing vacant housing rate Russeifa 1 Reduce vacant housing rate to 8% Zarqa 0 Keep existing vacant housing rate 1 Reduce vacant housing rate to 8% Table 2.4: Master plan policy levers City Policy lever level Lever name 0 Current Master Plan and building norms Amman 1 Modified Master Plan increasing the number of dwellings by 2 allowed along the new BRT lines 2.3.2 Solid waste management improvements Currently, Jordan generates about 2 million tons of municipal solid waste (MSW), 45,000 tons of industrial waste, and 4,000 tons of medical waste per year. MSW collection coverage is estimated at about 90% in urban areas and 70% in rural areas [6]. The final destination of MSW is divided between recovered and landfilled waste: 7% is informally recovered, 48% is sent to the sole engineered landfill in the country, and 45% is taken to one of the 20 existing dumpsites in Jordan [6]. The total cost of solid waste management in the country is 55 million JD per year. In some Jordanian cities, the waste problem has escalated; this has been caused mainly by urban expansion. In cities such as Amman, Irbid, Mafraq, Zarqa, and Russeifa, the issue of solid waste management is of particular concern due to the lack of collection and transfer stations. The lack of infrastructure has resulted in the collection trucks having to travel longer distances to the dumpsites and the landfill, increasing collection intervals and total management costs. Part of the solution is the construction of transfer stations. These buildings help reduce the distance travelled by each collection truck. This will in turn allow the cities to utilize trucks that carry at least twice the volume carried by the current low capacity trucks. This can potentially reduce management costs and allow for more efficient use of the collection trucks. Another relevant improvement is the creation of new landfills, which would reduce the environmental and public health risks posed by open dumpsites. Although only the technical experts from the Greater Amman Municipality shared specific details of their plans for two new transfer stations, and only Mafraq officials provided information regarding current collections and trips to the dumpsite, all Municipalities expressed their concerns for improving their solid 20 CHAPTER 2. METHODOLOGY waste management systems. For this reason, relevant policy levers were proposed for the five cities, as shown in Table 2.5. Table 2.5: Solid waste management policy levers City Policy lever level Lever name 0 Existing landfill and transfer station Amman 1 Two new transfer stations 0 Existing landfill and transfer station Irbid 1 New transfer station 0 Existing dumpsite Mafraq 1 New transfer station and landfill 0 Existing landfill Russeifa 1 New transfer station Zarqa 0 Existing landfill 1 New transfer station 2.3.3 Public transportation expansion In Jordan, transportation problems are some of the most challenging to address. The main problems include congestion, an increase in car ownership ratio, road capacity problems, lack of reliable public transportation systems, and an absence of BRT and light and national railway systems. These problems may cause increased travel times, travel costs, air pollution, and accident risks [7]. The solutions mentioned by local counterparts during the second round of workshops focused on increasing the width and capacity of the roads and providing more parking spaces. Several studies, however, have proven that individuals drive more when the stock of roads and parking in their city increases (which is called induced demand), and that this increment in driving offsets the benefits [8–10]. For this reason, the policy levers included in this study focus on measures from the demand side: providing more and better public transportation to reduce the number of individuals riding alone. The policy levers tested in this study include the public transportation expansion plans for Irbid bus routes, the BRT line under construction in Amman, and the planned BRT traveling from Amman to Zarqa. During the first workshop in the Zarqa Municipality, local officials mentioned that the BRT should not end in downtown Zarqa, but should continue to Hashemite University. Therefore, this extension to Hashemite University was included as another lever. In addition, two alternative BRT routes were proposed by this team: one from Zarqa to Amman but through Russeifa, and a BRT line east of Amman. The specific public transportation policy levers used for the five cities included: 21 2.3. STAGE 2 - POLICY LEVERS DEFINITION Table 2.6: Public transportation policy levers City Policy lever level Lever name 0 Existing transport routes Amman 1 Planned bus routes and BRT line 2 Proposed BRT line in East Amman 0 Existing transport routes Irbid 1 9 Planned bus lines Mafraq 0 Existing transportation routes 0 Existing transportation routes Russeifa 1 Planned BRT on highway 2 Proposed BRT through the city 0 Existing transportation routes Zarqa 1 Planned BRT on highway 2 Continue BRT to University 2.3.4 Green building code Until the 1970s, the way of life, urban planning, and building construction in the Arab world followed what would qualify as green practices. Unfortunately, after the oil boom, this trend was reversed due to increased wealth and consumerism in the region [11]. In the context of Jordan, traditional buildings were following the same tendencies as the Arab world. However, due to urbanization and population growth, there was a change in building patterns, land use, zoning, and technology. A shift towards green building concepts and sustainability in the way buildings are designed, constructed, and operated is crucial in minimizing the negative impact on the natural environment, especially given the water and energy constraints of the country. As part of Jordan’s efforts to tackle the water and energy limitations, the Jordan National Building Council developed the “Energy Efficiency in Buildings Code” to improve thermal performance and minimize energy consumption in buildings; in November 2010, the new Green Building Guideline and Rating System of Jordan was approved [12]. The green building guideline and rating system is currently referred to as Jordan’s compulsory Building Code; the challenge now is to enforce the Building Code in all future houses. For this reason, the proposed policy lever tests different percentages of penetration of the Green Building Code, ranging from 0% to 90% of the new houses expected to be built by 2030. It was assumed that a house built according to the Green Building Code would install the following mandatory energy saving technologies: Energy • Efficient light bulbs: 30% energy savings • Heater: 43% energy savings • Air conditioning: 30% energy savings In order to address the water issues, the following water saving technologies that have proven their viability in other developing countries were added: Water 22 CHAPTER 2. METHODOLOGY • Toilet: 20% water savings • Sink: 40% water savings • Shower head: 60% water savings • Washing machine: 50% water savings If all of the above measures were combined, energy consumption per housing unit would decrease from 12,724 KWh per year to 11,686 KWh per year; additionally, water consumption would decrease from 518m3 per year to 330m3 per year. An extended description of this calculation can be examined in Annex A. The specific green building code policy levers used for the five cities included: Table 2.7: Green building code policy levers City Policy lever level Lever name 0 Penetration in 0% of new housing units Amman 1 Penetration in 14% of new housing units 2 Penetration in 90% of new housing units 0 Penetration in 0% of new housing units Irbid 1 Penetration in 14% of new housing units 2 Penetration in 70% of new housing units 0 Penetration in 0% of new housing units Mafraq 1 Penetration in 14% of new housing units 2 Penetration in 70% of new housing units 0 Penetration in 0% of new housing units Russeifa 1 Penetration in 14% of new housing units 2 Penetration in 70% of new housing units 0 Penetration in 0% of new housing units Zarqa 1 Penetration in 14% of new housing units 2 Penetration in 70% of new housing units 2.3.5 Clean energy generation In 2006, Jordan contributed about 28,717 gigagrams or 28.72 million tons of CO2 eq of GHG to the atmosphere, of which 72.9% came from the energy sector, followed by 10.6% from the waste sector and 8.9% from industrial processes. Regarding the energy sector, the subsectors that contributed the most are energy industries (27.6%), transportation (16.4%), and commercial and residential activities (10%) [13]. Moreover, Jordan’s energy sector imports a large amount of oil and natural gas, and even electricity, which poses an enormous challenge in energy security. In recent years, the government has been promoting different measures to tackle this challenge, such as improving the efficiency of energy consumption and using renewable energy. Regarding the use of renewable energy, the government expects PV solar self-generation projects with electric power in homes and government institutions, banks, hotels, and hospitals to rise to 125 MW by 2020 [13]. Local counterparts from Amman expressed the city’s plans to have 16 MW in solar generation capacity, Irbid expects a 16 MW solar plant, and Zarqa anticipates a 15 MW 23 2.3. STAGE 2 - POLICY LEVERS DEFINITION solar plant. These plans have been added as policy levers in this study, as they affect the national energy mix and hence its GHG emissions. It was assumed that the added solar generation replaces the same amount of electricity imported. In addition, the effect of a 10 MW solar plant was tested for Mafraq and Russeifa. The specific policy levers used for the five cities included: Table 2.8: Clean energy policy levers City Policy lever level Lever name 0 Do not increase solar power Amman 1 Increase solar power in 16 MW 0 Do not increase solar power Irbid 1 Increase solar power in 16 MW 0 Do not increase solar power Mafraq 1 Increase solar power in 10 MW 0 Do not increase solar power Russeifa 1 Increase solar power in 10 MW Zarqa 0 Do not increase solar power 1 Increase solar power in 15 MW 2.3.6 Efficient public lighting In Jordan, almost all households are served by fully or partially lit roads; on average, only 7% of the households are not served by lit roads [14]. Jordan consumes 337 GWh of electricity each year in street lighting alone, which represents 2% of the total electricity consumed [15]; thus, reducing this form of consumption is an important action for Jordan. The main improvement in public lighting consumption is the replacement of common street light bulbs with LED bulbs. This change can reduce electricity consumption by almost 58% [16], generating economic, electricity, and GHG savings. The five cities in this study mentioned that they had plans to replace all or a portion of their light bulbs; however, in this study, the policy lever tested for all cities was based on changing 100% of the street light bulbs to LED. The specific public lighting policy levers used for the five cities included: Table 2.9: Public lighting policy levers City Policy lever level Lever name 0 Changing to LED 50% of total light bulbs Amman 1 Changing to LED 100% of total light bulbs 0 Changing to LED 6% of total light bulbs Irbid 1 Changing to LED 100% of total light bulbs 0 Changing to LED 0% of total light bulbs Mafraq 1 Changing to LED 100% of total light bulbs 0 Changing to LED 0% of total light bulbs Russeifa 1 Changing to LED 100% of total light bulbs Zarqa 0 Changing to LED 0% of total light bulbs 1 Changing to LED 100% of total light bulbs 24 CHAPTER 2. METHODOLOGY 2.3.7 Reduce hazards Air and water pollution continues to receive a great deal of attention worldwide due to its negative impacts on human health. Several studies reported significant correlations between air pollution and certain diseases, including shortness of breath, sore throat, chest pain, nausea, asthma, bronchitis, and lung cancer [17]. Also, water pollution can propitiate diseases, especially when water is contaminated with heavy metals [18]. In Jordan, there is a present concern about air pollution from cement and stone cutting industries and river contamination from wastewater. To diminish the effects of such pollution, some Jordanian cities have taken or plan to take some measures according to the type of contamination (airborne or waterborne). For artisanal industries, cities like Irbid, Mafraq, and Zarqa aim to enforce regulations or relocate the sources out of the city. Water contamination is much more difficult to control. In Russeifa and Zarqa, for example, a contaminated section of the Zarqa River passes by; the cities are aiming for a complete cleaning of the Zarqa river, and hope to generate a public space for people to gather [19]. In addition, Russeifa is concerned about other sources of pollution such as the phosphate piles generated by a mining company and the former landfill land [20]. The city wants to remove the phosphate piles and clean the former landfill soil to prevent any risks for the population. The policy lever considered in Irbid, Mafraq, and Zarqa was the control of artisanal industry air pollution. In Russeifa, the policy lever tested was the cleaning of phosphate lands and landfill soils. A lever modelling the recovery of the Zarqa River was also added in Russeifa and Zarqa. The specific hazard control policy levers used for the four cities included: Table 2.10: Hazards control policy levers City Policy lever level Lever name 0 Existing artisanal industry Mafraq 1 Control artisanal industry air pollution 0 Existing phosphate lands, polluted Zarqa River, and former landfill Russeifa 1 Clean Phosphate lands, and landfill 2 Clean Zarqa River, phosphate lands, and landfill 0 Existing artisanal industry and polluted Zarqa River Zarqa 1 Control artisanal industry air pollution 2 Clean Zarqa River and control artisanal industry air pollution 2.3.8 Public space The accessibility of people to urban services and facilities is an important component of the quality of life in a city [21]. In particular, public spaces have a central role, both physically and functionally, in urban planning and development. Urban theorists point out their significant role as one of the principal components of a healthy urban setting. Moreover, public spaces increase a sense of community when intensive social interactions take place in these areas. As is stated by many 25 2.3. STAGE 2 - POLICY LEVERS DEFINITION urban theorists, public spaces, such as neighborhood parks or community gardens, are one of the major elements that define the city’s unique attraction points. Furthermore, the importance of public spaces was examined during the first round of workshops, specifically in Russeifa and Zarqa, where an intense discussion took place. Some of the attendants questioned the impact of recently built large parks that were not located in proximity to the most populated areas of the city. From this experience and later meetings with local counterparts, several options for new parks were defined as policy levers for both cities. These included small but widely distributed parks in Russeifa, creating large parks in the ex-phosphate lands and in the former landfill, two parks at two specific points along the Zarqa River, and a linear park along the fully restored Zarqa River. In the city of Irbid, the policy lever embraces the parks that are already planned by the municipality. For Mafraq, the parks marked in the Master Plan were considered as one policy lever, but also local officials shared the idea of building a linear park in the former railway. The specific public space policy levers used for the five cities included: Table 2.11: Public spaces policy levers City Policy lever level Lever name Amman 0 Existing landmarks 0 Existing landmarks Irbid 1 Planned parks 0 Existing landmarks Mafraq 1 Planned parks and railway linear park 0 Existing landmarks 1 Planned parks Russeifa 2 Parks in ex-phosphate lands, landfill and planned parks 3 Zarqa River linear park, park in ex-phosphate lands and planned parks 0 Existing landmarks in Open Street Maps Zarqa 1 Two parks along River Zarqa and planned parks 2 Zarqa River linear park and planned parks 26 CHAPTER 2. METHODOLOGY 2.4 Stage 3 - Data gathering The third stage focused on collecting, validating, organizing, and integrating data in a single platform. Information was gathered from several government offices. This task depended on constant communications with local stakeholders and a demanding process of data homogenization, downscaling, and merging. It was a cumbersome task in the absence of an up-to-date database, at the Ministry level and at the Municipal level. As an added benefit, this project has helped the municipalities with creating their own databases, in addition to delivering a set of useful planning tools. A single, integrated database was developed for each city. It is important to mention that to ensure precision of the scenarios, special attention was given to both quantity and quality of information. Outputs from Stage 1 and the integration of data in a single platform provided a realistic perspective for the definition of indicators and scenarios. The final databases encompassed the following information and sources: • Total population, total number of housing units, and total number of vacant housing units by neighborhood – Population and Housing Census 2015, Department of Statistics. • Population projections for the Kingdom by 2030 – Population projections 2015- 2050, Department of Statistics. • Public transportation routes (bus, service and collective taxi) – Municipalities and Ministry of Transport city registries. • Job density as an estimate from the gross domestic product (GDP) derived from satellite data, National Oceanic and Atmospheric Administration (NOAA). • Location of schools, health facilities, cultural facilities, places of worship, sport facilities and public spaces – Municipalities. • Primary, secondary, tertiary and pedestrian roads – Ministry of Transport. • Average water consumption per housing unit – Jordan Water Sector Facts and Figures 2015, Ministry of Water and Irrigation, Miyahuna Business Plan 2013- 2017. • Total electricity generation – National Electric Power of Jordan. • Emissions factors per type of generation – IPCC. • Construction cost of electric, water and sanitation networks – Public Works Departments within each Municipality. • Characteristics of the solid waste management system and the public lighting - Public Works Departments within each Municipality. 27 2.5. STAGE 4 - METHODS DEVELOPMENT The full list of data and assumptions used in this study can be found in Appendix C. Data gathering represented several challenges, which are explained in Chapter 3. 2.5 Stage 4 - Methods development Stage four focused on developing accurate methods to calculate each indicator. The calculation methods were defined in line with the policy levers identified by the stakeholders. For example, to estimate the energy savings from replacing public lighting bulbs with LED bulbs, the key variable in the calculation method was the percentage of light bulbs that are LED. However, to reflect how much energy will be needed to illuminate the new streets that will be built by 2030, the method should also consider the number of streets as one variable that could change depending on how much the city grows in each scenario. In other words, it was necessary to identify the variables that would turn each policy lever on and off, and to design the calculation method based on such variables. It was also important to reflect the possible drawbacks of the policy levers. For example, creating a transfer station in the solid waste collection system can reduce the volume of diesel consumed by the collection trucks, but the new transfer station will consume energy to operate. Therefore, it is important to have both aspects in the calculation method of the energy consumption indicator. The calculation method of each indicator is documented in the Appendix A to ease future replication. 2.6 Stage 5 - Scenario development The scenarios are representations of possible futures that take into consideration how current decisions will change the future of the city. Scenarios are created by combining one or more policy levers. There can be as many scenarios as the number of policy lever combinations. More than five scenarios for 2030 were modelled for each Jordanian city. The scenarios for the Jordanian cities were defined by modelling a business-as-usual scenario (BAU scenario) and alternative scenarios that combine the policy levers mentioned in Section 2.3. The BAU scenario assumes that the city’s future growth will repeat its past growth patterns. The scenario assumes that urban services or landmarks will be built in a similar proportion and distribution as in the last urban growth period (2000 to 2015 for this exercise in Jordan). The scenario uses machine learning algorithms to forecast the urban expansion for the horizon year. These methods learn from the land use changes of the past to predict the non-urban areas that are likely to become urban in the future. Urban growth in this scenario is not restricted by natural reserves or land uses; therefore, if human settlements have occurred in unzone areas in the past, the model replicates this trend. The resulting scenario represents what will happen if the 28 CHAPTER 2. METHODOLOGY public policies that have shaped the past remain unchanged. Section 2.8 describes the methodology utilized to model the urban expansion used in the BAU scenario. Two alternative scenarios were created by combining the three urban growth policy levers described in Subsection 2.3.1. The Master Plan scenario assumes that no human settlements will occur outside of the zoned areas and the current building code restrictions. The Compact growth scenario models what would happen if the construction of new housing units is prioritized close to employment and public transportation, and a policy to reduce the vacant housing rate to 8% is enforced. Section 2.9 describes in detail how urban growth is modelled for these scenarios. A Moderate scenario and a Vision scenario were created by blending the rest of the policy levers described in Section 2.3 with the Master Plan and Compact growth scenarios, respectively. The Vision scenario takes some of the policy levers to a more ambitious level; for example, it simulates having a mandatory Green Building Code instead of the voluntary code from the Moderate scenario. Apart from these urban scenarios created for the forecasted year, a Base scenario represents the situation in 2015. The following paragraphs express the definition of each scenario. Figure 2.4 shows a graphical representation of the five scenarios for 2030, and figures 2.5, 2.6, 2.7, 2.8, and 2.9 summarize the policy levers that form the main scenarios for each city. Base scenario. Current conditions of the city by the year 2015. The boundaries of what is considered an urban area are defined in this scenario. It summarizes the population, employment density, landmarks, and other characteristics of the city in the base year. BAU scenario. Possible conditions by 2030 if the city expands according to historical growth patterns and new population occupies the urbanized areas. This scenario uses machine learning algorithms to forecast the urban expansion for the horizon year. These methods learn from the land use changes of the past to predict the non-urban areas that are likely to become urban in the future. Master Plan scenario. Possible conditions by 2030 if urban growth happens according to the Master Plan: New population settles according to historical growth patterns but within the zoned areas indicated in the Master Plan. If the maximum densities of the Master Plan are reached, settlement occurs in un-zoned areas within the municipality boundary. Moderate scenario. Possible conditions by 2030 if urban growth happens according to the Master Plan, the public transportation system grows according to current plans, parks included in the Master Plan are built, all public lighting uses LED bulbs, the Green Building Code is enforced in 14% of the new houses, solid waste transfer stations are built as well as solar farms with 10 MW to 16 MW of generating capacity, artisanal industry air pollution is controlled, and the ex-phosphate lands and former landfill in Russeifa are cleaned. Compact growth scenario. Possible conditions by 2030 if infill close to jobs and 29 2.6. STAGE 5 - SCENARIO DEVELOPMENT Figure 2.4: Urban scenarios public transportation is prioritized, and the maximum housing densities indicated in the Master Plan are respected. If the maximum housing densities are reached, expansion takes place; otherwise, no new land is consumed. The vacant housing rate is reduced to 8%, assuming that the new population will occupy a portion of the existing vacant dwellings in the city. Vision scenario. Possible conditions by 2030 if the city follows compact growth (as in the Compact growth scenario); alternative BRT routes are built in Amman, Zarqa, and Russeifa in addition to the planned public transportation routes; planned parks are finished; the Zarqa River is fully cleaned and turned into a linear park in Russeifa and Zarqa; all public lighting uses LED bulbs; the Green Building Code is enforced in 70 to 90% of new houses; solid waste transfer stations are built, as well as solar farms with 10 MW to 16 MW of generating capacity; artisanal industry air pollution is controlled; and the ex-phosphate lands and former landfill in Russeifa are cleaned, along with the Zarqa River. 30 CHAPTER 2. METHODOLOGY Figure 2.5: Urban scenarios for Amman Figure 2.6: Urban scenarios for Irbid 31 2.6. STAGE 5 - SCENARIO DEVELOPMENT Figure 2.7: Urban scenarios for Mafraq Figure 2.8: Urban scenarios for Russeifa 32 CHAPTER 2. METHODOLOGY Figure 2.9: Urban scenarios for Zarqa 33 2.7. STAGE 6 - RESULTS DISSEMINATION 2.7 Stage 6 - Results dissemination Findings were communicated to policymakers and other stakeholders during the workshops and follow-up meetings in July and October 2017. Databases, this final comprehensive report, and dissemination materials were handed to local counterparts for the continuation of urban studies based on this methodology. A project brief and a short video were prepared as dissemination materials. Also, an online results visualization tool was created to display the scenarios’ results. The dissemination materials and the visualization tool are accessible via the website http://jordan.capitalsustentable.com.mx/. A series of training sessions took place with key members of the Municipal and National governments. The Urban Growth Scenarios Handbook was created for this purpose, with a graphical explanation of the general methodology and the calculation methods of the main indicators. Another relevant outcome of this project was the Urban Growth Scenarios Guidebook, which contains a detailed explanation of the process followed with the local counterparts to promote engagement and build new capacities. The objective of this document is to serve as a guide to replicate urban growth scenarios modelling in other cities. 2.8 BAU Expansion models This section describes the methodology used to model the expansion in the BAU scenario explained in Section 2.6 for the five Jordanian cities. As a result of the limited availability of data and the wide diversity of factors, considerations, and methods, it is possible to find different results for the same growth projection. For example, if two growth models are carried out using the same input variables, but using a different statistical method, it can be expected that the results of both projections will be different. To address these challenges, three different growth models were tested in this project to estimate the changes in the urban footprint of the cities of Amman-Russeifa-Zarqa (as one metropolitan area), Irbid, and Mafraq by 2030. It is important to note that implementing different methods in a “redundant” manner has been especially useful to build consensus with the various actors. The growth modelling methods implemented are Random Forests, Extratrees, and Logistic Regression with regularization [5]. The models work through machine learning algorithms and are considerably robust and complex. The models are based on the trends in the change of historical density and land use. The input variables of the models were adjusted to each city according to the specific characteristics of their urban environment. In this way, the projections consider the specific characteristics of each city. Urban growth projection models are not new; in fact, some of these processes were 34 CHAPTER 2. METHODOLOGY conceptualized in the 1940s, and since then they have been used by different authors in a variety of contexts to estimate patterns of urban expansion. Some examples of recent studies are Kamusoko et al. (2015) for Harare, Zimbabwe [22]; Mustafa et al. (2017) for Wallonia, Belgium [23]; Wang et al. (2017) for North Brabant, the Netherlands [24]; and Shafizadeh-Moghadam et al. (2017) for Tehran and Isfahan, Iran [25]. 2.8.1 Models Each of the machine learning methods implemented for this project is described in the following paragraphs. Random Forest. The Random Forest method is made up of a collection of decision trees, which are used to control the variance. This method can be described as a set (collection) of models that use aggregated sampling bootstraps to construct different decision trees, and later combine these models in a final classification. The Random Forest method has several advantages: they can handle many variables, they are quickly trained, they do not require distribution assumptions like the rest of the methods, they are generally robust in the treatment of outlier data and noise, and they provide a way to calculate the importance that each variable has in the model. Extratrees. The Extratrees method is a variant of the Random Forest classifier [10] that uses the complete sample in each step with random decision limits (variables). Some advantages of Extratrees, compared to Random Forest, are that it represents a lower computational cost, the randomization makes the limits of each decision smoother, and the use of fewer variables in each tree avoids overfitting. Logistic Regression. The Logistic Regression method allows researchers to predict the outcome of a dependent variable (categorical) based on a series of independent variables. In general terms, it allows one to model the probability of an event that occurs as a function of other factors. To reduce the possibility of overfitting, a Ridge and Lasso regularization process was integrated. The Logistic Regression method allows one to identify the variables that are significant for a particular model. What the Random Forest, Extratrees, and Logistical Regression methods all have in common is that they use a series of observations of land use change over three moments in time. They also depend on a series of explanatory variables that can be described as those conditions that influenced the change in the observations. These models “learn” about the tendencies that are seen and “train” to predict the change in land use in the future. From this training, they predict the possible changes in future land use. 2.8.2 Data sources Some of the most frequently used explanatory variables include proximity to urban centers, roads, and metro stations [26], or proximity to built-up areas, roads, industrial centers, schools, universities, hospitals, airports, and downtown area of 35 2.8. BAU EXPANSION MODELS the city, topographic characteristics, per capita income, altitude, average slope, and population density [22]. In order to ensure that results were comparable among cities, global data sources with similar temporal information were used. The data sources used in this work included: Built-up Grid: Data contains an information layer on built-up presence as derived from Sentinel1 image collections. • Source: Global Human Settlements [27]. • Temporality: 1990, 2000, and 2014. • Format: Raster with a 250 by 250 meter pixel resolution. Population Grid: Generated using census data combined with built-up index and aerial weights to generate the spatial distribution expressed as the number of people per cell. • Source: Global Human Settlements [28]. • Temporality: 1990, 2000, and 2015. • Format: Raster with a 250 by 250 meter pixel resolution. Digital Elevation model (DEM): From the GTOPO30, which is a global digital elevation model (DEM) with a horizontal grid of 1 square kilometer. GTOPO30 was derived from several raster and vector sources of topographic information. With the elevation model, a raster file with slopes in percentage was generated, which considers the maximum difference between each cell to its adjacent cells. • Source: U.S. Geological Survey’s Center for Earth Resources Observation and Science (EROS) [29]. • Temporality: 1996. • Format: Raster with a 1000 by 1000 meter pixel resolution. Gross Domestic Product distribution: Gross Domestic Product spatial distribution derived from night-lights satellite data. • Source: National Oceanic and Atmospheric Administration (NOAA) [30]. • Temporality: 1995, 2000, and 2013. • Format: Raster with a 1000 by 1000 meter pixel resolution. Highways • Source: Open Street Maps. • Temporality: Beginning in 2008. • Format: Lines geometry. Geolocations: airports, schools, universities, worship places and hospitals. • Source: Open Street Maps. 36 CHAPTER 2. METHODOLOGY • Temporality: starting from 2008. • Format: points geometry. Water Bodies: Provides a base map for the lakes, seas, oceans, large rivers, and dry salt flats of the world. • Source: Esri Data and Maps [31]. • Format: Polygons geometry. 2.8.3 Definition of urban There are no standard criteria to define an urban area, but many definitions include elements like population size and density, economic activity, level of infrastructure, or some combination of these. For this project, an adaptation to the harmonized urban cluster defined by the European Commission [32] was used, where a cell is considered urban if: 1. The maximum value of built-up is greater than or equal to 40%. 2. The mean value of the population is greater than or equal to 75 people per 0.16 km2 . 3. The total number of people in adjacent cells is greater than or equal to 5,000. The three models for the five Jordanian cities were estimated using the data sources and the definition of urban mentioned in this section. The resulting urban expansion predictions are shown in Section 2.8.4. 2.8.4 Expansion model results Table 2.12 contains accuracy parameters for the results of the three implemented growth models, which help to evaluate the performance of each model and choose the best fitting one. Table 2.12: Accuracy of the urban expansion models City Model TN TP FP FN Number Precision Recall F1 of units Extratrees 34713 4296 390 254 39653 0.91 0.94 0.93 Amman Random Forest 34696 4287 406 264 39653 0.91 0.94 0.92 Logistic Regression 34684 4004 546 419 39653 0.90 0.87 0.89 Extratrees 18378 1941 279 85 20683 0.87 0.95 0.91 Irbid Random Forest 18399 1898 259 127 20683 0.88 0.93 0.90 Logistic Regression 18374 1793 283 233 20683 0.86 0.88 0.87 Extratrees 4457 198 37 28 4720 0.84 0.87 0.85 Mafraq Random Forest 4443 203 51 23 4720 0.79 0.89 0.84 Logistic Regression 4463 162 64 31 4720 0.84 0.7 0.76 Where: • TN = True Negative, number of cells correctly predicted as rural 37 2.8. BAU EXPANSION MODELS • TP = True Positive, number of cells correctly predicted as urban • FP = False Positive, number of cells where a rural area was incorrectly predicted as an urban area • FN = False Negative, number of cells where an urban area was incorrectly predicted as a rural area • Precision: Measures the proportion of urban cells that are correctly classified from all of the actual positive cells. precision = T P T P + F P • Recall: Measures the proportion of urban cells that are correctly identified as such to the cells that are classified as urban. recall = T P T P + F N • F1: It is the harmonic mean between precision and recall: a single measure of performance of the test for the positive class, and in this case, urban cells. F 1 = 2T P 2T P + F P + F N The urban expansions predicted by the Extratrees model were used for the analyses of the five Jordanian cities in this study. Random Forests and Extratrees achieve better results than Logistic Regression in overall terms. A high score in recall means that the model does not usually mispredict an area that is rural as urban. Additionally, the Extratrees model yields the highest F1 Score. Table 2.13 presents the expansion predicted by the Extratrees model compared to the historical expansion of each city in 1990-2000 and 2000-2014. Table 2.13: Urban expansion between 1990 and 2014 and expansion predicted for 2030 by the Extratrees model 1990 - 2000 2000 - 2014 2015 - 2030 expansion expansion Extratrees [km2 ] [km2 ] prediction [km2 ] Amman-Russeifa-Zarqa 85.69 80.42 94.86 Irbid 33.99 28.72 52.24 Mafraq 10.99 3.53 4.83 Maps displayed in figures 2.10 to 2.18 plot the urban footprint of each city in the years 1990, 2000, and 2014, and the expansion predicted for 2030 by the three expansion models explained here. 38 CHAPTER 2. METHODOLOGY Figure 2.10: Russeifa's urban footprint in 1990, 2000, and 2014 (upper left), urban footprint in 2014 and prediction for 2030 using the Extratrees model (upper right), urban footprint in 2014 and prediction for 2030 using Logistic Regression model (lower left), and urban footprint in 2014 and prediction for 2030 using the Random Forest model (lower right) Figure 2.11: Amman's urban footprint in 1990, 2000, and 2014 (left) and urban footprint in 2014 and prediction for 2030 using Extratrees model (right) 39 2.8. BAU EXPANSION MODELS Figure 2.12: Amman's urban footprint in 2014 and prediction for 2030 using Logistic Regression model (left) and urban footprint in 2014 and prediction for 2030 using the Random Forest model (right) Figure 2.13: Irbid's urban footprint in 1990, 2000, and 2014 (left) and urban footprint in 2014 and prediction for 2030 using the Extratrees model (right) 40 CHAPTER 2. METHODOLOGY Figure 2.14: Irbid's urban footprint in 2014 and prediction for 2030 using Logistic Regression model (left) and urban footprint in 2014 and prediction for 2030 using the Random Forest model (right) Figure 2.15: Mafraq's urban footprint in 1990, 2000, and 2014 (left) and urban footprint in 2014 and prediction for 2030 using the Extratrees model (right) 41 2.8. BAU EXPANSION MODELS Figure 2.16: Mafraq's urban footprint in 2014 and prediction for 2030 using Logistic Regression model (left) and urban footprint in 2014 and prediction for 2030 using the Random Forest model (right) Figure 2.17: Zarqa's urban footprint in 1990, 2000, and 2014 (left) and urban footprint in 2014 and prediction for 2030 using the Extratrees model (right) 42 CHAPTER 2. METHODOLOGY Figure 2.18: Zarqa's urban footprint in 2014 and prediction for 2030 using Logistic Regression model (left) and urban footprint in 2014 and prediction for 2030 using the Random Forest model (right) 43 2.9. POPULATION SETTLEMENT MODELING 2.9 Population settlement modeling Various urban growth scenarios can be created according to how the population projected for 2030 will settle. If no specific measures are taken, the population can be expected to occupy the urban expansion predicted by the urban growth models explained in Section 2.8 and used in the BAU scenario. On the contrary, the urban expansion can be modified by changes in the land uses and densities indicated by the city’s Master Plan. Additionally, if urban containment is enforced by the government, settlement could be concentrated within the current city boundaries. This could be done by prioritizing construction of new houses within the existing extension of the city or by encouraging the occupation of vacant dwellings. To envision these different growth scenarios, a second model was built to predict where the new population will settle. These scenarios are defined through three policy levers: a settlement lever, a vacant housing lever, and a master plan lever. The settlement lever directs where settlement should occur first with the use of priority polygons. The model allocates population into each priority polygon until it reaches the maximum allowed housing density or the indicated proportion of that density, doing the same in each polygon until the models reach the population projected for 2030. It has three options: • 0 where the new population occupies the predicted urban expansion polygon; • 1 where infill close to jobs and public transportation is prioritized, first within the current boundaries of the city, and then in the urban expansion polygon, and • 2 where the first priority polygon is the predicted urban expansion but within the zoned areas in the Master Plan, the second priority polygon is the complete Master Plan, and the last one corresponds to the un-zoned areas within the municipality boundary. Option 0 was used for the BAU scenario, option 1 for the Vision scenario, and option 2 for the Moderate scenario. The quantity of new population that the model allocates in each polygon is based on the maximum number of housing units allowed by the Master Plan (max_hu), the existing number of housing units (hu), the existing number of inhabitants (population), and the average number of inhabitants per housing unit (hu_size). Only a fraction (i) of the maximum number of housing units allowed is used in this project i = 95%, assuming 5% will remain un-built (see equation 2.1). P opulation = population + (i ∗ max_hu − hu) ∗ hu_size (2.1) When the maximum number of housing units is not defined such as when there is no Master Plan or it is an un-zoned area, a maximum number is assumed (assumed_hu) as shown in equation 2.2. 44 CHAPTER 2. METHODOLOGY P opulation = population + (i ∗ max_hu − hu) ∗ hu_size (2.2) The second policy lever involved in the population settlement model aims at reducing the vacant housing rate to a maximum of 8%. When the vacant housing lever is on, this means its value equals 1; thus, the model will allocate population first into the vacant houses within the city and then it will continue with the priority polygons process described above. When it is off, its value equals 0; thus, no population is allocated in vacant units, and the process proceeds to the priority polygons. The Master plan lever models changes in land use or building norms, i.e. a new or modified Master Plan, by introducing a different set of values for max_hu. The new urban footprint of the city (Footprint) is calculated as the current footprint (existing) plus the expansion areas (new), as shown in equation 2.3. Expansion areas are defined as the areas that had a population of 0 in the base year and have some amount of population at the end of the modelling process. F ootprint = existing + new (2.3) Areas inside the existing city boundaries that experienced a twofold increase in their population are added up; the total is referred to as Infill area (see equation 2.4). The Infill area is calculated to recognize that the infrastructure carrying capacity has to be upgraded to cope with the population increase. ∑ n Inf illarea = areai , where : populationi => 2populationb ase (2.4) i=0 Figure 2.19 depicts a graphical representation of the flow diagram of the population spatial distribution process described in this section. 45 2.9. POPULATION SETTLEMENT MODELING Figure 2.19: Flow diagram of the population settlement model Note: A square refers to each of the analysis points both inside and outside of the city. Population refers to the number of inhabitants living in that square in the base scenario. The maximum number of housing units that can be built in the square according to the Master Plan is called max_hu, and it is multiplied by a fraction called i. hu is the number of existing housing units in the square. hu_size is the average number of inhabitants per housing unit. If the square is outside of the Master Plan, it has no max_hu and a maximum number of housing units is assumed and referred to as assumed_hu. 46 3. Adaptation to Jordan 3.1 Population projections The urban population growth for each city by 2030 was estimated from the Population Projections 2015-2050, published by the Department of Statistics [33]. The report includes three scenarios: the Basic High Scenario, the Medium Growth Scenario, and the Low Growth Scenario. The figures include non-Jordanians residing in the Kingdom; they do not include Jordanians abroad. The Medium Growth Scenario was used for this study, which projects Jordan’s total population to grow by 27.59%, from 9,401,993 to 11,995,549 inhabitants, by 2030. The assumptions made by the Department of Statistics in this scenario included: • Reduction of fertility to 2.4 children per woman. • Life expectancy increases 1.5 years, reaching 74.37 for men and 75.77 for women. • Current net emigration flow of 11,863 men and 1,318 women per year between 2015 and 2024, and a reduction to 5,932 men and 659 women per year between 2025 and 2050. • Syrian refugees begin returning to their home country after year 17; by 2050 the number of Syrians is half the number in 2015. • Palestinians, Iraqis, Yemenis, and Libyans increase at a rate of 2% between 2015 and 2025, at 1.6% between 2025 and 2030, and at 1.2% between 2030 and 2050. Projections were completed by the Department of Statistics only for the Kingdom; projections per Governorate were still under preparation at the time this report was written. Population growth projections for each city were made by multiplying their urban population in 2015 [2] by the 27.59% national growth figure. Only the population within the areas that comply with the definition of urban by the European Commission [32] were considered to match the urban areas defined for the urban expansion model explained in Section 2.8. Table 3.1 summarizes the population growth estimated per city. 3.2. DATA LIMITATIONS Table 3.1: Population projections 2015-2030 by city City Population in 2015 Population in 2030 Increment % Amman 3,423,389 4,367,902 944,513 27.59% Irbid 815,815 1,040,898 225,083 27.59% Mafraq 100,071 127,681 27,610 27.59% Russeifa 451,315 575,832 124,517 27.59% Zarqa 588,232 750,526 162,294 27.59% 3.2 Data limitations The most common difficulties for urban scenario modelling are those related to data gathering, specifically disaggregated census data, GIS Master Plans, and updated spatial information. There are important dissimilarities among the level of systematization and formats used by the different Municipalities. For example, Irbid has a comprehensive Urban Master Plan in geographic information systems (GIS); Zarqa has a Master Plan that covers only a fraction of the city; and Mafraq Municipality manages its land uses with traditional computer-aided design (CAD) software. The Master Plans provided by MOMA in GIS were used for the population settlement models in Irbid, Mafraq, and Russeifa. Greater Amman Municipality provided the Master Plan used to model that city. Zarqa was modelled with the partial zoning provided by Zarqa Municipality, and maximum housing densities were assumed for the rest of the city. In a similar way, the Municipalities and MOMA provided the location of landmarks for Amman, Irbid, Mafraq, and Russeifa. These included the location of schools, health facilities, parks, and urban open spaces such as plazas, sports facilities, and public service offices. No information was provided for Zarqa Municipality. The information used to inform the urban models for Jordan was obtained through comprehensive Population and Housing Census for 2004 and 2015, the Establishments Census for 2011, and year books containing statistical data. The only limitation is that this information is not open for public access at the disaggregation level required for urban spatial analysis. Bearing in mind these limitations, indirect data sources were used as a complement to direct official measurements. Open Street Maps (OMS) was used to obtain the missing amenities for Zarqa, and to complement the streets that were not identified in the .shp files provided by the Land Transport Regulatory Commission. The GHS built-up grid from the Global Human Settlement [27] and the Gross Domestic Product layer from the National Oceanic and Atmospheric Administration (NOAA) [30] were used to downscale and complement the population, housing, and employment data provided by the Department of Statistics at the neighborhood level. The adaptation process is described in the following sections. 48 CHAPTER 3. ADAPTATION TO JORDAN 3.3 Population and housing data In order to calculate the urban indicators, it is necessary to obtain data at a resolution of block level or similar. The population and housing data provided by the Department of Statistics was at the neighborhood level; therefore, a downscaling process was performed. The GHS built-up 250x250m grid, created by the Joint Research Centre of the European Commission [27], served to downscale the official information to the disaggregation level required. Using QGIS open source geospatial software [34], the total population per neighborhood was divided into the GHS grid points according to their built-up area value. Values smaller than 1% were assigned 0 population. The same process was followed with the number of housing units and vacant housing units. The result was a 250x250m grid containing the number of inhabitants, housing units, and vacant houses per point. Each point from this grid was used as one analysis point for the urban indicator calculations. 3.4 Job density data The Department of Statistics provided Table 14: Number of Active Economic Establishments by Governorate and Employees Category (number of employees in the establishment) from the Establishments Census 2011 at the neighborhood level. The information was downscaled to the 250x250m grid following the same process performed for the population and housing data. However, the data was not appropriate for this study as it contained only private establishments; the public sector was not included, which accounted for 38% of the total number of jobs in 2009 [35]. Technical staff from Irbid and Amman analyzed the resulting maps and confirmed they were not representative of the job distribution within the cities. The Gross Domestic Product layer (GDP) from the National Oceanic and Atmospheric Administration (NOAA) [30] was used to estimate job density across the five cities of this study due to the lack of comprehensive data from the local or national authorities in Jordan. This GDP map was created by Ghosh et al. as a spatially disaggregated 1 km2 map of estimated total (formal plus informal) economic activity. It was created from satellite images by calibrating the sum of night-lights to official measures of economic activity at the sub-national level for China, India, Mexico, and the United States and at the national level for the other countries of the world [36]. For this project, job density (jobs per hectare) at block scale from Mexican cities was used to obtain a linear regression model that related the GDP map to job density. Data from Mexico was used for this step because, out of the four countries with sub-national data in the GDP layer, Mexico was the only developing country with a similar population density to Jordan. The function obtained is shown in equation 3.1. 49 3.5. HOUSING DENSITIES job_density = 0.9058 ∗ e(0.067∗GDP ) (3.1) The resulting 1 km2 data was then downscaled into the 250x250m grid to standardize the scale with the population and housing datasets. Technical staff from each city observed that the map obtained from this process was consistent with their knowledge regarding job distribution along the urban area. 3.5 Housing densities The maximum number of housing units that can be built per lot (lot_max_hu) is obtained from the land uses indicated in the Master Plan of each city using equation 3.2: area ∗ c ∗ n lot_max_hu = (3.2) hu_area Where area refers to the area of the lot in square meters, c to the lot coverage ratio (maximum fraction of the lot to be built), n to the maximum number of stories allowed for housing, and hu_area to the average housing unit size in square meters. The value of n in non-residential land uses is 0, therefore (lot_max_hu) = 0. The second step is to aggregate these numbers into the 250x250m grid previously explained to obtain the total maximum housing units allowed per analysis point (max_hu). ∑ x max_hu = lot_max_hui (3.3) i=0 The resulting figures are used in the population settlement model (described in Section 2.9) as the maximum number of housing units allowed (max_hu) per analysis point. An example of the population, housing, and max_hu data previously described is presented in table 3.2. Figure 3.1 shows a portion of the downscaling process. 50 CHAPTER 3. ADAPTATION TO JORDAN Table 3.2: Example of the data obtained for Russeifa Square ID Population Housing max_hu units 1 264 51 39 2 890 197 297 3 3554 647 639 4 261 50 128 5 890 197 0 6 890 197 293 7 2071 377 121 8 4068 948 455 9 3473 810 384 10 2438 464 529 11 858 190 244 12 2376 452 274 13 3493 636 566 14 890 197 532 15 2883 672 475 16 261 50 222 17 220 42 0 18 532 118 0 19 604 134 168 Figure 3.1: Analysis points example for Russeifa 51 3.5. HOUSING DENSITIES 52 4. Results 4.1 Urban growth scenarios results The scenarios defined with the local stakeholders in each city (see Section 2.6) were assessed on the basis of more than 15 indicators. The results for the three main scenarios for each city based on five main indicators are summarized in Figure 4.1. The full performance of the five scenarios for the cities of Amman, Irbid, Mafraq, Russeifa, and Zarqa can be assessed through tables 4.1, 4.2, 4.3, 4.4 and 4.5. These are discussed in chapter 5. 4.1. URBAN GROWTH SCENARIOS RESULTS Figure 4.1: Main results by scenario and city 54 CHAPTER 4. RESULTS Table 4.1: Results by scenario and city (Part I) City Scenario name Total Urban Population Energy GHG Integrated population footprint density consumption emissions costs [km2 ] [pop/km2 ] [kWh/capita [kgCO2 eq 2016- per yr] /capita 2030 per yr] [million JD] Amman Base 3,423,389 212.48 16,111 4,426 1,348 - BAU 4,367,902 253.91 17,202 4,323 1,308 3,659 Master Plan 4,367,902 236.66 18,456 4,333 1,310 3,442 Moderate 4,367,902 236.66 18,456 4,312 1,304 3,378 Compact growth 4,367,902 212.48 20,557 3,719 1,127 2,948 Vision 4,367,902 212.48 20,557 3,695 1,120 2,877 Irbid Base 815,815 58.42 13,963 3,983 1,210 - BAU 1,040,898 75.61 13,766 4,039 1,217 1,089 Master Plan 1,040,898 65.49 15,895 3,969 1,198 948 Moderate 1,040,898 65.49 15,895 3,895 1,177 885 Compact growth 1,040,898 58.42 17,816 3,573 1,076 831 Vision 1,040,898 58.42 17,816 3,486 1,056 767 Mafraq Base 100,071 4.36 22,975 3,368 1,034 - BAU 127,681 8.17 15,631 3,557 1,081 227 Master Plan 127,681 4.92 25,961 3,415 1,042 132 Moderate 127,681 4.92 25,961 3,405 1,039 130 Compact growth 127,681 4.36 29,314 3,306 1,013 115 Vision 127,681 4.36 29,314 3,217 985 105 Russeifa Base 451,314 20.87 21,627 3,628 1,091 - BAU 575,832 21.87 26,332 3,673 1,100 339 Master Plan 575,832 22.39 25,713 3,709 1,109 347 Moderate 575,832 22.39 25,713 3,664 1,096 323 Compact growth 575,832 20.87 27,594 3,550 1,069 333 Vision 575,832 20.87 27,594 3,479 1,044 302 Zarqa Base 588,232 29.56 19,897 3,978 1,211 - BAU 750,526 35.50 21,141 3,944 1,194 574 Master Plan 750,526 32.32 23,223 3,989 1,205 534 Moderate 750,526 32.32 23,223 3,948 1,193 499 Compact growth 750,526 29.56 25,386 3,861 1,169 502 Vision 750,526 29.56 25,386 3,792 1,148 461 55 4.1. URBAN GROWTH SCENARIOS RESULTS Table 4.2: Results by scenario and city (Part II) City Scenario name Land Infill area Infrastructure Infra. Infra. Municipal consumption [km2 ] costs costs costs service [km2 ] [million (expansion) (infill) costs JD] [million [million [JD/capita JD] JD] per yr] Amman Base - - - - - 62 BAU 41.44 - 231.67 231.67 - 58 Master Plan 24.19 - 135.23 135.23 - 56 Moderate 24.19 - 135.23 135.23 - 55 Compact growth - 7.13 16.99 - 16.99 49 Vision - 6.94 16.55 - 16.55 48 Irbid Base - - - - - 71 BAU 17.19 - 97.38 97.38 - 70 Master Plan 7.06 - 40.01 40.01 - 64 Moderate 7.06 - 40.01 40.01 - 60 Compact growth - 4.5 11.09 - 11.09 58 Vision - 4.44 10.93 - 10.93 53 Mafraq Base - - - - - 83 BAU 3.81 - 46.26 46.26 - 105 Master Plan 0.56 - 6.83 6.83 - 72 Moderate 0.56 - 6.83 6.83 - 72 Compact growth - 0.06 0.32 - 0.32 67 Vision - 0.06 0.32 - 0.32 60 Russeifa Base - - - - - 47 BAU 1.00 - 4.8 4.8 - 43 Master Plan 1.57 0.58 8.55 7.33 1.22 43 Moderate 1.57 0.58 8.55 7.33 1.22 40 Compact growth - 3.54 7.38 - 7.38 41 Vision - 3.15 6.56 - 6.56 38 Zarqa Base - - - - - 56 BAU 5.94 - 31.54 31.54 - 53 Master Plan 2.88 0.63 16.06 14.63 1.43 51 Moderate 2.88 0.63 16.06 14.63 1.43 47 Compact growth - 4.64 10.63 - 10.63 48 Vision - 4.64 10.63 - 10.63 44 56 CHAPTER 4. RESULTS Table 4.3: Results by scenario and city (Part III) City Scenario name Total Total Total Vacant Water for Energy population housing vacant housing dwellings for units housing rate [m3 /capita dwellings units per yr] [kWh/capita per yr] Amman Base 3,423,389 910,941 209,516 23 % 139 3,434 BAU 4,367,902 1,099,896 258,373 23 % 130 3,204 Master Plan 4,367,902 1,099,953 258,386 23 % 130 3,204 Moderate 4,367,902 1,099,953 258,386 23 % 129 3,198 Compact growth 4,367,902 958,732 76,572 8% 113 2,793 Vision 4,367,902 959,455 76,629 8% 111 2,785 Irbid Base 815,815 187,312 33,716 18 % 121 2,971 BAU 1,040,898 232,687 42,561 18 % 115 2,844 Master Plan 1,040,898 233,177 42,650 18 % 116 2,850 Moderate 1,040,898 233,177 42,650 18 % 114 2,844 Compact growth 1,040,898 213,720 17,094 8% 106 2,613 Vision 1,040,898 213,752 17,096 8% 103 2,594 Mafraq Base 100,071 19,474 16,163 8% 108 2,657 BAU 127,681 26,001 2,195 8% 105 2,591 Master Plan 127,681 26,224 2,213 8% 106 2,613 Moderate 127,681 26,224 2,213 8% 105 2,606 Compact growth 127,681 26,104 2,054 8% 105 2,601 Vision 127,681 26,104 2,054 8% 99 2,564 Russeifa Base 451,314 90,141 9,013 10 % 103 2,544 BAU 575,832 113,901 11,257 10 % 102 2,517 Master Plan 575,832 114,003 11,267 10 % 102 2,519 Moderate 575,832 114,003 11,267 10 % 101 2,513 Compact growth 575,832 112,697 9,001 8% 101 2,490 Vision 575,832 112,209 8,961 8% 95 2,452 Zarqa Base 588,232 139,830 19,717 14 % 123 3,038 BAU 750,526 171,180 24,575 14 % 118 2,902 Master Plan 750,526 171,360 24,601 14 % 118 2,905 Moderate 750,526 171,360 24,601 14 % 117 2,899 Compact growth 750,526 168,640 18,142 11 % 116 2,859 Vision 750,526 168,640 18,142 11 % 111 2,831 57 4.1. URBAN GROWTH SCENARIOS RESULTS Table 4.4: Results by scenario and city (Part IV) City Scenario name Energy Energy Energy Energy for Energy Energy consumption for for water commuting for for solid [kWh public provision [kWh/capita dwellings waste /capita per lighting [kWh/ per yr] [kWh/ collection yr] [kWh/ capita per capita per [kWh/ capita yr] yr] capita per per yr] yr] Amman Base 4,426 14 321 635 3,434 21.91 BAU 4,323 12 300 785 3,204 21.75 Master Plan 4,333 12 299 796 3,204 21.61 Moderate 4,312 8 297 796 3,198 13.38 Compact growth 3,719 10 261 633 2,793 21.43 Vision 3,695 7 257 633 2,785 13.2 Irbid Base 3,983 74 280 624 2,971 33.95 BAU 4,039 74 268 818 2,844 33.95 Master Plan 3,969 64 268 753 2,850 33.61 Moderate 3,895 33 265 732 2,844 20.65 Compact growth 3,573 57 245 625 2,613 33.38 Vision 3,486 29 238 604 2,594 20.41 Mafraq Base 3,368 100 254 339 2,657 17.71 BAU 3,557 138 252 558 2,591 18.94 Master Plan 3,415 83 248 454 2,613 17.13 Moderate 3,405 83 245 454 2,606 18.15 Compact growth 3,306 73 246 369 2,601 16.82 Vision 3,217 36 231 369 2,564 17.84 Russeifa Base 3,628 41 237 772 2,544 33.66 BAU 3,673 34 234 855 2,517 33.32 Master Plan 3,709 35 234 887 2,519 33.36 Moderate 3,664 17 232 880 2,513 21.85 Compact growth 3,550 32 231 763 2,490 33.25 Vision 3,479 16 219 770 2,452 21.74 Zarqa Base 3,978 49 283 577 3,038 29.87 BAU 3,944 46 271 696 2,902 29.72 Master Plan 3,989 42 270 742 2,905 29.53 Moderate 3,948 21 268 741 2,899 19.07 Compact growth 3,861 38 266 669 2,859 29.36 Vision 3,792 19 254 668 2,831 18.9 58 CHAPTER 4. RESULTS Table 4.5: Results by scenario and city (Part V) City Scenario name Job Public School Public Sports proximity transport proximity space facility proximity proximity proximity Amman base 27% 84% 96% 0% 76% bau 21% 65% 74% 0% 59% Master Plan 21% 65% 74% 0% 59% Moderate 21% 65% 74% 0% 59% Compact growth 32% 83% 74% 0% 77% Vision 32% 82% 96% 0% 77% Irbid base 43% 82% 98% 34% 6% bau 33% 64% 76% 29% 5% Master Plan 33% 64% 76% 28% 5% Moderate 33% 66% 94% 50% 5% Compact growth 49% 84% 98% 35% 5% Vision 49% 87% 98% 57% 5% Mafraq base 84% 75% 0% 27% 1% bau 61% 56% 0% 20% 1% Master Plan 61% 54% 0% 20% 1% Moderate 61% 55% 0% 55% 3% Compact growth 88% 84% 0% 20% 1% Vision 88% 82% 0% 50% 1% Russeifa base 55% 75% 99% 45% 0% BAU 43% 59% 78% 42% 0% Master Plan 48% 62% 86% 39% 0% Moderate 48% 66% 93% 66% 0% Compact growth 64% 76% 99% 76% 0% Vision 64% 94% 99% 83% 0% Zarqa base 77% 91% 29% 0% 0% bau 60% 71% 23% 0% 0% Master Plan 61% 76% 23% 0% 0% Moderate 61% 76% 23% 20% 0% Compact growth 68% 86% 24% 0% 0% Vision 68% 87% 24% 37% 0% 59 4.1. URBAN GROWTH SCENARIOS RESULTS Table 4.6: Results by scenario and city (Part VI) City Scenario name Worship Health Public Cultural place facility building space proximity proximity proximity proximity Amman base 100 % 94 % - - bau 77 % 85 % - - Master Plan 77 % 87 % - - Moderate 77 % 72 % - - Compact growth 77 % 72 % - - Vision 100 % 94 % - - Irbid base 99 % 91 % - 16 % bau 76 % 70 % - 14 % Master Plan 76 % 70 % - 15 % Moderate 76 % 86 % - 15 % Compact growth 99 % 92 % - 13 % Vision 99 % 92 % - 13 % Mafraq base 1% 66 % 14 % 4% bau 0% 48 % 11 % 3% Master Plan 0% 48 % 11 % 3% Moderate 0% 51 % 12 % 3% Compact growth 0% 75 % 11 % 3% Vision 0% 75 % 11 % 3% Russeifa base 100 % 85 % 49 % - BAU 78 % 66 % 38 % - Master Plan 87 % 72 % 40 % - Moderate 87 % 72 % 40 % - Compact growth 100 % 86 % 47 % - Vision 100 % 86 % 49 % - Zarqa base 53 % 70 % 35 % - bau 41 % 54 % 27 % - Master Plan 45 % 55 % 28 % - Moderate 45 % 55 % 28 % - Compact growth 51 % 57 % 33 % - Vision 51 % 57 % 33 % - 60 5. Discussion This Chapter discusses the results obtained during the urban growth exercise performed with the government of Jordan and the local authorities of Amman, Irbid, Mafraq, Russeifa, and Zarqa. In the analyzed scenarios, all other “external factors” such as population increase, economic development, climate variability, and so on are held constant so that each scenario is discussed in a clearer manner. The results of one indicator should not be analyzed apart from the other indicators. Decisions should be made using the integrated results of all of the scenarios. All scenarios have trade-offs; the best path for each city will depend on the correct assessment of them and on the population’s priorities and preferences. 5.1 Amman Amman is the most populated city in Jordan. The urban population in Greater Amman Municipality is expected to grow from 3,423,389 inhabitants in 2015 to 4,367,902 by 2030, according to the projections explained in Section 3.1. Five future scenarios were modelled for the city to accommodate the additional 944,513 inhabitants expected by 2030 and to assess their needs. The scenarios’ results were presented in Section 4.1, and their main outcomes are discussed in the following paragraphs. 5.1.1 Land consumption Based on how the urban extension of Amman has been growing since 1990, we expect that in the BAU scenario land consumption will increase by 14% between 2015 and 2030, amounting to 41.44 km2 (BAU scenario in Figure 5.1). Almost half of this growth (17 km2 ) is expected to occur outside of zoned areas. Green or arable lands to the east and south of Amman are likely to be converted to urban use. The Master Plan scenario indicates that enforcing the Greater Amman Municipality’s current Master Plan could reduce new land consumption to 24.19 km2 , preventing valuable lands from becoming urban. However, total land consumption in the Master Plan scenario could reach up to 99 km2 if all residential land uses are urbanized by 2030. There are at least 75 additional square kilometers with Residential A, B, C, D, or Popular land use beyond the expansion area estimated by the growth model (marked in light blue in Figure 5.1. AMMAN 5.1). That area could easily become urbanized if no containment policy is implemented. Figure 5.1 compares land consumption from the BAU scenario (left) with the Master Plan / Moderate scenario (right). Figure 5.1: Urban footprint for Amman in the Base scenario (2015), BAU scenario (2030) and Moderate scenario (2030) Residential land use: areas zoned in GAM’s Master Plan as Residential A, B, C, D, or Popular that would not become populated by 2030 if urban growth follows historical trends, but are likely to become urbanized if there is no containment policy. This full Master Plan scenario is worse than the BAU scenario because it would lead to a leapfrog development of the city, as well as higher infrastructure and municipal service costs, as will be explained in Section 5.1.2. The Compact growth scenario leads to no consumption of new land, as shown in Figure 5.2. This scenario proves that all the population growth expected for 2030 can be housed within the existing city boundaries. The main policy lever that contributes to no additional land consumed in the Compact growth scenario is reducing the current rate of vacant housing from 23 to 8%. About 742,910 future inhabitants could be housed in 141,238 existing but vacant houses instead of building new dwellings on the outskirts of the city. This measure will house three-quarters of the expected population growth of the city by 2030. The second measure is a Transport Oriented Development policy in which densities along the new BRT lines experience a twofold increase. This lever yields benefits in 62 CHAPTER 5. DISCUSSION Figure 5.2: Urban footprint for Amman in the Base scenario (2015), BAU scenario (2030) and Compact growth scenario (2030) reducing commuting distances and increasing proximity to urban services, as will be explained in Section 5.1.3. Solely inducing the new population to settle close to employment areas and public transportation could be enough to prevent the city from expanding horizontally. The number of housing units that can be built according to the land uses of the current Master Plan and the building norms limitations is enough to house the population growth expected for 2030. 5.1.2 Infrastructure costs and municipal service costs The BAU scenario requires the highest investment in infrastructure (infrastructure costs) and has the most expensive service costs per capita for the Municipality. About 231.67 million JD are required to provide the 41.44 km2 of new urban area with streets, walkways, water, drainage, and electricity networks. This scenario costs almost twice as much as the Master Plan scenario, and 13 times more than the Vision scenario. The Compact growth and Vision scenarios incur less costs because they have no new land consumption, meaning no new km2 of the city to be built. The nearly 17 million JD in infrastructure costs required will serve to upgrade the water, drainage, and electricity infrastructure in the inner areas of the city that will house the new population. The objective is to keep up with infrastructure needs as the population increases. Concentrating infill along the BRT lines allows the Vision scenario to reduce the 63 5.1. AMMAN investment needed to upgrade the networks to 16.5 million JD. The Vision scenario also yields the lowest annual municipal service cost per capita. These costs are the average annual municipal budget for providing water to Amman’s homes, collecting their solid waste, lighting Amman’s streets, and maintaining them. The 14% reduction from 58.19 JD in the BAU scenario to 49.76 JD in the Vision scenario may not sound like a significant amount of money, but when this is multiplied by the total urban population each year up to 2030, the accumulated difference amounts to almost 500 million JD. As can be seen in Table 5.1, in the long run, reducing the costs of providing municipal services is highly relevant. Table 5.1: Integrated costs for Amman Municipal service Infra. Infra. Municipal cost Infra. costs costs service 2016- Land Infill costs (expansion) (infill) costs 2030 consump. area [million [million [million [JD/capita/ [million Scenario name [Km2 ] [Km2 ] JD] JD] JD] annum] JD] BAU 41.44 - 231.67 231.67 - 58.19 3,427.52 Master Plan 24.19 - 135.23 135.23 - 56.15 3,307.75 Moderate 24.19 - 135.23 135.23 - 55.05 3243 Compact growth - 7.13 16.99 - 16.99 49.76 2,931.09 Vision - 6.94 16.55 - 16.55 48.57 2,860.86 Municipal expenses in the services provision are affected mainly by the expansion of the city. The BAU scenario, with the largest urban area, is the most expensive one, whereas the Compact and Vision scenarios are the cheapest, serving the same population. The Compact scenario’s municipal costs are 11% lower than the Master Plan scenario and 16% lower than the BAU scenario. Enforcing the Green Building Code in 90% of the houses that will be built between 2015 and 2030 is the second policy lever with the highest impact on cost savings to the Municipality. Costs of municipal services are reduced by 3% if almost all new dwellings are built according to the code; this figure becomes less than 1% if only 14% of new houses adhere to the code, as modelled in the Moderate scenario. The savings potential of a mandatory Green Building Code can be summarized in two main aspects: 1. Providing water to Amman’s homes, including the water that is lost due to the network leakages, accounts for 50% of the municipal service costs. 2. Enforcing the Green Building Code in almost all new homes would reduce the overall housing water demand in the city by 6%. Planning for compact growth of the city reduces the infrastructure costs since the investment needed is directly proportional to the km2 of a new and upgraded city. It can be achieved by reducing the vacant housing rate, promoting housing in the BRT corridors, and prioritizing infill, as explained in Section 5.1.1. 64 CHAPTER 5. DISCUSSION 5.1.3 Proximity to urban services and amenities The Vision scenario, in which a part of the vacant houses are occupied and the current Master Plan is modified to encourage the construction of new houses along the BRT lines, reveals considerable improvement in the indicators of proximity to public services and facilities such as health facilities, schools, sport facilities, mosques, and even to the main sources of employment in the city. The number of spaces and facilities in the city remains constant in all scenarios; the only change is where in the city the new families will live. The BAU scenario has a poorer performance than the Base scenario, mainly because the population is assumed to settle in the outskirts of the city, where service provision is limited. The Vision scenario is different from the Compact growth scenario because it includes the intensification in the BRT corridor. As revealed in Figure 5.3, this measure could substantially improve the quality of life of Amman’s inhabitants by increasing accessibility, which also reduces the time and money spent in commuting. Figure 5.3: Percentage of the total population in the city of Amman that lives in proximity to urban amenities in the different urban growth scenarios 5.1.4 Proximity to public transport The construction of the planned BRT lines increases the number of people living close to a public transportation route by merely 1%. This is because the planned BRT will cover the routes currently run by buses. New public transportation routes need to be provided in the currently unserved areas of Amman, and in the area where the city will grow, in order to cover a third part of the population that otherwise would not have access to public transportation. The indicator improves with the Compact growth and Vision scenarios because the new population would settle close to the BRT corridor. 65 5.1. AMMAN Although the indicator is barely affected by the BRT project, the quality of the service offered is expected to improve substantially. More information regarding the quality of the current buses service is required to build a comprehensive indicator. 5.1.5 Water and energy consumption Greater Amman Municipality is carrying out a series of projects with the objective of improving the overall efficiency of the city’s energy consumption. These projects were assessed as part of the Moderate scenario, and include the replacement of all public lighting bulbs with LED bulbs, achieving a 14% penetration of the Green Building Code in the new dwellings, a 16 MW solar farm, and the construction of two municipal solid waste transfer stations. The Vision scenario combines the same policy levers with the Compact growth scenario and takes the penetration of the Green Building Code to 90%. The combination of these two policy levers achieves almost a 15% reduction in energy consumption, a 14% reduction in water consumption, and a 25% reduction in GHG emissions. The variations in energy consumption, water consumption, and GHG emissions between the Moderate scenario and the Master Plan scenario are negligible. Figure 5.4 reveals that even though LED lighting and the two new transfer stations reduce the energy demand of those systems by a third (comparing Moderate and Vision scenarios vs Master Plan and Compact growth scenarios), their contribution to overall energy savings is less than 1%. Compact growth has the largest impact on the energy demand of the city, as clearly shown in Figure 5.4. The electricity consumed by Amman’s households is the second largest contributor to consumption. For this reason, even though the reduction per household is barely 1%, enforcing this code is the efficiency measure that saves the most energy, apart from controlling the expansion of the city. 66 CHAPTER 5. DISCUSSION Figure 5.4: Energy consumption breakdown for the city of Amman by scenario 5.1.6 GHG emissions The GHG emissions indicator assesses the annual volume of these gases produced by the following energy-consuming activities: providing water in the city, lighting up the streets, collecting solid waste, and daily inhabitant commuting. Energy savings in these areas will reflect a reduction in the GHG emission indicator. The Vision and Compact growth scenarios are clearly the most sustainable options in terms of avoiding GHG emissions (see Figure 5.5). Reducing the number of housing units by occupying the vacant dwellings instead of building more leads to an overall reduction of almost 14%, although an in-depth analysis of the energy consumption of vacant houses is needed to obtain a more accurate estimation. A compact development of the city, with no reduction in the vacant housing rate, can still reduce emissions by 3%. The construction of a 16 MW solar farm reduces the carbon factor of the electricity mix only to a limited extent, from 313.018 kgCO2 eq/GWh to 312.87 kgCO2 eq/GWh. A total of 1,000 MW of solar capacity built across the country could reduce 2.5% of the city’s GHG emissions related to solid waste collection, commute in the city, electricity in households, and the municipal water system. 67 5.2. IRBID Figure 5.5: GHG emissions in Amman by scenario 5.2 Irbid Irbid is the second largest city in Jordan, with an urban population of 815,815 inhabitants in 2015. The Department of Statistics projected that the population in the Kingdom will grow 27.59% by 2030; this means that the city will experience 225,083 new dwellers, assuming the same growth rate. Five main scenarios have been defined with local stakeholders to assess various possible futures for the city in 2030: BAU, Master Plan, Moderate, Compact growth, and Vision scenarios. In the BAU scenario, the city grows according to historical patterns; the Master Plan scenario models land occupation according to the land uses indicated in this instrument; the Moderate scenario combines the Master Plan with the policy levers suggested by local authorities; the Compact growth scenario models urban containment policies and no new land is consumed; and the Vision scenario is the combination of the Compact growth scenario and the best performing public policies. The performance of each scenario is discussed by each indicator in the following paragraphs, identifying the public policies or projects (policy levers) that yield the highest benefits. 5.2.1 Land consumption The results presented in Section 4.1 show that the city will expand 17.19 km2 between 2015 and 2030 if the trends from the last decades continue (BAU scenario in Figure 5.6). A total of 46.62 km2 could be added to the city by 2030 if all of the residential land uses of the Master Plan become urban (blue areas in Figure 5.6). This figure excludes 68 CHAPTER 5. DISCUSSION land uses labelled as residential agricultural and residential rural in the Master Plan. If only the residential land uses within the projected expansion are urbanized, the expansion of the city could be limited to 7.06 km2 (Moderate scenario in Figure 5.6). The Compact growth scenario in Figure 5.7 indicates that all of the projected population growth for 2030 could be accommodated within the current boundaries of the city. No new land has changed from current use to urban, preserving in this way the valuable agricultural land that surrounds Irbid. This was modelled respecting the current land uses, maximum heights, and lot coverage indicated in the Master Plan. Reducing the rate of vacant housing and imposing a containment policy to prioritize infill are the two policy levers modelled in the Compact growth scenario and Vision scenario; both had no new land consumption. The rate of vacant housing is reduced from 18 to 8%, allowing 101,413 of the new projected inhabitants to dwell in 19,280 existing housing units, thus reducing the number of new units and city areas to be built. Even if no policy to occupy the vacant houses is enacted, new land would not be required because of inner vacant land or under-utilized plots available inside the city where new housing units could be built. It is recommended to invest in upgrading the water, drainage, and electricity networks of the areas where infill is encouraged. The amount of funds needed for this is analyzed in the following paragraphs. Figure 5.6: Urban footprint for Irbid in the Base scenario (2015), BAU scenario (2030), and Moderate scenario (2030) 69 5.2. IRBID Figure 5.7: Urban footprint for Irbid in the Base scenario (2015), BAU scenario (2030), and Compact growth scenario (2030) 5.2.2 Infrastructure costs and municipal service costs Investment in infrastructure is needed to expand the city and to upgrade the existing systems in order to cope with the requirements of the population in 2030. Table 5.2 compares the total investment needed in each scenario, under the indicator Infrastructure costs. This total is the sum of the expansion costs and the infill costs (upgrading existing infrastructure), which are directly proportional to the amount of new land to be urbanized (land consumption) and total area to be upgraded in the city (infill area). The Compact growth and Vision scenarios require almost ten times less JD than the BAU scenario to satisfy the infrastructure needs of the same number of inhabitants. The BAU scenario is the most expensive one because it consumes a larger amount of new land. Even though the infill costs are assumed to be 1.5 times higher than the costs of urbanizing new land from scratch, the enormous difference in the area required makes the Vision and Compact growth scenario more attractive in terms of money. The annual per capita costs of providing municipal services to the population are also lower in the Compact growth and Vision scenarios than in the BAU and Master Plan scenarios. The difference is more remarkable when the money required between 2015 and 2030 is totaled, as shown in Table 5.2. The Vision scenario saves 24% of the municipal budget for solid waste collection, public lighting, providing water to Irbid’s dwellings, and street’ maintenance 70 CHAPTER 5. DISCUSSION compared to the BAU scenario. Table 5.2: Integrated costs for Irbid Municipal service Infra. Infra. Municipal cost Infra. costs costs service 2016- Land Infill costs (expansion) (infill) costs 2030 consump. area [million [million [million [JD/capita/ [million Scenario name [Km2 ] [Km2 ] JD] JD] JD] annum] JD] BAU 17.19 - 97.38 97.38 - 70.71 992.57 Master Plan 7.06 - 40.01 40.01 - 64.7 908.24 Moderate 7.06 - 40.01 40.01 - 60.24 845.67 Compact growth - 4.50 11.09 - 11.09 58.43 820.17 Vision - 4.44 10.93 - 10.93 53.89 756.55 Almost half of the savings are due to growing the city in a compact way, a third can be attributed to changing all public lighting to LED bulbs, and one-tenth derive from enforcing the Green Building Code in 70% of the new dwellings in Irbid by 2030. 5.2.3 Proximity to urban services and amenities The parks that Greater Irbid Municipality has planned for the upcoming years were added in the Moderate scenario. This measure has the potential of increasing the percentage of the population that lives in proximity to a park or public space (public space proximity indicator) by 30 points more than the Master Plan and BAU scenarios, reaching half of Irbid’s population. Figure 5.8 shows a comparison of the scenarios by indicator. If compact growth is combined with this increment in the number of parks (Vision scenario), about 57% of the population would be able to walk to a park from their homes. The location and number of schools planned for Irbid would cover almost all dwellings in the city, as shown in the Moderate and Vision scenarios. Compact growth also increases the proximity to schools to 98% even if no new schools are built. This is accomplished only by fostering the new dwellers to live in the areas of the city currently served. Also, the percentages for proximity to jobs, public transportation, worship places, and health facilities are larger in the Compact growth and Vision scenarios. 71 5.2. IRBID Figure 5.8: Percentage of the total population in the city of Irbid that lives in proximity to urban amenities in the different urban growth scenarios 5.2.4 Proximity to public transport Greater Irbid Municipality and the Land Transport Regulatory Commission provided 9 new bus routes for the city of Irbid. They also provided all the routes of the buses, minibuses, and shared taxis currently serving Irbid. In the Moderate scenario, which includes these 9 new bus routes, 64% of Irbid’s inhabitants would have access to the public transportation system, serving 2% more than the BAU and Master Plan scenarios. Fostering infill, like in the Compact growth and Vision scenarios, brings 20% more of the population close to a public transportation route than in the rest of the scenarios. The resulting percentages for all the scenarios can be seen in Figure 5.8. To make these figures real, it is necessary that bus drivers follow the routes planned by the authorities. 5.2.5 Water and energy consumption The Moderate scenario has the potential of saving 1% of the water consumed by Irbid’s dwellings; the difference in water consumption between the BAU and Master Plan scenarios is negligible. Figure 5.9 indicates the water savings estimated under each scenario. The Moderate scenario models that only 14% of the houses that will be built between 2015 and 2030 will implement water saving measures like the ones described in the Green Building Code in Section 2.3.4. Residential water savings could increase to 5% if the penetration of the Green Building Code is at least 70% of new houses, and to 11% if in addition the vacant houses are used to house a part of the incoming population, as modelled in the Vision scenario. It is important to note that domestic consumption accounted for half of the total water consumption in the Kingdom in 2015 [37]. 72 CHAPTER 5. DISCUSSION Figure 5.9: Energy consumption breakdown for the city of Irbid by policy lever The Vision scenario could save 575 GWh per year compared to the BAU scenario (13% reduction), which is almost 3% of the total electricity consumption. From this total, potentially, 78 GWh/annum can be saved in electricity for public lighting and municipal water supply, 14 GW/annum in diesel for solid waste collection, 260 GW/annum in the electricity consumption of Irbid’s homes, and 223 GWh/annum in fuels for public and private transportation in the city. Compact growth is responsible for one-third of the Vision scenario’s saving potential. A compact growth policy that encourages future inhabitants to settle close to high employment areas and public transportation is expected to favor fewer trips to work or study and more sustainable ways of commuting, resulting in less fuel burned. Additionally, a smaller city footprint requires the lighting of fewer streets and waste collection trucks will have shorter trips Enforcing a Green Building Code with mandatory energy and water saving measures can potentially save 47 GWh per year compared to the BAU scenario; this amounts to 5% less in energy demand. 5.2.6 GHG emissions The Vision scenario results in 12% less GHG emissions than the BAU scenario, as is clearly shown in Figures 5.9 and 5.10. This accounts for 148,532 tons of CO2 eq per year. The main driver of this reduction is the compact development of the city. Greater Irbid Municipality is undergoing the replacement of public lighting bulbs with LEDs, 73 5.3. MAFRAQ implementing energy saving measures in the building code, and improving the solid waste collection system, which is also modelled in the Vision scenario in their most efficient version. In spite of this, these measures combined account for less than a quarter of the Vision scenario savings. It would be necessary to build at least 1,000 MW of solar capacity throughout the country to reduce GHG emissions related to water provision, public lighting, and electricity in urban houses by about 2%. The Vision scenario modelled the construction of a 16 MW solar farm, but the benefit is negligible. Figure 5.10: GHG emissions in Irbid by scenario 5.3 Mafraq Mafraq is the smallest city included in this study. Its urban population in 2015, according to the definition of urban explained in Section 2.8.3, was only 100,071 inhabitants. The Department of Statistics projected a national population increment of 27.59% for 2030, which means Mafraq is expected to reach 127,681 inhabitants that year. Five possible future scenarios were analyzed for 2030 with the objective of identifying actions that can direct Mafraq into a more sustainable perspective. These scenarios include a business-as-usual option, following historical tendency (BAU scenario), a Master Plan scenario modelling growth according to this instrument (Master Plan scenario), a Compact growth scenario to assess the outcomes of prioritizing infill over expansion, a Moderate scenario with energy efficiency measures suggested by local actors, and a Vision scenario combining the best performing options. The following paragraphs discuss the performance of the various scenarios by the indicators described in Section 2.2.1. 74 CHAPTER 5. DISCUSSION 5.3.1 Land consumption The urban extension of Mafraq is predicted to have almost a twofold increase by the year 2030, according to the expansion of the city in the past decades (BAU scenario). Section 2.8 explains how spatial data from 1990 to 2014 is used to model the urban expansion for 2030 with three different methodologies. The Extratrees algorithm was selected for its accuracy rates and used to model the BAU scenario. Under the BAU scenario, Mafraq’s urban footprint is predicted to grow by 3.81 km2 , reaching a total urban footprint of 8.17 km2 , as shown in Figure 5.11. Furthermore, the Compact growth scenario reveals that all of the projected population growth for 2030 could be accommodated within the current boundaries of the city, as shown in Figure 5.12. Figure 5.11: Urban footprint for Mafraq in the Base scenario (2015), BAU scenario (2030), and Master Plan scenario (2030) The Compact growth scenario has no land consumption; there is no need to expand the city as the new inhabitants can be accommodated in vacant or under-utilized lots within the existing city areas. The land uses indicated in the Mater Plan and the current building norms are respected. A policy to reduce the vacant housing rate is not necessary in Mafraq, as the proportion of unoccupied houses in 2015 was already close to 8%, which has been assumed to be a desirable rate. Prioritizing infill close to high employment areas and public transportation is the policy that allows the Vision scenario to maintain the urban footprint of 2015. Land consumption directly affects the energy consumed by the city, its GHG emissions, 75 5.3. MAFRAQ Figure 5.12: Urban footprint for Mafraq in in the Base scenario (2015), BAU scenario (2030), and Compact growth scenario (2030) and costs, as explained in the following sections. Therefore, even though the value of the land around Mafraq for other uses could be questioned, it is beneficial to plan for a compact city. 5.3.2 Infrastructure costs and municipal service costs The scenarios with the smallest urban footprint, the Compact growth and Vision scenarios, require less investment to cope with the infrastructure needs of the new population. They require 10 times less money than the BAU scenario because their land consumption is 0 km2 . The 317,119 JD infrastructure costs in the Vision and Compact growth scenarios would upgrade the water, drainage, and electricity networks to cope with the population increase. The Vision scenario also yields the lowest cost for providing municipal services like public lighting, solid waste collection, municipal water, and road maintenance. If these annual costs are totalled through the year 2030, the municipality could potentially save almost 80 million JD, as shown in Table 5.3. Maintenance of the streets and roads in the city accounts for half of the urban service costs. 76 CHAPTER 5. DISCUSSION Table 5.3: Integrated costs for Mafraq Municipal service Infra. Infra. Municipal cost Infra. costs costs service 2016- Land Infill costs (expansion) (infill) costs 2030 consump. area [million [million [million [JD/capita/ [million Scenario name [Km2 ] [Km2 ] JD] JD] JD] annum] JD] BAU 3.81 - 46.26 46.26 - 105.49 181.65 Master Plan 0.56 - 6.83 6.83 - 72.87 125.48 Moderate 0.56 - 6.83 6.83 - 72.03 124.02 Compact growth - 0.06 0.32 - 0.32 67.09 115.52 Vision - 0.06 0.32 - 0.32 60.88 104.83 5.3.3 Proximity to urban services and amenities Local authorities in Mafraq suggested the idea of building a linear park along the railway that crosses the city and is used only occasionally. This option was included in the Moderate scenario. A linear park in the railway has the potential to serve around a third of Mafraq’s total population. The percentage of inhabitants that could potentially walk less than 10 minutes to find a playground, park, or open space would increase from 20% in the BAU scenario to 50% and 55% in the Vision and Moderate scenarios, respectively, as shown in Figures 5.13 and 5.14. Proximity to jobs, public transportation, and health facilities increase between 20 and 30 percentage points in the Compact growth and Vision scenarios compared to the BAU scenario. Figure 5.13 summarizes the results by indicator and scenario. Figure 5.13: Percentage of the total population in the city of Mafraq that lives in proximity to urban amenities in the various urban growth scenarios 77 5.3. MAFRAQ Figure 5.14: Map of the existing and proposed parks in the city of Mafraq, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. 5.3.4 Water and energy consumption The Vision scenario is the option that saves the most energy out of the five scenarios analyzed, as shown in Figure 5.15. More than 43 GWh per year can be saved compared to the BAU scenario, with a third of this due to the replacement of all public lighting with LED bulbs and half due to the compact development of the city. After compact growth and LED public lighting, enforcing energy and water saving measures in new housing units is the third most efficient energy saving measure. A total of 6 GWh per year can be saved in Mafraq’s dwellings. This also represents economic savings for families, amounting to about 76 JD per year per household. The current solid waste management system in Mafraq is causing two main problems, according to technical officials in the Municipality: 1. Elevated costs and diesel consumption from collection trucks that travel more than 25 kilometers to the final disposal site, and 2. pollution due to the open dumpsite. To tackle these issues, the Moderate scenario includes the construction of a transfer station and landfill to replace the dumpsite. The transfer station would be located close to the current urban boundary of the city, as shown in Figure 5.16, about 2 km from the city center. The scenario takes into consideration the additional energy that would be consumed to transfer the collected waste from low capacity urban trucks to large capacity trucks, and the energy to operate the landfill. 78 CHAPTER 5. DISCUSSION Figure 5.15: Energy consumption breakdown for the city of Mafraq by combination of policy levers Adding the transfer station and landfill increases the overall energy consumption of the solid waste management system by around 5%. This is because the additional energy required to operate the transfer station and landfill offsets the energy savings from the collection. However, the system’s energy demand can potentially be reduced by 6% if these improvements are combined with a compact growth strategy, like in the Vision scenario. This is because a more compact city would have fewer streets that require collection than the expanded city in the BAU scenario. The energy saved in the collection would be larger than the increments from the station and landfill. Additionally, Mafraq’s inhabitants would no longer be exposed to litter near their environments and the associated risks. 79 5.3. MAFRAQ Figure 5.16: Existing dumpsite and proposed locations for a transfer station and landfill in the city of Mafraq 5.3.5 GHG emissions The Vision and Compact growth scenarios have the largest potential to reduce GHG emissions. The policy levers with the highest saving potential are compact growth development, replacing all public lighting bulbs with LEDs, and implementing a Green Building Code with mandatory energy and water saving measures. Figure 5.17 summarizes the benefits of the main scenarios in terms of GHG emission reduction. 80 CHAPTER 5. DISCUSSION Figure 5.17: GHG emissions in Mafraq by scenario 5.4 Russeifa Russeifa Municipality’s urbanization originated due to its location between Amman and Zarqa. In 2015, it had an urban population of 451,315 inhabitants; this is expected to increase to 575,832 by 2030. Like the other cities, Russeifa’s possible future in 2030 was modelled for five scenarios: BAU, Master Plan, Compact growth, Moderate, and Vision scenarios. The results have been presented in Section 4.1 and are discussed in the following paragraphs. 5.4.1 Land consumption Russeifa has a growth pattern that is considerably different from the other cities in this study. In the past decades, the natural boundaries around the city have limited its horizontal growth, resulting in an expansion of only 0.7 km2 between 2000 and 2014. In line with this pattern, the predicted expansion in the BAU scenario for 2030 is barely 1 km2 , as shown in Figure 5.18. This 4% increase is considerably lower than the expansion predicted for the other Jordanian cities in this study, which ranges from 14 to 21%. The new land consumption in the Master Plan and Moderate scenarios increases to 1.6 km2 . These scenarios model growth according to the zoning stated in the Master Plan. This expansion occurs mainly toward the northwest edge of the city, in a scattered manner, where residential land use prevails. Figure 5.18 indicates in light green the areas where the new settlements are expected to emerge, and in light blue all the residential areas denoted in the Master Plan. If all of these residential areas become urban, then land consumption would double. New land consumption decreases to 0 km2 in the Compact growth scenario (see 81 5.4. RUSSEIFA Figure 5.18). This scenario reduces the vacant housing rate from the current 10% to 8% and prioritizes infill within the maximum housing densities and heights indicated in the Master Plan. The combination of these two policy levers has the potential of housing the incoming population within the existing city. It is relevant to analyze the investment needed to satisfy the new infrastructure demand in each scenario. The population density will increase in the three scenarios, augmenting the needs of the existing and new urban services, including the water and drainage networks. The first approach to this assessment is included in Section 5.4.2. Figure 5.18: Urban footprint for Russeifa in the Base scenario (2015), and by 2030 in the BAU, Master Plan, and Compact growth scenarios 5.4.2 Infrastructure costs and municipal service costs The BAU scenario for Russeifa estimates that a total of 4,803,094 JD is necessary to expand the urban footprint of the city by 4% between 2015 and 2030, with an average cost of expected public services of 43.1 JD per inhabitant per year needed for waste collection, water supply, public lighting, and road maintenance. Assuming the population in Russeifa will increase at the same annual rate, from 451,314 inhabitants in 2015 to 575,832 inhabitants in 2030, the total money spent by the municipality to provide the services mentioned would be 334,668,866 JD. The integrated infrastructure investment and services costs in the BAU scenario would be 339,471,960 JD, as shown in Table 5.4. The projection of the Master Plan will cause a 78% increase in urban investment costs, reaching 8,552,021 JD after 15 years, due to the proposed new urban areas. 82 CHAPTER 5. DISCUSSION Table 5.4: Integrated costs for Russeifa Municipal service Infra. Infra. Municipal cost Infra. costs costs service 2016- Land Infill costs (expansion) (infill) costs 2030 consump. area [million [million [million [JD/capita/ [million Scenario name [Km2 ] [Km2 ] JD] JD] JD] annum] JD] BAU 1 - 4.8 4.8 - 43.09 334.67 Master Plan 1.57 0.58 8.55 7.33 1.22 43.6 338.6 Moderate 1.57 0.58 8.55 7.33 1.22 40.54 314.84 Compact growth - 3.54 7.38 - 7.38 41.94 325.72 Vision - 3.15 6.56 - 6.56 38.09 295.8 The annual municipal service cost per inhabitant will have a slight increase to 43.6 JD (1% increase) in this scenario. Adding the investment cost and the public services cost for Russeifa´s population indicates that by the year 2030 the Master Plan scenario will have a total cost of 347,147,151 JD, which is 2% larger than the BAU scenario. The Master Plan scenario is more expensive than the BAU scenario due to the added kilometers that public service vehicles will need to drive to serve the same population, the added roads to maintain, and the added energy costs of more public lights per inhabitant. The Compact growth scenario utilizes 7,382,695 JD to retrofit the city, mainly investing in upgrading the current water and electricity networks. The scenario has no new land consumption; therefore, all of the economic resources are used to improve the existing systems. This figure is 53% and 14% higher than the BAU and Master Plan scenarios, respectively. However, it is important to recognize that current dwellers of the upgraded areas would benefit from the investment as well. Municipal service costs in the Compact growth scenario decrease by 3% compared to the BAU scenario and by 4% compared to the Master Plan option, to an annual average of 41.94 JD per inhabitant. These savings are achieved because fewer streets need to be lit up and maintained, and less driving is needed to collect solid waste. The additional investment costs in the Compact growth scenario to upgrade the city are offset by the savings in the provision of municipal services. The overall cost of the Compact growth scenario is 333,105,845 JD; this is 2% lower than the BAU scenario and 4% lower than the Master Plan scenario. By adding energy efficiency measures to compact growth, savings in the Vision scenario increase to 11% and 13% compared to the BAU and Master Plan scenarios, respectively. Annual municipal expenditure on services is 38.09 JD per inhabitant. Almost half of these potential savings come from a lower energy bill for public lighting due to the combination of more efficient light bulbs and fewer streets to light up. A third part is due to the reduction in the domestic water demand by implementing a mandatory Green Building Code, hence reducing the energy bill for supply water. The Vision scenario also yields energy savings of 5%, a potential reduction of 5% in 83 5.4. RUSSEIFA the GHG emissions, and increases the proximity of Russeifa’s population to urban amenities. These and other co-benefits are described in the following sections. 5.4.3 Proximity to urban services and amenities Local stakeholders from Russeifa Municipality expressed their specific interest in converting into urban parks the ex-phosphate land, the Zarqa River, and the former landfill. Their overall objective is to turn currently polluted and hazardous spaces like these into leisure spaces, increasing the number of public spaces and parks that Russeifa’s inhabitants can enjoy. The first policy lever tested was the creation of the parks indicated in the Master Plan. This increased the public space proximity indicator from 39% to 66%, thanks mainly to the small but distributed parks in the northeast edge of the city (see Figure 5.20). Turning the ex-phosphate lands into a park had almost no impact in terms of increasing the percentage of the population within walking distance to a park. This is because these lands are located between the Farah Park and the park on Army Street; hence, the population that lives around the ex-phosphate lands already has a park nearby. By comparing the served areas in Figures 5.19 and 5.21, it can be appreciated that the phosphate lands are already inside the served area revealed in Figure 5.19. Furthermore, creating a park at the former landfill site had no impact on proximity to public spaces either, as it is located far from the city borders. This is illustrated in Figure 5.21. On the other hand, restoring the Zarqa River as a linear park increased the indicator to 76%. The river has a strategic location running through the whole city, including the unserved areas along the eastern and western edges of the city. This is illustrated in Figure 5.22. All of these figures were created on the basis of the population settling according to the Master Plan scenario. Changing this to compact growth settlement triggers the indicator up to 83%, as shown in the Vision scenario in Figure 5.23. Growing in a compact manner also brings more inhabitants close to jobs, public transportation, health facilities, public offices, and mosques. The increments are shown in Figure 5.23 for the Compact growth and Vision scenarios. 84 CHAPTER 5. DISCUSSION Figure 5.19: Map of the existing parks in the city of Russeifa, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. Figure 5.20: Map of the existing and planned parks in the city of Russeifa, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. 85 5.4. RUSSEIFA Figure 5.21: Map of the existing and planned parks, ex-phosphate lands and ex-landfill lands turned into parks in the city of Russeifa, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. Figure 5.22: Map of the existing and planned parks, ex-phosphate lands turned into a park, and the Zarqa River Linear Park in the city of Russeifa, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. 86 CHAPTER 5. DISCUSSION Figure 5.23: Percentage of the total population in the city of Russeifa that lives in proximity to public spaces in the different scenarios. 87 5.4. RUSSEIFA 5.4.4 Proximity to Public transport The ratio of the local population with proximity to public transportation can potentially increase from 66% to 94% by building the future BRT line through the city (Vision scenario) instead of only along the highway (Moderate scenario). This means that 164,400 more inhabitants could use the BRT to connect to work, study, or leisure activities, which often occur in the neighboring Amman or Zarqa. If only the existing bus routes continue into 2030, the percentage of the population with access to this service would be only 59% (BAU scenario). Figure 5.24 shows the comparison between the different scenarios. Figure 5.24: Map of the proposed BRT stations through the city of Russeifa, and the areas they serve Note: The served areas are determined using a radius of 300 m from the bus routes and 800 m from the BRT stops. 5.4.5 GHG emissions and water and energy consumption Three main efficiency measures were combined with the Master Plan and Compact growth scenarios to create the Moderate and Vision scenarios: • New solid waste transfer station. • Green Building Code in 14% and 70% of the new housing units. • Changing 100% of the public lighting bulbs to LED. The Vision scenario has the potential to reduce the domestic water consumption by 6%, the energy consumption in urban services and dwellings by 5%, and the GHG emissions by 5%, compared to the BAU scenario. The performance of the Master 88 CHAPTER 5. DISCUSSION Plan scenario is worse than the BAU scenario in the three indicators mainly because its urban footprint is larger. The Green Building Code directly affects the amount of water that the future homes will consume. The domestic water demand can be reduced by 5% compared to the BAU scenario if at least 70% of the new dwellings implement the code. Savings drop to 1% if only 14% follow this recommendation. The code is also the second largest energy saving measure after compact growth. Building a 10 MW solar plan has no considerable impact on decreasing the GHG emissions related to energy consumption, as shown in Figure 5.26. It would be necessary to build 1,000 MW in solar plants across the country to reach a reduction of 8.9%. Figure 5.25: GHG emissions in Russeifa by scenario 89 5.5. ZARQA Figure 5.26: Energy consumption breakdown for the city of Russeifa by combination of policy levers 5.5 Zarqa Zarqa is the capital of the Governorate with the same name, and the third largest city analyzed in this study after Amman and Irbid. It had an urban population of 588,232 inhabitants in 2015, and this is estimated to increase by 27.6% by 2030 according to the Department of Statistics (see Section 3.1). Five scenarios were modelled for the city in 2030 and assessed on the basis of the indicators explained in Section 2.2.1. The scenarios model three possible urban growth patterns, a Business-as-usual growth (BAU scenario), a Master Plan scenario, and a Compact growth scenario, and then combined with the projects and policies defined with the local actors to create a Moderate scenario and a Vision scenario. The policies and scenarios are detailed in chapter 4. The results of the various scenarios are mentioned in Section 4.1 and discussed in the following paragraphs by indicator. 5.5.1 Land consumption Land consumption refers to the amount of land estimated to change from non-urban uses in 2015 to urban in 2030. The expansion model explained in Section 2.8 predicted Zarqa’s urban footprint to grow by 16%, consuming 5.94 km2 of land. Figure 5.27 shows the predicted expansion in the BAU scenario. It is important to note that the study area is limited to the municipal boundary of Zarqa. There is no Master Plan for Zarqa, but some sections in the southern and northern 90 CHAPTER 5. DISCUSSION edges of the Municipality are officially zoned. In order to model a Master Plan scenario, the maximum housing density was calculated for the zoned areas according to its land use, and a maximum density of 30 hu/ha (housing units per hectare) was assumed for the un-zoned areas. The result of this assumed density in the Master Plan scenario was half the land consumption of the BAU scenario, or 2.88 km2 , as shown in Figure 5.27. Figure 5.27: Urban footprint for Zarqa in in the Base scenario (2015), BAU scenario (2030), and Master Plan scenario (2030) New land consumption in the Compact growth scenario is 0 km2 since all future inhabitants can be housed within the current boundaries of the city, thus maintaining the city’s footprint of 29.56 km2 as shown in Figure 5.28. This scenario respects the land uses and building norms applicable to the zoned areas and assumes a maximum density of 30 hu/ha for un-zoned areas. The extension of the city modifies other indicators such as costs and energy consumption. The trade-offs are analyzed in this chapter. 91 5.5. ZARQA Figure 5.28: Urban footprint for Zarqa in in the Base scenario (2015), BAU scenario (2030), and Compact growth scenario (2030) 5.5.2 Infrastructure costs and municipal service costs Two cost indicators are assessed: the infrastructure costs as the investment needed to build the roads and networks of the expansion area (land consumed) and to upgrade the water and electric networks in the city, and the cost of providing municipal services. It is assumed that all costs that are not modified in these indicators remain the same. Infrastructure costs in the Compact growth and Vision scenarios are a third of those in the BAU scenario. The BAU scenario requires 31,540,921 JD to build the streets, walkways, and water and electricity networks for the new 5.94 km2 of city. The Compact growth and Vision scenarios have no expansion, but 10,630,878 JD is estimated to increase the capacity of the water and electric systems in 4.64 km2 of the existing city. This area is where the incoming population is projected to settle. Table 5.5 shows the main figures per scenario. Municipal service costs are 17% lower in the Vision scenario compared with the BAU scenario, and 9% lower in the Compact growth scenario compared with the BAU scenario. The cost per capita for collecting solid waste, providing domestic water, lighting the streets, and maintaining roads in the BAU scenario is estimated to be 53.63 JD/capita*annum. The compact growth scenario reduces this figure to 48.64 JD/capita because its urban footprint is smaller than in the BAU scenario, resulting in fewer streets to maintain and less energy for public lighting. The Vision scenario yields the highest economic savings because it combines compact 92 CHAPTER 5. DISCUSSION Table 5.5: Integrated costs for Zarqa Municipal service Infra. Infra. Municipal cost Infra. costs costs service 2016- Land Infill costs (expansion) (infill) costs 2030 consump. area [million [million [million [JD/capita/ [million Scenario name [Km2 ] [Km2 ] JD] JD] JD] annum] JD] BAU 5.94 - 31.54 31.54 - 53.63 542.79 Master Plan 2.88 0.63 16.06 14.63 1.43 51.18 518 Moderate 2.88 0.63 16.06 14.63 1.43 47.75 483.3 Compact growth - 4.64 10.63 - 10.63 48.64 492.3 Vision - 4.64 10.63 - 10.63 44.58 451.23 growth policies with efficiency measures such as LED public lighting, a Green Building Code, and a solid waste transfer station. The resulting annual service costs are 44.58 JD/capita. A total of 112,472,059 JD can be saved between 2015 and 2030 between the infrastructure costs and the total services costs of those years, as shown in Table 5.5. This indicates savings of more than seven million JD per year on average. The savings potential of the Vision scenario is due mainly to its compact urban form. The second highest savings measure is changing the conventional light bulbs to LED bulbs. 5.5.3 Proximity to urban services and amenities No official information on the number and location of urban amenities was provided by Zarqa Municipality or by national offices. Data from the Open Street Maps platform was used instead to calculate the proximity indicators. Since there may be missing information, the indicator results should be considered a moderate estimation. Compact growth in Zarqa increases the percentage of the population living close to urban amenities by approximately 10 points. Figure 5.29 summarizes the changes in the proximity to areas of high employment, public transportation, and urban amenities. The Moderate scenario also models the creation of two parks planned for the city and two additional parks along the Zarqa River. This scenario would serve 20% of the population. A second option, with a linear park along the Zarqa River instead of only two small sections of it, was assessed in the Vision scenario. The percentage of the population in proximity to this type of space has a twofold increase since the Zarqa River crosses through the entire city; thus, a large proportion of the city’s population would benefit from this. The extension of the areas served in each scenario is shown in Figures 5.30 and 5.31. 93 5.5. ZARQA Figure 5.29: Percentage of the total population in the city of Zarqa that lives in proximity to urban amenities in the various urban growth scenarios Figure 5.30: Map of the planned parks in the city of Zarqa, two parks in Zarqa River, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. 94 CHAPTER 5. DISCUSSION Figure 5.31: Map of the planned parks in the city of Zarqa, Zarqa River linear park, and the areas they serve Note: The served areas are determined using a radius of 700 m from the edge of every park. 95 5.5. ZARQA 5.5.4 Proximity to public transport Local authorities mentioned during the first round of workshops in Zarqa that the BRT should not only reach downtown Zarqa, but should continue into the city and reach Hashemite University. This alternative future was included in the Vision scenario. Extending the BRT line to the University increased the indicator by only 1%. 5.5.5 GHG emissions, water and energy consumption The Vision scenario yields a 5% savings in energy consumption, 6% in water consumption, and reduces by 5% the GHG emissions. This scenario includes growing in a compact form, replacing 100% of the public lighting with LED bulbs, constructing a new transfer station, and implementing the Green Building Code in 14% of the new housing units by 2030. The main driver of these savings is compact growth. Compact growth is achieved through instruments to stimulate infill in areas close to jobs and public transportation. This brings more of the population close to their places of work, reducing the average number of trips they make. The second highest savings option is the Green Building Code, which reduces the domestic energy and water demand. Figure 5.32: Energy consumption breakdown for the city of Zarqa by combination of policy levers The effects of building a 10 MW solar farm are negligible, as shown in Figure 5.33. 96 CHAPTER 5. DISCUSSION Figure 5.33: GHG emissions in Zarqa by scenario 97 5.5. ZARQA 98 6. Conclusions 6.1 Main findings The urban growth scenarios for the Hashemite Kingdom of Jordan assessed the possible outcomes of implementing various projects, instruments, and policies that aim to deal with the main concerns of the cities of Amman, Irbid, Mafraq, Russeifa, and Zarqa for 2030. The possible solutions include changing the public lighting to LED bulbs, implementing a Green Building Code, increasing the number of parks or schools, improving the solid waste management system and the public transportation system, building solar plants, enacting policies to reduce the vacant housing rate, and compact growth policies. They were conceptualized into policy levers that can change the future conditions of the cities by creating different scenarios. Three main urban growth scenarios were analyzed: a Business-as-usual (BAU) scenario following historical growth, a Moderate scenario according to the cities’ Master Plans and projects planned for each city, and a Vision scenario that couples compact growth policies with a more ambitious implementation of projects and policies. The overall objective was to identify sustainable growth paths for the five cities. The scenarios with the smallest urban footprints yielded the lowest energy consumption, greenhouse gases (GHG) emissions, and integrated municipal expenditure. Overall economic savings ranged from 12 to 49% when comparing compact growth development against the expansion of the BAU scenario, and increased to 20 to 54% in the Vision scenario. This is true for all except Russeifa, where all scenarios have a compact urban form because of the natural restrictions on expansion in the area. A compact growth policy has the potential of reducing energy consumption from public services and dwellings, and the related GHG emissions, between 2 and 14% compared to the BAU scenario. The Vision scenario has the highest population proximity to employment and urban services in all of the cities in the present study. Additionally, intensification along the BRT corridors that are currently under construction in Amman proved to substantially increase the proximity to schools, sports facilities, health facilities, and mosques, benefiting around 800,000 inhabitants. Intensification showed a potential savings of 788,640 tons of CO2 eq from being emitted every year in 6.1. MAIN FINDINGS Amman by reducing the length of daily trips within the city or by commuting via more efficient modes. The costs associated with upgrading the capacity of the water and electricity networks in the intensification corridors were offset by the savings obtained mainly from having fewer streets per person to maintain and provide lighting. The Master Plans of all the cities have zoned expansion areas that are between 3 and 11 times larger than the BAU scenario. If no policy to manage urbanization in time and space is enforced, all these areas are at risk of being urbanized, increasing the costs of providing urban services, investment costs, energy consumption, and GHG emissions. Reducing the vacant housing rate from the current percentages to 8% has a significant impact on the land consumption of Amman and Irbid, reducing by 85% the amount of land that would change from other uses to urban. In Russeifa and Zarqa the reduction is moderate, and in Mafraq this measure has no impact because the current rate is already 8%. The public transportation expansion plans provided by the municipalities and included in the Moderate scenarios did little to increase the accessibility to these services (by less than 2% on average). Congestion and air pollution problems can be expected to augment if this trend is not modified. On the other hand, compact growth proved to enhance public transport proximity, increasing the indicator by 20 points on average in the five cities. More information regarding the quality of the current bus service is required to build indicators that reflect in a comprehensive manner the benefits of replacing current bus routes by integrated public transportation systems that could include BRT lines. Changing public lighting to LED saves around 5% of the money spent on municipal services when compared to the BAU scenario. Only in the city of Amman is this less than 3%, because the city has already changed almost half of its public lighting to LED. It is highly recommended to integrate water saving measures into Green Building Codes. Apart from reducing the annual volume of supplied water by 6%, it has an important impact in reducing energy consumption and energy bills that the municipalities pay to supply the vital liquid due to the high amount of energy embodied in water supply. Making the Green Building Code mandatory has the potential to save 1% of the total energy consumed in Jordan. A total of 853.32 GWh/yr and 0.41 MTCO2eq/yr in the five cities can be saved by changing from a voluntary instrument in which only 14% of the new housing units apply the recommendations to a mandatory instrument achieving a 90% penetration. Irbid’s planned schools were shown to be in locations that allow the population to reach them by walking. The parks planned in Irbid’s Master Plan will not be enough to grant access to these kinds of spaces to all of its population; it is recommended to plan additional parks in the unserved areas. Even with the nine new bus routes 100 CHAPTER 6. CONCLUSIONS planned for the city, a third of Irbid’s population will not be within walking distance to the public transportation system. It is recommended to favor new dwellings close to the main transportation corridors to increase the percentage of the population with access to this service, and to allow bus routes to be more profitable. In Russeifa, adding a BRT line that goes through the city instead of along the highway would benefit 164,400 inhabitants. Cleaning the ex-phosphate lands is highly recommended as they are a hazardous source of air pollution located in the middle of the city. However, turning these lands into a park, as indicated in Russeifa’s Master Plan, has barely any impact in terms of improving the accessibility of the population to these kinds of spaces because these lands are located close to the largest existing parks in the city. Another option is to assign mixed uses to foster employment, services, and housing, linking this new development with the other cities and Russeifa itself with an alternative new BRT line that goes through the city. The best option to increase accessibility to parks in Russeifa and Zarqa is making the Zarqa River a linear park, as both cities have grown around the river. Zarqa’s plans for creating two new parks would benefit almost 20% of the population, increasing to 37% if the river is involved. The creation of a landfill and a transfer station modelled in Mafraq’s Vision scenario would save the municipality energy and money, and potentially reduce health problems related to the current open dumpsite. Collection and transportation energy can be reduced by 1.4 GWh per year with the building of the transfer station, but an additional 1.3 GWh is required each year to operate the station and the new landfill. However, the energy saved in Mafraq’s solid waste collection system can offset the operational landfill and transfer station energy if the city grows in a compact form, yielding a reduction of 25.9 GWh per year. Compact growth proved to be a cross-sectoral strategy that yields environmental, social, and economic benefits. Even though the environmental or agricultural value of the land around some cities could be questioned, it is important to plan for compact growth development due to its potential in decreasing energy consumption, GHG emissions, and municipal costs. The total GHG emissions that can potentially be prevented each year with the Vision scenario in the five cities is of 1.1 MTCO2eq, which is 4% of the total annual GHG emissions in Jordan [13]. This figure achieves 3% only through compact growth policies. 6.2 Further work The creation of a Geoportal is necessary to facilitate comprehensive urban planning in the Kingdom. A Geoportal is an open-access web-based platform integrating urban spatial information into one database. This kind of platform can foster the diagnosis and planning of urban issues, and the creation of indicators that assess the possible futures outlined by the Master Plans. Its open accessibility to Jordanian municipalities 101 6.2. FURTHER WORK and planners in general is essential. An urban growth scenario tool for the Ministry of Municipal Affairs can be developed in the future to support planning for Jordanian cities. The compliance with social, environmental, and economic standards can be assessed through indicators built into the tool and used to guide the future of the cities into the development objectives of the Hashemite Kingdom of Jordan. 102 A. Indicators methodology This Appendix contains a detailed description of the calculation methods and data sources used to build each of the indicators explained in section 2.2.1. Appendix B summarizes the values used to calculate the indicators in each city and policy lever. A.0.1 Land consumption Description. Amount of land predicted to change from natural habitats or agricultural uses into urban human settlements. Measurement units. Square kilometers [km2 ] Methodology. Land consumption (land_consumption_km) is calculated as the difference between the city footprint in the horizon year (fp_horizon) and the footprint in the base year (fp_base). The city footprint refers to the total built-up area of a city, including streets, open space and inner vacant land. Urban footprint for the horizon year is estimated using artificial neural networks based on orography, roads, built-up area, population and employment historical data, using at least two points in time (e.g. years 2000 and 2015). The number of years between these two points in the past determines how far into the future can the forecast go; for example, if 2015 is the base year, and information from 15 years before 2015—Year 2000—is available, then the forecast can go as far as 15 years after the base year 2015—year 2030. Calculation. land_consumption_km = fp_horizon - fp_base Sources. • Built-up area: Developed by CAPSUS as explained in chapter 2.8 A.0.2 Population density Description. Number of inhabitants per built-up area, expressed as inhabitants per square kilometer. Measurement units. Inhabitants per square kilometers [pop/km2 ] Methodology. The population density (pop_dens) is calculated by dividing the total number of inhabitants (tot_pop) by the built-up area of the city (footprint_km2 ). Calculation. pop_dens = tot_pop / footprint_km2 Sources. • Population: Population and housing census 2015 and projections by the Department of Statistics and the Housing and Urban Development Corporation (HUDC) [2, 33]. • Built-up area: Developed by CAPSUS as explained in chapter 2.8 Desirable range. According to the studies Better Neighborhoods: Making higher density work [38], Compact Sustainable Communities [39] and Towards a strategy of Transport Oriented Design for Mexico City [40], recommended urban densities range between 80 to 110 housing units per hectare (15,000 to 35,000inhabitants per km2 ). 104 APPENDIX A. INDICATORS METHODOLOGY A.0.3 GHG emissions Description. Average per capita greenhouse gases emissions released annually related to the energy consumed for public lighting, municipal water supply, solid waste management, electricity in dwellings and commuting (public transportation and private vehicles). Measurement units. Kilograms of CO2 eq per person per year [kgCO2 eq/capita/annum] Methodology. Annual per capita GHG emissions (ghg_tot) are the result of multiplying annual energy consumption per person by the carbon factor of each type of energy. Carbon factors refer to the amount of CO2eq released by unit of energy consumed. For electricity consumption, the carbon factor (carbon_factor_elect) is specific to the national energy mix. Gasoline (carbon_factor_gasoline) and diesel (carbon_factor_diesel) carbon factors are used to estimate emissions from the consumption of fuels in transportation. The types of energy considered are: electricity for public lighting (energy_lighting), electricity for water supply (energy_water), electricity for housing units (energy_buildings), energy for solid waste collection, transportation and final disposal (energy_swaste) and energy for commuting (energy_gasoline and energy_diesel). The carbon electric factor (carbon_factor_elect) is calculated by dividing the electric emissions (elec_emi) between the total electricity generated in the country (gen_tot) plus the total electricity imported in the country (imp_tot). The electric emissions (elec_emi) value is calculated by adding the emissions of the energy generated in the country (gen_emi) plus the emissions from the energy imported to the country (imp_emi). To calculate the emissions of the energy generated in the country (gen_emi) is necessary to make a calculus adding the generation of each type of energy multiplied by the emissions factor of each type of energy: steam energy generation (ste_gen) is multiplied by the steam energy emissions (ste_emi), plus the diesel powered gas turbine generation (gdie_gen) multiplied by the diesel powered gas turbine emissions (gdie_emi), plus the natural gas powered gas turbine electric generation (gnat_gen) multiplied by its emissions (gnat_emi), plus the heavy fuel oil powered diesel engine electric generation (hfo_gen) multiplied by its emissions (hfo_emi), plus the natural gas powered diesel engine electric generation (dnat_gen) multiplied by its emissions (dnat_emi), plus the diesel engine electric generation (die_gen) multiplied by its emissions (die_emi), plus the electric generation of hydropower units (hyd_gen) multiplied by its emissions (hyd_emi), plus the eolic electric generation (win_gen) multiplied by its emissions (win_emi), 105 plus biogas plants electric generation (bio_gen) multiplied by its emissions (bio_emi), plus combined cycle electric generation (com_gen) multiplied by its emissions (com_emi), plus the existing generation of solar energy (sol_gen) plus the calculation of new solar power plants (capacity of the solar plant (sol_cap) multiplied by the efficiency of the plant (sol_eff) and by the hours of sun that the plant receives every year (sol_hours)) multiplied by 365 and by the emissions of solar electric generation (sol_emi). The emissions of the imported energy (imp_emi) are calculated by subtracting the generation of the new solar power plants (capacity of the solar plant (sol_cap)) multiplied by the efficiency of the plant (sol_eff) and by the hours of sun that the plant receives every year (sol_hours) from the total energy imported in the country (imp_tot), all multiplied by the imported energy emissions factor (imp_fact). Carbon_factor_diesel and carbon_factor_gasoline are constant values assumed from the national average. Calculation. ghg_tot=(energy_water+energy_lighting+energy_buildings)*carbon_factor_elect + energy_gasoline* carbon_factor_gasoline + (energy_diesel+energy_swaste)*carbon_factor_diesel carbon_factor_elect=(elec_emi/(gen_tot+imp_tot))/1000000 elec_emi=gen_emi+imp_emi gen_emi=(ste_gen*ste_emi)+(gdie_gen*gdie_emi)+(gnat_gen*gnat_emi)+(hfo_gen *hfo_emi)+(dnat_gen*dnat_emi)+(die_gen*die_emi)+(hyd_gen*hyd_emi)+(win_gen* win_emi)+(bio_gen*bio_emi)+(com_gen*com_emi)+((sol_gen+((sol_cap/1000)* (sol_eff/100)* (sol_hours*365))*sol_emi) imp_emi=(imp_tot-((sol_cap/1000)*(sol_eff/100)* (sol_hours*365)))*imp_fact Sources. • Electricity generation in Jordan obtained from the National Electric Power of Jordan [41] • Emissions factors per type of generation obtained from the IPCC [42] • Efficiency of solar plant of 80% [43] • Sun hours that Jordan receives in average [44] 106 APPENDIX A. INDICATORS METHODOLOGY A.0.4 Energy consumption Description. Total average energy consumed per person during a year for public lighting, municipal water supply, solid waste management, electricity in dwellings and commuting by public transportation and private vehicles. The solid waste management energy consumption involves collection, transportation to transfer stations and final disposal sites, transference, and final disposal in landfill (if applicable). Measurement units. Kilowatts hour per person per year [kWh/capita/annum] Methodology. The indicator energy_consumption embraces the energy consumed by the city’s population for commuting (energy_transport), for the electricity they consume in their homes (energy_buildings), to supply the water they consume (energy_water), for public lighting (energy_lighting), and to manage the solid waste produced by the city (energy_swaste). Each of these consumptions is explained as one indicator in the next charts. Calculation. energy_consumption=energy_water+energy_lighting+energy_swaste+ energy_buildings+energy_transport 107 A.0.5 Energy consumption for commuting Description. Total average energy consumed per person during a year for commuting within the city, by public transportation or private vehicle. Measurement units. Kilowatts hour per person per year [kWh/capita/annum] Methodology. Energy consumption associated with transportation (energy_transport) is calculated by adding the energy consumed by type of fuel available in transportation vehicles in the city (energy_diesel and energy_gasoline) divided by the total population(tot_pop). The overall consumption in the city, by type of fuel, is the sum of the energy consumed in all the analysis points in the city. The energy associated to transportation by fuel type in each analysis point was calculated by multiplying the costs incurred in each type of fuel (transport_cost_diesel and gasoline) per person, by the population in the analysis point, by a factor that converts them to energy. This factor combines the diesel and gasoline calorific value (diesel_cv and gasoline_cv) and the diesel and gasoline density (gasoline_density and diesel_density). The transport cost associated to each type of fuel was calculated by multiplying the transport (transport_cost) by the fraction each type of transport represents of the whole array (gasoline and diesel_transp_frac). Transport costs were calculated per each analysis point by applying a linear multivariable regression modelIed with expenditure and income data from Mexico. The model’s resulting units are costs incurred per household per trimester in Mexican pesos. As the units desired are the local currency per person per year, the result is multiplied by 4 trimesters in a year, the exchange rate to the desired currency (JDMXN_exrate), average inflation of the Mexican Peso from the date the model was developed until the current date (avge_inflation), and divided by average household size in the city (hu_size). Calculation. energy_transport=(sum(energy_dieseli )+sum(energy_gasolinei ))/tot_pop energy_diesel=sum(energy_dieseli )/tot_pop energy_gasoline=sumenergy_gasolinei )/tot_pop energy_dieseli =popi *transport_cost_dieseli *(diesel_cv/diesel_cost* diesel_density/1000) energy_gasolinei =popi *transport_cost_gasolinei *(gasoline_cv/gasoline_cost* gasoline_density/1000) transport_cost_dieseli =transport_costi *diesel_transp_frac/100 108 APPENDIX A. INDICATORS METHODOLOGY transport_cost_gasolinei =transport_costi *gasoline_transp_frac/100 transport_costi =max(0,[4/JDMXN_exrate*(1+avge_inflation/100)*(- 620.06+3.06*transit_distance+(-19.10)*job_density_avge+(- 0.69)*pop_density_avge+213.09*avge_area+661.16*socioeco_level)]/hu_size) Sources. • Percentages of diesel and gasoline vehicles in Jordan [45] • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. • Diesel and gasoline calorific values and densities [46] 109 A.0.6 Energy consumption for water distribution Description. Per capita annual amount of energy required to supply the volume of water demanded by the city’s dwellings. Water losses due to the municipal network lakages are included in the calculation. Measurement units. Kilowatts hour per person per year [kWh/capita/annum] Methodology. The total annual energy consumption for water distribution (energy_water) is calculated by multiplying the energy needed to supply and distribute one cubic meter of water (water_factor) by the sum of the total volume of water consumed by the city (tot_water * tot_pop) and the water lost through leakages; which is estimated as the multiplication of the kilometres of roads in one square kilometre of the city (prim_road_km2 + sec_road_km2 + ter_road_km2 ), the square kilometres of the city (footprint_km2 ) and the volume of water lost by kilometre (loss). This total is then divided by the total population of the city (tot_pop). Calculation. energy_water=water_factor*(tot_water*tot_pop+footprint_km2 *(prim_road_km2 + sec_road_km2 +ter_road_km2 )*loss)/tot_pop Sources. • Total water consumed by the city [47]. • Loss of water per net length [47]. • Energy consumption of the municipal water grid to supply 1 m3 of water [48]. • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. • Roads: Open Street Maps and Jordan Ministry of Transport spatial registries. • Built-up area: Developed by CAPSUS as explained in chapter 2.8. 110 APPENDIX A. INDICATORS METHODOLOGY A.0.7 Energy consumption for public lighting Description. Annual average energy consumption for public lighting per person. Measurement units. Kilowatts hour per person per year [kWh/capita/annum] Methodology. The energy consumption for public lighting (energy_lighting) considers the total number of bulbs in the city (tot_bulb), how many of these are LED bulbs (num_led), the voltage of conventional bulbs (volt_bulb) and LED bulbs (volt_led), the dalily number of hours day that the bulbs are on (h) and 365 days to calculate the annual energy required to illuminate the streets. This number is divided by the total kilometers of primary, secondary and tertiary roads in the city (prim_road_km + sec_road_km + ter_road_km) to obtain the energy required per kilometer of street. As the precise number of kilometers of street that the city will have is uncertain, this number is estimated by multiplying the total built-up area of the city (footprint_km2 ) by the kilometers of primary, secondary and tertiary roads per square kilometer that the city has in the base year (prim_road_km2 + sec_road_km2 + ter_road_km2 ). At last, the energy required per kilometer of street is multiplied by the estimated kilometers of street and divided by the total population (tot_pop) to obtain the annual per capita energy consumption for public lighting. Calculation. energy_lighting=((tot_bulb- num_led)*volt_bulb+num_led*volt_led)*hours_day*365/(prim_road_km+ sec_road_km+ter_road_km+ped_road_km)*(prim_road_km2 +sec_road_km2 + ter_road_km2 )*footprint_km2 /tot_pop Sources. • LED and common bulbs voltage [49] • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. • Roads: Open Street Maps and Jordan Ministry of Transport spatial registries. • Built-up area: Developed by CAPSUS as explained in chapter 2.8. 111 A.0.8 Energy consumption for solid waste collection Description. Average per capita energy consumed annually by the solid waste management system of the city, including collection, transportation and energy consumed in the landfill and transfer stations. Measurement units. Kilowatts hour per person per year [kWh/capita/annum] Methodology. The energy consumption associated with solid waste management (energy_swaste) embraces the energy consumed in every step the management system: solid waste collection (collection_energy), its transportation to the transfer stations and/or landfills (transport_energy), the energy consumed in the transfer station (TS_energy) and the energy used in the landfill (landf_energy). The result of this sum is divided by the total population (tot_pop). The first step is to calculate the energy used in the collection stage (collection_energy). This includes the efficiency of the collection truck (truck1_ef) in liters of diesel consumed per km, multiplied by the diesel density (diesel_den), the diesel calorific value (diesel_cv) and the total of kilometers traveled in a year, which is estimated by multiplying the kilometers of primary roads per km2 (prim_road_km2 ) by the percentage of the primary roads that the truck uses (prim_road_fact) plus the kilometers of secondary roads per km2 (sec_road_km2 ) multiplied by the percentage of the secondary roads that the truck uses (sec_road_fact) plus the kilometers of tertiary roads per km2 (ter_road_km2 ) multiplied by the percentage of the tertiary roads that the truck uses (ter_road_fact). All the last is multiplied by the number of times the truck collects garbage per week (collections) and multiplied by the weeks of the year. Then, this total is multiplied by the total built-up area (footprint_m3 ) to obtain the energy used by the truck per year. Additionally, is important to add the energy used by the truck’s compactor system, to calculate this part is necessary to multiply the compactor efficiency in m3 of diesel by m3 of garbage (comp_ef) by the diesel density (diesel_den) and the diesel calorific value (diesel_cv). Then multiply the result by the total population (tot_pop) multiplied by the waste generation per person per day (waste_per) multiplied by 365 days per year, all between the waste density (waste_density). The second stage is the energy used by the waste transport (transport_energy) from the center of the city to the transfer stations and from the transfer stations to the landfill, if there are not transfer stations it is assumed that the transport is 112 APPENDIX A. INDICATORS METHODOLOGY from the center of the city to the landfill area. The first part assumes that there are not transfer stations, it includes the efficiency of the collection truck (truck1_ef) converted to m3 per km multiplied by the diesel density (diesel_den) and the diesel calorific value (diesel_cv). Then is multiplied by the total waste volume collected annually (tot_wvol) divided by the capacity of the collection truck (truck1_cap), multiplied by the average of the distances from the center of the city to the landfill or landfills (dist_land). The second part of the calculation assumes one or more transfer stations exist, therefore it is the same than the first part but multiplied by the average of the distances from the center of the city to the transfer stations (dist_ts) and plus the efficiency of the transfer station truck (truck2_ef) converted to m3 per km multiplied by the diesel density (diesel_den) and the diesel calorific value (diesel_cv) and multiplied by the total population (tot_pop), the waste generation per person per day (waste_per), 365 days per year, and divided by the capacity of the transfer station truck (truck2_cap) multiplied by the average of the distances from transfer stations to the landfill (dist_tsland). The third stage is the energy consumed in the transfer stations (TS_energy) which includes the multiplication of the total population (tot_pop) by the waste generated per person per day (waste_per) multiplied by 365 days per year, multiplied by the energy consumed by the waste segregation machinery (energy_tonTS). The fourth and last stage is the calculation of the energy consumed in the landfill. This is obtained by multiplying the total population (tot_pop) by the waste generation per person per day (waste_per) by 365 days per year, divided by the efficiency of the landfill in ton per year (land_ef), all multiplied by the efficiency of the landfill trucks (truck3_ef). Calculation. energy_swaste=(collection_energy+transport_energy+ TS_energy+landf_energy)/tot_pop collection_energy=truck1_ef/1000*diesel_den*diesel_cv*(prim_road_km2 * prim_road_fact/100+sec_road_km2 *sec_road_fact/100+ter_road_km2 * ter_road_fact/100)*collections*52*footprint_km2 +comp_ef/1000*diesel_den* diesel_cv*tot_pop*waste_per*365/1000/waste_density transport_energy=(((truck1_ef/1000)*diesel_den*diesel_cv)*(((tot_pop*waste_per *365)/1000))/truck1_cap)*dist_land)+(((truck1_ef/1000)*diesel_den*diesel_cv)* (((tot_pop*waste_per*365)/1000))/truck1_cap)*dist_ts)+(((truck2_ef/1000)* diesel_den*diesel_cv)*(((tot_pop*waste_per*365)/1000))/truck2_cap)*dist_tsland) TS_energy=((tot_pop*waste_per*365)/1000))*energy_tonTS landf_energy=(((tot_pop*waste_per*365)/1000))/land_ef)*truck3_ef 113 Sources. • Solid waste generation and efficiencies and capacities of the trucks: Obtained from the cities municipalities. • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. • Roads: Open Street Maps and Jordan Ministry of Transport spatial registries. • Built-up area: Developed by CAPSUS as explained in chapter 2.8. 114 APPENDIX A. INDICATORS METHODOLOGY A.0.9 Energy consumption for dwellings Description. Average annual housing electricity consumption per capita. Measurement units. Kilowatts hour per person per year [kWh/capita/annum] Methodology. The annually energy consumed per person in housing units (energy_buildings) reflects the energy savings expected from implementing a Green Building Code in the houses to be built between the base year and the horizon year (HU_new). It is estimated by multiplying the number of housing units existing in the base year (HU_existing) by the average energy consumption per household established as baseline (ener_baseline), plus the multiplication of the new houses (HU_new) by the penetration percentage of the green building code (GBC_pen /100) by the reduced energy consumption per household (GBC_ener), plus the multiplication of the new houses (HU_new) by the percentage that do not implement the green building code (1-GBC_pen/100) by the baseline housing unit energy consumption (ener_baseline). This total volume is then divided by the total population (tot_pop) to obtain the annual housing energy consumption per capita (energy_buildings). The number of new housing units (HU_new) is calculated as the difference between the total number of housing units in the horizon year (HU_tot_h) and the total number of housing units in the base year (HU_tot_b). Calculation. energy_buildings=(HU_existing*ener_baseline+(HU_new*(1- GBC_pen/100)*ener_baseline+HU_new*GBC_pen/100*GBC_ener))/tot_pop HU_existing=HU_tot_b HU_new=HU_tot_h-HU_tot_b Sources. • Energy of equipment: Energy of air conditioner, heater and lighting [44]. • Saving of equipment: Obtained from the USA Environmental Protection Agency (EPA) • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. • Roads: Open Street Maps and Jordan Ministry of Transport spatial registries. • Built-up area: Developed by CAPSUS as explained in chapter 2.8. 115 A.0.10 Infrastructure costs Description. Total investment required to build the roads, water network, sewer network, public lighting and electricity grids for the square kilometers that the city will expand, and the investment required to increase the capacity of the existing networks were the urban population will have a two-fold increase. Measurement units. Jordan dinars [JD]. Methodology. The infrastructure costs indicator is the sum of the investment required to build the infrastructure of the city’s expansion (infrastructure_new_costs) and to upgrade the areas of the city that will increase their population (infrastructure_infill_costs). Calculation. Infrastructure_costs=Infrastructure_new_costs + Infrastructure_infill_costs 116 APPENDIX A. INDICATORS METHODOLOGY A.0.11 Infrastructure costs for urban expansion Description. Total costs to build the roads and water, sewage, public lighting and electricity networks of the km2 that the city is estimated to grow. Measurement units. Jordan dinars [JD]. Methodology. Combines the parametric costs to build the infrastructure of one new km2 of city by the total km2 of urban expansion. Infrastructure costs embrace construction costs per kilometer of primary road (cost_prim_road), secondary road (cost_sec_road), tertiary road (cost_ter_road), municipal water network (cost_water), sewage (cost_swge), electric grid (cost_elec) and public lighting (cost_light). Construction of roads considers walkways and road pavement. As the precise number of kilometers of street that the city will have is uncertain, this number is estimated by multiplying the total built-up area of the city (footprint_km2 ) by the kilometers of primary, secondary and tertiary roads per square kilometer that the city has in the base year (prim_road_km2 + sec_road_km2 + ter_road_km2 ). This estimate is multiplied by each of the parametric costs to obtain the total infrastructure costs of expanding the city. Calculation. infrastructure_new_costs = ((cost_prim_road * prim_road_km2 ) + (cost_sec_road * sec_roads_km2 ) + (cost_ter_road * ter_roads_km2 ) + ((cost_light + cost_elec+cost_watr + cost_swge) * (prim_road_km2 + sec_roads_km2 + ter_roads_km2 ))) * land_consumption_km 117 A.0.12 Infrastructure costs for upgrading existing capacity Description. Total costs to upgrade the water, sewage and electricity networks of the areas of the existing city that are estimated to have a two-fold increase in their population. Measurement units. Jordan dinars [JD]. Methodology. Considers costs associated with retrofitting the infrastructure needed to promote infill development. Includes parametric costs of water pipes retrofit (retro_watr), electricity lines retrofit (retro_elec), and sewer infrastructure retrofit (retro_swge) per km2 that needs to be retrofitted to cope with the demand of the new population density. The parametric costs are then multiplied by the number of kilometers of primary, secondary and tertiary roads per square kilometer of city (prim_road_km2 + sec_roads_km2 + ter_roads_km2 ), and by the amount of total land designated for infill development (infill_area_km2 ). If the infill area is not indicated by the user, it is calculated as the sum of the areas of all points of analysis that had population since the base scenario (pop0 >0) and their population had a two-fold increase or more, i.e. that the population in the scenario of analysis (popi ) is twice or more than the population in the base scenario (pop0 ), which was higher than 0. Calculation. infrastructure_infill_costs=(retro_elec+retro_watr+retro_swge)*(prim_road_km2 +sec_roads_km2 +ter_roads_km2 )*infill_area_km2 infill_area_km2 =0.01*sum of area where pop0 >0 and popi /pop0 >=2 118 APPENDIX A. INDICATORS METHODOLOGY A.0.13 Water consumption Description. Total average volume of water consumed per capita in the households of the city in one year. Measurement units. Cubic meters per person per year [m3 /capita/annum] Methodology. The total volume of water consumed annually per capita (tot_water) reflects the water savings expected from implementing a Green Building Code in part of the new houses to be built between the base year and the horizon year (HU_new). It is estimated by multiplying the number of housing units existing in the base year (HU_existing) by the average consumption of water per household established as baseline (HU_water0), plus the multiplication of the new houses (HU_new) by the penetration percentage of the green building code (GBC_pen /100) by the reduced water consumption per household (HU_water1), plus the multiplication of the new houses (HU_new) by the percentage that do not implement the green building code (1-GBC_pen/100) by the baseline housing unit water consumption (HU_water0). This total volume of water is then divided by the total population (tot_pop) to obtain the annual water consumption per capita. The number of new housing units (HU_new) is calculated as the difference between the total number of housing units in the horizon year (HU_tot_h) and the total number of housing units in the base year (HU_tot_b). Calculation. tot_water=((HU_existing*HU_water0)+(HU_new*(1- (GBC_pen/100))*HU_water0+(HU_new*(GBC_pen/100)*HU_water1))/tot_pop HU_existing=HU_tot_b HU_new=HU_tot_h-HU_tot_b Sources. • Average water savings per equipment (toilets, showerheads, sinks and washing machine) [50] • Total houses from the Population and housing census 2015 [2]. 119 A.0.14 Job proximity Description. Percentage of the population that lives within a distance of 1,000 meters from the areas of high job density of the city. Measurement units. Percentage [%] Methodology. This indicator identifies the areas of the city that concentrate employment and then quantifies the population that lives close to these areas as a percentage of the total city population. The process is divided in 4 stages: First, job density is quantified for each analysis point within a 250x250 meters grid. Job density (job_density) is measured as the number of jobs in a radius of 1,000 meters divided by the area of that circle, i.e. by 314.16 hectares. Second, a buffer of the maximum distance recommended (max_dist_job) is created from the center of each analysis point with a job density equal or higher than the minimum job density recommended (min_job_density). Third, the population (pop) of all the analysis points contained in the buffer is added up. This is the population that lives close to high job density areas (pop_prox_job). Fourth, this population is divided by the total population of the city (tot_pop) to obtain the percentage of the population that lives close to employment (job_prox). Calculation. job_prox=pop_prox_job/tot_pop pop_prox_job=sumpopif(distance<=max_dist_job) from (job_density>min_job_density) max_dist_job=800m min_job_density=10 jobs per hectare If jobs_lever = 0 job_prox = ( subset(pop_prox_job, footprint_base) + pop_expansion * d_job_prox) / tot_pop Sources. • Number of jobs: Obtained of an estimate of gross domestic product (GDP) derived from satellite data made by the National Oceanic and Atmospheric Administration (NOAA) [36] [30]. 120 APPENDIX A. INDICATORS METHODOLOGY • Population: Population and housing census 2015, table 3.1 “Total population” by block or neighborhood [2]. 121 A.0.15 Proximity to public transport Description. Percentage of the population that lives within walking distance from a public transport station. Walking distance is considered to be 800 m for structured transport systems like a BRT or subway, and 300 m for buses and similar. Measurement units. Percentage [%] Methodology. Public transport proximity (transit_prox) is calculated by dividing the population (pop_prox_transit) that lives within the maximum distance recommended to a public transport station (max_dist_transit), by the total population (tot_pop). First, a buffer of the maximum distance recommended (max_dist_transit) is created from the center of each public transport station. Second, the population (pop) of all the neighborhoods or blocks contained in the buffer is added up to obtain the population that lives close to public transport (pop_prox_transit). Third, this population is divided by the total population of the city (tot_pop) to obtain the percentage of the population that lives close to public transport (transit_prox). Calculation. transit_prox=pop_prox_transit/tot_pop pop_prox_transit=sumpopif(distance<=max_dist_transit) If transit_lever=0 transit_prox=(subset(pop_prox_transit,footprint_base)+pop_expansion* d_transit_prox)/tot_pop Sources. • Public transport stations: Municipalities of the cities and Ministry of Transport city registries. • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. Desirable range. 80% to 100% 122 APPENDIX A. INDICATORS METHODOLOGY A.0.16 Services proximity Description. Percentage of the population with access to each of the following urban public services and amenities: schools (elementary, secondary or high school), universities, health facilities (clinics and hospitals), nurseries, public buildings, cultural spaces (community centers, libraries, theaters), worship places, markets, sport facilities, and public spaces. One indicator is calculated per each urban service or amenity. Measurement units. Percentage [%] Methodology. One indicator is calculated for each of the following classes of amenity: school, university, health facility, nursery, public building, cultural facility, place of worship, market, sports and public space. The proximity is calculated for each amenity class by dividing the population (pop_prox_ami ) that lives within the maximum distance recommended for that type of amenity (max_disti ), by the city’s total population (tot_pop). Table A.1 shows the maximum distance considered for each indicator. Table A.1: Classes and maximum distance recommended Class Landmarks Maximum distance School Elementary school, secondary school and high school 700m Health Clinic, doctors, hospital 1500m Public building Public building, town hall 2000m Cultural facility Community center, library, social facility, theater 1000m Place of worship Mosque, Church 1000m Sports Sport field or court, Pitch, swimming pool 1000m Public space Park, garden, public space 700m Calculation. amen_proxi =(pop_prox_ami )/tot_pop pop_prox_ami =sumpopif(distance<=max_disti) If amenity_lever=0 amen_prox=(subset(pop_prox_am,footprint_base)+pop_expansion* d_amen_prox)/tot_pop 123 A.0.17 Municipal service costs Description. Average per capita annual municipal expenditure needed to provide public lighting, potable water, and solid waste collection services and maintenance to the city roads. Measurement units. Jordan dinars per capita [JD/person] Methodology. The annual municipal expenditure in public services per person (municipal_service_costs) is estimated from the energy needed to provide public lighting (energy_lighting) in all the streets of the city multiplied by the cost the municipality pays per each kWh of electricity consumed for public lighting (elighting_cost), plus the energy needed to provide potable water (energy_water) multiplied by the cost the municipality pays per each kWh of electricity consumed for provide potable water (ewater_cost), plus the diesel consumed by the trucks used for solid waste collection and management (energy_swaste) multiplied by the cost of one liter of diesel (diesel_cost), plus the average amount of money that the municipality spends to maintain one kilometer of road (road_maintenance) multiplied by the road kilometers per square kilometer of the city (prim_road_km2 +sec_roads_km2 +ter_roads_km2 ), by the built-up area of the city (footprint_km2 ) and divided by the total population (tot_pop) to obtain the municipal expense per capita. Calculation. municipal_service_costs = energy_lighting*elighting_cost + energy_water*ewater_cost + ( diesel_cost*1000 * (collection_energy+transport_energy) / (diesel_den*diesel_cv) + road_maintenance * (prim_road_km2 + sec_roads_km2 + ter_roads_km2 ) * footprint_km2 ) / tot_pop Sources. • Population: Population and housing census 2015, table 3.1 “Total population” by neighborhood [2]. • Roads: Open Street Maps and Jordan Ministry of Transport spatial registries. • Built-up area: Developed by CAPSUS as explained in chapter 2.8. • Diesel calorific value and density [46] 124 APPENDIX A. INDICATORS METHODOLOGY A.0.18 Exposure to hazards Description. Percentage of the population that is exposed to hazardous pollutants for living in the outreach of human-made stationary sources of pollution. Measurement units. Percentage [%] Methodology. Exposure (hazard_exp) is calculated by dividing the population that lives within the outreach distance (outreach) of a hazardous source of pollution (pop_prox_hazards) by the total population (tot_pop). First, a buffer of the outreach distance (outreach) is created from the center of each human-made stationary source of pollutants. Second, the population (pop) of all the analysis points contained in the buffer is added up to obtain the population exposed to hazards (pop_prox_hazards). Third, this population is divided by the total population of the city (tot_pop) to obtain the percentage of the population that is exposed to hazardous pollutants (hazard_exp). Table A.2 shows the outreach distance per type of hazard. Table A.2: Outreach distance Type of hazard Outreach distance Outreach of sources of suspended particulate 1000m Outreach of water borne hazards 700m Calculation. hazard_exp=(pop_prox_hazards)/tot_pop pop_prox_hazards=sum pop if (distance <= outreach) Sources. • Hazards location obtained from the cities’ municipalities. 125 126 B. Policy levers Tables B.1 and B.2 summarize all the policy levers per city. Table B.1: Policy levers for the five cities City Policy lever Policy lever level Lever name Solid waste management 0 Existing landfill and transfer station improvements 1 Two new transfer stations 0 Existing transport routes Public transport expansion 1 Planned bus and BRT lines 2 Proposed BRT line in East Amman 0 Penetration in 0% of new housing units Amman Green building code 1 Penetration in 14% of new housing units 2 Penetration in 90% of new housing units 0 Do not increase solar power Clean energy generation 1 Increase solar power in 16 MW 0 Changing to LED 50% of total light bulbs Efficient public lighting 1 Changing to LED 100% of total light bulbs Landmarks 0 Existing landmarks Solid waste management 0 Existing landfill and transfer station improvements 1 New transfer station 0 Existing transport routes Public transport expansion 1 9 Planned bus routes 0 Penetration in 0% of new housing units Green building code 1 Penetration in 14% of new housing units 2 Penetration in 70% of new housing units Irbid 0 Do not increase solar power Clean energy generation 1 Increase solar power in 16 MW 0 Changing to LED 6% of total light bulbs Efficient public lighting 1 Changing to LED 100% of total light bulbs 0 Existing artisanal industry Reduce hazards 1 Control artisanal industry air pollution 0 Existing landmarks Landmarks 1 Planned parks Solid waste management 0 Existing dumpsite improvements 1 New transfer station and landfill Public transport expansion 0 Existing transport routes 0 Penetration in 0% of new housing units Green building code 1 Penetration in 14% of new housing units 2 Penetration in 70% of new housing units 0 Do not increase solar power Mafraq Clean energy generation 1 Increase solar power in 10 MW 0 Changing to LED 0% of total light bulbs Efficient public lighting 1 Changing to LED 100% of total light bulbs 0 Existing artisanal industry Reduce hazards 1 Control artisanal industry air pollution 0 Existing landmarks Landmarks 1 Planned parks and railway linear park 128 APPENDIX B. POLICY LEVERS Table B.2: Policy levers for the five cities City Policy lever Policy lever level Lever name Solid waste management 0 Existing landfill improvements 1 New transfer station 0 Existing transport routes Public transport expansion 1 Planned BRT in highway 2 Proposed BRT through the city 0 Penetration in 0% of new housing Green building code units 1 Penetration in 14% of new housing units Russeifa 2 Penetration in 70% of new housing units 0 Do not increase solar power Clean energy generation 1 Increase solar power in 10 MW 0 Changing to LED 0% of total light Efficient public lighting bulbs 1 Changing to LED 100% of total light bulbs 0 Existing phosphate lands, polluted Reduce hazards Zarqa River and old landfill 1 Clean Phosphate lands and landfill 0 Existing landmarks 1 Planned parks Landmarks 2 Parks in ex-phosphate lands, landfill and planned parks 3 Zarqa River linear park, park in ex- phosphate lands and planned parks Solid waste management 0 Existing landfill improvements 1 New transfer station 0 Existing transport routes Public transport expansion 1 Planned BRT in highway 2 Continue BRT to University 0 Penetration in 0% of new housing Green building code units 1 Penetration in 14% of new housing units Zarqa 2 Penetration in 70% of new housing units 0 Do not increase solar power Clean energy generation 1 Increase solar power in 15 MW 0 Changing to LED 0% of total light Efficient public lighting bulbs 1 Changing to LED 100% of total light bulbs 0 Existing artisanal industry and Reduce hazards polluted Zarqa River 1 Control artisanal industry air pollution 2 Clean Zarqa River 0 Existing landmarks in Open Street Landmarks Maps 1 Two parks along River Zarqa and planned parks 2 Zarqa River linear park and planned parks 129 130 C. Data and assumptions Table C.1: Data used for Amman Description Value Units Category ID Lever Source Electricity network construction 30000 JD/km costs cost_elec 0 Public lighting construction 15000 JD/km costs cost_light 0 Primary road (6 lanes) construction 295000 JD/km costs cost_prim_road 0 Secondary (4 lanes) construction 229000 JD/km costs cost_sec_road 0 Sewer network construction 15000 JD/km costs cost_swge 0 Tertiary road (2 lanes) construction 154000 JD/km costs cost_ter_road 0 Water network construction 30000 JD/km costs cost_watr 0 Commercial cost per liter of diesel 0.5 JD/L costs diesel_cost 0 [51] Cost that the municipality pays per kWh of 0.114 JD/kWh costs elighting_cost 0 electricity consumed for public lighting Cost that the municipality pays per kWh of 9.4E-2 JD/kWh costs ewater_cost 0 electricity consumed for provide potable water Increment cost factor for consolidation 1.5 factor costs incr_consolidation 0 Electric network improvement 45000 JD/km costs retro_elec 0 Sewage network improvement 22500 JD/km costs retro_swge 0 Water network improvement 45000 JD/km costs retro_watr 0 Average maintenance costs for the municipality 22600 JD/km costs road_maintenance 0 Average inflation from (dic 2010/2012) to Agu 2017 27.84 % general avge_inflation 0 [52] in Mexico Diesel net calorific value (NET CV) 11.93 kWh/kg general diesel_cv 0 [46] Diesel density 837.52 kg/m3 general diesel_den 0 [46] Avge. Gasoline market price (90&95 octane, 50/50 0.79 JDL/L general gasoline_cost 0 [51] split) Gasoline net calorific value (NET CV) 12.62 kWh/kg general gasoline_cv 0 [46] Gasoline density 676.13 kg/m3 general gasoline_den 0 [46] Housing unit size: number of habitants per housing 5.26 Inhabitants/hu general hu_size 0 [2] unit Exchange rate for 1 Jordanian Dinar (JOD) to 25.7456 MXN/JOD general JDMXN_exrate 0 [53] Mexican Peso (MXN) Pedestrian roads total lenght in the city 27.62 km general ped_road_km 0 [54] Primary roads total lenght in the city 485.93 km general prim_road_km 0 [54] Secondary roads total lenght in the city 271.56 km general sec_road_km 0 [54] Average street area % of km2 of development 36 % general %_street_area 0 Tertiary roads total lenght in the city 3746.80 km general ter_road_km 0 [54] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 0 [42] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 1 [42] Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 0 [41] Jordan Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 1 [41] Jordan Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 0 [46] Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 1 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 0 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 1 [46] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 0 [42] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 1 [42] Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 0 [41] year in Jordan Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 1 [41] year in Jordan Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 0 [42] Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 1 [42] Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 0 [41] in Jordan Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 1 [41] in Jordan Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 0 [42] Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 1 [42] Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 0 [41] per year in Jordan Continues in the next page Description Value Units Category ID Lever Source Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 1 [41] per year in Jordan Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 0 [42] Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 1 [42] Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 0 [41] in Jordan Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 1 [41] in Jordan Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 0 [41] generated in the country Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 1 [41] generated in the country Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 0 [42] Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 1 [42] Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 0 [41] in Jordan Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 1 [41] in Jordan HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 0 [42] HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 1 [42] HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 0 [41] in Jordan HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 1 [41] in Jordan Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 0 [42] Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 1 [42] Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 0 [41] Jordan Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 1 [41] Jordan Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 0 [55] Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 1 [55] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 0 [41] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 1 [41] Capacity of the solar plant 0 MW ghg_emissions sol_cap 0 Capacity of the solar plant 16 MW ghg_emissions sol_cap 1 Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 0 [43] Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 1 [43] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 0 [42] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 1 [42] Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 0 [41] Jordan Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 1 [41] Jordan Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 0 [44] average Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 1 [44] average Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 0 [42] Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 1 [42] Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 0 [41] Jordan Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 1 [41] Jordan Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 0 [42] Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 1 [42] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 0 [41] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 1 [41] Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 0 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 1 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 2 [44] established as baseline Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 0 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 1 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 2 [44] implementing green building code measures Penetration percentage of Green Building Code 0 % green_b_code GBC_pen 0 measures in housing units Penetration percentage of Green Building Code 14 % green_b_code GBC_pen 1 measures in housing units Penetration percentage of Green Building Code 90 % green_b_code GBC_pen 2 measures in housing units Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 0 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 1 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 2 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 0 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 1 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 2 [50] Number of hours a day that the street lighting 12 h public_lighting hours_day 0 works Continues in the next page 132 APPENDIX C. DATA AND ASSUMPTIONS Description Value Units Category ID Lever Source Number of hours a day that the street lighting 12 h public_lighting hours_day 1 works Number of led bulbs used for street lighting 25000 units public_lighting num_led 0 Number of led bulbs used for street lighting 50000 units public_lighting num_led 1 Total number of bulbs used for street lighting 50000 units public_lighting tot_bulb 0 Total number of bulbs used for street lighting 50000 units public_lighting tot_bulb 1 Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 0 [49] lighting Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 1 [49] lighting Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 0 [49] Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 1 [49] Percent of diesel vehicles Jordan as of 2009 29.87 % transport_energy diesel_transp_frac 0 [45] Percent of gasoline vehicles Jordan as of 2009 70.13 % transport_energy gasoline_transp_frac 0 [45] Times the solid waste is collected per week 4 times per week waste collections 0 Times the solid waste is collected per week 4 times per week waste collections 1 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 0 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 1 Average distance from the city center to the 25 km waste dist_land 0 landfill or dumpsite Average distance from the city center to the 0 km waste dist_land 1 landfill or dumpsite Average distance from the city center to the 10 km waste dist_ts 0 transfer station Average distance from the city center to the 10 km waste dist_ts 1 transfer station Average distance from the transfer station to the 35 km waste dist_tsland 0 landfill or dumpsite Average distance from the transfer station to the 31.13 km waste dist_tsland 1 landfill or dumpsite Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 0 [56] segregated waste Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 1 [56] segregated waste Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 0 waste that can handle by hour Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 1 waste that can handle by hour Percentage of primary roads used by the collection 90 % waste prim_road_fact 0 truck Percentage of primary roads used by the collection 90 % waste prim_road_fact 1 truck Percentage of secondary roads used by the 90 % waste sec_road_fact 0 collection truck Percentage of secondary roads used by the 90 % waste sec_road_fact 1 collection truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 0 truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 1 truck Capacity of the collection truck 6.3 ton waste truck1_cap 0 Capacity of the collection truck 6.3 ton waste truck1_cap 1 Efficiency of the collection truck 0.84 L/km waste truck1_ef 0 Efficiency of the collection truck 0.84 L/km waste truck1_ef 1 Capacity of the transfer truck 54 ton waste truck2_cap 0 Capacity of the transfer truck 54 ton waste truck2_cap 1 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 0 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 1 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 0 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 1 Waste Density (compacted volume) 350 kg/m3 waste waste_density 0 Waste Density (compacted volume) 350 kg/m3 waste waste_density 1 Total solid waste generated per person per day 0.66 kg/day waste waste_per 0 Total solid waste generated per person per day 0.66 kg/day waste waste_per 1 Energy needed to supply one m3 of water 2.24 kWh/m3 water water_factor 0 Water distribution loss 2217.54 m3 /km*yr water_loss loss 0 End of the table 133 Table C.2: Data used for Irbid Description Value Units Category ID Lever Source Electricity network construction 30000 JD/km costs cost_elec 0 Public lighting construction 15000 JD/km costs cost_light 0 Primary road (6 lanes) construction 295000 JD/km costs cost_prim_road 0 Secondary (4 lanes) construction 229000 JD/km costs cost_sec_road 0 Sewer network construction 15000 JD/km costs cost_swge 0 Tertiary road (2 lanes) construction 154000 JD/km costs cost_ter_road 0 Water network construction 30000 JD/km costs cost_watr 0 Commercial cost per liter of diesel 0.5 JD/L costs diesel_cost 0 [51] Cost that the municipality pays per kWh of 0.114 JD/kWh costs elighting_cost 0 electricity consumed for public lighting Cost that the municipality pays per kWh of 9.4E-2 JD/kWh costs ewater_cost 0 electricity consumed for provide potable water Increment cost factor for consolidation 1.5 factor costs incr_consolidation 0 Electric network improvement 45000 JD/km costs retro_elec 0 Sewage network improvement 22500 JD/km costs retro_swge 0 Water network improvement 45000 JD/km costs retro_watr 0 Average maintenance costs for the municipality 22600 JD/km costs road_maintenance 0 Average inflation from (dic 2010/2012) to Agu 2017 27.84 % general avge_inflation 0 [52] in Mexico Diesel net calorific value (NET CV) 11.93 kWh/kg general diesel_cv 0 [46] Diesel density 837.52 kg/m3 general diesel_den 0 [46] Avge. Gasoline market price (90&95 octane, 50/50 0.79 JDL/L general gasoline_cost 0 [51] split) Gasoline net calorific value (NET CV) 12.62 kWh/kg general gasoline_cv 0 [46] Gasoline density 676.13 kg/m3 general gasoline_den 0 [46] Housing unit size: number of habitants per housing 5.26 Inhabitants/hu general hu_size 0 [2] unit Exchange rate for 1 Jordanian Dinar (JOD) to 25.74 MXN/JOD general JDMXN_exrate 0 [53] Mexican Peso (MXN) Pedestrian roads total lenght in the city 0.29 km general ped_road_km 0 [54] Primary roads total lenght in the city 103.86 km general prim_road_km 0 [54] Secondary roads total lenght in the city 55.90 km general sec_road_km 0 [54] Average street area % of km2 of development 36 % general %_street_area 0 Tertiary roads total lenght in the city 1119.61 km general ter_road_km 0 [54] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 0 [42] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 1 [42] Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 0 [41] Jordan Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 1 [41] Jordan Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 0 [46] Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 1 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 0 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 1 [46] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 0 [42] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 1 [42] Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 0 [41] year in Jordan Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 1 [41] year in Jordan Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 0 [42] Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 1 [42] Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 0 [41] in Jordan Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 1 [41] in Jordan Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 0 [42] Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 1 [42] Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 0 [41] per year in Jordan Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 1 [41] per year in Jordan Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 0 [42] Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 1 [42] Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 0 [41] in Jordan Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 1 [41] in Jordan Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 0 [41] generated in the country Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 1 [41] generated in the country Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 0 [42] Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 1 [42] Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 0 [41] in Jordan Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 1 [41] in Jordan HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 0 [42] HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 1 [42] Continues in the next page 134 APPENDIX C. DATA AND ASSUMPTIONS Description Value Units Category ID Lever Source HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 0 [41] in Jordan HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 1 [41] in Jordan Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 0 [42] Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 1 [42] Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 0 [41] Jordan Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 1 [41] Jordan Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 0 [55] Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 1 [55] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 0 [41] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 1 [41] Capacity of the solar plant 0 MW ghg_emissions sol_cap 0 Capacity of the solar plant 16 MW ghg_emissions sol_cap 1 Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 0 [43] Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 1 [43] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 0 [42] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 1 [42] Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 0 [41] Jordan Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 1 [41] Jordan Number of sun hours that Jordan receives in 8.5 h/year ghg_emissions sol_hours 0 average Number of sun hours that Jordan receives in 8.5 h/year ghg_emissions sol_hours 1 average Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 0 [42] Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 1 [42] Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 0 [41] Jordan Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 1 [41] Jordan Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 0 [42] Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 1 [42] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 0 [41] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 1 [41] Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 0 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 1 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 2 [44] established as baseline Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 0 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 1 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 2 [44] implementing green building code measures Penetration percentage of Green Building Code 0 % green_b_code GBC_pen 0 measures in housing units Penetration percentage of Green Building Code 14 % green_b_code GBC_pen 1 measures in housing units Penetration percentage of Green Building Code 70 % green_b_code GBC_pen 2 measures in housing units Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 0 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 1 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 2 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 0 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 1 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 2 [50] Number of hours a day that the street lighting 12 h public_lighting hours_day 0 works Number of hours a day that the street lighting 12 h public_lighting hours_day 1 works Number of led bulbs used for street lighting 3000 units public_lighting num_led 0 Number of led bulbs used for street lighting 50000 units public_lighting num_led 1 Total number of bulbs used for street lighting 50000 units public_lighting tot_bulb 0 Total number of bulbs used for street lighting 50000 units public_lighting tot_bulb 1 Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 0 [49] lighting Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 1 [49] lighting Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 0 [49] Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 1 [49] Percent of diesel vehicles Jordan as of 2009 29.87 % transport_energy diesel_transp_frac 0 [45] Percent of gasoline vehicles Jordan as of 2009 70.13 % transport_energy gasoline_transp_frac 0 [45] Times the solid waste is collected per week 4 times per week waste collections 0 Times the solid waste is collected per week 4 times per week waste collections 1 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 0 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 1 Average distance from the city center to the 27 km waste dist_land 0 landfill or dumpsite Continues in the next page 135 Description Value Units Category ID Lever Source Average distance from the city center to the 0 km waste dist_land 1 landfill or dumpsite Average distance from the city center to the 1.9 km waste dist_ts 0 transfer station Average distance from the city center to the 6.3 km waste dist_ts 1 transfer station Average distance from the transfer station to the 30.1 km waste dist_tsland 0 landfill or dumpsite Average distance from the transfer station to the 30.1 km waste dist_tsland 1 landfill or dumpsite Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 0 [56] segregated waste Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 1 [56] segregated waste Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 0 waste that can handle by hour Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 1 waste that can handle by hour Percentage of primary roads used by the collection 90 % waste prim_road_fact 0 truck Percentage of primary roads used by the collection 90 % waste prim_road_fact 1 truck Percentage of secondary roads used by the 90 % waste sec_road_fact 0 collection truck Percentage of secondary roads used by the 90 % waste sec_road_fact 1 collection truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 0 truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 1 truck Capacity of the collection truck 6.3 ton waste truck1_cap 0 Capacity of the collection truck 6.3 ton waste truck1_cap 1 Efficiency of the collection truck 0.84 L/km waste truck1_ef 0 Efficiency of the collection truck 0.84 L/km waste truck1_ef 1 Capacity of the transfer truck 54 ton waste truck2_cap 0 Capacity of the transfer truck 54 ton waste truck2_cap 1 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 0 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 1 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 0 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 1 Waste Density (compacted volume) 350 kg/m3 waste waste_density 0 Waste Density (compacted volume) 350 kg/m3 waste waste_density 1 Total solid waste generated per person per day 1.18 kg/day waste waste_per 0 Total solid waste generated per person per day 1.18 kg/day waste waste_per 1 Energy needed to supply one m3 of water 2.24 kWh/m3 water water_factor 0 Water distribution loss 2217.54 m3 /km*yr water_loss loss 0 End of the table 136 APPENDIX C. DATA AND ASSUMPTIONS Table C.3: Data used for Mafraq Description Value Units Category ID Lever Source Electricity network construction 30000 JD/km costs cost_elec 0 Public lighting construction 15000 JD/km costs cost_light 0 Primary road (6 lanes) construction 295000 JD/km costs cost_prim_road 0 Secondary (4 lanes) construction 229000 JD/km costs cost_sec_road 0 Sewer network construction 15000 JD/km costs cost_swge 0 Tertiary road (2 lanes) construction 154000 JD/km costs cost_ter_road 0 Water network construction 30000 JD/km costs cost_watr 0 Commercial cost per liter of diesel 0.5 JD/L costs diesel_cost 0 [51] Cost that the municipality pays per kWh of 0.114 JD/kWh costs elighting_cost 0 electricity consumed for public lighting Cost that the municipality pays per kWh of 9.4E-2 JD/kWh costs ewater_cost 0 electricity consumed for provide potable water Increment cost factor for consolidation 1.5 factor costs incr_consolidation 0 Electric network improvement 45000 JD/km costs retro_elec 0 Sewage network improvement 22500 JD/km costs retro_swge 0 Water network improvement 45000 JD/km costs retro_watr 0 Average maintenance costs for the municipality 22600 JD/km costs road_maintenance 0 Average inflation from (dic 2010/2012) to Agu 2017 27.84 % general avge_inflation 0 [52] in Mexico Diesel net calorific value (NET CV) 11.93 kWh/kg general diesel_cv 0 [46] Diesel density 837.52 kg/m3 general diesel_den 0 [46] Avge. Gasoline market price (90&95 octane, 50/50 0.79 JDL/L general gasoline_cost 0 [51] split) Gasoline net calorific value (NET CV) 12.62 kWh/kg general gasoline_cv 0 [46] Gasoline density 676.13 kg/m3 general gasoline_den 0 [46] Housing unit size: number of habitants per housing 5.26 Inhabitants/hu general hu_size 0 [2] unit Exchange rate for 1 Jordanian Dinar (JOD) to 25.7456 MXN/JOD general JDMXN_exrate 0 [53] Mexican Peso (MXN) Pedestrian roads total lenght in the city 1.706 km general ped_road_km 0 [54] Primary roads total lenght in the city 32.18 km general prim_road_km 0 [54] Secondary roads total lenght in the city 5.06 km general sec_road_km 0 [54] Average street area % of km2 of development 36 % general %_street_area 0 Tertiary roads total lenght in the city 159.19 km general ter_road_km 0 [54] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 0 [42] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 1 [42] Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 0 [41] Jordan Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 1 [41] Jordan Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 0 [46] Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 1 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 0 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 1 [46] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 0 [42] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 1 [42] Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 0 [41] year in Jordan Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 1 [41] year in Jordan Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 0 [42] Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 1 [42] Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 0 [41] in Jordan Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 1 [41] in Jordan Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 0 [42] Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 1 [42] Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 0 [41] per year in Jordan Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 1 [41] per year in Jordan Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 0 [42] Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 1 [42] Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 0 [41] in Jordan Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 1 [41] in Jordan Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 0 [41] generated in the country Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 1 [41] generated in the country Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 0 [42] Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 1 [42] Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 0 [41] in Jordan Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 1 [41] in Jordan HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 0 [42] HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 1 [42] Continues in the next page 137 Description Value Units Category ID Lever Source HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 0 [41] in Jordan HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 1 [41] in Jordan Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 0 [42] Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 1 [42] Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 0 [41] Jordan Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 1 [41] Jordan Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 0 [55] Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 1 [55] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 0 [41] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 1 [41] Capacity of the solar plant 0 MW ghg_emissions sol_cap 0 Capacity of the solar plant 10 MW ghg_emissions sol_cap 1 Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 0 [43] Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 1 [43] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 0 [42] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 1 [42] Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 0 [41] Jordan Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 1 [41] Jordan Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 0 [44] average Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 1 [44] average Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 0 [42] Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 1 [42] Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 0 [41] Jordan Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 1 [41] Jordan Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 0 [42] Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 1 [42] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 0 [41] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 1 [41] Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 0 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 1 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 2 [44] established as baseline Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 0 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 1 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 2 [44] implementing green building code measures Penetration percentage of Green Building Code 0 % green_b_code GBC_pen 0 measures in housing units Penetration percentage of Green Building Code 14 % green_b_code GBC_pen 1 measures in housing units Penetration percentage of Green Building Code 70 % green_b_code GBC_pen 2 measures in housing units Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 0 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 1 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 2 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 0 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 1 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 2 [50] Number of hours a day that the street lighting 12 h public_lighting hours_day 0 works Number of hours a day that the street lighting 12 h public_lighting hours_day 1 works Number of led bulbs used for street lighting 0 units public_lighting num_led 0 Number of led bulbs used for street lighting 7701 units public_lighting num_led 1 Total number of bulbs used for street lighting 7701 units public_lighting tot_bulb 0 Total number of bulbs used for street lighting 7701 units public_lighting tot_bulb 1 Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 0 [49] lighting Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 1 [49] lighting Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 0 [49] Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 1 [49] Percent of diesel vehicles Jordan as of 2009 29.87 % transport_energy diesel_transp_frac 0 [45] Percent of gasoline vehicles Jordan as of 2009 70.13 % transport_energy gasoline_transp_frac 0 [45] Times the solid waste is collected per week 4 times per week waste collections 0 Times the solid waste is collected per week 4 times per week waste collections 1 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 0 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 1 Average distance from the city center to the 25.1 km waste dist_land 0 landfill or dumpsite Continues in the next page 138 APPENDIX C. DATA AND ASSUMPTIONS Description Value Units Category ID Lever Source Average distance from the city center to the 0 km waste dist_land 1 landfill or dumpsite Average distance from the city center to the 0 km waste dist_ts 0 transfer station Average distance from the city center to the 2.1 km waste dist_ts 1 transfer station Average distance from the transfer station to the 0 km waste dist_tsland 0 landfill or dumpsite Average distance from the transfer station to the 23.3 km waste dist_tsland 1 landfill or dumpsite Energy used by the transfer station by each ton of 0 kWh/ton waste energy_tonTS 0 segregated waste Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 1 [56] segregated waste Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 0 waste that can handle by hour Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 1 waste that can handle by hour Percentage of primary roads used by the collection 90 % waste prim_road_fact 0 truck Percentage of primary roads used by the collection 90 % waste prim_road_fact 1 truck Percentage of secondary roads used by the 90 % waste sec_road_fact 0 collection truck Percentage of secondary roads used by the 90 % waste sec_road_fact 1 collection truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 0 truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 1 truck Capacity of the collection truck 6.3 ton waste truck1_cap 0 Capacity of the collection truck 6.3 ton waste truck1_cap 1 Efficiency of the collection truck 0.84 L/km waste truck1_ef 0 Efficiency of the collection truck 0.84 L/km waste truck1_ef 1 Capacity of the transfer truck 54 ton waste truck2_cap 0 Capacity of the transfer truck 54 ton waste truck2_cap 1 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 0 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 1 Efficiency of the landfill truck (compactor) 0 kWh/day waste truck3_ef 0 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 1 Waste Density (compacted volume) 350 kg/m3 waste waste_density 0 Waste Density (compacted volume) 350 kg/m3 waste waste_density 1 Total solid waste generated per person per day 1.18 kg/day waste waste_per 0 Total solid waste generated per person per day 1.18 kg/day waste waste_per 1 Energy needed to supply one m3 of water 2.24 kWh/m3 water water_factor 0 Water distribution loss 2217.54 m3 /km*yr water_loss loss 0 End of the table 139 Table C.4: Data used for Russeifa Description Value Units Category ID Lever Source Electricity network construction 30000 JD/km costs cost_elec 0 Public lighting construction 15000 JD/km costs cost_light 0 Primary road (6 lanes) construction 295000 JD/km costs cost_prim_road 0 Secondary (4 lanes) construction 229000 JD/km costs cost_sec_road 0 Sewer network construction 15000 JD/km costs cost_swge 0 Tertiary road (2 lanes) construction 154000 JD/km costs cost_ter_road 0 Water network construction 30000 JD/km costs cost_watr 0 Commercial cost per liter of diesel 0.5 JD/L costs diesel_cost 0 [51] Cost that the municipality pays per kWh of 0.114 JD/kWh costs elighting_cost 0 electricity consumed for public lighting Cost that the municipality pays per kWh of 9.4E-2 JD/kWh costs ewater_cost 0 electricity consumed for provide potable water Increment cost factor for consolidation 1.5 factor costs incr_consolidation 0 Median of municipal expenses 2945960000 JD costs median_expense 0 Median of municipal revenues 3891960000 JD costs median_revenue 0 Primary Road Width 21 m costs prim_road_wid 0 Electric network improvement 45000 JD/km costs retro_elec 0 Sewage network improvement 22500 JD/km costs retro_swge 0 Average maintenance costs for the municipality 22600 JD/km costs road_maintenance 0 Average inflation from (dic 2010/2012) to Agu 2017 27.84 % general avge_inflation 0 [52] in Mexico Diesel net calorific value (NET CV) 11.93 kWh/kg general diesel_cv 0 [46] Diesel density 837.52 kg/m3 general diesel_den 0 [46] Avge. Gasoline market price (90&95 octane, 50/50 0.79 JDL/L general gasoline_cost 0 [51] split) Gasoline net calorific value (NET CV) 12.62 kWh/kg general gasoline_cv 0 [46] Gasoline density 676.13 kg/m3 general gasoline_den 0 [46] Housing unit size: number of habitants per housing 5.26 Inhabitants/hu general hu_size 0 [2] unit Exchange rate for 1 Jordanian Dinar (JOD) to 25.7456 MXN/JOD general JDMXN_exrate 0 [53] Mexican Peso (MXN) Pedestrian roads total lenght in the city 0.09 km general ped_road_km 0 [54] Primary roads total lenght in the city 40.41 km general prim_road_km 0 [54] Secondary roads total lenght in the city 2.16 km general sec_road_km 0 [54] Average street area % of km2 of development 36 % general %_street_area 0 Tertiary roads total lenght in the city 344.17 km general ter_road_km 0 [54] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 0 [42] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 1 [42] Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 0 [41] Jordan Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 1 [41] Jordan Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 0 [46] Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 1 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 0 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 1 [46] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 0 [42] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 1 [42] Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 0 [41] year in Jordan Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 1 [41] year in Jordan Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 0 [42] Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 1 [42] Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 0 [41] in Jordan Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 1 [41] in Jordan Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 0 [42] Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 1 [42] Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 0 [41] per year in Jordan Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 1 [41] per year in Jordan Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 0 [42] Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 1 [42] Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 0 [41] in Jordan Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 1 [41] in Jordan Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 0 [41] generated in the country Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 1 [41] generated in the country Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 0 [42] Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 1 [42] Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 0 [41] in Jordan Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 1 [41] in Jordan HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 0 [42] Continues in the next page 140 APPENDIX C. DATA AND ASSUMPTIONS Description Value Units Category ID Lever Source HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 0 [41] in Jordan HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 1 [41] in Jordan Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 0 [42] Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 1 [42] Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 0 [41] Jordan Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 1 [41] Jordan Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 0 [55] Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 1 [55] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 0 [41] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 1 [41] Capacity of the solar plant 0 MW ghg_emissions sol_cap 0 Capacity of the solar plant 10 MW ghg_emissions sol_cap 1 Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 0 [43] Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 1 [43] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 0 [42] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 1 [42] Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 0 [41] Jordan Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 1 [41] Jordan Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 0 [44] average Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 1 [44] average Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 0 [42] Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 1 [42] Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 0 [41] Jordan Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 1 [41] Jordan Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 0 [42] Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 1 [42] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 0 [41] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 1 [41] Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 0 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 1 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 2 [44] established as baseline Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 0 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 1 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 2 [44] implementing green building code measures Penetration percentage of Green Building Code 0 % green_b_code GBC_pen 0 measures in housing units Penetration percentage of Green Building Code 14 % green_b_code GBC_pen 1 measures in housing units Penetration percentage of Green Building Code 70 % green_b_code GBC_pen 2 measures in housing units Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 0 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 1 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 2 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 0 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 1 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 2 [50] Number of hours a day that the street lighting 12 h public_lighting hours_day 0 works Number of hours a day that the street lighting 12 h public_lighting hours_day 1 works Number of led bulbs used for street lighting 0 units public_lighting num_led 0 Number of led bulbs used for street lighting 15200 units public_lighting num_led 1 Total number of bulbs used for street lighting 15200 units public_lighting tot_bulb 0 Total number of bulbs used for street lighting 15200 units public_lighting tot_bulb 1 Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 0 [49] lighting Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 1 [49] lighting Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 0 [49] Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 1 [49] Percent of diesel vehicles Jordan as of 2009 29.87 % transport_energy diesel_transp_frac 0 [45] Percent of gasoline vehicles Jordan as of 2009 70.13 % transport_energy gasoline_transp_frac 0 [45] Times the solid waste is collected per week 4 times per week waste collections 0 Times the solid waste is collected per week 4 times per week waste collections 1 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 0 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 1 Average distance from the city center to the 30.4 km waste dist_land 0 landfill or dumpsite Continues in the next page 141 Description Value Units Category ID Lever Source Average distance from the city center to the 0 km waste dist_land 1 landfill or dumpsite Average distance from the city center to the 0 km waste dist_ts 0 transfer station Average distance from the city center to the 6.9 km waste dist_ts 1 transfer station Average distance from the transfer station to the 0 km waste dist_tsland 0 landfill or dumpsite Average distance from the transfer station to the 27.1 km waste dist_tsland 1 landfill or dumpsite Energy used by the transfer station by each ton of 0 kWh/ton waste energy_tonTS 0 [56] segregated waste Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 1 segregated waste Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 0 waste that can handle by hour Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 1 waste that can handle by hour Percentage of primary roads used by the collection 90 % waste prim_road_fact 0 truck Percentage of primary roads used by the collection 90 % waste prim_road_fact 1 truck Percentage of secondary roads used by the 90 % waste sec_road_fact 0 collection truck Percentage of secondary roads used by the 90 % waste sec_road_fact 1 collection truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 0 truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 1 truck Total annual solid waste collected in the base year 860000 ton/yr waste tot_wvol_base 0 Total annual solid waste collected in the base year 860000 ton/yr waste tot_wvol_base 1 Capacity of the collection truck 6.3 ton waste truck1_cap 0 Capacity of the collection truck 6.3 ton waste truck1_cap 1 Efficiency of the collection truck 1.2 L/km waste truck1_ef 0 Efficiency of the collection truck 1.2 L/km waste truck1_ef 1 Capacity of the transfer truck 54 ton waste truck2_cap 0 Capacity of the transfer truck 54 ton waste truck2_cap 1 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 0 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 1 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 0 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 1 Waste Density (compacted volume) 350 kg/m3 waste waste_density 0 Waste Density (compacted volume) 350 kg/m3 waste waste_density 1 Total solid waste generated per person per day 1.18 kg/day waste waste_per 0 Total solid waste generated per person per day 1.18 kg/day waste waste_per 1 Energy needed to supply one m3 of water 2.24 kWh/m3 water water_factor 0 Water distribution loss 2217.54 m3 /km*yr water_loss loss 0 End of the table 142 APPENDIX C. DATA AND ASSUMPTIONS Table C.5: Data used for Zarqa Description Value Units Category ID Lever Source Electricity network construction 30000 JD/km costs cost_elec 0 Public lighting construction 15000 JD/km costs cost_light 0 Primary road (6 lanes) construction 295000 JD/km costs cost_prim_road 0 Secondary (4 lanes) construction 229000 JD/km costs cost_sec_road 0 Sewer network construction 15000 JD/km costs cost_swge 0 Tertiary road (2 lanes) construction 154000 JD/km costs cost_ter_road 0 Water network construction 30000 JD/km costs cost_watr 0 Commercial cost per liter of diesel 0.5 JD/L costs diesel_cost 0 [51] Cost that the municipality pays per kWh of 0.114 JD/kWh costs elighting_cost 0 electricity consumed for public lighting Cost that the municipality pays per kWh of 9.4E-2 JD/kWh costs ewater_cost 0 electricity consumed for provide potable water Increment cost factor for consolidation 1.5 factor costs incr_consolidation 0 Electric network improvement 45000 JD/km costs retro_elec 0 Sewage network improvement 22500 JD/km costs retro_swge 0 Water network improvement 45000 JD/km costs retro_watr 0 Average maintenance costs for the municipality 22600 JD/km costs road_maintenance 0 Average inflation from (dic 2010/2012) to Agu 2017 27.84 % general avge_inflation 0 [52] in Mexico Diesel net calorific value (NET CV) 11.93 kWh/kg general diesel_cv 0 [46] Diesel density 837.52 kg/m3 general diesel_den 0 [46] Avge. Gasoline market price (90&95 octane, 50/50 0.79 JDL/L general gasoline_cost 0 [51] split) Gasoline net calorific value (NET CV) 12.62 kWh/kg general gasoline_cv 0 [46] Gasoline density 676.13 kg/m3 general gasoline_den 0 [46] Housing unit size: number of habitants per housing 5.26 Inhabitants/hu general hu_size 0 [2] unit Exchange rate for 1 Jordanian Dinar (JOD) to 25.7456 MXN/JOD general JDMXN_exrate 0 [53] Mexican Peso (MXN) Pedestrian roads total lenght in the city 0.37 km general ped_road_km 0 [54] Primary roads total lenght in the city 56.76 km general prim_road_km 0 [54] Secondary roads total lenght in the city 27.31 km general sec_road_km 0 [54] Average street area % of km2 of development 36 % general %_street_area 0 Tertiary roads total lenght in the city 518.35 km general ter_road_km 0 [54] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 0 [42] Biogas units emissions factor 0 kgCO2 eq/GWh ghg_emissions bio_emi 1 [42] Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 0 [41] Jordan Biogas units electricity generation per year in 4.09 GWh ghg_emissions bio_gen 1 [41] Jordan Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 0 [46] Greenhouse gases emissions per kWh of diesel 0.26 kgCO2 eq/kWh ghg_emissions carbon_factor_diesel 1 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 0 [46] Greenhouse gases emissions per kWh of gasoline 0.24 kgCO2 eq/kWh ghg_emissions carbon_factor_gasoline 1 [46] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 0 [42] Combined cycle units emissions factor 371600 kgCO2 eq/GWh ghg_emissions com_emi 1 [42] Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 0 [41] year in Jordan Combined cycle units electricity generation per 15108.2 GWh ghg_emissions com_gen 1 [41] year in Jordan Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 0 [42] Diesel engines units emissions factor 0 kgCO2 eq/GWh ghg_emissions die_emi 1 [42] Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 0 [41] in Jordan Diesel engines units electricity generation per year 28.6 GWh ghg_emissions die_gen 1 [41] in Jordan Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 0 [42] Natural gas diesel engines emissions factor 469000 kgCO2 eq/GWh ghg_emissions dnat_emi 1 [42] Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 0 [41] per year in Jordan Natural gas diesel engines electricity generation 648.70 GWh ghg_emissions dnat_gen 1 [41] per year in Jordan Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 0 [42] Diesel gas turbines emissions factor 0 kgCO2 eq/GWh ghg_emissions gdie_emi 1 [42] Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 0 [41] in Jordan Diesel gas turbines electricity generation per year 1 GWh ghg_emissions gdie_gen 1 [41] in Jordan Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 0 [41] generated in the country Quantity of energy that Jordan consumes 19171.5 GWh ghg_emissions gen_tot 1 [41] generated in the country Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 0 [42] Natural gas turbines emissions factor 469000 kgCO2 eq/GWh ghg_emissions gnat_emi 1 [42] Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 0 [41] in Jordan Natural gas turbines electricity generation per year 329.5 GWh ghg_emissions gnat_gen 1 [41] in Jordan HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 0 [42] HFO diesel engines emissions factor 0 kgCO2 eq/GWh ghg_emissions hfo_emi 1 [42] Continues in the next page 143 Description Value Units Category ID Lever Source HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 0 [41] in Jordan HFO diesel engines electricity generation per year 94.5 GWh ghg_emissions hfo_gen 1 [41] in Jordan Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 0 [42] Hydro units emissions factor 19000 kgCO2 eq/GWh ghg_emissions hyd_emi 1 [42] Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 0 [41] Jordan Hydro units electricity generation per year in 41.6 GWh ghg_emissions hyd_gen 1 [41] Jordan Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 0 [55] Emissions factor of imported energy 96000 kgCO2 eq/GWh ghg_emissions imp_fact 1 [55] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 0 [41] Quantity of imported energy that Jordan consumes 435 GWh ghg_emissions imp_tot 1 [41] Capacity of the solar plant 0 MW ghg_emissions sol_cap 0 Capacity of the solar plant 15 MW ghg_emissions sol_cap 1 Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 0 [43] Efficiency of the solar plant 86.12 % ghg_emissions sol_eff 1 [43] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 0 [42] Solar energy plants emissions factor 29000 kgCO2 eq/GWh ghg_emissions sol_emi 1 [42] Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 0 [41] Jordan Solar energy units electricity generation per year in 491 GWh ghg_emissions sol_gen 1 [41] Jordan Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 0 [44] average Number of sun hours that Jordan receives in 8.5 h/day ghg_emissions sol_hours 1 [44] average Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 0 [42] Steam units emissions factor 769 kgCO2 eq/GWh ghg_emissions ste_emi 1 [42] Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 0 [41] Jordan Steam units electricity generation per year in 2033.6 GWh ghg_emissions ste_gen 1 [41] Jordan Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 0 [42] Wind units emissions factor 15000 kgCO2 eq/GWh ghg_emissions win_emi 1 [42] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 0 [41] Wind units electricity generation per year in Jordan 390.7 GWh ghg_emissions win_gen 1 [41] Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 0 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 1 [44] established as baseline Average energy consumption per household 12723.94 kWh/yr per hu green_b_code ener_baseline 2 [44] established as baseline Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 0 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 1 [44] implementing green building code measures Reduced energy consumption per household by 11686.67 kWh/yr per hu green_b_code GBC_ener 2 [44] implementing green building code measures Penetration percentage of Green Building Code 0 % green_b_code GBC_pen 0 measures in housing units Penetration percentage of Green Building Code 14 % green_b_code GBC_pen 1 measures in housing units Penetration percentage of Green Building Code 70 % green_b_code GBC_pen 2 measures in housing units Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 0 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 1 [50] Baseline water demand per housing unit 518.35 m3 /yr per hu green_b_code HU_water0 2 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 0 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 1 [50] Efficient water demand per housing unit 329.9 m3 /yr per hu green_b_code HU_water1 2 [50] Number of hours a day that the street lighting 12 h public_lighting hours_day 0 works Number of hours a day that the street lighting 12 h public_lighting hours_day 1 works Number of led bulbs used for street lighting 0 units public_lighting num_led 0 Number of led bulbs used for street lighting 23535 units public_lighting num_led 1 Total number of bulbs used for street lighting 23535 units public_lighting tot_bulb 0 Total number of bulbs used for street lighting 23535 units public_lighting tot_bulb 1 Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 0 [49] lighting Voltage of the common bulbs used for street 0.28 kW public_lighting volt_bulb 1 [49] lighting Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 0 [49] Voltage of the led bulbs used for street lighting 0.13 kW public_lighting volt_led 1 [49] Percent of diesel vehicles Jordan as of 2009 29.87 % transport_energy diesel_transp_frac 0 [45] Percent of gasoline vehicles Jordan as of 2009 70.13 % transport_energy gasoline_transp_frac 0 [45] Times the solid waste is collected per week 4 times per week waste collections 0 Times the solid waste is collected per week 4 times per week waste collections 1 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 0 Efficiency of the collector truck compaction system 0 L/m3 waste comp_ef 1 Average distance from the city center to the 25.3 km waste dist_land 0 landfill or dumpsite Continues in the next page 144 APPENDIX C. DATA AND ASSUMPTIONS Description Value Units Category ID Lever Source Average distance from the city center to the 0 km waste dist_land 1 landfill or dumpsite Average distance from the city center to the 0 km waste dist_ts 0 transfer station Average distance from the city center to the 3.6 km waste dist_ts 1 transfer station Average distance from the transfer station to the 0 km waste dist_tsland 0 landfill or dumpsite Average distance from the transfer station to the 22.6 km waste dist_tsland 1 landfill or dumpsite Energy used by the transfer station by each ton of 0 kWh/ton waste energy_tonTS 0 [56] segregated waste Energy used by the transfer station by each ton of 12 kWh/ton waste energy_tonTS 1 segregated waste Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 0 waste that can handle by hour Efficiency of the landfill expressed in quantity of 143 ton/h waste land_ef 1 waste that can handle by hour Percentage of primary roads used by the collection 90 % waste prim_road_fact 0 truck Percentage of primary roads used by the collection 90 % waste prim_road_fact 1 truck Percentage of secondary roads used by the 90 % waste sec_road_fact 0 collection truck Percentage of secondary roads used by the 90 % waste sec_road_fact 1 collection truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 0 truck Percentage of tertiary roads used by the collection 90 % waste ter_road_fact 1 truck Capacity of the collection truck 6.3 ton waste truck1_cap 0 Capacity of the collection truck 6.3 ton waste truck1_cap 1 Efficiency of the collection truck 1.2 L/km waste truck1_ef 0 Efficiency of the collection truck 1.2 L/km waste truck1_ef 1 Capacity of the transfer truck 54 ton waste truck2_cap 0 Capacity of the transfer truck 54 ton waste truck2_cap 1 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 0 Efficiency of the transfer truck 1.2 L/km waste truck2_ef 1 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 0 Efficiency of the landfill truck (compactor) 2264 kWh/day waste truck3_ef 1 Waste Density (compacted volume) 350 kg/m3 waste waste_density 0 Waste Density (compacted volume) 350 kg/m3 waste waste_density 1 Total solid waste generated per person per day 1.18 kg/day waste waste_per 0 Total solid waste generated per person per day 1.18 kg/day waste waste_per 1 Energy needed to supply one m3 of water 2.24 kWh/m3 water water_factor 0 Water distribution loss 2217.54 m3 /km*yr water_loss loss 0 End of the table 145 146 Bibliography [1] Ministry of Environment of Jordan. 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