I Ben-Gurion University of the Negev Faculty Of

BEN-GURION UNIVERSITY OF THE NEGEV

FACULTY OF HUMANITIES AND SOCIAL SCIENCES

DEPARTMENT OF GEOGRAPHY AND ENVIRONMENTAL DEVELOPMENT

SUSTAINABLE CITIES: A NEIGHBOURHOODS CARBON FOOTPRINT

ANALYSIS –THE CITY TEL AVIV - JAFFA

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE MASTER OF ARTS DEGREE

TAL HAR EVEN LEVY

UNDER THE SUPERVISION OF: PROF. MEIDAD KISSINGER

APRIL 2020

BEN- GURION UNIVERSITY OF THE NEGEV THE FACULTY OF HUMANITIES AND SOCIAL SCIENCES DEPARTMENT OF GEOGRAPHY AND ENVIRONMENTAL DEVELOPMENT

SUSTAINABLE CITIES: A NEIGHBOURHOODS CARBON FOOTPRINT

ANALYSIS –THE CITY TEL AVIV - JAFFA

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE MASTER OF ARTS DEGREE

TAL HAR EVEN LEVY

UNDER THE SUPERVISION OF: PROF. MEIDAD KISSINGER

Signature of student: ______Date: _19/04/20_ Signature of supervisor: ______Date: _19/04/20_ Signature of chairperson of the committee for graduate studies: ______Date: ______

APRIL 2020

Abstract

Population growth, lifestyle change and an increase in resource consumption have led to an increase in greenhouse gas emissions, resulting in climate change. Most of the world’s population lives in cities, the urban lifestyle is characterised by excessive materials and energy consumption. Most of the greenhouse gas emissions are emitted directly or indirectly as a result of urban activity. Therefore, the role of cities in greenhouse gas (GHG) emissions reduction is significant. Many cities around the world are committed to reduce urban emissions. Analysing the source and amount of emission from different urban activities is essential to applying reduction measures. This growing interest in the interaction between urban activity and climate change processes and the understanding of the importance of the city in mitigating the problem have led to the growth and development of various approaches and methods of measuring cities' "carbon footprint" in the last few years.

As a result of the increasing interest in the connection urban and climate change processes and the recognition of the importance of the city to reduce the problem, various approaches and methods of urban carbon accounting have developed over the last few years. Carbon is the most important component of greenhouse gases, so it is important to compare the carbon emitted by human activity with carbon fixed by plants in order to promote the principle of sustainability and preserve resources for future generations. This study focuses on the carbon accounting method "carbon footprint". Carbon footprint is the total amount of carbon dioxide (CO2) and other greenhouse gases emitted during the entire lifecycle of a product or process, from extracting the raw material to its decomposition. Carbon footprint is expressed in units of carbon dioxide (CO2eq).

Until recently, research in the topic and the urban monitoring was in the whole city scale. Neighbourhoods are urban units that attract people with similar socioeconomic characteristics. Recent studies examine the GHG emissions of urban neighbourhoods in order to determine the most appropriate means of reduction, thereby contributing to the reduction of total urban emissions.

The study examines the carbon footprint of the domestic sector in the neighbourhoods of Tel Aviv - Jaffa. The consumption components examined in this study are electricity, food and private transportation. After examining the carbon footprint, the study examines the social, economic and spatial characteristics of the neighbourhoods and their relation to the results of the carbon footprint. Finally, the study examines policy measures, technology and behavioural changes and their impact on the reduction of neighbourhood and urban emissions.

The research found that the overall annual carbon footprint of Tel Aviv-Jaffa is approximately 1,980,000-ton CO2eq, or the equivalent of 4.6 ton of CO2eq per resident. Electricity consumption related emissions found to be responsible of almost half of the overall urban emissions. The neighbourhoods with the highest CO2eq emissions per person are Glilot Tzuki Aviv and Sde Dov Area - neighbourhood no. 1

(6.8 ton). The scenario with the highest mitigation potential is the electricity based technological scenario with a potential of reducing 6.3% of all urban emissions.

Table of contents 1. Introduction ...... 9 2. Literature review ...... 11 2.1 Measuring GHG Emissions ...... 11 2.1.1Cities and climate change ...... 11 2.1.2 Emissions on the household scale ...... 12 2.1.3 Neighbourhood carbon footprint ...... 14 2.2 Approaches for calculating cities’ GHG emissions...... 16 2.3 Socio-economic, demographic and spatial factors’ relation to GHG emissions ...... 17 2.3.1 Neighbourhood emissions and socio-economic, demographic and spatial factors...... 19 2.4 Cities working towards climate change mitigation plans ...... 20 2.4.1 Neighbourhoods GHG emissions reduction plans and scenarios ...... 21 3. Methods ...... 27 3.1 The research questions and purpose ...... 27 3.2 The importance of the research ...... 27 3.3 The place of the research ...... 28 3.4 Neighbourhoods' carbon footprint analysis ...... 28 3.5 Data collection ...... 32 3.6 Processing the data ...... 33 3.7 Characterizing the city's neighbourhoods ...... 35 3.7.1 Statistical analysis ...... 35 3.8 GHG emissions reduction scenarios ...... 36 3.9 Research assumptions and limitations ...... 37 4. Results ...... 39 4.1 Neighbourhood's carbon footprint analysis ...... 39 4.1.1 Total carbon emissions ...... 39 4.1.2 Electricity ...... 41 4.1.3 Transportation ...... 44 4.1.4 Food ...... 46 4.2 Neighbourhood Characteristics-Carbon Footprint Correlation ...... 48 4.3 Neighbourhoods GHG mitigation scenarios ...... 55 4.3.1 Electricity scenarios ...... 55 4.3.2 Transportation scenarios ...... 56

VII

4.3.3 Food scenarios ...... 57 4.3.4 Overall mitigation potential scenarios ...... 58 5. Discussion and conclusion ...... 63 6. References ...... 71 7. Appendix ...... 78

Figure index Figure 1 Carbon footprint analysis ...... 29 Figure 2 Neighbourhoods ...... 30 Figure 3 Carbon footprint components ...... 40 Figure 4 Total carbon footprint ...... 40 Figure 5 Total electricity CO2eq emissons, per household and per person ...... 44 Figure 6 Transportation components ...... 45 Figure 7 Total transportation CO2eq emissons, per household and per person ...... 46 Figure 8 Total food CO2eq emissons, per household and per person ...... 48 Figure 9 Income level...... 49 Figure 10 Average house size ...... 50 Figure 11 Level of education ...... 51 Figure 12 Public transportation ...... 52 Figure 13 Pedestrian or bicycle commuters ...... 53 Figure 14 New houses ...... 54 Figure 15 Household density ...... 54 Figure 16 Neighbourhood elecrticity related emission reduction percentage ...... 56 Figure 17 Neighbourhood transportation related emission reduction percentage ..... 57 Figure18 Neighbourhood food related emission reduction percentage ...... 58 Figure 19 Total neighbourhood emission reduction percentage ...... 59 Figure 20 The most benefical secnarios by neighbourhoods ...... 60 Figure 21: Carbon footprint by statistical areas, neighbourhoods, sub-quarters and quarters ...... 69

Table index Table 1 Emission source by article ...... 15 Table 2 Neighbourhood GHG emission reports...... 26 Table 3 Neighbourhood name and numbering ...... 31 Table 4 Scenarios ...... 37 Table 5 Neighbourhood electricity consumption ...... 42 Table 6 Food consumpion (ton) by type ...... 46 Table 7 Reduction potential by scenario ...... 61 Table 8 Neighbourhoods' GHG emission report - extended ...... 79 Table 9 Food convertion by type ...... 84 Table 10 Tel Aviv - Jaffa's carbon footprint ...... 85 Table 11 Transportation Components ...... 89 Table 12 Emissions and neighbourhoods characteristics correlations ...... 90 Table 13 Neighbourhoods population and socio economic cluster ...... 91

VIII

Acknowledgements I would like to thank my supervisor, Prof. Meidad Kissinger, for his guidance throughout the long process of the research, thoughtful conversations and inspiring ideas. Thank you for your patience, all the support and for reading my work countless times.

I would like to thank all my colleges from the Sustainability and Environmental

Policy Lab for the many hours we spent together, for the creative discussions and all the help. Thanks for introducing me to new and interesting topics.

Finally, thank you to my husband and family for allowing me to delve into the subject. Without them and their support, I would not have managed to reach this result.

1. Introduction

Worldwide cities share the concern related to climate change and collaborate in the global effort to mitigate greenhouse gas (GHG) emissions. Cities provide economic and social opportunities that draw people. As a result, most of the world's population nowadays lives in cities (Rees & Wackernagel, 1996). Excessive materials and energy consumption characterize the urban lifestyle. It follows that most of the world's resources are directly or indirectly consumed by cities and therefore most GHG emissions can also be related to cities )Rees & Wackernagel, 1996: Grimm et al, 2008:

Moore, 2009). The cities high population density and concentration of economic activity increases its ability to advance sustainability by increasing the urban efficiency and reduce its overall impact (Rees, 1997). Therefore, cities' role in GHG mitigation and advancing sustainability is crucial. Recent research suggests that cities cannot be sustainable as a whole if their sub-components are not acting in a sustainable manner

(Sharifi, 2013). Several studies have analysed and assessed different dimensions of urban sustainability and particularly on urban GHG emissions, focusing on the overall city scale, and on households' sustainability. However, the middle sub-urban component, which focuses on neighbourhoods, or specific areas within the city has received less research attention (Sharifi, 2013) and non for the case of cities in Israel.

This research calculated and analysed the carbon footprint, directly and in directly related to residents' activities of different parts of a city over a year. It is important as a means of comparison to emissions of other cities and neighbourhoods.

Moreover, such analysis is the initial stage of planning the carbon footprint reduction.

This research focuses on the city of Tel Aviv-Jaffa's carbon footprint as a result of electricity, food and private transport consumption. By focusing on households at

9 the neighbourhood scale, it allows to evaluate the households' GHG emissions of different parts of the city and to analyse emissions by neighbourhoods' socio-spatial characteristics. Based on this evaluation and analysis, GHG mitigation measures (i.e., policy and plans) are suggested.

The overall research question is: What are the sub-city GHG emissions and their reduction potential?

Specific questions are:

(1) What is the carbon footprint of different urban neighbourhoods in the city of

Tel Aviv - Jaffa?

(2) To what extent sub-city socio-spatial diversity is reflected in the studied neighbourhoods' GHG emissions?

(3) How various measures can contribute to GHG mitigation of the studied neighbourhoods?

2. Literature review

2.1 Measuring GHG Emissions

2.1.1Cities and climate change The awareness of the importance of sustainability has increased since the 1987

Brundtland report. Nonetheless, there is increasing evidence that the world has become less sustainable since the report was published (Rees, 1997).

Since 1950, the world’s population increased by 5.2 billion, reaching 7.7 billion in 2019 (UN, 2019). Economic growth and lifestyle changes have led to a substantially enlarged consumption of many types, including energy, transport and others. As a result, CO2 emissions have increased greatly as the result of economic and industrial development (UNDP, 2009).

More than half of the world’s population already lives in urban areas, a process that is expected to increase in the coming decades (UNDP, 2014). Cities provide economic and social opportunities that draw people. The urban lifestyle combined with population size and the magnitude of urban economic activities suggest that most of the world’s resources are consumed in cities, either directly or indirectly )Rees &

Wackernagel, 1996: Grimm et al., 2008: Moore, 2009; Kissinger & Stossel 2019). It has been estimated that approximately 75% of global resource consumption is related to cities’ activities as well as most of the waste. Consequently, 78% of global GHG emissions can be attributed to urban activities (Grimm et al., 2008; Moore, 2009;

Newman, 1999; Kennedy et al., 2007).

GHG emissions that exceed the norm cause changes in the atmosphere, disruption in the balance, higher temperatures and diverse components of climate change (Pachauri et al., 2014). In recent decades, it has become clear that the

11 increasing concentrations of carbon dioxide in the atmosphere, which are a result of human activity, particularly burning fossil fuels, changes the greenhouse effect, slowly increasing the global temperatures and making the world warmer. These processes are not only a matter of scientific interest but also widely debated public concern for international regulatory needs. Climate change is considered one of the most important challenges humanity will face in the coming decades (UNFCCC, 2018).

When dealing with the impact of climate change humanity one must consider cities, because they are becoming the principle human habitat (Rees, 1997).

Furthermore, cities are sensitive to the outcomes of climate change. Phenomenon such as urban heat islands, sea-level rise, and extreme weather events (which involve flooding or long heat wave), are expected to increase and influence the well-being and sustainability of urban residents (Rizwan et al., 2008).

However, the “urban sustainability multiplier” (Rees, 1997) suggests that cities enjoy two key advantages for sustainability: economies of agglomeration and scale.

These two advantages suggest that a city’s high population density and concentration of economic activity can increase its ability to advance sustainability by improving urban efficiency and reducing overall impact. Consider for example the use of mass transit, which is feasible only in cities with large, concentrated populations. Further, municipal governments have the advantage of a faster decision-making process than national governments. As a result, in many cases cities are leading mitigation plans and policy-making related to climate change (Rauland & Newman, 2015).

2.1.2 Emissions on the household scale Interest in understanding and analysing household sector consumption patterns has grown in recent decades, with varied studies focusing on different dimensions of household consumption and their environmental implications (e.g. Stossel et al., 2014;

Shirley et al., 2012; Jeong et al., 2011; Spangenberg & Lorek, 2002; Wier et al., 2001).

Moreover, several reviews published by international organizations recognize the household sector as a significant factor in the global effort for sustainability, and one of the frameworks that need to be considered when working toward achieving this goal

(e.g. OECD, 2013; OECD, 2008; UN, 2008).

Globally, households consume more than one-third of the end-user electricity.

In temperate countries, more than half of the energy is used for heating (Unander et al., 2004). Climatic conditions affect energy requirements through the demand for heating and cooling of the home (Di Donato et al., 2015; Kennedy et al., 2009).

Changes in the city infrastructure can reduce a large part of the direct energy use by households for transport and heating (Moll et al., 2005). At the household level, food choices, even with similar caloric content, can have a substantial influence on the amount of GHG emissions (Carlsson-Kanyama, 1998; Carlsson-Kanyama &

González, 2009).

Several studies have examined how different characteristics influence consumption patterns and their effect on GHG emissions. The type of settlement and its spatial characteristics, socio-economic level and demography may also be influenced by globalisation or culture. (Muñiz & Galindo, 2005; Brown et al., 2009;

Heinonen & Junnila, 2011; Poom et al., 2014; Lettenmeier et al., 2012; Baiocchi et al.,

2010; Tukker et al., 2010; Weisz & Steinberger 2010; Druckman & Jackson 2009;

Kotakorpiet al., 2008; Lenzen et al., 2006; Deaton & Paxson, 1998; Weber &

Matthews 2008; Tukker et al., 2010). For example, global demographic changes influence the GHG emissions not only through population growth but also through changes in family size (Keilman, 2003; Liu et al., 2003). Household size has declined from an average of 2.8 persons in the mid-1980s to 2.6 in the mid-2000s in the OECD

13 countries (OECD, 2011), and diminished household size is reported globally

(Bradbury et al., 2014). These demographic changes affect emissions attributed to per capita energy use. A two-person household will use 17% less energy per capita than a one-person household (O’Neill & Chen, 2002). Emissions from transportation are similar, for growth in family size up to three persons (Weber & Matthews, 2008).

2.1.3 Neighbourhood carbon footprint A city cannot be sustainable in isolation but rather as a part of a global urban system, in which one city can affect the overall system to some degree. However, a city cannot contribute to the global system if its components are not sustainable

(Sharifi, 2013). Neighbourhood status tends to be stable over time implying that processes of residential mobility often do not lead to neighbourhood change, as households with similar socioeconomic characteristics move in and out of these neighbourhoods, thereby maintaining the status quo over longer periods of time

(Zwiers et al., 2016; Meen et al., 2013).

The neighbourhood is a key spatial scale that clearly has both social and spatial dimensions. Although far more difficult to define than statistical or cluster analysis, neighbourhoods have a crucial resource that other scales lack, community. A neighbourhood is a living space where people access material and social resources, through which they pass to reach other opportunities, and it represents part of the identities of those who live there, for both themselves and outsiders (Meegan &

Mitchell, 2001; Davies & Herbert, 1993). Neighbourhood-scale analysis is necessary for evaluating and developing sustainable urban infrastructure like buildings, transportation, urban vegetation and water (Engel-Yan et al., 2005). A number of studies have focused on the GHG emissions on the sub-urban scale, the city was divided into different segment such as census tracts (Codoban & Kennedy, 2008),

14 neighbourhoods (Pérez & Rey, 2012) or by specific features (Gouda & Masoumi,

2017; Ottelin, Heinonen & Junnila, 2018). Most of these studies have focused on only some of the city’s neighbourhoods (e.g. Cerón-Palma et al., 2013; Pérez & Rey, 2012), while a few have examined all parts of the city (e.g. Gouda & Masoumi, 2017;

Codoban & Kennedy, 2008). Furthermore, some studies analysed only one component of consumption (e.g. Zhang, Guhathakurta & Ross, 2016; Lindsey et al., 2011).

Several cities are advancing neighbourhood-scale GHG mitigation plans. Table

1 shows a list of cities that have analysed the carbon footprint on the sub-urban/ neighbourhood level, and published the results in the form of either a city report or an academic study. Two main emission sources were examined: electricity and transportation. Emissions related to electricity consumption were found to be most significant. These reports and studies also describe the measures necessary to meet the city’s commitment to reduce greenhouse gas emissions. Academic studies examined the influence of neighbourhood characteristics on the steps that need to be taken to achieve the most significant carbon emission reduction in the neighbourhood level.

The studies show that suburban neighbourhoods emit more carbon then city centre neighbourhoods. City reports are diverse in scope and relation to neighbourhood characteristics.

Table 1 Emission source by article

City Electricity Heating Transportation Food Waste Building- and Construction cooling and Operation Studies Toronto (Codoban and      Kennedy, 2008) Vancouver (Senbel et   al., 2014) Vancouver (Kellett et      al., 2013) Phoenix  (Zhang, Guhathakurta and Ross, 2016)

City Electricity Heating Transportation Food Waste Building- and Construction cooling and Operation Beijing (Qin & Han,   2013) San Francisco (Jones    & Kammen, 2015) Chicago (Lindsey et  al., 2011) U.S. (Jones &    Kammen, 2014) U.S. (Lee & Lee,    2014) Lausanne (Pérez &      Rey, 2012) Helniski (Ottelin,     Heinonen & Junnila, 2018). Merida (Cerón-Palma    et al., 2013). City action plans Chinatown, San   Francisco (sustainable Chinatown steering committee, 2017) Baltimore (Baltimore     Office of Sustainability, 2013) City/organisation websites Kronsberg, Hannover    (Rumming, 2006) Vancouver (City of      Vancouver, 2012). Basel (Armando  Mombelli, 2016) Wallington (BedZED)    (Bioregional, 2018a) Zibi (Bioregional,   2018b) Jinshan    (Bioregional, 2013) 2.2 Approaches for calculating cities’ GHG emissions This research will use carbon footprint calculation and monitoring tools that provide quantitative measurement of GHG emissions. Carbon footprint is defined as the total amount of carbon dioxide )CO2( and other GHG emissions emitted during the production or the consumption of a product. Calculating carbon footprint includes all the direct and indirect emissions (Wiedmann & Minx, 2008), and can be at the individual, community, company, industry or other level.

When the calculating carbon footprint of a city we need to consider the different scopes of the GHG emissions. Each scope defines the boundaries of the research and

16 prevents double calculation of GHG. (Chavez, 2012). Scope 1 includes all GHG direct emissions produced within the city limits, including fossil fuels, industry and waste. Scope 2 emissions also include all the direct emissions and electricity imported for use within the city bounds. Scope 3 considers all direct and indirect GHG emissions which related to the life cycle of the supply system.

Cities are responsible for 40%-80% of global GHG emissions according to estimates. The range of the percentages is partially the result of the different approaches taken when making the calculations, whether a city’s emissions are considered based on the place of manufacture or the place of consumption. Evaluation by the place of consumption can show the differences between a regional industrial city and a large city with more people and less industry. Although there is less industry, the consumption by the city’s residents includes a significant quantity of materials and services from outside of the city. We can see the substantial difference between the approaches when calculating countries’ emissions, in part because developed countries tend to transfer their industrial production to developing countries, so a country’s industrial emissions may not reflect its consumption (Rauland & Newman, 2015).

2.3 Socio-economic, demographic and spatial factors’ relation to GHG emissions Socio-economic, demographic and spatial factors are connected to consumption and GHG emissions. Several studies have examined the relationship between household characteristics and consumption patterns. While some studies found that certain characteristics are evidently related to consumption levels, others have shown less clear correlations. The following paragraphs elaborate on this relationship by focusing on three consumption categories relevant to this study: transportation, electricity and food consumption.

Transportation: Different aspects of transportation have been examined including various modes (private, public and leisure transportation) and their relationship to socio-economic, demographic and spatial characteristics. A study in the UK found that socio-economic variables (e.g., age, employment status and socioeconomic group) have a stronger relationship to transportation patterns than to city land-use (Stead, 2001). A study in the Netherlands showed that spatial variables (e.g. population density, settlement type and train station access) correspond to the mode of transportation used, regardless of socio-economic variables (e.g. income, age and

17 education) (Limtanakool et al., 2006). Family sizes larger than three people, have nearly no impact on emissions from transportation (Weber & Matthews, 2008). A detailed analysis of commute-related GHG emissions in Tel Aviv-Jaffa found that 60% of total urban emissions related to private vehicles can be attributed to commuting (Kissinger & Reznik 2019). A study in Oslo found that the daily use of transportation decreases as the density of population rises in the urban centre, while on the other hand, leisure transport (e.g., flights) increases the population density and socio- economic levels increase. This can be explained by the low availability of green spots, such as gardens in a dense urban area, thereby increasing the need to go on holiday (Holden & Norland, 2005).

Food consumption: Household characteristics such as income, age, education, family type and labour force status have great impact on food consumption. The share of food expenses in the household budget decreases as income level increases (Resich et al., 2013). For this reason, increasing food prices have a major influence on low-income households in which food is a substantial part of their expenditure (Michaelis & Lorek, 2004). Moreover, wealthier households consume more ﬁsh, fruit and vegetables, and less meat per capita than low-income households, which is considered a healthier diet (Damari & Kissinger, 2018b; Du et al., 2004). The differences in food consumption cannot be explained merely by income. While income inﬂuences the types of food purchased, other variables should be examined in order to properly assess the differences in consumption. Food consumption emissions per capita also vary due to family structure, with the lowest emissions for households with children and highest for single households (Damari & Kissinger, 2018b; Gough et al., 2011). Family structure together with a variety of constraints, preferences and characteristics make up what is known as a “lifestyle”, which is crucial for understanding consumption patterns (Damari & Kissinger, 2018a).

Electricity: Demand for electricity is growing rapidly and the household sector is becoming a more substantial end user (Fischer 2008). In the OECD countries, the increase in the demand for electricity by households is the result of economic growth and demographic changes (Zacarias-Farah & Geyer-Allely, 2003). This is supported by findings from other countries (Ewing & Rong 2008, Lenzen et al., 2006, O’Neill & Chen 2002). Income levels are found to be highly related to the amount of electricity consumed (Damari & Kissinger, 2018b). The total electricity consumption of private

18 homes is higher than that of apartments. The consumption of households with children is higher than that of households without (McLoughlin et al., 2012). Elderly residents were also found to have higher demand for electricity (Damari & Kissinger, 2018b ; Santin et al., 2009; Lenzen et al., 2006; O’Neill & Chen 2002).

2.3.1 Neighbourhood emissions and socio-economic, demographic and spatial factors Neighbourhood emissions can vary throughout a city, due to various socio- spatial factors. The socio-economic aspect of GHG emissions has been explored mostly on the household scale (e.g., Lettenmeier et al., 2012), while neighbourhood scale analysis has focused mostly on the size and morphology of neighbourhood blocks that influence the density, shape, land use and building type, thus shaping energy demand for heating and cooling (Kellett et al., 2013).

Geographic location is a key factor for characterising neighbourhood emissions. Suburbs emit more GHG emission than central neighbourhoods, mainly due to the extensive use of transportation. Suburban residents usually own more cars and have larger homes (Jones & Kammen, 2015). Suburbs typically include detached homes rather than apartment buildings, which are more energy efficient during both construction and operation (Senbel et al., 2014).

Zhang, Guhathakurta and Ross (2016) found that the difference between suburban and urban transportation consumption is becoming smaller because transportation behaviour is becoming similar. If fewer local jobs are available there will more commuting trips. Location and density influence the neighbourhood’s emissions not only through transportation, but also in other ways (e.g. electricity and food) (Senbel et al., 2014). High-density areas are associated with low energy use for heating and cooling per built area. High-density areas also associate with use of transport modes that are low in carbon emissions (Qin & Han, 2013).

An extensive study (Jones & Kammen, 2014) of all cities in the U.S. (by zip code) examined the influence of density and suburbanization on households’ carbon footprint. They found that carbon emissions increase linearly with population density only up to 3000 persons per square mile (ppm2). In areas with density higher than 3000 ppm2, the carbon footprint decreases logarithmically, levelling out at a lower limit of about 30 tCO2 per household at densities over 50,000 persons per square mile. Urban areas are wealthier than rural areas, and thus tend to have higher carbon emissions.

This explains the linear increase of emissions as density rises. Urban areas that are denser than 3000 ppm2, shorter driving distances and somewhat lower incomes, which can explain the lower carbon emissions. Dense metropolitan areas tend to have suburbs that are more extensive, and which emit 25% more carbon emissions than the urban core.

Qin and Han (2013) found that mixed land use reduces carbon emissions from transportation because of the balance between the housing and workplace. Baltimore’s city action plan encourages mixed land use in order to shorten the distance between residential areas, transit, goods and services. Apart from reducing emissions, mixed- use neighbourhoods also provide transportation that is more equitable and reinforces neighbourhood centres and main streets. This is done by planning a “20-minute neighbourhood”, in which residents need to walk only 20 minutes to places and services that they use on a daily basis. When the plan was made, 56% of Baltimore’s residents commuted by private car, so encouraging an alternative has a significant impact on the city’s transport-related reduction. The Baltimore city action plan creates an alternative by reinforcing main routes and raising public awareness.

2.4 Cities working towards climate change mitigation plans

Cities play a crucial role in climate change global mitigation. While the world’s response to climate change focuses mainly on the national scale, the complexity of economics and geopolitical interests limit their ability to achieve GHG mitigation objectives. Cities, on the other hand, prepare risk assessments that set GHG emission reduction targets and promise action. The scientific foundation for cities’ role was established by the Intergovernmental Panel on Climate Change (IPCC). City leaders are more willing to take action against the threats of climate change than national politicians (Rosenzweig et al., 2010).

Extensive research analysed 200 medium-to-large scale cities around Europe and found that 65% of the cities have a mitigation plan. These plans include mostly technological solutions, for example improving energy efficiency and renewable energy generation (Reckien et al., 2014). The policies developed based on these mitigation plans can vary from promoting efficiency in energy use in the transportation, agriculture, and industrial sectors, to building green infrastructure and automated wastewater recycling facilities and more (Dhakal & Ruth, 2017).

Nevertheless, contemporary mitigation plans are far from reaching the cities’ reduction goals (Reckien et al., 2014).

While cities’ mitigation plans are mostly designed to reduce GHG emissions, implementing the plans’ objectives can contribute indirectly to other city welfare issues, such as energy savings, reduced air pollution, and improved public health. While the influences of the direct contributions of the mitigation plans occur over the long term, indirect effects have immediate benefits (Dhakal & Ruth, 2017).

2.4.1 Neighbourhoods GHG emissions reduction plans and scenarios Any neighbourhood has unique characteristics. For that reason, GHG emissions inventories and management plans need to offer reduction steps that match the neighbourhood’s unique features (Jones & Kammen, 2015; Lee & Lee, 2013; Codoban & Kennedy, 2008). A good example is the report on San Francisco’s Chinatown, which includes a detailed analysis of the neighbourhood’s characteristics. By comparing these characteristics to the city average, the report points out the neighbourhood’s unique features, enabling reduction steps that match these. Due to the neighbourhood’s low socio-economic status and high-density residential buildings compared to the city average, residents generally consume fewer resources. The physical features of the neighbourhood and the socio-economic traits of its residents led to the conclusion that the highest reduction potential would be achieved by aiming for 100% renewal energy by installing photovoltaic (PV) panels on rooftops.

Codoban and Kennedy (2008) examined the metabolism (material and energy flow and related emissions) of four neighbourhoods in Toronto, and suggested ways to reduce emissions, with varying foci based on the characteristics of each neighbourhood. For example, in older neighbourhoods, it is necessary to retrofit existing buildings or rebuild them with energy efficient designs. Suburban neighbourhoods can improve their energy consumption by creating new public transportation routes. In detached houses and low-rise buildings, solar energy and natural cooling techniques can provide 100% of the demand for heating and cooling .

A study in Helsinki, Finland corroborates the claim that different parts of a city with varied characteristics demand correspondingly diverse policy-making. To achieve a city-wide goal of 40% fewer carbon emissions, the city’s action plan is nearly sufficient. Another purpose of this study is to examine the influence of climate

21 change mitigation policy on consumption-based carbon footprints in different sections of the city, as well as the influence of the economic crisis of 2008. The decrease in total carbon footprint in Helsinki between 2006 and 2012 is not only the consequence of directed mitigation policies, but even more the outcome of economic, social and demographic changes.

Reduction plans in regions with high energy-related emissions need to focus on lower household energy consumption, unlike regions with a clean source of energy. If this were the only mitigation plan, two-thirds or more of households’ carbon footprints would be unaddressed. Suburbs are responsible for half of the carbon footprint of the city and tend to have high vehicle emissions, larger houses and high incomes. Therefore, the suitable action needs to include energy efficient technology and electric vehicles. Suburban neighbourhoods can improve their transport energy consumption by creating new public transportation routes (Codoban & Kennedy, 2008). Urban cores tend to have lower transportation and energy emissions but food consumption accounts for a larger share of their carbon footprint. Food also account for a greater share of the emissions in rural areas where house size tends to be large and total consumption relatively low. In urban areas, it would be better to focus on campaigns and policies of healthy diet and sustainable consumption (Jones & Kammen, 2014, 2015)

To highlight the importance of sustainable urban development in mitigating GHG emissions Lee and Lee (2014) analysed 125 large urban areas in the U.S. They found that by doubling population-weighted density, travel-related emissions are reduced by 48% and energy consumption-related emissions are reduced by 35%. Population-weighted density refers to the mean density of all sub-areas weighted by population size. By doubling the transit subsidy per capita, 18% of the transportation related city emissions and nearly 46% of private transportation use can be reduced. Redeveloping an area of detached homes as a blend of large and small-detached homes, townhouses, and small apartment buildings could achieve a reduction of 22% in emissions (Senbel et al., 2014)

Only a few researchers have created scenarios to analyse how different steps influence the neighbourhoods’ emissions, and suggest how to reduce GHG emissions to a minimum. According to the scenarios developed by Kellett et al. (2013), the most substantial reduction potential wold be adopting technology-based improvements in fuel efficiency. After analysing different scenarios for Chicago, Illinois they found that

22 the most significant reduction could be achieved by adopting 2012 European fuel economy standards, which would potentially reduce GHG emissions there by 48%.

The Jinshan neighbourhood in Guangzhou, China is a sustainable building project of 8,000 houses, which aims to reduce energy consumption by 65% and water consumption by 50% compared to current norms. The project includes a school, offices, leisure facilities, parks, and wetland areas for residents to grow their own food. Solar thermal panels are installed on buildings’ rooftops to provide hot water for the residential area and water source heat pumps are used to provide cooling for the commercial buildings (Bioregional, 2013).

In the Kronsberg neighbourhood of Hannover, Germany was developed for EXPO 2000 that addressed the theme of Humankind-Nature-Technology in the spirit of Agenda 21. The neighbourhood was planned using methods such as high-density development, low emission transportation, open spaces and mixed-use development.

The project’s efficiency optimisation goal was for CO2 emissions to be at least 60% lower than standard residential buildings (in comparison to German building regulations of 1995). The overall planning of the neighbourhood was rigorously monitored and supervised using contracts for the sale of land, planning permissions and bylaws, which was possible because the city of Hannover owned most of the building plots in the development area. Energy consumption was reduced by using a low-energy method for home construction, optimised energy provision and strict monitoring. Another 20% reduction was achieved by building two wind turbines and a thermal solar project (Rumming, 2006).

Global initiatives have also been established to create a framework for reduced carbon emissions on the neighbourhood level. The Beddington Zero Energy Development (BedZED) housing development in Wallington, UK led to the establishment of one of these frameworks. Steelwork and softwood walling studs from local demolition sites were remanufactured into useful new structural components, to lower the impact of transporting construction materials to the site (Zed factory, 2002). Apartment sizes vary, with one to four bedrooms. Half of the apartments were sold on the open market, one-quarter were rented at a low cost and the remaining quarter were designated for shared ownership, a lower cost way of owning a home. Most of the apartments have passive solar heat using multi-storey glazed sun spaces facing south, as well as photovoltaic panels incorporated into windows and rooftops. Energy

23 efficient appliances and lighting were installed in the apartments. All apartments are very highly insulated and well ventilated, using wind cowls on the roofs. The development’s hot water is supplied via an underground, gas-fired heating system. A water tank in each home stores hot water and keep it warm.

The One Planet Living framework was created based on the experience gained and lessons learning from the BedZED eco-village (Bioregional, 2018a). It envisions “a world in which people enjoy happy, healthy lives within their fair share of the earth’s resources, leaving space for wildlife and wilderness”. The framework consists of ten principles that provide businesses, cities and councils, real estate developers and communities with practical guidelines to create a sustainability action plan (Bioregional,2018).

The neighbourhood of Zibi in Ottawa, Canada is currently being redeveloped from industrial land usage, using the principles of the One Planet Living framework. The area will include residential, commercial and retail properties, a hotel, waterfront parks, open spaces and a network of pedestrian and cycling paths. Zibi’s action plan targets zero carbon energy use by 2020. Reaching this goal will be facilitated by an energy system that utilises waste heat from a nearby paper mill. The action plan also includes GHG emissions from transportation that are 90% lower than the regional average, by giving priority to walking, cycling and charge points for electric vehicles. The area is planned to be highly walkable, with an average distance of 500 metres between homes and workplaces (Bioregional, 2018c).

The city of Tel Aviv-Jaffa operates a sustainable neighbourhood program based on the One Planet Living framework and is committed to reducing carbon emissions. Its program aims to achieve zero carbon energy, zero waste, sustainable transport, green building and encourage local food growth and consumption. Having started in four of the city’s neighbourhoods, the program is currently spreading to reach as many neighbourhoods as possible. The program incorporates public participation, and is a collaboration of the municipality and residents that strives to match the city’s goals For example, after the sustainable .(רונן ושות', with the resident’s visions/needs (2012 aspects of the first neighbourhood, Bizaron, were mapped, twenty-five residents met with representatives of the municipality and chose the steps necessary for becoming a sustainable neighbourhood. The four steps selected are sustainable gardening, sustainable consumption, waste management and adjusting main routes by raising

24 public awareness with signs, ecological gardening in public and private areas and restoring public thoroughfares. Sustainable consumption is achieved by founding a neighbourhood cooperative for vegetables and fruits. Cleaning the neighbourhood and רונן ושות', ) adding more recycling bins are also ways of improving waste management 2012).

Municipal and organisational websites describe the measures that neighbourhoods have taken to become sustainable. All refer to reducing emissions from energy and transportation as main steps, while some mention sustainable food consumption. Most neighbourhoods use solar photovoltaic panels and solar water heating systems on rooftops, and others have a central system to produce renewable energy for their residents use. Mixed land use is also encouraged to shorten the distance between workplaces, goods, services and homes, as well as encouraging use of low carbon transport. However, the emissions reduction plan for neighbourhoods in Tel Aviv-Jaffa has a different focus that other neighbourhoods reviewed in this paper. It does not refer to reducing emissions from home energy consumption or mixed land use. Instead, the focus is on sustainable food consumption, recycling and rehabilitation of main transport routes.

Table 2 summarises all studies that examine neighbourhood emissions described above. For each neighbourhood, it gives the total estimated annual CO2eq, according to the relevant study, and reviews the suggested action plan. Additional details may be found in table of the action plans in the appendix.

Table 2 Neighbourhood GHG emission reports

City Total CO2eq Reduction action plans

Toronto 23.4 megatons Transportation: technology and (Codoban and Kennedy, 2008) CO2eq policy

Vancouver Infrastructure: planning and policy (Senbel et al., 2014) Vancouver Transportation: technology and policy (Kellett et al., 2013)

Phoenix The change in GHG (Zhang, Guhathakurta and Ross, emissions of 2016) Phoenix and Gilbert residents between 2001- 2009, is 157% and -6%. Beijing (Qin and Han, 2013) San Francisco Electricity: technology and policy (Jones & Kammen, 2015) Chicago Transportation: behaviour and policy (Lindsey et al., 2011) U.S. 5588 MT CO2eq Transportation: technology (Jones & Kammen, 2014) U.S. 49,733 lbs CO2 for Transportation: policy (Lee & Lee, 2014) all 125 areas Helsinki 10.9 CO2eq ton per Transportation: policy (Ottelin, Heinonen & Junnila, capita in 2006 and 2018) 10.2 CO2eq ton per capita in 2012 Merida The household Electricity: technology and policy (Cerón-Palma et al., 2013) sector represents of Food: technology and policy 100,352 tons/CO2eq/year Chinatown, San Francisco Electricity: technology (sustainable Chinatown steering Waste: policy committee, 2017) Baltimore 7,579,144 MT Electricity: technology and policy (Baltimore Office of Sustainability, CO2eq /year Transportation: behaviour and policy 2013) Kronsberg, Hannover Electricity: technology (Rumming, 2006) Vancouver Electricity: technology (City of Vancouver, 2012). Basel Electricity: technology and policy (Armando Mombelli, 2016) Wallington (BedZED) Electricity: technology and policy (Bioregional, 2018a) Transportation: behaviour and policy

Zibi (Bioregional, 2018a) Jinshan Food: behaviour and policy (Bioregional, 2013) Electricity: technology Tel Aviv-Jaffa Electricity: policy Food: behaviour and policy (רונן ושות', 2012) Waste: policy

3. Methods

3.1 The research questions and purpose

What are the city of Tel Aviv - Jaffa sub-city GHG emissions and their reduction potential?

Specific questions are:

(1) What is the carbon footprint of different urban neighbourhoods?

(2) To what extent sub-city socio-spatial diversity is reflected in the studied neighbourhoods' GHG emissions?

(3) How various measures can contribute to GHG mitigation of studied neighbourhoods?

3.2 The importance of the research

While a significant number of studies have examined city's GHG emissions at both the city and the household levels, only a number of studies have focused on the household level by spatial distribution in neighbourhoods. In Israel, several cities have formed GHG emission inventories and some have even suggested mitigation plans.

Yet all of these plans and inventories are at the city scale. No study in Israel has analysed GHG emissions at the sub-city scale.

Moreover, contemporary mitigation plans are far from reaching the cities' reduction goals (Reckien et al, 2014). The neighbourhood's unique characteristics may assist in achieving a better understanding of the city's consumption and emission reduction potential.

This research results also aim to support policy makers by highlighting the potential contributions of advancing various measures (at the household, neighbourhood, city and country level) to achieving GHG mitigation goals. By 27 examining the neighbourhoods' characteristics and consumption and developing reduction scenarios, the research assess which policies and different mitigation plans will be the most beneficial to the city's goal of reduction.

3.3 The place of the research

The research took place in the city of Tel Aviv - Jaffa. Tel Aviv - Jaffa is the second largest city in Israel, with a population of over 426,100 people and located in the 8th social-economic cluster out of ten (Central Bureau of Statistics, 2014). Tel Aviv

- Jaffa city's leaders see the environmental issue as one of the most important subjects,

.)עיריית תל אביב-יפו, which is reflected in the city's vision )2005

The city takes part in the Forum 15 initiative, which includes 15 cities in

Israel that make up 40% of Israel's population. The cities in Forum 15 commit to set goals for GHG emission reduction in their boundaries and make mitigation plans to meet their goals. The cities obligated to reduce at least 20% of the city's emissions by

.)פורום הcompared to the emissions in the year 2000 )2009 ,15 2020

The city is diverse in terms of socio-economic level and physical features.

The socio-economic clusters in the city vary between 2 in Ezra VeHa’argazim neighbourhood to 10 in Neve Dan (out of a total of ten clusters). There are neighbourhoods with detached houses and low density as well as dense neighbourhoods. Mixed land-use and building age also vary between neighbourhoods.

3.4 Neighbourhoods' carbon footprint analysis

The first part of this research focused on analysing the carbon footprint of each neighbourhood in the city of Tel Aviv - Jaffa. Figure 1 shows the steps taken in order to calculate the carbon footprint. The consumption was divided into three main household categories: food, transportation and electricity. Research has shown that

28 these emission sources have the greatest effect on total GHG emissions in cities. All categories were calculated per person, neighbourhoods and for total city residents.

Calculating per person, allows aggregation of consumption and emissions according to different variables, to enable statistic tests. The consumption unit differs from one category to another: food is accounted for in kilograms (kg), transportation in kilometres (km) and electricity in Kilowatt per hour (KWh). Accordingly, all categories included in this research were converted into an annual time frame. The last part in calculating the Neighbourhoods' carbon footprint was to convert the consumption to units of ton CO2eq.

Figure 1 Carbon footprint analysis

Kg FOOD CO2eq Central Bureau of Statistics Tel Aviv Yafo

KWh ELECTRICITY CO2eq Neighbourhood's carbon footprint Israel's Ministry TRANSPORTATION of Kilometers CO2eq Transportation

Each neighbourhood was given a reference number to simplify the presentation of the data on the different city maps. The following are a list of all city neighbourhoods and their corresponding reference number and a map showing their location.

Figure 2 Neighbourhoods

Table 3 Neighbourhood name and numbering Number Neighbourhood name Number Neighbourhood name 1 Glilot Tzuki Aviv and 29 Ganei Sarona Sde Dov Area 2 Ramat Aviv G 30 Nahalat Itzhak 3 Nofei Yam 31 Bitsaron and Ramat Israel 4 Neve Avivim 32 Tel Haim 5 Afeka 33 Yad Eliyahu 6 Tochnit Lamed 34 Ramat Hatayasim 7 Ramat Aviv 35 Hatikva 8 Kochav HaTzafon 36 Neve Barbour, Kfar Shalem West 9 Tel Baruch North Tel 37 Neve Eliezer and Kfar Baruch and Maoz Aviv Shalem 10 Ganei Tzhala, Ramot 38 Neve Hen Tzahala 11 Hamashtela 39 Kfir 12 Tzahala 40 Levana, Yedidya 13 Neot Afeka B 41 Nir Aviv 14 Neot Afeka A 42 Ezra and Ha'argazim 15 Hadar Yosef 43 Old Jaffa, Jaffa Port 16 Revivim 44 Jaffa North 17 Neve Dan 45 Florentin 18 Ramat HaHayal 46 Neve Sha'anan 19 Neve Sharet 47 Giv'at Herzl, Industrial Area Yaffo 20 Hatzafon HaYashan 48 Shapira (North) 21 Hatzafon HaYashan 49 Ajami and Giv'at Aliya (South) 22 Hatzafon HaHadash 50 Tzahalon (North) 23 Bavli 51 Yaffo-Tel Aviv College and Dakar 24 Hatzafon HaHadash 52 Tel Kabir, Neve Ofer, (Kikar Hamedina) Yaffo B 25 Hatzafon HaHadash 53 Yaffo G and Neve Golan (South) 26 Lev Tel Aviv 54 Yaffo D' (Giv'at HaTmarim) 27 Kerem Hateimanim 55 Kiryat Shalom and Hahorshot Park 28 Neve Tzedek

3.5 Data collection

This research focuses on three main emission sources: transportation, electricity and food consumption. The data was collected both on a city scale and an inner-city/neighbourhoods scale. The Central Bureau of Statistics was the data source of both food and electricity, and Israel's Ministry of Transportation supplied the transportation data.

Central Bureau of Statistics

The vast majority of the database for this research was the Central Bureau of

Statistics (CBS). The CBS database contains households’ expenditure surveys for the years 2010-2015. Through grouping the data from the surveys, a sample of more than

3221 households located in the city of Tel Aviv - Jaffa were used. These households are dispersed in all the city's neighbourhoods. The data contains a two-week diary of detailed descriptions of expenditures and quantities of consumption of various types: food, transportation, electricity, textiles, furniture, electrical appliances, communication services, healthcare and more. For this research, only electricity and food were used. The food data contained 141 food categories. Price and quantity were given for each product. The electricity data contained one month's payment for each household.

Israel's Ministry of Transportation

The second source is the licensing office of Israel's Ministry of Transportation.

The database includes registry information of all of the motorized vehicles in Israel.

The database contains over half a million motorized vehicles which are registered in the city of Tel Aviv - Jaffa. The database was collected throughout the annual roadworthiness test of the year 2015. The information includes the type of ownership

(private, company-lease) and registry information about the owner (of which this

32 research will use the postal zip code information) and information about the vehicle

(type, mileage, manufacturer and more).

3.6 Processing the data

The second step was made up of calculating the city's carbon footprint as well as its neighbourhoods. This step also includes characterizing these divisions by social, demographic and spatial features. Calculating carbon footprint initially requires conversion of the data into an equivalent Carbon dioxide unit.

Transportation

Vehicle owners' details were used to associate the transportation emissions to each neighbourhood. Due to confidentiality, the only detail used was the owners' zip code. The data contains the total distance travelled for each vehicle from two consequent roadworthiness tests from which the annual kilometres of travel were calculated. From this data, the median distance of travel in kilometres was calculated for each zip code. In case of one test, this calculation was used. Private transport is made up of private ownership vehicles and vehicles on lease for company use. The ratio between the two enabled to extract the number of leasing vehicle per neighbourhood. A national median distance of travel for leasing vehicles was used for this type of vehicle. To calculate the average CO2 equivalent emissions (tonCO2/year) per vehicle in each zip code, a conversion formula provided by the British department for environment was used. The calculation was according to the vehicle's engine volume and mileage, the age of the vehicle based on the average vehicle age in Britain.

Each zip code was assigned to a neighbourhood according to its geographic location by using GIS zip code and neighbourhood layers. The average amount of vehicles per person in each neighbourhood as well as the neighbourhoods' population was used to calculate the neighbourhoods total transportation related emissions. This information

33 was provided by the CBS and also served as a means of calculating the average transport related emission per person. This method was based on the studies of

Kissinger and Resnik (2019) and Reznik et. Al (2018).

Electricity

CBS households’ expenditure surveys from the years 2010-2015 provided the research with a total annual consumption of 3,130 households in Tel Aviv - Jaffa. The surveys database contains the monthly payment of each household and its statistical area. With the records of the KWh prices from the Israeli Electricity Company, the amount of KWh consumed was calculated with the corresponding price (which range between 53.78 and 63.22). The fixed monthly payment (ranging between 16.02 and

16.44), due to its indifference to the monthly consumption, was subtracted from the total payment. Subsequently, the remaining sum of consumption-based payment was divided by the price of kilowatt-hour in the corresponding month. Next, for each statistic area the average consumption per month was calculated, followed by the average monthly consumption. The average monthly consumption was then multiplied by 12 to give an annual figure. The total annual KWh consumption was multiplied by the population of each statistical area provided by CBS surveys from 2015.

Finally, the results were converted to CO2 equivalent using a factor taken from

This .(חברת חשמל, ח"ת) the Israeli Electricity Company (IEC) environmental report factor represents the natural resources used to produce the total KWh consumed and

CO2 emitted in the production process. This method was based on the study of Damari and Kissinger (2018).

Food

CBS households’ expenditure surveys database contains all food consumed throughout a fortnight by households. The information consists of statistical area,

34 product name, packages, price and quantity. First food package units were converted to kg using typical food packages and price. After all food was in kg units, all types of food products were converted to CO2 equivalent, using different factors matching each type of food product (Appendix Table 9). The factors to calculate the CO2 equivalent were mostly taken from Heller and Keoleian (2014). A review that cites a large number of food products' LCA studies calculating CO2eq referring to the consumption of food products. After converting the data, all consumption of food in each neighbourhood for the survey population in a two-week period was available. By dividing the total food's CO2 equivalent emissions by the number of people in the survey in each neighbourhood and multiplying by 26 (52 weeks in a year divided to two) a year's emissions per person in each neighbourhood. Then, by multiply by the number of people in each neighbourhood, the total neighbourhoods' annual CO2eq emissions were obtained. This method was based on the study of Damari and Kissinger (2018).

3.7 Characterizing the city's neighbourhoods

In order to achieve a better understanding of the city's consumption and the most suitable emission reduction, the unique neighbourhood characteristics need to be considered. This step characterized the neighbourhoods by social, demographic and spatial features. The socio-economic data is based on the most recent CBS database, which is based on the year 2008. Population size was based on the CBS database from the year of 2015.

3.7.1 Statistical analysis

All the data was defined in a set of variables and characteristics to meet the statistical test. In order to find a correlation between emissions to the neighbourhoods'

35 characteristics, the Pearson correlation between variables was assessed in a significance level of 95%.

3.8 GHG emissions reduction scenarios

The study compares between the city's various areas, analysing the different characteristics (social, demographic and spatial features) as reflected by variation in consumption. The next step of the study was analysing the city's mitigation plans as well as those of its neighbourhoods. The study shows empirically to what extent the plans can reduce the GHG emissions and if they have the ability to reach the city's reduction goal. The study develops scenarios that show how different mitigation plans will influence the city's carbon footprint. These scenarios provide an analysis of how much different policies (in the neighbourhood, city and country level), behaviour changes or technology changes can reduce the city's emissions. Furthermore, the scenarios give an insight into which policies can help reach the city's goals. For example, to what extent can solar panels reduce the emissions from electricity if installed on all the building roofs? By combining the neighbourhoods' emissions, the scenarios reduction abilities the study can result with a mitigation plan that can help reduce GHG emissions optimally.

Table 4 Scenarios

Consumption Scenario Type Details Type Food Policy changes "Meatless Monday" – 50% of the population changing their daily diet once a week into a vegetarian one. Food Technology 20% of the neighbourhoods' rooftops will be used for changes growing crops. Food Behaviour 10% of the population changing their diet to a change vegetarian diet. 50% of the rest of the population substituting beef with chicken. Electricity Policy changes A change in the combination of fuel types. 20% of energy will be from renewable sources. Electricity Technology Installing solar panels on 20% of the roofs area. changes Electricity Behaviour Reducing 10% of electricity use. change Transportation Policy changes All commuters to the three major employment centres within the city were assumed to take a train or light-rail to work. Transportation Technology A change of the urban vehicle fleet engine changes composition to prefer smaller vehicles. Transportation Behaviour All commute routes within the same neighbourhood change or adjacent neighbourhoods adopting non-motorized means of transportation. employer-sponsored shuttles transporting employees on all routes to the three main work destinations in Tel Aviv- Jaffa

3.9 Research assumptions and limitations

The socio-economic data is based on the most recent CBS database, which is based on the year 2008. Since the main demographic, social and economic characteristics of the neighbourhood's residents remained relatively constant, only a minor change is expected. The transportation-related assumption and limitations of this research are due to the scope of the study and its sources. The research examined private transport alone as public transport was excluded in the scope of this research.

Zip codes and neighbourhoods are not in full correspondence to each other. To convert the zip codes to neighbourhoods, the zip codes were first transferred, using GIS, to a central point representing each zip code. Distance travelled for leasing cars was

37 calculated by the national average and not by the actual kilometres travelled by each vehicle per neighbourhood.

The scope of this study also confines food consumption to that of a household use rather than eating out. Moreover, the food consumption addressed was limited to the available data. Each product's carbon footprint is affected by the place manufacture, i.e. olive oil made with olives grown in Israel may have a different impact on co2 emissions, than olive oil made with olives grown in Greece. The database used in this research did not include the source of each product. Product information was classified in general groups that do not distinguish between sub- groups of the same product. For example, strawberry yogurt and banana yogurt are both sub-groups of the yogurt group, yet obviously have a different carbon footprint.

The database contains a monthly consumption of electricity that was multiplied by 12 to make up the yearly consumption. This research assumes that each neighbourhood has at least minimal data for each month of the year in a way that balances the lack of data.

The research extends its assessment to scope 3, which means all the GHG emissions that are related to direct and indirect activities within the city are examined.

However, the research focuses on three main emissions and does not include all the city's emissions. The three emission sources that are examined are the most significant according to previous studies. The research does not include tourists and commuters from out of the city, and rather focus on the city's citizens. The extent of this study does not allow such a degree of analysis.

4. Results

The research findings presented in this chapter are divided into three consumption categories - food, electricity and private vehicles transportation.

First, the overall carbon footprint of the residential sector of each neighbourhood is presented, and then the share of each specific category is analysed. This is followed by an analysis of the extent to which various socio-economic and spatial factors shape the neighbourhoods' carbon footprint. Finally, selected scenarios, illustrating the potential contribution of different mitigation measures are proposed.

4.1 Neighbourhood's carbon footprint analysis

4.1.1 Total carbon emissions

The research found that the overall annual carbon footprint of Tel Aviv-Jaffa is approximately 1,980,000-ton CO2eq, or the equivalent of 4.6 ton of CO2eq per resident. As presented in the following figure 3, Electricity consumption related emissions found to be responsible of almost half of the overall urban emissions

(945,000 ton of CO2eq), private vehicles transportation related emissions found to be responsible for about 34.5% of the urban emissions (685,000 ton CO2eq) and the rest of the emissions were related to food (350,000 ton CO2eq).

Figure 3 Carbon footprint components

However, as presented in figure 4, the rates of emissions are not spread equally throughout the city and the carbon footprint of different neighbourhoods is diverse.

Figure 4 Total carbon footprint

By analysing each neighbourhood’s overall CO2eq emissions (figure 4a) it appears that most emissions are related to the city’s central neighbourhoods, driven by a combination of individual rates of consumption and the number of people living in each neighbourhood. The neighbourhoods with the highest emissions are Lev

Tel Aviv - neighbourhood no. 26 (153,200 ton), Hatzafon Hayashan-south - neighbourhood no. 21 (140,600 ton) and Hatzafon Hayashan-north - neighbourhood no. 20 (138,700 ton), which are also the neighbourhoods with the largest population.

When analysed per household emissions (figure 4b), the highest emissions are spread between few neighbourhoods in the north and in the southern parts of the city.

The highest emitting households are located in Glilot Tzuki Aviv and Sde Dov Area - neighbourhood no. 1 in the north (22 ton) followed by Kiryat Shalom and Hahorshot

Park - neighbourhood no. 55 (21.3 ton) in the south.

When examining the rates of emissions per capita (Figure 4c) there is a clear gradient from north to south, where the north has higher rates of emissions.

This is also true from west to east, although less significantly. An exception from this pattern is the neighbourhood of Ajami (no. 49 in the south. Ajami is located in the south-west of the city and has higher levels of emissions in relation to adjacent neighbourhoods. The neighbourhoods with the highest CO2eq emissions per person are Glilot Tzuki Aviv and Sde Dov Area - neighbourhood no. 1 (6.8 ton), Neve Dan - neighbourhood no. 17 (6.7 ton) and Tochnit Lamed - neighbourhood no. 6 (6.5 ton), which are located in the north of the city.

When analysing the range of emissions per person 66% of the neighbourhoods were found to be in the range of standard deviation 1. The results vary between 3 tons in Neve Sha’anan- neighbourhood no. 46 to 6.8 tons in Glilot-Tzoki Aviv –no. 1. Neve

Sha’anan is located in the south-east of Tel Aviv- Jaffa and ranks 3.5 (out of 10) in socio-economic cluster and Glilot-Tzoki Aviv is located in the north-west of the city and ranks 9 in socio economic cluster. These extremes match the pattern previously observed as they are located at the two edges of the gradient.

4.1.2 Electricity

The household sector in Tel Aviv - Jaffa consumed 1,359,559,900 kWh in the year of 2015, while the neighbourhood average is 24,719,300 kWh. Table 5 shows the electricity consumption per kWh per neighbourhood.

Table 5 Neighbourhood electricity consumption Neighbourhood no. Total kWh kWh per household kWh per person

1 10,718,615 3,845 3,878

2 52,578,644 9,773 4,238

3 37,359,307 9,579 3,773

4 39,595,960 7,580 3,140

5 11,874,459 16,379 4,826

6 26,487,734 12,257 4,789

7 28,359,307 6,359 3,147

8 22,962,482 10,174 4,645

9 38,709,957 3,470 4,343

10 23,196,248 14,737 4,278

11 9,186,147 10,427 3,526

12 9,554,113 13,233 4,575

13 19,502,165 8,741 3,481

14 11,536,797 10,264 3,788

15 23,158,730 7,551 3,169

16 17,848,485 13,912 4,177

17 13,522,367 14,525 5,249

18 16,581,530 10,080 4,056

19 18,269,841 6,123 2,559

20 102,308,802 5,891 3,046

21 92,512,266 5,198 3,232

22 36,632,035 6,201 3,451

23 25,942,280 6,621 2,781

24 48,102,453 6,845 3,443

25 47,334,776 7,305 3,799

26 123,848,485 6,709 3,692

27 16,329,004 5,682 3,368

28 15,851,371 8,559 3,928

29 5,721,501 6,179 3,404

30 29,675,325 9,213 3,511

31 19,803,752 8,324 3,327

Neighbourhood no. Total kWh kWh per household kWh per person

32 10,450,216 5,128 2,331

33 38,995,671 6,023 2,479

34 5,212,121 5,672 2,562

35 30,398,268 6,611 2,785

36 11,435,786 5,868 2,266

37 20,569,986 8,603 3,072

38 15,991,342 6,849 2,607

39 8,653,680 7,782 2,530

40 9,399,711 5,275 1,827

41 12,734,488 6,858 2,629

The Distribution pattern of total CO2eq electricity related emissions for each neighbourhood has high resemblance to the pattern of the total emissions, yet per capita, there are subtle differences. The range of electricity related emissions per person varies between 1.1 tons in Neve Ofer - neighbourhood no. 52 to 3.5 tons in

Neve Dan. Figure 8c shows the electricity related emissions per person. The city's north has higher emissions, neighbourhoods with the highest amounts of emissions are scattered. Another concentration of neighbourhoods that stand out from their surroundings are located in the centre. These neighbourhoods have a slightly lower amount of emissions from the north.

Figure 5 Total electricity CO2eq emissons, per household and per person

4.1.3 Transportation The components included in calculating CO2 emissions from private vehicles were engine volume, age of vehicle, and distance travelled. The total kilometres travelled by the entire population of Tel Aviv - Jaffa was 3,717,448,400 kilometres.

The results show a wide range of driving characteristics of different neighbourhoods.

The neighbourhood whose residents travelled the most is Hatzafon HaYashan (South)

- neighbourhood number 21, which travelled 320,859,713 kilometres in the examined year. The neighbourhood whose residents travelled the least kilometres is Giv'at Herzl and Industrial Area of Jaffa (neighbourhood number 47) which travelled 3,049,450 kilometres, this neighbourhood has also the least number of residents. Tel Aviv - Jaffa resident's vehicles have an average age of 6 years, ranging between 4 to 8 years. The average vehicles engine volume is 1573.

It can be seen that in the city centre, the number of kilometres travelled by residents is larger, however, the average engine volume is smaller in relation to the rest of the city. A different pattern can be seen when looking at the average vehicle age, this map shows that the average vehicle age decreases from south to north.

Figure 6 Transportation components

Transportation related emissions per person vary between 0.5 tons in Nir Aviv

- neighbourhood no. 17 to 3.3 tons in Glilot-Tzoki Aviv –no. 1. The total transport related CO2eq emissions are presented in figure 7a. The Distribution pattern of the neighbourhoods’ emissions has high resemblance to the pattern of the total emissions and electricity related emissions. Figure 7b shows the transportation related emissions per household. The neighbourhoods with the highest emissions are located at the north, some neighbourhoods with high emissions are located in the south. Figure 7c shows the transportation related emissions per person. The city's north has higher emissions, neighbourhoods with the highest amounts of emissions are mostly concentrated along the shore.

Figure 7 Total transportation CO2eq emissons, per household and per person

4.1.4 Food To calculate the amount of emissions from food consumption, the quantity of food in each neighbourhood was examined. The following table shows the amounts of food each neighbourhood consumed in a year by type of food. Most of the neighbourhoods’ food consumption is of vegetables and fruits (43%), followed by dairy products (13%). The neighbourhoods with the highest food consumption are

Hatzafon HaYashan (North) - no. 20 and Lev Tel Aviv - no. 26, which are also the neighbourhoods with the largest population.

Table 6 Food consumpion (ton) by type Number Meat Legumes Grain Milk Fruits & Other Total Vegetabl es

1 203 8 201 316 1060 546 2334 2 366 7 373 737 2256 1171 4910 3 249 8 189 396 1337 645 2824 4 84 2 86 142 445 273 1033 5 230 3 288 397 1321 666 2905 6 348 6 115 246 792 461 1968 7 294 13 306 498 1655 918 3683 8 627 24 543 692 2450 1212 5548 9 530 20 574 819 2690 1450 6085 10 298 5 242 326 1126 814 2813 11 101 3 101 169 360 258 992 12 108 7 160 107 565 268 1214

13 203 8 201 316 1060 546 2334 14 53 1 84 112 463 167 880 15 144 4 289 363 1305 646 2750 16 182 8 156 232 784 475 1837 17 156 1 72 105 710 208 1253 18 134 7 148 184 667 384 1524 19 192 5 303 400 1095 871 2867 20 1051 41 976 1481 5416 2311 11276 21 630 23 979 1410 4289 2107 9438 23 413 12 489 611 1844 1362 4730 24 409 11 538 795 2332 1430 5515 25 310 16 412 715 2233 1270 4955 26 872 65 1290 1406 4962 2546 11139 27 150 11 168 138 945 486 1899 28 122 13 286 198 636 410 1664 29 10 2 41 50 159 106 368 30 332 13 415 488 1388 745 3381 31 192 19 363 257 942 448 2221 32 235 14 170 294 885 464 2063 33 523 10 709 821 2160 1199 5420 34 74 2 107 109 333 187 810 35 456 23 880 416 1791 939 4505 36 218 0 204 321 838 641 2223 37 381 8 311 356 987 689 2731 38 289 12 322 332 970 579 2504 39 317 13 375 256 743 565 2270 40 196 10 208 252 714 551 1930 41 239 9 248 289 766 493 2043 42 143 4 214 138 464 329 1291 43 24 3 38 27 106 80 278 44 224 24 346 358 1505 740 3197 45 216 15 457 472 1466 686 3312 46 139 7 498 156 758 331 1889 47 136 0 108 274 722 775 2014 48 224 10 253 234 890 383 1993 49 319 31 642 317 1102 762 3172 50 384 17 691 399 1277 820 3588 51 244 13 276 273 896 609 2312 52 455 10 669 484 1805 964 4386 53 412 3 465 328 966 846 3022 54 312 6 363 321 1133 637 2772

Food related emissions per person varies between 0.4 tons in Ganei Sarona - no. 29 to 1.4 tons in Kfir - no. 39. Figure 8 shows the food related emissions. It is noticeable that total emissions are higher in the centre than the rest of the city (figure

8a). However, when compared to the food emissions per household (figure 8b) and per person (figure 8c), a different pattern is present. The highest level of emissions is located in the south, followed by the north, while the lowest level of emissions was recorded in the city's centre.

Figure 8 Total food CO2eq emissons, per household and per person

4.2 Neighbourhood Characteristics-Carbon Footprint Correlation

Correlation between the neighbourhoods’ carbon footprint and their characteristics was examined. Details of this analysis can be found in the appendix.

Evidently, such a correlation was found between the neighbourhoods’ unique characteristics and its carbon footprint.

Income level

Income level (Socio-economic cluster) is generally high in the north of the city and degrades as one moves to the south. The same pattern is noticeable from west to east, yet to a minor degree. The clusters represent ten groups of income level.

A linear correlation between income level and all emission components was found. The correlation between income level and electricity and transportation consumption is the strongest from all of the characteristics examined. Income level has a strong positive correlation of 0.82 to electricity related emissions. As shown in figure

9, the slope of the linear line is 0.19, meaning that a change in one socio economic cluster will be followed by a change of 0.19 ton CO2eq. The socio-economic cluster can explain 67% of the electricity related emissions as seen by the value of R2. This characteristic has a strong positive correlation of 0.58 to transportation related emissions. The slope of the linear line is 0.13 and R2 is 33%. Income level has a negative correlation to food relation emissions, the slope of the linear line is -0.02 and

R2 is 10%.

Figure 9 Income level

Average house size

Small houses are located mainly in the centre and the south of the city while in the north houses tend to be larger as seen in figure 10. The neighbourhoods of Hatzafon

HaHadash (no. 24) in the centre, Jaffa (no. 43) and Ajami (no. 49) in the south have larger houses compared to their surroundings. These neighbourhoods also have a relatively high carbon footprint (figure 4b and 4c).

A positive correlation was found between the average house size to all types of emissions apart from those that are food-related. The correlation to the total emissions is 0.73, with the strongest correlation to electricity (0.78) and the weakest to transport

(0.45). The slope of the linear line of the house size-electricity correlation is 0.014 and

R2 is 61%, as the graph in figure 10 shows. House size is an important factor in determining a neighbourhood's footprint. Furthermore, the most substantial correlation to the total neighbourhoods' emissions was the percent of large houses (100 sqm and more).

Figure 10 Average house size

Education level

Education level has a similar geographic distribution pattern to income level.

Correlation between income level and all emission components was found. Education level has a strong positive correlation of 0.61 to total emissions. This characteristic has a strong positive correlation of 0.72 to electricity related emissions. As shown in figure

11, the slope of the linear line is 0.05 and the level of education can explain 52% of the electricity related emissions as seen by the value of R2. Education level also has a positive correlation of 0.43 to transportation emissions, the slope of the linear line is

0.03 and R2 is 18%. Education level has a negative correlation of -0.33 to food relation emissions, the slope of the linear line is -0.01 and R2 is 10%.

Figure 11 Level of education

Percent of public transport commuters

The percent of public transportation commuters is generally high in the south of the city, followed by the city centre as the map in figure 12 shows. This characteristic has a mostly negative correlation to the emission components, except for food related emissions, which are positively correlated.

The percent of public transportation commuters has a strong negative correlation of -0.73 to total emissions. This characteristic has a strong negative

51 correlation of -0.79 to electricity related emissions. As shown in figure 12, the slope of the linear line is -0.03 and the percent of public transport commuters can explain

63% of the electricity related emissions as seen by the value of R2. This characteristic also has a negative correlation of -0.58 to transportation emissions. The slope of the linear line is -0.02 and R2 is 34%. However, this characteristic has a positive correlation of 0.36 to food relation emissions, the slope of the linear line is 0.004 and

R2 is 13%.

Figure 12 Public transportation

Percent of pedestrian or bicycle commuters

The percent of pedestrian or bicycle commuters can be abstractly described by three geographic rings, with the highest percentage in the centre and gradually decreasing towards the suburbs of the city, as the map in figure 14 shows. Two exceptions from this pattern are Ramat Hahayal (no.18) and Neve Sharet (no.19) with relatively high percentage of pedestrian or bicycle commuters. This characteristic

52 correlates negatively with food related emissions (-0.37), the slope of the linear line is

-0.01 and R2 is 13%.

Figure 13 Pedestrian or bicycle commuters

Percent of new houses

The percent of new houses (houses are considered new if built during the

1980’s or later) is higher in the north, as well as Kfir (no.39) and the surrounding neighbourhoods, which are located in the south-east of the city. A positive correlation was found between the Percent of new houses to all emission components apart from food-related emissions. The correlation to the total emissions is 0.49, with a correlation to electricity of 0.42 and 0.36 to transport. The linear correlation to electricity related emissions has a slope of 0.01 and a R2 value of 17%, as the graph in figure 14 shows.

For transport, the slope of the linear line is 0.01 and R2 is 13%.

Figure 14 New houses

Persons per room

The average persons per room is higher in the north and south and decreases towards the centre. This characteristic correlates positively with food related emissions

(0.40), the slope of the linear line is 0.16 and R2 is 16%.

Figure 15 Household density

4.3 Neighbourhoods GHG mitigation scenarios

The study examines three types of scenarios for each emission component - policy changes, technological changes, and behavioural changes.

4.3.1 Electricity scenarios

Technological changes: this scenario includes installing solar panels on 20% of the roof’s area. It is the scenario with the highest mitigation potential with a potential of reducing 6.3% of all urban emissions. Examining the differences between neighbourhoods revealed that Old Jaffa (no. 43) has the potential to reduce 53% of its emissions. Applying this step in Lev Tel Aviv neighbourhood (number 26) will lead to the most significant potential reduction on a city scale, a reduction of 0.5% from the overall city emissions.

Policy changes: Increase the use of renewable sources of energy. In this scenario, 20% of electricity will be from renewable sources. It could lead to a 6% reduction of the city's overall emissions. Neighbourhoods with high electricity consumption levels will achieve a greater reduction since this reduction is a fixed share of the electricity related emissions.

Behaviour change: Reducing 10% of electricity use. This can be achieved, for example, by raising awareness to the subject as well as replacing old inefficient equipment. The reduction potential is 4% of all urban emissions. In the neighbourhoods of Old Jaffa, it can reduce 6.1% of the neighbourhood's emissions.

Applying this step in Lev Tel Aviv neighbourhood (number 26) will lead to the biggest reduction on a city scale, a reduction of 0.34% from the total city emissions.

Another advantage is that this scenario will reduce emissions consistently throughout the city’s neighbourhoods, in contrast to the technological and behaviour change scenarios, that differ between neighbourhoods due to economic, social and

55 personal considerations. In these scenarios, the city and its residents have direct influence on the implementation of the scenario. The following graph number 16 shows the reduction potential of each neighbourhood by electricity-related scenarios.

The difference in reduction potential between the neighbourhoods is because of the current electricity consumption and the rooftop area available for the technological scenario.

Figure 16 Neighbourhood elecrticity related emission reduction percentage

4.3.2 Transportation scenarios

Technological changes: this scenario includes two steps. First, a change of the neighbourhood's vehicle fleet engine composition to prefer smaller vehicles. This scenario assumed that 50% of the vehicles in each neighbourhood are small with an engine volume of<1.4L; 30%, are medium sized vehicle with an engine volume of 1.4–

2.0L and 20% are large with an engine volume of>2.0L. The Second step assumes that

10% of all the neighbourhoods' residents use electric vehicles. This scenario has a reduction potential of 4.8% out of all urban emissions. In the neighbourhood of Glilot

Tzuki Aviv and Sde Dov Area (no. 1) it can reduce 25.7% of the neighbourhood's emissions. Changing the neighbourhood's vehicle fleet engine is responsible of 85% of the total reduction from this scenario.

Policy changes: this scenario assumes that all commuters to the three major employment centres within the city will take a train or light-rail to work. This scenario

56 has a reduction potential of 1.1% out of all urban emissions. In the neighbourhood of

Florentin (no. 45) it can reduce 3% of the neighbourhood's emissions.

Behavioural changes: this scenario includes two steps. First, all commute routes within the same neighbourhood or adjacent neighbourhoods adopt a non- motorized means of transportation. Second, employer-sponsored shuttles will transport employees on all routes to the three main work destinations in Tel Aviv-

Jaffa. This scenario has a reduction potential of 1.9% out of all urban emissions. In the neighbourhood of Florentin (no. 45) it can reduce 4.3% of the neighbourhood's emissions. Shuttle transport is responsible for 67% of the total reduction from this scenario.

There is a clear advantage to technology scenario over other scenarios as shown in figure 17. This advantage is because that it applies to all emissions related to travel and not only to commuting trips as other scenarios analysed.

- Figure 17 Neighbourhood transportation related emission reduction percentage

4.3.3 Food scenarios

Technological changes: in this scenario, 20% of the neighbourhoods' rooftops will be used for growing crops. The reduction potential is less than 1% of all urban emissions. In the neighbourhoods of Tel Kabir, Neve Ofer, Yaffo B' (no. 52) it can reduce 0.53% of the neighbourhood's emissions.

Policy changes: The Meatless Monday Association promotes the reduction of meat consumption. The association works to increase the supply of vegetarian dishes in home kitchens and institutional food rooms, public information and education for sustainable nutrition. The reduction potential is less than 1% of all urban emissions. In the neighbourhoods of Neve Sha'anan (no. 26) it can reduce 0.2% of the neighbourhood’s emissions.

Behavioural changes: 10% of the population will revert to a vegetarian diet and another 45% of the population will substitute beef with chicken. This scenario has the highest reduction potential from the food scenarios, yet, it is less than 1% of all urban emissions. In the neighbourhoods of Kfir (no. 39) it can reduce 4.6% of the neighbourhood's emissions.

All food-related scenarios reduce less than 1% of the city's emissions.

The behavioural scenario has the greatest potential out of these scenarios (as shown in figure 18), as opposed to the electricity and transportation scenarios were the behavioural scenario's potential is the smallest.

Figure18 Neighbourhood food related emission reduction percentage

4.3.4 Overall mitigation potential scenarios In terms of the various scenarios, it can be seen that in most of the city's neighbourhoods, the scenario with the highest potential is the installation of solar panels on rooftops. The scenario with the lowest potential reduction is growing food on rooftops. Therefore, when looking at the roof area resource, it is more beneficial in

terms of reducing emissions when utilizing the roof area for solar panels if possible.

Implementing new technological measures found to be the most significant potential

to reduce the emissions. It can be seen in figure 19 showing the percentage of emission

reduction potential of each neighbourhood by scenario type, which together have the

reduction potential of 11% of the city's total emissions.

When examining all the reduction scenarios, it can be seen that implementing

them all will reduce 24% of the total urban emissions and therefore achieve the target

set by the city. When two scenarios are substitutional, for example utilising roofs for

solar panels or for growing crops on them instead, the scenario with the lowest

reduction potential was excluded for this calculation. In addition, when assessing each

individual neighbourhood the average reduction potential is 32% of its emissions.

Figure 19 Total neighbourhood emission reduction percentage

Percent of Total Neighbourhood Emission Reduction

Policy Technology Behaviour 60% 40% 20%

0% Percentage

Neighbourhoods

The following figure 20 shows the optimal scenario for each neighbourhood.

It is prominent that most of the city's neighbourhoods achieve the best results from an

electricity related scenario, mainly technology based and policy for a small number of

neighbourhoods. The northern shore achieves the best results from a technology-based

transport related scenario.

Figure 20 The most benefical secnarios by neighbourhoods

The following table shows the percentage of potential emission reduction by neighbourhood and scenario type. The colours reflect which step has the highest reduction potential in a particular neighbourhood is the most significant of all the steps in the various neighbourhoods. Thus, it can be seen that the technological scenario does indeed have the highest potential for reduction

Table 7 Reduction potential by scenario FOOD TRANSPORTATION ELECTRICITY

no.

Policy % of total of % Policy total of % Policy

Policy % off total off % Policy

Neighbourhood

Behaviour % of total of % Behaviour total of % Behaviour total of % Behaviour

Technology% of total of Technology% total of Technology% total of Technology%

1 0.1% 0.0% 0.8% 1.0% 25.8% 2.8% 5.8% 5.9% 3.9%

2 0.2% 0.0% 1.2% 0.8% 6.6% 2.2% 7.3% 4.6% 4.9%

3 0.1% 0.0% 0.5% 0.8% 6.7% 2.2% 7.3% 4.3% 4.8%

4 0.1% 0.0% 1.0% 0.8% 6.5% 2.1% 7.1% 6.1% 4.8%

5 0.2% 0.1% 1.0% 0.6% 5.4% 1.8% 8.3% 17.4% 5.5%

6 0.1% 0.0% 0.8% 0.7% 6.2% 2.0% 7.6% 7.1% 5.0%

7 0.1% 0.1% 0.9% 0.6% 4.8% 1.6% 8.6% 13.2% 5.7%

8 0.4% 0.0% 2.1% 0.6% 5.1% 1.7% 7.9% 4.9% 5.3%

9 0.0% 0.0% 0.2% 0.9% 16.2% 1.7% 7.9% 3.8% 5.3%

10 0.2% 0.0% 1.5% 1.0% 5.9% 1.9% 7.0% 9.7% 4.6%

11 0.1% 0.1% 0.3% 1.0% 6.2% 2.0% 7.3% 11.0% 4.9%

12 0.1% 0.1% 0.4% 0.9% 5.7% 1.8% 7.6% 25.5% 5.1%

13 0.1% 0.0% 0.7% 1.3% 8.0% 2.6% 5.7% 5.6% 3.8%

14 0.1% 0.0% 0.6% 1.2% 7.5% 2.4% 6.8% 10.7% 4.5%

15 0.1% 0.0% 0.6% 1.1% 6.6% 2.1% 6.8% 10.2% 4.5%

16 0.2% 0.1% 1.5% 0.7% 4.2% 1.3% 8.5% 11.7% 5.7%

17 0.2% 0.1% 0.9% 0.9% 5.5% 1.8% 8.0% 15.1% 5.4%

18 0.1% 0.1% 0.5% 1.1% 6.7% 2.2% 7.1% 18.0% 4.7%

19 0.1% 0.0% 1.0% 0.9% 5.6% 1.8% 6.8% 6.5% 4.6%

20 0.2% 0.1% 1.3% 1.5% 2.9% 2.8% 7.7% 11.1% 5.1%

21 0.1% 0.0% 0.8% 2.0% 3.8% 3.6% 6.8% 10.7% 4.6%

22 0.1% 0.0% 0.4% 0.9% 4.3% 1.6% 7.9% 10.0% 5.3%

23 0.2% 0.0% 1.1% 0.8% 4.1% 1.5% 6.7% 7.5% 4.5%

24 0.1% 0.0% 0.9% 1.0% 4.8% 1.8% 7.3% 9.5% 4.9%

25 0.2% 0.1% 1.1% 0.8% 4.2% 1.5% 7.9% 12.6% 5.3%

26 0.1% 0.1% 0.8% 2.1% 3.0% 2.8% 8.4% 13.9% 5.6%

27 0.2% 0.1% 1.3% 1.9% 2.8% 2.6% 8.1% 19.0% 5.4%

28 0.1% 0.2% 0.7% 2.2% 3.2% 3.0% 8.0% 36.3% 5.3%

29 0.1% 0.2% 0.3% 2.5% 3.5% 3.3% 8.2% 44.5% 5.5%

30 0.2% 0.1% 1.4% 1.2% 3.0% 1.4% 8.2% 12.6% 5.5%

31 0.2% 0.1% 1.3% 1.8% 4.5% 2.1% 6.8% 20.8% 4.5%

32 0.3% 0.0% 1.8% 1.6% 4.1% 1.9% 5.9% 10.0% 3.9%

33 0.2% 0.1% 1.4% 1.6% 4.1% 1.9% 6.7% 14.7% 4.5%

34 0.2% 0.1% 0.8% 1.7% 4.3% 2.0% 6.5% 12.5% 4.4%

35 0.4% 0.1% 3.2% 1.2% 3.1% 1.4% 7.4% 19.8% 4.9%

36 0.2% 0.0% 1.5% 1.4% 3.6% 1.7% 6.4% 9.2% 4.3%

37 0.3% 0.0% 1.9% 1.1% 2.8% 1.3% 8.0% 6.0% 5.3%

38 0.3% 0.0% 2.0% 1.8% 4.4% 2.1% 6.0% 6.8% 4.0%

39 0.6% 0.0% 4.7% 0.8% 1.9% 0.9% 6.7% 6.2% 4.5%

40 0.4% 0.0% 2.9% 2.0% 5.0% 2.3% 4.8% 9.4% 3.2%

41 0.3% 0.0% 2.5% 0.8% 1.9% 0.9% 8.2% 7.0% 5.5%

42 0.4% 0.1% 3.2% 1.6% 4.1% 1.9% 5.4% 30.6% 3.6%

43 0.4% 0.2% 3.0% 1.3% 2.5% 1.7% 9.2% 53.0% 6.1%

44 0.2% 0.1% 1.1% 2.1% 3.9% 2.7% 7.1% 29.9% 4.8%

45 0.1% 0.1% 0.7% 3.0% 4.9% 4.4% 5.8% 14.8% 3.8%

46 0.3% 0.2% 2.4% 1.6% 2.5% 2.3% 7.9% 48.7% 5.2%

47 0.3% 0.2% 1.1% 2.1% 3.9% 2.7% 6.3% 43.5% 4.2%

48 0.3% 0.1% 2.7% 1.9% 3.1% 2.8% 6.8% 33.7% 4.5%

49 0.2% 0.1% 1.7% 2.4% 4.5% 3.1% 6.4% 19.2% 4.3%

50 0.4% 0.1% 2.6% 2.4% 4.6% 3.2% 5.6% 16.5% 3.7%

51 0.4% 0.1% 2.7% 1.8% 3.5% 2.4% 5.9% 24.4% 4.0%

52 0.5% 0.0% 4.1% 2.2% 4.1% 2.9% 5.0% 9.3% 3.3%

53 0.5% 0.0% 3.3% 1.5% 2.9% 2.0% 6.7% 8.3% 4.5%

54 0.5% 0.0% 4.0% 1.8% 3.4% 2.4% 6.2% 9.9% 4.1%

5. Discussion and conclusion Population growth and changing lifestyles have increased demands for resources and, as a result, increased GHG emissions worldwide (UNPD, 2009).

GHG emissions that exceed the norm lead to changes in the atmosphere, higher temperatures and diverse components of climate change (Pachauri et al., 2014).

Climate change is considered one of the most important challenges humanity will face in the coming decades (UNFCCC, 2018).

Most of the world’s population nowadays lives in cities. The urban lifestyle is characterised by excessive material and energy consumption. It follows that most of the world’s resources are directly or indirectly consumed by cities and therefore most

GHG emissions can also be related to cities (Rees & Wackernagel, 1996; Grimm et al., 2008; Moore, 2009). Cities play a crucial role in climate change global mitigation

(Rosenzweig et al., 2010).

This research assesses sub-city GHG emissions and their reduction potential.

It contributes to the growing discussion on the importance of the cities in GHG mitigation and policy leadership, and presents an innovative analysis of applying the carbon footprint model to urban neighbourhoods in Tel Aviv-Jaffa, Israel. Most prior studies assessed GHG emissions at the city level, and only in recent years have any focused on the inner-city scale. This is the first such study concern Israel.

The carbon footprint method allows us to compare not only different consumption elements, but also neighbourhoods, cities and countries. This method provides a foundation for mitigation plans by showing neighbourhoods where they should focus and what steps have the greatest reduction potential.

The research results are mostly consistent with the literature. As found in all of the studies reviewed, electricity accounts for the largest share of consumption.

Geographic location was found to be a key factor, in terms of neighbourhood emissions, distance from city centre, distance from the shore. Suburbs emit more GHG emission per household and per person than central neighbourhoods, mainly due to the extensive use of electricity and transportation. The few neighbourhoods where transportation-related emissions are higher than those from electricity are located in the north and south of the city. Some studies discuss mixed land use and population density as factors in household GHG emissions, but the current study did not found a correlation between these infrastructure elements and the neighbourhoods’ emissions.

Different aspects of transportation have been examined in the literature, including the different modes (private, public and leisure transportation) and their relation to socio-economic, demographic and spatial characteristics. Some of the research results match existing studies, yet some reveal a different picture. This research has focused only on private cars (including leased cars) and found a correlation to the characteristics mentioned above. We show that transport patterns in

Tel Aviv-Jaffa are most strongly connected to socio-economic cluster. This is consistent with prior studies, which found socio-economic variables to be more influential than other variables such as land use (e.g., Stead, 2001). Nevertheless, unlike other studies, which found a correlation between transport, population density and land use (e.g., Holden & Norland, 2005; Stead, 2001), we did not find such a correlation.

Although the city centre is associated with good public transportation, high density population and mixed land use, private transport-related emissions are highest in the city centre. The large population living in the city centre can explain this, and indeed because private transport emissions per capita the centre rank lower. The

64 highest private transport- related emissions per capita are in the northern suburban neighbourhoods.

Glilot–Tzuki Aviv (no. 1) and Neot Afeka B (no. 13) are the neighbourhoods with the highest private transport emissions per capita. These are suburban neighbourhoods associated with high socio-economic status (9-10 out of 10 clusters), with a high percentage of the population with master’s degrees, a median age above the city average, large house size and high percentage of residential land use. In these neighbourhoods, the percent of commuters working outside of the city is similar to other parts of the city. However, the use of public transportation for commuting is lower here and residents regularly choose to use private cars, meaning that greater distances are travelled by private transportation. The literature also mentions vehicle engine volume as another important factor that influences the high emissions

(Kissinger & Reznik, 2019), and it indeed tends to be higher in these neighbourhoods.

Research shows that socio-economic characteristics, such as income, age, education, family type and labour force status, have a very significant impact on food consumption. This study shows that food-related emissions in Tel Aviv-Jaffa are most closely related to average residential density (number of people per room). Socio- economic cluster and the percentage of population with a master’s degree have a negative correlation to food-related emissions. An interesting relationship was found between transportation modes and food emissions. Population in neighbourhoods with high food-related emission often use public buses and rarely commute by walking or cycling.

The neighbourhoods with the most food-related emissions per person are Kfir

(no. 39), Givat Herzl (no. 47) and Jaffa Gimel (no. 53), located at the south of Tel

Aviv-Jaffa. These are neighbourhoods associated with low socio-economic status (2.4-

3 out of 10 clusters), with a low percentage of the population with a master’s degree and small house size. Givat Herzl (no. 47) has a very mixed land use whereas Kfir (no.

39) and Jaffa Gimel (no. 53) have a high percentage of residential land use. Population in these neighbourhoods commute more by public transportation and less by private cars, walking or cycling.

When considering amounts of food (in kg), Kfir (no. 39) remains at the top, but the third neighbourhood is Tzahala (no. 12). Comparing these two neighbourhoods sheds light on the topic, revealing significant differences in the food components and socio-economic-spatial characteristics. Residents of Kfir consume a high proportion of beef products but in Tzahala (no. 12) they eat a large percentage of fruit and vegetable products. One kilogram of beef products is equal to at least 16.25 times 1 kg of fruit and vegetable products in terms of CO2 emissions. This might explain some of the gap between the wealthy northern neighbourhoods and the poor southern neighbourhoods. However, most of the southern neighbourhoods consume more kilograms of food than other Tel Aviv-Jaffa neighbourhoods. This current study was limited to food consumed in the household, which might explain this difference, because people in households with a high income eat outside the home more often than those in low-income households.

The demand for electricity is growing rapidly and the share of the household sector is becoming more significant. In the current study, CO2 emissions from electricity consumption were found to be the most significant, with food consumption accounting for 50% fewer emissions. The research results match the results found in the literature, emissions related to electricity consumption were found to be most significant (e.g. Senbel et al., 2014; Codoban & Kennedy, 2008; Jones & Kammen,

2015). The literature shows that total electricity consumption is higher in detached

66 houses than in apartments (McLoughlin) et al., 2012). This is also true in Tel Aviv-

Jaffa where the neighbourhoods with the highest electricity emissions per capita are

Tzahala (no. 12), Neve Dan (no. 17 and Afeka (no. 5), which are suburban neighbourhoods with detached houses and high socio-economic status (9-10 out of 10 clusters).

Tel Aviv-Jaffa aims to lower its GHG emission by 40% by 2030. This study assessed nine reduction scenarios and suggests behavioural, technological and policy solutions. The solutions with the highest reduction potential are in general technology- based solutions for transportation and electricity, but a behavioural scenario has greater impact for reducing food related emissions. In most neighbourhoods, the scenario with the highest reduction potential (8%) is installing photovoltaic panels on rooftops, a technological solution. The second-best solution is a governmental policy that guarantees electricity production from 20% renewable energy. On one hand, this solution provides a 6% reduction for the whole city regardless of neighbourhood characteristics. On the other hand, the city has only a minor influence on national policy. The third-best solution is using technological improvements to decrease engine volume and introduce hybrid vehicles, which would reduce transportation related emissions by 5%. Neighbourhoods with the highest reduction potential are located in the city centre. Plans to encourage a change in food consumption habits are most beneficial in the south of the city.

When comparing different scales, a higher resolution shows more variability within units of the city. By looking at the city on a number of scales, one can compare regional characteristics and identify exceptional units within the region. This broad perspective can contribute to the analysis of the factors affecting the various emission components. For example, Ramat Aviv is part of the quarter with the highest

67 emissions. Yet, at the neighbourhood level, the GHG emissions per capita in Ramat

Aviv are similar to those of the neighbourhoods in the south of the city, which have lower emissions.

Neighbourhood analysis helps the city focus its efforts on those changes that have the potential to achieve the highest reduction. On the level of city quarters, the highest potential seems to be in the northern quarter. Analysing the neighbourhood level while taking population size into account shows that the highest potential will be achieved by focusing on one neighbourhood in each region: Ramat Aviv G (no. 2) in the north, Old North (no. 20) in the centre, and Yad Eliyahu (no. 33) in the south.

A small area provides a far more accurate carbon footprint measurement.

However, the neighbourhood scale is favoured over statistical areas despite size, due to the social and community aspects. Community involvement in neighbourhoods can take many forms including, but not limited to, formal volunteering, neighbourhood groups, and neighbourhood institutions, providing the ability to organize and drive change processes in the neighbourhood. Therefore, it is necessary to take advantage of neighbourhood organizations to implement environmental policy.

Figure 21: Carbon footprint by statistical areas, neighbourhoods, sub-quarters and quarters

This study examines the main factors affecting greenhouse gas emissions

attributed to the city’s residents. The scope of the study precluded considering other

factors such as public transportation and waste. Future research could examine the

additional factors that were not analysed here, in order to produce a more accurate

picture of the urban emissions attributed to the residents and neighbourhoods of Tel

Aviv-Jaffa. Further, they might examine additional sectors, such as industry, the public

sector, and the private, business sector. These studies could complement the

neighbourhood vision related to other components of neighbourhood life. It is probable

that a reduction of industrial emissions will have a greater impact on the city’s total

carbon footprint.

Another possibility for further research is to examine the neighbourhood infrastructure. Cross-referencing the carbon footprint of the neighbourhoods together with the infrastructure can give us a better view of the impact of infrastructure on the residents’ emissions and ways to reduce them through the urban organisation. The municipality has a great influence on the infrastructure, by which it will be possible to reduce urban emissions and plan neighbourhoods whose residents’ habitats will be smaller.

To conclude, the analysis of urban emissions at the neighbourhood level allows examination of the relationship between social, economic and spatial characteristics and neighbourhoods’ carbon footprint. This analysis can help target the urban emissions with appropriate reduction measures. The neighbourhood unit has socio- economic characteristics that allow for policy adjustment, and also has the ability to organize and drive processes among the residents, which helps to achieve better results. The results show that the city will achieve its reduction goals only if all mentioned scenarios are applied, not accounting for the rapid growth of population.

The scenarios examined require changes in all aspects of life, from personal behaviour to technology and national policy. These aspects are tightly linked, as even technological change requires an adjustment of policy and adaptation of personal behaviour and lifestyle in order to be most effective. Only a holistic approach together with further drastic measures will achieve the city’s goal and make a significant long- term changes.

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7. Appendix

Table 8 Neighbourhoods' GHG emission report - extended

Total CO2eq Reduction action plans Reduction potential Studies

Toronto 23.4 megatons CO2eq Reducing tailpipe NOx emissions from diesel trucks and/or reducing diesel truck 10-40 kg CO2eq (Codoban and traffic during smog conditions. Kennedy, 2008) Reducing natural gas use for heating through conservation and through fuel substitution using renewable energy sources. Adopting parking, licensing, and related municipal measures that encourage taxis, corporate fleets, and individuals to purchase fuel efficient, low polluting vehicles, enhanced by growing biofuel use. Shifting to hybrid and plug-in hybrid technologies in the City’s own corporate fleet. Closing lanes in favour of public transit. Generating renewable energy. Equipping Landfills with GHG recovery systems. 4 kg CO2eq per tonne of waste Integrating light control technologies in municipal buildings. Developing of a permanent and automated information and knowledge base for energy and emissions, and the establishment of permanent technical and managerial capacity within City Hall to sustain a long-term commitment to emission reduction. Vancouver Shifting from large single-family homes to a mixture of large and small single-family 22% reduction in (Senbel et al, homes, townhouses, and small apartment buildings emissions 2014) Vancouver 1. "Modified baseline" – elimination of emissions attributable to through traffic. New Baseline (Kellett et al, 2. Scenario 1+ Adopting the best existing policy and regulation standards. 37% reduction in 2013) emissions

3. Scenario 2+ Transit oriented infill and re-development. 52% reduction in emissions 4. Scenario 3+ technical innovations and improvements. 69% reduction in emissions Phoenix The change in GHG (Zhang, emissions of Phoenix and Guhathakurta Gilbert residents between and Ross, 2001- 2009, is 157% and 2016) -6%. Beijing (Qin and Han, 2013) San Francisco Energy: (Jones & 1. Installing solar photovoltaic panels by introducing assistance and financing Kammen, incentives. 2015) 2. Obliging building owners to report the total amount of building energy use. Waste: Zero waste 1. Free on-site assistance, including outreach materials and staff/ management training. 2. Reducing trash sent to the landfill can lead to a 75% bill discount. 3. Zero Waste Facilitators Pilot for multifamily buildings. Chicago Replacing SUVs with SUV hybrids 7% (Lindsey et Replacing SUVs with compact hybrids 14% al, 2011) CAFE standards 2016 28% EUROPEAN standards 2012 48% U.S. 5588 MT CO2eq Suburbs action plans: energy efficient technology and electric vehicles. (Jones & Kammen, 2014)

U.S. 49,733 lbs CO2 for all 125 Doubling the transit subsidy per capita 46% lower VMT and (Lee & Lee, areas an 18% reduction in 2014) transportation CO2 emissions Finland 10.9 CO2eq ton per capita Urban density policies. (Ottelin, in 2006 and 10.2 CO2eq Increasing public transport availability. Investing in cycling routes. Heinonen & ton per capita in 2012 Junnila, 2018) Merida (Cerón- 3033 9 CO2eq ton per Two strategies were assessed: eco-tech and green spaces. The latter affecting food A1: 33% Palma et al, household distribution and thus decreasing regional transportation. Several scenarios were A2: 31% 2013) assigned to each strategy. Scenarios A1 and A2 consisted of replacing lighting, B1: 11% refrigerators and air conditioning to more efficient models. Scenario A2 includes B2: 19% replacing air-conditioners as opposed to scenario A1. Scenarios B1-B3 define the B3: 24.6% different crops (sedum/tomatoes) and areas of green spaces (rooftops/ground plots). Finally, in scenarios including the growth of tomato crops the impact of avoiding the transport to the consumer was calculated.

City's action plans Chinatown, ENERGY: ENERGY: San Francisco 1. Lower energy use 1. 20% (sustainable 2. Potential for solar power and 100% GHG-free electricity. Solar photovoltaics and 2. 14% Chinatown solar hot water. 3. 11% steering 3. Lower utilization of city programs and high saving potential. 4. 10% 4. Potential for solar power and 100% GHG- free electricity. In energy saving

81 committee, WASTE: 2017) 1. Limited data 2. More landfilled waste. 3. Significant waste challenges. 4. Culturally appropriate reduction strategies.

Baltimore 7,579,144 MT CO2eq Energy: Energy 51% (Baltimore /year 1. Reducing energy consumption of existing buildings. Office of 2. Promoting generation of renewable energy. Land Use & Sustainability 3. Expanding and upgrading energy performance for major renovation and new Transportation 5% , 2013) construction. 4. Promoting efficient community energy districts. 5. Baltimore Sustainability Plan Quantification Energy Savings and Supply Activities. Land Use & Transportation 1. Promoting mixed-use development near transit. 2. Supporting commuting alternatives. 3. Exploring parking strategy options. 4. Increasing walking and cycling. 5. Increasing efficiency in city fleet 6. Supporting cleaner vehicles City/Organisation Websites Kronsberg, 1. 'Low Energy House' manufacture method, optimised energy provision and strict 1. Reduction of 60% Hannover monitoring. 2. Reduction of 20% (Rumming, 2. building two wind turbines and a solar thermal project 2006) Vancouver Convert existing steam heat systems to low carbon energy sources 120,000 CO2 ton Establish and expand new Neighbourhood Energy System

(City of Vancouver, 2018). Basel New technical solutions and a series of actions taken to improve energy efficiency. reducing energy (Armando 1. 100% renewable energy from solar photovoltaic panels and a renewable consumption to Mombelli, electricity factory. average of 2000-watt 2016) 2. All the buildings were built according to green building standards per person Wallington 1. Solar heating as well as photovoltaic panels incorporated in windows and (BedZED) rooftops. 2. Energy efficient appliances and lighting. 3. All apartments are highly insulated and well ventilated, using the wind cowls on the roofs. 4. Hot water is supplied via an underground heating system, which is gas-fired. Zibi 1. Energy system that uses waste heat. 1. zero carbon energy (Bioregional, 2. Giving priority to walking, cycling and charging points for electric vehicles. The use BedZED) area is planned to be highly walkable, with 500 metres between homes and 2. 90% reduction in workplaces. transport greenhouse gas emissions Jinshan Wetland areas for residents to grow their own food. Reduce energy (Bioregional, Rooftops solar thermal panels are installed consumption by 65% 2013) Tel Aviv - Adjusting main routes, sustainable gardening, sustainable consumption and waste 'zero carbon' energy Jaffa management zero waste )רונן ושות', 2012)

Table 9 Food convertion by type Product KgCO2eq/ Product KgCO2eq/ Product KgCO2eq/ Kg Kg Kg Bread 0.386 Green Peas 0.90 Fresh Figs 0.39 Pita Bread 0.386 Dried Onion and 0.39 Strawberries 0.49 Green Onion Flour 0.58 Garlic 0.33 Apples 0.36 Rice 1.14 Carrot 0.53 Persimmon 0.5 Pasta 0.725 Lettuce 1.08 Mango 0.97 Soya Oil 1.63 Eggplant 1.3 Other Fresh Fruit 0.5 Olive Oil 1.08 Cabbage 0.12 Kiwi 0.6 Margarine 1.36 Cauliflower 0.39 Pomelo 0.5 Fresh Beef 19.7 Cucumbers 0.66 Kumquat 0.5 Frozen Beef 19.7 Beet 0.43 Cherries 0.36 Frozen Ground 36.4 Tomatoes 0.67 Guava 0.5 Beef Beef Liver 19.7 Pepper 0.88 Fresh Dates 0.42 Inner Parts of 19.7 Radish 0.33 Olives 0.27 Bones and Legs Mutton and 19.7 Squash 0.09 Figs 0.39 Lamb Fresh Chicken 1.5 Corn 0.73 Dates 0.42 Frozen Chicken 1.5 Fresh Beans 0.06 Raisins 1.03 Chicken and 1.5 Mushrooms 0.73 Plums 0.36 Turkey Canned Fish 4.11 Pumpkin 0.09 Dried Plums 1.03 Fresh Fish 3.83 Broccoli 0.4 Other Dried Fruit 1.03 Milk 1.02 Kohlrabi 0.43 Roasted Seeds 1.17

Sour Cream 4.59 Okra 0.73 Nuts 1.17 Yellow Cheese 10.71 Citrus Fruit 0.5 Almonds 1.17 Eggs 3.54 Watermelons 0.27 Peanuts 1.94 Sugar 0.21 Pears 0.29 Roasted Legumes 1.17 Tea 0.87 Peaches 0.36 Pistachios 1.17 Coffee 8.1 Bananas 1.32 Preserved Frozen 1.05 Fruit Cocoa Powder 8.74 Melons 0.39 Dried Fruit 1.03 Dry Beans 0.78 Apricot 0.36 Natural Fruit Juice 1.03 Sweet Potato 0.33 Grapes 0.29 Butter 2.04 Potato 0.33 Pomegranate 0.5 Ice Cream 1.8

Table 10 Tel Aviv - Jaffa's carbon footprint

Ton CO2eq per person Ton CO2eq total population Transportati Total Transportati Total Neighbourhood Food on Electricity emissions Food on Electricity emissions Glilot Tzuki Aviv and Sde Dov Area 0.9 3.3 2.7 6.8 2441 9201 7428 19071 Nofei Yam 0.8 2.0 2.6 5.3 7640 20005 25890 53535 Tochnit Lamed 1.0 2.3 3.3 6.5 5433 12688 18356 36477 Kochav HaTzafon 1.2 1.7 3.2 6.1 5729 8613 15913 30256 Ramat Aviv G' 0.9 2.2 2.9 6.1 10934 27732 36437 75102 Neve Avivim 0.7 1.7 2.2 4.7 9270 21115 27440 57825 Afeka 0.9 1.8 3.3 6.0 2147 4477 8229 14854 Ramat Aviv 0.6 1.0 2.2 3.8 5295 9283 19653 34231

Tel Baruch North Tel Baruch and Maoz Aviv 0.8 1.9 3.0 5.7 7176 16777 26826 50779 Neot Afeka B' 0.8 3.1 2.4 6.3 4471 17324 13515 35309 Neot Afeka A' 0.5 2.7 2.6 5.8 1515 8174 7995 17684 Hadar Yosef 0.7 2.0 2.2 4.8 5133 14389 16049 35570 Hamashtela 0.7 1.9 2.4 5.0 1784 4941 6366 13091 Ganei Tzhala, Ramot Tzahala 1.1 2.3 3.0 6.5 6145 12445 16075 34665 Tzahala 0.9 2.2 3.2 6.2 1896 4554 6621 13071 Neve Sharet 0.8 1.3 1.8 3.9 5631 9550 12661 27841 Revivim 0.9 1.3 2.9 5.2 3895 5585 12369 21849 Neve Dan 0.9 2.3 3.6 6.7 2272 5889 9371 17533 Ramat HaHayal 0.7 2.4 2.8 5.9 2780 9996 11491 24267 Hatzafon HaYashan (North) 0.7 1.3 2.1 4.2 23345 44456 70900 138701 Hatzafon HaYashan (South) 0.6 2.1 2.3 4.9 18039 58471 64111 140621 Bavli 1.1 1.3 1.9 4.3 9978 12258 17978 40214 Hatzafon HaHadash (North) 0.7 1.4 2.4 4.5 7181 15366 25386 47933 Hatzafon HaHadash (Kikar Hamedina) 0.8 1.7 2.4 4.9 10886 24430 33335 68651 Hatzafon HaHadash (South) 0.8 1.5 2.7 5.0 10087 19043 32803 61933 Tzamarot Ayalon 0.7 2.6 2.2 5.5 1513 5514 4606 11633 Lev Tel Aviv 0.6 1.4 2.6 4.6 20713 46663 85827 153204 Kerem Hateimanim 0.8 1.2 2.3 4.3 3810 5811 11316 20936

Neve Tzedek 0.8 1.6 2.7 5.1 3107 6610 10985 20701 Ganei Sarona 0.4 1.6 2.4 4.4 720 2589 3965 7274 Jaffa North 0.9 1.5 2.2 4.5 6074 10013 14601 30688 Giv'at Herzl, Industrial Area Yaffo 1.4 1.7 2.2 5.3 4991 6047 7748 18786 Old Jaffa, Jaffa Port 0.7 0.8 2.5 4.1 778 893 2642 4313 Ajami and Giv'at Aliya 1.0 1.9 2.2 5.1 6104 11079 12793 29976 Tzahalon 1.0 1.5 1.5 4.0 7634 11782 11623 31039 Yaffo G' and Neve Golan 1.2 0.9 1.7 3.7 7355 5670 10435 23460 Yaffo-Tel Aviv College and Dakar 1.0 0.9 1.3 3.2 5001 4579 6248 15827 Tel Kabir, Neve Ofer, Yaffo B' 1.1 1.2 1.1 3.4 10230 10811 10350 31392 Yaffo D' (Giv'at HaTmarim) 1.0 0.9 1.3 3.2 6564 6118 8860 21542 Florentin 0.7 2.9 2.3 5.9 6085 25266 19537 50889 Neve Sha'anan 0.7 0.8 1.6 3.1 3250 3781 7729 14759 Shapira 0.8 1.1 1.6 3.5 4219 5942 8465 18625 Kiryat Shalom and Hahorshot Park 1.0 1.2 1.6 3.7 8468 10157 14034 32658 Nahalat Itzhak 0.8 1.2 2.5 4.5 6853 10350 20565 37767 Bitsaron and Ramat Israel 0.7 2.1 2.3 5.1 4279 12196 13724 30200 Tel Haim 1.0 1.5 1.6 4.1 4379 6802 7242 18423 Ramat Hatayasim 0.7 1.6 1.8 4.1 1482 3190 3612 8283

Hatikva 0.9 1.1 1.9 3.9 9841 11764 21066 42671 Yad Eliyahu 0.7 1.4 1.7 3.9 10990 22237 27024 60251 Ezra and Ha'argazim 1.1 1.4 1.4 3.9 3070 4086 3988 11144 Levana, Yedidya 0.9 1.8 1.3 4.0 4737 9308 6514 20559 Kfir 1.5 0.7 1.7 3.9 5145 2294 5997 13435 Neve Barbour, Kfar Shalem West 0.9 1.2 1.6 3.7 4645 6052 7925 18623 Neve Eliezer and Kfar Shalem 0.9 1.0 2.1 4.0 5886 6734 14255 26874 Neve Hen 0.9 1.8 1.8 4.5 5514 11007 11082 27603 Nir Aviv 0.9 0.6 1.8 3.3 4413 2822 8825 16060

Table 11 Transportation Components Neighbourhood Neighbourhood name Average Average Percent of Total kilometres number engine age of leasing car travelled in a year volume vehicles from total vehicles 1 Glilot Tzuki Aviv 1598 4 19% 48,214,765 2 Ramat Aviv G 1598 5 22% 149,444,934 3 Nofei Yam 1598 4 19% 107,393,356 4 Neve Avivim 1593 5.5 21% 117,869,562 5 Afeka 1598 5 19% 23,010,219 6 Tochnit Lamed 1591 5 19% 67,918,332 7 Ramat Aviv 1588 6 19% 51,448,932 8 Kochav HaTzafon 1598 4 19% 42,774,773 9 Tel Baruch North Tel Baruch and 1597 5.5 26% 87,558,074 Maoz Aviv 10 Ganei Tzhala, Ramot Tzahala 1597 5 25% 66,180,395 11 Hamashtela 1598 5 25% 26,259,319 12 Tzahala 1798 5 26% 23,874,901 13 Neot Afeka B 1591 5 27% 93,584,602 14 Neot Afeka A 1595 6 27% 44,162,806 15 Hadar Yosef 1591 6 27% 76,417,292 16 Revivim 1598 5 28% 29,838,942 17 Neve Dan 1598 5 28% 31,166,629 18 Ramat HaHayal 1591 6 28% 54,753,531 19 Neve Sharet 1542 7 25% 52,933,940 20 Hatzafon HaYashan (North) 1451 5.8 23% 252,041,952 21 Hatzafon HaYashan (South) 1432 5.8 41% 320,859,713

22 Hatzafon HaHadash (North) 1498 6 23% 86,561,475

23 Bavli 1593 5.5 23% 66,873,744 24 Hatzafon HaHadash (Kikar 1590 5.8 23% 132,753,570 Hamedina) 25 Hatzafon HaHadash (South) 1548 5.8 23% 104,771,703 26 Lev Tel Aviv 1469 5.9 20% 259,801,967 27 Kerem Hateimanim 1462 6.3 12% 29,753,693 28 Neve Tzedek 1547 6 19% 35,986,636 29 Ganei Sarona 1498 6 26% 14,316,482 30 Nahalat Itzhak 1591 6 18% 56,961,479 31 Bitsaron and Ramat Israel 1589 6 18% 62,422,732 32 Tel Haim 1590 6 16% 37,480,888 33 Yad Eliyahu 1589 7.2 16% 120,352,123 34 Ramat Hatayasim 1498 7 16% 17,984,780 35 Hatikva 1591 7.5 12% 64,756,103 36 Neve Barbour, Kfar Shalem West 1591 7 17% 32,774,460 37 Neve Eliezer and Kfar Shalem 1589 7 17% 37,390,210 38 Neve Hen 1591 6.5 17% 60,260,253 39 Kfir 1596 7 17% 12,444,855 40 Levana, Yedidya 1591 7 11% 51,529,139

41 Nir Aviv 1591 7 17% 15,228,714 42 Ezra and Ha'argazim 1591 8 11% 21,132,731 43 Old Jaffa, Jaffa Port 1597 6 11% 4,675,303 44 Jaffa North 1533 6.2 20% 77,287,020 45 Florentin 1429 6.6 17% 158,865,108 46 Neve Sha'anan 1562 7.7 19% 20,379,202 47 Giv'at Herzl, Industrial Area Yaffo 1526 7 20% 3,049,450 48 Shapira 1594 8 20% 41,525,032 49 Ajami and Giv'at Aliya 1598 7 11% 58,582,844 50 Tzahalon 1597 7 12% 63,260,757 51 Yaffo-Tel Aviv College and Dakar 1596 7 14% 16,972,236 52 Tel Kabir, Neve Ofer, Yaffo B 1595 7.3 14% 65,521,405

53 Yaffo G and Neve Golan 1594 8 12% 31,083,111 54 Yaffo D (Giv'at HaTmarim) 1594 7.5 14% 33,288,291

55 Kiryat Shalom and Hahorshot 1597 7.5 14% 53,713,923 Park

Table 12 Emissions and neighbourhoods characteristics correlations

Emissions Emissions Emissions Total electricity transportation food per emissions per per person per person person person Emissions electricity per person 1.00 0.54 NA 0.86 Emissions transportation per person 0.54 1.00 NA 0.86 Emissions food per person NA NA 1.00 NA Total emissions per person 0.86 0.86 NA 1.00 Socio-economic cluster 0.82 0.58 -0.32 0.76 Average house size 0.78 0.45 NA 0.73 Percent of New Houses (80s) 0.42 0.36 NA 0.49 Percent of small Houses (up to 70 -0.75 -0.55 NA -0.78 sqm) Percent of medium sized houses (70- -0.31 NA NA NA 100 sqm) Percent of Large Houses (100 sqm and 0.80 0.54 NA 0.79 more) Academic degree (MA or more) 0.72 0.43 -0.33 0.61 Percent of private vehicle commuters 0.70 0.58 NA 0.70

Percent of public transport commuters -0.79 -0.58 0.36 -0.73 Percent of pedestrian or bicycle NA NA -0.37 NA commuters Persons per room NA NA 0.40 NA

Table 13 Neighbourhoods population and socio economic cluster

Number Name Population size Socio-economic cluster 1 Glilot Tzuki Aviv and 19,071 9 Sde Dov Area 2 Ramat Aviv G 75,102 10 3 Nofei Yam 53,535 9.3 4 Neve Avivim 57,825 9 5 Afeka 14,854 8.4 6 Tochnit Lamed 36,477 9 7 Ramat Aviv 34,231 10 8 Kochav HaTzafon 30,256 9.6 9 Tel Baruch North Tel 50,779 9.6 Baruch and Maoz Aviv 10 Ganei Tzhala, Ramot 34,665 9 Tzahala 11 Hamashtela 13,091 9 12 Tzahala 13,071 7.8 13 Neot Afeka B 35,309 9 14 Neot Afeka A 17,684 9 15 Hadar Yosef 35,570 9 16 Revivim 21,849 10 17 Neve Dan 17,533 8 18 Ramat HaHayal 24,267 5.4 19 Neve Sharet 27,841 9 20 Hatzafon HaYashan 138,701 8.1 (North) 21 Hatzafon HaYashan 140,621 7.3 (South) 22 Hatzafon HaHadash 47,933 9 (North) 23 Bavli 40,214 9 24 Hatzafon HaHadash 68,651 9.2 (Kikar Hamedina) 25 Hatzafon HaHadash 61,933 8.8 (South) 26 Lev Tel Aviv 153,204 7.2 27 Kerem Hateimanim 20,936 6 28 Neve Tzedek 20,701 6 29 Ganei Sarona 7,274 8 30 Nahalat Itzhak 37,767 4.2 31 Bitsaron and Ramat 30,200 6 Israel 32 Tel Haim 18,423 5.6 33 Yad Eliyahu 60,251 3 34 Ramat Hatayasim 8,283 3.5 35 Hatikva 42,671 4 36 Neve Barbour, Kfar 18,623 3 Shalem West 37 Neve Eliezer and Kfar 26,874 2.4 Shalem 38 Neve Hen 27,603 3.4 39 Kfir 13,435 3.6 40 Levana, Yedidya 20,559 3 41 Nir Aviv 16,060 3 42 Ezra and Ha'argazim 11,144 4.2

43 Old Jaffa, Jaffa Port 4,313 6 44 Jaffa North 30,688 6.6 45 Florentin 50,889 3 46 Neve Sha'anan 14,759 4 47 Giv'at Herzl, 18,786 6 Industrial Area Yaffo 48 Shapira 18,625 5 49 Ajami and Giv'at 29,976 4.6 Aliya 50 Tzahalon 31,039 6 51 Yaffo-Tel Aviv 15,827 4 College and Dakar 52 Tel Kabir, Neve Ofer, 31,392 4.6 Yaffo B 53 Yaffo G and Neve 23,460 3.1 Golan 54 Yaffo D' (Giv'at 21,542 5 HaTmarim) 55 Kiryat Shalom and 32,658 2 Hahorshot Park

ןכות ע נ י י נ םי המדקה ...... 10 קס י ר ת פס ר ו ת ...... 12 םידדומ לפ תוטי ג ז י הממח ...... 12 רע םי ישו נ ו י םילקא ...... 12 לפ תוטי הנקב הדימה לש יקשמ תיבה ...... 13 תעיבט לגר נמחפ תי לש וכש נ תו ...... 15 תוטיש ושיחל ב לפ תוטי ג ז י הממח ...... 17 רשקה יב ן יפאמ י נ םי יתרבח םי - לכלכ י ,םי ומד יפרג םי יבחרמו םי תטילפל זג י הממחה ...... 18 אמ פ י י נ י ם ח ב תר י י ם - ילכלכ ,םי ומד יפרג ,םי יבחרמ םי תטילפו זג י הממחה לש וכש נ תו ...... 20 םירע תודבוע לע נכת י תו תתחפהל תטילפ ג ז י הממחה ...... 21 נכת י תו תתחפה תוטילפ ישיחרתו התחפה לש וכש נ תו ...... 22 תמ ו ד ו ל ו ג י ה ...... 28 אש ל ו ת קחמ ר ו טמ ר ת ו ...... 28 שח י ב ו ת רקחמה ...... 28 קמ ו ם רקחמה ...... 29 בושיח תעיבט לגרה נמחפה תי לש כשה ו נ ו ת ...... 29 ףוסיא ותנה נ םי ...... 33 דוביע נותנה םי ...... 34 יא פ י ו ן לש וכשה נ תו ...... 36 חותינ יטסיטטס ...... 36 ישיחרת התחפה לש תוטילפ ג ז י הממחה ...... 37 לבגמ תו תוחנהו רקחמה ...... 38 תואצות ...... 40 חותינ תעיבט לגרה תינמחפה לש תונוכשה ...... 40 ךס לכ תוטילפ יזג הממחה ...... 40 משח ל ...... 42 בחת ו הר ...... 45 זמ ו ן ...... 47 םירשק יב ן יפאמה י נ םי וכשה יתנ םי תעיבטל לגרה נמחפה תי ...... 49 ישיחרת התחפה לש תוטילפה וכשה יתנ תו ...... 56 חרת י ש י תתחפה פ ל י ט ו ת רצמ י תכ משח ל ...... 56 חרת י ש י תתחפה פ ל י ט ו ת רצמ י תכ בחת ו הר ...... 57 חרת י ש י תתחפה פ ל י ט ו ת רצמ י תכ זמ ו ן ...... 59 לאיצנטופ תתחפה תוטילפ ...... 60 יד ו ן םוכיסו ...... 64 קמ ו ר ו ת מ י ד ע ...... 72 םיחפסנ ...... 78

שר י תמ א י ו ר י ם יא רו 1 חותינ תעיבט לגרה תינמחפה ...... 30 יא רו 2 כש ו נ ו ת ...... 31 יא רו 3 כרמ י ב י בט י תע רה ג ל מחפה נ י ת ...... 41 יא רו 4 ךס כ ל בט י תע רה ג ל מחפה נ י ת ...... 41 יא רו 5 לפ תוטי נה עבו תו תכירצמ למשח לכואל יסו ,הי קשמ תיב ו םדאל ...... 45 יא רו 6 כרמ י ב י בושיח תוטילפ הרובחתמ ...... 46 יא רו 7 לפ תוטי נה עבו תו תכירצמ בחת ו הר אל ו לכ ו ס י י ה , קשמ ב י ת ו םדאל ...... 47 יא רו 8 לפ תוטי נה עבו תו תכירצמ זמ ו ן לכואל יסו ,הי קשמ תיב ו םדאל ...... 49 יא רו 9 תמר כה נ הס ...... 50 יא רו 10 ממ ו צ ע ג ו ד ל ה ד י ר ה ...... 51 יא רו 11 תמר כשה הל ...... 52 יא רו 12 בחת ו הר צ י ב ו ר י ת ...... 53 יא רו 13 תוממוי םיינפואב וא לגרב ...... 54 יא רו 14 חא ו תבה י ם שדחה י ם ...... 55 יא רו 15 פצ י פ תו ...... 55 יא רו 16 חא ו ז ה פ התח – חרת י ש י ם ושקה ר י ם ל משח ל ...... 57 יא רו 17 חא ו ז ה פ התח – חרת י ש י ם ושקה ר י ם ל בחת ו הר ...... 59 יא רו 18 חא ו ז ה פ התח – םישיחרת םירושקה זמל ו ן ...... 60 יא רו 19 חא ו ז ה פ התח ךסמ כ ל ה פ ל י ט ו ת כשה ו נ ת י ו ת ...... 61 יא רו 20 םישיחרתה ילעב לאיצנטופ תחפה הובגה יב רתו יפל וכש הנ ...... 61 יא רו 21 תעיבט לגר נמחפ תי יפל םירוזא ,םיטסיטטס וכש נ ו , תת עבור עבורו ...... 70 יא רו 20 רשק י ם ב י ן פ ל י ט ו ת יזג הממחה םינייפאמהו םייתנוכשה ...... 92 שר י תמ בט אל ו ת בט הל 1 קמ ו ר ה פ ל י ט ו ת ל פ י רמאמ ...... 16 בט הל 2 תוחוד תוטילפ יזג הממח לש נוכש תו ...... 27 בט הל 3 םש ו סמ פ ר שה כ ו נ ו ת ...... 32 בט הל 4 חרת י ש י ם ...... 37 בט הל 5 רצ י תכ למשח לש כשה ו נ תו ...... 43 בט הל 6 תכירצ ןוזמ ןוט( ) יפל גוס ...... 47 בט הל 7 לאיצנטופ התחפה יפל שיחרת ...... 62 בט הל 8 תוחוד תוטילפ יזג הממח לש נוכש תו - הבחרה ...... 79 בט הל 9 תרמה תכירצ וזמ ן תוטילפל יפל גוס ...... 84 בט הל 10 בט י תע רה ג ל לש לת בא י ב י פ ו ...... 85 בט הל 11 כרמ י ב י בחת ו הר ...... 88

תקציר גידול האוכלוסייה, שינוי באורח החיים ועלייה בצריכת המשאבים הביאו לעלייה בפליטות גזי חממה וכתוצאה מכך לשינויי אקלים. רוב אוכלוסיית העולם מתגוררת בערים, סביבת חיים המאופיינת בצריכה משמעותית של משאבים ואנרגיה. לכן רוב פליטות גזי החממה נפלטות בצורה ישירה או עקיפה כתוצאה מהפעילות העירונית. המשמעות היא שלערים תפקיד חשוב בהפחתת הפליטות. אכן, ערים רבות ברחבי העולם מקדמות צעדים שונים להפחתת הפליטות. ניתוח מקורות והיקפי הפליטה מפעילויות עירוניות שונות מהווה בסיס ליישום הצעדים השונים. העניין ההולך וגובר בממשק שבין פעילות עירונית לתהליכי שינוי האקלים וההכרה בחשיבות העיר לצמצום הבעיה הלכו והתפתחו במהלך השנים האחרונות גישות ושיטות שונות לחישוב "טביעת הרגל הפחמנית" של ערים. עד כה עיקר המחקר בתחום והניטור העירוני התקיים ברמה הכלל עירונית. שכונות הינן יחידות פנים עירוניות אשר מושכות אליהם אנשים עם מאפיינים חברתיים כלכליים דומים. מחקרים החלו לבחון את הפליטות של השכונות בערים במטרה לדייק את אמצעי ההפחתה המתאימים ביותר ובכך לתרום להפחתת הפליטות העירוניות. המחקר בוחן את טביעת הרגל הפחמנית של המגזר הביתי בשכונות בעיר תל אביב יפו. מרכיבי הצריכה שנבדקים במחקר זה הינם: חשמל, מזון ותחבורה פרטית. לאחר בחינת טביעת הרגל הפחמנית, המחקר בוחן את המאפיינים החברתיים, הכלכליים והמרחביים של השכונות ואת הקשר שלהם לתוצאות טביעת הרגל הפחמנית. לבסוף המחקר בוחן צעדים מדיניות, טכנולוגיה ושינויים התנהגותיים והשפעתם על הפחתת הפליטות השכונתיות והעירוניות. המחקר מצא כי טביעת הרגל השנתית הכוללת של תל אביב-יפו היא כ- 1,980,000 טון CO2eq, או 4.6 טון CO2eq לתושב. נמצא כי פליטות הקשורות לצריכת חשמל אחראיות כמעט למחצית מהפליטות העירוניות הכוללות. השכונות עם הפליטות הגבוהות ביותר לאדם הן גלילות, צוקי אביב ואזור שדה דב - שכונה מספר. 1 )6.8 טון(. התרחיש עם פוטנציאל ההפחתה הגבוה ביותר הוא תרחיש בתחום החשמל מבוסס טכנולוגי מבוסס עם פוטנציאל להפחתה של 6.3% מכל הפליטות העירוניות.

III

וא נ תטיסרבי ןב ג ירו ו ן

וקפה הטל עדמל י רה ו ח ו הרבחה

חמה הקל ל ג א ו ג ר פ י ה ו פ י ת ו ח בס י תב י

יק תומי וריע נ :תי נ חותי תעיבט לגר תינמחפ לש וכש נ תו – עב י ר לת יבא ב י פ ו

רוביח הז הווהמ קלח תושירדהמ תלבקל ראותה ךמסומ" יעדמל חורה "הרבחהו (M.A)

תאמ : ט ל ה ר בא ן ל ו י

יחנהב :תי רוספורפ דדימ יסיק נ רג

תח י תמ טסה ו ד נ ט : ______ראת י ך : _5.12.2019_

תח י תמ מה נ הח : ______ראת י ך : _5.12.2019_

תח י תמ י ו " ר א"מה חמה תקל י : ______ראת י ך : ______

רפא י ל 2020

וא נ תטיסרבי ןב ג ירו ו ן

וקפה הטל עדמל י רה ו ח ו הרבחה

חמה הקל ל ג א ו ג ר פ י ה ו פ י ת ו ח בס י תב י

יק תומי ע ורי נ :תי נ חותי יבט תע לגר תינמחפ לש וכש נ תו – עב י ר לת יבא ב י פ ו

רוביח הז הווהמ קלח תושירדהמ תלבקל ראותה ךמסומ" יעדמל חורה "הרבחהו (M.A)

תאמ : ט ל ה ר בא ן ל ו י

הב נ ח י י ת : רוספורפ דדימ יסיק נ רג

ןסינ פ"שת רפא י ל 2020