Projection to 2020 for setting emission reduction targets...

Report EUR 26461 EN

20 13

Alessandro K. Cerutti, Greet Janssens-Maenhout,

An approach with a

Business-as-Usual scenario

Projection to 2020 for setting emission reduction targets in the Southern Mediterranean Partner Countries

European Commission

Joint Research Centre

Institute for Environment and Sustainability

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This publication is a Reference Report by the Joint Research Centre of the European Commission.

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JRC87130

EUR 26461 EN

ISBN 978-92-79-35153-2 (pdf)

ISSN 1831-9424 (online)

doi: 10.2788/60771

Luxembourg: Publications Office of the European Union, 2013

© European Union, 2013

Reproduction is authorised provided the source is acknowledged.

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Table of contents

1. Introduction ............................................................................................................................................... 2

1.1 Geocoverage of the Covenant of Mayors South Programme ........................................................... 2

1.2 Transferring the origin of the Covenant of Mayors EU initiative ...................................................... 3

1.3 Proposed adaptation of the methodology for Southern Partnership ............................................... 4

1.4 Local projections: an alternative? ..................................................................................................... 4

2. Description of the Business-as-usual (BAU) scenario ................................................................................ 5

2.1 Data and models used ....................................................................................................................... 5

2.2 Outcome of the emission projections ............................................................................................... 6

2.3 Discussion on the limitation of the projections............................................................................... 11

3. Emission coefficients derived from a business-as-usual projection for the CoM South countries ......... 12

3.1 Calculation of National coefficients ................................................................................................. 12

3.2 Including the urban dimension in BAU factors ................................................................................ 13

3.3 How to calculate BAU emissions using reference factors ............................................................... 14

4. Conclusion ................................................................................................................................................... 17

References ....................................................................................................................................................... 18

ANNEX I ............................................................................................................................................................... I

Aggregated emission tables for Mashreq and Maghreb regions ................................................................... I

ANNEX II ............................................................................................................................................................ IV

Country and sector specific emissions ......................................................................................................... IV

An approach with a Business-as-Usual scenario projection to 2020 for setting emission reduction targets

in the Southern Mediterranean Partner Countries

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1. Introduction

1.1 Geocoverage of the Covenant of Mayors South Programme

As occurred for the extension of the Covenant of Mayors project to the Eastern partnership, also within the Covenant of Mayors South programme, aspects need to be revised in the original methodology described in the Guidebook "How to develop a Sustainable Energy Action Plan (SEAP)". This revision aims to tackle in a more appropriate way the specific institutional and economic situation of the 10 countries involved in the initiative, which in particular are less politically stable and therefore less accessible for sustainable energy projects with longer term investments. Some face lack of resources, absence of national framework, and all have an aspiration to strong economic growth, which the CoM would like to guide into a green growth. The countries included in the CoM – South are the following:

- Algeria - Egypt - Israel - Jordan - Lebanon - Libyan Arab Jamahiriya - Morocco - Palestine1 - Syrian Arab Republic - Tunisia

The situation between these countries is quite different from that in the EU. These Mediterranean countries are characterised by very different CO2/cap numbers and CO2 reductions are only acceptable using a business-as-usual scenario to allow the necessary growth in economic activities. Again the cities should plan sustainable energy actions using a baseline year emission inventory and the signatories are given the possibility to use a business as usual (BAU) scenario to estimate their emissions in 2020. They will be allowed to use their own way to estimate their emissions in 2020 or to use factors provided by JRC, This second option would avoid a burden to the signatories of smaller cities that which to adhere, have economic aspiration but lack the resources for a full-fledged business-as-usual projection of their emission inventory. National coefficients are derived by the European Commission’s Joint Research Centre, based on the energy consumption projections with an in-house EC model for energy-related activity increase. As baseline emission inventory (BEI) is recommended a recent year (later than 1990), which is representative for the current economic situation and for which reliable statistical data are available. The BEI should include the key sectors (residential and transport) as defined for the CoM initiative.

1 Palestine is a member of the CoM South partnership, but activity data on energy consumption and waste management from

international statistics are scarce. A comprehensive emission estimate could not be made and as a consequence the calculation of implied emission factors for the BAU scenario were not as reliable as an average over the neighbouring countries. We recommend for the energy production-related sectors to apply similar emission factors as those of Israel. For the other sector (transport and waste), we recommend to apply similar emission factors as those for Jordan. For the BAU coefficients, we also recommend to apply those of Jordan.



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1.2 Transferring the origin of the Covenant of Mayors EU initiative

Key of the CoM methodology is the Sustainable Energy Action Plan (SEAP), in which signatories commit to a minimum CO2 emission reduction target of 20% by 2020 and define the actions they need to put in place to reach their commitment. A more specific overview of the original initiative can be found in CoM core text and is briefly described underneath. A city who signs up the Covenant of Mayors commits to:

- reduce the CO2 emissions in its territory by at least 20% by 2020. - prepare a Baseline Emission Inventory (BEI) as a basis for the SEAP. - submit the SEAP, officially approved by the Local Authority, within the year following the adhesion

to the Covenant of Mayors.

To elaborate and implement a successful SEAP, a signatory should also: - adapt city structures, including allocation of sufficient human resources, in order to undertake the

necessary actions to take part in developing the Action Plan; - mobilise the civil society.

For signatories of the Eastern and Southern Partnership countries of the EU, the recommended baseline year is no longer 1990 but a more recent year that is representative for the current economic situation and for which the most comprehensive and reliable data can be provided. The emission reduction target is set against the baseline year and it can be set either as absolute reduction or per capita reduction. The Baseline Emission Inventory (BEI) covers the CO2 emissions that occur due to energy consumption in the territory of the local authority. The following sectors (often referred to as key sectors of activity) are recommended:

- municipal buildings, equipment and facilities; - tertiary (non-municipal buildings, equipment and facilities); - residential buildings; - municipal public lighting; - urban road transportation (including municipal fleet, public transport, private transport). - Solid waste disposal (landfills for CH4 or incineration for CO2) - Wastewater treatment

The energy-related emissions coming from other sectors might be included in the BEI, if the SEAP foresees measures for them (e.g. small industries under the mandate of the municipality). Some emission sources not related to energy consumption can be included in the BEI and in the SEAP, for example wastewater and solid waste treatment. These last two sectors are added, in order to encounter the daily and most pertinent needs of the highly populated areas in the North-African border of the Mediterranean sea The Local Authority may wish to include actions aiming at reducing the CO2 emissions also on the supply side (e.g. development of the district heating network, wind farms, PV, etc…). In this case, local energy (electricity, heat/cold) production should be accounted for in the BEI. The scope of the Sustainable Energy Action Plan (SEAP) is then to define, to describe and to estimate quantitatively energy-related greenhouse gas reduction measures. A large dataset of very different actions proposed by cities has been compiled and can be consulted upon demand. A SEAP should contain both short term actions and mid-long term strategies. The key sectors of activity are the ones, whose inclusion in the BEI is "strongly recommended". Moreover, the local authority is expected to play an exemplary role, by taking outstanding measures on its own buildings, facilities and fleet.



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1.3 Proposed adaptation of the methodology for Southern Partnership

In the specific context of the Southern Partnership and North African border of the Mediterranean Sea, a new approach is needed in order to allow social and economic progress. However, the signatories should adopt measures so that this progress occurs in a sustainable manner, under conditions of “green growth” and that energy efficiency criteria are applied to existing buildings and infrastructures, as well as to all new developments. The opportunity to calculate a target based on a reference scenario, called business-as-usual (BAU) (defined as a continuation of the current trend) to 2020 will be given to signatories willing to do so (besides the absolute and the per capita reduction options). Starting from present data, the BAU scenario will analyse the evolution of energy and emission levels until 2020, under the hypothesis of continuing current trends in population, economy, technology and human behaviour, without the implementation of a SEAP. The target would therefore be calculated compared to the emission levels forecasted by the scenario for 2020. National coefficients that allow estimating the energy consumption in 2020 (starting from real present data) are provided in this report. Signatories will thus develop a simplified BAU scenario, accounting for the attainment of a normal level of quality of the services (streets with public lighting, water supplied every day, etc…). In principle, the key sectors that should be included in the BEI will remain the standard ones of the CoM. The same key sectors should be tackled by the set of actions of the SEAP. In this way, signatories will be able to do their emission inventories of the present situation, and estimate which their emissions in 2020 will be. Then they will commit to an emission reduction target based on their projections of emissions for 2020 following the BAU. The factors will be country-specific, calculated both for CO2 and CO2eq (CO2, CH4, N2O using the GWP100metric) in order to allow signatories to choose the approach they prefer. The factors do account for differences between country evolutions and are based on the sectors which are targeted by the CoM (buildings and transport and waste). A global emission projection with EC in-house data and EC in-house anthropogenic projection model is thereto used.

1.4 Local projections: an alternative? From open source information, emission projection tools and results are available for local regions, such as e.g. by UN-Habitat and ICLEI. UN-Habitat launched the Sustainable Cities programme, which has some similarities with the CoM initiative. This is also supported by ICLEI (unifying the Local Governments for Sustainability), who is collecting cities scenarios. However, the collection of local scenarios does not provide a global pool of information that is consistent amongst countries. Such collection cannot be labelled impartial, uniform and neutral coverage of all countries from the CoM. For consistency it is recommended to use one single tool, in which the scenarios follow projections set by the European Commission, based on a Commissions model and database for emission growth.



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2. Description of the Business-as-usual (BAU) scenario The business-as-usual (BAU) scenario indicates that no or just actual measurements are taken into account for the future emission trends and that world energy consumption will be more than doubled in the 2000-2050 period. The emission inventory projections for the coming four decades are calculated, starting from the base year 2005 with the sector-specific growth rates and technology-based emission factors taking into account different abatement measures per regions, in the frame of the FP7 research project CIRCE (www.circeproject.eu), documented by Doering et al (2010). This BAU scenario was also used by the global climate model ECHAM to investigate areas of high air pollution with high health risks for the near future. Pozzer et al. (2012) indicated hot spots with high pollution index per capita for several cities in particular in the Middle East and South East Asia.

2.1 Data and models used

As basis the Emission Database for Global Atmospheric Research (EDGAR-CIRCE) was used, which contains global anthropogenic emissions inventories of various air pollutants and greenhouse gases. Based on the EDGARv4version inventories have been calculated for the CIRCE project providing historical (1990-2005) global anthropogenic emissions of CO2, CH4, and N2O for the greenhouse gases (the air pollutants and particulate matter are not of importance for the CoM). Emissions trends up to the year 2050 were calculated starting from base year 2005. Activity growth rates from the POLES model (Russ et al., 2007) were used to calculate activity data for the energy sector starting from the EDGAR-CIRCE base year dataset (residential, transport, ships, aviation, transformation and refineries). The POLES (Prospective Outlook for the Long term Energy System) model is also an EC in-house macro-economic model based on partial energy equilibrium. It contains technologically-detailed modules for energy-intensive sectors, including power generation, iron-steel, aluminium and cement production, and transportation sectors to simulate the development of energy scenarios until 2050 on world-scale (for 47 different regions) with one single oil market and three regional gas markets. The growth rates of POLES are differentiated for one power generation sector, three fuel production sectors, three energy consumption sectors, for four transport sectors, for the different fuel types, and for 29 countries and 23 regions. The same growth rates for the fuel consumption were used for the industrial production sector, assuming that the activity/emission trends in industrial production follows the combustion trends in that sector. It should be noted that neither a technology shift nor a fuel shift is explicitly modelled for a given industry sector. The growth rates are entirely based on the economic dynamics of fuel costs and carbon taxes of POLES and the fuel shifts modelled in there. Trends in agriculture, land use and waste are provided from the IMAGE model that is compatible with the POLES baseline and climate stabilization scenarios (P. Russ, D. van Vuuren, personal communication and van Vuuren et al., 2009) with emission trends given by world region for a baseline scenario. The IMAGE model (Integrated Model to Assess Global Environment) is a model of MNP which comprises an Energy-Industry System, a Terrestrial Environment System and an Atmosphere-Ocean System, of which the model one was the most important to project agricultural land-use change, crop production and animal elevation. Also sectors such as ‘use other products’ (including solvents) use the population growth rate from IMAGE (van Vuuren et al, 2009) for the growth rates of the emissions. For the sectors ‘solid waste disposal’ (main sector: waste) emissions of all substances are scaled with the population growth rate, while emissions of ‘waste water treatment’ are scaled with growth rates of sewage. The ‘agriculture’ sectors are treated in different ways. In the sectors ‘agricultural soils’ the emissions of the respective substances are scaled with the specific growth rates of the corresponding emitters, e.g. N2O



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emissions are scaled with the growth rate of fertilizer combined with the growth rate of crop residues. Emissions of ‘enteric fermentation’ are scaled with the growth rates of the corresponding animals. Emissions of ‘manure management’ of the respective substances are scaled with the growth rates of animal waste. Emissions of ‘agricultural waste burning’ are scaled with the growth rates of CH4 emissions from agricultural waste burning (IMAGE model). Indirect emissions are assumed constant in time. The “Business as Usual” scenario, hereafter referred to as BAU, explores the situation when no further climate and air pollution policies are implemented beyond what is in place since the year 2005. This means that energy consumption from 2005 to 2050 is driven by population and economic growth but not by energy efficiency/climate change policies (POLES baseline scenario). The combustion technologies/abatement measures are assumed not to change beyond the year 2005 technologies.

2.2 Outcome of the emission projections

Several studies estimate Greenhouse gas (GHG) emissions at the global scale according to a BAU scenario (e.g. IPCC, 2005; IPCC 2007, van Vuuren et al., 2009). Therefore comparisons with the proposed method at the global scale can be made and detailed discussions of this issue can be found in Janssens-Maenhout et al., (2012). For the countries of CoM Southern partnership, projections using CIRCE were calculated for the period 2009-2020 in order to extend calculations from activity data from EDGAR v 4.2 that are available up to 2008. Emissions in BAU scenario were calculated separately for individual gases (here CO2, CH4 and N2O) were converted into CO2-equivalents based on the conversion using the Global Warming Potential (GWP), which express the contribution to global warming of the specific greenhouses in relation to carbon dioxide. Throughout this background document the 100 year GWP values as used in the Kyoto Protocol are applied (IPCC, 1995). Furthermore, according to CoM main methodology (CoM, 2010), also for BAU projections, emission inventories are accounted considering exclusively sectors in which mayors can influence significant changes. In particular the following sectors are considered: residential, transport, solid waste management and wastewater treatment.

Considering CO2 emissions, most important emitting sectors are residential and transport (figure 1). The expected trend for 2020 is a significant increase in emissions in both sectors, in particular, compared to 1990 an increase of 51 Mt CO2 (+115%) is estimated in the residential sector and an increase of 86 Mt CO2 (+153%) is estimated in the transport sector. BAU projections highlight in 2020, compared to 1990, an increase of 76 Mt CO2 (+129%) in the Mashreq region (figure Annex I.1) and an increase of 61Mt CO2 (+146%) in the Maghreb region (figure Annex I.2).



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Figure 1. CO2 emissions (expressed in Mt CO2) for residential and transport sector in CoM South countries.

Considering CH4 emissions, the most important sector is solid waste management. This sole sector is estimated to increase in 2020 of more than 450% (+25 Mt CO2-eq) compared to 1990 (figure 2). On the contrary, CH4 emissions in other sectors are relatively stable of even reducing, such as in the residential sector (-1.66 Mt CO2-eq corresponding to 70% less emissions). In aggregate terms, CH4 emissions are estimated to increase in 2020 of 16 Mt CO2-eq (+162%) in Mashreq region (figure Annex I.3) and 14MtCO2-eq (+159%) in Maghreb region (figure Annex I.4).

Figure 2. CH4 emissions (expressed in Mt CO2-eq) for CoM sectors in the Southern partnership.

Considering N2O emissions, most important sectors are wastewater treatments and the residential sector. is wastewater treatment. In fact impacts from wastewater treatments are estimated to account for more that the half of all N2O emissions in CoM sectors (figure 3). Nevertheless the highest increase in relative terms is expected to occur in the residential sector, with +175% of N2O emissions (1.25 MtCO2-eq) in 2020 compared to 1990. The country breakdown of N2O emissions projections, for Mashreq and Maghreb region is reported in figure Annex I.5 and figure Annex I.6 respectively.



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Figure 3. N2O emissions (expressed in Mt CO2-eq) for CoM sectors in the Southern partnership.

Furthermore, some interesting remarks can be done for the total GHG emissions estimated according to BAU projections. Total GHG emission increase in 2020 for all CoM South countries is estimated in 172 Mt CO2-eq (+139%) compared to 1990 (figure 4). To this amount, major contribution are given by the transport sector (87 Mt CO2-eq) and the residential sector (50 Mt CO2-eq). Nevertheless the biggest relative increase occurs in solid waste management (+451% of CO2-eq emissions) and also wastewater treatments represent an important source of emissions. The country breakdown of GHG emissions projections, for Mashreq and Maghreb region is reported in figure Annex I.7 and figure Annex I.8 respectively.

Figure 4. GHG emissions (expressed in Mt CO2-eq) for CoM sectors in the Southern partnership.

Furthermore, it is interesting to highlight differences from individual gas to GHG emissions (figure 5). As expected, CO2 is the gas that contributes the most (137 Mt CO2, nevertheless, the gas with the biggest increase is expected to be CH4 (+160% of CO2-eq emissions).



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Figure 5. Individual gas breakdown of GHG emissions (expressed in Mt CO2-eq) for

CoM sectors in the Southern partnership.

Considering these results, it is possible to define an significant role of secondary sectors (solid waste management and wastewater treatment) and secondary gasses (CH4 and N2O) to reach emission reduction targets in 2020. As a consequence also for the extension to the Southern partnership of the CoM, it is highly recommended to signatories to take in considerations actions in these two latter sectors and, consequently, the account of these two sectors in their Baseline Emission Inventory (BEI) (see CoM South, 2013).

According to the projections, an intense increase of emissions in CoM sectors is expected to occur in the BAU scenario (table 1). The emission increase related to the first period of the study (1990-2008) in higher than the second for most of the countries (excluding Syria that shows a different trend) including an impressive increase of Morocco, Egypt and Israel. Although the emission increase of the second timeframe (2008-2020) is smaller than the first, it is interesting to highlight that, for most of the countries, this increase is higher than 20%, which is the main CoM target.

Table 1: Emission increase in CoM-South sectors for the South Partnership Countries in periods 1990 -2008, 2008-2020 and in the whole timeframe.

1990-2008 2008-2020

Total (1990-2020)

COUNTRY CO2 CO2-eq CO2 CO2-eq CO2 CO2-eq

Algeria 84% 80% 8% 14% 99% 104%

Egypt 116% 98% 31% 33% 183% 162%

Israel 102% 98% 27% 30% 157% 158%

Jordan 76% 81% 37% 39% 141% 151%

Lebanon 60% 59% 37% 40% 120% 122%

Libya 87% 83% 14% 19% 114% 118%

Morocco 109% 93% 41% 42% 194% 173%

Syrian Arab Republic -12% -4% 32% 34% 17% 29%

Tunisia 86% 65% 48% 44% 175% 139%

Furthermore, the projected emission time series can be used to calculate emissions per capita using UNDP World Population Prospects data (UNDP, 2013). In table 2 and 3, emissions per capita are calculated using EDGAR v4.2 up to 2010 and are projected with the BAU assumptions for 2020. It is important to remark



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that these emissions refer exclusively to CoM-South sectors, therefore they are significantly lower that emissions per capita including emissions from all sectors of the country. The calculation of emissions from all sectors in CoM South countries is reported in Annex II as a reference value and emissions per capita from all countries in the World are available on the webpage of the EDGAR database2.

Table 2: Overview of emissions (expressed in tCO2/capita) for the South Partnership Countries in 1990 -2020. These CO2 factors include CoM-South sectors only

Calculated according to EDGAR v4.2 and UNDP World Population Prospects

Estimated using BAU assumptions

COUNTRY 1990 1995 2000 2005 2010 2020

Algeria 0.87 0.79 0.81 1.03 1.11 1.09

Egypt 0.43 0.47 0.53 0.60 0.65 0.74

Israel 2.18 2.74 2.88 2.68 2.67 2.91

Jordan 1.28 1.20 1.30 1.50 1.27 1.43

Lebanon 1.13 1.75 1.55 1.46 1.29 1.62

Libya 1.73 2.40 2.51 2.07 2.11 2.27

Morocco 0.54 0.64 0.77 0.84 0.91 1.11

Syrian Arab Republic 2.06 1.90 1.81 1.60 1.47 1.72

Tunisia 0.39 0.45 0.48 0.47 0.47 0.55

Table 3: Overview of emissions (expressed in tCO2-eq/capita) for the South Partnership

Countries in 1990 – 2020. These CO2-eq factors include CoM-South sectors only

Calculated according to EDGAR v4.2 and UNDP World Population Prospects

Estimated using BAU assumptions

COUNTRY 1990 1995 2000 2005 2010 2020

Algeria 1.03 0.96 0.98 1.21 1.31 1.33

Egypt 0.58 0.62 0.68 0.77 0.81 0.92

Israel 2.46 3.03 3.19 2.98 2.98 3.30

Jordan 1.44 1.36 1.50 1.71 1.48 1.68

Lebanon 1.32 1.95 1.76 1.66 1.51 1.91

Libya 1.95 2.65 2.76 2.31 2.36 2.60

Morocco 0.70 0.79 0.93 1.01 1.10 1.34

Syrian Arab Republic 2.29 2.15 2.08 1.90 1.79 2.11

Tunisia 0.51 0.57 0.60 0.59 0.54 0.62

Also the increase of emissions per capita in the timeframe of the study reflects the trends highlighted considering emission increase in absolute terms. The average emission increase per capita in Mashreq is 34% of CO2 (38% of CO2-eq) and in Maghreb is 76% of CO2 (65% of CO2-eq). Therefore also considering emissions per capita, the effort in reaching the CoM 20% reduction target is clearly worthy.

2 EDGAR database, version 4.2. http://edgar.jrc.ec.europa.eu

http://edgar.jrc.ec.europa.eu/



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2.3 Discussion on the limitation of the projections

It should be noted that the projections are done with global growth rates, taking into account the historical trends in Maghreb and Mashreq regions from 1990-2005. The projection is a global one (because of the coupling of fuel markets globally), based on IEA data (2007), following an EC internal scenario (developed by DGENV and JRC-IPTS for the climate-energy package of Europe 2020). The advantage is that the projections can be defended for all countries equally with one single methodology, consistently applied. Therefore the results are not only valid for the Europe's Southern Partnership but also for EU-28 itself and countries of the Eastern Partnership. Moreover the projections are done for all sectors, energy-related and agricultural related sectors (the latter is in particular important when including non-CO2 gases such as CH4

and N2O. Not only all greenhouse gases, but also all other air pollutants and aerosols were considered, this allowing a global assessment of climate and air quality changes on citizen’s health and wealth. Even though the projections use 2005 as baseline year, the EDGARv4.2 emission inventory is available till 2010 and so allows to adjust the first five years of projection to the CO2 emission estimates calculated with reported IEA energy consumption data for the same period. In addition, in case 2011 or 2012 is chosen as baseyear, we recommend to consult also the CO2 2013 report by Olivier et al. (2013) in which the CO2 emissions for 2011 and 2012 are in a country and sector-specific way compiled and analysed. .



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3. Emission coefficients derived from a business-as-usual projection for the CoM South countries

3.1 Calculation of National coefficients

Emissions projections from the POLES model applied the sectors which are relevant for the CoM (buildings, transport, solid waste management and wastewater treatments) can be used to calculate the National Coefficients. These factors are the base for estimating emissions in 2020 according to the BAU scenario, calculated for each year from the ratio between projected emissions in 2020 and emissions in the same year. The evolution of this factor for CoM-South countries is shown in figures 6 and 7, taking into account just CO2 emissions (trend of GHG emissions is similar, therefore it is not shown). Obviously, the closer the base year for the BEI to 2020, the more the factor approaches 1, which represents the projected emission according to the BAU scenario.

Figure 6. National coefficients for CoM-South signatories to estimate their CO2 emissions in 2020 based on their BEI (in the time frame 1990-2020) for Mashreq region.



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Figure 7. National coefficients for CoM-South signatories to estimate their CO2 emissions in 2020

based on their BEI (in the time frame 1990-2020) for Maghreb region.

3.2 Including the urban dimension in BAU factors

As highlighted in section 2, emissions for BAU scenario and relative BAU factors have been calculated on a country base. Nevertheless the focus of the CoM project is the urban dimension; therefore an adaptation of BAU factors for cities is necessary. Data on urban and rural population from all countries in the world is available from the UNDP World Urbanization Prospects database (UNDP Review, 2012). Incorporating UNDP data in the EDGAR database, in relation to urban and rural population and assuming that most of the increase on emission production will take place in urban places rather than in rural communities, we propose to use the following formula to estimate the emission increase which will take place in cities:

Xcity = (Xcountry * Pop_totyear -Pop_ruryear)/Pop_urbyear (1) where Xcountry is the factor calculated for countries for a certain year, Xcity is the factor adapted for a city in a certain country and Pop_totyear, Pop_ruryear and Pop_urbyear are total, rural and urban populations for a certain country in a certain year. As populations are only available until 2015, these factors have only been calculated for these 3 years, and taking into account projections of urban to rural ratios until 2015. In table 4 the comparison of national and urban factors for 2000, 2005, 2010 and 2015 is shown. In countries with high urban population (such as Israel, with more than 80% of population in cities) national and urban factors do not change significantly. On the contrary, in countries with significant share of the population spread outside urban areas, such as Egypt and Morocco, national and urban factors differ significantly.



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Table 4: Comparison of national and urban factors for calculating BAU scenario emissions in each of the CoM-South countries.

National BAU Factor Urban BAU Factor

COUNTRY 2000 2005 2010 2015 2000 2005 2010 2015

Algeria 1.78 1.29 1.11 1.06 2.28 1.44 1.16 1.08

Egypt 1.95 1.56 1.32 1.15 3.22 2.30 1.74 1.34

Israel 1.45 1.43 1.28 1.14 1.50 1.47 1.30 1.15

Jordan 1.68 1.32 1.34 1.16 1.85 1.39 1.41 1.19

Lebanon 1.26 1.24 1.35 1.17 1.30 1.28 1.40 1.19

Libya 1.22 1.35 1.20 1.10 1.29 1.45 1.25 1.13

Morocco 1.77 1.54 1.34 1.16 2.45 1.98 1.60 1.27

Syrian Arab Republic 1.15 1.25 1.29 1.14 1.24 1.38 1.43 1.21

Tunisia 1.71 1.50 1.38 1.18 2.37 1.94 1.69 1.31

Considering the economic situation of each CoM-South country and the assumption that cities will lead the economic growth of these regions for the timeframe of the CoM project, signatories are encouraged to apply urban factors for accounting BAU emissions in 2020. In particular urban BAU factors, considering a linear extrapolation of the missing years from UNDP data, are presented in table 4 and 5. As most of the BEIs in CoM South are likely to be expected from recent years, BAU factors are given from 2002, nevertheless specific BAU factors for previous years can be requested to the JRC-IES team in support to the project. Please note that BAU factor with a BEI in 2020 is not reported as mathematically equal to 1.

3.3 How to calculate BAU emissions using reference factors With this approach the local authority has the option to calculate its final target starting from the results of the BEI and foreseeing CO2 emissions for the territory of the local authority in 2020 (referring to t CO2 or t CO2-eq.) using a BAU scenario. The possibility of setting the reduction target using a BAU scenario is characteristic for the CoM signatories outside EU28 (in particular CoM-East and CoM-South). It has the aim of allowing those municipalities that are on a rapid economic growth path to develop their economies in a sustainable manner. When preparing a BAU scenario, the CoM signatory decides if use its own approach (specifically develop for scenario investigations) or to use the national coefficients provided in this report for each country (tables 5 and 6). In the latter case, signatories can simply multiply the total emission in BEI by the national coefficient according to the chosen baseline year. In fact, the coefficient indicates the relative projected increase in GHG emissions between the baseline year and 2020. In operative terms, in order to obtain the GHG emissions foreseen for the year 2020, the emission in the baseline year has to be multiplied by the national coefficient K according to the following formula:

EmissionBAU2020 = EmissionBEI * k (2) where k is the national coefficient from table 5 or 6, selected according to the chosen baseline year; EmissionBEI are the emissions in the baseline year, EmissionBAU2020 are the estimated emissions for 2020. Therefore, the final reductions target of at least 20 % refers to EmissionBAU2020 the emissions foreseen for the year 2020 according to the BAU scenario.

Table 5. BAU coefficients to be applied to BEI in order to calculate BAU scenario emissions in CoM-South countries

in case of CO2 emission accounting

Country 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Algeria 1.94 1.77 1.61 1.44 1.38 1.32 1.27 1.21 1.16 1.14 1.13 1.11 1.10 1.08 1.07 1.05 1.03 1.02

Egypt 2.85 2.67 2.48 2.30 2.19 2.08 1.96 1.85 1.74 1.66 1.58 1.50 1.42 1.34 1.27 1.20 1.13 1.06 Israel 1.49 1.48 1.47 1.47 1.43 1.40 1.37 1.33 1.30 1.27 1.24 1.21 1.18 1.15 1.12 1.09 1.06 1.03 Jordan 1.67 1.57 1.48 1.39 1.39 1.40 1.40 1.41 1.41 1.37 1.32 1.28 1.24 1.19 1.15 1.11 1.07 1.04 Lebanon 1.29 1.29 1.28 1.28 1.30 1.33 1.35 1.38 1.40 1.36 1.31 1.27 1.23 1.19 1.15 1.11 1.07 1.04 Libya 1.36 1.39 1.42 1.45 1.41 1.37 1.33 1.29 1.25 1.23 1.20 1.18 1.15 1.13 1.10 1.08 1.05 1.03 Morocco 2.26 2.16 2.07 1.98 1.90 1.83 1.75 1.68 1.60 1.53 1.46 1.40 1.34 1.27 1.22 1.16 1.10 1.05 Syrian Arab Republic 1.30 1.32 1.35 1.38 1.39 1.40 1.41 1.42 1.43 1.39 1.34 1.30 1.26 1.21 1.17 1.13 1.08 1.04 Tunisia 2.20 2.11 2.02 1.94 1.89 1.84 1.79 1.74 1.69 1.60 1.53 1.45 1.38 1.31 1.24 1.18 1.12 1.06

Table 6. BAU coefficients to be applied to BEI in order to calculate BAU scenario emissions in CoM-South countries

in case of GHG emission accounting (therefore using CO2-eq) Country 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Algeria 1.97 1.81 1.66 1.50 1.45 1.39 1.33 1.27 1.21 1.19 1.17 1.15 1.13 1.11 1.09 1.06 1.04 1.02

Egypt 2.71 2.55 2.38 2.22 2.12 2.03 1.93 1.83 1.74 1.65 1.57 1.49 1.42 1.34 1.27 1.20 1.13 1.06

Israel 1.52 1.51 1.50 1.49 1.46 1.43 1.39 1.36 1.32 1.29 1.25 1.22 1.19 1.15 1.12 1.09 1.06 1.03

Jordan 1.71 1.62 1.53 1.44 1.44 1.43 1.43 1.43 1.42 1.37 1.33 1.28 1.24 1.19 1.15 1.11 1.07 1.04

Lebanon 1.35 1.34 1.34 1.33 1.34 1.36 1.38 1.39 1.41 1.36 1.32 1.27 1.23 1.19 1.15 1.11 1.07 1.04

Libya 1.41 1.44 1.47 1.50 1.46 1.42 1.37 1.33 1.29 1.26 1.23 1.21 1.18 1.15 1.12 1.09 1.06 1.03

Morocco 2.25 2.16 2.07 1.97 1.90 1.83 1.76 1.68 1.61 1.54 1.47 1.40 1.34 1.28 1.22 1.16 1.10 1.05

Syrian Arab Republic 1.40 1.42 1.43 1.45 1.45 1.45 1.45 1.45 1.45 1.40 1.35 1.31 1.26 1.21 1.17 1.13 1.08 1.04

Tunisia 1.90 1.83 1.76 1.68 1.67 1.66 1.65 1.64 1.63 1.56 1.49 1.42 1.35 1.29 1.23 1.17 1.11 1.05



16

As a consequence, applying a BAU approach for setting the reduction target not necessary lead to an actual reduction of GHG emissions compared to BEI, but to a greener growing of the city. It is possible to visualize this aspect in figure 8 and further details are available in the CoM South guidebook under development (CoM South, 2013). Furthermore, it has to be noticed that setting a 'per capita' target on the basis of a BAU scenario it is not recommended because the elaboration of the BAU scenario, general or custom-made for a city, implies already a certain assumption on the population trend until 2020.

Figure 8. Absolute 20 % reduction target based on the BEI results compared to absolute target based on BAU projections



17

4. Conclusion On the basis of the work carried out for the CIRCE project we have obtained factors to be used to estimate emission in 2020 for 10 countries of the Southern Partnership for the Covenant of Mayors. An additional assessment has been carried out to estimate factors which could be adapted for cities, assuming that most of the emission increase in those countries will take place in cities. Some more general methodological recommendations are:

- Accurate, complete and up-to-date baseyear emissions are fundamental: the more complete and recent, the less biases are present at the start of the projections. The inventory making of emissions takes time and a delay of one to two years is common. Although there are not specific recommendations for choosing a baseyear for CoM South, the period between 2001 and 2010 is considered the more suitable.

- A first indication on how much the country contributes to the global warming is obtained by

comparing the emission level per capita (data available in the EDGAR database3). It is interesting to highlight that the general idea of low emission country is not applicable in all CoM South partnership countries. E.g., the per capita emissions levels for Israel (10.62 tCO2-eq/cap) and Libya (12.41 tCO2-eq/cap), exceed the European mean average (9.9 tCO2eq/cap) mainly because of the oil & gas production industry. Therefore extra growth for emissions according to the BAU approach has to be questioned case by case, also according to the willingness and the technological level of each particular city, and the standard CoM procedure as for EU-28 could be more appropriate in some cases.

- The country-specific coefficients for CoM-South cities are most coherently derived when using a

global prospective outlook model addressing all countries. As such no fast and local variations, such as recessions are perturbing the projections for 2020.

- A distinction between the countries based on their economic growth seems necessary. For those

countries which are no longer in the initial phase of economic development (and have higher HDI and high GHG emission levels), the urban factors are not applicable (i.e. Israel).

- All cities of one single country are allowed to use the same BAU coefficient, eventually corrected

with the urban factor, as set for the country, in order to obtain a national consistency.

- This exercise can be repeated for all world countries, to give a more equilibrated view on the cities which are already signatories in the CoM of Western-European countries and on cities which could join a similar CoM designed for countries in other EC Partnerships, such as CoM South.

3 EDGAR database, version 4.2. http://edgar.jrc.ec.europa.eu

http://edgar.jrc.ec.europa.eu/



18

References

CoM South, 2013. How to develop a sustainable energy action plan (SEAP) in the Southern Mediterranean Partner Countries: The Baseline Emission Inventory. INPRESS

CoM, 2010. How to develop a Sustainable Energy Action Plan (SEAP) - Guidebook. Luxembourg: Publications Office of the European Union.

Doering, U.M., U.M. Doering, G. Janssens-Maenhout, J.A. van Aardenne, V. Pagliari (2010), CIRCE report D.3.3.1, Climate Change and Impact Research in the Mediterranean Environment: Scenarios of Future Climate Change IES report 62957.

IPCC, 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds). IGES, Japan.

Janssens-Maenhout G., Meijide-Orive A., Guizzardi D., Pagliari V., Iancu A., 2012. An approach with a Business-as-Usual scenario projection to 2020 for the Covenant of Mayors from the Eastern Partnership. JRC schientifc and technical reports, EUR 25315 EN doi:10.2788/26047

Olivier, J.G.J., Van Aardenne, J.A., Dentener, F., Ganzeveld, L. and J.A.H.W. Peters 2005. Recent trends in global greenhouse gas emissions: regional trends and spatial distribution of key sources. In: Non-CO2 Greenhouse Gases (NCGG-4), A. van Amstel (coord.), page 325-330. Millpress, Rotterdam, ISBN 905966 043 9.

Pozzer A., Zimmermann P., Doering U.M., van Aardenne J., Tost H., Dentener F., Janssens-Maenhout G., Lelieveld J., 2012. Effects of Business-as-usualanthropogenic emissions on air quality. Atmos. Chem. Phys., 12:6915-6937, DOI:10.519/acp-12-6915-2012.

Russ, P., Wiesenthal, T., van Regenmorter, D., Ciscar, J. C., 2007. Global Climate Policy Scenarios for 2030 and beyond. Analysis of Greenhouse Gas Emission Reduction Pathway Scenarios with the POLES and GEM-E3 models, JRC Reference report EUR 23032 EN (http://ipts.jrc.ec.europa.eu/publications/pub.cfm?id=1510)

Schade, B., Wiesenthal, T. (2007), Comparison of long-term world energy studies. Assumptions and results from four world energy models, IPTS EUR report 22398 EN.

Shindell, D., Kuylenstierna, J.C., Vignati, E., van Dingenen, R., Amann, M., Klimont, Z., Anenberg, S.C., Muller, N., Janssens-Maenhout, G., Raes, F., Schwartz, J., Faluvegi, G., Pozzoli, L., Kupiainen, K., Höglund-Isaksson, L., Emberson, L., Streets, D., Ramanathan, V., Hicks, K., Oanh, N.T.K., Milly, G., Williams, M., Demkine, V., Fowler, D. (2012) Simultaneously mitigating Near-Term Climate Change and Improving Human Health and Food Security, Science, Vol. 335, p. 183-189

United Nations Dep. of Econ. & Soc. Affairs/ Population Division (UNDP) (2013), World Population Prospects: The 2012 Revision, No. E.13.XIII.17

Van Vuuren, D.P., den Elzen, M.G.J., van Vliet, J., Kram, T., Lucas, P., Isaac M. (2009), Comparison of different climate regimes : the impact of broadening participation, Energy Policy, Vol. 37, p. 5351-5362

http://ipts.jrc.ec.europa.eu/publications/pub.cfm?id=1510



I

ANNEX I

Aggregated emission tables for Mashreq and Maghreb regions

Figure AI.1. CO2 emissions (expressed in Mt CO2) for residential and transport sector in Mashreq region

Figure AI.2. CO2 emissions (expressed in Mt CO2) for residential and transport sector in Maghreb region

Figure AI.3. CH4 emissions (expressed in Mt CO2-eq) for CoM sectors in Mashreq region



II

Figure AI.4. CH4 emissions (expressed in Mt CO2-eq) for CoM sectors in Maghreb region

Figure AI.5. N2O emissions (expressed in Mt CO2-eq) for CoM sectors in Mashreq region

Figure AI.6. N2O emissions (expressed in Mt CO2-eq) for CoM sectors in Maghreb region



III

Figure AI.7. GHG emissions (expressed in Mt CO2-eq) for CoM sectors in Mashreq region

Figure AI.8. GHG emissions (expressed in Mt CO2-eq) for CoM sectors in Maghreb region



IV

ANNEX II

Country and sector specific emissions

GHG Emission, in term of tCO2-eq per capita, is calculated according the historical data from EDGAR v4.2 and forecast scenario using the POLES model (chapter 2.1). The conversion from emission at the national level to emission at the urban level is performed using UNDP data on urban population and the methodology described in chapter 3.2.

Table AII.1: GHG Emissions (tCO2-eq/capita) at the urban level for CoM South countries and

sectors – Years 1990-2000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Algeria

Transport 0.684 0.697 0.664 0.658 0.580 0.548 0.538 0.526 0.504 0.495 0.517

Residential 0.779 0.838 0.814 0.801 0.747 0.700 0.666 0.609 0.669 0.686 0.651

Soild waste management 0.068 0.069 0.070 0.071 0.072 0.073 0.074 0.074 0.075 0.076 0.077

Wastewater treatment 0.164 0.161 0.161 0.158 0.155 0.152 0.149 0.147 0.146 0.145 0.142

Egypt

Transport 0.498 0.511 0.520 0.533 0.562 0.590 0.617 0.575 0.652 0.691 0.692

Residential 0.393 0.372 0.361 0.324 0.361 0.378 0.380 0.401 0.417 0.384 0.390



Israel

Transport 1.295 1.290 1.294 1.317 1.365 1.439 1.466 1.481 1.380 1.452 1.481

Residential 0.741 0.706 0.832 0.988 1.012 1.123 1.157 1.222 1.118 1.085 1.208



Jordan

Transport 0.881 0.761 0.749 0.724 0.680 0.702 0.698 0.713 0.706 0.707 0.747

Residential 0.626 0.610 0.721 0.646 0.640 0.604 0.584 0.621 0.572 0.587 0.643



Lebanon

Transport 0.619 0.745 0.995 1.119 1.015 1.159 1.179 1.106 1.166 1.090 1.007

Residential 0.552 0.832 0.642 0.515 0.591 0.588 0.504 0.662 0.538 0.551 0.526



Libya

Transport 1.524 1.504 1.495 1.723 2.007 2.120 2.074 2.017 2.053 2.084 2.174

Residential 0.375 0.399 0.409 0.430 0.470 0.499 0.521 0.532 0.529 0.559 0.555



Morocco

Transport 0.445 0.444 0.457 0.454 0.450 0.420 0.417 0.423 0.428 0.456 0.451

Residential 0.548 0.578 0.638 0.655 0.649 0.657 0.653 0.652 0.684 0.715 0.810



Syrian Arab

Republic

Transport 1.284 1.459 1.494 1.481 1.334 1.342 1.310 1.258 1.257 1.236 1.204

Residential 1.132 1.292 1.016 0.842 0.664 0.695 0.591 0.547 0.593 0.579 0.541



Tunisia

Transport 0.417 0.421 0.421 0.460 0.464 0.463 0.470 0.495 0.501 0.525 0.540

Residential 0.487 0.505 0.510 0.560 0.561 0.570 0.583 0.583 0.581 0.591 0.601





V

Table AII.2: GHG Emissions (tCO2-eq/capita) at the urban level for CoM South countries and sectors – Years 2001-2010

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Algeria

Transport 0.506 0.545 0.554 0.555 0.548 0.542 0.597 0.616 0.587 0.560

Residential 0.653 0.671 0.695 0.757 0.810 0.800 0.845 0.853 0.817 0.786

Soild waste management 0.077 0.077 0.078 0.078 0.079 0.079 0.080 0.080 0.085 0.089

Wastewater treatment 0.139 0.137 0.135 0.132 0.130 0.128 0.126 0.124 0.123 0.121

Egypt

Transport 0.675 0.685 0.698 0.718 0.691 0.736 0.810 0.785 0.762 0.740

Residential 0.390 0.394 0.384 0.465 0.557 0.563 0.595 0.606 0.597 0.589



Israel

Transport 1.381 1.401 1.398 1.328 1.351 1.334 1.369 1.335 1.322 1.309

Residential 1.287 1.234 1.375 1.475 1.137 1.297 1.326 1.264 1.209 1.162



Jordan

Transport 0.753 0.746 0.739 0.829 0.893 0.845 0.877 0.890 0.873 0.858

Residential 0.593 0.583 0.627 0.658 0.674 0.617 0.571 0.461 0.446 0.434



Lebanon

Transport 0.927 0.909 0.955 0.939 0.929 0.880 0.680 0.825 0.828 0.831

Residential 0.594 0.534 0.629 0.586 0.500 0.431 0.311 0.424 0.419 0.415



Libya

Transport 2.146 2.113 2.070 1.836 1.728 1.713 1.739 1.962 1.887 1.816

Residential 0.558 0.559 0.558 0.560 0.510 0.498 0.503 0.496 0.466 0.439



Morocco

Transport 0.449 0.448 0.433 0.427 0.438 0.443 0.463 0.402 0.403 0.404

Residential 0.833 0.836 0.845 0.876 0.906 0.934 0.970 1.004 1.010 1.023



Syrian Arab

Republic

Transport 1.112 1.114 1.077 1.049 0.957 0.997 0.939 0.829 0.819 0.809

Residential 0.570 0.542 0.467 0.398 0.394 0.376 0.354 0.346 0.340 0.337



Tunisia

Transport 0.548 0.553 0.563 0.559 0.573 0.561 0.568 0.573 0.576 0.578

Residential 0.608 0.590 0.598 0.629 0.629 0.589 0.585 0.594 0.614 0.637





VI

Table AII.2: GHG Emissions (tCO2-eq/capita) at the urban level for CoM South countries and sectors – Years 2011-2020

2011 2012 2013 2014* 2015* 2016* 2017* 2018* 2019* 2020*

Algeria

Transport 0.560 0.561 0.561 0.563 0.564 0.567 0.571 0.574 0.578 0.583

Residential 0.759 0.734 0.712 0.691 0.671 0.655 0.640 0.625 0.612 0.600



Egypt

Transport 0.749 0.759 0.769 0.780 0.791 0.802 0.813 0.824 0.836 0.848

Residential 0.586 0.583 0.580 0.578 0.576 0.573 0.570 0.568 0.566 0.564



Israel

Transport 1.334 1.361 1.389 1.417 1.447 1.479 1.511 1.545 1.580 1.617

Residential 1.145 1.129 1.114 1.100 1.087 1.075 1.065 1.055 1.046 1.038



Jordan

Transport 0.871 0.884 0.898 0.913 0.928 0.945 0.963 0.981 1.000 1.020

Residential 0.427 0.420 0.414 0.409 0.404 0.400 0.396 0.393 0.390 0.388



Lebanon

Transport 0.855 0.880 0.905 0.932 0.959 0.988 1.019 1.050 1.082 1.115

Residential 0.415 0.415 0.416 0.416 0.417 0.418 0.420 0.422 0.424 0.426



Libya

Transport 1.845 1.875 1.906 1.937 1.969 1.987 2.005 2.025 2.045 2.066

Residential 0.419 0.400 0.383 0.366 0.351 0.334 0.318 0.304 0.290 0.277



Morocco

Transport 0.412 0.420 0.429 0.438 0.447 0.456 0.466 0.476 0.486 0.496

Residential 1.031 1.039 1.048 1.058 1.069 1.079 1.091 1.103 1.115 1.129



Syrian Arab

Republic

Transport 0.819 0.830 0.841 0.853 0.866 0.877 0.889 0.902 0.915 0.928

Residential 0.322 0.308 0.296 0.284 0.272 0.261 0.250 0.240 0.231 0.222



Tunisia

Transport 0.592 0.605 0.619 0.634 0.649 0.665 0.681 0.697 0.714 0.732

Residential 0.645 0.653 0.661 0.670 0.679 0.688 0.698 0.709 0.719 0.731


Wastewater treatment 0.135 0.135 0.135 0.135 0.135 0.135 0.136 0.136 0.137 0.137 *Please remember that figures reported for these years are projections according to the method described in Chapter 2. These figures should be used as general indication of trends according to a BUA scenario, but they cannot be directly used for the elaboration of the BEI. For GHG emissions referring to the most updated historical data please refer to the EDGAR website http://edgar.jrc.ec.europa.eu

European Commission

EUR 26461 – Joint Research Centre – Institute for Environment and Sustainability

Title: How to develop a SEAP in the Southern Mediterranean Partner Countries: The Baseline Emission Inventory

Author(s): Alessandro K. Cerutti, Greet Janssens-Maenhout

Luxembourg: Publications Office of the European Union

2013 – 25 pp. – 21.0 x 29.7 cm

EUR – Scientific and Technical Research series – ISSN 1831-9424 (online)

ISBN 978-92-79-35153-2 (pdf)

DOI: 10.2788/60771

Abstract

The Covenant of Mayors for the Members of the Southern Partnership required an adaption of the Covenant of Mayors

methodology to the local situation and a complete revision of the Guidebook "How to develop a Sustainable Energy Action

Plan (SEAP)". A similar adaption was made when extending the Covenant of Mayors to the Eastern Partnership Members,

which allowed for a green growth while keeping up with the fixed 2020 targets. This revision aims to tackle in a more

appropriate way the specific institutional and economic situation of the Southern Partnership Members involved in the

initiative (Algeria, Egypt, Israel, Jordan, Lebanon, Libyan Arab Jamahiriya, Morocco, Palestine, Syrian Arab Republic and

Tunisia).

In particular this report describes the business-as-usual scenario approach and explains the underlying method and data

applied for calculating specific coefficients in order to estimate emissions in 2020. As for the extension of the Covenant of

Mayors project to the Eastern partnership, this option of using business as usual scenarios would avoid a burden to the

signatories in their aspiration for further development and would allow social and economic progress. A scientific region-

specific business-as-usual projection for the Mediterranean area, using European Commission tools and data, from the CIRCE

integrated project for Climate Change and Impact Research: the Mediterranean Environment was applied.

The coefficients calculated in this report are necessary for the alternative approach setting the emission reduction target on a

projected inventory in SEAPs instead of the traditional approach with direct emission reduction on the current situation.

Therefore this report has to be considered as a background document of the Covenant of Mayors South Guidebook, which is

forthcoming.

z

As the Commission’s in-house science service, the Joint Research Centre’s mission is to provide

EU policies with independent, evidence-based scientific and technical support throughout the

whole policy cycle.

Working in close cooperation with policy Directorates-General, the JRC addresses key societal

challenges while stimulating innovation through developing new standards, methods and tools,

and sharing and transferring its know-how to the Member States and international community.

Key policy areas include: environment and climate change; energy and transport; agriculture

and food security; health and consumer protection; information society and digital agenda;

safety and security including nuclear; all supported through a cross-cutting and multi-

disciplinary approach.

LB

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-26461-E

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