MACROECONOMIC IMPACTS OF THE LOW CARBON TRANSITION IN BELGIUM ANNEX 1 MAIN RESULTS

MACROECONOMIC IMPACTS OF THE LOW CARBON TRANSITION IN BELGIUM ANNEX 1 MAIN RESULTS

Analyzing the macroeconomic impacts of the transition to a low carbon society in Belgium Annex 1 Main results October 2016 A project by Climact, Thierry Bréchet, Federal Planning Bureau and Oxford Economics CLIMACT sa www.climact.com info@climact.com T: +32 10 750 740

3 Background The study analyses the macroeconomic impacts of the transition to a low carbon society by 2050 in Belgium The methodology is voluntarily broad and builds on three complementary models: HERMES, GEIM & OPEERA with multipliers Thematic workshops with stakeholders and experts have been organised Several documents are available: Main findings Report (methodology and results) Annexes (1. Main results, 2. HERMES results, 3. GEIM Results, 4. OPEERA-IO results, 5. Literature Review) The study was commissioned by the Federal Public Service Health, Food Chain Safety and Environment and realised between January 2015 and September 2016 The study was conducted by CLIMACT, the Federal Plan Bureau, Oxford Economics and Prof. Thierry Brechet

Background This document presents the analysis of the main results from the models The document divided into 6 chapters: 1. Overall results 2. Results for the construction sectors 3. Results for the transport sector 4. Results for the power sector 5. Results for the manufacturing sector 6. Results for the agricultural sector 4

5 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

Scenario definition Key elements defining the CORE LOW CARBON SCENARIO 1 CO 2 emissions evolution & low carbon measures Hermes GEIM OPEERA-IO -46% in BE (2030 vs 1990) (in line with -80% in 2050) -80% in EU (2050 vs 1990) -46% in BE (2030 vs 1990) -80% in BE (2050 vs 1990) Measures and actions defined in the CORE scenario from the study Scenarios for a low carbon Belgium by 2050 2 Carbon price & fiscal policy Carbon price in all sectors (gradually to 40 in 2030) ETS (+5 in 2030, from 35 to 40 ) Rises to 150 in 2050 Recycling of carbon revenues through reduction in personal and employer s social security contributions Recycling of carbon revenues through reduction of government deficit N/A N/A 3 International context Global action: low carbon transition policies in EU and the rest of the world N/A 6 Complementary analyses have been performed and are presented where appropriate (1) From the study Scenarios for a low carbon Belgium by 2050 available on www.climat.be/2050

1. Overall impacts - Growth It is possible to stimulate growth through investments in the low carbon economy GDP and CO 2 : putting the scenario in a historical perspective (million 2005 / million tons CO 2 in that year) GDP (million 2005 ) 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 Oil crisis 1 Post oil crisis period Oil crisis 2 GDP - Historical data GDP - Hermes Reference GDP - Hermes CORE Low Carbon Kyoto 97 Financial crisis CO2 - Historical data CO2 - Hermes Reference CO2 - Hermes CORE Low carbon +2% GDP Nuclear phase out 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 GHG (million tons CO 2 ) 160 140 120 100 80 60 40 20 0-46% CO2 Key messages: Reaching GHG emission objectives can be done with limited (potentially positive) impact on growth Main growth enablers 1. Energy savings 2. Demand push 3. Recycling 4. EU and global action 7 Source: Climact, Federal Plan Bureau, Prof. T. Bréchet

1. Overall impacts - Growth Continuation of decoupling trend between growth and energy/ghg Energy and CO 2 intensity of the GDP, historical and scenarios (Hermes, % evolution in base 100, M of energy / M of GDP & tons CO2 / M of GDP) 100 90 80 70 60 50 40 30 20 Energy intensity - Historical data Energy intensity - Hermes CORE LOW CARBON Energy intensity - Hermes REFERENCE CO2 intensity - Historical data CO2 intensity - Hermes CORE LOW CARBON CO2 intensity - Hermes REFERENCE Further improvement of 15% vs Reference Key message: The low carbon scenario is a continuation of the current trend of decoupling between GDP and Energy / CO2 observed since 1970 10 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 Further improvement of 9% vs Reference Source: Climact, Federal Plan Bureau, Prof. T. Bréchet 8

1. Overall impacts - Growth The impact is positive for households, firms and public finance Impacts on households, firms and public finance in 2030 (Hermes, % change in 2030 wrt Reference scenario) Impacts % changes Drivers Households: Net disposable income Firms: Gross operating surplus Public finance: Government balance +0.27% +1.22 (here as change in percentage point ) +0.4% Energy savings Recycled carbon revenue invested in reduction of personal social security contributions Higher internal activity Energy savings Recycled carbon revenue invested in reduction of employer social security contributions Social contribution (more jobs) Direct and indirect taxes Key messages: The low carbon investments and the carbon tax increase the energy and the overall production prices Energy savings and the recycling of the carbon tax more than compensate these increase for most agents 9 Source: Climact, Federal Plan Bureau, Prof. T. Bréchet

1. Overall impacts - Jobs ~80,000 additional jobs are created in 2030 Jobs creation by sectors in 2030, CORE LOW CARBON scenario (Hermes, thousands of jobs in that year wrt Reference scenario) 68 0 6 17 3 81 1 7 24 5 81 1 11 27 7 Agriculture Energy Manufacturing industry Construction Transports et communications Other market services Key messages: The main driver for job creation is the demand push: mostly in market services, construction and manufacturing industries Market services benefit from all low carbon measures and actionsand is job intensive 42 46-1 -2 40-3 There is a loss of 3.000 jobs in the energy sector (which includes both power and refining in Hermes) 2020 2025 2030 10 Source: Climact, Federal Plan Bureau, Prof. T. Bréchet

1. Overall impacts - Jobs Different drivers for growth and job creation Main growth and job creation drivers in 2030 (% of GDP change explained by the parameter, HERMES) Key messages: Growth drivers 15% 5% 10% 70% Jobs drivers 15% 25% 10% 50% Energy savings Demand push Recycling of carbon revenues Global action 1. Energy savings are reinvested in the low carbon products and services 2. Demand is pushed through additional (low carbon) investments 3. Accompanying recycling policy support economic growth 4. EU and global action support low carbon exports Inter-sectoral effects will drive a decarbonized economic growth 11 Source: Climact, Federal Plan Bureau, Prof. T. Bréchet

1. Overall impacts - Jobs low carbon measures and actions contribute to emissions reduction and job creation Proportion of emission reduction and job creation by type of abatement levers (Hermes, in 2030 wrt Reference scenario) Abatement levers % of total CO 2 emissions reduction % of total jobs creation Transport 31% 10% Building 30% 51% Industry 26% 27% Power 13% 12% Key messages: Transport and building measures contribute for more than 60% of CO 2 emissions reduction Building and industry measures contribute for more than 75% of jobs creation 12 Source: Climact, Federal Plan Bureau, Prof. T. Bréchet

13 1. Overall impacts competitiveness Energy prices are impacted by the carbon price Impact of CORE low carbon scenario on energy prices in BE (Hermes, % change in that year CORE Low Carbon vs Reference scenario) Vector Use 2020 2025 2030 Solid fuels Liquid fuels Natural gas Electricity (a) Households & services 12.4 25.2 39.0 (b) industry 5.2 10.2 15.7 (a) gasoline 2.6 5.3 8.0 (b) Diesel oil 3.3 6.7 10.1 (c) Fuel for heating 6.2 12.5 18.3 (d) Heavy fuel 1.2 2.3 3.4 (a) Industry 0.9 1.8 2.7 (b) Services 4.7 8.6 12.2 (c) Households 5.3 11.0 16.7 (a) High tension 8.9 1.8 2.7 (b) Low tension 8.4 4.7 1.8 Average energy price 5.4 6.2 7.2 Of which households 5.4 7.2 8.3 Carbon value ( 05 /ton) Non-ETS +16,65 +26,3 +40 ETS +1.65 +3.3 +5 Key messages: Price levels increase more for carbon intensive fuels and for households Average energy price increases less for the industry as the ETS sectors already face a carbon price in the baseline (35 in 2030) In 2030, the impact of the carbon tax and increased balancing costs on electricity prices are partly compensated by the decrease in production costs compared to 2020

1. Overall impacts competitiveness Oil prices are lower in all low carbon scenarios, due to lower demand 14 Impact on world oil prices (GEIM scenarios, $ 2014 per barrel) Source: Climact, Oxford Economics, Prof. T. Bréchet Key messages: A Global action scenario leads to a slightly lower energy demand than in the REFERENCE Low carbon measures and actions at the EU level impacts the energy demand at global level: other regions free ride on EU s efforts Low carbon measures and actions global level further decrease demand and price

1. Overall impacts competitiveness EU-only and global action scenarios lead to different energy prices evolution for Europe Impact on energy prices (GEIM scenarios, % evolution in 2050 wrt REF scenario, real prices) Energy prices EU-only Global action - EU28 8.0-1.5 - US -8.5 9.6 - China -1.7 23.0 Key messages: EU-only: US and China benefit from global fuel price decrease due to falling demand Global action: China (and the US to some extent), are impacted by the higher carbon intensity of their energy sources 15 Source: Climact, Oxford Economics, Prof. T. Bréchet

1. Overall impacts competitiveness The impact of the low carbon scenarios is limited at EU level Impact on overall industry GVA in EU28 (GEIM scenarios, in 2010 billions) Key messages: 140 120 100 80 +2,5% +2,7% The impact of the low carbon scenario is limited and positive for the EU added value of the overall industrial production 60 40 20 0 2015 2030 2050 Reference EU-only Global action In the global action scenario, the EU performs better than the rest of the world 16 Source: Climact, Oxford Economics, Prof. T. Bréchet

1. Overall impacts competitiveness The impact of the global action scenario on GVA is larger than the impact of EU-only for most sectors 17 Impact on industrial sectors GVA in EU28 (GEIM scenarios, in 2010 billions) Construction Chemicals Food, beverages & tobacco Mechanical engineering Electrical Engineering Metal products Rubber & Plastics Precision equipment Printing Basic metals Paper Refining Wood & Wood products 0 200 400 600 800 Source: Climact, Oxford Economics, Prof. T. Bréchet 2030 2050 Reference EU-ONLY GLOBAL ACTION 0 200 400 600 800 Key messages: The Global action scenario offsets some of the negative impacts of EU-only for more energy intensive sectors Impact on chemical sector is negative (- 3,5%) but sector performs better in EU than in rest of world (-4%) See Annex 3 for more details

1. Overall impacts competitiveness The energy savings have a major positive impact on the energy balance deficit 18 Belgian energy external balance, historical and scenarios (Hermes, in % of the GDP in that year) Source: Climact, Federal Plan Bureau, Prof. T. Bréchet Key messages: The external energy bill is cut by half in 2030 (= more than 12 billions) representing a large decrease of imports Overall, exports are boosted by the international coordinated policy (+2.7%) Increase of imports (+2.8%) is driven by intermediary and equipment goods (growing domestic activity) The effect on the global external trade balance is neutral

1. Overall impacts competitiveness External trade balance Exports and imports in 2030, CORE LOW CARBON scenario (Hermes, % change wrt Reference scenario) Impact Comments Exports +2.75% Stronger foreign activity (coordinated policy in the EU) Belgian competitiveness improvements in a low carbon economy Imports +2.79% Lower fossil fuel imports Offset by the increase in equipment and intermediary goods Current external trade balance -0.10 (here as change in percentage point of GDP ) Savings in energy imports offset by increase in imports in equipment goods Terms of trade are not impacted by the scenario Key messages: No significant impact is expected on the current external balance of Belgium Belgium is saving on energy imports but needs to increase the imports of equipment goods to respond to the growing demand of the economy 19 Source: Climact, Federal Plan Bureau, Prof. T. Bréchet

1. Overall impacts Summary Details on impact of key scenario drivers Impact of scenarios on main macroeconomic indicators (Hermes, difference wrt the Reference, in 2030) 3.0 2.5 2.0 1.5 1.0 0.5 0.0-0.5 GDP Exports Jobs Households income Firms gross operating surplus (% wrt Ref) (% wrt Ref) (% wrt Ref) (% wrt Ref) (diff pt. wrt Ref) Key messages: A global coordinated policy with adequate mitigations and fiscal measures can yield positive impacts in the economy BOTTOM-UP MEASURES = REFERENCE scenario + low carbon technical assumptions in BE CO2 PRICE = BOTTOM-UP MEASURES scenario + gradual carbon price of 40 in 2030 in BE Scenarios: RECYCLING = CO 2 PRICE scenario + recycling of revenues in BE (lower labor cost) EU-ONLY = RECYCLING scenario + similar policies and measures in the whole EU GLOBAL ACTION = EU POLICY scenario + similar policies and measures in the rest of the world Source: Climact, Federal Plan Bureau, Prof. T. Bréchet 20

22 Table of content 1. Overall results 2. Results for the construction sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

23 1.1 Main conclusions for the construction sector The construction sector has a very large potential for job creation Key messages from macroeconomic models: The construction sector gains about 27 000 jobs in 2030 driven by demand push through low carbon investments The sector also benefits from carbon revenues recycling Impact of building, power and transport levers on jobs in construction sector (OPEERA-IO, in jobs in 2030 wrt Reference scenario, both direct and indirect jobs) Key insights from the workshops Challenge of social dumping / posting of jobs Risk of capacity constraints Important role of public procurement to stimulate domestic investments

24 Table of content 1. Overall results 2. Results for the construction sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

1.2 Key assumptions for the construction sector Illustration of assumptions on residential and commercial buildings levers 2030 investments for residential buildings (in million EUR) 2030 investments for commercial buildings (in million EUR) +25% 12,800 10,250 7,194 7,608 New build 4,995 +47% 7,348 605 2,451 2,343 2,849 Retrofit Heating system 25 REFERENCE CORE Expenditures drivers: Increased renovation rate (+1%=>=2%) Increased level of renovation Large increase in the proportion of heat pumps at the expense of oil and gas heating systems Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO REFERENCE CORE Expenditures drivers: Investment costs are not split in commercial buildings Main drivers are also increased renovation rate and electrification of heating systems + electrification of cooling systems

1.2 Key assumptions for the construction sector Assumption on expenditures level evolution 2030 system costs for all buildings levers (in million EUR) 21,553 5,773 535 15,245 Reference +10% Technology 34,546 2,342 536 23,683 24,454 23,116 3,009 2,729 3,208 525 533 536 +32% 31,668 20,149 Core 19,854 Behaviour 20,710-95% Fuel Operations & Maintenance Investment CORE vs REF scenario Overall buildings system costs +10% Investments in the buildings value chain +32% Fuel costs -48% 26 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

27 Table of content 1. Overall results 2. Results for the construction sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

1.3 Construction sector impacts from macroeconomiceconomic models Impact on production prices Impact of scenarios on production prices in construction (Hermes, % changes wrt Reference scenario, in 2030) 5.0 4.0 3.0 2.0 1.0 0.0 2.9 3.0 4.0 Production prices BOTTOM UP MEASURES CO2 PRICE + RECYCLING CORE LOW CARBON Production prices +4% in 2030 in the CORE LOW CARBON scenario 1. The strong demand push leads to a pressure on production capacities 2. Recycling offsets the CO 2 tax burden 3. Import prices increase in the CORE LOW CARBON scenario 28

1.3 Construction sector impacts from macroeconomic models Impact on sectoral jobs creation Impact of scenarios on jobs in the construction sector (Hermes, % change wrt Reference scenario, in 2030) 10.0 8.0 6.0 4.0 2.0 0.0 8.8 9.2 9.4 Employment BOTTOM UP MEASURES CO2 PRICE + RECYCLING CORE LOW CARBON Employment +26,500 jobs (+9.4%) in 2030 in the CORE LOW CARBON scenario 1. Mitigation measures create a demand push for the sector 2. Recycling has a positive but slight effect 3. The international environment also has a positive impact 29

1.3 Construction sector impacts from macroeconomic models Impact on sectoral value added Impact of scenarios on value-added for the construction sector (Hermes, % changes in volume wrt Reference scenario, in 2030) 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 3.1 1.9 1.3 Value added BOTTOM UP MEASURES CO2 PRICE + RECYCLING CORE LOW CARBON Value added +3.1% in 2030 in the CORE LOW CARBON scenario Recycling and the international environment both have a significantly positive impact 30

31 Table of content 1. Overall results 2. Results for the construction sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

1.4 Construction sector impacts from OPEERA-IO model Structure of the results presented in next slides The results focus on jobs evolution which is a relevant indicator to reflect the impact of the transition on the sector The results focus on buildings levers impacts (most relevant for the construction sector) as follow : 1. Jobs difference by EXPENDITURES categories 2. Jobs difference by LEVERS 3. Jobs difference by SECTORS 4. Variances analysis The results also give some power levers impacts relevant for the construction sector 32

33 1.4 Construction sector impacts from OPEERA-IO model Impact on employment from low carbon scenario in Belgium Jobs difference in CORE vs REF scenario - impact of buildings EXPENDITURES categories (jobs in that year, direct & indirect) 26,461 28,289 27,690 31,737 25,987 32,586 Fuel Operations & Maintenance Investment This is in the same order of magnitude as what the macroeconomic models indicate Shift from fuel expenditures to investments leads to jobs creation -1,810-18 -3,992 2020 2025-55 -6,506-93 2030 Impact of operation and maintenance expenditures is negligible

34 1.4 Construction sector impacts from OPEERA-IO model Impact on employment from low carbon scenario Jobs difference in CORE vs REF scenario impacts of buildings LEVERS (in jobs in that year, including investments, operations and fuel expenditures, both direct and indirect) 26,460 3,907 8,020 1,818 27,689 2,581 12,031 2,282 25,988 2,678 11,964 2,802 Domestic - New Build Domestic - Retrofit Domestic - Heating systems Domestic - Fuel spendings Commercial buildings Buildings retrofit is the largest potential driver in terms of job creation 14,075-1,360 13,891-3,096 13,665-5,121 (The commercial buildings lever also includes a large amount of retrofits) 2020 2025 2030

35 1.4 Construction sector impacts from OPEERA-IO model Impact on employment from low carbon scenarios Jobs difference in CORE vs REF scenario impact of buildings developments on SECTORS (in jobs in that year, including investments, operations and fuel, both direct and indirect) 27.688 25.988 26.769 11.588 3.536 5.224 13.218 13.353 3.941 4.061 5.569 5.652 2.521 2.810 2.885 5.402 6.143 6.542-1.117-215 -3.223-465 -5.392-170 -305-467 -646 2020 2025 2030 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services The overall building value chain benefits from new investments Construction sector gains the largest share of the new created jobs

1.4 Construction sector impacts from OPEERA-IO model Variance analysis: imported content in the buildings value chain Jobs difference in CORE vs REF scenario impacts of increase or decrease of BELGIAN content (in jobs in 2030, including investments, operations and fuel, both direct and indirect impacts) Changes in 36 39,849 18,101 5,984 8,018 4,071 9,838-5,110 +10% +53% 25,988 13,353 4,061 5,652 2,885 6,542-28% -597-5,392-646 -456-467 CORE 18,778 10,979 3,100 4,468 4,894-5,812-473 -5% 2,293 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services -671 = % change in Belgian content of goods and services in the non-energy value chain Remark : the impact highlighted here is underestimated as potential international development (exports opportunities) is not taken into account imported content could impact the construction sector: +10% of BELGIAN content = potential gain of 13,800 jobs -5% of BELGIAN content = potential loss of 7,000 jobs

37 1.4 Construction sector impacts from OPEERA-IO model Variance analysis of impact of different low carbon scenarios Jobs difference in scenario vs REF impacts of CORE, Behaviour and Technological scenarios (in jobs in that year, including investments, operations and fuel, both direct and indirect) 25,988 13,353 5,652 6,542-5,392 CORE +19% -12% +217% 30,821 22,857 82,321 38,320 11,139 15,872 13,932 13,049 4,715 7,970 4,061 5,967 3,766 2,885 3,216 5,486 16,744 8,459 2,712 5,568-646 -4,304-660 -6,664-651 -6,664-651 -467-504 -409-409 TECH BEHAV -95% Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services Technological and behavioural scenarios do NOT lead to a significant difference in terms of job creation for the building value chain More ambitious scenario (-95%) demand larger energy savings and investments that represent a larger demand push for the economy

1.4 Construction sector impacts from OPEERA-IO model Impact on employment from low carbon scenarios Jobs created by CORE vs REF scenario impact of power capacity developments in main economic sectors (in jobs in that year, including investments, operations and fuel spending, both direct and indirect) 4.429 3.050 343 2.804 241 586 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services The demand push for new infrastructures in the energy sector gives the opportunity to create new jobs in the construction sector in 2030 +500 jobs 2020 2025 2030 38 Remark: The impacts of buildings levers and power levers should be analysed separately as they do not take into account economic feedback loops. Only Hermes simulations take those into account.

39 1.4 Construction sector impacts from OPEERA-IO model Impact on employment from low carbon scenarios Jobs difference in CORE vs REF scenario impact of transport developments on SECTORS (in jobs in that year, including investments, operations and fuel, both direct and indirect) All vehicles 75-331 -356-2.431-2.775 262 6 456-607 -551 122-5 -3.426-4.011 425 167-886 -18-904 -5.327 46 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services The model also expects a positive but limited demand push in new infrastructures in the transport sector that also represent an opportunity to create new jobs in the construction sector in 2030 2020 2025-6.497 2030

41 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

42 2.1 Main conclusions for the transport sector Different impacts for transport services and transport vehicles supply chain Key messages from macroeconomiceconomic models: The transport services sector gains 3 000 additional jobs in 2030 A decrease in number of individual cars leads to a net decrease in numbers of jobs (-6 500 jobs) in the overall transport vehicles value chain (especially technical services with - 5 000 jobs) Investments in domestic capabilities for electric cars assembly and maintenance partially compensate such decrease Impact of low carbon transports vehicles development on jobs (OPEERA-IO, in jobs in 2030 wrt Reference scenario, both direct and indirect jobs) Key insights from the workshops Key impact of investments in low carbon and smart transport infrastructures Development of collective vehicle value chains Links with co-benefits

43 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

2.2 Key assumptions for the transport sector Technical assumptions extracted from Low Carbon Belgium project results Total cars by technology ( 000s units) 4,259 2010 5,356 REF 2030-45% Fuel Cell (Hydrogen) Electric 3,983 CORE 2030 6,245 REF 2050-85% Plug-in Hybrid Electric 3,451 CORE 2050 ICE, incl. biofuels, CNG & hybrid Decrease of the overall number of cars because of Lower travel demand Higher occupation of vehicles (25 to 35%) Higher and longer use of vehicles and infrastructure Almost complete shift to electric mobility is expected in 2050 44 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

2.2 Key assumptions for the transport sector Technical assumptions extracted from Low Carbon Belgium project results Total cars by technology in 2030 ( 000s units) 5,356-45% 3,983-59% 3,400-36% 4,686 3,400 The number of ICE cars decreases in all low carbon scenarios because of lower demand for transport Reference CORE Fuel Cell (Hydrogen) Electric BEHAV TECH -95% Plug-in Hybrid Electric ICE, incl. biofuels, CNG & hybrid But number of cars by technology is significantly different in the scenarios 45 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

2.2 Key assumptions for the transport sector Assumptions on expenditures level evolution 2030 total system costs for transport levers (in million EUR) -17% 21,673 4,108 7,868-10% 9,697 Reference 17,883 1,826 7,354 8,703 CORE -18% 0% 21,716 2,308 17,694 1,465 8,832 7,532 +9% -10% 10,576 8,697-21% 17,208 1,487 7,024-10% 8,697 Behaviour Technology -95% Fuel Operations & Maintenance Investment In the CORE and Behaviour scenarios, investment and fuel expenditures decrease by ~10% and ~55/60% with transport levers In the Technological and even more in the -95% scenarios, investments expenditures increase (higher cost of low carbon vehicles) and fuel expenditures decrease less 46 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

47 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

48 2.3 Transport sector impacts from macroeconomiceconomic models Impact on sectoral jobs creation and value added in transport services Impact on employment and value added, CORE LOW CARBON scenario (Hermes, thousands of jobs / M constant price in that year wrt Reference scenario) Jobs 3.0 Value Added 1,325 Impact on land transport services (e.g. NMBS/SNCB, logistic services, taxi services, etc.) 1.9 662 985 Employment +3,000 jobs in 2030 1.1 Value added +1,325 M 2005 in 2030 2020 2025 2030 2020 2025 2030 Rail and road transport services

49 2.3 Transport sector impacts from macroeconomic models Impact on sectoral production prices evolution in transport services Impact of scenarios on production prices in 2030 (Hermes, % changes wrt Reference scenario, in 2030) 0.0-0.5-1.0-1.5-2.0-2.5-3.0-3.5-3.1-2.1-1.0 Rail and road transport services BOTTOM UP MEASURES CO2 PRICE + RECYCLING CORE LOW CARBON Efficiency gains related to Mitigation measures enable land transport services to decrease sectoral cost price Production prices are decreasing less in both Recycling and CORE LOW CARBON scenarios due to higher energy price and higher pressure on capacities

50 2.3 Transport sector impacts from macroeconomic models Impact on sectoral jobs creation in transport services Impact of scenarios on employment in 2030 (Hermes, % change wrt Reference scenario, in 2030) 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 1.9 2.9 3.2 Rail and road transport services BOTTOM UP MEASURES CO2 PRICE + RECYCLING CORE LOW CARBON Employment in transport services is positively impacted by all 3 dimensions Mitigation measures have the largest impact because of their boosting effect to the overall economy that benefit transport services

51 2.3 Transport sector impacts from macroeconomic models Impact on sectoral value added in transport services Impact of scenarios on value-added in 2030 (Hermes, % changes in volume wrt Reference scenario, in 2030) 20.0 15.0 10.0 5.0 0.0 17.9 19.7 19.7 Rail and road transport services BOTTOM UP MEASURES CO2 PRICE + RECYCLING CORE LOW CARBON Efficiency gains related to mitigation measures is an important driver for increased value added in land transport services The effect of Tax + Recycling and CORE LOW CARBON scenarios are limited or neutral for value added

52 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO model 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector

2.4 Transport sector impacts from OPEERA-IO model Structure of the results presented in next slides The results focus is on jobs evolution which is a relevant indicator to reflect the impact of the transition on the sector (results also available on added value evolution in Appendix) The results focus on transport levers impacts as follow : 1. Jobs difference by EXPENDITURES categories 2. Jobs difference by LEVERS 3. Jobs difference by SECTORS 4. Variances analysis 53

2.4 Transport sector impacts from OPEERA-IO model Impact on employment from low carbon scenario in Belgium Jobs difference in CORE vs REF scenario - impact of transport EXPENDITURES categories (jobs in that year, direct & indirect) 9-2.024-761 -2.776 All vehicles -23-2.145-1.842-4.010-289 -2.785-3.423-6.497 Fuel Operations & Maintenance Investment In the CORE scenario, the decrease in demand for transport (and the related decrease of ICE vehicles) has a negative impact on the overall transport value chain The negative impact is worsening in the long term as the decrease is getting more important 2020 2025 2030 54

2.4 Transport sector impacts from OPEERA-IO model Impact on employment from low carbon scenarios Jobs difference in CORE vs REF scenario impact of transport developments on SECTORS (in jobs in that year, including investments, operations and fuel, both direct and indirect) 75-331 -356-2.431-2.775 All vehicles 262 6 456 425 46 122 167-607 -5-886 -551-18 -904-3.426-4.011-5.327 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services The decrease of new vehicle particularly impacts technical services (retail, distribution and maintenance) Manufacturing is not very impacted because of the high imported content of vehicles (positive impact of increasing collective vehicles) 55 2020-6.497 2025 2030 Shift to electric has a positive impact for electricity production and negative impacrt for other fossil fuels

2.4 Transport sector impacts from OPEERA-IO model Variance analysis: impact of different low carbon scenarios 56 Jobs difference in scenario vs REF impacts of CORE, Behaviour and Technological scenarios (in jobs in 2030, including investments, operations and fuel, both direct and indirect) CORE, Behavioural and - -904-5,327-6,497 CORE All vehicles 6,642 903 790 94 279 46 5,341 425 167 0-886 -765 0 0-18 TECH 511-1,113-6,772-1,036-7,636 BEHAV 376 466 75 75 323 511 326-1,036-1,113-6,772-7,543-95% Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods 95% lead to a decrease of demand of mobility, meaning a decrease of number of cars and loss of jobs, especially in technical market services In the Technological scenario, the lower decrease of vehicles units and the higher cost of low carbon vehicles represent a boost to the economy Market services - non techical services +6,600 jobs Market services - technical services

57 2.4 Transport sector impacts from OPEERA-IO model Impact on employment from low carbon scenario in Belgium Jobs difference in CORE vs REF scenario - impact of transport EXPENDITURES categories (jobs in that year, direct & indirect) Individual low carbon* vehicles 23,417 20,004 640 14,259 604 11,233 324 8,826 5,144 8,791 2020 10,574 11,544 2025 2030 *Low carbon = Electric, Plug-in electric and fuel cell vehicles Collective low carbon* vehicles 254 2020 285 2025 484 2030 Fuel Operations & Maintenance Investment Focusing on low carbon vehicles only, the CORE scenario has a positive impact on job creation +23,000 jobs Collective vehicles have a positive but relatively limited impact on jobs creation (not including services related to collective transport that are job intensive)

58 18/02/2016 2.4 Transport sector impacts from OPEERA-IO model Variance analysis of imported content in transport value chain Jobs created by CORE vs REF scenario impacts of changes in Belgian content (in jobs in 2030, including investments, operations and fuel, both direct and indirect) The development of a more Belgian value chain (+10% of Belgian content) could create: +32% +163% 23.417 30.958 +32% 1.272,0 +163% 2030 CORE +10% more jobs in electric, hybrid and fuel cells individual vehicles 484,0 2030 CORE +10% more jobs in electric, hybrid and fuel cells collective vehicles Indiv vehicles Collective vehicles

60 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO 5. Results for the manufacturing sector 6. Results for the agriculture sector

61 4.3 Energy sector The energy sector needs to adapt to the changes in energy demand and supply Key messages from macroeconomiceconomic models: The energy sector is expected to loose some existing jobs driven by the fall in energy demand Investments in domestic capabilities for electrification (renewables, grid, services, etc.) create new jobs (+ 4 500 jobs in 2030) Impacts of changes in Belgian content on jobs in power sector (OPEERA-IO, in jobs in 2030 wrt Reference scenario, both direct and indirect jobs) Key insights from the workshops Impact of other configurations of the production on the energy sector (decentralization, intermittency, grids, storage) Impact of decreasing renewables costs Specificities of the high efficiency of the Belgian refinery sector within the EU Importance of the quality of the new jobs

62 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO 5. Results for the manufacturing sector 6. Results for the agriculture sector

TWh 3.2 Key assumptions Solar PV for the energy sector Electricity production mix in the CORE scenario in Belgium, TWh per year 160 140 120 2045 2050 Hydroelectric power stations Onshore wind Electricity production by source Offshore wind Total consumption Renewable energy sources Intermittent sources Reference scenario 100 80 Gas 60 Nuclear 40 20 Offshore wind 0 2010 2015 2020 2025 2030 2035 2040 2045 2050 Imports of decarbonized electricity Coal+Gas+Oil power stations Nuclear power Carbon Capture Storage (CCS) Gas : used as an intermediary Industry CHP source of electricity, Residential CHP but replaced over time and used as Geothermal electricity back-up Biomass power stations Intermittent RES : solar PV Hydroelectric and wind power make stations up ~50% of the production Solar PV mix in 2050 Onshore wind Non-intermittent RES : Offshore wind Biomass and geothermal are Total key consumption to complement the mix, and support grid Renewable energy sources stability with back-up Intermittent sources Reference scenario 63 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

3.2 Key assumptions for the energy sector Electricity capacity in Belgium in 2050 in the CORE scenario GW Intermittent +119% 18 2-26% 40 28 Total capacity increases as capacity factors of solar and wind 1 are low If all 28 GW produce at max potential : Minimal Belgian demand of ~10 GW Interconnection potential of 15 to 30 GW to the rest of Europe Additional increase in DSM and storage Non-intermittent 16 7 5 Back-up gas plants Non-intermittent production is completed by a large amount of back-up capacity Altogether firm capacity still decreases compared to 2010, but with better interconnections and DSM 2010 2050 64 1 Solar PV: ~10% (2010) to ~15% (2050); Onshore wind : 25% (2010) to 30% (2050) SOURCE: CREG (2010), ECF Roadmap 2050 phase II (McKinsey/KEMA/ICL), Climact

3.2 Key assumptions for the energy sector System costs assumptions Average yearly system costs (incl. primary energy costs) (undiscounted 2010-2050, in M ) Average yearly investment costs (undiscounted 2010-2050, in M ) 6,815-14% 5,888 +40% 2.334 248 Grid and balancing implications Fuel 4,291 2,617 1.670 248 786 Wind power (on and offshore) +40% 937 641 544 Solar PV O&M Investment 854 1,670 2,334 321 264 4 456 492 Hydro and geothermal Biomass and gas power stations REF Core REF Core 65 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

3.2 Key assumptions for the energy sector Electricity production in Belgium in the various technical scenario, TWh Imports of decarbonized electricity 1 Solar PV Wind on/offshore Geothermal and hydro electricity 2030 2050 Industrial and residential CHP Biomass power stations Carbon Capture and Storage (CCS) Gas 108 4 1 22 1 13 4 0% -8% -15% 108 99 5 1 5 1 92 4 25 25 24 5 5 11 5 12 9 12 9 7-12% 96 4 1 23 4 11 10 135 6 2 33 1 17 6-22% 105 2 13 41-31% 93 9 36-6% 126 2 13 41 25-34% 89 2 8 35 63 42 40 52 43 Reference Core Behaviour Technology -95% Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO 66 70 Reference 25 12 25 17 13 12 11 13 15 13 15 10 6 2 0 Core Behaviour Technology -95%

67 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO 5. Results for the manufacturing sector 6. Results for the agriculture sector

3.3 Energy sector impacts from macroeconomic models Impact on sectoral production prices 68 Impact of scenarios on production prices in the energy sector (power and refineries) (Hermes, % changes wrt Reference scenario, in 2030) Production prices = -3,3% 0.0 in the energy sector in 2030 in the BOTTOM UP MEASURES CORE LOW CARBON scenario -1.0-2.0-3.0-4.0-5.0-6.0-5.7-4.6-3.7-3.3 Production prices CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON The lower demand reduces pressure on production capacities utilisation in the mitigation scenario (~-12% in 2030) Recycling partially offsets the CO 2 tax burden on cost price of the sector EU Policy and CORE LOW CARBON scenarios see pressure on both capacity and cost prices that increase the overall production prices

3.3 Energy sector impacts from macroeconomic models Impact on sectoral employment Impact of scenarios on jobs in the energy sector (power and refineries) (Hermes, % change wrt Reference scenario, in 2030) 0.0-2.0-4.0-6.0-8.0-7.5-7.6-7.3 Employment -7.3 BOTTOM UP MEASURES CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON Employment = -2.900 jobs in the energy sector in 2030 in the CORE LOW CARBON scenario Mitigation measures create a large reduction in energy demand which leads to decrease of employment in the energy sector The CO2 tax has a negative impact that is partially compensated by the recycling policy EU POLICY and CORE LOW CARBON scenarios have a limited but positive impact 69

3.3 Energy sector impacts from macroeconomic models Impact on sectoral value added Impact of scenarios on value-added in the energy sector (power and refineries) (Hermes, % changes in volume wrt Reference scenario, in 2030) 0.0-2.0-4.0-6.0-8.0-10.0-9.4-10.0-9.7 Value added -9.6 BOTTOM UP MEASURES CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON Value added = -9.6% in the energy sector in 2030 in the CORE LOW CARBON scenario As for employment, the CO 2 tax has a negative impact on the sector partially compensated by the recycling policy EU POLICY and CORE LOW CARBON scenarios have a limited but positive impact 70

71 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector Main conclusions for the sector Key assumptions Results from macroeconomic models Results from OPEERA-IO 5. Results for the manufacturing sector 6. Results for the agriculture sector

3.4 Energy sector impacts from OPEERA-IO model Structure of the results presented in next slides The results focus on jobs evolution which is a relevant indicator to reflect the impact of the transition on the sector (results also available on output and added value evolution in Appendix) The results focus on power capacity development levers impacts as follow : 1. Jobs difference by EXPENDITURES categories 2. Jobs difference by LEVERS 3. Jobs difference by SECTORS 4. Variances analysis 72

73 3.4 Energy sector impacts from OPEERA-IO model Impact on employment from low carbon scenario in Belgium Jobs difference in CORE vs REF scenario - impact of transport EXPENDITURES categories (jobs in that year, direct & indirect) 4.428 2.912 203 560 2.149 2.804 765 2.059-20 1.079 3.568-219 Fuel Operations & Maintenance Investment New biomass fuel needs more than compensate the loss in gas fuel consumption Investments in and maintenance of new capacity for renewable energy sources create more than 1800 and 600 jobs in 2030 2020 2025 2030

74 3.4 Energy sector impacts from OPEERA-IO model Impact on employment from low carbon scenario Jobs difference in CORE vs REF scenario impact of transport developments by LEVERS (in jobs in that year, including investments, operations and fuel spending, both direct and indirect) 4.429 2.913 1.748 117 3 130 425 711 487 370-1.078 2020 1.702 2.803 145 1.536 1.771 116 725 6 6 688 546 190 204 671 770 486 552-1.473-1.409 2025 2030 Biomass power stations Interconnections Gas power stations Geothermal electricity Hydroelectricity Offshore wind Onshore wind Solar PV Solar thermal Compared to the REF scenario the CORE installs more RES based electricity production, and reduces gas-based production Some technologies like solar PV and onshore wind are already assumed to be well developed in the REF scenario in 2030, and therefore their additional impact on jobs is limited

75 3.4 Energy sector impacts from OPEERA-IO model Impact on employment from low carbon scenarios Jobs difference in CORE vs REF scenario impact of transport developments on SECTORS (in jobs in that year, including investments, operations and fuel spending, both direct and indirect) 4.429 3.050 6 351 490 127 308 1.544-119 343 2.804 13 458 497 284 82 1.882-653 842 241 586 17 563 181 499 2.800-1.059 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services In 2030, the power developments lead to +500 more jobs in the construction sector +700 jobs in manufacturing value chain +1400 jobs in services value chain 2020 2025 2030

76 3.4 Energy sector impacts from OPEERA-IO model Variance analysis of impact of different low carbon scenarios Jobs difference in scenario vs REF impacts of CORE, Behaviour and Technological scenarios (in jobs in that year, including investments, operations and fuel, both direct and indirect) +9% 4,429 4,824 586 594 17 563 566 17 842 858 499 181 502 2,800 2,807-1,059-702 CORE TECH -48% 182 2,297-44% 2,477 439 11 401 12 289 624 593 463 288 79 282 1,798 1,680-1,200-1,122 BEHAV -95% 137 Construction Energy - electricity & gas fuels Energy - Solid & liquid fuels Energy - Biomass fuel Industry - equipment goods Industry - intermediary goods Market services - non techical services Market services - technical services Technological scenarios lead to same amount of job creation than CORE scenario The Behavioral and - 95% scenarios leads to a lower but still positive net jobs evolution for the power capacity value chain

77 3.4 Energy sector impacts from OPEERA-IO model Impact of increase or decrease in imported content in the power capacity value chain Jobs difference in CORE vs REF scenario impacts of increase or decrease of imported content (in jobs in that year, including investments, operations and fuel, both direct and indirect) The development of a more domestic value chain (+10% of Belgian content) could create: +56% +190% 770,0 2030 CORE 1.204,0 +10% More than 1,5x more jobs in solar PV value chain 204,0 2030 CORE 592,0 +10% Nearly 3x more jobs in onshore wind value chain Solar PV Onshore Wind Remark : the impact highlighted here is underestimated as potential international development (exports opportunities) is not taken into account

79 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector Main conclusions for the sector Key assumptions Results from macroeconomiceconomic models 6. Results for the agriculture sector

80 4.1 Main conclusions for manufacturing industries Energy savings have a positive impact on industry production prices Key messages from macroeconomic models: The manufacturing sector as a whole gains about 11 000 jobs by 2030 This gain is driven by lower production prices and higher overall economic activity The limited carbon price increase in the industry does not impact overall firms competitiveness Impact on jobs and value added (Hermes, thousands of jobs / M constant price in that year wrt Reference scenario) Key insights from the workshops Specific risk of carbon leakage for ETS sectors needs attention Firms and sectors challenges and opportunities are specific Value chain and industrial clusters effects have to be taken into account

81 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector Main conclusions for the sector Key assumptions Results from macroeconomiceconomic models 6. Results for the agriculture sector

3.2 Key assumptions for manufacturing industries Technical assumptions extracted from Low Carbon Belgium project results GHG emissions in the Belgium manufacturing industries, CORE scenario MtCO2e per year 45 1 2 3 4 4 9 12 13-3 -76% 3 1 0 11 6-9 2 3 3 1 Other Food, drinks and tobacco Pulp & Paper Lime and glass Cement Steel Oil & Gas Chemicals Biomass allocated to industry Delta vs 2010-33% -21% -23% -76% -65% -97% -54% +250% Assumptions on energy efficiency levels by sectors have been included in the models in line with GHG emissions target But models do not include formal sector specific CO 2 emissions reduction targets 82 2010 2050 Source: Scenarios for a Low Carbon. Belgium by 2050, CLIMACT-VITO

83 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector Main conclusions for the sector Key assumptions Results from macroeconomiceconomic models 6. Results for the agriculture sector

84 4.3 Manufacturing industries impacts from macroeconomic models Impact on sectoral jobs creation and value added Impact on employment and value added, CORE LOW CARBON scenario (Hermes, thousands of jobs / M constant price in that year wrt Reference scenario) 5,9 2,8 1,4 1,2 1,8 2,1 2020 Jobs 7,5 4,3 2025 Intermediary goods 10,7 7,6 0,4 2,7 2030 985 585 160 239 2020 Equipment goods Value Added 1.357 852 57 449 2025 1.508 927 605-25 2030 Consumption goods Employment in manufacturing industries +10,700 jobs in 2030 Value added +1,500 M 2005 in 2030

85 4.3 Manufacturing industries impacts from macroeconomic models Impact on sectoral production prices Impact of scenarios on production prices in 2030 (Hermes, % changes wrt Reference scenario, in 2030) 1.0 0.0-1.0-2.0-3.0-4.0-3.0-3.4-1.0-0.8 Intermediary goods -1.0-0.9 0.6 0.7 Equipment goods -1.8-1.7-0.4-0.3 Consumption goods BOTTOM UP MEASURES CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON Mitigation measures reduce production prices due to strong energy savings Smaller decrease of production prices in the recycling scenario The benefits of energy savings almost vanish in the EU POLICY & CORE LOW CARBON scenarios because of an increase in import prices

86 4.3 Manufacturing industries impacts from macroeconomic models Impact on Belgian external trade Impact of scenarios on external trade in 2030 (Hermes, % changes in volume wrt Reference scenario, in 2030) 3,0 2,5 2,0 1,5 1,0 0,5 0,0 0,17 0,08 2,75 2,54 Belgian exports 0,24 0,30 2,59 Belgian imports 2,79 BOTTOM UP MEASURES CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON Increase of exports in all scenarios Imports also increase because of high domestic demand and high imported content of our exports The EU POLICY & CORE LOW CARBON scenarios boost exports (and imports) because international trade of Belgium is very sensitive to EU and RoW growth

87 4.3 Manufacturing industries impacts from macroeconomic models Impact on manufacturing external trade Impact of scenarios on external trade (Hermes, M current in that year, CORE LOW CARBON and Reference scenario) 70.000,0 60.000,0 50.000,0 40.000,0 30.000,0 20.000,0 10.000,0 0,0-10.000,0-20.000,0-30.000,0 Intermediate goods Equipment goods Consumption goods Reference CORE LOW CARBON 2020 2025 2030 2020 2025 2030 2020 2025 2030 External trade of equipment goods is worsening because of high imported content of the sector External trade of intermediate goods is boosted by exports opportunities growth Consumption goods contribution to external trade is marginal

88 4.3 Manufacturing industries impacts from macroeconomic models Impact on sectoral employment Impact of scenarios on employment (Hermes, % change wrt Reference scenario, in 2030) 5.0 4.0 3.0 2.0 1.0 0.0 2.9 3.2 4.3 4.4 Intermediary goods 1.1 1.5 0.6 0.5 Equipment goods 1.5 1.8 1.8 1.8 Consumption goods BOTTOM UP MEASURES CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON Jobs creation is positive in all sectors and in all scenarios Tax + recycling always fosters jobs creation EU POLICY and CORE LOW CARBON scenarios are: Very positive for intermediary Less positive for equipment Neutral for consumption

89 4.3 Manufacturing industries impacts from macroeconomic models Impact on sectoral value added Impact of scenarios on value-added (Hermes, % changes in volume wrt Reference scenario, in 2030) Value added mostly positively impacted by mitigations measures 5.0 4.0 4.2 4.0 3.6 3.7 3.7 3.9 3.9 3.5 The impact of Tax + recycling scenario is relatively neutral 3.0 2.0 1.0 0.0-1.0 Intermediary goods 1.4 1.4-0.2-0.4 Equipment goods Consumption goods BOTTOM UP MEASURES CO2 PRICE + RECYCLING EU-ONLY CORE LOW CARBON Equipment goods experience a slight negative impact in the EU POLICY & CORE LOW CARBON scenario: strong increase in imports, and capacity constraints

91 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector Main conclusions for the sector Key assumptions

5.1 Main conclusion for the agricultural sector The agriculture sector must be analysed carefully Key insights from the workshops on impacts of 2 main emissions reduction levers: 1) Technical options Growing efforts for GHG emissions reduction in the sector : higher potential than identified in technical study Initiatives should be pursued to favour innovation Objectives of different agricultural models should be reconciled 2) Behavioural options The necessity of a protein transition towards a more sustainable agricultural and food system should be analysed in more details The impact of such transition on production level in BE will depend on the competitiveness of the sector The current trend for lower demand for animal proteins participates to the decrease of GHG emissions and has positive co-benefits on public health (see next section) Sectorial opportunities Measures to limit food waste and support circular economy initiatives in the sector could have an important economic development potential Importance of developing initiatives with the entire food supply chain (from producers to customers) when tackling the sustainability challenges 92

93 Table of content 1. Overall results 2. Results for the construction sector 3. Results for the transport sector 4. Results for the energy sector 5. Results for the manufacturing sector 6. Results for the agriculture sector Main conclusions for the sector Key assumptions

94 3.2 Key assumptions for a low carbon agriculture Main sources of direct GHG emissions from agriculture Non combustion GHG emissions of agriculture in Belgium (%, 2010) 40% are N 2 O emissions from soil management (application of manure and mineral nitrogen fertilizer) 36% are CH 4 emissions from enteric fermentation (mainly coming from cattle and sheep) Source: Agriculture sector document from Scenario for a low Carbon Belgium in 2050 24% are CH 4 and N 2 O from manure management (emitted during storage and treatment of manure)

3.2 Key assumptions for a low carbon agriculture Main levers for reduction of direct GHG emissions from agriculture Decrease CH4 emissions from enteric fermentation reduction of the amount of livestock productivity increase (decrease of CH4 per unit of product) improvement of rumen efficiency and feed conversion efficiency Decrease CH4 and N2O emissions from manure management reduction of the amount of livestock amount and characteristics of manure animal waste management Decrease direct N2O emissions of agricultural soils controls of nitrification and denitrification soil and crop management Source: Agriculture sector document from Scenario for a low Carbon Belgium in 2050 95