Low carbon economy 2030 in China - Jiangxi Province as an example

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1 Low carbon economy 23 in China - Jiangxi Province as an example Hancheng DAI NIES

2 Overview Motivation: develop methodology for provincial level low-carbon studies; Questions Future scenario of energy & emissions without policy intervention; Economic cost of carbon reduction; Measures to lower the economic cost; Co-benefits of low carbon economy. Methodology A 2-region CGE model including Jiangxi province and rest of China; A hybrid model including various technologies in power sector; period; Findings Energy and emission would increase due to GDP growth carbon price and GDP loss would be quite high in the most stringent carbon reduction scenarios With low-carbon countermeasures, carbon price and GDP loss would be lowered substantially. A lot of co-benefits associated with low-carbon economy, including air pollutants reduction and energy security improvement This model is relatively robust and can be applied to any other province given the data. 212/12/16 18th AIM International Workshop 2

3 Mtoe/year Mt-CO2/year 1. Introduction: energy and emissions in China Past (IEA, 29) Primary Energy % 16% 14% 12% 1% 8% 6% 4% 2% % CO2 Emissions Other Renewable Hydro Nuclear Gas Oil % 2% 15% 1% coal 2 share to 1 5% world % Other Transport Industry Other Energy Electricity& heat Share to World Future (EIA, 211) Primary energy 1% 8% 6% 4% 2% % Other Brazil South Korea Japan Europe Canada United States India China CO2 emissions 1% 8% 6% 4% 2% % Other Brazil South Korea Japan Europe Canada United States India China 212/12/16 18th AIM International Workshop 3

4 1. Introduction: Motivation and objective 1 To develop methodology at provincial level 2 To develop a framework for integrated and quantitative assessment of low-carbon policies 3 4 To assess the effectiveness and economic impacts of low-carbon policies To provide policy recommendations for promoting low carbon economy (LCE) 212/12/16 18th AIM International Workshop 4

5 1. Introduction: Question Statement 1 Future scenario of energy & emissions under BaU 2 Key technologies /countermeasures to achieve LCE 3 Economic cost of LCE 4 How to soften the economic cost? 5 Benefits and co-benefits ofof LCE 212/12/16 18th AIM International Workshop 5

6 2. Literature review: existing studies on LCE Country level Japan: it has the technological potential to reduce its CO 2 emissions by 7% compared to the 199 level in 25, quantitative roadmaps which include over 6 options. (NIES, 28) ; China: possible for China to reduce its emissions to 25 levels in 25 (Jiang, 29). India: transiting to low carbon future either through advanced technologies like CCS and nuclear energy, or through renewable technologies on the supply side and dematerialization, sustainable consumption and end use device efficiency on the demand side (Shukla, 29) ; South Korea: "Low Carbon, Green Growth Policy, reduce carbon emissions by 3% by 22 relative to BaU (Jones and Yoo, 21); Vietnam: low carbon society for Vietnam in 23 for the residential, commercial, industrial, passenger and freight transport sectors, emissions can be decreased by approximately 45% from BaU (Nguyen et al, 21); Indonesia: LCS visions 25, emissions would be 48-85% lower than the BAU, clean energy, low carbon lifestyle, electricity and fuels in industry and sustainable transport (Dewi et al, 21); Thailand: emissions in 23 can be decreased approximately by 42.5% to 324 million t-co 2 (Limmeechokchai, 21). 212/12/16 18th AIM International Workshop 6

7 2. Literature review: existing studies on LCE Country level UK: Pathways to a low-carbon economy, the CO2 reduction by 4%, 6% and 8% by 25 compared to 199 levels, deep reduction in power and transport sectors. Higher GDP in mitigation scenario due to faster adoption of new carbon- reducing technologies and higher investment (Dagoumas, 21); Australia: reduce accumulated CO 2 during by 5%, through renewable electricity or advanced fossil fuel with CCS, nuclear and natural gas, GDP loss 14-37% (Foran, 211). Sub-country level Ahmedabad, India: low carbon vision toward 235 through "Eight Actions, by 67% over 235 BaU level (Shukla, 29); Iskandar, Malaysia: Low-carbon Region by 225, emission decreasing by 6% and suppressed to 19.6 million t-co 2 (Ho, 29); Australia: transitioning to low carbon communities through behaviour change in households which result in less carbon intensive lifestyles & institutional and infrastructure systems (Moloney et al., 29); Broward, USA: policies and responses on planning for low carbon city at county level (Feliciano, 211); Jilin city, China: emissions reduction by 25-42% in 23 against BaU (Jiang et al, 29); Guangdong Province, China: CO 2 lowered by 29-5% in 23 against BaU (Jiang, 29). 212/12/16 18th AIM International Workshop 7

8 3. A two-region CGE model for Jiangxi Province Production block Output Market block Resource GHG Energy & VA σ = σ =.4 Non Energy σ = Output T = 2 Export Domestic Provincial outflow T = 3 Local market Value added σ =.6 Energy σ =.7 NE 1 NE k Import σ 4 Local Production Capital Labor Land Electricity Solid Fossil energy σ = 1.5 Liquid Gas GHG Local market σ >4 Provincial inflow Domestic consumption Income /Endowment Household Tax on products Income block Expenditure block Household σ = 1 Goods Goods Income Tax Government Government σ = 1 Goods Goods Gross saving Capital Formation σ = 1 Goods Goods 212/12/16 18th AIM International Workshop 8

9 3. Methodology: CGE model Region & time & GHGs Data Two regions: Jiangxi Province, rest of China Time: 25-23, one year time step Gas type CO 2, CH 4, N 2 O, CO, NH 3, NMVOC, NO X, SO 2 ; Energy related, process related, biomass related. Input-output table Energy balance table GHG emission factors of fossil fuels Data on characteristics of electricity generation technologies; Production: 4 sectors (excl. electricity) Input: Intermediate inputs, capital, labor, land, resource, traditional biomass; Land reliant: Agriculture, Forest, Livestock; Resource reliant: Coal mining, natural gas, crude oil, mineral mining, other agriculture Energy transformation : Coking, oil refinery, town gas Other sectors Technology Power generation Five Coal Oil power Natural gas Hydro Nuclear Wind Solar Biomass CCS technology Coal, Gas, Biomass with CCS; Cement, Chemistry, Iron and Steel Bio-fuel Biomass to liquid, to gas Need land inputs (competing with agriculture) 212/12/16 18th AIM International Workshop 9

10 3. Data requirement of CGE model Base year Input-output table: Statistics; Energy balance table: Statistics; CO 2 emission factors: IPCC recommendation; Energy price of coal, oil and gas: Statistics; Cost of renewable energy technology: Investigation & estimation. Future scenario Economy GDP growth rate; Labor force growth rate; Elasticity of substitution among inputs; Total factor productivity (TFP) improvement; Production trend of key energy intensive products (cement, iron & steel etc.); Domestic consumption scenario; Energy & Technology Autonomous energy efficiency improvement; Future international energy price; Extraction cost change of fossil fuels; Resource potential of renewable energy. Utilization target of each renewable energy type in different years; 212/12/16 18th AIM International Workshop 1

11 Tibet Qinghai Ningxia Hainan Gansu Guizhou Xinjiang Yunnan Jiangxi Chongqing Jilin Shanxi Shaanxi Tianjin Guangxi Helongjiang Mongolia Anhui Beijing Fujian Hunan Hubei Sichuan Shanghai Liaoning Hebei Henan Zhejiang Shandong Jiangsu Guangdong 3. Jiangxi Province 12.% GDP Share 1.% 8.% 6.% 4.% 2.%.% Jiangxi 212/12/16 18th AIM International Workshop 11

12 [MJ/$] [t CO2/GJ] [2 US $] [tco2] Jiangxi Province and Rest of China a Evolution of Kaya factors of world, OECD, China and Jiangxi per capita GDP b per capita CO2 Per capita GDP steady increases Jiangxi < China < World < OECD Per capita CO2 increases Jiangxi < China World < OECD c Energy intensity d Carbon intensity Energy intensity falls OECD < World < Jiangxi < China In 27 almost the same Carbon intensity China increases Jiangxi slightly decreases Higher than world and OECD 212/12/16 18th AIM International Workshop 12

13 4. Scenario towards 23 Consumption pattern (2 types) High carbon style Low carbon style Reference Scenario Carbon constraints (3 types) Level 1: carbon intensity of GDP reduces as Copenhagen target Level 2: ERI s Enhanced Low Carbon Scenario Level 3: Global collective reduction derived from 2 degree target Counter measures (4 types) None Non-fossil energy development Low carbon consumption pattern Carbon Capture and Storage (CCS) technology Inter-provincial emissions trading Mitigation Scenario 212/12/16 18th AIM International Workshop 13

14 4. Scenario towards 23 Nr. Sc enario Non-f os s il energy Cons umpt ion pattern CCS Emission trading CO2 emission constraint 1 RS_HC conventional scale High carbon off off off No constraint 2 RS_LC conventional scale Low carbon off off off No constraint 3 CM_CAP1 25 level High carbon off off on Level1: intensity target 4 CM_CAP2 25 level High carbon off off on Level2: mild reduction 5 CM_CAP3 25 level High carbon off off on Level3: most stringent 6 CM_CAP3_HC 25 level High carbon off off on Level3, GDP intensity convergence 7 CM_CAP3_HC_RE Large scale develop High carbon off off on Level3, GDP intensity convergence 8 CM_CAP3_LC_RE Large scale develop Low carbon off off on Level3, GDP intensity convergence 9 CM_CAP3_LC_CCS Large scale develop Low carbon on off on Level3, GDP intensity convergence 1 CM_CAP3_LC_ET Large scale develop Low carbon on on on Level3, GDP intensity convergence Reference Carbon constraints Countermeasure 212/12/16 18th AIM International Workshop 14

15 4. Scenario: household consumption pattern Direction Jiangxi Urban Rural Urban Rural Whole Region High Carbon Low Carbon Per capita expenditure (25 US dollar) Food 4.8% 49.1% 27.8% 32.4% 28.7% 28.% 32.% Clothing 1.6% 5.% 1.1% 5.9% 9.3% 1.% 6.% Housing 1.6% 13.1% 9.7% 22.8% 12.2% 1.% 12.% Furnishings 7.% 3.9% 8.% 5.3% 7.5% 8.% 5.% Health care and Medical services 5.3% 6.2% 6.5% 6.6% 6.5% 6.6% 6.5% Transport and Communications 9.3% 9.2% 18.5% 13.% 17.4% 18.% 13.% Education and recreation 13.2% 11.1% 14.6% 11.3% 13.9% 15.% 11.% Miscellaneous goods and services 3.2% 2.2% 5.% 2.7% 4.5% 4.4% 14.5% Rest of China Per capita expenditure (25 US dollar) Food 36.7% 45.1% 26.5% 31.7% 27.4% 26.% 32.% Clothing 1.1% 5.8% 1.1% 5.9% 9.4% 1.% 6.% Housing 1.2% 14.3% 9.6% 23.% 11.7% 1.% 12.% Furnishings 5.6% 4.4% 8.2% 5.3% 7.8% 8.% 5.% Health care and Medical services 7.6% 6.4% 6.4% 6.5% 6.4% 6.4% 6.5% Transport and Communications 12.6% 9.8% 19.1% 13.3% 18.2% 19.% 13.% Education and recreation 13.8% 12.% 14.9% 11.5% 14.4% 15.% 12.% Miscellaneous goods and services 3.5% 2.3% 5.1% 2.7% 4.8% 5.6% 13.5% 212/12/16 18th AIM International Workshop 15

16 1 Billion Yuan mtce CO2: Million ton 1 Billion Yuan mtce CO2: Million ton 5. Results: Reference scenario Jiangxi: GDP 3 1RS_GAP_HC 25 2RS_GAP_LC % Jiangxi: TPES % RS_GAP_HC 2 2RS_GAP_LC % 35 Jiangxi: CO2 +18% RS_GAP_HC 5 2RS_GAP_LC % Rest of China: GDP 1RS_GAP_H C +62% Rest of China: TPES +22% 2 1RS_GAP_HC 1 2RS_GAP_LC % Rest of China: CO2 +2% 1RS_GAP_HC 5 2RS_GAP_LC Energy saving and carbon reduction effect by low-carbon consumption % 212/12/16 18th AIM International Workshop 16

17 CO2: Million ton 5. Results: Mitigation scenario Nr. Sc enario Non-f os s il energy Cons umpt ion pattern CCS Emission trading CO2 emission constraint 3 CM_CAP1 25 level High carbon off off on Level1: intensity target 4 CM_CAP2 25 level High carbon off off on Level2: mild reduction 5 CM_CAP3 25 level High carbon off off on Level3: most stringent Carbon constraints CAP1: Carbon intensity of GDP reduces as Copenhagen target; CAP2: ERI s Enhanced Low Carbon Scenario; CAP3: Emissions in 23 are reduced by 9.5% compared with 25 s level, derived from 2 degree target Ref CAP1 CAP2 CAP3 China: CO2-17% % -72% /12/16 18th AIM International Workshop 17

18 1 Billion Yuan 1 Billion Yuan CO2: Million ton CO2: Million ton 5. Results: Mitigation scenario Jiangxi: CO2 Reference CAP1 CAP2 CAP3-14% -52% -65% 25 $/t CO Jiangxi: CO2 Price CAP1 CAP2 CAP Jiangxi: GDP Reference CAP1 CAP2 CAP3-1.4% -12% -19% Rest of China: CO2 Reference CAP1 CAP2 CAP3-17% -62% -72% 25 $/t CO Rest of China: CO2 Price CAP1 CAP2 CAP Rest of China: GDP Reference CAP1 CAP2 CAP3-1.% -14% -21% Carbon emissions in mitigation scenarios are exogenously assumed. Carbon prices increase with more reduction. GDP loss is higher under more stringent constraint. 212/12/16 18th AIM International Workshop 18

19 Billion kwh Billion kwh 5. Results: Mitigation scenario Nr. Sc enario Non-f os s il energy Cons umpt ion pattern CCS Emission trading 6 CM_CAP3_HC 25 level High carbon off off 7 CM_CAP3_HC_RE Large scale develop High carbon off off 8 CM_CAP3_LC_RE Large scale develop Low carbon off off 9 CM_CAP3_LC_CCS Large scale develop Low carbon on off 1 CM_CAP3_LC_ET Large scale develop Low carbon on on Five types of responses None; Non-fossil energy development; Low carbon consumption pattern; CCS technology; Free inter-provincial emissions trading Jiangxi: Non-fossil Solar 1 Biomass 8 Wind 6 Nuclear 4 Hydro 2 Rest of China: Non-fossil 25 23RS 23CM 25 23RS 23CM Solar Biomass Wind Nuclear Hydro Reference: Energy Research Institute, 29. Assumptions on CCS: Absorb 8% CO 2 ; Need times more capital and labor input; Need 3% more electricity input; Annual penetration rate: less than 2% of existing power plant capacity. 212/12/16 18th AIM International Workshop 19

20 GDP: 1 Billion yuan CO2 price: US $/ton GDP: 1 Billion yuan CO2 price: US $/ton 5. Overall impacts of countermeasures Jiangxi 23 ROC GDP Carbon price GDP Carbon price Under carbon constraint scenario, carbon price and GDP loss would be quite high; With all countermeasures introduced, carbon price will fall by half, and GDP loss would be about 9% instead of 2%. 212/12/16 18th AIM International Workshop 2

21 Comparing with historical trends Per capita GDP steady increases China: higher than current OECD; Jiangxi: higher than world level Per capita CO2 increases China: close to OECD Jiangxi: close to world level Falling in mitigation scenario Energy intensity further falls China and Jiangxi lower than current world level; Jiangxi lower than China Carbon intensity decreases Due to non-fossil energy development. But still higher than world and OECD due to coal domination. 212/12/16 18th AIM International Workshop 21

22 Co-benefits of low-carbon economy - Oil import dependency - Air pollutants emission 212/12/16 18th AIM International Workshop 22

23 Import (mil. ton) Import ratio 5. Co-benefits: Oil import dependency 3 Jiangxi 8% 1, China 8% 2 6% 6% 4% 5 4% 1 2% 2% % % Import (RS) Import (LC) Import (RS) Import (LC) 212/12/16 18th AIM International Workshop 23

24 Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) 5. Co-benefits: air pollutants CO2: Jiangxi 25 23RS 23CM CO2: Rest of China 2 Household 18 Service Transport 12 OthEnergy 1 Electricity 8 6 OtherManu 4 Chemicals 2 Metal 25 23CM NOX: Jiangxi 25 23CM NOX: Rest of China 6 Household 5 Service Transport 4 OthEnergy 3 Electricity 2 OtherManu 1 Chemicals Metal 25 23CM Household Service Transport OthEnergy Electricity OtherManu Chemicals Metal CO: Jiangxi 25 23RS 23CM CO: Rest of China 35 Household 3 Service 25 Transport 2 OthEnergy 15 Electricity OtherManu 1 Chemicals 5 Metal 25 23RS 23CM SO2: Jiangxi 25 23CM SO2: Rest of China Household 7 Service 6 Transport 5 OthEnergy 4 Electricity 3 OtherManu 2 Chemicals 1 Metal Agriculture 25 23RS 23CM Household Service Transport OthEnergy Electricity OtherManu Chemicals Metal Agriculture 212/12/16 18th AIM International Workshop 24

25 Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) Emissions (Million Ton) 5. Co-benefits: air pollutants CH4: Jiangxi 25 23CM CH4: Rest of China 2 Household 18 Service Transport 12 OthEnergy 1 Electricity 8 6 OtherManu 4 Chemicals 2 Metal 25 23RS 23CM NMVOC: Jiangxi 25 23CM NMVOC: Rest of China 6 Household 5 Service Transport 4 OthEnergy 3 Electricity 2 OtherManu 1 Chemicals Metal 25 23CM Household Service Transport OthEnergy Electricity OtherManu Chemicals Metal N2O: Jiangxi 25 23CM N2O: Rest of China 3.5 Household 3 Service 2.5 Transport 2 OthEnergy 1.5 Electricity OtherManu 1 Chemicals.5 Metal 25 23CM NH3: Jiangxi.6-1% Household.5 Service.4 Transport OthEnergy.3 Electricity.2 OtherManu.1 Chemicals Metal 25 23CM NH3: Rest of China 25 Household Service 2 Transport 15 OthEnergy Electricity 1 OtherManu 5 Chemicals Metal 25 23CM Household Service Transport OthEnergy Electricity OtherManu Chemicals Metal 212/12/16 18th AIM International Workshop 25

26 Conclusions Energy and emission would increase due to GDP growth in future reference scenarios; therefore, China s participation is crucial for global climate mitigation; At national level, economic impacts are closely related to stringency of carbon constraints; at provincial level, economic impacts are also determined by burden sharing scheme; Without additional low-carbon countermeasures, carbon price and GDP loss would be quite high in the most stringent carbon reduction scenarios; With introduction of the low-carbon countermeasures, carbon price and GDP loss would be lowered substantially. There are a lot of co-benefits associated with low-carbon economy, including air pollutants reduction and energy security improvement. 212/12/16 18th AIM International Workshop 26

27 Future work Inter-provincial flow of labor and capital; Technology in transport sector; Apply to other provinces, or developing 31-region model. 212/12/16 18th AIM International Workshop 27

28 Publications Journal papers Dai Hancheng, Masui Toshihiko, Matsuoka Yuzuru, Fujimori Shinichiro (212). "The Impacts of China s Household Consumption Expenditure Patterns on Energy Demand and Carbon Emissions towards 25." Energy Policy 5(Special Issue): Dai Hancheng and Masui Toshihiko (212). "Assessing the Contribution of Carbon Emissions Trading in China to Carbon Intensity Reduction." Energy Science and Technology 4(1): 1-8. Dai Hancheng, Masui Toshihiko, Matsuoka Yuzuru, Fujimori Shinichiro (211). "Assessment of China's Climate Commitment and Non-Fossil Energy Plan towards 22 Using Hybrid AIM/CGE Model." Energy Policy 39(5): Proceeding papers and presentation Assessing the Contribution of Inter-provincial Carbon Emissions Trading in China to Carbon Intensity Reduction in 22. The 2nd Congress of the East Asian Association of Environmental and Resource Economics, Bandung, Indonesia, Feb 2-5, 212. Contribution of China s Renewable Energy Development in Power Generation to Carbon Intensity Reduction. The 1st Congress of the East Asian Association of Environmental and Resource Economics, Sapporo, Hokkaido, Aug , 21. Impact Assessment of China's Climate Target towards 22. The 15th Asia-Pacific Integrated Model International Workshop, Tsukuba, Japan, Feb. 2-22, /12/16 18th AIM International Workshop 28

29 Thank you for your attention!

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