China’s Energy system Optimization and co-benefit evaluation under INDC Commitments: based on China-MAPLE model Xi Yang1, Fei Teng2 1Assitant Professor, academy of Chinese Energy Strategy, China University of Petroleum, Beijing 102249 China 2 Associate Professor, Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084 China Tulsa Oct, 26th 2016

Outline Background 2. Introduction of China-MAPLE 3. Results of Scenarios analysis Conclusion Next step

Background

Background—Pressure of Carbon mitigation China submitted its INDC (Intended Nationally Determined Contribution) in June 2015. In the INDC, China promised to: Peak its carbon emission by 2030; To increase the share of non-fossil fuel consumption to around 20% by 2030. Carbon emission per unit GDP will be reduced by 60%–65% in 2030 compared to 2005 level. Forest volume in 2030 will increase to 4.5 billion m3 Source: NDRC, 2015

Background—Pressure of Air pollution National Air Pollution Control Action Plan, 2013. China State Council. In 2030, all cities should achieve national air quality standards. The major air pollutants should be reduced by around 80% from year 2010 level (The New Climate Economy Report, 2014) In 2013, air quality of 145 cities have exceeded national air quality standard. Source: Ministry of Environment Protection, 2013; China State Council, 2013

Background—Co-control Local pollutant emissions are highly related to fossil fuel combustion. Actions of energy conservation to reduce carbon emissions often reduce co- emitted air pollutants like SO2, NOx, and PM2.5 , bringing co-benefits for air quality. Contribution of coal combustion to the SO2, NOx, and PM2.5 emissions in 2012 Data source: MEIC model database (MEIC, 2013)

Introduction of China-MAPLE

Introduction of China-MAPLE China Multi-pollutant Abatement Planning and Long-term Benefit Evaluation (China-MAPLE) model To evaluate the effects of the energy conservation policies and local pollutant control measures on energy system Bottom-up model. Developed based on VEDA-TIMES. Minimizes the total energy system cost when simultaneously meeting the final energy service demands and external constraints. 5-year step, 2010-2050.

Structure of China-MAPLE

Characters of China-MAPLE China-MAPLE differs from other China bottom-up model in three aspects: First, local pollutant control module has been integrated into the energy system framework in China-MAPLE. Second, instead of based on fuel consumption or activity level, the link of local pollutant to energy module is based on technological level in MAPLE. This approach can help distinguish the local pollutant reduction due to energy conservation and end-of-pipe control measures. Third, instead of setting resource cost as fixed-cost or increasing rate, China-MAPLE introduces energy supply curve into the energy supply module.

Data source The data of the model mainly comes from: China Statistical Yearbook, China Energy Statistical Yearbook, China Electric Power Yearbook, Yearbook of Industrial Statistics China 21st Century Energy Technology Development, 2010 electric power production project cost briefing China Iron and Steel Statistics, China Chemical Industry Yearbook, China Nonferrous Metals Industry Yearbook Technical data on electricity production and economic analysis of the literature Technical parameter from production line of major industrial sectors As well as large amount of relevant reports and literature studies.

Results of Scenario Analysis REF Scenario EPC scenario versus REF scenario COC scenario versus EPC scenario Co-benefit evaluation

Design of Scenarios Abbreviation Scenarios Description REF Reference Scenario Taking the current energy policies, technologies and regulations into simulation. DDP Deep De-carbonization Scenario Taking deep energy conservation measures and technologies into account, especially strict coal control measures in power sector and industries. EPC End-of-Pipe Control Scenario The maximum level of end-of-pipe measures promotion; With the BATs (Best available Technologies) adopted and with maximum application rate among sectors. COC Co-Control Scenario Combination of both DEC and EPC Scenarios.

Social-economic assumptions Unit 2010 2020 2030 2040 2050 Population Million 1360 1520 1890 1470 1420 GDP growth rate %/per year 7.5 6.2 4.1 3.2 2.5 GDP per capita Thousand RMB/ person 29.5 57.4 98.8 150.8 198.1 Urbanization % 51.1 58.2 67.1 72.4 75.2 GDP growth: Considering the recent economy “New-normal” in China. GDP growth rate will decrease, 2020 around 6.2%, 2030 around 4.1%. (Cao et al. 2013) The model assumes the population growth scenario that having a second child is allowed publicly. China’s total population will peak around 2025–2030, and then reduce to 1.42 billion by 2050. (Zeng et al. 2013) Source: Cao et al. 2013; Zeng et al. 2013; Word Bank, 2012.

REF Scenario—Primary energy consumption Total : (2030)5.96 billion tce; (2050)7.29 billion tce. In 2030: Coal : 48.4% Gas: 7.3% Non-fossil: 17.3%

REF Scenario—Carbon emission In 2030, total energy related CO2 emission 11.9 billion tons. Million tons CO2

REF Scenario—Local pollutant emission With the current end-of-pipe control measures, SO2、NOX and PM2.5 in 2030 will increase 163.2%，81.9% and 60.2% to 2010 level. Air quality will deteriorate in 2030. Necessity of end-of-pipe control measures

Strengthening End-of-Pipe Control Scenario EPC vs. REF Scenario—end-of-pipe control measures   Reference Scenario Strengthening End-of-Pipe Control Scenario End-of-Pipe Control Technology Application of End Treatment Sector Current Level Best Available Technology Best Promotion of Application Electricity SO2 FGD removal rate of 70%-80%; FGD installation of 96% in 2030 Wet FGD removal rate of 92%–98%; Dry FGD removal rate of 85%–92% 100% installation of FGD of coal power plant NOX Low NOx combustion technology with removal rate of less than 60%; SCR removal rate of 85%. LNC installation of 75% by 2010; LNC installation of 84% by 2030; SCR+LNC installation of 12%. SCR removal rate of 80%– 95% 100% installation of SCR of coal-based power plant by 2030 PM2.5 Elec dust removal rate of 93%; Bag removal rate of 95%. Elec installation of 80%; bag removal installation of 20% by 2030. Elec and bag dust removal rate of 99.7% Bag dust removal and elec dust removal rate of 100% by 2030 Industry Boiler FGD removal rate of 65%–75% FGD installation around 50% FGD removal rate of 90% FGD installation of 100% by 2030 Wet dust removal efficiency of 80% Wet dust installation of 95% by 2030 Bag and dust removal rate of 99% Bag dust removal installation 100% by 2030 Iron and Steel Sector Sintering FGD efficiency of 80% Sintering FGD installation of 40% Wet FGD efficiency of 98% WFGD installation of 100% by 2030 Sintering, Elec, and Bag efficiency of 90% Installation of 80% by 2030 Sintering, bag, and emission (0.155–0.255 kg/t product） Dust removal in Sintering process installation of 100% by 2030  Building Sector Coal stove and biomass stove efficiency of 40%. Coal stove and biomass stove installation of 60% Low-pollution coal and biomass stove efficiency of 70%. Coal stove and biomass stove installation of 90% Transportation 2030 EU IV and V standard EU VI standard reduction of 80% Shift from V to VI standard by 2030 EU VI standard reduction of 66%

EPC vs. REF Scenario—Local pollutant emission Obvious reduction Reduction PM>NOx>SO2; SO2: 2020(51.5%),2030(68%); NOx: 2020(43%),2030(61%); PM2.5: 2020(54%),2030(73.4%);

EPC vs. REF Scenario—Reduction Effect Reduction in 2030, compared to 2010 level（%） SO2 NOx PM2.5 Electricity generation 91.4% 92.3% 98.7% Cement industry 90.0% 82.8% 99.3% Industry boilers 75.2% 81.5% 96.6% Non-mental industry 84.2% 90.2% Other industry Residential buildings 30.0% 10.0% 89.1% Iron and steel Industry 92.5% 93.3% Transportation 70.0% National average level 68.1% 61.3% 73.4% Target level 80.0% National average: SO2 reduced by 68.1%, NOx reduced by 61.3%, PM2.5 reduced by 73.4%. By sectors: iron and steel/electricity/cement > Industry boilers/industry process > residential/transport Not enough to fulfill the air quality target.

DDP Scenario—Primary Energy Consumption and Carbon emission In 2030, 6.12 billion tce (REF) to 5.86 billion tce (DDP); In 2050, 7.29 billion tce (REF); 6.17 billion tce (DDP) Carbon emission peaking in 2030 reduced from 11.9 to 10.6 billion ton, reduced by 1.3 billion ton. Carbon intensity (per GDP) 60% reduction 2030/2010.  billion ton 2010 2020 2030 2040 2050 REF Scenario 7.84 10.88 11.88 12.87 13.91 DDP Scenario 10.44 10.58 9.96 7.70

DDP Scenario—Electricity generation

COC vs. EPC Scenario—local pollutant reduction Source control: SO2 in 2030 reduced 14%； Nox in 2030 reduced 17.9%； PM2.5 in 2030 reduced 8.51%. SO2：power generation 20%, industry 67%； NOx：transport 24%、industry 40% and power 20%； PM25：residential 69%、industry 20%, power generation 7%

COC vs. EPC Scenario—local pollutant reduction In 2030, SO2, NOx, PM2.5 reduced to 21.15%、22.44% and 16.68% of 2010 level Contribution of end-of-pipe measures 69%-76%; Contribution from source control 24%-31%.

COC vs. EPC Scenario—contribution from source control contribution of source control and end-of-pipe control measures in Electricity sector and building sector

Monetary Co-benefit evaluation Case study in Cement Industry

Mitigation Technologies Co-benefit evaluation—cement sector Num Mitigation Technologies 1 Mining optimization 2 New steel tape hoist 3 Vertical mill for Raw material Grinding 4 Roller Press for Raw material Grinding 5 Power system of ore transportation 6 Purelow-temperature Cogeneration technology 7 Co-grinding system 8 The fourth-generation grate cooler technology 9 New efficient burner 10 Efficient precalciner pre-heater system 11 Fan inverter technology with High-temperature 12 New efficient drying technology 13 Alternative fuel technology for cement production 14 Energy management system for online detection and analysis 15 Increase in pre-heater stages 16 carbon capture and storage (CCS) for cement production 17 Oxy-fuel technology for Cement clinker 18 Pentane media pure low temperatureCogeneration technology

Co-benefit evaluation—cement sector Co-benefit in cement sector is around 19.9-256.7RMB/tCO2; consistent with result of studies on developed countries 2-128$/tCO2 Large variance of environmental effect among provinces (income/population intensity)

Co-benefit evaluation—MACCs of all sectors Revised Marginal Abatement Cost Curve compared to Original curve in 2030 in the case of a carbon tax level below 120 RMB/ton CO2, the carbon emissions mitigation cost can be balanced by the environmental benefit.

Co-benefit evaluation—MACCs of all sectors Mitigation Cost Contribution of carbon mitigation cost and co-benefit to GDP -Even with the most strict end-of-pipe measures in 2030, one unit ton of carbon abatement can still have 25RMB environmental co-benefit.

Conclusion

Conclusion In REF scenario, with the current effort, in 2030, the primary energy consumption will reach 6.12 billion tce, with carbon emission of 11.98 billion tons. And the air quality will further deviate. In EPC scenario, with strict end-of-pipe control, in 2030, the SO2, Nox and PM2.5 will be reduced to 68.1%，61.3%, and 73.4%, compare to 2010 level. However, still not enough to achieve air quality target. In COC scenario, the carbon emission in 2030 will be reduced 1.5 billion tons. Typical local pollutant SO2, NOx and PM2.5 will be reduced to 78.9%，77.6%, and 83.3%, compare to 2010 level. Roughly fulfill the air quality target. The average environmental co-benefit is around 100.8-175.2 RMB/ton CO2; Co- benefit in electricity generation sector is around 171.1-179.7 RMB/ton CO2. In building sector the co-benefit is about 733.7-1027.1 RMB/ton CO2 The study of co-benefits will effectively help the climate negotiations to get out from the “zero-sum game” dilemma, and promote their motivation to make carbon mitigation commitment.

Next step

Current work Next step Non-CO2 GHG in China-MAPLE; Model training; Data transparency Detailed supply curve for China-MAPLE; Health damage of co-benefit evaluation; Natural gas and energy security co-benefit evaluation. Next step

Thank you for your attention! Any comments? yangxi@cup.edu.cn