Energy system Optimization and co-benefit evaluation: based on China-MAPLE model Xi Yang 1, Fei Teng 2

Transcription

1 Energy system Optimization and co-benefit evaluation: based on China-MAPLE model Xi Yang 1, Fei Teng 2 1 Assitant Professor, academy of Chinese Energy Strategy, China University of Petroleum, Beijing China 2 Associate Professor, Institute of Energy, Environment and Economy, Tsinghua University, Beijing China Study on Energy Supply Curves in China s bottom-up model up to 2030 Xiaoqian Xi, Xi Yang* Academy of Chinese Energy Strategy, China University of Petroleum, Beijing China Cost benefit analysis of China s INDC based on Carbon MACCs Xi Yang*, Fei Teng, Xiaoqian Xi, Qi Zhang Academy of Chinese Energy Strategy, China University of Petroleum, Beijing China Institute of Energy, Environment and Economy, Tsinghua University, Beijing China 19 th June 2017

2 Outline 1. Brief overview 2. Background & questions 3. China-MAPLE & MACCs 4. Results & Analysis 5. Conclusion 6. Next step 2

3 Brief Overview A cost benefit analysis of China s INDC in 2030 considering auxiliary benefits that have been ignored for long time Marginal abatement cost can be offset by the marginal abatement cost curves revised by environmental benefit The cost of carbon abatement in NEPC scenario can be fully compensated by the benefit when mitigation rate is below 16.8% The cost-effectiveness of China s INDC depends on the strictness of its end-of-pipe technology China s INDC target in the NEPC scenario is both achievable and cost-effective. The cost of abatement in the EPC scenario can be partially offset 3

4 Background & questions 4

5 Background Pressure of Carbon mitigation Paris Agreement, enforced in November 4, 2016, aims to limit the rise of global temperature to lower than 2 degrees. Deep de-carbonization efforts were enacted to achieve these targets. China recently suffered from severe haze problem because of rapid development and urbanization. Heart diseases, such as stroke and ischemic heart and lung problem (e.g., chronic obstructive pulmonary disease and lung cancer) became the most common causes of death in China. Source: Ministry of Environment Protection, Anzhen hospital 5

6 Background Co-control Local pollutant emissions are highly related to fossil fuel combustion. Actions of energy conservation to reduce carbon emissions often reduce coemitted air pollutants like SO 2, NO x, and PM 2.5, bringing co-benefits for air quality. Contribution of coal combustion to the SO 2, NO x, and PM 2.5 emissions in 2012 Data source: MEIC model database (MEIC, 2013) 6

7 Questions? China has stepped into a new normal economic development stage. This development requires comprehensive analysis of main benefits to examine China s INDC targets. For the INDC framework, several questions: What would be the net(real) carbon abatement cost if environmental benefits are considered? Second, how much mitigation cost can be reduced and what is the best carbon mitigation rate for cost benefit analysis (CBA)? Third, is China s INDC target, particularly its carbon reduction target, costeffective, or not? If so, what would be the real cost of fulfilling this target? Otherwise, what is an alternative option for China s mitigation schedule? 7

8 China-MAPLE & MACCs 8

9 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,

10 Structure of China-MAPLE 10

11 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. 11

12 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. 12

13 Original MACCs and revised MACCs Marginal cost Shadow price of carbon MDC CC+AQ MAC CC MAC CC p* 0 MDC CC p*p MAC CC +MDC AQ q * q q q * Carbon mitigation amount (a) Carbon mitigation amount (b) Impact of co-benefit of air pollutant emission reduction on the marginal abatement cost 13

14 Benefit evaluation Emission->Concentration change-> Health end point->valuation ExBenefit i APi Cill ness MOC ExBenefit / ER i i i c IF Q BR P RR exp c RR 1 Y Y RR 0 14

15 Supply curves in China s Energy modelling MC, P MC, P MC 4 MC MC 3 MC 2 MC 1 Horizontal supply curve Q q 1 q 2 q 3 q 4 Discrete energy supply curve Q Some shortcomings of horizontal supply curve: 1. can not analyse the changes of resources supply from different production regions; 2. can not accurately reveal the resource substitution caused by technology improvement; 3. can not offer the accurate energy optimization solutions

16 Supply curves in China-MAPLE The short-run cost curve moves upwards when the cumulative production amount increases, and its slope also changes due to the new investment. Marginal Cost Marginal Cost High average cost Mine 2 Mine 4 Mine 3 High average cost Low average cost Mine 2 Mine 4 Mine 3 Low average cost Mine 1 Mine 1 Maximum production capacity Cumulative Production Base year MC curve for one supply region Maximum production capacity Cumulative Production The change of intercept based on base year curve

17 Supply curves in China-MAPLE intercept= previous period's intercept + previous period's slope*production in that year*intercept change factor (1) High average cost Low average cost Marginal Cost Mine 1 Mine 2 Mine 4 Mine 3 Maximum production capacity Cumulative Production The change of intercept based on base year curve the increase rate of intercept is influenced by factors such as the amount of recoverable resources the annual production some other geological factors

18 Supply curves in China-MAPLE The short-run cost curve moves upwards when the cumulative production amount increases, and its slope also changes due to the new investment. Marginal Cost Marginal Cost High average cost Mine 4 Mine 3 High average cost Mine 2 Low average cost Mine 1 Low average cost Maximum production capacity Cumulative Production Base year MC curve for one supply region Maximum production capacity Cumulative Production The change of slope based on base year curve

19 Coal Supply curves in China-MAPLE Figure China s coal supply curve in 2010

20 Coal Supply curves in China-MAPLE Figure China s coal supply curve in 2030

21 Results & Analysis 21

22 Social-economic assumptions Unit Population Million GDP growth rate GDP per capita %/per year Thousan d RMB/ person Urbanization % 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 , and then reduce to 1.42 billion by (Zeng et al. 2013). 22

23 Design of Scenarios Abbreviatio n Scenarios Description REF Reference Scenario Taking the current energy policies, technologies and regulations into simulation. DEC Deep Decarbonization 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. 23

24 Energy-related CO2 emission (billion ton) REF Scenario Carbon emission In 2030, total energy related CO2 emission 11.9 billion tons Agriculture Electricity Industry Tranportation Buildings Million tons CO2 24

25 PM2.5 emission(10^4 ton) SO2 Emission(!0^4 ton) NOx emission(10^4 ton) REF Scenario Local pollutant emission Cement Electricity Industry Boilers Non-Ferrous Cement Electricity Industry Boilers Non-Ferrous Iron and Steel Other Industry Buildings Transportation Iron and Steel Other Industry Buildings Transportation 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 Cement Electricity Industry Boilers Non-Ferrous Iron and Steel Other Industry Buildings Transportation Necessity of end-of-pipe control measures 25

26 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 End-of-Pipe Control Technology Application of End Treatment Sector Current Level Current Level Best Available Technology Best Promotion of Application Electricity SO 2 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 NO X PM 2.5 Low NOx combustion technology with removal rate of less than 60%; SCR removal rate of 85%. Elec dust removal rate of 93%; Bag removal rate of 95%. LNC installation of 75% by 2010; LNC installation of 84% by 2030; SCR+LNC installation of 12%. Elec installation of 80%; bag removal installation of 20% by SCR removal rate of 80% 95% Elec and bag dust removal rate of 99.7% 100% installation of SCR of coal-based power plant by 2030 Bag dust removal and elec dust removal rate of 100% by 2030 Industry Boiler SO 2 FGD removal rate of 65% 75% PM 2.5 Wet dust removal efficiency of 80% Iron and Steel Sector SO 2 Sintering FGD efficiency of 80% PM 2.5 Sintering, Elec, and Bag efficiency of 90% Building Sector PM 2.5 Coal stove and biomass stove efficiency of 40%. Transportation NO X 2030 EU IV and V standard PM 2.5 FGD installation around 50% FGD removal rate of 90% FGD installation of 100% by 2030 Wet dust installation of Bag and dust removal rate Bag dust removal 95% by 2030 of 99% installation 100% by 2030 Sintering FGD installation Wet FGD efficiency of 98% WFGD installation of 100% of 40% by 2030 Installation of 80% by 2030 Sintering, bag, and Dust removal in Sintering emission ( process installation of kg/t product) 100% by 2030 Coal stove and biomass Low-pollution coal and Coal stove and biomass stove installation of 60% biomass stove efficiency stove installation of 90% of 70%. EU VI standard reduction of 80% EU VI standard reduction of 66% Shift from V to VI standard by

27 PM2.5 emission (10^4 ton) SO2 emission (10^4 ton) Nox emission (10^4 ton) 3500 EPC vs. REF Scenario Local pollutant emission SO2 emission 3500 NOx emission Electricity Cement Coking Industry Boiler Nonmetallic Industry Other Industry Residential Iron and steel Transportation PM2.5 emission Electricity Cement Coking Industry Boiler Nonmetallic Industry Other Industry Residential Iron and steel Transportation Obvious reduction Reduction PM>NOx>SO2; SO2: 2020(51.5%),2030(68%); NOx: 2020(43%),2030(61%); PM2.5: 2020(54%),2030(73.4%); Cement Electricity Industry Boiler Nonmetallic Industry Other Industry Residential Iron and steel Transportation 27

28 Previous study Reduction Effect Reduction in 2030, compared to 2010 level(%) SO 2 NO x PM 2.5 Electricity 91.4% 92.3% 98.7% generation Cement 90.0% 82.8% 99.3% industry Industry 75.2% 81.5% 96.6% boilers Non-mental 84.2% 81.5% 90.2% industry Other industry 84.2% 81.5% 90.2% Residential 30.0% 10.0% 89.1% buildings Iron and steel 92.3% 92.5% 93.3% Industry Transportation 10.0% 70.0% 70.0% 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. National 68.1% 61.3% 73.4% average level Target level 80.0% 80.0% 80.0% 28

29 Primary energy Consumption (million ton) INDC DDP Scenario Primary Energy Consumption &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) REF/DDP REF DDP REF DDP REF DDP REF DDP Coal Gas Oil Nuclear Hydro Biomass Wind Solar Other billion ton REF Scenario DDP Scenario 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/

30 COC vs. EPC Scenario local pollutant reduction 100% 0% 8% 5% 90% 13% 21% 80% 70% 19% 3% 6% 47% 60% 15% 50% 14% 10% 40% 30% 20% 10% 0% 4% 15% 18% 1% 0% 5% 18% 4% 7% 10% 1% 0% 0% 1% 3% 36% 2% 1% 5% 4% 27% 3% 5% 2% 1% 1% 9% 3% 1% 2% 5% 4% 12% 2% 1% 2% 7% 10% 2% 10% 5% 6% 7% 0% 1% 0% 1% 1% 1% 2010 E P C C O C EPC2030 C O C E P C C O C S O 2 N O X P M 2 5 Electricity Cement Sintering Industry Boilers and process NonMentallic Industry Buildings Steel making Transportation 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%. 30

31 Emission and Concentration change Health end point 31

32 Original MACCs When the carbon tax in 2030 is below 100 RMB/ton CO2, carbon mitigation rate is 14.5%; when the carbon tax reaches 200 RMB/ton CO2, the carbon mitigation rate increases to 24.2%. Carbon mitigation rate will sharply increase to 43.8% as the carbon tax keeps on increasing from 200 RMB/ton CO2 to 800 RMB/ton CO2. When the carbon tax is above 800 RMB/ton CO2, the effect on carbon mitigation will fail, and MACCs will become perpendicular 32

33 Original MACCs vs. Revised MACCs When the carbon tax level is below 120 RMB/ton CO2, the carbon emissions mitigation cost can be balanced by environmental benefit. Positive cut-off from the original cost when carbon tax in the range of RMB/ton CO2. The average environmental co-benefit is in the range of RMB/ton CO2. When the carbon mitigation rate increases by 10%, environmental benefit will rise by 0.1% of the GDP. In this case, environmental cobenefit can fully compensate carbon abatement cost when the carbon mitigation rate is below 16.8%. 33

34 Revised MACCs under strict end-of-pipe control measures a trade-off exists between the improvement of carbon mitigation rate and the increase in environmental benefit co-benefit can still be observed even if the strictest end-of-pipe control measures are implemented. increase along with the carbon mitigation. However, lower. With mitigation rate below 3%, the carbon mitigation cost can still be compensated by the environmental benefit. The environmental benefit becomes increasingly obvious if the end-of-pipe control measure is weak. This phenomenon also explains why the benefit evaluation is much higher in several developing countries 34

35 Conclusion & Discussion 35

36 Conclusion & Discussion: the INDC case INDC case: the energy-related carbon emission in 2030 will reach billion tons of CO 2. China s INDC shows a 11.8% deviation from the REF scenario. (based on our previous study) Marginal abatement cost will reach 82.5 RMB/tCO 2 at 11.8% mitigation rate without considering environmental benefit. When transferred to the GDP loss, the abatement cost of INDC target becomes 0.08% of the GDP loss. When frozen end-of-pipe measures are considered, CBA shows a break-even point of 16.8% of reduction rate, which corresponds to 9.9 billion tons of CO 2 in net benefit can be observed. Optimal reduction rate is the cross point where the marginal benefit is equal to the marginal cost. A reduction of 500 million tons can be achieved in the INDC scenario with a positive net benefit. The net cost of INDC with the frozen end-of-pipe measures is negative, which shows a no-regret mitigation target. However, if the most stringent end-of-pipe measures are achieved, the benefit of the INDC will not compensate the mitigation cost. This result will lead to a net cost of 48.6 RMB/tCO 2. Overall GDP loss will be 0.06%. 36

37 Next step 37

38 Current work Data transparency Detailed supply curve for China-MAPLE; Health damage of co-benefit evaluation; Next step Natural gas and energy security co-benefit evaluation; Energy-water-climate change nexus 38

39 Thank you for your attention! Any comments? 39