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China’s low-carbon transition of transportation towards carbon neutrality

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China’s low-carbon transition of transportation towards carbon neutrality

  1. 1. China’s low-carbon transition towards carbon neutrality ETSAP, virtually, May 23, 2022 Hongbo Duan Co-authored with Quanying Lu, Huiting Shi, Yi Liu, Binbin Peng, Huibin Du, Tian Wu and Shouyang Wang Session: Decarbonisation
  2. 2. UCAS qBackground qMethodology qData and scenarios qResult and analysis qConcluding remarks Outline
  3. 3. UCAS • Over the past decade, transportation has seen the most significant increase in energy consumption (15% of total final); • A dramatic increase in carbon emissions of China’s transport sector, from 248 Mt in 2000 to 950 Mt in 2020 (9% of total CO2 emissions). • Poses severe challenges to the low-carbon transition and carbon neutrality. Background -03- 0 2 4 6 8 10 12 14 16 18 GtCO 2 -2 0 2 4 6 8 10 12 2030 2040 2050 GtCO 2 2030 2040 2050 2030 2040 2050 2030 2040 2050 2030 2040 2050 2030 2040 2050 2030 2040 2050 2030 2040 2050 AIM GCAM IMAGE POLES REMIND WITCH GCAM-TU IPAC Industry Residential & Commercial Transportation Electricity No Policy 1.5 ℃ limit 2030 2040 2050 Source: Duan et al., 2021 (Science)
  4. 4. UCAS • Transportation electrification is widely recognized as a powerful way to reduce GHGs. Electric vehicles have developed rapidly in recent years, driven by a series of road electrification and energy-saving and emission reduction policies. • The national sales volume of new energy vehicles (NEVs) in 2010 was only 7200, and it further expanded to 136,7000 in 2020 (69%, top 1), In 2020, the number of NEVs in China reached 4.92 million (1.75%). Background -04- Emission reduction policies NEVs development policies
  5. 5. UCAS • Given the substantial mitigation effect, transportation electrification has been broadly recognized as a powerful way to reduce GHGs, particularly when the power is carbon-free. • It is therefore one of typical pathways toward China’s carbon neutrality. Background -05- Source: Shrink That Footprint
  6. 6. UCAS • To the best of our knowledge, the emission reduction potential of China's road transport and the emission reduction contribution of electrification have not been systematically assessed. • The current research focuses more on analyzing greenhouse gas emissions in the life cycle of electric vehicles and the economic and environmental benefits. • Most studies use top-down macro calculations based on annual data from statistical yearbooks and are limited to a single city or region. • Few studies have used bottom-up microaccounting methods to study national road traffic emissions with high-frequency big data. Our tasks -06-
  7. 7. UCAS • This paper calculates the cross-city vehicle stock (different vehicle classes and different fuel types) and construct CO2 emission database of road transport from 2016 to 2019. • Analyze and measure the emission reduction contribution of policies (road electrification, etc.) and assess the difficulty and policy intensity of the road transport sector to achieve the "dual carbon" goal. • We developed a low-carbon transition planning model of China road transport (CRT-LCTP) and design three scenarios (Common Policy Scenario, CPS; Transition Policy Scenario, TPS; Enhanced Policy Scenario, EPS) for study of the low-carbon transportation transition pathways. Our tasks -07-
  8. 8. UCAS • The emissions is calculated via power consumption, emission factors as well the standard mileages, and the estimated emissions are not life cycle. Methodologies -08-
  9. 9. UCAS • The bottom-up microaccounting method is based on China's high- frequency passenger car sales data (city-month). • Xiaoxiong APP’s per 100 km by sampling millions of real-time monitoring data of passenger cars (actual fuel consumption and mileage data, etc.). Data and scenarios -09- Class Vehicle stock (thousands) Proporti on (%) Fuel type Annual vehicle travel (km) Fuel economy L(kWh)/100 km Passenger Traditional fuel Vehicle Mini 229.92 100 Gasoline 13273.27 6.66 Common 15302.97 99.98 Gasoline 19801.12 7.80 2.2813 0.015 Diesel 20283.16 5.57 Middle 6267.46 99.42 gasoline 15570.97 9.67 36.4148 0.58 Diesel 21690.34 8.68 Middle- high 73.92 97.75 Gasoline 18523.72 13.39 1.70 2.25 Diesel 27667.21 11.15 High 0.10 100 Gasoline 23459.28 16.56 NEVs BEV 223.55 100 Electricity 21424.02 15.71 HEV 94.12 100 Gasoline 21707.47 5.06 PHEV 63.75 100 Gasoline 24953.94 6.46 Table 1. Basic data for estimating CO2 of passenger sector
  10. 10. UCAS Data and scenarios -10- Class Vehicle stock (thousands) Proport ion (%) Fuel type Annual vehicle travel (km) Fuel economy L(kWh)/100km Freight Traditional fuel Vehicle Mini 7616.9 27.37 Diesel 75000 15 Light 1162.5 4.18 Diesel 35000 18 Medium 19007.5 68.3 Diesel 30000 21 Heavy 41.3 0.15 Diesel 20000 25 NEVs 410 0.015 Electricity 21424 15.71 Table 2. Basic data for estimating CO2 of freight sector • The carbon emissions of the freight sector will continue to increase and reach 1548.26 Mt in 2060, and it will overtake passenger transport and become the main force of road transport emissions from 2024. • Scenarios setting: —BAU, Business as Usual, —CPS, Common Policy Scenario —TPS, Transition Policy Scenario —EPS, Enhanced Policy Scenario
  11. 11. UCAS • The stock of NEVs is much larger in the east, given the 50,000 threshold, 4 cities in 2016 to 15 in 2019. • Given the influence of the restricted purchase policy, the sales growth of NEVs in some first-tier or new first-tier cities is relatively slow. • Zhengzhou and Chongqing are cities with enormous market demand for NEVs. Main results: NEV stock and CO2 emissions -11-
  12. 12. UCAS Ø A significant yearly trend, with emissions increasing from 414.59 Mt in 2016 to 725.77 Mt in 2019, annually grow by20.5%. • The eastern coastal region has the largest CO2 emissions, half of the total road traffic emissions, given the high income and stock. • The top 10 cities are all located in the central and eastern regions, except CQ and ZZ. Main results: NEV stock and CO2 emissions -12-
  13. 13. UCAS Ø Fuel vehicles decrease: -gasoline:-6.25% -diesel: -4.8% Ø NEVs increase: +45.76%, particularly for BEVs, whose sales accounted for 24.7% and 39.9% more than those of HEVs and PHEVs, respectively. Ø This is related to the car price promotion activities (like subsidies) at the end of the year. Every July is the liquidation time of the previous year, explains the surge in NEV sales in the second half of the year. Main results: NEV stock and CO2 emissions -13-
  14. 14. UCAS Ø The emission reduction increased from 1.81 Mt in 2016 to 10.14 Mt in 2019, annually growth by 77.45%. (Clean sources) Ø MCC and MHCC gasoline cars (engine displacement in the range of 1.6-4 L) are the main forces of passenger car consumption (averagely 96% of total sales), the significance of regulating high-emission vehicle sales to control road traffic emissions. Ø NEVs will show a significant growth trend in the future. Main results: NEV stock and CO2 emissions -14-
  15. 15. UCAS Main results: NEV and carbon interactions -15- Fig. 3 | Relation dynamics of road passenger stock GDP per capita (2008=100) and CO2 emissions 2016→2019. a. 11 provincial capitals of Eastern; b. 10 provincial capitals of Central; c. 9 provincial capitals of Western; d. Relationship between passenger car stock and economic development in 291 prefecture-level cities. Source data are provided in Source Data file. Ø The level of economic development of a region largely determines the purchasing power of consumers, which in turn determines the car inventory and sales and related carbon emissions. Ø The per capita GDP growth rates of Shenyang, Hangzhou, Tianjin, and Jinan are lower than the average in eastern China, but the CO2 emission growth rate is more prominent, the reverse trend of low-carbon transportation in these areas is worthy of attention.
  16. 16. UCAS -16- Fig. 3 | Relation dynamics of road passenger stock GDP per capita (2008=100) and CO2 emissions 2016→2019. a. 11 provincial capitals of Eastern; b. 10 provincial capitals of Central; c. 9 provincial capitals of Western; d. Relationship between passenger car stock and economic development in 291 prefecture-level cities. Source data are provided in Source Data file. Ø The CO2 emissions in half of the central provincial capitals increased by more than 100% (Wuhan-2,207%). Ø This is mainly attributed to the "stock" effect of motor vehicles. Ø The economic growth of Xi'an and Lanzhou has been slow, while their vehicle ownership and emissions have grown enormously. Ø This provides evidence that economic development is not always positively and linearly related to residents' willingness and ability to buy a car. Main results: NEV and carbon interactions
  17. 17. UCAS -17- Fig. 3 | Relation dynamics of road passenger stock GDP per capita (2008=100) and CO2 emissions 2016→2019. a. 11 provincial capitals of Eastern; b. 10 provincial capitals of Central; c. 9 provincial capitals of Western; d. Relationship between passenger car stock and economic development in 291 prefecture-level cities. Source data are provided in Source Data file. Ø A highly positive correlation between passenger car ownership and per capita GDP, and the relationship has been gradually strengthened over the period. Ø Economic development has effectively driven increases in regional consumption and structural changes in the automobile industry. Ø Much room for growth of autos, especially NEVs, given incentives. Main results: NEV and carbon interactions
  18. 18. UCAS -18- Ø BAU: China's road transport CO2 emissions will increase by about 1.26 times to 1,683.66 Mt in 2030 (2.3%). Ø The emissions will peak in 2058, corresponding to the peak level of 2563.08 Mt. Ø It could be advanced to 2045-CPS, peak level - 26.1%; 2034-TPS at 1330.98 Mt; 2031-EPS, peak level-48.3%). Ø We also compared our projections with typical SSP pathways. Main results: Pathways and mitigation Fig. 4 | CO2 emissions pathways (2020-2060) and distribution of carbon mitigation contribution (2060) for China’s road transportation. a-c. CO2 emissions across scenarios (Stock adjustment=Stock, Electrification of vehicles=EV, Technological development=TD, Other factors (public transport rate, emission standard control, efficiency, etc) =Other, Zero emissions gap=Gap)
  19. 19. UCAS -19- Ø Stock adjustment contributes the most to mitigation: 75.15%, 63.74%, and 59.57%. Ø Electrification: China's road transport 17.6%-CPS to 33.1%-EPS. Ø Technological progress, such as fuel efficiency improvement, to emission reduction is relatively limited. Ø Other factors: moderate contribution to mitigation approximately 5-16%, on average. Fig. 4 | CO2 emissions pathways (2020-2060) and distribution of carbon mitigation contribution (2060) for China’s road transportation. a-c. CO2 emissions across scenarios (Stock adjustment=Stock, Electrification of vehicles=EV, Technological development=TD, Other factors (public transport rate, emission standard control, efficiency, etc) =Other, Zero emissions gap=Gap) Main results: Pathways and mitigation
  20. 20. UCAS -20- Ø Due to the persistence of traditional fuel vehicles, it is difficult to achieve zero fuel vehicle ownership in the road traffic sector by 2060 (inertia), despite the optimistic autoelectric transformation effect is remarkable . Ø There are challenges in the low-carbon transition of freight transport, which makes it difficult to achieve carbon neutrality in road transport Main results: Pathways and mitigation Fig. 5 | Road transport stock under different scenarios and CO2 emission under BAU scenario (2020-2060) a. Road transport stock under different scenarios (2020-2060); b. CO2 emission under BAU scenario (2020-2060) (PE=Passenger emissions; FE=Freight emissions; PS=Passenger stock; FS=Freight stock). Source data are provided in Source Data file.
  21. 21. UCAS • During the studied period, the ownership of NEVs and CO2 emissions from road traffic showed a significant growth trend, with average annual growth rates of 57.27% and 20.52%, respectively. • China’s road transport CO2 emissions and NEV ownership show significant regional heterogeneity and combined characteristics (Stock effect in first- and second-tier cities). • Driven by economic development, the consumer market in the vast central and western regions still has much room for growth. • Economic level is not always positively and linearly related to residents' willingness and ability to purchase cars (e.g., Beijing, Guangzhou Shenzhen), the importance of policies. Concluding remarks -21-
  22. 22. UCAS • If the power of electric vehicles came from coal, emissions reductions from electrification increased nearly fourfold; and it can be tenfold, when electricity comes from clean sources. • Although the road traffic emissions will peak in 2058 under the BAU, this time point in the EPS can be advanced to the same time as committed in the NDC pledges. • Electrification and total motor vehicle control are the policies that contribute the most to emission reductions, 33% and 55%. • It is challenging to achieve net-zero emissions in the road transportation sector by 2060 due to the inertia of fuel vehicles, and the carbon neutrality may depend more on fuel vehicles' forced phase-out and a more substantial transition to transport electrification. Concluding remarks -22-
  23. 23. UCAS • Hongbo Duan, Deyu Yuan, Zongwu Cai, Shouyang Wang. Valuing the impact of climate change on China’s economic growth. Economic Analysis and Policy, 2022. 74: 155-174. • Kailan Tian, Yu Zhang, Yuze Li, Xi Ming, Shangrong Jiang, Hongbo Duan*, Cuihong Yang, Shouyang Wang. Regional Comprehensive Economic Partnership burdens global carbon emission mitigation. Nature Communications, 2022, 13: 408. • Hongbo Duan, Sheng Zhou, Kejun Jiang, et al. Assessing China’s efforts to pursue the 1.5C warming limit. Science, 2021, 372(6540): 378-385. • Hongbo Duan, Joeri Rogelj, Jason Veysey, Shouyang Wang. Modeling deep decarbonization: Robust energy policy and climate action. Applied Energy, 2020, 262: 114517. • Hongbo Duan, Gupeng Zhang, Shouyang Wang, Ying Fan. Integrated benefit-cost analysis of China’s optimal adaptation and targeted mitigation. Ecological Economics, 2019, 160: 76-86. • Hongbo Duan, Gupeng Zhang, Shouyang Wang, Ying Fan. Robust climate change research: A review on multi-model analysis. Environmental Research Letters, 2019, 14(3): 033001. • Hongbo Duan, Shouyang Wang. Potential impacts of China’s climate policies on energy security. Environmental Impact Assessment Review, 2018, 71: 94-101. • Hongbo Duan, Jianlei Mo, Shouyang Wang, Ying Fan. Achieving China's energy and climate policy targets in 2030 under multiple uncertainties. Energy Economics, 2018, 70: 45-60. • Hongbo Duan*. Emissions and temperature benefits: The role of wind power in China. Environmental Research, 2017, 152: 342-350. • Hongbo Duan, Lei Zhu, Gürkan Kumbaroglu, Ying Fan. Regional opportunities for China to go low- carbon: Results from the REEC Model. The Energy Journal, 2016, 37: 223-252. References -23-
  24. 24. E-mail: hbduan@ucas.ac.cn
  25. 25. UCAS Scenario setting -25-

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