Journal of Resources and Ecology ›› 2023, Vol. 14 ›› Issue (2): 372-382.DOI: 10.5814/j.issn.1674-764x.2023.02.015
• Resources and Environment • Previous Articles Next Articles
GUO Xiurui(), GONG Xiaoqian, LIU Yao, ZHANG Yiling
Received:
2022-04-28
Accepted:
2022-06-28
Online:
2023-03-30
Published:
2023-02-21
Contact:
GUO Xiurui
Supported by:
GUO Xiurui, GONG Xiaoqian, LIU Yao, ZHANG Yiling. Projections of the Emission Reductions of Carbon Dioxide and Conventional Pollutants in the Major Transport Sectors of the Beijing-Tianjin-Hebei Region, China[J]. Journal of Resources and Ecology, 2023, 14(2): 372-382.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2023.02.015
Sub-sector | Energy type | CO2 | CO | NOx | SO2 | PM2.5 |
---|---|---|---|---|---|---|
Highway | Gasoline | 2025.28 | 234.16 | 17.56 | 0.58 | 0.44 |
Diesel | 2168.69 | 29.27 | 23.42 | 5.41 | 1.06 | |
LPG | 1861.35 | 234.16 | 17.56 | 0.00 | 0.01 | |
CNG | 1624.54 | 11.71 | 17.58 | 0.00 | 0.03 | |
Railway | Diesel | 2168.69 | 29.27 | 35.12 | 5.41 | 0.01 |
Aviation | Kerosene | 2106.84 | 2.93 | 8.78 | 5.83 | 0.96 |
Waterway | Fuel oil | 2201.98 | 29.27 | 43.91 | 5.83 | 0.80 |
Table 1 Emission factors of CO2 and air pollutants (Unit: kg tce?1)
Sub-sector | Energy type | CO2 | CO | NOx | SO2 | PM2.5 |
---|---|---|---|---|---|---|
Highway | Gasoline | 2025.28 | 234.16 | 17.56 | 0.58 | 0.44 |
Diesel | 2168.69 | 29.27 | 23.42 | 5.41 | 1.06 | |
LPG | 1861.35 | 234.16 | 17.56 | 0.00 | 0.01 | |
CNG | 1624.54 | 11.71 | 17.58 | 0.00 | 0.03 | |
Railway | Diesel | 2168.69 | 29.27 | 35.12 | 5.41 | 0.01 |
Aviation | Kerosene | 2106.84 | 2.93 | 8.78 | 5.83 | 0.96 |
Waterway | Fuel oil | 2201.98 | 29.27 | 43.91 | 5.83 | 0.80 |
Region | CO2 | CO | NOx | SO2 | PM2.5 |
---|---|---|---|---|---|
Beijing | 615 | 0.00 | 0.05 | 0.03 | 0.01 |
Tianjin | 811 | 0.00 | 0.06 | 0.02 | 0.01 |
Hebei | 903 | 0.00 | 0.09 | 0.04 | 0.01 |
Table 2 Emission factors of CO2 and air pollutants from electric vehicles in the BTH region (Unit: g kWh?1)
Region | CO2 | CO | NOx | SO2 | PM2.5 |
---|---|---|---|---|---|
Beijing | 615 | 0.00 | 0.05 | 0.03 | 0.01 |
Tianjin | 811 | 0.00 | 0.06 | 0.02 | 0.01 |
Hebei | 903 | 0.00 | 0.09 | 0.04 | 0.01 |
Scenario | Scenario definition | Specific description |
---|---|---|
Transportation Structure Adjustment (TSA) | The proportion of railway transport in the BTH region would be increased, vigorously develop railway freight and waterway freight transport, build a national comprehensive three-dimensional transportation network with railway as the main trunk based on the highway, and make use of the comparative advantages of water transport and civil aviation | In 2060, railway freight would account for 80% in Beijing, 60% railway and 35% waterway freight in Tianjin, and 70% railway and 25% waterway freight in Hebei Province The population of private cars in Beijing, Tianjin and Hebei would be controlled within 5, 4 and 30 million, respectively The sharing rate of passenger public transport in the city would be increased |
Energy Efficiency Improvement (EEI) | With the continuous innovation and progress of science and technology, the energy consumption per unit activity level of the terminal equipment in various transportation sectors would decrease; the continuous optimization of fuel economy and the popularization of new technologies would be increasingly extensive in the transport sectors | Energy consumption of highway passenger turnover would decrease by 1.5%, road freight by 1%, air freight by 2%, fuel efficiency of private cars and taxis by 1.5%, and bus fuel efficiency by 1%. Energy consumption of highway passenger and freight traffic would decrease by 1% per year, air passenger and freight by 2% and 1%, fuel efficiency of private taxis by 1%, and bus fuel efficiency by 0.5% |
Optimizing the Energy Structure (ESO) | The rate of clean energy consumption in transportation sectors would increase, and the proportion of clean energy sources such as electricity, natural gas, biofuels and hydrogen energy used for all kinds of vehicles would increase | In 2040, all buses and taxis in Beijing would be all electricity-driven; in 2050, road passenger transport and private cars would be all driven by new energies; in 2060, road freight would all be new energy driven; railway electrification would account for 80%, and aviation biofuel would account for 80%. In 2060, new energy used in the road passenger and freight in Tianjin and Hebei Province would account for more than 80%, the electrification of railway passenger and freight would account for 80%, aviation biofuel would account for 70%, clean energy such as biofuel applied in the water transportation industry would account for 60% |
Table 3 Detailed descriptions of the three policy scenarios considered in this study
Scenario | Scenario definition | Specific description |
---|---|---|
Transportation Structure Adjustment (TSA) | The proportion of railway transport in the BTH region would be increased, vigorously develop railway freight and waterway freight transport, build a national comprehensive three-dimensional transportation network with railway as the main trunk based on the highway, and make use of the comparative advantages of water transport and civil aviation | In 2060, railway freight would account for 80% in Beijing, 60% railway and 35% waterway freight in Tianjin, and 70% railway and 25% waterway freight in Hebei Province The population of private cars in Beijing, Tianjin and Hebei would be controlled within 5, 4 and 30 million, respectively The sharing rate of passenger public transport in the city would be increased |
Energy Efficiency Improvement (EEI) | With the continuous innovation and progress of science and technology, the energy consumption per unit activity level of the terminal equipment in various transportation sectors would decrease; the continuous optimization of fuel economy and the popularization of new technologies would be increasingly extensive in the transport sectors | Energy consumption of highway passenger turnover would decrease by 1.5%, road freight by 1%, air freight by 2%, fuel efficiency of private cars and taxis by 1.5%, and bus fuel efficiency by 1%. Energy consumption of highway passenger and freight traffic would decrease by 1% per year, air passenger and freight by 2% and 1%, fuel efficiency of private taxis by 1%, and bus fuel efficiency by 0.5% |
Optimizing the Energy Structure (ESO) | The rate of clean energy consumption in transportation sectors would increase, and the proportion of clean energy sources such as electricity, natural gas, biofuels and hydrogen energy used for all kinds of vehicles would increase | In 2040, all buses and taxis in Beijing would be all electricity-driven; in 2050, road passenger transport and private cars would be all driven by new energies; in 2060, road freight would all be new energy driven; railway electrification would account for 80%, and aviation biofuel would account for 80%. In 2060, new energy used in the road passenger and freight in Tianjin and Hebei Province would account for more than 80%, the electrification of railway passenger and freight would account for 80%, aviation biofuel would account for 70%, clean energy such as biofuel applied in the water transportation industry would account for 60% |
Fig. 2 Projected total energy consumption from the transport sectors under each of the different scenarios in the BTH region, China Note: tce, called coal equivalent, a unit of energy measurement for summing and comparing the different types of energy source according to the calorific value of standard coal.
[1] | Aggarwal P, Jain S. 2016. Energy demand and CO2 emissions from urban on-road transport in Delhi: Current and future projections under various policy measures. Journal of Cleaner Production, 128(1): 48-61. |
[2] | Aliakbar K, Ali V, Ahmed K B I. 2012. An estimation of traffic related CO2 emissions from motor vehicles in the capital city of Iran. Iranian Journal of Environmental Health Science & Engineering, 9(1): 9-13. |
[3] | Azam M, Othman J, Ara B R, et al. 2016. Energy consumption and emission projection for the road transport sector in Malaysia: An application of the LEAP model. Environment, Development and Sustainability, 18(4): 1027-1047. |
[4] | Bu C J, Cui X Q, Li R Y, et al. 2021. Achieving net-zero emissions in China’s passenger transport sector through regionally tailored mitigation strategies. Applied Energy, 284: 116265. DOI: 10.1016/j.apenergy.2020.116265. |
[5] | Costa E, Seixas J, Baptista P, et al. 2018. CO2 emissions and mitigation policies for urban road transportation: Sao Paulo versus Shanghai. Urbe Revista Brasileira de Gestão Urbana, 10(1): 143-158. |
[6] | Du L L, Zhao H J, Tang H Y, et al. 2021. Analysis of the synergistic effects of air pollutant emission reduction and carbon emissions at coal-fired power plants in China. Environmental Progress & Sustainable Energy, 40(5): 711-723. |
[7] | Fan J L, Wang J X, Li F Y, et al. 2017. Energy demand and greenhouse gas emissions of urban passenger transport in the Internet era: A case study of Beijing. Journal of Cleaner Production, 165: 177-189. |
[8] | Feng X Z, Zhao M X, Wang M, et al. 2021. Simulation research on co-controlling pollutants and greenhouse gases emission in China’s transportation sector. Climate Change Research, 17(3): 279-288. (in Chinese) |
[9] | Gao Y. 2019. Analysis of traffic carbon emission accounting and influencing factors in Tianjin. Recyclable Resources and Circular Economy, 12(6): 18-21. (in Chinese) |
[10] | Guo J X, Zeng Y, Zhu K W, et al. 2021. Vehicle mix evaluation in Beijing’s passenger-car sector: From air pollution control perspective. Science of the Total Environment, 785: 147264. DOI: 10.1016/J.SCITOTENV.2021.147264. |
[11] | Guo X R, Liu F X, Fu L W, et al. 2017. Scenarios prediction of energy saving and emission reduction in the road transport sector of Beijing-Tianjin-Hebei region. Journal of Beijing University of Technology, 43(11): 1743-1749. (in Chinese) |
[12] | Hong S J, Chung Y H, Kim J, et al. 2016. Analysis on the level of contribution to the national greenhouse gas reduction target in Korean transportation sector using LEAP model. Renewable and Sustainable Energy Reviews, 60: 549-559. |
[13] | Huo H, Wang M, Zhang X, et al. 2012. Projection of energy use and greenhouse gas emissions by motor vehicles in China: Policy options and impacts. Energy Policy, 43: 37-48. |
[14] | Kazancoglu Y, Ozbiltekin P M, Ozkan-Ozen Y D. 2021. Prediction and evaluation of greenhouse gas emissions for sustainable road transport within Europe. Sustainable Cities and Society, 70: 102924. DOI: 10.1016/J.SCS.2021.102924. |
[15] | Li H, Tan X C, Guo J X, et al. 2019. Study on an implementation scheme of synergistic emission reduction of CO2 and air pollutants in China’s steel industry. Sustainability, 11(2): 352-358. |
[16] | Li Y Y, Song Y D. 2021. Study on the synergetic emission reduction effect of CO2 and air pollutants from the mobile source of urban roads in Beijing under the target of carbon neutralization. Chinese Journal of Environmental Management, 13(3): 113-120. (in Chinese) |
[17] | Liu L, Wang K, Wang S S, et al. 2018. Assessing energy consumption, CO2 and pollutant emissions and health benefits from China’s transport sector through 2050. Energy Policy, 116: 382-396. |
[18] | Lu I J, Lewis C, Lin S J. 2009. The forecast of motor vehicle, energy demand and CO2 emission from Taiwan’s road transportation sector. Energy Policy, 37(8): 2952-2961. |
[19] | Lv C, Li Y X, Yang N, et al. 2020. Assessment and scenario analysis of on-road vehicle greenhouse gases emission: A case study of Bejing. Environmental Engineering, 38(11): 25-32. (in Chinese) |
[20] | Lv Q. 2018. Study on the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. China Environmental Science, 38(10): 3689-3697. (in Chinese) |
[21] | Mao X Q, Xing Y K, Gao Y B, et al. 2021. Study on GHGs and air pollutants co-control: Assessment and planning. China Environmental Science, 41(7): 3390-3398. (in Chinese) |
[22] | Nie H W, Deng J, Shi Z Z. 2019. Research on the carbon emission inventory of passenger transportation in Guizhou. Highway, 64(2): 252-255. (in Chinese) |
[23] | Ning X J, Zhang J P, Qin Y C, et al. 2014. Spatial and temporal characteristics of carbon emissions from urban resident travel in Zhengzhou. Resources Science, 36(5): 1021-1028. (in Chinese) |
[24] | Tan Q L, Yang H W. 2017. Analysis on the synergistic effect of Beijing-Tianjin-Hebei traffic control of greenhouse gases and pollutants. China Energy, 39(4): 25-31. (in Chinese) |
[25] | Yang H Z, Liu J F, Jiang K J, et al. 2018. Multi-objective analysis of the co-mitigation of CO2 and PM2.5 pollution by China’s iron and steel industry. Journal of Cleaner Production, 185: 331-341. |
[26] | Yang L, Wang Y X, Lian Y J, et al. 2020. Factors and scenario analysis of transport carbon dioxide emissions in rapidly-developing cities. Transportation Research Part D, 80(C): 102252. DOI: 10.1016/j.trd.2020.102252. |
[27] | Yang W, Song J. 2021. Simulating optimal development of clean coal-fired power generation for collaborative reduction of air pollutant and CO2 emissions. Sustainable Production and Consumption, 28(10): 811-823. |
[28] | Yu Y, Jin Z X, Li J Z, et al. 2020. Low-carbon development path research on China’s power industry based on synergistic emission reduction between CO2 and air pollutants. Journal of Cleaner Production, 275: 123097. DOI: 10.1016/j.jclepro.2020.123097. |
[29] | Yuan Z Y, Li Z Y, Kang L P, et al. 2021. A review of low-carbon measurements and transition pathway of transport sector in China. Climate Change Research, 17(1): 27-35. |
[30] | Zhang L X, Li Z W, Jia X P, et al. 2020. Targeting carbon emissions mitigation in the transport sector: A case study in Urumqi, China. Journal of Cleaner Production, 259: 120811. DOI: 10.1016/j.jclepro.2020.120811. |
[31] | Zhao B, Wang P, Ma J Z, et al. 2012. A high-resolution emission inventory of primary pollutants for the Huabei region, China. Atmospheric Chemistry and Physics, 12(255): 481-501. |
[32] | Zhu C Z, Wang M, Du W B. 2020. Prediction on peak values of carbon dioxide emissions from the Chinese transportation industry based on the SVR Model and scenario analysis. Journal of Advanced Transportation, 2020: 1-14. DOI: 10.1155/2020/8848149. |
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