基于长期能源替代规划模型的江苏省能源CO_2排放达峰时间及峰值水平
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摘要
为了应对全球气候变暖,中国政府提出到2030年CO_2排放达到峰值(或峰值平台)的减排目标.为了探究中国能否实现该减排目标,本研究以江苏省为例,基于长期能源规划替代模型(LEAP),耦合对数平均迪氏分解模型,运用情景分析法科学设定快速、中速、慢速达峰3种发展情景,预测CO_2排放达峰时间及峰值水平.结果表明:2000—2015年间,经济规模效应是CO_2排放总量增长的主要驱动因素,其贡献度高达147.4%;技术进步效应是最重要的缓解因素,贡献度为-60.4%,一次能源结构效应、产业结构效应、人均收入效应、人口规模效应的贡献度分别为-5.3%、9.7%、11.0%、0.6%.在快速、中速达峰情景下,江苏省分别在2025和2029年达到CO_2排放峰值,峰值分别为7.01、7.95亿t,慢速达峰情景下未能实现2030年的CO_2排放达峰目标.综合研究分析,江苏省有着较大的减排潜力,经过相应的努力能够实现CO_2减排目标.为实现2030年的CO_2减排目标可采取以下措施:主动适应经济新常态,稳定发展增速;积极发展第三产业,平衡经济结构;持续推进节能减排技术,降低能源消费强度;大力发展天然气及核电等清洁能源,优化一次能源消费结构.
China has put forward CO_2 emissions reduction target(committing to achieve CO_2 emissions peak or plateau by 2030) to prevent global climate change. With Jiangsu Province as a case, we explored whether China could achieve the 2030 CO_2 emissions reduction target. We predicted the peak volume and time of CO_2 emission in three scenarios, i.e., quick scenario, medium scenario, slow scenario, respectively, based on the long-range energy alternatives planning system(LEAP) model and the logarithmic mean Divisia index decomposition approach. The results showed that, during the period 2000 to 2015, the economic scale effect was the most important driver, whose contribution to the increase of total CO_2 emissions was as high as 147.4%. The technology progress effect was the main mitigation factor for CO_2 emissions, which caused CO_2 emissions to decrease by 60.4%. In addition, the contributions of energy structure effect, industrial structure effect, per capita income effect and population scale effect to CO_2 emissions were-5.3%, 9.7%, 11.0%, and 0.6% respectively. In quick and medium scenarios, the peak CO_2 emissions of Jiangsu Province would be 701 million tons in 2025, and 795 million tons in 2029, respectively. In slow scenario, however, Jiangsu Province could not achieve the 2030 CO_2 emissions reduction target. To achieve the 2030 target, Jiangsu Province needs to adopt some strategies, including actively developing the tertiary industry to balance the economic structure, continuously promoting energy saving and emissions reduction technologies to reduce energy consuming intensity, and vigorously deploying clean energy to optimize the energy consuming structure.
China has put forward CO_2 emissions reduction target(committing to achieve CO_2 emissions peak or plateau by 2030) to prevent global climate change. With Jiangsu Province as a case, we explored whether China could achieve the 2030 CO_2 emissions reduction target. We predicted the peak volume and time of CO_2 emission in three scenarios, i.e., quick scenario, medium scenario, slow scenario, respectively, based on the long-range energy alternatives planning system(LEAP) model and the logarithmic mean Divisia index decomposition approach. The results showed that, during the period 2000 to 2015, the economic scale effect was the most important driver, whose contribution to the increase of total CO_2 emissions was as high as 147.4%. The technology progress effect was the main mitigation factor for CO_2 emissions, which caused CO_2 emissions to decrease by 60.4%. In addition, the contributions of energy structure effect, industrial structure effect, per capita income effect and population scale effect to CO_2 emissions were-5.3%, 9.7%, 11.0%, and 0.6% respectively. In quick and medium scenarios, the peak CO_2 emissions of Jiangsu Province would be 701 million tons in 2025, and 795 million tons in 2029, respectively. In slow scenario, however, Jiangsu Province could not achieve the 2030 CO_2 emissions reduction target. To achieve the 2030 target, Jiangsu Province needs to adopt some strategies, including actively developing the tertiary industry to balance the economic structure, continuously promoting energy saving and emissions reduction technologies to reduce energy consuming intensity, and vigorously deploying clean energy to optimize the energy consuming structure.
引文
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[17] Jeong YS.Assessment of alternative scenarios for CO2 reduction potential in the residential building sector.Sustainability,2017,9:394,DOI:10.3390/su9030394
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[24] Ang BW.LMDI decomposition approach:A guide for implementation.Energy Policy,2015,86:233-238
[25] Huang J-Y (黄健元),Feng C-Y (冯成颖),Xu G-Q (徐国强).The prediction of population development in Jiangsu Province.Northwest Population Journal (西北人口),2007,28(5):43-47 (in Chinese)
[26] Xinhua News Agency (新华社).Outline of the Thirteenth Five Year Plan for National Economic and Social Development in Jiangsu [EB/OL].(2016-03-31) [2018-02-08].http://jiangsu.sina.com.cn/news/m/2016_03_31/detail-ifxqxcnr5084893.shtml (in Chinese)
[27] Bai J (白娟).Analysis and Trend Research of China’s industrial structure adjustment based on 1978-2011 years’ data.Times Finance (时代金融),2015(34):17-18 (in Chinese)
[28] Tian L,Ding Z,Wang Y,et al.Analysis of the driving factors and contributions to carbon emissions of energy consumption from the perspective of the peak volume and time based on LEAP.Sustainability,2016,8:513,DOI:10.3390/su8060513
[29] Huang R (黄蕊),Lu Y-Q (陆玉麒),Chen M-Q (陆梦秋).Projection of energy consumption carbon emission peak for Jiangsu,Zhejiang and Shanghai under different energy policies.Resources and Environment in the Yangtze Basin (长江流域资源与环境),2017,26(1):15-25 (in Chinese)
[2] Xinhua News Agency (新华社).U.S.-China Joint Announcement on Climate Change [EB/OL].(2014-11-13) [2018-02-08].http://www.caixin.com/2014_11_13/100750277.html (in Chinese)
[3] Ma D (马丁),Chen W-Y (陈文颖).Analysis of China’s 2030 carbon emission peak level and peak path.China Population,Resources and Environment (中国人口·资源与环境),2016,26(suppl.1):1-4 (in Chinese).
[4] Liu Y (刘宇),Cai S-F (蔡松锋),Zhang Q-Z (张其仔).The economic impact of China’s carbon dioxide emissions to peak in 2025,2030 and 2040:Based on the dynamic GTAP-E model.Management Review (管理评论),2014,26(12):3-9 (in Chinese)
[5] Li L,Lei Y,He C,et al.Prediction on the peak of the CO2 emissions in China using the STIRPAT model.Advances in Meteorology,2016,8:1-9
[6] Khanna NZ,Zhou N,Fridley D,et al.Quantifying the potential impacts of China’s power-sector policies on coal input and CO2 emissions through 2050:A bottom-up perspective.Utilities Policy,2016,41:128-138
[7] Li W,Li H,Zhang HX,et al.The analysis of CO2 emissions and reduction potential in China’s transport sector.Mathematical Problems in Engineering,2016,2016:1-12,DOI:10.1155/2016/1043717
[8] Ma L (马立),Liu L-T (刘立涛).Peak forecast of Chinese energy consumption based on developed countries’ trends.Scientia Geographica Sinica (地理科学),2016,36(7):980-988 (in Chinese)
[9] Xi X-P (席细平),Xie Y-S (谢运生),Wang H-L (王贺礼),et al.Forecast of Jiangxi’s carbon emissions to peak based on IPAT model.Jiangxi Science (江西科学),2014(6):768-772 (in Chinese)
[10] Wen L,Liu Y.The peak value of carbon emissions in the Beijing-Tianjin-Hebei region based on the STIRPAT model and scenario design.Polish Journal of Environmental Studies,2016,25:823-834
[11] Chen L-J (陈丽君),Wu H-M (吴红梅),Fan L (范玲),et al.The research of carbon emissions peak judgment and countermeasure research of Zhejiang Pro-vince.Energy of China (中国能源),2017,39(4):43-47 (in Chinese)
[12] Huang WM,Lee GWM.GHG legislation:Lessons from Taiwan.Energy Policy,2009,37:2696-2707
[13] Li Y,Bao L,Li W,et al.Inventory and policy reduction potential of greenhouse gas and pollutant emissions of road transportation industry in China.Sustainability,2016,8:1218,doi.org/10.3390/su8121218
[14] Liang Y,Yu B,Wang L.Costs and benefits of renewable energy development in China’s power industry.Renewable Energy,2019,131:700-712
[15] Dong KY,Sun RJ,Li H,et al.A review of China’s energy consumption structure and outlook based on a long-range energy alternatives modeling tool.Petroleum Science,2017,14:214-227
[16] Subramanyam V,Ahiduzzaman M,Kumar A.Greenhouse gas emissions mitigation potential in the commercial and institutional sector.Energy and Buildings,2017,140:295-304
[17] Jeong YS.Assessment of alternative scenarios for CO2 reduction potential in the residential building sector.Sustainability,2017,9:394,DOI:10.3390/su9030394
[18] Park NB,Yun SJ,Jeong EC.An analysis of long-term scenarios for the transition to renewable energy in the Korean electricity sector.Energy Policy,2013,52:288-296
[19] Liu Z,Guan D,Wei W,et al.Reduced carbon emission estimates from fossil fuel combustion and cement production in China.Nature,2015,524:335-338
[20] Chen F (陈芳).Empirical analysis on energy consumption intensity of six provinces in Central China.Special Zone Economy (特区经济),2015(1):72-74 (in Chinese)
[21] Ang BW,Zhang FQ,Choi KH.Factorizing changes in energy and environmental indicators through decomposition.Energy,1998,23:489-495
[22] Fan JL,Liao H,Liang QM,et al.Residential carbon emission evolutions in urban-rural divided China:An end-use and behavior analysis.Applied Energy,2013,101:323-332
[23] Ang BW.Decomposition analysis for policymaking in energy:Which is the preferred method?Energy Policy,2004,32:1131-1139
[24] Ang BW.LMDI decomposition approach:A guide for implementation.Energy Policy,2015,86:233-238
[25] Huang J-Y (黄健元),Feng C-Y (冯成颖),Xu G-Q (徐国强).The prediction of population development in Jiangsu Province.Northwest Population Journal (西北人口),2007,28(5):43-47 (in Chinese)
[26] Xinhua News Agency (新华社).Outline of the Thirteenth Five Year Plan for National Economic and Social Development in Jiangsu [EB/OL].(2016-03-31) [2018-02-08].http://jiangsu.sina.com.cn/news/m/2016_03_31/detail-ifxqxcnr5084893.shtml (in Chinese)
[27] Bai J (白娟).Analysis and Trend Research of China’s industrial structure adjustment based on 1978-2011 years’ data.Times Finance (时代金融),2015(34):17-18 (in Chinese)
[28] Tian L,Ding Z,Wang Y,et al.Analysis of the driving factors and contributions to carbon emissions of energy consumption from the perspective of the peak volume and time based on LEAP.Sustainability,2016,8:513,DOI:10.3390/su8060513
[29] Huang R (黄蕊),Lu Y-Q (陆玉麒),Chen M-Q (陆梦秋).Projection of energy consumption carbon emission peak for Jiangsu,Zhejiang and Shanghai under different energy policies.Resources and Environment in the Yangtze Basin (长江流域资源与环境),2017,26(1):15-25 (in Chinese)
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