差异化碳减排目标对区域产业部门经济与碳减排的影响
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摘要
碳减排目标是实现减排任务的重要保障.为探讨差异化减排目标对区域产业部门经济与碳减排的影响,以在我国具有特定区位条件("21世纪海上丝绸之路"的重要节点)和经济发展现状(相对较为落后)的广西壮族自治区为主要研究区域,构建包括广西壮族自治区和我国其他地区〔除广西壮族自治区以外的其他省(自治区、直辖市),但不含港澳台地区,下同〕在内的可计算的一般均衡模型,设定P55C65、P75C65、P65C55、P65C65、P65C75("P"表示广西壮族自治区,"C"表示我国其他地区,"55""65""75"分别表示碳减排目标依次为55%、65%、75%)和基准情景(无减排目标),研究区域间宏观经济和微观产业部门指标之间的相互影响.结果表明:①无差异情景(P65C65)下,广西壮族自治区农林牧渔业2030年的产值为741. 17×108元,比基准情景下降0. 19%;我国其他地区电子设备制造业的产值为33 457. 49×108元,比基准情景下降2. 00%.对广西壮族自治区碳排放贡献较大的产业部门主要为食品制造业、金属冶炼及压延业和服务业,而我国其他地区碳排放主要受电力热力生产的影响.②差异化情景下,这两个区域的金属冶炼及压延部门受减排约束影响均较明显.相比无差异情景,P75C65情景下,广西壮族自治区2030年金属冶炼及压延部门的出口总值和省际输出分别降至17. 55×108和186. 32×108元,分别下降了6. 80%和1. 65%;本地供应和产出分别降至358. 72×108和562. 59×108元,分别下降了1. 85%和1. 95%;金属冶炼及压延部门碳排放降至4 997×104t,下降了12. 79%.而我国其他地区2030年P75C65情景下,金属冶炼及压延部门的出口总值、省际输出、本地供应和产出则分别上升了0. 05%、2. 06%、0. 06%和0. 06%,分别增至1 283. 24×108、28. 25×108、26 598. 95×108和27 910. 43×108元,该部门的碳排放上升了0. 05%,增至82 927×104t.尽管碳减排目标能有效降低广西壮族自治区的碳排放,但也会给其产业部门带来一定的经济损失,该区域的出口、省际输出、本地供应和产出不仅受自身减排目标的影响,也受到我国其他地区减排目标的约束.建议在落实各省(自治区、直辖市)减排任务时,也要实施差异化减排目标,发展当地的优势产业.
The carbon mitigation target plays a crucial role in achieving carbon dioxide emission reduction. In order to investigate the impacts of implementing differentiated emission targets on industrial economic and carbon reductions,this study takes Guangxi Zhuang Autonomous Region as a research subject. Despite a relatively poor economic performance,Guangxi Zhuang Autonomous Region serves as an essential node to the Maritime Silk Road. We thus constructed a computable general equilibrium model which containing Guangxi Zhuang Autonomous Region and the rest of China(include other provinces except Guangxi Zhuang Autonomous Region,and Hongkong,Macao,Taiwan(due to the unavailable data)) to estimate the mitigation effect. The scenarios of P55 C65,P75 C65,P65 C55,P65 C6,P65 C75(‘P'represents Guangxi Zhuang Autonomous Region,‘C'represents the rest of China) and baseline(no mitigation target) are set up. The values of‘55'‘65'‘75'represent the carbon reduction targets of 55%,65% and 75% respectively,which are applied to examine the correlation between the macro economy and micro industries. The results show that despite some industrial economic losses being generated when constraint with carbon reduction targets,substantial reduction gains under different scenarios are yield.(1) Under the no-difference scenario(P65 C65),the agricultural output in Guangxi Zhuang Autonomous Region would be 741. 17×108 RMB,0. 19%less than the baseline scenario; while the electronic manufacturing's output in the rest of China would be 33,457. 49×108 RMB,2. 00%less than the baseline. The food manufacturing sector,the metal smelting and rolling sector,and the service sector,however,are found to be the main sectors which contribute large amount to carbon dioxide emissions in Guangxi Zhuang Autonomous Region,while the electronic and heating sectors are the main contributors in the rest of China.(2) Under the differentiated scenario,the metal smelting and rolling sector in both regions is supposed to be affected significantly. Comparing with the no-difference scenario,the export value and the provincial export value would decrease respectively to 17. 55× 108 and 186. 32× 108 RMB,reducing by 6. 80% and 1. 65% respectively;the local supply value and the output value would decrease respectively to 358. 72×108 and 562. 59×108 RMB,with 1. 85% and 1. 95%less than the no-difference scenario respectively. Consequently,the corresponding CO_2 emissions of the metal smelting and rolling sector would decrease by 12. 79% to 4,997×104 t under the scenario of P75 C65 in 2030. In contrast,all these indicators would change in the opposite direction in the rest of China under the same scenario of the metal smelting and rolling sector. In 2030,the export value,the provincial export value,the local supply value and the output value would increase by 0. 05%,2. 06%,0. 06% and 0. 06%,respectively,reaching to 1,283. 24×108,28. 25×108,26,598. 95×108 and 27,910. 43×108 RMB,respectively,while the corresponding CO_2 emissions would increase by 0. 05% to 82,927. 00 × 104 t. Even with substantial reduction of carbon emissions in Guangxi Zhuang Autonomous Region,a certain amount of economic losses on industries still exists. The economic losses of export,provincial export,local supply and output would not only be affected by Guangxi Zhuang Autonomous Region's reduction targets,but also be correlated with the targets in the rest of China. These findings suggest that a comprehensive consideration of differentiated mitigation targets should be taken when implementing the Intended Nationally Determined Contribution plan,as well as developing domestic competitive industries.
The carbon mitigation target plays a crucial role in achieving carbon dioxide emission reduction. In order to investigate the impacts of implementing differentiated emission targets on industrial economic and carbon reductions,this study takes Guangxi Zhuang Autonomous Region as a research subject. Despite a relatively poor economic performance,Guangxi Zhuang Autonomous Region serves as an essential node to the Maritime Silk Road. We thus constructed a computable general equilibrium model which containing Guangxi Zhuang Autonomous Region and the rest of China(include other provinces except Guangxi Zhuang Autonomous Region,and Hongkong,Macao,Taiwan(due to the unavailable data)) to estimate the mitigation effect. The scenarios of P55 C65,P75 C65,P65 C55,P65 C6,P65 C75(‘P'represents Guangxi Zhuang Autonomous Region,‘C'represents the rest of China) and baseline(no mitigation target) are set up. The values of‘55'‘65'‘75'represent the carbon reduction targets of 55%,65% and 75% respectively,which are applied to examine the correlation between the macro economy and micro industries. The results show that despite some industrial economic losses being generated when constraint with carbon reduction targets,substantial reduction gains under different scenarios are yield.(1) Under the no-difference scenario(P65 C65),the agricultural output in Guangxi Zhuang Autonomous Region would be 741. 17×108 RMB,0. 19%less than the baseline scenario; while the electronic manufacturing's output in the rest of China would be 33,457. 49×108 RMB,2. 00%less than the baseline. The food manufacturing sector,the metal smelting and rolling sector,and the service sector,however,are found to be the main sectors which contribute large amount to carbon dioxide emissions in Guangxi Zhuang Autonomous Region,while the electronic and heating sectors are the main contributors in the rest of China.(2) Under the differentiated scenario,the metal smelting and rolling sector in both regions is supposed to be affected significantly. Comparing with the no-difference scenario,the export value and the provincial export value would decrease respectively to 17. 55× 108 and 186. 32× 108 RMB,reducing by 6. 80% and 1. 65% respectively;the local supply value and the output value would decrease respectively to 358. 72×108 and 562. 59×108 RMB,with 1. 85% and 1. 95%less than the no-difference scenario respectively. Consequently,the corresponding CO_2 emissions of the metal smelting and rolling sector would decrease by 12. 79% to 4,997×104 t under the scenario of P75 C65 in 2030. In contrast,all these indicators would change in the opposite direction in the rest of China under the same scenario of the metal smelting and rolling sector. In 2030,the export value,the provincial export value,the local supply value and the output value would increase by 0. 05%,2. 06%,0. 06% and 0. 06%,respectively,reaching to 1,283. 24×108,28. 25×108,26,598. 95×108 and 27,910. 43×108 RMB,respectively,while the corresponding CO_2 emissions would increase by 0. 05% to 82,927. 00 × 104 t. Even with substantial reduction of carbon emissions in Guangxi Zhuang Autonomous Region,a certain amount of economic losses on industries still exists. The economic losses of export,provincial export,local supply and output would not only be affected by Guangxi Zhuang Autonomous Region's reduction targets,but also be correlated with the targets in the rest of China. These findings suggest that a comprehensive consideration of differentiated mitigation targets should be taken when implementing the Intended Nationally Determined Contribution plan,as well as developing domestic competitive industries.
引文
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[3]莫建雷,段宏波,范英,等.《巴黎协定》中我国能源和气候政策目标:综合评估与政策选择[J].经济研究,2018(9):168-181.MO Jianlei,DUAN Hongbo,FAN Ying,et al. China's energy and climate targets in the Paris Agreement:integrated assessment and policy options[J].Economic Research Journal,2018(9):168-181.
[4]王方军,王文举.北京”十二五”时期碳强度年度目标分解研究[J].首都经济贸易大学学报,2013(2):121-123.WANG Fangjun,WANG Wenju. Decomposition of annual carbon intensity targets during the 12thFive-Year Plan in Beijing[J].Journal of Capital University of Economics and Business,2013(2):121-123.
[5]程纪华.中国省域碳排放总量控制目标分解研究[J].中国人口·资源与环境,2016,26(1):23-30.CHEN Jihua.Decomposition of carbon cap targets at provincial level in China:a case study of Zhejiang[J].China Population,Resources and Environment,2016,26(1):23-30.
[6]韩中合,祁超,刘明浩.“十三五”规划节能减排目标实现路径研究[J].干旱区资源与环境,2018,32(3):23-27.HAN Zhonghe,QI Chao,LIU Minghao. The route for realizing the13thFive-Year Plan's objective of energy saving and emission reduction[J]. Journal of Arid Land Resources and Environment,2018,32(3):23-27.
[7] BAO Qin,TANG Ling,ZHANG Zhongxiang,et al.Impacts of border carbon adjustments on China's sectoral emissions:simulations with a dynamic computable general equilibrium model[J]. China Economic Review,2013,24:77-94.
[8] LIU Yu,LU Yingying.The economic impact of different carbon tax revenue recycling schemes in China:a model-based scenario analysis[J].Applied Energy,2015,141:96-105.
[9] WU Rui,DAI Hancheng,GENG Yong,et al. Achieving China's INDC through carbon cap-and-trade:insights from Shanghai[J].Applied Energy,2016,184:1114-1122.
[10] TIAN Xu,DAI Hancheng,GENG Yong,et al.The effects of carbon reduction on sectoral competitiveness in China:a case of Shanghai[J].Applied Energy,2017,197:270-278.
[11] DONG Huijuan,DAI Hanchegn,GENG Yong,et al. Exploring impact of carbon tax on China's CO2reductions and provincial disparities[J]. Renewable and Sustainable Energy Reviews,2017,77:596-603.
[12] WANG Peng,DAI Hancheng,REN Songyan,et al. Achieving Copenhagen target through carbon emission trading:economic impacts assessment in Guangdong province of China[J]. Energy,2015,79:212-227.
[13]王锋,吴丽华,杨超.中国经济发展中碳排放增长的驱动因素研究[J].经济研究,2010(2):18-29.WANG Feng,WU Lihua,YANG Chao. Driving factors for China of carbon dioxide emissions during economic development in China[J].Economic Research Journal,2010(2):18-29.
[14]王雅楠,谢艳琦,谢丽琴,等.基于LMDI和Q型聚类的中国城镇生活碳排放因素分解分析[J].环境科学研究,2019,32(4):539-546.WANG Yanan,XIE Yanqi,XIE Liqin,et al.Factor decomposition of Chinese urban household emission-empirical analysis based on LMDI and Q-clustering[J]. Research of Environmental Sciences,2019,32(4):539-546.
[15]陈诗一.能源消耗、二氧化碳排放与中国工业的可持续发展[J].经济研究,2009(4):41-55.CHEN Shiyi. Energy consumption,CO2emission and sustainable development in Chinese industry[J]. Economic Research Journal,2009(4):41-55.
[16]余明成,徐占军,余健.山西省CO2排放影响因素研究及情景分析[J].环境科学研究,2018,31(8):1357-1365.YU Mingcheng,XU Zhanjun,YU Jian. Influcing factors of CO2emission and scenario analysis in Shanxi Province[J]. Research of Environmental Sciences,2018,31(8):1357-1365.
[17] ZHANG Yuejun,WEI Yiming.An overview of current research on EU ETS:evidence from its operating mechanism and economic effect[J].Applied Energy,2010,87:1804-1814.
[18]谢守红,王利霞,邵珠龙.中国碳排放强度的行业差异与动因分析[J].环境科学研究,2013,24(11):1253-1258.XIE Shouhong,WANG lixia,SHAO Zhulong. Analysis on industrial differences and motivation factor of carbon emission intensity[J].Research of Environmental Sciences,2013,24(11):1253-1258.
[19]郭莹,李进华,夏东坡,等.我国中部六省CO2排放动态变化趋势[J].环境科学研究,2016,29(9):1279-1287.GUO Ying,LI Jinhua,XIA Dongpo,et al. Dynamic changes of carbon dioxide emissions in six provinces of Central China[J].Research of Environmental Sciences,2016,29(9):1279-1287.
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