天津市能源消费碳排放影响因素及其情景预测
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摘要
在天津市2000—2016年碳排放测算的基础上,运用LMDI模型分析天津市碳排放影响因素。结果表明:经济规模和能源强度对天津市碳排放影响较大,二者对碳排放影响分别表现为促进和抑制作用;通过将经济变量分为高、中、低3种情景,减排变量分为中减排和强减排2种情景,组合得到高增长强减排、中增长强减排等6种发展情景。基于STIRPAT扩展模型,预测6种组合情景下天津市碳排放变化趋势。结果显示,中增长强减排情景模式在保证经济发展的同时,较其余几种模式碳排放峰值及其年份均有优化,是天津市最佳发展模式。
The LMDI model was applied to analyze the factors affecting carbon emission from energy consumption in Tianjin from 2000 to 2016 based on the estimated carbon emission data. The results indicated that the carbon emission in Tianjin was significantly affected by the scale of economy and energy intensity,which played the promotion and inhibition of carbon emission respectively. On which the economic variables were classified into high,medium and low scenarios,and the emission reduction variables were divided into two modes of medium emission reduction and strong emission reduction. By means of the combination,6 development modes including the high economic growth with strong emission reduction and the medium economic growth with strong emission reduction were obtained. Based on the STIRPAT extended model,the trend of carbon emissions in Tianjin under the 6 combination modes was predicted. The results showed that the mode of medium economic growth with strong emission reduction was the best for maintaining the economic development in Tianjin,and the carbon emission peaks and their occurring years were optimized compared to those of other modes.
The LMDI model was applied to analyze the factors affecting carbon emission from energy consumption in Tianjin from 2000 to 2016 based on the estimated carbon emission data. The results indicated that the carbon emission in Tianjin was significantly affected by the scale of economy and energy intensity,which played the promotion and inhibition of carbon emission respectively. On which the economic variables were classified into high,medium and low scenarios,and the emission reduction variables were divided into two modes of medium emission reduction and strong emission reduction. By means of the combination,6 development modes including the high economic growth with strong emission reduction and the medium economic growth with strong emission reduction were obtained. Based on the STIRPAT extended model,the trend of carbon emissions in Tianjin under the 6 combination modes was predicted. The results showed that the mode of medium economic growth with strong emission reduction was the best for maintaining the economic development in Tianjin,and the carbon emission peaks and their occurring years were optimized compared to those of other modes.
引文
[1]Ashfaq A,Zhao Y H,Muhammad S,et al.Carbon emissions energy consumption and economic growth:An aggregate and disaggregate analysis of the Indian economy[J].Energy Policy,2016,96:131-143.
[2]王仲瑀.京津冀地区能源消费、碳排放与经济增长关系实证研究[J].工业技术经济,2017,36(1):82-92.[Wang Zhongyu.The empirical study on the relationship between energy consumption,carbon emissions and economic growth in Beijing-Tianjin-Hebei[J].Industrial Technology&Economy,2017,36(1):82-92.]
[3]郭炳南,林基,刘堂发.长三角地区二氧化碳排放与经济增长脱钩关系的实证研究[J].生态经济,2017,33(4):25-29.[Guo Bingnan,Lin Ji,Liu Tangfa.An empirical analysis of decoupling relations between economic growth and carbon dioxide emissions in Yangtze River Delta Region[J].Ecological Economy,2017,33(4):25-29.]
[4]李彩云,陈兴鹏,张子龙,等.敦煌市旅游业碳排放与经济增长的耦合关系分析[J].生态科学,2016,35(1):109-116.[Li Caiyun,Chen Xingpeng,Zhang Zilong,et al.Estimation of tourism carbon emission and its decoupling with tourism development in Dunhuang[J].Ecological Science,2016,35(1):109-116.]
[5]Presley K,Wesseh J,Babette Z.Causal independence between energy consumption and economic growth in Liberia:Evidence from a non-parametric bootstrapped causality test[J].Energy Policy,2012,50:518-527.
[6]Haberl H,Krausman F,Gingrich S.Ecological Embeddedness of the Economy[J].Economic and Political Weekly,2005,41(47):4 896-4 904.
[7]Chang Y F,Lewis C,Lin S J.Comprehensive evaluation of industrial CO2emission(1989-2004)in Taiwan by input-output structural decomposition[J].Energy Policy,2008,36(7):2 471-2 480.
[8]钟超,刘宇,汪明月,等.中国碳强度减排目标实现的路径及可行性研究[J].中国人口·资源与环境,2018,28(10):18-26.[Zhong Chao,Liu Yu,Wang Mingyue,et al.Feasibility study on China’s potential paths to intensity-based carbon reduction targets[J].China Population Resources and Environment,2018,28(10):18-26.]
[9]Wei J,Huang K,Yang G S,et al.Driving forces analysis of energyrelated carbon dioxide(CO2)emissions in Beijing:An input-output structural decomposition analysis[J].Journal of Cleaner Production,2017,163:58-68.
[10]武红.中国省域碳减排:时空格局、演变机理及政策建议---基于空间计量经济学的理论与方法[J].管理世界,2015(11):3-10.[Wu Hong.Provincial carbon emission reduction of China:Spatiotemporal pattern,evolution mechanism and policy proposals based on the theory and method of spatial econometrics[J].Management World,2015(11):3-10.]
[11]袁长伟,张倩,芮晓丽,等.中国交通运输碳排放时空演变及差异分析[J].环境科学学报,2016,36(12):4 555-4 562.[Yuan Changwei,Zhang Qian,Rui Xiaoli,et al.Spatiotemporal evolution and difference of transport carbon emissions in China[J].Acta Scientiae Circumstantiae,2016,36(12):4 555-4 562.]
[12]刘永伟,闫庆武.基于GIS的中国碳排放时空分布规律研究[J].安全与环境学报,2015,15(3):199-205.[Liu Yongwei,Yan Qingwu.On the spatio-temporal distribution regularity of the carbon emissions based on the GIS in China[J].Journal of Safety and Environment,2015,15(3):199-205.]
[13]Tian X,Chang G M,Tanikawa H,et al.Structural decomposition analysis of the carbonization process in Beijing:A regional explanation of rapid increasing carbon dioxide emission in China[J].Energy Policy,2013,53(1):279-286.
[14]张巍.基于STIRPAT模型的陕西省工业碳排放量预测和情景分析[J].可再生能源,2017,35(5):771-777.[Zhang Wei.Prediction and scenario analysis of industrial carbon emissions in Shaanxi Province based on STIRPAT model[J].Renewable Energy Resources,2017,35(5):771-777.]
[15]邓小乐,孙慧.基于STIRPAT模型的西北五省区碳排放峰值预测研究[J].生态经济,2016,32(9):36-41.[Deng Xiaole,Sun Hui.Forecast of the northwest five provinces’carbon emissions based on STIRPAT model[J].Ecological Economy,2016,32(9):36-41.]
[16]刘晴川,李强,郑旭煦.基于化石能源消耗的重庆市二氧化碳排放峰值预测[J].环境科学学报,2017,37(4):1 582-1 593.[Liu Qingchuan,Li Qiang,Zheng Xuxu.The prediction of carbon dioxide emissions in Chongqing based on fossil fuel combustion[J]Acta Scientiae Circumstantiae,2017,37(4):1 582-1 593.]
[17]王长建,汪菲,张虹鸥.新疆能源消费碳排放过程及其影响因素---基于扩展的Kaya恒等式[J].生态学报,2016,36(8):2 151-2 163.[Wang Changjian,Wang Fei,Zhang Hongou.The process of energy-related carbon emissions and influencing mechanism research in Xinjiang[J].Acta Ecologica Sinica,2016,36(8):2 151-2 163.]
[18]顾阿伦,何崇恺,吕志强,等.基于LMDI方法分析中国产业结构变动对碳排放的影响[J].资源科学,2016,38(10):1 861-1 870.[Gu Alun,He Chongkai,LüZhiqiang,et al.Industrial structure changes impacts on carbon emissions in China based on LMDI method[J].Resources Science,2016,38(10):1 861-1 870.]
[19]刘彦君.京津冀地区碳排放峰值预测与分析[D].北京:华北电力大学,2017.[Liu Yanjun.The Forecast and Analysis of Carbon Emissions’Peak Value in the Beijing-Tianjin-Hebei Region[D].Beijing:North China Electric Power University,2017.]
[20]张巍,尚丽.陕西省工业碳排放影响因素分析与启示[J].生态经济,2017,33(5):80-83.[Zhang Wei,Shang Li.Impact factors analysis and enlightenment of industrial carbon emission in Shaanxi Province[J].Ecological Economy,2017,33(5):80-83.]
[21]林伯强,刘希颖.中国城市化阶段的碳排放:影响因素和减排策略[J].经济研究,2010,45(8):66-78.[Lin Boqiang,Liu Xiying.China’s carbon dioxide emissions under the urbanization process:Influence factors and abatement policies[J].Economic Research Journal,2010,45(8):66-78.]
[2]王仲瑀.京津冀地区能源消费、碳排放与经济增长关系实证研究[J].工业技术经济,2017,36(1):82-92.[Wang Zhongyu.The empirical study on the relationship between energy consumption,carbon emissions and economic growth in Beijing-Tianjin-Hebei[J].Industrial Technology&Economy,2017,36(1):82-92.]
[3]郭炳南,林基,刘堂发.长三角地区二氧化碳排放与经济增长脱钩关系的实证研究[J].生态经济,2017,33(4):25-29.[Guo Bingnan,Lin Ji,Liu Tangfa.An empirical analysis of decoupling relations between economic growth and carbon dioxide emissions in Yangtze River Delta Region[J].Ecological Economy,2017,33(4):25-29.]
[4]李彩云,陈兴鹏,张子龙,等.敦煌市旅游业碳排放与经济增长的耦合关系分析[J].生态科学,2016,35(1):109-116.[Li Caiyun,Chen Xingpeng,Zhang Zilong,et al.Estimation of tourism carbon emission and its decoupling with tourism development in Dunhuang[J].Ecological Science,2016,35(1):109-116.]
[5]Presley K,Wesseh J,Babette Z.Causal independence between energy consumption and economic growth in Liberia:Evidence from a non-parametric bootstrapped causality test[J].Energy Policy,2012,50:518-527.
[6]Haberl H,Krausman F,Gingrich S.Ecological Embeddedness of the Economy[J].Economic and Political Weekly,2005,41(47):4 896-4 904.
[7]Chang Y F,Lewis C,Lin S J.Comprehensive evaluation of industrial CO2emission(1989-2004)in Taiwan by input-output structural decomposition[J].Energy Policy,2008,36(7):2 471-2 480.
[8]钟超,刘宇,汪明月,等.中国碳强度减排目标实现的路径及可行性研究[J].中国人口·资源与环境,2018,28(10):18-26.[Zhong Chao,Liu Yu,Wang Mingyue,et al.Feasibility study on China’s potential paths to intensity-based carbon reduction targets[J].China Population Resources and Environment,2018,28(10):18-26.]
[9]Wei J,Huang K,Yang G S,et al.Driving forces analysis of energyrelated carbon dioxide(CO2)emissions in Beijing:An input-output structural decomposition analysis[J].Journal of Cleaner Production,2017,163:58-68.
[10]武红.中国省域碳减排:时空格局、演变机理及政策建议---基于空间计量经济学的理论与方法[J].管理世界,2015(11):3-10.[Wu Hong.Provincial carbon emission reduction of China:Spatiotemporal pattern,evolution mechanism and policy proposals based on the theory and method of spatial econometrics[J].Management World,2015(11):3-10.]
[11]袁长伟,张倩,芮晓丽,等.中国交通运输碳排放时空演变及差异分析[J].环境科学学报,2016,36(12):4 555-4 562.[Yuan Changwei,Zhang Qian,Rui Xiaoli,et al.Spatiotemporal evolution and difference of transport carbon emissions in China[J].Acta Scientiae Circumstantiae,2016,36(12):4 555-4 562.]
[12]刘永伟,闫庆武.基于GIS的中国碳排放时空分布规律研究[J].安全与环境学报,2015,15(3):199-205.[Liu Yongwei,Yan Qingwu.On the spatio-temporal distribution regularity of the carbon emissions based on the GIS in China[J].Journal of Safety and Environment,2015,15(3):199-205.]
[13]Tian X,Chang G M,Tanikawa H,et al.Structural decomposition analysis of the carbonization process in Beijing:A regional explanation of rapid increasing carbon dioxide emission in China[J].Energy Policy,2013,53(1):279-286.
[14]张巍.基于STIRPAT模型的陕西省工业碳排放量预测和情景分析[J].可再生能源,2017,35(5):771-777.[Zhang Wei.Prediction and scenario analysis of industrial carbon emissions in Shaanxi Province based on STIRPAT model[J].Renewable Energy Resources,2017,35(5):771-777.]
[15]邓小乐,孙慧.基于STIRPAT模型的西北五省区碳排放峰值预测研究[J].生态经济,2016,32(9):36-41.[Deng Xiaole,Sun Hui.Forecast of the northwest five provinces’carbon emissions based on STIRPAT model[J].Ecological Economy,2016,32(9):36-41.]
[16]刘晴川,李强,郑旭煦.基于化石能源消耗的重庆市二氧化碳排放峰值预测[J].环境科学学报,2017,37(4):1 582-1 593.[Liu Qingchuan,Li Qiang,Zheng Xuxu.The prediction of carbon dioxide emissions in Chongqing based on fossil fuel combustion[J]Acta Scientiae Circumstantiae,2017,37(4):1 582-1 593.]
[17]王长建,汪菲,张虹鸥.新疆能源消费碳排放过程及其影响因素---基于扩展的Kaya恒等式[J].生态学报,2016,36(8):2 151-2 163.[Wang Changjian,Wang Fei,Zhang Hongou.The process of energy-related carbon emissions and influencing mechanism research in Xinjiang[J].Acta Ecologica Sinica,2016,36(8):2 151-2 163.]
[18]顾阿伦,何崇恺,吕志强,等.基于LMDI方法分析中国产业结构变动对碳排放的影响[J].资源科学,2016,38(10):1 861-1 870.[Gu Alun,He Chongkai,LüZhiqiang,et al.Industrial structure changes impacts on carbon emissions in China based on LMDI method[J].Resources Science,2016,38(10):1 861-1 870.]
[19]刘彦君.京津冀地区碳排放峰值预测与分析[D].北京:华北电力大学,2017.[Liu Yanjun.The Forecast and Analysis of Carbon Emissions’Peak Value in the Beijing-Tianjin-Hebei Region[D].Beijing:North China Electric Power University,2017.]
[20]张巍,尚丽.陕西省工业碳排放影响因素分析与启示[J].生态经济,2017,33(5):80-83.[Zhang Wei,Shang Li.Impact factors analysis and enlightenment of industrial carbon emission in Shaanxi Province[J].Ecological Economy,2017,33(5):80-83.]
[21]林伯强,刘希颖.中国城市化阶段的碳排放:影响因素和减排策略[J].经济研究,2010,45(8):66-78.[Lin Boqiang,Liu Xiying.China’s carbon dioxide emissions under the urbanization process:Influence factors and abatement policies[J].Economic Research Journal,2010,45(8):66-78.]