碳强度约束的模拟:宏观效应、减排效应和结构效应
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摘要
基于碳强度约束的行政型减排措施是中国应对气候变化的主要手段之一,分析碳强度约束对经济系统的影响,是碳减排机制设计的重要内容。本文通过动态CGE模型,模拟2012-2030年碳强度约束和非化石能源比重提高对中国宏、微观经济和碳减排的影响。研究表明:在保持经济适度增长的情况下,2020年和2030年,碳强度分别比基准情景下降15. 176%和36. 586%,但仍略低于碳强度目标值;碳强度约束引起综合国内销售价格大幅上涨,投资、出口和进口小幅上升,消费少量下降;该约束还导致煤炭、原油和成品油价格大幅上涨;大多数非能源部门的产出和出口受到抑制,国内需求和进口显著上升。总体而言,行政型减排措施能够有效降低碳强度,但不能完全实现碳减排目标,需要适时推出碳税、碳交易等举措,形成碳减排的长效机制。
Administrative emission reduction measures based on carbon intensity restriction are one of China's major means of coping with climate change. The analysis of the impact of carbon intensity restriction on the economy is a significant content of the carbon emission reduction mechanism design. This paper adopts the dynamic CGE model to simulate influences of carbon intensity restriction goals and the increase in the proportion of non-fossil energy over China's macro-economy,micro-economy and carbon emission reduction from2012 to 2030. The study shows: while moderate economic growth is maintained,the carbon intensity falls by 15.176% and 36.586% respectively in 2020 and 2030 compared with the baseline scenario but it's still a bit lower than the target carbon intensity goal; besides,carbon intensity restriction leads to a dramatic rise in China's domestic sales prices,a small increase in investment,export and import,and a slight drop in consumption; the restriction gives rise to a sharp increase in coal,crude oil and refined oil products; the output and export of the majority of non-energy departments are restrained,and domestic demand and import witness an obvious rise. Generally speaking,administrative emission reduction measures can effectively weaken the carbon intensity but cannot completely reach the carbon emission reduction goals. Therefore,it's feasible to form a long-term mechanism by implementing carbon tax and carbon transaction.
Administrative emission reduction measures based on carbon intensity restriction are one of China's major means of coping with climate change. The analysis of the impact of carbon intensity restriction on the economy is a significant content of the carbon emission reduction mechanism design. This paper adopts the dynamic CGE model to simulate influences of carbon intensity restriction goals and the increase in the proportion of non-fossil energy over China's macro-economy,micro-economy and carbon emission reduction from2012 to 2030. The study shows: while moderate economic growth is maintained,the carbon intensity falls by 15.176% and 36.586% respectively in 2020 and 2030 compared with the baseline scenario but it's still a bit lower than the target carbon intensity goal; besides,carbon intensity restriction leads to a dramatic rise in China's domestic sales prices,a small increase in investment,export and import,and a slight drop in consumption; the restriction gives rise to a sharp increase in coal,crude oil and refined oil products; the output and export of the majority of non-energy departments are restrained,and domestic demand and import witness an obvious rise. Generally speaking,administrative emission reduction measures can effectively weaken the carbon intensity but cannot completely reach the carbon emission reduction goals. Therefore,it's feasible to form a long-term mechanism by implementing carbon tax and carbon transaction.
引文
[1] Liu X.,Mao G.,Ren J.,et al. How Might China Achieve Its 2020 Emissions Target? A Scenario Analysis of Energy Consumption and CO2Emissions Using the System Dynamics Model[J]. Journal of Cleaner Production,2015,103:401-410
[2] Yi B. W.,Xu J. H.,Fan Y. Determining Factors and Diverse Scenarios of CO2Emissions Intensity Reduction to Achieve the 40-45%Target by 2020 in China—A Historical and Prospective Analysis for the Period 2005-2020[J]. Journal of Cleaner Production,2016,122:87-101
[3]刘宇,蔡松锋,张其仔. 2025年、2030年和2040年中国二氧化碳排放达峰的经济影响——基于动态GTAP-E模型[J].管理评论,2014,26(12):3-9
[4] Shahiduzzaman M.,Layton A. Decomposition Analysis for Assessing the United States 2025 Emissions Target:How Big Is the Challenge?[J]. Renewable&Sustainable Energy Reviews,2017,67:372-383
[5]张友国,郑玉歆.碳强度约束的宏观效应和结构效应[J].中国工业经济,2014,(6):57-69
[6]周县华,范庆泉.碳强度减排目标的实现机制与行业减排路径的优化设计[J].世界经济,2016,39(7):168-192
[7]林伯强,孙传旺.如何在保障中国经济增长前提下完成碳减排目标[J].中国社会科学,2011,(1):64-76
[8] Dai H.,Masui T.,Matsuoka Y.,et al. Assessment of China’s Climate Commitment and non-fossil Energy Plan Towards 2020Using Hybrid AIM/CGE Model[J]. Energy Policy,2011,39(5):2875-2887
[9] Zhang D.,Rausch S.,Karplus V. J.,et al. Quantifying Regional Economic Impacts of CO2Intensity Targets in China[J]. Energy Economics,2012,40(2):687-701
[10] Springmann M.,Zhang D.,Karplus V. J. Consumption-Based Adjustment of Emissions-Intensity Targets:An Economic Analysis for China’s Provinces[J]. Environmental and Resource Economics,2015,61(4):615-640
[11]吴洁,范英,夏炎,等.碳配额初始分配方式对我国省区宏观经济及行业竞争力的影响[J].管理评论,2015,27(12):18-26
[12] Li A. J.,Li Z. General Equilibrium Analysis of the Effects of Technological Progress for Energy Intensity and Carbon Dioxide Emissions in China[J]. Advanced Materials Research,2011,347(5):1093-1097
[13]查冬兰,司建松,周德群,等.中国工业部门能源与非能源替代弹性研究——基于多弹性测度方法[J].管理评论,2016,28(6):180-191
[14] Fischer C.,Springborn M. Emissions Targets and the Real Business Cycle:Intensity Targets Versus Caps or Taxes[J]. Journal of Environmental Economics&Management,2009,62(3):352-366
[15]张国兴,高秀林,汪应洛,等.我国节能减排政策协同的有效性研究:1997-2011[J].管理评论,2015,27(12):3-17
[16]张欣.可计算一般均衡模型的基本原理与编程[M].上海:格致出版社,2010
[17]林伯强,姚昕,刘希颖.节能和碳排放约束下的中国能源结构战略调整[J].中国社会科学,2010,(1):58-71
[18]梁强,许文,苏明.基于CGE模型的税收政策控煤效果[J].财政科学,2016,(5):10-69
(1)例如,“十二五”规划和“十三五”规划分别提出2015年比2010年碳强度下降17%,2020年比2015年碳强度下降18%,该时期的碳强度下降目标会进一步细化分解至各省(区)和工业领域,这些都是为了保障完成中国政府承诺2020年和2030年的碳强度下降总目标。
(2)本文的“微观”主要指编制SAM表和构建CGE模型过程中,将“活动”和“商品”细分为31个部门,这与将“活动”和“商品”看作一个整体的“宏观”作对应。因此,本文的“微观”与微观经济学的研究对象(即单个消费者、家庭和企业)是不同的。
(3)估算碳排放的公式为Cj=Ej·ECj=Ej·CEFipcc-j·NCVj·COFj,其中,Cj、Ej和ECj分别是消费第j种能源后的碳排放、能源消费量和碳排放系数,CFEipcc-j、NCVj和COFj分别是能源的缺省排放因子、平均低位发热值和碳氧化因子。
(4)该列数值中,2005-2015年的碳排放是估算碳排放,2016-2030年的碳排放是依据碳强度下降目标推算的碳排放。将2005-2030年的碳排放作为目标情景的约束值。
(5)由于SAM表数据统计口径与实际宏观经济指标并不完全一致,因此通过动态CGE模型模拟得到的2005-2015年数值和实际值存在一定误差,2012年实际GDP为54. 037万亿元,模型估计值为56. 17万亿元,误差仅为3. 9%,模型估计是可靠的。
(6)为了与以往文献同期比较,该系列数值为2013-2020年的国内销售价格变动的平均值,与表4中时期范围不同,表4列出的是2013-2030年各部门指标的平均变化,因时期更长,各能源的价格变动幅度更大。
[2] Yi B. W.,Xu J. H.,Fan Y. Determining Factors and Diverse Scenarios of CO2Emissions Intensity Reduction to Achieve the 40-45%Target by 2020 in China—A Historical and Prospective Analysis for the Period 2005-2020[J]. Journal of Cleaner Production,2016,122:87-101
[3]刘宇,蔡松锋,张其仔. 2025年、2030年和2040年中国二氧化碳排放达峰的经济影响——基于动态GTAP-E模型[J].管理评论,2014,26(12):3-9
[4] Shahiduzzaman M.,Layton A. Decomposition Analysis for Assessing the United States 2025 Emissions Target:How Big Is the Challenge?[J]. Renewable&Sustainable Energy Reviews,2017,67:372-383
[5]张友国,郑玉歆.碳强度约束的宏观效应和结构效应[J].中国工业经济,2014,(6):57-69
[6]周县华,范庆泉.碳强度减排目标的实现机制与行业减排路径的优化设计[J].世界经济,2016,39(7):168-192
[7]林伯强,孙传旺.如何在保障中国经济增长前提下完成碳减排目标[J].中国社会科学,2011,(1):64-76
[8] Dai H.,Masui T.,Matsuoka Y.,et al. Assessment of China’s Climate Commitment and non-fossil Energy Plan Towards 2020Using Hybrid AIM/CGE Model[J]. Energy Policy,2011,39(5):2875-2887
[9] Zhang D.,Rausch S.,Karplus V. J.,et al. Quantifying Regional Economic Impacts of CO2Intensity Targets in China[J]. Energy Economics,2012,40(2):687-701
[10] Springmann M.,Zhang D.,Karplus V. J. Consumption-Based Adjustment of Emissions-Intensity Targets:An Economic Analysis for China’s Provinces[J]. Environmental and Resource Economics,2015,61(4):615-640
[11]吴洁,范英,夏炎,等.碳配额初始分配方式对我国省区宏观经济及行业竞争力的影响[J].管理评论,2015,27(12):18-26
[12] Li A. J.,Li Z. General Equilibrium Analysis of the Effects of Technological Progress for Energy Intensity and Carbon Dioxide Emissions in China[J]. Advanced Materials Research,2011,347(5):1093-1097
[13]查冬兰,司建松,周德群,等.中国工业部门能源与非能源替代弹性研究——基于多弹性测度方法[J].管理评论,2016,28(6):180-191
[14] Fischer C.,Springborn M. Emissions Targets and the Real Business Cycle:Intensity Targets Versus Caps or Taxes[J]. Journal of Environmental Economics&Management,2009,62(3):352-366
[15]张国兴,高秀林,汪应洛,等.我国节能减排政策协同的有效性研究:1997-2011[J].管理评论,2015,27(12):3-17
[16]张欣.可计算一般均衡模型的基本原理与编程[M].上海:格致出版社,2010
[17]林伯强,姚昕,刘希颖.节能和碳排放约束下的中国能源结构战略调整[J].中国社会科学,2010,(1):58-71
[18]梁强,许文,苏明.基于CGE模型的税收政策控煤效果[J].财政科学,2016,(5):10-69
(1)例如,“十二五”规划和“十三五”规划分别提出2015年比2010年碳强度下降17%,2020年比2015年碳强度下降18%,该时期的碳强度下降目标会进一步细化分解至各省(区)和工业领域,这些都是为了保障完成中国政府承诺2020年和2030年的碳强度下降总目标。
(2)本文的“微观”主要指编制SAM表和构建CGE模型过程中,将“活动”和“商品”细分为31个部门,这与将“活动”和“商品”看作一个整体的“宏观”作对应。因此,本文的“微观”与微观经济学的研究对象(即单个消费者、家庭和企业)是不同的。
(3)估算碳排放的公式为Cj=Ej·ECj=Ej·CEFipcc-j·NCVj·COFj,其中,Cj、Ej和ECj分别是消费第j种能源后的碳排放、能源消费量和碳排放系数,CFEipcc-j、NCVj和COFj分别是能源的缺省排放因子、平均低位发热值和碳氧化因子。
(4)该列数值中,2005-2015年的碳排放是估算碳排放,2016-2030年的碳排放是依据碳强度下降目标推算的碳排放。将2005-2030年的碳排放作为目标情景的约束值。
(5)由于SAM表数据统计口径与实际宏观经济指标并不完全一致,因此通过动态CGE模型模拟得到的2005-2015年数值和实际值存在一定误差,2012年实际GDP为54. 037万亿元,模型估计值为56. 17万亿元,误差仅为3. 9%,模型估计是可靠的。
(6)为了与以往文献同期比较,该系列数值为2013-2020年的国内销售价格变动的平均值,与表4中时期范围不同,表4列出的是2013-2030年各部门指标的平均变化,因时期更长,各能源的价格变动幅度更大。