从“鞭打快牛”到效率驱动:中国区域间碳排放权分配机制研究
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摘要
设计合理的区域间碳排放权分配机制对于中国温室气体达峰目标的实现和减排成本的优化具有重要的理论与现实意义。本文提出了一个考虑技术异质性在内的多因素碳排放权分配理论模型,明确了全国碳减排总量目标、本地区排放总量、本地区碳排放强度以及本地区碳排放效率四个因素对碳排放权分配的影响机制。利用中国企业级低碳专利数据作为低碳技术水平的代理变量,考虑碳市场试点的政策冲击,本文基于消费侧和生产侧碳排放视角,利用随机前沿模型估计了各地区的碳排放效率,并使用估算效率值对碳排放权的地区分配进行了数值模拟。结果显示,当碳排放基于消费侧责任计算时,基于产出或排放量的分配机制将分别比基于多因素的分配机制额外带来1.61%和0.47%的工业总产出损失,而当碳排放基于生产侧责任计算时,仅基于产出或排放量的分配机制将分别比基于多因素的分配机制额外带来0.68%和0.21%的工业总产出损失。与此同时,当分配机制中考虑了碳排放效率时,基于消费侧和生产侧的不同分配会使部分地区产生较大的分配差异,能源生产省份在生产侧核算下需要承担更高碳减排量,以内蒙古为例,其减排量比消费侧核算下高出约65%,而电力调入省份在消费侧核算下需要承担更高碳减排量,以河北为例,其减排量比生产侧核算下高出约83%。
Designing a reasonable regional CO_2 emission permit allocation mechanism is important for China to achieve its peak emission goal and to optimize its total emission abatement costs. The Chinese government officially launched the construction of the national emission trading scheme(ETS) at the end of 2017. The national ETS will first cover the electricity generation sector and gradually expand to other industrial sectors. The emission permits will be freely allocated using a benchmark method. There are three key questions for constructing the national ETS. First, how to ensure effectiveness so that the overall emission control target is achieved. Second, how to improve the efficiency of the carbon market to minimize total emission abatement costs. Third, how to balance social equity and the efficiency of environmental policy. To answer these questions, we must study the design of the optimal permit allocation mechanism for China's national ETS.In this paper, we start by analyzing the marginal abatement cost curve(MACC) which is separated into two parts by carbon emission efficiency. One region's potential carbon reduction amount is determined by the part that is not related to low-carbon technology. When the social planner allocates emission permits by maximizing the national total output, economic efficiency is achieved. The marginal abatement cost of each region is equalized under the first-order condition of this optimization problem. The corresponding permit allocation mechanism is a multi-criteria mechanism which includes the total emission reduction target, total emission amount, emission intensity, and carbon emission efficiency. In the empirical part, patent data related to environmentally sound technologies are used to measure the low-carbon technology development level in each region. Both consumption-based and production-based carbon emission efficiencies are estimated using stochastic frontier models and used in a numerical simulation. Looking at the carbon emission efficiency trend in different provinces, most regions show a downward drift during the 11 th Five-Year Plan period. During the 12 th Five-Year Plan period, most regions have a turning point in carbon emission efficiency and show a gradual upward trend because of the compulsory carbon emission intensity constraints. For consumption-based efficiency, output-based allocations(OBA) and emissions-based allocations(EBA) lead to 1.61 percent and 0.47 percent larger aggregate output losses than when using the multi-criteria allocation mechanism, respectively. For production-based efficiency, OBA and EBA lead to 0.68 percent and 0.21 percent larger aggregate output losses, respectively. The simulated allocation results differ between consumption-based and production-based allocations. Energy production provinces undertake more emission reduction responsibilities using production-based allocation. For example, Inner Mongolia reduces around 65 percent more emissions using consumption-based efficiency. Comparatively, electricity inflow provinces undertake more emission reduction responsibilities using consumption-based allocations. For example, Hebei reduces around 83 percent more emissions using production-based efficiency.Based on our theoretical analysis and empirical results, we give the following policy suggestions. First, when considering the allocation of carbon emission allowances, China should use multiple criteria such as output, historical emissions, and technological efficiency to avoid "spurring a willing horse". Second, under the optimal multi-criteria allocation framework, firms invest more in low-carbon technology to obtain more emission permits. Thus, endogenous low-carbon technological progress is realized in the long term. Third, permit allocation results are quite different between consumption-based and production-based allocations for some provinces. Therefore, the impacts of different allocation results on regional equity must be fully considered. Fourth, the multi-criteria allocation mechanism proposed in this paper can also be applied to firm-level permit allocations in the future.
Designing a reasonable regional CO_2 emission permit allocation mechanism is important for China to achieve its peak emission goal and to optimize its total emission abatement costs. The Chinese government officially launched the construction of the national emission trading scheme(ETS) at the end of 2017. The national ETS will first cover the electricity generation sector and gradually expand to other industrial sectors. The emission permits will be freely allocated using a benchmark method. There are three key questions for constructing the national ETS. First, how to ensure effectiveness so that the overall emission control target is achieved. Second, how to improve the efficiency of the carbon market to minimize total emission abatement costs. Third, how to balance social equity and the efficiency of environmental policy. To answer these questions, we must study the design of the optimal permit allocation mechanism for China's national ETS.In this paper, we start by analyzing the marginal abatement cost curve(MACC) which is separated into two parts by carbon emission efficiency. One region's potential carbon reduction amount is determined by the part that is not related to low-carbon technology. When the social planner allocates emission permits by maximizing the national total output, economic efficiency is achieved. The marginal abatement cost of each region is equalized under the first-order condition of this optimization problem. The corresponding permit allocation mechanism is a multi-criteria mechanism which includes the total emission reduction target, total emission amount, emission intensity, and carbon emission efficiency. In the empirical part, patent data related to environmentally sound technologies are used to measure the low-carbon technology development level in each region. Both consumption-based and production-based carbon emission efficiencies are estimated using stochastic frontier models and used in a numerical simulation. Looking at the carbon emission efficiency trend in different provinces, most regions show a downward drift during the 11 th Five-Year Plan period. During the 12 th Five-Year Plan period, most regions have a turning point in carbon emission efficiency and show a gradual upward trend because of the compulsory carbon emission intensity constraints. For consumption-based efficiency, output-based allocations(OBA) and emissions-based allocations(EBA) lead to 1.61 percent and 0.47 percent larger aggregate output losses than when using the multi-criteria allocation mechanism, respectively. For production-based efficiency, OBA and EBA lead to 0.68 percent and 0.21 percent larger aggregate output losses, respectively. The simulated allocation results differ between consumption-based and production-based allocations. Energy production provinces undertake more emission reduction responsibilities using production-based allocation. For example, Inner Mongolia reduces around 65 percent more emissions using consumption-based efficiency. Comparatively, electricity inflow provinces undertake more emission reduction responsibilities using consumption-based allocations. For example, Hebei reduces around 83 percent more emissions using production-based efficiency.Based on our theoretical analysis and empirical results, we give the following policy suggestions. First, when considering the allocation of carbon emission allowances, China should use multiple criteria such as output, historical emissions, and technological efficiency to avoid "spurring a willing horse". Second, under the optimal multi-criteria allocation framework, firms invest more in low-carbon technology to obtain more emission permits. Thus, endogenous low-carbon technological progress is realized in the long term. Third, permit allocation results are quite different between consumption-based and production-based allocations for some provinces. Therefore, the impacts of different allocation results on regional equity must be fully considered. Fourth, the multi-criteria allocation mechanism proposed in this paper can also be applied to firm-level permit allocations in the future.
引文
谌莹、张捷,2016:《碳排放、绿色全要素生产率和经济增长》,《数量经济技术经济研究》第8期。
李善同,2010:《2002年中国地区扩展投入产出表:编制与应用》,经济科学出版社。
刘卫东、唐志鹏、陈杰、杨波,2014:《2010年中国30省区市区域间投入产出表》,中国统计出版社。
苗壮、周鹏、李向民,2013:《借鉴欧盟分配原则的我国碳排放额度分配研究——基于ZSG环境生产技术》,《经济学动态》第4期。
彭水军、张文城、孙传旺,2015:《中国生产侧和消费侧碳排放量测算及影响因素研究》,《经济研究》第1期。
彭水军、张文城、卫瑞,2016:《碳排放的国家责任核算方案》,《经济研究》第3期。
乔晓楠、段小刚,2012:《总量控制、区际排污指标分配与经济绩效》,《经济研究》第10期。
汤维祺、吴力波、钱浩祺,2016:《从“污染天堂”到绿色增长——区域间高耗能产业转移的调控机制研究》,《经济研究》第6期。
魏楚、杜立民、沈满洪,2010:《中国能否实现节能减排目标:基于DEA方法的评价与模拟》,《世界经济》第3期。
吴力波、钱浩祺、汤维祺,2014:《基于动态边际减排成本模拟的碳排放权交易与碳税选择机制》,《经济研究》第9期。
徐盈之、张赟,2013:《中国区域碳减排责任及碳减排潜力研究》,《财贸研究》第2期。
张成、史丹、李鹏飞,2017:《中国实施省际碳排放权交易的潜在成效》,《财贸经济》第2期。
张伟、朱启贵、李汉文,2013:《能源使用、碳排放与我国全要素碳减排效率》,《经济研究》第10期。
张亚雄、齐舒畅,2012:《2002、2007年中国区域间投入产出表》,中国统计出版社。
周五七、聂鸣,2012:《中国工业碳排放效率的区域差异研究——基于非参数前沿的实证分析》,《数量经济技术经济研究》第9期。
朱承亮、岳宏志、师萍,2011:《环境约束下的中国经济增长效率研究》,《数量经济技术经济研究》第5期。
Battese, G. E., and T. J. Coelli, 1995, “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data”, Empirical Economics, 20(2), 325—332.
Evans, J., M. Filippini, and L. C. Hunt, 2013, “The Contribution of Energy Efficiency Towards Meeting CO2 Targets”, In: Fouquet, Roger (Ed.), Handbook on Energy and Climate Change, Edward Elgar Publishing.
Greene, W., 2005, “Fixed and Random Effects in Stochastic Frontier Models”, Journal of Productivity Analysis, 23(1), 7—32.
Groenenberg, H., and K. Blok, 2002, “Benchmark-based Emission Allocation in a Cap-and-trade System”, Climate Policy, 2(1), 105—109.
Jotzo, F., V. Karplus, M. Grubb, A. L?schel, K. Neuhoff, L. Wu, and F. Teng, 2018, “China's Emissions Trading Takes Steps Towards Big Ambitions”, Nature Climate Change, 8(4), 265.
Lehmann, P., 2012, “Justifying a Policy Mix for Pollution Control: A Review of Economic Literature”, Journal of Economic Surveys, 26(1), 71—97.
McKitrick, R., 1999, “A Derivation of the Marginal Abatement Cost Curve”, Journal of Environmental Economics and Management, 37(3), 306—314.
Meng, F., B. Su, E. Thomson, D. Zhou, and P. Zhou, 2016, “Measuring China's Regional Energy and Carbon Emission Efficiency with DEA Models: A Survey”, Applied Energy, 183, 1—21.
Ni, J., C. Wei, and L. Du, 2015, “Revealing the Political Decision Toward Chinese Carbon Abatement: Based on Equity and Efficiency Criteria”, Energy Economics, 51, 609—621.
Qu, S., S. Liang, and M. Xu, 2017, “CO2 Emissions Embodied in Interprovincial Electricity Transmissions in China”, Environmental Science & Technology, 51(18), 10893—902.
Quirion, P., 2009, “Historic Versus Output-based Allocation of GHG Tradable Allowances: A Comparison”, Climate Policy, 9(6), 575—592.
Roberts, M. J., and M. Spence, 1976, “Effluent Charges and Licenses Under Uncertainty”, Journal of Public Economics, 5(3-4), 193—208.
Sorrell, S., and J. Sijm, 2003, “Carbon Trading in the Policy Mix”, Oxford Review of Economic Policy, 19(3), 420—437.
Su, B., and B. W. Ang, 2014, “Input-output Analysis of CO2 Emissions Embodied in Trade: A Multi-region Model for China”, Applied Energy, 114, 377—384.
Weitzman, M. L., 1974, “Prices vs. Quantities”, Review of Economic Studies, 41(4), 477—491.
Yi, W., L. Zou, J. Guo, K. Wang, and Y. Wei, 2011, “How Can China Reach Its CO2 Intensity Reduction Targets by 2020 A Regional Allocation Based on Equity and Development”, Energy Policy, 39(5), 2407—2415.
Zhang, D., V. J. Karplus, C. Cassisa, and X. Zhang, 2014, “Emissions Trading in China: Progress and Prospects”, Energy Policy, 75, 9—16.
Zhang, Y. J., J. F. Hao, and J. Song, 2016, “The CO2 Emission Efficiency, Reduction Potential and Spatial Clustering in China's Industry: Evidence from the Regional Level”, Applied Energy, 174, 213—223.
Zhou, P., and M. Wang, 2016, “Carbon Dioxide Emissions Allocation: A Review”, Ecological Economics, 125, 47—59.
(1)2013年11月建设全国碳市场被列入全面深化改革的重点任务之一。2014年12月国家发改委发布《碳排放权交易管理暂行方法》,确立全国碳市场总体框架。2015年9月《中美元首气候变化联合声明》提出,中国于2017年启动全国碳排放交易体系。2017年12月国家发改委发布《全国碳排放权交易市场建设方案(发电行业)》,这标志着全国碳市场完成总体设计,正式启动。
(1)此外,Groenenberg&Blok(2002)指出基线法需要极高的基础数据要求,并且往往仅限于针对特定工艺流程和产品进行分配。
(2)由于国际间各国很难达成可以实现国际间公平的经济政策(例如国际间支付转移、技术补贴等政策),因此国际间碳减排责任分担需要在效率和公平间进行权衡。
(1)由于篇幅限制,感兴趣的读者可向作者索取完整的推导过程。
(2)其中k3=k3ei,0,由于k3表示的是拐折MACC右侧的上升速度,其几何性质不会随排放量单位的变化而改变,因此这里定义的k珋3对应的是百分比减排量的无量纲MACC。
(3)实际上C1与C2均为近似常数参数,因为这两个参数对地区i的参数并不敏感。其中C1=Σi(1/mi,1),C2=Σi(1/珟mi,1)。将它们视为常数不会对本文主要结论产生影响。
(4)由包含碳排放效率的括号项对碳排放效率进行求导,可得其导数为6TE[k2-1+k_3TE],由TE> 0且k_3> 0,可知k_2+k_3≤1时,导数必然小于0,因而TE上升将减少碳减排量分配额,即碳排放权分配额增加。由拐折MACC的几何含义可知,k_2表示右侧曲线最低点位置,而k_3表示MACC右侧部分的上升速度,当减排主体在投入有效减排技术之后会面临一段边际减排成本缓慢上升的过程,此时对应的k_2和k_3较小,即拐点右侧斜率远小于拐点左侧斜率,因而很容易满足k_2+k_3≤1的条件,这一条件也与现实情况相符。
(1)Hausman检验结果显示,卡方统计量为13. 99,小于自由度为12时且5%显著性水平所对应的临界值21. 03,因而无法拒绝原假设,模型存在随机效应。
(2)本文的无效率方程中,与低碳技术相关的变量取对数形式,与政策相关的变量为哑变量形式。
(1)在Qu et al.(2017)的研究中,采用了省际间电力流动完全消耗系数矩阵来计算省际间电力的隐含碳。但是考虑到电力与一般商品贸易不同,其生产和消费具有瞬时性、不可储存的特征,因此本文在计算电力间接排放因子时,假设电力的流动是单向且不可循环的,即采用的是电力流动直接消耗矩阵。
(2)电力间接排放因子采用国家发展和改革委员会应对气候变化司发布的《2010年中国区域及省级电网平均二氧化碳排放因子》中所提出的方法进行计算,跨省电力传输数据来源于2006—2015年《电力工业统计资料汇编》,各个能源品种的热值来自各年度《中国能源统计年鉴》以及《公共机构能源资源消耗统计制度》(2011年7月),含碳量与碳氧化率来自《省级温室气体清单编制指南(试行)》(2011年5月)。特高压输送可再生能源发电数据来自国家能源局《2016年度全国可再生能源电力发展监测评价报告》。
(3)具体调整公式为:工业总产值=工业销售产值+当年末存货-去年末存货。
(4)9 个子类指数分别为:燃料动力类、黑色金属材料类、有色金属材料类、化工原料类、木材及纸浆类、建材类、其他工业原料类、农副产品类和纺织原料类。
(5)对于缺少本年折旧数据的年份,则由前后年份进行插值运算得到缺失折旧率数据。
(1)这两个维度也是国家知识产权局数据库中对专利的分类标准,其中“创新程度”维度还包括“外观设计”这一分类,但由于低碳数据库中属于该分类的专利数量为0,因而在省级层面的分类中将该分类排除在外。
(2)七个省市分别为:北京市、上海市、天津市、重庆市、湖北省、广东省和深圳市。
(3)生产侧责任的回归结果与消费侧责任的结果基本一致,由于篇幅限制而不进行展示,感兴趣的读者可向作者索取回归结果。
(4)所有系数中仅效率方程中的“国内技术溢出”系数(即γ2)存在一定的差异,主要的原因在于对于国内技术溢出专利数据而言,授权类型(对应模型1、模型3和模型5)和已申请类型(对应模型2、模型4和模型6)的总量之间存在较大差异。同时我们看到采用同类型专利数据的模型之间,系数变动无显著差异,因而同样说明SFA模型是稳健的。
(1)变异系数的计算公式为:变异系数=标准差/平均值×100,该指标反映了变量的离散程度大小。在本文中该指标越小,表明由不同SFA模型估计得到的碳排放效率的离散程度越小,即SFA模型得到的效率估计值是稳健的。
(2)中国在哥本哈根气候变化会议之后,首次提出了控制温室气体排放的行动目标,即到2020年单位国内生产总值二氧化碳排放比2005年下降40%—45%,并且作为约束性指标纳入国民经济和社会发展中长期规划。
(1)MACC情形2的模拟结果与MACC情形1基本一致,由于篇幅限制而不进行展示,感兴趣的读者可向作者索取模拟结果。
(2)例如,基于产出分配差异=(基于产出分配配额-基于多因素分配配额)/基于多因素分配配额。
(3)基于两种排放责任视角下的全国工业总排放量存在差异,因此无法对排放权分配结果进行比较,但是在情景S1下,两种视角下的全国总减排量均为2. 59亿吨,因此具有可比性。故在这部分的讨论中着重讨论各区域在排放权分配后所承担减排量的差异。
(1)除以上几种参数设定外,我们还尝试了满足假设条件下不同参数在不同情景下的模拟结果,发现结果都是稳健的。由于篇幅限制,本文只列出了部分减排情景和几类参数选择下的模拟结果,感兴趣的读者可向作者索取完整结果。
李善同,2010:《2002年中国地区扩展投入产出表:编制与应用》,经济科学出版社。
刘卫东、唐志鹏、陈杰、杨波,2014:《2010年中国30省区市区域间投入产出表》,中国统计出版社。
苗壮、周鹏、李向民,2013:《借鉴欧盟分配原则的我国碳排放额度分配研究——基于ZSG环境生产技术》,《经济学动态》第4期。
彭水军、张文城、孙传旺,2015:《中国生产侧和消费侧碳排放量测算及影响因素研究》,《经济研究》第1期。
彭水军、张文城、卫瑞,2016:《碳排放的国家责任核算方案》,《经济研究》第3期。
乔晓楠、段小刚,2012:《总量控制、区际排污指标分配与经济绩效》,《经济研究》第10期。
汤维祺、吴力波、钱浩祺,2016:《从“污染天堂”到绿色增长——区域间高耗能产业转移的调控机制研究》,《经济研究》第6期。
魏楚、杜立民、沈满洪,2010:《中国能否实现节能减排目标:基于DEA方法的评价与模拟》,《世界经济》第3期。
吴力波、钱浩祺、汤维祺,2014:《基于动态边际减排成本模拟的碳排放权交易与碳税选择机制》,《经济研究》第9期。
徐盈之、张赟,2013:《中国区域碳减排责任及碳减排潜力研究》,《财贸研究》第2期。
张成、史丹、李鹏飞,2017:《中国实施省际碳排放权交易的潜在成效》,《财贸经济》第2期。
张伟、朱启贵、李汉文,2013:《能源使用、碳排放与我国全要素碳减排效率》,《经济研究》第10期。
张亚雄、齐舒畅,2012:《2002、2007年中国区域间投入产出表》,中国统计出版社。
周五七、聂鸣,2012:《中国工业碳排放效率的区域差异研究——基于非参数前沿的实证分析》,《数量经济技术经济研究》第9期。
朱承亮、岳宏志、师萍,2011:《环境约束下的中国经济增长效率研究》,《数量经济技术经济研究》第5期。
Battese, G. E., and T. J. Coelli, 1995, “A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data”, Empirical Economics, 20(2), 325—332.
Evans, J., M. Filippini, and L. C. Hunt, 2013, “The Contribution of Energy Efficiency Towards Meeting CO2 Targets”, In: Fouquet, Roger (Ed.), Handbook on Energy and Climate Change, Edward Elgar Publishing.
Greene, W., 2005, “Fixed and Random Effects in Stochastic Frontier Models”, Journal of Productivity Analysis, 23(1), 7—32.
Groenenberg, H., and K. Blok, 2002, “Benchmark-based Emission Allocation in a Cap-and-trade System”, Climate Policy, 2(1), 105—109.
Jotzo, F., V. Karplus, M. Grubb, A. L?schel, K. Neuhoff, L. Wu, and F. Teng, 2018, “China's Emissions Trading Takes Steps Towards Big Ambitions”, Nature Climate Change, 8(4), 265.
Lehmann, P., 2012, “Justifying a Policy Mix for Pollution Control: A Review of Economic Literature”, Journal of Economic Surveys, 26(1), 71—97.
McKitrick, R., 1999, “A Derivation of the Marginal Abatement Cost Curve”, Journal of Environmental Economics and Management, 37(3), 306—314.
Meng, F., B. Su, E. Thomson, D. Zhou, and P. Zhou, 2016, “Measuring China's Regional Energy and Carbon Emission Efficiency with DEA Models: A Survey”, Applied Energy, 183, 1—21.
Ni, J., C. Wei, and L. Du, 2015, “Revealing the Political Decision Toward Chinese Carbon Abatement: Based on Equity and Efficiency Criteria”, Energy Economics, 51, 609—621.
Qu, S., S. Liang, and M. Xu, 2017, “CO2 Emissions Embodied in Interprovincial Electricity Transmissions in China”, Environmental Science & Technology, 51(18), 10893—902.
Quirion, P., 2009, “Historic Versus Output-based Allocation of GHG Tradable Allowances: A Comparison”, Climate Policy, 9(6), 575—592.
Roberts, M. J., and M. Spence, 1976, “Effluent Charges and Licenses Under Uncertainty”, Journal of Public Economics, 5(3-4), 193—208.
Sorrell, S., and J. Sijm, 2003, “Carbon Trading in the Policy Mix”, Oxford Review of Economic Policy, 19(3), 420—437.
Su, B., and B. W. Ang, 2014, “Input-output Analysis of CO2 Emissions Embodied in Trade: A Multi-region Model for China”, Applied Energy, 114, 377—384.
Weitzman, M. L., 1974, “Prices vs. Quantities”, Review of Economic Studies, 41(4), 477—491.
Yi, W., L. Zou, J. Guo, K. Wang, and Y. Wei, 2011, “How Can China Reach Its CO2 Intensity Reduction Targets by 2020 A Regional Allocation Based on Equity and Development”, Energy Policy, 39(5), 2407—2415.
Zhang, D., V. J. Karplus, C. Cassisa, and X. Zhang, 2014, “Emissions Trading in China: Progress and Prospects”, Energy Policy, 75, 9—16.
Zhang, Y. J., J. F. Hao, and J. Song, 2016, “The CO2 Emission Efficiency, Reduction Potential and Spatial Clustering in China's Industry: Evidence from the Regional Level”, Applied Energy, 174, 213—223.
Zhou, P., and M. Wang, 2016, “Carbon Dioxide Emissions Allocation: A Review”, Ecological Economics, 125, 47—59.
(1)2013年11月建设全国碳市场被列入全面深化改革的重点任务之一。2014年12月国家发改委发布《碳排放权交易管理暂行方法》,确立全国碳市场总体框架。2015年9月《中美元首气候变化联合声明》提出,中国于2017年启动全国碳排放交易体系。2017年12月国家发改委发布《全国碳排放权交易市场建设方案(发电行业)》,这标志着全国碳市场完成总体设计,正式启动。
(1)此外,Groenenberg&Blok(2002)指出基线法需要极高的基础数据要求,并且往往仅限于针对特定工艺流程和产品进行分配。
(2)由于国际间各国很难达成可以实现国际间公平的经济政策(例如国际间支付转移、技术补贴等政策),因此国际间碳减排责任分担需要在效率和公平间进行权衡。
(1)由于篇幅限制,感兴趣的读者可向作者索取完整的推导过程。
(2)其中k3=k3ei,0,由于k3表示的是拐折MACC右侧的上升速度,其几何性质不会随排放量单位的变化而改变,因此这里定义的k珋3对应的是百分比减排量的无量纲MACC。
(3)实际上C1与C2均为近似常数参数,因为这两个参数对地区i的参数并不敏感。其中C1=Σi(1/mi,1),C2=Σi(1/珟mi,1)。将它们视为常数不会对本文主要结论产生影响。
(4)由包含碳排放效率的括号项对碳排放效率进行求导,可得其导数为6TE[k2-1+k_3TE],由TE> 0且k_3> 0,可知k_2+k_3≤1时,导数必然小于0,因而TE上升将减少碳减排量分配额,即碳排放权分配额增加。由拐折MACC的几何含义可知,k_2表示右侧曲线最低点位置,而k_3表示MACC右侧部分的上升速度,当减排主体在投入有效减排技术之后会面临一段边际减排成本缓慢上升的过程,此时对应的k_2和k_3较小,即拐点右侧斜率远小于拐点左侧斜率,因而很容易满足k_2+k_3≤1的条件,这一条件也与现实情况相符。
(1)Hausman检验结果显示,卡方统计量为13. 99,小于自由度为12时且5%显著性水平所对应的临界值21. 03,因而无法拒绝原假设,模型存在随机效应。
(2)本文的无效率方程中,与低碳技术相关的变量取对数形式,与政策相关的变量为哑变量形式。
(1)在Qu et al.(2017)的研究中,采用了省际间电力流动完全消耗系数矩阵来计算省际间电力的隐含碳。但是考虑到电力与一般商品贸易不同,其生产和消费具有瞬时性、不可储存的特征,因此本文在计算电力间接排放因子时,假设电力的流动是单向且不可循环的,即采用的是电力流动直接消耗矩阵。
(2)电力间接排放因子采用国家发展和改革委员会应对气候变化司发布的《2010年中国区域及省级电网平均二氧化碳排放因子》中所提出的方法进行计算,跨省电力传输数据来源于2006—2015年《电力工业统计资料汇编》,各个能源品种的热值来自各年度《中国能源统计年鉴》以及《公共机构能源资源消耗统计制度》(2011年7月),含碳量与碳氧化率来自《省级温室气体清单编制指南(试行)》(2011年5月)。特高压输送可再生能源发电数据来自国家能源局《2016年度全国可再生能源电力发展监测评价报告》。
(3)具体调整公式为:工业总产值=工业销售产值+当年末存货-去年末存货。
(4)9 个子类指数分别为:燃料动力类、黑色金属材料类、有色金属材料类、化工原料类、木材及纸浆类、建材类、其他工业原料类、农副产品类和纺织原料类。
(5)对于缺少本年折旧数据的年份,则由前后年份进行插值运算得到缺失折旧率数据。
(1)这两个维度也是国家知识产权局数据库中对专利的分类标准,其中“创新程度”维度还包括“外观设计”这一分类,但由于低碳数据库中属于该分类的专利数量为0,因而在省级层面的分类中将该分类排除在外。
(2)七个省市分别为:北京市、上海市、天津市、重庆市、湖北省、广东省和深圳市。
(3)生产侧责任的回归结果与消费侧责任的结果基本一致,由于篇幅限制而不进行展示,感兴趣的读者可向作者索取回归结果。
(4)所有系数中仅效率方程中的“国内技术溢出”系数(即γ2)存在一定的差异,主要的原因在于对于国内技术溢出专利数据而言,授权类型(对应模型1、模型3和模型5)和已申请类型(对应模型2、模型4和模型6)的总量之间存在较大差异。同时我们看到采用同类型专利数据的模型之间,系数变动无显著差异,因而同样说明SFA模型是稳健的。
(1)变异系数的计算公式为:变异系数=标准差/平均值×100,该指标反映了变量的离散程度大小。在本文中该指标越小,表明由不同SFA模型估计得到的碳排放效率的离散程度越小,即SFA模型得到的效率估计值是稳健的。
(2)中国在哥本哈根气候变化会议之后,首次提出了控制温室气体排放的行动目标,即到2020年单位国内生产总值二氧化碳排放比2005年下降40%—45%,并且作为约束性指标纳入国民经济和社会发展中长期规划。
(1)MACC情形2的模拟结果与MACC情形1基本一致,由于篇幅限制而不进行展示,感兴趣的读者可向作者索取模拟结果。
(2)例如,基于产出分配差异=(基于产出分配配额-基于多因素分配配额)/基于多因素分配配额。
(3)基于两种排放责任视角下的全国工业总排放量存在差异,因此无法对排放权分配结果进行比较,但是在情景S1下,两种视角下的全国总减排量均为2. 59亿吨,因此具有可比性。故在这部分的讨论中着重讨论各区域在排放权分配后所承担减排量的差异。
(1)除以上几种参数设定外,我们还尝试了满足假设条件下不同参数在不同情景下的模拟结果,发现结果都是稳健的。由于篇幅限制,本文只列出了部分减排情景和几类参数选择下的模拟结果,感兴趣的读者可向作者索取完整结果。