Economic Analysis of CO2 Emission Trends in China

Climate change is one of hot spots all around the world. China, the second biggest CO₂ emitter, is facing increasingly severe pressure to reduce CO₂ emission. The article first describes Kaya Identity and its policy implications. Second, it uses the modified Kaya Identity and makes decomposition without residues on CO₂ emission during the period 1971-2005. Taking into account the changes of macroeconomic background, it conducts a detailed analysis in terms of CO₂ emission trend from 4^th Five Year Plan through 10^th Five Year Plan. The decomposition results indicate that economic development and increase in population are major driving forces, and that improvement in energy efficiency contributes to the reduction of CO₂ emission, and that decarbonization in primary energy structure is also an important strategic choice. Finally, the article stresses that in CO₂ order to realize the binding target of 20% reduction in GDP energy intensity during the 11^th Five Year Plan, China should speed up the readjustment of the industrial structure and energetically develop the energy-efficient technologies and clean fuel technology, which will effectively promote the country to reduce CO₂ emission and contribute to the mitigation of climate change.     

西安高新区多情景碳达峰预测及减排路径分析

第十三届全国人民代表大会第五次会议提出要致力于推进碳达峰碳中和工作,促进经济社会向全面绿色低碳转型,实现高质量发展.西安高新区作为陕西省重要的科技创新和产业聚集区,经济发展在很大程度上依赖于能源消耗,碳减排的任务就显得尤为艰巨.以西安高新区为研究对象,首先通过系统核算园区内碳排放,对不同能源种类和不同行业企业碳排放现状进行分析;然后利用Kaya模型设定多种独立的碳达峰情景,预测不同情景下的碳排放总量值及碳达峰时间;最后结合西安高新区发展特点科学甄选相应的碳减排路径,给出合理的减排建议.结果表明,目前电力消耗碳排放占比最多且份额呈逐年上升趋势,工业碳排量始终占主导地位且第三产业发展日益蓬勃;碳排放因子情景、能源强度情景和经济水平情景这3种情景下可于2030年达到碳达峰,其中经济发展水平对西安高新区未来碳达峰的峰值和时间影响最大,产业结构情景、能源结构情景和人口规模情景在2030年前没有出现峰值;未来减排路径主要从电力部门脱碳、经济稳健高质量发展、能源及产业结构绿色升级和构建绿色交通体系入手,可为实现碳中和预留更多的准备时间,也为我国工业园区低碳发展提供决策参考.     

基于Kaya方法的四个直辖市碳效率评价与比较

应用Kaya恒等式的计算方法对我国4个直辖市2005-2009年的碳效率进行了评价和比较分析,结果显示:4个直辖市碳效率的排序在2005-2009年间没有发生变化,4个城市的碳效率都处于上升态势。     

基于扩展STIRPAT模型LMDI分解的碳排放脱钩因素

为研究经济发展与碳排放的脱钩情况,常用对数平均迪式指数分解法（LMDI）结合Kaya恒等式和Tapio脱钩模型计算碳变化量和弹性脱钩指数.借鉴上述方法,将STIRPAT模型与LMDI分解法相结合,建立STIRPAT模型的回归系数与碳变化量和脱钩弹性指数之间的数量关系,研究影响碳排放各因素的脱钩状态.结果表明：(1)STIRPAT模型LMDI分解法能够避免满足Kaya恒等式的IPAT模型中使用LMDI分解法时增加新变量的情况,部分新增变量往往缺乏明确的经济学含义;(2)LMDI分解将STIRPAT模型中的统计回归系数的含义,由变量的变动引起碳排放量变动的弹性系数,扩展到变量的变动引起碳变化量的倍数;(3)STIRPAT模型LMDI分解法,将数据的统计结果通过统计回归系数纳入到各因素的碳变化量和弹性脱钩指数之中,使弹性脱钩指数能够反映数据的统计信息;(4)以重庆市2001～2019年碳排放数据为例,来说明STIRPAT模型LMDI分解法可以用于判定碳排放变量的脱钩状态,能够体现数据本身所包含的统计信息,更能反映研究对象的实际情况.     

华中地区种植业生产碳排放驱动因素分析

农业是温室气体排放的第二大人为因素源,探寻农业生产碳排影响因素,对实现农业节能减排有重要意义。以中国粮食主产区华中地区为背景,综合运用IPCC(2006)推荐的方法估算华中地区1994~2013年种植业生产的碳排放量,基于Kaya恒等式、灰色关联模型对华中地区种植业生产的碳排放驱动因素进行识别并探讨主要影响因素的贡献。结果显示:(1)华中地区1994~2013年种植业生产碳排放呈上升趋势,2013年达到了11 257.63万t CO2-eq。其中,河南省、湖北省、湖南省的种植业生产碳排放增幅分别为101.29%、24.88%、21.73%;(2)在过去的20 a中,种植业生产效率、种植业结构、农业劳动力规模对农业生产碳排放具有一定抑制作用,而农业经济发展则促进了种植业生产碳排,具有一定的推动作用;(3)近20 a的农业发展过程中,华中地区种植业生产碳排放最主要的贡献因子是种植业结构,其次是农业从业人口、种植业产值、人均农用物资消耗量。     

基于中欧对比视角的货运机动车尾气排放PM2.5分析研究

基于中欧对比视角,本研究运用扩展的Kaya模型以及LMDI(Logarithmic-Mean Divisia Index)对货运机动车尾气排放PM_(2.5)的影响因素进行分析.分析表明:尽管中欧货运机动车PM_(2.5)排放近年来持续下降,但中国的排放强度远高于欧盟15国(EU15)平均水平.从分解因素来看,货运量增加是中国货运机动车PM_(2.5)排放增加的最主要影响因素,但该因素的作用自2013年以来极为微小.而EU15货运机动车PM_(2.5)排放量增加最主要的因素是较高的用车次数.此外,越来越严格的排放标准为中国和EU15降低PM_(2.5)排放都做出了突出的贡献.     

Can Advances in Science and Technology Prevent Global Warming?

The most stringent emission scenarios published by the Intergovernmental Panel on Climate Change (IPCC) would result in the stabilization of atmospheric carbon dioxide (CO₂) at concentrations of approximately 550 ppm which would produce a global temperature increase of at least 2 ^{^}C by 2100. Given the large uncertainties regarding the potential risks associated with this degree of global warming, it would be more prudent to stabilize atmospheric CO₂ concentrations at or below current levels which, in turn, would require more than 20-fold reduction (i.e., ≥95%) in per capita carbon emissions in industrialized nations within the next 50–100 years. Using the Kaya equation as a conceptual framework, this paper examines whether CO₂ mitigation approaches such as energy efficiency improvements, carbon sequestration, and the development of carbon-free energy sources would be sufficient to bring about the required reduction in per capita carbon emissions without creating unforeseen negative impacts elsewhere. In terms of energy efficiency, large improvements (≥5-fold) are in principle possible through aggressive investments in R&D and the removal of market imperfections such as corporate subsidies. However, energy efficiency improvements per se will not result in a reduction in carbon emissions if, as predicted by the IPCC, the size of the global economy expands 12–26-fold by 2100. Terrestrial carbon sequestration via reforestation and improved agricultural soil management has many environmental advantages, but has only limited CO₂ mitigation potential because the global terrestrial carbon sink (ca. 200 Gt C) is small relative to the size of fossil fuel deposits (≥4000 Gt C). By contrast, very large amounts of CO₂ can potentially be removed from the atmosphere via sequestration in geologic formations and oceans, but carbon storage is not permanent and is likely to create many unpredictable environmental consequences. Renewable energy can in theory provide large amounts of carbon-free power. However, biomass and hydroelectric energy can only be marginally expanded, and large-scale solar energy installations (i.e., wind, photovoltaics, and direct thermal) are likely to have significant negative environmental impacts. Expansion of nuclear energy is highly unlikely due to concerns over reactor safety, radioactive waste management, weapons proliferation, and cost. In view of the serious limitations and liabilities of many proposed CO₂ mitigation approaches, it appears that there remain only few no-regrets options such as drastic energy efficiency improvements, extensive terrestrial carbon sequestration, and cautious expansion of renewable energy generation. These promising CO₂ mitigation technologies have the potential to bring about the required 20-fold reduction in per capita carbon emission only if population and economic growth are halted without delay. Therefore, addressing the problem of global warming requires not only technological research and development but also a reexamination of core values that equate material consumption and economic growth with happiness and well- being.     

中国农业碳排放时空特征及影响因素分解

农业生产所导致碳排放大幅增加以及引发的环境问题,越来越受到人们的关注.本研究基于农业生产中6个主要方面的碳源,测算了我国1993-2008年农业碳排放量.发现自1993年以来我国农业碳排放处于阶段性的上升态势,总体上可分为快速增长期、缓慢增长期、增速反弹回升期、增速明显放缓期等四个变化阶段.其中农业碳排放总量和强度年平均增长率分别为4.08％、2.38％.农业碳排放总量较高地区主要集中在农业大省,农业碳排放强度较高地区主要集中在发达城市、东部沿海发达省份和中部农韭大省.进一步通过Kaya恒等式变形对农业碳排放影响因素进行分解研究,结果表明,效率因素、结构因素、劳动力规模因素对碳排放量具有一定的抑制作用,1994-2008年与基期相比分别累计实现12.95％、26.62％、33.29％的碳减排,而农业经济发展则对农业碳排放具有较强推动作用,累计产生154.94％的碳增量.最后,据此提出促进农业减排的政策建议.     

环渤海地区碳排放影响因素分解及其差异研究

在Kaya公式的基础上对环渤海地区碳排放影响因素进行了分解,并对各省市碳排放状况进行了对比分析。结果发现,环渤海地区碳排放量大致呈现持续均匀的增长趋势。其碳排放主要受能源结构、碳排放系数、能源强度、产业结构、经济规模5个因素影响。各省市碳排放量均呈加速增长趋势,能源结构仍以煤炭为主,产业结构有待优化。结合研究结果,从加强低碳技术创新、优化能源消费结构、开发绿色能源、宣传低碳观念角度提出了相关碳减排建议。     

International inequality in CO2 emissions: A new factorial decomposition based on Kaya factors

In this paper, we analyze the determinants of inequality in the global distribution of CO₂ emissions across the regions considered by the International Energy Agency during the period 1990–2010. The inequality analysis is carried out using a factorial decomposition of the second Theil index of inequality. Specifically, based on Kaya factors, CO₂ emissions by active population are decomposed into four factors: carbon intensity of electricity production, electricity intensity of GDP, economic growth in terms of labour productivity and employment rate. The results show that global inequality in CO₂ emissions by active population declined by 22 percent between 1990 and 2010, where the economic growth in terms of labour productivity is the main item responsible for the whole inequality value. Then, a second decomposition by multiplying factors for analyzing the within- and the between-group inequality components is described. In relation to the study of inequality by population groups, it was found that the within-group inequality component had been the main contributor to the whole inequality during all the period. Finally, some economic policy implications are discussed.