Spatial and temporal heterogeneity of the impact of per capita income on household indirect carbon emissions in western China

With China entering the stage of high-quality development,the issue of carbon emission has become a hot research topic.This paper analyzes the different temporal and spatial effects of per capita income on household indirect carbon emissions in western China.Based on the data of Chinese Family Panel Studies(CFPS)in 2016 and 2018 in the western China,this paper uses Regression analysis and Bayesian correlation analysis to study the relationship between per capita income and household indirect carbon emissions.The results showed that the indirect carbon emissions generated by the expenditure on food,housing and household equipment in the household consumption structure in the western China were relatively high.In 2016-2018,the per capita income and per capita household consumption indirect carbon emissions in the western China showed an increasing trend.There was a positive correlation between per capita income and indirect carbon emissions of per capita household consumption,and its correlation was gradually enhanced in time dimension.In the spatial dimension,the household indirect carbon emissions in Yunnan,Qinghai,Guangxi Zhuang and Ningxia in the western China were greatly affected by per capita income,while the household indirect carbon emissions in Guizhou was least affected by per capita income.Finally,the paper puts forward some problems that we should consider in the process of facing the per capita income growth and climate change:the collection of carbon tax,the optimization of household consumption structure,the research and development of low-carbon products,and the differentiated carbon reduction.

Factor Decomposition and Regression Analysis of the Energy Related Carbon Emissions in Shandong, China: A Perspective of Industrial Structure

An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China.Based on the perspective of industrial structure,the expanded KAYA equation to measure the energy related carbon emissions of the primary industries(Resources and Agriculture)and secondary industries(Manufacturing and Construction)and tertiary industries(Retail and Service)was utilized in Shandong Province from 2011 to 2017.The carbon emissions among industries in Shandong Province were empirically analyzed using the Logarithmic Mean Divisia Index decomposition approach.The results were follows:(1)Under the three industrial dimensions,the energy structure effect and the energy intensity effect have a restraining influence on the carbon emissions of the three industries.(2)The development level effect and the employment scale effect play a pulling role in carbon emissions.(3)From the perspective of the employment structure effect of the primary industry,there is a restraining effect on carbon emissions,while the employment structure effects of the secondary and tertiary industries play a pulling role in carbon emissions,and the employment structure effect of the tertiary industry has a greater pulling effect on carbon emissions than the secondary industry.

The Application of Two-stage Structure Decomposition Technique to the Study of Industrial Carbon Emissions

The total carbon emissions control is the ultimate goal of carbon emission reduction, while industrial carbon emissions are the basic units of the total carbon emission. On the basis of existing research results, in this paper, a two-stage input-output structure decomposition method is creatively proposed for fully combining the input-output method with the structure decomposition technique. In this study, more comprehensive technical progress indicators were chosen in comparison with the previous studies and included the utilization efficiency of all kinds of intermediate inputs such as energy and non-energy products, and finally were positioned at the factors affecting the carbon emissions of different industries. Through analysis, the affecting rate of each factor on industrial carbon emissions was acquired. Thus, a theory basis and data support is provided for the total carbon emissions control of China from the perspective of industrial emissions.

The Factor Decomposition on Carbon Emission of China——Based on LMDI Decomposition Technology

Carbon emission is the current hot issue of global concern. How to assess various contributing factors for carbon emission is of great importance to find out the key factors and promote carbon emission reduction. In this paper, the author constructs an identical equation for carbon emission, based on the economic aggregate, the economic structure, the efficiency of energy utilization, the structure of energy consumption, and the coefficient of carbon emission; by applying to LMDI decomposition technology, the author analyzes the carbon emission of China from 1995 to 2007 at industrial level and regional level. The results show that the expansion of economic aggregate is the main reason for China' s rapidly increasing carbon emission and the increase of energy utilization efficiency is the key factor that can hold back the increase of carbon emission. In addition, the change of industrial structure or regional structure and the change of traditional energy structure have limited influence on the carbon emission, and their potentials have not yet been exploited. At the end of this paper, the author proposes the efforts that China should make to reduce carbon emission.

Carbon Emission of Regional Land Use and Its Decomposition Analysis: Case Study of Nanjing City, China

Through the matching relationship between land use types and carbon emission items, this paper estimated carbon emissions of different land use types in Nanjing City, China and analyzed the influencing factors of carbon emissions by Logarithmic Mean Divisia Index（LMDI） model. The main conclusions are as follows： 1） Total anthropogenic carbon emission of Nanjing increased from 1.22928 ×10^7 t in 2000 to 3.06939 × 10^7 t in 2009, in which the carbon emission of Inhabitation, mining ＆ manufacturing land accounted for 93% of the total. 2） The average land use carbon emission intensity of Nanjing in 2009 was 46.63 t/ha, in which carbon emission intensity of Inhabitation, mining ＆ manufacturing land was the highest（200.52 t/ha）, which was much higher than that of other land use types. 3） The average carbon source intensity in Nanjing was 16 times of the average carbon sink intensity（2.83 t/ha） in 2009, indicating that Nanjing was confronted with serious carbon deficit and huge carbon cycle pressure. 4） Land use area per unit GDP was an inhibitory factor for the increase of carbon emissions, while the other factors were all contributing factors. 5） Carbon emission effect evaluation should be introduced into land use activities to formulate low-carbon land use strategies in regional development.

Study on the Carbon Emission Factors in Guangdong Province Based on Divisia Decomposition Method

[Objective] By decomposing and studying the relative factors of carbon emissions in Guangdong Province,the policy and suggestion on further keeping the sustainable development were put forward,which provided the reference for the carbon emission reduction in other provinces.[Method] Based on the carbon emissions formula which was put forward by Johan,three factors(the energy structure,energy efficiency and economy development) which affected the carbon emissions during 1996-2009 in Guangdong Province were studied by using Divisia decomposition method of logarithmic mean weight(LMD).[Result] The economy development was the main reason that caused the continuous significant increase of carbon emissions in Guangdong Province.The improvement of energy efficiency was the important manner for decreasing the energy consumption and the carbon emissions.The adjustment and optimization of energy consumption structure had the huge potential for reducing the carbon emissions in Guangdong Province.[Conclusion] The carbon emissions in Guangdong Province would continue to increase in the future for a long time.When formulated the development strategy in the future,it needed pay special attention to keep the accord development of economy and environment.

Decomposition analysis of carbon reduction measures and carbon emission changes in five areas in China

Based on the analysis of primary energy consumption structure in five main provinces or municipalities in China,the factors that affect carbon emissions in the five study areas are analyzed quantitatively and comparatively with the decomposition analysis method.Empirical results demonstrate that the decomposition models of carbon emissions can be defined as ＂municipality model＂ and ＂provincial model＂,and the population factor of ＂municipal model＂ plays a significant role in carbon emissions than that of ＂provincial model＂.Either positive or negative effects of energy structure can be found in five different areas.However,there is a general trend that energy structure effort is becoming more and more important.Based on the characteristics and trends of carbon emissions in different areas,the carbon reduction measures are proposed as well.

Analysis of transportation carbon emissions and its potential for reduction in China

The transportation industry is an essential sector for carbon emissions mitigation.This paper firstly used the LMDI(Logarithmic Mean Divisia Index)decomposition method to establish factors decomposition model on China's transportation carbon emission.Then,a quantitative analysis was performed to study the factors influencing China's transportation carbon emissions from 1991 to 2008,which are identified as transportation energy efficiency,transportation structure and transportation development.The results showed that:(1)The impact of transportation development on transportation carbon emissions showed pulling function.Its contribution value to carbon emissions remained at high growth since 1991 and showed an exponential growth trend.(2)The impact of transportation structure on transportation carbon emissions showed promoting function in general,but its role in promoting carbon emissions decreased year by year.And with the continuous optimization of transportation structure,the promoting effect decreased gradually and showed the inversed"U"trend.(3)The impact of transportation energy efficiency on transportation carbon emissions showed a function of inhibition before pulling.In order to predict the potential of carbon emission reduction,three scenarios were set.Analysis of the scenarios showed that if greater intensity emission reduction measures are taken,the carbon emissions will reduce by 31.01 million tons by 2015 and by 48.81 million tons by 2020.

Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses

Low-carbon economic development is a strategy that is emerging in response to global climate change. Being the third-largest energy base in the world, Central Asia should adopt rational and efficient energy utilization to achieve the sustainable economic development. In this study, the logarithmic mean Divisia index(LMDI) decomposition method was used to explore the influence factors of CO2 emissions in Central Asia(including Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan) during the period 1992–2014. Moreover, decoupling elasticity and decoupling index based on the LMDI decomposition results were employed to explore the relationship between economic growth and CO2 emissions during the study period. Our results show that the total CO2 emissions decreased during the period 1992–1998, influenced by the collapse of the Soviet Union in 1991 and the subsequent financial crisis. After 1998, the total CO2 emissions started to increase slowly along with the economic growth after the market economic reform. Energy-related CO2 emissions increased in Central Asia, mainly driven by economic activity effect and population effect, while energy intensity effect and energy carbon structure effect were the primary factors inhibiting CO2 emissions. The contribution percentages of these four factors(economic activity effect, population effect, energy intensity effect and energy carbon structure effect) to the total CO2 emissions were 11.80%, 39.08%, –44.82% and –4.32%, respectively, during the study period. Kazakhstan, Uzbekistan and Turkmenistan released great quantities of CO2 with the annual average emissions of 189.69×106, 45.55×106 and 115.38×106 t, respectively. In fact, their economic developments depended on high-carbon energies. The decoupling indices clarified the relationship between CO2 emissions and economic growth, highlighting the occurrence of a ’’weak decoupling’’ between these two variables in Central Asia. In conclusion, our results indicate that CO2 emissions are still not completely decoupled from economic growth in Central Asia. Based on these results, we suggest four key policy suggestions in this paper to help Central Asia to reduce CO2 emissions and build a resource-conserving and environment-friendly society.

How Does Environmental Regulation Affect Carbon Emissions? —Evidence from China

In this paper, the increment of CO2 emissions from 285 prefecture-level cities in China from 2004 to 2013 is decomposed into scale effect, composition effect and technique effect. Based on the decomposition results, the effects of different environmental regulation on carbon emission increment and its decomposition effect were studied by using multiple regression methods. The results show that carbon emission environmental regulation, sulfur dioxide emission environmental regulation and electricity prices also significantly reduce the city’s carbon emissions, in which carbon emission reduction environmental regulation has the best effect, followed by sulfur dioxide emission reduction environmental regulation. Different environmental regulations do not significantly reduce urban carbon emissions in terms of composition effects, and even increase carbon emissions of cities. In addition, the impacts of different environmental regulations on the cities in the two control zones and the non-control zone were studied. Compared with non-control zone cities, environmental regulations for carbon emission and SO2 have a better effect on carbon emission reduction in cities of two control zones.

Research on the Influencing Factors of Changes in Industrial Carbon Emissions in China: Based on Simplified D&L Model

The simplified D＆L method with special properties required by ideal decomposition method was used to decompose the impact of carbon emission intensity,input-output technology,the final demand structure and the final demand level on changes in industrial carbon emissions in China during 1997-2012. The results showed that the final demand level which was the most important factor leading to the growth of carbon emissions performed a steadily and significantly positive and sustained effect. The carbon emissions intensity which was the only factor that led to the reduction in carbon emissions showed a negative effect. The input-output technology showed a positive effect. The final demand structure underwent a transition from a negative effect to a weak positive effect and finally to a positive effect. In order to achieve the goal of total carbon emission control,China should take some measures such as reducing direct carbon emission coefficient to strengthen the negative impact of carbon emission intensity. Meanwhile,China should implement some structural adjustment measures such as optimizing the final demand structure and reducing the proportion of industries with a great increase of influence coefficient to change the input-output technology and the final demand structure from the positive effect to the negative effect.

Carbon Emissions in International Trade： Calculating Size and Causes

Due to ongoing growth in carbon emission, many governments＇ have taken measures to curb it. Thus, it is of great importance to measure carbon emissions in international trade and probe into the causes behind them. This paper first applies inputoutput model then it estimates the carbon emissions embodied in China＇s international trade in 2002, 2005 and 2007. Using structural decomposition analysis, this paper measures carbon emissions in international trade by their scale effect, composition effect and intensity effect. Our results illustrate： （1） a rapidly-rising net export of carbon emissions for China, and （2） scale effect and composition effect stimulates carbon emissions embodied in exports, while intensity effect discourages it.

Driving factors of direct greenhouse gas emissions from China's pig industry from 1976 to 2016

Livestock cultivation is a significant source of greenhouse gas(GHG) emissions, accounting for 14.5% of the total anthropogenic emissions. China is responsible for a considerable share of the global livestock emissions, particularly caused by pork production. We used the Kaya identity and the logarithmic mean Divisia index(LMDI) to decompose the national annual GHG emissions from enteric fermentation and manure management in pig farming in China from 1976 to 2016. We decomposed the sources of the emissions into five driving factors:(1) technological progress(e.g., feed improvement);(2) structural adjustment in the livestock sector;(3) structural adjustment in agriculture;(4) affluence;and(5) population growth. The results showed that the net GHG emissions from the pig sector in China increased 16 million tons(Mt) of carbon dioxide equivalents(CO2 eq) during the study period. The decomposition analysis revealed that structural adjustment in agriculture, growing affluence, and population growth contributed to an increase of the GHG emissions of pork production by 23, 41, and 13 Mt CO2 eq, respectively. The technological progress and structural changes in animal husbandry mitigated emissions by –51 and –11 Mt CO2 eq, respectively. Further technological progress in pig production and optimizing the economic structures are critical for further reducing GHG emissions in China's pig industry. Our results highlight the dominant role of technological changes for emission reductions in the pig farming.

Decomposition Analysis of Carbon Emissions Growth of Tertiary Industry in Beijing

The output as well as carbon dioxide emissions of tertiary industry have increased continuously in Beijing. Therefore, the tertiary industry has become a new ifeld that needs to be explored for energy saving and emission reduction. This paper calculates the direct and indirect carbon dioxide emissions of tertiary industry in Beijing from 2005 to 2012 using the input-output analysis method. The results indicate that both the direct and indirect carbon dioxide emissions increased continuously and the latter grew more quickly than the former. The growth of the direct and indirect carbon dioxide emissions of tertiary industry was decomposed, which showed that four factors inlfuence the growth of carbon emission of the tertiary industry, including the changes of industrial scale, industrial structure, energy consumption intensity and carbon coefifcient. The results show that the industrial scale advancement and the direct energy carbon coefifcient change promote the increase of direct carbon dioxide emissions, while the shift of industrial structure and the change of direct energy consumption intensity have the restrain on it. The industrial scale enlargement, industrial structure shift and the indirect energy carbon emission coefifcient change promote the growth of indirect carbon dioxide emissions, while the indirect energy consumption intensity change has a restrain.

中国四大城市群碳排放驱动因素时空分解研究

城市群是中国经济发展和能源消耗的集聚区域,也是碳排放的主要来源。研究中国典型城市群碳排放的时空演变特征及其影响因素对实现“双碳”目标具有重要意义。文中应用ST-IDA模型(时空指数分解分析法)和LMDI(对数平均迪氏指数法)分解法,分析2000—2019年京津冀、长三角、珠三角和成渝城市群的碳排放驱动因素(人口规模、经济水平、产业结构、能源强度和能源结构)。研究发现:2000—2019年间,四大城市群能源活动碳排放总体趋势均由高速增长阶段步入平稳增长阶段,其中成渝城市群已基本实现碳达峰;能源强度效应是影响碳排放空间差异的主要因素;人口规模扩张、经济发展水平提高和能源强度上升是促进碳排放增长的主要因素,产业结构和能源消费结构优化起到抑制作用;四大城市群碳排放的时空演变主要取决于工业部门。鉴于四大城市群呈现出不同的碳排放特征,未来应探索差异化、多元化的城市群减排路径,促进城市群碳减排。

工业园区碳排放多因素分解及其与经济发展的脱钩关系

综述分析了工业园区碳排放核算范围和基于一维或二维脱钩模型的工业园区碳排放与经济发展脱钩关系,并讨论了基于LMDI分解方法和Tapio脱钩模型构建的脱钩效应模型中各因素对脱钩关系的影响程度,为工业园区碳排放清单确立、脱钩程度分析、驱动因素识别和综合评估等方面提供参考。在此基础上,建议工业园区应合理制定碳排放清单,多维度考量碳排放驱动因素,构建更为全面的指标体系。

中国工业低碳转型进程再评估及其多样化分解

工业低碳转型是中国落实“双碳”目标、推进绿色低碳发展的关键所在,全面理解中国工业低碳转型进程具有重要价值。为此,首先构建工业低碳转型进程再评估及其分解的理论框架,随后基于跨国、省份和行业样本数据,采用SBM方向性距离函数和Luenberger生产率指标对2001—2021年中国工业低碳转型进程展开再评估,并从生产技术演化、要素效率变动和经济结构变迁三个维度对该进程进行分解。研究发现,国际视角下的中国工业低碳转型进程在研究期内稳步推进,节奏更为平稳、方式更可持续;国内视角下的中国工业低碳转型进程在研究期内具有先快后缓的推进特征,且省份间和行业间低碳转型分化程度持续加深;经典分解表明技术进步和技术效率提升在推动工业低碳转型过程中无法有效协同,存在普遍的正负或强弱特征;要素分解表明工业低碳转型的驱动要素逐渐多元化,能源效率和碳效率的贡献逐步凸显,但具有一定波动性;结构分解显示区域结构效率总体呈小幅下降趋势,产业结构效率始终为负且后期受政策影响而呈现周期性波动特征。基于研究结论,从协同推进技术进步与技术效率改善、提升区域及行业维度能源效率和碳效率、优化经济结构效率三方面为高质量推进中国工业低碳转型进程提出了具体建议。

面向碳中和目标的中国工业部门减排路径与战略选择

工业部门(包含电力工业)是中国碳排放量最多的国民经济部门.探寻工业部门合理有效的碳减排路径是中国碳达峰、碳中和目标顺利实现的关键.本文分解了中国2000年—2019年工业碳排放历史演变路径的驱动因素,预测了三种情景下2019年—2060年工业碳排放的潜在演变路径,估计了工业部门碳中和进程中所需的投资额,讨论了不同减排时点选择下投资需求的异质性.研究发现:1)规模效应和效率效应是历史演变路径中最关键的驱动因素,而化石能源清洁转化效应、化石能源内部结构效应和清洁能源替代效应的影响较小;2)非电力工业未来核心的碳减排技术路径为能源效率提升和清洁能源消费替代,电力工业未来核心的碳减排技术路径则为清洁能源发电替代;3)在当前的技术水平下,工业部门碳中和的进程中需要超过300万亿元的巨额投资,所需投资主要集中于高排放的非电力工业.本文最后提出了“以技术为核心,以市场为机制,以政策为引导”的中国工业部门实现碳中和的战略选择.

基于奇异谱分析和辛几何模态分解的短期碳排放预测模型

在短时期内对碳排放水平进行评估和规划,对制定精准的减排目标和有效的政策措施可以起到辅助作用。将奇异谱分析分解法(Singular spectrum analysis decomposition,SSAD)和辛几何模态分解(Symplectic geometry mode decomposition,SGMD)组合成新型的二次信号分解法,并应用于每日碳排放量预测。在对原始序列进行二次分解之后,利用快速傅里叶变换对子序列进行重构,并应用偏自相关函数来选择合适的输入变量。最后,利用麻雀搜索算法(Sparrow search algorithm,SSA)对长短期记忆网络(Long short-term memory network,LSTM)进行优化,建立了SSAD-SGMD-SSA-LSTM模型。通过与其他模型进行对比实验,发现SSAD-SGMD二次分解更加适合碳排放时间序列预处理,并且可以进一步提高预测精度。SSAD-SGMD模型与集成经验模态分解和变分模态分解相结合的二次分解模型相比,模型的可决系数R2提高了1.83%,平均绝对百分比误差(Mean absolute percentage error,MAPE)有所降低,均方根误差(Root mean square error,RMSE)降低了43.16%。此外,经过SSA优化后的LSTM模型,R2提高了1.49%,MAPE有所降低,RMSE降低了38.64%。所提出的模型能够有效提升短期碳排放预测的准确性。

“双碳”目标引领下家庭碳账户体系的构建对策及建议

碳账户体系建设是践行国家“双碳”目标的重要路径。随着个人碳账户的不断完善,开展家庭碳账户的试点探索,有助于个人碳账户应用体系与碳市场的建立与完善。从家庭碳账户体系构建的背景及意义出发,通过文献调研与实证调查等方法,分析个人碳账户存在问题及对家庭碳账户体系构建的启示。研究结果表明,个人碳账户建设存在多平台重复建设及标准不统一问题、公平性与合理性问题、信息准确性与偏差问题、以及减排政策框架的有效融合欠缺问题。在“双碳”目标引领下,应设立家庭碳户主并建立家庭碳共济总账户,整合碳账户平台并完善家庭碳账户数据信息,创新家庭碳普惠模式,以构建并完善家庭碳账户体系,促进全民参与绿色低碳生态文明行动。