中国能源消费碳排放强度及其影响因素的空间计量(英文)

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%–45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China's carbon intensity from energy consumption in 1997–2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP(11.72%). Secondly, the trend of Moran's I indicated that China's carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China's carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.     

中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素分析（英文）

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.     

中国不同区域能源消费碳足迹的时空变化(英文)

Study on regional carbon emission is one of the hot topics under the background of global climate change and low-carbon economic development, and also help to establish different low-carbon strategies for different regions. On the basis of energy consumption and land use data of different regions in China from 1999 to 2008, this paper established carbon emission and carbon footprint models based on total energy consumption, and calculated the amount of carbon emissions and carbon footprint in different regions of China from 1999 to 2008. The author also analyzed carbon emission density and per unit area carbon footprint for each region. Finally, advices for decreasing carbon footprint were put forward. The main conclusions are as follows: (1) Carbon emissions from total energy consumption increased 129% from 1999 to 2008 in China, but its spatial distribution pattern among different regions just slightly changed, the sorting of carbon emission amount was: Eastern China > Northern China > Central and Southern China > Southwest China > Northwest China. (2) The sorting of carbon emission density was: Eastern China > Northeast China > Central and Southern China > Northern China > Southwest China > Northwest China from 1999 to 2003, but from 2004 Central and Southern China began to have higher carbon emission density than Northeast China, the order of other regions did not change. (3) Carbon footprint increased significantly since the rapid increasing of carbon emissions and less increasing area of pro-ductive land in different regions of China from 1999 to 2008. Northern China had the largest carbon footprint, and Northwest China, Eastern China, Northern China, Central and Southern China followed in turn, while Southwest China presented the lowest area of carbon footprint and the highest percentage of carbon absorption. (4) Mainly influenced by regional land area, Northern China presented the highest per unit area carbon footprint and followed by Eastern China, and Northeast China; Central and Southern China, and Northwest China had a similar medium per unit area carbon footprint; Southwest China always had the lowest per unit area carbon footprint. (5) China faced great ecological pressure brought by carbon emission. Some measures should be taken both from reducing carbon emission and increasing carbon absorption.     

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.     

Economic development and energy efficiency in Jilin Province,China

The relationship between economic development and energy consumption is revealed by employing cointegration theory, the index decomposition method, and a log-linear regression approach based on a case study of Jilin Province, China. The results suggest： 1） the economic development and energy consumption are interdetermined, whose relationship is positive and long-term. The economic development is highly depending on the energy in Jilin Province. 2） Under the condition of other unchanged factors, the change of industrial energy efficiency contributes to the energy saving, while that of industrial structure increases the energy consumption. 3） The industrial structure change enhances the energy intensity, but the energy utility efficiency change lowers it. From the view of contribution to the energy consumption, the contribution of industrial structure was more than that of the energy utility efficiency in 2000-2011. 4） In 2000-2011, the comprehensive energy intensity change and hydroelectricity energy intensity change were related to all industrial structures＇ change, and the influencing factors about structure of oil energy intensity change were more than those of coal energy intensity change; from the impact degree, agricultural proportion decreased exerted an positive and greater effect on lowering the energy intensity of comprehensive energy and hydroelectricity, and industrial one did on coal and natural gas. Some conclusions can be drawn as follows： the major way to promote the coordinated development of the industrial economy and energy consumption is to optimize the industrial structure by increasing the proportion of the tertiary industry and low energy consumption industrial sectors and to enhance the energy utility efficiency.     

空间溢出视角下经济集聚对水污染物排放的影响——基于中国 339 个地级及以上城市数据的实证分析（英文）

Whether economic agglomeration can promote improvement in environmental quality is of great importance not only to China's pollution prevention and control plans but also to its future sustainable development. Based on the COD(Chemical Oxygen Demand) and NH3-N(Ammonia Nitrogen) emissions Database of 339 Cities at the city level in China, this study explores the impact of economic agglomeration on water pollutant emissions, including the differences in magnitude of the impact in relation to city size using an econometric model. The study also examines the spillover effect of economic agglomeration, by conducting univariate and bivariate spatial autocorrelation analysis. The results show that economic agglomeration can effectively reduce water pollutant emissions, and a 1% increase in economic agglomeration could lead to a decrease in COD emissions by 0.117% and NH_3-N emissions by 0.102%. Compared with large and megacities, economic agglomeration has a more prominent effect on the emission reduction of water pollution in small-and medium-sized cities. From the perspective of spatial spillover, the interaction between economic agglomeration and water pollutant emissions shows four basic patterns: high agglomeration–high emissions, high agglomeration–low emissions, low agglomeration–high emissions, and low agglomeration–low emissions. The results suggest that the high agglomeration–high emissions regions are mainly distributed in the Beijing–Tianjin–Hebei region, Shandong Peninsula, and the Harbin-Changchun urban agglomeration; thus, local governments should consider the spatial spillover effect of economic agglomeration in formulating appropriate water pollutant mitigation policies.     

柴达木盆地植被覆盖度的分析与模拟——空间自相关方法的运用(英文)

Relationship between vegetation and environmental factors has always been a major topic in ecology, but it has also been an important way to reveal vegetation’s dynamic response to and feedback effects on climate change. For the special geographical location and climatic characteristics of the Qaidam Basin, with the support of traditional and remote sensing data, in this paper a vegetation coverage model was established. The quantitative prediction of vegetation coverage by five environmental factors was initially realized through multiple stepwise regression (MSR) models. However, there is significant multicollinearity among these five environmental factors, which reduces the performance of the MSR model. Then through the introduction of the Moran Index, an indicator that reflects the spatial auto-correlation of vegetation distribution, only two variables of average annual rainfall and local Moran Index were used in the final establishment of the vegetation coverage model. The results show that there is significant spatial autocorrelation in the distribution of vegetation. The role of spatial autocorrelation in the establishment of vegetation coverage model has not only improved the model fitting R2 from 0.608 to 0.656, but also removed the multicollinearity among independents.     

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association(LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China's economic growth and carbon emission load will continue for some time.     

中国能源生态效率的空间格局与空间效应（英文）

Energy eco-efficiency is a concept integrating ecological and economic benefits arising from energy utilization and serves as a measure of efficiency in the energy–environment–economy system. Using the slacks-based measure(SBM) model considering undesirable output, this study first measures the energy eco-efficiency of provinces in China from 1997 to 2012. It then analyzes the spatial distribution and evolution of energy eco-efficiency from three aspects: scale, intensity, and grain of spatial patterns. Finally, it examines the spatial spillover effects and influencing factors of energy eco-efficiency in different provinces by means of a spatial econometric model. The following conclusions are drawn:(1) The overall energy eco-efficiency is relatively low in China, with energy-inefficient regions accounting for about 40%. Guangdong, Hainan and Fujian provinces enjoy the highest energy eco-efficiency, while Ningxia, Gansu, Qinghai, and Xinjiang are representative regions with low efficiency. Thus, the pattern of evolution of China's overall energy eco-efficiency is U-shaped. Among local regions, four main patterns of evolution are found: increasing, fluctuating, mutating, and leveling.(2) At the provincial level, China's energy eco-efficiency features significant spatial agglomeration both globally and locally. High–high agglomeration occurs mainly in the eastern and southern coastal regions and low–low agglomeration in the northwestern region and the middle reaches of the Yellow River. Changes in spatial patterns have occurred mainly in areas with high–low and low–high agglomeration, with the most remarkable change taking place in the Beijing–Tianjin–Hebei region.(3) There exist significant spatial effects of energy eco-efficiency among provinces in China. For the energy eco-efficiency of a given region, spatial spillovers from adjacent regions outweigh the influence of errors in adjacent regions. Industrial structure has the greatest influence on energy eco-efficiency.     

城市能源利用与CO_2排放的多尺度综合评价(英文)

Accurate and detailed accounting of energy-induced carbon dioxide(CO2) emissions is crucial to the evaluation of pressures on natural resources and the environment, as well as to the assignment of responsibility for emission reductions. However, previous emission inventories were usually production- or consumption-based accounting, and few studies have comprehensively documented the linkages among socio-economic activities and external transaction in urban areas. Therefore, we address this gap in proposing an analytical framework and accounting system with three dimensions of boundaries to comprehensively assess urban energy use and related CO2 emissions. The analytical framework depicted the input, transformation, transfer and discharge process of the carbon-based(fossil) energy flows through the complex urban ecosystems, and defined the accounting scopes and boundaries on the strength of ‘carbon footprint' and ‘urban metabolism'. The accounting system highlighted the assessment for the transfer and discharge of socio-economic subsystems with different spatial boundaries. Three kinds methods applied to Beijing City explicitly exhibited the accounting characteristics. Our research firstly suggests that urban carbon-based energy metabolism can be used to analyze the process and structure of urban energy consumption and CO2 emissions. Secondly, three kinds of accounting methods use different benchmarks to estimate urban energy use and CO2 emissions with their distinct strength and weakness. Thirdly, the empirical analysis in Beijing City demonstrate that the three kinds of methods are complementary and give different insights to discuss urban energy-induced CO2 emissions reduction. We deduce a conclusion that carbon reductions responsibility can be assigned in the light of production, consumption and shared responsibility based principles. Overall, from perspective of the industrial and energy restructuring and the residential lifestyle changes, our results shed new light on the analysis on the evolutionary mechanism and pattern of urban energy-induced CO2 emissions with the combination of three kinds of methods. And the spatial structure adjustment and technical progress provides further elements for consideration about the scenarios of change in urban energy use and CO2 emissions.     

中国能源消费碳排放强度及其影响因素的空间计量

碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO₂排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。     

中国省域碳强度空间依赖格局及其影响因素的空间异质性研究

在估算各省域碳强度的基础上,利用探索性空间数据分析(ESDA)和时空跃迁测度方法以及地理加权回归(GWR)模型分析了1995~2015年中国省域（不包括西藏、港、澳、台地区）能源消费碳强度的空间依赖格局及其驱动因素的空间异质性。结果显示：① 中国省域碳强度存在显著的空间正相关性,表现为先下降后上升再到小幅波动的特征,碳强度相似的省域趋向于集聚,表明中国省域碳强度具有明显的空间依赖特征;②省域碳强度存在不均衡的发展格局,高-高集聚的省域主要分布在中国西北部,低-低集聚的省域多分布于中国东南部。③碳强度空间集聚总体呈优化态势,高-高集聚的省域在减少,低-低集聚的省域在不断增多,但不同省域在碳强度的空间集聚中所起的作用不同。④ 碳强度影响因素(解释变量)的回归系数均为正值,4个解释变量对碳强度的影响程度依次为：能源强度>能源结构>产业结构>人均GDP;且各因素对碳强度的影响在不同省域具有明显的空间异质性。     

Spatiotemporal dynamics of carbon intensity from energy consumption in China

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China’s carbon intensity from energy consumption in 1997–2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China’s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP (11.72%). Secondly, the trend of Moran’s I indicated that China’s carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China’s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.     

中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素

基于1992-2013年中国城市遥感模拟反演碳排放数据,采用空间自相关、空间马尔科夫矩阵和动态空间面板数据模型,在同时考虑碳排放的时空滞后效应和不同地理经济空间权重矩阵的条件下,对城市碳排放的演化路径和关键影响因素进行了定量识别和减排政策探讨。研究表明,中国城市能源消费碳排放的区域差异正逐步缩小,空间上呈现出明显的高排放俱乐部集聚特征,同时碳排放类型演化具有明显的路径依赖特征;面板数据模型估计结果表明经济增长与人均碳排放呈现显著的倒“U”型曲线关系,而绝大多数城市的人均碳排放处于随经济发展而增加的阶段,二产偏重的经济结构和投资的粗放增长共同正向作用于城市碳排放,而人口的集聚效应、技术水平的提升、对外开放度和公路运输强度的增加则共同抑制城市碳排放水平的提高。因此未来要抑制促增因素和发挥促降因素的作用才能有效降低城市碳排放;优化产业结构、精简粗放投资、增加研发强度以及提升公路通达性是未来实现中国城市节能减排的有效途径。     

河南省能源消费碳排放动态变化及空间分异

在分析河南省1978—2015年能源消费碳排放总量和结构变化的基础上,利用Im PACT等式对河南省碳排放驱动因素进行了研究和对未来碳排放量进行了情景预测,并运用空间自相关分析法探讨了空间分异特征。结果表明:(1)1978—2015年,河南省碳排放量总体上呈现增加的趋势,年均增长5.11%,由煤炭和石油消费导致的碳排放比重一直稳定在95%以上。(2)弹性分析表明人均真实GDP增加1%将导致人均能源消费量增加0.48%,利用强度下降0.52%,而环境影响增加0.53%。(3)保持经济增长的同时,与2011—2015年相比,1978—2015年效率年均增长率提高5.25倍,是河南省实现循环经济建设的一种可行方案。(4)河南省2015年碳排放全局Moran’s I值为0.047,呈微弱空间正相关,各地市碳排放具有明显的二元结构特征,空间集聚特征不明显。     

Regional inequality,spatial spillover effects,and the factors influencing city-level energy-related carbon emissions in China

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation (Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club’ agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.     

基于IO-SDA模型的新疆能源消费碳排放影响机理分析（英文）

Analysis of carbon emission mechanism based on regional perspectives is an important research method capable of achieving energy savings and emission reductions.Xinjiang,an important Chinese energy production base,is currently going through a period of strategic opportunities for rapid development.Ensuring stable socio-economic development while achieving energy savings and meeting emission reductions targets,is the key issue currently facing the region.This paper is based on the input-output theory,and conducts a structural decomposition analysis on the factors affecting energy-related carbon emissions in Xinjiang from 1997 to 2007;this analysis employs a hybrid input-output analysis framework of "energy- economy- carbon emissions".(1) Xinjiang's carbon emissions from energy consumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007;carbon emissions growth was mainly concentrated in the production and processing of energy resources,the mining of mineral resources,and the processing industry.(2) The analysis of the direct effects of the influencing factors on carbon emissions showed that the change in per capita GDP,the final demand structure,the population scale,and the production structure were the important factors causing an increase in carbon emissions,while the decrease in carbon emission intensity during this period was the important influencing factor in stopping the growth of carbon emissions.This showed that while the sizes of Xinjiang's economy and population were growing,the economic structure had not been effectively optimized and the production technology had not been efficiently improved,resulting in a rapid growth of carbon emissions from energy consumption.(3) The analysis of the indirect effects of the influencing factors of carbon emission showed that the inter-provincial export,fixed capital formation,and the consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang.(4) The growth of investments in fixed assets of carbon intensive industry sectors,in addition to the growth of inter-provincial exports of energy resource products,makes the transfer effect of inter-provincial "embodied carbon" very significant.     

中国城市居民生活用电碳排放差异及时空演变

以地级市为研究单元,以人均生活用电碳排放为测度指标,借助经典统计学中的各类不平衡指数,对2002―2011年间中国城市居民生活用电碳排放的差异程度进行动态时序分析。同时,运用ESDA方法,从静态的碳排放量和动态的碳排放增长率2个角度,定量描述了2002年以来中国城市生活用电碳排放的时空演化特征。结果表明：中国城市居民人均生活用电碳排放差异逐渐缩小;就人均碳排放量来说,总体上表现为空间正相关,且集聚程度逐渐增强,冷热点格局基本稳定,高值区域从广东省逐渐向北延伸至其他东南沿海地区;就碳排放增长率来说,碳排放增长在总体上无显著的空间相关性,地理集中现象不明显,冷热点区域转换迅速,高值区域数目显著增加且表现出向北转移的趋势。     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.     

1990-2014年泛长三角地区能源利用碳排放时空格局及影响因素

区域碳排放时空格局及其关键影响因素是近年来学者们关注的热点。本文以中国经济发达、经济关联密切、产业格局变化剧烈的泛长三角地区为案例,分析1990年以来典型年份碳排放的空间分异、时间演变,解析碳排放空间分异的关键影响因素。结果表明：①区域碳排放总量快速增长,总体格局稳中有变,核心区16个城市排放量占比大都超过50%。②以2005年为拐点,之前外围城市增长幅度较低,之后外围城市碳排放量快速增长;外围地区碳排放量占比从2005年的33%快速增加至2014年的47%,区域碳排放量的空间集聚度呈现先增后减的态势。③碳排放格局变化受多种因素影响,不同变量对碳排放的影响各异。其中,工业生产、城镇化建设及人口集聚仍是现阶段泛长三角地区最主要的碳排放来源;固定资产与外商投资对区域碳排放的作用呈增强趋势,但其作用强度较工业生产、城镇化建设、人口集聚要小。地区生产总值对碳排放影响存在倒U型关系,随着经济发展水平的提高,碳排放与经济发展呈现脱钩趋势。研究结果可为揭示经济发展格局变化的环境效应、制定节能减排政策提供参考。