Since the Paris Climate Change Conference in 2015, reducing carbon emission and lowering carbon intensity has become a global consensus to deal with climate change. Due to different economic development stages, carbon intensity is regarded as a better index to measure regional energy-related carbon emissions. Although previous scholars have made great efforts to explore the spatiotemporal patterns and key driving factors of carbon intensity in China, the results lack the perspective from city level because of limited availability of statistical data of city-level carbon emission. In this study, based on carbon intensity of 283 cities in China from 1992-2013, we used the kernel density estimation, spatial autocorrelation, spatial Markov-chain and quantile regression panel model to empirically reveal its spatial spillover effects and explore the critical impact factors of carbon intensity at the city level. Our result indicates that although the total carbon emission increased during the study period, carbon intensity saw a gradual decline and regional differences were shrinking. Secondly, the city-level carbon intensity presented a strong spatial spillover effect and diverse regional backgrounds exerted heterogeneous effects on regions. Thirdly, quantile panel data analysis result showed that for low-intensity cities, on the one hand, FDI and transport sector were main contributing factors, and economic growth, technical progress and high population density negatively affected carbon intensity. On the other hand, industrial activity, extensive growth of investment and urban sprawl were key promoting factors for high-intensity cities, and population density was beneficial to emission reduction task. Furthermore, technological advance has not exerted negative influence on carbon intensity in high-intensity cities. At last, we suggested that Chinese government should take different carbon intensity levels into full consideration before policy making.

采用核密度估计、空间自相关、空间马尔科夫链和面板分位数回归等方法对1992-2013年全国283个城市碳排放强度的空间溢出效应和驱动因素进行了分析。① 核密度估计结果表明,中国城市碳排放强度总体均值下降,差异在逐步缩小。② 空间自相关Moran's I指数表明城市碳排放强度存在显著的空间集聚性且空间集聚性在逐渐增强,但空间集聚水平的变化逐年缩小。③ 空间马尔科夫链分析结果表明：第一,中国城市碳排放强度存在马太效应,低强度与高强度的城市在相邻年份转移过程中呈现维持初始状态的特征。第二,城市碳排放“空间溢出”效应明显,且不同区域背景下溢出效应存在异质性,即若与碳排放强度低的城市为邻,该城市的碳强度能够增加向上转移的概率,反之亦然。④ 面板分位数结果显示：在碳排放强度低的城市,经济增长、技术进步、适当的人口密度起到减排作用;外商投资强度与交通排放是使碳强度增大的主要因素。在碳排放强度高的城市,人口密度是重要的减排因素,技术进步暂时没起减排作用;工业排放、粗放式的资本投资以及城市土地蔓延则是碳强度上升的主要因素。

In this paper, the correction coefficient was applied to measure energy industry efficiency of emission reduction in each province. According to the energy industry carbon emission intensity and output value share of each province, the identity of national carbon emission intensity in China's energy industry was established from top to bottom, and the contribution rate of each province, energy industry carbon emission intensity and output value share in each province to the decline of national carbon emission intensity in energy industry was obtained by using LMDI-I decomposition method. Following the idea of “emission reduction efficiency - carbon emission intensity contribution - comprehensive contribution by province - the relationship between provincial emission reduction efficiency and the comprehensive contribution of province”, the emission reduction effectiveness of each province in the process of carbon emission intensity decline in China energy industry was evaluated. The results indicate that (1) the carbon emission intensity in China' energy industry shows a downward trend year by year, and the carbon emission intensity decreased by 45% in 2016 than that in 2005. (2) More than half of the provinces are efficiency areas for emission reduction and the number of provinces is increasing, while most of the provinces that do not meet the emission reduction standards are located in economically underdeveloped areas, and there are significant differences in emission reduction paths between provinces. (3) The contribution rate of energy industry carbon emission intensity in most provinces shows an upward trend, and the regional differences gradually decrease. (4) The competing momentum of comprehensive contribution by province is strong, and most provinces have contributed to the decline of carbon emission intensity in China energy industry, and the eastern region is the main contribution area, where the comprehensive contribution grade of many provinces develops positively. (5) The number of provinces with good emission reduction effectiveness is the largest, the general areas are scattered in the eastern coastal area and a few inland areas, and the spatial pattern of the poor areas remains stable in the western region. In the future, provinces should not only choose the low-carbon development path according to their own emission reduction effectiveness characteristics, but also form a benign interaction with the decline of national carbon emission intensity.

采用矫正系数衡量各省份的减排有效性,根据各省份能源行业碳排放强度及产值份额构建自上而下的全国能源行业碳排强度恒等式,运用LMDI-Ⅰ分解方法获取各省份、各省份能源行业碳排放强度及产值份额对全国能源行业碳排放强度下降的贡献率,并遵循“减排有效性-碳排放强度贡献-省份综合贡献-减排有效性与省份综合贡献关系”的思路对中国能源行业碳排放强度下降过程中各省份的减排成效进行评价。结果显示：① 中国能源行业碳排放强度呈逐年下降趋势,2016年能源行业碳排放强度较2005年下降45%。② 超过一半的省份为减排有效区且数量呈增加趋势,而减排未达标的省份多数为经济欠发达地区,各省份间的减排路径存在显著差异。③ 多数省份能源行业碳排放强度的贡献率呈上升趋势,省份差异逐渐缩小。④ 省份减排综合贡献的进位赶超势头强劲,绝大多数省份对全国能源行业碳排放强度的下降作出了贡献,且多个省份的综合贡献等级呈正向发展,东部地区是主要的降排贡献区。⑤ 减排成效良好省份数量最多,中等区多散布于东部沿海地区及少数内陆地区,欠佳区在西部地区空间格局保持相对稳定。未来各省份不仅要根据自身的减排成效特征因地制宜的选择低碳发展道路,还应与全国碳排放强度的下降形成良性互动。

The output as well as carbon dioxide emissions of tertiary industry have increased continuously in Beijing. Therefore, the tertiary industry has become a new field 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 influence the growth of carbon emission of the tertiary industry, including the changes of industrial scale, industrial structure, energy consumption intensity and carbon coefficient. The results show that the industrial scale advancement and the direct energy carbon coefficient 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 coefficient change promote the growth of indirect carbon dioxide emissions, while the indirect energy consumption intensity change has a restrain.

The global environment issue resulting from carbon emissions has aroused worldwide concern for governments, the public and scientific communities. A precise measurement of the time-resolved and spatial distribution characteristics of carbon dioxide (CO2) and carbon footprint as well as its long-period evolution mechanism, can help clarify the relationship between environmental carbon load and economic growth, and are critical references to the formulation of scientific carbon emission reduction targets with reasonable and differential emission reduction policies. In this study, the mainland of China is taken as the research object. According to the quantitative correlations between DMSP-OLS nighttime light image data and carbon emission statistics, the carbon emission panel data model was simulated for China's carbon emissions in the period 2000-2013, and then the spatiotemporal evolving trend and spatial distribution characteristics of carbon emissions in the 14-year research period were discussed using Theil-Sen Median trend analysis and Mann-Kendall test method. Based on the framework of exploratory spatial-temporal data analysis (ESTDA), the spatial pattern and spatiotemporal dynamic evolution of carbon footprint from 2001 to 2013 were analyzed from the perspective of spatiotemporal interaction. In the three periods, the decoupling effect between environmental carbon load and economic growth of 336 prefecture-level cities were analyzed using the improved Tapio decoupling model. The results show that the overall carbon emissions in China had been on the rise from 2000 to 2013, in which the stable-slow rise type was dominant. China's carbon footprint and carbon deficit increased year by year, and the central and western regions became the focus of the growth of carbon footprint and carbon deficit from 2001 to 2013. At different administrative city scales, the spatial distribution pattern of carbon footprint and carbon deficit show obvious administrative orientated and spatial zonal differentiation characteristics. The annual average of global Moran's I index of each level unit is 0.491, which indicates that there is a significant spatial auto-correlation feature in the carbon footprint of China's prefecture-level units. The relative length of the LISA time path is greater in the north than in the south, and it tends to increase from the coastal areas to the central and western regions. The curvature of LISA time path decreases from coastal areas to inland areas on the whole. The curvature of northeast and central regions is higher, while that of eastern and western regions is lower. There is a different trend of the decoupling effect of environmental carbon load in China. Meanwhile the expansion-connection and expansion of negative decoupling regions continuously increased and spatially agglomerated, presenting an "E"-shaped distribution pattern from the north to the south. The national average decoupling elastic value is gradually increasing, while the coefficient of variation continues to decline, and the decoupling type has a significant evolution trend. Therefore, the unbalanced trend of economic growth and carbon emissions in China will continue for a certain period.

利用DMSP-OLS夜间灯光数据和碳排放统计数据,构建碳排放面板数据模型,模拟了2000—2013年中国的碳排放量。运用探索性时空数据分析（ESTDA）框架体系,从时空交互视角分析2001—2013年碳足迹的空间格局和时空依赖动态演化;利用改进的Tapio脱钩模型对3个时间段336个地级单元环境碳负荷与经济增长之间的脱钩效应进行综合分析。研究表明：① 2000—2013年,中国的碳排放在时空演变上既表现出稳中有进的总体特征,也存在快速增长的阶段特征。② 碳足迹和碳赤字均呈逐年增长趋势,年均增长率分别为4.82%和5.72%;碳足迹和碳赤字整体北方大于南方,不同的行政单元尺度下碳足迹和碳赤字空间异质性特征明显。各地级单元碳足迹变异系数逐步增大,存在极为显著的空间自相关特征。③ LISA时间路径相对长度北方大于南方,且呈由沿海地区向中西部地区递增的趋势;LISA时间路径弯曲度整体上则由沿海地区向内陆地区递减。④ 综合脱钩指数整体以弱脱钩型为主,但弱脱钩型城市数量持续减少,扩张连接、扩张负脱钩区域数量逐渐增多且向中西部及东北地区聚集分布;全国平均脱钩弹性值逐步增长,变异系数持续下降。

<p>The integration of space and time generates much closer interaction between geography and other social sciences, and provides a new perspective for understanding the role of geography in tourist flow distribution. China has become one of the world's most popular tourist destinations. However, there are significant disparities in volume and structure of China's international tourist flow between the coastland and inland. Tourism behavior involves the cross-regional movement at spatio-temporal dimension. Accordingly, an insight into spatial-temporal distribution and inter-regional co-opetition relationships is very helpful to develop destination marketing strategy. Based on the perspective of space-time interaction, this paper explored the inter-provincial inequality of international tourist flow in China from 1997 to 2013. LISA time path, space-time transition and visualization were employed for spatial-temporal data analysis. It concludes that: ① the polarization effects of the main port cities (Beijing, Shanghai, Guangzhou) tend to be weaken, and along with the rise of new growth poles located in the central and western regions, inter-provincial inequality tend to be reduced. ② The spatial distribution of international tourist flow obeys to the distance-decay rule-the short-range international tourist market is more spatially concentrated than the long-haul market. ③ The local spatial structure of European market is simplest, Southeast Asian market is the most complex. Space-time dependence effect has the greatest impact on the dynamic of regional system of North-American market; the Japanese-Korean market is of the least influence. The inter-provincial growth of inner Yangtze River Delta shows a significant space-time dependence, but the space-time dependence of Beijing and her neighbors, Guangdong and her neighbors are rather faint, in spite of the two provinces' own high growth rate; the local spatial structures of these backward provinces situated in the central and western regions are stable due to their slower growth rate. ④ The inter-provincial collaborative growth is the main theme of Chinese international tourism development. However, the situation of co-opetition varies from one province to another. ⑤ There exists an intense spatial cohesion among provinces, and their own market roles are relatively stable; Space-time transition probability varies from one province to another and from one market to another. Some suggestions are made to promote regional balanced development, such as continuing to increase supports for backward provinces; breaking administrative barriers for the circulation of tourism factors; necessitating the cooperation between touristic backward and advanced provinces as well as self-improvement of the formers; cultivating and strengthening the booming tourism growth poles located in the central and western regions; making more efforts to the advertising & marketing for the long-haul market.</p>

运用LISA时间路径、时空跃迁、可视化等方法,对比研究1997-2013年中国主要国际客源市场的客流省份分布动态特征。研究表明：① 京沪粤极化减弱,中西部新兴增长极崛起,区域差异趋于缩小。② 客流分布遵循距离衰减规律,近程市场客流分布较远程集中。③ 欧洲市场局部空间结构最简单,东南亚最复杂;日韩市场受时空依赖影响最小,北美洲最大,东南亚次之;江浙沪间客流增长的时空依赖效应以溢出为主;而京粤对邻域以极化为主;中西部旅游欠发达省份局部空间结构稳定,增长缓慢。④ 国际旅游发展以省份间协同增长为主,局部空间竞合态势不同。⑤ 客流分布空间凝聚强,省份市场地位相对稳定;时空变迁概率因区域、市场而异。应打破行政壁垒,发挥空间溢出效应,促进要素流通;欠发达省份既要加强区域合作,也要不断自我完善;壮大中西部新兴旅游增长极;加大远程市场营销力度。

Carbon emission control is the main problem and measure of ecological protection and high-quality development in the Yellow River Basin. Carbon emission at county level research can provide more accurate theoretical support for collaborative governance and sustainable development of the Yellow River Basin. Spatial panel model, spatial autocorrelation analysis and spatial Markov chain with regional background and nearest neighbor as spatial lags were used to analyze the spatiotemporal pattern and spatial effect of carbon emissions in counties of the Yellow River Basin from 2000 to 2017, the results showed that: 1) the carbon emission in the Yellow River basin has increased dramatically since 2000; the high carbon emissions areas, Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia, expands to the outer circle layer and the axial direction, forming the spatial pattern of high in the east and low in the west; 2) there is a phenomenon of “club convergence”; the high carbon emission counties converge in Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia; the low carbon emission counties converge in the southwest; the comparison between 2000 and 2017 shows that county carbon emission type has strong stability; counties which tranfered from higher carbon emission type to lower carbon emission type are concentrated in the southeast region, while counties that change in the opposite direction are concentrated in Inner Mongolia. 3) high carbon spillover effect and low carbon locking effect are important forces to shape the spatiotemporal pattern and the former is stronger; the regional background enhances “club convergence” and the convergence of surrounded outliers and its acting force was stronger than the nearest neighbor; the probability of carbon emission type transition in insignificant regions increased; 4) the spatial panel model shows that increase of carbon emissions and its spatial effect are promoted by young population structure, large economy, industrial structure dominated by the secondary industry, high living standard and high public expenditure; economy and industrial structure are important driving factors.

利用空间面板模型、空间自相关分析和以区域背景与最近邻状况为空间滞后的空间马尔科夫链对2000—2017年黄河流域县域碳排放时空格局与空间效应进行分析，结果表明：① 2000年以来黄河流域碳排放量激增，由山东全域和陕甘宁蒙交界的高值区向外圈层与轴向扩张，形成东高西低碳排放格局；② 存在“俱乐部趋同”现象，高碳排放县集聚于山东全域和陕甘宁蒙交界，低碳排放县集聚于西南部；2000年与2017年对比发现县域碳排放类型稳定性强，较高碳排放变为较低碳排放的县集中在东南部区域，而相反方向转变的县集中在内蒙古；③ 高碳溢出效应与低碳锁定效应是塑造时空格局的重要作用力，前者作用力更强；区域背景增强了“俱乐部趋同”与被包围异常值趋同，作用力强于最近邻状况，不显著区域内碳排放类型转变概率提高。④ 空间面板模型结果显示年轻人口结构、大经济规模、二产为主产业结构、高生活水平和高公共支出促进了碳排放量增加与空间效应作用，其中经济规模与产业结构是重要驱动因素。

County-scale tourism has been recognized as one of the important base units that supports the tourism industry development in China and has also been regarded as the new direction of inbound tourism in the future. The lack of research on county-scale inbound tourism hinders people's cognition and attention to the development in small scale inbound tourism destination. With the help of ESTDA analytical framework and spatial variation function, the spatial correlation characteristics of county-scale inbound tourism in counties of Zhejiang province are simulated. Then the space-time evolution characteristics and types of inbound tourism are measured in each county by means of LISA time path and the space-time transition. In this way, nesting the quantile regression model and space-time evolution type is achieved, which also promotes the construction of space-time transition driving mechanism model. The result shows that the county-scale inbound tourism in Zhejiang has the obvious feature of spatial aggregation—with Hangzhou playing a dominant role. Moreover, the spatial self-organizing feature is increasingly reinforced while its spatial differences are gradually lessened, the latter of which can be reflected at the local level. On the one hand, the revolution pattern of county-scale inbound tourism in this province is feature by significantly positive space integration; on the other hand, there is a degree of transfer inertia between counties. Apart from that, it is shown that the inbound tourism of the backward counties located in the northwest and southeast parts has a relatively dynamic local spatial feature. To be more specific, Changshan county has the most significant volatility in the spatial dependence while the Daishan county has the best stability. In addition, the space-time transition driving patterns of county-scale inbound tourism in Zhejiang are divided into four categories (driving the industry-structure driving pattern, the economy-service restriction modes, the trade-industry driving pattern and the location-resource restriction modes), which present an obvious evolution process which starts from synthetic development and then gradually turns into synthetic restriction from northeast to southwest area. There are less driving areas and more restricted areas at low quantile level, while more driving areas and less restricted areas at high quantile level. And the four main patterns imply different influence mechanisms.

县域旅游是支撑中国旅游产业发展的重要基础单元,也是未来入境旅游发展的新方向,当下对县域入境旅游研究的缺失阻碍着对小尺度入境旅游地发展的认知和重视。以浙江省县域为研究对象,借助空间变差函数模拟了浙江县域入境旅游空间格局的演变特征。进一步通过LISA时间路径、时空跃迁测度了各县市入境旅游时空跃迁特征及类型,并将分位数回归与时空跃迁类型相嵌套,建立了四种时空跃迁驱动机制模式。结果表明：浙江省县域入境旅游具有明显的空间集聚特征,“一杭独大”特征显著,且空间自组织性逐年增强,区域入境旅游核心县市扩散效应不断增加,核心县市辐射区域差异逐渐缩小,空间差异主要体现在局部方向;浙江省县域入境旅游空间格局的演化具有较强的正向空间整合性,县域类别之间存在一定的转移惰性,东南—西北区域的入境旅游落后县市具有相对动态的局部空间特征,常山县在空间依赖方向上具有最大的波动性,岱山县在空间依赖方向上具有最大的稳定性;产业—结构驱动、经济—服务制约、贸易—产业驱动、区位—资源制约四种驱动模式呈现出自东北至西南逐渐由同向发展到同向制约的演化过程,此外四种模式背后隐含着不同的影响机理。

County is the pivotal platform and region unit to realize the new-type urbanization. The study of county-level CO2 emissions is of great significance to improve China's urbanization strategy, accelerate the achievement of ecological civilization and low-carbon transformation. Based on the data of China's county-level CO2 emissions from 2000 to 2017, this paper analyzed the overall tendency, regional differences, spatiotemporal pattern and agglomeration characteristics of per capita CO2 emissions. Meanwhile, under the STIRPAT model and EKC hypothesis, this study employed the panel quantile regressions to explain the dynamic impact of socio-economic development on per capita CO2 emissions. The main conclusions show that: (1) China's county-level CO2 emissions show an increasing trend of rapid growth followed by slow growth. The regional disparity of per capita CO2 emissions is distinct and shows a more uneven trend. (2) On the whole, China's county-level CO2 emissions present a spatial pattern of "high in the north and low in the south". The per capita CO2 emissions level in economically developed areas is much higher than that in other areas, thus brings about an obvious spatial polarization effect. (3) There is a significant positive spatial correlation of per capita CO2 emissions within counties. The number of counties with High-High concentration gradually increases and the distribution center gradually moves to Northwest China, while the number of Low-Low concentration counties decreases continuously and they were mainly distributed in the central and southern regions. The agglomeration type of county-level per capita CO2 emissions presents a spatial locking effect. (4) Population density and government expenditure have an inhibitory effect on county-level per capita CO2 emissions, while the scale of secondary industry output value and carbon emission intensity have significant promotive influence. And there is an inverted "N-shaped" relationship between economic development and per capita CO2 emissions in the counties with low- and middle-level emissions. The adjustment of socio-economic development structure plays a critical role in achieving China's total CO2 emission reduction target. Therefore, the policy makers of emission reduction strategy should consider the regional disparity to realize the development and transformation of backward counties. And the key urban agglomerations should play a leading role in carbon emission reduction simultaneously. In addition, improving energy use efficiency through technological innovation should be the key way to the reduction of carbon emissions in China's counties at the present stage.

县域是实现新型城镇化的重要平台和关键地域单元,揭示县域碳排放的时空格局演变及其驱动因素对于完善中国新型城镇化战略和促进生态文明建设、绿色转型发展具有重要意义。本文使用2000&#x02014;2017年中国县域碳排放数据,分析了县域人均碳排放的总体变化、区域差异、时空格局及集聚特征,并在STIRPAT模型和环境库茨涅茨曲线（EKC）假说下,运用面板分位数回归解释社会经济发展对县域人均碳排放的动态影响。结果表明：① 中国县域人均碳排放呈现先急后缓的增长趋势。人均碳排放水平差异加大,且呈上升趋势,西部地区县域人均碳排放差距悬殊。② 县域人均碳排放总体上呈现&#x0201c;北高南低&#x0201d;的空间格局,经济发达地区的人均碳排放远高于其他地区,空间极化效应明显。③ 县域人均碳排放具有显著的空间正相关性,高&#x02014;高集聚的区县数量逐渐增多且分布重心向西北移动,而低&#x02014;低集聚的区县数量不断减少,主要集中于中南地区,县域人均碳排放集聚类型具有空间锁定效应。④ 人口密度、政府财政支出对县域人均碳排放具有抑制作用,第二产业产值规模、碳排放强度则存在显著的正相关性,中低碳排放水平区县的经济发展和人均碳排放之间呈现倒&#x0201c;N&#x0201d;型曲线关系,社会经济发展结构的调整是实现整体碳减排的关键。因此,政府减排策略的落实应考虑区县碳排放的阶段性差异,实现落后地区发展和转型&#x0201c;两手抓&#x0201d;的同时发挥重点城市群、都市圈在碳减排中的先导作用。此外,通过技术创新提高能源利用效率应作为现阶段县域碳减排的主要手段。

Carbon emissions are increasing due to human activities related with the energy consumptions for economic development. Thus, attention has been paid to the reduction of the growth of carbon emissions and formulation of policies for addressing climate change. Although most studies have explored the driving forces behind carbon emissions in China, literature lacks studies at the city-level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we applied the spatial autocorrelation, spatial Markov-chain transitional matrices, dynamic spatial panel model and Sys-GMM to empirically estimate the key determinants of carbon emissions at the city-level and discuss its spatial spillover effects in consideration of spatiotemporal lag effects and different geographical and economic weighting matrices. Results indicated that the regional inequalities of city-level carbon emissions decreased over time and presented an obvious spatial spillover effect and high-emission "club" agglomeration. In addition, the evolution of the emission pattern has the characteristic of obvious path dependence. Panel data analysis results indicated that there was a significant U-shaped curve that can reflect the relationship between carbon emissions and GDP per capita. In addition, carbon emissions per capita are increasing with economic growth for most cities. High-proportion of secondary industry and extensive growth of investment exerted significantly positive effects on China's city-level carbon emissions. Conversely, rapid population agglomeration, the improvement of technology level, the increase of trade openness and road density play an inhibiting role in carbon emissions. Therefore, in order to reduce carbon emissions, the Chinese government should inhibit the effects of promotion factors and enhance the effects of mitigation factors. Combining with the analysis of results, we argued that optimizing the industrial structure, streamlining the extensive investment, increasing the level of technology and improving the road accessibility are the effective ways to increase energy savings and reduce carbon emissions in China.

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。