中国碳排放的空间格局及分布动态演进——基于县域碳排放数据的实证研究

基于中国1997—2017年2725个县域单元的碳排放数据,采用标准差椭圆、Theil指数三阶段嵌套分解和Kernel密度估计等方法,全面考察中国碳排放的空间格局及分布动态演进。研究发现：① 中国碳排放由1997年的30.97亿t增长到2012年的93.08亿t,年均增长达到7.86%,而后围绕93亿t上下波动,未出现下降的拐点。② 在空间分布上,中国碳排放呈现东高西低的分布格局,表现出东北-西南方向向心集聚、西北-东南方向空间发散的趋势。③ 在空间差异上,Theil指数三阶段嵌套分解结果表明,中国碳排放的总体差异呈下降趋势,地级行政单元内部差异的贡献率由1997年的43%增加到2017年49%,成为中国碳排放总体区域差异的主要来源。④ 在分布动态演进上,中国县域单元的碳排放存在空间收敛模式,具有显著的空间正相关性,地区间的相互作用影响中国碳排放的未来空间分布。     

基于EBA模型的中国碳排放稳健性影响因素研究

依据环境经济学理论,运用极值边界分析（the extreme bounds analysis, EBA）模型,拓展了Kaya恒等式,利用中国30个省际区域2001~2010年的面板数据,实证研究中国省际区域人均碳排放量“稳健性”的影响因素。结果表明,地方财政决算支出、产业结构、能源效率、能源消费结构、能源价格、客运量等6个因素对中国人均碳排放量具有抗干扰的“稳健性”显著影响,并根据实证结论提出了一些政策建议,如调整产业结构,控制高碳产业发展;优化能源消费结构,积极发展新能源和可再生能源;发展循环经济开发清洁技术,提高能源利用效率;提倡低碳生活,提倡低碳生活方式。这些建议为政府制定环境保护与经济发展政策提供经验证据和决策参考。     

新疆碳排放影响因素分析与政策建议

气候变化和二氧化碳减排问题已引起全世界的关注.本文运用岭回归分析1995-2008 年新疆碳排放与人口、经济、技术间的关系,并进一步探讨了产业结构和主导产业对碳排放的影响,以寻找减排的技术路线和对策,推动区域低碳经济的发展.结果表明：①不合理的经济结构和人口增长对碳排放有显著的推动作用,技术进步虽在一定程度上缓解了碳排放,但影响甚微;②新疆过分依赖自然资源的经济增长方式和以第二产业为主的经济结构是导致温室气体排放量增加的主要原因;③研究期间,新疆的主导产业均为以石油天然气开采、石油化工、煤化工为主的重工业,但其在工业总产值的比重却有较大提升,导致对能源的消耗急剧增加.未来在全球化背景下,新疆应转变经济增长模式,加大生态保护力度,更多地依靠科技创新、技术进步和制度的改进,大力发展低碳产业.     

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.     

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.     

甘肃省碳排放变化及影响因素分析

采用甘肃省人口、经济发展、能源消费等数据,通过相关方法对1997-2008年的碳排放总量、碳排放强度及其三大产业的碳排放进行了估算,并利用岭回归函数对STIRPAT扩展模型拟合,进一步分析影响甘肃省碳排放的因素。结果表明： （1）从1997-2008年甘肃省能源消费的碳排放量和人均碳排放量均呈逐年增长的趋势。碳排放量由1997年的1 767.14×104 t增加到2008年 4 341.64×104 t。人均碳排放量由1997年的0.7 t /人增长到2008年的1.65 t /人,且以煤炭消费的碳排放为主,占各能源碳排放的比例达到70%以上。（2）碳排放强度从1997-2001年呈波动变化,2001年以后则呈逐年下降趋势,总体上从1997年的2.214 t/104元下降到2008年的1.364 t / 104元。（3）三大产业的碳排放呈逐年上升趋势,且以第二产业的贡献为主。（4）人口增长、经济发展对碳排放影响较大,而生活水平的提高更加剧了碳排放的增长。     

经济增长、产业结构与碳排放相互关系研究进展

对碳排放与经济、产业系统相互影响机理的研究已成为气候变化领域关注的重点.在对国内外文献进行系统梳理的基础上,论述了经济增长、产业结构与碳排放相互作用的相关内容,并对研究方法进行回顾和总结.研究内容既涉及碳排放的经济学分析、三次产业发展的碳效应、交互关系、碳减排的经济损失等基础分析,又重点关注了低碳经济、低碳城市、国际碳流动等应用领域.研究多采用经济计量学、数理统计、情景分析、模型模拟、投入产出和碳足迹等定量方法,与定性研究相得益彰.研究内容呈现多层次性,理论不断深化,方法趋于多类型综合化,视角逐步向微观层面转向.最后指出今后需要深入研究的方向：完善研究的方法论体系;进行方法创新,增强区域性研究,探索适合中国的研究模式和框架;扩展研究尺度,填补研究空白和薄弱之处.     

中国经济“新三步走”战略下人均碳排放预测

Under the context of the New Three-step strategy of Chinese economic development, we use the reverse tracing method and petroleum, natural gas and coal as sources of carbon emissions. To eliminate the impact of regional and technological levels in the energy conversion process we improved the IPAT model and used it to predict and analyze China’s per capita carbon emissions in three assumed scenarios up to 2050. We found that China’s per capita carbon emissions peak will occur respectively in 2020 and 2030 in different assumed scenarios; national per capita carbon emissions will reach 2.0127–2.6791 tons; China’s per capita carbon emissions will grow continuously at an average rate of 2.25%–3.40% per year before the peak year and then will decline at a rate of 1.33%–1.78% per year. By the year of 2050, national per capita carbon emissions will be 1.3147–1.8817 tons.     

城市CO2排放结构与低碳水平测度——以京津沪渝为例

在科学设计城市CO₂排放、城市低碳水平数理模型基础上,结合BP神经网络法,综合考虑各种不确定因素的影响,通过1995~2008年京津沪渝4市CO₂排放结构和低碳水平测度以及BP神经网络模型预测,可以发现:4城市CO₂排放量逐年递增,但存在较大差异;城市CO₂排放量和发展态势取决于4城市CO₂排放结构及变化;低碳水平测度结果表明4城市的经济增长仍然依赖于碳基能源消耗,但产业结构的优化升级对提高低碳水平的作用是显著的;基于BP神经网络法的短期预测比传统预测法更为合理和精确。     

基于IO-SDA模型的新疆能源消费碳排放影响机理分析

基于区域视角的能源消费碳排放影响机理分析,是有效实现节能降耗减排的重要研究议题。本文基于投入产出理论,通过构建“能源—经济—碳排放”混合型投入产出分析框架,利用扩展的IO-SDA模型,对新疆维吾尔自治区（简称新疆）1997-2007年能源消费碳排放的影响因素进行结构分解分析。结果显示：① 新疆能源消费碳排放从1997年的2070.08万t增长到2007年的4034.33万t,碳排放的增长主要集中在能源资源生产与加工业和矿产资源开采与加工业。② 碳排放影响因素的直接效应分析,人均GDP、最终需求结构、人口规模和生产结构的变化是引起碳排放增长的重要影响因素,碳排放强度的降低是这一时期遏制碳排放增长的重要影响因素,说明在经济规模和人口数量不断增长的同时,经济结构未得到有效优化,生产技术未得到有效的提升,导致新疆能源消费碳排放的快速增长。③ 碳排放影响因素的间接效应分析,省域间调出、固定资本形成总额和城镇居民消费对于新疆能源消费碳排放的变化影响显著。④ 碳密集产业部门的固定资产投资增加,能源资源型产品的省域间调出增长,使得区域间“隐含碳”转移效应十分显著。     

我国城市居民直接能耗的碳排放类型及影响因素

针对我国287个地级以上城市,在测算了近9年居民直接能耗导致的CO₂排放量的基础上,进行聚类、对比,并分析城市居民直接能耗的碳排放影响因素,得到以下结论:全国分为6类城市居民直接能耗碳排放类型;高碳排放型城市的地均碳排放强度、人均工资碳排放强度及居民直接能耗CO₂排放总量等方面均比低碳排放型城市高,人均地方生产总值碳排放强度低于低碳排放型城市,并多为经济发达城市和资源丰富城市,其碳排放构成上分别以电、交通能耗碳排放和气碳排放为主导,高碳排放型城市居民直接能耗CO₂排放量占全国地级以上城市的86.20%。我国大部分地级城市居民直接能耗的碳排放属于相对低碳排放型,其人均CO₂排放量低于全国平均水平。城市所在地的降温度日数(CDD)、采暖期、采暖强度、人均能源供给量、居民的人均工资、城市人均地方生产总值是影响城市居民直接能耗CO₂排放量的主要因素。     

欧盟碳减排目标的经济可能性评估

经济发展与能源消耗有着密切的关系,文章通过选取1994年至2009年欧盟27国的人口、经济和能源数据,利用碳排放动力学模型对欧盟关于2050年前削减温室气体排放80-95%的承诺进行预测,并对欧盟目标下的减排情景进行了分析。结果表明：（1）以当前的技术进步速率下,沿最优平稳经济增长路线,到2050年欧盟的碳排放量将为775.608MtC,达不到预定的减排要求;（2）在最优经济增长速度得出总能源消费量的基础上,采用调整能源结构与碳捕捉技术,预期可以达到设定减排80%的任务。其每一期的煤炭占比、石油占比、天然气占比应分别有4%、2.26%、1.23%转移至非碳能源占比,非碳能源的上升速率应达到2.21%/年;（3）若以历史的能源结构转移趋势预测未来的能源结构占比,即使考虑能源利用效率和碳捕捉技术的预期目标,欧盟仍然达不到在2050年的减排目标;（4）考虑欧盟提出的四种减排路径上下限组合,可预计出到2050年欧盟的减排范围在80.51%-87.16%;（5）若欧盟重振工业（特别是制造业）,即使考虑欧盟制定的减排路径,仍存在着达不到减排预期的可能。     

Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis

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。     

西北地区居民生活碳排放现状分析及预测

我国西北地区经济发展、居民生活水平和碳排放均低于全国平均水平,随着国家政策倾斜,居民生活条件逐步改善,居民生活碳排放量提升空间较大,排放格局将受影响,这对西北地区本就脆弱的生态环境更加不利。目前有关居民碳排放的研究多集中在人类活动频繁、碳排放量大的我国东、南部地区,较少关注西北地区,但碳排放增加、环境成本加重对于欠发达地区的影响更加深远。其次,研究者关注居民碳排放预测时,通常着眼于数量预测,忽视了空间格局预测,不利于区域间协同发展。基于1997—2016年西北五省居民能源消耗和消费支出数据,首先利用直接系数法和投入产出法测算了1997—2016年西北地区居民生活碳排放,对其现状进行分析;第二,基于标准差椭圆和ARIMA模型从数量和空间格局上对2017—2021年西北居民生活碳排放进行预测。结果表明:1997—2016年,西北居民生活碳排放呈先缓慢后快速的上升趋势。直接碳排放稳定在0.3~0.4×10^8 t;间接碳排放达到2.38×10^8 t;空间分布总体稳定,呈西北—东南分布,移动趋势为西北—东南—西北,标准差椭圆中心在(99.07°E,38.19°N)附近移动。2017—2021年,直接碳排放达到0.543×10^8t;间接碳排放为3.631×10^8 t;主体区域沿X轴发散,Y轴收敛,旋转轴变化小,随着西部大开发和脱贫的推进,新疆排放量次于陕西,增速较快,推动碳排放主体区域向西北移动。旨在为实现西北地区人口、消费、环境协调发展、引导居民树立低碳消费的价值理念建言献策。     

Temporospatial changes of carbon footprint based on energy consumption in China

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 productive 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.     

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.     

中国居民食物消费碳排放时空演变趋势及其驱动机制分析

食物生产是影响温室效应气体排放的重要方面,由于食物类型不同其碳排强度存在较大的差异,因此当前中国居民食物消费快速演替必然会对生态碳环境产生重要影响。论文利用洛伦兹曲线和基尼系数,研究1997—2016年中国城乡居民食物消费在生产过程中碳排放时空演变及其主要驱动因子。主要结论如下：① 在数量上,中国居民食物消费碳排放总量和人均量整体都呈现逐渐的增长刚性,但仍属于低碳消费模式;② 在结构上,各类型食物碳排放演变呈现显著的分异态势,整体上动物性食物碳排放增长速度快于植物性食物碳排放减少速度;③ 在城乡差异上,城镇和乡村居民食物消费碳排放在数量、结构和趋势上表现出显著的城乡二元属性,但近年城乡差异表现出减缓趋势;④ 在空间上,各地食物消费碳排放呈现较显著的民族性、地域性、集聚性和中心“塌陷”的特征,碳排放密度高值主要分布在直辖市和沿海经济相对发达地区;⑤ 在驱动力上,结构演替是全国居民食物消费碳排放总量整体变化的主要驱动因素,而人口数量是各地地域空间差异分布的主要驱动因子。     

空心村综合整治与低碳乡村发展战略探讨

当前以低能耗、低污染、低排放为特征的低碳经济成为全球热点。我国日益加剧的农村空心化带来一系列的不良效应和生态环境问题。开展空心村综合整治,可以通过降低能耗、减少污染、减少排放和增加碳汇4个途径,引领乡村低碳产业发展,重塑农民生产、生活新观念与新模式。在统筹城乡发展和新农村建设的新形势下,深入探讨空心村整治的低碳经济战略,测算空心村整治增地潜力与固碳能力,研究空心村整治过程中的农村能源技术体系,构建空心村整治还林还田创新机制,可为空心村整治实践和低碳乡村发展提供科学决策依据。     

中国碳排放关键驱动因子的分析和政策反思——基于LMDI：1995—2010

影响中国能源消费和碳排放的两大主因是经济政策和能源政策,显然,经济因素是外因,能源供应结构和能源效率才是影响的内在因子。本文基于碳排放的完全分解分析模型——对数平均Divisia指数（LMDI）系统分析了影响1995～2010年中国碳排放变化的关键因子和贡献率,分解因子包括四种,即经济规模效应、结构效应、能源强度效应、碳强度效应。指数分解模型结果表明,不同时期这四种效应对碳排放变化的贡献率是不同的,1995—2010年对碳排放增加影响最大的因子是经济发展（贡献率155％）和产业结构改变（贡献率10．6％）,而碳排放量的减少主要是由能源强度下降贡献的（贡献率-63．7％）,碳强度效应影响很小。但最近几年碳排放量的增加除了经济规模这个主要因子,碳强度的改变（贡献率4％）、产业结构调整（贡献率1．3％）都促进了碳排放量的增加,只有能源强度的下降起到了抑制碳排放量增加的作用（贡献率-73．5％）。模型的这种分解结果提示我们需要对这一时期内的产业政策、能源发展措施等方面进行反思,未来还要加强“节能减排”政策的落实、加强低碳能源技术的投资力度和政策扶持。