安徽省会经济圈碳排放强度与生态补偿研究

通过对1997年和2007年碳排放强度分析,揭示安徽省会经济圈内各市县对碳排放的影响和区域差异,根据相关固碳价格计算省会经济圈市县的生态补偿标准。研究结果显示:(1)省会经济圈总体上为碳汇区,1997—2007年间碳排放总量增加1 049.9万t,年均增长14.4%,其中,合肥市碳排放量增长最大。(2)2007年经济圈内地均建设用地碳排放强度和地均碳排放强度分别增加为1997年的2.18,2.41倍。1997—2007年,省会经济圈内的碳排放量、地均碳排放强度和地均建设用地碳排放都呈现合肥市>巢湖市>六安市。(3)经济圈内各县市地均碳排放强度差异显著,建设用地平均碳排放强度以合肥市最高(784 t/hm2),其次是霍山县和金寨县,其他区域的建设用地平均碳排放强度相差不大。(4)2007年合肥市提供的生态补偿标准是12.8～105.1亿元,六安市和巢湖市得到的生态补偿标准范围分别为31.9～278.1,0.6～13.8亿元;各县市生态补偿差异也很大。     

海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例

海岛城市具有独特的社会经济发展轨迹以及碳排放特征。定量剖析海岛城市碳排放的演变趋势及其关键驱动因子,对于指导"低碳海岛城市"和"生态海岛城市"建设意义重大。以中国典型海岛城市舟山市为案例区,采用IPCC参考方法,测算2001-2015年间舟山市各部门的碳排放量,并运用STIRPAT扩展模型,定量分析能源强度、人均GDP以及城市化率等关键驱动因子对海岛城市碳排放的影响;在此基础上,结合情景分析技术对舟山市未来的碳排放量进行情景预测。结果表明:(1)2001-2015年间,舟山市碳排放量增加迅速,年均增长率达到32%,碳排放强度遵从先降后增的"U型"变化规律,年均值略高于厦门市。(2)岭回归分析表明城市化率对碳排放量的增加影响最大,其次为能源强度,人均GDP对碳排放的影响最弱。(3)舟山市经济增长与碳排放之间存在Kuznets曲线假说,且理论拐点将出现在人均GDP为10.46万元附近(2000年可比价)。(4)舟山市在2020年和2030年的碳排放总量将分别达到2142万t和4333万t。总体而言,产业结构低碳化和能源效率提升是缓解舟山市未来碳减排压力的有力抓手。     

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。     

河南省能源消费碳排放的历史特征及趋势预测

根据联合国政府间气候变化专门委员会(IPCC)2006年版碳排放计算指南计算了河南省1995—2006年的能源消费碳排放量。预测了2007—2050年河南省平稳经济增长路径下的最优经济增长率和2007—2050年的河南省能源消费结构和产业结构。进一步,计算了河南省2007—2050年在经济平稳增长路径下的能源消费与碳排放总量以及人均量。结果表明:1995年以来,河南省的碳排放量逐年增加,碳排放强度先是逐年下降,到2003年发生转折,出现上升的趋势;同时,预测的2007—2050年的能源结构显示,河南省煤炭资源在能源消费总量中所占的比重有较大幅度的下降,石油与其他清洁能源的比重不断上升;另外,河南省分别在2036年和2034年达到能源消费和碳排放总量高峰;人均能源消费量和碳排放量的高峰则出现在2033年和2032年。另外,尝试预测了河南省森林碳汇潜力,发现2006—2050年累计森林碳汇量持续上升,到2050年,累计碳汇量达131.14M tc。     

基于土地利用的哈尔滨市2004～2012年碳排放强度变化分析

采用2004~2012年哈尔滨市土地利用和能源消耗数据,分析了2004~2012年哈尔滨市主要土地利用方式的碳排放。结果显示:1哈尔滨市2004年碳排放量为361.451万t,2012年碳排放量增长至1 875.658万t。2建设用地为主要碳源区,其碳排放占每年碳排放总量的96.98%;林地是主要碳汇区,约占碳汇量的99.90%,其总吸收量约为每年1 523.02万t碳;3哈尔滨市碳排放强度由2004年的0.681 t/hm2上升至2012年的3.534 t/hm2,平均每年增长22.854%;4建设用地碳排放强度2008年以前呈快速增长,2008年以后为缓慢的波动增长;5预测2020年建设用地的碳排放量为3 558.264万t;碳排放总量为2 055.839万t,比2012年上涨180.181万t,年平均增长率为1.15%,增长速度较慢。     

江苏省碳排放清单测算及减排潜力分析

基于省域层面,构建了碳排放清单的核算框架和计算方法.以江苏省为例,对2000-2010年的碳排放清单进行了全面测算,并对江苏省碳减排潜力进行了情景分析.江苏省碳排放总量从2000年的8 005.29万t上升到2010年的20 888.88万t,涨幅为160％,其中工业能源消费碳排放占86％;江苏省单位GDP碳排放强度呈波动下降趋势,从2000年的0.94t/万元下降到2010年的0.71t/万元,降幅达24％;人均碳排放则呈逐年增长态势,从1.09t/a增长到2.69t/a;在低碳情景下,江苏省2015年和2020年碳减排量分别为4 930.75万t和16 101.13万t,碳减排比例分别达15％和29％;在江苏省“十二五”低碳经济规划中,应重点加强工业能源与交通能源消耗、垃圾焚烧与填埋等部门的碳减排力度,切实降低区域碳排放强度,为低碳经济发展提供技术支撑和示范效应.     

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。     

新疆农牧业碳排放及其与农业经济增长的脱钩关系研究

研究基于农地利用、畜牧养殖两方面15类碳源,测算了新疆1993-2011年以及各地州2011年的农牧业碳排放量,并运用Tapio脱钩模型对1994-2011年间新疆农牧业碳排放与农业经济增长之间的脱钩关系进行分析。结果显示:(1)2011年新疆农牧业碳排放总量为778.73×104t,比1993年增加了50.12%,年均增长2.28%,呈现出明显的"持续上升——波动上升"两阶段特征,碳排放强度和结构的空间差异明显;(2)农牧业碳排放与农业经济增长呈现出"弱脱钩与扩张连接交替—弱脱钩平稳—强脱钩转型"的演进过程。     

基于IPAT模型的安徽省不同情景碳排放趋势测度分析

基于1995-2010年能源消费数据,利用IPAT模型,分规划情景、惯性情景、低碳情景对安徽省未来碳排放量及碳排放强度进行测度,结果表明:2015年,安徽省在规划情景、惯性情景、低碳情景下的碳排放量分别为16 680.96万t、14 790.52万t、11 235.49万t,均呈增长态势,碳排放强度分别为0.6952t/万元、0.6661t/万元、0.6533t/万元,呈下降趋势,规划与惯性情景模式下的碳排放量和碳排放强度均高于低碳情景模式。3种情景碳排放曲线表明,不会出现库兹涅茨曲线(EKC)拐点。科技创新、机制创新是实现碳减排的重要途径,"十二五"期间,安徽省通过提高能源效率、优化能源结构、创新机制等举措,碳排放仍有较大削减空间。     

中国粮食主产区耕地利用碳排放与粮食生产脱钩效应研究

深入分析耕地利用碳排放与粮食生产的脱钩效应,可为协调粮食安全与生态环境关系提供理论依据.该文在系统梳理碳排放源的基础上,对2000-2019年中国13个省域粮食主产区的耕地利用碳排放进行核算,探索其时空格局演进过程,进而依托T apio脱钩理论分析碳排放与粮食生产的长期关系.结果表明:考察期间,粮食主产区耕地利用碳排放量均值为16459.942万t,历经波动下降、匀速增长、平缓下降3个阶段.排放结构中,秸秆燃烧碳排放量占比最高,而土壤碳排放量占比最低;高排放省域主要聚集在华中地区,低排放省域多分布于东北地区.碳排放与粮食产量的关系经历了"弱负脱钩—扩张负脱钩—增长连接—弱脱钩"的演变历程.根据碳排放与粮食产量的双重特征,可将13个省域划分为高排高产区、低排低产区及高排低产区3类.经过20年演化,河北、辽宁、吉林等10个省域已稳定保持弱脱钩状态,内蒙古、河南、湖南尚处于增长连接状态,粮食主产区总体呈现以弱脱钩为主、增长连接为辅的脱钩格局.     

中国2005—2010年二氧化碳排放强度下降的结构分解分析

本文以2005年和2010年的投入产出表为基础,核算了中国二氧化碳排放强度的变化。结果表明,2005年二氧化碳排放总量678964．38万t,强度每万元3．67t。2010年二氧化碳排放总量930194．96万t,强度每万元3．27t（2005年价格）。5年间,二氧化碳排放强度降低了11％。在此基础上进一步构建结构分解分析模型,将促使二氧化碳排放强度的降低的因素分解为4种效应,即能源效率因素、能源结构因素、经济结构效应和经济增长方式效应。计量结果表明,部门单位产出能源消费强度变化和能源消费结构变化是造成二氧化碳排放强度下降的因素,其中尤以前者的影响最为显著。而经济增长方式变化和经济结构变化是促使二氧化碳排放强度上升的因素,并且前者的影响更为显著。     

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.     

基于结构分解法的北京能源碳排放增量分析(英文)

确保减碳的首要任务是定量测度化石能源消费碳排放的增量影响因素及其大小。为分析北京市1997-2007年的碳排放增量,本文构建了一个扩展的(调入、进口)竞争型经济—能源—碳排放投入产出模型,从整体特征、不同产业、工业行业3个方面,对1997-2007年北京能源消费的碳排放增量进行了结构分解。分析发现:经济规模增长要素(消费、投资、调出和出口等)是拉动碳排放增长的主导因素,能源强度变动效应却是碳减排的决定性因素;在规模扩张因素中,消费和调出超过投资和出口,是碳排放增长的主要贡献者;2002以来新一轮"高碳"特征的工业化导致CO2排量呈急增之势;产业结构调整、三产比重最大使得服务业成为碳排放增长的最大部门,但工业排放的增长却后来居上;碳增排的重点行业是高能耗业,而碳减排的却是能源工业;两时段各效应在不同产业、不同工业行业的影响方向和大小不一。     

黑色还是绿色？低碳经济目标下的中国经济增长方式

Achieving dual goals of economic growth and carbon emission reduction is a major issue in China. In this paper we define three economic growth patterns (green growth, brown growth and black growth) according to the relationship between economic scale and carbon emissions, and then trace the historical trajectory of economic growth over the last 40 years. Granger causality is used to test China’s economic growth and carbon emission linkage. We argue that green growth means that the rate of carbon emission reduction is faster than the increase in GDP. When carbon intensity reduction is slower than GDP growth, brown growth occurs. Black growth occurs when both carbon intensity and economic scale increase. For China, we denote the four periods of black growth (1971–1977), brown growth (1978–2001), black growth (2002–2004), and brown growth (2004–2010). Granger causality tests indicate that mutual causality between carbon emissions and economic growth exists in the short term, but not the long term. In order to achieve the goals of economic growth and carbon intensity reduction, it is necessary for China to actively participate in global carbon reduction activities, technological innovation and ecological civilization construction.     

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.     

基于能源消费的中国不同产业空间的碳足迹分析

Using energy consumption and land use data of each region of China in 2007,this paper established carbon emission and carbon footprint model based on energy consumption,and estimated the carbon emission amount of fossil energy and rural biomass energy of dif-ferent regions of China in 2007.Through matching the energy consumption items with indus-trial spaces,this paper divided industrial spaces into five types:agricultural space,living & industrial-commercial space,transportation industrial space,fishery and water conservancy space,and other industrial space.Then the author analyzed the carbon emission intensity and carbon footprint of each industrial space.Finally,advices of decreasing industrial carbon footprint and optimizing industrial space pattern were put forward.The main conclusions are as following:(1) Total amount of carbon emission from energy consumption of China in 2007 was about 1.65 GtC,in which the proportion of carbon emission from fossil energy was 89%.(2) Carbon emission intensity of industrial space of China in 2007 was 1.98 t/hm2,in which,carbon emission intensity of living & industrial-commercial space and of transportation in-dustrial space was 55.16 t/hm2 and 49.65 t/hm2 respectively,they were high-carbon-emission industrial spaces among others.(3) Carbon footprint caused by industrial activities of China in 2007 was 522.34 106 hm2,which brought about ecological deficit of 28.69 106 hm2,which means that the productive lands were not sufficient to compensate for carbon footprint of industrial activities,and the compensating rate was 94.5%.As to the regional carbon footprint,several regions have ecological profit while others have not.In general,the present ecologi-cal deficit caused by industrial activities was small in 2007.(4) Per unit area carbon footprint of industrial space in China was about 0.63 hm2/hm2 in 2007,in which that of living & indus-trial-commercial space was the highest (17.5 hm2/hm2).The per unit area carbon footprint of different industrial spaces all presented a declining trend from east to west of China.     

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.     

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。