基于化石能源消耗的重庆市二氧化碳排放峰值预测

首先利用重庆市能源平衡表,采用IPCC方法 1对重庆市1997—2012年的碳排放进行核算;其次依据重庆市经济社会发展状况,通过LMDI因素分解法将影响碳排放的因素分解为:人口、人均GDP、产业结构、能源结构、能源强度和碳排放系数;然后利用扩展的重庆市STIRPAT碳排放模型,在9个情景模式下对2013—2050年重庆市碳排放进行预测;最后对比分析了各情景下的峰值大小及出现时间.研究发现:基准模式下的重庆市碳排放在2035年出现32135.38万t的峰值;提高能源利用技术、增加清洁能源使用比例和大力发展第三产业,能在不降低经济发展的情况下有效降低碳排放;消极因素中的第二产业占比下降比碳排放强度下降对碳排放的抑制作用更加明显;积极因素对碳排放峰值的影响比消极因素更有效.     

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.     

基于LMDI方法的广东省生活能源消费影响因素分析

运用对数平均迪氏分解法(LMDI)构建广东省生活能源消费的因素分解模型,定量分析2000~2014年间能源结构、人口规模、居住面积和设备消费等因素的变化对生活能源消费的影响。研究发现,4种因素的累计效应均为正,而采用逐年效应角度观察,设备消费是生活能源消费增加的最大拉动因素,其次为居住面积。基于研究结果,提出降低居民生活能源消费的相关政策建议。     

中国能源消费导致的CO2排放量的差异特征分析

运用一种不产生残差的方法——对数平均迪氏指数法LMDI(logarithmicmeanDivisiaindex),对中国部分省份、区域能源消费导致的二氧化碳排放量进行了分解分析。将二氧化碳排放总量的变化分解成五个主要影响因素,即化石燃料的排放系数、能源消费结构、能源强度、人均GDP和人口总数。研究表明我国各省(地区)的二氧化碳排放量在1996年后呈现零(或负)增长趋势,主要影响因素是能源强度的提高;各省(地区)的二氧化碳排放量地区差异显著。因此,要在全国实现二氧化碳排放量的总体减排,应从提高能源利用效率,调整产业结构,消除地区发展的不平衡,逐步改善能源消费结构等方面考虑。     

碳排放现状及驱动因素分解实证分析研究——以东莞市为例

根据IPCC碳排放计算指南核算东莞市2005年-2011年期间能源消费的碳排放量,并进行现状分析和采用对数平均权重分解法（LMDI）因素分解分析.研究结果表明：碳排放总量以5.8％速度增长,而碳排放强度以5.8％速度递减;煤是碳排放主源,但所占比重逐年下降;工业碳排放量比重最大,但是增速减慢;交通业、服务业和居民生活碳排放量及所占比重逐年增加;经济规模的扩大是拉动东莞市人均碳排放的决定因素,累计效应远高于能源效率和能源结构对碳减排影响.     

上海市碳排放强度的影响因素解析

采用对数均值迪氏分解（LMDI）法对1995—2008年上海市碳排放强度进行分解分析. 结果表明,产业部门能源强度的下降是上海市碳排放强度下降的主要原因,贡献率为67.6%. 进一步分析显示,上海市能源强度的下降主要来源于第二产业,但由于传统的工业节能改造的潜力有限,近年来工业能源强度下降的速度逐渐放缓,其对碳强度减排的贡献趋于减少. 能源结构和产业结构的调整是碳排放强度下降的次要原因,贡献率分别为18.2%和14.2%. 但是能源结构和产业结构仍然存在较大的调整空间,这2个因素有望对碳排放强度的下降作出持久的贡献.      

Two-step accelerated mineral carbonation and decomposition analysis for the reduction of CO2 emission in the eco-industrial parks

Carbon dioxide（CO2） emissions are a leading contributor to the negative effects of global warming. Globally, research has focused on effective means of reducing and mitigating CO2 emissions. In this study, we examined the efficacy of eco-industrial parks（EIPs） and accelerated mineral carbonation techniques in reducing CO2 emissions in South Korea.First, we used Logarithmic Mean Divisia Index（LMDI） analysis to determine the trends in carbon production and mitigation at the existing EIPs. We found that, although CO2 was generated as byproducts and wastes of production at these EIPs, improved energy intensity effects occurred at all EIPs, and we strongly believe that EIPs are a strong alternative to traditional industrial complexes for reducing net carbon emissions. We also examined the optimal conditions for using accelerated mineral carbonation to dispose of hazardous fly ash produced through the incineration of municipal solid wastes at these EIPs. We determined that this technique most efficiently sequestered CO2 when micro-bubbling, low flow rate inlet gas, and ammonia additives were employed.     

长江经济带工业污染排放空间分布格局及其影响因素

长江经济带发展强调不搞大开发,共抓大保护战略,长江生态环境修复成为该地区发展的重要内容,因此探明长江经济带工业污染排放空间分布格局及其影响因素具有重要意义.选取2013 ～2017年长江经济带102个城市的工业SO2、工业废水以及工业烟粉尘排放量作为基础数据,运用空间自相关分析和冷热点分析方法(Getis-Ord ...     

Decomposition Analysis of Sectoral Energy Use in Beijing （1981-2005） Using the LMDI Method

This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005. Sectoral energy use was investigated when regional economic structure changed significantly. The changes of total primary energy consumption in Beijing are decomposed into production effects, structural effects and intensity effects using the additive version of the logarithmic mean Divisia index （LMDI） method. Aggregate decomposition analysis showed that the major contributor of total effect was made by the production effect followed by the intensity effect, and the structural effect was relatively insignificant. The total and production effects were all positive. In contrast, the structural effect and intensity effect were all negative. Sectoral decomposition investigation indicated that the most effective way to slow down the growth rate of total primary energy consumption （TPEC） was to reduce the production of the energy-intensive industrial sectors and improving industrial energy intensity. The results show that in this period, Beijing＇s economy has undergone a transformation from an industrial to a service economy. However, the structures of sectoral energy use have not been changed yet, and energy demand should be increasing until the energy-intensive industrial production to be reduced and energy intensity of the region reaches a peak. As sequence energy consumption data of sub-sectors are not available, only the fundamental three sectors are considered： agriculture, industry and service. However, further decomposition into secondary and tertiary sectors is definitely needed for detailed investigations.     

中国服务业能源消费CO2排放及其因素分解

基于IPCC温室气体排放清单指南中的CO₂排放因子与核算方法,估算了1995—2010年中国服务业能源消费与CO₂排放量,并对其总体变化趋势进行时间序列分析;以LMDI(对数平均迪氏指数)法辨识与分解3个时段(1995—2000年、2000—2005年和2005—2010年)中影响中国服务业CO₂排放量变动的关键因素及其对CO₂排放量的贡献值. 结果表明:1995—2010年中国服务业能源消费CO₂排放量增长态势明显,累计排放总量为853197.55×104t;服务业能源消费主要依赖于高碳化能源燃料,各年度油品和煤品分别占能源消费总量的67%~74%和5%~27%;LMDI分析结果显示,1995—2010年产业规模和人口效应引起CO₂排放增加量分别为133357.10×104和7691.25×104t,能源效率和能源结构引起CO₂排放减少量分别为59034.50×104和23898.60×104t. 提出CO₂减排对策:①以经济、政策和监管手段促进服务业节能减排;②依托科技创新提高能源综合利用效率,降低服务业CO₂排放量.