Change in Carbon Dioxide （CO2） Emissions From Energy Use in China＇s Iron and Steel Industry

 As the largest energy consuming manufacturing sector and one of the most important sources of carbon dioxide (CO2) emissions, the China′s iron and steel industry has paid attention to the study of changing trend and influencing factors of CO2 emissions from energy use. The logarithmic mean Divisia index (LMDI) technique is used to decompose total change in CO2 emissions into four factors: emission factor effect, energy structure effect, energy consumption effect, and steel production effect. The results show that the steel production effect is the major factor which is responsible for the rise in CO2 emissions; whereas the energy consumption effect contributes most to the reduction in CO2 emissions. And the emission factor effect makes a weak negative contribution to the increase of CO2 emissions. To find out the detailed relationship between change in energy consumption or steel production and change in CO2 emissions, the correlation equations are also proposed.     

Assessing drivers of energy consumption and progress toward energy targets in Italy

ABSTRACT

The objectives of this study are to provide new insights on (i) the drivers of changes in final energy consumption in Italy over the period from 1995 to 2015 by employing a multi-sectoral decomposition analysis approach – Logarithmic Mean Divisia Index I (LMDI-I); and (ii) the progress of Italy toward the indicative energy efficiency and mandatory energy-saving targets set for 2020. The decomposition results show that from 1995 to 2015, an increase in final energy consumption caused by activity effects has been almost totally offset by structural changes. Energy intensity improvements occurred during the period from 2006 to 2015 when most of the energy efficiency policies in Italy were implemented. However, unlike the ex ante estimates of energy savings reported by the Italian government, the LMDI-I analysis shows that Italy is not on track to achieve the 2020 energy efficiency and energy-saving targets. Challenges and opportunities in policymaking are discussed.     

基于LMDI模型的电力行业碳排放的影响因素分析

通过LMDI模型计算了各因素对电力行业碳排放的影响值和贡献率,得出了经济发展对碳排放量的增加具有主要的拉动作用的结论.在经济稳定发展的条件下,发电能源结构是抑制碳排放的主要因素.为了降低碳排放量,应当大力开发新能源发电,改善我国发电结构.     

基于实际耗水的固原地区马铃薯生产水足迹研究

通过水足迹理论准确评估固原地区马铃薯生产的水资源利用效率情况,为当地马铃薯产业发展提供理论依据。采用基于实际耗水的水足迹计算原理、Mann-Kendall检验、Sen斜率法、LMDI模型,研究了固原地区马铃薯生产水足迹的变化趋势和主要影响因素。研究结果表明：1981-2017年固原市马铃薯生产水足迹均值为1.48 m³/kg,下辖县(区)均值在1.17~2.04 m³/kg之间,全市和各县(区)马铃薯生产水足迹均呈显著减小趋势,全市年均变幅为-0.04 m³/(kg·a);固原市单位面积绿水消耗量和单产量均值分别为2 778.4 m³/ hm²和2.18 t/hm²,下辖县(区)分别在2 622.2~3 401.3 m³/hm²和1.65~3.19 t/hm²之间,各县(区)单产量均呈显著增加趋势,从西北到东南单产量和单位面积绿水消耗量增大,水足迹减少;固原市单产量全时段贡献率为80.8%,贡献量在0~-2.30 m³/kg之间,单位面积绿水消耗量贡献率为19.2%,各县(区)单产量贡献率为76.3%~84.0%,单位面积绿水消耗量贡献率为16.0%~23.7%。水足迹计算方法中考虑了实际耗水条件,比充分灌溉条件更能反映实际情况。固原地区马铃薯水足迹显著下降主要是由于单产量增加,单产量作为主要驱动因子仍有提升潜力,今后应该加强农业现代化发展,提高水资源利用效率。     

基于LMDI法东莞市水资源生态足迹影响因素分析

水资源生态足迹是衡量水资源可持续发展的重要指标。研究东莞市水资源生态足迹现状,并应用LMDI法剖析水资源生态足迹影响因素。结果表明:2000-2011年间,东莞市水资源生态足迹整体上呈现先急速上升后缓慢下降的趋势,经济效应是水资源生态足迹增长的主要推动因素,水足迹技术效应是抑制水资源生态足迹增长的关键因素,水足迹结构效应和人口效应一定程度上助长水资源生态足迹的增长。     

基于对数平均迪氏指数分解法的陕西省居民用电影响因素分析

随着陕西省西安市新一线城市的确立和人才引进等计划的实施,将会引起居民用电量大幅增长,明确其增长动因及未来增长潜力对电力供应企业把控市场变化具有重要的参考价值。通过统计计算2010年至2016年陕西省人均住房面积、城镇化进程、常住人口以及居民用电量等相关数据,采用LMDI法分析了陕西省居民生活用电量增长动因,由分解结果可知,用电习惯、居住条件改善、城镇化进程、人口增长4个方面,对陕西省居民用电增长分别贡献了18.39%、52.51%、13.84%、15.26%,可以看出居住条件和用电习惯为重要影响因素。     

Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method

Knowledge of influencing factors of industrial carbon emissions (ICE) is crucial to the efforts of reducing anthropogenic greenhouse gas emissions. In this paper, main factors responsible for the ICE in Shanghai between 1996 and 2007 were identified and quantitatively analyzed using the Log-Mean Divisia Index method. It was found that the industrial output was the main driving force of ICE. The decline in energy intensity and the adjustment of energy and industrial structure are major determinants for reduction of ICE, with the former alone accounting for 90% of the reduction. To better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two equal time intervals and analyzed some high-carbon emission sectors. The results suggested that the intensity of energy use should be reduced further, for it was far higher than the world average. Adjustment of industrial structure by developing low-carbon emission industries is more crucial than energy mix.     

山东省能源消费CO2排放及驱动因素分析

基于IPCC提出的温室气体排放计算公式,利用1990～2009年山东省能源消费数据,测算了CO2排放量,并结合经济和人口数据,采用LMDI因素分解法分析了能源强度、能源结构、人口规模和人均GDP对CO2排放的影响。结果表明,CO2排放主要来源于火力发电和供热及第二产业,经济水平与人口规模表现为正效应,能源强度与能源结构表现为负效应,由此提出了山东省现阶段的减排建议。     

Decomposition and decoupling analysis of electricity consumption carbon emissions in China

Electricity consumption is one of the major contributors to greenhouse gas emissions. In this study, we build a power consumption carbon emission measurement model based on the operating margin factor. We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects (i.e., emission intensity, power generation structure, consumption electricity intensity, economic scale, population structure, and population scale) and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China’s economic development and population changes. Moreover, we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies. The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.     

基于三维水足迹模型的河南省水资源可持续利用水平评价

基于三维水足迹模型,以水生态赤字与盈余、水压力指数、水资源集约利用指数和水资源可持续指数4个指标构建水资源可持续利用水平指标评价体系,从时间上和空间上分析河南省的水资源可持续利用水平,并且利用对数平均迪氏指数分解法分析水足迹变化的驱动因素。结果表明：河南省水足迹由大到小为生产用水足迹、生活用水足迹和生态用水足迹,其中豫北和豫中各市的水足迹深度较大;河南省整体水资源可持续利用水平较差,其中豫北和豫中地区的水资源可持续利用水平最差;经济规模效应、人口规模效应对水足迹的变化具有正向驱动作用,而水足迹强度效率效应和水资源承载力效应具有负向驱动作用。