孙涵,黄潮(学)Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index.
我校英国威廉希尔公司孙涵老师在T3级别期刊——《Environment, Development and Sustainability》上发表题为“Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index”。论文第一作者孙涵为英国威廉希尔公司教授，博士生导师。
Abstract /摘要：
MT 分析不同行业PM2.5污染的驱动因素,对我国行业制定节能减排政策具有重要意义。在这项研究中,以消费为基础的中国工业的PM2.5排放量估计使用一个投入产出模型；在此基础上,运用广义迪维西亚指数法( GDIM )测算驱动因素对我国六大行业PM2.5排放变化的贡献。结果表明,2007 - 2015年我国以消费为主的PM2.5排放量呈下降趋势,工业PM2.5排放量变化对我国PM2.5排放总量变化的影响远高于其他行业,占据主导地位。广义Divisia指数分解分析结果表明,投资、产出和能源消费规模是6个部门PM2.5排放量增加的首要贡献因素,其中投资规模的贡献最大。投资PM2.5排放强度、产出PM2.5排放强度和能源消费PM2.5强度对抑制PM2.5排放起主要作用,而投资效率和能源强度的抑制作用较小。因此,政府应引导投资更多地向高端、低排放行业倾斜,鼓励企业增加绿色投资,使用可再生能源和清洁能源。避免相关行业的过度投资,提高投资效率也能有效缓解PM2.5排放。
原文 Analyzing the driving factors of PM2.5pollution in different industries is of great significance for developing energy conservation and emission reduction policies in China's industries. In this study, the consumption-based PM2.5emissions of China's industries are estimated by using an input–output model; on this basis, the generalized Divisia index method (GDIM) is used to measure the contributions of driving factors to the changes in PM2.5emissions from China's six major industries. The results show that China's consumption-based PM2.5emissions presented a downward trend from 2007 to 2015, the changes in industrial PM2.5emissions had a much higher impact on China's total PM2.5emissions changes than other industries and occupied a dominant position. The generalized Divisia index decomposition analysis results show that investment, output and energy consumption scale were the primary contributors to the increase of PM2.5emissions in six sectors, with investment scale contributing the most. The investment PM2.5emission intensity, output PM2.5emission intensity and energy consumption PM2.5intensity play a major role in suppressing PM2.5emissions, while investment efficiency and energy intensity have a smaller inhibitory effect. Therefore, the government should guide investments to more high-end, low-emission industries and encourage companies to increase green investments and use renewable energy and clean energy. Avoiding excessive investments and improving investment efficiency in related industries can also effectively alleviate PM2.5emissions.
论文信息;
Title/题目:
Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index
Authors/作者:
Key Words /关键词：
PM;emission;Generalized Divisia index;Input–output model;Factor decomposition
Indexed by /核心评价：
WAJCI; SCI; Scopus;
DOI:10.1007/S10668-021-01862-7
全文链接：https://link.springer.com/article/10.1007/s10668-021-01862-7
