基于LSDV估计法的中国主要品种能源消费影响碳排放强度效应分析-《郑州大学学报(工学版)》

文章信息/Info

Title:: Effects Evaluation of the Impact on Carbon Emission Intensity by Consumption of Main Types of Energy in China Based on LSDV Estimation

Author(s):: Wei Ran; Zhongyuan University of Technology System and Industrial Engineering Technology Research Center; Zhongyuan University of Technology School of Economics and Management

Keywords:: LSDV estimation method; effect analysis; Panel data; carbon intensity; Energy consumption

摘要:: 本文根据面板数据似然比检验和Hausman检验的结果，基于LSDV估计法，采用了固定影响效应模型回归分析了2005年-2017年中国统计年鉴和中国能源统计年鉴数据中的人口规模、人均财富、主要品种能源消费对碳排放强度的影响效应。分析过程中，对传统STIRPAT模型进行了改进，采用了人口规模和人均财富传统因素，煤炭、焦炭、原油、燃料油、汽油、煤油、柴油和天然气等能源消费因素，以及因变量碳排放强度。通过分析发现，样本期间我国各主要品种能源消费对于碳排放强度的影响差异较大，对于碳排放强度具有正向作用的能源消费因素包括煤炭消费、焦炭消费、汽油消费、柴油消费和天然气消费，具有负向作用的因素包括原油消费、燃料油消费和煤油消费，此外，从全国角度来看，近年来的人口规模变化对于碳排放强度的降低具有积极作用，而人均财富因素和煤炭消费因素仍然成为拉高碳排放强度的因素。

Abstract:: Impact effects on carbon emissions intensity by population, per capita GDP, and main types of energy in China were evaluated with the fixed effect model based on LSDV estimation with reasons of the results of Likelihood Ratio Test and Hausman Test. The traditional model of STIRPAT was improved by adding Carbon Emission Intensity and Energy Consumption Variables, which included consumptions of coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas, except population and per capita GDP. The results show that consumptions of different types of energy have different impacts on carbon emissions intensity from 2004 to 2016 in China. Five variables of energy consumption, which were corresponding to coal, coke, gasoline, diesel oil, and natural gas, had played positive effects on carbon emission intensity from the data of China Statistical Yearbook and China Energy Statistical Yearbook of 200 5 to 201 7. Other variables of crude oil consumption, fuel oil consumption, and kerosene consumption took opposite impact on carbon emission intensity. Moreover, change of population had the most significant favorable influence on carbon emission intensity in all studied variables. Unfortunately, per capita GDP and coal consumption contributed to the increasing of carbon emission intensity in China in the studied period.