摘要
全球变暖与二氧化碳减排是各国关注的重要议题。综合研究碳排放影响因素,有效减少碳排放是亟待解决的重要任务。本文收集了我国2005~2017年30个省市的相关数据,构建GAMLSS模型进行非参数回归分析,探讨人口规模、经济水平、城市化水平和能源强度对二氧化碳排放的影响机制。分析结果显示,各因素从长期来看对二氧化碳排放具有线性正向效应,且人口规模、经济水平和城市化水平在一定范围内对二氧化碳排放呈现倒“U”型的非线性影响模式。基于研究结论,结合我国国情提出了关于二氧化碳减排的一些建议。
Global warming and carbon dioxide reduction are important issues of concern to all countries. It is an important task to comprehensively study the factors affecting carbon emissions and effectively reduce carbon emissions. This paper collects relevant data from 30 provinces and cities in China from 2005 to 2017, constructs a GAMLSS model for non-parametric regression analysis, and explores the impact of population size, economic level, urbanization level, and energy intensity on carbon dioxide emissions. The analysis results show that all factors have a linear positive effect on carbon dioxide emissions in the long run, and that the population size, economic level, and urbanization level have an inverted “U”-shaped non-linear impact mode on carbon dioxide emissions within a certain range. Based on the conclusions of the study, combined with China’s national conditions, this paper puts forward some suggestions on carbon dioxide emission reduction.
出处
《应用数学进展》
2020年第8期1177-1186,共10页
Advances in Applied Mathematics