Regional impact of aging population on carbon dioxide emissions in China: Evidence from panel threshold regression (PTR)

Author:

Liang YifanORCID,Xinping HanORCID,Mazlan Nur Syazwani,Liang Bufan,Ting Liu

Abstract

Carbon dioxide emission is a high-profile issue that can affect both the human economy and human existence, but few scholars have studied the relationship between these two. Therefore, this study constructs the panel threshold regression (PTR) based on the National Bureau of Statistics of China’s panel data from 2002 to 2019 in 19 regions. One of the advantages of PTR is to leverage segmented functions, allowing for a more detailed analysis of the data. Besides, by introducing the idea of a threshold, PTR can effectively avoid structural changes in the data. The different between this study and other research is that this study divides 19 regions into two parts: Eastern China and Central China. Based on that, this study researches and compares the different influences of the aging population on carbon emissions in these two regions. The results show that although the Environment Kuznets Curve has been confirmed in both Eastern China and Central China, with the development of the economy, the carbon emissions will increase in Eastern China and decrease in Central China, respectively. In addition, the key factors affecting carbon emissions in Eastern China and Central China are trade dependence and urbanization rate separately. Hence, this study suggests that the regional governments in Eastern China may guide and encourage more international trading companies to move to Central China, and at the same time, the regional governments in Central China should issue more policies to attract these companies, such as: reducing land lease fees and building better transportation infrastructure. Apart from that, the governments in Central China should vigorously increase the rate of urbanization to reduce energy consumption and improve energy efficiency.

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

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