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Examining the influencing factors of CO2 emissions at city level via panel quantile regression: evidence from 102 Chinese cities

  • MoE Key Laboratory of Complex System Analysis and Management Decision
  • Beihang University
  • Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation

Research output: Contribution to journalArticlepeer-review

Abstract

Ascertaining the influencing factors of carbon dioxide emissions in Chinese cities is an important issue for policy-makers. This paper investigates the effect of several determinants on carbon emissions per capita in Chinese cities. Non-normally distributed and heterogeneous features of carbon emissions per capita in Chinese cities are considerably important. The empirical results demonstrate that GDP per capita has an increasingly positive impact on carbon emissions per capita due to the growth in household consumption. Urbanization has a slightly decreasing positive effect on carbon emissions per capita with a quantile increase resulting from continuous highway construction. Industrialization has a decreasing positive effect with carbon emission per capita quantile increases because of increasing energy efficiency and lower costs related to carbon reductions. The population has a decreasing negative effect on carbon emissions because of people’s increasing demand for environmental safety. The distributions of emissions per capita conditional on the 10th and 90th quantiles of independent variables also vary considerably. Specific policy implications are provided based on these results.

Original languageEnglish
Pages (from-to)3906-3919
Number of pages14
JournalApplied Economics
Volume51
Issue number35
DOIs
StatePublished - 27 Jul 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Chinese cities
  • STIRPAT model
  • carbon emissions accounting
  • panel quantile regression

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