Do carbon emissions and economic growth decouple in China? An empirical analysis based on provincial panel data

Yu Hao*, Zirui Huang, Haitao Wu

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    57 Citations (Scopus)

    Abstract

    Global warming has emerged as a serious threat to humans and sustainable development. China is under increasing pressure to curb its carbon emissions as the world's largest emitter of carbon dioxide. By combining the Tapio decoupling model and the environmental Kuznets curve (EKC) framework, this paper explores the relationship between China's carbon emissions and economic growth. Based on panel data of 29 provinces from 2007 to 2016, this paper quantitatively estimates the nexus of carbon emissions and economic development for the whole nation and the decoupling status of individual provinces. There is empirical evidence for the conventional EKC hypothesis, showing that the relationship between carbon emissions and per capita gross domestic product (GDP) is an inverted U shape and that the inflection point will not be attained soon. Moreover, following the estimation results of the Tapio decoupling model, there were significant differences between individual provinces in decoupling status. As a result, differentiated and targeted environmental regulations and policies regarding energy consumption and carbon emissions should be reasonably formulated for different provinces and regions based on the corresponding level of economic development and decoupling status.

    Original languageEnglish
    Article number2411
    JournalEnergies
    Volume12
    Issue number12
    DOIs
    Publication statusPublished - 2019

    Keywords

    • Decoupling theory
    • Differential GMM estimation
    • Environmental Kuznets curve (EKC)
    • Panel data
    • Tapio decoupling model

    Fingerprint

    Dive into the research topics of 'Do carbon emissions and economic growth decouple in China? An empirical analysis based on provincial panel data'. Together they form a unique fingerprint.

    Cite this