A comparative analysis of Chinese provincial carbon dioxide emissions allowances allocation schemes in 2030: An egalitarian perspective

Weijun He, Bin Zhang*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    46 Citations (Scopus)

    Abstract

    A fair and effective carbon dioxide emissions allowances (CEA) allocation scheme is critical to balance regional development and alleviate social poverty. From the perspective of egalitarianism, this study aims to investigate the Chinese provincial CEA allocation in 2030, as well as the equality of allocation results. For this purpose, a total of 11 allocation schemes based upon a composite indicator method are developed, which consider different combinations of four allocation criteria, namely, egalitarian, responsibility, capability and efficiency. Then, an environmental Gini coefficient derived from Lorenz curves of allocation results is proposed to examine the equality of the results. The main findings show that the egalitarian criterion plays a crucial role in constructing allocation schemes, and different schemes often give rise to diversified allocation results. Although all the 11 allocation schemes, with the environmental Gini coefficients ranging from 0.0642 to 0.4019, better ensure the equality of allocation results, the schemes considering egalitarian criterion present higher equality over the schemes without this criterion. Furthermore, the schemes are likely to present higher equality, when the efficiency criterion is measured by the reciprocal of emission intensity, rather than the indicator derived from a data envelopment analysis method. Emission intensity is a better proxy for efficiency criterion in improving the equality of allocation results.

    Original languageEnglish
    Article number142705
    JournalScience of the Total Environment
    Volume765
    DOIs
    Publication statusPublished - 15 Apr 2021

    Keywords

    • CEA
    • Equality
    • Fractional programming
    • Gini coefficient
    • Information entropy

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