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Assessing the convergence process of ESG metrics: a research note on the role of economic freedom

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  • Patroklos Patsoulis

    (Neapolis University Pafos)

  • Demetra Demetriou

    (Neapolis University Pafos
    Neapolis University Pafos)

Abstract

Do environmental, social, and corporate governance (ESG) metrics convergence at the country level? Is there a common mechanism influencing the key attributes of this transitional process? This paper delves into the convergence patterns of firm-specific ESG factors, aggregated at a country level, aiming to identify the drivers of convergence. Specifically, we investigate whether ESG metrics have reached a singular or multiple steady states. To address this critical issue, we employ the Phillips and Sul (Econometrica 75(6):1771–1855, 2007; J Appl Econom 24(7):1153–1185, 2009) (hereafter PS) methodology. The findings reject the hypothesis of full convergence and suggest the formation of four distinct clubs. To further scrutinize our results, we conduct statistical tests to determine whether previously identified transition paths (specifically, factors related to economic freedom) corroborate our story.

Suggested Citation

  • Patroklos Patsoulis & Demetra Demetriou, 2024. "Assessing the convergence process of ESG metrics: a research note on the role of economic freedom," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-15, December.
    • Handle: RePEc:spr:lsprsc:v:17:y:2024:i:1:d:10.1007_s12076-024-00383-x
    DOI: 10.1007/s12076-024-00383-x
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    Keywords

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    JEL classification:

    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • F64 - International Economics - - Economic Impacts of Globalization - - - Environment
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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