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Revisiting the literature on the dynamic Environmental Kuznets Curves using a latent structure approach

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Abstract

Theories of the association between environmental degradation and economic growth are not new and are very important under current global conditions to understand and tackle challenges like decarbonisation and the circular economy among others. Countries must balance growth with environmental degradation, and in the extensive literature that deals with this association, applied economists have largely used the Environmental Kuznets curve (EKC) setting, with different empirical methodologies in various data settings. This paper exploits one of the methodologies to unveil heterogeneity to determine groupings from the data. We consider the countries that account for nearly 80\(\%\) of global carbon dioxide emissions and apply the EKC setting. Using a Classifier Lasso framework that applies latent group methodologies to address unobservable heterogeneity, we find for two distinct groups substantial heterogeneity in types of energy consumption (renewable and total) with both positive and negative effects observed in the data. The results provide a new perspective on potential impacts illustrated in the EKC literature that might be relevant to policy makers.

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Notes

  1. The Kuznets curve describes the trend of inequality in relation to the rate of development, showing the evolution of income distribution over time (Kuznets 1955).

  2. New EKC applications are among others (Massimiliano and Antonio 2014; Mazzanti and Musolesi 2017; Wagner 2015; Wagner et al. 2020)

  3. (Musolesi and Mazzanti 2014) approach is the following: “disentangling income and time-related effects (which are possibly heterogeneous across countries) in the study of greenhouse gas dynamics, while allowing for possible residual serial correlation at the same time, using Generalized Additive Mixed Models”

  4. An important assumption is that individual group membership does not vary over time.

  5. For a better explanation please refer to SSP (2016) page 2220, they use a Gaussian quasi-maximum likelihood estimation (QMLE) technique they minimize \(\beta _i\), \(\phi _i\) and \(\tau _t\) from Eq. (1) with \(\psi (\omega _{it}, \beta _i, \phi _i, \tau _t) = \frac{1}{2} (y_{it}-\beta '_i x_{it} - \phi _i - \tau _t)^2\) and \(\omega _{it} = (y_{it}, x'_{it})'\). Where \(\psi (\omega _{it}, \beta _i,\phi _i, \tau _t)\) is assumed to be the logarithm of the pseudo-true conditional density function of \(y_{it}\) given \(x_{it}\), the history of (\(y_{it}\), \(x_{it}\)), and (\(\beta _i,\phi _i, \tau _t\)).

  6. Looking at Eco-innovation scoreboards, Italy and France are close to the Northern EU countries (https://ec.europa.eu/environment/ecoap/indicators/index_en).

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Authors and Affiliations

  1. Paris School of Economics, Paris, France

    Saptorshee Kanto Chakraborty

  2. Department of Economics and Management, University of Ferrara, SEEDS, Ferrara, Italy

    Massimiliano Mazzanti

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  1. Saptorshee Kanto Chakraborty
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Correspondence to Saptorshee Kanto Chakraborty.

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This paper was a part of the Doctoral thesis of the first author at University of Ferrara, Italy (2015-19, EMIS).

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Chakraborty, S.K., Mazzanti, M. Revisiting the literature on the dynamic Environmental Kuznets Curves using a latent structure approach. Econ Polit 38, 923–941 (2021). https://doi.org/10.1007/s40888-021-00232-w

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