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Re‐estimating the pollution haven–halo hypotheses for Brazil via a machine learning procedure

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  • Emmanuel Uche
  • Philip Chimobi Omoke
  • Charles Silva‐Opuala
  • Mamdouh Abdulaziz Saleh Al‐Faryan

Abstract

In this study, we re‐examined the pollution haven and halo hypotheses in Brazil for approximately five decades (1970–2019) while controlling for the effects of income, renewable energy and natural resource depletion. For clearer insights, the study employed both the conventional autoregressive distributed lag (ARDL) and the enhanced kernel regularized least squares (KRLS) techniques. Notably, the KRLS is a flexible machine learning nonlinear analytical technique that explains the interactions of the regressand and the regressors both at the average and across a range of quantiles. After ascertaining cointegration through the bounds tests and the Bayer–Hanck procedures, the following empirical outcomes emerged: The ARDL result suggests the acceptance of the pollution haven hypothesis in Brazil in both the short and long runs. However, the KRLS technique reveals that foreign direct investment (FDI) could enhance environmental quality (pollution halo) within the 25th quantile of the distributions of CO2 emissions. However, at the 50th and 70th quantiles, the pollution haven hypothesis is rectified. This suggests the adoption of varying policy options to ensure continuous inflows of FDI without compromising environmental quality. Additionally, among the control variables, a U‐shaped environmental Kuznets curve (EKC) structure is revealed from the influence of gross domestic product (GDP); renewable energy ensures a clean environment at all times, while resource rent ensures a clean environment only at the 25th and 50th quantiles of the distributions. Policies that could lead to clean environments in Brazil have been provided.

Suggested Citation

  • Emmanuel Uche & Philip Chimobi Omoke & Charles Silva‐Opuala & Mamdouh Abdulaziz Saleh Al‐Faryan, 2024. "Re‐estimating the pollution haven–halo hypotheses for Brazil via a machine learning procedure," Journal of International Development, John Wiley & Sons, Ltd., vol. 36(2), pages 1274-1292, March.
    • Handle: RePEc:wly:jintdv:v:36:y:2024:i:2:p:1274-1292
    DOI: 10.1002/jid.3868
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    References listed on IDEAS

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