The nexus of economic growth, foreign direct investment, and environmental sustainability: An empirical evidence

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Type of the article: Research Article

Abstract
Economic growth and foreign direct investment constitute critical determinants of environmental sustainability. This study empirically examines the nexus between these factors and environmental degradation, proxied by the ecological footprint and carbon emissions, while controlling for urbanization and natural resource depletion. Leveraging the strictly complete panel dataset from 17 selected Asian economies spanning 1990 to 2023, the analytical framework incorporates a comprehensive suite of diagnostic tests, including assessments of multicollinearity, cross-sectional dependence, stationarity, cointegration, and model specification. Diagnostic results confirm the absence of multicollinearity, establish stationarity at first differences, and validate long-run cointegration for both environmental models. Empirical evidence delineates five key findings. First, economic growth exhibits a positive and significant impact on both ecological footprint and carbon emissions, whereas its squared term exerts a negative and significant effect, corroborating the Environmental Kuznets Curve. Second, foreign direct investment increases the ecological footprint significantly, supporting the Pollution Haven Hypothesis. Conversely, foreign direct investment reduces carbon emissions, suggesting the presence of the Pollution Halo Hypothesis. Next, urbanization significantly amplifies the ecological footprint but mitigates carbon emissions. Finally, natural resource depletion significantly reduces the ecological footprint while intensifying carbon emissions. Collectively, these results underscore the complex roles of economic growth and foreign direct investment in environmental sustainability and affirm the concurrent applicability of the Environmental Kuznets Curve, Pollution Haven, and Pollution Halo theoretical frameworks within the Asian context.

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    • Figure 1. Empirical framework
    • Table 1. Definitions of variables
    • Table 2. Descriptive statistics and correlation analysis results
    • Table 3. VIF (variance inflation factor) results
    • Table 4. Unit root test results from Im, Pesaran and Shin W-stat
    • Table 5. Engle-Granger cointegration results
    • Table 6. Granger causality results
    • Table 7. Cross-sectional dependence result
    • Table 8. Chow, Hausman, and Lagrange multiplier panel data test results
    • Table 9. Panel data results
    • Table 10. Summary of the hypotheses results
    • Conceptualization
      Suyanto Suyanto, Michael Wijaya
    • Formal Analysis
      Suyanto Suyanto, Michael Wijaya
    • Funding acquisition
      Suyanto Suyanto
    • Investigation
      Suyanto Suyanto, Michael Wijaya
    • Methodology
      Suyanto Suyanto, Michael Wijaya
    • Project administration
      Suyanto Suyanto
    • Supervision
      Suyanto Suyanto
    • Validation
      Suyanto Suyanto
    • Writing – original draft
      Suyanto Suyanto, Michael Wijaya
    • Writing – review & editing
      Suyanto Suyanto, Michael Wijaya
    • Data curation
      Michael Wijaya
    • Resources
      Michael Wijaya
    • Software
      Michael Wijaya
    • Visualization
      Michael Wijaya