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<p>This study investigates heterogeneous effects of industrial investment, digital economy on carbon dioxide emissions using Method of Moments Quantile Regression (MMQR) across the CO₂ distribution. Using panel data analysis, we examine how GDP growth, industrial investment, digital economy development, and higher education influence emissions at different quantiles (Q0.10-Q0.90). Results reveal significant quantile-dependent relationships challenging conventional modeling assumptions. GDP growth demonstrates consistently positive associations with emissions across all quantiles, with effects strengthening at higher emission levels (Q0.75 = 0.023, Q0.90 = 0.045), indicating economic expansion drives disproportionately greater emissions in high-polluting contexts. Industrial investment exhibits stable negative coefficients around − 0.48 across quantiles, suggesting uniform emission reduction benefits from productive investments regardless of baseline levels. Digital economy development shows increasingly negative effects from − 0.172 (Q0.25) to -0.400 (Q0.90), while higher education demonstrates progressive emission reductions strengthening from − 0.638 to -0.786 across quantiles. These patterns indicate that digital transformation and educational investments provide greater environmental benefits in high-emission contexts. The heterogeneous effects underscore those environmental policies must be tailored to specific emission contexts for optimal effectiveness, highlighting the importance of distributional analysis in environmental economics.</p>

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Determinants of carbon dioxide emissions in China under climate change: an empirical analysis using quantile regression

  • 期刊论文

  • 发表日期：

    2026年1月2日

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  • Fang Liu,
  • Wenting Cao,
  • Jun Liu,
  • Yasir Khan,
  • Mohamed Marie

摘要

This study investigates heterogeneous effects of industrial investment, digital economy on carbon dioxide emissions using Method of Moments Quantile Regression (MMQR) across the CO₂ distribution. Using panel data analysis, we examine how GDP growth, industrial investment, digital economy development, and higher education influence emissions at different quantiles (Q0.10-Q0.90). Results reveal significant quantile-dependent relationships challenging conventional modeling assumptions. GDP growth demonstrates consistently positive associations with emissions across all quantiles, with effects strengthening at higher emission levels (Q0.75 = 0.023, Q0.90 = 0.045), indicating economic expansion drives disproportionately greater emissions in high-polluting contexts. Industrial investment exhibits stable negative coefficients around − 0.48 across quantiles, suggesting uniform emission reduction benefits from productive investments regardless of baseline levels. Digital economy development shows increasingly negative effects from − 0.172 (Q0.25) to -0.400 (Q0.90), while higher education demonstrates progressive emission reductions strengthening from − 0.638 to -0.786 across quantiles. These patterns indicate that digital transformation and educational investments provide greater environmental benefits in high-emission contexts. The heterogeneous effects underscore those environmental policies must be tailored to specific emission contexts for optimal effectiveness, highlighting the importance of distributional analysis in environmental economics.