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<p>This study examines how renewable energy adoption and strategic trade policies can mitigate climate change in 22 lower-middle-income (LMI) economies (2008–2022). We employ a novel methodological framework combining Method of Moments Quantile Regression (MMQR) with Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) to address endogeneity while capturing distributional heterogeneity, the first application of this triangulated approach to the energy-trade-emissions nexus. Key findings reveal that renewable energy reduces CO₂ emissions by 18.88% at low emission quantiles but only 9.73% at high quantiles, indicating diminishing returns as countries industrialize. Trade taxes demonstrate a robust − 0.77% emission reduction effect, functioning as implicit carbon border adjustments. Energy intensity emerges as the dominant driver, with 1% reduction yielding 0.84–0.89% emission decreases across all specifications. These results inform three immediate policy actions: (1) implement zero-tariff regimes for certified clean technologies (solar panels, wind turbines, energy-efficient equipment) while imposing 2–5% surcharges on carbon-intensive imports; (2) establish renewable energy auction mechanisms with 15-year power purchase agreements and grid connection guarantees to de-risk private investment; (3) launch national industrial energy efficiency programs targeting top-200 emitters with 30–50% co-financing for retrofits. The methodological innovation of combining MMQR’s distributional insights with FMOLS/DOLS robustness checks provides policymakers with quantile-specific guidance for sequencing climate interventions based on countries’ emission profiles, advancing beyond one-size-fits-all approaches prevalent in existing literature.</p>

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How can renewable energy and strategic trade practices mitigate climate change severity in lower-middle-income economies?

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Yasir Khan,
  • Humaira Yousafzai,
  • Constant Dingamadji Bounde,
  • Xiangdong Li,
  • Yi-Cheng Yeh

摘要

This study examines how renewable energy adoption and strategic trade policies can mitigate climate change in 22 lower-middle-income (LMI) economies (2008–2022). We employ a novel methodological framework combining Method of Moments Quantile Regression (MMQR) with Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) to address endogeneity while capturing distributional heterogeneity, the first application of this triangulated approach to the energy-trade-emissions nexus. Key findings reveal that renewable energy reduces CO₂ emissions by 18.88% at low emission quantiles but only 9.73% at high quantiles, indicating diminishing returns as countries industrialize. Trade taxes demonstrate a robust − 0.77% emission reduction effect, functioning as implicit carbon border adjustments. Energy intensity emerges as the dominant driver, with 1% reduction yielding 0.84–0.89% emission decreases across all specifications. These results inform three immediate policy actions: (1) implement zero-tariff regimes for certified clean technologies (solar panels, wind turbines, energy-efficient equipment) while imposing 2–5% surcharges on carbon-intensive imports; (2) establish renewable energy auction mechanisms with 15-year power purchase agreements and grid connection guarantees to de-risk private investment; (3) launch national industrial energy efficiency programs targeting top-200 emitters with 30–50% co-financing for retrofits. The methodological innovation of combining MMQR’s distributional insights with FMOLS/DOLS robustness checks provides policymakers with quantile-specific guidance for sequencing climate interventions based on countries’ emission profiles, advancing beyond one-size-fits-all approaches prevalent in existing literature.