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<p>This study investigates the long-run impacts of total primary energy supply (TES), trade openness, and economic growth on China’s ecological footprint (ECOF) over the period 1990–2021, while controlling for urbanization, renewable energy use, and technological progress. Given the presence of volatility and structural dynamics in environmental time-series data, the analysis employs a Maximum Likelihood Autoregressive Conditional Heteroskedasticity (ML-ARCH) framework. Unit-root and Johansen cointegration tests confirm the existence of a stable long-run relationship among the variables. The empirical results reveal that TES, trade, and renewable energy exert statistically significant negative effects on ecological footprint, indicating the roles of energy efficiency improvements, cleaner trade structures, and renewable energy adoption in mitigating environmental pressure. In contrast, economic growth, urbanization, and technological progress are found to increase ecological footprint, reflecting scale effects associated with rapid economic expansion and urban development. Diagnostic tests confirm the robustness and reliability of the estimated model. The findings underscore the importance of promoting renewable energy transitions, improving energy efficiency, and aligning trade and technological policies with environmental objectives. This study offers policy-relevant insights for China’s sustainable development strategy by highlighting the need to balance economic growth with environmental protection through targeted energy and structural reforms.</p>

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China’s Environmental Challenges: The Role of Energy Supply and Trade

  • 期刊论文

  • 发表日期：

    2026年3月7日

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  • Hafiz Muddassir Majeed Butt,
  • Irfan Khan,
  • Shahzad Khan Durrani,
  • Muhammad Hamza Naseem,
  • Shabnam Shahzadi

摘要

This study investigates the long-run impacts of total primary energy supply (TES), trade openness, and economic growth on China’s ecological footprint (ECOF) over the period 1990–2021, while controlling for urbanization, renewable energy use, and technological progress. Given the presence of volatility and structural dynamics in environmental time-series data, the analysis employs a Maximum Likelihood Autoregressive Conditional Heteroskedasticity (ML-ARCH) framework. Unit-root and Johansen cointegration tests confirm the existence of a stable long-run relationship among the variables. The empirical results reveal that TES, trade, and renewable energy exert statistically significant negative effects on ecological footprint, indicating the roles of energy efficiency improvements, cleaner trade structures, and renewable energy adoption in mitigating environmental pressure. In contrast, economic growth, urbanization, and technological progress are found to increase ecological footprint, reflecting scale effects associated with rapid economic expansion and urban development. Diagnostic tests confirm the robustness and reliability of the estimated model. The findings underscore the importance of promoting renewable energy transitions, improving energy efficiency, and aligning trade and technological policies with environmental objectives. This study offers policy-relevant insights for China’s sustainable development strategy by highlighting the need to balance economic growth with environmental protection through targeted energy and structural reforms.