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<p>Effective management of greenhouse gas (GHG) emissions from municipal solid waste (MSW) is critical for mitigating climate change impacts. Rapid population growth, urbanization, and industrial expansion have significantly increased MSW generation in the Middle Eastern country of United Arab Emirates (UAE), leading to rising methane (CH₄) emissions. Identifying key drivers of these emissions is essential for appropriate mitigation strategies. In this study, a causality-driven framework combined with an AI-based approach was applied to model greenhouse gas (GHG) emissions from solid waste management in the UAE. The analysis shows that growth in gross domestic product (GDP) per capita and urban population drives higher methane (CH<sub>4</sub>) emissions from municipal solid waste (MSW), while greater foreign direct investment (FDI) inflows and improved literacy rates are linked to reductions. Projections using a recurrent neural network (RNN) suggest CH<sub>4</sub> emissions will rise by 0.67% annually from 2020 to 2050, reaching nearly 180 Gg by mid-century. Model robustness was confirmed through k-fold validation, yielding a low standard deviation of ± 0.07 for the predictive coefficient. Despite ongoing mitigation measures, significant opportunities exist to advance MSW management via recycling, composting, waste-to-energy, and carbon capture, guided by circular economy–based policies.</p>

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Modeling and mitigating greenhouse gas emissions from solid waste management: a causality-driven and AI-based case study of the United Arab Emirates

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Muhammad Muhitur Rahman,
  • Abdulaziz I. Almulhim,
  • Bijoy Mitra,
  • Md Arif Hasan,
  • Mohammed Shahedur Rahman,
  • Mohammad Sayem Mozumder,
  • Syed Masiur Rahman

摘要

Effective management of greenhouse gas (GHG) emissions from municipal solid waste (MSW) is critical for mitigating climate change impacts. Rapid population growth, urbanization, and industrial expansion have significantly increased MSW generation in the Middle Eastern country of United Arab Emirates (UAE), leading to rising methane (CH₄) emissions. Identifying key drivers of these emissions is essential for appropriate mitigation strategies. In this study, a causality-driven framework combined with an AI-based approach was applied to model greenhouse gas (GHG) emissions from solid waste management in the UAE. The analysis shows that growth in gross domestic product (GDP) per capita and urban population drives higher methane (CH4) emissions from municipal solid waste (MSW), while greater foreign direct investment (FDI) inflows and improved literacy rates are linked to reductions. Projections using a recurrent neural network (RNN) suggest CH4 emissions will rise by 0.67% annually from 2020 to 2050, reaching nearly 180 Gg by mid-century. Model robustness was confirmed through k-fold validation, yielding a low standard deviation of ± 0.07 for the predictive coefficient. Despite ongoing mitigation measures, significant opportunities exist to advance MSW management via recycling, composting, waste-to-energy, and carbon capture, guided by circular economy–based policies.