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<p>This study investigates the efficiency and performance of waste supply chain management across eight major South Korean cities, focusing on the interplay between input variables, inefficiency determinants, and waste processing outputs.&#xa0;Employing a multidisciplinary framework grounded in Resource-Based View, Environmental Justice Theory, and Systems Theory, the research utilizes Stochastic Frontier Analysis (SFA), Grey Incidence Analysis (GIA), and Artificial Neural Network (ANN) to evaluate the relative importance of various influencing factors. SFA estimate results highlight that budget and manpower productivity significantly contribute to efficiency, while disparities in budget allocation and outdated infrastructure contribute to inefficiencies.&#xa0;GIA underscores the dominance of commercial incineration and landfill performance, driven by strict industrial regulations and waste-to-energy initiatives. Conversely, commercial recycling and domestic landfill perform the worst. ANN reveals that budget productivity and manpower productivity have stronger and more impactful relationships with efficiency scores in cities like Seoul, Busan, and Incheon. On the inefficiency side, high facility installation costs, operation costs, and miscellaneous costs demonstrate significant negative impact on overall effectiveness across multiple cities.</p>

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Improving the waste supply chain, a case of South Korea 2012–2021: stochastic frontier analysis, artificial neural network, and grey-incidence approach

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Leo Hong,
  • Gawon Yun,
  • Douglas N. Hales

摘要

This study investigates the efficiency and performance of waste supply chain management across eight major South Korean cities, focusing on the interplay between input variables, inefficiency determinants, and waste processing outputs. Employing a multidisciplinary framework grounded in Resource-Based View, Environmental Justice Theory, and Systems Theory, the research utilizes Stochastic Frontier Analysis (SFA), Grey Incidence Analysis (GIA), and Artificial Neural Network (ANN) to evaluate the relative importance of various influencing factors. SFA estimate results highlight that budget and manpower productivity significantly contribute to efficiency, while disparities in budget allocation and outdated infrastructure contribute to inefficiencies. GIA underscores the dominance of commercial incineration and landfill performance, driven by strict industrial regulations and waste-to-energy initiatives. Conversely, commercial recycling and domestic landfill perform the worst. ANN reveals that budget productivity and manpower productivity have stronger and more impactful relationships with efficiency scores in cities like Seoul, Busan, and Incheon. On the inefficiency side, high facility installation costs, operation costs, and miscellaneous costs demonstrate significant negative impact on overall effectiveness across multiple cities.