TY - JOUR

T1 - A systemic efficiency measurement of resource management and sustainable practices: A network bias-corrected DEA assessment of OECD countries

AU - Liu, Yin

AU - Alnafrah, Ibrahim

AU - Zhou, Yaying

N1 - The article has been prepared with the support of: University Natural Science Foundation of Anhui Province (Grant No. 2023AH051550 ), and the Ministry of Science and Higher Education of the Russian Federation ( Ural Federal University Program of Development within the Priority-2030 Program).

PY - 2024

Y1 - 2024

N2 - In the face of pressing challenges such as climate change and dwindling biodiversity, the global need for optimizing natural resource management is urgent. To address this, green innovations and digital government play a vital role in efficient resource utilization, aligning with Sustainable Development Goals (SDGs). Our study presents a comprehensive framework for evaluating natural resource management efficiency and its link to achieving SDGs. Using network bias-corrected Data Envelopment Analysis (DEA) and unsupervised machine learning techniques on 30 OECD countries, we found significant disparities in environmental and resource management efficiencies. Importantly, environmental efficiency strongly correlates with resource management efficiency, often surpassing the influence of green innovations. The results also reveal a pattern where major industrial economies like Germany, France, and the UK struggle with low natural resource efficiency. In contrast, smaller economies, particularly in the Nordic and Baltic regions, excel in resource management and employ green innovations and digital governance practices effectively to advance SDGs. This machine learning-driven dichotomy emphasizes the pivotal role of systemic resource management in achieving SDGs and highlights the leadership of smaller economies in sustainability. Our study significantly contributes to the understanding of green innovation systems and their role in enhancing natural resource management efficiency. Furthermore, it equips policymakers with the ability to pinpoint specific systemic shortcomings in natural resource management, facilitating the tailored development of policies to meet precise goals. © 2024 Elsevier Ltd.

AB - In the face of pressing challenges such as climate change and dwindling biodiversity, the global need for optimizing natural resource management is urgent. To address this, green innovations and digital government play a vital role in efficient resource utilization, aligning with Sustainable Development Goals (SDGs). Our study presents a comprehensive framework for evaluating natural resource management efficiency and its link to achieving SDGs. Using network bias-corrected Data Envelopment Analysis (DEA) and unsupervised machine learning techniques on 30 OECD countries, we found significant disparities in environmental and resource management efficiencies. Importantly, environmental efficiency strongly correlates with resource management efficiency, often surpassing the influence of green innovations. The results also reveal a pattern where major industrial economies like Germany, France, and the UK struggle with low natural resource efficiency. In contrast, smaller economies, particularly in the Nordic and Baltic regions, excel in resource management and employ green innovations and digital governance practices effectively to advance SDGs. This machine learning-driven dichotomy emphasizes the pivotal role of systemic resource management in achieving SDGs and highlights the leadership of smaller economies in sustainability. Our study significantly contributes to the understanding of green innovation systems and their role in enhancing natural resource management efficiency. Furthermore, it equips policymakers with the ability to pinpoint specific systemic shortcomings in natural resource management, facilitating the tailored development of policies to meet precise goals. © 2024 Elsevier Ltd.

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U2 - 10.1016/j.resourpol.2024.104771

DO - 10.1016/j.resourpol.2024.104771

M3 - Article

VL - 90

JO - Resources Policy

JF - Resources Policy

SN - 0301-4207

M1 - 104771

ER -