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

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.