China’s huge investment in the “belt and road initiative” (BRI) may have helped improve the economic level of participating countries, but it may also be accompanied by a substantial increase in greenhouse gas (GHG) emissions. The BRI corridors aim to bring regional stability and prosperity. In such efforts, energy efficiency due to increased transport has been overlooked in the recent literature. This paper employed a data envelopment analysis of the slack-based measurement (SBM) for bad output to assess the transport energy efficiency of 19 countries under the BRI economic corridors. By using the most cited transport-related input variables, such as vehicles, labor, motor oil, jet fuel, and natural gas, this study first analyzes the transport energy efficiency by first assuming the output variables individually and then takes two years as a pre- and post-BRI case by considering the aggregated output model. The results show an increase in economic activity but a decline in transport energy efficiency in terms of consumption and emissions.

Economic freedom is an important determinant of economic development, yet little academic research has considered its financial perspectives. Using a relatively new measure of finance, we find that a high-freedom environment fosters finance in developing economies. Unlike prior literature, the advanced panel threshold estimator shows a single significant economic freedom threshold level of 4.282 as a benchmark, where the relationship turns positive. The findings are robust to alternative estimators and split-sample analysis. The findings are useful for policymakers to attain optimal financial benefits by assuring a high-freedom environment.

Today’s agricultural management decisions impact food security and sustainable ecosystems, even when operating with back-to-basic operations. In such endeavors, policymakers usually need a quantitative tool, such as trade-offs margins, to effectively adjust resource consumption or production. This paper applies the weighted slack-based measurement (SBM-DEA) program to 136 developing countries’ agricultural performance. First, it finds the current agricultural efficiency and then makes marginal trade-offs on desirable-output variables (such as crop yield and forest area) to see the effective changes in undesirable-output (such as methane and nitrous oxide emissions). The results show that choosing effective marginal trade-offs does not deteriorate the relative efficiency of the decision-making units (DMUs) below the efficient frontier line. Thus, such a method enables the decision-makers to determine the best marginal trade-off points to reach the optimal efficiencies and decide which output factor needs special brainstorming to design effective policy.

The increase in domestic transportation in developing countries may adversely affect the energy efficiency of road transportation due to effective productivity and carbon dioxide emissions (CO₂). When evaluating quantitatively the countries on the efficiency frontier, poor efficiency can still be seen sometimes due to the slack available in undesirable output measures. This paper uses desirable and undesirable output variables, such as passenger-kilometers (PKM), tones-kilometers (TKM), and carbon dioxide (CO₂), to compute the weakly efficient decision-making units (DMUs). The data envelopment analysis (DEA) technology is used to assess the efficiencies of the decision-making units (DMUs), which are countries in our case. Then, the trade-off method with efficient binding surfaces is used to attain the optimal efficiencies of the weakly efficient DMUs. The marginal rates aid this trade-off analysis. Resultantly, such marginal trade-offs do not deteriorate the efficiency of the DMUs below the frontier line. We calculate the maximum change (margin) in a specific variable amount when another variable’s amount is changed. Thus, such a computation gives us different margins, with which each output variable can be a traded off to bring a DMU further toward the closest optimal point possible. The marginal trade-off can help the managers and policymakers in effective decision-making, and it is further recommended to address efficiency damages (by the undesired outputs).

Integrating green supply chain management (GSCM) with global sustainability initiatives has emerged as a critical area of research due to escalating environmental challenges and the need for sustainable industrial practices. However, empirical synthesis on the effectiveness of GSCM in achieving the United Nations Sustainable Development Goals (SDGs) remains limited. This study aims to fill this gap by conducting a comprehensive meta-analysis of 37 peer-reviewed articles to evaluate the role of GSCM practices in advancing key SDGs, including responsible consumption and production (SDG 12), climate action (SDG 13), and partnerships for the goals (SDG 17). Using JASP software and the Hunter-Schmidt method, the study provides robust quantitative evidence that GSCM practices—particularly green procurement, waste minimization, and resource efficiency—positively and significantly contribute to environmental sustainability, economic performance (SDG 8), and stakeholder collaboration. The results also reveal heterogeneity across regions and industries, underscoring the importance of contextual strategies. This study offers novel insights by systematically quantifying GSCM’s multi-dimensional impact on the SDGs and provides evidence-based recommendations for policymakers, business leaders, and sustainability advocates. The originality of this research lies in its integrative approach, combining statistical rigor with sustainability policy relevance, to demonstrate GSCM’s transformative potential in achieving global sustainability goals.