Middle-income economies contribute significantly to global CO₂ emissions as they pursue economic development, creating an urgent need to understand emission drivers. This article investigates the impact of renewable energy use, financial development, and industrialization on CO₂ emissions in 71 middle-income countries (32 upper-middle income, 39 lower-middle income) between 2002 and 2020. We used the advanced Generalized Method of Moments Panel Vector Autoregression (GMM-PVAR) approach to address endogeneity and reveal complex relationships among the variables. Our findings revealed that renewable energy utilization had no substantial influence on emissions reduction in either upper- or lower-middle-income countries, challenging conventional policy assumptions. Financial development consistently reduces emissions across both income groups (−0.08% and −0.06%, respectively). Industrialization has heterogeneous effects, increasing emissions by 2.03 percent in upper-middle-income countries and with no effect in lower-middle-income countries. Granger causality tests illustrated a bidirectional relationship connecting CO₂ emissions and financial development, whereas no causal link was found between CO₂ emissions and renewable energy use. These findings prove the importance of coordinated policies that strengthen financial systems and sustainable industrial practices.

Occupational Health and Safety (OHS) risk management is an ongoing challenge in resource-scarce chemical industries, especially in developing nations like Tunisia. The current research recommends a hybrid socio-techno-environmental model based on Fuzzy Logic and the Analytic Hierarchy Process (AHP) for occupational health and safety risk assessment and prioritization of occupational health and safety (OHS) risks. The model, based on a backup of expert judgment from pre-specified questionnaires and real data of Tunisian chemical industries, quantifies the risk factors based on criticality and probability of occurrence. Fuzzy–AHP not only evaluates qualitative estimates but also provides a systematic framework for evaluating and prioritizing occupational health and safety risks. Findings are consistent with the fact that socio-organizational factors such as occupational safety culture and compliance among workers play a pivotal role in determining total levels of risk, and in certain cases dominate over technical considerations alone. In offering a scalable and context-sensitive solution, this research has an additional contribution with a decision-support framework that assists organizations in evaluating and prioritizing occupational health and safety risks under uncertain conditions. Its use for policy makers and business interests that seek to maximise OHS performance in given constraints, and its use in actual implementation and compatibility with sustainability and worker health goals, speaks to its utility.