At the crossroads of innovation and responsibility, our study delves into CSR’s pivotal role in steering organizations toward sustainable value creation. Combining the frameworks of stakeholder theory, the resource-based view, and dynamic capabilities theory, this study examines the influence of corporate social responsibility (CSR) in enhancing sustainable value creation (SVC), with a keen focus on how organizational learning (OL) and innovation capability (IC) mediate this relationship and the degree to which stakeholder engagement (SE) moderates the impact of CSR on OL, IC, and SVC. Using a quantitative research approach, 416 managers in the Turkish manufacturing sector (e.g., textiles, building materials, and food processing firms) were surveyed using electronic and physical questionnaires. To test the research model, AMOS 24 was employed. The results confirmed that CSR positively influences SVC, OL, and IC; OL and IC positively influence SVC; OL and IC partially mediate the relationship between CSR and SVC. Likewise, the findings suggest that stakeholder engagement (SE) plays a moderate role in strengthening the relationship between CSR and organizational learning (OL), CSR and innovation capability (IC), and CSR and sustainable value creation (SVC), with increased SE enhancing the positive effect of CSR on OL, IC, and SVC. This study explains the complex ways that CSR enhances SVC, focusing on OL and IC’s mediating roles and SE’s moderating effects. It offers manufacturing sectors in Turkey useful information for promoting sustainability and innovation.

Malaysia’s commitment towards sustainable environment requires implementing clean energy sources and less raw materials usage, which could have a significant impact on the nation’s biocapacity and ecological footprint. As a result, this study examined the environmental impact of resource efficiency, biomass energy, and economic growth in Malaysia, within the load capacity curve hypothesis framework, while controlling economic globalization, financial globalization and trade globalization. This research utilizes the ARDL estimator and the bootstrapped time-varying causality (TVC) approach to analyse the dataset from 1970 to 2018. The findings of the ARDL estimator reveals that biomass energy and resource efficiency increase load capacity factor (LF), while trade globalization reduces LF in Malaysia. Furthermore, the result uncovered the validity of the load capacity curve hypothesis, highlighting that the persistent economic activities will help to achieving ecological quality in Malaysia. The result of the bootstrapped TVC approach reveals a feedback causal association between LF and its predictors in Malaysia. These results emphasize the need for an effective circular economy, which involves maximizing resource use, reducing waste, and incorporating biomass energy into this framework.

This study examines the role of government support (GS) in facilitating green supply chain management (GSCM) practices in Libya. It focuses on the moderating effect of green market orientation (GMO) and the mediating role of employee environmental commitment (EEC). The study explores how government policies drive sustainable supply chain practices, considering the influence of employees’ environmental awareness and market orientation. This study uses empirical data from various Libyan industries and applies structural equation modeling (SEM) to analyze relationships. The results show that GS positively impacts EEC, enhancing the adoption of GSCM practices. Furthermore, GMO strengthens the relationship between EEC and GSCM implementation. These findings highlight the importance of government support in fostering environmental sustainability within supply chains. This study contributes to the literature on sustainable supply chain management by providing empirical evidence from a developing country, offering valuable insights for policymakers and business leaders in Libya and similar regions.

Brazil, as an emerging and newly industrialized nation, presents a complex dynamic between economic advancement and environmental sustainability. This study investigates the influence of coal consumption (COAL), gross domestic product (GDP), renewable energy (REN), and foreign direct investment (FDI) on CO₂ emissions in Brazil using quarterly data from 1990Q1 to 2020Q4. Employing the Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) method and the Quantile-on-Quantile Granger Causality (QQGC) test, we uncover significant nonlinear and distributional heterogeneities in these relationships. Results show that COAL, GDP, and FDI consistently exert a positive impact on CO₂ emissions across most quantiles, whereas REN significantly reduces emissions, particularly at the upper emission quantiles. Causality analysis confirms that all four variables are significant predictors of CO₂ emissions. The study contributes methodologically by applying QQKRLS and QQGC to reveal nuanced interactions across the emissions distribution—an advancement over traditional linear approaches. Empirically, it provides Brazil-specific evidence of the dual role of FDI and economic growth in both driving emissions and offering potential for sustainable transition. Based on these findings, we recommend policies that prioritize sector-specific FDI screening to promote green technologies, accelerate investment in renewable energy infrastructure, and impose adaptive carbon pricing mechanisms that reflect the heterogeneous impact of coal and economic growth on emissions. These insights support Brazil’s climate targets and guide a balanced path toward inclusive and sustainable development.

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments.