In response to increasing concerns about environmental issues, businesses, and industries face pressure to mitigate their negative environmental impacts. Consequently, firms must reevaluate their operations to align with environmental standards. To address both economic and environmental objectives, industries need to green their supply chains. However, uncertainties in the real world, such as economic instability, add complexity to this greening process. This study proposes a novel risk-averse two-stage stochastic model for green supply chain (GSC) design under uncertainty, integrating Conditional Value at Risk (CVaR) with multi-objective programming. The model uses discrete Fuzzy Random Variables (FRVs) to capture both randomness and fuzziness in cost and emission parameters. To solve the model, we apply possibility theory and Fuzzy Chance-Constrained Programming (FCCP) to derive deterministic equivalents for optimistic, pessimistic, and hybrid decision-making attitudes. Numerical results from a flour supply chain case in Iran show that higher risk aversion increases both cost and CO₂ CVaR, while possibility levels affect outcomes differently across models. The approach provides managers with a flexible tool for balancing economic and environmental goals under uncertainty.
Although AI is widely adopted to improve efficiency and responsiveness in automotive supply chains, its specific contributions to sustainability across environmental, economic, and social dimensions remain underexplored. This study adopts a multi-method approach and highlights the crucial mediating role of AI adoption in the relationship between reverse logistics implementation and sustainable supply chain performance measures, moderated by top management support, among automotive suppliers and manufacturers. Drawing on the Technology-OrganizationEnvironment (TOE) framework, we collected 450 survey data from 120 automotive suppliers and manufacturers in T & uuml;rkiye and analyzed them to test our hypotheses. A moderated mediation analysis reveals that AI adoption partially mediates the relationship between reverse logistics implementation and sustainable supply chain performance in the automotive sector. By uncovering the role of top management support as a critical enabler, the findings highlight its role in the relationship between reverse logistics implementation and AI adoption. Complementary interviews with eleven automotive top managers provided deeper insight into how AI tools influence reverse logistics practices and sustainability performance. Together, these findings offer a comprehensive understanding of both the mechanisms and impacts of AI adoption in reverse logistics, providing valuable insights for automotive suppliers, manufacturers, and policymakers aiming to enhance sustainability through AI-driven innovations.
Integrating emerging market economies into global financial and economic relations can expose them to external shocks. This paper explores the connection between the US economic policy uncertainty (USEPU) and exchange market pressure (EMP) in nine major emerging markets from 2000 to 2019. To achieve this objective, we use the general-to-specific vector autoregressive (GETS-VAR) and Bayesian quantile regression methods. The empirical results reveal that USEPU predicts changes in the EMP of large emerging market economies, except for Turkey. However, no feedback causal effect from EMP to USEPU was observed. Also, the long-run steady-state effects and cumulative impulse responses show that an increase in USEPU intensifies the EMP in Brazil, India and Mexico. Furthermore, our findings reveal that the positive impact of USEPU is heterogeneous leading to asymmetric patterns across the distribution of EMP.
Global warming remains one of the greatest threats to the sustainability of the planet. The literature is replete with studies investigating the economic effects of global warming with very little attention being paid to the financial ramifications. To this end, the present study attempts to ascertain the predictive power of global warming for green financial assets (Clean Energy Index, Green Bond Index, World ESG Index, and Sustainability World Index) under bearish, normal and bullish market conditions. Employing a novel rolling windows wavelet quantile Granger causality testing procedure, which controls for time, frequency, and quantile asymmetries, findings reveal that the predictive power of global warming for green financial assets is more (less) stable across time at lower (higher) frequencies when markets are normal. In bearish and bullish markets, however, the predictability of global warming for green financial assets is observed to be more stable at higher frequencies and less stable at relatively lower frequencies. These results imply that global warming encourages low-frequency trading in normal markets, but induces relatively more speculative trading in bearish and bullish markets. Based on these findings, policy commendations are offered.
In this paper, we introduce a novel method for solving radon transport equation in various media. We derived a general solution, that can be expressed as a normalized superposition of source-term and environmental effects (Constraints). We refer to this approach as “Source-Environment-Response (SER) method”. We examine the families of solutions generated by the method and demonstrate its applicability to key geological scenarios, including Lithosphere-Atmosphere coupling, radon transport in water, and the simulation of trapped radon during seismic or faulting events. In all cases, the SER method shows strong agreement with established literature. Notably, the method is well suited for inverse modeling problems and can be extended to hybrid formulations for estimating source strength. The Versality and robustness of the SER method make it a valuable tool for modeling radon dynamics in diverse environmental and geophysical contexts.
