Examining the Energy-Environmental Kuznets Curve in OECD Countries Considering their Population.

This research aims to examine the validity of the Environmental Kuznets Curve (EKC) hypothesis in 37 Organization for Economic Co-operation and Development (OECD) countries over the period from 1960 to 2019. Panel Quantile Regressions (QR) show that for the lower quartile, economic growth does not impact emissions; for the central quartile a U-shaped curve emerges; while for the upper quartile, an N-shaped curve is found. In addition, cointegrating regressions highlight that economic growth, fossil fuel consumption, and population exert a detrimental effect on the environment, while renewable energy consumption reduces carbon dioxide (CO2) emissions. These results are confirmed by panel causality tests since a feedback mechanism is found between CO2 emissions and the remaining series. Furthermore, single-country estimates provide evidence of great variability in the sample.

Closing the loop of a global supply chain through a robust optimal decentralized decision support system.

This paper presents a novel decentralized decision support system to optimally design a general global closed-loop supply chain. This is done through an original risk-based robust mixed-integer linear programming that is formulated based on an initial uncertain bi-level programming. Addressing the decision-maker's (DM's) attitude toward risk, a scenario-based conditional value-at-risk is used to deal with demand and return uncertainty. Also, the Karush-Kuhn-Tucker (KKT) conditions are employed to transform the model into its single-level counterpart. The results obtained from solving a numerical example through the proposed framework are compared with those of the corresponding centralized system, which is formulated through deterministic multi-objective programming and solved by the Lp-metric method. The results show that the use of the proposed framework improves the robustness of profit, income, and cost by about 28%, 34%, and 36% on average. However, a more conservative DM faces a larger cost of robustness than an optimistic DM while experiencing a more significant improvement in the system responsiveness. Using the proposed framework, the manager can measure the advantages, disadvantages, and consequences of their decisions before their actual implementation. This is because the model is capable of establishing fundamental trade-offs among risk, cost, profit, income, robustness, and responsiveness according to the DM's attitude toward risk.

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study.

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.

Environmental technology and wastewater treatment: Strategies to achieve environmental sustainability.

Water is the vital liquid for human subsistence and is used as a resource in various production processes. However, the degradation of the environment is being reflected in the water resources of the planet. One of the leading causes of water pollution is ineffective wastewater treatment, which results in greywater being returned to the environment without having gone through a decontamination process. Ideally, wastewater should have the lowest concentration of polluting materials to be reused and exploited in other activities, such as agriculture or the generation of renewable energy. However, in its various forms, technological progress plays a vital role in improving wastewater treatment processes, becoming a determining factor in improving greywater quality. This study examines how environmental technology contributes to wastewater improvement in 16 selected OECD countries during 2000-2019. Annualized information is used and collected from various official sources of information, subsequently processed with various econometric approaches. The results obtained show a heterogeneous behaviour in the quantiles of wastewater treatment, environmental technology and renewable energy are positively related to an increase in wastewater treatment between 0.09% - 0.20% and 3.5 e-12% - 5.74 e-12%, respectively. Based on the results obtained, the policy implications suggest promoting environmental technology to improve wastewater treatment.

Determinants of economic growth and environmental sustainability in South Asian Association for Regional Cooperation: evidence from panel ARDL.

Considering the importance of green economic growth and environmental sustainability in the discussion, it is crucial to understand its critical contributing factors and to draw results implications for the green policy. This research used the data of the South Asian Association for Regional Cooperation (SAARC) member countries for a period from 2005 to 2017. It adopted the panel autoregressive distributed lag technique to examine the hypotheses. The findings revealed that environmental sustainability is strongly and positively associated with national scale-level green practices, including renewable energy, regulatory pressure, and eco-friendly policies, and sustainable use of natural resources. Conversely, in our model, the "regulatory pressure" has an insignificant effect on economic growth. A necessary contribution of the present study is that a positive effect of green practices on national scale economic and environmental variables, particularly in the scenario of SAARC member states, can be noticed. At the end of the present study, we have provided policy implications for regulatory authorities and discussed potential areas for future research.