China has received significant foreign direct investment in the last few decades; the FDI inflow could affect the environment, income, and people’s health. Therefore, this paper aims to investigate the relationship between FDI, renewable energy consumption, CO₂ emissions, and Population health quality in China for the period 1980–2020. We applied the VECM method for the data analysis for the short and long-run effects of the independent variables. In the short run, FDI and CO₂ emissions did not affect health quality in China; however, in the long run, FDI and CO₂ emissions improved life expectancy. Renewable energy has both run and long implications for the health quality in China. These results reflect that FDI creates more jobs in China and improves the overall income of Chinese citizens, contributing to more accessible healthcare services in the long run. Therefore, the government should provide incentives to increase the FDI inflow, which uses renewable energy in production. Furthermore, to mitigate the CO₂ emissions government should implement a carbon tax on the industries which has substantial CO₂ emissions in the country.

The adoption of blockchain technology (BCT) in a supply chain holds great potential for textile industries by executing transactions among stakeholders in a most reliable and verifiable way. Textile industries in emerging economies, like Pakistan, confront severe economic pressures and uncertain environment and strive to achieve sustainable supply chain excellence through blockchain implementation. This study is an initiative to analyze the key barriers in adopting BCT-related practices within the textile industry. This study conducts an extensive review of the literature using fuzzy Delphi approach for finalizing the barriers and applied fuzzy analytical hierarchy process (AHP) for prioritizing the barriers under uncertain environment. Based on the extensive review of the literature and panel discussions with experts, a total of five main barriers and 21 sub-barriers were categorized and ranked. The results and findings prioritize technological and system-related barriers (TSB) first, and human resources and R&D (HRB) barriers second among the other barrier dimensions. This paper highlights the need for an inclusive understanding of the various technological, environmental, and socio-economic perspectives to create blockchain applications that work for the textile sector. This study’s key findings and policy guidelines can assist concerned stakeholders in making strategic decisions for adopting BCT within the textile supply chain. The managerial implications are provided for the industrial decision-makers and policymakers aiming to integrate BCT into the supply chain processes. Presently, there exists no research in the context of Pakistan that highlights the challenges faced during the adoption of BCT in the supply chain. For this purpose, an approach in the form of an integrated model based on fuzzy set theory is developed. Finally, the robustness of the proposed model is checked through sensitivity analysis.

Studies investigating the interconnection of health poverty and climatic variability are rare in spatial perspectives. Given the importance of sustainable development goals 3, goal 10, and goal 13, we explored whether the geographic regions with diverse climate structure has a spatial association with health poverty; whether spatial disparities exist across districts of Pakistan. We implied the A-F methodology to estimate the MHP index using the PSLM survey, 2019–20. The climate variables were extracted from the online NASA website. We applied the spatial techniques of Moran’s I, univariate and bivariate LISA, to address the research questions. The findings revealed that the magnitude of MHP differs across districts. Punjab was found to be the better-ff whereas Baluchistan was the highest health poverty-stricken province. The spatial results indicated positive associations of MHP and climate indicators with their values in the neighbors, whereas a negative spatial association was found between the MHP and climate indicators. Also, spatial clusters and outliers of higher MHP were significant in Baluchistan and KP provinces. Government intervention and policymaker’s prioritization are needed towards health and health-related social indicators, mainly in the high poverty-stricken districts, with high temperature and low humidity and precipitation rates, especially in Baluchistan.

In the context of Sustainable Development Goals, declining poverty (Goal 1), achieving gender equality (Goal 5), and ensuring access to clean and affordable energy (SDG7) are still behind track, and the gaps are not yet ready to be rapidly fulfilled. As part of Goal 7, energy consumption-related targets still lack the potential to be targeted. Considering these elements, this study aims to determine the impact of poverty and gender equality on the energy consumption of households in several European countries. Using data from 2010 to 2022 and the moments’ quantile regression method combined with a pooled OLS based on Driskoll-Kraay estimators, we found statistically significant results regarding the impact of poverty and gender on household energy consumption. These findings’ significance will direct policy design towards those meaningful tools that will increase energy efficiency, address energy poverty, and ensure energy just transition, leaving no one behind. Based on the main findings, the policymakers can understand that a mix of policies is significantly more efficient. In such circumstances, social and economic inequalities will not successfully address development issues without including key environmental priorities, such as emissions mitigation and energy consumption patterns.

Renewable energy has become an imperative global focus in the battle against climate change, addressing energy security and the development of a sustainable energy economy. This study examines the economic effects of green energy investments in four distinct countries: India, the USA, Germany, and Denmark, using time-series data on employment generation, energy efficiency, and GDP growth for the period from 1996 to 2023. The study employed regression analysis, and the research isolates important differences in outcomes for these regions that are generated by renewable energy policies and investments. The findings suggest that labor-intensive renewable energy projects benefit emerging markets, such as India. Due to market saturation, the projects become counterproductive for mature markets like Germany. For large-scale project development in a stable policy environment, the USA scores highly, while Denmark excels in innovation and sustainability in wind energy. The study highlights the value of targeted policy interventions in maximizing the economic benefits from renewable energy. In addition, it emphasizes the need to tackle country-specific issues, encourage innovation, and ensure a fair pathway to a green energy system. The results of this research will be beneficial for policymakers and stakeholders in evaluating decisions regarding renewable energy investments.

Several successive crises during the first three decades of the third millennium created the premises for a world that, after expanding international relations, entered a new reality of slowbalization or deglobalization, shaping new development paradigms for national economies. In this context, where economic activity remains highly sensitive to energy market disruptions and strategic resource constraints, nations seek new opportunities to reduce their foreign dependencies through energy diversification and a green transition. Nations are seeking strategies to leverage their advantages and moderate their weaknesses. This research evaluates the relationship between energy-related features and economic growth in a complex context, describing dependency on foreign markets. Furthermore, the study discusses the effects of a selection of variables describing the green transition (energy import dependency, energy diversification, and the share of renewable energy) on economic growth. The data covers the period between 1995 and 2024 for 25 European countries. The study uses cross-sectionally ARD (CS-ARDL) for the main empirical analysis and augmented mean group (AMG) to check the robustness of the main results. Furthermore, the method of moments quantile regression (MMQR) is employed to capture the impact more precisely across various stages of countries’ development. The findings suggest a direct relationship between employment and renewable energy adoption across all quantiles. Moreover, the negative coefficient for the energy dependency in the first quantile documents an increased sensitivity of less developed economies to energy market uncertainties.