The increasing significance of green supply chain management in developing countries’ manufacturing sector is primarily driven by the deteriorating environment, signified by decreasing raw material resources, a surplus of waste sites, and rising pollution levels. Green supply chain management can provide competitiveness while boosting a company’s environmental sustainability if implemented effectively. Therefore, it is necessary to determine the effect of green supply chain management practices on the firm performance of the manufacturing sector. This research aims to determine the moderating effect of collaborative capability and the mediating influence of eco-technological innovation and environmental strategy on the relationship between green supply chain management and firm performance. Five hundred fifty survey questionnaires are gathered from manufacturing firms of China. Utilizing structural equation modeling (SEM), the proposed hypotheses have been analyzed and investigated. The results show that green supply chain management indirectly affects the firm performance. Moreover, green supply chain management is positively related to environmental strategy and eco-technological innovation, which effectively enhance firm performance. The findings further indicate that environmental strategy and eco-technological innovation significantly mediate the association between green supply chain management and firm performance. Furthermore, collaborative capability significantly and positively moderates the relationship between green supply chain management and firm performance. As a result, the adoption of these factors influences firm performance positively and will assist the manufacturing sector in meeting diverse yet radically changing requirements and overcoming obstacles originating from a dynamic global business environment. Consequently, it is of the utmost importance that businesses must utilize green practices with relatively low environmental impacts. Companies can considerably maintain and improve their firm performance by reducing the environmental impact if they have effective collaborative capabilities, eco-technological innovation, and environmental strategies.
The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO2 emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies’ observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.
This study examines the impact of literacy rate and innovations on environmental pollution in China from 1990 to 2021. We applied the Quantile Autoregressive Distributed Lag method for the analysis, and our findings suggest that an increase in literacy rate leads to short- and long-term innovations. At the same time, literacy has a positive effect in 1st to 4th quantile in the long run. Furthermore, innovation has a positive effect on environmental pollution. However, in the short run, the literacy rate has negative implications for environmental pollution. These findings imply that education is essential to increase innovation in the economy. Besides, the literacy rate increases the standard of living, and thus, it is recommended that government should adopt environmental protection laws to reduce environmental pollution along with literacy increase. Additionally, China has achieved significant economic growth in the last few decades. To ensure sustainable economic growth government should introduce a carbon tax to minimize production externalities such as environmental pollution.
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.
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