Search Results (2)

Global warming and extreme weather have increased most people’s awareness of the problem of environmental destruction. In the domain of sustainable development, environmental governance has received considerable scholarly attention. However, protecting and improving the environment requires not only substantial capital investment but also cooperation among stakeholders. Therefore, based on the network structure of stakeholders, the best–worst method (BWM) and modified Vlsekriterijumska Optimizacija I Kompromisno Resenje method were combined to form an environmental co-governance assessment framework that can be used to evaluate the effects of various policies and identify strategies for further improvement through data analysis (henceforth the BWM-mV model). This mechanism is not only useful for evaluating the effectiveness of environmental governance policies but also for generating suggestions to enhance these policies. Hence, the BWM-mV model is particularly suitable for local governments with limited resources in time, money, or labor. Pingxiang City Government is currently subject to such limitations and was therefore selected as the subject of an empirical case study. The results of this study revealed that the aspects (i.e., criteria) the Pingxiang City Government should urgently improve on pertain to a high-quality information communication platform (C₁₃) and smooth joint decision-making by stakeholders (C₂₄). Full article

Analyzing the spatial-temporal changes of resources–based industrial land is essential to the transformation and development of resources–exhausted cities. In this paper, we studied coal resources–based industrial land use changes and their driving factors in a typical coal resources–exhausted city, Anyuan District, Pingxiang city. The changes between coal resources–based industrial land and other land-use types were analyzed. The logistic regression models were applied to identify the main driving factors and quantify their contributions to coal resources–based industrial land-use changes during the two periods of 2003–2008 and 2008–2013. The results show that coal resources–based industrial land declined by 34.37% during the period 2008–2013 as coal resources were being exhausted. Altitude, distance to roads, distance to town, population density change, fixed-asset investment per area change, and GDP per capita change drove coal resources–based industrial land-use changes. However, the patterns of the driving effects differed, and even the same factors had different influences on coal resources–based industrial land-use changes during the two periods. The changes in the driving factors can be seen as responses to socioeconomic transformation and development in the city, which is experiencing the exhaustion of coal resources. As a result of the comprehensive effects of these driving factors, coal resources–based industrial land use has changed in complex ways. Full article