The effect of carbon performance on corporate financial performance in a growing economy

Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China

With the growing emphasis on sustainable development, there has been increasing attention given to measures aimed at promoting environmental improvements and reducing carbon emissions, including the adoption of intelligent industry. Recent studies have analyzed the influence of industrial intelligence on urban carbon emission performance while ignore the spatial spillover effects and lack in-depth discussion of the mechanisms, which reduces the reliability of the assessment of industrial intelligence's impact on carbon emission performance. To address this issue, the paper examines direct effect, spatial spillover effects, and mechanisms, utilizing a balanced panel data from 2008 to 2019 for 238 Chinese cities. The findings reveal that a 1 % improvement in industrial intelligence results in a 2.747 % enhancement of local carbon emission performance. Moreover, through the spatial spillover analysis, we determined that industrial intelligence has a notable negative impact on the carbon emission performance of surrounding areas. The mechanism analysis demonstrated that industrial intelligence affects the carbon emission performance of local and neighboring areas by influencing the agglomeration of productive services. Furthermore, our study illustrates that the industrial intelligence's enhancement effect on carbon emission performance shows more significantly in eastern, resource-dependent, and ordinary prefecture-level cities. Finally, endogeneity and robustness tests conducted yielded consistent conclusions. Our study provides a new perspective on industrial intelligence's carbon reduction effect and contributes to the development of policies related to industrial upgrading and green development.

The influence of corruption on environmental sustainability in the developing economies of Southern Africa

This paper analyses the impact of corruption on environmental sustainability in all 16 countries in the Southern region of Africa from 2010-2017. The paper uses two proxies of corruption: the Corruption Index and Corruption Ranking. Using two econometric methods, namely, the Dumitrescu and Hurlin (2012) Granger causality test and the Generalised Method of Moments (GMM) techniques this study found largely congruent results on both causation and relationships, respectively. Firstly, the two indicators of corruption harmoniously show that corruption Granger causes the existing state of environmental sustainability in Southern African economies, and vice-versa. Moreover, in the short-run corruption was also found to worsen environmental sustainability for both regression models deployed using the two corruption indicators. In the long-term, the two measures of corruption conflicted with their findings. In this regard, though the relationship is contradicting in the long-run the corruption negative (becoming bad) effect of corruption ranking surpasses the corruption positive (becoming clean) effect of corruption index by nearly three times. This show how detrimental corruptible actions are to the natural environment. Overall, this paper consent to global reports explaining how Southern African environments are gradually deteriorating by putting corruption as one central practice causing extensive damage.