In order to promote ecological sustainability, the issue of sulphur dioxide emissions is of increasing interest to researchers. Majority of the current research, however, focuses on the relationship between sulphur dioxide (SO₂) emissions, foreign direct investment (FDI), and trade, as well as the effects of trade on SO₂ emissions, thus rarely takes it into account that the greater impact of the institutional environment and economic growth on SO₂ emissions. Using the 2008–2017 provincial panel data, this paper uses a fixed effects model to empirically test the institutional environment and economic growth of sulphur dioxide (SO₂) emissions. The results show that GDP growth and SO₂ emissions had an inverted “U”-shaped relationship. The institutional environment and the higher level of government intervention in the region led to SO₂ emissions decreasing significantly, and the institutional environment and the level of government intervention on economic growth and SO₂ emissions form a negative regulatory role. In this paper, environmental governance research, specified by the regional environmental governance, and government environmental performance audit policy provide empirical evidence, thus promoting sustainable ecological and environmental development.

The world is severely impacted by the coronavirus (COVID19). During the epidemic, logistics service, an often-overlooked pillar of the modern society, steps into the spotlight. However, the service capability is inevitably weakened by the epidemic. The fatigued service providers are increasingly unable to meet the high expectations of users, who therefore leave harsh comments on logistics services. It is important for managers to find information that helps to improve management, out of the biased and angry comments. Text sentiment analysis is a fundamental work in natural language processing (NLP). In recent years, graph neural network (GNN) has achieved excellent performance in various NLP tasks. Nevertheless, GNN only considers the adjacent words, as it updates graph nodes. The model thereby emphasizes local features over global features, and misses the intent of the comment text. This paper constructs a triple graph neural network (TGNN) to serve the sentiment analysis of service texts. Firstly, the corresponding node connection windows were applied on different network layers to consider both local and global features. Next, the graph attention network (GAT) was adopted as the message delivery mechanism to fuse the features of all word nodes in the graph. Experimental results show that, the TGNN can evaluate the comment texts on logistics service quality more accurately than the other models.