The multi-level perspective (MLP) is a prominent framework for transition research. However, few studies have used bibliometrics for conducting a global picture of the MLP research. This study identifies the worldwide trends at three levels: sources, authors, and documents, and uses the bibliometrix based on 757 articles published in WOS and Scopus from 2002 to 2020. The results show that the MLP research literature is proliferating, and the number of journals and countries concerned in this field is increasing. MLP research has mainly focused on transition, sustainability transition, socio-technical transition, energy transition, innovation, and governance; and will increase focus on agency, power, and policy. MLP research will focus on multi-niche, multi-regime, and multi-landscape interactions at the hierarchy levels. The results assist scholars in systematically understanding the current research status, research frontiers, and future trends of MLP from a macro perspective.

This study examines the impact of environmental information disclosure quality on firm value for Chinese listed companies in heavily polluting industries from 2010 to 2021. By controlling for the level of leverage, growth, and corporate governance, a fixed effects model is constructed to test this relationship. Furthermore, this study analyzes the moderating effects of annual report text features, such as length, similarity, and readability, on the relationship between environmental information disclosure and firm value and the heterogeneous impact of firm ownership on this relationship. The main findings of this study are as follows: There is a positive correlation between the level of environmental information disclosure and firm value for Chinese listed companies in heavily polluting industries. Annual report text length and readability positively moderate the relationship between environmental information disclosure and firm value. Annual report text similarity negatively moderates the relationship between environmental information disclosure and firm value performance. Compared with state-owned enterprises, the impact of environmental information disclosure quality on the firm value of no-state-owned enterprises is more significant.

To explore the impact mechanisms of China’s low-carbon pilot policies on urban carbon emissions, this paper employs the propensity-score-matched difference-in-differences (PSM-DID) methodology, in conjunction with a dynamic marginal effect analysis, to examine the mechanisms through which China’s low-carbon pilot policies, initiated in three phases at disparate points in time, influence urban carbon emissions. The analysis is based on urban panel data from 2009 to 2020. The case demonstrates that the low-carbon pilot policies have had a considerable positive impact on the reduction of urban carbon emissions. Improving the efficiency of energy use and promoting the transformation of the industrial structure towards modern services are pivotal to curbing the intensity of carbon emissions. While the impact on the secondary industry is not statistically significant, these policies do have a significant impact on the restructuring of the tertiary industry. Increasing the amount of carbon sink in urban green spaces likewise has no discernible impact on lowering carbon emissions. Consequently, it is recommended that low-carbon technological innovation be further strengthened, including industrial upgrading, energy consumption control, and renewable energy development. Through these strategies, not only can carbon emissions be effectively reduced, they can thereby facilitate the creation of an environmentally friendly and resilient low-carbon city.

Due to the increasing deployment of Internet of Things in the mining industry, portable gas monitoring devices have been widely used. According to the character of time series of gas stream, the paper studies on mathematics analysis method of time series similarity based on pattern beam and pattern set. Combining with short-time stationary of gas data, the feature selection method of short-time gas data based on sliding time window is proposed. On the basis of the fuzzy C-Means clustering algorithm, short-time gas stream in the fuzzy C-Means clustering algorithm is put forward to analyze the convergence effects of the data of gas stream based on binary statistic and multivariate statistic, which provides qualified data that available for analysis and calculation for data correction of gas sensors afterward.