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Environmental complaints serve as a crucial means for citizens to participate in environmental regulation, providing precise insights for real-time identification of pollution issues and understanding public environmental concerns. This study analyzes 102,782 environmental complaint texts from China's e-government platform between 2016 and 2022, firstly employing an ensemble machine learning model (Stacking-BERT) to examine the hot topics and sentiment characteristics of these environmental complaints. The results indicate that: (1) The number of environmental complaints exhibits an "M-shaped" fluctuation, with the proportion of complaints related to noise, waste, and radiation continuously rising. The sentiment orientation of these complaints has shifted from predominantly negative (2016-2020) to more neutral (2021-2022), indicating a positive trend. (2) Complaints regarding air, water, land, noise, and waste demonstrate significant seasonal cyclical fluctuations, characterized by a homogenized pattern. (3) Approximately 70.41% of environmental complaints express negative emotions. Noise complaints are the dominant topic of negative emotions, while air and radiation complaints are the core themes of extreme negative emotions. (4) There is a high overlap between major complaint regions and hotspots of negative emotions. The dual hotspot areas (Guangdong, Hebei, Shandong, Henan) are identified as critical regions requiring urgent attention to resolve environmental complaints., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

There is unequal spatial distribution of resource endowment, population density, industrial structure, and economic development with diverse differences in labor, energy, and capital productivities in China. However, previous studies paid little attention to the determinants of CO 2 transfers embodied in electricity trade. In this study, we use both the absolute and comparative advantage theories to reveal the determinants of embodied CO 2 transfers through electricity trade within China. Results show that China's electricity sector has higher labor productivity but lower asset efficiency and energy productivity than that of mining and manufacturing sectors. The large-scale electricity trade alleviates the shortage of electricity supply in developed regions by outsourcing to the less-developed regions, reduces the unequal spatial distribution of coal and natural gas reserves, and changes CO 2 flow embodied in power grid. Econometric analysis shows that coal reserve contributes to the increase of embodied CO 2 emission, while natural gas reduces the embodied CO 2 emission. The regional differences in the opportunity cost of labor productivity of non-electricity sector are the dominant factor of the embodied CO 2 transfers through electricity trade within China, while asset efficiency and energy productivity are not significant in the regressions. Our findings could provide details about China's power grid expansion when confronting climate mitigation in the future., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xu Peng reports financial support was provided by Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province. Xu Peng reports financial support was provided by Fundamental Research Funds for the Central Universities., (Copyright © 2023 Elsevier Ltd. All rights reserved.)