Clean heating renovation is important for optimizing the regional energy structure and improving the quality of the atmospheric environment in Northern China. According to survey data of 1298 rural households in the “coal-to-gas” reform in Hengshui of Hebei Province, in this paper, we construct a clean heating subsidy model which focuses on clean heating choices, the reburning rate, and the subsidy cost. The key parameters affecting farmers’ choice of clean heating, including the lifeline, classification of rural households, and gas price elasticity, are estimated. Furthermore, we simulate changes between the existing and new subsidy policies, including the impact on the welfare of rural residents. We present the following findings: (1) under the current subsidy standard, the level of gas heating lifeline in Hengshui is 700 m³, the proportion of low-gas-consumption households is approximately 47.38%, and the reburning rate ranges from 47.38% to 63.80%; (2) under a new subsidy standard which we designed to ensure that households will not reburn the coal, the optimal subsidy value is 1.23 CNY/m³, the optimal subsidy volume is 970 m³, and the total subsidy demonstrates an increase of 46.21%; and (3) the government’s subsidy standards should focus on low-income households.

To address the limitations of traditional policy instrument analysis—such as labor-intensive coding, high subjectivity, and time-consuming procedures—this study develops a policy instrument analysis framework that integrates large language models (LLMs) and proposes a LLM-driven analytical workflow comprising six stages: case repository construction, policy instrument selection, content element generation, clause-level coding, reliability and validity testing, and quantitative analysis. Using governance texts on teachers’ ethical misconduct from 27 universities specializing in finance and economics as the empirical context, the study employed DeepSeek-R1 to identify policy instruments, classify content elements, perform clause-level coding, and conduct two-dimensional cross-tabulation analysis. The results indicate that these governance texts exhibit pronounced regulatory, procedural, and accountability-oriented characteristics, while also revealing a structural imbalance marked by strong front-end norm construction and relatively weak back-end remedial mechanisms. Overall, the proposed framework improves the efficiency and consistency of policy text analysis and provides a novel technical pathway for methodological innovation in education policy research.

Returnee faculty play a pivotal role in international knowledge transfer and the advancement of scientific research within domestic universities. However, the effectiveness of returnees in enhancing institutional research performance remains inadequately understood. This study seeks to quantify and examine the collaborative academic networks of returnee faculty, assessing their impact on research output and funding performance. Based on an extensive academic dataset, comprehensive scientific collaboration networks (SCN) of returnees were constructed and empirically analyzed to elucidate the influence mechanisms underpinning research performance. The findings indicate that the presence of returnee faculty substantially enhances overall publication output and funding acquisition. Further, within the SCN of returnees, both academic influence and network expansion positively correlate with research productivity and funding success, whereas an increase in cooperation density appears to exert a negative effect. Additionally, the evolution of these collaboration networks was explored, revealing that returnees' SCN display lower similarity and retention over time compared to those of native faculty. These insights offer a valuable theoretical basis for improving the management and integration of returnee faculty and optimizing the allocation of higher education resources, thereby fostering more effective pathways for enhancing institutional research outcomes.

The prospectus, as the primary vehicle for issuers to disclose information to the public, plays a crucial role in protecting investors' rights. Review inquiries serve as an important tool to ensure the quality of the prospectus, as the inquiry and feedback mechanism helps to identify potential risks and enhance the quality of information disclosure. This paper, based on the theory of responsive regulation and the attention-based view, takes companies applying for Initial Public Offering (IPO) on the Science and Technology Innovation Board (STAR) Market and ChiNext Board between 2019 and 2023 as the research samples. Using text analysis methods such as the Latent Dirichlet Allocation (LDA) topic model and dictionary-based methods, this study measures the intensity of review inquiries and the extent of information disclosure. It examines the impact of inquiry topics on the disclosure of corresponding information in the prospectus and explores the moderating effects of company ownership structure, sponsor reputation, and auditor reputation on these relationships. Empirical results indicate that: (1) an increase in the formality of review inquiries enhances the optimization of information disclosure in the prospectus; (2) the focus of review inquiries on specific topics has a significant positive impact on the update of relevant information disclosure in the prospectus; and (3) at the ownership structure level, state-owned enterprises dampen the positive influence of review inquiries on the textual features of the prospectus.

The descriptions of Research and Development (R&D) activities in the annual reports of listed companies provide crucial insights into a company's internal governance, external competitiveness, and long-term sustainability strategies. However, R&D disclosures in China's securities market are largely semi-mandatory, often leading listed companies to adopt either "self-enhancement" or "self-suppression" approaches in their R&D activity descriptions. This inconsistency between actions and disclosures erodes trust among market participants and exacerbates information asymmetry. Based on annual report data from Chinese manufacturing firms, this study assesses the intensity of R&D activity disclosures from both accounting data and textual information using Latent Dirichlet Allocation (LDA) topic modeling and principal component analysis (PCA). Normalized difference metrics are applied to quantify the level of inconsistency between these two dimensions. Empirical findings reveal a prevalent degree of inconsistency in R&D activity descriptions within manufacturing firms' annual reports. Furthermore, both accounting and textual disclosure intensities have increased over time, with inconsistency levels initially rising and then showing a marked decline. The findings offer a theoretical basis for enhancing and standardizing R&D activity descriptions and disclosures, serving as a resource for government, companies, thirdparty agencies, and investors.