Evaluation of primary-level credit environment, indicator system and empirical analysis: A case study of credit construction in China county and district.

Townships (towns, streets) represent the foundational layer of China's administrative structure, and the quality of their credit environment is crucial for underpinning the development of a primary-level social credit system. This initiative aims to accelerate the establishment of the social credit system and cultivate a trustworthy economic and social environment. Starting from the three major fields of government, business and society, and focusing on integrity culture and credit innovation, the article proposes an innovative evaluation framework for primary-level credit environment and it can become a point of reference as a policy tool in international evaluation programs. Using clustering and the coefficient of variation methods, we quantitatively refine our indicator system, establishing a set of criteria to assess the primary-level credit environment. We incorporate hierarchical analysis, the entropy weight method, and machine learning models to conduct a comprehensive evaluation of the credit environments within 24 townships (towns, streets) of Fuyang District in Hangzhou City for the year 2023. The findings underscore the need for a realistic appraisal of the current state and deficiencies of the primary-level credit environment. We advocate for the bolstering of credit development within governmental, business, and societal realms. It's imperative to leverage the normative influence of honesty and integrity culture, enhance the breadth and application of credit innovations, and thereby foster the high-quality growth of the primary-level social credit system.

Analyzing public demands on China's online government inquiry platform: A BERTopic-Based topic modeling study.

This study aims to enhance governmental decision-making by leveraging advanced topic modeling algorithms to analyze public letters on the "People Call Me" online government inquiry platform in Zhejiang Province, China. Employing advanced web scraping techniques, we collected publicly available letter data from Hangzhou City between June 2022 and May 2023. Initial descriptive statistical analyses and text mining were conducted, followed by topic modeling using the BERTopic algorithm. Our findings indicate that public demands are chiefly focused on livelihood security and rights protection, and these demands exhibit a diversity of characteristics. Furthermore, the public's response to significant emergency events demonstrates both sensitivity and deep concern, underlining its pivotal role in government emergency management. This research not only provides a comprehensive landscape of public demands but also validates the efficacy of the BERTopic algorithm for extracting such demands, thereby offering valuable insights to bolster the government's agility and resilience in emergency responses, enhance public services, and modernize social governance.