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The aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.

This research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.

Findings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.

Empirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.

Charitable giving in China has moved from being subjected to government attention and public skepticism to receiving government encouragement and public support. The role played by political connections in philanthropy is indisputable, although very few studies have explored their association from the perspective of the country’s first Charity Law of 2016. This study aims to contribute to the ongoing debate about the 2016 Charity Law and offers an understanding of the future trends in corporate charitable giving.

Using empirical analysis of data collected from listed companies in China, this study analyzes the impact of political connections on corporate charitable giving before and after the 2016 Charity Law. The study adopts three leading theories from previous research into corporate charitable giving and political connections: corporate social responsibility, resource dependence theory and stakeholder theory. A conceptual framework is outlined, and hypotheses are formulated accordingly.

The results show that political connections have a substantial positive impact on corporate charitable giving, both before and after the implementation of the 2016 Charity Law, which has significantly promoted and increased the amount and proportion of charitable giving. Although the 2016 Charity Law attempted to weaken the political connections of enterprises, the influence of political connections on corporate charitable giving has proved difficult to diminish or eliminate, as charity is dominated by the state.

This study explores the association between political connections and corporate charitable giving from the perspective of China’s Charity Law of 2016.

As the global emphasis on environmental consciousness intensifies, many corporations claim to be environmentally responsible. However, some merely partake in “greenwashing” – a facade of eco-responsibility. Such deceptive behavior is especially prevalent in Chinese heavy-pollution industries. To counter these deceptive practices, this study aims to use machine learning (ML) techniques to develop predictive models against corporate greenwashing, thus facilitating the sustainable development of corporations.

This study develops effective predictive models for greenwashing by integrating multifaceted data sets, which include corporate external, organizational and managerial characteristics, and using a range of ML algorithms, namely, linear regression, random forest, K-nearest neighbors, support vector machines and artificial neural network.

The proposed predictive models register an improvement of over 20% in prediction accuracy compared to the benchmark value, furnishing stakeholders with a robust tool to challenge corporate greenwashing behaviors. Further analysis of feature importance, industry-specific predictions and real-world validation enhances the model’s interpretability and its practical applications across different domains.

This research introduces an innovative ML-based model designed to predict greenwashing activities within Chinese heavy-pollution sectors. It holds potential for application in other emerging economies, serving as a practical tool for both academics and practitioners.

The findings offer insights for crafting informed, data-driven policies to curb greenwashing and promote corporate responsibility, transparency and sustainable development.

While prior research mainly concentrated on the factors influencing greenwashing behavior, this study takes a proactive approach. It aims to forecast the extent of corporate greenwashing by using a range of multi-dimensional variables, thus providing enhanced value to stakeholders. To the best of the authors’ knowledge, this is the first study introducing ML-based models designed to predict a company’s level of greenwashing.