Research on the Digital Governance Mechanism of Public Welfare Forests from the Perspective of Multi-Level Governance

Abstract

As the core resource for maintaining ecological balance and supporting the construction of ecological civilization, the governance efficiency of public welfare forests is directly related to ecological security and sustainable development. Traditional governance models for public welfare forests face issues such as fragmented systems, inefficient decision-making, and significant information barriers, making them difficult to meet the practical needs of ecological protection in the new era. This paper takes Longquan City, Zhejiang Province, as a case study. Based on multi-level governance theory and digital governance theory, it adopts a case study method and combines field research, in-depth interviews, and literature review to explore the specific path of digital technology-driven innovation in public welfare forest governance models. The study finds that digital technology breaks down information barriers by constructing a comprehensive data integration platform, optimizes management and protection processes to reduce transaction costs, and effectively enhances the cross-level collaboration efficiency among county, township, and village levels. Relying on the hierarchical responsibility system of the forest chief system, digital governance achieves precise calculation and transparent distribution of compensation funds, enhances supervision efficiency through intelligent monitoring networks, and promotes the formation of a diversified co-governance pattern through innovative public participation mechanisms, thereby facilitating the transformation of governance structure towards efficient collaboration. Based on this, suggestions are made to build a multi-level digital collaboration platform, improve information sharing mechanisms, and optimize the compensation fund supervision system, providing empirical references and practical insights for the innovation of public welfare forest governance models.