AI tools, licensing processes, legal expertise, and policy stability in environmental legal service efficiency in Jiangxi Province of China
Ruan Yan
City University of Malaysia
Aimi Fadzirul
Faculty of Forestry and Environment, University Putra Malaysia
DOI: https://doi.org/10.59429/pest.v8i1.13417
Keywords: environmental legal services; AI-assisted triage; administrative licensing; policy uncertainty; data envelopment analysis; Jiangxi
Abstract
Environmental disputes and compliance requirements in China increasingly hinge on timely legal assistance that can translate fast-evolving regulatory rules into actionable guidance for firms and residents. This article develops a practical framework for evaluating environmental legal service (ELS) efficiency in Jiangxi Province by linking four operational determinants: the deployment of AI tools for intake, retrieval, and drafting; the clarity and throughput of environmental licensing processes (e.g., permitting and planning EIA procedures); the depth of legal expertise embedded in service teams; and the stability of the policy environment faced by service providers and users. We propose an efficiency measurement pipeline that combines data envelopment analysis with a quasi-experimental pre–post comparison of pilot jurisdictions that introduced AI-assisted service workflows. Illustrative results indicate that AI-enabled triage and knowledge retrieval can raise ELS efficiency, but only when paired with streamlined licensing interfaces and professional capacity building. Policy stability further conditions returns by reducing repeated rework, inconsistent enforcement expectations, and advisory volatility. The findings provide implementationoriented guidance for provincial justice administrations seeking to scale trustworthy AI in public legal services while maintaining compliance with data governance and algorithm accountability requirements.
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