Artificial Intelligence Empowers Environmental Crime Investigation: Applications, Challenges and Optimization Strategies

Authors

  • Guan Ping

DOI:

https://doi.org/10.37420/j.mlr.2026.001

Keywords:

Artificial Intelligence;Environmental Crime Investigation;Data Governance

Abstract

The continuous advancement of industrialization has intensified global ecological and environmental pressures, making environmental issues increasingly severe in various countries, and environmental crime has become a key concern worldwide. Endowed with powerful data analysis, pattern recognition, and efficient processing capabilities, artificial intelligence (AI) is gradually penetrating the entire process of environmental crime investigation. It provides a new path to break through the limitations of traditional investigation models and improve crackdown efficiency, while also giving rise to multiple challenges such as technological adaptation, institutional regulation, and ethical balance. Based on the practical applications of AI in core links of environmental crime investigation—including clue mining, evidence collection and analysis, and criminal suspect tracking—this paper systematically analyzes key problems such as uneven data quality, insufficient algorithm reliability, lagging legal regulation, and shortage of interdisciplinary talents. Furthermore, targeted countermeasures are proposed from three dimensions: technological optimization, institutional improvement, and talent cultivation. This study aims to provide practical references for promoting the in-depth integration of AI and environmental crime investigation, as well as enhancing the modernization level of ecological environment governance.

Author Biography

Guan Ping

People's Public Security University of China, Beijing, China

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Published

2026-02-28

How to Cite

Ping, G. (2026). Artificial Intelligence Empowers Environmental Crime Investigation: Applications, Challenges and Optimization Strategies. Modern Law Research, 7(1). https://doi.org/10.37420/j.mlr.2026.001

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