Transitioning to Adaptive Ecosystems: The Role of Artificial Intelligence in Sustainable Environmental Management and Pollution Control

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive framework for evaluating AI's net environmental impact, useful for future research on sustainable AI.

🏢実務担当者:Offers guidance on balancing AI deployment benefits with environmental costs, relevant for corporate sustainability teams.

🏛政策担当者:Underlines the urgent need for regulatory standards on AI energy use and e-waste, informing climate policy development.

The escalating threat of global environmental emergencies—driven by extreme climate volatility and the rapid depletion of biodiversity—forces a critical departure from rigid conservation models toward highly responsive, adaptive ecosystem governance. While artificial intelligence (AI) rapidly accelerates this operational shift by providing exceptionally advanced tools for sustainability, the functional deployment of these systems simultaneously generates deeply complicated ecological trade-offs. This scoping review systematically examines the contradictory environmental impacts inherent to AI-directed ecological management to properly contextualize this tension. We weigh the immediate operational advantages of algorithmic systems directly against their total life-cycle environmental toll, synthesizing broad interdisciplinary literature published between 2018 and 2026 while maintaining strict compliance with PRISMA reporting standards. The active integration of AI networks with Internet of Things (IoT) sensor arrays—according to our evaluation—makes continuous, real-time environmental surveillance and highly predictive biodiversity tracking functionally possible. Sophisticated machine learning algorithms refine Life Cycle Assessments (LCA) to yield highly precise carbon footprint calculations; meanwhile, the deployment of physics-informed edge computing actively supports autonomous, decentralized pollution control. These mitigation advantages, however, are severely offset by the vast computational energy required to train such models, alongside the intensive extraction of regional water resources and the exponential generation of electronic waste. This analysis determines that deploying algorithms without strict regulation carries the severe risk of entrenching techno-solutionism, thereby worsening an already severe carbon paradox. Ensuring these digital technologies actively support ecologically grounded environmental stewardship requires the immediate implementation of globally standardized computational carbon accounting protocols, alongside stringent corporate oversight, to unlock genuine sustainability yields. 

• semanticscholar https://doi.org/10.53797/jthkkss.v7i1.1.2026first seen 2026-06-29 09:10:16