AI-Driven Participatory Environmental Management: Innovations, Applications, and Future Prospects
At EBIS-HSE, we’re passionate about advancing environmental management through global research and leading-edge thinking. This series brings together the newest findings and practical innovations that support a cleaner, more sustainable planet for generations to come.
Artificial intelligence is reshaping environmental management through smarter decisions, real-time monitoring, and wider public participation.
To achieve lasting benefits, AI must be guided by ethical standards, transparent governance, and inclusive policies that promote fairness and sustainability.
Collaboration across nations and communities is essential to ensure AI advances both environmental protection and social well-being worldwide.
Summary
The study concludes that AI has substantial potential to transform participatory environmental management by improving data analysis, stakeholder engagement, decision-making accuracy, and environmental governance.
Key contributions include:
- Identification of ten major thematic clusters, ranging from AI-enhanced urban health to smart infrastructure, disaster resilience, circular economy, precision agriculture, and strategic waste planning.
- AI tools, such as machine learning, digital twins, GIS integration, chatbots, and IoT systems, provide powerful mechanisms for monitoring, predicting, and managing environmental conditions collaboratively.
- Yet, risks related to energy use, transparency, algorithmic bias, ethics, and data privacy must be resolved to ensure equitable and sustainable deployment.
- The authors emphasize the need for policies, capacity development, and interdisciplinary collaboration to bridge current gaps and realize AI’s full potential in environmental governance.
Aim & Context
The study investigates how AI technologies can be incorporated into participatory environmental management to improve:
- Environmental decision-making
- Stakeholder collaboration
- Monitoring and management of environmental risks
- Transparency and governance in sustainability initiatives
It seeks to answer three core questions:
- What common research themes characterize the field of AI-driven participatory environmental management?
- How can AI be effectively integrated into participatory environmental practices?
- What research gaps remain that may influence stakeholder collaboration in the future?
Methodology
The authors applied AI-assisted literature analysis, combining:
- A dataset of 80 peer-reviewed articles gathered using the Elicit LLM-powered research tool.
- Text mining and co-word analysis using VOSviewer to reveal thematic clusters and trends.
- Manual qualitative assessment of methods, hypotheses, policy recommendations, and future research directions.
This hybrid approach allowed the researchers to map the intellectual landscape of AI applications in participatory environmental management efficiently and comprehensively.
Key Findings
1. AI as a Strategic Enabler in Environmental Management
- AI significantly enhances monitoring, prediction, and decision-making across environmental domains.
- It strengthens participatory processes by improving accuracy, speed, and stakeholder access to actionable data.
2. Ten Core Thematic Areas of Impact
- AI applications cluster around major themes including urban health, climate resilience, community resource management, precision agriculture, smart infrastructure, and waste management.
- These clusters reflect AI’s expanding role across governance, sustainability, and community engagement.
3. Demonstrated Benefits Across Sectors
- AI tools; such as machine learning, IoT sensors, GIS, chatbots, and digital twins, improve environmental data quality, forecast reliability, and stakeholder participation.
- Empirical studies show strong gains in forest management, pollution monitoring, and disaster preparedness.
4. Adoption Influenced by Human and Contextual Factors
- Public and stakeholder acceptance depends on awareness, education, trust, and perceived usefulness of AI systems.
- Community engagement and adaptation to local contexts are essential for successful deployment.
5. Policy and Governance Gaps Remain Significant
- Clear needs emerge for ethical guidelines, data privacy standards, transparency requirements, and regulatory oversight.
- Investment in infrastructure, workforce training, and public–private collaboration is critical for scaling AI safely and effectively.
6. Future Research Priorities
- Studies call for interpretable, low-energy, and sustainable AI models; integrated frameworks combining AI with GIS, IoT, and remote sensing; and robust policies supporting responsible, inclusive AI use.
- Interdisciplinary cooperation is highlighted as foundational for long-term success.
Takeaways for Practice
- Use AI (ML models, IoT, GIS) for real-time environmental monitoring, forecasting, and participatory planning.
- Deploy AI-powered platforms (e.g., chatbots) to strengthen community reporting and involvement.
- Integrate digital twins and AI models into public governance systems for climate adaptation and resource management.
- Adopt ethical frameworks emphasizing transparency, fairness, and data privacy.
- Strengthen workforce training and encourage public–private partnerships to accelerate responsible innovation.
Read the full research study here:
Santos, M. R. C., & Carvalho, L. C. (2025). AI-driven participatory environmental management: Innovations, applications, and future prospects. Journal of Environmental Management, 373, 123864.
https://doi.org/10.1016/j.jenvman.2024.123864