Harnessing Artificial Intelligence for Climate Governance: Legal Innovations and Challenges in Environmental Law

Abstract

Introduction

Climate change, driven by human development and industrialization, poses serious threats to the environment and humanity. It causes ecosystem disruption, violates environmental rights, and demands urgent solutions. While traditional legal and policy measures are important, they are no longer sufficient. Artificial Intelligence (AI) is emerging as a powerful tool to accelerate climate adaptation, improve data analysis, and support evidence-based decision-making in environmental governance.

II. Legal Foundations of Climate Governance

Climate governance is shaped by international treaties and national laws:

• Montreal Protocol (1987): Addressed greenhouse gases.

• UNFCCC (1992): Established global cooperation to combat climate change.

• Kyoto Protocol (1997): Introduced binding emission targets for developed nations.

• Paris Agreement (2015): Legal treaty requiring all countries to submit climate action plans (NDCs), with mechanisms for global progress review and transparency.

III. India’s Role in Climate Law

India, a key player in global climate governance, balances development needs with environmental responsibility:

• Constitutional Provisions:

  • Article 48A: State must protect the environment.

  • Article 51A(g): Citizens must safeguard nature.

• Judicial Interpretation: Indian courts interpret the right to life (Article 21) to include the right to a healthy environment. Landmark rulings reinforce this, such as in M.K. RanjitSinh vs Union of India.

IV. AI in Climate Governance: Opportunities

A. Energy Efficiency and Renewable Power

• AI optimizes solar/wind power systems by analyzing environmental data.

• Improves energy demand prediction and grid management.

• Reduces energy waste and promotes sustainability.

B. Advanced Climate Modelling

• AI enhances climate predictions using machine learning on massive datasets.

• Helps scientists better understand weather patterns, ocean currents, and emission scenarios.

C. Risk and Vulnerability Mapping

• AI models combine climate and socio-economic data to identify high-risk regions, guiding adaptation efforts.

V. AI in Carbon Capture and Containment

• AI supports all stages of Carbon Capture and Storage (CCS):

  • Site selection using geological data.

  • Monitoring CO? leaks through sensors and satellite data.

  • Innovating new methods like mineral carbonation to convert CO? into rock.

• AI helps make CCS safer and more efficient, storing millions of tons of CO? annually.

VI. AI in Weather Forecasting

• AI dramatically improves forecast speed and accuracy.

• Combines satellite and sensor data (e.g., MIIDAPS-AI) to provide real-time weather updates.

• Detects early signs of extreme weather events using deep learning.

VII. Legal Innovations via AI

A. Enforcement and Monitoring

• AI tools use satellite imagery and real-time sensors to detect environmental violations (e.g., illegal deforestation).

B. Impact Assessments

• AI improves Environmental Impact Assessments (EIAs) with predictive models for more accurate planning.

C. Legal Research and Litigation

• AI assists in analyzing case law, supporting environmental lawsuits, and predicting litigation outcomes.

VIII. Challenges and Ethical Considerations

A. Legal and Institutional Risks

• Accountability issues: Who is responsible if AI systems fail or cause harm?

• Risk of AI overriding democratic decision-making and weakening legal oversight.

B. Ethical Concerns

• Privacy violations from surveillance technologies.

• Bias in AI models may lead to unjust allocation of climate aid or misrepresentation of vulnerable groups.

• Communities lacking digital access may be excluded from AI-driven solutions.

C. Equity and Rights

• There's a risk of reinforcing inequalities if AI is not inclusively deployed.

• Potential conflict with emerging legal frameworks that grant rights to nature or non-human entities.

Conclusion

The incorporation of AI into environmental governance marks a significant evolution in how societies confront climate change and ecological challenges. While AI technologies offer powerful tools for monitoring, decision-making, and enforcement, they also unsettle established legal and ethical norms. The issues of accountability, transparency, fairness, and institutional legitimacy cannot be overlooked as governance systems adapt to these new realities. Addressing these challenges requires more than technical fixes; it demands a comprehensive rethinking of legal frameworks to ensure they remain robust and just in the face of automation. Clear lines of responsibility must be established so that when AI systems err or cause harm, affected parties can seek redress and hold the right actors accountable. Procedural safeguards need to be strengthened to ensure that decisions influenced or made by AI remain transparent and open to meaningful scrutiny, preserving fundamental rights such as due process and participation. Ethically, the deployment of AI in environmental contexts must be guided by principles of equity and inclusion. Policymakers should actively work to prevent algorithmic biases from exacerbating existing social inequalities and address the digital divide that threatens to leave vulnerable communities behind. Recognizing the rights of nature alongside technological advancements also calls for innovative legal thinking that respects ecological values and balances them against machine-driven objectivity.

Copyright

Copyright © 2025 Simranpreet Kaur. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.