As we observed, various parts of India are facing “heatwaves” owing to abrupt climate change that takes place due to massive usage of carbon emissions leading to the Indian Meteorological Department (IMD) focusing on “carbon neutrality”. As a result, the paper will explore the evolving policy and legal regulatory landscape governing the deployment of AI in carbon management practices. Authors in this paper seek to discuss the functionality of AI in carbon reduction and sequestration by delving into the intricacies of policy frameworks at international, regional, and national levels. The paper will begin with the authors analyzing the key directives, agreements, and initiatives shaping AI integration into carbon management strategies, emphasizing the alignment with global climate goals such as the Paris Agreement. It would help the readers to understand the burning issue since its known significant inception. Additionally, the authors also contemplate the legal challenges and ethical considerations accompanying AI applications in carbon management. It explores pertinent issues of data privacy, algorithm transparency, accountability, and liability, offering insights into the evolving juris-prudence surrounding these matters. Authors have considered drawing on case studies and best practices; the research highlights successful regulatory approaches and identifies areas requiring further development. Its under-scores the importance of interdisciplinary collaboration among policymakers, legal experts, technologists, and environmental stakeholders to navigate the complex terrain of AI-enabled carbon management responsibly and sustain-ably. Finally, while concluding the chapter, the authors have taken the liber-ty to gauge the feasibility and accuracy of understanding the policy and legal frameworks necessary to harness the full potential of AI in addressing the pressing challenges of carbon emissions and climate change mitigation.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Policy and Legal Regulatory Landscape for Artificial Intelligence in Carbon Management

  • Aranya Nath,
  • Srishti Roy Barman,
  • Rasika Pramod Bangre

摘要

As we observed, various parts of India are facing “heatwaves” owing to abrupt climate change that takes place due to massive usage of carbon emissions leading to the Indian Meteorological Department (IMD) focusing on “carbon neutrality”. As a result, the paper will explore the evolving policy and legal regulatory landscape governing the deployment of AI in carbon management practices. Authors in this paper seek to discuss the functionality of AI in carbon reduction and sequestration by delving into the intricacies of policy frameworks at international, regional, and national levels. The paper will begin with the authors analyzing the key directives, agreements, and initiatives shaping AI integration into carbon management strategies, emphasizing the alignment with global climate goals such as the Paris Agreement. It would help the readers to understand the burning issue since its known significant inception. Additionally, the authors also contemplate the legal challenges and ethical considerations accompanying AI applications in carbon management. It explores pertinent issues of data privacy, algorithm transparency, accountability, and liability, offering insights into the evolving juris-prudence surrounding these matters. Authors have considered drawing on case studies and best practices; the research highlights successful regulatory approaches and identifies areas requiring further development. Its under-scores the importance of interdisciplinary collaboration among policymakers, legal experts, technologists, and environmental stakeholders to navigate the complex terrain of AI-enabled carbon management responsibly and sustain-ably. Finally, while concluding the chapter, the authors have taken the liber-ty to gauge the feasibility and accuracy of understanding the policy and legal frameworks necessary to harness the full potential of AI in addressing the pressing challenges of carbon emissions and climate change mitigation.