AI's Role in Enhancing Climate Resilience

The urgency of climate change demands innovative strategies to enhance resilience in various sectors. Artificial Intelligence (AI) stands out as a transformative tool, capable of significantly strengthening adaptation and mitigation efforts. However, the literature on AI's applications in climate resilience remains fragmented.

A systematic review of 385 peer-reviewed papers reveals that 64.4% of studies focus on adaptation, while 16% address mitigation, and 19.4% incorporate both approaches. The research is unevenly distributed, with Asia (32%) and global-scale studies (34%) receiving the most attention. Africa (7.5%) and South America (1.8%) remain significantly underrepresented.

AI methodologies vary, with 51.4% of studies employing Classical Machine Learning or General ML techniques, and 22.3% utilizing deep learning/neural network models. Despite AI's potential, key gaps persist in data accessibility, model interpretability, and ethical considerations, particularly in developing regions.

Unlike earlier reviews that focused on specific sectors or aspects of AI in climate adaptation, this study offers a comprehensive cross-sectoral analysis. It covers nine sectors and integrates perspectives on both adaptation and mitigation. This breadth, along with an emphasis on system-level resilience and underrepresented regions, distinguishes this work from prior reviews.

The paper underscores the need for interdisciplinary collaboration, regionally tailored AI models, and ethical AI frameworks to ensure equitable climate resilience. It provides a roadmap for policymakers, researchers, and industry stakeholders to strategically deploy AI in strengthening global climate adaptation and mitigation efforts.

AI's role in climate resilience is multifaceted. For instance, in agriculture, AI can predict weather patterns and optimize crop management. In urban planning, AI can model the impact of extreme weather events and inform infrastructure design. In energy, AI can enhance grid efficiency and renewable energy integration.

The challenges, however, are significant. Data accessibility remains a major hurdle, especially in developing regions where data collection and infrastructure are limited. Model interpretability is another concern, as black-box models can be difficult to trust and regulate. Ethical considerations, such as bias and privacy, must also be addressed to ensure that AI solutions are fair and transparent.

To bridge these gaps, interdisciplinary collaboration is essential. Experts from climate science, computer science, and social sciences must work together to develop robust AI models. Regionally tailored solutions are also crucial, as the impact of climate change varies widely across different geographies. Ethical AI frameworks can guide the development and deployment of AI solutions, ensuring they are aligned with societal values and norms.

In conclusion, AI holds great promise for enhancing climate resilience, but its potential can only be fully realized through collaborative efforts and a focus on ethical, accessible, and interpretable AI solutions.