Applying Artificial Intelligence (AI) for Mitigation Climate Change Consequences of the Natural Disasters
Table 1. Framework for Using AI in Combating Climate Change(Maher et al. 2022)
| Mitigation | Adaptation and Resilience | Fundamentals | |||
| Measurement | Reduction | Removal | Hazard Forecasting | Vulnerability and Exposure management | Climate research and modeling |
| Macro-level measurement e.g., estimating remote carbon natural stock | Reducing GHG emissions intensity e.g., supply forecasting for solar energy | Environmental removal e.g., monitoring encroachment on forests and other natural reserves | Projecting localized long-term trends e.g., regionalized modeling of sea-level rise or extreme events such as wildfires and floods | Managing crises e.g., monitoring epidemics | Climate research and modeling e.g., modeling of economic and social transition |
| Micro-level measurement e.g., calculating the carbon footprint of individual products | Improving energy efficiency e.g., encouraging behavioral change | Technological removal e.g., assessing carbon-capture storage sites | Building early warning systems e.g., near-term prediction of extreme events such as cyclones | Strengthening infrastructure e.g., intelligent irrigation | Climate finance e.g., forecasting carbon prices |
| Reducing greenhouse effects e.g., accelerating aerosol and chemistry research | Protecting populations e.g., predicting large-scale migration patterns | Education, nudging, and behavioral change e.g., recommendations for climate-friendly consumption | |||
| Preserving biodiversity e.g., identifying and counting species | |||||