Integrating AI in Energy and Agriculture: Climate Change and Economic Growth
Integrating AI in Energy and Agriculture: Climate Change and Economic Growth
서대성
초록
Purpose: This study investigates the causes of climate-related disasters driven by accelerating climate change and proposes an AI-based energy management model designed to enhance food security and stabilize global trade systems. Methodology: Using EM-DAT disaster data from 1979 to 2024, excluding earthquakes, the study applies statistical and regression analyses to examine how CO₂ emissions and radiative forcing influence the frequency and severity of climate-related disasters. Results: The analysis demonstrates that a 1 W/m² increase in radiative forcing is associated with approximately 433 additional climate-related disaster events worldwide. In Korea, fruit cultivation zones have shifted nearly 250 km northward over the past four decades, advancing at an average rate of 6.25 km per year. If CO₂ concentrations continue rising and annual temperature increases exceed 3°C, this agricultural migration is expected to accelerate, posing significant risks to national and regional food security. Furthermore, short-term CO₂ emissions generated by rapid digitalization intensify climate pressures despite ongoing advances in renewable energy technologies. Conclusions: The study proposes an AI-based platform to optimize CO₂ emissions, strengthen agricultural adaptation capacity, and mitigate escalating climate disaster risks. Integrating AI into energy, environmental, and agricultural systems can improve efficiency, enhance carbon absorption, transform climate-related challenges into opportunities for sustainable development and long-term resilience.