Maximum Entropy Method for Wind Farm Site Selection: Implications for River Basin Ecosystems Under Climate Change

Abstract

As the global shift from fossil fuels to the Paris Agreement has accelerated, wind energy has become a key alternative to hydroelectric power. However, existing research often needs to improve in integrating diverse environmental, economic, and climate-related variables when modeling wind energy potential, particularly under future climate change scenarios. Addressing these gaps, this study employs the maximum entropy (MaxEnt) method, a robust and innovative tool for spatial modeling, to identify optimal wind farm sites in T & uuml;rkiye. This research advances site selection methodologies and enhances predictive accuracy by leveraging a comprehensive dataset and incorporating climate change scenarios. The results indicate that 89% of the current licensed projects will maintain compliance in the future, while 8% will see a decrease in compliance. Furthermore, the wind energy potential in T & uuml;rkiye is expected to increase because of climate change. These results confirm the suitability of existing project locations and identify new high-potential areas for sustainable wind energy development. This study provides policymakers, investors, and developers actionable insights to optimize wind energy integration into the national energy portfolio, supporting global climate goals by accelerating the adoption of renewable energy sources.