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Machine learning analysis of New York clean energy trends

Author

Listed:
  • Gokavarapu, Kamal Nikhil
  • Gore, Nidhish Abhijit
  • Ohri, Chirag
  • Carbonara, Joaquin
  • Ahmed, Saquib

Abstract

The transition from fossil fuels to renewable energy sources is central to mitigating greenhouse gas emissions, yet state-level adoption pathways remain uneven. This study develops a data-driven framework to assess the determinants of renewable market share in New York State, integrating socioeconomic, policy, and resource-based variables. A dataset of 200 observations (2016–2020) was analyzed using eight machine learning algorithms, including Linear Regression, Ridge Regression, ensemble methods (Random Forest, Gradient Boosting, AdaBoost, XGBoost), Support Vector Machines, and K-Nearest Neighbors. Model evaluation employed a 60:20:20 train-validation-test split and four metrics—RMSE, MAE, MAPE, and R2—to ensure robust generalization.

Suggested Citation

  • Gokavarapu, Kamal Nikhil & Gore, Nidhish Abhijit & Ohri, Chirag & Carbonara, Joaquin & Ahmed, Saquib, 2025. "Machine learning analysis of New York clean energy trends," Energy Economics, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:eneeco:v:152:y:2025:i:c:s0140988325008692
    DOI: 10.1016/j.eneco.2025.109039
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    References listed on IDEAS

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