Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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A Machine Learning Framework for Multidimensional Climate Change Analysis: Integrating Predictive Modeling, Causal Inference, and Policy Optimization

Authors
Yashwant A. Waykar1, *, Sucheta S. Yambal2, Ganesh Gadekar3, Vijay Rambhau Bhosale4, Prajakta U. Waghe5
1Department of Management Science, Dr. Babasaheb Ambedkar Marathwada University, Chhatrapati Sambhaji Nagar, India
2Department of Management Science, Dr. Babasaheb Ambedkar Marathwada University, Chhatrapati Sambhaji Nagar, India
3MCA Department (Commerce and Management), Vishwakarma University, Laxminagar, Kondhwa (Bk.), Pune, India
4U.D. Pathrikar School of Business Management, Dongargaon (Kawad), Tal – Phulambri, Dist – Chhatrapati Sambhajinagar, India
5Department of Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
*Corresponding author. Email: yawaykar.mgmtsci@bamu.ac.in
Corresponding Author
Yashwant A. Waykar
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_58How to use a DOI?
Keywords
Climate Change; Machine Learning; Predictive Modeling; Granger Causality; Policy Optimization; Environmental Analytics
Abstract

This study develops a unified machine learning framework to analyze the primary drivers of global CO2 emissions and provide targeted policy guidance for 195 countries. We integrate a comprehensive suite of socioeconomic and environmental data from 1900 to 2023, employing a Random Forest model that achieves exceptional predictive accuracy (R2 = 0.9966). Our analysis identifies the carbon intensity of economic activity as the paramount driver, accounting for 78.0% of feature importance. While Granger causality tests revealed no significant short-term causal dynamics, cluster analysis delineated four distinct national groupings based on emission profiles. These findings underscore the primacy of economic structure in long-term emissions trajectories and demonstrate the critical advantage of integrating machine learning with statistical analysis to move beyond one-size-fits-all solutions. Consequently, this research provides an empirical foundation for crafting nuanced, cluster-specific climate policies.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_58How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yashwant A. Waykar
AU  - Sucheta S. Yambal
AU  - Ganesh Gadekar
AU  - Vijay Rambhau Bhosale
AU  - Prajakta U. Waghe
PY  - 2026
DA  - 2026/01/06
TI  - A Machine Learning Framework for Multidimensional Climate Change Analysis: Integrating Predictive Modeling, Causal Inference, and Policy Optimization
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 830
EP  - 852
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_58
DO  - 10.2991/978-94-6463-948-3_58
ID  - Waykar2026
ER  -