Abstract
This study analyzes the capacity of renewable energy facilities to reduce greenhouse gas emissions using feature-based analysis approaches. The main goal is to identify the technological, economic, and environmental elements that most substantially influence emission reduction, serving as a basis for strategic planning and policy development. The dataset includes multiple renewable energy sources and financial variables. Predictive modeling was conducted via CatBoost Regression (CAT R) and Random Forest Regression (RFR), along with hybrid optimization via Transit Search Optimization (TSP) and Arithmetic Optimization Algorithm (AOA). Among the assessed configurations, the CAAO configuration not only achieved the highest predictive performance but also converged faster, demonstrating computational efficiency advantageous for real-time and large-scale energy planning. Feature analysis utilizing SHAP values, K-fold cross-validation, and sensitivity evaluation via the FAST method revealed that energy storage efficiency is the predominant factor, followed by financial incentives, underscoring the significance of both technological and economic aspects in emission reduction strategies. These findings offer an initial investigation and pragmatic suggestions rather than conclusive determinations. The findings indicate that feature-oriented assessments, when integrated with sophisticated predictive modeling, may substantially improve renewable energy planning and facilitate the formulation of context-specific, low-carbon policies. Importantly, by jointly employing variance-based global sensitivity analysis (FAST) and explainable machine learning (SHAP), the study reconciles an apparent discrepancy between structural system drivers (e.g., energy storage capacity) and predictive policy drivers (e.g., financial incentives). This dual-perspective analysis demonstrates that while storage dominates the physical response of emission reduction, incentive mechanisms primarily govern short-term predictive variability, offering a nuanced interpretability framework rarely achieved by single-method studies.
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Data can be obtained from the corresponding author upon reasonable request (subashsubashch12@gmail.com).
References
Wang, J. & Azam, W. Natural resource scarcity, fossil fuel energy consumption, and total greenhouse gas emissions in top emitting countries. Geosci. Front. 15(2), 101757 (2024).
Patil, G., Pode, G., Diouf, B. & Pode, R. Sustainable decarbonization of road transport: Policies, current status, and challenges of electric vehicles. Sustainability 16(18), 8058 (2024).
Jha, M. K. & Dev, M. Impacts of climate change. In Smart Internet of Things for Environment and Healthcare 139–159 (Springer, 2024).
Hariram, N. P., Mekha, K. B., Suganthan, V. & Sudhakar, K. Sustainalism: An integrated socio-economic-environmental model to address sustainable development and sustainability. Sustainability 15(13), 10682 (2023).
Anser, M. K., Khan, K. A., Umar, M., Awosusi, A. A. & Shamansurova, Z. Formulating sustainable development policy for a developed nation: Exploring the role of renewable energy, natural gas efficiency and oil efficiency towards decarbonization. Int. J. Sustain. Dev. World Ecol. 31(3), 247–263 (2024).
Yang, H.-C., Feng, G.-F., Zhao, X. X. & Chang, C.-P. The impacts of energy insecurity on green innovation: A multi-country study. Econ. Anal. Policy 74, 139–154 (2022).
Yin, H.-T., Chang, C.-P. & Wang, H. The impact of monetary policy on green innovation: Global evidence. Technol. Econ. Dev. Econ. 28(6), 1933–1953 (2022).
Islam, M. M. et al. Improving reliability and stability of the power systems: A comprehensive review on the role of energy storage systems to enhance flexibility. IEEE Access 12, 152738–152765 (2024).
Raihan, A. & Tuspekova, A. The nexus between economic growth, renewable energy use, agricultural land expansion, and carbon emissions: New insights from Peru. Energy Nexus 6, 100067 (2022).
Raihan, A. & Voumik, L. C. Carbon emission dynamics in India due to financial development, renewable energy utilization, technological innovation, economic growth, and urbanization. J. Environ. Sci. Econ. 1(4), 36–50 (2022).
Saleh, H. M. & Hassan, A. I. The challenges of sustainable energy transition: A focus on renewable energy. Appl. Chem. Eng. 7(2), 2084 (2024).
Posacka, K. Reducing CO2 emissions into the atmosphere using the main engine settings of maritime vessels. Zesz. Nauk. Politech. Morskiej w Szczecinie (2024).
Lee, H. et al. Climate change 2023 synthesis report summary for policymakers. In Clim. Chang. 2023 Synth. Rep. Summ. Policymakers (2024).
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L. & Jin, R. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning, 27268–27286 (2022).
Giannelos, S., Pudjianto, D., Zhang, T. & Strbac, G. Energy hub operation under uncertainty: Monte Carlo risk assessment using gaussian and KDE-based data. Energies 18(7), 1712 (2025).
Dong, Z., Zhang, X., Zhang, L., Giannelos, S. & Strbac, G. Flexibility enhancement of urban energy systems through coordinated space heating aggregation of numerous buildings. Appl. Energy 374, 123971 (2024).
Giannelos, S., Konstantelos, I., Zhang, X. & Strbac, G. A stochastic optimization model for network expansion planning under exogenous and endogenous uncertainty. Electr. Power Syst. Res. 248, 111894 (2025).
Du, Z. et al. Decarbonisation of data centre networks through computing power migration. In 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence (CCAI), 871–876 (2025).
Wang, Y., Li, K. & Peng, L. Revesting the spatial spillover effects of renewable energy in curbing carbon emissions: Evidence from China. Energy Environ. https://doi.org/10.1177/0958305X241296447 (2024).
Abidi, I. & Nsaibi, M. Assessing the impact of renewable energy in mitigating climate change: A comprehensive study on effectiveness and adaptation support. Int. J. Energy Econ. Policy 14(3), 442–454 (2024).
Heshmati, A., Abolhosseini, S. & Altmann, J. Impact of renewable energy development on carbon dioxide emission reduction. In The Development of Renewable Energy Sources and its Significance for the Environment 119–146 (Springer, 2015).
Kumi, E. N. & Mahama, M. Greenhouse gas (GHG) emissions reduction in the electricity sector: Implications of increasing renewable energy penetration in Ghana’s electricity generation mix. Sci. Afr. 21, e01843 (2023).
Dulal, A. et al. Impact of renewable energy on carbon dioxide emission reduction in Bangladesh. J. Power Energy Eng. 9(5), 134–165 (2021).
Kurte, K. R., Raju, M. M., Dongritot, P. & Kulkarni, K. Budget-constrained Emission Reduction in Economic and Environmental Dispatch. In 2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE), 1–6 (2023).
Marouani, I. Contribution of renewable energy technologies in combating phenomenon of global warming and minimizing GHG emissions. Clean Energy Sci. Technol. 2(2), 164 (2024).
Breiman, L., Friedman, J., Olshen, R. & Stone, C. Classification and Regression Trees (CRC Press, 1984).
Biau, G. & Scornet, E. A random forest guided tour. TEST 25, 197–227 (2016).
Prokhorenkova, L., Gusev, G. Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, Vol. 31 (2018).
Dorogush, A. V., Ershov, V. & Gulin, A. CatBoost: Gradient boosting with categorical features support. arXiv Prepr. http://arxiv.org/abs/1810.11363 (2018).
Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S. & Al-Atabany, W. Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Appl. Intell. 51(3), 1531–1551 (2021).
Qais, M. H., Hasanien, H. M. & Alghuwainem, S. Transient search optimization for electrical parameters estimation of photovoltaic module based on datasheet values. Energy Convers. Manage. 214, 112904 (2020).
Mohamed, S. Tawfik, R. M., Elbayoumi, M. & Darweesh, M. S. Efficient UAV-aided data acquisition based on transit search optimization algorithm. In 2023 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 184–187 (2023).
Giannelos, S., Konstantelos, I., Pudjianto, D. & Strbac, G. The impact of electrolyser allocation on Great Britain’s electricity transmission system in 2050. Int. J. Hydrogen Energy 202, 153097 (2026).
Amann, G. et al. E-mobility deployment and impact on grids: impact of EV and charging infrastructure on European T&D grids: innovation needs (2022).
Giannelos, S., Konstantelos, I. & Strbac, G. Optimal supply chain design using machine learning, risk assessment and optimisation applied to coal distribution. EURO J. Decis. Process. https://doi.org/10.1016/j.ejdp.2025.100062 (2025).
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SC conceived and supervised the study, developed the methodology, and managed the overall project. ARA contributed to data collection, preprocessing, and analysis of environmental and mechanical parameters. RH conducted the literature review, structured the research framework, and validated the model assumptions. NB refined the methodology, ensured technical validation, and provided critical revisions to the manuscript. AM performed data curation, applied predictive modeling techniques, and interpreted the results. AJ carried out simulation experiments, evaluated comparative models, and visualized the outcomes. MA prepared and edited the draft and integrated environmental factors into the predictive framework. TE conducted statistical analysis, contributed to model optimization, and proofread the manuscript. BM verified the findings, contributed to the discussion, and ensured the overall quality of the final draft. All authors reviewed and approved the final version of the manuscript.
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Chandra, S., Abdulhadi, A.R., Hdeib, R. et al. Automated assessment of technological and financial drivers of greenhouse gas reduction in sustainable renewable energy systems. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40170-w
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DOI: https://doi.org/10.1038/s41598-026-40170-w
