Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

Al-Qudah, A. A., Hamdan, A., Al-Okaily, M., Alhaddad, L. (2023). The impact of green lending on credit risk: evidence from UAE’s banks. Environmental Science and Pollution Research, 30(22), 61381–61393. https://doi.org/10.1007/s11356-021-18224-5

Ben Jabeur, S., Khalfaoui, R., Ben Arfi, W. (2021). The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning. Journal of Environmental Management, 298. https://doi.org/10.1016/j.jenvman.2021.113511

Bucur, C., Tudorica, B. G., Oprea, S. V., Nancu, D., & Dusmanescu, D. M. (2021). Insights into Energy Indicators Analytics towards European Green Energy Transition Using Statistics and Self-Organizing Maps. IEEE Access, 9, 64427–64444. https://doi.org/10.1109/ACCESS.2021.3075175

Bui-Duy, L., Le, L. T., Vu-Thi-Minh, N., Hoang-Huong, G., Bui-Thi-Thanh, N., & Nguyen, P. N. (2023). Economic and environmental analysis of biomass pellet supply chain using simulation-based approach. Asia Pacific Management Review, 28(4), 470–486. https://doi.org/10.1016/j.apmrv.2023.02.002

D’Orazio, P., & Popoyan, L. (2019). Fostering green investments and tackling climate-related financial risks: Which role for macroprudential policies? Ecological Economics, 160, 25–37. https://doi.org/10.1016/j.ecolecon.2019.01.029

Feng, Y. (2022). Bank Green Credit Risk Assessment and Management by Mobile Computing and Machine Learning Neural Network under the Efficient Wireless Communication. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/3444317

Hassan, B. A., Rashid, T. A., Mirjalili, S. (2021). Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets. Data in Brief, 36. https://doi.org/10.1016/j.dib.2021.107044

Hassan, Q., Viktor, P., J. Al-Musawi, T., Mahmood Ali, B., Algburi, S., Alzoubi, H. M., Khudhair Al-Jiboory, A., Zuhair Sameen, A., Salman, H. M., & Jaszczur, M. (2024). The renewable energy role in the global energy Transformations. Renewable Energy Focus, 48. https://doi.org/10.1016/j.ref.2024.100545

Hoang Huong, G., Ta Anh, S., Le, L. T., Linh, B. D., & Vu Thi Minh, N. (2021). Supply chain disruption for crude oil due to the effect of Covid-19 pandemic: Evidence from Vietnam. International Journal of Logistics Systems and Management, 1(1), 1. https://doi.org/10.1504/ijlsm.2021.10044296

Kai, Z., Sharaf, M., Wei, S. Y., Shraah, A. Al, Le, L. T., Arvind Bedekar, D. A., Bani Ahmad, A. Y. A. (2024). Exploring the asymmetric relationship between natural resources, fintech, remittance and environmental pollution for BRICS nations: New insights from MMQR approach. Resources Policy, 90. https://doi.org/10.1016/j.resourpol.2024.104693

Lai, X., Yue, S., Chen, H. (2022). Can green credit increase firm value? Evidence from Chinese listed new energy companies. Environmental Science and Pollution Research, 29(13), 18702–18720. https://doi.org/10.1007/s11356-021-17038-9

Le, L. T., Lee, G., Park, K. S., Kim, H. (2020). Neural network-based fuel consumption estimation for container ships in Korea. Maritime Policy and Management, 47(5), 615–632. https://doi.org/10.1080/03088839.2020.1729437

Le, L. T., Xuan-Thi-Thu, T. (2024). Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood. Maritime Business Review, EarlyCite. https://doi.org/https://doi.org/10.1108/MABR-10-2023-0074

Le, T. L. (2025). Uncovering Import Document Fraud: Leveraging the Deep Learning Approach. Global Trade and Customs Journal, 20(1), 1–8. https://kluwerlawonline.com/journalarticle/Global+Trade+and+Customs+Journal/20.1 [pre-publication]/GTCJ2025002

Leng, C., Wei, S. Y., Al-Abyadh, M. H. A., Halteh, K., Bauetdinov, M., Le, L. T., & Alzoubi, H. M. (2024). An empirical assessment of the effect of natural resources and financial technologies on sustainable development in resource abundant developing countries: Evidence using MMQR estimation. Resources Policy, 89. https://doi.org/10.1016/j.resourpol.2023.104555

Li, X., Abed, A. M., Shaban, M., Le, L. T., Zhou, X., Abdullaev, S., Alhomayani, F. M., Yasser Elmasry, Mahariq, I., & Rahman, A. A. (2024). Artificial Intelligence Application for Assessment/Optimization of a Double-Flash Geothermal Scheme Tailored Combined Heat/Power Plant. Energy, 133594. https://doi.org/https://doi.org/10.1016/j.energy.2024.133594

Ma, X., Ma, W., Zhang, L., Shi, Y., Shang, Y., & Chen, H. (2021). The impact of green credit policy on energy efficient utilization in China. Environmental Science and Pollution Research, 28(37), 52514–52528. https://doi.org/10.1007/s11356-021-14405-4

Machado, M. R., & Karray, S. (2022). Assessing credit risk of commercial customers using hybrid machine learning algorithms. Expert Systems with Applications, 200. https://doi.org/10.1016/j.eswa.2022.116889

Mirza, N., Afzal, A., Umar, M., & Skare, M. (2023). The impact of green lending on banking performance: Evidence from SME credit portfolios in the BRIC. Economic Analysis and Policy, 77, 843–850. https://doi.org/10.1016/j.eap.2022.12.024

Moradi, S., & Mokhatab Rafiei, F. (2019). A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0121-9

Sadeghi, M., Naghedi, R., Behzadian, K., Shamshirgaran, A., Tabrizi, M. R., & Maknoon, R. (2022). Customisation of green buildings assessment tools based on climatic zoning and experts judgement using K-means clustering and fuzzy AHP. Building and Environment, 223. https://doi.org/10.1016/j.buildenv.2022.109473

Samal, U., & Kumar, A. (2024a). Empowering software reliability: Leveraging efficient fault detection and removal efficiency. Quality Engineering, 1–12. https://doi.org/https://doi.org/10.1080/08982112.2024.2358889

Samal, U., & Kumar, A. (2024b). Fault Removal Efficiency: A Key Driver in Software Reliability Growth Modeling. In Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-031-55048-5_7

Samal, U., & Kumar, A. (2024c). Incorporating human dynamics into software reliability analysis: learning, fatigue, and efficiency considerations. International Journal of System Assurance Engineering and Management. https://doi.org/https://doi.org/10.1007/s13198-024-02368-x

Samal, Umashankar, & Kumar, A. (2024a). A Neural Network Approach for Software Reliability Prediction. International Journal of Reliability, Quality and Safety Engineering, 31(3). https://doi.org/https://doi.org/10.1142/S0218539324500098

Samal, Umashankar, & Kumar, A. (2024b). Enhancing Software Reliability Forecasting Through a Hybrid ARIMA-ANN Model. Arabian Journal for Science and Engineering, 49(5), 7571–7584. https://doi.org/10.1007/s13369-023-08486-1

Su, T., Meng, L., Wang, K., & Wu, J. (2023). The role of green credit in carbon neutrality: Evidence from the breakthrough technological innovation of renewable energy firms. Environmental Impact Assessment Review, 101. https://doi.org/10.1016/j.eiar.2023.107135

Sun, C. (2022). The correlation between green finance and carbon emissions based on improved neural network. Neural Computing and Applications, 34(15), 12399–12413. https://doi.org/10.1007/s00521-021-06514-5

Tao, M., Silva, E., Sheng, M. S., Wen, L., & Qi, L. (2023). How financial clustering influences China’s green development: Mechanism investigation and empirical discussion. Journal of Environmental Management, 347. https://doi.org/10.1016/j.jenvman.2023.119081

Tian, C., Li, X., Xiao, L., & Zhu, B. (2022). Exploring the impact of green credit policy on green transformation of heavy polluting industries. Journal of Cleaner Production, 335. https://doi.org/10.1016/j.jclepro.2021.130257

Wang, M., Li, X., & Wang, S. (2021). Discovering research trends and opportunities of green finance and energy policy: A data-driven scientometric analysis. Energy Policy, 154. https://doi.org/10.1016/j.enpol.2021.112295

Yang, M. S., Chang-Chien, S. J., & Nataliani, Y. (2019). Unsupervised fuzzy model-based Gaussian clustering. Information Sciences, 481, 1–23. https://doi.org/10.1016/j.ins.2018.12.059

Zhao, L. T., Li, F. R., & Wang, D. S. (2023). How to achieve the common developments of green finance and clean energy in China? Evidence from coupling coordination evaluation. Ecological Indicators, 155. https://doi.org/10.1016/j.ecolind.2023.111011

Zhao, X., Zeng, S., Ke, X., & Jiang, S. (2023). The impact of green credit on energy efficiency from a green innovation perspective: Empirical evidence from China based on a spatial Durbin model. Energy Strategy Reviews, 50. https://doi.org/10.1016/j.esr.2023.101211

Zhou, X. Y., Caldecott, B., Hoepner, A. G. F., & Wang, Y. (2022). Bank green lending and credit risk: an empirical analysis of China’s Green Credit Policy. Business Strategy and the Environment, 31(4), 1623–1640. https://doi.org/10.1002/bse.2973