A Hybrid Regression–Fuzzy Framework for Climate Impact Assessment Using Primary energy consumption and CO₂ Emissions

Asido Saragih; Lambok Franciskus Simangunsong

doi:10.33541/edumatsains.v11i2.8769

Authors

Asido Saragih Institut Teknologi Del
Lambok Franciskus Simangunsong

DOI:

https://doi.org/10.33541/edumatsains.v11i2.8769

Keywords:

primary energy consumption, CO₂ emissions, fuzzy Mamdani classification, climate impact, linear regression analysis

Abstract

This research aims to establish a relationship between primary energy consumption and CO₂ emissions and classify countries by their potential climate impact applying hybrid regression and Mamdani fuzzy logic. Data of 178 countries were analyzed using cross-sectional data of 2022, where primary energy consumption and CO₂ emissions were measured per capita. The system involved two inputs with three triangular and shoulder-shaped membership functions (Low, Moderate, High) and nine fuzzy rules. The results indicated that 96 (53.9%) countries belonged to High, 27 (15.2%) to Moderate, and 55 (30.9%) to Low potential climate impact categories. A bivariate linear regression model was estimated to analyze the relationship between primary energy consumption and CO₂ emissions by setting the former as an independent variable and the latter as a dependent variable. The results found a very weak positive association r = 0.0721 and p = 0.3385. The regression results demonstrated that 0.52% of the changes in CO₂ emissions per capita could be predicted by primary energy consumption per capita. The results suggest that potential climate impact is influenced by various factors, including energy sources, economic activities, industrialization, and energy efficiency. Therefore, the analysis employed a hybrid approach which combined statistical and fuzzy logic methodologies to address the climate change problem.

References

Ali, J., Wu, X., Kakar, S. K., Yani, S., Jahanger, A., & Rehman, M. (2026). Exploring the Relationship Between Industrialization, Primary energy consumption, and Carbon Emissions Across 35 Asian Countries: A Panel Quantile ARDL Approach. Natural Resources Forum, 50(3), 1104–1117. https://doi.org/10.1111/1477-8947.70014

Anbari Moghadam, M., Bagherpour, M., & Ghannadpour, S. F. (2024). Sustainability assessment in construction projects: a sustainable earned value management model under uncertain and unreliable conditions. Environment Systems and Decisions, 44(1), 45–68. https://doi.org/10.1007/s10669-023-09913-2

Bartan, A., Kucukali, S., Ar, I., & Baris, K. (2023). An integrated environmental risk assessment framework for coal‐fired power plants: A fuzzy logic approach. Risk Analysis, 43(3), 530–547. https://doi.org/10.1111/risa.13908

Barzegar, Y., Gorelova, I., Bellini, F., & D’Ascenzo, F. (2023). Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy). International Journal of Environmental Research and Public Health, 20(15), 6522. https://doi.org/10.3390/ijerph20156522

Carrasco-Garrido, C., Moreno-Cabezali, B. M., & Martínez Raya, A. (2025). New perspectives on university quality assessment: A Mamdani Fuzzy Inference System approach. PLOS One, 20(5), e0321013. https://doi.org/10.1371/journal.pone.0321013

Chanapathi, T., Thatikonda, S., Pandey, V. P., & Shrestha, S. (2019). Fuzzy-based approach for evaluating groundwater sustainability of Asian cities. Sustainable Cities and Society, 44, 321–331. https://doi.org/10.1016/j.scs.2018.09.027

Hussain, A., & Hussain, I. (2023). Sustainability assessment for construction projects: A cost-sustainability tradeoff approach. Journal of Cleaner Production, 423, 138727. https://doi.org/10.1016/j.jclepro.2023.138727

Jie, W., & Rabnawaz, K. (2024). Renewable energy and CO2 emissions in developing and developed nations: a panel estimate approach. Frontiers in Environmental Science, 12. https://doi.org/10.3389/fenvs.2024.1405001

Jiskani, I. M., Moreno-Cabezali, B. M., Ur Rehman, A., Fernandez-Crehuet, J. M., & Uddin, S. (2022). Implications to secure mineral supply for clean energy technologies for developing countries: A fuzzy based risk analysis for mining projects. Journal of Cleaner Production, 358, 132055. https://doi.org/10.1016/j.jclepro.2022.132055

Karakas, G., Kocaman, S., & Gokceoglu, C. (2023). A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye. International Journal of Disaster Risk Science, 14(2), 326–341. https://doi.org/10.1007/s13753-023-00477-y

Lappas, P. Z., & Symeonidis, G. (2025). Introducing AI in Pension Planning: A Comparative Study of Deep Learning and Mamdani Fuzzy Inference Systems for Estimating Replacement Rates. Mathematics, 13(23), 3737. https://doi.org/10.3390/math13233737

Mahi, H. M., Nasrin, S., & Zaman, A. S. (2025). Construction of fuzzy logic model for estimating environmental improvement index in industries of Bangladesh. Discover Sustainability, 6(1), 1165. https://doi.org/10.1007/s43621-025-02020-z

Muniz, R. N., de Sá, J. A. S., da Rocha, B. R. P., Buratto, W. G., Nied, A., & da Costa, C. T. (2024). A new brazilian amazon energy sustainability index based on fuzzy systems. Journal of Intelligent & Fuzzy Systems, 46(1), 2433–2446. https://doi.org/10.3233/JIFS-235750

Ozturk, I., Al-Mulali, U., & Saboori, B. (2016). Investigating the environmental Kuznets curve hypothesis: the role of tourism and ecological footprint. Environmental Science and Pollution Research, 23(2), 1916–1928. https://doi.org/10.1007/s11356-015-5447-x

Pedrycz, W. (1994). Why triangular membership functions? Fuzzy Sets and Systems, 64(1), 21–30. https://doi.org/10.1016/0165-0114(94)90003-5

Phillis, Y. A., Grigoroudis, E., & Kouikoglou, V. S. (2011). Sustainability ranking and improvement of countries. Ecological Economics, 70(3), 542–553. https://doi.org/10.1016/j.ecolecon.2010.09.037

Puntoon, W., Tarkhamtham, P., & Tansuchat, R. (2022). The impacts of economic growth, industrial production, and energy consumption on CO2 emissions: A case study of leading CO2 emitting countries. Energy Reports, 8, 414–419. https://doi.org/10.1016/j.egyr.2022.10.219

Saragih, A., Santoso, J., Muliyana, A., Pardede, W. J., Hutapea, O. J., & Siagian, W. R. (2026). Fuzzy Based Evaluation of Digital Connectivity Across Countries Using Mamdani Fuzzy Inference System. Brillo Journal, 5(02), 44–58.

Savran, E., Karpat, E., & Karpat, F. (2024). Sustainable Energy Management in Electric Vehicles Through a Fuzzy Logic-Based Strategy. Sustainability, 17(1), 89. https://doi.org/10.3390/su17010089

Şener Fidan, F. (2025). Fuzzy Logic–Enhanced PMC Index for Assessing Policies for Decarbonization in Higher Education: Evidence from a Public University. Sustainability, 17(19), 8966. https://doi.org/10.3390/su17198966

Shahbaz, M., Salah Uddin, G., Ur Rehman, I., & Imran, K. (2014). Industrialization, primary energy consumption and CO2 emissions in Bangladesh. Renewable and Sustainable Energy Reviews, 31, 575–586. https://doi.org/10.1016/j.rser.2013.12.028

Tan, Y., Shuai, C., Jiao, L., & Shen, L. (2017). An adaptive neuro-fuzzy inference system (ANFIS) approach for measuring country sustainability performance. Environmental Impact Assessment Review, 65, 29–40. https://doi.org/10.1016/j.eiar.2017.04.004

Tekin, A. T. (2026). Fuzzy Rule-Based Explanations for Tabular Black-Box Classifiers: A Comprehensive Empirical Framework with Prediction-Boundary-Aware Partitioning and Rule-Level Uncertainty Indication. Applied Sciences, 16(12), 5896. https://doi.org/10.3390/app16125896

Wang, Q., & Yang, X. (2020). Investigating the sustainability of renewable energy – An empirical analysis of European Union countries using a hybrid of projection pursuit fuzzy clustering model and accelerated genetic algorithm based on real coding. Journal of Cleaner Production, 268, 121940. https://doi.org/10.1016/j.jclepro.2020.121940

Wang, T., Gault, R., & Greer, D. (2026). Data Informed Initialization of Fuzzy Membership Functions. International Journal of Fuzzy Systems, 28(2), 597–608. https://doi.org/10.1007/s40815-024-01971-1

Yesbolova, A. Y., Abdulova, T., Nurgabylov, M., Yessenbekova, S., Turalina, S., Baytaeva, G., & Myrzabekkyzy, K. (2024). Analysis of the Effect of Renewable Energy Consumption and Industrial Production on CO2 Emissions in Turkic Republics by Panel Data Analysis Method. International Journal of Energy Economics and Policy, 14(1), 480–487. https://doi.org/10.32479/ijeep.15261

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X