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A Hybrid Fuzzy AHP–TOPSIS Approach for Ranking Sustainable Green Technology Innovations

Year 2025, Volume: 21 Issue: 2, 62 - 83, 21.12.2025

Abstract

Today, environmental sustainability has become a strategic necessity in the face of global challenges such as climate change, depletion of natural resources, and rising carbon emissions. In this context, green technology innovations play a critical role in developing environmentally friendly production processes, enhancing energy efficiency, and supporting sustainable development goals. However, accurately assessing the performance of these technologies requires a multidimensional and uncertainty-based decision-making framework.
In this study, a model based on a hybrid Fuzzy AHP–Fuzzy TOPSIS approach was developed to evaluate the performance of green technology innovations. In the first stage, ten main criteria were identified and pairwise compared by three experts, and the criterion weights were calculated using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). In the second stage, these weights were integrated into the Fuzzy TOPSIS method to rank nine alternative green technology innovations according to their closeness coefficients.
The findings reveal that, when environmental, technological, and economic dimensions are jointly considered, green hydrogen, carbon capture, and energy-efficient coding technologies achieve the highest performance values. The proposed hybrid model provides a reliable and flexible methodological framework for guiding sustainable technology investments and designing green transformation policies.

Thanks

This study was supported by TÜBİTAK under the 2209-A Research Project Support Programme for Undergraduate Students. The authors would like to thank TÜBİTAK for its support

References

  • [1] Chen, Y. S., Lai, S. B., & Wen, C. T. (2006). The influence of green innovation performance on corporate advantage in Taiwan. Journal of Business Ethics, 67(4), 331–339.
  • [2] OECD. (2023). Green Technology and Innovation: OECD Insights.
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  • [4] Kemp, R., & Pearson, P. (2007). Final report MEI project about measuring eco-innovation. UM Merit, Maastricht, 10(2), 1-120.
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  • [6] Tariq, G., Sun, H., Ali, I., Pasha, A. A., Khan, M. S., Rahman, M. M., ... & Shah, Q. (2022). Influence of green technology, green energy consumption, energy efficiency, trade, economic development and FDI on climate change in South Asia. Scientific reports, 12(1), 16376.
  • [7] Li, C., Solangi, Y. A., & Ali, S. (2023). Evaluating the factors of green finance to achieve carbon peak and carbon neutrality targets in China: a delphi and fuzzy AHP approach. Sustainability, 15(3), 2721.
  • [8] Çelik, M. T., & Yıldız, A. (2024). Evaluation of Factors Affecting Innovation Productivity by Pythagorean Fuzzy AHP Method. Verimlilik Dergisi, 89-106.
  • [9] 15 ‘Green’ Tech Innovations And Practices That Can Help Protect The Environment. https://www.forbes.com/councils/forbestechcouncil/2025/10/10/cloud-observability-challenges-at-scale-and-how-to-solve-them/
  • [10] Staffell, I., Scamman, D., Abad, A. V., Balcombe, P., Dodds, P. E., Ekins, P., ... & Ward, K. R. (2019). The role of hydrogen and fuel cells in the global energy system. Energy & Environmental Science, 12(2), 463-491.
  • [11] Zainal, B. S., Ker, P. J., Mohamed, H., Ong, H. C., Fattah, I. M. R., Rahman, S. A., ... & Mahlia, T. I. (2024). Recent advancement and assessment of green hydrogen production technologies. Renewable and Sustainable Energy Reviews, 189, 113941.
  • [12] Zhang, M., Wang, S., Wu, J., Wei, W., Zhang, D., Zhou, Z., ... & Yang, Y. (2025). Toward Energy-Efficient Spike-Based Deep Reinforcement Learning With Temporal Coding. IEEE Computational Intelligence Magazine, 20(2), 45-57.
  • [13] Wang, Y., Wu, D., & Li, H. (2022). Efficiency measurement and productivity progress of regional green technology innovation in China: a comprehensive analytical framework. Technology Analysis & Strategic Management, 34(12), 1432-1448.
  • [14] Elshehabi, T., & Alfehaid, M. (2025). Sustainable geothermal energy: a review of challenges and opportunities in deep wells and shallow heat pumps for transitioning professionals. Energies, 18(4), 811.
  • [15] Gebler, M., Schoot Uiterkamp, A. J. M., & Visser, C. (2014). A global sustainability perspective on 3D printing technologies. Energy Policy, 74, 158–167. [16] Zhou, H., Cheng, X., Jiang, X., Zheng, G., Zhang, J., Li, Y., ... & Lv, F. (2022). Green manufacturing-oriented polyetheretherketone additive manufacturing and dry milling post-processing process research. Processes, 10(12), 2561.
  • [17] Mulligan, C., Morsfield, S., & Cheikosman, E. (2024). Blockchain for sustainability: A systematic literature review for policy impact. Telecommunications Policy, 48(2), 102676.
  • [18] Liu, Y., Fang, Z., Cheung, M. H., Cai, W., & Huang, J. (2023). Mechanism design for blockchain storage sustainability. IEEE Communications Magazine, 61(8), 102-107.
  • [19] Chen, L., Zhang, Y., Wang, L., Ruan, S., Chen, J., Li, H., ... & Tsang, D. C. (2022). Biochar-augmented carbon-negative concrete. Chemical Engineering Journal, 431, 133946.
  • [20] Dang, C. N., Wipulanusat, W., Nuaklong, P., & Witchayangkoon, B. (2025). Assessing green innovation practices in construction firms: a developing-country perspective. Engineering, Construction and Architectural Management, 32(5), 3104-3131.
  • [21] Leung, D. Y., Caramanna, G., & Maroto-Valer, M. M. (2014). An overview of current status of carbon dioxide capture and storage technologies. Renewable and sustainable energy reviews, 39, 426-443.
  • [22] Mclaughlin, H., Littlefield, A. A., Menefee, M., Kinzer, A., Hull, T., Sovacool, B. K., & Griffiths, S. (2023). Carbon capture utilization and storage in review: Sociotechnical implications for a carbon reliant world. Renewable and Sustainable Energy Reviews, 177, 113215.
  • [23] Varjani, S., Shahbeig, H., Popat, K., Patel, Z., Vyas, S., Shah, A. V., ... & Tabatabaei, M. (2022). Sustainable management of municipal solid waste through waste-to-energy technologies. Bioresource technology, 355, 127247.
  • [24] Hsu, H. W., Binyet, E., Nugroho, R. A. A., Wang, W. C., Srinophakun, P., Chein, R. Y., ... & Laemthong, T. (2024). Toward sustainability of Waste-to-Energy: An overview. Energy Conversion and Management, 321, 119063.
  • [26] Azadi, H., Moghaddam, S. M., Burkart, S., Mahmoudi, H., Van Passel, S., Kurban, A., & Lopez-Carr, D. (2021). Rethinking resilient agriculture: From climate-smart agriculture to vulnerable-smart agriculture. Journal of Cleaner Production, 319, 128602.
  • [25] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80.
  • [28] Wang, Y., & Yang, Y. (2021). Analyzing the green innovation practices based on sustainability performance indicators: a Chinese manufacturing industry case. Environmental Science and Pollution Research, 28(1), 1181-1203.
  • [29] Fercoq, A., Lamouri, S., & Carbone, V. (2016). Lean/Green integration focused on waste reduction techniques. Journal of Cleaner production, 137, 567-578.
  • [30] Mala, D., & Bencikova, D. (2018). Innovations of a green product. Ekonomicko-manazerske spektrum, 12(1), 64-74.
  • [31] Guo, M., Nowakowska-Grunt, J., Gorbanyov, V., & Egorova, M. (2020). Green technology and sustainable development: Assessment and green growth frameworks. Sustainability, 12(16), 6571.
  • [32] Stringfellow, W. T., & Dobson, P. F. (2021). Technology for the recovery of lithium from geothermal brines. Energies, 14(20), 6805.
  • [33] Ali, A., Jiang, X., & Ali, A. (2023). Social ties, absorptive capacity, and the adoption of green innovation: a social capital perspective. International Journal of Manpower, 44(2), 214-230.
  • [34] Jie, H. (2021). Overall optimization model of efficiency and performance of green technology innovation. Sustainable Cvomputing: Informatics and Systems, 30, 100501.
  • [35] Taherdoost, Hamed, and Mitra Madanchian. "Multi-criteria decision making (MCDM) methods and concepts." Encyclopedia 3.1 (2023): 77-87.
  • [36] Thakkar, J. J. (2021). Multi-criteria decision making, 336, 1-365. Singapore: Springer.
  • [37] Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) Methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25-48.
  • [38] Dağdeviren, M., Yavuz, S., & Kılınç, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert systems with applications, 36(4), 8143-8151.
  • [39] Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process (Vol. 282). Berlin, Germany: Springer Science+ Business Media, LLC.
  • [40] Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247.
  • [41] Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303–318.
  • [42] Yıldız, A., & Demir, Y. (2019). Bulanık TOPSIS yöntemiyle Türkiye’nin yerli otomobili için en uygun fabrika yerinin seçimi. Business & Management Studies: An International Journal, 7(4), 1427-1445.
  • [43] Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.

Sürdürülebilir Yeşil Teknoloji İnovasyonlarının Sıralanmasında Hibrit Bulanık AHP–TOPSIS Yaklaşımı

Year 2025, Volume: 21 Issue: 2, 62 - 83, 21.12.2025

Abstract

Günümüzde çevresel sürdürülebilirlik, iklim değişikliği, doğal kaynakların tükenmesi ve artan karbon emisyonları gibi küresel sorunlar karşısında stratejik bir gereklilik haline gelmiştir. Bu kapsamda yeşil teknoloji inovasyonları, çevre dostu üretim süreçlerinin geliştirilmesi, enerji verimliliğinin artırılması ve sürdürülebilir kalkınma hedeflerinin desteklenmesinde kritik bir rol üstlenmektedir. Ancak bu teknolojilerin performansının doğru biçimde ölçülmesi, çok boyutlu ve belirsizlik içeren bir karar ortamını gerektirmektedir.
Bu çalışmada, yeşil teknoloji inovasyonlarının performansını değerlendirmek amacıyla Hibrit Bulanık AHP–Bulanık TOPSIS yaklaşımına dayalı bir model geliştirilmiştir. İlk aşamada, belirlenen on temel kriter üç uzman tarafından ikili karşılaştırmalarla değerlendirilmiş ve kriter ağırlıkları bulanık AHP yöntemi kullanılarak hesaplanmıştır. İkinci aşamada ise, bu ağırlıklar bulanık TOPSIS yöntemi kapsamında kullanılarak dokuz alternatif yeşil teknoloji inovasyonları yakınlık katsayılarına göre sıralanmıştır.
Elde edilen bulgular, çevresel, teknolojik ve ekonomik boyutların birlikte değerlendirildiği analizlerde yeşil hidrojen, karbon yakalama ve enerji verimli kodlama teknolojilerinin en yüksek performans değerlerine ulaştığını göstermektedir. Geliştirilen hibrit model, sürdürülebilir teknoloji yatırımlarının yönlendirilmesi ve yeşil dönüşüm politikalarının tasarımında karar vericilere güvenilir ve esnek bir yöntemsel çerçeve sunmaktadır.

Thanks

Bu çalışma, TÜBİTAK 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında desteklenmiştir. Yazarlar, desteklerinden dolayı TÜBİTAK’a teşekkür eder.

References

  • [1] Chen, Y. S., Lai, S. B., & Wen, C. T. (2006). The influence of green innovation performance on corporate advantage in Taiwan. Journal of Business Ethics, 67(4), 331–339.
  • [2] OECD. (2023). Green Technology and Innovation: OECD Insights.
  • [3] Zhang, W., Zhang, S., Chen, F., Wang, Y., & Zhang, Y. (2023). Does Chinese companies’ OFDI enhance their own green technology innovation? Finance Research Letters, 104113.
  • [4] Kemp, R., & Pearson, P. (2007). Final report MEI project about measuring eco-innovation. UM Merit, Maastricht, 10(2), 1-120.
  • [5] Xu, Q., Li, X., Dong, Y., & Guo, F. (2025). Digitization and green innovation: how does digitization affect enterprises’ green technology innovation?. Journal of Environmental Planning and Management, 68(6), 1282-1311.
  • [6] Tariq, G., Sun, H., Ali, I., Pasha, A. A., Khan, M. S., Rahman, M. M., ... & Shah, Q. (2022). Influence of green technology, green energy consumption, energy efficiency, trade, economic development and FDI on climate change in South Asia. Scientific reports, 12(1), 16376.
  • [7] Li, C., Solangi, Y. A., & Ali, S. (2023). Evaluating the factors of green finance to achieve carbon peak and carbon neutrality targets in China: a delphi and fuzzy AHP approach. Sustainability, 15(3), 2721.
  • [8] Çelik, M. T., & Yıldız, A. (2024). Evaluation of Factors Affecting Innovation Productivity by Pythagorean Fuzzy AHP Method. Verimlilik Dergisi, 89-106.
  • [9] 15 ‘Green’ Tech Innovations And Practices That Can Help Protect The Environment. https://www.forbes.com/councils/forbestechcouncil/2025/10/10/cloud-observability-challenges-at-scale-and-how-to-solve-them/
  • [10] Staffell, I., Scamman, D., Abad, A. V., Balcombe, P., Dodds, P. E., Ekins, P., ... & Ward, K. R. (2019). The role of hydrogen and fuel cells in the global energy system. Energy & Environmental Science, 12(2), 463-491.
  • [11] Zainal, B. S., Ker, P. J., Mohamed, H., Ong, H. C., Fattah, I. M. R., Rahman, S. A., ... & Mahlia, T. I. (2024). Recent advancement and assessment of green hydrogen production technologies. Renewable and Sustainable Energy Reviews, 189, 113941.
  • [12] Zhang, M., Wang, S., Wu, J., Wei, W., Zhang, D., Zhou, Z., ... & Yang, Y. (2025). Toward Energy-Efficient Spike-Based Deep Reinforcement Learning With Temporal Coding. IEEE Computational Intelligence Magazine, 20(2), 45-57.
  • [13] Wang, Y., Wu, D., & Li, H. (2022). Efficiency measurement and productivity progress of regional green technology innovation in China: a comprehensive analytical framework. Technology Analysis & Strategic Management, 34(12), 1432-1448.
  • [14] Elshehabi, T., & Alfehaid, M. (2025). Sustainable geothermal energy: a review of challenges and opportunities in deep wells and shallow heat pumps for transitioning professionals. Energies, 18(4), 811.
  • [15] Gebler, M., Schoot Uiterkamp, A. J. M., & Visser, C. (2014). A global sustainability perspective on 3D printing technologies. Energy Policy, 74, 158–167. [16] Zhou, H., Cheng, X., Jiang, X., Zheng, G., Zhang, J., Li, Y., ... & Lv, F. (2022). Green manufacturing-oriented polyetheretherketone additive manufacturing and dry milling post-processing process research. Processes, 10(12), 2561.
  • [17] Mulligan, C., Morsfield, S., & Cheikosman, E. (2024). Blockchain for sustainability: A systematic literature review for policy impact. Telecommunications Policy, 48(2), 102676.
  • [18] Liu, Y., Fang, Z., Cheung, M. H., Cai, W., & Huang, J. (2023). Mechanism design for blockchain storage sustainability. IEEE Communications Magazine, 61(8), 102-107.
  • [19] Chen, L., Zhang, Y., Wang, L., Ruan, S., Chen, J., Li, H., ... & Tsang, D. C. (2022). Biochar-augmented carbon-negative concrete. Chemical Engineering Journal, 431, 133946.
  • [20] Dang, C. N., Wipulanusat, W., Nuaklong, P., & Witchayangkoon, B. (2025). Assessing green innovation practices in construction firms: a developing-country perspective. Engineering, Construction and Architectural Management, 32(5), 3104-3131.
  • [21] Leung, D. Y., Caramanna, G., & Maroto-Valer, M. M. (2014). An overview of current status of carbon dioxide capture and storage technologies. Renewable and sustainable energy reviews, 39, 426-443.
  • [22] Mclaughlin, H., Littlefield, A. A., Menefee, M., Kinzer, A., Hull, T., Sovacool, B. K., & Griffiths, S. (2023). Carbon capture utilization and storage in review: Sociotechnical implications for a carbon reliant world. Renewable and Sustainable Energy Reviews, 177, 113215.
  • [23] Varjani, S., Shahbeig, H., Popat, K., Patel, Z., Vyas, S., Shah, A. V., ... & Tabatabaei, M. (2022). Sustainable management of municipal solid waste through waste-to-energy technologies. Bioresource technology, 355, 127247.
  • [24] Hsu, H. W., Binyet, E., Nugroho, R. A. A., Wang, W. C., Srinophakun, P., Chein, R. Y., ... & Laemthong, T. (2024). Toward sustainability of Waste-to-Energy: An overview. Energy Conversion and Management, 321, 119063.
  • [26] Azadi, H., Moghaddam, S. M., Burkart, S., Mahmoudi, H., Van Passel, S., Kurban, A., & Lopez-Carr, D. (2021). Rethinking resilient agriculture: From climate-smart agriculture to vulnerable-smart agriculture. Journal of Cleaner Production, 319, 128602.
  • [25] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80.
  • [28] Wang, Y., & Yang, Y. (2021). Analyzing the green innovation practices based on sustainability performance indicators: a Chinese manufacturing industry case. Environmental Science and Pollution Research, 28(1), 1181-1203.
  • [29] Fercoq, A., Lamouri, S., & Carbone, V. (2016). Lean/Green integration focused on waste reduction techniques. Journal of Cleaner production, 137, 567-578.
  • [30] Mala, D., & Bencikova, D. (2018). Innovations of a green product. Ekonomicko-manazerske spektrum, 12(1), 64-74.
  • [31] Guo, M., Nowakowska-Grunt, J., Gorbanyov, V., & Egorova, M. (2020). Green technology and sustainable development: Assessment and green growth frameworks. Sustainability, 12(16), 6571.
  • [32] Stringfellow, W. T., & Dobson, P. F. (2021). Technology for the recovery of lithium from geothermal brines. Energies, 14(20), 6805.
  • [33] Ali, A., Jiang, X., & Ali, A. (2023). Social ties, absorptive capacity, and the adoption of green innovation: a social capital perspective. International Journal of Manpower, 44(2), 214-230.
  • [34] Jie, H. (2021). Overall optimization model of efficiency and performance of green technology innovation. Sustainable Cvomputing: Informatics and Systems, 30, 100501.
  • [35] Taherdoost, Hamed, and Mitra Madanchian. "Multi-criteria decision making (MCDM) methods and concepts." Encyclopedia 3.1 (2023): 77-87.
  • [36] Thakkar, J. J. (2021). Multi-criteria decision making, 336, 1-365. Singapore: Springer.
  • [37] Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) Methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25-48.
  • [38] Dağdeviren, M., Yavuz, S., & Kılınç, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert systems with applications, 36(4), 8143-8151.
  • [39] Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process (Vol. 282). Berlin, Germany: Springer Science+ Business Media, LLC.
  • [40] Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247.
  • [41] Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303–318.
  • [42] Yıldız, A., & Demir, Y. (2019). Bulanık TOPSIS yöntemiyle Türkiye’nin yerli otomobili için en uygun fabrika yerinin seçimi. Business & Management Studies: An International Journal, 7(4), 1427-1445.
  • [43] Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Theory of Computation (Other)
Journal Section Research Article
Authors

Kadriye Doğanay This is me 0009-0004-8641-8711

Aytaç Yildiz 0000-0002-0729-633X

Submission Date October 13, 2025
Acceptance Date December 1, 2025
Early Pub Date December 16, 2025
Publication Date December 21, 2025
Published in Issue Year 2025 Volume: 21 Issue: 2

Cite

APA Doğanay, K., & Yildiz, A. (2025). Sürdürülebilir Yeşil Teknoloji İnovasyonlarının Sıralanmasında Hibrit Bulanık AHP–TOPSIS Yaklaşımı. Electronic Letters on Science and Engineering, 21(2), 62-83.