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A multi-criteria decision support model for adopting energy efficiency technologies in the iron and steel industry

Author

Listed:
  • Hongtao Ren

    (East China University of Science and Technology)

  • Wenji Zhou

    (Renmin University of China)

  • Marek Makowski

    (International Institute for Applied Systems Analysis
    Polish Academy of Sciences)

  • Shaohui Zhang

    (International Institute for Applied Systems Analysis
    Beihang University)

  • Yadong Yu

    (East China University of Science and Technology)

  • Tieju Ma

    (East China University of Science and Technology
    International Institute for Applied Systems Analysis)

Abstract

Promoting energy efficiency in iron and steel production provides opportunities for mitigating environmental impacts from this energy-intensive industry. Energy efficiency technologies differ in investment costs, fuel-saving potentials, and environmental performance. Hence the decision-making of the adoption strategy needs to prioritize technological combinations concerning these multi-dimensional objectives. To address this problem, this study proposes a hybrid multi-criteria decision-support model for adopting energy efficiency technologies in the iron and steel industry. The modeling framework integrates a linear programming model that determines the optimal technology adoption rates based on the techno-economic, energy, and environmental performance details and an interactive multi-criteria model analysis tool for diverse modeling environments. A real case study was performed in which a total number of 56 energy efficiency technologies were investigated against various criteria concerning economics, energy, and environmental performances. The results examine the tradeoffs and synergies were examined with regard to seven criteria. A balanced solution shows that a total investment of 13.4 billion USD could save 2.51 Exajoule fuel consumption, cut 67.4 million tons (Mton) CO2 emissions, and reduce air pollution of 1.5 Mton SO2, 1.41 Mton NOx, and 0.86 Mton PM, respectively. The case study demonstrates the effectiveness and applicability of the proposed multi-criteria decision-making support framework.

Suggested Citation

  • Hongtao Ren & Wenji Zhou & Marek Makowski & Shaohui Zhang & Yadong Yu & Tieju Ma, 2023. "A multi-criteria decision support model for adopting energy efficiency technologies in the iron and steel industry," Annals of Operations Research, Springer, vol. 325(2), pages 1111-1132, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:2:d:10.1007_s10479-022-04548-z
    DOI: 10.1007/s10479-022-04548-z
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    1. Riccardi, R. & Bonenti, F. & Allevi, E. & Avanzi, C. & Gnudi, A., 2015. "The steel industry: A mathematical model under environmental regulations," European Journal of Operational Research, Elsevier, vol. 242(3), pages 1017-1027.
    2. Granat, Janusz & Makowski, Marek, 2000. "Interactive specification and analysis of aspiration-based preferences," European Journal of Operational Research, Elsevier, vol. 122(2), pages 469-485, April.
    3. Saraswat, S.K. & Digalwar, Abhijeet K., 2021. "Evaluation of energy alternatives for sustainable development of energy sector in India: An integrated Shannon’s entropy fuzzy multi-criteria decision approach," Renewable Energy, Elsevier, vol. 171(C), pages 58-74.
    4. Haoqi Qian & Shaodan Xu & Jing Cao & Feizhou Ren & Wendong Wei & Jing Meng & Libo Wu, 2021. "Air pollution reduction and climate co-benefits in China’s industries," Nature Sustainability, Nature, vol. 4(5), pages 417-425, May.
    5. Vishnupriyan, J. & Manoharan, P.S., 2018. "Multi-criteria decision analysis for renewable energy integration: A southern India focus," Renewable Energy, Elsevier, vol. 121(C), pages 474-488.
    6. Ghenai, Chaouki & Albawab, Mona & Bettayeb, Maamar, 2020. "Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method," Renewable Energy, Elsevier, vol. 146(C), pages 580-597.
    7. Cinzia Colapinto & Raja Jayaraman & Fouad Ben Abdelaziz & Davide La Torre, 2020. "Environmental sustainability and multifaceted development: multi-criteria decision models with applications," Annals of Operations Research, Springer, vol. 293(2), pages 405-432, October.
    8. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    9. Mukhamet, Tileuzhan & Kobeyev, Sultan & Nadeem, Abid & Memon, Shazim Ali, 2021. "Ranking PCMs for building façade applications using multi-criteria decision-making tools combined with energy simulations," Energy, Elsevier, vol. 215(PB).
    10. Ana Garcia-Bernabeu & Antonio Benito & Mila Bravo & David Pla-Santamaria, 2016. "Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain," Annals of Operations Research, Springer, vol. 245(1), pages 163-175, October.
    11. An, Runying & Yu, Biying & Li, Ru & Wei, Yi-Ming, 2018. "Potential of energy savings and CO2 emission reduction in China’s iron and steel industry," Applied Energy, Elsevier, vol. 226(C), pages 862-880.
    12. Ren, Lei & Zhou, Sheng & Peng, Tianduo & Ou, Xunmin, 2021. "A review of CO2 emissions reduction technologies and low-carbon development in the iron and steel industry focusing on China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    13. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    14. Marinakis, Vangelis & Doukas, Haris & Xidonas, Panos & Zopounidis, Constantin, 2017. "Multicriteria decision support in local energy planning: An evaluation of alternative scenarios for the Sustainable Energy Action Plan," Omega, Elsevier, vol. 69(C), pages 1-16.
    15. Xu, Ye & Li, Ye & Zheng, Lijun & Cui, Liang & Li, Sha & Li, Wei & Cai, Yanpeng, 2020. "Site selection of wind farms using GIS and multi-criteria decision making method in Wafangdian, China," Energy, Elsevier, vol. 207(C).
    16. Parkinson, Simon C. & Makowski, Marek & Krey, Volker & Sedraoui, Khaled & Almasoud, Abdulrahman H. & Djilali, Ned, 2018. "A multi-criteria model analysis framework for assessing integrated water-energy system transformation pathways," Applied Energy, Elsevier, vol. 210(C), pages 477-486.
    17. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    18. Zhang, Shaohui & Worrell, Ernst & Crijns-Graus, Wina & Wagner, Fabian & Cofala, Janusz, 2014. "Co-benefits of energy efficiency improvement and air pollution abatement in the Chinese iron and steel industry," Energy, Elsevier, vol. 78(C), pages 333-345.
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