A Multi-Objective Optimization Framework for Low-Carbon Index Construction and Application in Green Finance

[1] Wang, W., Huang, H., Peng, X., Wang, Z., & Zeng, Y. (2024). Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance. Journal of Environmental Management, 360, 121225. https://doi.org/10.1016/j.jenvman.2024.121225 Wang W. Huang H. Peng X. Wang Z. Zeng Y. ( 2024 ). Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance . Journal of Environmental Management , 360 , 121225 . https://doi.org/10.1016/j.jenvman.2024.121225 Search in Google Scholar

[2] Lannelongue, L., Grealey, J., & Inouye, M. (2021). Green algorithms: quantifying the carbon footprint of computation. Advanced Science, 8(12), 2100707. https://doi.org/10.1002/advs.202100707 Lannelongue L. Grealey J. Inouye M. ( 2021 ). Green algorithms: quantifying the carbon footprint of computation . Advanced Science , 8 ( 12 ), 2100707 . https://doi.org/10.1002/advs.202100707 Search in Google Scholar

[3] He, Y., Liu, X., Sun, E., & Wu, D. (2024). Balancing economic growth and environmental conservation through the optimization of rail transit routes for sustainable development. Scientific Reports, 14(1), 31926. https://doi.org/10.1038/s41598-024-83462-9 He Y. Liu X. Sun E. Wu D. ( 2024 ). Balancing economic growth and environmental conservation through the optimization of rail transit routes for sustainable development . Scientific Reports , 14 ( 1 ), 31926 . https://doi.org/10.1038/s41598-024-83462-9 Search in Google Scholar

[4] Johnstone, I. M., & Titterington, D. M. (2009). Statistical challenges of high-dimensional data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1906), 4237-4253. https://doi.org/10.1098/rsta.2009.0159 Johnstone I. M. Titterington D. M. ( 2009 ). Statistical challenges of high-dimensional data . Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 367 ( 1906 ), 4237 - 4253 . https://doi.org/10.1098/rsta.2009.0159 Search in Google Scholar

[5] Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G. (2022). Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives. Energy and AI, 10, 100195. https://doi.org/10.1016/j.egyai.2022.100195 Liu Z. Sun Y. Xing C. Liu J. He Y. Zhou Y. Zhang G. ( 2022 ). Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives . Energy and AI , 10 , 100195 . https://doi.org/10.1016/j.egyai.2022.100195 Search in Google Scholar

[6] Tan, R. R., Aviso, K. B., & Ng, D. K. S. (2019). Optimization models for financing innovations in green energy technologies. Renewable and Sustainable Energy Reviews, 113, 109258. https://doi.org/10.1016/j.rser.2019.109258 Tan R. R. Aviso K. B. Ng D. K. S. ( 2019 ). Optimization models for financing innovations in green energy technologies . Renewable and Sustainable Energy Reviews , 113 , 109258 . https://doi.org/10.1016/j.rser.2019.109258 Search in Google Scholar

[7] Bell, M. L., Hobbs, B. F., Elliott, E. M., Ellis, H., & Robinson, Z. (2001). An evaluation of multi-criteria methods in integrated assessment of climate policy. Journal of Multi-Criteria Decision Analysis, 10(5), 229-256. https://doi.org/10.1002/mcda.305 Bell M. L. Hobbs B. F. Elliott E. M. Ellis H. Robinson Z. ( 2001 ). An evaluation of multi-criteria methods in integrated assessment of climate policy . Journal of Multi-Criteria Decision Analysis , 10 ( 5 ), 229 - 256 . https://doi.org/10.1002/mcda.305 Search in Google Scholar

[8] Aïd, R., & Biagini, S. (2023). Optimal dynamic regulation of carbon emissions market. Mathematical Finance, 33(1), 80-115. https://doi.org/10.1111/mafi.12364 Aïd R. Biagini S. ( 2023 ). Optimal dynamic regulation of carbon emissions market . Mathematical Finance , 33 ( 1 ), 80 - 115 . https://doi.org/10.1111/mafi.12364 Search in Google Scholar

[9] Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., Dijmărescu, A., & Dijmărescu, I. (2022). Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the Internet of Robotic Things. Electronics, 12(1), 22. https://doi.org/10.3390/electronics12010022 Andronie M. Lăzăroiu G. Karabolevski O. L. Ștefănescu R. Hurloiu I. Dijmărescu A. Dijmărescu I. ( 2022 ). Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the Internet of Robotic Things . Electronics , 12 ( 1 ), 22 . https://doi.org/10.3390/electronics12010022 Search in Google Scholar

[10] Sovacool, B. K., Baum, C. M., & Low, S. (2023). Risk-risk governance in a low-carbon future: Exploring institutional, technological, and behavioral tradeoffs in climate geoengineering pathways. Risk Analysis, 43(4), 838-859. https://doi.org/10.1111/risa.13932 Sovacool B. K. Baum C. M. Low S. ( 2023 ). Risk-risk governance in a low-carbon future: Exploring institutional, technological, and behavioral tradeoffs in climate geoengineering pathways . Risk Analysis , 43 ( 4 ), 838 - 859 . https://doi.org/10.1111/risa.13932 Search in Google Scholar

[11] Mahmood, F., Zaied, Y. B., & Abedin, M. Z. (2024). Role of green finance instruments in shaping economic cycles. Technological Forecasting and Social Change, 209, 123792. https://doi.org/10.1016/j.techfore.2024.123792 Mahmood F. Zaied Y. B. Abedin M. Z. ( 2024 ). Role of green finance instruments in shaping economic cycles . Technological Forecasting and Social Change , 209 , 123792 . https://doi.org/10.1016/j.techfore.2024.123792 Search in Google Scholar

[12] Xu, Z., Cheng, X., Wang, K., & Yang, S. (2020). Analysis of the environmental trend of network finance and its influence on traditional commercial banks. Journal of Computational and Applied Mathematics, 379, 112907. https://doi.org/10.1016/j.cam.2020.112907 Xu Z. Cheng X. Wang K. Yang S. ( 2020 ). Analysis of the environmental trend of network finance and its influence on traditional commercial banks . Journal of Computational and Applied Mathematics , 379 , 112907 . https://doi.org/10.1016/j.cam.2020.112907 Search in Google Scholar

[13] Li, W., & Wu, Z. (2022). A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria. Applied Soft Computing, 128, 109476. https://doi.org/10.1016/j.asoc.2022.109476 Li W. Wu Z. ( 2022 ). A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria . Applied Soft Computing , 128 , 109476 . https://doi.org/10.1016/j.asoc.2022.109476 Search in Google Scholar

[14] Saleem, R., Yuan, B., Kurugollu, F., Anjum, A., & Liu, L. (2022). Explaining deep neural networks: A survey on the global interpretation methods. Neurocomputing, 513, 165-180. https://doi.org/10.1016/j.neucom.2022.09.129 Saleem R. Yuan B. Kurugollu F. Anjum A. Liu L. ( 2022 ). Explaining deep neural networks: A survey on the global interpretation methods . Neurocomputing , 513 , 165 - 180 . https://doi.org/10.1016/j.neucom.2022.09.129 Search in Google Scholar

[15] Cheng, Q., Wang, X., Wang, S., Li, Y., Liu, H., Li, Z., & Sun, W. (2023). Research on a carbon emission prediction method for oil field transfer stations based on an improved genetic algorithm—The decision tree algorithm. Processes, 11(9), 2738. https://doi.org/10.3390/pr11092738 Cheng Q. Wang X. Wang S. Li Y. Liu H. Li Z. Sun W. ( 2023 ). Research on a carbon emission prediction method for oil field transfer stations based on an improved genetic algorithm-The decision tree algorithm . Processes , 11 ( 9 ), 2738 . https://doi.org/10.3390/pr11092738 Search in Google Scholar

[16] Tan, K. C., Lee, T. H., & Khor, E. F. (2002). Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artificial Intelligence Review, 17(4), 251-290. https://doi.org/10.1023/A:1015516501242 Tan K. C. Lee T. H. Khor E. F. ( 2002 ). Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons . Artificial Intelligence Review , 17 ( 4 ), 251 - 290 . https://doi.org/10.1023/A:1015516501242 Search in Google Scholar

[17] Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A comprehensive review. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, 185-231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4 Abdel-Basset M. Abdel-Fatah L. Sangaiah A. K. ( 2018 ). Metaheuristic algorithms: A comprehensive review . Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications , 185 - 231 . https://doi.org/10.1016/B978-0-12-813314-9.00010-4 Search in Google Scholar

[18] Vemulapalli, G. (2023). Operationalizing machine learning best practices for scalable production deployments. International Machine Learning Journal and Computer Engineering, 6(6), 1-21. https://mljce.in/index.php/Imljce/article/view/30 Vemulapalli G. ( 2023 ). Operationalizing machine learning best practices for scalable production deployments . International Machine Learning Journal and Computer Engineering , 6 ( 6 ), 1 - 21 . https://mljce.in/index.php/Imljce/article/view/30 Search in Google Scholar

[19] Chen, J., Peng, D., Liu, Z., Wu, L., & Jiang, M. (2024). A sustainable model for forecasting carbon emission trading prices. Sustainability, 16(19), 8324. https://doi.org/10.3390/su16198324 Chen J. Peng D. Liu Z. Wu L. Jiang M. ( 2024 ). A sustainable model for forecasting carbon emission trading prices . Sustainability , 16 ( 19 ), 8324 . https://doi.org/10.3390/su16198324 Search in Google Scholar

[20] Mayer, M. J., Szilágyi, A., & Gróf, G. (2020). Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm. Applied Energy, 269, 115058. https://doi.org/10.1016/j.apenergy.2020.115058 Mayer M. J. Szilágyi A. Gróf G. ( 2020 ). Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm . Applied Energy , 269 , 115058 . https://doi.org/10.1016/j.apenergy.2020.115058 Search in Google Scholar

[21] Rubtsov, A., & Shen, S. (2024). Dynamic portfolio decisions with climate risk and model uncertainty. Journal of Sustainable Finance & Investment, 14(2), 344-365. https://doi.org/10.1080/20430795.2022.2045890 Rubtsov A. Shen S. ( 2024 ). Dynamic portfolio decisions with climate risk and model uncertainty . Journal of Sustainable Finance & Investment , 14 ( 2 ), 344 - 365 . https://doi.org/10.1080/20430795.2022.2045890 Search in Google Scholar

[22] Oyebode, O., Babatunde, D. E., Monyei, C. G., & Babatunde, O. M. (2019). Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals. Heliyon, 5(11). https://doi.org/10.1016/j.heliyon.2019.e02796 Oyebode O. Babatunde D. E. Monyei C. G. Babatunde O. M. ( 2019 ). Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals . Heliyon , 5 ( 11 . https://doi.org/10.1016/j.heliyon.2019.e02796 Search in Google Scholar

[23] Fikri, N., Rida, M., Abghour, N., Moussaid, K., & El Omri, A. (2019). An adaptive and real-time based architecture for financial data integration. Journal of Big Data, 6, 1-25. https://doi.org/10.1186/s40537-019-0260-x Fikri N. Rida M. Abghour N. Moussaid K. , El Omri A. ( 2019 ). An adaptive and real-time based architecture for financial data integration . Journal of Big Data , 6 , 1 - 25 . https://doi.org/10.1186/s40537-019-0260-x Search in Google Scholar

[24] Mardani, A., Liao, H., Nilashi, M., Alrasheedi, M., & Cavallaro, F. (2020). A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 275, 122942. https://doi.org/10.1016/j.jclepro.2020.122942 Mardani A. Liao H. Nilashi M. Alrasheedi M. Cavallaro F. ( 2020 ). A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques . Journal of Cleaner Production , 275 , 122942 . https://doi.org/10.1016/j.jclepro.2020.122942 Search in Google Scholar

[25] Wenstøp, F., & Seip, K. (2001). Legitimacy and quality of multi-criteria environmental policy analysis. Journal of Multi-Criteria Decision Analysis, 10(2), 53-64. https://doi.org/10.1002/mcda.289 Wenstøp F. Seip K. ( 2001 ). Legitimacy and quality of multi-criteria environmental policy analysis . Journal of Multi-Criteria Decision Analysis , 10 ( 2 ), 53 - 64 . https://doi.org/10.1002/mcda.289 Search in Google Scholar

[26] Li, R., Wang, W., Wu, X., Tang, F., & Chen, Z. (2019). Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis. Energy, 168, 30-42. https://doi.org/10.1016/j.energy.2018.11.069 Li R. Wang W. Wu X. Tang F. Chen Z. ( 2019 ). Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis . Energy , 168 , 30 - 42 . https://doi.org/10.1016/j.energy.2018.11.069 Search in Google Scholar

[27] Shuai, H., Chuanzheng, L., & Goda, K. (2023). Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action. Chinese Journal of Geotechnical Engineering, 45(2), 336-344. Shuai H. Chuanzheng L. Goda K. ( 2023 ). Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action . Chinese Journal of Geotechnical Engineering , 45 ( 2 ), 336 - 344 . Search in Google Scholar