{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:47:42Z","timestamp":1776491262134,"version":"3.51.2"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"45","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Sci Pollut Res"],"DOI":"10.1007\/s11356-025-37156-y","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T08:00:51Z","timestamp":1762416051000},"page":"25618-25642","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting CO2 emissions in Morocco: exploring the use of ridge regression with data preprocessing and feature impact analysis"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4908-5164","authenticated-orcid":false,"given":"Yassine","family":"Dani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoual","family":"Belouaggadia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mustapha","family":"Jammoukh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"37156_CR1","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.jclepro.2019.03.352","volume":"225","author":"AO Acheampong","year":"2019","unstructured":"Acheampong AO, Boateng EB (2019) Modelling carbon emission intensity: application of artificial neural network. J Clean, Prod 225:833\u2013856. https:\/\/doi.org\/10.1016\/j.jclepro.2019.03.352","journal-title":"J Clean, Prod"},{"issue":"1","key":"37156_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12010197","volume":"12","author":"TA Adjuik","year":"2022","unstructured":"Adjuik TA, Davis SC (2022) Machine learning approach to simulate soil CO2 fluxes under cropping systems. Agronomy 12(1):197. https:\/\/doi.org\/10.3390\/agronomy12010197","journal-title":"Agronomy"},{"key":"37156_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.spc.2021.10.001","volume":"29","author":"\u00dc A\u011fbulut","year":"2022","unstructured":"A\u011fbulut \u00dc (2022) Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustain Prod Consum 29:141\u2013157. https:\/\/doi.org\/10.1016\/j.spc.2021.10.001","journal-title":"Sustain Prod Consum"},{"issue":"6","key":"37156_CR4","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1016\/j.apenergy.2010.12.076","volume":"88","author":"E Akbostanc\u0131","year":"2011","unstructured":"Akbostanc\u0131 E, Tun\u00e7 G\u0130, T\u00fcr\u00fct-A\u015f\u0131k S (2011) CO2 emissions of Turkish manufacturing industry: a decomposition analysis. Appl Energy 88(6):2273\u20132278. https:\/\/doi.org\/10.1016\/j.apenergy.2010.12.076","journal-title":"Appl Energy"},{"key":"37156_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/ijfe.2134","author":"MU Ali","year":"2020","unstructured":"Ali MU, Gong Z, Ali MU, Asmi F, Muhammad R (2020) CO2 emission, economic development, fossil fuel consumption and population density in India, Pakistan and Bangladesh: a panel investigation. Int J Finance Econ. https:\/\/doi.org\/10.1002\/ijfe.2134","journal-title":"Int J Finance Econ"},{"key":"37156_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eti.2021.101674","volume":"23","author":"B Aslam","year":"2021","unstructured":"Aslam B (2021) The nexus of industrialization, GDP per capita and CO2 emission in China. Environ Technol Innov 23:101674. https:\/\/doi.org\/10.1016\/j.eti.2021.101674","journal-title":"Environ Technol Innov"},{"key":"37156_CR7","doi-asserted-by":"publisher","unstructured":"Ayompe, L. M., Davis, S. J., & Egoh, B. N. (2020). Trends and drivers of African fossil fuel CO2 emissions 1990- 2017. Environmental Research Letters, 15(12), 9326 64. https:\/\/doi.org\/10.1088\/1748-","DOI":"10.1088\/1748"},{"key":"37156_CR8","doi-asserted-by":"publisher","DOI":"10.1051\/matecconf\/202134500002","volume":"345","author":"A Backa","year":"2021","unstructured":"Backa A, Drga J, Martvo\u0148ov\u00e1 L, Pola\u010dikov\u00e1 M (2021) Machine learning model designed to predict the amount of CO2 produced by a small pellet boiler. MATEC Web Conf 345:00002. https:\/\/doi.org\/10.1051\/matecconf\/202134500002","journal-title":"MATEC Web Conf"},{"key":"37156_CR9","doi-asserted-by":"crossref","unstructured":"Bappon, S. D., Dey, A., Sabuj, S. M., & Das, A. (2022). Toward a machine learning approach to predict the CO2 rating of fuel-consuming vehicles in Canada. IEEE Xplore. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10054732\/","DOI":"10.1109\/ICCIT57492.2022.10054732"},{"key":"37156_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-020-10773-5","author":"S Bouyghrissi","year":"2020","unstructured":"Bouyghrissi S, Berjaoui A, Khanniba M (2020) The nexus between renewable energy consumption and economic growth in Morocco. Environ Sci Pollut Res Int. https:\/\/doi.org\/10.1007\/s11356-020-10773-5","journal-title":"Environ Sci Pollut Res Int"},{"key":"37156_CR11","unstructured":"Carmen. (2022). Tahaddart combined cycle power plant, Morocco. Power Technology, Jan. 13. https:\/\/www.power-technology.com\/marketdata\/tahaddart-combined-cycle-power-plant-morocco\/"},{"key":"37156_CR12","doi-asserted-by":"publisher","unstructured":"Chinedu Pascal Ezenkwu, S. C., & Ibeke, E. (2024). Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments\u2019 carbon reduction policies. Environmental Monitoring and Assessment, 196(3, Feb), 024\u201312388\u20136. https:\/\/doi.org\/10.1007\/s10661-","DOI":"10.1007\/s10661"},{"issue":"1","key":"37156_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-73060-0","volume":"14","author":"L Costantini","year":"2024","unstructured":"Costantini L, Laio F, Mariani MS, Ridolfi L, Sciarra C (2024) Forecasting national CO2 emissions worldwide. Sci Rep 14(1):22438. https:\/\/doi.org\/10.1038\/s41598-024-73060-0","journal-title":"Sci Rep"},{"key":"37156_CR14","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.rser.2017.03.111","volume":"77","author":"E Dogan","year":"2017","unstructured":"Dogan E, Aslan A (2017) Exploring the relationship among CO 2 emissions, real GDP, energy consumption and tourism in the EU and candidate countries: evidence from panel models robust to heterogeneity and cross- sectional dependence. Renew Sustain Energy Rev 77:239\u2013245. https:\/\/doi.org\/10.1016\/j.rser.2017.03.111","journal-title":"Renew Sustain Energy Rev"},{"issue":"1","key":"37156_CR15","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1146\/annurev.marine.010908.163834","volume":"1","author":"SC Doney","year":"2009","unstructured":"Doney SC, Fabry VJ, Feely RA, Kleypas JA (2009) Ocean acidification: the other CO2 problem. Annu Rev Mar Sci 1(1):169\u2013192. https:\/\/doi.org\/10.1146\/annurev.marine.010908.163834","journal-title":"Annu Rev Mar Sci"},{"key":"37156_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.124471","volume":"282","author":"M Gao","year":"2021","unstructured":"Gao M, Yang H, Xiao Q, Goh M (2021) A novel fractional grey Riccati model for carbon emission prediction. J Clean Prod 282:124471. https:\/\/doi.org\/10.1016\/j.jclepro.2020.124471","journal-title":"J Clean Prod"},{"issue":"352","key":"37156_CR17","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1080\/01621459.1975.10480301","volume":"70","author":"DK Guilkey","year":"1975","unstructured":"Guilkey DK, Murphy JL (1975) Directed ridge regression techniques in cases of multicollinearity. J Am Stat Assoc 70(352):769\u2013775. https:\/\/doi.org\/10.1080\/01621459.1975.10480301","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"37156_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.marpolbul.2015.09.018","volume":"101","author":"EA Hern\u00e1ndez-Delgado","year":"2015","unstructured":"Hern\u00e1ndez-Delgado EA (2015) The emerging threats of climate change on tropical coastal ecosystem services, public health, local economies and livelihood sustainability of small islands: cumulative impacts and synergies. Mar Pollut Bull 101(1):5\u201328. https:\/\/doi.org\/10.1016\/j.marpolbul.2015.09.018","journal-title":"Mar Pollut Bull"},{"key":"37156_CR19","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.egyr.2019.05.004","volume":"5","author":"SM Hosseini","year":"2019","unstructured":"Hosseini SM, Saifoddin A, Shirmohammadi R, Aslani A (2019) Forecasting of CO2 emissions in Iran based on time series and regression analysis. Energy Rep 5:619\u2013631. https:\/\/doi.org\/10.1016\/j.egyr.2019.05.004","journal-title":"Energy Rep"},{"issue":"4","key":"37156_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/su15043631","volume":"15","author":"Z Huo","year":"2023","unstructured":"Huo Z, Zha X, Lu M, Ma T, Lu Z (2023) Prediction of carbon emission of the transportation sector in Jiangsu Province-regression prediction model based on GA-SVM. Sustainability 15(4):3631. https:\/\/doi.org\/10.3390\/su15043631","journal-title":"Sustainability"},{"key":"37156_CR21","unstructured":"Jorf Lasfar coal-fired power plant expansion. (2023). Power Technology. https:\/\/www.power-technology.com\/projects\/jorf_lasfar\/"},{"key":"37156_CR22","unstructured":"Kadam, P., & Vijayumar, S. (2018). Prediction model: CO2 emission using machine learning. IEEE Xplore, Apr, 01. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8529498"},{"key":"37156_CR23","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1016\/j.rser.2010.06.006","volume":"14","author":"A\u00c7 K\u00f6ne","year":"2010","unstructured":"K\u00f6ne A\u00c7, B\u00fcke T (2010) Forecasting of CO2 emissions from fuel combustion using trend analysis. Renew Sustain Energy Rev 14:2906\u20132915. https:\/\/doi.org\/10.1016\/j.rser.2010.06.006","journal-title":"Renew Sustain Energy Rev"},{"key":"37156_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102253","author":"S Kumar","year":"2023","unstructured":"Kumar S (2023) A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies. Ecol Inform. https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102253","journal-title":"Ecol Inform"},{"key":"37156_CR25","doi-asserted-by":"publisher","unstructured":"Kumari, S., & Singh, S. K. (2022). Machine learning-based time series models for effective CO2 emission prediction in India. Environmental Science and Pollution Research, 21723\u201321728. https:\/\/doi.org\/10.1007\/s11356-022-","DOI":"10.1007\/s11356-022"},{"issue":"15","key":"37156_CR26","doi-asserted-by":"publisher","first-page":"6182","DOI":"10.1073\/pnas.0807695106","volume":"106","author":"JA Langley","year":"2009","unstructured":"Langley JA, McKee KL, Cahoon DR, Cherry JA, Megonigal JP (2009) Elevated CO2 stimulates marsh elevation gain, counterbalancing sea-level rise. Proc Natl Acad Sci U S A 106(15):6182\u20136186. https:\/\/doi.org\/10.1073\/pnas.0807695106","journal-title":"Proc Natl Acad Sci U S A"},{"key":"37156_CR27","doi-asserted-by":"publisher","unstructured":"Li, Y., & Sun, Y. (2021). Modeling and predicting city-level CO2 emissions using open access data and machine learning. Environmental Science and Pollution Research, 12294\u201312297. https:\/\/doi.org\/10.1007\/s11356-020-","DOI":"10.1007\/s11356-020"},{"issue":"9","key":"37156_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/en11092475","volume":"11","author":"M Li","year":"2018","unstructured":"Li M, Wang W, De G, Ji X, Tan Z (2018) Forecasting carbon emissions related to energy consumption in Beijing-Tianjin-Hebei region based on grey prediction theory and extreme learning machine optimized by support vector machine algorithm. Energies 11(9):2475. https:\/\/doi.org\/10.3390\/en11092475","journal-title":"Energies"},{"key":"37156_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fenvs.2021.721517","author":"S Li","year":"2021","unstructured":"Li S, Siu YW, Zhao G (2021) Driving factors of CO2 emissions: further study based on machine learning. Front Environ Sci. https:\/\/doi.org\/10.3389\/fenvs.2021.721517","journal-title":"Front Environ Sci"},{"key":"37156_CR30","doi-asserted-by":"publisher","first-page":"4222","DOI":"10.1016\/j.egypro.2017.03.906","volume":"105","author":"Y Libao","year":"2017","unstructured":"Libao Y, Tingting Y, Jielian Z, Guicai L, Yanfen L, Xiaoqian M (2017) Prediction of CO 2 emissions based on multiple linear regression analysis. Energy Procedia 105:4222\u20134228. https:\/\/doi.org\/10.1016\/j.egypro.2017.03.906","journal-title":"Energy Procedia"},{"key":"37156_CR31","doi-asserted-by":"publisher","DOI":"10.1029\/2021gb007146","author":"SM Ludwig","year":"2022","unstructured":"Ludwig SM (2022) Using machine learning to predict inland aquatic CO 2 and CH 4 concentrations and the effects of wildfires in the Yukon-Kuskokwim Delta, Alaska. Glob Biogeochem Cycles. https:\/\/doi.org\/10.1029\/2021gb007146","journal-title":"Glob Biogeochem Cycles"},{"key":"37156_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2021.756311","author":"N Ma","year":"2021","unstructured":"Ma N, Shum WY, Han T, Lai F (2021) Can machine learning be applied to carbon emissions analysis: an application to the CO2 emissions analysis using Gaussian process regression. Front Energy Res. https:\/\/doi.org\/10.3389\/fenrg.2021.756311","journal-title":"Front Energy Res"},{"key":"37156_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2020.11.050","author":"C Magazzino","year":"2020","unstructured":"Magazzino C, Mele M, Schneider N (2020) A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. Renew Energy. https:\/\/doi.org\/10.1016\/j.renene.2020.11.050","journal-title":"Renew Energy"},{"key":"37156_CR34","doi-asserted-by":"publisher","unstructured":"Mohamed, M. G. S., Bialy, B. A. E., Hassanein, A., Baker, A. A., Ali, W. M., Aboushadi, N. R., & Mohamad, E. F. E. (2025). Antecedents and forecasting of carbon emissions using machine learning algorithms: insights from the Top Ten Carbon-Emitting Nations. https:\/\/doi.org\/10.32479\/ijeep.19622","DOI":"10.32479\/ijeep.19622"},{"key":"37156_CR35","doi-asserted-by":"publisher","unstructured":"Mohsin, M., Orynbassarov, D., Anser, M. K., & Oskenbayev, Y. (2022). Does hydropower energy help to reduce CO2 emissions in European Union countries? Evidence from quantile estimation. Environmental Development, 100794,. https:\/\/doi.org\/10.1016\/j.envdev.2022.100794.","DOI":"10.1016\/j.envdev.2022.100794"},{"key":"37156_CR36","unstructured":"Morocco | Data. (2018). https:\/\/data.worldbank.org\/country\/morocco"},{"issue":"2","key":"37156_CR37","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1007\/s11069-015-1898-0","volume":"79","author":"R Ohlan","year":"2015","unstructured":"Ohlan R (2015) The impact of population density, energy consumption, economic growth and trade openness on CO2 emissions in India. Nat Hazards 79(2):1409\u20131428. https:\/\/doi.org\/10.1007\/s11069-015-1898-0","journal-title":"Nat Hazards"},{"key":"37156_CR38","doi-asserted-by":"publisher","first-page":"86","DOI":"10.11591\/ijece.v14i1.pp86-98","volume":"14","author":"H Oubnaki","year":"2024","unstructured":"Oubnaki H, Haouraji C, Mounir B, Mounir I, Farchi A (2024) Energy use and CO2 emissions of the Moroccan transport sector. International Journal of Electrical and Computer Engineering (IJECE) 14:86\u201398. https:\/\/doi.org\/10.11591\/ijece.v14i1.pp86-98","journal-title":"International Journal of Electrical and Computer Engineering (IJECE)"},{"key":"37156_CR39","unstructured":"Petroleum, B. (2022). Statistical Review of World Energy. BP Global. https:\/\/www.bp.com\/en\/global\/corporate\/energy-economics\/statistical-review-of-world-energy.html"},{"issue":"2","key":"37156_CR40","doi-asserted-by":"publisher","first-page":"652","DOI":"10.5771\/0257-9774-2006-2-652","volume":"101","author":"L Pries","year":"2006","unstructured":"Pries L (2006) Schuerkens, Ulrike (ed.): Transnational migrations and social transformations. Anthropos 101(2):652\u2013653. https:\/\/doi.org\/10.5771\/0257-9774-2006-2-652","journal-title":"Anthropos"},{"issue":"5","key":"37156_CR41","doi-asserted-by":"publisher","first-page":"26892","DOI":"10.1016\/j.heliyon.2024.e26892","volume":"10","author":"BD Ross-Veit\u00eda","year":"2024","unstructured":"Ross-Veit\u00eda BD (2024) Machine learning regression algorithms to predict emissions from steam boilers. Heliyon 10(5):26892. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e26892","journal-title":"Heliyon"},{"key":"37156_CR42","doi-asserted-by":"publisher","unstructured":"Safa, M., Nejat, M., Nuthall, P., & Greig, B. (2016). Predicting CO2 emissions from farm inputs in wheat production using artificial neural networks and linear regression models. International Journal of Advanced Computer Science and Applications, 7(9). https:\/\/doi.org\/10.14569\/ijacsa.2016.070938.","DOI":"10.14569\/ijacsa.2016.070938"},{"key":"37156_CR43","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/114\/1\/012148","volume":"114","author":"C Saleh","year":"2016","unstructured":"Saleh C, Dzakiyullah NR, Nugroho JB (2016) Carbon dioxide emission prediction using support vector machine. IOP Conf Ser Mater Sci Eng 114:012148. https:\/\/doi.org\/10.1088\/1757-899x\/114\/1\/012148","journal-title":"IOP Conf Ser Mater Sci Eng"},{"issue":"4","key":"37156_CR44","doi-asserted-by":"publisher","first-page":"359","DOI":"10.3233\/mas-180446","volume":"13","author":"DN Schreiber-Gregory","year":"2018","unstructured":"Schreiber-Gregory DN (2018) Ridge regression and multicollinearity: an in-depth review. Model Assist Stat Appl 13(4):359\u2013365. https:\/\/doi.org\/10.3233\/mas-180446","journal-title":"Model Assist Stat Appl"},{"issue":"12","key":"37156_CR45","doi-asserted-by":"publisher","first-page":"33833","DOI":"10.1007\/s11356-022-24577-2","volume":"30","author":"C Shi","year":"2022","unstructured":"Shi C, Zeng Q, Zhi J, Na X, Cheng S (2022) A study on the response of carbon emission rights price to energy price macroeconomy and weather conditions. Environ Sci Pollut Res Int 30(12):33833\u201333848. https:\/\/doi.org\/10.1007\/s11356-022-24577-2","journal-title":"Environ Sci Pollut Res Int"},{"issue":"S1","key":"37156_CR46","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s13280-011-0243-4","volume":"41","author":"J Siikam\u00e4ki","year":"2012","unstructured":"Siikam\u00e4ki J, Newbold SC (2012) Potential biodiversity benefits from international programs to reduce carbon emissions from deforestation. Ambio 41(S1):78\u201389. https:\/\/doi.org\/10.1007\/s13280-011-0243-4","journal-title":"Ambio"},{"issue":"3","key":"37156_CR47","doi-asserted-by":"publisher","first-page":"302","DOI":"10.4491\/eer.2016.153","volume":"22","author":"W Sun","year":"2017","unstructured":"Sun W, Sun J (2017) Prediction of carbon dioxide emissions based on principal component analysis with regularized extreme learning machine: the case of China. Environ Eng Res 22(3):302\u2013311. https:\/\/doi.org\/10.4491\/eer.2016.153","journal-title":"Environ Eng Res"},{"key":"37156_CR48","doi-asserted-by":"publisher","unstructured":"Sun, W., & Wang, Y. (2021). Prediction and analysis of CO2 emissions based on regularized extreme learning machine optimized by adaptive whale optimization algorithm. Polish Journal of Environmental Studies, 30(3), 2756\u20132767,. https:\/\/doi.org\/10.15244\/pjoes\/129917.","DOI":"10.15244\/pjoes\/129917"},{"issue":"1","key":"37156_CR49","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.matpr.2017.11.047","volume":"5","author":"MM Wagh","year":"2018","unstructured":"Wagh MM, Kulkarni VV (2018) Modeling and optimization of integration of renewable energy resources (RER) for minimum energy cost, minimum CO2 emissions and sustainable development, in recent years: a review. Mater Today Proc 5(1):11\u201321. https:\/\/doi.org\/10.1016\/j.matpr.2017.11.047","journal-title":"Mater Today Proc"},{"issue":"29","key":"37156_CR50","doi-asserted-by":"publisher","first-page":"28985","DOI":"10.1007\/s11356-018-2738-z","volume":"25","author":"S Wei","year":"2018","unstructured":"Wei S, Yuwei W, Chongchong Z (2018) Forecasting CO2 emissions in Hebei, China, through moth-flame optimization based on the random forest and extreme learning machine. Environ Sci Pollut Res Int 25(29):28985\u201328997. https:\/\/doi.org\/10.1007\/s11356-018-2738-z","journal-title":"Environ Sci Pollut Res Int"},{"key":"37156_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119492","volume":"250","author":"L Wen","year":"2020","unstructured":"Wen L, Cao Y (2020) Influencing factors analysis and forecasting of residential energy-related CO2 emissions utilizing optimized support vector machine. J Clean Prod 250:119492. https:\/\/doi.org\/10.1016\/j.jclepro.2019.119492","journal-title":"J Clean Prod"},{"key":"37156_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.140010","volume":"434","author":"L Ye","year":"2024","unstructured":"Ye L, Du P, Wang S (2024) Industrial carbon emission forecasting considering external factors based on linear and machine learning models. J Clean Prod 434:140010\u2013140010. https:\/\/doi.org\/10.1016\/j.jclepro.2023.140010","journal-title":"J Clean Prod"},{"issue":"2","key":"37156_CR53","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy13020328","volume":"13","author":"U-H Yeo","year":"2023","unstructured":"Yeo U-H (2023) Applicability of machine-learned regression models to estimate internal air temperature and CO2 concentration of a pig house. Agronomy 13(2):328\u2013328. https:\/\/doi.org\/10.3390\/agronomy13020328","journal-title":"Agronomy"},{"issue":"8","key":"37156_CR54","doi-asserted-by":"publisher","DOI":"10.3390\/su15086720","volume":"15","author":"X Zhang","year":"2023","unstructured":"Zhang X (2023) The impact of energy intensity, energy productivity and natural resource rents on carbon emissions in Morocco. Sustainability 15(8):6720. https:\/\/doi.org\/10.3390\/su15086720","journal-title":"Sustainability"},{"issue":"6","key":"37156_CR55","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/j.jlp.2012.05.004","volume":"25","author":"R Zhao","year":"2012","unstructured":"Zhao R, Neighbour G, Han J, McGuire M, Deutz P (2012) Using game theory to describe strategy selection for environmental risk and carbon emissions reduction in the green supply chain. J Loss Prev Process Ind 25(6):927\u2013936. https:\/\/doi.org\/10.1016\/j.jlp.2012.05.004","journal-title":"J Loss Prev Process Ind"},{"issue":"3","key":"37156_CR56","doi-asserted-by":"publisher","first-page":"2899","DOI":"10.1007\/s11356-017-0642-6","volume":"25","author":"X Zhao","year":"2017","unstructured":"Zhao X, Han M, Ding L, Calin AC (2017) Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA. Environ Sci Pollut Res Int 25(3):2899\u20132910. https:\/\/doi.org\/10.1007\/s11356-017-0642-6","journal-title":"Environ Sci Pollut Res Int"}],"container-title":["Environmental Science and Pollution Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-025-37156-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11356-025-37156-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-025-37156-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T05:45:15Z","timestamp":1764308715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11356-025-37156-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"references-count":56,"journal-issue":{"issue":"45","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["37156"],"URL":"https:\/\/doi.org\/10.1007\/s11356-025-37156-y","relation":{},"ISSN":["1614-7499"],"issn-type":[{"value":"1614-7499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"8 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}