{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:04:27Z","timestamp":1787504667858,"version":"build-2736575974"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Environmental Management"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.jenvman.2025.124514","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T10:21:52Z","timestamp":1741861312000},"page":"124514","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Investigating the causal relationship between electricity pricing policy and CO2 emission: An application of machine learning-driven metalearners"],"prefix":"10.1016","volume":"380","author":[{"given":"Iman Emtiazi","family":"Naeini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parisa","family":"RahimKhoei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khadijeh","family":"Hassanzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Saberi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jenvman.2025.124514_bib1","series-title":"Trends in Fossil Fuel Extraction","author":"Achakulwisut","year":"2021"},{"key":"10.1016\/j.jenvman.2025.124514_bib2","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1016\/j.jpolmod.2021.03.011","article-title":"Science Direct renewable energy, CO2 emissions and economic growth in sub-saharan Africa: does institutional quality matter?","volume":"43","author":"Acheampong","year":"2021","journal-title":"J. Pol. Model."},{"key":"10.1016\/j.jenvman.2025.124514_bib3","series-title":"Internat\u0131onal Conference on Euras\u0131an Econom\u0131es 2015","article-title":"Yenilenebilir energi tuketimi, iktisadi uyume ve karbondioksit emisyonu arasindaki nedensel iliski: orta dogu ve kuzey afrika ulkeleri ornegi","author":"Akay","year":"2015"},{"issue":"4","key":"10.1016\/j.jenvman.2025.124514_bib4","first-page":"292","article-title":"Electricity consumption, carbon emissions and economic growth in Nigeria","volume":"2","author":"Akpan","year":"2012","journal-title":"Int. J. Energy Econ. Pol."},{"key":"10.1016\/j.jenvman.2025.124514_bib5","series-title":"International Conference on Machine Learning","first-page":"129","article-title":"Limits of estimating heterogeneous treatment effects: guidelines for practical algorithm design","author":"Alaa","year":"2018"},{"issue":"2","key":"10.1016\/j.jenvman.2025.124514_bib7","first-page":"1148","volume":"47","author":"Athey","year":"2019","journal-title":"Generalized random forests"},{"issue":"10","key":"10.1016\/j.jenvman.2025.124514_bib8","doi-asserted-by":"crossref","first-page":"2899","DOI":"10.3390\/en14102899","article-title":"Households' electricity consumption in Hungarian urban areas","volume":"14","author":"Bak\u00f3","year":"2021","journal-title":"Energies"},{"key":"10.1016\/j.jenvman.2025.124514_bib9","series-title":"Double\/debiased Machine Learning for Treatment and Structural Parameters","author":"Chernozhukov","year":"2018"},{"issue":"118122","key":"10.1016\/j.jenvman.2025.124514_bib10","first-page":"1","article-title":"The in\ufb02uence of shadow economy, environmental policies and geopolitical risk on renewable energy: a comparison of high- and middle-income countries","volume":"342","author":"Chu","year":"2023","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.jenvman.2025.124514_bib12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.2516\/stet\/2024014","article-title":"Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector","volume":"79","author":"\u00c7\u0131narer","year":"2024","journal-title":"Science and Technology for Energy Transition"},{"key":"10.1016\/j.jenvman.2025.124514_bib13","doi-asserted-by":"crossref","first-page":"63739","DOI":"10.1007\/s11356-020-11094-3","article-title":"Do renewable energy and natural gas consumption mitigate CO2 emissions in the USA? New insights from NARDL approach","volume":"28","author":"\u00c7\u0131tak","year":"2021","journal-title":"Environ. Sci. Pollut. Control Ser."},{"key":"10.1016\/j.jenvman.2025.124514_bib15","doi-asserted-by":"crossref","first-page":"3785","DOI":"10.1007\/s11356-017-0801-9","article-title":"Are shocks to renewable energy consumption permanent or temporary? Evidence from 54 developing and developed countries","volume":"25","author":"Demir","year":"2018","journal-title":"Environ. Sci. Pollut. Control Ser."},{"key":"10.1016\/j.jenvman.2025.124514_bib16","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.renene.2021.12.136","article-title":"The role of data frequency and method selection in electricity price estimation: comparative evidence from Turkey in pre-pandemic and pandemic periods","volume":"186","author":"Depren","year":"2022","journal-title":"Renew. Energy"},{"issue":"2","key":"10.1016\/j.jenvman.2025.124514_bib18","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1260\/2046-0430.2.2.123","article-title":"Can electricity powered vehicles serve traveler needs?","volume":"2","author":"Du","year":"2013","journal-title":"International Journal of Transportation Science and Technology"},{"key":"10.1016\/j.jenvman.2025.124514_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2023.113154","article-title":"Offsetting carbon emissions from household electricity consumption in Europe","volume":"175","author":"Gordic","year":"2023","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"2","key":"10.1016\/j.jenvman.2025.124514_bib20","doi-asserted-by":"crossref","first-page":"315","DOI":"10.2307\/2998560","article-title":"On the role of the propensity score in efficient semiparametric estimation of average treatment effects","volume":"66","author":"Hahn","year":"1998","journal-title":"Econometrica"},{"issue":"3","key":"10.1016\/j.jenvman.2025.124514_bib21","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1214\/19-BA1195","article-title":"Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion)","volume":"15","author":"Hahn","year":"2020","journal-title":"Bayesian Analysis"},{"issue":"1","key":"10.1016\/j.jenvman.2025.124514_bib22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1257\/jel.47.1.5","article-title":"Recent developments in the econometrics of program evaluation","volume":"47","author":"Imbens","year":"2009","journal-title":"J. Econ. Lit."},{"key":"10.1016\/j.jenvman.2025.124514_bib23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inteco.2017.02.001","article-title":"CO2 emissions, renewable and non-renewable energy consumption, and economic growth: evidence from panel data for developing countries","volume":"151","author":"Ito","year":"2017","journal-title":"International Economics"},{"key":"10.1016\/j.jenvman.2025.124514_bib24","series-title":"Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)","first-page":"324","article-title":"Causal inference and causal machine learning with practical applications: the paper highlights the concepts of causal inference and causal ML along with different implementation techniques","author":"Karmakar","year":"2023"},{"issue":"10","key":"10.1016\/j.jenvman.2025.124514_bib25","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.1073\/pnas.1804597116","article-title":"Metalearners for estimating heterogeneous treatment effects using machine learning","volume":"116","author":"K\u00fcnzel","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"issue":"4","key":"10.1016\/j.jenvman.2025.124514_bib26","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.5194\/essd-12-3269-2020","article-title":"Global carbon budget 2020","volume":"12","author":"Le Qu\u00e9r\u00e9","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"issue":"1","key":"10.1016\/j.jenvman.2025.124514_bib27","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.ijtst.2022.09.002","article-title":"Emerging technologies and policies for carbon\u2013neutral transportation","volume":"12","author":"Li","year":"2023","journal-title":"International Journal of Transportation Science and Technology"},{"key":"10.1016\/j.jenvman.2025.124514_bib29","doi-asserted-by":"crossref","DOI":"10.3389\/fenrg.2021.802697","article-title":"Urban household energy consumption forecasting based on energy price impact mechanism","volume":"9","author":"Lianwei","year":"2021","journal-title":"Front. Energy Res."},{"key":"10.1016\/j.jenvman.2025.124514_bib30","series-title":"Fossil-fuel Subsidies and Climate Change","author":"Merrill","year":"2015"},{"issue":"2","key":"10.1016\/j.jenvman.2025.124514_bib31","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1093\/biomet\/asaa076","article-title":"Quasi-oracle estimation of heterogeneous treatment effects","volume":"108","author":"Nie","year":"2021","journal-title":"Biometrika"},{"issue":"2","key":"10.1016\/j.jenvman.2025.124514_bib32","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1093\/biomet\/asaa076","article-title":"Quasi-oracle estimation of heterogeneous treatment effects","volume":"108","author":"Nie","year":"2021","journal-title":"Biometrika"},{"issue":"7691","key":"10.1016\/j.jenvman.2025.124514_bib33","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/d41586-018-01495-3","article-title":"Fossil-fuel subsidies assessed","volume":"554","author":"Parry","year":"2018","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.jenvman.2025.124514_bib34","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s00704-021-03764-0","article-title":"Anthropogenic climate change: the impact of the global carbon budget","volume":"146","author":"Redlin","year":"2021","journal-title":"Theor. Appl. Climatol."},{"issue":"1","key":"10.1016\/j.jenvman.2025.124514_bib35","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s00704-021-03764-0","article-title":"Anthropogenic climate change: the impact of the global carbon budget","volume":"146","author":"Redlin","year":"2021","journal-title":"Theor. Appl. Climatol."},{"issue":"4","key":"10.1016\/j.jenvman.2025.124514_bib36","doi-asserted-by":"crossref","first-page":"931","DOI":"10.2307\/1912705","article-title":"Root-N-Consistent semiparametric regression","volume":"56","author":"Robinson","year":"1988","journal-title":"Econometrica"},{"issue":"1","key":"10.1016\/j.jenvman.2025.124514_bib37","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","article-title":"The central role of the propensity score in observational studies for causal effects","volume":"70","author":"Rosenbaum","year":"1983","journal-title":"Biometrika"},{"key":"10.1016\/j.jenvman.2025.124514_bib41","doi-asserted-by":"crossref","first-page":"52576","DOI":"10.1007\/s11356-023-26050-0","article-title":"Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA","volume":"30","author":"Ulussever","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"10.1016\/j.jenvman.2025.124514_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2021.110888","article-title":"Electricity price and habits: which would affect household electricity consumption?","volume":"240","author":"Wang","year":"2021","journal-title":"Energy Build."},{"issue":"4","key":"10.1016\/j.jenvman.2025.124514_bib44","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.ijtst.2017.03.003","article-title":"Quantifying the impact of road capacity loss on urban electrified transportation networks: an optimization based approach","volume":"5","author":"Wei","year":"2016","journal-title":"International Journal of Transportation Science and Technology"},{"key":"10.1016\/j.jenvman.2025.124514_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.resourpol.2023.103614","article-title":"Using econometric and machine learning models to forecast crude oil prices: insights from economic history","volume":"83","author":"Xu","year":"2023","journal-title":"Resour. Policy"},{"key":"10.1016\/j.jenvman.2025.124514_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.131256","article-title":"Socioeconomics determinants of household carbon footprint in Iskandar Malaysia","volume":"347","author":"Zen","year":"2022","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.jenvman.2025.124514_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.120797","article-title":"Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm","volume":"229","author":"Zhang","year":"2021","journal-title":"Energy"},{"key":"10.1016\/j.jenvman.2025.124514_bib51","article-title":"Interplay between economic progress, carbon emissions and energy prices on green energy adoption: evidence from USA and Germany in context of sustainability","author":"Zhang","year":"2024","journal-title":"Renew. Energy"},{"key":"10.1016\/j.jenvman.2025.124514_bib52","first-page":"1","article-title":"The relationship between CO2 emissions, renewable energy and economic growth in the US: evidence from symmetric and asymmetric spectral granger causality analysis","author":"Zuhal","year":"2024","journal-title":"Environ. Dev. Sustain."}],"container-title":["Journal of Environmental Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301479725004906?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301479725004906?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T16:21:36Z","timestamp":1750263696000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0301479725004906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":40,"alternative-id":["S0301479725004906"],"URL":"https:\/\/doi.org\/10.1016\/j.jenvman.2025.124514","relation":{},"ISSN":["0301-4797"],"issn-type":[{"value":"0301-4797","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Investigating the causal relationship between electricity pricing policy and CO2 emission: An application of machine learning-driven metalearners","name":"articletitle","label":"Article Title"},{"value":"Journal of Environmental Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jenvman.2025.124514","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"124514"}}