{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:10:42Z","timestamp":1777605042736,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It analyzes the interplay between regional marginal prices (LMP) and carbon emissions within electricity markets. The paper explores how market designs and operational strategies influence carbon output by leveraging a dataset that encompasses hourly LMP and carbon emissions data across various regions of New York State. The analysis utilizes neural networks to simulate and predict the effects of different market scenarios on carbon emissions, highlighting the role of LMP, loss costs, and congestion costs in environmental policy effectiveness. The results underscore the potential of system engineering to provide a holistic framework that integrates market dynamics, policy adjustments, and environmental impacts, thereby offering actionable insights into optimizing market designs for reduced carbon footprints. This approach not only enhances the understanding of the complex interactions within electricity markets but also supports the development of targeted strategies for achieving sustainable energy transitions.<\/jats:p>","DOI":"10.3390\/systems12120544","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T08:33:51Z","timestamp":1733387631000},"page":"544","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7291-7025","authenticated-orcid":false,"given":"Zhiyu","family":"An","sequence":"first","affiliation":[{"name":"Department of Systems Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9569-5295","authenticated-orcid":false,"given":"Clifford Alan","family":"Whitcomb","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Cornell University, Ithaca, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"132450","DOI":"10.1016\/j.jclepro.2022.132450","article-title":"Have those countries declaring \u201czero carbon\u201d or \u201ccarbon neutral\u201d climate goals achieved carbon emissions-economic growth decoupling?","volume":"363","author":"Zhao","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/14693062.2021.1990831","article-title":"Countries with sustained greenhouse gas emissions reductions: An analysis of trends and progress by sector","volume":"22","author":"Lamb","year":"2022","journal-title":"Clim. Policy"},{"key":"ref_3","unstructured":"Ministry of Ecology and Environment of People\u2019s Republic of China (2023). Notice on Doing the Work Related to the Allocation of National Carbon Emission Trading Allowances for the Years 2021 and 2022, Ministry of Ecology and Environment of People\u2019s Republic of China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1017\/bca.2022.19","article-title":"How is the U.S. Pricing Carbon? How Could We Price Carbon?","volume":"13","author":"Aldy","year":"2022","journal-title":"J. Benefit-Cost Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"eaas9793","DOI":"10.1126\/science.aas9793","article-title":"Net-zero emissions energy systems","volume":"360","author":"Davis","year":"2018","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1038\/s41558-018-0317-4","article-title":"A new scenario resource for integrated 1.5 \u00b0C research","volume":"8","author":"Huppmann","year":"2018","journal-title":"Nat. Clim. Change"},{"key":"ref_7","unstructured":"IEA (2023). CO2 Emissions in 2022, IEA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111778","DOI":"10.1016\/j.enpol.2020.111778","article-title":"Reducing carbon dioxide emissions beyond 2030: Time to shift U.S. power-sector focus","volume":"148","author":"Anderson","year":"2021","journal-title":"Energy Policy"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ji, C., Liu, Y., Hao, Y., Song, Y., Cao, Y., and Qi, H. (2024). Dynamic interactions of carbon trading, green certificate trading, and electricity markets: Insights from system dynamics modeling. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0304478"},{"key":"ref_10","unstructured":"IEA (2020). Implementing Effective Emissions Trading Systems, IEA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.2516\/stet\/2024035","article-title":"Carbon emission measurement method of regional power system based on LSTM-Attention model","volume":"79","author":"Yu","year":"2024","journal-title":"Sci. Technol. Energy Transit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"117033","DOI":"10.1016\/j.energy.2020.117033","article-title":"Designing tax and subsidy incentives towards a green and reliable electricity market","volume":"195","author":"Masoumzadeh","year":"2020","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/14693062.2021.1891018","article-title":"Economic and Environmental Impacts of a Proposed \u2018Carbon adder\u2019 on New York\u2019s Energy Market","volume":"21","author":"Rutherford","year":"2021","journal-title":"Clim. Policy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fridstr\u00f8m, L. (2021). The Norwegian Vehicle Electrification Policy and Its Implicit Price of Carbon. Sustainability, 13.","DOI":"10.3390\/su13031346"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1109\/TPWRS.2020.2966663","article-title":"Carbon-Oriented Operational Planning in Coupled Electricity and Emission Trading Markets","volume":"35","author":"Wang","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106202","DOI":"10.1016\/j.epsr.2020.106202","article-title":"An economic-environmental asset planning in electric distribution networks considering carbon emission trading and demand response","volume":"181","author":"Lehtonen","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1080\/14693062.2019.1682491","article-title":"Emissions trading in regulated electricity markets","volume":"20","author":"Acworth","year":"2020","journal-title":"Clim. Policy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100483","DOI":"10.1016\/j.esr.2020.100483","article-title":"A clean innovation comparison between carbon tax and cap-and-trade system","volume":"29","author":"Chen","year":"2020","journal-title":"Energy Strategy Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105426","DOI":"10.1016\/j.eneco.2021.105426","article-title":"Effects of government subsidies on green technology investment and green marketing coordination of supply chain under the cap-and-trade mechanism","volume":"101","author":"Li","year":"2021","journal-title":"Energy Econ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ungureanu, S., Topa, V., and Cziker, A.C. (2021). Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market. Energies, 14.","DOI":"10.3390\/en14216966"},{"key":"ref_21","first-page":"9149","article-title":"An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission","volume":"45","author":"Nguyen","year":"2023","journal-title":"Energy Sources Part A Recover. Util. Environ. Eff."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115527","DOI":"10.1016\/j.apenergy.2020.115527","article-title":"Short-term forecasting of CO2 emission intensity in power grids by machine learning","volume":"277","author":"Leerbeck","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102052","DOI":"10.1016\/j.scs.2020.102052","article-title":"A review on renewable energy and electricity requirement forecasting models for smart grid and buildings","volume":"55","author":"Ahmad","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, J., and Wang, Q. (2021). Forecasting carbon price with secondary decomposition algorithm and optimized extreme learning machine. Sustainability, 13.","DOI":"10.3390\/su13158413"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1109\/TIA.2021.3079329","article-title":"Economic-Emission Dispatch Problem in Power Systems with Carbon Capture Power Plants","volume":"57","author":"Zare","year":"2021","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"121392","DOI":"10.1016\/j.energy.2021.121392","article-title":"Modeling and optimization of combined heat and power with power-to-gas and carbon capture system in integrated energy system","volume":"236","author":"Ma","year":"2021","journal-title":"Energy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhong, J., Qi, Y., Hao, X., Liu, R., Luo, Z., and Lin, H. (2022, January 8\u201311). Power Generation Planning Optimization Model Considering Carbon Emission. Proceedings of the 2022 IEEE\/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Shanghai, China.","DOI":"10.1109\/ICPSAsia55496.2022.9949840"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chu, M., Wang, M., Guan, D., Cui, W., Liu, L., and Yao, H. (2021, January 22\u201324). Regional Integrated Energy System Day-ahead Optimal Dispatch Considering Carbon Emission. Proceedings of the 2021 China Automation Congress (CAC), Beijing, China.","DOI":"10.1109\/CAC53003.2021.9728660"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, C., Zhou, X., Li, A., and Liu, C. (2023, January 27\u201330). Electricity Price and Dynamic Carbon Emission Factor Guided Bi-level Optimization Model for Demand Response of Integrated Energy System. Proceedings of the 2023 Panda Forum on Power and Energy (PandaFPE), Chengdu, China.","DOI":"10.1109\/PandaFPE57779.2023.10141313"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100161","DOI":"10.1016\/j.ijft.2022.100161","article-title":"A systems thinking approach to address sustainability challenges to the energy sector","volume":"15","author":"Laimon","year":"2022","journal-title":"Int. J. Thermofluids"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106368","DOI":"10.1016\/j.infsof.2020.106368","article-title":"Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions","volume":"127","author":"Lwakatare","year":"2020","journal-title":"Inf. Softw. Technol."},{"key":"ref_32","first-page":"3293","article-title":"An Open Source Representation for the NYS Electric Grid to Support Power Grid and Market Transition Studies","volume":"38","author":"Liu","year":"2023","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_33","unstructured":"U.S. Environmental Protection Agency (2024). Clean Air Status and Trends Network (CASTNET)."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"42420","DOI":"10.1109\/ACCESS.2022.3168013","article-title":"Analytics on Non-Normalized Data Sources: More Learning, Rather Than More Cleaning","volume":"10","author":"Allauzen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, X., and Yu, J. (2023, January 26\u201328). A New Integrated Locational Marginal Price Based on the Node Carbon Emission Intensity. Proceedings of the 2023 3rd New Energy and Energy Storage System Control Summit Forum (NEESSC), Mianyang, China.","DOI":"10.1109\/NEESSC59976.2023.10349327"},{"key":"ref_36","unstructured":"Warfield, J.N. (1974). Structuring Complex Systems, Battelle Memorial Institute."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ford, L., and Ertas, A. (2024). Utilizing a Transdisciplinary (TD) Systems Engineering (SE) Process Model in the Concept Stage: A Case Study to Effectively Understand the Baseline Maturity for a TD SE Learning Program. Systems, 12.","DOI":"10.3390\/systems12010013"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ertas, A., and Gulbulak, U. (2020). Managing Complexity Through Integrated Transdisciplinary Design Tools, Atlas Publishing.","DOI":"10.22545\/2021b\/M7"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0040-1625(78)90028-8","article-title":"Interpretive structural modeling\u2014A useful tool for technology assessment?","volume":"11","author":"Watson","year":"1978","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1080\/1463922X.2022.2044932","article-title":"Application of interpretive structural modelling (ISM) for developing ergonomic workstation improvement framework","volume":"24","author":"Joshi","year":"2022","journal-title":"Theor. Issues Ergon. Sci."},{"key":"ref_41","unstructured":"Harary, F., Norman, R.Z., and Cartwright, D. (1965). Structural Models: An Introduction to the Theory of Directed Graphs, Wiley."},{"key":"ref_42","unstructured":"Duperrin, J.-C., and Godet, M. (1973). M\u00e9thode de Hi\u00e9rarchisation des \u00c9l\u00e9ments d\u2019un Syst\u00e8me: Essai de Prospective du Syst\u00e8me de L\u2019\u00e9nergie Nucl\u00e9aire dans son Contexte Soci\u00e9tal, CEA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1108\/01443579410062086","article-title":"Vendor selection using interpretive structural modelling (ISM)","volume":"14","author":"Mandal","year":"1994","journal-title":"Int. J. Oper. Prod. Manag."},{"key":"ref_44","unstructured":"Ren, J., and Xia, F. (2024). Brain-inspired Artificial Intelligence: A Comprehensive Review. arXiv."},{"key":"ref_45","first-page":"39553","article-title":"Mimonets: Multiple-input-multiple-output neural networks exploiting computation in superposition","volume":"36","author":"Menet","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_46","unstructured":"Cohen, D., Ai, Q., and Croft, W.B. (2016). Adaptability of neural networks on varying granularity IR tasks. arXiv."},{"key":"ref_47","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","unstructured":"Agarap, A. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_49","unstructured":"Kingma, D.P. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","article-title":"Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","volume":"15","author":"Hodson","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_51","unstructured":"New York Independent System Operator (2019). Market Operations Report, New York Independent System Operator."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/12\/544\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:47:46Z","timestamp":1760114866000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/12\/544"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"references-count":51,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["systems12120544"],"URL":"https:\/\/doi.org\/10.3390\/systems12120544","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]}}}