IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v400y2025ics0306261925013200.html

An agent-based approach to modeling power firms' emission reduction strategies and market dynamics

Author

Listed:
  • Liu, Songyuan
  • Zhou, Peng
  • Wang, Mei
  • Xu, Aobo

Abstract

This paper develops an agent-based model to study the impact of China's national carbon market on the decision-making behaviors of power generation firms by accounting for their multidimensional heterogeneity. The multi-agent deep deterministic policy gradient algorithm is employed to optimize firm strategies in production, low-carbon technology adoption, and allowance trading. An application study is conducted by using the real data from over 3000 Chinese power firms and the real price data from the China national carbon market. The modeling results show that tightening emission reduction targets leads to higher carbon and power prices, greater renewable energy generation, and increased adoption of low-carbon technologies. Additionally, our study highlights the critical role of allowance allocation methods, with auction-based rule providing more stable and higher carbon price signals that incentivize earlier emission reductions. The research also identifies a disparity between large-scale and small-medium-scale firms in terms of participation in allowance trading and low-carbon technology adoption, with larger firms leading in both areas. The findings offer valuable insights for enhancing the cost-effectiveness and incentive mechanisms of carbon market.

Suggested Citation

  • Liu, Songyuan & Zhou, Peng & Wang, Mei & Xu, Aobo, 2025. "An agent-based approach to modeling power firms' emission reduction strategies and market dynamics," Applied Energy, Elsevier, vol. 400(C).
    • Handle: RePEc:eee:appene:v:400:y:2025:i:c:s0306261925013200
    DOI: 10.1016/j.apenergy.2025.126590
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925013200
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.126590?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Loomans, Naud & Alkemade, Floor, 2024. "Exploring trade-offs: A decision-support tool for local energy system planning," Applied Energy, Elsevier, vol. 369(C).
    2. Yu, Song-min & Fan, Ying & Zhu, Lei & Eichhammer, Wolfgang, 2020. "Modeling the emission trading scheme from an agent-based perspective: System dynamics emerging from firms’ coordination among abatement options," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1113-1128.
    3. Kelly Sims Gallagher & Fang Zhang & Robbie Orvis & Jeffrey Rissman & Qiang Liu, 2019. "Assessing the Policy gaps for achieving China’s climate targets in the Paris Agreement," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    4. Bunn, Derek W. & Oliveira, Fernando S., 2007. "Agent-based analysis of technological diversification and specialization in electricity markets," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1265-1278, September.
    5. Sun, Qingkai & Wang, Xiaojun & Liu, Zhao & Mirsaeidi, Sohrab & He, Jinghan & Pei, Wei, 2022. "Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market," Applied Energy, Elsevier, vol. 324(C).
    6. Wang, Baixue & Duan, Maosheng, 2022. "Consignment auctions of emissions trading systems: An agent-based approach based on China’s practice," Energy Economics, Elsevier, vol. 112(C).
    7. Rego, Erik Eduardo & Costa, Oswaldo L.V. & Ribeiro, Celma de Oliveira & Lima Filho, Roberto Ivo da R. & Takada, Hellinton & Stern, Julio, 2020. "The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints," Energy, Elsevier, vol. 213(C).
    8. Dissanayake, Sumali & Mahadevan, Renuka & Asafu-Adjaye, John, 2020. "Evaluating the efficiency of carbon emissions policies in a large emitting developing country," Energy Policy, Elsevier, vol. 136(C).
    9. Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    10. Petrović, Marko & Ozel, Bulent & Teglio, Andrea & Raberto, Marco & Cincotti, Silvano, 2020. "Should I stay or should I go? An agent-based setup for a trading and monetary union," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    11. Mei Wang & Peng Zhou, 2020. "Impact of Permit Allocation on Cap-and-trade System Performance under Market Power," The Energy Journal, , vol. 41(6), pages 215-232, November.
    12. Wu, Jiaqian & Zheng, Xiaolin & Yu, Songmin & Yu, Lean, 2024. "Modeling multi-market coupling effects considering the consumption above quota trading market in renewable portfolio standards: An agent-based perspective," Energy Economics, Elsevier, vol. 138(C).
    13. Richard G. Newell & William A. Pizer & Daniel Raimi, 2014. "Carbon Markets: Past, Present, and Future," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 191-215, October.
    14. repec:aen:journl:ej41-6-zhou is not listed on IDEAS
    15. Junming Zhu & Yichun Fan & Xinghua Deng & Lan Xue, 2019. "Low-carbon innovation induced by emissions trading in China," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    16. Franco, Carlos J. & Castaneda, Monica & Dyner, Isaac, 2015. "Simulating the new British Electricity-Market Reform," European Journal of Operational Research, Elsevier, vol. 245(1), pages 273-285.
    17. Chenhao Fang & Tieju Ma, 2021. "Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price," Annals of Operations Research, Springer, vol. 300(2), pages 577-600, May.
    18. Auke Hoekstra & Maarten Steinbuch & Geert Verbong, 2017. "Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation," Complexity, Hindawi, vol. 2017, pages 1-23, December.
    19. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
    20. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    21. Innocent Bakam & Robin Matthews, 2009. "Emission trading in agriculture: a study of design options using an agent-based approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 14(8), pages 755-776, December.
    22. Guo, Xiaopeng & Zhang, Xinyue & Zhang, Xingping, 2024. "Incentive-oriented power‑carbon emissions trading-tradable green certificate integrated market mechanisms using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 357(C).
    23. Naegele, Helene & Zaklan, Aleksandar, 2019. "Does the EU ETS cause carbon leakage in European manufacturing?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 93, pages 125-147.
    24. Romagnoli, Francesco & Barisa, Aiga & Dzene, Ilze & Blumberga, Andra & Blumberga, Dagnija, 2014. "Implementation of different policy strategies promoting the use of wood fuel in the Latvian district heating system: Impact evaluation through a system dynamic model," Energy, Elsevier, vol. 76(C), pages 210-222.
    25. Branger, Frédéric & Quirion, Philippe, 2015. "Reaping the carbon rent: Abatement and overallocation profits in the European cement industry, insights from an LMDI decomposition analysis," Energy Economics, Elsevier, vol. 47(C), pages 189-205.
    26. Flora, Maria & Vargiolu, Tiziano, 2020. "Price dynamics in the European Union Emissions Trading System and evaluation of its ability to boost emission-related investment decisions," European Journal of Operational Research, Elsevier, vol. 280(1), pages 383-394.
    27. Zhou, Yanting & Ma, Zhongjing & Shi, Xingyu & Zou, Suli, 2024. "Multi-agent optimal scheduling for integrated energy system considering the global carbon emission constraint," Energy, Elsevier, vol. 288(C).
    28. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    29. Li, Li & Wang, Jing & Zhong, Xiaoyi & Lin, Jian & Wu, Nianyuan & Zhang, Zhihui & Meng, Chao & Wang, Xiaonan & Shah, Nilay & Brandon, Nigel & Xie, Shan & Zhao, Yingru, 2022. "Combined multi-objective optimization and agent-based modeling for a 100% renewable island energy system considering power-to-gas technology and extreme weather conditions," Applied Energy, Elsevier, vol. 308(C).
    30. Arkady Zgonnikov & Ihor Lubashevsky, 2014. "Unstable Dynamics Of Adaptation In Unknown Environment Due To Novelty Seeking," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(03n04), pages 1-17.
    31. Zhou, P. & Wen, Wen, 2020. "Carbon-constrained firm decisions: From business strategies to operations modeling," European Journal of Operational Research, Elsevier, vol. 281(1), pages 1-15.
    32. Zhu, Rongqi & Wei, Yigang & Tan, Longyan, 2024. "Low-carbon technology adoption and diffusion with heterogeneity in the emissions trading scheme," Applied Energy, Elsevier, vol. 369(C).
    33. Richstein, Jörn C. & Chappin, Emile J.L. & de Vries, Laurens J., 2014. "Cross-border electricity market effects due to price caps in an emission trading system: An agent-based approach," Energy Policy, Elsevier, vol. 71(C), pages 139-158.
    34. Nguyen, Le Khanh Ngan & Howick, Susan & Megiddo, Itamar, 2024. "A framework for conceptualising hybrid system dynamics and agent-based simulation models," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1153-1166.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Guixiang & Zhang, Hao & Qiu, Lin, 2026. "Graph-based multi-agent reinforcement learning with an enriched environment for joint ride-sharing and charging optimization," Applied Energy, Elsevier, vol. 405(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Mei & Liu, Songyuan & Liu, Jiageng & Li, Zhengjun, 2025. "Agent-based modeling of firm's heterogeneous preferences: Implications for trading and technology adoption in electricity-carbon markets," Energy Economics, Elsevier, vol. 148(C).
    2. Wei, Yigang & Liang, Xin & Xu, Liang & Kou, Gang & Chevallier, Julien, 2023. "Trading, storage, or penalty? Uncovering firms' decision-making behavior in the Shanghai emissions trading scheme: Insights from agent-based modeling," Energy Economics, Elsevier, vol. 117(C).
    3. Li, Pei-Hao & Barazza, Elsa & Strachan, Neil, 2022. "The influences of non-optimal investments on the scale-up of smart local energy systems in the UK electricity market," Energy Policy, Elsevier, vol. 170(C).
    4. Duan, Mengmeng & Wang, Mei & Zhou, Xianyang & Zhou, Dequn & Wu, Fei, 2025. "Synergy between emissions trading system and low-carbon consumption awareness: A multi-agent simulation analysis," Energy Economics, Elsevier, vol. 151(C).
    5. Yu, Song-min & Fan, Ying & Zhu, Lei & Eichhammer, Wolfgang, 2020. "Modeling the emission trading scheme from an agent-based perspective: System dynamics emerging from firms’ coordination among abatement options," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1113-1128.
    6. Weiss, Olga & Bogdanov, Dmitry & Salovaara, Kaisa & Honkapuro, Samuli, 2017. "Market designs for a 100% renewable energy system: Case isolated power system of Israel," Energy, Elsevier, vol. 119(C), pages 266-277.
    7. Wu, Jiaqian & Liang, Yiduo & Yu, Songmin & Yu, Lean, 2025. "System dynamics emerging from compliance behaviors coordination of electricity consumers under renewable portfolio standards in China: An agent-based analysis," Energy, Elsevier, vol. 321(C).
    8. Zhu, Rongqi & Wei, Yigang & Tan, Longyan, 2024. "Low-carbon technology adoption and diffusion with heterogeneity in the emissions trading scheme," Applied Energy, Elsevier, vol. 369(C).
    9. Barazza, Elsa & Strachan, Neil, 2020. "The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition," Energy Policy, Elsevier, vol. 138(C).
    10. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    11. Qiang Tu & Limei Zuo & Jianlei Mo & Yingnan Wang, 2026. "Carbon pricing, green innovation, and production efficiency—new evidence from China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 31(3), pages 1-32, March.
    12. Du, Minzhe & Wu, Fenger & Luo, Lichun & Wang, Qiya & Liao, Liping, 2025. "Spatial effects of the market-based energy allocation on energy efficiency: A quasi-natural experiment of energy quota trading," Energy, Elsevier, vol. 318(C).
    13. Xiande, Zhang & Chonghui, Fu & Pengcheng, Xie & Yajie, Bo & Feng, Pan & Wenjun, Wang, 2025. "Carbon price prediction model based on multi-agent and environment co-evolution," Energy, Elsevier, vol. 328(C).
    14. Marc Deissenroth & Martin Klein & Kristina Nienhaus & Matthias Reeg, 2017. "Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration," Complexity, Hindawi, vol. 2017, pages 1-24, December.
    15. Zhang, Yue-Jun & Cheng, Hao-Sen & Wang, Xia & Liu, Jing-Yue, 2025. "The impact of China’s carbon emissions trading system on energy justice," Structural Change and Economic Dynamics, Elsevier, vol. 74(C), pages 483-492.
    16. Wang, Dan & Sun, Mili & Meng, Bo & An, Yunbi & Cheng, Wenyin & Ye, Bin, 2024. "Can carbon market efficiency promote green technology innovation for Chinese companies?," Energy, Elsevier, vol. 309(C).
    17. Nathalia Wolf & Pablo Escalona & Mónica López-Campos & Alejandro Angulo & Jorge Weston, 2023. "On Carbon Tax Effectiveness in Inducing a Clean Technology Transition: An Evaluation Based on Optimal Strategic Capacity Planning," Sustainability, MDPI, vol. 15(15), pages 1-23, July.
    18. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2017. "How to benefit from a common European electricity market design," Energy Policy, Elsevier, vol. 101(C), pages 629-643.
    19. Wu, F. & Wang, S.Y. & Zhou, P., 2023. "Marginal abatement cost of carbon dioxide emissions: The role of abatement options," European Journal of Operational Research, Elsevier, vol. 310(2), pages 891-901.
    20. Liu, Yinan & Lv, Peiyao & Zhao, Hao, 2024. "Green innovation through trade: The impact of European Union emissions trading scheme on Chinese exporters," Journal of International Money and Finance, Elsevier, vol. 149(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:400:y:2025:i:c:s0306261925013200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.