IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v298y2021ics0306261921005584.html
   My bibliography  Save this article

How does a carbon tax affect Britain’s power generation composition?

Author

Listed:
  • Atherton, John
  • Xie, Wanni
  • Aditya, Leonardus Kevin
  • Zhou, Xiaochi
  • Karmakar, Gourab
  • Akroyd, Jethro
  • Mosbach, Sebastian
  • Lim, Mei Qi
  • Kraft, Markus

Abstract

The purpose of this paper is to determine the effect of different carbon tax rates on the power generation composition of Britain. This was accomplished via a regional, geospatial model, accounting for regional loads, transmission losses and generators of Britain’s current energy infrastructure. This regional model is also compared to a pure dispatch, nationally aggregated model which considers only costs on the generator side inclusive of the carbon tax, thus allowing the effect of including geospatial conditions to be identified. The effect of this tax (in both the geospatial and nationally aggregated cases) is a transition from coal to combined cycle gas turbine (CCGT) generated power to fulfil demand unmet by nuclear or renewable sources. The more sophisticated regional model, however, differs from the nationally aggregated case by having a significantly larger window of carbon tax rates over which this coal to CCGT transition occurs. Due regional differences in demand and installed capacity technology types it is determined that more than 50% of this transition occurs prior to CCGT becoming more economical than coal from a pure dispatch (nationally aggregated) perspective. Primarily due to CCGT generators typically being closer to larger southern loads than northern coal, transmission losses and the economic disincentive of a carbon tax combine in encouraging this transition. The transition window, therefore, is not only broadened by the consideration of geospatial effects, but furthermore, this broadening significantly and disproportionately occurs by decreasing the lower bound of this transition window. These findings validate the significance of utilising a geospatial model, particularly of regional resolution. They further identify the deployment of current energy infrastructure in Britain under differing carbon tax regimes and by extension, the transition window (found to be from coal to CCGT) an increasing carbon tax rate would create. These results bear not only significance in understanding the UK’s currently incrementing (top-up) carbon tax rate, but also shed light on future policies due to the UK’s leaving of the EU’s Emissions Trading Scheme (ETS), with immediate plans to continue with a domestic carbon tax and trading scheme. Thus, these results hold importance in the understanding the effect of carbon taxation on existing infrastructure, energy modelling and national policy in the UK.

Suggested Citation

  • Atherton, John & Xie, Wanni & Aditya, Leonardus Kevin & Zhou, Xiaochi & Karmakar, Gourab & Akroyd, Jethro & Mosbach, Sebastian & Lim, Mei Qi & Kraft, Markus, 2021. "How does a carbon tax affect Britain’s power generation composition?," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921005584
    DOI: 10.1016/j.apenergy.2021.117117
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921005584
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117117?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    2. Cosimo Magazzino, 2017. "The relationship among economic growth, CO2 emissions, and energy use in the APEC countries: a panel VAR approach," Environment Systems and Decisions, Springer, vol. 37(3), pages 353-366, September.
    3. Ekins, Paul, 1994. "The impact of carbon taxation on the UK economy," Energy Policy, Elsevier, vol. 22(7), pages 571-579, July.
    4. Barker, Terry & Baylis, Susan & Madsen, Peter, 1993. "A UK carbon/energy tax : The macroeconomics effects," Energy Policy, Elsevier, vol. 21(3), pages 296-308, March.
    5. Tezuka, Tetsuo & Okushima, Keisuke & Sawa, Takamitsu, 2002. "Carbon tax for subsidizing photovoltaic power generation systems and its effect on carbon dioxide emissions," Applied Energy, Elsevier, vol. 72(3-4), pages 677-688, July.
    6. G. Cornelis van Kooten, 2016. "Wind versus Nuclear Options for Generating Electricity in a Carbon Constrained World: Proceedings of the CSME International Congress 2016," Working Papers 2016-06, University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group.
    7. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
    8. Cabral, Renato P. & Mac Dowell, Niall, 2017. "A novel methodological approach for achieving £/MWh cost reduction of CO2 capture and storage (CCS) processes," Applied Energy, Elsevier, vol. 205(C), pages 529-539.
    9. Azadeh, A. & Tarverdian, S., 2007. "Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, Elsevier, vol. 35(10), pages 5229-5241, October.
    10. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    11. Köne, Aylin Çigdem & Büke, Tayfun, 2010. "Forecasting of CO2 emissions from fuel combustion using trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2906-2915, December.
    12. Wang, B. & Liu, L. & Huang, G.H. & Li, W. & Xie, Y.L., 2018. "Effects of carbon and environmental tax on power mix planning - A case study of Hebei Province, China," Energy, Elsevier, vol. 143(C), pages 645-657.
    13. Li, Chiao-Ting & Peng, Huei & Sun, Jing, 2013. "Reducing CO2 emissions on the electric grid through a carbon disincentive policy," Energy Policy, Elsevier, vol. 60(C), pages 793-802.
    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. Huang, Qian & Xu, Jiuping, 2023. "Carbon tax revenue recycling for biomass/coal co-firing using Stackelberg game: A case study of Jiangsu province, China," Energy, Elsevier, vol. 272(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. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    2. Xu, Mengmeng & Lin, Boqiang & Wang, Siquan, 2021. "Towards energy conservation by improving energy efficiency? Evidence from China’s metallurgical industry," Energy, Elsevier, vol. 216(C).
    3. Zhou, Yinbo & Li, Hansheng & Huang, Jilei & Zhang, Ruilin & Wang, Shijie & Hong, Yidu & Yang, Yongliang, 2021. "Influence of coal deformation on the Knudsen number of gas flow in coal seams," Energy, Elsevier, vol. 233(C).
    4. Azimi, Seyyed Shahabeddin & Namazi, Mohammad Hosain, 2015. "Modeling of combustion of gas oil and natural gas in a furnace: Comparison of combustion characteristics," Energy, Elsevier, vol. 93(P1), pages 458-465.
    5. Ying, Zhou & Xin-gang, Zhao, 2021. "The impact of Renewable Portfolio Standards on carbon emission trading under the background of China’s electricity marketization reform," Energy, Elsevier, vol. 226(C).
    6. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2016. "Ant Lion Optimization Algorithm for Renewable Distributed Generations," Energy, Elsevier, vol. 116(P1), pages 445-458.
    7. Kim, Hyung Woo & Seo, Su Been & Kang, Seo Yeong & Go, Eun Sol & Oh, Seung Seok & Lee, YongWoon & Yang, Won & Lee, See Hoon, 2021. "Effect of flue gas recirculation on efficiency of an indirect supercritical CO2 oxy-fuel circulating fluidized bed power plant," Energy, Elsevier, vol. 227(C).
    8. Bilgili, Faik & Koçak, Emrah & Bulut, Ümit & Sualp, M. Nedim, 2016. "How did the US economy react to shale gas production revolution? An advanced time series approach," Energy, Elsevier, vol. 116(P1), pages 963-977.
    9. Song, Rui & Feng, Xiaoyu & Wang, Yao & Sun, Shuyu & Liu, Jianjun, 2021. "Dissociation and transport modeling of methane hydrate in core-scale sandy sediments: A comparative study," Energy, Elsevier, vol. 221(C).
    10. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    11. Golberg, Alexander, 2015. "Environmental exergonomics for sustainable design and analysis of energy systems," Energy, Elsevier, vol. 88(C), pages 314-321.
    12. Shen, Peiliang & Jiang, Yi & Zhang, Yangyang & Liu, Songhui & Xuan, Dongxing & Lu, Jianxin & Zhang, Shipeng & Poon, Chi Sun, 2023. "Production of aragonite whiskers by carbonation of fine recycled concrete wastes: An alternative pathway for efficient CO2 sequestration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    13. Bartolini, Andrea & Mazzoni, Stefano & Comodi, Gabriele & Romagnoli, Alessandro, 2021. "Impact of carbon pricing on distributed energy systems planning," Applied Energy, Elsevier, vol. 301(C).
    14. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    15. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    16. Miaomiao Tao & Pierre Failler & Lim Thye Goh & Wee Yeap Lau & Hanghang Dong & Liang Xie, 2022. "Quantify the Effect of China’s Emission Trading Scheme on Low-carbon Eco-efficiency: Evidence from China’s 283 Cities," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(6), pages 1-33, August.
    17. Freyre, Alisa & Klinke, Sandra & Patel, Martin K., 2020. "Carbon tax and energy programs for buildings: Rivals or allies?," Energy Policy, Elsevier, vol. 139(C).
    18. Sun, Wantong & Wei, Na & Zhao, Jinzhou & Kvamme, Bjørn & Zhou, Shouwei & Zhang, Liehui & Almenningen, Stian & Kuznetsova, Tatiana & Ersland, Geir & Li, Qingping & Pei, Jun & Li, Cong & Xiong, Chenyang, 2022. "Imitating possible consequences of drilling through marine hydrate reservoir," Energy, Elsevier, vol. 239(PA).
    19. Shmelev, Stanislav E. & Speck, Stefan U., 2018. "Green fiscal reform in Sweden: Econometric assessment of the carbon and energy taxation scheme," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 969-981.
    20. Marco Mele & Cosimo Magazzino & Nicolas Schneider & Antonia Rosa Gurrieri & Hêriş Golpira, 2022. "Innovation, income, and waste disposal operations in Korea: evidence from a spectral granger causality analysis and artificial neural networks experiments," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 427-459, July.

    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:298:y:2021:i:c:s0306261921005584. 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.