IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1720-d1062785.html
   My bibliography  Save this article

Malaysia’s Electricity Decarbonisation Pathways: Exploring the Role of Renewable Energy Policies Using Agent-Based Modelling

Author

Listed:
  • Kazeem Alasinrin Babatunde

    (Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Moamin A. Mahmoud

    (Institute of Informatics and Computing in Energy, Department of Computing, College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Nazrita Ibrahim

    (Institute of Informatics and Computing in Energy, Department of Informatics, College of Computing and Informatics, Universiti Tenaga Nasional, Kajang 43000, Malaysia)

  • Fathin Faizah Said

    (Center for Sustainable and Inclusive Development Studies (SID), Faculty of Economics and Management, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia)

Abstract

Coal’s rising prominence in the power industry has raised concerns about future CO 2 emissions and energy reliability. As of 2017, it is estimated that Malaysia’s existing natural gas production can only be maintained for another 40 years. Consequently, the carbon intensity of electricity production has increased due to the increasing share of coal-fired plants and electricity infrastructure inefficiencies. To summarise, energy industries have been the highest emitters of CO 2 emissions, with a 54-percent share. In response to these challenges, the government implemented a series of renewable energy (RE) policy measures. Whether these policies are sufficient in driving Malaysian energy decarbonisation is yet to be seen. In this study, we simulated different scenarios from 2015 to 2050 with an agent-based model to explore the roles of renewable energy policies towards emission reduction in the energy sector. The simulation results reveal that when all renewables initiatives were implemented, the share of RE increased to 16 percent, and emissions intensity fell by 26 percent relative to its level in 2005, albeit with increasing absolute carbon emissions. This milestone is still far below the government’s 45 percent reduction target. The simulation results demonstrate that renewable energy policies are less effective in driving Malaysian electricity towards desired low-carbon pathways. Furthermore, it is evidenced that no single approach can achieve the emission reduction target. Therefore, a combination of energy efficiency and renewable energy policy measures is unavoidable to decarbonise the electricity sector in Malaysia.

Suggested Citation

  • Kazeem Alasinrin Babatunde & Moamin A. Mahmoud & Nazrita Ibrahim & Fathin Faizah Said, 2023. "Malaysia’s Electricity Decarbonisation Pathways: Exploring the Role of Renewable Energy Policies Using Agent-Based Modelling," Energies, MDPI, vol. 16(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1720-:d:1062785
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1720/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1720/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xifeng Wu & Sijia Zhao & Yue Shen & Hatef Madani & Yu Chen, 2020. "A Combined Multi-Level Perspective and Agent-Based Modeling in Low-Carbon Transition Analysis," Energies, MDPI, vol. 13(19), pages 1-21, September.
    2. Hansjörg Blöchliger & Sigurdur Johannesson & Marias Halldor Gestsson, 2022. "The effect of a carbon tax rise on Iceland’s economy," OECD Economics Department Working Papers 1708, OECD Publishing.
    3. Li, Lili & Taeihagh, Araz, 2020. "An in-depth analysis of the evolution of the policy mix for the sustainable energy transition in China from 1981 to 2020," Applied Energy, Elsevier, vol. 263(C).
    4. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    5. Kalisri Logeswaran Aravindan & Ramayah Thurasamy & Murali Raman & Narinasamy Ilhavenil & Sanmugam Annamalah & Arul Selvam Rathidevi, 2022. "Modeling Awareness as the Crux in Solar Energy Adoption Intention through Unified Theory of Acceptance and Use of Technology," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    6. 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.
    7. Mohd Chachuli, Fairuz Suzana & Ahmad Ludin, Norasikin & Md Jedi, Muhamad Alias & Hamid, Norul Hisham, 2021. "Transition of renewable energy policies in Malaysia: Benchmarking with data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    8. Ball-Burack, Ari & Salas, Pablo & Mercure, Jean-Francois, 2022. "Great power, great responsibility: Assessing power sector policy for the UK’s net zero target," Energy Policy, Elsevier, vol. 168(C).
    9. Derek W. Bunn and Fernando Oliveira, 2001. "An Application of Agent-based Simulation to the New Electricity Trading Arrangements of England and Wales," Computing in Economics and Finance 2001 93, Society for Computational Economics.
    10. Ardak Akhatova & Lukas Kranzl & Fabian Schipfer & Charitha Buddhika Heendeniya, 2022. "Agent-Based Modelling of Urban District Energy System Decarbonisation—A Systematic Literature Review," Energies, MDPI, vol. 15(2), pages 1-27, January.
    11. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    12. Mohd Idris, Muhammad Nurariffudin & Hashim, Haslenda & Leduc, Sylvain & Yowargana, Ping & Kraxner, Florian & Woon, Kok Sin, 2021. "Deploying bioenergy for decarbonizing Malaysian energy sectors and alleviating renewable energy poverty," Energy, Elsevier, vol. 232(C).
    13. Lindberg, Marie Byskov & Markard, Jochen & Andersen, Allan Dahl, 2019. "Policies, actors and sustainability transition pathways: A study of the EU’s energy policy mix," Research Policy, Elsevier, vol. 48(10).
    14. Babatunde, Kazeem Alasinrin & Begum, Rawshan Ara & Said, Fathin Faizah, 2017. "Application of computable general equilibrium (CGE) to climate change mitigation policy: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 61-71.
    15. Hon Chung Lau, 2022. "Evaluation of Decarbonization Technologies for ASEAN Countries via an Integrated Assessment Tool," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    16. Widha Kusumaningdyah & Tetsuo Tezuka & Benjamin C. McLellan, 2021. "Investigating Preconditions for Sustainable Renewable Energy Product–Service Systems in Retail Electricity Markets," Energies, MDPI, vol. 14(7), pages 1-21, March.
    17. Wan Syakirah Wan Abdullah & Miszaina Osman & Mohd Zainal Abidin Ab Kadir & Renuga Verayiah, 2019. "The Potential and Status of Renewable Energy Development in Malaysia," Energies, MDPI, vol. 12(12), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    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. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    2. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    3. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    4. 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.
    5. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    6. Доможиров Д. А. & Ибрагимов Н. М. & Мельникова Л. В. & Цыплаков А. А., 2017. "Интеграция подхода «затраты – выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы. Integration of input–output approach into agent-based modeling. Part 1. Methodological pr," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(1), pages 86-99.
    7. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    9. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    10. Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021. "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers 450, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    11. Oeffner, Marc, 2008. "Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation," MPRA Paper 18199, University Library of Munich, Germany, revised Oct 2009.
    12. Paul De Grauwe, 2012. "Booms and busts: New Keynesian and behavioural explanations," Chapters, in: Robert M. Solow & Jean-Philippe Touffut (ed.), What’s Right with Macroeconomics?, chapter 6, pages 149-180, Edward Elgar Publishing.
    13. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    14. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    15. Furtado, Bernardo Alves & Eberhardt, Isaque Daniel Rocha, 2015. "Modelo espacial simples da economia: uma proposta teórico-metodológica [A simple spatial economic model: a proposal]," MPRA Paper 67005, University Library of Munich, Germany.
    16. Pascal Seppecher & Isabelle L Salle & Marc Lavoie, 2018. "What drives markups? Evolutionary pricing in an agent-based stock-flow consistent macroeconomic model," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(6), pages 1045-1067.
    17. Mellár, Tamás & Hau, Orsolya & Sebestyén, Tamás, 2013. "Láthatóvá tehető-e a láthatatlan kéz? Egy ágensalapú piaci modell tapasztalatai [Can the invisible hand be rendered visible? Experiences of an agent-based market model]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 992-1024.
    18. repec:hal:spmain:info:hdl:2441/5rtilga41c899ab0rctd3cp2r3 is not listed on IDEAS
    19. Mauro Napoletano, 2018. "A Short Walk on the Wild Side: Agent-Based Models and their Implications for Macroeconomic Analysis," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(3), pages 257-281.
    20. Li, Hongyan & Sun, Junjie & Tesfatsion, Leigh, 2010. "Testing institutional arrangements via agent-based modeling: a U.S. electricity market example," ISU General Staff Papers 201002020800001088, Iowa State University, Department of Economics.
    21. Nigar Hashimzade & Gareth Myles, 2017. "Risk-based Audits in a Behavioral Model," Public Finance Review, , vol. 45(1), pages 140-165, January.

    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:gam:jeners:v:16:y:2023:i:4:p:1720-:d:1062785. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.