DOI: https://doi.org/10.14710/ijred.2023.57601

Assessing the energy efficiency of fossil fuel in ASEAN

1Department of Defence Science, Faculty of Defence Science and Technology, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia

2Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, Kuala Lumpur, Malaysia

3Department of Logistics Management and Business Administration, Faculty of Defence Studies and Management, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia

4 Department of Maritime Science & Technology, Faculty of Defence Science and Technology, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia

View all affiliations
Received: 24 Jul 2023; Revised: 5 Sep 2023; Accepted: 15 Sep 2023; Available online: 22 Sep 2023; Published: 1 Nov 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

The world's industries, transportation systems, and households rely heavily on fossil fuels despite their limited availability and high carbon content. Therefore, it is of the utmost importance to improve fossil fuel energy efficiency in order to facilitate the shift towards a sustainable energy system with reduced greenhouse gas emissions. This paper employs a slacks-based measure network data envelopment analysis model with undesirable outputs to assess the efficiencies of fossil fuel energy in the Association of Southeast Asian Nations (ASEAN) countries during a span of seven years, specifically from 2015 to 2021. The inclusion of undesirable outputs in this study is important because it allows for a more realistic assessment of efficiency by considering factors like CO2 emissions, which are undesirable outcomes associated with fossil fuel use. The datasets utilised in this study are sourced from the U.S. Energy Information Administration and the open data website of Our World in Data. Based on the findings, it can be observed that Singapore and the Philippines have demonstrated outstanding performance in maximising the utilisation of fossil fuels. In contrast, Myanmar exhibits the lowest level of efficiency in this analysis. By identifying top-performing countries in terms of fossil fuel efficiency, it is possible to implement measures to boost efficiency in under-performing countries. This can be achieved through the promotion and adoption of cleaner energy alternatives, specifically renewable energy sources that exhibit a low or negligible carbon footprint. These findings offer significant contributions to policymakers exploring sustainable energy usage, environmental stewardship, and the formulation and execution of comprehensive strategies that aim to mitigate carbon dioxide emissions arising from the consumption of fossil fuels in the ASEAN region.

Fulltext View|Download
Keywords: energy efficiency; fuel fossil; ASEAN; Data Envelopment Analysis (DEA); ranking
Funding: Universiti Pertahanan Nasional Malaysia under contract UPNM/2021/GPJP/STG/4

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Assessing the energy efficiency of fossil fuel in ASEAN Unlocking Africa’s solar and wind energy potential: A panel data analysis on the determinants of the production of electricity through solar and wind energy HOMER optimization of standalone PV/Wind/Battery powered hydrogen refueling stations located at twenty selected French cities Transition metal-based materials and their catalytic influence on MgH2 hydrogen storage: A review Retraction Notice to Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application, IJRED 11(2), 533-546 More recent articles
Most cited articles
Developing A Family-Size Biogas-Fueled Electricity Generating System Corrosion of the metal parts of diesel engines in biodiesel-based fuels Combustion of Pure, Hydrolyzed and Methyl Ester Formed of Jatropha Curcas Lin oil Castor Seed from Melkasa Agricultural Research Centre, East Showa, Ethiopia and it’s biodiesel performance in Four Stroke Diesel Engine Tax Incentive Policy for Geothermal Development: A Comparative Analysis in ASEAN More cited articles
  1. Abankina, I., Aleskerov, F., Belousova, V., Gokhberg, L., Kiselgof, S., Petrushchenko, V., Shvydun, S., & Zinkovsky, K. (2016). From equality to diversity: Classifying Russian universities in a performance oriented system. Technological Forecasting and Social Change, 103, 228–239. https://doi.org/10.1016/j.techfore.2015.10.007
  2. Abdul Rahman, A. S., Syed Ali, S. A., Isa, M. R., Ali, F., Kamaruddin, D., & Baharuddin, M. H. (2023). Performance Assessment of Malaysian Fossil Fuel Power Plants: A Data Envelopment Analysis (DEA) Approach. International Journal of Renewable Energy Development, 12(2), 247–260. https://doi.org/10.14710/ijred.2023.48487
  3. Afonso, G. P., Ferreira, D. C., & Figueira, J. R. (2023). A Network-DEA model to evaluate the impact of quality and access on hospital performance. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05362-x
  4. Amin, G. R., & Ibn Boamah, M. (2023). Modeling business partnerships: A data envelopment analysis approach. European Journal of Operational Research, 305(1), 329–337. https://doi.org/10.1016/j.ejor.2022.05.036
  5. ASEAN Centre for Energy. (2022). ASEAN Centre for Energy Institutional Profile. https://aseanenergy.sharepoint.com/PublicationLibrary/Forms/AllItems.aspx?id=%2FPublicationLibrary%2F2022%2FPublication 2022%2FACE Institutional Profile.pdf&parent=%2FPublicationLibrary%2F2022%2FPublication 2022&p=true&ga=1
  6. Bewick, V., Cheek, L., & Ball, J. (2003). Statistics review 7: Correlation and regression. Critical Care, 7(6), 451–459. https://doi.org/10.1186/cc2401
  7. Boubaker, S., Le, T. D. Q., & Ngo, T. (2023). Managing bank performance under COVID-19: A novel inverse DEA efficiency approach. International Transactions in Operational Research, 30(5), 2436–2452. https://doi.org/10.1111/itor.13132
  8. Chen, W., Zhou, K., & Yang, S. (2017). Evaluation of China’s electric energy efficiency under environmental constraints: A DEA cross efficiency model based on game relationship. Journal of Cleaner Production, 164, 38–44. https://doi.org/10.1016/j.jclepro.2017.06.178
  9. Cooper, W.W., Seiford, L.M., Zhu, J. (2004). Data Envelopment Analysis - History, Models and Interpretations. In J. (eds) Cooper, W.W., Seiford, L.M., Zhu (Ed.), Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science (pp. 1–39). Springer. https://doi.org/https://doi.org/10.1007/1-4020-7798-X_1
  10. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software: Second edition. In Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software: Second Edition. https://doi.org/10.1007/978-0-387-45283-8
  11. Cui, C., Li, S., Zhao, W., Liu, B., Shan, Y., & Guan, D. (2023). Energy-related CO2 emission accounts and datasets for 40 emerging economies in 2010-2019. Earth System Science Data, 15(3), 1317–1328. https://doi.org/10.5194/essd-15-1317-2023
  12. Dogan, N. O., & Tugcu, C. T. (2015). Energy efficiency in electricity production: A data envelopment analysis (DEA) approach for the G-20 countries. International Journal of Energy Economics and Policy, 5(1), 246–252. https://www.econjournals.com/index.php/ijeep/article/view/1043
  13. Du, M., Liu, Y., Wang, B., Lee, M., & Zhang, N. (2021). The sources of regulated productivity in Chinese power plants: An estimation of the restricted cost function combined with DEA approach. Energy Economics, 100, 105318. https://doi.org/10.1016/j.eneco.2021.105318
  14. Eurostat Statistics Explained. (2018). Glossary: Primary energy consumption. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Primary_energy_consumption#:~:text=Primary energy consumption measures the,final consumption by end users
  15. Eurostat Statistics Explained. (2023). Glossary: Carbon dioxide emissions. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Carbon_dioxide_emissions#:~:text=Carbon dioxide (CO2) is,area and period of time
  16. Geng, Q., Ren, Q., Nolan, R. H., Wu, P., & Yu, Q. (2019). Assessing China’s agricultural water use efficiency in a green-blue water perspective: A study based on data envelopment analysis. Ecological Indicators, 96(November 2017), 329–335. https://doi.org/10.1016/j.ecolind.2018.09.011
  17. Ghasemi, N., Najafi, E., Hoseinzadeh Lotfi, F., & Movahedi Sobhani, F. (2020). Assessing the performance of organizations with the hierarchical structure using data envelopment analysis: An efficiency analysis of Farhangian University. Measurement: Journal of the International Measurement Confederation, 156, 107609. https://doi.org/10.1016/j.measurement.2020.107609
  18. Hesampour, R., Hassani, M., Hanafiah, M. M., & Heidarbeigi, K. (2022). Technical efficiency, sensitivity analysis and economic assessment applying data envelopment analysis approach: A case study of date production in Khuzestan State of Iran. Journal of the Saudi Society of Agricultural Sciences, 21(3), 197–207. https://doi.org/10.1016/j.jssas.2021.08.003
  19. Iftikhar, Y., Wang, Z., Zhang, B., & Wang, B. (2018). Energy and CO2 emissions efficiency of major economies: A network DEA approach. Energy, 147, 197–207. https://doi.org/10.1016/j.energy.2018.01.012
  20. Institut national de la statistique et des etudes economiques. (2020). Total primary energy consumption. https://www.insee.fr/en/metadonnees/definition/c1705
  21. Jahangoshai Rezaee, M., & Dadkhah, M. (2019). A hybrid approach based on inverse neural network to determine optimal level of energy consumption in electrical power generation. Computers and Industrial Engineering, 134, 52–63. https://doi.org/10.1016/j.cie.2019.05.024
  22. Kumar, K., Sunil, D., & Aggarwal, A. (2020). Efficiency analysis in the management of COVID-19 pandemic in India based on data envelopment analysis. Current Research in Behavioral Sciences,2, 100063. https://doi.org/10.1016/j.crbeha.2021.100063
  23. Laloui, L., & Rotta Loria, A. F. (2020). Energy and geotechnologies. Analysis and Design of Energy Geostructures, 3–23. https://doi.org/10.1016/b978-0-12-816223-1.00001-1
  24. Linda Doman. (2017). EIA projects 48% increase in world energy consumption by 2040. U.S. Energy Information Administration
  25. Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega (United Kingdom), 41(5), 893–902. https://doi.org/10.1016/j.omega.2012.11.004
  26. Liu, Y., & Noor, R. (2020). Energy Efficiency in ASEAN: Trends and Financing Scheme. 1196, 75–75. https://www.adb.org/sites/default/files/publication/648701/adbi-wp1196.pdf
  27. Mahmoudi, R., Emrouznejad, A., Khosroshahi, H., Khashei, M., & Rajabi, P. (2019). Performance evaluation of thermal power plants considering CO2 emission: A multistage PCA, clustering, game theory and data envelopment analysis. Journal of Cleaner Production, 223, 641–650. https://doi.org/10.1016/j.jclepro.2019.03.047
  28. Mammadov, R., & Aypay, A. (2020). Efficiency analysis of research universities in Turkey. International Journal of Educational Development, 75(March). https://doi.org/10.1016/j.ijedudev.2020.102176
  29. Mintz-Woo, K. (2022). Fossil Fuels. The Routledge Companion to Environmental Ethics. https://doi.org/10.4324/9781315768090-32
  30. MIT Climate Portal Writing Team. (2022). Why does burning coal generate more CO2 than oil or gas? Massachusetts Institute of Technology Cambridge. https://climate.mit.edu/ask-mit/why-does-burning-coal-generate-more-co2-oil-or-gas#:~:text=Coal contains more carbon than,O along with CO2
  31. Muhammad Adib, H. R., Sharifah Aishah, S. A., Ahmad Shafiq, A. R., Lee, L. S., & Zuraida, A. A. (2023). A comparative analysis of the performance of Takaful and conventional companies in Malaysia. In A. A. Zuraida, S. A. Sharifah Aishah, & L. S. Lee (Eds.), Operational Research and Analytics in Practice: Theory, Methods, and Applications (pp. 139–163). UTeM Press
  32. National Climate Change Secretariat (NCCS). (2016). Singapore’s Climate Action Plan: Take action today for a carbon-efficient Singapore
  33. National Climate Change Secretariat (NCCS). (2021). Singapore to Phase Out Unabated Coal Power by 2050. https://www.nccs.gov.sg/media/press-release/sg-phase-out-unabated-coal#:~:text=Since independence%2C Singapore’s reliance on,Event on 4 November 2021
  34. National Climate Change Secretariat (NCCS). (2022). Singapore And International Efforts. https://www.nccs.gov.sg/singapores-climate-action/singapore-and-international-efforts/#:~:text=In October 2022%2C Singapore announced,part of its 2030 NDC
  35. Onder, O., Cook, W., & Kristal, M. (2022). Does quality help the financial viability of hospitals? A data envelopment analysis approach. Socio-Economic Planning Sciences, 79(June 2021), 101105. https://doi.org/10.1016/j.seps.2021.101105
  36. Pan, Z., Tang, D., Kong, H., & He, J. (2022). An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model. International Journal of Environmental Research and Public Health, 19(2). https://doi.org/10.3390/ijerph19020958
  37. Ritchie, H., Roser, M., & Rosado, P. (2023). Energy. Our World In Data. https://ourworldindata.org/energy
  38. Robaina, M., & Arshad, Z. (2020). The role of energy efficiency in CO2mitigation - Economy wide rebound effect in ASEAN countries. International Conference on the European Energy Market, EEM, 2020-Septe(i). https://doi.org/10.1109/EEM49802.2020.9221941
  39. Sueyoshi, T., Liu, X., & Li, A. (2020). Evaluating the performance of Chinese fossil fuel power plants by data environment analysis: An application of three intermediate approaches in a time horizon. Journal of Cleaner Production, 277, 121992. https://doi.org/10.1016/j.jclepro.2020.121992
  40. Sueyoshi, T., Qu, J., Li, A., & Xie, C. (2020). Understanding the efficiency evolution for the Chinese provincial power industry: A new approach for combining data envelopment analysis-discriminant analysis with an efficiency shift across periods. Journal of Cleaner Production, 277, 122371. https://doi.org/10.1016/j.jclepro.2020.122371
  41. Sun, Q., & Sui, Y.-J. (2023). Agricultural Green Ecological Efficiency Evaluation Using BP Neural Network–DEA Model. Systems, 11(6), 291. https://doi.org/10.3390/systems11060291
  42. The ASEAN Secretariat. (2020). About ASEAN. https://asean.org/about-asean
  43. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509. https://doi.org/https://doi.org/10.1016/S0377-2217(99)00407-5
  44. U.S. Energy Information Administration. (2021). International Energy Outlook 2021. https://www.eia.gov/outlooks/ieo/
  45. U.S. Energy Information Administration. (2023). Data. https://www.eia.gov/international/data/world
  46. Ullah, S., Majeed, A., & Popp, J. (2023). Determinants of bank’s efficiency in an emerging economy: A data envelopment analysis approach. PLoS ONE, 18(3 March), 1–17. https://doi.org/10.1371/journal.pone.0281663
  47. Vinay Trivedi. (2021). Coal-based thermal power in southeast Asian countries (Arif Ayaz Parrey (ed.)). Centre for Science and Environment
  48. Wang, Z. H., Zeng, H. L., Wei, Y. M., & Zhang, Y. X. (2012). Regional total factor energy efficiency: An empirical analysis of industrial sector in China. Applied Energy, 97, 115–123. https://doi.org/10.1016/j.apenergy.2011.12.071
  49. World Nuclear Association. (2022). Carbon Dioxide Emissions From Electricity. https://www.world-nuclear.org/information-library/energy-and-the-environment/carbon-dioxide-emissions-from-electricity.aspx#:~:text=Worldwide emissions of carbon dioxide,2 and other greenhouse gases
  50. Yang, T., Chen, W., Zhou, K., & Ren, M. (2018). Regional energy efficiency evaluation in China: A super efficiency slack-based measure model with undesirable outputs. Journal of Cleaner Production, 198, 859–866. https://doi.org/10.1016/j.jclepro.2018.07.098
  51. Yang, Z., & Wei, X. (2019). The measurement and influences of China’s urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA. Journal of Cleaner Production, 209, 439–450. https://doi.org/10.1016/j.jclepro.2018.10.271
  52. Yuichi Shiga. (2022). Philippines’ shift away from coal expands with Ayala deal. Nikkei Asia. https://asia.nikkei.com/Business/Energy/Philippines-shift-away-from-coal-expands-with-Ayala-deal
  53. Zhang, N., Zhao, Y., & Wang, N. (2022). Is China’s energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets. Energy Economics, 112(June), 106143. https://doi.org/10.1016/j.eneco.2022.106143

Last update:

No citation recorded.

Last update: 2024-11-11 10:33:22

No citation recorded.