A Long-Term Electricity Consumption Model adapted from the MMM-ARCH-M Framework for Strategic Greenhouse Gas Emission Reduction towards Smart City Goals in Thailand
Received: 27 January 2025 | Revised: 17 February 2025 and 7 March 2025 | Accepted: 13 March 2025 | Online: 4 June 2025
Corresponding author: Supannika Wattana
Abstract
This research aims to identify the factors for formulating management strategies that enhance energy consumption efficiency in the electricity sector to achieve the long-term goal of reducing greenhouse gas emissions and transitioning towards Smart City Thailand. The research employs a quantitative approach by developing an advanced model known as the Moderated Mediation Model based on Autoregressive Conditionally Heteroscedastic in Mean (MMM-ARCH-M). This model incorporates white noise and the best model methodology, serving as a decision-making tool for future national development. It fills gaps found in previous models, yielding more accurate and precise future forecasts. Additionally, the model demonstrates high validity, making it applicable to other sectors. The research findings reveal that the government needs to establish the most appropriate new scenario policies to develop long-term (2025-2044) national management strategies under sustainability policies aimed at achieving Smart City Thailand. The research identifies critical indicators that need to be immediately and urgently implemented nationwide through enforceable legislation. These indicators include clean technology, waste biomass, renewable energy, green material rate, and biomass energy. If the government adopts these indicators for national management, the total energy consumption growth rate (2044/2025) will increase by only 90.59%, which is lower than the defined carrying capacity of 150.45%. Furthermore, CO2 gas emissions are found to decrease by 35.09%, with CO2 emissions reaching 42.50 Mt CO2 Eq. by 2044, which is within Thailand's carrying capacity limit of 50.07 Mt CO₂ Eq. Thus, this model is highly beneficial as a decision-making tool for national management, supporting the realization of Smart City Thailand and ensuring long-term sustainability.
Keywords:
new scenario policy, Smart City Thailand, smart government, sustainability policy, smart environment, smart energyDownloads
References
"Energy use (kg of oil equivalent per capita)." World Bank Open Data. https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE.
"Office of the National Economic and Social Development Council." Nesdc. https://www.nesdc.go.th/nesdb_en/main.php?filename=index.
Ministry of Digital Economy and Society, Announcement of the Subcommittee on Smart City Promotion and Management on the Award of the Smart City Certification; No. 1, 2021, Bangkok, Thailand.
"Depa Thailand - Digital service View." Depa. https://www.depa.or.th/en/digitalservice/smartcity/smart%20city%20manaul.
Thailand Greenhouse Gas Management Organization (Public Organization). "Net Zero GHG Emissions." Thaicarbonlabel. https://thaicarbonlabel.tgo.or.th/tools/files.php?mod=YjNKbllXNXBlbUYwYVc5dVgyUnZkMjVzYjJGaw&type=WDBaSlRFVlQ&files=TkRrPQ.
"Department of Alternative Energy Development and Efficiency." Dede. https://www.dede.go.th/.
"National Statistical Office Ministry of Information and Communication Technology." Nso. https://www.nso.go.th/nsoweb/index?set_lang=en.
"Historical crude oil import prices (indicator)." Organization for Economic Co-operation and Development. https://www.oecd.org/en/data/indicators/crude-oil-import-prices.html.
"Thailand’s Fourth Biennial Update Report." Office of Natural Resources and Environmental Policy and Planning. https://www.onep.go.th/book/thailands-fourth-biennial-update-report/.
P. Sutthichaimethee, "A Framework on Setting Strategies for Enhancing the Efficiency of State Power use in Thailand’s Pursuit of a Green Economy," International Journal of Energy Economics and Policy, vol. 14, no. 1, pp. 108–120, Jan. 2024.
United Nations Framework Convention on Climate Change, "The Paris Agreement: An Early Assessment," Environmental Policy and Law, vol. 46, no. 1, pp. 485-488, 2014.
United Nations Framework Convention on Climate Change, "Global Progress in Environmental Law," Environmental Policy and Law, vol. 46, no. 1, pp. 23–27, 2016.
P. Sutthichaimethee, "Forecasting Economic, Social and Environmental Growth in the Sanitary and Service Sector Based on Thailand’s Sustainable Development Policy," Journal of Ecological Engineering, vol. 19, no. 1, pp. 205–210, Jan. 2018.
Pollution Control Department Ministry of Natural Resources and Environment. "Enhancement and Conservation of National Environmental Quality Act, B.E. 2535." Pcd. https://www.pcd.go.th/laws/5406/.
Thailand Development Research Institute. "Carbon Capture Utilization, Carbon Capture Utilization and Storage." Tdri. https://tdri.or.th/lowcarbon-landing/.
Pollution Control Department Ministry of Natural Resources and Environment. "Principle 4: In Order to Achieve Sustainable Development, Environmental Protection Shall Constitute an Integral Part of the Development Process and Cannot Be Considered in Isolation from It." Pcd. https://www.pcd.go.th/laws/.
B. Wattana, S. Wattana, W. Sa-Ngiamvibool, P. Thanarak, and J. Luo, "Electric vehicles penetration in Thailand: Rationale, challenges and strategies," Journal of Infrastructure, Policy and Development, vol. 8, no. 8, p. 6829, Aug. 2024.
B. Wattana and S. Wattana, "Implications of electric vehicle promotion policy on the road transport and electricity sectors for Thailand," Energy Strategy Reviews, vol. 42, Jul. 2022, Art. no. 100901.
C. Junsiri, P. Sutthichaimethee, and N. Phong-a-ran, "Modeling CO2 Emission Forecasting in Energy Consumption of the Industrial Building Sector under Sustainability Policy in Thailand: Enhancing the LISREL-LGM Model," Forecasting, vol. 6, no. 3, pp. 485–501, Sep. 2024.
Department of Alternative Energy Development and Efficiency. " Energy balance of Thailand 2022." Dede. https://www.dede.go.th/.
M. Mishina, S. Mityagin, A. Belyi, A. Khrulkov, and S. Sobolevsky, "Towards Urban Accessibility: Modeling Trip Distribution to Assess the Provision of Social Facilities," Smart Cities, vol. 7, no. 5, pp. 2741–2762, Oct. 2024.
Y. Wu, P. Martens, and T. Krafft, "Transitioning to a Low-Carbon Lifestyle? An Exploration of Millennials’ Low-Carbon Behavior—A Case Study in China," Smart Cities, vol. 7, no. 4, pp. 2015–2041, Aug. 2024.
Z. A. Shaikh, "Keyword Detection Techniques: A Comprehensive Study," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2590–2594, Feb. 2018.
Z. A. Shaikh, A. Kraikin, A. Mikhaylov, and G. Pinter, "Forecasting Stock Prices of Companies Producing Solar Panels Using Machine Learning Methods," Complexity, vol. 2022, no. 1, Oct. 2022, Art. no. 9186265.
Z. A. Shaikh, "Towards Sustainable Development: A Review of Green Technologies," Trends in Renewable Energy, vol. 4, no. 1, pp. 1–14, Nov. 2017.
D. A. Dickey and W. A. Fuller, "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, vol. 49, no. 4, pp. 1057–1072, Jul. 1981.
J. G. MacKinnon, "Critical Values for Cointegration Tests," in Long- Run Economic Relationships, R. F. Engle and C. W. J. Granger, Eds. Oxford, U.K.: Oxford University Press, 1991, pp. 267–276.
S. Johansen, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford, UK: Oxford University Press, 1995.
S. Johansen and K. Juselius, "Maximum Likelihood Estimation and Inference on Cointegration — with Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, vol. 52, no. 2, pp. 169–210, May 1990.
W. Enders, Applied Econometric Time Series, 4th ed. Hoboken, NJ, USA: Wiley, 2014.
A. C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge, UK: Cambridge University Press, 1990.
K. Dorji, S. Jittanon, P. Thanarak, P. Mensin, and C. Termritthikun, "Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 17931–17938, Dec. 2024.
M. Frikha, K. Taouil, A. Fakhfakh, and F. Derbel, "Predicting Power Consumption Using Deep Learning with Stationary Wavelet," Forecasting, vol. 6, no. 3, pp. 864–884, Sep. 2024.
R. A. Ramadan and S. Boubaker, "Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13578–13583, Apr. 2024.
Downloads
How to Cite
License
Copyright (c) 2025 Pruethsan Sutthichaimethee, Worawat Sa-Ngiamvibool, Buncha Wattana, Jianhui Luo, Supannika Wattana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
