IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2021-05-69.html
   My bibliography  Save this article

Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework

Author

Listed:
  • Pruethsan Sutthichaimethee

    (Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Bangkok 10330, Thailand)

  • Danupon Ariyasajjakorn

    (Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Bangkok 10330, Thailand)

  • Apinyar Chatchorfa

    (Department of Political Sciences, Faculty of Social Sciences, Mahachulalongkornrajavidyalaya University, Phahon Yothin Road, Kilometer 55 Lam Sai, Wang Noi, Phra Nakhon Si Ayutthaya 13170, Thailand,)

  • Boonton Dockthaisong

    (Department of Political Sciences, Faculty of Social Sciences, Mahachulalongkornrajavidyalaya University, Phahon Yothin Road, Kilometer 55 Lam Sai, Wang Noi, Phra Nakhon Si Ayutthaya 13170, Thailand,)

  • Sthianrapab Naluang

    (School of Law, Assumption University, 592/3 Ramkhamhaeng 24, Hua Mak, Bangkok 10240; Thailand)

  • Sirapatsorn Wongthongdee

    (Faculty of Public Administration, Dhurakij Pundit University, 110/1-4 Prachachuen Road, Laksi District, Bangkok, Thailand)

  • Nachatchaya Thongjan

    (School of Law, Assumption University, 592/3 Ramkhamhaeng 24, Hua Mak, Bangkok 10240; Thailand)

Abstract

This research was conducted to examine the true benefit of energy consumption within the scope of energy cost, as well as model a forecasting tool for energy cost in food manufacturing industry. It was limited to the analysis of true benefit of the consumption, energy cost, forward-and-backward relationship, and prediction of future energy cost during the next 10 years ranging from 2021 to 2030, and 20 years ranging from 2021 to 2040. The analysis was made possible via an application of ARIMAX model optimizing the input-output table of Thailand. As for the result, it reveals that the product of tobacco is found with the highest true value of benefit. While candy and sweets, sugar, breweries, corn, distilled spirit, slaughtering, milled rice, coffee and tea, and canned meat are respectively detected. In taking forward-and-backward relationship into account, a close monitoring is required for the sector of canned meat and milled rice, respectively. Since the developed model is confirmed for its validity, an optimization of RMSE, MAE, and MAPE measurement for 10 years (2021-2030) and 20 years (2021-2040) prediction of energy cost would result in the following outcomes; 1) a gradual increase of 41.86 percent is estimated for the energy cost by 2030 compared to 2021 per illustration in Model 1, and 2) energy cost is calculated at a steadily increased 70.79% by 2040 in comparison with 2021 per presentation in Model 2.

Suggested Citation

  • Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn & Apinyar Chatchorfa & Boonton Dockthaisong & Sthianrapab Naluang & Sirapatsorn Wongthongdee & Nachatchaya Thongjan, 2021. "Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 600-608.
  • Handle: RePEc:eco:journ2:2021-05-69
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/11515/6062
    Download Restriction: no

    File URL: https://www.econjournals.com/index.php/ijeep/article/view/11515/6062
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    2. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    3. Jin Zhang and David C. Broadstock, 2016. "The Causality between Energy Consumption and Economic Growth for China in a Time-varying Framework," The Energy Journal, International Association for Energy Economics, vol. 0(China Spe).
    4. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    6. Zhang, Chuanguo & Lin, Yan, 2012. "Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China," Energy Policy, Elsevier, vol. 49(C), pages 488-498.
    7. Talha Yalta, A. & Cakar, Hatice, 2012. "Energy consumption and economic growth in China: A reconciliation," Energy Policy, Elsevier, vol. 41(C), pages 666-675.
    8. Xu, Shi-Chun & He, Zheng-Xia & Long, Ru-Yin, 2014. "Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI," Applied Energy, Elsevier, vol. 127(C), pages 182-193.
    9. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    10. Jie Ma & Amos Oppong & Kingsley Nketia Acheampong & Lucille Aba Abruquah, 2018. "Forecasting Renewable Energy Consumption under Zero Assumptions," Sustainability, MDPI, vol. 10(3), pages 1-17, February.
    11. Pruethsan Sutthichaimethee & Sthianrapab Naluang, 2019. "The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model," Energies, MDPI, vol. 12(16), pages 1-21, August.
    12. Zhang, Chuanguo & Xu, Jiao, 2012. "Retesting the causality between energy consumption and GDP in China: Evidence from sectoral and regional analyses using dynamic panel data," Energy Economics, Elsevier, vol. 34(6), pages 1782-1789.
    13. Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn, 2020. "A Forecasting Model on Carrying Capacity for Government's Controlling Measure under Environmental Law in Thailand: Adapting Non-Recursive Autoregression based on the Var-X Model," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 645-655.
    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. Pruethsan Sutthichaimethee & Harlida Abdul Wahab, 2021. "A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand s Environmental Law: Enriching the Path Analysis-VARIMA-OVi Model," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 398-411.
    2. Pruethsan Sutthichaimethee & Chanintorn Jittawiriyanukoon, 2022. "Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 282-293, July.
    3. Ruixiaoxiao Zhang & Geoffrey QP Shen & Meng Ni & Johnny Wong, 2020. "The relationship between energy consumption and gross domestic product in Hong Kong (1992–2015): Evidence from sectoral analysis and implications on future energy policy," Energy & Environment, , vol. 31(2), pages 215-236, March.
    4. Kerry Patterson & Michael A. Thornton, 2013. "A review of econometric concepts and methods for empirical macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 2, pages 4-42, Edward Elgar Publishing.
    5. Fang, Zheng & Chen, Yang, 2017. "Human capital, energy, and economic development – Evidence from Chinese provincial data," RIEI Working Papers 2017-03, Xi'an Jiaotong-Liverpool University, Research Institute for Economic Integration.
    6. Committee, Nobel Prize, 2003. "Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity," Nobel Prize in Economics documents 2003-1, Nobel Prize Committee.
    7. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).
    8. Zeren, Fatma & Korap, Levent, 2010. "A cost-based empirical model of the aggregate price determination for the Turkish economy: a multivariate cointegration approach," MPRA Paper 23655, University Library of Munich, Germany.
    9. Arize, Augustine C., 1998. "The long-run relationship between import flows and real exchange-rate volatility: The experience of eight European economies," International Review of Economics & Finance, Elsevier, vol. 7(4), pages 417-435.
    10. Pershin, Vitaly & Molero, Juan Carlos & de Gracia, Fernando Perez, 2016. "Exploring the oil prices and exchange rates nexus in some African economies," Journal of Policy Modeling, Elsevier, vol. 38(1), pages 166-180.
    11. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "A Relational Analysis Model of the Causal Factors Influencing CO 2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model," Energies, MDPI, vol. 11(7), pages 1-16, July.
    12. Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn, 2018. "Relationships between Causal Factors Affecting Future Carbon Dioxide Output from Thailand’s Transportation Sector under the Government’s Sustainability Policy: Expanding the SEM-VECM Model," Resources, MDPI, vol. 7(4), pages 1-18, December.
    13. Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn, 2021. "The Management Efficiency of the Sustainable Development Policy under Thailand s Energy Law: Enriching the SEM-based on the ARIMAXi model," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 472-482.
    14. Xu, Haifeng & Hamori, Shigeyuki, 2012. "Dynamic linkages of stock prices between the BRICs and the United States: Effects of the 2008–09 financial crisis," Journal of Asian Economics, Elsevier, vol. 23(4), pages 344-352.
    15. Ali MNA & Moheddine YOUNSI, 2018. "A monetary conditions index and its application on Tunisian economic forecasting," Journal of Economics and Political Economy, KSP Journals, vol. 5(1), pages 38-56, March.
    16. Hauser, Shmuel & Kedar-Levy, Haim & Milo, Orit, 2022. "Price discovery during parallel stocks and options preopening: Information distortion and hints of manipulation," Journal of Financial Markets, Elsevier, vol. 59(PA).
    17. M. T. Alguacil & V. Orts, 2003. "Inward Foreign Direct Investment and Imports in Spain," International Economic Journal, Taylor & Francis Journals, vol. 17(3), pages 19-38.
    18. Ansgar Belke & Robert Czudaj, 2010. "Is Euro Area Money Demand (Still) Stable? Cointegrated VAR Versus Single Equation Techniques," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 56(4), pages 285-315.
    19. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, June.
    20. Ramona Dumitriu & Razvan Stefanescu, 2015. "The Relationship Between Romanian Exports And Economic Growth After The Adhesion To European Union," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 17-26.

    More about this item

    Keywords

    economic problem; environmental law; sustainability; Food Manufacturing sector; energy cost;
    All these keywords.

    JEL classification:

    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

    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:eco:journ2:2021-05-69. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.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.