Article Sidebar

Submitted
July 15, 2024
Accepted
October 23, 2024
Published
October 31, 2024
Download
Full text PDF
Statistic
Read Counter : 0 Download : 0

Downloads

Download data is not yet available.

Main Article Content

Abstract

Purpose — The study explores the effects of greenhouse gas emissions, renewable energy, and economic growth on health expenditures across Southeast Asia while comparing the performance of different econometric models for accuracy in analysis.
Method — To analyze the relationships among variables, the study employs three econometric models, the Autoregressive Distributed Lag Model, the co-integration Model, and the Quantile Regression Model, using annual data from 2000 to 2020.
Findings — The results reveal that greenhouse gas emissions and GDP significantly influence health expenditure in all three models. However, the significance of renewable energy consumption varies, with only the quantile regression model indicating a significant relationship with health expenditure. Model comparison based on Mean Squared Error (MSE) suggests that the autoregressive distributed lag (ARDL) model provides the most accurate predictions. Also, it found that there is a short-run and long-run causal effect of GHG and GDP on health expenditure, as well as health spending on GDP.
Implication — This study helps to understand how economic growth, environmental factors, and healthcare spending interact to develop sustainable policies to address health and environmental problems in Southeast Asia.
Originality — This research contributes to the body of knowledge examining the impact of economic and environmental factors on health expenditures in Southeast Asia through a comparative analysis of different econometric models.

Keywords

Autoregressive distributed lag model co-integration model quantile regression model mean squared error

Article Details

License

Copyright (c) 2024 Resa Mae R. Sangco

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. 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.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Creative Commons License

Economic Journal of Emerging Markets by Center for Economic Studies, Universitas Islam Indonesia is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Sangco, R. M. R. (2024). Impact of greenhouse gas emission, renewable energy, and economic growth on health expenditure in Southeast Asia: A comparative analysis of econometric models. Economic Journal of Emerging Markets, 16(2), 191–202. https://doi.org/10.20885/ejem.vol16.iss2.art8
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Adom, P. K., & Bekoe, W. (2012). Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: A comparison of ARDL and PAM. Energy, 44(1), 367–380. https://doi.org/10.1016/j.energy.2012.06.020
  2. Alhassan, H., & Kwakwa, P. A. (2023). The effect of natural resources extraction and public debt on environmental sustainability. Management of Environmental Quality: An International Journal, 34(3), 605–623. https://doi.org/10.1108/MEQ-07-2022-0192
  3. Apergis, N., Gupta, R., Lau, C. K. M., & Mukherjee, Z. (2018). U.S. state-level carbon dioxide emissions: Does it affect health care expenditure? Renewable and Sustainable Energy Reviews, 91, 521–530. https://doi.org/10.1016/j.rser.2018.03.035
  4. Arı, Y. (2021). Engle-granger cointegration analysis between GARCH-type volatilities of gold and silver returns. Alanya Academic Review, 5(2), pp.589-618.
  5. Atuahene, S. A., Yusheng, K., Bentum-Micah, G. (2020). Health expenditure, CO2 emissions, and economic growth: China vs. India. Preprints 2020, 2020090384. https://doi.org/10.20944/preprints202009.0384.v1
  6. Behera, D. K., & Dash, U. (2020). Is health expenditure effective for achieving healthcare goals? Empirical evidence from South-East Asia Region. Asia-Pacific Journal of Regional Science, 4(2), 593–618. https://doi.org/10.1007/s41685-020-00158-4
  7. Belaïd, F., Youssef, A. Ben, & Lazaric, N. (2020). Scrutinizing the direct rebound effect for French households using quantile regression and data from an original survey. Ecological Economics, 176, 106755. https://doi.org/10.1016/j.ecolecon.2020.106755
  8. Bilgili, F., Kuşkaya, S., Khan, M., Awan, A., & Türker, O. (2021). The roles of economic growth and health expenditure on CO2 emissions in selected Asian countries: a quantile regression model approach. Environmental Science and Pollution Research, 28(33), 44949–44972. https://doi.org/10.1007/s11356-021-13639-6
  9. Camba Jr, A. C., & Camba, A. L. (2021). An Engle-granger and Johansen cointegration approach in testing the validity of Fisher hypothesis in the Philippines. The Journal of Asian Finance, Economics and Business, 8(12), 31-38.
  10. Chernozhukov, V., Fernández-Val, I., & Melly, B. (2022). Fast algorithms for the quantile regression process. Empirical Economics, 62(1), 7–33. https://doi.org/10.1007/s00181-020-01898-0
  11. Çobanoğulları, G. (2024). Exploring the link between CO2 emissions, health expenditure, and economic growth in Türkiye: evidence from the ARDL model. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-04835-8
  12. Dorbonova, İ., & Sugözü, İ. H. (2024). Analyzing the relationship between health expenditure, renewable energy and life expectancy: evidence from Asian countries. ESAM Ekonomik ve Sosyal Araştırmalar Dergisi, 5(1), 111–134. https://doi.org/10.53662/esamdergisi.1459607
  13. Ebi, K. L., & Hess, J. J. (2020). Health risks due to climate change: Inequity in causes and consequences. Health Affairs, 39(12), 2056–2062. https://doi.org/10.1377/hlthaff.2020.01125
  14. European Commission. (2023). Emission Database for Global Atmospheric Research (EDGAR) – Report. https://edgar.jrc.ec.europa.eu/report_2023
  15. Farooq, M. U., Shahzad, U., Sarwar, S., & ZaiJun, L. (2019). The impact of carbon emission and forest activities on health outcomes: Empirical evidence from China. Environmental Science and Pollution Research, 26(13), 12894–12906. https://doi.org/10.1007/s11356-019-04779-x
  16. Gillingham, K., & Stock, J. H. (2018). The cost of reducing greenhouse gas emissions. Journal of Economic Perspectives, 32(4), 53–72. https://doi.org/10.1257/jep.32.4.53
  17. Haseeb, M., Kot, S., Hussain, H. I., & Jermsittiparsert, K. (2019). Impact of economic growth, environmental pollution, and energy consumption on health expenditure and R&D expenditure of ASEAN countries. Energies, 12(19), 3598. https://doi.org/10.3390/en12193598
  18. Ibukun, C. O., & Osinubi, T. T. (2020). Environmental quality, economic growth, and health expenditure: Empirical evidence from a panel of African countries. African Journal of Economic Review, 8(2). https://www.ajol.info/index.php/ajer/article/view/197206
  19. Jian, J., Fan, X., He, P., Xiong, H., & Shen, H. (2019). The Effects of Energy Consumption, Economic Growth and Financial Development on CO2 Emissions in China: A VECM Approach. Sustainability, 11(18), 4850. https://doi.org/10.3390/su11184850
  20. Kong, Y.-C., Kimman, M., Subramaniam, S., Yip, C.-H., Jan, S., Aung, S., Khoa, M. T., Ngelangel, C. A., Nyein, H. L., Sangrajrang, S., Tanabodee, J., Bhoo-Pathy, N., Arounlangsy, P., Aung, S., Balete, S. L., Bhoo-Pathy, N., Bounxouei, B., Bui, D., Datukan, J., … Woodward, M. (2022). Out-of-pocket payments for complementary medicine following cancer and the effect on financial outcomes in middle-income countries in southeast Asia: a prospective cohort study. The Lancet Global Health, 10(3), e416–e428. https://doi.org/10.1016/S2214-109X(21)00595-7
  21. Lamb, W. F., Wiedmann, T., Pongratz, J., Andrew, R., Crippa, M., Olivier, J. G. J., Wiedenhofer, D., Mattioli, G., Khourdajie, A. Al, House, J., Pachauri, S., Figueroa, M., Saheb, Y., Slade, R., Hubacek, K., Sun, L., Ribeiro, S. K., Khennas, S., de la Rue du Can, S., … Minx, J. (2021). A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environmental Research Letters, 16(7), 073005. https://doi.org/10.1088/1748-9326/abee4e
  22. Li, F., Chang, T., Wang, M.-C., & Zhou, J. (2022). The relationship between health expenditure, CO2 emissions, and economic growth in the BRICS countries—based on the Fourier ARDL model. Environmental Science and Pollution Research, 29(8), 10908–10927. https://doi.org/10.1007/s11356-021-17900-w
  23. Lim, M. Y., Kamaruzaman, H. F., Wu, O., & Geue, C. (2023). Health financing challenges in Southeast Asian countries for universal health coverage: a systematic review. Archives of Public Health, 81(1), 148. https://doi.org/10.1186/s13690-023-01159-3
  24. Loucks, D. P. (2021). Impacts of climate change on economies, ecosystems, energy, environments, and human equity: A systems perspective. In The Impacts of Climate Change (pp. 19–50). Elsevier. https://doi.org/10.1016/B978-0-12-822373-4.00016-1
  25. Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and health impacts of air pollution: A review. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.00014
  26. McNown, R., Sam, C. Y., & Goh, S. K. (2018). Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50(13), 1509–1521. https://doi.org/10.1080/00036846.2017.1366643
  27. Mentes, M. (2023). Sustainable development economy and the development of green economy in the European Union. Energy, Sustainability and Society, 13(1). https://doi.org/10.1186/s13705-023-00410-7
  28. Mujtaba, G., & Shahzad, S. J. H. (2021). Air pollutants, economic growth and public health: implications for sustainable development in OECD countries. Environmental Science and Pollution Research, 28(10), 12686–12698. https://doi.org/10.1007/s11356-020-11212-1
  29. Shahzad, K., Jianqiu, Z., Hashim, M., Nazam, M., & Wang, L. (2020). Impact of using information and communication technology and renewable energy on health expenditure: A case study from Pakistan. Energy, 204, 117956. https://doi.org/10.1016/j.energy.2020.117956
  30. Slathia, P., Vashishtha, A., Jena, P. K., & Sahu, P. K. (2024). Examining the dynamic impact of carbon emissions, renewable energy and economic growth on healthcare expenditure in Asian countries. Heliyon, 10(9), e30136. https://doi.org/10.1016/j.heliyon.2024.e30136
  31. Vo, A., Vo, D., & Le, Q. (2019). CO2 Emissions, Energy Consumption, and Economic Growth: New Evidence in the ASEAN Countries. Journal of Risk and Financial Management, 12(3), 145. https://doi.org/10.3390/jrfm12030145
  32. Wang, C.-M., Chang, C.-Y., Yuan, C.-C., Wang, J., & Feng, Y. (2020). Examining CO 2 Emissions, Health Expenditure, and Economic Growth Nexus for China: A Co-integration Approach. IOP Conference Series: Earth and Environmental Science, 555(1), 012020. https://doi.org/10.1088/1755-1315/555/1/012020
  33. WHO. (2020). Operational Framework for Primary Health Care. In World Health Organization. https://www.who.int/publications/i/item/9789240017832
  34. Xie, S.-P. (2024). Global Warming. In Coupled Atmosphere-Ocean Dynamics (pp. 339–366). Elsevier. https://doi.org/10.1016/B978-0-323-95490-7.00013-8
  35. Zaidi, S., & Saidi, K. (2018). Environmental pollution, health expenditure and economic growth in the Sub-Saharan Africa countries: Panel ARDL approach. Sustainable Cities and Society, 41, 833–840. https://doi.org/10.1016/j.scs.2018.04.034
  36. Zhang, Z., Huang, J., Wagner, P. D., & Fohrer, N. (2022). A method for detecting the non-stationarity during high flows under global change. Science of The Total Environment, 851, 158341. https://doi.org/10.1016/j.scitotenv.2022.158341
No Related Submission Found