Growth, Carbon Emissions, and Public Health Spending: Long-Run Evidence from South Asia
Abstract
Rising public healthcare expenditure in South Asia reflects not only pressures within health systems but also broader patterns of economic growth, investment, trade, and environmental degradation. This study examines the long-run effects of economic growth and carbon emissions on government health expenditure in Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka using annual panel data for 1995–2023. Gross fixed capital formation and trade openness are incorporated as control variables. The empirical strategy applies the Im–Pesaran–Shin panel unit root test, the Pedroni panel cointegration test, and Fully Modified Ordinary Least Squares estimation. The unit root results show that all variables are non-stationary at level but stationary after first differencing, indicating integration of order one. The cointegration results provide sufficient evidence of a stable long-run relationship among government health expenditure, gross domestic product, CO₂ emissions, gross fixed capital formation, and trade openness. FMOLS estimates indicate that all four explanatory variables have positive and statistically significant long-run effects on government health expenditure. Specifically, economic growth expands fiscal capacity to finance public healthcare, whereas higher CO₂ emissions are associated with additional health-system pressures arising from pollution-related risks. Capital formation also strengthens public spending capacity, while trade openness has a positive but comparatively small effect. These findings show that public health expenditure is shaped jointly by economic opportunity and environmental pressure. The study concludes that South Asian governments should integrate health planning with cleaner growth strategies, stronger environmental regulation, sustainable investment, and trade policies that improve access to health-related goods and technologies while limiting pollution-intensive development. References Akash, M. M. R. (n.d.). Cardiotoxicity prediction models in cancer patients using artificial intelligence and genomics. International Journal of Drug Delivery Technology, 16, 60–73. Al Kium, A., Sarker, S., Shikha, S. A., Kamal, M. A. T., Jabed, M. I. K., Munifa, N. K., ... John, D. B. (2023). Health equity and digital disparities in cancer screening and cardiovascular care across socioeconomic and ethnic groups: A systematic review. Vascular and Endovascular Review, 6(2), 35–44. Ali, N. M., Ahmed, M. E., Fakhrul Islam, M., Gomes, C. A., & Islam, M. S. (2026). Toward a circular US economy: Green and artificial intelligence innovation, renewable energy, and domestic material consumption. Energy Sources, Part B: Economics, Planning, and Policy, 21(1), Article 2685043. Aydin, M., & Bozalti, O. (2023). The impacts of the refugee population, renewable energy consumption, carbon emissions, and economic growth on health expenditure in Turkey: New evidence from Fourier-based analyses. Environmental Science and Pollution Research, 30(14), 41286–41298. https://doi.org/10.1007/s11356-023-25181-8 Bayraktar, Y., Aydin, S., Çakır, M. A., Recepoğlu, M., Ozyilmaz, A., Buyukakin, F., & Çakır, S. (2024). Consequences of information and communication technologies and environmental degradation on health expenditure. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 61, Article 00469580241277449. https://doi.org/10.1177/00469580241277449 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 Chaabouni, S., Zghidi, N., & Ben Mbarek, M. (2016). On the causal dynamics between CO2 emissions, health expenditures, and economic growth. Sustainable Cities and Society, 22, 184–191. https://doi.org/10.1016/j.scs.2016.02.001 Cheng, C., Ren, X., Zhang, M., & Wang, Z. (2024). The nexus among CO2 emissions, health expenditure, and economic development in the OECD countries: New insights from a cross-sectional ARDL model. Environmental Science and Pollution Research, 31(11), 16746–16769. https://doi.org/10.1007/s11356-024-32081-y Derindag, O. F., Maydybura, A., Kalra, A., Wong, W.-K., & Chang, B. H. (2023). Carbon emissions and the rising effect of trade openness and foreign direct investment: Evidence from a threshold regression model. Heliyon, 9(7), Article e17448. https://doi.org/10.1016/j.heliyon.2023.e17448 Dritsaki, M., & Dritsaki, C. (2024). The relationship between health expenditure, CO2 emissions, and economic growth in G7: Evidence from heterogeneous panel data. Journal of the Knowledge Economy, 15, 4886–4911. https://doi.org/10.1007/s13132-023-01349-y Govdeli, T. (2023). The nexus between economic growth, health expenditure, and environmental quality: A comparative study for E7 countries. Reviews on Environmental Health, 39(3), 551–560. https://doi.org/10.1515/reveh-2022-0246 Hu, Q., & Wang, L. (2024). Economic growth effects of public health expenditure in OECD countries: An empirical study using the dynamic panel threshold model. Heliyon, 10(4), Article e25684. https://doi.org/10.1016/j.heliyon.2024.e25684 Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7 Jabed, M. I. K., Imran, M., Gomes, C. A., Ponduru, P. S., Mandal, S., & Hassan, S. (2025a). Deep learning and explainable AI framework for predicting lung cancer severity. Journal of Computer Science and Technology Studies, 7(12), 573–598. Jabed, M. I. K., Imran, M., Gomes, C. A., & Ponduru, P. S. (2025b). Machine learning-based prediction of poor self-rated health among U.S. adults using behavioral and socioeconomic factors. World Journal of Advanced Research and Reviews, 27(1), 2817–2829. https://doi.org/10.30574/wjarr.2025.27.1.2649 Jabed, M. I. K., Imran, M., Khan, A. A., Mehedi, M., Islam, A., & Pervez, R. (2026a). Explainable machine learning framework for early heart disease detection using SMOTE and SHAP. Vascular and Endovascular Review, 9(1), 316–324. Jabed, M. I. K., Manzoor, M. A., Tofa, F. M., & Khan, M. H. (2026b). Interpretable ensemble learning approach for breast cancer diagnosis using SHAP-based explainable AI. Journal of Computer Science and Technology Studies, 8(8), 244–255. Jiang, W., Li, W., & Chen, Y. (2024). Air pollution, public health expenditure, and physical health: Evidence from China. Environmental Quality Management, 34(1), Article e22185. https://doi.org/10.1002/tqem.22185 Karaaslan, A., & Çamkaya, S. (2022). The relationship between CO2 emissions, economic growth, health expenditure, and renewable and non-renewable energy consumption: Empirical evidence from Turkey. Renewable Energy, 190, 457–466. https://doi.org/10.1016/j.renene.2022.03.139 Khan, M. K., Teng, J.-Z., Khan, M. I., & Khan, M. O. (2019). Impact of globalization, economic factors, and energy consumption on CO2 emissions in Pakistan. Science of the Total Environment, 688, 424–436. https://doi.org/10.1016/j.scitotenv.2019.06.065 Khoshnevis Yazdi, S., & Khanalizadeh, B. (2017). Air pollution, economic growth and health care expenditure. Economic Research-Ekonomska Istraživanja, 30(1), 1181–1190. https://doi.org/10.1080/1331677X.2017.1314823 Kutlu, G., & Örün, E. (2023). The effect of carbon dioxide emissions, GDP per capita, and urban population on health expenditure in OECD countries: A panel ARDL approach. International Journal of Environmental Health Research, 33(12), 1233–1242. https://doi.org/10.1080/09603123.2022.2083088 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 Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670. https://doi.org/10.1111/1468-0084.0610s1653 Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. In B. H. Baltagi, T. B. Fomby, & R. C. Hill (Eds.), Nonstationary panels, panel cointegration, and dynamic panels (Advances in Econometrics, Vol. 15, pp. 93–130). Emerald Group Publishing Limited. https://doi.org/10.1016/S0731-9053(00)15004-2 Polcyn, J., Voumik, L. C., Ridwan, M., Ray, S., & Vovk, V. (2023). Evaluating the influences of health expenditure, energy consumption, and environmental pollution on life expectancy in Asia. International Journal of Environmental Research and Public Health, 20(5), Article 4000. https://doi.org/10.3390/ijerph20054000 Ponduru, P. S., Nandanavanam, P. P. V., & Ponduru, S. K. K. (2026). Ecological proportionality in generative AI: The ethics of marginal capability and environmental sufficiency. Rahman, M. A., Jabed, M. I. K., Devnath, R. K., Mehedi, C. M., Begum, M., & Mahmud, T. (2026, May). MSRFF: An interpretable CNN-Vision Transformer framework for diabetic retinopathy detection. In 2026 2nd International Conference on Computational Intelligence Approaches and Applications (ICCIAA) (pp. 1–7). IEEE. Raihan, A., Ridwan, M., Rahman, S. M., & Sarker, T. (2026). Navigating the complexities of healthcare costs in Bangladesh: A closer look at environmental quality, economic growth, energy use, industrialization, urbanization, and forest area. Innovation and Green Development, 5(1), Article 100328. https://doi.org/10.1016/j.igd.2026.100328 Rasheed, M. D., Akram, M., Imran, M., Ahmed, R. H., & Rauf, A. (2025). Towards human-centric smart manufacturing: A digital twin enabled affective ergonomic framework for adaptive human robot collaboration. International Journal of Advances in Signal and Image Scien