Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models

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Rapid industrial growth, expanding financial systems, and weak environmental controls place growing pressure on public and private healthcare budgets. Understanding how finance, governance related channels, and environmental stress interact with health spending remains limited. Motivation You need evidence that links economic structure, environmental pressure, and financial development to health expenditure over the long run. This study responds to that gap by examining lower-middle- and upper-middle-income countries over the period 1995 to 2023, with attention to asymmetric and cross-country dynamics. Findings Health expenditure rises with industrialisation, foreign direct investment, carbon dioxide emissions, financial development, and trade openness. A 10 percent increase in industrial output raises health spending by about 1.8 percent. The same increase in foreign direct investment and trade openness increases health spending by roughly 1.5 percent and 1.6 percent. Carbon dioxide emissions exert the strongest effect. A 10 percent increase raises healthcare costs by nearly 6.7 percent. Renewable energy consumption reduces health expenditure. A 10 percent increase lowers health spending by about 1.7 percent. Results remain stable under DCE, DCE-IV, and asymmetric NARDL estimations. Implications You should treat health expenditure as a downstream outcome of financial expansion, environmental stress, and governance related policy choices. Stronger environmental regulation, cleaner energy transition, and finance aligned with sustainability can ease long-run healthcare burdens. Integrating health, energy, and financial policies can support fiscal stability and sustainable development in middle-income economies. 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F1000Research 2026, 15 :197 ( https://doi.org/10.12688/f1000research.177010.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models [version 1; peer review: awaiting peer review] Md Qamruzzaman https://orcid.org/0000-0002-0854-2600 1 , Ms. Tanzila Amir 1 , Abdulrahman Alomair https://orcid.org/0000-0001-7493-7822 2 , Mohammed Alomair 2 Md Qamruzzaman https://orcid.org/0000-0002-0854-2600 1 , Ms. Tanzila Amir 1 , Abdulrahman Alomair https://orcid.org/0000-0001-7493-7822 2 , Mohammed Alomair 2 PUBLISHED 05 Feb 2026 Author details Author details 1 united international University, Dhaka, 1212, Bangladesh 2 King Faisal University, Al Ahsa, Eastern Province, Saudi Arabia Md Qamruzzaman Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Ms. Tanzila Amir Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Abdulrahman Alomair Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Resources, Writing – Original Draft Preparation, Writing – Review & Editing Mohammed Alomair Roles: Conceptualization, Data Curation, Formal Analysis, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Global Public Health gateway. Abstract Background Rising health expenditure has become a persistent challenge in middle-income economies. Rapid industrial growth, expanding financial systems, and weak environmental controls place growing pressure on public and private healthcare budgets. Understanding how finance, governance related channels, and environmental stress interact with health spending remains limited. Motivation You need evidence that links economic structure, environmental pressure, and financial development to health expenditure over the long run. This study responds to that gap by examining lower-middle- and upper-middle-income countries over the period 1995 to 2023, with attention to asymmetric and cross-country dynamics. Findings Health expenditure rises with industrialisation, foreign direct investment, carbon dioxide emissions, financial development, and trade openness. A 10 percent increase in industrial output raises health spending by about 1.8 percent. The same increase in foreign direct investment and trade openness increases health spending by roughly 1.5 percent and 1.6 percent. Carbon dioxide emissions exert the strongest effect. A 10 percent increase raises healthcare costs by nearly 6.7 percent. Renewable energy consumption reduces health expenditure. A 10 percent increase lowers health spending by about 1.7 percent. Results remain stable under DCE, DCE-IV, and asymmetric NARDL estimations. Implications You should treat health expenditure as a downstream outcome of financial expansion, environmental stress, and governance related policy choices. Stronger environmental regulation, cleaner energy transition, and finance aligned with sustainability can ease long-run healthcare burdens. Integrating health, energy, and financial policies can support fiscal stability and sustainable development in middle-income economies. READ ALL READ LESS Keywords Industrialization; Carbon Emission; Trade Openness; Health Expenditure; Corresponding Author(s) Md Qamruzzaman ( [email protected] ) Abdulrahman Alomair ( [email protected] ) Close Corresponding authors: Md Qamruzzaman, Abdulrahman Alomair Competing interests: No competing interests were disclosed. Grant information: This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [KFU260335] and Institute for Advanced Research (IAR), United international University (UIU). Ref: IAR-2026-Pub-009 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Qamruzzaman M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Qamruzzaman M, Amir MT, Alomair A and Alomair M. Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :197 ( https://doi.org/10.12688/f1000research.177010.1 ) First published: 05 Feb 2026, 15 :197 ( https://doi.org/10.12688/f1000research.177010.1 ) Latest published: 02 May 2026, 15 :197 ( https://doi.org/10.12688/f1000research.177010.3 )  There is a newer version of this article available. Suppress this message for one day. I. Introduction The world economy and billions of people are facing rising pollution costs. It takes one of the dominant positions in the United Nations Sustainable Development Goals (SDGs), which require urgent intervention. The article entitled “ENVIRONMENTAL, the cost of pollution: industrialisation, environmental stressors and increasing healthcare expenditures” seeks to expound on the complex and intertwined nature of industrialisation, environmental stressors, and increasing healthcare expenditures, and touches on SDG 3, which fosters good health and well-being. The impact of pollution goes well beyond degradation; it harms health systems, amplifying healthcare expenditures and draining resources. Industrialisation is paradoxical: it increases industry’s productivity while contributing to environmental pollution. Empirical evidence indicates that industrial agglomeration increases social health costs through environmental pollution, thereby perpetuating inflated health care costs. The presence of a strong correlation, particularly between air pollution and health expenditures, can be revealed due to the research that shows that such pollutants as PM 2.5 and nitrogen dioxide (NO2) are significant causes of healthcare costs that in turn, lead to a higher rate of hospitalization and chronic morbidity, which manifests itself in overburdened health expenses. In the United Kingdom, predictive estimates of poor air quality are projected to impose considerable financial costs on the National Health Service, as an evident increase in non-communicable illnesses is expected. Besides, the economic damage from pollution is not limited to health expenditures but also affects corporate income when investors consider environmental risks during debt pricing ( Tan and Chan, 2021 ; Wang, Zhang, and Zhou, 2022 ). A growing body of studies shows that when populations are healthy, they are more productive across diverse spheres of life because of a healthy dose of health spending on prevention, diagnosis, and treatment of diseases, which leads to less absenteeism and more people joining the workforce productively. Healthy populations are essential for long-term economic growth and development ( Bambra, 2022 ). Health spending is also a very effective factor in managing health disparities and enhancing social equity. Sufficient government healthcare spending is necessary to close the gap in healthcare demands across socioeconomic groups; therefore, even marginalized or disadvantaged groups should receive the necessary healthcare services ( Raghupathi and Raghupathi, 2020 ). Economic factors greatly influence health expenditure. One is Gross Domestic Product (GDP), which is a major driver of health care expenditure: as an economy grows, so does health expenditure. For instance, Stepovic et al. (2020) indicate that expenditures on health in Balkan and Eastern European countries have increased more rapidly than gross domestic product (GDP), suggesting an increasing burden of health on the economies, and that the problem primarily affects low- and middle-income countries. Raghupathi and Raghupathi (2020) demonstrate that government spending on the health sector can positively contribute to economic performance by increasing human capital and productivity. Conversely, Maruthappu et al. (2014) cautioned that economic declines can trim health-care investment because, when budgets are tightened, public spending is curtailed. In this way, the economic environment shapes how health funds are allocated and acquired. Financial reasons are not the only determinants of health expenditure; geography is also a factor. For example, Yazdi and Khanalizadeh (2017) concluded their regression analysis and found that CO 2 and PM10 emissions in the MENA region incur high health costs. A. Mujtaba, Jena, Bekun, and Sahu (2022) affirmed that the health issues caused by pollutants, in their turn, raise the medical expenditures (G. Mujtaba & Shahzad, 2020 ). According to Socol (2023) , the European Union observed that climate change contributes to increased healthcare costs by raising temperatures and air quality. Together, these studies indicate that deteriorating environmental conditions lead to increased health expenditure and greater stress on the system. Social factors are important in determining the amount of money spent on health, particularly through lifestyle decisions and demographic changes. Roy and Khatun (2022) emphasised the necessity to manage adolescent fertility and the overall condition of pregnant women, and, according to them, a greater share of health resources will decrease maternal and neonatal mortality in low-resource populations. According to Lopreite and Zhu (2020) , the ageing population in China is driving up healthcare costs and making the sustainability of healthcare systems difficult. This is illustrated by these examples, which reveal how changes in lifestyle and demographics determine spending trends and how they interact with other factors. This paper examines the connections among economic, environmental, and health variables and their influence on society’s welfare. It focuses on the relationships among CO 2 emissions, industrialisation, foreign direct investment, renewable energy, health spending, and life expectancy. The article has been aware that the contemporary world is complex, and that achieving industrial development, environmental protection, and health outcomes is delicate. The study examines how the life span and well-being of people around the world are affected when policies across the three areas intersect, shaping the outcome of the interaction. It also explores the complex nature of the system of health expenditure that comprises total, household expenditure and government expenditure and their high global effect. Knowledge of resource distribution and investments made helps nations address current healthcare problems. The study aims to examine the broad impacts of health expenditure and inform researchers about its role in promoting individual, socioeconomic, and national welfare, thereby fostering healthier and fairer societies that advance the United Nations’ sustainable development goals. The existing study provides a broad overview of the intricate relationships among industrialisation, health expenditure preferences, environmental conditions, and resource consumption across low-, middle-, and high-income metropolitan economies. First, it reveals how industrialisation and health expenditure are interdependent; it demonstrates that, with more industrial activity, which is, in most cases, accompanied by pollution, the costs of health are increasing, even as the overall economy is growing. It is important to implement policy measures to control industrial activities that negatively affect population health, as evidenced by ecological indicators. Second, the article affirms a strong positive relationship between CO 2 emissions and health spending, indicating that pollution harms health. Control of carbon emissions would also result in significant savings in preventive health expenditures, which regulation and clean energy ought to achieve. Third, the implications of foreign direct investment (FDI) are discussed. On the one hand, FDI can bring technology and strengthen health infrastructure; on the other hand, it increases costs if it encourages industrial development, causing pollution. This two-fold impact highlights the need for sustainable investment policies that support both health and economic objectives. Fourth, the research seeks to examine the role of clean energy in health spending. Increased utilisation of renewable energy sources is linked to lower healthcare costs, mainly because air pollution triggers fewer medical conditions. The outcomes of this discovery highlight the potential of clean energy measures to protect lives and reduce healthcare costs. Lastly, the empirical study of the effectiveness of trade openness in health expenditure was conducted. Although trade openness may increase healthcare demand by boosting economic and income growth, it can also heighten health risks by facilitating the cross-border flow of goods. This trade balance act underscores the need for trade policy to promote economic health without posing a significant risk to people’s health. The rest of the body of this manuscript is as follows: Section II deals with a survey of relevant literature, as well as the data, model, and estimation strategies displayed in Section III. Empirical model estimation and interpretation are presented in Section IV; Section V discusses the study findings; and the conclusion and policy suggestions are presented in Section VI. II. Literature survey 2.1 Theoretical assessment Industrialisation has long been hailed as a driver of economic growth, technological advances, and social progress. Nevertheless, the price paid for industrialisation goes well beyond economic wealth: it undermines environmental sustainability and public health ( Soni, 2024 ; Van Tran, Tran, Bui Hoang, & Mai, 2024 ; Zhang, Zheng, Xia, & Cheng, 2024 ). The cyclical degradation of ecosystems, increasing pollution, and the subsequent healthcare costs need to be understood within a theoretical framework. Drawing on established theories of the economy and the environment, this study offers a categorical assessment of the relationships among industrial growth, environmental damage, and increasing pressure on health care systems. Being one of the most popular theoretical frameworks that maps the interaction between economic progress and environmental performance, the Environmental Kuznets Curve (EKC) conjecture provides an interesting storyline. In a given framework, nascent and growing economies experience a period of expanding environmental degradation, followed by a decline, as mature economies adopt cleaner technologies ( Hassan, Yang, Usman, Bilal, and Ullah, 2023 ). In the early phases of industrialisation, most countries are more focused on GDP growth rates at the expense of protecting ecological integrity, thereby creating significant pollution and ecological loss. With increases in per-capita income, expenditure shifting in favour of less-polluting sectors, and regulatory pressures converging, long-term pollutant emissions decrease. Despite the EKC’s positive outlook, it has limited applicability, particularly in developing economies with weak regulatory systems. The assumption that environmental degradation inevitably shrinks with economic growth does not hold when one considers long-term pollutants such as greenhouse gases and hazardous waste, which only increase as industrial bases expand. Conversely, the Pollution Haven Hypothesis (PHH) explains that polluter-rich industries often move to less robust jurisdictions or develop there because of the high income and more stringent environmental regulations in the relatively wealthy states ( Bradu et al., 2023 ; Dritsaki and Dritsaki, 2023 ; Zeeshan, Han, Rehman, Ullah, and Mubashir, 2022 ). A high degree of unfairness has been created through this dynamic in the distribution of industrial pollution, where the developing world is the victim of ecological devastation and its resultant impacts on the population and their well-being. In most of these locales, morbidities associated with pollution, especially respiratory and cardiovascular diseases, are significantly more common than in urbanised localities, resulting in substantial medical costs. It has been shown empirically that governments that have lax environmental policies serve as an attraction to the most polluting industries and thus contribute to the escalation of the health crisis in the respective countries ( Hamid and Wibowo, 2023 ; Hassan et al., 2023 ; Tackie, Chen, Ahakwa, and Atingabili, 2022 ). The PHH thus disputes the wishful thinking that growth can create positive or negative conditions on its own, focusing instead on the necessity of institutional frameworks, multilateral coordination, and coordinated industrial and environmental regulations. One way to further understand the economics of industrial pollution is through the Grossman Health Production Function. This model concludes that health outcomes are a function of multiple interacting inputs, including lifestyle and behaviour, medical care, and environmental context. If industrialisation increases pollution, many individuals and governments will need to invest more in healthcare to prevent pollution-related diseases. The cost of such industrial harm is a negative externality, as producers do not pay the full social costs of the environmental harm they cause. This causes pollution levels above the socially optimal level and additional medical expenditures. These externalities frequently cause governments to step in by subsidizing medical treatment or imposing pollution taxes to internalize them. Nevertheless, without meaningful enforcement mechanisms, industries are allowed to externalize the costs to the environment and, ultimately, our healthcare systems that are overwhelmed with preventable diseases ( Dritsaki & Dritsaki, 2023 ; A. Mujtaba et al., 2022 ; S. Roy & Khatun, 2022 ; Y. Shang, Razzaq, Chupradit, An, & Abdul-Samad, 2022 ). The discussion of industrialization and environmental degradation should not be divorced from the common talking points on sustainable development worldwide. A multidimensional framework for mapping the impact of Industrial Growth is drawn from the United Nations Sustainable Development Goals (SDGs). SDG 3, which addresses good health and well-being; SDG 9, which promotes sustainable industrialization; and SDG 13, which calls for climate action, are key to considering the impact of pollution on healthcare costs. The dilemma that arises thereupon is how to balance robust economic growth with the damaging environmental safeguards and health needs of the citizenry. Some countries have managed to minimise the financial pressure of medical care due to the process of implementation of sustainable industrial relations, in addition to investments in renewable sources of energy, as well as in green technologies. 2.2 Empirical assessment A. Carbon dioxide emissions and health expenditure The former group speaks about the positive relationship. The relationship between carbon dioxide emissions and health expenditure is analyzed across total, government, and household health expenditure. For example, Raihan et al. (2022) show that increased carbon emissions intensity is associated with higher healthcare costs and worsening environmental conditions. Furthermore, Pichler, Jaccard, and Weisz (2019) believe that chronic diseases can also be precipitated by carbon emissions, requiring long-term medical therapy, continuous medical intervention, and increased medical costs. As the publications by Wang, Dong, and Dong (2021) and Zhao, Jiang, Dong, and Dong (2021) show, suboptimal air quality due to carbon emissions can lead to the emergence of various diseases, and people also need health assistance, which increases the cost of health care. Vyas, Mehta, and Sharma (2023) found that a limited budget allocated to healthcare may affect long-run capital spending on health, leading to a significant increase in health expenditures. The strong, positive relationship between CO2 and healthcare spending posits that CO2 increases healthcare spending, as supported by Kutlu and Örün (2023) . The report outlines the health risks to the population from increased CO2 emissions and environmental deterioration. As demonstrated by Dritsaki and Dritsaki (2023) , G7 CO 2 emissions are positively correlated with health expenditure, as CO2 emissions add to the costs of health and to the negative impact of environmental pollution on human health and the economy. According to Chaabouni and Saidi (2017) , the correlation between health expenditure and CO2 emissions is significant and positive, indicating the negative impact of pollution on human health and the costs imposed on society. Interestingly, the findings suggest that a 1 per cent increase in CO 2 emissions is linked with a high increase in healthcare bills by 2.5 percent. Apergis, Gupta, Lau, and Mukherjee (2018) assess the influence of CO 2 emissions on state healthcare expenditure between 1966 and 2009 and find a positive relationship, but only in states that spend more on healthcare. The emissions of CO 2 and expenditure on health are influenced by complex regional factors such as climate and energy requirements. The possible advantage of CO 2 reduction is healthcare savings. The report outlines the need for a coordinated effort and political will to cut CO2 emissions cost-effectively and to address the complex relationship between environmental issues and health care expenditures. The quantitative impact of CO 2 emissions in China on healthcare expenditure (HCE) is that, though not as much as income, the impact of CO 2 emissions on the extension of HCE is positive, particularly at higher quantiles. As demonstrated by Zeeshan et al. (2022) , family health expenditure increases with rising CO 2 emissions, highlighting the health hazards of environmental pollution and its economic costs. Another statistically significant, non-zero positive correlation between CO2 emissions, environmental pollution, and household spending in Chinese health is also found in their study. On the other hand, there is no confirmation of an adverse nexus between carbon dioxide emissions and health spending. B. Industrialisation and health expenditure Having two lines of evidence on the relationship between health expenditure and industry is taken over. A positive correlation is observed in the former group, indicating that health spending increases with industrial development. For example, Raghupathi and Raghupathi (2020) , Jakovljevic et al. (2017) , and Hassan et al. (2023) associate health-care costs with industrialisation and economic growth, driven by adverse environmental effects. On the other hand, the fragmentation of roles and worsening working conditions are forms of industrialisation of health that can also raise costs. Abbas Khan et al. (2019) found that, as trade volume increases, CO 2 emissions rise, thereby increasing health expenditures. Y. The study by Shang et al. (2022) also revealed that the health burden worsens due to carbon emissions from urbanization and industrialization, leading to increased health costs. In the article, Hassan et al. (2023) demonstrate that health-care expenditure increased in the 10 leading countries in terms of spending between 1995 and 2018, driven by industrialization. Their findings reveal a positive correlation between industry and health costs, and that clear policies are required to address the health-economic implications of industrialization. Nadeem, Ali, Khan, and Guo (2020) note that the effect is complex. Industrial clusters that lead to pollution can increase or reduce residents’ health expenditures. Industrialization influences the cost of health care through rising incomes and the negative impacts of pollution. Kraipornsak (2017) determined that increased use of green energy in logistics and business processes can reduce spending on general health and increase labour productivity, as well as environmental health. The authors suggested that the creation and implementation of green technologies have a strong positive effect on the environment and personal health ( Dong, Xue, Xiao, and Liu, 2021 ; Shahzad et al., 2020 ). Tackie et al. (2022) applied a PMG-ARDL model to demonstrate that industrialization is associated with lower panel expenditure on health and recommended that industrialized countries invest more in health. Their statistics confirm that industrialization and health expenditure in West African economies are positively linked. T. Shang, Samour, Abbas, Ali, and Tursoy (2024) noted that lower health expenditure can be achieved through pollution-driven industrial clusters, provided technological innovation, increased employment, expanded medical services, and environmental infrastructure are put in place to improve the situation. Nevertheless, there are still locations where expenditures are higher due to rising pollution or higher incomes. Ampon-Wireko et al. (2022) found a unidirectional causal relationship between public expenditure on health and industrialisation, meaning that the higher the level of industrialisation, the greater the health spending. This implies that health care is a subject in which more resources are invested in more developed nations. Another body of research links the concept of green industrialisation to health expenditure. The primary objective of green industrialisation is to minimise environmental damage, while also improving population health and reducing healthcare costs. Renewable energy and cleaner technologies reduce respiratory and cardiovascular disease hospitalisations by decreasing air pollution ( Sarfraz, Ivascu, and Cioca, 2022 ). Water is available responsibly and without pollution, which preserves clean water supplies, reducing water-borne illnesses and expenses ( Ferreira, Graziele, Marques, and Goncalves, 2021 ). In industrial processes, safer chemicals reduce occupational illnesses, thereby minimising healthcare costs. Environmentally-friendly transportation reduces air pollution and traffic crashes, enhances respiratory health, and decreases expenditures ( Tchapchet Tchouto et al., 2024 ). Safety procedures at the workplace reduce accidents and injuries, consequently reducing absenteeism and health care costs ( Hadi and Nayeri, 2023 ). Green industrialisation reduces the impact of polluters and toxins on the environment, thereby preventing diseases and associated health costs ( Bradu et al., 2023 ). Sustainable agriculture and healthier food can support population health and reduce health spending ( Ashraf et al., 2021 ). C. FDI and health expenditure Proving the correlation between Foreign Direct Investment and total, government, and household health expenditure. The article by Ehsani, Dashtban Farouji, Khoshnoodi, and Dashtban Farouji (2023) has conducted an empirical model study with the help of the nonlinear autoregressive distributed lag (NARDL) model that revealed positive and significant results, indicating the positive impact of Foreign Direct Investment (FDI) on health expenditure and, consequently, population health and life expectancy in the long term. The statistics indicate that FDI enhances economic growth, leading to high spending on health and health promotion. According to research by ALZIYANI and Murad (2021) , African health spending increases with FDI, good governance, and larger city populations. The results indicate that FDI increases health spending in regions, thereby enhancing health development. Similarly, Giammanco and Gitto (2019) suggest that FDI rises with EU public health expenditure. The connection between money and commitment to healthcare infrastructure is pointed out in the project ( Barkat, Alsamara, Al Kwifi, & Jarallah, 2024 ). According to Immurana (2021) , Foreign Direct Investment (FDI) has a positive impact on the African health sector, enhancing a country’s health capacity in the short and long term and contributing to life expectancy and mortality. It means that higher levels of FDI can enhance health outcomes, and policies to promote these investments should be emphasised, along with additional steps to maximise welfare benefits, including health benefits. Unver and Erdogan (2015) note that health spending in these areas can be negatively affected by foreign direct investment (FDI). A study has shown that foreign direct investment (FDI) enhances the long-term health of the Bangladeshi people. The inquiry claims that FDI enhances health outcomes and that healthcare and hygiene policies can maximise its impact on the country’s health sector. D. Clean energy and health expenditure The first category shows a positive correlation, indicating a relationship between renewable energy and total health expenditure (both government and household). The results of the study conducted by Zhu (2023) demonstrate that when renewable energy sources are used in cooking, including electricity, natural gas, liquid gas, methane, or solar energy, the health outcome of rural people also improves since discomfort decreases, and physical activity increases. The use of clean energy did not influence self-reported health, bronchitis, asthma, or medical costs, yet it has a positive effect and may lead to cost savings in rural regions. According to this study by Zhongwei and Liu (2022) , the use of clean energy, including renewable energy, raises the Chinese life expectancy. This positive outcome demonstrates that promoting renewable energy production and consumption can enhance health. Therefore, clean energy projects can contribute to improving the general health of the population and reducinglthcare costs. Li, Ozturk, Majeed, Hafeez, and Ullah (2022) emphasise that renewable energy can improve health outcomes, including reducing chronic disease rates. Thus, the fact that clean energy is more health-promising refers to potential cost savings in health-related costs. It demonstrates that clean-energy logistics enhances the sustainability and profitability. Green energy can benefit both health and the economy by promoting sustainability. Greener energy can reduce health care costs, as environmental performance adversely affects health expenditure. ICT and renewable energy in Pakistan cut on health expenditure. The research by Shahzad et al. (2020) indicated that health-related costs are reduced due to clean and renewable energy and breakthroughs in ICT. According to the findings, politicians are advised to invest in renewable energy and ICT to improve the environment and reduce healthcare costs—the researchers of A. Khan, Chenggang, Hussain, and Kui (2021) found that countries that used renewable energy-based Belt and Road Initiative (B&RI) projects had better environmental quality and lower CO2 emissions. Cleaner energy could help reduce healthcare costs by improving the environment. Clean energy protects the environment and reduces healthcare costs, enhancing sustainability and health. In the study, Ullah, Rehman, Khan, Shah, and Khan (2020) note that Renewable energy (RE) saves money in Pakistan by reducing health costs. The statistics indicate that the utilisation of renewable energy decreases health expenditure. This implies that the inclusion of renewable energy sources in the energy mix will lower CO2 emissions and health costs. III. Data and methodology of the study 3.1 Theoretical development and model specification The theoretical framework of the study investigates the relationships among health expenditure (HE), industrialisation (IND) and environmental degradation (CO 2), as well as foreign direct investment (FDI) and financial development (FD), clean energy (CE) and trade openness (TO). Using the significant economic and environmental theories, we can see the picture of these relations. The environmental Kuznets Curve (EKC) hypothesis is frequently used to evaluate the relationship that exists between industrialisation and health spending. The implications of this hypothesis are as follows: when an economy begins to grow and becomes industrialised, the rate of environmental degradation increases; however, this decreases once an economy has reached a certain level of development. This has been caused by lack of environmental awareness, increased regulation and the adoption of greener technologies. There is also a possibility that industrialisation will increase CO 2 emissions, negatively impacting health and, therefore, spending on health ( Dam, Durmaz, Bekun, and Tiwari, 2024 ; Y. Shang et al., 2022 ; J. Wang et al., 2021 ; Zeeshan et al., 2022 ). One of the significant aspects of the global economy is FDI. It can enhance growth and industrialisation. There are, however, environmental risks when multinational companies take advantage of lax environmental rules in host nations, a scenario described by the Pollution Haven Hypothesis (PHH). According to this assumption, FDI can increase pollution rates, which in turn can raise healthcare costs through ecological destruction ( Chireshe and Ocran, 2020 ; A. Khan et al., 2021 ; T. Shang et al., 2024 ). The role of financial development is two-fold. In the long term, it is possible to reduce health expenditure by investing sufficiently in clean technologies and medical infrastructure. Nevertheless, rapid financial development in the absence of environmental regulations may contribute to greater environmental degradation, thereby increasing health spending. The use of clean energy is traditionally associated with reduced environmental damage and improved health outcomes for the population. With the adoption of renewable energy sources, CO2 emissions will decrease significantly, reducing the adverse effects of pollution on health and potentially lowering healthcare costs. Trade openness may also significantly influence health spending by affecting economic growth and environmental quality. Greater trade can potentially cause economic growth, though at the cost of some problems (possibly of environmental degradation at the beginning and greater health spending). In the long run, openness to trade may facilitate the exchange of cleaner technologies and other environmental best practices, thereby reducing health spending. All in all, the theoretical development of the study shows that different effects interact, indicating that industrialisation, FDI, financial development, clean energy, and trade openness influence health expenditures. These aspects affect health spending through negative environmental degradation and economic development. These relationships can only be understood deeply when policymakers aim to achieve a balance among economic development, human health, and environmental sustainability. The objectives of the study are to evaluate the Impact of industrialisation (IND), environmental degradation (CO2), foreign direct investment (FDI), financial development (FD), clean energy (CE), and trade openness (TO) on health expenditure (HE) in Lower Middle-income countries and higher-middle-income nations for the period 1995-2020. The general form of empirical relations is presented below; (1) HE i , t = ∫ CO 2 , CE , IND , FDI , TO , FD After the natural log transformation of Equation (1) , it can be rewritten in regression form in the following manner, see Equation (2) : (2) lnHE i , t = α 0 + β 1 lnCO 2 i , t + β 2 lnCE i , t + β 3 lnIND i , t + β 4 lnIND i , t + β 5 lnFDI i , t + β 6 lnTO i , t + β 7 lnFD i , t + ε i , t Where the coefficients of β 1 … … . . β 7 explain the changes in HE due to variations in target variables, α 0 explained the constant term and ε i , t for white noise in the equation. 3.2 Variables definition and anticipated sign of coefficients Carbon dioxide (CO2) emissions are the first explanatory variable, reflecting the release of CO2 into the atmosphere. This mainly occurs due to human activities such as industrial production, transportation, and energy production. Carbon dioxide emissions are typically measured in metric tons per capita or in total carbon dioxide emitted over a given period. Other sources are official environmental agencies within a country, international bodies such as the World Bank, and research institutions. The increase in carbon dioxide emissions is one of the causes of environmental pollution, which has adverse consequences for human health, including respiratory diseases, cardiovascular diseases, and other health complications. Research shows that the costs of treating pollution-related diseases, including hospitalisation, medication, and medical care, may increase health spending. Therefore, the cost of treating pollution-induced illnesses can strain healthcare budgets, leading to increased government spending on healthcare systems and facilities. Nevertheless, other research has shown that applying controls to curb pollution and encouraging clean energy programmes can reduce CO2 emissions and, consequently, medical expenditures for diseases caused by pollution. Furthermore, improvements in environmental health can be achieved through increased research and development funding to develop pollution-mitigation technologies, which eventually translate into lower healthcare costs ( Eckelman et al., 2020 ). Government initiatives to address pollution-related health challenges can also create employment opportunities in the healthcare sector, leading to economic growth and, indirectly, reducing healthcare spending by boosting tax collection. However, our findings show that carbon dioxide emissions and health expenditure are positively related. This increase in CO2 emissions is expected to raise healthcare costs due to the adverse health effects of pollution, with β1 = 1 = 2HE/2CO2. The second independent variable is industrialisation, which refers to the process of economic development that entails the development of an economy’s manufacturing and industrial sectors ( Hauge & Chang, 2019 ). One of the possible methods to evaluate the state of the process of industrialization is through various indicators, including the ratio of industry to the Gross Domestic Product (GDP), Study ( Tulchinsky & Varavikova, 2014 ), ( Dembe, 2001 ) went further and claimed that industrialization might increase pollution rates, occupational health risk, and exposure to harmful substances which subsequently translates into greater healthcare costs. Accidents, injuries, and occupational diseases may also result from the development of industrial operations and require medical care and recovery. Quite the contrary, some studies have shown that the dividends of industry growth may lead to the development of new technologies in healthcare provision, such as healthcare equipment and medicines, as well as more efficient treatment strategies. It can eventually improve healthcare outcomes and reduce the costs of long-term care. Further, industrialisation may help enhance living standards, sanitation, and access to healthcare, thereby achieving better health outcomes and possibly lowering the cost of developing widespread healthcare spending to treat preventable illnesses. However, in our investigation, industrialisation will have a positive impact occasionally on health expenditure owing to possible growth in pollution diseases as well as work health risks. The third explanatory variable is foreign direct investment (FDI). It occurs when a foreign entity invests its capital into the domestic economy as a way of setting up business or purchasing assets. FDI is measured by aggregating FDI inflows or as a percentage of a nation’s GDP. FDI has many sources of data, among them national investment promotion agencies, central banks and international bodies such as UNCTAD and OECD. According to some studies, FDI can enhance economic growth, advance infrastructure, and generate jobs ( Zekarias, 2016 ). This, in its turn, may increase accessibility to healthcare services and funding. The fourth independent variable is clean energy, incorporating renewable sources such as solar, wind, and hydroelectric power, providing options beyond traditional energy sources that depend on fossil fuels. Consumption of Clean energy may be measured either as a share of overall energy consumption or in units such as terawatt-hours. Information on the use of clean energy can be found through various sources, such as national energy agencies, international organisations such as IRENA, and energy research institutions. Previous evidence indicates that clean energy has the potential to reduce air pollution, respiratory diseases, and the medical costs associated with pollution-related illnesses. The investment in clean energy technologies will also generate employment, boost economic development, and improve the population’s health outcomes, thereby reducing health spending. Other sources, however, assert that a short-term investment in clean energy infrastructure and technology may incur these costs at the outset, temporarily increasing healthcare expenditure. We have analysed that clean energy will save on health expenditure by minimising pollution-related diseases and health care costs. The dependent variable is health expenditure, defined as the monetary amount spent on the referral and support of healthcare services ( Ke, Saksena, and Holly, 2011 ). It is measured by three major elements: total health expenditure, government health expenditure, and household health expenditure. Health spending is typically defined as a percentage of GDP or in absolute terms, referring to the total amount spent on healthcare within a given economy. Government spending on health is the money the government allocates to fund healthcare facilities and services ( Babatunde, 2018 ). Household spending on health is the expenditures made by people and families to cover health services, including those not covered by insurance. 3.3 Estimation strategies To ensure econometrically valid and robust inference, this study adopts a comprehensive multi-stage estimation strategy explicitly designed for heterogeneous, cross-sectionally dependent panel data. The empirical framework sequentially examines slope heterogeneity, cross-sectional dependence, unit roots, and cointegration, and then proceeds to dynamic long-run estimation using the Dynamic Common Correlated Effects (DCCE) approach. 1. Slope Heterogeneity Test To assess whether slope coefficients differ across cross-sectional units, the study applies the slope heterogeneity (SH) test proposed by Bersvendsen and Ditzen (2021) . This test evaluates whether individual-specific slope estimates significantly deviate from the pooled estimator. Consider the baseline panel regression: (1) y it = α i + x it ′ β i + u it , i = 1 , … , N ; t = 1 , … , T where β i is allowed to vary across units. The standardised dispersion statistic is defined as: (2) Δ i = N ( β i − β - ) σ ̂ Δ with (3) β - = 1 N ∑ i = 1 N β i , σ ̂ Δ 2 = 1 N ∑ i = 1 N ( β i − β - ) 2 The null hypothesis H 0 : β i = β ∀ i implies slope homogeneity, while rejection indicates heterogeneous slope behaviour across cross-sections. 2. Cross-Sectional Dependence Test Given the strong likelihood of interdependence among economic units, cross-sectional dependence (CD) is tested using the statistic developed by Juodis and Reese (2022) , which generalizes Pesaran’s CD test. Let u ̂ it denote residuals obtained from the panel regression. Pairwise correlation coefficients are computed as: (4) ρ ̂ ij = ∑ t = 1 T u ̂ it u ̂ jt ∑ t = 1 T u ̂ it 2 ∑ t = 1 T u ̂ jt 2 , i ≠ j The CD test statistic is given by: (5) CD = 2 N ( N − 1 ) ∑ i = 1 N − 1 ∑ j = i + 1 N ρ ̂ ij Under the null hypothesis H 0 : ρ ̂ ij = 0 , the statistic converges to a standard normal distribution. Rejection implies the presence of cross-sectional dependence, necessitating estimators robust to standard shocks. 3. Panel Unit Root Test To determine the order of integration, the study employs the panel unit root test of Herwartz and Siedenburg (2008) , which accommodates heteroskedasticity, cross-sectional dependence, and structural instability. The augmented regression is specified as: (6) Δ y it = α i + ρ i y i , t − 1 + ∑ k = 1 p i ϕ ik Δ y i , t − k + ε it where Δ denotes the first-difference operator, ρ i is the autoregressive coefficient, and p i is the optimal lag length. The hypotheses are: (7) H 0 : ρ i = 0 ( unit root ) , H 1 : ρ i < 0 ( stationarity ) Rejection of H 0 indicates stationarity of the series. 4. Panel Cointegration Test Long-run equilibrium relationships are examined using the Westerlund and Edgerton (2008) cointegration test, which is robust to cross-sectional dependence and structural breaks. The test is based on the Durbin–Hausman principle applied to the estimated error-correction term: (8) e ̂ it = ϑ i e ̂ i , t − 1 + η it The group-mean test statistic is defined as: (9) D H g = ∑ i = 1 N S ̂ i ( ϑ ~ i − ϑ ̂ i ) 2 ( ∑ t = 2 T e ̂ i , t − 1 2 ) where ϑ ̂ i and ϑ ~ i are OLS and IV estimators, respectively, and S ̂ i is a long-run variance correction. The null hypothesis assumes cointegration for all cross-sections. 5. Dynamic Common Correlated Effects (DCCE) Estimation After establishing cointegration, long-run coefficients are estimated using the Dynamic Common Correlated Effects (DCCE) estimator proposed by Chudik and Pesaran (2015) . The cross-sectionally augmented distributed lag (CS-ARDL) specification is: (10) y it = ∑ l = 1 p λ il y i , t − l + ∑ l = 0 q δ il ′ x i , t − l + ∑ l = 0 r γ l ′ z t − l - + μ i + ε it where z t - = ( y t - , x t - ) represents cross-sectional averages that proxy unobserved common factors. The long-run coefficient vector is recovered as: (11) θ i = ∑ l = 0 q δ il 1 − ∑ l = 1 p λ il 6. Endogeneity Correction via DCCE-IV To mitigate endogeneity arising from lagged dependent variables, the study employs an instrumental-variable-adjusted DCCE estimator following Ditzen (2018) . Historical lags of regressors are used as instruments: (12) E ( ε it | x i , t − s ) = 0 , s ≥ 2 This approach yields consistent and efficient estimates even in the presence of feedback effects and weak exogeneity. IV. Estimation and interpretation 4.1 Pre – estimation assessment This section investigates cross-sectional dependency and the slope of the heterogeneity test, following Juodis and Reese (2022) and Bersvendsen and Ditzen (2021) . Table 1 displays the results with two-panel outputs for the CD test and the SH test, respectively. According to the test statistics, all variables are cross-sectionally dependent and exhibit heterogeneity in their properties. Table 1. Results of CSD and SH test. Panel A: SH test of Bersvendsen and Ditzen (2021) Delta statistic Adjusted delta statistic SH exits Model 3.7022*** 5.6842*** Yes Model 3.9396*** 4.2483*** Yes Panel B: CD test of Juodis and Reese (2022) HE IND FDI REC FD TO CO2 test stat value 3.441 -1.2356 -5.985 -2.3845 3.2778 -1.697 6.6708 Probability *** *** *** *** *** *** *** CD exist YES YES YES YES YES YES YES The study performed a panel unit root test and a cointegration test with a structural break, following Helmut Herwartz, Maxand, Raters, and Walle (2018) and Westerlund and Edgerton (2008) . The output of the panel unit root and cointegration test is reported in Table 2 . From the results, it is apparent that all variables become stationary after the first difference. Furthermore, the panel cointegration test established a long-run association in the empirical relations. Table 2. Panel unit root test and cointegration test. Panel A: Integration (or unit-root) test of Herwartz and Siedenburg -2008 HE IND FDI REC FD TO CO2 At level 0.4937 1.7963 1.3513 1.5564 1.879 1.8403 0.1319 First difference -3.8705 7.0344 8.8425 -3.2095 -3.0493 4.4278 -3.2097 Panel B: Cointegration test of Westerlund and Edgerton (2008) No shift Mean shift Regime shift LMг LMΦ LMг LMΦ LMг LMΦ stat. Stat. Stat. Stat. Stat. Stat. Model 1 -4.1509 -4.941 -2.8775 -4.6002 -2.0494 -3.8778 Model 2 -4.6978 -2.0105 -4.5972 -2.752 -2.7137 -4.6631 4.2 Empirical model estimation with DCE and DCE-IV The coefficient, see Table 4 , for industrialization was positive and statistically significant at the 1% level, suggesting that industrialization may increase health care costs. Precisely, a 10% increase in industrial output will result in a 1.826% increase in HE cost with DCE and a 1.729% increase with DCE-IV estimation. Our findings are consistent with the existing literature ( Rastegar, 2004 ; Shen, Wang, & Shen, 2021 ; Zhou et al., 2020 ). Effects of industrial processes on health may be explained by their impact on the environment and health, such as air pollution. Most of the time, pollution is associated with high healthcare costs, as it requires greater spending on healthcare. Policymakers should thus consider the impacts of industrialisation on healthcare budget allocation. In doing so, they will be able to minimise adverse effects on the population’s health and ensure that the healthcare industry is ready to address increased demand for services. FDI also has a positive impact on healthcare costs, with the effect statistically significant at the 1% level. An 10% increase in FDI results in a nearly 1.509% increase in healthcare spending when employing the DCE technique and a 1.735% increase using the DCE-IV associated technique. This could be because FDI drives economic growth and increases the demand for healthcare services, and this also brings new medical technologies and practices that initially make the cost high. The results are consistent with those of ALZIYANI & Murad (2021) , Ehsani et al. (2023) , Van Tran et al. (2024) , and Zekarias (2016) . The two variables, renewable energy consumption and healthcare costs, are statistically significant at the 1 percent level. There is a 10% increase in renewable energy consumption, resulting in a 1.723% reduction in healthcare spending in the DCE model and a 1.003% reduction in the DCE-IV model. According to studies, increased renewable energy will reduce healthcare costs, likely by decreasing pollution and its associated health issues. Healthcare expenditures show positive correlations with the CO2 emission coefficients in both models, and these correlations are significant at the 1% level. This means that spending in the healthcare sector will increase as the environment continues to be degraded. Namely, a change of 10 0.795 0.5 stayed costs by a significant margin of 6.691, a 10 0.5 rate of increase in CO 2 emissions, increases healthcare expenses by 0.795 0.5 ratio and 6.691 0.5 ratio respectively, with respect to the DCE and DCE-IV techniques. These conclusions highlight that the effects of pollution on health services and costs are significant, and these arguments effectively support the implementation of policies to reduce emissions ( Dritsaki and Dritsaki, 2023 ; Hamid and Wibowo, 2023 ; Ullah et al., 2020 ). Financial development positively affects healthcare costs, with the effect statistically significant at the 1% level. A 10% increase in financial development leads to a 0.995% increase in healthcare expenditures with the DCE method and a 1.598% increase with the DCE-IV method. The study by Chireshe & Ocran (2020) and Xu & Tan (2020) contended that financial development improves access to healthcare services, leading to higher utilisation and, consequently, higher costs. Trade openness is positively associated with healthcare costs, and the association is statistically significant at the 1% level. A 10% increase in trade openness results in a 1.575% increase in healthcare expenditures with the DCE method and a 1.683% increase with the DCE-IV method. Trade openness can drive economic growth and increase healthcare demand, but it may also introduce health risks through the exchange of goods and services that require greater healthcare spending. 4.3 Coefficients estimation with NARDL Among the high-income group, the long-run asymmetric coefficients (see Table 5 , Panel A) indicate that health expenditure is positively associated with CO2 emissions (both positive and negative), with coefficients of 0.1212 and 0.1325, respectively, suggesting that higher CO2 emissions are associated with greater health expenditure. Coefficients of -0.1238 and -0.0999, respectively, for positive and negative changes in renewable energy consumption (REC), imply a negative correlation between changes in REC and health care spending. Health spending is positively affected by changes in foreign direct investment (FDI) (0.1269), though negative FDI has a minor positive effect (0.0971). There is a complicated link between industrial development (IND) and trade openness (TO). IND changes, whether good or negative, increase health spending, with the negative impact being more pronounced (0.1333). Nevertheless, trade openness shows a negative coefficient for positive changes (-0.0991) and a substantial drop for negative changes (-0.1158), suggesting that health spending is reduced in the long term by more open trade policies. However, the trend is different for the low-income group. Health spending is also substantially increased by positive CO2 emissions (0.1006), but at a slower pace than the high-income group; on the other hand, harmful CO2 emissions have a somewhat more significant effect (0.1165). Health spending is substantially increased (0.1159) by positive REC changes but only slightly (0.0983) by negative REC changes. Foreign direct investment (FDI) has a favorable influence on both positive and negative changes in the low-income group. However, the adverse effect is much higher (0.1737) than in the high-income group. Similarly, health spending is favorably affected by industrial growth, with a higher positive effect (0.0999) than a negative one (0.0849). Not only does trade openness have a positive effect on health expenditure (0.0722) in the low-income group, but it also has an adverse effect (0.1053). This shows that low-income groups are more affected by trade dynamics. Panel B shows that extra insights are revealed in the near term by the asymmetric consequences. Positive CO2 emissions significantly raise health spending in high-income nations (0.0394), whereas harmful CO2 emissions significantly lower it (-0.0474). Positive CO2 emissions have little effect on low-income nations (0.003), but harmful emissions considerably raise health care costs (0.0441). Both positive and negative changes in renewable energy consumption continue to have favourable consequences; however, low-income groups are hit more by negative changes (0.0261) than high-income groups. Foreign direct investment (FDI) has a favourable effect regardless of the direction of the flow and the recipient’s income level. However, low-income recipients are particularly walloped by negative FDI changes (0.0321). Health spending is positively affected by industrial growth across all income levels, with low-income nations being more sensitive to its adverse effects (0.0513). In high-income nations, trade openness raises health spending by 0.047, whereas in low-income countries it has a negligible, negative effect of -0.0103. In conclusion, nations with lower incomes react more strongly (0.0523) to financial progress, although this effect is statistically significant across both categories. From the adjustment speed to equilibrium (cointEq (-1)), when health spending deviates from its long-run path, low-income countries restore long-term equilibrium more quickly (-0.3042) than high-income countries (-0.4161). We further test the duality of causality by conducting directional causality tests in Model 1 ( Table 3 ) and find diverse relationships among these variables. The relationship between health expenditure (HE) and carbon dioxide emissions (CO2) is also bidirectional, as shown by a critical W-Stat and Zbar-Stat at both HE CO2. That is to say, the variations in health expenditure could anticipate CO2 emissions and vice versa. Also, there is bidirectional causality between HE and IND; CO2 and TO indicate each other’s predictive influence. It is also noted that throughout the countries under test. At the same time, there exists a unidirectional causal configuration from CO2 to HE via REC and FDI, and neither direction can predict any of those variables. Table 3. Results of DCE and DCE-IV estimation. Coefficient Std. error t-statistic Coefficient Std. error t-statistic DCE DCE-IV Panel –A: Higher-income nations HE(-1) 0.1498 0.0424 3.5344 0.1268 0.0423 2.9981 IND 0.1826 0.0417 4.381 0.1729 0.0422 4.0992 FDI 0.1509 0.0305 4.9481 0.1735 0.0373 4.6517 REC -0.1723 0.0291 -5.9216 -0.1003 0.0166 -6.0439 CO2 0.0795 0.0197 4.0385 0.6691 0.0139 48.1302 FD 0.0995 0.0193 5.1601 0.1598 0.0151 10.586 TO 0.1575 0.0331 4.7598 0.1683 0.0209 8.0531 c -13.217 0.24013 -55.041 -12.849 0.24013 -53.5085 R2 0.9036 0.899 Adj R 2 0.9342 0.9424 Table 4. Output of the asymmetric assessment. HIC LMIC Variables Coefficient st. error t-stat Coefficient st. error t-stat Panel A: Long-run asymmetric coefficients lnCO2+ 0.1212 0.0806 3.59037 0.1006 0.0375 2.6826 lnCO2- 0.1325 0.0625 2.1221 0.1165 0.0426 2.7347 lnREC+ -0.1238 0.0611 -2.0261 0.1159 0.0357 3.2464 lnREC- -0.0999 0.035 -2.8542 0.0983 0.0431 2.2846 lnFDI+ 0.1269 0.0405 3.1333 0.1033 0.0345 2.9942 lnFDI- 0.0971 0.0436 2.3267 0.1737 0.0376 6.9601 lnIND+ 0.1066 0.0441 2.4385 0.0999 0.0169 5.9112 lnIND- 0.1333 0.0258 6.3536 0.0849 0.0118 7.1949 lnTO -0.0991 0.036 -2.7527 0.0722 0.018 4.0111 lnFD -0.1158 0.0296 -3.9121 0.1053 0.0337 3.1246 Symmetry test- long-run W CO 2 10.576 14.0287 W REC 14.9598 12.7268 W FDI 14.08 14.9248 W IND 9.3849 11.7901 Panel -B: Short-run asymmetric coefficients ΔlnCO2+ 0.0394 0.0036 10.9444 0.003 0.0063 0.4761 ΔlnCO2- -0.0474 0.0027 -17.555 0.0441 0.0077 5.7272 ΔlnREC+ 0.0178 0.00272 6.5441 0.0177 0.0026 6.8076 ΔlnREC- -0.0086 0.00716 -1.2011 0.0261 0.0055 4.7454 ΔlnFDI+ 0.045 0.00689 6.5312 0.0109 0.0077 1.4155 ΔlnFDI- 0.0486 0.00731 6.6484 0.0321 0.0056 5.7321 ΔlnIND+ 0.0323 0.00827 3.9056 0.0365 0.006 6.0833 ΔlnIND- -0.0086 0.00765 -1.1241 0.0513 0.0063 8.1428 ΔlnTO 0.047 0.00441 10.6575 -0.0103 0.0071 -1.4507 ΔlnFD 0.0506 0.0035 14.4571 0.0523 0.0061 8.5737 cointEq (-1) -0.4161 0.0161 -25.8447 -0.3042 0.0077 -39.5064 W CO 2 8.8689 13.5018 W REC 13.3801 14.3392 W FDI 12.212 8.2001 W IND 9.2302 10.8148 Table 5. Output of D-H directional casualty test. Null hypothesis W-Stat. Zbar-Stat. Remarks W-Stat. Zbar-Stat. Remarks HECO2 3.9415 4.1543 <-- 5.4187 5.7113 CO2HE 10.1126 10.6586 8.8342 9.3112 HEREC 3.2571 3.4329 RECHE 6.3326 6.6745 1.7194 1.8122 HEFDI 3.0605 3.2257 <-- 8.1402 8.5797 FDIHE 10.4431 11.007 4.0701 4.2898 HEIND 5.8894 6.2074 9.5462 10.0616 INDHE 9.3931 9.9003 5.8724 6.1895 HETO 2.0882 2.2009 <-- 1.0712 1.129 <-- TOHE 7.2252 7.6153 8.9914 9.4769 HEFD 8.3985 8.852 1.5802 1.6655 FDHE 7.9681 8.3983 1.1105 1.1704 RECCO2 4.899 5.1635 --> 8.5674 9.03 CO2REC 1.7704 1.866 9.6801 10.2028 CO2FDI 3.5494 3.741 <-- 1.0435 1.0998 FDICO2 7.7577 8.1766 2.1041 2.2177 CO2IND 4.5568 4.8028 --> 4.1987 4.4254 INDCO2 1.2975 1.3675 7.9766 8.4073 CO2TO 8.8363 9.3134 8.5281 8.9886 TOCO2 9.1349 9.6281 3.0456 3.21 CO2FD 8.3602 8.8116 6.8501 7.22 FDCO2 7.2784 7.6714 8.6578 9.1253 RECFDI 3.2029 3.3758 8.882 9.3616 FDIREC 3.8756 4.0848 3.9798 4.1947 RECIND 7.3836 7.7823 5.8724 6.1895 INDREC 8.0106 8.4431 3.5419 3.7331 RECTO 6.0425 6.3687 5.7088 6.017 TOREC 6.4633 6.8123 6.4527 6.8011 RECFD 8.2848 8.7321 5.3166 5.6036 --> FDREC 3.7109 3.9112 1.9054 2.0082 FDIIND 8.0935 8.5305 5.2656 5.5499 INDFDI 7.764 8.1832 3.6408 3.8374 FDITO 2.2008 2.3196 <-- 1.6269 1.7147 <-- TOFDI 10.5047 11.0719 8.6153 9.0805 FDIFD 1.1721 1.2353 3.5876 3.7813 FDFDI 3.0775 3.2436 4.679 4.9316 INDTO 5.6269 5.9307 9.492 10.0045 TOIND 9.8097 10.3394 6.5037 6.8548 INDFD 4.6811 4.9338 2.9128 3.07 <-- FDIND 4.0212 4.2383 8.0085 8.4409 TOFD 8.8788 9.3582 7.7523 8.1709 FDTO 8.3315 8.7814 5.1732 5.4525 Table 6. Results of the robustness test. FGLS PCSE FMOLS Coefficient st. error t-stat Coefficient st. error t-stat Coefficient st. error t-stat Panel –A: For Lower Middle-income countries IND 0.1183 (0.0373) [3.1715] 0.1696 (0.0259) [6.5482] 0.1612 (0.0342) [4.7134] FDI 0.1487 (0.0222) [6.6981] 0.1563 (0.0371) [4.2129] 0.1743 (0.0257) [6.7821] REC -0.1143 (0.0358) [3.1927] -0.1256 (0.019) [6.6105] -0.1209 (0.0143) [8.4545] FD 0.1681 (0.0314) [-5.3503] 0.1198 (0.0219) [-5.4703] 0.1542 (0.0421) [-3.6627] TO -0.1084 (0.025) [4.336] -0.0986 (0.019) [5.1894] -0.1214 (0.0299) [4.0602] CO2 0.1814 (0.0356) [5.0955] 0.1512 (0.0335) [4.5134] 0.1058 (0.0197) [5.3705] r2 0.699 0.569 Adj R2 0.451 Wald X2 24364.3741 14910.76984 Prob. 0 0 0 Panel –B: For higher-income countries FGLS PCSE FMOLS IND 0.1301 (0.041) [-3.1731] 0.1784 (0.0186) [-9.5913] 0.1493 (0.0238) [-6.2731] FDI 0.0987 (0.0235) [-4.2] 0.1622 (0.0226) [-7.1769] 0.1708 (0.023) [-7.426] REC -0.1692 (0.0265) [6.3849] -0.1303 (0.0333) [3.9129] -0.123 (0.0317) [3.8801] FD 0.1088 (0.0389) [-2.7969] 0.1808 (0.017) [-10.6352] 0.1548 (0.0167) [-9.2694] TO -0.0971 (0.0157) [6.1847] -0.1092 (0.0265) [4.1207] -0.1181 (0.0188) [6.2819] CO2 0.1678 (0.0409) [-4.1026] 0.0868 (0.0146) [-5.9452] 0.0962 (0.0382) [-2.5183] r2 0.647 0.547 Adj R2 0.464 Wald X2 21309.36116 22820.01895 Table 7. Endogeneity assessment. LMIC HIC HE (-1) -0.1613 (0.0168) [9.6011] 0.1668 (0.0318) [-5.2452] IND -0.1032 (0.0188) [5.4893] 0.1734 (0.0307) [-5.6482] FDI -0.1197 (0.0258) [4.6395] 0.1821 (0.0242) [-7.5247] REC 0.1598 (0.0345) [-4.6318] 0.1602 (0.0453) [-3.5364] FD 0.1074 (0.0434) [-2.4746] 0.1539 (0.0362) [-4.2513] TO 0.1477 (0.017) [-8.6882] 0.1343 (0.0338) [-3.9733] CO2 -0.1338 (0.0298) [4.4899] -0.0978 (0.0444) [2.2027] Anderson canon. Corr. LM statistics 14.7069 14.7069 Cragg-Donald Wald F-statistics 1283.7665 1283.7665 Stock-Yogo weak ID test critical values 17.1766 17.1766 Model 2 showed causal relationships between the variables as well. Some of them were significantly higher or lower compared to Model 1. Obviously, the two-way causality between HE and CO2 is essential for retaining and suggesting an intimate connection between health expenditure and carbon dioxide emissions (for instance, []). Furthermore, precipitation is bidirectionally causal with HE and IND, and CO2 is bidirectionally causal with FD, allowing them to predict each other. REC to HE unidirectional causality is established, indicating that health expenditure can be predicted by renewable energy consumption moving from the past into the future, while not evolving vice versa. A more complex interplay between these variables is suggested by the current model, which shows that CO2 Granger-causes REC and that REC Granger-causes CO2. Our results also provide evidence for the sparsely linked causal network among health expenditure, CO2 emissions, renewable energy consumption, and some economic dimensions. 4.4 Robustness assessment The following section deals with the assessment of the robustness of the estimation through different techniques, such as FGLS, PCSE, and FMOLS, as well as endogeneity assessment with IV estimation. The output reported in Table 6 showed a similar vine of association, as observed in the earlier estimation. Thus, it assesses the robustness and internal consistency of the empirical model. The endogeneity problem in the study has also been addressed using IV techniques, as shown in Table 7 . It is apparent that an endogeneity problem is absent. V. Discussion of the study findings The study’s findings suggest a positive relationship between industrialisation and rising healthcare costs. More specifically, a 10% increase in industrial output is associated with an almost 1.8% rise in health spending. The one-sided correlation is statistically significant at the 1% level. It is also in line with the existing literature, including the works by Rastegar (2004) , Shen et al. (2021) , and Zhou et al. (2020) . Possible causes of this relationship include environmental factors and the health issues mentioned above related to industrial activity. Industrial processes often result in air pollution, which is a known cause of health complications. For example, when emitted by factories, harmful substances aggravate the condition of individuals with respiratory infections and cardiovascular diseases. Industrialisation is closely connected with an increasing number of individuals demanding medical services, thereby putting fixed pressure on healthcare spending. The nature of value-added processes inherent in industrial activity often involves heavy exploitation of raw materials, chemicals, machinery, and energy, leading to increased emissions of environmental pollutants ( Jiang, Mei, and Feng, 2016 ). Since manufacturing processes transform raw materials into finished products, emissions are released into the air, water, and land, thereby exacerbating environmental degradation. Therefore, more intensive production and added value in industries are associated with increased volumes and greater richness of emissions. The worst industrial pollutants include particulate matter, heavy metals, and volatile organic compounds, which are rife in the industrial environment. During prolonged exposure to these pollutants, health risks that develop result in respiratory disorders, cardiovascular diseases, and chronic conditions like cancer are significantly high. In poor health conditions, the affected population becomes highly dependent on healthcare services to alleviate pollution-related illnesses, thereby increasing medical spending. To address this increased demand, health systems are forced to increase capacity, modernise their facilities, and invest in infrastructure to ensure satisfactory service delivery. In this regard, industrialisation is a factor in increasing healthcare costs due to a greater focus on disease burden, and the healthcare infrastructure must improve as well. There is also empirical evidence that foreign direct investment (FDI) causes a considerable positive impact on healthcare expenditures. In particular, a 1 in 10 per cent increase in FDI is accompanied by a growth of about 1.509 per cent in healthcare expenditure, where the relationship is found to be statistically significant at the 1 in 10 per cent level ( Outreville, 2007 ). FDI also sparks economic growth, thus increasing household incomes and expanding individuals’ ability to invest in health-related services. Disposable income rises; therefore, demand for higher-quality healthcare services and their increased frequency grow, resulting in higher aggregate health spending. The results are consistent with previous reports indicating an indirect influence of FDI on healthcare demand via income and economic development channels ( Outreville, 2007 ; Shenkar, Liang, and Shenkar, 2022 ). FDI can introduce new healthcare practices and technologies in other nations. Although these innovations can strengthen health care in the long term, in the short term, the expenses associated with them can increase due to the investment of health care specialists in innovation, training, and facilities. Facilities and services in the health sector can be developed through FDI and made available to a greater number of people. Industrialisation and urbanisation, along with FDI, can heighten environmental pollution and degradation. Deteriorating environmental conditions increase healthcare costs incurred in treating diseases caused by pollution ( Singhania & Saini, 2021 ). Increased FDI may boost economic growth, raising incomes and demand for healthcare services, but it may also increase costs. Also, new healthcare technologies and practices introduced through FDI may initially increase costs but eventually enhance healthcare outcomes ( K. A. Khan et al., 2019 )—the determinant and effect of health spending on investment in financial assets choices. The effects of FDI on healthcare spending can vary depending on the level of economic development, the structure of the healthcare system, and regulatory frameworks. Further studies are needed to investigate these ways of contextualization and their impact on the relationship between FDI and healthcare spending. Our study found harmful effects on healthcare costs: a 10 per cent increase in renewable energy utilisation will cause healthcare spending to drop by 1.723 per cent, a statistically significant effect at the 1 per cent level, which is quite notable. This implies that healthcare costs can be minimised by promoting renewable energy sources, which may be explained by the consequent decrease in pollution and medical complications (A. Roy, 2024 ). Consuming renewable energy has also been shown to reduce healthcare costs by mitigating environmental degradation and pollution. Suggesting that healthcare costs can be reduced by increasing the utilisation of renewable energy sources. The most probable explanation for this connection is the decrease in pollution and related health issues. Because less pollution will result from greater use of renewable energy sources, air quality might improve, and, consequently, the number of respiratory and cardiovascular diseases, among other health gains, will go down. The net effect is that the reduction in healthcare spending can be achieved by improving the healthcare system as a whole. The study results indicate a positive correlation between CO 2 emissions and the escalation of healthcare expenditures. This is a one-sided correlation that is statistically significant at the 1% level. To be more exact, a 10 per cent increase in CO2 emissions leads to a nearly 0.795 per cent increase in health expenditures. Some byproducts of human activities, such as CO2 emissions, have been shown to have severe effects on public health. High concentrations of CO2 and other air pollutants can also cause respiratory and cardiovascular issues, as well as cancer (C.-M. Wang, Hsueh, Li, and Wu, 2019 ). These health problems are more prevalent as emission levels increase, necessitating increased medical services, which in turn increase healthcare spending. To mitigate the consequences, policymakers must focus on the adoption of technologies that help to decrease emissions to achieve this, including the encouragement of renewable energy sources, the enhancement of energy efficiency, and the stricter regulations on the emissions caused by industry and transportation ( Hussain, Marcel, Majeed, and Tsimisaraka, 2023 ). By curbing air pollution, governments can not only enhance population health but also reduce the economic burden on the health sector ( Ullah et al., 2020 ). 5.1 Conclusion The purpose of this study was to explore the intricate relationships among economic, environmental, and healthcare characteristics in lower-middle (LMI) or upper-middle-income countries. The objective of this research was to investigate the effects of industrialisation, environmental degradation, foreign direct investment (FDI), financial development, clean energy, and trade openness on health expenditure in Malaysia from 1995-2020. The approach used a diverse set of econometric methods to yield robust, accurate results. These are tests of slope heterogeneity, cross-sectional dependence, unit roots, and cointegration. Methods. We used the Dynamic Common Correlated Effects (DCE) panel regression and instrumental variable-adjusted DCE (DCE-IV) approaches to address potential endogeneity issues. Moreover, the asymmetric relationships of such variables were further investigated using a nonlinear autoregressive distributed lag (NARDL) model. Survey results showed significant correlations between the variables and health expenditures. Increased industrialisation is an expected cost factor due to pollution and its associated health impacts on the population. Through backward linkages to new capital and technology, FDIs are likely to positively affect health expenditure by altering the healthcare structure. This is likely related to environmental effects, as CO2 emissions were significantly positively associated with healthcare payments, and it also points to health problems caused by pollution sources. On the other hand, an inverse relationship between health expenditure and renewable energy consumption was found, suggesting that rising healthcare costs can be reduced by adopting clean energy. Health expenditure is positively associated with financial development and trade openness. In layperson’s terms, this means the more developed and globalised an economy becomes, the more significant its healthcare spending becomes! The study also revealed that there were asymmetric effects in these links, which mainly applied to high and low-income groups. The sensitivity of some factors (foreign direct investment and industrial growth) to changes was greater in low-income countries. These findings highlight the intricate relationships among economic development, environmental determinants, and healthcare spending. The results of the scenarios underscore the need for policymakers to integrate such cross-sector interests into strategies for sustainable development and public health improvement. 5.2 Policy suggestions Based on the results and discussion of the study, the following policy suggestion can be proffered to address some adverse effects associated with CO2 emissions, foreign direct investment (FDI), and clean energy, as well as to promote positive impacts within the nations investigated. First, by contrast, policymakers in LMIC and UMIC should work top down - for example, on more complex relationships between carbon emissions and economic development or healthcare expenditures. Clean energy aid and sustainable pathways to industrialisation are central to addressing the adverse co-benefits of CO2 pollution. Governments in LMICs and UMICs need to offer private companies more incentives to invest in clean energy (through tax breaks, grants, and other financial mechanisms). This will decrease the use of fossil fuels and, at the same time, reduce carbon emissions while maintaining economic growth. Second, there is the introduction and strict implementation in LMICs and UMICs with FDI that would restrict pollution by foreign companies and keep their healthcare prices under control. Part of this involves conducting an environmental impact assessment, standardising emissions, and requiring the use of best available technology to reduce pollution. At the same time, FDI can enable policymakers to accelerate the adoption of clean technologies and green practices by selectively stimulating green investments or projects focused on environmental protection. Three, the government and the business community in LMICs and UMICs must emphasise research and development of clean energy technologies. One possible solution to popularise this is making sure that governments increase investments in renewable energy research, create university-industry alliances, and create innovation hubs that have a specific mandate to create and commercialise clean-energy solutions. It can lower the cost of renewable energy, making it an even more sound option than fossil fuels and enabling its quick adoption across sectors of our economy. This paper contends that, if healthcare costs are addressed through policy to address the environmental burden of health, the costs should be confronted through the application of preventive measures and early interventions. This would involve an investment in the public health systems, air and water quality-ready metering systems and early warning of environmental dangers. Environmental sustainability in healthcare policies should also be documented, including telemedicine’s ability to reduce carbon footprints by reducing travel and using energy-efficient medical equipment. Nations should initiate educational and awareness programs about the connections among environmental quality, health outcomes, and economic growth. Alternatively, they may also raise awareness and knowledge in a broader scope in order to create sustainable change at an individual level, which, in its turn, will result in the decrease of carbon emissions and the enhancement of health outcomes. This boils down to encouraging international collaborations and knowledge exchange to address such challenges. Developed economies need to provide financial support, capacity-building, and technology transfer to enable developing states to make the transition possible. Economic growth will be achieved at the expense of environmental sustainability, and more people will have access to healthcare. Ethics approval and consent to participate Ethical approval and consent were not required. Declaration on the use of AI statement The authors confirmed that no generative Artificial Intelligence (AI) tools were used in the conceptualisation of this research or writing, data analysis, and interpretation of this study. Data availability This work contains the following underlying data: Zenodo: World development Indicator [Data set]. https://doi.org/10.5281/zenodo.18204974 (Md. Qamruzzaman, 2026). This project contains the following data: • P_Data_Extract_From_World_Development_Indicators(2).xlsx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References Abbas Khan K, Zaman K, Shoukry AM, et al. : Natural disasters and economic losses: controlling external migration, energy and environmental resources, water demand, and financial development for global prosperity. Environ. Sci. Pollut. Res. 2019; 26 : 14287–14299. 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Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 05 Feb 2026 ADD YOUR COMMENT Comment Author details Author details 1 united international University, Dhaka, 1212, Bangladesh 2 King Faisal University, Al Ahsa, Eastern Province, Saudi Arabia Md Qamruzzaman Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Ms. Tanzila Amir Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Abdulrahman Alomair Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Resources, Writing – Original Draft Preparation, Writing – Review & Editing Mohammed Alomair Roles: Conceptualization, Data Curation, Formal Analysis, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [KFU260335] and Institute for Advanced Research (IAR), United international University (UIU). Ref: IAR-2026-Pub-009 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 02 May 2026, 15:197 https://doi.org/10.12688/f1000research.177010.3 version 2 Revised Published: 19 Feb 2026, 15:197 https://doi.org/10.12688/f1000research.177010.2 version 1 Published: 05 Feb 2026, 15:197 https://doi.org/10.12688/f1000research.177010.1 Copyright © 2026 Qamruzzaman M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Qamruzzaman M, Amir MT, Alomair A and Alomair M. Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :197 ( https://doi.org/10.12688/f1000research.177010.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: AWAITING PEER REVIEW AWAITING PEER REVIEW ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 05 Feb 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 3 (revision) 02 May 26 read Version 2 (revision) 19 Feb 26 read read Version 1 05 Feb 26 Mario Coccia , National Research Council of Italy, Turin, Italy Jain Yassin , Universiti Teknologi MARA, Shah Alam, Malaysia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Yassin J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 May 2026 | for Version 3 Jain Yassin , Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia 0 Views copyright © 2026 Yassin J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The introduction does not clearly specify the geographical focus of the study. The manuscript references multiple regions and countries. However, it remains unclear. The introduction attempts to cover multiple broad themes simultaneously; as a result, the narrative becomes fragmented and loses conceptual focus. The conclusion repeatedly introduces and discusses external studies instead of emphasizing the findings generated by the present study.The emphasis should be on the manuscript’s original contribution rather than re-explaining prior literature. The future research section is comprehensive but somewhat overloaded with multiple directions. Competing Interests No competing interests were disclosed. Reviewer Expertise Research areas include development economics, multidimensional poverty, sustainable and inclusive development, green and blue economy, regional economic development, and socio-economic policy analysis. reply Respond to this report Responses (0) Yassin J. Peer Review Report For: Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :197 ( https://doi.org/10.5256/f1000research.197691.r481062) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-197/v3#referee-response-481062 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Yassin J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Mar 2026 | for Version 2 Jain Yassin , Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia 0 Views copyright © 2026 Yassin J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Despite its empirical contributions, the study has several limitations. First, the conceptual framework could be strengthened, as the relationships between financial development, environmental pressures, and health expenditure are not sufficiently grounded in established theoretical perspectives. The analysis relies heavily on empirical results without clearly explaining the underlying mechanisms linking these variables. Second, although governance is highlighted in the title, its role is not thoroughly discussed in the empirical interpretation and policy analysis. The study does not clearly explain how governance indicators influence health expenditure or interact with environmental and financial factors. The description of the study scope is somewhat confusing and inconsistent. The paper initially states that it aims to explore the relationships among economic, environmental, and healthcare characteristics in lower-middle- and upper-middle-income countries. However, it later specifies that the empirical analysis focuses on Malaysia for the period 1995–2020. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Research areas include development economics, multidimensional poverty, sustainable and inclusive development, green and blue economy, regional economic development, and socio-economic policy analysis. reply Respond to this report Responses (1) Author Response 02 May 2026 Md Qamruzzaman, School of Business and Economics, united international University, Dhaka, 1212, Bangladesh Comment 1 The conceptual framework should be strengthened and grounded in theory. Response We thank the reviewer for this valuable comment. In response, we have added a new section titled “Conceptual Framework of the Study.” This section provides theoretical grounding for the relationships among: • Financial development • Environmental pressures • Governance quality • Health expenditure The framework integrates insights from: • Health production theory • Environmental Kuznets Curve theory • Public finance and health expenditure literature The conceptual model explains both direct and indirect channels through which financial development and environmental pressures influence healthcare spending. A visual conceptual framework diagram has also been added to improve clarity. Comment 2 Governance is highlighted in the title but not sufficiently discussed in the empirical interpretation. Response Thank you for this insightful observation. The role of governance has now been expanded in the empirical interpretation and policy analysis sections . Specifically, we now: • Explain how governance quality influences health system efficiency . • Discuss how governance affects environmental regulation and pollution control . • Highlight how governance conditions the allocation of financial resources to healthcare systems . These discussions are included in both the Results Interpretation and Policy Implications sections. Comment 3 The description of the study scope is inconsistent (Malaysia vs panel countries). Response We thank the reviewer for identifying this issue. The inconsistency has been corrected. The revised manuscript clearly states that the analysis uses panel data for lower-middle-income and upper-middle-income countries over the period 1995–2023 . All sections of the manuscript have been carefully checked to ensure internal consistency in describing the sample and empirical scope of the study . View more View less Competing Interests No reply Respond Report a concern Yassin J. Peer Review Report For: Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :197 ( https://doi.org/10.5256/f1000research.196490.r460925) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-197/v2#referee-response-460925 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Coccia M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Mar 2026 | for Version 2 Mario Coccia , National Research Council of Italy, Turin, Italy 0 Views copyright © 2026 Coccia M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models The topics of this paper are interesting, though well known. The structure and content must be revised, and results have to be better explained by authors before to be reconsidered. The title has to be shorter, avoid acronyms. Abstract has to clarify the goal, methods and health and social implications. Introduction has to better clarify the research questions of this study, indicating the gap presents in literature that this study endeavors to cover, and provide more theoretical background, also about the role of health expenditure to face crisis, also considering the role of public debt. After that authors can focus on the topics of this study to provide a correct analysis for fruitful discussion (Please refer to the suggested readings, all of which should be thoroughly reviewed and incorporated into your text.). Authors have to avoid a lot of subheadings that create fragmentation and confusion. If necessary, you can use bullet points (same comments for all sections). Section 2 it is too long and redundant. The methods of this study are not clear. The section of Materials and methods must be re-structured with the following three sections: • Sample and data • Measures of variables • Models and Data analysis procedure. Section IV. The study relies on secondary macro level data sourced from international databases, which may contain measurement errors, reporting inconsistencies, and differences in definitions across countries and years. Health expenditure, pollution indicators, and financial development measures are often subject to revision, incomplete reporting, or methodological changes over time. Such inconsistencies can influence both the magnitude and stability of the estimated coefficients, despite the robust testing framework employed. Although the empirical strategy includes tests for slope heterogeneity and cross sectional dependence, the institutional, demographic, and health system differences across countries remain only partially captured. Even with the Dynamic Common Correlated Effects (DCCE) estimator, unobserved heterogeneity cannot be fully eliminated. For instance, national health care governance, preventive care strategies, and cultural differences in health seeking behaviors are difficult to quantify yet may systematically influence health expenditure. Moreover, the study uses log linear panel models, which assume constant elasticity across the sample period. However, relationships between pollution, industrialization, and health costs may be nonlinear or vary structurally across development stages. While the Nonlinear ARDL (NARDL) model introduces asymmetry, it is still limited by its requirement that asymmetries are stable across countries and time. In reality, shocks caused by pollution, energy transitions, or financial markets often produce highly heterogeneous responses. In addition, although lag structures and instrumental variable adjustments are included to address endogeneity, the possibility of residual endogeneity remains. Economic growth, health expenditure, and financial development are mutually reinforcing processes. Reverse causality—such as higher health expenditure attracting FDI or improved health outcomes stimulating industrialization—may not be fully addressed, even with DCE IV instruments, which rely on lagged regressors that may be weak or imperfect proxies. The study’s time period (1995–2023) captures multiple global shocks—financial crises, oil price fluctuations, pandemics, and geopolitical disruptions—that may introduce structural breaks. While the cointegration tests incorporate structural shifts, the modelling framework cannot fully disentangle crisis driven temporary effects from long run relationships. The pandemic years, in particular, saw unprecedented spikes in health spending unrelated to typical economic or environmental drivers, potentially inflating coefficient estimates. The study aggregates countries into lower middle and upper middle income groups, which may obscure within group diversity. Some middle income economies have advanced health systems comparable to high income nations, while others face severe infrastructure gaps. Thus, panel wide averages may not accurately reflect national realities. The study also focuses primarily on CO₂ emissions, while excluding other pollutants such as PM₂.₅, NO₂, ground level ozone, or water contamination, which often have more direct health implications. This limits the environmental dimension of the model and may underestimate the true magnitude of pollution related health costs. Again. authors have to avoid a lot of subheadings that create fragmentation and confusion. If necessary, you can use bullet points. The paper has a lot of tables that are difficult to digest, some of them can be put in appendix and inserting in the text the most important ones to improve the readability… Discussion is vague. First, authors have to synthesize the main results in a simple table to be clear for readers and then show what this study adds compared to other studies. Although the Results section provides a detailed description of the data collected and analyzed, there needs to be a more critical synthesis and comparison of the findings with the literature. The discussion section has to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and explain any new understanding or insights that emerged from your research. The discussion has better to connect to the introduction through the research questions, hypotheses, and the literature you reviewed. The discussion clearly has to explain how this study advances these research topics. Comment on whether the results were expected for each set of findings; go into greater depth to explain unexpected findings. Moreover, either compare your results with the findings from other studies or use the studies to support your results. A claim for how the results can be applied more generally, also in different geoeconomic regions. Describe lessons learned, proposing recommendations that can help improve the sustainability of health expenditure to face turbulent environment, highlighting best practices. Conclusion has not to be a summary, but authors have to focus on manifold limitations of this study. The Conclusion does not adequately discuss the theoretical implications of the study. Discuss how the gap in the literature has been addressed about the research problem. Make sure you create 3 subsections in the Conclusion: 1) Theoretical Implications, 2) Policy Implications, and 3) Ideas for Future Research. Overall, then, the paper is interesting, but the structure is too fragmentated. Theoretical framework is weak, and some results create confusion… structure of the paper has to be improved; study design, discussion and presentation of results have to be clarified using suggested comments. I kindly recommend revising the paper by thoroughly addressing all comments provided, including engaging with the suggested readings . These references should be carefully reviewed and appropriately incorporated into both the main text and the bibliography. For new versions, I will conduct a detailed evaluation of the revised version, and only then will the paper be considered for further review. Ref 1: Wei, J., Rahim, S., & Wang, S. (2022). Ref 2: Lo, S., et al. (2019). Ref 3: Coccia, M., & Benati, I. (2024). Ref 4: Zhang, D., & Dong, X. (2023). Ref 5: Nica, E., et al. (2023). Ref 6: Wang, F., Gillani, S., Nazir, R., & Razzaq, A. (2024). Ref 7: Coccia, M., & Benati, I. (2024). Ref 8: Ajide, K. B., & Alimi, O. Y. (2020). Ref 9: Socol, A., Iuga, H., Socol, D., & Iuga, I. C. (2023). Ref 10: Alimi, O. Y., & Ajide, K. B. (2021). Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Wei J, Rahim S, Wang S: Role of Environmental Degradation, Institutional Quality, and Government Health Expenditures for Human Health: Evidence From Emerging Seven Countries. Frontiers in Public Health . 2022; 10 . Publisher Full Text 2. Lo S, Gaudin S, Corvalan C, Earle A, et al.: The Case for Public Financing of Environmental Common Goods for Health. Health Systems & Reform . 2019; 5 (4): 366-381 Publisher Full Text 3. Coccia M, Benati I: Negative effects of high public debt on health systems facing pandemic crisis: Lessons from COVID-19 in Europe to prepare for future emergencies. AIMS Public Health . 2024; 11 (2): 477-498 Publisher Full Text 4. Zhang D, Dong X: From the Perspectives of Pollution Governance and Public Health: A Research of China’s Fiscal Expenditure on Energy Conservation and Environmental Protection. International Journal of Environmental Research and Public Health . 2023; 20 (11). Publisher Full Text 5. Nica E, Poliakova A, Popescu G, Valaskova K, et al.: The impact of financial development, health expenditure, CO2 emissions, institutional quality, and energy Mix on life expectancy in Eastern Europe: CS-ARDL and quantile regression Approaches. Heliyon . 2023; 9 (11). Publisher Full Text 6. Wang F, Gillani S, Nazir R, Razzaq A: Environmental regulations, fiscal decentralization, and health outcomes. Energy & Environment . 2024; 35 (6): 3038-3064 Publisher Full Text 7. Coccia M, Benati I: Effective health systems facing pandemic crisis: lessons from COVID-19 in Europe for next emergencies. International Journal of Health Governance . 2024; 29 (2): 89-111 Publisher Full Text 8. Ajide K, Alimi O: The Conditioning Role of Institutions in Environment-Health Outcomes Nexus in Africa. International Economic Journal . 2020; 34 (4): 634-663 Publisher Full Text 9. Socol A, Iuga H, Socol D, Iuga I: Does climate change drive up government healthcare costs in the European Union?. Frontiers in Environmental Science . 2023; 11 . Publisher Full Text 10. Alimi O, Ajide K: The role of institutions in environment–health outcomes Nexus: empirical evidence from sub-Saharan Africa. Economic Change and Restructuring . 2021; 54 (4): 1205-1252 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Public Health, Health Systems reply Respond to this report Responses (1) Author Response 02 May 2026 Md Qamruzzaman, School of Business and Economics, united international University, Dhaka, 1212, Bangladesh Reviewer 1 Comment 1 The title has to be shorter, avoid acronyms. Response Thank you for this helpful suggestion. Following your recommendation, we have revised the title to make it shorter and clearer while avoiding technical acronyms that may reduce readability. The revised title now reflects the core focus of the study in a concise and accessible manner. The new title reads: “Financial Development, Governance, Environmental Pressure, and Health Expenditure: A Panel Analysis.” This revision improves clarity and better communicates the scope of the study. Comment 2 Abstract has to clarify the goal, methods and health and social implications. Response We appreciate this important suggestion. The abstract has been completely rewritten to clearly state: The main objective of the study The methodological approach used Key empirical findings Health and social policy implications The revised abstract now explicitly explains the use of Dynamic Common Correlated Effects (DCCE) and Nonlinear ARDL (NARDL) models, highlights the role of financial development, governance quality, and environmental pressures in shaping health expenditure, and clarifies the implications for sustainable healthcare financing and environmental policy. Comment 3 Introduction has to better clarify the research questions of this study, indicating the gap presents in literature that this study endeavors to cover, and provide more theoretical background, also about the role of health expenditure to face crisis, also considering the role of public debt. Response Thank you for this valuable suggestion. The Introduction section has been substantially revised and expanded to improve the clarity of the research motivation and theoretical background. Specifically, we have: • Clearly formulated the research questions guiding the study. • Identified the research gap in the existing literature regarding the interaction between financial development, environmental pressures, governance quality, and health expenditure dynamics. • Expanded the theoretical discussion , particularly concerning the role of healthcare spending in addressing systemic shocks such as financial crises, environmental degradation, and public health emergencies. • Added discussion on public debt and fiscal sustainability , highlighting how debt dynamics influence governments' capacity to finance health systems during economic and environmental crises. In addition, all suggested references provided by the reviewer have been carefully reviewed and incorporated into the revised introduction and literature review sections where relevant. Comment 4 Section 2 is too long and redundant. The methods of this study are not clear. The section of Materials and Methods must be restructured. Response We thank the reviewer for highlighting this issue. In response, we have fully restructured the methodology section to improve clarity and readability. The section is now organized into the following subsections: Sample and Data Description of the dataset Country coverage Time period (1995–2023) Data sources Measures of Variables Definition of each variable Measurement proxies Data transformations Summary statistics Models and Data Analysis Procedure Description of the DCCE estimator Explanation of Nonlinear ARDL (NARDL) modeling Discussion of cross-sectional dependence and heterogeneity Cointegration testing procedures Robustness analysis This restructuring significantly improves methodological transparency and ensures that the research design is clearly communicated. In addition, we reduced redundancy and simplified the presentation to improve readability. Comment 5 The paper has many tables that are difficult to digest. Some of them can be moved to the appendix. Response Thank you for this suggestion. To improve readability, we have revised the presentation of empirical results as follows: • The main tables presenting core empirical results remain in the main manuscript. • Several supplementary diagnostic tables and robustness results have now been moved to the Appendix . • A summary results table has been added to synthesize the main findings of the study for readers. These changes significantly improve the readability and structure of the results section. Comment 6 Discussion is vague and needs stronger synthesis and comparison with literature. Response We greatly appreciate this important comment. The Discussion section has been completely rewritten to improve analytical depth and clarity. The revised discussion now includes: • A summary table of key empirical findings . • A detailed comparison of our results with existing literature . • An explanation of how the findings confirm, extend, or differ from previous studies . • Interpretation of expected and unexpected findings . • A discussion of how the results contribute to understanding health expenditure sustainability in turbulent economic and environmental contexts . Furthermore, the revised discussion clearly reconnects the empirical findings to the research questions and theoretical framework introduced earlier in the paper . Comment 7 Provide lessons learned and recommendations for improving sustainability of health expenditure. Response Following this suggestion, we expanded the policy discussion to highlight practical implications. The revised manuscript now provides clear policy recommendations , including: • Strengthening green financial systems to reduce environmental health costs. • Improving governance quality to ensure efficient allocation of healthcare resources. • Integrating environmental policy and public health planning . • Expanding renewable energy investment to reduce pollution-related healthcare expenditures. These recommendations are discussed in the Policy Implications subsection of the Conclusion . Comment 8 Conclusion should not be a summary but should discuss implications and limitations. Response We appreciate this important suggestion. The Conclusion section has been fully rewritten and reorganized into three subsections as recommended: Theoretical Implications Contribution to the literature linking financial development, environmental pressure, governance, and health expenditure. Policy Implications Recommendations for sustainable healthcare financing and environmental governance. Directions for Future Research Identification of limitations and potential research extensions. Additionally, the revised conclusion explicitly discusses: • Data limitations • Structural heterogeneity across countries • Potential endogeneity concerns • Exclusion of other pollutants beyond CO₂ These revisions strengthen the academic contribution and transparency of the study. View more View less Competing Interests No reply Respond Report a concern Coccia M. Peer Review Report For: Financial Development, Governance, and Environmental Pressures in Health Expenditure Dynamics, Evidence from DCE and Asymmetric NARDL Models [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :197 ( https://doi.org/10.5256/f1000research.196490.r456197) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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