Nature’s Regenerative Capacity Linked to Lower Government Health Spending in G7 Economies

Nature’s capacity to regenerate itself may be quietly shaping the size of national health bills. A new panel data study of the Group of Seven advanced economies finds that biocapacity — the ability of ecosystems to produce the biological resources people use and absorb the waste they generate — is negatively associated with government health expenditure as a share of GDP. The research covers Canada, France, Germany, Italy, Japan, the United Kingdom and the United States over the period 2000 to 2023, drawing on World Bank and Footprint Network data for 168 country-year observations. Using a random coefficient panel model that allows each country’s response to differ, the authors report a biocapacity coefficient of −3.52 for the panel as a whole, statistically significant at the 5 percent level. Life expectancy, in contrast, was positively associated with health spending, while annual GDP growth showed a negative association. The authors emphasize that these are statistical associations rather than causal proof, but the results inject an ecological dimension into a literature long dominated by income and demographics.

The theoretical starting point is Grossman’s 1972 health investment model, which treats health as a form of human capital that can be invested in and which raises productivity. Public health expenditure is often framed as a burden when it feeds deficits and debt, yet when health is treated as an investment it can deliver social and economic returns across the population, supporting Sustainable Development Goal 3 on healthy lives and Goal 10 on reducing inequalities. Since Newhouse’s pioneering work in 1977, income has been cited as the most important determinant of health expenditure levels, and subsequent studies have confirmed a strong link with economic performance. The direction of causality, however, varies across countries. Baltagi and Moscone’s analysis of 20 OECD countries from 1971 to 2004 found a unidirectional relationship from GDP to health expenditure in Germany but a bidirectional one in Japan, the United Kingdom and the United States, while Amiri and Ventelou reported mutually reinforcing interactions between per capita income and per capita health expenditure across OECD economies.

What distinguishes the new study is its choice of environmental indicator. Most previous research on the environment-health expenditure relationship has relied on pollution-based metrics, especially CO2 emissions, which measure the intensity of pollution. The ecological footprint reflects human pressure on ecosystems. Biocapacity, by contrast, measures the regeneration and absorption capacity of ecological systems: it tracks the structure and availability of ecological resources at national scale, capturing nature’s ability to replenish what humans demand from cropland, forest, grazing land and fishing grounds while absorbing the waste they generate. In National Ecosystem Area and Biocapacity Accounts, it is calculated by multiplying the physical area by a yield factor and an appropriate equivalence factor, and it is expressed in global hectares. Because it can vary from year to year with climate, governance and economic conditions, biocapacity offers a broader picture of sustainability than pollution intensity alone, reflecting environmental resilience and the long-term capacity of ecosystems to support human well-being.

The data reveal striking contrasts between the seven economies. Domestic general government health expenditure averaged 7.64 percent of GDP, ranging from 5.42 to 10.60 percent. Life expectancy at birth averaged 80.78 years, from 76.33 to 84.56. Biocapacity averaged 3.87 global hectares but ranged enormously, from 0.61 to 17.76, while annual GDP growth averaged 1.41 percent, spanning −10.05 to 8.93 percent. When the three research variables are plotted together, biocapacity shows no significant positive relationship with life expectancy — the correlation across all observations is about −0.10. Public health spending and life expectancy co-occur weakly to moderately, with a correlation of roughly 0.21, though the authors caution that this cannot support a causal claim that spending raises longevity. The biocapacity-health expenditure correlation is very weak and negative, about −0.11. Canada is particularly noteworthy: despite far higher biocapacity than its peers, it does not have the highest life expectancy. Japan shows the opposite pattern, combining relatively low biocapacity with the highest average life expectancy at 83.5 years.

Methodologically, the study follows a rigorous panel sequence. Breusch-Pagan and Pesaran tests detected cross-sectional dependence in all series at the 1 percent level, necessitating second-generation unit root procedures. The Im, Lee and Tieslau panel unit root test, which determines structural break dates endogenously for each country and tests stationarity under single-break and two-break specifications with constants and trends, rejected unit roots for all variables at level, meaning the series are stationary once breaks from crises, reforms and policy changes are accounted for. The Blomquist-Westerlund slope homogeneity test then rejected homogeneous slope coefficients at the 1 percent level, with a Delta statistic of 6.29 and an adjusted Delta of 7.07. Because the relationships between the explanatory variables and health expenditure differ across countries, the authors employed the Random Coefficient Model developed by Swamy in 1970, which treats unit-specific coefficients as random deviations around a mean and constructs an efficient estimator that weights each country’s estimate by its precision.

For the panel as a whole, life expectancy carries a positive coefficient of 0.368, statistically significant at the 10 percent level. This suggests that longer life spans generally co-occur with higher health expenditure, consistent with rising chronic disease prevalence, population ageing and growing demand for long-term care, although the relationship is not strong or uniform across the entire panel. Annual GDP growth carries a negative coefficient of −0.048, also significant at the 10 percent level, indicating that faster growth mitigates health spending ratios. The authors attribute this to improved resource allocation efficiency, better income and living conditions, and easier access to preventive healthcare, which can partially relieve financial pressure on health systems. Biocapacity carries a negative coefficient of −3.519, significant at the 5 percent level. As the natural environment’s capacity to regenerate and absorb ecological pressure increases, health expenditure falls. In countries where natural resources are conserved and air and water quality is higher, people face fewer environmental risks, reducing respiratory diseases, chronic illnesses and other environmentally linked health problems.

The country-specific estimates reveal substantial heterogeneity that a common-coefficient framework would conceal. Life expectancy is positive and statistically significant in France, Germany, Italy, Japan and the United Kingdom, with coefficients of 0.281, 0.375, 0.219, 1.341 and 0.558 respectively, but statistically insignificant in Canada and the United States. Japan presents one of the most striking findings in the model: its life expectancy coefficient is about 2.4 times that of the United Kingdom and roughly 6.1 times Italy’s. The authors link this to Japan’s high proportion of elderly people, long life expectancy and the resulting increased need for care, treatment and social support. Japan also records the highest average life expectancy in the sample at 83.5 years, so demographic structure and macroeconomic developments appear more tightly bound to its healthcare spending than in any other G7 economy. The pattern indicates that demographic ageing translates into spending pressure very differently across high-income countries, depending on healthcare systems, institutional arrangements and the composition of care demand.

Economic growth affects health expenditure in every country except the United Kingdom, where the coefficient is statistically insignificant. The effect is negative in Canada, France, Germany, Italy and the United States, with the United States showing the largest magnitude at −0.117, followed by Canada at −0.101. In these economies, rising individual incomes, improved living standards and better access to nutrition, housing and healthcare services appear to improve overall societal health, reducing the need for publicly funded care. Technology accompanying the growth process also matters: digital health applications, early detection systems and preventive services contribute to more controlled spending management, and stronger early detection policies can reduce excessively costly treatment expenses over the long term. Japan is the exception, with a positive and significant growth coefficient of 0.051. Economic growth there does not reduce health spending; rather, it supports more comprehensive and higher-quality services, funding advanced healthcare technologies, expanding elderly care services and strengthening healthcare infrastructure for one of the world’s longest-lived populations.

Biocapacity improvements are negatively associated with health expenditure in every country except Italy, where the coefficient is statistically insignificant, and France, where it is only marginally significant at the 10 percent level. The negative coefficients are statistically significant in Canada, Germany, Japan, the United Kingdom and the United States, reaching −7.22 in the United States and −5.96 in the United Kingdom. The authors interpret this as evidence that environmentally friendly technological transformation and innovative production structures can create cost-reducing effects on health systems, since conservation of natural resources, better air and water quality and stronger ecological balance reduce environmentally induced disease. They caution, however, that the estimated relationships represent statistical associations within the variables and period examined, not proof that changes in biocapacity directly cause changes in health expenditure. The analysis is restricted to seven high-income economies over 2000 to 2023 and does not capture all relevant factors. Its two main contributions — bringing biocapacity into health expenditure analysis and explicitly modelling cross-country heterogeneity — suggest that environmental policies aimed at increasing biocapacity may yield significant gains in health economics.

Subject of Research: Government health expenditures, economic growth, biocapacity and life expectancy in the G-7 countries: evidence from panel data analysis (2000–2023)

Article Title: Government health expenditures, economic growth, biocapacity and life expectancy in the G-7 countries: evidence from panel data analysis (2000–2023)

Article References: Akyol, M., Beken, H. G., Henni, M. D., & Si Mohammed, K. (2026). Government health expenditures, economic growth, biocapacity and life expectancy in the G-7 countries: evidence from panel data analysis (2000–2023). Environmental Challenges, 25, Article 101693. https://doi.org/10.1016/j.envc.2026.101693

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101693

Keywords: Government, health, expenditures, economic, growth, biocapacity, life, expectancy, countries, evidence, panel, data