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Frontiers in Environmental Economics ·

Volume 5 - 2026 | https://doi.org/10.3389/frevc.2026.1714884

Impact of demography and emission trading scheme on heat-related mortality in China

  • Shuyang Chen1*corresponding author
  1. Department of Land Resources Management, School of Public Administration, South China University of Technology, Guangzhou, China
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Abstract

Both demography and emission trading scheme impact heat-related mortality (HRM), but the interactive impacts of demography and ETS on HRM are usually neglected in the literature. In this paper, we attempt to narrow the research gap by studying the collective impacts of the demographic factors and the ETS implemented in China during 2025–2060. To achieve this research target, a Computable General Equilibrium (CGE) model was employed to simulate the operation of the Chinese economy system. The model results indicate that population aging increases the monetary value of heat-related mortality (MVHRM), while population shrinkage decreases MVHRM. These two demographic factors collectively have a positive impact on MVHRM; they will increase MVHRM by over 115% in 2060. Both population aging and shrinkage reduce GDP and emissions, with the former's impact more pronounced than the latter's impact. By curbing anthropogenic emissions, the ETS will reduce MVHRM by around 0.03% in 2060. The ETS incurs a short-term GDP loss but boosts economic growth in the long term, though the magnitude of this ETS's effect will be minimal in 2060. The ETS strengthens the demography-induced GDP loss in the long term, but it weakens the negative impact of the demography on HRM and emissions. These demographic factors collectively enable the ETS to reduce more HRM; however, they lower emission mitigation and increase ETS-induced GDP loss. Hence, the current demographic shift causes climate policy to be less effective in emission mitigation despite its expansion of HRM reduction by climate policy; adaptation measures for the elderly are more effective in alleviating HRM than emission mitigation.

Keywords

1 Introduction

Widely recognized as the biggest global threat in the 21st century (Gasparrini et al., 2017), unprecedented climate change increases environmental exposures and amplifies health vulnerabilities (McDermott-Levy et al., 2021). Particularly, mortality is one of the most severe consequences of warming because it is associated with the increasing frequency and severity of extreme weather events, such as heatwaves, under climate change (Houghton et al., 2012). Climate change was expected to increase heat-related fatalities throughout the 21st century (Petkova et al., 2014). The magnitude of heat-related mortality (HRM) is enormous: for example, the HRM could be up to 100 billion euros annually by the end of the 21st century for Europe (Watkiss and Hunt, 2012). HRM brings about tremendous economic loss since it negatively affects labor health and thus labor supply, thereby impeding economic growth. On a global scale, the heat-induced reduction in worker productivity will result in an average decline of 1.4% in global GDP by 2100 in the RCP8.5 scenario (Orlov et al., 2020).

Warming-induced mortality is affected by demographic factors, which is supported by the current demographic transition involving a large shift from natural mortality to low mortality (Chapman and Lummaa, 2024). The evidence of demography impacting HRM was provided by Zhang et al. (2023), who found that in Central and South China, population change will have a greater influence on the variation in heat-related deaths than temperature change. Being one of the most popular demographic trends, population aging substantially amplifies future temperature-related mortality burdens since older adults are generally amongst the most vulnerable to warming (Chen et al., 2024). The vulnerability of the elderly to heat exposure is due to a combination of impaired thermoregulatory capacity to heat and a high prevalence of comorbidities (Yang et al., 2021a). Because of the soaring heat-related deaths caused by the expanding elderly population, aging is one of the main influential factors to HRM (Liu et al., 2023). To relieve the devastating consequences of HRM in aging societies, it is urgent to take measures to reduce HRM effectively. Apart from population policies that can relieve population aging, climate policies contribute to the reduction of HRM as emission mitigation decelerates warming, thereby negatively affecting warming-induced deaths. Indeed, climate policies are regarded as one of the most effective measures that humans can undertake to cope with warming (Tol, 2017).

As a popular climate policy globally, emission trading scheme (ETS) relieves global temperature rise and thus effectively mitigates HRM. When modeling the health benefits of ETS, most previous researchers usually focused on co-benefits from air pollution reduction (Dong et al., 2019). Nevertheless, the role of HRM is usually omitted in previous ETS evaluations, letting aside studying how demography complicates the interplay between ETS and HRM. To our best knowledge, no previous researchers have unambiguously uncovered the interactive impacts of demography and ETS on HRM. This paper attempts to close the aforementioned research gap by endogenizing demography and HRM in the ETS research framework. Although global temperature rise is due to the cumulative impact of global carbon emissions, we have studied the effect of a single-country climate policy (the Chinese ETS) on HRM without considering the impact of emission mitigation elsewhere. This research focus is mainly attributed to the significant disparities in country-specific demographic trends; therefore, it is meaningful to scrutinize the interactive impact of demography and ETS at the country level. The potential emission leakage from unilateral climate policy is not a serious issue that arouses attention. This is because emission mitigation is a global trend, particularly for the major carbon emitters; we designed a loose cap-and-trade policy, which is unlikely to induce emission leakage.

This paper's contributions lie in the following aspects. Firstly, we have developed Computable General Equilibrium (CGE) modeling by investigating the role of demography in the economy, energy, and environment (3E) nexus. Previous CGE models were usually adopted to analyze 3E issues; however, demography is usually excluded in CGE modeling. By incorporating demography into the CGE research framework, we have enlightened future researchers to expand the research domain of CGE models. Secondly, we have enriched environmental policy evaluations by illustrating the role of demographic structure and scale in shaping HRM. This illustration uncovers the significance of endogenizing demographic factors as moderating variables in assessing the health effects of climate policy. Thirdly, we have developed population economics by elucidating the mechanism through which demography impacts HRM during ETS implementation. Studying the demographic impact on HRM may enlighten demographers to rethink the economic costs of demographic shift in the context of climate change and emission mitigation. Fourthly, we have contributed to health economics by articulating how population aging and shrinkage impact the monetary value of heat-related mortality (MVHRM). Elucidating the demographic impact on MVHRM is beneficial to the meticulous design of health policies aimed at managing warming-related health risks. Lastly, we have found that the demographic impact on MVHRM is much more pronounced than the ETS's effect. This finding implies that, to reduce HRM effectively in aging societies, policymakers should focus on providing adaptation measures for the elderly rather than mitigating anthropogenic carbon emissions.

The rest of this paper is divided into the following sections: In the literature review section, we provide previous empirical evidence on how demography and ETS affect HRM. In the method section, we depict the details of the adopted research framework, including the model structure, the policy details of the designed ETS, linking emissions to warming, and quantifying the warming impact on mortality. In the result section, the model results are presented and analyzed. In the discussion section, the findings are compared to the relevant literature, and the limitations are acknowledged. Finally, this paper ends with the conclusions.

2 Literature review

Heat-related fatalities are a significant component of mortality risks from global warming (Masterman and Viscusi, 2025); HRM significantly increases with climate change proceeding (Tong et al., 2021). Many previous studies showed the considerable magnitude of HRM. For example, extreme heat was responsible for more deaths than any other weather event in the US (Jones et al., 2021); the heatwave was associated with a 5% increase in all-cause mortality in Queensland, Australia (Franklin et al., 2023). Being one of the devastating consequences of global warming, HRM negatively impacts labor input. In China, the annual heat-induced work hours loss was projected to increase by 121.1%, 10.8%, and −17.8% by the end of the century in the RCP8.5, RCP4.5, and RCP2.6 scenarios (Cheng et al., 2023). The economic loss of HRM is considerable. In France, the economic cost of heatwave-induced mortality was 23.2 billion euros in 2015–2019 (Adélaïde et al., 2022). The heatwave in 2013 caused excess mortality and morbidity, and its total economic loss was 27.49 billion Yuan for the Chinese city of Nanjing (Xia et al., 2018).

As demographic change influences climate change (Rauscher, 2020), population trends and patterns matter greatly for projecting HRM (Morefield et al., 2018); particularly, population aging has a much higher impact on HRM than climate change (Liu et al., 2023). This is because, compared to younger adults, older adults are at substantially greater risks of mortality from ambient heat, owing to their unique physiological profiles, living environments, and behaviors (Kephart and Okoye, 2024). For the elderly, the sensitivity of mortality to temperature is much higher on hot days than on cold days (Botzen et al., 2020). By increasing the vulnerability to warming, aging positively impacts HRM. Such evidence was provided by Chen et al. (2024), who found that one-fifth to one-fourth of heat-related deaths would be attributed to population aging; aging would intensify more heat-related deaths than climate change in 50 countries.

Conversely, climate policy is supposed to have a negative effect on HRM as it curbs carbon emissions from anthropogenic sources and thus decelerates global warming. The negative impact of emission mitigation on HRM was verified in China, where many heat-related deaths annually would be avoided by emission reduction (Zhang et al., 2023). Similarly, Gosling and Lowe (2011) found that climate policies serve to reduce the number of heat-related deaths attributable to climate change in the three European cities (London, Lisbon, and Budapest). As a positive spillover of climate policy, HRM reduction should be endogenized in policy evaluations. Indeed, incorporating the economic cost of HRM provides a more precise and accurate measurement of the social cost of carbon, thereby changing the optimal policy (Bressler, 2021).

Despite the previous evidence on climate policy affecting HRM, the HRM-related benefits of climate policy are usually excluded in research frameworks, which causes biased policy evaluations. As one of the most prevalent climate policies, ETS is widely studied regarding its socioeconomic and environmental effects, but there is a paucity of quantitative research on how ETS affects HRM. In most prior studies, the impact of demography and the effect of climate policy are isolated from each other in modeling HRM. How demography and climate policy collectively impact HRM is seldom explored in the literature. Neglecting the potential linkages between demography and climate policy is likely to cause estimation biases in the quantification of HRM.

This paper aims to bridge the aforementioned research gap by examining the contribution of demographic factors and ETS to HRM. To achieve this research target, we focus on how demography influences the ETS's effect on HRM and whether ETS moderates the relationship between demography and HRM. Answering these two research questions, we enrich the literature by revealing the interactive impact of demography and ETS on HRM. China is the research area. As the largest carbon emitter, the country has implemented numerous measures to reduce its carbon emissions. Particularly, the Chinese nationwide ETS (CNETS) may become one of the most significant efforts that the country has ever undertaken to mitigate its emissions and will play a leading role in achieving carbon neutrality (Guo, 2022). Unfortunately, formally launched in July 2021, the CNETS lacks relevant research regarding its effect on HRM. In addition, China is a country with typical demographic features (such as population aging) that are also prevalent globally. Studying the interrelations between demography and HRM in China uncovers how demographic factors contribute to HRM in the context of emission mitigation. This paper's findings and implications could be thought-provoking elsewhere, considering the worldwide prevalence of population aging, accelerating warming, and emission mitigation.

3 Method

3.1 Research framework

This paper's research framework is displayed in Figure 1. According to Figure 1, population structure and scale are the main demographic factors considered in this paper. Demography affects labor input because the variations of population structure and scale change the size of the working-age population. Demography also impacts HRM as it changes the number of people vulnerable to warming. Economic output requires capital, energy, and labor inputs. Because of fossil fuel combustion, economic growth is accompanied by carbon emissions. Anthropogenic emissions are the main culprits of global temperature rise, and the geophysical impact of emissions on warming is quantified by the Dynamic Integrated model of Climate and the Economy (DICE)/Regional Integrated model of Climate and the Economy (RICE) model (Nordhaus, 1992). Global warming causes climate damage, consequently reducing economic output; it generates HRM, which is linked to the monetary value of heat-related mortality (MVHRM). Notably, since MVHRM is deemed as an outcome of warming, we cannot capture the potential impact of MVHRM on the macroeconomic equilibrium, though MVHRM may endogenously affect labor input and thus commodity production. The research period of this paper is 2025–2060, as climate impact usually extends over a long-term horizon, and thus a long research period is rational.

The research framework.
Figure 1. The research framework.

Considering the non-excludable and non-rival features of climate, rational entities tend to over-emit carbon dioxide when governmental intervention is unavailable. Climate policy (ETS) is implemented to correct the market failure by abating carbon emissions, and thus it relieves the negative impact of global warming. ETS is a policy shock to the economic system because it distorts the market mechanism by increasing the consumption cost to discourage fossil energy combustion. Since ETS causes market distortion and green innovation, its implementation affects economic output.

3.2 CGE model structure

Originating from the pioneering work of Johansen (1960), Computable General Equilibrium (CGE) models are widely utilized in modern economic modeling (Choi, 2015) because they depict the complicated real world by endogenously defining all the targeted research variables in polynomial equations. In addition, CGE models have stood out in climate policy research (An et al., 2023; Choi, 2015) because they facilitate tracing policy-induced inter-sector linkages (Cao et al., 2021). Hence, a CGE model is employed to simulate the complex economic system and capture the economy-wide effect of the designed ETS in this paper.

Targeted to analyze the roles of demographic factors in emission abatement in China, this paper's CGE model is named the China Energy, Economy, and Demography (CEED) 2.0 model, following the previous version of the model in Chen (2022). As a single-country model, the CEED 2.0 model focuses on China and quantifies how the country is economically related to the rest of the world (ROW). Although China is a big country, it is small compared to the ROW; therefore, it is meaningful to assume that the Chinese ETS does not affect commodity prices in the ROW. The social accounting matrix (SAM) of the model is based on the China Input-Output Table 2020. The sector division is displayed in Table S1 in the supplementary materials. The embedded equations of the model are also presented in the supplementary materials.

The model has four economic entities, namely the household, enterprise, government, and foreigner; the interrelations among the entities are shown in the divided blocks. The production block depicts commodity production. The enterprise, representing all the Chinese enterprises, covers all the divided sectors in Table S1, and one sector is assumed to produce one kind of commodity. In the top production relation, a Leontief function is adopted to model the relation between intermediate input and added value; constant elasticity of substitution (CES) functions are used to quantify the other production relations. Originating from Chen (2022), the CES elasticities were confirmed to have parametric robustness; however, the robustness of the CES elasticities is still checked by the sensitivity tests in this paper.

The income-expense block focuses on the income and expenditure of the domestic economic entities. The household, representing all the Chinese households, earns income from labor compensation, capital return, and income redistribution; its expenditures include commodity consumption and consumption tax (Chen and Wang, 2023). The enterprise has its income from capital return only, and its expenditures include labor compensation, income redistribution, and production tax. The government, representing all the Chinese central and local governments, has its income from consumption tax and production tax; its expenditures are commodity consumption and income redistribution.

In the trade block, the foreigner, representing all the countries in the ROW, earns income from the production of commodities consumed by the domestic household and government; its expenditures include the consumption of commodities produced by the domestic enterprise (Chen, 2022). International trade occurs because commodities produced from different regions cannot perfectly substitute for each other (Armington, 1969). Only when the monetary value of aggregate export equals that of aggregate import will the economy arrive at the trade balance (Chen, 2025).

In the environment block, domestic commodity production and consumption are accompanied by fossil energy exploitation and thus generate anthropogenic emissions. Domestic entities are assumed to be rational; without governmental interventions, they over-emit carbon dioxide if the private cost of emissions is lower than the social cost. Anthropogenic emissions cause global warming, which has devastating consequences, like HRM, on humans. To correct the market failure from the negative spillovers of emissions, the government takes measures, like climate policy, to effectively abate emissions.

In the climate-mortality block, the geophysical equations in the DICE/RICE model are utilized to simulate the geophysical process of warming caused by anthropogenic emissions. This is because, being stable during its evolution, the DICE/RICE model has well-established geophysical equations, and these equations have no distinct differences in the results of major environmental variables compared to other Integrated Assessment Models (Nordhaus, 2018b). HRM is utilized to denote the negative consequences of global warming in this paper.

The CEED 2.0 model is a dynamic recursive CGE model. “Dynamic recursive” means that variables and parameters are updated using terminal values in 1 year as starting values for another year's solution and simulation (Aminu, 2019); in other words, current economic conditions are path-dependent but not forward-looking. In the dynamic block, the dynamic variables and parameters are assumed to equal the base-year (2020) values multiplied by the projected growth rates. In the business-as-usual (BAU) scenario, the growth rates of the dynamic variables are assumed to follow the external sources, as shown in Table 1. Nevertheless, the projected growth rates of these variables are affected by the ETS: for instance, the ETS discourages fossil energy exploitation, thereby reducing the growth rate of non-renewable energy consumption. In contrast, the growth rates of the dynamic parameters are assumed to be unaffected by the ETS and fixed in all the designed scenarios. For example, the growth rate of the Chinese population is assumed to follow the projection by the World Population Prospects (WPP) 2024.

Table 1. Growth rates of the dynamic variables and parameters in the BAU scenario.

Dynamic variableGrowth rate
GDPChina's GDP by OECD's long-term GDP forecast
Energy consumptionChina's energy consumption by international energy outlook 2023
Carbon emissionsChina's energy consumption multiplied by carbon emission factor
PriceChina's price by OECD's long-term baseline projections, no.114
Dynamic parameterGrowth rate
PopulationChina's population by the WPP 2024
LaborChina's working-age population by the WPP 2024

To construct the CEED 2.0 model, we have made some assumptions: Firstly, labor and capital are assumed to be completely mobile across the divided sectors within the Chinese economy, but they are not allowed to flow internationally. According to the China Statistical Yearbook, the international flows of labor and capital are minimal compared to the overall labor supply and capital formation.

Secondly, like most CGE models in the literature (Jia et al., 2022; Lin and Jia, 2019), this paper's CGE model is established based on the Neoclassical macro closure. Neoclassical economics contends that the market mechanism is capable of allocating resources efficiently; therefore, full labor employment is attained in the designed scenarios without climate policy implementation. As the ETS interrupts the market mechanism, it causes inefficient resource allocation, which leads to employment loss and, consequently, rising unemployment.

Thirdly, to our best knowledge, there is no empirical evidence on how HRM affects the induced technological change of climate policy. Like most previous CGE models, this paper's model is characterized by the exogenous determination of technological progress, denoted by the Automatic Energy Efficiency Improvement (AEEI), based on the national plan for energy conservation and emission reduction in China (Jia and Lin, 2020). The AEEI reflects China's long-term effort to improve energy efficiency; therefore, the magnitude of its improvement could be much larger than that of a single climate policy. Hence, the AEEI is set to be exogenous and unaffected by the designed ETS in this paper, even though climate policy triggers green innovation and thus positively impacts energy efficiency. A similar research setting could be found in Jia and Lin (2020), who evaluated the policy effects of the carbon tax and ETS, with the technological progress represented by the AEEI.

Lastly, carbon dioxide is the primary greenhouse gas driving global warming (He et al., 2022); approximately 70% of the emission mitigation will be achieved by reducing gross CO2 emissions in the 1.5 °C pathway (Ganti et al., 2024). Hence, this paper considers carbon dioxide emissions, excluding non-CO2 greenhouse gas emissions from anthropogenic sources. This consideration is rational as evaluating the climate effect of non-CO2 emissions is not yet possible in most state-of-the-art climate models (Smith and Gasser, 2022).

3.3 ETS design

In this paper, the ETS is designed to cover all the secondary sectors and a tertiary (transport) sector. The quota allocation is based on the base-year sectoral emissions minus annual quota decline; the annual quota decline factor is assumed to equal 2% (Chen, 2023). For the given carbon quotas, the free quota ratio is 90%; in other words, 90% of the carbon quotas are free, and the other 10% of quotas are allocated by auction. The free quota ratio is assumed to decrease by 10% annually over the research period. Quota auctions will be canceled if the highest bids are significantly lower than the market price (ICAP, 2022); therefore, the quota auction price is assumed to equal the market price (Chen, 2025).

Previously, quota trading price was usually assumed to vary freely to balance the quota market (Dai et al., 2018; Mu et al., 2018). Such endogenous price determination usually induces a balanced price severely deviating from the actual price. The configured price in the ex-ante analysis may prospectively inform policymakers of potential ETS contributions to low-carbon transition or carbon neutrality (Ma and Qian, 2022; Fang et al., 2021). Unfortunately, policy implications of ex-ante analysis are usually meaningless as mitigation targets can be more cost-effectively achieved via multiple measures rather than a single policy instrument, like the CNETS. Hence, given its advantage in realism, the ex-post analysis, in which the quota trading price is fixed at the actual price, is more practically meaningful than the ex-ante analysis. The exogenous price determination could also be found in Lin and Jia (2019), who studied the impact of the given ETS prices in China. In this paper, the quota trading price of the designed ETS is fixed at the current CNETS price which is around 90 CNY/t CO2 or approximately 12 USD/t CO2. The designed ETS price is much lower than that in the rest of the world, such as the trading price of the EU ETS; therefore, we have designed a loose mitigation measure in this paper. How the variation of the ETS price impacts the results is studied in the sensitivity analysis.

The exogenous price determination induces an unbalanced quota market; more details on how to model an unbalanced quota market can be found in Chen and Wang (2023). The unbalanced quota market is characterized by quota demand unequal to quota supply (Chen, 2025). When quota demand is lower than supply, there are redundant quotas that cannot be traded or used. Nevertheless, in most cases, quota demand is higher than supply; consequently, quota shortage incentivizes entities to take mitigation measures to avoid punishment for quota-uncovered excess emissions. Existing worldwide (ICAP, 2022; Kim and Yu, 2018; Li and Jia, 2016), ETS penalties are implemented through penalty prices, which are much higher than normal prices to penalize emission non-compliance (Chen, 2024). In the designed ETS, the penalty price is assumed to be three times the normal price.

3.4 Emission impact on warming

Regional anthropogenic emissions cause atmospheric accumulation of greenhouse gases, thereby inducing global temperature rise. The DICE/RICE model can quantify this geophysical mechanism through a few simple equations because the geophysical variables are quite stable in the development of the DICE/RICE model (Nordhaus, 2018a). The simple structure of the DICE/RICE model provides transparent and understandable guidance on policymaking (Yang et al., 2021b). Hence, the geophysical equations in the DICE-2016R2 model (Nordhaus, 2018b) are employed to quantify how Chinese carbon emissions cause global warming through the carbon cycle and radiative forcing.

According to Chen et al. (2023), some settings of the DICE-2016R2 model are modified so that the geophysical equations fit well with the CEED 2.0 model. World emissions (WE*t*) are divided into emissions in China (CE*t*), emissions in the rest of the world (RWE*t*), and land-use emissions (EtLand). CE*t* is endogenously determined by Chinese energy consumption, and it is influenced by the designed ETS. With the five-year time interval in the DICE-2016R2 model, RWE*t* and EtLand are assumed to change linearly in a five-year period, as the time interval of the CEED 2.0 model is 1 year. RWE*t* and EtLand are assumed to exogenously follow the projected baseline growth routes in the DICE-2016R2 model; they are unaffected by the Chinese ETS. This assumption is rational as we attempt to study the interrelations among demography, ETS, and HRM in China; therefore, emission abatement elsewhere is irrelevant to this paper's research focus. This assumption is further justified by the recent climate inaction worldwide, such as the US withdrawal from the Paris Agreement in 2024, which causes difficulties in projecting global abatement efforts or achieving warming targets.

Global warming causes climate damage, and the damage function in the DICE-2016R2 model is adopted to quantify the warming impact on economic output. Although the DICE-2016R2 model has its optimal condition, namely the maximization of social utility (Nordhaus, 2018b), only the geophysical equations are adopted to quantify the anthropogenic cause of warming in this paper. Hence, incorporating the geophysical equations does not change the macro closure of the CEED 2.0 model.

3.5 HRM quantification

According to Liu et al. (2023), heat-related mortality (HRM*jt*) is calculated based on the exposure-response function, as shown in Eq. (1). The subscripts t and j denote a year in 2025–2060 and an age group, respectively. In the medium-variation population projection by the WPP 2024, the population by single age is aggregated into 5 age groups ranging from 0–4 to 100 +. GMR*jt* denotes the mortality rate of an age group, and it is defined as the number of deaths divided by the aggregated number of deaths and population in an age group. GP*jt* denotes the population of an age group. As the demography changes GP*jt*, it affects HRM*jt*. AF*jt* is attributable fraction and defined in (Equation 2). RR*jt* represents relative risk; we assume RRjt=eERFjt×ΔCTt. α is the relative risk of extreme hot temperature, and its value is 1.21 in China (Ma et al., 2015). ERF*jt* is exposure-response factor, and it means the unit HRM change with a 1 °C rise; we can get ERF*jt* = α × GMR*jt*.

HRMjt=GMRjt×GPjt×AFjt(1)

AFjt=RRjt-1RRjt=1-e-ERFjt×ΔCTt =1-e-α×GMRjt×β(WTt-WT0)(2)

In (Equation 2), we assume that the temperature rise in China is linearly related to that in the world. This assumption is rational as the absolute intensity of the warmest and coldest temperatures in China changes linearly with the global mean near-surface temperature rise (Hu and Sun, 2020). The parameter β denotes the ratio of temperature change in China to global temperature change. The mean surface temperature over China had a significant warming trend at an annual rate of 0.28 °C/decade in 1979–2020 (Wang et al., 2022); the IPCC report shows that global temperature increased by 0.30 °C/decade in 1980–2020 (Sun et al., 2023). Hence, we assume β = 0.28/0.30 ≈ 0.93.33; the parametric value of β is assumed to remain unchanged in 2025–2060. In the sensitivity analysis, we have tested how the model results respond to the parametric change of β. ΔCT*t* is temperature change relative to the base-year temperature in China; we assume ΔCT*t* = β × ΔWT*t*. ΔWT*t* is world temperature change; we can get ΔWT*t* = WT*t*−WT0. WT*t* is annual world temperature change compared to the 1,900 level, and its value is calculated based on the geophysical equations of the DICE-2016R2 model. WT0 is the world temperature change in the base year (2020) compared to the temperature in 1900.

As defined in (Equation 3), heat-related years of life loss (HRYLL*jt*) measures disease burden by assigning more weight to younger deaths and less weight to older deaths, considering life expectancy at death ages (Liu et al., 2023). LE*jt* represents the life expectancy of a divided age group; it is assumed to equal the projected life expectancy of the population at the group's median age in the WPP 2024.

HRYLLjt=HRMjt×LEjt(3)

The monetary value of heat-related mortality (MVHRM*t*) is defined in (Equation 4), where VSLY*jt* denotes the economic value of a statistical life year. VSLY*jt*, rather than the value of a statistical life (VSL*t*), is utilized to quantify the economic burden of HRM. VSLY*jt* is measured as VSL*t* divided by the discounted remaining future life years (Liu et al., 2023), as shown in (Equation 5). r is the discount rate, and its default value is 0.05 (Liu et al., 2023). According to Kim et al. (2020), VSL*t* is defined in (Equation 6), where WTP*t* refers to willingness to pay (WTP), and its value in China is around 0.25 million USD (Xie et al., 2019) or equivalently 1.72 million CNY in 2020. PGDP*t* is per capita GDP; PGDP0 is the per capita GDP in the base year (2020), and its value is 0.072 million CNY, according to the China Statistical Yearbook.

MVHRMt=∑j(HRYLLjt×VSLYjt)(4)

VSLYjt=VSLt×r1-(1+r)-LEjt(5)

VSLt=WTPt×(PGDPtPGDP0)0.5(6)

To study whether the given parametric values of α, β, and r unduly influence the model results, we have performed the sensitivity tests to check the robustness of the model results. In the sensitivity analysis, the changes in the model results are compared to the corresponding parametric changes. The robustness of the results is confirmed when the result changes are less than the parametric changes.

3.6 Demographic setting

In this paper, the demographic impact is decomposed into the impacts of population structure and scale. As population structure and scale affect the projected growth rates of the working-age population (labor force), these demographic factors influence labor input, which is directly linked to economic output and thus anthropogenic emissions. Besides, as shown in (Equation 1), the demographic factors affect GP*jt* and thus change heat-related mortality (HRM*jt*).

To study the impact of population structure, the age structure of the Chinese population is assumed to evolve following the WPP 2024, but the population scale is fixed at the base-year (2020) level. Supplementary Figure 1 in the supplementary materials shows the projected changes of the young population (aged 0–15), working-age population (aged 16–59), and elderly population (aged 60 or above), compared to the base-year level. According to the projections by the WPP 2024, the young and working-age population was expected to decline steadily, whereas the number of the elderly was projected to soar over time. Supplementary Figure 1 implies that population aging will occur in China over the research period. Hence, the variation of population structure is signified by population aging.

To study the impact of population scale, we assume that the population structure does not change over time, and it is fixed at the 2020 level; the Chinese population scale follows the medium-variant projection in the WPP 2024. The projected change in population scale is displayed in Supplementary Figure 2 in the supplementary materials. According to Supplementary Figure 2, the scale of the Chinese population was expected to diminish; in other words, China will experience population shrinkage over time. Hence, the variation of population scale is denoted by population shrinkage.

When we study the collective impact of population structure and scale, these demographic factors are assumed to vary based on the medium-variant projections in the WPP 2024.

3.7 Scenarios

The designed scenarios are displayed in Table 2. In the business-as-usual (BAU) scenario, no ETS is implemented; the Chinese population is assumed to be fixed at the base-year level. The BAU scenario acts as the reference scenario for the comparison analysis in the result section. Full labor employment is assumed in the BAU scenario, where no ETS is implemented.

Table 2. Designed scenarios.

ScenarioPopulation structurePopulation scaleETS
BAU
CLP✓
PST✓
PSC✓
PSS✓✓
PSTCL✓✓
PSCCL✓✓
PSSCL✓✓✓

The climate policy implementation (CLP) scenario is characterized by the ETS implementation, as shown in Section 3.3, and the same demographic setting as the BAU scenario. Hence, the only difference between the CLP and BAU scenarios is the ETS implementation. The ETS interrupts the market mechanism in resource allocation since it increases the consumption cost of non-renewable energy commodities. According to the Atkinson and Stiglitz theorem, commodity taxes are redundant or should be uniform (Cremer and Gahvari, 2015); therefore, the ETS can cause economic distortions and thus impede GDP growth. Conversely, the ETS may also boost GDP growth by promoting green innovation and facilitating a low-carbon transition.

In the population structure (PST) scenario, no ETS is implemented; the impact of population structure is considered with the constant population scale. With the same policy setting as the BAU scenario, the population scale (PSC) scenario is designed to consider the impact of population scale with the population structure unchanged over time. The population structure and scale (PSS) scenario is designed to study the combined impact of population structure and scale without ETS implementation.

The PSTCL, PSCCL, and PSSCL scenarios are designed with the ETS implementation and the same demographic settings as the PST, PSC, and PSS scenarios.

4 Result

4.1 Demographic impact

In this section, we analyzed the demographic impacts absent from the ETS implementation and then elaborated on the moderating effect of the ETS on the demographic impacts. The absolute and percentage values of the demographic impacts were studied via scenario comparisons. Specifically, comparing the PST, PSC, and PSS scenarios with the BAU scenario uncovers the demographic impacts without the ETS implementation. Comparing the PSTCL, PSCCL, and PSSCL scenarios with the CLP scenario reveals the demographic impacts during the ETS implementation. The results of the comparison analyses were graphically presented in Figures 2, 3 and Supplementary Figures S3–S16 in the supplementary materials; we also listed the data supporting the figures in Supplementary Tables S2–S9 in the supplementary materials. In these figures, “Population Structure”, “Population Scale”, and “Population Structure and Scale” denote the cases where we considered the impacts of population structure, population scale, and population structure and scale, respectively.

Demographic impact on MVHRM without ETS implementation.
Figure 2. Demographic impact on MVHRM without ETS implementation.
Demographic impact on GDP without ETS implementation.
Figure 3. Demographic impact on GDP without ETS implementation.

Supplementary Figures 3–6 displays how the demographic factors impact warming; the data supporting these figures are listed in Supplementary Tables 2, 3. The demographic factors were expected to decelerate warming; the population structure and scale were expected to decrease global warming by approximately −0.5%. This is because the projected population aging and shrinkage negatively impact GDP and thus reduce anthropogenic carbon emissions. The ETS slightly weakens the demographic impact on warming because it abates emissions and thus narrows the scope for the demographic factors to influence emissions.

Supplementary Figures 2 and 7 show the absolute and percentage impacts of demography on MVHRM when the designed ETS is not implemented. With the proportion of the elderly rising over time, the population structure positively impacts MVHRM; it was expected to increase MVHRM by over 180% in 2060. The underlying mechanism is that the elderly are more susceptible to warm temperatures; therefore, a higher proportion of the elderly population induces more heat-related deaths. Conversely, as the Chinese population was expected to shrink, the population scale negatively impacts MVHRM over time, with the magnitude to be approximately −25% in 2060. This is because a reduction in the population means fewer individuals suffer from global temperature rise, and thus it results in less warming-induced mortality. The line for the combined impact of population structure and scale stays above the x-axis of Figure 2, which implies that population structure outperforms population scale in affecting MVHRM. In 2060, population structure and scale will collectively increase MVHRM by more than 115%, compared to the BAU scenario.

Supplementary Figures 8, 9 displays how the demography impacts MVHRM during the ETS implementation; Supplementary Tables 4, 5 presents the data supporting these figures. The shapes of the lines in these two figures are quite close to the lines in Supplementary Figures 2, 7, which implies that the ETS does not have a distinct moderating effect on the relationship between demography and MVHRM. In Supplementary Tables 4, 5, the ETS slightly decreases the absolute values of the demographic impacts, but it has almost no effect on the percentage values of the demographic impacts.

Supplementary Figure 10, Figure 3 present the absolute and percentage impacts of the population structure and scale on GDP without the ETS implementation. These two figures show that both population structure and scale have negative and time-increasing impacts on GDP. This is because the aging and shrinking Chinese population is accompanied by the decreasing working-age population, and labor is an essential input factor of economic output. The impact of the population structure is larger than that of the population scale, which implies that population aging causes more economic loss than population shrinkage. Since the population structure and scale affect GDP in the same direction, the combined impact is larger than the individual impact. In 2060, population structure and scale will collectively reduce GDP by over 20%.

Supplementary Figures 11, 12 shows the demographic impact on GDP during the ETS implementation; Supplementary Tables 6, 7 displays the data supporting these figures. The ETS weakens the demographic impact on GDP in the short term but eventually strengthens this demographic impact in the long term. Nevertheless, the magnitude of this ETS's effect is less than 0.1%, which implies that the moderating effect of the ETS is minimal.

Supplementary Figures 13–16 present the demographic impact on carbon emissions without and with ETS implementation, respectively; the data supporting these figures are listed in Supplementary Tables 8, 9. The demographic factors decrease anthropogenic emissions because they have negative impacts on economic growth. The population structure has a larger impact on emissions than the population scale, which implies that population aging outperforms population shrinkage in affecting emissions. The population structure and scale will decrease emissions by around 10% in 2060; the demographic impact on emissions is more distinct than that on warming. This is because Chinese emissions only partially contribute to global warming, which is also driven by anthropogenic emissions elsewhere, leaving aside natural forces. According to Supplementary Tables 8, 9, the ETS slightly weakens the demographic impact on emissions over the research period; the magnitude of this ETS's effect is less than 0.1%.

4.2 ETS's effect

In this section, we analyzed the effects of the designed ETS on warming, MVHRM, GDP, and carbon emissions; subsequently, we studied the moderating impacts of the demographic factors (namely population structure and scale) on the ETS's effects. We presented both the absolute and percentage values of the ETS's effects by comparing the differences in the CLP-BAU, PSTCL-PST, PSCCL-PSC, and PSSCL-PSS scenarios. As shown in Supplementary Tables 10–15 in the Supplementary material and Figures 4, 5, the scenario differences are denoted by the labels “Invariant Population”, “Population Structure Change”, “Population Scale Change”, and “Population Structure and Scale Change”. These labels mean that the ETS is implemented with the constant population, changing population structure, changing population scale, and changing population structure and scale, respectively.

ETS's effect on MVHRM (unit: 10<sup>9</sup> CNY).
Figure 4. ETS's effect on MVHRM (unit: 109 CNY).
ETS's effect on GDP (unit: 10<sup>9</sup> CNY).
Figure 5. ETS's effect on GDP (unit: 109 CNY).

The ETS's effect on warming is depicted in Supplementary Tables 10, 11. According to these two tables, the ETS decelerates global warming as it curbs anthropogenic emissions in China. With its emission abatement accumulated over time, the ETS has a cumulative effect on warming; in 2060, the ETS was expected to lower global temperature rise by over 0.03%. The population structure and scale slightly relieve this ETS's effect because these demographic factors decelerate climate change, which partially counteracts the ETS's effect on warming.

How the ETS affects MVHRM is shown in Figure 4 and Supplementary Table 12. Regardless of considering demographic impact, the ETS negatively affects MVHRM because it decelerates global temperature rise and thus reduces warming-related economic costs. The magnitude of this ETS's effect will be around −0.03% in 2060, which is comparable to the magnitude of the ETS's effect on warming. The population structure strengthens the ETS's effect on MVHRM, whereas the population scale weakens this ETS's effect; as the former has a larger impact than the latter, these two demographic factors collectively reinforce this ETS's effect.

Figure 5 and Supplementary Table 13 display the ETS's effect on GDP when the demographic factors act as moderators. Owing to its intervention in the market mechanism, the ETS induces a short-term economic downturn after its implementation; it was projected to decrease GDP by approximately 1% in 2025. Nevertheless, the ETS boosts economic growth in the long term, even though its effect will be negligible in 2060. The underlying economic intuition is that the ETS triggers green innovation and thus fosters the development of the green economy. The population structure and scale relieve the ETS-induced economic loss in the short term. Nevertheless, in the long term, these demographic factors reverse this ETS's effect; in other words, the ETS negatively impacts GDP considering demographic impact.

Supplementary Tables 14, 15 show the moderating impact of the demography on the ETS's emission abatement. The ETS reduces carbon emissions over the research period because it imposes additional costs for emitting carbon. As climate policy has a rebound effect in the long term (Barker et al., 2009), this ETS's effect diminishes over time; it was expected to change from roughly −1.28% in 2025 to about −0.08% in 2060. The demographic factors weaken this ETS's effect; the ETS will reduce emissions by approximately 0.06% in 2060, considering the collective impact of the population structure and scale. The underlying mechanism is that the demography negatively affects anthropogenic emissions, thereby reducing the scope for the ETS to affect emissions.

4.3 Sensitivity analysis

In the sensitivity analysis, the changes in the model results are compared to the corresponding parametric changes within the range of −50% to 50%. Supplementary Tables 16–19 in the supplementary materials shows the results of the sensitivity tests on α, β, r, and CES elasticity parameters in 2060 in the PSSCL scenario. According to these tables, α and β may have undue influences on MVHRM, but they have minimal impacts on the other indices. The indices are insensitive to the given values of r and CES elasticity parameters. Overall, the sensitivity tests imply that the model results are robust to the given parameters, except that MVHRM is dependent on the parametric values of α and β.

How the exogenous value of the ETS price influences the results is displayed in Supplementary Table 20 in the supplementary materials. When the price changes by −50% or 50%, it exerts a slight influence on the results; therefore, we have confirmed the robustness of the model results to the given value of the ETS price. As shown in Supplementary Table 21 in the supplementary materials, the ETS's effect on MVHRM remains minimal when the ETS price changes in the range of −50% to 50%. If an extraordinarily high price is implemented (i.e., 700 CNY/t CO2 or approximately 100 USD/t CO2), the ETS's effect on MVHRM will be −0.22% in 2060; the magnitude of this effect is still much lower than the demographic impact on MVHRM. Hence, we believe that the designed ETS has a limited impact on MVHRM. This is because the designed ETS is a unilateral climate policy implemented in China with no mitigation measures assumed to be taken in the rest of the world; consequently, it has a trivial effect on warming, thereby posing a limited influence on MVHRM.

5 Discussion

This paper quantifies the interrelations among demography, ETS, and HRM in China using the CEED2.0 model. The two demographic factors considered are population structure and scale; the change of the former is signified by population aging, whereas the change of the latter is denoted by population shrinkage. Aging was expected to increase HRM. This is because the elderly are more susceptible to warming, considering that extreme heat may cause heat stroke, heat exhaustion, and dehydration; it may also worsen cardiovascular and kidney diseases (Orlov et al., 2020). Conversely, population shrinkage decreases HRM since it causes fewer people to be affected by warming. As the impact of the population aging is larger than that of the shrinkage, the population structure and scale collectively have a positive impact on HRM; in 2060, these two demographic factors will collectively increase MVHRM by over 115%.

The population structure and scale negatively impact the Chinese GDP; they will collectively lower GDP by over 20% in 2060. The underlying economic intuition is that population aging and shrinkage decrease the number of laborers, and labor is an essential input factor of economic output. This finding complies with the previous research showing that the growth-deteriorating impact of aging was transmitted through the labor force and productivity growth channels in Malaysia (Yip et al., 2024). As the demography negatively impacts GDP, it decreases carbon emissions and thus decelerates global warming. The negative aging impact on emissions is also shown by Fan et al. (2021), who found that aging changes lifestyles and energy consumption patterns. The negative impact of the population shrinkage on emissions implies the positive relationship between population scale and emissions; a similar relationship exists in South Asia, where population size was one of the leading drivers of emissions (Anser et al., 2020).

The ETS negatively affects GDP in the short term, which implies that regional climate policy induces mitigation costs outweighing the health benefits of mitigating climate change. Similarly, Lomborg (2020) argued that climate policy usually has abatement costs that often vastly surpass climate benefits. Non-etheless, the negative effect of the ETS on GDP was expected to diminish to zero before 2035; thereafter, the ETS boosts economic growth, even though the magnitude of this ETS's effect will be minimal in 2060. The underlying mechanism is that the ETS promotes green innovation and thus develops the green economy. The ETS abates Chinese carbon emissions and relieves anthropogenic climate change, thereby reducing HRM. The benefit of HRM reduction from limiting global warming could be found in the US, where limiting climate change could significantly reduce heat-related deaths (Lo et al., 2019). Indeed, decelerating warming could prevent large increases in temperature-related mortality in most regions worldwide (Vicedo-Cabrera et al., 2018).

In this paper, the magnitude of the ETS's effect is quite minimal: e.g., the ETS was expected to reduce MVHRM by about 0.03% in 2060. This weak policy signal is due to the unique policy background and content; it may be easily interrupted by policy noise. For instance, if the growth route of the emissions in the rest of the world had been based on the recent global climate action, the magnitude of global warming would have been lower than what we have estimated; with a lower temperature rise, the ETS might have had a smaller effect on MVHRM. Climate sensitivity may also influence the policy signal. A lower risk of extreme heat or linkage between warming in China and the world (denoted by a lower value of α or β) could have led to smaller MVHRM, thereby diminishing the ETS's effect on MVHRM. To strengthen the signal of the ETS, the policy could be designed with a higher carbon price; a tighter ETS may exert more distinct policy effects.

The demography has a much more pronounced impact on MVHRM than the ETS, which is based on the exclusion of air-pollution-related mortality. Nevertheless, the clean-air benefits of ETS could be substantial in China (Chen, 2026); population aging has become the most significant driver, nearly offsetting the diminishing economic cost from the declining PM2.5 pollution in China (Lian et al., 2022). If we had simultaneously considered the monetary values of heat-related and air-pollution-related mortality, we might have drawn a different conclusion about the relative magnitudes of the demographic impact and ETS's effect.

The ETS slightly strengthens the negative impact of the population structure and scale on GDP in the long term, even though the magnitude of its effect will be less than 0.01% in 2060. Conversely, the ETS weakens the negative impacts of the demographic factors on emissions and warming. The population structure and scale both increase the ETS-induced reduction in MVHRM; however, this demographic impact is minimal over the research period. These demographic factors reverse the promoting effect of ETS on GDP in the long term; therefore, the demographic trends cause emission abatement to become costlier. This finding agrees with Lupi and Marsiglio (2021), who argued that the social cost of environmental policy substantially increases considering endogenous population change. The demographic factors reduce emission mitigation and thus attenuate the ETS's effect on warming. The adverse impact of aging on abatement efforts can also be found in Albalate et al. (2023), who showed a negative association between the share of the elderly and policy ambitions in climate agreements and regulatory initiatives to fight climate change.

Overall, we have confirmed the robustness of the model results to the given values of the exogenous parameters, except that the value of MVHRM is sensitive to the parametric values of α and β. α denotes the relative risk of extreme heat. A higher value of α means that extreme heat is more deadly and causes more heat-related deaths, thereby expanding the value of MVHRM. β stands for the linkages between the temperature rise in China and the world. A higher value of β means that global warming poses more influences on the climate in China; therefore, it generates more MVHRM in the country. The uncertainties of MVHRM imply that the health benefits of regional climate policy from HRM reduction are dependent on the susceptibility to extreme hot weather and the interactions between the local and global climate systems. To reduce the economic cost of warming-induced deaths, policymakers may take measures, such as increasing healthcare for the susceptible population and reforestation, to lower the risk of extreme heat and protect the local climate.

This paper has a few limitations to be addressed in future research. Firstly, emissions elsewhere are assumed to follow the baseline projections in the DICE-2016R2 model. This assumption is due to the recent climate inaction, like the US withdrawal from the Paris Agreement in 2024, which causes uncertainties in global emission mitigation and limiting temperature rise. Future researchers may unbiasedly model the potential growth route of world emissions, which affect the speed of warming and thus HRM.

Secondly, the scope of negative externalities induced by warming is narrowed to focus on HRM, though warming causes other negative spillovers, like sea-level rise. By mitigating warming, climate policy generates positive externalities other than HRM reduction; however, these externalities are beyond the research focus. The climate benefits of emission mitigation are dispersed worldwide; therefore, global climate benefits should be quantified in evaluating regional climate policy. Unfortunately, we have only quantified local climate benefits from HRM reduction and thus underestimated the potential benefits of the ETS.

Thirdly, owing to the data constraint, labor employment is assumed to be unaffected by climate change and demographic shift. This assumption is rational as most previous integrated assessment models, like the DICE model, do not incorporate the adverse impact of warming on employment in damage functions (Nordhaus, 2018a). In evaluating the effect of demographic policy on carbon emissions, the demographic impact on employment is beyond the research scope (Tang et al., 2024). In reality, global warming and population aging may lead to voluntary or involuntary unemployment, which could exacerbate the ETS-induced economic downturn and thus alter MVHRM. Future researchers may extend this paper's research framework by endogenizing the impact of warming and demography on employment.

Fourthly, in this paper's ex-post analysis, the quota trading price of the designed ETS is fixed at the actual price. In the real world, economic fluctuations and demographic shifts may pose influences on the ETS price, which consequently changes the policy effects of the designed ETS. In future research, ETS prices should be endogenized to consider the impact of sociodemographic factors; meanwhile, price variations should be constrained to avoid simulated prices severely deviating from the real price.

Lastly, the temperature rise in China is assumed to be linearly correlated with global temperature rise, without capturing the impact of regional climate feedback loops or extreme weather patterns on HRM. Integrated assessment models may be capable of unbiasedly quantifying the thermodynamic cause of HRM, but they cannot comprehensively uncover the socioeconomic effects of climate policy. In contrast, as a macroeconomic model, the adopted CGE model in this paper can simulate the operation of the economic system and thus trace the inter-sector impacts of policy shocks; however, such economic details compromise the thermodynamic quantification of the climate system. In future research, a CGE model and an integrated assessment model may be coupled to measure the economic and thermodynamic effects of climate policy accurately.

6 Conclusion

In this paper, the China Energy, Economy, and Demography (CEED) 2.0 model is adopted to analyze the interactive impacts of demography and ETS on HRM, taking China as a case study. The considered demographic factors are population structure and scale, whose changes are denoted by population aging and shrinkage, respectively. Population aging increases MVHRM, while population shrinkage decreases MVHRM. As the impact of aging is larger than that of shrinkage, these two demographic factors collectively have a positive impact on MVHRM; they will increase MVHRM by over 115% in 2060. Both population aging and shrinkage negatively impact GDP and emissions; however, population aging has a larger impact than shrinkage. In 2060, these two demographic trends will collectively reduce GDP by over 20% and lower emissions by approximately 10%.

Designed to curb anthropogenic carbon emissions, the ETS decelerates warming and thus reduces MVHRM; in 2060, it will reduce warming and MVHRM by around 0.03%. The emission abatement of the ETS comes at the price of GDP loss in the short term; the ETS was projected to lower GDP by approximately 1% in 2025. Nevertheless, in the long term, the ETS eventually boosts economic growth, even though its effect will be minimal in 2060. The ETS strengthens the negative impact of the demographic factors on GDP in the long term, though its magnitude is less than 0.1%; conversely, it weakens the demographic impacts on emissions and warming. The demography reinforces the negative effect of the ETS on MVHRM; however, it reverses the long-term promotional effect of the ETS on GDP and lowers the ETS-induced reduction in emissions. For instance, in 2060, the ETS will lower emissions by about 0.08%, but it will reduce emissions by approximately 0.06% considering demographic impact.

These results imply that population aging increases HRM, whereas population shrinkage saves HRM; as aging outperforms shrinkage in affecting HRM, the demography expands HRM. The current Chinese demographic transition, featured by population aging and shrinkage, enables the ETS to reduce more HRM; however, it impedes the ETS implementation by increasing economic costs, denoted by GDP loss, and decreasing emission mitigation. To mitigate emissions effectively during the demographic shift, the ETS needs to be meticulously designed. For instance, ETS's revenues could be utilized to subsidize health insurance or medical care for the elderly who suffer from heat-related diseases, which may alleviate the economic pressure of emission mitigation intensified by aging. ETS's revenues may also be recycled to support the development of the silver economy, thereby achieving the synergy between climate policy implementation and economic growth in an aging society.

Compared to the ETS, demographic structure has a much more pronounced impact on HRM; therefore, adaptation measures for the elderly are more effective in alleviating HRM than emission mitigation. To reduce the susceptibility of the elderly to warming, governments may subsidize the installation of air conditioners for the elderly, thereby protecting vulnerable groups from extreme heatwaves.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frevc.2026.1714884/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Phoebe Koundouri, Athens University of Economics and Business, Greece

Reviewed by: Zhaobin Sun, Chinese Academy of Meteorological Sciences, China

Ioannis Arampatzidis, Athens University of Economics and Business, Greece

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This paper is funded by the Fundamental Research Funds for the Central Universities (Project No. QNMS202407).

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

SC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

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Citation

Chen, S. (2026). Impact of demography and emission trading scheme on heat-related mortality in China. Frontiers in Environmental Economics, Volume 5. https://doi.org/
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*Correspondence: Shuyang Chen

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