Introduction

Energy poverty, which refers to the situation where individuals lack access to modern energy sources or essential energy services like electricity and clean cooking facilities (IEA 2010), exerts a detrimental effect on people’s physical and mental health (Kose 2019; Churchill and Smyth 2021). Households in a state of energy poverty often face serious health threats. With regard to physical health, the absence of modern energy services such as electricity and the overreliance on solid fuels can result in indoor air pollution, which poses a great threat to physical health. Households’ inability to pay for heating expenses can lead to indoor temperatures being either too high or too low, which significantly increases the incidence of respiratory and cardiovascular illnesses (Llorca et al. 2020). With regard to mental health, energy poverty, being one of the key factors that contributes to poor living conditions, may increase psychological stress, which in turn leads to heightened anxiety and emotional distress (Liddell and Guiney 2015; Zhao et al. 2022).

Although research on energy poverty and health is abundant, it mainly addresses temporary effects, overlooking the cumulative impact of chronic energy poverty. Life Course Theory highlights that current health outcomes reflect the cumulative influence of past and present experiences (Sampson and Laub 2005), and early socioeconomic inequalities tend to amplify health disparities over time (Willson et al. 2007; Larson et al. 2018). Chronic poverty, in particular, causes more severe health damage the longer it persists (López del Amo González et al. 2018; Shi and Dong 2022), with long-term income exerting a greater influence on health than current income (Benzeval and Judge 2001). Compared with temporary energy poverty, which can be alleviated more quickly, chronic energy poverty is continuous and persistent (Alem and Demeke 2020), posing a more significant threat to health. Ignoring its cumulative impact may therefore lead to a serious public health crisis.

The cumulative health effects of energy poverty are difficult to assess due to the lack of standardized measurement methods and tools to capture its dynamic nature. Existing indices, such as the Energy Poverty Index (EPI), Low Income-High Cost (LIHC) indicator, and Multidimensional Energy Poverty Index (MEPI), mainly emphasize single dimensions like accessibility or affordability, limiting comprehensive assessments of energy vulnerability and obscuring its complex links to poverty (Bouzarovski 2014). Most studies also rely on static cross-sectional data (Lin and Wang 2020; Ye and Koch 2021), which fail to reveal transitions of households into or out of deprivation (Thomson et al. 2017), leaving a gap in measuring its dynamic evolution (Alem and Demeke 2020; Drescher and Janzen 2021).

To address this, we develop a chronic multidimensional energy poverty measure within the multidimensional poverty framework (Alkire and Foster 2011; Foster 2009; Alkire et al. 2017). Following Zhang et al. (2019), multidimensionally energy-poor households are identified using equal weights for accessibility and affordability, and we further incorporate duration to capture chronic deprivation. Using China Family Panel Studies (CFPS) data from 2012 to 2018, we measure individual-level chronic multidimensional energy poverty, analyze its trends and spatial disparities, and assess its health implications, considering heterogeneity across individuals and regions. Finally, we explore the health effects of energy poverty when only duration is considered and compare the relative impacts of duration and depth.

The novel contribution of this paper lies in two key aspects. First, we advance the measurement of energy poverty by introducing a chronic multidimensional energy poverty (CMEP) based on a dual-embedded framework that captures both the incidence and persistence of multidimensional energy poverty over time. Unlike existing indices that primarily provide static assessments of energy accessibility or affordability, the CMEP offers a dynamic perspective by integrating the Alkire–Foster multidimensional counting approach (Alkire and Foster 2011; Alkire et al. 2017) with Foster’s duration approach. This integration allows the index to not only measure the extent and intensity of deprivation at a given point in time but also capture the temporal accumulation of deprivation, reflecting the sustained hardship faced by chronically energy-poor households across multiple dimensions. As such, the CMEP functions as a diagnostic tool that distinguishes between transient and structural (chronic) energy poverty. By extending conventional static measures into a dynamic framework, this study deepens the understanding of chronic multidimensional energy poverty and its long-term impacts.

Second, this paper empirically investigates the impact of chronic multidimensional energy poverty on health outcomes. While most existing studies focus on the immediate or short-term health consequences of energy poverty (e.g., Churchill and Smyth 2021; Zhang et al. 2019; Zhang et al. 2021), they largely overlook its cumulative and persistent effects over time. Our approach differs by employing an independent variable with a longitudinal interpretation—namely, an index that reflects a household’s chronic multidimensional energy poverty level. Regressing this index on health outcomes allows us to assess the long-term health implications of sustained energy deprivation. Using micro-level data from Chinese households, our findings reveal that chronic multidimensional energy poverty has a significantly adverse effect on both physical and mental health. Notably, the chronic dimension contributes more strongly to these negative outcomes than the multidimensional aspect alone. These results underscore the importance of addressing long-term energy deprivation in public health policy and offer valuable evidence for governments in designing interventions aimed at reducing health disparities associated with persistent energy poverty.

Literature review

Measuring energy poverty

Since the concept of energy poverty was introduced (Lewis 1982), no unified definition or universally applicable measurement has been established. The literature generally identifies two main dimensions—accessibility and affordability—measured in different ways (Li et al. 2014). Research in developing countries often emphasizes access to modern energy (e.g., Adusah-Poku and Takeuchi 2019), while studies in developed countries focus more on affordability (e.g., Hills 2012; Teller-Elsberg et al. 2016). Single accessibility indicators, such as electricity or clean cooking fuel use (IEA 2010), are straightforward but insufficient to capture the complexity of energy poverty. To address this, multidimensional approaches and composite indices have been developed. Pachauri et al. (2004) proposed a two-dimensional measure of energy availability and consumption to assess energy poverty in India. The International Energy Agency (2010) introduced the Energy Development Index (EDI), which incorporates five indicators, including energy consumption, electricity use, and energy production ratios. Building on Alkire and Foster’s (2011) multidimensional poverty framework, Nussbaumer et al. (2012) developed the Multidimensional Energy Poverty Index (MEPI), which captures access to modern cooking fuel, electricity, appliances, education, and communication tools.

Existing research on energy poverty measurement in developed countries mainly emphasizes affordability. For instance, while some households have physical access to electricity, they cannot afford its use (Winkler et al. 2011). Lewis (1982) first defined energy poverty as the inability to afford adequate energy for basic needs, a concept later formalized by Boardman (1991) through the 10% indicator. Hills (2012) refined this with the Low Income-High Costs (LIHC) approach, identifying households with both low income and above-average essential energy expenditures. More recent work, such as Ye and Koch (2021), incorporated household energy demand into a Foster–Greer–Thorbecke (FGT)-based measure, showing that over half of South African households experience energy poverty.

However, these measures are largely unidimensional, focusing on either affordability or accessibility, which limits their ability to capture the complexity of energy poverty. Measurement approaches also differ in their conceptual foundations and applicability. Table 1 illustrates how various energy poverty indicators are calculated. For instance, the Energy Poverty Index (EPI) provides a basic assessment of energy supply shortages, which is particularly useful for identifying regions with infrastructure deficits. The Multidimensional Energy Poverty Index (MEPI) extends the EPI by incorporating additional dimensions, such as lack of access to modern energy sources, reliance on solid fuels, and affordability constraints, thereby allowing for a broader evaluation of energy deprivation. In contrast, the LIHC approach emphasizes economic burden by identifying households that, although not income-poor by conventional standards, face energy poverty due to disproportionately high energy costs. The FGT measure can place greater emphasis on those in more severe deprivation, thus offering a more nuanced assessment of energy poverty distribution. While each of these indices offers valuable insights, focusing on a single dimension may underestimate both the breadth and depth of energy poverty, limiting the effectiveness of policy interventions aimed at its alleviation.

Table 1 Comparison of different energy poverty indices.
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In recent years, scholars have revisited frameworks for measuring energy poverty, noting that reducing it to a single dimension, such as affordability, obscures its multifaceted nature. Siksnelyte-Butkiene et al. (2021) highlight its dual nature, encompassing availability and accessibility, while Drago and Gatto (2023) stress the need for composite indicators. Although energy poverty differs across developed and developing countries, multidimensional frameworks remain broadly applicable. To address this, Zhang et al. (2019) developed a multidimensional measure incorporating both accessibility and affordability, using China Family Panel Studies (CFPS) 2012–2016 data. Their findings reflect China’s context: despite full electricity coverage, income inequality and geographic disparities persist. Many low-income and rural households still rely on solid fuels, with CFPS 2018 data showing nearly 30% of households—and over 50% in rural areas—using solid fuels. Moreover, persistent income inequality (He et al. 2018; Qi et al. 2022) means that financial poverty continues to translate into energy poverty, as access to modern energy does not guarantee affordability.

Regarding the temporal dimension, Thomson et al. (2017) emphasized that energy poverty should be analyzed with attention to both temporal and spatial dynamics, recognizing that households may transition in or out of deprivation as circumstances change. Most existing studies, however, rely on cross-sectional data, limiting the ability to capture persistence and evolution. Research has largely focused on developed countries. Phimister et al. (2015) used longitudinal data in Spain to compare energy and financial poverty, Chaton and Lacroix (2018) applied a movers-stayers model in France and found energy poverty largely temporary, while Drescher and Janzen (2021) employed dynamic models in Germany and reported limited persistence with varying causes across time. In contrast, evidence from developing countries suggests greater persistence: Alem and Demeke (2020), using Ethiopian panel data, showed that households in energy poverty were 16% more likely to remain deprived in the following period. These findings underscore the importance of adopting a temporal perspective when measuring energy poverty. While cross-sectional analyses can effectively capture short-term fluctuations, they may overlook long-term structural challenges and cumulative effects. This can result in underestimating both the persistence of energy deprivation and the enduring impact of policy interventions.

Despite growing recognition of these dynamics, research on the long-term consequences of energy poverty remains limited. Unlike temporary energy poverty, which may arise from short-term economic shocks, chronic energy poverty reflects sustained deprivation often rooted in structural economic and social constraints (McCulloch and Baulch 2000; Alkire et al. 2017). The drivers of chronic energy poverty are typically more complex, and its socioeconomic impacts more profound and persistent (Van et al. 2016; Shi and Dong 2022). Addressing this gap requires a measurement approach that explicitly incorporates the temporal dimension of energy deprivation. This study contributes to the literature by constructing a CMEP index that accounts for both the depth and duration of energy poverty. Building on the Alkire–Foster method (Alkire and Foster 2011) and the duration-based approach proposed by Foster (2009), the CMEP index integrates both affordability and accessibility dimensions. It captures the temporal accumulation effect of energy poverty, offering a diagnostic tool to distinguish between short-term fluctuations and long-term structural deprivation.

Health impacts of energy poverty

Energy poverty has been identified as a significant factor contributing to health disparities, with existing research broadly categorizing its health effects into two main dimensions: energy accessibility and energy affordability.

In many developing countries, limited access to modern energy sources forces households to rely on traditional biomass fuels, such as firewood and coal, for cooking and heating. The incomplete combustion of these fuels releases harmful pollutants, including carbon monoxide and nitrogen oxides, leading to indoor air pollution. Prolonged exposure to these pollutants has been linked to adverse respiratory health outcomes, particularly an increased prevalence of respiratory infections, chronic obstructive pulmonary disease (COPD), and other pulmonary conditions (Oum 2019). Empirical studies have provided further evidence of this relationship. For example, Phoumin and Kimura (2019) examined the prevalence of energy poverty in Cambodia and its implications for education, health, and income. Their findings indicate that members of energy-poor households are more likely to suffer from respiratory illnesses and tend to have higher healthcare expenditures compared to non-energy-poor households. These findings highlight the link between energy poverty and increased health risks, particularly in regions where modern energy infrastructure remains underdeveloped.

Even in countries where modern energy services are widely available, affordability constraints can still pose significant health risks. Households that struggle to afford sufficient energy expenditures are particularly vulnerable during periods of extreme weather. Both excessively high and low temperatures have been shown to adversely affect health outcomes, particularly for vulnerable populations such as the elderly, children, and individuals with preexisting medical conditions (Churchill and Smyth 2021; Thomson et al. 2017). Wilkinson et al. (2007) highlighted that, even in high-income countries, a substantial number of individuals experience energy poverty, which has measurable negative effects on health. Exposure to prolonged cold due to inadequate heating is associated with increased rates of respiratory illnesses, cardiovascular diseases, and chronic conditions (Lacroix and Chaton 2015). Furthermore, Liddell and Guiney (2015) investigated the psychological effects of energy poverty, revealing that cold and damp living conditions contribute to heightened stress levels and the development of negative emotional states.

Beyond direct physiological and psychological impacts, energy poverty also has broader economic consequences that further compound health risks. Households experiencing energy poverty often face difficult trade-offs between essential expenditures. Research has shown that energy-poor households may be forced to reallocate financial resources from food and healthcare to cover energy costs, leading to inadequate nutrition and limited access to medical services. This economic strain not only exacerbates physical health conditions but also contributes to lower subjective well-being (Churchill et al. 2020).

Methodology and data

Measurements of chronic multidimensional energy poverty

We combine the Alkire–Foster approach (Alkire and Foster 2011) with the duration approach (Foster 2009), following Alkire et al. (2017) idea of constructing a chronic multidimensional poverty index to divide the construction of such an index into three stages. In our study, we assume that the economy consists of n individuals, the observation periods are denoted as T, the individual’s welfare level is measured by d dimensions, and each dimension is measured by m indicators, \({x}_{ij}^{t}\) is the value of individual i in period t on welfare indicator j, and zj is the deprivation threshold of the welfare indicator j.

  1. 1.

    Identification of unidimensional energy poverty. First, we compare the value of each index with the corresponding deprivation threshold to determine the individual’s energy poverty status according to a single index. When \({x}_{ij}^{t}\le {z}_{j}\), we set \({r}_{ij}^{t}=1\), meaning that the individual is in a deprived state according to the indicator j (j = 1…m); otherwise, we set \({r}_{ij}^{t}=0\). Then, we assign a weight of wj to each indicator. \({g}_{ih}^{t}\) denotes the total deprivation score of individual i in period t on the dimension h, which is \({g}_{ih}^{t}=\mathop{\sum }\limits_{j=1}^{m}{w}_{j}{r}_{ij}^{t}\). We denote σ as the unidimensional energy poverty threshold (0 < σ ≤ 1). When \({g}_{ih}^{t}\ge \sigma\), the individual is identified as being unidimensionally energy poor.

  2. 2.

    Identification of multidimensional energy poverty. We calculate the individual’s weighted deprivation score on the dimension h (h = 1…d). We assign a weight of wh to each dimension. \({c}_{i}^{t}\) denotes the total deprivation score of individual i in period t on each dimension as \({c}_{i}^{t}=\mathop{\sum }\limits_{h=1}^{d}{w}_{h}{g}_{ih}^{t}\). Then, we set k as the threshold (0 < k ≤ 1). When \({c}_{i}^{t}\ge k\), the individual is identified as being multidimensionally energy poor. It (k) is the indicator function for identifying multidimensional energy poverty in period t. If the individual is in a state of multidimensional energy poverty, then It (k) = 1.

Table 2 lists the dimensions, indicators, and weights selected for this study. In the construction of the multidimensional index, we adopt an equal-weight approach following the existing research (Zhang et al. 2019) to measure energy accessibility and affordability, selecting cooking fuel and the 10% indicator as specific indicators. The dimension weight wh is 1/2, and the indicator weight wj is 1. In this study, energy expenditure broadly refers to energy spending, which includes utilities, fuel, and heating costs in a year, and income refers to the total income of a household in a year.

  1. 3.

    Identification of chronic multidimensional energy poverty. First, we construct the equation \({l}_{i}=\frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}{I}^{t}(k)\), which represents the ratio of periods in which an individual is in multidimensional energy poverty to all T periods. For example, when li = 0.5, individual i is in multidimensional energy poverty in half of all T periods. Second, we assume the timeshare τ is the threshold value of chronic multidimensional energy poverty (0 < τ ≤ 1). We construct an indicator function \({\rho }_{i}^{T}(k,\tau )=I({l}_{i}\ge \tau )\) to identify individuals who experience chronic multidimensional energy poverty in T periods. If the proportion of time an individual is in multidimensional energy poverty exceeds the threshold τ, the individual is identified as being in a state of chronic energy poverty (\({\rho }_{i}^{T}(k,\tau )=1\)).

Table 2 Measurement dimensions and indicators of CMEP.
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Taken together, the chronic multidimensional energy poverty index measurement model applied at the household level is:

$$CMEP(x,z,w,k,\tau )={\rho }_{i}^{T}(k,\tau )\times \frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}\mathop{\sum }\limits_{h=1}^{d}{w}_{h}{g}_{ih}^{t}(x,z).$$
(1)

Following Alkire and Foster’s (2011) multidimensional counting approach and Foster’s (2009) definition of the duration approach and focusing here only on households that are in a state of chronic multidimensional energy poverty, the following decomposition can be made (Alkire et al. 2017):

$$CMEP(x,z,w,k,\tau )={A}^{c}\times {D}^{c}$$
(2)

where Ac is the depth of chronic multidimensional energy poverty, which is the average share of chronically multidimensionally energy-poor households during the period of energy poverty; the higher the number of dimensions of household deprivation, the greater Ac will be:

$${A}^{c}=\frac{\mathop{\sum }\nolimits_{t=1}^{T}\mathop{\sum }\nolimits_{h=1}^{d}{w}_{h}{g}_{ih}^{t}(x,z)}{\mathop{\sum }\nolimits_{t=1}^{T}{I}^{t}(k)}.$$
(3)

In Eq. (4) below, Dc is the duration of chronic multidimensional energy poverty, which is measured as the ratio of the number of periods of household energy poverty to all observation periods:

$${D}^{c}=\frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}{I}^{t}(k).$$
(4)

According to Eq. (4), it is clear that the deeper and longer the duration of chronic multidimensional energy poverty, the greater the index value will be. Furthermore, extending the sample to n individuals in a region, the index measurement model applied to the macro level is (Alkire et al. 2017):

$$\begin{array}{c}NCMEP(x,z,w,k,\tau ,n)=\frac{1}{n}\mathop{\sum }\limits_{i=1}^{n}\left[{\rho }_{i}^{T}(k,\tau )\times \frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}\mathop{\sum }\limits_{h=1}^{d}{w}_{h}{g}_{ih}^{t}(x,z)\right]\\ ={H}^{n}\times {A}^{n}\times {D}^{n}\end{array}$$
(5)

where Hn is the incidence of chronic multidimensional energy poverty in a region, An is the average depth of chronically multidimensionally energy-poor households in a region, and Dn is the average duration of chronically multidimensionally energy-poor households in a region. The specific meaning of the indicator decomposition of Eq. (5), as well as the process of measuring the deprivation ratio of the indicator, are shown in Appendix A. Compared with the household-level index measurement, the macro-level index is influenced by the incidence rate: the higher the incidence rate of local chronic multidimensional energy poverty and the deeper the depth and the longer the duration, the greater the index value will be.

Variable information

Physical health and mental health

We used health as the dependent variable. The WHO (2001) emphasized that health should include both physical and mental health, and thus, the importance of mental health cannot be ignored. We therefore construct variables Physical to reflect the level of physical health and Mental to reflect the level of mental health. Referring to related studies (Kose 2019; Zhang et al. 2019; Tang et al. 2025), our main proxy of physical health is the respondent’s attitude regarding physical health measured on a five-point scale, with higher scores indicating better physical health. The main proxy for mental health is based on the respondent’s Ces-d-8 depressive mood score. Prior studies have shown the validity of the Ces-d-8 scale (e.g., Nie et al. 2021; Wang et al. 2023). To further verify the appropriateness of its application in this study, Appendix C presents an assessment of the scale’s reliability and validity. Specifically, Table C1 reports a Cronbach’s alpha coefficient of 0.948, indicating a high level of internal consistency. Table C2 shows a Kaiser-Meyer-Olkin (KMO) value of 0.922, and Bartlett’s test of sphericity is significant at the 1% level. These results collectively support the validity of the Ces-d-8 scale in this study. With eight questions, each rating one symptom, the scores can be summed to 24. The Ces-d-8 depressive mood score is a negative indicator, with higher scores indicating more severe depression.

Chronic multidimensional energy poverty index

Our independent variable is the chronic multidimensional energy poverty index (CMEP) measured by applying Eq. (1) above. In contrast to the approaches taken in previous studies on multidimensional energy poverty measures (e.g., Zhang et al. 2021; Lin and Wang 2020; Ye and Koch 2021), ours is a dynamic indicator that considers the time factor and focuses on chronic multidimensional energy poverty. According to the decomposition of Eq. (2), it is clear that higher values of CMEP and levels of chronic multidimensional energy poverty are associated with increased duration or depth. The time threshold τ in our study is 0.75, and a household is classified as being chronically multidimensionally energy poor if the proportion of time it spends in multidimensional energy poverty is 75% or above during the observation periods, where 0 < CMEP < 1. The greater the index, the more prominent the chronic multidimensional energy poverty of the household is. If the proportion of time a household is in multidimensional energy poverty during the observation periods does not reach 75%, the household's chronic multidimensional energy poverty level is recorded as 0 (CMEP = 0).

Control variables

Drawing upon previous research on the relationship between energy poverty and health (Churchill and Smyth 2021; Zhang et al. 2019), we select a series of individual and household characteristics as control variables for the regressions, including age, age squared/100, gender, educational experience, marital status, employment status, number of members in a household, number of children, urban–rural status, and region. Table 3 shows the definitions of the main variables and their descriptive statistics.

Table 3 Description and summary statistics of variables.
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Data source

The data used for this study come from the China Family Panel Studies (CFPS) initiated by the Institute of Social Science Research, Peking University, China. CFPS conducts tracking surveys on Chinese communities, households, and individuals every two years. The current, complete tracking data include four survey data periods in 2012, 2014, 2016, and 2018, which cover 25 provinces, municipalities, or autonomous regions in China and include economic and noneconomic information such as education, marriage, employment, and health, and are nationally representative. We restrict the sample to adults aged 18 years or older and retain only individuals and households continuously surveyed for three years or more. We also exclude households with zero annual income to measure energy poverty, thereby resulting in panel data of 10,548 individuals with 40,166 observations.

Distribution characteristics of the chronic multidimensional energy poverty index

Overall situation

According to the indicators and weights set in Table 2, we only consider the case given a multidimensional energy poverty threshold k = 0.5. To better observe the dynamic development of energy poverty in China, we apply Eq. (5) above and calculate China’s chronic multidimensional energy poverty index under different time thresholds τ. Figure 1 presents the averages of the incidence, depth, duration, and composite index (NCMEP). The results indicate that nearly 80% of households experience at least one stage of multidimensional energy poverty, and more than 40% experience three or more stages of multidimensional energy poverty, which is a suboptimal situation for household energy poverty in China. Moreover, the figure shows that the depth of chronic multidimensional energy poverty increases as the time threshold τ increases, which implies that the longer a household is in multidimensional energy poverty, the greater the depth of multidimensional energy poverty will be. Chronically energy-poor households tend to face more severe energy poverty conditions.

Fig. 1
figure 1

Overall energy poverty situation.

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Table 4 shows the deprivation ratio of each indicator at different time thresholds τ. The data in the table show that the cooking fuel indicator accounts for more than 50% irrespective of whether the time threshold τ is 0.25, 0.5, 0.75, or 1. This result suggests that households fall into chronic multidimensional energy poverty mainly because of a lack of access to clean energy. On the other hand, the data in the table show that the proportion of deprivation in the indicators for the energy accessibility dimension increases with the duration of energy poverty. In contrast, the proportion in the energy affordability dimension gradually decreases, thus indicating the importance of universal access to clean energy in reducing the proportion of households in a state of chronic multidimensional energy poverty.

Table 4 The calculated deprivation ratio of each indicator at different thresholds τ (k = 0.5).
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Urban–rural disparities

Despite China’s recent efforts to promote urban–rural integration, income inequality between urban and rural areas remains significant (Yang et al. 2021). To explore the impact of this income disparity on energy poverty, we present the energy poverty rate by annual gross income decile. The results are shown in Table B1 in the Appendix. The results in the table show that the energy poverty rate decreases as income increases and that low-income households are more likely to experience energy poverty. Combined with Ma et al.'s (2023) study on energy transition in rural households, we hypothesize that rural households will likely fall into more severe chronic energy poverty because they cannot afford to pay for energy.

To demonstrate the urban–rural disparities in the level of chronic multidimensional energy poverty in China, we refer to studies on energy poverty (e.g., Foster 2009; Alkire et al. 2017), set the threshold to k, and separately measure the level of chronic multidimensional energy poverty in urban and rural areas. As illustrated in Fig. 2, in terms of incidence, urban households are mainly temporarily multidimensionally energy poor or energy poor, and their incidence of chronic multidimensional energy poverty is low. In rural areas, multidimensional energy poverty is predominantly chronic, with about 60% of rural households experiencing such conditions. The chronic energy poverty problem in rural China is therefore severe. In terms of depth and duration, chronic multidimensional energy poverty is more protracted in rural areas, and urban–rural disparities in chronic multidimensional energy poverty are significant. This result is consistent with our conjecture that low-income households are more likely to experience energy poverty. Chronic energy poverty is more severe in rural China, where incomes are lower and solid fuel use is more prevalent.

Fig. 2
figure 2

Urban–rural disparities.

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Table 5 shows the calculated deprivation ratio of each indicator at the urban, rural, and national levels. There are also significant differences between urban and rural areas regarding deprivation ratios for specific indicators, with households in urban areas having more severe problems with energy affordability than energy accessibility, and rural households falling into chronic multidimensional energy poverty more frequently due to energy unavailability. China’s urban areas have excellent infrastructure as well as convenient transportation and logistics, thus making access to modern energy services more possible. However, due to the severe wealth gap, many households cannot afford energy and are trapped in a state of chronic multidimensional energy poverty. On the other hand, rural areas are more influenced by geography, resource distribution, and economic development (He et al. 2018) and still use solid fuels as the primary energy source for cooking and heating. In the context of unbalanced energy development between urban and rural areas, the problems in the energy accessibility dimension are more prominent than those in the energy affordability dimension in a national context. On the one hand, this phenomenon is likely related to China’s economic development and poverty alleviation efforts in recent years (Liu et al. 2018), where per capita income has increased, poverty has decreased, and household affordability of energy has improved. On the other hand, the chronic use of lower-cost solid fuels as the primary energy source by a large proportion of the population has further reduced household energy expenditures, such that the proportion of households that are chronically energy poor is under the 10% indicator.

Table 5 The deprivation ratio of each indicator at the urban, rural, and national levels.
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Regional disparities

As mentioned above, we take the thresholds of the chronic multidimensional energy poverty index as k = 0.5, τ = 0.75. Figure 3 depicts the chronic multidimensional energy poverty index for 25 provinces, municipalities, or autonomous regions in China from 2012 to 2018. The chronic multidimensional energy poverty index varies widely across regions in China. In general, the index is lower in the more economically developed regions. Still, there are cases in which the chronic multidimensional energy poverty index is higher in the relatively economically developed provinces than in the less developed ones. For example, Beijing has a strong economy, but its chronic multidimensional energy poverty index is higher than that in Tianjin and Zhejiang, which may be caused by the sizeable internal wealth gap. In addition, the situation is more optimistic in the eastern region. However, the index is higher in provinces located in the northeast, which may be related to their higher energy requirement for heating due to their high latitude and low winter temperatures, thus leading to energy poverty (Feeny et al. 2021).

Fig. 3
figure 3

Regional disparities.

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Regression results

Baseline results

We adopt the regression model used in most of the literature to test the impact of chronic multidimensional energy poverty on health with the following model specification:

$$Healt{h}_{i}={\beta }_{0}+{\beta }_{1}CME{P}_{i}+{\beta }_{2}{X}_{i}+{\beta }_{3}{F}_{i}+{\beta }_{4}Pr{o}_{i}+\varepsilon$$
(6)

where Healthi denotes the health status of individual i (including both physical and mental health) and CMEPi denotes the chronic multidimensional energy poverty index. Xi is a vector of individual characteristics, Fi is a vector of household characteristics, Proi is a provincial fixed effect, and ε is a randomly distributed error term. The coefficient β1 is the central concern of this study. When the dependent variable is Physical, the coefficient β1 is significantly negative, which implies that chronic multidimensional energy poverty has a significantly negative effect on an individual’s physical health. When the dependent variable is Mental, the coefficient β1 is significantly negative, which implies that chronic multidimensional energy poverty has a significantly negative effect on the individual’s mental health.

Table 6 presents the regression results. Columns (1) and (2) use physical health as the dependent variable, with the former controlling for individual characteristics only and the latter adding family characteristics. In both models, the coefficients on CMEP are negative and significant at the 1% level, indicating that chronic multidimensional energy poverty significantly harms physical health, with higher levels associated with poorer self-reported health. Columns (3) and (4) use mental health as the dependent variable under the same control settings. The coefficients on CMEP are positive and significant at the 1% level, suggesting that chronic multidimensional energy poverty also deteriorates mental health. Specifically, higher levels of deprivation are associated with increased Ces-d-8 depressive symptoms, reflecting greater psychological harm.

Table 6 Baseline regression of CMEP and health.
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Consistent with previous studies (e.g., Zhang et al. 2019; Kose 2019), Table 5 shows that age, gender, and employment status are related to health. Family size and number of children have a significantly positive effect on both physical and mental health, aligning with Kendall and Anglewicz (2018) and studies linking family size to lower depressive symptoms (Nie et al. 2021). Other controls show that education, marital status, and urban–rural residence are not significantly related to physical health but do affect mental health. Education enhances cognitive abilities and social status, reducing depression (Lee 2011). Marriage may improve psychological health through companionship and support. Urban residents benefit from better infrastructure, services, and higher income, which can reduce depression (Huang et al. 2017). Poverty alleviation policies and improved rural medical insurance have narrowed urban–rural differences in physical health (Fang et al. 2019), explaining the lack of a significant relationship between residence and physical health in this study.

Endogeneity

While the baseline regression establishes a significant relationship between CMEP and residents’ physical and mental health, concerns regarding endogeneity remain. These concerns primarily stem from two sources: reverse causality and omitted variable bias. Poorer health conditions may reduce income-earning capacity, limiting access to modern energy sources. Omitted variable bias occurs when unobserved factors, such as regional development disparities or household preferences, simultaneously affect both CMEP and health outcomes, leading to biased estimates.

To address these endogeneity concerns, we employ the instrumental variable (IV) approach following Lewbel (2012), combined with a two-stage least squares (2SLS) regression. This method allows for the generation of internal instruments based on heteroscedasticity, providing a viable solution when traditional IVs are unavailable. Additionally, we incorporate external instruments derived from exogenous policy shocks—specifically, the Carbon Emission Trading Policy (CETP) and the New Energy Demonstration Policy (NEDP). CETP, introduced in 2013 across pilot regions such as Beijing and Tianjin, aims to enhance energy efficiency and reduce carbon emissions. NEDP promotes the adoption of renewable energy technologies. The validity of these policies as instruments lies in the fact that, while they do not directly affect individual health, they influence household energy choices and affordability, thereby impacting CMEP.

Our empirical strategy follows a stepwise approach. First, we implement the Lewbel internal IV method and, subsequently, extend the analysis by incorporating external IVs based on CETP and NEDP. A key requirement for the validity of the Lewbel IV approach is the presence of heteroscedasticity, which we confirm through the Pagan–Hall and Breusch–Pagan tests. Table 7 reports the results of these endogeneity tests, showing strong statistical evidence of heteroscedasticity, thereby justifying the use of this method. Additionally, the first-stage F-statistics consistently exceed 10 across all specifications, indicating the absence of weak instrument bias.

Table 7 IV tests of CMEP and health.
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The IV regression results are presented in Table 7. Columns (1) and (2) report the estimates using the Lewbel internal IV, confirming that CMEP continues to exert a significant negative impact on both physical and mental health. Columns (3) through (6) present the results incorporating external IVs, separately examining CETP and NEDP. The findings remain robust across all specifications, reinforcing the conclusion that the adverse health effects of CMEP persist even after addressing potential endogeneity concerns.

Robustness checks

Replace the dependent variable

To address potential bias from measurement errors in health indicators, we perform robustness checks using physical health from medical examinations and life satisfaction as complementary measures for physical and mental health. In the CFPS dataset, physical health is assessed on a seven-point scale based on self-reported evaluations after a medical examination, providing a more structured measure than subjective health assessments. Life satisfaction, measured on a five-point scale, serves as a proxy for mental health, consistent with prior studies linking it to psychological well-being (Nie et al. 2022). Table 8 presents the results. Column (1) shows that chronic multidimensional energy poverty significantly worsens physical health, with a positive coefficient significant at 1%. Column (2) indicates a negative coefficient significant at 10% for life satisfaction, suggesting that higher energy poverty reduces mental well-being.

Table 8 Robustness checks of CMEP and health.
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In the baseline regression, we used 2018 physical health attitudes and Ces-d-8 depressive scores as proxies for physical and mental health, implicitly assuming continuous incremental effects. To address potential measurement errors, we instead use the mean values of these indicators across four observation periods in columns (3) and (4). Both coefficients on CMEP remain significant at 1%, confirming that individuals in severe long-term energy poverty exhibit poorer physical and mental health. The smaller magnitude compared to the baseline reflects the smoothing effect of averaging over multiple periods, as expected.

Adding control variables

Omitted variable bias arises when relevant factors are excluded from the model, potentially distorting the relationship between independent and dependent variables. In our study, economic factors such as household income and consumption expenditure may influence both CMEP and individuals’ physical and mental health, potentially confounding the results. By including household income and consumption expenditure as controls, we account for the broader economic context shaping health outcomes. These variables reflect a household’s resources, which affect access to healthcare, nutrition, and other services critical to health (Tang et al. 2024; Shen et al. 2025). Controlling for them ensures that the estimated effect of CMEP is not conflated with underlying economic poverty.

The results, shown in columns (5) and (6) of Table 8, indicate that even after controlling for these economic factors, the coefficient of CMEP remains significant at the 1% level. This finding provides strong evidence that the relationship between CMEP and health outcomes is robust and not simply driven by underlying economic factors such as income or consumption expenditures. In other words, even after accounting for the potential confounding effects of economic poverty, CMEP continues to exert a significant impact on both physical and mental health, further reinforcing the validity of our conclusions.

Change the estimation method

We change the estimation method to propensity score matching (PSM), which has the advantage of including fewer model constraints and mitigating sample self-selection bias on observable variables (Wang et al. 2025). Using the PSM method first requires the discretization of the chronic multidimensional energy poverty index. We select the time threshold \(\tau\) for chronically multidimensionally energy-poor households as the classification criterion. To ensure robustness, we apply four matching methods—nearest neighbor, radius, kernel, and local linear regression—using the same control variables as in the baseline regression.

Table 9 reports the PSM results. We focus on the average treatment effect on the treated (ATT). For physical health, ATT is negative, indicating lower health attitude scores among chronically multidimensionally energy-poor households compared to non-poor households. For mental health, ATT is positive, indicating higher depression scores among the chronically energy-poor. Across all matching methods and poverty criteria, the core ATT remains significant at the 1% level, confirming that chronic multidimensional energy poverty substantially impairs both physical and mental health.

Table 9 PSM tests of CMEP and health.
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Adjusting measurement weights

Since the baseline model uses equal weighting for the dimensions of long-term multidimensional energy poverty, it is crucial to assess whether different weightings for these dimensions could lead to significant variations in the results. The concern arises from the possibility that certain dimensions of energy poverty, such as energy accessibility or affordability, may have stronger effects on residents’ physical and mental health than others, thus altering the overall impact when given different weights.

To address this issue, we re-estimate the CMEP index by adjusting the weights assigned to each dimension, thereby shifting from an equal-weighted structure to schemes that emphasize one dimension over the other. We then rerun the regression analysis using the modified CMEP indices to assess whether the results remain robust. Table 10 reports the outcomes of this sensitivity analysis. The results indicate that, under all alternative weighting schemes, the estimated effects of chronic multidimensional energy poverty on both physical and mental health remain statistically significant. This consistency across specifications reinforces the reliability of the findings and suggests that the negative association between long-term multidimensional energy poverty and health outcomes is robust to variations in the relative importance assigned to its constituent dimensions.

Table 10 Weightings adjusted tests of CMEP and health.
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Mechanism analysis

This study’s mechanism analysis examines how long-term multidimensional energy poverty affects individual health through food expenditure and perceived environmental pollution, highlighting the indirect and interconnected pathways linking energy poverty to physical and mental well-being. Food expenditure is a key mechanism. Existing research consistently shows that energy-poor households face constrained budgets, forcing them to make trade-offs between essential expenses such as energy, food, and healthcare (Fry et al. 2023). In these households, energy costs are often prioritized, resulting in reduced food budgets. This effect becomes more pronounced under long-term energy poverty, where sustained financial pressure limits resources over extended periods (Nie et al. 2021). Consequently, households may opt for cheaper, less nutritious food alternatives instead of higher-quality options, leading to adverse health effects due to inadequate nutrition (Sambodo and Novandra 2019).

Perceived environmental pollution is another critical mechanism. Households lacking access to clean energy sources frequently rely on solid fuels like coal, wood, or biomass for cooking and heating. These fuels release indoor air pollutants, such as carbon monoxide and particulate matter, which are linked to respiratory and cardiovascular conditions, including asthma and chronic obstructive pulmonary disease. Beyond indoor effects, the use of such fuels contributes to outdoor air pollution, further degrading environmental quality. This persistent exposure to polluted conditions not only impairs physical health but also affects mental well-being, as poor air quality is associated with increased stress and anxiety, particularly among those with limited means to mitigate their circumstances (Liddell and Guiney, 2015).

To empirically examine these mechanisms, this study uses household food expenditure and environmental pollution as mediators in a mediation analysis, with results presented in Table 11. The findings show that food expenditure significantly mediates the relationship between energy poverty and health: long-term multidimensional energy poverty reduces food expenditure, while higher food expenditure is associated with better health outcomes, reflecting households’ ability to afford more nutritious food when resources allow. Similarly, the analysis confirms that prolonged energy poverty intensifies environmental pollution, further contributing to health deterioration. In summary, food expenditure and perceived environmental pollution are key mechanisms linking long-term multidimensional energy poverty to individual health.

Table 11 Mechanism analysis of CMEP and health.
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Heterogeneity analysis

Heterogeneity of the individual characteristics

Much of the literature has found that personal characteristics have a significant impact on health (e.g., Hu et al. 2016; Black et al. 2017), and to explore the impact these characteristics have on our results, we conducted a further heterogeneity analysis. Black et al. (2017) examined the importance of age, cognitive ability, and socioeconomic status on health disparities using data from the Australian Household Longitudinal Survey and found significant differences in self-reported health across individuals of different ages and education levels. On this basis, we conjecture that the effect of chronic multidimensional energy poverty on health will show different variability according to respondents’ age and income. To check this conjecture, we conducted the following heterogeneity analysis.

(1) Age. Columns (1) and (2) of Table 12 report the results of the interaction term based on age. The coefficients on the interaction terms for respondents aged up to 50 years pass the 1% significance level test, thus indicating that increasing age strengthens the negative impact of chronic multidimensional energy poverty on health—that is, that respondents aged up to 50 years are more vulnerable to the effects of chronic multidimensional energy poverty on health. This result is consistent with our perception that older people have unique physical and psychological vulnerabilities due to aging and their social circumstances. When living in energy poverty for long periods of time, deterioration in the living environment may further induce illness, and extreme heat or cold temperatures may even increase isolation, anxiety, and fear.

Table 12 Heterogeneity analysis of CMEP and health.
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(2) Income. Columns (3) and (4) of Table 12 report the results of the interaction term based on income classification. In the data processing, we define income below the mean as lower income and above the mean as higher income. The results show that the interaction term coefficients for the lower-income group pass the significance test at the 1% level. Low-income respondents are more likely to be negatively affected when their health status is exposed to chronic multidimensional energy poverty, which aligns with previous research (Churchill and Smyth 2021) that shows that lower-income groups are less able to cope with adverse living conditions than higher-income groups.

Regional heterogeneity

China’s vast geographical expanse, shaped by diverse topography, climate, and economic conditions, leads to notable regional variations in the dynamic characteristics of multidimensional energy poverty (see Fig. 3). Given the disparities in economic development levels between China’s eastern, central, and western regions, the spatial and temporal distribution of multidimensional energy poverty is likely uneven. To explore these variations, this study divides the sample into eastern, central, and western regions and examines the heterogeneous effects on the physical and mental health of residents in these areas.

The regression results in columns (6) and (7) of Table 12 reveal distinct patterns. Long-term multidimensional energy poverty significantly reduces physical health in eastern and central provinces, whereas its effect in western provinces is statistically insignificant. Regarding mental health, energy poverty increases depression levels across all regions, with the western provinces showing a statistically significant difference compared to the central region. These results indicate that the health consequences of energy poverty vary by region.

Regional differences in adaptability to air pollution, influenced by geographic and socioeconomic conditions, help explain these effects (Ji et al. 2022). In the more developed eastern and central provinces, households lacking access to modern energy often rely on coal and similar fuels, producing indoor air pollution that harms physical health and may worsen mental health over time. In contrast, residents in the western region, dominated by heavy industry, appear less sensitive to energy-related pollution in terms of physical health—likely due to adaptation to chronically high pollution—but this adaptation does not mitigate the mental health impact, as depression levels remain elevated.

Further analysis

Health effects of the duration of energy poverty

In chronically multidimensionally energy-poor households, the CMEP index rises as the depth or duration of energy poverty increases, suggesting that health impacts may depend on either factor or their combination. While the adverse effect of energy poverty depth on health has been documented (Zhang et al. 2021), and longer exposure to energy poverty has been associated with greater health deterioration (Churchill and Smyth 2021), existing research lacks a rigorous quantitative analysis of the separate effect of duration. Therefore, we provide an in-depth examination of the role of energy poverty duration.

First, we examine the role of duration in households experiencing chronic multidimensional energy poverty. To isolate the effect of duration from depth, we include the measured depth as a control variable and apply Eq. (6) in a regression analysis. The results, reported in columns (1) and (2) of Table 13, indicate that when the dependent variable is physical health, the coefficient on CMEP is not significant at the 10% level, suggesting that beyond the chronic threshold, longer duration no longer affects physical health. This may be due to the relatively short observation period, with limited variation across three or four periods. In contrast, when the dependent variable is mental health, the coefficient on CMEP is positive and statistically significant at the 1% level, indicating that prolonged energy poverty continues to adversely affect mental health even after reaching the chronic threshold. This aligns with previous findings that extended exposure to stressors, such as noise pollution, can have a more pronounced impact on mental than physical health (Jensen et al. 2019).

Table 13 Further analysis of CMEP and health.
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Next, we explore the importance of the duration of energy poverty for all households. According to the definition of Eq. (1), the chronic multidimensional energy poverty index for a household with temporary multidimensional energy poverty is directly set as 0 (Alkire et al. 2017). To make the estimation results more objective and comprehensive, we refer to the construction principle of Eq. (1) to construct a multidimensional energy poverty index that incorporates a time factor (TMEP). Unlike CMEP, TMEP includes the energy poverty experiences of all observed households, including households with temporary and chronic multidimensional energy poverty. Therefore, the index value is no longer assumed to be zero for households experiencing temporary multidimensional energy poverty, but rather a more precise measure of the average level of multidimensional energy poverty during the observation period, as expressed by:

$$TMEP(x,z,w,k)=\frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}\mathop{\sum }\limits_{h=1}^{d}{w}_{i}{g}_{ih}^{t}(x,z).$$
(7)

Equation (7) is decomposed in this paper based on the decomposition principle of Eq. (2), and the results are as follows:

$$TMEP(x,z,w,k)={A}^{t}\times {D}^{t}=\frac{\mathop{\sum }\nolimits_{t=1}^{T}\mathop{\sum }\nolimits_{h=1}^{d}{w}_{i}{g}_{ih}^{t}(x,z)}{\mathop{\sum }\nolimits_{t=1}^{T}{I}^{t}(k)}\times \frac{1}{T}\mathop{\sum }\limits_{t=1}^{T}{I}^{t}(k).$$
(8)

where At is the depth of multidimensional energy poverty of a household in T periods, and Dt is the duration of multidimensional energy poverty of a household in T periods. Like the concept of CMEP, the longer the household experiences multidimensional energy poverty and the greater the depth of that poverty during the observation periods, the larger the value of TMEP will be.

Controlling for depth, we use regression to examine the effect of TMEP on individuals’ physical and mental health. The results, reported in columns (3) and (4) of Table 13, show that all coefficients on TMEP are significant at the 1% level, indicating that multidimensional energy poverty negatively affects both physical and mental health, and that higher average levels over the observation periods exacerbate these effects. Since depth is controlled, this negative impact reflects the role of duration, consistent with findings in the poverty literature (e.g., Benzeval and Judge 2001; López del Amo González et al. 2018), where prolonged poverty significantly harms health, while temporary poverty spanning only one or two periods has no significant effect (Shi and Dong 2022).

Notably, the coefficient on TMEP is significant at the 1% level for mental health, differing from the results for physical health in column (1). Temporal variation within chronically energy-poor households does not affect physical health, but when temporary energy-poor households are included, duration becomes significant. This suggests a cumulative effect: chronic energy poverty has more severe health consequences than temporary poverty. These findings highlight the importance of incorporating duration when assessing the health impact of energy poverty; ignoring it may underestimate the welfare losses for households with low depth but prolonged exposure.

Contrast the effects of duration and depth

The regression results above show a negative relationship between the duration of multidimensional energy poverty and health, but they do not clarify whether duration or depth has a stronger impact. To address this, we normalize the depth and duration measures from Eq. (8) and re-estimate Eq. (6), allowing for direct comparison of coefficient magnitudes.

Columns (5) and (6) of Table 13 present the results. Clear differences emerge between the effects of depth and duration. For physical health, the coefficient on duration is negative and significant at the 1% level, confirming that prolonged exposure to energy poverty consistently harms physical health. In contrast, the coefficient on depth is not significant at the 10% level, suggesting that the intensity of energy poverty does not have a persistent effect on physical health. For mental health, both duration and depth are positively associated with depression, with duration significant at the 1% level and depth at the 5% level, indicating that longer and deeper energy poverty increases depressive symptoms. However, the effect of duration is stronger. These findings indicate that the adverse health impacts of the duration of energy poverty outweigh those of its depth, highlighting the importance of accounting for duration when assessing the well-being losses caused by energy poverty.

Conclusion and policy implications

While energy poverty’s impact on human welfare and social sustainability has received increasing attention, research on its dynamic evolution remains limited, especially in developing countries like China, due to data constraints. We innovatively combine the Alkire and Foster (2011) dual cut-off method with Foster’s (2009) concept of duration to construct a long-term multidimensional energy poverty index across multiple periods, which is then used to examine its effects on individuals’ physical and mental health.

The analysis reveals several important insights. Remarkably, more than 40% of Chinese households experience long-term multidimensional energy poverty, a proportion exceeding that of households facing short-term deprivation. This suggests that, despite ongoing efforts to alleviate energy poverty, a substantial portion of the population continues to face persistent energy constraints. Moreover, long-term energy poverty exerts a pronounced negative effect on health, operating primarily through two channels: reduced household food expenditure and heightened exposure to environmental pollution. The impact is not uniform across the population, with young individuals, higher-income households, and residents in central provinces appearing particularly vulnerable. Crucially, the duration of energy poverty emerges as a key factor: the longer households remain in a state of deprivation, the more severe the deterioration of both physical and mental health. This underscores the cumulative and enduring nature of energy poverty’s effects, emphasizing that its harm grows over time rather than being confined to short-term episodes.

The findings of this study offer practical insights for policymakers addressing energy poverty and its associated challenges. First, despite notable progress, energy poverty remains pervasive, highlighting the need for continued investment in modern energy services. Targeted interventions are essential to reduce regional disparities and support vulnerable populations. For example, under the rural revitalization strategy, the government could provide clean energy subsidies to low-income rural households, expand natural gas infrastructure, and promote non-solid fuels such as natural gas. Such measures would decrease reliance on biomass and mitigate health risks, including respiratory diseases linked to solid fuel use. Enhancing social welfare systems and reducing income inequality would further improve households’ capacity to afford energy, addressing the root causes of energy poverty.

Second, households experiencing long-term multidimensional energy poverty face severe health burdens, which increase medical costs and may crowd out other essential expenditures, perpetuating a cycle of reduced living standards. To interrupt this cycle, policymakers should prioritize these households by linking energy subsidies with public healthcare initiatives, such as free health check-ups and respiratory disease prevention programs tailored to energy-poor families.

Third, older adults and low-income individuals are particularly vulnerable to the health impacts of prolonged energy poverty, compounded by their disadvantaged social positions. To protect these groups, governments could strengthen elderly care and medical services, including home insulation improvements and energy-efficient equipment installations. For low-income households aged 60 and above, providing clean heating solutions and integrated energy-health assistance could safeguard physical health, while psychological counseling services could address mental health challenges exacerbated by poor living conditions.

While this study provides valuable insights into the dynamics of energy poverty and its health consequences, several limitations highlight directions for future research. First, the construction of the long-term multidimensional energy poverty index is shaped by China’s specific context. Given the country’s geographic diversity, large population, and recent energy-related policies, future studies could enhance the index’s generalizability by incorporating data from similar countries, such as India, enabling cross-national comparisons. Second, extending the dataset would allow researchers to assess the effects of recent policy changes and socioeconomic transformations, providing a more comprehensive understanding of energy poverty trends. Finally, the impact of the CMEP index on health outcomes may vary across dimensions such as energy affordability and accessibility. While this study conducted robustness checks using unequal weights, future research could apply systematic weighting methods to improve the precision and interpretability of the multidimensional index.