Introduction

Climate change has become the most serious threat to the sustainability of the human race, as reported in the Global Risks Report 2023 by the World Economic Forum.Footnote 1 Despite the ongoing conflict of interests among nations, a global consensus has been slowly established to actively combat climate change. For example, the Paris Agreement was signed by almost all countries worldwide. From developed to developing nations, climate change-related policies have been introduced at an increasing pace. For instance, over seventy carbon pricing initiatives have been implemented globally, according to the World Bank.Footnote 2More than 110 countries, including the world largest emitters, such as China and India, have committed to carbon-neutral targets. The number of countries pursuing these targets is on the rise. Various policies, including legislation, regulations, and strategies, have been implemented to support these climate goals (Eskander and Fankhauser, 2020).

Two important questions arise following these activities: first, can climate actions bring additional benefits to society, for instance, alleviating energy poverty? Second, if the answer to the first question is positive, which type of climate policies are more likely to bring synergies to resolve energy access problems? Bearing these questions in mind, this research performs a cross-country empirical study using a sample of 75 nations from 2000 to 2020. The first key contribution is to examine whether policy spillover exists from climate actions to energy poverty from an international perspective. Rather than focusing on the impacts of a particular climate policy, the accumulation of policies from various categories is considered. This approach allows us to comment more broadly on the policy spillover effects and to explore differences across policy types. Overall, this work hopes to provide new evidence for policymakers to properly evaluate climate actions. Among different pathways towards a sustainable future, there are potential win-win strategies that should be actively pursued.

Fundamentally, no matter what forms of policies are implemented against climate change, the fundamental issue or objective of these actions is to control GHG emissions (Martin and Saikawa, 2017). Given the fact that these emissions are mainly from the use of fossil fuel energy, transitioning towards a sustainable energy system (Solomon and Krishna, 2011) or using renewable energy (Staples et al., 2017) has become an inevitable option. However, the process of energy transition is fraught with significant challenges (Gillingham and Stock, 2018) and is often related to issues of justice and equity (Carley and Konisky, 2020; Markkanen and Anger-Kraavi, 2019). From this perspective, the impacts of climate change policies are multi-dimensional. Climate actions and policies can affect sustainable development (Swart et al., 2003), improve health conditions through better air quality (Nemet et al., 2010), influence the financial sector (Stolbova et al., 2018), and have spillover effects in many other aspects of the global community.

Referring to energy-related issues, some recent studies point out that the implementation of climate change policies can affect energy poverty (Streimikiene et al., 2020), which is central to the UN SDG target 7–affordable and clean energy. While the world seeks climate solutions, we must realize that a significant share of the population is unable to access modern energy or afford it (Zhang et al., 2019b). IEA (2023), for example, reports about 760 million people without access to electricity, and ~2.3 billion people unable to use clean cooking fuels. These unfortunate figures lead to an urgent need for the international community to respond (Sovacool et al., 2012).

The question is whether and how climate change policies may affect energy poverty. First, by supporting the development of renewable energy, it allows the energy-poor to access electricity (Azad and Chakraborty, 2020). In the past decade, renewable energy, such as solar and wind power, has gained significant financial support. Favorable policies, along with advances in technological progress, have made the renewable energy sector more reliable and economically viable (Adefarati and Bansal, 2019). Second, climate policies can also alleviate energy poverty by improving energy efficiency (Zhao et al., 2022). Green innovation and new technology have changed the way energy is used. Machines are more powerful and less emissive. Third, the development of climate finance provides valuable resources to improve the current energy system (Long et al., 2022). In addition, subsidies to renewable energy and green finance can encourage innovation (Yu et al., 2021), which can further support reducing energy poverty.

In addition to the positive role of climate change policies in resolving energy poverty issues, they can also bring opposite impacts. Belaïd (2022), for example, argues that climate policies, if poorly designed, can worsen energy poverty through the price channel. In other words, energy prices can increase due to climate policy changes, leading to affordability problems. Similar arguments can also be found in Berry (2019) for France, Henry et al. (2021) for Guatemala, and other country-level studies. Carbon tax or environmental restrictions to cope with climate changes can add costs to fossil fuel energy, leading to consequential problems in energy poverty.

Overall, the impacts of climate change policies on energy poverty are not entirely clear, and conclusions from some recent studies are also inconsistent. For example, Chien et al. (2022) estimate the effect of green fiscal policy on energy poverty based on panel data for 2010–2020 in 17 countries. They find that green fiscal policy can reduce energy poverty by improving efficiency. Poblete-Cazenave et al. (2021) evaluate the accessibility of energy services under different climate mitigation policy scenarios in Africa and South Asia. They find that ambitious climate mitigation scenarios cannot fundamentally change access to energy services for households in developing countries.

Arguably, even if policy spillovers exist, not all climate policies can alleviating energy poverty. Authorities should pay attention to their policy mix and incorporate concerns of energy poverty when planning climate actions. Bonatz et al. (2019) explore a policy framework to simultaneously achieve energy poverty alleviation and carbon emissions reduction in the scenario of China. Streimikiene et al. (2020) analyze the energy poverty and climate change mitigation issues in EU households based on a systematic literature review. They argue that it is necessary to adopt more targeted climate change policies to alleviate energy poverty. Economidou et al. (2022) take the European policy designs as an example to show the need for strategic planning of energy and climate policies to achieve a synergetic effect. These studies point to a strand of recent literature explicitly discussing spillovers across different SDGs. Lusseau and Mancini (2019), for example, provide evidence that SDGs and their sub-categories can be used to form an interactive network, and the network differs across nations. Similar works can also be seen in Wu et al. (2022) and Cao et al. (2023), supporting the existence of policy spillovers. They suggest that the synergetic effect is more likely to be achieved with proper policy design.

The remainder of this paper is organized as follows: Section 2 explains the construction of main variables based on relevant literature. Section 3 reports descriptive information on the data, and the empirical models used in the paper. Section 4 reports and analyzes the main results based on the empirical models. Section 5 concludes and discusses the policy implications.

Key variables

Measurement of energy poverty

The concept of energy poverty can be traced back to Lewis (1982), who suggests that the well-being of people can be affected by insufficient use of energy. Boardman (1991) follows this logic to introduce the 10% threshold, which is the ratio of energy consumption to household income, as a measure of energy poverty. A household is considered to be in energy poverty if the ratio exceeds 10%, meaning that they cannot afford sufficient energy use, or because of high energy prices, the household must sacrifice other important expenditures to heat their home. In addition to affordable energy use, broader concepts of energy poverty have been developed to incorporate energy access into this framework (Zhang et al., 2019a). Given that most of the developed world has almost 100% access to modern energy, the second dimension of energy poverty is primarily relevant to developing nations (Zhang et al., 2019b). However, this broader concept has led to the development of multi-dimensional energy poverty indices (e.g., Sadath and Acharya, 2017). These measures are often based on fine micro-level or household-level survey data and thus are limited to a smaller sample of countries.

To construct a national-level measurement of energy poverty, while maintaining the multi-dimensional nature of this issue, Banerjee et al. (2021) developed the Energy Development Index (EDI). The EDI measure is designed to encompass the following four dimensions: (1) total primary energy use per capita; (2) renewable energy consumption (percentage of total end-used energy consumption); (3) electric power energy consumption per capita; (4) access to electricity (percentage of the population). Among these factors, a higher level of primary energy use per capita is relevant to affordability, while the renewable energy share indicates cleaner energy use. A higher level of electric power consumption reflects both accessibility and affordability elements, whereas the last factor directly captures energy accessibility in a country. In general, less energy poverty in a country is associated with a higher level of EDI. The measurements of these four dimensions (j) are scaled first (see Equation (1)), and then averaged to get the index for a country i. Ej,it refers to the four dimensions of each country i in year t, where EIj,it is the scaled measure ranging from 0 to 100, representing the worst to the best in terms of energy development.

$$E{I}_{j,it}=\frac{{E}_{j,it}-\min ({E}_{j,t})}{\max ({E}_{j,t})-\min ({E}_{j,t})}\times 100,i=1,\cdots \,,N;j=1,2,3,4$$
(1)
$$ED{I}_{it}=\frac{1}{4}\mathop{\sum }\limits_{j=1}^{4}E{I}_{j,it}$$
(2)

Factors affecting energy poverty

Climate policy has become an important factor related to energy poverty in recent years, attracting more attention. For example, Belaïd (2022) suggests that poorly designed climate policy can exacerbate energy poverty by raising energy prices. Xing et al. (2024) present a stock-flow consistent model examining the economics impacts of varies climate policies. Through simulation, climate policies can have significant economic consequences, for instance, improving general social welfare. Climate policies may also lead to higher risks in financial sector, which requires policymakers to pay further attention. Overall, properly designed climate policies, in their model, can generate positive economic impacts and welfare improvements. Tracing back to the economic literature on climate policy, Jakob et al. (2020) discuss its relationship with economic growth and human well-beings. Their key point is to align environmental stainability with social welfare. Based on these theoretical discussions, climate policies can be designed to resolve energy poverty issues.

Empirically, Churchill et al. (2022) explore the impacts of climate change on household energy poverty in Australia. Climate risks are found to affect energy poverty in China through technological progress in the renewable energy sector (Lee et al., 2022). The development of this sector can have high exposure to climate policy uncertainties (Ma et al., 2023). Vandyck et al. (2023) argue that climate policies such as carbon pricing can exacerbate energy poverty by raising fuel prices, using the EU climate policies to explore the potential impacts on energy poverty.

To measure climate policy, we follow Eskander and Fankhauser (2020) in using the stock of climate change policies in each country. Two sub-categorical variables are derived according to the issuing authorities: the stock of climate change policies passed by legislative bodies (CP_legi) and those passed at executive levels (CP_exec). Climate change policies can also be divided into categories based on short- or long-term nature of the policies, namely, the stock of climate change policies passed within the previous 3 years (CP_short), and those implemented more than 3 years ago (CP_long), respectively. Additionally, Chen et al. (2022) suggest dividing climate change policies into laws, regulations, and strategic policies according to their nature. Laws are generally similar to legislative-level policies, so we treat them in the same way, while policies passed at executive levels can be divided into regulations (Regulation) and strategic policies (Strategy). These different types of policies are also studied in this paper to understand which forms can benefit energy poverty.

Besides climate policies, a wide range of other factors is considered in the existing literature as affecting energy poverty, from economic variables to development issues. These variables will be used as control variables in our empirical model. The choice of these control variables is based on existing studies, such as Chaudhry and Shafiullah (2021); Chen et al. (2022); Eskander and Fankhauser (2020). We limit the choice to (1) the economic development level, or log of per capita GDP (Lgdp), expected to reduce energy poverty; (2) the level of openness, measured by the share of import and export of goods and services to GDP (Open), where a country with strong import and export is expected to have lower energy poverty; (3) the industrial structure (Service), the proportion of the tertiary industry output value, expected to be positively related to EDI; (4) the urbanization level (Urban), the percentage of the urban population, another positive factor to energy development; (5) the labor force participation rate (Labor), or the percentage of labor force participation rate for age 15–64; (6) the age dependency ratio (Dependency), or the percentage of people younger than 15 or older than 64. The impacts of labor force participation and age dependency ratio are not entirely clear and will be discussed with respect to the empirical results. Finally, energy development or poverty is related to climate change. Thus, the last control variable is climate-relevant and measured by abnormal temperature (Temperature), or the difference between annual average temperatures and the long-term (1980–2015) average value.Footnote 3.

Model and data

Model

To investigate the aforementioned problems, panel fixed-effect models are utilized to analyze the empirical dataFootnote 4The baseline model is formulated as follows:

$$ED{I}_{it}=\alpha +{\beta }_{0}C{P}_{i,t-1}+\gamma {X}_{i,t-1}+{\theta }_{i}+{\eta }_{t}+{\varepsilon }_{it}$$
(3)

Whereas EDIit refers to the EDI for country i at year t. CPi,t−1 is the measure of climate change policies for country i at year t − 1. Xi,t−1 is the vector of control variables, as explained in section 2. These explanatory variables are lagged for one-period in all models similar to (Chen et al., 2022). The model controls for country-specific and year-specific fixed effects via θi and ηt, and εit is the error term. In addition to the model above, we also consider various types of climate policies as discussed above:

$$ED{I}_{it}=\alpha +{\beta }_{1}CP\,{\_}leg{i}_{i,t-1}+{\beta }_{2}CP{\_}\,exe{c}_{i,t-1}+\gamma {X}_{i,t-1}+{\theta }_{i}+{\eta }_{t}+{\varepsilon }_{it}$$
(4)
$$ED{I}_{it}=\alpha +{\beta }_{1}CP\,{\_}shor{t}_{i,t-1}+{\beta }_{2}CP{\_}\,lon{g}_{i,t-1}+\gamma {X}_{i,t-1}+{\theta }_{i}+{\eta }_{t}+{\varepsilon }_{it}$$
(5)

Moreover, following Chen et al. (2022), the policies passed at the executive level will be further divided into regulations (Regulation) and strategic policies (Strategy). These are also examined in the baseline models.

Data

The empirical study utilizes annual data from 2000 to 2020 to analyze relevant issues. The primary data source is the World Development Indicators from the World Bank, which provides rich information about national-level socio-economic status. We first calculate the EDI following the methodology outlined by Banerjee et al. (2021). The cross-country EDIs are depicted in Fig. 1, illustrating the distribution of energy development across all sample countries during the period. From this, we observe that the level of EDI has generally been increasing over time, indicating overall improvements in energy poverty at the international level.

Fig. 1
figure 1

Energy development index.

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Among all countries in the sample, Iceland has the best level of energy development, or the lowest level of energy poverty, while Norway and Canada took the second and the third places, respectively. However, it is worth noting that many developing countries still experience serious energy poverty in 2020, particularly in regions such as Africa, Southeast and South Asia, Central and South America. As shown in Fig. 2, developed countries are generally in a better position relative to developing countries.

Fig. 2
figure 2

EDI for developed and developing countries.

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Regarding climate change policies, data is collected from the “Climate Change Laws of the World” dataset, which provides information on the national-level climate change laws and policies. The dataset includes detailed information about the titles, contents, release time, and types of policies. It has been used in recent literature and has proven to be a useful collection of climate policies worldwide (e.g., Eskander and Fankhauser, 2020). The original data covers 133 countries, but only 75 of them are used in our analysis due to the availability of information on other variables. Note that the dataset includes both mitigation and adaption policies; of the 2670 items of policies, 1994 are mitigation policies. Of these mitigation policies, 37% of them are legislative type, or passed by parliaments, while the remainder are in the form of executive orders.

Figure 3 plots the time trend of the implementation of climate change policies over the sample period, by policy types (panel A) and by the development status of countries (panel B). It shows that climate change policies have increased rapidly during this period, especially after the 2008 global financial crisis, when the world became more focused on sustainable development and climate change. Among all these policies, legislative policies are relatively stable over the entire sample period, whereas executive policies are more common than in the later stage. Developing countries started at a lower level compared to developed countries, but they have started to catch up and become more active in the recent decade.

Fig. 3: Number of climate policies passed per year.
figure 3

A By policy types and B by development status.

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For the information of other control variables and their descriptive statistics, please refer to Table 1 for more information. The range of EDI values is from 0 to 83.401, indicating a very large gap between countries in terms of energy poverty. The lowest value is recorded in Bangladesh, with all four dimensions of the EDI construction being the lowest among all countries. Iceland leads all countries with the highest EDI of 83.401. In terms of the number of climate policies, the average number of new policies issued every year is 0.647, and the average stock of climate policies is 5.993.

Table 1 Descriptive statistics.
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The correlation matrix of the key variables is presented in Fig. 4, where all the diagonal elements (self-correlation) have been removed. The matrix reveals generally strong correlations among different types of policies. GDP, Urbanization and Labor participation are the top three variables correlated with EDI. For the main climate policy measure (CP), the correlation with EDI is 0.16.

Fig. 4
figure 4

Correlation matrix in heatmap.

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Empirical results

Baseline regressions

In this section, we examine the baseline regressions outlined in Equations (3–5) using the sample data. The primary focus of these models is to test whether climate policies can affect energy poverty (EDI) and to determine which types of policies are more effective. Our goal is to identify the overall impacts, as well as the impacts from sub-categories. The results are reported in Table 2. All models presented here include both country-fixed effects and year-fixed effects.

Table 2 Baseline regressions.
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In all models, climate policies are positively associated with EDI, indicating clear evidence of policy spillovers from climate actions to energy poverty. In other words, the adoption of climate policies can benefit a country’s energy development. These results are consistent across all sub-categories, though the coefficients show that policies legislated and long-term policy counts have relatively stronger effects compared to those in executive or short-term counts. This difference can be interpreted as an indicator of effectiveness in policy spillovers. Climate policies passed by legislations tend to be enforced more effectively and thus generate a stronger effect on energy development. These climate policies tend to take a longer time to bring extra benefits to EDI.

An interesting finding (Model 5–6) emerges when dividing executive policies into regulations and strategies. The results indicate that regulations tend to reduce EDI scores, whereas strategies increase them. The rationale behind this is straightforward: regulatory climate policies often suppress the use of fossil fuel energy, leading to lower EDI scores. This is consistent with findings such as those in Belaïd (2022); Vandyck et al. (2023), which suggest that regulations against climate change can induce energy poverty by raising energy prices and other costs. Conversely, strategic climate policies usually emphasize the development of renewable energy or infrastructure, which benefits the general EDI performance of a country. A recent work by Xing et al. (2024) shows that policy mix matters in terms of effectiveness. A poorly designed policy may generate unwanted effects, weakening or neutralizing the initial goals (Chen et al., 2024). Results here also indicate that policymakers need to take a more general view and carefully choose their policy mix.

To illustrate the effect of climate policies on EDI, we hypothetically assume no climate policy and calculate the counterfactual results based on the above regressions. The average differences across countries over time can be seen in Fig. 5. The gap between the scenarios ‘without climate policy’ and ‘with climate policy’ widens over time, with the differences being quite significant, around 5% of the average EDI in the sample.

Fig. 5
figure 5

Counterfactual evaluation of the climate policy impacts on EDI.

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Regarding the effects of other control variables, their impacts are intuitively consistent with expectations. Better economic development, as measured by per capita GDP, is positively and significantly associated with energy development. In other words, rich (developed) countries have better energy development (Simionescu et al., 2023); they tend to consume more energy resources, have better renewable energy development, and have broader access to modern energy forms. Countries with stronger imports and exports are also likely to have stronger energy development. Urbanization, often positively related to economic development and energy infrastructure, is expected to be linked to energy development (Zhao et al., 2022). The dependency ratio, which is often higher for developed nations (Muszyńska and Rau, 2012), becomes a critical issue for these countries. Although this implies a higher dependency ratio is often related to development and thus positively associated with EDI, it is somewhat surprising to see the negative relationship between labor force participation and energy development. Typically, the literature suggests that energy poverty negatively impacts labor force participation (Bakehe, 2022), as poorer access to energy can affect health status and lead to work absences. However, our results show the opposite: a higher labor participation rate is associated with lower EDI performance. This finding is consistent with Chaudhry and Shafiullah (2021), who also identified similar results. Climate itself, as measured by extreme temperatures, has no significant impact on energy development.

Further investigations

Building on the baseline regression results, we further explore which types of policies may be more effective in the positive spillovers identified earlier. First, we investigate the short-term and long-term effects. Specifically, we differentiate each policy type by the number of short-term policies (policies introduced within 3 years) and long-term policies (policies introduced more than 3 years ago).

Generally, we expect climate policies to take longer to be effective, as indicated in the baseline regressions. The results, reported in Table 3, align with expectations. For legislative policies, it takes longer to see effectiveness, hence the coefficients for long-term legislative policies are significantly larger than those for short-term ones, where some coefficients of the short-term legislative policies are not significant. A similar pattern is observed for strategic policies, with the coefficients for Strategies_long being almost double those for Strategies_short.

Table 3 Further analysis: terms of policies.
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Second, countries have different levels of development and different types of energy poverty. Developing nations, for example, countries in Africa, have much more complicated energy poverty status than wealthy nations. There, even the accessibility to modern energy cannot be easily resolved, and they also have trouble accessing financial resources. On the contrary, developed nations have other major concerns, such as the cost of energy use. In addition, considering that developed countries are the main players in global actions against climate change and tend to have stronger EDI compared to developing nations, it is also interesting to investigate the differences in policy spillovers between developed and developing countries.

The results dealing with sub-samples according to development status are reported in Table 4. While the results are generally consistent with the baseline regressions in showing that climate policies can generate spillover effects on energy poverty, the implications for developed and developing nations appear to be markedly different. The spillovers of climate policy to energy poverty are significantly stronger for developing countries, which is not entirely surprising given that these countries generally have a smaller number of policies. The marginal effects of introducing more climate policies should, therefore, be larger. This finding provides a stronger incentive for developing countries to actively engage in climate change actions. However, regulations tend to have a more negative impact on the EDI for developed countries, whereas their impact on developing countries is insignificant. To further validate these findings, we also employed an alternative division approach by comparing industrialized countries, specifically the EU/OECD, with other countries. The results, which are similar, are provided in Appendix Table (C5).

Table 4 Further analysis: developed vs. developing nations.
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Possible mechanisms

Upon confirming the existence of policy spillovers from climate policies to the alleviation of energy poverty, there is also great interest in understanding how the system operates. To uncover the underlying mechanisms, this section considers three potential factors and performs relevant empirical tests. These variables are the natural resource rents (NRR) as a percentage of GDP, the share of electricity from renewable energy (Renewable), and a measure of innovation in terms of the number of researchers per million of the population (Innovation).

The rationale behind the selection of these variables is as follows: first, climate policies can improve market efficiency and the productivity of the energy sector (Shen et al., 2021), thereby facilitating energy transition and enhancing energy development (Huang, 2022). Second, climate policies can regulate the use of fossil fuel energy while simultaneously supporting the development of the renewable energy sector, which benefits overall energy development. Third, it is widely accepted that climate policies can promote benefits in the energy sector by supporting innovation (Yu et al., 2021). A few cases can be found in some recent studies to support the arguments above, for instance, Mukhtar et al. (2023) use Sub-Sahara Africa as an example to discuss renewable energy as a possible solution to energy poverty, they suggest that renewable energy development is crucial to achieve the net-zero climate goals, whereas it is a feasible solution to energy poverty. The key problem, though, is the significant cost of investment. Streimikiene et al. (2020) showcase that the EU climate policies can help energy-poor households by prompting energy efficiency technology and renewable energy use. In China, the 2017 green finance pilot policy is found to alleviate energy poverty, by enhancing efficiency and switching to cleaner energy sources (Xue et al., 2023).

Using data from the World Bank’s World Development Indicators, we empirically test these channels and report the results in Table 5. A two-step approach is used. Generally, the results align with our expectations and those documented in the literature. As discussed earlier, climate policies can increase NRR, the share of renewable energy, and Innovation, all of which then significantly increase EDI in all specifications. However, it is important to acknowledge that the role of climate policy can be multi-dimensional. In other words, other possible channels exist, especially when investigating different types of policies. For instance, we would expect regulations to suppress the use of fossil fuel energy, leading to negative impacts on EDI, as demonstrated in the earlier analysis.

Table 5 Testing for possible channels.
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Robustness check

Although the results found in this paper are significant and consistent across different specifications, it is necessary to check for robustness, particularly considering endogeneity. To address this, we perform a simple exercise using an instrumental variable and the 2-stage least square (2SLS) approach. The instrument used here is the Red List Index, which shows trends in overall extinction risk for species. It ranges from 0 to 1, with 0 representing all species having goen extinct and 1 representing no risk of extinction in the near future. The index, measuring the risks of biodiversity loss, is not directly related to energy poverty but is relevant to climate policies due to the urgent need to integrate these issues into policy designs (Turney et al., 2020). Following this logic, a country facing a higher exposure to biodiversity risk is more likely to introduce climate-related policies, thus making the index a valid instrument. We use one minus the index as the actual instrument (Redlist), which should be positively related to climate policies. The data is collected from the UN SDG website. Results are reported in Table 6.

Table 6 2SLS regression results and additional checks.
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Note that in Table 6, we also replaced the main dependent variable, EDI, with two single-dimensional energy poverty measures, namely access to clean cooking and access to electricity. Models (1–2) confirm the baseline regression results. Turning to the IV regression results, the 2SLS estimation strongly supports the baseline regressions. The underidentification test, Kleibergen-Paap LM-statistic, is statistically significant, and the Cragg-Donald Wald statistic exceeds the 15% Stock-Yogo critical value, indicating no weak identification problem. The Kleibergen-Paap F-statistic also validates this finding. In other words, the choice of IV is appropriate.

Conclusions and implications

Addressing climate change is presently the most important issue for the international community and will continue to be in the future. Taking action is costly and requires substantial resources, heavily reliant on government support. Consequently, climate policies are essential for making progress. After decades of debate and discussion, the world has gradually reached a consensus, and an increasing number of policies have been implemented. While these policies are positive, their effects need to be evaluated. In addition to direct impacts on climate, these policies can also affect other aspects of society through the complex economic system, leading to important questions about the existence of policy spillovers.

This paper aims to explore this line of research, with a special interest in understanding how climate policy may affect energy poverty. Alleviating energy poverty is a crucial target for many countries and is one of the most important parts of the UN SDGs. Intrinsically, climate change and energy use are closely connected, making policy spillovers more likely to exist. Unfortunately, the impacts are not entirely clear, as climate policies can bring both benefits and difficulties. In this paper, we use cross-country panel data to empirically test the possible impacts of climate policies on energy development and explore the detailed mechanisms behind them.

The results can be summarized as follows: First, there is clear evidence that more climate policies are associated with better energy development (or lower energy poverty). Second, the impacts of climate policies are sensitive to the nature of these climate policies. Policies passed by legislative bodies have stronger positive impacts compared to those passed by executive orders. Additionally, we find that climate regulations tend to reduce EDI, whereas strategic policies work in favor of EDI. Third, policy effects take longer to manifest, as demonstrated by stronger effects for longer-term climate policies. The impacts of climate policy are stronger for developing nations relative to developed countries. Fourth, the policy spillovers are shown to be effective through improving efficiency, increasing renewable energy shares, and supporting innovation. Our results are robust to a series of checks.

Several important policy implications can be drawn from this study. The first and most crucial is that recognizing the positive spillovers provides policymakers with an incentive to be more proactive in addressing climate change. Such policy actions can have positive externalities beneficial to other sustainable development goals (SDGs), such as alleviating energy poverty. Second, not all policies against climate change have the same effect. It takes longer for climate policies to generate larger benefits for energy development. Legislative or more formal policies work more effectively towards improvements in energy development. Short-term regulations tend to temporarily deteriorate energy poverty, as these policies often suppress the use of fossil fuel energy. When alternative energy is not fully ready to replace the gap, it can generate negative impacts on energy poverty. Third, given that policy spillovers work more effectively for developing nations, which are more affected by energy poverty, policymakers in these states should be more active. This also provides an important incentive for the international community to further support climate actions in developing nations. Energy market reforms, energy transition, and stronger support for green innovation should be considered by policymakers to further reinforce the benefits of these policies.

While the current study is valuable in revealing the impacts of climate action on energy poverty, a few limitations exist and deserve future investigation. First, the current empirical framework is based on national-level evidence, thus unable to show the very detailed responses at the micro-level, for example, households. This can, of course, be resolved by using survey data, which has been extensively utilized in single-country studies. Lacking a comprehensive and globally representative micro-level survey, however, makes this almost impossible in the current setup. A possible solution is to select representative countries and perform cross-country comparisons, which allow the topic to be extended further. Second, it is also worth noting that the effectiveness of different policy approaches may be affected by the country-specific status. Clearly, the EU and China have no major concerns about energy access, but there is still a significant share of the population in Africa who remains unable to access modern energy. Therefore, the major issue for Africa is how to finance the significant cost of investment, whereas the EU and China need to provide stronger policy support for technological innovation. Third, panel time series models might be useful to explore dynamic patterns, but the time dimension of our sample is rather small, resulting relatively inaccurate estimations. Anyhow, some additional analyses have been added in the appendix. Other possible extensions to the current framework are to include other SDGs and expand the scope of this research, for instance, Li et al. (2024).