Policy spillovers from climate actions to energy poverty: international evidence

The study investigates whether climate actions produce positive spillovers on energy poverty and which types of climate policies are most effective. Against a backdrop of accelerating global climate policy adoption (e.g., Paris Agreement, carbon pricing, carbon neutrality pledges), the paper posits that policies intended to curb GHG emissions—largely from fossil fuels—may influence energy affordability and access. Prior discussions highlight both potential benefits (renewables expansion, efficiency gains, innovation, finance) and risks (price-induced affordability issues). The authors conduct a cross-country empirical analysis for 75 nations over 2000–2020 to test for spillovers from climate policy to energy poverty and to compare effects across policy types and country development status. The purpose is to inform policy design to achieve climate goals while advancing SDG7 on affordable and clean energy.

The paper reviews theories and evidence on how climate policies interact with energy poverty. Positive pathways include support for renewable energy deployment, efficiency improvements, innovation, and climate finance that can alleviate energy poverty (e.g., Azad and Chakraborty, 2020; Zhao et al., 2022; Yu et al., 2021; Long et al., 2022). Conversely, poorly designed policies (e.g., carbon taxes or regulations) may increase energy prices and exacerbate energy poverty (Belaïd, 2022; Berry, 2019; Henry et al., 2021; Vandyck et al., 2023). Empirical findings are mixed: some studies find that green fiscal policies reduce energy poverty via efficiency (Chien et al., 2022), while others suggest ambitious mitigation alone does not ensure improved energy access (Poblete-Cazenave et al., 2021). Literature also stresses policy mix and targeted design for synergies among SDGs (Streimikiene et al., 2020; Economidou et al., 2022; Lusseau and Mancini, 2019; Wu et al., 2022; Cao et al., 2023).

Design: Panel data analysis for 75 countries from 2000 to 2020 using fixed-effects models with country and year effects. Explanatory variables are lagged one period. Driscoll–Kraay standard errors are used. The baseline relates a country’s Energy Development Index (EDI) to the lagged stock of climate policies (CP) and controls. Extended models decompose CP by issuing authority (legislative vs. executive), by policy nature (executive regulations vs. executive strategies), and by duration (short-term: introduced within 3 years; long-term: more than 3 years). Subsample analyses compare developed versus developing countries. Mechanism tests and instrumental variable (IV) robustness checks are conducted. Outcome variable: Energy poverty is measured inversely via the Energy Development Index (EDI; Banerjee et al., 2021), a national-level composite of four normalized dimensions: (1) total primary energy use per capita, (2) renewable energy consumption share, (3) electric power consumption per capita, and (4) access to electricity. Higher EDI indicates lower energy poverty. Climate policy variables: Total stock of climate policies (CP) from the Climate Change Laws of the World database; subcategories include legislative (CP_legi) versus executive (CP_exec), short-term (CP_short) versus long-term (CP_long), and, following Chen et al. (2022), executive policies split into regulations (Regulations) and strategies (Strategies). Policy stock counts accumulate enacted items over time. Controls: Log GDP per capita (Lgdp), trade openness (Open, exports+imports as % of GDP), services share of GDP (Service), urbanization rate (Urban), labor force participation rate (Labor, ages 15–64), age dependency ratio (Dependency), and abnormal temperature (Temperature, deviation from 1980–2015 mean). Data are primarily from World Development Indicators. Empirical models: Fixed-effects regressions of EDI on lagged policy stock(s) and controls, including variants separating policy types and terms. Additional analyses include: (i) short vs. long-term effects for each policy type; (ii) developed vs. developing country subsamples; (iii) mechanism tests via a two-step approach where CP predicts channels—natural resource rents (NRR % of GDP), renewable electricity share (Renewable), and innovation (researchers per million)—which in turn predict EDI; and (iv) IV-2SLS using the Red List Index of species extinction risk (1−index, labeled Redlist) as an instrument for CP to address endogeneity. Weak identification diagnostics (Kleibergen–Paap LM/F, Cragg–Donald) are reported. Data summary: The original policy dataset covers 133 countries; 75 are retained given data availability for other variables, yielding 1,505 observations (or 1,428 in models with lags). Descriptive statistics show mean EDI 32.324 (0–83.401 range), mean CP stock 5.993, and average new policies per year 0.647. Legislative policies constitute ~37% of mitigation policies; executive policies surge after 2008.

- Climate policy spillovers: Across baseline models with country and year fixed effects, more climate policies are associated with higher EDI (lower energy poverty). The CP coefficient ranges from about 0.139 to 0.168 and is significant at 1%. - Policy type heterogeneity: Legislative policies exhibit stronger positive associations with EDI than executive policies (CP_legi ~0.191***; CP_exec ~0.138***). Within executive policies, regulations are negatively associated with EDI (≈−0.095***), while strategies are strongly positive (≈0.537–0.565***). - Policy duration: Both short-term and long-term policy stocks are positive (CP_short ≈0.103**, CP_long ≈0.104**), but further decomposition shows long-term effects are larger and more consistently significant—especially for legislative and strategy policies. For example, CP_legi_long ≈0.218–0.247***, Strategies_long ≈0.702–0.764***, whereas some short-term legislative effects are insignificant and Regulations_short can be negative. - Developed vs. developing: Spillovers are substantially stronger in developing countries. For CP, coefficients are ~0.202–0.370*** in developing vs. ~0.036–0.053 (often smaller/insignificant) in developed. Regulations have a more negative impact in developed countries (≈−0.293***), but are insignificant in developing countries; strategies are positive in both. - Mechanisms: CP increases (i) natural resource rents (NRR) and NRR positively relates to EDI (NRR→EDI ≈0.062***), (ii) renewable electricity share (Renewable→EDI ≈0.241***), and (iii) innovation (researchers per million; Innovation→EDI ≈0.001**). Direct CP effects on EDI remain positive in these specifications (e.g., ≈0.113–0.190***), supporting efficiency, renewable deployment, and innovation channels. - Robustness (IV-2SLS): Using Redlist as an instrument for CP, first-stage relevance is strong (Kleibergen–Paap LM significant; Cragg–Donald above Stock–Yogo 15% threshold; Kleibergen–Paap F ≈11.25). Second-stage CP→EDI remains positive (≈0.414*). Replacing EDI with single-dimension outcomes shows CP improves access to clean cooking (≈0.329***) and access to electricity (≈0.136***). - Controls: Higher GDP per capita, openness, and urbanization are positively associated with EDI; labor force participation relates negatively; dependency ratio is generally positive; abnormal temperature is not significant. - Counterfactual: A scenario without climate policies shows a widening EDI gap over time compared to observed with-policy levels, reaching around 5% of the sample’s average EDI by later years.

The findings directly address the core research question by demonstrating that climate policies yield positive spillovers on energy development, thereby alleviating energy poverty. The magnitude and direction depend on policy design and timing: legislative and strategic policies deliver stronger, sustained benefits, while regulatory measures can temporarily depress energy development, likely via price and cost channels that constrain affordability when substitutes are not fully available. The stronger spillovers in developing countries suggest high marginal returns to climate policy in contexts with lower initial policy stocks and greater energy access deficits. Mechanism tests support that these spillovers are mediated through enhanced efficiency/productivity (proxied by NRR improvements), increased renewable energy shares, and stimulated innovation. Together, the results underscore that policy mix and horizon matter for aligning climate action with SDG7, and that carefully crafted strategies can create synergies rather than trade-offs.

This paper provides international evidence that climate actions reduce energy poverty: a larger stock of climate policies is associated with higher EDI. Legislative and strategic policies are more effective than executive regulations, and long-term policies have stronger impacts than short-term ones. Spillovers are larger in developing countries. Mechanisms include improved efficiency, greater renewable penetration, and enhanced innovation. Robustness checks, including IV estimations and alternative energy access outcomes, support the conclusions. Policy implications are: (1) recognizing positive spillovers strengthens the case for proactive climate policy; (2) policy mix and time horizon are crucial—legislative and strategic approaches and sustained efforts are more beneficial; (3) regulations should be accompanied by measures that mitigate short-run affordability impacts; (4) international support can amplify benefits in developing nations; (5) complementary reforms and innovation support can reinforce outcomes. Future research should exploit micro-level data to capture household impacts, account for country-specific contexts when tailoring policy portfolios, and employ longer panels or dynamic models to better characterize temporal effects and feedbacks.

- Data granularity: National-level EDI and policy stocks cannot capture micro-level (household) heterogeneity; lack of globally comparable surveys limits microanalysis. - Context dependence: Policy effectiveness varies by country circumstances (e.g., access deficits and financing challenges in parts of Africa versus cost and innovation priorities in the EU/China), potentially affecting generalizability of specific policy-type effects. - Time dimension: The 2000–2020 panel length constrains dynamic panel approaches, possibly yielding imprecise estimates of long-run dynamics. While robustness checks are provided, additional SDG linkages and extended time coverage would further validate results.