The Impact of Climate Risk on Urban Population: Evidence from Sub-Saharan Africa

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VICTOR KAMUYEAH JR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9695684/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the impact of climate risk on urban population share across 29 Sub-Saharan African (SSA) countries over the period 2003–2023, using an unbalanced panel dataset of 609 country-year observations. Employing both random effects (RE) and pooled ordinary least squares (OLS) estimators, and confirming model selection via the Hausman (1978) specification test, we find that climate risk exerts a statistically significant and positive effect on urban population share. A one-unit increase in the climate risk index raises urban population share by approximately 0.15 percentage points, controlling for GDP per capita and energy intensity. Results are robust across multiple model specifications, including standard error correction for heteroscedasticity and autocorrelation. The findings suggest that climate-induced rural distress is a significant driver of urbanization in SSA, with important implications for urban planning, climate adaptation policy, and sustainable development in the region. Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Earth and environmental sciences/Environmental sciences/Environmental impact Climate risk urbanization Sub-Saharan Africa panel data random effects Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Sub-Saharan Africa (SSA) is simultaneously one of the world's most rapidly urbanizing regions and one of the most climate-vulnerable. According to the World Bank and IPCC projections, the region is transitioning toward a majority urban population by the early 2030s, with urban dwellers expected to constitute over 60 percent of the population by 2050. Yet this transformation is occurring in an environment of escalating climate hazards rising temperatures, intensifying droughts, increased flooding frequency, and growing exposure to extreme weather events that threaten agricultural livelihoods and rural settlement viability. A substantial body of literature documents how climate change is reshaping economic geographies. Agricultural productivity losses, land degradation, and climate-driven livelihood insecurity push rural populations toward cities in search of more stable income and better services. However, the empirical relationship between a composite measure of climate risk and urban population share in SSA has received comparatively little direct econometric attention. Most existing studies focus on specific climate variables typically temperature anomalies or precipitation deficits and their effects on rural-urban migration flows or national urbanization rates, rather than examining the overall climate risk burden faced by households. This paper contributes to filling this gap by using the Germanwatch Global Climate Risk Index a widely used composite indicator capturing losses from extreme weather events as our primary climate risk measure, and examining its effects on urban population share across a panel of 29 SSA countries over 2003–2023. Our empirical framework employs random effects generalized least squares (GLS) and pooled OLS estimators, with the Hausman ( 1978 ) test used to formally guide model selection. We control for GDP per capita and energy intensity, which capture development-driven and structural determinants of urbanization. The remainder of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 describes the data and empirical methodology. Section 4 presents and discusses the empirical results. Section 5 concludes with policy implications. 2. Literature Review 2.1 Climate Change and Urbanization The relationship between climate change and urbanization has garnered growing scholarly attention over the past two decades. Seminal work by Barrios, Bertinelli, and Strobl ( 2006 ) demonstrated that rainfall deficits in SSA historically contributed to rural depopulation and increased urbanization rates, estimating that reduced rainfall accounts for a significant share of Africa's above-average urbanization relative to other developing regions. This work established the conceptual foundation: deteriorating rural climate conditions push households toward urban centers, even when urban areas themselves face considerable environmental pressures. Henderson, Squires, Storeygard, and Weil ( 2017 ) extended this analysis using high-resolution panel data on African cities, finding strong but differentiated links between moisture availability and local urbanization. Their work showed that drying increases urbanization in cities with industrial capacity those that can absorb agricultural migrants productively while having little effect in small market towns primarily serving agricultural hinterlands. This heterogeneity underscores the complex mediating role of urban economic structure in climate-driven migration dynamics. More recently, the IPCC Sixth Assessment Report (AR6, 2022) synthesized evidence showing that urban areas in SSA face a double burden: they are both destinations for climate-displaced populations and are themselves highly exposed to climate hazards. Growing informal settlements without basic services increase the vulnerability of large populations to climate extremes, particularly the elderly, women, and children. Climate Analytics (2023) further documented that as agricultural livelihoods in SSA become more precarious under warming scenarios, rural-urban migration rates are expected to accelerate, adding to already significant urbanization pressures. 2.2 Determinants of Urbanization in Sub-Saharan Africa The drivers of urbanization in SSA are multifaceted. Economic development theory, building on the Lewis ( 1954 ) dual-economy model, posits that structural transformation the shift of labor from low-productivity agriculture to higher-productivity manufacturing and services drives rural-urban migration. GDP per capita growth creates urban employment opportunities that attract rural migrants. Empirically, Jedwab and Vollrath ( 2019 ) documented that SSA's urbanization pattern is unusual: it has largely occurred without commensurate industrialization, creating what they call "urbanization without growth" in some contexts, though others show the more canonical pattern. Energy intensity, as a proxy for the energy efficiency of the economy and urbanization process, also matters for the sustainability and form of urban development. Countries with lower energy intensity tend to have more service-based economies, while high energy intensity often reflects resource-dependent or heavy manufacturing economies. The spatial distribution of energy infrastructure shapes where urban agglomerations emerge and grow. 2.3 Research Gap and Contribution Despite this rich literature, a specific gap remains: no published study has employed a composite climate risk index as opposed to individual climate variables to examine its direct effect on aggregate urban population share in a contemporary multi-country SSA panel. Composite climate risk indices capture a broader range of climate hazards, including both slow-onset and sudden-onset events, providing a more comprehensive measure of the climate burden faced by rural and urban households. Furthermore, studies using more recent data (post-2015) are sparse, limiting understanding of whether the Paris Agreement era has seen any change in the climate-urbanization nexus. This study directly addresses these gaps by using the Germanwatch Climate Risk Index across 29 SSA countries, 2003–2023, and applying appropriate panel econometric methods to identify the causal relationship between climate risk and urban population dynamics. 3. Data and Methodology 3.1 Data Sources and Variables Our dataset is an unbalanced panel comprising 29 SSA countries observed over 2003–2023, yielding 609 country-year observations for the primary regression sample and 564 observations in specifications including energy intensity (due to data availability). The countries represent a diverse cross-section of SSA sub-regions, income levels, and climate profiles. Dependent Variable. Urban population share ( urban_pop ) is the percentage of a country's total population residing in urban areas, sourced from the World Bank World Development Indicators. The sample mean is 42.3 percent with a standard deviation of 16.5, ranging from 12.6 percent (low-urbanization agrarian economies) to 91.6 percent (highly urbanized small states). Key Independent Variable. Climate risk ( climate_risk ) is derived from the Germanwatch Global Climate Risk Index, which quantifies the degree to which countries have been affected by extreme weather events such as storms, floods, and heat waves in a given year. Higher values indicate greater climate losses per unit of GDP and greater fatalities per 100,000 inhabitants, thereby reflecting a heavier climate burden. The sample mean is 11.1, with substantial variation across countries and years (standard deviation: 4.9). Control Variables. GDP per capita ( gdp_pc ) in constant 2015 USD, sourced from the World Bank, proxies for the level of economic development and structural transformation. Energy intensity ( energy_intensity ), measured as megajoules of energy consumption per dollar of GDP, captures the energy efficiency of the economy and the nature of its production structure. A year trend variable is included to capture common time effects, including global technological and institutional trends. 3.2 Descriptive Statistics Table 1 presents descriptive statistics for all variables. The distribution of urban population share is slightly right-skewed (skewness: 0.574), consistent with the presence of some highly urbanized outliers such as Gabon (mean: 87%) and South Africa. Climate risk shows greater right-skew (0.443), indicating that most country-years face moderate climate risk with a minority experiencing extreme events. GDP per capita exhibits pronounced right-skew (2.303) and kurtosis (8.71), reflecting the highly unequal income distribution across SSA. Table 1 Descriptive Statistics Variables Obs Mean Std. Dev. Min Max p1 p99 Skew. Kurt. urban pop 609 42.289 16.524 12.59 91.638 16.216 89.001 .574 3.157 climate risk 609 11.053 4.863 .131 32.508 1.575 25.128 .443 3.893 gdp pc 609 2585.717 3252.982 249.012 19141.512 289.088 15333.105 2.303 8.71 energy intensity 564 5.763 3.074 2.06 23.38 2.19 14.82 1.658 7.595 3.3 Correlation Structure Table 2 presents pairwise correlation coefficients. Climate risk is positively correlated with urban population (r = 0.160, p < 0.01), supporting the hypothesized positive relationship. GDP per capita is more strongly correlated with urbanization (r = 0.456, p < 0.01), consistent with development-driven urbanization theories. Energy intensity is negatively correlated with urban population (r = − 0.324, p < 0.01), suggesting that countries with higher energy intensity often less service-oriented economies tend to have lower urbanization rates. Critically, the correlations among explanatory variables are modest (|r| ≤ 0.318), mitigating concerns about multicollinearity, which is confirmed by the VIF analysis. Table 2 Pairwise correlations Variables (1) (2) (3) (4) (1) urban_pop 1.000 (2) climate_risk 0.160* 1.000 (3) gdp_pc 0.456* 0.256* 1.000 (4) energy_intensity -0.324* -0.191* -0.318* 1.000 *** p < 0.01, ** p < 0.05, * p < 0.1 3.4 Empirical Model Our baseline empirical model is: Urban_Pop it = α + β₁ ClimateRisk it + β₂ GDPpc it + β₃ EnergyIntensity it + β₄ Year t + µi + εit where subscripts i and t denote country and year respectively; µ i is the unobserved country-level heterogeneity; and ε it is the idiosyncratic error. The parameter of central interest is β₁, which measures the marginal effect of climate risk on urban population share, holding other factors constant. We estimate both random effects GLS (RE) and pooled OLS versions of this model. The choice between fixed effects (FE) and RE is guided by the Hausman ( 1978 ) test, which assesses whether country-level unobserved heterogeneity is correlated with the regressors. The null hypothesis of the Hausman test is that the RE estimator is consistent (i.e., country effects are uncorrelated with regressors). A Hausman test statistic of 4.865 (p-value: 0.301) fails to reject the null, supporting the use of the RE estimator as our preferred specification. To address potential heteroscedasticity and serial correlation in the error term, we additionally report clustered standard error corrections (Driscoll-Kraay type), which ensure valid inference even under arbitrary within-cluster dependence. 4. Empirical Results 4.1 Main Regression Results This section presents the main regression results across four model specifications. Table (3) report random effects GLS estimates and Table (4) Pooled OLS with standard errors; Table (5) and (6) replicate these with heteroscedasticity- and autocorrelation-robust standard errors. All specifications include a year trend to absorb common temporal shocks. Table 3 Regression results urban_pop Coef. St.Err. t-value p-value [95% Conf Interval] Sig climate_risk .156 .036 4.33 0 .085 .226 *** gdp_pc 0 0 2.74 .006 0 .001 *** energy_intensity − .912 .13 -6.99 0 -1.168 − .656 *** year .07 .025 2.79 .005 .021 .118 *** Constant -95.677 50.05 -1.91 .056 -193.774 2.42 * Mean dependent var 41.775 SD dependent var 16.396 Overall r-squared 0.195 Number of obs 564 Chi-square 121.969 Prob > chi2 0.000 R-squared within 0.181 R-squared between 0.201 *** p<.01, ** p<.05, * p<.1 Table 4 Regression results urban_pop Coef. St.Err. t-value p-value [95% Conf Interval] Sig climate_risk .153 .036 4.28 0 .083 .224 *** gdp_pc 0 0 2.32 .021 0 .001 ** energy_intensity − .898 .131 -6.84 0 -1.156 − .64 *** year .074 .025 2.98 .003 .025 .123 *** Constant -105.198 50.049 -2.10 .036 -203.517 -6.879 ** Mean dependent var 41.775 SD dependent var 16.396 R-squared 0.181 Number of obs 564 F-test 29.332 Prob > F 0.000 Akaike crit. (AIC) 2859.764 Bayesian crit. (BIC) 2881.439 *** p<.01, ** p<.05, * p<.1 Table 5 Regression results urban_pop Coef. St.Err. t-value p-value [95% Conf Interval] Sig climate_risk .156 .048 3.21 .001 .061 .251 *** gdp_pc 0 0 1.06 .291 0 .001 energy_intensity − .912 .421 -2.17 .03 -1.737 − .086 ** year .07 .026 2.70 .007 .019 .12 *** Constant -95.677 50.52 -1.89 .058 -194.695 3.341 * Mean dependent var 41.775 SD dependent var 16.396 Overall r-squared 0.195 Number of obs 564 Chi-square 35.663 Prob > chi2 0.000 R-squared within 0.181 R-squared between 0.201 *** p<.01, ** p<.05, * p<.1 Table 6 Regression results urban_pop Coef. St.Err. t-value p-value [95% Conf Interval] Sig climate_risk .153 .049 3.10 .004 .052 .254 *** gdp_pc 0 0 0.95 .352 0 .001 energy_intensity − .898 .424 -2.12 .043 -1.766 − .03 ** year .074 .026 2.87 .008 .021 .127 *** Constant -105.198 51.466 -2.04 .05 -210.621 .225 * Mean dependent var 41.775 SD dependent var 16.396 R-squared 0.181 Number of obs 564 F-test 9.088 Prob > F 0.000 Akaike crit. (AIC) 2857.764 Bayesian crit. (BIC) 2875.104 *** p<.01, ** p<.05, * p<.1 The results are strikingly consistent across all four specifications. The coefficient on climate risk is positive and highly statistically significant (p < 0.01) in all columns, with a point estimate clustering around 0.153–0.156. This implies that a one-unit increase in the climate risk index raises a country's urban population share by approximately 0.15 percentage points. Given the mean climate risk of 11.1 and the degree of variation across the sample (standard deviation of 4.9), a one standard deviation increase in climate risk is associated with an increase in urban population share of roughly 0.75 percentage points, a substantively meaningful effect within a context where annual urbanization gains in SSA average around 0.5–1.0 percentage points per year. GDP per capita carries a positive and statistically significant coefficient in Table (3) and (4), consistent with development-driven urbanization theories. However, this coefficient loses statistical significance in the robust standard error specifications (Tables 4 and 5 ), suggesting that within-country year-to-year income variation explains less of urbanization dynamics once autocorrelation in the error is accounted for. The energy intensity coefficient is negative, large in magnitude (approximately − 0.9), and significant across all specifications, indicating that countries with more energy-intensive economies typically those more dependent on subsistence agriculture and primary commodities tend to have lower urban population shares. The positive and significant year trend (coefficient ≈ 0.07) captures the broad upward trend in SSA urbanization independent of the included explanatory variables. 4.2 Hausman Test and Model Selection The Hausman ( 1978 ) specification test yields a chi-square statistic of 4.865 with a p-value of 0.301, failing to reject the null hypothesis that the random effects estimator is consistent. This result provides statistical support for the use of the RE estimator as our preferred specification, suggesting that country-level unobserved heterogeneity is not systematically correlated with the included regressors. This is a plausible finding given that the regressors climate risk (largely driven by geographic and meteorological factors), GDP per capita, and energy intensity contain substantial cross-country variation that is partially independent of country-specific fixed characteristics. Table 7 Hausman ( 1978 ) specification test Chi-square test value Coef. 4.865 P-value .301 4.3 Variance Inflation Factor Analysis To formally assess multicollinearity, we compute variance inflation factors (VIF) for all regressors. Results show: GDP per capita (VIF = 1.17), Climate Risk (VIF = 1.13), Energy Intensity (VIF = 1.13), Year Trend (VIF = 1.06), with a mean VIF of 1.12. All VIF values are well below the conventional threshold of 5 (or the stricter threshold of 2 used in some studies), confirming that multicollinearity is not a concern in our model and that coefficient estimates are reliable. Table 8 Variance inflation factor gdp pc VIF 1/VIF 1.17 .855 climate risk 1.131 .885 energy intensity 1.129 .886 year 1.057 .946 Mean VIF 1.122 . 4.4 Graphical Analysis Figures 1 through 5 provide visual representations of the key patterns in the data and the regression results. Figure 1 shows the divergent temporal trajectories of the two key variables: while urban population share exhibits a steady upward trend, the climate risk index displays greater year-to-year volatility, with notable spikes corresponding to years of severe weather events across the region. The overall upward drift in both series is consistent with a positive relationship. Figure 2 confirms a positive bivariate association between climate risk and urban population share, with the OLS trend line showing a slope of approximately 0.16 closely matching the multivariate regression estimates. Figure 3 illustrates the substantial heterogeneity across countries: smaller island states and coastal economies like Gabon tend to have both high urbanization and moderate climate risk, while Sahelian and landlocked countries show low urbanization and varied climate risk. Figure 4 presents the coefficient estimates from the preferred RE GLS specification with 95% confidence intervals, highlighting the precision of the climate risk estimate and the negative energy intensity effect. Figure 5 shows that both control variables exhibit the expected directional relationships with urbanization: positive for GDP per capita and negative for energy intensity. 5. Conclusion and Policy Implications This paper provides novel econometric evidence on the impact of climate risk on urban population dynamics in Sub-Saharan Africa. Using a panel of 29 countries over 2003–2023 and employing random effects GLS as our preferred estimator validated by the Hausman specification test we find that climate risk exerts a statistically significant and positive effect on urban population share. Our findings are robust to the use of heteroscedasticity- and autocorrelation-consistent standard errors and across multiple model specifications. The positive climate risk urbanization relationship is consistent with the climate-driven rural-push hypothesis: as climate hazards intensify and impose greater economic and livelihood losses on rural households, migration toward urban centers accelerates. This is particularly consequential in SSA, where the majority of the poor remain in rural areas heavily dependent on rain-fed agriculture, and where adaptive capacity to absorb climate shocks is limited. These findings carry important policy implications. First, they reinforce the need for integrated climate-urban planning frameworks in SSA. Cities receiving climate migrants are often themselves exposed to flooding, heat stress, and infrastructure stress, requiring proactive investment in climate-resilient urban infrastructure, housing, and services. Second, the findings underscore the importance of rural climate adaptation investments including drought-resistant agriculture, water management, and social protection that can reduce climate-driven migration pressure while improving rural welfare. Reducing the climate risk index through mitigation and adaptation thus has a dual benefit: improving rural livelihoods and moderating the pace of unplanned urbanization. Third, the negative energy intensity coefficient suggests that structural transformation toward more service-based and energy-efficient economies is associated with higher urbanization. Policies promoting economic diversification away from subsistence agriculture and energy-intensive primary production can accelerate sustainable urbanization. Fourth, the loss of significance of GDP per capita under robust standard errors suggests that short-run income fluctuations matter less for urbanization than structural and climatic factors, implying that urbanization in SSA is partly driven by necessity rather than opportunity a pattern that demands policy responses focused on urban social protection and inclusive city governance. Several limitations warrant acknowledgment. The Germanwatch Climate Risk Index primarily captures losses from sudden-onset weather events and may not fully reflect slow-onset climate stressors such as sea-level rise, gradual temperature increases, or desertification. Future research should explore the role of these slow-onset variables, potentially employing instrumental variable approaches to more cleanly identify causal effects. Additionally, country-level data mask important within-country heterogeneity across urban and rural areas; sub-national analysis as data availability improves will enrich our understanding of these dynamics. References Barrios, S., Bertinelli, L., & Strobl, E. (2006). Climatic change and rural-urban migration: The case of sub-Saharan Africa. Journal of Urban Economics , 60(3), 357–371. Germanwatch (2023). Global Climate Risk Index 2023 . Germanwatch e.V., Bonn, Germany. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica , 46(6), 1251–1271. Henderson, J. V., Squires, T., Storeygard, A., & Weil, D. (2017). The global distribution of economic activity: nature, history, and the role of trade. Quarterly Journal of Economics , 133(1), 357–406. IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report . Cambridge University Press. Jedwab, R., & Vollrath, D. (2019). The mortality transition, malthusian dynamics, and the rise of poor mega-cities. Journal of Development Economics , 141, 102364. Lewis, W. A. (1954). Economic development with unlimited supplies of labour. The Manchester School , 22(2), 139–191. World Bank (2024). World Development Indicators. World Bank Group, Washington, D.C. Available at: https://databank.worldbank.org World Meteorological Organization (WMO) (2024). State of the Climate in Africa 2023 . WMO, Geneva. Additional Declarations Yes there is potential Competing Interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Introduction","content":"\u003cp\u003eSub-Saharan Africa (SSA) is simultaneously one of the world's most rapidly urbanizing regions and one of the most climate-vulnerable. According to the World Bank and IPCC projections, the region is transitioning toward a majority urban population by the early 2030s, with urban dwellers expected to constitute over 60 percent of the population by 2050. Yet this transformation is occurring in an environment of escalating climate hazards rising temperatures, intensifying droughts, increased flooding frequency, and growing exposure to extreme weather events that threaten agricultural livelihoods and rural settlement viability.\u003c/p\u003e \u003cp\u003eA substantial body of literature documents how climate change is reshaping economic geographies. Agricultural productivity losses, land degradation, and climate-driven livelihood insecurity push rural populations toward cities in search of more stable income and better services. However, the empirical relationship between a composite measure of climate risk and urban population share in SSA has received comparatively little direct econometric attention. Most existing studies focus on specific climate variables typically temperature anomalies or precipitation deficits and their effects on rural-urban migration flows or national urbanization rates, rather than examining the overall climate risk burden faced by households.\u003c/p\u003e \u003cp\u003eThis paper contributes to filling this gap by using the Germanwatch Global Climate Risk Index a widely used composite indicator capturing losses from extreme weather events as our primary climate risk measure, and examining its effects on urban population share across a panel of 29 SSA countries over 2003\u0026ndash;2023. Our empirical framework employs random effects generalized least squares (GLS) and pooled OLS estimators, with the Hausman (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) test used to formally guide model selection. We control for GDP per capita and energy intensity, which capture development-driven and structural determinants of urbanization.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the data and empirical methodology. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents and discusses the empirical results. Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with policy implications.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Climate Change and Urbanization\u003c/h2\u003e \u003cp\u003eThe relationship between climate change and urbanization has garnered growing scholarly attention over the past two decades. Seminal work by Barrios, Bertinelli, and Strobl (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) demonstrated that rainfall deficits in SSA historically contributed to rural depopulation and increased urbanization rates, estimating that reduced rainfall accounts for a significant share of Africa's above-average urbanization relative to other developing regions. This work established the conceptual foundation: deteriorating rural climate conditions push households toward urban centers, even when urban areas themselves face considerable environmental pressures.\u003c/p\u003e \u003cp\u003eHenderson, Squires, Storeygard, and Weil (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) extended this analysis using high-resolution panel data on African cities, finding strong but differentiated links between moisture availability and local urbanization. Their work showed that drying increases urbanization in cities with industrial capacity those that can absorb agricultural migrants productively while having little effect in small market towns primarily serving agricultural hinterlands. This heterogeneity underscores the complex mediating role of urban economic structure in climate-driven migration dynamics.\u003c/p\u003e \u003cp\u003eMore recently, the IPCC Sixth Assessment Report (AR6, 2022) synthesized evidence showing that urban areas in SSA face a double burden: they are both destinations for climate-displaced populations and are themselves highly exposed to climate hazards. Growing informal settlements without basic services increase the vulnerability of large populations to climate extremes, particularly the elderly, women, and children. Climate Analytics (2023) further documented that as agricultural livelihoods in SSA become more precarious under warming scenarios, rural-urban migration rates are expected to accelerate, adding to already significant urbanization pressures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Determinants of Urbanization in Sub-Saharan Africa\u003c/h2\u003e \u003cp\u003eThe drivers of urbanization in SSA are multifaceted. Economic development theory, building on the Lewis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1954\u003c/span\u003e) dual-economy model, posits that structural transformation the shift of labor from low-productivity agriculture to higher-productivity manufacturing and services drives rural-urban migration. GDP per capita growth creates urban employment opportunities that attract rural migrants. Empirically, Jedwab and Vollrath (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) documented that SSA's urbanization pattern is unusual: it has largely occurred without commensurate industrialization, creating what they call \"urbanization without growth\" in some contexts, though others show the more canonical pattern.\u003c/p\u003e \u003cp\u003eEnergy intensity, as a proxy for the energy efficiency of the economy and urbanization process, also matters for the sustainability and form of urban development. Countries with lower energy intensity tend to have more service-based economies, while high energy intensity often reflects resource-dependent or heavy manufacturing economies. The spatial distribution of energy infrastructure shapes where urban agglomerations emerge and grow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research Gap and Contribution\u003c/h2\u003e \u003cp\u003eDespite this rich literature, a specific gap remains: no published study has employed a composite climate risk index as opposed to individual climate variables to examine its direct effect on aggregate urban population share in a contemporary multi-country SSA panel. Composite climate risk indices capture a broader range of climate hazards, including both slow-onset and sudden-onset events, providing a more comprehensive measure of the climate burden faced by rural and urban households. Furthermore, studies using more recent data (post-2015) are sparse, limiting understanding of whether the Paris Agreement era has seen any change in the climate-urbanization nexus.\u003c/p\u003e \u003cp\u003eThis study directly addresses these gaps by using the Germanwatch Climate Risk Index across 29 SSA countries, 2003\u0026ndash;2023, and applying appropriate panel econometric methods to identify the causal relationship between climate risk and urban population dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Sources and Variables\u003c/h2\u003e \u003cp\u003eOur dataset is an unbalanced panel comprising 29 SSA countries observed over 2003\u0026ndash;2023, yielding 609 country-year observations for the primary regression sample and 564 observations in specifications including energy intensity (due to data availability). The countries represent a diverse cross-section of SSA sub-regions, income levels, and climate profiles. Dependent Variable. Urban population share (\u003cem\u003eurban_pop\u003c/em\u003e) is the percentage of a country's total population residing in urban areas, sourced from the World Bank World Development Indicators. The sample mean is 42.3 percent with a standard deviation of 16.5, ranging from 12.6 percent (low-urbanization agrarian economies) to 91.6 percent (highly urbanized small states). Key Independent Variable. Climate risk (\u003cem\u003eclimate_risk\u003c/em\u003e) is derived from the Germanwatch Global Climate Risk Index, which quantifies the degree to which countries have been affected by extreme weather events such as storms, floods, and heat waves in a given year. Higher values indicate greater climate losses per unit of GDP and greater fatalities per 100,000 inhabitants, thereby reflecting a heavier climate burden. The sample mean is 11.1, with substantial variation across countries and years (standard deviation: 4.9). Control Variables. GDP per capita (\u003cem\u003egdp_pc\u003c/em\u003e) in constant 2015 USD, sourced from the World Bank, proxies for the level of economic development and structural transformation. Energy intensity (\u003cem\u003eenergy_intensity\u003c/em\u003e), measured as megajoules of energy consumption per dollar of GDP, captures the energy efficiency of the economy and the nature of its production structure. A year trend variable is included to capture common time effects, including global technological and institutional trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for all variables. The distribution of urban population share is slightly right-skewed (skewness: 0.574), consistent with the presence of some highly urbanized outliers such as Gabon (mean: 87%) and South Africa. Climate risk shows greater right-skew (0.443), indicating that most country-years face moderate climate risk with a minority experiencing extreme events. GDP per capita exhibits pronounced right-skew (2.303) and kurtosis (8.71), reflecting the highly unequal income distribution across SSA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep99\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSkew.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKurt.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eurban pop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclimate risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egdp pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2585.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3252.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e249.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19141.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e289.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15333.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenergy intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation Structure\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents pairwise correlation coefficients. Climate risk is positively correlated with urban population (r\u0026thinsp;=\u0026thinsp;0.160, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting the hypothesized positive relationship. GDP per capita is more strongly correlated with urbanization (r\u0026thinsp;=\u0026thinsp;0.456, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), consistent with development-driven urbanization theories. Energy intensity is negatively correlated with urban population (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.324, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that countries with higher energy intensity often less service-oriented economies tend to have lower urbanization rates. Critically, the correlations among explanatory variables are modest (|r| \u0026le; 0.318), mitigating concerns about multicollinearity, which is confirmed by the VIF analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) urban_pop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) climate_risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.160*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) gdp_pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.456*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.256*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) energy_intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.324*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.191*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.318*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Empirical Model\u003c/h2\u003e \u003cp\u003eOur baseline empirical model is:\u003c/p\u003e \u003cp\u003e \u003cb\u003eUrban_Pop\u003c/b\u003e \u003cb\u003eit\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β₁\u003c/b\u003e \u003cb\u003eClimateRisk\u003c/b\u003e \u003cb\u003eit\u0026thinsp;+\u0026thinsp;β₂\u003c/b\u003e \u003cb\u003eGDPpc\u003c/b\u003e \u003cb\u003eit\u0026thinsp;+\u0026thinsp;β₃\u003c/b\u003e \u003cb\u003eEnergyIntensity\u003c/b\u003e \u003cb\u003eit\u0026thinsp;+\u0026thinsp;β₄\u003c/b\u003e \u003cb\u003eYear\u003c/b\u003e \u003cb\u003et\u0026thinsp;+\u0026thinsp;\u0026micro;i\u0026thinsp;+\u0026thinsp;εit\u003c/b\u003e \u003c/p\u003e \u003cp\u003ewhere subscripts \u003cb\u003ei\u003c/b\u003e and \u003cb\u003et\u003c/b\u003e denote country and year respectively; \u0026micro;\u003cb\u003ei\u003c/b\u003e is the unobserved country-level heterogeneity; and ε\u003cb\u003eit\u003c/b\u003e is the idiosyncratic error. The parameter of central interest is β₁, which measures the marginal effect of climate risk on urban population share, holding other factors constant.\u003c/p\u003e \u003cp\u003eWe estimate both random effects GLS (RE) and pooled OLS versions of this model. The choice between fixed effects (FE) and RE is guided by the Hausman (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) test, which assesses whether country-level unobserved heterogeneity is correlated with the regressors. The null hypothesis of the Hausman test is that the RE estimator is consistent (i.e., country effects are uncorrelated with regressors). A Hausman test statistic of 4.865 (p-value: 0.301) fails to reject the null, supporting the use of the RE estimator as our preferred specification. To address potential heteroscedasticity and serial correlation in the error term, we additionally report clustered standard error corrections (Driscoll-Kraay type), which ensure valid inference even under arbitrary within-cluster dependence.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Main Regression Results\u003c/h2\u003e \u003cp\u003eThis section presents the main regression results across four model specifications. Table\u0026nbsp;(3) report random effects GLS estimates and Table\u0026nbsp;(4) Pooled OLS with standard errors; Table\u0026nbsp;(5) and (6) replicate these with heteroscedasticity- and autocorrelation-robust standard errors. All specifications include a year trend to absorb common temporal shocks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eurban_pop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSt.Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e[95% Conf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclimate_risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egdp_pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenergy_intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-95.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e50.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-193.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e41.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eSD dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e16.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOverall r-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e121.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR-squared within\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eR-squared between\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*** p\u0026lt;.01, ** p\u0026lt;.05, * p\u0026lt;.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eurban_pop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSt.Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e[95% Conf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclimate_risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egdp_pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenergy_intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-105.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e50.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-203.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-6.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e41.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eSD dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e16.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e29.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2859.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eBayesian crit. (BIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e2881.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*** p\u0026lt;.01, ** p\u0026lt;.05, * p\u0026lt;.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eurban_pop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSt.Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e[95% Conf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclimate_risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egdp_pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenergy_intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-95.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e50.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-194.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e41.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eSD dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e16.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOverall r-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR-squared within\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eR-squared between\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*** p\u0026lt;.01, ** p\u0026lt;.05, * p\u0026lt;.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eurban_pop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSt.Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e[95% Conf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclimate_risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egdp_pc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenergy_intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-105.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e51.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-210.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e41.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eSD dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e16.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2857.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eBayesian crit. (BIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e2875.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*** p\u0026lt;.01, ** p\u0026lt;.05, * p\u0026lt;.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results are strikingly consistent across all four specifications. The coefficient on climate risk is positive and highly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in all columns, with a point estimate clustering around 0.153\u0026ndash;0.156. This implies that a one-unit increase in the climate risk index raises a country's urban population share by approximately 0.15 percentage points. Given the mean climate risk of 11.1 and the degree of variation across the sample (standard deviation of 4.9), a one standard deviation increase in climate risk is associated with an increase in urban population share of roughly 0.75 percentage points, a substantively meaningful effect within a context where annual urbanization gains in SSA average around 0.5\u0026ndash;1.0 percentage points per year.\u003c/p\u003e \u003cp\u003eGDP per capita carries a positive and statistically significant coefficient in Table\u0026nbsp;(3) and (4), consistent with development-driven urbanization theories. However, this coefficient loses statistical significance in the robust standard error specifications (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting that within-country year-to-year income variation explains less of urbanization dynamics once autocorrelation in the error is accounted for. The energy intensity coefficient is negative, large in magnitude (approximately\u0026thinsp;\u0026minus;\u0026thinsp;0.9), and significant across all specifications, indicating that countries with more energy-intensive economies typically those more dependent on subsistence agriculture and primary commodities tend to have lower urban population shares. The positive and significant year trend (coefficient\u0026thinsp;\u0026asymp;\u0026thinsp;0.07) captures the broad upward trend in SSA urbanization independent of the included explanatory variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Hausman Test and Model Selection\u003c/h2\u003e \u003cp\u003eThe Hausman (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) specification test yields a chi-square statistic of 4.865 with a p-value of 0.301, failing to reject the null hypothesis that the random effects estimator is consistent. This result provides statistical support for the use of the RE estimator as our preferred specification, suggesting that country-level unobserved heterogeneity is not systematically correlated with the included regressors. This is a plausible finding given that the regressors climate risk (largely driven by geographic and meteorological factors), GDP per capita, and energy intensity contain substantial cross-country variation that is partially independent of country-specific fixed characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHausman (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) specification test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-square test value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.865\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Variance Inflation Factor Analysis\u003c/h2\u003e \u003cp\u003eTo formally assess multicollinearity, we compute variance inflation factors (VIF) for all regressors. Results show: GDP per capita (VIF\u0026thinsp;=\u0026thinsp;1.17), Climate Risk (VIF\u0026thinsp;=\u0026thinsp;1.13), Energy Intensity (VIF\u0026thinsp;=\u0026thinsp;1.13), Year Trend (VIF\u0026thinsp;=\u0026thinsp;1.06), with a mean VIF of 1.12. All VIF values are well below the conventional threshold of 5 (or the stricter threshold of 2 used in some studies), confirming that multicollinearity is not a concern in our model and that coefficient estimates are reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance inflation factor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egdp pc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/VIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.855\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclimate risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenergy intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean VIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Graphical Analysis\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e through \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provide visual representations of the key patterns in the data and the regression results.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the divergent temporal trajectories of the two key variables: while urban population share exhibits a steady upward trend, the climate risk index displays greater year-to-year volatility, with notable spikes corresponding to years of severe weather events across the region. The overall upward drift in both series is consistent with a positive relationship. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e confirms a positive bivariate association between climate risk and urban population share, with the OLS trend line showing a slope of approximately 0.16 closely matching the multivariate regression estimates. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the substantial heterogeneity across countries: smaller island states and coastal economies like Gabon tend to have both high urbanization and moderate climate risk, while Sahelian and landlocked countries show low urbanization and varied climate risk. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the coefficient estimates from the preferred RE GLS specification with 95% confidence intervals, highlighting the precision of the climate risk estimate and the negative energy intensity effect. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that both control variables exhibit the expected directional relationships with urbanization: positive for GDP per capita and negative for energy intensity.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Policy Implications","content":"\u003cp\u003eThis paper provides novel econometric evidence on the impact of climate risk on urban population dynamics in Sub-Saharan Africa. Using a panel of 29 countries over 2003\u0026ndash;2023 and employing random effects GLS as our preferred estimator validated by the Hausman specification test we find that climate risk exerts a statistically significant and positive effect on urban population share. Our findings are robust to the use of heteroscedasticity- and autocorrelation-consistent standard errors and across multiple model specifications.\u003c/p\u003e \u003cp\u003eThe positive climate risk urbanization relationship is consistent with the climate-driven rural-push hypothesis: as climate hazards intensify and impose greater economic and livelihood losses on rural households, migration toward urban centers accelerates. This is particularly consequential in SSA, where the majority of the poor remain in rural areas heavily dependent on rain-fed agriculture, and where adaptive capacity to absorb climate shocks is limited.\u003c/p\u003e \u003cp\u003eThese findings carry important policy implications. First, they reinforce the need for integrated climate-urban planning frameworks in SSA. Cities receiving climate migrants are often themselves exposed to flooding, heat stress, and infrastructure stress, requiring proactive investment in climate-resilient urban infrastructure, housing, and services. Second, the findings underscore the importance of rural climate adaptation investments including drought-resistant agriculture, water management, and social protection that can reduce climate-driven migration pressure while improving rural welfare. Reducing the climate risk index through mitigation and adaptation thus has a dual benefit: improving rural livelihoods and moderating the pace of unplanned urbanization.\u003c/p\u003e \u003cp\u003eThird, the negative energy intensity coefficient suggests that structural transformation toward more service-based and energy-efficient economies is associated with higher urbanization. Policies promoting economic diversification away from subsistence agriculture and energy-intensive primary production can accelerate sustainable urbanization. Fourth, the loss of significance of GDP per capita under robust standard errors suggests that short-run income fluctuations matter less for urbanization than structural and climatic factors, implying that urbanization in SSA is partly driven by necessity rather than opportunity a pattern that demands policy responses focused on urban social protection and inclusive city governance.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant acknowledgment. The Germanwatch Climate Risk Index primarily captures losses from sudden-onset weather events and may not fully reflect slow-onset climate stressors such as sea-level rise, gradual temperature increases, or desertification. Future research should explore the role of these slow-onset variables, potentially employing instrumental variable approaches to more cleanly identify causal effects. Additionally, country-level data mask important within-country heterogeneity across urban and rural areas; sub-national analysis as data availability improves will enrich our understanding of these dynamics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarrios, S., Bertinelli, L., \u0026amp; Strobl, E. (2006). Climatic change and rural-urban migration: The case of sub-Saharan Africa. \u003cem\u003eJournal of Urban Economics\u003c/em\u003e, 60(3), 357\u0026ndash;371.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGermanwatch (2023). \u003cem\u003eGlobal Climate Risk Index 2023\u003c/em\u003e. Germanwatch e.V., Bonn, Germany.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausman, J. A. (1978). Specification tests in econometrics. \u003cem\u003eEconometrica\u003c/em\u003e, 46(6), 1251\u0026ndash;1271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson, J. V., Squires, T., Storeygard, A., \u0026amp; Weil, D. (2017). The global distribution of economic activity: nature, history, and the role of trade. \u003cem\u003eQuarterly Journal of Economics\u003c/em\u003e, 133(1), 357\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC (2022). \u003cem\u003eClimate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report\u003c/em\u003e. Cambridge University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJedwab, R., \u0026amp; Vollrath, D. (2019). The mortality transition, malthusian dynamics, and the rise of poor mega-cities. \u003cem\u003eJournal of Development Economics\u003c/em\u003e, 141, 102364.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis, W. A. (1954). Economic development with unlimited supplies of labour. \u003cem\u003eThe Manchester School\u003c/em\u003e, 22(2), 139\u0026ndash;191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank (2024). \u003cem\u003eWorld Development Indicators.\u003c/em\u003e World Bank Group, Washington, D.C. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://databank.worldbank.org\u003c/span\u003e\u003cspan address=\"https://databank.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Meteorological Organization (WMO) (2024). \u003cem\u003eState of the Climate in Africa 2023\u003c/em\u003e. WMO, Geneva.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate risk, urbanization, Sub-Saharan Africa, panel data, random effects","lastPublishedDoi":"10.21203/rs.3.rs-9695684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9695684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the impact of climate risk on urban population share across 29 Sub-Saharan African (SSA) countries over the period 2003\u0026ndash;2023, using an unbalanced panel dataset of 609 country-year observations. Employing both random effects (RE) and pooled ordinary least squares (OLS) estimators, and confirming model selection via the Hausman (1978) specification test, we find that climate risk exerts a statistically significant and positive effect on urban population share. A one-unit increase in the climate risk index raises urban population share by approximately 0.15 percentage points, controlling for GDP per capita and energy intensity. Results are robust across multiple model specifications, including standard error correction for heteroscedasticity and autocorrelation. The findings suggest that climate-induced rural distress is a significant driver of urbanization in SSA, with important implications for urban planning, climate adaptation policy, and sustainable development in the region.\u003c/p\u003e","manuscriptTitle":"The Impact of Climate Risk on Urban Population: Evidence from Sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 07:26:36","doi":"10.21203/rs.3.rs-9695684/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0743131a-ba8b-42f5-92f4-df3c8759c071","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"checksComplete","content":"","date":"2026-05-13T03:16:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Communications Earth \u0026 Environment","date":"2026-05-12T18:42:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68128728,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"},{"id":68128729,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2026-05-14T07:26:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 07:26:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9695684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9695684","identity":"rs-9695684","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}