Fossil fuel dependence and energy insecurity
Energy, Sustainability and Society volume 12, Article number: 27 (2022)
Access to reliable energy services is increasingly seen as a prerequisite for well-being and human development. Copious research documents the negative consequences that occur when nations specialize in natural resource production, creating a “natural resource curse” or “paradox of plenty”. In this analysis, we evaluate how natural resource dependence, measured as oil and gas production, impacts energy security.
Using entropy-balanced fixed effects models, we find that oil and coal production is not associated with shorter times to establish a connection to the electricity grid, fewer outages, or improve electricity access among the population.
Nations that produce oil and coal do not seem to have better energy insecurity as a result, representing a distributional inequality. Fossil fuel-producing nations should consider implementing policies that would allow them to retain more wealth from fossil fuel production.
In an oft-cited quote, Schumacher (1982) noted that energy is “not just another commodity, but the precondition of all commodities, a basic factor equal with air, water, and earth” . Access to energy services is recognized as a fundamental prerequisite for human flourishing and maximization of capabilities [2,3,4], although the degree of energy consumption in many developed nations far exceeds levels needed to maximize well-being [5,6,7,8]. More recent literature has centered energy security, or conversely, energy insecurity, as a key topic for scholars working at the linkage of well-being, development, sustainability, and the energy system. Indeed, the United Nations seventh sustainable development goal is to “Ensure access to affordable, reliable, sustainable and modern energy” .
Energy insecurity can occur at both a system and a household scale. An energy system is thought to be insecure if it cannot provide consistent, affordable, reliable and sustainable energy services to those who need it [10,11,12,13]. At the household level, households are energy secure if they have reliable access to adequate energy services to provide for thermal comfort, reading light, and other needs [13, 14]. Importantly, household energy insecurity has been connected to a variety of negative health outcomes [15,16,17,18,19].
A long line of scholarship documents the deleterious consequences of natural resource dependence at multiple scales of analysis. At the international scale, nations that build their economies around natural resource extraction—such as mining or forestry—typically have slower economic growth, worse population health outcomes, and are more likely to have authoritarian governments. [2, 20,21,22,23]. Indeed, some authors refer to a “paradox of plenty” or “natural resource curse” to describe this phenomenon [24, 25]. Notably, the natural resource curse appears to persist at subnational scales also, such as within the U.S. or Canada [21, 26].
In this analysis, we ask the following research question: Is natural resource dependence related to energy insecurity at the country scale? To the best of our knowledge, this is the first study to evaluate this linkage. This is an important gap in our knowledge because, from an energy justice perspective, nations that produce rely upon fossil fuel production may not experience better energy security as a result. We suggest that natural resource dependence could be coupled with energy insecurity through a variety of mechanisms. For instance, natural resource dependence involves an inherent volatility that many not allow governments to collect consistent revenue that can be reinvested into electric grid infrastructure. Further, the political authoritarianism associated with natural resource dependence may create political incentives for authoritarian regimes that do not encourage improving energy security for its populations. In the next section, we describe the relevant literature on energy insecurity.
The origins of the concept of energy insecurity are diverse, and some researchers have used terms like “fuel poverty” to describe much the same issue. Energy systems that seem ostensibly secure at a macro-scale may still not provide an equitable distribution of energy services. For instance, several studies in some of the world’s most affluent nations find evidence of significant energy insecurity at the household scale, which in turn has deleterious consequences on individuals in those households. Energy insecurity is a mediating mechanism between socio-economic status and health outcomes—that is, low-socio-economic status households often struggle to attain needed energy services, which in turn has negative health consequences . Using French data,  and  find that energy insecurity is associated with lower self-rated health, these findings were echoed by . Using a sample from a low-income neighborhood in a major metropolitan region of the U.S.,  find that energy insecurity is associated with lower self-rated health, sleep problems, depression, and asthma. Also studying a metropolitan area in the U.S.,  connect energy insecurity problem behaviors in children. Several studies have connected issues like lack of thermal comfort, drafty and cold buildings, and potential intermittency to various indicators of well-being, such as self-rated health [12, 16, 17, 19], asthma, depression and sleep problems , increased youth hospitalizations  and excess mortality during cold months .
Energy insecurity has increasingly been recognized as a barrier to human development and flourishing in low- and middle-income nations. The challenges created by energy insecurity are diverse and often context specific. For instance, inadequate electric grids create household reliance on stoves that burn combustible fuels like wood—these stoves are a fire hazard and the smoke that they create endangers the health of women who often perform cooking duties [28,29,30]. The poor indoor air quality engendered by cook stoves is also a threat to the health of children and the elderly . Lack of reliable lighting at night can make it difficult for children to study at night, and lack of electricity is likely a contributor to illiteracy and low formal education [32,33,34]. In Ghana and Nigeria, energy-insecure households forgo basic necessities to pay for energy and their children are more likely to miss school days  Access to electricity is essential for the development of firms and effective institutions. Electricity allows for households to start businesses or otherwise engage in activities that generate employment and income [33, 35, 36].
Natural resource dependence
Natural resource dependence can occur when a bounded economy (at local, regional or even national scales) has its economic development primarily drive by extraction or production of natural resources, such as fossil fuels, minerals and related activities [37, 38]. Natural resource dependence is associated with a wide range of undesirable social and economic outcomes, although there is some variation in these relationships across contexts. Auty  coined the term “resource curse”, observing that, counter to conventional economic theory (e.g., ), nations that were blessed with ample resource endowments tended to have significantly slower economic growth than nations without a rich abundance of natural resources. Freudenberg described communities with “resource addiction”, whose economy was largely centered around unstable and ultimately unsustainable extractive activities [40, 41].
Auty  considered oil-producing nations and their industrial and fiscal policies. They argue that oil-rich nations often suffer due to the inherent instability of commodity prices and that, in many nations, foreign interests own or have significant influence on oil extraction. This causes a situation wherein oil-producing nations are unable to retain and reinvest much of the wealth generated from oil. Volatility is a common theme in the resource curse literature, and certainly one explanation for why resource-rich nations (or subnational regions) lag behind their less resource-rich counterparts .
Political factors also play a role in fomenting a “natural resource curse” scenario. Resource-rich nations tend to have authoritarian governments, or at least lack full democratization and struggle with problems of corruption [43, 44]. To some degree, this emerges because various political interests seek to develop a natural resource as quickly as quickly and cheaply as possible—Lane and Tornell  call this a “feeding frenzy”. However, the causal relationship between natural resource dependence and governing regimes is complex. For instance, Bulte and Damiana  argue that corruption and lack of political allow for rent-seeking firms to encourage extractive activities that bolster their profits at the expense of the well-being of the nation. However, Brooks and Kurtz  suggest that natural resources—in their application, oil production—is endogenous to industrialization and democratization, in part because industrialized and democratic regimes are more apt to invest in the technologies and human capital necessary for oil resource development. The literature has not converged on a simple consensus on the link between natural resource dependence and democratization.
At the subnational scale, the evidence for a resource curse appears to be less ambiguous. For instance, a long line of studies conducted in the U.S. has connected mining activities to slow economic growth and poor performance on other socio-economic indicators [21, 22, 47,48,49,50,51,52]. For instance, James and Aadland  evaluated natural resource dependence (operationalized as the number of jobs in resource-related occupations) and economic performance for U.S. counties, ultimately concluding that resource-dependent counties had much slower economic growth. Considering 207 counties in the Western region of the U.S., Haggerty et al.  find that economic specialization in oil and gas development was associated with anemic economic development and social problems like crime. Natural resource curse dynamics have also been observed in mining regions of Australia [53, 54], and Canada . Presumably, problems of natural resource dependence might exist in some regions of nation that are not writ large subject to the natural resource curse, while the natural resource curse can be observed cross-sectionally between nations.
The resource curse can be conceptualized in energy justice terms. Energy justice scholarship borrows from a range of empirical and theoretical perspectives and highlights questions of who benefits and who suffers from energy production [56,57,58]. Some regions serve as “sacrifice zones” for other places, absorbing the ill environmental and health effects of fossil fuel development while often receiving limited direct benefits [59,60,61]. Energy insecurity at the national scale can be viewed via the lens of energy justice. If a nation is producing fossils but their own energy security does not improve as a result, this situation is a distributional injustice.
Linking energy insecurity and the natural resource curse
We are unaware of any studies that have connected, either theoretically or empirically, natural resource dependence and energy insecurity at the national or subnational scale. Here, we suggest several mechanisms that might connect the two. Natural resource dependence might create a situation where energy-producing resources are sent abroad. These problems could be exacerbated in authoritarian regimes, whose tenuous grip on power might require that they sell off natural resources to gain the favor of a small economic elite [22, 23]. Further, as many scholars have noted, price volatility is an unavoidable part of extractive-based economies [40, 42]. This price volatility may create a situation wherein states, even democratic states, cannot effectively invest in long-run infrastructure development, such as improving the electric grid. Given the centrality of energy services for economic and human development, this lack of investment might create worse social and economic outcomes in the long run. Energy insecurity could be another mechanism by which natural resource dependence reduces the long-run fortunes of nations, or the well-being of their populations, but this relationship has undergone remarkably little evaluation. In the next section, we describe the data we use to connect natural resource dependence to energy insecurity at the national scale.
We use several dependent variables. The first is a measure of the percentage of the population that has access to electricity. The IEA’s 2009 report uses this variable as part of an index . To some extent, access to electricity is not a direct measurement of energy insecurity as some energy systems may have ample access, but said access might be intermittent, unreliable, or otherwise insufficient. However, access to electricity is a useful indicator for two reasons. First, and most practically, this data is available for a wide cross-section of nations. More conceptually, access to electricity is a prerequisite for energy security. That is, an energy system cannot be secure if it does not provide access to electricity for large sections of its relevant population.
The final two variables have significantly less data available and are compiled from the World Bank’s individual-country surveys. The first is a measure of the number of days that are typically required to establish a new electric connection. The second is a measure of the percentage of firms that have experienced a power outage within the last month. We provide the distribution of all three of these outcome measures in Fig. 1.
Natural resource dependence has been measured in a variety of ways. Because the focus of our paper is energy-producing resources—specifically fossil fuels—we rely on indicators of the economic rents as a percentage of GDP produced by the oil and coal sector in each nation, again from data available on the World Bank data repository . The World Bank calculates economic rents as the difference between the average cost of producing a commodity and the price of that commodity. We also include a binary variable for whether the nation was a producer of coal or oil—we calculated this variable by access oil and coal production data from BP’s Statistical Review of World Energy . We adopted this strategy of using a binary indicator of production because the oil and coal rents variables are highly skewed, and this allows us to ascertain the effect of simply hosting coal or oil production.
Energy insecurity is likely influenced by a range of variables at the national scale. To mitigate against potential omitted variable bias, we employ a range of control variables, some of which are also entered into the entropy balancing algorithm (described further below). From the World Bank data repository, we access variables for GDP per capita in current USD, military expenditures as a percentage of GDP, and the percentage of a given nations population that lives in a rural area. We include GDP per capita because of the strong relationship between economic development and energy systems. Several studies find that nations that invest significant resources in their military have a range of worse environmental and social outcomes [65,66,67]. In our case, we might expect that public investment in militarization might crowd out investment in energy infrastructure, contributing to energy insecurity. We include the percentage of population that lives in a rural area because rural populations are typically more difficult to connect to electricity grids.
Earlier we noted an often-observed linkage between natural resource dependency and authoritarian regimes. There are several ways to operationalize democracy using well-vetted indicators. We opt to use the indicators provided by the Varieties of Democracy project . We turn to this data set because it includes a measure of corruption, which is especially appropriate for our analysis as corruption is associated with natural resource-dependent regimes. A second advantage of this dataset is that it has rich coverage during the time when the first two outcomes (that is, days to establish an electricity connection and the percentages of firms experiencing an outage). From the Varieties of Democracy, we borrow the electoral democracy and corruption index. Table 1 displays descriptive statistics for all variables, and Appendix lists the countries included for each outcome variable.
Our indicators of energy security are very different and require different modeling strategies. Access to electricity and number of days required to make a electricity connection has thousands of observations across many countries and years. For these indicators, we opt for a panel regression model with country fixed effects. The sample sizes for firm outages and the number of days required is much sparser. For firm outages, there are 366 observations spread between 2006–2019, with many countries having only a single observation. For this indicator, we eschew the country fixed effects.
We also take additional steps to improve causal inference for each model. We use Tubbike’s  recent extension of the entropy balancing method for continuous treatments. The original entropy balancing method was developed by  and . Entropy balancing is somewhat akin to other causal inference techniques like propensity score matching, in that it seeks to create covariate balance between treatment and control groups. Entropy balancing accomplishes these through a data-preprocessing, algorithmic procedure that generates weights that balance covariates on sample moments specified by the analyst. Often, entropy balancing is used to balance on means and standard deviations, creating statistical equivalence between treatment and control groups for the covariates chosen by the research. In this way, the binary case of entropy balancing mimics random assignment with respect to the covariates chosen by the researcher—it effectively mitigates the “selection on observables” problem in causal inference. Entropy balancing has many attractive advantages over more established methods like propensity score matching in that it eliminates many of the rather arbitrary choices made by researchers in developing a matching model. However, its limitation is that it only accommodates binary treatments.
Tubbike  developed a powerful extension of entropy balancing that allows for the use of continuous treatment—entropy balancing for continuous treatments, of EBCT. Tubbike  explains that “Essentially, EBCT re-weights all units to achieve zero correlations between the treatment variable and covariates.” (p.8). This is akin to a situation wherein the treatment variable was randomly assigned with respect to potential confounders. In our application, we use EBCT to mimic a situation wherein natural resource dependence (in our case, oil and coal rents) is randomly distributed with respect to GDP per capita, population, rural population (%), and political variables for electricity access and days required to receive a new electricity hook-up. The outages data are sparser and cannot be balanced on the full range of covariates without losing significant data points due to list-wise deletion. Accordingly, we balance on GDP per capita, population size, and the percentage of rural population for this variable and eschew adding a full suite of predictors in our regression equations.
Our measures of days to establish an electricity connection is highly skewed and over-dispersed—that is, the variance far exceeds the mean. In these instances, conventional regression models (e.g., OLS) will produce biased estimates. To avoid these problems, we estimate the models for days to establish an electricity connection and the percentages of outages using negative binomial regression, an appropriate model for over-dispersed count data . The data for firm outages and electricity access is not over-dispersed. Thus, we employ a more conventional OLS model for this variable.
Oil rents results
Table 2 displays coefficients, standard errors, and R2 statistics for our oil rents models. Model 2 implies that oil rents have no direct influence on the days to establish a new energy connection (b = -0.61, n.s.). Indeed, few variables—except for population size—emerge as statistically significant in this model. Model 2 uses the percentage of firms experiencing an outage in the last month as the dependent variable. This model drops military expenditure per capita as a predictor and eschews the country fixed effects due to the relatively small sample size. Here again, we find that oil rents do not improve or damage this measure of energy insecurity. Finally, we turn our attention to Model 3. Here, we find that oil rents are associated with increased energy access (b = 0.16, p < 0.05). Population size is associated with increased electricity access, and access declines as rural populations increase, and military expenditures grow.
Coal rents results
For coal rents, we first consider Model 4, which uses days to establish an electricity connection as the outcome variable and includes the entropy balancing weights. Here we find that coal rents and coal production do not have a statistically significant effect on the days to establish an electricity connection. Model 5 suggests that as coal rents increase, the percentage of firms experiencing outages declines, the dummy variable for coal production has a similar negative effect. In model 6, the effect of coal rents is not significant, but coal-producing nations have lower electricity access.
We conducted additional checks to determine the relative robustness of our results. We implement the konfound method [73, 74]. Analysts often evaluate robustness by estimating models with proxy variables or different model specifications in a somewhat arbitrary fashion. The advantage of konfound is that it estimates a percentage of cases that would have to be replaced to invalidate an inferences—that is, to render a non-significant result significant or vice versa. For linear models (e.g., OLS), konfound also estimates the correlation between the predictor and outcome that would be necessary to change the inference. Please note that, in our applications, this correlation coefficient cannot be estimated for our negative binomial models—konfound cannot yet estimate the correlation threshold for non-linear models (i.e., models 1 and 4).
The konfound analysis (Table 3) suggests that the null results for firm outages in model 2 are uniquely robust, with a very high degree of misclassification (98.69%) necessary to make the effect of oil rents statistically significant. For model 3, the statistically significant effect of oil rents could be invalidated if 15.14% of the cases were misclassified and an omitted variable was correlated at 0.081 with oil rents and -0.081 with electricity access, implying that this result may be more brittle than others. However, there are likely few omitted variables having a positive correlation with oil rents and a negative relationship with electricity access. Similarly, the statistically significant effect of coal rent on firm outages on Model 5 would become non-significant if only 1.9% of the cases were misclassified, and the correlation to invalidate the inference is 0.055. Finally, the results for model 6, wherein our binary variable for coal production was statistically significant, appear to be relatively robust. Overall, the konfound analysis implies that the few statistically significant effects could be rendered non-significant with relatively modest changes in model specification.
The purpose of this paper is to understand the effect of natural resource dependence—specifically, dependence upon fossil fuel production—on indicators of energy insecurity. Scholarship has increasing foregrounded energy security as an important factor in economic development, well-being and livelihoods [12, 13]. Further, energy justice scholarship foregrounds concerns about who benefits and who suffers from energy production . Although the deleterious consequences of natural resource dependence are well-documented , it is not well-understood if fossil fuel production improves or damages energy security at the national scale. In this section, we discuss our findings considering the conceptual framework detailed earlier in the paper.
Generally, our indicators of natural resource dependence had effects that were not statistically significant. We recognize that many scholars implicitly or explicitly view statistically significant results as more “interesting” in some ways, and perhaps may view analyses that produce statistically non-significant results as somehow flawed or in error. Of course, many leading methodologists have challenged the long tradition in the sciences of foregrounding p-values as the centerpiece of a given analysis and some journals and disciplines have followed suit by banning p-values or encouraging other ways to assess findings [75,76,77,78]. We do not seek to wade into these debates, we only point out that there are important reasons why scholars should recognize that null findings are, in fact, informative.
In the current paper, the mostly null effects of our indicators for oil and coal dependence have practical and theoretical implications. Our models imply that, across multiple indicators of energy insecurity, the production of fossil fuels did not appear to consistently increase or improve energy security within the nations under study. This was especially true for oil production, although coal production had more nuanced effects. This analysis implies that the production of fossil fuels does not necessarily engender “natural resource curse” dynamics wherein a nation’s population is more energy insecurity because of fossil fuel production. However, our analysis does illustrate a distributional inequality—nations that are producing fossil fuels do not see improvements in their energy systems, at least in terms of energy insecurity. From an energy justice perspective, this is a uniquely substantive finding. Nations that specialize in fossil fuel production do not necessarily experience a “curse” with regard to energy insecurity but are in the seemingly paradoxical situation where their energy production does not enhance their own energy security. Our results imply that fossil fuel-producing nations could implement policies to retain and redistribute more of the benefits of their production.
We suggest that more empirical and theoretical work is needed to unpack a middle ground between an optimistic account of fossil fuels that paints them as a driver of broadly shared benefits and the prototypical natural resource curses scenario, wherein nations perform more poorly a broad swath of social and economic indicators because of fossil fuels. Understanding how fossil fuel production does not translate into greater energy security is a needed task for future research.Footnote 1 A limitation of this study is our use of country-level data—we cannot evaluate within-country differences in natural resource dependence and energy insecurity. National subregions that specialize in resource production may suffer from problems of energy insecurity.
An important caveat is that our modeling strategy is uniquely robust to issues of omitted variable bias and statistical non-equivalence among confounders because we employ a combination of entropy balancing and fixed effects. This means that, barring the effect of some unmeasured confounder, our estimates of the effect of natural resource dependency represent a robust direct effect of these variables. However, we did not model the indirect effects of oil and coal dependence. That is, perhaps oil and gas dependence is a distal cause of social and economic maladies. Further, we did not allow the effect of oil and coal dependence to vary across nations—as we noted in our literature review, there is evidence that natural resource curse dynamics can occur at a subnational scale, implying that in some places become “cursed” while others do not experience profoundly deleterious outcomes. Further, we remind that reader that we only consider indicators related to energy insecurity, not a broad suite of social and economic variables. While energy insecurity is important, other research might find the oil and coal dependence, as operationalized here, might be associated with any number of undesirable social or economic consequences.
Nations that produce fossil fuels should ostensibly benefit in the form of improved energy insecurity. Yet our results suggest that this is not the case—nations that specialize in the production of fossil fuels typically do no better than their counterparts in providing a secure energy system for their populations. However, we do not observe a prototypical “resource curse” scenario wherein energy security is worse because of fossil fuel production. More work is needed to understand how nations or national subregions that specialize in resource production can use this wealth to improve services and infrastructure.
Availability of data and materials
Data are available from the authors upon request.
The null effects of the entropy balanced predictors are partially a result of the entropy balancing, which reweights to remove the correlation between these covariates and the outcomes.
Schumacher EF (1982) Schumacher on energy: speeches and writings of E.F. Schumacher. Cape, London
Auty RM (2007) Natural resources, capital accumulation and the resource curse. Ecol Econ 61:627–634
Sweidan OD, Alwaked AA (2016) Economic development and the energy intensity of human well-being: evidence from the GCC countries. Renew Sustain Energy Rev 55:1363–1369
Ouedraogo NS (2013) Energy consumption and human development: Evidence from a panel cointegration and error correction model. Energy 63:28–41
Goldemberg J, Johansson TB, Reddy AK, Williams RH (1985) Basic needs and much more with one kilowatt per capita. Ambio 14:4/5:190–200
Rao ND, Min J (2018) Decent living standards: material prerequisites for human well-being. Soc Indic Res 138:225–244
Dietz T, Rosa EA, York R (2009) Environmentally efficient well-being: Rethinking sustainability as the relationship between human well-being and environmental impacts. Hum Ecol Rev 16:114–123
Steinberger JK, Roberts JT (2010) From constraint to sufficiency: The decoupling of energy and carbon from human needs, 1975–2005. Ecol Econ 70:425–433
Goal 7|Department of Economic and Social Affairs. https://sdgs.un.org/goals/goal7. Accessed 27 Apr 2022
Ang BW, Choong WL, Ng TS (2015) Energy security: definitions, dimensions and indexes. Renew Sustain Energy Rev 42:1077–1093
Chester L (2010) Conceptualising energy security and making explicit its polysemic nature. Energy Policy 38:887–895
Sovacool BK (2016) Differing cultures of energy security: An international comparison of public perceptions. Renew Sustain Energy Rev 55:811–822
Hernández D (2016) Understanding ‘energy insecurity’ and why it matters to health. Soc Sci Med 167:1–10
Boateng GO, Balogun MR, Dada FO, Armah FA (2020) Household energy insecurity: dimensions and consequences for women, infants and children in low- and middle-income countries. Soc Sci Med 258:113068
Cook JT, Frank DA, Casey PH et al (2008) A brief indicator of household energy security: associations with food security, child health, and child development in US infants and toddlers. Pediatrics 122:e867–e875
Hernández D, Siegel E (2019) Energy insecurity and its ill health effects: a community perspective on the energy-health nexus in New York City. Energy Res Soc Sci 47:78–83
Lacroix E, Jusot F (2014) Fuel Poverty is it harmful for health? Evidence from French health survey data. JESF. p 19 (hal-01523725)
Lacroix E, Chaton C (2015) Fuel poverty as a major determinant of perceived health: the case of France. Public Health 129:517–524. https://doi.org/10.1016/j.puhe.2015.02.007
Mayer A, Smith EK (2019) Exploring the link between energy security and subjective well-being: a study of 22 nations. Energy Sustain Soc 9:1–13
Bulte E, Damania R (2008) Resources for sale: corruption, democracy and the natural resource curse. BEJEAP. https://doi.org/10.2202/1935-1682.1890
James A, Aadland D (2011) The curse of natural resources: An empirical investigation of US counties. Res Energy Econ 33:440–453
Papyrakis E, Gerlagh R (2004) The resource curse hypothesis and its transmission channels. J Comp Econ 32:181–193
Sachs JD, Warner AM (2001) The curse of natural resources. Eur Econ Rev 45:827–838
Dauvin M, Guerreiro D (2017) The paradox of plenty: a meta-analysis. World Dev 94:212–231
Karl TL (1999) The perils of the petro-state: reflections on the paradox of plenty. J Int Aff 53(1):31–48
Haggerty J, Gude PH, Delorey M, Rasker R (2014) Long-term effects of income specialization in oil and gas extraction: the US West, 1980–2011. Energy Economics 45:186–195
Healy JD (2003) Excess winter mortality in Europe: a cross country analysis identifying key risk factors. J Epidemiol Community Health 57:784–789
Kanagawa M, Nakata T (2007) Analysis of the energy access improvement and its socio-economic impacts in rural areas of developing countries. Ecol Econ 62:319–329
Clark ML, Peel JL, Burch JB et al (2009) Impact of improved cookstoves on indoor air pollution and adverse health effects among Honduran women. Int J Environ Health Res 19:357–368
Walker ES, Clark ML, Young BN et al (2020) Exposure to household air pollution from biomass cookstoves and self-reported symptoms among women in rural Honduras. Int J Environ Health Res 30:160–173
Smith KR, McCracken JP, Thompson L et al (2010) Personal child and mother carbon monoxide exposures and kitchen levels: methods and results from a randomized trial of woodfired chimney cookstoves in Guatemala (RESPIRE). J Exposure Sci Environ Epidemiol 20:406–416
Bhusal P, Zahnd A, Eloholma M, Halonen L (2007) Replacing fuel based lighting with light emitting diodes in developing countries: energy and lighting in rural Nepali homes. Leukos 3:277–291
Kaygusuz K (2012) Energy for sustainable development: a case of developing countries. Renew Sustain Energy Rev 16:1116–1126
Behera B, Ali A (2017) Factors determining household use of clean and renewable energy sources for lighting in Sub-Saharan Africa. Renew Sustain Energy Rev 72:661–672
Vernet A, Khayesi JN, George V et al (2019) How does energy matter? Rural electrification, entrepreneurship, and community development in Kenya. Energy Policy 126:88–98
Atiase VY, Mahmood S, Wang Y, Botchie D (2018) Developing entrepreneurship in Africa: investigating critical resource challenges. J Small Bus Enterp Dev. https://doi.org/10.1108/JSBED-03-2017-0084
Humphrey CR, Berardi G, Carroll MS et al (1993) Theories in the study of natural resource-dependent communities and persistent rural poverty in the United States. Persist Pov Rural Am. https://doi.org/10.4324/9780429301391-6
Peluso NL, Humphrey CR, Fortmann LP (1994) The rock, the beach, and the tidal pool: People and poverty in natural resource-dependent areas. Soc Nat Resour 7:23–38
Rostow WW (1959) The stages of economic growth. Econ Hist Rev 12:1–16
Freudenburg WR (1992) Addictive economies: extractive industries and vulnerable localities in a changing world economy. Rural Sociol 57:305–332
Freudenburg WR, Gramling R (1998) Linked to what? Economic linkages in an extractive economy. Soc Nat Resour 11:569–586
Van der Ploeg F, Poelhekke S (2009) Volatility and the natural resource curse. Oxf Econ Pap 61:727–760
Pendergast SM, Clarke JA, Van Kooten GC (2011) Corruption, development and the curse of natural resources. CJPS/RCSP 44:411–437
Busse M, Gröning S (2013) The resource curse revisited: governance and natural resources. Public Choice 154:1–20
Lane PR, Tornell A (1996) Power, growth, and the voracity effect. J Econ Growth 1:213–241
Kurtz MJ, Brooks SM (2011) Conditioning the “resource curse”: globalization, human capital, and growth in oil-rich nations. Comp Pol Stud 44:747–770
Deaton BJ, Niman E (2012) An empirical examination of the relationship between mining employment and poverty in the Appalachian region. Appl Econ 44:303–312
Lobao L, Zhou M, Partridge M, Betz M (2016) Poverty, place, and coal employment across Appalachia and the United States in a new economic era. Rural Sociol 81:343–386
Partridge MD, Betz MR, Lobao L (2013) Natural resource curse and poverty in Appalachian America. Am J Agr Econ 95:449–456
Stedman RC, Parkins JR, Beckley TM (2004) Resource dependence and community well-being in rural Canada. Rural Sociol 69:213–234
Mayer A, Olson-Hazboun SK, Malin S (2017) Fracking fortunes: economic well-being and oil and gas development along the urban-rural continuum. Rural Sociol 83(3):532–567
Mayer A, Malin SA, Olson-Hazboun SK (2018) Unhollowing rural America? Rural human capital flight and the demographic consequences of the oil and gas boom. Popul Environ 39:219–238
Langton M, Mazel O (2008) Poverty in the midst of plenty: aboriginal people, the ‘resource curse’and Australia’s mining boom. J Energy Nat Resour Law 26:31–65
Ennis G, Finlayson M (2015) Alcohol, violence, and a fast growing male population: exploring a risky-mix in “boomtown” Darwin. Soc Work Public Health 30:51–63
Shrivastava M, Stefanick L (2012) Do oil and democracy only clash in the global south? Petro politics in Alberta, Canada. New Glob Stud 6(1):1–26
Fuller S, McCauley D (2016) Framing energy justice: perspectives from activism and advocacy. Energy Res Soc Sci 11:1–8
Goldthau A, Sovacool BK (2012) The uniqueness of the energy security, justice, and governance problem. Energy Policy 41:232–240
Healy N, Barry J (2017) Politicizing energy justice and energy system transitions: Fossil fuel divestment and a “just transition.” Energy Policy 108:451–459
Fox J (1999) Mountaintop removal in West Virginia: an environmental sacrifice zone. Organ Environ 12:163–183
Hernández D (2015) Sacrifice along the energy continuum: a call for energy justice. Environ Justice 8:151–156
Holifield R, Day M (2017) A framework for a critical physical geography of ‘sacrifice zones’: physical landscapes and discursive spaces of frac sand mining in western Wisconsin. Geoforum 85:269–279
Agency IE (2009) World energy outlook. OECD/IEA, Paris
World Bank Open Data|Data. https://data.worldbank.org/. Accessed 26 Apr 2022
Statistical Review of World Energy|Energy economics|Home. https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed 26 Apr 2022
Bullock B, Firebaugh G (1990) Guns and butter? The effect of militarization on economic and social development in the Third World. J Polit Mil Soc 18(2):231–266
Carlton-Ford S, Durante KA, Evans TD, Graham C (2019) Guns and butter: child mortality and the mediators of militarization. Armed Forces Soc 45:177–197
Clark B, Jorgenson AK, Kentor J (2010) Militarization and energy consumption: a test of treadmill of destruction theory in comparative perspective. Int J Sociol 40:23–43
Coppedge M, Gerring J, Lindberg SI, et al (2015) Varieties of democracy. Codebook Version
Tübbicke S (2022) Entropy balancing for continuous treatments. J Econ Methods 11(1):71–89
Hainmueller J (2012) Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit Anal 20:25–46
Hainmueller J, Xu Y (2013) Ebalance: a Stata package for entropy balancing. J Stat Softw 54(7):1–13
Hilbe JM (2011) Negative binomial regression. Cambridge University Press, Cambridge
Frank KA, Xu R (2017) KONFOUND: stata module to quantify robustness of causal inferences. https://EconPapers.repec.org/RePEc:boc:bocode:s458298
Xu R, Frank KA, Maroulis SJ, Rosenberg JM (2019) Konfound: command to quantify robustness of causal inferences. Stand Genomic Sci 19:523–550
Abadie A (2020) Statistical nonsignificance in empirical economics. Am Econ Rev Insights 2:193–208
Kline RB (2004) Beyond significance testing: reforming data analysis methods in behavioral research. American Psychological Associatin
Vakhitova ZI, Alston-Knox CL (2018) Non-significant p-values? Strategies to understand and better determine the importance of effects and interactions in logistic regression. PLoS ONE 13:e0205076
Wasserstein RL, Lazar NA (2020) ASA statement on statistical significance and P-values. In: The theory of statistics in psychology. Springer, pp 1–10
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See Table 4.