Next Article in Journal
The Role of Collector-Drainage Water in Sustainable Irrigation for Agriculture in the Developing World: An Experimental Study
Previous Article in Journal
Waqf: An Advanced Approach to Combating Agricultural Land Fragmentation in Islamic Countries
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
World
Volume 5
Issue 4
10.3390/world5040071
world-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Multiple Indicator Vulnerability to Energy Poverty: Assessing Spatial Variability Across Chile

by
Aner Martinez-Soto
1,*,
Emily Nix
2,
Yarela Saldias-Lagos
1 and
Daniel Ignacio Sanhueza-Catalán
1
1
Department of Civil Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4780000, Chile
2
Department for Public Health, Policy and Systems, University of Liverpool, Liverpool L69 3BX, UK
*
Author to whom correspondence should be addressed.
World 2024, 5(4), 1404-1420; https://doi.org/10.3390/world5040071
Submission received: 30 October 2024 / Revised: 21 November 2024 / Accepted: 26 November 2024 / Published: 16 December 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
This study investigates the multifaceted issue of energy poverty, focusing on its spatial and socioeconomic dimensions, with a particular emphasis on the case of Chile. Despite global initiatives like Sustainable Development Goal 7, which advocates for universal access to affordable and reliable energy, millions remain vulnerable to energy poverty. In Chile, this phenomenon is exacerbated by geographic and climatic variability, resulting in significant disparities in energy access, affordability, and efficiency. Using a multidimensional framework adapted from Bouzarovski and Petrova, we assessed energy poverty through factors including household income, fuel costs, energy infrastructure reliability, and regional climate needs. This analysis integrated composite indicators to map vulnerability at the regional level, highlighting high-risk areas primarily in the central and southern regions, where low incomes and dependency on biomass for heating amplified exposure occur. The findings reveal that approximately 4 million Chileans face a high risk of energy poverty, underscoring the need for regionally tailored policies that address both immediate economic constraints and structural energy inequalities. This research contributes to a comprehensive understanding of energy poverty in Chile and offers policy recommendations aimed at reducing socioeconomic disparities and achieving sustainable, equitable energy access.

1. Introduction

The United Nations’ Sustainable Development Goal 7 aims to ensure universal access to affordable, reliable, and modern energy services by 2030 [1] However, disparities in energy access and affordability persist, contributing to energy poverty, a condition where households cannot secure adequate energy services to maintain basic living standards [2]. In Chile, energy poverty is influenced by a combination of factors, including regional income disparities, dependence on biomass for heating, and varying levels of energy efficiency in the housing sector [3]. Approximately 4 million Chileans face a high or very high risk of energy poverty, with central regions experiencing heightened vulnerability due to low energy efficiency and affordability challenges [4,5]). These vulnerabilities are compounded by Chile’s geographical and climatic diversity, which leads to significant variation in the energy needs across its regions. A comprehensive assessment is necessary to develop targeted policies that address the multifactor nature of energy poverty in Chile, emphasizing affordability, energy efficiency, and the reliability of energy sources [6].
Energy poverty is a multifaceted issue, encompassing challenges such as inadequate access to reliable and modern energy, unaffordable energy costs, and energy inefficiency [2]. In developed countries, energy poverty often manifests as a high household expenditure on energy, disproportionately affecting vulnerable groups such as the elderly, low-income families, and those living in poorly insulated homes. For instance, in Portugal, studies indicate that 15.3% to 23.4% of households are unable to maintain adequate indoor temperatures during the winter, leading to one of the highest excess winter mortality rates in Europe [7]. In contrast, energy poverty in developing countries typically revolves around limited access to modern energy services, with nearly 60% of Africa’s population lacking reliable electricity as of 2018 [8]. Dependence on traditional biomass for cooking and heating—common in rural areas—exacerbates indoor air pollution, contributing to approximately 1.3 million annual deaths globally due to health complications [9]. This global disparity in energy access underscores the importance of addressing energy poverty from both affordability and accessibility perspectives, as these dimensions are critical for the improving quality of life and supporting sustainable development [10]. Emerging studies have proposed additional factors, such as climate risk and thermal comfort, as relevant to energy poverty. However, their inclusion in analytical frameworks remains limited due to the ongoing development of empirical support and consensus [11,12,13,14,15].
The lack of a unified definition and measurement framework complicates efforts to address energy poverty consistently across regions. In Europe, energy poverty has been linked to affordability, with the “10% indicator” suggesting that if a household spends more than 10% of its income on energy to maintain adequate warmth, it is considered energy-poor [16]. Meanwhile, developing countries face more profound challenges tied to energy accessibility, as seen in Nigeria, where the power generation capacity fluctuates between 3000 MW and 4000 MW, far below the estimated national requirement of 98,000 MW for a stable supply [17]. This divergence in definitions necessitates a multidimensional approach to better capture the local realities and facilitate targeted policy interventions aimed at mitigating energy poverty’s diverse impacts.
To comprehensively assess energy poverty, a variety of indicators and indices have been developed, each offering a different perspective on the issue [18]. In developed countries, composite indicators, such as the Low Income High Cost (LIHC) measure, account for both household income and energy expenditures, highlighting disparities between the income groups. In the United Kingdom, a study revealed that older households are disproportionately affected when assessed using expenditure-based measures, yet these same households often do not self-identify as energy-poor, revealing discrepancies between objective and subjective indicators [18]. In developing nations, single indicators like the share of income spent on energy are commonly employed, but they often fail to capture the broader social and environmental impacts associated with limited energy access [10]. For example, rural households in Nigeria rely heavily on biomass, with 80% lacking access to cleaner cooking technologies, leading to significant health risks and environmental degradation [17].
Mapping regional vulnerability has proven effective in identifying the areas most in need of policy intervention. In Poland, vulnerability mapping using the Energy Poverty Vulnerability Index has shown that northern and eastern districts are at higher risk due to lower income levels and poor housing quality [19]. Similarly, an assessment in Portugal revealed that urban areas, despite higher overall incomes, face significant risks due to older, less energy-efficient housing stock [7]. These regional and socioeconomic variations emphasize the need for context-specific policies, which must consider local economic conditions, energy infrastructure, and climate factors. For instance, composite indices like the Multidimensional Energy Poverty Assessment Index (MEAI) have shown that while global energy poverty has improved, spatiotemporal disparities remain, particularly in regions like East Asia, which experienced a notable decline in their MEAI scores between 2014 and 2016 due to targeted energy efficiency programs [20]. The complexity of these variations necessitates the development of robust, multidimensional indicators to guide effective and contextually relevant policy measures.
While research on energy poverty has extensively developed indicators to measure and compare its impacts, significant knowledge gaps persist, especially in evaluating energy poverty without relying on data-intensive indices. Current methods often emphasize composite indices like MEPI (Multidimensional Energy Poverty Index), which, though insightful, require substantial data inputs that may not be feasible in all contexts, particularly in regions with diverse geographic and socioeconomic characteristics like Chile [10,18]. In developing regions, energy poverty studies often concentrate on accessibility to electricity, affordability, and heating/cooling needs, yet few examine whether the spatial mapping of key variables alone can effectively highlight vulnerable areas [7,21]. This is particularly relevant in Chile, where geographic and climatic diversity significantly influences energy needs and costs, underscoring the necessity for methods that capture local variability without excessive data demands [22,23]. This study seeks to address this gap by assessing whether energy poverty can be effectively evaluated through the mapping of key entry variables—such as electricity access and affordability—bypassing the need for detailed composite indicators. Employing regional vulnerability mapping and spatial analysis, this research aims to identify vulnerable populations and provide practical, scalable recommendations for policy interventions. This approach contributes to the literature by offering a simplified yet robust framework for understanding energy poverty, aligning with Sustainable Development Goal 7 to ensure universal access to modern energy services by 2030.

2. Materials and Methods

To generate a holistic understanding of the energy poverty issues in Chile, we utilized the conceptual framework a which identifies five key drivers contributing to energy poverty: access, affordability, flexibility, energy efficiency, and needs [24]. Each of these factors reflects a critical dimension of energy poverty, with relevance to both developed and developing contexts. In this study, we adapted their framework to better address the specific conditions found in Chile, including the regional diversity of energy use and the health risks associated with traditional biomass fuels. The measurable parameters for each factor were based on the available datasets, and the methodologies for combining these data are outlined in the following sections. A summary of factors, driving forces, and measurable indicators is provided in Table 1.

2.1. Access

Access is defined here as the availability of energy carriers capable of meeting household needs in a safe and sustainable manner. We considered three main dimensions of access: the types of fuels used, the health risks from these fuels, and the reliability of the energy source. The variability in energy carriers across Chile’s climate regions is significant, with wood and gas being prevalent for heating and cooking, particularly in southern regions where climatic conditions are harsher [29]. According to some authors, indoor exposure to particulate matter (PM2.5), commonly resulting from biomass use, accounts for around 1.8 attributable deaths per 100,000 population in Chile, making it a critical health consideration in energy poverty assessments [31].
The risk levels were categorized using thresholds established from the literature on indoor air quality. For instance, the PM2.5 concentrations in the homes using biomass for cooking were classified into five risk levels: very low risk for <1 µg/m3, low risk for 1–2 µg/m3, moderate risk for 2–3 µg/m3, high risk for 3–4 µg/m3, and very high risk for >4 µg/m3 [32]. For energy reliability, we utilized the System Average Interruption Duration Index (SAIDI) as an indicator of the stability of the electricity supply, with values ranging from 0.22 to 11.1 across Chile. The areas with SAIDI values >4 were classified as very high risk due to their association with frequent and prolonged outages, significantly affecting household energy security [33]. The variability in energy sources across the regions is detailed in Table 2, which outlines the predominant energy carriers and their usage by thermal zone.
The thermal zone classification system developed by MINVU divides Chile into seven distinct zones based on the predominant climatic conditions such as temperature, humidity, and precipitation. This classification serves as a critical framework for guiding architectural and energy-efficient design. It ranges from zone 1 (Hot Desert), characterized by high temperatures and minimal heating needs, to zone 7 (Cold Austral), where extremely low temperatures drive high heating demand. Intermediate zones, such as zone 4 (Temperate Mediterranean) and zone 6 (Rainy Temperate), present a mix of heating and insulation needs due to humid winters. This zoning system plays a fundamental role in improving energy efficiency in buildings and informing policies aimed at enhancing thermal comfort and addressing energy poverty across Chile’s diverse climatic regions [29].

2.2. Affordability

Affordability in this study is defined as the economic capacity of households to afford adequate energy services without financial strain. This is assessed through three key parameters: the average household income, unemployment rates, and fuel costs. Data were sourced from the CASEN 2017 survey and monthly regional employment bulletins [34,35]. The household income was categorized into five risk levels, with very high-risk regions having average monthly incomes between 575,000 and 700,000 CLP, as energy costs in these regions often exceeded 10% of the household income, a standard international threshold for energy poverty [16]. Similarly, unemployment rates were used as a proxy for economic insecurity, with the areas displaying unemployment rates ≥8.5% classified at the highest risk level.
The fuel costs were adjusted by energy efficiency factors to reflect the true household expenditures. For instance, the regions where average fuel costs exceeded 78 CLP/kWh were deemed to be at very high risk, reflecting the burden of energy costs on low-income households [36,37]. These parameters were aggregated using a weighted model, assigning equal importance to income, unemployment, and fuel costs to derive a single affordability indicator at the provincial level.

2.3. Flexibility

Flexibility is the capacity of households to switch energy service providers or energy carriers in response to economic or environmental pressures. This factor was assessed by considering the economic constraints related to fuel costs and household income, and the costs of transitioning between energy systems. In Chile, the costs of switching between heating systems—such as wood to gas or electricity—vary widely, with wood-fired systems costing up to three times more than gas-based systems for heating (Figure 1). To assess the feasibility of switching energy systems, these economic factors were analyzed in relation to household income levels (Table 3). This analysis draws on reliable data from the 2010–2015 period, a timeframe chosen because improvements in housing quality tend to progress slowly, constrained by the long lifecycle of housing infrastructure and prevailing market dynamics [25,38]. Despite efforts to develop an updated, centralized, and reliable database on this topic, the dataset selected represents the most recent and dependable resource available for this research [39,40,41].
The regions were categorized by their risk levels based on the percentage of annual household income required to transition to alternative energy systems. A very low risk was defined for the regions where any energy system could be adopted with ≤10% of annual income, while very high-risk regions required >15% of the income for system transitions. This categorization aligns with international practices that assess economic flexibility as a key indicator of energy resilience [42].
Table 3. Characteristics of common energy use technologies and related costs and efficiency. Based on [29,43].
Table 3. Characteristics of common energy use technologies and related costs and efficiency. Based on [29,43].
Energy End-UseTechnologyCost of ImplementationCost of FuelEfficiency of System (η)
Space heatingWood-burning635 USD0.06 USD/kWh0.62
Gas238 USD0.13 USD/kWh0.9
Electricity289 USD0.18 USD/kWh0.99
Kerosene543 USD0.08 USD/kWh0.85
Water heatingGas200 USD0.13 USD/kWh0.9
Wood150 USD0.06 USD/kWh0.62
Electricity
CookingGas317 USD0.13 USD/kWh0.9
Electricity556 USD0.22 USD/kWh0.98
Wood556 USD0.06 USD/kWh0.62

2.4. Efficiency

Efficiency, in this context, refers to the conversion of primary fuels into useful household energy, accounting for losses during the energy transformation process. This study utilized data on the efficiency of heating, cooking, and hot water systems across Chile’s thermal zones (Table 2). The efficiency of the energy systems was calculated by averaging the efficiencies for each thermal zone, weighted by the prevalence of each fuel type. The zones with efficiency values ≥ 85% were classified as very low risk, while those with <55% efficiency were considered very high risk, consistent with the efficiency standards outlined in international energy studies [10].
This efficiency assessment is particularly relevant in Chile’s colder southern regions, where energy losses can exacerbate heating costs. For example, wood-fired heating systems, which are common in rural areas, were shown to have lower efficiency than gas or electric systems, increasing the risk of energy poverty in those regions [29]. These findings were integrated into the final efficiency indicator using a weighted sum approach.

2.5. Needs

The needs factor evaluates the adequacy of the energy services relative to the specific requirements of households, which are heavily influenced by local climatic conditions. In this study, we used Heating Degree Days (HDD) and Cooling Degree Days (CDD) to assess the energy necessary to maintain indoor thermal comfort, a critical indicator of energy poverty. While this study provides a multidimensional analysis of energy poverty, the availability of comprehensive national data on housing efficiency, indoor environmental conditions (e.g., temperature, dampness, mold), and occupants’ energy practices (which can influence energy poverty) in Chile remains limited [44,45,46]. Current national surveys do not capture these variables in detail. For instance, regional studies provide insights into thermal insulation and indoor air quality but are restricted to specific case studies and lack national representativeness [47].
Indoor thermal conditions have been linked to health risks, with temperatures below 12 °C significantly increasing the likelihood of respiratory and cardiovascular diseases [48]. To capture these risks, we established five levels of vulnerability: very low risk for zones with ≤100 CDD and ≤500 HDD, low risk for >100 to ≤200 CDD and >500 to ≤1000 HDD, moderate risk for CDD values between >200 and ≤300 and HDD between >1000 and ≤1500, high risk for zones with CDD from >300 to ≤400 and HDD from >1500 to ≤2000, and very high risk for CDD > 400 and HDD > 2000. These thresholds were selected based on the international best practices for assessing thermal comfort needs and their applicability to Chile’s diverse climate zones.
In addition to thermal requirements, we considered access to energy services essential for food preparation and hygiene, as these are fundamental household needs. Cooking and food preservation are critical to maintaining health and quality of life, and access to reliable refrigeration systems directly impacts a household’s ability to store perishable food safely [49]. This assessment included the presence of kitchen appliances like ovens, stoves, and refrigerators, as well as access to hot water systems, which are critical for maintaining adequate hygiene standards. Households using traditional braziers or basic stoves were considered at higher risk due to the inefficiency and health risks associated with these appliances, particularly in rural areas where modern cooking and heating systems are less prevalent.
The evaluation of the energy needs also incorporated the availability of heating and cooling systems, which varied significantly across Chile’s climate zones. In colder regions, where heating is a necessity, the proportion of homes without adequate heating systems was a critical indicator of energy vulnerability. Conversely, in warmer areas, the absence of cooling systems was used as a measure of unmet energy needs. The risk levels were determined by combining the thermal comfort requirements (HDD and CDD) with the percentage of households equipped with adequate heating and cooling systems. The regions where fewer than 20% of the homes had sufficient heating or cooling capabilities were categorized as very high risk, while the areas with ≥80% coverage were classified as very low risk. This categorization follows the guidelines used in previous studies that emphasize the importance of household energy infrastructure in mitigating energy poverty risks [10].
To provide a comprehensive assessment of the energy needs, additional parameters related to hygiene and food security were included. The access to piped hot water was evaluated, as the absence of hot water systems is a significant indicator of unmet basic energy needs, particularly in areas with colder climates. The households lacking reliable hot water sources were categorized as high risk, given the implications for health and sanitation. Similarly, the absence of refrigeration facilities was considered a substantial risk factor, as it directly affects a household’s ability to preserve food, increasing the likelihood of foodborne illnesses.
The final indicator of needs was calculated by equally weighing five critical parameters: a system for cooking, household refrigeration, hot water access, heating/cooling availability, and HDD/CDD requirements. Each parameter was assigned a score from very low to very high risk based on the established thresholds, and the values were combined to produce a single indicator for each climate zone. This approach aligns with international standards for assessing energy poverty, integrating both environmental conditions and access to essential energy services. The geographic distribution of the energy needs was mapped by thermal zone, using data obtained from the Coporación de Desarrollo Tecnológico CDT [29]. The final risk levels highlighted areas where energy services were inadequate, guiding targeted interventions for reducing vulnerability to energy poverty.
Climatic factors such as outdoor and indoor temperatures, extreme weather events, and air quality may also influence energy poverty [44,50]. These factors can affect household energy needs, infrastructure reliability, and health outcomes, potentially driving increased energy consumption or the adoption of energy efficiency improvements [51,52,53]. While these factors were not included in this analysis due to limited empirical evidence, they represent important areas for future research and empirical validation [54,55,56].

2.6. Combined Vulnerability to Energy Poverty

The five indicators—access, affordability, flexibility, efficiency, and needs—were combined to create a single measure of vulnerability to energy poverty for each commune in Chile (see Figure 2). Each of these indicators was derived from specific measurable drivers, such as the System Average Interruption Duration Index (SAIDI) for access and Heating Degree Days (HDD) for needs. An equal weighting approach was employed to ensure that each factor contributed equally to the final composite indicator. This decision aligns with the established literature, which supports equal weighting in multidimensional indices to avoid introducing bias, unless strong empirical evidence justifies alternative weighting [10].
To categorize vulnerability, each parameter was divided into five intervals, ranging from very low risk to very high risk. These intervals were primarily defined based on established thresholds from the literature. For instance, affordability was categorized using thresholds for the proportion of household income spent on energy, while thermal comfort needs were assessed using HDD and Cooling Degree Days (CDD). The combined vulnerability indicator was then mapped using ArcGIS, visualizing the spatial distribution of the energy poverty risk across the country. Data aggregation was conducted at the commune level, allowing for a high-resolution analysis, with progressive aggregation to the provincial and regional levels where necessary. This hierarchical spatial analysis provided a detailed examination of the energy poverty patterns and facilitated targeted policy interventions. The methodological approach ensured a comprehensive and transparent assessment, consistent with international best practices [57].

3. Results

In this section, we discuss the spatial distribution of these factors, using the examined factors, and identify the most at-risk areas. We first discuss each factor individually before outlining the combined risk of energy poverty and the proportion of pollution considered to be vulnerable.

3.1. Spatial Distribution of Drivers of Energy Poverty

Figure 3 (left) shows the distribution of risk for drivers related to energy access, reliability (SAIDI), and access to healthy fuels. As illustrated, the central regions of Chile, including Bío-Bío, La Araucanía, Los Ríos, and Los Lagos, exhibit the highest risk of energy poverty. The northern regions generally show lower risks, except for Parinacota, which has a high-risk profile. In Bío-Bío and La Araucanía, frequent power interruptions, often due to falling trees on the grid during high winter winds, contribute significantly to the risk. Additionally, in these central and southern regions, the predominant use of firewood, which is detrimental to health, exacerbates risk.
The spatial distribution of the affordability-related risks is illustrated in Figure 3 (center), accounting for factors like household income, unemployment, and fuel costs. Significant income inequalities reflect regional economic activities, impacting energy affordability. For instance, forestry and fishing in the Bío-Bío region, and agriculture in Maule and O’Higgins, contribute to lower incomes and higher affordability risks. In contrast, Antofagasta and the Metropolitan Region, where mining and industry drive the economy, have average incomes 38% above the national level, resulting in lower risk. Higher fuel transport costs in the northern and southern regions also contribute to increased energy costs.
The Bío-Bío and La Araucanía regions have a greater number of hours without electricity service supply (SAIDI) (Figure 4). An explanation for this could be falling trees on the power grid due to high wind speeds, especially in the winter: in the La Araucanía region, for example, 21.2% of the surface corresponds to forest plantations located mainly in the mountain range (east) and the coastal area (west) and these areas also observe the highest wind speeds. For the appropriateness of household fuels for health, firewood (which is most detrimental to health) is most predominantly used in the center and south of Chile, which leads to increased risk in these regions.
Household flexibility, assessed through the income and the switching costs between the energy sources, is shown in Figure 3 (right). Regions like Antofagasta and the Metropolitan Region display low flexibility risks due to higher incomes. The Magallanes region in the far south also shows a low risk because of lower fuel costs for kerosene and biomass. Residents in regions like La Araucanía and Ñuble allocate a significant portion of their income (16–17%) to energy system transitions, underscoring the flexibility risks in these areas.
The results for energy efficiency indicate low risks in O’Higgins, while high-risk levels were found in the south and far north (Figure 5, left). In southern Chile, the reliance on biomass for heating elevates risk due to its low system efficiency (η = 0.62), contrasting with regions using liquefied gas, where efficiency is higher (η = 0.9). The O’Higgins region is relatively low-risk since liquefied gas is the main fuel for heating, cooking, and hot water systems, leading to greater overall energy efficiency.
For the needs indicator (Figure 5, right), most of the country exhibits a low or very low risk, with notable exceptions in certain climate zones. In climate zone 1, only 12.5% of the households use heating systems compared to 97.8% in zone 7, reflecting significant variability in the energy needs across zones. In northern Chile’s hot, desert climates, energy demand for heating is low. Conversely, the south has high heating requirements due to colder, wetter climates, impacting household energy needs. The ownership of cooling and refrigeration systems shows more homogeneity across the zones, generally reducing the risk in this category.

3.2. Combined Risk of Energy Poverty

Figure 6 illustrates the combined risk across all the factors (access, affordability, flexibility, efficiency, and needs). The combined risk peaked in Chile’s south–central and La Araucanía regions, where factors like low incomes, high unemployment, poor power reliability, and low efficiency of equipment converge, heightened energy poverty vulnerability. In contrast, the far north and extreme south benefited from higher incomes and government support, resulting in relatively low combined risks.

3.3. Population Vulnerability to Energy Poverty

Table 4 presents the proportion of the population vulnerable to energy poverty across Chile, disaggregated by individual and combined factors. These findings reveal that nearly 7.8 million people (44.9%) face at least a moderate risk of energy poverty when considering all the factors combined. Affordability and efficiency stand out as the most significant contributors, with approximately 10.9 million individuals (63.08%) experiencing a moderate affordability risk and 9.8 million (56.65%) falling under a moderate efficiency risk. Additionally, 6.2 million people (35.95%) were at a high risk in terms of energy efficiency, highlighting critical deficiencies in heating and cooling systems.
When combining all the factors, 3.2 million people (18.5%) fell into the high-risk category, while 934,200 (5.4%) were classified as very high risk. Notably, affordability presented a significant challenge, with over 5.4 million individuals (31.57%) at a high risk, disproportionately affecting the central and southern regions where energy expenditures and reliance on inefficient systems were prevalent. On the lower end of the spectrum, 10.1 million people (58.42%) experienced a low risk in terms of access, indicating progress in ensuring reliable energy connections in many areas. However, only 925,550 individuals (5.35%) experienced a low risk in affordability, underscoring the economic strain the households faced despite access improvements.

4. Discussion

This study’s findings emphasize the multifaceted nature of energy poverty, with certain regions, particularly the central and southern areas, facing high risks. This aligns with existing research that underscores the complexity of energy poverty due to intertwined factors such as affordability, accessibility, and regional disparities [10,58]. The pronounced vulnerability observed in these regions suggests a need for targeted intervention strategies that focus on both economic support and accessible energy infrastructure, reinforcing the multidimensional approach advocated in other studies on energy poverty.
Another critical yet often overlooked factor is national internal mobility. Although internal mobility is recognized as a key driver of vulnerability, detailed data on its effects remain scarce in Chile. Current statistics, such as those from the National Census and the CASEN survey, primarily focus on rural-to-urban migration, leaving significant gaps in understanding the impact of intra-urban and inter-urban movements [35,59]. These forms of mobility, motivated by economic or social factors, can lead to shifts in the spatial distribution of energy poverty [60]. For instance, population displacement to peripheral areas with limited energy infrastructure could exacerbate vulnerability [61]. The absence of granular data on such patterns restricts the ability to fully capture the dynamic nature of energy poverty and design adaptive policies. Addressing this data gap by developing more specific data collection systems would allow for a more nuanced analysis of how mobility influences household energy conditions.
The composite index approach used here is consistent with Xiahui Che et al.’s framework, which highlights the importance of incorporating availability, affordability, and efficiency in a single measure to capture the comprehensive scope of energy poverty. This allows for an in-depth understanding of the spatial and economic dynamics involved, as reflected in assessments of global energy poverty by some scholars [19,21]. The integration of multidimensional indicators not only help in identifying the specific needs within regions but also guides more effective policy responses.
In terms of policy implications, this study reveals critical areas for intervention. For instance, the central and southern regions, where energy poverty risk is high, would benefit from focused policies that address income disparities and improve energy infrastructure. Some studies has shown that interventions are more effective when they address both economic and infrastructure gaps [2]. This could include subsidies for heating or cooling costs, expanded access to affordable fuels, and incentives for adopting energy-efficient appliances tailored to high-risk areas.
Despite the presence of several energy policies in Chile, their effectiveness in mitigating energy poverty remains limited due to their broad, nationwide scope. For instance, the Plan de Prevención y Descontaminación Atmosférica (PPDA) aims to reduce air pollution levels to protect public health, with region-specific plans tailored to address local atmospheric conditions [62]. Similarly, the Plan Nacional de Eficiencia Energética 2022–2026 provides a strategic framework to promote energy efficiency as part of Chile’s goal to achieve carbon neutrality by 2050 [63]. However, these policies often lack direct mechanisms to address the unique energy needs of vulnerable regions. For example, southern regions with extreme cold would benefit from policies that prioritize heating systems and energy-efficient insulation, while northern areas with higher temperatures require initiatives promoting cooling technologies and solar energy adoption. A climate zone-oriented approach could significantly enhance the efficiency of these policies by addressing the specific energy challenges unique to each region, ensuring that interventions align more closely with local energy needs to reduce energy poverty and promote energy equity.
Additionally, aligning energy poverty alleviation efforts with broader social programs, as suggested by a research done on Portugal, could improve resilience among vulnerable populations by addressing root socioeconomic challenges [7]. For example, such integrated efforts might focus on energy literacy programs and resources for home insulation, which could help lower long-term energy costs and increase energy security for low-income households.
In conclusion, this study highlights the need for spatially aware energy poverty policies that address both immediate economic burdens and structural factors over the long term. Future research should further explore how local environmental and social dynamics intersect to exacerbate energy poverty. Developing increasingly refined multidimensional indicators will continue to aid policymakers in tailoring interventions effectively and achieving a more comprehensive alleviation of energy poverty across diverse regions.

Challenges and Opportunities for Improvement

The reliance on secondary data sources could limit the ability to capture rapidly changing socioeconomic conditions [59,64,65]. However, these datasets represent the most comprehensive and validated information available, ensuring consistency and reliability across national-scale analyses [60,61]. While socioeconomic dynamics evolve, changes in housing quality and energy infrastructure typically occur at slower rates, reinforcing the relevance of these data [38,66]. Future research could incorporate primary data collection to complement secondary sources, offering more dynamic and localized insights into the evolving nature of energy poverty [67]. Although valuable regional-level insights are provided, the absence of finer-scale data, such as at the commune or neighborhood level, may omit critical intraregional variations in vulnerability [50]. Addressing these gaps would enable a more detailed understanding of localized disparities, particularly in peripheral areas with limited energy infrastructure. Incorporating high-resolution data in future analyses could enhance the specificity of vulnerability assessments, supporting the design of targeted and effective policy interventions [19,68,69].
The policy recommendations serve as a strategic framework for addressing energy poverty. While broad in scope, these recommendations could be further refined by including detailed implementation plans that align with the unique needs of specific regions [70,71,72]. Future work could outline timelines, resource allocations, and measurable outcomes, facilitating more actionable and region-specific interventions. Such enhancements would strengthen stakeholder engagement and ensure that policies effectively address the vulnerabilities identified [73,74]. The current approach offers a snapshot of the existing conditions, providing a static assessment of energy poverty. While comprehensive, this method does not account for how energy poverty might evolve over time due to factors such as climate change, urbanization, and economic shifts [12,13,14]. Longitudinal analyses could track these temporal dynamics, providing valuable insights into the effectiveness of interventions and the resilience of vulnerable populations [75,76].
Finally, the use of equal weighting in the composite vulnerability index ensures methodological simplicity and avoids introducing subjective bias [10]. However, this approach may not fully reflect the varying significance of factors across different contexts. Future analyses could explore alternative weighting schemes, guided by empirical evidence or expert judgment, to refine vulnerability assessments further [55,77,78,79]. Such refinements would preserve the robustness of this framework while enhancing its sensitivity to regional nuances. By addressing these challenges and opportunities, this research provides a strong foundation for advancing energy poverty assessments and policymaking in Chile [4,80,81]. Tailoring future approaches to incorporate these considerations will enhance their impact, ensuring that interventions are equitable, effective, and aligned with the diverse needs of Chile’s regions [82].

5. Conclusions

This study presents a comprehensive multifactor assessment of energy poverty across Chile, revealing significant regional disparities. Approximately 4 million individuals, or nearly 21% of the population, face high or very high energy poverty risks. These risks are predominantly concentrated in the central and southern regions, with Bío-Bío, La Araucanía, and Los Ríos showing the highest vulnerability levels. The combined risk indicator highlights that 18.5% of the population falls under high risk, while 5.4% is categorized as very high risk.
Affordability remains a critical issue, with 63% of the population experiencing moderate affordability risk. High energy costs, exceeding 10% of the household income in some regions, exacerbate the vulnerability. Energy efficiency also poses significant challenges, particularly in southern regions where wood-fired heating systems—accounting for up to 90% of heating sources in some zones—operate at efficiencies as low as 62%, compared to 90% for gas-based systems.
The spatial analysis underscores the diversity in energy needs across Chile’s climatic zones. In zone 7 (Cold Austral), over 97% of households require heating systems due to extreme low temperatures, contrasting sharply with zone 1 (Hot Desert), where only 12% of households report heating needs. Similarly, regions with high Heating Degree Days (HDD) and Cooling Degree Days (CDD) exhibit pronounced vulnerabilities, with nearly 26% of the population in these zones categorized under moderate to very high-risk levels for energy needs.
The policy recommendations derived from these findings emphasize the need for targeted interventions. Subsidies for heating and cooling systems, along with incentives for energy-efficient appliances, could significantly alleviate the economic burden on vulnerable households. Investments in grid reliability could also reduce the impact of frequent outages, particularly in regions with high System Average Interruption Duration Index (SAIDI) values, such as La Araucanía and Bío-Bío, where outages average over 4 h per year.
Future research should prioritize the collection of granular data on indoor environmental conditions and housing efficiency. Incorporating such data would refine the multidimensional assessment framework and enable more precise, region-specific policy responses. This would contribute to achieving Sustainable Development Goal 7 by ensuring equitable access to affordable and reliable energy for all Chileans.

Author Contributions

Conceptualization, A.M.-S. and E.N.; methodology, A.M.-S. and E.N.; software, D.I.S.-C. and Y.S.-L.; formal analysis, A.M.-S. and D.I.S.-C.; investigation, D.I.S.-C., A.M.-S. and Y.S.-L.; resources, A.M.-S.; data curation, A.M.-S.; writing—original draft preparation, A.M.-S., D.I.S.-C.; writing—review and editing, E.N.; project administration, A.M.-S.; funding acquisition, A.M.-S. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Universidad de La Frontera. This work was supported by ANID (previously CONICYT) Chile and is part of the project “Chilean-UK research network for the development of parameters to assess energy poverty in Chile”, grant number: REDI170532.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this study are available in Section 2.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations Development Programme Sustainable Development Goals. In Climate Change 2013–The Physical Science Basis; Cambridge University Press: Cambridge, UK, 2015; pp. 1–30.
  2. González-Eguino, M. Energy Poverty: An Overview. Renew. Sustain. Energy Rev. 2015, 47, 377–385. [Google Scholar] [CrossRef]
  3. MDS. Base de Datos Archivo Histórico CASEN. 2017. Available online: https://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-2017 (accessed on 16 May 2024).
  4. Reyes, R.; Schueftan, A.; Ruiz, C.; González, A.D. Controlling Air Pollution in a Context of High Energy Poverty Levels in Southern Chile: Clean Air but Colder Houses? Energy Policy 2019, 124, 301–311. [Google Scholar] [CrossRef]
  5. Porras-Salazar, J.A.; Contreras-Espinoza, S.; Cartes, I.; Piggot-Navarrete, J.; Pérez-Fargallo, A. Energy Poverty Analyzed Considering the Adaptive Comfort of People Living in Social Housing in the Central-South of Chile. Energy Build. 2020, 223, 110081. [Google Scholar] [CrossRef]
  6. Ministerio de Energía. Energia 2050; Ministerio de Energía Gobierno de Chile: Santiago, Chile, 2014. [Google Scholar]
  7. Horta, A.; Gouveia, J.P.; Schmidt, L.; Sousa, J.C.; Palma, P.; Simões, S. Energy Poverty in Portugal: Combining Vulnerability Mapping with Household Interviews. Energy Build. 2019, 203, 109423. [Google Scholar] [CrossRef]
  8. The World Bank Annual Report 2020; World Bank: Washington, DC, USA, 2020; ISBN 978-1-4648-1619-2.
  9. World Health Organization. Health in 2015: From MDGs, Millennium Development Goals to SDGs, Sustainable Development Goals; World Health Organisation: Geneva, Switzerland, 2015. [Google Scholar]
  10. Siksnelyte-Butkiene, I.; Streimikiene, D.; Lekavicius, V.; Balezentis, T. Energy Poverty Indicators: A Systematic Literature Review and Comprehensive Analysis of Integrity. Sustain. Cities Soc. 2021, 67, 102756. [Google Scholar] [CrossRef]
  11. Teixeira, I.; Ferreira, A.C.; Rodrigues, N.; Teixeira, S. Energy Poverty and Its Indicators: A Multidimensional Framework from Literature. Energies 2024, 17, 3445. [Google Scholar] [CrossRef]
  12. Abbas, K.; Li, S.; Xu, D.; Baz, K.; Rakhmetova, A. Do Socioeconomic Factors Determine Household Multidimensional Energy Poverty? Empirical Evidence from South Asia. Energy Policy 2020, 146, 111754. [Google Scholar] [CrossRef]
  13. Hou, X.; Wu, H.; Wang, W. Identification and Measurement of Multidimensional Relative Poverty of Chinese Rural Adults Considering Climate Factors. Front. Environ. Sci. 2022, 10, 891077. [Google Scholar] [CrossRef]
  14. Taltavull de La Paz, P.; Juárez Tárrega, F.; Su, Z.; Monllor, P. Sources of Energy Poverty: A Factor Analysis Approach for Spain. Front. Energy Res. 2022, 10, 847845. [Google Scholar] [CrossRef]
  15. Zhou, L.; Zhou, C.; Yang, F.; Che, L.; Wang, B.; Sun, D. Spatio-Temporal Evolution and the Influencing Factors of PM2.5 in China between 2000 and 2015. J. Geogr. Sci. 2019, 29, 253–270. [Google Scholar] [CrossRef]
  16. Taylor, L. Fuel Poverty: From Cold Homes to Affordable Warmth | by B. Boardman Belhaven Press, 1991, £38. Energy Policy 1993, 21, 1071–1072. [Google Scholar] [CrossRef]
  17. Nwozor, A.; Olanrewaju, J.S.; Ake, M.B. National Insecurity and the Challenges of Food Security in Nigeria. Acad. J. Interdiscip. Stud. 2019, 8, 9–20. [Google Scholar] [CrossRef]
  18. Deller, D.; Turner, G.; Waddams Price, C. Energy Poverty Indicators: Inconsistencies, Implications and Where Next? Energy Econ. 2021, 103, 105551. [Google Scholar] [CrossRef]
  19. Karpinska, L.; Śmiech, S. Breaking the Cycle of Energy Poverty. Will Poland Make It? Energy Econ. 2021, 94, 105063. [Google Scholar] [CrossRef]
  20. Che, X.; Zhu, B.; Wang, P. Assessing Global Energy Poverty: An Integrated Approach. Energy Policy 2021, 149, 112099. [Google Scholar] [CrossRef]
  21. Sy, S.A.; Mokaddem, L. Energy Poverty in Developing Countries: A Review of the Concept and Its Measurements. Energy Res. Soc. Sci. 2022, 89, 102562. [Google Scholar] [CrossRef]
  22. Karpinska, L.; Śmiech, S.; Gouveia, J.P.; Palma, P. Mapping Regional Vulnerability to Energy Poverty in Poland. Sustainability 2021, 13, 10694. [Google Scholar] [CrossRef]
  23. Mardones, C. Economic, Environmental, and Social Assessment of the Replacement of Coal-Fired Thermoelectric Plants for Solar and Wind Energy in Chile. J. Clean. Prod. 2023, 411, 137343. [Google Scholar] [CrossRef]
  24. Bouzarovski, S.; Petrova, S. A Global Perspective on Domestic Energy Deprivation: Overcoming the Energy Poverty–Fuel Poverty Binary. Energy Res. Soc. Sci. 2015, 10, 31–40. [Google Scholar] [CrossRef]
  25. Thomson, H.; Bouzarovski, S.; Snell, C. Rethinking the Measurement of Energy Poverty in Europe: A Critical Analysis of Indicators and Data. Indoor Built Environ. 2017, 26, 879–901. [Google Scholar] [CrossRef]
  26. Ministerio de Energía. Balance Nacional de Energía; Ministerio de Energía Gobierno de Chile: Santiago, Chile, 2019. [Google Scholar]
  27. C.N.E. Anuario Estadístico de Energía 2018; Comisión Nacional de Energía. Available online: https://www.cne.cl/wp-content/uploads/2019/04/Anuario-CNE-2018.pdf (accessed on 16 May 2024).
  28. Labraña, J.; Urquiza, A.; Cortés, J.; Álamos, N.; Amigo, C.; Chahuan, J. Caracterización Del Mercado de Leña: Un Abordaje Integral; Red de Pobreza Energética–RedPE: Santiago, Chile, 2019; Available online: https://pobrezaenergetica.cl/wp-content/uploads/2023/01/DT7.-Caracterizacion-mercado-de-la-lena-en-Chile.pdf (accessed on 16 May 2024).
  29. CDT. Estudio de usos Finales y Curva de Oferta de la Conservación de la Energía en el Sector Residencial; Corporación de Desarrollo Tecnológico: Santiago, Chile, 2010. [Google Scholar]
  30. Ramos, N.P.R. Consumo de Energía a Nivel Residencial en Chile y Análisis de Eficiencia Energética en Calefacción; Universidad de Chile: Santiago, Chile, 2011. [Google Scholar]
  31. Boso, A.; Hofflinger, A.; Garrido, J.; Álvarez, B. Breathing Clean Air or Cheaply Heating Your Home: An Environmental Justice Dilemma in Chilean Patagonia. Geogr. Rev. 2022, 112, 667–687. [Google Scholar] [CrossRef]
  32. Pokhrel, A.K.; Bates, M.N.; Acharya, J.; Valentiner-Branth, P.; Chandyo, R.K.; Shrestha, P.S.; Raut, A.K.; Smith, K.R. PM 2.5 in Household Kitchens of Bhaktapur, Nepal, Using Four Different Cooking Fuels. Atmos. Environ. 2015, 113, 159–168. [Google Scholar] [CrossRef]
  33. Energía Abierta, Comisión Nacional de Energía (CNE). Available online: http://energiaabierta.cl/ (accessed on 16 May 2024).
  34. MIDESO. Ingresos de los Hogares. Síntesis de Resultados Casen 2017; MIDESO: Santiago, Chile, 2018. [Google Scholar]
  35. INE. Boletín Estadístico: Empleo Trimestral Regional; Instituto Nacional de Estadísticas: Santiago, Chile, 2019. [Google Scholar]
  36. CNE Mapa Precio de Combustibles. Comisión Nacional de Energía. 2018. Available online: http://energiaabierta.cl/visualizaciones/mapa-precio-de-combustibles/ (accessed on 16 May 2024).
  37. Ministerio de Energía. Encuesta Nacional de Energía; Ministerio de Energía: Santiago, Chile, 2016. [Google Scholar]
  38. Clark, J.; Kearns, A. Housing Improvements, Perceived Housing Quality and Psychosocial Benefits from the Home. Hous. Stud. 2012, 27, 915–939. [Google Scholar] [CrossRef]
  39. Pérez-Fargallo, A.; Bienvenido-Huertas, D.; Rubio-Bellido, C.; Trebilcock, M. Energy Poverty Risk Mapping Methodology Con-Sidering the User’s Thermal Adaptability: The Case of Chile. Energy Sustain. Dev. 2020, 58, 63–77. [Google Scholar] [CrossRef]
  40. Santiago, C.M.; Raggi, J.P.F.; Erices, L.V. Urban Growth Trends in Midsize Chilean Cities: The Case of Temuco. Urbe 2016, 8, 375–389. [Google Scholar] [CrossRef]
  41. Sarricolea, P.; Herrera-Ossandon, M.; Meseguer-Ruiz, Ó. Climatic Regionalisation of Continental Chile. J. Maps 2017, 13, 66–73. [Google Scholar] [CrossRef]
  42. Recalde, M.; Peralta, A.; Oliveras, L.; Tirado-Herrero, S. Structural Energy Poverty Vulnerability and Excess Winter Mortality in the European Union: Exploring the Association between Structural Determinants and Health. Energy Policy 2019, 133, 110869. [Google Scholar] [CrossRef]
  43. Martinez-Soto, A.; Jentsch, M.F. Quantifizierung der langfristigen Entwicklung des Nutzungsgrades von Anlagen und Geräten im Wohnungssektor in Deutschland und Bestimmung zukünftiger Energieeinsparpotenziale im Hinblick auf die Klimaschutz-ziele der Bundesregierung. In Proceedings of the Bauphysiktage Kaiserslautern 2015, Kaiserslautern, Germany, 21–22 October 2015; pp. 137–142. [Google Scholar]
  44. Encinas, F.; Truffello, R.; Aguirre-Nuñez, C.; Puig, I.; Vergara-Perucich, F.; Freed, C.; Rodríguez, B. Mapping Energy Poverty: How Much Impact Do Socioeconomic, Urban and Climatic Variables Have at a Territorial Scale? Land 2022, 11, 1449. [Google Scholar] [CrossRef]
  45. Mutani, G.; Alehasin, M.; Yang, H.; Zhang, X.; Felmer, G. Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile. Buildings 2024, 14, 185. [Google Scholar] [CrossRef]
  46. Villalobos, C.; Chávez, C.; Uribe, A. Energy Poverty Measures and the Identification of the Energy Poor: A Comparison between the Utilitarian and Capability-Based Approaches in Chile. Energy Policy 2021, 152, 112146. [Google Scholar] [CrossRef]
  47. Martinez-Soto, A.; Jimenez-Gallardo, C.; Villarroel-Lopez, A.; Reyes-Riveros, A.; Höhl, J. Toward Sustainable Indoor Environments: Assessing the Impact of Thermal Insulation Measures on Air Quality in Buildings—A Case Study in Temuco, Chile. Sustainability 2024, 16, 547. [Google Scholar] [CrossRef]
  48. Aristondo, O.; Onaindia, E. Inequality of Energy Poverty between Groups in Spain. Energy 2018, 153, 431–442. [Google Scholar] [CrossRef]
  49. Reicks, M.; Kocher, M.; Reeder, J. Impact of Cooking and Home Food Preparation Interventions Among Adults: A Systematic Review (2011–2016). J. Nutr. Educ. Behav. 2018, 50, 148–172.e1. [Google Scholar] [CrossRef] [PubMed]
  50. Cornelis, M. Energy Efficiency, the Overlooked Climate Emergency Solution. Ekon. Polit. 2020, 2020, 48–67. [Google Scholar] [CrossRef]
  51. Ruiz-Rivas, U.; Tirado-Herrero, S.; Castaño-Rosa, R.; Martínez-Crespo, J. Disconnected, yet in the Spotlight: Emergency Research on Extreme Energy Poverty in the Cañada Real Informal Settlement, Spain. Energy Res. Soc. Sci. 2023, 102, 103182. [Google Scholar] [CrossRef]
  52. Tvinnereim, E.; Liu, X.; Jamelske, E.M. Public Perceptions of Air Pollution and Climate Change: Different Manifestations, Similar Causes, and Concerns. Clim. Change 2017, 140, 399–412. [Google Scholar] [CrossRef]
  53. Vurro, G.; Santamaria, V.; Chiarantoni, C.; Fiorito, F. Climate Change Impact on Energy Poverty and Energy Efficiency in the Public Housing Building Stock of Bari, Italy. Climate 2022, 10, 55. [Google Scholar] [CrossRef]
  54. Ehsanullah, S.; Tran, Q.H.; Sadiq, M.; Bashir, S.; Mohsin, M.; Iram, R. How Energy Insecurity Leads to Energy Poverty? Do Environmental Consideration and Climate Change Concerns Matters. Environ. Sci. Pollut. Res. 2021, 28, 55041–55052. [Google Scholar] [CrossRef]
  55. Haddad, S.; Paolini, R.; Ulpiani, G.; Synnefa, A.; Hatvani-Kovacs, G.; Garshasbi, S.; Fox, J.; Vasilakopoulou, K.; Nield, L.; Santamouris, M. Holistic Approach to Assess Co-Benefits of Local Climate Mitigation in a Hot Humid Region of Australia. Sci. Rep. 2020, 10, 14216. [Google Scholar] [CrossRef]
  56. Liu, S.; Kwok, Y.T.; Lau, K.K.L.; Ouyang, W.; Ng, E. Effectiveness of Passive Design Strategies in Responding to Future Climate Change for Residential Buildings in Hot and Humid Hong Kong. Energy Build. 2020, 228, 110469. [Google Scholar] [CrossRef]
  57. Zeleňáková, M.; Labant, S.; Zvijáková, L.; Weiss, E.; Čepelová, H.; Weiss, R.; Fialová, J.; Minďaš, J. Methodology for Environmental Assessment of Proposed Activity Using Risk Analysis. Environ. Impact Assess. Rev. 2020, 80, 106333. [Google Scholar] [CrossRef]
  58. Primc, K.; Dominko, M.; Slabe-Erker, R. 30 Years of Energy and Fuel Poverty Research: A Retrospective Analysis and Future Trends. J. Clean. Prod. 2021, 301, 127003. [Google Scholar] [CrossRef]
  59. INE. Migración Interna en Chile; INE: Santiago, Chile, 2017. [Google Scholar]
  60. Bouzarovski, S.; Simcock, N. Spatializing Energy Justice. Energy Policy 2017, 107, 640–648. [Google Scholar] [CrossRef]
  61. Bouzarovski, S.; Petrova, S.; Sarlamanov, R. Energy Poverty Policies in the EU: A Critical Perspective. Energy Policy 2012, 49, 76–82. [Google Scholar] [CrossRef]
  62. Montenegro, E.R.; Palavecino, M.A. Impacto del Plan de Prevención y Descontaminación Atmosférica Sobre la Contaminación de Santiago. RIAT Rev. Interam. Medioambiente Tur. 2011, 7, 10–17. [Google Scholar]
  63. Acuña, H.; Easton, P.; Ramos, C.; Torres, C. El Sector de la Construcción Ante el Desafío Climático Global; Cámara Chilena de la Construcción: Las Condes, Santiago, 2019; Volume 45, Available online: https://catalogo.extension.cchc.cl/documentos/documentos/44024-2.pdf (accessed on 16 May 2024).
  64. Minsterio de Desarrollo Social Sintesis de Resultados, Casen 2017. Ministerio de Desarrollo Social y Familia, 2017. Available online: https://www.dipres.gob.cl/597/w3-multipropertyvalues-14837-23712.html (accessed on 16 May 2024).
  65. Sanhueza, C. Casen 2009. Obs. Econ. 2010, 43, 4–6. [Google Scholar] [CrossRef]
  66. Park, K.B. A Study on the Small-Scale Housing Improvement. J. Archit. Inst. Korea 2023, 39, 87–94. [Google Scholar] [CrossRef]
  67. Thomson, H.; Thomas, S.; Sellstrom, E.; Petticrew, M. Housing Improvements for Health and Associated Socio-Economic Outcomes. Cochrane Database Syst. Rev. 2013, 28, CD008657. [Google Scholar] [CrossRef]
  68. Castaño-Rosa, R.; Sherriff, G.; Thomson, H.; Guzmán, J.S.; Marrero, M. Transferring the Index of Vulnerable Homes: Application at the Local-Scale in England to Assess Fuel Poverty Vulnerability. Energy Build. 2019, 203, 109458. [Google Scholar] [CrossRef]
  69. Gouveia, J.P.; Palma, P.; Simoes, S.G. Energy Poverty Vulnerability Index: A Multidimensional Tool to Identify Hotspots for Local Action. Energy Rep. 2019, 5, 187–201. [Google Scholar] [CrossRef]
  70. Best, R.; Sinha, K. Fuel Poverty Policy: Go Big or Go Home Insulation. Energy Econ. 2021, 97, 105195. [Google Scholar] [CrossRef]
  71. Calvo, R.; Álamos, N.; Huneeus, N.; O’Ryan, R. Energy Poverty Effects on Policy-Based PM2.5 Emissions Mitigation in Southern and Central Chile. Energy Policy 2022, 161, 112762. [Google Scholar] [CrossRef]
  72. IPCC. Climate Change 2014 Synthesis Report Summary Chapter for Policymakers; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
  73. Brolan, C.E. Looking Back—Australia’s Sustainable Development and Climate Change Policy Agendas. Sustainability 2023, 15, 5688. [Google Scholar] [CrossRef]
  74. Mohamad Taghvaee, V.; Assari Arani, A.; Nodehi, M.; Khodaparast Shirazi, J.; Agheli, L.; Neshat Ghojogh, H.M.; Salehnia, N.; Mirzaee, A.; Taheri, S.; Mohammadi Saber, R.; et al. Sustainable Development Goals: Transportation, Health and Public Policy. Rev. Econ. Political Sci. 2023, 8, 134–161. [Google Scholar] [CrossRef]
  75. Bienvenido-Huertas, D.; Sánchez-García, D.; Marín-García, D.; Rubio-Bellido, C. Is the Analysis Scale Crucial to Assess Energy Poverty? Analysis of Yearly and Monthly Assessments Using the 2 M Indicator in the South of Spain. Energy Build. 2023, 285, 112889. [Google Scholar] [CrossRef]
  76. Sánchez-Torija, J.G.; Nieto, M.A.F. Energy Consumption of Buildings and Urban Energy Poverty Assessment: Case Study of a Madrid Neighbourhood. Energy Effic. 2022, 15, 53. [Google Scholar] [CrossRef]
  77. Cedano, K.G.; Robles-Bonilla, T.; Santillán, O.S.; Martínez, M. Assessing Energy Poverty in Urban Regions of Mexico: The Role of Thermal Comfort and Bioclimatic Context. Sustainability 2021, 13, 10646. [Google Scholar] [CrossRef]
  78. März, S. Assessing the Fuel Poverty Vulnerability of Urban Neighbourhoods Using a Spatial Multi-Criteria Decision Analysis for the German City of Oberhausen. Renew. Sustain. Energy Rev. 2018, 82, 1701–1711. [Google Scholar] [CrossRef]
  79. Rani, E.; Kaushik, S.; Goyat, B. Poverty Assessment of Rural Households through Multidimensional Indicators in Haryana. Ann. Agri Bio Res. 2015, 20, 121–130. [Google Scholar]
  80. Cortes, A.; Rismanchi, B. Residential Energy Efficiency in Chile. Policies to Reduce Firewood Dependency. Estoa 2020, 9, 86–106. [Google Scholar] [CrossRef]
  81. Pérez-Fargallo, A.; Marín-Restrepo, L.; Contreras-Espinoza, S.; Bienvenido-Huertas, D. Do We Need Complex and Multidimensional Indicators to Assess Energy Poverty? The Case of the Chilean Indicator. Energy Build. 2023, 295, 113314. [Google Scholar] [CrossRef]
  82. Cortés, A.; Amigo, C. Energy Culture and the Dynamics of Energy Poverty in South Chile: A Blind Spot for Decontamination Energy Efficiency Policies. People Place Policy Online 2022, 16. [Google Scholar] [CrossRef]
World 05 00071 g001
Figure 1. Price comparison according to heat production systems in kWh of useful thermal energy per Chilean pesos [29,36].
Figure 1. Price comparison according to heat production systems in kWh of useful thermal energy per Chilean pesos [29,36].
World 05 00071 g001
World 05 00071 g002
Figure 2. Parameters, factors, and their combination in assessing vulnerability to energy poverty.
Figure 2. Parameters, factors, and their combination in assessing vulnerability to energy poverty.
World 05 00071 g002
World 05 00071 g003
Figure 3. Risk of energy poverty from factors of access (left), affordability (center), and flexibility (right).
Figure 3. Risk of energy poverty from factors of access (left), affordability (center), and flexibility (right).
World 05 00071 g003
World 05 00071 g004
Figure 4. Wind speed in July (left), SAIDI (center), and forests (right) in the region of La Araucanía.
Figure 4. Wind speed in July (left), SAIDI (center), and forests (right) in the region of La Araucanía.
World 05 00071 g004
World 05 00071 g005
Figure 5. Risk of energy poverty from factors of energy efficiency (left) and needs (right).
Figure 5. Risk of energy poverty from factors of energy efficiency (left) and needs (right).
World 05 00071 g005
World 05 00071 g006
Figure 6. Combined risk of energy poverty as a combination of the five factors (access, affordability, flexibility, efficiency, and needs).
Figure 6. Combined risk of energy poverty as a combination of the five factors (access, affordability, flexibility, efficiency, and needs).
World 05 00071 g006
Table 1. Factors that contribute to the vulnerability of energy poverty, measurable drivers and outcomes, and data available for Chile, adapted from [25].
Table 1. Factors that contribute to the vulnerability of energy poverty, measurable drivers and outcomes, and data available for Chile, adapted from [25].
FactorDriving ForcesMeasurable DriversData AvailableSource
AccessPoor availability of energy carriers appropriate to meet household needs.Choice and availability of carriersAccess fuel type of each region and climate zone
Power supply reliability SAIDI		[3,26]
AffordabilityHigh ratio between the cost of fuels and household incomes, including the role of tax systems or assistance schemes. Inability to invest in the construction of new energy infrastructures.Household income
Energy costs—actual and theoretical		Average household income
Percentage of unemployment Cost for each fuel type		[3,27,28]
FlexibilityInability to move to a form of energy service provision that is appropriate to household needs.Tenure typeTenure type of equipment to heating, hot water provision, cooking, lighting and appliances[29]
Energy efficiencyDisproportionately high loss of useful energy during energy conversions in the home.Energy efficiency rating of built fabric and equipmentEfficiency of the equipment[29]
NeedsMismatch between household energy requirements and available energy services for social, cultural, economic or health reasons.Household type
Additional energy needs		Climate zone and percentage of household with heating/cooling
Percentage of household with refrigeration and hot water system
System of cooking		[29,30]
Table 2. Predominant energy carries and coverage by energy end-use requirements across climate regions in Chile [29].
Table 2. Predominant energy carries and coverage by energy end-use requirements across climate regions in Chile [29].
Thermal ZoneSpace Heating (% Use)Water HeatingCookingLighting and Appliances
Zone 1No system (87%)/Gas (5.5%), Electricity (6.5%)No system (31%)/Wood (6%), gas (52%), electricity (3%), kerosene (8%)Wood (8%), gas (92%)Electricity (100%)
Zone 2No system (34%)/Wood (11%), gas (34), electricity (20%), kerosene (1%)No system (14%)/Wood (11%), gas (68%), electricity (6%), kerosene (1%)Wood (13%), gas (87%)Electricity (100%)
Zone 3No system (12%)/Wood (13%), gas (41%), electricity (30%), kerosene (4%)No system (8%)/Wood (9%), gas (68%), electricity (11%), kerosene (3%)Wood (5%), gas (32%), electricity (63%)Electricity (100%)
Zone 4No system (6%)/Wood (61%), gas (16), electricity (15%), kerosene (2%)No system (23%)/Wood (9%), gas (17%), electricity (48%), kerosene (3%)Wood (49%), gas (51%)Electricity (100%)
Zone 5Wood (86%), gas (7%), electricity (4%), kerosene (1%)No system (49%)/Wood (36%), gas (5%), electricity (9%), Wood (86%), gas (14%)Electricity (100%)
Zone 6Wood (90%), gas (4%), electricity (2%), kerosene (1%)No system (37%)/Wood (52%), gas (5%), electricity (5%), Wood (90%), gas (10%)Electricity (100%)
Zone 7Wood (31%), gas (66%), electricity (1%)No system (26%)/Wood (43%), gas (28%), electricity (3%), kerosene (1%)Wood (63%), gas (37%)Electricity (100%)
Table 4. Proportion of population vulnerable to energy poverty across Chile and the approximated population (AP) [35].
Table 4. Proportion of population vulnerable to energy poverty across Chile and the approximated population (AP) [35].
Risk LevelFactor
AccessAPAffordabilityAPFlexibilityAPEnergy EfficiencyAPNeedsAPCombinedAP
Very low risk0%00%044.88%7,764,2400%026.64%4,608,72044.90%7,767,700
Low risk58.42%10,106,6605.35%925,55023.85%4,126,0505.20%899,60073.36%12,691,28020.90%3,615,700
Moderate risk25.17%4,354,41063.08%10,912,84014.06%2,432,38056.65%9,800,4500%010.30%1,781,900
High risk14.11%2,441,03031.57%5,461,61017.21%2,977,33035.95%6,219,3500%018.50%3,200,500
Very high risk2.30%397,9000%00%02.19%378,8700%05.40%934,200
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Martinez-Soto, A.; Nix, E.; Saldias-Lagos, Y.; Sanhueza-Catalán, D.I. Multiple Indicator Vulnerability to Energy Poverty: Assessing Spatial Variability Across Chile. World 2024, 5, 1404-1420. https://doi.org/10.3390/world5040071

AMA Style

Martinez-Soto A, Nix E, Saldias-Lagos Y, Sanhueza-Catalán DI. Multiple Indicator Vulnerability to Energy Poverty: Assessing Spatial Variability Across Chile. World. 2024; 5(4):1404-1420. https://doi.org/10.3390/world5040071

Chicago/Turabian Style

Martinez-Soto, Aner, Emily Nix, Yarela Saldias-Lagos, and Daniel Ignacio Sanhueza-Catalán. 2024. "Multiple Indicator Vulnerability to Energy Poverty: Assessing Spatial Variability Across Chile" World 5, no. 4: 1404-1420. https://doi.org/10.3390/world5040071

APA Style

Martinez-Soto, A., Nix, E., Saldias-Lagos, Y., & Sanhueza-Catalán, D. I. (2024). Multiple Indicator Vulnerability to Energy Poverty: Assessing Spatial Variability Across Chile. World, 5(4), 1404-1420. https://doi.org/10.3390/world5040071

Article Metrics

Back to TopTop