A Comprehensive Quadrilemma Index of Renewable Energy: The Latin American Case

Abstract

This study developed an Energy Quadrilemma Index (EQI) for Latin American countries, analyzing data from six countries from 2014 to 2020. Using the Principal Component Analysis method, this work reduced the dimensionality of 20 indicators grouped into four dimensions: energy security, energy equity, sustainable development, and a new social context axis. The results reveal significant disparities among the countries in the study. For example, Uruguay shows robust indicators, Paraguay exhibits low utilization of the energy it produces, and Chile displays the poorest results in the sustainable development axis. Many countries’ widespread dependence on hydroelectricity makes them vulnerable to water crises. The results show that social, economic, and structural inequalities represent the main barriers to the energy transition, often marginalizing low-income populations. Ensuring a fair and inclusive transition requires implementing targeted policies and solutions adapted to each country’s specific context. Although Costa Rica leads in performance, it faces significant challenges in the field of sustainability. In contrast, Honduras has made some progress with sustainable development but still demonstrates weaknesses in other areas. These results highlight that standardized solutions can exacerbate regional inequalities, demanding approaches more tailored to local needs. This work’s novelty lies in the use of the social context dimension as a feature to assess energy poverty in selected countries.

1. Introduction

The recent energy crisis, exacerbated by the conflict in Ukraine and the disruption of production chains in the post-pandemic period, has highlighted the vulnerability of global energy security, with repercussions across all nations, including Europe. This scenario raises questions regarding the feasibility of the energy transition strategies outlined by the 2015 Paris Agreement. These uncertainties are studied in the concept known as the energy trilemma, which seeks to balance energy security, equity, and environmental sustainability. While Latin America benefits from a predominantly renewable energy matrix and emits comparatively fewer pollutants than developed regions, it still has unique political, economic, and social challenges.

Socioeconomic factors present additional barriers to the energy transition, highlighting an energy quadrilemma. This expanded concept incorporates the social context as a critical dimension, particularly pertinent to Latin America, where significant pre-existing inequalities cast doubt on the energy transition’s capacity to benefit the entire population. Often, only a privileged minority gains access to or can take advantage of the energy transition. The objective of this study is to develop and compute an indicator of the energy quadrilemma that reflects aspects of the regional social context, providing a more comprehensive perspective on the challenges of implementing balanced and effective energy policies. The aim is to quantify how energy security, equity, environmental sustainability, and social dynamics interact in Latin America.

The concept of the energy trilemma, developed by the World Energy Council, comprises three facets. The first, energy security, pertains to the ability to reliably meet current and future energy demands while possessing the resilience to withstand and recover from market shocks. This dimension emphasizes the robustness and resilience of national energy infrastructure. The second component, energy equity, examines the capability to ensure universal access to energy services at affordable and fair prices for the entire population, focusing on the equitable distribution of energy resources to ensure that all societal strata enjoy the opportunities provided by energy. Finally, environmental sustainability forms the third pillar of the trilemma, representing efforts to promote an energy transition that minimizes and prevents environmental damage and the impacts of climate change [1].

In a similar vein, the concept of the energy trilemma developed by the World Economic Forum (WEF) addresses the same three pillars. Beyond the trilemma, the index created by the WEF introduces a fourth pillar, which assesses a country’s capacity to advance in the energy transition. This new index includes elements such as the regulatory environment for renewable energies, access to financing for clean energy projects, and the promotion of innovation and development of new businesses in the energy sector. It also considers job creation in industries contributing to reducing environmental impacts [2].

These elements directly influence both the efficiency and the speed of the energy transition. However, there is a significant challenge: the outcomes of these metrics may not accurately reflect the existing socioeconomic context. Most performance metrics assign greater weight to sustainable development’s economic dimension than social and environmental dimensions because they support the hypothesis that economic growth is essential for driving environmental and social improvements [3].

Although the region’s economies are predisposed to adopt new energy technologies [4], a substantial portion of the population may not fully enjoy their benefits. This imbalance underscores the urgent need for more inclusive energy transition strategies, which should advance technological innovation and ensure that all segments of society equitably enjoy the benefits of the transition. An attractive alternative for the Latin American context is to incorporate regional characteristics, expanding the traditional analysis of the energy trilemma to include a social context dimension. This approach translates into the concept of the energy quadrilemma, integrating the pillars of energy equity, energy security, environmental sustainability, and a new dimension of social context [5].

Within this new dimension, key indicators such as poverty, income distribution, and access to basic services provide essential insights into the broader contextual factors affecting the energy quadrilemma index. Restricting comparisons to countries within the region is a fair way to assess performance, considering the historical colonial process the region has undergone. Its current issues tend to have common roots, and importing solutions may not yield the desired effects. This study expanded the traditional analysis of the energy trilemma to include a social context dimension, forming an energy quadrilemma that integrates equity, security, environmental sustainability, and the new topic, social context.

Therefore, the main objective of this study is to develop a composite indicator, the Energy Quadrilemma Index (EQI) that captures the interplay between energy security, energy equity, environmental sustainability, and a social context dimension tailored to Latin America. This index aims to reflect the region’s structural, economic, and social challenges.

Although indices such as the Energy Trilemma World Energy Council [1] and the Energy Transition Index World Economic Forum, [2] assess key aspects of energy systems, they often fail to capture the socio-political, historical, and infrastructural specificities of Latin American countries. Thomson et al. [6] identified only 62 scientific articles addressing energy poverty in Latin America since 1991, revealing a significant research gap. In addition, Quijano [7] argues that Latin American societies continue to operate under a “coloniality of power”, a persistent structure of domination rooted in the colonial era that manifests in social inequality and institutional asymmetries. These structural conditions influence both access to energy and the capacity to benefit from energy transitions.

In response to these theoretical and methodological gaps, we propose the Energy Quadrilemma Index (EQI), composed of four dimensions, adding a social pillar that incorporates indicators of poverty, inequality, and basic infrastructure. The index is constructed using Principal Component Analysis (PCA), a well-established technique for composite index construction, recommended by institutions such as the OECD and JRC [8]. PCA reduces dimensionality, minimizes noise, and enhances interpretability.

Using the indicators suggested by Lazaro & Soares [5], an Energy Quadrilemma Index (EQI) specific to Latin America was developed, employing a principal component analysis to synthesize multifaceted data into a robust index that reflects the complex interactions between these four pillars. The Energy Quadrilemma Index (EQI) was obtained using the Principal Component Analysis method.

The remainder of this paper is structured to provide a logical and comprehensive exposition of the research process and findings. Section 2 presents the theoretical framework and conceptual underpinnings of the Energy Quadrilemma, emphasizing the limitations of existing composite indices and justifying the inclusion of a fourth pillar addressing a social context. This section also discusses how regional inequalities and institutional legacies shape energy access in Latin America. Section 3 outlines the methodological approach, detailing the variable selection process, data sources, normalization procedures, and the implementation of Principal Component Analysis (PCA) as a dimensionality reduction technique. It also explains the rationale behind the weighting structure and the robustness checks employed. Section 4 is devoted to presenting and interpreting the results, including a detailed analysis of the principal components and the resulting country-level scores across the four dimensions of the EQI. Additionally, this section contextualizes the findings by comparing national profiles and identifying structural patterns of vulnerability. Section 5 synthesizes the main conclusions and elaborates on the policy implications of the results, emphasizing the relevance of multidimensional indicators for designing inclusive energy transition strategies. It also offers reflections on the limitations of the study and proposes avenues for future research.

2. Theory Framework

The concept of economic development gained importance in the 1970s, with various authors proposing their definitions. However, a definition that gained greater acceptance is based on a Delphi process conducted by the American Economic Development Council in 1984. This definition establishes economic development as creating wealth by mobilizing human, financial, physical, and natural capital to produce marketable goods. Thus, economic development benefits society by providing more job opportunities and increasing tax revenue, which can be reinvested in more goods and services for the community [9].

Bossel [10] emphasizes that development indicators are evolving to accommodate and manage the complex interactions among many systems, including environmental, economic, and social. He highlights the importance of identifying variables that provide critical information on the viability and sustainability of such systems. Furthermore, Bossel argues that this evolution should include the creation of indicators designed to measure and assess environmental sustainability. This change marks a shift from simple, often economic-focused parameters to more intricate sets of indicators that capture multiple dimensions of sustainable development.

The concept of sustainable development raises a fundamental question about how to measure it. Since the United Nations Conference on Environment and Development in 1992, significant efforts have been made to develop assessment tools for sustainable development [9]. These tools play a crucial role in supporting the development, monitoring, and evaluation of policies and setting goals to achieve objectives defined by national or supranational bodies.

Many studies use the energy trilemma to examine various aspects, including energy justice [11,12], aiming to understand the relationship between energy and economic development. These researches assess the energy policies adopted by countries [13] and the resultant energy performance [14]. Recently, the focus has expanded to issues related to the energy transition. In this context, the energy trilemma serves as a foundation for various academic works, as indicated. Adopting an energy transition perspective, the World Economic Forum (WEF) proposes a composite indicator that expands the energy trilemma by adding a new pillar: the capability to promote the energy transition [2]. This new component evaluates aspects related to business actions and practices, as well as the ability to generate and adopt new technologies and the available infrastructure.

The climate emergency has propelled energy issues to the forefront of economic development. According to Lazaro and Serrani [15], energy, previously seen merely as an input, now assumes strategic importance. The author explains that volatility, rising energy costs, and unpredictability in energy markets have spurred a desire for energy self-sufficiency. At the same time, this situation has underscored the importance of sustainable practices, elevating energy to a prominent economic position. In this respect, incorporating the social context becomes vital when assessing economic development, as access to and consumption of energy tends to be very unequal within the Latin American population. The same author reports that wealthier population strata tend to consume a larger proportion of modern energies, notably electricity and natural gas. In contrast, the less affluent typically consume a higher proportion of biomass and fuels. In Lazaro & Soares [5], a fourth pillar based on the social context is proposed, using Brazil as a case study. The author formulates a quadrilemma addressing characteristics of households and access to essential services such as treated water and sanitation, in addition to considering income inequality. These elements constitute an aspect often overlooked by prevailing models in the current literature.

Social conjecture in Latin America is shaped by historical events. The territories comprising the region share challenges that trace back to the period of colonization, a historical milestone that laid the fundamental roots for enduring implications in the region. These implications encompass property concentration, persistent poverty, social marginalization, the practice of discrimination, the presence of authoritarian regimes, and dependence on and submission to external influences [16].

A comprehensive analysis of the use of renewable energies can offer a detailed insight into the historical context and enable comparisons that identify the most impactful actions in the process. Industrialization in the region assumed a “forced” character, driven by fluctuations in the international market and the need to replace imports during periods of conflict when developed nations directed their productions towards military efforts. Similarly, the energy transition appears to align with this adaptation trend to external circumstances [17]. This situation could result in the energy transition merely replicating existing inequalities; in this regard, adding a social context element to the discussion is important.

Many studies aim to address socioeconomic issues in the energy transition debate. A critical review shows that in LAC, these studies focus on evaluating issues related to energy poverty and ensuring a fair energy transition. Interestingly, over 80% of the region’s scientific research on this topic was conducted after 2015 [6], which seems to be influenced by the Paris Agreement and the development goals set by the UN. Thomson et al. [6] classify the studies for LAC into three main categories: (i) Energy services provision: This aims to capture energy deprivation through kitchen services, indoor lighting, equipment, education/entertainment, and communication [18,19,20]. (ii) Energy expenditure: This approach is tailored to the European view, where electricity spending is evaluated as a percentage of total household expenses [21,22]. (iii) Access and reliability of electricity: Without reliable indicators, access to the electrical grid and its reliability are used as a proxy for energy poverty [23,24,25]. In short, it becomes clear that an index consolidating elements related to the energy transition and social context can be helpful in formulating public policies and evaluating their performance over time, aligning with this study’s objectives.

3. Method and Data

The Energy Quadrilemma Index (EQI) was created using a three-step method commonly used in multidimensional data reduction. This method, known as Principal Component Analysis (PCA), is useful when multiple variables are evaluated simultaneously and uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The PCA approach is widely used for explanatory data analysis (independent) and projection purposes because of its ability to show the structure of data and explain its variation [26]. However, their use in constructing index numbers composed of several possibly correlated variables justifies the use of the PCA method in this study.

3.1. Variables and Sources

The data were selected based on the energy trilemma indicators discussed in the previous sections, as proposed by the World Energy Council WEC [27] and the World Economic Forum WEF, [2]. Additionally, the newly introduced social context pillar incorporates the variables suggested by Lazaro & Soares [5], including the proportion of expenditures categorized as “social protection”. This metric is used to assess the impact of government transfers on reducing poverty. The sample selection was constrained by data availability, covering Bolivia, Brazil, Colombia, Costa Rica, Honduras, and the Dominican Republic from 2014 to 2020.

The Figure 1 summarizes the variables selected for each of the four pillars of the Energy Quadrilemma Index (EQI). The choice of indicators was guided by theoretical frameworks proposed by the WEC and the WEF and extended to include variables that capture the unique social and infrastructural conditions of Latin America.

The Social Context pillar, the novel contribution to this index, reflects key elements of structural inequality and energy injustice, as discussed in the literature on energy poverty and energy justice [12,20]. Furthermore, they respond to calls from Latin American scholars for region-specific approaches in energy poverty analysis [6]. Each variable is described below.

Component 1 (Energy Security): Energy sufficiency (ENESUFF): relates primary energy production to total energy supply. External energy dependence (ENEDEP): the ratio of net energy imports to total domestic energy consumption. This indicator enables the measurement of the extent to which net energy imports contribute to a country’s internal energy supply. Positive values indicate a predominance of imports over domestic consumption, negative values suggest an excess of production over consumption, and values close to zero indicate a balance between exports and imports. System average interruption duration index (SINTDUR): the system average interruption duration index is the average total duration of outages (in hours) experienced by a customer in a year. System average interruption Frequency index (SINTFRE): the average number of service interruptions experienced by a customer in a year.

Component 2 (Energy Equity): Access to energy (ENEACRUR and ENEACURB): percentage of the population with access to electricity, divided into Urban and Rural populations. Access to clean cooking technologies (CCTACRUR and CCTACURB): percentage of the population with access to clean cooking technologies (electricity and gas), divided into urban and rural populations. Electricity prices (ELEPRICE): average price paid by the residential consumer (Electricity: Megawatt hour (MWh). Residential consumption of biomass (BIOMCONS): this refers to the amount of animal, plant, industrial, and urban waste used for energy purposes in the residential sector.

Component 3 (Sustainable Development): CO₂ Emissions (EMIC02): assesses the contribution to CO₂ emissions per capita. Energy intensity of GDP (ENEINTT): reflects the efficiency with which an economy uses energy to generate wealth; calculated as the ratio between total energy produced and GDP in purchasing power parity. Electric sector efficiency (ELCEFF): measures the efficiency of the electric sector by comparing the total amount of electrical energy generated (in energy units, such as megawatt-hours) with the total energy inputs used for this generation in conventional and authorized power plants. Emissions per energy consumed (EMIENE): amount of CO₂ emissions produced per ton of oil equivalent (toe) of energy consumed. Total supply renewability index (SUPRENEW): defined as the ratio between the total supply of renewable sources (primary and secondary, discounting their production to avoid duplication), divided by the total energy supply. The total supply of primary renewable energies includes hydro, geothermal, wind, solar, biomass, and in the case of secondary renewable energies, electricity, and biofuels.

Component 4 (Social Context): Poverty (USD365POV): proportion of the population living on less than USD $3.65 per day. GINI Index (GINIRUR and GINIURB): assessed by two indicators, the GINI index; Income quartile ratio: the ratio between the average income of the fifth quartile and the average income of the first quartile of the population’s earnings. Access to water services (WATACSS): proportion of households with access to water treatment services. Access to sanitation services (SANACSS): proportion of households with access to sanitation services. Quintile ratio income (QTLRATRUR and QTLRATURB): relationship of average income between quintile 5 and quintile 1. Adjusted Human Development Index (HDIADJ): HDI adjusted for income inequality within the country. Government spending on social protection (GOVSCSP): ratio of public social expenditure to GDP, according to the classification of government functions.

When looking at the descriptive statistics in

Table 1. Descriptives statistics.

Energy Security
Variable	Obs	Min	Mean	Median	Max	Skew	Kurtosis	Standard Deviation
ENESUFF	84	0.11	1.16	0.88	4.05	1.09	0.33	0.94
ENEDEP	84	−294.2	−12.5	10.94	90.46	−1.12	0.27	93.54
SINTFRE	69	0	18.75	6.96	133	2.46	5.56	30.05
SINTDUR	69	0	22.06	7.05	145	1.93	2.62	34.98
Energy Equity
Variable	Obs	Min	Mean	Median	Max	Skew	Kurtosis	Standard Deviation
ELEPRICE	53	6.88	15.71	11.29	35.32	0.91	−0.64	8.68
BIOMCONS	56	0.08	0.42	0.35	0.87	0.72	−0.99	0.27
CCTACRUR	56	8.7	60.18	67.25	100	−0.49	−1.06	29.83
CCTACURB	56	72.4	93.1	98.25	100	−1.29	−0.01	9.58
ENEACRUR	56	59.3	86.93	90.7	100	−0.77	−0.69	12.31
ENEACURB	56	94.4	99.16	99.8	100	−2.09	3.31	1.39
Sustainable Development
Variable	Obs	Min	Mean	Median	Max	Skew	Kurtosis	Standard Deviation
SUPRENEW	77	10.88	35.83	29.37	69.12	0.28	−1.2	17.64
EMIENE	77	1.17	2.49	2.54	3.86	−0.07	−1.22	0.78
ELCEFF	77	0.37	0.59	0.56	0.99	0.71	−0.63	0.17
ENEINTT	77	0.04	0.07	0.07	0.12	0.15	−0.93	0.02
EMIC02	77	746.78	1982.7	1774.3	4919.7	1.55	2.16	1006.99
Social Context
Variable	Obs	Min	Mean	Median	Max	Skew	Kurtosis	Standard Deviation
USD365POV	72	0.43	8.5	6.86	28.79	1.51	1.93	7.11
HDIADJ	77	0.46	0.6	0.59	0.72	0.07	−0.83	0.07
GINIURB	70	0.38	0.45	0.45	0.55	0.16	−1.03	0.04
GINIRUR	70	0.32	0.44	0.46	0.56	−0.44	−0.86	0.06
WATACSS	68	82.8	95.39	97.15	99.5	−1.05	0.66	3.88
SANACSS	68	56	83.38	85.85	99.2	−0.47	−1	12.92
QTLRATURB	70	7.4	11.7	11.1	28.4	1.51	4.49	3.61
QTLRATRUR	70	5	12.01	12.85	23.7	0.21	−0.6	4.34
GOVSCSP	77	0.39	4.61	3.84	17.43	1.22	1.61	3.49

Source: Authors’ calculations.

, it is possible to note most of the differences.

The “ENESUFF” indicator, with a median of 0.88 and a mean of 1.16, highlights that in the countries analyzed, internal energy production often falls short of meeting total energy demands. High variability suggests significant disparities among countries regarding their capacity to fulfil local energy needs. “ENEDEP” exhibits a wide range, from −294.24 to 90.46, reflecting the contrasting roles of nations like Colombia, Bolivia, and Guyana as major energy exporters. Additionally, “SINTDUR” and “SINTFRE” reveal frequent energy interruptions in areas with an inadequate infrastructure, with averages of 22.06 and 18.75, respectively—substantially higher than the OECD averages of 0.90 and 0.68.

Energy access indicators, such as “ENEACURB” and “ENEACRUR,” alongside “CCTACRUR” and “CCTACURB”, underscore significant rural–urban disparities in energy and clean cooking technology availability. “BIOMCONS,” with an average of 0.42, reflects continued reliance on biomass. Research on energy poverty in the region indicates that biomass use, particularly charcoal, is more culturally driven than linked to energy poverty, stabilizing at a plateau level in some nations after initial reductions.

“SUPRENEW” demonstrates median renewable energy participation below 30%, compared to global and OECD figures of 14% and 11.5%, respectively, [28]. “EMIC02” averages 1.98 tons of CO₂ per capita, significantly lower than the OECD average of 6.35 tons (Global, 2020), illustrating the higher emissions of advanced economies. These results highlight the challenges of balancing growth and sustainability.

Indicators like “USD365POV” and “GINIURB” reveal persistent poverty and inequality, especially in rural areas where incomes are lower and basic services are less accessible. “WATACSS” and “SANACSS” show progress in service access, though regional gaps persist, particularly in peripheral and rural zones. “QTLRATRUR” and “QTLRATURB” underscore pronounced income disparities in urban contexts. “GOVSCSP” reflects substantial differences in social spending driven by public redistribution policies. Finally, “HDIADJ” highlights the detrimental effects of inequality on national progress while remaining stable over the evaluated period.

3.2. Principal Component Analysis—PCA

The PCA method is not recommended for datasets with many missing values or outliers, as it may not be effective in such cases [29]. To avoid biased results, countries with no data are excluded, and Kelman’s filter is applied to address certain cases of missing values [30].

3.3. Normalization

Given that the chosen indicators span diverse units and scales, it is imperative to transform them into normalized variables before aggregating them to create a composite index [26]. This study followed the min–max technique to normalize variables, ensuring robustness checks and sensitivity analyses.

$${Norm}_Z={\displaystyle \frac{Z_i-MinZ}{MaxZ-MinZ}}$$

(1)

$${Norm}_Z=1-{\displaystyle \frac{Z_i-MinZ}{MaxZ-MinZ}}$$

(2)

where $Z_i$ is the raw data and ${Norm}_Z$ is the normalize indicator. The procedure involved in normalizing values ensures that all values lie within the closed interval between 0 and 1. This way, countries with high values are closer to 1, while those with low values are closer to zero. However, in some cases, such as with variables like carbon emissions or the emission of greenhouse gases, high values indicate a worsening of the indicator. In such cases, Equation (2) is used.

3.4. PCA Model

The PCA construction is adapted from Le et al. [26]. Consider a random vector $X$ with values in $\left(R^m\right)$, characterized by a mean ${\mu }_m$ and a covariance matrix ${\sum }_X$. The eigenvalues of ${\sum }_X$, in descending order, are denoted as ${\lambda }_1>{\lambda }_2>\dots >{\lambda }_m>0$, where each eigenvalue corresponds to its respective eigenvector. Let ${\alpha }_i$ represent the eigenvector corresponding to the i-th the eigenvalue of ${\sum }_X$. Maximizing $\mathrm{v}\mathrm{a}\mathrm{r}\left[{\alpha }_1^{T}X\right]={\alpha }_1^T{\sum }_X{\alpha }_1$ subjected to ${\alpha }_1^T{\alpha }_1=1$. Mathematically, this is formulated as a Lagrange optimization problem:

$$\begin{gathered} L({\alpha }_1,{\varphi }_1)={\alpha }_1^T{\sum }_X{\alpha }_1+{\varphi }_1({\alpha }_1^T{\alpha }_1-1) \\ {\displaystyle \frac{\partial L}{\partial {\alpha }_1}}=2{\sum }_X{\alpha }_1+2{\varphi }_1{\alpha }_1=0 \\ \Rightarrow {\sum }_X{\alpha }_1=-{\varphi }_1{\alpha }_1 \\ \Rightarrow \mathrm{v}\mathrm{a}\mathrm{r}\left[{\alpha }_1^{T}X\right]=-{\varphi }_1{\alpha }_1^T{\alpha }_1=-{\varphi }_1 \end{gathered}$$

(3)

Consider the eigenvalue $-{\varphi }_1$ is the eigenvalue of ${\sum }_X$ with ${\alpha }_1$ being the corresponding normalized eigenvector. The variance $\mathrm{v}\mathrm{a}\mathrm{r}\left[{\alpha }_1^{T}X\right]$ is maximized by selecting ${\alpha }_1$ as the first eigenvector of ${\sum }_X$. In this scenario, $z_1={\alpha }_1^{T}X$ is referred to as the first principal component of $X$ while ${\alpha }_1$ is the vector of coefficients for $z_1$ and $\mathrm{v}\mathrm{a}\mathrm{r}\left(z_1\right)={\lambda }_1.$

To ascertain the second principal component, $z_2={\alpha }_2^{T}X$, address the optimization task of maximizing $\mathrm{v}\mathrm{a}\mathrm{r}\left[{\alpha }_2^{T}X\right]={\alpha }_2^T{\sum }_X{\alpha }_2$ subject to $z_2$ being uncorrelated with $z_1$. Given that $\mathrm{c}\mathrm{o}\mathrm{v}({\alpha }_1^{T}X,{\alpha }_2^{T}X)=0$, solving this challenge is analogous to maximizing ${\alpha }_2^T{\sum }_X{\alpha }_2$ under the constraints ${\alpha }_1^T{\alpha }_2=0$, and ${\alpha }_2^T{\alpha }_2=1$. The approach involves applying the Lagrange multiplier method as follows.

$$\begin{gathered} L({\alpha }_2,{\varphi }_1,{\varphi }_2)={\alpha }_2^T{\sum }_X{\alpha }_2+{\varphi }_1{\alpha }_1^T{\alpha }_2+{\varphi }_2({\alpha }_2^T{\alpha }_2-1) \\ {\displaystyle \frac{\partial L}{\partial {\alpha }_2}}=2{\sum }_X{\alpha }_2+{\varphi }_1{\alpha }_1+2{\varphi }_2{\alpha }_2=0 \\ \Rightarrow {\alpha }_1^T(2{\sum }_X{\alpha }_2+{\varphi }_1{\alpha }_1+2{\varphi }_2{\alpha }_2)=0\Rightarrow {\varphi }_1=0 \\ \Rightarrow {\sum }_X{\alpha }_2=-{\varphi }_2{\alpha }_2 \\ \Rightarrow {\alpha }_2^T{\sum }_X{\alpha }_2=-{\varphi }_2 \end{gathered}$$

(4)

Consider the eigenvalue $-{\varphi }_2$ is the eigenvalue of ${\sum }_{\mathit{X}}$ with ${\alpha }_2$ being the corresponding normalized eigenvector. The variance $\mathrm{v}\mathrm{a}\mathrm{r}\left[{\alpha }_2^{T}X\right]$ is maximized by selecting ${\alpha }_2$ as the first eigenvector of ${\sum }_{\mathit{X}}$. In this scenario, $z_2={\alpha }_2^{T}X$ is referred to as the first principal component of $X$ while ${\alpha }_1$ is the vector of coefficients for $z_1$ and $\mathrm{v}\mathrm{a}\mathrm{r}\left(z_1\right)={\lambda }_1$. Continuing with the analogous process, the i-th principal component, denoted as $z_i$, can be formulated by selecting ${\alpha }_i$ as the i-th eigenvector of ${\sum }_{\mathit{X}}$, with a variance of $z_i={\alpha }_i^{T}X$. The fundamental insight of PCA underscores that these principal components constitute the sole set of uncorrelated linear transformations of the input factors, each characterized by orthogonal coefficient vectors.

3.5. The Index Composition

Defining the weights is a crucial aspect of creating the composite indicator, as it can significantly affect the outcome. There are various techniques that can be utilized for weighting, as described in [8]. Some studies like WEF [2] use arbitrary weights, or use other PCA to define the weights, like Svirydzenka [31]. In this study, the Index results from summing the four PCA score weights, each weighted equally.

$${EQI}_{it}=a_1{E}_1+a_2{E}_2+a_3{E}_3+a_4{E}_4$$

(5)

Here, $E$ represents the diverse input indicators (sub indicators) utilized in constructing the composite index, while $a_1,a_2,\dots a_i$ denote the coefficients (the weights) defined by 1/4.

The final index is given by

$$Energy Security=\sum a_j{ES}_j$$

(6)

$$Energy Equitity=\sum a_j{EE}_j$$

(7)

$$Sustainable Development=\sum a_j{SDV}_j$$

(8)

$$Social Context=\sum a_j{SCT}_j$$

(9)

$$EQI=a_{ES}ES+a_{EE}EE+a_{SDV}SDV+a_{SC}SC$$

(10)

This method assumes that all indicators can substitute for each other. Therefore, if one indicator performs poorly, it can be compensated by a high value of another indicator. Another alternative model is geometric aggregation, which allows some degree of substitution imperfection between indicators. However, this method does not provide any compensation for poor performance.

3.6. Assumptions in Principal Component Analysis

The in-depth study on assumptions conducted by OECD/JRC [8] highlights the lack of scientific consensus, reflecting the varied opinions among experts. Regarding the minimum number of cases for Principal Component Analysis, various criteria are recommended in the literature: at least 10 observations, ratios of 3:1 or 5:1 case per variable, and an additional 51 cases beyond the number of variables to conduct chi-squared tests. The panel examined meets all these established criteria.

All dimensions were tested for internal consistency by Cronbach’s alpha and Kaiser–Meyer–Olkin (KMO) sampling adequacy tests, which assess whether the data was suitable for factor analysis booth ranges from 0 to 1. The Cronbach’s alpha value of more than 0.60 indicates an acceptable reliability and the KMO with a value greater than 0.5 indicates suitability for factor analysis [13].

To ensure clarity and ease of interpretation, all variables were standardized in terms of their directional meaning. For instance, higher values of HDI (Human Development Index) are considered preferable, following the principle of “more is better”. Conversely, lower values of GINI are deemed desirable. This approach was consistently applied to all variables, with the model adjusted to maintain uniformity in directional preference. See

Table 2. Variables and tests.

Quadrilemma Dimension	Variables	Direction	Interpretation	Sources
Energy Security	1—Energy sufficiency index;	+	Higher energy sufficiency indicates self-reliance in energy supply [26]	OLADE
	2—External energy dependency index;	–	High dependence reduces energy security resilience [27]
	3—System average interruption duration index;	–	More interruptions reflect infrastructure fragility [18]	Doing Business
	4—System average interruption frequence index	–	Frequent interruptions indicate system unreliability [18]
Cronbach’s alpha	0.8
Kaiser–Meyer–Olkin factor	0.54
Energy Equity	1—Energy access (rural and urban);	+	Access to electricity reduces deprivation and improves equity [12]	WDI
	2—Clean energy for cooking (rural and urban);	+	Clean cooking is critical to health and sustainable development [20]
	3—Biomass residential consumption index;	–	Higher biomass use implies lower modern energy access [32]	OLADE
	4—Electricity prices	–	High prices limit access and increase energy poverty [21]
Cronbach’s alpha	0.94
Kaiser–Meyer–Olkin factor	0.81
Sustainable Development	1—CO2 Emissions per capita;	–	More emissions worsen sustainability [3]	OLADE
	2—Final energy intensity GDP PPP;	–	High intensity implies inefficient energy use [10]
	3—Electricity Efficiency sector;	+	Greater efficiency means better energy use [10]
	4—Index of emissions per energy consumed;	–	Higher emissions per toe imply environmental inefficiency [26]
	5—Total supply renewability index	+	More renewable supply enhances sustainability [28]
Cronbach’s alpha	0.77
Kaiser–Meyer–Olkin factor	0.59
Social Context	1—Population with incomes below 3.65 per day;	–	High poverty indicates social exclusion [6]	WDI
	2—Households, by availability of basic services in urban housing (water and sanitation);	+	Access to services reflects better infrastructure [33]
	3—HDI Inequality adjusted;	+	Higher HDIADJ shows inclusive human development [33]	UNDP
	4—GINI (rural and urban);	–	Greater inequality undermines equitable transition [7]	CEPAL
	5—Relationship of average income between quintile 5 and quintile 1 (rural and urban);	–	Wide income gaps reflect structural exclusion [6]
	6—Social protection expenditures	+	More social protection indicates better income redistribution [5]
Cronbach’s alpha	0.74
Kaiser–Meyer–Olkin factor	0.51

Source: Authors’ calculations.

.

The sum of eigenvalues can reach, at most, the total number of variables present. Thus, an eigenvalue greater than one (λi > 1) indicates that the component captures information from more than one variable. In contrast, an λi < 1 indicates that the component contains little information, explaining less than one variable. Kaiser [34] uses this logic to determine the number of principal components. This method selects components whose eigenvalues exceed one (λi > 1), as these components capture a greater amount of information about the variability of the data. In this way, only the principal components are considered in the analysis.

4. Results and Discussion

This part is divided into three sections. The first presents the PCA results. The following section focuses on a decentralized analysis of the regions, offering a detailed view of the energy quadrilemma from a performance group perspective. The final section assesses the challenges and opportunities associated with the energy transition.

4.1. Principal Component Analysis

The Principal Component Analysis (PCA), focused on energy security, reveals that the first two components, PC1 and PC2, account for 97.01% of the total variance. PC1 is predominantly influenced by Energy Sufficiency and Energy Dependence, reflecting a high correlation with the original variable. Conversely, PC2 is characterized by average interruption duration and frequency of the interruptions, see

Table 3. Energy Security—ES.

	Eigenvalues
	PC1	PC2	PC3	PC4
SS Loading	2.54	1.34	0.12	0.01
Cumulative variance %	63.43%	97.01%	99.89%	100.00%
	Loading		${\mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}^2$		Communality
Variables	PC1	PC2	PC1	PC2
ENESUFF	1	0.03	1.0000	0.0009	1.0009
ENEDEP	1	0.02	1.0000	0.0004	1.0004
SINTDUR	0.26	0.94	0.0676	0.8836	0.9512
SINTFRE	0.29	0.93	0.0841	0.8649	0.9490
				Variance Explain
				2.54	1.34
				63.43%	33.54%

Source: Authors’ elaboration.

below.

High communalities demonstrate that the selected components substantially retain the variability of the analyzed variables, with values close to 1 indicating an effective retention of information. This analysis suggests that PC1 and PC2 encapsulate a significant amount of data variability. It can be observed that ENESUFF and ENEDEP are thoroughly explained.

In

Table 4. Energy Equity—EE.

	Eigenvalues
	PC1	PC2	PC3	PC4	PC5
SS Loading	4.11	0.47	0.29	0.1	0.03
Cumulative variance %	82.21%	91.56%	97.32%	99.30%	100.00%
	Loading	${\mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}^2$	Communality
Variables	PC1	PC1
BIOMCONS	−0.94	0.8836	0.8836
CCTACRUR	0.91	0.8281	0.8281
CCTACURB	0.93	0.8649	0.8649
ENEACRUR	0.83	0.6889	0.6889
ELEPRICE	0.92	0.8464	0.8464
			Variance Explain
				4.11
				82.21%

Source: Authors’ elaboration.

it is noteworthy that PC1 accounts for 91.56% of the total variance, highlighting its significant explanatory power.

Interestingly, biomass consumption exhibits an inverse loading, indicating a negative correlation with the other variables. High values of the component suggest a reduction in biomass consumption, pointing to a shift away from traditional energy sources.

In

Table 5. Sustainable Development—SD.

	PC1	PC2	PC3	PC4	PC5
SS Loading	3.05	1.37	0.3	0.25	0.04
Cumulative variance %	60.96%	88.27%	94.25%	99.30%	100%
	Loading		${\mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}^2$		Communality
Variables	PC1	PC2	PC1	PC2
EMIENE	0.96	0.07	0.9216	0.0049	0.9265
SUPRENEW	0.95	−0.03	0.9025	0.0009	0.9034
ELCEFF	0.88	−0.1	0.7744	0.0100	0.7844
MFOOTPC	−0.4	0.86	0.1600	0.7396	0.8996
EMIC02	0.53	0.78	0.28	0.61	0.8893
				Variance Explain
				3.05	1.34
				63.43%	33.54%

Source: Authors’ elaboration.

, the context of sustainable development, the first three components, PC1 and PC2, account for 88.27% of the total variance.

PC1 is dominant, displaying significant correlations with CO₂ emissions from energy consumed and the energy supply renewability. In contrast, PC2 is heavily influenced by per capita CO₂ emissions and footprint.

The social context axis reveals that PC1, PC2, and PC3 account for 84.05% of the total variance, highlighting urban and rural dynamics. PC1 is strongly linked to poverty indicators, and PC2 to rural disparities and government spending on social protection, while PC3 is shaped by inequalities, see

Table 6. Social Context—SC.

	Eigenvalues
	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
SS Loading	3.44	2.89	1.23	0.68	0.46	0.18	0.07	0.04	0.03
Cumulative variance %	38.2%	70.3%	84.0%	91.5%	96.6%	98.7%	99.3%	99.7%	100.0%
	Loading			${\mathrm{L}\mathrm{o}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}^2$			Communality
Variables	PC1	PC2	PC3	PC1	PC2	PC3
HDIADJ	0.91	0.07	0.3	0.8281	0.0049	0.09	0.923
SANACSS	0.89	0.15	0.34	0.7921	0.0225	0.1156	0.9302
WATACSS	−0.68	0.52	0.34	0.4624	0.2704	0.1156	0.8484
USD365POV	−0.62	0.32	0.02	0.3844	0.1024	0.0004	0.4872
GOVSCSP	−0.2	0.93	0.21	0.04	0.8649	0.0441	0.949
QTLRATURB	0.15	0.91	0.06	0.0225	0.8281	0.0036	0.8542
GINIRUR	−0.52	0.59	−0.2	0.2704	0.3481	0.04	0.6585
GINIURB	0.19	0.07	0.96	0.0361	0.0049	0.9216	0.9626
QTLRATRUR	0.12	0.09	0.96	0.0144	0.0081	0.9216	0.9441
							Variance Explain
							3.44	2.89	1.23
							38.18%	32.16%	13.62%

Source: Authors’ elaboration.

below.

The high communality values indicate that most of the variability is captured. The exception is the poverty indicator; however, this variable is kept because of its theoretical importance.

4.2. Quadrilemma Index

The Quadrilemma Index combines the classic components of the energy trilemma: energy security, energy equity, and sustainable development. Additionally, a social context axis is included, with each axis contributing equally, and each having a weight of 1/4. The evaluation of the results can be viewed below.

Figure 2 presents the evolution of the Quadrilemma Index for selected countries from 2014 to 2020. Costa Rica stands out with the highest index, demonstrating robustness and economic and social stability. On the other hand, Honduras records the lowest index, with a slight upward trend yet remaining below 0.25. Colombia, Bolivia, and Brazil maintain moderate and relatively stable indices around 0.6 to 0.7. The temporal analysis shows no clear trends over the studied period.

In 2014, Brazil (BRA) ranked 3rd in the trilemma but improved to 2nd place in the quadrilemma. A more detailed analysis reveals that the country achieved progress across all dimensions except for energy equity; however, significant advancements in sustainable development offset this performance. On the other hand, Colombia (COL), which led the trilemma ranking in 2014, dropped to 4th position in the quadrilemma due to an unsatisfactory performance in the social context indicator, which compromised its overall result. In contrast, Costa Rica (CRI) demonstrated consistency, maintaining its leadership in the quadrilemma for both 2014 and 2020. This result reflects a balanced integration between energy and social dimensions, although energy security showed a slight downward trend over time.

Among the other countries, Honduras (HND) stands out for its stagnated position, remaining last in both the trilemma and quadrilemma rankings across both years. This result highlights persistent challenges in the energy and social domains despite achieving its best scores in the sustainable development dimension. However, such outcomes are largely due to low economic dynamism and limited levels of industrialization.

It is worth noting the increasing correlation between the indicators over time, rising from 0.60 in 2014 to 0.77 in 2020. This growth suggests a stronger convergence between social factors and traditional energy indicators, reflecting a more robust integration between energy sustainability and social equity, see

Table 7. Trilemma and quadrilemma.

Country	2014		2020
	Trilemma	Quadrilemma	Trilemma	Quadrilemma
DOM	6	5	4	5
BOL	4	3	5	4
BRA	3	2	1	3
COL	1	4	3	2
CRI	2	1	2	1
HND	5	6	6	6
Correlation	0.60		0.77

Source: Authors’ calculations with WEC reports.

.

Furthermore, economic progress tends to intensify this correlation, although challenges related to income distribution remain, as the trilemma does not adequately capture them. This gap becomes even more evident when considering regional disparities within countries where urban areas tend to achieve better results than rural zones.

Honduras (HND) shows poor performance across most dimensions despite a relatively strong performance in the Social Context. On the other hand, Costa Rica (CRI) stands out as the most balanced, achieving high scores across all dimensions, solidifying its leadership. Colombia (COL) performs well in Sustainable Development and Energy Equity but poorly in the Social Context. Brazil (BRA) and Bolivia (BOL) exhibit similar profiles, with moderate performance in most dimensions, although Brazil lags in Energy Equity. In turn, the Dominican Republic (DOM) stands out in Energy Equity but has weak results in the Social Context. These findings indicate that the social context must be regarded as a significant challenge within the energy transition framework, see Figure 3 for more details.

The results obtained for the Energy Security pillar align with studies focused on national energy resilience. Le et al. [26] identified that self-sufficiency in energy production and supply stability are critical determinants of energy security. This pattern is reflected in our EQI: countries such as Bolivia and Colombia, which exhibit high dependence on imported petroleum derivatives and limited refining capacity, score poorly in this dimension. Similarly, Sovacool & Mukherjee [35] demonstrated that external vulnerability significantly undermines the energy robustness of developing countries. These findings reinforce the results of this study, which indicate that supply instability and external dependence remain key constraints to energy security across much of Latin America.

The results of the Energy Equity pillar are also supported by studies such as Nussbaumer et al. [32] and Santillán et al. [20], which demonstrate that energy poverty in the region cannot be explained solely by national electrification rates. Significant disparities persist between urban and rural areas, as well as among socioeconomic groups. Our findings corroborate this evidence. Although countries such as Honduras and the Dominican Republic report relatively high rates of electricity access, the effective use of modern energy remains severely limited in rural regions and among low-income populations, particularly regarding access to clean cooking technologies. This is clearly reflected in the low scores of these countries in the EQI, driven by high reliance on biomass, indicating persistent energy deprivation even in technically electrified contexts.

Additionally, Piai Paiva et al. [21], in their assessment of electricity accessibility in Brazil, confirm that the relative cost of electricity can result in exclusion from energy use, even when physical access is available. This phenomenon, referred to as “access limited by payment capacity” reveals that a significant portion of the population is connected to the grid but restricts energy consumption due to insufficient income.

The Environmental Sustainability dimension of the EQI resonates with the findings of Sheinbaum-Pardo et al. [3], who show that, despite improvements in energy intensity, progress has not been distributed evenly across regions and social sectors, particularly between urban and rural consumers. This observation is mirrored in the EQI results, which highlight substantial disparities between countries such as Costa Rica, with a high overall environmental performance, and Bolivia or the Dominican Republic, where performance is weakened by low efficiency and high emission levels. Such heterogeneity has also been observed in other developing regions. However, the authors caution that this relationship is highly context-dependent, influenced by institutional quality and infrastructure capacity, factors also identified in the EQI, where countries with a limited investment capability face structural obstacles to advancing their energy transitions.

The Social Context pillar of the EQI introduces a critical perspective: the recognition that energy sustainability cannot be dissociated from social inclusion. Building on this conceptual advancement, the EQI reveals that countries such as Honduras and Bolivia exemplify a persistent paradox; while they show modest improvements in renewable energy uptake and some environmental indicators, they continue to experience severe inequality in access to modern energy, minimal investment in social protection, and a high proportion of the population living in poverty. This reinforces the notion that the energy transition, if not guided by inclusive frameworks, may reproduce, or even deepen, pre-existing inequalities.

Previous studies, such as Benfica & Marques [36], have already warned that current energy transition policies risk perpetuating the status quo and even exacerbating inequality, as they tend to marginalize populations without access to financing mechanisms that would enable them to change their energy consumption patterns.

Moreover, the explicit incorporation of the social dimension in the EQI directly responds to the recommendations of Song et al. [14], who advocate for the use of multicriteria methodologies to construct indices capable of capturing the multidimensional nature of national energy performance. According to these authors, effective indices must reflect not only technical and environmental conditions but also countries’ capacities for social inclusion and institutional robustness.

4.3. Challenges and Opportunities

Even within the context of Latin America, regional disparities are evident, highlighting distinct challenges for each locality in the energy transition. Costa Rica, for example, shows positive indicators compared to other countries, yet it still faces significant challenges, and its poverty rates have gradually increased during the analyzed period. In terms of energy balance, the country demonstrates vulnerability due to its total dependence on imported petroleum derivatives, with over 80% of this energy being directed towards transportation and the industrial sector. It is further exposed when considering that approximately 65% of the electricity generated comes from hydroelectric sources, followed by wind energy, which accounts for about 11%.

Costa Rica ranks among the highest-performing countries in the Energy Quadrilemma Index (EQI), reflecting a generally balanced performance across the four pillars. In the Energy Security dimension, however, the country remains vulnerable due to its total dependence on imported petroleum derivatives, which is highly sensitive to external energy dependence. While the electricity matrix is predominantly renewable, with significant contributions from hydroelectric and wind power, this structure poses risks during periods of drought, revealing limitations in the resilience of the energy system. Despite having untapped geothermal potential, progress in diversifying the energy mix has been limited.

In the Social Context pillar, Costa Rica underperforms relative to its overall EQI score. This discrepancy is explained by below-average access to basic services such as treated water, despite relatively uniform conditions across urban and rural areas. The principal components of this dimension indicate that these deficiencies, along with persistent income inequality, negatively influence the country’s social score. These findings suggest that, while Costa Rica leads in technical and environmental aspects of the energy transition, structural inequalities remain key obstacles. To ensure a fully inclusive transition, investments in social infrastructure must accompany improvements in energy systems.

Colombia performs moderately well in the EQI, with contrasting results in the four pillars. In the Energy Security dimension, the country benefits from its status as an oil exporter, which contributes positively to the energy sufficiency indicator and is reflected in this pillar. However, a limited refining capacity and depletion of natural gas reserves undermine long-term self-sufficiency, while the country’s growing dependence on coal signals a growing vulnerability in its energy strategy. The energy matrix, although relatively diversified, remains heavily dependent on fossil fuels; approximately 50% of total supply comes from natural gas, coal, and oil. These characteristics limit the country’s ability to migrate to cleaner sources, affecting the Sustainable Development pillar. In fact, although electricity consumption has increased, the persistent use of biomass in homes suggests a transition process that has not fully reached low-income or rural populations. Finally, in the Social Context pillar, Colombia’s performance is hampered by sharp income inequality in rural areas. Indicators such as the Gini index and income quintiles contribute to lower scores, especially in the second and third components, which highlight inequality and limited government social spending. These structural disparities suggest that the benefits of energy modernization continue to be unevenly distributed across the population, highlighting the government’s failure to support low-income populations.

Bolivia exhibits a weak and stagnant performance, particularly in the pillars of Energy Security and Sustainable Development. Its energy matrix remains heavily reliant on fossil fuels, with approximately 50% of energy production derived from natural gas and 30% from oil, resources primarily allocated to the transportation and industrial sectors. This configuration adversely affects the country’s score in the Energy Security dimension, which is sensitive to energy sufficiency and external dependency. The declining trend in domestic oil production and overall energy supply further exacerbates the country’s vulnerability, leading to greater reliance on imports and lower energy resilience.

In the Sustainable Development pillar, Bolivia’s performance is compromised by the predominance of non-renewable electricity generation, which accounts for around 72% of total output. The stagnation in electricity generation, combined with the continuous growth in fossil fuel consumption, negatively influences this component and is associated with emissions per energy consumed and renewable supply ratios, key drivers of the sustainable development score. Energy Equity components signal that large segments of the population remain excluded from the benefits of energy transition. These issues are strongly reflected in the first principal component of the equity dimension, which penalizes low electrification rates and high traditional fuel reliance, particularly in rural areas.

Although Bolivia’s performance in the Social Context pillar is relatively moderate compared to its other dimensions, persistent inequality and limited progress in access to basic services remain structural concerns. The combination of a fossil-fuel-dependent energy matrix, stagnant infrastructure, and insufficient advances in social inclusion suggests that Bolivia’s current trajectory does not support a fair or sustainable transition in the long term. Strengthening long-term energy planning and prioritizing inclusive policies are critical to reversing this trend.

Brazil demonstrates a relatively balanced performance in the EQI, primarily due to its diversified energy matrix and substantial share of renewable sources. In the Energy Security pillar, the country benefits from high energy production levels and a consistent increase in energy exports. However, the country’s considerable reliance on hydroelectricity, responsible for approximately 60% of electricity generation, exposes it to risks during periods of drought. This vulnerability is indirectly reflected in Energy Security, which accounts for system interruptions and the resilience of infrastructure.

In the Sustainable Development dimension, Brazil scores well due to the predominance of renewable energy in its electricity mix (83%). However, the ethanol supply chain, often celebrated as a model of biofuel integration, shows signs of structural fragility. Ethanol production remains highly sensitive to market fluctuations, particularly due to the competitive dynamic between sugar and ethanol production. This dynamic limit its scalability, especially in periods of currency volatility or rising international sugar prices. As the principal components in this dimension incorporate the renewability of supply and emissions intensity, this instability may temper Brazil’s long-term sustainability outlook.

Energy Equity, on the other hand, is where Brazil exhibits one of its weakest performances. Despite near-universal access to electricity in urban areas, rural regions continue to face gaps in clean cooking access and affordability of modern energy technologies. The continued use of biomass in certain communities and the socioeconomic segmentation in access to efficient appliances penalize inequality in access and reliance on traditional fuels. These disparities explain the lower score in this dimension compared to other EQI pillars.

In the Social Context pillar, Brazil’s high levels of income inequality, particularly between urban and rural populations, continue to undermine progress toward a just energy transition. Indicators such as the income quintile ratio and Gini index significantly affect the principal components explaining variance in this dimension. While rich-income segments of the population can rapidly adopt advanced technologies and benefit from energy policy incentives, a substantial share remains excluded due to financial constraints. As a result, energy transition policies have disproportionately benefited higher-income groups, reinforcing structural inequalities. Enhancing the distributive capacity of energy and social policies is essential for the country to align its energy transition trajectory with inclusive development goals.

Honduras exhibits the lowest overall performance in the Energy Quadrilemma Index (EQI), with a particularly weak outcome in the Energy Security pillar. The principal component associated with this dimension highlights the country’s critical dependence on energy imports as a key vulnerability. Furthermore, the poor condition of its distribution infrastructure, captured by high average interruption duration and frequency, further exacerbates its fragility in this area. Within the Energy Equity pillar, limited access to clean cooking technologies in rural areas and the widespread use of residential biomass significantly hinder equitable access to modern energy services. Regarding Sustainable Development, Honduras does not perform poorly; however, this is primarily attributed to its low level of industrialization rather than to meaningful progress in environmental or energy efficiency indicators. Lastly, in the Social Context dimension, the country displays alarming indicators, including high poverty rates, minimal public expenditure on social protection, and stark income inequalities. These structural challenges contribute to its low performance in this pillar. Addressing basic infrastructure deficits and narrowing income disparities must become top priorities to enable a just and sustainable energy transition.

5. Conclusions

The Energy Quadrilemma Index (EQI) addresses the complexities of energy development in Latin America, highlighting existing regional disparities. This study emphasizes the importance of adopting a decentralized approach to tackle the energy quadrilemma. Its main contribution lies in incorporating a social context dimension into the traditional energy trilemma model. While the results partially align with those of the trilemma, the social context axis shifts outcomes by reducing the purely economic perspective, thus highlighting challenges related to energy access and income inequality in the energy transition. This approach underscores the need to consider not only energy generation but also the socioeconomic and infrastructural challenges specific to each region.

The findings indicate that although countries like Costa Rica achieve high EQI scores, reflecting a robust, clean, and inclusive energy sector, regional disparities within these nations reveal specific challenges that demand attention. For instance, Brazil benefits from an effective integration of renewable energy sources, whereas Honduras, with significantly lower indices, has severe gaps in energy security and equitable energy access, particularly in rural areas.

The complexity of the energy transition requires an in-depth analysis of injustices and inequalities within the energy sector to promote a fair and sustainable transition. This situation is evident in EQI variations, where countries with lower scores face critical challenges tied to socioeconomic disparities. These findings emphasize the need for public policies aimed at mitigating energy poverty, particularly in isolated rural areas. The results highlight the urgency of energy transitions that address social and infrastructural inequalities to avoid uneven economic development that could deepen existing disparities. It becomes clear that countries exhibit distinct realities and performances, suggesting that applying uniform measures and policies may not be the most effective approach to fostering a fair and sustainable transition.

From a policy perspective, the EQI serves as a multidimensional diagnostic tool capable of guiding strategic decision-making at both national and regional levels. Policymakers can use the results to identify specific structural bottlenecks, such as inadequate rural infrastructure, a high dependence on fossil fuels, or a persistent reliance on biomass for cooking, and design targeted interventions accordingly. For instance, countries scoring low in the Energy Equity pillar may prioritize subsidies for clean cooking technologies in rural areas, while those underperforming in Sustainability may need to reassess the efficiency of their electric sector and the emissions profile of their energy mix.

Furthermore, the findings highlight that many energy challenges in Latin America transcend national borders. Thus, national actions should be complemented by regional cooperation mechanisms focused on: (i) aligning energy transition goals and policy frameworks, (ii) sharing best practices and facilitating technology transfer, and (iii) providing technical and institutional support to countries with limited capacity. The establishment of regional data platforms and harmonized energy indicators is also crucial to enhance the EQI’s analytical power and enable meaningful cross-country comparisons.

Recognizing these indicators as regional challenges is a critical starting point for energy planning discussions across the continent. Understanding the local energy planning landscape is essential to identifying barriers, enabling the implementation of sustainable energy solutions, and developing strategies to overcome them. This approach ensures a more equitable and efficient energy transition across Latin America.