Environmental Sustainability in South America
La sostenibilidad medioambiental en América del Sur
Paola Marcela Hermosa del Vasto*
Rui Cunha Marques**
Juan Luis Peñaloza***
*Correspondence contact. PhD in Economics and Business Studies. Assistant Professor in the Faculty of Social and Legal Sciences of the University of Granada. Melilla, Spain, paolahermosa@ugr.es © https://orcid.org/0000-0001-5696-2258 © https://orcid.org/0000-0001-5696-2258
** PhD and Postdoc of the Scientific Area of Systems Engineering. Full Professor in the Faculty of the Department of Industrial Engineering and Management, rui.marques@ulusofona.pt © https://orcid.org/0000-0003-0344-5200
*** PhD in Economic Sciences. Associate Professor in the Faculty Trade and Tourism of the Financial Economics, Actuarial Economics and Statistics, jluispf@ucm.es © https://orcid.org/0000-0001-9109-7247
How to Cite: Hermosa Del Vasto, P., Cunha Marques, R., & Peñaloza, J. L. (2024). Environmental Sustainability in South America. Apuntes del Cenes, 43(78). Págs. 127-162. https://doi.org/10.19053/uptc.01203053.v43.n78.2024.17728
Reception date: February 1st, 2024 Approval date: June 24, 2024
Abstract:
The purpose of this article is to determine the similarities in the context of environmental practices and sustainability of twelve South American countries and compare the environmental sustainability performance with six principal components. A multivariate analysis as a hierarchical method was carried out with seventeen sustainable environmental indicators, using official secondary data sources, namely the databases of ECLAC (Economic Commission for Latin America and the Caribbean), applying eigenvectors and eigenvalues from the correlation matrix and Ward's method with squared Euclidean distances.
The results suggest that the initial jumps in terms of distance are small, so the twelve countries analyzed in the study are grouped into five clusters. Deepening then in the perspective of the characteristics of each cluster, CL1: Colombia, Venezuela, Suriname, Argentina and Bolivia; CL2: Guyana, Paraguay, Uruguay and Peru; CL3: Brazil; CL4: Ecuador; CL5: Chile. The research highlights significant differences among these South American countries, clustering those with similar patterns of behavior and identifying the best performers. We argue there is a need to protect and promote biodiversity, raise awareness of the importance of environmental sustainability and support the impacts of climate change. Argentina, Uruguay and Chile face severe water scarcity problems, and temperatures have risen in all the countries, but especially in Brazil, Colombia, Ecuador, Paraguay, Suriname and Venezuela.
Keywords: environmental sustainability, South America, multivariate analysis, sustainable environmental indicators.
Resumen:
El propósito de este artículo es determinar las similitudes en el contexto de las prácticas ambientales y la sostenibilidad de doce países de América del Sur, y comparar el desempeño de la sostenibilidad ambiental con seis componentes principales. Se realizó un análisis multivariado como método jerárquico con diecisiete indicadores ambientales sostenibles; se consultaron fuentes de datos oficiales secundarios, a saber, la base de datos de la CEPAL (Comisión Económica para América Latina), aplicando el vector propio y los valores propios de la matriz de correlaciones y el método de Ward con distancias euclidianas al cuadrado.
Los resultados sugieren que los saltos iniciales en términos de distancia son pequeños. En consecuencia, los doce países analizados en este estudio se agrupan en cinco clusters. Profundizando luego en la perspectiva de las características de cada cluster, CL1: Colombia, Venezuela, Surinam, Argentina y Bolivia; CL2: Guyana, Paraguay, Uruguay y Perú; CL3: Brasil; CL4: Ecuador; CL5: Chile. La investigación pone de relieve diferencias significativas entre estos países sudamericanos, agrupando a los que presentan patrones de comportamiento similares e identificando a los que obtienen mejores resultados. Argumentamos que hay que proteger y fomentar la biodiversidad, y concienciar sobre la importancia de la sostenibilidad medioambiental y apoyar el impacto del cambio climático. Argentina, Uruguay y Chile se enfrentan a graves problemas de escasez de agua y las temperaturas han aumentado en todos los países, pero especialmente en Brasil, Colombia, Ecuador, Paraguay, Surinam y Venezuela.
Palabras clave: sostenibilidad ambiental, América del Sur, análisis multivariante, indicadores ambientales sostenibles.
Codigos JEL: P48, C38, Q1, H11, N56.
INTRODUCTION
In considering global sustainability, it is necessary to address human, animal, plant and environmental health as inseparable aspects of an interlinked challenge (United Nations Environment Program [UNEP], 2022). The vital role played by environmental, social and economic sustainability is acknowledged by academics and policymakers alike. Environmental sustainability theory argues that for an organization to gain and maintain sustainable competitive advantage, it must balance economic, environmental and social interests, and recognize current and future needs in each of these areas (Olafsson et al., 2014).
In this context, benchmarking is the application of uniform measures to assess performance. Successful benchmarking of environmental sustainability performance requires adequate disclosure of information, regarding all levels of governance (Alcaraz-Quiles et al., 2014). Relevant aspects of environmental performance include climate change, air quality, waste generation, water quality, water resources, forest resources, energy resources (Ponomarenko et al., 2022), biodiversity and environmentally-related taxes. These areas of concern have been addressed by many public administrations, and various international conventions in this respect have been approved and ratified in recent years (Organisation for Economic Co-operation and Development [OECD], 2022). Focusing both on individual countries and on continental regions, international organizations have sought to evaluate environmental sustainability performance, emphasizing the need to strengthen efforts to mitigate and adapt to climate change and urban pollution (Gómez Peláez et al., 2020; Klumpp et al., 2023), and to protect and restore the natural world.
This paper examines and compares the environmental sustainability performance of twelve South American countries. Previous research in this area is limited in its instrumental and rationalistic conceptualization and very few cross-country benchmarking performance case studies have been presented. Moreover, few studies have reported on the current understanding of environmental sustainability (Crabb & Leroy, 2008; Da Cruz & Marques, 2014; Ammons & Roenigk, 2015), or on policies, agreements and successes in this area. In this paper, we argue that environmental sustainability performance depends on ecosystem services at local, national and international scales, and that governance systems have a duty of care towards environmental infrastructure (Olafsson et al., 2014), including information disclosure on sustainability-related outcomes.
Assessing the environmental outcomes of sustainability initiatives is a complex issue. The broad scope of the Economic Commission for Latin America and the Caribbean Statistical Databases and Publications (CEPALSTAT) indicators reflect the UN's aim to enhance environmental protection policies worldwide and help measure national progress in South America. Especially important among these indicators are the six principal components (PC) or constructs that are characterized by 17 environmental indicators (Table 1).
With these considerations in mind, this paper aims to evaluate the environmental sustainability performance of twelve South American countries, considering data published in 2019. According to Alcaraz-Quiles et al. (2014), an evaluation of national performance in implementing sustainable development policies and of the corresponding legal measures adopted would lack practical application without reference to the sustainable development guidelines that have arisen from considerations of global governance. In the present analysis, therefore, we evaluate environmental sustainability performance to better understand the complex modus operandi of international organizations, countries and regions in developing new indicators of environmental phenomena and their subnational, national and supranational effects.
The Global Reporting Initiative (GRI, 2022) provides the world's most widely used standards - the GRI Standards -for sustainability reporting. Specific environmental areas are commonly assessed in terms of the key indicators proposed by the OECD (2022), together with the World Development Indicators that have been published by the World Bank since the 1960s. The use of these and other indices of environmental sustainability informs policymakers and society at large of the relationships and trade-offs among the environmental, social and economic dimensions of sustainability (Goodland, 1995).
In this paper, we focus on six principal components of environmental sustainability: secondary energy production and change in energy intensity, the proportion of marine protected areas for the conservation of biodiversity, the contribution to the persistence of marine biodiversity, the proportion of areas dedicated to the conservation of terrestrial and marine biodiversity, the impact of global warming, and measures taken to address pollution and overexploitation of the oceans.
After critically discussing the core ideas of environmental sustainability, we elaborate on the main features highlighted in our review of the literature, including a subsection of studies focused on the context of South America. Finally, we identify and discuss the significant differences observed among these countries in terms of their environmental sustainability performance, and construct a profile to define the results obtained, in five clusters.
In short, this paper reveals patterns of behavior in the countries considered, according to the different initiatives adopted, and illustrates the benefits of benchmarking these outcomes. This research has the following practical implications: we identify the best performing countries, thus facilitating the creation of benchmarks for their neighbors, and suggest possible environmental performance improvements, hence strengthening sustainability. In this regard, Ammons et al. (2001) and Rutherford (2000), among others, have commented that effective benchmarking requires that uniform measures be applied. In another study, Alcaraz-Quiles et al. (2014) considered the disclosure of information about sustainability by different government agencies.
The literature on environmental sustainability in South America is mainly descriptive and non-academic, a research gap that the present study seeks to fill. Accordingly, we address the following research questions:
RQ1: How have these 12 South American countries evolved in terms of environmental sustainability and how are they now positioned regarding the provision of indicators to support benchmarking?
RQ2: What similarities and differences can be observed among the indicators of environmental sustainability in these countries?
RQ3: What behavior patterns can be observed regarding the use of environmental sustainability indicators?
RQ4: Can some countries be identified as benchmarks for others, in terms of their environmental sustainability behavior?
The rest of this paper is organized as follows. In the next section, we present a literature review of environmental sustainability in the 12 countries considered. We then conduct an empirical analysis, explaining the data and methodology used. This is followed by a presentation and discussion of the findings obtained. Finally, we summarize the main conclusions drawn.
LITERATURE REVIEW
Environmental Sustainability
The UNEP publishes data on 30 major indicators, including air pollution, climate change, greenhouse gases, biodiversity, energy and minerals, forests, governance and inland water resources. According to the UNEP report (2022), the devastating impacts of climate change, pollution, waste and the degradation of the natural world have been compounded by widening inequality, conflict in Ukraine and elsewhere and rising prices for food and energy. And as always, the poorest and most vulnerable populations have been hardest hit by impacts such as drought, flooding, wildfires, and the loss of biodiversity.
Numerous academic studies and environmental sustainability institutions, including Sun et al. (2020), Olafsson et al. (2014) and Alcaraz-Quiles et al. (2014), have considered the measurement and assessment of sustainability performance, noting that the selection of appropriate indicators is a dynamic question that must be addressed in accordance with the requirements and priorities assumed. If an accurate evaluation can be achieved, this will enable policymakers to identify and apply the policy measures needed to strengthen environmental sustainability in the context considered. In response to this perceived need, international organizations such as the OECD (2022), the World Bank (2022), GRI (2022) and UNEP (2022) have developed performance indicators to assess environmental sustainability.
The environmental sustainability indicators published by CEPALSTAT (2022), the statistical databases and publications portal of ECLAC, are the metrics most used to measure specific areas of physical conditions, ecosystems and biodiversity, including environmental quality, energy resources, biological resources, water resources, atmospheric emissions, waste production and disposal, and natural events and disasters. Ecological indicators and 3 dimensions assess sustainability in 11 Latin American countries (Toumi et al., 2017).
In addition, consideration must be given to novel challenges such as the role of AI and its impact on sustainability action and governance as a referent in policies for international sustainability to ecological modernization, the green government movement and civic environmentalism need consideration (Francisco & Linnér, 2023). These emerging interactions have showed the relation of environmental sustainability to Corporate Social Responsibility (CSR) practices and green innovation (Shahzad et al., 2020).
Environmental Sustainability in South America
In this section, we review the literature on environmental sustainability issues as they affect diverse countries in South America.
The challenge of assessing environmental performance has been taken up by many public administrations and international organizations, using appropriate indicators of environmental phenomena and their impacts, whether regional, national or supranational. Among them, the OECD Key Indicators (2022) are commonly used to measure specific areas of environmental concern, such as climate change, air quality, waste generation, water quality, water resources, forestry resources, energy resources, biodiversity and environmentally related taxation. In addition, the World Bank has proposed World Development Indicators (WDI) focusing on agriculture, climate, energy and mining, environment, urban and rural development, water and sanitation, among other areas. These indicators can be analyzed to reveal a country's progress towards achieving the environmental goals set out in the 2030 Agenda for Sustainable Development (World Bank, 2022). The GRI Standards are currently the most widely used measures of sustainability reporting and corporate accountability.
According to Sun et al. (2020), the above indicators are an invaluable resource for policymakers, providing them with meaningful information for monitoring and comparing the outcomes of procedures adopted. Another useful tool is the 2022 Environmental Performance Index (EPI), which shows, for example, that Nordic countries score highly for sustainability, via longstanding investments in policies to protect environmental health, preserve biodiversity and natural habitats, conserve natural resources and decouple greenhouse gas emissions from economic growth. Denmark tops the 2022 EPI rankings, thanks to its strong performance across nearly all the issues considered.
Gallego-Álvarez et al. (2018) used the EPI indicators and the HJ-Biplot multivariate analysis methodology to analyze the variables that might influence microeconomic policies and their effect on investment in 24 Latin American countries. These authors concluded that Argentina, Chile and Brazil were the countries in this region most concerned about climate change. Hermosa et al. (2024) propose e-AI based on Global Reporting Initiative GRI survey, enhancing governance and website accessibility for South American disclosure. In recent years, the forest area has shrunk dramatically and became fragmented, due in part to the prolonged drought that has affected many parts of South America, for example, Central Chile from 2010 to 2017. This deforestation and the consequent loss of biodiversity is just one of many negative effects of climate change. Miranda et al. (2020) discussed the effects of geographical variation and forest type as indicators of drought, using data from the MODIS satellite sensor, and temporal trends in the Normalised Difference Vegetation Index in a highly threatened Mediterranean landscape of South America.
Water resources are of vital importance, both for the environment in general and for human life in particular (Shiklomanov, 1998). South America has almost one third of the world's renewable water resources, and Brazil, Colombia and Peru are among the top ten countries in this respect (Global Water Partnership [GWP], 2022). Nevertheless, several regions, for example in Argentina, Uruguay and Chile, are subject to severe water scarcity, causing crop losses, jeopardizing food security and human health and endangering ecosystems (United Nations, 2023). Furthermore, Ecuador, Paraguay, Bolivia and Venezuela present high levels of eutrophication (excessive presence of nutrients such as phosphorus and nitrogen) due to uncontrolled agricultural activities and wastewater discharge.
Paredes-Beltran et al. (2021) applied the Water Availability and Adaptation Policy Analysis (WAAPA) model to evaluate water storage and its influence on the river systems of South America. The study results show that water availability is greater in the southeast, which the authors suggest is due to the widespread development of hydraulic infrastructure in this area of the continent. Reflecting this understanding, several countries have announced their intention to build hundreds of new dams, for a variety of purposes. Darré et al. (2019) discussed experiences and situations related to impacts on water use and quality by reference to rainfed and irrigated systems for corn and soybean production in temperate regions. These authors concluded that soybean cultivation was more susceptible to ecotoxicity and has a greater environmental impact than corn production.
Analysis of the EPI (2022) shows that accumulated emissions in the form of air pollution, effluent flows into waterways, mismanaged waste, chemical releases and greenhouse gases harm human health and ecosystems. To counter these negative impacts, many countries have enacted policies to significantly reduce their greenhouse gas emissions. In this respect, Denmark and the United Kingdom plan to reach greenhouse gas neutrality by 2050; however, China, India and Russia are still heading in the wrong direction.
Best practices for environmental sustainability include sustainability benchmarking and information disclosure at various levels of government (Alcaraz-Quiles et al., 2014), to facilitate meaningful international comparisons and long-term monitoring (Del Campo et al. 2021; Gallego-Álvarez et al., 2018). The adequate availability of information enables policymakers and others to assemble metrics and tools to gauge the adequacy of national policies, thus providing a mechanism for holding governments to account (EPI, 2022; CEPALSTAT, 2022; World Bank, 2022). Stakeholder concerns in the development of sustainability policies led to the adoption of the UN Framework Convention on Climate Change (UNFCCC, 2021), as an international initiative to collaborate for the greater good. Policymakers in the European Union have observed that policies favoring environmental sustainability can also produce competitive advantages for their proponents (Boasson & Wettestad, 2016), and promote the legislation of the Inflation Reduction Act (IRA), which aims to promote renewable energy and reduce dependence on fossil fuels in the global economy (Ma et al., 2024)
Thus, indicators of environmental sustainability can function as a social tool, enabling stakeholders to identify and analyze differences in environmental performance assessments by policy category, objective and country. Porter (1991) suggested this would generate a win-win situation, in the sense that environmental policies benefit the environment and at the same time enhance competitiveness. These outcomes are evidently much sought after by policymakers, in view of the growing number of green "new deals" and "net zero" carbon emission pledges being made at the national level. However, there is a marked gap between the environmental policy data needed and the real supply of reliable indicators and indices (Herman & Shenk, 2021).
Most South American countries have made significant recent progress in regulating environmental sustainability (see Appendix A), with particular reference to critical water issues, to protect and manage this increasingly scarce resource. In many cases, governments are making increasing use of visioning, back casting and adaptive environmental management to anticipate and respond to change, complexity and uncertainty, whilst pursuing effective implementation, monitoring and evaluation (Mitchell, 2013). Furthermore, many have ratified international agreements in this respect, such as the Paris Agreement on Climate Change (except for Chile and Ecuador) and have established agencies to protect biodiversity and protected areas and to prosecute cases of criminal responsibility. However, climate change remains uncontrollable, since factors such as global warming, excessive greenhouse gas emissions, and the use of fossil fuels (Kiehbadroudinezhad et al., 2024), losses of biodiversity and improper waste management will continue to prevail. Notwithstanding, and as highlighted by Paredes-Beltrán (2021), EPI (2022), Del Campo et al. (2021) and Miranda et al. (2020), the countries of South America are well aware of the extreme gravity of the environmental problems facing the region. Few studies have assessed and compared regional trends in environmental sustainability in South America. Major barriers to this type of research include, weak regional monitoring, limited coverage, varied measurement techniques, and complex access to air quality data (Clean Air Institute, 2013).
DATA AND METHODOLOGY
Sample Location and Availability
This paper aims to evaluate the environmental sustainability performance of twelve South American countries (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay and Venezuela), according to data published in 2019, the CEPALSTAT database (see Table 1) and survey data published by the ECLAC, one of five regional commissions established by the United Nations (UN) to contribute to economic development (Martínez, 2019), and the similarities and differences can be observed among the indicator practices in these countries of two statistical methods, Principal Component Analysis (PCA) and Cluster Analysis (CA).
The study data reflect the similarities and disparities among the countries in terms of the following dimensions: production of secondary energy and change in energy intensity, proportion of protected marine areas for the preservation of biological diversity, contribution to the persistence of marine biodiversity, proportion of protected areas to preserve terrestrial and marine biodiversity, global warming, pollution and overexploitation of the sea.
Principal Component Analysis (PCA)
In this study, indicators of environmental sustainability are determined by means of principal component analysis (PCA), a multivariate technique which identifies latent variables that while not directly observable can be inferred (via a mathematical model) from other variables that are observable and can be measured directly. With this approach, the degree of environmental responsibility and sustainability (of an individual, organization, community or country) can be quantified.
The increasing demand by countries for information on the status of their natural resources and marine biodiversity has led to the creation of databases and the development of methodological and statistical capacity to produce reliable indicators of the results of the environmental sustainability policies applied by countries or regions. Such actions produce long-term effects, justifying our use of multivariate techniques in their analysis. This study of environmental sustainability and its correlation with supporting statistical data was conducted in order to quantify sustainability in terms of environmental indicators. Moreover, the development of an optimal system of indicators helps to explain the dynamics of environmental sustainability and the goals achieved.
The multidimensional approach taken to the system of environmental sustainability indicators reflects the complex reality of national systems and of each of the components involved in the transformation of urban and rural productive activity. Each dimension of the system has its own characteristics; it both constrains and is conditioned by the other dimensions (Sepúlveda et al., 2002). To achieve sustained growth and development, institutional actors and economic agents must be aware of current stocks of renewable natural resources and the environment in which they operate and be capable of managing them from a long-term perspective.
In our analysis, the measurement of environmental sustainability is associated with the concept of construct, as a means of understanding the data, goals and thresholds of sustainability with a high level of abstraction. Constructs are subjective variables designed to measure the changes implicit in a given phenomenon or process. They are objectively verifiable and replicable, and are considered analytical tools that facilitate the measurement of the hidden changes within a system that manifest themselves in observable variables. Principal Component Analysis (PCA) is a technique from statistics for simplifying a data set. It was developed by Pearson (1901) and Hotelling (1933).
In our study, in order to avoid problems arising from the use of different measurement scales, all variables are standardized by transformation into z-scores with a mean of 0 and a standard deviation of 1. This approach is used to configure the correlations present in a set of observed variables. Appropriate environmental sustainability constructs are derived by means of a multivariate analytical approach (Glave & Escobal, 1995), i.e., principal component analysis (Pearson, 1901; Hotelling, 1933, 1936). The first such approach employed is that of an unsupervised learning algorithm, which uses multivariate statistical techniques to reduce the original seventeen variables to six, the minimum number that reflects the variability in the data set with minimal loss of information (see tables 1 and 2).
Cluster Analysis (CA)
Cluster analysis is well established and can be applied as a hierarchical method or as a k-means method. The hierarchical method divides a set of individuals (in this case, countries) into smaller groups, so that those belonging to the same set are very similar to each other, but very different from those assigned to other groups -i.e., there is inter-group homogeneity and intra-group heterogeneity- (Kaufman & Rousseeuw, 2009).
In our study, cluster analysis makes it possible to obtain a typology of countries such that each cluster corresponds to certain patterns of behavior and performance in terms of environmental sustainability. Among other outcomes, this analysis identifies the countries with best practices in terms of the environmental indicators considered and shows how they have evolved over time.
The results obtained could facilitate the benchmarking of environmental practices and hence foster improvement. Based on the indicators described, we identify the groups of countries that present similar characteristics and those that behave as outliers and highlight their most important features and their role in the South American context.
Furthermore, as the study variables are cross-sectional (referring to 2019), we also considered model-based clustering. Finally, the fact that only twelve countries were included in the analysis means that the complexity related to large databases, which is one of the main drawbacks of hierarchical methods (Aghabozorgi et al., 2015), was not experienced.
In the multivariate methodology applied in this study, eigenvectors and eigenvalues are extracted from the correlation matrix. From the eigenvalues, we obtain the proportion of variance explained by each component of the total variance present in the data set of interest. Since the sum of the eigenvalues quantifies the total variance explained by the factors, we seek to maximize this proportion. In this selection process, it is important to consider only those components that contribute most to explaining the total variance. A criterion commonly adopted is that at least 80% of the total variance should be explained, such that the significant reduction obtained in the dimensionality of the original data set produces the least possible loss of information.
The results thus obtained are then evaluated with the eigenvectors, to determine how the variables behave in each of the different components, and to obtain elements for their definition. An important consideration is that the lower the eigenvalues the more difficult it becomes to define the components on the basis of the eigenvectors, i.e., the less the component explains the variability of the data. Having identified the indicators that impact on environmental sustainability, we now make further use of multivariate statistics (cluster analysis) to generate country profiles according to the environmental practices observed.
Under the clustering method applied, we first grouped the two closest elements and then successively combined pairs of elements, gradually forming larger groups until all the elements were incorporated. This process can be interrupted at any level of the clustering "tree" created, thus enabling the desired number of groups to be obtained directly. In this process, it is useful to calculate a metric of cluster quality (minimizing the distance between data elements in the same cluster and maximizing the distance with respect to the other clusters) to determine an appropriate cutoff level.
In this study, hierarchical cluster analysis was applied, using Ward's method with squared Euclidean distances, which from practical experience produces the best results in this type of situation (Ward, 1963). A formal, rigorous description of these techniques can be found in Everitt et al. (2011), Hair et al. (2009) and Kaufman and Rousseeuw (2009). See also Aghabozorgi et al. (2015) for an exhaustive review of clustering time series.
The above-described cluster analysis enables us to obtain the proximity matrix from the data matrix X of order nxp and to construct the distance matrix S of order nxn, where each coefficient of S, sij, indicates the distance between the countries of interest according to the latent variables considered, such that each value of a dissimilarity coefficient for cases i and j measures the degree of dissimilarity/distance of the individuals. This matrix is symmetric, given that sij = sji.
The distance matrix was obtained using the "average link between groups" clustering method and taking the squared Euclidean distance as the clustering measure (see Note 1). In statistical analysis, a similarity measure or function is a real-valued function that quantifies the similarity between two objects, even though there is no single definition of similarity.
RESULTS
Principal Components Analysis (PCA)
From the Kaiser criterion of eigenvalues greater than one and considering the slopes of the sedimentation plot (see Figure 1), we find that 91,7% of the total variance can be explained by six principal components, which represents a significant percentage of the variability present in the data set under study (see Table 4). The following principal components were selected:
In terms of environmental sustainability, these indicators relate to the conservation of natural resources and biodiversity and reflect an awareness of the finite nature of these resources, of the fragility of the physical environment and of how it is affected by human activities.
This analytical perspective is related to biodiversity, climate, pollution, energy, and the efficient use of natural resources. To properly interpret the elements considered, we must examine the correlations obtained between the variables and the PCs to determine the strength of the relationship between them (Table 3). In this regard, the following results were obtained.
PC1 is significantly correlated with secondary energy production and change in energy intensity (>90%).
PC2 incorporates variables related to marine protected areas and the variety of marine life (correlations >0.9). The relationship is positive, meaning that increasing protection for marine areas would enhance marine biodiversity.
PC3 reflects the severe effects produced on the marine ecosystem by the aquaculture industry and the intensive use of fertilizers in agriculture. Among other criticisms, aquaculture is associated with the destruction of mangroves and with a negative environmental impact on receptor ecosystems, due to the excessive consumption of resources, the transformation of the habitat and the generation of the final product. The intensive use of fertilizers generates an excess of nutrients that subsequently contaminate surface water and groundwater. This component, therefore, reflects the need to protect the marine environment.
PC4 includes terrestrial biodiversity preservation practices, the generation of energy from renewable sources, and the total percentage of protected areas. This construct reflects practices of terrestrial and marine biodiversity protection.
PC5 concerns the variations in average temperature by area and in the total area of land and inland water. Thus, this component captures the impact of global warming.
Finally, PC6 combines the production of fish (including crustaceans and mollusks) with the concentration of air pollution particles. This component is an indicator of pollution and overexploitation of the sea.
Our analysis revealed moderate to strong correlations between the four indicators of environmental sustainability and PC1 (secondary energy production and change in energy intensity). Positive correlations were also observed between three indicators and PC2 (proportion of marine protected areas for the preservation of biological diversity) and PC3 (Contributing to the persistence of marine biodiversity) and PC4 (proportion of areas protected to conserve terrestrial and marine biodiversity).
Conversely, the correlation with renewable proportion of primary energy supply was negative. Correlations were strong with average temperature variation and country area (total) and with PC5 (global warming), while the correlations between both fish capture production and concentration of fine particulate matter and PC6 were very high (see Table 3).
Table 4 and Figure 1 show the seventeen components with eigenvalues (coefficients applied to eigenvectors that give the vectors their length or magnitude) through the extraction method, i.e., principal component analysis (PCA). Larger eigenvalues correlate with more important directions.
Finally, the total variability is captured by the projections of the data onto the first principal components, so that the first component is a direction that maximizes the variance of the data, and that the i-th component can be considered as a direction orthogonal to the first i -1 principal components that maximizes enabling us to associate countries with the variance of the projected data. comparable characteristics.
Cluster Analysis (CA)
The distance or similarity matrix is examined in order to determine the true structure of the data under study, since many of the coefficients reported may be related. Consequently, the concordance of the results may reflect the type of grouping structure being sought, assuming that scores will be lower when these groups are more similar, and that higher scores will reflect dissimilarity. The distance matrix, thus, functions as a quantitative tool to evaluate the similarity of countries in terms of their environmental practices, enabling us to associate countries with comparable characteristics.
In the present study, the squared Euclidean distance was calculated to obtain the coefficients and hence the required grouping structure. The results of this calculation are shown in Table 5. The countries that present similarities in terms of their long-term environmental practices and sustainability are Venezuela and Colombia (0.497), Paraguay and Guyana (0.531), Suriname and Colombia (3.442), Guyana and Argentina (3.469), Venezuela and Suriname (3.670) and Bolivia and Argentina (3.705).
Ecuador, Brazil and Chile present very high similarity coefficients (above two digits), which indicates that their environmental sustainability-related practices differ significantly, both among themselves and with the other countries analyzed. This disparity is reflected in the biodiversity indicators published by international organizations. Among these three countries, Ecuador presents greater degradation of biodiversity than Chile and Brazil.
In the next phase of the analysis, we construct a hierarchical classification of the twelve countries, in which the individuals are not simultaneously partitioned into clusters; instead, successive partitions are made at different levels of aggregation, following a priority or hierarchy, becoming progressively less homogeneous as the groups become larger. This successive partitioning is carried out by combining the six latent indicators identified by principal component analysis. The outcome of this process is a dendrogram showing the degree of clustering of the countries according to their similarities and differences regarding environmental sustainability practices.
To perform the above partitioning, it is first necessary to determine the number of clusters that reflect the profiles of the countries under study. This decision-making process is usually represented by an iterative algorithm and by the distances at which countries should be grouped according to their environmental profiles.
Figure 2 shows that in this dendrogram, the initial jumps in terms of distance are small, while later jumps are more widely spaced. The cutoff point taken is the point at which sharp jumps or large distances begin to occur.
The dendogram shows that five clusters are located below the continuous line (see Figure 2). Analysis of the successive increases in the distances between the clusters (jumps on the vertical axis of the values for the intergroup sums of squares values) shows that a reasonable choice would be the five-cluster solution at a distance of ten rescaled points. This is because when more than five clusters are considered, the jumps in terms of the inter-group distances are very significant. Accordingly, the twelve countries analyzed in this study are grouped into five clusters:
Cluster 1 - Colombia, Venezuela, Suriname, Argentina and Bolivia
Cluster 2 - Guyana, Paraguay, Uruguay and Peru
Cluster 3 - Brazil
Cluster 4 - Ecuador
Cluster 5 - Chile
In the following, we detail the characteristics of each cluster.
Cluster 1. Colombia, Venezuela, Suriname, Argentina and Bolivia. These countries employ similar environmental practices, moving towards low-emission and climate change resilient economies, and promoting innovative solutions in key sectors such as renewable energy, agriculture, electromobility, financial institutions and biodiversity. Among related projects, Argentina has launched the program "Strengthening governance to implement land degradation neutrality objectives through sustainable management of forests and agricultural systems", financed by the Global Environment Facility (GEF). The aim of this program is to reduce climate vulnerability, improve land productivity and protect social fairness and environmental quality in agroecosystems, in three river basins in Argentina. Other initiatives include the joint Argentina-Bolivia project "Integrated management of water resources in the Bermejo River basin", financed by GEF, aimed at achieving the integrated planning and management of the Bermejo River basin and the sustainable use of natural resources. In Colombia, the project "Energy efficiency for the transition to carbon neutral cities" seeks to reduce CO2 emissions by increasing energy efficiency in the construction sector in the regions of Barranquilla, Montería and Pasto, via actions addressing the different stages of the life cycle of buildings and interventions in public spaces, focusing on three key components: energy efficiency in buildings and public spaces; the management of sustainable projects; and the dissemination and management of knowledge related to the environment and global warming.
Cluster 2. Guyana, Paraguay, Uruguay and Peru. These countries all present environmental problems related to the overexploitation of water resources, the emission of greenhouse gases by extensive livestock farming, and the loss of biodiversity associated with agriculture due to the intensive use of chemical fertilizers and artificial farming methods and the inadequate treatment of industrial waste. These countries, hence, feature weak environmental performance and poor protection of marine and terrestrial areas.
Cluster 3. Brazil. According to the System for Estimating Greenhouse Gas Emissions (SEEG, 2021) Municipalities, part of the Climate Observatory initiative, and the online journal Expansion, Brazil is one of the worst performers among 184 countries in terms of CO2 emissions. This situation is aggravated by the uncontrolled burn-off of the rainforests, which pollutes the air breathed by millions of people and impacts on human health throughout the Amazon region, according to the Amazon Environmental Research Institute (IPAM, 2021).
Cluster 4. Ecuador. The main environmental problems facing Ecuador are deforestation, agribusiness, hydroelectric power generation, mining, and weak environmental institutions.
However, the authorities have pledged to reform the energy matrix, reduce deforestation, and promote responsible, sustainable consumption. In short, this country aims to improve its environmental performance, although it is currently failing to provide adequate protection for marine and terrestrial biodiversity.
Cluster 5. Chile. Chile's main environmental problems are air pollution, water scarcity and pollution, soil loss and contamination, noise pollution, inadequate solid waste management and the loss of biodiversity. However, Chile has strengthened its environmental institutions on the basis of a multisectoral environmental coordination model. It has also stepped up its environmental initiatives in the areas of air, water, waste and biodiversity management, using innovative instruments innovative instruments (e.g. trade) and successful reforms (e.g. water services).
Figure 3 illustrates the significant heterogeneity in environmental sustainability in South America, and the performance of the countries in each of the clusters identified.
In every case, complex situations must be addressed. However, all twelve countries face certain problems that are common to all. These include deforestation, the intensive use of fertilizers in agriculture, the greenhouse effect, the pollution associated with extensive live-stock farming, inadequate management of solid waste and an enormous loss of biodiversity.
The efforts of these countries to develop a sustainable economic and social policies consistent with environmental objectives are reflected in the development of appropriate regulations and in their efforts to generate quantitative indicators to monitor progress in this context.
DISCUSSION
Historically, models of national growth and development have not been linked to environmental objectives but have been used to identify the spatial distribution of economic activities, the territorial concentration of the population, the location and degree of growth of urban centers, and the types of links between certain territorial units and the rest of the national territory. However, this type of territorial distribution takes little account of its environmental consequences, especially in the medium and long term.
In practical terms, this phenomenon has generated a process of spatial differentiation in which each country or region acquires specific productive, economic and socio-political characteristics, as a functional component of a complex national development matrix, with limited environmental objectives.
The spatial differentiation thus created and the types of geographic connections established are evident in three main ways:
These characteristics accentuate environmental problems and produce disparities in how current models of growth address questions such as global warming and the loss of biodiversity.
In the present study, indicators of environmental problems are used to group countries with similar patterns of behavior in order to identify the main characteristics of the configuration of clusters and the role they play in South America.
All twelve South American countries considered in this study have signed and ratified various international treaties and conventions on climate change biodiversity and ecosystems, sustainable development, ocean protection and the management of waste and hazardous chemicals (see Note 2 and Appendix B).
The study's findings highlight the uneven progress made by these countries in addressing environmental problems. In this respect, the 2023 Regional Conference on South-South Cooperation in Latin America and the Caribbean called for economic integration and partnerships for sustainable growth, with a view to fulfilling the United Nations 2030 Agenda.
The Peruvian Agency for International Cooperation (APCI) was created in 2002, the Ecuadorian Agency for International Cooperation (AGECI) in 2007 (in July 2010, its name was changed to that of the Technical Secretariat for International Cooperation), the Colombian Presidential Agency for Cooperation (APC) in 2011, the Mexican Agency for International Development Cooperation (AMEXCID) in 2011, and the Uruguayan Agency for International Cooperation (UCI) in 2011 (Rivero & Xalma, 2019). In the remaining LAC countries, development cooperation policies, programs and activities have been strengthened with the creation of new offices or departments within one or more government ministries (see Table 7).
In line with the findings obtained by other authors, there is currently a high level of environmental concern in South America. However, some countries have shown to be more concerned about climate change; these countries include Argentina, Chile and Brazil (Gallego-Álvarez et al., 2018). Chile is promoting the development of green hydrogen, while Brazil is a leader in renewable energy, with hydroelectric power. According to Darré et al. (2019) Uruguay's agricultural land increased by 138%, led by the soybean expansion (1,140,000 ha), making it one of the world's top six global exporters in the world. However, this land is not exempt from various environmental problems such as soil degradation, water pollution, deforestation, and loss of biodiversity.
The 2022 Environment Performance Index (EPI) shows that the highest scoring countries in the region are Chile and Suriname. Many countries with midrange scores include Brazil, Colombia, Argentina, Paraguay, Bolivia, and Peru. At the other end of the Index, countries with low scores include Guyana and Uruguay, which have made little significant progress. There is a very high correlation between a country's wealth and its EPI score, so the environmental challenges vary according to a country's wealth and level of development of the countries. Ideas about environmental sustainability are therefore likely to reinforce already existing institutional and discursive settings (Crabb & Leroy, 2008); Alcaraz-Quiles et al., 2014; Olafsson et al., 2014; Boasson & Wettestad, 2016; Miranda et al., 2020).
CONCLUSIONS
This paper aims to evaluate the environmental sustainability performance of twelve South American countries, considering data published in 2019. Indicators of environmental sustainability highlight the evaluation of each country in this regard, and can be used to promote benchmarking. Multivariate statistical techniques are used to group the countries into five homogeneous clusters. The results obtained show that there are clear differences between these countries in terms of environmental sustainability, but that overall performance has improved in each case.
In relation to the production and secondary consumption of energy (PC1), Brazil, Argentina and Venezuela recorded progressive growth in indicators 10, 11, 12 and 13 (see Table 1). Brazil recorded the highest production capacity of renewable energy. By contrast, the production capacity of Guyana and Suriname has fallen in recent years due to their increased use of fossil fuels, although Suriname has received IDB assistance in energy reform projects (regarding institutional and regulatory reform, digitalization, sustainable infrastructure, energy access and renewable energies) to promote clean energy.
Ecuador, Peru and Brazil report the highest proportions of marine protected areas (Indicator 2), while neither Bolivia nor Paraguay have any such protected area. In absolute terms, Chile and Colombia have the largest Marine Protected Areas. The Nazca-Desventuradas Marine Park (Chile) is the largest single marine protected area in the Americas. According to Indicator 5 (Proportion of important places for marine biodiversity that constitute a protected area), Suriname, Ecuador, Brazil and Colombia each have a significant number of these locations, but Bolivia, Guyana and Paraguay have none.
Peru, Chile, Ecuador and Brazil score highly for total aquaculture production (Indicator 3), in contrast to Suriname, Uruguay and Paraguay. Chile, Brazil and Colombia each have a significant proportion of protected areas in relation to marine areas (Indicator 3), while Bolivia and Paraguay have none. The lowest levels for intensity of fertilizer use (Indicator 17) are recorded for Bolivia, Guyana, Peru and Suriname, and the highest values, for Chile, Brazil, Uruguay and Ecuador.
Venezuela, Suriname, and Bolivia record the highest values, and Guyana and Uruguay, the lowest ones, for the proportion of important places for terrestrial diversity that constitute protected areas (Indicator 4). Paraguay, Guyana and Suriname score well for hydropower, solar, wind, geothermal, bioenergy, wave and tidal energy resources (Indicator 14), unlike Venezuela, Argentina and Bolivia. The leading countries for the proportion of terrestrial and marine protected areas (Indicator 7) are Venezuela, Bolivia and Brazil, while Argentina, Guyana and Chile score poorly in this respect.
The strongest average temperature variations (Indicator 1) are presented by Paraguay, Suriname, Brazil, Colombia, Venezuela and Ecuador, although in general the entire continent has experienced rising temperatures over the past fifteen years. Brazil, Argentina and Peru are the largest in terms of CAT (total surface area, including inland waters), while Suriname, Uruguay, Guyana and Ecuador are the lowest ranking in this regard.
Among the indicators regarding Factor 6, the highest values for total fisheries capture production (Indicator 15) are reported by Peru, Chile and Argentina, at levels that have not significantly increased during the last fifteen years. Bolivia reports the lowest values in this respect. With respect to air pollution, another indicator contributing to Factor 6, Peru, Bolivia, Suriname and Chile report the highest concentrations of fine particulate matter (Indicator 9), and Uruguay, the lowest.
In summary, the above findings reflect clear differences among these twelve South American countries in terms of their environmental sustainability performance, in some cases due to legal initiatives and practices (see Appendix A). In this regard, Chile can be considered a positive benchmark, having enacted new laws on climate change, having created a new, powerful public agency, SBAP, to protect biodiversity (finally established in June 2023, after 13 years' preparation), and having established the National System of Protected Areas, with severe sanctions for non-compliance.
Although predominantly descriptive, this analysis of environmental sustainability performance in twelve South American countries makes a real contribution to our understanding by revealing how and why some countries differ in this respect, and by highlighting the changes needed to enhance performance. In any geographic region with commonalities such as those we describe, it is important to identify and characterize top-performing countries in order to create benchmarks on which others may judge their results.
Among the countries considered, Chile has most improved its environmental sustainability practices, for example by creating a significant marine protected area, thus obtaining competitive and comparative advantages for the development of sustainable aquaculture. Moreover, Chile is a signatory to international agreements against climate change and is making great efforts to achieve carbon neutrality.
The present research also identifies possible advances in theoretical concepts based on the environmental sustainability indicators analyzed, providing valuable empirical information. This is especially important as very few previous studies have adopted such a comparative-international perspective towards the South American region.
Our journey towards sustainability is a process based on a new environmental paradigm, which must guide us towards the changes needed in how resources are managed, in the economic criteria applied, in prevailing values and in the ecological and social practices developed. To be successful in this endeavor, we must alleviate the current deterioration of the global environment and adopt policies in line with the real possibilities of the natural world.
NOTES
Note 1. A Euclidean distance represents the space between two points, measured as a line segment.
Note 2. These treaties and agreements include the Basel Convention, the Stockholm Convention, the Rotterdam Convention, the Minamata Convention, the Vienna Ozone Convention, the Convention on Biological Diversity, the Sustainable Development Goals and the Convention on International Trade in Endangered Species (CITES).
ACKNOWLEDGEMENTS
The authors would like to express their sincere gratitude to the anonymous reviewers for their constructive feedback.
FUNDING
This work was made by the generous financial support of the program "Short Stays in National and Foreign Research Centres" of the Own Research Plan Year 2022 at the Instituto Superior Técnico of the University of Lisbon (IST). CERIS - Systems and Management Research Group with the application of Research Stay (No. 58) of the University of Granada.
DECLARATION OF CONFLICTS OF INTEREST
Authors declare no conflict of interest.
AUTHORS' CONTRIBUTIONS
Conceptualization: Paola Marcela Hermosa Del Vasto; Rui Cunha Marques. Software: Juan Luis Peñaloza; Paola Marcela Hermosa del Vasto. Validation: Juan Luis Peñaloza. Formal Analysis: Juan Luis Peñaloza. Data Curation: Juan Luis Peñaloza. Writing - Preparation of the Original Draft: Paola Marcela Hermosa Del Vasto. Writing-Review and Editing: Paola Marcela Hermosa Del Vasto; Rui Cunha Marques. Visualization: Juan Luis Peñaloza. Supervision: Rui Cunha Marques. Project Management: Paola Marcela Hermosa Del Vasto. All authors have read and accepted the published version of the manuscript.
REFERENCES
[1] Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-Series Clustering. A Decade Review. Information Systems, 53, 16-38. https://doi.org/10.1016/j.is.2015.04.007
[2] Alcaraz-Quiles, F., Navarro-Galera, A., & Ortiz-Rodríguez, D. (2014). A Comparative Analysis of Transparency in Sustainability Reporting by Local and Regional Governments. Lex Localis, Journal of Local Self-Government. 12(1), 55-78. https://doi.org/10.4335/12.1.55-78(2014)
[3] Ammons, D. N., Coe, C., & Lombardo, M. (2001). Performance-Comparison Projects in Local Government: Participants' Perspectives. Public Administration Review, 61(1), 100-110. http://www.jstor.org/stable/977540
[4] Ammons, D. N., & Roenigk, D. J. (2015). Benchmarking and Interorganizational Learning in Local Government. Journal of Public Administration Research and Theory, 25(1), 309-335. https://doi.org/10.1093/jopart/muu014
[5] Boasson, E. L., & Wettestad, J. (2016). EU Climate Policy: Industry, Policy Interaction and External Environment. Routledge.
[6] CEPALSTAT. (2022). Bases de datos y publicaciones estadísticas. Cepal. https://statistics.cepal.org/portal/cepalstat/index.html?lang=es
[7] Clean Air Institute. (2013). Air Quality in Latin American: An Overview. https://www.semanticscholar.org/paper/Air-Quality-in-Latin-America-%3A-An-Overview-Green/6a995e3798617e8496bbe02be96e21e7228cb86b.
[8] Crabb, A., & Leroy, P. (2008). The Handbook of Environmental Policy Evaluation. Routledge. https://doi.org/10.4324/9781849773072
[9] Da Cruz, N. F., & Marques, R. C. (2014). Scorecards for Sustainable Local Governments. Cities, 39, 165-170. https://doi.org/10.1016/j.cities.2014.01.001
[10] Darré, E., Cadenazzi, M., Mazzilli, S., Rosas, J., & Picasso, V. (2019). Environmental Impacts on Water Resources from Summer Crops in Rainfed and Irrigated Systems. Journal of Environmental Management, 232, 514-522. https://doi.org/10.1016/j.jenvman.2018.11.090
[11] Del Campo, C., Hermosa del Vasto, P., Urquía-Grande, E., & Jorge, S. (2021). Country performance in the South American region: A multivariate analysis. International Journal of Public Administration, 44(5), 390-408. https://doi.org/10.1080/01900692.2020.1728314
[12] Environmental Performance Index EPI. (2022). 2022 EPI Results. https://epi.yale.edu/epi-results/2022/component/epi
[13] Everitt, B., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis (5th ed.). John Wiley & Sons.
[14] Francisco, M., & Linnér, B. (2023). AI and the Governance of Sustainable Development. An Idea Analysis of the European Union, the United Nations, and the World Economic Forum. Environmental Science & Policy, 150, 103590. https://doi.org/10.1016/j.envsci.2023.103590
[15] Gallego-Álvarez, I., García-Rubio, R., & Martínez-Ferrero. (2018). Environmental Performance Concerns in Latin America: Determinant Factors and Multivariate Analysis. Spanish Accounting Review, 21(2), 206-221. https://doi.org/10.1016/j.rcsar.2018.05.003
[16] Glave, M., & Escobal, J. (1995). Indicadores de sostenibilidad para la agricultura andina. Debate Agrario, (23), 89-112. https://www.proquest.com/scholarly-journals/indicadores-de-sostenibilidad-para-la-agricultura/docview/217872634/se-2?accountid=14542
[17] Global Reporting Initiative GRI (2022). Full Set of GRI Standards. https://www.globalreporting.org/how-to-use-the-gri-standards/gri-standards-english-language/
[18] Global Water Partnership GWP (2022). Annual Report of the Global Water Partnership. https://www.gwp.org/en/About/more/Events-and-Calls/2022/
[19] Gómez Peláez, L. M., Santos, J. M., De Almeida Albuquerque, T. T., Reis, N. C., Andreão, W. L., & De Fátima Andrade, M. (2020). Air Quality Status and Trends over Large Cities in South America. Environmental Science & Policy, 114, 422-435. https://doi.org/10.1016/j.envsci.2020.09.009
[20] Goodland, R. (1995). The Concept of Environmental Sustainability. Annual Review Ecology and Systematics, 26(1), 1-24. https://doi.org/10.1146/annurev.es.26.110195.000245
[21] Hair, J., Black, W., Babin, B., & Anderson, R. (2009). Multivariate Data Analysis (7th ed.). Pearson.
[22] Herman, K. S., & Shenk, J. (2021). Pattern Discovery for Climate and Environmental Policy Indicators. Environmental Science & Policy, 120, 89-98. https://doi.org/10.1016/j.envsci.2021.02.003
[23] Hermosa, P., Jorge, S., Urquía-Grande, E., & Campo, C. D. (2024). Measuring Governments' Online Accountability. Electronic Government, an International Journal, 20(1), 109-138. https://doi.org/10.1504/EG.2024.135314
[24] Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, 24(6), 417-441. https://doi.org/10.1037/h0071325
[25] Hotelling, H. (1936). Simplified Calculation of Principal Components. Psychometrika, 1, 27-35. https://doi.org/10.1007/BF02287921
[26] Kaufman, L., & Rousseeuw, P. (2009). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons
[27] Kiehbadroudinezhad, M., Merabet, A., Al-Durra, A., Hosseinzadeh-Bandbafha, H., Wright, M. M., & El-Saadany, E. (2024). Towards a Sustainable Environment and Carbon Neutrality: Optimal Sizing of Standalone, Green, Reliable, and Affordable Water-Power Cogeneration Systems. Science of the Total Environment, 912, 168668. https://doi.org/10.1016/j.scitotenv.2023.168668
[28] Klumpp, A., Domingos, M., & Pignata, M. L. (2023). Air Pollution and Vegetation Damage in South America. State of Knowledge and Perspectives. In Environmental Pollution and Plant Responses (pp. 111-136). Routledge. https://doi.org/10.1201/9780203756935-7
[29] Ma, W., Wu, T., Stan, S., & Gao, B. (2024). Ensuring Environment Sustainability through Natural Resources, Renewable Energy Consumption, and Inflation Dynamics. Resources Policy, 90, 104676. https://doi.org/10.1016/j.resourpol.2024.104676
[30] Martínez, R. (ed.) (2019). Institutional Frameworks for Social Policy in Latin America and the Caribbean (ECLAC Books, No. 146). Economic Commission for Latin America and the Caribbean ECLAC.
[31] Miranda, A., Lara, A., Altamirano, A., Di Bella, C., Mauro, E., & Camarero, J. (2020). Forest Browning Trends in Response to Drought in a Highly Threatened Mediterranean Landscape of South America. Ecological Indicators, 115, 1-10. https://doi.org/10.1016/j.ecolind.2020.106401
[32] Mitchell, B. (2013). Managing for Environmental Justice. Resource and Environmental Management (pp. 369-387). Routledge. https://doi.org/10.4324/9781315847771
[33] Olafsson, S., Cook, D., Davidsdottir, B., & Johannsdottir, L. (2014). Measuring Countries' Environmental Sustainability Performance. A Review and Case Study of Iceland. Renewable and Sustainable Energy Reviews, 39, 934-948. https://doi.org/10.1016/j.rser.2014.07.101
[34] Organisation for Economic Co-operation and Development OECD. (2022). Environmental Indicators. https://www.oecd.org/site/envind/
[35] Organisation for Economic Co-operation and Development OECD. (2023). OECD Key Indicators. https://www.compareyourcountry.org/key-indicators
[36] Oviedo, E. (2021). Evaluating South-South Cooperation in Six Latin American and Caribbean Countries: Shared Challenges for Implementation of the 2030 Agenda for Sustainable Development (LC/TS. 2021/121). Repositorio Cepal. https://org/bitstream/handle/11362/47446/3/S2100360_en.pdf
[37] Paredes-Beltrán, B., Sordo-Ward, A., De Lama, B., & Garrote, L. (2021). A Continent Assessment of Reservoir Storage and Water Availability in South America. Water, 13 (14) https://doi.org/10.3390/w13141992
[38] Pearson, K. (1901). LIII. On Lines and Planes of Closest Fit to Systems of Points in Space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572. https://doi.org/10.1080/14786440109462720
[39] Ponomarenko, T., Reshneva, E., Mosquera Urbano, A. P. (2022). Assessment of Energy Sustainability Issues in the Andean Community: Additional Indicators and Their Interpretation. Energies, 15, 1077. https://doi.org/10.3390/en15031077
[40] Porter, M. (1991). America's Green Strategy. Scientific American, 264(4).
[41] Rivero, M., & Xalma, C. (2019). Iberoamérica y la Cooperación Sur-Sur frente a las encrucijadas de la agenda internacional para el desarrollo. Documentos de Trabajo, (16).
[42] Rutherford, B. (2000). Construction and Presentation of Performance Indicators in Executive Agency External Reports. Financial Accountability & Management, 16(3), 225-249. https://doi.org/10.1111/1468-0408.00106
[43] Sepúlveda, S., Chavarría, H., Castro, A., Rojas, P., Picado, E., & Bolaños, D. (2002). Metodología para estimar el nivel de desarrollo sostenible en espacios territoriales. Instituto Interamericano de Cooperación para la Agricultura (IICA). http://repositorio.iica.int/handle/11324/7696
[44] Shahzad, M., Qu, Y., Javed, S. A., Zafar, A. U., & Rehman, S. U. (2020). Relation of Environment Sustainability to CSR and Green Innovation: A Case of Pakistani Manufacturing Industry. Journal of Cleaner Production, 253, 119938. https://doi.org/10.1016/j.jclepro.2019.119938
[45] Shiklomanov, I. (1998). World's Water Resources. A New Appraisal and Assessment for the 21st Century. UNESCO.
[46] System for Estimating Greenhouse Gas Emissions SEEG. (2021). Brazilian Civil Society Initiative the Climate Observatory. SEEG's Municipality Data. https://www.cdp.net/en/cities/ghg-emissions-tools-and-datasets-guide-for-cities/seeg
[47] Sun, H., Mohsin, M., Alharthi, M., & Abbas, Q. (2020). Measuring Environmental Sustainability Performance of South Asia. Journal of Cleaner Production, 251, 119519. https://doi.org/10.1016/j.jclepro.2019.119519
[48] The Amazon Environmental Research Institute IPAM. (2021). Amazonian Municipalities Dominate Carbon Emissions in Brazil. https://ipam.org.br/amazonian-municipalities-dominate-carbon-emissions-in-brazil/
[49] Toumi, O., Le Gallo, J., & Ben Rejeb, J. (2017). Assessment of Latin American sustainability. Renewable and Sustainable Energy Reviews, 78, 878-885. https://doi.org/10.1016/j.rser.2017.05.013
[50] United Nations. (2023). Climate Change Aggravates Water Scarcity in Argentina, Uruguay and Chile. IAGUA. https://www.iagua.es/noticias/onu/cambio-climatico-agrava-escasez-agua-argentina-uruguay-y-chile
[51] United Nations Framework Convention on Climate Change UNFCCC. (2021). Glasgow Climate Change Conference October-November 2021. https://unfccc.int/conference/glasgow-climate-change-conference-october-november-2021
[52] United Nations Environment Programme. (2022). UNEP in 2022. United Nations Environment Programme. https://doi.org/unep.org/annualreport
[53] Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236-244. https://doi.org/10.1080/01621459.1963.10500845
[54] World Bank. (2022). Environmental Indicators. https://data.worldbank.org/indicator?tab=all