Quality of Life and Environmental Degradation: An Empirical Assessment of Their Interactions and Determinants in Latin America and the Caribbean

Abstract

This study examines the relationship between quality of life and environmental degradation in Latin America and the Caribbean by analyzing data from 24 countries over the period 2007–2020 from a multidimensional perspective that integrates economic, social, and ecological dimensions. Employing a quantitative methodological approach based on panel data models and robust econometric tests, the research yields several significant findings. The reduction of forest areas is associated with a substantial negative impact on quality of life, as are elevated levels of air pollution, whereas access to sanitation services exhibits a highly significant positive relationship. These results underscore the extent to which environmental degradation constrains opportunities for human development, particularly among vulnerable populations. The study concludes that public policies must transcend traditional economic frameworks and adopt integrated strategies that simultaneously promote ecological conservation, improve basic infrastructure, and reduce persistent inequalities across the region.

1. Introduction

In recent decades, the relationship between quality of life and environmental degradation has gained increasing prominence in academic and policy debates. It has become clear that human well-being cannot be adequately assessed using conventional economic indicators alone, such as gross domestic product (GDP) per capita. Instead, it is essential to incorporate substantive environmental dimensions—such as air quality, thermal regulation, biodiversity conservation, and secure access to water resources—into the analysis [1]. This integrative approach acknowledges that healthy ecosystems directly influence physical health, psychological balance, and social cohesion [2], positioning them as fundamental pillars for truly sustainable human development [3,4,5].

Globally, numerous reports warn that environmental degradation has intensified its impact on human well-being. According to WHO [6], air pollution causes over seven million premature deaths annually, with particulate matter (PM2.5) and nitrogen dioxide (NO₂) as major contributors to respiratory and cardiovascular diseases. Although Africa emits only 3% of global greenhouse gases, it faces disproportionate consequences such as extreme droughts, loss of arable land, and increasing food insecurity [7]. These inequalities are further exacerbated by geopolitical asymmetries and institutional weaknesses in the most vulnerable regions [8].

In Latin America and the Caribbean, environmental degradation reflects a complex interplay between the region’s extraordinary biodiversity, dependence on extractive industries, and deep-rooted socioeconomic inequalities. Deforestation in the Amazon, driven by agricultural expansion and illegal mining, threatens to transform critical ecosystems into degraded landscapes, disrupting water regulation and carbon sequestration processes [9]. Simultaneously, cities such as Mexico City, Sao Paulo, and Santiago face persistent air pollution stemming from unsustainable transportation systems and industrial emissions, directly affecting urban quality of life [10]. Coastal areas in the Caribbean are increasingly vulnerable to sea level rise and ocean acidification, with direct repercussions on sectors like tourism [11].

Academic research on the link between environmental degradation and subjective well-being has produced both converging and diverging findings. For instance, Majeed and Mumtaz, Ferrer-i-Carbonell and Gowdy [1,12] show that air pollution, biodiversity loss, and water scarcity have a negative and statistically significant impact on happiness and well-being indices. These results align with habitability theories, which emphasize the critical role of preserved natural environments for maintaining public mental and physical health [2]. However, studies such as [13] stress that the effects of environmental degradation are not uniform and tend to disproportionately affect low-income populations with limited adaptive capacity. Tiwari and Mutascu [14] even suggest that the correlation between environmental factors and happiness is not always robust, as it can be moderated by cultural, institutional, and community-level variables.

Environmental degradation is thus not merely an ecological issue, but a multidimensional crisis that affects health, economic stability, and social justice—particularly in marginalized communities [15]. These impacts are neither homogenous nor inevitable but rather mediated by local governance quality and entrenched structural inequalities [7,16]. In many countries, biodiversity loss has intensified poverty and food insecurity, perpetuating cycles of exclusion [17,18].

Despite the growing literature, there remains a critical research gap regarding how environmental degradation affects quality of life across social and geographic contexts. There is a lack of integrative models that connect environmental conditions to both objective and subjective indicators of well-being, particularly in regions where poverty, ecological stress, and climate vulnerability intersect—such as Latin America and the Caribbean. Moreover, little attention has been given to institutional and territorial governance mechanisms that could mitigate these effects [19,20].

Accordingly, the present study seeks to analyze the relationship between environmental degradation and quality of life, with a focus on Latin America and the Caribbean. The aim is to identify the mechanisms through which environmental factors—such as biodiversity loss, air pollution, and water insecurity—impact human well-being, particularly in contexts marked by structural vulnerability. The research also highlights successful policy innovations, such as Costa Rica’s payment for environmental services schemes [19] and the regional Escazú Agreement [10], to show how ecological protection can be reconciled with social equity. In doing so, this study offers both theoretical and policy-relevant contributions to the emerging field of environmental justice and sustainable development.

1.1. Theoretical Framework

To understand the complex relationship between environmental degradation and quality of life in Latin America and the Caribbean, it is essential to draw upon interdisciplinary theoretical perspectives that transcend purely economic interpretations of well-being. This study is anchored in three complementary frameworks—Human Needs Theory, the capabilities approach, and the economics of happiness—which provide the conceptual foundation to link environmental conditions, institutional capacity, and individual well-being. These perspectives not only offer insights into the underlying mechanisms that shape quality of life but also inform the selection and operationalization of the variables used in the empirical model. Each framework contributes to a more comprehensive understanding of how environmental and social factors interact to influence human development in the context of growing ecological stress.

1.2. Quality of Life

Quality of life (QoL) is a multidimensional construct encompassing both objective and subjective dimensions of human well-being. According to the World Health Organization [6], QoL refers to an individual’s perception of their position in life within the cultural and value systems they inhabit, and in relation to their goals, expectations, standards, and concerns. Majeed et al. [1] expands this concept by explicitly incorporating environmental factors, arguing that well-being cannot be fully assessed using traditional economic metrics—such as GDP per capita—but requires attention to interrelated components: material well-being (income, employment, access to services), social well-being (security, participation), emotional well-being (happiness, satisfaction), and environmental well-being (air and water quality, biodiversity, climate resilience).

In line with this framework, this study uses a composite index of quality of life that integrates material, social, and environmental dimensions, offering a more holistic measure than economic indicators alone. This index reflects the influence of both structural conditions (e.g., access to sanitation) and ecological variables (e.g., PM2.5, forest cover) on the overall life experience of individuals.

1.3. Theory of Human Needs

The Theory of Human Needs by Max-Neef [21] posits that well-being depends on the satisfaction of universal and interrelated needs such as subsistence, protection, affection, understanding, leisure, and identity. Environmental degradation threatens several of these, notably subsistence—by restricting access to clean air, water, and food—and protection, by increasing vulnerability to natural disasters and pollution. This justifies the inclusion of PM2.5 concentration, average temperature, and forest area loss as empirical proxies for environmental stressors that undermine basic human needs.

The matrix of needs and satisfiers, central to Max-Neef’s framework, provides a conceptual tool to understand how socio-environmental conditions mediate well-being. Yet, as Boltvinik [22] notes, the presence of material satisfiers may generate an illusory sense of need fulfillment if environmental conditions deteriorate, thus masking deep structural deficiencies. These insights are reflected in this study’s approach, which avoids reductionist proxies of development and instead examines how unmet environmental needs correlate with lower quality of life scores.

1.4. Economics of Happiness

The “Easterlin Paradox” by Easterlin [23] demonstrated that beyond the satisfaction of basic needs, income growth does not necessarily lead to greater happiness. This insight challenges GDP-centric development models and underscores the value of non-material determinants of well-being—such as environmental quality, public trust, and emotional health. Gutiérrez [13] highlights that access to clean air and water, natural landscapes, and community cohesion are intangible goods that significantly enhance life satisfaction. Recent studies in Latin America confirm that air pollution, water insecurity, and climate stress diminish subjective well-being, even amid periods of economic growth [24,25]. These findings reinforce the inclusion of PM2.5 concentration and temperature, as they represent environmental burdens that materially and emotionally affect populations, particularly in urban and vulnerable settings. Thus, by including GDP per capita alongside environmental indicators, this study explicitly examines whether income growth offsets or exacerbates declines in subjective well-being—an essential debate within the economics of happiness.

1.5. Capabilities Approach

The capabilities approach [26] defines quality of life not in terms of material assets, but as the effective freedom to achieve valuable life outcomes. Environmental degradation constrains these freedoms by limiting access to essential natural resources, undermining health, and amplifying climate vulnerability. For example, rising temperatures and declining forest cover reduce agricultural productivity, damage ecosystems, and restrict people’s ability to live healthy, autonomous lives [17].

Access to improved sanitation, meanwhile, reflects both material and institutional capacities to support fundamental capabilities such as health, safety, and dignity [27]. In this sense, the inclusion of sanitation access in the empirical model operationalizes key elements of the capabilities approach, linking environmental conditions to the effective exercise of individual freedoms.

Furthermore, the approach supports multidimensional assessments of QoL, integrating health, education, environmental quality, and political voice. The study’s inclusion of structural and ecological variables—rather than relying solely on income—responds directly to [26] critique of narrow metrics and reflects a rights-based approach to human development.

2. Methodology

This research analyzed the relationship between quality of life and environmental degradation factors in Latin American and Caribbean countries. To achieve this objective, a quantitative methodological approach was adopted to identify and measure the impact of environmental variables on well-being levels. Within this framework, statistical techniques were applied to examine the associations among the selected indicators. Additionally, GDP per capita was included as a control variable since, from an econometric perspective, it allows for isolating the direct effect of environmental factors on quality of life. This is essential to avoid omitted variable bias, as national income influences both socioeconomic conditions and a country’s capacity to invest in environmental infrastructure and public services, which may simultaneously affect the ecological environment and population well-being.

In addition, for the econometric analysis, a panel data model was estimated, which, as highlighted by [28], offers substantial advantages by simultaneously capturing both the cross-sectional and temporal dimensions of the data. The final selection of the fixed-effects model was based on the results of the Hausman test [29], in line with [30], who emphasize the superiority of this approach when there is correlation between unobserved effects and explanatory variables. This modeling strategy allowed for the control of unobserved heterogeneity across countries and addressed common issues of autocorrelation and heteroskedasticity in economic time series. Consequently, a Prais–Winsten regression with corrected standard errors was applied—an appropriate technique when residuals exhibit first-order autocorrelation, as it adjusts the estimates to obtain more efficient and consistent coefficients, while maintaining the validity of statistical inference [30].

A sample of 24 countries in Latin America and the Caribbean was selected for this study, based on the availability of consistent and comparable data provided by the World Bank, ECLAC, and WHO for the period 2007–2020. The sample comprised Argentina, the Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Trinidad and Tobago, and Uruguay. The selection of these countries followed an analytical criterion aimed at assessing the relationship between quality of life and environmental degradation while capturing the region’s socioeconomic and ecological diversity. It should be noted that countries such as Suriname and Guyana were excluded due to the lack of complete data for the key variables, a methodologically justified decision intended to preserve the validity and robustness of the analysis.

For the quantitative analysis, critical environmental indicators were considered:

• Quality of life variable:composite index that integrates dimensions of material, social, and environmental well-being.
• Environmental degradation variables: average annual temperature, percentage change in forest areas, and PM2.5 concentration.
• Variable of access to basic services: percentage of the population with access to improved sanitation.
• Economic Variable: GDP per capita.

3. Results

Between 2005 and 2020, quality of life remained stable or showed slight improvements in most Latin American and Caribbean countries, while temperature exhibited moderate fluctuations (Figure 1). Countries such as Nicaragua and El Salvador experienced greater thermal instability. This suggests that progress in quality of life has not necessarily been accompanied by environmental improvements. The findings highlight the urgent need to incorporate sustainability criteria into well-being policies.

Figure 2 shows that between 2005 and 2020, quality of life remained stable or slightly improved in most countries. In contrast, forest areas exhibit a decreasing and more volatile trend. This divergence is particularly notable in countries such as Brazil, Paraguay, and Nicaragua. The evolution suggests that improvements in well-being have not been accompanied by environmental conservation. The need to integrate sustainability into development policies is evident.

Figure 3 shows that between 2005 and 2020, quality of life remained stable or slightly improved in most countries. Air pollution showed greater variability, with notable increases in Guatemala and Honduras. In countries like Uruguay and Costa Rica, a slight reduction was observed. No direct relationship between both variables is evident. Integrating environmental sustainability into well-being policies is necessary.

Figure 4 shows that between 2005 and 2020, quality of life in Latin America and the Caribbean remained stable, while access to sanitation showed greater variability across countries. Brazil, Colombia, and Peru exhibited steady improvements in sanitation, although not always accompanied by increases in well-being. In El Salvador and Paraguay, fluctuations were more pronounced. These results suggest that access to basic services is essential but not sufficient on its own to enhance quality of life. Comprehensive policies are needed.

The following paragraphs detail the results of the econometric model developed.

Table 1. Variance Inflation Factors (VIFs).

Variable	VIF	1/VIF
Access to sanitation services	1.68	0.596
Air pollution	1.57	0.637
Temperature	1.39	0.719
Forest area	1.27	0.790
GDP per capita	1.00	0.996

presents the results of the multicollinearity analysis using the Variance Inflation Factor (VIF) for the independent variables included in the model. The VIF values range from 1.00 to 1.68, all of which are well below the commonly accepted thresholds of 5 or 10, indicating the absence of multicollinearity problems. The highest VIF corresponds to access to sanitation services (1.68), followed by air pollution (1.57), temperature (1.39), and forest area (1.27). GDP per capita registers the lowest VIF (1.00), suggesting complete independence from the other explanatory variables.

The results presented in

Table 2. Unit root stationarity test results.

Variable	Unadjusted t	Adjusted t	p-Value
Quality of life	1.124	5.231	0.004
Temperature	0.987	4.765	0.012
Forest areas	1.056	6.002	0.008
Air pollution	1.098	5.487	0.006
Access to sanitation services	1.034	4.998	0.010
GDP per capita	1.345	3.878	0.003

confirm the statistical significance of all the variables included in the model, as evidenced by their adjusted t-values (all above 3.8) and p-values below 0.05. Quality of life (t = 5.231, p = 0.004), temperature (t = 4.765, p = 0.012), forest areas (t = 6.002, p = 0.008), and air pollution (t = 5.487, p = 0.006) all exhibit strong and consistent associations with the dependent variable. Similarly, access to sanitation services (t = 4.998, p = 0.010) and GDP per capita (t = 3.878, p = 0.003) demonstrate significant explanatory power, underscoring the multidimensional nature of development and well-being in the context analyzed. These results highlight the importance of integrating environmental, infrastructural, and economic factors in empirical assessments of quality of life, particularly in regions characterized by structural inequality and ecological vulnerability.

The results of the Breusch–Pagan/Cook–Weisberg heteroskedasticity test, shown in

Table 3. Breusch–Pagan/Cook–Weisberg heteroskedasticity test.

Statistic	p-Value
${\chi }^2$(1) = 0.79	0.374

, indicate that there is no statistically significant evidence of heteroskedasticity in the model. The chi-square statistic (${\chi }^2$ = 0.79) and the associated p-value (0.374) exceed the conventional significance threshold ($\alpha$ = 0.05). Consequently, the null hypothesis of homoscedasticity cannot be rejected, implying that the model’s residuals exhibit constant variance. This finding supports the conclusion that the assumption of homoscedasticity is satisfied, thereby reinforcing the reliability of the estimated standard errors and the validity of the inferences drawn from the linear regression analysis.

The results of the Wooldridge test for autocorrelation in panel data, shown in

Table 4. Wooldridge test for autocorrelation in panel data.

F (1, 23)	p-Value
539.058	0.0000

, provide clear evidence of the presence of autocorrelation within the model. The F statistic (F = 539.058) and the associated p-value (p < 0.0001) indicate that the null hypothesis of no first-order autocorrelation is decisively rejected. This finding suggests that the residuals are serially correlated over time, highlighting the need to apply estimation techniques robust to autocorrelation, such as corrected standard errors or generalized least squares, to ensure the validity of the model’s inferences.

The results of the fixed effects and random effects models are shown in

Table 5. Fixed effects and random effects model estimates.

Variable	Fixed Effects (FEs)	t-Value (FE)	p-Value (FE)	Random Effects (REs)	z-Value (RE)	p-Value (RE)
Temperature	0.7123	4.56	0.000	0.0463	1.76	0.078
Forest areas	0.1601	3.71	0.000	−0.0015	−0.20	0.844
Air pollution	0.0707	3.66	0.000	−0.0016	−0.91	0.910
Access to sanitation services	0.4677	19.39	0.000	0.0391	3.27	0.001
GDP per capita	0.00055	6.31	0.000	0.000013	1.28	0.200
Constant	6.4047	1.38	0.170	70.0367	50.03	0.000

Note: Comparison between fixed-effects (FEs) and random-effects (REs) model estimates.

, as a basis for conducting the Hausman test.

The results of the Hausman test, as reported in

Table 6. Hausman test for fixed vs. random effects.

Predictor	Fixed Effects (b)	Random Effects (B)	Difference (b-B)
Temperature	0.712	0.046	0.666
Forest areas	0.160	−0.001	0.161
Air pollution	0.076	−0.002	0.078
Access to sanitation services	0.468	0.031	0.437
GDP per capita	0.00055	0.000013	0.00054
Chi2	646.92
Prob > Chi2	0.0000

Note: The Hausman test evaluates whether the unique errors are correlated with the regressors. A significant result (p < 0.05) suggests the fixed-effects model is preferred.

, indicate a clear preference for the fixed-effects model over the random-effects specification. The test yields a chi-square statistic of 646.92 with a corresponding p-value of 0.0000, which is well below the conventional significance threshold ($\alpha$ = 0.05). This result leads to the rejection of the null hypothesis that the random-effects estimator is consistent, thereby validating the use of the fixed-effects model for this analysis. The magnitude of the coefficient differences between models is substantial for several variables, particularly temperature (difference = 0.666), access to sanitation services (0.437), and air pollution (0.078). These discrepancies suggest the presence of correlation between the unobserved individual effects and the explanatory variables, violating the assumptions underlying the random-effects model. Consequently, the fixed-effects model is more appropriate, as it controls for unobserved heterogeneity across countries and produces consistent and unbiased estimates. This decision reinforces the robustness of the study’s findings regarding the relationship between environmental degradation, access to basic services, and quality of life in Latin America and the Caribbean.

The model shown in

Table 7. Prais–Winsten regression with corrected standard errors.

Variable	Coefficient	Het-Corrected Standard Error	p-Value	95% Confidence Interval
Temperature	0.030	0.050	0.548	[−0.068, 0.129]
Forest areas	−0.015	0.006	0.013	[−0.027, −0.003]
Air pollution	−0.025	0.010	0.014	[−0.045, −0.005]
Access to sanitation services	0.082	0.017	<0.001	[0.048, 0.116]
GDP per capita	0.00055	0.00008	0.000	[−5.91 $\times {10}^{-6}$ , 0.0000294]
Constant	64.689	2.278	<0.001	[60.223, 69.155]

Note: $\rho =0.853$, Wald ${\chi }^2\left(4\right)=27.27$, $p<0.001$.

represents the final estimation of the study due to its ability to correct for first-order autocorrelation present in the residuals of time series, which enhances the statistical validity of the analysis and improves the efficiency of the estimates. The application of the Prais–Winsten regression with corrected standard errors enables the generation of more robust and consistent coefficients, while also controlling for unobserved cross-country variations—common aspects in longitudinal panel data studies.

The results indicate that access to sanitation services has a positive and highly significant effect on quality of life (coef. = 0.082; p < 0.001), suggesting that improvements in basic infrastructure directly impact population well-being. In contrast, environmental variables such as air pollution (coef. = −0.025; p = 0.014) and the reduction of forest areas (coef. = −0.015; p = 0.013) show negative and statistically significant effects, demonstrating that environmental degradation reduces quality of life in the region. On the other hand, temperature does not show a significant effect (p = 0.548), which could be attributed to its more stable behavior or to gradual social adaptation. Finally, GDP per capita, included as a control variable, shows a positive and significant relationship (coef. = 0.00055; p = 0.000), supporting the hypothesis that economic growth contributes to well-being, although its effect must be nuanced considering the associated environmental impact. These findings reinforce the need to integrate environmental sustainability criteria into human development policies.

4. Discussion

The econometric results establish a clear relationship between key environmental variables and quality of life in Latin America and the Caribbean, revealing patterns consistent with previous studies. In particular, the reduction of forest areas is significantly associated with a decline in well-being, which aligns with the findings of [31], who highlight the role of urban ecosystems in promoting health and social cohesion. Likewise, recent research indicates that the loss of vegetation cover affects biodiversity, microclimate, and equitable access to green spaces, especially in vulnerable regions [32,33]. This effect is more severe in communities with a high dependence on ecosystem services, as observed in contexts of artisanal mining or accelerated deforestation [7,34].

On the other hand, air pollution is confirmed as a key factor in the deterioration of quality of life. The negative and statistically significant relationship is consistent with the studies of [35], who identifies both physical and mental health effects, and with the findings of [36], which show an inverse relationship between subjective well-being and exposure to fine particulate matter. More recent research has also documented that even emerging pollution events generate multidimensional impacts on health, the economy, and the environment [2,37]. This reflects a persistent paradox in the region: economic growth indicators may not translate into improvements in well-being if the negative effects of environmental degradation are not addressed.

In contrast, access to basic sanitation services shows a strong positive relationship with quality of life. This association has been documented by [38] and more recently by [39], who emphasize their role in reducing disease, improving public health, and promoting social equity. In contexts such as the Brazilian Amazon, the availability of drinking water and sewage systems has proven to be a key factor in reducing territorial gaps in well-being [40]. In addition, various studies in community health confirm that improvements in basic infrastructure have significant effects on the perception of well-being and quality of life [27,41].

Finally, GDP per capita, included as a control variable, maintains a positive and significant relationship with quality of life, in line with the empirical literature [36,42]. However, recent research warns that while economic growth is necessary, it is not sufficient to achieve sustainable development. For example, Morales-Urrutia et al. [3] shows that the natural environment acts as a determinant of productive investment in emerging economies. Similarly, Xu and Xu and Ghosh and Pearson [43,44] highlight the need to rethink economic foundations to integrate environmental, social, and institutional dimensions. In this regard, the study demonstrates that quality of life does not depend solely on income, but also on environmental and structural factors that must be addressed through integrated public policies.

One of the main limitations of this study lies in the availability and timeliness of the environmental and social indicators used, which, in some cases, do not reflect the most recent changes in sustainability practices or institutional transformations that have occurred after 2020. Likewise, the temporal coverage of the analysis (2005–2020) may limit the detection of emerging effects related to climate change, the energy transition, or the implementation of new public policies in the region. These data constraints may affect the explanatory power of the model and the generalizability of the results to more recent contexts.

5. Conclusions

This study reveals the importance of adopting a multidimensional and context-sensitive approach when analyzing the relationship between environmental conditions and quality of life in Latin America and the Caribbean. The evidence demonstrates that environmental sustainability cannot be treated as a secondary or complementary component of development, but must be integrated into the design of public policies and economic strategies. In this regard, the findings underscore the need for more holistic development planning, in which social and ecological criteria are considered fundamental elements of well-being.

In terms of public policy, the results call for differentiated strategies that respond to specific territorial realities. For example, strengthening basic sanitation infrastructure should be prioritized in regions with significant service gaps, not only to improve health but also to promote social cohesion. At the same time, advancing reforestation initiatives and the protection of green areas—particularly in urban and peri-urban zones—would help mitigate environmental deterioration and enhance environmental equity. Likewise, adopting stricter urban air quality standards and increasing public investment in clean technologies could reduce the harmful effects of pollution on the population’s health and emotional well-being.

Finally, the study highlights the need to move toward a model of sustainable development that transcends the exclusive use of economic indicators such as GDP per capita. Future policies should incorporate ecological, social, and institutional dimensions in a balanced manner, promoting long-term resilience. This implies strengthening environmental governance, improving access to reliable and updated indicators, and ensuring citizen participation in environmental decision-making. Only through an integrated and inclusive vision will it be possible to reduce the region’s structural vulnerabilities and promote a more dignified and sustainable quality of life.