The Natural Environment and Investment in Economic Growth: From the Perspective of the Prosperity of Developed and Developing Countries

Abstract

This research aims to identify the environmental factors that affect economic growth in developed and developing countries using a panel data regression model. In advanced economies, restrictions on international investment, emissions, the use of natural resources, and environmental preservation efforts stand out as influential variables. In contrast, in developing countries, contract enforcement, emissions, and exposure to air pollution are key determinants of economic performance. This study concludes that the natural environment plays a more significant role in the economic growth of developed countries, due to their greater capacity to implement sustainability policies. In developing countries, institutional conditions and environmental management are more relevant, highlighting the need to strengthen legal frameworks and invest in mitigation measures to achieve sustainable economic development.

1. Introduction

The investment environment is a key element for the economic development of nations, as it encompasses essential factors such as political stability, financial support, and infrastructure. These components facilitate the attraction of foreign investment and foster economic growth [1]. However, the success of this environment lies not only in its ability to mobilize capital, but also in its integration with policies that promote environmental sustainability and social inclusion [2]. In this regard, a development model that prioritizes economic growth while disregarding its ecological and social impact is unsustainable in the long term [3].

Developed economies have adopted strategies aimed at investing in technology and clean energy as a means to mitigate the negative effects of industrial growth on the environment [4]. In contrast, developing countries face significant challenges due to the expansion of polluting industries that compromise ecological balance. This situation creates constant tension between economic progress and sustainability, compelling nations to adopt more holistic approaches in their investment and growth policies [5,6].

The use of natural resources is fundamental to economic dynamics, but their uncontrolled exploitation may compromise future availability. Although natural resources have historically served as a driver of growth for several advanced economies [7], the relationship between resource abundance and economic development is not linear, nor does it guarantee a country’s financial success [8]. In fact, the poor management of these resources can lead to accelerated degradation and structural problems that hinder sustainable development [9].

In response to these challenges, environmental regulations and policies have been implemented to reduce the impact of economic activity on the natural environment [10,11], reinforcing the need to move toward more sustainable economic models in which ecosystem conservation is not subordinate to immediate economic growth [11]. In contrast to these sustainability strategies, some countries rely heavily on the exploitation of natural resources as their primary source of income, posing significant economic and environmental challenges [12] and promoting extractive activities that accelerate ecosystem degradation.

In this context, ensuring sustainable economic growth requires investment policies that incorporate environmental criteria to prevent irreversible ecosystem damage. When economies become overly dependent on extractive activities, they compromise their natural capital and limit long-term development opportunities. A balanced policy enables economic diversification, the preservation of natural capital, and a reduction in structural vulnerabilities. Moreover, it fosters technological innovation and improves quality of life without depleting resources. Thus, the balance between investment and the environment becomes a necessary condition for lasting prosperity.

On the other hand, in the case of developing countries, although the negative impact of this dependency is acknowledged, the implementation of effective actions for environmental conservation remains insufficient.

In this context, the objective of this study is to identify the environmental factors that affect economic growth in developed and developing countries, while recognizing the structural, institutional, and ecological differences that shape the relationship between nature and the economy. Prosperity should not be measured solely in terms of economic growth, but rather in its ability to ensure sustainable development that benefits both present and future generations. Investment in clean technologies, the implementation of effective environmental policies, and the promotion of a more diversified economy are essential measures to achieve this goal.

1.1. Background

1.1.1. Definition of the Natural Environment

The natural environment refers to the set of physical, biological, and chemical elements that surround living organisms and are crucial for their survival. This includes tangible resources such as water, soil, and air, as well as ecosystems and biodiversity, which play a key role in maintaining ecological balance [13]. The concept is fundamental to understanding the interdependence between human activities and environmental processes. Economically, it provides essential resources that form the foundation for the growth and development of societies, but at the same time, it can be altered and degraded, affecting its capacity to sustain future generations.

1.1.2. Importance of the Natural Environment

The well-being and sustainability of a nation are closely linked to the condition of the natural environment, with a direct impact on health, the economy, and resilience to climate change. Natural environments have a significant relationship with physical activity, social interaction, and stress reduction, all of which contribute positively to public health [14,15]. Population health largely depends on the integrity of ecosystems, biodiversity, and climate stability, which are fundamental factors for collective well-being [16]. There is also a significant correlation between environmental quality, population distribution, and economic growth. Regions with healthier natural environments tend to have a higher population density and more dynamic economies [17].

In low-income countries, rapid population growth leads to environmental degradation, reducing the quality and quantity of available resources, which negatively affects economic stability and food security [18]. Therefore, the sustainable management of natural resources is crucial to ensuring long-term economic and social stability. Overuse or poor management of these resources can lead to severe environmental degradation.

1.1.3. Definition of Economic Growth

Economic growth refers to the continuous increase in an economy’s production capacity over a specific period. This phenomenon is commonly measured by the increase in Gross Domestic Product (GDP) or GDP per capita and constitutes a crucial aspect of the analysis of national development. It involves an increase in the volume of goods and services and a transformation in the economic structure of a country, evolving toward higher productivity sectors. Furthermore, growth is defined in terms of its ability to generate improvements in population well-being, as supported by endogenous growth theory [19].

1.1.4. Importance of Economic Growth

Economic growth plays a fundamental role in improving the quality of life of the population, as it can help reduce poverty and provide governments with the resources needed to invest in infrastructure, education, and healthcare. If a country experiences sustained economic growth, developing nations may achieve greater prosperity, while in already developed countries, it helps ensure that living conditions remain good or continue to improve. According to [20], long-term economic growth depends on capital accumulation, technological innovation, and increased labor productivity. This growth, in turn, can be influenced by factors such as investment in human capital and the quality of institutions [21].

1.1.5. Indicators

Gross Domestic Product (GDP): One of the main indicators used to measure economic growth is Gross Domestic Product (GDP), defined as the total value of all goods and services produced within a country during a given period. This indicator is crucial because it reflects economic activity and is used to compare the economic performance of different countries or to assess economic development over time. As noted by [22], GDP measures production and provides insight into the dynamism and efficiency of an economy. However, despite its usefulness, GDP has been criticized for not fully capturing aspects related to social well-being, such as income distribution or environmental impact [23].

GDP per Capita: This is another indicator that provides a clearer view of economic well-being, with the distinction that it is measured per citizen. It adjusts GDP based on a country’s population. As noted in [24], GDP per capita is essential for analyzing the quality of life and the level of development of a nation, as it offers an approximation of average per capita income. It is calculated by dividing the total GDP by the number of inhabitants, allowing for comparisons between countries with different population sizes.

In this regard, the choice of total GDP and GDP per capita as central indicators in this study is justified by their ability to directly and consistently reflect the level and evolution of a country’s economic growth, enabling valid comparisons across economies of different sizes and development levels. Total GDP provides an aggregate measure of economic performance, while GDP per capita adjusts this value according to population size, which is crucial for assessing relative economic well-being and its relationship with the environmental context. Alternative indicators such as the production index, employment rate, or fixed capital investment were not used because, although useful for sectoral or specific analyses, they do not comprehensively capture the magnitude of overall economic growth.

1.1.6. Relationship Between the Natural Environment and Economic Growth

In this regard, studies have shown that the Environmental Kuznets Curve (EKC) tends to be confirmed in upper-middle- and high-income countries, where robust regulatory frameworks and greater environmental awareness are present. Ref. [25] argue that this relationship depends on the stage of economic development and the institutional context, as the transition toward cleaner technologies and sustainable consumption patterns requires state capacity and political will. However, the EKC has not been universally confirmed. For instance, when analyzing the evolution of global and cumulative pollutants such as carbon dioxide (CO₂), empirical evidence tends to contradict the theoretical inverted U-shape of the curve. There is no robust inverted U relationship for greenhouse gases, as their concentrations continue to rise alongside per capita income in many countries.

Moreover, Ref. [26] warn that some developed countries may appear to show environmental improvements simply because they have outsourced their polluting industries to developing countries, a phenomenon known as the environmental boomerang effect. Refs. [27,28,29] also support the view that the EKC relationship is influenced by economic development stages and institutional settings, as transitioning to cleaner technologies and sustainable consumption requires institutional strength and political commitment.

Furthermore, the use of the IPAT formula has allowed for the decomposition of the effects of economic growth on the environment, showing that factors such as population growth and increased wealth tend to intensify resource consumption and pollution. Despite this, it is acknowledged that technological advances may act as a moderating factor, mitigating some of the negative impacts of economic growth on the natural environment. However, the extent of this mitigation remains under debate, as the net effects depend on how these technologies are implemented in different economies [30].

2. Materials and Methods

This research aimed to establish the degree of association between the independent variables and the dependent variable. To achieve this, a descriptive analysis was conducted to identify the existence of a relationship between investment environment elements and the natural environment with economic growth in G7 and ALADI countries.

To determine whether the data followed a normal or non-normal distribution, the Kolmogorov–Smirnov test was applied, as the dataset contained more than 50 observations. Additionally, this test helped identify the statistical distribution that best fit the data.

Upon identifying that the data did not follow a normal distribution, Spearman’s rank correlation coefficient was used. This non-parametric statistic enabled the measurement of both the direction and strength of the relationships among the ranked variables considered in the study. It is denoted by the letter r with subscripts.

For the interpretation of Spearman’s correlation levels, the classification provided in [31] was used. The authors offered a guideline for interpreting correlation (Rho) values.

To identify and establish which elements significantly influence economic growth in developed and developing countries, this research employed an explanatory-level analysis through a panel data regression model. This econometric tool allowed the consideration of both the cross-sectional and time dimensions of the dataset. Hence, time series and cross-sectional data were combined for the analysis.

According to [32], the panel data regression model presents the following characteristics:

• It explicitly accounts for data heterogeneity.
• By combining cross-sectional and time series data, it provides higher variability, lower collinearity, and greater efficiency.
• It analyzes dynamic changes within the data.
• It efficiently detects and measures data effects.
• It handles the behavior of complex datasets.
• It reduces bias potentially introduced by data augmentation.

Therefore, the panel data model is more reliable and efficient for estimating complex models, thereby strengthening the empirical investigation of the variables.

For the analysis of the investment and natural environments in relation to economic growth, a dataset covering 20 countries was used: 7 developed and 13 developing. The data is cross-sectional, interpreted as a time series across 16 years from 2007 to 2023.

There are two types of panel data regression models: fixed effects and random effects. To determine the most appropriate model, the Hausman test was conducted. This test helps identify which model better fits the available data and determines the existence of a relationship between the variables and the model to be applied [32].

Based on the results of the Hausman test, the fixed effects model was found to be the most suitable, as the p-value was below the 5% threshold. This indicates a significant correlation between the unobserved individual effects and the explanatory variables, thereby violating the independence assumption required by the random effects model.

The fixed effects model performs data analysis assuming the existence of country-specific characteristics that influence and determine national economic growth. These characteristics are considered time-invariant; therefore, any unobserved heterogeneity can be captured through the fixed effects panel data model.

Population

For this study, a sample of 20 countries is selected based on [33]. This sample includes the developed countries of the G7 (United States, Canada, France, Italy, United Kingdom, Germany, and Japan) and the developing countries from the ALADI group (Argentina, Peru, Bolivia, Ecuador, Paraguay, Brazil, Chile, Colombia, Uruguay, Venezuela, Cuba, Mexico, and Panama). The objective is to analyze the impact of two fundamental pillars, the investment environment and natural environment, on economic growth. The selection of G7 and ALADI countries follows a comparative logic that allows for the analysis of structural asymmetries between developed and developing economies in relation to these two critical pillars of economic growth: the investment environment and the environmental context.

For the data analysis, country scores from 2007 to 2023 were considered for the pillar identified as the investment environment. This pillar includes the following elements: property rights, investor protection, contract enforcement, financing ecosystem, and restrictions on international investment.

On the other hand, for the assessment of the natural environment, the following components were analyzed: preservation efforts, oceans, freshwater, forests, land and soil, exposure to air pollution, and emissions.

A total of 4046 data points were collected to meet the research objectives.

This study aims to address the following research questions:

• Which factors of the environmental context influence the economic growth of G7 developed countries?
• Which factors of the environmental context influence the economic growth of ALADI developing countries?

3. Results

3.1. Correlation Between Investment Environment Elements and Natural Environment Variables-G7

Table 1. Shapiro–Wilk W test for normal data.

Variable	Obs	W	V	Z	Prob > z
pibc	119	0.91773	7.861	4.617	0.00000
emi	119	0.72768	26.020	7.298	0.00000
expo_cont	119	0.93413	6.294	4.120	0.00002
bosq_sue	119	0.91171	8.436	4.776	0.00000
bosq_sue	119	0.86947	12.472	5.651	0.00000
Ocea	119	0.98021	1.891	1.427	0.07686
esf_preser	119	0.93095	6.598	4.225	0.00001

presents the results generated by SPSS 27 for testing the assumption of normality using the Shapiro–Wilk test. This test determines whether the dataset follows a normal distribution, which in turn informs the appropriate application of either parametric or non-parametric statistical models. The results indicate that most variables do not follow a normal distribution. Consequently, the use of Spearman’s rank correlation coefficient, a non-parametric statistical method, is justified.

Table 2. Spearman correlation coefficient between natural environment variables and economic growth in G7 countries.

	Emissions	Air Pollution Exposure	Forests and Land and Soil	Freshwater	Oceans	Preservation Efforts
GDP per capita	−0.505	0.498	0.375	0.169	0.614	−0.007
Sig. (two-tailed)	0.000	0.000	0.000	0.067	0.000	0.938

Non-parametric Spearman’s Rho correlation between natural environment variables and economic growth in G7 countries, 2007–2023. Source: Own elaboration (2024).

presents the results of the Spearman correlation analysis between the elements of the natural environment (preservation efforts, oceans, freshwater, forests, land and soil, air pollution exposure, and emissions) and economic growth in the G7 countries: Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States.

The Spearman correlation coefficient allows for the interpretation of both the strength and direction of the correlation between the components of the independent variables and the dependent variable (economic growth).

The results from the Spearman correlation analysis reveal statistically significant associations between per capita GDP and several environmental dimensions. A significant negative correlation was observed between per capita GDP and emissions (ρ = −0.505, p < 0.001), suggesting that higher income levels in these countries are associated with lower levels of environmental emissions.

Additionally, per capita GDP shows significant positive correlations with the following:

• Air pollution exposure (ρ = 0.498, p < 0.001);
• Forests and land and soil indicators (ρ = 0.375, p < 0.001);
• Ocean conditions (ρ = 0.614, p < 0.001).

These relationships may reflect the coexistence of economic growth with localized environmental pressures, or potentially a greater monitoring and reporting capacity in higher income countries.

In contrast, the correlation between per capita GDP and freshwater availability (ρ = 0.169, p = 0.067) is not statistically significant. Similarly, no significant association is found with preservation efforts (ρ = −0.007, p = 0.938), indicating that income levels are not systematically linked with this environmental dimension across the G7 countries analyzed.

3.2. Correlation Between Investment Environment Elements and Natural Environment Variables-ALADI

Table 3. Spearman correlation coefficient between natural environment variables and economic growth in ALADI countries.

	Emissions	Air Pollution Exposure	Forests and Land and Soil	Freshwater	Oceans	Preservation Efforts
GDP per capita	0.327	0.345	0.033	0.422	0.209	−0.446
Sig. (two-tailed)	0.000	0.000	0.621	0.000	0.004	0.000

Non-parametric Spearman’s Rho correlation between natural environment variables and economic growth in ALADI countries, 2007–2023. Source: Own elaboration (2024).

presents the results of Spearman’s Rho correlation analysis between elements of the natural environment—namely preservation efforts, oceans, freshwater, forests, land and soil, air pollution exposure, and emissions—and economic growth in ALADI countries, including Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Panama, Paraguay, Peru, Uruguay, and Venezuela.

The results show a moderate and statistically significant positive correlation between per capita GDP and environmental emissions (ρ = 0.327, p < 0.001), as well as with air pollution exposure (ρ = 0.345, p < 0.001). These associations suggest that higher income levels in the analyzed ALADI countries are linked to increased environmental pressure, particularly in terms of emissions and air quality. This may reflect resource-intensive consumption and production patterns associated with advanced stages of industrialization.

A significant positive correlation was also found with freshwater availability (ρ = 0.422, p < 0.001), indicating that some economies may possess a better capacity to manage water resources efficiently. Similarly, a weaker but still statistically significant positive correlation was observed with the state of the oceans (ρ = 0.209, p = 0.004), which might be associated with localized monitoring, marine conservation efforts, or the presence of high-value coastal economic activities.

In contrast, no significant relationship was identified between GDP per capita and forests and land and soil (ρ = 0.033, p = 0.621). More notably, a significant negative correlation was found with preservation efforts (ρ = −0.446, p < 0.001), suggesting that higher income levels do not necessarily translate into greater investment in environmental conservation policies.

3.3. Unit Root Stationarity Test of Environmental and Economic Variables

The stationarity test applied to both environmental and economic variables demonstrates that all time series are at a stationary level, as the p-values associated with the adjusted t-statistics are below the 5% significance threshold. This allows for the rejection of the null hypothesis of a unit root presence and confirms that the series exhibit constant statistical properties over time (see

Table 4. Unit root stationarity test results.

Variable	Unadjusted t	Adjusted t	p-Value
Emissions	−3.3780	−2.0874	0.0184
Air Pollution Exposure	−6.0081	−3.6008	0.0002
Forests and Land and Soil	−11.9125	−7.9889	0.0000
Freshwater	−6.2407	−3.5992	0.0002
Oceans	−6.8218	−3.1707	0.0008
Preservation Efforts	−9.8688	−5.2520	0.0000
GDP per Capita	−12.4808	−8.9385	0.0000

).

Table 5. Variance Inflation Factor (VIF) for Natural Environment Variables.

Variable	VIF
Freshwater	4.30
Preservation Efforts	2.40
Air Pollution Exposure	2.37
Emissions	2.21
Oceans	2.12
Forests and Land and Soil	1.93

Multicollinearity test results (VIF) after multiple regression model. Source: Own elaboration (2024).

presents the results of the multicollinearity analysis. The findings indicate that no multicollinearity exists among the independent variables included in the model. This validates the application of regression models without concern for inflated variances or distorted coefficient estimates due to inter-variable correlation.

The results indicate that all Variance Inflation Factor (VIF) values are below the conventional threshold of 5, suggesting no severe multicollinearity among the explanatory variables. Therefore, the model estimates are not significantly biased due to inter-variable linear dependence, as detailed in Table 5.

To assess other assumptions of the panel data regression model, tests for autocorrelation and heteroskedasticity were performed. As shown in

Table 6. Wooldridge test for autocorrelation in panel data.

Hypothesis	F (1, 6)	Prob > F
H0: No first-order autocorrelation	19.883	0.0043

, the Wooldridge test for autocorrelation indicated the presence of first-order autocorrelation in the panel data, with an F-statistic of 19.883 and a p-value of 0.0043—allowing for the rejection of the null hypothesis of no autocorrelation.

Likewise, the presence of groupwise heteroskedasticity was tested using the Modified Wald test, whose results are presented in

Table 7. Modified Wald test for groupwise heteroskedasticity (fixed effects model).

Hypothesis	Chi2 (7)	Prob > Chi2
H0: σi2 = σ2 for all i (homoskedasticity)	181.69	0.0000

. The test yields a Chi-squared value of 181.69 with a p-value of 0.0000, rejecting the null hypothesis of homoskedasticity across panels.

These findings confirm that both autocorrelation and heteroskedasticity are present in the data. To address these issues and ensure the accuracy of standard error estimates, the model was corrected using Panel Corrected Standard Errors (PCSE), improving the robustness and reliability of the econometric results.

The results confirm the presence of both autocorrelation and heteroskedasticity in the panel data in

Table 8. Fixed effects vs. random effects panel regression models.

Variable	Fixed Effects (FE)	t-Value	p-Value	Random Effects (RE)	z-Value	p-Value
Emissions	111.894	1.67	0.096	146.623	2.28	0.023
Air Pollution Exposure	45.748	0.57	0.570	121.173	1.53	0.125
Forests and Land and Soil	110.123	1.29	0.198	104.365	1.26	0.209
Freshwater	87.199	1.73	0.085	85.761	1.69	0.092
Oceans	32.759	1.22	0.224	28.778	1.06	0.290
Preservation Efforts	101.066	3.20	0.002	96.527	3.04	0.002

Comparative table of fixed vs. random effects panel data model results. Source: Own elaboration (2024).

. To address these issues and ensure robust standard error estimates, the models were corrected using Panel Corrected Standard Errors (PCSEs). This correction enhanced the accuracy of inference and strengthenedthe robustness of the econometric results. The corrected fixed effects models are presented at the end of the Results section.

Although both models show similar results in terms of global significance and the influence of key variables on economic growth, the observed differences in coefficient estimates justify the need for a Hausman test to determine which model better fits the data.

The results shown in

Table 9. Hausman test—fixed effects vs. random effects model.

Variable	(b) Fixed Effects	(B) Random Effects	(b − B) Difference	Std. Error
Emissions	111.894	146.623	−34.729	23.578
Air Pollution Exposure	45.748	121.173	−75.425	22.524
Forests and Land and Soil	110.123	104.365	5.758	27.026
Freshwater	87.199	85.761	1.438	8.791
Oceans	32.759	28.778	3.981	4.212
Preservation Efforts	101.066	96.527	4.539	5.910
Chi2	24.59
Prob > Chi2	0.0062

Comparative table of fixed vs. random effects panel data model results. Source: Own elaboration (2024).

indicate significant differences in the coefficients estimated by the fixed effects and random effects models. This difference suggests that the two models yield substantially different interpretations of the relationship between the explanatory variables and economic growth.

Given the Chi-square statistic of 24.59 and a p-value of 0.0062, we reject the null hypothesis that the random effects model is more appropriate. Since the p-value is less than 5%, we conclude that the fixed effects model provides a better fit for the panel data under study. This outcome confirms the presence of a correlation between unobserved individual effects and the explanatory variables, violating the assumption of independence required by the random effects model.

Accordingly, the fixed effects panel model is selected as the most appropriate specification for analyzing the relationship between environmental factors and economic growth across the countries in the study.

The fixed effects panel regression model applied to the G7 countries reveals that three environmental variables are statistically significant in explaining changes in economic growth. As shown in

Table 10. Fixed effects panel regression results—G7 countries.

Significant Variables	Coefficient	t-Statistic	p-Value
Emissions	−710.923	−6.35	0.000
Air Pollution Exposure	235.4492	1.91	0.056
Forests and Land and Soil	−419.813	−6.13	0.000
Freshwater	−392.2384	−2.23	0.066
Oceans	22.78261	0.30	0.767
Preservation Efforts	241.984	4.30	0.065
Significant Variables	Coefficient	t-statistic	p-value
R-Squared	0.8464
p-Value (Overall Model)	0.0000

Fixed effects panel regression model results for G7 countries. Source: Own elaboration (2024).

, two of these variables exhibit a negative and statistically significant relationship with per capita GDP.

Specifically, the variable “Emissions” presents a coefficient of −710.923 and a p-value of 0.000, indicating a strong inverse relationship with economic growth. This suggests that higher emission levels are associated with lower per capita GDP, pointing to the environmental and economic cost of pollution in developed countries.

Likewise, the variable “Forests and Land and Soil” also displays a significant negative effect on economic growth, with a coefficient of −419.813 and a p-value of 0.000. This underscores the importance of sustainable land management and environmental preservation for maintaining long-term growth in developed economies.

In addition, the variable “Air Pollution Exposure” shows a positive effect that is marginally significant (p = 0.056), while “Freshwater” (p = 0.066) and “Preservation Efforts” (p = 0.065) approach significance and may warrant further investigation. The variable “Oceans” is not statistically significant in this model.

The model’s explanatory power is high, with an adjusted R-squared of 0.8464, indicating that approximately 85% of the variation in per capita GDP can be explained by the selected environmental variables. The overall model significance (p = 0.0000) further confirms the robustness and reliability of the fixed effects specification.

The results of the fixed effects panel regression model for ALADI countries are presented in

Table 11. Fixed effects panel regression results—ALADI countries.

Significant Variables	Coefficient	t-Statistic	p-Value
Emissions	−212.4162	−2.42	0.015
Air Pollution Exposure	172.8877	3.37	0.001
Forests and Land and Soil	59.1343	1.44	0.149
Freshwater	−36.0960	−0.73	0.468
Oceans	−11.2956	−0.29	0.776
Preservation Efforts	28.5451	0.74	0.460
Significant Variables	Coefficient	t-statistic	p-value
R-Squared	0.3941
p-Value (Overall Model)	0.0000

Fixed effects panel regression results for ALADI countries. Source: Own elaboration (2024).

. Two variables emerge as statistically significant in influencing economic growth in this group of developing countries.

The first is “Emissions”, which displays a negative relationship with economic growth, with a coefficient of −212.4162 and a p-value of 0.015. This indicates that higher levels of greenhouse gas emissions are significantly associated with lower levels of per capita GDP in these countries. The result suggests that economic and environmental costs are closely linked, and that high emission levels can constrain long-term sustainable growth. To mitigate this impact and support economic expansion, it would be prudent for these nations to adopt standards and targets aimed at reducing emissions and their negative effects on economic performance.

The second significant variable is “Air Pollution Exposure”, which shows a positive coefficient of 172.888 and a p-value of 0.001. This suggests that increases in industrial activity, urbanization, and economic production, often occurring in settings with weak environmental regulations, may drive short-term GDP growth. However, while this dynamic may stimulate economic expansion in the near term, it raises concerns about sustainability, as the associated environmental and health impacts may lead to higher long-term social and economic costs, undermining overall well-being and development.

As shown in Table 11, the variables “Forests and Land and Soil”, “Freshwater”, “Oceans”, and “Preservation Efforts” did not show statistically significant relationships with economic growth in this model.

The model reports an R-squared of 0.3941, indicating a moderate explanatory power, meaning that approximately 39% of the variation in per capita GDP is explained by the environmental variables included in the model. Furthermore, the overall model significance (p = 0.0000) confirms the validity of the fixed effects specification for this group of developing countries.

4. Discussion

This research reveals significant and structural differences in the relationship between natural environment variables and economic growth among developed G7 countries and developing ALADI nations. These disparities underscore the importance of adopting differentiated and sustainable approaches tailored to the unique institutional, productive, and environmental contexts of each economic bloc.

In the case of the G7, results indicate that variables such as greenhouse gas emissions and natural resource degradation (forests and land and soil) exert a negative and statistically significant effect on economic growth. This evidence aligns with the Environmental Kuznets Curve (EKC) hypothesis, which posits that environmental degradation tends to decline at higher stages of economic development [34].

This outcome suggests that advanced economies have begun to internalize environmental costs into their productive systems through mechanisms such as green taxation, stricter regulations, clean production technologies, and emissions trading schemes. Supporting these findings, recent studies show that international agreements like the Paris Agreement have had a positive impact on emissions reduction without hindering economic growth [35]. This transition is facilitated by stable institutional frameworks and strong investments in environmental innovation and renewable energy. In this way, G7 economies have not only partially decoupled economic growth from environmental harm, but have also positioned sustainability as a strategic pillar of competitiveness and resilience.

Furthermore, the analysis shows that environmental preservation efforts have a positive and significant impact on economic growth in these countries. The expansion of protected areas, sustainable ocean management, and strengthened climate policies have contributed to job creation, eco-tourism, and productivity gains in green sectors. Indeed, recent reports by the Rhodium Group and the International Energy Agency [36] confirm that countries like Germany, France, and Canada have successfully reduced absolute carbon emissions while maintaining positive economic growth rates, largely due to transport electrification, industrial energy efficiency, and the mass adoption of renewable energy.

In contrast, in ALADI developing countries, the results reflect a positive correlation between emissions and air pollution exposure and economic growth, highlighting a strong dependency on carbon-intensive and -extractive industries. While this relationship may be functional in the short term, it entails considerable environmental and social costs, such as biodiversity loss, public health deterioration, and natural capital depletion [37,38].

Additionally, the negative correlation between conservation efforts and GDP per capita in these countries points to structural weaknesses in environmental governance. Limited financial resources, low political prioritization of environmental issues, and the absence of effective incentives for sustainable practices severely constrain the ability to integrate economic growth with sustainability [33,39]. This clearly contrasts with the G7 and supports the argument of [40], who emphasize that institutional quality is a key determinant in transitioning to low-carbon economies. Where institutions are strong, environmental policy can stimulate innovation and redirect investment; where they are weak, such policies tend to be ineffective or merely symbolic.

This study has limitations related to the availability and homogeneity of data across countries, as well as the complexity of capturing all dimensions of sustainable development. Future research could explore more specific institutional variables, differentiated sectoral impacts, and dynamic analyses that integrate climate scenarios.

5. Conclusions

The results of this study reaffirm that sustainable economic growth is neither automatic nor linear, but critically depends on the balance between environmental protection and the institutional capacity of nations. The research provides robust empirical evidence that underscores the importance of accounting for structural heterogeneity between developed (G7) and developing (ALADI) countries. It demonstrates that environmentally sustainable growth trajectories are conditioned by institutional quality and the extent to which environmental policies are integrated into national economic strategies. This approach not only contributes to the theoretical framework of the Environmental Kuznets Curve (EKC), but also offers practical insights for designing development-level-differentiated policy strategies.

In G7 economies, there is a clear transition toward development models that partially decouple economic growth from environmental degradation. This is made possible through investments in technological innovation, robust regulatory frameworks, and the increasing institutionalization of environmental commitments. These countries have shown that sustainability can be incorporated as a strategy for competitiveness and productivity.

In contrast, ALADI countries face deeper structural challenges, including a persistent reliance on pollution-intensive extractive sectors, a weak regulatory capacity, and limited investment in clean technologies. In these contexts, environmental degradation not only constrains long-term growth prospects but also exacerbates social and territorial inequalities.

One of the most relevant contributions of this study is its empirical support for the thesis that effective institutions are a fundamental condition for sustainable development. The presence of strong institutional frameworks—capable of enforcing coherent environmental policies, attracting responsible investment, and fostering green innovation—emerges as a key determinant of 21st century economic success. Conversely, in contexts where institutions are fragile, environmental objectives tend to be sidelined in favor of short-term priorities, leading to unsustainable growth cycles.

Therefore, strengthening environmental and economic governance must become a strategic priority for developing countries, aligned with international mechanisms for cooperation, climate finance, and technology transfer.

In this context, several policy reflections can be drawn from the analysis: Developed countries should continue advancing their energy transition strategies, strengthening green innovation systems, and assuming an active leadership role in international climate finance. Additionally, they must commit to more ambitious decarbonization targets and support technology transfer mechanisms to countries with lower institutional capacity. Developing countries need policies that strengthen environmental institutions, enhance transparency and regulatory effectiveness, and promote economic diversification to reduce dependence on extractive activities. This will require access to concessional climate finance, technical capacity-building, and progressive integration into sustainable value chains. Only through an approach of shared but differentiated responsibility will it be possible to move toward a more equitable global economy that respects the planet’s ecological limits.