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

Sustainability 2025, 17(12), 5513; https://doi.org/10.3390/su17125513

by Ximena Morales-Urrutia^1,*, Aracelly Núñez-Naranjo²

, Rubén Nogales-Portero³

and Evelin Yanez-Toapanta¹

Reviewer 1:

Fusheng Xie

Reviewer 2:

Paweł Węgrzyn

Reviewer 3:

Alina Hlushko

Sustainability 2025, 17(12), 5513; https://doi.org/10.3390/su17125513

Submission received: 25 April 2025 / Revised: 18 May 2025 / Accepted: 11 June 2025 / Published: 15 June 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments and Suggestions for Authors

1.Theoretical Background & Literature Review

(1)The paper provides a good overview of the relationship between economic growth and environmental factors, but the literature review could be strengthened by incorporating more recent studies (post-2020) on green growth and sustainability policies.

(2)The Environmental Kuznets Curve (EKC) discussion is relevant but could benefit from a deeper critique, including recent empirical challenges to its validity.

2.Research Design & Methodology

The methodology is well-detailed, but the research questions could be stated more explicitly early in the paper. The justification for selecting G7 and ALADI countries is clear, but a brief discussion on why these specific groups were chosen (beyond data availability) would strengthen the rationale.

3.Results & Discussion

The results are well-presented, but the discussion could better integrate the findings with existing literature. For example: Why do preservation efforts positively impact GDP in developed but not developing countries? How do institutional weaknesses in ALADI nations contribute to the observed trends?

4.Conclusions

(1)The conclusions are supported but could be expanded to include policy recommendations tailored to developed vs. developing economies.

(2)A brief note on limitations (e.g., data homogeneity, omitted variables) would improve transparency.

5.Language & Clarity

(1)Some sentences are overly complex or awkwardly phrased (e.g., "This negative correlation indicates that in the G7 countries, pollutant emissions tend to be inversely proportional to GDP per capita"). Consider revising for conciseness.

(2)Ensure consistency in terminology (e.g., "ALADI" vs. "LAIA").

6.References

The references are appropriate but could include more recent works (2023–2024) on sustainable development and green investment.

Comments on the Quality of English Language

(2)Ensure consistency in terminology (e.g., "ALADI" vs. "LAIA").

Author Response

Responses to Reviewer 1

We sincerely thank Reviewer 1 for the valuable and constructive feedback provided. Each of the comments has contributed to significantly improving the quality and clarity of the manuscript. Below we present our detailed responses and the corresponding modifications made to the revised version of the paper.

1.Theoretical Background & Literature Review

1.1The paper provides a good overview of the relationship between economic growth and environmental factors, but the literature review could be strengthened by incorporating more recent studies (post-2020) on green growth and sustainability policies.

1.2 The Environmental Kuznets Curve (EKC) discussion is relevant but could benefit from a deeper critique, including recent empirical challenges to its validity.

Response 1.1:

We appreciate this suggestion. In response, we have updated the literature review to include several recent studies from 2021 to 2024 that address green growth, environmental policy integration, and sustainable development. These additions strengthen the theoretical framework and ensure the discussion reflects the latest academic contributions. See revised section 2. Theoretical Framework, particularly pages 2–4.

Response 1.2:

Thank you for this important observation. We have expanded the EKC discussion to include recent empirical challenges and alternative interpretations from recent studies (e.g., Jahanger et al., 2022; Jakada et al., 2022). These have been incorporated in the theoretical background section to provide a more balanced and critical view. Please refer to line 144 to 167 of the revised manuscript.

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 [14]. 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 [15 - 16] Population health largely depends on the integrity of ecosystems, biodiversity, and climate stability, which are fundamental factors for collective well-being [17]. There is also a significant correlation between environmental quality, population distribution, and economic growth. Regions with healthier natural environments tend to have higher population density and more dynamic economies [18].

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 [19]. 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 [20]

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 [21], 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 [22].

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 [23], 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 [24].

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 [25], 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. [26] 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, [27] 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 [28- 29 -30] 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 [31].

2.Research Design & Methodology

2.1The methodology is well-detailed, but the research questions could be stated more explicitly early in the paper.

2.2 The justification for selecting G7 and ALADI countries is clear, but a brief discussion on why these specific groups were chosen (beyond data availability) would strengthen the rationale.

Response 2.1:

We agree with the reviewer’s point. To improve clarity, we have explicitly stated the two main research questions at the end of the introduction section. These appear now on page 5, final paragraph.

A total of 4,046 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?

Response 2.2:

A paragraph has been added to justify the selection of G7 and ALADI countries beyond data availability. We now emphasize the contrast in institutional, economic, and environmental contexts, which enhances the comparative analysis. This addition is found on page 5, lines 220-224, in the Methodology section.

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.

3.Results & Discussion

Response 3:

We have enriched the discussion by integrating these critical questions into the comparative analysis.

The findings of this research reveal structural and significant differences in the relationship between natural environment variables and economic growth across developed G7 countries and developing ALADI nations. These disparities validate the necessity of applying differentiated and sustainable approaches, tailored to the 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, 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 [39 - 40]. This clearly contrasts with the G7 and supports the argument of [41], 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.

4.Conclusions

4.1The conclusions are supported but could be expanded to include policy recommendations tailored to developed vs. developing economies.

The conclusions section was rewritten based on the suggestions.

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, 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, strengthen green innovation systems, and assume 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.

4.2 A brief note on limitations (e.g., data homogeneity, omitted variables) would improve transparency.

It should be noted that the paragraph on limitations has been moved to the methodology section, as requested by another reviewer.

5.Language & Clarity

5.1 Some sentences are overly complex or awkwardly phrased (e.g., "This negative correlation indicates that in the G7 countries, pollutant emissions tend to be inversely proportional to GDP per capita"). Consider revising for conciseness.

5.2 Ensure consistency in terminology (e.g., "ALADI" vs. "LAIA").

Response 5.1:

We have carefully revised the manuscript for clarity and conciseness. Several long or awkward sentences have been simplified throughout the text. The sentence mentioned by the reviewer has been rewritten. A professional language check was also conducted.

Response 5.2:

Thank you for noticing this inconsistency. We have standardized all terminology throughout the manuscript, consistently using "ALADI" (in both English and Spanish versions) to refer to the Latin American Integration Association.

6.References

6.1 The references are appropriate but could include more recent works (2023–2024) on sustainable development and green investment.

Response 6.1:

We have updated the reference list to include multiple recent works from 2023 and 2024, focusing on green growth, sustainable investment, and climate policy. These additions are reflected in both the theoretical framework and discussion sections, as well as in the bibliography.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript addresses an interesting topic regarding the relationship between the natural environment, institutional factors, and economic growth in developed (G7) and developing (ALADI) countries. However, several substantive and technical issues require careful revision before publication.

Major comments:

• The abstract clearly signals interesting findings about the importance of the natural environment in developed countries and the additional relevance of institutional factors in developing nations. It is commendable to introduce institutional theory here; however, referencing the seminal works of Acemoglu, Johnson, and Robinson on institutions would significantly strengthen the theoretical foundation. It is regrettable that such important authors were omitted.

• The authors correctly identify significant environmental challenges related to polluting industries in developing countries. The application of the Environmental Kuznets Curve (EKC) concept is appropriate and valuable. However, it would significantly enrich the manuscript to discuss when and under what conditions the EKC is empirically supported or why some studies fail to confirm its theoretical shape. Moreover, it might be advisable to include a nonlinear econometric specification (e.g., by adding emission squared) to test the EKC hypothesis explicitly. If such a nonlinear specification is not considered, this limitation should be explicitly acknowledged.

• Providing formulas for GDP and GDP per capita calculation seems redundant, as this information is standard economic knowledge. If authors choose to retain these formulas, they should at least provide a more detailed explanation of the IPAT formula (page 4).

• The Methodology section lacks a justification for using Spearman's correlation coefficient instead of a parametric alternative, such as Pearson’s correlation coefficient. Is this choice driven by non-stationary data or another methodological reason?

• Critically, the equation presented on page 6 describes a fixed effects model, not a random effects model, as stated in the manuscript.

• Beyond applying the Hausman test, robustness checks and sensitivity analyses should be included to enhance the credibility of the econometric findings. It is essential to check the normal distribution, autocorrelations and heteroscedasticity of the residuals.

• The authors have not provided information regarding data stationarity, a crucial aspect in panel data econometrics. How was stationarity tested, and were any transformations applied to the data?

• The significance level (p-value = 0.05) applied in econometric models is mentioned too late (page 9). It should be stated explicitly in the Methodology section.

• Correlation interpretations (pages 7-8) are frequently misleading, suggesting causal relationships rather than mere associations. For example: ‘Oceans: It has a strong positive correlation (0.614) with economic growth in the core countries, which means that as ocean indicators such as maritime activity, tourism, trade and fishing grow, the impact on GDP will be significant, thus increasing its value’.

• Some interpretations appear superficial or inconsistent. For instance, preservation efforts’ correlation with GDP per capita in G7 countries is reported as insignificant in correlation analysis (page 7), yet significant in regression analysis (page 10). These inconsistencies must be explicitly discussed and clarified.

• Even though only a few variables (three in G7, two in ALADI) were statistically significant, the regression tables should report complete results, including variables with p-values above 0.05.

• The discussion is a good summary of findings but lacks broader engagement with current scholarly debates. Including broader references to global environmental policies, clean energy adoption, or institutional capacities would enrich the manuscript significantly.

Technical and editorial comments:

• Bullet points should be used when specifying characteristics of panel data models (page 5, after reference 39) to enhance readability.

• Table 2's title should be corrected to English to maintain language consistency.

• Table 6 should appear immediately after the paragraph starts with 'Table 6 shows…', to improve readability.

• The acronym "ALADI" is introduced for the first time on page 4 (Methodology section), but its explanation (list of member countries) appears only on page 6. Definitions should appear at the first mention to avoid confusion.

Comments on the Quality of English Language

• The manuscript contains typographical errors and linguistic inaccuracies (e.g., “Husman” instead of “Hausman” on page 5 and awkward phrasing such as “measured through the increase” on page 2). Careful proofreading and language correction are necessary.

• Some sentences are overly complex and unclear, e.g., page 6: “The fixed effects model performs a data analysis assuming the existence of specific characteristics of each country that determine and influence the growth of the economy of the nations, being that, these characteristics do not vary over time, therefore, those characteristics that are not observed can be captured in the fixed effects panel data model”. Suggest rewriting clearly and concisely, such as: “The fixed effects model assumes country-specific characteristics that influence economic growth and remain constant over time. Thus, it captures unobserved country-specific effects.”

• The note under Table 5 requires rephrasing: "The results of the Hausman test indicate that the fixed effects panel data model is appropriate for the countries studied."

Author Response

Response to Reviewer 2

We sincerely thank Reviewer 2 for the detailed and thoughtful review of our manuscript. The observations provided have been extremely helpful in improving the academic and methodological rigor of our study. Below we present our point-by-point responses to each of the major and technical comments, including the modifications made to the revised version of the manuscript.

The abstract clearly signals interesting findings about the importance of the natural environment in developed countries and the additional relevance of institutional factors in developing nations. It is commendable to introduce institutional theory here; however, referencing the seminal works of Acemoglu, Johnson, and Robinson on institutions would significantly strengthen the theoretical foundation. It is regrettable that such important authors were omitted.

We appreciate the reviewer’s observation, and contributions from authors such as Acemoglu and Robinson have been included, with their most emblematic work that contributes to this research.

The authors correctly identify significant environmental challenges related to polluting industries in developing countries. The application of the Environmental Kuznets Curve (EKC) concept is appropriate and valuable. However, it would significantly enrich the manuscript to discuss when and under what conditions the EKC is empirically supported or why some studies fail to confirm its theoretical shape. Moreover, it might be advisable to include a nonlinear econometric specification (e.g., by adding emission squared) to test the EKC hypothesis explicitly. If such a nonlinear specification is not considered, this limitation should be explicitly acknowledged.

First, the manuscript now includes a discussion on when and under what conditions the EKC is empirically supported.

Moreover, [27] 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 [28 - 29 - 30] 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.

Second, in the present study, a non-linear econometric specification—such as the inclusion of the squared term of emissions—was not incorporated, as the methodology strictly follows the approach adopted by previous high-impact studies, particularly those that examine the relationship between environmental variables and economic growth in comparative contexts between developed and developing countries.

Providing formulas for GDP and GDP per capita calculation seems redundant, as this information is standard economic knowledge. If authors choose to retain these formulas, they should at least provide a more detailed explanation of the IPAT formula (page 4).

In accordance with the suggestion, the formulas have been removed from the text.

The Methodology section lacks a justification for using Spearman's correlation coefficient instead of a parametric alternative, such as Pearson’s correlation coefficient. Is this choice driven by non-stationary data or another methodological reason?

The paragraph explains that the data are not normally distributed; therefore, Spearman's correlation should be applied.

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 direction and strength of the relationships among the ranked variables considered in the study. It is denoted by the letter r with subscripts.

Critically, the equation presented on page 6 describes a fixed effects model, not a random effects model, as stated in the manuscript.

We appreciate the observation. Due to a writing error, random effects were mentioned when fixed effects would have been correct. Additionally, the formula has been removed from the text.

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.

Beyond applying the Hausman test, robustness checks and sensitivity analyses should be included to enhance the credibility of the econometric findings. It is essential to check the normal distribution, autocorrelations and heteroscedasticity of the residuals.

A normality test was conducted on the data to determine the appropriate type of statistical tests to apply.

Table 1 presents the results generated by SPSS 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 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

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, Land & Soil	1.93
Variable	VIF

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

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, the Wooldridge test for autocorrelation indicates 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 the rejection of the null hypothesis of no autocorrelation.

Table 6. Wooldridge Test for Autocorrelation in Panel Data

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

Likewise, the presence of groupwise heteroskedasticity was tested using the Modified Wald test, whose results are presented in Table 7. 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.

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 results confirm the presence of both autocorrelation and heteroskedasticity in the panel data Table 8. To address these issues and ensure robust standard error estimates, the models were corrected using Panel Corrected Standard Errors (PCSE). This correction enhances the accuracy of inference and strengthens the robustness of the econometric results. The corrected fixed effects models are presented at the end of the Results section.

The authors have not provided information regarding data stationarity, a crucial aspect in panel data econometrics. How was stationarity tested, and were any transformations applied to the data?

A unit root stationarity test was conducted to verify whether the study variables are stationary. According to the results, there was no need to transform the data.

3.3. Unit Root Stationarity Test of Environmental and Economic Variables

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, Land & 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

The stationarity test applied to both environmental and economic variables demonstrates that all time series are stationary at level, as the p-values associated with the adjusted t-statistics are below the 0.05 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).

The significance level (p-value = 0.05) applied in econometric models is mentioned too late (page 9). It should be stated explicitly in the Methodology section.

The entire paragraph was corrected based on the explanation of the model selection according to the p-value.

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.

Correlation interpretations (pages 7-8) are frequently misleading, suggesting causal relationships rather than mere associations. For example: ‘Oceans: It has a strong positive correlation (0.614) with economic growth in the core countries, which means that as ocean indicators such as maritime activity, tourism, trade and fishing grow, the impact on GDP will be significant, thus increasing its value’.

Based on the correction, the analysis of the two correlation tables in the document has been revised to explain associations rather than causal relationships.

Table 2. Spearman Correlation Coefficient Between Natural Environment Variables and Economic Growth in G7 Countries

	Emissions	Air Pollution Exposure	Forests, Land & Soil	Freshwater	Oceans	Preservation Efforts
GDP per capita	-0.505	0.498	0.375	0.169	0.614	-0.007
Sig. (2-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).

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:

Air pollution exposure (ρ = 0.498, p < 0.001),
Second bullet; Forests, 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 was 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

Table 3 presents the results of the 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.

Table 3. Spearman Correlation Coefficient Between Natural Environment Variables and Economic Growth in ALADI Countries

	Emissions	Air Pollution Exposure	Forests, Land & Soil	Freshwater	Oceans	Preservation Efforts
GDP per capita	0.327	0.345	0.033	0.422	0.209	-0.446
Sig. (2-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).

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 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 or 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, 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.

Some interpretations appear superficial or inconsistent. For instance, preservation efforts’ correlation with GDP per capita in G7 countries is reported as insignificant in correlation analysis (page 7), yet significant in regression analysis (page 10). These inconsistencies must be explicitly discussed and clarified.

The model was run again, and the table was corrected due to an error in the transcription of the results obtained from the software, correcting the inconsistency involving the variables "Preservation Efforts" and "GDP per Capita."

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, Land & 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).

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, Land & 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.

Even though only a few variables (three in G7, two in ALADI) were statistically significant, the regression tables should report complete results, including variables with p-values above 0.05.

The tables were prepared with all the variables, including those that were significant and those that were not

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, Land & 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).

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, Land & 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).

The discussion is a good summary of findings but lacks broader engagement with current scholarly debates. Including broader references to global environmental policies, clean energy adoption, or institutional capacities would enrich the manuscript significantly.

Current references have been included, and aspects related to environmental policies, clean energy, and others have been expanded.

General Technical & Editorial Comments

Response:
All technical and stylistic corrections suggested have been addressed:

Bullet points are now used in Section 3.1 to explain panel data model characteristics.
The title of Table 2 has been translated to English.
Table 6 has been repositioned for coherence with its reference in the text.
The definition of ALADI now appears at its first mention (page 4).
A full language review was conducted to correct typographical errors, improve clarity, and ensure consistency (e.g., corrected “Husman” to “Hausman”).

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

To summarize, the article is a valuable contribution to the study of environmental factors affecting the economic growth of developed and developing countries. However, in order to improve the scientific quality of the work, it is advisable to finalize certain points: to deepen the theoretical framework, improve the arguments for the choice of indicators, add graphical visualization to confirm the results, and address the identified problems with the discussion and conclusions. Taking these comments into account will enhance the quality and relevance of the research.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 3

We sincerely thank Reviewer 3 for the detailed and thoughtful review of our manuscript. The observations provided have been extremely helpful in improving the academic and methodological rigor of our study. Below we present our point-by-point responses to each of the major and technical comments, including the modifications made to the revised version of the manuscript.

Comments on the Introduction Section

Comment 1.1:

1.1. There is no clearly stated goal and objectives of the study. After justifying the importance of ensuring sustainable economic growth on the basis of a balanced investment and environmental policy (lines 55-60), the authors should define the purpose of the study and outline the tasks to be solved.

Response 1.1:

We appreciate this recommendation. We have revised the introduction to clearly define the main goal of the study and enumerate the specific research objectives. These are now clearly stated at the end of the introduction section (see page 2, lines 66-73).

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.

Comment 1.2:

1.2. The introduction is oversaturated with references to literature sources, and the use of a number of them in this section is inappropriate. In particular, work 14 (C. F. Gómez-Segura, O. H. Cerquera-Losada, and E. F. Acero-Cebay, “La curva medioambiental de Kuznets y el crecimiento 526 económico sostenible en Colombia,” Apunt. del Cenes, vol. 40, no. 71, pp. 165-188, 2021, doi: 527 10.19053/01203053.v40.n71.2021.11387), cited by the authors in line 52, is devoted to testing the hypothesis of the Kuznets environmental curve (EKC) for carbon dioxide (CO2) and nitrous oxide (N2O) emissions and can be used in paragraph 2.5 Relationship of the natural environment with Economic Growth (lines 136-139), where the authors analyze the advantages and disadvantages of using this methodology.

Response 1.2:

The author Gómez-Segura et al. (2021) was removed because it was not relevant.

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 subordinated to immediate economic growth [12]. 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 [13] and promoting extractive activities that accelerate ecosystem degradation.

Comments on the Background Section

Comment 2.1:

2.1. In paragraph 2.2. Importance of the natural environment, the authors cite three sources at once, namely 21, 22, 23, as confirmation of one statement (lines 83-85). Such multi-source citation is inappropriate and needs to be clarified. It seems to be a formal reference without a proper connection to the specific content of each source. I recommend one most relevant source that really confirms the opinion expressed.

Response 2.1:

We have refined the paragraph in Section 1.1.2, selecting a single, most relevant source to support the statement. We also restructured the sentence to reflect the specific contribution of that source, improving citation precision and scholarly coherence.

Comment 2.2:

2.2. In paragraphs 2.3 and 2.4 (line 94 and line 110), there are errors in the formatting of the source citation. This complicates the verification of sources and reduces the overall level of scientific quality of the work.

Response 2.2:

All references have been carefully reviewed and reformatted in accordance with the required citation style (IEEE). The inconsistencies in formatting have been corrected throughout Sections 1.1.3 and 1.1.4.

Comment 2.3:

2.3. In paragraph 2.4 Indicators, the authors describe Gross domestic product (GDP) and GDP per capita as the main indicators of economic growth. However, the material presented does not provide a justification for the choice of these indicators. It would be advisable to provide a brief description of alternative indicators of economic growth (e.g., industrial production index, employment rate, fixed capital investment, etc.), as well as arguments why GDP and GDP per capita are the most relevant for the purposes of this study.

Response 2.3:

A full paragraph was added to justify the selection of total GDP and GDP per capita for the analysis.

Comment 2.4:

2.4. In paragraph 2.5 Relationship of the natural environment with Economic Growth, the authors make an important attempt to compare traditional economic paradigms and modern approaches to understanding economic growth with regard to environmental factors. However, the references to scientific studies from 2011 and 2013 are somewhat outdated, given the dynamic development of the discourse on sustainable development and the green economy in recent decades. It is recommended to replace the references with more recent studies that highlight modern concepts of the relationship between economic growth and the natural environment.

Response 2.4:

Thank you for this insight. We have updated the literature in Section 1.1.6 to include more recent and relevant studies published from 2020 to 2023, aligning the theoretical discussion with contemporary debates on green growth and sustainable development.

Comment 2.5:

2.5. Subsections 2.5 Relationship of the natural environment with economic growth and 2.6 Impact of the natural environment on economic growth overlap to a large extent in terms of content and concept. Both subchapters address the relationship between the natural environment and economic growth. However, there is no clear logical and structural boundary between the concepts of “relationship” and “impact” in the presented material. It would be advisable to combine the paragraphs to avoid duplication of concepts and improve the logical presentation of the material. In addition, the authors again unreasonably use multi- source citation (lines 172-175) without clearly distinguishing the contribution of each source, which reduces the transparency of the statement.

Response 2.5:

We have merged Sections 2.5 and 2.6 into a single (Section 1.1.6), streamlined subsection to avoid duplication and improve conceptual clarity. The structure now clearly distinguishes between theoretical relationships and empirical impacts.

1.1.6. Relationship Between the Natural Environment and Economic Growth

Moreover, [27] 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.[28 - 29 - 30] 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.

Comments on the Results Section

Comment 3:

To improve the interpretation of the data presented, it is necessary to add appropriate graphic materials. In particular, the regression analysis should be accompanied by visualization in the form of scatter plots. This will allow the authors to clearly demonstrate the direction and strength of the relationship, increase the reliability and transparency of the analysis, and ensure better readability for readers.

Response 3:

We appreciate the reviewer’s comment; however, we have not included graphical material, as we believe the tables provide a clear and direct explanation of the phenomenon under study.

Comments on the Discussion Section

Comment 4.1:

4.1. In this section, the authors describe the differential impact of the natural environment on economic growth in the G7 and ALADI countries. However, the discussion is mostly descriptive. It would be advisable to supplement the chapter with a critical interpretation, and it is important to compare the key differences between the two groups of countries.

Response 4.1:

In the following paragraphs, a critical explanation has been provided in response to the suggestions made by the reviewer

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, sustainable approaches tailored to the unique institutional, productive, and environmental contexts of each economic bloc.

Comment 4.2:

4.2. Although the authors provide references to the literature, they do not explain how the results extend existing approaches and statements. The authors' contribution should be more clearly emphasized.

Response 4.2:

We have revised the whole section of the discussion to clearly state the original contribution of this study to the literature, particularly in integrating institutional factors with environmental determinants of growth in a comparative panel data framework.

Comment 4.3:

4.3. The discussion should indicate possible limitations and prospects for further research to increase transparency and objectivity of interpretation. To do this, the authors should move the information from the Conclusions to this section, namely lines 477-480.

Response 4.3:

As recommended, we have moved the relevant lines (previously in the conclusion) to the end of the Discussion section (lines 461 to 464), where we now address study limitations and suggest future research avenues, improving the structure and academic transparency.

Comment 4.4:

4.4. It is recommended to briefly define what is meant by “weak environmental institutions” to ensure the correct perception of the readers.

Response 4.4:

"Weak environmental institutions" was defined correctly; it is possible that an inconsistency in the wording was caused by the translation into English.

Comment 4.5:

4.5. Some sentences are too long and overused, making it difficult to read (lines 427-430).

Response 4.5:

We have revised the long and complex sentences throughout the manuscript for improved readability. We now use clear, concise constructions to express technical content.

Comments on the Conclusions Section

Comment 5.1:

The general conclusions need to be specified. The authors should focus on the results obtained (in order to strengthen the statements in lines 465-470) and emphasize the scientific and practical value of the study. The thesis about “effective institutions” (lines 471-472) needs to be elaborated.

Response 5.1:

We have revised the Conclusion section to be more result-oriented, focusing on the statistical findings and some reflective notes are included. In particular, we clarified the statement about "effective institutions" and its relevance to long-term sustainable development.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have sufficiently addressed all crucial reviewer comments. The manuscript has significantly improved in clarity, consistency, and overall quality. Only minor editorial details remain, such as using '0.05' consistently instead of '5%' when reporting p-values throughout the manuscript (page 5). Addressing these minor editorial aspects will further enhance readability and uniformity. Nonetheless, the paper is suitable and ready for publication.

Author Response

Responses to Editor

We sincerely thank Editor for the valuable and constructive feedback provided. Each of the comments has contributed to significantly improving the quality and clarity of the manuscript. Below we present our detailed responses and the corresponding modifications made to the revised version of the paper.

The authors have sufficiently addressed all crucial reviewer comments. The manuscript has significantly improved in clarity, consistency, and overall quality. Only minor editorial details remain, such as using '0.05' consistently instead of '5%' when reporting p-values throughout the manuscript (page 5). Addressing these minor editorial aspects will further enhance readability and uniformity. Nonetheless, the paper is suitable and ready for publication.

Response to the Editor:

We proceeded to make the correction suggested by the editor as follows

Page 8

The stationarity test applied to both environmental and economic variables demonstrates that all time series are stationary at 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).

Pages 9-10

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.

Author Response File: Author Response.pdf

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

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