By quantitatively assessing their impact using Random Effects Model REM, we found that CO2 emissions were significantly impacted by the Economic Landscape, FDI, and Industrialization, in
INTRODUCTION
Human behavior and economic activities significantly influence global environmental variability, with fossil fuel CO2 emissions experiencing rapid growth since 2000 (Canadell, 2007) Research highlights CO2's critical role in climate change, which can be traced back to the Industrial Revolution that began in 1750 Prior to this period, CO2 emissions were significantly lower, but industrialization spurred substantial increases in per capita incomes, particularly in Western economies, leading to higher consumer expenditures and increased environmental strain (Luhui Wang et al, 2022) In 2019, atmospheric CO2 concentrations reached 409.8 ppm, the highest in at least 800,000 years (Opoku, 2019), compared to 280 ppm before the Industrial Revolution Over the last century, CO2 levels have risen from 180 to 280 ppm, increasing by approximately 0.7 ppm per year in the 1950s and escalating to 2.1 ppm annually in the last decade (NOAA, 2013) Although CO2 is not a direct pollutant, it contributes to the greenhouse effect, driving climate change (Cassia, 2018) The consequences of climate change are severe, including global warming, melting polar ice caps, altered weather patterns, threats to biodiversity, freshwater scarcity, soil erosion, rising sea levels, and increased natural disasters (Haugan, 1996; Quéré, 2010; FITTER, 2008).
The research team believes that three main factors lead to environmental degradation: FDI, industrialization, and the country's economic landscape Wherein the environmental impact is measured by CO2 emission.
Foreign Direct Investment (FDI) and industrialization are crucial drivers of economic growth, significantly enhancing financial offerings, facilitating international trade integration, fostering technological advancements, and contributing to human capital development However, while FDI promotes economic expansion, it can also adversely affect the environment due to its dependence on natural resources Research indicates that environmental emissions associated with FDI are often overlooked in light of its growth benefits Therefore, it is essential to analyze the impact of FDI in industry and total FDI on environmental degradation.
Empirical research has primarily focused on the impact of industrialization on the environment, with most studies indicating that it causes harm (Farhan Ahmed et al, 2021) However, some research suggests a contrasting view, highlighting that the effects may not be as detrimental as previously thought (NASIR et al, 2020; Udcmba and Kclc$, 2021) For instance, Ahmed (2022) found that the environmental impact of industrialization was statistically insignificant, while other studies argue that it poses long-term environmental risks (Voumik, 2023; Sethi, 2020).
The economic landscape plays a crucial role in environmental degradation, as highlighted by various studies that establish a connection between population dynamics, GDP per capita, and environmental health (Nengsih, 2023).
Between 2000 and 2010, FDI inflows to OECD countries represented 69.5% of global FDI, highlighting their significance in the international investment landscape (World Bank, 2011; Investment, 2007) Despite this, there is a scarcity of research articles addressing the environmental impacts of FDI in these nations Therefore, our focus is on OECD countries, which are primarily developed, to better understand the relationship between FDI, industrialization, and their environmental consequences.
This study aims to identify the factors influencing environmental conditions through the Environmental Kuznets Curve (EKC), Pollution Haven Hypothesis (PHH), and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model Additionally, it seeks to examine the relationships between CO2 emissions and foreign direct investment (FDI) in industry, total FDI, industrialization, and the broader economic landscape Ultimately, the research intends to pinpoint the primary causes of environmental degradation to assist the government in formulating more effective and stringent environmental protection regulations.
LITERATURE REVIEW
Definitions
Foreign Direct Investment (FDI), as defined by the International Monetary Fund (IMF) and OECD, refers to the long-term interest of a resident entity in one economy (the direct investor) in an enterprise located in another economy (the direct investment enterprise) FDI is vital to the growth of the global economy, often associated with globalization Consequently, developing economies view the attraction of FDI as a key component of their economic development strategies, actively working to enhance market conditions and investment environments to draw in foreign investors (Luhui Wang et al., 2022).
Industrialization refers to the transition of a nation's economy from agriculture to manufacturing, driven by mechanized mass production technologies This process is essential for economic development, similar to foreign direct investment (FDI) (Opoku & Boachie, 2020) By enhancing the quantity and diversity of goods, industrialization facilitates significant changes in social production (Wang et al., 2022), ultimately boosting labor productivity and fostering overall economic growth (Chakraborty & Nunnenkamp, 2008; De Mello Jr, 1997).
The economic landscape, as defined by Blagikh (2018), is a specific geographic area characterized by natural boundaries where interconnected natural components create a cohesive whole This landscape encompasses at least three essential infrastructures: administrative, market, and transportation Additionally, the economic space represents a diverse zone that includes various elements such as towns, industrial enterprises, developed recreational areas, and intricate transportation and engineering networks.
In this study, the team will measure these variables:
• FDI refers to the stated ratio of FDI Hows into the economy In particular, the authors divide FDI into two categories: FDI Industrial and FDI total.
• Industrialization: The country’s industrialization level is split into three categories: Trade, Manufacturing, and Industry.
• Economic landscape: The country's economic landscape is classified into three categories: GDP - GDP Per Capita - Population.
The dependent variable Environmental Impacts is evaluated by CO2 emissions.
Analytical Framework for Environmental degradation
In environmental economics, numerous studies have explored the effects of economic and human activities on the environment, with the Environmental Kuznets Curve (EKC) being a notable hypothesis Research by Grossman and Krueger revealed an inverted U-shaped relationship between economic growth and environmental degradation, indicating that as economies grow, environmental impact initially increases before eventually decreasing.
Figure I Graphical representation of the Environmental Kuznets Curve (EKC);
Source: economichelp.com, accessed on 20 November 2022.
Environmental economists employ the Environmental Kuznets Curve (EKC) to analyze the connection between economic growth and environmental quality The EKC hypothesis suggests that there is an inverted U-shaped relationship between income per capita and environmental quality, indicating that as income rises, environmental quality initially deteriorates before improving at higher income levels.
Proponents of the Environmental Kuznets Curve (EKC) theory suggest that during the initial phases of industrialization, environmental degradation tends to rise as societies prioritize economic growth and job creation over environmental concerns This increased economic activity leads to higher energy consumption and pollutant emissions, which negatively affect environmental quality However, as income surpasses a certain threshold, known as the "turning point," the demand for a cleaner environment grows disproportionately compared to income, prompting more effective regulatory measures to reduce pollution Consequently, this results in an overall improvement in environmental conditions Notably, research indicates that manufacturing and foreign direct investment (FDI) in OECD countries, primarily developed nations, have minimal impact on environmental degradation.
Foreign Direct Investment (FDI) can have contrasting effects on the environment, with potential negative impacts being significant Larger FDI inflows may lead to increased environmental emissions, particularly in developing countries where multinational firms may relocate pollution-intensive production due to lax environmental regulations This phenomenon, known as the Pollution Haven Hypothesis (PHH), suggests that as trade and FDI expand, developing nations could experience heightened greenhouse gas emissions Consequently, these countries may gain a competitive edge in producing pollution-heavy goods, further exacerbating environmental degradation and increasing pollution levels.
The STIRPAT model highlights the effects of population growth, economic development, and industrialization on the environment in post-industrial societies (Temurshoev, 2006) Research by Elif Akbostanci et al (2011) indicates that industrialization is the main factor driving fluctuations in CO2 emissions This conclusion is corroborated by various studies that demonstrate a correlation between increased industrialization, elevated emissions of harmful substances, and subsequent environmental degradation (Cedrica Bento & Moreira, 2019; Arif et al., 2022; Muhammad et al., 2021).
Following the definitions of variables and theoretical underpinning, the team presented the following research model:
Hypothesis 1: FDI Total has a positive impact on Environmental Impacts.
Hypothesis 2: FDI Industrial has a positive impact on Environmental Impacts.
Hypothesis 3: Trade has a negative impact on Environmental Impacts.
Hypothesis 4: Manufacturing has a negative impact on Environmental Impacts.
Hypothesis 5: Industry has a negative impact on Environmental Impacts.
Hypothesis 6: GDP has a positive impact on Environmental Impacts.
Hypothesis 7: GDP Per Capita has a negative impact on Environmental Impacts.
Hypothesis 8: Population has a positive impact on Environmental Impacts.
Figure 2 The theoretical framework for factors contributing to environmental impacts;
Previous method
Researchers have explored the effects of foreign direct investment (FDI) and industrialization on the environment using various methodologies, with the ARDL GMM model being the most prevalent (Ahmed et al., 2022; Muhammad et al., 2021; Sahoo & Sethi, 2020; Seker et al., 2015; Voumik & Ridwan, 2023; Arellano & Bond, 1991) Shahbaz et al (2014) identified an inverted U-shaped relationship between industrialization and CO2 emissions, indicating that increased industrialization initially fosters economic growth but ultimately leads to environmental degradation and higher CO2 emissions Their study focused on the correlation between industrialization and CO2 emissions in Sub-Saharan Africa, utilizing the GMM technique to analyze the industry-to-emissions link.
A study conducted by Azam et al (2014) revealed a significant positive relationship between GDP ratio and greenhouse gas emissions in OPEC countries, highlighting the causal links among commerce, industrialization, urbanization, and CO2 levels The research utilized robust least squares techniques and the fixed effect estimator, validated through the Hausman test, alongside various analytical methods including the Westerlund cointegration test and D-H causality test The findings indicated bidirectional causality and long-term cointegration between industrialization and environmental contamination, with evidence showing that industrialization generally increases CO2 emissions However, contrary results from Elfaki et al (2020) suggested that industrialization can also reduce CO2 emissions and negatively correlate with environmental degradation To refine the analysis, differentiation of the model is recommended to address correlations between residuals and explanatory variables Additionally, studies employing the Pooled Mean Group model (PMG), Dynamic Ordinary Least Squares Estimator (DOLS), and Vector Error Correction (VECM) have demonstrated stronger cause-and-effect relationships and enhanced accuracy in this context.
Data source
Empirical research on the impact of Foreign Direct Investment (FDI) and industrialization on environmental degradation utilizes diverse data across multiple levels Some studies analyze global trends (Muhammad et al., 2021; Quito et al., 2023), while others focus on national contexts (Bulus & Koc, 2021; Ndiaya & Lv, 2018; Voumik & Ridwan, 2023) Additionally, specific research targets particular groups of countries, including OPEC nations, Newly Industrialized Countries (NICs) (Wang et al., 2022), and BRICS countries (Muhammad et al., 2021).
Table 1 Table of data description from eleven previous studies.
Title Research scope Timeline Authors
36 African industrialization and foreign direct countries investment.
Impact of FDI, industrialization, and education on the environment in Argentina
The Impact of Foreign Direct
China Pollution in China: Corruption
The Impact of Foreign Direct
The link between environmental quality, economic growth, and
' 0PEC energy use: new evidence from five
Urbanization and carbon emission: causality evidence from the new NIC’S industrialized economies.
Impact of foreign direct investment, natural resources, renewable energy consumption, and economic growth
Minh Nguyen, Duc Hong Vo
Khalid Khan, 1991-2016 Chi-Wei Su, Ran
Khan evidence from BRICS, developing. developed and global countries.
Impact of Industrialization and Hafiz Muhammad
The Impact of Foreign Direct Hadi Sasana, Rr.
Investment to the Quality of the Indonesia 1990-2015 Retno Sugiharti and
Environment in Indonesia Yuliani Sctyaningsih
FDI, Growth and the Environment: Mizan Bin Hitam
Impact on Quality of Life in Malaysia 1965-2010 Halimahton Binti
Impacts of industrialization, renewable energy and urbanization on the global ecological footprint:
Byron Quito, Maria de la Cruz del Rio- Rama, Jose Alvarez- Garcia, Amador
Previous results
Research indicates that foreign direct investment (FDI) often negatively affects the environment by increasing CO2 and greenhouse gas emissions However, Opoku and Boachie's study found that industrialization's environmental impact was statistically insignificant Additionally, some studies suggest that GDP growth has a more significant effect on FDI than on CO2 emissions, with GDP growth influencing emissions and subsequently affecting FDI (Chirilus & Costea, 2023).
Research indicates that industrialization positively influences CO2 emissions, with studies showing that its flexibility can actually reduce emissions and negatively correlate with environmental degradation However, findings from Ahmed et al (2022) highlight that foreign direct investment (FDI) generally has a harmful effect on the environment, leading to increased methane and CO2 emissions Overall, while the environmental impacts of industrialization, assessed through industry and production factors, are substantial, they tend to be moderate in scale.
Furthermore, study by Siddique (2021) demonstrates that renewable energy influences the risk of environmental degradation.
Research gaps and unique contributions
This study makes several significant contributions by focusing on developed nations, contrasting with the prevalent research on national or developing countries and Asian markets By analyzing extensive data from 2006 to 2020, the research provides a comprehensive understanding of the environmental impact of Foreign Direct Investment (FDI) across different country groups Additionally, it highlights the influence of industrial FDI on ecosystems, urging policymakers to establish effective regulations to attract sustainable FDI sources.
RESEARCH METHOD
Data source
The research uses data from 33 developed countries in the OECD The variables are described in the Table 2:
Table 2 Variables description table for 33 countries from 2006 to 2020.
Carbon dioxide emissions measured in kilotons
FDI Total FDI(FDI) to GDP in all fields OECD
FDI Industry FDI to industry OECD
Independent variables GDP Per Gross Domestic Product (GDP) per _
World Bank Capita capita of each country
Trade Trade-to-GDP ratio It is measured by World Bank imports plus exports over GDP
Gross Domestic Product (GDP) of
GDP World Bank each country
Indicator of industrialization, Industry measured by the value added of the World Bank industry
Indicator of industrialization, Manufacturing measured by the value added of World Bank manufacturingPopulation Number of the population World Bank
Data analyzing
This study utilizes four-panel data estimation techniques in Stata 17, including the Autoregressive Distributed Lag Model (ARDL), Pooled Ordinary Least Squares (OLS), Fixed Effect Model (FEM), and Random Effects Model (REM), to analyze data from 33 developed countries Following the completion of cross-section dependence, stationary tests, and co-integration tests, the findings indicate that the Random Effects Model is the most appropriate for this analysis The data processing procedure is illustrated in Figure 3 below.
Fixing errors and completing the model
Figure 3 Data analyzing procedure; Source: authors.
CO2 = Pl +P2FDI Total +P3FDI Industry + P4GDP Per Capita+ psTrade + psAGDP +
RESULTS
Cross-sectional Dependence test
The presence of cross-section dependence between the series will provide biased and inconsistent empirical results.
Table 3 Cross-sectional dependence test results.
The Cross-sectional Dependence test results for 10 variables indicated a significant finding with prob>F = 0.000, leading to the rejection of the null hypothesis of “no cross-sectional dependence” at the 1% significance level Consequently, all 10 variables exhibit cross-sectional dependence Based on this preliminary analysis, all variables will be included in the subsequent Unit Root test, with the exception of CO2, as the following test is only applicable to independent variables.
Unit Root test
Unit root tests play a vital role in time series analysis by enabling researchers to determine if a variable is stationary or non-stationary Non-stationary variables are characterized by fluctuations in mean, variance, or autocorrelation over time, making these tests essential for accurate data interpretation and forecasting.
Table 4 Unit Root test of 8 variables results.
In this analysis, if the p-value is less than the alpha level of 0.05 and the absolute value of the variables exceeds the critical values, we reject the null hypothesis (H0) indicating the presence of a unit root, thus confirming that the series is stationary As shown in Tables 4 and 5, the p-values for the variables FDI total, FDI industry, GDP per capita, and Trade are significant, confirming their stationarity However, the remaining four variables were found to be non-stationary, prompting a further Unit Root test for deeper investigation.
Table 6 Unit Root test of the remaining 4 variables results.
The Unit Root test for the first difference of the four remaining variables revealed that they are stationary The p-value was found to be less than 0.05, and the absolute value of the t-statistic exceeded the critical value, leading to the rejection of the null hypothesis (H0).
Westerlund Cointegration test
Cointegration is a statistical method used to analyze the long-term relationships between non-stationary time series Given the cross-sectional dependence among the variables, the Westerlund test was applied to the data, with the findings detailed in Table 7.
The analysis of the results table indicates that the p-value is greater than the 5% significance level (p > 0.05), leading us to retain the null hypothesis of no cointegration (H0) This finding suggests that the data lacks cointegration, indicating there are no long-run equilibrium relationships among the variables As a result, we utilized standard regression methods, including Pooled OLS, FEM, and REM, to estimate the data.
Before comparing regression models, it's crucial to analyze the data for correlation Our examination revealed several variables with high correlation coefficients Consequently, we performed a multicollinearity test to evaluate the effects of multicollinearity on the model The results of this test are presented in Table 8.
The analysis presented in Table 8 indicates a relatively low degree of multicollinearity among the variables, with values below 8 Consequently, we can advance to the subsequent phase of comparing three regression methods: Pooled Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM).
In our analysis, we first compared the efficiency of Pooled OLS and Fixed Effects Models (FEM), finding that the FEM model yielded superior results Subsequently, we evaluated Pooled OLS against Random Effects Models (REM), with the Breusch-Pagan test indicating a significant preference for REM over random effects (p < 0.05) Finally, the Hausman test further supported the superiority of REM compared to FEM (p < 0.05), establishing REM as the most suitable model for data analysis.
Figure 4 Regression models selecting result; Source: authors.
4.6 Fixing errors and completing the model
The Random Effects Model (REM) is a statistical tool used for analyzing panel data that accounts for differences among observational units Prior to estimating the REM model, it is essential to evaluate the correlation and variance changes within the data Our analysis revealed first-order correlation and heteroscedasticity, prompting us to adjust the model accordingly The results of this adjustment are detailed in Table 9.
Table 9 Regression results table of the REM model after correcting for first-order correlation and heteroscedasticity.
The regression analysis reveals that Foreign Direct Investment (FDI) in the industry, GDP per capita, trade, adjusted GDP (AGDP), and population (APopulation) are significant predictors of CO2 emissions, all exhibiting p-values below the alpha level of 0.1.
Research indicates that GDP Per Capita and Trade negatively affect CO2 emissions, with coefficients of -0.392 and -0.02, respectively Conversely, Foreign Direct Investment (FDI) in industry, agricultural GDP (AGDP), and population all contribute positively to CO2 levels, with coefficients of 0.043, 0.050, and 130.651, respectively.
Figure 5 Research model and regression results; Source: authors.
Comparing regression models
In our analysis, we initially compared Pooled Ordinary Least Squares (OLS) and Fixed Effects Model (FEM), finding that the FEM model demonstrated greater efficiency Subsequently, we examined Pooled OLS against Random Effects Model (REM), where the Breusch-Pagan test (p < 0.05) indicated that the null hypothesis of random effects could be rejected, affirming the superiority of REM Finally, the Hausman test (p < 0.05) further supported REM over FEM, establishing REM as the most suitable model for data analysis.
Figure 4 Regression models selecting result; Source: authors.
Fixing errors and completing the model
The Random Effects Model (REM) is a statistical approach used to analyze panel data, particularly when there are differences among observational units Prior to estimating the REM model, it is essential to evaluate the correlation and variance changes within the data Our analysis revealed first-order correlation and heteroscedasticity, prompting us to adjust the model accordingly The adjusted results are displayed in Table 9.
Table 9 Regression results table of the REM model after correcting for first-order correlation and heteroscedasticity.
The regression analysis reveals that Foreign Direct Investment (FDI) in industry, GDP per capita, trade, aggregate GDP (AGDP), and population (APopulation) are significant predictors of CO2 emissions, as evidenced by p-values below the alpha level of 0.1.
The analysis reveals that GDP Per Capita and Trade significantly reduce CO2 emissions, with coefficients of -0.392 and -0.02, respectively In contrast, Foreign Direct Investment (FDI) in Industry, Adjusted GDP (AGDP), and APopulation contribute positively to CO2 levels, showing coefficients of 0.043, 0.050, and 130.651, respectively.
Figure 5 Research model and regression results; Source: authors.
CONCLUSION
Discussion
The regression model reveals that five variables—FDI industry, trade, GDP, GDP Per Capita, and population—significantly influence economic growth, with GDP Per Capita and population having the most pronounced effects As countries pursue economic advancement, they increase international trade and GDP, which in turn raises GDP Per Capita However, these economic developments lead to environmental repercussions, as intensified trade boosts global supply chains and transportation emissions Additionally, GDP growth is associated with energy-intensive industries, urbanization, and consumerism, resulting in higher energy consumption, urban expansion, and waste generation, while infrastructure development disrupts ecosystems Furthermore, as GDP Per Capita rises, wealthier populations tend to consume more, increasing energy demands and contributing to air pollution.
Population growth exacerbates these trends A larger population means greater overall consumption, energy use, and waste generation Urbanization, agricultural demand, and increased transportation further contribute lo CƠ2 emissions (Krewski, 2007).
While total foreign direct investment (FDI) may not influence CO2 emissions, the Environmental Kuznets Curve (EKC) indicates that economic growth can inversely relate to environmental degradation after a certain development threshold In developed nations, advanced technology and strict regulations significantly mitigate the impact of industry and manufacturing on CO2 emissions These countries adopt sustainable practices, such as efficient logging methods, reducing the need for deforestation, while stringent environmental laws protect forests from excessive industrial damage Consequently, this balance between economic growth and ecological preservation allows for certain economic activities, like trading, to have minimal environmental impact (Tomasclli, 2019).
Implications
Vietnam is rapidly emerging as one of the fastest-growing economies globally, characterized by a youthful population and a robust influx of foreign direct investment (FDI) With an impressive average GDP growth rate of 6-7% per year, as reported by the World Bank in 2022, the country has successfully implemented economic reforms that foster an inviting environment for foreign investors Boasting a population of approximately 100 million, Vietnam offers a substantial labor force to support its economic expansion Its competitive advantages, including low labor costs, a strategic geographical location, and a stable business climate, have made it a prime destination for FDI from various countries As a result, Vietnam has transitioned from a traditional agricultural economy to a diversified one, marked by significant advancements in industrial production, services, and information technology.
The Vietnamese government should adopt policies to attract foreign direct investment (FDI) due to its economic advantages and potential for positive environmental impacts as development progresses Initially, it is crucial to facilitate the transfer of modern, eco-friendly technologies to mitigate environmental degradation Additionally, establishing a cap on FDI inflows will help balance economic growth with environmental protection To further reduce the industrial FDI's contribution to CO2 emissions, the government can impose stricter approval criteria for foreign investors, emphasizing low-emission technologies Moreover, encouraging investment in agriculture and services through preferential policies can help divert capital flows away from more environmentally harmful sectors.
Vietnam's youthful population and stable birth rate present a unique opportunity for strategic investment in education aimed at fostering environmental awareness By highlighting the significance of forest conservation, the government can motivate citizens to engage in the protection of natural ecosystems Additionally, to combat air pollution stemming from population growth, prioritizing the development of efficient public transportation systems is essential Investing in reliable buses, trains, and other transport options will encourage reduced reliance on personal vehicles, ultimately leading to lower emissions and enhanced air quality.
In the manufacturing sector, a substantial amount of carbon emissions stems from on-site activities and energy purchases, primarily due to the reliance on carbon-intensive fossil fuels like natural gas and diesel To achieve meaningful decarbonization, it is essential for manufacturers to transition to low- or no-carbon alternatives such as biomass, heat pumps, and electric heating Utilizing renewable energy sources, verified by Guarantees of Origin (GO) or Renewable Energy Certificates (RECs), can further reduce carbon emissions Additionally, electrifying vehicle fleets enhances emissions reductions Implementing a carbon tax can also promote cleaner energy practices by incentivizing reductions in carbon emissions and encouraging the adoption of renewable energy The revenue from such taxes can be reinvested in clean technologies and support for vulnerable communities.
Carbon trading serves as a powerful tool in the fight against climate change By implementing a system of carbon allowances, companies that exceed emission limits are required to pay fees, while those that successfully lower their emissions can benefit financially by selling their excess allowances Additionally, as governments progressively decrease the total number of available permits, the rising costs of these permits create further incentives for businesses to reduce their emissions even more.
Limitations and further research guidance
This study highlights two key methodological limitations that open avenues for future research in development economics To gain a comprehensive understanding of the complex interactions between independent and dependent variables, it is essential to incorporate moderating factors such as institutions and economic policies Previous research has established that these elements significantly influence economic development and, consequently, the environment (Terzic, 2017; Bhattacharjee, 2016; Nelson).
Research indicates that various sub-sectors, such as green credit policies and government subsidies, significantly affect the natural environment (Su, 2022; Khastar, 2020; Wenqi, 2022) However, the reliance on only eight dependent variables limits the analysis of environmental impacts, suggesting a need for more comprehensive measurement methods Future studies should explore additional factors contributing to environmental degradation, thereby expanding the theoretical framework for a better understanding of environmental impacts.
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