Muhammad Zeeshan
Jiabin Han1†
Alam Rehman
Irfan Ullah
Zeeshan Fareed- 1College of Business Administration, Liaoning Technical University, XingCheng, China
- 2Faculty of Management Sciences, National University of Modern Language, Islamabad, Pakistan
- 3Reading Academy, Nanjing University of Information Science and Technology, Nanjing, China
- 4Department of Management Sciences, National University of Modern Languages Peshawar Campus, Islamabad, Pakistan
- 5School of Economics and Management, Huzhou University, Huzhou Zhejiang, China
This study compares the nexus among trade liberalization, CO2 emissions, energy consumption, and economic growth in Southeast Asian and Latin American countries. We apply the structural equation modeling approach for estimation analysis of the data from 1991 to 2018. The empirical findings of this study validate that trade has a positive and statistically significant effect on energy consumption, CO2 emissions, and gross domestic product (GDP) in Southeast Asian countries. Whereas in Latin American countries, trade shows a positive insignificant impact on energy consumption, but the coefficients for both CO2 emissions and GDP are positive and statistically significant. Energy consumption also exhibits a positive significant effect on CO2 emissions and a positive statistically insignificant effect on GDP in the Southeast Asian region. However, in Latin American countries, energy consumption predicts a positive and statistically significant impact on both CO2 emissions and GDP. Whereas, CO2 emissions indicate a positive significant effect on GDP in both regions. Therefore, each country’s government in both areas should formulate appropriate policies to promote green technologies in the production and exports, which could help economies to achieve a clean environment and sustainable long-term development.
Introduction
Trade has been extensively discussed with CO2 emissions, energy consumption, and gross domestic product (GDP) in the literature; however, the regional comparison is still not being undertaken. We extended the existing literature on trade, CO2 emissions, energy consumption, and economic growth by using the case of more regions, i.e., Southeast Asian and Latin American countries. The comparative analysis of the two regions may provide a clear picture of the success of trade policies and implications on growth, environmental, and energy consumption in the two regions. Trade openness is considered to be an engine to the growth of an economy, which consequently contributes to more CO2 emissions and energy consumption that affect environmental quality, GDP, and CO2 emissions (Ullah et al., 2019b). Many countries in Latin America and Southeast Asia adopted trade liberalization in the 1990s, and it resulted in significant improvement in economic growth. Besides the positive effect on the economy, international trade expansion contributes to the CO2 emissions and energy consumption, which affects the environmental quality of a country (Copeland and Taylor, 1997; Frankel and Rose, 2002). Trade openness can account for an increase in CO2 emissions from three aspects. First, trade increases in CO2 emissions as a result of a high level of economic activities, especially in the export sector industries (Ullah et al., 2019a). Second, countries change the production pattern due to the specialization after the trade liberalization, which increases CO2 emissions (Grossman and Krueger, 1991; Copeland and Taylor, 2004). Third, countries use a higher level of technologies that require a high level of energy consumption, which leads to a higher level of CO2 emissions in the economy. The emergence of trade openness in developing countries has got attention as many Southeast Asian economies achieved substantial economic growth. Besides this achievement, trade activities created a lot of challenges for the environment, particularly in terms of CO2 emissions. The environmental quality is worsening due to emissions of dangerous gases and results in more severe consequences for human health and sustainable development (Rock and Angel, 2007). Environmental Kuznets Curve provides a framework showing CO2 emissions for countries in different stages of the economy, in the transition period due to industrial development and economic growth that leads to higher CO2 emissions in the economy. In contrast, in later stage, countries give more attention to reduces CO2 emissions; therefore, in later stage, it reduces CO2 emissions. Both trade and economic growth are based on agriculture production, which requires high-level energy consumption. Energy consumption has no crucial adverse effect on economic growth Menegaki (2011). However, some of the studies found that trade liberalization, more FDI, and energy consumptions do not negatively contribute to the environmental degradation (Ikram et al., 2021; Shahzad et al., 2021).
Trade expansion and GDP have the same effect on energy consumption; in this regard, the conventional theories suggest that trade and GDP increase energy consumption. The majority of the studies in existing literature concluded the positive nexus of trade with energy consumption, CO2 emissions, and GDP. The contradiction of empirical findings requires further empirical investigation by comparing the two regions like Southeast Asian countries and Latin American Countries. Besides, this study will help to better understand the success of trade openness in two regions and its implication for economic growth, energy consumption, CO2 emissions, and energy consumption. Many countries in various regions have varying environmental legislation and robust strategic framework, which may have diverse implications for the CO2 emissions. Therefore, the nexus of these variables in these Southeast Asian countries and Latin American Countries are investigated in this study. The study contributes to the literature in many ways. First, there is no such study exists, which explores the dynamic interrelationship among trade, CO2, energy consumption, and economic growth by comparing the emerging regions. Second, the comparative analysis of various regions such as Southeast Asia and Latin American may provide the success of trade policies and their implications for the environment, CO2 emissions, energy consumption, and GDP. Third, this paper uses a novel statistical technique known as the “structural equation model (SEM)”, which is a novel approach that provides a more robust empirical estimation for complex models and prominent policy implications for the policymakers in these various countries and regions.
Literature Review
Trade openness has a manifold effect on the economy, including economic growth, CO2 emissions, and energy consumption. Trade and economic growth have been widely discussed in the literature. Kemal (2007) analyzed the trade openness effect on economic growth for the South Asian countries by using the restricted VAR model and co-integration technique and confirmed the growth-lead hypothesis. Awokuse (2008) demonstrated that trade gives the prevalence of import-led and export-led hypotheses for Latin American countries. Besides the significant contribution of trade to economic development, it also has some serious threats for the country’s environmental degradation concerns. Many studies found that trade leads to an increase the CO2 emissions. Ullah et al. (2019a) confirmed that trade openness increased the CO2 emissions in China for 1990–2017. Ferda Halicioglu (2009) investigated trade, CO2 emissions, and energy consumption for Turkey and found that trade openness leads to an increase in CO2 emissions. Omri et al. (2015) reported that trade, financial development, and economic growth cause the degradation of environmental quality in MENA countries. Shahbaz et al. (2013) confirmed that trade openness increases the CO2 emissions in Indonesia. Trade activities also increase CO2 emissions and found that trade openness increases CO2 emissions in China (Ullah et al., 2019a; Zeeshan et al., 2021a; Zeeshan et al., 2021b). Another study by Ullah et al. (2019b) found that trade liberalization in Pakistan also leads to CO2 emissions in Pakistan. Whereas in a similar study, Grossman and Krueger (1991) reported a positive relationship between trade openness and CO2 emissions. Likewise, Copeland and Taylor (2004) also confirmed a positive nexus of CO2 emissions and trade, but they came up with contrast findings and found that CO2 emissions can be reduced by trade openness and claimed that technological innovation and energy-efficient technologies in the production process negatively affect CO2 emissions and trade.
Trade and GDP both can increase energy consumption, and many studies found a positive association between trade and GDP growth for energy consumption. The pioneering research of Kraft and Kraft (1978) indicates that GDP increases energy consumption. However, later empirical findings show a mix of conclusions on the relationship between trade GDP and energy consumption. Khan et al. (2006) found that energy consumption is the main factor for accelerating economic growth in Pakistan, Sri Lanka, and Bangladesh. Whereas in a similar study, Noor and Siddiqi (2010) predicted a short-run unidirectional causality running from GDP to energy consumption in Pakistan, Bangladesh, Nepal, Sri-Lanka, and India. Lee (2005) analyzed the relationship between GDP and energy consumption by using the co-integration technique and VECM for the period of 1975 to 2001 and concluded the existence of the long-run relationship between energy consumption and economic growth; however, no short-run relationship was observed. Salahuddin et al. (2015) and Ahmad et al. (2021) tested the relationship among economic growth, energy consumption, carbon dioxide emissions, and financial development in the GCC area, and their findings suggest that economic growth and energy consumption stimulate CO2 emissions in GCC countries. Pao and Tsai (2010) also found a co-integration relationship between GDP, energy consumption, and CO2 emissions for BRIC countries with the help of Granger causality tests and panel co-integration technique. Their long-run estimation results suggest a positive link between carbon emissions and energy consumption. Jammazi and Aloui (2015) investigated the cross-linkages among CO2 emissions, economic growth, and energy consumption for GCC countries with the approach of wavelet window cross-correlation. Their results report a unidirectional nexus between energy consumption and CO2 emissions. Aqeel and Butt (2001) also confirmed that the economic growth of a country directly influences the growth of petroleum consumption. Likewise, Paul and Bhattacharya (2004) also reported bi-directional causality between energy consumption and economic growth in India. However, Pirlogea and Cicea (2012) found a unidirectional relationship between energy consumption to economic growth for European countries. Whereas, few studies justified the bi-directional causality between renewable energy consumption and economic growth (Apergis and Payne, 2009; Apergis et al., 2010). Trade and energy consumption have been widely examined in the liberalization. Likewise, Grossman and Krueger (1995), Frankel(2009), and Zeeshan et al. (2021a) found that trade liberalization reduces environmental quality. Cole (2006) and Doney et al. (2009) argued that trade openness potentially affects CO2 emissions, particularly in the industrial production and exports that heavily rely on the fossil fuels that is the major underlying cause of pollution. Redding and Venables (2002) and Grossman and Helpman (1991) comprehended that energy consumption may be affected by trade openness policies like reducing non-tariff and tariff barriers on energy-efficient products and suggesting a positive association between energy consumption and trade openness. Greenaway et al. (2002) and Wacziarg (2001) found a positive relationship between energy consumption and trade openness. Antweiler et al. (2001) suggested a theoretical framework that an increase in energy usage is due to trade openness. However, they are not the same, as the exercises of changing policies and the structure of the economy represent liberalization.
Economic growth and CO2 emission have also explored in the previous studies by using EKC theory (Grossman and Krueger, 1995; Seldon and Song, 1994; Shafiq et al., 1994; Al-Mulali et al., 2016; Solarin et al., 2017). Saboori and Sulaiman (2013) investigated that there exists bidirectional causality between carbon dioxide emissions and economic growth. However, many studies explored the nexus of economic growth, CO2, and energy consumption (Halicioglu, 2007; Apergis et al., 2010; Menyah and Wolde-Rufael, 2010; Ozturk and Acaravci, 2010; Arouri et al., 2012; Sheinbaum-Pardo et al., 2012). Few studies also examined the panel data and found a dynamic relationship between carbon dioxide emissions and economic growth and suggested that GDP determines the long-run association with CO2 emissions (Coondoo and Dinda, 2002; Coondoo and Dinda, 2008; Lee and Lee, 2009; Narayan et al., 2010; Jaunky, 2011; Apergis, 2016). On the basis of the above literature, we have developed the following hypothesis (Ahmad et al., 2021).
Hypothesis Testing
H1: Trade has a positive effect on energy consumption.
H2: Trade positively contributes to CO2 emission.
H3: Energy consumption positively affects CO2 emission.
H4: Energy consumption positively affects GDP.
H5: Trade positively affects GDP.
H6: CO2 emission positively affects GDP.
Stylized Fact for Trade, Energy, CO2 Emissions, and GDP in Latin American and Southeast Asian Countries
Trade Liberalization Trend
In Figure 1, the Southeast Asian Country’s trade liberalization can be observed with an upward but more flat behavior from 1991 to 2018. The association of Southeast Asian countries consists of many diverse economies, as compared to Europe or North America. Moreover, they are ethnically, historically, culturally, and economically different from one another as to Europe or American Northern countries. However, in terms of economic development, Singapore is leading among the Association of Southeast Asian Nations (ASEAN) countries. It is noted that domestic policies change that ASEAN has to make difficult politically, in the prism of economic crisis. To increase the trade, ASEAN countries expanded trade in the region that significantly improved the region’s income. Singapore has obtained tremendous growth due to aggressive and well-planned trade strategies. Indonesia shows a similar practice for trade openness in the same period, which means that trade openness increases the trade activities in the region. Lao PDR also exhibits similar practices, as Indonesia, in terms of trade. However, Malaysia statistics show that the country showed a significant increase in trade activates from 1991 to 2006; then, a downward trend is seen for trade openness due to international finical crises. Philippines, Thailand, and Vietnam show somehow similar procedures and governance for trade openness up to 2015, and then, Thailand and Vietnam show a rising trend due to special reforms by their current regimes. Singapore shows serious upward trade liberalization practices till 2008, and then, a declining approach is seen.
FIGURE 1. Trade liberalization of Southeast Asian countries.
Figure 2 contains the trade liberalization trend for the Latin American countries; Honduras and Costa Rica trade liberalization have dynamic trends as showing upward and downward movement for trade activities from 1991 to 2018. Bolivia, Cuba, Peru, Dominican Republic, and Uruguay show a smooth trend for trade in the study period, which means that no special creativities have been taken concerning trade in Guatemala. Honduras, Paraguay, Venezuela, Haiti, and Chile exhibit similar practices as Guatemala in terms of trade with no remarkable changes. However, Panama figures show that it increased trade from 1991 to 1997, and then, a downward curve in terms of trade openness is seen up to 2002. Bolivia, El Salvador, Paraguay, Uruguay, Dominican Republic, Guatemala, Ecuador, and Puerto Rico show somehow similar procedures and governance for trade openness up to 2018, but Peru starts a sudden upward trend in 2014, for 2 years. This jump was characterized by trade facilitation efforts by the ruling regime, and then, a downward trend is seen.
FIGURE 2. Trade liberalization of Latin American countries.
Energy Consumption
In Figure 3, the energy consumption has been shown for the Southeast Asian region. As a whole of 10% of the world’s population reckon with the assessment of global outlook energy consumption, rapidly growing regional economies contribute to various aspects of energy outlook and economic factors. The Southeast Asia energy outlook reported a growing perspective of energy consumption in Southeast Asia. International Energy Agency reported that the risks and opportunities that are faced by the Association of Southeast Asian nations, i.e., Cambodia, Malaysia, Singapore, Indonesia, Thailand, Lao PDR, Philippines, Myanmar, and Vietnam, look in achieving to meet their affordable and sustainable energy demand. The sustainability demand is represented in Figure 3. The graph showed its slow and steady energy demand and consumption. This intensifies the effort by these countries to ensure secure, sustainable, and affordable energy sector pathway plans. It includes investments in power supply, infrastructure, and fuel, mainly focuses on efficiency that resulted in a well-managed region’s energy system as regards the quality of life, and improves welfare for citizens. However, there are also some warning signs, an increase in fuel demand, and outpaced production within the Southeast Asia region. Thailand, Vietnam, and Singapore show overall upward trends from 1991 to 2018, with a slight upward and downward practice. It is the reason for serious efforts, which are being made by these countries to stimulate trade using more energy in their production units. More production has moved the energy consumption curve upward in these countries than in other countries in the same region. However, Malaysia and the Philippines show an upward but flat kind of energy consumption practices, showing their less intensity toward energy consumption. Whereas, Cambodia and Lao PDR show a flat but slightly increasing trend in their energy consumption.
FIGURE 3. Energy consumption of Southeast Asian countries.
Figure 4 shows that Haiti’s energy consumption can be seen as having an upward but flat behavior from 1991 to 1994. Dominican Republic shows similar practices for energy consumption in the period, which means that no special initiatives have been taken regarding energy consumption and trade in the Dominican Republic, Costa Rica, El Salvador Nicaragua, Paraguay, Puerto Rico, Venezuela, Chile Cuba, Ecuador, Guatemala, Honduras, Panama, Peru, and Uruguay, due to which their static position is there. Therefore, flat practices are seen with a slight rising trend, which is very much clear that these countries did not entice foreign trade.
FIGURE 4. Energy consumption of Latin American countries.
CO2 Emission
In Figure 5, the CO2 emissions are shown. Vietnam, Singapore, and Thailand show a downward curve from 1991 to 2007, and then, a sudden drastic upward trend is witnessed in the graph, which is due to more energy consumption in this period by these three countries and the promulgation of various trade approaches and policies to stimulate the economy. Malaysia and the Philippines show a flat but a bit raising trend in CO2 emissions, which is very much coherent with their energy consumption practices, as both countries have similar trends for their energy consumption. Indonesia, Lao DPR, and Cambodia show, somehow, similar volumes in terms of their CO2 emissions, which is due to their approach toward energy consumption.
FIGURE 5. CO2 emission in Southeast Asian countries.
In Figure 6, the CO2 emissions for the Latin belt are shown. Venezuela shows a downward curve from 1991 to 1996, and then, a sudden drastic upward trend is witnessed in the graph for 3 years, which is due to more energy consumption in this period by Venezuela, as oil resources have extracted in Venezuela. Similarly, Chile shows an upward trend from 1991 to 2018. Bolivia, Cuba, El Salvador, Honduras, Paraguay, Uruguay, Dominican Republic, Guatemala, Nicaragua, Peru, Costa Rica, Ecuador, Haiti, Panama, and Puerto Rico show a flat but slightly increasing upward trend in their CO2 emissions in the study period of 1991–2018.
FIGURE 6. CO2 emission in Latin American countries.
Economic Growth
Figure 7 presents the economic growth for the Southeast Asian countries. Singapore shows a tremendous rise in economic growth. It is due to its knowledge economy using sophisticated technologies in their production and more driven toward innovation and creativity of industrial output, agricultural production, services, and wise promulgation of economic policies. Malaysia is also showing an upward trend in terms of economic growth. It is due to the coherent and robust economic measures to fuel the economy. In comparison, Thailand stands third in the graph in terms of economic growth. However, the rest of the countries show almost similar economic growth, which reflects their economic weight and width. In Figure 8, the economic growth for Latin American countries is shown. Puerto Rico shows a tremendous rise in economic growth from 1991 to 2018. It is due to its knowledge economy consuming advanced machinery in their production and more driven to invention and creativity of industrial output, agricultural production, services, and wise promulgation of economic policies. Peru, Venezuela, and Uruguay also show an upward trend in terms of economic growth. It is due to the coherent and robust economic measures to fuel their economies. However, the rest of the countries such as Bolivia, Dominican Republic, Haiti, Paraguay, Chile, Ecuador, Honduras, Costa Rica, El Salvador, Cuba, Guatemala, and Panama demonstrated nearly parallel economic growth, which reflects their economic quantity and amount.
FIGURE 7. Economic growth of Southeast Asian countries.
FIGURE 8. Economic growth in Latin American countries.
Methodology of the Paper
Data and Variables
We collected data from the WDI data stream for the analysis of this study. We used data span from 1991 to 2018. We collected data of two regions i.e., Southeast Asia and Latin American, and dropped these in two panels. Panel A consists of Southeast Asian Countries (Cambodia, Indonesia, Lao PDR, Malaysia, Philippines, Singapore, Thailand, and Vietnam). Whereas, Latin America, panel B, consists of various countries, namely, Bolivia, Chile, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Uruguay, and Venezuela.
We collected the data of trade liberalization, CO2 emissions, energy consumption, and economic growth to conduct empirical analysis for hypothesis testing. We denote T, for trade liberalization, which measured % of GDP. The same measurement approach has been used by previous researchers (Aye and Edoja, 2017; Ben Jebli and Hadhri, 2018; Ullah et al., 2019b). We use E, for energy consumption, measured in % of total final energy consumption, in the spirit of previous studies (Aye and Edoja, 2017; Ullah et al., 2019b; Zeeshan et al., 2021a), and CO2, for carbon dioxide emission, which is measured as metric tons per capita (Hanle et al., 2004; Shi et al., 2019, whereas economic growth is measured in US dollars (Heidari et al., 2015; Aye and Edoja, 2017; Ben Jebli and Hadhri, 2018).
Emperical Model
We have developed the following model and we estimated the following model, which covers our underline hypothesis.
Estimation Techniques
Various estimation techniques have been used to analyze data of different panels in numerous fields across the globe. However, very few studies available on OLS, GLS (fixed effect and random effect), panel unit root, panel ARDL, GMM, pooled OLS, and SEM. The SEM is the modern-day technique widely used in panel data and cross-sectional data analysis to provide robust and accurate results for various variables, dependent on each other, showing inter-relationship with each other. The importance of SEM in data analysis has been pointed by various researchers. In this regard, Fan et al. (2016) considered SEM, as a strong multivariate technique being used in various scientific investigations to comprehend and evaluate the causal structural relationships. It is a unique estimator, as, at the same time, it finds out direct, indirect, and total effects of the causal relationship. Byrne (1998) argued that SEM is an appropriate and robust model and clearly resolved the structural relationship of the variables in a single attempt approach. Rehman et al. (2021) also highlighted the significant contribution of SEM in structural relationship of the variables. Likewise, Hair et al. (2006) justified the appropriateness of SEM as a fit estimator when a researcher determines a structural relationship among various variables. Bryan (1990) also confirmed the reliability of SEM in the time when it delivers better fit indices. On the basis of the aforementioned justification, hence, we applied SEM approach to conduct the data analysis to comprehend the inter relationship of trade, CO2 emissions, energy consumption, and economic growth in these regions (Figure 9).
FIGURE 9. Conceptual model of the study.
Empirical Analysis
Diagnostic Tests
We performed various data diagnostic tests to validate the data and declare its suitability for further statistical estimation techniques. We applied Wooldridge test to know the serial correlation in the data. In this regard, we performed a separate test on each panel, and, in both cases, the reported values of the test are Prob > F = 0.087, for panel A, and Prob > F = 0.093, for panel B. These values show that there is no autocorrelation in the data and the data is free from such problem. Further, it provides signal for statistical tools to be used. We also conducted Breusch–Pagan/Cook–Weisberg test for heteroscedasticity and treated both samples separately for understanding the problem of heteroscedasticity in the data. We obtained Prob > chi2 = 0.0890, for panel A, and Prob > chi2 = 0.0760, for panel B. The results confirm that there is no existence of hetero problems in the data.
Correlation Analysis of Southeast Asian Countries’ Panel
Table 1 shows the correlation analysis of Southeast Asian Countries for the observed correlation of various variables used in this study. The results demonstrate a strong and positive correlation between trade and CO2 in the Southeast Asian panel. Similarly, trade also shows a positive strong correlation with energy and GDP (Trade with Energy, r = 0.563, p < 0.01, and Trade with GDP, r = 0.619, p < 0.01). CO2 shows a positive strong correlation with energy consumption and a positive weak, moderate correlation with GDP (Cohen, 1988). However, energy shows a positive but insignificant correlation with GDP (r = 0.077, p > 0.01).
TABLE 1. Correlation SEAC panel.
Goodness of Fit Measures
Table 2 shows the goodness of fit measures for the structural model for Southeast Asian countries, various variables relationship. The reported value of root mean square error of approximation (RMSEA) is 0.061, which is an acceptable level for the fitness of the model, as Hair et al. (2006) suggested the acceptable value of RMSEA as 0.08 or less. RMR also shows the feasible level of value, as lower than 0.05, is a good fit as for as model fitness is concerned. Hair et al. (2006) recommended ≥0.90 for concerned CFI, NFI, GFI, and TLI. However, the observed values of the model are above the threshold, suggesting that the model is fit.
TABLE 2. Southeast Asian countries: Goodness of fit measure.
Regression Weights/Path Analysis for Testing Hypothesis
Table 3 depicts the regression path of different variables on one another, generated through SEM, for the hypothesis testing. The results demonstrate that trade has a positive and statistically significant effect on energy consumption (p < 0.05), CO2 emissions (p < 0.05), and GDP (p < 0.05). Energy consumption also exhibits a significant positive effect on CO2 emissions in the Southeast Asian region. Whereas, energy consumption shows a positive but statistically insignificant effect on the GDP of the countries in the Southeast Asian belt. The results also predict a significant positive impact of CO2 emissions on GDP in this region.
TABLE 3. Regression weight SEA region.
Correlation Analysis of Latin American Countries’ Panel
Table 4 shows the correlation analysis of Latin American countries. The reported correlation depicts the relationship of various variables. The results predict a strong and positive correlation between trade and CO2 emissions in these countries’ panel. Likewise, trade also shows a strong positive correlation with GDP (trade with GDP, r = 0.419). However, trade shows a positive but weak correlation with energy consumption. CO2 reports a strong correlation with energy consumption and a moderate correlation with GDP (Cohen, 1988). In contrast, energy shows a positive and strong correlation with GDP.
TABLE 4. Correlation Latin American panel.
Goodness of Fit Measures
Table 5 shows the goodness of fit measures for the structural model for Latin American countries, various variables relationship. The reported value of the RMSEA is 0.068, which stands valid for the fitness of the model, as Hair et al. (2006), in their research, recommended that the acceptable value of RMSEA is less than 0.08. RMR also shows a value lower than 0.05, which shows a good fit for the fitness of the model. Hair et al. (2006) recommended ≥0.90 for various indices, i.e., CFI, NFI, GFI, and TLI. However, the observed values of the model are above the threshold, suggesting that the model is fit.
TABLE 5. Latin American: Goodness of fit measure.
Regression Weights/Path Coefficients for Testing Hypothesis
Table 6 portrays the path analysis of SEM, explaining the impact of one variable on another in the context of Latin American countries. The results demonstrate that trade liberalization has a positive but statistically insignificant effect on energy consumption. The results of our study align with many previous studies, showing the positive nexus between trade and energy consumption (Baek and Kim, 2013; Al-mulali and Sheau-Ting, 2014; Seker et al., 2015). Likewise, trade shows a positive but statistically significant effect on CO2 and GDP in the context of the Latin American region. Exploring the nexus of similar variables, Muhammad (2019) and Shakeel et al. (2013) validated the positive nexus of trade with carbon dioxide emission and GDP. Energy consumption also shows a positive statistically significant effect on CO2 emissions and GDP in Latin American countries. The same kind of nexus of these variables has been featured by previous studies (Shakeel et al., 2013; Ozcan et al., 2020). Although, energy consumption indicating a positive and statistically insignificant effect on the GDP of the countries in the Latin American region. The outcomes also predict a significant positive effect of CO2 emissions on GDP in these countries. Shakeel et al. (2013) and Muhammad (2019) validated the positive nexus between CO2 emissions and GDP.
TABLE 6. Regression weights for Latin American region.
Conclusion
Trade liberalization has been adopted in both Latin American and Southeast Asian countries, which significantly contributed to the development in those regions. Besides, trade liberalization has been found as a key determinant of environmental quality, which significantly contributing to CO2 emissions, energy consumption, and economic growth in any country. Therefore, this study investigates the nexus between trade liberalization, CO2 emissions, energy consumption, and economic growth in Southeast Asian countries and Latin American Countries for the period of 1991 to 2018. We applied SEM, which is a strong sophisticated estimation technique that provides better statistical results as compared to the conventional estimation techniques. Moreover, SEM is considered as a suitable estimation method, especially when variables are determined to affect each other. The empirical results confirmed that trade has a positive and statistically significant effect on energy consumption, CO2 emissions, and GDP in southeast countries, and it implies that trade expansion results in a high level of energy consumption, CO2 emissions, and GDP. The results are in line with many previous studies Muhammad (2019) Shakeel et al. (2013). However, in the context of Latin American countries, trade openness shows a positive but statistically insignificant effect on energy consumption, but the coefficients for both CO2 and GDP are statistically significant. Energy consumption also exhibited a positive significant effect on CO2 emissions, but the GDP coefficient is insignificant in Southeast Asian countries. The results of this study confirmed the previous studies’ findings such as Ozcan et al. (2020) and Shakeel et al. (2013), who predict similar results. However, our results in the context of Latin American countries demonstrated a positive and significant effect on energy consumption in both CO2 emissions and GDP. The results also predicted a positive significant effect of CO2 emissions on GDP in both regions. A similar relationship of these variables was registered in many previous studies (Ameyaw and Yao, 2018; Hasson and Masih, 2017).
The study has some policy suggestions; the Southeast Asian and Latin American countries should mitigate the CO2 emissions by levying specific taxes for CO2 reduction and robust trade liberalization strategies. The governments in these regions should have a special focus on those industries that significantly contribute to CO2 emissions and adopt green technologies in the production and export sector. Moreover, governments in both regions should encourage the use of green technologies by providing special incentives and loan provisions at favorable rates to those industries driven toward the use of environment-friendly technologies. Governments in these regions should implement regulations that will support those industries that emit fewer amounts of CO2 emissions. These policy frameworks will not only decrease CO2 emissions but will assist in achieving sustainable development goals of numerous countries in these regions.
Moreover, the countries should encourage green energy to make the environment health friendly, which will help in improving the labor productivity and long-term development. Future studies can be conducted to extend the literature by analyzing these variables using cases of various regions, i.e., SAARC, MENA, GULF, BRICS, Middle East, and Sub-Saharan countries. In future studies, assessing moderating and mediating variables is also suggested to conduct a unique robust study.
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at https://databank.worldbank.org/source/world-development-indicators.
Author Contributions
MZ: visualization, data curation, and writing the original draft. JH: writing review, conceptualization, data curation, and editing. AR: methodology, visualization, supervision, and editing. IU: review and editing. FA: writing review and editing and software. ZF: review writing and proofreading of the whole draft.
Funding
National Social Science Fund Project (18BJL032).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
I would like to extend my appreciation to my supervisor JH for his help and supervision in making sure this work has been carried out in a timely and appropriate method.
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Keywords: trade liberalization, CO2 emissions, energy consumption, economic growth, structural equation modeling
Citation: Zeeshan M, Han J, Rehman A, Ullah I, Afridi FEA and Fareed Z (2022) Comparative Analysis of Trade Liberalization, CO2 Emissions, Energy Consumption and Economic Growth in Southeast Asian and Latin American Regions: A Structural Equation Modeling Approach. Front. Environ. Sci. 10:854590. doi: 10.3389/fenvs.2022.854590
Received: 14 January 2022; Accepted: 24 January 2022;
Published: 28 February 2022.
Edited by:
Magdalena Radulescu, University of Pitesti, RomaniaReviewed by:
Sher Ali, Islamia College University, PakistanZeeshan Ahmed, University of Lahore, Pakistan
Copyright © 2022 Zeeshan, Han, Rehman, Ullah, Afridi and Fareed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Irfan Ullah, irfanecon@nuist.edu.cn; Zeeshan Fareed, zeeshanfareed@zjhu.edu.cn
†ORCID: Muhammad Zeeshan, orcid.org/0000-0003-3525-9429; Jiabin han, orcid.org/0000-0003-3000-4355; Alam Rehman, orcid.org/0000-0001-9490-979X; Irfan Ullah, orcid.org/0000-0003-3961-888X; Fakhr E. Alam Afridi, orcid.org/0000-0001-9776-5074