A Quantum Leap Towards Sustainability? Exploring the Interplay between Green Trade Exports and Environmental Performance in OECD Countries

A Quantum Leap Towards Sustainability? 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Exploring the Interplay between Green Trade Exports and Environmental Performance in OECD Countries Chang Hwan Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4270045/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The pursuit of economic growth, environmental pollution reduction, and the achievement of sustainable development are central concerns for numerous countries. In 2001, the WTO proposed the elimination of non-tariff barriers on environmental goods and services to mitigate trade barriers and reduce pollutant emissions, thereby enhancing the global trade industry chain. Several scholars have scrutinized the consequences of green trade on sustainable development. This study centers on assessing the impact of green trade exports (GTE) on green total factor productivity (GTFP) and greenhouse gas emissions, utilizing a panel dataset for OECD countries. Initially, a linear regression model is employed to observe that GTE fails to contribute to GTFP and is ineffective in mitigating CO 2 emissions. The relationship between GTE and GTFP exhibits an inverted N-shaped curve. Subsequently, a non-linear threshold model is established, revealing that GTE can foster GTFP growth when clean energy and research and development (R&D) exceed the first threshold value. Consequently, an augmentation in clean energy and technological intensity can lead to sustainable development in OECD nations. This study offers vital insights for developing countries seeking to participate effectively and efficiently in the global industrial chain, thereby reducing domestic development costs. Green trade exports Green total factor productivity (GTFP) Sustainable development Clean energy Research and development (R&D) Figures Figure 1 1. Introduction Human activities have exacerbated global warming through greenhouse gas emissions, traditional energy consumption, land use practices, lifestyles, and consumption trends, impacting regions and nations worldwide. These activities significantly contribute to rising global greenhouse gas levels (IPCC, 2023 ). Ahmed and Le ( 2020 ) contend that human activity is the primary driver of environmental deterioration. In 2015, the United Nations declared that international trade plays a pivotal role in global sustainability by advancing nine environmental sustainable development goals (SDGs) (Xu et al., 2020 ). International trade not only alleviates regional resource constraints but also stimulates economic growth and enhances social welfare (Steen-Olsen et al., 2012 ; Blanco and Razzaque, 2009 ). While the reduction of trade barriers has improved global commerce, spurring economic activity (Buysse et al., 2018 ) and influencing CO 2 emissions (Xie and Wu, 2021 ), trade expansion can escalate environmental pressures (Mrabet et al., 2021 ). This, in turn, can negatively impact social well-being due to increased CO 2 emissions. Notably, as environmental regulations become more lenient, it is becoming increasingly common for economically disadvantaged nations to serve as global manufacturing hubs (Ahmad, Jabeen, and Wu, 2021 ), resulting in an environmental and economic development disparity between developed and developing countries. Consequently, the World Trade Organization announced tariff-free treatment for environmental goods to create more equitable opportunities for developing countries. While some researchers argue that green trade exports (GT exports) can more efficiently reduce greenhouse gas emissions (Li et al., 2022 ; Liu et al., 2022c ; Huang et al., 2020 ), others hold opposing views (Hu et al., 2020 ; Wan et al., 2018 ). Green commodities hold the potential to promote long-term environmental sustainability by reducing energy consumption during the manufacturing process (Paramati et al., 2020 ). In 2008, the World Bank suggested that green trade is less detrimental to the environment (World Bank, 2008 ). However, Zugravu-Soilita ( 2018 ) asserts that while GT exports may reduce CO 2 emissions, they can indirectly increase water contamination. Furthermore, some scholars raise concerns about potential environmental hazards associated with Green Trade (GT) exports, stemming from unforeseen adverse effects in the manufacturing process (Liu et al., 2022a ; Wan and Wen, 2017 ; Wan et al., 2018 ). Green total factor productivity (GTFP) serves as a crucial indicator of the quality of economic growth. While several studies have explored the relationship between GT products and the environment, there is limited research on the impact of GT exports on GTFP, as highlighted by Liu et al. ( 2022a ). Environmental factors can potentially hinder GTFP growth in China. However, the negative impact of trade can be mitigated by promoting the equitable distribution of regional resources and strengthening environmental controls. Despite these considerations, there is a scarcity of research on industrialized nations exploring the relationship between GT exports and GTFP. Liu et al. ( 2023 ) support the notion that reducing GT barriers can enhance agricultural GTFP, and the scale of agricultural trade can significantly affect agricultural GTFP. Given the dearth of literature on GT trade and GTFP, most studies have only examined linear relationships, leading to limited conclusions. The purpose of this paper is to investigate the linear relationship between GT exports and GTFP as a baseline model, using data from 37 OECD countries spanning from 2003 to 2016, addressing this research gap. As the second objective, we introduce clean energy and research and development (R&D) as threshold variables to examine the non-linear relationship between GT exports and GTFP. Clean energy and R&D are chosen as threshold variables because of their substantial influence on Green Total Factor Productivity (GTFP) growth. In addition to the GTFP variable, we also assess the impact of GT trade on traditional total factor productivity (TFP), CO 2 emissions reduction, and PM2.5. Our empirical results indicate that GT exports impede the development of GTFP and traditional TFP while failing to reduce CO 2 and PM2.5 emissions. Notably, when considering clean energy and R&D as threshold variables, they exacerbate the adverse impact on GTFP at the initial threshold stage, but strongly enhance GTFP beyond the first threshold value. The choice of OECD countries is motivated by the fact that most OECD countries are developed nations that prioritize low-carbon research and environmental issues, often offering government financial support (Can et al., 2021b ). This choice allows us to examine whether GT exports can promote GTFP development in OECD countries, which is of significant concern to developing nations. The remainder of this study is organized as follows: Section 2 presents the literature review; Section 3 covers data, GTFP measurement methodology, and the empirical model; Section 4 presents the model findings and discussion; and Section 5 provides the conclusion. 2. Literature Review 2.1 International Trade and Environment The relationship between international trade and environmental pollution has come to the forefront with the globalization of trade (Xie and Wu, 2021 ). However, the findings regarding this relationship are not consistently clear. As global trade expands, it can directly impact the environment, resulting in increased pollution and the depletion of natural resources. This phenomenon has given rise to the "pollution-haven hypothesis," suggesting that developing countries with diverse and stringent environmental policies may experience a rise in pollution due to trade globalization. Conversely, the advancement of international trade and investment has the potential to enhance environmental quality by fostering economic growth and social welfare, which are integral aspects of effective environmental management (OECD). Furthermore, participation in trade globalization can influence a country's pollution emissions per capita or per unit of gross domestic product (GDP), promoting cleaner production processes and the adoption of environmentally friendly technologies to reduce pollution (Antweiler et al., 2001 ). Divergent viewpoints argue that carbon dioxide and sulfur dioxide emissions are the primary contributors to climate change and air pollution, with international trade primarily contributing to the reduction of carbon dioxide emissions (Ma and Wang, 2021 ). However, Lin ( 2017 ) observed that trade openness might increase concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and aerosols. Studies by Omri et al. ( 2015 ) and Tamazian and Rao ( 2010 ) also suggest that international trade can lead to increased CO 2 emissions. Additionally, international trade can have indirect effects on the environment by enhancing labor productivity, competitiveness, and resource efficiency, thereby reducing pollution emissions in OECD countries (Erdogan, 2014 ; Cole, 2004 ). 2.2 Green trade and environment In recent years, the concept of 'green trade' has garnered significant attention. Green trade represents a nation's commitment to environmental protection and sustainable development through the production and export of environmentally friendly goods (PAGE, 2017a; European Parliament, 2019 ). Moreover, the liberalization of green trade offers a promising pathway to achieve a triple win – benefiting trade, the environment, and sustainable growth in countries like China (Yu, 2007 ). Liu et al. ( 2022b ) have noted that green trade can effectively mitigate environmental pollution, regardless of whether it involves import or export activities, thereby contributing to the preservation of China's environment. Similar to traditional international trade, green trade also has indirect environmental impacts, primarily through its influence on income. While green trade can reduce carbon dioxide (CO 2 ) emissions, it can simultaneously lead to an increase in water pollution due to this indirect effect (Zugravu-Soilita, 2018 ). Green trade policies are instrumental in reducing the consumption of natural resources, as confirmed by the results of Granger causality tests showing that natural resources contribute to green trade (Huang and Zhao, 2022 ). Huang et al. ( 2020 ) have proposed that green trade could reduce economic reliance on natural oil, foster the conservation of natural resources, and promote sustainable development. In 2021, Can et al. ( 2021a ) introduced the concept of the green openness index, which pertains to the export of environmentally friendly goods within a given region. The green openness index plays a pivotal role in environmental protection, especially in OECD countries, where it has been observed that greater green openness significantly affects environmental protection (Can et al., 2021b ). Increasing the number of environmentally friendly patents has been shown to reduce CO 2 emissions (Hashmi, R., and Alam, K., 2019 ). However, Li et al. ( 2022 ) arrived at contrasting findings, suggesting that green trade can substantially reduce pollution emissions based on a panel dataset of China spanning from 2007 to 2016, employing the SYS-GMM model. 2.3 Green trade and green total factor productivity Environmentally friendly goods can be categorized into two main groups: traditional and environmentally preferable products (Liu et al., 2022a ). Traditional environmental goods often consist of innovative and intricate products, as noted by Hamwey ( 2005 ). According to the United Nations Environment Programme (UNEP, 2018 ), traditional environmental goods encompass five subgroups: air pollution, wastewater management, solid and hazardous waste management, and clean technologies and resources. These products are designed to be used for environmental protection, but they can still induce pollution, as seen in the production of items like wind turbines (Liu et al., 2022a ; May et al., 2021 ). In contrast, environmentally preferable products include natural dyes, natural rubber, jute, and sisal fibers, which, due to their superior environmental qualities compared to available alternatives, are more attractive options for developing countries (Melo & Vijil, 2014 ). Recent studies have yielded noteworthy findings in this realm. Hao et al. (2020) concluded that the impact of green productivity growth on CO 2 emissions is decreasing, underscoring the contribution of green growth to environmental improvements in G7 countries. Cheng and Kong ( 2022 ), employing panel data from 30 regions spanning from 2000 to 2019, asserted that the Chinese government must shift away from traditional extensive industry structures, promote green industries, and enhance production efficiency to ensure sustainable development in China. In a study by Liu et al. ( 2022a ) that examined the impact of green trade on green total factor productivity (GTFP) using panel data from China for the period 2003 to 2015, it was found that green trade does not exert a strong influence on sustainable and green growth in China and does not significantly reduce pollution, such as CO 2 and PM2.5. Interestingly, when green trade exceeds the second threshold, its effect on GTFP becomes positive. Drawing from the existing literature, it's evident that the discourse in this field has been growing, offering valuable insights. Primary research areas include investigating whether green trade can enhance environmental quality by reducing emissions of CO 2 , SO 2 , and PM2.5 (Liu et al., 2022a , 2022b ; Yu, 2007 ; Xie and Wu, 2021 ; de Alwis, 2014 ). The question of whether trade can lead to pollution havens in developing countries has also been explored (Abid and Sekrafi, 2021 ; Brunnermeier and Levinson, 2016 ). However, the study of the relationship between green trade and green total factor productivity remains limited. Consequently, to address this gap in the current literature, we measure the GTFP index in OECD countries and construct models to analyze the impact of green trade on both the environment and GTFP. 3. Empirical Research 3.1 Measurement of green total factor productivity Traditional methods of measuring total factor productivity typically exclude undesirable outputs like CO 2 , SO 2 , and industrial dust. However, Green Total Factor Productivity (GTFP) offers a more comprehensive approach to assess the efficiency of economic growth, taking into account waste and pollution outputs. Therefore, GTFP provides a more accurate reflection of the genuine quality of economic growth. The primary method for calculating GTFP involves the use of Data Envelopment Analysis (DEA), a non-parametric approach used to compare the efficiency of decision-making units (DMU) (Zhu, 1998). Tone (2001) introduced a non-oriented SBM (Slacks-based Measure) model to address slack variable issues and enhance accuracy. In 2007, Tone et al. (2007) proposed an innovative SBM that incorporates unexpected output variables, offering an indicator of sustainable environmental and economic growth. However, this GTFP calculation method is not suitable for dynamic research. To facilitate dynamic analysis, Chung et al. (1997) introduced the Malmquist Luenberger index, but this method is challenged by variations in production technologies. To address this heterogeneity, Oh and Lee (2010) established the Malmquist meta-frontier index, though it still grapples with infeasibility issues. To mitigate these infeasibility challenges, Oh ( 2010 ) devised the Global Malmquist-Luenberger productivity index (GML), a more efficient measure of GTFP. Consequently, we have opted to employ the SBM-GML index method, with an input orientation, to calculate the GTFP index. Table 1 presents the input, expected output, and unexpected output variables used in this calculation. Table 1 Input and output variables for measuring GTFP Vector Indicator Unit Data source Input variable Capital stock million/10 6 World Bank Labor force persons/10 4 persons/10 3 World Bank Oil Ton World Energy Agency Expected output variable GDP kw·h/10 6 million/10 6 World Bank Unexpected output variable CO 2 Ton/10 4 Ton/10 3 World Bank 3.2 Explanatory variable The explanatory variables include green trade, clean energy, gross domestic product (GDP), unemployment rate, industrial structure, foreign direct investment, and population. Specifically, the core explanatory variable is green trade, and all variables are defined in the following sections. 3.2.1 Core explanatory variable To date, no consistent definition is available in the existing literature of which products are in the basket of green trade (Can et al., 2021b ). Diverse ranges of green-trade products are revealed, according to several international organisations. For example, the Asia-Pacific Economic Cooperation (APEC, 2012 ) produced 54 green products on the “APEC List of Environmental Goods”. Whereas, the OECD’s “Combined List of Environmental Goods” (CLEG) covers 40 goods, consisting of 255 products, which is the largest green product basket (Can et al., 2021b ). According to the OECD’s CLEG list, there are 11 primary categories for environmental goods: 1) air pollution control; 2) cleaner or more resource-efficient technologies and products; 3) environmentally preferable products based on end-use or disposal characteristics; 4) heat and energy management; 5) environmental monitoring, analysis, and assessment equipment; 6) natural resource protection; 7) noise and vibration abatement; 8) renewable energy plants; 9) management of solid and hazardous waste and recycling systems; 10) clean-up or remediation of soil and water; and 11) wastewater management and potable water treatment. Here, we adopt all the above values for exporting environmental goods to total exports to calculate the value of green trade. Data related to green trade were collected from OECD statistics. 3.2.2 Other variables Our model had six control variables: (1) clean energy (CE), percent of renewable energy consumption of total final energy consumption; (2) economic growth level (GDP), proxied by the rate of national GDP growth; (3) unemployment rate (UN), percent of the total labour force; (4) industrial structure (IS), percent of GDP; (5) foreign direct investment (FDI), net flow of percent GDP; and (6) population density (pop), people per sq. km of land area. Data were collected from the World Bank. 3.3 Model specification 3.3.1 GT and GTFP In the existing literature, most studies focus on the relationship between green trade, GTFP, and the environment. A fixed model and system-generalised method of moments (GMM) were adopted. This study aims to evaluate whether green trade can promote an increase in GTFP. Can environmental pollutant emissions decrease? For this purpose, the following models were established: To avoid endogeneity, we reduce the bias by omitting time and individual effects. We adopted a fixed model to establish Model 1 as follows: $${GTFP}_{it}=\alpha +{\beta }_{1}{GT}_{it}+{\beta }_{2}{CE}_{it}+{\beta }_{3}{GDP}_{it}+{\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}{+\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+\mu +\eta +ϵ$$ 1 To solve the problem of reverse causality between GT and GTFP, for Model 2, we also lagged GT it by one and two years. This model is shown in Models 2 and 3. $${GTFP}_{it}=\alpha +{\beta }_{1}{GT}_{it-1}+{\beta }_{2}{CE}_{it}+{\beta }_{3}{GDP}_{it}+{\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}{+\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+\mu +\eta +ϵ$$ 2 $${GTFP}_{it}=\alpha +{\beta }_{1}{GT}_{it-2}+{\beta }_{2}{CE}_{it}+{\beta }_{3}{GDP}_{it}+{\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}{+\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+\mu +\eta +ϵ$$ 3 Furthermore, to further study the relationship between GT and GTFP, in Model 4, we construct a linear parametric fixed effect model with the quadratic cubic polynomial of GT based on Model 1 to ensure the status of the OECD country. This model can be expressed as follows: $${GTFP}_{it}=\alpha +{\beta }_{1}{GT}_{it}+{\beta }_{2}{GT}_{it}^{2}+{\beta }_{3}{GT}_{it}^{3}+{\beta }_{4}{CE}_{it}+{\beta }_{5}{GDP}_{it}+{\beta }_{6}{UN}_{it}+{\beta }_{7}{IS}_{it}{+\beta }_{8}{FDI}_{it}{+\beta }_{9}{POP}_{it}+\mu +\eta +ϵ$$ 4 Based on the linear regression model, to elucidate the relationship between the GT and GTFP, we established a threshold model to explore the non-linear relationship between GT exports and GTFP, which can be expressed as Models 5 and 6. $${GTFP}_{it}={\mu }_{it}+{\beta }_{1}{GT}_{it}\times I\left({CE}_{it}\le \gamma \right)+{{\beta }_{2}{GT}_{it}\times I\left({CE}_{it}>\gamma \right)+{\beta }_{3}{GDP}_{it}+\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}+{\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+{ϵ}_{it}$$ 5 $${GTFP}_{it}={\mu }_{it}+{\beta }_{1}{GT}_{it}\times I\left({R\&D}_{it}\le \gamma \right)+{{\beta }_{2}{GT}_{it}\times I\left({R\&D}_{it}>\gamma \right)+{\beta }_{3}{GDP}_{it}+\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}+{\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+{ϵ}_{it}$$ 6 Here, i and t, indicate the country and year, CE it, R&D it is the threshold variables and \(\gamma\) represents the calculated threshold value. \(\text{I}(.)\) is the indication coefficient, \({{\beta }}_{1}, {{\beta }}_{2}\) is the coefficient of GT it , when the threshold value is different in each stage. 3.3.2 GT and CO 2 To explore whether GT can directly decrease CO 2 emissions and contribute to sustainable development. We took the emission of CO 2 as the explained variable, as in Model 5. In addition, we lagged GT it by one and two years to test the reverse causality between CO 2 and GT, as in Models 7, 8, and 9. $${CO}_{2it}=\alpha +{\beta }_{1}{GT}_{it}+{\beta }_{2}{CE}_{it}+{\beta }_{3}{GDP}_{it}+{\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}{+\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+\mu +\eta +ϵ$$ 7 $${CO}_{2it}=\alpha +{\beta }_{1}{GT}_{it-1}+{\beta }_{2}{CE}_{it}+{\beta }_{3}{GDP}_{it}+{\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}{+\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+\mu +\eta +ϵ$$ 8 $${CO}_{2it}=\alpha +{\beta }_{1}{GT}_{it-2}+{\beta }_{2}{CE}_{it}+{\beta }_{3}{GDP}_{it}+{\beta }_{4}{UN}_{it}+{\beta }_{5}{IS}_{it}{+\beta }_{6}{FDI}_{it}{+\beta }_{7}{POP}_{it}+\mu +\eta +ϵ$$ 9 4. Results and Discussion 4.1 Linear results and quadratic and cubic polynomials of GT Table 2 lists the basic results of Models 1, 2, 3, and 4. Column (1) shows the results of the fixed effect model. Columns (2) and (3) show the results of lagged GT for one and two years, respectively. These results reveal that the export of GT cannot promote the development of GTFP and even has an evident negative effect on green growth (Liu, et al., 2022). Among the control variables, clean energy, unemployment, and FDI have positive effects on GTFP. Otherwise, the level of economic growth has a negative influence on GTFP. First, clean energy can promote green growth because it can reduce the use of gas, coal, and fuel, and reduce pollution emissions to achieve environmental sustainability (Alper and Oguz., 2016, Ahmed, et al., 2022 ). Second, FDI has a positive economic significance on GTFP, which can be explained that FDI exerts the positive spill-over effect of FDI on GTFP (Zhou, 2019). The economic level can decrease GTFP because the economy may be characterised by “high pollution, high consumption, high emission” in the early stage (Wang, et al., 2020 ). The output of the GT's quadratic cubic polynomial is shown in Column (6). It reveals that significant GT and GT2 coefficients at the 1% and 2% levels and their positive and negative signs, respectively, point to a "U-shaped" link between GT and GTFP. Model 4 has two turning points, and the coefficients of GT, GT2, and GT3 are statistically significant. The signs of the coefficients are negative, positive, and negative, indicating an inverse "N-shaped" relationship between GT and GTFP. Basic Models 1 and 4 exhibited the same GT symptoms. GT and GTFP were in stage 3, and a negative association was observed between GT and GTFP. GT exports stimulate GTFP growth in the initial stage because of insufficient absorption capacity. Because of technological breakthroughs, GT exports exceed the first turning point, and encourage the development of GTFP. With the progressive expansion of trade in the third stage, trade development reached a bottleneck. Table 2 Results of baseline models and polynomial models GTFP GTFP GTFP GTFP GTFP Green trade -0.845 * (0.456) -2.997 *** (1.073) -6.680 *** (2.428) Green trade t−1 -1.201 *** (0.459) Green trade t−2 -1.184 ** (0.462) Green trade squared 10.242 ** (4.626) 49.764 ** (23.831) Green trade cubic -111.03 * (65.678) Clean energy 0.005 * (0.003) 0.006 ** (0.003) 0.006 * (0.003) 0.005 * (0.003) 0.005 * (0.003) GDP -0.005 * (0.003) -0.004 * (0.003) -0.004 (0.003) -0.004 * (0.003) -0.004 * (0.003) UN 0.019 *** (0.003) 0.019 *** (0.003) 0.020 *** (0.003) 0.019 *** (0.003) 0.019 *** (0.003) Industry structure 0.008 * (0.005) 0.007 (0.005) 0.006 (0.005) 0.007 (0.005) 0.006 (0.005) FDI 0.002 * (0.001) 0.002 * (0.001) 0.003 ** (0.001) 0.002 * (0.001) 0.002 ** (0.001) POP 0.002 (0.001) 0.002 (0.001) 0.002 (0.001) 0.002 * (0.001) 0.002* (0.001) Country-fixed Yes Yes Yes Yes Yes Year-fixed Yes Yes Yes Yes Yes N 518 481 444 518 518 R 2 0.646 0.620 0.593 0.650 0.652 Note: Robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1 4.2 Threshold effect of Green trade on GTFP 4.2.1 Threshold effect test and threshold value estimation In Models 5 and 6, we investigate whether cleaner energy and R&D have a threshold effect on GT exports and GTFP. Hence, we tested single-, double-, and triple-threshold values and used 500 bootstrapping iterations because of the small sample size. Table 3 presents the F-statistics and significance level of the p-value, which reveal that clean energy R&D is significant for a single threshold. The single threshold value for clean energy is 8.660. The single threshold value for R&D level was 0.664. Furthermore, we tested the confidence intervals of the threshold variables, as shown in Fig. 1 . Table 3 Single threshold of green trade Threshold variable Independent variable Threshold value Hypothetical test F-statistics Critical values (90%) Critical values (95%) Critical values (99%) Clean energy Green export 8.660 Single 79.98 ** 56.031 68.681 102.744 R&D Green export 0.664 Single 56.30 * 55.262 67.019 86.625 4.2.2 Results of threshold regression Table 4 presents the nonlinear relationship between green trade and GTFP in OECD countries using different threshold variables. Column (1) shows that for clean energy, the effect of GT exports on GTFP changes from negative to positive. When clean energy is below the first threshold value of 8.660, the effect of GT exports on GTFP is significant at a 5% level, and the coefficient is -1.518. This indicates that as clean energy is at the first threshold stage, an increase of one unit in GT exports will decrease the GTFP by 1.518 units. However, when clean energy exceeds the first threshold value, the effect of GT exports is positive (1.056) at the 10% level. Column (2) reveals that as the level of R&D changes, the coefficient of GT exports on GTFP will change from − 3.165 to 1.193. It appears that the effect of GT exports on GTFP is stronger and more positive with the effect of R&D level. In. In conclusion, it appears that clean energy and R&D promote the growth of GTFP when it exceeds the first threshold. Table 4 Results of threshold regression GTFP GTFP Green trade (energy ≤ 8.660) -1.518 ** (0.630) Green trade (energy ≥ 8.660) 1.056 * (0.563) Green trade (R&D ≤ 0.664) -3.165 *** (0.805) Green trade (R&D ≥ 0.664) 1.193 ** (0.576) GDP -0.004 (0.002) -0.004 (0.003) UN 0.027 *** (0.003) 0.025 *** (0.003) Industry structure -0.014 ** (0.006) -0.021 *** (0.006) FDI 0.002 (0.002) 0.002 (0.002) POP 0.009 *** (0.001) 0.009 *** (0.001) N 518 518 Note: Robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1. 4.3 Results of green trade and TFP, pollutant emissions As shown in Table 5 , GT exports do not have an obvious effect on TFP, which is measured using the same method as GTFP and does not include unexpected variables. Therefore, it can be explained that the inefficiency of GTFP is likely due to the poor environmental performance of GT exports (Liu, et al., 2022). Furthermore, we investigated the effects of GT exports on environmental indicators. Columns (4) and (5) show that, although GT exports do not have a significant effect on increasing CO 2 emissions in the current period, they have an evident influence on increasing CO 2 emissions through the time-lagged effect. Compared to the coefficient of GT exports, the effect of GT exports with a time lag is more influential than that in the current period. Compared with Columns (7) and (8), the coefficient of GT exports is not statistically significant, but still has an economic impact on increasing PM2.5. Clean energy is an important issue that can contribute to environmental protection and improve green economic growth (Ahmed, et al., 2022 ). Hence, we adopted clean energy as an interaction term to test the interaction effects of clean energy on TFP, CO 2 emissions, and PM2.5. Column (3) shows that the interaction between clean energy and GT exports did not have a significant effect on TFP improvement. By comparing Columns (6) and (9), we can see that the interaction effect of clean energy and green trade can decrease the emissions of greenhouse gases and PM2.5. Thus achieving sustainable growth. Table 5 Results of traditional TFP and environmental factors TFP TFP TFP CO 2 CO 2 CO 2 PM2.5 PM2.5 PM2.5 Green trade -0.025 (0.250) 3.252 (2.067) 6.071 (4.137) Green trade t−1 -0.035 (0.255) 4.513 ** (2.059) 4.725 (4.057) Green trade* energy -0.003 (0.007) -0.595 *** (0.065) -0.300 ** (0.117) Clean energy -0.001 (0.002) -0.001 (0.002) -0.156 *** (0.012) -0.155 *** (0.013) -0.157 *** (0.025) -0.147 *** (0.025) GDP 0.003 ** (0.001) 0.003 ** (0.001) -0.023 ** (0.011) -0.020 * (0.011) -0.012 (0.023) -0.029 (0.023) UN -0.001 (0.002) -0.002 (0.002) -0.059 *** (0.013) -0.063 *** (0.013) 0.039 (0.026) 0.026 (0.026) Industry structure -0.006 ** (0.003) -0.007 ** (0.003) 0.089 *** (0.021) 0.077 *** (0.021) 0.028 (0.042) 0.042 (0.041) FDI -0.001 * (0.001) -0.001 * (0.001) -0.003 (0.005) -0.002 (0.005) -0.005 (0.011) -0.0004 (0.011) POP 0.0005 (0.0006) 0.00008 (0.000) -0.029 *** (0.005) -0.031 *** (0.005) -0.046 *** (0.010) -0.050 *** (0.010) Country-fixed Yes Yes Yes Yes Yes Yes Yes Yes Yes Year-fixed Yes Yes Yes Yes Yes Yes Yes Yes Yes N 518 481 518 518 481 518 518 481 518 R 2 0.202 0.194 0.179 0.623 0.625 0.481 0.665 0.620 0.631 Note: Robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1. 4.4 Robustness test To further enhance the credibility of the empirical results, we test the robustness based on baseline models by dropping the 2008 dataset due to the financial crisis because international trade is the main path to the contagion economic crisis (Chote and Daniel, 1998 ). As shown in Table 6 , GT exports still have a negative effect on GTFP when the 2008 dataset is dropped. As for the control variables, clean energy, unemployment, and FDI can also enhance the development of GTFP, and the economic-level variable will decrease GTFP, exactly as in the baseline models; therefore, these models were robust. Table 6 Results of robust test of green trade on GTFP GTFP GTFP GTFP Green trade -0.886 * (0.469) Green trade t−1 -1.197 ** (0.489) Green trade t−2 -1.074 ** (0.492) Clean energy 0.005 * (0.003) 0.006 ** (0.003) 0.006 * (0.003) GDP -0.005 * (0.003) -0.003 * (0.003) -0.004 (0.003) UN 0.018 *** (0.003) 0.019 *** (0.003) 0.020 *** (0.004) Industry structure 0.007 (0.005) 0.007 (0.005) 0.007 (0.005) FDI 0.002 (0.001) 0.002 * (0.001) 0.003 ** (0.001) POP 0.002 (0.001) 0.002 (0.001) 0.002 (0.001) Country-fixed Yes Yes Yes Year-fixed Yes Yes Yes N 481 407 370 R 2 0.655 0.640 0.614 Note: Robust standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1. 5. Conclusions Sustainable development is a common goal amid the increasing conflict between environmental challenges and economic development. As an indicator of green productivity, GTFP incorporates naturalal resources and environmental issues into a total factor productivity analytical framework to balance the quantity and quality of economic growth (Wang, et al., 2020 ; Chen et al., 2019). The GT refers to the production and exportation of environmentally friendly goods to reduce pollution and achieve sustainable development. However, GT exports may cause environmental pollution during the production processes (Liu et al., 2022a ). Consequently, this study examines the effect of GT exports on GTFP and environmental issues using a panel dataset from 2003 to 2016 in OECD nations using linear and nonlinear models. First, we explore the influence of GT exports on GTFP using a fixed effects model. The empirical results show that GT exports have an evident negative effect on GTFP—and Liu et al. ( 2022a ) approached the same results. This can be explained by the fact that environmental products are produced in the process owing to the industrial capabilities (Liu, et al., 2022a ., Wan and Wen, 2017 ., Wan, et al., 2018 ) For the control variables, clean energy, unemployment, FDI, and population can promote the growth of GTFP; otherwise, the level of economic growth will decrease the growth of GTFP due to “high pollution, high consumption, and high emission”. Second, we investigated the relationship between GT exports and GTFP by using the polynomial of GT exports and observed that an inverse “N-shaped” relationship between GT exports and GTFP in OECD countries. When GT exports do not exceed the first turning point, they will decrease the growth of GTFP, and when GT exports are between the first and second turning points, the effect of GT exports on GTFP is positive. When GT exports exceed the second turning point, their effect on GTFP is again negative. Third, this study explores the non-linear relationship between GT exports and GTFP using clean energy and the level of R&D as threshold variables. The results showed that both clean energy and R&D have a single threshold value for GTFP. When the threshold variable is in the first stage, GT exports have a negative effect on GTFP; however, when the threshold value is in the second stage, GT exports can promote growth in GTFP. Hence, according to our results, this study argues that with the development of clean energy and R&D levels, the marginal effect of GT exports on GTFP will be stronger and improve the growth of GTFP. The results of this study also have policy implications for policymakers to promote the growth of GTFP in OECD countries. First, although GT exports have a negative effect on GTFP, GT export enterprises can still contribute to GTFP by supplying more impeccable GT chains globally. Countries should expand the domestic market and alleviate the export dependence of export-oriented industries. Second, governments should establish stricter environmental policies, increase the standards of their environmental standards, and focus on economic and environmental benefits (Wang, et al., 2020 ., Wangand Luo., 2020). Especially in developing countries, the policymakers should rapidly increase the development of the country's tertiary industry and emerging science and technology, reduce the proportion of the domestic secondary industry within a reasonable range, and avoid the country becoming the world's factory. Third, to reduce the intensity of traditional energy use, the government must improve infrastructure construction for clean energy sources, such as wind, water, and solar energy, and increase the intensity of clean energy use. Fourth, the government should increase spending on science and technology education and vigorously improve the country's scientific and technological R&D capabilities, thereby promoting production efficiency. This study proves that GT exports affect GTFP and environmental factors. Nonetheless, it has academic limitations. For example, data relative to the GT are limited, and a small sample size will affect the results. Although OECD countries are primarily developed countries, we do not address the heterogeneity between OECD countries, such as the factors of average income, trade dependence, educational level, and the effect of GT exports on whether GTFP can be promoted. These fields should be explored in future studies. Declarations Funding There is no funding sources. Author information Authors and Affiliations Chang Hwan Choi, Ph.D., Professor, International Trade Dept. Dankook University U.S.A. Attorney at Law (Washington D.C.) 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, 16890, South Korea Economic Building # 615 Tel (82) 31-8005-3379 (e-mail) [email protected] Contributions Chang Hwan Choi : writing—original draft, writing—review and editing, formal analysis, validation. data curation, visualization, supervision, resources Corresponding author Correspondence to Chang Hwan Choi, Ph.D., Ethics declarations Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Conflict of interest The authors declare no competing interests. References Abid M, Sekrafi H (2021) Pollution haven or halo effect? A comparative analysis of developing and developed countries. Energy Repprts, 7 Ahmad M, Jabeen G, Wu Y (2021) Heterogeneity of pollution haven/halo hypothesis and environmental Kuznets curve hypothesis across development levels of Chinese provinces. 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ICTSD Programme on the Trade and Environment Zhou Y, Xu Y, Liu C, Fang Z, Fu X, He M (2019) The threshold effect of China's financial development on green total factor productivity. Sustainability, 11 Zhu J (1998) Data envelopment analysis vs. principal component analysis: an illustrative study of economic performance of Chinese cities. Eur J Oper Res 111:50–61 Zugravu-Soilita N (2018) The impact of trade in environmental goods on pollution: What are we learning from the transition economies’. experience? Environmental Economics and Policy Studies, p 20 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Exploring the Interplay between Green Trade Exports and Environmental Performance in OECD Countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHuman activities have exacerbated global warming through greenhouse gas emissions, traditional energy consumption, land use practices, lifestyles, and consumption trends, impacting regions and nations worldwide. These activities significantly contribute to rising global greenhouse gas levels (IPCC, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ahmed and Le (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) contend that human activity is the primary driver of environmental deterioration. In 2015, the United Nations declared that international trade plays a pivotal role in global sustainability by advancing nine environmental sustainable development goals (SDGs) (Xu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). International trade not only alleviates regional resource constraints but also stimulates economic growth and enhances social welfare (Steen-Olsen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Blanco and Razzaque, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While the reduction of trade barriers has improved global commerce, spurring economic activity (Buysse et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and influencing CO\u003csub\u003e2\u003c/sub\u003e emissions (Xie and Wu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), trade expansion can escalate environmental pressures (Mrabet et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This, in turn, can negatively impact social well-being due to increased CO\u003csub\u003e2\u003c/sub\u003e emissions. Notably, as environmental regulations become more lenient, it is becoming increasingly common for economically disadvantaged nations to serve as global manufacturing hubs (Ahmad, Jabeen, and Wu, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), resulting in an environmental and economic development disparity between developed and developing countries. Consequently, the World Trade Organization announced tariff-free treatment for environmental goods to create more equitable opportunities for developing countries.\u003c/p\u003e \u003cp\u003eWhile some researchers argue that green trade exports (GT exports) can more efficiently reduce greenhouse gas emissions (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), others hold opposing views (Hu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Green commodities hold the potential to promote long-term environmental sustainability by reducing energy consumption during the manufacturing process (Paramati et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In 2008, the World Bank suggested that green trade is less detrimental to the environment (World Bank, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, Zugravu-Soilita (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) asserts that while GT exports may reduce CO\u003csub\u003e2\u003c/sub\u003e emissions, they can indirectly increase water contamination. Furthermore, some scholars raise concerns about potential environmental hazards associated with Green Trade (GT) exports, stemming from unforeseen adverse effects in the manufacturing process (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Wan and Wen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreen total factor productivity (GTFP) serves as a crucial indicator of the quality of economic growth. While several studies have explored the relationship between GT products and the environment, there is limited research on the impact of GT exports on GTFP, as highlighted by Liu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Environmental factors can potentially hinder GTFP growth in China. However, the negative impact of trade can be mitigated by promoting the equitable distribution of regional resources and strengthening environmental controls. Despite these considerations, there is a scarcity of research on industrialized nations exploring the relationship between GT exports and GTFP. Liu et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) support the notion that reducing GT barriers can enhance agricultural GTFP, and the scale of agricultural trade can significantly affect agricultural GTFP. Given the dearth of literature on GT trade and GTFP, most studies have only examined linear relationships, leading to limited conclusions.\u003c/p\u003e \u003cp\u003eThe purpose of this paper is to investigate the linear relationship between GT exports and GTFP as a baseline model, using data from 37 OECD countries spanning from 2003 to 2016, addressing this research gap. As the second objective, we introduce clean energy and research and development (R\u0026amp;D) as threshold variables to examine the non-linear relationship between GT exports and GTFP. Clean energy and R\u0026amp;D are chosen as threshold variables because of their substantial influence on Green Total Factor Productivity (GTFP) growth. In addition to the GTFP variable, we also assess the impact of GT trade on traditional total factor productivity (TFP), CO\u003csub\u003e2\u003c/sub\u003e emissions reduction, and PM2.5. Our empirical results indicate that GT exports impede the development of GTFP and traditional TFP while failing to reduce CO\u003csub\u003e2\u003c/sub\u003e and PM2.5 emissions. Notably, when considering clean energy and R\u0026amp;D as threshold variables, they exacerbate the adverse impact on GTFP at the initial threshold stage, but strongly enhance GTFP beyond the first threshold value. The choice of OECD countries is motivated by the fact that most OECD countries are developed nations that prioritize low-carbon research and environmental issues, often offering government financial support (Can et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). This choice allows us to examine whether GT exports can promote GTFP development in OECD countries, which is of significant concern to developing nations.\u003c/p\u003e \u003cp\u003eThe remainder of this study is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the literature review; Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e covers data, GTFP measurement methodology, and the empirical model; Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the model findings and discussion; and Section \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides the conclusion.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 International Trade and Environment\u003c/h2\u003e \u003cp\u003eThe relationship between international trade and environmental pollution has come to the forefront with the globalization of trade (Xie and Wu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the findings regarding this relationship are not consistently clear. As global trade expands, it can directly impact the environment, resulting in increased pollution and the depletion of natural resources. This phenomenon has given rise to the \"pollution-haven hypothesis,\" suggesting that developing countries with diverse and stringent environmental policies may experience a rise in pollution due to trade globalization.\u003c/p\u003e \u003cp\u003eConversely, the advancement of international trade and investment has the potential to enhance environmental quality by fostering economic growth and social welfare, which are integral aspects of effective environmental management (OECD). Furthermore, participation in trade globalization can influence a country's pollution emissions per capita or per unit of gross domestic product (GDP), promoting cleaner production processes and the adoption of environmentally friendly technologies to reduce pollution (Antweiler et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDivergent viewpoints argue that carbon dioxide and sulfur dioxide emissions are the primary contributors to climate change and air pollution, with international trade primarily contributing to the reduction of carbon dioxide emissions (Ma and Wang, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, Lin (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed that trade openness might increase concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and aerosols. Studies by Omri et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Tamazian and Rao (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) also suggest that international trade can lead to increased CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eAdditionally, international trade can have indirect effects on the environment by enhancing labor productivity, competitiveness, and resource efficiency, thereby reducing pollution emissions in OECD countries (Erdogan, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cole, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Green trade and environment\u003c/h2\u003e \u003cp\u003eIn recent years, the concept of 'green trade' has garnered significant attention. Green trade represents a nation's commitment to environmental protection and sustainable development through the production and export of environmentally friendly goods (PAGE, 2017a; European Parliament, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the liberalization of green trade offers a promising pathway to achieve a triple win \u0026ndash; benefiting trade, the environment, and sustainable growth in countries like China (Yu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Liu et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) have noted that green trade can effectively mitigate environmental pollution, regardless of whether it involves import or export activities, thereby contributing to the preservation of China's environment.\u003c/p\u003e \u003cp\u003eSimilar to traditional international trade, green trade also has indirect environmental impacts, primarily through its influence on income. While green trade can reduce carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions, it can simultaneously lead to an increase in water pollution due to this indirect effect (Zugravu-Soilita, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Green trade policies are instrumental in reducing the consumption of natural resources, as confirmed by the results of Granger causality tests showing that natural resources contribute to green trade (Huang and Zhao, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Huang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have proposed that green trade could reduce economic reliance on natural oil, foster the conservation of natural resources, and promote sustainable development.\u003c/p\u003e \u003cp\u003eIn 2021, Can et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) introduced the concept of the green openness index, which pertains to the export of environmentally friendly goods within a given region. The green openness index plays a pivotal role in environmental protection, especially in OECD countries, where it has been observed that greater green openness significantly affects environmental protection (Can et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Increasing the number of environmentally friendly patents has been shown to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions (Hashmi, R., and Alam, K., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) arrived at contrasting findings, suggesting that green trade can substantially reduce pollution emissions based on a panel dataset of China spanning from 2007 to 2016, employing the SYS-GMM model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Green trade and green total factor productivity\u003c/h2\u003e \u003cp\u003eEnvironmentally friendly goods can be categorized into two main groups: traditional and environmentally preferable products (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Traditional environmental goods often consist of innovative and intricate products, as noted by Hamwey (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). According to the United Nations Environment Programme (UNEP, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), traditional environmental goods encompass five subgroups: air pollution, wastewater management, solid and hazardous waste management, and clean technologies and resources. These products are designed to be used for environmental protection, but they can still induce pollution, as seen in the production of items like wind turbines (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; May et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, environmentally preferable products include natural dyes, natural rubber, jute, and sisal fibers, which, due to their superior environmental qualities compared to available alternatives, are more attractive options for developing countries (Melo \u0026amp; Vijil, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have yielded noteworthy findings in this realm. Hao et al. (2020) concluded that the impact of green productivity growth on CO\u003csub\u003e2\u003c/sub\u003e emissions is decreasing, underscoring the contribution of green growth to environmental improvements in G7 countries. Cheng and Kong (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), employing panel data from 30 regions spanning from 2000 to 2019, asserted that the Chinese government must shift away from traditional extensive industry structures, promote green industries, and enhance production efficiency to ensure sustainable development in China. In a study by Liu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) that examined the impact of green trade on green total factor productivity (GTFP) using panel data from China for the period 2003 to 2015, it was found that green trade does not exert a strong influence on sustainable and green growth in China and does not significantly reduce pollution, such as CO\u003csub\u003e2\u003c/sub\u003e and PM2.5. Interestingly, when green trade exceeds the second threshold, its effect on GTFP becomes positive.\u003c/p\u003e \u003cp\u003eDrawing from the existing literature, it's evident that the discourse in this field has been growing, offering valuable insights. Primary research areas include investigating whether green trade can enhance environmental quality by reducing emissions of CO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and PM2.5 (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Yu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xie and Wu, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; de Alwis, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The question of whether trade can lead to pollution havens in developing countries has also been explored (Abid and Sekrafi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brunnermeier and Levinson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the study of the relationship between green trade and green total factor productivity remains limited. Consequently, to address this gap in the current literature, we measure the GTFP index in OECD countries and construct models to analyze the impact of green trade on both the environment and GTFP.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Empirical Research","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Measurement of green total factor productivity\u003c/h2\u003e \u003cp\u003eTraditional methods of measuring total factor productivity typically exclude undesirable outputs like CO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and industrial dust. However, Green Total Factor Productivity (GTFP) offers a more comprehensive approach to assess the efficiency of economic growth, taking into account waste and pollution outputs. Therefore, GTFP provides a more accurate reflection of the genuine quality of economic growth.\u003c/p\u003e \u003cp\u003eThe primary method for calculating GTFP involves the use of Data Envelopment Analysis (DEA), a non-parametric approach used to compare the efficiency of decision-making units (DMU) (Zhu, 1998). Tone (2001) introduced a non-oriented SBM (Slacks-based Measure) model to address slack variable issues and enhance accuracy. In 2007, Tone et al. (2007) proposed an innovative SBM that incorporates unexpected output variables, offering an indicator of sustainable environmental and economic growth. However, this GTFP calculation method is not suitable for dynamic research. To facilitate dynamic analysis, Chung et al. (1997) introduced the Malmquist Luenberger index, but this method is challenged by variations in production technologies. To address this heterogeneity, Oh and Lee (2010) established the Malmquist meta-frontier index, though it still grapples with infeasibility issues. To mitigate these infeasibility challenges, Oh (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) devised the Global Malmquist-Luenberger productivity index (GML), a more efficient measure of GTFP.\u003c/p\u003e \u003cp\u003eConsequently, we have opted to employ the SBM-GML index method, with an input orientation, to calculate the GTFP index. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the input, expected output, and unexpected output variables used in this calculation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInput and output variables for measuring GTFP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVector\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInput variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapital stock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emillion/10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLabor force\u003c/p\u003e \u003cp\u003epersons/10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epersons/10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Energy Agency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpected output variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003cp\u003ekw\u0026middot;h/10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emillion/10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexpected output\u003c/p\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eTon/10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTon/10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Explanatory variable\u003c/h2\u003e \u003cp\u003eThe explanatory variables include green trade, clean energy, gross domestic product (GDP), unemployment rate, industrial structure, foreign direct investment, and population. Specifically, the core explanatory variable is green trade, and all variables are defined in the following sections.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Core explanatory variable\u003c/h2\u003e \u003cp\u003eTo date, no consistent definition is available in the existing literature of which products are in the basket of green trade (Can et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Diverse ranges of green-trade products are revealed, according to several international organisations. For example, the Asia-Pacific Economic Cooperation (APEC, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) produced 54 green products on the \u0026ldquo;APEC List of Environmental Goods\u0026rdquo;. Whereas, the OECD\u0026rsquo;s \u0026ldquo;Combined List of Environmental Goods\u0026rdquo; (CLEG) covers 40 goods, consisting of 255 products, which is the largest green product basket (Can et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the OECD\u0026rsquo;s CLEG list, there are 11 primary categories for environmental goods: 1) air pollution control; 2) cleaner or more resource-efficient technologies and products; 3) environmentally preferable products based on end-use or disposal characteristics; 4) heat and energy management; 5) environmental monitoring, analysis, and assessment equipment; 6) natural resource protection; 7) noise and vibration abatement; 8) renewable energy plants; 9) management of solid and hazardous waste and recycling systems; 10) clean-up or remediation of soil and water; and 11) wastewater management and potable water treatment.\u003c/p\u003e \u003cp\u003eHere, we adopt all the above values for exporting environmental goods to total exports to calculate the value of green trade. Data related to green trade were collected from OECD statistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Other variables\u003c/h2\u003e \u003cp\u003eOur model had six control variables: (1) clean energy (CE), percent of renewable energy consumption of total final energy consumption; (2) economic growth level (GDP), proxied by the rate of national GDP growth; (3) unemployment rate (UN), percent of the total labour force; (4) industrial structure (IS), percent of GDP; (5) foreign direct investment (FDI), net flow of percent GDP; and (6) population density (pop), people per sq. km of land area. Data were collected from the World Bank.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Model specification\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 GT and GTFP\u003c/h2\u003e \u003cp\u003eIn the existing literature, most studies focus on the relationship between green trade, GTFP, and the environment. A fixed model and system-generalised method of moments (GMM) were adopted. This study aims to evaluate whether green trade can promote an increase in GTFP. Can environmental pollutant emissions decrease? For this purpose, the following models were established:\u003c/p\u003e \u003cp\u003eTo avoid endogeneity, we reduce the bias by omitting time and individual effects. We adopted a fixed model to establish Model 1 as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${GTFP}_{it}=\\alpha +{\\beta }_{1}{GT}_{it}+{\\beta }_{2}{CE}_{it}+{\\beta }_{3}{GDP}_{it}+{\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}{+\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo solve the problem of reverse causality between GT and GTFP, for Model 2, we also lagged GT\u003csub\u003eit\u003c/sub\u003e by one and two years. This model is shown in Models 2 and 3.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${GTFP}_{it}=\\alpha +{\\beta }_{1}{GT}_{it-1}+{\\beta }_{2}{CE}_{it}+{\\beta }_{3}{GDP}_{it}+{\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}{+\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${GTFP}_{it}=\\alpha +{\\beta }_{1}{GT}_{it-2}+{\\beta }_{2}{CE}_{it}+{\\beta }_{3}{GDP}_{it}+{\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}{+\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFurthermore, to further study the relationship between GT and GTFP, in Model 4, we construct a linear parametric fixed effect model with the quadratic cubic polynomial of GT based on Model 1 to ensure the status of the OECD country. This model can be expressed as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${GTFP}_{it}=\\alpha +{\\beta }_{1}{GT}_{it}+{\\beta }_{2}{GT}_{it}^{2}+{\\beta }_{3}{GT}_{it}^{3}+{\\beta }_{4}{CE}_{it}+{\\beta }_{5}{GDP}_{it}+{\\beta }_{6}{UN}_{it}+{\\beta }_{7}{IS}_{it}{+\\beta }_{8}{FDI}_{it}{+\\beta }_{9}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBased on the linear regression model, to elucidate the relationship between the GT and GTFP, we established a threshold model to explore the non-linear relationship between GT exports and GTFP, which can be expressed as Models 5 and 6.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${GTFP}_{it}={\\mu }_{it}+{\\beta }_{1}{GT}_{it}\\times I\\left({CE}_{it}\\le \\gamma \\right)+{{\\beta }_{2}{GT}_{it}\\times I\\left({CE}_{it}\u0026gt;\\gamma \\right)+{\\beta }_{3}{GDP}_{it}+\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}+{\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+{ϵ}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${GTFP}_{it}={\\mu }_{it}+{\\beta }_{1}{GT}_{it}\\times I\\left({R\\\u0026amp;D}_{it}\\le \\gamma \\right)+{{\\beta }_{2}{GT}_{it}\\times I\\left({R\\\u0026amp;D}_{it}\u0026gt;\\gamma \\right)+{\\beta }_{3}{GDP}_{it}+\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}+{\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+{ϵ}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, i and t, indicate the country and year, CE\u003csub\u003eit,\u003c/sub\u003e R\u0026amp;D\u003csub\u003eit\u003c/sub\u003e is the threshold variables and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e represents the calculated threshold value. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}(.)\\)\u003c/span\u003e\u003c/span\u003e is the indication coefficient, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\beta }}_{1}, {{\\beta }}_{2}\\)\u003c/span\u003e\u003c/span\u003e is the coefficient of GT\u003csub\u003eit\u003c/sub\u003e, when the threshold value is different in each stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 GT and CO\u003csub\u003e2\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eTo explore whether GT can directly decrease CO\u003csub\u003e2\u003c/sub\u003e emissions and contribute to sustainable development. We took the emission of CO\u003csub\u003e2\u003c/sub\u003e as the explained variable, as in Model 5. In addition, we lagged GT\u003csub\u003eit\u003c/sub\u003e by one and two years to test the reverse causality between CO\u003csub\u003e2\u003c/sub\u003e and GT, as in Models 7, 8, and 9.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${CO}_{2it}=\\alpha +{\\beta }_{1}{GT}_{it}+{\\beta }_{2}{CE}_{it}+{\\beta }_{3}{GDP}_{it}+{\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}{+\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${CO}_{2it}=\\alpha +{\\beta }_{1}{GT}_{it-1}+{\\beta }_{2}{CE}_{it}+{\\beta }_{3}{GDP}_{it}+{\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}{+\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${CO}_{2it}=\\alpha +{\\beta }_{1}{GT}_{it-2}+{\\beta }_{2}{CE}_{it}+{\\beta }_{3}{GDP}_{it}+{\\beta }_{4}{UN}_{it}+{\\beta }_{5}{IS}_{it}{+\\beta }_{6}{FDI}_{it}{+\\beta }_{7}{POP}_{it}+\\mu +\\eta +ϵ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Linear results and quadratic and cubic polynomials of GT\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the basic results of Models 1, 2, 3, and 4. Column (1) shows the results of the fixed effect model. Columns (2) and (3) show the results of lagged GT for one and two years, respectively. These results reveal that the export of GT cannot promote the development of GTFP and even has an evident negative effect on green growth (Liu, et al., 2022). Among the control variables, clean energy, unemployment, and FDI have positive effects on GTFP. Otherwise, the level of economic growth has a negative influence on GTFP. First, clean energy can promote green growth because it can reduce the use of gas, coal, and fuel, and reduce pollution emissions to achieve environmental sustainability (Alper and Oguz., 2016, Ahmed, et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, FDI has a positive economic significance on GTFP, which can be explained that FDI exerts the positive spill-over effect of FDI on GTFP (Zhou, 2019). The economic level can decrease GTFP because the economy may be characterised by \u0026ldquo;high pollution, high consumption, high emission\u0026rdquo; in the early stage (Wang, et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe output of the GT's quadratic cubic polynomial is shown in Column (6). It reveals that significant GT and GT2 coefficients at the 1% and 2% levels and their positive and negative signs, respectively, point to a \"U-shaped\" link between GT and GTFP. Model 4 has two turning points, and the coefficients of GT, GT2, and GT3 are statistically significant. The signs of the coefficients are negative, positive, and negative, indicating an inverse \"N-shaped\" relationship between GT and GTFP. Basic Models 1 and 4 exhibited the same GT symptoms. GT and GTFP were in stage 3, and a negative association was observed between GT and GTFP. GT exports stimulate GTFP growth in the initial stage because of insufficient absorption capacity. Because of technological breakthroughs, GT exports exceed the first turning point, and encourage the development of GTFP. With the progressive expansion of trade in the third stage, trade development reached a bottleneck.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of baseline models and polynomial models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.845\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.997\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.680\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.428)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003csub\u003et\u0026minus;2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.184\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.242\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(4.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.764\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(23.831)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade cubic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-111.03\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(65.678)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry-fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Robust standard errors in parentheses; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Threshold effect of Green trade on GTFP\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Threshold effect test and threshold value estimation\u003c/h2\u003e \u003cp\u003eIn Models 5 and 6, we investigate whether cleaner energy and R\u0026amp;D have a threshold effect on GT exports and GTFP. Hence, we tested single-, double-, and triple-threshold values and used 500 bootstrapping iterations because of the small sample size. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the F-statistics and significance level of the p-value, which reveal that clean energy R\u0026amp;D is significant for a single threshold. The single threshold value for clean energy is 8.660. The single threshold value for R\u0026amp;D level was 0.664. Furthermore, we tested the confidence intervals of the threshold variables, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSingle threshold of green trade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreshold value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypothetical test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003cp\u003evalues (90%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003cp\u003evalues (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003cp\u003evalues (99%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen export\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.98\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026amp;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen export\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.30\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Results of threshold regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the nonlinear relationship between green trade and GTFP in OECD countries using different threshold variables. Column (1) shows that for clean energy, the effect of GT exports on GTFP changes from negative to positive. When clean energy is below the first threshold value of 8.660, the effect of GT exports on GTFP is significant at a 5% level, and the coefficient is -1.518. This indicates that as clean energy is at the first threshold stage, an increase of one unit in GT exports will decrease the GTFP by 1.518 units. However, when clean energy exceeds the first threshold value, the effect of GT exports is positive (1.056) at the 10% level. Column (2) reveals that as the level of R\u0026amp;D changes, the coefficient of GT exports on GTFP will change from \u0026minus;\u0026thinsp;3.165 to 1.193. It appears that the effect of GT exports on GTFP is stronger and more positive with the effect of R\u0026amp;D level. In. In conclusion, it appears that clean energy and R\u0026amp;D promote the growth of GTFP when it exceeds the first threshold.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of threshold regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003cp\u003e(energy\u0026thinsp;\u0026le;\u0026thinsp;8.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.518\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003cp\u003e(energy\u0026thinsp;\u0026ge;\u0026thinsp;8.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.056\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003cp\u003e(R\u0026amp;D\u0026thinsp;\u0026le;\u0026thinsp;0.664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.805)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003cp\u003e(R\u0026amp;D\u0026thinsp;\u0026ge;\u0026thinsp;0.664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.193\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.576)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.021\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Robust standard errors in parentheses; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results of green trade and TFP, pollutant emissions\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, GT exports do not have an obvious effect on TFP, which is measured using the same method as GTFP and does not include unexpected variables. Therefore, it can be explained that the inefficiency of GTFP is likely due to the poor environmental performance of GT exports (Liu, et al., 2022). Furthermore, we investigated the effects of GT exports on environmental indicators. Columns (4) and (5) show that, although GT exports do not have a significant effect on increasing CO\u003csub\u003e2\u003c/sub\u003e emissions in the current period, they have an evident influence on increasing CO\u003csub\u003e2\u003c/sub\u003e emissions through the time-lagged effect. Compared to the coefficient of GT exports, the effect of GT exports with a time lag is more influential than that in the current period. Compared with Columns (7) and (8), the coefficient of GT exports is not statistically significant, but still has an economic impact on increasing PM2.5.\u003c/p\u003e \u003cp\u003eClean energy is an important issue that can contribute to environmental protection and improve green economic growth (Ahmed, et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, we adopted clean energy as an interaction term to test the interaction effects of clean energy on TFP, CO\u003csub\u003e2\u003c/sub\u003e emissions, and PM2.5. Column (3) shows that the interaction between clean energy and GT exports did not have a significant effect on TFP improvement. By comparing Columns (6) and (9), we can see that the interaction effect of clean energy and green trade can decrease the emissions of greenhouse gases and PM2.5. Thus achieving sustainable growth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of traditional TFP and environmental factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003cp\u003e(0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.252\u003c/p\u003e \u003cp\u003e(2.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.071\u003c/p\u003e \u003cp\u003e(4.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003cp\u003e(0.255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.513\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.725\u003c/p\u003e \u003cp\u003e(4.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade* energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.595\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.300\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.156\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.155\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.157\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.147\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.063\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003cp\u003e(0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00008\u003c/p\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.031\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.046\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry-fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: Robust standard errors in parentheses; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness test\u003c/h2\u003e \u003cp\u003eTo further enhance the credibility of the empirical results, we test the robustness based on baseline models by dropping the 2008 dataset due to the financial crisis because international trade is the main path to the contagion economic crisis (Chote and Daniel, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, GT exports still have a negative effect on GTFP when the 2008 dataset is dropped. As for the control variables, clean energy, unemployment, and FDI can also enhance the development of GTFP, and the economic-level variable will decrease GTFP, exactly as in the baseline models; therefore, these models were robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of robust test of green trade on GTFP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTFP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.886\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.197\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen trade\u003csub\u003et\u0026minus;2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.074\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.492)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry-fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-fixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Robust standard errors in parentheses; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eSustainable development is a common goal amid the increasing conflict between environmental challenges and economic development. As an indicator of green productivity, GTFP incorporates naturalal resources and environmental issues into a total factor productivity analytical framework to balance the quantity and quality of economic growth (Wang, et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., 2019). The GT refers to the production and exportation of environmentally friendly goods to reduce pollution and achieve sustainable development. However, GT exports may cause environmental pollution during the production processes (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Consequently, this study examines the effect of GT exports on GTFP and environmental issues using a panel dataset from 2003 to 2016 in OECD nations using linear and nonlinear models.\u003c/p\u003e \u003cp\u003eFirst, we explore the influence of GT exports on GTFP using a fixed effects model. The empirical results show that GT exports have an evident negative effect on GTFP\u0026mdash;and Liu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) approached the same results. This can be explained by the fact that environmental products are produced in the process owing to the industrial capabilities (Liu, et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e., Wan and Wen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e., Wan, et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) For the control variables, clean energy, unemployment, FDI, and population can promote the growth of GTFP; otherwise, the level of economic growth will decrease the growth of GTFP due to \u0026ldquo;high pollution, high consumption, and high emission\u0026rdquo;. Second, we investigated the relationship between GT exports and GTFP by using the polynomial of GT exports and observed that an inverse \u0026ldquo;N-shaped\u0026rdquo; relationship between GT exports and GTFP in OECD countries. When GT exports do not exceed the first turning point, they will decrease the growth of GTFP, and when GT exports are between the first and second turning points, the effect of GT exports on GTFP is positive. When GT exports exceed the second turning point, their effect on GTFP is again negative. Third, this study explores the non-linear relationship between GT exports and GTFP using clean energy and the level of R\u0026amp;D as threshold variables. The results showed that both clean energy and R\u0026amp;D have a single threshold value for GTFP. When the threshold variable is in the first stage, GT exports have a negative effect on GTFP; however, when the threshold value is in the second stage, GT exports can promote growth in GTFP. Hence, according to our results, this study argues that with the development of clean energy and R\u0026amp;D levels, the marginal effect of GT exports on GTFP will be stronger and improve the growth of GTFP.\u003c/p\u003e \u003cp\u003eThe results of this study also have policy implications for policymakers to promote the growth of GTFP in OECD countries. First, although GT exports have a negative effect on GTFP, GT export enterprises can still contribute to GTFP by supplying more impeccable GT chains globally. Countries should expand the domestic market and alleviate the export dependence of export-oriented industries. Second, governments should establish stricter environmental policies, increase the standards of their environmental standards, and focus on economic and environmental benefits (Wang, et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e., Wangand Luo., 2020). Especially in developing countries, the policymakers should rapidly increase the development of the country's tertiary industry and emerging science and technology, reduce the proportion of the domestic secondary industry within a reasonable range, and avoid the country becoming the world's factory. Third, to reduce the intensity of traditional energy use, the government must improve infrastructure construction for clean energy sources, such as wind, water, and solar energy, and increase the intensity of clean energy use. Fourth, the government should increase spending on science and technology education and vigorously improve the country's scientific and technological R\u0026amp;D capabilities, thereby promoting production efficiency.\u003c/p\u003e \u003cp\u003eThis study proves that GT exports affect GTFP and environmental factors. Nonetheless, it has academic limitations. For example, data relative to the GT are limited, and a small sample size will affect the results. Although OECD countries are primarily developed countries, we do not address the heterogeneity between OECD countries, such as the factors of average income, trade dependence, educational level, and the effect of GT exports on whether GTFP can be promoted. These fields should be explored in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding sources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChang Hwan Choi, Ph.D.,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProfessor, International Trade Dept. Dankook University\u003c/p\u003e\n\u003cp\u003eU.S.A. Attorney at Law (Washington D.C.)\u003c/p\u003e\n\u003cp\u003e152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do, 16890, South Korea\u003c/p\u003e\n\u003cp\u003eEconomic Building # 615\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTel (82) 31-8005-3379\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(e-mail) [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChang Hwan Choi\u003c/p\u003e\n\u003cp\u003e: writing\u0026mdash;original draft, writing\u0026mdash;review and editing, formal analysis, validation. data curation, visualization, supervision, resources\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;Chang Hwan Choi, Ph.D.,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbid M, Sekrafi H (2021) Pollution haven or halo effect? 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Environmental Economics and Policy Studies, p 20\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Green trade exports, Green total factor productivity (GTFP), Sustainable development, Clean energy, Research and development (R\u0026D)","lastPublishedDoi":"10.21203/rs.3.rs-4270045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4270045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pursuit of economic growth, environmental pollution reduction, and the achievement of sustainable development are central concerns for numerous countries. In 2001, the WTO proposed the elimination of non-tariff barriers on environmental goods and services to mitigate trade barriers and reduce pollutant emissions, thereby enhancing the global trade industry chain. Several scholars have scrutinized the consequences of green trade on sustainable development. This study centers on assessing the impact of green trade exports (GTE) on green total factor productivity (GTFP) and greenhouse gas emissions, utilizing a panel dataset for OECD countries.\u003c/p\u003e \u003cp\u003eInitially, a linear regression model is employed to observe that GTE fails to contribute to GTFP and is ineffective in mitigating CO\u003csub\u003e2\u003c/sub\u003e emissions. The relationship between GTE and GTFP exhibits an inverted N-shaped curve. Subsequently, a non-linear threshold model is established, revealing that GTE can foster GTFP growth when clean energy and research and development (R\u0026amp;D) exceed the first threshold value. Consequently, an augmentation in clean energy and technological intensity can lead to sustainable development in OECD nations. This study offers vital insights for developing countries seeking to participate effectively and efficiently in the global industrial chain, thereby reducing domestic development costs.\u003c/p\u003e","manuscriptTitle":"A Quantum Leap Towards Sustainability? 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