How does foreign trade affect green total factor energy efficiency? Evidence from China

As the largest trading nation in the world, there have been substantial foreign trades (export and import) between China and other countries. Meanwhile, it is also one of the major forces for China’s emission reduction. This article applies the panel data of 30 provinces for the period 2004–2017 to investigate the effect of foreign trade on China’s green total factor energy efficiency (GTFEE). The slack-based measure (SBM) model is employed to calculate the provincial GTFEEs. Subsequently, the empirical results of the basic linear regression model revealed that both export and import promoted the region’s GTFEE, on which the import particularly has more effects than the export. Moreover, the spatial Durbin model (SDM) exhibited that the increase in import will not only present a positive influence on the GTFEE of the region, but also will improve the GTFEEs of the surrounding provinces through the spatial spillover mechanism. Although the increase in export will also exert a positive influence on the GTFEE of the local area, it will impose a significant negative impact on the GTFEEs of the surrounding regions. The results of this study provide important policy implications for the optimization of trade structure and high-quality development of the Chinese economy.

1 Introduction

Since the reform and opening-up, China has already made substantial achievements in economic development to become the second largest economy in the world, and scholars indicate that foreign trade has turned into a powerful engine to continually drive economic growth (Hu and Tan, 2016; Kong et al., 2020). In 2001, China obtained membership in the World Trade Organization (WTO), since which its trade development has achieved remarkable results (Yu and Luo, 2018). In 2010, China became the largest country in trading goods (Jarreau and Poncet, 2012; Caporale et al., 2015). By the end of 2019, China’s values of export and import had reached 2.50 and 2.08 trillion dollars, respectively (China’s Ministry of Commerce, 2019), ranking first and second in terms of countries. With the rapid economic development, China’s environmental problems have become increasingly severe. In 2014, China had become the world’s largest CO₂ emitter, whose carbon emissions accounted for about 27.5% of the global emissions (Li and Wang, 2019). According to the Paris Agreement, the intensity of carbon emissions per GDP in China will drop by 60%–65% in 2030 compared with 2005 (Ma et al., 2017; Wang K et al., 2019). Generally speaking, the export-oriented model to drive economic growth is no longer sustainable for China.

At present, how to optimize trade structure and promote the green economy has posed a challenge for China’s government. Thus, this article selects the GTFEE to reflect the emissions and economic achievements in China (Wu et al., 2020a). It will focus on studying the impact of foreign trade on China’s GTFEE, which attaches great significance to improving the trade structure and achieving the high-quality development of the economy in China.

The contributions of this study are as follows. First, most of the previous research on the export impact on economic growth without considering import separately (Mania and Rieber, 2019; Jalles and Ge, 2020). We attempt to research the impacts of foreign trade on GTFEE. Second, the empirical result of this article draws new conclusions, foreign trade has two ways of exporting and importing, which positively impact China’s GTFEE. More importantly, the impacts of import on GTFEE are greater than those of export. Finally, the generalized method of moments (GMM) is used to solve endogeneity problems between variables. The export and import have spatial effects on GTFEE, which are examined with the Spatial Durbin model (SDM).

This article endeavors to provide a better understanding of connections between foreign trade and GTFEE, which is of great significance to the transformation of trade pattern and high-quality development of economy in China.

The remainder of this article is organized as follows: Section 2 presents a literature review. Section 3 introduces the methodology. Section 4 describes the variables and data sources. Section 5 presents an empirical analysis. Section 6 concludes the paper.

2 Literature review

At present, scholars mainly focus on three aspects of the foreign trade impact on GTFEE: carbon emissions, energy efficiency, and technology.

First, scholars have long been concerned with the problem of carbon emissions brought by foreign trade. The impact of foreign trade on global carbon emissions during 1995–2009 was examined by Wang and Ang (2018), whose results showed that a large amount of emissions had been generated. Based on the instrumental variables, Managi et al. (2009) suggested that the influence of foreign trade was significantly heterogeneous among different countries. From the perspective of export effects on carbon emissions, Dietzenbacher et al. (2012) found that ordinary export brought more emissions than the processing trade. Besides, from the regional and industrial perspectives, Yan et al. (2020) calculated the amount of carbon emissions produced by export in China. In summary, scholars have conducted profound studies on the impact of foreign trade on CO2 emissions and generally believed that foreign trade was one of the most important factors affecting carbon emissions.

Second, numerous scholars have also studied the impact of foreign trade on energy efficiency. Most of them focused on exports. Taking Latin America as the research object, Egger and Url (2010) found that the growth of export had become a crucial factor in the lack of energy supply in Austria. Kohler (2013) got the same conclusion when he took South Africa as the object. As for China, the significant correlation between its foreign trade and energy consumption during 1971–2011 was proved by Shahbaz et al. (2013). Besides, Farrow et al. (2018) and Zhang et al. (2017) pointed out that export structure was also an important factor affecting energy efficiency.

Third, Almodovar et al. (2014) firstly researched the effect on technological progress from foreign trade. Since then, it has been regarded as one of the most significant channels for transnational technology spillover (Parrado and De Cian, 2014; Yuan and Ya-Li, 2014; Tientao et al., 2016; Ho et al., 2018). Clerides et al. (1998) and Sharma (2018) indicated that trading enterprises could learn advanced clean technology and management experience from the developed countries through trade. Besides, participating in the fierce international competition required domestic enterprises to continuously improve technology and product quality to meet stricter demands (Revesz, 1992; Korves, 2011).

In addition, the spatial spillover effect on technology is one of the main topics that scholars focus on. By using SDM, Pan et al. (2020a) found that the Outward Foreign Direct Investment (OFDI) exerted a significant spillover effect on the Green Total Factor Productivity (GTFP) of neighboring provinces. With the application of SLM, Zhang et al. (2018) found that carbon productivity also exerted the spatial spillovers effect. Trade is an important way of cooperation, and its technology spillover effect should also attract more attention.

According to the related research above, it is not difficult to discover that many scholars have already studied the relationship between foreign trade and green development from different perspectives. However, few scholars have ever researched the impact of foreign trade on GTFEE and distinguished the heterogeneity between the export and import. Compared with the direct adoption of carbon emissions, energy consumption, and technology as variables, GTFEE could have considered more factors. Meanwhile, studying the impact of foreign trade on GTFEE is more consistent with China’s current reality of high-quality economic development. Moreover, this article studies the local and peripheral effects of reverse technology spillovers from foreign trade and whether the export and import can improve the GTFEEs of neighboring areas through a spatial spillover mechanism.

3 Methodology and data

3.1 Calculating method of the GTFEE

Academia has obtained fruitful results in research on energy consumption and efficiency (Wu et al., 2020b). However, the undesired output is rarely included in the indices of energy efficiency at present (Li and Hu, 2012; Li and Lin, 2017). Chung et al. (1997) firstly considered the undesired outputs and named them the Directional Distance Function (DDF). Since then, multiple expansion models continued to be produced, such as the Banker–Charnes–Cooper (BCC) model, the Malmquist index model, and the SBM models (Banker and Cooper, 1984; Tone, 2001; Long and Xiaozhen, 2010; Wang Z et al., 2019). According to the previous research and the reality of China’s economic development, this article applies the superefficiency SBM-undesirable model to measure the GTFEEs of 30 provinces in China from 2004 to 2017.

Hypothetically, there are n number of DMUs (provinces in China) at time t, l kinds of input factors to production, M types of desirable outputs, and K types of non-desirable outputs for each DMU. The input set, desirable output set, and non-desirable output set of each DMU are expressed as $x_i=\left(x_{\mathit{1}k},x_{\mathit{2}k}\cdots ,x_{\mathit{30}k}\right)$, $y_i=\left(y_{\mathit{1}k},y_{\mathit{2}k}\cdots ,y_{\mathit{30}k}\right)$, and $x_i=\left(b_{\mathit{1}k},b_{\mathit{2}k}\cdots ,b_{\mathit{30}k}\right)$, respectively. Among them, l = 3 corresponds to the capital stock (K), labor (L), and energy consumption (EU). Using the “Perpetual Inventory Method” (PIM) and following the research methods of Dey-Chowdhury (2008) and Dong and Cen (2011), the capital stock is calculated from the fixed-asset investments and their price indexes of every province by the equation presented as follows:

$K_{i,t}=I_{i,t}+\left(\mathit{1}-{\delta }_{i,t}\right)K_{i,t-\mathit{1}}(1)$

where K_i,t, K_i,t-1, and I_i,t denote the capital stock (K) of province i in year t, the capital stock (K) of province i in year t-1, and the values of new capital investment of province i in year t, respectively. $\delta$ is the depreciation rate, whereas the research of this article is inclined to set the capital depreciation rate of each province uniformly as 10.96% (Shan, 2008; Liu and Xin, 2019).

In addition, m = 1 corresponds to the GDP of each province, whereas k = 3 corresponds to the discharge of industrial wastewater, the discharge of industrial solid waste, and carbon emissions.

Finally, this article will be based on the superefficiency SBM-undesirable model, which incorporates undesired outputs into the efficiency study. As shown in formula 2, each province’s GTFEE is calculated individually:

${gtfee}^{\ast }=mingtfee{\displaystyle \sum }_{k=\mathit{1}}^k{\lambda }_k{x}_{ik}\le gt{fee{x}_n}_{\begin{array}{}\\ s.t.{\displaystyle \sum }_{k=\mathit{1}}^k\begin{array}{c}{\lambda }_k{y}_{ik}\ge y_m\\ {\displaystyle \sum }_{k=\mathit{1}}^k\begin{array}{c}{\lambda }_k{z}_{ik}=y_j\\ \mathrm{n}=1,2\dots ,\mathit{N};\mathit{m}=\mathrm{1,2}\dots ,\mathit{M};\mathit{j}=\mathrm{1,2}\dots ,\mathit{J};\mathit{k}=\mathit{1,2}\dots ,\mathit{K}\end{array}\end{array}\end{array}}(2)$

3.2 Econometric model setting

Exploring the impact of foreign trade on GFTEE and following the research of Copeland and Taylor (2004), Chen and Golley (2014), and Li and Wu (2020), this study establishes the specific model as follows:

${GTFEE}_{i,t}={\alpha }_0+{{\alpha }_{1}Export}_{i,t}+{{\alpha }_{2}EI}_{i,t}+{{\alpha }_{3}RD}_{i,t}{{+\alpha }_{4}IS}_{i,t}+{{\alpha }_{5}ER}_{i,t}{+{\alpha }_{6}ER}_{i,t}^2+{{\alpha }_{7}UR}_{i,t}+{\delta }_i+{\upsilon }_t+{\epsilon }_{i,t},(3)$

${GTFEE}_{i,t}={\beta }_0+{{\beta }_{1}Import}_{i,t}+{{\beta }_{2}EI}_{i,t}+{{\beta }_{3}RD}_{i,t}{{+\beta }_{4}IS}_{i,t}+{{\beta }_{5}ER}_{i,t}{+{\beta }_{6}ER}_{i,t}^2+{{\beta }_{7}UR}_{i,t}+{\delta }_i+{\upsilon }_t+{\epsilon }_{i,t},(4)$

where export and import individually indicate the foreign trade levels, i represents every province, t represents the year, EI indicates the economic development, RD indicates the investment in Research and Development (R&D), IS indicates the industrial structure, ER indicates the environmental regulation, UR indicates the urbanization rate, and α and β are parameters, respectively. Besides, EI is expressed as the scale effect, RD is expressed as the technology effect, IS and UR are expressed as the structure effect, respectively (Feng et al., 2017; Cheng et al., 2018; Yuan and Xiang, 2018).

Based on existing research, the GTFEE may be affected at an earlier stage. According to the method proposed by Wu et al. (2020b), this study also integrates the lagging one-stage variable (GTFEE_i,t-1) into the following formula:

${GTFEE}_{i,t}={\alpha }_{\mathit{0}}+{\alpha }_{\mathit{1}}{GTFEE}_{i,t-\mathit{1}}+{\alpha }_{\mathit{2}}{Tra}_{i,t}+{\alpha }_{\mathit{3}}{EI}_{i,t}+{\alpha }_{\mathit{4}}{RD}_{i,t}+{\alpha }_{\mathit{5}}{IS}_{i,t}+{\alpha }_{\mathit{6}}{ER}_{i,t}+{\alpha }_{\mathit{7}}{ER}_{i,t}^{\mathit{2}}+{\alpha }_{\mathit{8}}{UR}_{i,t}+{\delta }_i+{\upsilon }_t+{\epsilon }_{it}(5)$

3.2.1 Spatial Durbin model

1) Spatial analysis methods

As mentioned earlier, there are spatial heterogeneity and spatial correlations of foreign trade between different regions. GTFEE of a certain region will be affected not only by the level of its local trade, but also by its neighboring regions. Before performing the spatial econometric analysis, it is essential to test the spatial correlation of these variables (Pan et al., 2019a; Pan et al., 2019b).

The Moran Index (Moran’s I) is applied to identify the spatial correlation between the province samples. The calculation method of Moran’s I is shown as follows:

${Moran}^{\prime }sI=\frac{{{\displaystyle \sum }}_{\mathrm{i}=\mathit{1}}^{\mathit{n}}{{\displaystyle \sum }}_{\mathit{j}=\mathit{1}}^{\mathit{n}}{W}_{i,j}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{S^2{{\displaystyle \sum }}_{\mathit{i}=\mathit{1}}^{\mathit{n}}{{\displaystyle \sum }}_{\mathit{j}=\mathit{1}}^{\mathit{n}}{W}_{i,j}}.(6)$

In formula 6, $S^{\mathit{2}}={{\displaystyle \sum }}_{i=\mathit{1}}^n{\left(x_i-\overline{x}\right)}^{\mathit{2}}$ ,$\overline{x}=\frac{\mathit{1}}{n}{{\displaystyle \sum }}_{i=\mathit{1}}^n{x}_i$. GTFEE of province is represented by x_i and n represents all the 30 provinces. $\overline{x}$ represents the average of GTFEEs, and the variance of GTFEE is represented by $S^{\mathit{2}}$. The range of Moran’s I is from −1 to 1. When Moran’s I > 0, it shows a positive spatial dependence. When Moran’s I < 0, it implies a negative spatial dependence. When Moran’s I = 0, it indicates no spatial autocorrelation.

2) Construction of spatial weight matrix

Constructing a spatial weight matrix is the basis for establishing a spatial econometric model. Given the geographical spaces and economic discrepancies between different provinces, there have been three spatial weight matrices to be selected, including the ones of geographic adjacency, geographic distance, and economic distance (Li K et al., 2018) as follows:

Geographic adjacency spatial weight matrix (W1).

$W_{ij}=\mathrm{1}\left(i\ne j\right).(7)$

In the matrix above, if province i owns a common boundary with province j, it means W1 = 1; otherwise, W1 = 0.

Geographic distance’s spatial weight matrix (W2):

$W_{\mathit{2}}=1/{dis}_{i,j}.(8)$

In the matrix above, dis_i,j is the straight-line distance between the capitals of provinces i and j.

Economic distance’s spatial weight matrix (W3):

$W_{\mathit{3}}=1/\left|\overline{y_i}-\overline{y_j}\right|\left(i\ne j\right).(9)$

In the matrix above, y_i and y_j are individually represented as the per capita GDP in provinces i and j and W3 is expressed as the reciprocal of the absolute value of the gap between the economic levels of these two provinces.

3) The design of SDM

After the spatial correlation test, this article intends to apply a spatial econometric model to study the spatial effects. There have been three applicable kinds of spatial econometric models: the Spatial Lag Model (SLM), Spatial Error Model (SEM), and SDM. However, as some scholars have argued, SDM can capture the spatial correlation between dependent variables and the spatial spillover effects of independent variables more effectively than SLM and SEM. Thus, this article employs SDM to study the influence of trade on GTFEE and its spillover effects. This model is accordingly established as follows:

${GTFEE}_{i,t}=\alpha +\rho {{\displaystyle \sum }}_{j=\mathit{1}}^n{W}_{i,j}{GTFEE}_{j,t}+{\beta }_{\mathit{1}}{tra}_{i,t}+{\beta }_{\mathit{2}}{{\displaystyle \sum }}_{j=\mathit{1}}^n{W}_{i,j}{tra}_{i,j}+{\beta }_{\mathit{3}}{X}_{i,t}+{\delta }_i+{\upsilon }_t+{\epsilon }_{it}(10)$

where tra_i,t indicates either export or import; X is a series of control variables; Wi is a spatial weight matrix; and α, ρ, and β are the parameters^.

3.3 Data sources and variables selection

3.3.1 Explained variables

Green total factor energy efficiency (GTFEE) is the explained variable in our basic model and SDM model. It is calculated by formula 2 in Section3.1.

3.3.2 Explanatory variables

Foreign trade (tra_i,t) is the most important explanatory variable in our model, including export and import values. Export mainly refers to the flow of goods from the region to other countries, and import mainly refers to the flow of goods from other countries to the region.

3.3.3 Control variables

We added multiple control variables to the model to minimize or even avoid the estimation bias caused by missing variables. These control variables are explained as follows.

1) Economic development level (EL): the per capita GDP measures the level of provincial economic development (Josep et al., 2005; Hao et al., 2020).

2) Research and Development ((R& D) investment: R&D investment is also one of the important factors in promoting technological progress. The ratio of research investment to GDP is used to measure the R&D ((Lin and Zhao, 2016).

3) Industrial structure (IS): the share of GDP in the secondary industry is used to measure the industrial structure. The secondary industry is a large energy user. This is the original intention of adding the variables. Thus, the higher the proportion of the total secondary industry, the more the emissions and the lower the GTFEE (Liu and Bae, 2018; Wu et al., 2019; Hao et al., 2020).

4) Environmental regulation (ER): Porter and Linde (1999) put forward the Porter hypothesis, suggesting that the enterprise can achieve a win-win situation between economic growth and environmental protection through ER. Here, the ratio of environmental governance investment to GDP is used to measure the ER (Lanoie et al., 2008; Nesta et al., 2014; Wu et al., 2020a).

5) Urbanization rate (UR): according to the research of Zhang et al. (2015) and Li M et al. (2018), the share of the non-agricultural population relative to the total population is used to measure the UR.

3.3.4 Data sources

The sample data set is the panel data of China’s 30 provinces from 2004 to 2017. The main data comes from the China Statistical Yearbook, China Science and Technology Statistics Yearbook, China Energy Statistical Yearbook, China Environmental Statistics Yearbook, Wind Database, and National Bureau of Statistics. Taking into the lack of data in Tibet and the availability of data in Hong Kong, Macau, and Taiwan. The research objects are 30 provinces except for Tibet, Hong Kong, Macau, and Taiwan. In order to eliminate the impact of the price factor on the results, increase the data stability, and reduce size impact, EI, export, and import values have been represented by logarithm. Here, the descriptive statistics of the data are given in Table 1.

TABLE 1

TABLE 1. The descriptive statistical analysis of variables.

4 Results and discussions

4.1 Foreign trade in China

4.1.1 Export in China

Table 2 reveals that the proportion of exports in eastern China to the total exports is much higher than that in the central and western regions during the sample period. From 2004 to 2017, the average proportion of exports in eastern China to the total exports was 89.90%, compared with just 5.50% and 4.50% in central and western China. The top three regions of total exports are Guangdong (29.21%), Jiangsu (15.91%), and Zhejiang (11.22%), respectively, all in eastern China. By contrast, the last three ones are Hainan (0.15%), Ningxia (0.11%), and Qinghai (0. 04%), respectively, all in western China. Obviously, the eastern regions located in the coastal areas play an irreplaceable role in China’s exports.

TABLE 2

TABLE 2. China’s exports in 2004–2017.

4.1.2 Import in China

Table 3 shows that the average proportion of imports from eastern China in the total is 88.10%, compared with only 6.10% and 5.80% from central and western regions, respectively. More notably, the top three proportions of imports are from Guangdong (24.54%), Beijing (16.46%), and Shanghai (14.04%), all in eastern China. However, the last three proportions of imports are from Guizhou (0.09%), Ningxia (0.05%), and Qinghai (0.02%). Generally speaking, the eastern coastal regions hold a dominant position in China’s export and import, which is the main frontier for reform and opening-up. Among all these regions, the development of trade in China is very uneven, which must attract attention and solutions during the process of high-quality economic development.

TABLE 3

TABLE 3. China’s imports in 2004–2017.

4.2 China’s GTFEE

Based on the calculation methods of GTFEE above, this study has measured the GTFEEs of these three regions¹. The results are exhibited in Figure 1. From 2004 to 2017, the results show an upward trend in China’s average GTFEE from 0.526 to 0.595. From the perspective of the region, both the central and western ones presented the GTFEEs lower than the national level. However, the GTFEE of the eastern region turned out to be higher than the national average. From 2004 to 2017, the gap between GTFEEs of eastern and central regions widened from 0.107 to 0.237, whereas the gap between GTFEEs of eastern and western regions broadened from 0.227 to 0.358. There has been an upward trend in GTFEE moving to the eastern regions from the central and western regions. Although there is a high level of coordination between economic growth and environmental performance in the eastern regions, there remain significant regional differences in China, which should be considered when making related policies.

FIGURE 1

FIGURE 1. Three regional static green total factor energy efficiency.

1 According to the No. 33 (2000) document of China, this study divides 30 provinces into three regions: the eastern region (including 11 provinces, i.e., Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangzhou, Hainan, and Liaoning.), the central region (including eight provinces, i.e., Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Jilin, and Heilongjiang.), and the western region (including 11 provinces, i.e., Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.).

4.3 Baseline regression results and discussion

According to the methodology, this study applies the regression methods of Fixed Effect (FE) and Random Effect (RE) to estimate the formulas of (3) and (4). Meanwhile, in order to address the possible endogeneity problem between export, import, and GTFEE while improving the robustness of the results, the generalized moment method is adopted to conduct the estimation.

The empirical results are shown in Table 4 and Table 5, respectively. The selection of instrumental variables is reasonable because the Hansen test and the Auto-Regressive AR(2) estimation results present no second-order sequence correlation of the random error terms (Blundell and Bond, 1998). The specifics of the results are as follows: firstly, the coefficients of export and import are both positive and they are both significant at the 1% level. In other words, an improvement in export or import will inhibit an improvement in GTFEE. Secondly, the coefficient of export (0.003) is lower than that of import (0.009), which means that the import impacts GTFEE more significantly than the export trade does. In fact, China has strongly promoted the processing trade since the last century (Byrne et al., 1996; Ma et al., 2014). Processing trade accounts for over 50% of China’s total exports (He and Wang, 2020). Some scholars found that an excessive proportion of processing trade was one of the most important factors to lower productivity (Lu et al., 2010; Ma et al., 2014; Dai et al., 2016; Manova and Yu, 2016). Meanwhile, other scholars indicated that the enterprises can absorb advanced technology through import, which was a significant source of technological progress and a key driving force for improving GTFEE (Grossman and Helpman, 1991; Coe and Helpman, 1995; School of Earth Environment, University of Leeds, Leeds, UK, 2016). Thirdly, the sign of the EI coefficient among other variables is positive and statistically significant at the 1% level, which shows a higher level of economic development and a higher degree of GTFEE. The sign of the UR coefficients is negative and statistically significant at the 1% or 5% level. With progressive urbanization, China’s population and industries have inundated the cities and significantly increased energy consumption and environmental pollution in China (Sheng et al., 2020; Sun and Huang, 2020). The sign of R&D coefficients is also positive and statistically significant at the 1% level, which exhibits that technological innovation considerably reduces carbon emission by improving the GTFEE. The coefficients of the IS are significantly negative at the 1% or 10% level, which indicates that IS plays a significantly negative role in raising the GTFEE. The results also present that the coefficients of ER are negative, and the coefficients of square term (ER ²) are positive, which are both significant at the 1%, 5%, or 10% levels. It indicates a “U-shaped” relationship between the ER and GTFEE. Before the turning point, ER showed a “green paradox” effect and after the turning point, it showed an “emission reduction effect” (Cheng et al., 2017; Huang and Lei, 2021; Wu et al., 2020a).

TABLE 4

TABLE 4. Estimation results of the basic model for export.

TABLE 5

TABLE 5. Estimation results of the basic model for import.

4.4 Spatial results and analysis

4.4.1 Spatial autocorrelation analysis

Moran’s I is an effective tool for explaining spatial correlation. According to formula (6), Moran’s I test results of spatial correlation of GTFEE under three different matrices of weights are individually shown in Table 6. During 2004–2017, Moran’s I of the GTFEE are positive and statistically significant, which indicates that the GTFEE of China presented significant characteristics of spatial agglomeration.

TABLE 6

TABLE 6. Moran’s I value of GTFEE during 2004–2017.

Moreover, the degree of spatial dependence exhibits a growing uptrend.

4.4.2 Estimation of the SDM model

In order to make the estimation structure more reliable, this study applies three different matrices of spatial weight to estimate the SDM. Following the Hausmann test, this study chooses the FE model in the form of space-fixed, time-fixed, and time-space double-fixed effects. Finally, according to the likelihood function values and goodness of different fixed effects, this study reports the results of the double-fixed one. Its regression results are listed in Table 7.

TABLE 7

TABLE 7. Estimation of the SDM.

The coefficients of export and import are all significantly positive under W1, W2, and W3, presenting that both export and import can promote the growth of provincial GTFEEs. From another perspective, no matter what spatial matrix is adopted, the spatial lag terms of export and import are always positively significant, which also passed the significance test of the 1% or 5% level. This finding implies that the growth of provincial export or import will exert a positive effect on promoting the GTFEEs in the surrounding areas. However, this study can only make a preliminary judgment according to Table 7. On the contrary, Lesage (2008) and Yang (2019) pointed out that applying point estimation to test the spillover effects may cause model estimation errors.

4.4.3 Direct and indirect effects

Table 8 reveals that the direct estimations under three different spatial weights of export are all positive and significant, which indicates a positive influence on the region’s GTFEE growth. However, the indirect effects of export are negative and significant under three different spatial weights, and all passed the significance test of the 1% or 5% level. This proves that export in this region imposes a significant negative impact on GTFEE in surrounding areas. This occurs mainly for some reasons. China’s has obvious development differences between regions and uneven trade development structure between regions,the raw materials and intermediate products with low added-value to the regional source from the surrounding areas, which would generate much energy consumption and emissions and objectively reduce their GTFEEs. For example, China’s central and western regions have been crucial sources of energy and labor for the eastern coastal areas. Moreover, compared with competitors in regions with backward trade development, those trading enterprises in developed areas enjoy more opportunities to learn and absorb advanced technology and management experience, which play a significant role in improving their GTFEEs (Poon et al., 2006; Wang and Ang, 2018). In summary, the total effect of export is positive and composed of the most direct impacts.

TABLE 8

TABLE 8. Effect decomposition.

As shown in Table 8, this article can also find that the direct estimations under three different spatial weights of import are all positive and significant, indicating another positive influence on the region’s GTFEE growth. This presents that the growth of imports is conducive to improving the GTFEE. More importantly, the indirect effects of imports are positive and significant under three different spatial weights, and all passed the significance test of the 1%, 5%, or 10% level. This result indicates that the growth of imports in this region will also affect the GTFEEs in surrounding areas. The possible explanations are as follows. On the one hand, the import of capital-intensive and technology-intensive goods can facilitate the imitations and innovations by domestic enterprises, such as pollution-treatment equipment and other high-tech products, which may be called the “technology spillover effect,” thus eventually improving the GTFEE (Parrado and De Cian, 2014; Huang et al., 2017; Zhao and Lin, 2019). On the other hand, the import of consumer goods may replace domestic production and therefore reduce domestic energy consumption and emissions (Al-mulali and Sheau-Ting, 2014). Besides, during the fierce international competition, the domestic enterprises may raise their technological levels, which will bring pressure on their competitors in the surrounding regions to improve their production technology. Therefore, the GTFEEs of these adjacent regions could be improved as well.

5 Conclusion and policy recommendations

Based on the previous research, this study applied the panel data of 30 provinces from 2004 to 2017 to calculate the GTFEE with the SBM-undesirable model, based on which this study established the basic linear regression model to empirically test the influences of export and import on GTFEE. In addition, the geographic proximity matrix (W1), geographic distance weight matrix (W2), and economic, geographic distance weight matrix (W3) were introduced, respectively, into this study. The SDM was applied to test the direct and indirect spatial effects of export and import on GTFEE. The conclusions are as follows. Firstly, the inter-provincial GTFEE in China showed a ladder-like distribution pattern of eastern-central-western. Secondly, both export and import improved the regional GTFEEs, but import exhibited more effect than export on the enhancement of GTFEE. Thirdly, in terms of spatial effect, the increase in import would not only exert a positive effect on the GTFEE of the local regions, but also raise the GTFEE of surrounding provinces through the spatial spillover mechanism. Although the increase in export would have a positive influence on the GTFEE of the local regions, it would impose a significant negative impact on the GTFEE of surrounding areas. Based on the above empirical analysis, this study put forward several policy recommendations as follows:

1) The government should focus more on regionally coordinated development and keep narrowing the development gaps between regions, especially between the eastern and central-western regions. Significantly, the old path of “treatment after pollution” concentrated in the central and western regions should be avoided. The road to sustainable development requires drawing lessons from the development experience of the eastern regions. The policy needs to optimize the regional coordination of technologies and innovations and finally improve the GTFEEs in the central and western regions. For example, the government could encourage more investments in universities and scientific research institutions in the central and western provinces.

2) Moreover, the government should greatly enhance the role of imports in improving the quality of economic development, especially the imports of capital goods and high-tech products. Honestly speaking, China has been over-relying on export for numerous years. However, now, the international environment for cooperation has undergone profound changes. Therefore, as the important driving force of economic development, the import should be emphasized more than before, which will optimize the trade structure and promote the GTFEEs.

3) Additionally, the government should also promote the optimization of industrial distributions and encourage the high-tech enterprises and talents to transfer from the eastern regions to the central and western ones. The western and central provinces could fully use the late-comer advantages in trade and strengthen the cooperation with eastern provinces by introducing advanced technology and management experience. Especially for western regions, it is essential to transform their resource advantages into industrial ones, thus effectively promoting their local GTFEEs.

Though this study quantitatively investigated the relationships between export, import, and GTFEE, there remain some limitations, which could become the possible directions of future research. Firstly, the inter-provincial data applied in this study is not adequate and may easily cause some sample bias. Thus, future researchers should gather city-level or firm-level data to conduct precise investigations. Moreover, this study mainly applied total export and import to measure the level of trade. Consequently, the heterogeneity of trading partner countries may be overlooked. Therefore, future research will need to explore deeper into these fields.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Author contributions

JL: conceptualization, project administration, formal analysis, writing-review and editing. Jx: conceptualization, writing-review and editing, writing-original draft. CL: writing-review and editing, methodology, validation.

Funding

This study was supported by the National Natural Science Foundation of China (Grant no. 71964032).

Acknowledgments

The authors acknowledge financial support from the National Natural Science Foundation of China (Grant no. 71964032), the Key Project of Philosophy and Social Science of the Ministry of Education of China (Grant no. 17JZD033).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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