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Original research article, research on the impact of green technology innovation on energy total factor productivity, based on provincial data of china.


  • 1 Guangzhou Institute of International Finance, Guangzhou University, Guangzhou, China
  • 2 School of Economics and Statistics, Guangzhou University, Guangzhou, China

Against the background of carbon peaking and carbon neutralization, green technology innovation plays an important role in promoting the energy total factor productivity (TFP). This study verifies the impact of green technology innovation on energy TFP in a complete sample and the subsamples by region, by constructing a panel threshold model, and analyzes its influence mechanism on the basis of the mediating effect test based on annual provincial data of mainland China from 2005 to 2018. The empirical results reveal the following: first, with the level of economic development as the threshold variable, there is a threshold effect in the impact of green technology innovation on the energy TFP; second, green technology innovation has an impact on the energy TFP through industrial structure upgrading; that is, industrial structure has a mediating effect in the influence mechanism; and third, there is heterogeneity in the impact of green technology innovation on the energy TFP among different regions in China, and the threshold effect only exists in the western region, since the central and eastern regions have crossed a certain developmental stage.


Energy TFP is usually defined by the ratio of energy input to output ( Perez-Lombard et al., 2013 ). The promotion of energy TFP plays an important role in the sustainable development of the economy. The energy TFP includes the input of a series of means of production such as labor, energy, and capital, and is accompanied by the expected output GDP and the non-expected output carbon dioxide emissions. From the perspective of input, the promotion of energy efficiency avoids excessive energy consumption, and in terms of the output, it reduces the excessive damage to the environment. The energy TFP can reflect the efficiency of comprehensive development and utilization of energy to a large extent, and also helps to identify the state of economic growth, that is, whether economic growth depends on the consumption of energy scale or the improvement of energy use efficiency. A lot of literature works related to energy TFP have discussed ways to improve efficiency. Xie et al. (2014) measured energy TFP in the OECD and BRIC countries and found that the adjustment of the energy structure has a certain impact on energy TFP. Energy consumption can be reduced by restructuring industries so as to improve energy TFP ( Xiong et al., 2019 ; Zhu et al., 2019 ; Yu, 2020 ). The efficiency of technological innovation is the main reason for energy TFP improvement in the industrial sector, and the effect of the technological progress on energy TFP is gradually increasing ( Fisher-Vanden et al., 2006 ; Baccarelli et al., 2016 ; Naranjo et al., 2019 ). Miao et al. (2018) demonstrate the significant positive driving effect of technology innovation on energy TFP. Hellsmark et al. (2016) believed that industrial technology innovation can rapidly improve energy TFP so as to achieve rapid industrial growth and expansion. From the perspectives of changing energy structure, adjusting industrial structure, and technology innovation, the related literature fully demonstrates the effectiveness of technology innovation in improving energy TFP. However, the adjustment of energy structure and industrial structure is more dependent on the resource endowment of economic subjects, while technological innovation improves the energy utilization efficiency through improvement at the technological level, which has a more realistic value.

Energy TFP measurement needs to focus not only on the desired output but also on the undesired output. Based on the consideration of environmental factors, energy TFP measurement involves two aspects of the output: desired output and undesired output. The desired output refers to the output, such as GDP, which can increase human material products and services to a certain extent. The undesired output refers to additional products that have a negative effect on the human environment and health due to the consumption of energy and other elements in the production process, such as the greenhouse effect caused by carbon dioxide emissions in the production process. Too many studies have mainly focused on the contribution of energy to economic development, with less consideration on the impact of energy consumption on environmental quality, that is, ignoring the issue of the relationship between energy consumption and sustainable development. With the increasing prominence of environmental problems, more and more studies have included environmental factors into the consideration of energy TFP measurement to reduce the deviation in the process of energy TFP measurement ( Zhang et al., 2011 ; He et al., 2013 ; Yang and Wang, 2013 ; Simsek, 2014 ; Vlontzos et al., 2014 ). Environmental regulation significantly promotes green technology innovation and reduces environmental pollution in the process of economic development ( Du et al., 2021 ). Hu and Wang (2006) constructed a full-factor energy efficiency analysis framework to maximize the energy TFP output to improve energy TFP, which means that improving energy TFP should not only increase the desired output but also pay attention to the weakening of the undesired output. Environmental regulation can reduce the undesired output such as carbon dioxide, while improving energy TFP through technological innovation can play an essential role and have global strategic significance.

Technological innovation promotes the maturity of production technology and the development of new products, thus improving efficiency significantly. From the perspective of the innovation system, the improvement of energy TFP by technological innovation is to reduce the leakage in the process of energy use by means of process transformation, and then to improve the total factor productivity of energy. From the perspective of industrial ecological chain, technological innovation promotes the upgrading of the regional structure and the exchange and cooperation between the industrial structure, and innovation jointly promotes technological innovation and industrial structure upgrading ( Greunz, 2004 ; Motohashi and Yun, 2007 ; Altenburg et al., 2008 ), and adjusting industrial structure can improve energy utilization efficiency as well ( Zhao et al., 2010 ; Zhou et al., 2013 ; Yu et al., 2016 ; Wang et al., 2020 ). The technical level of energy TFP production depends on the technological innovation ability; empirical results show that the enhancement of technological innovation ability can effectively improve energy efficiency and reduce energy consumption intensity ( Du and Yan, 2009 ; Zhong and Li, 2020 ). Wagner et al. (2014) found from innovative activities, with potential environmental impact that the potential of technological innovation is unlimited. Although technological innovation has a strong effect on efficiency, pure technological innovation does not take into account the external effects of the environment, so green technology innovation is more in line with the goal of sustainable development ( Li and Liao, 2020 ). Considering that technological innovation promotes the upgrading of the industrial structure and thus has an impact on energy TFP, this study chooses the upgrading of the industrial structure as a mediating variable to study the indirect impact of green technological innovation on total factor productivity of energy.

Green technology innovation belongs to the scope of technological innovation, which is the general name of management and technology innovation aimed at protecting the environment. In the process of innovation practice, although some innovations can greatly improve productivity, they do not consider the external effects on the environment; for example, technological innovation is simply about increasing the output in energy-intensive industries. As a result, various industries abide by the green principle and pay more and more attention to economic development and environmental problems ( Gorelick and Walmsley, 2020 ; Sukharev, 2020 ). Green technology follows the ecological principle and the law of ecological economy, considers the saving of resources and energy in the process of innovation, avoids, eliminates or reduces the pollution and damage to the ecological environment in the process of innovation, and maintains the minimum ecological negative effect in technological innovation. Green technology innovation aims to achieve long-term sustainable development; produce economic, environmental, and social benefits; save resources and energy; and eliminate or reduce environmental pollution and degradation ( Zhou et al., 2014 ; Li et al., 2018 ). Because green technology innovation considers the external efficiency of the environment, there may be conflicts between self-interests and social benefits in the process of enterprises’ implementing green technology innovation, thus reducing the power of green technology innovation ( Braun and Wield, 1994 ; Li et al., 2019a ).

The significant effect of technological innovation on energy TFP is based on the consistency of interests which belong to stakeholders, but the impact of green technology innovation on the interests of stakeholders may be different. Green technology innovation may increase the production cost of enterprises, and the improvement of energy TFP requires more consideration of environmental externalities. Therefore, whether green technology innovation has an impact on energy TFP needs to be explored in both theory and practice. Based on this, this article studies the impact of green technology innovation on energy TFP.

This article focuses on the impact of green technology innovation on energy TFP. Its marginal contributions are as follows: first, green technology innovation that will involve subject behavior and external environmental effects related to the subject’s behavior is considered in a framework. Most of the existing literature works only consider green technology innovation or the total factor productivity of energy, and less considers external effects such as environment. In this article, green technology innovation is separated from the innovation system, the undesired output is included in TFP measurement, green technology innovation is included in the behavior of the innovation subject, and external effect of environment is considered in energy TFP measurement. Second, the threshold effect of green technology innovation on energy TFP is studied. In the process of empirical analysis, it is found that green technology innovation does not necessarily have a significant impact on energy TFP, but through the threshold effect model, it is found that when the level of economic development is the threshold variable, there is a threshold effect in the impact of green technology innovation on energy TFP. Third, the mediating effect mechanism of green technology innovation on energy TFP was studied. Through the selection and experiment of different mediating variables, this article empirically tests the mediating effect of the industrial structure in the impact of green technology innovation on energy TFP. Fourth, there is spatial heterogeneity in the impact of green technological innovation on energy TFP. Since China’s economy has very strong regional heterogeneity, according to the basic situation of economic development in the area of space, this article divides the full sample into three subsamples: the eastern, the central, and the western regions, to study the heterogeneity.

The structure of the rest of this article is as follows: the second section is about the measurement of the impact of green technology innovation on energy TFP, including model setting, variables, data sources, and test results. The third section focuses on the influence mechanism analysis of green technology innovation on energy TFP. In terms of the technology of testing the mediating effect, this part estimates the parameters and analyzes the mediating effect by setting the mediating effect model. The fourth section is about the heterogeneity analysis of the impact of green technology innovation on energy TFP. According to the basic situation of economic development, the full sample is divided into three subsamples, and the heterogeneity is analyzed. The fifth section draws the basic conclusion.

Econometric Test of the Impact of Green Technology Innovation on Energy Total Factor Productivity

Panel threshold model setting.

The improvement of energy TFP by technological innovation has been proven in a lot of literature works, but whether green technology innovation affects energy TFP needs to be tested with more empirical evidence. From the perspective of the relationship between technological innovation and energy TFP, technological innovation requires costs; the greater uncertainty of green technological innovation means that enterprises are facing greater uncertainty in technological innovation; this uncertainty makes enterprises, as the main body of technological innovation, more inclined to realize their self-interests when making decisions, and then tend to ignore the strategic interests. Accordingly, in the face of various external constraints, enterprises have very great differences in their green technology innovation motivation, so green technology innovation has an impact on total factor productivity, but this impact needs to be verified through econometric tests.

In different stages of economic development, the strength and consciousness of enterprises to support green technology innovation are different. For example, in regions with a high degree of economic development, people have higher demand for products and environmental quality, and the corresponding innovation subjects can bear greater risks of technological innovation and research. Therefore, there is a certain threshold for the impact of green technology innovation on energy TFP in theory.

On that basis, this study assumes that green technology innovation has a significant effect on energy TFP, and this effect is nonlinear and has a threshold effect, and the variable of the core threshold effect is the level of economic development. The threshold effect model can examine the function between the two and the threshold effect ( Liu et al., 2020 ). In this study, the level of economic development is taken as the threshold variable, and the panel threshold model proposed by Hansen is adopted ( Hansen, 1999 ). The basic form of the model is as follows:

where EE it represents the energy TFP, which is used to measure energy efficiency; GTI it represents the green technology innovation; threshold variable EDI it is the economic development level; γ is the threshold value to be estimated; I ( · ) is the indicator function; and when the condition in parentheses is satisfied,  I ( · ) = 1 ; otherwise, it is 0. μ i is a fixed effect, which is used to describe the heterogeneity of different provinces at different levels of economic development; ε it is the error term. In addition, i represents different provinces and t represents different years.

After the threshold and slope values are estimated, the significance of the threshold effect should be tested. The basic principle of testing the threshold effect is as follows. Taking single threshold as an example, the null hypothesis and test statistics of the model are obtained as follows:

If the null hypothesis is rejected, there is threshold effect in the impact of green technological innovation on energy TFP. S 0 is the sum of squares of residuals obtained under the null hypothesis H 0 , and S 0 ≥ S 1 ( γ ^ ) . Under the null hypothesis, the threshold value γ of the economic development level needs to be evaluated, so the distribution of F 1 is nonstandard, but the bootstrap method can be used to simulate its asymptotic distribution, so the confidence interval of distribution F 1 in Eq. 2 can be obtained.

After determining the threshold effect of the economic development level, it is necessary to test whether the threshold estimated value γ ^ is equal to its true value. The null hypothesis of the single threshold model and the corresponding test statistics are as follows:

where the LR distribution is also nonstandard. This study adopts a formula proposed by Hansen (1999) ; that is, when L R 1 ( γ ) > − 2 l n ( 1 − 1 − α ) ( α is the significant level), the null hypothesis is rejected.

Variable and Data Description

Variable description.

Energy TFP is the explained variable, which is measured by the ratio of energy consumption to GDP in many studies. This method of measuring energy efficiency is not responsive to the dynamic change of efficiency ( Hang and Tu, 2007 ; Adom and Kwakwa, 2014 ). In the continuous research of energy TFP, some methods such as index decomposition analysis (IDA), parametric stochastic Frontier analysis (SFA), and nonparametric data envelopment analysis (DEA) have been proposed to measure energy efficiency or total factor productivity ( Zhou et al., 2012 ; Filippini and Hunt, 2015 ; Li et al., 2019b ; Liao and Drakeford, 2019 ; Zheng et al., 2020 ). These methods can dynamically investigate the dynamic changes of energy efficiency or total factor productivity, and then study the effects of other factors on energy TFP, but these methods do not consider the interest correlation between the evaluation subjects. The cross-efficiency evaluation can dynamically investigate the dynamic changes of energy TFP on the basis of self-evaluation and other evaluation so that the evaluation results are comparable, and a complete ranking result can be obtained. Therefore, this study selects the DEA cross-efficiency model to measure energy TFP. The basic form is as follows:

Let S be the number of provinces selected in this study; then the vectors of m energy input indexes and n energy output of the decision-making unit DMU i are expressed as X i = ( x 1 i , x 2 i , · · · · · · , x m i ) T > 0 , Y i = ( y 1 i , y 2 i , · · · · · · , y n i ) T > 0,1 ≤ i ≤ s ,

• The constraints are

where θ ^ d i represents the cross-efficiency value of D M U i ( 1 ≤ i ≤ s ) based on D M U d . The value of the final energy TFP of D M U i is expressed by the average value of the cross-efficiency values of decision-making units D M U d from D M U 1 to D M U s .

In the energy TFP calculation, it usually takes into account the input of the means of production, while this study also focuses on the expected and unexpected outputs. Labor, capital stock, and energy consumption are used as input indicators. The number of urban employment is used to measure the labor force, the perpetual inventory is used to estimate capital stock, and the basic equation is K i , T = K i , T − 1 ( 1 − δ i , T ) + I i , T , where i and T are the i-th province and the T-th period, δ is the economic depreciation rate, I is the total fixed capital formation, the initial capital stock is obtained by dividing the fixed capital in the initial year by 10%, and the economic depreciation rate δ is set at 9.6% ( Zhang, 2008 ). Energy consumption is measured by the total amount of energy consumption in each province.

The desired output of energy TFP is measured by GDP, and the undesired output is measured by carbon dioxide emission. The details are shown in Table 1 . Data of GDP are converted to real regional GDP with the year 2000 as the base year. Carbon emissions are estimated by the direct method: C O 2 i = σ c V c + σ o V o + σ q V q , where V c ,     V o ,    and  V q represent the energy consumption of coal, oil, and natural gas, respectively, for the production of region i , and σ c , σ o , and σ q represent the carbon emission coefficients of coal, oil, and natural gas, respectively.


TABLE 1 . Input–output variables used to measure energy TFP.

The explanatory variable is green technology innovation, expressed by the number of green patent application, including green invention patents and green utility model patents. The control variables of the model include foreign direct investment (FDI), industrial structure (IS), and energy price. FDI which is closely related to economic development is measured by the proportion of foreign direct investment in regional GDP ( Li et al., 2019c ), IS was measured by the proportion of the output value of secondary industry in GDP, and the energy price is calculated by this formula: P E = λ c P c + λ o P o + λ e P e , where λ c ,   λ o , and  λ e represent the proportion of coal, oil, and electricity in total energy consumption in each year, respectively, and P c ,   P o ,  and  P e represent the average price of coal, oil, and electricity in turn, respectively. The energy price is calculated by multiplying the annual fuel and power purchase index of each province by the energy price of the previous year. The threshold variable is the level of economic development, measured by per-capita GDP in each region and adjusted to a constant price based on 2000 ( Matei, 2020 ). The mediating variable is the upgrading of the industrial structure, which is measured by the hierarchical coefficient of the industrial structure. The specific formula is W = 3 q ( 3 ) + 2 q ( 2 ) + q ( 1 ) , where q ( 1 ) ,   q ( 2 ) ,  and  q ( 3 ) are the proportions of the added value of the primary, secondary, and tertiary industries, respectively.

Data Sources

The sample data are from 30 provinces of mainland China (the sample does not include Hong Kong, Macao, Taiwan, and Tibet due to data problems) in 2005–2018. The time frequency of the data is set to year. The green patent data come from CNRDS green patent-GPRD database. The data of urban employment, total energy consumption, and the added value of the primary, secondary, and tertiary industries are derived from the China Energy Database. The output value of the secondary industry and the regional GDP are derived from the annual statistical yearbooks published by the National Bureau of Statistics. Gross fixed capital formation of the whole society comes from the Wind Database. Foreign direct investment data are from the provincial statistical yearbooks. The purchase price indexes of fuel and power come from the China Price Yearbook. The “China Price Yearbook (2004)” has a relatively comprehensive record of various energy prices, so they can be used to calculate the average price of the three energy sources in 2003 and convert into the form of yuan/ton standard coal to get the energy price in 2003. Other variables are shown in Table 2 .


TABLE 2 . Explanatory variables, control variables, threshold variables, and mediating variables used in empirical studies.

According to the data source, relevant data are collected and relevant variables are measured. The descriptive statistics of each variable are shown in Table 3 .


TABLE 3 . Descriptive statistics of variables.

It can be seen from Table 3 that the variation degree of each variable is quite great, especially the variable of the level of economic development. Table 3 describes the basic characteristics of the data for 30 provinces in China from 2005 to 2018, including the average values, standard deviation, minimum, and maximum values. For the green technology innovation data, there is a large gap between the maximum value 5.603 and the minimum value 0.001, which indicates that there are significant gaps in the level of green technology innovation among different regions. In terms of standard deviation, the standard deviation of energy price is the largest, followed by that of the level of economic development. From the discrete degree of these indicators, it can be seen that there is a large standard deviation in the three variables of the economic development level, energy price, and green technology innovation level, which indicates that heterogeneity exists in the field of green technology innovation and energy technology. Heterogeneity research can be analyzed from different perspectives and methods to explore the development law of things and the internal relations of some influencing factors in a more comprehensive way ( Li et al., 2020a ; Li et al., 2020b ; Li and Zhong, 2020 ; Li et al., 2021a ; Li et al., 2021b ).

Empirical Results

On the basis of the constructed threshold model, it is necessary to determine the existence of threshold effect and the number and size of the threshold value. Using sample data, the existence of threshold effect is tested, and the test results are shown in Table 4 .


TABLE 4 . Test results of threshold effect of the full sample.

Table 4 shows that with the level of economic development as the threshold variable, green technology innovation has a single threshold effect on energy TFP. The F statistics value of the single threshold effect test is 68.59, passing the significance test at 95% confidence level, while the F statistics value of the double threshold effect is 9.98, failing the significance test. Judging from the F statistics in Table 4 , the impact of green technology innovation on energy TFP does cause a single threshold effect based on the level of economic development. The threshold estimate of the variable of the economic development level is 7.248. The single threshold effect model is used to estimate the parameters of the model, and the results are shown in Table 5 .


TABLE 5 . Parameter estimation results of a single threshold model with full sample.

Table 5 shows that with the economic development level as the threshold variable, green technology innovation has a threshold effect on energy TFP. From the regression results of the panel threshold effect, the promotion of green technology innovation to energy TFP is restricted by the threshold effect of the economic development level. When the level of economic development is below the threshold value of 7.248, the influence coefficient of green technology innovation on energy TFP is relatively low, which is 0.113. When the level of economic development crosses the threshold value of 7.248, the influence coefficient of green technology innovation on energy TFP is increased to 0.536. This indicates that the impact of green technology innovation on energy TFP is different at different levels of economic development. Under the low level of economic development, due to extensive economic management, it is difficult for the economic entities to achieve the balance between their own interests and the social benefits in production decision-making, and they pay more attention to their own short-term economic interests; correspondingly, the low level of economic development leads to low promotion of green technology innovation on energy TFP. With the improvement of the level of economic development, the decision-making behavior of economic entities is more focused on strategic development. Local governments gradually implement environmental regulations and other measures to promote the economic transformation of various regions. Therefore, the promotion of green technology innovation on energy TFP is significantly improved.

Mechanism Analysis of the Impact of Green Technology Innovation on Energy Total Factor Productivity

Mediating effect model setting.

Green technology innovation has positive promoting effect on economic growth, and this positive role has a threshold effect, so the impact of green technology innovation on energy TFP is not direct, but through other channels. Enterprises are the main body of technological innovation. When technology and capital are combined, they will have a very important impact on the industrial ecology. From the perspective of industrial evolution, technological innovation promotes the upgrading of the industrial structure. In terms of energy consumption, the period of industrialization has greatly increased energy consumption. With the evolution of industrial structure, the industry has gradually developed to the tertiary industry. On the one hand, through the improvement of industrial technology, the comprehensive utilization efficiency of energy has been greatly improved, and then promoting carbon emissions reach the peak faster, which has effectively improved the total factor productivity of energy. On the other hand, the industrial structure gradually shifts from a higher proportion of high-energy industries to a higher proportion of low-energy industries, thus reducing the amount of energy consumption. Green technology innovation needs strategic adjustment. For the micro entity, it is difficult to cover the short-term investment cost of green technology innovation before it reaches a certain level of economic development. Therefore, there is a threshold in the impact of green technology innovation on energy TFP. Based on the above analysis, the impact of green technology innovation on energy TFP is affected by the way of the industrial structure; that is, there is a mediating effect of the industrial structure, but there may be differences among different regions.

In order to study the influence mechanism of green technology innovation on energy TFP and explore the mediating effect, this part introduces industrial structure upgrading as the mediating variable on the basis of the theoretical analysis. The mediating effect models are as follows:

where E E i t represents the energy TFP; G T I i t represents the green technology innovation; I N S i t represents the industrial structure upgrading, that is, the mediating variable; C O N T R i k t represents the control variables, including industrial structure (IS), foreign direct investment (FDI), and energy prices (PRI); subscripts i , t , and k represent different provinces, time, and control variables, respectively, i = 1,2,…,31, t = 1,2,…,9, k = 1,2,3; and ε represents the random error term.

In the mediating effect analysis, Model (10) is first regressed to test whether the regression coefficients of energy TFP and green technological innovation are positive, and only when the coefficients are significantly positive the next test can be carried out; otherwise, the test is terminated. Second, Model (11) is regressed to test whether the regression coefficients of the mediating variable industrial structure upgrading and green technology innovation are significantly positive, and if they are significantly positive, green technology innovation supports the upgrading of the industrial structure. Then, Model (12) is regressed, and if the coefficients c ′ and b are significant and the value of c ′ decreases compared with that of c , there is a partial mediating effect, and if the coefficient c ′ is not significant while the coefficient b is significant, there is a complete mediating effect.

Mediating Effect Test and Result Analysis

The parameters in Models (10)–(12) are estimated by using the same sample data, and the results are shown in Table 6 .


TABLE 6 . Mediating effect results (with the stepwise regression coefficient method).

Table 6 shows that green technology innovation influences energy TFP through the channel of industrial structure upgrading; that is, industrial structure upgrading has a mediating effect in the influence mechanism. The overall regression results show that the regression coefficients of both green technology innovation and industrial structure upgrading on energy TFP are significant, indicating that the total effect is significant. In Model (10), the impact of green technology innovation on energy TFP is verified. The coefficient of GTI is 0.192, which is significantly positive at the level of 1%, indicating that green technology innovation promotes energy TFP. In Model (11), there is a significant positive correlation between green technology innovation and industrial structure upgrading at the level of 1%, which indicates that green technology innovation accelerates industrial structure upgrading. In Model (12), after adding the industrial structure upgrading variable to Model (11), the coefficients of green technology innovation and industrial structure upgrading are significantly positive, and the coefficient of green technology innovation is reduced from 0.192, when there is no mediating variable, to 0.0758. It indicates that industrial structure upgrading plays a partial mediating effect in the impact of green technology innovation on energy TFP.

It can be concluded from Table 7 that the mediating effect of the industrial structure in the influence mechanism of green technology innovation on energy TFP is robust. Compared with the empirical results in Table 6 , the significance and direction of parameter estimation in Table 7 have not changed, and the mediating effect has not changed significantly. Through the Bootstrap test, the direct effect value of green technology innovation on energy TFP is 0.12, while the indirect effect value of green technology innovation on energy TFP through industrial structure upgrading is 0.09, and the mediating effect accounts for 42.86% of the total effect, and the effect is significant. According to Table 7 , the confidence intervals of direct and indirect effects are observed, excluding 0, indicating that the mediating effect of green technology innovation on energy TFP through industrial structure upgrading is tenable and robust.


TABLE 7 . Robustness test results with the Bootstrap sampling method.

Heterogeneity Analysis on the Impact of Green Technology Innovation on Energy Total Factor Productivity

Sample partition based on regions.

According to the above test results, green technology innovation has a threshold effect on energy TFP, so to a large extent, the impact of green technology innovation on energy TFP is heterogeneous. It can also be known from the aforementioned descriptive statistics that there are differences in the level of economic development, green technology innovation, and energy TFP in different regions. As a country with extremely uneven economic development, China has significant differences in the level of economic development among different provinces. Therefore, this part analyzes the heterogeneity of the impact of green technology innovation on the energy TFP based on regional differences.

Provincial administrative regions are the main basis of regional division in China. Most of the research literature works on Chinese regions are based on Chinese mainland provincial administrative regions. This study also divides regions into 30 provincial administrative regions (excluding Tibet). According to the descriptive statistics of variables in Table 3 , combined with the level of economic development and the practice of most literature, 30 provinces are divided into three regions: the eastern, the central, and the western regions. There are 11 provinces and municipalities in the eastern region, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region includes eight provinces such as Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. There are 11 provinces and municipalities in the western region, including Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shanxi, Gansu, Ningxia, Qinghai, and Xinjiang.

Empirical Result Analysis

According to the division of regions, three subsamples are obtained. The test of the threshold effect shows that there is heterogeneity in the threshold values of the three subsamples. Therefore, the threshold effect of the three subsamples should be tested separately. The specific test results are shown in Table 8 .


TABLE 8 . The threshold effect test results of the three sub-samples.

From Table 8 , it can be concluded that with the level of economic development as the threshold variable, there is heterogeneity in the threshold of three subsamples. With the level of economic development as the threshold variable, the green technology innovation in the western region has a single threshold effect on the energy TFP, but there is no threshold effect in the eastern and central regions. The F statistic value of the single threshold effect test is 67.63, which is significant at the level of 1%, and the F statistic value of the double threshold effect test is 12.99, which has not passed the significance test. The F statistic values of the threshold effect test in both the eastern and the central regions do not pass the significance test. Therefore, it can be concluded that the threshold effect of green technology innovation on energy TFP has spatial regional differences with the level of economic development as the threshold variable. At the same time, the results of the threshold effect test show that the central and eastern regions have crossed a certain stage of economic development.

Based on the above analysis, the parameters of the threshold effect in the western region are estimated, and the results are shown in Table 9 .


TABLE 9 . Parameter estimation results of the single threshold model in the western region.

Combining the results in Table 5 and Table 9 , it can be seen that the threshold effect range of green technology innovation on the energy TFP in the western region is lower than that of the full sample. In Table 5 , the threshold effect values of the full sample are 0.113 and 0.536, and in Table 9 , the threshold effect values of the western region are 0.239 and 4.062. The range of the threshold effect value in the western region becomes smaller, while the threshold effect in the eastern and central regions no longer exists, indicating that when economic growth goes beyond a certain limit, the positive effect of green technology innovation on energy TFP will play a promoting role, which can be gradually independent on the level of economic development. According to the estimated results of the parameters in Table 9 , when the level of economic development is below the threshold value of 4.062, the influence coefficient of green technology innovation on energy TFP is 0.239. When the level of economic development increases above the threshold value of 4.062, the influence coefficient of green technology innovation on energy TFP is 4.495. This indicates that with the development of the western region, the role of green technology innovation in promoting energy TFP is becoming more and more significant, so in the construction of the western economy, the investment of green technology innovation should be increased, and the improvement of energy TFP should be promoted, so as to achieve a win–win situation.

By constructing the panel threshold effect model and using Chinese mainland provincial data, this study examines the impact of green technology innovation on energy TFP and analyzes its mechanism and heterogeneity. The conclusions are as follows:

First, with the level of economic development as the threshold variable, green technology innovation has heterogeneous threshold effect on energy TFP. Based on the data of the full sample, it is estimated that the impact of green technology innovation on energy TFP is restricted by the level of economic development, and there is a single threshold effect. However, combining with the empirical analysis results, it can be found that this threshold effect does not exist in the central and eastern regions. Green technology innovation has positive effect on energy TFP, which indicates that green innovation must be promoted in order to achieve long-term sustainable development of economy. From the threshold effect, the promotion effect of green technology innovation on energy TFP increases with the improvement of the economic development level.

Second, green technology innovation has an impact on energy TFP through industrial structure upgrading; that is, industrial structure has mediating effect in the influence mechanism. Industrial structure upgrading realizes industrial structure optimization by increasing the proportion of the tertiary industry, improving energy utilization efficiency through technology innovation and product upgrading, and improving energy TFP.

Third, the impact of green technology innovation on energy TFP is heterogeneous in the western, central, and the eastern regions of China, and the threshold effect only exists in the western region, since the economic development of the central and eastern regions has crossed a certain stage. In the eastern and central regions, there is no threshold effect in the impact of green technology innovation on energy TFP, while in the western region, there is a single threshold effect, and the impact of green technology innovation on energy TFP increases significantly with the level of economic development. In the eastern and central regions of China, the effectiveness of green technology has exceeded a certain stage, and green technology innovation has gradually played a strategic role in promoting the total factor productivity of energy.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , and further inquiries can be directed to the corresponding author.

Author Contributions

MW: grasp the theme and research direction, YL: empirical research and data, GL: data.

This research was funded by the Chinese National Funding of Social Sciences, grant number 18ATJ002, and the 13th Five-year Plan of Guangzhou Social Science, grant number 2018GZYB129.

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2021.710931/full#supplementary-material

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Keywords: green technology innovation, energy TFP, threshold effect, mediating effect, heterogeneity

Citation: Wang M, Li Y and Liao G (2021) Research on the Impact of Green Technology Innovation on Energy Total Factor Productivity, Based on Provincial Data of China. Front. Environ. Sci. 9:710931. doi: 10.3389/fenvs.2021.710931

Received: 17 May 2021; Accepted: 03 June 2021; Published: 25 June 2021.

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Copyright © 2021 Wang, Li and Liao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yanling Li, [email protected]

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Environmental technology and a multiple approach of competitiveness

  • Milad Abdelnabi Salem 1 ,
  • Fekri Shawtari 1 ,
  • Hafezali Bin Iqbal Hussain 2 &
  • Mohd Farid Shamsudin 3  

Future Business Journal volume  6 , Article number:  17 ( 2020 ) Cite this article

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Metrics details

This paper investigates the relationships between environmental technology and competitiveness focusing on 224 industrial corporations. To date, there is still a debate regarding the benefits of being green. Previous literature has investigated this relationship mostly in developed countries. Additionally, the majority of these studies do not disaggregate the environmental practices and competitiveness. Less attention has been given to the environmental issues in developing countries. This study aims to fill these gaps by breaking down the environmental technology into two processes and products-focused practices and investigating their effects on the multiple approaches of competitiveness represented by image-, profits-, and satisfaction-related aspects of competitiveness. The study adopts a cross-sectional study using a self-reported questionnaire. The collected data are analysed using structural equation modelling technique based on AMOS methods. The results revealed that only products-focused practices could improve the three dimensions of competitiveness. The processes-focused practices did not contribute to any of the competitiveness aspects. Such results provide new insight for the application of resource-based view theory in green-based developing countries.


Competitiveness reflects the match between the change in the surrounding environment and internal capabilities of corporations [ 48 ]. Companies use their tangible and intangible resources to promote their competitive position [ 97 , 98 ] since they reflect the weaknesses and strengths of the corporations [ 33 ].

Resources, directly and indirectly, support corporations in improving their competitiveness [ 15 , 16 , 48 , 51 , 72 , 79 , 97 ]. It is commonly known that environmental technology could be a source of competitiveness [ 24 , 48 , 63 , 85 , 88 , 92 ]. Environmental technology means using raw materials that have low environmental impact, processing them efficiently, and promoting reutilisation and minimal waste of their final products, thus changing the products and processes of a given production cycle [ 32 ]. The environmental technologies aim to reduce negative impacts of company’s products and services on the environment [ 12 , 44 , 57 , 90 ]. Processes- and products-focused practices are interrelated because engaging in pollution prevention activities requires the consideration of both the products and the processes for manufacturing [ 12 ].

Nevertheless, several studies suggest addressing the two concepts in a separated fashion [ 21 , 24 , 41 , 57 , 58 ]. Klassen and Whybark [ 57 ] stated that activities related to products process include pollution prevention technologies, which require adaptation in both processes- and products-focused practices.

This study relies on resource-based view theory in developing the framework of the study. The researchers have reviewed the related articles to build the hypotheses of the study. The study adopted a cross-sectional survey method, which means the data are collected at one point in time. A survey method is an appropriate tool when the researcher aims to collect data on particular attributes and opinions of a population, and these data are unavailable in secondary sources [ 29 , 96 ]. The following sections discuss the literature review and the methodology of the study.

Literature review

This review focuses on the literature that pertains to the concepts that form the theoretical frame of this paper. By and large, outside of definitions, it elucidates what we currently know about green technology and its relationship with organisational performance according to the resources based view.

Green technology

Green technology refers to the activities related to both products and processes practices. Processes-focused practices refer to activities that intend to install a greater sense of environmental protection in the production processes. This involves measuring things such as using less polluting inputs, redesigning production processes to be less polluting, and recycling products [ 24 , 48 , 85 , 88 , 92 ]. Christmann [ 24 ] noted that such practices could be divided into pollution prevention and innovation of environmental technology. Klassen and Whybark [ 57 ] pointed out that process adaption refers to the fundamental changes to the manufacturing process that reduce any negative impacts on the environment during material acquisition, production, or delivery. Additionally, González-Benito and González-Benito [ 41 ] provided a detailed picture of such practices, classifying processes-focused practices into internal processes-related practices and external processes-related practices.

Internal processes practices consider such things as the installation of emission filters or waste separation, installation preparation systems, acquisition of clean technology, using the renewable resource of energy, and concentration of environmental criteria for production planning, while the external processes practices refer to the activities that consider aspects related to the distribution and supply actions. Such activities can be reflected in the purchase of ecological products, incorporation of environmental performance criteria in supplier selection processes, consolidation of shipments, using cleaner transportation methods, and the establishment of recuperation and recycling systems.

Products-focused practices are related to product aspects aiming to design or develop more environmentally friendly products [ 21 ], which include things such as redesigning product packaging and products to be more environmentally responsible, developing new environmentally responsible products, and advertising the environmental benefits of the production [ 21 , 28 , 57 , 88 ]. González-Benito and González-Benito [ 41 ] classified the products-focused practices into several dimensions, namely using alternative materials that reduce pollution and hazard, reducing resource consumption, designing for disassembly, designing the product in a manner enabling the reusability and recyclability of the product, remanufacturing, and disposal. As a result, products-focused practices intend to make production or the goods less damaging to the environment, which gives extra value to these products or goods. Klassen and Whybark [ 57 ] identify such practices as all investments that significantly modify an existing product’s design to reduce any negative impacts on the environment during any stage of product manufacturing, using, disposing, and reusing.

In general, products- and processes-focused practices can be captured by several indicators that have been widely used by previous literature. These include things such as substituting polluting and hazardous materials/parties with environmentally friendly materials/parties; designing products with a constant focus on reducing resource consumption and waste reduction; designing products that are dismantled, reused, and recycled; preferring green products in purchasing, consolidating the shipments; selecting cleaner transportation methods; using recyclable and reusable packaging/containers in logistics; implementing cleaner processes and technologies; and adopting recuperation and recycling systems (e.g. [ 28 , 41 , 58 , 76 , 87 , 88 ]).

Green technology and competitiveness

Despite the high cost of products- and processes-focused green practices, some historical examples have shown that behaving in an environmentally friendly way could save companies additional costs such as costs related to cleaning up their waste and loss of natural resources. For example, replacing its non-environmentally friendly parts by 3M Pharmaceutical Corporation in California imposed cost of $60,000. However, it removed the corporation annual solvent purchase of $15,000 and the need for $180,000 in emission control equipment. Additionally, it protected the environment since it eliminated around 24 tons of air pollution from the California atmosphere [ 62 ]. Shrivastava [ 90 ] found that focusing on green technological practices could benefit both the surrounding environment and competitiveness.

Similar results are articulated in environmental literature [ 24 , 41 , 54 , 57 , 60 , 76 , 82 , 85 , 90 ]. Porter and Van der Linde [ 73 ] emphasised that environmental innovation can be a means to improve the competitiveness of corporations. Such innovation has been found to have a direct relationship with corporate competitiveness [ 28 ]. Rubashkina et al. [ 80 ] concluded that environmental regulations have a positive influence on the output of innovation activity represented by patents of European manufacturing sectors, which is considered support of Porter’s model. Additionally, Shrivastava [ 90 ] found that adoption of environmental technology can improve the public image of the corporation, which can be considered an aspect of competitiveness. Although Shrivastava considered one aspect of competitiveness represented by the public image to be a result of environmental technology practices, the findings provide indicators that such practices can improve the competitiveness of the corporation. Additionally, eco-design as products-focused practices were found to be significantly related to cost reduction [ 100 ], and the same relationship was observed with reverse logistics [ 35 ]. Also, such a relationship was observed in the study of Lin et al. [ 60 ] that investigated the relationships between market demand, green products, and corporate performance of a sample of 208 Vietnamese motorcycle corporations. The study concluded that there is a positive relationship between green products and corporate performance represented by market position, cost reduction, profits, and reputation. Additionally, Fraj et al. [ 39 ] confirmed that a proactive environmental strategy and innovation favour organisational competitiveness.

Although previous studies came out with similar findings indicating that green practices could improve competitiveness, some argue that engaging in such practices might impose costs and consequently negatively affect the corporation. Sarkis and Cordeiro [ 85 ] investigated the relationships between short-run financial performance represented by return on sales and pollution prevention and the end-of-pipe policies within 482 US firms in 1992. The study found that the end-of-pipe and pollution prevention policies have a negative relationship with financial performance and that pollution prevention had a larger negative relationship with return on sales than did end-of-pipe policy.

Interestingly, González-Benito and González-Benito [ 41 ] found that products- and processes-focused practices had different impacts on different dimensions of corporate performance. While the study found that product design practices had a significant relationship with the market performance (reputation, image, market expectations, and new products), such a relationship seemed to be insignificant with regard to the relationship between processes-focused practices and market performance. However, the study found that both practices do not have significant relationships with other performance measures (e.g. quality, cost, financial performance). Such inconclusiveness in the results of previous studies creates fertile ground for further investigation.

Many regard corporate social/environmental concepts as a Western phenomenon that results from developed institutions and robust systems many of which are hard to find in developing countries [ 4 , 64 ]. Such an understanding would have guided studies to focus on corporate social/environmental concepts and their relationship with the health of an organisation exclusively in developed countries. This disproportionate focus on developing nations means that the same relationship in developing countries has been overlooked. This imbalance is evident in Orlitzky et al. [ 68 ] and Horváthová [ 50 ]. Interestingly, such studies noted that the country location or/and regulations influence how environmental issues relate to corporate performance [ 50 ]. Given this background, our research enriches our understanding of the relationship between corporate social/environmental concepts and organisation in Libya as a developing country.

Moreover, an overview of previous literature has revealed a dearth in research on the subject in developing countries, and specifically in Arab countries such as Libya. For instance, Etzion [ 36 ] stated that very few studies have, until recently, considered how corporate performance requires the consideration of environmental issues in the context of non-developed countries. This oversight necessitates empirical research detailing the relationship between sustainable corporate performance and firm performance for the context of developing countries [ 42 ].

Resource-based view theory

The resource-based view (RBV) theory has been extensively applied in the aim of investigating the relationships between the resources and competitiveness. RBV theory relies on the assumption that performances of companies are varied due to resources heterogeneity across the corporations [ 15 , 16 , 48 , 51 , 97 , 98 , 101 ]. An organisation’s resources constitute its dynamic capabilities and its ability to create, extend, or modify its resources [ [ 9 ], p. 3]. It includes routines that determine an organisation’s accomplish its goals. This depends heavily on tacit knowledge [ 22 , 37 , 56 , 99 ].

The resource-based view theory often overlooks and under-appreciates the importance of the natural environment [ 48 ]. Hart [ 48 ] summarised several capabilities that can be possible sources of competitiveness, namely technology, design, production, procurement, distribution, and services. Consequently, the study assumes that green technology practices could be considered environmental capabilities that capitalise on tacit knowledge that is difficult to observe or replicate [ 22 , 55 ]. Organisations can boost their competitiveness by capitalising on this often overlooked resource [ 22 , 48 , 55 , 78 , 13 ]. Accordingly, the resource-based method promotes the efficient use of resources for improved environmental sustainability and greater competitiveness [ 65 ].

Notwithstanding the importance of RBV, the theory was criticised for its focus on overall performance instead of focusing on the different outputs. For instance, Ray et al. [ 79 ] suggested with the disaggregated dependent variable when testing the RBV. Moreover, there is evidence of the lack of environmental management studies in developing countries [ 4 , 36 , 42 , 64 ]. For instance, Etzion [ 36 ] has stated that only a few studies have investigated the link between green practices and corporations’ performances. Therefore, further empirical research is required in developing countries [ 42 ]. As a response to such calls, the current study aims to determine the influences of green technology practices and competitive aspects of industrial corporations. Consequently, it aims to answer the following question:

To which extent can the green technology explain the competitiveness?

Extensive literature review and in line with RBV theory, the following hypotheses are developed:

Green technology aspects contribute positively to the different aspects of competitiveness.

Products-focused practices contribute positively to the image aspects of competitiveness.

Products-focused practices contribute positively to the profit aspects of competitiveness.

Products-focused practices contribute positively to the satisfaction aspects of competitiveness.

Processes-focused practices contribute positively to the image aspects of competitiveness.

Processes-focused practices contribute positively to the profit aspects of competitiveness.

Processes-focused practices contribute positively to the satisfaction aspects of competitiveness.

The questionnaire development

The items of the questionnaire have been selected from previous environmental management literature to measure the variables as follows: 13 items have been used to represent conventional green practices (e.g. [ 20 , 28 , 41 , 58 , 76 , 87 , 88 ]) and 11 items have been used for the competitiveness (e.g. [ 8 , 28 , 31 , 54 , 61 , 76 , 87 , 88 , 93 , 94 ]).

All questionnaire-based surveys require testing for reliability and validity before conducting the actual survey. Content validity means ensuring the scale can measure what it is supposed to measure [ 46 ]. In other words, the data are considered to be contently validated if experts agree that the instruments of the study include items that can cover all variables [ 14 , 47 , 83 ]. Additionally, Hair et al. [ 46 ] noted that validation refers to referring specialists or experts to review the suitability of the items within the construct. Validity means that the indicators represent the concept accurately while reliability pertains to the consistency between the indicators [ 46 ]. When a questionnaire is valid and reliable, it means that its question is understood clearly by the respondents, and the response options are appropriate [ 96 ].

All items were subjected to reliability and validity test prior to the main data collection. With regard to the content validity of the questionnaire, experts in the same field have checked the questions in the instrument to ensure that they are comprehensive, are relevant, and reflect the phenomena to be measured. Additionally, the researcher conducted two interviews with those in charge of environmental activities in two corporations with characteristics similar to the target population. The respondents’ feedback suggested that the questionnaire is understandable and did not need much time to be completed. The experts equally indicated that since the respondents are familiar with environmental issues, they are likely to be comfortable with the proposed seven-point Likert scale.

Additionally, a sample of 50 environmental managers were randomly choosing to answer the questionnaire for the pilot test. Several studies have recommended that a sample size of 50 could be an adequate for factor analysis [ 30 , 40 , 89 ] and reliability tests [ 47 , 49 , 84 ].

First, we validated the factor structure using exploratory factor analysis. This method is commonly used in environmental literature. For instance, Mardani et al. [ 65 ] researched several prominent databases to determine the frequency of SEM techniques used in studies in the period from 2005 to 2016. Interestingly, they found that around 61% of the published papers have used exploratory factor analysis to validate their data. The items of competitiveness are loaded on three dimensions named image-, satisfaction-, and profits-related aspects with total variance explained value of 55.286. Additionally, the items of green practices variable are loaded on two factors named processes-focused and products-focused explaining the total variance of 62.458.

Second, the reliability test was conducted to insure the existence of the consistency between the indicators [ 46 ]. A Cronbach’s alpha range < 0.6 is poor, moderate between 6 and 7, good when ranging between 7 and 8, very good between 8 and 9, and excellent when equal to greater than 9 [ 46 , 67 ]. If alpha > 0.95, the items should be checked to ensure that they measure different aspects of the concept [ 46 ]. Reliability test resulted in Cronbach’s alpha’s values greater than 0.6, which is considered acceptable as mentioned by Nunnally et al. [ 67 ] and Hair et al. [ 46 ]. Table  1 shows a summary of factor analysis and reliability.

Analysing the main data

After the confirmation of both validity and reliability of the instrument, the actual survey is carried out. The data were collected from a sample of 224 Libyan industrial corporations that represent a response rate of 82%. The target of the study was organisational level as represented by either production manager, environmental management manager, or general manager in small companies [ 95 ]. The following section presents the descriptive statistics of the questionnaire items.

First, Green practices are the activities undertaken by the corporations to make environmental sound regarding their products and manufacturing processes. In general, this variable scored a mean value of 4.14 for all items with a standard deviation of 1.47338. The previous scores indicate that the corporations give moderate importance to these practices.

The mean values of the items ranged from 3.91 to 4.38. The highest value was for preferring green products in purchasing, while the lowest value was for consolidating the shipments. The remaining items were located between these two values as follows: recyclable packaging with a mean value of 3.93, followed by product’s ability to dismantle with a mean value of 3.98, adopting recycling systems with a mean value of 4.07, cleaner transportation methods with a mean value of 4.13, each of reducing resource consumption during the production and product usage stages scored a mean value of 4.18, ecological material in primary packaging with a mean value of 4.19, and finally each of clean processes and technologies and substituting polluting material scored a mean value of 4.32, and finally reducing waste generation during production scored a mean value of 4.34. Table  2 summarises the descriptive statistics of green practices.

Second, competitiveness reflects the degree to which environmental management was beneficial for a number of corporate goals. Items related to the competitiveness have mean values that ranged from 4.10 to 4.92, which indicate that some improvements were gained as results of engaging in environmental activities, especially in aspects related to employees’ retention, sales, and management satisfaction.

Table  3 shows that better recruitment and staff retention recorded the highest mean value of 4.92, followed by achieving higher long-term profits with a mean value of 4.72, increasing sales with a mean value of 4.67, and increasing management satisfaction with a mean value of 4.61, followed by productivity with a mean value of 4.59; both reducing cost and increasing market share have the same mean value of 4.58, followed by each of achieving higher short-term profits and increasing shareholders satisfaction with a mean value of 4.57, improving corporate image with a mean value of 4.53, and finally improving product image with a mean value of 4.10.

In addition to the descriptive part, data were screened for problems in the data that might undermine its validity.

We performed an independent-sample T test to identify the differences between the early and late respondents [ 10 , 17 , 46 ]. The test revealed no significant difference between the two groups. Also, there were no outliers in the data after using Mahalanobis distance, which represent the distance from the case to the centroid of all cases for predictor variables [ 47 , 91 ]. The Harman single factor was also used to identify serious threats in the data due to common method variance [ 45 , 69 , 70 ]. Interestingly, the single-factor model resulted in more than one factor, and the first factor explained 30.497 of the variance, which indicates that common method bias was not a serious threat in this study.

Additionally, correlation matrix shows that there is no evidence of existence of multicollinearity between the variables as all correlation values are less than 0.8 according to the rule of thumb by Hair et al. [ 46 ], who stated that when the correlation between two independent variables is higher than 0.8, it can be an indicator of the existence of multicollinearity, which can deteriorate the results of the analysis. Table  4 shows the correlation results.

Structure equation modelling technique

Structural equation modelling is when multiple variables are studied using statistical methods to determine how they relate to each other [ 47 ]. This technique enables software such as AMOS to be utilised for assessing the confirmatory factor analysis and building the measurement model that is currently allocated before evaluating the structural model (the proposed theoretical framework), which will help in validating the hypothesised model [ 19 , 45 ].

The framework of this research was developed from a review of the literature from which we derived the concepts that framed the research and the analytical tools to process the data, particularly structural equation modelling (SEM) using AMOS. We adopt a reflective model given that our indicators are interchangeable and measure common themes [ 53 , 77 ]. Interchangeable indicators help measure the construct based on several relevant items underlying the domain of the construct [ 25 , 66 ]. It also means that adding or deleting an item will not affect the conceptual domain of the construct [ 53 , 77 ]. This approach is justified as several studies have used it to measure models that comprise few items.

Confirmatory factor analysis (CFA)

CFA was applied for both endogenous and exogenous variables using structural equation modelling (SEM) AMOS 20 technique. The following section discusses the results of confirmatory factor analysis.

First, for the exogenous variables (green practices) Fig.  1 shows that eight items were subject to CFA. It also shows that the P value is significant, which indicates the lack of fit in the exogenous variables. Therefore, Q10 is deleted as it represents the highest modification index item.

figure 1

The hypothesised model of green practices

After deleting Q10, the fit is improved and constructs left with seven items (four items from processes-focused practices and three items from products-focused practices). Figure  2 shows the results of CFA for exogenous variables. It shows that after deleting Q10, all criteria are improved ( P , Chi-square/ df , GFI, TLI, CFI, and RMSEA).

figure 2

CFA of green practices

Second, for the endogenous variables (competitiveness aspects) Fig.  3 shows that 11 items were subject to CFA. It also shows that P value is significant, which indicates the lack of fit in the endogenous variables. Therefore, Q7 was deleted as it represents the highest modification index items.

figure 3

The hypothesised model of competitiveness

After deleting Q7, the fit is improved and constructs left with ten items (four items from profits-related aspects, three items from image-related aspects, and three items for satisfaction-related aspects). Figure  4 shows the results of CFA for endogenous variables. It shows that after deleting Q7, all criteria improved ( P , Chi-square/ df , GFI, TLI, CFI, and RMSEA).

figure 4

CFA of competitiveness

The structural model of the study

After conducting the confirmatory factor analysis for both the exogenous and endogenous variables, the study reached the final structural model as shown in Fig.  5 . The figure shows that five constructs left with 17 items after deleting questions based on their factor loadings and higher modification indexes. Seven items resulted from CFA as probable measurements of green practices within the Libyan industrial sectors and ten items reflect the constructs of the competitiveness. Figure  5 illustrates the final structural model that resulted from AMOS 20.

figure 5

The structural model

The loadings of items range from the lowest 0.47 of profit question 10 to the highest 0.79 of question 3 of the processes-focused practices construct, which reflects that the factor loading of each item is higher than the suggested 0.40 cut-off criteria for SEM loadings [ 47 ].

The reliability test of both environmental technology and competitiveness constructs recorded Cronbach’s alpha values greater than 0.6 for each factor. This is an acceptable range according to Nunnally et al. [ 67 ] and Hair et al. [ 46 ]. Furthermore, correlation matrix recorded no evidence of multicollinearity between the variables as all correlation values are less than 0.8. According to Hair et al. [ 46 ], if the correlation > 0.8, then severe multicollinearity may be present. Table  5 shows the loading of items, correlations, and reliability of the structural model.

These results show that the model is statistically accepted [ 18 , 45 , 47 ]. Additionally, other criteria such as CFI, GFI, TLI, and RMSEA support that the model fits the data very well.

Hair et al. [ 47 ] recommended less than three indicators per construct. Chin [ 23 ] found that structure equation modelling should include a maximum of four items per construct in for acceptable results. More than that, risks produce unacceptable results. With this, we can conclude that the model of this research is acceptable.

The regression weights table (Table  6 ) shows that products-focused practices positively influence the three aspects of competitiveness, which reflects the support of the first three hypotheses (H1.1, H1.2, and H1.3). On the other hand, the table shows that there is not enough evidence to support significant relationships between the processes-focused green practices and the aspects of competitiveness ( P  > 0.05 for all processes-related aspects constructs). Therefore, the last three hypotheses were rejected (H2.1, H2.2, and H2.3). Moreover, it shows that the expected relationships seem to be negative.

The result shows that products-focused practices influence all aspects of organisational competitiveness. Such a result is consistent with previous literature. For instance, Chuang and Huang [ 26 ] found that the competitiveness of Taiwan manufacturing companies was enhanced by incorporating environmental practices. Famiyeh et al. [ 38 ] reached the same conclusion. Additionally, environmental innovation has a positive impact on the competitiveness of Chinese manufacturing enterprises [ 27 ]. Moreover, Lee et al. [ 59 ] found that the competitive advantages of Italian manufacturing SMEs was positively affected by the dimensions of sustainability including the environmental once. Ashton et al. [ 11 ] concluded that clean development mechanisms affect the performance of Malaysian companies positively. Moreover, Junquera and Barba-Sánchez [ 52 ] revealed that Spanish companies experienced cost-based and differentiation-based competitive advantages when they adopted a proactive environmental policy.

On the other hand, processes-focused practices do not have any significant effects on the dimensions of competitiveness. The results indicate any improvements in the processes-focused practices will not lead to any improvements in the competitiveness-related aspects. This result leads to rejecting the last three hypotheses (H2.1, H2.2, and H2.3). These results are consistent with the findings of other studies. For instance, Aboelmaged [ 1 ] found that when Egyptian SMEs integrated technology and environmental regulations, they did not see significant improvements in sustainable manufacturing practices. Additionally, González-Benito and González-Benito [ 41 ] concluded that processes-focused practices do not have significant relationships with performance measures such as quality, cost, financial, and market performance.

According to Poole and Van de Ven [ 74 ], in the case of failed hypotheses, it could be due to temporal differences. For instance, new organisations behave differently to seasoned and established organisations. This reasoning applies to this study as the majority of environmentally related studies focused on established organisations in developed countries which behave differently to organisations in developing countries that do not have the same regulatory framework and corporate environment as those found in developed nations. As seen in this research, this is true for the case of Libya. In summary, the different stages of development could explain why the results of this study differ from those of the majority of the literature.

Exploratory and confirmatory factor analyses were applied to confirm each construct of the model. Doing so resulted in two constructs representing green technology, namely processes- and products-focused practices. Additionally, competitiveness was laid on profits-, satisfaction-, and image-related practices. The study expected that each construct of competitiveness would be explained by each of the green processes- and products-focused practices. This was in line with RBV theory, which assumes that engaging in environmental practices will improve the competitive position of the company [ 48 ], and that only proactive environmental governance is a source of competitiveness, because it was unique to the firm and difficult to obtain by competitors [ 43 ]. However, the results show that only products-focused practices could improve the three dimensions of competitiveness. Additionally, it revealed that processes-focused practices do not contribute to any of the competitiveness aspects. Such results are in line with previous literature [ 41 , 85 ]. It corresponds with the assumption that the profits of the company might be affected by type of environmental innovation rather than the environmental innovation in general [ 81 ]. It also could be due to that products are something that can be seen and evaluated by the customers compared to the processes, which reflect internal intangible resources that cannot be evaluated directly by the customers. Therefore, the consequences of such processes are not valuable unless transformed into tangible outputs. These outputs are represented by products.

The paper contributes to the body of knowledge by stating and testing the potential relationships between each practice of green technology and a multidimensional approach to competitiveness. It contributes to the debate of whether it pays to be green. Additionally, it highlighted the lack of research on environmental issues in developing countries [ 36 , 42 ]. It articulated the competitiveness of Libyan industrial companies as weak [ 2 , 3 , 5 , 6 , 7 , 75 ], and such weakness could be attributed to environmental issues [ 34 , 71 ]. Therefore, it may help to create or improve the awareness of the decision-makers in Libyan industrial corporations towards their environmental actions, and ways to utilise such actions in improving both the surrounding environment and the corporations’ goals.

Despite the contributions of the paper, it has several limitations that should be taken into consideration when referring to this paper. First, the study used a self-reported questionnaire filled in by managers in the study sample. Therefore, survey data might be subject to social desirability bias [ 12 , [ 86 ]]. Second, this study was conducted in Libya, which is considered a developing country, caution should be taken when generalising the results of the study, and the results may be generalised only to a similar environment and stage of development. Third, another limitation of the study is that some items have been deleted from the hypothesised models during the process of CFA, which may affect the validity of the construct. However, reflective models are not disturbed the addition or deletion of an item as it preserves the conceptual integrity of the construct [ 53 , 77 ]. Finally, although 224 industrial corporations can represent an acceptable sample size for this type of study, future studies should increase the sample size to obtain stronger results. This is based on the fact that the sample size can affect the results of a study, and the bigger sample size, the more likely the results are credible and generalisable [ 47 ].


Analysis of moment structures

Structural equation modelling

Confirmatory factor analysis

Goodness-of-fit index

Tucker–Lewis index

Comparative fit index

Root mean square error of approximation

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Authors’ contributions

All the mentioned authors have substantial contributions to the conception of the manuscript, analysis, or interpretation of data for the manuscript. MAS is the corresponding author, and he has built the framework of the study and analysed the main data. FS has major contributions to the study, and the co-author has substantial contribution in screening the data and conducting the pilot study’ test. HBIH has a great contribution in reviewing the data analysis section, and his comments and recommendations have improved the data analysis process and facilitated the interpretation of the data. MFS is the author who summarised the outputs of literature review and reviewed the whole manuscript. All authors have read and approved the manuscript.


We wish to dedicate our acknowledgements and appreciations to all participants in our survey at the industrial companies. Without their cooperation and valuable feedback, it was impossible to complete the study. Additionally, we would like to present our special thanks to the staff at University Utara Malaysia and University Kuala Lumpur for their support.

Competing interests

The authors declare that there are no significant competing financial, professional or personal interests that may affect the manuscript.

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The data that support the findings of this study are available from the corresponding author [Milad Abdelnabi Salem] on request.

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Salem, M.A., Shawtari, F., Hussain, H.B.I. et al. Environmental technology and a multiple approach of competitiveness. Futur Bus J 6 , 17 (2020). https://doi.org/10.1186/s43093-020-00012-1

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Received : 04 October 2019

Accepted : 25 February 2020

Published : 02 April 2020

DOI : https://doi.org/10.1186/s43093-020-00012-1

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