The Impact of Global Value Chain Embedment on Energy Conservation and Emissions Reduction:Theory and Empirical Evidence
-
Junhong Bai
Abstract
An in-depth investigation into the effect of embedment in global value chain (GVC) on energy conservation and emissions reduction is of great significance for scientifically assessing the environmental impact of GVC participation, and promoting high-quality development in China. This paper incorporates GVC embedment, energy consumption and carbon emissions into the same analysis framework for the first time. Based on the WIOD database, this paper theoretically and empirically examines the impact and mechanism of global value chain embeddedness on carbon emission reduction from two dimensions: energy consumption intensity and energy consumption structure. The study found that GVC embedment significantly reduced the industry’s carbon emission intensity; developing economies’ embedment in GVC helped reduce their carbon emission intensity, while the effect was not obvious in developed economies. GVC embedment had a significant inhibitory effect on the carbon emissions in both upstream and downstream industries, but not conducive to carbon reduction of low-tech manufacturing. The mechanism test shows that the GVC embedment not only exhibits the dual effects of energy conservation and emissions reduction, but also has a significant impact on carbon emissions by reducing the energy consumption intensity and improving the energy consumption structure.
1 Introduction
In recent years, with the acceleration of globalization and the rapid development of economy and society, the pressure of energy conservation and emission reduction on a global scale has become increasingly prominent. The continuous growth of energy consumption and carbon emissions not only poses a serious threat to the quality of life and health of residents, but also has an important impact on the sustainable development of national and regional economies. In fact, governments have paid full attention to this issue and taken different kinds of measures to deal with it. As the world’s largest consumer of primary energy and carbon dioxide emitter, China put forward binding energy conservation and emission reduction targets in the 11th to 13th Five-Year Plans. In 2020, China put forward low-carbon development goals to further increase the Nationally Determined Contributions (NDCs)[1], achieve carbon peak and carbon neutrality by 2030 and 2060, respectively, and include policies and actions to address climate change in the 14th Five-Year Plan. In 2009, the U.S. House of Representatives passed the U.S. Clean Energy Security Act, which allows the U.S. government to impose carbon tariffs on energy-intensive imports. At the same time, France and other EU countries are planning to impose punitive carbon tariffs on energy-intensive traded products. However, in sharp contrast to the energy conservation and emission reduction policies of various governments, global energy demand and greenhouse gas emissions continue to increase, and the situation of energy conservation and emission reduction is still grim. According to the Global Carbon Project (GCP), total global primary energy consumption and total carbon dioxide emissions increased by 1.3% and 0.6% respectively in 2019, both hitting record highs. The continuous increase of global energy consumption and carbon emissions is inseparable from the development of the global economy, especially in the context of the continuous expansion and refinement of the international division of labor in the process of globalization. How to achieve deep participation in the global value chain and at the same time, reduce energy consumption and carbon emissions become an important issue that cannot be ignored in the process of promoting the sustainable development of the global economy.
In the process of economic globalization, the environmental pollution of regions or industries can cross national borders and eventually evolve into a global environmental pollution problem through trade, foreign direct investment and other media (Shapiro and Walker, 2018). Based on the theoretical framework of the environmental Kuznets curve (EKC), previous studies found that international trade and foreign capital flows are conducive to economies of scale and technological spillovers, thereby showing a positive improvement effect on environmental pollution (Li and Lu, 2010), however, the rapid development of international trade and investment expands the scale of domestic pollution-intensive production activities, resulting in the deterioration of environmental quality (Hu et al., 2019). As economic globalization has deepened, scholars have shifted their focus from simply examining the scale effect of trade to exploring the significant impact of global value chain embedment on pollution (Yu, 2017; Lv and Lv, 2019).
Based on this, the marginal contributions of this paper are as follows: First, based on the factor attributes of energy, the research framework of environmental impact effects of GVC embeddedness is expanded. Most of the existing literature separates energy consumption and carbon emissions, and examines the impact of GVC embeddedness on energy consumption or carbon emissions, but has not fully paid attention to the possible role and role of energy consumption in the process of GVC embeddedness affecting carbon emissions. This paper integrates GVC embeddedness, energy consumption and carbon emissions into a unified analytical framework, and deeply analyzes the energy mechanism of GVC embeddedness affecting carbon emissions, so as to expand and enrich the research on the relationship between globalization and energy conservation and emission reduction to a certain extent. Second, this paper attempts to comprehensively consider energy consumption from two dimensions of energy consumption intensity and energy consumption structure, and comprehensively reveals the internal mechanism of GVC embeddedness affecting carbon emissions through energy consumption, so as to provide a theoretical reference for us to more clearly identify the path of GVC embeddedness on energy conservation and emission reduction.
2 Theoretical Analysis
As mentioned earlier, GVC are the product of the further deepening and extension of the international division of labor. In the process of globalization, there is no such thing as an absolutely “self-sufficient” economy, and participation in GVC becomes an inevitable choice for economic growth in the context of division of labor. The form of GVC not only includes productive links such as raw material procurement, transportation and manufacturing, but also includes non-productive links such as design, marketing, and after-sales service. Therefore, GVC embeddeness can act on production activities and have an important impact on industry factor input and pollutant emissions of the industry.
To a certain extent, GVC embeddeness means the further opening up of international trade and investment, and the broader international market demand may promote the expansion of the production scale of domestic related industries, thereby increasing the demand for factor inputs including energy to a certain extent. However, in this process, along with the deep embeddedness of GVC, the transnational elements in the division of labor network are also flowing and integrating rapidly, which not only contributes to the host country’s technological R&D innovation, industrial transformation and upgrading (Jin et al., 2013), and the improvement of industrial productivity (Baldwin and Lopez-Gonzalez, 2014), but also may promote the adjustment of relevant trade policies (Sheng and Chen, 2015) and industrial policies (Gereffi, 2013) based on the linkage between upstream and downstream partners in the GVC. This will promote the international standardization of “measures within the boundary” such as energy and environmental regulations, which in turn will help reduce the industry’s consumption of non-clean energy and carbon dioxide pollution emissions. More importantly, the advanced knowledge elements in the global value chain, including green technology innovation and environmental protection standards, are continuously flowing, which also enables enterprises embedded in the global value chain to continue to climb to the high end of GVC through continuous learning, so as to achieve long-term sustainable development goals. Therefore, for the carbon reduction problem of the industry, the opportunities brought by GVC embeddeness outweigh the challenges. Based on the above analysis, hypothesis 1 is proposed.
H1: GVC embeddedness has a significant negative impact on carbon emission intensity, that is, with the increase of GVC embeddedness, GVC participation can help reduce carbon emission pollution.
How does GVC embeddeness have an impact on carbon emissions? The combustion of energy inputs represented by coal and crude oil in the process of production and utilization will directly produce pollutants such as carbon dioxide, so energy consumption is usually regarded as the main source of carbon emission pollution. With the continuous extension and refinement of GVC among countries, the economic and trade cooperation between the world’s major economies become more and more closer. In order to meet the product demand of the international market and obtain more benefits of the division of labor, the main body of the chain usually has enough motivation to participate in GVC and strive to climb to the high-end. At this time, the technology spillover and the reversal forcing effect brought by GVC embeddeness will help reduce energy consumption intensity and optimize the energy input mix, thereby reducing the proportion of non-clean energy such as coal and oil in the production process of the industry to a certain extent, and promoting the reduction of carbon emission intensity of the industry.
GVC embeddeness is beneficial for reducing energy consumption of per unit of output, which is the most important source of increased pollution emissions (Akhmat et al., 2014). The role of GVC in the transfer of advanced knowledge and technology was widely recognized by the academic community (Pietrobelli and Rabellotti, 2011). Participating in GVC makes it easier for local industries to access advanced technologies and patents from overseas, promotes technological progress in the industry, and thus improves the energy efficiency of the industry (Wurlod and Noailly, 2018). The above impact is not only reflected in the development and application of energy technology in a certain production link, but also in the participation of energy, as a factor of production, in the entire production process, including transportation, production, organization and management (Li and Zhou, 2006). Moreover, in order to ensure the overall operational efficiency of GVC in the international division of labor network, the main body in the chain that master more key technologies usually has enough motivation to realize the cross-border dissemination of technology, which provides strong technical support for improving the energy consumption intensity of various industries in the chain, thereby helping to reduce the pollution emission intensity of the industry.
GVC embeddeness helps optimize the energy structure, which in turn has an impact on the carbon intensity of the industry. The market with a higher division of labor usually has higher requirements for environmental protection standards in the production process, which will undoubtedly form a reversal forcing mechanism for other industries in the chain, prompting them to reduce the use of fossil energy such as coal and oil, and strive to realize the transformation of the energy structure from the consumption of nonclean energy to the consumption of clean energy. In addition, in order to effectively avoid competition from industries with similar industry category development levels in the value chain, industries often take the initiative to adjust their energy input mix to reduce the pollution intensity of their products at the root (Zhang et al., 2016) to improve the low-carbon competitiveness of their products. Therefore, driven by the goal of deeply participating in economic globalization and achieving the rise of GVC’s status, GVC embeddeness is conducive to optimizing the energy consumption structure of the industry and reducing the proportion of fossil energy consumption such as coal and oil, thereby promoting the realization of carbon emission reduction goals.
In summary, GVC embeddeness not only has a direct impact on the carbon emission intensity of the industry, but also indirectly affects the carbon emission intensity of the industry by affecting the “quantity” and “quality” of energy consumption. Specifically, on the one hand, participating in GVC helps the industry give full play to the technology spillover effect and learning effect, promotes the continuous progress of production technology, improves the energy efficiency of the industry, and reduces the carbon emission intensity of the industry. On the other hand, the advanced environmental protection standards and potential competition in the chain are also conducive to forcing industries with low division of labor to reduce the use of nonclean energy such as coal, further optimizing the energy structure of the industry, and thus showing a significant inhibitory effect on the carbon emission intensity of the industry. Based on this, the following hypothesis 2 is proposed.
H2: Energy consumption has a significant mediating effect on the impact of GVC embeddedness on carbon emission intensity, that is, GVC embeddeness has an impact on carbon emissions by reducing energy consumption intensity and optimizing energy consumption structure.
3 Estimation Strategies
3.1 Emission Reduction Effect Model of GVE Embeddeness
The multi-dimensional fixed effect is conducive to controlling the unique and unobservable heterogeneity of each individual, so as to effectively avoid the adverse effects of omitted variable bias on causal inference to a certain extent. Therefore, this paper mainly uses the multi-dimensional fixed effect estimation method (Reghdfe) to investigate the effect of GVC embeddeness on environmental pollution. Specifically, in order to investigate the impact of GVC embeddeness on carbon emissions, the following econometric model is established:
Among them, c is the country, i is the industry, t is the time, and Coicit represents the carbon emissions of the industry i of country c in t years. GVCparcit is the degree of GVC embeddedness. Xcit represents a range of other control variables, which we will cover in more detail later. λc is the national fixed effect, ηi is the industry fixed effect, δt is the time fixed effect, and εcit is the random disturbance term.
Equation (1) is a static panel model to investigate the effect of GVC embeddeness on environmental pollution. Considering that environmental pollution variables such as carbon emissions may have certain path-dependent characteristics, on the basis of Equation (1), this paper adds the first-order lag term of carbon emissions to establish a dynamic panel model. The econometric model is shown in Equation (2):
Among them, Coci,t−1 is the first-order lag term of Cocit. Other variable definitions are the same as above.
3.2 The Mediating Effect of Energy Consumption and the Energy Saving Effect Model of GVC Embeddeness
As mentioned in the previous hypothesis 2, GVC embeddeness may have an impact on the carbon emission intensity of the industry through two pathways: energy consumption intensity and energy consumption structure. In order to test whether energy consumption plays a significant mediating effect in the above process, this paper uses the mediating effect model to test the above mechanism. The model is built as follows:
Among them, Med represents the mediating variables, including energy consumption intensity (Ei) and energy consumption structure (Es). Other variable definitions are the same as above. Equation (3) in the model also tests the energy-saving effect of GVC embeddeness.
3.3 Variable Selection
3.3.1 Explained Variable: Carbon Emissions
Carbon emission intensity can overcome the influence of factors such as industry scale and industry development benefits to a certain extent, so this paper mainly uses carbon dioxide emissions per unit of output as a measurement index of industry carbon emissions.
3.3.2 Core Explanatory Variable: GVC Embeddedness Degree
The widely adopted vertical specialization index only describes the total value of trade products and does not consider the added value of trade, which has the problem of double counting of trade flows, and may even distort the benefits obtained by the subjects participating in trade. In this regard, Koopman et al. (2014) decompose a country’s exports into different value-added parts from the perspective of value-added trade, and calculate the GVC participation index. On this basis, Wang et al. (2013) further extended the decomposition of total exports to the sector, bilateral, and bilateral industry levels. Therefore, this paper mainly refers to the value-added decomposition method of Wang et al. (2013) and uses the value chain participation index to measure the degree of GVC embeddedness.
Assuming that there are two countries in the world, domestic and foreign, and each country has N tradable sectors, the products produced by each sector can be used as both intermediate goods and final goods, and can be used both domestically and exported abroad. Based on the multi-regional input-output model, the Leontief inverse matrix of the country Lrr = (I – Arr)−1 can be obtained, where Arr is the input-output coefficient matrix of country r of N × N, and the total export Esr of country s to country r is further decomposed into two parts: final product export and intermediate goods export, that is, Esr = Ysr + Asr Lrr Yrr + Asr Lrr Er*.
Among them, Asr means the intermediate input matrix from country s to country r, and Yrr means the final demand matrix for domestically produced goods in country r.
Thus, extending the above equation to the case of G numbers of countries, the total industry-level exports from country s to country r can be broken down into different value-added parts and double-counted items.
Based on the above decomposition framework of total exports, the GVC participation index (Koopman et al., 2010) is constructed as follows:
Among them, c and i represent the country and industry, respectively. IVci represents indirect value-added exports of industry i in country c, which measures how much value added is included in the intermediate goods exported by industry i in country c, and processed by the direct importing country and then exported to a third country. FVci represents the foreign value added included in the exports of intermediate goods and final goods in the industry i of country c. Eci represents the total exports of industry i in country c. GVCparci reflects the depth of GVC embeddedness in industry i in country t, with a value between 0 and 1. The higher the value indicates the higher the degree of GVC embeddeness of the industry in that country, and vice versa.
3.3.3 Mediating Variables: Energy Consumption Intensity and Energy Consumption Structure
For energy consumption intensity (Ei), measuring energy consumption from the perspective of factor input efficiency can overcome the influence of factors such as industry scale and production mode to a certain extent, so the ratio of total energy consumption to total output (constant price in 2000) is used as a measure of energy consumption intensity.
For the energy consumption structure (Es), the larger the proportion of fossil energy represented by coal in the energy consumption structure, the more carbon emissions per unit of energy consumption. Therefore, this paper uses the proportion of coal in total energy consumption to measure the energy consumption structure.
3.3.4 Other Control Variables
In order to reduce the estimation bias caused by the omission of variables, other influencing factors are controlled:
First, at the industrial level: (1) output per capita (Pgo). Here, the ratio of the total output of the industry to the number of employees is used to control the per capita output level, and the primary, quadratic and tertiary terms are introduced into the model at the same time to test the possible nonlinear relationship between per capita output and environmental quality. (2) Factor endowment structure (Kl). The traditional factor endowment theory (Ohlin, 1935) argues that regions or industries with higher capital-labor ratios tend to have a comparative advantage in producing capital-intensive products, that is, changes in the factor endowment structure will affect the output structure of the industry, and different output structures will have an impact on energy consumption and pollution emissions. We use the ratio of the physical capital stock to the number of employees in the industry to measure the factor endowment structure. (3) Position. The higher the position of a country’s industry in the GVC, the more high-value-added, low-polluting products the sector can produce and export, and its pollution-intensive industries can also be shifted to countries with a lower status of division of labor, thus reducing local carbon emissions. Based on the previous decomposition of total exports (Wang et al., 2013), this paper uses the GVC status index (Koopman et al., 2014) to measure the status of division of labor in GVC. Here’s how it is measured:
Among them, Positionci is the status of division of labor of the industry i embedded in GVC in country c. Other variable definitions are the same as above.
Second, at the national or regional level: (1) the level of economic development (Lngdp). The natural logarithm of each economy’s GDP is used to measure the level of regional economic development. (2) Foreign direct investment (Fdi). The proportion of foreign direct investment in GDP is used to control the level of foreign investment. (3) Green technology innovation (Green). Refering to Popp (2002), this paper uses the proportion of green patents in all patents in that year to measure the level of green technology innovation.[1]
3.4 Data Description
Considering the availability of data, the data used in this study are the 3-dimentional panel data of 54 industries in 43 regions around the world from 2000 to 2014. The data are mainly derived from the World Input-Output Sheet (WIOT) and the Socio-Economic Subsidiary Account (SEA) in the latest WIOD database released by the European Union in 2016 and the latest WIOD environmental account released in 2019. In order to match the latest data years of the above three databases at the same time, 2000–2014 is selected as the sample period. In addition, 54 industries (C1-C54) in the WIOD database are selected as the objects of study, while the two service industries of household self-use activities (C55) and international organizations and institutions (C56) are not considered in this paper due to serious lack of data.
4 Results and Discussion
4.1 Analysis of the Emission Reduction Effect of GVC Embeddeness
4.1.1 Benchmark Regression Results
To enhance comparison, we also report estimation results of OLS mixed regression, static general fixed effects, static multi-dimensional fixed effects, and dynamic multidimensional fixed effects models, as shown in columns (1), (2), (3), and (4) of Table 1, respectively. It should be pointed out that in order to verify the nonlinear characteristics of GVC embeddedness on carbon emissions, referring to Xu and Mao (2016), we divide the samples into high GVC embeddeness group (High) and low GVC embeddeness group (Low) according to the 50% quantile of GVC embeddedness, and investigate the impact of GVC embeddeness on industry environmental pollution in different groups, and the estimation results are shown in column (5) in Table 1.
Benchmark Regression Results
| Variables | OLS (1) |
FE (2) |
REGH (3) |
D-REGH (4) |
D-REGH (5) |
|---|---|---|---|---|---|
| L.Coi | 0.8282*** (0.0819) |
0.8283*** (0.0819) |
|||
| GVCpar | 1.3441 (0.7572) |
–0.3422 (0.2581) |
–0.3422** (0.1456) |
–0.2324*** (0.0871) |
|
| GVCpar×Low | 1.0132 (3.9337) |
||||
| GVCpar×High | –0.2366*** (0.0903) |
||||
| Constant | 0.7353*** (0.0479) |
2.9388*** (0.5745) |
3.0284*** (0.2392) |
0.7731*** (0.1722) |
0.7717*** (0.1713) |
| Control variables | Control | Control | Control | Control | Control |
| Time fixed | NO | NO | YES | YES | YES |
| State fixed | NO | NO | YES | YES | YES |
| Industry fixed | NO | NO | YES | YES | YES |
| R2 | 0.0016 | 0.0203 | 0.8489 | 0.9517 | 0.9517 |
| Observations | 34830 | 34830 | 34830 | 32508 | 32508 |
Note: ***, **, and * represent the significance levels of 1%, 5%, and 10%, respectively, and the values in () are the corresponding robust standard errors. The same below.
As can be seen from Table 1, compared with the estimates results of columns (3) and (4) considering multi-dimensional fixed effects, the adjusted R2 of columns (1) without considering fixed effects and (2) without considering multi-dimensional fixed effects are at a lower level, and the goodness of fit is poor. To a certain extent, it is shown that if the problem of multi-dimensional fixed effects is not considered when using multi-dimensional panel data, it may cause deviation in model setting to a certain extent.
Furthermore, columns (3) and (4) based on the Reghdfe estimation do have more significant regression coefficients than columns (1) and (2), and the coefficient of the lag term in column (4) is significantly positive at the significance level of 1%, which verifies the inference that the change of carbon emission intensity has a time lag feature mentioned above. Compared with the first three models, the estimation results of columns (4) and column (5) that consider both the multidimensional fixed-effect feature and the time lag effect of the explained variable show the best statistical characteristics, so the regression results of the dynamic multidimensional fixed-effect model are mainly analyzed and discussed in this paper.
According to the estimation results in column (4) of Table 1, GVC embeddeness has a significant negative impact on the carbon emission intensity of the industry, that is, participating in GVC can help reduce the carbon emission intensity of the industry. Further observation shows that every 1% increase in GVC embeddedness can reduce the carbon emission intensity of the industry by about 0.23%, which also means that participating in GVC is of great significance to reduce the carbon emission intensity of the industry. Moreover, the estimation results in column (5) in Table 1 show that the impact of GVC embeddedness on the industry carbon emission intensity is not significant when the GVC embeddedness degree is low (GVCpar×Low), while the impact on the industry carbon emission intensity is significantly negative when the GVC embeddedness degree is high (GVCpar×High). This further confirms the carbon reduction effect of deep participation in GVC, and hypothesis 1 is validated.
4.1.2 Heterogeneity Analysis
First, regional heterogeneity. Compared with developed economies in the value chain, developing economies are still facing the development dilemma of low added value of products, backward green technologies and low environmental thresholds, so they may suffer from the “low-end lock-in” effect at the environmental level. Based on this, this paper divides the sample into two groups: developed economies and developing economies according to the per capita income level of each region, and regresses them respectively[1]. As can be seen from the results in Table 2, the impact of GVC embeddedness on the sectoral carbon intensity of developed economies is negative but not significant during the study period. However, for developing economies, GVC embeddedness reduce the carbon emission intensity of the industry in the region as a whole. To some extent, this result suggests that developing economies in the GVC chain are not necessarily “locked in at the low end” at the environmental level. This may be due to the fact that developing countries can easily access more advanced experience, green technologies and environmental protection standards in line with international standards by participating in the rapid flow and integration of transnational elements in the network of division of labor, which in turn will help them reduce the carbon emission intensity of their industries.
Heterogeneity Estimation
| Variables | Regional heterogeneity | Status of division of labor heterogeneity | ||
|---|---|---|---|---|
| Developed economies | Developing economies | Upstream industries | Downstream industries | |
| L.Coi | 0.8240*** | 0.8577*** | 0.8042*** | 0.7935*** |
| (0.0920) | (0.0412) | (0.0609) | (0.0558) | |
| GVCpar | 0.0716 | –0.5594** | –0.4617** | –0.4072* |
| (0.4855) | (0.2506) | (0.2855) | (0.2357) | |
| Constant | 0.9201*** | 0.6876*** | 0.8654*** | 0.5755*** |
| (0.2351) | (0.1369) | (0.2215) | (0.1992) | |
| Control variables | Control | Control | Control | Control |
| Time fixed | YES | YES | YES | YES |
| State fixed | YES | YES | YES | YES |
| Industry fixed | YES | YES | YES | YES |
| R2 | 0.9311 | 0.9773 | 0.9773 | 0.9573 |
| Observations | 25704 | 16201 | 16201 | 16159 |
| Variables | Industrial heterogeneity | |||
|---|---|---|---|---|
| Non-manufacturing | Manufacturing | |||
| Total sample | Low-tech | High - tech | ||
| L.Coi | 0.8304*** (0.0905) |
0.8053*** (0.1089) |
0.6563*** (0.0461) |
0.8057*** (0.1107) |
| GVCpar | –0.2028** (0.0914) |
–0.4539 (0.6727) |
4.2504** (2.0921) |
–0.6582* (0.8358) |
| Constant | 0.8825*** (0.2142) |
0.6201** (0.2726) |
0.2691*** (0.0678) |
0.8310** (0.3743) |
| Control variables | Control | Control | Control | Control |
| Time fixed | YES | YES | YES | YES |
| State fixed | YES | YES | YES | YES |
| Industry fixed | YES | YES | YES | YES |
| R2 | 0.9508 | 0.9577 | 0.9084 | 0.9574 |
| Observations | 21070 | 11438 | 6622 | 4816 |
Second, the heterogeneity of the division of labor. The carbon emission reduction effect of GVC embeddeness may be different under different status of division of labor, and those industries with higher status of division of labor often have a greater carbon emission reduction effect of GVC. Therefore, this paper uses quantiles for sample processing, and divides the samples into upstream industries and downstream industries according to the 50% quantile of the status index of division of labor, and examines the impact effect of GVC embeddeness on the carbon emissions of these industries. According to the estimation results in Table 2, although the impact of GVC embeddedness on the carbon emissions of both upstream and downstream industries is significantly negative during the study period, the carbon emission reduction effect of upstream industries participating in GVC is higher than that of downstream industries, which may be due to the relatively low pollution intensity of upstream industries and their ability of reducing their pollutant emission intensity through the integration effect of international division of labor.
Third, industrial heterogeneity. Compared with the manufacturing industry, the non-manufacturing industry, which is dominated by high-tech services, is mainly embedded in GVC through the forward participation model, and the higher the degree of forward embeddeness, the higher the proportion of domestic intermediate goods in the exports of other countries, so the environmental benefits obtained by non-manufacturing enterprises participating in GVC division of labor may be higher than that of manufacturing. Moreover, compared with low-tech manufacturing, high-tech manufacturing has a higher innovation momentum and technology absorption capacity (Baldwin and Lopez-Gonzalez, 2014), which transfers low-value-added links to low-end industries through GVC, thereby reducing its own carbon emission pollution. Based on this, this paper divides the total sample into non-manufacturing and manufacturing[1], and further divides the manufacturing industry into two groups[1]: low-tech and high-tech manufacturing. According to Table 2, for non-manufacturing, the impact of GVC embeddeness on carbon emission intensity is significantly negative. However, for the manufacturing industry, when the technical level is low, the impact of GVC embeddeness on carbon emissions is significantly positive, and when the technical level is high, it has a significant negative impact, so that the positive and negative effects also make the overall carbon emission reduction effect of GVC embeddeness in the manufacturing industry not significant.
4.1.3 Robustness Test
The following robustness tests are performed in this paper. First, consider the robustness of the replacement indicators. In this paper, the natural logarithm ( Co ) of total carbon emissions is used as the explained variable, and the dynamic multidimensional fixed effect model is used to test the robustness of the relationship between GVC embeddeness and total carbon emissions. Second, the degree of GVC embeddedness based on the quartile is divided. Based on the 25% (quartile) of GVC embeddedness, the samples are further divided into the lowest (D1), lower (D2), higher (D3), and highest GVC embeddedness (D4) to re-test the carbon reduction effect of GVC embeddedness. Third, the robustness test considering the endogeneity problem. In order to overcome endogeneity as much as possible, this paper intends to use the two-stage least squares method of instrumental variables (IV-2SLS) and multi-dimensional fixed effect estimation of instrumental variables (IV-DREGH) to estimate the model. Appropriate instrumental variables need to meet the assumption that they are highly correlated with GVC embeddeness in various industries, but not related to random error terms. Considering that the dynamic panel used in this paper is a short-panel model, in order to ensure the degree of freedom, we refer to Lv et al. (2018) and select the first-order lag term of GVC embeddeness as IV in IV-2SLS and IV-DREGH estimation. At the same time, in order to solve the endogeneity problem that may be caused by the lag period of the explained variable in the dynamic panel, this paper uses System Generalized Moment Estimation (SYS-GMM) to re-estimate the benchmark model. The results are shown in Table 3.
Robustness Test Results
| Variables | Robustness test 1 | Robustness test 2 | Robustness test 3 | |||
|---|---|---|---|---|---|---|
| IV-2SLS | IV-DREGH | SYS-GMM | ||||
| (1) | (2) | (3) | (4) | (5) | (6) | |
| L.Co | 0.8288*** (0.0081) |
0.8286*** (0.0081) |
||||
| L.Coi | 0.8281*** (0.0818) |
0.8266*** (0.0817) |
0.8282*** (0.0336) |
0.0291*** (0.0071) |
||
| GVCpar | –0.1320** (0.0557) |
–0.3073*** (0.0885) |
–0.2339* (0.1292) |
–1.7930*** (0.4377) |
||
| GVCpar* Low | 2.0156 (5.6906) |
|||||
| GVCpar×High | –0.1628*** (0.0584) |
|||||
| GVCpar×D1 | 4.3613 (6.5232) |
|||||
| GVCpar×D2 | 8.3737 (6.0011) |
|||||
| GVCpar×D3 | –4.8607 (3.1293) |
|||||
| GVCpar×D4 | –0.2138*** (0.0809) |
|||||
| Constant | 0.8273*** (0.0706) |
0.8184*** (0.0705) |
0.8273*** (0.0706) |
5.3608*** (0.0783) |
||
| Control variables | Control | Control | Control | Control | Control | Control |
| Time fixed | YES | YES | YES | YES | YES | YES |
| State fixed | YES | YES | YES | YES | YES | YES |
| Industry fixed | YES | YES | YES | YES | YES | YES |
| AR(2) | 0.4308 | |||||
| Sargan text | 0.5441 | |||||
| R2 | 0.9919 | 0.9919 | 0.9919 | 0.6846 | 0.6822 | |
| Observations | 32508 | 32508 | 32508 | 32508 | 32508 | 32508 |
From the robustness test 1 and robustness test 2 in Table 3, the impact of GVC embeddeness on total carbon emissions is significantly negative, whether the total carbon emissions are replaced as the explained variables or the samples are divided by quartiles. When the degree of GVC embeddedness is low, its impact on the total carbon emissions is positive or not obvious, while when the degree of GVC embeddedness is high, it will significantly inhibit the carbon emissions of the industry, which is consistent with the above estimates. In addition, the robustness test 3 shows that the impact of GVC embeddeness on carbon emission intensity is significantly negative after controlling for the possible endogeneity problems of the model, and the conclusions of this paper are robust.
4.2 Mediating Effect of Energy Consumption and Energy-Saving Effect of GVC Embeddedness
How does GVC embeddeness affect carbon intensity? Further, this paper mainly uses the stepwise coefficient test method (Wen and Ye, 2014) from the two dimensions of energy consumption intensity and energy consumption structure, according to the mediating effect model mentioned in Equations (2)~(4) above. At the same time, the energy-saving effect of GVC embeddeness can also be investigated by observing the characteristics of the estimation coefficient of GVC embeddeness in Equation (3).
The results of the mediating effect test based on carbon emission intensity are shown in Table 4. Columns (1) to (3) examine the mediating role of the energy intensity effect. The results of the second step test show that the impact of GVC embeddeness on the energy intensity of the mediating variable is significantly negative, and the mediating variable in the third test has a significant positive impact on the carbon emission intensity of the industry. The coefficient symbol of β2μ3 is the same as that of α2, indicating that the energy intensity effect has a significant partial mediating effect in the impact of GVC embeddeness on the carbon emission intensity of the industry, that is, GVC embeddeness promotes the reduction of the carbon emission intensity of the industry by promoting the reduction of the energy intensity of the industry. Columns (4) to (6) in Table 4 examine the mediating role of energy structure effects. Columns (4) to (6) in Table 4 examine the mediating role of energy structure effects. The results of the second step test show that the impact of GVC embeddeness on the energy structure of the mediating variable is significantly negative, and the mediating variable has a significant positive impact on the carbon emission intensity of the industry after the mediating variable is introduced into the model. The coefficient symbol of β2μ3 is the same as that of α2, that is, with the deepening of GVC embeddedness, the share of coal consumed in the industry production activities decreases, and the industry energy structure tends to be optimized, which in turn inhibits the increase of carbon emission intensity. The test results of this paper also show that the indirect effect of energy consumption intensity is −0.1093 and the indirect effect of energy consumption structure is −0.0018, both of which are significant. The mediating effect of energy intensity caused by GVC embeddeness accounts for 47.07% of the total effect, indicating that the mediating effect of energy consumption is significant.
Mechanism Test Results
| Variables | Energy intensity mechanism | Energy structure mechanism | ||||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| L.Coi | 0.8282*** (0.0819) |
0.6387*** (0.0603) |
0.8282*** (0.0819) |
0.8281*** (0.0820) |
||
| L.Ei | 0.7828*** (0.0725) |
|||||
| L.Es | 0.7423*** (0.0282) |
|||||
| GVCpar | –0.2324*** (0.0871) |
−3.3151*** (1.2422) |
–0.1122* (0.0639) |
–0.2324*** (0.0871) |
–0.0154* (0.0085) |
–0.2314*** (0.0872) |
| Ei | 0.0330*** (0.0064) |
|||||
| Es | 0.1131* (0.0600) |
|||||
| Constant | 0.7731*** (0.1722) |
12.2223*** (2.3894) |
–0.0555 (0.1909) |
0.7731*** (0.1722) |
0.1788*** (0.0147) |
0.7522*** (0.1685) |
| Control variables | Control | Control | Control | Control | Control | Control |
| Time fixed | YES | YES | YES | YES | YES | YES |
| State fixed | YES | YES | YES | YES | YES | YES |
| Industry fixed | YES | YES | YES | YES | YES | YES |
| R2 | 0.9517 | 0.9673 | 0.9682 | 0.9517 | 0.9474 | 0.9517 |
| Observations | 32508 | 32508 | 32508 | 32508 | 32508 | 32508 |
In summary, both energy consumption intensity and energy consumption structure have a significant mediating role in the impact of GVC embeddedness on carbon emissions, and energy consumption intensity and energy consumption structure play a significant reverse mediating role, so that the impact of GVC embeddedness on the carbon emission intensity of the industry presents significant negative characteristics. That is, participation in the global value chain promotes emission reduction through energy conservation and effectively promotes the realization of carbon emission reduction goals.
5 Conclusions and Implications
This paper uses a combination of theoretical analysis and empirical analysis to investigate the impact of global value chain embeddedness on energy conservation and emission reduction and the internal mechanism, and draws the following conclusions: First, GVC embeddedness significantly inhibits the carbon emission intensity of the industry as a whole, that is, deep participation in GVC is conducive to promoting the realization of carbon emission reduction goals. Second, GVC embeddeness not only directly promotes carbon emission reduction, but also has a significant impact on carbon emissions by reducing energy consumption intensity and optimizing energy consumption structure, thereby contributing to the realization of energy conservation and emission reduction goals. Thirdly, there are obvious regional heterogeneity, status of division of labor heterogeneity and industrial heterogeneity in the impact of GVC embeddedness on carbon emissions. Specifically, the impact of GVC embeddedness on carbon emission intensity is mainly concentrated in low-income areas. GVC embeddeness significantly reduces the carbon emission intensity of upstream and downstream industries, and even if the degree of GVC embeddeness is low, it still contributes to the carbon emission reduction of upstream industries. The impact of GVC embeddeness on carbon emissions in non-manufacturing and high-tech manufacturing industries is significantly negative on the whole, but has an adverse impact on low-tech manufacturing to a certain extent.
The above conclusions have important policy implications for China. The global call for energy conservation and emission reduction, as well as the country’s concern for own sustainable development, have inspired China to actively adopt effective energy conservation and emission reduction measures, thereby promoting the green development of China’s economy. To illustrate China’s situation more visually, Figure 1 depicts the average GVC embeddedness and carbon emission intensity of China and the 43 economies in the WIOD database from 2000 to 2014. During this period, China’s carbon emission intensity showed a significant downward trend, which is related to China’s active adoption of a series of energy conservation and emission reduction policies.
Average GVC Embeddedness and Carbon Emission Intensity between China and Other Economies from 2000 to 2014
Of course, compared with the average level of various economies, China’s GVC embeddedness is still relatively low, but the carbon emission intensity is relatively high, which shows that China’s GVC embeddedness still has a lot of room for improvement. In 2011, China’s GVC embeddedness reached the lowest level in recent years, when China overtook the United States to become the world’s largest emitter of carbon pollution and a consumer of primary energy. It is worth noting that from the perspective of industries, both China’s non-manufacturing and manufacturing industries can have a positive and significant carbon emission reduction effect by embedding GVC. To a certain extent, these facts provide a realistic impetus for China to firmly increase the breadth and depth of participation in global value chains, thereby promoting the achievement of energy conservation and emission reduction goals.
Based on this, we make the following policy recommendations. First of all, from an overall perspective, GVC embeddeness and energy conservation and emission reduction are “like-minded” in the process of sustainable development, especially for developing economies like China, GVC embeddeness can help achieve their energy conservation and emission reduction goals. Therefore, under the current situation of continuous expansion and refinement of the value chain among economies around the world, China should deeply participate in the system of division of labor of globalization with a more active attitude, and strive to climb to the middle and high end of the global value chain, so as to give full play to the role of GVC embeddeness in carbon emission reduction and promote sustainable economic development. Secondly, there are two direct and indirect pathways for GVC embeddedness to affect carbon emissions, and the energy intensity effect and energy structure effect caused by GVC embeddeness are important driving forces for reducing the carbon emission intensity of the industry. Therefore, when the government formulates various energy conservation and emission reduction policies, it needs to pay further attention to the coordination between energy conservation policies and emission reduction policies, and establish the concept of “energy conservation” to drive “emission reduction”. Actively guiding the industry to participate in international market competition, maximize the digestion and utilization of knowledge and technology spillovers in the value chain, so as to improve energy efficiency, encourage the industry to actively approach international high environmental protection standards, and constantly adjust and optimize the factor input structure, which will help enhance the low-carbon competitiveness of the industry. Finally, compared with the industries with low status of division of labor, GVC embedding has a more significant effect on the carbon emission reduction of industries with high status of division of labor, and compared with low-tech manufacturing, GVC embeddeness significantly reduces the carbon emission intensity of high-tech manufacturing. Therefore, in the process of actively guiding various industries to participate in globalization, the government must also focus on the downstream industries of the value chain and the industries with low technological level, and improve their technology absorption and transformation capabilities through the formulation and implementation of targeted measures to encourage innovation, so as to promote their deep integration into the global value chain and continue to climb upstream with the help of chain advantages, which is not only conducive to giving full play to the energy conservation and emission reduction effect of GVC embeddeness, but also conducive to promoting the green transformation and development of China’s economy in the long run.
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© 2024 Junhong Bai, Xuewei Yu, published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Articles in the same Issue
- Frontmater
- Frontmatter
- Column: China's Economic Development
- Energy Price Fluctuation and Real Estate Market Risk Prevention—Empirical Evidence at the Prefecture-Level City Level in China
- Are the Green TBTs a Stimulus or a Trap for Enterprises’ Green Technology Development?
- The Impact of Global Value Chain Embedment on Energy Conservation and Emissions Reduction:Theory and Empirical Evidence
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