Can the extension of the global value chain production length promote carbon emissions reduction in China's equipment manufacturing industry?

Qingbo Huang; Xinxin Xia; Xiaohan Zhang; Yan Li

doi:10.1007/s10668-022-02795-5

. 2022 Dec 31:1–28. Online ahead of print. doi: 10.1007/s10668-022-02795-5

Can the extension of the global value chain production length promote carbon emissions reduction in China's equipment manufacturing industry?

Qingbo Huang ¹, Xinxin Xia ¹, Xiaohan Zhang ¹, Yan Li ^1,^✉

PMCID: PMC9804243 PMID: 36618555

Abstract

In the context of localization of Global Value Chain (GVC) and stricter carbon emission requirements, the impact of participating in GVC on carbon emission reduction has become one of the most crucial criteria for China's manufacturing industry to consider whether to deepen its participation in GVC. In order to clearly and directly reflect the change in the production distance between the original input and the final product, we use the GVC production length to express the degree of participation in GVC. And in order to make the research more targeted and typical, we select the equipment manufacturing industry as the research object. Using the data from the World Input–Output Database (WIOD), we empirically analyze the GVC production length under different cross-border production activities on the basis of the theoretical mechanism. The results show that the extension of the GVC production length can significantly promote the carbon emissions reduction. In the decomposition part, the extension of simple GVC production length can effectively promote carbon emissions reduction. Therefore, it is suggested that China's equipment manufacturing industry should continue to deeply participate in the high-end production links of GVC and improve its status in the complex GVC production activities.

Keywords: Equipment manufacturing industry, Carbon emissions, Global Value Chain, Global Value Chain production length

Introduction

In the twenty-first century, the refinement and specialization of the international division of production has made the production chains of various countries continue to extend to domestic and foreign markets (Baldwin & Evenett, 2015). Global value chain (GVC) arise from this (Kostoska et al., 2020). The degree of fragmentation of the GVC division of labor increases with the number of suppliers (Fally and Hillberry 2016). Moreover, the greater the number of suppliers is, the more complex the production structure of the international division of labor would be. This means there are more intermediate goods trade links from the initial input to the final product, which promotes the extension of the GVC production length (Wang et al., 2020).

However, since the outbreak of the Covid-19 pandemic in 2019, affected by the ebb of economic globalization, the development of multi-polarization, and the impasse in the conflict between Russia and Ukraine, uncertain factors in the international social environment, economic environment, and political environment have increased day by day (Altig et al., 2020). The GVC division system of the industry shows a trend of localization. In this context, China's manufacturing industry faces a difficult decision, it is whether to adhere to the development strategy of deeply participating in GVC. At the same time, since the signing of the “Paris Agreement” in 2016, the global energy conservation and emission reduction boom has hit, requiring China's economic growth to be decoupled from excessive energy use and pollutant emissions (Lin & Liu, 2016; Liu et al., 2018). Based on this, China proposes the goal of “carbon peaking in 2030 and carbon neutrality in 2060” (Xinhua, 2021), and requires the manufacturing industry to make a huge contribution to carbon emission reduction. Given the far-reaching impact of participating in GVC on carbon emissions (Bi et al., 2015; Long et al., 2018; Sun et al., 2019), we need to examine whether participating in GVC is beneficial for carbon emission reduction (Wang & Chen, 2022). This has become one of the crucial criteria for China's manufacturing industry to consider whether to deepen the participation in GVC.

Existing studies on the impact of participation in GVC on manufacturing carbon emissions have mixed conclusions. On the one hand, some scholars believe that deep participation in GVC can improve environmental efficiency (Liu et al., 2018). Participation in GVC has led to a rapid increase in the trade volume of the intermediate products of China's manufacturing industry, and brought huge economic profits and technical experience returns (Du et al., 2012; Humphrey & Schmitz, 2002; Wei et al., 2016). These value-creating factors will enhance the impact on environmental sustainability (Stock et al., 2018; Ye et al., 2020), and reduce carbon emissions (Xiao et al., 2020), which in turn reduces environmental pollution caused by production (Ho et al., 2018). On the other hand, some scholars believe that although integration into GVC is the driving force of economic growth in developing countries, it will also cause huge carbon emission reduction pressure (Sun et al., 2019; Wang et al., 2019). China's manufacturing industry has a very high degree of "backward participation" in GVC (Li et al., 2020a; Zhang et al., 2021a, 2021b), and the effect of "low-end lock-in" is obvious (Li et al., 2020b). China's manufacturing industry has gradually become a typical high-consumption and high-pollution industry in China in the process of participating in GVC (Wang & Feng, 2020). With the GVC turning into "global pollution chains" (Duan et al., 2020), the increase in GVC production activities has led to a substantial increase in carbon emissions from China's manufacturing industry (Chen & Chen, 2011; Su & Thomson, 2016).

The existing research still has three points that need to be expanded. (1) Most empirical studies measured the participation of GVC based on vertical specialization index (Hummels et al., 2001), GVC participation and location indices (Koopman et al., 2010; Wang et al., 2017a) Nonetheless, in essence, in the process of deep participation in GVC, the fragmentation of the international division of labor increases the number of suppliers (Fally, 2011). This means an increase in the number of intermediate trade links in GVC. The production distance between the original input and the final product is lengthened, which increases the GVC production length (Wang et al., 2017b). Therefore, using GVC production length is more realistic and direct, and can improve the accuracy of GVC participation estimates (Wang et al., 2020). (2) Most studies have not been clear enough to sort out the theoretical mechanism of the impact of participating in GVC on carbon emissions, and they have not been able to use theoretical models to clarify the theoretical mechanism. (3) Most studies take the manufacturing industry as the main research object. Few studies have discussed the topic from the perspective of the equipment manufacturing industry. However, as the cornerstone of national economic development, equipment manufacturing industry undertakes an important function of providing equipment and technology for downstream industries. Compared with other manufacturing industries, the production division of equipment manufacturing industry is more refined, specialized, and internationalized, and the value chain is more fragmented. As a result, the carbon emissions of the equipment manufacturing industry are greatly affected by participating in GVC. Taking the equipment manufacturing industry as the research object is more typical and pertinent.

The main contribution of this paper lies in the following three aspects. (1) We use GVC production length to measure the participation of GVC, which improves the estimation accuracy. According to the different production activities, we comprehensively describe the situation of the equipment manufacturing industry participating in GVC by distinguishing the concepts of the GVC production length, GVC simple production length, the GVC production length returned to the exporting country and the pure foreign GVC production length. (2) We update the theoretical model of carbon emissions effects of participating in GVC, derive relevant economic indicators that affect carbon emissions, and further clarify the theoretical mechanism. (3) From the perspective of the equipment manufacturing industry, we empirically test the impact of participating in GVC on the carbon emissions and its industry heterogeneity. It will help to put forward more targeted suggestions for China's equipment manufacturing industry to reasonably control carbon emission levels in the transformation to a technology-based industry.

The rest of the paper is arranged as follows. The second part introduces the theoretical mechanism. The third part explains the empirical model and data sources. The fourth part shows the empirical analysis and discussion. The last part is wrapped up with the conclusions and policy implications.

Theoretical mechanism

Theoretical model

Referring to the theoretical model constructed by Antweiler et al. (2001), we introduce the impact function of the extension of GVC production length on production, and construct a carbon emissions effect model of the extension of GVC production length according to the theory of perfect competition.

To construct the model, the following assumptions are made:

(1) There are only two industries in the world, namely industry 1 and industry 2, of which industry 1 is a high-carbon industry and industry 2 is other industries. Then the world only produces two products, that is, industry 1 produces product X and industry 2 produces product Y. In addition, the production process of the two types of products obeys the principle of constant return to scale.
(2) Product X is a high-carbon product, that is, the production of product X will discharge a large amount of carbon dioxide. Y is a low-carbon product, whose production does not emit any carbon dioxide.
(3) The production only needs two factors, which are labor (L) and capital (K).
(4) In an open economy with complete market competition, both industry 1 and industry 2 participate in the international division of labor.

Suppose the production function of potential output in the economy is:

S_{t_{0}} = F (K_{t_{0}}, L_{t_{0}})

In Eq. (1), F is the production function; S is the total output of the industry; K is the capital input, and L is the labor input, t₀ indicates the moment of initial production before the promulgation of environmental regulations.

As the wave of carbon emission reduction intensifies, the international carbon emission reduction requirements are increasing day by day. In order to comply with the trend of carbon emission reduction in international production, the government has formulated a series of policies and regulations to guide firms to achieve low-carbon production (Zhang et al., 2021a, Zhang et al., 2021b). In response to government carbon reduction regulations, the potential output of the product is reduced. $r_{p} (0 < r_{p} < 1)$ represents the share of potential output reductions resulting from environmental regulations. Thus, after the implementation of environmental regulations (t₁), the production function of potential output of product X is:

S_{X t_{1}} = (1 - r_{p}) F (K_{X t_{1}}, L_{X t_{1}})

Firms will make sure to get maximum benefit. The production level is at its maximum possible given the cost imposed by the regulatory framework. The regulations takes into account the social cost of that maximum, such that it set a limit which considers both social and private cost to get the new lower social maximum production level, which firms should look into (Feng et al., 2019; Yao et al., 2020). Therefore, the actual output of the firms should be up to but not lower than the potential output level ( $r_{a} \leq r_{p}$ ). Thus, the production function of actual output of product X is:

S_{X t_{1}} = (1 - r_{a}) F (K_{X t_{1}}, L_{X t_{1}})

Since industry 1 participates in the international division when producing X, the impact of the extension of GVC production length on production is $ω (V)$ . Under the condition of an open economy, the production function of actual output of product X is:

S_{X t_{1}} = (1 - r_{a}) F (K_{X t_{1}}, L_{X t_{1}}) ω_{X t_{1}} (V)

As policies and regulations related to carbon emission reduction drive production at the social level, firms can ensure long-term production operations through more environmentally friendly management (Wreford et al., 2021). Management (M) increases production efficiency by supporting technological change (T) and ensures that carbon emissions per unit of output are minimized (Wulf, 2020). Based on this, the level of carbon emissions (carbon emission per unit of output) can be expressed as:

ϕ_{X t_{1}} (r_{a}) = \frac{1}{M_{X t_{1}} (T)} {(1 - r_{a})}^{\frac{1}{α}}

where, $ϕ (r)$ is a decreasing function of $r$ , while the reciprocal of $M_{X t_{1}} (T)$ represents that management is reducing carbon emissions by improving production technology, and $0 < α < 1, ϕ^{'} (r) < 0, ϕ^{″} (r) > 0$ . The carbon emissions during the production of product X is:

C_{X t_{1}} = ϕ_{X t_{1}} (r_{a}) F (K_{X t_{1}}, L_{X t_{1}}) = \frac{1}{M_{X t_{1}} (T)} {(1 - r_{a})}^{\frac{1}{α}} F (K_{X t_{1}}, L_{X t_{1}})

Incorporating formula 6 into formula 4, the relationship between actual output of product X and carbon emissions can be obtained:

S_{X t_{1}} = M_{X t_{1}}^{α} (T) C_{X t_{1}}^{α} F {(K_{X t_{1}}, L_{X t_{1}})}^{1 - α} ω_{X t_{1}} (V)

Since carbon emissions will cause negative externalization to the society, corresponding opportunity costs must be paid, so the tax rate for carbon emissions is set to γ. According to the principle of minimizing the cost, under normal circumstances, firms will choose the optimal arrangement of potential output and carbon emissions levels to achieve the lowest production cost of product. Therefore, we can construct the following function:

\begin{matrix} min_{C} \{E_{X t_{1}} (E_{K}, E_{L}) F (K_{X t_{1}}, L_{X t_{1}}) + γ M_{X t_{1}} (T) C_{X t_{1}}\} \\ s . t . S_{X t_{1}} = M_{X t_{1}}^{α} (T) C_{X t_{1}}^{α} F {(K_{X t_{1}}, L_{{Xt}_{1}})}^{1 - α} ω_{{Xt}_{1}} (V) \end{matrix}

where, $E_{X t_{1}} (E_{K}, E_{L})$ is the unit production cost of the potential output of product X, and $E_{K}, E_{L}$ is the production cost of capital and labor, respectively. By constructing a Lagrangian function:

L = E_{X t_{1}} (E_{K}, E_{L}) F (K_{X t_{1}}, L_{X t_{1}}) + γ M_{X t_{1}} (T) C_{X_{1}} + θ M_{X t_{1}}^{α} (T) C_{X t_{1}}^{α} F {(K_{X_{1}}, L_{X t_{1}})}^{1 - α} ω_{X t_{1}} (V)

where, θ is the Lagrangian multiplier. We can obtain the derivation of carbon emissions C and output $F (K_{X}, L_{X})$ :

\begin{matrix} γ T_{X t_{1}} = & - α θ M_{X t_{1}}^{α} (T) C_{X t_{1}}^{α - 1} F {(K_{X t_{1}}, L_{X t_{1}})}^{1 - α} ω_{X t_{1}} (V) \\ E_{X t_{1}} (E_{K}, E_{L}) = & - (1 - α) θ M_{X t_{1}}^{α} (T) C_{X t_{1}}^{α} F {(K_{X t_{1}}, L_{X t_{1}})}^{- α} ω_{X t_{1}} (V) \end{matrix}

The cost minimization conditions for the production of product X is obtained by dividing the two formulas in Eq. (10).

E_{X t_{1}} (E_{K}, E_{L}) = \frac{γ (1 - α) M_{X t_{1}} (T) C_{X t_{1}}}{α F (K_{X t_{1}}, L_{X t_{1}})}

Under perfectly competitive market conditions, the result of market competition is in line with the Pareto optima. Then the net profit of the production of product X must be zero, so the profit function of product X is set as:

Π = P_{X t_{1}} S_{X t_{1}} - E_{X t_{1}} (E_{K}, E_{L}) F (K_{X t_{1}}, L_{X t_{1}}) - γ M_{X t_{1}} (T) C_{X t_{1}} = 0

where $P_{X}$ is the relative price of product X relative to product Y, and the price of product Y is defined as 1. Combining Eq. (11) with Eq. (12), we obtain:

S_{X t_{1}} = \frac{γ M_{X t_{1}} (T) C_{X t_{1}}}{α P_{X t_{1}}}

Thus, the carbon emissions level is:

ϕ_{X t_{1}} (r_{a}) = \frac{C_{X t_{1}}}{S_{X t_{1}}} = \frac{α P_{X t_{1}}}{γ M_{X t_{1}} (T)}

The carbon emissions function in Eq. (6) can be rewritten as:

C_{X t_{1}} = ϕ_{X t_{1}} (r_{a}) F (K_{X t_{1}}, L_{X t_{1}}) = \frac{α P_{X t_{1}}}{γ M_{X t_{1}} (T)} \cdot \frac{S_{X t_{1}}}{(1 - r_{a}) ω_{X t_{1}} (V)}

Equation (15) is the decomposition model of the carbon emissions effect of product X participating in GVC. After taking the logarithm of both sides, we obtain:

ln C_{X t_{1}} = ln (\frac{α P_{X t_{1}}}{γ}) - ln M_{X t_{1}} (T) + ln S_{X t_{1}} - ln (1 - r_{a}) - ln ω_{X t_{1}} (V)

where, $ln (α P_{X} / γ)$ is a constant term. As shown in Eq. (16), the sign of production scale (S) is positive, which means that as the production scale expands, carbon emissions will increase. The signs of management (M) and regulations ( $r$ ) are negative, which means that carbon emissions will be reduced with the improvement of management and regulations. Besides, it is expected that the extension of GVC production length will also have a negative effect on carbon emissions.

Mechanism description

According to the derivation of the above model, it is found that the influencing factors of industrial carbon emissions in the context of participating in GVC mainly include regulations, the GVC production length, production scale and management. However, in actual situations, the specific effects of various influencing factors on carbon emissions may be different. Based on this, we carry out a detailed analysis in combination with the actual situation (Fig. 1).

Fig. 1 — The mechanism diagram of the impact of participating in GVC on the carbon emissions

Effect of GVC production length

The essential feature of the GVC is the fragmentation of international division of labor and production. Countries participating in GVC will integrate into different production links according to their own comparative advantages, and the extension of GVC production length is also different, which will create a different effect. Therefore, since the production length of the forward and backward GVC is different in connotation, the different types of production lengths of a country participating in GVC will have different impacts on its carbon emissions. When a country's GVC production length based on forward linkage is longer, it means that the country is more involved in the division of labor in GVC by providing high-tech intermediate product. It also illustrates that the country is at a relatively high-end link in GVC and mainly engages in R&D and design activities. The country plays the role of "value export", and accordingly its carbon emissions will be relatively small. On the contrary, the longer the GVC production length of a country based on backward linkage, the lower the country is located in the division of labor in GVC. It also manifests that the country mainly produces through the import of intermediate products from other countries for processing, assembly and other production activities. The country acts as a "value input" and has a relatively large amount of carbon emissions.

H₁: The extension of the forward GVC production length is conducive to reducing carbon emissions, while the extension of the backward GVC production length is not conducive to reducing carbon emissions.

Scale effect

Scale effect means that with the substantial increase in industrial output value, industrial production needs to invest more energy and fuel, leading to a rapid increase in carbon emissions (Lin & Xie, 2016; Xu & Lin, 2017). According to the Environmental Kuznets Curve (EKC) model (Grossman & Krueger, 1995), when a country's economic development level is poor, there are fewer polluting production activities and the air quality is better. However, with the gradual development of the country's economy, it is necessary to increase its economic level through the mass production of high-energy, low-tech products. This large-scale production will bring serious environmental pollution problems. During this period, carbon emissions increase significantly with the development of the economy. When the country's economy has developed to a certain level, that is, after reaching a certain critical point, the country's science and technology has also progressed to a certain level of mainly producing high-tech, low-energy-consuming products. Although this kind of production will also slightly increase carbon emissions, in conjunction with other environmental protection measures, the overall environmental quality of the country will gradually improve.

H₂: The expansion of production scale will contribute to an increase in carbon emissions.

Regulation effect

The effect of policy regulation demonstrates that with the continuous refinement of international and domestic carbon emissions policy requirements, carbon emissions in the industrial production process will continue to decrease (Jorgenson & Wilcoxen, 1990). Environmental regulation is a tangible system or intangible binding force formed by the government or organization to protect the environment (Wang & Ang, 2018). The carbon emissions level of a country is greatly affected by domestic and foreign policies and regulations. When a country's industry participates in GVC, carbon emissions will also be reduced due to stricter domestic and foreign policies and regulations. Therefore, although policy regulations will not be affected by the level of participating in GVC, it will affect the carbon emissions of a certain industry due to the transmission of GVC (Ye et al., 2020). First, the countries in the upper reaches of the GVC pay much more attention to environmental pollution as well as the research and development of clean energy utilization technologies than those in the middle and lower reaches. In order to meet the requirements of upstream countries for product quality, environmental protection, safety and other aspects, countries that export to them will actively introduce advanced clean production technologies, increase investment in research and development of advanced low-carbon technologies, and thereby reduce carbon emissions (Pei et al., 2019). Second, with the rapid development of the world economy and increasingly severe environmental problems, countries located in the middle and lower reaches of the GVC are gradually increasing environmental protection standards and production carbon emissions requirements, no longer destroying the environment in pursuit of economic development, which in turn, promotes the accelerated reduction of domestic carbon emissions.

H₃: Stricter environmental regulations will help reduce carbon emissions.

Management effect

In the context of participating in GVC, firms coordinate the rational use of resources through management to ensure the smooth operation of social production networks and maintain the interests in environmental protection (Kano et al., 2020). Particularly after being severely impacted by the COVID-19 pandemic, the role of management in safeguarding production cannot be ignored (Kano et al., 2022). Good management is conducive to further improving social productivity (Elena et al., 2020 Pananond et al., 2020). Management guides firms to better participate in GVC by improving the level of production technology (Gereffi et al., 2005). At the same time, firms with high production technology can better reduce carbon emissions in production (Wang et al., 2021). Management mainly promotes the improvement of production technology level through two channels. The first is the technological spillover effect brought about by attracting foreign investment. FDI (foreign direct investment) technology spillover effect refers to the transfer of some advanced technologies as countries with high technology invest abroad, so as to facilitate low-tech countries to produce mid-to-high-end products that meet their needs (Inge & Claes, 2010; Jakub, 2012). At this time, low-tech countries will take the initiative to learn advanced technology and production experience to gradually improve the country's technological level (Pietrobelli & Rabellotti, 2011) and reduce production carbon emissions (Zhu et al., 2016). The second is the R&D effect generated by increasing investment in R&D. The R&D effect means that under the division of labor in GVC, the increase in R&D investment helps countries at the middle and low end of the value chain train R&D personnel, strengthen patent R&D capabilities, promote technological innovation, and reduce industrial carbon emissions (Huang et al., 2015).

H₄: Management improves the level of production technology through FDI technology spillover effect and R&D effect, thereby promoting the reduction of carbon emissions.

Methodology and data

Econometric model

Based on the actual situation of the impact of participating in GVC on carbon emissions, we improve and replace some of the influencing factors according to the theoretical analysis, and finally build the following empirical model:

ln C_{it} = β_{0} + β_{1} ln G V C_{it} + β_{2} ln R_{it} + β_{3} ln S c a l e_{it} + β_{4} ln M_{it} + ε_{it}

In Eq. (17), the explained variable C represents the carbon dioxide emissions of each sub-industry of China's equipment manufacturing industry; GVC represents the core explanatory variables related to the GVC, including forward and backward GVC production length and its decomposed parts; R represents the policy regulation, which is expressed by the amount of industrial pollution control investment, and the weight is the ratio of the total investment in fixed assets of equipment manufacturing industry to China's total investment in fixed assets, then the industrial pollution control investment can be calculated according to China's total pollution control investment; Scale represents the production scale, measured by per capita output value; M stands for management effect, which is measured by FDI and R&D investment. FDI is measured by the proportion of total assets of Hong Kong, Macao, Taiwan and foreign-invested industrial firms in total assets of all industrial firms above designated size, and R&D investment is measured by the R&D expenditures of various industries; $β_{0}$ represents a constant term; $β_{1}$ to $β_{4}$ represent the coefficient of each variables; ε represents a random disturbance term; i represents an industry, and t represents year.

Measuring GVC production length

According to the calculation method of Wang et al. (2017b), a country's participation in GVC can be decomposed following the KWW trade value-added accounting framework. Assuming that there are three countries (s, t, r) in the world and each country has two industrial sectors (i, j), all products can be consumed as final demand or continue to be produced as intermediate products. Then the conditions for the country's market equilibrium are:

X = A X + Y = A^{D} X + Y^{D} + A^{F} X + Y^{F} = A^{D} X + Y^{D} + E

Vector X represents the total output of one country; A represents the direct consumption coefficient matrix; Y represents the sum of final products used in a country from other countries, $A^{D}$ represents the domestic input coefficient, $Y^{D}$ represents the total domestic final products consumed by each country, $A^{F}$ represents the import input coefficient, $Y^{F}$ represents the sum of final products exported, and E represents total exports. According to the Leontief inverse matrix (B), we can rewrite Eq. (18):

\begin{matrix} X = & B Y = {(I - A)}^{- 1} Y = {(I - A^{D})}^{- 1} Y^{D} + {(I - A^{D})}^{- 1} E \\ = & B^{D} Y^{D} + B^{D} E = B^{D} Y^{D} + B^{D} Y^{F} + B^{D} A^{F} X \end{matrix}

where, $B^{D} = {(I - A^{D})}^{- 1}$ represents the domestic Leontief inverse matrix. According to the world input–output table, the relationship between the value-added (Va) and the final product (Y) is:

V a^{'} = \hat{V} X = \hat{V} B Y

where V represents the matrix of value-added coefficients. Therefore, the equation of the value-added of participating in production of s country i industry can be summarized as follows:

\hat{V} \hat{Y} + \hat{V} A \hat{Y} + \hat{V} A A \hat{Y} + \dots = \hat{V} (I + A + A A + \dots) \hat{Y} = \hat{V} {(I - A)}^{- 1} \hat{Y} = \hat{V} B \hat{Y}

where, $\hat{V} B \hat{Y}$ matrix represents the sum of value-added in all production stages, each element of which represents the value-added from an industry in one country, and the value-added is directly or indirectly used by an industry in another country to produce final products. Take the production length of each stage as the weight and add it up to get the total output of a specific industrial department, we obtain:

\begin{matrix} \hat{V} \hat{Y} + 2 \hat{V} A \hat{Y} + 3 \hat{V} A A \hat{Y} + \dots & = \hat{V} (I + 2 A + 3 A A + \dots) \hat{Y} \\ = \hat{V} {(B + A B + A A B + \dots)}^{- 1} \hat{Y} = \hat{V} B B \hat{Y} \end{matrix}

The average production length of the value-added in the final product transferred from the i industry in country s to the j industry in country r is expressed as:

P L v y = \frac{\hat{V} B B \hat{Y}}{\hat{V} B \hat{Y}}

Summing up the products of the j industry in country r can represent the overall average production length of the value-added of i industry in country s, so the average production length of the value-added of the i industry in country s based on the forward industrial linkage is:

P L v = \frac{\hat{V} B B Y}{\hat{V} B Y} = \frac{\hat{V} B X}{\hat{V} X}

The longer the forward production length is, the more downstream production stages the industry's value-added participates in as output, and the higher its upstream production position. Summing up all the value-added of industry i in country s, which is used by the final product of industry in country r and j, we can get the production length based on backward industry linkage:

P L y = \frac{V B B \hat{Y}}{V B \hat{Y}}

The longer the backward production length is, the greater the number of upstream production stages a particular final product has, and the lower the downstream production position of the product is. Since a country's production activities can be broken down into five parts according to the situation of cross-border production activities (Fig. 2), the forward production length (PLv) and the backward production length (PLy) can also be divided into 5 parts. The parts involved in GVC activities include the GVC production length (PLv_GVC, PLy_GVC), the simple GVC production length (PLv_GVC_S, PLy_GVC_S), the GVC production length returning to the exporting country (PLv_GVC_D, PLy_GVC_D) and the pure foreign GVC production length (PLv_GVC_F, PLy_GVC_F).

\begin{matrix} P L v = & \underset{(1) - P L v_D}{\underset{⏟}{\frac{\hat{V} B^{D} B^{D} Y^{D}}{\hat{V} B^{D} Y^{D}}}} + \underset{(2) - P L v_R T}{\underset{⏟}{\frac{\hat{V} B^{D} B^{D} Y^{F}}{\hat{V} B^{D} Y^{F}}}} + \underset{(3) - P L v_G V C}{\underset{⏟}{\frac{\hat{V} B B Y - \hat{V} B^{D} B^{D} Y^{D}}{\hat{V} B^{D} A^{F} B Y}}} \\ = & \underset{(1) - P L v_D}{\underset{⏟}{\frac{\hat{V} B^{D} B^{D} Y^{D}}{\hat{V} B^{D} Y^{D}}}} + \underset{(2) - P L v_R T}{\underset{⏟}{\frac{\hat{V} B^{D} B^{D} Y^{F}}{\hat{V} B^{D} Y^{F}}}} + \underset{(3 a) - P L v_G V C_S}{\underset{⏟}{\frac{\hat{V} B^{D} B^{D} A^{F} B^{D} Y^{D} + \hat{V} B^{D} A^{F} B^{D} B^{D} Y^{D}}{\hat{V} B^{D} A^{F} B Y}}} \\ + \underset{(4 a) - P L v_G V C_D}{\underset{⏟}{\frac{\hat{V} B^{D} B^{D} A^{F} (B Y - B^{D} Y^{D})}{\hat{V} B^{D} A^{F} (B Y - B^{D} Y^{D})}}} + \underset{(4 b) - P L v_G V C_F}{\underset{⏟}{\frac{\hat{V} B^{D} A^{F} (B B Y - B^{D} B^{D} Y^{D})}{\hat{V} B^{D} A^{F} (B Y - B^{D} Y^{D})}}} \end{matrix}

\begin{matrix} P L y = & \underset{(1) - P L y_D}{\underset{⏟}{\frac{V B^{D} B^{D} {\hat{Y}}^{D}}{V B^{D} {\hat{Y}}^{D}}}} + \underset{(2) - P L y_R T}{\underset{⏟}{\frac{V B^{D} B^{D} {\hat{Y}}^{F}}{V B^{D} {\hat{Y}}^{F}}}} + \underset{(3) - P L y_G V C}{\underset{⏟}{\frac{V B B \hat{Y} - V B^{D} B^{D} {\hat{Y}}^{D}}{V B^{D} A^{F} B \hat{Y}}}} \\ = & \underset{(1) - P L y_D}{\underset{⏟}{\frac{V B^{D} B^{D} {\hat{Y}}^{D}}{V B^{D} {\hat{Y}}^{D}}}} + \underset{(2) - P L y_R T}{\underset{⏟}{\frac{V B^{D} B^{D} {\hat{Y}}^{F}}{V B^{D} {\hat{Y}}^{F}}}} + \underset{(3 a) - P L y_G V C_S}{\underset{⏟}{\frac{V B^{D} B^{D} A^{F} B^{D} {\hat{Y}}^{D} + V B^{D} A^{F} B^{D} B^{D} {\hat{Y}}^{D}}{V B^{D} A^{F} B \hat{Y}}}} \\ + \underset{(4 a) - P L y_G V C_D}{\underset{⏟}{\frac{V B^{D} B^{D} A^{F} (B \hat{Y} - B^{D} {\hat{Y}}^{D})}{V B^{D} A^{F} (B \hat{Y} - B^{D} {\hat{Y}}^{D})}}} + \underset{(4 b) - P L y_G V C_F}{\underset{⏟}{\frac{V B^{D} A^{F} (B B \hat{Y} - B^{D} B^{D} {\hat{Y}}^{D})}{V B^{D} A^{F} (B \hat{Y} - B^{D} {\hat{Y}}^{D})}}} \end{matrix}

where the extension of the simple GVC production length means the increase in the production activities of simple intermediate goods, which is the increase in the production activities of intermediate goods directly used and consumed by the importing country; the extension of GVC production length returning to the exporting country and the pure foreign GVC production length means an increase in the production activities of complex intermediate products, which is an increase in the production activities of intermediate products returning to the exporting country or exported to a third country.

Fig. 2 — Production length divided by production activities

Sources: We draw it based on the paper named Characterizing Global Value Chains: Production Length and Upstreamness (Wang et al., 2017b).

Data sources

Since the World Input–Output Database (WIOD) is based on the International Standard Industry Classification (ISIC.Rev4) promulgated by the United Nations, the standard divides the equipment manufacturing industry into six categories. The Chinese National Economic Industry Classification (GB/T4754-2011) divides the equipment manufacturing industry into seven categories. In this study, we integrate the classification of the two, and finally divide the equipment manufacturing industry into five categories (Table 1).

Table 1.

Equipment manufacturing segmentation

Code	Name	ISIC.Rev4	GB/T4754-2011	Technology level
C16	Manufacture of metal products	Manufacture of fabricated metal products, except machinery and equipment	Manufacture of metal products	Medium-tech
C17	Manufacture of computer, electronic and optical products	Manufacture of computer, electronic and optical products	Manufacture of computer, communications and other electronic equipment	High-tech
C17	Manufacture of computer, electronic and optical products	Manufacture of computer, electronic and optical products	Manufacture of instrumentation	High-tech
C18	Manufacture of electrical equipment	Manufacture of electrical equipment	Manufacture of electrical equipment	Medium-tech
C19	Manufacture of machinery and equipment	Manufacture of machinery and equipment n.e.c	Manufacture of Ordinary Machinery	Medium-tech
C19	Manufacture of machinery and equipment	Manufacture of machinery and equipment n.e.c	Manufacture of equipment for special purposes	Medium-tech
C20	Manufacture of transport equipment	Manufacture of motor vehicles, trailers and semi-trailers	Manufacture of automotive	High-tech
C20	Manufacture of transport equipment	Manufacture of other transport equipment	Manufacture of railway, shipbuilding, aerospace and other transportation equipment	High-tech

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Both the OECD and the National Bureau of Statistics of China classify air and spacecraft and related machinery in the transportation equipment manufacturing industry into high-tech industries, and other industries in the transportation equipment manufacturing industry are mainly listed as Medium–high R&D intensive industries by the OECD. Moreover, because China's railway equipment manufacturing level is at the forefront of the world, and the development of ship and automotive manufacturing technology is rapid, we classify China's transportation equipment manufacturing industry as a high-tech industry

According to the "High-tech Industry (Manufacturing) Classification (2017)" (National Bureau of Statistics of China, 2018) and OECD (2017), the equipment manufacturing industry is divided into high-tech industries and medium-tech industries. We select samples of various sub-industries of the equipment manufacturing industry from 2000 to 2014 for a total of 15 years. The carbon emissions data is the original data of the latest environmental account released by the WIOD database in 2019. The data of GVC production length and the value-added of each industry are all calculated by the input–output account released by WIOD in 2016. R&D expenditure data comes from the "Statistical Yearbook of Scientific and Technological Activities of Industrial Enterprises". The rest of the data all comes from the "China Statistical Yearbook".

Data description

Carbon emissions of China's equipment manufacturing industry

The carbon emissions of China's equipment manufacturing industry show evident characteristics of industry clusters and phases distribution (Fig. 3).

Fig. 3 — The growth rate of carbon emissions of China's equipment manufacturing sub-industries from 2000 to 2016

First, the industry cluster of carbon emissions from China's equipment manufacturing industry is mainly reflected in the medium-technology industries. The carbon emissions of the medium-technology industries account for about 70% of the total carbon emissions of the equipment manufacturing industries, which is about twice the carbon emissions of the high-tech industries. This is the main reason for the significant increase in the carbon emissions of the equipment manufacturing industry. The manufacturing of machinery and equipment ranks first, and its carbon emissions reached the peak in 2007, accounting for approximately 38% of the equipment manufacturing industry's carbon emissions. The carbon emissions of the manufacture of metal products showed a fluctuating growth trend, accounting for 17% of the carbon emissions of the equipment manufacturing industry. The average annual growth rate of carbon emissions in the manufacture of electrical equipment exceeded 7%, and the carbon emissions increase in 17 years was the largest, about three times that of other industries. In contrast, the increase in carbon emissions from high-tech industries is relatively small, and the combined increase in carbon emissions from manufacture of computer, electronic and optical products and manufacture of transport equipment is only about half of that of manufacture of electrical equipment. The reason is that the clean production capacity of medium-tech industries is insufficient and the level of production technology is low, resulting in production energy consumption much greater than that of high-tech industries.

Besides, the carbon emissions trend of China's equipment manufacturing industry is mainly divided into three phases (Fig. 4). The first phase is the slow growth stage from 2000 to 2006. At this stage, the carbon emissions of various industries are increasing rapidly, and the annual growth rate has also been maintained at a relatively stable acceleration. In 2006, the carbon emissions of the equipment manufacturing industry were approximately twice that of 2000. The second phase is from 2006 to 2010. Benefiting from the great attention given by countries around the world to green development, China has gradually begun to formulate green development plans, leading to a cliff-like decline in the growth rate of carbon emissions in all the equipment manufacturing industries, and the carbon emissions of the equipment manufacturing industry eventually reached its peak in 2011. The third phase is the volatility reduction stage from 2012 to 2016. Except for the carbon emissions growth rate of the manufacture of electrical equipment which started to pick up in 2014, the carbon emissions growth rate of other sub-sectors all showed negative growth, indicating that China's equipment manufacturing industry has a good prospect for carbon emissions reduction.

GVC production length of China's equipment manufacturing industry

China's equipment manufacturing industry has deeply participated in GVC. The change trend of forward and backward GVC production length is similar, but the backward GVC production length is always longer than the forward GVC production length (Figs. 5 and 6). It shows that China is more deeply involved in the downstream activities of GVC than in the upstream activities. In 2001, the forward and backward GVC production length increased rapidly, which resulted from the tremendous progress that China made after joining the WTO in 2000. The convenience of participating in the international division of production has been promoted, and the technology spillovers from developed countries has increased. After a difficult growth process, the GVC participating level of China's equipment manufacturing industry achieved a major leap again in 2009. Comparing the GVC production length under different participating activities, the largest increase part is the GVC production length returning to the exporting country, followed by the pure foreign GVC production length. The variation of the GVC production length and the simple GVC production length is almost the same. It shows that the impetus provided by participating in GVC is far greater than that of China's independent research and development. In addition, the polarization of China's equipment manufacturing industry's participation in GVC is serious. The upstream production activities of GVC in which the equipment manufacturing industry participates are relatively high-end, but the downstream production activities are relatively low-end. It shows that although some industries in China's equipment manufacturing industry have been among the high-end production links of GVC and the production level is in the forefront of the world, there are still some industries locked in the low-end production links of GVC, and the production level is difficult to improve.

Fig. 5 — The forward GVC production length of China's equipment manufacturing industry

Fig. 6 — The backward GVC production length of China's equipment manufacturing industry

Empirical analysis and discussion

Analysis of benchmark regression results

We use the panel data of five sub-industries of China's equipment manufacturing industry from 2000 to 2014 to perform regression analysis on Eq. (17). In view of the possibility of groupwise heteroscedasticity, intra-group autocorrelation, or contemporaneous correlation in disturbance terms, we use Panel-Corrected Standard Error (PCSE) for regression. The regression results are more significant than OLS regression (Table 2), indicating that there is groupwise heteroscedasticity and contemporaneous correlation in the disturbance term. Subsequently, considering the influence of intra-group autocorrelation on the estimation results, we use Feasible Generalized Least Squares (FGLS) to perform regression. The test results in column (3) show that the disturbance item has characteristics of groupwise heteroscedasticity, intra-group autocorrelation and contemporaneous correlation. Therefore, we use FGLS to perform the following regression in this part.

Table 2.

The impact of the extension of GVC production length on carbon emissions

Variables	(1)	(2)	(3)	(4)	(5)
Variables	OLS	PCSE	FGLS	FGLS	FGLS
plv_GVC				− 3.519*** (0.348)
ply_GVC					− 1.728*** (0.455)
R	− 0.174 (0.154)	− 0.174** (0.081)	− 0.008** (0.050)	− 0.246*** (0.046)	− 0.105* (0.060)
Scale	0.256 (0.190)	0.256*** (0.074)	0.160** (0.075)	0.660*** (0.049)	0.382*** (0.067)
M_Fdi	0.716* (0.308)	0.716*** (0.078)	0.527*** (0.113)	− 0.034 (0.094)	0.501*** (0.094)
M_RD	0.203* (0.090)	0.203*** (0.021)	0.083** (0.038)	0.132*** (0.018)	0.195*** (0.027)
Constant	− 0.077 (1.406)	− 0.077 (0.585)	1.433** (0.633)	1.746*** (0.467)	1.427** (0.638)
Wald test			2802.04***	46.76***	615.44***
Wooldridge test			182.389***	227.930***	169.189***
Pesaran's test			1.210***	3.718***	2.342**
N	75	75	75	75	75

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The robust standard errors in parentheses, ***, **, and * indicate significant at the level of 1%, 5%, and 10% respectively

On the one hand, the regression results with only control variables added show that regulation and scale have a significant impact on carbon emissions (Table 2), which verifies that the regulation effect and scale effect. First of all, the coefficient of regulations is negative, which shows that increasingly stringent environmental regulation can help drive carbon reduction (Li & Lin, 2016). This is consistent with Hypothesis 3. But during the reporting period, the promotion of carbon emissions reduction is not obvious. Because China is in the industrialization stage from 2000 to 2011, the environmental regulation did not take effect until the end, resulting in a certain lag in its carbon reduction effect. Secondly, the scale effect will promote the increase of production carbon emissions, which is consistent with Hypothesis 2. Because in the process of joining the international division of labor, China's equipment manufacturing industry has undertaken the transfer of high-carbon industries from developed countries, and its production is dominated by high energy-consuming and high-polluting activities. The expansion of production scale will lead to an increase in carbon emissions, which is in line with the reality (Edgar, 2020). On the other hand, there is a significant positive correlation between FDI, R&D investment and carbon emissions, which is contrary to Hypothesis 4. This means that the technological improvements brought about by management have not contributed to the reduction of carbon emissions, indicating that the advanced technologies learned from investing countries in the low-end production links of China's equipment manufacturing industry are mainly high-carbon technologies, and self-developed technologies are also high-carbon technologies, which shows that the improvement of production technology will still promote the increase of production carbon emissions (Dammert & Marchand, 2015). It further shows that the firms have improperly managed cleaner production activities, and the application ability of value-added service systems such as the Sustainable Product Service System (SPSS) needs to be improved (Batlles-delaFuente et al., 2021).

After adding the core variable, the results show that the impact of forward GVC production length on carbon emissions is significantly negative. And for every 1 percentage extension in the forward GVC production length of China's equipment manufacturing industry, carbon emissions will be reduced about 3.5%, which is consistent with Hypothesis 1 (Table 2). This shows that the carbon emissions reduction effect brought by the extension of the forward GVC production length is better. The reason is that the extension of the forward GVC production length represents the move to the upstream production links in GVC. In this process, China's equipment manufacturing industry has achieved technological improvement through imitation, learning, and secondary innovation, and provided downstream producers with more high-tech intermediate products (Zheng et al., 2022). At this time, China's equipment manufacturing industry plays the role of “value output”, which in turn promotes the reduction of carbon emissions (Rafiq et al., 2016).

It is worth noting that for every 1 percentage point extension of the backward GVC production length, the level of carbon emissions will decrease by about 1.7%, which is contrary to Hypothesis 1 (Table 2). The extension of the backward GVC production length has a weak carbon emission reduction effect, indicating that the extension of the processing and assembly production chain of China's equipment manufacturing industry will promote the reduction of carbon emissions. This is because with the reduction of China's labor resources and the rise of basic production costs, the simple processing and production part of China's equipment manufacturing industry has begun to transfer to other developing countries in GVC, and production carbon emissions will also be transferred. In this case, although the backward GVC production length in the equipment manufacturing industry has been extended, the level of clean production technology has not been improved, which will also promote the reduction of carbon emissions. In the long run, this situation is not conducive to green development.

From the perspective of the impact of the decomposition part of GVC production length on carbon emissions, the coefficient of simple GVC production length is significantly negative at the level of 1%, while the impact of other parts on the carbon emissions of China's equipment manufacturing industry is not significant (Table 3). This indicates that the improvement of the level of cleaner production technology of China's equipment manufacturing industry mainly stays in the simple intermediate production stage, clean production technology for complex intermediate production activities is still insufficient. There are two possible reasons for this. On the one hand, China's equipment manufacturing industry does not have a high status in the international production division of labor, and participates in GVC mainly through simple processing and production links. The processing technology of complex intermediate products of China's equipment manufacturing industry is still immature. When processing and assembling imported high value-added intermediate inputs, a large amount of carbon emissions are left in the country. On the other hand, China's equipment manufacturing industry has developed to a certain level. However, when the leaders of GVC find that the development of the midstream and downstream players poses a threat to power, they will prevent them from upgrading to the high-end production links through technical barriers and other means. Midstream and downstream players will be locked in the low-end production links of GVC with high carbon emissions (Humphrey & Schmitz, 2001). Therefore, China's equipment manufacturing industry cannot achieve carbon emission reduction if it continues to deeply participate in the production activities of complex intermediate products in GVC (Wang et al., 2015).

Table 3.

The impact of different decomposition parts of the GVC production length on carbon emissions

Variables	Forward GVC production length			Backward GVC production length
Variables	(1)	(2)	(3)	(4)	(5)	(6)
plv_GVC_S	− 2.968*** (0.423)
plv_GVC_D		− 0.096 (0.089)
plv_GVC_F			0.007 (0.096)
ply_GVC_S				− 1.434*** (0.434)
ply_GVC_D					− 0.052 (0.141)
ply_GVC_F						0.023 (0.130)
R	− 0.205*** (0.056)	0.011 (0.052)	0.009 (0.050)	− 0.094 (0.062)	− 0.023 (0.054)	− 0.003 (0.053)
Scale	0.584*** (0.060)	0.221*** (0.060)	0.175** (0.070)	0.339*** (0.069)	0.224*** (0.062)	0.193*** (0.070)
M_Fdi	0.196** (0.098)	0.608*** (0.091)	0.574*** (0.107)	0.478*** (0.107)	0.645*** (0.088)	0.590*** (0.106)
M_RD	0.083*** (0.022)	0.074*** (0.026)	0.078** (0.033)	0.186*** (0.031)	0.099*** (0.027)	0.092*** (0.034)
Constant	1.757*** (0.507)	1.147** (0.563)	1.338** (0.640)	1.137* (0.654)	0.900 (0.568)	1.027 (0.657)
Wald test	59.17***	578.48***	1367.30***	517.19***	267.03***	496.11***
Wooldridge test	214.408***	183.143***	180.087***	173.755***	129.890***	177.516***
Pesaran's test	3.098**	1.152*	1.185*	2.511**	1.202*	1.196*
N	75	75	75	75	75	75

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The robust standard errors in parentheses, ***, **, and * indicate significant at the level of 1%, 5%, and 10% respectively

Analysis of the industry heterogeneity

In order to investigate whether there is industry heterogeneity in the impact of participating in GVC on carbon emissions of China's equipment manufacturing industry, the equipment manufacturing industry is divided into high-tech and medium-tech equipment manufacturing industries. High-tech industries include the manufacture of computer, electronic and optical products and manufacture of transport equipment, while medium-tech industries include manufacture of metal products, manufacture of electrical equipment and manufacture of machinery and equipment. In addition, it is found in empirical studies that the sample size of high-tech industries is small, and there is no groupwise heteroscedasticity and contemporaneous correlation in the disturbance terms. Therefore, the FGLS method that only solves the intra-group autocorrelation is used for regression. While the disturbance terms in the middle-tech industry have characteristics of groupwise heteroscedasticity, intra-group autocorrelation and contemporaneous correlation, we use FGLS for regression (Tables 4 and 5).

Table 4.

The impact of the forward GVC production length on the carbon emissions

Variables	High-tech equipment manufacturing industry				Medium-tech equipment manufacturing industry
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
ply_GVC	− 0.855 (0.675)				− 1.740*** (0.435)
ply_GVC_S		− 1.213* (0.695)				− 1.403*** (0.433)
ply_GVC_D			− 0.016 (0.172)				− 0.066 (0.136)
ply_GVC_F				− 0.009 (0.179)				0.083 (0.140)
R	− 0.056 (0.097)	− 0.091 (0.096)	− 0.001 (0.094)	− 0.001 (0.094)	− 0.222*** (0.064)	− 0.214*** (0.067)	− 0.168** (0.076)	− 0.177** (0.075)
Scale	0.038 (0.187)	0.087 (0.164)	− 0.147 (0.120)	− 0.152 (0.119)	0.549*** (0.064)	0.510*** (0.067)	0.361*** (0.070)	0.360*** (0.070)
M_Fdi	1.471*** (0.292)	1.389*** (0.282)	1.770*** (0.213)	1.768*** (0.214)	0.186* (0.110)	0.196 (0.122)	0.355*** (0.114)	0.363*** (0.115)
M_RD	0.212** (0.100)	0.200** (0.092)	0.244** (0.098)	0.247** (0.097)	0.101*** (0.016)	0.072*** (0.019)	0.078*** (0.020)	0.084*** (0.020)
Constant	3.698*** (0.643)	3.765*** (0.567)	4.055*** (0.662)	4.074*** (0.678)	0.387 (0.655)	0.250 (0.661)	− 0.240 (0.683)	− 0.540 (0.663)
Wald test	0.02	0.00	0.71	0.79	0.53*	1.69*	15.22**	10.97**
Wooldridge test	72.895*	82.128*	58.330*	65.156*	80.328**	67.648**	56.597**	58.499**
Pesaran's test	0.557	0.948	− 0.179	− 0.151	3.808***	3.553***	3.324***	3.486***
N	30	30	30	30	45	45	45	45

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The robust standard errors in parentheses, ***, **, and * indicate significant at the level of 1%, 5%, and 10% respectively

Table 5.

The impact of the backward GVC production length on the carbon emissions

Variables	High-tech equipment manufacturing industry				Medium-tech equipment manufacturing industry
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
ply_GVC	0.416 (0.697)				− 0.159 (0.633)
ply_GVC_S		− 0.034 (0.659)				0.178 (0.640)
ply_GVC_D			0.091 (0.248)				0.050 (0.214)
ply_GVC_F				− 0.093 (0.235)				0.229 (0.183)
R	0.022 (0.093)	− 0.005 (0.095)	− 0.006 (0.094)	0.004 (0.094)	− 0.184** (0.076)	− 0.173** (0.077)	− 0.176** (0.075)	− 0.180** (0.075)
Scale	− 0.219 (0.165)	− 0.137 (0.164)	− 0.169 (0.123)	− 0.139 (0.119)	0.387*** (0.089)	0.353*** (0.089)	0.360*** (0.072)	0.358*** (0.069)
M_Fdi	1.882*** (0.252)	1.771*** (0.261)	1.763*** (0.215)	1.786*** (0.212)	0.383*** (0.120)	0.363*** (0.118)	0.357*** (0.114)	0.365*** (0.113)
M_RD	0.247** (0.096)	0.242** (0.097)	0.263*** (0.098)	0.231** (0.098)	0.089*** (0.026)	0.078*** (0.025)	0.084*** (0.023)	0.089*** (0.020)
Constant	4.044*** (0.640)	4.015*** (0.617)	3.922*** (0.703)	4.221*** (0.719)	− 0.380 (0.831)	− 0.590 (0.794)	− 0.502 (0.735)	− 0.836 (0.700)
Wald test	1.79	0.63	0.68	0.98	6.75*	9.59**	11.94***	9.46**
Wooldridge test	158.686**	75.006*	51.702*	82.815*	60.039**	54.794**	53.725**	59.377**
Pesaran's test	− 0.206	− 0.047	− 0.078	− 0.169	3.919***	3.856***	3.483***	3.655***
N	30	30	30	30	45	45	45	45

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The robust standard errors in parentheses, ***, **, and * indicate significant at the level of 1%, 5%, and 10% respectively

For high-tech industries, only the forward simple GVC production length has a significant negative impact on carbon emissions. This shows that the production of high-tech industries in China's equipment manufacturing industry is mainly to provide high-level intermediate products to other countries. In this process, the level of production increases with the deepening of the level of participating in GVC, which in turn reduces carbon pollution in the production process. For the medium-tech industries, the extension of the forward GVC production length is conducive to reducing carbon emissions, while the extension of the backward GVC production length has no significant effect on carbon emissions. This is because most of the current medium-tech equipment manufacturing industries are resource-intensive industries. The way to deeply participating in GVC is to provide more resource-intensive intermediate products to other importing countries, which is not conducive to green development (Qu et al., 2020). The medium-tech industries have not completely deviated from the production model of low value-added, and it is more difficult to reduce the carbon emissions of production.

To sum up, it can be seen that the extension of GVC production length of the five sub-industries has not played a significant role in reducing carbon emissions. The reason is that the equipment manufacturing industry have higher requirements for precision parts, but the production of China's equipment manufacturing industry is highly dependent on FDI technology spillovers, whereas the core technology manufacturing capabilities are still weak. The speed of the transformation of basic core components, basic core processes, and basic core materials to high-end components and high-tech products is slow, which has caused China to provide related products to high-end manufacture countries mainly through OEM (Original Equipment Manufacturer) production mode, carbon emissions in production are difficult to reduce.

Robustness analysis

For the purpose of further examining the robustness of the empirical results, we remove 5% of the extreme values from both ends, and perform regression test on the sub-samples to eliminate the influence of non-randomness on the regression results. The robustness test results show that the impact of the forward GVC production length on carbon emissions is significantly negative at the level of 1% while the backward GVC production length have little impact on carbon emissions. In addition, we draw on the calculation method of Wang et al. (2017b) to calculate the GVC position index, and use it to replace the original core explanatory variables for regression. The results show that embedding in GVC can help reduce the carbon emissions of China's equipment manufacturing industry. In other words, the results from these two robustness tests largely support the robustness (Table 6).

Table 6.

The results of robustness test

Variables	(1)	(2)	(3)
plv_GVC	− 3.273*** (0.366)
ply_GVC		− 1.640*** (0.492)
GVC position			− 2.131*** (0.442)
R	− 0.179*** (0.042)	− 0.027 (0.058)	− 0.020 (0.047)
Scale	0.617*** (0.050)	0.335*** (0.057)	0.265*** (0.065)
M_Fdi	− 0.004 (0.097)	0.547*** (0.089)	0.361*** (0.099)
M_RD	0.107*** (0.015)	0.187*** (0.026)	0.012 (0.038)
Constant	1.903*** (0.427)	1.699*** (0.637)	0.707 (0.500)
Wald test	87.04***	734.43***	2338.44***
Wooldridge test	254.739***	198.392***	95.642***
Pesaran's test	4.312***	3.127**	0.810*
N	75	75	75

Open in a new tab

The robust standard errors in parentheses, ***, **, and * indicate significant at the level of 1%, 5%, and 10% respectively. Based on the measurement of the forward and backward GVC production length, Wang et al. proposed the GVC position index, which can be defined as the ratio of the two kinds of production length. The greater the value of the index, the more upstream is the country's industry

Conclusion and policy implications

Based on the theoretical model, we derive the core indicators that affect carbon emissions in the context of participating in GVC, and empirically analyze the specific impact of different GVC participating modes on the carbon emissions of the equipment manufacturing industry. The main research conclusions are summarized in the following four aspects. (1) China's equipment manufacturing industry is deeply participating in GVC. It participates in relatively high-end production activities in the upstream of GVC, but mainly participates in low-end production activities in the downstream of GVC. (2) The extension of GVC production length can effectively promote carbon emissions reduction. In the decomposition of GVC production length, the carbon emissions reduction effect of the extension of simple GVC production length is the most significant. (3) Changes in production scale and management will increase carbon emissions. Otherwise, improved regulation will promote the reduction of carbon emissions. (4) The extension of forward simple GVC production length in high-tech industries will significantly reduce carbon emissions, and the extension of forward GVC production length in medium-tech industries will also reduce carbon emissions.

Based on the above conclusions, we propose the following four policy implications to promote China's equipment manufacturing industry to achieve carbon emissions reduction during deeply participation in GVC. (1) In the context of participating in GVC, the extension of the GVC production length will bring great potential for carbon reduction worldwide, especially in manufacturing sector (Rilong et al., 2020). It provides strong evidence for China's unswerving participation in the international division of labor and adherence to opening up. Therefore, China's equipment manufacturing industry should actively respond to the "Belt and Road" initiative, cooperate with countries along the "Belt and Road" in production activities, and undertake more high-value-added, low-carbon-emission production activities from developed countries, and transfer low-end production activities to other developing countries where resources and labor are cheaper. (2) In the global recovery stage after the Covid-19 pandemic, Chinese firms should seize the opportunity of global supply and demand mismatch, grasp the opportunity of expanding domestic and international market demand for Chinese products, attract global consumers and investors, and occupy the foreign high-tech intermediate product market while stabilizing domestic demand. (3) For high-tech industries, the main task in the next stage is to get out of the predicament of "low-end lock-in" and break away from the assembly and modification production links. The medium-tech industry needs to expand the production scale of high value-added intermediate products through the extensive introduction of advanced low-carbon production technologies, reduce dependence on the export of pollution-intensive intermediate products, and gradually transform from the high-carbon GVC participation channels to high-tech channels. (4) China should gradually raise the threshold of environmental control and improve the environmental pollution legal system. Meanwhile, the whole process of coping with the GVC is not only about production, but also includes stepping through different stages with the help of management. As an important manifestation of management results, the improvement of production technology plays an important role in reducing carbon emissions (Zheng et al., 2022). China should continue to optimize its business environment and attract FDI in high-end technology industries, and build a capital and technology accumulation platform for equipment manufacturing companies (Liu & Wei, 2022).

There are still some shortcomings in this study. The empirical results show that technological improvements brought about by management have not contributed to the reduction of carbon emissions, which is contrary to H₄. This is because we study the impact of equipment manufacturing industry's participation in GVC on carbon emissions from the industry level. However, the actors participating in GVC are different manufacturing firms, and there are management differences between firms, which leads to the asynchronous technological changes driven by management (Wulf, 2020). Therefore, from a micro level, the activities of China's manufacturing firms to achieve carbon reduction in the process of participating in GVC have individual differences. Especially when the management efficiency of most firms is poor, the implementation of the industry-wide carbon emission reduction regulation and the realization of carbon emission reduction goals are facing huge obstacles. Based on this, the lines of future research can be drawn by comprehensively collecting the effective data of firms' participation in GVC, and studying the impact of management differences on the realization of low-carbon production in China's manufacturing industry from the micro level. Through in-depth exploration of the development obstacles encountered by firms in the process of participating in GVC, it will provide implications for China's manufacturing industry to realize a comprehensive low-carbon transformation.

Acknowledgements

This work was supported by Social Science Foundation of Shenzhen (SZ2021B015, 202107), Department of Education of Liaoning Province (2020-10151-002), and Social Science Foundation of Liaoning Province (L21AJY001, L21AJL001).

Author contribution

Q.H.: Conceptualization, Writing—Reviewing and Editing. X.X.: Data curation, Software, Writing—Original draft preparation. X.Z.: Methodology, Writing—Reviewing and Editing. Y.L.: Supervision, Writing—Reviewing and Editing.

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This article does not contain any studies with human or animal subjects performed by any of the authors.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Qingbo Huang, Email: huangqingbo@dlmu.edu.cn.

Xinxin Xia, Email: 2285008896@qq.com.

Xiaohan Zhang, Email: ssszx1.3zxh@163.com.

Yan Li, Email: lilyyan@dlmu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

[CR1] Altig D, Baker S, Barrero JM, et al. Economic uncertainty before and during the COVID-19 pandemic. Journal of Public Economics. 2020;191:104274. doi: 10.1016/j.jpubeco.2020.104274. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR2] Antweiler W, Copelan BR, Taylor MS. Is free trade good for the environment? American Economic Review. 2001;91(4):877–908. doi: 10.1257/aer.91.4.877. [DOI] [Google Scholar]

[CR3] Baldwin R, Evenett S. Value creation and trade in 21st century manufacturing. Journal of Regional Science. 2015;55:31–50. doi: 10.1111/jors.12175. [DOI] [Google Scholar]

[CR4] Batlles-delaFuente A, Belmonte-Ureña LJ, Plaza-Úbeda JA, Abad-Segura E. Sustainable business model in the product-service system: analysis of global research and associated EU legislation. International Journal of Environmental Research and Public Health. 2021;18:10123. doi: 10.3390/ijerph181910123. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR5] Bi K, Huang P, Ye H. Risk identification, evaluation and response of low-carbon technological innovation under the global value chain: A case of the Chinese manufacturing industry. Technological Forecasting and Social Change. 2015;100(4):238–248. doi: 10.1016/j.techfore.2015.07.005. [DOI] [Google Scholar]

[CR6] Chen ZM, Chen GQ. An overview of energy consumption of the globalized world economy. Energy Policy. 2011;39(10):5920–5928. doi: 10.1016/j.enpol.2011.06.046. [DOI] [Google Scholar]

[CR7] Dammert AC, Marchand BU. Privatization in China: Technology and gender in the manufacturing sector. Contemporary Economic Policy. 2015;33(2):250–264. doi: 10.1111/coep.12071. [DOI] [Google Scholar]

[CR8] Du LM, Wei C, Cai SH. Economic development and carbon dioxide emissions in China: Provincial panel data analysis. China Economic Review. 2012;23:371–384. doi: 10.1016/j.chieco.2012.02.004. [DOI] [Google Scholar]

[CR9] Duan Y, Ji T, Yu T. Reassessing pollution haven effect in global value chains. Journal of Cleaner Production. 2020;284:124705. doi: 10.1016/J.JCLEPRO.2020.124705. [DOI] [Google Scholar]

[CR10] Edgar GH. Carbon fueling complex global value chains tripled in the period 1995–2012. Energy Economics. 2020;86:104651. doi: 10.1016/j.eneco.2019.104651. [DOI] [Google Scholar]

[CR11] Elena B, Liam C, Gerard H. Global value chains as entrepreneurial capture: Insights from management theory. Review of International Political Economy. 2020;27(4):903–925. doi: 10.1080/09692290.2019.1657479. [DOI] [Google Scholar]

[CR12] Fally, T. (2011). On the Fragmentation of Production in the US. University of Colorado working paper, CU Denver

[CR13] Fally, T., Hillberry, R. H. (2016) A Coasian Model of International Production Chains. World Bank Policy Research Working Paper 7434, 299–315

[CR14] Feng W, Ji G, Pardalos PM. Effects of government regulations on Manufacturer's behaviors under carbon emission reduction. Environment Science and Pollution Research. 2019;26:17918–17926. doi: 10.1007/s11356-017-0891-4. [DOI] [PubMed] [Google Scholar]

[CR15] Gereffi G, Humphrey J, Sturgeon T. The governance of global value chains. Review of International Political Economy. 2005;12(1):78–104. doi: 10.1080/09692290500049805. [DOI] [Google Scholar]

[CR16] Grossman GM, Krueger AB. Economic growth and the environment. Quarterly Journal of Economics. 1995;110(2):353–377. doi: 10.2307/2118443. [DOI] [Google Scholar]

[CR17] Ho CY, Wang W, Yu J. International knowledge spillover through trade: A time-varying spatial panel data approach. Economics Letters. 2018;162:30–33. doi: 10.1016/j.econlet.2017.10.015. [DOI] [Google Scholar]

[CR18] Huang T, Onishi A, Shi F, Morisugi M, Cherry M. Regional characteristics of industrial energy efficiency in China: Application of stochastic frontier analysis method. Frontiers of Environmental Science and Engineering. 2015;9(3):506–521. doi: 10.1007/s11783-014-0656-y. [DOI] [Google Scholar]

[CR19] Humphrey J, Schmitz H. Governance in global value chains. IDS Bulletin. 2001;32(3):19–29. doi: 10.1111/j.1759-5436.2001.mp32003003.x. [DOI] [Google Scholar]

[CR20] Humphrey J, Schmitz H. How does insertion in global value chains affect upgrading in industrial clusters? Regional Studies. 2002;36(9):1017–1027. doi: 10.1080/0034340022000022198. [DOI] [Google Scholar]

[CR21] Inge I, Claes GA. Supplier upgrading in the home-furnishing value chain: An empirical study of IKEA's sourcing in China and South East Asia. World Development. 2010;38:1575–1587. doi: 10.1016/j.worlddev.2010.04.007. [DOI] [Google Scholar]

[CR22] Jakub G. The world technology frontier: What can we learn from the US states? Oxford Bulletin of Economics and Statistics. 2012;74(6):777–807. doi: 10.1111/j.1468-0084.2011.00686.x. [DOI] [Google Scholar]

[CR23] Jorgenson DW, Wilcoxen PJ. Environmental regulation and U.S. economic growth. Rand Journal of Economics. 1990;21(2):314–340. doi: 10.2307/2555426. [DOI] [Google Scholar]

[CR24] Kano L, Narula R, Surdu I. Global value chain resilience: Understanding the impact of managerial governance adaptations. California Management Review. 2022;64(2):24–45. doi: 10.1177/00081256211066635. [DOI] [Google Scholar]

[CR25] Kano L, Tsang EWK, Yeung HW. Global value chains: A review of the multi-disciplinary literature. Journal of International Business Studies. 2020;51:577–622. doi: 10.1057/s41267-020-00304-2. [DOI] [Google Scholar]

[CR26] Koopman, R., Powers, W., Wang, Z., Wei, S. (2010). Give credit where credit is due: Tracing value added in global production chains. NBER Working Paper, 16426 10.3386/w16426

[CR27] Kostoska O, Viktor S, Ljupco K. On the structure of the world economy: An absorbing markov chain approach. Entropy. 2020;22(4):482. doi: 10.3390/e22040482. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR28] Li K, Lin B. Impact of energy conservation policies on the green productivity in China's manufacturing sector: Evidence from a three-stage DEA model. Applied Energy. 2016;168:351–363. doi: 10.1016/j.apenergy.2016.01.104. [DOI] [Google Scholar]

[CR29] Li Y, Sun H, Huang J, Huang Q. Low-end lock-in of Chinese equipment manufacturing industry and the global value chain. Sustainability. 2020;12(7):2981. doi: 10.3390/su12072981. [DOI] [Google Scholar]

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PERMALINK

Can the extension of the global value chain production length promote carbon emissions reduction in China's equipment manufacturing industry?

Qingbo Huang

Xinxin Xia

Xiaohan Zhang

Yan Li

Abstract

Introduction

Theoretical mechanism

Theoretical model

Mechanism description

Fig. 1.

Effect of GVC production length

Scale effect

Regulation effect

Management effect

Methodology and data

Econometric model

Measuring GVC production length

Fig. 2.

Data sources

Table 1.

Data description

Carbon emissions of China's equipment manufacturing industry

Fig. 3.

Fig. 4.

GVC production length of China's equipment manufacturing industry

Fig. 5.

Fig. 6.

Empirical analysis and discussion

Analysis of benchmark regression results

Table 2.

Table 3.

Analysis of the industry heterogeneity

Table 4.

Table 5.

Robustness analysis

Table 6.

Conclusion and policy implications

Acknowledgements

Author contribution

Data availability

Declarations

Conflict of interest

Ethical approval

Footnotes

Contributor Information

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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