Supply Chain Concentration, Financing Constraints, and Carbon Performance

Abstract

Companies have been implementing various strategies, such as supply chain reconfiguration and process optimization, striving to find an efficient and effective solution for enhancing carbon performance over the last decade. Although various factors that may influence supply chain carbon performance have been investigated, the impact of supply chain concentration remains unclear due to insufficient research and inconsistencies in conclusions from the existing research. It is essential for enterprises to understand whether and to what extent supply chain concentration is an effective measure for improving carbon performance. Equally important is understanding the situations in which supply chain concentration works more effectively. In this research, we will construct fixed effects models using data from Chinese A-share listed companies from 2012 to 2021 to investigate the effects and mechanisms of supply chain concentration on corporate carbon performance. Our results suggest that supply chain concentration has a significant positive effect on carbon performance, with financing constraints playing a partial mediating role in this relationship. In addition, we have found that managerial myopia has a negative moderating effect on the positive relationship between supply chain concentration and carbon performance, whereas unexpected public events positively moderate this relationship. Further research indicates that the effect of supply chain concentration on carbon performance is stronger for state-owned enterprises and low-growth enterprises in China.

1. Introduction

Since the 1990s, China’s economy has experienced sustained and rapid growth, but it has also caused damage to the ecological environment. To seek a balance between economic growth and environmental protection and to achieve the harmonious coexistence of man and nature, China upholds the development philosophy that “Lucid waters and lush mountains are invaluable assets”. China has proposed the goal of peak carbon emissions and carbon neutrality and has implemented the carbon quota and trading system to promote low-carbon and high-quality economic development. As micro-level actors, enterprises need to strengthen their green supply chain management in order to comply with environmental regulations, taking into account factors such as stakeholders, innovation capabilities, strategies, and laws and regulations [1]. By collaborating with key customers upstream and downstream in the supply chain to implement green inputs, such relationship-specific inputs will in turn foster trust between the two partners [2,3], generate close supply chain partnerships, and increase supply chain concentration. The concentration of the supply chain has pros and cons. On the one hand, it is conducive to integrating upstream and downstream suppliers and customer resources in the supply chain, improving the competitiveness of enterprise products, promoting information sharing among supply chain partners, reducing transaction costs, and avoiding opportunistic behavior risks [4,5]. On the other hand, it may weaken bargaining power and increase business risks for companies [3]. Against the backdrop of global climate change and carbon regulations, studying whether the concentration of the supply chain is conducive to improving carbon performance is of great significance in preventing climate change and seeking a collaborative low-carbon development path for supply chain enterprises.

The existing literature mainly explores the factors affecting carbon performance from two perspectives: internal and external to the company. This includes research on internal factors such as property rights nature [6], carbon risk awareness [7], carbon disclosure [8,9], digitalization level [10], technological progress [11], and corporate governance [12,13]. It also includes research on external factors such as carbon trading policies [14], environmental subsidies [15], and mixed ownership reform [16]. There is a considerable amount of literature studying firm performance from a supply chain perspective, but the conclusions are not consistent, and there is a lack of research on carbon performance. Firstly, in terms of environmental performance, some studies have found that sustainable supply chain management has no significant impact on environmental performance [17]. The integration of manufacturing firms’ suppliers and customers may even have a negative impact on environmental performance [18]. However, green supply chain management has a significant impact on improving environmental performance [19], and green customer integration improves environmental performance through green process innovation [20]. Secondly, in other aspects of firm performance, some scholars believe that sustainable supply chain management has a significant positive impact on economic performance and social performance [17]. Internal integration in green supply chains can also improve financial performance [21]. Centralization of procurement can accelerate inventory turnover, and centralization of sales can enhance market performance [22]. However, some scholars have obtained opposite conclusions, suggesting that customer concentration has a detrimental effect on financial performance, especially when relationship-specific investments are intensified [3]. At the same time, some scholars view the supply chain as a mechanism and explore its relationship with firm performance. It has been found that supply chain integration plays an intermediary role in the impact of digital transformation on firm performance, and digital transformation has a significant driving effect on the degree of supply chain integration [23].

According to the theories of capital supply and redundant resources [24,25], having sufficient capital serves as the economic foundation for enterprises to undertake environmental responsibility and can provide more resources for their green transformation. The more significant the financing constraints on a company, the greater the challenges it faces in terms of green technology innovation and transitioning to a low-carbon economy, as both are influenced by the limitations on funding [26]. Additionally, supply chain concentration can affect a company’s financing constraints. The higher the concentration of suppliers, the lower the difficulty for the company to obtain bank loans, resulting in weaker external financing constraints [27]. Furthermore, the existence of major customers is beneficial for reducing the risk of the company and sending positive signals to the capital market, making investors and financial institutions more willing to provide financing opportunities to these companies [28]. Therefore, it remains to be further explored what role financing constraints play in the relationship between supply chain concentration and carbon performance and whether they have a certain impact.

In summary, there is a considerable amount of literature on carbon performance research, and it has yielded beneficial conclusions, providing a theoretical basis for the green and low-carbon transformation of businesses. However, there is a lack of consistent literature on the relationship between environmental performance and supply chain concentration. Additionally, carbon performance, as an evaluation indicator of environmental performance, is often overlooked in assessing environmental performance [29,30]. Therefore, it is necessary to further explore the relationship between supply chain concentration and carbon performance. Considering this, this study examines the impact of supply chain concentration on carbon performance, as well as the mediating role of financial constraints in this relationship. Possible marginal contributions include (1) verifying the relationship between supply chain concentration and carbon performance using extensive datasets, providing a theoretical basis for low-carbon management in supply chain companies; (2) testing the mediating effect of financial constraints on the relationship between supply chain concentration and carbon performance, as well as the moderating effect of managerial myopia and unexpected public events on the relationship between supply chain concentration and carbon performance, revealing the mechanisms through which supply chain concentration affects carbon performance; and (3) addressing the deficiency in the existing literature where conclusions were insufficient to guide low-carbon supply chain management practices due to the neglect of carbon performance factors in environmental performance measurement.

2. Theoretical Analyses and Research Hypotheses

2.1. Supply Chain Concentration and Carbon Performance

Firstly, environmental regulations require businesses to increase the concentration of their supply chains. According to stakeholder theory, pressure from stakeholders serves as a significant driving force for businesses to engage in green supply chain management. As the concepts of green and sustainability become increasingly ingrained in society, governments and markets are imposing stricter requirements on businesses. On one hand, environmental regulations compel companies to continuously update and improve their production and operational methods, prioritizing suppliers and customers that meet environmental regulatory requirements and possess green production capabilities, resulting in a relatively centralized green supply chain. On the other hand, there is a constant increase in market demand for green products from both consumers and the public. As a result, businesses are increasingly willing to intensify green technological innovation, implement green production practices, and provide green products and services [31]. This trend further drives them towards establishing centralized supply chains, allowing for greater flexibility and efficiency in responding to fluctuations in market demand. In turn, the market absorbs a substantial quantity of green products from companies, improving their carbon performance.

Secondly, supply chain consolidation facilitates enterprises in achieving carbon emission reduction. Green manufacturing theory emphasizes considering the environmental impact and resource efficiency from the perspective of the entire supply chain, aiming to minimize the negative environmental effects and maximize resource efficiency throughout the entire lifecycle of a product. Within the supply chain, carbon emissions arise from diverse activities spanning from raw material processing to the ultimate customer [32]. By concentrating the supply chain among fewer suppliers and customers, enterprises can optimize logistics and transportation, diminish traffic congestion, and curtail energy consumption. Consequently, this leads to a reduction in carbon emissions while enhancing enterprise carbon performance [33]. Furthermore, supply chain concentration nurtures specialized production processes, rectifying the disparities and inadequate utilization of resources caused by fragmented supply chains and outsourced production links [34]. In addition, this results in a further reduction in waste and carbon emissions during the production process.

Finally, supply chain centralization plays a crucial role in strengthening inter-firm synergies. In order to achieve carbon emission reduction, effective communication of carbon information within the supply chain is imperative [35]. Drawing inspiration from the theory of supply chain collaboration, in order to better control the information throughout the entire supply chain, companies need to strive to achieve a relatively centralized supply chain as much as possible. This facilitates a more efficient assessment of the carbon performance of the entire supply chain, enabling the implementation of targeted measures. Additionally, a more concentrated supply chain enhances the interconnectivity between enterprises and their chain partners. This increases the likelihood of reaching a consensus on cooperation, allowing for better coordination and integration of resources and technologies among the enterprise, suppliers, and customers [36]. Consequently, more environmentally friendly measures can be adopted, reducing environmental impact and promoting sustainable, low-carbon, and environmentally friendly development throughout the entire supply chain. Based on the analysis provided above, this paper proposes the following Hypothesis 1:

H1: 

Supply chain concentration has a positive effect on carbon performance.

2.2. The Mediating Effect of Financing Constraints in the Relationship between Supply Chain Concentration and Carbon Performance

Supply chain concentration can alleviate firms’ financing constraints. On the one hand, firms achieving a relatively centralized supply chain can improve financial performance. When the supply chain is relatively centralized, numerous product transactions within an enterprise require the allocation of specialized assets to meet the needs of a limited number of key suppliers and customers. These specialized assets become critical resources for companies [37], enabling them to gain additional profits and achieve strong financial performance, aligning with the perspective of embeddedness theory. Simultaneously, transaction cost theory suggests that enterprises adopting a centralized supply chain strategy and optimizing the supply chain structure can reduce transaction costs [38], achieve economies of scale, and bring more profit space. On the other hand, due to the high dependency of companies on a small number of suppliers and customers, the upstream and downstream relationships are therefore more closely connected. According to the resource dependence theory, the development of supply chain finance and green finance will further alleviate the financing constraints faced by companies on the supply chain. Connecting upstream and downstream enterprises and integrating financial institutions and supply chain resources provides enterprises with more comprehensive, accurate, and efficient financial services, assisting them in overcoming financing challenges. In summary, an increase in supply chain concentration will alleviate the external financing constraints of enterprises.

The level of financing constraints also influences the carbon performance of enterprises. Specifically, companies facing less stringent financing constraints are more inclined to allocate additional resources and funds toward implementing environmental protection measures and technological innovations [39]. Such enterprises also tend to prioritize long-term development and social responsibility, motivating a focus on environmental protection. In contrast, higher financing costs may restrict the ability and willingness of companies to undertake environmental protection measures. Severe financing constraints can even crowd out investments in innovation and environmental protection inputs [40]. This, in turn, can hinder their capacity to develop new products and upgrade environmental protection technologies, impeding the sustainable development of the enterprise and impacting its carbon performance. Moreover, companies facing fewer financing constraints generally exhibit stronger market competitiveness [41] and are more agile in meeting customer needs and responding swiftly to market changes. By leveraging customers’ environmental awareness and demand for eco-friendly products, enterprises can further enhance their carbon performance. Based on the above analysis, this paper proposes the following Hypothesis 2:

H2: 

There is a mediating effect of financing constraints in the relationship between supply chain concentration and carbon performance.

3. Research Design

3.1. Sample Selection and Data Sources

Due to the new regulations on annual reports for listed companies issued by the China Securities Regulatory Commission in 2012, companies are now required to disclose transaction information for their top five suppliers and customers. In the same year, the National Development and Reform Commission issued the Interim Measures for the Administration of Greenhouse Gas Voluntary Emission Reduction Trading and the Notice on Carbon Emission Trading Pilot Work, marking the beginning of China’s construction of a standardized carbon market management mechanism.

Considering the practical significance of the study and data availability, this paper takes Chinese A-share listed companies from 2012 to 2021 as the research sample. Carbon emission data were mainly obtained by manually extracting information from social responsibility reports, sustainable development reports, and corporate environmental reports. Financial data required for analysis were sourced from the CSMAR database.

In order to ensure the reliability of the data, the following treatments were applied to the samples in this paper: (1) exclusion of companies in special treatment status (ST, *ST) during the sample period; (2) exclusion of observations with missing data; (3) exclusion of samples of companies in the financial industry; and (4) shrinking continuous variables to the upper and lower 1% quartiles to control the influence of extremes. This resulted in a final total of 2759 companies with 14,390 samples. The tools used in this paper are Excel 2016 with Stata 16.0 software.

3.2. Variable Definition and Measurement

3.2.1. Response Variable

Following the methodology of Yan et al. (2018) [42], the logarithm of operating income per unit of carbon emissions of enterprises is used as a proxy variable for carbon performance (CEPI). A higher value of this variable indicates superior carbon performance. Carbon emissions data were obtained using the processing method outlined by Wang et al. (2022) [43], involving the manual collection and processing of social responsibility reports, sustainable development reports, and corporate environmental reports disclosed by enterprises annually.

3.2.2. Explanatory Variable

Referring to the study of Fang et al. (2017) [44], we first calculate supplier concentration (the percentage of purchases from the top five suppliers to the annual purchases) and customer concentration (the percentage of sales from the top five customers to the annual sales). Subsequently, we average the supplier concentration and customer concentration to measure supply chain concentration (SCii).

Various index-type indicators, such as the KZ index, WW index, and SA index, are widely used to measure the degree of financing constraints. Drawing from the research of Ju et al. (2013) [45] and given that the SA index does not contain financing variables with endogenous characteristics, making it easy to calculate and more robust, this paper uses the SA index to measure the financing constraints (FC) of enterprises. The specific formula for the SA index is as follows:

$$SA=-0.737\times Size+0.043\times {Size}^2-0.04\times Age$$

3.2.3. Control Variables

Building on the studies of Pan and Yuan (2023), Yan et al. (2018), and Zhou et al. (2019) [7,8,42], this paper controls for the following variables: firm size (Size), nature of property rights (Soe), financial leverage (FL), sales growth rate (Growth), net interest rate on total assets (ROA), shareholding concentration (Top1), firm age (Age), education of the chairman (EDU), sole director ratio (Idr), board size (Board), and equipment renewal rate (New), while controlling for year, individual, and industry. Detailed explanations of these variables are provided in

Table 1. Variable definition.

Variable Symbol	Variable	Variable Definition
CEPI	carbon performance	ln (revenue/carbon emissions)
SCii	supply chain concentration	(proportion of purchases from top five suppliers to annual purchases) + (proportion of sales from top five customers to annual sales)/2
Size	enterprise size	ln (assets)
Soe	nature of ownership	state-owned enterprises take 1; otherwise, take 0
Age	enterprise age	ln (years of establishment of the company)
ROA	return on assets	net profit/average total assets
FL	financial leverage	(net profit + income tax expense + financial expense)/(net profit + income tax expense)
Top1	shareholding concentration	shareholding ratio of the first largest shareholder
EDU	chairman’s degree	0 = other, 1 = secondary and below, 2 = college, 3 = bachelor’s degree, 4 = master’s degree, 5 = doctoral degree
Growth	sales growth rate	change in sales revenue for the current period/sales revenue for the previous period
New	equipment renewal rate	net fixed assets/total fixed assets
Idr	ratio of sole director	number of independent directors/number of director members
Board	board size	number of director members
Company	individual fixed effect	individual fixed effects
Industry	industry fixed effect	industry fixed effect
Year	year fixed effect	yearly fixed effects
FC	financing constraints	SA index

.

3.3. Model Settings

To test Hypothesis 1, this paper constructs the model as follows:

$$CEPI={\alpha }_0+{\alpha }_1{SCii}_{i,t}+\sum {Controls}_{i,t}+\sum {Year}_{i,t}+\sum {Company}_{i,t}+\sum {Industry}_{i,t}+{\epsilon }_{i,t}$$

(1)

To test the partial mediating role of financing constraints in Hypothesis 2, we follow the three-step method of mediation effect testing proposed by Wen et al. (2004) [46]. We establish the following two models based on Equation (1):

$$FC={\alpha }_0+{\alpha }_1{SCii}_{i,t}+\sum {Controls}_{i,t}+\sum {Year}_{i,t}+\sum {Company}_{i,t}+\sum {Industry}_{i,t}+{\epsilon }_{i,t}$$

(2)

$$\begin{array}{c}CEPI={\alpha }_0+{\alpha }_1{SCii}_{i,t}+{\alpha }_2{FC}_{i,t}+\sum {Controls}_{i,t}+\sum {Year}_{i,t}+\sum {Company}_{i,t}\\ +\sum {Industry}_{i,t}+{\epsilon }_{i,t}\end{array}$$

(3)

If the model test results satisfy the conditions that α₁ in (1) is significant, α₁ in (2) is significant, α₁ and α₂ in (3) are significant, and the absolute value of α₁ in (3) is smaller than that of α₁ in (1), then it proves that there is a partially mediating role of financing constraints in the relationship between supply chain concentration and carbon performance, and Hypothesis 2 can be tested.

4. Result Analysis

4.1. Descriptive Statistics

Table 2. Variable descriptive statistics.

Variable	Sample Size	Mean	Median	SD	Min	Max
CEPI	14,390	10.028	10.045	0.356	8.704	10.888
SCii	14,390	0.312	0.293	0.155	0.038	0.808
FC	14,390	4.154	3.858	1.497	1.698	9.684
FL	14,390	1.353	1.070	0.981	0.448	8.068
Top1	14,390	35.007	33.305	14.419	9.050	74.976
Soe	14,390	0.288	0.000	0.453	0.000	1.000
EDU	14,390	3.090	3.000	1.322	0.000	5.000
Growth	14,390	0.197	0.130	0.359	−0.414	2.376
Size	14,390	22.170	21.951	1.251	20.061	26.368
Age	14,390	2.817	2.833	0.341	1.792	3.497
New	14,390	0.217	0.190	0.144	0.003	0.690
Board	14,390	8.467	9.000	1.600	4.000	18.000
Idr	14,390	37.534	33.330	5.289	33.330	57.140
ROA	14,390	0.060	0.049	0.047	−0.005	0.232

presents the descriptive statistics of the main variables. In particular, the standard deviation of carbon performance is 0.356, with a maximum value of 10.888 and a minimum value of 8.704, indicating an imbalance in carbon performance among firms. The minimum value of supply chain concentration is 0.038 and the maximum value is 0.808, highlighting a significant difference in supply chain concentration between enterprises. The average value is 0.312, suggesting that the supply chain concentration for most enterprises is relatively low.

Financing constraints range from 1.698 to 9.684 with a standard deviation of 1.497, signifying a significant difference in the constraints faced by firms in terms of access to finance. The mean value of 4.154 is higher than the median value of 3.858, suggesting that a majority of the firms in the sample are indeed facing the issue of financing constraints.

4.2. Analysis of Regression Results

4.2.1. Supply Chain Concentration and Carbon Performance

Table 3. Regression results of supply chain concentration, financing constraints, and carbon performance.

	(1)	(2)	(3)
	CEPI	FC	CEPI
SCii	0.105 ***	−0.021 **	0.103 ***
	(2.72)	(−2.12)	(2.67)
FC			−0.094 ***
			(−2.64)
FL	−0.001	−0.003 ***	−0.001
	(−0.32)	(−2.60)	(−0.38)
Top1	0.001	0.001 ***	0.001
	(0.92)	(4.08)	(1.02)
Soe	−0.001	−0.015 **	−0.002
	(−0.04)	(−2.23)	(−0.09)
EDU	−0.000	0.001	0.000
	(−0.01)	(1.21)	(0.02)
Growth	−0.162 ***	−0.007 ***	−0.163 ***
	(−18.52)	(−2.98)	(−18.59)
Size	0.015	1.187 ***	0.127 ***
	(1.57)	(479.88)	(2.92)
Age	0.062	−0.141 ***	0.049
	(1.04)	(−9.13)	(0.82)
New	0.215 ***	0.043 ***	0.219 ***
	(4.48)	(3.47)	(4.56)
Board	0.007	−0.001	0.007
	(1.60)	(−0.65)	(1.58)
Idr	0.002	−0.000	0.002
	(1.42)	(−0.43)	(1.41)
ROA	0.052	0.031	0.055
	(0.50)	(1.15)	(0.53)
Constant	9.262 ***	−21.615 ***	7.228 ***
	(29.98)	(−268.85)	(8.70)
Observations	14,390	14,390	14,390
R-squared	0.051	0.973	0.051
Company FE	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES

Note: t-statistic provided in parentheses; ***, and ** indicate that regression coefficient is significant at the 1%, and 5% levels, respectively.

reports the regression results of supply chain concentration, financing constraints, and carbon performance. In Column (1) of the analysis, the effect of supply chain concentration (SCii) on carbon performance (CEPI) is examined. The coefficient of SCii is reported as 0.105, and it is significantly positive at the 1% level. This positive coefficient suggests that supply chains with a higher level of concentration are significantly more likely to have enhanced carbon performance. This finding supports Hypothesis 1, which posits that relative concentration within supply chains has a positive impact on carbon performance.

4.2.2. Partial Mediating Role of Financing Constraints

Table 3 shows the test of the mediating effect of financing constraints between supply chain concentration and carbon performance. In column (1), the coefficient of SCii is reported as 0.105, significantly positive at the 1% level, supporting Hypothesis 1. This indicates that supply chain concentration has a facilitating effect on carbon performance. In column (2), the coefficient of SCii is −0.021, which is significantly negative at the 5% level, suggesting that supply chain concentration helps alleviate the financing constraints faced by firms. In column (3), the FC coefficient is −0.094 and the SCii coefficient is 0.103; both are significantly positive at the 1% level. Comparing these coefficients to the SCii coefficient in column (1), the value of 0.103 is less than 0.105. This finding supports Hypothesis 2, indicating that financing constraints play an intermediary role in the relationship between supply chain concentration and carbon performance.

5. Endogeneity and Robustness Tests

5.1. Instrumental Variables Approach

The increase in supply chain concentration can enhance the carbon performance of enterprises. However, it is worth noting that enterprises with good carbon performance may also exhibit higher supply chain concentration. This suggests a potential mutualistic relationship where supply chain concentration and carbon performance influence each other. In addition, due to the incompleteness of the control variables, it is challenging to completely eliminate the effects of omitted variables. To address potential endogeneity problems, this paper adopts the instrumental variable method for further tests to verify the impact of supply chain concentration on carbon performance.

Following the approach of Xu and Hua (2020) [47], the median annual supply chain concentration of the industry is selected as an instrumental variable for the test. The regression results for the first stage (Column 1 of

Table 4. Instrumental variable estimation results.

	(1)	(2)
	SCii	CEPI
IV	0.602 ***
	(7.53)
SCii		0.625 *
		(1.86)
Constant	0.997 ***	8.802 ***
	(23.64)	(23.00)
N	14,390	14,390
R-squared	0.205	0.039
Controls	YES	YES
Company FE	YES	YES
Industry FE	YES	YES
Year FE	YES	YES
First-stage F-statistics	85.82

Note: t-statistic provided in parentheses; ***, and * indicate that regression coefficient is significant at the 1%, and 10% levels, respectively.

) show that the coefficient of the instrumental variable (IV) is significantly positive at the 1% level. Additionally, the F-statistic is reported as 85.82, substantially exceeding the critical value of 10. These findings indicate that the instrumental variable satisfies the correlation condition and is not weak. Column (2) of Table 4 reports the second stage regression, where the coefficient of SCii is significantly positive at the 10% level. This suggests that after addressing the endogeneity issue, achieving a relatively centralized supply chain still significantly improves firms’ carbon performance. Therefore, the conclusions drawn in this paper remain unchanged.

5.2. Measurement of Financing Constraints of Replacement Variables

The WW index is used as the measurement variable for the degree of financing constraints in the robustness test, replacing the SA index. The specific formula of the WW index is as follows:

$$\begin{array}{c}WW=-0.062\times {DIV}_{i,t}-0.091\times {CF}_{i,t}+0.021\times {LS}_{i,t}-0.044\times {LNTA}_{i,t}-\\ 0.0035\times {SG}_{i,t}+0.102\times {ISG}_{i,t}\end{array}$$

In this formula, DIV_i,t is a binary dummy variable representing whether company i pays cash dividends in year t. It takes the value of 1 if the company pays dividends, and 0 otherwise. CF_i,t is the cash flow from operating activities divided by total assets; LS_i,t is the long-term liabilities divided by total assets; LNTA_i,t is the natural logarithm of total assets; and SG_i,t and ISG_i,t denote the sales growth rate of the industry and the individual company, respectively. A higher WW index indicates a higher degree of corporate financing constraints. The regression results of supply chain concentration and carbon performance after replacing the variable of financing constraints are shown in

Table 5. Regression results for the replacement variable financing constraints.

	(1)	(2)	(3)
	CEPI	FC	CEPI
SCii	0.085 **	−0.079 ***	0.081 **
	(2.08)	(−4.29)	(1.98)
FC			−0.050 **
			(−2.17)
FL	−0.001	0.002	−0.000
	(−0.13)	(1.24)	(−0.10)
Top1	0.000	0.000	0.000
	(0.25)	(0.28)	(0.25)
Soe	0.011	−0.005	0.010
	(0.39)	(−0.42)	(0.38)
EDU	−0.001	−0.003	−0.001
	(−0.13)	(−1.62)	(−0.17)
Growth	−0.177 ***	−0.099 ***	−0.182 ***
	(−18.69)	(−23.06)	(−18.68)
Size	0.008	−0.096 ***	0.003
	(0.80)	(−20.90)	(0.32)
Age	0.058	0.075 ***	0.062
	(0.92)	(2.59)	(0.97)
New	0.147 ***	−0.042 *	0.145 ***
	(2.86)	(−1.79)	(2.82)
Board	0.008 *	−0.006 ***	0.008
	(1.67)	(−2.83)	(1.61)
Idr	0.001	−0.000	0.001
	(0.84)	(−0.42)	(0.83)
ROA	0.109	0.034	0.110
	(0.92)	(0.64)	(0.94)
Constant	9.502 ***	1.087 ***	9.557 ***
	(29.44)	(7.43)	(29.53)
Observations	11,712	11,712	11,712
R-squared	0.057	0.161	0.057
Company FE	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES

Note: t-statistic provided in parentheses; ***, **, and * indicate that regression coefficient is significant at the 1%, 5%, and 10% levels, respectively.

. As shown in column (1), the coefficient of SCii is significantly positive at the 5% level, supporting Hypothesis 1; a comprehensive comparison of the data in columns (3) and (1) indicates that Hypothesis 2 is also robust.

5.3. Measurement of Carbon Performance of Replacement Variables

In order to improve the reliability of the regression results, this paper replaces the original measure of carbon performance with the logarithm of operating costs borne by the enterprise per unit of carbon emissions [48], and the results are presented in

Table 6. Regression results for the replacement variable carbon performance.

	(1)	(2)	(3)
	CEPI	FC	CEPI
SCii	0.136 ***	−0.021 **	0.134 ***
	(3.29)	(−2.12)	(3.25)
FC			−0.081 **
			(−2.11)
FL	0.002	−0.003 ***	0.002
	(0.49)	(−2.60)	(0.44)
Top1	0.000	0.001 ***	0.000
	(0.20)	(4.08)	(0.28)
Soe	0.048 *	−0.015 **	0.046 *
	(1.78)	(−2.23)	(1.73)
EDU	−0.003	0.001	−0.003
	(−0.60)	(1.21)	(−0.57)
Growth	−0.137 ***	−0.007 ***	−0.137 ***
	(−14.64)	(−2.98)	(−14.69)
Size	0.016	1.187 ***	0.111 **
	(1.54)	(479.89)	(2.40)
Age	0.075	−0.141 ***	0.064
	(1.18)	(−9.13)	(1.00)
New	0.292 ***	0.043 ***	0.296 ***
	(5.70)	(3.46)	(5.77)
Board	0.008 *	−0.001	0.008 *
	(1.70)	(−0.65)	(1.69)
Idr	0.001	−0.000	0.001
	(1.18)	(−0.43)	(1.18)
ROA	−1.322 ***	0.030	−1.319 ***
	(−11.98)	(1.13)	(−11.96)
Constant	8.881 ***	−21.615 ***	7.141 ***
	(26.92)	(−268.85)	(8.05)
Observations	14,390	14,390	14,390
R-squared	0.068	0.973	0.068
Company FE	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES

Note: t-statistic provided in parentheses; ***, **, and * indicate that regression coefficient is significant at the 1%, 5%, and 10% levels, respectively.

.

The SCii coefficient in column (1) of Table 6 is significantly positive at the 1% level, supporting Hypothesis 1. Considering the findings from columns (1) and (3), we conclude that financing constraints play an intermediary role in the relationship between supply chain concentration and the carbon performance of enterprises. This conclusion aligns with Hypothesis 2 and remains robust.

5.4. Replacing the Sample Interval

In light of the New Environmental Protection Law introduced in 2015, which mandates enterprises to implement measures to reduce emissions and treat pollution, it is possible that such initiatives could impact the green governance performance of companies. To ensure the relevance of the data used in this study to the current regulation environment, samples from 2014 and earlier were excluded, and the sample interval was set to 2015–2021. A regression analysis was then conducted on the remaining 11,093 sample data, and the results are presented in

Table 7. Regression results of changing sample intervals.

	(1)	(2)	(3)
	CEPI	FC	CEPI
SCii	0.182 ***	−0.012	0.180 ***
	(3.27)	(−1.02)	(3.24)
FC			−0.120 **
			(−2.38)
FL	−0.000	−0.002	−0.001
	(−0.10)	(−1.46)	(−0.14)
Top1	0.002 **	0.001 ***	0.002 **
	(2.13)	(3.61)	(2.22)
Soe	0.004	−0.005	0.004
	(0.14)	(−0.77)	(0.12)
EDU	−0.007	0.001	−0.007
	(−1.32)	(0.72)	(−1.30)
Growth	−0.140 ***	−0.007 ***	−0.141 ***
	(−13.67)	(−3.00)	(−13.75)
Size	0.011	1.185 ***	0.153 **
	(0.82)	(398.86)	(2.50)
Age	0.279 ***	−0.183 ***	0.257 **
	(2.80)	(−8.50)	(2.57)
New	0.323 ***	0.020	0.326 ***
	(4.97)	(1.44)	(5.01)
Board	0.006	0.002	0.006
	(0.97)	(1.17)	(1.00)
Idr	0.002	0.000	0.002
	(1.46)	(0.92)	(1.48)
ROA	−0.014	0.049 *	−0.008
	(−0.11)	(1.82)	(−0.07)
Constant	8.587 ***	−21.607 ***	5.995 ***
	(19.34)	(−225.12)	(5.09)
Observations	11,093	11,093	11,093
R-squared	0.055	0.969	0.055
Company FE	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES
Ind_eff Test (p-val)	0.003 Indirect effect holds	0.003 Indirect effect holds	0.003 Indirect effect holds

Note: t-statistic provided in parentheses; ***, **, and * indicate that regression coefficient is significant at the 1%, 5%, and 10% levels, respectively.

.

The SCii coefficient in column (1) of Table 7 is significantly positive at the 1% level, supporting Hypothesis 1. The coefficient for FC is found to be statistically insignificant in the regression analysis. To further examine the indirect effect, a bootstrap test was conducted, resulting in a p-value of 0.003. This indicates that the indirect effect is significant at the 1% level. Considering the findings from columns (1) and (3), we conclude that financing constraints play an intermediary role in the relationship between supply chain concentration and the carbon performance of enterprises. This conclusion aligns with Hypothesis 2 and remains robust in light of our additional analyses.

6. Further Research

6.1. Moderating Effect of Management Myopia

The upper echelons theory posits that due to the complexity of both internal and external enterprise environments, it is impractical for managers to possess a comprehensive understanding of all aspects. The personal characteristics of managers, such as cognitive ability, values, experience, and vision, significantly influence the strategic choices and behaviors of the enterprise [49]. Conversely, management myopia is characterized by an excessive focus on short-term performance and profits at the expense of long-term development and sustainable competitiveness. Given that the green and low-carbon transformation of enterprises is a lengthy and uncertain process requiring substantial financial and technological investments, management myopia inevitably undermines the enhancement of enterprise carbon performance.

To verify the moderating role of management myopia in the relationship between supply chain concentration and carbon performance, this paper constructs the following model:

$$\begin{array}{c}CEPI={\beta }_0+{\beta }_1{SCii}_{i,t}+{\beta }_2{Myopia\_Index }_{i,t}+{\beta }_3{SCii}_{i,t}\times {Myopia\_Index }_{i,t}+\\ \sum {Controls}_{i,t}+\sum {Year}_{i,t}+\sum {Company}_{i,t}+\sum {Industry}_{i,t}+{\epsilon }_{i,t}\end{array}$$

In the model, Myopia_Index denotes management short-sightedness. Drawing on the research of Hu et al. (2021) [50], this paper utilizes the ratio of the word frequency of “short-term perspective” words to the total word frequency of the relevant parts disclosed in the annual reports of listed companies × 100 and thus obtains the indicator of managerial myopia. The larger the indicator value, the more serious the problem of managerial short-sightedness.

Table 8. Moderating effect of management myopia.

	CEPI
SCii	0.118 ***
	(3.03)
Myopia_Index	−4.575
	(−1.52)
SCii × Myopia_Index	−37.639 **
	(−2.13)
FL	−0.002
	(−0.45)
Top1	0.001
	(0.96)
Soe	−0.001
	(−0.03)
EDU	−0.000
	(−0.03)
Growth	−0.164 ***
	(−18.65)
Size	0.017 *
	(1.74)
Age	0.066
	(1.09)
New	0.215 ***
	(4.47)
Board	0.007
	(1.56)
Idr	0.002
	(1.34)
ROA	0.022
	(0.22)
Constant	9.226 ***
	(29.73)
Observations	14,281
R-squared	0.052
Company FE	YES
Industry FE	YES
Year FE	YES

Note: t-statistic provided in parentheses; ***, **, and * indicate that regression coefficient is significant at the 1%, 5%, and 10% levels, respectively.

demonstrates the results of the moderating effect of managerial myopia obtained by regression using the fixed effects model. As can be seen from the table, the cross-multiplier term SCii × Myopia_Index is significantly negative at the 5% statistical level. This indicates that management myopia negatively moderates the contribution of supply chain concentration to firms’ carbon performance. As management myopia increases, the positive relationship is weakened.

6.2. Moderating Effect of Unexpected Public Incidents

The outbreak of the COVID-19 pandemic has significantly impacted the global economy and society. As an important component of the economic ecosystem, businesses have encountered unprecedented challenges. In the early stages of the outbreak, businesses in many countries or regions faced full or partial closures, causing disruptions in global supply chains and affecting business operations and performance. At the same time, businesses have also faced difficulties in obtaining funding and credit, as well as issues such as declining customer demand, disrupted logistics, and other market uncertainties.

However, government authorities have taken a series of measures to help businesses to overcome these challenges, ensure stability, and provide a better environment for sustainable development. Hence, a critical question arises: will the impact of the COVID-19 pandemic affect the relationship between supply chain concentration and corporate carbon performance? To examine the regulatory effects of the COVID-19 pandemic, this study constructs the following model:

$$\begin{array}{c}CEPI={\beta }_0+{\beta }_1{SCii}_{i,t}+{\beta }_2{COVID }_{i,t}+{\beta }_3{SCii}_{i,t}\times {COVID }_{i,t}+\sum {Controls}_{i,t}+\\ \sum {Year}_{i,t}+\sum {Company}_{i,t}+\sum {Industry}_{i,t}+{\epsilon }_{i,t}\end{array}$$

Among them, COVID represents the outbreak of the COVID-19 pandemic and is set as a dummy variable. The variable takes the value of 1 after the outbreak and 0 otherwise.

Table 9. Moderating effect of unexpected public incidents.

	CEPI
SCii	0.111 ***
	(2.86)
COVID	0.025
	(0.69)
SCii × COVID	0.081 *
	(1.68)
FL	−0.001
	(−0.30)
Top1	0.001
	(0.94)
Soe	−0.001
	(−0.05)
EDU	0.000
	(0.03)
Growth	−0.162 ***
	(−18.57)
Size	0.015
	(1.61)
Age	0.063
	(1.05)
New	0.214 ***
	(4.46)
Board	0.007
	(1.62)
Idr	0.002
	(1.45)
ROA	0.055
	(0.53)
Constant	9.247 ***
	(29.92)
Observations	14,390
R-squared	0.051
Company FE	YES
Industry FE	YES
Year FE	YES

Note: t-statistic provided in parentheses; ***, and * indicate that regression coefficient is significant at the 1%, and 10% levels, respectively.

demonstrates the results of the moderating effect of COVID-19 obtained by regression using the fixed effects model. According to Table 9, the interaction term SCii × COVID is significantly positive at the 10% significance level. This indicates that the COVID-19 pandemic positively moderates the impact of supply chain concentration on corporate carbon performance, suggesting that the government has played an important role in alleviating business pressure, promoting innovative development, and driving economic recovery.

6.3. Analysis of the Sample According to the Type of Enterprise Ownership

According to the existing research, the carbon performance of enterprises with different property rights exhibits certain differences [6]. Consequently, the impact of supply chain concentration on financing constraints and carbon performance may vary depending on the actual controller of the enterprise. In this paper, based on the differences in the nature of property rights, the sample is divided into state-owned enterprises and non-state-owned enterprises to explore the impact of supply chain concentration on carbon performance under different actual controllers of enterprises. The regression results are shown in

Table 10. Regression results for firms with different nature of ownership.

	(1) CEPI		(2) FC		(3) CEPI
	State-Owned Enterprises	Non-State Enterprise	State-Owned Enterprises	Non-State Enterprise	State-Owned Enterprises	Non-State Enterprise
SCii	0.168 **	0.075	−0.080 ***	0.002	0.166 **	0.076
	(2.53)	(1.54)	(−2.71)	(0.30)	(2.50)	(1.56)
FC					−0.021	−0.394 ***
					(−0.55)	(−4.56)
Constant	9.111 ***	9.552 ***	−22.396 ***	−21.346 ***	8.636 ***	1.146
	(15.60)	(24.06)	(−85.79)	(−418.60)	(8.24)	(0.61)
Observations	4142	10,248	4142	10,248	4142	10,248
R-squared	0.060	0.054	0.921	0.993	0.060	0.057
Controls	YES	YES	YES	YES	YES	YES
Company FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Ind_eff Test (p-val)	0.046 Indirect effect holds		0.046 Indirect effect holds		0.046 Indirect effect holds

Note: t-statistic provided in parentheses; ***, and ** indicate that regression coefficient is significant at the 1%, and 5% levels, respectively.

.

For the regression results of model (1) in column (1) of Table 10, in the column for state-owned enterprises, the coefficient of SCii is 0.168, which is significantly positive at the 5% level, indicating that the establishment of relatively centralized supply chains by state-owned enterprises will have a significant contribution to carbon performance. In the case of non-state-owned enterprises, the coefficient of SCii is 0.075 and statistically insignificant. This is due to the fact that non-state-owned enterprises often face challenges in supply chain management and have fewer advantages in resource allocation, technological innovation, and policy support. As a result, their ability to drive carbon reduction actions within the supply chain is limited.

Secondly, non-state-owned enterprises typically operate in a competitive environment, where they often prioritize financial gains and short-term profits. Cost reduction and efficiency improvement take precedence over investments in environmental protection or carbon reduction for private enterprises.

In column (3) of Table 10, the coefficient for financing constraints (FC) in state-owned enterprises is −0.021, although it is not statistically significant. However, the bootstrap test for examining the mediation effect reveals a p-value of 0.046 for the indirect effect. Combining with the analysis in column (2) of Table 10, we conclude that part of the mediation effect produced by financing constraints between supply chain concentration and carbon performance is statistically significant in state-owned enterprises, but this effect is not obvious. As an important pillar of the national economy, state-owned enterprises often benefit from substantial government support and resource allocation, such as the priority of financing channels. Compared with non-state-owned enterprises, state-owned enterprises have lower financing costs and are more likely to secure financial support. In addition, an increase in supply chain concentration enhances the ability of firms to obtain external financing.

On the other hand, non-state-owned enterprises face stricter financing restrictions and higher financing costs, making it challenging for supply chain concentration to support the improvement of corporate carbon performance through increased financing.

6.4. Analysis of Sample Divided According to Enterprise Growth Ability

Given that enterprises with varying growth capabilities prioritize aspects such as supply chain management, economic profit, and corporate social responsibility trade-offs differently, it is crucial to analyze the outcomes and pathways of supply chain concentration on carbon performance from the perspective of enterprise growth rate. To achieve this, the total sample can be divided into two groups: high-growth enterprises and low-growth enterprises, using the mean value of enterprise sales growth rate (Growth) as the criterion.

Table 11. Regression results for firms with different growth capabilities.

	(1) CEPI		(2) FC		(3) CEPI
	Low-Growth	High-Growth	Low-Growth	High-Growth	Low-Growth	High-Growth
SCii	0.105 **	0.001	−0.047 ***	0.008	0.102 **	0.004
	(2.05)	(0.02)	(−2.96)	(0.71)	(1.99)	(0.05)
FC					−0.062	−0.324 ***
					(−1.57)	(−2.82)
Constant	9.435 ***	8.516 ***	−21.501 ***	−21.540 ***	8.101 ***	1.530
	(23.21)	(12.87)	(−169.05)	(−211.25)	(8.58)	(0.60)
Observations	9005	5385	−0.047 ***	5385	9005	5385
R-squared	0.028	0.061	0.950	0.993	0.029	0.063
Controls	YES	YES	YES	YES	YES	YES
Company FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Ind_eff Test (p-val)	0.003 Indirect effect holds		0.003 Indirect effect holds		0.003 Indirect effect holds

Note: t-statistic provided in parentheses; ***, and ** indicate that regression coefficient is significant at the 1%, and 5% levels, respectively.

presents the regression results for these two distinct groups categorized by growth rate.

According to column (1) of Table 11, the SCii coefficients for low-speed growth firms and high-speed growth firms are 0.105 and 0.001, respectively. The coefficient for low-speed growth firms is significant at the 5% level, indicating that supply chain concentration has a more substantial impact on improving the carbon performance of low-speed growth firms compared to high-speed growth firms. This may be due to the fact that suppliers and customers usually rely more on fast-growing enterprises, giving these enterprises greater influence in choosing suppliers and customers with better carbon performance. This flexibility allows them to achieve good carbon performance without strictly adhering to a centralized supply chain, thereby attenuating the effect of supply chain concentration on carbon performance.

Columns (2) and (3) of Table 11 show that financing constraints do not partially mediate the impact of supply chain concentration on carbon performance for fast-growing firms. Fast-growing firms tend to invest more resources and energy in technological innovation. Therefore, even with lower supply chain concentration, these firms are still able to achieve good carbon performance through technological innovation, which is not entirely dependent on large financing support.

In column (3) of Table 11, the FC coefficient for low-speed growth firms is −0.062, which is not significant. However, a bootstrap test was conducted, and the p-value for the indirect effect is 0.003. Combining these results with those from columns (1) and (2) suggests a partial mediating role of financing constraints in the relationship between supply chain concentration and carbon performance for low-speed growth firms. This is because low-speed growth firms often face greater financing constraints, making it challenging to obtain loans from banks or other financial institutions due to issues such as information asymmetry and risk premiums. By increasing supply chain concentration, firms can build close cooperation within the supply chain, which in turn mitigates information asymmetry and promotes transparent operations. This initiative helps enterprises to obtain financing support externally, reducing their financial pressure. With more financial support, companies become more inclined to invest in environmental protection and take measures to reduce carbon emissions, ultimately improving their carbon performance.

7. Conclusions and Implications

7.1. Conclusions

Based on our extensive study of the impact of supply chain concentration on carbon performance and its underlying mechanisms using data from A-share listed companies spanning 2012 to 2021, we conclude the following: (1) Supply chain concentration has a significantly positive effect on carbon performance, with financing constraints playing a partial mediating role in this effect. (2) Managerial myopia weakened the impact of supply chain concentration on carbon performance, while unexpected public events strengthened this impact. (3) The impact of supply chain concentration on carbon performance is much stronger in state-owned enterprises and low-growth enterprises. Additionally, this positive impact is realized to a certain extent with the help of financing constraints.

7.2. Discussions

To the best of our knowledge, this is the first study on the impact of supply chain concentration on carbon performance and the underlying mechanisms of this impact. While similar research has been conducted, such as by Wong et al. (2020) [20], who investigated the impact of green supply chain integration on environmental performance, they did not include carbon performance in their measurement. This paper fills the research gap in the carbon performance literature. Furthermore, Su and Yu (2023) [18] found a negative correlation between supply chain integration and environmental performance in manufacturing firms, which contrasts with our research findings. Potential reasons for this discrepancy could be that the measurement of environmental performance did not consider carbon performance factors (which was not included in [18]) or that we used a more comprehensive sample dataset. Furthermore, we considered the mediating role of financing constraints in the relationship between supply chain concentration and carbon performance, as well as the moderating effects of managerial myopia and unexpected public events. Overall, our research findings provide a theoretical basis for the co-governance of carbon emissions within supply chains, considering both internal and external factors.

7.3. Implications

Based on the research findings and discussion above, we suggest the following: (1) Enterprises should centralize their supply chains to enhance operational efficiency and elevate management standards. For instance, enterprises can increase investment in infrastructure for production, logistics, communication, etc., facilitating supply chain centralization. Establishing data-sharing platforms can build mutually beneficial relationships among supply chain partner companies and enhance overall supply chain efficiency. (2) Enterprises should establish and improve green governance mechanisms to avoid management’s short-sighted behavior. Keep paying attention to their cognitive traits, such as environmental awareness and sustainable development concepts, when selecting and cultivating senior managers. At the same time, establishing a management equity incentive mechanism to ensure that the behavior of the management is consistent with the sustainable development goals of the enterprise. (3) Governments should take action to alleviate enterprise financing constraints. For example, establishing investment attraction policies and a functional governmental information-sharing platform for customer credit, risk assessment, and dynamic policy fitness assessment. Standardizing the order of the financial market orders and mitigating financial risks, developing supply chain finance and green finance to alleviate the financing constraints of supply chain enterprises, and provide financial support for the green and low-carbon transformation of enterprises. Continuously optimizing the business environment and investment promotion policies, establishing industrial parks, guiding and encouraging full supply chain enterprises to settle in and integrate development.

7.4. Shortcomings and Perspectives

This research is based on A-share listed companies in China. It remains unclear whether supply chain concentration has a similar effect on carbon performance in a different country. We encourage researchers to investigate the impact of supply chain con-centration on carbon performance using different datasets, particularly focusing on companies in other countries or globally distributed international companies, for example. Moreover, to accelerate enterprise green transition, more research should be conducted on the impact of other factors on carbon performance, e.g., geopolitical factors.