Green Transformation of Enterprises and the Bullwhip Effect: Empirical Evidence from Listed Companies in China

Abstract

In the face of growing economic downturn pressure in China, disruptions in certain segments of the supply chain have intensified the bullwhip effect, severely destabilizing supply chains and posing risks to the sustainable development of the real economy. This study utilizes data from Chinese A-share listed enterprises from 2008 to 2022, employing a multiple linear regression model alongside robustness and endogeneity tests to investigate the mechanism through which corporate green transformation alleviates the bullwhip effect. The empirical results indicate that a one-unit increase in the green transformation leads to a significant 0.073-unit reduction in the bullwhip effect. Mechanism analysis further reveals that green transformation mitigates the bullwhip effect by enhancing supply-chain information sharing, strengthening organizational resilience, and improving managerial effectiveness. Heterogeneity analysis shows that the impact of green transformation on the bullwhip effect varies significantly depending on ownership structure and industry characteristics. This study contributes to the integration of green development theory and supply chain management by providing theoretical insights and practical implications for fostering corporate green transformation and optimizing supply-chain strategies. Specifically, it suggests that policymakers enhance regulatory guidance and incentives, encourage enterprises to prioritize green transformation, and implement tailored strategies based on firm characteristics to achieve supply chain stability and sustainable development.

1. Introduction

In an era of green and low-carbon development, green transformation serves as an effective strategy to enhance supply chain resilience and efficiency [1,2]. Beyond promoting environmental protection and sustainability, green transformation significantly improves supply-chain performance and adaptability through technological advancements. China’s 14th Five-Year Plan for Ecological and Environmental Protection explicitly emphasizes the acceleration of green and low-carbon development, recognizing the advancement of green-supply chain management as a key national priority. This strategic focus provides enterprises with explicit policy support and strategic guidance, encouraging the optimization of supply-chain structures and operational models to comply with increasingly stringent environmental regulations.

Currently, China faces various challenges arising from both internal and external factors. International trade frictions, fluctuations in energy and raw material prices, geopolitical risks, and domestic economic pressures have disrupted supply chains, exacerbating the bullwhip effect and severely affecting supply-chain stability and the healthy development of the real economy [3,4,5]. Disruptions in production and logistics have gradually distorted demand information across the supply chain, leading to an increasing mismatch between upstream enterprises and actual demand. The bullwhip effect, first introduced by Forrester [6] and later empirically validated by Lee et al. [7], refers to the amplification and delay of demand information as it propagates upstream in the supply chain. This distortion results in overstocked inventories, overcapacity, or supply shortages, contributing to resource misallocation and imbalances between supply and demand [8]. Therefore, effectively mitigating the bullwhip effect and restoring supply-chain stability are crucial for sustaining economic growth.

A significant amount of research has been devoted to understanding the bullwhip effect [9,10,11]. Its causes are multifaceted, including inaccurate and delayed information, irrational decision-making by supply-chain members [12,13], and structural characteristics of supply chains [14]. Longer supply-chain tiers and more complex network structures increase the number of information transmission links and the time required for information propagation, thereby exacerbating the bullwhip effect [15]. Market uncertainties, such as rapid shifts in consumer preferences and economic volatility, further amplify this phenomenon [16]. To mitigate the bullwhip effect, strategies such as enhancing information sharing, establishing long-term stable partnerships, and optimizing inventory management have been proposed, with collaborative demand forecasting demonstrating particular promise in improving forecast accuracy [17].

Extensive research has explored the causes, mechanisms, and mitigation strategies of the bullwhip effect; however, a significant gap remains in understanding the role of green transformation in alleviating this phenomenon. Despite the growing attention paid to green transformation, its potential to enhance supply chain efficiency—particularly in mitigating the bullwhip effect—has not been sufficiently studied. This study uses the natural logarithm of the number of invention patents filed by a company as a proxy for green transformation, effectively reflecting the company’s efforts and achievements in green technological innovation. Invention patents, as key indicators of technological progress, often represent a company’s development of environmentally friendly technologies and its contribution to green transformation. Green invention patents are particularly significant, as they embody innovations in environmental protection, resource conservation, and sustainable development. These patents help companies reduce energy consumption, lower emissions, and improve resource efficiency, directly advancing their green transformation goals. Compared to general technological innovations, green technologies provide stronger environmental benefits and enhance market competitiveness. By securing green invention patents, companies not only strengthen their technological capabilities but also respond to increasing global demands for stricter environmental regulations and sustainable development. Therefore, the natural logarithm of the number of green invention patents serves as an important indicator of green transformation, reliably reflecting a company’s investment and output in green innovation. This, in turn, offers valuable insights into the impact of green transformation on supply-chain performance and stability [18].

Although green transformation is widely seen as a path to more resilient supply chains, it can sometimes introduce new risks. Under certain institutional and operational conditions, it may increase uncertainty and coordination challenges. According to institutional theory, some firms adopt green practices mainly due to regulatory or normative pressures, without aligning them with internal capabilities. This can result in superficial compliance, weak information systems, and fragmented decisions, which disrupt demand communication and amplify the bullwhip effect. The resource-based view also shows that green transformation demands substantial investment in technology, systems, and skills. Firms with limited resources—especially upstream or downstream—may face difficulties in implementation, leading to inconsistent adoption, poor data integration, and forecasting errors. Moreover, information asymmetry may rise, as dominant firms control key environmental data and limit sharing, weakening supply-chain collaboration. The complexity of green technologies and compliance systems may further increase operational variability, particularly in firms with low digital maturity or long lead times. During this transition, small disruptions in inventory or information flow can quickly spread, creating instability.

Additionally, while China’s 14th Five-Year Plan for Ecological and Environmental Protection advocates green supply-chain management, it lacks specific guidance on how enterprises can effectively apply these policies to mitigate the bullwhip effect. Enterprises require clearer direction on how to utilize policy support to optimize supply-chain operations within the framework of green transformation and reduce the impact of the bullwhip effect.

This study aims to address this research gap by thoroughly examining the relationship between green transformation and the bullwhip effect. It will investigate whether green transformation can effectively mitigate the bullwhip effect and identify the specific pathways and mechanisms through which green practices influence this phenomenon. In doing so, this study will provide valuable insights for enterprises to make informed decisions regarding their green transformation efforts and will offer recommendations to policymakers for designing more targeted strategies to support supply-chain stability and sustainable development.

2. Theoretical Analysis and Hypothesis

In the current economic environment, enterprises face intense market competition and mounting environmental pressures. Green transformation, as an emerging business paradigm, has garnered significant attention from firms striving for sustainable development. The bullwhip effect, characterized by amplified fluctuations in supply chains due to information asymmetry, inaccurate demand forecasting, and overreactions, significantly undermines supply-chain stability. Corporate green transformation holds the potential to mitigate this effect through various mechanisms, thereby improving the overall efficiency and resilience of the supply chain.

2.1. The Role of Green Transformation and Supply-Chain Information Sharing in Mitigating the Bullwhip Effect

From the perspective of transaction cost economics, information asymmetry in the supply chain increases transaction costs. When supply-chain members cannot access timely and accurate information, decision-making becomes more difficult, leading to inefficient resource allocation and higher costs related to negotiation, monitoring, and risk management. Therefore, information sharing is crucial for reducing transaction costs and enhancing supply-chain performance. According to resource-based theory, efficient information sharing is a valuable strategic resource that enables firms to adapt to market changes, respond to customer demands, and collaborate effectively with partners, ultimately strengthening their competitive advantage.

Green transformation encourages firms to prioritize information sharing and transparency in supply-chain management [19]. During this process, firms often adopt advanced technologies such as the Internet of Things (IoT), big data analytics, and cloud computing. From a transaction-cost, economics perspective, these technologies reduce the costs of information search, negotiation, and monitoring [20]. By enabling real-time data collection and sharing, these technologies allow supply-chain members to gain more accurate insights into market demand, production progress, and inventory levels. This reduces forecasting errors and mitigates excessive reactions to information delays or uncertainty, alleviating the bullwhip effect.

Furthermore, green transformation promotes closer collaboration among supply-chain stakeholders [21]. As firms emphasize environmental protection and resource efficiency, they are more likely to form long-term, stable partnerships with suppliers and other partners [22]. These collaborations, consistent with resource-based theory, become strategic assets that contribute to a firm’s success. Strengthened collaboration enhances information sharing and demand coordination, improving supply-chain resilience and enabling firms to better manage market fluctuations and mitigate the bullwhip effect. A well-integrated supply chain can respond more quickly to market changes, preventing instability and the amplification of the bullwhip effect.

Additionally, green transformation requires firms to consider the environmental impact of their production and logistics processes [23]. This necessitates refined production scheduling and inventory-management practices. Information sharing should extend to upstream and downstream supply-chain partners, enabling better coordination of production schedules, reducing inventory fluctuations, and minimizing excessive responses to market-demand changes [24].

However, challenges persist in implementing information-sharing technologies. Due to data complexity and diversity, firms may struggle with data interpretation. Etzioni and Etzioni [25] noted that firms without professional data analysts may misinterpret IoT and big data analytics, leading to inaccurate demand forecasts and supply-chain disruptions. Moreover, delays in technology adoption pose significant challenges. Firms with limited financial resources, technological capabilities, or conservative cultures may struggle to implement new information-sharing technologies promptly, limiting their ability to mitigate the bullwhip effect [26].

Based on the above analysis, this study proposes the following hypothesis:

Hypothesis 1.

Corporate green transformation significantly mitigates the bullwhip effect by enhancing supply chain information sharing.

2.2. The Role of Green Transformation and Organizational Resilience in Mitigating the Bullwhip Effect

Corporate green transformation significantly enhances organizational resilience, which refers to a firm’s ability to rapidly adjust, adapt, and maintain stable operations in the face of external changes or internal disruptions. Green technological innovation strengthens a firm’s adaptability and resilience by improving its capacity for resource integration and its flexibility in responding to market fluctuations, thereby enhancing its ability to manage unexpected events.

First, green transformation drives the adoption of green technologies, enabling firms to maintain efficient operations amid environmental policy shifts and changes in market demand. Green technologies are characterized by high sustainability and adaptability, allowing firms to quickly perceive external changes and adjust production and supply-chain activities accordingly, thus mitigating the impact of environmental uncertainties [27]. Through these technologies, firms improve resource utilization during production and optimize supply-chain management. This reduces supply-chain volatility caused by delayed responses or information asymmetry, effectively alleviating the bullwhip effect [28]. At this stage, firms demonstrate stronger dynamic capabilities, maintaining supply-chain stability through continuous innovation and adjustment, thus minimizing the adverse impacts of demand amplification.

Second, green transformation enhances firms’ resource allocation capabilities, enabling flexible adjustments in resource usage when confronted with market demand volatility, raw material shortages, or other unforeseen disruptions. This flexibility helps firms avoid over-reliance on single suppliers or rigid production processes [29]. By mitigating overreactions driven by information asymmetry, green transformation helps reduce supply-chain risks. During the implementation of green technological innovations, firms often improve supply-chain transparency, enabling upstream and downstream partners to respond more quickly and accurately to market signals. This enhances coordination and responsiveness across the supply chain [30]. According to the Dynamic Capabilities Theory, improved resource allocation and mobilization capabilities allow firms to swiftly adapt to external market dynamics, thereby strengthening organizational resilience and reducing supply-chain instability.

Finally, green transformation reinforces a firm’s sustainable competitive advantage, enabling it to maintain stable operations under long-term environmental pressures. In light of increasingly stringent global environmental regulations, green innovation allows firms to meet societal and market expectations for environmental stewardship while improving strategic adaptability and organizational flexibility—core components of resilience. When faced with regulatory changes, market shifts, or resource constraints, green technologies provide firms with sustainable, stable competitive advantages. By reducing resource waste and production costs, green transformation enhances supply-chain flexibility and responsiveness, mitigating the risks associated with supply-chain disruptions and market volatility.

Therefore, corporate green transformation strengthens organizational resilience and effectively reduces the negative impacts of the bullwhip effect. Green technological innovation not only enables firms to better cope with external environmental changes but also alleviates supply-chain instability caused by market fluctuations through optimized resource allocation and flexible production adjustments. Throughout the green transformation process, firms’ dynamic capabilities are significantly enhanced, providing a robust foundation for navigating challenges in an increasingly complex and volatile market environment.

However, despite the generally positive impact of green transformation on organizational resilience, its effectiveness may vary across firms and contexts. First, the high upfront costs associated with green technological innovation can impose short-term financial pressures, potentially undermining a firm’s ability to respond to unexpected events. Second, the implementation of green technologies may encounter technical bottlenecks or adaptation barriers, limiting firms’ ability to make timely adjustments. Third, an over-reliance on a single green technology may constrain firms’ flexibility in responding to diverse market dynamics. Additionally, external uncertainties and insufficient policy support may hinder the effective application of green technologies, weakening their contribution to organizational resilience.

Based on the above analysis, this study proposes the following hypothesis:

Hypothesis 2.

Corporate green transformation significantly mitigates the bullwhip effect by promoting organizational resilience.

2.3. The Role of Green Transformation and Management Quality in Mitigating the Bullwhip Effect

Corporate green transformation not only reflects a strategic response to environmental challenges but also serves as a catalyst for improving internal-management quality. By driving changes in organizational routines, performance evaluation systems, and strategic execution capabilities, green transformation enhances operational precision and managerial coordination, helping to mitigate the bullwhip effect.

First, green transformation promotes the modernization of managerial philosophies and decision-making mechanisms. As firms adopt sustainable development as a core strategic goal, they are compelled to reform existing management models by introducing long-term planning, cross-functional integration, and process standardization. According to resource dependence theory, firms adapt their internal management systems to better respond to external environmental pressures and resource constraints [31]. Through green transformation, enterprises optimize resource allocation, reduce operational waste, and enhance production efficiency—key components of managerial quality that improve a firm’s responsiveness to market fluctuations and reduce overreactions in the supply chain [32,33].

Second, the implementation of green initiatives strengthens firms’ supply-chain governance capabilities. Rather than merely fostering information sharing, green transformation enhances managerial oversight of upstream and downstream coordination. Firms adopting green supply-chain management often institutionalize mechanisms such as vendor assessment, performance monitoring, and environmental compliance audits [34]. These practices improve consistency in supply-chain execution, reduce variability in procurement and logistics, and help prevent disruptions caused by misaligned incentives or fragmented planning, thereby alleviating the bullwhip effect.

Finally, green transformation strengthens firms’ overall risk-management architecture, an integral dimension of managerial quality [35]. Enterprises embracing green transformation often establish more robust internal control systems, including risk-identification tools, early warning indicators, and contingency plans. These mechanisms enable firms to anticipate supply-demand mismatches, respond proactively to shocks (e.g., raw material shortages or policy shifts), and reduce inefficiencies caused by reactive decision-making [36]. As a result, firms can maintain operational stability and buffer the supply chain from amplification effects under volatile conditions.

Based on the above analysis, this study proposes the following hypothesis:

Hypothesis 3.

Corporate green transformation significantly mitigates the bullwhip effect by improving management quality.

3. Data, Variables, and Methodology

3.1. Data Sources

In this study, China’s A-share listed companies from 2008 to 2022 were selected as the research subjects. The sample was obtained from the China Stock Market & Accounting Research (CSMAR) database, which provides comprehensive and reliable firm-level panel data covering all A-share listed companies in China. The choice of this sample period is closely linked to the significant economic developments and policy changes in China. The global financial crisis of 2008 had a profound and far-reaching impact on China’s economy, prompting substantial adjustments in both the economic environment and corporate operations. In response, the Chinese government implemented a series of policy measures, including proactive fiscal policies and moderately loose monetary policies, aimed at stimulating economic growth. Furthermore, during this period, China made notable progress in enhancing its environmental protection policies and sustainable development strategies, providing a unique context for examining the relationship between corporate green transformation and the bullwhip effect. Analyzing data from this period allows for a better understanding of how companies adapted to external economic shocks and policy shifts in their business operations.

The CSMAR database, developed by Shenzhen Xishima Data Technology Co., Ltd., Shenzhen, China, is the first comprehensive economic and financial database in China. It is widely recognized and extensively utilized in both academia and financial institutions, serving more than 1000 prominent universities and financial organizations domestically and internationally. Its comprehensive data coverage and rigorous verification processes ensure high data quality and reliability.

The sample data were processed according to the following standards:

(1) Exclusion of Financial Companies: Financial companies were initially excluded from the sample due to their distinct financial structures, business models, regulatory requirements, and risk characteristics, which significantly differ from those of non-financial companies. These differences make financial companies incomparable to non-financial firms in the context of this study. As a result, 1044 financial company samples were removed from the original dataset;

(2) Exclusion of ST Companies: Companies labeled as “ST” (Special Treatment) during the sampling period were also excluded. These companies typically face significant financial distress and operational challenges, such as consecutive losses, excessive debt-to-asset ratios, and other financial difficulties. As their performance diverges substantially from that of normal companies, including them could have distorted the analysis results. A total of 1478 ST company samples were removed;

(3) Exclusion of Incomplete Data: Corporate samples with incomplete data during the sampling period were removed to ensure the integrity and accuracy of the analysis. In this step, 22,644 samples with missing or inconsistent data were discarded.

The final sample was obtained after the exclusion of financial companies, ST companies, and observations with missing data, as detailed in

Table 1. Sample screening.

Original sample number	48,011
	(5337)
Exclusion of financial companies	1044
	(134)
Exclusion of ST companies	1478
	(607)
Exclude samples with missing variables	22,644
	(1368)
Valid sample number	22,845
	(3228)

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Analysis of

Table 2. Distribution of green transformation by year.

Year	N	Mean	Year	N	Mean
2008	806	0.037	2016	1604	0.153
2009	795	0.067	2017	1586	0.192
2010	841	0.087	2018	1757	0.204
2011	964	0.100	2019	2115	0.213
2012	1383	0.110	2020	2146	0.248
2013	1597	0.112	2021	2560	0.243
2014	1618	0.122	2022	1781	0.190
2015	1292	0.133

reveals that, after 2016, corporate green transformation has shown a rapid growth trend. This can be attributed to multiple factors: On the policy front, the government has vigorously promoted the green development strategy, implementing a series of incentive policies, such as subsidies, to support enterprises in their green transformation. Additionally, there has been a stronger emphasis on environmental regulatory enforcement, increasing the cost of non-compliance for businesses. On the societal level, there has been a continuous increase in environmental awareness, with social-responsibility investing gradually gaining traction. Companies are now under increasing pressure from public opinion and investor demands for greener practices and sustainability in their operations.

3.2. Variable Setting

Dependent variables. In this study, we adopted the Bray and Mendelson [37] method to define the bullwhip effect variable. This approach measures the deviation between production and demand fluctuations to assess supply and demand variations within the industry chain.

The study chose this method for several reasons. Firstly, the Bray and Mendelson method is widely recognized in the academic community for studying supply chain dynamics. Its theoretical foundation, based on the universal economic principles of supply and demand, effectively captures the bullwhip effect and amplification of demand variability as it moves upstream in the supply chain. Furthermore, the method has proven adaptable across various economic contexts. Its simplicity and comprehensiveness make it well-suited to the complex and diverse Chinese market, where it can be easily implemented. It does not depend on specific market structures or regulatory environments, making it a versatile tool for our research. Additionally, the Chinese A-share market, central to our study, includes companies with well-established supply chains and comprehensive data disclosure. Relevant data, such as production and demand figures, can be reliably sourced from the CSMAR database, ensuring the feasibility of applying this method to our research.

In this study, the bullwhip effect is measured by the ratio of production volatility to demand volatility. Specifically, production volatility is defined as the standard deviation of quarterly production, while demand volatility is defined as the standard deviation of quarterly demand. By comparing the ratio of production volatility to demand volatility, this paper quantifies the amplification of production fluctuations relative to demand fluctuations in the supply chain, thus measuring the intensity of the bullwhip effect. When production volatility is significantly greater than demand volatility, it indicates that fluctuations are amplified at the production stage, which is a characteristic feature of the bullwhip effect. To eliminate the influence of long-term trends and seasonal fluctuations, both production and demand data undergo logarithmic differencing transformations. This method removes time trends, allowing the analysis to focus on short-term fluctuations and accurately reflect on how demand volatility impacts production volatility, thereby revealing the potential presence of the bullwhip effect in the supply chain. The specific measurement methods are presented in Formulas (1) and (2).

First, the deviation of supply and demand fluctuations for a firm is measured by the ratio of annual production volatility (quarterly standard deviation) to demand volatility (quarterly standard deviation):

$${\mathrm{BWE}}_{\mathrm{i},\mathrm{t}}={\displaystyle \frac{\mathrm{\sigma }({\mathrm{Production}}_{\mathrm{i},\mathrm{t}})}{\mathrm{\sigma }({\mathrm{Demand}}_{\mathrm{i},\mathrm{t}})}}$$

(1)

Here, $\mathrm{\sigma }$( ) represents the quarterly standard deviation, and Production is obtained using the Formula (2):

$${\mathrm{Production}}_{\mathrm{i},\mathrm{t}}={\mathrm{Costs}}_{\mathrm{i},\mathrm{t}}+{\mathrm{Inventory}}_{\mathrm{i},\mathrm{t}}-{\mathrm{Inventory}}_{\mathrm{i},\mathrm{t}-1}$$

(2)

Here, Costs_i,t represents the cost of goods sold for firm i in year t, and Inventory_i,t represents the net inventory value of firm i in year t. Demand is expressed in terms of the cost of goods sold (Costs). To eliminate time trends, both Production and Demand undergo logarithmic and first-order differencing transformations, i.e., {Production_i,t} is transformed into {ln(Production_i,t) − ln(Production_i,t−1)}, and {Demand_i,t} is transformed into {ln(Demand_i,t) − ln(Demand_i,t−1)}.

Independent variables. Corporate green transformation is an ongoing process. Green patents, which reflect both investment and outcomes in green innovation, serve as indicators of this transformation [38]. Among these, green invention patents, especially the number of patent applications, more accurately capture a company’s progress in green technology. Unlike granted patents, the number of applications reflects research and development activity as well as innovation intentions [39]. Patent applications also provide foresight, as they often mark the beginning or key phase of green technological innovation [40]. The number of green invention patents is a strong indicator of a company’s technological efforts in green transformation, offering an objective measure of input and output. This makes it highly comparable and quantifiable. Thus, the number of green invention patent applications is widely used in research as a core measure of green innovation. To process the data, the study added one and took the natural logarithm to reduce skewness and ensure robust results.

Mediating variables. This study explores the role of supply-chain relationship closeness (Supply) in the information-sharing process. The existing literature suggests that closer, more stable relationships between major customers and suppliers lead to stronger interdependence, facilitating the efficient exchange of both public and private information within the supply chain. Based on this premise, the degree of relationship closeness between firms is used as an indicator for measuring the level of information sharing. Specifically, the ratio of accounts payable to total assets serves as the baseline variable. To control for industry characteristics and bargaining power, we regressed this ratio on firm ownership, size, and industry. The resulting residuals acted as a proxy for relationship closeness, providing a more accurate measure of the true degree of closeness between firms and, therefore, the level of information sharing.

The organizational resilience indicator (Resilience) is constructed based on dynamic capabilities theory, measured in two dimensions: rebound resilience and overtaking resilience. Rebound resilience reflects a firm’s ability to return to its original state or functional level following shocks or disruptions. Four indicators were selected to assess this dimension, based on prior research on corporate resilience: quick ratio, embedded redundant resources, non-embedded redundant resources, and return on equity. Overtaking resilience represents a firm’s ability to achieve performance exceeding its pre-disruption level after recovery, thus indicating its growth potential. Following Ping and Nan’s approach [41], we used the year-on-year growth rates of total assets, operating revenue, and net profit as indicators of overtaking resilience. All indicators were standardized and averaged to form a composite organizational resilience index, which captures a firm’s dynamic adaptability and capacity for sustainable development. The sample median of this composite index was then calculated, where firms above the median were coded as 1 and those at or below the median were coded as 0.

The management efficiency indicator (IME) follows the method proposed by Sun et al. [42], using management expenses to assess a firm’s operational efficiency. Specifically, management expenses are regressed on total employees, operating income, cost markup, industry, and year to obtain residuals for each firm. Within each industry, the residuals are ranked, and the average residual of the top 10% of firms is used to represent the industry’s management efficiency frontier (ME). Each firm’s residual is then divided by the industry ME to derive its relative management efficiency. For interpretability, this value is multiplied by −1 to construct a positively oriented indicator (IME), where higher values indicate greater management efficiency and stronger resource-allocation capabilities.

Control variables. This study comprehensively considers key factors that may influence the relationship between corporate green transformation (GT) and the bullwhip effect (BWE) to mitigate the issue of omitted variable bias and enhance the accuracy and robustness of the regression model. First, firms’ fundamental characteristics may affect their supply-chain management capabilities and production decisions. Therefore, this study controls for total assets (Size), measured as the natural logarithm of total assets at the beginning of the year, and workforce size (Staff), represented by the natural logarithm of the total number of employees in the listed company. Additionally, firms’ financial conditions and asset structures may influence the bullwhip effect, prompting the inclusion of profitability (Profit), calculated as (operating revenue − operating costs)/operating revenue, and the proportion of tangible assets (Tangible), measured as (total assets − net intangible assets − net goodwill)/total assets, to capture the stability of firms’ asset structures.

Furthermore, the gender composition of executives may influence corporate decision-making styles and risk preferences. To account for this, the proportion of male members on the board of directors, supervisors, and senior management (Man) is included to examine its potential impact on supply-chain management and green-transformation strategies. The market competition environment is also a crucial determinant of corporate decision-making. To address this, the Lerner index (Power) is incorporated to measure firms’ market monopoly power, where higher values indicate greater market competitiveness. Additionally, government subsidies may influence corporate green investments and supply-chain management decisions. Therefore, the ratio of government subsidies to operating revenue (Subsidy) is included as a control variable to isolate the effect of government support on firms’ behavior.

Regarding supply chain management characteristics, this study includes accounts’ receivable turnover (Receivable) and accounts’ payable turnover (Payable), measured as the ratio of operating revenue to the ending balance of accounts receivable and the ratio of operating costs to the ending balance of accounts payable, respectively. These variables account for firms’ liquidity management within the supply chain. Moreover, supply-chain concentration (Concentration), calculated as (the proportion of procurement from the top five suppliers + the proportion of sales to the top five customers)/2, is incorporated to reflect firms’ dependence on key suppliers and customers, which may influence the bullwhip effect.

Beyond internal firm characteristics, external environmental factors also play a crucial role in shaping corporate green transformation and supply-chain management. To capture the impact of government environmental expenditure, this study includes the ratio of regional environmental protection expenditure to regional GDP (Environment). Additionally, in June 2017, the Chinese government established the first batch of green finance pilot zones in Zhejiang, Guangdong, Xinjiang, Jiangxi, and Guizhou following discussions at a State Council executive meeting. To account for the potential impact of this policy, a difference-in-differences variable for the implementation of the green finance pilot zone policy (Gfrl) was introduced. In June 2017, the Chinese government designated Zhejiang, Guangdong, Xinjiang, Jiangxi, and Guizhou as the first batch of pilot zones for green-finance reform and innovation. To isolate the policy effect on corporate green transformation and the bullwhip effect, this study constructed a difference-in-differences (DID) variable, Gfrl, which equals 1 for firms located in the pilot zones after 2017, and 0 otherwise. This variable helps to identify the potential influence of the Green Finance Reform and Innovation Pilot Zone policy on firms’ strategic and operational behavior. Finally, considering that cultural factors may influence firms’ long-term strategic choices, regional cultural values (Value) were controlled for, represented by the natural logarithm of the number of Confucian academies in the region, to examine the potential impact of Confucian culture on corporate green transformation.

In summary, this study incorporates control variables across multiple dimensions, including firm characteristics, financial conditions, market competition, supply-chain management, government policies, and regional culture, to minimize the risk of omitted variable bias and enhance the explanatory power of the regression model. While the inclusion of these factors captures key determinants influencing the relationship between corporate green transformation and the bullwhip effect, the possibility of unobserved factors remains, which future research may further explore. The variable definitions in this paper are shown in

Table 3. Variable definitions.

Variable Type	Variable Symbol	Variable Definition
Dependent variable	BWE	The deviation of demand fluctuations caused by the production fluctuations of enterprises
Independent variable	GT	The natural logarithm of the number of green invention patents granted to the firm plus one
Mediating variables	Supply	Represents the closeness of the relationship between firms in the supply chain, constructed using the residuals from regressing the ratio of accounts payable to total assets on firm characteristics (ownership type, size, and industry), serving as a proxy for supply-chain information sharing
	Resilience	Reflects organizational resilience, measured by rebound and overtaking resilience. Composite index based on indicators including the quick ratio, redundant resources (both embedded and non-embedded), return on equity, and the year-on-year growth rates of total assets, operating revenue, and net profit. Firms above the sample median receive a value of 1, indicating strong resilience, while others are coded as 0
	IME	Derived from the residuals of management expenses regressed on firm-level factors (number of employees, revenue, cost markup, industry, and year), adjusted relative to the industry efficiency frontier. Higher values indicate greater managerial efficiency and resource allocation capability
Control variables	Size	The natural logarithm of asset at the beginning of the year
	Staff	The natural logarithm of the total number of employees in the listed company
	Profit	(Operating revenue − operating costs)/operating revenue
	Tangible	(Total assets − net intangible assets − net goodwill)/total assets
	Man	The proportion of male members on the board of directors, supervisors, and senior management (%)
	Power	The firm’s competitiveness is measured by the Lerner index, with a higher value indicating greater monopoly power
	Subsidy	The ratio of government subsidies to operating revenue
	Receivable	The ratio of operating revenue to the ending balance of accounts receivable (%)
	Payable	The ratio of operating costs to the ending balance of accounts payable (%)
	Concentration	(The proportion of procurement from the top 5 suppliers + the proportion of sales to the top 5 customers)/2 (%)
	Environment	The ratio of regional environmental protection expenditure to regional GDP
	Gfrl	In June 2017, after discussions in the State Council executive meeting, the Chinese government decided to establish the first batch of green-finance pilot zones in five provinces: Zhejiang, Guangdong, Xinjiang, Jiangxi, and Guizhou. Therefore, a difference-in-differences variable for the implementation of the green finance pilot zone policy was set
	Value	Regional cultural value is represented by the natural logarithm of the number of Confucian academies in the region
	Year/Industry	Virtual variable

Note: The variables listed in Table 3 are derived from the China Stock Market & Accounting Research (CSMAR) database. The CSMAR database is the primary source for the sample used in this study, providing comprehensive and reliable data on Chinese listed companies.

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3.3. Empirical Methodology

To examine the impact of green transformation on the bullwhip effect, Ordinary Least Squares (OLS) regression was employed as the primary estimation method. Under classical assumptions, OLS provides the Best Linear Unbiased Estimator, relying on key conditions such as linearity, zero conditional mean of the error term, homoscedasticity, and the absence of perfect multicollinearity. Diagnostic tests confirmed that these assumptions are largely satisfied by the dataset, making OLS a statistically reliable and interpretable approach for baseline estimation.

However, while OLS provides consistent and interpretable baseline estimates under standard assumptions, it does not fully address potential endogeneity concerns, particularly those arising from reverse causality and sample-selection bias. In this study’s context, green transformation may be both a cause and a consequence of supply-chain volatility, introducing reverse causality. Additionally, firms engaged in green transformation may possess unobserved characteristics, such as superior managerial capabilities, fewer financial constraints, or better access to policy support, all of which simultaneously influence supply-chain stability and lead to sample-selection bias. If these endogenous factors remain unaccounted for, causal inference could be compromised, resulting in biased estimates.

To address these concerns and strengthen the robustness of the results, subsequent robustness checks incorporated two complementary econometric approaches. First, the two-stage least squares (2SLS) method was applied to mitigate reverse causality by using instrumental variables for green transformation. Second, the Heckman two-step selection model was employed to correct for sample-selection bias arising from non-random firm characteristics. These additional estimation strategies improved the empirical identification and provided more credible evidence regarding the stabilizing effect of green transformation on supply-chain dynamics.

$$\begin{array}{c}{\mathrm{BWE}}_{\mathrm{i},\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1{\mathrm{GT}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_2{\mathrm{Size}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_3{\mathrm{Staff}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_4{\mathrm{Profit}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_5{\mathrm{Tangible}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_6{\mathrm{Man}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_7{\mathrm{Power}}_{\mathrm{i},\mathrm{t}}\hfill \\ +{\mathrm{\alpha }}_8{\mathrm{Subsidy}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_9{\mathrm{Receivable}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_{10}{\mathrm{Payable}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_{11}{\mathrm{Concentration}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_{12}{\mathrm{Environment}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\alpha }}_{13}{\mathrm{Gfrl}}_{\mathrm{i},\mathrm{t}}\hfill \\ +{\mathrm{\alpha }}_{14}\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\mathrm{\epsilon }\hfill \end{array}$$

(3)

Specifically, BWE_i,t is the dependent variable, representing the bullwhip effect, with indices i and t denoting the firm and year, respectively. GT_i,t is the independent variable, representing corporate green transformation.

α0 is the constant term; α₁ is the coefficient for the impact of corporate green transformation on the bullwhip effect, and it is expected to be significantly negative, indicating that corporate green transformation effectively reduces the bullwhip effect; ∑Year and ∑Industry represent year and industry fixed effects, where year and industry dummy variables were introduced into the regression model to effectively eliminate the influence of industry and year factors, ensuring the accuracy of the regression results; and ε is the random error term.

4. Empirical Results

4.1. Descriptive Statistics

Table 4. Descriptive statistics.

Variable	N	Mean	Std	Min	Max	VIF
BWE	22,845	1.253	0.988	0.173	6.343
GT	22,845	0.167	0.509	0	2.890	1.23
Supply	22,845	0.000	0.064	−0.106	0.221	1.10
Resilience	22,845	0.504	0.500	0	1	1.30
IME	22,845	0.000	0.512	−1.163	1.077	1.48
Size	22,845	22.365	1.295	20.012	26.398	4.37
Staff	22,845	7.839	1.231	4.984	11.283	3.39
Profit	22,845	0.273	0.166	−0.010	0.790	3.33
Tangible	22,845	0.924	0.090	0.164	1	1.18
Man	22,845	81.594	11.246	27.780	100	1.21
Power	22,845	0.120	0.122	−0.271	0.507	3.01
Subsidy	22,845	0.008	0.012	0	0.071	1.15
Receivable	22,845	39.495	144.636	0.898	1167.180	1.11
Payable	22,845	7.905	0.797	0.797	85.039	1.24
Concentration	22,845	28.693	16.670	2.240	78.320	1.40
Environment	22,845	0.005	0.003	0.002	0.017	1.56
Gfrl	22,845	0.165	0.371	0	1	1.31
Value	22,845	2.675	1.439	0	4.762	1.51

lists the descriptive statistics of the main variables. Among them, the mean, minimum, and maximum values of BWE are 1.253, 0.173, and 6.343, respectively, suggesting that some enterprises are facing severe bullwhip effects. The mean, minimum, and maximum values of GT are 0.167, 0, and 2.890, respectively, indicating significant differences in the degree of green transformation among enterprises, with most enterprises showing weak green transformation.

The variable Size represents the natural logarithm of a firm’s assets at the beginning of the year, measured in yuan. It has a mean value of 22.365 and a standard deviation of 1.295, indicating a relatively consistent distribution. However, the range of values, from 20.012 to 26.398, suggests some variability in firm size across the sample. The variable Receivable was calculated as the ratio of accounts receivable to operating revenue, with a mean value of 39.495 and an exceptionally high standard deviation of 144.636. This large standard deviation reflects significant variation in accounts receivable turnover across firms, which may be influenced by factors such as firm size, industry, or the business model. The Man variable, which reflects the gender composition of board members, supervisors, and senior management, has a mean value of 81.594 and a standard deviation of 11.246. This suggests a highly skewed gender distribution, with most firms having a predominantly male leadership. The Concentration variable, which measures supplier and customer concentration, has a mean value of 28.693 and a standard deviation of 16.670. This indicates significant variation among firms, with some exhibiting greater reliance on their top suppliers and customers. Such variability may be reflective of differences in supply-chain strategies or industry-specific dynamics, as firms in certain sectors may face higher concentration in their supply chains.

Furthermore, the variance inflation factor (VIF) was used to test whether there is multicollinearity between variables. The VIF value is less than the critical value of 5, indicating that there is no significant multicollinearity between the variables.

4.2. Baseline Regression

Table 5. Baseline results.

Variables	(1)	(2)
	BWE	BWE
GT	−0.085 ***	−0.073 ***
	(−7.364)	(−5.806)
Size		0.057 ***
		(5.983)
Staff		−0.048 ***
		(−5.107)
Profit		1.036 ***
		(12.422)
Tangible		−0.180 **
		(−2.215)
Man		−0.002 ***
		(−2.603)
Power		−0.686 ***
		(−6.762)
Subsidy		−1.171 *
		(−1.822)
Receivable		−0.000
		(−1.593)
Payable		0.002 ***
		(4.173)
Concentration		0.000
		(0.734)
Environment		4.779 *
		(1.735)
Gfrl		−0.036 *
		(−1.862)
Value		0.018 ***
		(3.302)
Industry/Year	Yes	Yes
Constant	1.460 ***	0.662 ***
	(19.625)	(3.340)
N	22,845	22,845
Adj. R2	0.009	0.025

Note: *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

presents the baseline regression results examining the relationship between green transformation and the bullwhip effect. In Column (1), only year and industry fixed effects were controlled for. The regression coefficient of green transformation (GT) is −0.085 and is statistically significant at the 1% level. In Column (2), an additional set of control variables is introduced, and the coefficient of GT remains statistically significant. Specifically, a one-unit increase in the level of green transformation is associated with an average decrease of 0.073 units in the bullwhip effect, indicating that green transformation significantly mitigates supply-chain fluctuations within firms. This finding is consistent with the theoretical expectation that green transformation enhances the accuracy of demand forecasting by improving information flows and internal coordination across the supply chain, thereby reducing order variability. In practice, firms engaged in green transformation often adopt more structured, transparent, and long-term-oriented management practices, which substantially improve their capacity to respond to market volatility and reduce overall supply-chain instability. Moreover, green transformation frequently requires enterprises to strengthen their digital infrastructure, enhance supply-chain traceability, and implement environmental-compliance systems. These initiatives not only improve data accuracy and supply-chain visibility but also enable firms to access and respond to market-demand information more promptly, thereby reducing uncertainty in production and inventory management. Concurrently, by fostering closer collaboration with upstream and downstream partners, firms can optimize information-sharing mechanisms and limit excessive inventory adjustments, further alleviating the bullwhip effect.

As discussed in the previous analysis, existing theories suggest that, under specific institutional constraints or resource limitations, green transformation may bring about new coordination challenges, increase information asymmetry, and potentially amplify supply-chain instability, especially in a rapidly developing market like China with a constantly evolving institutional environment. These challenges often arise from regulatory pressures, unequal resource allocation, and coordination difficulties between upstream and downstream entities in the supply chain during the green transformation process. However, the empirical results of this study, based on a sample of Chinese A-share listed companies, did not find any such negative effects. This indicates that, in the current institutional context in China, the positive impact of green transformation is more pronounced, particularly as policy support strengthens and green-finance and environmental-protection policies improve. The Chinese government’s strong push for green transformation, along with policy support for green technologies and supply-chain management, has led companies to place greater emphasis on resource integration and information sharing during the transformation process. This has effectively enhanced supply-chain transparency and coordination efficiency, not only alleviating potential coordination challenges but also reducing the risks associated with information asymmetry, further strengthening the stability of the supply chain and its ability to respond to market fluctuations.

In sum, advancing corporate green transformation contributes not only to achieving environmental sustainability goals but also to improving supply chain stability and operational efficiency, thereby providing firms with a robust competitive advantage in an increasingly dynamic market environment.

Regarding the control variables, the coefficient of Profit is significantly positive, indicating a positive correlation between profit margins and the bullwhip effect. This finding suggests that firms with higher profit margins may be more inclined to adopt expansionary production and inventory strategies in anticipation of continued growth in demand [43]. In particular, within the context of China’s manufacturing sector, profitability is often viewed as a signal of market opportunity, prompting firms to increase output levels and place larger upstream orders. These aggressive adjustments, aimed at maximizing short-term gains, can inadvertently lead to greater variability in order volumes and can amplify fluctuations across the supply chain. Furthermore, higher profitability may reduce firms’ sensitivity to cost risks, weakening internal constraints on overproduction and speculative inventory buildup [44], thereby intensifying the bullwhip effect.

The coefficient of Tangible is significantly negative, suggesting that firms with a higher proportion of tangible assets are less prone to experiencing severe bullwhip effects. Such firms, often operating in asset-heavy industries, typically exhibit more structured production systems, better inventory planning capabilities, and greater operational stability, all of which help dampen supply-chain volatility.

The coefficient of Man is also significantly negative, indicating that a higher proportion of male executives on the board is associated with a smaller bullwhip effect. This may reflect gender-based behavioral tendencies, where male executives are more likely to adopt rational, risk-averse decision-making styles, thereby avoiding excessive or reactive production adjustments.

The coefficient of Subsidy is significantly negative, suggesting that a greater ratio of government subsidies to revenue contributes to a reduction in the bullwhip effect [11]. Financial support from the government eases firms’ operational pressures, allowing for more stable production planning and reducing the need to overcompensate for demand uncertainties.

The coefficient of Payable is significantly positive, implying that a higher accounts-payable ratio is associated with a stronger bullwhip effect. This may indicate tighter liquidity or weaker supplier relationships, both of which can introduce disruptions in order cycles and inventory management, thus amplifying fluctuations along the supply chain.

Finally, the coefficient of Gfrl is significantly negative, supporting the conclusion that green finance pilot policies play a role in mitigating the bullwhip effect. These policies promote sustainable practices and long-term coordination within supply chains, thereby enhancing operational resilience and reducing order variability.

4.3. Sensitivity Analyses

Next, this study conducted a series of sensitivity analyses to further verify the robustness of the baseline analysis results. The sensitivity tests primarily address three concerns:

(1) Regional Environmental Pressure Differences. The Yangtze River Economic Belt plays a critical role in China’s national ecological strategy and bears significant responsibility for protecting the ecological environment of the Yangtze River. Compared to other regions, enterprises located in this area are generally subject to stricter environmental regulations and greater public scrutiny. As a result, they tend to have stronger incentives to pursue green transformation, with higher levels and greater intensity of such efforts. If not properly controlled, this regional specificity may compromise the generalizability of the empirical findings. Therefore, this study excludes firms located in the Yangtze River Economic Belt in the sensitivity analysis to account for potential biases arising from regional environmental pressure differences. As shown in Column (1) of

Table 6. Sensitivity analyses.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	BWE	BWE	BWE	BWE	BWE	BWE
	Exclude the Yangtze River Economic Belt Region	Non-Heavy-Pollution Enterprises	(i) Years Before and Including 2015; (ii) Years After and Including 2016		Received Government Subsidies	No Government Subsidies
GT	−0.086 ***	−0.089 ***	−0.079 ***	−0.075 ***	−0.071 ***	−0.103 ***
	(−5.474)	(−6.474)	(−3.079)	(−5.175)	(−5.356)	(−2.629)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes	Yes	Yes	Yes
Constant	0.660 **	0.618 ***	1.151 ***	−0.039	0.637 ***	0.421
	(2.482)	(2.777)	(3.541)	(−0.158)	(2.979)	(0.747)
N	12,543	17,608	9296	13,549	20,057	2788
Adj. R2	0.028	0.034	0.023	0.027	0.026	0.022

Note: **, and *** indicate significance at the 0.05, and 0.01 levels, respectively.

, the regression coefficient of GT remains significantly negative, indicating that the results are robust and continue to support the study’s hypothesis;

(2) Industry Pollution Characteristics. Heavy-pollution industries, due to the significant environmental impact of their production activities, are often subject to early and deeper green transformation under the influence of policy guidance and social attention. This could lead to the higher level of green transformation in these firms potentially confounding the relationship between corporate green transformation and the bullwhip effect. To address this concern, the study selected a sample of enterprises from non-heavy-pollution industries for sensitivity analysis. As shown in Column (2) of Table 6, the coefficient of GT remains significantly negative, and the results continue to support the research hypothesis

(3) Time Trends. In recent years, with the deepening of the green development concept and strong policy support, the level of corporate green transformation has shown rapid growth. This surge may cause the observed relationship between corporate green transformation and the bullwhip effect to be concentrated in the later years of the sample period, potentially affecting the generalizability of the findings. To address this concern, the sample was divided into early and late periods, and separate regression analyses were conducted. As shown in Columns (3) and (4) of Table 6, the coefficient of GT remains significantly negative, and the results continue to support the research hypothesis;

(4) In the context of China’s economy, fiscal policies, particularly government subsidies, play a key role in the operation and development of enterprises. These subsidies are used by the government to support innovation and transformation within firms, especially during the green transformation process. Government subsidies can effectively stimulate investment in green-technology research and development and production-model upgrades. Therefore, accounting for the direct impact of fiscal policies is essential when studying the relationship between green transformation and the long-tail effect in enterprises. Government subsidies provide financial support, reduce the costs and risks associated with the green-transformation process, and incentivize firms to accelerate research, development, and the application of green technologies. Additionally, these subsidies may influence firms’ strategic decisions and resource allocation, thereby affecting their operations within the supply chain and, consequently, the long-tail effect. As a result, government subsidies may influence the findings of this study. To assess the robustness of the results, the sample was divided into two groups: firms that received government subsidies and firms that did not. Regressions were conducted separately for each group to examine the potential moderating effect of government subsidies on the relationship between green transformation and the bullwhip effect. As shown in Columns (5) and (6) of Table 6, in the sample of firms without government subsidies, the coefficient of the green transformation variable (GT) remains significantly negative, indicating that green transformation continues to significantly mitigate the bullwhip effect, even in the absence of fiscal support. In the sample of firms that received government subsidies, the GT coefficient remains negative and statistically significant.

4.4. Robustness Test

Firstly, it is essential to consider the impact of macroeconomic policies. There may be differences in the business environment, environmental policies, and other factors across regions, while the industrial support policies and environmental constraints can also vary by industry. To effectively control for potential policy disturbances arising from the time-varying industry and regional factors, this paper introduces “Industry × Year” and “Province × Year” interaction fixed effects in model (3). As shown in Column (1) of

Table 7. Robustness test.

Variables	(1)	(2)	(3)	(4)
	BWE	BWE	BWE	BWE
	Interaction Fixed Effects	Exclude Samples from Directly Governed Municipalities	Total Factor Productivity	The Ratio of Green Invention Patents to Total Invention Patents
GT	−0.072 ***	−0.100 ***	−0.060 ***	−0.009 ***
	(−5.733)	(−6.920)	(−3.991)	(−6.339)
Controls	Yes	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes	Yes
Industry × Year	Yes	No	No	No
Province × Year	Yes	No	No	No
Constant	2.082 ***	0.686 ***	0.539 **	0.756 ***
	(3.658)	(3.044)	(2.490)	(3.870)
N	22,845	17,964	22,845	22,845
Adj. R2	0.028	0.023	0.024	0.025

Note: **, and *** indicate significance at the 0.05, and 0.01 levels, respectively.

, the coefficient of green transformation (GT) is −0.072 and is significantly negative at the 1% level, indicating that green transformation has a significant negative impact on the bullwhip effect in firms.

Secondly, considering the special characteristics of municipalities directly under the central government, it was necessary to exclude enterprises located in these regions for the robustness test. Municipalities directly under the central government often differ from other regions in terms of policy implementation, economic development, and the business environment, which could have a significant impact on the research results. As pilot areas for policy initiatives, these municipalities typically receive special treatment with respect to government policies, industry support, and environmental constraints, which may lead to a different relationship between corporate green transformation and the bullwhip effect compared to other regions. To mitigate the potential interference of these special factors on the research findings, excluding the sample from these municipalities helps enhance the robustness and generalizability of the study, ensuring that the results more accurately reflect the impact of corporate green transformation on the bullwhip effect in other regions. As shown in Column (3) of Table 7, after excluding the sample from municipalities directly under the central government, the coefficient of GT remains significantly negative, and the robustness test results further support the research conclusion.

Thirdly, considering the potential measurement bias of the independent variable, this study conducted a robustness test by altering the measurement method. On the one hand, total factor productivity (TFP) was used as a proxy for corporate green transformation, primarily because TFP is a comprehensive indicator of a firm’s resource utilization efficiency and productivity improvement, which can effectively reflect the efficiency changes in the green transformation process. Green transformation typically involves more efficient resource utilization and a reduction in environmental impact, both of which can be captured by TFP as it reflects the firm’s improvements in these areas. By using TFP as a proxy variable, this study was able to comprehensively evaluate the impact of green transformation on productivity enhancement and resource-allocation efficiency. As shown in Column (4) of Table 7, the research results remain robust even after changing the measurement method for corporate green transformation. On the other hand, the study used the ratio of green invention patents filed by a firm in a given year to the total number of invention patents filed by the firm as an alternative measure of green transformation. The number of green invention patents filed by a firm directly reflects its input and outcomes in green technological innovation. Compared to the total number of patents, the proportion of green invention patents more accurately reflects the firm’s emphasis on and progress in green technology. Therefore, using this ratio as a measure of green transformation helps to effectively capture the firm’s performance in green innovation. As shown in Column (5) of Table 7, the research results remain robust when using this alternative variable, with the coefficient for green transformation (GT) being significantly negative, further validating the negative impact of green transformation on the long-tail-effect instrumental variables to eliminate interference from reversed causal relationships, thereby ensuring the validity and consistency of the estimation results.

As shown in Columns (1) of

Table 8. Considering the issue of endogeneity.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	BWE	BWE	GT	BWE	Dum_GT	BWE
	Kernel Matching	Entropy Balancing	2SLS		Heckman Selection Model
GT	−0.075 ***	−0.063 ***		−0.413 **		−0.122 ***
	(−5.933)	(−4.378)		(−2.175)		(−6.252)
IV			0.052 ***		0.164 ***
			(11.513)		(7.726)
IMR						0.151 **
						(2.250)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes	Yes	Yes	Yes
Constant	0.642 ***	0.557 *	−4.252 ***	−0.967	−13.797 ***	−1.388 *
	(3.211)	(1.688)	(−27.943)	(−1.214)	(−30.181)	(−1.886)
N	22,539	22,845	20,183	20,183	20,131	20,131
Adj. R2/Pseudo R2	0.025	0.024			0.202	0.025
Cragg-Donald Wald F statistic			119.50
Kleibergen-Paap rk LM			139.36

Note: *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

, after applying PSM, the coefficient of GT remains significantly negative, indicating that green transformation has a significant inhibiting effect on the bullwhip effect. Similarly, the research findings remain unchanged after using the Entropy Balancing Method (see Column (2) of Table 8). Overall, considering the potential endogeneity issues arising from sample selection bias, the conclusions still support the theoretical analysis.

This study acknowledges a potential endogeneity issue between corporate green transformation and the bullwhip effect. On the one hand, corporate green transformation can mitigate the bullwhip effect by improving supply-chain management, enhancing information transparency, and optimizing resource allocation. These improvements reduce information lags and overreactions within the supply chain. On the other hand, the bullwhip effect itself may drive companies to pursue green transformation. Specifically, demand fluctuations and supply-chain instability can motivate companies to invest in green patent research and development to improve supply-chain adaptability and mitigate the impact of these fluctuations. In such cases, the bullwhip effect may promote green transformation, particularly when companies face supply-chain challenges, where green innovation becomes a strategy to cope with uncertainty and enhance market stability. Thus, the bullwhip effect could exacerbate market fluctuations, increase green-patent activity, and further drive corporate green transformation.

To address the endogeneity issue, a commonly used method is to identify a suitable instrumental variable and apply Two-Stage Least Squares (2SLS) to obtain more consistent and reliable estimates. Following the approach of Tao et al. [45], this study used the number of telephones per ten thousand people in each city in 1984 as the instrumental variable for corporate green transformation. The historical telecommunications infrastructure in each region can influence the adoption and diffusion of green technologies. This is because the level of technological development and communication habits from the past affect the flow of information and technological diffusion, which, in turn, impacts corporate green transformation. Moreover, the postal and telecommunications infrastructure from 1984 reflects the communication technology development in various regions at that time, which may have long-term effects on the technological upgrading paths of local industries. Regions with better communication networks are more likely to develop technology-based clusters, enhancing the green innovation capabilities of firms. Thus, the historical telecommunications data indirectly influence corporate behavior through channels such as information dissemination and industrial upgrading, rather than directly determining whether firms undergo green transformation, which satisfies the exclusion restriction. To improve the instrument, following the method of Nunn and Qian [46], this study introduced a time-varying variable to create a panel instrumental variable. Specifically, the interaction of the previous year’s national green technology patent applications and the number of telephones per ten thousand people in 1984 was used to construct the instrumental variable for green transformation. This interaction reflects the relative competitive advantage of different regions in green innovation, enhancing the explanatory power of the instrument.

From the results in columns (3) and (4) of Table 8, it can be seen that the regression coefficient of the first-stage instrumental variable (IV) is significantly positive at the 1% level, and the Cragg–Donald Wald F-statistic for the weak instrument test is 119.50, significantly greater than the critical value of 16.38 at the 10% significance level, indicating the absence of weak instruments. The Kleibergen–Paap rk LM statistic is 139.36, and the null hypothesis is rejected at the 1% level, indicating that there is no issue of under-identification with the instrument. The coefficient of the second-stage green transformation (GT) is significantly negative at the 1% level, indicating that, after addressing endogeneity, the conclusions remain robust.

Furthermore, as some listed companies have not applied for green patents, sample self-selection bias may occur, which could affect the validity of the estimated results. Therefore, this study used the Heckman two-stage model for estimation. In the first-stage Probit regression, whether a company has a green patent (Dum_GT) was used as the dependent variable to estimate the inverse Mills ratio (IMR). In the second-stage regression, IMR was added to the model (3) to control for potential self-selection bias. The results in column (6) of Table 8 show that the estimated coefficient of GT remains significantly negative, indicating that, after considering sample self-selection, the research conclusions remained robust, suggesting that potential self-selection bias does not substantially affect the core conclusions of this paper.

4.5. Pathway Mechanism Testing

Based on the baseline regression results, this study found that green transformation significantly reduces the bullwhip effect of firms, and the results remained robust after a series of robustness checks. However, the internal mechanisms through which green transformation affects firms’ bullwhip effect have not been fully explored. Therefore, this study empirically examined the potential mechanisms through which green transformation influences bullwhip effect and established the following model for further investigation.

$$\begin{array}{c}{\mathrm{Mediator}}_{\mathrm{i},\mathrm{t}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{GT}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_2{\mathrm{Size}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_3{\mathrm{Staff}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_4{\mathrm{Profit}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_5{\mathrm{Tangible}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_6{\mathrm{Man}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_7{\mathrm{Power}}_{\mathrm{i},\mathrm{t}}\hfill \\ +{\mathrm{\beta }}_8{\mathrm{Subsidy}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_9{\mathrm{Receivable}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_{10}{\mathrm{Payable}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_{11}{\mathrm{Concentration}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_{12}{\mathrm{Environment}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\beta }}_{13}{\mathrm{Gfrl}}_{\mathrm{i},\mathrm{t}}\hfill \\ +{\mathrm{\beta }}_{14}\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\mathrm{\epsilon }\hfill \end{array}$$

(4)

$$\begin{array}{c}{\mathrm{BWE}}_{\mathrm{i},\mathrm{t}}={\mathrm{\gamma }}_0+{\mathrm{\gamma }}_1{\mathrm{GT}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_2{\mathrm{Mediator}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_3{\mathrm{Size}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_4{\mathrm{Staff}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_5{\mathrm{Profit}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_6{\mathrm{Tangible}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_7{\mathrm{Man}}_{\mathrm{i},\mathrm{t}}\hfill \\ +{\mathrm{\gamma }}_8{\mathrm{Power}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_9{\mathrm{Subsidy}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_{10}{\mathrm{Receivable}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_{11}{\mathrm{Payable}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_{12}{\mathrm{Concentration}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_{13}{\mathrm{Environment}}_{\mathrm{i},\mathrm{t}}\hfill \\ +{\mathrm{\gamma }}_{14}{\mathrm{Gfrl}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\gamma }}_{15}\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}+\sum \mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}+\sum \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}+\mathrm{\epsilon }\hfill \end{array}$$

(5)

In this context, Mediator represents the mediating variables, including the supply-chain information sharing, organizational resilience, and management quality. Specifically, model (3) still considers the total effect of green transformation on the bullwhip effect of firms, with the coefficient α₁ indicating the magnitude of the total effect. In model (4), the coefficient β₁ represents the impact of corporate green transformation on the mediating variables. In model (5), the coefficient γ₁ represents the direct effect of green transformation on the firm’s bullwhip effect, while the product of coefficients β₁ and γ₂ from model (4), denoted by β₁γ₂, indicates the magnitude of the mediating effect. If the absolute value of the regression coefficient γ₁ is less than that of α₁, it suggests that the mediating variables play a mediating role in the relationship between green transformation and the bullwhip effect of firms.

As shown in the first column of

Table 9. Channel 1: supply-chain information sharing.

Variables	(1)	(2)	(3)
	BWE	Supply	BWE
GT	−0.073 ***	0.006 ***	−0.071 ***
	(−5.180)	(6.400)	(−5.040)
Supply			−0.342 ***
			(−3.250)
Controls	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes
Constant	0.412	0.134 ***	0.458
	(1.460)	(7.600)	(1.630)
N	22,845	22,845	22,845
Adj. R2	0.025	0.088	0.025
Sobel Test	−0.002 ***
	(−2.898)

Note: *** indicate significance at the 0.01 levels.

, green transformation significantly reduces the bullwhip effect of firms. The second step was to regress model (4) to examine the impact of green transformation on supply-chain information sharing. As indicated in the second column of Table 9, the coefficient for GT is significantly positive, suggesting that green transformation significantly enhances supply-chain information sharing. The third step was to verify whether supply-chain information sharing plays a mediating role in the process through which green transformation reduces the bullwhip effect. According to the results shown in the third column of Table 9, when Supply is included in the model, the coefficient for GT remains significantly negative, but its value decreases, indicating that supply-chain information sharing plays a partial mediating role. In conclusion, the mediating effect test supports the finding that green transformation improves supply-chain information sharing and reduces the bullwhip effect.

This mechanism is both economically and managerially plausible. The implementation of green transformation compels firms to comply with increasingly stringent environmental regulations and heightened stakeholder expectations, which, in turn, drives improvements in operational transparency and traceability. In response to these external pressures, firms are motivated to adopt digital technologies to facilitate real-time data exchange with upstream and downstream supply-chain partners. These technological investments enable supply-chain participants to access accurate and timely information on demand forecasts, inventory levels, and production schedules. As a result, the increased visibility of this information reduces uncertainty and mitigates the distortion of demand signals throughout the supply chain, thereby significantly decreasing the amplification of order fluctuations characteristic of the bullwhip effect.

While theoretical analysis suggests that uneven technological capabilities or limited data-processing capacity could constrain the positive impact of green transformation on supply-chain information sharing, the empirical results indicate that these barriers have not substantively affected the effectiveness of the mechanism. This can be explained by the widespread availability and decreasing costs of digital technologies. Even firms with weaker technological capabilities can enhance their information-sharing abilities through platforms or outsourcing. Furthermore, firms generally adopt a gradual approach to implementing these technologies, continuously refining their information-sharing systems over time. More importantly, during the green transformation process, firms develop closer collaboration with their upstream and downstream partners, with stronger firms providing technical support to others, thus bridging technological gaps. Despite initial disparities, the growing urgency and mutual benefits of information sharing drive firms to overcome these gaps, ensuring the successful implementation of this mechanism in reducing the bullwhip effect.

As shown in the first column of

Table 10. Channel 2: organizational resilience.

Variables	(1)	(2)	(3)
	BWE	Resilience	BWE
GT	−0.073 ***	0.031 ***	−0.070 ***
	(−5.180)	(4.880)	(−4.980)
Resilience			−0.092 ***
			(−6.250)
Controls	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes
Constant	0.414	1.572 ***	0.558 **
	(1.470)	(12.410)	(1.980)
N	22,845	22,845	22,845
Adj. R2	0.025	0.227	0.026
Sobel Test	−0.003 ***
	(−3.843)

Note: **, and *** indicate significance at the 0.05, and 0.01 levels, respectively.

, green transformation significantly reduces the bullwhip effect of firms. The next step was to regress model (4) to examine the impact of green transformation on organizational resilience. As indicated in the second column of Table 10, the coefficient for GT is significantly positive, suggesting that green transformation significantly improves organizational resilience. Finally, we tested whether organizational resilience plays a mediating role in the process through which green transformation reduces the bullwhip effect. According to the results in the third column of Table 10, when organizational resilience (Resilience) is included in the model, the coefficient for GT remains significantly negative, but its value decreases, indicating that organizational resilience partially mediates the relationship between green transformation and the bullwhip effect.

The results of this study demonstrate that green transformation significantly alleviates the bullwhip effect, with organizational resilience playing a mediating role in this process. Specifically, the reduction of the bullwhip effect is not only a direct result of green transformation but is also closely associated with the enhancement of organizational resilience. Green transformation helps firms establish more adaptable organizational structures, thereby strengthening their ability to respond to supply-chain disruptions and market demand fluctuations, effectively mitigating the instability caused by these external disturbances.

From a theoretical perspective, green transformation enhances a firm’s dynamic capabilities, particularly in terms of more efficient resource allocation and flexible production adjustments. This enables firms to respond more nimbly to external environmental changes and market-demand fluctuations. At the same time, organizational resilience, by strengthening a firm’s adaptability, helps maintain stable operations in the face of external shocks, thus reducing uncertainty and fluctuations in the supply chain. This is crucial for mitigating the bullwhip effect.

Despite the negative effects of green transformation discussed in the theoretical analysis, such as high initial costs, technical adaptation issues, and over-reliance on a single green technology, these factors did not significantly manifest in the mediating mechanism tests. The reasons for this may be as follows: first, although the initial costs of green transformation are high, the marginal cost of related investments gradually decreases as firms adapt to and optimize green technologies. Moreover, through long-term resource optimization and supply-chain stability improvements, the financial pressure of these initial costs is alleviated, weakening their impact. Second, while technical adaptation issues may pose obstacles in the early stages of transformation, firms can gradually overcome these challenges through continuous technological development and upgrades, making the technical bottleneck less impactful on the mediating mechanism. Lastly, firms typically adopt diversified technological paths in the green transformation process, avoiding over-reliance on a single technology, which enhances the flexibility of green transformation and reduces the potential risks associated with such reliance.

Over time, the direct benefits of green transformation, such as resource optimization and supply-chain stability, become increasingly evident. The role of organizational resilience becomes more pronounced in this process, indicating that green transformation is not a one-time strategic adjustment but a continuous process. By continuously strengthening adaptability and the ability to cope with market fluctuations, green transformation enhances a firm’s long-term stability.

As shown in the first column of

Table 11. Channel 3: management quality.

Variables	(1)	(2)	(3)
	BWE	IME	BWE
GT	−0.073 ***	0.065 ***	−0.068 ***
	(−5.180)	(10.760)	(−4.820)
IME			−0.076 ***
			(−4.960)
Controls	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes
Constant	0.414	−4.187 ***	0.095
	(1.470)	(−34.530)	(0.330)
N	22,845	22,845	22,845
Adj. R2	0.025	0.323	0.026
Sobel Test	−0.005 ***
	(−4.507)

Note: *** indicate significance at the 0.01 levels.

, green transformation significantly reduces the bullwhip effect. The next step involved regression model (4) to examine the impact of green transformation on management quality. As presented in the second column of Table 11, the coefficient of GT is significantly positive, indicating that green transformation significantly reduces the management quality of firms. Finally, we tested whether management quality plays a mediating role in the relationship between green transformation and the reduction of the bullwhip effect. According to the results in the third column of Table 11, when management quality (IME) is included in the model, the coefficient of green transformation remains significantly negative but decreases in magnitude, suggesting that management quality partially mediates the effect of green transformation in mitigating the bullwhip effect.

From a theoretical perspective, the mediating role of management quality highlights its pivotal position in the process through which green transformation influences supply-chain stability. Green transformation initiates adjustments in firms’ operational mechanisms, and the successful implementation of these adjustments relies on the improvement of management quality. Strengthened managerial capabilities, including improved resource-allocation efficiency, enhanced operational flexibility, and faster responsiveness to external changes, facilitate the effective implementation of green technologies. These improvements contribute to reduced supply-chain volatility and a mitigation of the bullwhip effect.

This mediating mechanism further suggests that the effectiveness of green transformation is not solely determined by the adoption of green technologies but also depends on the simultaneous optimization of internal management systems. By refining managerial practices, firms can integrate green technologies more efficiently into daily operations, thereby improving overall adaptability and reinforcing the capacity to manage supply-chain uncertainties. In the initial stages of green transformation, enhanced management quality plays a crucial role in minimizing the uncertainty and execution costs associated with green technologies, thereby providing a more stable foundation for supply-chain operations.

Accordingly, management quality functions as a key mediating factor in the pathway through which green transformation impacts supply-chain stability. It serves as an essential safeguard for achieving the intended outcomes of green initiatives. Firms aiming to attain sustainable and stable performance improvements through green transformation should prioritize the development of robust managerial systems, which are necessary for supporting the effective implementation of technological innovation.

The results in

Table 12. The influence relationship among mediating variables.

Variables	(1)	(2)
	BWE	BWE
GT	−0.063 ***	−0.063 ***
	(−5.030)	(−5.037)
Supply	−0.337 ***	−0.302 **
	(−3.374)	(−2.250)
Resilience	−0.092 ***	−0.092 ***
	(−6.218)	(−6.190)
IME	−0.075 ***	−0.057 ***
	(−4.787)	(−2.861)
Supply × Resilience		−0.072
		(−0.378)
Supply × IME		−0.173
		(−0.961)
Resilience × IME		−0.037
		(−1.445)
Controls	Yes	Yes
Industry/Year	Yes	Yes
Constant	0.544 ***	0.537 **
	(2.599)	(2.567)
N	22,845	22,845
Adj. R2	0.028	0.028

Note: **, and *** indicate significance at the 0.05, and 0.01 levels, respectively.

show that the regression coefficients of all interaction terms are not significant, indicating that there is no strong interactive effect between the different mediator variables. This suggests that the mediating paths are relatively independent within the framework of this study. Therefore, it is reasonable to treat the mediator variables as independent mechanisms for further analysis, systematically revealing how green transformation impacts the bullwhip effect through different paths and providing more detailed empirical evidence.

4.6. Heterogeneity Analysis

In this section, the study further discusses the potential heterogeneity results based on differences in corporate characteristics.

First, this study examined whether ownership structure affects the relationship between green transformation and the bullwhip effect. Specifically, we introduced an interaction term, GT × State, where State is a dummy variable equal to 1 for state-owned enterprises, and 0 is otherwise. Column (1) of

Table 13. Company ownership nature.

Variables	(1)	(2)	(3)	(4)	(5)
	BWE	BWE	BWE	BWE	BWE
	Total Sample	Large-Scale Enterprises	Small-Scale Enterprises	High Government Subsidies	Low Government Subsidies
GT	−0.050 ***	−0.050 ***	−0.046 **	−0.043 ***	−0.056 ***
	(−5.252)	(−4.829)	(−2.269)	(−3.387)	(−3.088)
State	−0.002	−0.015	−0.002	−0.012	−0.008
	(−0.124)	(−0.738)	(−0.072)	(−0.494)	(−0.403)
GT × State	0.021 *	0.023 *	0.002	−0.024	0.042 **
	(1.713)	(1.769)	(0.055)	(−1.357)	(2.036)
Controls	Yes	Yes	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes	Yes	Yes
Constant	0.664 ***	1.244 ***	0.272	−0.192	1.013 ***
	(3.274)	(4.628)	(0.781)	(−0.584)	(3.488)
N	22,845	11,424	11,421	10,029	10,028
Adj. R2	0.025	0.026	0.026	0.030	0.028

Note: *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively. This study uses the median of scale enterprises and government subsidies for grouping. Scale enterprises are represented by the natural logarithm of total employees. Government subsidies are represented by the natural logarithm of government subsidies, excluding samples with zero government subsidies.

shows that the impact of green transformation (GT) on mitigating the bullwhip effect varies across ownership types. The negative coefficient of GT suggests that, overall, green transformation helps reduce the bullwhip effect. However, the significantly positive coefficient of GT*State indicates that the marginal effect is weaker for state-owned enterprises compared to non-state-owned ones.

This difference may stem from the unique characteristics of state-owned enterprises, including more complex organizational structures, longer decision-making chains, and weaker market competition. These factors slow the implementation of green transformation and diminish its effectiveness in mitigating the bullwhip effect. Moreover, the reliance on external support reduces internal motivation for transformation.

To further investigate the role of ownership structure, the sample was divided by median employee size and government subsidy levels. In the large enterprise group (Column 2), the coefficient of GT*State remains significantly positive, indicating that green transformation has a weaker impact on the bullwhip effect in large state-owned enterprises. This is likely due to their complex structures and limited competition pressure, which hinder the rapid adoption of transformation measures. In contrast, in the small enterprise group (Column 3), the coefficient is not significant. Smaller firms tend to be more agile, with flexible decision-making processes that facilitate quicker and more responsive green transformation.

Columns (4) and (5) examine the moderating effect of government subsidies. In the low-subsidy group, the coefficient of GT*State is significantly positive, suggesting that green transformation is less effective in state-owned enterprises under financial constraints. Limited subsidies reduce their ability to implement green measures efficiently, making them more dependent on administrative channels rather than market-driven incentives. Conversely, in the high-subsidy group, the interaction term is not significant. Ample subsidies ease financial pressures and support smoother implementation, allowing both state-owned and non-state-owned enterprises to benefit more equally from green transformation.

Next, this study explored whether industry characteristics influence the relationship between green transformation and the bullwhip effect, by introducing the interaction term GT*Capital_Ind. Here, Capital_Ind is a dummy variable equal to 1 for capital-intensive industries and 0 otherwise. Column (1) of

Table 14. Industry nature.

Variables	(1)	(2)	(3)	(4)	(5)
	BWE	BWE	BWE	BWE	BWE
	Total Sample	High Product Market Development	Low Product Market Development	High Regulatory Pressure	Low Regulatory Pressure
GT	−0.045 ***	−0.063 ***	−0.037 ***	−0.070 ***	−0.036 ***
	(−6.592)	(−6.232)	(−3.968)	(−6.100)	(−4.406)
Capital_Ind	0.028	0.095 ***	−0.036	−0.045	0.049 **
	(1.516)	(3.458)	(−1.460)	(−1.213)	(2.337)
GT × Capital_Ind	0.035 **	0.038	0.041 **	0.019	0.036 **
	(2.355)	(1.234)	(2.355)	(0.690)	(2.096)
Controls	Yes	Yes	Yes	Yes	Yes
Industry/Year	Yes	Yes	Yes	Yes	Yes
Constant	0.694 ***	0.410	0.937 ***	0.878 **	0.702 ***
	(3.476)	(1.389)	(3.388)	(2.037)	(3.061)
N	22,845	11,054	11,791	5569	17,276
Adj. R2	0.025	0.027	0.028	0.025	0.025

Note: **, and *** indicate significance at the 0.05, and 0.01 levels, respectively. This study groups the sample based on the median of regional product market development and regional environmental regulatory pressure. The level of product market development is derived from the China Provincial Marketization Index Database. Regulatory pressure is measured by the frequency of terms related to environmental regulation in provincial government work reports.

shows that the impact of green transformation (GT) on the bullwhip effect varies significantly across industries. The negative coefficient of GT confirms that green transformation helps mitigate the bullwhip effect overall. However, the significantly positive coefficient of GT*Capital_Ind indicates that this effect is weaker in capital-intensive industries.

This may be attributed to the higher transformation costs faced by capital-intensive industries, due to large fixed investments and operational rigidity, which reduce the effectiveness of green transformation in mitigating supply-chain volatility.

To further examine this relationship, the sample was divided based on the median levels of product market development and environmental regulatory pressure. As shown in columns (2) and (3) of Table 14, in regions with less developed product markets, the coefficient of GT*Capital_Ind remains significantly positive. This may be because such regions lack mature industrial and supply-chain systems, resulting in limited infrastructure and technical support for green transformation. Consequently, the process becomes more complex and uncertain, which amplifies the bullwhip effect in capital-intensive industries.

Columns (4) and (5) present results based on environmental regulatory pressure. In regions with low regulatory pressure, the coefficient of GT*Capital_Ind is significantly positive, suggesting that, in the absence of strong external incentives, capital-intensive enterprises are less motivated to pursue effective green transformation. This slower pace may worsen supply-chain instability and exacerbate the bullwhip effect. In contrast, in regions with high regulatory pressure, the interaction term is not significant. Strong regulation appears to encourage more consistent and proactive green transformation, aligning the behavior of capital-intensive firms with sustainability goals and mitigating adverse effects on the supply chain.

5. Conclusions and Suggestion

5.1. Discussion

This study enhances the understanding of the impact of corporate green transformation on supply-chain dynamics, specifically focusing on its role in mitigating the bullwhip effect. While much of the existing literature has explored the causes and mitigation strategies of the bullwhip effect, this piece of research is one of the few to examine the potential stabilizing role of green transformation. The findings indicate that green transformation, as proxied by green invention patents, can reduce the bullwhip effect by improving coordination, enhancing the efficiency of information transmission, and fostering more adaptive supply-chain responses under uncertain conditions.

The study identifies three key mediating mechanisms through which green transformation influences the bullwhip effect: improved information sharing, enhanced organizational resilience, and more efficient management practices. These mechanisms operate independently, allowing firms to target specific aspects of green transformation to strengthen their supply-chain stability. This extends the existing literature, which has largely concentrated on the economic or reputational benefits of green transformation, by emphasizing its role in fostering systemic coordination within supply chains.

Additionally, the research highlights the importance of contextual factors, particularly the moderating effects of ownership type and capital intensity. The results suggest that the effectiveness of green transformation varies across different types of organizations. State-owned enterprises and capital-intensive firms, for instance, may be better equipped to leverage green innovations due to greater institutional resources and absorptive capacities. These insights provide a more nuanced view of how organizational context influences the strategic value of green transformation, enriching the ongoing discourse on heterogeneity in environmental practices.

From a practical standpoint, the findings offer valuable implications for both corporate managers and policymakers. Enterprises, particularly those in complex supply networks, should view green transformation not only as a compliance measure but as a strategic tool to reduce supply-chain volatility. Policymakers should focus on providing targeted incentives and infrastructure to support the adoption of green technologies, particularly for firms that face resource constraints, ensuring that green transformation can be effectively scaled across different industries.

In conclusion, this study establishes green transformation as a key factor in mitigating the bullwhip effect and stabilizing supply chains. The research contributes to the growing body of knowledge on sustainable supply-chain management, underscoring the importance of integrating environmental strategies with operational efficiency in future supply chain practices.

5.2. Theoretical Contributions

This study explores the relationship between corporate green transformation and the bullwhip effect from a novel perspective. The existing literature primarily focuses on the impact of green transformation on corporate economic performance and high-quality development [47,48,49], yet relatively few studies examine how green transformation influences the bullwhip effect and its underlying mechanisms. Based on data from Chinese A-share listed companies from 2008 to 2022, this study employed a multiple linear regression model for empirical analysis. The regression results indicate that, for each unit increase in corporate green transformation, the bullwhip effect significantly decreases by 0.073 units. This finding remains robust across a series of robustness tests, including the instrumental variable approach, propensity score matching, and the Heckman selection model, further strengthening the credibility of the causal inference. This discovery not only confirms the positive role of green transformation in enhancing corporate environmental performance but also unveils its profound implications for supply-chain management, expanding the research perspective at the intersection of environmental sustainability and supply-chain dynamics.

Furthermore, to gain deeper insights into the mechanisms through which corporate green transformation mitigates the bullwhip effect, this study conducted a pathway analysis and identified three main channels: (1) enhanced information sharing, (2) increased organizational resilience, and (3) improved management quality. The results demonstrate that corporate green transformation fosters information sharing between firms and their supply-chain partners, effectively reducing supply-chain fluctuations caused by information asymmetry. Additionally, green transformation enhances organizational resilience and optimizes corporate management practices, further stabilizing the supply chain and improving operational efficiency. To verify the independence of these mediating variables, this study introduced interaction terms among them in the regression analysis. The results show that all interaction terms are statistically insignificant, suggesting that there is no significant synergy among different mediators, thereby supporting the relative independence of each mediating pathway.

This study makes several theoretical contributions. First, this study expands the scope of green transformation research by extending its impact to the field of supply-chain management, providing robust empirical evidence. Second, it explicitly reveals the mechanisms through which green transformation alleviates the bullwhip effect via information sharing, management-practice optimization, and organizational-resilience enhancement, enriching the intersection of green management and supply-chain dynamics research. Finally, by incorporating multiple robustness tests, this study enhances the credibility of its findings and integrates statistical results with theoretical contributions, offering valuable insights for policymakers and corporate managers in formulating more effective sustainability and supply-chain management strategies.

5.3. Managerial Implications

This study provides practical guidance for policymakers and corporate managers. The findings indicate that corporate green transformation not only enhances environmental reputation but also plays a crucial role in improving supply-chain efficiency and stability. Companies should recognize that green transformation is not merely about fulfilling environmental compliance requirements or enhancing brand image; it is a strategic initiative essential for optimizing supply-chain management and strengthening overall operational resilience.

Empirical evidence demonstrates that, for every unit increase in the corporate green transformation index, the bullwhip effect decreases by 0.073 units. This result clearly illustrates the positive impact of green transformation on supply-chain stability. Therefore, companies should integrate green transformation into their core strategic planning, embedding it into top-level strategy design and ensuring its incorporation across all aspects of corporate operations.

For state-owned enterprises (SOEs), the marginal impact of green transition on the bullwhip effect is generally lower compared to non-state-owned enterprises (NSOEs), mainly due to unique organizational structures, decision-making mechanisms, and market environments. Therefore, the government should actively promote internal management reforms within SOEs, particularly large-scale enterprises. These reforms should include streamlining decision-making hierarchies, eliminating unnecessary intermediaries, and establishing dedicated green transition committees led by senior executives. Furthermore, introducing market-competition mechanisms and performance-evaluation metrics will incentivize SOEs to accelerate their green transition efforts.

Regarding government subsidies, policies should be optimized to offer targeted subsidies to SOEs with limited financial support, ensuring that funds are directed toward green technology R&D and supply-chain green transformation. Additionally, tax incentives should be implemented post-policy to ease the financial burden on companies. For SOEs receiving substantial subsidies, the government should establish a financial monitoring platform to ensure that funds are used exclusively for green transition projects.

In mature markets, capital-intensive firms should accelerate their green transition by increasing investments in green technology R&D to improve the market competitiveness of green products. Firms operating under low environmental regulatory pressures should integrate sustainable development principles into their core strategies. In contrast, companies facing high environmental regulatory pressures should seize the opportunity to enhance green supply-chain management by integrating green standards across all stages of production, from raw material procurement to product sales. This approach will enhance corporate environmental reputation and competitive advantage.

To further promote the green transformation of enterprises, the government should intensify policy support, particularly by establishing green transformation funds, providing subsidies for green technology research and development, and offering tax incentives to help enterprises, overcome financial barriers, and accelerate the innovation and application of green technologies. Additionally, the government should establish and promote unified green industry standards, advance the construction of green certification systems, and provide enterprises with clear green development directions and market access thresholds. In the financial sector, the government can broaden the financing channels for enterprises’ green transformation by issuing green bonds, creating green investment funds, and using other means. At the same time, there should be enhanced supervision and evaluation of green transformation policies to ensure effective implementation and timely adjustments based on practical outcomes. Finally, the government should strengthen public awareness campaigns and educational initiatives on green transformation, enhance the green awareness of enterprises and society, and encourage the participation of the entire society in building a green economy.

5.4. Limitations and Future Research

Although this study yields valuable insights, it inevitably has certain limitations.

First, this research focuses solely on Chinese A-share listed companies, which may limit the generalizability of the findings. Different countries and regions have unique economic conditions and regulatory environments, and the characteristics of Chinese A-share listed companies may not fully reflect the situation in other areas. Therefore, future research could consider expanding the scope of the study to include companies from other regions or conduct cross-national comparative studies to more comprehensively assess the global applicability of the findings. Second, this study employs a multiple linear regression model to analyze the relationship between green transformation and the bullwhip effect. However, this relationship may be influenced by some unobserved factors, such as external market conditions. Future studies could incorporate interdisciplinary econometric methods to delve into these potential factors, thereby providing a more precise understanding of the underlying mechanisms linking green transformation and the bullwhip effect. Third, environmental policies and supply-chain dynamics are constantly evolving. This study primarily focuses on the current state; however, future research could further explore the long-term impact of green transformation on supply-chain performance. Such studies would be better suited to adapt to the changing external environment and would provide more forward-looking insights for corporate decision-making and policy formulation.