Can New Energy Vehicle Promotion Policy Enhance Firm’s Supply Chain Resilience? Evidence from China’s Automotive Industry

Abstract

Whether the New Energy Vehicle Promotion Policy (NEVPP) enhances supply chain resilience is pivotal to China’s green transition and global industrial security. Using data on A-share listed automobile manufacturers from 2012 to 2024, this study employs a multi-period difference-in-differences approach to identify the policy’s impact. Results show that NEVPP significantly strengthens supply chain resilience, and the findings remain robust across alternative specifications. Mechanism analysis reveals that the policy raises managerial attention, eases financing constraints, and stimulates technological innovation, thereby enhancing resilience through managerial, financial, and technological channels. Heterogeneity analysis by ownership, geography, R&D intensity, analyst coverage, and institutional ownership shows that the effect is stronger for state-owned enterprises, firms in central and western regions, low-R&D firms, those without analyst coverage, those with high analyst attention, and firms with low institutional ownership. This study provides firm-level evidence on the economic consequences of NEVPP, advances understanding of industrial policy and corporate resilience, and offers policy implications for supporting the global energy transition and safeguarding supply chain stability.

1. Introduction

The global economy has entered a new stage of structural instability. The interaction of the COVID-19 pandemic, geopolitical conflicts, extreme climate events, and trade protectionism has severely disrupted global industrial and supply chains [1,2]. These systemic shocks spread through the “ripple effect,” exposing the vulnerabilities of highly optimized and globally integrated supply chains. Consequently, the risks of industrial disruptions and supply imbalances have heightened [3]. Recent indicators confirm the growing severity of these risks. For instance, the Global Supply Chain Volatility Index, jointly released by Global Excellence Partners and S&P Global, fell to −0.45 in February 2025, its lowest level since July 2023, signaling rising risks of capacity underutilization. A report by Gartner, a leading global research and consulting firm, shows that only 29% of supply chain organizations possess key resilience capabilities, with most firms insufficiently prepared for disruptions [4]. Increasingly frequent and severe disruptions have thus become the “new normal,” inflicting substantial economic losses rather than occasional setbacks. Building supply chain resilience, defined as the capacity to withstand shocks and recover quickly, has therefore shifted from a matter of risk management to a strategic imperative for corporate survival, industrial development, and national economic security [5].

Environmental challenges further underscore the urgency of resilience. Conventional fuel vehicles impose significant costs on society and ecosystems through carbon emissions, air pollution, and fossil energy dependence, making carbon control a global consensus [6,7]. As a strategic substitute, new energy vehicles (NEVs) have become a key driver of the global green transition, reducing fossil fuel dependence and substantially cutting emissions. According to the IEA’s Global EV Outlook 2025, global NEV sales exceeded 17 million units in 2024, surpassing 20% of total automobile sales for the first time. Notably, China contributed nearly two-thirds of this market share [8]. This underscores the strategic role of the NEV industry in advancing global low-carbon development. However, its supply chain remains fragile. Heavy reliance on concentrated raw materials (lithium, cobalt, nickel) and critical components (batteries, motors) leaves the industry vulnerable to price volatility, capacity bottlenecks, and geopolitical risks [9]. To withstand such shocks, firms require strong adaptability and recovery capacity. In this study, supply chain resilience is defined as a firm’s dynamic capability to withstand external shocks, maintain core functions, and recover quickly to a stable state through resource reallocation and structural optimization [10]. Strengthening supply chain resilience has therefore become essential for the sustainable growth of the NEV industry.

Given this global context, China’s role becomes particularly critical. As one of the world’s largest carbon emitters and the largest market for NEVs, China holds a strategic position in global energy transition and climate governance. To address worsening climate challenges, China has committed to carbon peaking by 2030 and carbon neutrality by 2060. Guided by this national strategy, the transportation sector has been prioritized for decarbonization due to its high energy consumption and emissions. Consequently, NEV development has been identified as the core pathway for this transition [11]. To promote the adoption of NEVs, since 2016, the Ministry of Industry and Information Technology (MIIT) has implemented the “Catalogue of Recommended Models for the Promotion and Application of New Energy Vehicles” (hereafter, “Recommended Catalogue”) [12]. This policy has standardized market entry, stabilized supply chains, and laid the institutional foundation for the rapid growth of the NEV industry. Driven by both policy and market forces, China’s NEV industry has continued to expand. According to statistics from the China Association of Automobile Manufacturers (CAAM), NEV production and sales reached 12.888 million and 12.866 million units, respectively, in 2024, marking year-on-year increases of 34.4% and 35.5%. China has maintained its global leadership in this sector for ten consecutive years, with NEVs accounting for 40.9% of total automobile sales, 9.3 percentage points higher than in 2023 [13].

Academic research on NEV policies has deepened alongside industry growth. Existing literature primarily focuses on how these policies affect market scale and technological innovation. Scholars generally classify NEV policies into fiscal subsidies, traffic control, and technical standards [14,15]. Extensive studies have demonstrated that these policies significantly improve firm financial performance and stimulate R&D efficiency [16,17]. However, despite the growing body of literature, the relationship between industrial policy and supply chain resilience remains underexplored. Most studies view policy effects through the lens of profitability or patent output, often overlooking how policy interventions shape a firm’s ability to resist supply risks. Only a few recent studies have touched upon this area. Two studies are particularly relevant to this research. Li et al. (2024) utilized monthly sales data to demonstrate that fiscal subsidies strengthen consumption resilience [18]. Meanwhile, Li and Li (2025) found that subsidy phase-outs significantly reduced firms’ organizational resilience, a decline partly driven by increased financing constraints [19]. Nevertheless, there is still a lack of empirical evidence regarding whether and how the Recommended Catalogue affects the supply chain resilience of manufacturing firms.

As a representative measure of NEVPP, the “Recommended Catalogue” plays a crucial role in stabilizing supply chains and ensuring sustainable industry development [20]. However, its specific impact on the supply chain resilience of automobile manufacturing firms remains uninvestigated. This paper addresses this gap by examining the effect of NEVPP on the supply chain resilience of A-share listed automobile manufacturing firms in China during 2012–2024. Using a multi-period difference-in-differences (DID) model, we find that NEVPP significantly enhances resilience. Mechanism analysis shows that the policy strengthens resilience by increasing managerial attention, alleviating financing constraints, and promoting technological innovation. In doing so, the policy exerts distinct managerial, resource, and technological empowerment effects. Heterogeneity tests reveal that the effect is stronger for state-owned enterprises, firms in central and western regions, and those with lower R&D investment. The policy also exerts positive effects for firms with no and high analyst coverage, but not for those with low coverage. Similarly, it benefits firms with low and high institutional ownership, but not those with medium ownership.

This study makes three marginal contributions to the literature. First, it extends the research boundaries of industrial policy evaluation from “performance enhancement” to “resilience building.” While prior studies have extensively examined the effects of NEV policies on innovation output and market scale [21,22], the nexus between industrial policy and supply chain stability remains under-researched. This paper provides micro-level evidence that policy interventions can effectively fortify supply chain resilience. Second, it constructs a cohesive theoretical framework integrating institutional theory and dynamic capabilities theory. By conceptualizing the NEVPP as an institutional trigger that activates firms’ sensing, seizing, and transforming capabilities, this study bridges the gap between macro-level policy shocks and micro-level organizational responses, enriching the application of dynamic capabilities theory in the field of policy analysis. Third, it unpacks the “black box” of policy transmission mechanisms. Unlike studies that focus solely on financial channels, this paper incorporates managerial attention into the analytical framework. It uncovers a multi-dimensional transmission path of “managerial empowerment, resource empowerment, and technological empowerment,” offering a more granular understanding of how policies reshape corporate resilience.

The remainder of this paper is organized as follows. Section 2 introduces the policy background, reviews the relevant literature, and develops the hypotheses. Section 3 presents the model specification, key variables, and data sources. Section 4 reports the empirical results and robustness checks. Section 5 discusses the mechanism tests and heterogeneity analysis. Finally, Section 6 concludes with the main findings, policy implications, and limitations of this study.

2. Policy Background, Theoretical Analysis, and Hypotheses Development

2.1. Policy Background

From a policy evolution perspective, China’s NEV industry policy has gradually shifted from “market promotion” to “technological breakthroughs” and, more recently, to “supply chain restructuring,” with the underlying logic consistently targeting both “industrial upgrading” and “risk mitigation.” In 2016, the MIIT issued the “Catalogue of Recommended Models for the Promotion and Application of New Energy Vehicles”, establishing a market access mechanism through “technical standards plus fiscal subsidies” [12]. For the first time, key metrics such as battery energy density, driving range, and safety performance were directly linked to the level of subsidies. This policy not only raised entry barriers for the industry but also pushed firms to break free from their path dependence on internal-combustion-to-electric conversions, marking the transition of NEVs from demonstration projects to large-scale commercialization.

Subsequently, the “2017 Action Plan for Promoting the Development of the Automotive Power Battery Industry” did not alter this technology-oriented orientation [23]; instead, building on the established standards in the Recommended Catalogue, it reinforced the incentive logic of “performance for support,” motivating firms to enhance endogenous competitiveness through battery structural optimization and vehicle platform-based design. By 2020, the issuance of the “New Energy Vehicle Industry Development Plan (2021–2035)” explicitly called for the construction of an independent, controllable, secure, and efficient supply chain system, elevating the policy focus from mere market expansion to industrial security and high-quality development at a strategic level [24].

Against the backdrop of global supply chain disruptions, policy priorities gradually tilted toward supply chain resilience. In 2021, MIIT organized a special campaign to ensure supply and stabilize industrial chains, creating platforms to match supply and demand along the industry chain, alleviating challenges such as chip shortages, rising raw material prices, and logistics bottlenecks [25]. Simultaneously, efforts were made to accelerate domestic lithium resource development and battery recycling, laying the foundation for a resource security system centered on domestic circulation. Entering a new stage of global competition, policies began to emphasize outward deployment and international standard-setting. Afterwards, China accelerated the integration of domestically led charging interface and battery safety standards into the international standard system, thereby strengthening its influence in global NEV supply chain rule-making. In 2024, t In 2024, the government issued the “Guiding Opinions on Quality Infrastructure Assisting the Coordinated Improvement of Quality in Industrial and Supply Chains”, focusing on strategic industries such as new energy vehicles, to promote the resilience of industrial and supply chains, thereby enhancing the ability to enhance risk resistance amid geopolitical tensions and resource volatility [26].

2.2. Theoretical Analysis and Hypotheses Development

To provide a cohesive theoretical basis for this study, we adopt dynamic capabilities theory as the overarching framework, while integrating perspectives from institutional theory, the attention-based view, signaling theory, and the Porter hypothesis. According to the dynamic capabilities theory, organizational resilience relies on three micro-foundations, namely sensing opportunities and threats, seizing resources, and reconfiguring assets. In the context of this study, institutional theory explains the external coercive and normative pressures exerted by the NEVPP, which acts as the environmental trigger. Facing this trigger, firms must activate their dynamic capabilities. Specifically, the attention-based view explains how firms sense policy signals; Signaling theory explains how firms seize financial resources to alleviate constraints; and the Porter hypothesis elucidates how firms transform through technological innovation. This integrated framework creates a logical chain proceeding from institutional stimulus to dynamic capability activation, and ultimately to supply chain resilience.

2.2.1. The Direct Effect of NEVPP

According to institutional theory, firms’ development is shaped not only by market competition mechanisms but also by constraints and guidance from the external institutional environment [27]. As an essential component of this environment, policy influences firms’ strategic choices and operational models by regulating behavior and directing resource allocation [28,29]. For NEV manufacturers, the role of policy is particularly salient. Given the industry’s characteristics of high technological barriers, substantial investment requirements, and a long value chain, market forces alone are often insufficient to achieve rapid industrial maturity. Government interventions such as subsidies, tax incentives, infrastructure investment, and market access regulation can effectively reduce firms’ operational risks and accelerate industrial development [20]. Specifically, NEVPP not only stimulates consumer demand and expands market scale but also enhances the resilience of automotive firms’ supply chains.

First, NEVPP strengthens firms’ risk resistance by providing fiscal subsidies and support for technological innovation, enabling them to maintain stable operations amid raw material price fluctuations or external shocks. Such policy-induced incentives and institutional pressures, by enforcing production standards and encouraging compliance [30], improve supply chain stability and shock absorption capacity [31]. Second, supply chain resilience emphasizes the ability to recover and reconfigure in the face of disruptions [32]. On the one hand, NEVPP promotes industry standardization and infrastructure development, which facilitates faster recovery from interruptions. On the other hand, under the pressure of NEVPP, firms seeking inclusion in the Recommended Catalogue, eligibility for subsidies, and broader market legitimacy [33] often optimize their supply chain networks and strengthen cooperation with upstream suppliers of critical components, thereby enhancing adaptability and recovery capability. Furthermore, NEVPP highlights green transition and low-carbon objectives, which provide a long-term strategic orientation for firms’ supply chains and improve coordination and stability across the industry [34]. Therefore, from the perspectives of risk resistance, recovery capacity, and sustainable development, NEVPP significantly enhances supply chain resilience. To formally establish this baseline causal relationship, we propose the following hypothesis:

H1: 

NEVPP significantly improves the supply chain resilience of automotive manufacturing firms.

2.2.2. The Mechanisms of NEVPP

Drawing on dynamic capabilities theory [35], supply chain resilience refers to a firm’s ability to sustain and enhance operational functionality under environmental uncertainty and external shocks through preparedness, rapid response, and recovery [36]. Drawing on the unifying framework of dynamic capabilities theory, we analyze how the NEVPP enhances supply chain resilience through three specific mechanisms, corresponding to the capabilities of sensing, seizing, and transforming. First, regarding sensing capability, the policy redirects managerial attention toward low-carbon transformation. According to the attention-based view, this allows firms to anticipate opportunities and risks, enhancing preparedness. Second, regarding seizing capability, the policy functions through a signaling mechanism to alleviate financing constraints. This enables firms to mobilize financial resources rapidly to buffer against shocks. Third, regarding transforming capability, consistent with the Porter hypothesis, the policy incentivizes technological innovation. This drives firms to reconfigure their supply chain systems through technical substitution and process optimization. Consequently, we propose that the policy strengthens resilience through management empowerment, resource empowerment, and technological empowerment.

NEVPP can exert a management empowerment effect by improving managerial attention, thus enhancing supply chain resilience. According to the attention-based view (ABV), strategic decisions and organizational behaviors are shaped by managerial attention, which is inherently limited in scope. The distribution of this scarce resource dictates an organization’s responsiveness to key issues [37]. As an influential external signal, policy reshapes the attention structure at the top management level, driving changes in resource allocation and decision-making [38]. As a key policy tool issued by the Chinese government, NEVPP guides management’s attention allocation through policy signals, influencing strategic decisions related to supply chains [39].

First, the “subsidy eligibility signal” of NEVPP shifts management’s attention from the “price war in traditional internal combustion engine vehicles” to “how to have more models included in the Recommended Catalogue to receive policy benefits.” By identifying potential opportunities from environmental changes, firms can adjust competition strategies and thereby influence strategic direction and resource allocation [40,41]. Second, the clear “technical standard threshold” directs management’s attention from short-term technical compliance to long-term technical roadmap planning, accelerating innovation outputs [42] and new product development [43]. It also promotes technological collaboration with supply chain partners [44], enhancing supply chain flexibility and risk resistance, and helping firms remain competitive in volatile markets. Lastly, NEVPP sends a signal of “full product coverage and multi-scenario application,” expanding management’s focus from single-vehicle production to building industry chain synergy and ecosystems. This shift drives a more integrated supply chain model in which managers strengthen cooperation with upstream suppliers and downstream partners, thereby strengthening supply chain coordination, interconnectivity, and adaptability to market changes [45]. Therefore, acting as a multidimensional policy signal, the NEVPP shifts managerial attention towards resilience-enhancing capabilities within the supply chain network. Accordingly, we propose the following hypothesis:

H2: 

NEVPP enhances the supply chain resilience of automotive manufacturing firms by improving managerial attention.

NEVPP can generate a resource empowerment effect by alleviating financing constraints, thereby strengthening firms’ supply chain resilience. According to signaling theory [46], firms reduce information asymmetry by transmitting signals of their qualifications and capabilities to external stakeholders. Government support exerts a significant signaling effect, enhancing firms’ creditworthiness and encouraging commercial banks to extend credit [47]. At the same time, building supply chain resilience requires continuous capital investment, as the resource-capability configuration underpinning resilience depends on sufficient financial support [32]. Thus, the development of resilience relies on external funding from NEVPP. First, NEVPP provides firms listed in Recommended Catalogue with a legitimacy endorsement, lowering perceived risk for financial institutions and enabling stable funding for long-term supply chain infrastructure [48,49], thereby improving risk resistance [50]. Second, its technical standard screening mechanism conveys quality signals to the market, mitigating financing premiums caused by information asymmetry, enhancing capital availability for upstream and downstream partners, and reducing the risk of collaboration disruptions triggered by short-term liquidity shortages [51], thereby improving financing efficiency [52]. Finally, subsidies and preferential credit offered under NEVPP prioritize firms’ investment needs through low-cost financing, directly improving cash flow and credit standing, reducing reliance on costly external capital [53], and safeguarding the continuity of core supply chain R&D and supplier cooperation, thus preventing resource volatility from undermining supply chain stability [54]. Accordingly, we propose the following hypothesis:

H3: 

NEVPP enhances the supply chain resilience of automotive manufacturers by easing financing constraints.

NEVPP can generate a technological empowerment effect by fostering innovation, thereby enhancing firms’ supply chain resilience. According to the Porter hypothesis [55], well-designed environmental regulations stimulate technological innovation, allowing firms to offset compliance costs through improvements in technology and efficiency, ultimately strengthening competitiveness. As a representative environmental regulation tool, NEVPP creates a “comply or exit” mechanism by imposing mandatory technical indicators such as battery range, which compels firms to reallocate R&D resources toward compliant technologies [56], lock in long-term technological roadmaps, build innovation-based advantages, and increase patent output [57,58]. In addition, firms whose models are listed in the Recommended Catalogue receive financial subsidies, reducing R&D investment costs and easing funding pressures, thereby facilitating green technology innovation [59], improving production and supply efficiency, and enhancing the resilience and competitiveness of supply chains in the face of shocks [60,61]. Moreover, NEVPP and its associated regulatory requirements mandate automakers to connect with the national monitoring platform for new energy vehicles, enabling real-time data sharing and strengthening compliance in data reporting, which drives process innovation and digital capability development [62]. This accelerates supply chain visibility, coordination, and reconfiguration, thereby improving resilience and development quality for automotive manufacturers [63,64]. Accordingly, we propose the following hypothesis:

H4: 

NEVPP enhances the supply chain resilience of automotive manufacturers by stimulating technological innovation.

3. Research Design

3.1. Model Specification

Between 2016 and 2022, the MIIT issued 78 batches of the Recommended Catalogue. This study takes the Recommended Catalogue as a representative instrument of the NEVPP and employs a difference-in-differences (DID) approach, treating the release of Recommended Catalogue batches as a quasi-natural experiment to identify how NEVPP affects the supply chain resilience of firms that are included in the Recommended Catalogue. Following the methodology of Beck et al. (2010) [65], we construct the following DID model, as shown in Model (1):

$$Scor{e}_{i,t}={\beta }_0+{\beta }_{1}NEVP{P}_{i,t}+{\beta }_{2}Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(1)

where subscript i denotes firm and t denotes year. The dependent variable Score measures the level of supply chain resilience. $NEVP{P}_{i,t}$ is a dummy variable defined as the interaction between $Trea{t}_i$ and ${Post}_t$. $Trea{t}_i$ is a dummy variable used to distinguish the treatment group from the control group, taking the value of 1 if firm i is included in the Recommended Catalogue and 0 otherwise. ${Post}_t$ is a time dummy variable that equals 1 in the year a firm is first included in the Recommended Catalogue and in all subsequent years, and 0 for the years prior to inclusion. The coefficient ${\beta }_1$captures the effect of NEVPP on firms’ supply chain resilience. A significantly positive ${\beta }_1$indicates that NEVPP enhances firms’ supply chain resilience. $Control{s}_{i,t}$ represents a set of control variables, ${\mu }_i$ and ${\lambda }_t$ denote firm and year fixed effects, respectively, and ${\epsilon }_{i,t}$ is the random error term. To mitigate potential heteroskedasticity, standard errors are clustered at the firm level.

3.2. Variable Selection and Measurement

3.2.1. Dependent Variable

In this study, supply chain resilience is the dependent variable. Following established studies [36,66,67,68], we conceptualize supply-chain resilience as the ability to absorb shocks, maintain critical functions, and recover or adapt to a more desirable post-disruption state. Directly observing such dynamic responses at the firm level is difficult in panel data. We therefore construct an index of resilience-related capabilities that support resistance and recovery [69,70], encompassing (i) network exposure to partner concentration, (ii) operational agility, and (iii) financial buffer capacity.

First, at the network vulnerability level, resilience derives from the robustness of firms’ supply chain structures. Effective collaboration is essential to mitigating disruptions and accelerating recovery, with network position and supplier–distributor ties central to resilience [71]. A highly concentrated customer or supplier base magnifies the impact of a single partner’s failure and creates exposure to cascading risks, thereby undermining resilience [72]. Conversely, diversified supply chains, heterogeneous supplier portfolios, and multiple partnerships help disperse risks associated with disruptions and significantly enhance recovery capabilities [67]. Drawing on relevant literature, customer concentration and supplier concentration are used to measure firms’ structural risks, with the share of revenue from the top five customers representing customer concentration and the share of procurement from the top five suppliers representing supplier concentration. Higher values indicate less stable supply chain relationships and weaker resistance to external shocks.

Second, beyond external network structures, firms’ operational agility and efficiency directly reflect their ability to withstand disruptions. This study employs the inventory turnover ratio and returns on assets to capture this dimension. The inventory turnover ratio reflects the efficiency of inventory management and sales; a higher turnover indicates faster conversion of inventory into sales and cash, reducing capital lock-up and inventory risk, rebalancing operating resources more rapidly, and thereby enabling firms to respond more flexibly to demand fluctuations and supply interruptions [73,74]. Return on assets measures how efficiently firms use total assets to generate profits. Firms that can utilize assets effectively and achieve rapid working capital turnover generally demonstrate stronger operational agility. Together, these two indicators represent firms’ capacity to reallocate resources and sustain core operations under supply chain disruptions, thereby enhancing higher resistance [75].

Finally, the speed of recovery after a disruption largely depends on financial soundness. Financially robust firms are better able to maintain adequate inventory levels and redundant supply chain resources to cope with uncertainty, thereby strengthening both resistance and recovery [36,76]. To measure this dimension, this study adopts the debt-to-asset ratio and return on equity (ROE). The debt-to-asset ratio is a key indicator of financial leverage and long-term solvency. A lower debt level implies greater financial flexibility and borrowing capacity, giving firms more room to finance recovery activities during crises [77,78]. ROE reflects the profitability of shareholders’ equity, and sustained high ROE allows firms to accumulate internal financial slack, absorb losses, and fund recovery efforts [79,80]. Together, these two indicators capture firms’ ability to absorb shocks and restore financing capacity under adverse conditions.

In sum, to integrate the six indicators across these three dimensions, this study applies the entropy weight method. Specifically, positive and negative indicators are first standardized to eliminate scale effects; second, weights are determined by calculating the information entropy of each indicator, where smaller entropy implies greater weight; and finally, the standardized values are linearly weighted by their corresponding weights to construct a composite index capturing supply-chain resilience-related capabilities (Score), integrating network vulnerability, operational agility, and financial buffer capacity.

3.2.2. Explanatory Variable

The core explanatory variable is NEVPP. Following Ren et al. (2024), we compile data from the Recommended Catalogue [22]. In each batch of the Recommended Catalogue, every vehicle model is explicitly linked to its producing firm, which allows us to establish a one-to-one match between models and firms. Firms included in the Recommended Catalogue are assigned to the treatment group, whereas those not included constitute the control group. For firms that appear in the Recommended Catalogue in a given year, the variable NEVPP is coded as 1 for that year and all subsequent years, and 0 otherwise. If a firm is listed multiple times across different years, the first year of inclusion is defined as the treatment year. Thus, the interaction term $NEVP{P}_{i,t}$ captures the comprehensive policy support received by the treated firms.

3.2.3. Control Variables

To mitigate potential confounding effects of other factors on firms’ supply chain resilience, we follow prior studies [81,82] and include eleven covariates as control variables. These variables comprise leverage (Lev), firm size (Size), board size (Board), CEO duality (Dual), ownership type (Soe), cash flow (Cashflow), return on equity (Roe), asset turnover ratio (Atr), ownership concentration (Top10), revenue growth rate (Growth), and firm age (Age). The definitions of the main variables are provided in Appendix A,

Table A1. Variable definitions.

Variable Name	Measurement Methods
Score	The composite index of supply chain resilience
NEVPP	If a company is included in the recommended directory in a specific year, set the variable NEVPP to 1 for that year and subsequent years; otherwise, set it to 0
Lev	The ratio of total liabilities to total assets
Size	The natural logarithm of total assets
Board	The natural logarithm of the number of board members
Dual	If the Chairman and General Manager are the same person, the value is 1; otherwise, the value is 0
Soe	For state-owned enterprises, the value is 1; otherwise, it is 0
Cashflow	The ratio of net cash flows from operating activities to total assets
Roe	The ratio of the enterprise’s net profit to shareholders’ equity
Atr	The ratio of net sales revenue to average total assets
Top10	The shareholding ratio of the top ten shareholders of the company
Growth	The ratio of the increase in revenue to the total revenue from the previous year
Age	The natural logarithm of the company’s founding year plus 1

.

3.3. Data Sources and Description

Following the 2012 industry classification issued by the China Securities Regulatory Commission, this study selects A-share listed firms in the automotive manufacturing sector during the period 2012–2024 as the sample. To enhance the reliability of the analysis, the initial sample is refined as follows: (1) Firms designated as ST or *ST are excluded because these firms face abnormal financial conditions and regulatory restrictions. Including them could distort resilience measures and confound policy effect identification; (2) Firms with missing values in key variables are removed. The final dataset comprises 1509 firm-year observations. Specifically, the data sources for the quantitative tests are categorized as follows: (1) Policy Documents: The explanatory variable (NEVPP) is manually compiled from the 78 batches of the Recommended Catalogue documents published by the MIIT. (2) Annual Financial Reports: Quantitative financial data measuring supply chain resilience and firm characteristics are sourced from firms’ audited annual reports, accessed via the CSMAR and Wind databases. (3) Management Discussion and Analysis (MD&A): The managerial attention variable is constructed by text-mining the MD&A sections of these annual reports. (4) Patent Documents: Technological innovation data are derived from patent grant filings with the China National Intellectual Property Administration (CNIPA), accessed via CSMAR. To mitigate the influence of extreme values, all continuous variables are winsorized at the 1% level on both tails.

4. Empirical Results

4.1. Descriptive Statistics

Table 1. Descriptive statistics of variables.

Variables	Observations	Mean	Std. Dev.	Min	Max
Score	1509	0.365	0.094	0.178	0.717
NEVPP	1509	0.156	0.363	0.000	1.000
Lev	1509	0.442	0.180	0.097	0.874
Size	1509	22.318	1.335	19.952	26.564
Board	1509	2.104	0.210	1.609	2.708
Dual	1509	0.328	0.470	0.000	1.000
Soe	1509	0.250	0.433	0.000	1.000
Cashflow	1509	0.054	0.057	−0.130	0.210
Roe	1509	0.055	0.130	−0.755	0.280
Atr	1509	0.652	0.248	0.215	1.454
Top10	1509	0.623	0.155	0.250	0.999
Growth	1509	0.125	0.245	−0.423	1.170
Age	1509	3.019	0.288	2.079	3.584

reports the descriptive statistics of the main variables. As shown in Table 1, the mean value of supply chain resilience is 0.365, with a maximum of 0.717 and a minimum of 0.177, indicating substantial variation in supply chain resilience across firms.

4.2. Baseline Regression

Based on the baseline model, we examine the effect of NEVPP on supply chain resilience. Controlling for firm and year fixed effects, the DID regression results are reported in

Table 2. Baseline results.

	(1)	(2)	(3)
	Without Controls	With Controls	With Lagged Controls
	Score	Score	Score
NEVPP	0.022 ***	0.033 ***	0.021 ***
	(3.422)	(5.129)	(2.885)
Constant	0.418 ***	−0.282 ***	0.0362
	(69.484)	(−2.656)	(0.295)
Observations	1509	1509	1291
Adj-R2	0.449	0.527	0.477
Controls	NO	YES	YES
Firm FE	YES	YES	YES
Year FE	YES	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbol *** denote significance at the 1% level. The number of observations is slightly reduced due to missing lagged control variables for some firms.

. Column (1) presents the estimates without any control variables, where the coefficient of the key explanatory variable NEVPP is 0.022 and significantly positive at the 1% level. To mitigate potential biases from observable firm-level factors contemporaneous with or prior to the implementation of NEVPP, Columns (2) and (3) of Table 2 incorporate matched covariates from the current and the previous period, respectively, as additional controls. The results show that under different sets of control variables, the coefficient of NEVPP remains significantly positive at the 1% level. Overall, these findings indicate that NEVPP exerts a significant positive effect on firms’ supply chain resilience, effectively enhancing resilience, which is consistent with the theoretical expectation in Hypothesis 1.

4.3. Robustness Checks

4.3.1. Dynamic Effects Analysis

The consistency of the DID estimates relies on the parallel trend assumption, which requires that in the absence of external shocks, the outcome variable would exhibit similar trends in the treatment and control groups. Prior to the implementation of the NEVPP, the trends in supply chain resilience of firms included in the Recommended Catalogue and those excluded should be consistent. Following Jacobson et al. (1993) [83] and Beck et al. (2010) [65], we employ an event-study approach and construct the dynamic DID model as follows:

$$Scor{e}_{i,t}={\alpha }_0+\sum _{k=-5}^{k=5}{\alpha }_k{NEVPP}_{i,t_0+k}+{\alpha }_{2}Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(2)

where ${NEVPP}_{i,t_0+k}$ denotes the “event” of the Recommended Catalogue and is represented by a series of dummy variables. $t_0$ denotes the first year in which firm i implements NEVPP, namely the policy implementation year, k represents the k-th year after the implementation of NEVPP. The sample spans a ten-year window around the policy, and to avoid multicollinearity, we follow Stevenson and Wolfers (2006) [84] and use the year immediately preceding NEVPP implementation as the benchmark. The control variables and fixed effects are set consistently with the baseline regression model (1).

Figure 1 plots the dynamic treatment effects with 95 percent confidence intervals. Prior to policy implementation, the estimated coefficients are statistically insignificant, suggesting no significant difference in supply chain resilience between the treatment and control groups, thereby confirming the parallel trend assumption. Starting from the second year after the policy implementation (i.e., 2018), the coefficients become significantly positive, indicating that NEVPP exerts a substantial promoting effect on firms’ supply chain resilience and that the policy impact exhibits a certain lag.

4.3.2. Placebo Test

To assess the extent to which the above results may be influenced by omitted variables or random factors, we conduct the following placebo test:

(1)

A placebo test based on a fictitious policy year. To rule out confounding from unobservable factors, we follow Topalova (2010) [85] and Ruan et al. (2025) [86] by advancing the implementation year of NEVPP by four years and replacing the actual policy year with this fictitious one to perform a counterfactual test. The results are reported in Column (1) of

Table 3. Placebo test: fictitious policy implementation time.

Variables	(1)
	Score
NEVPP_-4	0.012 (0.877)
Constant	−0.197
	(−1.214)
Observations	1509
Adj-R2	0.518
Controls	YES
Firm FE	YES
Year FE	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses.

. The estimated coefficient of the newly constructed dummy variable NEVPP_-4 is 0.012 and statistically insignificant, indicating that firms subject to NEVPP and those not subject to it do not exhibit systematic differences in the time trend of supply chain resilience. This finding corroborates the robustness of the baseline regression results.

(2)

Mixed placebo test. In the baseline regression, we control for multiple firm characteristics beyond NEVPP that may affect the treatment and control groups, but unobservable firm-year factors may still bias the estimates. To ensure robustness, we follow Ferrara et al. (2012) [87] and Li et al. (2016) [88] and randomly assign firms and years to participate in NEVPP, thereby constructing randomized experiments at both the firm and year levels. We perform 1000 random draws to enhance the reliability of the results. The outcomes of the mixed placebo test are illustrated in Figure 2, where the estimated coefficients of the fictitious interaction terms are concentrated around zero, deviating from the actual baseline coefficient of 0.0333. Moreover, the p-values of most estimated coefficients exceed 0.1. These findings suggest that serious omitted variable problems do not exist in the model specification, and the core conclusion remains robust.

4.3.3. PSM-DID

Potential endogeneity remains a concern in this study. Specifically, firms with stronger supply chain capabilities might be more likely to meet the technical standards and enter the Recommended Catalogue, potentially introducing reverse causality. However, the Catalogue primarily evaluates technical indicators such as battery density and driving range, rather than direct supply chain metrics. Nevertheless, to further mitigate selection bias and verify the causal effect, we employ the Propensity Score Matching (PSM-DID) approach. Systematic differences inevitably exist between the treatment and control groups in this study. To address the potential bias in regression results arising from sample selection, we use the control variables in baseline regression model (1) as matching criteria and apply nearest-neighbor matching, radius matching, and kernel matching methods to minimize pre-policy differences in characteristics between the two groups. Specifically, we implement the PSM-DID model to correct endogenous selection bias through sequential matching. Following Brucal et al. (2019) [89], we first conduct 1:1 nearest-neighbor matching with replacement. In addition, when the potential control sample substantially exceeds the treatment sample, a one-to-many matching strategy can reduce sampling variance at the expense of some matching precision, thereby enhancing matching power [90,91]. In view of this, we further conduct the regression by changing the matching ratio to 1:3. Columns (1) to (4) of

Table 4. The results of PSM-DID.

	(1)	(2)	(3)	(4)
	1:1 Nearest Neighbor Matching	1:3 Nearest Neighbor Matching	Radius Matching	Kernel Matching
	Score	Score	Score	Score
NEVPP	0.090 ***	0.070 **	0.039 **	0.037 **
	(4.121)	(2.197)	(2.552)	(2.466)
Constant	−0.832	−0.707	−0.545	−0.489
	(−1.524)	(−1.658)	(−1.634)	(−1.587)
Observations	209	356	698	716
Adj-R2	0.646	0.537	0.509	0.519
Controls	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbols **, and *** respectively denote significance at the 5%, and 1% levels.

report the regression results under different matching methods, where the coefficients of NEVPP are 0.090, 0.070, 0.039, and 0.037, all significantly positive at the 1% and 5% levels. These findings indicate that after mitigating endogeneity concerns, the conclusion that NEVPP enhances firms’ supply chain resilience remains robust.

4.3.4. Heterogeneous Treatment Effect Test

Given that firms in the automobile manufacturing industry are affected by NEVPP at different times, the traditional two-way fixed effects (TWFE) model may yield biased estimates of the impact of this exogenous shock on supply chain resilience due to heterogeneous treatment effects. To address this concern, we conduct the following test.

(1)

Goodman-Bacon (2021) [92] DID decomposition method. This method evaluates the extent of bias in multi-period DID estimates under TWFE. As shown in Figure 3 and

Table 5. Goodman-Bacon Decomposition Result.

Type of Comparison Group	Weight	2 × 2 DID Estimate
Treated vs. Never treated	0.926	0.021
Treated earlier vs. Later	0.012	0.003
Treated later vs. Earlier	0.018	0.020
Treated vs. Already treated	0.044	0.034
Total (TWFE DID estimate)	1	0.021

Note: This table reports the Goodman-Bacon decomposition of the two-way fixed effects DID estimator. Weights denote the relative contribution of each 2 × 2 DID comparison to the overall estimate.

, the estimated coefficients across different groups are all greater than zero, indicating that the results are not subject to negative weighting. The decomposition further reveals that the overall DID estimates are primarily driven by comparisons using never-treated firms as the control group, which account for as much as 92.6% of the weight. In contrast, comparisons where early-treated firms serve as controls, which may generate bias, contribute only 1.8% of the weight. This limited influence suggests that the estimates are not materially distorted and do not overstate the impact of NEVPP on supply chain resilience. Hence, the core conclusion of this study remains robust.

(2)

The counterfactual imputation method of Borusyak et al. (2024) [93]. This approach addresses estimation bias in the two-way fixed effects (TWFE) model by estimating group fixed effects, time fixed effects, and treatment-control group fixed effects, thereby yielding more accurate estimates. As reported in Column (1) of

Table 6. Robust Estimates.

	(1)	(2)
	DID_Imputation	CSDID
	Score	Score
NEVPP_ATT	0.032 *** (3.012)	0.156 *** (3.200)
Controls	YES	YES
Firm FE	YES	YES
Year FE	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbol *** denotes significance at the 1% level.

, after applying the imputation method to account for heterogeneous treatment effects, the results still show a significantly positive impact, confirming the robustness of the findings in this study.

(3)

The weighted difference-in-differences method developed by Callaway and Sant’Anna (2021) [94], hereafter referred to as CSDID. This approach provides unbiased estimates by comparing outcome changes between the treated and never-treated groups across two periods. Based on inverse probability weighted least squares, the results reported in Column (2) of Table 6 continue to show a significantly positive effect, and the absolute value of the estimated coefficient increases markedly relative to the baseline regression.

4.3.5. Indicator Replacement

To validate the reliability of the supply chain resilience scoring and incorporate cross-verification through independent statistical methods, the dependent variable is replaced with alternative composite indices, and the impact of NEVPP is re-estimated. In addition to the entropy weighting method, the standardized inverse-covariance weighting approach (ICW) is a commonly used method for constructing a composite index [95], and some scholars also adopt rank-based methods [96]. Beyond these two alternatives, principal component analysis (PCA) is further applied to construct a composite index [97]. The KMO statistic exceeds 0.6 and the Bartlett test of sphericity is significant, indicating that the variables are suitable for PCA. Following the criteria of retaining principal components with eigenvalues greater than 0.8 and a cumulative variance contribution above 70%, the first three components are extracted and aggregated linearly using their respective variance contributions as weights to form the composite supply chain resilience index. Columns (1)–(3) of

Table 7. Robustness checks: Alternative composite indices and clustering specifications.

	(1)	(2)	(3)	(4)	(5)
	ICW	Rank-Based	PCA	Province-Level	Province-Year Level
	Score	Score	Score	Score	Score
NEVPP	0.093 ***	0.027 ***	0.235 ***	0.033 **	0.033 *
	(4.049)	(2.823)	(6.271)	(2.262)	(1.955)
Constant	−0.035	0.202	−2.026 ***	−0.345	−0.345
	(−0.093)	(1.277)	(−3.298)	(−1.645)	(−1.660)
Observations	1509	1509	1509	1509	1509
Adj-R2	0.637	0.414	0.776	0.821	0.821
Controls	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbols *, **, and *** respectively denote significance at the 10%, 5%, and 1% levels.

report the regression results based on the alternative composite indices. Even after replacing the composite supply chain resilience index, NEVPP continues to exhibit a significantly positive effect, further corroborating the robustness of the conclusions.

4.3.6. Changing the Clustering Level

To account for potential intra-regional correlation in the error terms, the clustering level in the baseline regression model (1) is changed. Specifically, the firm-level clustering is replaced by province-level clustering and two-way clustering at the province-year level, and the regression results are re-estimated accordingly [98]. As reported in Table 7, Column (4) shows that after switching to province-level clustering, the coefficient of NEVPP is 0.033 and remains significantly positive at the 5% level. Column (5) indicates that under province-year two-way clustering, the coefficient of NEVPP is also 0.033 but significant at the 10% level. These findings demonstrate that the positive effect of NEVPP on supply chain resilience is robust to alternative clustering specifications.

4.3.7. Adjusting the Time Window

Following Ruan et al. (2025) [86], this study adopts the following approach to mitigate the influence of external events on the regression results. First, since COVID-19 caused significant disruptions in the production of Chinese automobile manufacturers, the regressions are re-estimated after excluding the 2020–2021 sample. Second, given that the extraordinary volatility in China’s stock market in 2015 may have temporarily affected the capital structure and financing behavior of listed automobile firms, the 2015 sample is excluded to control for such exogenous financial shocks. The results, reported in

Table 8. Adjusting the time window.

	(1)	(2)
	Score	Score
	Excluding 2020–2021	Excluding 2015
NEVPP	0.027 ***	0.036 ***
	(2.6994)	(2.9904)
Constant	−0.3732 *	−0.3424 *
	(−1.9664)	(−1.9081)
Observations	1212	1426
Adj-R2	0.8224	0.8211
Controls	YES	YES
Firm FE	YES	YES
Year FE	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbols * and *** respectively denote significance at the 10% and 1% levels.

, show that after excluding 2020–2021 and 2015, the coefficients of NEVPP are 0.027 and 0.036, respectively, both remaining significant at the 1% level. These findings indicate that even after accounting for external events, NEVPP continues to significantly enhance supply chain resilience.

5. Future Analyses and Tests

5.1. Mechanism Analysis

As discussed in Section 2.2, the theoretical analysis suggests that NEVPP may enhance firms’ supply chain resilience by increasing managerial attention, alleviating financing constraints, and promoting technological innovation. Whether these three mechanisms hold in practice, however, requires validation through appropriate econometric methods.

5.1.1. Managerial Attention

As indicated by the preceding theoretical analysis, NEVPP may strengthen firms’ supply chain resilience by enhancing managerial attention. Following Li (2010) [99] and Hassan et al. (2019) [100], this study constructs a managerial attention index based on word frequency analysis. Specifically, the procedure involves four steps. First, financial reports of listed automobile manufacturers are collected, and the MD&A sections are extracted, converted from PDF to TXT format, and subjected to paragraph cleaning and error correction. Second, reviewing relevant Chinese official policy documents, industry reports, and authoritative as well as foundational academic literature, a supply chain resilience keyword dictionary is developed, grounded in established theoretical frameworks and tailored to the context of China’s automobile industry and policy environment (see Appendix A.2). Third, the MD&A texts are segmented into sentences and words using Python’s jieba package (Python 3.11), and after text cleaning, keywords related to supply chain resilience are extracted from the dictionary and their frequencies calculated. Fourth, the managerial attention index is computed and standardized according to the following formula:

$${Attention}_{i,t}={\displaystyle \frac{\sum \mathrm{F}\mathrm{r}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}\mathrm{o}\mathrm{f}\mathrm{s}\mathrm{u}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{y}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{k}\mathrm{e}\mathrm{y}\mathrm{w}\mathrm{o}\mathrm{r}\mathrm{d}\mathrm{s}\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{i}\mathrm{r}\mathrm{m}i\mathrm{’}\mathrm{s}\mathrm{M}\mathrm{D}\&\mathrm{A}\mathrm{i}\mathrm{n}\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}t}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{words}\mathrm{in}\mathrm{firm}i\text{’}\mathrm{s}\mathrm{MD}\text{\&}\mathrm{A}\mathrm{in}\mathrm{year}t}}\times 1000$$

(3)

In this context, ${Attention}_{i,t}$denotes the level of managerial attention to supply chain resilience for firm i in year t. It is measured as the ratio of the frequency of supply chain resilience keywords to the total number of words in the firm’s MD&A for that year, multiplied by a constant of 1000 to facilitate interpretation and improve the readability of regression coefficients. Generally, a higher value of ${Attention}_{i,t}$indicates a greater focus of the management team on supply chain resilience. To empirically test the transmission mechanism of managerial attention, this study specifies the following models (4) and (5):

$${Attention}_{i,t}={\mu }_0+{\mu }_{1}NEVP{P}_{i,t}+{\mu }_{2}Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(4)

$$Scor{e}_{i,t}={\theta }_0+{\theta }_{1}N\mathrm{E}VPP+{\theta }_2{Attention}_{i,t}+\theta Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(5)

Specifically, the control variables and fixed effects are specified in the same way as in the baseline regression model (1). Columns (1)–(3) of

Table 9. Results of mechanism analysis.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Score	Attention	Score	SA	Score	Lngp	Score
NEVPP	0.033 *** (5.129)	0.044 *** (2.591)	0.030 *** (3.567)	−0.439 *** (−2.703)	0.032 *** (4.005)	0.364 *** (2.864)	0.024 *** (3.305)
Attention			0.049 *** (3.188)
SA					−0.003 **
					(−2.132)
Lngp							0.003 **
							(1.974)
Constant	−0.345 ***	0.350	−0.267 **	12.203 ***	−0.282 **	−1.575	−0.219 *
	(−3.019)	(1.278)	(−2.015)	(4.418)	(−2.097)	(−0.772)	(−1.851)
Observations	1509	1171	1171	1224	1224	1369	1369
Adj-R2	0.820	0.512	0.836	0.840	0.812	0.827	0.847
Controls	YES	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbols *, **, and *** respectively denote significance at the 10%, 5%, and 1% levels.

report the results of the mechanism analysis based on managerial empowerment. As indicated in column (2), when the dependent variable is Attention, the coefficient of NEVPP is 0.044, significant at the 1% level, suggesting that NEVPP strengthens managerial attention through policy signaling. Column (3) further reveals that the coefficients of NEVPP and Attention are 0.030 and 0.049, respectively, both statistically significant, and the absolute magnitude of the NEVPP coefficient becomes smaller compared with Column (1). These results indicate that the NEVPP can leverage policy signals to redirect managerial attention, thereby guiding firms’ strategic orientation and resource allocation. By enhancing managerial attention, the policy strengthens firms’ supply chain resilience. Consequently, Hypothesis 2 is supported.

5.1.2. Financing Constraints

From the previous theoretical analysis, NEVPP can enhance firms’ supply chain resilience by alleviating financing constraints. To empirically verify this transmission mechanism, the following models (6) and (7) are specified:

$${SA}_{i,t}={\eta }_0+{\eta }_{1}NE{VPP}_{i,t}+{\eta }_{2}Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(6)

$$Scor{e}_{i,t}={\rho }_0+{\rho }_{1}NE{VPP}_{i,t}+{\rho }_2{SA}_{i,t}+\rho Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(7)

Specifically, ${SA}_{i,t}$ denotes the financing constraint of firm i in year t. The control variables and fixed effects are specified in the same way as in the baseline regression model (1). Given the well-known limitations of the KZ index, particularly for firms subject to government influence, we construct an alternative measure of financing constraints based on the SA index proposed by Hadlock and Pierce (2010) [48], which is constructed from exogenous firm characteristics to mitigate potential endogeneity. In general, a higher SA index indicates more severe financing constraints. Finally, regressions are conducted using models (1), (6), and (7).

Table 9, columns (4)–(5), reports the results of the mechanism analysis based on resource empowerment. Column (4) shows that when the dependent variable is SA, the coefficient of NEVPP is −0.439 and statistically significant at the 1% level, suggesting that NEVPP alleviates financing constraints. Column (5) further indicates that the coefficients of NEVPP and SA are 0.032 and−0.003, respectively, both statistically significant, and the absolute magnitude of the NEVPP coefficient decreases compared with column (1). The above results show that NEVPP can improve capital allocation efficiency and alleviate firms’ financing constraints, thereby fostering sustained investment in supply chain development and coordination. Through the path of mitigating financing constraints, the policy enhances firms’ supply chain resilience. Accordingly, Hypothesis 3 is supported.

5.1.3. Technological Innovation

The theoretical analysis above suggests that NEVPP can strengthen firms’ supply chain resilience by fostering technological innovation. To examine this transmission mechanism, the following models (8) and (9) are employed:

$${Lngp}_{i,t}={\delta }_0+{\delta }_{1}NE{VPP}_{i,t}+{\delta }_{2}Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(8)

$${Score}_{i,t}={\gamma }_0+{\gamma }_{1}NE{VPP}_{i,t}+{\gamma }_2{Lngp}_{i,t}+\gamma Control{s}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(9)

Specifically, ${Lngp}_{i,t}$ denotes the technological innovation of firm i in year t. The control variables and fixed effects are specified in the same way as in the baseline regression model (1). Patent grants can effectively reflect firms’ technological innovation capability [101]. Considering the time lag inherent in patent authorization, and following related studies [58,102], this study measures technological innovation using the number of patent grants. Specifically, the number of patents granted to listed firms is lagged by two periods, incremented by one, and then transformed by the natural logarithm, yielding the variable $Lngp$. In general, a larger value of $Lngp$ indicates stronger technological innovation capability. Finally, regressions are conducted using models (1), (8), and (9).

Table 9 presents the mechanism analysis results based on technological empowerment. As shown in column (6), when the dependent variable is Lngp, the coefficient of NEVPP is 0.364 and significant at the 1% level, indicating that NEVPP fosters technological innovation. Column (7) further reveals that the estimated coefficients of NEVPP and Lngp are 0.024 and 0.003, respectively, both statistically significant, and that the absolute value of the NEVPP coefficient becomes smaller relative to column (1). The empirical evidence provides support that the NEVPP directs R&D resources toward compliant technological domains, enabling firms to establish long-term technological trajectories and innovation-based competitive advantages. By fostering technological innovation, the policy strengthens firms’ supply chain resilience. Hence, Hypothesis 4 is confirmed.

5.2. Heterogeneity Analysis

The preceding analysis shows that NEVPP has a positive effect on firms’ supply chain resilience. However, this relationship may differ under varying contexts. To explore such heterogeneity, this study examines differences across ownership type, geographic location, R&D intensity, analyst coverage, and institutional ownership.

5.2.1. Ownership Type

The effect of NEVPP on supply chain resilience may differ across firms with different ownership types. Ownership structure significantly influences policy transmission and the efficiency of resource allocation [103,104]. This paper divides the sample into state-owned and non-state-owned firms and conducts group regressions, the results are reported in

Table 10. Heterogeneity analysis by ownership type, geographic location, and R&D intensity.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	SOEs	Non-SOEs	East	Central	West	High R&D	Low R&D
	Score	Score	Score	Score	Score	Score	Score
NEVPP	0.033 **	0.025	0.019	0.054 *	0.075 ***	0.005	0.049 ***
	(2.487)	(1.358)	(1.254)	(2.002)	(3.690)	(0.356)	(3.263)
Constant	−0.621 *	−0.226	−0.295	−0.545	−0.323 *	−0.039	−0.439 *
	(−1.871)	(−1.108)	(−1.297)	(−1.428)	(−2.075)	(−0.161)	(−1.672)
Observations	373	1124	1100	181	127	740	731
Adj-R2	0.735	0.839	0.839	0.851	0.833	0.849	0.796
Controls	YES	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbols *, **, and *** respectively denote significance at the 10%, 5%, and 1% levels.

. For state-owned enterprises, the coefficient on NEVPP is 0.033 and is significant at the 5% level, whereas for non-state-owned firms the coefficient is 0.025 and is not statistically significant. New energy vehicles are a core industry in China’s industrial upgrading and carbon-neutrality strategy. State-owned automobile manufacturers tend to have larger scale, complete qualifications, stable R&D investment, and stronger institutional links to government, so they play a key role in policy demonstration and diffusion, are more likely to be included in the Recommended Catalogue, and are more likely to receive fiscal subsidies and complementary support; these factors enhance supply chain coordination and risk mitigation and thus lead to more pronounced improvements in supply chain resilience. In contrast, non-state-owned firms, especially small and medium-sized enterprises, rely primarily on market mechanisms, face weaker policy transmission channels, and have greater difficulty entering the Recommended Catalogue. Even if selected, they are less able to obtain comparable support for financing, capacity expansion, and supply chain integration, so the effect is not significant. Therefore, NEVPP has a more pronounced impact on the supply chain resilience of state-owned enterprises.

5.2.2. Geographical Location

Differences in the effect of NEVPP on firms’ supply chain resilience may arise from geographic location. Regional disparities significantly influence the impact of industrial policy and economic resilience [10]. This paper divides the sample into three regional subsamples, namely eastern, central, and western regions, and conducts group regressions, with the results reported in Table 10. In the western region the coefficient on NEVPP is 0.075 and significant at the 1% level, while in the central region the coefficient is 0.054 and significant at the 10% level. In the eastern region the NEVPP coefficient is 0.019 and not statistically significant. The observed pattern may be explained by the relatively weaker industrial base in the central and western regions, where firms’ financing, technology, and market channels are less developed than in the eastern region and are therefore more dependent on policy-driven mechanisms for market access and resource allocation [105]. The Recommended Catalogue, as a national recognition mechanism, is often accompanied by local fiscal subsidies, supporting policies for industrial parks, and capacity coordination measures. These policies enable automobile manufacturers to obtain preferential local resources, enhance market access and upstream-downstream cooperation, and thereby improve supply chain coordination and risk mitigation [106]. By contrast, firms in the eastern region benefit from a stronger industrial base and more mature supply chain systems and therefore exhibit limited marginal responses to policy, so policy-induced improvements in resilience are modest. Accordingly, the effect of NEVPP on supply chain resilience is more pronounced in the western and central regions.

5.2.3. R&D Intensity

The impact of NEVPP on supply chain resilience may differ depending on firms’ R&D intensity. R&D investment plays a critical role in enabling firms to absorb and transform the effects of industrial policies [107]. This paper measures R&D intensity as the ratio of R&D expenditure to operating revenue and classifies firms into high- and low-intensity groups based on the sample median, with group regression results reported in Table 10. For firms with lower R&D intensity, the coefficient on NEVPP is 0.049 and significant at the 1% level, whereas for firms with higher R&D intensity, the coefficient is 0.005 and statistically insignificant. These results suggest that NEVPP exerts a stronger effect on supply chain resilience in firms with low R&D intensity. With weaker innovation capacity, these firms are more reliant on external policy support. The Recommended Catalogue, as a policy tool with explicit support and resource orientation, provides stronger guidance for resource-constrained firms [108,109], alleviating financing constraints and granting market access. This endorsement facilitates external funding and technological innovation, promoting capability building and innovation investment. Policy support enables these firms to quickly optimize supply chain structure and risk management capacity, thereby improving overall coordination and resilience.

In contrast, firms with high R&D intensity already possess stronger internal capabilities and established market foundations, display lower marginal sensitivity to the Catalogue policy [110], and rely primarily on internal capacity building rather than external policy support to strengthen supply chain resilience. Hence, NEVPP is more likely to reinforce resilience among firms with low R&D intensity.

5.2.4. Analyst Coverage

The effect of NEVPP on supply chain resilience varies with analyst coverage. Analyst attention enhances information production, public oversight, and market discipline, thereby improving information transparency and guiding corporate decisions [111,112]. This paper measures analyst coverage as the natural logarithm of one plus the number of analysts following the firm and classifies firms into high- and low-coverage groups based on the annual sample median, with firms lacking analyst coverage treated separately due to their distinct information environment [113]. Regression results, reported in

Table 11. Heterogeneity analysis by analyst coverage and institutional ownership.

	(1)	(2)	(3)	(4)	(5)	(6)
	Non Analyst Attention	High Analyst Attention	Low Analyst Attention	Low IO	Mid IO	High IO
	Score	Score	Score	Score	Score	Score
NEVPP	0.083 ***	0.038 **	0.015	0.043 ***	0.026	0.031 **
	(4.417)	(2.471)	(0.764)	(2.921)	(1.518)	(2.396)
Constant	−0.795 ***	−0.671 **	−0.286	−0.542 *	−0.475 *	0.152
	(−3.677)	(−2.190)	(−0.781)	(−1.816)	(−1.861)	(0.567)
Observations	538	409	442	462	460	459
Adj-R2	0.847	0.870	0.777	0.832	0.796	0.833
Controls	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES

Note: Robust standard errors clustered at the firm level are reported in parentheses. The symbols *, **, and *** respectively denote significance at the 10%, 5%, and 1% levels.

, show that for firms without analyst coverage, the coefficient on NEVPP is 0.083 and significant at the 1% level, for high-coverage firms, the coefficient is 0.038 and significant at the 5% level, and for low-coverage firms, the coefficient is 0.015 and statistically insignificant. These results indicate that NEVPP has the strongest positive effect for firms without analyst coverage, a weaker but still positive effect for those with high coverage, and no significant effect for those with low coverage.

One possible explanation is that firms without analyst coverage operate in a capital-market “information vacuum,” where external investors and partners face difficulty in accessing timely firm information [114]. The Recommended Catalogue, as a government endorsement, provides a certification effect that significantly enhances the credibility and visibility of these firms, thereby alleviating financing constraints and improving access to external capital. Eased financing conditions in turn facilitate technological innovation and capability building, which, together with strengthened supply chain cooperation, ultimately reinforce supply chain resilience.

High-quality analyst coverage serves as an external monitor of management, improving governance, mitigating information asymmetry, and encouraging more efficient resource allocation through information production, media scrutiny, and market pressure [115]. However, firms with high analyst coverage are often industry leaders or technological frontrunners with strong R&D capabilities, integrated supply chains, and brand advantages. Their supply chain resilience improvements primarily rely on internal capability building rather than short-term policy signals [116], which limits the marginal effect of the Recommended Catalogue. Firms with low analyst coverage, in contrast, lack consistent research coverage and credible market oversight [117], leaving them unable to benefit from market discipline like high-coverage firms or from the certification effect available to firms with no coverage, ultimately constraining the translation of policy benefits into substantive supply chain optimization and resilience gains.

5.2.5. Institutional Ownership

The effect of NEVPP on supply chain resilience may vary with the level of institutional ownership. Institutional investors possess the opportunity, resources, and capability to monitor and influence financial decisions and firm operations [118]. Institutional ownership is measured as the share of institutional investors’ holdings in total outstanding equity, and the sample is partitioned into low, medium, and high groups. Regression results, presented in Table 11, indicate that the NEVPP coefficients are 0.043 and 0.031 in the low-and high-ownership groups, respectively, significant at the 1% and 5% levels, whereas the coefficient in the medium-ownership group is 0.026 and not significant. These results indicate significant heterogeneity in NEVPP’s impact across firms with different institutional ownership levels. One explanation is that firms with low institutional ownership exhibit relatively weak corporate governance and external monitoring, making management more likely to overlook long-term supply chain security and compliance. The Recommended Catalogue helps redirect management’s focus to policy compliance and supply chain adjustments, inducing “passive improvements” that, together with marginal governance gains and external financing support, boost supply chain resilience [119].

By contrast, medium-ownership firms neither face the survival pressures of low-ownership firms nor benefit from the strong monitoring and resource support characteristic of high-ownership firms, which leads to cautious attention allocation by management and insufficient financing and innovation incentives, rendering the policy effect statistically insignificant and consistent with the nonlinearity of institutional monitoring effects [120]. In high-ownership firms, the policy effect is amplified through institutional monitoring and superior resource allocation. Institutional investors help refocus management on supply chain restructuring and alleviate financing constraints via reputational endorsement and capital market channels, securing continued investment in key technologies [121]. This leads to stronger resilience in industrial integration and supplier coordination.

6. Conclusions

Using a multi-period difference-in-differences approach and treating the Catalogue of Recommended Models for the Promotion and Application of New Energy Vehicles as a quasi-natural experiment, this study empirically examines the impact of NEVPP on the supply chain resilience of listed firms in China’s automobile manufacturing sector. The results show that the policy significantly enhances supply chain resilience overall and exhibits heterogeneous effects across firm characteristics. The effect is more pronounced for state-owned enterprises, firms located in the central and western regions, and firms with higher R&D intensity. Analyst attention and institutional ownership further reinforced these heterogeneous effects: the policy effect is strongest among firms without analyst coverage, remains positive but weaker among high-coverage firms, and is not significant among low-coverage firms. Likewise, institutional ownership materially affects the policy outcome, with significant positive effects observed in both low- and high-ownership groups but not in the medium-ownership group. Mechanism tests suggest that NEVPP enhances firms’ supply chain resilience through three primary channels: it raises managerial attention, prompting greater emphasis on supply chain security and robustness in strategic decision-making; it alleviates financing constraints and improves resource access, strengthening the supply chain’s ability to withstand shocks; and it stimulates technological innovation, enhancing firms’ adaptive and collaborative capabilities within supply chains. These findings extend the extant literature, which primarily focuses on the financial or innovation outcomes of industrial policies, by empirically confirming their critical role in strengthening supply chain resilience, a dimension previously underexplored. Furthermore, by identifying the “managerial attention” channel, our study bridges the gap between institutional theory and behavioral perspectives, revealing that policy signals act as cognitive guides rather than just resource allocators.

6.1. Policy Implications

Based on these conclusions, we propose the following policy implications, structured along a logical progression from micro-level targeted support to meso-level industrial synergy, and finally to macro-level global integration.

First, policymakers should implement differentiated and targeted support strategies. Our heterogeneity analysis reveals that policy effects vary significantly across firm ownership and R&D intensity. Therefore, a “one-size-fits-all” approach should be avoided. For non-SOEs and firms with financing difficulties, the government should provide precise credit guarantees and targeted subsidies. Conversely, for leading firms with strong innovation capabilities, policies should focus on guiding them to assume demonstration roles in setting industrial standards, ensuring resources are allocated to the most critical links.

Second, policy design should guide the allocation of managerial attention toward resilience building. Since managerial attention is a key transmission channel, governments should encourage firms to incorporate supply chain resilience into their ESG reporting and performance evaluation systems. By institutionalizing resilience management, firms can shift from passive response to proactive governance, ensuring that low-carbon transformation becomes a core strategic priority for corporate leadership.

Third, the government should foster cross-regional collaboration to enhance supply chain synergy. Given the regional disparities identified in this study, policymakers should break down local protectionism and market fragmentation. Leading “chain-head” firms should be leveraged to drive the coordinated development of upstream and downstream enterprises across different regions. By establishing cross-regional industrial alliances and information-sharing platforms, the resilience of the entire industrial ecosystem can be strengthened against localized disruptions.

Fourth, China should proactively integrate into the global innovation network by benchmarking against international best practices. Drawing on the experiences of developed economies like Europe and the United States, China should accelerate the formulation of carbon footprint standards and green supply chain regulations that align with international norms. By fostering high-level openness and encouraging international technical cooperation, Chinese NEV firms can better navigate global trade barriers (such as the EU’s Carbon Border Adjustment Mechanism), ultimately enhancing the global competitiveness of China’s NEV industry.

6.2. Limitations and Future Research

This study is subject to several limitations. First, the analysis is restricted to A-share listed firms in China’s automobile manufacturing industry, which may limit the external validity of the results due to sector- and region-specific characteristics. Future research could extend the scope to other industries or geographic contexts to enable comparative assessments. Second, regarding the statistical method, we employed a DID approach. While this effectively captures the average treatment effect of market access, it treats policy exposure as a dichotomous state (presence vs. absence), overlooking the intensive margin of support (e.g., the number of certified models or subsidy amounts). Future studies could employ continuous DID models to capture these intensity effects more precisely. Third, the measurement of supply chain resilience relies on the entropy weight method, which may involve a degree of subjectivity. Subsequent studies could incorporate a broader set of multidimensional indicators or granular transaction data to enhance robustness. Fourth, although we employed multiple identification strategies to mitigate endogeneity, the possibility of firms preemptively adjusting supply chain strategies to secure policy incentives (i.e., reverse causality) cannot be completely ruled out. Moreover, given the dense policy environment of China’s NEV sector (e.g., subsidy phase-outs, dual-credit schemes), our estimates should be interpreted as capturing the combined impact of NEVPP and related policy packages centred on the Recommended Catalogue, rather than a perfectly isolated policy effect. Future research could further explore the potential nonlinear effects of policy on supply chain resilience and assess how policy effectiveness varies across different phases of the economic cycle, thereby enriching the theoretical framework of policy outcomes.