A Study on the Impact of Local Policy Response on the Technological Innovation of the New Energy Vehicle Industry

Abstract

The successful implementation of lane change and overtaking maneuvers, as well as the technological advancements in new energy vehicles in China, are outcomes influenced by multiple factors. Among these factors, the responsiveness of local policies plays a crucial role and serves as a pivotal element in ensuring the effective execution of central policies. Nevertheless, there is a dearth of systematic research within the academic community regarding the innovative impacts of local policy responses. We utilize industrial policy and patent data from China’s NEV sector, employing text analysis to measure local policy response in terms of intensity, velocity, and degree. Regression analysis is conducted to investigate the impact of local policy responses on technological innovation. The findings reveal an inverted U-shaped correlation between policy issuance frequency, adoption speed, policy reproduction degree, and technological innovation. Regional disparities play a moderating role in the local policy response impact, with the eastern region exhibiting superior policy response compared to the central and western regions. Notably, an inverted U-shaped relationship is observed between adoption speed and policy reproduction degree in the eastern region, as well as between policy issuance frequency in the central region and technological innovation. Conversely, no significant policy response effect is detected in the western region. These outcomes underscore the necessity for effective local policy response, emphasizing the need for local governments to adapt and customize central policies in alignment with local contexts while navigating the balance between central coherence and local diversity, as well as policy adjustments and temporal constraints. This article contributes to the existing literature on policy implementation and innovative governance, offering empirical insights to enhance the optimization of regionally tailored policy frameworks and to bolster the coherence and efficacy of central and local policies.

1. Introduction

The new energy vehicle industry represents a significant advancement in addressing climate change and the energy crisis, serving as a strategic emerging sector crucial for energy security. This industry promotes environmentally sustainable practices and enhances global competitiveness, garnering substantial attention from nations worldwide and governmental bodies at various levels [1,2]. Being technology-intensive, new energy vehicles rely less on conventional technological foundations, offering China a potential avenue to surpass competitors. China has prioritized scientific research and technological development in this sector since the late 20th century, leading to notable achievements and establishing itself as a key manufacturer and exporter in the field [3]. By 2023, China had achieved a remarkable sales penetration rate of 31.6% for new energy vehicles, representing over 60% of global sales and solidifying its position as the world’s largest and most accessible automotive market. Moreover, consumers exhibit a strong inclination towards intelligent technologies, fostering technological collaborations between multinational automakers and local enterprises, thereby propelling the intelligent evolution of the industry’s supply chain [4].

Policies play a crucial role in shaping the evolution of technologies and industries [5]. China initiated new energy vehicle (NEV) development goals in 2009, introducing tax incentives in 2014 and the dual-credit policy in 2018, which pushed enterprises towards technological advancement and market-driven strategies. Local governments have actively supported these initiatives. Shanghai, for example, offers free green license plates for NEVs, reducing consumer purchase barriers [6]. Beijing reserves a significant portion of vehicle purchase quotas for NEVs, securing a stable high-end market for automakers [7]. In Guangdong, despite the cessation of central fiscal subsidies, local purchase subsidies and charging incentives persist [8]. The province fosters industrial clusters, supporting companies like BYD and GAC Aion [9], and has developed robust supply chains in power batteries and core components, enhancing the industrial cluster effect and innovation ecosystem.

The development of a technological innovation system in the new energy vehicle sector depends on both macro-level guidance from the central government and localized implementation by local authorities. Local governments refine and adapt central policies, facilitating their dissemination across various administrative levels. Within China’s unique central-local relations, local governments must balance adherence to central authority with discretionary power, leading to industrial policies that reflect both central uniformity and local distinctiveness. How do local governments integrate individuality into central policies, and how does this integration affect incentives for industrial development and technological innovation? The academic community has yet to systematically address this question. We examine how local policy responses, within China’s policy implementation system and hierarchical framework, can effectively enhance technological innovation. The exploration will focus on the following three questions:

• How do local government responses to central policies affect technological innovation in new energy vehicles?
• What are the varied impacts of policy responses across regions on technological innovation?
• What mechanisms underlie the regional differences in the effectiveness of policy responses?

This study advances the understanding of government response behaviors and hierarchical policy diffusion by introducing three quantitative indicators: policy quantity, adoption speed, and policy reproduction extent. These metrics capture local government actions in terms of intensity, speed, and content adaptation. Adoption speed assesses the time lag in local governments’ implementation of central policies, though a quick response may only indicate superficial compliance. To address this, the study introduces policy reproduction extent, which measures the divergence between local and central policies through text similarity analysis. These indicators address the limitations of single metrics, offering a comprehensive view of the varied response behaviors of Chinese local governments.

In the context of policy hierarchy diffusion, research indicates that central policies primarily drive macro-level guidance, while local governments focus on refining and updating these policies through specific implementation strategies [10]. There is also notable consistency between central and local government policies [11]. This study underscores the necessity for local governments to adaptively reconstruct central policies within their institutional frameworks, considering local conditions for effective implementation. This work extends research on the innovative effects of policy hierarchy diffusion and complements the dynamic optimization needed for policy sustainability.

Fang et al. highlight the growing centralization of China’s industrial policies [12], a trend supported by Luo et al., who report a 40% increase in top-down policies since 2013. These policies, however, are less attuned to local conditions than those developed by local governments [13]. Luo emphasizes the necessity for local governments to tailor policies to regional contexts, a key theme of this paper. Despite the dominance of central government policies, local governments’ responses are crucial. This paper builds on these insights, showing that within the hierarchical structure, local governments effectively balance central directives with local conditions, demonstrating both institutionalization and flexibility.

The paper is structured as follows: Section 2 reviews literature on the need for policy intervention in technological innovation and the effects of local policy responses. Section 3 outlines the theoretical framework and research hypotheses. Section 4 details data sources and processing methods. Section 5 describes the research methodology. Section 6 discusses variable configurations, presents descriptive statistics, and performs regression and regional heterogeneity analyses. Section 7 summarizes findings and offers further discussion.

2. Literature Review

2.1. Necessity and Effect Deviation of Policy Intervention in Technological Innovation

Technological innovation levels are influenced by various factors such as the external market and policy environment, internal organizational scale, R&D personnel, and knowledge accumulation [14,15]. However, it is important to note that due to the positive externalities of technology, innovators may not capture all the monopoly benefits, leading to suboptimal technological innovation choices and market failure [16]. Consequently, neoclassical theory considers policy intervention as a primary method to address market failure [17]. Similarly, in line with Schumpeter’s endogenous growth theory, it is argued that government intervention is justified in enhancing private investment in R&D and innovation [18]. In the realm of industrial technological innovation, government policies typically signal support, alleviate financial constraints, incentivize firms to boost R&D investments, and thereby enhance technological innovation levels.

Not all policies yield positive incentives. While fiscal policy plays a critical role in enhancing technological innovation efficiency, it can also have adverse effects, with varying impacts across different sub-sectors [19]. China’s new energy vehicle policy has notably enhanced air quality [20] and promoted technological innovation [21]. However, certain policies may backfire. For instance, license plate restrictions can impede the proliferation of electric vehicles [22], and pilot city initiatives may boost the number of technological innovations without necessarily enhancing innovation efficiency [23]. The academic community has raised concerns about deviations in policy implementation. Existing research primarily addresses two key aspects: the characteristics of policy tools and the execution of the policy framework.

On the one hand, policy tools vary in attributes and action mechanisms, leading to diverse policy effects. Subsidy policies primarily stimulate strategic innovation, while non-subsidy policies target substantial innovation [24]. Consumer subsidies have a more significant effect than manufacturer subsidies, but policy mix is more efficient [25]. However, government subsidies often fail to enhance the efficiency of technological innovation [23], likely due to information asymmetry between the government and the market, which incurs transaction costs throughout the policy process. In contrast, tax incentives can significantly catalyze innovation, as products have already been vetted by the competitive market, reducing information costs compared to government assessments [26].

On the other hand, new energy vehicle policies form a complex system where local governments wield discretionary power to replicate central policies, potentially resulting in deviations during policy implementation. Local protectionism, exemplified by the inclusion of preferential protection policies through local clauses and tax base safeguarding, engenders a local bias effect. Excessive government involvement further stifles market incentive, competition, and regulatory functions, hindering the industry’s long-term growth [27,28]. Government intervention has, to some extent, supplanted market resource allocation, with the crux of government failure stemming from an unbalanced principal-agent relationship [29], leading to issues like overcapacity, inadequate R&D, and subsidy misuse in the new energy vehicle sector. Hence, instituting an incentive-constraint mechanism for the dual principal-agent relationship of local governments and crafting a policy framework with diverse stakeholders and comprehensive oversight are effective strategies to mitigate moral hazard and adverse selection issues between the government and businesses. Tailoring policies based on the industry’s developmental stage is also crucial.

The selection of policy instruments and the establishment of policy frameworks constitute the local government’s response to central policies, showcasing the active involvement of local governments in policy dissemination. Nevertheless, variations in technological advancements, regional development stages, and local government preferences result in diverse reactions to central policies across regions. Consequently, the manner in which local policies adapt and leverage the beneficial aspects of technological innovation remains an unresolved inquiry within current scholarly investigations.

Government policies, as authoritative documents, guide technological innovation by redistributing social resources. However, the complexity of innovation, regional disparities, and varied interests among local officials can lead to discrepancies between policy formulation and execution, complicating the precise implementation of central policies at the local level. Academic research has investigated the reasons for these policy effect deviations both theoretically and empirically, highlighting the active role of local government responses. Thus, beyond understanding “why” these deviations occur, it is crucial to explore “how” they manifest. We extend existing research by examining local policy response variations and their effectiveness in promoting technological innovation.

2.2. Local Policy Response and Technological Innovation

Local policy response is essential for implementing central policies. Existing research primarily examines central-local coordination and policy diffusion. Central-local coordination is crucial for establishing a connection between higher and lower levels of government. The effectiveness of policies depends on the level of coordination between central and local governments [30]. Nonetheless, variations in temporal and spatial dimensions characterize central-local collaboration. For instance, shifts in central policy orientation may lead to instances of non-collaboration between the central and local levels, with collaboration dynamics differing across policy domains [31]. Disparities are also evident in the departments and scopes of responses to central policies [32]. In the new energy vehicle industry, various forms of collaboration exist, including equal, enhanced, and selective collaboration [33]. However, the impacts of different types of central-local collaboration have not been extensively discussed. Previous research indicates that central-local collaboration can stimulate enterprise innovation, whereas the absence of collaboration has a restraining effect [34]. Additionally, a U-shaped correlation has been identified between the extent of central-local collaboration and technological innovation [35]. Scholars have highlighted the positive role and varied forms of local government responses to central policies, illustrating that local policy formulation and implementation involve a dynamic interplay of checks and balances within China’s distinct central-local relations. The central policy addresses regional governance differences through a broad narrative, yet its ambiguous content undermines rational decision-making and leads to information loss during hierarchical transmission [36]. Consequently, implementing the central macro-strategy locally relies on intergovernmental policy transmission, facilitating the quantification of local policy responses. Consequently, top-down policies demonstrate lower local adaptability than bottom-up approaches [13], requiring local governments to adaptively reinterpret central policies. Empirically, researchers have quantitatively analyzed local policy adoption through the lens of vertical policy transmission and diffusion. Policy citation serves as a valuable tool for examining the trajectory of policy adoption, facilitating the tracking of policy diffusion and illustrating varied governmental responses [37,38]. It captures the dissemination of policy text concerning content, consensus, and ideas. Provincial governments adapt central policies by tailoring them to local contexts to foster regional development [39]. Liu et al. demonstrated that various policy themes differently affect the intensity and speed of policy responses through citation relationships, offering targeted policy dissemination suggestions [40]. Similarly, Wang et al. examined how policy responses influence the digital divide using citation methods [37], revealing spatiotemporal disparities in China’s local policy responses. They found that moderate response behaviors can significantly reduce the digital divide. This study, however, concentrates on vertical bureaucratic power dynamics in the new energy vehicle sector, providing specific optimal response strategies and actionable recommendations for regional policy responses.

3. Theoretical Analysis and Research Hypotheses

3.1. Theoretical Analysis

Industrial policies are influenced by incomplete contracts and externalities. Evaluating policy effectiveness necessitates examining the policy formulation and implementation process, which must consider China’s distinctive central-local decentralization dynamics [41]. In contrast to Western nations, China’s central-local relationship features territorial hierarchical management and administrative contracting across various levels, establishing an administrative contracting framework where policies cascade through different government tiers, maintaining a power equilibrium between central and local authorities. Additionally, the tax-sharing reform has addressed local finance resource constraints, enabling local governments to autonomously make administrative decisions [34]. Consequently, a strategic interplay exists between central and local governments. Particularly noteworthy is the scenario where local officials are primarily assessed based on GDP growth, creating a conundrum between prioritizing short-term economic expansion and long-term technological innovation that inevitably influences policy development and execution.

In this context, local governments develop new energy vehicle policies that align with central objectives while considering local conditions and performance metrics. This approach leads to policies characterized by administrative rigidity and local variation. Policy responses involve allocating limited public resources and attention based on central goals and local realities. By creating new policies, resources are provided to enterprises for technological innovation, offering dual incentives from both central and local governments. Additionally, these policies send strong signals to enterprises, encouraging innovation efforts, easing financing constraints, and enhancing technological advancement.

Quantifying local governments’ responses to central policies and their impact on technological innovation remains a challenge in academia. Based on existing research, we assess local policy responses through three dimensions: the volume of government documents issued, the speed of policy adoption, and the degree of policy replication. This analysis elucidates how local governments’ reactions to central policies impact technological innovation. This methodology elucidates how local responses to central policies affect technological innovation. Given regional disparities, these responses cannot be uniformly generalized. Consequently, we examine the mechanisms by which policy responses influence technological innovation across various regions. Our research framework is depicted in Figure 1.

3.2. Research Hypotheses

Local policy response is essential for fostering central-local collaboration. The implementation of central policies by local governments serves as a crucial component in the transmission of policy hierarchy. This practice reflects the allocation of attention by local governments. In the field of policy science, both political operations and policy processes are influenced by attention [42]. The continuity of policies is contingent upon sustained attention, whereas shifts in attention can lead to policy discontinuities [43]. Consequently, when local governments prioritize the advancement of the new-energy vehicle industry, they channel social resources towards innovation activities through strategic policy formulation. The frequency of policy issuance directly mirrors the government’s focus on a specific sector [44] and typically signifies the level of policy implementation [45]. Signaling theory suggests that issuing multiple policy documents in quick succession indicates strong governmental support for a particular industry, thereby directing market resources towards it. However, technological innovation, being a complex system, requires more than a single policy. A synergy of multiple policies is essential to establish an innovation ecosystem and enhance the policy environment for industrial innovation. A balanced issuance of policies can mitigate innovation risks, clarify support signals, address market failures, and offer incentives and guidance for innovation.

An excessive number of policy documents can hinder technological innovation. Overabundant policies fragment corporate attention and lead to information overload, consuming significant managerial resources. In environments with complex policy signals, firms may adopt short-term, reactive R&D strategies to quickly leverage policy benefits, diverting resources from technological innovation. This can lead to practices like data falsification or subsidy fraud to secure government resources, prioritizing innovation quantity over quality, ultimately degrading innovation performance. From a complex systems perspective, effective policy synergy requires alignment and coordination. An overabundance of policies complicates coordination, causing conflicts and misaligned goals. For instance, numerous local policies may lead to redundant efforts, resource waste, and increased R&D costs, thereby reducing innovation efficiency. We hypothesize a nonlinear relationship between the volume of local policy documents and technological innovation, following an inverted U-shaped trend.

Hypothesis 1.

There is an inverted U-shaped relationship between the volume of local policy documents and technological innovation, suggesting an optimal issuance level beyond which the incentive effect on innovation declines.

The primary objective of local governments is to execute tasks delegated by the central government and establish political accomplishments through policy outcomes. Although policy effects typically require time to manifest, superficial adoption behaviors like “whether to adopt” and “adoption speed” are readily monitored by higher authorities [46,47,48,49], thereby signaling compliance to superiors [50]. Individually, local officials, driven by the desire for recognition and avoidance of accountability, prioritize initiatives that showcase political achievements and align with leadership priorities [43]. By swiftly embracing the new-energy vehicle industry, local governments capitalize on development prospects, fostering local economic growth and officials’ career progression. Research generally views rapid adoption as advantageous [51], fostering institutional benefits and attracting innovation clusters. However, early adoption’s implementation impact may not surpass that of delayed adoption during policy dissemination, potentially obscuring government departmental priorities [52]. The evolving landscape of new-energy vehicle technologies necessitates proactive policy responses to seize opportunities and mobilize resources comprehensively. Merely focusing on linear diffusion rates inadequately captures local governments’ behaviors [53]. Swift issuance of policy documents may stem from legitimacy pressures rather than genuine commitment, undermining implementation drive and hindering technological innovation advancement effectively. Thus, the speed of policy adoption can initially foster but later hinder innovation.

Hypothesis 2.

The relationship between the speed of local policy adoption and technological innovation is inverted U-shaped, suggesting that a moderate policy response speed optimizes innovation performance.

The achievement of policy effectiveness hinges not merely on superficial adoption but on the ability of local governments to undertake adaptive reconstruction in alignment with central directives [53]. Central policies’ directionality, principle-orientation, and guidance necessitate local governments with ample local knowledge to refine and update them accordingly [54]. Thoughtlessly replicating policies without regard for local circumstances can detrimentally impact policy outcomes [55]. Drawing on their comprehension of superior policies, historical insights, and public demands, local governments, in conjunction with local realities, delineate the boundaries of policy reproduction, enhance policy frameworks, and contextualize them effectively [56].

Local governments adapt central government strategic goals to local contexts by translating them into management objectives and specific tasks, assigning responsible departments and deadlines, and overseeing policy implementation through a performance evaluation index system. This process aims to enhance collaborative governance effectiveness by aligning central goals with local resource allocation. However, deepening local policy reproduction may result in overly detailed implementation plans under the refinement logic, leading to policy rigidity, misalignment with actual needs, and fostering a blame-avoidance culture among grassroots officials, thereby diminishing policy impact. Conversely, intensive policy adaptation under the innovation logic can escalate local attention competition, diffuse government focus, overwhelm grassroots administrations with multiple tasks, and undermine policy implementation efficacy. The local government’s replication of central policies can enhance adaptability and implementation effectiveness, fostering innovation incentives. However, excessive replication may cause policy deviations, increase uncertainty, and potentially lead to local protectionism, thereby undermining policy synergy.

Hypothesis 3.

An inverted U-shaped relationship exists between local policy reproduction and technological innovation, suggesting that moderate policy reproduction optimally enhances regional innovation levels.

Local governments refine central policies, leading to divergent policy implementation through adaptive reconstruction based on rational decision-making [57]. Studies indicate that policies in the new energy vehicle industry are shaped by factors such as population density [58,59], residents’ income and education levels [60], and the availability of charging infrastructure [61], resulting in regional variations in policy outcomes. Generally, local governments with greater resources and capabilities tend to have a stronger impact on policy promotion through central-local coordination [62]. This typically indicates effective policy implementation and corporate adaptability. From a governmental standpoint, the eastern regions demonstrate strong fiscal capacity and administrative efficiency, allowing for more sophisticated support measures. A mature market environment aids in integrating industrial chain resources, expediting policy execution through the formation of industrial clusters and R&D platforms. Conversely, the western regions struggle with limited financial resources, outdated policies, inadequate inter-regional coordination, and uniform policies, hindering the development of a locally distinctive innovation ecosystem.

The eastern region is characterized by a concentration of leading universities, research institutions, and skilled talent, alongside a dense network of automotive enterprises. This environment, coupled with favorable financing and the presence of key suppliers like CATL, facilitates rapid technological advancement. Conversely, the western region faces a shortage of high-end research institutions and a significant brain drain, undermining its talent and material resources for corporate R&D. Additionally, financial constraints and limited market size impede the growth of new energy vehicle enterprises in the west, rendering them dependent on policy support and limiting their ability to invest in sustained innovation.

Hypothesis 4.

Regional variances moderate the impact of local policy response on innovation.

Hypothesis 4a.

The institutional environment and resource endowment in eastern regions are stronger, thus the promotion effect of local policy responses on technological innovation is more significant.

Hypothesis 4b.

Due to developmental constraints, the positive influence of local policy responses on technological innovation is relatively weaker in western regions.

4. Data Sources and Processing

(1)

Policy text data

Policy documents related to the new energy vehicle industry were collected from the Pkulaw database and official government websites. Searches were performed using keywords like “new energy vehicles,” “electric vehicles,” and “hybrid vehicles” for the period from 2009 to 2023, with the search conducted on 21 November 2023. After screening for duplicates and excluding administrative license approvals, irrelevant texts such as “letters,” “approvals,” “training courses,” and “salons” were removed. A total of 2135 policies were identified, comprising 203 national policies, 630 regional policies, and 1302 local city policies. To enhance theme recognition accuracy, a domain-specific stop-word list and term dictionary were developed for data preprocessing.

Local governments utilize policy texts as a crucial medium to respond to central policies. The alignment, evolution, and advancement of policy objectives can be discerned through interconnections within the literature. The citation networks within policy literature serve as a conduit for the dissemination of political values and the circulation of ideas [63]. Consequently, this study employs citations within policy literature to delineate the local government’s response. By analyzing key phrases such as “according to,” “in accordance with,” and “to implement and enforce,” the citation patterns among policies are identified [64], leading to the establishment of a citation dataset encompassing interactions between central and provincial governments. Following the exclusion of documents such as budgetary allocations and official notices, a total of 630 provincial and municipal policies, 173 citing provincial policies, 48 cited central policies, and 259 citation relationships are ultimately derived (Hong Kong, Macao, Taiwan, and the Tibet Autonomous Region were excluded due to insufficient data.).

The annual issuance volume of new energy vehicle policies serves as the local response intensity, while the adoption speed and content reproduction degree represent the local response speed and degree, respectively. These metrics are utilized as markers to assess how local governments react to central policies. Response speed is measured by the time interval between the issuance of local policies and the central policies they reference, while policy reproduction degree is evaluated through text similarity analysis.

(2)

Patent data

The patent data analyzed in this study were extracted from the Incopat patent database. Search queries were structured into topic searches focusing on content and IPC searches targeting technical fields. Using keywords such as “new energy vehicles, pure electric vehicles, fuel cell vehicles, and hybrid vehicles,” and aligning with the “Patent Classification System for Related Technologies of the ‘New Three Categories’ (2024)” issued by the National Intellectual Property Administration for IPC searches [65], which encompasses electric vehicles, lithium batteries, and photovoltaics, spanning from raw materials to vehicle manufacturing in the new energy vehicles domain. The patent search was executed on 9 December 2024, within the timeframe of 2009 to 2023, and limited to applicants from China, yielding a total of 188,743 relevant patents pertaining to new energy vehicles.

5. Research Methods

(1)

Text analysis method

This study employs text analysis to quantify policy reproduction and utilizes a regression model to examine the influence of local policy response on technological innovation. Adoption speed is operationally defined as the temporal gap between the issuance dates of local policies and corresponding central policies. Policy reproduction level is assessed using a text similarity calculation approach. Specifically, the word2vec model is utilized to create word vectors, which map words to a vector space based on contextual information. Word2Vec comprises two distributed word representation learning models: CBOW and Skip-gram. Given that Skip-gram demonstrates superior predictive performance for low-frequency words, we selected the Skip-gram model to train policy texts. For a given word sequence $w_1,w_{2,}\dots ,w_N$:

$${\displaystyle \frac{1}{N}}\sum _{t=1}^N\sum _{j\in \left[-k,k\right],j\ne 0}logp\left(w_{t+j}\right|w_t)$$

In this context, N is the length of the text sequence, k is the window size around the central word, set to k = 5, and training employs the negative sampling method.

Subsequently, the Word Mover’s Distance (WMD) algorithm is applied to measure document similarity [66]. This method excels in capturing word associations via word embedding and reflecting policy text adjustments through semantic transfers at the word level. By treating words as vectors, the algorithm computes the minimum cumulative distance between words as the dissimilarity metric between two policy texts. A smaller WMD signifies greater textual similarity and lower policy reproduction extent.

$$WMD(D,D^{\prime }){= }_{T\ge 0}^{min}{\sum }_{i=1}^n{\sum }_{j=1}^n{T}_{ij}c(i,j)$$

$$\mathrm{s}.\mathrm{t}. {\sum }_{j=1}^n{W}_{ij}=d_i,\forall i\in \{1,\dots ,n\}$$

$${\sum }_{i=1}^n{W}_{ij}=d_j,\forall j\in \{1,\dots ,n\}$$

Here, $W_{ij}$ represents the weight assigned when the word $i$ is migrated from document D to D’, while the semantic distance between words i and j is $c(i,j)$ calculated using Euclidean distance $c\left(i,j\right)=\left|\right|x_i-x_j{\left|\right|}_2$. The WMD similarity is computed by invoking the gensim package in Python 3.12.7.

(2)

Regression model

Due to patent data being non-negative integers, they are better suited for count models like Poisson regression and negative binomial regression. While Poisson regression assumes a Poisson distribution for the dependent variable, which is suitable when the variance equals the mean, the variance of the patent data in this study exceeds the mean. Hence, negative binomial regression is deemed more suitable as it can address over-dispersion by incorporating an extra parameter, thus effectively capturing the data’s variability and intricacy. Consequently, this study employs negative binomial regression, with the model specified as follows:

$${\mathrm{l}\mathrm{n}\left(E\right[patent}_{it}])={\beta }_0+{\beta }_j{x}_{ijt}+{ϵ}_{it}$$

The variable ${\mathrm{l}\mathrm{n}\left(E\right[patent}_{it}])$ denotes the expected value of patent applications in the i-th province during the t-th year. Here, ${\beta }_0$ is the constant term, ${\beta }_j$ is the coefficient corresponding to the j-th independent variable, j = 1,2,3, corresponding to publication volume, policy reproduction degree, and adoption speed, respectively. And $x_{ijt}$ represents the j-th independent variable of province i in year t. These variables encompass factors such as the number of published papers, policy reproduction degree, adoption speed, and its square term, alongside control variables. The error term, denoted by ${ϵ}_{it}$, is assumed to adhere to a negative binomial distribution. Omitting year control, this study delves into the intergovernmental transmission mechanism rather than sole local policy autonomy.

In the benchmark regression and analysis of regional disparities, explanatory variables were individually integrated into the model to assess the innovative effects of policy responses across various dimensions. For the mechanism analysis of regional disparities, all independent variables were incorporated into the regression model to examine the impact of interaction terms, expressed by the following formula:

$${\mathrm{l}\mathrm{n}\left(E\right[patent}_{it}])={\beta }_0+\sum _{j=1}^k{\beta }_j{x}_{ijt}+{ϵ}_{it}$$

Among them, $x_{ijt}$ represents the j-th independent variable of province i in year t, including the number of publications, degree of policy reproduction, adoption speed and its squared term, control variables, and interaction terms.

6. Empirical Analysis

6.1. Variable Setting and Descriptive Statistics

(1)

Explained variable

Patents are frequently employed by scholars to assess technological innovation due to their high degree of innovation and measurability. Patent applications can indicate the innovation activity of a subject to some extent, while granted patents and invention patents can signify the quality of innovation. Therefore, this study utilizes the quantity of new energy vehicle patent applications as the dependent variable and granted patents and invention patents as innovation metrics for robustness assessment.

(2)

Explanatory variables

This study evaluates local governments’ responsiveness to central policies through three dimensions: response intensity, speed, and extent. Building on Liu et al. [67], we use the annual volume of local policy documents, the degree of policy content reproduction, and adoption speed as explanatory variables. The volume of policy documents indicates government attention, adoption speed suggests the priority given to central policies, and the degree of policy reproduction reflects updates and refinements made by local governments at the content level. Given the relatively lengthy patent application cycle, the adoption speed is converted to a monthly unit to more accurately assess its impact.

(3)

Control variables

To isolate the net impact of local policy on technological innovation, control variables were selected to account for other factors influencing regional new energy vehicle innovation output. Research shows that cities with higher economic development and R&D investment tend to experience increased technological innovation [68,69]. Local fiscal expenditure significantly boosts green innovation [70], while a strong financial foundation in enterprises can greatly enhance innovation efforts [71]. Conversely, inadequate operating revenue may lead to an inefficient structure of innovation input and output [72]. Therefore, we identified control variables across four dimensions: economic development, innovation R&D investment, industrial structure, and government governance. Specifically, the selected variables include per capita GDP, fiscal expenditure level (total local fiscal expenditure as a percentage of the population), R&D investment intensity (R&D investment funds as a percentage of regional GDP), asset intensity (total assets of large-scale industrial enterprises as a ratio to the number of enterprises), and income intensity (main business income of large-scale industrial enterprises as a ratio to the number of enterprises). Please refer to the accompanying

Table 1. Main variables and descriptions.

Type of Variables	Variable Name	Variable Definition and Formula
Explained variable	technological innovation	patent applications
Explanatory variables	Policy volume	the annual volume of local policies
	Policy content reproduction degree	Policy content reproduction degree/the annual volume of local policies
	Speed of policy adoption	Speed of policy adoption/30/the annual volume of local policies
Control variables	economic development	per capita GDP/10,000
	fiscal expenditure level	total local fiscal expenditure/population size
	R&D investment intensity	R&D investment/GDP
	asset intensity	total assets of large-scale industrial enterprises/the number of enterprises
	income intensity	main business income of large-scale industrial enterprises/the number of enterprises

for a detailed list of these variables.

(4)

Descriptive statistics

The volume of policy issuance, rate of adoption, and degree of policy reproduction are determined through the examination and analysis of policy documents, while the control variables are extracted from pertinent statistical yearbooks. To ensure temporal consistency with the control variables, data pertaining to patents and policy documents in the new energy vehicle sector from 2009 to 2022 were chosen for analysis. Following the removal of erroneous data and incomplete samples, a total of 158,851 patent applications and 579 policy documents issued by provincial government entities were retained, yielding 405 valid observations. The period from 1 January 2009 to 31 December 2022, includes 29 provinces and municipalities directly under central government control. Due to data unavailability, Hong Kong, Macao, Taiwan, Tibet Autonomous Region, and Heilongjiang Province were excluded. Detailed descriptive statistics are presented in the accompanying

Table 2. Descriptive statistics.

	Mean	Std.	Min	Max	Obs.
Patent number	392.22	693.52	1	4501	405
Policy volume	1.43	1.94	0	12	405
Policy reproduction	0.34	0.32	0	0.93	405
Policy adoption speed	9.16	11.80	0	79.67	405
GDP per capita	5.75	3.07	1.10	19.03	405
fiscal expenditure	1.27	0.66	0.31	3.78	405
income intensity	3.19	1.27	0.66	9.35	405
R&D investment	1.73	1.14	0.34	6.83	405
asset intensity	4.06	3.01	0.66	21.03	405

.

The data presented in Table 2 reveals that, on average, each region within the country introduces 1.43 new policies related to energy vehicles annually, with a standard deviation of 1.94, indicating varying frequencies of policy issuance across provinces. While some provinces exhibit high activity in policy formulation, as evidenced by a maximum of 12 policies, such instances are uncommon. The mean policy reproduction rate stands at 0.34, denoting a moderate average level with relatively consistent fluctuations among regions. The average duration for policy adoption is 9.16 months, with a variance of 11.8 months. Notably, the most extended adoption period extends to six and a half years, underscoring substantial regional disparities.

6.2. Benchmark Regression Analysis

To account for policy lag effects and prevent oversight, this study presents regression outcomes for both the current period and lag periods showing significant impacts.

① Correlation analysis between the issuance volume of local policies and technological innovation

The regression analysis presented in

Table 3. Presents the results of a regression analysis examining the influence of policy issuances on technological innovation.

	Patent Application		Valid Patents		Invention Patents
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
policy volume	0.212 ***		0.261 ***		0.222 ***
	(0.057)		(0.066)		(0.065)
policy volume2	−0.017 ***		−0.021 ***		−0.019 ***
	(0.006)		(0.007)		(0.007)
L1. policy volume		0.254 ***		0.305 ***		0.254 ***
		(0.050)		(0.059)		(0.054)
L1. policy volume2		−0.021 ***		−0.024 ***		−0.021 ***
		(0.006)		(0.007)		(0.007)
GDP per capita	0.165 ***	0.139 ***	0.190 ***	0.161 ***	0.123 ***	0.101 ***
	(0.032)	(0.033)	(0.041)	(0.041)	(0.035)	(0.036)
fiscal expenditure	1.245 ***	1.174 ***	1.651 ***	1.548 ***	1.376 ***	1.285 ***
	(0.230)	(0.229)	(0.304)	(0.304)	(0.247)	(0.257)
income intensity	0.184 *	0.053	0.179	0.013	0.201 *	0.074
	(0.098)	(0.104)	(0.135)	(0.145)	(0.105)	(0.112)
R&D investment	0.362 ***	0.404 ***	0.317 ***	0.367 ***	0.445 ***	0.481 ***
	(0.061)	(0.061)	(0.081)	(0.080)	(0.069)	(0.068)
asset intensity	−0.299 ***	−0.251 ***	−0.335 ***	−0.274 ***	−0.323 ***	−0.273 ***
	(0.050)	(0.049)	(0.065)	(0.067)	(0.052)	(0.053)
constant	−6.001 ***	−5.579 ***	−7.177 ***	−6.649 ***	−6.492 ***	−6.067 ***
	(0.236)	(0.256)	(0.328)	(0.356)	(0.254)	(0.276)
N	405	371	405	371	405	371
Log Likelihood	−2415.689	−2267.494	−2130.019	−2026.083	−2237.108	−2106.110
LR chi2	700.617	640.490	510.472	462.256	618.740	579.217

Note: Standard errors are in parentheses; * p < 0.10, *** p < 0.01.

reveals a statistically significant inverted U-shaped correlation between the frequency of local policy implementations and technological innovation, consistently supporting Hypothesis 1. As the number of policy implementations rises, the impact on technological innovation demonstrates an initial increase followed by a decrease, with a threshold at approximately 6. This suggests that the introduction of multiple new energy vehicle policies concurrently can bolster innovation endeavors in the short term, with the policy directives issued serving to invigorate innovation stakeholders. However, surpassing 6 policy implementations may lead to a diminishing innovation effect. Calculations indicate that with 3 policy implementations, each additional unit is anticipated to result in a 11.6% increase in patent applications, a 14.5% rise in valid patents, and an 11.4% uptick in invention patents. With 5 policy implementations, these growth rates decrease to 4.3%, 5.2%, and 3.3%, respectively, indicating a waning influence on patent numbers as policy implementations escalate. Moreover, at 8 policy implementations, the growth rates for patents decrease by 5.8%, 5.2%, and 7.2%, respectively, signifying that exceeding the threshold leads to a suppression of patent numbers with additional policy implementations.

Policies for new energy vehicles generally emphasize industrial planning, fiscal subsidies, charging infrastructure, safety regulations, and market promotion, with approximately 6 policy documents addressing the primary support areas for industrial development. However, each new policy necessitates coordination across various departments. An overabundance of policies can elevate coordination costs, intensify implementation fragmentation, and ultimately diminish the innovation efficiency of policy coordination.

The aforementioned findings suggest that a moderate increase in the number of policies can signal support and incentivize businesses to partake in technological innovation. Conversely, an excessive issuance of policies may elevate institutional complexity, potentially resulting in information overload and resource dispersion. This, in turn, can diminish implementation efficiency and yield adverse effects. Moreover, an extended lag period correlates with a gradual increase in the peak value of the innovation effect, underscoring the necessity for a phased release of policy effects. As supporting policies advance and market scale expands, the impact of policy volume becomes more pronounced. Consequently, the development of policies for the new energy vehicle industry necessitates a long-term outlook to sustainably drive innovation.

② The incentive effect of adoption speed and content reproduction degree on technological innovation

Models 7–12 demonstrate the impact of adoption speed on patent applications, valid patents, and invention patents in the current period and with a one-period lag.

Table 4. Regression analysis of the impact of policy adoption speed on technological innovation.

	Patent Application		Valid Patents		Invention Patents
	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
adoption speed	0.030 ***		0.036 ***		0.028 ***
	(0.009)		(0.011)		(0.010)
adoption speed2	−0.000		−0.000		−0.000
	(0.000)		(0.000)		(0.000)
L1. adoption speed		0.042 ***		0.048 ***		0.043 ***
		(0.009)		(0.012)		(0.010)
L1. adoption speed2		−0.001 **		−0.001 **		−0.001 *
		(0.000)		(0.000)		(0.000)
GDP per capita	0.176 ***	0.157 ***	0.211 ***	0.192 ***	0.133 ***	0.117 ***
	(0.032)	(0.031)	(0.040)	(0.039)	(0.034)	(0.033)
fiscal expenditure	1.183 ***	1.061 ***	1.536 ***	1.391 ***	1.303 ***	1.147 ***
	(0.223)	(0.215)	(0.285)	(0.279)	(0.234)	(0.231)
income intensity	0.168 **	0.050	0.140	−0.008	0.172 *	0.055
	(0.084)	(0.087)	(0.115)	(0.118)	(0.089)	(0.091)
R&D investment	0.372 ***	0.417 ***	0.334 ***	0.375 ***	0.458 ***	0.503 ***
	(0.060)	(0.060)	(0.081)	(0.079)	(0.066)	(0.066)
asset intensity	−0.300 ***	−0.248 ***	−0.325 ***	−0.264 ***	−0.325 ***	−0.267 ***
	(0.049)	(0.046)	(0.061)	(0.060)	(0.052)	(0.049)
constant	−5.980 ***	−5.599 ***	−7.120 ***	−6.636 ***	−6.419 ***	−6.052 ***
	(0.222)	(0.236)	(0.309)	(0.323)	(0.236)	(0.246)
N	405	371	405	371	405	371
Log Likelihood	−2409.346	−2260.861	−2126.593	−2022.522	−2228.371	−2095.951
LR chi2	686.440	679.593	515.067	516.535	631.725	658.278

Note: Standard errors are in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

demonstrates that the rate of adoption significantly influences technological innovation, exhibiting a positive impact in the present period. A one-month increase in adoption speed correlates with a 2.8% to 3.7% rise in patent numbers. In the subsequent period, a notable inverted U-shaped pattern emerges, with a threshold around 24 months. Below this threshold, adoption speed positively affects patent numbers. For instance, at an adoption speed of 2, a one-month increase boosts patent applications by 4.5%. However, at an adoption speed of 10, the increase diminishes to 2.8%, indicating a waning effect. Conversely, surpassing the threshold diminishes the innovation effect. For example, at an adoption speed of 30, patent applications decrease by 1.2%, indicating an inhibitory effect on innovation. This inverted U-shaped correlation validates Hypothesis 2, which is further supported when considering valid patents and invention patents. This suggests that a moderate delay enhances technological innovation by aligning governmental actions with industrial demands and negotiating interest distribution effectively. Prolonged delays, on the other hand, dissipate policy benefits and hinder innovation in the long term. Therefore, local governments should respond by comprehensively understanding central government directives, conducting thorough research, tailoring strategies to their unique circumstances, and striking a dynamic equilibrium between precise policy adjustments and time efficiency.

The extent of policy content reproduction delineates the divergence between local and central policies. This metric, distinct from the sheer volume and pace of policy issuance, which are merely formal attributes, elucidates the variances in innovation outcomes resulting from local policy response entities at a substantive level. Models 13–18 analyze the impact of current and lagged first-period policy reproduction degree on patent applications, valid patents, and invention patents. As depicted in

Table 5. Regression analysis of the impact of policy reproduction degree on technological innovation.

	Patent Application		Valid Patents		Invention Patents
	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18
policy reproduction	1.900 ***		2.657 ***		1.892 ***
	(0.641)		(0.798)		(0.693)
policy reproduction2	−1.837 **		−2.758 **		−1.782 *
	(0.901)		(1.133)		(0.972)
L1. policy reproduction		1.687 ***		2.112 ***		1.479 **
		(0.650)		(0.812)		(0.687)
L1. policy reproduction2		−1.136		−1.527		−0.752
		(0.959)		(1.204)		(1.022)
GDP per capita	0.159 ***	0.134 ***	0.178 ***	0.157 ***	0.115 ***	0.096 ***
	(0.034)	(0.032)	(0.044)	(0.041)	(0.037)	(0.035)
fiscal expenditure	1.262 ***	1.123 ***	1.688 ***	1.482 ***	1.387 ***	1.208 ***
	(0.226)	(0.215)	(0.301)	(0.285)	(0.243)	(0.233)
income intensity	0.173 *	0.035	0.173	−0.013	0.184 *	0.036
	(0.089)	(0.086)	(0.124)	(0.118)	(0.096)	(0.089)
R&D investment	0.388 ***	0.422 ***	0.369 ***	0.394 ***	0.478 ***	0.501 ***
	(0.067)	(0.065)	(0.089)	(0.084)	(0.076)	(0.072)
asset intensity	−0.294 ***	−0.239 ***	−0.332 ***	−0.257 ***	−0.316 ***	−0.254 ***
	(0.047)	(0.044)	(0.062)	(0.059)	(0.049)	(0.046)
constant	−6.066 ***	−5.602 ***	−7.295 ***	−6.668 ***	−6.542 ***	−6.041 ***
	(0.229)	(0.235)	(0.328)	(0.330)	(0.247)	(0.247)
N	405	371	405	371	405	371
Log Likelihood	−2414.941	−2261.937	−2128.973	−2022.672	−2235.965	−2098.660
LR chi2	778.698	753.331	574.451	576.660	680.512	674.000

Note: Standard errors are in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

, the degree of policy reproduction exhibits a curvilinear pattern in the present timeframe, corroborating Hypothesis 3. At lower levels of reproduction, there is a positive correlation between reproduction degree and patent numbers. The peak innovation impact is observed at approximately 0.5 reproduction degree, beyond which innovation outcomes diminish. Notably, during the lag phase, a notable promotional effect is evident, underscoring the enduring influence of policy reproduction on innovation. These findings underscore that both excessively low and high levels of policy reproduction in the current period do not foster advancements in innovation. A low reproduction degree signifies local governments predominantly mirroring central policies without tailoring them to local contexts, thus acting as mere conduits without contextual adaptation. Conversely, an elevated reproduction degree indicates a myopic focus on local circumstances at the expense of overarching guidance, lacking a comprehensive strategic outlook. Furthermore, the positive innovation impact during the lag phase signifies the sustained release of policy benefits, continual enhancement of supportive measures, and a trend towards policy stability. The lucid direction provided by heightened reproduction levels facilitates the execution of scientific and technological innovations.

Hence, the local government should align the refinement and updating of central policies with its specific circumstances. Achieving short-term innovation benefits without accompanying support measures can cap policy reproduction at approximately 0.5. For regions requiring sustained development, a moderate increase in the reproduction rate is advisable, contingent upon continued support to sustain the momentum of innovative incentives.

6.3. Regional Differences

Group regression analysis was conducted to examine variations in the effects of policy response across the eastern, central, and western regions, as detailed in

Table 6. Regional Differences in the Innovation Effects of Policy Response.

	Esat			Central			West
	Model 19	Model 20	Model 21	Model 22	Model 23	Model 24	Model 25	Model 26	Model 27
policy volume	0.086 *			0.289 **			0.073
	(0.052)			(0.128)			(0.114)
policy volume2	−0.002			−0.045 *			−0.009
	(0.005)			(0.025)			(0.016)
adoption speed		0.022 ***			0.031 **			0.018
		(0.008)			(0.013)			(0.020)
adoption speed2		−0.001 ***			−0.000			−0.001
		(0.000)			(0.000)			(0.001)
policy reproduction			2.319 ***			1.096			−0.022
			(0.637)			(1.004)			(1.143)
policy reproduction2			−2.910 ***			−0.523			0.412
			(0.793)			(1.498)			(1.718)
GDP per capita	0.293 ***	0.349 ***	0.270 ***	−0.123	−0.114	−0.134	0.311 ***	0.311 ***	0.298 ***
	(0.034)	(0.032)	(0.032)	(0.095)	(0.078)	(0.092)	(0.083)	(0.082)	(0.088)
fiscal expenditure	0.419 **	0.241	0.559 ***	2.860 ***	2.582 ***	2.829 ***	2.026 ***	2.019 ***	2.028 ***
	(0.208)	(0.181)	(0.194)	(0.419)	(0.353)	(0.412)	(0.377)	(0.377)	(0.373)
income intensity	−0.182 **	−0.223 ***	−0.162 *	0.269 *	0.212	0.201	0.211	0.210	0.242 *
	(0.081)	(0.073)	(0.087)	(0.163)	(0.143)	(0.153)	(0.132)	(0.136)	(0.135)
R&D investment	0.215 ***	0.152 ***	0.228 ***	1.446 ***	1.562 ***	1.493 ***	0.789 ***	0.808 ***	0.793 ***
	(0.047)	(0.044)	(0.051)	(0.284)	(0.235)	(0.245)	(0.184)	(0.179)	(0.183)
asset intensity	−0.021	0.053	−0.024	−0.263 ***	−0.220 ***	−0.233 ***	−0.512 ***	−0.507 ***	−0.512 ***
	(0.047)	(0.042)	(0.045)	(0.062)	(0.059)	(0.066)	(0.083)	(0.082)	(0.081)
constant	−5.595 ***	−5.671 ***	−5.717 ***	−7.845 ***	−7.852 ***	−7.789 ***	−7.432 ***	−7.466 ***	−7.506 ***
	(0.238)	(0.230)	(0.235)	(0.511)	(0.453)	(0.476)	(0.347)	(0.350)	(0.349)
N	153	153	153	112	112	112	140	140	140
Log Likelihood	−981.839	−977.865	−981.592	−665.231	−656.534	−663.474	−657.542	−657.339	−657.260
LR chi2	532.225	609.515	565.720	287.864	325.380	283.618	672.562	533.625	538.232

Note: Standard errors are in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

. Models 19 to 21 revealed a positive correlation between the number of policy documents issued and technological innovation at the 10% significance level in the eastern region. Notably, both the rate of policy adoption and the level of policy reproduction exhibited significant inverted U-shaped relationships, with thresholds of 11 months and 0.4, respectively. Specifically, innovation outcomes in the eastern provinces decelerate or diminish when the response time to central policies exceeds 11 months or the level of policy reproduction surpasses 0.4. In Models 22 to 24 concerning the central region, a curvilinear relationship was observed between the number of policy documents issued and technological innovation, with an optimal threshold of approximately 3.21. The speed of policy adoption demonstrated a significant positive association, leading to a 3.1% increase in patent numbers. Conversely, the degree of policy reproduction did not yield a significant effect, suggesting that central provinces could enhance innovation outcomes by streamlining policy issuance, avoiding redundancy, focusing on crafting 3 to 4 high-quality policy documents, extending response times, and making judicious decisions. Models 25 to 27 indicated that the local policy response in the western region did not exert a significant influence on innovation.

The efficacy of local policy response in the eastern region surpasses that in the western region, with variations in the innovation incentive effect compared to the central region, as seen in Figure 2. While the eastern region exhibits a significant incentive effect from the issuance of policy documents, it is comparatively lower than that of the central region, suggesting a lesser reliance on policies in the economically advanced eastern region. Instead, it shows greater sensitivity to shallow and deep adoption policy processes. Adoption speed and policy reproduction levels are notably higher in the eastern region than in the central region, indicating efficient policy implementation post issuance. Conversely, although policy reproduction is present in the central region, it does not significantly incentivize technological innovation, suggesting a disconnect between policy decision-making and execution. In the western region, local policy response effects are insignificant, with innovation performance relying more on economic foundations than policy responses, potentially constrained by a weak innovation base. Thus, enhancing foundational support, developing local-appropriate policy models, and improving implementation efficiency are imperative.

This study examines the interaction between local policy responses and factors such as economic development, R&D investment, fiscal expenditure, and asset intensity to uncover the mechanisms driving regional disparities, as shown in

Table 7. The Intrinsic Mechanisms of Regional Disparities.

	East	Central	West
	Model 31	Model 32	Model 33
policy volume	0.362 ***	0.599 *	−0.205
	(0.103)	(0.322)	(0.390)
policy volume2	−0.002	−0.038	−0.029
	(0.006)	(0.028)	(0.024)
adoption speed	0.046 ***	0.167 ***	−0.053
	(0.016)	(0.047)	(0.060)
adoption speed2	−0.001 **	−0.001 *	−0.000
	(0.000)	(0.000)	(0.001)
policy reproduction	−0.384	−3.552 **	5.268 *
	(0.970)	(1.808)	(2.856)
policy reproduction2	0.133	−1.199	−0.881
	(0.971)	(1.519)	(2.491)
GDP per capita	0.316 ***	−0.014	0.356 **
	(0.090)	(0.107)	(0.144)
fiscal expenditure	1.036 **	1.692 ***	2.371 ***
	(0.458)	(0.379)	(0.660)
income intensity	−0.339 ***	0.119	0.308 **
	(0.075)	(0.156)	(0.145)
R&D investment	0.086	1.844 ***	0.818 ***
	(0.143)	(0.312)	(0.239)
asset intensity	0.180 **	−0.127 *	−0.587 ***
	(0.082)	(0.072)	(0.096)
policy volume * R&D investment	0.017	0.523 ***	0.270
	(0.029)	(0.153)	(0.241)
policy volume * GDP per capita	−0.041 *	−0.257 ***	−0.028
	(0.022)	(0.081)	(0.098)
policy volume * fiscal expenditure	0.112	0.183	0.190
	(0.119)	(0.302)	(0.470)
policy volume * asset intensity	−0.041 **	−0.021	−0.018
	(0.017)	(0.018)	(0.061)
adoption speed * R&D investment	−0.009	0.003	0.012
	(0.013)	(0.026)	(0.037)
adoption speed * GDP per capita	−0.002	−0.015	−0.007
	(0.002)	(0.013)	(0.019)
adoption speed * fiscal expenditure	0.009	−0.059	0.024
	(0.014)	(0.052)	(0.082)
adoption speed * asset intensity	0.002	0.009	0.013
	(0.003)	(0.007)	(0.015)
policy reproduction * R&D investment	0.188	−3.690 ***	−1.001
	(0.305)	(1.172)	(1.680)
policy reproduction * GDP per capita	0.249	0.995 **	−0.151
	(0.158)	(0.473)	(0.855)
policy reproduction * fiscal expenditure	−1.861 **	4.366 ***	−1.105
	(0.902)	(1.649)	(4.232)
policy reproduction * asset intensity	−0.001	−0.352 *	−0.464
	(0.147)	(0.187)	(0.622)
constant	−6.370 ***	−7.854 ***	−8.072 ***
	(0.279)	(0.535)	(0.391)
N	153	112	140
Log Likelihood	−953.967	−639.885	−648.089
LR Chi2	0.154	0.138	0.140

Note: Standard errors are in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

. Model 31 reveals that GDP per capita and asset intensity negatively moderate the issuance of policy documents. This indicates that in eastern regions, higher economic development and stronger corporate assets diminish the influence of policy issuance on technological innovation, suggesting that enterprises there possess greater capabilities and rely less on policies. Furthermore, fiscal expenditure and policy reproduction also show negative moderating effects. In regions with high fiscal investment, automobile enterprises may become dependent on policy subsidies, and frequent policy updates could disrupt R&D continuity. Conversely, in regions with low fiscal investment, moderate policy reproduction can align with central policies and encourage independent innovation.

Regions with high fiscal expenditures often have advanced industrial technologies, where excessive policy intervention can disrupt natural market selection. Conversely, underdeveloped regions with low fiscal spending typically lag in the new energy vehicle industry, where market mechanisms are incomplete, and policy updates can address these gaps. This illustrates the “resource curse” in industrial policy: in financially affluent areas, excessive intervention may misdirect corporate focus and resources, while in less affluent regions, policies primarily serve as signals. In the rapidly evolving new energy vehicle industry, governments in mature markets should adopt a moderate, regulatory-focused approach, while in peripheral regions, they should guide industrial and technological development.

Model 32 reveals that in the central region, the interaction between policy document issuance and R&D investment is significantly positive, whereas the interaction with per capita GDP is significantly negative. This suggests that enterprises in economically advanced regions depend less on policy documents. However, given the central region’s comparatively weaker innovation capabilities, the synergy of policy issuance and R&D investment offers stronger innovation incentives. Furthermore, the interaction between policy reproduction intensity and R&D investment is significantly negative, while its interaction with fiscal expenditure is significantly positive. This indicates that frequent policy updates may diminish innovation output when R&D investment increases, yet fiscal expenditure can alleviate the adverse effects of high policy reproduction intensity.

Central region hosts numerous traditional automakers, yet its industrial chain remains underdeveloped. Policy changes in this area often align with central government directives. Local enterprises face the dual challenges of weak technological foundations and policy uncertainties. High policy replication forces companies to invest in multiple technological avenues, such as lithium batteries, hydrogen energy, and battery-swapping models, dispersing R&D resources and hindering significant technological breakthroughs, creating an “R&D investment trap.” Conversely, increased fiscal spending can aid in building technological innovation platforms, converting policy uncertainties into opportunities for experimentation, thereby alleviating the R&D investment trap linked to risks in technology pathway selection.

Model 33 reveals that none of the interaction terms in the western region are statistically significant. Nevertheless, fiscal expenditure, policy reproduction intensity, and R&D investment intensity show significantly positive correlations with innovation effects. This implies that the western region has yet to develop a synergistic mechanism between policy and market forces, with technological innovation largely dependent on government resource inputs.

New energy vehicle companies in the eastern region, benefiting from advanced development and robust innovation, show less reliance on policy incentives. Local governments should tailor guidance to economic conditions and industrial stages to prevent a “resource curse.” In the central region, where enterprises heavily depend on policy, local strategies should avoid frequent updates that could dilute R&D efforts. For the western region, increasing R&D investment and fiscal spending is recommended, alongside refining policies to create a policy-market synergy for technological innovation, thus strengthening collaborative outcomes.

7. Research Conclusions and Discussions

7.1. Research Conclusions

In order to alleviate the transaction cost increased due to asymmetric information between upper and lower governments and implement the governance efficiency of central policies, central-local coordination is an inevitable choice. By analyzing the impact of policy quantity, adoption speed and policy reproduction degree on technological innovation, this paper finds that effective local policy response is the adaptive reconstruction of central policy by local government under the framework of political authority, combined with local actual situation and according to development expectation.

The quantity of policy issuance serves as a key indicator of local policy response, shaping institutional commitments through official policy releases that establish the foundational framework of the institutional environment and offer support for local technological innovation. In the context of the burgeoning new-energy vehicle industry, heightened policy issuance by Chinese local governments plays a pivotal role in refining the policy landscape and fostering innovation by facilitating resource accumulation. However, it is crucial to note that a higher volume of policy issuance does not invariably translate to better outcomes. Research indicates that an optimal innovation incentive effect is achieved when the number of policy issuances hovers around 6. As the lag period extends, the anticipated increase in patent numbers escalates, underscoring the enduring nature of the collaborative impact. Nonetheless, an excessive proliferation of policies can result in resource duplication and policy redundancy. Analysis on a national scale reveals that once the number of policy issuances surpasses 6, the innovation incentive effect begins to wane, signaling a marginal diminishing return. Notably, while a positive incentive effect of policy quantity persists in the eastern region, it only attains statistical significance at the 10% level. Consequently, there is a need to prioritize optimizing policy structure over a blind pursuit of policy quantity. The observed inverted U-shaped relationship in the central region suggests that maintaining the innovation incentive effect necessitates keeping the number of policy issuances below 3. Therefore, it is imperative for the government to tailor policy issuance to the specific industrial landscape and regional attributes to enhance the policy framework effectively.

The pace of adoption reflects the political responsiveness of local governments under incentivized promotion. Empirical findings demonstrate a significant positive correlation between current adoption speed and technological innovation. Notably, a pronounced inverted U-shaped relationship emerges with a one-period delay, with a national sample threshold of approximately 24 months. This suggests challenges in effectively implementing rapid responses due to insufficiently detailed implementation plans tailored to the requirements of new energy vehicle enterprises, potentially resulting in a uniform approach. Conversely, a moderate delay enables the government to align with industrial needs, facilitating negotiations on interest distribution among diverse stakeholders. Excessive delays, however, may lead to prolonged and concentrated instances of resource misallocation, blurred corporate expectations, dissipation of policy benefits, and delayed fiscal subsidies. Empirical evidence indicates that surpassing the adoption speed threshold results in a decline in innovation outcomes. Thus, within the threshold range, the advantages of judicious policy delays outweigh institutional costs, fostering enhanced innovation levels. Nevertheless, once the threshold is surpassed, innovation becomes constrained. For instance, optimal response times for eastern provinces should not exceed 11 months, while central regions may extend these timelines judiciously. Consequently, local governments should tailor responses based on insights from central policies and local circumstances, avoiding hasty reactions solely for superiors’ acknowledgment or excessive delays leading to missed policy cycles. Striking a dynamic equilibrium between precision in policy adjustments and time considerations is essential.

Policy reproduction is indicative of local institutional innovation capacity. Proper reproduction can bridge the gap between the ambiguity of central policies and local micro-situations while aligning with major central government policies, thereby fostering innovation through resource alignment across government hierarchies. Empirical analysis demonstrates an inverted U-shaped relationship between the extent of new energy vehicle policy reproduction and current period patent numbers, with a threshold at 0.5, followed by a positive incentive effect. This suggests that both excessively low and high levels of policy reproduction are detrimental to innovation enhancement. Overadjustment may cause short-term disruptions, which can be mitigated through long-term adaptation mechanisms. Furthermore, there are regional disparities in the impacts of policy reproduction levels. In the eastern region, a significant inverted U-shaped relationship exists with a threshold around 0.4, while the central region shows a significant positive correlation, and the western region exhibits no significant effect. Therefore, local governments should tailor policy refinement and updates to their development goals and central government directives. Additionally, they should strategically harness the incentive effects of policy reproduction on innovation based on regional development contexts.

Enterprises in the eastern region demonstrate superior innovation capabilities due to a mature market environment and well-established industrial chains, which lessen their reliance on policies. Conversely, enterprises in the central region, characterized by weaker technological foundations and lower marketization, depend more on government resources. In the western region, there is no significant correlation between policy responsiveness and technological innovation, highlighting the need to enhance policy implementation capacity.

In conclusion, this paper contends that local governments should prioritize alignment with local industrial conditions and innovation needs over mere speed and volume in implementing central policies, thereby enhancing local applicability and avoiding formalism. Furthermore, adequate time must be allocated for research and coordination to effectively study and assimilate central policies, preventing inefficiencies like “hasty implementation and frequent revisions” and “campaign-style governance.” Lastly, local adaptation of central policies should focus on interpretative refinement and localized updates rather than substantive modifications or excessive layering of requirements.

7.2. Discussion

This paper examines the impact of hierarchical government relations in China’s sectional system on technological innovation, using policy texts as the primary focus. The findings indicate that local government responses can incentivize technological innovation to a certain degree. It highlights that local governments are motivated by both political authority and developmental needs, necessitating the issuance of appropriately tailored policies within an institutional framework. These governments must balance policy adaptation with time costs by learning from central policies and addressing local issues. Grounded in neoclassicism and Schumpeter’s endogenous growth theory [17,18], the study offers actionable recommendations for government intervention to mitigate market failure. Local governments should moderately engage in industrial development and technological innovation, avoiding both inaction and excessive intervention. They should adhere to market and technological principles, emphasize local development traits, foster core technologies, and leverage local comparative advantages, all while aligning with central government policies.

Luo et al. identified local bureaucrats as key drivers of policy initiation and diffusion. They noted that centrally initiated industrial policies often lack alignment with local contexts, while bottom-up policies tend to be more locally applicable [13]. Building on this, our study argues that even with information asymmetry between central and local governments, policy consistency can improve through local responsiveness. This necessitates that local governments, within the centralized and decentralized institutional framework, thoroughly understand local industrial and economic conditions, exercise agency, and adapt central policies through differentiated responses.

This study examines inter-regional variability, highlighting the heterogeneous effects of local policy responses, aligning with existing research [73]. It underscores that local governments craft and implement policies based on central directives, tailored to their developmental stage’s characteristics and expectations. While previous studies have focused on policy diffusion mechanisms like competition, imitation, and learning among regions [74], this paper contends that effective diffusion necessitates an active role from local governments, including scrutinizing local officials’ policy implementation. For instance, the western region must enhance foundational support to boost policy implementation efficiency. This research enriches the discourse on policy responses by exploring regional differences at the meso-level, offering insights for local governments to adopt differentiated policy strategies.

This study highlights regional disparities in the innovation-driven impacts of policy responses, underscoring the need for local governments to tailor policies to economic conditions and industrial stages. In the developed eastern regions, enterprises exhibit strong market-driven innovation capabilities, with local policies primarily serving regulatory and protective functions to prevent excessive intervention and resource curse effects. Conversely, enterprises in less developed central regions depend more on government resources, with lower industrial marketization levels, requiring policy support and guidance. Nonetheless, it is vital to avoid policy fragmentation, which could disperse R&D resources and diminish innovation efficiency.

Existing research on the interplay between local and central government policies predominantly adheres to a static synergy framework [34,75], overlooking the dynamic behavior of local governments crucial to policy implementation outcomes. This paper addresses this gap by integrating the institutional context of local governments with the adaptive requirements of industrial development. It quantifies policy response through metrics such as the volume of policy issuance, adoption speed, and extent of policy replication. By analyzing both surface-level adoption behaviors and in-depth content, this multidimensional approach offers a comprehensive view of local government responses to central policies, addressing gaps in existing research on local government behavior.

Despite significant advancements in new energy vehicle technology, research indicates that even with sophisticated autonomous driving systems, further optimization in energy consumption is necessary [76]. This suggests that improvements are still needed in the energy efficiency and practical application of these innovations. Local governments must remain aligned with central policies, attuned to industry trends, and adaptable to local conditions. Based on the aforementioned considerations, the following policy recommendations are proposed: ① A “flexible nesting” mechanism is essential for effective central-local policy transmission during policy implementation and feedback. Local governments should leverage the institutional flexibility allowed by central policies to balance central uniformity with local diversity. Concurrently, dynamic monitoring of responses to new energy vehicle policies is crucial. If the number of issued documents surpasses a set threshold, higher-level governments should conduct compliance reviews to eliminate redundant policy support. ② Enhance the decision-making efficiency of local governments. Policy transmission should follow a dynamic cycle of “learning, digestion, absorption, reproduction, and feedback.” In industrial planning, local governments must engage in field visits and research to grasp local automaker needs, perform policy simulations, and estimate fiscal expenditures and technological advancements. Avoiding institutional friction from “second-hand transmission” and formalism is crucial to improve policy implementation precision. ③ Implement region-specific policy responses and offer tailored guidance for local actions. In the eastern regions, where sensitivity to policy issuance is low, policies should be streamlined. In contrast, central enterprises depend heavily on government resources, requiring industrial policies to align with central directives to maximize the synergy between policy response and R&D investment. Western regions should avoid the mere accumulation of policies and instead focus on improving infrastructure to support innovation and enhance policy implementation capacity. ④ Implement a policy cycle evaluation mechanism to dynamically optimize policy responses. Throughout policy execution, monitor technological innovation within the new energy vehicle industry, focusing on innovation indicators like patent applications, commercialization, and R&D investment. Adjust policies promptly based on these monitoring results to decide whether they should be continued, revised, or terminated.

This study has several limitations. Firstly, while the prominence of technology-intensive characteristics and governmental support at various levels in the new energy vehicle industry, it possesses unique industry-specific traits. Therefore, the generalizability of the findings to other industries necessitates additional validation. Secondly, this study utilizes provincial-level policies as proxies for local policies. Future research will delve into prefecture-level and district-county-level policies to enhance the examination of intergovernmental collaboration across different administrative tiers. Thirdly, although this study assesses local policy responses from various angles, it falls short of fully capturing the relationship between policy content and industrial characteristics, technological innovation, and policy practicality. The study’s ability to evaluate policy quality is limited. Future research will address these gaps using methodologies like text analysis and machine learning.