Does Spatial Location Competition Shape Enterprises’ Green Technology Innovation? The Moderating Roles of Digital Transformation and Environmental Uncertainty

Abstract

The research objective of this paper focuses on the core relationship between “spatial location competition and enterprises’ green technological innovation”. It aims to systematically reveal the intrinsic impact mechanism of spatial location competition on enterprises’ green technological innovation and further clarify the moderating effects of two key variables which are digital transformation and environmental uncertainty in this relationship. Specifically, this study explores the impact mechanism of spatial location competition on enterprises’ green technological innovation, with a particular focus on the moderating role of digital transformation. Based on the resource-based theory, this paper constructs a theoretical framework of “location competition—digital transformation—green innovation”. Using panel data of Chinese A-share listed companies as the research sample, the empirical results show that there is a significant negative correlation between spatial location competition and enterprises’ green technological innovation. Furthermore, the level of enterprises’ digital transformation can effectively mitigate this negative impact and enhance the incentive effect of spatial location competition on green technological innovation. The findings of this study have important theoretical and practical implications. They suggest that the government should establish a regional policy system to promote the coordinated development of “digital + green” initiatives and strengthen the integration and supervision of digital technology and green technology, thereby providing institutional guarantees for enterprises to improve green technological innovation through spatial location optimization and digital transformation.

1. Introduction

Green technology plays a crucial role in boosting economic growth, advancing China’s carbon peak goal, and driving high-quality economic development. To date, China has achieved remarkable progress in green technology development, which is well evidenced by the rapid growth of related patents. Nevertheless, on a global scale, both the quantity and quality of China’s green technology patents still lag behind those of leading countries such as the United States, Japan, and Germany. Furthermore, the domestic green technology market is underdeveloped, the “industry–university–research application” mechanism has its shortcomings, the conversion rate of green technology remains low, and there are significant spatial disparities in the distribution of green technology. Given China’s ambition to achieve its carbon peak by 2030 and the projected growth of the green technology market to approximately USD 63.94 billion by that same year, it is vital to tackle the challenges facing innovation in green technology, whether through policy initiatives or by attracting economic value.

From the standpoints of economic growth and technological advancement, enterprises play an essential and irreplaceable role, with their innovation often shaped by competitive pressures. Research has indicated that competition compels companies to ramp up their innovation efforts to retain their market positions. Additionally, studies examining the same-group effect have revealed that, in competitive landscapes, firms are more inclined to emulate the innovations of successful competitors, thus enhancing their own innovative pursuits; however, some scholars argue that intense competition can hinder innovation efforts, as companies may opt to cut back on innovation investments and conserve funds to mitigate risks. Ultimately, in both scenarios, the actions taken by firms are aimed at benefiting themselves. When innovation risks are manageable, companies may be more inclined to innovate to boost their competitiveness, especially in the globalized context and in light of the economic repercussions of the pandemic. Without a competitive edge, businesses are more likely to be sidelined by the market.

From the perspective of an enterprise’s survival and growth, these outcomes are deeply influenced by their environment, which encompasses market competition, regulations, technological conditions, the frequency of economic fluctuations, policy shifts, and resource availability. Specifically, in a highly competitive market, firms may enhance their innovation efforts [1]; governmental policies promoting green development can incentivize companies to pursue innovations in green technology [2]; the pace of technological advancement may steer a company’s strategic decisions toward strengthening its innovation capabilities [3]; during periods of significant economic volatility and policy alterations, firms might adopt more conservative strategies and curtail high-risk innovation investments [4]; and innovation is crucially affected by the availability of external resources, such as financing, talent, and technology, whereby a scarcity of these resources can restrict enterprises’ innovative activities, while abundant resources can facilitate the achievement of innovation objectives [5]. In summary, a company’s innovation is intricately linked to its surrounding environment.

From a practical perspective, the rapid evolution of next-generation information technologies, such as big data, cloud computing, the Internet of Things, and artificial intelligence, is driving significant digital transformations in the global economy [6,7]. For various sectors, the digital transformation of businesses has become a strategic imperative through which to effectively navigate market competition, meet customer expectations, and foster sustainable growth [8,9]. This transformation involves not only technological advancements but also substantial innovations in business models, organizational frameworks, and operational procedures [10]. By harnessing digital technologies, organizations can optimize their processes, enhance operational efficiency, and adapt more rapidly to market fluctuations [11]. Additionally, there is a strong link between digital transformation and corporate innovation. Firstly, digital platforms and tools empower companies to drive innovations in products, services, and processes [12]. Secondly, digital transformation promotes the integration of internal and external resources, breaks down information silos, and enhances knowledge sharing and collaborative innovation capabilities [13]. Additionally, it catalyzes innovations in business models, leading to new approaches for creating and capturing value, thereby allowing companies to better adapt to and influence market shifts [14,15]. Therefore, it is crucial to include digital transformation in our research agenda.

Previous studies often used environmental uncertainty and the degree of enterprises’ digital transformation as explanatory variables to analyze their impacts on corporate innovation or as moderating variables that affect other factors (such as corporate financialization and managerial myopia) in relation to corporate innovation [4]. While some researchers have explored all three variables within a single framework, their studies have frequently focused on state-owned enterprises [2]. Other scholars have examined market competition as a moderating variable in understanding the influence of digital transformation on corporate innovation [6]. Overall, findings reveal a robust connection among environmental uncertainty, enterprise digital transformation, competition, and innovation. However, there has been limited exploration of the integration of environmental uncertainty and enterprise digital transformation in the context of how competitive dynamics affect innovation, particularly regarding the impacts of both generalized and localized competition on firms’ green technology innovation.

In this context and building on prior research related to spatial competition (Zhang and Hong, 2022), we conduct this study with the aim of investigating how competition based on the spatial location of enterprises influences green technology innovation while also considering environmental uncertainty and enterprise digital transformation as moderating factors [16]. By incorporating these two crucial elements into the research framework, we address a gap in the existing literature concerning the interactions between environmental uncertainty, digital transformation, corporate competition, and green technology innovation.

This research contributes to a deeper understanding of the factors affecting green technology innovation in enterprises, enriches the discussion on environmental uncertainty and digital transformation as moderating variables, and offers new theoretical insights and practical pathways for achieving green and high-quality economic development in China.

2. Theoretical Model and Research Hypotheses

2.1. Spatial Location Competition

Spatial location competition is an important research field in economics and regional science, exploring how a company’s geographical location choices affect its competitive behavior and market structure. The concept of spatial location competition originated from an extension of the traditional oligopoly monopoly model. The Cournot model (1838) initially explored competition between firms in terms of output but did not consider spatial factors [17]. Bertrand (1883) proposed the price competition model, but it triggered the Bertrand paradox, which is the dilemma of zero profit for enterprises under price competition. To explain this paradox [18], Hotelling (1929) introduced spatial location competition, emphasizing that the geographical location choices of firms have crucial impacts on competitive behavior and market equilibrium, opening up the field of spatial economics [19]. Subsequently, scholars have explored the impacts of spatial location competition on firm site selection, pricing, and innovation strategy by constructing and expanding models [2,20]. The aforementioned studies theoretically demonstrate that spatial location competition affects firm decisions, but there has been no empirical research using actual data to explain the impact of spatial location competition on firm decisions. Spatial location competition to the influence of firm competition on capital market pricing rates for the first time, bringing spatial location competition from theory to empirical evidence as a more appropriate variable for the degree of competition between firms [16].

In classic competition models such as Cournot (1838), Bertrand (1883) and Hotelling (1929), the core objective of enterprises is to capture market share and maximize economic profits [17,18,19]. However, due to the high upfront investment and long payback period, green technological innovation is often regarded as an unnecessary cost [21]. But by integrating the Triple Bottom Line (TBL) approach, it can be observed that contemporary spatial location competition has evolved from a single economic competition to a three-dimensional value competition. The TBL approach, centered on the synergy of “economic-environmental-social” values, breaks the limitation of traditional competition theories that focus solely on economic profits, providing a theoretical basis that is more in line with sustainable development goals for understanding the driving mechanism of spatial location competition on enterprise green technological innovation. Elkington (1998) was the first to systematically propose the “triple bottom line” responsibility concept, clearly stating that enterprise performance should be comprehensively evaluated from financial, environmental, and social dimensions [22]. This framework has fundamentally changed the traditional single-dimensional understanding of enterprise goals in competition theories. In classic competition models such as Cournot and Bertrand, the core objective of enterprises is to capture market share and maximize economic profits. However, due to the high upfront investment and long payback period, green technological innovation is often regarded as an unnecessary cost. But by integrating the TBL approach, it can be observed that contemporary spatial location competition has evolved from a single economic competition to a three-dimensional value competition.

Integrating institutional theory in the Chinese context can further refine the impact path of spatial location competition on green technological innovation. The institutional competition between local governments sets “green rules” for enterprise competition, and the competition between enterprises promotes the diffusion of green technological innovation within the location through compulsory and imitative isomorphism mechanisms [23]. This addition not only explains why there are significant regional differences in the green innovation behavior of Chinese enterprises but also provides an institutional theoretical basis for subsequent analysis of the moderating role of environmental uncertainty [24]. Environmental uncertainty, essentially the instability of the institutional environment, can be explained through the logic of institutional pressure changes in institutional theory for its moderating effect on the relationship between spatial location competition and green technological innovation.

ESG (Environmental, Social, and Governance) related theories focus on the correlation between an enterprise’s sustainable development capabilities and its performance in the capital market, emphasizing that ESG performance is not only a manifestation of social responsibility but also a key factor influencing investors’ decisions and financing costs. Incorporating ESG theory into the theoretical framework can add a dimension of capital market competition to the product market competition in classic competition models, thereby providing a more comprehensive explanation of the economic incentive mechanism driving green technological innovation through spatial location competition. In the context of spatial location competition, the supplementary value of ESG theory mainly lies in two aspects: strengthening the economic return expectations of green technological innovation and refining the differentiated competition paths of green innovation.

2.2. Green Technology Innovation

Currently, research on green technology innovation focuses on the factors driving this innovation and the economic benefits associated with a low-carbon economy. The driving factors encompass external green and low-carbon policies, the governance environment, and internal governance conditions [25,26]. The economic benefits represent the objectives of green technology innovation, including the quality and sustainability of economic development, as well as carbon emissions [4]. Scholars’ findings indicate that government subsidies and tax incentives significantly impact green technology innovation. The ability to deduct related R&D expenses and receive low-carbon emission subsidies alleviates the financial burden on companies, thereby enhancing their motivation for green technology innovation, although this effect varies across different industries [13]. Additionally, more pronounced low-carbon pilot policies are likely to greatly encourage enterprises in their green technology innovation efforts [13]. City governments involved in policy pilots will adhere to pilot requirements, issue various incentive documents, and promote green and low-carbon initiatives within the city, thereby fostering stronger societal oversight of enterprises’ green practices.

2.3. Spatial Location Competition and Enterprise Green Technology Innovation

From a theoretical perspective, spatial location competition exerts an inhibitory effect on green technological innovation by undermining the resource allocation efficiency, collaborative networks, and market environment necessary for green technological innovation. The specific mechanisms are as follows:

Under the global wave of sustainable development, green technological innovation has become the core path to resolving the contradiction between economic growth and environmental constraints. However, this innovation process is highly dependent on the efficient allocation of VRIN resources (resources that are valuable, scarce, difficult to imitate, and irreplaceable). Nevertheless, the widespread existence of regional market segmentation fundamentally undermines the mechanism for the optimal allocation of VRIN resources, setting multiple obstacles for green technological innovation and severely restricting the release of its development potential [27]. From the perspective of spatial competition logic, the spatial location competition between local governments based on performance evaluations easily gives rise to local protectionism, thereby solidifying the regional market segmentation pattern based on administrative boundaries. To safeguard the short-term interests of local industries, local governments often distort market mechanisms through three means: first, by setting differentiated technical standard barriers to artificially raise the threshold for external green technologies to enter the local market; second, by directly restricting the circulation of green products from other regions, fragmenting the unified market demand; third, by interfering with the cross-regional flow of talents, technologies, and funds, creating administrative barriers to the flow of VRIN resources. These actions not only undermine the fair competitive environment of the market but also directly prevent the VRIN resources necessary for green technological innovation from being optimally allocated according to market rules, thereby weakening the resource foundation for innovation from the source. Secondly, excessive competition leads to “short-sighted” behavior, squeezing the space for green technological innovation investment. When spatial location competition enters the stage of excessive competition, regions may fall into a “race to the bottom” trap in pursuit of short-term economic growth indicators. Local governments may lower environmental regulation standards and prioritize supporting traditional industries with high short-term returns, while lacking policy support for high-investment, long-cycle green technological innovation [27]. Enterprises, facing intense competition for market share, may concentrate resources on non-innovative competitive measures such as short-term capacity expansion and price wars rather than long-term green technology research and development. Moreover, under excessive competition, the profit margins of enterprises are compressed, further weakening their ability to bear the risks of green technological innovation, leading to a continuous squeeze on green technological innovation investment and making it difficult to form a stable innovation impetus.

Thirdly, negative spillover effects from neighboring regions disrupt the collaborative network for green technological innovation. Under spatial location competition, the relationship between neighboring regions tends to exhibit a “zero-sum game” characteristic, triggering negative spillover effects. On the one hand, local regions may engage in a “policy subsidy race” to attract green innovation enterprises or research institutions from neighboring regions, leading to the loss of innovation resources in neighboring regions and undermining the collaborative foundation for green technological innovation between regions. On the other hand, some regions may achieve economic growth by taking over high-pollution and high-energy-consuming industries from neighboring regions, forming “pollution havens” which indirectly weakens the environmental incentives for green technological innovation in neighboring regions. The rising pollution levels in neighboring regions may reduce the perceived environmental benefits of local green technological innovation and also make cross-regional collaborative green technology research and development difficult due to conflicts of interest, disrupting the cross-regional collaborative network necessary for green technological innovation and ultimately inhibiting the overall improvement of green technological innovation levels.

Based on the above theoretical analysis, spatial location competition inhibits green technological innovation by weakening the resource allocation efficiency and market foundation for innovation through regional market segmentation, squeezing the space for green technological innovation investment through excessive competition, and disrupting the collaborative network for innovation through negative spillover effects from neighboring regions. Therefore, this paper proposes the core research hypothesis:

Hypothesis 1 (H1).

Spatial location competition has a negative impact on green technology innovation.

2.4. The Moderating Effect of Digital Transformation

In the era of the digital economy, the digital transformation of enterprises exerts an intervention effect on the relationship between spatial location competition and green technological innovation by reconstructing the information transmission path, resource integration model and competition rules. The core logic lies in that on the one hand, digital transformation breaks the physical limitations of traditional space on resource flow, weakening the inhibitory effect of spatial location competition on green technological innovation. On the other hand, it reshapes the attributes and transfer methods of competitive elements, shifting the focus of location competition from geographic space contention to digital ecosystem construction, thereby changing the intensity and direction of the relationship between the two, forming a typical moderating effect.

Firstly, by reducing spatial dependence, it weakens the negative constraints of spatial location competition on green technological innovation. The inhibition of spatial location competition on green technological innovation largely stems from spatial dependence, that is, the resources needed for green technology research and development, such as talents, technologies, and data, are highly bound to specific geographic regions. The market segmentation and factor barriers between regions further intensify this dependence, leading enterprises into the dilemma of abandoning long-term green innovation for the sake of local resource competition. However, digital transformation weakens the negative impact of spatial location competition through the following mechanisms. First, virtual collaboration networks break the geographical boundaries of resource integration. Digital transformation promotes enterprises to build virtual collaboration systems based on cloud platforms and industrial internet, transforming green technology research and development from local resource-dependent to cross-regional resource collaborative [10]. Second, digital technology enhances the accessibility and utilization efficiency of innovation elements. Big data analysis and artificial intelligence technologies in digital transformation can convert green technology information and research and development experience scattered in different regions into shareable digital assets [11]. Third, cross-regional market connectivity compensates for the positive externalities of green innovation. Digital transformation promotes the development of online markets, breaking the restrictions of traditional regional market segmentation on the sales of green products [24].

Secondly, by enhancing the transfer of competitive elements, it reshapes the connotation of spatial location competition and optimizes the environment for green technological innovation. The core of spatial location competition is the competition for geographic location elements, and the scarcity of such elements easily leads to excessive competition, squeezing the space for green technological innovation. However, digital transformation reshapes the connotation of spatial location competition and changes the influence logic of spatial location competition on green technological innovation by enhancing the transfer and reconstruction of competitive elements. First, the transferability of digital elements reduces the monopolistic nature of geographic locations. Digital transformation shifts competitive elements from untransferable geographic elements to flowable digital elements [28]. Second, the expansion of the digital ecosystem dilutes the intensity of location competition. Digital transformation expands the scope of spatial location competition from local geographic space to cross-regional digital ecosystem, that is, the competitive advantage of enterprises no longer depends on local resource occupancy, but on participation and collaboration ability in the digital ecosystem [28]. Third, digital technology’s support for green innovation enhances the innovation orientation of competitive elements. Green technological innovation is increasingly dependent on the empowerment of digital technologies. This makes digital technology application capabilities a more important competitive factor than geographical location [14]. To gain an edge in competition, enterprises will shift resources from competing for geographical locations to enhancing the integration of digital technology application and green innovation capabilities.

Hypothesis 2 (H2).

Digital transformation has a positive moderating effect on the relationship between spatial location competition and green technology innovation. When the level of digital transformation of enterprises is high, the driving effect of spatial location competition on green technology innovation is more significant; in contrast, when the level of digital transformation is low, even facing high-intensity location competition, enterprises find it difficult to efficiently transform competitive pressure into green innovation drivers.

2.5. The Moderating Effect of Environmental Uncertainty

In the complex and volatile market environment, environmental uncertainty, as a core external risk variable for enterprises, specifically refers to the unpredictability and dynamic changes in external factors such as policies and regulations, market demand, technological iterations, and supply chain stability [24]. Its moderating logic on the relationship between spatial location competition and enterprise green technological innovation lies in that environmental uncertainty intensifies enterprises’ risk perception and resource constraints, altering their strategic choices in spatial location competition. When the external environment fluctuates significantly, enterprises, in order to avoid risks and maintain operational stability, will further reduce their investment in green technological innovation, thereby strengthening the negative inhibitory effect of spatial location competition on green technological innovation. Conversely, if the environment is relatively stable, enterprises will have a higher tolerance for innovation risks, and the negative impact of spatial location competition will be relatively weakened, forming a typical positive moderating effect.

Environmental uncertainty intensifies risk aversion tendencies, strengthening the inhibitory effect of spatial location competition on green innovation. Green technological innovation inherently features high investment, long cycles, and uncertain returns. Environmental uncertainty further amplifies this innovation risk, significantly enhancing the risk aversion tendencies of enterprise leaders [24]. Against the backdrop of spatial location competition, this superimposed risk perception generates a “double inhibition” effect. First, the “defensive cut” of innovation expenditure and the “resource squeeze” of location competition resonate. When facing uncertainties such as sudden policy changes and fluctuations in market demand, enterprises will prioritize cutting “non-essential expenditures” such as green technology research and development to retain financial resources to deal with potential crises [29]. Meanwhile, spatial location competition itself has already led enterprises to invest a large number of resources in short-term capacity expansion, local market competition, and other areas. At this time, the “defensive cut” caused by environmental uncertainty will further squeeze the resource space for green innovation. Second, the negative revision of innovation return expectations weakens the motivation for green innovation. Environmental uncertainty reduces the stability of enterprises’ expectations for the returns of green technology innovation. Enterprises find it difficult to judge the long-term benefits of new energy technology research and development. Even if they do not face direct resource competition pressure in spatial location competition, they will reduce innovation investment due to the ambiguity of return expectations. Moreover, the low-profit space formed by the “race to the bottom” in spatial location competition will further amplify the impact of this negative expectation revision.

Environmental uncertainty intensifies cost and cash flow pressure, further restricting the resource input for green innovation. Environmental uncertainty directly leads to a decline in the stability of enterprises’ income and an increase in cost fluctuations and stable cash flow is a key prerequisite for the continuous advancement of green technological innovation [29]. In the context of spatial location competition, this increase in cost and cash flow pressure forms a resource constraint intensification effect. Firstly, cost fluctuations compress profit margins, indirectly weakening the capacity for green innovation investment. When facing uncertainties, such as supply chain disruption risks and rising compliance costs, enterprises’ production costs will significantly increase. Meanwhile, price competition between local peers in spatial location competition limits the space for product price hikes, further compressing profits [29]. This dual pressure of rising costs and shrinking profits further strengthens the resource constraints of spatial location competition on green innovation. Secondly, cash flow risks force a conservative resource allocation prioritizing the abandonment of green innovation. Environmental uncertainty increases the risk of cash flow fluctuations for enterprises [30]. To maintain stable cash flow, managers will adopt a conservative resource allocation strategy, prioritizing limited funds for core expenditures such as raw material procurement and employee salaries that are essential for maintaining operations. Green technological innovation, as a long-term investment, often becomes the first to be abandoned [31]. In spatial location competition, enterprises need to reserve funds for channel expansion, promotional activities, etc., to compete for local market share. The cash flow risk caused by environmental uncertainty further solidifies this short-term-oriented resource allocation, making the inhibitory effect of spatial location competition on green innovation even stronger [32].

Hypothesis 3 (H3).

Environmental uncertainty negatively affects the relationship between spatial location competition and green technology innovation. When the environmental uncertainty is high, enterprises face high-intensity spatial location competition and may reduce their investment in green innovation due to the instability of the external environment; in contrast, when the environmental uncertainty is low, enterprises are more likely to clearly judge the direction of competition and control innovation risks, thereby efficiently transforming competitive pressure into green innovation momentum.

3. Methods

3.1. Methodology and Theoretical Model

To test the role of spatial location competition in green technology innovation, the benchmark regression model constructed in this study is as follows:

Gtii,t/Tpgi,t = α0 + α1Distance5 + ∑controls + ∑year + ∑Industry + εi,t

(1)

In Equation (1), Gtii,t and t represent the number of green invention patent applications filed by enterprise i in year t, measuring the quality of green technology innovation in that year; Tpgi,t represents the sum of the applications for green invention patents and green utility model patents by enterprise i in year t, measuring the number of green technology innovations by enterprise i in that year; Distance5 represents the average geographical distances between the 5 nearest listed companies in the same industry and listed company i; controls is the control variable, while year and industry are the year and industry dummy variables, respectively; and εi,t is the random disturbance term.

To test the moderating role of digital transformation on the relationship between spatial location competition and green technology innovation. The regression model constructed in this study is as follows:

Gti_i,t/Tpg_i,t = β₀ + β₁Distance5 + β₂Digital + β₃Distance5 × Digital +∑controls + ∑year + ∑Industry + ε_i,t

(2)

In Equation (2), the moderating variable is the degree of digital transformation (Digital). Textual analysis is used to identify and calculate the frequency of keywords related to digital transformation in the annual reports of listed companies [27,29,30,33].

This paper builds a relatively complete digital dictionary by leveraging the semantic expressions of national policies related to the digital economy and uses a machine learning-based text analysis method to construct an index that comprehensively reflects the digitalization level of listed companies in China. The specific steps are as follows:

• Step 1: Constructing a Digitalization Term Dictionary

Due to the lack of a specialized dictionary in the digital economy field, this study first constructs a corporate digitalization term dictionary based on the national policy semantic system. We manually screened 30 key national-level digital economy policy documents released between 2012 and 2018 to extract 197 keywords related to corporate digitalization [31]. Subsequently, after processing with Python-based word segmentation and manual verification, we finalized the dictionary by selecting 197 terms that appeared with a frequency of five or more.

• Step 2: Conducting Text Analysis of Annual Reports

Next, we expanded the 197-term dictionary into the “jieba” Chinese word segmentation library within the Python software 3.9 version. We then performed text analysis on the “Management Discussion and Analysis” (MD&A) sections of the listed companies’ annual reports using machine learning methods. This process yielded the frequency of the 197 digitalization-related terms within each report.

• Step 3: Constructing the Enterprise Digitalization Index

To account for variations in the length of the MD&A sections, we constructed an enterprise digitalization index (DCG). For each firm-year, this index is calculated by dividing the total frequency of digitalization-related terms by the total word count of the MD&A section. For ease of interpretation, the index is multiplied by 100. A higher DCG value indicates a higher degree of enterprise digital transformation.

To test the moderating role of environmental uncertainty on the relationship between spatial location competition and green technology innovation. The regression model constructed in this study paper is as follows:

Gti_i,t/Tpg_i,t = γ₀ + γ₁Distance5 + γ₂EU + γ₃Distance5 × EU + ∑controls + ∑year + ∑Industry + ε_i,t

(3)

Equation (3) introduces environmental uncertainty as a moderating variable. The measurement of this variable (EU) refers to the research method of Ghosh et al. (2009): firstly, calculate the unadjusted value of the industry, which is the ratio of the standard deviation of abnormal sales revenue in the past five years to the average sales revenue of the company in the same period [33]; then, divide this value by the median of the unadjusted environmental uncertainty of all companies in the same industry in the same year to obtain the industry-adjusted environmental uncertainty index [34]. The higher the ratio value, the higher the level of environmental uncertainty.

3.2. Spatial Location Competition (Distance5) Measurement

The independent variable is spatial location competition (Distance5). Using the average geographical distances between the five listed companies in the same industry closest to listed company i and listed company i, taking the natural logarithm as the inverse indicator of the degree of spatial location competition faced by listed company i, assuming that the latitude and longitude of the operating address of listed company i are (Loni, Lati), and the latitude and longitude of the operating address of listed company j in the same industry are (Loni, Lati), Formulas (2) and (3) can be directly used to calculate the geographical distance between the two listed companies, where R is the radius of the Earth, with a value of 6371 km:

Ci,j = sin(Lati) × sin(Latj) + cos (Lati) × cos(Latj) × cos (Loni − Lonj)

(4)

Distancei,j = R × Arccos (Ci,j) × π/180

(5)

Therefore, the calculation formula for the explanatory variable Distance5 is “Distance5i = ln [1 + (∑5j = 1Distancei, j)/5]”. Adding 1 can avoid negative values for the variable Distance5.

3.3. Green Technology Innovation Measurement and Control Variables Measurement

In this article, we use the number of green invention patent applications to measure the quality of enterprise green technology innovation (Gti), and measure the quantity of enterprise green technology innovation (Tpg) using the sum of the number of applications for green invention patents and green utility model patents [16].

Referring to the existing research, we mainly controlled the influencing factors, such as company size (Size), debt-to-asset ratio (Lev), return on total assets (Roa), and so on. The details for the all of the variables considered in this study are listed in

Table 1. Research variables and measurement standards.

Variable Types	Variable Name	Variable Symbol	Variable Calculation
Dependent Variable	The quality of Enterprise Green Technology Innovation	Gti	The number of green invention patents applied for by enterprises
	The quantity of Enterprise Green Technology Innovation	Tpg	The sum of green invention patents and green utility models applied for by enterprises
Independent Variable	Spatial location competition	Distance5	The natural logarithm of the average distance between the 5 closest peer companies to the company plus 1
Moderating Variable	Digital transformation	DCG	Digital transformation index [24]
Control Variables	Enterprise size	Size	Logarithmic calculation of total assets
	Asset liability ratio	Lev	Total liabilities/total assets
	Return on Total Assets	Roa	Net profit margin of total assets
	CEO duality	Dual	The chairman and general manager are the same person with 1, otherwise it is 0
	Cash flow ratio	Cashflow	Net cash flows from operating activities/end of period current liabilities
	Company listing period	ListAge	Ln (year − company listing year + 1)
	Company growth	Growth	Current year’s operating income/previous year’s operating income −1
	Shareholding ratio of the top three shareholders	Top3	Shareholding ratio of the top three shareholders/total number of shares
	Book-to-market	BM	Book assets/current year market value
	Separation degree of two rights	Seperate	The degree of separation between equity and control
	Occupation of funds by major shareholders	Occupy	Capital occupation by major shareholders/second type agency costs
	Proportion of independent directors	Indep	The ratio of the number of independent directors to the total number of directors in the board of directors

.

3.4. Data Collection

For this article, we selected Chinese A-share listed companies from 2007 to 2022 as the initial sample. Subsequently, financial listed companies and special status samples, such as ST* and ST, were excluded. Finally, a total of 28,605 annual observations were obtained from the CSMAR database. We applied tail reduction to all continuous variables at the 1% and 99% levels.

4. Empirical Testing and Results

4.1. Relevance Analysis

Table 2. Description of variables.

Variable	Obs	Mean	St	Min	Max
Gti	28,605	0.459	0.893	0	6.805
Tpg	28,605	0.709	1.105	0	7.223
Distance5	28,605	3.188	1.023	0.160	5.678
Size	28,605	22.34	1.316	19.32	26.45
Lev	28,605	0.459	0.199	0.0274	0.908
Roa	28,605	0.0409	0.0613	−0.373	0.257
Dual	28,605	0.223	0.416	0	1
Cashflow	28,605	0.0490	0.0718	−0.223	0.283
ListAge	28,605	2.291	0.757	0	3.401
Growth	28,605	0.169	0.423	−0.658	4.024
Top3	28,605	49.35	15.61	15.13	87.84
BM	28,605	0.643	0.252	0.0641	1.246
Seperate	28,605	5.157	7.687	−10.31	30.25
Occupy	28,605	0.0164	0.0251	7.29 × 10−5	0.212
Indep	28,605	37.27	5.342	25	60

shows the descriptions of the variables considered in this study. A variance inflation factor test (VIF) was performed, and the variable values in the model were all less than 10, with a mean of 3.05, indicating that there is no severe collinearity between the variables. According to the descriptive statistics of the main variables presented in Table 2, the mean and standard deviation of green technology innovation quality (Gti) were 0.459 and 0.893, respectively, with minimum and maximum values of 0 and 6.805, indicating significant differences in green technology innovation between different enterprises. The average number of green technology innovations (Tpg) was 0.709, indicating that the majority of listed companies have engaged in green technology innovation. The mean, minimum, and maximum values of Distance5 for spatial location competition were 3.188, 0.160, and 5.678, respectively, indicating that there are significant differences in the degrees of spatial location competition faced by listed companies, which preliminarily demonstrates the value of research on spatial location competition.

4.2. Regression Analysis

In

Table 3. Regression results in this study.

	Gti	t-Value	Tpg	t-Value
Distance5	−0.0821 *** (0.00509)	−16.13	−0.0869 *** (0.00606)	−14.35
Size	0.332 *** (0.00537)	61.88	0.407 *** (0.00639)	63.68
Lev	−0.0485 (0.0303)	−1.60	0.0529 (0.0360)	1.47
Roa	−0.119 (0.0949)	−1.25	0.0524 (0.113)	0.46
Dual	−0.00730 (0.0112)	−0.65	−0.0279 ** (0.0134)	−2.09
Cashflow	−0.115 (0.0708)	−1.62	−0.144 * (0.0843)	−1.71
ListAge	−0.00964 (0.00733)	−1.32	−0.0274 *** (0.00872)	−3.14
Growth	−0.0306 *** (0.0112)	−2.74	−0.0263 ** (0.0133)	−1.98
Top3	−0.00144 *** (0.000330)	−4.38	−0.00268 *** (0.000392)	−6.83
BM	−0.340 *** (0.0259)	−13.12	−0.322 *** (0.0308)	−10.46
Seperate	−0.000340 (0.000593)	−0.57	0.000547 (0.000706)	0.77
Occupy	0.0851 (0.191)	0.45	0.170 (0.227)	0.75
Indep	0.00341 *** (0.000850)	4.01	0.00334 *** (0.00101)	3.30
Year	Yes		Yes
Industry	Yes		Yes
_cons	−6.478 *** (0.106)	−60.94	−7.846 *** (0.127)	−62.00
N	28,605		28,605
adj. R2	0.291		0.343

Notes: Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

, which presents the regression results, the coefficients in columns 1 and 2 are significantly negative, indicating a noteworthy negative correlation between spatial location competition and green technology innovation. As previously defined, spatial location competition serves as a negative predictive variable; therefore, a significant negative correlation suggests that a smaller average geographical distance between a company and the five nearest listed companies in the same industry intensifies the spatial location competition faced by the company, thereby strengthening the impetus for technological transformation and potentially accelerating the pace of green technology innovation. The coefficients of Distance5 in Table 3 are significantly negative at the 1% level; hence, our finding suggests that hypothesis 1 is supported.

4.3. Moderating Effect Results

The first and second columns of

Table 4. Testing results of the moderating effects in this study.

	(1)	(2)		(3)	(4)
	Gti	Tpg		Gti	Tpg
Distance5	−0.0718 *** (0.00514) (−13.95)	−0.0740 *** (0.00613) (−12.07)	Distance5	−0.0878 *** (0.00522) (−16.82)	−0.0934 *** (0.00622) (−15.02)
Digital	0.104 *** (0.00548) (18.94)	0.124 *** (0.00653) (−18.93)	EU	−0.00762 ** (0.00383) (−1.99)	−0.00883 * (0.00456) (−1.94)
Distance5 * Digital	−0.0203 *** (0.00336) (−6.03)	−0.0100 ** (0.00401) (−2.50)	Distance5 * EU	0.0103 *** (0.00329) (3.12)	0.0112 *** (0.00392) (2.85)
Controls	Yes	Yes	Controls	Yes	Yes
Year	Yes	Yes	Year	Yes	Yes
Industry	Yes	Yes	Industry	Yes	Yes
_cons	−6.442 *** (0.108) (−59.89)	−7.797 *** (0.128) (−60.84)	_cons	−6.485 *** (0.109) (−59.31)	−7.862 *** (0.130) (−60.40)
N	27,884	27,884	N	27,317	27,317
adj. R2	0.297	0.347	adj. R2	0.297	0.348

Notes: Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

present the linear regression results after incorporating the degree of digital transformation as a moderating variable in Equation (1). The regression results indicate that the coefficient of the interaction term between spatial location competition and the degree of digital transformation is significantly negative at the 1% level. As previously defined, spatial location competition serves as a negative predictive variable; therefore, a significant negative correlation suggests that a smaller average geographical distance between a company and the five nearest listed companies in the same industry intensifies the spatial location competition faced by the company, thereby strengthening the impetus for technological transformation and potentially accelerating the pace of green technology innovation. This suggests that the degree of digital transformation in enterprises can effectively enhance the incentive effect of spatial location competition on green technology innovation; hence, our finding suggests that hypothesis 2 is supported.

Columns 3 and 4 represent the linear regression results of adding micro-enterprise environmental uncertainty as a moderating variable to Equation (1). From the regression results, it can be seen that the coefficient of the interaction term between spatial location competition and environmental uncertainty is significantly positive at the 1% level. As previously defined, spatial location competition serves as a negative predictive variable; therefore, a significant negative correlation suggests that a smaller average geographical distance between a company and the five nearest listed companies in the same industry intensifies the spatial location competition faced by the company, thereby weakening the impetus for environmental uncertainty and potentially accelerating the pace of green technology innovation. Even in the face of a fierce competitive environment, enterprises are unable to mobilize sufficient resources to cope with these external competitions; hence, our finding suggests that Hypothesis 3 is supported.

4.4. Robustness, Endogeneity, and Heterogeneity Tests

4.4.1. Robustness

In this article, we recalculated the average geographical distance between the focal company and its three closest competitors in the same industry, added 1, and used the natural logarithm of this value, referred to as Distance3, as an explanatory variable. The re-regression results, displayed in columns (1) and (2) of

Table 5. Robust testing of distance in this study.

	(1)	(2)		(3)	(4)	(5)	(6)
	Gti	Tpg		GTI	Tpg	Gti	Tpg
Distance5	−0.0825 *** (0.00534) (−15.44)	−0.0844 *** (0.00634) (−13.31)	−0.0524 *** (0.00788) (−6.64)	−0.0568 *** (0.00964) (−5.89)	−0.0626 *** (0.00892) (−7.02)	−0.0621 *** (0.0109) (−5.70)	−0.0825 *** (0.00534) (−15.44)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes
_cons	−6.817 *** (0.114) (−59.82)	−8.167 *** (0.135) (−60.38)	−5.130 *** (0.146) (−35.13)	−6.764 *** (0.179) (−37.87)	−5.555 *** (0.171) (−32.55)	−7.254 *** (0.208) (−34.81)	−6.817 *** (0.114) (−59.82)
N	25,622	25,622	14,041	14,041	11,058	11,058	25,622
adj. R2	0.304	0.353	0.232	0.298	0.248	0.306	0.304

Notes: Standard errors in parentheses, *** p < 0.01.

, indicate that the coefficient for Distance3 is significantly negative, suggesting that the findings of this study remain unaffected by the selection of the number of listed companies within the same sector. Additionally, we employed the methodology of Wang (2023)to utilize ln(1 + 0.5 * number of green invention patents + 0.5 * number of green utility model patents) as a proxy variable for assessing green technology innovation [32]. The re-regression outcomes presented in column (3) of Table 5 further validate the robustness of hypothesis 1. Lastly, we also accounted for province-fixed effects, as evidenced by the regression results in columns (4) and (5) of Table 5, which continue to support the hypothesis.

The explanatory variable Distance5 in this article may have regional characteristics, such as the geographical distance between listed companies in western regions and their competitors in the same industry may be remote, while listed companies in economic agglomeration areas such as Beijing, Shanghai, Guangzhou, and Shenzhen may be closer to their competitors in the same industry. The conclusion of this article may only indicate that the green technology innovation ability of enterprises in remote areas is insufficient, while listed companies in economic agglomeration areas may be forced to carry out green technology innovation due to institutional coercion in their location.

In order to further exclude alternative explanations that may arise from regional characteristics, this article conducted individual regional exclusion processing on the sample. Firstly, removing the samples from relatively remote or sparsely populated areas (Xinjiang, Xizang, Inner Mongolia, Qinghai, Sichuan, Heilongjiang, Yunnan and Gansu, the top eight in terms of geographical area) and regressing allowed for obtaining the results shown in columns (1) and (2) of

Table 6. Robust testing of regional characteristics in this study.

	(1)	(2)		(3)		(4)	(5)
	Gti	Tpg		GTI		Gti	Tpg
Distance3	−0.0713 *** (0.00482) (−14.80)	−0.0757 *** (0.00574) (−13.20)	Distance5	−0.0699 *** (0.00482) (−14.49)	Distance5	−0.0700 *** (0.00554) (−12.63)	−0.0728 *** (0.00660) (−11.03)
Controls	Yes	Yes	Controls	Yes	Controls	Yes	Yes
Year	Yes	Yes	Year	Yes	Year/Industry/Province	Yes	Yes
Industry	Yes	Yes	Industry	Yes		Yes	Yes
_cons	−6.541 *** (0.106) (−61.75)	−7.911 *** (0.126) (−62.75)	_cons	−6.372 *** (0.101) (−63.26)	_cons	−6.449 *** (0.107) (−60.25)	−7.803 *** (0.127) (−61.21)
N	28,605	28,605	N	28,605	N	28,604	28,604
adj. R2	0.290	0.342	adj. R2	0.335	adj. R2	0.300	0.350

Notes: Standard errors in parentheses, *** p < 0.01.

. Secondly, excluding samples from highly concentrated economic regions (Guangdong, Shanghai, Jiangsu, Zhejiang, and Beijing, which have the highest number of listed companies), the regression results are shown in columns (3) and (4) of Table 6. Thirdly, by simultaneously removing samples from the aforementioned two regions, the regression results are obtained as shown in columns (5) and (6) of Table 6. The results show that the coefficients of Distance5 in each column are significantly negative, and the robust test results can to some extent exclude the alternative explanations mentioned above.

4.4.2. Endogeneity

In order to tackle the potential issue of sample self-selection, we employed propensity score matching (PSM) to pair the samples. The average geographical distance to the nearest five listed companies in the same industry was calculated by adding one and taking the natural logarithm of Distance5, which serves as the explanatory variable for enterprise classification. Enterprises with a Distance5 exceeding the median were designated as the experimental group (marked as 1), while those below the median were classified as the control group (marked as 0). Additionally, enterprise size, asset capital structure, and return on equity were selected as covariates. The balance test revealed that the standardized deviation values for all covariates significantly decreased post-matching, with absolute values remaining below 10%. The mean values for both the experimental and control groups were also relatively aligned, suggesting that the chosen matching variables and methods were suitable and fulfilled the hypothesis criteria. In the 1:1 matching conducted in this study, the regression coefficients for Distance5 were −0.124 and −0.145, respectively, both significant at the 1% level, demonstrating that spatial location competition significantly enhances enterprises’ green technology innovation.

In this study, we also addressed the potential reverse causality between spatial location competition and green technological innovation, as such endogeneity may undermine the accuracy of regression results. Following the established methodology in existing literature, we introduced regional per capita GDP as an instrumental variable to conduct a two-stage least squares (2SLS) analysis. As shown in Columns (3) to (6) of

Table 7. Endogeneity testing in this study.

	Propensity Score Matching (PSM)			Two-Stage Instrumental Variables
	(1)	(2)		(3)	(4)	(5)	(6)
	Gti	Tpg		Distance5	Gti	Distance5	Tpg
Distance5	−0.124 *** (0.00990) (−12.53)	−0.145 *** (0.0118) (−12.34)	GDP per capita	−0.00 *** (0.000000179) (−42.29)		−0.000 *** (0.000000179) (−42.29)
			Distance5		−0.265 *** (0.0246) (−10.81)		−0.239 *** (0.0274) (−8.71)
Controls	Yes	Yes	Controls	Yes	Yes	Yes	Yes
Year	Yes	Yes	Year	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Industry	Yes	Yes	Yes	Yes
_cons	−6.745 *** (0.104) (−64.84)	−8.103 *** (0.124) (−65.47)	_cons	5.129 *** (0.117) (−43.78)	−5.478 *** (0.210) (−26.11)	5.129 *** (0.117) (−43.78)	−7.318 *** (0.236) (−31.03)
N	28,594	28,589	N	28,247	28,247	28,247	28,247
adj. R2	0.288	0.341	adj. R2	0.312	0.261	0.312	0.330

Notes: Standard errors in parentheses, *** p < 0.01.

, the regression coefficient of regional per capita GDP on spatial location competition is significant at the 1% level. Moreover, the absolute value of the t-statistic reaches 42.29, and the corresponding F-statistic far exceeds the critical value of 10, indicating that there is no concern about weak instrumental variables. The results of the second-stage regression show that the coefficients of spatial location competition on green technological innovation are −0.265 and −0.239, respectively, both significant at the 1% level, which effectively rules out the interference of endogeneity issues.

4.4.3. Heterogeneity Tests

In this article, we posit that state-owned enterprises primarily exist within the capital-intensive and energy sectors, which are characterized by significant entry barriers. Consequently, green technology innovation tends to concentrate on these enterprises as the main subjects of study. For the heterogeneity analysis, the grouping was determined by political affiliation (SOE). The results of the grouped regression, presented in

Table 8. Heterogeneity testing of political connection in this study.

	Gti		Tpg
	SOE = 1	SOE = 0	SOE = 1	SOE = 0
Distance5	−0.106 *** (0.00778) (−13.66)	−0.0634 *** (0.00663) (−9.55)	−0.110 *** (0.00899) (−12.25)	−0.0671 *** (0.00816) (−8.23)
Controls	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes
_cons	−6.904 *** (0.152) (−39.15)	−6.259 *** (0.146) (−35.78)	−8.061 *** (0.177) (−41.29)	−7.896 *** (0.179) (−37.32)
N	13,289	15,314	13,289	15,314
adj. R2	0.361	0.222	0.419	0.272
Intergroup coefficient difference	p = 0.0001 (15.54)		p = 0.0006 (11.65)

Notes: Standard errors in parentheses, *** p < 0.01.

, reveal that enterprises linked to political entities in the SOE = 1 category are more likely to pursue green technology innovation to counteract or lessen the effects of competition arising from their spatial positioning in the industry.

We categorized government subsidies based on the median, assigning a value of 1 to those above the median and a value of 0 to those below it. The findings, presented in

Table 9. Heterogeneity testing of government subsidies in this study.

	Gti		Tpg
	Subsidy = 1	Subsidy = 0	Subsidy = 1	Subsidy = 0
Distance5	−0.0832 *** (0.00704) (−11.81)	−0.0649 *** (0.00728) (−8.91)	−0.0839 *** (0.00822) (−10.21)	−0.0741 *** (0.00892) (−8.30)
Controls	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes
_cons	−6.502 *** (0.163) (−43.18)	−5.680 *** (0.153) (−40.74)	−7.730 *** (0.188) (−44.25)	−7.068 *** (0.188) (−43.07)
N	15,446	13,159	15,446	13,159
adj. R2	0.310	0.286	0.359	0.339
Intergroup coefficient difference	p = 0.0906 (2.86)		p = 0.4385 (0.60)

Notes: Standard errors in parentheses, *** p < 0.01.

, indicate that government subsidies positively influence listed companies. Regardless of the subsidy amounts, these companies demonstrate a greater willingness to align with local or national environmental protection policies, actively pursue innovations in green technology, lower carbon emissions, and support the development philosophy that values ecological preservation as highly as economic wealth.

Listed manufacturing companies typically fall into 1 of 31 industry categories.

Table 10. Heterogeneity testing of enterprise type in this study.

	Gti		Tpg
	Manufacturing = 1	Non-Manufacturing Industry = 0	Manufacturing = 1	Non-Manufacturing Industry = 0
Distance5	−0.148 *** (0.00665) (−22.23)	−0.0387 *** (0.00651) (−5.94)	−0.176 *** (0.00794) (−22.16)	−0.0168 ** (0.00801) (−2.10)
Controls	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
_cons	−7.317 *** (0.142) (−52.31)	−5.259 *** (0.170) (−32.03)	−8.886 *** (0.170) (−53.91)	−6.443 *** (0.209) (−32.40)
N	18,372	10,233	18,372	10,233
adj. R2	0.272	0.194	0.310	0.222
Intergroup coefficient difference	p = 0.000 (130.24)		p = 0.000 (197.54)

Notes: Standard errors in parentheses, ** p < 0.05, *** p < 0.01.

indicates that the spatial competition between these enterprises significantly enhances the advancement of green technology innovation compared to among non-manufacturing firms. This is largely because listed manufacturing companies are often significant carbon emitters; thus, their proactive involvement in green technology innovation can lead to reduced energy usage and foster the growth of green technologies, helping them maintain a competitive edge.

5. Conclusions, Implications, and Future Research

5.1. Conclusions

To deeply explore the intrinsic connection and influencing mechanism between spatial location competition and corporate green technological innovation, this paper conducts a systematic empirical analysis and heterogeneity test. The core research conclusions are as follows:

(1)

The basic relationship between spatial location competition and green technological innovation: Empirical results indicate a significant and robust negative correlation between spatial location competition and corporate green technological innovation. This conclusion remains consistent across cross-industry and cross-regional sample tests. Specifically, when spatial location competition intensifies in the region where an enterprise is located, the enterprise’s subjective willingness and actual investment intensity in green technological innovation will significantly decrease—reflecting the inhibitory effect of fierce location competition on enterprises’ long-term green innovation behaviors.

(2)

Moderating effect of digital transformation: After introducing corporate digital transformation as a moderating variable, empirical analysis shows that the coefficient of the interaction term between spatial location competition and digital transformation degree is significantly negative at the 1% statistical level. Combined with the basic negative impact of spatial location competition on green technological innovation, this result clearly indicates that corporate digital transformation can effectively mitigate the inhibitory effect of spatial location competition on green technological innovation and may even convert this “inhibitory effect” into an “incentive effect”. That is, the higher the degree of digital transformation, the weaker the negative impact of spatial location competition on green technological innovation; enterprises can thus leverage digital empowerment to carry out green technological innovation amid competition.

(3)

Moderating effect of environmental uncertainty: Empirical analysis further reveals that environmental uncertainty exerts a significant moderating effect on the relationship between spatial location competition and green technological innovation. When enterprises face higher environmental uncertainty, their capability and willingness to engage in green technological innovation will be significantly weakened. Even in a spatially competitive environment with intense rivalry, enterprises—constrained by limited resources and driven by risk-averse tendencies—struggle to allocate sufficient resources to simultaneously address external competitive pressures and green innovation demands. This resource allocation dilemma ultimately hinders the smooth progression of green technological innovation activities.

(4)

Heterogeneous impact of enterprise ownership (state-owned enterprises): From the perspective of ownership heterogeneity, state-owned enterprises (SOEs), which possess inherent political connections, exhibit a stronger inclination than non-SOEs to conduct green technological innovation when confronting spatial location competition. This strategic choice helps them avoid or mitigate the impacts of intra-industry competition. Such differentiated tendencies originate from the policy attributes, resource acquisition advantages, and long-term operation orientation of SOEs. These inherent characteristics enable SOEs to prioritize green innovation strategies in competitive contexts, thereby building and consolidating their core competitiveness.

(5)

Incentive effect of government subsidies: Empirical results demonstrate that government subsidies have a significant positive incentive effect on the green technological innovation of listed companies, and this effect is free from a “scale threshold”. Regardless of the subsidy amount, enterprises receiving government subsidy support tend to be more proactive in complying with regional and national environmental protection policies and actively increase investment in green technological innovation. This finding highlights the key role of government subsidies in stimulating enterprises’ green innovation motivation and guiding their environmentally friendly behaviors.

(6)

Industry heterogeneity of listed manufacturing companies: Sub-sample analysis of listed manufacturing companies shows that as the core carbon emitters in the industrial sector, these enterprises have a more urgent need to pursue green technological innovation to maintain their leading position in the industry. By proactively engaging in green technological innovation, manufacturing enterprises can not only effectively reduce energy consumption and lower carbon emission intensity in their production processes but also promote the upgrading of their own green technology levels and drive the green development of the entire industry. This ultimately forms a virtuous cycle of “innovation—energy conservation—industry leadership”.

5.2. Implications

5.2.1. Management Implications for Enterprises

Firstly, enterprises should rationally respond to spatial location competition and balance short-term survival with long-term innovation. They need to clearly recognize the inhibitory effect of spatial location competition on green technological innovation and avoid falling into the trap of “short-term technological transformation dependence”. On the one hand, when competition intensifies, they should reserve special resources for green technological innovation in line with their strategic planning. On the other hand, they can reduce the pressure of location competition through differentiated competition strategies and avoid direct competition with peers in homogeneous products, thus creating space for green technological innovation. Secondly, enterprises can accelerate the process of digital transformation to activate the driving force of green innovation. Given that digital transformation can convert the “inhibitory effect” of spatial location competition into an “incentive effect”, enterprises should take digital transformation as the core approach to promoting green technological innovation [35]. Meanwhile, state-owned enterprises should fully leverage their policy attributes and resource advantages, taking green technological innovation as the core strategy to respond to spatial location competition and quickly form a green competitive advantage in location competition.

5.2.2. Management Implications for Government

Firstly, governments can alleviate the negative effects of spatial location competition through multiple measures and create a benign competitive atmosphere. Governments need to reduce the vicious competition between regions through policy intervention and create a favorable environment for enterprise green technological innovation. On the one hand, they should break down regional market segmentation and establish a unified national green product market system to eliminate the restrictions of local protectionism on the circulation of green products. On the other hand, they should guide the differentiated layout of regional industries, clarify the key directions of green industries based on the resource endowments and industrial foundations of each region, and avoid excessive competition between enterprises within the same region in the same green field, reducing the internal consumption of innovation resources. Secondly, governments should strengthen the support policies for digital transformation to help enterprises unleash their green innovation potential [36]. Given the enabling role of digital transformation in green innovation, governments need to provide support from multiple dimensions such as infrastructure construction, technology research and development subsidies, and talent cultivation. At the infrastructure level, they should increase the coverage of 5G, industrial internet and other digital infrastructure in industrial parks and industrial clusters. At the technology subsidy level, they should establish a special fund for the integration of digitalization and green innovation and organize training for compound talents in digitalization and green innovation to solve the problem of talent shortage in enterprises. Thirdly, governments can improve policies for responding to environmental uncertainties to stabilize the expectations of enterprise green innovation [37]. Governments need to stabilize policies and share risks to reduce the impact of environmental uncertainties on enterprise green innovation. At the same time, they should establish government-guided green risk investment funds to provide equity financing support for enterprise green innovation projects facing uncertainties and alleviate the financial pressure on enterprises. Finally, governments should consider the threshold-free incentive effect of government subsidies on green innovation and further optimize the subsidy distribution mechanism to enhance policy effectiveness.

5.2.3. Management Implications for the Manufacturing Industry

Focus on Industry Characteristics and Promote the Synergistic Development of Green Innovation. Firstly, build a green innovation collaboration network in the manufacturing industry to enhance the overall innovation capacity of the industry. As the core emitter of carbon emissions, the manufacturing industry needs to rely on industry associations and leading enterprises to build collaborative innovation platforms and solve the problems of green technological innovation. Secondly, formulate industry standards for green innovation in the manufacturing industry to guide enterprises to innovate in a standardized manner. Industry associations need to formulate differentiated green technological innovation standards and evaluation systems based on the characteristics of different sub-sectors in the manufacturing industry.

5.3. Future Research

5.3.1. Expansion of Research Perspectives

Refined analysis from the perspective of the deep integration of the digital economy and the green economy: Existing studies have confirmed the moderating role of digital transformation in the relationship between spatial location competition and corporate green technological innovation. However, future research can conduct more targeted and in-depth explorations from the perspective of the deep integration of the digital economy and the green economy. Specifically, it can focus on the specific application scenarios and forms of various digital technologies (such as big data, artificial intelligence, blockchain, and the Internet of Things) and systematically analyze the heterogeneous moderating mechanisms of different digital technologies on the relationship between spatial location competition and green innovation. This will help clarify how to match digital technology tools with green innovation needs to more effectively mitigate the inhibitory effect of location competition on green innovation.

Cross-national comparative studies from the perspective of global value chains: With the deepening of economic globalization, the spatial location competition of enterprises has transcended domestic regional boundaries and been integrated into the global value chain division system. Therefore, introducing the global value chain perspective to carry out cross-national comparative studies is a valuable direction for future research. On one hand, it is necessary to compare the differences in the impact of spatial location competition models on corporate green innovation across different countries and regions (such as China, the European Union, and the United States) and deeply analyze the moderating effects of contextual factors including institutional environment, market structure, and technological foundation. On the other hand, it is essential to explore the interactive relationship between the position of enterprises in the global value chain and their green innovation performance, focusing on how enterprises in different value chain positions (e.g., upstream, midstream, and downstream) respond to spatial location competition at the global level. Additionally, it is worth verifying whether the green technology spillover effects of multinational corporations can alleviate the inter-regional competitive pressure in host countries. Such research can provide international experience references for Chinese enterprises to formulate green innovation strategies amid global competition and enhance their green competitiveness in the global market.

5.3.2. Optimization of Research Methods

In-depth application of dynamic panel and causal identification methods: Most existing empirical studies rely on static panel data, which struggle to fully capture the dynamic evolutionary relationship between spatial location competition and green technological innovation. Future research can adopt dynamic panel models (e.g., the system GMM method), incorporate lagged terms of the explained variable (green technological innovation), and thereby analyze the path-dependent characteristics of green technological innovation, as well as the long-term dynamic impact of spatial location competition on such innovation. Meanwhile, to more accurately identify the causal relationship between the two core variables, further exploration of quasi-natural experiment scenarios is warranted. For instance, by taking regional industrial policy adjustments and major green technological breakthroughs as exogenous shock variables and applying methods such as difference-in-differences (DID) and regression discontinuity design (RDD), researchers can eliminate the interference of omitted variables and reverse causality, thereby enhancing the causal identification power of research conclusions.

Combined application of spatial econometrics and microdata: Given the significant spatial spillover effects inherent in spatial location competition, future research can more extensively employ spatial econometric methods (e.g., spatial lag model [SLM], spatial error model [SEM], and spatial Durbin model [SDM]). By incorporating spatial weight matrices (which can be constructed based on geographical distance, economic connection, or institutional proximity), researchers can quantitatively measure the spatial spillover effects of spatial location competition and systematically analyze how these spillover effects impact corporate green technological innovation. This approach helps overcome the limitation of ignoring spatial correlation in traditional econometric models, thereby providing a more comprehensive understanding of the spatial transmission mechanism of location competition on green innovation.

5.3.3. Integration of Case Studies and Quantitative Analysis

Current research mainly focuses on quantitative analysis and lacks detailed descriptions of specific scenarios of enterprise green innovation practices. Future research can adopt a mixed research method combining quantitative analysis and case studies. On the basis of large-sample quantitative analysis, typical enterprises or regions can be selected for in-depth case studies. For example, enterprises that have successfully transformed spatial location competition into green innovation drivers during digital transformation, or enterprises that maintain green innovation under high environmental uncertainty can be selected as cases. Through methods such as interviews, field research, and archival analysis, the specific decision-making processes, resource allocation methods, and organizational change paths of enterprises in response to spatial location competition and green innovation can be explored. A research system that mutually corroborates macro laws and micro cases can be formed to enhance the practical guidance of research conclusions.

As for the measurement of digital transformation, this paper using MD&A keyword frequency is innovative, but its objectivity and replicability could be questioned without further validation. Future research can combine the MD&A keyword frequency method with objective digital indicators publicly available by enterprises, forming a dual measurement standard that combines qualitative expression and quantitative investment. Introduce IT investment data, such as the amount of intangible assets, software, and systems in the annual report of the enterprise. The proportion of R&D expenditure and digital technology R&D directly reflects the resource investment of enterprises in digital technology, which can effectively compensate for the deficiency of keyword frequency emphasizing expression and neglecting investment.

As for the measurement of green technology innovation, currently, the focus is limited to the number of green patents. However, these may not always reflect actual environmental impact. In the future research, future research will measure green technology innovation from four aspects—CO₂ reduction indicators, green revenue share, environmental certification and green innovation performance—and adjust indicator design based on specific industries (such as manufacturing and new energy).

As for the measurement of spatial location competition, the Distance5 variable used in this study can effectively capture the core characteristics of close-range spatial competition, but it does not fully consider the spatial clustering effect and spatial autocorrelation effect of green technology innovation of enterprises in the region, which may lead to the bias of traditional OLS regression results. In future research, a two-step analysis framework of “spatial dependency detection spatial econometric modeling” can be introduced. The existence of spatial effects can be verified through Moran’s I test, and regression bias can be corrected using SAR and SEM models. The specific implementation path is as follows: firstly, Moran’s I index is a core tool for measuring spatial autocorrelation, which can be used to verify whether there are spatial clustering characteristics in green technology innovation of enterprises, providing a basis for subsequent selection of spatial econometric models. Step two: Based on the Moran’s I test results, select an appropriate spatial econometric model and incorporate spatial dependence into the regression framework of “spatial location competition green technology innovation”. The above spatial econometric analysis has strong practicality. Regarding spatial data, the latitude and longitude coordinates of enterprises can be obtained through the registered address of the “National Enterprise Credit Information Publicity System” and the office address of listed companies’ annual reports, combined with Baidu Maps API or Gaode Maps API conversion. The accuracy can reach street level and meet the calculation requirements of a 5 km distance threshold.

5.3.4. Deepening of Research Content

Current research mostly treats green technological innovation as an overall variable without fully considering its heterogeneity. Future research can conduct detailed studies from the dimensions of type, intensity, and stage of green technological innovation. Existing research mainly focuses on the moderating effects of external factors and pays insufficient attention to internal governance factors of enterprises. Future research can deeply analyze the moderating effects of internal governance characteristics of enterprises on the relationship between spatial location competition and green technological innovation.