Achieving Optimal Distinctiveness in Green Innovation: The Role of Pressure Congruence

Abstract

As a critical external mechanism driving green innovation, institutional and competitive pressure often coexist and jointly shape firms’ strategic responses. However, existing studies primarily focus on the individual effects of these pressures, with limited attention to their interactive impacts on green innovation. Drawing on optimal distinctiveness theory, this study proposes a “pressure–response” analytical framework that classifies institutional and competitive pressure combinations into congruent (i.e., high–high or low–low) and incongruent (i.e., high–low or low–high) pressure contexts based on their relative intensities. It further examines how these distinct configurations affect two types of green innovation: strategic green innovation (StrGI) and substantive green innovation (SubGI). Using panel data from Chinese A-share listed firms between 2010 and 2022, the empirical results reveal that under congruent pressure contexts, the alignment of institutional and competitive pressures tends to suppress green innovation. In contrast, under incongruent contexts, the misalignment between the two pressures significantly promotes green innovation. Regarding innovation motivation, the high institutional–low competitive pressure context more significantly promotes StrGI, while the low institutional–high competitive pressure context has a more prominent effect on SubGI. In addition, this study also investigates the mediating roles of StrGI and SubGI on ESG performance. The findings provide theoretical support and policy implications for improving green transition policies and institutional frameworks, as well as promoting sustainable corporate development.

1. Introduction

As global environmental challenges become increasingly severe, green innovation has been widely recognized as a central pathway to achieving sustainable development and has emerged as a critical strategic choice for promoting high-quality economic growth and addressing ecological crises [1,2]. Given its characteristics—such as high investment requirements, long development cycles, and significant uncertainty—green innovation often confronts firms with resource constraints and risk challenges, making it difficult to achieve green transformation based solely on internal drivers. In this context, government regulatory policies and market competition have become key external forces driving green innovation due to their dominant roles in regulatory guidance and resource allocation [3,4]. However, firms exhibit substantial variation in their strategic approaches to green innovation when responding to institutional and market pressures. These variations directly influence the effectiveness of green innovation strategies. For example, under the strong influence of national new energy vehicle policies, Evergrande Auto overly prioritized policy alignment and institutional compliance, while neglecting market demands for technological competence and product competitiveness, ultimately failing to commercialize its offerings. In contrast, Uber placed excessive emphasis on market expansion and technological iteration, while disregarding regulatory compliance, which led to frequent bans and ultimately resulted in operational restrictions and reputational damage [5]. These cases highlight a critical challenge: as institutional regulations tighten and market competition intensifies, how can firms reconcile the tension between “institutional orientation” and “market orientation” and respond effectively to dual external pressures? Addressing this question has become a central concern in the strategic implementation of green innovation.

Green innovation is not merely a reactive decision but a strategic response shaped by multiple institutional logics and resource constraints, such as regulatory mandates and market competition [6]. These institutional logics are not independent; rather, they interact in complex ways. On the one hand, regulatory arrangements can reinforce market selection pressures by setting green thresholds and guiding industry standards [7]. On the other hand, market actors’ responses can influence the effectiveness and direction of policy implementation, creating a dynamic interplay among government, market, and firms [8]. Within this context of institutional plurality and dynamic interaction, green innovation is highly complex and strategic. However, existing studies have predominantly examined the isolated effects of either institutional or competitive pressure, with limited attention to the interaction between the two and its influence on green innovation [9,10]. This raises a critical but underexplored question: when institutional and competitive pressures coexist, do they reinforce or constrain each other, and how does their interaction shape firms’ green innovation strategies? Optimal distinctiveness theory offers a valuable framework for addressing this question. It posits that firms must navigate the inherent tension between legitimacy, derived from conforming to institutional expectations, and differentiation, driven by market competition [11,12]. In the context of green transformation, firms are required not only to comply with environmental regulations [13] but also to build unique market value through green technologies, products, and business model innovation [14]. Yet, given the constraints of limited resources, capabilities, and managerial attention, it is often unfeasible to optimize both dimensions simultaneously. Firms must therefore make strategic trade-offs. How to dynamically balance the tension between institutional legitimacy and market differentiation—achieving optimal distinctiveness—becomes a central challenge in formulating effective green innovation strategies.

Moreover, as a key strategic response to external pressures, green innovation is not a uniform or homogeneous process. Instead, variations in green innovation reflect underlying differences in strategic motivations and resource allocation preferences [15]. However, existing studies predominantly focus on the intensity, content, and forms of green innovation, while paying limited attention to the motivational heterogeneity behind such initiatives [16]. To better understand the diversity and complexity of green innovation, this study adopts a motivation-based perspective and distinguishes between strategic green innovation (StrGI) and substantive green innovation (SubGI). This distinction helps reveal the different behavioral logics and response patterns that firms adopt under the interplay of institutional and competitive pressures. Specifically, StrGI is primarily driven by short-term compliance goals and resource-seeking motives, such as regulatory responses or subsidy acquisition, often characterized by low cost, low risk, and short time horizons. In contrast, SubGI focuses on enhancing green technologies and environmental performance, typically involving higher R&D investment and longer payback periods [17,18]. This motivation-based typology offers deeper insight into the diverse strategic orientations and adaptive paths firms pursue in response to external pressures.

In light of the release of the International Patent Classification (IPC) Green Inventory by the World Intellectual Property Organization (WIPO) in 2010, which offers a standardized framework for identifying patents related to environmentally sound technologies (ESTs) [19], this study defines and measures firms’ green innovation using patent classifications derived from this inventory. Accordingly, we set 2010 as the starting point of the sample period. Based on panel data of Chinese A-share listed firms spanning 2010 to 2022, this study draws on optimal distinctiveness theory to develop a “pressure–response” analytical framework that systematically examines how various configurations of institutional and competitive pressures shape green innovation outcomes. Particular attention is given to the differentiated strategic responses between StrGI and SubGI. By integrating a motivation-based perspective and the logic of external pressure matching, this study not only extends the application of optimal distinctiveness theory to the green management domain but also deepens our understanding of heterogeneity in green innovation behaviors, providing theoretical support for optimizing institutional design and implementing differentiated policy tools to advance China’s “dual carbon” goals.

The marginal contributions of this study are threefold: First, it systematically investigates the interaction mechanism between institutional and competitive pressures and constructs a dual-pressure framework that reveals how firms dynamically balance conformity and distinctiveness under external institutional–market tension. This advances prior research that has largely treated institutional and competitive pressures in isolation and highlights the selective coupling of external pressures and their complex influence on firms’ strategic responses. Second, although prior research has primarily applied this theory to organizational strategy or general institutional responses [20,21], its application to understanding how firms develop green strategies under the simultaneous influence of institutional and competitive pressures remains limited. This study extends the application of optimal distinctiveness theory to the context of green innovation. Specifically, it conceptualizes institutional pressure and competitive pressure as external drivers of legitimacy pursuit and market differentiation, respectively, and develops a dual-dimensional pressure configuration framework focused on green innovation. This framework illustrates how firms adopt differentiated green innovation strategies under varying pressure alignments to achieve a dynamic balance between conformity and distinctiveness. Thus, the study enriches the theoretical applicability of optimal distinctiveness theory in the green management context and expands its relevance within the domains of environmental strategy and sustainable management. Third, the study emphasizes the motivational heterogeneity of green innovation by distinguishing between StrGI and SubGI, highlighting the theoretical significance of innovation motives. Through a motivation-based “pressure–response” framework, it uncovers the mechanisms through which external pressures shape firms’ green innovation strategies, deepening our understanding of how such pressures influence innovation intent and behavior. This contributes to addressing the gap in the literature concerning the role of motivation in green innovation and provides theoretical support for governments seeking to refine institutional design and implement differentiated policy instruments.

2. Literature Review and Research Hypothesis

2.1. Green Innovation

Green innovation, as a critical strategy enabling firms to mitigate environmental degradation and achieve environmental sustainability through developing green products and processes and optimizing organizational management, exhibits significant dual externalities [22,23]. On the one hand, it generates economic returns and competitive advantages for firms; on the other hand, its environmental benefits primarily manifest at the societal level, making them difficult for firms to internalize fully. This externality structure leads to pronounced behavioral heterogeneity driven by firms’ internal motivations in pursuing green innovation [19]. Prior research indicates that different green innovation motives influence firms’ preferences for green investment, resource allocation, and responsiveness to environmental regulation, resulting in differentiated behavioral outcomes [24]. Accordingly, from a motivational perspective, Liao [24] categorizes green innovation into substantive green innovation (SubGI) and strategic green innovation (StrGI). SubGI refers to high-quality green innovation aimed at advancing technological progress, gaining competitive advantage, and improving environmental performance; StrGI, often described as symbolic environmental behavior, involves pursuing other interests by emphasizing the “quantity” and “speed” of innovation to comply with government policies and regulations [25,26]. Unlike StrGI, which focuses on incremental improvements in existing products or technologies and seeks short-term benefits and market legitimacy, SubGI emphasizes transformative innovation in products or technologies, requiring greater resource investment, higher costs, and the acceptance of elevated uncertainty and risks. These motivationally driven green innovations differ not only in resource commitment and technological pathways but also produce distinct impacts on firms’ environmental performance, economic outcomes, and long-term competitiveness [27,28]. However, existing studies lack a systematic and in-depth exploration of the mechanisms guiding green innovation strategy selection and its driving pathways.

Existing research indicates that the external pressures exerted by stakeholders are widely recognized as crucial drivers stimulating green innovation, facilitating a strategic shift from passive to proactive environmental approaches [29]. In China’s unique economic context, characterized by both policy guidance and market mechanisms, the synergy between government-led green development strategies and market-driven differentiation pressures serves as a key impetus accelerating firms’ green technology R&D and adoption [30]. Most studies have examined the independent effects of institutional and competitive pressures on green innovation from the perspectives of legitimacy theory and industrial organization theory, respectively [31,32]. On one hand, following the Porter Hypothesis, appropriate environmental regulations can stimulate innovation compensation effects [33]. Institutional pressure, manifested through government supervision and regulatory mechanisms, encourages firms to enhance their environmental and social responsibility activities, thereby promoting green innovation [34,35,36]. However, some scholars argue, based on cost effects, that excessive regulatory pressure may increase compliance costs, thereby suppressing green innovation investments [37,38]. On the other hand, the mechanism of market competition is complex, influenced jointly by firms’ strategic motives and institutional contexts [39]. In developed economies, managers tend to view green innovation as a strategic path to enhance long-term competitiveness and profitability; thus, competition pressure fosters greater green innovation investment [40]. Conversely, in emerging markets such as China, institutional deficiencies—such as weak investor protection, incomplete environmental and social responsibility regulations, and low market transparency [41]—lead firms under market pressure to prioritize short-term financial performance over long-term green innovation investment [39]. It is evident that firms’ green innovation investment is influenced not only by the institutional environment and market structure independently but is embedded within the dual interplay of institutional and market forces, resulting in differentiated response mechanisms.

Although recent studies have begun exploring the interactive effects of institutional pressure and market competition on green innovation, aiming to move beyond single-factor analyses, relevant research remains limited. For instance, Akhtar et al. [30] find that the interaction between institutional and competitive pressures positively impacts green innovation performance in SMEs. Despite limited resources, SMEs’ greater flexibility enables them to adopt green innovation strategies under competitive market pressure to maintain their market position. Conversely, Liu et al. [42], using a sample of listed companies, find that institutional pressure significantly raises compliance costs, while intense market competition reduces firms’ bargaining power and increases operating costs. This shifts capital allocation toward compliance and competitive expenditures, crowding out green innovation investment and exacerbating financing constraints. Such contrasting findings suggest significant heterogeneity in the intensity of institutional and market pressures faced by firms during green innovation. Their interaction may lead to differentiated pathways affecting green innovation outcomes. Specifically, varying levels of institutional pressure may strengthen or weaken the driving effect of market competition on green innovation; simultaneously, the intensity of market competition may influence the effectiveness and enforcement of policy instruments, thereby affecting the actual outcomes of government green policies [43]. Therefore, this study aims to further investigate how different configurations of institutional and market pressure intensities shape firms’ green innovation strategies, analyze their differentiated innovation paths and policy effects, and provide theoretical support for optimizing green innovation policy design.

2.2. Model Framework Based on the Optimal Distinctiveness Theory

Optimal distinctiveness theory emphasizes that organizations must dynamically balance the pursuit of legitimacy and distinctiveness when responding to external environmental pressures, thereby overcoming the “dual constraints” to achieve a sustained competitive advantage [13]. The theory posits that organizations typically face two conflicting strategic orientations: on the one hand, aligning with institutional norms to gain legitimacy; on the other hand, differentiating from competitors to secure a market advantage [6]. In the context of green transformation, the tension between legitimacy and distinctiveness is particularly salient. Institutional pressure manifests through increasingly stringent government environmental regulations, policy directives, and compliance standards, requiring firms to respond to fulfill environmental responsibilities legally and secure external legitimacy. According to institutional theory, institutional pressure encompasses not only formal pressures such as government laws and regulations but also informal pressures, including social expectations, cultural traditions, and industry conventions [44,45]. Within the context of policy effectiveness research, institutional theory commonly regards the government as the regulatory institutional pillar and the most salient institution for firms [46]. Particularly in China’s unique government-led economic model, the government plays a central role in economic regulation by closely linking policy formulation and resource allocation with business activities [47,48]. Competitive pressure arises from peer firms’ green transformation efforts, rapid iterations in green technologies, and growing consumer demand for green products and services, compelling firms to adopt differentiated green innovation strategies to gain a competitive advantage in a fierce market environment [49,50,51]. These two pressures represent legitimacy-driven and distinctiveness-driven orientations, forming the core tension in firms’ green innovation strategy formulation. An excessive focus on institutional pressure may lead to conformity and the loss of market differentiation, whereas an overemphasis on competitive pressure risks neglecting compliance and undermining external legitimacy [52,53]. Therefore, firms must strategically balance the contradiction between “conformity” and “distinctiveness”, responding to institutional demands to ensure legitimacy while achieving innovation breakthroughs through differentiation.

Based on the alignment of intensity between institutional and competitive pressures, this study classifies external pressure configurations into four distinct contextual types, as illustrated in Figure 1. Among them, the “high institutional pressure–high competitive pressure” (High IP-High CP) and “low institutional pressure–low competitive pressure” (Low IP-Low CP) scenarios represent congruent pressure contexts. Congruence denotes symmetry in pressure magnitude rather than alignment in strategic direction or intent. The High IP–High CP context reflects a dual-pressure environment where firms are simultaneously subject to strong regulatory demands and fierce market competition, requiring a balanced strategic response to meet compliance obligations while enhancing market adaptability. In contrast, the Low IP–Low CP context denotes a relaxed external environment with minimal institutional and market constraints, offering greater strategic leeway. In incongruent pressure contexts, the “high institutional pressure–low competitive pressure” (High IP–Low CP) and “low institutional pressure–high competitive pressure” (Low IP–High CP) combinations reflect asymmetric pressure dynamics. The former represents an institutionally dominant context, where firms operate under strong regulatory oversight but face limited competitive intensity, often prioritizing institutional compliance over market differentiation. The latter corresponds to a competition-driven context, where intense market rivalry coexists with weak institutional enforcement, pushing firms to focus on improving their competitive positioning. This classification framework helps to uncover the differences in firms’ green innovation behaviors under varying configurations of external pressures, thereby laying a solid foundation for subsequent mechanism analysis.

Within a complex and multifaceted institutional context, firms’ behavioral adjustments in green innovation are not merely passive adaptations to external pressures but strategic choices shaped by institutional logic, competitive dynamics, and resource constraints. Existing studies indicate that varying types and intensities of external pressures elicit differentiated organizational responses, primarily manifesting as symbolic compliance and substantive improvement pathways [54]. The former emphasizes legitimacy acquisition through symbolic means such as compliance disclosures and certification labels, reflecting StrGI. The latter focuses on technological innovation and process optimization to achieve substantive environmental performance improvements, thereby enhancing intrinsic competitiveness, embodying SubGI [19]. Firms’ perceptions of different pressure structures influence their prioritization between legitimacy and distinctiveness demands, which in turn drives their motivational orientation toward green innovation. Therefore, drawing on optimal distinctiveness theory, this study conceptualizes institutional and competitive pressure as dual triggering mechanisms for organizational behavioral adjustment and identifies StrGI and SubGI as two distinct response paths under different pressure scenarios. This framework elucidates how firms dynamically balance legitimacy constraints and differentiation pursuits to select adaptive green innovation strategies, providing a systematic theoretical foundation and analytical tool for examining the specific impact mechanisms of varying pressure intensities on green innovation behavior.

2.3. Pressure Combination and Green Innovation

In the process of green transformation, the interplay between institutional constraints and market competition compels firms to dynamically balance legitimacy acquisition and distinctiveness positioning, giving rise to a “dual embeddedness” strategic response pattern [50,53,55]. Under congruent pressure contexts, where institutional and competitive pressures are aligned in intensity, firms face a more coherent set of external constraints. This structural synergy of pressures reduces firms’ flexibility in navigating between compliance and innovation, thereby narrowing their strategic options and undermining their proactive engagement in green innovation [56,57]. Specifically, in a High IP–High CP context, firms are subject to both stringent environmental regulations and intense market rivalry. From a resource constraint perspective, such contexts may create a compounded “institution–market” high-pressure environment that strains firms’ available resources. High institutional pressure often entails significant regulatory costs—such as investments in pollution control, carbon quotas, or environmental taxes—which may crowd out funds otherwise allocated to technological innovation [56,58]. Simultaneously, heightened competition compels firms to prioritize operational efficiency, often leading to short-term market expansion and cost control efforts at the expense of long-term innovation [59]. This dual pressure results in strategic lock-in under synergistic high pressure, weakening firms’ willingness and ability to sustain green innovation. From a strategic decision-making standpoint, compliance-related investments tend to offer more predictable returns, while green innovation is typically associated with longer payback periods and greater uncertainty [60]. Faced with the dual constraints of strong regulatory oversight and fierce market competition, firms are more likely to adopt passive compliance strategies to mitigate regulatory risks, rather than pursuing green technological innovation that exceeds regulatory requirements.

In a Low IP–Low CP context, firms lack external drivers from both policy and market forces, which may lead to weak incentives for green innovation and reinforce path dependence. Low institutional pressure reflects lax environmental regulation, reducing the need for firms to allocate significant resources toward compliance and minimizing the risk of policy sanctions or reputational damage for failing to meet environmental standards. At the same time, low competitive pressure diminishes the urgency to enhance market competitiveness through technological innovation, reducing firms’ motivation to upgrade production processes or develop green products. From a behavioral perspective, the absence of institutional and market pressures may result in inertial reliance, wherein firms continue to follow established technologies and business models instead of actively exploring innovative trajectories [61]. As March and Cyert [62] suggest, firms tend to avoid uncertainty rather than confront it. Given the high levels of uncertainty and upfront investment typically associated with green innovation, firms in low-pressure environments are more likely to maintain existing production modes to minimize transformation costs and operational risks. From a competitive dynamics’ standpoint, green innovation often serves as a differentiation strategy in highly contested markets, enabling firms to stand out amid homogeneous competition [63]. However, diminished competitive pressure weakens the strategic necessity of green innovation as a means to capture market share or command brand premiums. In such contexts, firms can sustain traditional production practices without substantial risk to their market position, thereby eroding the external incentives for green technological advancement and potentially delaying innovation efforts. Accordingly, this study proposes the following hypothesis:

H1: 

When firms face a congruent pressure context (i.e., the “High IP—High CP” or “Low IP—Low CP” combination), it will suppress green innovation.

In an incongruent pressure context, the misalignment between institutional constraints and market signals presents firms with conflicting external demands. Specifically, when a firm faces strong pressure from one dimension (e.g., regulatory institutions) but weak incentives from another (e.g., market forces), it must balance legitimacy compliance with distinctiveness-seeking to formulate adaptive strategies. As Oliver [64] argues, firms do not passively accept institutional demands but rather engage in strategic responses to navigate multiple—and sometimes contradictory—institutional logics. Under conditions of pressure asymmetry, firms often adopt a selective coupling approach: they actively respond to the stronger pressure dimension while seeking strategic compensation in the weaker one to optimize resource allocation and manage risk [65]. This dynamic adaptation reflects firms’ innovative responses to institutional complexity and pressure misfit. In a High IP–Low CP context, stringent environmental regulations tend to drive homogeneity across market participants [66]. To differentiate in a low-competition environment, firms may adopt green innovation strategies that go beyond compliance, aiming to build a sustainable brand image and enhance market recognition and corporate reputation [67]. Conversely, in a Low IP–High CP context, firms face minimal compliance risks but must enhance their market positioning through competitive strategies. Existing research shows that firms under intense competitive pressure are more likely to pursue innovation-driven strategies to meet consumer demand for sustainable products and to establish technological barriers that secure long-term competitiveness [68]. From a resource allocation perspective, low institutional pressure reduces the fixed compliance costs imposed by regulators, enabling firms to more flexibly invest in internal innovation. Meanwhile, high competitive pressure compels firms to continuously adjust their strategies in response to market dynamics, with green innovation serving as a key mechanism for achieving differentiation and sustainable growth. This forward-looking innovation may also position firms advantageously in anticipation of the future tightening of environmental regulations [69]. In sum, under incongruent pressure contexts, green innovation functions as a critical adaptive strategy that enables firms to cope with environmental uncertainty arising from institutional–market misalignment and to maintain long-term development. Accordingly, this study proposes the following hypothesis:

H2: 

When firms face an incongruent pressure context (i.e., the “High IP—Low CP” or “Low IP—High CP” combination), it will promote green innovation.

When facing incongruent pressures, firms must strategically balance legitimacy and differentiation and craft responses that reconcile competing external expectations [70]. According to strategic choice theory, firms operating under resource constraints tend to prioritize responses that address the most salient pressure and yield immediate relief or returns [71]. Specifically, in a High IP—Low CP context, firms are subject to stringent regulatory oversight but encounter limited market competition. With weaker incentives for innovation-driven returns, they are more likely to adopt StrGI, focusing on compliance and the signaling of environmental responsibility to mitigate regulatory risks and enhance legitimacy [72,73]. In contrast, under Low IP–High CP, firms operate in less regulated environments but face intense market rivalry. Lacking strong policy constraints, they are more motivated to pursue SubGI to achieve technological breakthroughs, cost efficiency, and brand enhancement [74]. In this case, green innovation is primarily market-driven—a strategic investment to gain a competitive advantage rather than a response to regulatory mandates. SubGI enables firms to build technological barriers, strengthen product competitiveness, and foster consumer loyalty, making it a preferred strategy in highly competitive markets [18]. In summary, incongruent pressure contexts may lead firms to adopt differentiated green innovation paths, shaped by the asymmetric influence of institutional and market forces. This reflects the dynamic trade-off between legitimacy and efficiency. Accordingly, this study proposes the following hypothesis:

H3: 

In an incongruent pressure context, the “High IP—Low CP” combination is more likely to promote StrGI, while the “Low IP—High CP” combination is more likely to promote SubGI.

Accordingly, the research model of this study is depicted in Figure 2.

3. Research Design

3.1. Sample Selection

This study begins with the full sample of A-share listed companies in China from 2010 to 2022. To ensure data reliability, the sample is refined through the following procedures: (1) excluding firms in the financial sector (e.g., banks, insurance companies, and securities firms); (2) removing firms under special treatment (ST, *ST, or PT); (3) eliminating observations with missing values in key variables; and (4) applying 1% winsorization at both tails for all continuous variables to mitigate the influence of outliers. Given the typical 1–2-year development cycle of patents and the lagged effect of policy implementation, green innovation is measured using a one-year lag for green patent applications. The final sample comprises 27,213 firm–year observations across 3886 non-financial listed firms. The data are primarily sourced from the CNIPA, CSMAR, and CNRDS databases.

3.2. Variable Design

3.2.1. Dependent Variable: Green Innovation

The dependent variable in this study is green innovation. We use the number of green patent applications by listed companies as a proxy for green innovation. According to China’s Patent Law, green patents are categorized into invention patents, utility model patents, and design patents, with decreasing levels of innovativeness. Among these, invention patents are the most innovative, with a complex and rigorous application process, making them the best indicator of a company’s creativity [75]. Therefore, based on the study by Zhang et al. [19], we define the number of green invention patent applications as SubGI and the number of green utility model patent applications as StrGI. Given that green design patents have a lower technological content, with their reports and submitted documents not requiring stringent examination, this study does not consider the number of green design patent applications [18].

3.2.2. Independent Variable: Pressure Environment

The key explanatory variable in this study is the pressure combination, which involves two interacting external pressure factors: institutional pressure and competitive pressure. (1) Institutional pressure: Since regulatory pressure primarily stems from the enforcement of standards and regulations rather than their mere existence [56], this study adopts the number of environmental administrative penalty cases accepted in each province as a proxy for institutional pressure, following Liao et al. [76]. (2) Competitive pressure: Drawing on Huang [77], this study adopts the Herfindahl–Hirschman Index (HHI) to proxy the level of industry competition. The HHI reflects the degree of market concentration by accounting for both the distribution and dominance of firms within an industry. A higher HHI value denotes a more concentrated market structure, typically associated with weaker competitive intensity, whereas a lower HHI suggests a more fragmented market with heightened rivalry among firms. To better represent competitive pressure as a positive indicator, the HHI is transformed by subtracting it from one. Based on this, the pressure combination is defined as the product of institutional pressure and competitive pressure.

3.2.3. Control Variables

To mitigate omitted variable bias, this study follows Wang et al. [78] and includes a set of control variables: firm size, firm age, leverage ratio, proportion of fixed assets, ownership structure, board size, CEO duality, market value, and regional GDP. In addition, year and province fixed effects are included to account for temporal and regional heterogeneity.

Table 1. Variables and measurements.

Variables	Symbol	Measurement
Substantive green innovation	SubGI	ln (green invention patents applications + 1)
Strategic green innovation	StrGI	ln (green utility patent application + 1)
Institutional pressure	IP	ln (environmental administrative penalty cases + 1)
Competitive pressure	CP	1-HHI
Enterprise scale	Size	ln (total enterprise assets)
Enterprise maturity	Age	ln (years since firm establishment)
Asset-liability ratio	Lev	total liabilities/total assets
Fixed Asset Ratio	Fixed	net fixed assets/total assets
Nature of shareholding	Soe	1 if state-owned enterprises, 0 otherwise
Board size	Board	total number of board directors
Management ownership ratio	Mshare	shares held by management/total shares outstanding
Dual role	Dual	1 if CEO and chairman are the same person, 0 otherwise
Enterprise market value	TobinQ	market value of firm/total assets
Economic development	GDP	ln (regional GDP)
Year	Year	year dummy variable
Province	Province	province dummy variables

provides detailed definitions of the relevant variables.

3.3. Model Building

To investigate the impact of the combination of institutional pressure and competitive pressure on green innovation, this study follows the approach of Li et al. [74] and constructs the following regression model, as shown in Equation (1).

$${GI}_{i,t}(\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{G}\mathrm{I}/\mathrm{S}\mathrm{u}\mathrm{b}\mathrm{G}\mathrm{I})={\alpha }_0+{\alpha }_1({IP}_{i,t-1}\ast {CP}_{i,t-1})+\sum {\alpha }_2{Controls}_{i,t}+{\mu }_t+{\gamma }_t+{\epsilon }_{i,t}$$

(1)

where i and t represent firms and years, respectively. GI_i,t denotes the level of green innovation (StrGI/SubGI) for firm i in year t. IP ∗ CP represents the interaction term between institutional pressure and competitive pressure (IPCP). Controls refer to a set of control variables, as shown in Table 1. ${\mu }_t$ and ${\gamma }_t$ represent year and province fixed effects, respectively, and $\epsilon$ is the error term. Given the potential lag in the effect of institutional environments, this study follows Zhao et al. [79] and examines the effect of the pressure combination (year t − 1) on green innovation (year t), thereby more accurately reflecting the causal relationship between the explanatory and dependent variables. To explore how different intensities of pressure combinations affect green innovation, this study employs the median split method based on Iacobucci et al. [80]. Specifically, the median values of institutional and competitive pressure across the study period are used as thresholds. Firms are then classified into “high” or “low” categories based on these thresholds. Firms are classified as having “high” or “low” institutional or competitive pressure based on whether their values are above (or equal to) or below the respective median. Accordingly, firms are grouped into four institutional–competitive pressure combinations: (1) High IP—High CP: Firms face strong institutional regulation and intense market competition. (2) High IP—Low CP: Firms are subject to strong institutional regulation but operate in less competitive markets. (3) Low IP—High CP: Firms face intense market competition but relatively weak institutional pressure. (4) Low IP—Low CP: Firms operate in an environment characterized by low institutional pressure and low competitive pressure.

4. Empirical Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Mean	Std. Dev.	Min	Max
StrGI	0.546	0.882	0.000	3.664
SubGI	0.539	0.910	0.000	4.043
IP	8.578	1.037	5.673	10.012
CP	0.584	0.112	0.026	0.673
Size	22.267	1.283	20.108	26.330
Age	2.904	0.324	1.946	3.526
Lev	0.331	0.140	0.052	0.611
Fixed	0.177	0.119	0.003	0.506
Soe	0.321	0.467	0.000	1.000
Board	1.136	0.064	0.959	1.292
Mshare	0.298	0.457	0.000	1.000
Dual	1.649	1.163	0.000	4.235
TobinQ	1.021	0.335	0.000	2.129
GDP	2.189	0.404	1.292	2.997

presents the results of the descriptive statistical analysis. The mean values of StrGI and SubGI are 0.546 and 0.539, respectively, with StrGI slightly higher than SubGI. This indicates that sample firms are more likely to apply for green utility model patents than green invention patents, suggesting a tendency for quantity over quality in green innovation. Regarding IP and CP, the substantial differences between their maximum and minimum values indicate significant variation in the intensity of IP and CP across firms. This provides a solid empirical basis for further investigation into how different pressure combinations affect green innovation.

4.2. Correlation Analysis

Figure 3 presents the results of the correlation analysis. The results show that the correlation coefficients between IP and CP and both StrGI and SubGI are significantly positive at the 1% level, indicating that both types of pressure have a significant promoting effect on green innovation, congruent with theoretical expectations. Additionally, the correlation coefficients between the independent and dependent variables are all below 0.6. Although StrGI and SubGI are highly correlated (correlation coefficient = 0.702), they are not included in the same regression model, and thus their correlation does not affect the regression results. Therefore, the selection of variables in this study is appropriate. Furthermore, to address potential multicollinearity among the explanatory variables, a variance inflation factor (VIF) test was conducted (as shown in

Table 3. VIF test.

	IP	CP	Size	Age	Lev	Fixed
VIF	1.17	1.01	1.99	1.15	1.50	1.10
1/VIF	0.853	0.993	0.502	0.873	0.668	0.906
	Soe	Board	Mshare	Dual	TobinQ	GDP
VIF	1.72	1.16	1.77	1.14	1.20	1.28
1/VIF	0.581	0.866	0.502	0.878	0.835	0.783

). The VIF values range from 1.01 to 1.99, all well below the threshold of 10, indicating that multicollinearity is not a serious concern in this study.

4.3. Hypothesis Testing Results

Table 4. Regression results for the group with congruent pressure intensity.

Variables	High IP–High CP			Low IP–Low CP
	(1)	(2)	(3)	(1)	(2)	(3)
	GI	StrGI	SubGI	GI	StrGI	SubGI
IPCP	−0.343 *** (−5.07)	−0.375 *** (−5.46)	−0.327 *** (−4.69)	−0.021 ** (−2.54)	−0.026 *** (−3.08)	−0.012 (−1.41)
Size	0.456 *** (28.55)	0.400 *** (24.73)	0.451 *** (27.36)	0.452 *** (32.70)	0.404 *** (28.23)	0.446 *** (32.09)
Age	−0.051 *** (−3.94)	−0.056 *** (−4.26)	−0.033 ** (−2.50)	−0.083 *** (−6.55)	−0.090 *** (−6.83)	−0.068 *** (−5.29)
Lev	0.091 *** (6.52)	0.113 *** (7.95)	0.064 *** (4.41)	0.050 *** (3.91)	0.069 *** (5.12)	0.027 ** (2.07)
Soe	−0.021 (−0.61)	−0.115 *** (−3.31)	0.060 * (1.70)	0.077 *** (2.67)	0.058 * (1.91)	0.097 *** (3.31)
Fixed	0.014 (1.06)	0.071 *** (5.46)	−0.037 *** (−2.76)	−0.093 *** (−8.58)	−0.076 *** (−6.72)	−0.109 *** (−9.99)
Board	0.007 (0.58)	0.018 (1.49)	−0.003 (−0.26)	−0.024 ** (−2.14)	−0.025 ** (−2.10)	−0.018 (−1.58)
Mshare	0.080 *** (5.52)	0.049 *** (3.37)	0.086 *** (5.81)	0.087 *** (5.77)	0.075 *** (4.84)	0.081 *** (5.33)
Dual	0.057 *** (2.32)	0.055 ** (2.20)	0.078 *** (3.07)	−0.085 *** (−3.17)	−0.100 *** (−3.58)	−0.053 ** (−1.98)
TobinQ	0.092 *** (7.37)	0.064 *** (5.05)	0.099 *** (7.66)	0.002 (0.18)	−0.027 ** (−2.16)	0.025 ** (2.06)
GDP	0.697 * (1.91)	0.546 (1.47)	0.748 ** (1.98)	−0.224 * (−1.80)	−0.207 (−1.61)	−0.082 (−0.65)
Year	Yes	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes	Yes
Constant	1.081 (1.34)	0.932 (1.14)	1.233 (1.48)	−0.321 *** (−5.67)	−0.314 *** (−5.34)	−0.265 *** (−4.64)
Observations	6830	6830	6830	6758	6758	6758
F	53.53	47.81	44.96	56.40	46.37	49.78
R2	0.206	0.188	0.179	0.292	0.253	0.267

Note: In the table, t statistics are in brackets and the parentheses are the robust standard error values. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the regression results indicating that congruent pressure combinations (High IP–High CP and Low IP–Low CP) exert a significant negative impact on green innovation, with coefficients of −0.343 and −0.021, respectively (p < 0.01), thus supporting H1. This suggests that both high and low levels of congruent pressure may hinder green innovation. These results suggest that congruent pressure combinations, regardless of intensity, may impede green transition without appropriate incentives or balancing mechanisms.

Table 5. Regression results for the group with incongruent pressure intensity.

Variables	High IP–Low CP			Low IP–High CP
	(1)	(2)	(3)	(1)	(2)	(3)
	GI	StrGI	SubGI	GI	StrGI	SubGI
IPCP	0.074 *** (6.82)	0.093 *** (8.23)	0.049 *** (4.47)	0.191 *** (4.42)	0.190 *** (4.45)	0.193 *** (4.37)
Size	0.429 *** (29.86)	0.349 *** (23.42)	0.433 *** (29.88)	0.355 *** (24.17)	0.314 *** (21.63)	0.349 *** (23.25)
Age	−0.063 *** (−5.05)	−0.056 *** (−4.29)	−0.063 *** (−5.03)	−0.099 *** (−7.91)	−0.086 *** (−6.98)	−0.088 *** (−6.86)
Lev	0.075 *** (5.67)	0.108 *** (7.87)	0.029 ** (2.17)	0.018 (1.43)	0.042 *** (3.32)	−0.005 (−0.38)
Soe	0.088 *** (2.75)	0.071 ** (2.14)	0.127 *** (3.92)	0.134 *** (4.73)	0.093 *** (3.32)	0.153 *** (5.27)
Fixed	−0.054 *** (−4.66)	−0.021 * (−1.77)	−0.075 *** (−6.47)	0.011 (1.05)	0.054 *** (5.15)	−0.024 ** (−2.21)
Board	0.008 (0.64)	0.0030 (0.25)	−0.001 (−0.08)	−0.015 (−1.36)	−0.008 (−0.71)	−0.015 (−1.29)
Mshare	0.061 *** (4.48)	0.069 *** (4.93)	0.042 *** (3.04)	0.074 *** (5.19)	0.084 *** (5.90)	0.056 *** (3.81)
Dual	0.071 *** (2.98)	0.024 (0.96)	0.096 *** (3.99)	−0.035 (−1.43)	−0.018 (−0.73)	−0.030 (−1.20)
TobinQ	0.054 *** (4.17)	0.016 (1.17)	0.068 *** (5.19)	0.040 *** (3.46)	0.015 (1.33)	0.061 *** (5.22)
GDP	−0.778 ** (−2.21)	−1.060 *** (−2.91)	−0.477 (−1.35)	−0.085 (−0.67)	−0.144 (−1.16)	−0.057 (−0.44)
Year	Yes	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes	Yes
Constant	−1.494 * (−1.92)	−2.074 *** (−2.57)	−0.902 (−1.15)	−0.360 *** (−5.57)	−0.288 *** (−4.50)	−0.289 *** (−4.38)
Observations	6848	6848	6848	6777	6777	6777
F	67.67	51.25	59.57	30.71	27.83	25.78
R2	0.247	0.199	0.224	0.183	0.169	0.158

Note: In the table, t statistics are in brackets and the parentheses are the robust standard error values. *** p < 0.01, ** p < 0.05, * p < 0.1.

shows that incongruent pressure combinations, namely, High IP–Low CP and Low IP–High CP combinations, significantly enhance green innovation, with coefficients of 0.074 and 0.191, respectively (p < 0.01), thus supporting H2. Moreover, this study further examines the differentiated impacts on types of green innovation. The High IP–Low CP combination shows coefficients of 0.093 and 0.049 for StrGI and SubGI, respectively (p < 0.01)—suggesting a stronger effect on StrGI. In contrast, the Low IP–High CP combination yields coefficients of 0.190 and 0.193 for StrGI and SubGI, respectively, indicating a more pronounced effect on SubGI. These findings provide additional support for H3. Figure 4 illustrates the differential impacts of distinct pressure configurations on StrGI and SubGI.

4.4. Robustness Test

4.4.1. Deal with Endogeneity Issues

There may be a potential reverse causality between IPCP and green innovation. To address this endogeneity concern, this study adopts an instrumental variable (IV) approach. Following Zhao et al. [81], the one-period lag of IPCP is employed as the instrument. The 2SLS regression results are presented in Appendix A 

Table A1. Regression results of endogeneity.

Variables	High IP–High CP			High IP–Low CP			Low IP–High CP			Low IP–Low CP
	First	Second		First	Second		First	Second		First	Second
	IPCP	StrGI	SubGI	IPCP	StrGI	SubGI	IPCP	StrGI	SubGI	IPCP	StrGI	SubGI
IPCP-iv	0.156 *** (22.96)	−1.263 *** (−3.31)	−1.360 *** (−3.50)	0.505 *** (44.89)	0.175 *** (7.33)	0.107 *** (4.18)	0.034 *** (6.77)	1.108 ** (2.29)	1.259 ** (2.34)	0.363 *** (34.59)	−0.047 * (−1.90)	0.015 (0.61)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	0.213 (1.53)	1.019 (1.22)	1.808 ** (2.00)	0.074 (0.10)	−1.996 ** (−2.35)	−0.922 (−1.15)	−1.029 *** (−38.74)	0.784 (1.48)	1.013 * (1.73)	0.650 *** (5.74)	−0.148 (−1.43)	−1.182 * (−1.83)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	5016	5016	5016	5152	5152	5152	4886	4886	4886	4879	4879	4879
F	147.90	30.10	23.68	74.89	29.61	30.35	150.54	16.33	14.24	42.99	27.36	25.75
R2	0.479	0.181	0.164	0.312	0.202	0.230	0.594	0.129	0.103	0.295	0.262	0.274
Kleibergen-Paaprk LM statistic	319.25 ***			243.18 ***			9.88 ***			102.061 ***
Wald F statistic	527.10			2015.04			45.84			1196.25
Stock-Yogo 10% maximal IV size critical value	16.38

Note: In the table, IPCP-iv refers to the instrumental variables of IPCP. t statistics are in brackets and the parentheses are the robust standard error values. *** p < 0.01, ** p < 0.05, * p < 0.1.

. The first-stage regression results indicate that IPCP is significantly and positively correlated with its lagged value at the 1% level, confirming instrument relevance. The second-stage estimates remain congruent with the baseline regression results, suggesting that endogeneity does not substantially alter the main findings. Furthermore, the Wald F-statistics from the first stage all exceed the Stock–Yogo critical value of 16.38 for a 10% maximal IV size distortion, indicating that the instrument is sufficiently strong and that the weak instrument problem is unlikely. Since the model is exactly identified, the Hansen J statistic equals 0.000, and thus, overidentification testing is not applicable. Overall, the use of the instrumental variable approach in this study is methodologically sound and yields robust and reliable results.

4.4.2. Change the Sample Size

To ensure the robustness of the research findings, this study conducts robustness checks using a balanced panel dataset spanning 12 consecutive years (2010–2022), thereby mitigating potential biases arising from single-year observations. The regression results congruently pass the robustness tests (see Appendix A 

Table A2. Robustness test: Change the sample size.

Variables	High IP–High CP		High IP–Low CP		Low IP–High CP		Low IP–Low CP
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI
IPCP	−0.361 *** (−4.03)	−0.279 *** (−2.99)	0.117 *** (7.13)	0.063 *** (3.96)	0.225 *** (5.26)	0.229 *** (5.25)	−0.287 *** (−3.75)	−0.250 *** (−3.15)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	−0.840 (−1.10)	0.009 (0.01)	−0.871 (−1.20)	−0.505 (−0.71)	−0.230 *** (−3.99)	−0.227 *** (−3.85)	−0.058 (−0.73)	−0.076 (−0.91)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	3397	3397	3526	3526	3449	3449	3397	3397
F	23.99	21.77	24.89	30.08	27.23	21.44	37.71	34.53
R2	0.204	0.189	0.204	0.237	0.149	0.121	0.197	0.184

Note: In the table, t statistics are in brackets and the parentheses are the robust standard error values. *** p < 0.01.

). All models control for year and province fixed effects, and the corresponding regression statistics (e.g., F-values and R²) indicate strong model fit. These findings further confirm the reliability and robustness of the baseline results.

4.4.3. Long-Term Effect Robustness Check

Given the long-term nature of green innovation, this study further examines whether firms engage in short-term “greenwashing” by analyzing the effects of pressure combinations on green innovation with two- and three-period lags. The regression results remain congruent with the baseline findings (see Appendix A 

Table A3. Robustness test: long-term effects.

Variables	Two-Period Lagged Green Innovation								Three-Period Lagged Green Innovation
	High IP–High CP		High IP–Low CP		Low IP–High CP		Low IP–Low CP		High IP–High CP		High IP–Low CP		Low IP–High CP		Low IP–Low CP
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)
	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI	StrGI	SubGI
IPCP	−0.224 *** (−5.90)	−0.226 *** (−5.83)	0.091 *** (7.64)	0.054 *** (4.73)	0.098 *** (4.75)	0.099 *** (4.65)	−0.019 ** (−2.00)	0.000 (0.05)	−0.178 *** (−4.46)	−0.196 *** (−4.81)	0.096 *** (7.42)	0.061 *** (4.92)	0.059 *** (3.39)	0.060 *** (3.29)	−0.020 * (−1.95)	0.004 (0.41)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	0.964 (1.28)	1.516 ** (1.98)	−1.772 ** (−2.40)	−0.648 (−0.91)	−0.231 *** (−4.76)	−0.219 *** (−4.36)	−0.264 *** (−3.68)	−0.239 *** (−3.40)	0.912 (1.14)	1.574 * (1.92)	−2.072 *** (−2.59)	−0.843 (−1.10)	−0.350 *** (−7.70)	−0.268 *** (−5.64)	−0.210 ** (−2.37)	−0.205 ** (−2.36)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	5721	5721	6188	6188	5522	5522	5745	5745	4755	4755	5571	5571	4568	4568	5039	5039
F	40.42	37.03	44.19	52.91	50.12	44.23	38.20	41.31	34.60	31.03	41.49	50.94	44.26	39.45	34.57	36.97
R2	0.190	0.177	0.192	0.221	0.161	0.145	0.244	0.258	0.190	0.174	0.193	0.227	0.163	0.148	0.246	0.258

Note: In the table, t statistics are in brackets and the parentheses are the robust standard error values. *** p < 0.01, ** p < 0.05, * p < 0.1.

). This indicates that firms do not exhibit short-term greenwashing behavior, and that the effects of green innovation are more likely to be sustained and long-term. These results provide additional support for the reliability and robustness of the study’s main conclusions.

4.5. Extended Analysis

As a strategic behavior, green innovation ultimately aims to enhance firms’ overall performance. Under varying external pressure conditions, whether firms can achieve superior performance outcomes by adopting different green innovation strategies remains a key question worthy of further investigation. ESG performance, widely recognized as a critical indicator of corporate sustainability, has been increasingly incorporated into capital market evaluations and investment decisions, exerting significant influence on corporate reputation, financing capacity, and long-term value creation [82,83]. Based on this, the study further constructs a structural equation model (SEM) to analyze the pathways through which institutional pressure combinations influence corporate ESG performance via different types of green innovation via StrGI and SubGI. The SEM diagram is shown in Figure A1, with path estimates reported in

Table A4. Model path analysis between IPCP, GI and ESG.

Path	Estimate	S.E.	Est./S.E.	p-Value	Std. Coef.
IPCP → StrGI	0.039	0.0076	5.14	0	0.039
IPCP → SubGI	0.0169	0.0076	2.24	0.025	0.0169
StrGI → ESG	0.0274	0.0082	3.33	0.001	0.0274
SubGI → ESG	0.0505	0.0083	6.12	0	0.0505
IPCP → ESG	0.0612	0.0079	7.74	0	0.0612

. Both StrGI and SubGI have significant positive effects on ESG performance, with SubGI exhibiting a higher standardized path coefficient (β = 0.0505, p < 0.001) than StrGI (β = 0.0274, p < 0.01), indicating a more pronounced role of SubGI in enhancing ESG outcomes. Additionally, the direct effect of IPCP on ESG is 0.0612 (p < 0.001), demonstrating a significant direct driving force. To further verify the mediating role of green innovation, the bootstrap method was employed to test indirect effects (see

Table A5. Bootstrap analysis of mediation effect testing.

Indirect Path	Estimate	p-Value	95% Confidence Interval (Normal)
SIPCP → StrGI → ESG	0.00107	0.008	[0.00028, 0.00186]
SIPCP → SubGI → ESG	0.00086	0.038	[0.00005, 0.00166]

). The results indicate that IPCP significantly influences ESG performance indirectly through both StrGI and SubGI, with indirect effects of 0.00107 (p = 0.008) and 0.00086 (p = 0.038), respectively; their 95% confidence intervals exclude zero, confirming partial mediation by both green innovation types. The findings validate the dual-path innovation response mechanism—strategic and substantive—adopted by firms under pressure combinations, jointly constituting a vital channel for improving sustainable performance. This study elucidates the micro-level mechanisms by which pressure combinations affect ESG performance, emphasizing the differentiated roles of green innovation strategies. It contributes to a deeper understanding of the relationship between green strategies and corporate performance, while providing theoretical and practical insights for firms to select effective green innovation paths amid institutional transformation and intensified ESG evaluations.

5. Discussion and Implications

5.1. Conclusions

Amid intensifying ecological pressures and the ongoing transformation of economic development, effectively stimulating green innovation has become a critical pathway to advancing sustainable development. Institutional and market forces serve as the primary external sources of pressure, and varying combinations of these pressures can trigger heterogeneous innovation responses among firms. Against this backdrop, this study develops an analytical framework that integrates the interaction between institutional pressure and competitive pressure. The study empirically investigates how different pressure combinations affect green innovation. Building on this, it further incorporates the dimension of innovation motivation to examine the differentiated effects of various pressure combinations on StrGI and SubGI. Multiple robustness checks are conducted to ensure the reliability and validity of the findings.

The empirical results indicate that firms exhibit differentiated innovation response patterns under various pressure combinations. Specifically, in congruent pressure contexts (i.e., “high IP–high CP” or “low IP–low CP”), green innovation is suppressed. This finding aligns with Liu et al. [42], who argue that dual pressures from institutional and market sources may strain resources and weaken green innovation capacity. However, unlike Liu et al. [42], who did not differentiate pressure combinations by intensity and structure, this study introduces the concept of “congruence” versus “incongruence” in pressure combinations. By doing so, it not only confirms the “overload effect” under mutually reinforcing pressures but also reveals a “complacency effect” under insufficient pressure. In contrast, in incongruent pressure contexts (i.e., “high IP–low CP” or “low IP–high CP”), firms are more likely to strengthen green innovation to adapt to environmental changes and institutional demands. Moreover, in the “high IP–low CP” context, firms are more inclined to pursue StrGI to enhance legitimacy, while in the “low IP–high CP” context, they focus more on SubGI to enhance core competitiveness. This finding aligns with the “balance view” of optimal distinctiveness theory, which posits that maintaining an appropriate level of differentiation between legitimacy and distinctiveness enables organizations to achieve optimal performance [84]. Compared with congruent pressures, which may lead to excessive conformity or resource constraints, incongruent pressure combinations offer firms greater flexibility and strategic options, thereby fostering green innovation [6]. Further analysis reveals that both StrGI and SubGI significantly improve ESG performance, with SubGI having a more pronounced effect. Additionally, there exists a certain substitution effect between the two, as an over-reliance on StrGI may suppress the implementation of SubGI, thereby adversely affecting the firm’s sustainable development.

5.2. Theoretical Implications

Grounded in institutional theory and optimal distinctiveness theory, this study develops a pressure–response model to systematically examine the interactive effects of institutional and competitive pressures and their underlying mechanisms in shaping firms’ green innovation behavior. The theoretical contributions are as follows: First, in contrast to prior studies that primarily focus on the isolated effects of institutional pressure (e.g., environmental regulations) or competitive pressure (e.g., market intensity) [50], this study responds to Su et al. [6]’s call for research on the “dual-pressure configuration effect” by proposing an integrated analytical framework capturing the synergy between institutional and competitive pressures. It emphasizes that these pressures are not independent but dynamically aligned, jointly shaping firms’ strategic choices through the tension between legitimacy and differentiation. Under different pressure configurations, firms face distinct external expectations and internal adjustment mechanisms, which, in turn, influence their orientation and pathways in green innovation. This insight moves beyond the traditional “single-pressure” paradigm, expanding the theoretical boundaries of external drivers of green innovation.

Second, this study extends the application of optimal distinctiveness theory to the context of green innovation. While this theory emphasizes the dynamic balance firms must strike between legitimacy acquisition and competitive differentiation [85], prior research has primarily applied it to general strategic management or organizational choice contexts [20,21]. However, there has been limited theoretical exploration of its relevance to green innovation—a strategic behavior characterized by both regulatory compliance and market orientation. To address this gap, this study draws on optimal distinctiveness theory to conceptualize institutional pressure and competitive pressure as external representations of legitimacy pursuit and differentiation drive, respectively, and develops a dual-dimensional framework of external pressures to depict the structural tensions that firms face in seeking optimal distinctiveness. By contextualizing the alignment of institutional and competitive pressures, the study reveals how firms navigate the tension between conformity and differentiation across varying pressure configurations to achieve a synergistic balance between legitimacy and distinctiveness. Through this integration, it enriches the theoretical connotation of optimal distinctiveness theory within environmental strategy research and advances its application in the fields of green management and sustainable development.

Third, this study reveals the heterogeneous response mechanisms of firms’ green innovation motivations under different external pressure configurations. By introducing a motivation-based perspective, green innovation is classified into StrGI and SubGI, and the study systematically analyzes how various combinations of institutional and competitive pressures exert differentiated impacts on these two types. While prior research has acknowledged motivational differences in green innovation [18], limited attention has been paid to how external pressure structures shape these motivations, particularly the response paths in contexts where institutional and competitive pressures interact. This study integrates the dual-motivation perspective with pressure configurations to develop an analytical framework that links external structural pressures with firms’ green innovation path selection, thereby expanding the theoretical scope of the “pressure–response” fit in green innovation research. Moreover, by moving beyond the traditional view of green innovation as a homogeneous activity, this research enhances our understanding of how firms strategically adjust their innovation behaviors in response to complex environmental conditions, offering more nuanced insights into the internal logic of corporate green transformation.

5.3. Managerial Implications

First, policymakers should enhance the coordination between institutional design and market mechanisms. The study reveals that different combinations of pressures exert nonlinear effects on green innovation, indicating that institutional and competitive pressures are not inherently additive and may even suppress innovation when misaligned. Therefore, in formulating green transition policies, policymakers should account for both market capacity and regulatory intensity, promoting more flexible and inclusive institutional arrangements that enable firms to achieve compliance while unlocking their innovation potential.

Second, firms should dynamically assess the structure of external pressures and adapt their green innovation strategies accordingly to avoid strategic lock-in. Particular attention should be paid to the risks of strategic rigidity and innovation convergence under conditions of “dual high pressure” or “dual low pressure”, where resource constraints or excessive responses may occur. To address these challenges, firms should establish effective environmental sensing mechanisms to monitor changes in external pressures in real time, enabling timely strategic adjustments. Additionally, optimizing resource allocation and improving the efficiency of green innovation investments are essential to mitigate the risks of resource waste and homogenized innovation under compounded pressure contexts.

Finally, governments and enterprises should jointly promote the precise alignment of green innovation motivations to achieve synergy between policy guidance and strategic choice. The findings indicate that firms tend to adopt different types of green innovation under varying pressure combinations. This suggests that, in formulating green transition strategies, firms should align innovation motivations with external pressures and internal resources, and design tailored technological pathways and investment mechanisms to improve the adaptability and effectiveness of green transformation. Meanwhile, policymakers should pay closer attention to the structural heterogeneity of firms’ innovation motivations and avoid one-size-fits-all policy enforcement. For example, in regions characterized by high IP-low CP, stronger incentives for SubGI should be provided, whereas in low IP–high CP contexts, firms should be guided to leverage market advantages into technological capabilities. Such targeted approaches can facilitate the shift from compliance-driven to value-creating innovation and enhance the quality of green development.

5.4. Limitations and Future Research Directions

Despite offering valuable insights into the mechanisms through which institutional and competitive pressures influence green innovation, this study has several limitations. First, the analysis primarily focuses on external institutional and market factors, without fully considering internal elements such as corporate governance structures, strategic orientations, and resource allocation. For firms engaged in diversified operations or cross-regional activities, the complexity of internal governance and the challenges of resource coordination may shape how external pressures affect green innovation. Future research could explore how internal factors—such as strategic conflicts and organizational slack—mediate the evolution of green innovation under varying institutional contexts, thereby advancing the integration of green innovation and organizational adaptability theories. Second, this study is based on a sample of listed firms in China, and its findings may not be directly generalizable to other market economies. Institutional environments vary significantly across countries, potentially leading to heterogeneous firm responses to external pressures. Future studies could expand the sample to include firms from diverse countries, industries, and stages of institutional development to conduct cross-national comparisons. Such efforts would help validate the external applicability of the findings and provide a more globally informed understanding of institutional drivers of green innovation. Finally, this study has certain limitations in the measurement of green innovation and model specification. On the one hand, although green patents are widely used and supported by existing theoretical and empirical literature, patent-based indicators still have inherent constraints. For instance, firms across industries may differ in their motivations and behaviors regarding patent applications; patent filings may be influenced by multiple factors such as regulatory compliance or brand image considerations, which may not fully reflect the actual intensity or substance of technological innovation. Moreover, patent data may not capture the full scope of green innovation, especially in sectors where innovation is not oriented toward patent output. Future research could incorporate alternative or complementary indicators—such as green product launches, environmental certifications, or green technology investments—to more comprehensively capture firms’ green innovation activities. On the other hand, given the potential interdependence between StrGI and SubGI, the use of separate regression models may not fully account for the covariance structure of the error terms, potentially reducing estimation efficiency. Future studies may consider employing a Seemingly Unrelated Regressions (SUR) approach to improve the robustness and precision of model estimation. In sum, future research could extend this study along the dimensions of internal governance, diverse samples, and innovation measurement to better uncover the complex interactions between external environments and corporate green innovation, thereby providing more targeted theoretical and practical guidance for green development strategies across varying contexts.