An Empirical Investigation into the Impact Mechanisms of Energy Transition in Corporate Performance

Abstract

This paper empirically investigates the impact of energy transition on corporate performance by utilizing panel data from A-share listed companies in Shanghai and Shenzhen between 2008 and 2022. Employing a fixed-effects model, the analysis incorporates key mediating variables including financing constraints, research and development (R&D) investment, corporate reputation, and investor attention. The results demonstrate that the energy transition exerts a significantly positive effect on firm performance, primarily through alleviating financing constraints and stimulating R&D activities. These effects are notably stronger among firms with higher market responsiveness and in regions exhibiting greater levels of economic development—particularly small- and medium-sized enterprises (SMEs), non-state-owned firms, enterprises located in eastern China, and those operating in high-carbon-emitting industries. Furthermore, enhanced corporate reputation and heightened investor attention serve as important amplifiers, reinforcing the positive relationship between energy transition and firm performance. This suggests the existence of a virtuous cycle wherein “transition investment” facilitates “resource integration,” ultimately leading to superior “performance outcomes.” The findings highlight the strategic value of aligning energy transition efforts with firm-level capabilities, indicating that sustainable investments can serve as a pathway to both environmental and economic gains through enhanced competitiveness and stakeholder engagement.

1. Introduction

Amid escalating global climate challenges and China’s pursuit of its “dual carbon” goals, energy transition has emerged as a strategic imperative for firms seeking to bolster their core competitiveness. As foundational entities within the economic system, corporations are not only major consumers of energy and emitters of carbon but also play a crucial role in driving low-carbon technological innovation and reshaping development paradigms. Nonetheless, the impact of energy transition on corporate performance is inherently multifaceted and temporally dynamic. In the short term, firms often encounter substantial pressures arising from high capital expenditures—such as investments in renewable energy infrastructure and carbon capture R&D—alongside the technological uncertainties associated with nascent fields like hydrogen value chains and the financial costs of phasing out legacy energy assets [1]. These challenges can negatively affect immediate financial outcomes. In contrast, long-term advantages may be realized through a combination of supportive policy instruments, including green subsidies [2] and revenues from carbon trading mechanisms, as well as from market-oriented benefits such as enhanced brand equity for low-carbon products and technological breakthroughs that improve energy storage efficiency. Collectively, these developments open new pathways for sustainable value creation and performance enhancement [3].

The capital-intensive and inherently uncertain characteristics of energy transition render it highly sensitive to firms’ financing conditions [4]. On one hand, financial constraints—often manifested in the disparity between internal and external financing costs—directly affect a firm’s capacity to translate transition-related opportunities into tangible investments [5]. Enterprises with lower financial constraints are typically able to access more diverse and cost-effective funding channels, thereby sustaining momentum in transition projects and achieving competitive advantages through increased market penetration and enhanced performance. Conversely, firms operating under tighter financial constraints may be compelled to postpone or downsize their transition initiatives due to liquidity shortfalls. Moreover, the asset specificity of many transition-related investments may diminish their collateral value, further exacerbating financing difficulties [6]. This dynamic is consistent with information asymmetry theory, which underscores the impact of financing frictions on investment behavior, and financial flexibility theory, which identifies liquidity as a prerequisite for effective strategic implementation.

On the other hand, China’s “dual-carbon” policy architecture is actively working to alleviate financing pressures through the deployment of green financial instruments such as carbon-reduction support tools and green bonds. Nevertheless, the distribution of these benefits remains uneven across industries and ownership structures. For example, firms operating in high-emission sectors may experience credit rationing due to heightened perceptions of stranded asset risk whereas emerging clean-energy enterprises are more likely to benefit from ESG-related valuation premiums and preferential access to capital [7]. In this context, financing constraints not only represent a significant bottleneck within the energy transition process but also function as a crucial transmission channel linking policy initiatives to corporate strategic behavior.

At its essence, the energy transition constitutes a fundamental shift in technological paradigms. Whether manifested in the expansion of renewable energy sources, the deployment of carbon capture technologies, or the decarbonization of industrial value chains, research and development (R&D) functions as a pivotal enabler of this transformation [8,9]. As an intermediary mechanism linking transition strategies to performance outcomes, R&D investment fulfills a dual role. First, the imperative to decarbonize compels firms to escalate R&D expenditures to surmount technological bottlenecks. The resulting innovations may enhance product competitiveness, increase operational efficiency, or reduce costs, thereby contributing positively to corporate performance [10]. Second, R&D activities generate knowledge spillovers that improve resource allocation efficiency and fortify firms’ adaptive capacity under conditions of uncertainty [11]. This transmission mechanism exemplifies the dynamic trajectory of “strategic investment → capability accumulation → performance realization,” in line with core tenets of innovation economics, which position R&D as a critical engine of long-term value creation.

In the Chinese context, the pursuit of carbon neutrality has catalyzed a surge in demand for low-carbon technological innovation, supported by an array of government instruments such as R&D subsidies and preferential tax policies aimed at incentivizing corporate innovation efforts [12]. Nonetheless, the effectiveness of R&D investment exhibits substantial heterogeneity across sectors. Traditional high-emission industries often encounter delays in realizing the benefits of R&D due to weaker innovation ecosystems and institutional inertia. In contrast, emerging clean-energy enterprises tend to be more adept at leveraging intensive R&D to construct technological moats and consolidate first-mover advantages. Failure to account for the mediating role of R&D risks obscuring the true efficacy of energy transition strategies and may lead to misaligned policy or managerial expectations—either undervaluing the transformational potential of sustainability-oriented innovation or overestimating the capacity of technology to independently drive performance outcomes.

Against this backdrop, a nuanced investigation into the relationship between energy transition and corporate performance holds substantial academic and practical significance. From a practical perspective, it enables firms to identify effective transition pathways—such as prioritizing R&D investment and restructuring financing strategies—while informing policymakers in the design of targeted incentive mechanisms, including transitional subsidies and green financial instruments. Theoretically, this inquiry advances interdisciplinary scholarship at the nexus of sustainable development and corporate strategy by elucidating the complex mechanisms through which environmental responsibility can translate into economic returns. Such analysis is particularly salient for emerging economies undergoing accelerated decarbonization, where understanding the causal linkages and moderating channels between energy transition and firm performance is essential for reconciling the tension between short-term transitional costs and long-term developmental imperatives. By clarifying these dynamics, the paper contributes to the theoretical foundation for fostering green competitiveness and sustainable value creation in the corporate sector.

This paper employs a text-based analytical framework to construct a multidimensional measure of energy transition and investigates its impact on corporate performance using panel data from Chinese A-share listed firms on the Shanghai and Shenzhen stock exchanges from 2008 to 2022. The empirical findings indicate that energy transition significantly improves corporate performance through two primary mediating mechanisms: the alleviation of financing constraints and the stimulation of research and development (R&D) investment. Further analysis reveals that these effects are more pronounced in firms operating under conditions of greater market flexibility and higher regional development maturity. Specifically, the positive impact of energy transition is amplified among small- and medium-sized enterprises (SMEs), non-state-owned enterprises, firms located in eastern China, and companies within high-carbon-emission industries.

The contributions of this paper are threefold. First, it systematically elucidates the internal mechanisms through which energy transition enhances corporate performance, identifying two primary channels: the mitigation of financing constraints and the intensification of R&D investment. Employing fixed-effects models and complementary econometric techniques, the analysis demonstrates that energy transition alleviates financial pressures, stimulates innovation efforts, and ultimately strengthens firm performance. These findings reveal a mutually reinforcing relationship between environmental responsibility and financial resilience, thereby extending the theoretical framework of sustainable development by articulating a bidirectional transmission pathway between environmental strategies and economic outcomes. Second, the paper advances beyond traditional single-moderator approaches by empirically verifying that corporate reputation—through signaling effects—and investor attention—through market value discovery—serve as significant amplifiers of the performance gains associated with energy transition. This highlights the critical role of stakeholder engagement and market visibility in magnifying the strategic and financial returns of low-carbon transformation, offering novel insights into how firms can harness external perceptions to enhance the effectiveness of their transition initiatives.

2. Literature Review

2.1. Energy Transition

With the implementation of China’s “dual carbon” objectives, the energy transition has emerged as a critical pillar in the pursuit of sustainable economic development. In the post-COVID-19 recovery phase, as the global economy gradually regains momentum, countries are actively reorienting their growth trajectories toward low-carbon pathways. Currently, the world is experiencing the third phase of the energy transition, characterized by broad-based international engagement and strategic policy realignments. In this context, identifying scientifically grounded and operationally efficient transition pathways is imperative for ensuring a smooth and resilient transformation of energy systems.

Recent empirical studies have examined various mechanisms through which energy transition influences firm behavior and macroeconomic dynamics. Long et al. (2023) [13] identified three channels through which energy transition affects business risk profiles, financial investment behavior, and the cost of corporate debt—particularly through the dampening of global oil price volatility. Their findings suggest that these effects are more pronounced among firms with weaker risk resistance capabilities [13]. Lee et al. (2022) [14] investigated the spatial and local spillover effects of China’s low-carbon-city pilot policies on energy consumption. Their analysis revealed that such pilots significantly accelerate energy transition processes, primarily through improvements in total factor productivity. Importantly, this relationship is conditional on the development of public transportation infrastructure, highlighting the role of urban planning in facilitating transition outcomes [14].

In the Canadian context, Thomas and Marcelin (2022) [15] demonstrated that most provinces would experience net gains from energy transition policies, particularly those that currently rely on fossil fuels for electricity generation. These provinces are expected to achieve greater economic and environmental benefits post transition compared to regions already dominated by renewables [15]. Broska et al. (2022) [16] using a quasi-dynamic model based on the cross-impact balance (CIB) method, emphasized that the success of community-led energy transition initiatives depends on the interplay of favorable socio-economic conditions. Specifically, community cohesion and strong social influence are prerequisites for such grassroots efforts to be effective; absent these conditions, citizen-driven energy transitions are unlikely to achieve scale or impact [16].

From a policy design perspective, Chen et al. (2019) argued that the development of a robust low-carbon pathway requires the integration of new energy technologies, effective economic management, and environmental science as core elements [17]. Yuan et al. (2019) [18] further emphasized the necessity for China to construct a synergistic, multi-energy system that leverages the complementarities of diverse energy sources. They also advocated for the systematic evaluation of the environmental benefits yielded by such integrated energy systems, suggesting that a more holistic approach to energy governance is essential for optimizing long-term sustainability outcomes [18].

2.2. Corporate Performance

Corporate performance has long been a focal point in academic research, serving as a critical metric for assessing operational efficiency, strategic execution, and a firm’s capacity for sustainable development. With evolving economic paradigms and shifting policy priorities, scholarly inquiry into corporate performance has expanded beyond traditional financial indicators to encompass multidimensional evaluations—particularly those integrating environmental, social, and governance (ESG) considerations and energy transition dynamics.

Recent studies underscore the increasing relevance of non-financial performance factors. Moussa et al. (2024) establish a strong positive relationship between comprehensive ESG performance and market capitalization, suggesting that responsible corporate behavior is increasingly rewarded in capital markets [1]. Zhang et al. (2024) investigate the intersection of sustainability, green innovation, and financial constraints, revealing that ESG performance facilitates green innovation by easing financing barriers—thereby reinforcing the connection between sustainability orientation and innovation-led value creation [19].

Building on these insights, Wu and Huang (2022) employ a generalized method of moments (GMM) approach to demonstrate that financial constraints diminish the beneficial impact of digital finance on the financial performance of new energy enterprises [20]. In a similar vein, Qian (2024) argues that enhanced ESG performance mitigates financing frictions and spurs green innovation, ultimately boosting firm value—a finding that aligns with the resource-based view, wherein reputational capital alleviates information asymmetry and enhances access to capital markets [21].

The mediating role of technological innovation is further supported by Xu and Yin (2025), who uncover a dual-chain mediating effect—technological innovation and financing constraints—linking digital transformation to improvements in ESG outcomes [22]. Fu (2024) examines the impact of internationalization on firm performance, concluding that global expansion improves return on assets and market value, with R&D investment serving as a key enabler of technological adaptability in international markets [23].

Policy-driven innovation incentives also constitute a significant area of inquiry. Wang et al. (2022), using panel data methods, identify positive correlations between government subsidies, R&D intensity, and innovation performance, thereby highlighting the catalytic role of public policy in shaping corporate innovation behavior [24]. Finally, Sun et al. (2019) point to the lagged effects of R&D on corporate performance, emphasizing that the efficacy of internal control systems moderates this relationship—offering valuable insight into how governance mechanisms can accelerate the translation of innovation into measurable performance gains [25].

2.3. Energy Transition Policy and Corporate Performance

The urgent need to address environmental challenges has prompted governments worldwide to implement policies aimed at accelerating the energy transition. As a result, scholars from various countries have increasingly examined the relationship between energy transition policies and corporate performance, offering valuable insights for companies seeking to expedite their transition. Zhang and Kong (2022) [26] found that renewable energy policies have positively impacted the total factor productivity (TFP) of energy companies, with the continued implementation of these policies further amplifying their effects. However, the positive outcomes of these policies may be undermined by their influence on resource allocation efficiency and technological innovation. Additionally, variations in corporate types, external environments, and geographical contexts contribute to the heterogeneous effects of renewable energy policies on total factor productivity [26].

Liu et al. (2023) observed that energy transition policies have led to a reduction in energy consumption intensity in pilot cities, particularly benefiting high-energy-consuming companies [27]. Similarly, Zhou et al. (2023) concluded that these policies have significantly enhanced the green innovation capacity of energy-intensive corporations [28]. The literature indicates that energy transition policies have fostered green technological innovation by facilitating research and development (R&D) while simultaneously improving the efficiency of the transition through government governance and technological advancements [29,30,31]. Furthermore, some scholars suggest that the relationship between energy transition and green total factor productivity is nonlinear rather than straightforwardly linear. Li et al. (2013) identified a threshold effect between energy transition and green TFP, indicating that green TFP is only influenced when the energy transition surpasses a certain threshold [32]. Xiong et al. (2023) [33] and Zheng et al. (2023) [34] observed a U-shaped relationship between energy transition and green TFP. Chen et al. (2025) found that the new energy demonstration city policy significantly improved the green TFP of companies, with this improvement being further enhanced by the U-shaped characteristics of environmental regulation and the optimization of urban new quality productivity [35].

Upon reviewing the existing literature, it is apparent that current research on energy transition exhibits a notable divergence in focus: on the one hand, scholars have systematically explored the driving factors (e.g., policy regulation and technological innovation) and pathways of energy transition; on the other hand, research on corporate performance has predominantly concentrated on the one-dimensional impacts of ESG governance efficiency, financing constraints, and R&D investment intensity. However, these two research strands have yet to form a cohesive framework, particularly with regard to the in-depth deconstruction of the transmission mechanisms through which energy transition influences corporate performance. Specifically, the intermediary role of financing constraints in energy technology iteration remains underexplored, and the dynamic effects of R&D investment on the nonlinear relationship between “transition costs” and “performance outputs” have not been adequately quantified. This theoretical fragmentation complicates the identification of the key pathways through which firms can achieve performance transitions via energy transition strategies. Therefore, there is an urgent need to establish an integrated analytical framework that encompasses the full spectrum of “policy-driven technological breakthroughs, capital allocation, and performance feedback.”

3. Theory and Hypothesis

This paper adopts an integrated theoretical framework, combining stakeholder theory, resource-based theory, and dynamic capabilities theory, to explore the impact of energy transition on corporate performance.

3.1. Stakeholder Theory

Stakeholder theory suggests that firms must align their strategies with the expectations of diverse stakeholders, including investors, governments, creditors, and consumers. Socially responsible investors, who prioritize environmental, social, and governance (ESG) performance, are more inclined to direct capital towards firms undergoing energy transitions [36]. Financial instruments such as green credit and sustainable bonds offer cost-effective financing solutions for these companies. Government initiatives, including carbon emission reduction subsidies and tax incentives, bolster corporate cash flows and enhance debt repayment capacity [37]. Furthermore, public and consumer recognition of low-carbon brands serves to mitigate information asymmetry in the financing process, reducing the risk of adverse selection through the reputational effect [38]. The combined support of these stakeholders alleviates financing constraints, enabling transitioning firms to secure capital at a lower cost and ensuring the continuity of both their day-to-day operations and strategic investments.

3.2. Resource-Based Theory

Resource-based theory posits that a firm’s unique resources and capabilities are critical to securing a competitive advantage. In the context of energy transition, financial resources—such as green subsidies and carbon trading revenues—along with reputational assets, including enhanced ESG ratings and green certifications, represent the firm’s heterogeneous advantages [39,40,41]. These resources replenish cash flows and strengthen creditors’ confidence, thereby alleviating financing constraints. The combined effect of these resources creates a dual empowerment mechanism driven by ‘policy–market’ interactions. Concurrently, the energy transition stimulates the need for technological innovation, prompting companies to increase their investments in research and development (R&D) [10]. Technological resources, such as green patents and proprietary know-how derived from R&D, not only provide product differentiation but also improve production efficiency through technology spillover effects. The accumulation and integration of these diverse resources enable firms to establish a distinctive core competence that is difficult to replicate in the increasingly competitive low-carbon economy.

3.3. Dynamic Capabilities Theory

Dynamic capabilities theory provides a valuable framework for understanding a firm’s ability to adapt to rapidly changing environments [42,43]. In the context of the energy transition, companies must cultivate two critical capabilities through the integration of both internal and external resources: (1) Green Financing Capability: This refers to the firm’s ability to identify and leverage green financial instruments, as well as to optimize its capital structure to effectively manage periods of high investment. Strengthening this capability directly alleviates financing constraints, thereby mitigating the risk of cash flow disruptions during the early stages of the transition. (2) Technological Innovation Capability: This entails the firm’s capacity to establish a robust low-carbon-technology R&D system and foster interdisciplinary technological research. The augmentation of R&D investment reflects this capability, directly fueling performance growth by driving technological advancements that lead to new product revenues and cost reductions.

3.4. Hypotheses

Based on the theoretical framework, the following hypotheses are proposed:

Hypothesis 1. 

Energy transition will alleviate financing constraints faced by companies, leading to enhanced corporate performance.

Hypothesis 2. 

Energy transition will stimulate an increase in R&D investment by companies, thereby improving their performance.

4. Model

4.1. Materials

This paper employs a balanced panel dataset comprising A-share listed firms on the Shanghai and Shenzhen stock exchanges from 2008 to 2022. The data are subjected to a series of preprocessing steps to ensure robustness and validity:

(1)

Sample Screening: Firms designated as ST or *ST (Special Treatment) are excluded to avoid distortions arising from financial distress or regulatory interventions;

(2)

Data Cleaning: Observations with missing or anomalous values in key financial indicators during the sample period are removed;

(3)

Data Imputation: For variables with sporadic missing values, linear interpolation is applied to maintain continuity and address potential outlier effects.

Following these procedures, the final dataset consists of 37,065 firm-year observations. Financial variables are sourced from the CSMAR and Wind databases while non-financial indicators are drawn from the China Statistical Yearbook. All data processing and empirical analyses are conducted using Stata 17.

4.2. Methodology

To empirically assess the impact of energy transition on corporate performance, this paper employs a fixed-effects panel regression model using data from Chinese A-share listed firms spanning the period 2008 to 2022. The model is specified as follows:

$${\mathrm{Q}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{G}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\lambda }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

Here, i and t denote the firm and year, respectively. The dependent variable ${\mathrm{Q}}_{\mathrm{i}\mathrm{t}}$ captures firm performance while the key independent variable reflects the degree of energy transition undertaken by the firm. ${\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ denotes a vector of control variables. ${\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$ captures firm-specific fixed effects and ${\mathsf{\lambda }}_{\mathrm{i}\mathrm{t}}$ represents time-fixed effects. The term ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$ is the idiosyncratic error term. To address potential econometric concerns such as autocorrelation and heteroscedasticity, robust standard errors clustered at the firm level are employed.

To investigate the mediating roles of financing constraints and R&D investment, this paper adopts the mediation analysis framework proposed by Wen Zhonglin (2014) [44] and constructs the following set of mediation models:

$${\mathrm{G}\mathrm{T}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\lambda }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(2)

$${\mathrm{Q}}_{\mathrm{i}\mathrm{t}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{G}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_3{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\lambda }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

Here, ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ denotes the mediating variable; ${\mathsf{\alpha }}_0$ and ${\mathsf{\gamma }}_0$ represent constant terms.

To explore the moderating effects of corporate reputation and investor attention, the following interaction model is specified:

$${\mathrm{Q}}_{\mathrm{i}\mathrm{t}}={\mathsf{\sigma }}_0+{\mathsf{\sigma }}_1{\mathrm{T}\mathrm{J}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\sigma }}_2{\mathrm{G}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\sigma }}_3{\mathrm{T}\mathrm{J}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{G}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\sigma }}_4{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\lambda }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(4)

Here, ${\mathrm{T}\mathrm{J}}_{\mathrm{i}\mathrm{t}}$ represents the moderating variable, ${\mathrm{T}\mathrm{J}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{G}\mathrm{T}}_{\mathrm{i}\mathrm{t}}$ denotes the interaction term between the moderating variable and energy transition, and ${\mathsf{\sigma }}_1$ and ${\mathsf{\sigma }}_2$ are the coefficient of the moderating variable and the explanatory variable, respectively. ${\mathsf{\sigma }}_3$ is the coefficient of the interaction term. The definitions of the other variables remain consistent with previous explanations. It is important to focus on the coefficient ${\mathsf{\sigma }}_3$. If ${\mathsf{\sigma }}_3$ > 0 and is statistically significant, it indicates that the moderating variables play a positive role in the process by which energy transition affects corporate performance. Conversely, if ${\mathsf{\sigma }}_3$ < 0 and is significant, it suggests that the moderating variables have a negative impact. If ${\mathsf{\sigma }}_3$ is not significant, it implies that the moderating variables do not influence the relationship between energy transition and corporate performance.

4.3. Variables

4.3.1. Dependent Variable

Corporate performance is proxied by Tobin’s Q, defined as the ratio of the market value of a firm’s assets to the replacement cost of those assets. This metric captures the firm’s market-based valuation relative to its asset base and is widely employed in empirical research as a forward-looking indicator of corporate performance [45].

4.3.2. Independent Variables

Due to data availability constraints, this paper uses a Green Transition Index as a proxy for corporate energy transition. Following the textual analysis methodology of Loughran and McDonald (2011) [46], the index is constructed based on textual disclosures in firms’ annual reports. Drawing on the framework proposed by Hart (1995) [47], five dimensions are identified as essential drivers of corporate green transition: (1) product- and process-related green capabilities, (2) employee training and engagement on environmental issues, (3) cross-functional environmental organizational capabilities, (4) formalized environmental management systems and procedures, and (5) strategic planning aligned with environmental objectives. Zhou Kuo (2022) [48] further argues that corporate green transition is fundamentally guided by sustainable development strategies adopted by top management, which shift firm priorities from pure profit maximization to addressing the environmental externalities of business operations. Green transition practices may include changes in governance structures, environmental education programs for employees, and the development of environmentally friendly products aimed at reducing aggregate emissions and enhancing sustainability performance.

Based on policy documents such as the 12th Five-Year Plan, the Environmental Protection Law, the Technical Guide for Environmental Behavior Assessment of Enterprises, the White Paper on Standardization of Green Manufacturing, and the “Made in China 2025” strategy, a set of 113 keywords related to corporate green transition was compiled. These keywords span five thematic categories: strategic initiatives, conceptual alignment, technological innovation, pollution control, and environmental monitoring. The frequency of these terms in the annual reports of listed firms is calculated and used to construct the Green Transition Index. To account for scale effects and reduce skewness, the natural logarithm of the keyword frequency (plus one) is used as the final measure of corporate green transition.

4.3.3. Mediating Variables

This paper investigates two core mediating variables—financing constraints and R&D investment—to elucidate the mechanisms through which energy transition affects corporate performance.

• Financing constraints are measured using the KZ index [49], a composite indicator that captures a firm’s internal financial pressure based on net operating cash flow, cash holdings, dividend payout ratio, total debt, and capital expenditure. A higher KZ index implies more severe financing constraints, reflecting limited internal capital availability and reduced external financing capacity.
• R&D investment is proxied by the ratio of R&D expenditure to total operating revenue, a standard measure of innovation intensity. This variable reflects a firm’s commitment to technological advancement and its potential to generate future performance gains through innovation.

These variables serve as the transmission channels in the mediation analysis, allowing the paper to empirically assess how green transition initiatives translate into improved firm performance.

4.3.4. Regulating Variables

Corporate Reputation

Corporate reputation is measured through a comprehensive evaluation framework that aggregates the perceptions of various stakeholders, including customers, investors, and the broader public. This framework incorporates 12 key indicators that reflect critical dimensions of reputation such as trustworthiness, product quality, ethical conduct, and corporate social responsibility. Factor analysis is employed to derive a composite reputation score, which is then classified into ten distinct levels (ranging from low to high reputation) and assigned values from 1 to 10. This approach ensures a multidimensional and robust assessment of corporate reputation, capturing both the public’s perception and the firm’s long-term credibility.

Investor Attention

Investor attention is quantified using the Baidu Index, a widely recognized measure of online interest in Chinese companies. The Baidu Index reflects the frequency with which a company is searched on Baidu, China’s dominant search engine, providing a proxy for the level of investor attention. The frequency of searches is assumed to indicate market participants’ interest in the firm’s operations, financial performance, and strategic decisions. An increase in the Baidu Index signals heightened investor awareness, which can influence stock prices, trading volumes, and overall market behavior. Previous studies have established that investor attention, as captured by the Baidu Index, significantly affects stock returns, especially in volatile market conditions.

4.3.5. Control Variables

Corporate performance is influenced by a multitude of factors, including energy transition, financial investments, human capital development, foreign trade participation, and the level of innovation. Building on the work of several scholars [22], this paper incorporates eight control variables to ensure a more precise evaluation of the impact of energy transition on corporate performance. These control variables are as follows:

Firm Size: The scale of the company, typically measured by total assets or revenue, as larger firms may have more resources to implement energy transition strategies.

Asset–Liability Ratio: A financial metric that assesses the company’s capital structure and financial risk, influencing its capacity to invest in green technologies.

Ownership Concentration: The proportion of ownership held by the top ten shareholders, which may affect strategic decision-making, including sustainability efforts.

Separation of Ownership and Control: This variable measures the degree to which ownership and management are separated, influencing corporate governance practices.

Debt-Paying Capacity: A company’s ability to meet its debt obligations, which can impact its financial flexibility and ability to fund energy transition initiatives.

Overall Leverage: The ratio of a company’s total debt to its equity, affecting its financial stability and capacity to invest in green projects.

CEO Duality: Whether the roles of the CEO and Chairman are held by the same person, which could influence strategic direction, including sustainability initiatives.

Shareholding Ratio of the Largest Shareholder: The ownership stake held by the largest shareholder, which may play a role in corporate decision-making and governance, especially regarding long-term investment strategies such as energy transition.

These control variables help isolate the specific effect of energy transition on corporate performance while accounting for other potential influencing factors.

These variables are summarized in

Table 1. Variable definitions.

	Name	Symbol	Definition
Explained variable	Corporate performance	Q	Tobin Q = firm’s market value/the replacement cost of assets
Explanatory variables	Energy transition	GT	Text analysis of corporate disclosures with the approach of Zhou Kuo et al. [48]
Intermediate variables	Financing constraints	KZ	Internal and external financing ratio by KZ index
	R&D investment	RD	R&D expenses
Regulating variables	Corporate reputation	REP	Construction of evaluation system with reference to Guan Kaolei [50]
	Investor attention	IAA	Analysis of word frequency statistics with reference to the text of Kong Dongmin [51]
Control variables	Corporate scale	SALE	Logarithm of ending capital
	Asset–liability ratio	CS	Ratio of total debt to total assets
	Proportion of the top ten shareholders	RATE	The shareholding ratio of the top ten shareholders
	Separation of ownership and control	SEP	Separation of ownership and control = Control rights—ownership
	Debt-paying ability	CR	Cash flow ratio
	Overall leverage	CL	Change rate of net profit/change rate of main business income
	CEO Duality	CONP	If the chairman and the general manager are the same person (1 = the same; 0 = not the same)
	Shareholding ratio of the largest shareholder	LHR	LHR = the proportion of shares held by the largest shareholder/the total number of shares outstanding × 100%

.

5. Results

5.1. Benchmark Regression Analysis

To evaluate the impact of energy transition on corporate performance, we have conducted a benchmark regression analysis using panel data from Chinese A-share listed companies spanning from 2008 to 2022. The regression model, as outlined in Equation (1), incorporates both corporate and time-fixed effects, with standard errors clustered at the corporate level. The results, presented in

Table 2. Benchmark regression analysis.

	(1)	(2)	(3)	(4)
	Q	Q	Q	Q
GT	0.055 **	0.231 ***	0.117 ***	0.177 ***
	(0.019)	(0.043)	(0.026)	(0.022)
SALE		−0.790 ***		−1.985
		(0.238)		(1.048)
CS		1.471 **		1.121
		(0.507)		(1.174)
RATE		0.090		0.075
		(0.085)		(0.088)
SEP		0.002		−0.018
		(0.002)		(0.020)
CR		0.059		0.039
		(0.059)		(0.053)
CL		−0.002		−0.001
		(0.001)		(0.000)
CONP		−0.203		−0.247
		0.205		(0.225)
LHR		−0.004 *		−0.002
		(0.001)		(0.011)
_cons	1.979 ***	18.426 ***	1.801 ***	45.220 *
	(0.111)	(5.008)	(0.078)	(22.695)
i.company	NO	NO	YES	YES
i.year	NO	NO	YES	YES
N	37,065	37,067	36,662	36,102
adj. R2	0.000	0.028	0.235	0.256

Note: OLS regression results. The sample includes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges. Q: corporate performance; GT: the energy transition index. *, **, and ***: significant at the 10%, 5%, and 1% levels, respectively.

, consistently show a positive and statistically significant coefficient for energy transition across all four columns, indicating that energy transition exerts a beneficial effect on corporate performance. This finding remains robust across different model specifications.

When comparing Column (1) with Column (2), the coefficient becomes more significant after incorporating control variables, suggesting that the baseline model may have omitted important factors that are positively correlated with both energy transition and corporate performance. In the absence of these controls, the effects of these omitted variables might have been mistakenly attributed to the energy transition. The inclusion of control variables helps mitigate this potential omitted variable bias.

Similarly, when comparing Column (1) with Column (3), the coefficient becomes even more pronounced with the introduction of individual and time-fixed effects. These controls account for firm-specific heterogeneity and macroeconomic time trends, enhancing the precision of the estimated effect of energy transition on corporate performance.

In Column (4), the coefficient for the Green Transition Index (GT) is 0.177, which is statistically significant at the 1% level. Economically, this implies that a one-standard-deviation increase in the energy transition index corresponds to a 0.04-standard-deviation improvement in corporate performance.

Overall, these findings provide robust evidence supporting the positive contribution of energy transition to corporate performance, affirming the value of energy transition initiatives in driving organizational success.

5.2. Endogenous Analysis

To address potential endogeneity concerns, particularly reverse causality between energy transition and corporate performance, we employ an instrumental-variable (IV) approach using digital transformation as an instrument. This approach is designed to account for the possibility that energy transition may be influenced by, or be influencing, corporate performance in ways that the standard regression models might not fully capture.

In terms of relevance, synergy theory posits that technological innovations in different domains within a firm often exhibit complementary effects. Digital transformation and energy transition, as two core aspects of technological upgrading, are deeply integrated in areas such as data collection, process optimization, and decision-making support [52,53]. Acemoglu et al. (2021) [54] have further proposed the “digital–green technology complementarity hypothesis,” which suggests that digital technologies enhance resource allocation efficiency and support iterative innovation. This, in turn, lowers the threshold for adopting green technologies. This theoretical framework supports the use of digital transformation as a valid instrument for energy transition as it satisfies the relevance criterion for instrumental variables.

As shown in Column (1) of

Table 3. Endogenous analysis.

	(1)	(2)
Variables	GT	Q
DT	0.496 ***
	(25.94)
GT		1.385 **
		(1.96)
SALE	0.874 ***	−2.435 **
	(40.67)	(−1.96)
CS	0.380 ***	0.478
	(2.76)	(0.50)
RATE	0.724 ***	−0.284 **
	(13.15)	(−2.19)
SEP	0.002	−0.022
	(1.02)	(−1.02)
CR	−0.001	0.036
	(−0.79)	(0.70)
CL	−0.0005 *	−0.000
	(−1.95)	(−0.19)
CONP	−0.119	−0.086
	(−4.81)	(−0.58)
LHR	−0.033	0.017
	(24.05)	(0.90)
K-P rk LM statistic	461.91 [0.000]
C-D-Wald-F statistic	912.74 {16.38}
Hansen J statistic	0.000
Observations	35,869	35,869
R-squared	0.013	0.013
Number of ID	4366	4366

Note: 2SLS regression results. The sample includes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges. Q: corporate performance; GT: energy transition index; DT: digital transformation index. Column (1) reports the first-stage regression results and Column (2) reports the second-stage results. *, **, and ***: significant at the 10%, 5%, and 1% levels, respectively.The t-value is shown in parentheses.

, the coefficient for the instrumental variable (DT) is 0.496 and is statistically significant at the 1% level, confirming a strong relationship between digital and energy transitions. Furthermore, the first-stage F-statistic exceeds the conventional threshold, ensuring that the instrument is not weak.

In terms of validity, from the resource-based view, digital assets developed through transformation efforts possess value, rarity, and inimitability. However, the impact of digital resources on firm performance depends on specific value-creation activities [55]. Energy transition offers a crucial context for the application of digital resources, forming a transmission chain of “digital resources–transition practices–performance improvement” [56]. Since the accumulation of digital capabilities primarily relies on sustained investment in technology, their influence on firm performance is indirect and delayed, rather than directly linked to short-term fluctuations in performance. Furthermore, digital capabilities are not correlated with unobservable firm-level heterogeneity, satisfying the exogeneity assumption of instrumental variables.

Additionally, according to endogenous growth theory, technological progress is the fundamental driver of economic growth. Digital technology, as a general-purpose technology, tends to influence performance in indirect and delayed ways [57]. This suggests that the impact of digital technology on firm outcomes is realized through specific application scenarios—such as energy transition—reinforcing the theoretical pathway “digital transformation → energy transition → firm performance.” By using digital transformation as an instrumental variable, this paper isolates the exogenous technological pressure inherent in the energy transition process, allowing for a more accurate estimation of its net effect. This approach enhances our theoretical understanding of the micro-mechanism through which “technological complementarity drives value creation.”

The results are presented in Table 3. The first-stage regression results, shown in Column (1), indicate that the coefficient for the instrumental variable (GT_iv) is 0.558 and statistically significant at the 1% level, confirming a robust relationship between digital and energy transitions. The F-statistic for the first stage exceeds the critical value, ruling out concerns over weak instruments. In the second stage (Column 2), the coefficient for the Green Transition Index (GT) remains positive and statistically significant at the 5% level, reinforcing the conclusion that energy transition positively impacts corporate performance. Additional diagnostic tests, including the Hansen J statistic and the K-P rk LM statistic, further confirm the validity of the instrument and the robustness of the results.

5.3. Robustness Test

5.3.1. Replacing the Interpreted Variable

To verify the robustness and consistency of our results, we replaced Tobin’s Q with total factor productivity (TFP) as the dependent variable. TFP, also known as the “Solow residual,” measures the portion of output growth that cannot be attributed to increases in inputs, reflecting improvements in technological efficiency and overall productivity. To estimate TFP, we employed the Levinsohn–Petrin (LP) method, which addresses potential endogeneity concerns associated with traditional Ordinary Least Squares (OLS) and fixed-effects approaches. The regression results, presented in Column (1) of

Table 4. Robustness test result.

	(1)	(2)	(3)	(4)	(5)	(6)
	TFP	Q	Q	Q	Q	Q
GT	0.014 ***			0.362 ***	0.386 ***	0.378 ***
	(0.003)			(0.029)	(0.031)	(0.032)
EGT		1.974 ***
		(0.327)
L.GT			0.409 ***
			(0.035)
SALE	0.583 ***	−1.158 ***	−1.759 ***	−1.592 ***	−1.445 ***	−1.608 ***
	(0.004)	(0.050)	(0.073)	(0.058)	(0.059)	(0.063)
CS	0.038 **	0.965 ***	0.311	0.898 ***	0.760 ***	0.823 ***
	(0.012)	(0.174)	(0.278)	(0.181)	(0.185)	(0.192)
RATE	−0.036	0.757 **	0.614	0.616 *	0.507	0.595 *
	(0.020)	(0.271)	(0.346)	(0.285)	(0.287)	(0.303)
SEP	0.001 *	−0.019 *	−0.023 *	−0.020 *	−0.019 *	−0.019 *
	(0.001)	(0.008)	(0.010)	(0.008)	(0.009)	(0.009)
CR	−0.004 ***	0.034 ***	0.040 ***	0.032 ***	0.034 ***	0.032 ***
	(0.000)	(0.007)	(0.008)	(0.007)	(0.007)	(0.007)
CL	−0.000	−0.001	−0.000	−0.000	−0.001	−0.000
	(0.000)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
CONP	−0.000	−0.242 *	−0.261 *	−0.233 *	−0.252 *	−0.280 *
	(0.000)	(0.103)	(0.129)	(0.106)	(0.111)	(0.115)
LHR	−0.010	−0.029 ***	−0.015 *	−0.021 ***	−0.020 ***	−0.022 ***
	(0.007)	(0.005)	(0.007)	(0.006)	(0.006)	(0.006)
_cons	−4.710 ***	28.026 ***	40.464 ***	36.617 ***	33.348 ***	37.039 ***
	(0.085)	(1.044)	(1.532)	(1.219)	(1.281)	(1.375)
N	32,355	36,507	29,659	35,363	33,374	32,230
adj. R2	0.476	0.130	0.145	0.127	0.142	0.145

Note: OLS regression results. The sample includes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges. Q: corporate performance; GT: energy transition index; TFP: total factor productivity; EGT indicates the entropy-based Green Transition Index used as an alternative proxy for energy transition. L.GT: the one-period lag of the Green Transition Index. *, **, and ***: significant at the 10%, 5%, and 1% levels, respectively.

, show that the coefficient for the Green Transition Index (GT) is 0.014 and statistically significant at the 1% level. This result confirms that the positive relationship between energy transition and corporate performance is consistent when using TFP as an alternative performance metric.

5.3.2. Replacing Explanatory Variables

To further validate our findings, we have constructed an alternative Green Transition Index using the entropy method, as suggested by Lin and Pan (2024) [58]. This new index incorporates five dimensions: technological innovation, production efficiency, pollution and carbon reduction, environmental protection, and social evaluation. Each dimension includes multiple indicators, such as innovation input, labor productivity, cleaner production techniques, and environmental management practices. We have calculated entropy-weighted scores for each of these indicators. The second-level indicator variables are initially defined using leading indicators for each dimension.

Next, the standardized entropy method is applied to calculate the weight of each variable, ensuring a comprehensive and balanced measurement of corporate green transition efforts.

$$X_{ij}^{\prime }={\displaystyle \frac{X_{ij}-min{X}_{ij}}{max{X}_{ij}-min{X}_{ij}}}$$

(5)

$$E_j=-ln{\left(n\right)}^{-1}{\sum }_{i=1}^n{p}_{ij}ln{p}_{ij}$$

(6)

Among them, $p_{ij}={\displaystyle \frac{X_{ij}^{\prime }}{{\sum }_{i=1}^n{X}_{ij}^{\prime }}}$. Then, we calculate the redundancy and weight entropy: ${ W}_j={\displaystyle \frac{1-E_j}{k-\sum E_j}}$ , where j = 1, 2, ..., k. Finally, the comprehensive score is computed based on the scores of each index, resulting in the corporate Green Transition Index required for this paper (

Table 5. Evaluation system of Green Transition Index.

Main Indicators	Second Indicators	Mean
Technological innovation	Innovation investment	R&D investment
	Innovation output	the proportion of green patent grants to total patent grants
Production capacity	Production efficiency	total factor productivity (calculated by LP method)
	Labor productivity	the ratio of total operating income to the number of employees
Reduce dye and carbon	Pollution control	disclosed corporate environmental information (wastewater, waste gas, smoke and dust, solid waste, noise and light pollution) (0 = no description, 1 = qualitative description, 2 = quantitative description)
	Cleaner production	disclosure of cleaner production facilities
Environmental protection	Environmental regulation	whether through ISO or otherwise
	Environmental management	Disclosure of Emergency Mechanism of Environmental Events and “Three Synchronisms” System
Social evaluation	Social responsibility	total score of social responsibility

).

Additionally, the GT variable is lagged by one period to account for potential delayed effects of energy transition. Columns (2) and (3) of Table 4 present the results using the revised explanatory variables. The estimated coefficients of EGT and L.GT are 1.974 and 0.409, respectively, both of which are significantly positive at the 1% level. These results indicate that, regardless of the method used to measure the degree of energy transition, it exerts a positive influence on corporate performance. This further confirms the robustness and validity of the conclusions drawn in this paper.

5.3.3. Excluding Specific Samples

This paper tests the robustness of the results by excluding certain samples. The global financial crisis of 2008 triggered significant volatility in capital markets, a tightening of credit, and a sharp decline in demand. As a result, firms widely faced liquidity shortages, investment stagnation, and abnormal performance fluctuations. Companies experiencing a sudden drop in performance may have been forced to reduce investments in energy transition, leading to a pseudo-causal chain of “crisis → performance decline → stalled transition”, rather than accurately reflecting the true effect of energy transition on performance. Although fixed-effects models control for firm-specific and time-invariant characteristics, the 2008 crisis represents a heterogeneous temporal shock, with its intensity and mechanisms differing significantly from those of typical years. Excluding data from this year helps prevent crisis-induced outliers from skewing the time effects, ensuring that the model captures the net effect of energy transition under normal market conditions. Therefore, data from 2008 are excluded, and regression analysis is re-conducted using data from 2009 to 2022. The results, presented in Column (4) of Table 4, show that the estimated coefficient of GT is 0.362, statistically significant at the 1% level. This suggests that the conclusions remain stable even after excluding the impact of the economic crisis.

Similarly, the COVID-19 pandemic of 2019 caused an abrupt and widespread global economic shutdown, marked by its sudden onset, systemic impact, and unpredictability. Firms were widely affected by demand contraction, supply chain disruptions, and government-imposed restrictions. For many companies, the resulting decline in performance led to forced reductions in energy transition investments, forming a pseudo-causal chain of “pandemic → performance decline → transition stagnation”, rather than reflecting the true impact of energy transition on corporate performance. As a heterogeneous structural shock, the pandemic introduced abnormal fluctuations in firm outcomes that might have been mistakenly absorbed into the model’s time effects. This misattribution could lead to a biased estimation of the coefficient for energy transition, distorting its actual influence. To minimize the potential impact of this event on the paper, the data for 2019 are excluded and the regression analysis is repeated. The results, shown in Column (5) of Table 4, indicate that the coefficient of GT is 0.386, statistically significant at the 1% level. This confirms that the results remain robust even after excluding the impact of the pandemic.

Finally, Column (6) presents the regression results after excluding the data for both the 2008 financial crisis and the 2019 COVID-19 pandemic. The estimated coefficient of GT is 0.378, which remains significantly positive at the 1% level, further corroborating the robustness of the findings in this paper.

6. Further Analysis and Discussion

6.1. Mechanism

Previous empirical studies have demonstrated that energy transition can significantly improve corporate performance; however, the underlying mechanisms remain largely unexplored. Drawing on the work of Jiang Ting [59], this paper has employed a two-step method to test the mediating effects of corporate financing constraints and R&D investment in the relationship between energy transition and corporate performance.

The paper’s use of the Kaplan–Zingales (KZ) index to measure financing constraints provides valuable insights into how energy transition influences corporate performance. The negative coefficient of −0.077 for the energy transition variable (GT) in the regression analysis indicates that as firms undergo energy transition, their financing constraints decrease, thereby facilitating investment. Referring to the research of Liu Xiuli (2024) [60], a significant negative correlation is also found between financing constraints and corporate performance. This suggests that reducing financing constraints, by increasing investment, ultimately leads to improved corporate performance. Thus, Hypothesis 1 is supported.

Additionally, the paper has utilized the SA index, which takes firm size and age into account to assess financing constraints. This provides a more parsimonious measure that complements the KZ index. The negative coefficient of −0.052 for the energy transition variable (GT) in the regression analysis again suggests that as firms undergo energy transition, their financing constraints decrease. Referencing prior research by Ju Xiaosheng et al. (2013) [61], a significant negative relationship has also been found between the SA index and corporate performance, reinforcing the consistency of our results.

The paper also employs the WW index, a dynamic measure of investment–cash flow sensitivity, to capture the nuanced effects of energy transition on financing constraints. The positive coefficient of 0.024 suggests that, during the transition, firms may experience increased capital outlays due to the disposal of high-carbon assets and investment in low-carbon infrastructure. Given its sensitivity to short-term cash flow fluctuations during transitions, the WW index effectively captures residual financial risks not fully mitigated by policy support. Referring to the research of Li Chengming (2023) [62], the positive correlation between the WW index and corporate performance reflects a “catfish effect,” where financing constraints incentivize firms to optimize resource allocation. Moreover, the WW index serves as a policy filter, selectively highlighting transition risks that remain unaddressed by existing government interventions.

The regression results for corporate R&D investment as a mediating variable are presented in Columns (5) and (6) of

Table 6. Mechanism.

	Financing Constraints				R&D Investment
	(1)	(2)	(3)	(4)	(5)	(6)
	Q	KZ	SA	WW	Q	RD
GT	0.370 *	−0.077 ***	−0.052 ***	0.024 ***	0.370 *	0.035 *
	(0.164)	(0.015)	(0.001)	(0.003)	(0.164)	(0.015)
SALE	−1.431 *	−0.144 **	−0.113 ***	−0.057 ***	−1.431 *	0.199 ***
	(0.717)	(0.052)	(0.006)	(0.006)	(0.717)	(0.044)
CS	0.831	2.156 **	0.067 ***	−0.086 *	0.831	−0.132
	(1.106)	(0.783)	(0.020)	(0.035)	(1.106)	(0.109)
RATE	0.534	0.064	−0.071 ***	0.087 ***	0.534	0.082
	(0.388)	(0.095)	(0.006)	(0.017)	(0.388)	(0.074)
SEP	−0.019	−0.000	0.000	−0.001	−0.019	0.002
	(0.020)	(0.004)	(0.000)	(0.001)	(0.020)	(0.004)
CR	0.034	−0.032	0.001	−0.003 *	0.034	0.019
	(0.049)	(0.018)	(0.001)	(0.001)	(0.049)	(0.017)
CL	−0.001	0.001	−0.000	−0.000	−0.001	0.000
	(0.000)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)
CONP	−0.208	−0.053	0.002	−0.001	−0.208	−0.078
	(0.209)	(0.044)	(0.003)	(0.008)	(0.209)	(0.085)
LHR	−0.019 ***	−0.014 ***	0.004***	−0.004 ***	−0.019 ***	−0.005
	(0.003)	(0.003)	(0.000)	(0.000)	(0.003)	(0.003)
_cons	32.985 *	3.892 **	−1.266 ***	0.479 *	32.985 *	−3.810 ***
	(15.261)	(1.031)	(0.125)	(0.120)	(15.261)	(0.979)
N	36,507	32,486	32,486	32,486	36,507	36,501
adj. R2	0.023	0.063	0.640	0.021	0.023	0.008

Note: OLS regression results. The sample includes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges. Q: corporate performance; GT: energy transition index; KZ, SA, and WW indicate three alternative measures of financing constraints; RD: R&D expenditure. *, **, and ***: significant at the 10%, 5%, and 1% levels, respectively.

. The result in Column (4) shows that the regression coefficient of GT is 0.035, which is statistically significant at the 10% level. This indicates a significant positive correlation between energy transition and R&D investment. This phenomenon can be attributed to three primary factors.

Under the goals of carbon peaking and carbon neutrality, corporates face restrictions on carbon emission allowances. This regulatory pressure compels high-carbon industries, in particular, to intensify their research and development (R&D) efforts in low-carbon technologies.

Shifting consumer preferences towards low-carbon products, exemplified by the growing demand for new-energy vehicles, act as a market-driven impetus for firms to engage in green-technology R&D.

The development of renewable energy sources, such as offshore wind power, presents substantial technological hurdles. High R&D thresholds necessitate continuous investment from corporates to overcome technical bottlenecks.

With reference to the research of Song Jinglun and Zhang Qirui (2023) [63], which shows a significant positive correlation between R&D investment and corporate performance, it becomes evident that corporate R&D achievements directly enhance the product value added, with core technologies being translated into product competitiveness. R&D investment also drives improvements in production processes and process innovation can reduce production costs. By investing in R&D, corporates can meet ESG rating requirements, access low-cost financing, and enjoy government subsidies, thereby reaping dual dividends from both policies and the market through green technologies. Therefore, energy transition increases R&D investment through the dual drivers of external pressure and internal opportunities. R&D investment, in turn, improves corporate performance through pathways such as technology transfer, efficiency enhancement, and policy-driven gains, forming a virtuous cycle of “transition pressure → R&D investment → performance improvement.” Hypothesis 2 is thus supported.

6.2. Regulatory Effect

The moderating effect of corporate social reputation in the relationship between energy transition and corporate performance is presented in

Table 7. Regulatory effect.

	Corporate Reputation		Investor Attention
	(1)	(2)	(3)	(4)
	Q	Q	Q	Q
GT	0.351 ***	0.393 ***	0.435 ***	0.454 ***
	(0.025)	(0.026)	(0.038)	(0.039)
REP	0.272 ***	0.292 ***
	(0.023)	(0.024)
GT × REP		0.039 ***
		(0.008)
IAA			0.006 ***	0.006 ***
			(0.001)	(0.001)
GT × IAA				0.001 *
				(0.001)
SALE	−1.413 ***	−1.489 ***	−1.915 ***	−1.926 ***
	(0.059)	(0.061)	(0.079)	(0.080)
CS	0.512 **	0.540 **	1.060 ***	1.072 ***
	(0.171)	(0.171)	(0.241)	(0.241)
RATE	0.382	0.350	0.783 *	0.771 *
	(0.225)	(0.225)	(0.386)	(0.386)
SEP	−0.008	−0.008	−0.025 *	−0.026 *
	(0.007)	(0.007)	(0.011)	(0.011)
CR	0.086 ***	0.085 ***	0.034 ***	0.034 ***
	(0.009)	(0.008)	(0.008)	(0.008)
CL	−0.000	−0.000	−0.000	−0.000
	(0.002)	(0.002)	(0.002)	(0.002)
GTREP_c_cons	31.449 ***	32.864 ***	43.398 ***	43.558 ***
	(1.228)	(1.259)	(1.747)	(1.749)
N	30,489	30,489	27,778	27,778
adj. R2	0.138	0.137	0.146	0.146

Note: OLS regression results. The sample includes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges. Q: corporate performance; GT: energy transition index; GT × REP: the interaction term between the Green Transition Index and corporate reputation; GT × IAA: the interaction term between the Green Transition Index and investor attention. *, **, and ***: significant at the 10%, 5%, and 1% levels, respectively.

. Column (1) shows the benchmark regression results under the influence of social reputation. Column (2) presents the results after adding the interaction term between corporate social reputation and energy transition. According to the results in Column (2), the regression coefficient of the interaction term between energy transition and corporate social reputation is 0.039, which is statistically significant at the 1% level. This indicates that corporate social reputation positively moderates the relationship between energy transition and corporate performance.

Corporates with high social reputations can more efficiently integrate resources and mitigate risks during energy transitions, significantly enhancing the efficiency of converting transition investments into performance gains. In the context of the “green consumption” trend, these reputable firms can more readily translate low-carbon products into market share, thereby commanding stronger bargaining power within the industrial chain. Moreover, highly reputable corporates are more successful in attracting green technology talent. Their positive standing serves as a form of credit endorsement, facilitating collaborations with research institutions. Finally, when facing the short-term challenges of energy transition, these firms are more likely to gain the understanding of stakeholders. Their strong social reputation also reduces regulatory scrutiny costs; during processes such as carbon audits and green certifications, they are more likely to pass inspections smoothly, minimizing compliance expenditures.

The moderating effect of investor attention in the relationship between energy transition and corporate performance is also shown in Table 7. Column (3) presents the benchmark regression results considering investor attention. Column (4) shows the results after adding the interaction term between investor attention and energy transition. According to the results in Column (4), the regression coefficient of the interaction between energy transition and investor attention is 0.001, which is statistically significant at the 10% level. This suggests that investor attention positively moderates the relationship between energy transition and corporate performance.

First, firms with high investor attention benefit from the more timely and comprehensive market recognition of their energy transition efforts, accelerating the conversion of long-term strategic value into both short-term market reactions and sustained performance growth. High investor attention enables markets to quickly identify the hidden value of energy transition—green investments are interpreted as forward-looking “strategic positioning” whereas similar efforts by low-attention firms may be misperceived as “resource misallocation.”

Second, high investor attention increases subscription rates while creditors are more inclined to provide low-interest loans due to enhanced visibility and reduced information asymmetry. Third, under the high degree of attention, management faces more market supervision, which encourages the reduction of “rent-seeking” behavior during the transition and guides capital to flow toward more efficient and productive transition efforts. This further reinforces the idea that investor attention not only enhances firm performance but also improves the effectiveness of energy transition strategies by reducing inefficiencies and fostering accountability.

6.3. Heterogeneity Test

6.3.1. Ownership Structure

The impact of energy transition on corporate performance exhibits significant variation across different ownership structures owing to divergent organizational objectives, governance mechanisms, and decision-making logics. To explore this heterogeneity, firms are categorized into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs), and regression analyses are conducted to examine how ownership characteristics influence the relationship between energy transition and firm performance.

The results reveal that both SOEs and non-SOEs benefit from performance improvements associated with energy transition; however, the effect is more substantial among non-SOEs. Specifically, the regression coefficient for non-SOEs is 0.204, compared to 0.096 for SOEs, with both results statistically significant at the 1% level. This divergence can be attributed to fundamental differences in institutional incentives, governance frameworks, and operational flexibility. As key instruments of national policy implementation, SOEs operate within a principal-agent framework that links governmental objectives with enterprise behavior. Their investment decisions are often shaped by a dual mandate that encompasses both economic performance and political compliance. Consequently, SOEs may prioritize projects aligned with policy goals even at the expense of short-term profitability. The pursuit of broader social responsibilities—such as employment stability or environmental targets—can dilute the direct financial returns of energy transition investments.

Moreover, the prevalence of “soft budget constraints” in SOEs facilitates access to capital and enhances risk tolerance but may also encourage inefficient investments or policy arbitrage behaviors. Energy transition initiatives in these firms are sometimes driven by macro-level goals—such as piloting industrial policies or stabilizing regional economies—rather than by operational efficiency, thereby diminishing potential returns. In contrast, non-SOEs are predominantly market-oriented, with governance structures that emphasize profit maximization and accountability. The alignment of ownership and control reduces agency costs and enables the more precise targeting of profitable transition opportunities. Additionally, the presence of “hard budget constraints” compels non-SOEs to rigorously assess the cost-effectiveness of transition projects, leading to more efficient resource allocation. Under such market-driven selection mechanisms, energy transition is more likely to translate into tangible cost savings and productivity enhancements, thereby yielding superior financial performance. These findings underscore the need for policymakers to adopt differentiated incentive mechanisms that reflect the institutional characteristics of state and non-state firms in facilitating sustainable transitions.

6.3.2. Firm Size

Firm size also plays a critical role in shaping the effectiveness of energy transition on corporate performance due to variations in operational agility, resource endowment, and strategic orientation. This paper categorizes firms into small- and medium-sized enterprises (SMEs) and large enterprises and performs sub-sample regressions accordingly. As reported in Columns (3) and (4) of

Table 8. Heterogeneity test.

	Ownership Structure		Firm Size		Region		Industry
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	State-Owned	Non-State-Owned	Small and Medium	Large	Eastern	Mid-West	High	Low
GT	0.096 ***	0.204 ***	0.382 **	0.067 ***	0.198 ***	0.856	0.107 ***	0.398 *
	(0.013)	(0.037)	(0.132)	(0.010)	(0.025)	(0.629)	(0.021)	(0.188)
SALE	−0.546 ***	−2.810	−5.138	−0.101 **	−0.671 ***	−3.039	−0.422 ***	−1.558
	(0.059)	(1.753)	(3.288)	(0.033)	(0.090)	(2.271)	(0.094)	(0.845)
CS	0.129	0.795	0.580	−0.204 *	0.305	5.045	0.330	0.722
	(0.250)	(1.279)	(1.420)	(0.094)	(1.185)	(4.286)	(0.388)	(1.159)
RATE	0.084	0.246	0.314	0.050	0.164 *	1.779	−0.074	0.554
	(0.066)	(0.246)	(0.278)	(0.045)	(0.070)	(1.651)	(0.113)	(0.416)
SEP	−0.002	−0.029	−0.030	0.001	0.001	−0.057	−0.006	−0.023
	(0.003)	(0.034)	(0.031)	(0.002)	(0.004)	(0.057)	(0.006)	(0.025)
CR	0.002	0.041	0.031	−0.001	−0.009	0.884	−0.043 ***	0.035
	(0.010)	(0.055)	(0.046)	(0.003)	(0.005)	(0.850)	(0.013)	(0.051)
CL	−0.002 **	−0.000	−0.000	−0.001	−0.003	0.000	0.001	−0.001
	(0.001)	(0.000)	(0.001)	(0.001)	(0.002)	(0.001)	(0.004)	(0.000)
CONP	−0.029	−0.359	−0.514	−0.033	−0.011	−0.787	0.017	−0.250
	(0.041)	(0.309)	(0.457)	(0.033)	(0.074)	(0.720)	(0.094)	(0.240)
LHR	−0.005 *	0.000	−0.036 **	−0.004 **	−0.020 ***	−0.021	−0.009 **	−0.022 ***
_cons	14.053 ***	62.765	111.579	4.027 ***	16.878 ***	64.783	11.247 ***	35.862 *
	(1.310)	(37.651)	(69.612)	(0.748)	(1.977)	(46.129)	(2.025)	(18.062)
N	12,644	23,363	20,096	15,637	25,641	9784	4938	31,085
adj. R2	0.614	0.240	0.294	0.658	0.435	0.263	0.588	0.236

Note: OLS regression results. The sample includes data from A-share listed companies on the Shanghai and Shenzhen stock exchanges. GT: energy transition index. *, **, and ***: significant at the 10%, 5%, and 1% levels, respectively.

, the estimated coefficients of energy transition (GT) are 0.382 for SMEs and 0.067 for large firms, both statistically significant at the 5% level. These results suggest that while energy transition contributes positively to performance across firm sizes, the effect is considerably stronger for SMEs.

This discrepancy can be explained by organizational dynamics and market pressures. SMEs typically feature flatter hierarchies and shorter decision-making chains, enabling more rapid adaptation to environmental and regulatory changes. Their “asset-light, high-flexibility” operating models allow for relatively low sunk costs and high marginal gains from transition efforts. Furthermore, given the intense survival pressures they face, SMEs are particularly responsive to cost-saving opportunities. Energy transition measures that reduce energy expenditures can thus be quickly reflected in profitability metrics.

Conversely, large firms, despite having superior financial and technological resources, tend to experience more subdued performance gains. Their substantial fixed-cost base dilutes the marginal impact of cost reductions. Additionally, their larger organizational scale often introduces inertia that slows the realization of benefits from transition initiatives. Large enterprises typically manage diversified business portfolios, which may obscure or fragment the focus of their transition strategies, reducing their effectiveness. External financing conditions also contribute to scale-based differences. SMEs frequently encounter credit constraints and must rely on internal capital or public subsidies, which limits the scale of investments but imposes stricter discipline on resource use, fostering efficiency. Large firms, by contrast, enjoy preferential access to low-cost capital through formal financial markets, which may lead to overinvestment and diminishing marginal returns. These dynamics suggest that firm size moderates the transmission of energy transition efforts into performance outcomes.

6.3.3. Regional Heterogeneity

To assess regional heterogeneity, the sample is divided into firms located in eastern China and those in central and western regions, with regression analyses conducted separately for each subgroup. As shown in Columns (5) and (6) of Table 8, energy transition significantly enhances corporate performance in the eastern region while the effect is statistically insignificant in the central and western regions.

This regional divergence is shaped by several structural and institutional factors. First, the eastern region benefits from a well-established industrial base and agglomeration economies, fostering a mature green innovation ecosystem. High-tech industries and intensive R&D investment endow eastern firms with full-chain capabilities—from new-energy adoption to technological commercialization—allowing them to convert energy transition into immediate cost reductions and revenue growth. In contrast, firms in central and western regions are predominantly engaged in traditional manufacturing, with the proportion of high-tech firms being less than one-third that in the east. Limited access to local digital and technical service providers further raises transition costs and slows implementation, eroding the potential for performance enhancement.

Second, the policy environment in the eastern region is more conducive to transition. Historically export-oriented, eastern provinces have taken the lead in aligning with China’s “dual-carbon” strategy, supported by both administrative directives and market-based instruments such as carbon emission trading. Firms can not only benefit from subsidies but also monetize carbon assets, generating additional revenue streams. In contrast, the central and western regions are still navigating mid-industrialization stages, with policy focus skewed toward end-of-pipe pollution control, which increases compliance costs and crowds out investment in proactive transition measures.

Finally, regional market dynamics differ significantly. Consumers in eastern China show stronger environmental preferences and greater willingness to pay for green products and export-oriented firms must comply with international carbon tariffs. These external pressures incentivize firms to embrace energy transition as a strategy for both regulatory compliance and market competitiveness. By contrast, firms in central and western regions predominantly serve domestic markets with weaker environmental preferences, and access to abundant traditional energy sources lowers the urgency of transition, thereby weakening the performance-enhancing effects.

6.3.4. Industry Heterogeneity

Industry-level variation also influences the relationship between energy transition and firm performance. This paper groups firms into high- and low-carbon-emission industries based on industry classification codes and conducts separate regressions. As shown in Columns (7) and (8) of Table 8, the energy transition coefficient (GT) is 0.107 for high-emission industries and 0.398 for low-emission industries, both statistically significant, though at different confidence levels (1% and 10%, respectively). These findings indicate that while energy transition enhances performance across the board, its effect is more salient in high-carbon-emission sectors.

This difference is primarily driven by disparities in regulatory intensity, transformation potential, and technological characteristics. High-emission firms face more stringent regulatory pressures and are often subject to sector-specific mandates that elevate energy transition from a strategic choice to a survival necessity. Although these firms incur substantial upfront costs, they benefit from favorable policy tools such as carbon markets, which allow the monetization of surplus emission rights. In addition, high-emission industries frequently receive targeted government support in the form of grants, tax incentives, and preferential financing, which accelerates the realization of financial returns from transition projects.

Furthermore, energy expenditures represent a larger proportion of total operating costs in high-emission firms, making energy efficiency improvements and shifts toward renewable sources more impactful. These industries also tend to be capital-intensive, with greater absorptive capacity for integrating advanced energy technologies. In contrast, low-emission firms—often engaged in service-oriented or light manufacturing activities—focus more on process improvements, which yield slower and more diffuse returns.

Finally, high-emission industries are typically positioned upstream in the value chain and characterized by standardized products and intense price competition. In such environments, energy transition can serve as a key source of competitive differentiation and even drive the emergence of new business models through R&D investment. By contrast, energy transition in low-emission industries is often incremental and subject to diminishing marginal returns, thus contributing less significantly to overall performance enhancement.

7. Conclusions

Energy transition represents a multifaceted strategy through which firms align with the global shift toward a low-carbon economy. Its influence on corporate performance is shaped by a complex interplay of policy frameworks, market forces, and internal organizational capacities. Understanding these mechanisms—and the heterogeneous effects across diverse corporate contexts—is critical for both academic research and the formulation of practical strategies aimed at securing competitive advantage in a carbon-constrained world. This paper finds that energy transition can enhance corporate performance by alleviating financing constraints and stimulating research and development (R&D) investment. However, the magnitude and channels of this effect vary significantly depending on factors such as the ownership structure, firm size, regional location, and carbon intensity.

The findings offer actionable insights for both policymakers and corporate stakeholders. From the governmental perspective, a more targeted and adaptive support system should be established, encompassing several key dimensions. First, differentiated subsidies and tax incentives should be implemented. For non-state-owned and small- and medium-sized enterprises (SMEs), policies should expand the scope of additional R&D expense deductions and explicitly include green process innovations and carbon management infrastructure within the subsidy framework. For high-carbon-emission firms, transitional tax incentives—such as allowing a 150% pre-tax deduction for losses incurred from phasing out outdated capacity—can help alleviate short-term financial pressures. Moreover, both central and local governments could jointly establish a ‘Low-Carbon Transition Special Fund’ aimed at supporting the adoption of green technologies, particularly in the central and western regions.

Second, regional coordination and industrial support mechanisms should be strengthened. For instance, the government could pilot green technology trading platforms in the eastern region to promote the market-driven commercialization of corporate R&D outcomes. Simultaneously, national-level low-carbon innovation centers should be established in central and western regions to foster indigenous technological capabilities. Cross-regional cooperation mechanisms could also be developed to facilitate the relocation of production capacity from the east to designated low-carbon industrial parks in the central and western areas, thus promoting a coordinated national transition.

From the corporate perspective, firms should tailor their transition strategies rather than adopting a ‘one-size-fits-all’ model. Non-state-owned enterprises should leverage their operational agility and decision-making autonomy to pursue asset-light, flexible transition pathways. In contrast, state-owned enterprises should capitalize on their strengths in capital access and resource integration to spearhead the development and commercialization of core low-carbon technologies. SMEs should actively utilize available policy tools—such as R&D tax incentives and green transition subsidies—to ease financial burdens and improve investment efficiency. Large enterprises should embed energy transition into their corporate strategy, establish comprehensive carbon management systems at the group level, and strengthen stakeholder trust and regulatory compliance.

Region-specific approaches are also warranted. Firms in the eastern region should proactively address potential international green trade barriers by planning ahead for low-carbon certification and the establishment of green supply chains, thereby solidifying their competitive advantage in technology exports. In contrast, firms in the central and western regions can align with national strategies to receive technology transfers from the east and develop regionally tailored transition pathways based on local resource endowments. High-carbon industries must adopt a phased transition approach: in the short term, reduce carbon costs through asset optimization and energy efficiency measures; in the medium term, invest in disruptive technologies; and in the long term, pursue the development of zero-carbon industrial parks.

This paper is not without limitations. Although it has employed a basic fixed-effects model to control for unobserved heterogeneity, more sophisticated empirical strategies—such as quasi-natural experiments or difference-in-differences (DID) approaches—could enhance causal inference. The instrumental variable analysis could also benefit from robustness checks, such as placebo tests, to more rigorously validate instrument relevance and exogeneity. Furthermore, the sample has primarily included A-share listed firms, potentially excluding unlisted SMEs and county-level industrial enterprises, thereby limiting the generalizability of the findings. The classification of industries into high- and low-carbon sectors remains relatively coarse and could be refined by differentiating among specific sub-sectors (e.g., coal versus steel). Regional analysis is similarly broad, divided only into eastern, central, and western regions, without accounting for intra-regional heterogeneity such as in provincial differences or the influence of targeted environmental policies in air pollution transmission corridors like the Beijing–Tianjin–Hebei region.

Future research could refine the industry classification using detailed industrial codes to explore performance differences across transition pathways. Moreover, incorporating quasi-natural experiments—such as evaluating the impact of ‘Pilot Zones for Green Finance Reform and Innovation’—would offer deeper insights into the spatial spillover effects of policy implementation.