Green Drive Force, Energy Efficiency, and Corporate Sustainable Development

Abstract

This study investigates how improvements in energy efficiency (EE) contribute to the sustainable growth rate (SGR) of manufacturing firms. Using panel data from Chinese A-share listed companies between 2012 and 2023, we provide empirical evidence that higher EE significantly enhances firms’ ability to maintain long-term and stable growth. Furthermore, the findings reveal that executives’ green perception (EGP) and environmental protection investment (EPI) strengthen this positive relationship, while an excessive green innovation bubble (GIB) weakens it. By integrating insights from corporate governance and sustainability research, this study highlights the critical roles of managerial orientation, resource allocation, and innovation quality in shaping the pathway from EE to sustainable growth. The results extend the understanding of how micro-level corporate actions support global sustainability goals and provide a nuanced perspective on balancing efficiency and innovation. Practically, the findings suggest that managers should embed EE into strategic decisions, while policymakers should strengthen financial and institutional support to facilitate corporate green transition. This research contributes to the literature by offering new evidence from an emerging market context and by demonstrating the multidimensional mechanisms through which EE fosters corporate sustainable development.

1. Introduction

In light of the progress made in the global sustainable development agenda, energy efficiency and resource optimization have emerged as fundamental strategies for organizations to address environmental challenges and market volatility. The manufacturing industry is a major source of energy consumption and carbon emissions, and improving resource efficiency (REE) is an important way for manufacturing enterprises to achieve green transformation and sustainable growth [1,2].

According to the International Energy Agency (IEA), China’s energy-related CO₂ emissions rose by approximately 565 Mt (+4.7%) to 12.6 Gt in 2023; in the same year, the industrial sector accounted for roughly 48% of the country’s total final energy consumption [3,4]. For context, China’s manufacturing sector accounted for approximately 27% of national GDP in 2022, while the industrial sector was responsible for nearly 56% of total final energy consumption in 2020 [5,6]. These figures underscore both the urgency of improving energy efficiency and its long-standing role in China’s industrial policy. Since 2012, the Chinese government has gradually established resource efficiency as a fundamental component of its industrial policy, issuing several initiatives, including the “12th Five-Year Plan for Energy Conservation and Emission Reduction” and “Made in China 2025.” Beyond domestic initiatives, these policies align with the broader global sustainability agenda. Since the adoption of the United Nations 2030 Agenda, energy efficiency has been recognized as a central pathway to delivering over one-third of the CO₂ reductions needed by 2030 on a net-zero trajectory, according to the IEA [7,8]. This alignment underscores the convergence between China’s industrial upgrading and international sustainability commitments. Consistent with this alignment, China has pledged to peak CO₂ emissions by 2030 and reach carbon neutrality by 2060; under the 14th Five-Year Plan, the target is to reduce energy intensity and CO₂ intensity by 13.5% and 18%, respectively, between 2021 and 2025 [9]. These initiatives focus on improving energy utilization efficiency, reducing carbon emission intensity, and positioning resource efficiency as a core element of industrial policy. Such policy efforts are consistent with evidence that Chinese firms are increasingly motivated to engage in environmental management in response to institutional and market pressures [10]. These policy developments guide corporate green transformation and establish a solid foundation for academic research on the mechanisms through which energy efficiency affects corporate financial performance.

Although relationships among energy efficiency, environmental performance, and economic growth have been extensively studied [11,12,13,14,15], micro-level research concerning corporate financial sustainability is scarce. Moreover, the mechanisms of energy efficiency translating into financial performance and growth potential—such as internal strategies, external financing channels, and managerial cognition—are underexplored [16,17,18].

Sustainable growth rate (SGR) serves as a widely recognized metric for assessing a company’s capacity for sustainable development. It refers to capturing a company’s long-term growth potential within a stable financial policy framework [19]. SGR has been linked with capital structure, profitability, and corporate governance, yet few studies have examined it from the perspective of energy-use efficiency. Using SGR as a dependent variable helps reveal how energy efficiency translates into sustainable growth through financial mechanisms, highlighting the relationships among energy efficiency, financial performance, and sustainability [20].

Furthermore, the impact of energy efficiency on sustainable development can be influenced by various internal strategic orientations and external financing environments. First, the green perception of corporate executives reflects their emphasis on energy-saving technologies, which, in turn, affects how energy efficiency can enhance growth capabilities [21]. Second, investments in environmental protection affect the efficiency of capital allocation to green projects and the marginal returns on green investments [22]. Additionally, the disparity between corporate innovation input and actual market returns, known as the green innovation bubble, should not be too high; a significant disparity may hinder progress by causing resource misallocation, resulting in adverse consequences [23].

To examine the dynamic effects of policy evolution, technological advancement, and market changes on firm behavior, panel data from A-share-listed manufacturing firms in China from 2012 to 2023 are used in this study. This period encompasses the entire trajectory of China’s green policy—from its inception to deepening—ensuring policy consistency and data reliability. This 12-year analysis enhances the robustness and generalizability of the findings. Green policies, as external institutional pressures, influence firms’ green investment decisions and promote energy efficiency and sustainable development [24,25].

This study makes three primary contributions to the literature. First, it establishes an integrated analytical framework combining the Resource-Based View (RBV), Strategic Fit Theory, and Stakeholder Theory to elucidate how energy efficiency influences sustainable growth. The RBV emphasizes that firms gain sustainable advantages by effectively utilizing strategic resources; here, we view energy efficiency as a valuable resource that reduces costs and supports long-term development. Strategic Fit Theory stresses the importance of aligning internal capabilities with external conditions, which explains why executives’ green perception and environmental protection investment matter when translating efficiency into growth. Stakeholder Theory highlights the role of external actors—such as governments, investors, and customers—in shaping green strategies, which is essential for understanding how firms respond to institutional and market pressures in sustainability transitions. Second, it introduces behavioral variables such as executive green perception and green innovation bubble (GIB) to enhance the behavioral perspective within the energy efficiency–financial performance nexus. Third, by employing fixed-effect panel regressions and conducting multiple robustness checks, this study provides reliable results and empirical insights into the formulation of corporate green strategies, the design of government green finance policies, and the evaluation of corporate green capabilities by investors.

2. Theoretical Background and Research Hypotheses

This study draws on three theoretical perspectives to build its research framework. The Resource-Based View (RBV) argues that firms achieve sustainable advantages by leveraging strategic resources; in this research, we consider energy efficiency to be a valuable and inimitable resource that enhances competitiveness and long-term growth. Strategic Fit Theory highlights the importance of aligning internal capabilities with external environmental conditions, which helps explain how executives’ green perception and environmental protection investment reinforce the positive impact of energy efficiency. Finally, Stakeholder Theory emphasizes the influence of external stakeholders such as governments, investors, and customers on corporate decisions, underscoring why firms’ sustainable growth depends not only on internal efficiency, but also on external recognition and support. Together, these theories provide a multi-dimensional foundation for analyzing how energy efficiency contributes to corporate sustainable development.

2.1. Energy Efficiency and Corporate Sustainable Development

Porter and Van der Linde argued that economic development and ecological protection should progress synergistically. Enhancing energy efficiency helps firms control costs and maximize profits and acts as a vital component of global sustainable development initiatives [26]. Improving energy efficiency not only facilitates low-carbon growth and helps meet climate change objectives, but also allows businesses to sustain profitability amid fluctuations in energy prices, policy changes, and market uncertainties. Additionally, it reduces dependence on energy and enhances financial stability [27,28,29]. Global evidence also supports this perspective. Stern (2006) highlighted that the economic costs of inaction on climate change far exceed the investments required to improve energy efficiency [30]. Similarly, the Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (2007) identified energy efficiency as one of the most cost-effective mitigation strategies across sectors [31]. More recent empirical studies further confirm this global relevance: Charfeddine and Rahman (2025) demonstrated that green and energy efficiency policies significantly enhance environmental quality across countries, while Zhou (2025) showed that in energy-intensive industries of emerging economies, corporate management strategies play a crucial role in linking energy efficiency to sustainable development outcomes [32,33].

However, the impact of energy efficiency on sustainability varies with changes in the policy environment, market demand, and technological advancements [34,35]. This suggests that the marginal effects of energy efficiency on growth differ across various development stages and external conditions. Hence, a panel data analysis is crucial for capturing temporal heterogeneity and stability. Additionally, variations in firms’ innovation capabilities, financing channels, and managerial cognition may moderate this relationship, as discussed in the following hypothesis.

H1. 

Enhancing energy efficiency significantly improves a firm’s capacity for sustainable development.

2.2. The Moderating Role of Executives’ Green Perception

According to the upper echelons theory [36], enterprises’ strategic decisions reflect the values, cognitive styles, and experiences of top executives. The green perceptions of executives can influence the strategic direction and resource allocation of enterprises. Meta-analyses confirm that executive traits significantly affect corporate strategy and performance [37,38]. For instance, executives with strong green awareness are more likely to integrate energy efficiency into core strategies and leverage resource integration, technological innovation, and process optimization to enhance environmental and competitive performance [39,40,41].

Furthermore, executives’ environmental cognition sends strong market signals regarding the firm’s environmental responsibility, enhances reputation and investor relations, reduces financing costs, and increases the capital market’s recognition of sustainability potential [42,43]. Thus, executives’ green perceptions may amplify the positive relationship between energy efficiency and sustainable growth through internal and external synergistic mechanisms.

H2. 

Executives’ green perception positively moderates the relationship between energy efficiency and sustainable corporate development.

2.3. The Moderating Role of Environmental Protection Investment

Environmental protection investment (EPI) is the additional cost incurred by enterprises to mitigate environmental pollution, including expenditures related to environmental protection [44,45]. First, environmental protection investment provides essential capital support to enhance energy efficiency. Improving energy efficiency involves technological innovation, process optimization, and equipment upgrades, which require substantial initial capital investment [46]. Companies with higher environmental protection investment are more likely to effectively diversify financial risks and reduce their reliance on internal capital, enhancing their investment capacity in energy efficiency projects [47,48]. Second, investments in environmental protection enable firms to respond effectively to external uncertainties such as policy changes and market fluctuations [49]. Stable environmental protection investment ensures the continuity of energy efficiency enhancement projects and prevents disruptions caused by financial factors [50,51].

According to stakeholder theory [52], the availability of green financing reflects the capital market’s acknowledgment of environmentally friendly practices by companies. In other words, companies with improved green financing capabilities are more likely to secure external funding for technological advancements and enhancing energy efficiency [53,54,55].

Moreover, securing green financing can yield advantageous policy benefits, including tax incentives, enhanced credit ratings, and green certifications, thereby establishing a robust foundation for the sustainable development of enterprises [56]. With dual incentives from policy and the market, green financing can enhance the positive effects of energy efficiency improvements on sustainable corporate growth [57].

In summary, investment in environmental protection moderates the relationship between energy efficiency and sustainable growth through two mechanisms: a resource assurance mechanism, which provides financial support to energy efficiency projects; and a market signaling mechanism, which demonstrates a firm’s commitment to sustainability and enhances stakeholder recognition of its green strategies. Therefore, we propose the following hypothesis:

H3. 

Environmental protection investments positively moderate the relationship between energy efficiency and sustainable corporate growth.

2.4. The Moderating Role of the Green Innovation Bubble

The GIB refers to the misallocation of resources or inflated market expectations that occur in the pursuit of green innovation. This underscores the gap between the resources invested in innovation and the actual market returns [23]. While green innovation enhances enterprises’ environmental performance and market competitiveness, overly optimistic expectations of its potential can cause resource misallocation, increase the financial burden on enterprises, and undermine the long-term sustainability of green innovation [23,58]. Christensen et al. [59] observed that differences in ESG ratings and biases in market perception exacerbate this imbalance.

According to the RBV and behavioral finance theory [60,61], a GIB may moderate the impact of energy efficiency on corporate sustainability by influencing resource allocation efficiency and market expectations. When the GIB is low, enterprises’ green innovation inputs can be aligned effectively with their outputs [62]. Conversely, when the GIB is excessively inflated, inefficient resource allocation or unrealistic expectations may diminish the benefits of enhanced energy efficiency, potentially reversing the anticipated gains [59].

Previous studies suggest that higher levels of GIB could weaken the positive effect of energy efficiency, indicating a possible moderating role. Geng et al. [23] argued that when green innovation efforts diverge from a firm’s core strategy or market demand, the marginal returns on innovation investments diminish. Additionally, Song et al. [63] indicated that while moderate levels of green innovation can enhance performance, misaligned resource allocation or non-strategic innovation can impede firm growth.

Therefore, we propose that GIBs may reduce the positive impact of energy efficiency on sustainable corporate growth. Specifically, when the bubble level is moderate, the positive impact of energy efficiency is relatively stable; however, when the bubble level is excessively high, this positive impact may diminish or even reverse. Based on this, we propose the following hypothesis:

H4. 

A higher level of green innovation bubble (GIB) could reduce the positive impact of energy efficiency on sustainable corporate growth.

Figure 1 is the research framework.

3. Methodology

3.1. Definition and Measurement of Variables

3.1.1. Independent Variable

Energy efficiency (EE), the core independent variable, is defined as the economic output produced per unit of energy consumed, reflecting a firm’s ability to utilize resources efficiently. This study measured energy efficiency at the firm level as the ratio of operating revenue to total annual energy consumption.

Specifically, operating revenue data were obtained from the CSMAR database, while energy consumption data (including coal, electricity, oil, and gas) were collected from firms’ annual reports. To ensure comparability across heterogeneous energy types, all energy inputs were converted into tons of standard coal equivalent (tce) according to the conversion factors proposed by Zhang et al. [64]. This conversion allows different energy sources to be expressed in a unified metric, thereby enhancing cross-firm and cross-period comparability. To mitigate the influence of firm-level heterogeneity, skewed distributions, and extreme outliers, the EE variable was log-transformed (natural logarithm) [29].

This statistical treatment improves the robustness of the estimation results by reducing the sensitivity of the regression model to abnormal values. This measurement approach has been widely adopted in prior studies on energy use and corporate performance, ensuring both reliability and comparability across firms and over time.

3.1.2. Dependent Variable

SGR, the dependent variable in this study, measures a firm’s capacity to sustain revenue and profit growth under the existing financial policies. SGR reflects a firm’s profitability, capital structure, and growth potential and is a key indicator of corporate sustainability [65]. Data were obtained from annual financial reports in the CSMAR and Wind databases. Most scholars have used the model proposed by Higgins (1977) or James C. Van Horne (1988) to measure the sustainability of enterprises [66,67]. Considering the simplicity of the Higgins model and consideration of dynamic growth in the Horne model, this study calculates SGR based on the Higgins model and combines financial indicators such as net profit margin, retained rate of return, and asset-liability ratio [68,69] as follows.

$$\mathrm{S}\mathrm{G}\mathrm{R}={\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{m}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\times \mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t} \mathrm{t}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\times \mathrm{R}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\times \mathrm{E}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{m}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{i}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{r}}{1-(\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{i}\mathrm{t} \mathrm{m}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\times \mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t} \mathrm{t}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\times \mathrm{R}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\times \mathrm{E}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{m}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{i}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{r})}}$$

3.1.3. Moderating Variables

In this study, three types of moderating variables are set up to explore their moderating effects on the relationship between energy efficiency and sustainable corporate growth rate.

Executives’ green perception (EGP) captures the concern of firm’s top management toward environmental protection, green innovation, and sustainable development. This index essentially reflects managerial environmental cognition—that is, executives’ awareness and perception of environmental responsibility and sustainable strategies. Drawing on the textual analysis methods established by Loughran et al. [70], this study examines the content of annual reports and Management Discussion and Analysis (MD&A) sections [71]. A keyword dictionary covering green innovation, green finance, and ESG-related themes was developed by combining prior literature [72,73], national policy documents, and manual expert screening (

Table 1. Keyword dictionary for measuring executives’ green perception through textual analysis.

Green Competitive Awareness	Corporate Social Responsibility Consciousness	Perceived Environmental Pressure
Green innovation	Corporate social responsibility	Environmental regulation
Green technology	ESG	Emission standard
Green product	Sustainable development	Carbon emission
Energy saving	Green supply chain	Carbon neutrality
Low-carbon economy	Stakeholder engagement	Green policy pressure

). Textual analysis was then applied to the annual reports and MD&A sections using established methods [70,71]. The EGP index was calculated as the standardized frequency of these keywords relative to total text length, ensuring comparability across firms and years. This approach is consistent with recent empirical studies on executive environmental awareness [74,75].

Environmental protection investment (EPI) reflects the extent of a firm’s resource commitment to environmental protection, measured as the ratio of green investment to owners’ equity. It captures the intensity of capital allocation toward green initiatives and, consistent with prior studies, serves as a proxy for a firm’s strategic commitment to sustainability [76,77,78,79,80]. By channeling resources into environmental initiatives, such investments improve environmental performance, enhance firm performance and capital market outcomes, and contribute to carbon emission reduction and pollution mitigation.

Drawing on the measurement approaches of Chen et al. [77] and Ren et al. [79], we constructed the EPI index using the ratio of green investment to owners’ equity as follows:

EPI = Green Investment Amount/Owners’ Equity

Green investment refers to firm-level expenditures on energy conservation, pollution control, and application of green technologies, sourced from the CSMAR Corporate Social Responsibility database. Owners’ equity represents a firm’s capital foundation and overall resource capacity. EPI captures the intensity of capital allocation toward green initiatives and serves as a proxy for a firm’s strategic commitment to sustainability [47].

The corporate green innovation bubble (GIB) denotes the misalignment between input and output in corporate green innovation. When firms overemphasize the volume of green patent applications without corresponding success in authorization or commercialization, a “quantity-over-quality” bubble emerges, which hampers the effectiveness of green technological progress [23,81].

To construct a GIB, this study draws on the research methodology of Geng et al. [23] using the difference between the number of green patents granted and the number applied as a direct measure of the GIB. This study distinguishes between independent and joint patents by calculating the differences in their granted and applied numbers (i.e., the actual recognized level of innovation) separately, and then summing them to form a more comprehensive and structurally layered bubble indicator. The calculation formula is as follows:

GIB = (Independent Applications − Independent Grants) + (Joint Applications − Joint Grants)

Independent Applications and Independent Grants represent the number of green patents independently applied for and granted by the company, whereas Joint Applications and Joint Grants represent the number of green patents jointly applied for and granted. If the number of applications significantly exceeds the number of grants, it indicates a deviation between green innovation output and quality, reflecting the low conversion efficiency of corporate green innovation and certain bubble characteristics. It should be noted, however, that this indicator may also be affected by the inherent time lag between patent application and authorization, which typically spans one to three years. This measure may also be influenced by the inherent lag between patent application and authorization, and should therefore be interpreted with caution [23].

3.1.4. Control Variables

To reduce the impact of other potential factors on the regression results, this study controls for firm size (log of total assets), firm age (years since establishment), and operating cash flow (net operating cash flow/total assets). Additional controls include industry competition intensity (HHI), ownership concentration (Top1 shareholding), and regional dummies (East = 1, Central = 2, West = 3). Year and industry fixed effects are also included. All data were obtained from the CSMAR and Wind databases.

3.2. Data Sources and Sample Selection

China’s listed A-share manufacturing companies from 2012 to 2023 were selected as the research object in this study, and company-level balanced panel data were constructed to verify the impact of energy efficiency on the SGR of enterprises and its moderating mechanisms. The year 2012 was chosen as the starting point for the sample period because, since then, the Chinese government has introduced several important policies related to green low-carbon transformation, energy conservation and emission reduction, and green finance development [10]. Manufacturing enterprises were chosen as the research sample because the manufacturing industry is a major source of energy consumption and carbon emissions, and it has more complete data in areas such as green innovation, green financing, and environmental information disclosure. In contrast, the energy consumption patterns of service and technology industry enterprises are less pronounced, and the frequency of green investment activities is relatively low, which can interfere with the explanatory power of the model.

In the data preprocessing stage, financial and insurance companies were excluded (because their financial structure differed from that of industrial enterprises), and samples with financial anomalies, such as ST, *ST, and PT companies, were also removed. Next, observations with missing key variables or extreme outliers (including extreme fluctuations in financial variables, missing energy data, and missing text) were deleted. To control for the impact of extreme values, we applied winsorization to continuous variables at the 1% and 99% levels and performed logarithmic transformation and standardization on some variables to enhance the robustness and comparability of the estimation results [82].

Finally, a balanced panel dataset containing 9928 annual observations, encompassing complete information on all variables, was obtained. The data used in this article were mainly obtained from CSMAR (Guotai Junan), the Wind database, and the China Research Data Service Platform (CNRDS), with data processing and analysis completed using the STATA 16.0 software. The datasets are accessible to readers through their institutional subscriptions to these databases.

3.3. Model Specification

3.3.1. Baseline Model

To test Hypothesis 1, we constructed the following model based on Busch et al. [83] to analyze the direct impact of energy efficiency on the SGR of enterprises, controlling for heterogeneity at the firm and macro levels:

$$\mathrm{S}\mathrm{G}{\mathrm{R}}_{it}={\alpha }_0+{\alpha }_1\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}+\sum \beta k\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}{\mathrm{s}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

SGR represents the sustainable growth rate of enterprise i in year t; REE represents the energy efficiency indicator; Controls is a set of control variables, including company size, age, and cash flow; μ_i is the firm fixed effect; λₜ is the year fixed effect; and ε is the error term. Endogeneity and IV: To address reverse causality between EE and SGR, we additionally estimate firm fixed-effects two-stage least squares (IV–FE) models. We treat ln_REE (and its interactions with EGP/GIB/EPI) as endogenous and instrument them with their first lag; as robustness and over-identification, we also use first and second lags (L1 & L2). All IV models include firm and year fixed effects and cluster standard errors at the firm level.

3.3.2. Moderating Effect Model

(A)

Introducing the moderating model of executive green perception (EGP):

$$\mathrm{S}\mathrm{G}{\mathrm{R}}_{it}={\beta }_0+{\beta }_1\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}+{\beta }_2\mathrm{E}\mathrm{G}{\mathrm{P}}_{it}+{\beta }_3\left(\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}\times \mathrm{E}\mathrm{G}{\mathrm{P}}_{it}\right)+\sum \beta k\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}{\mathrm{s}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

EGP_it represents the executive green perception variable, and (REE_it× EGP_it) tests whether EGP moderates the relationship between energy efficiency and sustainable growth. A significantly positive β₃ indicates that executive green perception can enhance the positive effect of energy efficiency on the sustainable growth of enterprises.

(B)

Introducing the moderating model of environmental protection investment (EPI):

$$\mathrm{S}\mathrm{G}{\mathrm{R}}_{it}={\gamma }_0+{\gamma }_1\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}+{\gamma }_2\mathrm{E}\mathrm{P}{\mathrm{I}}_{it}+{\gamma }_3\left(\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}\times \mathrm{E}\mathrm{P}{\mathrm{I}}_{it}\right)+\sum \gamma k\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}{\mathrm{s}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

EPI_it represents the enterprise’s environmental protection investment variable and the interaction term is used to test its moderating effect. A significantly positive ${\gamma }_3$ indicates that environmental protection investment has a positive moderating effect, enhancing the firm’s ability to convert energy efficiency advantages into actual growth outcomes.

(C)

Introducing the moderating model of the green innovation bubble (GIB):

$$\mathrm{S}\mathrm{G}{\mathrm{R}}_{it}={\delta }_0+{\delta }_1\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}+{\delta }_2\mathrm{C}{\mathrm{I}\mathrm{B}}_{it}+{\delta }_3\left(\mathrm{R}\mathrm{E}{\mathrm{E}}_{it}\times \mathrm{C}\mathrm{I}{\mathrm{B}}_{it}\right)+\sum {\delta }_k\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}{\mathrm{s}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(4)

GIB_it is the corporate green innovation bubble variable. A significantly negative δ₃ is consistent with the interpretation that higher GIB levels are associated with a weaker positive impact of energy efficiency on sustainable growth.

4. Results

4.1. Descriptive Statistics

First, a descriptive statistical analysis was conducted. The results are presented in

Table 2. Descriptive statistics.

Variable	N	Mean	SD	Min	p50	Max
SGR	9928	0.293	0.349	−0.282	0.204	2.230
ln REE	9928	14.63	1.344	12.04	14.47	18.33
EGP	9928	4.188	4.932	0	2	22
GIB	9928	0.109	0.892	−0.795	−0.0990	5.930
EPI	9928	0.545	2.008	0	0	15.10
lnREE EGP	9928	1.473	7.268	−42.49	0.450	63.47
lnREE GIB	9928	0.515	3.627	−19.80	0.0550	150.7
lnREE EPI	9928	0.320	4.088	−97.57	0.123	142.8
size	9928	22.47	1.217	20.24	22.31	26.03
age	9928	2.437	0.612	0.693	2.565	3.367
cashflow	9928	0.0590	0.0590	−0.0880	0.0540	0.234
hhi	9928	0.0930	0.0760	0.0140	0.0680	0.487
top	9462	34.60	14.15	0.00600	33.22	87.70
area	9928	1.439	0.684	1	1	3

. The mean of the dependent variable SGR is 0.293, with a standard deviation of 0.349, indicating significant differences in development capabilities among the sample enterprises. The mean of the core explanatory variable, energy efficiency (lnREE), is 14.63, with a standard deviation of 1.344. This shows that the level of energy utilization among enterprises is generally high, but with some volatility and significant differences in energy efficiency among enterprises.

The means of the three moderating variables are 4.188 (EGP), 0.109 (GIB), and 0.545 (EPI), indicating that most corporate management teams prioritize green development. For EPI, both the minimum and median are zero, reflecting that many firms made no environmental protection investment in a given year, while a few firms invested substantially, leading to a right-skewed distribution. There are significant differences among companies in accessing green financing channels. The standard deviation of GIB is 0.892, with a maximum value of 5.930 and a minimum value that is negative, suggesting that some enterprises’ green innovation activities may exhibit the “quantity expansion and quality deficiency” characteristics of a bubble.

The GIB indicator is constructed based on the difference between the number of green patent applications and the number of patents granted, which may result in negative values in certain years. When the number of patents granted in the current period exceeds the number of new applications (e.g., owing to a concentration of previously accumulated patents being approved), it leads to a negative GIB. To enhance the robustness and comparability of the measurements, the GIB variable was standardized during its construction, and negative values did not affect its validity as a measure of structural innovation deviation. Nevertheless, some negative or positive values may arise from the time mismatch between patent application and approval processes, which should be considered when interpreting the results [23].

Regarding the control variables, firm size (mean = 22.47) and firm age (mean = 2.437) show that most companies in the sample are medium-sized and relatively mature. Cash flow (mean = 0.059) indicates that the majority of firms maintain relatively stable liquidity. The average industry concentration index (HHI = 0.093) suggests that most industries remain competitive, while the mean ownership concentration (Top = 34.6%) reflects the high dominance of the largest shareholder in Chinese listed firms. The regional dummy variable (area) confirms that the sample covers enterprises across eastern, central, and western regions, where the East is coded as 1, the Central as 2, and the West as 3. All continuous variables were winsorized at the 1% and 99% levels to minimize the influence of outliers.

4.2. Correlation

Table 3. Correlation.

SGR	ln REE	EGP	GIB	EPI	lnREE ~P	lnREE ~B
SGR	1
ln REE	0.247 ***	1
EGP	0.020 *	0.200 ***	1
GIB	−0.074 ***	0.179 ***	−0.00800	1
EPI	0.109 ***	0.109 ***	0.145 ***	0.00300	1
lnREE EGP	0.081 ***	0.107 ***	0.172 ***	−0.00300	0.047 ***	1
lnREE GIB	−0.070 ***	0.118 ***	−0.00700	0.499 ***	−0.020 **	0.0150	1
lnREE EPI	0.105 ***	0.045 ***	0.00300	−0.062 ***	0.303 ***	0.089 ***	−0.019 *
size	0.142 ***	0.912 ***	0.223 ***	0.175 ***	0.118 ***	0.117 ***	0.135 ***
age	0.152 ***	0.435 ***	0.174 ***	0.037 ***	0.036 ***	0.032 ***	0.0160
cashflow	0.187 ***	0.195 ***	0.070 ***	0.00300	−0.00500	0.052 ***	−0.00300
hhi 1	−0.039 ***	0.131 ***	−0.030 ***	0.017 *	−0.00300	−0.0100	0.0150
top	0.0100	0.119 ***	−0.027 ***	0.027 ***	−0.0140	0.063 ***	−0.00100
area	−0.00500	0.061 ***	0.078 ***	−0.0100	0.044 ***	−0.00400	−0.030 ***
lnREE ~I	size	age	cashflow	hhi 1	top	area
lnREE EPI	1
size	0.042 ***	1
age	−0.00500	0.488 ***	1
cashflow	−0.0120	0.123 ***	0.093 ***	1
hhi 1	0.0140	0.083 ***	0.043 ***	0.032 ***	1
top	−0.00300	0.058 ***	−0.137 ***	0.078 ***	0.081 ***	1
area	0.052 ***	0.073 ***	0.161 ***	−0.031 ***	0.00200	−0.00400	1

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the Pearson correlation coefficients between the variables. Energy efficiency (lnREE) is significantly positively correlated with the corporate sustainable growth rate (SGR) (r = 0.247, p < 0.01), providing preliminary support for H1. EGP and EPI are significantly positively correlated with SGR (r = 0.020, p < 0.1; r = 0.109, p < 0.01)—executives’ green perception and green financing capabilities enhance corporate sustainability. GIB is negatively correlated with SGR (r = −0.074, p < 0.01), providing preliminary evidence that it has a suppressive effect on corporate sustainable development. The interaction terms lnREE × EGP and lnREE × EPI are significantly positively correlated with SGR (r = 0.081 and 0.105, respectively; p < 0.01), while lnREE × GIB is negatively correlated with SGR (r = −0.070, p < 0.01), suggesting consistent and economically meaningful moderating effects, laying the foundation for multivariable regression analysis.

Among the control variables, firm size, age, and cash flow show significantly positive correlations with SGR, while industry competition intensity (HHI) is weakly negative (r = −0.039, p < 0.01). Ownership concentration (Top1) and regional dummies (area) exhibit very small correlations with SGR, indicating that these additional controls are unlikely to introduce multicollinearity. The variance inflation factors (VIFs) were found to be less than 3, and there was no apparent multicollinearity problem.

4.3. Regression Results and Analysis

To determine the rationality of the model specification, we conducted a Hausman test. The results indicate that the fixed-effects model is superior to the random-effects model. Therefore, a fixed-effects panel regression model including industry and year fixed effects was adopted, and the regression results are shown in

Table 4. Regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	SGR	SGR	SGR	SGR	SGR	SGR	SGR
ln_REE	0.2720 ***	0.2718 ***	0.2704 ***	0.2697 ***	0.2660 ***	0.2666 ***	0.2619 ***
	(0.0208)	(0.0207)	(0.0205)	(0.0202)	(0.0203)	(0.0202)	(0.0201)
EGP		0.0050 **	0.0049 **	0.0042 **	0.0027	0.0028	0.0026
		(0.0020)	(0.0020)	(0.0019)	(0.0020)	(0.0020)	(0.0020)
GIB			−0.0666 ***	−0.0664 ***	−0.0661 ***	−0.0635 ***	−0.0527 ***
			(0.0084)	(0.0084)	(0.0084)	(0.0083)	(0.0083)
EPI				0.0165 ***	0.0159 ***	0.0135 ***	0.0133 ***
				(0.0037)	(0.0037)	(0.0037)	(0.0036)
lnREE_EGP					0.0050 ***	0.0047 ***	0.0045 ***
					(0.0012)	(0.0012)	(0.0012)
lnREE_EPI						0.0058 *	0.0059 *
						(0.0032)	(0.0032)
lnREE_GIB							−0.0099 ***
							(0.0026)
size	−0.2242 ***	−0.2239 ***	−0.2231 ***	−0.2275 ***	−0.2261 ***	−0.2270 ***	−0.2222 ***
	(0.0232)	(0.0233)	(0.0229)	(0.0229)	(0.0227)	(0.0227)	(0.0225)
age	0.1013 ***	0.1066 ***	0.1175 ***	0.1132 ***	0.1315 ***	0.1305 ***	0.1284 ***
	(0.0254)	(0.0256)	(0.0253)	(0.0255)	(0.0264)	(0.0265)	(0.0265)
cashflow	0.4392 ***	0.4286 ***	0.4053 ***	0.4175 ***	0.3972 ***	0.4016 ***	0.4015 ***
	(0.0711)	(0.0707)	(0.0700)	(0.0698)	(0.0687)	(0.0681)	(0.0682)
hhi	0.0588	0.0495	0.0319	0.0304	0.0235	0.0166	0.0071
	(0.0935)	(0.0935)	(0.0937)	(0.0930)	(0.0930)	(0.0955)	(0.0982)
top	−0.0010	−0.0009	−0.0007	−0.0008	−0.0009	−0.0009	−0.0008
	(0.0007)	(0.0007)	(0.0006)	(0.0006)	(0.0007)	(0.0007)	(0.0006)
area	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
	(.)	(.)	(.)	(.)	(.)	(.)	(.)
_cons	1.0958 ***	1.0691 ***	1.0560 ***	1.1670 ***	1.1612 ***	1.1716 ***	1.1413 ***
	(0.3624)	(0.3634)	(0.3615)	(0.3657)	(0.3653)	(0.3649)	(0.3618)
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	9462	9462	9462	9462	9462	9462	9462
adj. R2	0.105	0.108	0.147	0.160	0.169	0.175	0.182
F	18.1259	17.1531	19.6392	19.2279	18.2743	17.8049	16.9800

The standard errors are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

.

The first column of baseline model (1) shows that lnREE is 0.2720 (p < 0.01), which passes the significance test at the 1% level—a company’s energy efficiency positively impacts its SGR, thus validating Hypothesis 1. Thus, improvements in energy efficiency can significantly enhance a company’s capacity for sustainable development.

Columns (2)–(4) of Table 4 introduce the three moderating variables. Model (2) introduces the green perception of executives (EGP); its main effect is significantly positive. Furthermore, in Model (5), lnREE × EGP is introduced. Its coefficient is 0.0050 (p < 0.01)—the stronger the green awareness of executives, the more significant the promoting effect of energy efficiency on sustainable growth, thus validating Hypothesis 2. Model (4) examines the main effect and moderating role of EPI, and the results show that EPI has a significantly positive impact on SGR (coefficient = 0.0165, p < 0.01). In Model (6), after introducing lnREE × EPI, the coefficient is 0.0058 (p < 0.1), which is significantly positive, supporting Hypothesis 3. Model (3) examines the GIB. The results indicate that the main effect of the GIB is negative (coefficient = −0.0666, p < 0.01), suggesting that “quantity inflation and insufficient quality” in innovation undermines the effectiveness of corporate green strategies. After adding lnREE × GIB to the model, the coefficient is −0.0099 (p < 0.01), which is significantly negative, validating Hypothesis 4; that is, a higher level of GIB could reduce the positive impact of energy efficiency on corporate sustainable growth.

Overall, introducing moderating variables gradually improved the model’s fit, with the adjusted R² value increasing from 0.105 in the baseline model to 0.182 in the full model, further validating the existence of moderating effects and enhancing the model’s explanatory power. Company age and cash flow showed significant positive effects, while company size was significantly negative across all models. This pattern suggests that SMEs may be more adaptive to green policies and efficiency upgrades, whereas larger firms face higher adjustment costs and path dependence; thus, the size effect should be interpreted as context-dependent rather than universally negative.

4.4. Robustness Test

To validate robustness and address endogeneity,

Table 5. Robustness (lagged SGR) and FE-2SLS (IV) results.

	(1)	(2)	(3)	(4)
Variables	L1.sgr	L2.sgr	2SLS: L1	2SLS: L1 + L2
REE	0.275 ***	0.270 ***	0.172 ***	0.197 ***
	(0.026)	(0.027)	(0.036)	(0.034)
EGP	0.011 ***	0.012 ***	0.008 **	0.003
	(0.004)	(0.004)	(0.003)	(0.002)
GIB	−0.033 ***	−0.035 ***	−0.074 ***	−0.073 ***
	(0.009)	(0.008)	(0.011)	(0.008)
EPI	0.005	0.008 **	0.012 ***	0.018 ***
	(0.004)	(0.004)	(0.004)	(0.005)
REE × EGP	0.004 ***	0.006 ***	−0.001	−0.001
	(0.001)	(0.001)	(0.002)	(0.001)
REE × GIB	−0.007 *	−0.007 **	0.017 ***	0.018 ***
	(0.004)	(0.003)	(0.005)	(0.004)
REE × EPI	0.006 *	0.011 ***	−0.001	−0.004
	(0.004)	(0.003)	(0.002)	(0.002)
Size	−0.226 ***	−0.217 ***	−0.158 ***	−0.154 ***
	(0.028)	(0.028)	(0.030)	(0.029)
Age	0.137 ***	0.108 ***	0.215 ***	0.154 ***
	(0.033)	(0.034)	(0.045)	(0.044)
Cashflow	0.345 ***	0.315 ***	0.400 ***	0.415 ***
	(0.087)	(0.080)	(0.099)	(0.097)
Industry	Yes	Yes	—	—
Year	Yes	Yes	Yes	Yes
Observations	8412	7646	8412	7156
Adj. R2	0.219	0.230
F	7.378	9.195

Standard errors are reported in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

reports two sets of estimations. Columns (1)–(2) present a re-estimation of the model using lagged SGR (L.sgr and L2.sgr) under firm fixed effects as conventional robustness checks. Columns (3)–(4) show the implementation of fixed-effects two-stage least squares (FE–2SLS, hereafter IV–FE) [84], where ln_REE and its interactions are treated as endogenous and instrumented with their lagged values—the first lag in Column (3) and the first and second lags in Column (4). All specifications include firm and year fixed effects, and standard errors are clustered at the firm level.

The IV–FE results confirm that EE positively and statistically significantly affects SGR. The FE-OLS checks in Columns (1)–(2) deliver comparable magnitudes, reinforcing robustness. Regarding moderators, the interaction EE × GIB is positive and statistically significant in the IV–FE specifications, indicating that higher green investment strengthens the EE–SGR link. The interactions with EGP and EPI are small and statistically insignificant under IV–FE, suggesting no stable moderating effects from these two dimensions after controlling for endogeneity. Overall, the findings are robust to alternative instrument sets (L1 and L1 and L2) and support the core conclusion that energy efficiency promotes sustainable growth.

5. Discussion and Conclusions

5.1. Discussion

This study explores the impact of energy efficiency on sustainable growth in enterprises by constructing a multidimensional moderation model of EGP, EPI, and GIB. This study transforms from a macro-policy to a micro-enterprise behavior perspective. The traditional literature neglects the intrinsic value of energy efficiency as a strategic resource allocation outcome. By incorporating energy efficiency into the discussion on sustainable growth, this study highlights the critical role of resource utilization efficiency in converting environmental performance into financial performance. This emphasizes how optimizing the internal energy utilization structure is a foundational resource for building green competitive advantage, enriching the application dimensions of green governance theory.

Consistent with our theoretical framework, the empirical findings provide strong support for the proposed hypotheses. Specifically, the significant positive coefficient of energy efficiency (lnREE) confirms H1, indicating that improvements in energy efficiency directly enhance firms’ sustainable growth capacity. The estimated coefficient of lnREE (≈0.27) implies that a 1% increase in energy efficiency is associated with an approximately 0.27 percentage point increase in the sustainable growth rate (SGR). The positive and significant moderating effect of executives’ green perception validates H2, showing that managerial environmental cognition strengthens the translation of efficiency gains into growth outcomes. Similarly, the results support H3, as environmental protection investment amplifies the positive impact of energy efficiency by providing financial and institutional resources. In contrast, the significantly negative moderating effect of the green innovation bubble corroborates H4, demonstrating that excessive innovation distortion weakens the benefits of energy efficiency and creates risks of resource misallocation. These findings collectively verify the multi-dimensional mechanisms hypothesized in Section 2 and reinforce the explanatory power of our integrated framework.

With respect to the control variables, firm size consistently exhibits a significantly negative coefficient. While this pattern may partially reflect the fact that small and medium-sized enterprises (SMEs) are more flexible and adaptive in responding to green transformation pressures, large firms also possess advantages such as economies of scale, stronger financing capabilities, and richer technological reserves. These mixed effects suggest that the relationship between firm size and green transformation performance is complex rather than uniformly negative, and its direction may vary across industries or policy contexts.

In terms of the moderation mechanism, EGP, EPI, and GIB are simultaneously introduced in this study, constructing a three-dimensional structural path of “cognition–resources–execution deviation” to identify the key moderating links between green strategy from the cognitive to performance end. EGP reflects the cognitive level of green strategic orientation; environmental protection investment acquisition capability represents the intensity of external resource support; and GIB reflects issues of resource misallocation or inflated performance in strategic execution.

Overall, this study broadens the scope of variables associated with research on green strategy performance and enhances the comprehension of the mechanisms underlying the transformation of green behavioral performance. It fosters the convergence and synthesis of micro-level green governance using sustainable development theory. Conducted within the context of China, a developing economy, this research holds significant implications for enterprises in other emerging market nations seeking to advance green transformation and bolster developmental resilience, particularly considering the global “dual carbon” initiative.

Furthermore, although extreme observations were minimized through winsorization at the 1% and 99% levels and by excluding financially abnormal firms (e.g., ST, ST, PT), the possibility of residual outliers cannot be fully eliminated. Outliers may arise from sudden shocks such as drastic fluctuations in energy prices, unexpected policy interventions, or one-time concentrated patent approvals, which could temporarily distort the measurement of energy efficiency or green innovation. While robustness checks using lagged models confirmed the stability of the main findings, future studies should consider employing additional diagnostic approaches, such as quantile regression or influence statistics (e.g., Cook’s distance), to more systematically detect and evaluate the potential impact of extreme values. This would further enhance the credibility and generalizability of the conclusions.

5.2. Conclusions

Based on panel data of A-share listed manufacturing companies in China from 2012 to 2023, this study examines the relationship between energy efficiency and sustainable corporate growth using a fixed-effects model. The results show that energy efficiency has a significant positive effect on sustainable corporate growth. This conclusion remains robust after controlling for industry heterogeneity, year effects, and corporate financial characteristics. The result is further confirmed when using one- and two-period lagged growth rates as the dependent variable, validating the fact that energy efficiency is both a technical indicator of cost savings and an endogenous performance driver of long-term growth and green transformation.

Further analysis indicates that when management adopts environmentally responsible and green strategy orientation, energy efficiency is more likely to be embedded in corporate development logic, creating a synergistic effect [85]. Additionally, the ability to secure green financing has a significant moderating effect—companies can obtain greater capital support for environmental protection investment and effectively transform energy efficiency advantages into growth drivers for technological investment and structural optimization [56]. Conversely, the results suggest that higher GIB levels could reduce the positive impact of energy efficiency on corporate growth, as companies that pursue “quantity expansion” rather than “quality improvement” in green patent applications may face resource misallocation and weakened innovation effectiveness. This finding highlights the hidden risk of resource misallocation in the process of corporate green transformation [81]. These findings qualitatively support the Porter Hypothesis, which suggests that improvements in resource efficiency can enhance firms’ long-term competitiveness. Furthermore, the observed risk associated with excessive GIB is consistent with prior case evidence, where overemphasis on patent quantity rather than quality led to resource misallocation and weaker innovation outcomes.

These conclusions are broadly consistent with prior evidence on the benefits of energy efficiency for firms’ competitiveness and sustainable performance [26,27,28,29], while extending such evidence to the firm-level financial sustainability perspective. The positive moderating roles of executives’ green perception and environmental protection investment align with studies emphasizing the importance of managerial cognition and green financing in supporting sustainability transitions [43,44,47,56]. By contrast, the negative moderating effect of the green innovation bubble complements earlier work by highlighting the risks of innovation distortion and resource misallocation when quantity is prioritized over quality [23,63].

To place these findings in a broader perspective, we also consider their applicability beyond Chinese listed firms and across different institutional contexts. China represents an economy with strong government intervention, evolving green finance mechanisms, and high institutional pressure for environmental compliance. In contrast, firms in developed economies (e.g., the US and Europe) often face more market-driven environmental strategies and shareholder-oriented governance, whereas firms in emerging economies may encounter weaker institutional enforcement and resource constraints. These contextual differences suggest that the positive role of energy efficiency and the moderating effects of managerial cognition, green financing, and green innovation bubbles may vary in intensity across regions. Future research could extend this framework by testing it in different institutional and cultural environments, thereby improving the generalizability of the findings to a global audience.

5.3. Implications

This study has theoretical and practical contributions. The empirical results enrich the identification of the role of energy efficiency in the sustainable growth of enterprises and provide important practical insights for the management of corporate green strategies and the construction of policy systems.

Theoretically, this study proposes a framework for the green transformation path from resource input to performance output, emphasizing the boundary-regulating role of cognition, capital, and execution structure in the energy efficiency transformation process. Among these factors, cognition plays a particularly critical role, as it directly shapes managerial perceptions and strategic choices. In this study, cognition refers specifically to managerial environmental cognition—that is, senior managers’ awareness and perception of environmental responsibility and sustainable strategy. Such cognition shapes strategic decision-making: when environmental cognition is strong, firms are more likely to integrate energy efficiency improvements into long-term development strategies, whereas weak or short-term-oriented cognition may foster the excessive pursuit of patent quantity, thereby increasing the risk of green innovation bubbles.

Practically, the research indicates that enterprises should not view energy efficiency merely as a compliance indicator or a cost-cutting tool to promote sustainable development, but rather should incorporate it into their core resource management logic. The impact of enhancing energy efficiency on corporate growth depends on the strengthening of internal governance capabilities and the realization of external resource coordination, especially in companies in which senior management has a strong green perception and green financing channels are smooth, making the performance transformation path more seamless. Additionally, companies should pay attention to the balance between “quality” and “quantity” in advancing green innovation strategies to prevent the formation of “innovation bubbles,” which could undermine their resource efficiency advantages. More specifically, firms can integrate energy efficiency targets into long-term strategic planning alongside financial and market objectives. They should also strengthen managerial environmental cognition through training programs and leadership development to ensure that decision-making reflects sustainable priorities. In addition, companies are advised to establish internal evaluation systems that emphasize the quality and impact of green innovations rather than focusing solely on the number of patent applications. Finally, firms should actively leverage green financing instruments, such as green bonds or sustainability-linked loans, to secure resources for efficiency-oriented investments. Together, these measures provide firms with implementable strategies to embed energy efficiency into corporate strategy.

The research findings also provide policymakers with multidimensional recommendations. Energy efficiency policies should prioritize the establishment of stringent performance consumption indicators while considering the variations in enterprises’ green governance capabilities. Additionally, it is essential to enhance the foundational framework of green finance and create a risk-sharing mechanism for green projects. Furthermore, resources should be concentrated on enterprises capable of undergoing green transformation, thereby improving the overall green production efficiency of the economic system. Beyond these macro-level recommendations, differentiated countermeasures should also be considered. For small and medium-sized enterprises (SMEs), policy support should focus on alleviating financing constraints and providing targeted incentives to help translate their flexibility advantage into sustained growth. For large enterprises, emphasis should be placed on encouraging long-term green R&D investment, reducing path dependence through governance reforms, and leveraging economies of scale to diffuse green technologies across supply chains.

5.4. Limitations and Future Research Directions

This study has several limitations. First, the sample is restricted to Chinese listed manufacturing firms, which limits cross-country generalizability. Broader datasets across institutional and market contexts are required. Second, although this study employs novel indicators for green innovation distortion, measurement errors and data heterogeneity issues remain. In particular, the green innovation bubble (GIB) indicator may suffer from a time mismatch problem, since patent authorization typically delays applications by one to three years or more. This implies that part of the problem with the GIB is that it may lead to delays in administrative approval rather than genuine innovation. Future research could refine this measure by adopting lag structures (e.g., comparing applications with authorizations after 2–3 years) or using quality-oriented indicators, such as citation-weighted patents or commercialization outcomes, to better disentangle administrative delays from innovation bubbles [23]. Third, although the model captures selected moderating factors, it may overlook deeper systemic drivers of green transition, such as governance dynamics or supply chain coordination. Advanced modeling approaches can be used to further explore these interactions. Finally, static panel models constrain dynamic insights.

Future research should adopt longitudinal or causal inference methods to examine temporal effects and feedback loops in energy-efficiency-driven sustainability. Additionally, future studies could benefit from interdisciplinary integration (e.g., combining energy economics with behavioral science or policy modeling) to better capture the socio-institutional dynamics underlying corporate green transitions under evolving regulatory regimes. Another limitation is the potential influence of residual outliers, which may not be fully eliminated despite the winsorization and sample screening procedures. Future studies should apply advanced diagnostic methods (e.g., Cook’s distance or quantile regression) to further examine these effects.