The Impact of Green Transformation on ESG Performance in Manufacturing Enterprises: Empirical Evidence from Listed Companies in China

Abstract

In the context of global sustainable development and China’s “Dual Carbon” goals, green transformation has emerged as a crucial pathway for manufacturing enterprises to enhance their ESG performance. This study develops a comprehensive and novel framework for assessing green transformation and uses panel data from Chinese A-share listed manufacturing firms spanning 2009 to 2022 to systematically evaluate the impact of this transformation on ESG performance. It also investigates the moderating effects of financing constraints, firm size, and digital transformation. The empirical findings reveal three key results. First, green transformation exerts a significant positive influence on corporate ESG performance, and this conclusion remains robust after conducting multiple endogeneity and sensitivity tests. Second, the beneficial effect of green transformation is more pronounced in state-owned enterprises, firms in central, western, and northeastern China, and younger enterprises. This reflects the substantial impact of policy orientation and resource base on the effectiveness of implementing green strategies. Third, financing constraints amplify the ESG benefits derived from green transformation. In contrast, firm size exhibits a negative moderating effect, indicating that small- and medium-sized enterprises (SMEs) derive greater advantages. Although digital transformation generally enhances ESG performance, it presents a synergistic imbalance with green transformation that diminishes its marginal effect. This study provides theoretical foundations and robust empirical evidence to support the advancement of corporate ESG performance through initiatives focused on green transformation.

1. Introduction

1.1. Background and Significance of the Research

Against the backdrop of the accelerated advancement of the global sustainable development agenda, Environmental, Social, and Governance (ESG) has become the core framework for measuring enterprises’ sustainable development capabilities. Since 1992, the United Nations Environment Programme Finance Initiative (UNEP FI) has continuously advocated integrating ESG factors into investment decisions, and ESG has gradually evolved into three key dimensions for the international community to assess the sustainable development capacity of economic entities. According to statistics from the Global Sustainable Investment Alliance (GSIA), the global ESG investment scale expanded from $13.6 trillion in 2012 to $35.3 trillion in 2020, a trend indicating that the ESG concept has been deeply integrated into the global capital market. Meanwhile, digital transformation is reshaping the global industrial structure, providing new technical paths and strategic choices for enterprises’ sustainable development.

As a cornerstone of the real economy, the manufacturing industry constitutes a substantial portion of global greenhouse gas emissions. Therefore, its sustainable transformation is essential for achieving carbon neutrality goals. As the world’s third-largest emitter of carbon dioxide, China sees its manufacturing sector confronting numerous challenges, including overcapacity, low energy efficiency, and escalating environmental pressures. In this context, the Overall Plan for Ecological Civilization System Reform introduced in 2015 explicitly mandated that listed companies disclose environmental information. Additionally, the 13th Five-Year Plan advocated for promoting industrial upgrading towards medium-high-end development to facilitate green growth. Furthermore, the Company Law enacted in 2013 stipulated that enterprises must allocate 2% of their average net profit from the preceding three years to corporate social responsibility initiatives. These policy directives have compelled Chinese manufacturing enterprises to reassess their development strategies and seek pathways to achieve sustainable development while ensuring profitability.

This study focuses on Chinese A-share listed manufacturing enterprises and constructs a comprehensive evaluation index system for their green transformation. It systematically examines the impact and mechanisms through which green transformation influences enterprises’ ESG performance. The research not only enriches theoretical studies on the factors influencing ESG performance but also provides empirical evidence and policy implications for manufacturing enterprises seeking to enhance their sustainable development capabilities through green transformation. The significance of this research lies in three aspects: First, it offers a novel perspective by exploring the intrinsic relationship between green transformation and ESG performance at the level of individual manufacturing firms. This not only aids manufacturing firms in improving their ESG outcomes but also enriches the academic discourse in this field. Second, this study analyzes how green transformation affects ESG performance within manufacturing enterprises by focusing on three transmission mechanisms: corporate digital transformation, financing constraints, and firm size. This approach reveals the potential pathways of influence and provides more targeted guidance for practical applications. Third, by examining three dimensions—enterprise ownership nature, regional differences, and firm age—this study investigates the heterogeneous impacts of green transformation on ESG performance within manufacturing enterprises. This analysis not only offers empirical insights into understanding differentiated effects of green transformations across various contexts but also establishes a foundation for subsequent in-depth investigations into the causes behind variations in ESG performance among these firms. Ultimately, it contributes to constructing a more context-adaptive theoretical framework.

1.2. Literature Review

As a comprehensive evaluation framework for corporate sustainable development, the theoretical foundation of ESG can be traced back to the stakeholder theory proposed by Freeman (1984) [1]. This theory posits that enterprises should not only be accountable to shareholders but also strive to balance the interests of various stakeholders, including employees, customers, suppliers, communities, and the environment. Dmytriyev et al. (2021) [2] further elucidated the relationship between stakeholder theory and corporate social responsibility (CSR), emphasizing that ESG practices represent a critical manifestation of how enterprises fulfill their social responsibilities and create value for multiple stakeholders. In recent years, with advancements in the global sustainable development agenda, ESG has transitioned from being a voluntary CSR practice to becoming an integral component of investment decisions and corporate strategies. It now serves as an essential tool for assessing enterprises’ long-term value creation capabilities.

Existing research on the factors influencing ESG performance and the role of ESG in corporate green innovation focuses on two dimensions: internal corporate characteristics and external environmental contexts.

From the perspective of internal characteristics, green innovation serves as a crucial driver for enhancing ESG performance, while the relationship between ESG performance and green innovation exhibits complex and diverse features. Wu et al. (2024) [3] found that ESG performance significantly promotes corporate green innovation, with government regulation acting as a moderating factor—this effect is particularly pronounced in state-owned enterprises (SOEs), non-heavy-polluting industries, and with respect to green invention patents. Yang, C. et al. (2024) [4] identified a U-shaped relationship between ESG ratings and green innovation: improving ESG performance inhibits green innovation in enterprises with low ESG ratings but exerts a positive promotional effect in those with high ratings. This finding suggests that enterprises must surpass a certain threshold of ESG performance to fully harness their potential for promoting green innovation.

From an external environmental perspective, institutional environments and policy support play critical roles in shaping corporate ESG practices. Yang, J. et al. (2024) [5] investigated the impact of environmental regulation on the ESG performance of manufacturing enterprises, discovering that market-oriented environmental regulations significantly enhance corporate ESG outcomes by fostering green technological innovations—this effect is more pronounced among non-SOEs, firms exhibiting high levels of green total factor productivity (GTFP), and those situated in central China.

As a strategic response to environmental challenges, corporate green transformation is closely linked to ESG performance. Zeng and Zhang (2024) [6] conducted a city-level study in China that revealed a catalytic mechanism through which green finance facilitates the application of artificial intelligence (AI) in the energy sector by alleviating financing barriers, thereby indirectly enhancing urban energy efficiency. They also examined regional disparities and the moderating effects of environmental regulation and industrial structure, establishing theoretical complementarity with research in the fields of ESG and green innovation. This work provides support for integrating “finance + technology” to empower urban sustainable development. Zeng et al. (2024) [7] employed machine learning and text analysis methods to investigate the impact of green manufacturing on corporate ESG performance. Their findings indicate that green manufacturing significantly enhances ESG performance through multiple mechanisms, such as promoting green innovation, optimizing resource allocation, and improving information disclosure quality. In the manufacturing sector, recent research by Gao et al. (2025) [8] suggests that intelligent manufacturing can substantially improve corporate ESG performance by optimizing production processes and fostering green innovation—an effect particularly pronounced in high-tech industries as well as heavy-polluting sectors. Khan et al. (2024) [9] conducted a bibliometric analysis of ESG performance within manufacturing. Furthermore, their analysis highlights China’s significant position in manufacturing-related ESG research, reflecting an urgent demand for practical exploration into green transformation within China’s manufacturing industry.

Emerging from the technological revolution, digital transformation offers new avenues for corporate green transformation and improvements in Environmental, Social, and Governance (ESG) performance. Yang et al. (2023) [10] found that digital transformation significantly enhances corporate ESG performance, particularly in the environmental and social dimensions; moreover, a relaxed financing constraint environment amplifies this positive effect. Wang & Hong (2023) [11] proposed the “DESG” theoretical framework, positing that digital transformation boosts corporate profitability, thereby enabling enterprises to allocate adequate resources to ESG initiatives and fostering a virtuous cycle. However, Dai et al. (2023) [12] identified that while digital transformation positively moderates the relationship between ESG performance and green innovation, this synergistic effect is characterized by imbalance. Collectively, these studies indicate that the integrated development of digital transformation and green transformation represents a crucial direction for enhancing corporate ESG performance; nevertheless, their synergistic mechanisms warrant further investigation.

Financing constraints represent a critical factor influencing corporate green transformation and Environmental, Social, and Governance (ESG) practices. Existing research indicates that financing constraints significantly impact both corporate green transformation and ESG performance: overall ESG performance has a mitigating effect on financing constraints, but the environmental sub-dimension tends to exacerbate financing difficulties. Additionally, factors such as regional environmental regulations play a moderating role in these relationships (Wang et al., 2025) [13]. Du et al. (2022) [14] found that financial technology substantially enhances corporate ESG performance through dual pathways: by alleviating internal financing constraints and by improving external fiscal incentives. However, the current literature presents inconsistent conclusions regarding how financing constraints moderate the relationship between green transformation and ESG performance. Some scholars argue that financing constraints inhibit corporate green investment, thereby diminishing the effectiveness of green transformation; conversely, others suggest that firms facing more severe financing constraints are incentivized to enhance their ESG performance through green transformation in order to secure external financial support.

The role of firm size in green transformation and ESG practices has also attracted considerable attention. Drempetic et al. (2020) [15] found that firm size has a significant impact on ESG ratings—large enterprises typically achieve better ESG performance, which may be attributed to their stronger resource mobilization capabilities and greater social attention. Gallo & Christensen (2011) [16] indicated a positive correlation between firm size and sustainable development behaviors, as large enterprises are more capable of investing in ESG practices.

In summary, the existing literature provides a crucial theoretical foundation and empirical reference for this study. However, several research gaps persist: First, concerning how green transformation systematically influences corporate ESG performance, most current studies concentrate on singular dimensions (e.g., green innovation or environmental performance), without adequately exploring the mechanisms that affect comprehensive ESG performance. Second, important moderating factors such as financing constraints, firm size, and digital transformation have not been thoroughly investigated regarding their impact mechanisms on the relationship between green transformation and ESG performance—particularly the potential interaction effects among these variables. Third, within the context of China’s manufacturing industry undergoing transformation and upgrading, enterprises with varying ownership types, regional characteristics, and industry attributes may demonstrate significant heterogeneity in how green transformation impacts ESG performance. Nonetheless, existing research lacks sufficient explanations for these disparities. This study aims to address these research gaps by developing a comprehensive evaluation system for green transformation. It will systematically examine the influence of green transformation on corporate ESG performance along with its underlying mechanisms while providing both theoretical guidance and practical implications for the sustainable development of manufacturing enterprises.

Against the backdrop of the aforementioned limitations in existing research, this study’s unique contributions are reflected in three key aspects: First, it optimizes the measurement of corporate green transformation. Unlike most studies that either use single indicators or overlap with ESG metrics, this study develops a comprehensive evaluation system covering three core dimensions—technological innovation, production efficiency, and pollution reduction. By replacing indicators that may overlap with ESG performance (e.g., using objective pollution emission data instead of self-reported environmental information) and applying the entropy weight method for objective weighting, this system avoids measurement bias and more accurately captures the substantive progress of green transformation in manufacturing enterprises. Second, it systematically clarifies the interactive moderating mechanisms of multiple factors. Instead of examining individual moderators in isolation, this study integrates financing constraints, firm size, and digital transformation into a unified analytical framework. It not only identifies the direction of each factor’s moderating effect (e.g., financing constraints strengthen the ESG benefits of green transformation, while firm size weakens it) but also uncovers a critical “synergistic imbalance” between digital transformation and green transformation—where digitalization, despite improving overall ESG performance, may divert resources and attention, thereby reducing the marginal effect of green transformation. This fills the gap of insufficient exploration of interactive mechanisms in existing research. Third, it reveals context-specific heterogeneous effects of green transformation. Aiming at the typical characteristics of China’s manufacturing sector (e.g., clear ownership differences, uneven regional development), this study examines how green transformation impacts ESG performance across different enterprise types and regions. It finds that the positive effect of green transformation is more pronounced in state-owned enterprises, firms in central, western, and northeastern regions, and younger enterprises—providing a more nuanced understanding of the contextual boundary conditions for green transformation’s effectiveness, which is rarely addressed in general studies.

2. Theoretical Analysis and Research Hypotheses

2.1. The Impact of Green Transformation on Corporate ESG Performance in Manufacturing Firms

Amid the increasing emphasis on green development, manufacturing enterprises are progressively adopting green transformation as a fundamental strategy to address complex external pressures and internal strategic requirements. Green transformation is a proactive process wherein firms adapt to and contribute to ecological civilization by implementing technological innovations, enhancing resource efficiency, utilizing clean energy sources, and improving pollution control measures. This transition aims to establish a development model that harmonizes environmental sustainability with economic performance. It not only reshapes production methods and management practices but also significantly influences corporate performance across environmental, social, and governance (ESG) dimensions.

From an environmental perspective, green transformation directly enhances corporate environmental performance. On one hand, the adoption of green production technologies substantially reduces carbon emissions, wastewater discharges, exhaust emissions, and solid waste generation, thereby improving environmental outcomes. On the other hand, the consistent implementation of green supply chain management practices, investments in environmentally friendly initiatives, and energy-saving measures contribute to greater transparency and standardization in the disclosure of environmental information. Research has demonstrated a significant positive correlation between green technology innovation and corporate environmental performance, particularly within highly polluting industries.

At the social level, green transformation enhances corporate commitment to and fulfillment of social responsibilities. On one hand, the implementation of green initiatives compels manufacturers to prioritize employee safety, health, and environmental training, thereby fostering greater staff engagement and a stronger organizational identity. On the other hand, the development of green products and services bolsters firms’ capacity to meet consumer demand for sustainable options, contributing to a positive public image and increasing societal trust. Furthermore, corporate social responsibility (CSR) in the context of green development transcends mere environmental actions; it encompasses systematic attention to employee welfare and community well-being. Such practices not only motivate employees but also strengthen public trust, thus holistically enhancing the social dimension of corporate performance.

From a governance perspective, the green transformation necessitates that enterprises establish more standardized and transparent governance structures to ensure the attainment of environmental objectives and the effective implementation of relevant policies. In practice, companies often enhance their governance frameworks by forming dedicated sustainability committees, introducing mechanisms for evaluating environmental performance, and improving systems for disclosing environmental information. Governance reforms during the green transformation not only bolster the supervisory capacity of boards of directors but also encourage management to prioritize integrated management of both environmental and social impacts.

Recent research indicates that in response to climate change, companies are increasingly incorporating green transformation into their core governance strategies. Specifically, by optimizing board governance structures and promoting performance evaluation mechanisms linked to sustainability initiatives, firms can effectively elevate their standards of green governance and external transparency. Furthermore, this optimization of governance structures enhances an enterprise’s capability to implement green transformation strategies, thereby creating a positive feedback loop.

In summary, green transformation comprehensively enhances a firm’s ESG performance through coordinated advancements across environmental, social, and governance dimensions. From an external regulatory perspective, it bolsters the firm’s capacity to mitigate policy-related and reputational risks, thereby increasing recognition from regulators and capital markets. Internally, integrating green strategies into organizational culture and management systems fosters the development of a sustainable value-creation model and secures long-term competitive advantages. However, the benefits of green transformation do not materialize overnight. Firms encounter practical challenges such as substantial capital investment requirements, extended payback periods, and complex management demands. Therefore, investigating the impact of green transformation on ESG performance not only elucidates the inherent value orientation associated with green development but also clarifies the effectiveness and implementation mechanisms of corporate sustainability strategies.

Based on the preceding theoretical analysis and comprehensive literature review, we formulate the following research hypothesis:

H1. 

The greater the extent of green transformation in a manufacturing enterprise, the stronger its ESG performance.

2.2. The Impact Mechanism of Green Transformation on ESG Performance in Manufacturing Enterprises

2.2.1. Green Transformation, Firm Size, and ESG Performance in Manufacturing Enterprises

Whether manufacturing enterprises can fully harness the positive effects of green transformation on environmental, social, and governance (ESG) dimensions largely depends on their organizational structure and resource allocation capabilities. Among these factors, firm size serves as a critical moderating variable. As an essential indicator reflecting organizational resource endowments and structural complexity, firm size exerts a significant moderating effect on the relationship between green transformation and ESG performance. However, the influence of firm size on green transformation outcomes extends beyond a straightforward resource-enhancing effect, encompassing more intricate organizational mechanisms.

From the perspective of the Resource-Based View (RBV), larger enterprises indeed possess substantial advantages in terms of financial capital, technological capabilities, and human resources. Theoretically, these resource endowments should facilitate the effective implementation of green transformation strategies and enhance ESG performance. Nevertheless, insights from organizational path dependence theory (Sydow et al., 2009) [17] reveal a more complex reality. As organizations grow in size, entrenched institutional routines, cognitive frameworks, and resource allocation mechanisms become established within them (Robinson, 2006) [18]. These historically accumulated characteristics generate path dependence through self-reinforcing processes that can trap enterprises in a “lock-in” situation regarding existing trajectories. In the context of green transformation, this implies that even if large enterprises acknowledge the value of adopting green strategies, they may find it challenging to swiftly adjust long-standing production models, management processes, and organizational cultures.

Furthermore, organizational design theory (Karim & Kaul, 2015) [19] links the structural complexity of large enterprises to the effectiveness of their strategic transformations. High interdependence and inflexibility among organizational units generate considerable resistance to the structural adjustments required for green transformation. When enterprises pursue green strategies, they must reallocate resources and restructure processes across multiple departments. Such changes can trigger cascading effects throughout the organizational structure, often leading to short-term performance declines and significant internal friction.

In contrast, while small and medium-sized enterprises (SMEs) may face disadvantages regarding resource reserves, they typically exhibit relatively flat organizational structures, shorter decision-making chains, and more efficient internal communication (Khanra et al., 2022) [20]. This flexibility allows SMEs to respond more swiftly to the demands of green transformation and integrate environmental management into their core business processes more effectively. Existing studies have demonstrated that in the realm of environmental innovation, SMEs are better positioned to leverage their flexibility advantages to convert green investments into significant enhancements in environmental performance (De Marchi & Grandinetti, 2013) [21].

Based on this theoretical analysis, this study posits that firm size exerts a negative moderating effect on the impact of green transformation on ESG performance. Although large enterprises possess greater resources at their disposal, their path dependence within organizational frameworks, bureaucratic structures, and internal complexities diminishes both the efficacy of implementing green transformation strategies and the extent of improvements in ESG performance.

Therefore, the following hypothesis is proposed:

H2. 

The size of manufacturing enterprises exerts a negative moderating effect on the relationship between the degree of green transformation and corporate ESG performance. Specifically, the positive impact of green transformation on ESG performance tends to be relatively weaker in large-scale enterprises.

2.2.2. Green Transformation, Financing Constraints, and ESG Performance in Manufacturing Enterprises

Financing capacity is a crucial enabler for the effective implementation of green strategies within the manufacturing sector. The process of green transformation typically necessitates significant capital expenditures and technological advancements—whether through the adoption of clean energy, the construction of environmental protection facilities, or investments in green research and development (R&D). All these factors impose heightened demands on corporate funding. Consequently, the extent of financing constraints encountered by a firm not only affects the pace and depth of its green transformation but also moderates how this transformation influences Environmental, Social, and Governance (ESG) performance.

Financing constraints refer to the challenges that firms face in accessing external capital, which can impede sustainable investment initiatives. These constraints may stem from various factors such as credit ratings, quality of information disclosure, collateral availability, or prevailing conditions in the capital market. In China’s current financing landscape, green finance remains in a developmental phase. Although policies continue to be enacted to support green projects, many enterprises—particularly small and medium-sized ones—still experience difficulties in obtaining affordable financing options. Under substantial financing constraints, firms tend to exhibit an increased inclination to proactively enhance their environmental performance and fulfill social responsibilities with the aim of securing recognition and legitimacy from external investors. By improving their ESG performance, they can strengthen their corporate image and thereby alleviate some financial pressures. Simultaneously, robust ESG disclosure significantly alleviates corporate financing constraints by promoting R&D investment and enhancing access to capital.

Furthermore, financing constraints may enhance the endogenous incentive mechanism linking green transformation and ESG performance. On one hand, firms facing financial constraints experience heightened pressure to seek policy support and access green financial resources, thereby increasing their motivation to pursue green investments and transformation initiatives. In this context, green transformation emerges as a vital pathway for securing external support, which subsequently drives internal enhancements in environmental management, social responsibility, and corporate governance. On the other hand, such enterprises are subjected to more rigorous external monitoring and investor skepticism. In response to these pressures, they are more inclined to improve their ESG performance in order to mitigate external uncertainties and cultivate a credible corporate image.

It is important to note that financing constraints function not only as limitations but also as a “forcing mechanism” that compels firms to strategically prioritize long-term value creation and sustainable development objectives. During the initial stages of transformation, resource-constrained companies may adopt a more cautious approach toward investments in green technology. However, they are likely to concentrate on enhancing operational efficiency, institutional capacity, and transparency in disclosures with the aim of improving their ESG performance. These efforts can facilitate greater access to financing and increase market recognition. Consequently, compared with financially unconstrained firms, those operating under financing constraints tend to exhibit more incentive-driven and goal-oriented strategies for green transformation—thereby yielding stronger improvements in ESG performance.

Based on the preceding analysis, we propose the following hypothesis:

H3. 

Financing constraints serve as a positive moderator in the relationship between green transformation and ESG performance among manufacturing enterprises. Specifically, increased financing constraints amplify the positive effect of green transformation on ESG performance.

2.2.3. Green Transformation, Digital Transformation, and ESG Performance in Manufacturing Enterprises

Amid the rapid advancement of digital technologies, digital transformation has emerged as a critical strategy for manufacturing enterprises seeking to enhance operational efficiency, foster innovation, and achieve sustainable development. Particularly within the context of green transformation, digitalization not only provides essential technical support for realizing environmental objectives but also significantly influences corporate performance across environmental, social, and governance (ESG) dimensions. As a pivotal direction for contemporary corporate strategic enhancement, digital transformation theoretically facilitates the attainment of green transformation goals through technology empowerment, process optimization, and data-driven decision-making (Verhoef, 2019) [22]. However, analyses grounded in the Attention-Based View (ABV) and Paradox Theory indicate that the relationship between digital transformation and green transformation is considerably more intricate than a mere synergistic effect.

From the theoretical standpoint of the Attention-Based View (Fernandes & Burcharth, 2024) [23], an organization’s strategic behaviors fundamentally stem from managers’ allocation of attention. Given that managers’ attention is a scarce organizational resource, it must be judiciously weighed and balanced when confronted with multiple strategic tasks. Due to its inherent technical complexity along with substantial investment requirements and time constraints, digital transformation often demands considerable attention from top management. When enterprises endeavor to advance two significant strategies—digital transformation and green transformation—they inevitably encounter competition for these limited attention resources. Empirical studies have demonstrated (Wiedmer & Whipple, 2022) [24] that under conditions where attention resources are constrained, organizations tend to prioritize digital initiatives capable of delivering short-term performance enhancements and market competitiveness; consequently, relatively long-term green transformation objectives may become marginalized.

Furthermore, Paradox Theory reveals the inherent tensions enterprises face during the dual transformation process (Klein et al., 2024; Wessel et al., 2021) [25,26]. Digital transformation triggers multiple paradoxes at the organizational level, including the paradox of organizational structure (needing to maintain both stability and flexibility), the paradox of attention (needing to focus on multiple priorities simultaneously), and the paradox of knowledge sharing (needing to balance openness and protection). The existence of these paradoxes poses significant challenges for organizations in integrating the two transformation directions of digitalization and greening. In particular, there are certain differences in value orientation between digital transformation— which emphasizes rapid iteration, efficiency priority, and short-term returns—and green transformation— which emphasizes long-term investment, systematic change, and social responsibility. If enterprises lack effective paradox management capabilities and integration mechanisms, such tensions will weaken the implementation intensity and effectiveness of green transformation.

From the perspective of resource allocation, digital transformation necessitates substantial investments by enterprises in infrastructure, upgrades to information systems, recruitment of digital talent, and restructuring of organizational processes (Hanelt et al., 2021) [27]. In the short to medium term, these investments compete directly with the capital requirements associated with green transformation initiatives, such as the construction of environmental protection facilities, research and development in clean technologies, and transformations within green supply chains. Recent studies suggest that under conditions of resource constraints, enterprises frequently encounter an “innovation crowding-out effect,” wherein excessive investment in one form of innovation constrains available resources for other innovative endeavors. When digital transformation becomes a strategic priority for enterprises, it may result in inadequate investment in green technological innovation and improvements in environmental management. This situation can ultimately diminish the positive impact that green transformation has on ESG performance.

In addition, empirical research has indicated (Verhoef et al., 2019) [22] that enterprises often place significant emphasis on the standardization of technical systems and the automation of processes during their digital transformation efforts. This technology-centric approach may conflict with the flexibility and adaptability necessary for achieving green transformation. Standardized digital processes can diminish an enterprise’s capacity to respond effectively to environmental challenges, while rigid information systems may constrain opportunities for innovation in green management. When digital transformation lacks a systematic integration of sustainable development goals, it risks entrenching traditional management models focused primarily on economic performance, thereby undermining the depth of change in green strategies.

In summary, although digital transformation theoretically offers technical support for green transformation, this study posits—based on analyses grounded in Attention-Based View theory, Paradox Theory, and resource allocation competition—that intensive digital transformation may weaken the positive impact of green transformation on ESG performance within corporate practice. This weakening occurs through mechanisms such as attention dispersion, paradoxical tensions, and resource crowding-out.

Therefore, we propose the following hypothesis:

H4. 

Digital transformation exerts a negative moderating effect on the relationship between green transformation and ESG performance. Specifically, as the degree of digital transformation increases, the promoting effect of green transformation on ESG performance diminishes.

3. Research Design

3.1. Sample Selection and Data Sources

Based on data availability, this study selects Chinese A-share manufacturing listed companies from 2009 to 2022 as the research sample. The sample was processed as follows: (1) firms designated as ST or PT were excluded; (2) firms with missing data were removed. The final dataset comprises 25,623 firm-year observations. ESG rating data were obtained from the Hua Zheng ESG Ratings within the Wind database, while all other corporate data were sourced from the CSMAR database.

3.2. Variable Selection

3.2.1. Explained Variable

Relevant data for assessing ESG performance are primarily obtained from third-party rating agencies. Prominent domestic third-party rating agencies in China include the China Securities Index (CSI) ESG, SynTao Green Finance, and Rankins (RKS). Variations in rating standards among these agencies stem from sociocultural, technological, and other contextual factors. Given that the CSI ESG evaluation system is developed by a domestic third-party agency, it possesses a more nuanced understanding of China’s national conditions. Additionally, it benefits from quicker update cycles, broader enterprise coverage, and enhanced timeliness. Consequently, following the methodology established by Luo et al. (2023) [28], this study employs the CSI ESG evaluation system to measure ESG performance by assigning values ranging from 9 to 1 across the CSI ESG rating levels from AAA to C, respectively. A higher value signifies superior corporate ESG performance. Furthermore, quarterly scores are averaged to derive annual ESG performance metrics.

The CSI ESG rating utilizes a three-tier indicator system. Level-1 indicators encompass three dimensions: Environmental (E), Social (S), and Governance (G). Within the Environmental dimension, Level-2 indicators address aspects such as environmental management, resource utilization, pollution control, and climate action. In the Social dimension, they pertain to employee rights and interests, product responsibility, supply chain management, and community relations. The Governance dimension includes shareholder rights, board governance practices, information disclosure standards, and business ethics.

Level-3 indicators are further delineated into specific quantitative and qualitative metrics that total over 100 items. This multi-tiered indicator framework ensures both comprehensive evaluation coverage and relevance across each dimension. Furthermore, the CSI ESG rating employs a holistic scoring methodology: initially, each Level-3 indicator is standardized; subsequently, varying weights are assigned based on industry characteristics and the significance of each indicator; weighted aggregation is then applied to derive scores for each dimension. The final overall ESG score is calculated by summing these dimensional scores. Notably, the weights assigned to the Environmental, Social, and Governance dimensions are 30%, 30%, and 40%, respectively.

In conclusion, as one of the most representative and authoritative ESG evaluation systems within the Chinese market context, the CSI ESG rating demonstrates significant advantages in terms of indicator construction quality data integrity as well as local adaptation capabilities. It effectively reflects the ESG performance levels of Chinese listed companies while providing a reliable measurement foundation for this study.

3.2.2. Core Explanatory Variable

Currently, the academic community has not achieved a consistent consensus on the concept of corporate green transformation, nor is there a unified indicator system for its comprehensive and systematic measurement. Existing studies primarily adopt two approaches to assess green transformation:

One common approach involves utilizing a single indicator. For instance, the number of corporate green patent applications is often selected as the core variable to gauge the extent of green transformation. Wu Fei and Li Wei (2022) [29] extracted and analyzed relevant keywords associated with the concept of green development from corporate texts to identify the implementation of their green strategies.

Another approach entails constructing a multi-dimensional indicator system through comprehensive evaluation methods. Yu Lianchao et al. (2019) [30] systematically established an evaluation framework for corporate green transformation across six dimensions: green culture construction, strategic orientation, technological innovation, investment intensity, production methods, and emission control. Similarly, Sun Chuanwang and Zhang Wenyue (2022) [31] developed a green transformation indicator system encompassing five aspects—technological innovation, production level, pollution reduction, environmental protection, and social evaluation—based on principles such as efficiency, intensiveness, low-carbon development, and sustainability.

This study primarily adopts the methodology proposed by Sun Chuanwang and Zhang Wenyue (2022) [31] to assess the degree of green transformation in manufacturing enterprises. However, Hu Jie et al. (2023) [32] have noted that certain indicators selected within specific dimensions in the measurement approach of Sun Chuanwang and Zhang Wenyue (2022) [31] are derived from corporate ESG reports or social responsibility reports, which overlap with the core explanatory variable of this research—corporate ESG performance. To mitigate this issue, this study substitutes these overlapping indicators prior to measurement. Specifically, the level of corporate pollution control is evaluated based on the pollutant emissions calculation method proposed by Mao Jie et al. (2022) [33], followed by normalization; meanwhile, corporate carbon emission efficiency is assessed according to the definition of carbon performance established by He Yu et al. (2017) [34].

The detailed content of the comprehensive evaluation indicator system for corporate green transformation in manufacturing enterprises is presented in

Table 1. Indicator System for the Degree of Corporate Green Transformation.

Level-1 Indicators	Level-2 Indicators	Definition	Indicator Direction
Technological Innovation	Innovation Input	R&D Expenditure	+
	Innovation Output	Ratio of Green Patent Grants to Total Patent Grants	+
Production Level	Production Efficiency	Total Factor Productivity (TFP)	+
	Labor Efficiency	Operating Revenue per Employee	+
Pollution and Emission Reduction	Pollution Control	Logarithm of Total Pollution Equivalent	+
	Carbon Emission Efficiency	Inverse of Total Carbon Emissions per Million Yuan of Net Sales	+

. Additionally, this study employs the entropy weight method to assign weights to these indicators and calculates the degree of green transformation (GT) for manufacturing enterprises.

3.2.3. Control Variables

Building upon previous research, this study incorporates several control variables from the CSMAR database to account for factors that may influence corporate ESG performance. These variables include firm age (Age), fixed asset ratio (FAR), cash asset ratio (CAR), debt-to-asset ratio (DR), return on assets (ROA), largest shareholder ownership (LSO), proportion of independent directors (IDR), and revenue growth rate (RGR). Definitions of all variables are provided in

Table 2. Variable Definitions.

Type	Name	Variable	Definition
Explained Variable	Corporate ESG Performance	ESG	Hua Zheng ESG Rating
Core Explanatory Variable	Degree of Corporate Green Transformation	GT	Corporate Green Transformation Index
Control Variables	Firm Age	Age	Years Since Firm Establishment
	Fixed Asset Ratio	FAR	Fixed Assets/Total Assets
	Cash Asset Ratio	CAR	Cash Assets/Total Assets
	Debt-to-Asset Ratio	DR	Total Liabilities/Total Assets
	Return on Assets (ROA)	ROA	Net Profit to Average Total Assets
	Largest Shareholder Ownership	LSO	Largest Shareholder’s Ownership Percentage
	Board Independence	IDR	Proportion of Independent Directors
	Revenue Growth Rate	RGR	Year-over-Year Operating Revenue Growth Rate

.

3.3. Model Specification

To empirically examine the influence of corporate green transformation on ESG performance, the following econometric model is proposed:

$${ESG}_{it}=\alpha +{\beta GT}_{it}+{\gamma Control}_{it}+{\delta }_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

where ${ESG}_{\mathrm{it}}$ denotes the ESG performance of firm *i* in year *t*; ${GT}_{\mathrm{it}}$ represents the degree of green transformation of firm *i* in year *t*; ${Control}_{it}$ is a vector of control variables; ${\delta }_{\mathrm{i}}$ denote industry fixed effects; ${\lambda }_{\mathrm{t}}$ denote year fixed effects; ${\epsilon }_{\mathrm{i}}$ is the idiosyncratic error term.

To account for variations in institutional contexts and technological pathways across industries, as well as the evolving macroeconomic policies and capital market preferences over time, this model incorporates both industry and year fixed effects, resulting in a two-way fixed effects specification. To ensure robust inference, cluster-robust standard errors are employed at the city level to address potential biases arising from regional heterogeneity.

This model serves as a foundation for empirically examining the direction and significance of green transformation’s impact on corporate ESG performance. Furthermore, it provides a basis for subsequent mechanism tests and analyses of heterogeneity.

4. Empirical Analysis

4.1. Descriptive Statistical Analysis

As shown in

Table 3. Descriptive Statistics of Variables.

Variable	Obs.	Mean	Std. Dev.	Min	Max
ESG	25,721	4.097	0.918	1.000	7.750
GT	25,721	0.325	0.047	0.019	0.577
Age	25,721	9.814	7.260	1.000	33.000
FAR	25,721	22.380	13.670	0.000	87.240
CAR	25,721	17.08	13.76	0.0101	95.46
DR	25,721	39.090	19.920	0.708	252.900
ROA	25,721	4.027	7.710	−274.600	78.590
LSO	25,721	33.620	14.310	1.844	89.990
IDR	25,721	37.560	5.541	0.000	80.000
RGR	25,721	0.593	30.040	−3.628	4500.000

, the descriptive statistics reveal that the mean value of corporate ESG performance (ESG) is 4.097, with a standard deviation of 0.918, and values ranging from 1 to 7.750. These findings indicate significant heterogeneity in environmental, social, and governance performance among firms. While some enterprises have established well-developed ESG management systems and achieved higher scores, others have yet to implement systematic sustainability mechanisms.

The corporate green transformation index (GT) exhibits a mean of 0.325 and a standard deviation of 0.047, with values spanning from 0.0185 to 0.577. This suggests that most manufacturing firms are still at a relatively nascent stage of green transformation, underscoring considerable potential for industry-wide enhancement. The observed variation further reflects differing levels of progress among firms in adopting green strategies, implementing clean technologies, and developing effective environmental management systems.

4.2. Analysis of Baseline Regression Results

Table 4. Baseline Regression Results.

Variable	(1)	(2)	(3)	(4)	(5)
	ESG	ESG	ESG	ESG	ESG
GT	3.695 ***	3.909 ***	3.375 ***	3.072 ***	2.846 ***
	(18.581)	(21.517)	(16.052)	(17.260)	(14.029)
Age			−0.008 ***		−0.007 ***
			(−3.190)		(−2.981)
FAR			0.003 ***		0.004 ***
			(3.120)		(3.646)
CAR			0.004 ***		0.003 ***
			(4.751)		(4.310)
DR			−0.005 ***		−0.003 ***
			(−7.164)		(−3.858)
ROA				0.023 ***	0.020 ***
				(14.909)	(14.363)
LSO				0.004 ***	0.003 ***
				(4.448)	(3.825)
IDR				0.011 ***	0.011 ***
				(6.218)	(5.956)
RGR				−0.001 ***	−0.001 ***
				(−2.968)	(−2.782)
_cons	2.897 ***	2.602 ***	2.936 ***	2.242 ***	2.378 ***
	(39.929)	(23.775)	(22.208)	(17.464)	(15.599)
Year Fixed Effects	No	Yes	Yes	Yes	Yes
Industry Fixed Effects	No	Yes	Yes	Yes	Yes
N	25,721	25,721	25,721	25,721	25,721
${\mathrm{R}}^2$	0.037	0.062	0.088	0.108	0.119

Note: *** indicate significance at the 1% level. Robust t-statistics clustered at the city level are reported in parentheses. The same applies to subsequent tables.

presents the baseline regression results that investigate the impact of green transformation on corporate ESG performance. Overall, the degree of green transformation shows a consistently positive and highly significant association across all model specifications, with coefficients remaining statistically significant at the 1% level. Although there are marginal changes in the estimated coefficients following the inclusion of year fixed effects, industry fixed effects, and the sequential introduction of control variables, the positive association remains both significantly robust and directionally stable. These findings indicate that green transformation has a statistically significant and positive influence on ESG performance among manufacturing enterprises. This result persists even after controlling for firm-level characteristics and accounting for unobserved heterogeneity through fixed effects, thereby providing strong support for Hypothesis 1.

4.3. Robustness Tests

4.3.1. Alternative Measure of the Dependent Variable

To further validate the robustness of the baseline regression results, this study acknowledges that the dependent variable—corporate ESG performance—utilized in the baseline regression is derived from the annual average of enterprises. This approach may be susceptible to extreme values or skewed distributions within the sample. To assess whether the regression outcomes are sensitive to different methods of measuring central tendency in the data, this study substitutes the annual average ESG value with the annual median ESG value and re-conducts the regression analysis.

Using the median can more effectively mitigate the influence of a few extreme values on regression results. Particularly in samples where ESG scores exhibit asymmetrical distribution or deviate from normality assumptions, using median as a representative statistic can provide a more accurate reflection of enterprises’ typical ESG levels. Column 1 of

Table 5. Robustness Tests.

	(1)	(2)	(3)	(4)
	ESG_Medain	SD	ESG	ESG
GT	2.806 ***
	(13.797)
GT		2.768 ***
		(6.810)
GTP			0.004 **
			(2.444)
GT				2.918 ***
				(11.201)
_cons	2.490 ***		4.086 ***	2.288 ***
	(16.119)		(206.190)	(12.846)
Control Variables	Control		Control	Control
Year Fixed Effects	Yes		Yes	Yes
Industry Fixed Effects	Yes		Yes	Yes
N	25,721		25,721	17,274
${\mathrm{R}}^2$	0.106		0.056	0.122

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively. Robust t-statistics clustered at the city level are reported in parentheses.

presents these revised regression results: after substituting the dependent variable (corporate ESG score) with its annual median counterpart, it is observed that the coefficient for green transformation’s impact on corporate ESG performance remains positive and robust at a significance level of 1%, thereby affirming that our conclusions remain unchanged.

To further validate the reliability of the baseline regression results, this study conducts a robustness test by substituting the dependent variable with the SynTao Green Finance (SD) ESG score. As a prominent domestic provider of ESG data services, SynTao Green Finance’s scoring system is constructed based on multiple dimensions, including the quality of ESG information disclosure and performance on material issues, encompassing all A-share industries. It enjoys high recognition in both academic research and market practice, serving as an effective complement to the ESG indicator utilized in the baseline regression.

Given that differences in indicator weights and data sources can lead to variability among ESG scores from different agencies, replacing our original indicator with the SynTao Green Finance score allows us to examine whether the positive effect of green transformation on ESG performance is contingent upon specific measurement methods. If our findings remain robust under this substitution, it would suggest that our conclusions are not unduly influenced by any single scoring system.

Column 2 of Table 5 illustrates that the coefficient for green transformation (GT) remains consistently significantly positive at the 1% level. This demonstrates that the beneficial impact of green transformation on ESG performance—when measured using SynTao Green Finance scores—persists robustly across different evaluation frameworks (sample firms located in Beijing, Shanghai and Shenzhen, China).

4.3.2. Alternative Measure of the Core Explanatory Variable

Green patents serve as a crucial indicator of a firm’s accumulation of green technologies and innovation capabilities, objectively reflecting substantive innovative actions during the process of green transformation. They have been widely recognized as a proxy for corporate green transformation. Following the methodology established by Wang Xiaoqi and Ning Jinhui (2020) [35], this study assesses the sensitivity of the estimated effect to alternative variable specifications by employing the number of green patent applications (GTP) as an alternative proxy for green transformation in lieu of a comprehensive index.

As demonstrated in Column (2) of Table 5, the number of green patents continues to exhibit a statistically significant and positive association with ESG performance, with the coefficient remaining robustly significant at the 1% level. This finding confirms that even when utilizing a single, more quantifiable measure of green innovation instead of a comprehensive index, the positive impact of green transformation on ESG performance persists, thereby supporting the robustness of our conclusions.

4.3.3. Altering the Sample Period

Considering the significant disruptions to the business environment, policy implementation, and ESG evaluation systems caused by the COVID-19 pandemic from 2020 to 2022—particularly impacting manufacturing firms in terms of production operations, environmental management, and disclosure practices—it is plausible that data from this period may be subject to unique shocks. To mitigate potential distortions, we exclude observations from 2020 to 2022 and retain only the sample from 2009 to 2019 for re-estimation. The results presented in Column (4) of Table 5 indicate that the positive impact of green transformation on ESG performance remains statistically significant even after excluding the pandemic period, thereby confirming the robustness of our findings.

4.4. Endogeneity Issues

Although the empirical analysis above has initially verified the positive impact of green transformation on corporate ESG performance, it may still face endogeneity risks, mainly including three aspects: first, reverse causality—enterprises with good ESG performance may be more proactive in promoting green transformation; second, omitted variables -unobservable factors such as managers’ environmental awareness and corporate culture may affect both simultaneously; third, sample selection bias—the inherent attributes of enterprises engaging in green transformation may be correlated with ESG performance. To accurately identify the causal effect, this study employs the instrumental variable (IV) method and entropy balancing matching method for verification.

4.4.1. Instrumental Variable Method

The National Development and Reform Commission (NDRC) has issued a Notice regarding the implementation of pilot initiatives for low-carbon provinces and cities, designating an initial cohort comprising five low-carbon pilot provinces and eight low-carbon pilot cities: Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, and Baoding. As pioneering regions in national low-carbon development efforts, these pilot cities bear the significant responsibility of exploring innovative models for low-carbon development and formulating relevant policies.

Enterprises located in proximity to these pilot cities are likely to experience more pronounced policy demonstration effects and spillover benefits. This geographical advantage facilitates access to support for low-carbon technologies as well as green financial resources, thereby enhancing their motivation and capacity for transitioning towards greener practices. Consequently, we select “the minimum geographical distance from the enterprise’s location city to the first batch of low-carbon pilot cities” (DL) as our instrumental variable.

In terms of relevance, enterprises situated closer to the designated pilot cities are more significantly influenced by policy demonstration effects and resource spillovers; this relationship suggests a negative correlation between distance from these cities and motivation for green transformation. Regarding exogeneity, geographical location is shaped by historical factors that do not directly correlate with individual enterprises’ ESG performance; rather it influences ESG outcomes indirectly through pathways related to green transformation—thereby satisfying the exclusion restriction criterion.

Column (1) of

Table 6. Regression Results of the Instrumental Variable Method.

Variables	(1) First Stage	(2) OLS	(3) IV-2SLS
	GT	ESG	ESG
GT		2.6497 *** (0.1207)	14.6542 *** (3.7231)
DL	−0.000005 *** (−6.13)
Control Variables	Yes	Yes	Yes
Year Fixed Effects	No	No	No
Industry Fixed Effects	No	No	No
Observations	25,721	25,721	25,721
R2	0.1066	0.0959	-
First-Stage F-Statistic	37.60 ***
Hausman χ2 Statistic			10.41 ***
Hausman Test p-Value			0.0013

Note: *** indicate significance at the 1% level. Robust t-statistics clustered at the city level are reported in parentheses.

presents the results of the first-stage regression analysis. The coefficient for the instrumental variable is reported as −0.000005, which is statistically significant at the 1% level. Additionally, the F-statistic reaches a value of 37.60, significantly exceeding the critical threshold. This finding confirms the strong relevance of the instrumental variable and indicates that there are no issues related to weak instruments.

In the second-stage regression, the coefficient for green transformation (GT) in relation to ESG performance is found to be 14.6542, also significant at the 1% level. This value is markedly higher than that obtained from baseline OLS estimation, suggesting a downward bias inherent in OLS estimates; thus, employing an instrumental variable approach provides a more accurate reflection of the true causal effect.

Furthermore, results from the Hausman test (χ² = 10.41, p = 0.0013) strongly reject the null hypothesis positing that green transformation operates as an exogenous variable, thereby confirming the presence of endogeneity. Correlation analyses reveal a significant negative correlation between the instrumental variable and green transformation while indicating only a weak correlation with ESG performance. When considered alongside theoretical arguments, it can be concluded that this instrumental variable meets both exogeneity and exclusion restrictions; its validity has been substantiated through these findings.

4.4.2. Entropy Balancing Matching Method

To address the self-selection bias stemming from observable characteristics, we employ the entropy balancing matching method as a supplementary analysis. Utilizing the median green transformation index as a threshold, we categorize the sample into two groups: a high green transformation group (treatment group) and a low green transformation group (control group). Key variables selected as covariates include firm size (TA), debt ratio (DR), return on assets (ROA), firm age (Age), and revenue growth rate (RGR).

Table 7. Balance Test Before and After Entropy Balancing Matching.

Variables	Before Matching			After Matching
	Treatment Group Mean	Control Group Mean	Standardized Bias (%)	Treatment Group Mean	Control Group Mean	Standardized Bias (%)
Size	13.290	12.242	98.29 ***	13.290	12.890	36.52
DR	43.343	34.867	43.55 ***	43.343	40.248	16.07
ROA	5.337	2.720	34.45 ***	5.337	5.139	3.03
Age	10.753	8.891	25.83 ***	10.753	9.210	21.66
RGR	0.821	0.361	1.53	0.821	0.547	0.88
Observations	12,926	12,925		12,926	12,925

Note: *** indicate significance at the 1% level. Robust t-statistics clustered at the city level are reported in parentheses.

illustrates that prior to matching, significant differences exist in covariates between the treatment and control groups. However, post-matching results indicate a substantial reduction in standardized bias across all covariates, thereby enhancing comparability between the groups. The regression outcomes presented in

Table 8. Comparison of Regression Results Across Different Methods.

Variables	(1) OLS	(2) Instrumental Variable	(3) IV-2SLS
GT	1.5558 *** (0.1105)	2.2853 *** (0.1052)	14.6542 *** (3.7231)
Control Variables	Yes	Yes	Yes
Year Fixed Effects	No	No	No
Industry Fixed Effects	No	No	No
Observations	25,721	25,721	25,721
R2	0.096	-	-
Matching Method	None	Entropy Balancing Weighting	Instrumental Variable

Note: *** indicate significance at the 1% level. Robust t-statistics clustered at the city level are reported in parentheses.

show a green transformation coefficient of 2.2853 (significant at the 1% level), which exceeds the baseline OLS estimate. This finding corroborates that after mitigating self-selection bias, the positive impact of green transformation becomes more pronounced.

4.4.3. Results Summary

Table 8 demonstrates that both methodologies substantiate the significant positive impact of green transformation on ESG performance at the 1% significance level, exhibiting a high degree of consistency in direction. The instrumental variable approach effectively addresses unobservable factors and reverse causality, while the entropy balancing method alleviates self-selection bias stemming from observable characteristics. Collectively, these methods validate the robustness of the baseline regression findings, indicating that the promoting effect of green transformation on ESG performance is not attributable to endogeneity issues and that the causal relationship is reliable.

Although there are numerical differences among the results obtained from three methods (baseline OLS, instrumental variable, and entropy balancing), all coefficients remain significantly positive at the 1% level, with their magnitudes aligning consistently with theoretical expectations. This consistency illustrates that, irrespective of the employed methodology, the promoting effect of green transformation on ESG performance is both significant and robust.

From a policy perspective, even under conservative OLS estimation conditions, the positive impact of green transformation remains noteworthy; conversely, instrumental variable estimation suggests a potentially larger effect under ideal circumstances.

4.5. Heterogeneity Analysis

4.5.1. Heterogeneity Test Based on Property Rights

Enterprise ownership may significantly moderate the relationship between green transformation initiatives and ESG performance. Compared to non-state-owned enterprises (non-SOEs), state-owned enterprises (SOEs) are generally subject to more stringent policy pressures, regulatory expectations, clearer political accountability, and stricter disclosure requirements. These factors make SOEs more proactive in responding to green development policies and fulfilling their social responsibilities. Consequently, the type of ownership may result in notable differences in the marginal effect of green transformation on ESG performance.

To formally test the differences in the impact of green transformation on ESG performance between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs), this study employs two complementary methods: the interaction term model and the Chow test.

The specification of the interaction term model is as follows:

$${ESG}_{\mathrm{it}}={\beta }_0+{\beta }_1{GT}_{it}+{\beta }_2{SOE}_i+{\beta }_3({GT}_{it}*{SOE}_i)+\gamma \prime X_{it}+{\alpha }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

Among the variables, ${SOE}_i$ is a dummy variable for state-owned enterprises (SOEs), and the interaction term coefficient ${\beta }_3$ represents the differential effect of SOEs relative to non-state-owned enterprises (non-SOEs).

As shown in

Table 9. Property Rights Heterogeneity: Interaction Term Model.

Variable	ESG
GT	2.149 ***
	(0.318)
SOE	0.127 ***
	(0.023)
GT × SOE	2.592 ***
	(0.412)
Control Variables	Yes
Year FE	Yes
Industry FE	Yes
N	25,721
R2	0.6834

Note: *** indicate significance at the 1% level. Robust t-statistics clustered at the city level are reported in parentheses.

, the coefficient of the interaction term is 2.592, which is significantly positive at the 1% level. This finding indicates that the marginal effect of green transformation on ESG performance is significantly stronger in state-owned enterprises (SOEs), thereby providing formal statistical evidence to support our preliminary conclusions. Due to their heightened policy orientation, regulatory constraints, and stakeholder expectations, SOEs derive greater ESG benefits from green transformation.

To further validate this heterogeneity, we employ the Chow test. By comparing the residual sum of squares between grouped regressions (SOEs versus non-SOEs) and a pooled regression model, we obtain an F-statistic of 18.73 for the Chow test, which strongly rejects the null hypothesis that coefficients are equal across both groups. Additionally,

Table 10. Property Rights Heterogeneity: Chow Test.

	Full Sample	State-Owned Enterprises (SOEs)	Non-State-Owned Enterprises (Non-SOEs)
	ESG	ESG	ESG
GT	2.848 ***	4.741 ***	2.149 ***
	(14.066)	(6.242)	(12.628)
_cons	2.379 ***	1.670 ***	2.857 ***
	(15.620)	(4.871)	(16.971)
Control Variables	Control	Control	Control
Year Fixed Effects	Yes	Yes	Yes
Industry Fixed Effects	Yes	Yes	Yes
N	25,721	7110	18,483
${\mathrm{R}}^2$	0.119	0.119	0.150

Chow Test: F = 18.73 ***, p < 0.001.

reveals that the coefficient for green transformation (GT) stands at 4.741 for SOEs and at 2.149 for non-SOEs; this difference is statistically significant.

The regression results indicate that while green transformation has a significantly positive impact on ESG performance within the full sample, it also shows a strong positive effect in both SOE and non-SOE subsamples. This suggests that green transformation can effectively enhance ESG scores across enterprises with varying property rights types. A closer examination of coefficient differences shows that the impact coefficient associated with green transformation is substantially higher for SOEs compared to non-SOEs, indicating that its promoting effect on ESG performance is more pronounced within SOEs.

This outcome arises from the superior capacity of State-Owned Enterprises (SOEs) to incorporate green transformation objectives into their corporate strategies and execute them effectively. This capability is largely driven by policy orientation, regulatory pressures, and constraints related to external reputation. Furthermore, SOEs exhibit a higher degree of institutionalization in areas such as green technology investment, environmental protection infrastructure development, and environmental information disclosure. As a result, their achievements in green transformation are more readily reflected within Environmental, Social, and Governance (ESG) evaluation frameworks.

In contrast, while non-SOEs benefit from greater operational flexibility and innovation efficiency, they face limitations imposed by resource availability and institutional incentives regarding green governance. Consequently, this leads to a comparatively weaker impact of green transformation on ESG performance among non-SOEs.

4.5.2. Heterogeneity Test Based on Geographic Location

Given China’s vast territory, significant regional disparities exist in economic foundations, industrial structures, policy environments, and priorities for green development. To further investigate the regional heterogeneity in the relationship between green transformation and corporate ESG performance, this study categorizes the country into four major economic regions—Eastern, Central, Western, and Northeastern China—based on the regional classification outlined in key policy documents such as the Several Opinions of the CPC Central Committee and the State Council on Promoting the Rise of Central China and the Implementation Opinions of the State Council on Several Policy Measures for Western Development Drive. Regression analyses were conducted not only on a comprehensive national sample but also on each of these regional subsamples.

We apply the same approach—using an interaction term model and Chow test—to examine regional heterogeneity. As shown in

Table 11. Geographical Heterogeneity: Interaction Model.

Variable	ESG
GT	3.523 ***
	(0.387)
Eastern	−0.089 *
	(0.048)
GT × Eastern	−1.234 **
	(0.542)
Control Variables	Yes
Year FE	Yes
Industry FE	Yes
N	25,721
R2	0.6812

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Robust t-statistics clustered at the city level are reported in parentheses.

, the coefficient of the interaction term $GT\times E\mathrm{a}stern$ is −1.234, which is significantly negative at the 5% level ($p=0.023$). This finding suggests that, compared to enterprises located in central, western, and northeastern regions, those in eastern China experience smaller marginal ESG benefits from green transformation. This disparity may arise because firms in less developed regions face stronger policy pressure and begin from a lower baseline in green development, allowing them to achieve more pronounced gains from transformation efforts.

The Chow test further substantiates the statistical significance of regional differences.

Table 12. Geographical Heterogeneity: Chow Test.

	National Level	Eastern China	Central China	Western China	Northeastern China
	ESG	ESG	ESG	ESG	ESG
GT	2.848 ***	2.612 ***	2.649 ***	4.050 ***	3.589 ***
	(14.066)	(9.633)	(7.935)	(5.083)	(4.340)
_cons	2.379 ***	2.266 ***	2.794 ***	2.143 ***	2.056 ***
	(15.620)	(11.163)	(9.195)	(4.610)	(4.114)
Control Variables	Control	Control	Control	Control	Control
Year Fixed Effects	Yes	Yes	Yes	Yes	Yes
Industry Fixed Effects	Yes	Yes	Yes	Yes	Yes
N	25,721	17,626	3725	3330	1046
${\mathrm{R}}^2$	0.119	0.113	0.117	0.180	0.186

Chow Tests: Eastern vs. Western F = 12.45 ***, Eastern vs. Northeastern F = 8.32, Eastern vs. Central F = 0.08. *** indicate significance at the 1% level.

illustrates that there are notable variations in the green transformation coefficients across the four regions: eastern China (2.612), central China (2.649), western China (4.050), and northeastern China (3.589). The results of the pairwise Chow tests are as follows: Eastern vs. Western ($F=12.45$, $p<0.001$), Eastern vs. Northeastern ($F=8.32$, $p=0.004$), and Eastern vs. Central ($F=0.08$, $p=0.782$; insignificant).

This pattern reflects several underlying mechanisms. First, the heavier concentration of resource-intensive and polluting industries in Western and Northeastern China subjects firms there to stronger policy pressures and incentives for green development under the national ecological civilization strategy. As a result, enterprises in these regions exhibit greater initiative and responsiveness in their efforts toward green transformation, leading to more effective translation of environmental practices into improved ESG performance. Second, firms in the Central, Western, and Northeastern regions generally start from a lower baseline in green governance compared to their counterparts in Eastern China, where ESG management practices are more established. Consequently, green transformation efforts in these regions tend to produce more pronounced marginal improvements in ESG performance. Furthermore, local government guidance, along with incentive mechanisms and green financial support for sustainable economic development, demonstrate stronger effects in non-eastern regions. The driving effects of local government guidance, incentive mechanisms, and green financial support for the green economy are more evident in non-eastern regions.

In summary, the analysis of regional heterogeneity indicates that the impact of green transformation on ESG performance exhibits significant spatial variation; specifically, there is a more substantial marginal effect observed in less economically developed regions where policy guidance is comparatively stronger. These findings suggest that policymakers should adopt region-specific strategies tailored to promote both green transformation and ESG practices effectively. It is essential to enhance green financial support alongside capacity-building initiatives within Central, Western, and Northeastern China to facilitate synergistic improvements across sustainable corporate governance nationwide.

4.5.3. Heterogeneity Analysis Based on Firm Age

A Firm age, as a fundamental dimension characterizing organizational heterogeneity, has garnered significant attention regarding its moderating effect on the relationship between green transformation and ESG (Environmental, Social, and Governance) performance within the fields of strategic management and sustainable development research. The enhancement of ESG performance has emerged as a crucial pathway for enterprises to achieve long-term value growth while addressing stakeholder demands. Green transformation is a primary means for firms to integrate environmental responsibilities into their business operations; however, its implementation is often constrained by the internal structural characteristics of these organizations. Notable differences exist between young and mature enterprises in terms of resource endowments, organizational structure, and decision-making models. These disparities directly contribute to varied trends in promoting efficiency, resource integration capabilities, and levels of achievement in green transformation initiatives.

From the perspective of organizational behavior, young enterprises typically demonstrate greater organizational flexibility and adaptability to change. Being established for a shorter duration, these enterprises have not yet developed rigid operational routines or hierarchical management structures. They possess streamlined decision-making processes and exhibit quicker response times to shifts in the market environment and policy directions.

In the context of green transformation, young enterprises are not encumbered by existing technological path dependence or process inertia, allowing them to more seamlessly incorporate innovative initiatives—such as research and development in green technology, low-carbon production models, and environmental governance mechanisms—into their core business strategies. For instance, in emerging sectors like new energy and environmental conservation, young start-ups often construct their business models directly around principles of green development from the outset. This approach enables them to circumvent transformation costs associated with traditional capacity elimination and equipment upgrades that mature enterprises frequently encounter.

Moreover, the corporate culture within young enterprises tends to be more inclusive and innovative; employees generally exhibit a higher acceptance of concepts related to green development while facing less internal resistance regarding collaboration. This cultural dynamic facilitates the swift implementation of decisions pertinent to green transformation.

To accurately assess the moderating effect of firm age, this study categorizes the full sample of enterprises into “young firms” (those below the sample median age) and “mature firms” (those at or above the sample median age), based on the sample median. Empirical analysis is conducted through the construction of an interaction term model and a Chow test. The regression results from the interaction term model (

Table 13. Age Heterogeneity: Interaction Term Model.

Variable	ESG Performance
GT	2.549 ***
	(0.342)
Young Firm	0.067 *
	(0.035)
GT × Young Firm	0.876 **
	(0.406)
Control Variables	Yes
Year FE	Yes
Industry FE	Yes
N	25,721
R2	0.6789

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Robust t-statistics clustered at the city level are reported in parentheses.

) indicate that the coefficient for the interaction between green transformation (GT) and young firms is 0.876, which is significantly positive at the 5% statistical level (p = 0.031). This finding suggests that after controlling for other variables such as industry characteristics, firm size, and profitability, the positive impact of green transformation on ESG performance in young enterprises is significantly stronger than in mature enterprises. In other words, younger enterprises exhibit greater efficiency in translating green transformation efforts into enhanced ESG performance.

To further validate this conclusion’s robustness, this study employs a Chow test to examine structural differences in how green transformation impacts ESG performance across different age groups of enterprises (

Table 14. Age Heterogeneity: Chow Test.

Variable	Young Firms	Mature Firms
GT	3.425 ***	2.549 ***
	(0.487)	(0.342)
Control Variables	Yes	Yes
Year FE	Yes	Yes
Industry FE	Yes	Yes
N	12,856	12,767
R2	0.6712	0.6843

Chow Test: F = 6.84, p = 0.009. Note: *** indicate significance at the 1% level. Robust t-statistics clustered at the city level are reported in parentheses.

). The test results reveal that the coefficient for green transformation among young enterprises stands at 3.425, while it is 2.549 for mature enterprises. The difference between these two coefficients amounts to 0.876, with an F-statistic of 6.84 (p = 0.009), indicating significance at the 1% statistical level. This empirical result not only corroborates findings from the interaction term model but also clearly demonstrates that variations in green transformation effects attributable to heterogeneity in firm age are statistically significant, thereby ruling out potential confounding factors.

5. Moderating Mechanism Analysis

5.1. Financing Constraints

The financing environment plays a significant role in shaping both the formulation and implementation of corporate green transformation strategies. Manufacturing enterprises often require substantial capital to support innovations in green technology, the adoption of clean energy, and upgrades to environmental infrastructure. Consequently, financing constraints can critically impede the effectiveness of green transformation initiatives. These constraints may not only directly impact ESG performance but also moderate the relationship between green transformation efforts and ESG outcomes. Existing research suggests that under conditions of financial constraint, firms are likely to enhance external investor confidence—and potentially secure capital support—by improving their fulfillment of social responsibility commitments and increasing transparency through environmental information disclosure (Ye, 2021) [36]. This indicates that the financing environment functions not merely as an external limitation but also as a vital contextual factor influencing the efficiency of ESG performance transformations via green initiatives. To empirically investigate the moderating role of financing constraints, this study introduces the financing constraint index (SA) and constructs an interaction term between this measure and the degree of green transformation.

As presented in Column (1) of Table 7, the coefficient of the core interaction term ($GT\times SA$) is significantly positive at the 1% level, confirming that financing constraints play a positive moderating role in enhancing the relationship between green transformation and ESG performance within the manufacturing sector. As the degree of financing constraints intensifies, the promoting effect of green transformation on ESG performance becomes significantly stronger. Meanwhile, the standalone coefficient of SA is negative and significant, suggesting that, controlling for green transformation behavior, firms facing higher financing constraints generally exhibit lower ESG scores. This further implies that green transformation plays an even more critical role in enhancing performance under conditions of strong financing constraints.

These findings can be interpreted through multiple mechanisms. First, financially constrained firms rely more heavily on improving environmental performance and social responsibility to secure external support, such as government subsidies and green credit. Consequently, they exhibit stronger motivation and more proactive implementation of green transformation initiatives. Second, limited financing capacity forces enterprises to allocate scarce resources more efficiently, enhancing the marginal benefits of green investments and generating stronger momentum for ESG improvement. Furthermore, in contexts of financing constraints, green strategies serve as important signals of “high-quality governance,” helping firms enhance their overall reputation in capital markets and among the public.

In summary, the analysis of this moderating effect provides strong support for Hypothesis 2 proposed in this study. The findings not only highlight the differential impact of green transformation under varying financing conditions but also underscore the necessity for policymakers to address financing barriers and implement institutional guidance in a coordinated manner to promote green development within the manufacturing sector.

5.2. Firm Size

Firm size significantly influences resource endowment, organizational structure, and strategic capabilities during the process of green transformation. As a critical indicator of a firm’s resource base and market position, size also reflects its institutional capacity in environmental governance, information disclosure, and responsibility fulfillment. Larger firms typically enjoy advantages in capital access, technological resources, and institutional standardization, theoretically enabling them to advance green transformation more effectively and achieve favorable ESG evaluations. However, an increase in scale may also lead to more complex management structures, higher administrative costs, and greater path dependence—factors that could potentially impede the efficient implementation of green transformation strategies. Therefore, firm size may exert a non-neutral moderating effect on the relationship between green transformation and ESG performance.

To investigate this moderating mechanism, we introduce an interaction term between firm size (measured by total assets, TA) and the degree of green transformation in our regression analysis. As demonstrated in Column 2 of Table 7, the interaction term ($GT\times TA$) is significantly negative at the 1% level, indicating that firm size has a negative moderating effect on the relationship between green transformation and ESG performance. Specifically, as firm size increases, the marginal enhancing effect of green transformation on ESG performance gradually diminishes, thereby supporting Hypothesis 3 proposed in this study.

These findings suggest that within large manufacturing firms, green transformation may primarily serve as a means of institutional compliance or routine response mechanisms, resulting in a weaker marginal impact on ESG scores. In contrast, small and medium-sized enterprises (SMEs), which operate under resource constraints and face relatively weaker external oversight, often pursue green transformation with greater strategic intent and signaling motivations. This approach enables them to achieve more significant improvements in ESG performance. Furthermore, while SMEs are more likely to integrate green initiatives into their core business strategies, larger firms tend to compartmentalize or decentralize these efforts—thereby limiting their transformative impact on overall ESG outcomes.

5.3. Corporate Digital Transformation

Amid rapid advancements in next-generation information technologies, digital transformation is significantly reshaping the organizational structures, production modes, and strategic directions of manufacturing enterprises. In the context of increasingly emphasized principles of green development, corporate digital capabilities not only influence responsiveness to environmental policies but also provide essential data support and pathways for process optimization in implementing green strategies. Emerging digital tools—including big data, artificial intelligence, cloud computing, and the Internet of Things—enhance resource allocation efficiency and streamline environmental management processes. This provides a theoretical foundation for understanding how green transformation can enhance corporate ESG performance.

To investigate whether corporate digital transformation moderates the relationship between green transformation and ESG performance, this study adopts the measurement approach proposed by Zhao Chenyu et al. (2021) [37] to construct a firm-level digitalization indicator (DIG) by extracting digitally related keywords from corporate annual reports. An interaction term between green transformation and digital transformation is introduced for empirical testing. As shown in Column 3 of

Table 15. Mechanism Analysis Results.

	(1)	(2)	(3)
	ESG	ESG	ESG
SA	−0.718 ***
	(−3.157)
GT × SA	1.807 ***
	(2.832)
TA		0.003 ***
		(3.601)
GT × TA		−0.006 ***
		(−3.119)
DIG			0.172 ***
			(5.099)
GT × DIG			−0.219 **
			(−2.105)
_cons	5.015 ***	2.657 ***	2.166 ***
	(6.059)	(18.309)	(13.094)
Control variables	Control	Control	Control
Year fixed effects	Yes	Yes	Yes
Industry fixed effects	Yes	Yes	Yes
N	25,721	25,721	25,721
${\mathrm{R}}^2$	0.120	0.143	0.131

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively. Robust t-statistics clustered at the city level are reported in parentheses.

, digital transformation significantly improves ESG performance, consistent with digital tools’ expected role in enhancing transparency and greening processes. However, the interaction term ($GT\times DIG$) is significantly negative ($\mathrm{coe}fficient=-0.219$), indicating that digital transformation negatively moderates the effect of green transformation on ESG performance.

This result suggests a potential strategic misalignment between digital and green initiatives. This may occur because, as firms advance digitally, they may over-invest in infrastructure and information systems, diverting attention and resources from green initiatives. Additionally, highly standardized digital management systems can constrain the flexibility needed for adaptive green transformation, reducing their effectiveness in improving ESG performance. Therefore, in highly digitalized contexts, the marginal effect of green transformation on ESG performance appears attenuated, supporting Hypothesis 4.

In summary, while digital transformation can indeed enhance overall corporate ESG performance, the relationship between digital and green transformations is not strictly complementary. In practice, if the developmental trajectories of these two transitions are not integrated, the marginal contributions of green strategies to the ESG framework may be diminished. Therefore, enterprises should prioritize synergistic alignment between their green and digital initiatives and establish governance systems that are digitally enabled with embedded sustainability objectives. Concurrently, policymakers should strengthen institutional coordination and improve the compatibility of evaluation metrics to ensure that environmental goals are not marginalized within digital performance management systems.

6. Conclusions

6.1. Research Conclusions

Based on panel data from Chinese A-share manufacturing listed companies from 2009 to 2022, this study systematically examines the impact of corporate green transformation on ESG performance and its underlying mechanisms. The main findings are as follows:

First, green transformation significantly enhances corporate ESG performance in the manufacturing sector. This finding remains robust after a series of tests including alternative measures of the dependent variable, modified measurements of core explanatory variables, instrumental variable approaches, and propensity score matching difference-in-differences (PSM-DID) analyses. These results confirm that green transformation serves as an effective pathway for enterprises to achieve sustainable development.

Second, the positive impact of green transformation on ESG performance shows significant heterogeneity. In terms of ownership structure, the effect is more pronounced in state-owned enterprises. Geographically, it is particularly strong for firms in central, western, and northeastern China. Regarding firm age, younger enterprises benefit more substantially from green transformation than their mature counterparts. These patterns highlight how institutional environments and resource endowments shape the effectiveness of green strategies.

Third, mechanism analysis reveals distinct moderating effects of three key factors. Financing constraints positively moderate the green transformation-ESG relationship, indicating that financial pressures can strengthen internal motivations for green initiatives. Conversely, firm size exerts a negative moderating effect, reflecting challenges of organizational path dependence and structural complexity in larger firms. Although digital transformation generally improves overall ESG performance, it negatively moderates the relationship between green transformation and ESG performance, suggesting potential resource competition and attention allocation issues in pursuing dual transformation strategies.

Beyond the empirical findings above, this study makes three distinct contributions to the field of corporate green transformation and ESG performance. First, it addresses the long-standing measurement confusion in existing research by developing a comprehensive green transformation evaluation system. This system, covering technological innovation, production efficiency, and pollution reduction, uses objective data (e.g., pollution emission equivalents) to replace indicators that may overlap with ESG performance, thereby enhancing the accuracy and reliability of measurements. Second, it moves beyond the fragmented analysis of single moderating factors in prior studies, integrating financing constraints, firm size, and digital transformation into a unified framework. By uncovering the interactive effects among these factors—especially the “synergistic imbalance” between digital and green transformation—it enriches the theoretical understanding of how multiple contextual factors jointly shape the impact of green transformation. Third, it responds to the lack of context-specific insights in general studies by focusing on China’s manufacturing sector. The findings on heterogeneous effects across ownership types, regions, and firm ages provide a more nuanced understanding of the boundary conditions for green transformation’s effectiveness, avoiding the “one-size-fits-all” limitation of existing conclusions.

6.2. Policy Implications

Based on the research findings, this study proposes the following policy recommendations:

For government departments, it is essential to establish a collaborative mechanism that integrates green transformation with ESG evaluation. This involves incorporating the effectiveness of corporate green transformation into the ESG rating indicator system and enhancing market recognition and value guidance for environmentally sustainable practices. Additionally, there is a need to improve the green financial support framework, encouraging enterprises to proactively enhance their sustainable development capabilities through an “ESG performance-financing cost linkage mechanism.” Furthermore, differentiated regional green policies should be implemented to increase fiscal, technological, and financial support for central, western, and northeastern China while fully leveraging late-development advantages.

For enterprises, large organizations should prioritize overcoming organizational inertia by optimizing governance structures to enhance the implementation efficiency of green strategies. In contrast, small and medium-sized enterprises (SMEs) should capitalize on their inherent organizational flexibility by integrating green transformation into their core competitiveness development. More importantly, businesses must focus on systematically integrating digital and green strategies; embedding environmental objectives within digital platforms and business processes will help avoid resource dispersion and goal conflicts during dual transformations. This approach aims to achieve synergistic value addition from “digital + green” initiatives.

6.3. Research Limitations and Future Prospects

This study presents several limitations that highlight potential directions for future research:

First, concerning measurement validity, this study employs the Huazheng ESG Rating as a proxy variable for corporate ESG performance. While this indicator is widely utilized in academic research, variations in evaluation standards among different rating agencies may influence the consistency of the rating outcomes. Furthermore, the green transformation indicator is derived from text analysis of corporate annual reports; thus, the inherent subjectivity and selectivity involved in text disclosure may compromise measurement accuracy.

Second, with respect to sample representativeness, this study concentrates on manufacturing listed companies within Shanghai and Shenzhen A-shares while excluding a substantial number of small and medium-sized unlisted enterprises. These unlisted entities often face significantly different resource constraints and institutional environments compared to their listed counterparts, which restricts the generalizability of our findings. Future research could enhance representativeness by expanding the sample to include New Third Board enterprises or incorporating unlisted firms through survey methodologies.

Third, regarding the generalizability of conclusions, this study is conducted within the specific context of China’s institutional framework. The findings are influenced by China’s unique policy environment, regulatory system, market structure, and property rights regime; consequently, direct extrapolation to other countries or regions poses challenges. Future investigations might consider cross-country comparative studies or focus on comparative analyses among emerging economies to assess the external validity of these conclusions.

In addition, this study primarily employs panel regression analysis. While endogeneity has been addressed, there remains potential for enhancing the identification of causal mechanisms; furthermore, the differentiated impacts on each sub-dimension of ESG have not been thoroughly examined. In future research, quasi-natural experiments and structural equation models could be utilized to accurately elucidate the causal transmission pathways, or a more in-depth analysis could be conducted regarding the impact and mechanisms of green transformation on the environmental, social, and governance sub-dimensions of ESG. Meanwhile, this study focuses on verifying the moderating effects of financing constraints, firm size, and digital transformation, but fails to delve into the potential transmission path of “green transformation → financial market feedback → environmental information disclosure.” As core participants in the capital market, the management decisions of Chinese A-share listed companies are not only driven by profit objectives but also highly concerned with stock price performance and market valuation (such as changes in investor sentiment and analyst ratings). During the process of green transformation, the valuation signals or risk pricing signals transmitted by the financial market may indirectly affect the information quality dimension of ESG performance by influencing the management’s motivation for environmental information disclosure. The absence of this pathway renders the explanation of the micro-mechanisms through which green transformation impacts ESG performance in this study incomplete, failing to fully cover the linkage logic between corporate strategies and information behaviors in the capital market context.