Trap Behind Triumph: Attribution and Formation Pathway Exploration of Corporate ESG’s Dilemmas

Abstract

As a new performance evaluation system, ESG has garnered significant goodwill and tax benefits for a set of benchmark enterprises through its forward-looking corporate values and overall enhancement of public trust. However, as more companies pay attention and invest more in ESG, the pursuit of these ratings also entails increasing costs. Whether the impressive upward trend in ESG ratings genuinely enriches and enhances a company’s reputation, or if the ratings driven by ESG costs are unsustainable over the long term, remains uncertain. In the pursuit of sustainable development, several enterprises may find themselves in a predicament where ESG ratings are on the rise while corporate performance is declining. This paper selects listed companies in China’s petrochemical industry, which exhibit the distinctive characteristics of ESG and corporate performance divergence, as its research sample. It aims to identify the quantitative features of this performance–ESG divergence dilemma and empirically uncover its causes and development pathways. The findings of this research will guide enterprises back to the path of ESG alignment, providing a theoretical foundation for ensuring that companies adhere to a high-quality ESG development path. Furthermore, it offers insights into addressing the gaps in the rating system behind the phenomenon of inflated ESG scores and presents policy-oriented perspectives to help enterprises avoid the pitfalls mentioned above.

1. Introduction

ESG performance has emerged as a pivotal measure for assessing the comprehensive effectiveness and sustainable development capabilities of enterprises [1]. A high ESG rating not only enhances a company’s social standing and bolsters investor and consumer trust but also translates to lower financing costs, higher market valuations, and amplified business growth opportunities. As a result, many companies are making concerted efforts to elevate their ESG ratings, creating a symbiotic relationship between ESG performance and corporate performance. However, while some enterprises witness an ongoing ascend in their ESG ratings, they often fixate on superficial ESG metrics and incur substantial expenses, neglecting the fundamental elements that underpin long-term enterprise development, such as technological innovation, product quality, and strategic market positioning. This approach ultimately leads to a decline in enterprise performance and undermines its enduring competitiveness and sustainable development potential [2]. Therefore, this study selects the petrochemical industry, a sector notorious for high pollution and energy consumption, yet highly responsible for environmental and social matters, as a focal point for in-depth exploration [3]. With the 10th company in the attached data package (a collection of ESG ratings for listed companies in the petrochemical industry) as a case study illustrating the “ESG trap” (as shown in Figure 1), the company’s ESG ratings and sales have climbed steadily, while corporate performance and profitability have waned. Consequently, this paper defines this counterintuitive phenomenon as the “ESG trap”.

The ESG trap refers to the predicament where an enterprise’s performance deviates from its ESG standards. That is, the ESG performance of the enterprise steadily improves, but the enterprise’s performance keeps declining. Some enterprises, in the process of striving for an increase in their ESG ratings, focus on making superficial efforts in ESG and invest a large amount of costs, such as large-scale charitable donations, formal environmental protection publicity, creating fictitious ESG positions to meet the rating requirements, and allocating a lot of resources to formal information disclosure. Instead, they neglect the core elements for the long-term development of the enterprise, such as technological innovation, product quality, and market strategy. These investments are difficult to be effectively transformed into core competitiveness. This kind of “throwing the baby out with the bathwater” approach occupies the space for the allocation of key resources, making the product and service quality unable to meet the increasingly growing demands of consumers. The enterprise gradually loses its technological leading edge in the market competition and ultimately leads to a decline in its performance and undermines its sustainable development ability. The predicament of the deviation between enterprise performance and ESG not only reflects the trade-off and conflict between economic interests and social responsibility of the enterprise, but also reveals the structural imbalance in the strategic layout of the enterprise. The ESG trap is conceptually distinct from related ideas. It differs from greenwashing, which involves misrepresentation or exaggeration of ESG efforts, in that the ESG trap concerns the misalignment between actual ESG investments and the firm’s core performance outcomes rather than deceptive disclosure. It also differs from ESG–performance trade-offs or ESG overinvestment, as these concepts focus on general resource allocation conflicts, while the ESG trap emphasizes situations where ESG improvements absorb resources without producing proportional performance gains. Furthermore, it is distinct from ESG rating divergence, which highlights discrepancies between ratings from different agencies; the ESG trap specifically addresses the internal disconnect between rising ESG ratings and declining corporate performance. The petrochemical industry, as a typical industry with high pollution and high energy consumption, faces strict environmental compliance requirements. Enterprises must invest a large amount of rigid capital in upgrading environmental protection equipment, reducing pollutants, and other hard expenditures. However, some petrochemical enterprises fail to formulate differentiated ESG strategies based on industry characteristics and instead fall into the shortsighted behavior of “emphasizing ratings but neglecting effectiveness”, making the predicament of the deviation between enterprise performance and ESG more prominent within the industry.

Based on this definition, the paper explores two key inquiries: “Does the concept of the ESG trap truly exist?” and “How is the ESG trap formed?” By analyzing medium and long-term statistics on ESG ratings and financial performance of listed companies in the petrochemical sector, it is observed that an increasing number of enterprises are experiencing a rise in their ESG ratings concurrent with a decline in their performance over time. As depicted in Figure 2, the phenomenon of the ESG trap within the petrochemical industry has witnessed a significant increase in recent years. From 2018 to 2023, the deviation between enterprise performance and ESG performance in the petrochemical industry is relatively common, and the proportion of enterprises falling into the ESG trap is as high as 34.41%, which is significantly higher than that of other industries in the same period. Consequently, this study concentrates on the petrochemical industry examining the ESG trap. Given the broad and diverse scope of activities within the petrochemical industry, to clarify the statistical categories, the paper strictly adheres to the “Guidelines on Industry Classification of Listed Companies” revised by the China Securities Regulatory Commission in 2012, covering sectors such as petroleum processing, coking, and nuclear fuel processing (C25), chemical raw materials and chemical products manufacturing (C26), and chemical fiber manufacturing (C28). The petrochemical industry, as an important sector of energy consumption and greenhouse gas emissions, has a profound impact on the environment. Despite the trend of the ESG trap phenomenon being highlighted in Figure 2 (where the data can be found in the Supplementary Materials, namely a excel tale: Table S1: Data on corporate performance and ESG from 2011 to 2023), the subsequent data analysis reveals a negative correlation between ESG ratings and enterprise performance, shedding light on the mechanisms that give rise to this phenomenon.

The structure of this paper is as follows: Section 2 reviews the literature on factors influencing ESG performance and their economic consequences; Section 3 proposes a hypothesis regarding the pathways through which ESG trap form; Section 4 quantifies and attributes ESG trap; and Section 5 establishes the formation pathways of ESG and their sub-pathways through empirical testing. Section 4 and Section 5 collectively address two critical scientific questions: ‘Does the defined ESG trap exist?’ and ‘How is the ESG trap formed?’ Section 6 proposes targeted solutions to resolve the dilemma of diverging corporate performance and ESG outcomes.

The main innovations of this paper are twofold. First, the research perspective is novel because it focuses on the phenomenon of ESG deviation from enterprise performance, capturing the distribution and trends of the ESG trap in the high-pollution, high-energy petrochemical sector and revealing cases where firms over-rate ESG achievements while neglecting effectiveness. This approach fills a gap in the literature and highlights the trade-offs enterprises face between economic performance and social responsibility. Second, the research approach is innovative by establishing the core mechanism linking ESG improvement to performance decline, exploring sub-pathways, and systematically analyzing the mediating effects of research and development intensity and financing constraints as well as the moderating effects of risk-taking and competitive position, providing a comprehensive framework for understanding ESG–performance deviation.

2. Literature Review

The literature pertinent to this study is primarily bifurcated into two domains: the first explores the factors influencing the ESG performance of corporations, while the second delves into the economic outcomes stemming from ESG performance. At the macroenvironmental level, factors such as the robustness of the legal framework, the extent of corruption, and the strength of the labor protection system have been found to significantly impact the ESG performance of corporations [4]. Yu’s work highlights the issue of “greenwashing” in ESG disclosures, underscoring the critical importance of transparent and accurate reporting practices [5]. A nation’s broader governance context can exert influence over a firm’s ESG practices, as Moone Eapen’s study demonstrates that companies in countries with lower levels of democracy and political stability tend to exhibit better ESG performance, suggesting that a country’s governance framework can shape firms’ ESG behaviors [6]. His research reveals that the implementation of the Environmental Protection Tax Law has a positive effect on corporate ESG performance by encouraging businesses to increase labor and employment [7]. At the corporate governance level, the size of the enterprise emerges as the pivotal internal driving force affecting ESG performance. Large enterprises typically possess more resources to obtain ESG data, thereby facilitating the enhancement of corporate ESG performance [8,9,10]. Institutional investors can effectively mitigate potential agency conflicts by compelling enterprises to enhance the transparency of ESG information [11,12]. Furthermore, positive public sentiment can elevate the valuation premium of firms with commendable sustainability performance, indicating that investor perceptions and sentiment play a crucial role in propelling firms’ financial performance based on ESG practices [13]. Wang and Lu’s findings suggest that digital transformation can significantly improve the ESG performance of corporations [14,15].

At the same time, research into the economic consequences of firm ESG performance has made significant progress. At the level of corporate performance, most studies suggest that robust ESG performance can curb short-term opportunistic behavior within firms, thereby bolstering overall corporate performance [16]. Zhang’s analysis of the impact of ESG performance on the financialization of Chinese enterprises points to the necessity of implementing stringent regulations on ESG activities and investments in financial assets to ensure sustainable economic development [17]. Ahmad’s study reveals that enterprises with commendable ESG performance report higher excess returns and lower volatility [18]. Izek’s investigation into the link between ESG performance and economic growth in East Asia, the Pacific, and South Asia underscores the potential synergy between ESG performance and economic development objectives [19]. However, from the lens of agency cost theory, some scholars advocate the opposite viewpoint, asserting that ESG performance may engender agency cost issues, with executives compromising shareholder interests in pursuit of personal reputation, thereby adversely affecting corporate performance [20,21]. Chen’s examination of the relationship between ESG responsibility fulfillment and enterprise performance uncovers a “substitution effect” in the short term and a “promotion effect” in the long term [22]. At the enterprise risk level, Atif’s research demonstrates that strong ESG performance can markedly decrease default risk [23]. During the COVID-19 pandemic, companies with high ESG performance not only realized higher earnings growth but also effectively mitigated the downside risk of stock prices [24] and fund downside risk [25]. Luo’s study found that commendable ESG performance aids in reducing the stock price crash risk of Chinese listed companies [26].

The existing literature on ESG research is relatively abundant, with scholars having explored this topic in depth from multiple perspectives and drawn numerous valuable conclusions. However, despite the growing prominence of ESG issues, research into the pathways leading to the formation of an ESG trap remains scarce. Against this backdrop, this paper endeavors to investigate the pathways of ESG trap formation, thereby revealing the potential obstacles and pitfalls enterprises may encounter when advancing ESG strategies. Compared to existing research, this paper’s principal contribution lies in the following: Firstly, it provides an in-depth analysis of the pathways leading to ESG pitfalls, offering a novel analytical perspective for future research. This fills a gap in the field of ESG trap studies and provides significant insights for subsequent ESG research and corporate practice. Second, through empirical analysis, this paper proposes strategies and methodologies for navigating ESG pitfalls, offering robust support for enterprises to formulate sound strategies within complex and volatile environments. This not only deepens corporate understanding of risks and challenges in ESG implementation but also effectively assists enterprises in avoiding ESG pitfalls, thereby achieving sustainable development.

3. Research Hypotheses

3.1. Assumptions of ESG Trap

Based on sustainable development theory, enterprises must coordinate the synergistic advancement of economic, environmental and social dimensions. Enhancing ESG performance relies on sustained resource allocation across environmental, social and governance spheres. On the environmental front, investments in eco-friendly technologies and equipment facilitate compliant operations and green transformation. Socially, employee welfare and philanthropic contributions enhance belonging and corporate reputation. Governance improvements ensure lawful operations. However, such investments yield limited short-term cash flow returns, often diverting resources from production and market expansion, thereby constraining profitability. Under stakeholder theory, enterprises must address differentiated ESG demands from governments, employees, consumers and other parties. The cumulative effect of multiple investments significantly increases cost pressures. When limited resources are heavily allocated to ESG activities, investments in profitable projects diminish, intensifying resource allocation conflicts. Expanding debt to bridge funding gaps elevates financial leverage, exacerbating financial risks and undermining stable operations. Under agency theory, the separation of ownership and management rights may induce self-serving behavior among executives. To bolster professional reputations, management may engage in inefficient overinvestment in ESG projects, favoring high-visibility short-term actions like donations and publicity campaigns while neglecting technological innovation and core business development. This elevates agency costs, diverts critical resources, and ultimately stifles corporate performance improvement. Information asymmetry theory highlights barriers between enterprises and external stakeholders. Inadequate and opaque ESG disclosure makes it difficult for external parties to accurately gauge the true efficiency of ESG investments. This may lead to either underestimating long-term value-driven investments or overestimating purely formalistic efforts, causing market valuation distortions and further exacerbating the imbalance between ESG costs and corporate performance.

In this study, the ESG trap refers to a situation in which an increase in ESG-related expenditure improves ESG performance, disclosure quality, or external recognition, but the corresponding marginal economic benefit is insufficient to offset the current resource burden borne by the firm, so that ESG engagement is accompanied by weaker profitability or operating performance in the relevant period. In economic terms, this can be understood as a state in which the marginal ESG benefit exceeds zero, while the net marginal effect on firm performance is negative or too small to cover the cost of resource input under existing financial and operational constraints. Under this definition, donations, environmental investment, and ESG-related expenses are treated as costs because they all consume current corporate resources and enter the firm’s decision problem as expenditures. Donations involve direct resource outflows, environmental investment requires capital and operating commitments whose returns are often delayed, and ESG-related organizational, staffing, and compliance expenses similarly absorb cash flow, labor, and managerial attention. Therefore, these items are classified as ESG cost components in the economic sense of resource consumption, even though some of them may also create long-term strategic or reputational benefits.

Based on the above reasoning, the following hypothesis is proposed:

Hypotheses 1. 

The costs incurred in ESG rating have created an ESG trap, manifesting as an improvement in ESG performance that simultaneously constrains corporate performance.

3.2. Hypothesis of Moderating Effect

3.2.1. The Regulatory Effect of Risk-Taking Level

Based on stakeholder theory, enterprises with higher risk-taking levels often adopt aggressive business strategies during expansion, leading to an overall increase in operational risk. When external environments experience volatility, this can trigger crises of confidence among stakeholders such as investors, governments, consumers, and creditors. This subsequently leads to negative feedback loops including valuation downgrades, tighter regulation, and credit contraction, significantly amplifying the adverse impact of ESG costs on corporate performance. Concurrently, high-risk-taking firms typically operate with elevated financial leverage, where ESG investments further exacerbate financial pressures and complicated risk management. Based on agency theory, management in high-risk environments tends to favor high-risk, high-return projects, allocating resources without fully considering the firm’s risk tolerance. This exacerbates agency conflicts between shareholders and management. Such behavior increases operational costs and financial risks, triggers trust crises, and damages corporate reputation, further amplifying the performance-inhibiting effect of ESG costs.

Based on this reasoning, the following hypothesis is proposed:

Hypotheses 2a. 

A higher level of risk-taking significantly amplifies the negative impact of ESG costs on corporate performance.

3.2.2. The Moderating Effect of Enterprises’ Competitive Position

Based on stakeholder theory, enterprises with highly competitive standing possess profound brand influence and extensive market recognition. They have established stable trust relationships with core stakeholders such as creditors and investors, making the costs of ESG investments more readily identifiable and recognized by stakeholders. Through promotional efforts, companies can translate ESG investment costs into enhanced brand value, creating a differentiated competitive advantage that attracts more consumers and investors. This brand strength helps sustain or even elevate profit margins. Concurrently, firms with strong competitive positions possess robust profitability and resource allocation capabilities. Leveraging substantial capital and efficient resource distribution mechanisms, they can shorten value return cycles through optimized supply chain management and technological innovation, thereby mitigating the adverse impact of ESG costs on corporate performance. Moreover, enterprises with highly competitive standing typically possess more mature and forward-looking strategic planning capabilities. By leveraging economies of scale and scope, they reduce unit ESG costs through expanded production and sales volumes. This scale effect enables enterprises to maintain market competitiveness while actively fulfilling social responsibilities, further mitigating the negative impact of ESG costs on corporate performance.

Based on the above theoretical mechanism, the following hypothesis is proposed:

Hypotheses 2b. 

A stronger competitive position significantly mitigates the negative impact of ESG costs on corporate performance.

3.3. Hypothesis of Mediation Effect

3.3.1. The Mediating Effect of R&D Investment Intensity

Based on agency theory, when corporate resources are constrained, ESG performance—as a core metric for regulatory assessment and societal evaluation—enables management to build personal professional reputations through its high social visibility and strong public scrutiny. To mitigate agency risks, management tends to prioritize resource allocation towards ESG initiatives. As ESG investment costs rise, corporate resource allocation structures undergo adjustment. Substantial funds are directed towards optimizing environmental protection measures, enhancing social responsibility fulfilment capabilities, and refining corporate governance frameworks. However, this resource tilt diverts funds from other critical domains, notably resulting in a significant reduction in research and development (R&D) funding. Given R&D’s high investment risk, substantial uncertainty in returns, and extended payback periods, its value realization requires stable resource support. In decision-making trade-offs, management may even proactively reduce R&D budgets. This decision-making bias not only crowds out R&D investment but also delays or terminates R&D projects due to funding shortages. In a market environment characterized by rapid technological iteration, insufficient R&D investment prolongs product renewal cycles. This renders product functionality and quality inadequate to meet consumers’ escalating demands, thereby jeopardizing the enterprise’s technological leadership within the industry. Consequently, it places the enterprise at a competitive disadvantage, ultimately leading to a contraction in market share.

Based on this reasoning, the following hypothesis is proposed:

Hypotheses 3a. 

Increased corporate ESG costs have displaced research and development expenditure, consequently leading to a decline in corporate performance.

3.3.2. The Mediating Effect of Financing Constraints

Based on stakeholder theory, rising ESG costs intensify corporate cash flow pressures, leading financial institutions to classify them as financial risks. This subsequently raises credit thresholds, tightens lending conditions, exacerbates financing constraints for enterprises, and triggers market pessimism alongside share price volatility. Under the theory of information asymmetry, the complex composition of ESG costs and inadequate disclosure reinforce information barriers between enterprises and external investors. This leads to heightened investor risk premium demands and tighter credit and investment conditions, further elevating financing costs and narrowing funding avenues. ESG investments divert operational capital, diminishing corporate liquidity and debt-servicing capacity while transmitting negative market signals, ultimately suppressing business performance.

Based on this reasoning, the following hypothesis is proposed:

Hypotheses 3b. 

Increased ESG costs for enterprises will exacerbate financing constraints, thereby leading to a decline in corporate performance.

4. Quantification and Attribution of ESG Trap

4.1. Quantitative Features of ESG Trap

While certain enterprises are witnessing an upward trajectory in their ESG ratings, there’s a concerning trend of focusing primarily on surface-level ESG initiatives. These companies often allocate significant resources without effectively addressing the fundamental elements critical for enduring growth. This approach can inadvertently lead to a decline in corporate performance and the emergence of an ESG trap. This section primarily investigates the phenomenon of an ESG trap by scrutinizing the quantitative link between ESG performance and firm performance. This will be accomplished through a regression analysis, aimed at establishing a correlation between ESG performance and corporate performance.

4.1.1. Sample Selection and Data Source

This study focuses on the listed companies in the petrochemical industry in Shanghai and Shenzhen A-shares from 2018 to 2023. According to the “Guidelines for Industry Classification of Listed Companies” revised by the China Securities Regulatory Commission in 2012, it covers the petroleum processing, coking and nuclear fuel manufacturing industries (C25), chemical raw materials and chemical products manufacturing industry (C26), and chemical fiber manufacturing industry (C28). The sample selection criteria are as follows: the average ESG performance of the enterprises from 2022 to 2023 is higher than that from 2018 to 2019, and the average ESG performance from 2020 to 2021 is no more than 2 points lower than that from 2018 to 2019, without any significant decline. Such enterprises are considered to have an improved ESG performance; if the average enterprise performance from 2022 to 2023 is lower than that from 2018 to 2019, and at least one year from 2020 or 2021 is lower than 1.67 times the average from 2018 to 2019, without any abnormal high values, then the enterprise is considered to have a decline in enterprise performance. Enterprises that meet both conditions have the characteristics of improved ESG performance but a consecutive decline in enterprise performance. Finally, 148 enterprises were selected (as shown in Figure 3). All data are from the Guotai Analytics Database, Wind Database and China Research Data Service Platform. ST, *ST and PT stocks were excluded, and missing values (refer to [27,28,29] for alternative treatments of missing values) were filled using Microsoft Excel combined with VBA code. The empirical analysis was completed using Stata 17 statistical software. To eliminate the influence of extreme values, all continuous variables were truncated at the 1% and 99% levels (refer to [30,31,32] for similar approaches).

To further reveal the long-term development trend of the ESG trap, this paper uses panel data of corporate performance from 2018 to 2023 and applies a linear regression model to fit the historical data (refer to [33,34,35] for similar analysis), predicting the change trend of corporate performance during the period from 2024 to 2028. As shown in Figure 4, the prediction results indicate that as high as 85.81% of the sample enterprises will experience a decline in performance over the next five years. This result demonstrates that the ESG trap is not a short-term shock-induced temporary phenomenon, but has a certain degree of persistence, further highlighting the structural challenges that enterprises face in the process of sustainable development.

4.1.2. Variable Selection and Model Setting

(1) Explained Variables: Enterprise performance encompasses the achievements realized by a company through its production and operational endeavors during a particular timeframe. In accordance with the approach outlined by Wang Chao [36], this study employs return on total assets (Roa) as the principal gauge of enterprise performance in the benchmark regression model (see [37,38,39] for its applications with more details).

(2) Explanatory Variables: ESG, as a comprehensive investment concept and evaluation standard, aims to measure the sustainable development capabilities of enterprises. Various professional institutions have successively launched ESG rating products. Not only international authoritative institutions such as Bloomberg, Thomson Reuters, KLD, and ASSET4 are involved, but also domestic institutions such as Huazheng and Shangdao Ronglv have actively participated in the assessment of enterprises’ ESG practices. The core working method of these rating institutions is to collect the financial data disclosed by enterprises and the relevant information provided by third parties, and then conduct quantitative scoring of the enterprises’ performance in the three dimensions of environment, society, and governance. On this basis, each institution assigns different weights to various indicators and comprehensively calculates the ESG score of the enterprise. Although the setting of indicator weights varies among different rating institutions, ESG rating has not yet formed a unified standard. However, in the subdivision of ESG quantitative indicators, the institutions tend to be consistent. The Huazheng ESG rating system, with its in-depth understanding of the Chinese market, comprehensive data coverage, and wide market recognition, provides a precise and reliable ESG assessment for enterprises. Consistent with the research conducted by Xu Jiayun [40], this study uses the ESG rating data of Chinese securities as the benchmark for quantifying the ESG performance (ESG) of enterprises. The ESG rating of Chinese securities is divided into nine grades, from poor to excellent, in the order of C, CC, CCC, B, BB, BBB, A, AA, and AAA, and each grade corresponds to a numerical range from 1 to 9.

(3) Control Variables: To minimize the risk of biased parameter estimates stemming from the exclusion of critical variables, this study integrates the following control variables, sourced from existing research: Asset–liability ratio (Lev), equity concentration (Top), proportion of independent directors (Con), corporate growth rate (Growth), executive compensation (Wage), total asset turnover (Tat), fixed asset ratio (Fixed), and corporate size (Size). Moreover, this study also controlled for time and firm fixed effects. The titles and descriptions of these variables are detailed in

Table 1. Variable definitions (1).

Variable Type	Variable Symbol	Variable Definition
Explained variable	Roa	Return on Total Assets = Net Profit/Total Assets
Explanatory variable	ESG	ESG scoring standard of China Securities Index
Control variable	Growth	Revenue Growth Rate = (Current Period Operating Income − Previous Period Operating Income)/Previous Period Operating Income
	Top	The proportion of the largest shareholder
	Lev	Total liabilities/total assets
	Con	Number of independent directors/size of directors
	Wage	Natural logarithm of total compensation of the top three executives plus 1
	Tat	Closing balance of operating income/total assets
	Fixed	Fixed assets/total assets
	Size	Natural logarithm of total assets plus 1
	Year	Year dummy variable
	Firm	Enterprise dummy variable

.

To assess the impact of ESG performance on corporate performance, model (1) is constructed as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}ES{G}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(1)

4.1.3. Analysis of Regression Results

To verify the impact of corporate ESG performance on business outcomes, this paper conducts a regression analysis on the relationship between ESG performance and corporate performance based on Model (1), with results presented in

Table 2. Regression results of the impact of ESG performance on enterprise performance.

	(1)	(2)		(1)	(2)
Variables	Roa	Roa	Variables	Roa	Roa
ESG	−0.005 **	−0.007 ***	Tat		0.056 ***
	(−2.05)	(−3.17)			(6.19)
Con		0.016	Fixed		−0.029 *
		(0.32)			(−1.68)
Lev		−0.116 ***	Size		0.019 ***
		(−5.83)			(3.67)
Top		−0.048	Growth		0.003 **
		(−1.57)			(2.49)
Wage		0.027 ***	Constant	0.061 ***	−0.360 ***
		(5.39)		(3.66)	(−4.47)
Observations	728	728	Observations	728	728
R-squared	0.738	0.789	R-squared	0.738	0.789
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. Column (1) indicates that, without incorporating control variables, the regression coefficient for ESG performance and corporate performance is −0.005, exhibiting a significant negative correlation at the 5% level. This suggests that substantial human, material, and financial resources invested by enterprises in enhancing ESG performance fail to translate fully into economic benefits in the short term, thereby exerting a negative impact on corporate performance. Column (2) indicates that after incorporating control variables, year effects, and firm fixed effects, the regression coefficient for ESG performance and corporate performance stands at −0.007. This represents a slight decrease compared to Column (1) and exhibits a significant negative correlation at the 1% level. This suggests that strong ESG performance significantly reduces corporate performance.

4.2. Attribution of ESG Trap

This section offers a thorough exploration of how different variables influence ESG performance, as well as their implications for overall firm performance, thereby highlighting the phenomenon of the ESG trap.

4.2.1. Variable Selection and Model Setting

(1) Explained Variable: ESG performance (ESG) and business performance (Roa).

(2) Explanatory Variables: The existing literature has not provided clear definitions for the following explanatory variables, and there is a lack of universally applicable measurement methods. In view of this, this paper makes the following definitions for the relevant variables. Firstly, investment in environmental protection equipment technology (Investment): Investment in environmental protection equipment and technology refers to the capital expenditure incurred by enterprises to meet environmental compliance requirements and advance the transition to a green, low-carbon economy. It primarily comprises investment in environmental protection facilities, involving the purchase and upgrading of pollution control equipment such as dust removal, desulphurization and wastewater treatment systems; costs associated with establishing and optimizing environmental management systems, including expenses related to environmental management system certification; investment in resource recycling projects, such as waste recovery and reuse and the development of biomass energy; and expenditure on ecological restoration and conservation projects. This paper selects expenditure items that meet the definition of investment in environmental protection equipment and technology, adds 1 to them, and then takes the natural logarithm of the result to measure investment in environmental protection equipment and technology (Investment). Secondly, social donations (Donation): Social donations represent the outflow of resources incurred by enterprises in fulfilling their social responsibilities and enhancing their public image. As a key component of corporate social responsibility, social donations reflect an enterprise’s support for and contribution to social welfare causes during a given statistical year, encompassing financial contributions to sectors such as education, poverty alleviation, culture and healthcare, as well as donations in kind and other forms of charitable giving. This paper uses the natural logarithm of the total corporate charitable donations for the statistical year plus one to measure the level of charitable donations. Thirdly, environmental education and training expenses (Education): Environmental education and training costs refer to the expenses incurred by enterprises in environmental training, education and related activities aimed at enhancing employees’ environmental awareness and professional skills. These costs primarily encompass three core areas: firstly, compliance training, which focuses on fundamental guidelines such as environmental protection laws and regulations and industry pollutant emission standards, ensuring that employees fully understand the minimum requirements for environmental compliance; secondly, practical skills training, which involves practical courses on energy conservation and emission reduction methods, as well as emergency response to sudden environmental incidents, to enhance employees’ ability to implement environmental requirements in their daily work; thirdly, ESG philosophy and strategy training, aimed at management and key staff, covering ESG strategic planning, the breakdown of sustainable development goals, and the identification and management of ESG risks, to promote the deep integration of ESG principles into the entire process of business decision-making. According to industry management standards, a typical petrochemical enterprise usually participates in two environmental education and training sessions per year, delegating one to two representatives each time; therefore, the actual number of participants is calculated as 1.5 person-times. The calculation of environmental education and training costs is based on the average of three representative case companies, multiplied in turn by 1.5 × 2 and a dummy variable ranging from 0 to 1 indicating whether the company participates in environmental education and training, serving as the basis for the final cost estimate. This paper uses the natural logarithm of the total cost of environmental education and training activities in which the enterprise participates, plus one, to measure environmental education and training costs (Education). Specific examples are as follows: organized by the Environmental Engineering Assessment Centre of the Ministry of Ecology and Environment, with training content covering the environmental law enforcement policy framework, standardized management of pollutant-emitting enterprises, and environmental damage compensation, at a cost of 3500 yuan per person; Jointly organized by the Chinese Society for Environmental Sciences and Jiangsu Jiumu Environmental Technology Co., Ltd. located in Nanjing, China, covering topics such as environmental protection law, environmental impact assessments for construction projects, and the preparation of environmental emergency response plans, with a training fee of 2600 yuan per person; Organized by the China Environmental Protection Federation, covering topics such as corporate environmental responsibility, environmental risk prevention, and technical exchanges on ecological and environmental governance, with a training and materials fee of 2800 yuan per person. Fourthly, environmental protection special action cost (Protection): The costs of environmental protection campaigns refer to the financial investments made by enterprises in the course of carrying out special environmental remediation and improvement initiatives, in response to national environmental policies, to implement green development requirements, and to address pressing environmental issues. Regarding the costs of environmental protection campaigns, a survey of 70 enterprises across relevant industries was conducted, yielding over 50 supporting cases of expenditure related to environmental awareness activities. The following five cases are particularly noteworthy, reflecting from different perspectives the investments made by enterprises in environmental protection campaigns and the results achieved: Sinopec launched the ‘Clear Waters and Blue Skies’ environmental protection campaign, completing a cumulative total of 870 environmental remediation projects with an investment of 20.92 billion yuan, making it the largest single-phase environmental remediation initiative in terms of investment scale and coverage among domestic enterprises to date; Baowu Group’s Guangdong Zhongnan Iron & Steel Co., Ltd. located in Guangzhou, China, invested approximately 3.459 billion yuan to advance 102 environmental upgrading and renovation projects. Among these, the flue gas purification renovation project for Sintering Machines No. 5 and No. 6 involved an investment of 335 million yuan and received 32 million yuan in support from the central government’s special fund for air pollution prevention and control, achieving significant emission reduction results; Sangang-Minguang has cumulatively invested 4.286 billion yuan in environmental protection construction and renovation, dedicated to implementing environmental upgrading and renovation projects to enhance the standard of environmental protection equipment; Zhenhua Heavy Industries has cumulatively invested 700 million yuan in environmental protection, used to strengthen environmental infrastructure and carry out comprehensive pollution prevention and control across water, air, noise and slag management; Hualu Group has invested 1.6 billion yuan in environmental protection, achieving a win-win outcome for both the environment and the economy. These funds have been used to improve facilities and equipment, reduce pollutant emissions, and ensure that pollutant levels consistently meet standards and environmental risks remain controllable. The cost of environmental protection initiatives is calculated by averaging the ratio of expenditure on such initiatives to operating revenue across all case study companies and then multiplying this figure by the company’s operating revenue. This paper measures the cost of environmental protection initiatives (Protection) using the natural logarithm of the total expenditure on environmental protection initiatives and other social welfare activities in which the company participates, plus one. Fifthly, we examine the financial implications of creating new jobs associated with ESG initiatives (Offerings). The costs associated with creating new ESG roles refer to the human resources costs incurred by a company in establishing dedicated positions to implement its environmental, social and governance strategies. Given the highly polluting and heavily regulated nature of the petrochemical industry, dedicated ESG roles primarily include environmental management positions, such as environmental protection engineers and carbon reduction managers, who are responsible for monitoring and controlling pollutant emissions, maintaining environmental protection equipment, and calculating carbon footprints; social responsibility roles, such as employee rights protection officers and public welfare project managers, which focus on occupational health and safety for employees, as well as social donations and the implementation of public welfare projects; and risk management roles, such as ESG risk assessors and public sentiment monitors, which are responsible for identifying potential risks and establishing risk early-warning and response mechanisms to mitigate the impact of adverse events on the enterprise. Drawing on the disclosure characteristics of corporate sustainability reports and combining this with an online survey of ten enterprises, ESG roles account for 8.84% of newly created positions in the petrochemical industry. The cost of establishing additional roles due to ESG requirements can be calculated by multiplying 8.84% by the number of new roles, and then by the average salary. This paper uses the natural logarithm of the total cost of dedicated ESG positions plus one to measure the cost of positions created due to ESG requirements (Offerings). The list of companies participating in the online survey is as follows: COFCO Biochemical (Bengbu City, China); Huajin Co., Ltd. (Panjin City, China); Sanyou Chemical (Tangshan City, China); Shanghai Jahwa (Shanghai City, China); Yueshi Technology (Nanjing City, China); Yabon Chemical (Taicang City, China); Aoke Co., Ltd. (Liaoyang City, China); Longxing Chemical (Shahe City, China); Hailide (Haining City, China); Yongtai Technology (Linhai City, China). For more details of the measurement of ESG Cost Components, please refer to Appendix A.

(3) Control Variables: Refer to the control variables in Section 4.1.2. The titles and descriptions of these variables are detailed in

Table 3. Variable definitions (2).

Variable Type	Variable Symbol	Variable Definition
Explained variable	Roa	Return on Total Assets = Net Profit/Total Assets
	ESG	ESG scoring standard of China Securities Index
Explanatory variable	Investment	Natural logarithm of environmental equipment and technology investment plus one
	Donation	Natural logarithm of the total amount of social donations of the enterprise in the statistical year plus 1
	Education	Natural logarithm of the total cost of environmental education and training activities participated by the enterprise plus 1
	Offerings	Natural logarithm of total expenses for full-time ESG positions plus 1
	Protection	Natural logarithm of total expenses for environmental protection special activities and environmental public welfare activities participated by the enterprise plus 1
Control variable	Growth	Revenue Growth Rate = (Current Period Operating Income − Previous Period Operating Income)/Previous Period Operating Income
	Top	The proportion of the largest shareholder
	Lev	Total liabilities/total assets
	Con	Number of independent directors/size of directors
	Wage	Natural logarithm of total compensation of the top three executives plus 1
	Tat	Closing balance of operating income/total assets
	Fixed	Fixed assets/total assets
	Size	Natural logarithm of total assets plus 1
	Year	Year dummy variable
	Firm	Enterprise dummy variable

.

To assess the impact of various variables on ESG performance, models (2) through (6) are constructed as follows:

$$\begin{array}{l}ES{G}_{i,t}={\alpha }_0+{\alpha }_{1}Offering{s}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(2)

$$\begin{array}{l}ES{G}_{i,t}={\alpha }_0+{\alpha }_{1}Educatio{n}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(3)

$$\begin{array}{l}ES{G}_{i,t}={\alpha }_0+{\alpha }_{1}Protectio{n}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(4)

$$\begin{array}{l}ES{G}_{i,t}={\alpha }_0+{\alpha }_{1}Donatio{n}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(5)

$$\begin{array}{l}ES{G}_{i,t}={\alpha }_0+{\alpha }_{1}Investmen{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(6)

To assess the impact of various variables on enterprise performance, models (7) through (11) are constructed as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Offering{s}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(7)

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Educatio{n}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(8)

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Protectio{n}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(9)

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Donatio{n}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(10)

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Investmen{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Ag{e}_{i,t}+{\alpha }_{4}Boar{d}_{i,t}+{\alpha }_{5}Le{v}_{i,t}+{\alpha }_{6}To{p}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Wag{e}_{i,t}+{\alpha }_{8}Ta{t}_{i,t}+{\alpha }_{9}Fixe{d}_{i,t}+{\alpha }_{10}Siz{e}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(11)

4.2.2. Analysis of Regression Results

(1) Regression analysis of various variables on ESG performance

To verify the impact of various variables on ESG performance, this paper conducts regression analyses on the relationships between the costs of adding ESG positions, environmental education and training expenses, environmental action-specific costs, social donations, environmental equipment and technology investments, and ESG performance based on models (2) to (6). The results are shown in

Table 4. Regression results of the effects of various variables on ESG performance.

	(1)	(2)	(3)	(4)	(5)
Variables	ESG	ESG	ESG	ESG	ESG
Offerings	0.012
	(0.84)
Education		0.274 **
		(2.25)
Protection			0.304
			(1.45)
Donation				0.032 **
				(2.10)
Investment					0.016 *
					(1.91)
Con	−0.002	−0.118	0.062	0.015	0.187
	(−0.00)	(−0.12)	(0.06)	(0.01)	(0.18)
Lev	−0.327	−0.291	−0.289	−0.318	−0.315
	(−0.82)	(−0.73)	(−0.73)	(−0.80)	(−0.79)
Top	0.039	0.031	−0.066	0.108	−0.001
	(0.06)	(0.05)	(−0.11)	(0.18)	(−0.00)
Wage	0.059	0.047	0.059	0.051	0.065
	(0.59)	(0.48)	(0.59)	(0.52)	(0.65)
Tat	0.277	0.254	−0.118	0.291	0.310 *
	(1.54)	(1.41)	(−0.36)	(1.62)	(1.72)
Fixed	0.306	0.236	0.214	0.247	0.278
	(0.88)	(0.68)	(0.61)	(0.72)	(0.81)
Size	0.251 **	0.261 **	−0.015	0.250 **	0.274 ***
	(2.36)	(2.49)	(−0.07)	(2.37)	(2.61)
Growth	−0.017	−0.017	−0.018	−0.013	−0.015
	(−0.69)	(−0.69)	(−0.75)	(−0.53)	(−0.60)
Constant	0.107	0.086	1.443	0.027	−0.396
	(0.07)	(0.05)	(0.75)	(0.02)	(−0.24)
Observations	728	728	728	728	728
R-squared	0.680	0.683	0.681	0.682	0.682
Firm FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES

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

. From column (1), it can be seen that, after controlling for the years and firm fixed effects, the regression coefficient of the cost of adding ESG positions is not significant, indicating that the institutional construction and compliance management effects promoted by ESG positions cannot be directly transformed into a significant improvement in ESG performance in the current period. Enterprises need to further optimize their internal power and responsibility allocation systems and gradually accumulate external reputation capital in order to ultimately achieve a leap in ESG performance. From column (2), it can be known that the regression coefficient of environmental education and training expenses and ESG performance is 0.274, which is significantly positively correlated at the 5% level, indicating that the investment in environmental education and training can enhance employees’ awareness of the importance of environmental protection and their sense of social responsibility, thereby contributing to the improvement of ESG performance. From column (3), it can be seen that the regression coefficient of environmental action-specific costs is not significant, indicating that the impact of environmental action-specific activities on ESG performance is lagging. Its promoting effect needs to be fully released through certain periods of process adjustment and system integration. The efficacy of the investment transformation undergoes a complete operational cycle rather than being immediately apparent in the current period. From column (4), it can be known that the regression coefficient of social donations and ESG performance is 0.032, which is significantly positively correlated at the 5% level, indicating that enterprises shaping a positive image and strengthening their sense of responsibility through donation funds can enhance their overall ESG performance. From column (5), it can be known that the regression coefficient of environmental equipment and technology investment and ESG performance is 0.016, which is significantly positively correlated at the 10% level, indicating that increasing environmental equipment and technology investment can promote technological innovation and industrial structure upgrading of enterprises, reduce pollutant emissions, and improve resource utilization efficiency, thereby having a positive impact on ESG performance.

(2) Regression analysis of various variables on enterprise performance

To verify the impact of various variables on enterprise performance, this paper conducts regression analyses on the relationships between the costs of adding ESG positions, environmental protection education and training expenses, environmental protection special action costs, social donation funds, environmental protection equipment and technology investment, and enterprise performance based on models (7) to (11). The results are shown in

Table 5. Regression results of the influence of various variables on enterprise performance.

	(1)	(2)	(3)	(4)	(5)
Variables	Roa	Roa	Roa	Roa	Roa
Offerings	−0.002 ***
	(−2.75)
Education		−0.001
		(−0.15)
Protection			−0.004 ***
			(−3.55)
Donation				−0.003 ***
				(−3.57)
Investment					−0.001 *
					(−1.79)
Con	−0.033	−0.031	−0.021	−0.030	−0.039
	(−0.69)	(−0.65)	(−0.44)	(−0.62)	(−0.81)
Lev	−0.085 ***	−0.085 ***	−0.108 ***	−0.090 ***	−0.085 ***
	(−4.79)	(−4.68)	(−5.71)	(−5.06)	(−4.78)
Top	−0.051 **	−0.050 **	−0.031	−0.052 **	−0.049 **
	(−2.11)	(−2.05)	(−1.25)	(−2.17)	(−2.02)
Wage	0.020 ***	0.020 ***	0.022 ***	0.021 ***	0.020 ***
	(4.38)	(4.36)	(4.77)	(4.58)	(4.35)
Tat	0.061 ***	0.063 ***	0.060 ***	0.060 ***	0.061 ***
	(7.42)	(7.50)	(7.37)	(7.28)	(7.33)
Fixed	−0.038 **	−0.034 **	−0.033 *	−0.033 *	−0.034 **
	(−2.22)	(−2.00)	(−1.93)	(−1.92)	(−2.01)
Size	0.014 ***	0.012 **	0.018 ***	0.014 ***	0.012 **
	(2.93)	(2.44)	(3.48)	(2.88)	(2.45)
Growth	0.003 ***	0.003 ***	0.003 ***	0.003 **	0.003 **
	(2.61)	(2.60)	(2.60)	(2.35)	(2.52)
Constant	−0.278 ***	−0.247 ***	−0.298 ***	−0.266 ***	−0.239 ***
	(−3.77)	(−3.38)	(−4.03)	(−3.66)	(−3.27)
Observations	776	776	776	776	776
R-squared	0.785	0.783	0.787	0.787	0.784
Firm FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES

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

. From column (1), it can be seen that, after controlling for the years and the fixed effects of enterprises, the regression coefficient of the cost of adding ESG positions and enterprise performance is −0.002, which is significantly negatively correlated at the 1% level. This indicates that setting ESG-related positions in enterprises leads to an increase in human resource investment and management costs, which temporarily increases the operational burden of the enterprises and has a significant inhibitory effect on enterprise performance. From column (2), it can be seen that the regression coefficient of environmental protection education and training expenses is not significant, suggesting that there may be no direct linear relationship between environmental protection education and training expenses and enterprise performance. The impact of environmental protection education and training expenses on enterprise performance is not significant. From column (3), it can be seen that the regression coefficient of environmental protection special action costs and enterprise performance is −0.004, which is significantly negatively correlated at the 1% level. This indicates that when enterprises implement environmental protection special actions, the funds invested may exceed the benefits brought by environmental protection, thereby having a negative impact on enterprise performance. From column (4), it can be seen that the regression coefficient of social donation funds and enterprise performance is −0.003, which is significantly negatively correlated at the 1% level. This indicates that enterprises’ social donations increase operational costs and give up other investment opportunities that may bring higher returns, which may lead to an unreasonable allocation of resources and have a negative impact on enterprise performance. From column (5), it can be seen that the regression coefficient of environmental protection equipment and technology investment and enterprise performance is −0.001, which is significantly negatively correlated at the 10% level. This indicates that environmental protection equipment and technology investment requires enterprises to invest a large amount of funds in the purchase of environmental protection equipment, improvement of production processes, etc. Due to the lag effect of environmental protection equipment and technology investment, it will have a negative impact on the short-term performance of the enterprise.

5. Empirical Test of ESG Trap Formation Path

After a comprehensive discussion on the quantification and attribution of ESG traps, this section will shift focus to the empirical testing of ESG traps. It will begin with an exploration of four key aspects: variable selection and model specification, action path analysis, robustness testing, and sub-path extraction. The goal is to uncover the underlying logic of ESG traps through scientific methodologies and detailed data support.

5.1. Variable Selection and Model Setting

(1) Explained Variable: ESG performance (ESG) and business performance (Roa).

(2) Explanatory Variables: Existing literature has yet to provide a clear definition of ESG costs, and there is a lack of universally applicable measurement methods. In light of this, this paper draws upon the framework of sustainability reporting, integrating the three dimensions of Environmental, Social, and Governance. It measures ESG costs (Cost) using the natural logarithm of the sum of environmental education and training expenses, costs of special environmental initiatives, costs of positions created due to ESG requirements, social donations, and investments in environmental equipment and technology, plus one.

(3) Adjustment Variables: Firstly, the risk level (Risk) is considered. Drawing from the work of Yu Minggui [41], this paper utilizes the volatility of profitability to gauge the risk-taking level. The Return on Assets (ROA) is defined as the proportion of a company’s earnings before interest, taxes, depreciation, and amortization (EBIT) relative to its total assets (ASSET) at the end of the reporting period. To attenuate the impact of industry variations on data analysis, the ROA for each company is adjusted annually against the industry average, culminating in the adjusted metric, ADJ_ROA. Following this, the standard deviation of the industry-adjusted ROA is calculated for each observation period, with each period encompassing three years. The calculation method is outlined in Formula (12):

$$\begin{array}{l}Ris{k}_{i,n}=\sqrt{{\displaystyle \frac{1}{N-1}}{\displaystyle {\sum }_{n=1}^N(ADJ\_RO{A}_{i,n}}-{\displaystyle \frac{1}{N}}{\displaystyle {\sum }_{n=1}^{N}ADJ\_RO{A}_{i,n}{)}^2}\left|N=3\right.}\\ ADJ\_RO{A}_{i,n}={\displaystyle \frac{EBI{T}_{i,n}}{ASSE{T}_{i,n}}}-{\displaystyle \frac{1}{X_n}}{\displaystyle {\sum }_{k=1}^X\frac{EBI{T}_{k,n}}{ASSE{T}_{k,n}}}\end{array}$$

(12)

Among them, i represents the enterprise, n represents the year within the observation period, X represents the total number of enterprises in the industry, and k represents the KTH enterprise in the industry.

Secondly, the competitive position of enterprises (Pcm). Adhering to the methodology of Peress [42], this paper utilizes the Lerner index as a metric for the competitive position of enterprises. The Lerner index is calculated as the ratio of net operating income after subtracting operating costs, selling expenses, and administrative expenses to operating income.

(4) Mediating Variables: Firstly, R&D investment intensity (Rd). Employing the methodology of Ke Dongchang [43], this paper utilizes the ratio of R&D expenses to operating income as a measure to assess the intensity of R&D investment within enterprises. Secondly, financing constraints (KZ). Existing studies primarily gauge financing constraints through two types of indicators: univariate and multivariate. Univariate indicators include the current ratio, enterprise size, and cash holdings, while multivariate indicators encompass the KZ index, SA index, and WW index. Given that univariate indicators do not comprehensively capture the financing constraints faced by enterprises, this paper opts for the KZ index as an indicator to measure the degree of financing constraints experienced by enterprises, drawing upon Kaplan [44].

(5) Control Variables: Refer to the control variables in Section 4.1.2. The titles and descriptions of these variables are detailed in

Table 6. Variable definitions (3).

Variable Type	Variable Symbol	Variable Definition
Explained variable	Roa	Return on Total Assets = Net Profit/Total Assets
	ESG	ESG scoring standard of China Securities Index
Explanatory variable	Cost	Natural logarithm of the sum of environmental education and training expenses, environmental protection action costs, costs of additional positions for ESG, social donations, and investment in environmental protection equipment and technology plus 1
Mediating variable	Rd	Research and development expenses/operating revenue
	KZ	KZ Index
Covariate	Risk	Profit Volatility = Three-year volatility of (Earnings Before Interest and Taxes/Total Assets)
	Pcm	Lerner Index = (Operating Revenue − Operating Costs − Selling Expenses − General and Administrative Expenses)/Operating Revenue
Control variable	Growth	Revenue Growth Rate = (Current Period Operating Income − Previous Period Operating Income)/Previous Period Operating Income
	Top	The proportion of the largest shareholder
	Lev	Total liabilities/total assets
	Con	Number of independent directors/size of directors
	Wage	Natural logarithm of total compensation of the top three executives plus 1
	Tat	Closing balance of operating income/total assets
	Fixed	Fixed assets/total assets
	Size	Natural logarithm of total assets plus 1
	Year	Year dummy variable
	Firm	Enterprise dummy variable

.

To assess the impact of ESG costs on ESG performance, model (13) is constructed as follows:

$$\begin{array}{l}ES{G}_{i,t}={\alpha }_0+{\alpha }_1\mathrm{Cos}{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(13)

To assess the impact of ESG costs on enterprise performance, model (14) is constructed as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(14)

To investigate the moderating influence of risk-bearing level on ESG costs and firm performance, a cross-product term (inter) between ESG costs and risk is incorporated into model (14). Subsequently, model (15) is structured as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Ris{k}_{i,t}+{\alpha }_{3}inte{r}_{i,t}+{\alpha }_{4}Co{n}_{i,t}+{\alpha }_{5}Le{v}_{i,t}+{\alpha }_{6}To{p}_{i,t}+{\alpha }_{7}Wag{e}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{8}Ta{t}_{i,t}+{\alpha }_{9}Fixe{d}_{i,t}+{\alpha }_{10}Siz{e}_{i,t}+{\alpha }_{11}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(15)

To investigate the moderating impact of competitive position on ESG costs and firm performance, a cross-product term (inter) between ESG costs and firm competitive position is incorporated into model (14). Consequently, model (16) is constructed as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Pc{m}_{i,t}+{\alpha }_{3}inte{r}_{i,t}+{\alpha }_{4}Co{n}_{i,t}+{\alpha }_{5}Le{v}_{i,t}+{\alpha }_{6}To{p}_{i,t}+{\alpha }_{7}Wag{e}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{8}Ta{t}_{i,t}+{\alpha }_{9}Fixe{d}_{i,t}+{\alpha }_{10}Siz{e}_{i,t}+{\alpha }_{11}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(16)

To examine the mediating effect of R&D investment intensity, model (17) is constructed as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \\ R{d}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \\ Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}R{d}_{i,t}+{\alpha }_{3}Co{n}_{i,t}+{\alpha }_{4}Le{v}_{i,t}+{\alpha }_{5}To{p}_{i,t}+{\alpha }_{6}Wag{e}_{i,t}+{\alpha }_{7}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{8}Fixe{d}_{i,t}+{\alpha }_{9}Siz{e}_{i,t}+{\alpha }_{10}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(17)

To examine the mediating role of financing constraints, model (18) is constructed as follows:

$$\begin{array}{l}Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \\ K{Z}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}Co{n}_{i,t}+{\alpha }_{3}Le{v}_{i,t}+{\alpha }_{4}To{p}_{i,t}+{\alpha }_{5}Wag{e}_{i,t}+{\alpha }_{6}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{7}Fixe{d}_{i,t}+{\alpha }_{8}Siz{e}_{i,t}+{\alpha }_{9}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \\ Ro{a}_{i,t}={\alpha }_0+{\alpha }_{1}Cos{t}_{i,t}+{\alpha }_{2}K{Z}_{i,t}+{\alpha }_{3}Co{n}_{i,t}+{\alpha }_{4}Le{v}_{i,t}+{\alpha }_{5}To{p}_{i,t}+{\alpha }_{6}Wag{e}_{i,t}+{\alpha }_{7}Ta{t}_{i,t}\\ \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}+{\alpha }_{8}Fixe{d}_{i,t}+{\alpha }_{9}Siz{e}_{i,t}+{\alpha }_{10}Growt{h}_{i,t}+\sum Year+\sum Firm+{\epsilon }_{i,t} \end{array}$$

(18)

5.2. Analysis of the Role Path of ESG Costs in the Process of Trap Formation

5.2.1. Regression Analysis of ESG Costs on ESG Performance

To verify the impact of ESG costs on ESG performance, this paper conducts a regression analysis on the relationship between ESG costs and ESG performance based on Model (13), with results presented in

Table 7. Regression results of the impact of ESG costs on ESG performance.

	(1)	(2)		(1)	(2)
Variables	ESG	ESG	Variables	ESG	ESG
Cost	0.226 ***	0.201 ***	Tat		0.100
	(4.30)	(3.21)			(0.54)
Con		0.457	Fixed		0.227
		(0.45)			(0.66)
Lev		−0.302	Size		0.089
		(−0.77)			(0.76)
Top		−0.092	Growth		−0.015
		(−0.15)			(−0.63)
Wage		0.064	Constant	2.084 ***	0.481
		(0.65)		(3.47)	(0.30)
Observations	728	728	Observations	728	728
R-squared	0.684	0.685	R-squared	0.684	0.685
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

*** indicate significance at the 1% levels.

. Column (1) indicates that, without controlling variables, the regression coefficient between ESG costs and ESG performance is 0.226, exhibiting a statistically significant positive correlation at the 1% level. This suggests that increased ESG costs are accompanied by stricter environmental regulatory standards, more comprehensive social responsibility implementation schemes, and more transparent corporate governance structures, thereby making improvements across all aspects of ESG performance. This provides preliminary validation for Hypothesis H1. Column (2) shows that, after incorporating control variables, year effects, and firm fixed effects, the regression coefficient for ESG costs and ESG performance is 0.201. This represents a slight decrease compared to Column (1) yet remains significantly positive at the 1% level. This indicates that higher ESG costs significantly enhance the ESG performance of the enterprises, further verifying Hypothesis H1.

5.2.2. Regression Analysis of ESG Costs on Enterprise Performance

To verify the impact of ESG costs on corporate performance, this paper conducted a regression analysis on the relationship between ESG costs and corporate performance based on Model (14), with results presented in

Table 8. Regression results of the impact of ESG costs on firm performance.

	(1)	(2)		(1)	(2)
Variables	Roa	Roa	Variables	Roa	Roa
Cost	−0.003 **	−0.005 ***	Tat		0.059 ***
	(−2.49)	(−4.38)			(7.21)
Con		−0.028	Fixed		−0.032 *
		(−0.59)			(−1.92)
Lev		−0.111 ***	Size		0.019 ***
		(−5.95)			(3.75)
Top		−0.027	Growth		0.003 **
		(−1.11)			(2.56)
Wage		0.022 ***	Constant	0.069 ***	−0.301 ***
		(4.84)		(3.99)	(−4.12)
Observations	776	776	Observations	776	776
R-squared	0.742	0.789	R-squared	0.742	0.789
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. Column (1) indicates that, without controlling variables, the regression coefficient between ESG costs and corporate performance is −0.003, exhibiting a significant negative correlation at the 5% level. This suggests that enterprises facing elevated ESG cost pressures may struggle to offset these costs through efficiency gains in the short term, potentially compressing profit margins and thereby diminishing corporate performance. This preliminary finding supports Hypothesis H1. Column (2) indicates that, after incorporating control variables, year effects, and firm fixed effects, the regression coefficient for ESG costs and corporate performance is −0.005. This represents a slight decrease compared to Column (1) and exhibits a significant negative correlation at the 1% level. This indicates that the increased environmental, social and governance costs will significantly undermine the performance of the enterprises, further confirming Hypothesis H1.

5.3. Robustness Test

To ensure the robustness and reliability of the regression results, this paper employs three methods: replacing the dependent variable, lagging the explanatory variable by one period, and excluding samples (refer to [45,46,47] for similar approaches) from special periods for conducting robustness tests.

5.3.1. Replacing the Dependent Variable

To ensure the robustness and reliability of the regression results of ESG costs on ESG performance, and to eliminate the biases caused by different rating agencies, this paper selects the ESG rating (CESG) released by the CNRDS China Research Data Service Platform as an alternative to the Huazheng ESG rating (ESG) as the measurement indicator of ESG performance, covering six aspects: employee relations, corporate governance, environment, diversification, products, charity, and volunteer activities, as well as social disputes. This paper conducts in-depth analysis using the ESG rating provided by the CNRDS China Research Data Service Platform as the dependent variable, and the results are shown in

Table 9. Regression Analysis Test Results for Replacing the Explanatory Variable (1).

	(1)	(2)		(1)	(2)
Variables	CESG	CESG	Variables	CESG	CESG
Cost	2.164 ***	2.562 ***	Tat		0.443
	(4.24)	(4.65)			(0.19)
Con		50.738 ***	Fixed		−1.606
		(3.80)			(−0.36)
Lev		2.683	Size		−1.235
		(0.52)			(−0.84)
Top		−20.651 **	Growth		−0.160
		(−2.58)			(−0.50)
Wage		2.075 *	Constant	10.688 *	1.200
		(1.66)		(1.71)	(0.06)
Observations	739	739	Observations	739	739
R-squared	0.638	0.653	R-squared	0.638	0.653
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. From column (1), it can be seen that without adding control variables, the regression coefficient of ESG costs and ESG performance is 2.164, and it is significantly positively correlated at the 1% level, indicating that the cost of increasing ESG investment significantly promotes the improvement of enterprises’ ESG performance. From column (2), it can be seen that when control variables and firm fixed effects are added, the regression coefficient of ESG costs and ESG performance is 2.562, which is slightly higher than the regression coefficient in column (1), and it is significantly positively correlated at the 1% level. It can be seen that the cost of ESG investment promotes the formation of the ESG trap, indicating that using different indicators to measure ESG performance still has robustness, and further verifying Hypothesis H1.

To ensure the robustness and reliability of the regression results of ESG costs on enterprise performance, this paper selects the return on equity (Roe) instead of the return on assets (Roa) to measure enterprise performance. The return on equity is the ratio of net profit to shareholders’ equity. As a core financial indicator for measuring a company’s profitability, the higher the value, the higher the efficiency of the company in utilizing shareholders’ investment and the stronger its profitability. This paper uses the return on equity as the dependent variable for in-depth analysis, and the results are shown in

Table 10. Regression Analysis Test Results for Replacing the Explanatory Variable (2).

	(1)	(2)		(1)	(2)
Variables	Roe	Roe	Variables	Roe	Roe
Cost	−0.007 ***	−0.010 ***	Tat		0.076 ***
	(−2.90)	(−3.57)			(3.99)
Con		−0.140	Fixed		−0.023
		(−1.26)			(−0.58)
Lev		−0.069	Size		0.031 ***
		(−1.60)			(2.62)
Top		−0.083	Growth		0.005 *
		(−1.46)			(1.72)
Wage		0.045 ***	Constant	0.156 ***	−0.508 ***
		(4.21)		(4.09)	(−2.98)
Observations	776	776	Observations	776	776
R-squared	0.666	0.695	R-squared	0.666	0.695
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. From column (1), it can be seen that without adding control variables, the regression coefficient of ESG costs and enterprise performance is −0.007, and it is significantly negatively correlated at the 1% level, indicating that increasing ESG costs significantly reduces enterprise performance. From column (2), it can be seen that when control variables, years, and enterprise fixed effects are added, the regression coefficient of ESG costs and enterprise performance is −0.010, which is slightly lower than the regression coefficient in column (1), and it is also significantly negatively correlated at the 1% level. It can be seen that the cost of ESG investment promotes the formation of the ESG trap, indicating that using different indicators to measure enterprise performance still has robustness, and thus verifying Hypothesis H1 again.

5.3.2. Lagged Explanatory Variable

Given that the impact of ESG costs on ESG performance 1may have a lag effect, this paper has conducted a one-period lagging treatment on the explanatory variable ESG costs, and re-examined the relationship between ESG costs and ESG performance. The results are shown in

Table 11. Regression Analysis Test Results for Lagged Explanatory Variables (1).

	(1)	(2)		(1)	(2)
Variables	ESG	ESG	Variables	ESG	ESG
LCost	0.070 ***	0.061 **	Tat		0.155
	(2.75)	(2.33)			(0.80)
Con		0.573	Fixed		0.733 **
		(0.55)			(2.04)
Lev		−0.290	Size		0.200
		(−0.69)			(1.61)
Top		0.740	Growth		−0.033
		(0.99)			(−0.57)
Wage		−0.044	Constant	3.119 ***	−0.262
		(−0.43)		(9.15)	(−0.14)
Observations	581	581	Observations	581	581
R-squared	0.764	0.769	R-squared	0.764	0.769
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

, where we refer to [48,49,50] for similar analysis of lagged explanatory variables.

Given that the impact of ESG costs on enterprise performance may have a lag effect, this paper processed the explanatory variable ESG costs with a one-period lag (refer to [51,52,53] for similar approaches). It then re-examined the relationship between ESG costs and enterprise performance, and the results are shown in

Table 12. Regression Analysis Test Results for Lagged Explanatory Variables (2).

	(1)	(2)		(1)	(2)
Variables	Roa	Roa	Variables	Roa	Roa
LCost	−0.003 *	−0.003 **	Tat		0.048 ***
	(−1.86)	(−2.09)			(4.39)
Con		−0.099	Fixed		−0.032
		(−1.60)			(−1.48)
Lev		−0.136 ***	Size		0.022 ***
		(−5.84)			(3.06)
Top		−0.045	Growth		0.009 ***
		(−1.34)			(2.68)
Wage		0.021 ***	Constant	0.055 ***	−0.317 ***
		(3.59)		(2.73)	(−3.07)
Observations	605	605	Observations	605	605
R-squared	0.762	0.807	R-squared	0.762	0.807
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. As can be seen from column (1), without including control variables, the regression coefficient of the lagged ESG cost and enterprise performance is −0.003, which is significantly negatively correlated at the 10% level. When controlling for the years and firm fixed effects, the regression coefficient of the lagged ESG cost and enterprise performance is −0.003, which is significantly negatively correlated at the 5% level. This indicates that the current ESG cost not only shows a significant negative correlation with the current enterprise performance, but also the lagged ESG cost is significantly negatively correlated with the current enterprise performance. The negative impact of ESG costs on enterprise performance has long-term and stability. This once again verifies Hypothesis H1 and confirms the robustness of the regression results.

5.3.3. Exclude Samples from Special Periods

The outbreak of the COVID-19 pandemic in 2020 had a significant impact on the macroeconomy, and enterprises’ production and operation activities encountered unprecedented challenges. They generally faced difficulties such as shrinking market demand, disrupted supply chains, and tight cash flow. To eliminate the influence of exogenous shocks during the special period on the empirical results, this paper excluded the sample observations for the year 2020 and conducted a regression analysis again on the relationship between ESG costs and ESG performance. The results are shown in

Table 13. Regression Analysis Test Results Excluding Samples from Special Periods (1).

	(1)	(2)		(1)	(2)
Variables	ESG	ESG	Variables	ESG	ESG
Cost	0.239 ***	0.183 **	Tat		0.204
	(3.98)	(2.53)			(0.94)
Con		0.270	Fixed		0.171
		(0.23)			(0.42)
Lev		−0.214	Size		0.173
		(−0.47)			(1.26)
Top		0.112	Growth		−0.017
		(0.16)			(−0.30)
Wage		0.067	Constant	1.943 ***	−0.742
		(0.58)		(2.82)	(−0.39)
Observations	603	603	Observations	603	603
R-squared	0.691	0.693	R-squared	0.691	0.693
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. From column (1), it can be seen that without adding control variables, the regression coefficient of ESG costs and ESG performance is 0.239, and it is significantly positively correlated at the 1% level, indicating that the cost of increasing ESG investment significantly improves the ESG performance of enterprises. From column (2), it can be known that when control variables and firm fixed effects are added, the regression coefficient of ESG costs and ESG performance is 0.183, which is slightly lower than the regression coefficient in column (1), and it is significantly positively correlated at the 5% level. It can be seen that the cost of ESG investment promotes the formation of the ESG trap, indicating that the regression results have not been offset by the external environment shocks during the special period, and the Hypothesis H1 has been verified, confirming the robustness of the regression results.

The outbreak of the COVID-19 pandemic in 2020 had a significant impact on the macroeconomy, and enterprises’ production and operation activities encountered unprecedented challenges. They generally faced difficulties such as shrinking market demand, disrupted supply chains, and tight cash flow. To eliminate the influence of exogenous shocks during the special period on the empirical results, this paper excluded the sample observations for the year 2020 and conducted a regression analysis again on the relationship between ESG costs and enterprise performance. The results are shown in

Table 14. Regression Analysis Test Results Excluding Samples from Special Periods (2).

	(1)	(2)		(1)	(2)
Variables	Roa	Roa	Variables	Roa	Roa
Cost	−0.003 **	−0.005 ***	Tat		0.058 ***
	(−2.48)	(−4.07)			(6.23)
Con		−0.061	Fixed		−0.045 **
		(−1.13)			(−2.29)
Lev		−0.124 ***	Size		0.018 ***
		(−5.89)			(3.31)
Top		−0.029	Growth		0.005 *
		(−1.06)			(1.82)
Wage		0.021 ***	Constant	0.075 ***	−0.262 ***
		(4.01)		(3.86)	(−3.22)
Observations	639	639	Observations	639	639
R-squared	0.750	0.800	R-squared	0.750	0.800
Firm FE	YES	YES	Firm FE	YES	YES
Year FE	YES	YES	Year FE	YES	YES

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

. From column (1), it can be seen that without adding control variables, the regression coefficient of ESG costs and enterprise performance is −0.003, and it is significantly negatively correlated at the 5% level, indicating that increasing ESG costs significantly reduces enterprise performance. From column (2), it can be seen that when control variables, years, and firm fixed effects are added, the regression coefficient of ESG costs and enterprise performance is −0.005, which is slightly lower than the regression coefficient in column (1), and it is significantly negatively correlated at the 1% level. It can be seen that the cost of ESG investment promotes the formation of the ESG trap, indicating that the regression results have not been offset by the external environment shocks during the special period, and the Hypothesis H1 has been verified, confirming the robustness of the regression results.

5.4. Subpath Extraction Based on Intermediate-Regulatory Effect Analysis

5.4.1. The Mediating Effect of R&D Investment Intensity and Financing Constraints

In order to verify the mediating role of R&D investment intensity, according to the model (17), the mediating effect of R&D investment intensity between ESG costs and firm performance is assessed using the stepwise regression method (see [54,55,56] for its applications) and the Sobel test (refer to [57,58,59] for similar analysis) as proposed by Wen Zhonglin [60]. The results are presented in

Table 15. Regression results of intermediate effect of R&D input intensity.

	(1)	(2)	(3)
Variables	Roa	Rd	Roa
Cost	−0.005 ***	−0.001 ***	−0.005 ***
	(−4.38)	(−3.21)	(−4.06)
Rd			0.471 **
			(2.35)
Con	−0.028	0.019 *	−0.037
	(−0.59)	(1.94)	(−0.78)
Lev	−0.111 ***	−0.011 ***	−0.105 ***
	(−5.95)	(−3.00)	(−5.64)
Top	−0.027	0.001	−0.028
	(−1.11)	(0.22)	(−1.13)
Wage	0.022 ***	−0.001	0.023 ***
	(4.84)	(−0.95)	(4.95)
Tat	0.059 ***	−0.011 ***	0.064 ***
	(7.21)	(−6.59)	(7.59)
Fixed	−0.032 *	0.005	−0.035 **
	(−1.92)	(1.44)	(−2.06)
Size	0.019 ***	0.001	0.018 ***
	(3.75)	(1.27)	(3.64)
Growth	0.003 **	−0.000 *	0.003 ***
	(2.56)	(−1.70)	(2.72)
Constant	−0.301 ***	0.007	−0.304 ***
	(−4.12)	(0.49)	(−4.18)
Observations	776	776	776
R-squared	0.789	0.950	0.791
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
Sobel Test	0.104680 ** (Z = 2.520)
Goodman-1	0.104680 ** (Z = 2.474)
Goodman-2	0.104680 ** (Z = 2.568)
Mediation effect	0.104680 ** (Z = 2.520)
Direct effect	0.471240 ** (Z = 2.351)
Total effect	0.575921 *** (Z = 2.861)
Mediation effect/Total effect	0.181761

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

. Column (1) indicates that, controlling for year and firm fixed effects, the regression coefficient between ESG costs and corporate performance is −0.005, showing a significant negative correlation at the 1% level. This suggests that increased ESG costs markedly diminish corporate performance. Column (2) shows that the regression coefficient between ESG costs and R&D intensity is −0.001, exhibiting a significant negative correlation at the 1% level. This indicates that higher ESG costs reduce a firm’s R&D intensity, as increased ESG costs compel firms to reallocate resources, thereby diverting funds originally earmarked for R&D [61]. Column (3) reveals that the regression coefficient for ESG costs and corporate performance is −0.005, exhibiting a significant negative correlation at the 1% level. The regression coefficient for R&D intensity and corporate performance is 0.471, showing a significant positive correlation at the 5% level. This indicates that higher R&D intensity significantly enhances corporate performance. Increased R&D intensity drives continuous refinement and optimization of production processes, enabling enterprises to maintain competitive advantages in dynamic market environments and fueling long-term development. Further Sobel tests confirm that Sobel, Goodman1, and Goodman2 all pass significance level tests. The mediation effect coefficient is 0.104680, indicating a significant positive correlation at the 5% level. The mediating effect of R&D intensity accounted for 18.18% of the variance, demonstrating that R&D intensity partially mediates the relationship between ESG costs and corporate performance. Increased corporate ESG costs displaced R&D investment, thereby negatively impacting corporate performance. This reveals that the costs associated with ESG investments contribute to the formation of the ESG trap, validating Hypothesis H3a.

To verify the mediating role of financing constraints, this paper employs stepwise regression and Sobel tests based on Model (18) to examine the mediating effect of financing constraints between ESG costs and corporate performance, with results presented in

Table 16. Regression results of mediating effect of financing constraints.

	(1)	(2)	(3)
Variables	Roa	KZ	Roa
Cost	−0.005 ***	0.101 **	−0.004 ***
	(−4.38)	(2.54)	(−3.85)
KZ			−0.007 ***
			(−6.30)
Con	−0.028	1.101	−0.021
	(−0.59)	(0.67)	(−0.44)
Lev	−0.111 ***	8.699 ***	−0.049 **
	(−5.95)	(13.55)	(−2.36)
Top	−0.027	−1.492 *	−0.038
	(−1.11)	(−1.76)	(−1.59)
Wage	0.022 ***	−0.229	0.020 ***
	(4.84)	(−1.45)	(4.61)
Tat	0.059 ***	−1.219 ***	0.050 ***
	(7.21)	(−4.33)	(6.24)
Fixed	−0.032 *	2.374 ***	−0.015
	(−1.92)	(4.08)	(−0.93)
Size	0.019 ***	−0.668 ***	0.014 ***
	(3.75)	(−3.84)	(2.85)
Growth	0.003 **	−0.023	0.003 **
	(2.56)	(−0.56)	(2.50)
Constant	−0.301 ***	6.937 ***	−0.252 ***
	(−4.12)	(2.75)	(−3.52)
Observations	776	776	776
R-squared	0.789	0.789	0.802
Firm FE	YES	YES	YES
Year FE	YES	YES	YES
Sobel Test	−0.000445 ** (Z = −2.119)
Goodman-1	−0.000445 ** (Z = −2.071)
Goodman-2	−0.000445 ** (Z = −2.171)
Mediation effect	−0.000445 ** (Z = −2.119)
Direct effect	−0.007142 *** (Z = −6.296)
Total effect	−0.007588 *** (Z = −6.649)
Mediation effect/Total effect	0.058695

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

. Column (1) indicates that, controlling for year and firm fixed effects, the regression coefficient between ESG costs and corporate performance is −0.005, showing a significant negative correlation at the 1% level. This suggests that increased ESG costs significantly reduce corporate performance. Column (2) reveals that the regression coefficient for ESG costs and financing constraints is 0.101, exhibiting a significant positive correlation at the 5% level. This indicates that when corporate ESG expenditure reaches a certain threshold, it diverts funds originally allocated for investment activities, triggering liquidity constraints and exacerbating financing difficulties. Column (3) shows that the regression coefficient between ESG costs and corporate performance is −0.004, slightly higher than that in Column (1), and exhibits a significant negative correlation at the 1% level. The regression coefficient between financing constraints and corporate performance is −0.007, exhibiting a significant negative correlation at the 1% level. This indicates that enterprises face restricted access to external funding at elevated costs, with capital shortages limiting the scale and scope of production, operations, and investment activities, thereby diminishing corporate performance. Further analysis via the Sobel test confirms that Sobel, Goodman1, and Goodman2 all pass the significance level test. The mediation effect coefficient is −0.000445, indicating a significant negative correlation at the 5% level. Financing constraints mediated 5.87% of the relationship, suggesting they partially mediate between ESG costs and corporate performance. Increased ESG costs exacerbate financing constraints, ultimately negatively impacting corporate performance. This demonstrates that ESG investment costs contribute to the formation of the ESG trap, validating Hypothesis H3b.

5.4.2. The Moderating Role of Risk-Taking Levels and the Competitive Position of Firms

Based on Model (15), this paper conducts an in-depth analysis of the moderating effect of risk-taking levels on the relationship between ESG costs and corporate performance. The regression results are presented in

Table 17. Regression results of moderating effect of risk-taking level.

	(1)		(1)
Variables	Roa	Variables	Roa
Cost	−0.005 ***	Wage	0.022 ***
	(−4.69)		(4.84)
Risk	−0.090 ***	Tat	0.059 ***
	(−3.50)		(7.21)
inter1	−0.077 ***	Fixed	−0.038 **
	(−3.52)		(−2.26)
Con	−0.050	Size	0.020 ***
	(−1.05)		(4.04)
Lev	−0.116 ***	Growth	0.003 ***
	(−6.23)		(2.63)
Top	−0.027	Constant	−0.313 ***
	(−1.09)		(−4.31)
Observations	776	Observations	776
R-squared	0.793	R-squared	0.793
Firm FE	YES	Firm FE	YES
Year FE	YES	Year FE	YES

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

. Column (1) indicates that, controlling for year and firm fixed effects, the regression coefficient for ESG costs and corporate performance is −0.005, showing a significant negative correlation at the 1% level. This suggests that higher ESG costs significantly reduce corporate performance. The regression coefficient for the interaction term between ESG costs and risk-taking level (inter1) is −0.077, exhibiting a significant negative correlation at the 1% level. This indicates that risk-taking level significantly amplifies the negative impact of ESG costs on corporate performance. That is to say, enterprises with a higher risk tolerance are more significantly affected by ESG costs, thereby verifying Hypothesis H2a.

Based on Model (16), this paper conducts an in-depth analysis of the moderating effect of corporate competitive position on the relationship between ESG costs and corporate performance. The regression results are presented in

Table 18. Regression results of moderating effect of competitive position.

	(1)		(1)
Variables	Roa	Variables	Roa
Cost	−0.002 *	Wage	0.010 ***
	(−1.91)		(2.86)
Pcm	0.362 ***	Tat	0.055 ***
	(19.71)		(8.75)
inter2	0.073 ***	Fixed	−0.030 **
	(8.69)		(−2.31)
Con	−0.096 ***	Size	0.005
	(−2.60)		(1.30)
Lev	−0.047 ***	Growth	0.001
	(−3.23)		(0.61)
Top	−0.059 ***	Constant	−0.107 *
	(−3.13)		(−1.88)
Observations	776	Observations	776
R-squared	0.876	R-squared	0.876
Firm FE	YES	Firm FE	YES
Year FE	YES	Year FE	YES

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

. Column (1) indicates that, controlling for year and firm fixed effects, the regression coefficient for ESG costs and corporate performance is −0.002, showing a significant negative correlation at the 10% level. This suggests that higher ESG costs significantly reduce corporate performance. The regression coefficient for the interaction term between ESG costs and competitive position (inter2) is 0.073, exhibiting a significant positive correlation at the 1% level. This indicates that competitive position significantly mitigates the negative impact of ESG costs on corporate performance. That is to say, enterprises in a more competitive position are less affected by the ESG costs in terms of their performance, which further validates Hypothesis H2b.

6. Solutions to ESG Trap

The primary pathway through which ESG trap form manifests as a significant increase in costs incurred to enhance ESG ratings. Based on prior research findings, excessive ESG cost investments can lead to declining corporate performance. Consequently, reducing ESG costs becomes crucial for improving corporate performance. Achieving this objective requires ensuring that reduced ESG costs enhance corporate performance while avoiding excessive deterioration in ESG outcomes. To quantitatively analyze this process, this study systematically examines the differential impacts of varying cost-reduction intensities on corporate performance and ESG outcomes. Five gradient levels—15%, 12%, 10%, 8%, and 5%—were selected as the extent of ESG cost reduction. Calculations were performed using the regression equation model established earlier:

$$\begin{array}{l}Ro{a}_{i,t}=-0.301-0.005Cos{t}_{i,t}-0.028Co{n}_{i,t}-0.111Le{v}_{i,t}-0.027To{p}_{i,t}+0.022Wag{e}_{i,t}\\ \begin{array}{ccc}& & \end{array} +0.059Ta{t}_{i,t}-0.032Fixe{d}_{i,t}+0.019Siz{e}_{i,t}+0.003Growt{h}_{i,t}+{\epsilon }_{i,t}\\ ES{G}_{i,t}=0.481+0.201Cos{t}_{i,t}+0.457Co{n}_{i,t}-0.302Le{v}_{i,t}-0.092To{p}_{i,t}+0.064Wag{e}_{i,t}\\ \begin{array}{ccc}& & \end{array} +0.100Ta{t}_{i,t}+0.227Fixe{d}_{i,t}+0.089Siz{e}_{i,t}-0.015Growt{h}_{i,t}+{\epsilon }_{i,t}\end{array}$$

(19)

By substituting values adjusted for ESG cost reductions, we calculated revised corporate performance and ESG metrics, comparing these with original data to determine the extent of performance growth and ESG decline. Based on the actual distribution characteristics of sample companies’ performance and ESG metrics, this study employs a 50% increase in ROA and a 5% decrease in ESG as classification criteria, dividing sample companies into four zones: Zone One comprises companies with an ROA increase ≥ 50% and an ESG decrease < 5%; Zone Two includes companies with an ROA increase ≥ 50% and an ESG decrease ≥ 5%; Zone 3 comprises firms with ROA growth below 50% and ESG decline ≥ 5%; Zone 4 comprises firms with ROA growth below 50% and ESG decline < 5%. Within each zone, points are plotted with firm count as radius and the average of ESG decline and ROA growth as center.

Significant divergences emerge between corporate performance and ESG outcomes across different ESG cost-cutting strategies. As illustrated in Figure 5, when ESG cost reductions reached 15%, companies in Zone 2 achieved an average performance increase of 194.6%—far exceeding other strategies—yet their ESG performance declined by 8.2%. This short-sighted strategy resulted in 24.5% of firms facing the dilemma of high performance coupled with low ESG standards. While delivering the most pronounced performance gains, this approach carries substantial costs, potentially exposing companies to long-term regulatory risks or reputational damage. When ESG cost reductions narrowed to 12%, Zone 2 enterprises accounted for 18% of the sample, achieving an average performance increase of 174.7%. However, this outcome was marginally less effective than the 15% reduction strategy, with an average decline in ESG performance of 6.9%. Although this strategy substantially enhances corporate performance, it struggles to stem the decline in ESG performance. Consequently, it is not recommended and represents an irrational choice for enterprises prioritizing sustainable development and social responsibility. When ESG cost reductions were maintained at 10%, companies in both Region 1 and Region 2 achieved an average performance increase exceeding 142.4%, with ESG performance declining below 6.2%, accounting for 19.4% of firms. Although performance growth was substantial, it could not prevent significant ESG performance deterioration. Excessive ESG cost cuts lack sustainability, rendering this strategy unsuitable as a priority option for enterprises. Further reducing ESG cost cuts to 8% enabled Zone 1 enterprises to achieve 137.2% performance growth. This substantial improvement in corporate performance was accompanied by a mere 4.1% decline in ESG performance, demonstrating favorable balance. 14.4% of enterprises successfully established themselves as dual-excellence benchmarks. This approach suits enterprises pursuing high performance while maintaining ESG standards, enabling substantial performance gains without unduly compromising sustainability. In contrast, the 5% ESG cost reduction strategy proves more robust. 11.5% of enterprises concentrated in Zone One achieved an average performance increase of 102.8%—a considerable growth rate—with ESG performance declining by only 2.7%. 88.5% of enterprises fell within Quadrant IV, where corporate performance saw a modest 5.6% increase and ESG performance experienced only a slight 2.8% decline, exhibiting relatively stable overall fluctuations. This strategy suits risk-averse enterprises prioritizing ESG performance and accepting the risk of slower growth, particularly in ESG-sensitive sectors like new energy where its stability proves more appealing.

Therefore, this paper recommends that enterprises adopt strategies with an ESG cost reduction of either 8% or 5%. Compared to the 5% reduction strategy, while the 8% reduction strategy faces certain pressures in terms of ESG scoring, it delivers a significant improvement in performance returns. Industry leaders, leveraging robust resource reserves and mature technological systems, may prioritize the 8% ESG cost reduction strategy to further expand their competitive edge. Conversely, start-ups or ESG-sensitive enterprises, constrained by limited resource endowments and insufficient risk resilience, should adopt the 5% ESG cost reduction strategy to steadily fortify the foundations of sustainable development. Aggressive strategies exceeding 10% ESG cost reduction may yield rapid short-term performance gains but risk precipitating a cliff-edge decline in ESG performance. This creates severe imbalance between ESG metrics and corporate performance, rendering such development models ultimately unsustainable under prevailing ESG investment trends.

To reduce ESG costs, enterprises may implement a series of optimization measures: Regarding environmental equipment technology investment, companies can reduce initial capital expenditure and subsequent operational costs through technological innovation and process optimization. Introducing energy-efficient, consumption-reducing environmental equipment, deploying intelligent management systems, and employing digital monitoring technologies for dynamic regulation can enhance equipment utilization efficiency. Regarding charitable donations, enterprises should proactively establish long-term partnerships with local governments, social organizations, and non-profit institutions. By implementing public welfare projects, they can optimize the allocation of donation resources. This approach not only leverages economies of scale to reduce per-unit donation costs but also strengthens the brand impact of fulfilling social responsibilities, steadily enhancing the enterprise’s societal influence. Regarding environmental education and training expenses, enterprises may innovate training models by conducting large-scale training through digital tools such as online learning platforms and virtual simulation systems. They should actively establish cooperative relationships with professional environmental organizations and higher education institutions to jointly develop standardized training courses. This approach reduces training costs and project implementation expenses while enhancing employees’ environmental awareness and participation. Regarding costs associated with creating new ESG-related positions, enterprises should abandon extensive management approaches. Instead, they should consolidate existing role resources, eliminate redundant positions, and implement cross-functional, multi-skilled training programs to systematically enhance employees’ comprehensive competencies. By promoting flexible staffing models that enable ‘multiple competencies per role,’ they can effectively control labor costs for new positions while improving organizational operational efficiency. Regarding costs associated with dedicated environmental initiatives, enterprises should strengthen full-lifecycle cost control, optimize project implementation plans, eliminate redundant procedures and unnecessary expenditures, develop scientifically grounded cost budgeting schemes, and establish dynamic monitoring and real-time accounting mechanisms. This ensures the precise allocation and efficient utilization of dedicated funds.

Beyond reducing total ESG expenditure, optimizing the internal allocation structure of ESG spending across cost components constitutes a complementary and potentially more sustainable strategy for alleviating the ESG trap. The regression analyses in Section 4.2 (Table 4 and Table 5) reveal that the five ESG cost components exhibit markedly heterogeneous “dual-effect profiles”—that is, their respective contributions to ESG performance enhancement and their impacts on corporate performance differ substantially. To systematically evaluate the relative efficiency of each component,

Table 19. Dual-effect efficiency classification of ESG cost components.

Component	ESG Effect (β)	Sig.	Roa Effect (β)	Sig.	Efficiency Tier
Education	0.274	**	−0.001	n.s.	High
Investment	0.016	*	−0.001	*	Moderate
Donation	0.032	**	−0.003	***	Moderate
Protection	0.304	n.s.	−0.004	***	Low
Offerings	0.012	n.s.	−0.002	***	Low

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Note: Efficiency tier is determined by the joint consideration of each component’s ESG enhancement effect and its corporate performance cost. “High” denotes significant ESG improvement with negligible performance cost; “Moderate” denotes significant ESG improvement accompanied by significant but limited performance cost; “Low” denotes no statistically significant ESG improvement in the current period combined with significant performance cost.

synthesizes the estimated coefficients and classifies each component into one of three efficiency tiers. Environmental education and training expenses (Education) emerge as the most efficient component, generating the strongest statistically significant ESG improvement (β = 0.274, p < 0.05) with no significant adverse effect on corporate performance. Environmental equipment and technology investment (Investment) and social donations (Donation) occupy a moderate efficiency tier: both produce significant ESG gains, albeit smaller in magnitude, while imposing statistically significant but limited performance costs. Environmental protection special action costs (Protection) and costs of adding ESG positions (Offerings) constitute the lowest efficiency tier, as neither produces a statistically significant ESG improvement in the current period, yet both exert highly significant negative effects on corporate performance. This classification implies that a considerable share of the performance penalty attributed to ESG spending in the petrochemical industry originates from structurally inefficient allocation rather than from the absolute level of ESG commitment.

Based on these efficiency profiles, this study constructs four illustrative allocation scenarios to examine how the redistribution of ESG resources—holding total expenditure constant—affects the aggregate ESG–performance trade-off. Two summary indices are computed for each scenario: a weighted ESG contribution index, defined as the sum of each component’s allocation weight multiplied by its statistically significant ESG coefficient (Education, Donation, and Investment), and a weighted performance drag index, defined as the sum of each component’s allocation weight multiplied by the absolute value of its statistically significant Roa coefficient (Offerings, Protection, Donation, and Investment). The baseline scenario (Scenario A) approximates the current average allocation structure observed in the petrochemical industry sample: Investment (35%), Protection (25%), Donation (15%), Offerings (15%), and Education (10%). Scenario B (Efficiency-Oriented) shifts resources from the two low-efficiency components by reducing Protection to 15% and Offerings to 7%, and redistributes the freed resources to Education (22%) and Investment (41%). Scenario C (ESG-Maximizing) concentrates resources on the three components with significant ESG effects, raising Education to 25% and Donation to 25%, while compressing Protection to 10% and Offerings to 5%, with Investment held at 35%. Scenario D (Balanced Optimization) moderately adjusts the structure, reducing Protection to 17% and Offerings to 10%, while increasing Education to 18%, Investment to 38%, and Donation to 17%. As presented in

Table 20. Illustrative ESG cost allocation scenarios and estimated effects.

Scenario	Investment	Protection	Donation	Offerings	Education	ESG Index	Roa Drag Index	ESG Change	Drag Change
A (Baseline)	35%	25%	15%	15%	10%	0.0378	0.0021	—	—
B (Efficiency)	41%	15%	15%	7%	22%	0.0716	0.0016	+89.5%	−23.8%
C (ESG-Max)	35%	10%	25%	5%	25%	0.0821	0.0016	+117.2%	−23.8%
D (Balanced)	38%	17%	17%	10%	18%	0.0608	0.0018	+61.0%	−15.7%

Note: The weighted ESG contribution index = Σ w_i × β_ESG,i for components with statistically significant ESG coefficients (Education, Donation, Investment). The weighted performance drag index = Σ w_i × |β_Roa,i| for components with statistically significant Roa coefficients (Offerings, Protection, Donation, Investment). Percentage changes are relative to Scenario A. These indices are illustrative approximations derived from separate regression estimates and should be interpreted directionally rather than as precise predictions.

, Scenario B achieves an 89.5% increase in the weighted ESG contribution index relative to the baseline while reducing the performance drag index by 23.8%, demonstrating that substantive resource reallocation can simultaneously strengthen ESG outcomes and attenuate the performance penalty without reducing total ESG spending. Scenario C maximizes ESG contributions (117.2% increase) with an equivalent drag reduction, though its heavy concentration on Education and Donation may limit diversification across ESG dimensions. Scenario D offers a more conservative adjustment, yielding a 61.0% ESG contribution improvement and a 15.7% performance drag reduction, suitable for enterprises seeking incremental optimization with minimal structural disruption. Across all three reallocation scenarios, the common pattern is clear: redirecting resources from Protection and Offerings—where current-period ESG returns are statistically unverifiable—toward Education, the component with the highest ESG return per unit of performance cost, constitutes the most effective lever for alleviating the ESG trap.

Combining the total cost reduction analysis above with the allocation optimization analysis, this paper recommends a two-pronged approach to resolving the ESG trap: enterprises should pursue moderate total ESG cost reductions (5–8%) to relieve immediate financial pressure, while simultaneously restructuring their ESG spending portfolio to prioritize high-efficiency components. Regarding environmental education and training—the highest-efficiency component identified in this study—enterprises should expand their investment share and innovate training delivery through digital tools such as online learning platforms and virtual simulation systems. They should actively establish cooperative relationships with professional environmental organizations and higher education institutions to jointly develop standardized training courses, thereby maximizing ESG returns per unit of expenditure while enhancing employees’ environmental awareness and participation. Regarding environmental equipment and technology investment, companies should focus on technological innovation and process optimization by introducing energy-efficient, low-consumption environmental equipment, deploying intelligent management systems, and employing digital monitoring technologies for dynamic regulation, thereby enhancing equipment utilization efficiency and reducing both initial capital expenditure and subsequent operational costs. Regarding charitable donations, enterprises should proactively establish long-term partnerships with local governments, social organizations, and non-profit institutions, implementing targeted public welfare projects that leverage economies of scale to reduce per-unit donation costs while strengthening the brand impact of fulfilling social responsibilities. Regarding the costs of creating new ESG-related positions—a low-efficiency component—enterprises should abandon extensive management approaches, consolidate existing role resources, eliminate redundant positions, and implement cross-functional, multi-skilled training programs that enable “multiple competencies per role,” effectively controlling labor costs for new positions while improving organizational operational efficiency. Regarding dedicated environmental initiative costs—likewise classified as low efficiency—enterprises should strengthen full-lifecycle cost control, optimize project implementation plans, eliminate redundant procedures and unnecessary expenditures, develop scientifically grounded cost budgeting schemes, and establish dynamic monitoring and real-time accounting mechanisms, ensuring the precise allocation and efficient utilization of dedicated funds. In sum, the path out of the ESG trap lies not in retreating from ESG commitment but in spending more wisely: reducing wasteful, formalistic expenditures that inflate ratings without generating substantive value, and channeling resources toward investments that embed ESG principles into core organizational capabilities and long-term competitive advantage.

7. Conclusions and Implications

This study examines 148 listed companies in the Shanghai and Shenzhen A-share petrochemical industry from 2018 to 2023, focusing on the formation pathways of the ESG trap. It empirically tests these pathways and further explores the moderating effects of risk-taking levels and competitive positioning, as well as the mediating effects of R&D investment intensity and financing constraints. The findings are as follows: (1) Environmental education and training expenses, charitable donations, and investments in environmental equipment and technology all exert a positive influence on corporate ESG performance. Conversely, costs associated with special environmental initiatives, expenses incurred from creating new ESG-related positions, charitable donations, and investments in environmental equipment and technology exert a negative influence on corporate performance, constituting the causes of the divergence dilemma between corporate performance and ESG. (2) Primary pathway 1 for this divergence arises when increased ESG costs displace R&D expenditure, thereby diminishing corporate performance (as shown in Figure 6 below). (3) Primary pathway 2 for this divergence occurs when heightened ESG costs exacerbate financing constraints, consequently reducing corporate performance. (4) Primary pathway 3: Cost investments targeting ESG ratings significantly enhance ESG performance. (5) Risk-taking levels markedly amplify the negative impact of ESG costs on corporate performance. (6) Competitive position significantly mitigates the negative impact of ESG costs on corporate performance.

Considering the research findings detailed above, this study proposes the following countermeasures and recommendations. Firstly, for companies actively engaging in clean energy initiatives and environmental protection technologies, it is essential to develop targeted incentive policies, such as tax breaks and subsidies. This strategy will help alleviate the barriers imposed by high ESG-related costs, guiding businesses towards a path of high-quality and sustainable ESG development. Secondly, the government should encourage financial institutions to innovate in ESG financing products, offering more favorable financing conditions to enterprises demonstrating robust ESG performance. This could involve reducing loan interest rates and extending loan terms, thereby lowering the financing costs for these enterprises. The cycle of “ESG costs–financing constraints–ESG trap” is thus transformed into a cycle of “ESG improvement–financing cost reduction–enterprise performance enhancement”. Thirdly, enterprises should formulate a scientific resource allocation plan, integrating ESG costs with other critical business indicators to ensure the balance and harmony of resource allocation. When determining the cost of ESG investment, the financial condition and market environment of the company must be taken into account, ensuring that the cost of ESG investment does not erode profit margins but effectively fosters the sustainable development of the business. Fourthly, companies should minimize the implementation costs of ESG projects through technological innovation and management optimization, thereby enhancing the return on investment. The adoption of advanced production processes and equipment can help improve the efficiency of resource utilization, reducing energy consumption and waste emissions. Lastly, it is imperative to refine ESG rating metrics to make it unviable for companies to boost their ratings through inflated ESG costs. In the context of corporate governance (G) ratings, there should be no additional points for the creation of specific positions, preventing the inefficient management trend of establishing vacancies and overstaffing [62], and emphasizing the scoring of management effectiveness.

Finally, a higher ESG rating does not necessarily indicate a proportional improvement in substantive environmental performance. In practice, ESG scores may also rise because of better disclosure quality, more visible governance arrangements, and stronger external communication. This suggests that the gap identified in this study between ESG improvement and economic pressure may partly reflect a short-run divergence between reporting-based ESG enhancement and actual environmental performance improvement.