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Article

Research on the Configuration Paths of Low-Carbon Transformation of Heavily Polluting Enterprises

by
Xianna Su
* and
Shujuan Ding
Business School, Yangzhou University, Yangzhou 225127, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5826; https://doi.org/10.3390/su16145826
Submission received: 20 May 2024 / Revised: 20 June 2024 / Accepted: 4 July 2024 / Published: 9 July 2024
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
In the context of escalating environmental and climate concerns, it is imperative for enterprises to embark on carbon emission reduction initiatives. Exploring the driving pathways for corporate low-carbon transformation is crucial for the development of a green economy. In this paper, various configuration pathways that may drive heavily polluting industrial enterprises towards green and low-carbon transformation were investigated based on the Technology–Organization–Environment (TOE) theoretical framework and the fuzzy set qualitative comparative analysis (fsQCA) method. The results indicated the following: (1) the low-carbon transformation of heavily polluting enterprises is the result of the joint action of multiple factors; (2) there are eight pathways that can promote corporate low-carbon transformation, roughly divided into single-factor driving types (including MEA drive, DT drive, and GI drive), dual-factor driving types (DT–ER drive and DT–ESGR drive), and multi-factor driving types (including GI–DT–MEA–ER drive, GI–FS–ER drive, and GI–FS–ESGR drive). It can be concluded that there can be certain substitutions between green technology innovation and digital transformation, and environmental regulations and ESG ratings. (3) GI and DT are crucial to the low-carbon transformation of heavily polluting enterprises, and the latter has a more significant impact on promoting low-carbon transformation. MEA is also worthy of attention. The research conclusions not only provide theoretical support for the low-carbon transformation of heavily polluting industrial enterprises but also have valuable reference significance for other industry enterprises, and even the whole of society, to achieve green sustainable development.

1. Introduction

Since the initiation of reform and opening up in 1978, China has made unprecedented achievements in various areas, particularly in economic advancement. The government has implemented a series of measures to stimulate economic growth. By 2010, China had become the world’s second-largest economy. By 2022, the GDP had surged to 121 trillion yuan, nearly a 329-fold increase from 40 years ago. The industrial sector serves as the backbone of the national economy, and the rapid progress of China’s economy is inseparable from the growth of the industrial sector. However, the extensive scale of traditional industrial enterprises and the relatively outdated technology not only result in substantial resource consumption in the production process but also lead to severe pollution of the ecological environment. Since 1970, the proportion of China’s annual carbon emissions has continuously risen, reaching 28% in 2019, making it the top emitter globally [1]. The Chinese government is under unprecedented international pressure to decrease greenhouse gas emissions. The previous extensive economic development model is unsustainable. Environmental and climate issues have become the focus of global attention. In order to fulfill its responsibilities as a major country and demonstrate its contribution, the Chinese government proposed the strategic objective of “peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060” at the 75th session of the United Nations General Assembly in 2020. The goal of “carbon peaking and carbon neutrality” is a significant strategic decision made by China, based on the inherent requirements of promoting the responsibility of building a community with a shared future for mankind and achieving sustainable development. The proposal of this objective can not only lead to multiple effects of improving environmental quality and industrial development but also help promote the green transformation of the economic structure in China. It can accelerate the establishment of green production methods and drive high-quality development, thereby creating more improved development opportunities for China.
The low-carbon transformation of enterprises is a crucial aspect of pollution control and the development of a green economy. Promoting the reduction in pollution and carbon emissions at the enterprise level is the foundation for China to achieve its “dual carbon” strategic goal. From the macro-environmental perspective, some scholars have pointed out that various measures, such as environmental regulations [2,3,4], the digital economy [5,6,7], carbon emissions trading [8,9], green finance [10], green credit [11,12], tax system greening [13], and industrial structure optimization [14], can effectively reduce regional or corporate carbon emissions. At the micro-enterprise level, other scholars have indicated that green technology innovation [15,16,17,18,19], digital transformation [20,21], and other factors can also help improve corporate carbon performance and assist enterprises in achieving green and low-carbon transformation. It is evident from the existing literature that the influencing factors driving enterprises to reduce carbon emissions are diverse. However, current scholarly research mostly employs traditional analytical methods, focusing on the linear relationship between a single influencing factor and enterprise carbon emission reduction. This approach fails to unveil the complex combination effect of multiple causal variables behind this phenomenon.
A growing number of academics have recently utilized fuzzy set qualitative comparative analysis (fsQCA), which offers the advantage of systematically examining the interactions between various elements in a wide range of domains. Throughout the existing literature, numerous scholars have employed fsQCA to examine high-performance configuration paths [22], green transformation paths [23], and enterprise digital transformation paths [24]. These findings have helped many businesses achieve high-quality development. The efficient low-carbon transformation of enterprises is crucial for the progression of the green economy. However, a majority of the current studies on green and low-carbon transformation are grounded in regional analysis of provincial and municipal data, with a limited focus on micro-enterprises. There remain gaps in the research concerning the use of fsQCA to investigate the configuration pathway towards low-carbon transformation for heavily polluting industrial enterprises, which deserves further comprehensive exploration.
Therefore, based on the TOE theoretical framework, this study focuses on heavily polluting industrial enterprises and uses the fsQCA method to empirically investigate the configuration pathways for green and low-carbon transformation among 65 heavily polluting industrial enterprises in China. The interactions between various elements are systematically analyzed, and the mechanisms of different configurations in low-carbon transformation are summarized. The multiple transformation paths of heavily polluting enterprises are identified, and typical enterprises are selected for in-depth analysis within each path. This study holds significant reference value for guiding enterprises in various industries on how to efficiently carry out green transformation and successfully upgrade themselves.

2. Literature Review and Theoretical Framework

2.1. Literature Review

The low-carbon transformation signifies a paradigm shift in the development model, moving away from a resource-intensive and environmentally polluting approach towards a more sustainable one that conserves resources and preserves the environment.
Carbon emission reduction stands as a pivotal aspect in addressing environmental challenges and fostering a low-carbon economy, garnering significant attention from scholars both domestically and internationally. Based on the current literature, the factors influencing carbon emission reduction can be broadly categorized into macro and micro factors.

2.1.1. Macro Factors

Most scholars adopt a macro perspective, emphasizing the influence of external environmental factors on carbon emission reduction. These factors can be further subdivided into policy and non-policy components.
Firstly, numerous scholars have conducted comprehensive analyses on the correlation between government policy implementation and carbon reduction, with their findings affirming the efficacy of environmental policies in the Chinese context. Tian et al. observed that the emission reduction impact of green credit policies exhibits heterogeneity, noting a more pronounced promotional effect on small and private enterprises [11]. Xuan et al. demonstrated through their research that China’s carbon emission trading policy contributes to a continuous and stable reduction in carbon dioxide emission intensity [25]. Zhang et al. further emphasized that this effect is particularly notable in the economically developed eastern regions of China [26]. Li’s research revealed that carbon emission trading and carbon tax policies can collaborate to facilitate carbon emission reduction [27]. Wu et al. found that local energy subsidies targeting green growth for clean energy and low-carbon industries positively influence carbon emission efficiency [28]. Li’s study indicated that government subsidies effectively encourage companies to further decrease their carbon emissions [29].
Furthermore, several scholars have explored the impact of non-policy external environmental factors on carbon emission reduction. Liu et al. identified a U-shaped correlation between industrial agglomeration and carbon emissions in their study [30]. Yan et al. noted that while the regional digital finance index reduces local carbon emissions, it increases emissions in adjacent areas [31]. Fan et al.’s research revealed that a lower dependence on natural resources corresponds to higher carbon emission efficiency [32]. Xie et al. found that the development of information infrastructure can lower carbon emission intensity [33]. Li’s study demonstrated that ESG ratings have a notable suppressive effect on corporate carbon emissions, with this effect being more pronounced in heavily polluting enterprises [34].

2.1.2. Micro Factors

Scholars have examined the factors influencing carbon emission reduction from the perspective of micro-enterprises, with current discussions focusing primarily on the degree of green technology innovation and enterprise digitization. Gao et al. underscored the significance of upgrading the industrial structure via green technology innovation to curtail carbon emissions in their study [35]. Du and Zhou posited that green technology innovation serves as an effective means to attain carbon neutrality and sustainable development [36]. Chen observed an inverted U-shaped relationship between green technology innovation and carbon emissions, indicating an initial increase followed by a subsequent decrease [37]. Wang’s research revealed that, in the long term, the digital transformation of enterprises enhances carbon emission efficiency [38]. Huang et al. emphasized that digital transformation holds the potential to lower carbon intensity, highlighting the critical role of domestic digital resources in this process [39].
Additionally, Zhang et al. developed an enterprise carbon budget system that considers carbon emissions, reduction costs, carbon emission rights trading, and the net profit and loss of carbon emission reduction. This system can dynamically adjust carbon emission reduction behaviors [33]. Dan et al. found that efficient asset utilization can alleviate the negative impact of the fixed asset ratio on emission performance [40]. M. Ma et al. revealed that investments in green technology can facilitate CO2 emission reduction [41]. Wu et al. studied the impact of different corporate social responsibility (CSR) commitment models on emission reduction. They concluded that retailers’ commitment to social responsibility is the most effective in promoting a low-carbon supply chain [42].
After conducting a thorough review of the relevant literature, it is evident that multiple factors impact carbon emission reduction. However, current research often focuses on isolated factors, neglecting a comprehensive analysis of the interconnectedness among various conditions. Additionally, there is a notable lack of studies specifically focused on heavily polluting enterprises. Therefore, further clarification is urgently needed to outline the complex configuration of multifaceted influences on the low-carbon transformation of these enterprises. This paper utilizes fuzzy set qualitative comparison analysis and the TOE framework to provide a holistic examination of this topic.

2.2. TOE Framework

Tomatzky and Fleischer first introduced the Technology–Organization–Environment (TOE) theory framework in 1990. Initially, this framework was utilized to analyze the factors influencing technological innovation in enterprises. Later, as the theoretical framework progressed and was enhanced, researchers conducted numerous empirical studies using the TOE framework. The essence of this model has been consistently enriched under various technical application scenarios.
The framework divides the factors that affect enterprise and organizational behavior and decision-making into three primary categories—technological, organizational, and environmental factors [43]. Technological factors primarily relate to the technology’s inherent traits and its connection to the organization. They center on whether the technology aligns with the organization’s structural attributes, whether it harmonizes with the organization’s application capabilities, and its potential to deliver benefits to the organization [44,45]. Organizational factors predominantly concern the organization’s internal characteristics, encompassing elements like enterprise size, business scope, formal and informal institutional arrangements, reserve savings, and various other aspects [44,46]. Environmental factors refer predominantly to the external conditions and market structures within which the organization operates, such as government regulatory policies [47,48].
Given that the TOE framework does not prescribe specific explanatory variables for its three dimensions, it exhibits high flexibility and broad applicability. Researchers can tailor the influencing factors at various levels to suit their unique research requirements, facilitating a more nuanced analysis of the underlying causes of diverse social phenomena. The environmentally responsible and low-carbon transition of heavily polluting enterprises represents a multifaceted process influenced not only by technological considerations but also by internal organizational dynamics and external environmental factors. Notably, several scholars have already adopted this framework to investigate the multi-dimensional factors influencing carbon emission reduction [49,50].
Therefore, based on the TOE framework model, this paper explores the various driving pathways for heavily polluting enterprises to achieve eco-friendly and low-carbon transformation. The specific analysis framework is illustrated in Figure 1.

2.3. Theoretical Model

2.3.1. Technology

Technological factors include green technology innovation and the digital transformation of enterprises.
(1)
Green Technology Innovation (GI): Scholars have examined carbon emissions from various perspectives, and technology innovation as a key factor in addressing climate change has received widespread attention. Green technology innovation is a form of innovation that adheres to ecological and economic principles and is markedly distinct from conventional technology innovation. Compared to traditional innovation models, green technology innovation is more targeted towards low-carbon development and can offer substantial support for countries to cope with climate change. According to the Porter hypothesis, the higher the level of green technology innovation, the more likely it is to produce a “green innovation compensation effect,” which can effectively mitigate negative environmental impacts while enhancing the competitiveness of enterprises [51], ultimately helping achieve the goal of carbon emission reduction.
(2)
Digital Transformation (DT): Digital transformation refers to the improvement in enterprise organizational structure and business models through the use of information technology and is of significant theoretical importance for the sustainable development of low-carbon enterprises. Digital transformation has the potential to enhance collaboration among enterprises, optimize supply chains, product development, and production processes by means of data and information sharing [38]. The integration of advanced technology and the utilization of digital communication technology can not only improve the efficiency of enterprise production factors and energy utilization, and facilitate the automation of the production process, but it can also promote the refinement of material input, product manufacturing, and sales processes. It enables enterprises to exert precise control over the production process, reduce energy consumption and waste, and have a positive impact on reducing carbon emissions for the enterprises.

2.3.2. Organization

Organizational factors include firm size and managers’ environmental awareness.
(1)
Firm Size (FS): Large-scale enterprises have more abundant funds and resources, which can not only enhance the environmental resilience and risk-bearing capacity of the enterprise but also provide greater flexibility for experimentation in the low-carbon transformation process. Additionally, it enables the timely acquisition and integration of external advanced technologies. It is also conducive to facilitating the refined division of labor, collaboration, and specialized production, ultimately promoting a reduction in carbon emissions.
(2)
Managers’ Environmental Awareness (MEA): Senior managers in the enterprise perceive the needs and expectations of community residents, employees, and public welfare organizations as their own responsibility. Their environmentally friendly actions, based on the health needs of stakeholders, can not only trigger a positive response from society and establish a good moral reputation but also increase employee satisfaction and cultivate a sense of identity with green culture. This, in turn, ensures the smooth progress of green innovation of enterprises [51] and further assists in achieving corporate low-carbon transformation.

2.3.3. Environment

Environmental factors encompass environmental regulations and corporate environmental, social, and governance ratings (ESGR).
(1)
Environmental Regulations (ER): Due to the negative externalities of environmental issues and the ambiguity of environmental property rights, it is difficult to rely solely on market forces to address environmental problems. Faced with the dilemma of market failure, we must rely on the power of environmental regulations issued by the government. The effectiveness of environmental regulations falls into the following two categories: The “environmental refuge hypothesis” holds that environmental regulations can internalize the externality of pollution. As the prices of environmental resources and natural elements increase, the pollution costs for companies will also correspondingly increase. Therefore, environmental regulations can constrain corporate carbon emissions through compliance cost effects [52]. The “Porter hypothesis” suggests that reasonable environmental regulations can promote technology innovation, reduce production costs through green innovation compensation effects, and effectively suppress carbon emissions [53].
(2)
Environmental, Social and Governance Ratings (ESGR): Promoting the eco-friendly and low-carbon transformation of enterprises is a critical approach to achieving high-quality development in China. Compared to formal environmental regulations that force enterprises to passively undergo low-carbon transformation, informal environmental regulations can more effectively stimulate the intrinsic motivation of enterprises. ESG ratings play a crucial role in connecting enterprises with the market, enhancing the external information environment of enterprises, and establishing incentive-compatible market governance mechanisms for the sustainable development of enterprises [54].

3. Methods

3.1. Sample Selection and Data Source

With the economy continuing to develop, an increasing number of people are paying attention to the issue of environmental protection and green development. The green and low-carbon transformation of enterprises has also become a hot topic for many scholars. This study focuses on the A-share-listed heavily polluting industrial enterprises in various provinces. Referring to the research of Lin et al., the identification of heavily polluting industries mainly relied on the “Industry Classification Guidelines for Listed Companies” revised by the China Securities Regulatory Commission in 2012, the “Industry Classification Management Catalog of Environmental Protection Inspection for Listed Companies” formulated by the Ministry of Environmental Protection in 2008, and the “Guidelines for Environmental Information Disclosure of Listed Companies”. It mainly includes 16 heavily polluting industries, such as coal, mining, textiles, tanning, papermaking, petrochemicals, pharmaceuticals, chemicals, metallurgy, and thermal power [55]. All antecedent and outcome variables were selected for the 2022 data. During the data filtering process, companies with missing data for key variables were excluded, as well as those labeled as ST or *ST. In the end, a total of 65 listed companies were included in the empirical analysis.
The sample data were sourced from various databases. Referring to the previous studies, the data on corporate green patents were obtained from the National Intellectual Property Administration [56]. The frequency of corporate digital transformation and the data on managers’ environmental awareness were extracted from the annual reports of the listed companies [57,58]. Corporate total assets and main business income data were sourced from corporate financial reports. The data of environmental regulations were collected from the annual reports of listed companies and provincial databases in China [59]. The data on ESG ratings were retrieved from the official website of Sino-Securities Index Information Service [34]. Corporate carbon dioxide emissions data were gathered from CEADs [60].

3.2. Qualitative and Comparative Analysis

Ragin proposed the method of Qualitative Comparative Analysis (QCA) in the 1980s [61]. Unlike the traditional linear regression method, which typically focuses on exploring the linear relationship between independent and dependent variables, the QCA method is based on configurational thinking. It can not only identify the necessity of each individual conditional variable but also match and combine multiple antecedent variables to explain the causal conditions and configuration of the resulting variables. This helps interpret the complex causal relationships behind the phenomenon from a holistic perspective. Due to the complexity of the low-carbon transformation process, which is influenced by various factors, and the different combinations of antecedent variables have varying effects on this process, this article utilized the fuzzy set qualitative comparative analysis method (fsQCA) to investigate the configuration pathways of low-carbon transformation in heavily polluting enterprises.

3.3. Data Measurement

3.3.1. Measurement of Outcome Variables

The outcome variable of this study was the corporate carbon emission intensity. According to the research by Li [34] and Zheng [62] et al., this intensity is measured as the ratio of corporate carbon dioxide emissions to the main business income of the enterprise. A smaller ratio indicates a lower intensity of carbon dioxide emissions. The formula for this calculation is presented below:
E n t e r p r i s e   c a r b o n   i n t e n s i t y = E n t e r p r i s e   c a r b o n   e m i s s i o n E n t e r p r i s e   m a i n   b u s i n e s s   i n c o m e  

3.3.2. Conditional Variables Measurement

(1)
Technological level
Green Technology Innovation: In the existing literature, numerous researchers have adopted the number of patent applications as a metric to gauge the level of innovation. This choice is justified by the fact that the timing of patent application aligns more closely with the actual time of patent invention compared to patent authorization. As such, it serves as a more accurate reflection of the current state of technological innovation. Additionally, patent authorization is susceptible to external influences, often characterized by delays and uncertainties. Following the precedent set by scholars such as Du [18] and Zhang [63] et al., who utilized the number of green technology patents to evaluate the level of green technology innovation in their respective studies, this paper employed the aggregate of green invention patent counts and green utility model patent counts as a measure of green technology innovation.
Digital Transformation: According to the information disclosure principles outlined in the annual report of the China Securities Regulatory Commission, enterprises are mandated to disclose the contents of “management discussion and analysis” in their respective annual reports. Zhong et al. noted in their research that enterprises emphasizing digital transformation are more likely to disclose such information. Following the methodology used by Zhao [57] and Zhong [64], this study employed text analysis to evaluate the extent of enterprise digital transformation, a widely accepted practice in academic circles. The specific methodology involves analyzing the frequency of 99 digital feature words across the following four dimensions: digital technology application, Internet business models, intelligent manufacturing, and modern information systems, as extracted from the enterprise’s annual report.
(2)
Organizational level
The measurement indicators for firm size commonly encompass factors such as the number of employees, total enterprise assets, and main business income. The concept of “firm size” frequently serves as a control variable in various studies. Drawing from previous research [55,65], this paper opted to utilize the total assets of enterprises as the primary metric for firm size.
Managers’ Environmental Awareness: According to the Whorf–Sapir hypothesis, an individual’s cognition can be inferred from the words they use in social interactions, as these words reflect their inner thoughts and beliefs. Therefore, analyzing the frequency of specific words can reveal an individual’s cognition. Building on Lin [66] et al., this study evaluated managers’ environmental awareness by analyzing the frequency of terms related to green cognition in the annual reports of enterprises. These terms encompass, among others, energy conservation, emission reduction, environmental protection strategies, and environmental management frameworks.
(3)
Environmental level
Environmental Regulations: Administrative environmental regulation is the most prevalent form of environmental control in China. It refers to the environmental laws, regulations, and policies established by governmental departments or environmental protection agencies, with its defining characteristic being mandatory compliance [67]. Following the methodology introduced by Liu et al. [59], this study calculated the proportion of investment in industrial pollution mitigation measures, such as waste gas and wastewater treatment, in the vicinity of listed companies compared to the total industrial output value. This ratio served as a metric for evaluating the impact of environmental regulations.
ESG Ratings: With the capital market’s increasing emphasis on the environmental, social, and governance (ESG) performance of enterprises intensifies, various market institutions have introduced ESG ratings for publicly traded companies. Sino-Securities is a leading evaluation agency in this field, employing a rating system where AAA, AA, A, BBB, BB, B, CCC, CC, and C correspond to scores ranging from 9 to 1, respectively. This paper followed the methodology outlined by Yang [68] and utilized the ESG rating scores of listed companies as a primary measurement index.

3.3.3. Calibration

The first step in fsQCA analysis was to calibrate the data by transforming the variables into fuzzy set variables ranging from 0 to 1. Following the approach of Andrews [69] and Chen [49] et al., the direct method was used to calibrate each variable in this research.
The specific practice was as follows: For the six antecedents, the calibration point of “fully affiliated” was 95%, the calibration point of “fully unaffiliated” was 5%, and the calibration point of “intersection” was 50%.
Considering that the carbon emission intensity of heavily polluting enterprises is negatively correlated with the low-carbon transition, meaning that the higher the carbon emission intensity, the lower the level of low-carbon transition. Therefore, the calibration point of “fully affiliated” was 5%, the calibration point of “fully unaffiliated” was 95%, and the calibration point of “intersection” was 50%.
The results of “fully affiliated”, “intersections” and “fully unaffiliated” of the six independent variables and the dependent variable are presented in Table 1.

4. Results

4.1. Necessity Condition Analysis

Before implementing the configuration of conditions, a necessity analysis was required to determine whether the presence of a single condition was essential for the outcome variable. According to the judgment criteria of fsQCA, if the consistency of a single variable exceeds 0.9, it can be concluded that the variable is a necessary condition for the outcome.
As observed from Table 2, the consistency of all six antecedent conditions falls below 0.9, implying that these six independent variables do not constitute necessary conditions for the low-carbon transformation of heavily polluting enterprises. It is crucial to highlight that in fsQCA, the necessity test value is relative rather than absolute. A value less than 0.9 does not signify the insignificance or inadequacy of these variables; they may still hold a pivotal role within a specific configuration. Given that these six variables alone do not form the requisite conditions for the low-carbon transition of heavily polluting enterprises, it becomes imperative to carry out a more comprehensive sufficiency analysis to examine their configurational effects.

4.2. Sufficiency Analysis Performance

After analyzing the necessity of each individual variable, it was important to conduct a sufficiency analysis of conditional configurations. The sufficiency analysis of conditional configurations can reveal the impact of various configurations made up of multiple conditions. Following the practices of Schneider [70], this study set the consistency threshold to 0.83, frequency threshold to 1, and PRI value to 0.75 when constructing the truth table. The results of the sufficiency analysis are shown in Table 3.
From Table 3, it was discovered that there are eight distinct configuration paths that can facilitate the low-carbon transformation of heavily polluting enterprises. These paths exhibit a consistency of 0.915625 and a coverage of 0.571567. This finding suggests that these eight configuration paths collectively possess an explanatory power of approximately 57.2% for achieving a high-level low-carbon transformation in heavily polluting enterprises, demonstrating significant explanatory strength.

4.3. Robustness Test

According to most empirical studies, the robustness of fsQCA results can be tested by adjusting the consistency threshold or PRI threshold. In this study, the original consistency threshold was increased from 0.83 to 0.88 while maintaining the raw frequency threshold unchanged. The results still reveal eight configurational paths, and consistency, raw coverage, unique coverage, solution consistency, and solution coverage all align with the threshold of 0.83. Therefore, the results can be considered robust.

5. Discussion

This section primarily consists of a detailed analysis of each configuration path and an examination of typical enterprise cases. The analysis framework is illustrated in Figure 2.

5.1. Single-Factor Driving Types

(1)
Managers’ environmental awareness drive: Configuration 1 (HLCT1) indicates that managers’ environmental awareness is the key factor driving the low-carbon transformation of enterprises. According to Table 3, Configuration 1 demonstrates a consistency of 0.907675 and a raw coverage of 0.303842, suggesting that this path can account for approximately 30% of the cases. Executives with high environmental awareness are more likely to be responsive to environmental policies, which is beneficial for steering enterprises towards eco-friendly development and promoting sustainable practices. Therefore, it is crucial for heavily polluting industrial enterprises to acknowledge the significance of managers’ environmental awareness, enhance managerial cognition guidance, particularly in recognizing opportunities for low-carbon development, and fully utilize managers’ influential role in strategic decision-making.
(2)
Digital transformation drive: In the context of the digital economy, heavily polluting enterprises can improve the utilization of digital resources and establish intelligent and eco-friendly operations through digital transformation. This is a crucial strategy for enterprises to achieve the goal of low-carbon development. The coefficient for configuration 2 (HLCT2) is 0.286556, indicating that this approach can explain approximately 28.7% of the cases.
(3)
Green technology innovation drive: Science and technology are the primary productive forces. Heavily polluting enterprises prioritize research and development, as well as technological innovation in their production processes. They implement technology to address carbon pollution and consistently make adjustments and upgrades in practice, which will effectively assist enterprises in achieving low-carbon transformation. The raw coverage of configuration 6 (HLCT6) is 0.161363, indicating that path 6 can explain about 16.1% of the cases.

5.2. Dual-Factord Driving Types

(1)
Digital transformation–Environment Regulation drive: The raw coverage of Configuration 3 (HLCT3) is 0.286556, indicating that the combination of digital transformation and environmental regulations can account for approximately 28.7% of the cases. Table 3 reveals that, in this scenario, both digital transformation and environmental regulations are considered marginal factors. Configuration 2 (HLCT2) demonstrates that high-level digital transformation can stimulate low-carbon innovation in enterprises. There appears to be a complementary relationship between the intensity of environmental regulations and the digital transformation of enterprises. When the level of digital transformation alone is insufficient to drive low-carbon transformation, the government can effectively compensate for this shortfall by enhancing the intensity of environmental regulations. This approach can help achieve the objective of green transformation and upgrading of enterprises.
(2)
Digital transformation–ESG ratings drive: The raw coverage of Configuration 4 (HLCT4) is 0.286556, indicating that the combined impact of digital transformation and ESG ratings can account for 28.7% of the cases. In comparison to Configuration 3, both digital transformation and ESG ratings are fundamental factors in this pathway, suggesting that their combined influence is more substantial. Unlike government mandates, ESG ratings act as informal environmental regulations that can indirectly encourage companies to voluntarily implement carbon reduction measures and actively explore various carbon reduction strategies. Consequently, the impact of low-carbon transformation is more effective and comprehensive.

5.3. Multi-Factor Driving Types

(1)
Technological development–Resource orientation–Environmental regulations–Incentive compound drive (GI–DT–MEA–ER): Configuration 5 (HLCT5) suggests that a combination of high green technology innovation, significant digital transformation, strong environmental awareness among managers, and intense environmental regulations can effectively promote low-carbon transformation. This configuration path has an explanatory efficiency of about 22%, with all four elements being core conditions for success. The synergistic effect of managers’ environmental awareness and environmental regulations propels the progress of low-carbon transformation in heavily polluting enterprises through enhanced green technology innovation and the adoption of digital transformation.
(2)
Environmental regulations–Innovation capability–Resource support synergy drive (GI–FS–ER): Configuration 7 (HLCT7) explains approximately 15.2% of high-level green transformation cases, indicating that under the auxiliary role of environmental regulations, high-level green technological innovation and large-scale enterprises can lead to high-level low-carbon transformation. Large-scale enterprises have more abundant capital resources. To comply with government environmental policies, large-scale enterprises will invest more resources into carbon reduction activities, allowing more room for trial and error in green technology research, development, and innovation. Moreover, large-scale enterprises can leverage existing green technologies from the market for internal carbon reduction activities through external channels to facilitate enterprises’ low-carbon transformation.
(3)
Environmental awareness–Institutional safeguards–Technological Innovation collaborative drive (GI–FS–ESGR): These are the key factors in Configuration 8 (HLCT8), indicating that a high level of green technological innovation, large firm size, and high ESG ratings can lead to high-level low-carbon transformation. This pathway can explain about 15.9% of the cases where green technology innovation is the core condition, and firm size and ESG ratings are auxiliary conditions. To achieve high ESG ratings, more enterprises are recognizing the importance of green transformation and taking action to reduce carbon dioxide emissions. In this process, firm size plays an auxiliary role; large-scale enterprises can enhance the level of green technology innovation by providing more financial resources and allocating technology research and development personnel, thereby facilitating corporate low-carbon transformation.

5.4. Typical Enterprise of Each Path

A typical example of HLCT1 (MEA drive) is Shenghe Resources Holding Co., Ltd., Chengdu, China. The company has consistently adhered to the concept that “Lucid waters and lush mountains are invaluable assets”. In 2022, efforts were made to strengthen environmental management and pollution prevention. An environmental supervisor management system was established, and employees’ environmental awareness was reinforced through various training programs. Various environmental regulations were strictly enforced in the production process, and the discharge of untreated pollutants was prohibited.
A typical case of HLCT2 (DT drive) is Zhejiang Huahai Pharmaceutical Co., Ltd., Taizhou, China. In 2022, Zhejiang Huahai Pharmaceutical Co., Ltd. fully utilized digital empowerment, tapped into their internal potential, promoted equipment automation transformation, optimized processes, effectively controlled costs, carried out initiatives to increase production and reduce consumption, and aimed to improve quality and efficiency. The company continued to steadily advance energy-saving and carbon-reduction work with the dual drivers of “technology and management”.
A typical case of HLCT 3 (DT + ER drive) is also Zhejiang Huahai Pharmaceutical Co., Ltd., Taizhou, China, as they have established a comprehensive quality management system based on regulatory laws and regulations requirements and the characteristics of its product types, and have been continuously optimizing it. The company utilizes digital and information tools to comply with the updates in domestic and foreign regulatory regulations and policies, integrates with the new development and supervision situations at home and abroad, and drives enterprises towards achieving low-carbon transformation.
A typical case of HLCT 4 (DT + ESGR drive) is China Resources Double-crane Pharmaceutical Co., Ltd., China. In 2022, China Resources Double-crane Pharmaceutical Co., Ltd. formulated a digital transformation plan and roadmap, optimized the digital talent structure, comprehensively promoted digital supply chain, digital marketing, intelligent manufacturing, and online platforms for research and development management, and conducted core digital transformation capability building. In its management practices, it has continuously improved the ESG management system and institutional framework, strengthened the integration of ESG leadership, coordination, and implementation, and promoted green and low-carbon development of the company.
The typical case of HLCT 5 (GI + DT + MEA + ER drive) is Sunstone Development Co., Ltd., Dezhou, China. In 2022, Sunstone Development Co., Ltd. was committed to innovating production technology and equipment, maximizing the recovery of residues in the production process, applying non-burning filling materials instead of metallurgical coke technology, and reducing the use of raw materials. Furthermore, while developing green technologies, the company actively promoted 13 digital projects represented by the smart financial system, industrial data research system, and digital supply chain system. The managers attached great importance to carbon emission reduction activities and actively promoted the development concept of “dual driving with two wings, low-carbon intelligent manufacturing” within the company, carried out technical exchange activities, and advocated employees to practice green office and low-carbon life.
A typical example of HLCT6 (GI drive) is Jinduicheng Molybdenum Co., Ltd., Xi’an, China. In 2022, the company continued to replace old and energy-intensive transformers and motors. In the overall mining and beneficiation upgrading project of Jinduicheng Molybdenum Mine, international advanced technology and equipment were selected to improve the level of tooling, promote cleaner production, and reduce pollutant intensity, such as material consumption, energy consumption, and water consumption of unit products to the first level of cleaner production and advanced level of peers. This initiative helped achieve the goal of carbon peaking and carbon neutrality.
A typical case of HLCT 7 (GI + FS + ER drive) is Yunnan Copper Co., Ltd., Kumming, China. The company’s operating income has exceeded 100 billion for two consecutive years. While achieving economic benefits, the company increasingly focuses on green development. In 2022, the company pioneered the development of the “combination of primary and secondary processes, comprehensive treatment at low, medium, and high levels, low-cost and efficient recycling of copper” technology, truly integrating economy, efficiency, and ecology, and actively promoting seven technologies, including “key technology for carbon smelting of high impurity copper raw materials”. In response to national strategies and policies and external environmental regulations, the company focused on implementing supervision policies and enhanced the carbon reduction awareness of executives and employees.
A typical case of HLCT8 (GI + FS + ESGR drive) is China Yangtze Power Co., Ltd., Beijing, China. This company is classified as a large enterprise, with ample internal funds and abundant resources. In 2022, the company closely followed the global energy structure transformation trend, enhanced the scientific and technological innovation system and mechanism, expedited the establishment of scientific and technology innovation platforms, conducted forward-thinking and strategic research on clean energy generation technology, continuously explored new business models, and dedicated efforts to constructing a zero-carbon industrial chain. Furthermore, China Yangtze Power Co., Ltd., Beijing, China, deeply integrated ESG management with enterprise production and operation management, as well as engaged in extensive practical applications.

5.5. Comparison between Paths

Firstly, both HLCT2 and HLCT6 are primarily driven by a single technological factor. However, HLCT2 demonstrates stronger explanatory efficiency compared to HLCT 6. Consequently, it can be inferred that digital transformation alone exhibits a superior carbon reduction effect compared to green technology innovation alone, suggesting a certain degree of substitutability between digital transformation and green technology innovation.
Secondly, an analysis of HLCT3 and HLCT4 reveals that the synergistic effects of digital transformation and environmental factors significantly facilitate the low-carbon transformation of heavily polluting enterprises. The explanatory efficiency of these two paths is comparable, indicating that when heavily polluting enterprises perceive a pronounced external influence, whether it be mandatory environmental regulation or non-mandatory ESG ratings, they are more likely to pursue digital transformation and upgrading to enhance carbon emission reduction efforts. Furthermore, there exists a certain degree of substitutability between environmental regulation and ESG ratings.
Lastly, when comparing HLCT8 with HLCT7, it becomes evident that as the environmental factor shifts from environmental regulation to ESG ratings, the importance of “firm size” transitions from being a core condition to being a marginal one. This suggests that the significance of firm size diminishes when heavily polluting enterprises are subjected to the non-mandatory pressure of ESG ratings. In contrast to HLCT3 and HLCT4, the paths represented by HLCT7 and HLCT8 indicate that large-scale heavily polluting enterprises, when influenced by external factors, tend to prioritize green technology innovation as a means to facilitate their low-carbon transformation.

6. Conclusions and Implications

6.1. Conclusions

This study examined 65 heavily polluting industrial enterprises in China using the fsQCA method. Based on the TOE theoretical framework, it investigated the synergistic driving effect of six conditional variables, including green technology innovation, digital transformation, firm size, managers’ environmental awareness, environmental regulations, and ESG ratings on the low-carbon transformation of enterprises. The study has revealed multiple configuration pathways leading to a high level of low-carbon transformation in heavily polluting enterprises. The results have revealed the following: (1) The low-carbon transformation of heavily polluting enterprises is the result of the joint action of multiple factors; (2) there are eight pathways that can promote corporate low-carbon transformation, roughly divided into single-factor driving types (including MEA drive, DT drive, and GI drive), dual-factor driving types (DT–ER drive and DT–ESGR drive), and multi-factor driving types (including GI–DT–MEA–ER drive, GI–FS–ER drive, and GI–FS–ESGR drive). It can be concluded that there may be certain substitutions between green technology innovation and digital transformation, and environmental regulations and ESG ratings. (3) GI and DT are crucial to the low-carbon transformation of heavily polluting enterprises, and the latter has a more significant impact on promoting low-carbon transformation. Additionally, MEA also plays a vital role in the green transformation process of heavily polluting enterprises.

6.2. Theoretical Contributions

The theoretical contributions of this paper are outlined as follows:
(1)
While most of the existing literature focuses on regional carbon emission reduction, few studies have explored the enterprise level. This paper used the TOE framework to examine heavily polluting enterprises. Taking into account China’s specific context, six secondary conditions that impact the low-carbon transformation of these enterprises were identified, providing a basis for qualitative comparative analysis.
(2)
Through the application of the fsQCA methodology, this study elucidated the driving forces behind efficient low-carbon transformation in heavy-polluting enterprises and uncovered substitution relationships among certain conditions. Our findings revealed that multiple configurations, with technology, organization, and environment as core components, can facilitate enterprises’ low-carbon transformation through diverse yet effective paths.
(3)
This research further illustrated each path with representative enterprise cases, enabling other enterprises to select the appropriate models for reference and learning based on their individual characteristics and external environmental factors.

6.3. Practical Inspirations

6.3.1. Enterprise Level

(1)
The synergy of multiple factors should be given significant consideration. For heavily polluting enterprises, managers should first realize that low-carbon transformation is caused by the interaction of multiple factors. They are supposed to use a configurational perspective to comprehensively consider various factors in different dimensions and take reasonable measures to achieve low-carbon transformation.
(2)
Emphasis should be placed on technological factors, especially digital transformation. The conclusions of this article highlight the significant role of technology development in the low-carbon transformation process of heavily polluting enterprises. Green technology innovation or digital transformation, when integrated with other factors, can effectively drive high-level low-carbon transformation in these enterprises. In comparison to green technology innovation, digital transformation synergistically drives change when combined with external environmental regulations. It is less constrained by the characteristics of the enterprises themselves and proves to be more efficient in reducing carbon emissions. Therefore, companies should understand local government environmental policies and ESG rating standards, then develop digital transformation strategies tailored to their needs.
(3)
The environmental awareness of senior executives (i.e., managers) is also crucial for the low-carbon transformation of heavily polluting enterprises. Therefore, managers should enhance their sensitivity to environmental policies, their ability to identify opportunities for green transformation and upgrading of enterprises, their capacity to predict and assess risks, continuously improve their dynamic management capabilities, acquire new knowledge to adapt to changing circumstances, uphold the firm’s long-term development, and steer the sustainable development of the enterprise.

6.3.2. Government Level

(1)
As a visible hand, the government should fully leverage environmental regulations to drive the low-carbon transformation of industrial firms. According to the findings of this study, environmental regulations can synergistically drive the low-carbon transformation of enterprises in conjunction with technological and organizational factors. Therefore, it is necessary for the government to strengthen the enforcement of environmental regulations, increase regulatory efforts, and fully leverage the role of environmental regulations in promoting the low-carbon transformation process of heavily polluting industrial companies.
(2)
The government should strengthen the support and guidance of enterprise technology innovation. Technological factors are of critical significance in the process of low-carbon transformation of heavily polluting industrial enterprises. However, there are many difficulties for enterprises to achieve technology-driven low-carbon transformation by relying solely on their own innovation. Therefore, it is undeniable that the government should prioritize the challenge of independent research and development innovation of enterprises as well as create a more equitable and open innovation environment through financial fund allocation, establishing specific projects, or recruiting and nurturing technology research and development talents. This will help steer enterprises towards actively engaging in technology innovation activities.
(3)
In addition to environmental regulations, the government should also focus on the role of subsidies in the low-carbon transformation of heavily polluting enterprises. Government subsidies for enterprises can not only help them solve the capital bottleneck in development but also send a positive signal to enterprise managers, indicating that the government encourages and supports carbon emission reduction activities, along with providing assistance for enterprise green transformation.

6.4. Limitations and Future Directions

There are certain limitations inherent in this study. Firstly, there is potential for enhancement in the selection of variables. Previous research has indicated the diversity of factors influencing carbon emission reduction. However, this paper, grounded in the TOE framework, has only examined six variables spanning technology, organization, and environment, excluding numerous other influential factors such as carbon emission trading policies and industrial structure from the discussion. Future investigations could explore the synergistic effects of a broader range of factors on corporate carbon emission reduction, leveraging alternative frameworks. Secondly, constrained by data availability, this study solely analyzed data from a single year, omitting cross-year case data. This limitation restricts the explanatory depth of the research findings in the diachronic dimension. In future investigations, panel data could serve as a valuable tool to dynamically scrutinize and unpack the evolving, multi-faceted journey of heavily polluting enterprises toward a low-carbon transformation.

Author Contributions

Conceptualization, X.S.; Methodology, S.D.; Writing—original draft, S.D.; Writing—review & editing, X.S.; Supervision, X.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Yangzhou Soft Science Project (Yangzhou, China)] grant number [YZ2019159, YZ2022229]. And the APC was funded by [Jiangsu Province Shuangchuang Doctoral Talent Project (Jiangsu, China)].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 16 05826 g001
Figure 1. TOE Theoretical Model.
Figure 1. TOE Theoretical Model.
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Figure 2. The configuration paths of low-carbon transformation of heavily polluting enterprises and typical cases.
Figure 2. The configuration paths of low-carbon transformation of heavily polluting enterprises and typical cases.
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Table 1. Calibration of result and condition variables.
Table 1. Calibration of result and condition variables.
SetFuzzy Set Calibrations
Fully
Affiliated		IntersectionsFully
Unaffiliated
Low-carbon transformation (LCT)5.138119.081579.99
Green technology innovation (GI)10.810
Digital transformation (DT)51.6142.2
Firm size (FS)290,609.325,188.763223.58
Managers’ environmental awareness (MEA)1982
Environmental regulations (ER)0.001840.00050.0001
ESG ratings (ESGR)6.255.253.55
Table 2. Necessity analysis of single variable.
Table 2. Necessity analysis of single variable.
Antecedent ConditionsConsistencyCoverage
GI0.5133700.718485
~GI0.7019200.710517
DT0.6408950.796783
~DT0.6278250.699087
FS0.4929150.686861
~FS0.7561620.767846
MEA0.5662500.716844
~MEA0.6456350.707549
ER0.6095180.661136
~ER0.6193920.793591
ESGR0.6477040.708799
~ESGR0.5594670.709432
Note: “~” means “not”.
Table 3. Configuration paths of low-carbon transformation of heavily polluting enterprises.
Table 3. Configuration paths of low-carbon transformation of heavily polluting enterprises.
Conditional
Variables		High Level of Low-Carbon Transformation (LCT)
HLCT1HLCT2HLCT3HLCT4
GISustainability 16 05826 i001Sustainability 16 05826 i001
DT ●●●
FSSustainability 16 05826 i001Sustainability 16 05826 i001Sustainability 16 05826 i001Sustainability 16 05826 i001
MEA●Sustainability 16 05826 i001Sustainability 16 05826 i001Sustainability 16 05826 i001
ERSustainability 16 05826 i001 ●Sustainability 16 05826 i001
ESGR Sustainability 16 05826 i001Sustainability 16 05826 i001●
consistency0.9076750.9621840.9346980.943396
Raw coverage0.3038420.2865560.2865560.286556
Unique coverage0.08438750.02176480.002854820.00840735
Conditional
Variables		High level of low-carbon transformation (LCT)
HLCT5HLCT6HLCT7HLCT8
GI●●●●
DT●Sustainability 16 05826 i001Sustainability 16 05826 i001Sustainability 16 05826 i001
FSSustainability 16 05826 i001Sustainability 16 05826 i001●●
MEA●Sustainability 16 05826 i001Sustainability 16 05826 i001Sustainability 16 05826 i001
ER●Sustainability 16 05826 i001●Sustainability 16 05826 i001
ESGR Sustainability 16 05826 i001Sustainability 16 05826 i001●
consistency0.9435150.9640120.9540230.935275
Raw coverage0.2231220.1613630.152170.158953
Unique coverage0.04583430.01024070.01204780.0172599
Consistency of the solution0.915625
Coverage of the solution0.571567
Note: ● means core existence condition, Sustainability 16 05826 i001 means core missing condition; ⊕ means marginal existence condition, ⊖ means marginal missing condition; blank spaces indicate a “don’t care” condition [71,72].
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Su, X.; Ding, S. Research on the Configuration Paths of Low-Carbon Transformation of Heavily Polluting Enterprises. Sustainability 2024, 16, 5826. https://doi.org/10.3390/su16145826

AMA Style

Su X, Ding S. Research on the Configuration Paths of Low-Carbon Transformation of Heavily Polluting Enterprises. Sustainability. 2024; 16(14):5826. https://doi.org/10.3390/su16145826

Chicago/Turabian Style

Su, Xianna, and Shujuan Ding. 2024. "Research on the Configuration Paths of Low-Carbon Transformation of Heavily Polluting Enterprises" Sustainability 16, no. 14: 5826. https://doi.org/10.3390/su16145826

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