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8 November 2024

A Matching Policy to Address ESG and Non-ESG Risks Impacted by a Relocation Policy in China’s Chemical Industry

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1
Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
2
Environmental Research Center, Duke Kunshan University, Kunshan 215316, China
3
Watershed Carbon Neutrality Research Center, Nanchang University, Nanchang 330031, China
4
Nicholas Institute for Energy, Environment & Sustainability, Duke University, Durham, NC 27708, USA
Sustainability2024, 16(22), 9760;https://doi.org/10.3390/su16229760
This article belongs to the Section Economic and Business Aspects of Sustainability
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Abstract

China’s chemical industry has faced severe environmental, social, and governance (ESG) issues, such as high safety and environmental accidents and risks. To address these issues and promote industrial upgrading, China’s central government has issued a national relocation and improvement policy targeting its chemical industry. However, its countrywide policy implementation may also lead to other ESG risks during the relocation of chemical enterprises, namely industrial transfer. The typical ESG risks that appear to occur in developed eastern region provinces include a one-size-fits-all solution and unemployment, while less developed central and western region provinces may encounter pollution transfer, carbon leakage, environmental injustice, and health disparities. These ESG risks might overlap with other economic and financial (non-ESG) risks, like stranded assets, industry hollowing-out, and debt sustainability issue. These ESG and non-ESG risks could result from potential mismatches between chemical enterprises and chemical parks, categorized as mismatching errors explained by social-ecological systems, behavioral economics, and information economics. To better manage these risks, we propose an ESG matching policy comprising a national standardized ESG scoring and ranking system, a deferred acceptance mechanism, and a score announcement instrument. Such a policy innovation aims at achieving fair and efficient chemical enterprise–chemical park pairs, which would help manage both ESG and non-ESG risks and provide a just transition toolkit for China and other developing countries.

1. Introduction

China’s chemical industry has held the title of the world’s largest revenue generator since 2011, and its sales (around USD 1.5 trillion of sales) represented approximately 40% of the global chemical industry revenue in 2017 (Hong et al., 2019) [1]. However, China’s chemical industry is confronted with a range of environmental, social, and governance (ESG) issues. These ESG issues include severe environmental pollution, high safety accident rates and risks, social conflicts arising from industrialization and urbanization, along with occasional unjustified social protests driven by the “not in my backyard” (NIMBY) sentiment, and failures in sustainability risk management.
Due to the ESG issues in China’s chemical industry and its production structure dominated by low-value-added chemicals, Chinese policymakers prioritize addressing its safety and environmental risks and boosting high-quality development together. Therefore, the Chinese central government issued Guiding Opinions of the General Office of the State Council on Advancing the Relocation and Improvement of Hazardous Chemical Manufacturers in Populous Urban Areas (hereafter referred to as this relocation policy) on 27 August 2017.
This relocation policy is designed to facilitate the construction of green or eco-industrial chemical parks that allow the traditional chemical industry to be environmentally bearable, economically viable, and socially equitable (Li et al., 2015) [2]. However, its content could have been more specific, such as the assessment methods and compliance requirements for specific safety, the required environmental protection distances, and the criteria for selecting development alternatives (Meng et al., 2018) [3]. Implementing this relocation policy may generate ESG opportunities according to its good policy intention but introduce other emerging ESG risks during the industrial transfer from China’s developed eastern region provinces to its less developed central and western region provinces. For example, the vagueness of this relocation policy might facilitate a one-size-fits-all solution, an ESG risk in terms of G, in China’s policy implementation context nowadays. The one-size-fits-all solution in the first policy implementation stage (the appraising and bargaining stage) may lead to many medium and small chemical enterprises or even some large chemical enterprises (that could be improved in their current sites) being relocated from the developed eastern region provinces to the less developed central and western region provinces in the second policy implementation stage (the searching and matching stage). Thus, some safety and environmental risks might transfer across regions under the banner of legitimate policy implementation instead of being eliminated or mitigated. In other words, this well-intentioned policy is likely to cause some unfavorable pollution transfer and carbon leakage in terms of E, consequent environmental injustice and health disparity in terms of S in the less developed central and western region provinces. Also, unemployment in terms of S due to chemical firms’ relocation or shutdown might occur in the developed eastern region provinces. Furthermore, these possible ESG risks may relate to other non-ESG (economic and financial) risks: stranded assets, industry hollowing-out, and debt sustainability issue. Such relation or causation would increase the corresponding sustainability risk management difficulty.
Indeed, similar ESG risks, particularly pollution transfer and carbon leakage, arise with industrial transfer in China and other countries. China pursued industry upgrades by relocating outdated industries from its developed regions to its less developed areas, resulting in a trend of pollution transfer from its east to its central and western regions (Fu et al., 2021; Wei et al., 2019) [4,5]. Additionally, domestic industrial transfer in China has led to the relocation of more carbon emissions from East China to West China, with most sectors experiencing pollution haven effects during the relocation process (Wang et al., 2019) [6]. On an international scale, India has implemented industrial relocation policies to combat environmental degradation. These policies have also given rise to a range of ESG and non-ESG risks, including unemployment and industry hollowing-out. For instance, India’s “Second Master Plan for Delhi–Perspective 2001” mandated the cessation of operations for “hazardous/noxious/heavy/large industries” within Delhi’s vicinity (Bhuwania, 2018) [7]. This regulation required existing industrial firms falling into these categories to relocate outside city limits; the initiative, enforced through city-zoning laws, significantly contributed to the deindustrialization of Delhi; many firms unable or unwilling to bear the costs of relocation opted to close down instead, leading to widespread job losses; estimates indicate that approximately two and a half million people were directly impacted by unemployment due to the closures (Bhuwania, 2018) [7].
An underlying reason for the potential ESG and non-ESG risks associated with implementing this relocation policy lies in the mismatch between chemical enterprises and chemical parks regarding ESG attributes. China has rapidly developed numerous industrial parks since 1978 as part of its industrialization journey, with many of these parks aspiring to transition into eco-industrial parks (EIPs) due to their effectiveness in promoting sustainable development (Wang et al., 2022) [8]. While some Chinese industrial parks prioritize alignment in terms of infrastructure and industrial chains with their member enterprises, they may place less emphasis on aligning ESG attributes. Given that member enterprises significantly influence the stability and efficiency of EIPs (Zhu et al., 2010) [9], mismatches between chemical enterprises and chemical parks regarding ESG attributes may contribute to various ESG and non-ESG risks. Therefore, our motivated research questions are: What are the probable mismatching errors and ESG and non-ESG risks in the two implementation stages of this relocation policy? Why do these probable mismatching errors and ESG and non-ESG risks occur? How can China’s central government design a policy change or innovation to address these probable mismatching errors and ESG and non-ESG risks?
The following organization of our research is first to analyze ESG issues and opportunities related to the causes and effects of this relocation policy. Secondly, we will study probable ESG and non-ESG risks due to mismatching errors. Thirdly, we will explain mismatching errors together with ESG and non-ESG risks from the perspectives of social-ecological systems, behavioral economics, and information economics. Fourthly, we will propose an ESG matching policy, including a deferred acceptance mechanism, a related national standardized ESG scoring and ranking system, and a score announcement instrument. Lastly, we will engage in a discussion and draw conclusions.
Though our research lacks significant methodological innovation and primarily uses classical theoretical theories, our study holds theoretical and practical contributions. The theoretical value lies in its provision of an analytical framework for explaining mismatching errors, along with ESG and non-ESG risks, drawing from insights from social-ecological systems, behavioral economics, and information economics. Additionally, it introduces a deferred acceptance mechanism into the ESG matching process between industrial enterprises and industrial parks, which is an innovative trial in interdisciplinary research between industrial organization and industrial ecology in less researched industrial enterprise–industrial park matching area. On the practical side, the study contributes by designing an ESG matching policy that could serve as a just transition toolkit, emphasizing the ESG dimension for the developing world to cope with sustainability and industrial risk management challenges accompanied by industrial transfer. As observed in China, other developing countries or regions have encountered similar challenges related to industrial transfer, industrialization, and industrial spatial planning, such as comparable ESG and non-ESG issues and risks. Importantly, our proposed ESG matching policy has the potential to manage these risks effectively, optimizing industrial transfer while fostering synergy among stakeholders to maximize overall environmental, social, and economic benefits.
Furthermore, this paper contributes to redefining governance (G) within the context of the principal–agent problem under the ESG framework and introduces a perspective for analyzing the impacts of a single policy across different regions. While the conventional understanding of G in ESG typically focuses on corporate governance (Kim and Li, 2021; CFA, 2022) [10,11], we expand this concept to encompass both corporate governance and public governance. This expansion is warranted as corporate governance and public governance complement each other in motivating firms to adhere to ESG principles (Kuzey et al., 2023) [12]. The G issue or risk addressed in this paper involve sustainability risk management failures within the chemical industry and the one-size-fits-all solution by local governments. Although these challenges may appear distinct on the surface, they stem from the same underlying logic: the principal–agent problem. Therefore, broadening the scope of G encourages further examination of the principal–agent problem in relation to industry policy from an ESG perspective. Additionally, this paper examines the policy impacts across two distinct Chinese regions simultaneously, rather than focusing solely on a single region. This approach enhances the relevance of spatial analysis and can provide valuable insights for other studies exploring research topics that span multiple regions.

4. Elaboration on Mismatching Errors Together with ESG and Non-ESG Risks: The Perspectives of Social-Ecological Systems, Behavioral Economics, and Information Economics

The type I and type II mismatching errors, together with ESG and non-ESG risks, could be explained by social-ecological systems, behavioral economics, and information economics. Social-ecological systems can display the characteristics of mismatching errors; behavioral economics can interpret mismatching errors through a behavioral incentive perspective, and information economics can clarify mismatching errors through an information incentive perspective.

4.1. The Perspective of Social-Ecological Systems

An industrial city, although constructed by human activities, exhibits characteristics akin to those of complex social-ecological systems (SESs). In detail, a Chinese industrial city SES’ subsystems include a resource system (that is, an industrial city with its specific land, natural resources, labor, and capital), resource units (that are represented by industrial products, for example, chemical products), users (that are typical industrial enterprises, for example, chemical enterprises), and governance systems (that are local governments responsible for industrial regulation, such as regulating the chemical industry). Notably, an industry park, like a chemical park, represents an innovative approach fostering better synergy between industrialization and urbanization, functioning as a small-scale resource system within its upper resource system (the broader industrial city scale).
Regarding stakeholders in the evolution process of a Chinese industrial city, local government, industrial enterprises and parks, colleges and universities, research institutions, consumers, and financial institutions all play their roles and constitute a typical Chinese stakeholder cooperation network mode in promoting economic growth. Additionally, the public is a stakeholder concept related to NIMBY and social protests, and the public members are from all fields of society.
Due to the disparity in economic development, Chinese industrial city SESs in the developed eastern region provinces and the less developed central and western region provinces are different in their subsystems and social, economic, and political settings. The subsystems of Chinese industrial city SESs in the developed eastern region provinces are more complicated, interrelated, and mature. In addition, their social, economic, and political settings are also more developed, stable, and robust. However, all Chinese industrial cities face the same problem: ensuring and promoting the sustainability of their industrial cities. This sustainability encompasses not only natural resources but also financial viability. In other words, promoting ESG and non-ESG performance and managing the corresponding ESG and non-ESG risks are the same goals of industrial cities in the developed eastern region provinces and the less developed central and western region provinces. The typical sustainability issue of industrial city SESs in the developed eastern region provinces is safety and environmental risks and accidents, and the representative sustainability issue of industrial city SESs in the less developed central and western region provinces is relatively immature industry development. Noticing these two kinds of sustainability issues, the Chinese central government may perceive both two regions’ industrial city SESs as lacking the self-organizing forces to be sustainable in the long term. Therefore, the Chinese central government issued this relocation policy aimed at simultaneously solving these sustainability issues.
However, the potential type I and type II mismatching errors and ESG and non-ESG risks may occur in the first appraising and bargaining stage and the second searching and matching stage of this relocation policy (see Table 1). In the first appraising and bargaining stage, the type I mismatching error occurs when local governments in the developed eastern region provinces reject MSCEs that could be improved in their existing venues. This rejection, within the context of SESs, may stem from an underestimation by governance systems, represented by local governments, of the long-term productivity potential of these MSCEs. According to Hypothesis 3, though with a small scale in revenues and profits, MSCEs might harbor significant innovation and entrepreneurial capabilities. On the other hand, the two kinds of type II mismatching errors (failing to reject substandard chemical enterprises or unstandardized chemical parks) in the second searching and matching stage demonstrate the relocation decision failure. Similarly, from the viewpoint of SESs, failing to reject substandard chemical enterprises that should be closed may result from an overestimation by governance systems, represented by administrative committees of chemical parks, of the short-term economic significance of relocated chemical enterprises (the users). Moreover, the probable reason for failing to reject unstandardized chemical parks is that the relocated chemical enterprises (the users) overestimate the capacity and boundary of unstandardized chemical parks (resource systems) in being standardized in the short term.
Table 1. A further explanation in mismatching errors together with ESG and non-ESG risks.
Indeed, the long-term sustainability of SESs depends on their matching to the attributes of the resource system, resource units, and users (Ostrom, 2009) [31]. Thus, these potential mismatching errors, as well as ESG and non-ESG risks, are probably due to mismatching policy implementation in terms of the local attributes of the resource system (chemical park), resource units (chemical products), and users (chemical enterprises).

4.2. The Perspective of Behavioral Economics

These underestimations and overestimations, explained from the perspective of social-ecological systems, could also be clarified more in behavioral economics’ cognitive–emotional framework. Generally, behavioral biases that may cause deviated decisions come from cognitive errors (namely, faulty cognitive reasoning) and emotional biases (that are based on feelings or emotions); cognitive errors are further classified into two categories: belief perseverance biases and processing errors (CFA Institute, 2023) [32].
In the first appraising and bargaining stage, local governments in the developed eastern region provinces easily underestimate the productivity of MSCEs in the long term and then reject MSCEs that could be improved in their existing venues (the type I mismatching error). From Table 1, the type I mismatching error that would be embodied in the one-size-fits-all solution may be due to some behavioral biases, such as the illusion of control and confirmation bias (that both are belief perseverance biases), framing bias (that is, processing error), as well as loss-aversion bias and overconfidence (that are emotional biases). Here, the illusion of control manifests in the belief of local governments that they can exert significant influence over economic transition by reallocating land previously occupied by MSCEs. Confirmation bias drives local governments to seek out industrial updating assessments that confirm their preconceived notions about relocating MSCEs to make way for newer, more advanced industries. This bias often leads to the neglect or devaluation of industrial analyses highlighting the innovation and entrepreneurial potential of MSCEs. As the specific chosen frame can be designed, framing bias mainly means that different choices of loss frame and gain frame will lead to different decision-making results. Here, framing bias is that local governments become more risk-taking in decision-making (embodying in the one-size-fits-all solution) when they choose a loss frame of reference of missing industrial updating chances when these MSCEs still occupy a large portion of land. In contrast, as different perceptions and risk attitudes lead to a specific domain that is not selectable but is determined by the actual situation, loss aversion bias mainly means that the perception and risk attitude under the loss domain differ from those under the gain domain. So, the loss-aversion bias is that local governments are willing to take more ESG and non-ESG risks to relocate MSCEs under the loss domain of industrial updating chances when they start their industrial updating procedure. Lastly, overconfidence is evident as local governments exhibit unwarranted faith in their ability to drive industrial restructuring through the relocation of MSCEs. This overconfidence may be reinforced by attributing excessive credit for past economic growth to their own actions while deflecting responsibility for failures onto other stakeholders.
In the second searching and matching stage, chemical parks regulated by local governments in the less developed central and western region provinces may overestimate the advantages of the size of relocated chemical enterprises in the short term, and the relocated chemical enterprises may overestimate the capacity of unstandardized chemical parks in being standardized in the short term. These overestimations can lead to the type II mismatching errors. From Table 1, the type II mismatching errors that would ease pollution transfer and carbon leakage may stem from some behavioral biases, such as conservation bias and confirmation bias (that both are belief perseverance biases), anchoring and adjustment bias together with framing bias (that are processing errors), as well as loss-aversion bias and self-control bias (that are emotional biases). Here, conservation bias is that administrative committees of chemical parks maintain their preference for the larger chemical project and its corresponding investment and tax intensity and pay less attention to the ESG attributes (for example, environmental impact) while they are in the decision-making process; and relocated chemical enterprises maintain their preference in the land, tax, and capital incentive provided by chemical parks and neglect the challenges in providing standardized safety prevention and environmental protection from those unstandardized chemical parks. Confirmation bias would be administrative committees of chemical parks search for favorable project assessment (that confirms their prior inclination toward the larger chemical enterprises) and neglect or undervalue the contracted scientific analysis results of these enterprises (that focus on the negative externalities); and relocated chemical enterprises look for the land, tax, and capital incentive policy of chemical parks (that confirms their prior expectation) and neglect or undervalue chemical parks’ current status or preparation of providing standard safety prevention and environmental protection. Anchoring and adjustment bias is that administrative committees of chemical parks rely too much on their expectation of desirable investment and tax intensity to make admission decisions for relocated chemical enterprises, and relocated chemical enterprises depend heavily on their expectation of sufficient land, tax, and capital incentive to make relocation decisions to chemical parks. Framing bias contributes to the risk-taking behavior of administrative committees of chemical parks and relocated chemical enterprises, particularly when viewing the situation through a selected frame that emphasizes potential losses associated with large chemical projects and high policy incentives. Loss-aversion bias encourages chemical park administrative committees and relocated chemical enterprises to accept higher risks, both ESG and non-ESG, when operating within a domain where the perceived losses from missing out on large projects or policy incentives are significant. Self-control bias may prompt chemical park administrative committees to engage in reckless borrowing to finance the standardization of unstandardized chemical parks, potentially leading to unsustainable debt levels.

4.3. The Perspective of Information Economics

Hidden actions and hidden characteristics are two kinds of asymmetric information involved in mismatching errors. Hidden actions are related to the principal–agent problem, and hidden characteristics facilitate adverse selection. From Table 1, the principal–agent problem and adverse selection may exist in the first appraising and bargaining stage and the second searching and matching stage.
In these two stages, the principal–agent problem is related to similar hidden actions (that are local governments’ perfunctory assessment of chemical enterprises). Here, for simplicity, the government levels of China are just classified into the central and local governments. In China, local governments are led by the central government. Specifically, local governments in the chemical park scale are administrative committees of chemical parks. Based on laws and regulations, all the Chinese governments should serve the people (the public). Therefore, there are indeed double principal–agent contracts among these stakeholders. In the first contract, the principal is the central government; the agents are local governments. In the second contract, the principal is the public; the agents are local governments. That means local governments are both agents of the central government and the public, who are principals. Meeting the interests of principals, in particularly the central government, would promote the officials of local governments to higher positions. All parties ideally share the same interests in three targets: robust economic development, a green natural environment, and an equitable societal environment. Nevertheless, the ranks and weights of these three targets may differ based on the distinct prioritized interests of these three parties.
Concerning the first principal–agent contract, the central government designed and issued this relocation policy and required local governments to implement it. The primary objective of this relocation policy is for all chemical enterprises to meet the requirements for safety and health-protective distance in populous urban areas. In addition, according to this relocation policy, chemical enterprises would be improved in their existing venues, relocated to standard industrial parks, and shut down to achieve this objective. However, based on their interests in industrial structure updating, local governments in the developed eastern region provinces may be incentivized to do a perfunctory assessment and then reject MSCEs that could be improved in their existing venues in the first appraising and bargaining stage. Similarly, in the second searching and matching stage, administrative committees of chemical parks in the less developed central and western region provinces may do a perfunctory assessment and then fail to reject chemical enterprises that have high safety and environmental risks (even these enterprises should be closed) because these enterprises can bring a large amount of investment and tax to their regulated chemical parks.
Moreover, regarding the second principal–agent contract, though the public needs robust domestic economic development (which generates job opportunities) and a green natural environment (which is beneficial to human health), it may put more weight on public members’ direct benefits, like relief fund and medical insurance system. However, it is challenging for local governments to fulfill all the public interests. They usually take a tradeoff rather than a synergy strategy in response to public interests. For example, local governments in the developed eastern region provinces have emphasized the green natural environment more these years and pursued high-quality development. Therefore, they may put MSCEs on the list for relocation or shutdown even at the cost of unemployment of some public members in the first appraising and bargaining stage. Additionally, local governments (including administrative committees of chemical parks) in the less developed central and western region provinces are urged to elevate the domestic economy so that they may overlook the severe high safety and environmental risks accompanied by some relocated chemical enterprises in the second searching and matching stage. Nevertheless, such neglect would threaten the health of the local public.
On the other hand, adverse selection in the first appraising and bargaining stage and the second searching and matching stage is connected to similar hidden characteristics (ESG and non-ESG information of chemical enterprises and chemical parks). In the first appraising and bargaining stage, some ESG and non-ESG information of MSCEs is hidden or undisclosed, such as their innovation and entrepreneurship, which are also sources in updating industrial structure. Without noticing these attributes, local governments in the developed eastern region provinces would classify MSCEs into the list of relocation or shutdown. In the second searching and matching stage, when some ESG and non-ESG information about chemical enterprises, such as their high safety and environmental risks, is hidden or undisclosed, administrative committees of chemical parks may fail to reject their application to the standard chemical parks. Likewise, when some ESG and non-ESG information about chemical parks is hidden or undisclosed, for example, their substandard safety prevention and environmental protection or incapability to be standard chemical parks due to debt sustainability issue, some relocated chemical enterprises may fail to reject the admission to these unstandardized chemical parks.

5. The ESG Matching Policy

A mismatch concerning ESG attributes between chemical enterprises and chemical parks could result in emerging ESG and non-ESG risks that are difficult to manage due to their individual and overlapping complexity. However, an ESG matching policy enabling the proper matching between chemical enterprises and chemical parks would be a policy innovation to reach a just transition. Notably, such policy innovation complements rather than substitutes the current widespread matching rules focusing on the degree of matching between chemical enterprises and chemical parks regarding the industry chain, infrastructure, and supply chain networks.
This policy innovation consists of three new components: (1) a national standardized ESG scoring and ranking system, (2) a deferred acceptance mechanism, and (3) a score announcement instrument before the preference submission process. Considering building matching pairs between chemical enterprises and chemical parks, this policy innovation will be similar to China’s college admission (Gaokao in Chinese) matching mechanism. Chemical enterprises resemble students, and their ESG scores resemble the Gaokao scores. Chemical parks resemble universities and colleges, and their ESG rankings resembles the rankings of Chinese universities.

5.1. The National Standardized ESG Scoring and Ranking System

The national standardized ESG scoring and ranking system focuses on financial materiality and risk concerning the ESG issues (Garz et al., 2018) [33] that pertain to a given enterprise or industrial park. This system is the base for this ESG matching policy as it allows both chemical enterprises and chemical parks to rank their preferences on each by the ESG scores and rankings rationally. Its fundamental role is ESG information disclosure and will be implemented in the chemical enterprise and chemical park scale to ensure mutual benefits from information sharing. Moreover, independent third parties are expected to monitor and regulate the ESG performance of chemical enterprises and chemical parks. For example, third-party auditors need to be involved in how enterprises and parks report their ESG performance to ensure the authenticity and standardization of the ESG scores and rankings to avoid greenwashing. The Ministry of Industry and Information Technology (MIIT) and its provincial, municipal, and county subsidiary departments can punish chemical enterprises and chemical parks (that use greenwashing) for guaranteeing the effective implementation of the national standardized ESG scoring and ranking system and ensuring ESG scores and rankings reflecting true ESG abilities.
In addition to information disclosure, the national standardized ESG scoring and ranking system can be utilized to evaluate whether chemical enterprises and chemical parks are well matched, under-matched, or over-matched. A match is measured by how far the distance between an enterprise’s percentile in the ESG score distribution of chemical enterprises and a park’s percentile in the ESG ranking distribution of chemical parks is. This technique is drawn from the method of measuring a student-college match (Dillon and Smith, 2017) [34]. A well-matched chemical enterprise–chemical park pair is defined as a chemical enterprise with the nth percentile in ESG score allocated to a chemical park with the same nth percentile in ESG ranking. Furthermore, such a well-matched pair can be relaxed to ± 3% (Cao, 2020) [35]. Therefore, the matching level can be explained empirically as below: well matched (|pj − ei| ≤ 3%), under-matched (pj − ei < −3%), and over-matched (pj − ei > 3%), with ei as an ith ESG score percentile chemical enterprise and pj as a jth ESG ranking percentile chemical park.

5.2. The Deferred Acceptance Mechanism

Several mechanisms exist to solve the student-to-school matching problem, such as the deferred acceptance (DA) mechanism, the Boston mechanism, and the serial dictatorship (SD) mechanism. Also, according to Lien et al. (2017) [36], matching outcomes can be clarified in terms of ex-ante fair (ability-based fair) and ex-post fair (score-based fair). Moreover, from Table 2, fairness (stability) and efficiency, the welfare measures of matching outcomes, could be assessed in two and three measurements, respectively (Lien et al., 2016) [37].
Table 2. The measurements of fairness and efficiency of matching outcomes regarding the student-to-school matching problem.
Studies have found that the DA mechanism improves the matching outcome by lowering the possibility of mismatch (Cao, 2020; Bo et al., 2019; Ha et al., 2020) [35,38,39]. Therefore, the DA mechanism, initially proposed by Gale and Shapley (1962) [40], is adopted for our proposed ESG matching policy. Under the DA mechanism, the matching pairs between chemical enterprises and chemical parks are achieved by their mutual section based on the ESG scores and rankings. The DA mechanism is categorized as positive screening in essence, and the ESG scores and rankings, reflecting ESG attributes, serve as the role of a defined ranking hurdle. Notably, chemical enterprises and chemical parks are assumed to be unwilling to accept counterparties whose ESG scores and rankings do not reach a certain sustainability threshold. Moreover, the MIIT and its provincial, municipal, and county subsidiary departments can monitor and regulate the implementation of the DA mechanism.
Learned from Gale and Shapley (1962) [40], for simplicity, we assume that if a chemical park is unwilling to accept a chemical enterprise whose ESG score is under a sustainability standard of the chemical industry, then this chemical enterprise will not even be allowed to apply to the chemical park. The procedure of the DA mechanism follows: a set of n chemical enterprises is to be assigned among m chemical parks, where qi is the quota of a chemical park (CPi). To begin with, all chemical enterprises apply to the chemical park of their first choice based on the ESG rankings of these chemical parks. CPi then places on its waiting list the qi applicants who rank highest based on their ESG scores, or all applicants if there are fewer than qi, and rejects the rest. Rejected chemical enterprises then apply to their second choice, and again, each chemical park selects the top qi from among the new applicants and those on its waiting list, puts these on its new waiting list, and rejects the rest. The procedure ends when every chemical enterprise is either on a waiting list or has been rejected by every chemical park to which it is willing and allowed to apply. At this point, each chemical park admits every chemical enterprise on its waiting list, and a stable and optimal (fair and efficient) assignment has been achieved.
Here, we use a simple example adjusted from (Cao, 2020) [35] to demonstrate the efficacy of the DA mechanism. Suppose there are three chemical enterprises E = {e1, e2, e3} and four chemical parks P = {p1, p2, p3, p4}, each with only one vacancy available.
The preferences of chemical enterprises are:
e1: p1  p2  p3
e2: p1  p2  p3
e3: p2  p3  p4
The preferences of chemical parks are:
p1: e1  e2  e3
p2: e1  e2  e3
p3: e1  e2  e3
p4: e1  e2  e3
Therefore, according to the DA mechanism, the results of matching pairs would be:
e1     p1
e2     p2
e3     p3

5.3. The Score Announcement Instrument Before the Application Process

Bo et al. (2019) [38] found that disclosure of the score before instead of after the preference submission process further lowered the probability of mismatch by 18% in their study, so we also propose a score announcement instrument before chemical enterprises’ application to chemical parks. This instrument can better supplement the national standardized ESG scoring and ranking system and the DA mechanism to realize their positive synergy effects. The regulator of the score announcement instrument could also be the MIIT and its local departments by regularly checking the authenticity of an information disclosure platform, such as a national digital application used in smartphones.
Moreover, the score announcement instrument before the application process can be extended to a suasive instrument that could be an online platform for every stage of information disclosure (i.e., ESG scores/rankings, available vacancies, and final results of matching pairs) to facilitate the ESG matching process between chemical enterprises and chemical parks. Nudged by this suasive instrument, chemical enterprises and chemical parks can grasp the progress of the ESG matching policy and have enough time to familiarize themselves with the national standardized ESG scoring and ranking system and the DA mechanism so as to be persuaded to actively adopt this ESG matching policy as a means to carry out the national relocation initiative.

5.4. Rationale of the ESG Matching Policy

All emerging ESG and non-ESG risks are related to mismatching errors that violate the matching principle that emphasizes the fairness and efficiency of matching outcomes. Our proposed ESG matching policy aims to correct mismatching errors and promote the fairness and efficiency of matching outcomes. This proposed policy could ensure that chemical enterprises and chemical parks reach agreements when matching pairs are made. Opting out of an agreement would require sound or even legal reasoning from the wishful party.
Underlying the ESG matching policy, there is a solid theoretical foundation. Firstly, this ESG matching policy embodies the principles of environmental economics and industrial ecology, as it emphasizes the matching of chemical enterprises and chemical parks concerning safety and environment, which can tap the roles of the internalization of external costs, the scale of economy and the industrial symbiosis to achieve energy and resources saving, pollution control, and carbon reduction at most. Secondly, this ESG matching policy is an information-based policy designed according to information economics, which can address stakeholders’ principal–agent problem and adverse selection as well as belief perseverance biases, processing errors, and emotional biases. Thirdly, as per industrial policy, this ESG matching policy would impact the whole China’s chemical industry through its policy implementation on a national scale.
Moreover, the ESG matching policy can also be understood as the combination of two regulatory instruments and a persuasive instrument. The national standardized ESG scoring and ranking system and the DA mechanism are two regulatory instruments. They are designed to gradually upgrade ESG information disclosure from comply-or-explain mode to comply-and-explain mode. Therefore, they could allow chemical enterprises and chemical parks to disclose their actual ESG information in an adaptive period, reducing the feeling of being overwhelmed by the policy innovation. On the other hand, the score announcement instrument before the application process is the persuasive instrument. It is crucial to communicate the benefits of ESG information disclosure and encourage chemical enterprises and chemical parks to disclose their accurate ESG information. With adequate time and incentive from these three instruments, chemical enterprises and chemical parks would adopt the ESG matching policy even though it is a new policy change.
Furthermore, from Table 3, the three components of this ESG matching policy, all information-based, can jointly address stakeholders’ principal–agent problem and adverse selection as well as belief perseverance biases, processing errors, and emotional biases that lead to the type I and type II mismatching errors. Firstly, the national standardized ESG scoring and ranking system, monitored by third-party auditors and regulated by the MIIT and its local subsidiary departments, could inform chemical enterprises and chemical parks by signaling that ESG attributes are also pivotal in building stable and efficient chemical enterprise–chemical park pairs. Thus, belief perseverance biases regarding enterprise size, investment amount, tax, and employment, together with favorable financing, land, and tax incentives, could be corrected or eliminated, related emotional biases could be recognized and adapted, and adverse selection in these two stages could be reduced or eliminated. Therefore, the type I and type II mismatching errors can be corrected. Secondly, the DA mechanism, monitored and regulated by the MIIT and its local subsidiary departments, is a decision-making algorithm to build stable and efficient chemical enterprise–chemical park pairs in terms of ESG attributes, thus processing errors regarding enterprise size, investment amount, tax, and employment, together with favorable financing, land, and tax incentives could be corrected or eliminated, related emotional biases could be recognized and adapted, and principal–agent problem (perfunctory assessment) and adverse selection in these two stages could be reduced or eliminated. Hence, the type I and type II mismatching errors could be corrected. Thirdly, the score announcement instrument before the application process, implemented by the MIIT and its local subsidiary departments, could correct the type I and type II mismatching errors in a similar logic as it applies to inform the benefits of the national standardized ESG scoring and ranking system and the DA mechanism.
Table 3. Rationale of the ESG matching policy.

5.5. The Expected Implementation Steps and Verification Logic of the ESG Matching Policy

Though this ESG matching policy is designed to be a national scale policy, implementing it in a trial firstly in specific provinces before its national implementation is wise to find its best implementation path. Here, Jiangsu Province, which stands for the developed eastern region provinces, and Jiangxi Province, which stands for the less developed central and western region provinces, are suggested as the trial provinces of this ESG matching policy. The reason for selecting Jiangsu Province as a trial province is that its local governments have become more risk-averse after the 2019 Xiangshui chemical plant explosion in its domain. The reason for selecting Jiangxi Province as a trial province is that its chemical parks have been the primary choices for relocated chemical enterprises from Jiangsu Province and Zhejiang Province. Moreover, if the trial implementation in both Jiangsu Province and Jiangxi Province can reduce ESG and non-ESG risks measured in Equation (1), the national-scale implementation of this ESG matching policy is suggested. However, while implementing it nationally, the complex local specificities and contexts of each province should be considered to smooth the implementation.
In detail, the first step of trial implementation is for the MIIT to build an online platform or application to introduce this ESG matching policy to all stakeholders, including three primary stakeholders (local governments, chemical enterprises, and chemical parks) and two secondary stakeholders (the public and scientists) in Jiangsu Province and Jiangxi Province. After these stakeholders are familiar with this ESG matching policy, the local departments of the MIIT in these two provinces begin the second step: They cooperate with third-party auditors to put the national standardized ESG scoring and ranking system into practice so as to score and rank the ESG attributes of chemical enterprises and chemical parks. When the ESG scores and ranks of chemical enterprises and chemical parks are fairly decided, the third step is that the local departments of the MIIT in these two provinces use the online platform or application as the score announcement instrument before the application process in order to allow all stakeholders share the information regarding the ESG scores and ranks of chemical enterprises and chemical parks. Lastly, regulated by the local departments of the MIIT in these two provinces, chemical enterprises and chemical parks reach their relocation matching pairs through the DA mechanism, and the final results of matching pairs should be seen instantly in the online platform or application.
As policy researchers, we turn our proposed ESG matching policy from suggestions to actions, relying on the decisions of Chinese policymakers. Moreover, even if the policymakers adopt this policy innovation, its effects could be able to be verified after its months’ or years’ implementation. However, we present a verification logic here extended from Equation (1). Though this ESG matching policy is a policy innovation, it complements and aligns with the previous relocation policy. Therefore, the basic logic to verify the effects of this policy innovation is comparing the values of ESG and non-ESG risks of specific stakeholders regarding the implementation of this relocation policy in the developed eastern region provinces (such as Jiangsu Province) and the less developed central and western region provinces (such as Jiangxi Province) before and after the trial implementation.
In detail, before the trial implementation, the values of all kinds of ESG and non-ESG risks of specific stakeholders concerning the implementation of this relocation policy in Jiangsu Province and Jiangxi Province are calculated based on Equation (1). Other things being equal, after the trial implementation, then the corresponding values of these risks are calculated again. If the later values are significantly higher than the previous values, the effects of this ESG matching policy are basically verified. For example, other things being equal, suppose the unemployment risk for laborers in MSCEs in Jiangsu Province is high before the trial implementation yet becomes medium or small after the trial implementation; this policy innovation is effective in addressing the unemployment risk. In addition, other things being equal, suppose the pollution transfer and carbon leakage risk from relocated chemical enterprises and admitted chemical parks in Jiangxi Province is high before the trial implementation yet becomes medium or small after the trial implementation; this policy innovation is effective in addressing the pollution transfer and carbon leakage risk. Moreover, Difference-in-Difference (DiD) and other casual inference methods should be used for profound causal relationship verification.

6. Discussion

The ESG matching policy is ideal for addressing potential ESG and non-ESG risks while implementing a national relocation and improvement policy in China’s chemical industry. The most expected apparent challenge of this ESG matching policy is greenwashing, and its universality is the potential to become a just transition toolkit for the developing world.

6.1. Anti-Greenwashing

6.1.1. Greenwashing: Counterargument of the ESG Matching Policy

The DA mechanism is categorized as positive screening in essence, and positive screening needs clarity, transparency, and objectivity instead of obscurity, opacity, and subjectivity in distinguishing companies within industries (Blank et al., 2016) [29]. Moreover, as this ESG matching policy is information-based, greenwashing would become an expected challenge to hamper the fairness and efficiency of the matching outcomes and invalidate the ESG matching policy, as greenwashing can distort the national standardized ESG scoring and ranking system as well as the DA mechanism.
In detail, greenwashing is a deliberate corporate action presenting misleading information concerning ESG, particularly environmental issues, focused on the deception of stakeholders (de Freitas Netto et al., 2020) [41]. Also, greenwashing, particularly deceptive manipulation (deceptive conduct in sustainability communication), is irresponsible behavior (Siano et al., 2017) [42]. Considering that the chemical industry is a traditional grey industry with high pollution, greenwashing incurs more adverse impacts on the fairness and efficiency of matching outcomes. Furthermore, as greenwashing can mask the actual ESG attributes, it would generate blocking pairs despite the DA mechanism.

6.1.2. The Major Stakeholder Network: Solution to Greenwashing

To better cope with greenwashing, a major stakeholder network, including chemical enterprises and chemical parks, regulators, and financial institutions, can be built to collaborate to combat greenwashing that appears to emerge in the implementation process of the ESG matching policy.
Firstly, regulators and financial institutions could shape a good policy context by putting outer pressure and incentives on chemical enterprises and chemical parks to combat greenwashing. In detail, the MIIT and its local departments, namely regulators, could be diligent in monitoring this policy innovation to direct chemical enterprises and chemical parks to adapt to the disclosure of real ESG information. Financial institutions could use sustainable finance instruments to punish greenwashing and encourage chemical enterprises and chemical parks to improve their ESG attributes. Lastly, chemical enterprises and chemical parks would reduce or eliminate greenwashing easier in their matching process when holding internal and external motivation.

6.1.3. Regulators: Alert on Their Reluctance

The responsiveness of political institutions, namely the strength of the political interest feedback in its balancing or reinforcing type that “past policy change modifies the status quo bias parameter to make future change” harder or easier, constitutes an important explanation of variation in emissions pathways (Moore et al., 2022) [43]. According to the policy design, the MIIT and its local departments are supposed to be the primary regulators of the ESG matching policy. So, the MIIT and its local departments should be alerted to detect and punish greenwashing while monitoring the policy implementation process. However, some officers of the MIIT and its local departments might be reluctant to implement this policy innovation when they feel it is daunting or overwhelming. Their reluctance would result in a new principal–agent problem that eases the greenwashing and causes blocking pairs, threatening the fairness and efficiency of matching outcomes. That means their reluctance would reduce the effectiveness of this ESG matching policy and hamper the sustainability transition of the entire Chinese chemical industry. Significantly, personal and contextual factors would impact the implementation effectiveness of the ESG matching policy as these younger city officials tend to work harder to meet the mandated targets than those older who saw a more limited prospect for promotion (Li et al., 2024) [44].
Therefore, to address the potential reluctance of some MIIT and local department officers to implement the policy innovation, we propose the introduction of an incentive mechanism with financial or non-financial incentives. This mechanism is designed to motivate officers, regardless of their age or career prospects, to fully engage in the implementation of the ESG matching policy. Under this mechanism, officers’ reluctance to implement the ESG matching policy would be significantly reduced and even eliminated, so they would then be more likely to address and combat greenwashing diligently based on their duties.

6.1.4. Financial Institutions: Sustainable Finance Constraint on Greenwashing

Though traditional finance may favor some high-growth sectors, sustainable finance, the financial activity taking sustainability into account (GARP, 2024) [23], is a driver in turning a pollution-intensive chemical industry into a relatively green one, as it promotes the fairness and efficiency of matching outcomes and increases the payoffs of parties whose actions align with sustainability. Here, sustainable finance is the broadest concept, including green finance, climate finance, transition finance, and social finance. Notably, using sustainable finance by financial institutions could combat greenwashing indirectly if they only provide financing to chemical enterprises and chemical parks that strictly disclose their real ESG information and ambitiously plan to improve their ESG attributes, scores, and rankings.
Moreover, as non-certified sustainable practices could be linked to greenwashing (Delmas and Gergaud, 2021) [45], to play the role of sustainable finance better, independent third-party audits or regulators should monitor this information disclosure and give related certification for chemical enterprises and chemical parks meeting specific sustainability standards. Then, based on such certification, chemical enterprises and chemical parks are permitted the privileges from financial institutions of issuing sustainability-linked bonds or using other sustainable finance instruments. In other words, financial institutions should stick to the constraint rule that no sustainable finance instrument is permitted if any greenwashing signal is detected. Conversely, those who meet the green or sustainable criteria are expected to receive favorable conditions from financial institutions to provide suitable sustainable finance instruments.

6.1.5. Chemical Enterprises and Chemical Parks: Internal and External Motivation to Reduce or Eliminate Greenwashing

To meet environmental regulation, innovation and lobbying are two substitutes in firm strategies (Hultgren, 2021) [46]. When chemical enterprises and chemical parks lobby and greenwash in the face of the ESG matching policy and pretend to be sustainable or green in terms of E, in that case, their high ESG scores and rankings cannot depict their accurate ESG attributes. Thus, the national standardized ESG scoring and ranking system is doomed to failure, and the DA mechanism cannot play its role in avoiding the mismatch between chemical enterprises and chemical parks. Such mismatch may still incur severe ESG and non-ESG risks. In short, greenwashing would make the ESG matching policy ineffective. Therefore, reducing or eliminating greenwashing from chemical enterprises and chemical parks is the basis of the successful implementation of the ESG matching policy.
Internal and external motivation to innovate to cope with the ESG matching policy can incentivize chemical enterprises and chemical parks to reduce or eliminate greenwashing. To achieve internal motivation, chemical enterprises and chemical parks need to admit their social responsibility to sustainability and have adequate extra financing beyond their everyday business. On the other hand, external motivation, like the consumers’ robust demand for sustainability in the life cycle value chain of chemical products, would push or nudge some chemical enterprises and chemical parks to surpass their peers regarding ESG attributes.

6.2. The Just Transition Toolkit for the Developing World: Universality of the ESG Matching Policy

Admittedly, our world is still unequal in wealth and other social dimensions. Also, human nature makes it easy to forget painful lessons. Considering the rising global concern about sustainability and industrial risk management, our proposed ESG matching policy has the universal meaning that it can be applied as a just transition toolkit for the developing world to promote equality and avoid repeated issues accompanied by industrial transfer.
Even though there are lessons from the US and Europe that they spent much effort to treat environmental pollution and carbon emissions after they achieved particular economic development targets in terms of industrialization and urbanization, the countries promoted their industrialization and urbanization targets later, like China and some emerging economies, still followed the old development path of “pollution first and treatment later” (Liu and Dong, 2019) [25] and repeated the struggling experience in combatting the environmental pollution and carbon emissions. Now, due to reduced demand growth rates and more new severe environmental regulations in China (Hong et al., 2019) [1], and tightened geopolitical and economic disputes manifesting in decoupling and de-risking from China, industrial transfer, especially the transfer of pollution-intensive industries, have generally occurred in China (Feng et al., 2024) [47]. In detail, many pollution-intensive manufacturing enterprises located in China, not limited to the chemical industry, have begun to search for suitable relocated industrial parks or sites throughout China and the world, particularly developing countries in Southeast Asia and Africa. Therefore, at this time, policy innovation in developing a just transition policy toolkit to avoid the painful lessons concerning “pollution first and treatment later” (Liu and Dong, 2019) [25] is vital to building a more just, resilient, robust, and sustainable developing world. Moreover, such policy innovation should be designed based on “the level of uncertainty and the position of the domestic industry in global supply chains” (Allan and Nahm, 2024) [48] and cautious of negative issues associated with some green industrial policies that lead to a green industrial race and competitive duplication instead of green spirals (Allan et al., 2024) [49] as well as excess rent capture, political lock-in, and political subsidy cutbacks (Meckling et al., 2017) [50].
During a massive industrial transfer, two apparent ESG risks should be addressed: pollution transfer (and carbon leakage) and unemployment. Pollution transfer, or pollution migration, is typically noticed in developing countries with less stringent environmental regulations that admit relocated industrial firms, and unemployment threatens social stability in countries where these relocated industrial firms originally resided.
In the previous massive industrial transfer, pollution transfer and carbon leakage have been noticed in China and other emerging economies partially due to their loose environmental regulation, and industrial decline or deindustrialization resulted in the massive loss of steel and auto-related jobs in the early 1980s and increasing crime rates that have been observed in the US under the Rust Belt shock (Feyrer et al., 2007) [51]. Thus, in the current massive industrial transfer originating from China, policy initiatives on preventing pollution transfer, carbon leakage, and unemployment would benefit the social welfare in China and the developing world together. Our proposed ESG matching policy would be the opportune just transition policy toolkit for China and the developing world in addressing these ESG risks.
Policies can be designed to provide incentives to curb pollution transfer and carbon leakage during industrial transfer. A typical policy approach providing a negative incentive for polluted behaviors is a rising environmental pollution tax in developing countries (Song and Wang, 2013) [52]. In contrast, the ESG matching policy would be a policy approach with a positive incentive to combat pollution transfer and carbon leakage as it allows the environmental attributes of relocated industrial firms and admitted relocated industrial parks or sites to be matched with each other. In that way, due to the internalization of external costs, the scale of the economy, and the industrial symbiosis, the average cost of pollution control and carbon reduction would be reduced as much as possible, and the pollution transfer and carbon leakage would be minimized to a tolerable level to the developing countries.
During industrial transfer, there is also a substantial job reallocation. This relocation would mean reduced hiring in industries negatively affected (Hafstead and Williams III, 2020) [53] by industrial transfer. Using our proposed ESG matching policy emphasizing ESG attributes, more MSCEs of China’s chemical industry that could be improved in their original sites would still operate as usual rather than relocate or close. Therefore, the workers of these MSCEs would still be employed. Such logic is suitable for Chinese manufacturing enterprises that face the improvement, relocation, or closure paths appraised by local governments, regardless of their scale. When more laborers can still work in their hiring enterprises despite the scale, unemployment risk due to environmental regulation would be effectively controlled. Not limited to China, the ESG matching policy would also benefit the workers of specific industries in other countries that experience similar unemployment risks due to industrial transfer.
In short, as our proposed ESG matching policy could allow industrial firms to locate in matched industrial parks and sites, eliminating or reducing ESG and non-ESG risks due to mismatches between them, it has the potential to be a universal just transition policy toolkit for building a more just, resilient, robust, and sustainable developing world given that it could promote carbon neutrality and social equality due to its expected policy effects in avoiding repeated issues like pollution transfer, carbon leakage, and unemployment accompanied by industrial transfer.

7. Conclusions

In this study, we proposed an ESG matching policy based on the DA mechanism between chemical enterprises and chemical parks to manage ESG and non-ESG risks that appear to occur due to the type I and type II mismatching errors during the implementation process of China’s relocation and improvement policy regarding its chemical industry. This ESG matching policy decides whether a chemical enterprise is fair and efficient enough to match a chemical park with the same or similar ESG score percentile. The three components of this ESG matching policy in terms of two regulatory instruments and one persuasive instrument, all information-based, can jointly address stakeholders’ principal–agent problem and adverse selection as well as belief perseverance biases, processing errors, and emotional biases that lead to the type I and type II mismatching errors. Moreover, this policy innovation is suggested to be implemented from the trial first in Jiangsu Province and Jiangxi Province in China to find its best implementation path and then extend to the whole of China. Though without the approval from Chinese policymakers, our proposed ESG matching policy can only be suggestions and cannot be verified by actual data, we proposed a verification logic in comparing the values of ESG and non-ESG risks of specific stakeholders regarding the implementation of this relocation policy in the developed eastern region provinces (such as Jiangsu Province) and the less developed central and western region provinces (such as Jiangxi Province) before and after the trial implementation of our proposed ESG matching policy.
Meanwhile, the sound function of this ESG matching relies on no greenwashing. Our proposed ESG matching policy is likely to be generalized as a just transition toolkit for the developing world to promote carbon neutrality and social equality due to its expected policy effects in avoiding repeated issues like pollution transfer, carbon leakage, and unemployment accompanied by industrial transfer. However, we need more sophisticated research in methodological innovation and empirical analysis. Further studies are needed into the methodological research and quantitative analysis of ESG and non-ESG risks, in particular quantifying hazard/external event, exposure, and vulnerability of ESG and non-ESG risks, the thorough design of industrial enterprise–industrial park matching mechanism, national standardized ESG scoring and ranking system, score announcement instrument, and the related causal verification methods to make this ESG matching policy innovation more scientific and feasible.

Author Contributions

Conceptualization, L.S., J.Z. and X.R.; formal analysis and project administration, X.R.; writing—original draft preparation, X.R. and K.L.D.; writing—review and editing, K.L.D., J.E. and J.Z.; validation, J.Z., L.S. and J.E.; supervision, J.Z. and L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 52270182) and the Key Research and Development Project of Jiangxi Province (Grant No. 20214BBG74006).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors thank the anonymous referees for their insightful comments and the editors for their great support throughout the publication process.

Conflicts of Interest

The authors declare no conflicts of interest.

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