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

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.

2. ESG Issues and Opportunities Related to the Causes and Effects of This Relocation Policy

Before delving into the ESG issues and opportunities associated with the causes and effects of this relocation policy, an overview of its stages, major stakeholders, and related objectives is beneficial for identifying these issues and opportunities. This relocation policy encompasses two policy implementation stages from the perspective of chemical enterprises (refer to Figure 1), with the key stakeholders being local governments, chemical enterprises, and chemical parks. At the end of the first policy implementation stage, the appraising and bargaining stage, the provincial governments’ target is to issue lists of chemical enterprises to be improved in their existing venues, relocated to standard industrial parks, and shut down. Subsequently, during the second policy implementation stage, termed the searching and matching stage, chemical enterprises listed for relocation to standardized industrial parks aim to search for suitable chemical parks for relocation, namely industrial transfer. Ideally, the bilateral matching between chemical enterprises and chemical parks should consider at least six dimensions: industry chain, infrastructure, supply chain, regulation, innovation, and ESG. This matching process is not confined to domestic provinces or regions in China but extends even globally. Furthermore, those chemical parks willing to accept relocated chemical enterprises must adhere to the standards of admitted parks outlined in this policy. For example, it mandates that all parks intending to accommodate chemical enterprises must comply with relevant laws, regulations, standards, and specifications issued by the state. Consequently, the objective for unstandardized chemical parks is to transform and upgrade to meet these standards if they intend to admit chemical enterprises. In practice, many chemical parks have already been closed by local governments due to their unstandardized characteristics.

The ESG issues targeted by this relocation policy and corresponding ESG opportunities generated because of the policy implementation are shown in Figure 2. These ESG issues include environmental pollution in terms of E, safety accidents and risks, social conflicts between industrialization and urbanization, together with occasional unjustified social protests due to NIMBY sentiment in terms of S, and failures in sustainability risk management in terms of G. Moreover, these ESG issues are interconnected. For instance, inadequate or poor sustainability risk management exacerbates severe environmental pollution and high safety accident rates and risks; the proximity of chemical production units and storage facilities to densely populated urban areas due to ongoing social conflicts between industrialization and urbanization further heightens safety and environmental risks. If this policy succeeds in alleviating or even eliminating these ESG issues, it will pave the way for the sustainability transition of the Chinese chemical industry, encompassing both industrial upgrading and ESG opportunities. These ESG opportunities include the mitigation of environmental and safety risks, the reduction of conflicts between industrialization and urbanization, the promotion of a more scientific understanding of the chemical industry, and the enhancement of sustainability risk management capabilities at the chemical industry level.

Safety and environmental risks and accidents are the current leading ESG issues this relocation policy targets. We choose to combine safety and environmental risks and accidents together for two main reasons. Environmental pollution directly impacts the health of workers, while safety accidents and risks pose threats to their lives. Additionally, many chemical enterprises have the same departments responsible for reducing environmental pollution and managing safety accidents and risks. Aligned with the overarching objective of this relocation policy, hazardous chemical manufacturers situated in densely populated urban areas and failing to meet safety and health-protective distance requirements are expected to significantly reduce their safety and environmental risks by 2025. This can be achieved through various means such as improving compliance with safety standards, relocating to standardized industrial parks, or ceasing operations altogether. Consequently, this relocation policy presents ESG opportunities to mitigate safety and environmental risks and associated stressors.

Secondly, economic–geographic conflicts between industrialization and urbanization, which exacerbate the adverse impact of high safety and environmental risks, represent typical ESG issues that necessitate policy intervention. These conflicts have arisen from cumulative failures in expedient industrial and urban planning throughout China’s rapid economic growth spanning over four decades. Specifically, industrial planning failures have led to cities being encircled by chemical enterprises, while urban planning failures have resulted in chemical enterprises being encircled by cities. Implementing this relocation policy by improving the extent of meeting the safety and health-protective distance requirements, relocating to standardized industrial parks, or shutting down would help alleviate these economic–geographic conflicts. Such alleviation also presents an ESG opportunity. It is important to note that the implementation of this relocation policy should be tailored to specific conflict scenarios. In the first scenario, where cities are surrounded by chemical enterprises, targeting chemical enterprises to mitigate safety and environmental risks is appropriate. However, in the second scenario, where chemical enterprises are surrounded by cities, chemical enterprises should be afforded greater flexibility, considering their vulnerable status resulting from irresponsible urban planning.

Thirdly, unjustified social protests against chemicals, particularly regarding substances like PX, also constitute ESG issues. These protests are often fueled by the NIMBY sentiment, which reflects public apprehension about safety and environmental risks and accidents associated with chemical facilities. In the implementation process of this relocation policy, the 2019 Xiangshui chemical plant explosion in Jiangsu province, China, one of the most severe chemical accidents in the country’s history (resulting in 78 fatalities), emphasized its necessity. Furthermore, this accident reinforced the NIMBY sentiment among citizens and heightened risk aversion among local governments not only in Jiangsu province but also in other developed eastern region provinces, which intensified pressure to expedite the implementation of the relocation policy nationwide. To some extent, the formulation and implementation of this relocation policy can be seen as a formal response by the Chinese central government to address public protests and the deeply ingrained NIMBY inclination. However, it is important to acknowledge that some social protests in certain cities are sometimes unjustified. The public can be easily swayed or even manipulated by specific stakeholders who stand to benefit disproportionately from the departure of the chemical industry. Nonetheless, the implementation of this relocation policy would engage citizens in both the developed eastern region provinces and the less developed central and western region provinces, potentially leading them to gain a more scientific understanding of the risks, values, costs, and benefits associated with chemicals. Consequently, the transition from a NIMBY inclination to a more scientifically informed perspective regarding chemicals represents an ESG opportunity.

Lastly, sustainability risk management failure, such as inadequate or failed sustainability risk management practices, serves as a catalyst for numerous safety and environmental risks and accidents, and is one of the ESG issues targeted by this policy. Given that human factors and management factors were identified as the primary causes of large and above hazardous chemical accidents in China from 2000 to 2020 (Yang et al., 2022) [13], adhering to the terms of this policy regarding safe production and environmental protection by chemical enterprises, rather than resorting to cheating, greenwashing, or lobbying to conceal sustainability risk management failures, could significantly enhance the sustainability risk management capabilities of China’s chemical industry. This represents a notable ESG opportunity for China’s chemical industry.

3. Probable ESG and Non-ESG Risks and Related Mismatching Errors

3.1. Characteristics and Hypotheses of Major Stakeholders

Three primary stakeholders are engaged in implementing this relocation policy: local governments, chemical enterprises, and chemical parks. In China, chemical parks are typically overseen by their administrative committees, which function as government agencies. Consequently, chemical parks exhibit more similarities with local governments regarding their management structure and responsibilities. The regulations, negotiations, and persuasions among these three major stakeholders play a pivotal role in determining the outcomes and impacts of policy implementation.

Given China’s political and economic context, there are four characteristics of these major stakeholders.

Characteristic 1: The quality of regional economic growth, industrial structure upgrading, and environmental regulation intensity in different regions of China are unbalanced (Fu et al., 2021; Ru et al., 2020; Yu and Wang, 2021) [4,14,15]. The developed eastern region provinces developed faster than the less developed central and western region provinces; and the environmental regulation policies greatly varies among regions: stricter environmental regulation in the developed eastern region provinces accelerated the transformation and optimization of the industrial structure, but the less stringent environmental regulation policies that sacrifice the environment for economic development in the less developed central and western region provinces (Yu and Wang, 2021) [15].

Characteristic 2: Local governments, chemical enterprises, and chemical parks are often faced with peer pressure. The interaction between local government competitive behaviors (such as investment and tax competition) and industrial agglomeration mode aggravates environmental pollution (Hong et al., 2020) [16]. On the other hand, when many chemicals in China are currently in oversupply or soon will be, and the Chinese chemical industry faces reduced demand growth rates, the new environmental regulations and more limited availability of finance push chemical enterprises to ensure that their operations are genuinely profitable (Hong et al., 2019) [1].

Characteristic 3: Size, R&D intensity, market structure, and trade shares are conducive to innovation in small to medium-sized businesses (Bhattacharya and Bloch, 2004) [17], and the size of China’s green innovative enterprises is relatively small (Jiao et al., 2020) [18].

Characteristic 4: China’s government has spent hundreds of billions of dollars to invest in new industrial parks (Zheng et al., 2017) [19], and there are various park brand promotion projects under different ministries management, which provide opportunities to attract indirect subsidies and tax credit from local governments (Wang et al., 2019) [20].

Based on four characteristics, we assume the corresponding four hypotheses of these major stakeholders, which are starting points for analyzing mismatching errors and the corresponding ESG and non-ESG risks probably emerging in the two implementation stages of this relocation policy.

Hypothesis 1.

Local governments, including administrative committees of chemical parks, prioritize economic growth in their distinct priorities: local governments in the developed eastern region provinces prioritize industrial upgrading, and local governments in the less developed central and western region provinces prioritize pure GDP growth.

Hypothesis 2.

Due to the competition among local governments, local governments, including administrative committees of chemical parks, prefer chemical enterprises with bigger sizes or larger investments in the admission decision. Because of the pursuit of profitability, chemical enterprises prefer more favorable financing, land, and tax incentives provided by chemical parks.

Hypothesis 3.

The size of enterprises is not the decisive factor in the business sector’s innovation capabilities; Chinese medium and small chemical enterprises can also innovate and advance technology.

Hypothesis 4.

Local governments usually provide direct or indirect financing to upgrade unstandardized chemical parks to standard ones. Such standardization financing support for a chemical park is generally more than that for a chemical enterprise.

3.2. Probable ESG and Non-ESG Risks and the Type I Mismatching Error Involved in the First Appraising and Bargaining Stage

As chemical enterprises are typically situated in the developed eastern region provinces, emerging ESG and non-ESG risks during the initial appraising and bargaining stage are observed in these provinces. Among the ESG risk categories, these risks include a one-size-fits-all solution related to governance and unemployment concerns associated with social factors. Additionally, non-ESG risks, such as stranded assets and industry hollowing-out, are also probable (refer to Figure 3). In particular, the one-size-fits-all solution represents a significant probable ESG risk, as it facilitates a type I mismatching error that furthermore contributes to increased unemployment, stranded assets, and industry hollowing-out. This error occurs when local governments in the developed eastern region provinces reject medium and small chemical enterprises (MSCEs) that could potentially be improved in their existing locations.

The one-size-fits-all solution needs to be analyzed under the policy content and context. According to the policy content, all provincial governments should survey hazardous chemical manufacturers failing to meet the safety and health-protective distance requirements in populous urban areas, one by one, based on relevant laws, regulations, and standards. After scientifically assessing these enterprises’ work safety and environmental protection conditions, hearing enterprise statements, and expert appraisal, these provincial governments should issue lists of enterprises to be improved in their existing venues, relocated to other places, and shut down. However, the Determination Method of External Safety Distance for Hazardous Chemicals Production Units and Storage Installations (national standard of China: GB/T 37243-2019) was posted on 25 February 2019, and implemented on 1 June 2019 [21], nearly two years after the issue date of this relocation policy which was 27 August 2017. The time lag in posting such a determination method indicates that the scientific assessment benchmarks in policy implementation before 25 February 2019, were unprepared. The unprepared determination method indeed nudged the one-size-fits-all solution.

Furthermore, within the context of the Chinese political economy, the adoption of a one-size-fits-all approach is a prevalent strategy among many local governments. Notably, as per Hypothesis 1, the majority of local governments in the developed eastern region provinces are keen on upgrading their industrial structures to more high-end alternatives. Consequently, larger-scale chemical enterprises with substantial investment projects are viewed favorably due to their potential to contribute significantly to local tax revenues, employment opportunities, and other benefits that MSCEs often lack. Building upon Hypothesis 2, it is understood that these local governments prefer the exit of MSCEs due to their smaller scale. Despite the potential for some MSCEs to improve their operations to meet safety and health-protective distance requirements, they may still find themselves listed for relocation or closure. In other words, MSCEs that could feasibly be upgraded in their existing locations to meet the safety and health-protective distance standards in the developed eastern provinces are erroneously earmarked for relocation or shutdown. This results in a type I mismatching error, wherein local governments in the developed eastern region provinces reject MSCEs that could actually be improved within their existing locations.

Impacted by the one-size-fits-all solution, unemployment, stranded assets, and industry hollowing-out would inevitably happen. Unemployment has already been anticipated as a consequence of this relocation policy, given that chemical enterprises may be slated for relocation or closure. This relocation policy aims to mitigate the risk of unemployment through measures such as job creation initiatives, vocational training programs, and job placement services. However, the prospects for unemployed workers, particularly those over 35 or 40 years old, to reenter the job market and secure new employment opportunities are bleak. Moreover, the challenges of job searching or unemployment are exacerbated in the post-COVID-19 era, as China grapples with a sluggish economic recovery. Additionally, the current Chinese labor market is characterized by an oversupply of young and well-educated college graduates, coupled with a scarcity of available job positions. Therefore, unemployed laborers find themselves in fierce competition with young graduates for limited job opportunities. Furthermore, unemployment could escalate into an ESG risk if a significant number of MSCEs that have the potential for improvement are mandated to relocate or shut down under the one-size-fits-all solution, which exacerbates the social and economic ramifications of this relocation policy, posing additional challenges for the affected unemployed workers and their communities.

Stranded assets are those that have “suffered from unanticipated or premature write-downs, devaluations, or conversion to liabilities” (Caldecott et al., 2013) [22]. It is a notion to understand better climate, environmental, and broader sustainability risks (GARP, 2024) [23]. Because MSCEs are likely relocated or closed due to the prevalent one-size-fits-all solution for the local governments in the developed eastern region provinces, the assets of those MSCEs, particularly the facilities and equipment, become obsolete during their relocation or closure process. That means these assets lose their economic value before the anticipated useful life (Generation Foundation, 2013) [24], which results from the implementation of this relocation policy. In contrast, if those MSCEs could be improved within their existing locations, this relocation policy would impact their assets less.

Finally, industry hollowing-out presents a significant non-ESG risk that could potentially emerge. As a foundational sector, the chemical industry produces materials that serve as inputs for a wide array of industries. Furthermore, as per Hypothesis 3, the development and innovation within the chemical industry necessitate the involvement of more than just large enterprises. Under the one-size-fits-all solution, MSCEs are likely to be relocated or shut down in an arbitrary manner, stifling their innovation potential. Consequently, the industry chains linking the chemical industry with other related sectors may weaken, ultimately leading to industry hollowing-out. This phenomenon could further undermine the industrial foundation of the developed eastern region provinces, triggering a detrimental cycle that negatively impacts domestic economic development and social welfare.

3.3. Probable ESG and Non-ESG Risks and the Type II Mismatching Errors Involved in the Second Searching and Matching Stage

During the second stage of searching and matching, chemical enterprises listed for relocation are tasked with searching for and identifying suitable chemical parks to move to. Given that our research focuses on ESG risks in China, particularly in the context of relocating chemical enterprises from the developed eastern region provinces to the less developed central and western region provinces, we will concentrate on this industrial transfer scenario. Under this scenario, various ESG and non-ESG risks may arise in the less developed central and western region provinces. These risks include environmental concerns such as pollution transfer and carbon leakage, as well as issues related to social justice such as environmental injustice and health disparities. Additionally, non-ESG risks such as debt sustainability issue may also manifest (refer to Figure 4). Pollution transfer and carbon leakage represent the primary ESG risks, which in turn contribute to environmental injustice and health disparities. Specifically, pollution transfer and carbon leakage stem from two kinds of type II mismatching errors. These errors occur when admission decisions fail to address substandard chemical enterprises or relocation decisions fail to address unstandardized chemical parks. Furthermore, the debt sustainability issue may arise due to imprudent investments made in the standardization of chemical parks.

Considering the disparities in Chinese economic geography, pollution transfer and carbon leakage emerge as the most probable ESG risks during the second searching and matching stage. Stark contrasts characterize China’s economic landscape. Its developed eastern region provinces, such as Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong, already have levels of wealth comparable to some developed countries in terms of GDP or GDP per capita. In contrast, some of its less developed central and western region provinces barely surpass the poverty line. As industrial transfer from advanced regions is beneficial to promote economic development in less advanced regions (Liu and Dong, 2019) [25], given China’s uneven economic geography, the industrial transfer of chemical enterprises has the potential to mitigate economic inequality. However, this process may also lead to pollution transfer and carbon leakage. Per Hypothesis 1, local governments in the developed eastern region provinces are urged to upgrade local industries after years of rapid economic growth, which has come at the expense of extensive energy and resource consumption. Consequently, they plan to relocate relatively low value-added, highly polluting, and carbon-intensive chemical enterprises to other regions of China, making way for high-end industries. On the other hand, based on Hypothesis 1, local governments in the less developed central and western region provinces aim to boost local GDP to close or shorten the development gaps between them and the economically mature East (Wei et al., 2019; Wang et al., 2019) [5,6]. Thus, the relocation of chemical enterprises, despite their environmental impacts, would simultaneously meet the economic interests of both sides of local governments of the developed eastern region provinces and the less developed central and western region provinces.

According to Hypothesis 1, despite negative environmental externality, these local governments in the less developed central and western region provinces may promote domestic economic growth at the expense of admitting some pollution-intensive and carbon-intensive chemical enterprises. In other words, regional differences in environmental regulation (that should be shrunk) ease the pollution heaven effect (Peng et al., 2023) [26]. Meanwhile, these high-emission chemical firms also prefer to relocate to regions with the actual looser environmental regulations (Wang et al., 2019) [6]. Furthermore, based on the case in China, while industrial transfer could effectively alleviate the degree of haze pollution in the transferred-out areas, it would significantly accelerate haze pollution in the transferred-in areas (Liu and Dong, 2019) [25]. Due to the above mutual interests, based on Hypothesis 2, there is a high chance of pollution transfer and carbon leakage, primarily when chemical enterprises exploit the ambiguity of this relocation policy, and administrative committees of chemical parks in the less developed central and western region provinces overlook the assessment of environmental impacts, especially for high-emission chemical firms with substantial size and investment. Moreover, though this relocation policy asked that all chemical parks intending to admit chemical enterprises meet the standardized environmental protection requirements, such standardization is challenging to realize in time for the less developed central and western region provinces due to their constrained financial budgets. Hypothesis 4 suggests that financial support to chemical enterprises is more affordable for local governments than that of chemical parks. Therefore, in line with Hypothesis 2, some unstandardized chemical parks may offer more attractive financing, land, and tax incentives to entice chemical enterprises, leveraging their preference for such incentives.

Furthermore, the public and scientists, the additional two secondary stakeholders, may unintentionally or deliberately influence the pace of industrial transfer and the potential for pollution transfer and carbon leakage based on their respective interests. In developed eastern region provinces, where the NIMBY sentiment is prevalent and social protests against chemical production are relatively common, the public’s actions can expedite the relocation of chemical firms, even regardless of their scale, as public appeals appear to shift the focus of regulators from boosting economic growth to avoiding pollution-induced public unrest (Buntaine et al., 2024) [27]. Also, the public in the less developed central and western region provinces, who live in relative financial distress and prefer more economic welfare due to their challenging circumstances, might be more adaptive to accepting those chemical enterprises even accompanied by high safety and environmental risks if these enterprises could provide them more economic benefits (like more job opportunities). Ideally, scientific assessments of the environmental impacts of chemical firms should play a crucial role in determining their relocation or improvement. However, environmental issues are often framed by scientists (Mickwitz, 2003) [28] rather than science itself. This phenomenon can lead to the manipulation of scientific data by researchers who receive substantial compensation from certain chemical enterprises, resulting in greenwashing. Consequently, environmental impact assessments serve as mere cover-ups for these firms’ actual negative environmental externalities. This type of greenwashing aided by scientists could more or less facilitate pollution transfer and carbon leakage.

Generally, there might be two mismatching scenarios in the searching and matching stage. The first scenario involves substandard chemical enterprises, with safety and environmental risks, relocating to standardized chemical parks in the less developed central and western region provinces, given that they ought to be shut down instead. The second scenario entails chemical enterprises that should be relocated to standardized chemical parks, migrating to unstandardized ones. These scenarios can lead to pollution transfer and carbon leakage, as they result in two kinds of type II mismatching errors. Firstly, standardized chemical parks overseen by local governments in the less developed central and western region provinces may fail to reject substandard chemical enterprises that warrant closure. Secondly, chemical enterprises may fail to reject admission from unstandardized chemical parks that do not meet the required standards for relocation.

Pollution transfer and carbon leakage caused by high-emission chemical enterprises or unstandardized chemical parks could further result in environmental injustice and health disparity. The less developed central and western region provinces are relatively ecologically fragile compared to the developed eastern region provinces. If pollution or carbon emissions from these relocated chemical enterprises cannot be treated strictly to meet national standards, the water, air, and land in the central and western region provinces will be easily contaminated, and local people in these provinces might be more vulnerable to pollution-driven diseases. Specifically, many local people in the less developed central and western region provinces are part of a low-income group. They usually struggle to afford the high medical expenses of treating these pollution-driven diseases, albeit they could get support from the national medical insurance system. Therefore, environmental injustice and health disparity appear: the health status of people in the developed eastern region provinces might be better because of the exit of MSCEs during the policy implementation; however, the industrial transfer and consequent potential pollution transfer and carbon leakage may harm the health status of people in the less developed central and western region provinces.

In general, the fiscal capacities of the less developed central and western region provinces are inadequate compared with those of the developed eastern region provinces. In other words, the fiscal self-sufficiency rates of the less developed central and western region provinces are relatively lower. Due to their lower fiscal self-sufficiency rates, these provinces must rely on government transfers from the central government to complement their yearly public expenditure. In addition, according to this relocation policy, local governments in the less developed central and western region provinces should ensure that chemical parks that receive relocated chemical enterprises comply with relevant laws, regulations, standards, and specifications issued by the state. That means assuring or building standardized chemical parks is a precondition for admitting relocated chemical enterprises. However, based on Hypothesis 4, if local governments’ financing support is the primary or significant source of the standardization investment in chemical parks, such financing support might be a formidable fiscal burden to local governments in the less developed central and western region provinces whose fiscal budgets are highly dependent on the central government’s transfer. Moreover, if these local governments disregard scientific investment analysis to guarantee that such investment can generate at least net present value within the investment time domain and invest indiscreetly, their reckless investment decisions in the building or upgrading chemical parks to be standardized ones would worsen their fiscal self-sufficiency status and further cause their insolvable debt sustainability issue.

3.4. The Facilitators of Mismatching Errors: The Exclusion Factor of Negative Screening and the Defined Ranking Hurdle of Positive Screening

The exclusion factor of negative screening and the defined ranking hurdle of positive screening may facilitate the type I and type II mismatching errors, respectively. Positive and negative screening are widely used in an investment decision-making process. Positive screening, namely best-in-class investment, generally selects the candidates overcoming a defined ranking hurdle to distinguish companies within industries; in contrast, negative screening, frequently used by ethical and faith-based investment, imposes a set of exclusions based on ethical preferences or normative worldview to deliberately opt not to invest in candidates that do not align with values, like “sin stocks” in tobacco and alcohol (CFA Institute, 2022; Blank et al., 2016) [11,29].

Negative screening may be responsible for the type I mismatching error occurring in the first appraising and bargaining stage of this relocation policy when the scale of chemical enterprises in medium and small becomes an exclusion reason. Therefore, the scale should not be reckoned as an exclusion factor to prevent the type I mismatching error. On the other hand, positive screening may facilitate the type II mismatching errors appearing in the second searching and matching stage of this relocation policy. Failing to reject substandard chemical enterprises that should be closed may be due to too much emphasis on the investment amount, tax, and employment they provide, and failing to reject admission from unstandardized chemical parks may be due to favorable financing, land, and tax incentives they offer. Thus, overall ESG attributes should serve as a defined ranking hurdle to prevent the type II mismatching errors.

3.5. The ESG Risk Assessment from the External Event, Exposure, and Vulnerability: A Transition Risk Approach

Though transition risk is usually perceived as a type of climate risk, which “results from the economic and societal changes required to transition to a sustainable economy with net-zero carbon emissions” (Caldecott et al., 2021) [30], this concept also aligns with all ESG and non-ESG risks described above. These risks are led by the type I and type II mismatching errors in the policy implementation with the objectives of transforming the Chinese chemical industry into a safer, more environmentally friendly, and more high-end one. Therefore, all ESG and non-ESG risks can be assessed from a transition risk perspective, particularly the external event, exposure, and vulnerability.

Moreover, ESG and non-ESG risks can be measured by Equation (1), which is revised from climate risk = hazard/external event × exposure × vulnerability (Caldecott et al., 2021) [30].

R_ij = h_ijk × e_ij × v_ij

(1)

where, 0 ≤ h_ijk ≤ 1, 0 ≤ e_ij ≤ 1, 0 ≤ v_ij ≤ 1.

R_ij represents the certain ESG or non-ESG risk i of specific stakeholder j, h_ijk means the magnitude of hazard/external event k related to the certain ESG or non-ESG risk i of specific stakeholder j, e_ij refers to the degree of exposure to the certain ESG or non-ESG risk i of specific stakeholder j, and v_ij shows the degree of vulnerability to the certain ESG or non-ESG risk i of specific stakeholder j. Noticeably, the higher magnitude of hazard/external event and the higher degrees of exposure and vulnerability lead to more severe risk.

For simplicity, we can classify ESG or non-ESG risks into three types: low risk (0 ≤ R ≤ 1/3), medium risk (1/3 < R ≤ 2/3), and high risk (2/3 < R ≤ 1) which are derived from the magnitude of hazard/external event, the degree of exposure, and the degree of vulnerability in terms of low, medium, and high with the same tertiles.

This relocation policy itself is a typical transition risk or external event. Exposure is “the classic financial sense of assets or firms in a vulnerable place or setting” (GARP, 2024) [23]. Meanwhile, the health of labor or the public is also an exposure considering the human capital. Vulnerability refers to the ease of reducing or eliminating transition risks at the facility level and the lack of preparation or financial resilience for transition risks at the corporate level (GARP, 2024) [23]. Here, in addition to chemical enterprises, as the other stakeholders, such as local governments, chemical parks, the public, and labor in the chemical industry, are also involved in or impacted by the policy implementation, so exposure and vulnerability would also cover these stakeholders.

Among the ESG and non-ESG risks in the first appraising and bargaining stage, the one-size-fits-all solution is an external event that impacts MSCEs more. Concerning unemployment, its exposure is the labor of MSCEs that would be relocated or closed, and the vulnerability is the difficulties of previous labor of MSCEs to regain jobs. Regarding stranded assets, the exposure is that MSCEs’ assets would be devalued or obsolete, and the vulnerability is that MSCEs may not be well prepared or financially resilient to adapt to their relocation or shutdown. Concerning industry hollowing-out, the exposure is a balance sheet recession threat, and the vulnerability is the industry sector’s lack of competitiveness and resilience in surviving and thriving.

Among the ESG and non-ESG risks in the second stage of searching and matching, pollution transfer and carbon leakage are typical sustainability-related transition risks; the exposure is high emission assets at the facility level and the high emission-dependent business at the corporate level, the vulnerability is the lack of capability to reduce pollution and carbon emission and the viability of transition plans to zero waste and carbon neutrality. Environmental injustice and health disparities are the public’s concern; the exposure is the public’s health threatened by environmental pollution and carbon emission, and the vulnerability is the lack of financial resilience in treating diseases resulting from environmental pollution and carbon emission. Regarding the debt sustainability issue, the exposure is the assets of newly built or renovated chemical parks funded by the debt, and the vulnerability is the chemical parks’ inadequate financial capability and resilience in paying back the principal and interests of the debt.

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. A further explanation in mismatching errors together with ESG and non-ESG risks.

Stages	Regions	Mismatching Errors and Screening Types	ESG/Non-ESG Risks	Stakeholders’ Perspectives
				Social-Ecological Systems	Behavioral Economics	Information Economics
The first appraising and bargaining stage	The developed eastern region provinces	The type I mismatching error: reject MSCEs Negative screening: the scale of chemical enterprises in medium and small becomes an exclusion reason	ESG risks: the one-size-fits-all solution, unemployment Non-ESG risks: stranded assets, industry hollowing-out	Underestimate the productivity of MSCEs in the long term	Belief perseverance biases: illusion of control, confirmation bias Processing error: framing bias Emotional biases: overconfidence, loss-aversion bias	Principal–agent problem: parties (the central government vs. local governments, the public vs. local governments), hidden actions (perfunctory assessment) Adverse selection: parties (local governments vs. chemical enterprises), hidden characteristics (ESG and non-ESG information of chemical enterprises)
The second searching and matching stage	The less developed central and western region provinces	The type II mismatching errors: fail to reject substandard chemical enterprises or unstandardized chemical parks Positive screening: emphasis on the investment amount, tax, and employment provided by substandard chemical enterprises or favorable financing, land, and tax incentives given by unstandardized chemical parks	ESG risks: pollution transfer and carbon leakage, environmental injustice and health disparity Non-ESG risk: the debt sustainability issue	Overestimate the advantages of the larger size of relocated chemical enterprises in the short term; overestimate the unstandardized chemical parks’ capacity in being standardized in the short term	Belief perseverance biases: conservation bias, confirmation bias Processing errors: anchoring and adjustment bias, framing bias Emotional biases: loss-aversion bias, self-control bias	Principal–agent problem: parties (the central government vs. local governments, the public vs. local governments), hidden actions (perfunctory assessment) Adverse selection: parties (administrative committees of chemical parks vs. chemical enterprises), hidden characteristics (ESG and non-ESG information of chemical enterprises and chemical parks)

). 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.

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 (|p_j − e_i| ≤ 3%), under-matched (p_j − e_i < −3%), and over-matched (p_j − e_i > 3%), with e_i as an ith ESG score percentile chemical enterprise and p_j 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. The measurements of fairness and efficiency of matching outcomes regarding the student-to-school matching problem.

Welfare	Measurement
Fairness	The probability of a matching outcome which has no blocking pairs
	The average number of blocking pairs occurring
Efficiency	The proportion of efficient matches
	The sum of payoffs across players in a match
	Pareto dominance as measured by payoffs of every student type

Note: this table is modified from Lien et al. (2016) [37]. Here, a blocking pair is defined by a (school, student) pair that has a mutual desire to alter their current assignment; efficient refers to the maximum possible sum of all students’ payoffs.

, fairness (stability) and efficiency, the welfare measures of matching outcomes, could be assessed in two and three measurements, respectively (Lien et al., 2016) [37].

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 q_i is the quota of a chemical park (CP_i). To begin with, all chemical enterprises apply to the chemical park of their first choice based on the ESG rankings of these chemical parks. CP_i then places on its waiting list the q_i applicants who rank highest based on their ESG scores, or all applicants if there are fewer than q_i, and rejects the rest. Rejected chemical enterprises then apply to their second choice, and again, each chemical park selects the top q_i 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. Rationale of the ESG matching policy.

Policy Components	Policy Attribute	Participants	Mismatching Errors	Behavioral Economics	Information Economics
The national standardized ESG scoring and ranking system	Information-based	Monitor: third-party auditors Regulator: the MIIT and its local subsidiary departments	The type I mismatching error: reject MSCEs The type II mismatching errors: fail to reject substandard chemical enterprises or unstandardized chemical parks	Belief perseverance biases, emotional biases	Adverse selection
The DA mechanism	Information-based	Regulator: the MIIT and its local subsidiary departments	The type I mismatching error: reject MSCEs The type II mismatching errors: fail to reject substandard chemical enterprises or unstandardized chemical parks	Processing errors, emotional biases	Principal–agent problem, adverse selection
The score announcement instrument before the application process	Information-based	Regulator: the MIIT and its local subsidiary departments	The type I mismatching error: reject MSCEs The type II mismatching errors: fail to reject substandard chemical enterprises or unstandardized chemical parks	Belief perseverance biases, processing errors, emotional biases	Principal–agent problem, adverse selection

, 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.

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.