Research on the Impact of Green Policies on the Transformation of Manufacturing Enterprises from the Perspective of Central-Local Collaboration

Abstract

Green policies serve as a crucial instrument for the state to incentivize enterprises toward green transformation. Utilizing panel data from manufacturing firms listed on the Shanghai and Shenzhen A-share markets between 2011 and 2022, this study employs a dual fixed-effects model to analyze the impact of central green policies on the green transformation of manufacturing enterprises. It further explores the role of central-local policy synergy and applies the WSR (Wuli–Shili–Renli) methodology to investigate the boundary effects of local manufacturing comparative advantages (Wuli dimension), local assessment pressures (Shili dimension), and local green awareness (Renli dimension) on the effectiveness of central green policies in promoting enterprise green transformation. The empirical analysis delves into the mechanisms through which central policies influence enterprise green transformation under the context of central-local decentralization. The results show that the central green policies are beneficial for the green transformation of enterprises, and the coordination of central and local green policies has a promoting effect on the green transformation of enterprises. The boundary effect analyses show that in the Wuli dimension, the comparative advantage of the local manufacturing industry has no significant boundary effect. In the Shili dimension, local assessment pressure has a nonlinear boundary effect on the positive relationship between the central green policy and enterprises’ green transformation. In the Renli dimension, local governments’ attention to green can significantly enhance the positive effect of central green policies on enterprises’ green transformation. The conclusions provide empirical insights for promoting enterprise green transformation and exploring the effective boundaries of green policies, offering policy implications for green development in developing countries: (1) improve the central green policy supply system and enhance support for enterprise green transformation through supply-side and environmental policies. (2) Strengthen central-local coordination mechanisms during the implementation of green policies. (3) Local governments should base their implementation plans on practical considerations, engaging in green governance with a clear understanding of Shili and Renli principles.

1. Introduction

Manufacturing is the backbone of China’s national economy, and its international competitiveness typically reflects a nation’s comprehensive strength. Since the 20th National Congress of the Communist Party of China emphasized the need to promote the high-end, intelligent, and green development of the manufacturing sector, the push for green transformation in manufacturing has been elevated to an unprecedented level. Within the current manufacturing structure, industries with high energy consumption and pollution still account for a significant proportion in China. This not only hinders the high-quality development of China’s economy but also places immense pressure on the country’s resources and environment. According to the “China Energy Statistical Yearbook (2024)”, the total energy consumption of China’s manufacturing sector reached 2.82447 billion tons of standard coal, accounting for 56.821% of the nation’s total energy consumption. The resulting carbon emissions amounted to 8.0113 billion tons, representing 69.838% of the country’s total carbon emissions. This indicates that there is still considerable room for improvement in green innovation and transformation within the manufacturing sector. The green transformation of manufacturing is not only a critical pathway for high-quality development but also an inevitable requirement for sustainable development [1].

Enterprises are the core carriers of socio-economic wealth creation and the extractors of natural resources, making them the most crucial factor in balancing economic development with ecological environmental protection. Given the public goods nature of the ecological environment, on one hand, the benefits of environmental governance by enterprises are shared by all economic stakeholders, while the costs are borne solely by the enterprises themselves. On the other hand, enterprises do not incur costs for their exploitation of natural resources [2]. Green transformation often conflicts with the profit-maximization goals of enterprises, making the drive for green transformation more reliant on various environmental policies implemented by the government. As an essential tool for guiding enterprises toward green development, environmental policies aim to address the negative environmental externalities of economic activities and internalize the costs of environmental externalities. By enacting relevant laws, regulations, policies, and guidelines, environmental policies effectively stimulate and intervene in enterprises’ energy-saving and emission-reduction activities, serving as a vital external force in promoting the transformation and development of manufacturing enterprises [3].

From an international perspective, to effectively address global environmental challenges such as climate change, environmental pollution, and ecological degradation, the United Nations has promoted global green ecological development through initiatives such as establishing a global green environmental policy framework and target system, organizing international environmental activities, and funding and supporting green ecological demonstration projects [4]. By providing financial and technical support, the United Nations assists countries in implementing green ecological demonstration projects, showcasing the feasibility and advantages of green ecological development, and offering references for other regions and countries. For instance, the United Nations established the Green Climate Fund (GCF) to support developing countries in adapting to and mitigating the impacts of climate change. As of November 2024, the GCF has approved 286 projects, with a total funding scale of $15.9 billion. In recent months, global trade pressures, energy security concerns triggered by the Russia–Ukraine conflict, industrial reshoring, and geopolitical factors have led to significant shifts in global green policy directions and theoretical reflections. From the perspective of China’s green policies, in 2015, the State Council issued Made in China 2025, listing “green manufacturing” as one of the five key projects and clarifying the strategic tasks for China’s manufacturing development. In 2021, the 14th Five-Year Plan for Industrial Green Development proposed that green transformation should be “guaranteed by regulatory and standard systems, vigorously implementing green manufacturing projects, and achieving significant results in industrial green development”. In 2024, the “Opinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Comprehensive Green Transformation of Economic and Social Development” was issued. These policies, as part of a series of central policy systems, systematically deploy measures to accelerate the comprehensive green transformation of economic and social development, explicitly requiring the improvement of the green transformation policy system and adherence to comprehensive green transformation. Central green policies, as top-level designs that govern the overall national strategy, provide policy incentives and institutional guarantees for the green transformation of manufacturing enterprises [5]. As a new round of technological revolution and industrial transformation approaches, China is bound to formulate and implement more policies in the field of green manufacturing development. In this context, clarifying the channels through which central green policies influence enterprise green transformation and whether there are differences in the effectiveness of different types of green policies holds significant theoretical and practical importance for the Chinese government in adopting appropriate green policies and promoting the green development of manufacturing enterprises.

China is the world’s largest unitary state, characterized by its vast territory and large population. Central policies are macro-level national strategies, while local governments possess certain discretionary powers to formulate regional policies based on local development conditions to further implement central policies. Consequently, there are variations in policies across different regions [6]. Under China’s model of decentralization, local governments face the dilemma of mismatched fiscal and administrative powers. Driven by “yardstick competition” and a GDP-oriented performance evaluation mechanism, local governments are more inclined to sacrifice environmental benefits for economic gains. This aligns with the profit-driven nature of enterprises, potentially leading to collusion between local governments and enterprises, resulting in misalignment with the objectives of the central government [7]. How to achieve a rational division of labor and collaborative governance between central and local governments and how to realize the complementary advantages and dynamic balance of professional and localized management are key issues that need to be addressed in advancing the modernization of the national governance system and capabilities.

The “Wuli–Shili–Renli” (WSR) systems methodology, which focuses on solving complex and systemic problems [8], aligns well with policy systems characterized by multiple intricate factors. It is particularly suitable for constructing a theoretical framework for green policies driving the green transformation of manufacturing enterprises. This is especially true for central green policies, as local governments, when faced with central policies, address three layers of issues: the “Wuli” (physical) layer, which deals with “what exists”, where local governments control the allocation of key physical elements such as land, energy, and data; the “Shili” (logical) layer, which addresses “how to do it”, where local governments balance economic and environmental performance pressures; and the “Renli” (human) layer, which tackles “why to do it”, reflecting the subjective willingness of local governments to promote enterprise green transformation [9]. Therefore, the WSR theory provides a comprehensive and clear framework for exploring the policy-making mechanisms of China’s green development in manufacturing from the perspective of central-local collaboration. It reveals the essence of such collaboration and offers diagnostic tools when transformation encounters obstacles.

Against this backdrop, this paper primarily explores the following questions: can the issuance of central green policies directly drive the green transformation of manufacturing enterprises? Should local governments coordinate with central policies or independently formulate green policies tailored to their own development? Alternatively, under the premise of aligning with the central government’s top-level design, how can local governments fully leverage their subjective initiative across multiple dimensions? The aim is to investigate the policy-making mechanisms through which the central and local governments promote the green transformation of manufacturing enterprises from a central-local collaboration perspective. To this end, this study employs a sample of A-share listed manufacturing companies in Shanghai and Shenzhen from 2011 to 2022, examining the impact of green policies on the green transformation of manufacturing enterprises and their underlying mechanisms from the perspective of central-local collaboration. First, the study tests the impact of central green policies on the green transformation of manufacturing enterprises and further categorizes green policies into three dimensions—supply-side, environmental, and demand-side—to examine their differential effects. Second, it explores the impact of central-local policy alignment on enterprise green transformation. Finally, based on the “Wuli–Shili–Renli” (WSR) methodology, the study investigates the boundary conditions for the effective implementation of central green policies. The findings reveal that central green policies generally facilitate the green transformation of manufacturing enterprises, with supply-side and environmental policies showing significant effectiveness. Central-local policy alignment has a notably positive impact on enterprise green transformation. The boundary effect analysis indicates that local manufacturing comparative advantages do not exert significant boundary effects, while local performance evaluation pressures exhibit a nonlinear boundary effect on the positive relationship between central green policies and enterprise green transformation. Local governments’ green focus significantly enhances the positive impact of central green policies on enterprise green transformation.

The marginal contributions of this paper are as follows: (1) using a sample of A-share listed manufacturing companies in Shanghai and Shenzhen from 2011 to 2022, this study creates a novel longitudinal dataset. This dataset integrates multi-dimensional data, including policy texts (central and local green policies), corporate annual reports (R&D investment, patent data, etc.), and regional economic statistics (e.g., local GDP growth rate, PM2.5 concentration, etc.), constructing a cross-level (central-local-enterprise) analytical framework. On the one hand, the longitudinal dataset allows for capturing dynamic changes, covering multiple policy cycles, and tracking the implementation intensity of central and local green policies at different time points and their long-term impacts on enterprise green transformation, thereby revealing the long-term interaction mechanisms between policies and enterprises. On the other hand, while existing studies have examined the impact of strategic planning or pilot policies in various subfields of green development on enterprise transformation, they have not comprehensively reflected the overall impact of central green policies on enterprise green transformation behavior. By analyzing policy texts, this study constructs an overall supply intensity index for central green policies, not only focusing on their impact on enterprise green transformation but also examining the differential effects of policies across different dimensions. (2) Grounded in China’s unique institutional context of central-local decentralization, this study investigates the intrinsic mechanisms through which green policy alignment under central-local decentralization influences the green transformation behavior of micro-enterprises. It extends the existing debate on “green policy effectiveness” to the exploration of “boundaries of green policy effectiveness”, enriching the boundary conditions for determining the effectiveness and desirability of green policies in transitioning countries. This provides a new cognitive foundation and empirical evidence for the ongoing academic debate on green policy effectiveness and offers a new theoretical framework and empirical evidence for the formulation and implementation of central and local policies. (3) The WSR methodology, which systematically and scientifically analyzes and designs, plans, and coordinates the development of things from an overall perspective, has been widely applied in fields such as knowledge management, enterprise management, public opinion governance, engineering practice, and system evaluation. However, existing research has yet to apply the WSR methodology to the theoretical study and practical guidance of central green policies. Based on the WSR methodology, this study explores the boundary effects of central green policies in enhancing the green transformation of manufacturing enterprises, contributing to broadening the research on the mechanisms through which central green policies drive enterprise green transformation and providing a theoretical basis for improving the green policy system.

2. Literature Review

Green transformation aims to shift from a high-emission, high-pollution, and environmentally destructive extensive development model to a low-emission, low-consumption green development model, thereby reconstructing the harmonious relationship between humans and nature and promoting sustainable development [10]. As the microeconomic entities in the market, enterprises are not only the main drivers of economic growth but also the primary polluters. Thus, they have become the key focus and breakthrough point for practicing green development concepts and achieving green transformation and upgrading. The green transformation of enterprises not only aligns with their intrinsic requirements for high-quality development but also resonates with national strategic orientations, making them a critical target for government environmental regulations and green policies [11].

In existing research, external factors such as fiscal and tax policies, regulatory factors, and resource factors, as well as internal factors such as corporate management structures and green technology innovation, are the primary focuses of domestic and international scholars studying enterprise green transformation. From the perspective of external factors, fiscal factors mainly refer to fiscal pressure. Yu et al. through a study of 285 cities in China, found that when fiscal pressure is high, local governments may divert funds originally intended to support green technology innovation to other areas with faster returns, which is detrimental to the green transformation of the manufacturing sector [12]. Regulatory factors primarily refer to environmental regulations. Strict environmental regulations can help reduce the pollution emission intensity while compelling manufacturing enterprises to improve their green productivity, thereby promoting green development [13]. Liu et al.’s research further revealed that the role of environmental regulations in enhancing the green transformation of manufacturing is mainly reflected in supervising and strengthening internal innovation, and this effect is more pronounced in enterprises with higher emissions, lower green productivity, and older equipment [14]. Resource factors mainly refer to resource dependence. Resource dependence refers to the phenomenon where resource-abundant economies crowd out factors that drive long-term economic growth during their development [15]. Zheng et al. further explored the reasons and found that resource-dependent cities excessively rely on natural resources while inadequately cultivating new drivers and industries, coupled with a lack of technological introduction, leading to increasingly prominent issues of high energy consumption and low output, ultimately trapping them in an unsustainable development model [16]. From the perspective of internal factors, green technology innovation is the primary driver and the foremost impetus for enterprise green transformation. Green technology innovation not only directly reduces pollutant emissions in manufacturing by replacing polluting equipment but also enhances pollutant recycling technologies, transforming undesirable outputs into intermediate inputs, thereby indirectly improving the overall green performance of enterprises [17]. Green human resource management [18], executives’ green investment vision [19], and organizational culture [20] are critical elements to achieve transformation.

From the perspective of the specific connotation of enterprise green transformation, the development model centered on energy conservation, environmental protection, and green low carbon embodies certain characteristics of quasi-public goods, representing an organic integration of innovation-driven and green development. Enterprises exhibit positive external effects during green transformation, and the benefits of such transformation often spill over to the entire society [21]. Therefore, for public goods with strong externalities, such as the ecological environment, it is particularly necessary for government departments to improve the design of supporting environmental management systems. Policy tools, as the collective term for political means to achieve policy objectives or governance effects, can effectively drive enterprise green transformation when appropriately selected. Policy tools are classified differently according to various application scenarios, with numerous classification systems requiring alignment with suitable logical frameworks. Based on different classification criteria, this paper summarizes the existing literature, as shown in

Table 1. Policy classification.

Classification Basis	Policy Classification
Degree of Policy Mandatoriness [22]	Voluntary, Mandatory, Mixed
Policy Executors and Targets [23]	Authority-based, Incentive-based, Capacity-building, Symbolic and Persuasive, Learning
Policy Governance Model [24]	Command-and-control, Guideline, Selective Intervention, Facilitation
Resources Utilized by Policies [25]	Information-based, Authority-based, Fiscal, Organizational
Functional Differences of Policies [26]	Foundational, Distributive, Market-oriented, Guiding
Policy Action Levels [27]	Strategic, Comprehensive, Basic
Policy Action Methods [28]	Standardized, Dynamic, Guided, Intelligent
Complexity of Policy Systems [29]	System-building, Information Exchange
Policy Impacts [30]	Supply-side, Environmental-side, Demand-side

.

Among these classifications, Rothwell and Zegveld categorized policies into three major types: supply-side, environmental, and demand-side. This classification not only aligns the selection of policy tools with the policy objectives of their applicable fields but also reduces the dimensionality of policies, exhibiting strong within-dimension convergent validity and between-dimension discriminant validity. As a result, it has been widely applied in policy research. In the context of China’s central green policies, supply-side central green policies refer to the government’s top-down support, directly acting on production factors to expand effective supply and directly supporting enterprise green development from the supply side. Bhandary et al., through literature reviews and case studies, examined the environmental and equity impacts of climate finance policies [31]. Xiang et al. examined the impact of enterprise financialization on green transformation intentions and found that government fiscal subsidies and green financial policies significantly mitigate the inhibitory effect of financialization on green transformation intentions, thereby facilitating enterprise green transformation [32]. Shao and Chen, through empirical research on heavily polluting enterprises, discovered that government talent subsidies significantly enhance the quantity and quality of green innovation by optimizing internal innovation resources and increasing external financing, thereby promoting green and low-carbon transformation [33]. Ulucak et al. argued that industrial intelligent technology, as a general-purpose technology, integrates with other technologies to trigger a green technology revolution, providing technical support for enterprise green transformation [34]. Advanced experimental equipment and favorable research environments are critical for green technology innovation. Jiang et al. empirically demonstrated that information infrastructure and integrated infrastructure create a favorable environment for enterprise green transformation [35]. Environmental central green policies aim to shape a favorable environment for green development, employing specific measures such as goal planning, intellectual property protection, regulation formulation, and effective supervision. Their primary purpose is to standardize and guide the overall industrial development layout, improve fair competition environments, and thereby enhance enterprises’ innovation capabilities to some extent. Oberlack et al. utilized meta-analysis (Archetype analysis) to study the impact of sustainable development goals on climate change [36]. Manigandan et al.’s research demonstrated that regulatory policies establish strict environmental standards and emission limits, set green product standards, regulate enterprises’ environmental behaviors, and continuously improve environmental performance through systematic management methods, achieving long-term green development [37]. Demand-side central green policies indirectly support target industries through policy tools such as government procurement, product subsidies, trade controls, and application promotion, stimulating the demand side to enhance enterprises’ enthusiasm for green and sustainable development. Cheng et al., through systematic literature reviews and content analysis, found that green procurement promotes the development of a circular economy, regulates environmental protection, and fosters the formation of an environmentally friendly society [38]. Chae’s research indicated that market cultivation policies also provide market application scenarios for green transformation behaviors, alleviating market failures. Comprehensive measures, such as guiding end consumers, promoting international cooperation, and establishing green trading platforms, can collectively advance enterprises’ green transformation [39].

Through the analysis of existing literature, three limitations in the research on central green policies and enterprise green transformation can be identified: first, a significant portion of the literature analyzes central green policies from a qualitative perspective. Due to the relative difficulty in quantifying demand-side and environmental policies, existing studies have focused more on the effects of specific pilot policies. However, the effects of pilot policies are often regional, necessitating further examination using broader and longer-term microdata. Second, most existing studies focus on the impact of central green policies on enterprise green transformation, overlooking the objective influence of central-local policy coordination under the framework of central-local decentralization on the formulation and implementation of central green policies. Yet, studying the pure effects of central green policies alone is a pseudo-proposition. Numerous external factors determine that the impact of central policies on enterprise green transformation is not simply a matter of “effective” or “ineffective”. Instead, it is essential to delve into the applicable conditions and boundaries for their effective implementation.

3. Theoretical Analysis and Research Hypotheses

3.1. Central Green Policies and Enterprise Green Transformation

Green policies aim to directly or indirectly address issues related to green development. In a narrow sense, they refer to laws and regulations formulated by national institutions to tackle green development challenges, while in a broader sense, they encompass all rules, systems, legislation, and actions related to green development. These policies not only serve the function of environmental protection but also play a role in harmonizing the relationship between the environment and economic development, improving the quality of life for people, and fostering international relations and cooperation. The environmental policies discussed in this paper fall within the broader category, encompassing a variety of measures. As the world’s largest unitary state, China grants the central government the decision-making authority and control over policy objectives. Through top-level design, policy coordination, and resource allocation, the central government systematically guides the direction of economic development and the implementation of major strategies. Under this central economic control model, the central government formulates long-term and medium-term economic development plans through national planning agencies and exercises direct control over economic activities through a series of directive plans. Moreover, the central government plays a dominant role in macroeconomic operations, employing a combination of economic, legal, and administrative means to scientifically regulate the economy [40]. Therefore, central green policies are not only a crucial driving force for promoting the green transformation of manufacturing enterprises but also an integral component of the nation’s overall governance policies.

Central green policies guide enterprises to adopt green production concepts while providing both incentives and constraints for their green transformation. Firstly, the central government, either directly or through mobilizing media, industry associations, and environmental protection organizations, extensively conducts environmental education, promotes green development concepts, and disseminates various environmental and green development information. This provides guidance for manufacturing enterprises to form green production concepts, behaviors, and strategies, thereby generating a guiding effect for their green transformation [41]. These policies not only encourage enterprises to embrace the concept of green transformation, learn and apply energy-saving and environmental protection knowledge, cultivate green and low-carbon production methods, and stimulate their enthusiasm and initiative in implementing environmental protection actions but also guide enterprises to adopt voluntary environmental protection behaviors. Additionally, they highlight exemplary cases of green transformation, helping enterprises understand that green production behaviors represent a dialectical unity of short-term and long-term benefits, thereby guiding them to pursue long-term gains and establish a long-term mechanism for environmental protection [42]. Secondly, central green policies directly or indirectly increase the production costs of high-energy-consuming and high-polluting enterprises, creating a pressure effect for their green transformation. Policy costs related to controlling pollution and carbon emissions, such as taxes, fees, fines, or expenditures on purchasing carbon quotas, are directly included in the production costs of enterprises, placing significant pressure on them to reduce costs. High-energy-consuming and high-polluting enterprises face substantial risks of shutdown, restructuring, or relocation, increasing their survival pressure. Moreover, green policies enhance societal awareness of environmental issues, exposing enterprises’ pollution discharge behaviors to greater risks of legal litigation, thereby constituting legal costs. Additionally, the negative public opinion stemming from environmental issues can harm the corporate reputation and brand image, not only directly reducing enterprise profits but also leading to a series of costs incurred to maintain reputation and image [43]. Lastly, green policies directly or indirectly increase the profits of green industrial enterprises, creating an incentive effect for their green transformation. Government rewards or subsidies for energy-saving, emission-reduction, or environmental-protection actions can directly boost enterprise profits. Leading enterprises in green transformation gain economic returns by selling energy-saving, pollution-reducing, and emission-reducing technologies and services, as well as surplus pollutant emission quotas or carbon quotas. Enterprises investing in green projects can prioritize access to green credit and green bonds, which have lower financing costs and relatively higher returns compared to non-green projects [44]. Based on this, the study proposes the following hypothesis:

H1: 

Central green policies can facilitate the green transformation of manufacturing enterprises.

According to public policy theory, the formulation and implementation of policies are a complex, multi-stage, and multi-actor dynamic process. Governments need to organically integrate policy tools to achieve functional complementarity. Drawing on the research ideas of Rothwell and Zegveld [22], this paper categorizes central green policies into supply-based policies, demand-based policies, and environment-based policies.

First, from the perspective of supply-side policies, central green policies can alleviate the resource constraints faced by enterprises and drive the green transformation of manufacturing firms. The goal of supply-side policies is to directly expand the supply of essential factors, including capital investment [45], talent cultivation [3], technical support [32], and infrastructure development [46]. In the short term, supply-side green policies can directly mitigate the resource shortages encountered by manufacturing enterprises during their transformation, helping them address practical issues such as high capital investment, high risk factors, and low success rates. These policies alleviate environmental uncertainties and survival pressures, reducing the tendency of managers to exploit environmental resources for short-term profits and guiding enterprises toward green transformation. In the long term, supply-side green policies create a relatively relaxed environment for survival and innovation, while dispersing the risks associated with green research and development and lowering the barriers to green innovation. Through substantial fiscal subsidies and measures to cultivate innovative talent, these policies provide continuous economic incentives and rewards for the green transformation of enterprises. This encourages managers to focus on long-term benefits and strategic planning, advancing the green transformation of manufacturing firms [35].

Second, from the perspective of environmental policies, central green policies can reduce transaction costs, provide high-quality public services, and drive the green transformation of manufacturing enterprises. Environmental policies aim to create a favorable business environment for enterprise development, including target planning [47], tax incentives [48], financial support [49], regulatory controls [37], and strategic measures [50]. In an environment where the business climate needs further improvement, “scarce resources” such as legal systems and property rights protection may favor non-green-oriented policy arbitrageurs with high economic value, which is detrimental to the green development of manufacturing enterprises. On one hand, environmental central green policies can reduce the transaction costs of green products, minimize market friction, and curb rent-seeking behavior and institutional costs in the market, guiding enterprises to allocate resources toward green production. On the other hand, environmental policies optimize the supply of public services such as legal systems and property rights protection, indirectly promoting enterprises’ green technology innovation and their willingness to take on transformation risks, thereby influencing their green transformation.

Third, from the perspective of demand-side policies, central green policies can stabilize the market expectations of manufacturing enterprises and drive their green transformation. Demand-side policies create institutional demand distinct from natural demand through government-led market construction, aiming to guide market demand, reduce enterprise uncertainty, lower risks, and enhance expected income. These policies include government green procurement [51], fostering green consumer markets [39], and promoting green demonstration projects [52]. As the largest single purchaser in the market, the government plays a dual role as both a market regulator and participant. Government green procurement not only directly impacts the environmental performance of the procured enterprises but also disrupts the cost–benefit equilibrium of non-procured enterprises within the industry. On one hand, to maintain market competitiveness, enterprises that do not receive orders are compelled to increase investment in green innovation and enhance the green value-added of their products or services to attract attention from the government and consumers [53]. On the other hand, the advanced green production technologies of procured enterprises can spill over to non-procured enterprises through industry exchanges and collaborative research, reducing the risks of green innovation for other enterprises in the industry and thereby motivating their green transformation [54]. Simultaneously, the “endorsement” of government client resources sends a positive signal to the market, which has a beneficial effect on alleviating financing constraints for enterprise transformation and promoting green technology innovation. Therefore, this study proposes the following hypotheses:

H1a: 

Supply-based central green policies can facilitate the green transformation of manufacturing enterprises.

H1b: 

Environment-based central green policies can facilitate the green transformation of manufacturing enterprises.

H1c: 

Demand-based central green policies can facilitate the green transformation of manufacturing enterprises.

3.2. Boundary Conditions of Central Green Policies on Enterprise Green Transformation

The central government is the formulator and leader of policies. However, due to information asymmetry across different administrative levels in China, the central government faces challenges in obtaining accurate and comprehensive data. Additionally, significant disparities in resource endowments and development stages across regions result in a certain degree of ambiguity in the formulation of central green policies, making it difficult to allocate their effectiveness differentially to various localities. China operates under a governance framework characterized by “centralized authority and local implementation”, where local governments possess discretionary power in the execution of policies. Due to variations in resource endowments, performance evaluation targets, and public demands across different regions, the intensity, priority, and tool selection for implementing central policies differ, leading to a divergence in policy outcomes. Specifically, local comparative advantages determine the capacity boundaries for enterprise transformation, assessment pressures reflect the prioritization of goals by local governments, and the degree of green focus shapes the social momentum for policy enforcement [55]. Consequently, this paper further explores how local governments, while coordinating with the central government’s top-level design, can exercise their subjective initiative—that is, the boundary conditions at the local government level for the effectiveness of central policies in promoting manufacturing enterprise green transformation.

The Wuli–Shili–Renli (WSR) systems methodology, abbreviated as WSR, emphasizes the comprehensive consideration of physical, logical, and human factors when addressing complex system issues involving nature, society, and their interactions [56]. Guided by the principles of “understanding the physical, clarifying the logical, and mastering the human”, this methodology systematically, holistically, and hierarchically examines complex problems. It has been widely applied in various fields such as knowledge management [57], enterprise management [9], public opinion governance [58], and emergency medical services [59]. The WSR methodology enables a systematic and scientific analysis and design, planning, and coordination of the development of things from an overall perspective, thereby scientifically exploring the laws of development and making informed decisions. On one hand, the implementation of central green policies differs from general scenarios, as it is influenced by multiple factors and constitutes a complex system. On the other hand, the impacts of central green policies on local governments and manufacturing enterprises are characterized by complexity, systematicity, and the need for multi-stakeholder collaboration [60]. Therefore, this paper employs the WSR theory to explore the policy-making mechanisms between central and local governments, aiming to achieve the green transformation of manufacturing enterprises. Specifically, the Wuli dimension refers to exogenous and long-term stable objective facts in the management process, which this paper examines through the comparative advantages of local manufacturing industries. The Shili dimension focuses on how to act, involving the formulation of appropriate management and execution plans based on the analysis of relevant knowledge and information, as well as the organizational structure and operational mechanisms of the system. This paper investigates this through the pressure factors faced by local governments. The Renli dimension pertains to subjective factors such as human behavior, attitudes, values, and social relationships in management practices, which this paper examines through the level of green attention in local areas.

3.2.1. Wuli (Regularities in Objective Existence): Comparative Advantage of Local Manufacturing

According to the resource -dependence theory, local comparative advantages determine the feasibility of enterprises’ green transformation. The resource-dependence theory posits that the survival and development of organizations rely on the acquisition of external critical resources, and the policy environment is a significant factor influencing the resource-dependence structure of enterprises. When central policies align with regional comparative advantages, they can reduce the resource constraints on enterprises’ green transformation and enhance the feasibility of their environmental strategies [61]. For instance, if a region already possesses clean energy or a circular economy infrastructure, the emission reduction targets mandated by central policies can be more easily achieved, sparing enterprises from bearing additional high costs for energy substitution or pollution control. When a region has a foundation for clean technology research and development, such as a concentration of universities, policy support can accelerate the transformation of technological achievements and reduce enterprises’ independent R&D costs [62]. If a region already exhibits a preference for green consumption, central policies (such as green product certification and carbon labeling) can further expand market demand and drive enterprise transformation. Moreover, green policies that follow comparative advantages ensure that the industrial structure of a region aligns with its endowment structure, enabling enterprises to achieve self-sustainability. This alignment accelerates regional capital accumulation and potentially fosters the fastest long-term economic growth, allowing local governments to allocate more fiscal expenditures to environmental governance. Additionally, self-sustaining enterprises are better equipped to internalize environmental pollution when faced with stricter environmental constraints, thereby effectively achieving green transformation [12]. Therefore, this study proposes the following hypothesis:

H2: 

The synergy between local manufacturing comparative advantages and central green policies promotes the green transformation of manufacturing enterprises.

3.2.2. Shili (Ways of Seeing and Doing): Local Pressure

Under the overarching context of green development and the assessment pressures from the central government, local governments exhibit contradictory behaviors: on one hand, there is an urgent need for green development and environmental improvement; on the other hand, to maintain a certain level of economic growth, there is a motivation to tolerate industries that are environmentally damaging but contribute significantly to tax revenue [63]. The Pressure–Response Model posits that external pressures influence the behavioral choices of organizations. Within the framework of central-local governance in China, local governments face multiple pressures, and their actions are essentially responses shaped by the trade-offs among these pressures. When local governments are subjected to high-intensity economic performance evaluation pressures, their policy implementation tends to prioritize economic growth, thereby weakening the binding force of central green policies and ultimately inhibiting corporate green transformation [64]. To attract more capital, local governments engage in a “race to the bottom” by lowering environmental standards, reducing corporate tax requirements, relaxing land use and planning restrictions, and diminishing environmental supervision, inspection, and enforcement efforts. They may turn a blind eye to violations or even provide subsidies and preferential policies to polluting enterprises or projects that fail to meet environmental standards, thereby squeezing resources for green projects and indirectly undermining the competitiveness of green products [65]. Under economic assessment pressures, local governments focus primarily on short-term gains, relaxing environmental standards. Lax environmental supervision sends a “growth-first” signal, leading enterprises to perceive green transformation as a non-urgent task, thereby delaying environmental investments and eroding their original motivation for green transformation [66]. Manufacturing enterprises face multidimensional challenges in green transformation, including costs, technology, markets, policies, and financing. Local economic assessment pressures exacerbate these difficulties by weakening supervision, distorting resource allocation, and encouraging short-term behaviors. Therefore, this study proposes the following hypothesis:

H3a: 

The economic assessment pressure on local governments negatively moderates the relationship between central green policies and the green transformation of manufacturing enterprises.

When local governments face relatively lenient environmental assessment pressures, the binding signals of central policies are flexibly mitigated, leading to a perceived reduction in the pressure for green transformation among enterprises [67]. For local governments, under low environmental assessment pressures, as long as the “one-vote veto” red line is not crossed, environmental enforcement only needs to meet the minimum compliance requirements. Provincial implementation rules may omit quantitative indicators, adopting symbolic enforcement strategies that are strict on explicit indicators (such as the issuance rate of pollution discharge permits) but lax on implicit indicators (such as actual emission reductions) [68]. In such cases, while written policies appear stringent, actual regulatory enforcement is relaxed, creating mixed signals for enterprises and prompting a wait-and-see attitude toward green transformation. On the other hand, under lax local environmental assessment standards, the costs of green investments often exceed the compliance costs and environmental penalties for enterprises that do not undergo green transformation [69]. As rational economic entities, enterprises are inclined to meet only the minimum legal requirements, even preferring to pay fines rather than engage in green transformation. This indicates a diminishing effect of local environmental assessment pressure on enterprise green transformation under such conditions. However, with the increasing refinement of central green policies, environmental performance has become a regular constraint in the evaluation of local officials, altering the behavioral preferences of local governments. By strengthening the enforcement of environmental regulations, raising entry barriers for polluting enterprises, and optimizing industrial structures, local governments enhance environmental governance. When local environmental assessment pressure reaches a certain threshold, the costs of green investments for enterprises become comparable to or even significantly lower than the costs of environmental penalties, compelling enterprises to prioritize green development and maintain a high level of compliance with green policies [70]. Simultaneously, local governments provide more policy support and incentives, fostering endogenous motivation for green transformation in enterprises. This demonstrates an “increasing” effect of local environmental assessment pressure on the green transformation of manufacturing enterprises under such circumstances [71]. Therefore, this study proposes the following hypothesis:

H3b: 

The environmental assessment pressure on local governments exerts a U-shaped moderating effect on the relationship between central green policies and the green transformation of manufacturing enterprises.

3.2.3. Renli (Patterns Underlying Human Relations): Local Green Attention

The green attention of local governments determines their level of emphasis on green policies, thereby influencing the transmission efficiency and enforcement intensity of central green policies. Local governments play the role of policy transmitters, integrating, refining, and categorizing the grand tasks of the central government within the macro institutional environment. They consolidate various resources, refine policy objectives, classify projects, and relay them to micro-level enterprises [72]. Therefore, the clear reception, accurate understanding, and effective execution of central policy signals by local governments are critical to ensuring policy implementation. Based on the attention allocation theory, local green attention reshapes the attention allocation patterns of local governments, thereby altering the transmission strength and execution priority of central green policy signals. Essentially, local green attention acts as a “signal amplifier” for central policies, addressing the attenuation of policy signals in multi-level governance by reconstructing attention allocation structures [73]. On the other hand, government attention, as a scarce resource, inherently signifies the prioritization of specific matters by decision-makers. Green attention reflects the priority local governments place on environmental protection. By creating a policy environment conducive to enterprises’ green transformation and supplying environmental resources, local governments can reduce financing constraints and alleviate the cost burdens of enterprise transformation [74]. Moreover, when enterprise management perceives the increasing emphasis of local governments on green development, they can promptly adapt to policy shifts, enhance their sustainable development capabilities, respond to government policies, shape a positive image of green development, and gain competitive advantages [75]. Therefore, this study proposes the following hypothesis:

H4: 

Local green attention positively moderates the relationship between central green policies and the green transformation of manufacturing enterprises.

4. Research Design

4.1. Regression Model Specification

To examine the mechanism through which central green policies influence the green transformation of manufacturing enterprises, this study draws on the empirical approach of Hao et al. [76] and employs a dual fixed-effects model to test the aforementioned theoretical hypotheses. The dual fixed-effects model integrates time-series and cross-sectional data to analyze differences between individuals and individual dynamics, while effectively expanding the sample size. This model addresses common issues in panel data, such as autocorrelation and heteroscedasticity, thereby enhancing model robustness. Additionally, it captures unobserved heterogeneity between individuals and temporal heterogeneity within individuals, improving model precision. Models (1) to (4) are constructed to test hypotheses H1, H1a, H1b, and H1c:

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolic{y}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(1)

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolicy\_Suppl{y}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(2)

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolicy\_Environmen{t}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(3)

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolicy\_Deman{d}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(4)

Here, i denotes the enterprise, and t denotes the year, with α₀ representing the constant term; GT stands for the dependent variable, which is the degree of the enterprise’s green transformation; CenPolicy represents the core explanatory variable, namely central green policies, which can be further divided into three dimensions: CenPolicy_Supply (central supply-side green policies), CenPolicy_Environment (central environmental green policies), and CenPolicy_Demand (central demand-side green policies); μ_t controls for year fixed effects; λ_i controls for enterprise fixed effects; control denotes control variables at both the enterprise and regional levels; and ε_i,t is the random disturbance term. The coefficient of primary interest in this study is α₁. If central green policies have a promoting effect on the green transformation of enterprises, α₁ should be significantly positive; conversely, it should be significantly negative.

To explore the moderating effects of local manufacturing comparative advantage, local assessment pressure, and local green attention on the relationship between central green policies and enterprise green transformation, interaction terms are added to the baseline regression model to construct the moderation effect model:

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolic{y}_{i,t}+\theta RC{A}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(5)

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolic{y}_{i,t}+\theta econom{y}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(6)

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolic{y}_{i,t}+\theta environmen{t}_{i,t}+{\theta }_{1}environmen{t_{i,t}}^2+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(7)

$$G T_{i,t}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_{1}CenPolic{y}_{i,t}+\theta attentio{n}_{i,t}+{\mathsf{\alpha }}_{2}contro{l}_{i,t}+{\mathsf{\lambda }}_i+{\mu }_t+{\epsilon }_{i,t}$$

(8)

Here, RCA represents the comparative advantage of local manufacturing, economy denotes the assessment pressure on local economic performance, environment signifies the assessment pressure on local environmental performance, and attention reflects the level of local government green attention.

4.2. Variable Descriptions

4.2.1. Dependent Variable

The dependent variable in this study is enterprise green transformation (GT). Drawing on the research of Guo et al. [77], the green transformation evaluation index is constructed from five dimensions: technological innovation, production level, pollution reduction, environmental protection, and social evaluation. The specific indicators are detailed in

Table 2. Construction of the enterprise green transformation index.

Primary Indicator	Secondary Indicator	Specific Meaning
Technological Innovation	Innovation Input	R&D investment amount
	Innovation Output	Ratio of green patents granted to total patents granted in the year
Production Level	Production Efficiency	Total factor productivity calculated using the LP method
	Labor Efficiency	Ratio of total operating revenue to the number of employees
Pollution Reduction	Pollution Control	Treatment and disclosure of wastewater, exhaust gas, and solid waste
	Clean Production	Disclosure of clean production facilities
Environmental Protection	Environmental Management	Environmental information in annual reports, as well as disclosure of environmental management systems, emergency mechanisms for environmental incidents, and the “three simultaneous” system
	Environmental Supervision	Disclosure of key pollution monitoring units, environmental accidents, environmental violations, environmental complaints, and ISO certification status
Social Evaluation	Social Responsibility	Total score of corporate social responsibility (CSR)

. Based on this index system, the entropy method is used to assign weights and calculate the enterprise green transformation index (GT). To avoid excessively small regression coefficients, the green transformation index is scaled up by a factor of 100.

4.2.2. Explanatory Variable

The explanatory variable in this study is central green policy (CenPolicy). The essence of green policy is the embodiment of national will in environmental management. Currently, the main forms of green policies issued by the central government in China include laws, regulations, rules, ordinances, opinions, measures, detailed rules, and notices. These policies carry significant binding force, and quantifying their effectiveness based on the number of policy documents is a feasible approach. This paper constructs a proxy indicator for central green policies by compiling various policies related to environmental governance issued by the central government from the PKULAW database. The steps are as follows:

First, central policies constitute a comprehensive system involving multiple tools and covering various fields. To accurately identify policy-optimization strategies, it is essential to establish a scientific and complete policy-analysis framework. This paper draws on the classic ideas of Rothwell and Zegveld, categorizing green policies into supply-side, demand-side, and environmental types. This theoretical framework is well-developed, practical, and facilitates integration, making it suitable for the policy classification method used in this study.

Second, the PKULAW database was searched using keywords such as “green”, “low-carbon”, “energy conservation and emission reduction”, “ecology”, “environment”, and “sustainable development”. The search included policy texts issued up to December 2022. The analysis sample consists of policies closely related to the green development of manufacturing enterprises issued by the Central Committee of the Communist Party of China, the State Council, and various national ministries and commissions, excluding policies promulgated by local governments and departments. The types of policy texts selected include decisions, notices, opinions, plans, work programs, management measures, laws, and regulations, totaling 169 green development-related policies.

Third, based on the collected policy text data, to avoid compromising the complete expression of policy meanings, this paper uses paragraphs of policy documents as the smallest coding units without further decomposition. These units were coded according to their issuance time and key content points, resulting in 1879 policy tool coding units (referred to as “coding units”). The coding format is “policy number-policy type-sub-policy type”, as shown in

Table 3. Central green policy text analysis unit coding table (excerpt).

No.	Policy Name	Issuance Date	Policy Text Content	Code	Policy Type	Sub-Policy Type
1	Implementation Plan for Further Improving the Market-Oriented Green Technology Innovation System (2023–2025)	2022	Strengthen fiscal and tax policy support. Governments at all levels should actively support qualified green technology research projects and encourage regions with the necessary conditions to support the promotion and application of green technology innovation achievements. Implement corporate income tax incentives for environmental protection, energy and water conservation, and comprehensive resource utilization, as well as personal income tax incentives for income from the transformation of green technology innovation achievements by scientific and technical personnel, to promote the research and application of green technologies, equipment, and products.	1-1-4 1-2-2	supply-based policy environment-based policy	Technical Support Tax Incentives
	…		…	…
169	Notice of the State Council on Issuing the National Environmental Protection 12th Five-Year Plan	2011	Establish a dynamic evaluation mechanism to strengthen the tracking, analysis, and supervision of the implementation of the plan. The Ministry of Industry and Information Technology should submit annual progress reports on the implementation of the plan and conduct mid-term evaluations as appropriate, continuously optimizing the implementation plan and safeguard measures to promote the smooth achievement of the plan’s goals and tasks.	169-2-5	environment-based policy	Strategic Measures
	…		…	…

Note: “…” indicates omitted rows; the complete table has 169 entries.

. Since a single coding unit may contain multiple policy tools, the total number of policy tools analyzed exceeds the total number of coding units. Using 2011 as the base year, this paper constructs a cumulative policy count on the central-year dimension to measure the supply intensity of central green policies (CenPolicy), and employs the central-year dimension’s policy increment count for robustness checks.

4.2.3. Control Variables

Drawing on existing literature [78], this study controls for factors that may influence enterprise digital transformation at both the enterprise and city levels. At the enterprise level, the control variables include the following: first, enterprise characteristic variables, such as age (the natural logarithm of the sample year minus the year of establishment plus one). Enterprises with longer establishment histories may have advantages in technology accumulation, management experience, and resource acquisition, which could influence their willingness and capacity for green transformation. Controlling for this variable helps eliminate the impact of enterprise history on green transformation. Size (the natural logarithm of total assets in billions of yuan). Larger enterprises typically possess more resources and capabilities for green investments and technological innovation, while smaller enterprises may face resource constraints. Controlling for this variable mitigates the potential influence of enterprise size on green transformation. Soe (a dummy variable, where state-owned enterprises are coded as 1 and otherwise as 0). State-owned enterprises may be more inclined to respond to national policies, whereas non-state-owned enterprises may prioritize economic benefits. Controlling for this variable addresses the differential impact of ownership structure on green transformation. Second, financial and operational conditions of enterprises, including Lev (total asset-liability ratio, %), which measures the enterprise’s debt level. Enterprises with higher debt ratios may face greater financial pressure, potentially reducing investments in green transformation. Controlling for this variable eliminates the influence of financial risk on green transformation. Roa (return on total assets, %), which measures the enterprise’s profitability. Enterprises with stronger profitability may have greater capacity for green investments, while those with weaker profitability may prioritize survival. Controlling for this variable addresses the impact of profitability on green transformation. Growth (revenue growth rate, %), which measures the enterprise’s operational performance. Enterprises with faster growth may be more willing to invest in green transformation to support future development, while those with slower growth may focus on short-term benefits. Controlling for this variable mitigates the influence of growth potential on green transformation. Fasset (fixed asset scale), which measures the enterprise’s financial stability. Enterprises with larger fixed asset scales may operate in capital-intensive industries, which may require green transformation to reduce environmental costs. Controlling for this variable eliminates the impact of asset structure on green transformation. Finally, corporate governance structure variables, including Dual (a dummy variable, where 1 indicates that the chairman and general manager are the same person, and 0 otherwise). The dual role of the chairman and general manager may improve decision-making efficiency but could also increase decision-making risks. Controlling for this variable addresses the potential influence of the governance structure on green transformation. Holder (ownership concentration, %), measured by the shareholding ratio of the largest shareholder. Enterprises with higher ownership concentration may find it easier to implement green transformation policies, as major shareholders may prioritize long-term value. Controlling for this variable eliminates the impact of the ownership structure on green transformation.

At the city level, the control variables include the following: Lngdp (the natural logarithm of the city’s GDP in billions of yuan), which measures the level of economic development. Cities with higher economic development levels may possess more resources and technological support to facilitate green transformation, while cities with lower economic development levels may face resource constraints. Controlling for this variable eliminates the influence of regional economic development on green transformation. Structure (the ratio of the added value of the tertiary industry to that of the secondary industry, %), which measures the level of industrial structure. This variable controls for the impact of differences in the development of manufacturing and service industries. Fdi (the ratio of foreign direct investment to GDP, %), which measures the level of market openness. Cities with higher foreign direct investment may be more influenced by international environmental standards, thereby promoting green transformation. Controlling for this variable mitigates the impact of external shocks on green transformation.

4.3. Data Sources and Processing

Manufacturing enterprises in China are typically characterized by high input, high energy consumption, and high emissions. Encouraging and promoting the transformation of manufacturing enterprises toward green and low-carbon practices and urging them to take responsibility for green transformation is one of the critical pathways to achieving sustainable development goals. Therefore, this study uses Chinese A-share listed manufacturing companies on the Shanghai and Shenzhen stock exchanges from 2011 to 2022 as the research sample. The initial sample is processed as follows: (1) excluding samples with abnormal operations (e.g., ST, *ST) and those with severe data deficiencies; (2) applying winsorization to all continuous variables at the 1% and 99% percentiles to mitigate the impact of outliers. Ultimately, 11,625 sample observations are obtained. Basic information and financial data are sourced from the CSMAR and Wind databases, central green policy data are collected from the PKULAW database, and other data are derived from provincial “Statistical Yearbooks”.

5. Analysis of Empirical Results

5.1. Baseline Regression

As shown in the regression results of column (1) in

Table 4. Descriptive statistics.

Variables	Observation	Mean	SD	Min	Max
GT	11,625	4.442	8.409	0.000	79.747
CenPolicy	11,625	7.024	9.342	0.000	58.000
Age	11,625	2.830	0.363	1.050	3.466
Size	11,625	3.598	1.144	1.114	7.629
Soe	11,625	0.291	0.454	0.000	1.000
Lev	11,625	0.394	1.916	0.505	0.906
Roa	11,625	4.415	6.390	−24.097	21.784
Growth	11,625	16.199	36.200	−56.843	271.453
Fasset	11,625	0.379	0.102	0.341	0.568
Dual	11,625	0.301	0.459	0.000	1.000
Holder	11,625	33.596	14.269	8.705	74.763
Lngdp	11,625	8.795	1.034	6.350	10.603
Structure	11,625	138.636	88.160	42.985	530.464
Fdi	11,625	2.540	1.602	0.113	8.164

, the coefficients of the central green policy variable are significantly positive (α = 0.081, p < 0.01). After incorporating enterprise-level control variables, the absolute value of the coefficient decreases slightly (α = 0.079, p < 0.01). Further, after adding city-level control variables, the absolute value of the coefficient also declines (α = 0.052, p < 0.1). These results indicate that the issuance of central green policies significantly enhances the green transformation of manufacturing enterprises. Therefore, the effectiveness of central green policies in promoting the green transformation of manufacturing enterprises is validated, and Hypothesis 1 of this paper is supported. On the one hand, central green policies subject enterprises to significant environmental assessment pressures by linking environmental quality to the performance of local governments. To prevent their performance from being affected by pollution issues within their jurisdictions, local governments amplify the environmental governance pressure from the central government and transmit it to local enterprises. On the other hand, central green policies provide government subsidies, tax incentives, and financial support for enterprises’ green transformation behaviors, helping to reduce the uncertainty risks of environmental investments and encouraging enterprises to actively participate in green transformation.

As shown in column (3) of

Table 5. Baseline regression results of the impact of central green policies on the green transformation of manufacturing enterprises.

Variables	(1)	(2)	(3)
CenPolicy	0.081 ***	0.079 ***	0.052 *
	(0.030)	(0.030)	(0.027)
Age		1.628	1.658
		(1.507)	(1.501)
Size		−0.039	−0.042
		(0.363)	(0.364)
SOE		0.011 **	0.011 **
		(0.005)	(0.005)
Lev		0.013	0.013
		(0.011)	(0.011)
ROA		−0.059 ***	−0.059 ***
		(0.021)	(0.021)
Growth		0.001	0.001
		(0.002)	(0.002)
Fasset		−0.015	−0.014
		(0.019)	(0.019)
Dual		−0.265	−0.265
		(0.242)	(0.243)
Holder		−0.041 *	−0.042 *
		(0.023)	(0.023)
lngdp			0.356
			(0.549)
Structure			0.011 *
			(0.006)
Fdi			0.105
			(0.076)
Year Fixed Effects	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes
Observation	11,625	11,625	11,625
R2	0.731	0.733	0.734

Note: ***, **, and * show 1%, 5%, and 10% significance.

, the control variables also have a certain impact on corporate green transformation. The coefficient of the enterprise ownership structure is significantly positive, indicating that state-owned enterprises exhibit significantly higher enthusiasm for green transformation compared to non-state-owned enterprises. This may be attributed to SOEs’ stronger financing capabilities, technological expertise, and government resource support, which position them as proactive leaders in green transformation. Additionally, SOEs bear social responsibilities and national missions, actively incorporating green transformation into their long-term strategic planning. The coefficient of return on total assets is significantly negative, suggesting that enterprises with stronger profitability show lower enthusiasm for green transformation. This may be because highly profitable enterprises tend to allocate resources to existing high-return businesses rather than long-term and uncertain investments like green transformation. The coefficient of ownership concentration is significantly negative, indicating that enterprises with higher ownership concentration exhibit lower enthusiasm for green transformation. This may be because major shareholders prioritize short-term financial returns, while the benefits of green transformation are often long-term and uncertain, failing to meet their short-term interests. The coefficient of industrial structure is significantly negative, suggesting that regions with higher industrial structure levels are more conducive to corporate green transformation. This may be because such regions typically possess more advanced green industrial chains and innovation ecosystems, providing enterprises with technological, financial, and market support for green transformation. Furthermore, the regression coefficients of other control variables are not significant. The general insignificance of control variables does not imply the ineffectiveness of the model but rather reflects the interaction between data and theory. For example, the insignificant impact of firm size on green transformation may reflect the counterbalancing effects of resource abundance in large enterprises and decision-making flexibility in small enterprises.

5.2. Robustness Checks

(1)

Replacing the Measurement Method of the Core Explanatory Variable

In

Table 6. Robustness checks: replacing the core explanatory variable and the explained variable.

Variables	(1)	(2)	(3)	(4)	(5)
	GT	GT	Green Patents	ESG	TFP
CenPolicy_new	0.124 **
	(0.048)
lnCenPolicy_new		0.304 **
		(0.120)
CenPolicy			0.041 ***
			(0.003)
CenPolicy				0.031 ***
				(0.004)
CenPolicy					0.015 ***
					(0.005)
Control Variables	Yes	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes	Yes
Observation	11,625	11,625	11,625	11,625	11,625
R2	0.734	0.734	0.786	0.880	0.866

Note: *** shows 1% significance, and ** shows 5% significance.

, columns (1) and (2), respectively, represent the replacement of the measurement method for central green policies with the number of newly issued green policies in the current year (CenPolicy_new) and the natural logarithm of the increment in central green policies (lnCenPolicy_new). The results show that after replacing the measurement indicators of the core explanatory variable, central green policies still have a positive effect on the green transformation of manufacturing enterprises, confirming the robustness of the baseline regression conclusions.

(2)

Replacing the Measurement Indicators of the Explained Variable

Columns (3) to (5) in Table 6, respectively, represent the replacement of the measurement method for manufacturing enterprises’ green transformation with the number of green patent authorizations, enterprise ESG rating scores, and total factor productivity. The results indicate that after replacing the measurement indicators of the explained variable, central green policies still have a significantly positive effect on the green transformation of manufacturing enterprises, further validating the robustness of the baseline regression conclusions.

(3)

Excluding Inherently Advantageous Enterprises

(4)

Enterprises in the environmental protection and new energy manufacturing sectors inherently produce green and clean products and possess favorable conditions in technological innovation, policy support, market demand, and environmental friendliness, giving them a clear advantage in green transformation. Therefore, these enterprises were excluded for robustness checks. As shown in column (1) of

Table 7. Robustness checks: excluding inherently advantageous enterprises and lagging the explanatory variable by one period.

Variables	(1)	(2)
	GT	GT
CenPolicy	0.056 **
	(0.028)
Lag1CenPolicy		0.044 **
		(0.022)
Control Variables	Yes	Yes
Fixed Effects	Yes	Yes
Observation	9314	11,625
R2	0.761	0.734

Note: ** shows 5% significance.

, after excluding inherently advantageous enterprises, the coefficient of central green policies remains significantly positive, confirming the robustness of the baseline regression.Lagging the Explanatory Variable by One Period

Considering the potential time lag in the impact of policy implementation and to mitigate reverse causality to some extent, the explanatory variable was lagged by one period and re-regressed. As shown in column (2) of Table 7, the coefficient of central green policies remains significantly positive, further confirming the robustness of the baseline regression conclusions.

5.3. Endogeneity Test

The global trend toward green and low-carbon transformation is irreversible, and the transition to a low-carbon economy and green development is an inevitable path toward creating a new form of human civilization. The green transformation of China’s manufacturing enterprises has made significant contributions to global environmental protection and sustainable development. The Chinese government has also issued several national-level strategic plans, providing clear direction for the green development of the manufacturing sector. However, the pace of green development at the local government level exhibits a “central-first, local-follow” delay effect, though some cities have taken the lead in promoting green transformation. This progress is closely linked to the personal characteristics of local officials, particularly their educational levels. In China, administrative power is concentrated in the Party committees at various levels, with provincial Party secretaries being the most influential officials in the provincial system. They possess substantial and flexible discretion in policy formulation and implementation. Officials with higher educational levels are more capable of quickly adopting and promoting new policies. Moreover, these officials are more innovative and able to explore new environmental policy tools or mechanisms at the local level. The experiences and lessons accumulated by local officials during policy implementation are fed back to the central government, helping to refine policy design and encouraging the central government to formulate more forward-looking green policies.

Therefore, this study collects and organizes data on the educational levels of provincial Party secretaries as an instrumental variable for the intensity of central green policy supply. From a correlation perspective, there is a certain relationship between the educational levels of local government Party secretaries and the green policies issued by the central government. On one hand, provincial Party secretaries with higher educational levels possess greater foresight and strategic vision. They are more sensitive to changes in the external environment, such as national policy directions and technological trends, and have a deeper understanding of green innovation technologies, enabling them to better grasp the trends and directions of green transformation. On the other hand, local officials with higher educational levels typically have richer professional knowledge and broader perspectives, allowing them to more accurately identify local environmental issues and reflect societal demands and expectations for green development. When formulating policies, governments must consider public willingness and needs to ensure policy adaptability and effectiveness. Local officials with higher educational levels are also more adept at communicating with the central government, effectively expressing local demands, and participating in the policy-making process. From an exogeneity perspective, the educational levels of provincial Party secretaries exhibit significant exogeneity and are not directly associated with corporate green transformation. The regression results of the instrumental variable are shown in columns (1) and (2) of

Table 8. Endogeneity test.

Variables	(1)	(2)
	CenPolicy	GT
CenPolicy		0.809 *
		(0.461)
IV	0.110 ***
	(0.023)
Control Variables	Yes	Yes
Kleibergen-Paap rk		23.11
LM Statistic		0.0000
Kleibergen-Paap rk		37.97
Wald F Statistic		16.38

Note: *** shows 1% significance, and * shows 10% significance.

. It can be observed that the years of education of provincial Party secretaries are significantly positively correlated with the intensity of the central green policy supply. After instrumental variable regression, the core explanatory variable remains significantly positive.

5.4. Heterogeneity Analysis

(1)

Ownership Differences

With the introduction of the “Dual Carbon” goals and the deepening awareness of sustainable development, national strategic planning and policy orientation have increasingly focused on green and low-carbon initiatives. Compared to non-state-owned enterprises (non-SOEs), state-owned enterprises (SOEs) are more susceptible to central green policy interventions and are prioritized for policy support and resource allocation. To explore this, the full sample is divided into SOEs and non-SOEs for subgroup regression analysis. As shown in columns (1) and (2) of

Table 9. Heterogeneity test.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	SOEs	Non-SOEs	Small-Scale Firms	Large-Scale Firms	Heavily Polluting Firms	Non-Heavily Polluting Firms	Low Innovation Capability	High Innovation Capability
GovPolicy	0.066 *	0.059	−0.009	0.071 **	0.049 *	0.862	−0.021	0.062 **
	(0.039)	(0.044)	(0.040)	(0.034)	(0.027)	(1.023)	(0.055)	(0.030)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observation	6350	5275	3569	8056	4787	6838	3367	8258
R2	0.752	0.752	0.782	0.746	0.729	0.933	0.646	0.774

Note: ** shows 5% significance, and * shows 10% significance.

, the coefficient for green transformation level in the SOE group is 0.066 and significant at the 10% level, while it is insignificant in the non-SOE group. This may be because most non-SOEs in the manufacturing sector are still in the early stages of transformation, facing shortcomings and bottlenecks in terms of capital, talent, and data resources. These factors create significant uncertainty in the input–output ratio of green transformation, and the supporting systems remain underdeveloped, thereby dampening the enthusiasm for green transformation among private manufacturing enterprises. In contrast, SOEs, backed by abundant government resources, are better positioned to apply green innovation technologies and integrate them into their production and operational activities to achieve their own green transformation. Additionally, SOEs often bear more national strategic tasks and social responsibilities, resulting in higher enthusiasm and proactive efforts in green transformation compared to non-SOEs.

(2)

Firm Size Differences

The effectiveness of central green policies may depend on differences in firm size. A firm’s production scale serves as a critical foundation for acquiring external resources, and firms with scale advantages possess greater resource acquisition capabilities compared to those without such advantages. Resources provided by green policies are more likely to be allocated to firms with scale advantages. This study uses the natural logarithm of a firm’s assets as a proxy for firm size and divides the sample into large-scale and small-scale firms based on the median firm size for heterogeneity analysis. The results, as shown in columns (3) and (4) of Table 9, reveal that the coefficient for green transformation level in the large-scale firm group is 0.071 and significant at the 5% level, while it is insignificant in the small-scale firm group. This indicates that green policies have a significant impact on the green transformation of larger firms, suggesting that firms with scale advantages can absorb government policy resources and translate them into transformation momentum. Since firms with scale advantages are often leading, star, or backbone enterprises in regional markets and serve as important pillars of local economic development and fiscal revenue, they are more likely to benefit promptly from government green policies and have greater opportunities to access resources such as government subsidies and tax incentives.

(3)

Pollution Heterogeneity of Firms

China has established stringent pollutant emission standards for heavily polluting industries, and firms under strict environmental regulations tend to adopt more proactive environmental protection strategies. Consequently, heavily polluting firms may have stronger motivations to reduce emission costs, enhance the economic benefits of emission reduction, and achieve forward-looking environmental goals. This study categorizes the sample into heavily polluting and non-heavily polluting firms based on the 16 heavily polluting industries listed in the Environmental Information Disclosure Guidelines for Listed Companies issued by China’s Ministry of Ecology and Environment, using the industry classification codes of the firms. The results, as shown in columns (5) and (6) of Table 9, indicate that the coefficient for green transformation level in the heavily polluting firm group is 0.049 and significant at the 10% level, while it is insignificant in the non-heavily polluting firm group. These findings suggest that central green policies have a more significant positive impact on the carbon performance of heavily polluting firms compared to non-heavily polluting firms. The reason lies in the fact that heavily polluting firms are subject to stricter government supervision and heavier penalties, facing more pronounced institutional constraints. This drives them to exhibit stronger willingness and motivation to adopt green technologies for transformation. Moreover, due to the environmentally sensitive nature of their business activities, once green transformation initiatives are implemented within heavily polluting firms, the resulting environmental benefits are likely to be higher than those of non-heavily polluting firms.

(4)

Regional Innovation Capability

Innovation-driven approaches are a critical pathway to reducing environmental pollution. Regions with strong innovation capabilities often foster green industrial clusters, enabling firms to share resources, collaborate on technology, and benefit from knowledge spillovers. Based on the median of the regional innovation capability index from the China Regional Innovation Capability Evaluation Report (2011–2022), the sample is divided into two groups: “low regional innovation capability” and “high regional innovation capability”. The results, as shown in columns (7) and (8) of Table 9, indicate that the coefficient for green transformation level in the high regional innovation capability group is 0.062 and significant at the 5% level, while it is insignificant in the low regional innovation capability group. This may be attributed to the fact that regions with high innovation capabilities possess well-developed innovation ecosystems, fostering close collaboration and interaction among innovation entities, which stimulates firms’ own innovation vitality. Additionally, these regions typically have a dense concentration of universities, research institutions, and innovative enterprises, enabling central green policies to be rapidly translated into applicable green innovation technologies through industry–university–research collaboration, thereby accelerating green transformation. In contrast, in regions with low innovation capabilities, firms generally lack technological R&D capabilities and green technology reserves, making it difficult to translate policy requirements into practical transformation actions.

5.5. Further Research

5.5.1. Differences in Policy Types

The effects of different types of central green policies on promoting enterprise green transformation may vary. Exploring the differential impacts of various sub-policy types is of great significance for a deeper understanding of the effectiveness of green policies.

This paper categorizes central policies into supply-based policies, environment-based policies, and demand-based policies. Supply-based policies act as a “push”, demand-based policies act as a “pull”, and environment-based policies play an indirect safeguarding role. These different policy emphases reflect the government’s goals and preferences for green transformation development. Therefore, this paper further investigates the differential impacts of these three major policy types on enterprise green transformation, with regression results shown in

Table 10. Differences in the effects of three major central green policies.

Variables	(1)	(2)	(3)
CenPolicy_Supply	0.081 **
	(0.040)
CenPolicy_Environment		0.136 *
		(0.071)
CenPolicy_Demand			0.027
			(0.080)
Control Variables	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes
Observation	11,625	11,625	11,625
R2	0.734	0.734	0.733

Note: ** shows 5% significance, and * shows 10% significance.

. Firstly, the results in column (1) of Table 10 show that the coefficient of supply-oriented green policies is positive and passes the test at the 5% level, indicating that supply-oriented green policies can promote enterprises to carry out green transformation. Currently, enterprise green transformation in China is still in its early stages. Supply-based policies can effectively alleviate resource constraints, provide various forms of support to enterprises, and yield quick results due to their short implementation cycles. These policies provide a foundation and guarantee for the smooth progress of enterprise green transformation, aligning with the current development stage of green transformation in Chinese enterprises. Thus, Hypothesis 1a is supported.

Secondly, the results in column (2) of Table 10 indicate that the coefficient of environmental-oriented green policies is positive and has passed the test at the 10% level, suggesting that environmental-oriented green policies can also significantly promote the green transformation of enterprises, and their effect is the greatest among the three types of policies. Environment-based policies reflect the interaction between policymakers and the external environment, focusing on the external conditions surrounding enterprises and exerting an indirect influence. The results demonstrate that a favorable policy environment has a strong radiating effect on the stable development of enterprise green transformation. Therefore, Hypothesis 1b is supported.

The results in column (3) of Table 10 indicate that the coefficient of demand-oriented green policies is positive but not significant. For a long time, China has primarily relied on supply-based policies, supplemented by environment-based policies, with demand-based policies being largely absent. First, the transmission chain of demand-side policies is relatively long, and the transmission mechanism is weak. Demand-side policies typically target downstream markets (such as consumers or purchasers) and indirectly influence corporate decision-making through market feedback. Consumer demand for green products may lag behind policy implementation, making it difficult for demand-side policies to significantly drive corporate green transformation in the short term. In contrast, firms are more directly constrained and incentivized by supply-side policies (e.g., technology subsidies) and environmental policies (e.g., emission standards) [79]. Second, demand-side policies can only generate limited short-term demand stimulation. The green product market is not yet mature, and consumer acceptance of high-priced green products remains limited. As a result, demand-side policies struggle to quickly achieve economies of scale, leaving firms with insufficient motivation for transformation [80]. Finally, the enforcement of demand-side policies is often inadequate. If green procurement or consumption subsidies have a narrow coverage or are implemented loosely, the scope of demand-side policies may be limited, targeting only specific industries or products. This makes it difficult for firms to form stable expectations for a green market, thereby reducing their willingness to transform [81]. In summary, the Chinese government currently tends to provide necessary resources and create favorable conditions for enterprise green transformation through hardware facilities (supply-based policies) and software services (environment-based policies) but has not yet given sufficient attention to the demand side. Thus, Hypothesis 1c is not validated.

Furthermore, this paper examines the effects of 12 sub-dimensions of central green policies, with regression results presented in

Table 11. Differences in sub-policies of supply-based central green policies.

Variables	(1)	(2)	(3)	(4)
	Capital Investment	Talent Cultivation	Infrastructure	Technical Support
CenPolicy	0.072 *
	(0.040)
		0.163
		(0.358)
			1.308
			(1.001)
				0.124 **
				(0.048)
Control Variables	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes
Observation	11,625	11,625	11,625	11,625
R2	0.734	0.733	0.734	0.734

Note: ** shows 5% significance, and * shows 10% significance.

,

Table 12. Differences in sub-policies of environment-based central green policies.

Variables	(1)	(2)	(3)	(4)	(5)
	Tax Incentives	Target Planning	Financial Support	Regulatory Control	Strategic Measures
CenPolicy	0.391
	(0.370)
		0.133 *
		(0.075)
			0.182 *
			(0.097)
				0.066 *
				(0.036)
					0.235
					(0.205)
Control Variables	Yes	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes	Yes
Observation	11,625	11,625	11,625	11,625	11,625
R2	0.733	0.734	0.734	0.734	0.734

Note: * shows 10% significance.

and

Table 13. Differences in sub-policies of demand-based central green policies.

Variables	(1)	(2)	(3)
	Government Procurement	Market Cultivation	Demonstration Promotion
CenPolicy	0.037
	(0.182)
		0.057
		(0.112)
			−0.016
			(0.165)
Control Variables	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes
Observation	11,625	11,625	11,625
R2	0.733	0.733	0.733

. The test results for the four secondary indicators of supply-based policies show that the sub-policies of capital investment and technical support significantly promote enterprise green transformation, while the sub-policies of talent cultivation and infrastructure do not pass the significance test. The test results for the five secondary indicators of environment-based policies indicate that the sub-policies of target planning, financial support, and regulatory control significantly promote enterprise green transformation, while the sub-policies of tax incentives and strategic measures do not pass the significance test. First, regarding government procurement, the green products purchased by the government may be limited to specific industries or products, and the scale of procurement may be relatively small, failing to comprehensively address the green transformation needs of manufacturing enterprises. Additionally, the standards for green products in government procurement may be unclear or loosely enforced, resulting in a lack of motivation for enterprises to pursue green transformation. Second, in terms of market cultivation, consumer awareness and demand for green products may be insufficient. Moreover, green products are often priced higher, and price-sensitive consumers may find traditional products more attractive in terms of cost and performance. This suppresses market demand, making it difficult to drive corporate green transformation through market forces. Finally, concerning demonstration and promotion, demonstration projects are often treated as political achievements, with government resources concentrated on a few “model enterprises”. However, replicable business models or technology diffusion mechanisms are rarely established, limiting the overall impact on the industry. After the conclusion of demonstration projects, it is crucial to provide continued policy support to ensure the sustainability of their effects. Overall, the varying effects of different sub-dimensions of central green policies indicate that some sub-policies are not significant, but this does not imply that central policies are ineffective.

5.5.2. Synergy Between Central and Local Policies and Enterprise Green Transformation

The central government formulates top-level designs aligned with national development strategies and the country’s specific conditions. Local governments, on the other hand, act as both agents for conveying and implementing central policies and self-interested actors addressing local economic development. They often weigh whether to adhere to central policies based on local circumstances. Therefore, to assess the effectiveness of central green policies, it is essential to fully consider the alignment of objectives between central and local governments during implementation and to explore how the synergy (or lack thereof) between central and local policies affects the outcomes of central green policies.

This paper employs content analysis to screen and analyze the texts of central and local green policies. It extracts and summarizes local governments’ responses to central green policies. Taking the Green Technology Promotion Catalog (2024 Edition) as an example, when local policies include expressions such as “organize and implement the Green Technology Promotion Catalog (2024 Edition)”, “implement the Green Technology Promotion Catalog (2024 Edition)”, or “be guided by the Green Technology Promotion Catalog (2024 Edition)”, it is considered that local and central governments share consistent green development goals, indicating central-local synergy. In such cases, a value of 1 is assigned; otherwise, the value is 0.

As shown in

Table 14. Regression results of central-local green policy synergy on enterprise green transformation.

Variables	(1)	(2)
	Synergistic	Non-Synergistic
CenPolicy	0.066 *	0.059
	(0.039)	(0.044)
Control Variables	Yes	Yes
Fixed Effects	Yes	Yes
Observation	5800	5275
R2	0.752	0.752

Note: * shows 10% significance.

, central green policies have a significant impact on enterprise green transformation in regions where central and local policies are synergistic, while their effect is insignificant in regions where central and local policies are not aligned. This demonstrates that under China’s decentralized governance system, the effective integration of the central government’s top-level institutional design and the local governments’ supporting implementation efforts can fully leverage the institutional and resource advantages of both political entities in promoting enterprise green transformation. When local policies deviate from the central government’s directives, enterprises face fragmented policy environments, which may lead to redundant compliance investments and hinder their strategic planning for green transformation due to policy uncertainties. On the other hand, the central government typically establishes unified policies and mechanisms to promote green finance [65]. If local policies contradict central directives, enterprises may encounter difficulties in accessing financial support such as green credit and green bonds, thereby constraining the funding sources necessary for green transformation [66].

6. Boundary Effect Tests

The findings above indicate that promoting green transformation at the micro-enterprise level requires collaborative efforts between the central and local governments to ensure the consistency of environmental governance policies and the rationality of resource allocation nationwide. Building on the assessment of the effectiveness and significance of central green policies in driving enterprise green transformation, this section explores how local governments can exercise their subjective initiative while aligning with the central government’s top-level design.

6.1. Comparative Advantage in Local Manufacturing

Drawing on Balassa’s [82] methodology, this paper employs the Revealed Comparative Advantage (RCA) index to measure the comparative advantage of manufacturing in a given region. First, the Location Quotient (LQ) is calculated using Formula (9):

$$L{Q}_{it}={\displaystyle \frac{\left(q_{it}/GD{P}_{it}\right)}{\left({\displaystyle \sum _i{q}_{it}}/{\displaystyle \sum _{i}GD{P}_{it}}\right)}}$$

(9)

where LQ_it represents the location quotient of the manufacturing sector in region i during period t, q_it denotes the value-added of manufacturing in region i during period t, and GDP_it is the gross domestic product of region i during period t.

Next, the Revealed Comparative Advantage is measured using Formula (10):

$$RC{A}_{it}=\left\{\begin{array}{l}1, if L{Q}_{it}\ge 1\\ 0, if L{Q}_{it}<1\end{array}\right.$$

(10)

When the location quotient is greater than or equal to 1, the manufacturing sector in region i during period t is considered to have a comparative advantage; otherwise, it does not.

The standalone term for the regional comparative advantage and its interaction term with central green policies are incorporated into the main regression. As shown in Column (1) of

Table 15. Boundary mechanism tests: local manufacturing comparative advantage, local pressure, and local green attention.

Variables	GT
	(1)	(2)	(3)	(4)	(5)	(6)
CenPolicy	0.052 *	0.107 ***	0.052 *	0.088	0.241 **	0.047 *
	(0.027)	(0.038)	(0.027)	(0.067)	(0.110)	(0.027)
RCA	0.001
	(0.002)
RCA × CenPolicy	−0.000
	(0.000)
economy		0.076
		(0.208)
economy × CenPolicy		−0.087 **
		(0.040)
environment			0.341 ***	0.379 ***	0.382 ***
			(0.115)	(0.111)	(0.111)
environment × CenPolicy				−0.009	−0.097 **
				(0.013)	(0.044)
environment 2 × CenPolicy					0.010 **
					(0.005)
attention						0.286
						(0.357)
attention × CenPolicy						0.089 *
						(0.049)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes
Observation	11,625	11,625	11,625	11,625	11,625	11,625
R2	0.734	0.734	0.734	0.734	0.735	0.872

Note: ***, **, and * show 1%, 5%, and 10% significance. And “environment ²” represents the square of environment.

, the coefficients of the interaction term between the regional comparative advantage and central policies are not significant, indicating that a region’s comparative advantage in manufacturing does not necessarily lead to a higher level of enterprise green transformation. Hypothesis 2 is not valid. While a region may possess a comparative advantage in manufacturing, the success of enterprise green transformation depends on multiple factors, including market demand, financial support, and industrial chain coordination. If these critical factors are lacking or underdeveloped, green transformation may still face significant challenges.

6.2. Local Pressure

Since the reform and opening-up, China has primarily utilized a promotion mechanism centered on economic performance as the core evaluation metric [68]. However, with the growing emphasis on sustainable development, environmental indicators have also been incorporated into the evaluation system. This study examines two types of local pressure: economic performance pressure (economy) and environmental performance pressure (environment). Economic performance pressure is measured by assigning weighted values based on three indicators: the regional GDP growth rate, fiscal surplus, and unemployment rate [83]. The interaction term between economic performance pressure and the central policies is included in the main regression. As shown in Column (2) of Table 15, the coefficient of the interaction term is significantly negative (θ = −0.087, p < 0.05), indicating that the implementation of central green policies hinders enterprise green transformation in regions with higher economic performance pressure. Hypothesis 3a holds.

Environmental performance pressure is measured based on the local PM2.5 concentration as the indicator [84]. First, the direct impact of central green policies on the green transformation of manufacturing enterprises is tested. The regression results in Column (3) of Table 15 show that central green policies have a significantly positive effect on enterprise green transformation, reaffirming Hypothesis 1. Next, the linear moderating effect of environmental pressure is examined by introducing the first-order moderating term of environmental performance pressure into the regression model. The results in Column (4) of Table 15 indicate that the inclusion of the first-order moderating term does not change the model’s R², and the term itself is not significant, suggesting that its linear moderating effect is negligible. To test whether environmental performance pressure has a significant nonlinear moderating effect on the positive relationship between central policies and enterprise green transformation, the second-order moderating term of environmental performance pressure is introduced. As shown in Column (5) of Table 15, the model’s R² increases, indicating improved overall explanatory power. The first-order moderating term is significantly negative (θ = −0.097, p < 0.05), while the second-order term is significantly positive (θ = 0.010, p < 0.05), confirming a U-shaped moderating effect of environmental performance pressure on the positive impact of central green policies on enterprise green transformation. Hypothesis 3b holds.

6.3. Local Government Green Attention

Local governments communicate their commitment and actions in environmental protection to the public through the release of environmental policies, regulations, standards, environmental reports, and monitoring data. These efforts also provide clear policy guidance for enterprises. This study measures local government green attention (attention) by calculating the frequency of green-development-related terms as a proportion of the total text in municipal government work reports [85]. The interaction term between local government green attention and central green policies is included in the main regression. As shown in Column (6) of Table 15, the coefficient of the interaction term is significantly positive (θ = 0.089, p < 0.1), indicating that after the implementation of central green policies, manufacturing enterprises in regions with higher government green attention exhibit a greater degree of green transformation compared to those in regions with lower government green attention. Hypothesis H4 holds.

7. Results and Discussion

Based on the above findings, the hypotheses tested in this study are summarized as follows, as shown in

Table 16. Hypothesis validation summary.

Hypothetical	Hypothesis Validation
Hypothesis H1: Central green policies can facilitate the green transformation of manufacturing enterprises.	support
Hypothesis H1a: Supply-based central green policies can facilitate the green transformation of manufacturing enterprises.	support
Hypothesis H1b: Environment-based central green policies can facilitate the green transformation of manufacturing enterprises.	support
Hypothesis H1c: Demand-based central green policies can facilitate the green transformation of manufacturing enterprises.	not support
Hypothesis H2: The synergy between local manufacturing comparative advantages and central green policies promotes the green transformation of manufacturing enterprises.	not support
Hypothesis H3a: The economic assessment pressure on local governments negatively moderates the relationship between central green policies and the green transformation of manufacturing enterprises.	support
Hypothesis H3b: The environmental assessment pressure on local governments exerts a U-shaped moderating effect on the relationship between central green policies and the green transformation of manufacturing enterprises.	support
Hypothesis H4: Local green attention positively moderates the relationship between central green policies and the green transformation of manufacturing enterprises.	support

.

Central policies are one of the key factors driving corporate green transformation and achieving high-quality development in the manufacturing sector. This study first confirms the role of central green policies in promoting the green transformation of manufacturing enterprises. It further categorizes green policies into three dimensions—supply-side, environmental, and demand-side—and demonstrates the differences in their effectiveness. Additionally, the research finds that central-local policy coordination significantly enhances corporate green transformation. Building on this, the study employs the Wuli–Shili–Renli (WSR) methodology to reveal the following insights: in the Wuli (physical) aspect, the comparative advantages of local manufacturing do not exhibit a significant boundary effect; in the Shili (logical) aspect, local performance evaluation pressure exerts a nonlinear boundary effect on the positive relationship between central green policies and corporate green transformation; and in the Renli (human) aspect, local governments’ green attention significantly strengthens the positive impact of central green policies on the green transformation of manufacturing enterprises. Based on these findings, the theoretical contributions of this study are as follows:

First, this study extends the literature on the impact of central green policies on corporate green transformation. Existing research has examined the effects of strategic planning or pilot policies in specific areas of central policies on corporate transformation, but it has failed to comprehensively and accurately reflect the holistic impact of central green policies on corporate behavior. This paper constructs a holistic intensity index for central green policies, not only focusing on their influence on corporate green transformation but also exploring the effectiveness differences among various policy dimensions. By doing so, this study not only extends the evaluation of the effectiveness of central green policies but also enriches empirical research on the driving factors of corporate green transformation.

Second, from the perspective of China’s unique central-controlled economic model, this paper investigates the intrinsic mechanisms through which the coordination between central and local governments under the decentralization framework influences the green transformation behavior of micro-enterprises. It enriches the boundary conditions for assessing the effectiveness and appropriateness of policies in transitioning economies, providing a new cognitive foundation and empirical evidence for the ongoing academic debate on the effectiveness of green policies. This contributes to the existing literature by deepening the understanding of the mechanisms through which green policies drive corporate green transformation.

Lastly, this study broadens the application of the Wuli–Shili–Renli (WSR) methodology. Based on the WSR framework, it explores the boundary effects of central green policies on enhancing corporate green transformation by examining the comparative advantages of local manufacturing (Wuli: physical factors), local performance evaluation pressure (Shili: logical factors), and local green attention (Renli: human factors). This provides a systematic analytical framework for understanding the mechanisms through which central and local governments collaboratively promote corporate green transformation. Furthermore, it validates the practicality and scientific rigor of the WSR methodology in the field of policy implementation.

8. Conclusions, Recommendations, and Research Limitations

8.1. Conclusions

Scientific and rational green policies are essential tools for driving high-quality economic and social development in China, as well as intrinsic motivators for promoting green transformation at the micro-enterprise level. Both central and local governments have introduced numerous environmental governance policies, providing supply-side push and demand-side pull for enterprise green transformation. These policies optimize the operational environment for green transformation, encouraging enterprises to actively pursue green development under the dual drivers of economic benefits and social responsibility. Based on data from A-share manufacturing listed companies in Shanghai and Shenzhen from 2011 to 2022, this paper delves into the impact of central green policies on enterprise green transformation and explores the underlying mechanisms from the perspective of central-local synergy. The findings are as follows:

(1)

Central green policies effectively drive green transformation in manufacturing enterprises. Supply-side and environmental central green policies significantly promote enterprise green transformation, while demand-side central green policies have no significant impact. Additionally, there are variations in the effects of central green policies across different sectors.

(2)

The synergy between central and local green policies has a significantly positive impact on the green transformation of manufacturing enterprises. This highlights that, under the framework of central-local decentralization, policy alignment between the central and local governments strengthens the orientation toward enterprise green transformation.

(3)

Under the premise of aligning with the central government’s forward-looking strategies, local governments need to exercise their subjective initiative. Based on the WSR (Wuli–Shili–Renli) methodology, this study identifies the boundary conditions for the effectiveness of central green policies at the local level. The findings reveal that the boundary effect in the Wuli aspect (W) is not significant, as the comparative advantage of local manufacturing does not exert a notable influence on the relationship between central policies and enterprise green transformation. In the Shili aspect (S), a nonlinear boundary effect exists, where regions with higher economic performance pressure hinder the green transformation of manufacturing enterprises, while environmental performance pressure exhibits a significant U-shaped moderating effect on the positive relationship between central green policies and enterprise green transformation. In the Renli aspect (R), a significantly positive boundary effect is observed, with regions demonstrating higher government green attention achieving a greater degree of green transformation in manufacturing enterprises compared to those with lower government green attention. These findings underscore the importance of local governments actively leveraging their unique conditions while aligning with central policies to enhance the effectiveness of green transformation initiatives, thereby creating a more conducive environment for driving enterprise green transformation and achieving sustainable development goals.

8.2. Policy Implications

This study, based on data from China, provides specific pathways and theoretical support for the transition of manufacturing industries in developing countries from high energy consumption and high pollution to green and high-end development. Green policies not only drive corporate green transformation and enhance competitiveness but also help developing countries strike a balance between economic growth and environmental protection. Furthermore, they offer theoretical foundations and practical experience for developing countries to address global environmental challenges and improve their international competitiveness. Therefore, this paper offers the following policy implications for developing countries:

(1)

To improve the central green policy supply system in developing countries and accelerate corporate green transformation, the following measures should be implemented. For supply-side policies, green technologies from developed countries should be introduced through international cooperation and technology transfer agreements and adapted to local conditions to reduce technical costs. Governments should establish special funds to support the green transformation of small and medium-sized enterprises (SMEs), particularly providing subsidies for technological upgrades in high-pollution and high-energy-consuming industries. Additionally, green skills training programs should be launched to cultivate green technology and management talents, while attracting international experts in the green field to participate in local green transformation efforts. For environmental policies, environmental protection laws tailored to national conditions should be formulated, clearly defining the legal responsibilities of enterprises in green transformation while avoiding overly stringent regulations that could impose excessive burdens on businesses. The technical capabilities and enforcement levels of environmental regulatory agencies should be enhanced to ensure the effective implementation of environmental laws, with the introduction of third-party oversight mechanisms. In regions where conditions are mature, pilot programs for carbon taxes or carbon emission trading should be initiated to incentivize emission reductions through economic means. Given the limited market demand and consumption capacity in developing countries, it is essential to explore and optimize demand-side policies to enhance their influence in the market. This will better complement supply-side and environmental policies, forming a comprehensive policy support system.

(2)

Strengthen the coordination mechanism between central and local green policies in developing countries to ensure the effective implementation of central policies at the local level. First, it is essential to clarify the division of responsibilities between central and local governments and adapt to local development levels. The central government of developing countries should establish macro-level green policy frameworks and strategic goals, providing financial, technical, and policy support while supervising local implementation. Local governments, based on their economic development levels and resource conditions, should formulate practical green policy implementation plans to avoid a “one-size-fits-all” approach. Given the significant disparities in economic development levels within developing countries, phased green transformation goals should be set for different regions to avoid the adverse impact of overly rapid transitions on local economies. Second, establish a central-local coordination platform to enhance communication efficiency. Specialized green policy coordination agencies should be set up within local governments to address the uneven distribution of resources in developing countries. These agencies would be responsible for liaising with the central government and ensuring policy implementation. A regular communication and feedback mechanism should be established to facilitate timely resolution of issues encountered during policy execution. Additionally, an information-sharing platform should be developed using digital technologies to ensure transparency and symmetry of information between central and local governments in policy formulation and implementation.

(3)

In light of China’s national conditions, local governments should adopt a pragmatic and people-oriented approach to green governance, tailoring their strategies to local contexts. In terms of pragmatism, given the significant disparities in regional economic development, central and local governments should collaboratively establish reasonable performance evaluation standards. Local governments should be encouraged to propose implementation plans and specific indicators based on their actual conditions, while formulating local policies and regulations. In local economic assessments, the weight of the GDP should be appropriately reduced, while increasing the weight of indicators such as green transformation, environmental quality, and sustainable development. This will prevent local governments from neglecting green governance due to excessive pressure from economic performance evaluations. In terms of people-oriented governance, it is essential to enhance the green awareness of both the government and the public. The mechanisms for public participation in policy formulation and supervision are still underdeveloped. Therefore, training and learning programs should be implemented to improve the green governance awareness and capabilities of local government officials, ensuring the scientific rigor of policy formulation and execution. Greater resource allocation should be directed toward green development in areas such as finance, human resources, and technological research. Through media campaigns and community activities, public awareness and support for green transformation should be elevated, encouraging public participation in policy formulation and supervision. Additionally, third-party oversight should be introduced, involving non-governmental organizations, media, and academic institutions in the supervision and evaluation of green policies, thereby enhancing policy transparency and execution.

8.3. Limitations

First of all, since it is difficult to obtain first-hand data of enterprises’ green transformation, this paper selects indicators based on publicly available secondary data and constructs a green transformation evaluation system to measure the level of enterprises’ green transformation. In the future, the scale of enterprise green transformation can be developed according to the specific purpose and needs of the research, and first-hand data can be obtained through questionnaire surveys to enhance the accuracy and flexibility of the research. Secondly, according to the classic ideas of Rothwell and Zegveld, this paper divides green policy into three dimensions: supply-oriented, environment-oriented, and demand-oriented. Future studies can enrich the subdivision methods of green policy, such as using the topic model BERTopic to identify and classify the topics of policy documents. And, the binary coding of central-local synergy (1 or 0) based on certain phrases may oversimplify complex policy dynamics and informal implementation behaviors. Therefore, in future research, we plan to extend binary coding to multi-level scales (such as 0 indicating no coordination, 1 indicating partial coordination, and 2 indicating complete coordination), in order to better reflect the degree of collaboration between the central and local governments. Finally, this paper focuses on local implementation strategies after the central government has issued green policies. In the face of different implementation conditions of local governments, whether the central government needs to carry out a new round of policy updates and adjustments, this paper lacks corresponding research. In the follow-up research, we can explore the interactive process of central and local policies from a dynamic perspective.