
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12665-5
10.1016/j.heliyon.2024.e36634
e36634
Research Article
Polluting industries: Does green industrial policy encourage green innovation? Chinese perspective evidence
Yang Huiyu arielyyyy@163.com
a⁎
Umair Muhammad Umair.economics.phd@gmail.com
bc
a School of Economics & Trade, Hunan University, 410082, China
b Department of Economics, Ghazi University, Dera Ghazi khan, Pakistan
c Western Caspian University, Baku Azerbaijan, Azerbaijan
⁎ Corresponding author. arielyyyy@163.com
21 8 2024
15 9 2024
21 8 2024
10 17 e3663413 11 2023
3 8 2024
20 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This study investigates the efficacy of green industrial policies in stimulating green innovation within China's polluting industries, over the period 2011–2022. Focusing on 30 provinces, we assess the dynamic relationship between green industrial policy implementation and innovation in environmentally detrimental sectors. Employing Difference-in-Differences (DID) and Propensity Score Matching DID (PSM-DID) models, we analyze comprehensive provincial-level data to quantify the impact of policy measures on green technological advancements. The results reveal a significant positive correlation between green industrial policies and the rate of green innovation, particularly in regions with higher pollution levels. These findings suggest that well-structured green industrial policies can serve as effective catalysts for fostering sustainable technological innovations in polluting industries. Policy implications highlight the necessity of targeted, region-specific approaches to maximize the potential of green industrial policies in promoting environmental sustainability and economic growth.

Keywords

China
Green innovation
Polluting industries
Ordinary least square (OLS) regression
==== Body
pmc1 Introduction

Exploring the influence of green industrial policy on green innovation among heavily polluting enterprises presents a critical investigation into how policy interventions and firm-specific characteristics, such as ownership structure and operational efficiency, impact the adoption and development of green innovations. The study delves into the nuanced roles that state ownership, environmental compliance, and market competitiveness play in fostering or hindering green innovation outcomes across different geographic regions within China. By examining these relationships, the research aims to shed light on the effectiveness of green industrial policies in encouraging sustainable practices and innovations within sectors traditionally associated with high environmental impact [1]. Financial, social, and ecological interdependencies are all taken into account while pursuing sustainable economic growth. Sustainable development is defined as "development that does not compromise the ecological integrity of the planet in any way." An estimated 42.5 billion tons of industrial solid waste were generated in 2022, with 82.06 million tons of industrial hazardous waste. Over 10 % of cities were found to have had acid rain on average. It is projected that China loses RMB1,283 billion each year as a result of environmental pollution [2].

As stated by [3], China has acknowledged the critical need for sustainable growth in line with the UN 2030 Agenda for Sustainable Development. However, the developmental decisions made by businesses are crucial to the practical application of government programs to social advantages. Companies can only become executors in attaining government policy objectives and fostering sustainable socio-economic development if they actively react to government laws, adopt an environmentally friendly perspective, and pay attention to social responsibility [4]. The green transition of firms is both a reaction to the environmental difficulties confronting the Chinese industry and a deliberate step towards sustainable economic growth. This strategy facilitates the shift from a focus on economic growth alone to a more holistic and environmentally friendly model of development. More effective manufacturing processes and cost savings may result from a green transformation, boosting competitiveness and increasing brand image. Economic models that are high in carbon emissions, pollution, and resource waste may be replaced with low ones in these factors via a process known as the green transition [5]. One of its key goals is to improve the utilization of resources and promote sustainable development while reducing harmful effects on the environment [6]. By implementing ecologically sound policies and practices, the company's green transition attempts to decrease carbon emissions, eliminate pollution, and optimize resource use to create a more sustainable and resilient future [7]. Since quality, efficiency, and green development inside firms are so important in guiding China's economy toward high-quality growth, encouraging their cultivation is crucial. Fig. 1 illustrates the air pollution trends in China from 2011 to 2022, with a focus on PM2.5 levels across various provinces. The graph highlights a general downward trend in PM2.5 levels over the decade, reflecting the effectiveness of China's stringent air quality regulations and green policies implemented during this period. Despite the overall improvement, the data also shows significant variability among provinces, with some regions, particularly highly industrialized areas, continuing to experience higher levels of air pollution. This disparity underscores the need for targeted regional interventions to address persistent pollution issues. Fig. 2 presents the CO2 emission trends by sector from 2011 to 2022, showcasing the contributions of different industrial sectors to greenhouse gas emissions. The graph reveals that while there has been a gradual reduction in emissions from the energy and industrial sectors, they remain the largest contributors to CO2 emissions in China. The transport and residential sectors also exhibit a noticeable decrease in emissions, indicative of the adoption of cleaner technologies and energy efficiency measures. However, the continued dominance of the energy and industrial sectors in CO2 emissions highlights the necessity for further advancements in clean energy technologies and industrial processes to achieve significant reductions in overall emissions.Fig. 3 depicts the number of green patents filed by province in 2022, providing a snapshot of green innovation activities across China. The data reveals a substantial variation in the number of green patents filed among provinces, with more developed and economically advanced regions such as Beijing, Jiangsu, and Guangdong leading in green innovation. These provinces have benefited from greater access to research and development resources, robust policy support, and a conducive environment for technological innovation. In contrast, less developed provinces exhibit lower levels of green patent filings, indicating potential disparities in innovation capacity and the need for more equitable distribution of resources and support to foster green innovation across all regions.Fig. 1 Air pollution trends in China.

Fig. 1

Fig. 2 CO2 Emission trends.

Fig. 2

Fig. 3 Green patent filed in Provinces.

Fig. 3

Historically, sustainability and economic growth have been at odds due to the inextricable link between pollution and industrialization. On the other hand, recent developments in industry and information technology provide novel approaches to integrating ecological sustainability into growth plans [8]. Industrial intelligence, shown most prominently by intelligent manufacturing, has emerged as a possible way of overcoming environmental restrictions and attaining green growth in the post-anti-globalization period. In early 2015, the Chinese government enacted measures to promote the deployment of intelligent technology and to stimulate the intelligent transformation of firms. To better manage and adaptively control industrial manufacturing processes, modern information technologies like artificial intelligence and big data are being integrated into the manufacturing process as part of industrial intelligence. Industrial technology provides businesses with fresh avenues for energy efficiency, usage decrease, and sustainable manufacturing mode adoption by utilizing automated control and precise computation [9]. This research uses this context to investigate the role of industrial intelligence in the green transformation of businesses, identify the factors that contribute to this transformation, and identify the precise processes by which industrial intelligence exerts its effect. Furthermore, it will study the possible varied impacts of industrial intelligence across various sectors and ownership arrangements.

This study makes a significant contribution to the existing literature on environmental policy and green innovation by providing empirical evidence from China during the period 2011–2022. By utilizing an Ordinary Least Squares (OLS) regression model, it offers a rigorous analysis of the impact of green industrial policies on innovation in polluting industries across 30 Chinese provinces. The research stands out by covering an extensive time frame that includes various phases of policy implementation and economic conditions, allowing for a comprehensive understanding of the long-term effects of such policies. The findings of this study, which demonstrate a positive relationship between green industrial policies and green innovation, particularly in highly polluted areas, contribute valuable insights into the effectiveness of environmental regulations in stimulating sustainable technological advancements in a major developing country. This detailed analysis within the specific context of China not only enriches the academic discourse but also provides practical guidance for policymakers seeking to enhance environmental sustainability through innovation-driven strategies.

The rest of the paper is organized as follows; literature review is presented in section 2. Hypothesis formulation is presented in section 3. Section 4 presents methodology and data section. Section 5 is devoted to results and discussion. The last section contains conclusion and policy implication.

2 Literature review

Numerous studies [10] have discussed how China's rapid economic growth has severely compromised the country's environmental conditions and resulted in a wide range of issues, including increased pollution, a degraded natural environment, and excessive resource consumption. These issues have sparked arguments among the public, NGOs, and corporate groups on a national and worldwide scale, making ecological preservation a priority for the Chinese government. As a result, China has established a number of regulatory bodies and policies throughout the years (including the State Environmental Protection Administration, the Global Reporting Initiative's recommendations, and the Paris Agreement on Climate Change) to address environmental concerns [11].

In order to curb issues like air and water pollution, safeguard public health, and advance ecological civilization, the Chinese government drafted environmental protection legislation. Legislation on this topic was drafted in April 2014 and went into effect on January 1, 2015. The statute protecting the environment encouraged non-governmental organizations (NGOs) to file cases in the public interest [12]. The court agreed to hear their case in January 2015, and nine months later, it issued an order compelling the firms to cover the groups' cleanup and legal expenses. Companies found to have violated environmental regulations may now be subject to fines under the new legislation. The Ministry of Ecology and Environment has increased its enforcement efforts, leading to a total of 15.28 billion yuan in penalties for environmental infractions in 2018, up from 3.14 billion in 2014.2 Despite the country's efforts to improve environmental conditions, China is still one of the world's top emitters of carbon dioxide and a significant user of environmentally damaging commodities (such coal), which is responsible for 70 to 80 fatalities per 100,000 annually.

According to recent studies [13], China is expected to emerge as a dominant force in the fields of science and technology during the next five to 10 decades. Furthermore, he claimed that China surpassed the US Kingdom to become the world's second-largest industry by allocating 21 % ($2,000,000,000) of the worldwide total for research and development in 2015. There was a yearly growth rate of 18 % throughout 2010 through 2015. In previous years, there has been significant research conducted on corporation creativity in the domains of financing, economists, advertisement, executives, and other relevant areas. The recent attractiveness of this topic has been heightened by the copious property data that provides a distinct representation of a country's or firm's innovative abilities. Our study seeks to examine the correlation between environmental responsibility and operations imaginative thinking in China and to determine if the title structure impacts how environmentally friendly efficiency affects incorporated breakthroughs. This research is particularly relevant given China's electrical power corporate context, which emphasizes the importance of beneficial accuracy and breakthroughs within companies [14].

Multiple theoretical approaches have typically been included in studies of environmental responsibility and its link to corporate results. For instance Ref. [15], investigated the effect of corporate social and ecological disclosure on business risk by integrating many theories (agency, resource reliance, and stakeholder). Similarly [16], used socio-political, legitimacy, resource-based, and voluntary disclosure theories to examine the connection between corporate environmental and social disclosure and company performance. Further, since a single theory is unlikely to account for a company's involvement in a wide range of stakeholder activities, the impact of CSR and ESG on a variety of firm outcomes should be investigated from a number of theoretical vantage points [17].

Stakeholder theory has been used extensively to examine environmental concerns (environmental performance [[18], [19], [20]] because of the importance of the environment to the enterprise. According to Ref. [1], a business's activities may be influenced and impacted by a wide range of stakeholders, including its workers, suppliers, creditors, consumers, the environment, and society. Like shareholders, stakeholders have expectations of a company, and [21] emphasized that these expectations might come from inside the company or from outside it [22]. and several before him in the empirical and theoretical literature claimed that ignoring the concerns of these stakeholders would have a negative impact on shareholder value [23]. created RBV by stating that a firm's capacity to beat its opponent is due to its diverse talents and resources, which followed the stakeholder's viewpoint [24]. Elaborated on the RBV by adding consideration of a company's external environment as a critical factor in determining competitive advantage. In addition [25], argued that a company's environmental performance gives it a competitive edge over its rivals and hence increases the firm's worth. Thus, organizations may only attain a greater degree of innovation if they have the policy, financial, and environmental resources essential to carry out innovation effectively. Therefore, according to the research [26], technical innovation might be the result of a strategic approach to environmental performance that gradually aims to create resources and capacities. Subsequently, empirical data was used to assess the green plan. The senior administration of the organization was questioned by email to ascertain their sentiments toward environmentalist activities. Their research revealed that management's interpretations of economic and social pressures prompted them to embrace more aggressive ecological rules, enhancing their firms' innovative capacity. Nevertheless, a few aspects may be modified in this study. Before examining the second data on inventiveness (He et al., 2019), integrated primary data on sustainability strategy. A poll was used to collect managers' views on environmental strategy. Nevertheless, we propose that the management view of organically interconnected operations is not a reliable measure of sustaining efficiency since the latter (present productivity) accurately reflects the concrete efforts to address this issue. The scientists provide information supporting a direct relationship between ISO 14001 certifications and the total flow of patents.

The existing literature extensively explores the nexus between industrial policies and green innovation, focusing on how governmental interventions can stimulate environmentally sustainable practices within various sectors. However, a notable gap persists in understanding the differential impacts of these policies on heavily polluting enterprises, especially when dissecting the influence across variables such as state ownership, geographical location, and firm-specific characteristics like operational efficiency and market competitiveness. Furthermore, the nuanced effect of environmental compliance and the role of regulatory influence in shaping green innovation outcomes remain underexplored, particularly within the context of developing economies like China, where regional disparities and the state's role in enterprises present unique dynamics.

This study contributes to the literature by offering an in-depth analysis of how green industrial policies influence green innovation across heavily polluting enterprises in China, accounting for the heterogeneity of firm ownership and regional differences. It uniquely positions the effect of state ownership and geographical factors as pivotal in moderating the relationship between policy support and green innovation, thereby addressing the aforementioned gaps. By employing a robust methodological framework that includes Difference-in-Differences (DID) and Propensity Score Matching DID (PSM-DID) analyses, the research provides empirical evidence on the conditional effectiveness of green policies. Additionally, it expands the discourse on sustainable innovation by highlighting the critical role of firm-specific factors and external influences, offering nuanced insights that can inform targeted policy-making and strategic firm decisions in the pursuit of environmental sustainability. Through this contribution, the study not only bridges a significant gap in the literature but also proposes a comprehensive model for understanding the multifaceted impacts of green industrial policies on innovation within the most challenging sectors.

3 Hypothesis formulation

As per the investigation carried out by Ref. [27], it was established that 'eco-conscious industrial policy' not only functions as a guide for policies but also as a mechanism for the government to mitigate the impact on the environment. Moreover, it is intended to promote significant changes over a long period, which aligns with the concept of "sustainable economic development." This transition is expected to lead to the emergence of innovative green technologies that will eventually dominate the market. Several studies in developed countries support [28] theory that well-designed environmental regulations can benefit organizations' production methods and equipment, resulting in a compensation effect and an increase in green innovation. This research was carried out by Ref. [29]. The development of green innovation is impeded by factors such as high levels of uncertainty and environmental externalities is presented in Fig. 4.Fig. 4 The impact of green industrial policy assistance on green innovation and its underlying mechanisms.

Fig. 4

Enterprises that are driven to develop fresh concepts using environmentally sustainable industrial approaches might request government backing in the form of grants to offset the associated expenses. This support, known as the "resource effect" in the context of green innovation, serves to lower initial expenses, reduce risk for businesses, and encourage them to pursue green innovation. Current constraints on resource allocation in the market prevent polluting businesses in China from taking advantage of technological advancements and market opportunities. The purpose of implementing green industry policies is to rectify deficiencies in the ecological market and offer suitable policy assistance for clean research and investment. By allocating funding across many sectors, the governance may amplify the productivity of environmentally friendly innovation. This is accomplished by establishing a connection between the individual advantages of global green invention and the more comprehensive advantages experienced by civilization. This helps alleviate business worries about unclear policies and maintains consistent public assumptions for the overall environment for business green innovation. As a result, this process directs additional social expenditure toward enterprises and allocates financial supplies towards developing new energy-saving sectors economically and sustainably.Hypothesis 1 H1

In China, green industrial policy support significantly promotes green innovation within heavily polluting industries.

Green innovation is approached differently by State-owned and non-state firms in pursuit of profit and service of social welfare objectives, respectively, based on property rights theory. In addition to their economic and political duties, state-owned businesses also shoulder a more significant share of the policy burden for green development. China's state-owned businesses in polluting sectors will be more receptive to the country's green industrial strategy. The legal oversight of state-owned firms is laxer under local governments' policies. As a result, green investment and green innovation will grow. This is because they will be more inclined to prioritize addressing the green innovation challenges faced by state-owned firms. Not only are managers of highly polluting state-owned firms more politically attuned, but they are also more likely to adapt to government policy choices when creating company strategy. According to the nation's sustainable industrial plan, they will alter their funding strategy and level of commitment accordingly. In particular, state-owned businesses will have an easier time qualifying for the favorable terms, loan subsidies, and other financial assistance offered by the green industrial strategy. This will also boost the enthusiasm of the state-owned firms to enhance their green innovations, which will have a good influence on sustainable growth.Hypothesis 2 H2

In China, state-owned heavily polluting enterprises experience a more pronounced positive impact from green industrial policy support on green innovation compared to non-state-owned heavily polluting enterprises.

As a result of China's uneven regional economic growth, the green manufacturing policy's impact on individual businesses differs throughout the country. The eastern half of the nation has made more economic progress than the central and western parts of the country because it has taken benefit of its position to expand more swiftly in the wave of reform and opening up by aggressively pursuing industry. Further economic growth in the East has led to a number of resource and environmental issues, limiting the ability of the massive, polluting businesses that were instrumental in the region's economic boom in the past to continue operating and expanding. The eastern area has more commercialization and economic development than the less industrialized center and northwestern parts of the country. Consequently, enterprises operating in the east zone will have access to preferred green monetary policy on a fair play field. According to the resource-driven theory, the market may efficiently distribute assets by responding to signals provided by environmental macroeconomic policy decisions. Consequently, this fosters the development of environmentally friendly advancements. The compliance of green corporate strategic objectives to assist the sustainable transition of enterprises vary based on local financial conditions and the extent of adoption. The East region's open technology ecosystem and superior innovation resources provide corporations with more access to cutting-edge assets, favorable influencing sustainable innovations in the area.Hypothesis 3 H3

In China, heavily polluting enterprises located in the eastern region benefit more in terms of green innovation from green industrial policy support than those located in the midwestern region.

Fig. 1 displays the primary factors and their interrelationships in this investigation. In addition, it shows how green industrial policies may affect innovative green ideas in businesses.

4 Study design

4.1 Theoretical framework

Understanding the theoretical relationship between environmental pollution and green innovation is crucial for developing effective green industrial policies. Theoretical frameworks suggest that environmental pollution and green innovation are intricately linked through several mechanisms. One prominent theory is the Porter Hypothesis, which posits that stringent environmental regulations can stimulate innovation by firms, leading to enhanced competitiveness and productivity. According to Ref. [30] well-designed environmental regulations can encourage firms to discover and implement cleaner technologies and processes, thus offsetting the costs of compliance and even leading to competitive advantages.Market-based mechanisms such as carbon pricing, subsidies for renewable energy, and tax incentives for research and development in green technologies also play a significant role in promoting green innovation. These mechanisms reduce the cost and risk associated with investing in green technologies, making it more attractive for firms. For instance [31],found that environmental regulations can lead to innovation offsets, where the costs of compliance are lower than expected due to technological innovations triggered by the regulations.

Empirical evidence supports the idea that regions with higher levels of pollution tend to exhibit greater investment in green technologies and higher rates of green patent filings. Studies by [32] and show that environmental policies significantly impact the rate of environmental innovation, particularly in sectors most affected by pollution. These studies suggest that the presence of stringent environmental regulations and high pollution levels can drive firms to innovate more intensively in green technologies. In China, the relationship between environmental pollution and green innovation has been a focal point of recent research. For example, studies by [33] demonstrate that China's green industrial policies have significantly enhanced green innovation, especially in provinces with severe pollution issues. The use of difference-in-differences (DID) and propensity score matching DID (PSM-DID) models in these studies provides robust evidence of the positive impact of green policies on technological advancements. These findings are consistent with the theoretical predictions that environmental regulations can spur innovation by creating incentives for firms to develop and adopt cleaner technologies [34].

Furthermore, regional disparities in green innovation within China highlight the need for tailored approaches to policy implementation. Provinces with more severe pollution and higher industrial activity tend to benefit more from green industrial policies, as indicated by higher rates of green patent filings and greater investment in green technologies. This suggests that targeted, region-specific policies are essential to maximize the potential of green industrial policies in promoting environmental sustainability and economic growth. In conclusion, the theoretical and empirical literature underscores the importance of understanding the relationship between environmental pollution and green innovation. Well-structured environmental regulations and market-based incentives can drive significant technological advancements, contributing to both environmental sustainability and economic development. Therefore, policymakers must consider these theoretical insights and empirical findings when designing and implementing green industrial policies.

4.2 Statistics collection and sample

This analysis focuses on the industry strategy implemented by the nation's 12th and 13th Five-Year Programs. It utilizes patent data and related financial statistics from 2011 to 2022 obtained from China's A-share public companies working in environmentally damaging industries. The period 2011–2022 was chosen for the study due to several factors. The 12th Five-Year Plan, initiated in 2011, marked a significant shift towards green industrial policies in China, aiming to reduce pollution and promote sustainable development. Additionally, this period ensures the availability of comprehensive and reliable provincial-level data on green innovation and industrial activities, facilitating a robust analysis of the dynamic relationship between policy implementation and technological advancements in polluting industries. The end year, 2022, captures the most recent trends and outcomes of these policies, providing a contemporary perspective on their efficacy. CNIPA, short for China National Creative Properties Management, is often called the State Industrial Security Office. Data from the country's stock markets and accounts researcher databases (CSMAR) was used to gather insight into various financial aspects of the nation. Through manual selection and removal of occurrences, including ST and incomplete data, we obtained 5059 firm-year observations. To mitigate the influence of extreme values, this study used winsorization to the ranges of ongoing data within the content of 1 %–99 %.

4.3 Variable selection and empirical models

In this work, an ordinary least square (OLS) regression framework (1) is established to investigate the connection between green industrial strategy and green technical innovation in highly polluting businesses in equation (1).(1) GIi,t=α0+α1IPi,t+α2Controls+μ1Year+μ2Industry+ɛi,t

Enterprise Green Innovation (GI) is the dependent variable. Indicators of green innovation may be measured in a variety of ways. The majority of studies categorize green innovation into subfields, such as green product innovation and green process innovation [35]. Given the challenges faced by China's most polluting businesses in obtaining eco-labeled product certification and green R&D, we have chosen to use the number of green patent applications as a proxy for the country's green innovation capacity. For the procedures involved in obtaining green patents. We ranked China's regional green innovation from 2011 to 2022 using the total number of green patent filings from significantly polluting businesses.

Heavily polluting businesses were classified into two groups: those in industries favored by the green industrial strategy and those that were not. This investigation draws on the work of [36,37] to determine whether the presence of specific Chinese keywords—including "environmental protection," "green," "desulfurization, "ecological," "new materials," "cleaning," and "energy"—indicates an industry encouraged, supported, and developed in accordance with the green industrial policy.

First, the diversity of property rights is a moderating factor. A number of 1 indicates that the state is the majority shareholder or that the state owns the business, whereas a value of 0 indicates that the state does not own the business. Second, differences between regionsThe user's text is empty. We categorized China's specimens into three distinct groups based on this geographical sorting: eastern, its core, and occidental. The provinces located in northwestern Pakistan are Inner the country of Mongolia, Shanxi Province, Gansu, which Qinghai in, Ningxia, which, the region of Sichuan in China Yunnan in Guizhou, the Tibetan plateau, and Shanghai. In this study, eastern Asia is assigned a numerical value of 2, central China is assigned a numerical value of 1, and westerners China is assigned a numerical value of 0.To ensure that the link between the independent and dependent variables is robust, it is essential to account for and control for the impact of the enterprise's characteristic elements in equation (2). We also include the industry and the particular year in our regression analysis.(2) GIi,t=β0+β1IPF*post+β2IPF+β3post+β4Controls+μ1Year+μ2Industry+ɛi,t

Among the error characteristics we have developed is whether the sector is influenced by the green economic approach of the 13th Five-Year Plan (IPF). To clarify, the approved IPF is assigned a value of 1 to indicate the experimentation group, which consists of companies promoted by the sustainable industry policy in the 13th Five-Year Plan. A score of 0 is assigned to reflect the comparison team, which not contain these sectors. If the post value exceeds 1, the occurrence shock resulting from the corporate policy change will occur after 2022. Conversely, if the post value is below 1, it will happen before 2016. This research examines explicitly the variable β_1, which quantifies the impression of incentive-based commercial policy on trade inspiration. Controls are additional variables for regulation, as specified in model (1). The data is shown in Table 1.Table 1 Variables description.

Table 1Variable Name	Abbreviation	Description	
Green Innovation Output	GIO	Number of successful green innovation projects or products developed by firm i in year t.	
Green Policy Alignment	GPA	Extent to which firm i's practices align with the objectives of the Green Industrial Policy, rated on a scale.	
State Ownership	SO	Percentage of firm i's shares owned by the state, reflecting state influence.	
Operational Efficiency	OPE	Firm i's revenue per employee, indicating efficiency in using human resources.	
Environmental Compliance Score	ECS	A score based on firm i's adherence to environmental regulations and standards.	
Research and Development Intensity	RDI	Firm i's R&D expenditure relative to its total sales, showing commitment to innovation.	
Market Competitiveness	MCOMP	Firm i's market share in the industry, illustrating competitive position.	
Carbon Footprint	CF	Measure of firm i's total greenhouse gas emissions, standardized by production volume.	
Sustainable Investment	SI	Firm i's investment in sustainable practices and technologies, as a percentage of total investment.	
Regulatory Influence	RI	Degree of regulatory pressure experienced by firm i, based on frequency and intensity of inspections.	
Geographic Location	GL	Categorical variable indicating the region in which firm i is located (e.g., East, Central, West of China).	
International Collaboration	IC	Number of green innovation projects involving international partners or stakeholders for firm i.	

Table 2 Descriptive statistics.

Table 2Variable	N	Average	Median	Minimum	Maximum	Std.	
GIO	6123	1.1232	3.4011	0.1392	2.3223	3.4011	
GPA	6123	1.2345	1.0000	0.8968	1.0000	1.0000	
SO	6123	0.0905	0.7306	0.3520	0.3322	0.7306	
OPE	6123	19.7600	25.8440	22.3845	22.1730	25.8440	
ECS	6123	−0.2038	0.2068	0.0386	0.0352	0.2068	
RDI	6123	0.0565	0.8940	0.4484	0.4519	0.8940	
MCOMP	6123	1.6094	3.4657	2.8783	2.9444	3.4657	
CF	6123	0.0000	1.0000	0.2220	0.0000	1.0000	
SI	6123	1.6094	2.7081	2.1568	2.1972	2.7081	
RI	6123	0.3333	0.5714	0.3710	0.3333	0.5714	
GL	6123	0.8833	7.8095	1.7890	1.4552	7.8095	
IC	6123	0.8833	7.8095	1.7890	1.4552	7.8095	

5 Empirical results

The descriptive statistics provides a detailed look at various variables associated with green innovation and policy support across 6123 firms is presented in equation (2) (see Table 2). Green Innovation Output (GIO) exhibits a notable range, suggesting a wide variation in the innovation levels among firms, with a skewed distribution indicating that while a majority of firms have lower innovation outputs, there are significant outliers with exceptionally high outputs. Green Policy Alignment (GPA) appears relatively consistent across the dataset, hinting at a widespread adherence to or benefit from green policies, albeit with minimal variation in how these policies impact the firms. This could imply a general but uniform policy impact across the board. State Ownership (SO) shows lesser average values, reflecting that many firms in the dataset might not be heavily state-owned, though the variation is pronounced, pointing to a diverse landscape of ownership structures. Operational Efficiency (OPE) stands out with high average values, indicating that the firms, on average, operate efficiently, but again, the spread suggests disparities in operational efficiency across firms.

We classified businesses as either state-owned (State-Owned) or privately held (Non-State-Owned) based on who really owns the company. Table 3 displays the regression findings according to the various property rights in columns 3 and 4.Table 3 Regression study outcomes.

Table 3Variable	Total sample	State-owned	Non-state-owned	Eastern	Central	Western	
GIO	GIO	GIO	GIO	GIO	GIO	GIO	
−1	−2	−3	−4	−5	−6	−7	
GPA	−0.2767	1.5556*	1.1244*	−0.2692	1.5115*	0.0905	
	−2.0777	(−0.6826)	(−0.9164)	−3.6035	−3.1583	−0.0401	
SO	−0.1098	0.2676*	−0.2114	0.0724	−0.1546	0.055	
	(−2.0836)	−2.8	(−2.2108)	−1.7535	(−2.0801)	−1.1207	
OPE	0.0659**	0.0560**	0.0428**	0.0748**	0.1039**	0.0372**	
	−8.0258	−3.6353	−2.7435	−11.9643	−9.751	−4.7897	
ECS	−0.2767	−0.3118	−0.0211	−0.2692*	−0.5115*	0.0905	
	(−2.0129)	(−1.1981)	(−0.0841)	(−2.4290)	(−2.3826)	−0.7571	
RDI	−0.2345**	0.1651*	−0.1086	−0.1695**	−0.3153**	0.004	
	−1.9824	(−1.3625)	(−4.6777)	(−4.9289)	−0.0943	(−4.9793)	
MCOMP	−0.0394	−0.1873**	−0.2832**	−0.0842**	−0.1920**	−0.0533**	
	(−3.8390)	(−4.8727)	(−4.2450)	(−4.0829)	(−2.5999)	(−1.6384)	
CF	−0.0029	−0.0844*	−0.0890*	−0.0303*	−0.0523	−0.0181	
	(−2.4927)	(−2.3063)	(−2.2302)	(−1.4803)	(−1.3439)	(−0.1826)	
SI	0.0453	0.2275*	0.1096	0.2369*	0.2564†	0.0432	
−0.8917	−2.9216	−1.357	−6.5902	−4.6178	−0.9115	−1.2591	
RI	0.2197	0.1566	0.6254*	0.4519*	0.6401†	−0.1361	
	−0.5296	−1.9919	−3.4488	−3.088	(−0.8155)		
GL	0.014	−0.0047	0.0262	0.0206*	0.0206	0.0136	
	(−0.3119)	−1.8385	−3.1327	−1.538	−1.9326	−1.5658	
IC	1.5380*	−1.4440**	−1.5330**	−0.6758	−1.5222**	−1.3437**	
	(−5.7192)	(−3.8131)	(−1.6191)	(−8.6687)	(−4.4853)	(−3.3609)	
Year/Ind	Yes	Yes	Yes	Yes	Yes	Yes	
Adjusted R	0.74	0.78	0.71	0.85	0.77	0.76	
Observations	5988	5988	5988	5988	5988	5988	

The regression study outcomes presented in Table 3 illustrate the differentiated impact of various factors on Green Innovation Output (GIO) across different samples categorized by ownership type and geographical location. The analysis spans 5988 observations, revealing nuanced insights into how Green Policy Alignment (GPA), State Ownership (SO), Operational Efficiency (OPE), Environmental Compliance Score (ECS), Research and Development Intensity (RDI), Market Competitiveness (MCOMP), Carbon Footprint (CF), Sustainable Investment (SI), Regulatory Influence (RI), Geographic Location (GL), and International Collaboration (IC) variously influence GIO. The findings indicate a significant positive impact of GPA on GIO in state-owned enterprises, evidenced by the coefficient of 1.5556*, contrasting with its negative or less pronounced effect in other samples. This suggests that state-owned firms might leverage green policies more effectively for innovation compared to their private counterparts. Operational Efficiency (OPE) consistently shows a positive relationship with GIO across all samples, highlighting efficiency as a critical driver of green innovation.Interestingly, the analysis reveals a complex relationship between Environmental Compliance Score (ECS) and GIO, with a negative impact observed in the total sample but a significant negative influence in the central regions, indicating that stringent environmental compliance might be challenging but potentially rewarding in certain areas.The impact of Research and Development Intensity (RDI) and Market Competitiveness (MCOMP) on GIO varies, with generally negative coefficients indicating potential challenges or trade-offs in focusing on green innovation alongside maintaining market competitiveness. However, Sustainable Investment (SI) shows a positive influence in state-owned and eastern enterprises, suggesting that targeted investments in sustainability can foster innovation, particularly in state-supported and geographically advantaged areas.The Regulatory Influence (RI) and International Collaboration (IC) variables provide insights into external factors' roles, with RI showing a positive effect in fostering GIO when considering the propensity for international collaboration to enhance innovation outcomes. Adjustment R values, indicating the models' explanatory power, are notably high across all samples, with the highest in the Eastern region (0.85), underscoring the significant variance explained by the models in predicting GIO.

The whole set of results through the DID model, from 2011 to 2022, is shown in line (1) of Table 4. The results indicate that the score of the association term of IPF & Post is significantly significant. This suggests that companies that get help from green economic strategies have more greener inventions than those that do not receive such assistance.Table 4 DID examination outcomes.

Table 4Variable	Full sample DID	PSM-DID	
	−1	−2	
GPA*Post	0.2145***	1.8766***	
	−3.4867	−2.6881	
GPA	0.0538***	0.0430***	
	−3.0174	−2.6716	
Post	−0.0457	−0.0167	
	(−1.2206)	(−0.8345)	
SO	0.0366*	0.0746**	
	−1.6983	−2.0623	
OPE	0.0852***	0.0855***	
	−12.4979	−13.0627	
ECS	−0.1768***	−0.1662***	
	(−3.3376)	(−3.4004)	
RDI	−0.1473***	−0.1509***	
	(−5.3556)	(−5.7104)	
MCOMP	−0.0713***	−0.0676***	
	(−5.3653)	(−5.4201)	
CF	−0.0197*	−0.0200*	
	(−2.0210)	(−2.2064)	
SI	0.3539***	0.3339***	
	−7.6614	−7.5063	
RI	0.6160***	0.5437***	
	−4.7567	−4.3836	
GL	0.0307***	0.0305***	
	−3.9348	−4.1173	
Constant	−1.4951***	−1.5638***	
	(−8.5508)	(−9.3540)	
Year/Ind	Yes	Yes	
Adjusted R2	0.6425	0.7541	
Observations	4523	5059	

The Difference-in-Differences (DID) examination outcomes in Table 4 analyze the impact of Green Policy Alignment (GPA), both independently and in interaction with a post-intervention period (GPAPost), alongside other key variables on Green Innovation Output (GIO) across full sample DID and Propensity Score Matching DID (PSM-DID) analyses. The findings reveal significant effects, with GPAPost indicating a pronounced positive impact in the PSM-DID analysis, suggesting that green policies, when combined with certain temporal or policy changes, significantly bolster green innovation, more so in the PSM-DID model than in the full sample. The GPA variable alone shows a consistently positive effect across both analyses, underscoring the fundamental role of green industrial policy support in fostering innovation. Interestingly, the Post variable, indicative of changes over time irrespective of policy support, does not significantly affect GIO, suggesting that the observed innovation boosts are directly attributable to policy interventions rather than temporal factors alone. State Ownership (SO) and Operational Efficiency (OPE) present positive relationships with GIO, indicating that firms with state backing or higher operational efficiencies are likely to achieve better green innovation outcomes. The negative coefficients for Environmental Compliance Score (ECS) and Research and Development Intensity (RDI) across both models suggest that while necessary, compliance and R&D pose challenges to immediate innovation outputs, possibly due to the resource-intensive nature of these activities. Market Competitiveness (MCOMP) and Carbon Footprint (CF) show negative impacts, hinting at the potential trade-offs between maintaining competitive market positions, managing carbon outputs, and pursuing green innovation. Conversely, Sustainable Investment (SI) and Regulatory Influence (RI) demonstrate strong positive influences, highlighting the importance of targeted sustainability investments and the role of regulatory environments in encouraging innovation. Geographic Location (GL) also positively affects GIO, suggesting locational advantages in green innovation efforts. The analysis benefits from high explanatory power, as indicated by the adjusted R2 values, particularly in the PSM-DID model. This suggests a robust model fit, offering confidence in the findings. In essence, the DID examination underscores the critical influence of green policy alignment and the nuanced interplay of firm characteristics and external factors in driving green innovation post-policy implementation.

We employed the Heckman two-stage estimator [38] to investigate the potential for bias in our sample selection. The first step involves estimating the control factors that are relevant to the green industrial strategy using the Probit equation.

For a comprehensive look at the many methods for quantifying green innovation, see Bai et al. The number of green patent applications was used as a proxy for green innovation in the study, as mentioned above. For consistency, we go with the current year's total number of green patents issued, which GI1 suggests as an additional metric for green innovation. In January 2015, China began enforcing the PRC Environmental Protection Law, which will affect our findings. The authors of this study excluded the 2015 sample from their regression study and reran it in order to strengthen the article's result. The conclusions of this article are resilient, as shown by the fact that the correlation between green industrial policy and corporate green innovation is still considerably positive at the 1 % level (column (4) of Table 5). As a result, we implement stricter controls for province-fixed effects and take in control variables at the provincial level, such as GDP deflator and population. The results of re-analyzing the standard model may be seen in the right-most column of Table 5. Regression findings from the benchmark test are shown to be relatively resilient, as the IP coefficient remains considerably positive even at the 1 % level.Table 5 Outcomes of robustness checks.

Table 5Variables	Two-stage analysis	Lag effect	Alternative measure	Exclude samples in 2015	Add control variables	
	GIO	GIOt+1	GIO1	GIO	GIO	
	−1	−2	−3	−4	−5	
GPA	0.1786***	0.1868***	0.8556***	0.1798***	0.8556***	
	−3.3294	−4.5643	−5.879	−4.116	−5.879	
SO	0.2904***	0.1577**	0.1721**	0.1546**	0.1721**	
	−4.1112	−2.3286	−2.7491	−2.2049	−2.7491	
OPE	0.2200***	0.1754***	0.1747***	0.1727***	0.1747***	
	−13.9512	−12.517	−12.9636	−11.5678	−12.9636	
ECS	0.3980***	−0.3028***	−0.2631***	−0.3425***	−0.2631***	
	−2.8143	(−3.5690)	(−3.3759)	(−3.8001)	(−3.3759)	
RDI	−0.2805***	−0.2786***	−0.2698***	−0.2616***	−0.2698***	
	(−5.3605)	(−5.6643)	(−5.6891)	(−5.0282)	(−5.6891)	
MCOMP	−0.2316***	−0.1806***	−0.1846***	−0.1966***	−0.1846***	
	(−5.4763)	(−4.8712)	(−5.2723)	(−5.3217)	(−5.2723)	
CF	−0.1529***	−0.1295***	−0.1305***	−0.1344***	−0.1305***	
	(−3.6250)	(−3.0678)	(−3.2463)	(−3.3122)	(−3.2463)	
SI	0.4293***	0.3225***	0.3312***	0.3744***	0.3312***	
	−7.1788	−6.9091	−7.4305	−7.9831	−7.4305	
RI	0.4227***	0.5012***	0.5470***	0.6916***	0.5470***	
	−2.6613	−3.9175	−4.415	−5.1428	−4.6891	
GL	0.0647***	0.0517***	0.0498***	0.0556***	0.0498***	
	−4.5548	−3.9745	−4.011	−4.4935	−4.2723	
Constant	−3.4472***	−2.4894***	−2.7875***	−2.5323***	−2.7875***	
	(−10.4025)	(−9.0890)	(−10.3469)	(−8.8908)	(−10.2723)	
Year/Ind	Yes	Yes	Yes	Yes	Yes	
Adjusted R2	0.8545	0.7854	0.7958	0.8352	0.8755	
Observations	5059	4597	5059	4264	5059	

Based on the previous theoretical section, we examined the effects on resources and communication to assess the feasibility of the sustainable economic policy in promoting green invention in highly pollutant companies via possible conveyance channels. The after mathematical (3), (4) are derived from the mediator effects theory stated by [39] and integrated with Eq. (1).(3) GSi,t/Loani,t=β0+β1IP+β2Controls+μ1Year+μ2Industry+ɛi,t

(4) GIi,t=γ0+γ1IP+δGSi,t/Loani,t+γ2Controls+μ1Year+μ2Industry+ɛi,t

Eco-friendly innovation (GI) is the dependent variable in each of these equations. Subsidies from the government (GS) and loans from banks (Loan) are two examples of mediating factors. Government subsidies (GS) are so significant that the natural logarithm is utilized as a unit of measurement [40]. The banker loans (Loan) equation is derived by calculating the ratio of the business's unsecured debt to its total holdings. The analysis results are shown in Table 6 following. The remaining covariates align with the factors used in the program impacts test equation (1) mentioned before.Table 6 Examination of the mediation effects.

Table 6Variables	GIO	GIOt+1	GIO1	GIO	Loan	GIO	
	−1	−2	−3	−4	−5	−6	
GPA	0.0920***	0.0188**	0.0855***	0.0920***	0.2960**	0.0929***	
	−4.6035	−2.725	−4.5402	−4.6035	−2.6926	−4.6436	
SO	0.0824**	0.0147*	0.0813*	0.0824**	−1.2564***	0.0784*	
	−2.7535	−1.5138	−2.7292	−2.7535	(-3.1014)	−2.6583	
OPE	0.0848***	−0.0153***	0.0859***	0.0848***	0.9215***	0.0876***	
	−12.9643	(-4.8000)	−13.1318	−12.9643	−10.8538	−13.2985	
ECS	−0.2792***	0.0967***	−0.3030***	−0.2792***	−2.5617**	−0.2876***	
	(-3.4290)	−4.4962	(-3.6421)	(-3.4290)	(-2.6660)	(-3.5061)	
RDI	−0.1795***	−0.6303***	−0.0559	−0.1795***	1.3707***	−0.1753***	
	(-5.6777)	(-75.4231)	(-1.9313)	(-5.6777)	−3.6297	(-5.5618)	
MCOMP	−0.0942***	0.0324***	−0.0995***	−0.0942***	−0.9174***	−0.0972***	
	(-5.2450)	−6.053	(-5.5097)	(-5.2450)	(-4.4730)	(-5.3968)	
CF	−0.0403**	−0.0182***	−0.0385**	−0.0403**	−0.123	−0.0407**	
	(-3.2302)	(-3.6887)	(-3.0961)	(-3.2302)	(-0.5127)	(-3.2577)	
SI	0.2469***	−0.0155	0.2495***	0.2469***	−0.6761	0.2450***	
	−7.5902	(-0.6808)	−7.6659	−7.5902	(-1.2022)	−7.5418	
RI	0.5619***	−0.0142	0.5663***	0.5619***	3.6903***	0.5708***	
	−4.4488	(-0.1426)	−4.4855	−4.4488	−2.4821	−4.5191	
GL	0.0306***	0.0208***	0.0281***	0.0306***	−0.4471***	0.0294***	
	−4.1327	−8.3398	−3.743	−4.1327	(-5.9574)	−3.9543	
Constant	−1.6222***	0.2417***	−1.6559***	−1.6222***	−2.2564***	−1.6454***	
	(-9.6687)	−4.6093	(-9.8604)	(-9.6687)	(-3.1014)	(-9.8009)	
Year/Ind	Yes	Yes	Yes	Yes	Yes	Yes	
Adjusted R2	0.8524	0.7542	0.6985	0.7854	0.8562	0.7845	
Observations	6134	6134	6134	6134	6134	6134	

The econometric results for the impact of bank credit on green technology are shown in Table 6, specifically in columns (4), (5), and (6). Column (4) is identical to column (1), displaying the analysis outcomes of Equation (1). The calculated effects of Equation (3) employing bank lending of firms as the independent variable are shown in Column 5. The green industrial strategy is positively associated with a rise in bank loans, as shown by a considerably positive coefficient. The regression findings for green innovation on IP and Loan as a mediator are shown in column 6. The calculated relationship between bank loans and green industrial policy is positively significant, showing that the green industrial policy may considerably impact the innovation of high-polluting businesses via this channel. With a Z-score of 2.983 and significance at the 1 % level, the Sobel test indicates that bank loans have a significant mediating effect. This confirms that the signaling effect acts as a mediator, as hypothesized.

6 Conclusion and policy recommendations

This study, conducted without specifying a time period to maintain consistency, utilizes a robust analytical framework, incorporating Difference-in-Differences (DID) and Propensity Score Matching DID (PSM-DID) models, to dissect the impact of green industrial policy on green innovation within heavily polluting enterprises in China from 2011 to 2022. The main findings reveal a nuanced landscape where green policy support significantly boosts green innovation, particularly in state-owned enterprises and firms located in the eastern regions of China. The analysis underscores the pivotal role of state ownership as a facilitator of policy efficacy, suggesting that governmental involvement in enterprise ownership could serve as a catalyst for enhanced green innovation outputs. Additionally, the geographical differentiation in policy impact highlights the influence of regional economic and infrastructural disparities on the effectiveness of green policies.

Operational efficiency and sustainable investment emerge as key drivers of green innovation across all firm categories, affirming the critical importance of internal firm capabilities and commitment to sustainability in leveraging policy support towards achieving innovative outcomes. Conversely, the study identifies certain challenges associated with environmental compliance and market competitiveness, indicating potential trade-offs firms face between maintaining competitive edges and pursuing green innovation.

The contribution of this research is manifold, providing empirical evidence on the conditional effectiveness of green industrial policies in stimulating green innovation among heavily polluting enterprises. By offering a detailed examination of how various factors, including state ownership, geographical location, and firm-specific characteristics, interact with policy support to influence innovation, the study fills a significant gap in the literature. It presents policymakers and business leaders with insights into the complex dynamics at play, suggesting that targeted, region-specific, and ownership-sensitive policies may be necessary to optimize the green innovation outcomes of industrial policy interventions.

6.1 Policy recommendations

Based on the findings of this study, several policy recommendations can be made to enhance the effectiveness of green industrial policies in promoting green innovation within China's polluting industries.1. The study highlights the positive impact of stringent environmental regulations on green innovation. Policymakers should continue to enforce and strengthen environmental regulations to incentivize firms to develop and adopt cleaner technologies. This can be achieved through stricter emission standards, enhanced monitoring, and increased penalties for non-compliance.

2. Given the significant variability in pollution levels and green innovation activities across different provinces, it is essential to implement region-specific policies. Tailored approaches that consider the unique environmental and economic conditions of each province will be more effective in addressing local challenges and leveraging local strengths. Provinces with higher pollution levels should receive targeted support to accelerate green innovation.

3. The findings suggest that SOEs may exhibit stronger positive effects from green industrial policies. Policymakers should focus on providing additional support and incentives to SOEs to enhance their role in driving green innovation. This could include financial support, tax incentives, and technical assistance to help SOEs implement green technologies and practices.

4. Increased investment in green technologies is crucial for sustaining innovation. The government should expand financial support mechanisms such as grants, subsidies, and low-interest loans to encourage firms to invest in research and development of green technologies. Public-private partnerships can also play a vital role in mobilizing resources for green innovation.

5. Promoting collaboration among firms, research institutions, and government agencies can facilitate the exchange of knowledge and best practices in green innovation. Establishing innovation hubs and networks can help create an ecosystem that fosters continuous improvement and adoption of green technologies.

6. Introducing carbon pricing mechanisms, such as carbon taxes or cap-and-trade systems, can provide economic incentives for firms to reduce emissions and invest in green technologies. These mechanisms should be designed to reflect the true cost of carbon emissions and encourage firms to innovate in order to minimize their carbon footprint.

7. Raising public awareness about the importance of green innovation and environmental sustainability can generate broader support for green industrial policies. Public campaigns and educational programs can help create a culture of sustainability and encourage consumers and businesses to adopt environmentally friendly practices.

8. Continuous monitoring and evaluation of green industrial policies are essential to assess their effectiveness and make necessary adjustments. Policymakers should establish robust data collection and analysis systems to track progress, identify gaps, and refine policies based on empirical evidence.

9. Enhancing access to green technologies, especially for small and medium-sized enterprises (SMEs), can drive broader adoption and innovation. This can be achieved by providing technical support, training programs, and establishing platforms for technology transfer.

10. Engaging in international cooperation and exchange of best practices can help China benefit from global advancements in green technologies and policies. Collaborating with other countries on joint research projects, technology development, and policy implementation can accelerate the progress towards a greener economy.

By implementing these policy recommendations, China can further leverage green industrial policies to foster sustainable technological innovations, reduce environmental pollution, and promote long-term economic growth.

6.2 Limitations and future research directions

Despite the valuable insights provided by this study, there are several limitations that should be acknowledged. First, the analysis relies on provincial-level data, which may mask significant intra-provincial variations and local-specific factors affecting green innovation. Future research could benefit from more granular data at the city or enterprise level to provide a deeper understanding of the dynamics at play. Second, the study covers the period from 2011 to 2022, and while this timeframe captures recent trends, it may not fully reflect long-term effects and the evolving nature of green industrial policies. Longitudinal studies spanning extended periods could offer more comprehensive insights into the sustained impacts of these policies. Additionally, the use of Difference-in-Differences (DID) and Propensity Score Matching DID (PSM-DID) models, while robust, may still be subject to biases related to unobserved confounders. Future research could explore alternative methodologies or incorporate qualitative approaches to complement the quantitative analysis and provide a more nuanced understanding of the mechanisms driving green innovation.

Moreover, the study focuses primarily on the relationship between green industrial policies and innovation in polluting industries, potentially overlooking the spillover effects on other sectors and the broader economy. Investigating these spillover effects could provide a more holistic view of the benefits and challenges associated with green industrial policies. The potential differences in policy effectiveness across various types of enterprises, such as small and medium-sized enterprises (SMEs) versus large corporations, were not extensively explored. Future research could delve into these distinctions to tailor policy recommendations more effectively. Additionally, while the study highlights the importance of regional-specific policies, it does not fully account for the political, economic, and social factors that may influence policy implementation and effectiveness across different provinces. Exploring these contextual factors in greater depth could enhance the understanding of the conditions under which green industrial policies are most successful.

Finally, with the rapid advancement of green technologies and the changing global landscape of environmental policies, ongoing research is necessary to keep pace with new developments and emerging challenges. Future research directions could include examining the impact of international cooperation on green innovation, the role of digital technologies and Industry 4.0 in promoting sustainable practices, and the long-term socio-economic implications of transitioning to a green economy. Addressing these limitations and exploring these future research avenues will contribute to a more comprehensive and actionable body of knowledge to guide policymakers and stakeholders in fostering green innovation and environmental sustainability.

Data availability statement’

The data that support the findings of this study are not publicly available due to restrictions imposed by the data providers. The authors are therefore unable to share the data.

CRediT authorship contribution statement

Huiyu Yang: Writing – review & editing, Visualization, Validation, Supervision, Data curation, Conceptualization. Muhammad Umair: Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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