Navigating the green transition: assessing the impact of Chinese green public procurement on corporate pollution emissions

Government public procurement (GPP) is an important policy instrument influencing market demand. It is also a way for the public sector to participate in market functioning and adjustments (Guerzoni and Raiteri, 2015; Krieger and Zipperer, 2022; Tuffour et al., 2024). GPP is a policy tool for energy efficiency and emission reduction and is implemented in many countries. China is the largest developing country in the world. Along with its rapid development, it is also facing environmental pollution. The Bulletin of China’s Ecological and Environmental Conditions 2021 shows that 35.7% of 339 cities in China exceeded the standard in 2021 without sand and dust. In 2021, the percentage of cities with acid rain in China is 30.8%. This means sulfur dioxide (SO₂) is still a pollutant that needs to be focused on. To transform economic growth and improve air quality, the Chinese government issued the first GPP list of environmentally labeled products in 2006. Existing literature (Czarnitzki et al., 2020; Tan et al., 2024a, 2024b) suggests that government intervention in the consumer market through GPP can reduce information asymmetry, guide enterprises to adopt green production processes, and improve environmental quality. However, studies have yet to examine whether China’s GPP policy can effectively guide enterprises to optimize cleaner production structures and promote energy conservation and emission reduction (Zhang and Jiang, 2022).

Current literature primarily focuses on the design issues of GPP and its associated economic and environmental benefits (Pihlajamaa and Merisalo, 2021; Kou et al., 2024; Schäfer et al., 2024). This focus is evident in three main areas. First, GPP holds significant political priority within the European Union public procurement system (Krieger and Zipperer, 2022; Tuffour et al., 2024). Key driving factors include the demand for environmentally friendly products from society, governmental regulations, and support from international guidelines and initiatives (Singh et al., 2024). Nicolas et al. (2025) found that the effectiveness of GPP implementation in the Czech Republic largely depends on the funding scale of European Structural and Investment Funds. Second, regarding the economic benefits of GPP, Krieger and Zipperer (2022) reported that innovative GPP in European countries can contribute to economic development through both “demand-pull” and “supply-push” mechanisms. Lindström et al. (2020) examined the organic food purchasing behavior of the public sector and found a positive impact on the share and quantity of land used for organic farming. Xu (2023) highlighted GPP’s critical role in promoting international sustainable development cooperation within the Belt and Road Initiative. However, differentiating green standards during the bidding process may reduce the social benefits of such policies (Cheng et al., 2018). Third, concerning the environmental benefits of GPP, the procurement of green products can significantly minimize environmental pollution during production processes (Alhola et al., 2019). Some literature combines qualitative analysis with case studies to confirm the positive impact of GPP on energy conservation and emissions reduction in polluted industries and urban areas. Orsatti et al. (2020) found that GPP in U.S. commuting zones enhanced local levels of green innovation. Cerutti et al. (2016) and Alvarez and Rubio (2015) assessed how public procurement of energy-efficient products and green public services in Italy and Spain contributed to reducing carbon emissions and footprints. Bryngemark et al. (2023) found that GPP practices in Swedish cities, such as the procurement of biofuels or electric vehicles, significantly promoted sustainability in the transportation sector. In the context of corporate sustainability, some scholars argue that government green procurement can significantly foster corporate green innovation, especially in sectors with stringent environmental regulations (Kou et al., 2024; Schäfer et al., 2024). Tan et al. (2024a) further analyzed the synergistic effects between GPP and government environmental subsidy policies in this process.

Environmental regulation is crucial for controlling corporate environmental pollution (Zhu et al., 2023; Qi et al., 2023; Zhang and Jiang, 2022), especially given the complexity between implementing GPP and existing regulatory frameworks. Environmental regulation can be categorized into command-and-control, market-based, and hybrid approaches. Strict total emission control policies, through direct emission restrictions and penalties, encourage compliance among enterprises (Chu et al., 2024a). However, such measures may lead to economic losses and constrain research and development investments. Market-based environmental regulations provide enterprises with greater autonomy, promoting more efficient resource allocation to achieve energy conservation and emission reduction (Bu et al., 2020). GPP, however, differs from these regulatory tools. It aims to leverage government market power to drive the development and diffusion of green products, technologies, and services, thereby facilitating environmental improvements. GPP generates demand for green products and services and interacts with various forms of environmental regulations, including both command-and-control and market-oriented approaches. Additionally, GPP serves as a model for low-carbon principles across society, motivating enterprises to adopt greener production practices and reduce pollution emissions (Ito et al., 2018). Nevertheless, most existing research has primarily focused on the economic and environmental benefits of GPP policies in developed countries (Leffel, 2022; Dimand and Neshkova, 2024). There is limited investigation into the challenges faced by developing countries in improving their GPP systems. This paper aims to address this gap. In 2006, the Chinese government jointly released the first public procurement list for eco-labeled products. Unlike other countries, China has developed specific production labels for different industries, emphasizing green practices during the production process. This study treats the 2006 GPP policy as a quasi-natural experiment and employs a difference-in-differences (DID) model using data from Chinese industrial enterprises between 2001 and 2010. Consequently, we explore the environmental impact of GPP policies on corporate environmental pollution. This research is significant for both theoretical and practical considerations in China and may provide valuable insights for other developing countries.

This study contributes in three aspects. First, we are among the first to explore how GPP policies affect corporate pollution reduction. This paper investigates the intrinsic action mechanism of GPP policies to reduce SO₂ emissions from enterprises. We do this from environmental protection, technology improvement, and energy efficiency perspectives. We also identify the heterogeneous features of GPP policies to promote enterprise pollution abatement at the regional, industry, and enterprise levels. It shows that GPP policies play a significant role in reducing corporate SO₂ emissions. Second, this paper constructs a DID model to study China’s first GPP list in 2006. It also studies a large sample of microenterprise data. This expands research on government GPP effects. Currently, less literature focuses on GPP policy effects in developing countries (Cheng et al., 2018). This paper enriches GPP research in China and emerging economies. Third, it expands research on firms’ pollution reduction drivers at the firm level. Most current studies explore the total factor aggregation effect of GPP at the city and industry levels. However, they ignore micro-subject firms’ heterogeneous responses to policies. Unlike existing studies on corporate pollution subject to environmental regulation, this paper focuses on how corporate pollution reduction is affected by demand-driven GPP policies, expanding the theoretical research.

The paper is structured as follows. Section 2 presents the study background and theoretical assumptions. Section 3 discusses data sources, variable design, and empirical strategy. Section 4 presents the empirical analysis results, including benchmark regression and robustness tests. Section 5 explores the mechanism and heterogeneity characteristics. Section 6 summarizes research conclusions and policy recommendations. The structure of this paper is shown in Figure 1.

In recent years, many countries have faced the pressures of global ecological degradation and climate change. Governments have implemented various environmental regulatory policies to improve environmental quality. Typical environmental regulations include emission standards, environmental protection laws, and pollutant controls. These policies often utilize legal enforcement, compliance checks, and fines to directly regulate corporate environmental behavior and reduce pollution. In contrast, Green Public Procurement (GPP) refers to the government’s priority in procuring products, services, and projects that emphasize environmental protection, energy efficiency, and sustainability. The core objective of GPP is to promote sustainable development and minimize environmental impact (Shadrina et al., 2022). GPP has two main characteristics: first, it is market-oriented, leveraging the government’s role as a buyer to drive the production and distribution of green products from the consumer demand side. Second, it employs an incentive mechanism, encouraging companies to engage in green innovation and improvements through positive incentives rather than merely ceasing or reducing pollution. It is important to emphasize the unique market influence of government green procurement as an environmental regulatory tool, distinguishing it from the coercive and direct nature of traditional environmental regulations. Globally, public procurement constitutes a significant portion of GDP. Statistics show that public procurement accounts for 15–25% of GDP in developed countries, such as those in the European Union (Krieger and Zipperer, 2022). Some developed nations, including the UK and the USA, have mandated or encouraged GPP through special legislation or government orders. Countries implement GPP through either national government or local entities.

From 2003 to 2021, China’s government procurement scale grew from 1% to 4% of GDP. The release of the “Opinions on the Implementation of Government Procurement of Environmental Labeling Products” in 2006 established the country’s first GPP product catalog [¹]. China’s GPP particularly focuses on environmental protection across different stages of the production process. This has expanded the market for energy-saving products and green development. The product catalog serves as a foundational framework for examining the impact of China’s green public procurement policy on corporate pollutant emissions. It allows for a clear distinction between enterprises participating in government green procurement (treatment group) and those that do not (control group), facilitating effective causal inference using a DID model. The DID model controls for time effects and individual heterogeneity, enabling a more accurate assessment of changes in corporate pollutant emissions before and after policy implementation. This contributes to understanding the dual roles of GPP in improving environmental quality: the effects of environmental regulation and market incentives.

Firms’ emission reduction activities consume many resources and thus require support from stakeholders in terms of resources. Due to the information asymmetry between firms and stakeholders on firms’ environmental behaviors (green technology innovation or energy transition) (Kogan et al., 2017; Roca et al., 2017). Stakeholders need to consider various information and risks when supporting a business. Signaling theory says GPP-supporting firms can send two positive signals to stakeholders. First, it demonstrates that the company has environmental advantages and development capabilities. GPP is a bidding program for green products. It contains the government’s comprehensive assessment and recognition of the company’s environmental advantages (Dimand and Neshkova, 2024), development prospects, and economic contribution potential in various aspects. Secondly, it signals the company’s trustworthiness. GPP shows a positive interaction and legitimacy between the company and the government (Krieger and Zipperer, 2022). Coupled with the government’s supervision and management of enterprises’ green production processes, it can alleviate the moral hazard problem stakeholders face. With the support of the GPP, local governments are gradually introducing preferential policies such as green credit support and tax relief (Drake et al., 2024). These policies alleviate the financing constraints of enterprises and further incentivize them to invest in environmental protection projects. In addition, these measures also encourage enterprises to shift to green production, thus reducing corporate pollution emissions (Singh et al., 2024; Hettler and Graf‐Vlachy, 2024). On this basis, the following hypotheses are proposed:

From the perspective of information asymmetry, enterprises in this industry are incentivized to optimize their energy structure and lower air pollutant emissions. This is to obtain GPP orders from the government. Enterprises receiving GPP support strengthen mutual trust between them and the government. This can effectively reduce the government’s supervision and evaluation costs on enterprises and increase enterprises’ investment in green resources. From the perspective of policy requirements, GPP sets higher standards for green products. Industrial firms optimize their energy structure, improve fossil fuel efficiency, or increase clean energy (Zhang and Jiang, 2022). Moreover, the literature (Chu et al., 2024b; Meng et al., 2023) also confirms that clean energy sources can reduce air pollution emissions, while improving energy efficiency. Therefore, this paper proposes the following hypothesis:

GPP can reduce corporate air pollution emissions by promoting green innovation in production technologies and processes. From the signaling perspective, government green purchasing usually synthesizes enterprises’ green technology level and environmental governance capacity for judging and screening. GPP can alleviate the problem of enterprise moral risk under information asymmetry. It can also improve external investors’ trust in enterprises and thus expand access to green innovation resources. This is conducive to promoting green innovation in production technologies and processes, thereby reducing corporate air pollution emissions. In terms of the nature of the policy itself, many scholars have pointed out that government procurement is essentially a “powerful tool for stimulating innovation” (Czarnitzki et al., 2020; Guerzoni and Raiteri, 2015; Stojčić et al., 2020; Tan et al., 2024a). In addition, studies (Zhang et al., 2021; Krieger and Zipperer, 2022) indicate that GPP promotes green innovation by increasing market size and reducing demand uncertainty. In addition, firms also take the lead in realizing green innovations based on green management and market share advantages. This is to capture government procurement market resources. The enhancement of green innovation ability has a pronounced positive effect on reducing corporate air pollution emissions. Therefore, this paper proposes the following hypotheses:

To examine how GPP affects corporate SO₂ emissions, we reference Zhang and Jiang (2022), as well as Tan et al. (2024a), in constructing a DID model based on green procurement policies for empirical analysis, and the empirical model is as follows:

where SO_2it represents the logarithmic SO₂ emissions for enterprise i in year t. GPP_it denotes enterprise i affected by the policy in this year t. X_it is a vector of control variables. year and firm represent year and firm fixed effects (FE), respectively. u_it is the error term. β₁ is the focus of this study. It reflects the net impact of GPP policy on the SO₂ emissions of enterprises. If β₁ is significantly negative, it indicates that GPP can reduce SO₂ emissions. Conversely, a positive β₁ would suggest otherwise.

Existing studies have pointed out that SO₂ emitted by industrial enterprises in the production process is one of the leading causes of air pollution and haze in China (Yan and Wu, 2017). Therefore, this paper uses each enterprise’s industrial SO₂ emissions to represent the degree of air pollution.

The independent variable is the interaction term GPP between the time and industry grouping variables. Following the methodology of Zhang and Jiang (2022), we utilize the detailed product catalog information from the green procurement list to construct the policy variable. Specifically: First, we matched the products in the green procurement list with the industries to identify those regulated by green procurement. Second, if the industry is involved in the government procurement list, and the time is in 2006 and later, then the GPP is 1. Otherwise, it is 0.

We also control the firm- and city-level factors influencing enterprise SO2 emissions. Following Chen et al. (2022) and Jiang et al. (2022), we select some control variables to add to the model. (1) Capital level of the enterprise (capital) (2) The enterprise size (size) (3) The age of the enterprise (age) (4) The enterprise’s equity properties (state-owned enterprises or not). (5) The degree of firm monopoly (output). In addition, we also control industry- and city-level control variables. (6) Market concentration (HHI). (7) Regional economic development (GDP). (8) Foreign direct investment (FDI). City-level variables are obtained from the China City Statistical Yearbooks (2002–2011). The definitions and explanations of variables are shown in Table 1.

This study focuses on Chinese industrial enterprises from 2001 to 2010. It examines the impact of the implementation of China’s first GPP product catalog on the emissions of industrial air pollutants. The city variables used in this paper are from the Chinese City Statistical Yearbook (2002–2011). Enterprise pollutants and energy consumption data are derived from the China Green Development Database. Green innovation patent data is from the State Intellectual Property Office of China. In this paper, the Chinese industrial enterprise database was carefully cleaned according to the practice of existing studies. For details, see Brandt et al. (2012). In addition, all the continuous variables are truncated to avoid outlier influence on the empirical results.

The descriptive statistics of the main variables are shown in Table 2. The mean value of SO₂ emissions is 10.1518, and the standard deviation is 2.0344. This indicates that the SO₂ emissions of the sampled enterprises are high, and there are significant differences in pollution gas emissions by Chinese industrial enterprises. Other control variables indicate considerable regional heterogeneity.

This section presents the fundamental research findings of this study. First, in Section 4.1, we conduct a preliminary verification of H1 using the DID model. Second, to ensure that the assumptions of the DID model are met, we perform a parallel trends test in Section 4.2. Finally, to enhance the robustness of our conclusions regarding H1, we conduct robustness checks in Section 4.3. These checks include variable substitution, consideration of other significant influencing factors, propensity score matching with DID, and a placebo test, among others.

Table 3 presents baseline regression results in Column (1). Regardless of other conditions, GPP significantly reduced enterprises’ SO₂ emissions. Column (2) reports baseline results controlling for year-fixed and firm-fixed effects. The coefficient of GPP is negative and significant at the 1% level. In column (3), a series of control variables are further added based on column (2), and it is found that the estimated coefficient of GPP is still significantly negative, which indicates that SO₂ emissions of enterprises are significantly reduced after the implementation of GPP. This also supports the hypothesis proposed in this study, which states that GPP can reduce corporate pollutant emissions.

The DID method requires the parallel trend assumption to be met. Before the implementation of GPP, the treatment and control groups should exhibit similar trends. This study examines the changes in SO₂ emissions between different groups of companies before and after the implementation of GPP. The results in columns (4) and (5) of Table 3 indicate that prior to the implementation of GPP, the impact on corporate SO₂ emissions was not significant. However, after the GPP policy was enacted, there was a noticeable decrease in SO₂ emissions among companies. During the period from 2001 to 2005, both companies listed in the GPP green product catalog and those not listed experienced similar trends (see Figure 2).

SO₂ emissions measurement needs to consider a firm’s total output. Therefore, we use firms’ industrial greenhouse gas emission (Pollu_ gas) and SO₂ emission efficiency (SO₂_EE, the ratio of total enterprise output to SO₂ emissions) as a proxy for the robustness test. Columns (1) and (2) of Table 4 are the results by replacing the dependent variable; the coefficients of GPP are significantly at the 1% level. It reveals that GPP can reduce firms’ industrial greenhouse gas emissions and improve firms’ SO₂ emissions efficiency, consistent with the baseline regression conclusion.

On the one hand, this paper adjusts standard errors to cluster at the firm level. As shown in Column (3) of Table 4, the coefficient of GPP is still significantly negative. On the other hand, in equation (1), we add the interaction term of industry and year fixed effect Ind × Year to control the interference factors at the industry level. The results are shown in column (4) of Table 4. The coefficient of GPP is also significantly negative at 1%. This confirms the conclusion of this paper again.

To eliminate the interference of other environmental policies on the conclusions of this article, we conducted the following four robustness tests. First, 447 samples of energy-consuming enterprises included in the key supervision by the government are excluded, and the results are shown in column (1) of Table 5. Second, eight industry samples that implemented cleaner production standards in 2006 were excluded, and the results are shown in column (2) of Table 5. Third, samples of waste gas enterprises subject to national key monitoring are excluded, and the results are shown in column (3) of Table 5. In addition, column (4) shows the regression result after considering the above policy interference. This coefficient is still significant, which further verifies the validity of the results in this paper.

Admittedly, the formulation of government procurement may have self-selection problems, affecting the reliability of the core conclusions. Therefore, we used the propensity score matching (PSM) method for robustness testing. Specifically, we use one-to-one and one-to-three nearest neighbor matching, radius matching and kernel matching to match. After ensuring the balance of sample data, each coefficient is still significantly negative according to the estimated results in columns (1) to (4) of Table 6.

To exclude the interference of random factors, following Li et al. (2016), we conduct a placebo test. The regression results were conducted by randomly generating policy variables and repeated 500 times to obtain the estimated coefficient distribution of the false GPP. In Figure 3, the red line is the normal distribution, and the left dotted line is the actual value. At the same time, it shows an approximate normal distribution trend close to 0. This result means the negative effect of GPP on air pollution is relatively robust.

Based on the previous theoretical analysis of mechanisms, GPP affects three dimensions: energy consumption structure, energy efficiency, and technological innovation. First, we want to know whether firms achieve SO₂ emissions reduction from low energy consumption or energy efficiency improvement on the production side. Columns (1) to (3) of Table 7 shows the relevant results. When the logarithm of coal consumption (Coal) and the standard coal consumption (Stan_coal) are used as the dependent variable, the interaction coefficient is significantly negative. In Column (3), we use natural gas consumption (Gas) as the dependent variable. The GPP coefficient is significantly negative at 5%. This indicates that implementing GPP can effectively reduce firms’ coal consumption, significantly boost natural gas consumption, and produce more cleanly. In addition, GPP may influence firms’ SO₂ emissions by affecting energy efficiency. Therefore, we further discuss the three methods to measure energy efficiency. They are the output obtained by consuming one unit of coal (EE), the output obtained by consuming one unit of exhaust (Exhaust_EE), and the output using one unit of standard coal (Stancoal_EE). The results are presented in Columns (4) to (6) of Table 7. The coefficients are significantly positive at the 1% level, which reveals that GPP significantly improves enterprises’ energy efficiency. These results support the hypothesis presented in this paper, H2, which states that GPP can optimize enterprises’ energy structure and energy efficiency, suppressing air pollution emissions.

The existing literature shows that technological innovation reduces air pollution. However, the impact of GPP on innovation is still unclear. Therefore, this paper further examines whether the policy can promote corporate innovation and reduce SO₂ emissions. The relevant regression results are listed in Columns (1) to (4) of Table 8. Columns (1) to (3) show that the interaction term coefficients are significantly positive at 1%. It means that GPP has a significant positive effect on technological innovation, including invention patents (Invent_patent), utility model patents (Utility_patent), and the total number of patents (Patent). In Column (4) of Table 8, the coefficient of green innovation (Green_patent) is significantly positive at the level of 5%. This indicates that the implementation of GPP can significantly promote the green innovation of enterprises (Jiang et al., 2022), thus reducing the emission of polluting gases. These results support the hypothesis proposed in this paper, H3, which states that GPP can enhance the green technology innovation capabilities of enterprises and thereby inhibit their air pollution emissions.

The previous empirical analysis has effectively validated that GPP can reduce air pollution emissions from enterprises. This reduction is primarily achieved through optimizing the energy structure, improving energy efficiency, and promoting technological innovation within companies. Then we conduct a heterogeneity analysis at three levels: enterprise, industry, and region, to provide more comprehensive evidence for the aforementioned findings.

Existing studies have pointed out that firms’ ownership affects their green behavior. For example, multinational enterprises are more likely to use green technologies in production (Tan et al., 2025; Pan et al., 2024). Therefore, we divided the sample into three groups based on the proportion of paid-up capital: domestic private enterprises (Private), state-owned enterprises (SOEs), and foreign-invested enterprises (Foreign). The results are reported in columns (1) to (3) of Table 9, which show that the policy significantly reduces the SO₂ emissions of domestic private enterprises but not for SOEs and foreign-funded enterprises. In addition, to examine how factor intensity affects the relationship between GPP and air pollution, we used net fixed asset value divided by the number of employees (logarithm) to measure enterprise capital intensity. Enterprises are divided into capital-intensive and labor-intensive based on the median capital intensity. The results are reported in Columns (4) and (5) of Table 9. The empirical results show that the policy significantly contributes to air pollution in capital-intensive enterprises but does not further reduce SO₂ emissions in labor-intensive enterprises. GPP reduces SO₂ emissions by promoting corporate innovation. In contrast, labor-intensive companies employ many workers and rely less on technology and equipment. On the contrary, nonlabor-intensive enterprises rely more on capital and technology, so GPP’s innovation spillover effect is prominent.

To study the impact of GPP on exhaust emissions of enterprises in different industries, we calculated the industry pollution emission intensity index. And we divided the sample into subsamples of high-polluting industries (High-pollution) and clean industries (Low-pollution) based on the median of this index. Columns (6) and (7) in Table 9 give the corresponding results. We found that in high-polluting industries, the negative impact of GPP on corporate SO₂ emissions is more significant. The reason may be that enterprises in high-polluting industries are the key regulatory objects of environmental departments. Under the incentive of government green procurement, they are more motivated to carry out green innovation to evade environmental regulations, thus promoting a more obvious reduction in pollution gas emissions.

Some studies found that the intensity of policy implementation between regions gradually increased the regional gap (Tesfaye and Seifu, 2016; Chu et al., 2024a). According to the results in columns (1) to (3) of Table 10, GPP inhibits SO₂ emission reduction of enterprises in the eastern and central regions but has no significant inhibitory effect on enterprises in the western region. The reasons may be as follows. On the one hand, the marketization level in the eastern and central regions is higher, the GPP system is more systematic, with greater demand and potential for greater environmental benefits (Tan et al., 2024a). On the other hand, enterprises in western regions need stronger competitive advantages in winning green public procurement tenders. In the context of market integration, enterprises in the eastern region may seize green procurement resources in the western region.

Industrial and population agglomeration is not conducive to reducing pollution emissions, which is one of the essential reasons for urban environmental degradation (Rosell, 2023). To investigate GPP’s impact on air pollution in different regions with different population agglomerations, we considered it. Based on the median of urban population intensity, we divided our sample into three groups: low–intensity, medium-intensity, and high-intensity areas. The results in columns (1) to (3) of Table 11 show that GPP’s negative effect on SO₂ emission is significant in medium and high-intensity areas. With residents’ increasing attention to environmental protection, population agglomeration is conducive to improving energy use efficiency and urban environmental quality (Chu et al., 2024b).

GPP is a demand-oriented environmental policy. Previous studies have not revealed the relationship between GPP and corporate air pollution emissions. Therefore, this paper considers the 2006 GPP policy an exogenous shock and constructs a DID model using data from Chinese industrial firms from 2001 to 2010. We explore the impact of GPP on firms’ environmental pollution and its internal mechanism and obtain the following three results. (1) GPP can significantly reduce firms’ SO₂ emissions and regression results robustness. (2) GPP reduces SO₂ emissions from enterprises by encouraging enterprises to improve their production processes. It encourages them to drive cleaner production with green technology innovation, optimize their energy structure, and improve energy efficiency. (3) GPP’s environmental cleaning effect is more pronounced in eastern and central regions or large and medium-sized urban areas. Furthermore, the impact effectively reduces SO₂ emissions from private, capital-intensive, and heavily polluting firms.

In the context of our study, GPP serves as an environmental policy that reflects the government’s commitment to sustainable development while providing strong external incentives for corporate environmental behavior. By integrating the content of green procurement policies with relevant theories, we can explore our research findings from multiple perspectives. The first aspect we explore is the relationship between GPP guidance and corporate environmental performance. The green product standards and catalogs established by GPP create clear market signals for companies, encouraging firms to adjust their production strategies to align with policy directives (Tan et al., 2024a). According to resource dependency theory, companies modify their operational strategies based on resource acquisition methods, market demands, and policy orientations when faced with changes in the external environment (Chu et al., 2024b; Tan et al., 2024b). By promoting a preference for green products, GPP effectively communicates market demand, driving companies to adopt more proactive environmental measures, which significantly reduce emissions of pollutants such as SO₂. Our findings indicate that GPP effectively lowers corporate SO₂ emissions as companies implement strategic adjustments in response to policy pressures and market opportunities.

The second aspect examines corporate strategic responses under GPP constraints. GPP plays a crucial role in encouraging companies to innovate clean technologies and transition toward energy efficiency. This can be understood through innovation theory, which posits that companies engage in technological innovation in response to market and policy stimuli (Kou et al., 2024). We found that GPP incentivizes firms to improve production processes and drive technological advancements to meet environmental standards. This technological innovation includes optimizing energy structures and enhancing energy efficiency, extending to broader developments in green technologies. Through these measures, companies not only comply with GPP requirements but also enhance their inherent sustainable development capabilities, effectively reducing SO2 emissions.

The third aspect addresses the differential effects of GPP across regions and firm types. Our results also indicate that GPP produces significant differential effects across various regions and types of enterprises, which can be explained by the Environmental Kuznets Curve (EKC) theory. The EKC theory suggests that environmental pollution may rise during certain phases of economic development but will decline after reaching a specific income level. The relatively mature economic development and environmental infrastructure in the eastern and central regions (Yang et al., 2024) create favorable conditions for the effective implementation of GPP, explaining its more pronounced effects in these areas. Additionally, private capital-intensive and heavily polluting enterprises, facing higher environmental pressures and policy incentives, can leverage the advantages of GPP more effectively, achieving better environmental performance.

Overall, our research reveals that GPP not only significantly reduces corporate air pollution emissions but also plays an important role in promoting corporate energy transition strategies and technological innovation within the dynamic context of regional development. This underscores GPP as a market-oriented environmental policy that aids in guiding companies toward sustainable development. Future policymakers should consider how to further optimize GPP implementation strategies to ensure its active contribution to broader environmental protection and sustainable development initiatives.

As a result, this paper proposes the following policy recommendations: First, the government should continuously improve the monitoring and feedback mechanisms for green procurement policies. A cross-departmental monitoring and feedback platform could be established to integrate and analyze data, allowing for real-time tracking of GPP policy implementation. Based on the feedback, the government can flexibly adjust policy measures. Additionally, establishing dedicated funding to provide subsidies for enterprises and products that meet green standards can reduce the costs associated with adopting green technologies, thereby encouraging more companies to participate in green procurement. Second, the government should collaborate with industry associations and other organizations to create a platform for exchanging green technologies and jointly develop standards for green products. Regularly publishing industry green development reports will clarify the specific requirements for green products, helping companies identify and select products that meet these standards. Third, companies should actively establish green management systems to enhance their overall environmental performance. Implementing an International Organization for Standardization (ISO) 14001 environmental management system and conducting regular environmental audits will ensure compliance with green procurement standards. Furthermore, companies should develop green procurement guidelines that specify the selection criteria for green products and incorporate these criteria into their supplier evaluation systems, ensuring the choice of environmentally compliant suppliers and materials.

In summary, this study proposes several directions for future exploration. First, future research could further assess the long-term impacts of green procurement policies and explore how they can be integrated with traditional environmental regulation policies to form a more comprehensive environmental policy framework aimed at maximizing environmental benefits. Second, while green procurement focuses on driving change through market mechanisms, traditional environmental regulations often rely on penalties and restrictions. Therefore, green procurement has the potential to influence larger markets and incentivize environmental improvements across entire industries, not just individual companies. Future research could examine the spillover and diffusion effects of government green procurement across different industries and regions. Third, some studies have begun to identify green public procurement projects based on government procurement contract information (Krieger and Zipperer, 2022; Tan et al., 2024a). However, due to data limitations associated with government information availability, future research could leverage multisource data and new methods such as machine learning to explore multidimensional aspects of green public procurement (e.g., energy transition procurement). This approach will help deepen the understanding of the differentiated policy effects of various green procurement policies.

Funding: This research was funded by the Fundamental Research Funds for the Central Universities (CXJJ-2024-334); National Key Research and development Program (Grant. No. 2022YFD1600604); National Natural Science Foundation of China (Grant No. 72063018).

Author contributions: Weijie Tan: Conceptualization, Methodology, Data curation, Writing. Xihui Chen: Conceptualization, Software, Methodology. Mingming Teng: Methodology, Software, Data curation, Editing. Weidong An: Data curation, Editing; Changhua Wu: Data curation, Editing, Supervision.

Competing interests: The authors declare that they have no conflict of interest.