Does China’s Zero Growth Policy Promote Green Enterprise Entry? Evidence from the Agricultural Input Sector

Abstract

Against the backdrop of global commitments to sustainable development and carbon neutrality objectives, the agricultural sector faces compelling imperatives to transition toward environmentally sustainable and resource-efficient production systems. Focusing on the critical role of agricultural inputs, this study investigates how China’s Zero Growth Policy for Fertilizer and Pesticide Use (ZGP), implemented in 2015, influences green transformation in the agricultural inputs sector through a quasi-natural experiment framework. Employing a staggered difference-in-differences (DID) design with comprehensive nationwide firm registration data from 2013 to 2020, we provide novel micro-level evidence on environmental regulation’s market-shaping effects. Our findings demonstrate that the ZGP significantly enhances green market selection, stimulating entry of environmentally certified firms, with effect heterogeneity revealing policy impacts are attenuated in manufacturing-intensive regions due to green entry barriers, while being amplified in major grain-producing areas and more market-oriented regions. Mechanism analyses identify three key transmission channels: intensified regulatory oversight, heightened public environmental awareness, and growing market demand for sustainable inputs. Furthermore, the policy has induced structural transformation within the industry, progressively increasing green enterprises’ market share. These results offer valuable insights for designing targeted environmental governance mechanisms to facilitate sustainable transitions in agricultural input markets.

1. Introduction

The global agricultural sector faces increasing pressures from resource environment constraints and persistent population growth. This makes the transition to sustainable agriculture an urgent priority [1]. As a key component of Sustainable Development Goal (SDG) 12, shifting toward green agricultural practices offers many benefits, including reductions in non-point source pollution, new income streams for rural communities, and alignment with growing consumer demand for eco-friendly products [2,3].

Agricultural inputs are fundamental to production systems, with their quality and efficiency directly determining agricultural sustainability. At the national level, they are instrumental in ensuring food security and stable supply chains. At the local level, they enhance productivity and rural livelihoods [4]. This dual significance makes the green transformation of agricultural inputs a crucial lever for sustainable agriculture. From a firm perspective, green input production has emerged as a strategic necessity. Based on public interest theory, it is acknowledged that government intervention plays a pivotal role in correcting market failures and shaping corporate behavior [5]. Environmental regulations generate dual market effects: they raise entry barriers for polluting firms and accelerate the exit of non-compliant enterprises [6]. The cost-effectiveness mechanism further explains how these policies reshape firm decisions through economic recalibration. While these regulations have been internalizing pollution costs successfully, they have also led to a reduction in profit expectations, thereby creating competing pressures on corporate strategy.

Extensive research has demonstrated how environmental policies shape agricultural market dynamics through firm location choices and market exits [7,8], with agricultural enterprises showing particular sensitivity to regulatory stringency [9]. Studies have documented significant market adjustments ranging from consolidation in U.S. swine operations [10] to relocations in Chinese livestock farms [11] and spatial redistribution in dairy [12] and pork production [13]. However, this body of work exhibits several limitations when applied to China’s transformative 2015 Zero Growth Policy (ZGP) for chemical inputs. While research has systematically documented ZGP’s effectiveness in reducing chemical applications through provincial-level [14] and crop-level difference-in-differences analyses [15], these studies suffer from three fundamental constraints: (1) their focus on pollution-intensive sectors neglects input suppliers, (2) their reliance on aggregate data, which obscures firm-level mechanisms; and (3) their emphasis on relocation and input metrics, which fails to capture broader market transformation. Most crucially, existing work overlooks how green production factors contribute to sustainable transformation. This study addresses this critical gap by examining ZGP’s impact on entrepreneurial activity in agricultural input markets.

This study bridges these gaps by examining how ZGP reshapes entrepreneurial activity in agricultural input markets—an underexplored dimension with theoretical and practical significance. Green input producers serve as primary conduits for sustainable technology diffusion, yet their emergence under environmental regulation remains poorly understood. We address this “black box” by investigating: How does ZGP reconfigure market entry dynamics in the agricultural inputs industry?

Our analysis makes three key contributions. First, leveraging the comprehensive CCAD, we provide the first systematic analysis of municipal-level market dynamics among 25,154 agricultural input firms (2013–2020), offering novel evidence on how fertilizer and pesticide manufacturers transition toward green production, which is a critical yet understudied dimension of sustainable agricultural transformation. Second, advancing Chen et al.’s [11] approach, we construct precise policy timelines across 338 cities (2015–2020) and implement a rigorous staggered difference-in-differences design [16] that addresses key identification challenges, yielding the most credible estimates to date of ZGP’s causal effects on green innovation. Third, we develop and empirically validate a new environmental policy framework that elucidates how regulatory interventions, stakeholder coordination mechanisms, and market demand factors collectively shape green enterprise entry patterns. This framework provides actionable insights for designing context-specific agricultural sustainability policies in emerging economies.

The paper is organized as follows: Section 2 describes the policy background and theoretical analysis. Section 3 introduces the research design. Section 4 elaborates on the baseline results and analysis. Section 5 explores mechanisms and further analysis. Section 6 concludes the paper.

2. Institutional Background and Theoretical Analysis

2.1. Institutional Background

Since the implementation of the Reform and Opening-up policy in 1978, China’s agricultural economy has experienced remarkable growth. More than 40% of the increase in grain production can be attributed to the use of chemical fertilizers [17]. Consequently, China has emerged as the world’s largest producer and consumer of both chemical fertilizers and pesticides. From 2000 to 2014, fertilizer consumption increased from 41.5 million tons to 60.0 million tons, comparable to the combined fertilizer consumption of the United States and India. The intensity of fertilizer application in China significantly exceeds the internationally recommended threshold of 225 kg per hectare. Similarly, pesticide use has shown a comparable upward trend (Figure A1). The excessive use of fertilizer and pesticides in agricultural practices has led to severe soil and water pollution, as well as risks to food safety and public health [18].

To address these environmental and health challenges, the Chinese Ministry of Agriculture (MOA) launched two significant initiatives in March 2015: the “Action to Achieve Zero Growth of Chemical Fertilizer Use by 2020” and the “Action to Achieve Zero Growth of Pesticide Use by 2020”. The primary goal of the first initiative is to restrict annual growth rates of chemical fertilizer use to below 1% from 2015 to 2019, ultimately achieving zero growth in fertilizer use for major crops by 2020. Similarly, the second initiative aims to keep pesticide application below the average levels recorded from 2012 to 2014, intending to attain zero growth in total pesticide use by 2020. In addition to these targets, both initiatives establish four specific requirements designed to improve the efficiency of chemical fertilizer and pesticide use, to reach a utilization rate of 40% for major crops (

Table A1. Specific targets for action to achieve zero growth of chemical fertilizer and pesticide use.

Items	Specific Objectives	Detailed Indicators
Chemical fertilizers	Structure optimization	By 2020, the coverage of soil testing and formulated fertilization shall exceed 90%; the application rate of livestock and poultry manure shall reach 60% (a 10% increase); and the application rate of straw shall reach 60% (a 25% increase). More than 40% of crop acreage shall be fertilized using machinery, a 10% increase; the application area for integrated irrigation and fertilization shall increase to 150 million mu, an increase of 80 million mu.
	Methods improvement
	Utilization rate enhancement	From 2015 onwards, the utilization rate of chemical fertilizer for main crops shall increase by at least 1% per year, achieving a rate above 40% by 2020.
Pesticides	Green prevention	The coverage of biological control and physical pest control methods on main crops shall exceed 30%, a 10% increase from coverage in 2014.
	Unified control	The coverage of professional unified pest control on main crops shall reach over 40%%, increasing by 10% from coverage in 2014.
	Scientific pesticide use	The utilization rate of pesticides on main crops shall reach over 40%, a 5% increase from the rate in 2013, with a significant increase in the proportion of effective, low-toxicity, and low-residue pesticides.

).

Following the release of the ZGP by the MOA, local agricultural departments swiftly implemented the central government’s policy requirements, successively formulating and implementing specific, actionable zero growth work plans. We collect data on city-level ZGP implementation beginning in 2015 from several sources, with PkuLaw (https://www.pkulaw.com/ (accessed on 9 April 2021)) as the primary source due to its extensive coverage of local Chinese laws and regulations. Our data collection involved searching for specific keywords such as “Huafei Lingzengzhang” (zero growth of chemical fertilizer) and “Nongyao Lingzengzhang” (zero growth of pesticide). As we focus on city-level implementation, documents issued by authorities at either higher or lower administrative levels were excluded. Additionally, we validated our findings through searches on the official websites of each city’s Bureau of Agriculture and Rural Affairs, adhering to the criteria outlined by Chen et al. [11]. When an official “red-title” document issued by a city’s Bureau of Agriculture and Rural Development specified a ZGP implementation start date, we recorded that date as the policy adoption time for the city. This rigorous multi-step filtering process enabled us to accurately identify each city’s ZGP implementation timeline based on formal legal documentation.

According to the above identification criteria, a total of 234 cities out of 338 cities nationwide have issued ZGP implementation plans from 2015 to 2020 (Figure A2). Specifically, in 2015, 31 cities were the first to respond, issuing official documents of ZGP. By 2016, the number of cities implementing the policy had rapidly increased to 109, with 78 new cities added. By the end of 2017, a cumulative total of 136 cities had implemented the zero growth actions. In 2018, 31 additional cities were added, and in 2019, 32 more cities were added. By 2020, a total of 234 cities had explicitly proposed implementing the zero growth actions, accounting for 69.23% of all cities. Spatially, the ZGP was first implemented in Hebei, Shandong, Anhui, Sichuan, and Chongqing, followed by subordinate cities in Hunan, Jiangxi, Yunnan, Guizhou, Liaoning, Jiangsu, Fujian, and Gansu. By the end of 2020, all provinces and regions except Tibet had cities implementing the ZGP (Figure A3).

2.2. Theoretical Analysis

As an important industrial policy, the ZGP exerts a restraining effect on extensive economic development models and polluting industries, thereby exerting a significant impact on corporate entry and changes in market structure [19]. In the long term, as polluting enterprises gradually exit the market, the resources they release can continuously flow toward green enterprises, enabling society to achieve a green transition through the market evolution process of “polluting enterprises exiting and green enterprises surviving”. Theoretically, the ZGP primarily promotes green production in agricultural input enterprises through channels such as enhancing government market supervision, increasing public environmental awareness, and adjusting product market demand (Figure 1).

First, as the primary policy implementer and market regulatory authority, the government plays a pivotal role in ensuring free market access and fair competition through diversified regulatory measures. This role is critical for the stringent enforcement of environmental regulations and the facilitation of local green transformation, thereby addressing the persistent issue of “regulatory enforcement biases” observed in local governance [20]. Strengthening environmental regulations and imposing stricter penalties on non-compliant enterprises not only enhances environmental quality [21] but also catalyzes industrial restructuring and upgrading by modifying corporate behavior [22]. The escalation of regulatory stringency and penalty severity significantly increases the risk of detection for environmental violations, compelling firms to internalize environmental costs as part of their operational expenses. Consequently, enterprises with inadequate pollution control capabilities and limited capacity to absorb environmental costs face heightened market pressures, ultimately leading to their exit from the market. Conversely, government-imposed administrative regulations establish potential entry barriers for green industries, incentivizing polluting firms to transition toward cleaner production methods. This shift influences the industry’s overall production structure and accelerates its green transformation [23]. Through these mandatory administrative measures, government environmental policies effectively function as a mechanism for “weeding out polluters and selecting clean producers”. Building on this framework, this study proposes the following hypothesis:

H1: 

The implementation of the ZGP promotes the market entry of green agricultural input enterprises by strengthening government market supervision.

Second, stakeholder behavior serves as a key driver of corporate green transformation. Environmental concerns reflect the public’s ecological awareness and their willingness to engage in environmental problem-solving. Stakeholders influence corporate green practices through two primary mechanisms. On the one hand, public pressure shapes corporate behavior by reinforcing government environmental governance and corporate environmental responsibility. Heightened environmental awareness among the public reduces information asymmetry between central/local governments and firms, curbs collusion between local governments and polluting enterprises, and ultimately incentivizes firms to increase green investments [24]. Environmental petitions and complaints further amplify this effect by holding both regulators and corporations accountable. On the other hand, shifting market preferences driven by environmental consciousness directly affect corporate financial and operational decisions. Rising demand for green products stimulates investment in environmentally friendly enterprises, whereas negative environmental publicity raises firms’ debt financing costs [25]. Consequently, regional market demand for polluting industries contracts, which reduces their market entry rates, while environmental governance firms expand to optimize market structure and improve environmental quality. The implementation of ZGP has further intensified public scrutiny of environmental issues, particularly among investors, who increasingly “vote with their money” in favor of green agricultural input companies [26]. This investor preference reflects optimism about the long-term viability of sustainable enterprises, compelling firms to adopt greener production practices to align with stakeholder expectations. Moreover, empirical studies demonstrate that R&D investments in eco-friendly products serve as a critical mechanism for corporate social responsibility fulfillment. Such investments not only enhance brand reputation and consumer trust but also improve the financial performance of traditional firms. Building on these insights, this study proposes the following hypothesis:

H2: 

The implementation of the ZGP promotes the market entry of green agricultural input enterprises by raising public awareness of environmental protection.

Finally, market demand at the downstream end of the supply chain is a key factor affecting production decisions in agricultural input industries. Downstream players, leveraging their market power, frequently transfer operational risks and cost pressures upstream [27]. Government environmental policies have significantly reshaped consumer preferences, stimulating growing demand for eco-friendly products. This market transformation not only broadens sales opportunities for sustainable goods but also enhances their mainstream acceptance. Over the long term, improving market recognition promises expanding profit margins for proactive firms. By promoting green products, companies can simultaneously capture more market share and build a unique brand, which in turn creates lasting momentum for sustainable transformation [28]. Unlike cyclical chemical industries, the downstream fertilizer and pesticide sector exhibits particular sensitivity to demand fluctuations. The ZGP implementation has depressed demand for conventional agrochemical products among farmers and agricultural enterprises, while accelerating demand for greener, low-toxicity, and high-efficiency alternatives. As people in agricultural markets prefer more eco-friendly products, firms are increasingly realizing that small improvements to traditional products are not enough. Instead, they must intensify R&D investments to improve green innovation capabilities [29]. Forward-looking companies are developing novel, differentiated green agricultural inputs to establish sustainable competitive advantages in environmental performance [30]. Based on this analysis, this study proposes the following hypothesis:

H3: 

The implementation of the ZGP promotes the market entry of green agricultural input enterprises through regulating the market demand for fertilizer and pesticide products.

3. Research Design

This study employs a quasi-natural experiment approach to evaluate the impact of the ZGP on the market entry of green agricultural enterprises. First, we integrate ZGP policy data (2015–2020) with firm registration records (CCAD, 2013–2020), identify green enterprises through industrial classification (GB/T 4754-2017 [31]) and product screening, and construct panel data covering 338 cities. Using staggered DID and Poisson regression, we analyze the policy effects while controlling for seven city-level variables. We addressed endogeneity through pre-trend tests and policy timing examinations. Figure 2 presents the research design flowchart.

3.1. Data Source

This paper primarily draws on data regarding the implementation of the ZGP from 2015 to 2020, as well as registration, deregistration, and operational data for agricultural input enterprises from 2013 to 2020, sourced from the China Academy for Rural Development-Qiyan China Agri-research Database (CCAD), Zhejiang University. The former provides detailed records of the policy’s implementation timelines in various cities, while the latter tracks information such as the registration and deregistration status of agricultural input enterprises.

We identify green input enterprises’ market entry dynamics through a three-step procedure. First, since each enterprise is assigned an industry code, we identify fertilizer and pesticide manufacturers based on the industry classifications specified in China’s National Industrial Classification Standard (GB/T 4754-2017). Subsequently, using the more detailed sub-industry codes provided in

Table A2. Classification of agricultural input enterprises.

Industry Code	Industry Name	Sub-Industry Code	Sub-Industry Name	Detailed Industry Description
262	Fertilizer Manufacturing	2621	Nitrogen Fertilizer Manufacturing	Refers to the production activities of mineral nitrogen fertilizers and chemically synthesized fertilizers containing the essential crop nutrient element nitrogen. Refers to production activities of fertilizers containing the essential crop nutrient element phosphorus, primarily using phosphate rock as raw material through chemical or physical processing methods.
		2622	Phosphate Fertilizer Manufacturing
		2623	Potash Fertilizer Manufacturing	Refers to production activities of fertilizers containing the essential crop nutrient element potassium, processed from natural potash ores through beneficiation and refining.
		2624	Compound/ Blended Fertilizer Manufacturing	Refers to production activities of fertilizers containing two or more major crop nutrient elements (N, P, K) processed through chemical or physical methods, including both general-purpose and special-purpose compound fertilizers.
		2625	Organic & Microbial Fertilizer Manufacturing	Refers to the production of fertilizers derived from plant/animal sources through fermentation or chemical treatment, suitable for soil application to provide plant nutrients, with nitrogen-containing compounds as primary components.
		2629	Other Fertilizer Manufacturing	Refers to the production of micronutrient fertilizers and other fertilizers not elsewhere classified.
263	Pesticide Manufacturing	2631	Chemical Pesticide Manufacturing	Refers to production activities of chemical pesticide technical materials, as well as chemical pesticide formulations processed into powder, emulsion or aqueous forms through mechanical crushing, mixing or dilution.
		2632	Biochemical & Microbial Pesticide Manufacturing	Refers to production activities of pesticide formulations derived from natural sources (bacteria, fungi, viruses, protozoa or genetically modified microorganisms) or plant extracts, used for controlling diseases, insects, weeds, rodents and other pests.

Note: Data are sourced from the National Economic Industry Classification (GB/T 4754-2017) issued by the National Bureau of Statistics (available at: https://www.stats.gov.cn/sj/tjbz/gmjjhyfl/ (accessed on 22 September 2022)). Based on this classification, enterprises engaged in the production of organic and microbial fertilizers (industry code 2625) or biochemical and microbial pesticides (industry code 2632) are categorized as green input producers, while all others are classified as conventional fertilizer and pesticide manufacturers.

, we classify enterprises producing organic fertilizers, microbial fertilizers, biochemical pesticides, or microbial pesticides as green input producers, while the remainder are categorized as conventional fertilizer and pesticide manufacturers. Third, we utilize official registration records to determine each enterprise’s exact market entry year, resulting in a preliminary dataset containing 136,071 firm-year observations for 25,154 agricultural input enterprises during 2013–2020. Importantly, our classification accounts for industry transitions by including both newly established green enterprises and transformed conventional firms in our green market entrant category.

For the city-level analysis, we aggregate enterprise entry counts by city and year. We then construct a balanced panel dataset with 2704 city-year observations from 338 cities between 2013 and 2020. This is achieved by geocoding enterprise addresses and merging them with administrative boundaries, ZGP implementation timelines, and city characteristics.

3.2. Variables

3.2.1. Explained Variables

The existing literature has conducted extensive research on firm entry activities under environmental regulations, primarily adopting two measurement approaches: (1) directly counting the number of firm entries [32], and (2) calculating the proportion of entering firms [33]. Given the prevalence of zero values in the count of agricultural input firms in our study, which makes proportional calculations inappropriate, the dependent variables in our empirical analysis are measured at the city level as the absolute numbers of firm entries in the agricultural inputs industry. For analytical purposes, we classify green agricultural input firm entries into two categories: newly established firms and pollution firms transformation.

From 2013 to 2020, the number of enterprises in the green agricultural inputs industry has grown significantly (see Appendix A Figure A4). Specifically, the number of green agricultural input enterprises increased from 7888 in 2013 to 14,809 in 2020, nearly doubling its size from 2013. The traditional agricultural inputs industry has remained largely unchanged, maintaining around 5400 companies in recent years. Regarding the proportion of the two types of companies, the share of green agricultural input firms in the national agricultural inputs industry continued to rise from 59.68% in 2013 to 72.95% in 2020, strengthening their dominant position. In contrast, while the absolute number of traditional agricultural input enterprises has remained largely unchanged, their share of the national agricultural input industry has steadily declined, reaching only 27.05% by 2020. This, to some extent, reflects the active industrial transformation of China’s agricultural input industry in recent years, moving toward more sustainable and environmentally friendly development.

3.2.2. Explanatory Variables

The core explanatory variable in this paper is the ZGP. Following existing research [11], this paper uses keywords such as “zero growth in fertilizer use” and “zero growth in pesticide use” to search for official documents published on the websites of local governments in various cities. The year of policy implementation is determined by the date of issuance of the document. Based on this, we further set a dummy variable, whose value is 1 for the year of policy issuance and subsequent years, and 0 otherwise.

3.2.3. Control Variables

The city-level socioeconomic control variables in this study are obtained from the China City Statistical Yearbooks from 2013 to 2020, covering approximately 300 cities across mainland China. Specifically, we include seven key variables: (1) Economic development level, measured by per capita GDP, as regions with higher economic development typically offer better industrial infrastructure and more dynamic markets; (2) Industrial structure, represented by the proportion of secondary industry GDP to total GDP, given that cities with stronger manufacturing bases tend to attract more manufacturing firms [32]; (3) Market demand, measured by city population size, since population agglomeration reflects market potential and influences firm entry decisions [34]; (4) Labor costs, proxied by average city wages, which directly affect firm profitability and competitiveness [33]; (5) Financial development level, indicated by year-end outstanding loans from financial institutions, as better financial access helps alleviate firm financing constraints; (6) Fiscal expenditure ratio, calculated as budgetary fiscal expenditure to GDP ratio, capturing local government support through subsidies and infrastructure investment [32]; and (7) Transportation infrastructure, measured by the number of highway interchanges, since superior transportation networks enhance factor mobility and location attractiveness [35]. These variables collectively control for major economic factors that may influence firm entry decisions in agricultural input markets.

3.2.4. Descriptive Statistics

Table 1. Summary statistics.

Variables	Variable Definition (Units)	N	Mean	Std	Min	Max
Green entry	Number of market entries of green agricultural input enterprises (units)	2704	4.083	6.476	0.000	74.000
ZGP	Zero Growth Policy (dummy)	2704	0.329	0.470	0.000	1.000
Manufacturing advantages	The proportion of secondary industry GDP to total GDP (%)	2664	42.951	11.474	6.715	79.360
Per GDP	Regional GDP per capita (Yuan)	2602	54,223.369	34,566.381	8398.125	467,749.000
Population	Registered population (ten thousand people)	2693	412.896	318.999	0.040	3416.000
Labor costs	Average wage of employees (Yuan)	2435	63,446.913	19,042.663	4958.000	185,026.000
Financial loan	Year-end balance of various loans of financial institutions (10,000 yuan)	2699	3272.071	7167.666	0.058	81,035.195
Fiscal stimulus	The proportion of fiscal expenditure within the budget to GDP (%)	2664	16.480	7.988	0.089	25.768
Transportation	Number of motorway entrances and exits (units)	2704	15.537	16.483	0.000	192.000

Note: In the empirical section, this paper applies natural logarithm transformation to some variables, including per capita GDP, population, average wage, year-end balance of various loans from financial institutions, and the number of motorway exits. For the number of motorway exits, this paper applies the log(x + 1) transformation to manage zero-value observations.

presents the descriptive statistical results of market entry and exit behavior of agricultural input enterprises and key control variables in Chinese cities from 2013 to 2020. From the perspective of market dynamics of agricultural input enterprises, an average of 4.08 new green agricultural input enterprises entered the market each year across cities. The standard deviation is 6.48, which indicates significant differences in the development of the green agricultural input industry across cities. Additionally, the statistical characteristics of the control variables show that the average manufacturing sector advantage, i.e., the proportion of GDP from the secondary industry, is 42.95%. Still, there are significant regional differences, and the industrialization levels of different cities may have heterogeneous impacts on the transformation of the agricultural input industry. The average per capita GDP, i.e., the level of economic development, is 54,223.37 yuan, but the gap between the maximum and minimum values is significant, which may affect enterprises’ ability to invest in green technologies. The average wage, as a measure of labor costs, is 63,446.91 yuan. Higher wage levels may force enterprises to reduce long-term production costs through green technology innovation. Finally, the average loan balance ratio, as a measure of financial support, is 16.48%, indicating that financing constraints in some regions may limit enterprises’ green transformation.

3.3. Model Setting

In this study, we utilize a difference-in-differences (DID) approach to identify the causal effect of the ZGP on the market entry of green agricultural input firms. For policies implemented at different times, the existing literature often utilizes the asymptotic difference-in-differences model (Staggered DID) to identify the causal effects. The expanded form of the difference-in-differences model is the Two-Way Fixed Effects Regression (TWFE) model. Since the dependent variable in this paper is a count-based indicator with some zero values, log-linear regression is not suitable for estimating percentage effects [36]. Instead, the coefficients from Poisson regression can be interpreted as semi-elasticity estimates, so this paper adopts a Poisson regression model to obtain consistent and efficient estimates [37]. Our most basic regression equation is given by Equation (1):

$${GreenEntry}_{ct}={\beta }_{1}ZG{P}_{ct}+{\mu }_c+{\lambda }_t+{\epsilon }_{ct}$$

(1)

where $c$ denotes city, and $t$ denotes year. The outcome variable ${GreenEntry}_{ct}$ denotes the green product production practices of agricultural input enterprises, which are represented by the number of market entries of green agricultural input enterprises. $ZG{P}_{ct}$ is a dummy variable indicating whether the city $\mathrm{c}$ implements the ZGP at year $t$. The core parameter is ${\beta }_1$, measuring the change in market entry of agricultural input enterprises in the treatment group before and after policy implementation, compared to agricultural input enterprises in the control group. In addition, we add city-level fixed effect ${\mu }_c$ to control for unobservable time-invariant city attributes. The year fixed effect ${\lambda }_t$ absorbs temporal shocks that are common to all firms. The last one, ${\epsilon }_{ct}$, is an unobservable error term.

The assumption of Equation (1) is that city-specific unobserved factors affecting market entry are constant over time, which means E [$ZG{P}_{ct}$·${\epsilon }_{ct}$|${\mu }_c$,${\lambda }_t$] = 0. However, this assumption may not hold if market entry in treated cities has a long-term trend that differs from the trend in control cities. Treated cities can be affected by several socio-economic factors that cause their market activities to change over time. Any such differences in trends can bias the estimates ${\beta }_1$. To improve the precision of the estimates and to control for factors that affect treated and controlled cities differently over time, we add additional variables to (1), resulting in Equation (2) below:

$${GreenEntry}_{ct}={\beta }_{1}ZG{P}_{ct}+\gamma X_{ct}+{\mu }_c+{\lambda }_t+{\epsilon }_{ct}$$

(2)

In (2), $X_{ct}$ denotes socio-economic characteristics at the city level. The identification assumption of the model (2) is that unobserved factors are not correlated with treatment, conditional on the covariates; that is, E [$ZG{P}_{ct}$·${\epsilon }_{ct}$|$X_{ct}$,${\mu }_c$,${\lambda }_t$] = 0. Finally, to address heteroskedasticity issues, all regressions in this chapter employ heteroskedasticity-robust standard errors [38], with standard errors clustered at the city level.

3.4. Possible Endogeneity Issues

The validity of the empirical strategy relies on the conditional heterogeneity in the timing of the ZGP implementation. Potential issues may arise if more developed regions characterized by greater agricultural production advantages and higher levels of environmental pollution adopt the ZGP earlier than other regions. Under such circumstances, the estimated policy effects could be biased if socioeconomic characteristics are correlated with trends in the outcome variables. To evaluate the potential impact of this factor on the primary results, we restrict the sample to the year preceding and the year of policy implementation, following the approach outlined by Shi et al. [39]. We then regress the year of policy introduction for each city on key socio-economic characteristics at the city level, agricultural production advantages, and pollution stemming from the agricultural sector. As shown in

Table 2. Determinants of city-level ZGP implementation timing.

Variables	Implementation of the ZGP
	(1)	(2)	(3)
GDP	−0.0580	−0.1908	−0.1906
	(0.1283)	(0.1803)	(0.1800)
Population	0.1347	0.2232	0.2285
	(0.4281)	(0.4390)	(0.4413)
Average wage	0.0339	0.1168	0.1146
	(0.1476)	(0.1424)	(0.1424)
Number of poor counties	0.0135	−0.0089	−0.0090
	(0.0161)	(0.0280)	(0.0281)
Share of agricultural GDP in total GDP		0.0063	0.0062
		(0.0156)	(0.0157)
Crop planting area		0.0978	0.0981
		(0.0903)	(0.0905)
The proportion of agricultural employees in the total population		−0.0037	−0.0035
		(0.0232)	(0.0234)
Use of chemical fertilizers and pesticides			0.0501
			(0.1556)
City FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	1053	871	871

Note: Each observation represents a city-year. The dependent variable equals 1 in the year the policy was implemented. The sample is limited to the year before policy implementation and the year of zero-growth policy implementation. The standard error is reported in parentheses on a city-by-city basis. GDP, population, and average wage are the natural logarithms of the GDP, total population, and average wage of a specific city. Fertilizer and pesticide usage are the natural logarithms of the total fertilizer and pesticide usage of a specific city in the given year. All variables are sourced from the City Statistical Yearbook. The number of poor counties is based on data from the 832 poor counties identified nationwide by the State Council’s Poverty Alleviation Office in 2014. This paper determines the number of poor counties for each city annually based on the exit list from 2016 to 2020 and conducts statistics at the city level. The standard errors in parentheses are clustered at the city level.

, none of these variables exhibit statistically significant associations with the policy timing, regardless of whether additional agricultural variables are included (columns (1)–(3)). This indicates that local policy adoption was often driven by non-economic factors such as central government mandates rather than endogenous regional characteristics.

4. Empirical Results and Analysis

4.1. Baseline Estimation

Table 3. Effects of the ZGP on the market entry of agricultural input enterprises.

	Green Entry		Green Entry (Newly Established)		Green Entry (Pollution Transformation)
	(1)	(2)	(3)	(4)	(5)	(6)
ZGP	0.5525 ***	0.5619 ***	0.5147 ***	0.5204 ***	1.0129 ***	1.0570 ***
	(0.0474)	(0.0477)	(0.0500)	(0.0504)	(0.1277)	(0.1319)
Manufacturing advantages		−0.0017		−0.0034		0.0162
		(0.0062)		(0.0065)		(0.0170)
Per GDP		0.3560 **		0.4145 **		−0.4599
		(0.1628)		(0.1648)		(0.4783)
Population		1.2006 ***		1.2847 **		0.4407
		(0.4529)		(0.5011)		(1.0702)
Labor costs		−0.4365		−0.5276 *		0.5743
		(0.2779)		(0.3100)		(0.8025)
Financial loan		−0.1371		−0.0878		−0.6267
		(0.1357)		(0.1286)		(0.4861)
Fiscal stimulus		−0.2109		−0.2875		0.6144
		(0.6018)		(0.6141)		(1.7688)
Transportation		0.0388		0.0434		−0.0801
		(0.0465)		(0.0498)		(0.1376)
City FE	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y
Observations	2656	2364	2632	2340	1824	1670

Note: The figures in brackets are robust standard errors at the city level. ***, **, and * indicate that the coefficients are significant at the 1%, 5%, and 10% levels, respectively.

presents the impact of the ZGP implementation on the market entry of green agricultural input enterprises, with differentiated analyses across various scenarios. Column (1) includes only city and year fixed effects. Policy-implementing regions experienced a 55.25% increase in green enterprises compared to non-implementing areas. This reveals that ZGP implementation significantly promotes market entry of green agricultural input enterprises. Column (2) displays regression results incorporating control variables, where the coefficient remains significantly positive at the 1% level. We further decompose market entry into two distinct pathways: (1) newly established green enterprises and (2) incumbent traditional enterprises transitioning to green production. Columns (3)–(6) demonstrate that ZGP implementation leads to a 52.04% increase in new green enterprises and a remarkable 105.70% marginal contribution to traditional enterprise transformation. Collectively, our estimates demonstrate that the ZGP effectively promotes green enterprise entry and facilitates green market transition in the agricultural inputs industry.

The estimation results for the control variables reveal a negative correlation between labor costs and the market entry of green agricultural input enterprises, indicating that rising labor costs constrain the market entry behavior of these firms. This relationship stems from the production function theory, where labor, as a crucial production input, significantly influences corporate production decisions through price fluctuations [33]. Conversely, financial loan availability and fiscal support demonstrate positive effects on the number of green agricultural input enterprises. Beyond internal cost considerations, external financial and policy support play a pivotal role in shaping production decisions [32]. A favorable financial environment coupled with supportive fiscal policies can effectively reduce production costs and lower industry entry barriers, thereby facilitating market entry activities for green agricultural input enterprises.

4.2. Event Study

A critical assumption for the validity of the difference-in-differences (DID) specification is that both the treatment group and the control group exhibit a similar time trend in market entry and exist in the absence of the ZGP. The condition is known as the parallel trend assumption. To evaluate this assumption, we conduct a graphical test. The model employed to assess the parallel trend hypothesis is as follows:

$${GreenEntry}_{ct}={\sum }_{k=-6,k\ne -1}^2{\beta }_{k}ZG{P}_{ck}+\gamma X_{ct}+{\mu }_c+{\lambda }_t+{\epsilon }_{ct}$$

(3)

where $k$ is the year dummy variable, which takes the value of 1 for the current year and 0 otherwise. The range of k is from −6 to 2, where k < 0 represents the year before the ZGP enforcement, k = 0 represents the year of the policy shock, and k > 0 denotes the year following the ZGP introduction. The base year is set as the year preceding the ZGP implementation, and thus, k = −1 is omitted. The definitions of other variables remain unchanged.

To rigorously validate the parallel trend assumption, we complement the graphical analysis with formal statistical tests of the pre-treatment coefficients.

Table 4. Dynamic Effects of ZGP Implementation.

Period	Coefficient	Standard Error	95% CI Lower Bound	95% CI Upper Bound	p-Value
Pre6	0.236	0.154	−0.066	0.538	0.126
Pre5	0.203	0.177	−0.144	0.549	0.252
Pre4	0.096	0.084	−0.068	0.261	0.252
Pre3	0.044	0.078	−0.109	0.197	0.574
Pre2	0.003	0.057	−0.108	0.114	0.954
Current	0.501	0.042	0.419	0.584	0.000
Post1	0.318	0.046	0.228	0.409	0.000
Post2	0.114	0.049	0.019	0.210	0.019

Note: The table reports coefficient estimates (${\beta }_k$), standard errors, and 95% confidence intervals from Equation (3). The omitted baseline period is k = −1.

reports the full dynamic effect estimates, including coefficients, standard errors, and 95% confidence intervals for each period. Figure 3 illustrates the results of the dynamic effect. The estimates ${\beta }_k$ are found to be insignificant in all years preceding the policy implementation (with all p-values > 0.1 and confidence intervals encompassing zero, as shown in Table 4), but they show significance in the year following the pilot enforcement. This statistical evidence, combined with the graphical pattern, strongly supports that there were no pre-treatment differences. The results indicate that the trends in market entry of green agricultural input enterprises for both the treatment and control groups were consistent before the introduction of the ZGP, with significant differences emerging only after the policy enforcement. Consequently, the parallel trend hypothesis is confirmed, affirming the reliability of the estimation results.

4.3. Robustness Checks

4.3.1. Substitution of the Explanatory Variable

The adoption of different measurement approaches for the dependent variable may yield divergent or even contradictory results. To address this methodological concern, following established practices in the literature [33], this study employs two alternative measures: (1) the ratio of green agricultural input enterprise entries to incumbent firms, and (2) the proportion of green enterprise entries relative to total new entrants in the agricultural inputs sector. As shown in columns (1)–(2) of

Table 5. Robustness Checks.

	Dependent Variable Replacement		Concurrent Policies		Address Migration	ZGP at the Provincial Level
	(1)	(2)	(3)	(4)	(5)	(6)
ZGP	0.5435 ***	0.1852 ***	0.5649 ***	0.5634 ***	0.0845 ***	0.4353 ***
	(0.0636)	(0.0313)	(0.0466)	(0.0475)	(0.0178)	(0.0447)
Control variables	Y	Y	Y	Y	Y	Y
City FE	Y	Y	Y	Y	Y	Y
Year FE	Y	Y	Y	Y	Y	Y
Observations	2352	1968	2364	2364	2380	2364

Note: The figures in brackets are robust standard errors at the city level. *** indicate that the coefficients are significant at the 1% levels.

, the positive effect on green market entry remains statistically significant under these alternative specifications. Specifically, column (1) demonstrates a 54.35% increase in the entry ratio relative to incumbent firms, while column (2) reveals an 18.52 percentage-point growth in the share of green entrants among total new firms, confirming that the ZGP significantly stimulates green rather than conventional agricultural input enterprise formation. These robustness checks using alternative dependent variable measurements consistently support our baseline findings, providing compelling evidence that the policy implementation has indeed triggered a surge in green enterprise entry activities in regulated cities.

4.3.2. Considering the Impact of Concurrent Policies

In 2010, China launched the Low-Carbon City Pilot Program, designating 5 provinces and 8 cities as the first batch of pilots. The program expanded in 2012 with a second batch comprising 1 additional province and 29 cities, followed by a third batch in 2017 that included 10 districts/counties and 35 cities. Existing research demonstrates that this pilot policy elevates green technology thresholds, facilitating market entry for high-tech low-carbon enterprises while phasing out environmentally backward firms [40]. The policy has also been shown to enhance corporate environmental performance [41] and reduce carbon emissions [42]. As chemical enterprises, agricultural input enterprises’ market dynamics may be affected by these pilot policies. To mitigate potential confounding effects, we incorporate dummy variables for all three pilot batches into our baseline regression model. As evidenced in column (3) of Table 5, the ZGP maintains its statistically significant positive impact on green agricultural input enterprises’ market entry even after accounting for these controls, underscoring the robustness of our core findings.

For an extended period, local governments prioritized economic growth in their performance evaluations, resulting in relatively weak environmental regulatory incentives. Against this backdrop, the central government launched a specialized environmental protection inspection campaign between 2016 and 2017, deploying four rounds of month-long, province-level inspections led by ministerial-level officials. Following the initial nationwide coverage, a 2018 “follow-up” inspection targeted 20 provincial units, including Hebei Province. Existing research demonstrates that these central environmental inspections improved corporate performance [43] and reduced market entry of highly polluting industries [44], suggesting potential impacts on agricultural input enterprises’ entry decisions. To control for this policy’s influence, we incorporate inspection implementation data through dummy variables in our model. As shown in column (4) of Table 5, our core findings regarding ZGP’s positive effect on green market entry remain robust even after accounting for the central environmental inspections, confirming the stability of our conclusions.

4.3.3. Eliminating the Impact of Corporate Address Relocation

Consistent with the Pollution Haven Hypothesis, which posits that firms tend to relocate from regions with stringent environmental regulations to those with laxer standards [45], this study addresses potential cross-regional migration of agricultural input firms. We identify relocated firms as those that changed their registration city during the study period. Among the 25,154 agricultural input firms in our sample, 204 underwent inter-city registration changes. After excluding these migrant firms, we recalculated city-level green market entry measures and present the revised estimates in column (5) of Table 5. The results demonstrate that while the regression coefficients become smaller after removing migration cases, the core findings remain statistically robust, confirming the stability of our primary conclusions.

4.3.4. Considering the Provincial ZGP

The identification of ZGP implementation discussed earlier primarily relied on policy documents at the municipal level. However, some provincial governments have also introduced related policies, which may result in all cities within the province being affected by the policy, thereby interfering with the benchmark regression results. To ensure the reliability of the research conclusions, this paper further considers the implementation of policies at the provincial level in the robustness tests. Specifically, this study searched PkuLaw and the official websites of provincial governments using keywords such as “zero growth in fertilizer use” and “zero growth in pesticide use” to identify “red-headed” documents or specialized implementation plans issued by provincial governments. If a province officially issued a provincial ZGP document during the sample period, the dummy variable for the policy for all cities under its jurisdiction is set to 1 for that year and subsequent years; otherwise, the original policy identification results at the city level were retained. Based on this adjusted policy variable, this study re-estimated the impact of the ZGP on the market entry of green agricultural input enterprises, with the regression results shown in column (6) of Table 5. The results show that the core conclusions remain unchanged. The direction and significance level of the regression coefficients after adjusting for provincial policies are highly consistent with the benchmark regression results. The market entry of green agricultural input enterprises is still significantly increasing.

4.3.5. Negative Binomial and Zero-Inflated Poisson Regression Model

To address potential overdispersion in conventional Poisson regression, which requires equal expectations and variances, we employ a Negative Binomial fixed effect model in

Table A3. Results of the Negative Binomial and Zero-Inflated Poisson regression model.

	Green Entry	Green Entry (Newly Established)	Green Entry (Pollution Transformation)
	(1)	(2)	(3)
Panel A: Negative Binomial regression
ZGP	0.5771 ***	0.5253 ***	1.0912 ***
	(0.0453)	(0.0477)	(0.1321)
City FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	2364	2340	1670
Panel B: Zero-Inflated Poisson regression
ZGP	0.5632 ***	0.5220 ***	1.0577 ***
	(0.0365)	(0.0383)	(0.1247)
City FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	2394	2394	2394

Note: *** indicate that the coefficients are significant at the 1% levels.

. The results demonstrate consistent regression coefficients and significance levels with the Poisson fixed effect estimates, though the latter may suffer from reduced efficiency when the equidispersion assumption is violated.

Given the substantial presence of zero values in the sample, which could stem from sample selection bias, we additionally estimate Equation (2) using a Zero-Inflated Poisson Regression. This approach mirrors Heckman’s (1979) two-stage framework: a binary selection model first distinguishes between zero and positive integers, followed by a count model for positive outcomes [46]. While Greene (1994) originally proposed using Vuong tests for model selection between standard and Zero-Inflated Poisson regressions [47], Wilson (2015) later demonstrated this test’s inapplicability due to boundary nesting when zero-inflation probability equals zero, rendering its distribution non-standard normal and unsuitable for inference [48].

Consequently, we adopt the AIC and BIC criteria for model comparison (

Table 6. Test results of the AIC and BIC information criteria.

Model	Obs	ll (Null)	ll (Model)	df	AIC	BIC
Poisson	2364	−10,447.630	−4378.021	8	8772.042	8818.187
Zip	2394	−9214.408	−4370.465	324	9388.930	11,261.880

). The test results reveal distinct information criterion values between the Poisson fixed effect and Zero-Inflated Poisson models. The former is more appropriate and indicates no sample selection bias. This conclusion is further supported by the robustness check in Table A3, where the Zero-Inflated Poisson estimates align with our primary Poisson results, confirming the study’s findings.

4.3.6. Placebo Test

To rule out potential omitted variable bias in the green enterprise market entry promotion effect of the ZGP, this study employs a randomized sampling approach to conduct a placebo test. This test involves randomly selecting policy implementation years and affected cities. Figure 4 presents the probability distribution of the estimated coefficients through 500 replications of the baseline regression. The results show that the coefficients from the randomized samples are densely distributed around zero, whereas the actual policy effect estimate significantly deviates from this distribution. This confirms that the market entry promotion effect of the ZGP on green agricultural input enterprises is not driven by unobserved confounding factors.

4.4. Moderation Effects and Heterogeneity Analysis

4.4.1. Moderation Effects of Industrial Agglomeration

Enterprises within industrial clusters achieve agglomeration economic benefits through the collaborative sharing of technology, resources, and information. They also benefit from cost savings and efficiency gains brought by spatial concentration. These factors collectively influence firms’ location decisions. However, under environmental regulation constraints and specialized industrial agglomeration, the homogeneous enterprises often lack interconnections and tend to form a structurally homogeneous production model. This can easily result in path dependence and technological lock-in, hindering the emergence and diffusion of new technologies. Consequently, firms face higher costs in adapting to external environmental challenges [49], which is unfavorable for their green transformation. Most existing studies use location entropy to reflect the development intensity of a specific industry in a given region, and find that the higher the value, the greater the development intensity [50]. Following the approach of [51], this paper employs the location entropy index to measure the spatial distribution of the agricultural inputs industry, with the specific calculation formula as follows:

$${LQ}_{ij}=(q_{ij}/q_j)/(q_i/q)$$

(4)

where $i$ represents the manufacturing sector, and ${LQ}_{ij}$ denotes the locational entropy of the manufacturing sector in region $j$ relative to the national average. $q_{ij}$ represents the number of manufacturing sector employees in region $j$, $q_j$ represents the total number of employees across all industries in region $j$, $q_i$ denotes the national total of manufacturing sector employees, and $q$ represents the national total of employees across all industries. By multiplying ${LQ}_{ij}$ by the ZGP implementation variable and incorporating it into the baseline model, the regression results are shown in column (1) of

Table 7. The moderating effect of manufacturing agglomeration degree and marketization level.

Variables	Green Entry
	(1)	(2)
ZGP	0.7020 ***	0.1799
	(0.0930)	(0.1953)
ZGP × Entropy of manufacturing location	−0.2054 **
	(0.0939)
ZGP × Marketisation index		0.0305 **
		(0.0151)
Control variables	Y	Y
City FE	Y	Y
Year FE	Y	Y
Observations	1971	2388

Note: The figures in brackets are robust standard errors at the city level. *** and ** indicate that the coefficients are significant at the 1% and 5% levels, respectively.

. The ZGP has a more significant impact on the market entry behavior of agricultural input enterprises in regions with lower manufacturing location entropy, indicating that specialization and agglomeration do not facilitate the entry of agricultural input enterprises into the green production industry. The high concentration of traditional manufacturing has formed a complete supply chain and fixed production patterns, with severe technological homogeneity among enterprises, and innovation is primarily focused on incremental improvements, such as enhancing fertilizer efficiency rather than disruptive green technologies. In non-advantaged manufacturing regions, where industrial structures are not yet solidified, new firms are more likely to introduce disruptive technologies for green production. Additionally, these regions have less policy resource competition, allowing green firms to secure more support. Therefore, the ZGP exhibits a stronger promotional effect on green market entry in such regions.

4.4.2. Moderation Effects of Marketization

The level of marketization directly reflects the maturity of a region’s economic system and institutional environment [52] and serves as a crucial factor influencing the effectiveness of environmental regulation policies [53]. First, regions with higher marketization levels have more robust intellectual property protection mechanisms. This ensures that innovators reap the benefits of their inventions, thereby amplifying the positive impact of environmental regulations on promoting green production practices among agricultural producers. Additionally, in more marketized regions, price transmission mechanisms work more efficiently, which enables agricultural producers to more clearly recognize the economic benefits of green production. This incentivizes active participation in green production and technological innovation, accelerating the transition toward sustainable agriculture [54].

In the baseline model of this study, an interaction term between the Marketization Index and the ZGP variable was introduced. The regression results are presented in Column (2) of Table 7. The coefficient of the interaction term is 0.0305 and is statistically significant at the 5% level, indicating that the ZGP has a more pronounced effect on the market entry of agricultural input enterprises in regions with a higher degree of marketization. This suggests that marketization strengthens the role of the policy in encouraging agricultural input enterprises to adopt green production practices, a finding consistent with prior research [55]. Through stronger intellectual property protection and more efficient market price signals, a higher level of marketization enhances the willingness of agricultural input enterprises to engage in green production, thereby promoting the green transition of the industry.

However, it can be observed that the main ZGP effect is insignificant (coefficient 0.1799). According to the framework proposed by Jiang (2022) [56], this implies that the influence of ZGP is weak when the degree of marketization is zero. This result is theoretically reasonable, as firms’ entry decisions inherently rely on effective market mechanisms. In the absence of marketization, ZGP cannot effectively influence firm dynamics. Therefore, these findings jointly demonstrate that the reinforcing effect of marketization on ZGP is conditional.

4.4.3. Heterogeneity Analysis Among Different Grain Production Functional Areas

The baseline results of this study demonstrate the average impact of the ZGP at the city level on the entry of agricultural input enterprises into the green product market. However, considering regional disparities in economic development, technological innovation, and industrial structure across China, the influence of government policies and environmental regulations on firms’ market decisions and production behaviors may vary. To explore the heterogeneous treatment effects of the ZGP from a regional perspective, we categorize sample enterprises into main grain-producing areas, main grain sales areas, and balancing areas based on the grain production function. The regression results are presented in

Table 8. Heterogeneity analysis of the ZGP on market entry of agricultural input enterprises in different regions.

	Main Grain Producing Areas	Main Grain Sales Areas	Balancing Areas
	(1)	(2)	(3)
ZGP	0.2226 ***	0.1078	0.2011 **
	(0.0616)	(0.0961)	(0.0785)
Control variables	Y	Y	Y
City FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	1375	351	587

Note: The figures in brackets are robust standard errors at the city level. *** and ** indicate that the coefficients are significant at the 1% and 5% levels, respectively.

.

The coefficients for main grain producing areas and balancing areas are positive and statistically significant at the 1% and 5% levels, respectively, with the former exhibiting a larger coefficient. This indicates that, compared to main grain sales areas, the ZGP more significantly promotes the market entry of green agricultural input enterprises in main grain producing and balancing areas. These findings align with the observed regional differences in the spatial distribution and market entry dynamics of agricultural input firms. Enterprises located in main grain producing areas are closer to agricultural input markets, making them more responsive to shifts in market demand. Following the implementation of the ZGP, the increased market demand for green fertilizers and low-toxicity pesticides is quickly transmitted to upstream agricultural input firms, prompting strategic adjustments and production transformation. In contrast, the demand-driven incentive effect is weaker in main grain sales areas, resulting in no significant increase in green market entry among local agricultural input enterprises.

5. Mechanisms and Further Analysis

5.1. Exploring the Possible Mechanisms

The baseline results demonstrate that the implementation of the ZGP can significantly promote agricultural input enterprises to engage in green agricultural product production. This naturally leads to a further question that requires exploration: through what mechanisms does the ZGP influence the production decisions of agricultural input enterprises? To address this, building upon the previous theoretical analysis, this paper examines the mechanisms from three key perspectives: the intensity of government market supervision, public environmental awareness, and product market demand.

First, in terms of market regulation, random spot checks represent a significant innovative measure. Typically, these inspections include verification of facility conditions and operational information. For market entities, if regulatory inspections reveal non-compliance between products and their associated technical specifications or quality standards, they become liable for corresponding legal responsibilities [57]. Theoretically, random inspections can amplify corporate environmental compliance pressure through a “deterrence effect”, thereby encouraging enterprises to adopt more proactive environmental protection measures [43]. This study employs randomized inspection records of agricultural input firms from the CCAD to construct a novel measure of regulatory intensity. Specifically, we compute the annual average frequency of inspections conducted by local authorities—including the Market Supervision and Administration Bureau, the Quality Supervision Bureau, the Industry and Commerce Bureau, and the Water Affairs Bureau—at the city level for each firm. It is important to note that the intensity of random inspections carried out by these agencies varies across regions. This metric captures the spatial heterogeneity in government supervision stringency, enabling us to rigorously examine its mediating role in shaping corporate production decisions under the ZGP framework. The regression results in columns (1)–(2) of

Table 9. Mechanism results of the impact of ZGP on the market entry of agricultural input enterprises.

Variables	Green Entry	Pollution Exist	Green Entry	Green Entry
	(1)	(2)	(3)	(4)
ZGP	0.5785 ***	−0.0290	0.5500 ***	3.1486 ***
	(0.0540)	(0.1460)	(0.0498)	(0.4663)
ZGP × Spot check Frequency	−0.0112	0.1075 **
	(0.0235)	(0.0523)
ZGP × Baidu Search Index			0.0204 *
			(0.0118)
ZGP × Use of chemical fertilizers and pesticides				−0.4578 ***
				(0.0811)
Control variables	Y	Y	Y	Y
City FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Observations	2352	2166	2332	2358

Note: The figures in brackets are robust standard errors at the city level. ***, **, and * indicate that the coefficients are significant at the 1%, 5%, and 10% levels, respectively.

demonstrate that while enhanced market supervision may initially suppress green product production among agricultural input firms, it significantly accelerates the market exit of traditional enterprises. This dynamic ultimately creates additional market space for other firms to transition toward green production practices.

Secondly, with the increasing accessibility of online information, search indices have been widely adopted in academic research in recent years [58]. This study employs Baidu’s PC and mobile search indices for “environmental pollution” to measure public environmental awareness [59], investigating the mechanism through which the ZGP promotes market entry of green agricultural input enterprises. The interaction term between Baidu search indices and the ZGP implementation variable yields regression results shown in column (3) of Table 9. The study finds that the estimated coefficient of the interaction term is statistically significant at the 10% level, indicating that the ZGP has enhanced public environmental awareness. As the public places greater emphasis on environmental issues and demonstrates higher demands for green products and ecologically sustainable production methods, this effect is transmitted to corporate market decision-making. This effectively encourages enterprises to make strategic choices by timely adjusting their production orientation and entering the green inputs industry.

Finally, as previous analysis indicates, the demand reduction for traditional chemical fertilizers and pesticides resulting from decreased usage under the ZGP directly influences production firms’ market selection decisions. To evaluate the mediating role of product market demand in the policy’s promotion of green market entry, this study interacts city-level fertilizer and pesticide usage data with the ZGP variable. The fertilizer and pesticide usage data, drawn from city statistical yearbooks covering 296 Chinese cities (2013–2020), yield significant results in Table 9, column (4). The negative coefficient (−0.4578, significant at 1%) for the interaction term demonstrates that policy-implemented regions experienced greater declines in traditional agrochemical use—a finding consistent with prior research [15]. The policy’s combined approach of subsidies and public awareness campaigns not only improved input efficiency but also expanded market capacity for green alternatives, particularly in the main grain producing areas. This demand-side shift toward eco-friendly products created viable market niches for new entrants, thereby incentivizing agricultural input firms to transition into green production.

5.2. Further Discussions

The preceding analysis provides a detailed discussion of how the ZGP implementation promotes green market entry by agricultural input enterprises. However, market entry only reflects incremental changes and fails to capture the overall green development trend of the industry. Drawing on the green industry measurement approaches of Cheng et al. [60], this study adopts the proportion of green agricultural input enterprises among all incumbent enterprises as an indicator of the agricultural inputs industry’s green transformation. After replacing the explained variable in the baseline model, the regression results are presented in

Table 10. Further analysis of the ZGP on the development of the agricultural inputs industry.

Variables	Market Share of Green Enterprises	HHI
	(1)	(2)
ZGP	0.0515 ***	−0.1588 ***
	(0.0120)	(0.0463)
Control variables	Y	Y
City FE	Y	Y
Year FE	Y	Y
Observations	2368	2301

Note: The figures in brackets are robust standard errors at the city level. *** indicate that the coefficients are significant at the 1% levels.

. Column (1) shows that the ZGP implementation significantly increased the market share of green agricultural input enterprises by 5.15%, an effect with substantial practical implications. First, it demonstrates continuous industrial structure optimization, where the growing proportion of green enterprises signifies the sector’s transition from traditional high-pollution models to environmentally friendly paradigms. Second, it generates technology diffusion effects, as the expanding market presence of green enterprises facilitates the penetration of cleaner production technologies throughout the industry. This transformation is not only crucial for the agricultural inputs sector itself but also generates ripple effects that drive the green transition of upstream and downstream industries.

Moreover, the market decisions of enterprises induced by the policy will profoundly reshape the competitive landscape of the industry. Therefore, this study also examines changes in the market structure of the agricultural inputs sector. Market concentration, which reflects the size distribution of enterprises, is commonly regarded as a key characteristic of market patterns and exerts significant influence on corporate decision-making and market operational efficiency [61]. The Herfindahl-Hirschman Index (HHI) measures market concentration by calculating the sum of the squares of the revenue or asset shares of all competitors within an industry. This index not only captures changes in market share but also reveals the size distribution characteristics of market participants, making it a widely used indicator of product market competition. Following existing research [62], this study employs the HHI to measure market concentration, with the specific calculation formula as follows:

$$HHI={\sum }_{i=1}^N{\left({\displaystyle \frac{X_i}{{\sum }_{j=1}^N{X}_j}}\right)}^2\times \mathrm{10,000}$$

(5)

where $X_i$ represents the sales revenue of firm $i$, and ${\sum }_{j=1}^N{X}_j$ denotes the total sales revenue of all $N$ firms in the market. The index construction involves four key elements: (1) each firm’s market share ${\displaystyle \frac{X_i}{{\sum }_{j=1}^N{X}_j}}$ reflects its relative position; (2) squaring these shares gives greater weight to dominant firms; (3) summing all squared shares yields an aggregate concentration measure ranging from 0 (perfect competition) to 10,000 (monopoly); and (4) the 10,000 scaling factor facilitates interpretation, with the U.S. Department of Justice considering markets above 2500 as highly concentrated.

This study employs the Herfindahl–Hirschman Index (HHI) as the dependent variable. The estimation results, presented in column (2) of Table 10, indicate that the implementation of the ZGP led to a significant reduction of 0.16 percentage points in the HHI of the pesticide and fertilizer industry, reflecting enhanced market competition. The underlying mechanism suggests that by establishing new standards for green development, the ZGP has effectively lowered entry barriers within green technology sectors, thereby creating opportunities for innovative small and medium-sized enterprises. Concurrently, it has incentivized some conventional firms to pursue transformation and innovation. The influx of new market entrants, together with strategic diversification among incumbents, has collectively contributed to reduced market concentration and intensified competitive dynamics. Consequently, the observed decline in HHI demonstrates that the policy has stimulated market vitality through a process of “creative destruction” [63], promoting the industry’s evolution from oligopolistic or monopolistic competition toward a more innovative and competitive market structure—consistent with the policy’s original objective of guiding green transformation and upgrading within the sector.

6. Conclusions and Policy Implications

6.1. Scientific Contributions

This paper is the first study to use microdata and quasi-experimental methods to examine the impact of ZGP on input reduction and changes in the structure of agricultural input markets. This study contributes to the literature in three key ways: First, utilizing firm-level data for the first time, we reveal how ZGP influences the entry of green enterprises into the agricultural inputs industry. Second, we innovatively combine mechanism analysis with regional heterogeneity examination through our quasi-experimental design. Third, by presenting robust empirical evidence, we elucidate how market incentives and regulation synergistically reshape agricultural industrial structure, thereby expanding the scope of environmental policy discussions in developing countries.

6.2. Conclusions

The findings of this study demonstrate that the implementation of the ZGP has effectively incentivized enterprises to adopt green production strategies. In policy-implemented cities, the number of new entrants into the green fertilizer and pesticide production sector increased by 56.19% This result has withstood various robustness tests. Notably, the policy impact demonstrates substantial regional heterogeneity, showing more pronounced effects on green market entry in areas characterized by lower manufacturing concentration compared to industrialized regions. Furthermore, the degree of marketization emerges as a critical moderating factor, amplifying the policy’s effectiveness in promoting green production, particularly among enterprises located in core production zones and production–sales-balanced regions.

Mechanism analysis indicates that the implementation of ZGP triggered a dual market adjustment process: local governments significantly strengthened environmental enforcement, accelerated the exit of traditional agricultural input enterprises, while enhancing public environmental awareness and stimulating demand for green alternatives, thereby creating favorable conditions for green enterprises to enter the market—these findings fully validate our theoretical assumptions. Further analysis reveals that the proportion of green agricultural enterprises in policy-implementing regions has increased gradually, contributing collectively to the green transformation of the industry’s market structure. In addition, market concentration has decreased, fostering a more competitive and dynamic market environment.

These findings not only confirm the positive role of the ZGP in promoting green production among agricultural input enterprises but also offer policymakers concrete strategies based on reliable quantitative data to balance sustainability and economic growth, particularly through differentiated policy implementation across regions with varying development levels.

6.3. Policy Implications

The implementation of the ZGP demonstrates that relying solely on administrative regulation cannot achieve optimal results in agricultural green transformation. Instead, an institutional-market synergy mechanism must be established. Therefore, future policies should further strengthen the dual role of regulation and market incentives. First, precision regulation and dynamic market access mechanisms should be enhanced. Governments can leverage big data to monitor the environmental compliance of agricultural input enterprises. They can also implement the phased elimination of high-pollution, high-residue products while lowering approval barriers for green agricultural inputs. Pilot regional differentiated regulatory policies could be introduced to prioritize green agricultural input substitution in key agricultural clusters. Second, a green consumer market should be cultivated to amplify demand-side pull. Through measures such as eco-labeling, premium subsidies for green agricultural products, and expanded certification coverage, governments can boost market recognition of green products. Coupled with consumer education campaigns, these efforts can establish stable green demand, thereby incentivizing agricultural input firms to adjust their production structures. Third, green transition barriers should be reduced, and industrial layouts optimized. In manufacturing-agglomerated regions, sunk costs may hinder green transformation. Governments can alleviate initial investment pressures for green enterprises through tax incentives, low-interest loans, and other support policies. Additionally, technology sharing within industrial clusters should be encouraged, for instance, by establishing regional green agricultural input innovation centers, to facilitate the transition of traditional enterprises toward green production.

6.4. Limits of the Study

This study has several limitations that should be acknowledged. First, due to data availability constraints, we are unable to provide more comprehensive evidence regarding the moderating effects of marketization and manufacturing agglomeration on ZGP’s implementation based on broader regional datasets. Future research could conduct more exhaustive analyses when additional relevant data becomes accessible. Furthermore, when examining how macro-level policies influence micro-level entities, the absence of detailed firm-level data on production inputs and subsidies limits our ability to validate impact pathways at the individual enterprise level. Subsequent studies could incorporate more granular data to enable deeper micro-level analysis of ZGP’s effects on agricultural enterprises’ green behaviors. Finally, constrained by data limitations, this study cannot fully explore the policy’s long-term environmental impacts and socioeconomic externalities. Future investigations could track ZGP’s effects on specific indicators such as carbon emissions, pesticide residues, and soil quality, while also examining aspects like input price adjustments, changes in competition levels, and impacts on small-scale producers.