Ecological Compensation Standard for Pesticide-Reduction Behavior of Chinese Vegetable Growers—Based on the Contingent Valuation Method and Heckman Two-Stage Model

Abstract

Promoting pesticide reduction is a key step toward green vegetable production and ecological safety. Based on survey data collected from 356 leek growers in Weifang City—the largest facility-based vegetable production base in Shandong Province—this study empirically estimates the ecological compensation standard associated with pesticide-reduction behavior. The estimation employs a contingent valuation method (CVM) using non-parametric kernel density estimation for conditional value assessment, combined with the Heckman two-step model to address potential sample selection bias. The results show that 79.3% of respondents are willing to participate in an eco-compensation program for pesticide reduction; the main reason for refusal is “the higher reduction costs and lower profits”. The expected compensation level ranges from 614.94 to 620.57 yuan per mu (1 mu is approximately 0.165 acres) per year. Gender, share of Chinese chives (Allium tuberosum) income, trust in extension agents, and government penalties for excessive spraying significantly raise the required compensation, whereas age and knowledge of eco-compensation significantly lower it. Therefore, a sustainable compensation scheme co-driven by government and market should be established, combining cash, technical and in-kind support, and adopting tiered compensation schemes that reflect different reduction intensities.

1. Introduction

Vegetables are the most consumed fresh produce in China. In 2023, the country’s vegetable acreage reached 22,873 thousand hectares, with an output of 790 million tons, accounting for 13.33% of all crops [1]. Such supply is heavily dependent on pesticides. However, the re-cultivation index of vegetables is high, and there are many pests and diseases. Moreover, currently, over 90% of vegetables in our country are still grown by small-scale farmers, making it difficult to supervise. As a result, it has become a major area where excessive pesticide use occurs. The average pesticide usage per hectare in the vegetable industry has reached 13.49 kg. Excessive application threatens food safety and aggravates agricultural non-point-source pollution, constraining the sector’s transformation [2]. With the proposal of the “carbon neutrality” goal, the ESG concept representing sustainable development has gained widespread consensus both domestically and internationally. It has been proven that the ESG strategy, which aims to seek ecological environmental protection, social harmonious development, and optimization of corporate governance, has a positive and significant impact on the long-term sustainable development of enterprises. To address these challenges, the Ministry of Agriculture launched the “Action Plan for Zero Growth of Pesticide Use by 2020” in 2015, followed by the “Action Plan for Reducing Chemical Pesticide Use by 2025” in 2023. Nevertheless, incidents such as “poisonous cucumber” and “poisonous chive” continue to emerge, highlighting the urgent need to address growers’ spraying behavior [2].

As the direct decision-makers in chemical pesticide application, vegetable growers represent a key barrier to ecological improvement and agricultural product safety, thereby exerting significant influence on the reduction of pesticide usage [3]. Pesticide reduction generates significant positive externalities for the environment and food safety. However, constrained by traditional mindsets, rational growers tend to focus on short-term risks—pest pressure, yield loss, and higher costs—while the external benefits cannot be internalized through markets, making pesticide reduction difficult [4]. Therefore, an eco-compensation mechanism that offsets potential income losses is essential to encourage the adoption of low-pesticide techniques. Different countries around the world have various policies regarding the reduction of pesticides. For instance, The “2030 Pesticide Regulatory White Paper” released by the European Union’s Agriculture Agency states that a 1% reduction in pesticide usage can earn a 0.8 euro per acre subsidy from the EU agricultural fund, with the subsidy cap raised to 5000 euros per farm per year. The US Environmental Protection Agency (EPA) updated the “Federal Insecticide Registration Law”, requiring all registered pesticides to provide an “Environmental Persistence Index (EPI)”. Among the 89 newly registered pesticide products, biopesticides accounted for 41%. Pilot programs are already in place in China: Beijing’s “vegetable-grower medical insurance” allocates 30 million yuan annually for pesticide reduction; Heilongjiang has subsidized precision nozzles; Hubei has provided a pesticide and fertilizer reduction subsidy for seven consecutive years, covering 7.7 million mu with a 40% cut in pesticide use [5]. These cases offer practical lessons for eco-compensation of pesticide reduction.

Eco-compensation is widely used as a governance tool [6]. It is an incentive-based system that provides payments to ecosystem-service suppliers through fiscal transfers or market transactions [7]. Empirical studies show that agricultural subsidies significantly increase farmers’ expected utility from participating in pesticide-reduction programs [8] and curb pesticide use [9]. However, eco-compensation research for cropping systems in China remains largely academic; in practice, it focuses on four areas: crop rotation and fallow, comprehensive straw utilization, pesticide-packaging recycling and farmland carbon sequestration [10,11]. An agricultural eco-compensation policy framework should therefore be built on agriculture’s externalities and their spillover targets [12]. From the payer perspective, eco-compensation is essentially a coordination game among stakeholders [13]. Identifying the principal payer is the first task in building a compensation scheme [14]; whoever the payer is, the ultimate goal is to find the lowest-cost supplier of compensation [15]. As the dominant actor in ecological protection [16], government is typically the most efficient payer. Concerning compensation standards, the losses and gains of each stakeholder must be specified, and stage-specific priorities should reflect their evolving interests [13]. Rational, scientific and standardized eco-product pricing is inseparable from compensation standard setting [17]. Most studies derive standards from opportunity costs [18,19], although some propose ecological value accounting [20]. Farmers’ compensation expectations for grain crops, open-field herbaceous cash crops, protected herbaceous crops and woody crops are generally higher than the ecological benefits actually generated [21]. As for payment methods, farmers on average prefer in-kind (farm input) and cash payments, followed by technical assistance; policy-based compensation is the least preferred [22]. Methodologically, contingent valuation, as seen in the Logit and Heckman models, is widely used to estimate government compensation standards, and has been applied to farmers’ adoption of fertilizer-reduction, straw collection and plastic-film recycling technologies [23,24,25].

Existing studies have systematically analyzed the scope, stakeholders, and methods of agri-ecological compensation, providing an important foundation for this paper. Nevertheless, three research gaps remain: first, crop coverage is narrow. Most compensation studies focus on watershed or forest eco-compensation; crop-related work deals almost exclusively with grain crops such as wheat and maize. Research on eco-compensation for cash crops, especially for pesticide reduction of vegetables, is currently limited. However, vegetables are essential for the daily diet of Chinese residents. They are widely cultivated, and there are many cases of pest and disease outbreaks. They are also the areas most severely affected by excessive pesticide use. Therefore, it is necessary to conduct targeted research on them. Second, the literature emphasizes external levers—government regulation, technology extension and social networks—to promote pesticide reduction among vegetable growers, but neglects the inner driver: growers’ own willingness to reduce spraying. In practice, adoption is stalled by growers’ rational fear of higher pest pressure, lower short-term yields, and increased production costs, while the positive externalities of pesticide reduction cannot be internalized through markets. Only by adopting growers’ rational-economic perspective and compensating them for potential income losses can passive compliance be turned into active participation. Third, methodological approaches are single-tool. Compensation standards are usually estimated with either CVM or the Heckman model alone; combined use is rare. Therefore, this paper focuses on estimating an eco-compensation standard for vegetable growers’ pesticide-reduction behavior. Recognizing that vegetables are diverse and geographically dispersed—and that compensation standards must be precise and scientific—we select Chinese chives, a pesticide-intensive, frequently contaminated vegetable, as our study crop [26]. By jointly employing non-parametric CVM and parametric Heckman two-stage estimation, we derive upper and lower bounds of the compensation standard for chive growers’ pesticide-reduction practices. The resulting interval provides a scientifically grounded, operationally relevant reference for designing a pesticide-reduction eco-compensation system for vegetable growers and for promoting green development of China’s vegetable industry.

2. Theoretical Analysis and Research Hypotheses

2.1. Theoretical Analysis

The theory of farmer behavior combines a set of theories that study the decision-making behaviors of farmers in aspects such as agricultural production, resource allocation, and risk management, and the influencing factors thereof. It mainly includes the flowing: the rational economic person assumption, which is that farmers will consider benefits, costs, and risks comprehensively in the decision-making process to pursue profit maximization or utility maximization; the limited rationality theory, which is that, due to information asymmetry and limited cognitive ability, farmers’ decisions may deviate from complete rationality and rely more on experience, social networks, and external influences; the expected utility theory, which is that when farmers face uncertainty, they will evaluate the expected returns of different choices based on personal experience, market information, and risk attitudes, and make corresponding decisions; and the planned behavior theory, which holds that farmers’ behaviors are influenced by attitudes, subjective norms, and perceived behavioral control.

In this study, pesticide-reduction ecological compensation is an incentive mechanism aimed at encouraging farmers to reduce pesticide use and adopt more eco-friendly planting methods. The theory of farmer behavior can better explain the decision-making process of farmers under the pesticide-reduction ecological compensation policy. The willingness of farmers to participate in compensation and the level of compensation received are influenced by multiple factors such as economic benefits, policy cognition, risk expectations, social trust, and government regulations. Therefore, when formulating the pesticide-reduction ecological compensation policy, it is necessary to combine the behavioral characteristics of farmers and, through reasonable compensation standards, effective information dissemination, technical training, and social support systems to enhance farmers’ participation enthusiasm and promote green agriculture and environmental protection.

2.2. Research Hypotheses

(1) Ecological cognition and farmers’ willingness to participate in pesticide-reduction ecological compensation.

Cognition is the process of screening, processing, and understanding known information. In the planned behavior theory, intuition generally involves three aspects: attitude, subjective norms, and perceived behavioral control. Ecological cognition is the behavior of an individual actively recognizing and understanding the environment through perception and action, and is a necessary prerequisite for forming ecological values [27]. In the production decision-making process of farmers, ecological cognition will have a certain impact on the final decision of farmers. At present, China attaches great importance to improving the level of agricultural green development and promoting sustainable agriculture, and the ecological cognition concept, to a certain extent, is conducive to farmers making decisions that are more beneficial to environmental protection. Farmers no longer only focus on issues related to their own costs, income, and benefits, but will more rationally consider environmental issues and the negative impacts of excessive pesticide application on the environment and food safety, thereby promoting environmental protection. Therefore, the following hypothesis is proposed in this paper:

H1: 

Ecological cognition has a positive impact on farmers’ willingness to participate in pesticide-reduction ecological compensation.

(2) Social trust and farmers’ willingness to participate in pesticide-reduction ecological compensation.

According to China’s agricultural conditions, farmers involved in agricultural production will inevitably have exchanges and cooperation with technicians, neighbors, village officials, etc., thereby generating social trust. Social trust helps information to be disseminated among farmers, converting information into knowledge for farmers, and then taking actions which, to a certain extent, rely on the policy publicity and technical dissemination of technicians. Therefore, the higher the degree of trust in technicians, the more willing farmers are to participate in pesticide-reduction ecological compensation. People in rural areas value interpersonal relationships, and the trust and communication among relatives and neighbors can effectively promote information dissemination, reducing information asymmetry. In addition, rural residents generally have a convergent and conformist social psychology, and pay more attention to the opinions of others around them. Therefore, when making decisions, they will try to be consistent with others’ behaviors. Thus, whether farmers participate in compensation will be influenced by relatives and neighbors. Therefore, the following hypothesis is proposed in this paper:

H2: 

Social trust has a positive impact on the willingness of farmers to participate in ecological compensation for reducing pesticide use.

(3) Government regulation and farmers’ willingness to participate in ecological compensation for reducing pesticide use.

Government regulation refers to measures and means by which the government, in order to maintain public interests, social order, and market fairness, intervenes and manages economic activities, social behaviors, and the use of public resources in accordance with laws and policies. The impact of government regulation on farmers’ willingness to participate in ecological compensation for reducing pesticide use mainly manifests in three aspects: first, incentive regulation. This is where the government reduces the information asymmetry between farmers and the market by providing information, offering subsidies, and implementing rewards, thereby reducing the risks associated with farmers’ implementation of pesticide reduction, achieving a mutual improvement of economic and ecological benefits. In this paper, “whether or not the government provides subsidies related to pesticide reduction” is used as the incentive regulation. Second, restrictive regulation. This is where the government strictly limits and controls the excessive pesticide-application behavior of farmers through the establishment of relevant laws and regulations, to restrain farmers’ unreasonable pesticide-application behavior, thereby improving the pesticide-application efficiency of farmers and reducing the application volume. In this paper, “whether or not the government imposes penalties for excessive pesticide application” is used as the incentive regulation. Third, guiding regulation. In addition to production costs, farmers in the process of reducing pesticides also need to pay for technical learning costs, time costs, information search costs, etc. Therefore, the government uses a series of guiding mechanisms, such as organizing publicity and conducting technical training, to improve farmers’ technical level and enhance their willingness to participate in ecological compensation for reducing pesticide use. In this paper, “whether or not the government conducts pesticide-reduction technical training” is used as the incentive regulation. Therefore, the following hypothesis is proposed:

H3: 

Government regulation has a positive impact on farmers’ willingness to participate in ecological compensation for reducing pesticide use.

3. Materials and Methods

3.1. Materials

There is a large variety of vegetables, and different types differ markedly in cultivation practices and pesticide application. To improve the precision and scientific validity of the study, we selected Chinese chive as a crop for the following reasons: (1) High representativeness. Chinese chive is one of the most important vegetables in China, with a large planting area and strong market demand. (2) During the cultivation of Chinese chives, the problem of pests and diseases is quite serious. As a result, farmers generally rely on frequent application of pesticides such as carbendazim, zinc acetate, and methyl thiophanate to control diseases. Long-term excessive or improper use of pesticides may lead to soil degradation, water pollution, and imbalance of the ecosystem. (3) Priority monitoring crop. Due to repeated harvests, pesticide residues accumulate readily in Chinese chives. The crop is highly susceptible to grey mold (Botrytis spp.), necessitating multiple applications of pyrimethanil or similar fungicides. Its short growth cycle leaves insufficient time for residues to dissipate before market, which is why Chinese chives are included among the 28 vegetables under special residue monitoring by the Ministry of Agriculture and Rural Affairs. Given growers’ heavy reliance on chemical pest control, using Chinese chive as the study crop provides strong representativeness and broad applicability for designing an eco-compensation mechanism aimed at pesticide reduction.

The data used in this paper come from a household survey conducted between July and October 2024. A total of 356 Chinese-chive growers in Weifang, Shandong Province, were interviewed. A stratified random sampling strategy was adopted. Before the formal investigation, the research team conducted a preliminary survey of 30 households in Wenjia Subdistrict, Shouguang City, Weifang. In addition, 80 questionnaires were distributed online. In total, 380 questionnaires were issued; after deleting questionnaires with missing key variables or obvious inconsistencies, 356 valid responses remained, giving an effective response rate of 93.68%. Weifang ranks first in Shandong and among the top municipalities nationwide in vegetable acreage, output and output value. Its Shouguang district is the largest protected-vegetable production base in China and is hailed as “the vegetable capital of China,” giving rise to the saying “China’s vegetables look to Shandong, Shandong’s vegetables look to Shouguang.” Shouguang City, located in Weifang, is the birthplace of greenhouse vegetables and a leading force in promoting agricultural industrialization. The area for vegetable cultivation here is 600,000 mu, with an annual output of 4.5 million tons and an annual transaction volume of over 9 million tons.

3.2. Methods

3.2.1. Contingent Valuation Method (CVM)

The Contingent Valuation Method (CVM) is a typical stated-preference valuation technique. It constructs a hypothetical market in which respondents are directly asked about their willingness to pay (WTP) for an environmental improvement or their willingness to accept (WTA) compensation for a loss, and thereby calculates the economic value of the improvement or protection, estimating both use and non-use values of the environmental good. The economic rationale of CVM rests on the assumption that an individual’s utility function depends on market goods (x), non-market environmental goods (q), and personal characteristics (s). The corresponding indirect utility function is also affected by the price vector of market goods (p), income (y), the environmental good (q), and a random component (ε) capturing preference and measurement errors, written as V(p, q, y, s, ε). When an environmental change moves the state from q₀ to q₁, assumed to be an improvement, the individual’s utility in the new state must satisfy V¹(p, q₁, y, s, ε) ≥ V⁰(p, q₀, y, s, ε). Achieving this improvement, however, requires the individual to bear a monetary cost. CVM uses survey questions to reveal the individual’s preference, identifies the income level at which the person is indifferent between the two states—i.e., V¹(p, q₁, y − ω, s, ε) = V⁰(p, q₀, y, s, ε)—and then derives the distribution of the willingness-to-pay (or willingness-to-accept) measure ω, yielding the economic value of the environmental good or service.

As CVM has become more widely used, questionnaire formats have evolved from early continuous formats into two broad categories—continuous and discrete—comprising four main variants: open-ended, payment-card, double-bounded dichotomous-choice, and iterative bidding games (

Table 1. Main forms of the Contingent Valuation Method.

CVM Type	Questionnaire Format	Main Characteristics
Continuous CVM	Open-ended questionnaire	Open-ended questionnaire: directly asks respondents for their maximum willingness to pay (or accept) in response to a described environmental change.
	Payment-card method	Payment-card method: presents a range of bid amounts and lets respondents pick the one that matches their maximum willingness to pay (or accept).
Discrete CVM	Single-bounded dichotomous choice	Single-bounded dichotomous choice: offers one fixed bid; respondents simply answer “yes” or “no” to whether they would pay (or accept) that amount.
	Double-bounded dichotomous choice	Double-bounded dichotomous choice: after the first “yes/no” reply, a higher or lower second bid is presented and the respondent answers again, giving two discrete responses.

). It can be observed that each method has its own advantages and disadvantages. Given the limited cultural literacy and subject-matter knowledge among the surveyed leek farmers, complex methodological approaches—such as the repeated bidding-game technique and the double-boundary binary method—proved difficult to comprehend. Consequently, the payment card method was adopted for this study. This approach is methodologically robust and intuitive, and offers multiple response options, thereby enhancing respondents’ comprehension and facilitating the collection of reliable, high-quality data.

Two sequential questions were asked: (1) “If the government provides compensation, would you be willing to reduce pesticide use in your chive field?” (Yes = 1, No = 0). (2) If yes: “What is the minimum compensation you would accept per mu per year to cut pesticide use by 20%?” Ten bid intervals were pre-printed on the card: 0–14.4684, 14.4684–28.9368, …, 130.2156–144.684, >144.684 US Dollar/mu/year. The mean WTA is computed as

$$E\left(WTA\right)={\displaystyle \sum _{i=1}^n{P}_i{W}_i}$$

(1)

In the above formula, $W_i$ is the i-th bid value chosen by the surveyed Chinese chive grower, $P_i$ is the probability that the grower selects $W_i$, and n denotes the number of different bid intervals.

3.2.2. Heckman Two-Stage Model

The Heckman two-stage model reduces selection bias that arises in CVM surveys by explicitly modeling the relationship between the selection process and the outcome variable. The model consists of two equations: a first-stage selection equation and a second-stage outcome equation.

In the first stage, a Probit model is constructed to identify the factors influencing growers’ willingness to participate in the pesticide-reduction eco-compensation scheme. The event “willing to reduce pesticide use if the government provides compensation” is coded as Y = 1, and unwillingness as Y = 0. This binary outcome is assumed to be generated by an underlying latent variable, expressed as follows:

$$Y_i^{\ast }=X_i\gamma +{\epsilon }_i$$

(2)

In the above equation, i denotes the i-th surveyed Chinese chive grower; $Y_i^{\ast }$ represents the latent propensity of that grower to participate in pesticide-reduction behavior; $X_i$ is a vector of variables influencing the grower’s decision to join the eco-compensation program; γ is the vector of parameters to be estimated; and the random error term ${\epsilon }_i$ is assumed to be independently and normally distributed with mean 0 and variance ${\sigma }_{\epsilon }^2$. The relationship between the observed binary variable $Y_i$ and the latent variable $Y_i^{\ast }$ is

$$Y_i=\left\{\begin{array}{c}1,Y_i^{\ast }>0\\ 0,Y_i^{\ast }<0\end{array}\right.,Y_i^{\ast }=X_i\gamma +{\epsilon }_i$$

(3)

To satisfy the model’s distributional assumptions, we assume that the independent error term ε_i in the Probit equation for growers’ willingness to participate in the pesticide-reduction eco-compensation program follows a standard normal distribution. To correct for potential sample-selection bias in the second-stage outcome equation estimation of growers’ compensation levels, we include the Inverse Mills Ratio (λ) as an instrumental variable. λ is computed from the first-stage Probit results according to the formula

$$\lambda ={\displaystyle \frac{\varphi \left(X_i\gamma /{\sigma }_0\right)}{\phi \left(X_i\gamma /{\sigma }_0\right)}}$$

(4)

In the above formula, $\varphi (X_i\gamma /{\sigma }_0)$ is the probability density function and $\phi (X_i\gamma /{\sigma }_0)$ is the cumulative distribution function of the standard normal distribution. The corrected estimation equation is then obtained as

$$Y_i=X_{2i}{\gamma }_0+{\lambda }_i+{\epsilon }_i+q$$

(5)

In the above formula,

$Y_i$ = expected compensation level for grower i’s pesticide-reduction behavior;

$X_{2i}$ = vector of explanatory variables in the second-stage OLS regression;

${\gamma }_0$ = column vector of coefficients to be estimated;

${\lambda }_i$ = Inverse Mills Ratio (IMR) correction term;

σ = coefficient on λ_i to be estimated;

${\epsilon }_i$ = independent error term;

q = constant term.

3.3. Variable Selection

3.3.1. Dependent Variables

After evaluating their ecological awareness, social trust, and government regulation, the surveyed Chinese-chive growers were asked to decide whether they would join a pesticide-reduction eco-compensation program. Based on preliminary field interviews, we constructed a binary dependent variable from the question “If the government provides corresponding compensation, are you willing to reduce pesticide use in your chive production?” 1 = willing to participate in the eco-compensation scheme, 0 = unwilling.

3.3.2. Independent Variables (See Table 2)

As “rational economic agents”, growers decide whether to join a pesticide-reduction eco-compensation scheme by weighing expected benefits; profit maximization is their primary decision rule. In reality, participation willingness is shaped not only by growers’ own characteristics, but also by the external policy environment. Drawing on previous studies of farmers’ pesticide-reduction behavior and eco-compensation, we examine five groups of influencing factors: individual traits of growers, farm-operation characteristics, ecological cognition, social trust and government regulation.

Table 2. Variable definition and assignment status.

Variable Name	Item	Options
Dependent variable	Participation in eco-compensation for pesticide reduction	Willing = 1, Unwilling = 2
	Expected compensation level	Divided into 11 intervals from 0–144.684 US Dollar.
Individual traits	Gender	Male = 1, Female = 0
	Age	Actual age in years
	Education level	No schooling = 1; Primary = 2; Junior high = 3; Senior high = 4; College and above = 5
	Household financial status	<7234.2001 US Dollar = 1; 7234.2001–14,468.4002 US Dollar = 2; 14,468.4002–21,702.6003 US Dollar = 3; 21,702.6003–28,936.8004 US Dollar = 4; >28,936.8004 US Dollar = 5
Operation characteristics	Share of chive income	<60% = 1; 60–80% = 2; 80% = 3
	Cultivated area	<10 mu = 1; 10–20 mu = 2; 20–30 mu = 3; 30–40 mu = 4; >40 mu = 5
	Co-operative membership	Yes = 1; No = 0
	Participation in quality inspection	Yes = 1; No = 0
Ecological cognition	Awareness of pesticide-reduction policy	Not aware at all = 1; Not aware = 2; Neutral = 3 Somewhat aware = 4; Very aware = 5
	Awareness of “eco-compensation”	Not aware at all = 1; Not aware = 2; Neutral = 3 Somewhat aware = 4; Very aware = 5
	Perceived environmental harm of pesticide overuse	No effect at all = 1; Minor effect = 2; Moderate effect = 3; Considerable effect = 4; Very strong effect = 5
	Belief that eco-compensation improves environment	No effect at all = 1; Minor effect = 2; Moderate effect = 3; Considerable effect = 4; Very strong effect = 5
Social trust	Trust in pesticide-reduction technicians	No trust at all = 1; Low trust = 2; Moderate trust = 3; High trust = 4; Very high trust = 5
Government regulation	Receipt of pesticide-reduction subsidy	Yes = 1; No = 0
	Penalty for excessive pesticide use	Yes = 1; No = 0
	Attendance at pesticide-reduction training	Yes = 1; No = 0

Note: 1 mu ≈ 0.165 acres.

Table 2. Variable definition and assignment status.

Variable Name	Item	Options
Dependent variable	Participation in eco-compensation for pesticide reduction	Willing = 1, Unwilling = 2
	Expected compensation level	Divided into 11 intervals from 0–144.684 US Dollar.
Individual traits	Gender	Male = 1, Female = 0
	Age	Actual age in years
	Education level	No schooling = 1; Primary = 2; Junior high = 3; Senior high = 4; College and above = 5
	Household financial status	<7234.2001 US Dollar = 1; 7234.2001–14,468.4002 US Dollar = 2; 14,468.4002–21,702.6003 US Dollar = 3; 21,702.6003–28,936.8004 US Dollar = 4; >28,936.8004 US Dollar = 5
Operation characteristics	Share of chive income	<60% = 1; 60–80% = 2; 80% = 3
	Cultivated area	<10 mu = 1; 10–20 mu = 2; 20–30 mu = 3; 30–40 mu = 4; >40 mu = 5
	Co-operative membership	Yes = 1; No = 0
	Participation in quality inspection	Yes = 1; No = 0
Ecological cognition	Awareness of pesticide-reduction policy	Not aware at all = 1; Not aware = 2; Neutral = 3 Somewhat aware = 4; Very aware = 5
	Awareness of “eco-compensation”	Not aware at all = 1; Not aware = 2; Neutral = 3 Somewhat aware = 4; Very aware = 5
	Perceived environmental harm of pesticide overuse	No effect at all = 1; Minor effect = 2; Moderate effect = 3; Considerable effect = 4; Very strong effect = 5
	Belief that eco-compensation improves environment	No effect at all = 1; Minor effect = 2; Moderate effect = 3; Considerable effect = 4; Very strong effect = 5
Social trust	Trust in pesticide-reduction technicians	No trust at all = 1; Low trust = 2; Moderate trust = 3; High trust = 4; Very high trust = 5
Government regulation	Receipt of pesticide-reduction subsidy	Yes = 1; No = 0
	Penalty for excessive pesticide use	Yes = 1; No = 0
	Attendance at pesticide-reduction training	Yes = 1; No = 0

Note: 1 mu ≈ 0.165 acres.

4. Results and Analysis

4.1. Willingness to Accept Pesticide-Reduction Compensation

Survey results show that 282 growers (79.3%) were willing to join the pesticide-reduction eco-compensation program, while 74 (20.7%) refused (

Table 3. Table of Compensation Intentions.

Willing to Participate		Unwilling to Participate
Quantity	Percentage	Quantity	Percentage
282	79.3	74	20.7

). Among the refusals, the most common reason—cited by 40.28%—was “the high reduction costs and lower profits”. A total of 31.25% expressed weak environmental awareness, regarding pesticide reduction as the government’s responsibility; additionally, 21.53% of the farmers believe that the current ecological environment has reached a green level, and that the environment is friendly to humans and aligns with the sustainable development goals. Therefore, no further protection is necessary; 6.94% distrusted local implementation, doubting that village cadres or cooperatives would actually deliver the promised payments, and therefore viewed the program as unlikely to reduce pesticide use or improve the environment.

4.2. Eco-Compensation Standard for Pesticide-Use Reduction in Chinese-Chive Production

4.2.1. Upper Bound—CVM Non-Parametric Estimate

In eliciting growers’ compensation levels for pesticide reduction, this study used interval-valued responses. The questionnaire divided 0–144.6840 US Dollars into ten equal intervals and assigned the mid-point of each interval as the representative compensation level expected by respondents; any amount above 144.6840 US Dollars was truncated to 144.6840 US Dollars. The resulting frequency distribution of overall willingness-to-accept (WTA) is shown in

Table 4. Distribution of growers’ willingness-to-accept levels.

Compensation Interval (US Dollar/mu/Year)	WTA (Dollar)	No. of Households	Sample Proportion (%)
No willingness	0	74	20.79%
0~14.4684	7.2342	3	0.84%
14.4684~28.9368	21.7026	3	0.84%
28.9368~43.4052	36.1710	14	3.93%
43.4052~57.8736	50.6394	15	4.21%
57.8736~72.342	65.1078	28	7.87%
72.342~86.8104	79.5762	61	17.13%
86.8104~101.2788	94.0446	63	17.70%
101.2788~115.7472	108.5130	47	13.20%
115.7472~130.2156	122.9814	27	7.58%
130.2156~144.6840	137.4498	13	3.65%
>144.684	144.6840	8	2.25%

Data source: Compiled from questionnaire survey statistics. Note: WTA = Expected compensation-level value. The same applies below.

.

As shown in Table 4, the most frequently selected option among surveyed chive growers was the interval of 86.8104~101.2788 US Dollar/mu/year, accounting for 17.7% of responses. Only three growers chose the 0~14.4684 US Dollar/mu/year interval (1.69%). Based on the frequency distribution of WTA values and using Equation (1) across the 282 households willing to participate, the mean expected compensation level is calculated to be 89.7866 US Dollar per mu per year.

4.2.2. Lower Bound—Heckman Parametric Estimate

Using the regression results from the Heckman two-stage model reported in the table, we inserted the sample means of all influencing factors: gender = 0.629, age = 3.264, education level = 2.607, household financial status = 2.598, share of chive income = 2.483, cultivated area = 2.390, cooperative membership = 0.228, participation in quality inspection = 0.671, awareness of pesticide-reduction policy = 3.112, awareness of “eco-compensation” = 3.104, perceived environmental harm of pesticide overuse = 3.228, belief that eco-compensation improves environment = 3.219, trust in pesticide-reduction technicians = 3.20, receipt of pesticide-reduction subsidy = 0.736, penalty for excessive pesticide use = 0.565, attendance at pesticide-reduction training = 0.635, and Inverse Mills Ratio λ = 0.358. Inserting these means into Equation (5) yields an expected compensation level of 88.9720 US Dollar/mu/year for chive growers willing to participate in the pesticide-reduction eco-compensation program.

4.2.3. Recommended Compensation Interval

By jointly employing the non-parametric Contingent Valuation Method (CVM) and the parametric Heckman two-stage model, we derive the expected compensation level for pesticide-reduction eco-compensation among Chinese-chive growers in Weifang City, as reported in

Table 5. Comparison of expected value of pesticide-reduction compensation level of growers.

Estimation Approach	Compensation Level for Willing Growers (Yuan/mu/Year)
Non-parametric (CVM)	620.57
Parametric (Heckman two-stage)	614.94

.

The calculations yield an expected eco-compensation standard of 89.7866 US Dollar/mu/year when using the CVM (non-parametric) approach, and 88.9720 US Dollar/mu/year when using the Heckman two-stage model (parametric). The difference between the two estimates is only 0.8146 US Dollar/mu/year. Because CVM relies on direct statements of desired compensation, its results are more subjective and may deviate from the true level. We therefore treat the CVM estimate as the policy ceiling—89.7866 US Dollar/mu/year. The Heckman estimate, which incorporates objective covariates and corrects for sample-selection bias, is taken as the policy minimum benchmark—88.9720 US Dollar/mu/year. Adopting the band 88.9720–89.7866 US Dollar/mu/year provides a flexible yet scientifically grounded compensation range for chive growers’ pesticide-reduction efforts, making the standard both reasonable and robust.

4.3. Influencing Factors of Pesticide-Reduction Compensation

This study analyses vegetable growers’ willingness to participate in pesticide-reduction eco-compensation and their corresponding compensation levels. While basic OLS was initially used, the Heckman selection model was ultimately adopted. The model yields a Wald χ² statistic of 37.17; ρ ≠ 0 and the likelihood-ratio test of ρ = 0 gives p < 0.01, indicating that the equation passes the 1% significance test and fits the data well. Consequently, the Heckman two-step estimator adequately corrects for sample-selection bias. In the resultant regression (

Table 6. Estimation results of Heckman.

	Stage 1: Probit Model Willingness to Participate in Eco-Compensation (n = 356)			Stage 2: OLS Model Expected Compensation Level (n = 282)
Variable	Coef.	Std. Err.	z-Value	Coef.	Std. Err.	z-Value
Gender	0.382 **	0.181	2.11	47.971 *	24.525	1.96
Age	−0.102	0.094	−1.09	−24.679 **	11.399	−2.17
Education level	0.284 ***	0.094	3.02	−3.642	12.594	−0.29
Household financial status	−0.156 **	0.072	−2.16	−6.087	9.357	−0.65
Share of chive income	−0.201 **	0.086	−2.32	32.876 ***	11.498	2.86
Cultivated area	−0.113	0.091	−1.24	−15.583	11.502	−1.35
Co-operative membership	0.056	0.230	0.24	32.453	26.632	1.22
Participation in quality inspection	0.382 **	0.187	2.04	−1.767	25.507	−0.07
Awareness of pesticide-reduction policy	−0.194 *	0.099	−1.95	−12.692	12.465	−1.02
Awareness of “eco-compensation”	0.074	0.089	0.83	−24.431 **	10.696	−2.28
Perceived environmental harm of pesticide overuse	0.157 *	0.088	1.78	6.025	10.863	0.55
Belief that eco-compensation improves environment	0.076	0.090	0.85	4.300	12.544	0.34
Trust in pesticide-reduction technicians	0.235 **	0.092	2.57	30.963 **	12.808	2.42
Receipt of pesticide-reduction subsidy	0.507 ***	0.194	2.62	−17.900	29.742	−0.6
Penalty for excessive pesticide use	0.161	0.180	0.89	56.443 **	22.405	2.52
Attendance at pesticide-reduction training	−0.069	0.190	−0.36	−16.816	22.665	−0.74
Whether influenced by neighbors	0.507 ***	0.103	4.92
Cons	−1.883 ***	0.728	−2.59	617.898	111.060	5.56
$\mathsf{\lambda }$	−8.149	71.761	−0.11
Rho	−0.046
Sigma	175.92
Wald chi2 (16)	57.8
Prob > chi2	0.0000

Notes: *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. The variable “whether influenced by relatives/neighbors (z)” is used as the instrumental variable in the second-stage OLS regression of the Heckman two-step model.

), the Inverse Mills Ratio (λ) is statistically significant at the 10% level. Key findings:

4.3.1. Impact of Individual Characteristics

Gender significantly influences both participation willingness (at the 5% level) and expected compensation (at the 10% level). Male growers, who are usually the principal farm laborers and decision-makers, command more resources, are more willing to take risks, and show greater acceptance of new technologies; consequently, they are more likely to adopt pesticide-reduction practices and to request higher compensation. Age exerts a negative effect on expected compensation (significant at 10%). Older growers prefer stable returns and are reluctant to jeopardize short-term income, so they demand less compensation. Younger growers are more familiar with bio-pesticides and novel technologies, but adopting these inputs requires additional capital and labor; hence they seek higher compensation to cover the extra cost.

4.3.2. Impact of Farm-Operation Characteristics

The share of household income derived from vegetables significantly reduces participation willingness (5% level), but increases expected compensation (10% level). A high dependence on vegetable income makes growers more reliant on pesticides to secure yields and quality. If the compensation offered is insufficient to offset the perceived risk of income loss, they are less willing to participate; if the payment is attractive, they will join but demand a premium to cover potential losses and adjustment costs.

4.3.3. Impact of Ecological Cognition

Better knowledge of “eco-compensation” significantly lowers expected compensation (10% level). Well-informed growers understand the program’s objectives, funding limits, and accompanying technical support, so they form more realistic expectations and do not inflate monetary claims. They also recognize that pesticide reduction is part of sustainable agriculture, and they value non-cash benefits (training, certification, and green marketing), reducing their reliance on cash payments.

4.3.4. Impact of Social Trust

Trust in extension agents significantly raises expected compensation (10% level). Agents are the channel for technical advice, subsidies, and policy information; higher trust makes growers believe they can secure more support and that recommended technologies are reliable, thereby increasing confidence in the program and the level of compensation they deem justified.

4.3.5. Impact of Government Regulation

Penalties for excessive pesticide use significantly increase expected compensation (5% level). Fines raise production costs and perceived risks; growers facing mandatory reductions fear yield or quality losses and therefore demand higher compensation to maintain profitability. Strict penalties also signal government resolve, leading growers to expect that accompanying compensation should be commensurately generous.

4.3.6. Impact of Relatives and Neighbors

If relatives or neighbors participate, the respondent’s own willingness to join rises significantly (1% level). Successful examples demonstrate program reliability, lower information costs and opportunities for joint-input purchasing or marketing. Peer participation also creates social norms: non-participants may feel marginalized, so conforming by joining the scheme becomes more likely.

5. Conclusions

Taking into account the Conditional Value of Money (CVM) method and the Heckman two-stage model, an analysis is conducted on the expected compensation level for pesticide reduction among vegetable growers.

(1) A total of 79.3% of growers are willing to participate in the eco-compensation program for pesticide reduction, whereas 40.28% are reluctant, because of “high reduction cost” and “income loss”. (2) The upper-bound willingness-to-accept (WTA) level for eco-compensation among vegetable growers to reduce pesticide use is estimated at 89.7866 US Dollar/mu/year, using CVM. (3) The lower-bound WTA level for eco-compensation among vegetable growers to reduce pesticide use is estimated at 88.9720 US Dollar/mu/year, using the Heckman two-stage model. (4) This study examines how growers’ WTA for pesticide reduction is shaped by their basic attributes, management characteristics, ecological awareness, social trust and government regulation. Regression results reveal that gender, the share of income from vegetable production, trust in pesticide-reduction technicians and penalties for overuse have significant positive effects on WTA. In contrast, grower’s age and understanding of eco-compensation exhibit significant negative effects.

The study comprehensively considered the CVM (Conditional Value at Risk) method for assessing the value of environmental benefits and the Heckman two-stage model to determine the expected compensation level for pesticide reduction by vegetable growers. This is of great value for the formulation of pesticide-reduction compensation policies in the study area in the future, and is of great significance for the sustainable development of China’s vegetable industry and the promotion of the “dual carbon” strategy worldwide. However, the research data used in this paper are cross-sectional data collected within a specific time period, and lack dynamic examination based on time. This limits the research on farmers’ participation in pesticide-reduction ecological compensation and the level of compensation to a static analysis level. In the future, through multi-period observations, it is expected that the scientific nature of pesticide-reduction compensation will be improved.