Empirical Research on Factors Influencing Chinese Farmers’ Adoption of Green Production Technologies

Abstract

During a critical period of structural reform in China’s agricultural supply chain, accelerating the promotion and application of green production technologies emerges as a pivotal strategy to ensure the quality and safety of agricultural products while advancing agricultural modernization. This study empirically examines the factors influencing farmers’ adoption of green production technologies using an ordered logistic model based on survey data collected from 533 respondents in Shandong Province. The survey targeted regions where major economic crops such as corn and soybeans are cultivated, employing simple random sampling to ensure the data’s representativeness and reliability. The findings underscore several critical factors influencing farmers’ willingness to adopt green production technologies, including the presence of quality inspections, evaluations of restrictions on prohibited pesticide use, sales performance of green products, availability of government subsidies, and traceability of agricultural products. To foster greater adoption of green production technologies and propel the transformation of China’s agriculture, it is recommended to advocate and guide green agricultural practices, enhance green agricultural subsidy policies, and strengthen agricultural product market management systems. These measures are essential for ensuring sustainable agricultural development in China.

1. Introduction

Since the 18th National Congress of the Communist Party of China, based on the basic national policy of resource conservation and environmental protection, the Chinese government has vigorously promoted the construction of an ecological civilization and has advocated for the upgrading of the agricultural industry structure and the practice of green production development concepts. However, the traditional resource-consuming production methods have long caused increasing concerns regarding non-point source pollution in agriculture and food safety, with ecological and environmental issues becoming increasingly prominent. Currently, China is at a critical period of agricultural supply-side structural reforms, thus necessitating effective measures to improve the agricultural production environment to ensure the safety and quality of agricultural products, thereby achieving a harmonious development of agriculture and rural areas that is environmentally friendly. Promoting green agricultural production technologies is not only an important measure to alleviate environmental pollution and transform agricultural production methods but also a main focus of the national ecological civilization construction and agricultural supply-side structural reforms.

The Ministry of Agriculture and Rural Affairs in China, along with other departments, has issued a series of important policy decisions that provide robust support for the adoption of green production technologies. However, in practice, farmers are the key entities in adopting these technologies, and their behaviors are influenced by various factors. Among these factors, farmers’ awareness of and willingness to adopt green production technologies are critical prerequisites for the successful promotion of these technologies.

Currently, Chinese agriculture primarily relies on traditional, resource-intensive, and extensive management practices. The fundamental transformation towards greener methods hinges on increasing farmers’ willingness to adopt green production technologies. Therefore, analyzing the subjective and objective factors influencing farmers’ willingness to adopt these technologies as well as understanding the mechanisms behind each influencing factor hold significant theoretical and practical relevance. This analysis is essential for promoting green production technologies and transforming agricultural production methods.

Green agricultural production technologies are crucial tools for the development of modern agriculture in China [1]. However, the adoption of these technologies by farmers has stagnated in practice, severely hindering the progress of modern agriculture. Consequently, many scholars have conducted research on the willingness to adopt green production technologies. Yi Funan’s empirical analysis [2] revealed that over 90% of farmers are willing to adopt green planting technologies. Lu Lei [3] used the Heckman two-stage model to explore the impact of information access channels on farmers’ adoption of green agricultural technologies, concluding that modern media, technical training, and communication among production and business entities significantly promote the adoption intensity of green agricultural technologies among farmers. Chen Nan [4] found that village regulations and climate awareness levels have a significant positive impact on farmers’ adoption of green production technologies, and factors such as gender and age also play important roles. Li Yanzi et al. [5] developed a multi-tiered structural model for the adoption of green industry technologies, discovering that farmers’ education level, family nature, and the intensity of government regulation are deep-seated influencing factors in technology adoption. Zhang Huiyi [6] explained the mechanism of farmers’ adoption of green pest-control technologies and empirically analyzed the influence of government intervention and market incentives on the adoption level of these technologies by farmers. Xiong Ying et al. [7] analyzed the game between relevant stakeholders in the process of adopting green production technologies and found that the main reason for farmers’ lack of motivation to adopt green pest-control technologies is the lack of high-quality but low-priced safe agricultural products under market information asymmetry. Additionally, the occurrence of moral hazard among farmers due to market failures also leads to their low willingness to adopt green pest-control technologies. Yao Xing’an et al. [8] believed that although government subsidies can significantly promote farmers’ adoption of green agricultural technologies, the risks associated with these technologies can also hinder their adoption. Zhou Li et al. [9] studied farmers’ adoption behavior regarding green agricultural technologies by taking soil testing and formulated fertilization technology as an example. They found that whether farmers adopt formulated fertilization is mainly influenced by technology promotion measures and also discovered that labor-supply factors play an important intermediary role. Fan Tianwei et al. [10], through field surveys, found that land trusteeship services can significantly promote farmers’ adoption of green agricultural production technologies and increase the probability of non-green production farmers adopting green production technologies. Wang Ruonan et al. [11] believed that demonstration effects and policy support measures can significantly enhance farmers’ willingness and ability to adopt green production technologies and suggested a reasonable layout of agricultural technology promotion institutions to fully realize the income-increasing effects of various green production technologies.

Agricultural pollution is also a major issue faced by many other developed and developing countries, including the United States, the European Union, the Netherlands, and New Zealand [12,13,14]. Wauters and Mathijs [15] conducted a review of the factors influencing the adoption of green measures in the United States, Canada, and Australia. In EU countries, Dessart et al. [16] provided a policy-oriented review examining the behavioral factors influencing the adoption of various sustainable agricultural practices in Europe.

Baregheh considered that the adoption of technology is a continuous process of innovation [17]. Esser [18] proposed that the adoption process of green technologies by farmers involves a set of acceptance criteria, including technological attributes, implementation costs, estimated benefits, time requirements, and risks. According to Ajzen [19], individuals periodically adjust their attitudes, subjective norms, and perceived behavioral control based on the outcomes of their behavior. Farmers’ environmental and economic attitudes as well as their sources of information significantly influence the adoption of green technologies in organic agriculture [20]. Therefore, the adoption of green production technologies by farmers primarily depends on their environmental [21,22] or environmental awareness [23,24] and economic attitudes [25,26,27].

At the macro level, regulatory policies rarely achieve beneficial outcomes directly [28,29]. Some scholars took a more pragmatic approach by returning to an understanding of the specific factors influencing farmers’ conservation behaviors through empirical research and analysis [30].

The present article identifies family farms as a crucial component of new agricultural business entities. As primary adopters of green production technology, family farms are the entry point for analysis. Shandong Province, China’s leading agricultural region, has seen rapid development of family farms in recent years. By 2021, the province nurtured 87,000 family farms, making it one of the leading regions in China for promotional efforts and well-developed supporting policies. Thus, using Shandong Province as the research sample helps to deeply and representatively reveal the factors influencing farmers’ willingness to adopt green production technologies. The study targets family farms across 16 cities in Shandong Province, using a combination of onsite interviews and random field inspections to gather data. Using the ordered logistic model to analyze factors affecting farmers’ willingness to adopt green production technologies, this paper proposes targeted policy recommendations. The aim is to contribute to the research on farmers’ green production behaviors and provide empirical evidence for promoting green agricultural production technologies and the modernization of agriculture.

2. Materials and Methods

2.1. Methodology

This study employed an ordered logistic model to analyze survey data. The survey targeted 533 respondents in Shandong Province, focusing on regions cultivating major economic crops like corn and soybeans.

The study also utilized simple random sampling to ensure the representativeness and reliability of the data.

2.2. Specific Model Used

The authors utilized an ordered logistic model to examine factors influencing farmers’ adoption of green production technologies. This model is suitable for analyzing relationships between multiple independent variables and an ordered categorical dependent variable (such as different levels of farmers’ adoption of green production technologies).

3. Data Selection and Model Construction

This paper selects the Shandong Province Family Farm Survey as the data support, combined with the literature and practical analysis; selects appropriate influencing variables; and establishes an ordered logistic model to analyze the factors influencing farmers’ willingness to adopt green production technologies.

3.1. Data Sources

This article utilizes data from a survey conducted on family farms in 16 cities across Shandong Province from July to August 2021. The survey employed simple random sampling, with rigorous execution and verification methods of randomization. A total of 549 questionnaires were distributed to family farms engaged in planting economic crops such as corn and beans, among others. The survey yielded 533 valid responses, achieving a validity rate of 97.1%, indicating high reliability. In analyzing factors influencing farmers’ willingness to adopt green production technologies, particular attention was given to the presence of outliers in some variables. In the process of selecting factors influencing farmers’ willingness to adopt green production technologies, the presence of outliers in some variables was considered. To avoid interference with the experimental results, 11 invalid samples were excluded, ultimately utilizing 522 valid datasets.

The limitations and deficiencies of the study are as follows:

• Limited coverage of influencing factors due to constraints in sample selection and data collection methods;
• Inadequate consideration of economic factors, costs of technological transformation, and impacts on agricultural ecological systems;
• Potential biases or limitations in the statistical methods used for analysis;
• Insufficient depth in exploring certain variables crucial to understanding the research question;
• Challenges in generalizing findings beyond the specific context of the study area.

3.2. Model Setting

Given that this paper analyzes factors influencing farmers’ willingness to adopt green production technologies, the dependent variable is the farmers’ willingness to adopt these technologies. The willingness presents the five possibilities of “very unwilling”, “unwilling”, “neutral”, “willing”, and “very willing” as non-continuous ordered categorical variables. Therefore, drawing on the model used by Li Jiazhen et al. [31] in discussing the analysis of loan accessibility, an ordered logistic model was established, specifically expressed as follows:

$$\ln \frac{P\left(y\le j\right)}{1-P\left(y\le j\right)}={\alpha }_j+{\displaystyle \sum _{i=1}^n{\beta }_i{x}_i}$$

(1)

This can be equivalently transformed:

$$P\left(y\le \left.j\right|{{\displaystyle x}}_i\right)=\frac{{{\displaystyle l}}^{\left({{\displaystyle \alpha }}_j+{\displaystyle \sum _{i=1}^n{{\displaystyle \beta }}_i}{{\displaystyle x}}_i\right)}}{1+{{\displaystyle l}}^{\left({{\displaystyle \alpha }}_j+{\displaystyle \sum _{i=1}^n{{\displaystyle \beta }}_i}{{\displaystyle x}}_i\right)}}$$

(2)

Y is the dependent variable representing farmers’ willingness to adopt green production technologies; ${{\displaystyle \alpha }}_j$ is the constant term; ${{\displaystyle x}}_i$ represents the independent variables, which are the factors influencing farmers’ willingness to adopt green production technologies; β represents the regression coefficients.

3.3. Variable Selection

3.3.1. Dependent Variable

In this study, the dependent variable is farmers’ willingness to adopt green production technologies. This is categorized into five levels of willingness, represented as Y = 1, 2, 3, 4, and 5, corresponding to “very unwilling”, “unwilling”, “neutral”, “willing”, and “very willing”, respectively.

3.3.2. Explanatory Variables

To explain the factors influencing farmers’ willingness to adopt green production technologies, this study considered the existing literature [32,33,34] and empirical survey data. Consequently, the variables selected for analysis include gender, age, education level, government subsidy amount, land management area, green product sales performance, presence of sales quality inspection, traceability of agricultural products, assessment of the enforcement of banned pesticide use restrictions, and membership in cooperatives. For detailed variable selection and settings, see

Table 1. Variables and Definitions of Model Variable.

Type	Variable Name	Variable Definition	Mean	Standard Deviation
Dependent variable	Willingness to adopt green production technologies (Y)	“Very Unwilling” = 1; “Unwilling” = 2; “Neutral” = 3; “Willing” = 4; “Very Willing” = 5	4.251	0.035
Explanatory variables	Sex (X1)	“Male” = 1; “Female” = 0	0.755	0.019
	Age (X2)	“40 years and under” = 1; “40 to 50 years” = 2; “50 to 60 years” = 3; “Above 60 years” = 4	2.188	0.035
	Level of education (X3)	“Junior High School and Below” = 1; “High School or Technical/Vocational School” = 2; “Community College or Technical Institute ” = 3; “Bachelor’s Degree and Above” = 4	2.056	0.038
	Government subsidy amount (X4)	“No Subsidy” = 1; “0 to 50,000 yuan” = 2; “50,000 to 100,000 yuan” = 3; “Above 100,000 yuan” = 4	1.851	0.048
	Land management area (X5)	“0 to 32.94 acres” = 1; “32.94 to 65.88 acres” = 2; “65.88 to 98.82 acres” = 3; “98.82 to 131.76 acres” = 4; “over 131.76 acres” = 5	2.307	0.061
	Sales performance of green products (X6)	“Very Difficult” = 1; “Quite Difficult” = 2; “Moderate” = 3; “Relatively Easy” = 4; “Very Easy” = 5	2.713	0.042
	Presence of sales quality inspection (X7)	“Yes” = 1; “No” = 0	0.854	0.015
	Traceability of agricultural products (X8)	“Yes” = 1; “No” = 0	0.715	0.020
	Assessment of enforcement intensity against the use of banned pesticides (X9)	“Very Small” = 1; “Somewhat Small” = 2; “Moderate” = 3; “Somewhat Large” = 4; “Very Large” = 5	3.891	0.046
	Membership in a cooperative (X10)	“Yes” = 1; “No” = 0	0.291	0.020

.

4. Empirical Analysis

The analysis began with the descriptive statistical evaluation of the selected variables, followed by testing each explanatory variable to address potential multicollinearity issues. The subsequent empirical analysis examined the mechanisms by which various factors influence farmers’ willingness to adopt green production technologies. Lastly, robustness checks were conducted to ensure the authenticity and reliability of the findings.

4.1. Descriptive Statistics of Sample Variables

After excluding 11 samples with outliers, descriptive statistics were performed based on 522 valid sample data. Detailed statistical analysis results are presented in

Table 2. Descriptive statistics of variables.

Sample Characteristics	Categorical Indicators	Frequency	Percentage	Sample Characteristics	Categorical Indicators	Frequency	Percentage
Sex (X1)	Male	394	75.5	Sales Performance of Green Products (X6)	Very difficult	55	10.5
	Female	128	24.5		Quite difficult	153	29.3
Age (X2)	40 years and under	100	19.2		Moderate	218	41.8
	40 to 50 years	247	47.3		Relatively easy	79	15.1
	50 to 60 years	152	29.1		Very easy	17	3.3
	Above 60 years	23	4.4	Presence of Sales Quality Inspection (X7)	Yes	446	85.4
Education Level (X3)	Junior high school and below	149	28.5		No	76	14.6
	High school or vocational/technical secondary school	226	43.3	Traceability of Agricultural Products (X8)	Yes	373	71.5
	Junior college or higher vocational education	116	22.2		No	149	28.5
	Bachelor’s degree and above	31	5.9	Assessment of Enforcement Intensity against the Use of Banned Pesticides (X9)	Very small	20	3.8
Government Subsidy Amount (X4)	No subsidy	279	53.4		Quite small	33	6.3
	0 to 50,000 yuan	118	22.6		Moderate	105	20.1
	50,000 to 100,000 yuan	49	9.4		Relatively large	190	36.4
	Above 100,000 yuan	76	14.6		Very large	174	33.3
Land Management Area (X5)	0 to 32.94 acres	196	37.5	Membership in a Cooperative (X10)	Yes	152	29.1
	32.94 to 65.88 acres	148	28.4		No	370	70.9
	65.88 to 98.82 acres	71	13.6
	98.82 to 131.76 acres	36	6.9
	Over 131.76 acres	71	13.6

.

4.2. Multicollinearity Test

Before conducting regression analysis on the data, it is essential to perform a multicollinearity test on the variable data. If the variance inflation factor (VIF) is less than 10, it indicates that there is no collinearity issue among the independent variables; if the VIF is 10 or higher, it indicates the presence of collinearity issues. The test results are shown in

Table 3. Multicollinearity Test.

Model	Unstandardized Coefficient		Standardized Coefficients	t	Significance	Collinearity Statistics
	B	Standard Error	Beta			Tolerance	VIF
X1	−0.031	0.080	−0.017	−0.389	0.698	0.965	1.036
X2	0.010	0.045	0.010	0.231	0.817	0.928	1.078
X3	0.029	0.041	0.031	0.714	0.475	0.926	1.080
X4	0.043	0.032	0.059	1.365	0.173	0.957	1.045
X5	0.017	0.025	0.030	0.692	0.490	0.949	1.054
X6	0.101	0.037	0.120	2.770	0.006	0.945	1.058
X7	0.235	0.099	0.103	2.370	0.018	0.945	1.058
X8	0.172	0.077	0.096	2.245	0.025	0.957	1.045
X9	0.138	0.033	0.181	4.205	0.000	0.959	1.043
X10	0.073	0.075	0.041	0.964	0.336	0.985	1.015

, indicating that there are no multicollinearity issues among the independent variables.

4.3. Analysis of Factors Influencing Farmers’ Willingness to Adopt Green Production Technologies

Following the successful multicollinearity test of the variable data, models were estimated using SPSS 22. An ordered logistic regression model was employed to conduct an empirical analysis of the factors influencing farmers’ willingness to adopt green production technologies. Model 1 incorporates all variables into the regression analysis to assess their collective impact, while model 2 refines the approach by excluding statistically insignificant variables and reassesses the regression with the remaining significant variables. Detailed results of these analyses are presented in

Table 4. Factors Influencing Farmers’ Willingness to Adopt Green Production Technologies.

Item	Model 1				Model 2
	Estimate	Standard Error	Wald	Significance	Estimate	Standard Error	Wald	Significance
X1	0.062	0.202	0.094	0.759
X2	0.041	0.112	0.136	0.712
X3	0.029	0.103	0.081	0.776
X4	0.137	0.081	2.882	0.090 *	0.138	0.079	3.047	0.081 *
X5	0.008	0.063	0.014	0.905
X6	0.242	0.092	6.819	0.009 ***	0.235	0.091	6.587	0.010 ***
X7	0.542	0.245	4.889	0.027 **	0.559	0.244	5.238	0.022 **
X8	0.529	0.192	7.611	0.006 ***	0.538	0.191	7.926	0.005 ***
X9	0.420	0.083	25.524	0.000 ***	0.415	0.083	25.236	0.000 ***
X10	0.177	0.190	0.876	0.349

Note: ***, **, and *, respectively, indicate that differences are statistically significant at the 1%, 5%, and 10% levels.

, providing insights into the dynamics of green technology adoption among farmers.

Based on the regression results from model 1 and model 2, the sales performance of green products, the traceability of agricultural products, and the evaluation of enforcement intensity against the use of banned pesticides all passed the 1% significance level test. The presence of sales quality inspections passed the 5% significance level, while the amount of government subsidies passed the 10% significance level. Variables such as gender, age, level of education, land management area, and membership in cooperatives did not pass the significance tests, indicating that they do not have a significant correlation with farmers’ willingness to adopt green production technologies.

(1) The sales performance of green products has a significant positive impact on farmers’ willingness to adopt green production technologies, indicating that each improvement in the sales status of green agricultural products increases the probability of farmers adopting green production technologies by 23.5%. As the quality of life and health awareness improve in China, consumers in first-, second-, and third-tier cities are increasingly focusing on the green, healthy, and safe aspects of agricultural products and are willing to pay a premium for green and organic agricultural products. As the ultimate recipients and payers for agricultural products, consumers’ awareness of green agricultural products and their related actions have a strong positive influence on producers. This awareness of green and safe food fully utilizes the consumer market’s role, prompting supply chain stakeholders to adopt green agricultural production technologies and produce high-quality green products.

(2) The traceability of agricultural products passed the 1% significance level test in both model 1 and model 2, with positive regression coefficients. On the one hand, utilizing traceability systems such as blockchain technology for agricultural product quality and safety can present all the relevant information about the product’s production and sales to consumers, significantly gaining their trust and effectively increasing the sales ratio of green agricultural products. On the other hand, the continuous improvement and development of agricultural product quality and safety traceability systems also urges farmers towards safe production practices, enhancing the use of green production technologies, ensuring the quality and safety of agricultural products, and fostering a sense of responsibility among producers.

(3) The assessment of enforcement intensity on the use of banned pesticides has a significant positive impact on farmers’ willingness to adopt green production technologies. Specifically, farmers perceive that stricter enforcement against the use of banned pesticides correlates with a stronger inclination to adopt green production techniques. The root cause of safety issues in agricultural products is the irrational application of pesticides, leading to residues that not only pose food safety risks but also cause ecological damage due to their externalities. Therefore, legal measures, as a means of controlling the quality and safety of agricultural products at the source, can effectively regulate and adjust pesticide use among farmers. This enforcement also motivates farmers to adopt green production technologies, thereby reducing the safety risks associated with agricultural products.

(4) The presence of sales quality inspections passed the 5% significance level test in both model 1 and model 2, with positive regression coefficients. Sales quality inspection, serving as the final safeguard for the quality and safety of agricultural products, is directly linked to consumer safety and the effective functioning of social order. Establishing and improving the system for quality inspection of agricultural products can urge producers and sellers to strictly adhere to production and sales standards, thereby reducing the likelihood of substandard products circulating in the market. Additionally, the enhancement of the agricultural product quality inspection system continuously encourages farmers to adopt green agricultural production technologies, reducing hazards such as pesticide residues and ensuring the quality and safety of agricultural products.

(5) The amount of government subsidies is significantly positively correlated with farmers’ willingness to adopt green production technologies. Specifically, for each unit increase in government subsidy amount, the probability of farmers adopting green production technologies increases by 13.8%. The government’s green agricultural subsidy system, as an effective mechanism for incentivizing the development of green agriculture, not only facilitates the implementation of green agricultural production standards and regulates the farming practices of agricultural producers but also enhances the quality and safety levels of agricultural products. Moreover, it encourages producers to actively adopt green technologies and methods of production, effectively controlling and eliminating endogenous pollution in agriculture.

4.4. Robustness Checks

The primary analysis in the preceding sections utilized an ordered logistic regression model to examine the factors influencing farmers’ willingness to adopt green production technologies. To further substantiate the reliability of the model estimates, an ordered probit model was initially employed. The statistical significance of the variables remained unchanged, which lends some degree of robustness to the empirical results. Additionally, to further assess the reliability of the original model estimates, this paper employed a method of removing certain extreme sample values. Specifically, a 1% winsorization was applied to both the government subsidy amount and land management area variables to eliminate outliers. Subsequently, the model was re-applied to perform a secondary test. The results, as presented in

Table 5. Robustness test.

	Ordered Logistic	Ordered Probit	Applying a 1% Winsorization to the Government Subsidy Amount	Applying a 1% Winsorization to the Land Management Area
X1	0.062	0.003	0.039	0.048
X2	0.041	0.022	0.042	0.064
X3	0.029	0.031	0.044	0.038
X4	0.137 *	0.075 *	0.140 *	0.134 *
X5	0.008	0.012	0.016	0.014
X6	0.242 ***	0.154 ***	0.245 ***	0.237 **
X7	0.542 **	0.317 **	0.531 **	0.495 **
X8	0.529 ***	0.273 **	0.511 ***	0.415 ***
X9	0.420 ***	0.225 ***	0.419 ***	0.526 ***
X10	0.177	0.105	0.164	0.167

Note: ***, **, and *, respectively, indicate that differences are statistically significant at the 1%, 5%, and 10% levels.

, show that the estimated coefficients of the variables are fairly consistent, and their significance did not change substantially. Therefore, the conclusions drawn in the previous sections are robust and valid. This consistency in findings supports the robustness of the conclusions and confirms the reliability of the model estimates, ensuring their applicability in scholarly discussions on sustainability.

5. Research Results

This study provides an analysis of the key factors influencing farmers’ adoption of green production technologies in China’s agricultural sector. The significant determinants we identified include the presence of quality inspections, evaluations regarding restrictions on prohibited pesticide use, the market performance of green products, availability of government subsidies, and the traceability of agricultural products.

The findings highlight the crucial role of these factors in influencing farmers’ decisions towards adopting sustainable agricultural practices. Specifically, it is recommended to promote and provide guidance on green agricultural practices, strengthen subsidy policies for supporting green technologies, and enhance agricultural product market management systems. These measures are essential for driving the transformation and sustainable development of agriculture in China.

6. Policy Recommendations for Promoting Green Agricultural Production

The Chinese government actively promotes the use of green production technologies in agriculture through various regulatory mechanisms. However, as previously discussed, there are several prominent weaknesses that, unless effectively addressed, could hinder the true green transformation of Chinese agriculture. Addressing the quality and safety issues of agricultural products not only requires farmers to control their own production behaviors but also necessitates dual constraints from both external government and market forces. Only through a complementary approach of production and management can farmers be motivated to adopt green agricultural production technologies and ensure the quality and safety of agricultural products. The specific recommendations are as follows.

6.1. Improving Green Certification Standards and Systems

The premium price of green products can motivate farmers to adopt green production technologies and safer pesticide practices. However, “free-riding” often results in a lemon market, which undermines consumer confidence in green agricultural products. To address this issue, it is crucial to strengthen quality certification management, improve certification systems, and ensure the quality of certified products. These measures will effectively enhance the authority and credibility of product certifications, thereby boosting consumer confidence. Additionally, developing a comprehensive green standards system will ensure that Chinese agricultural products are recognized as green and sustainable in the international market. Furthermore, optimizing market environments and standardizing trading mechanisms can expand the market for green agricultural products. By leveraging consumer demand, differentiated pricing strategies can incentivize farmers to adopt green production methods [33] and supply high-quality, safe green agricultural products to the market.

6.2. Enhancing the Traceability System for Agricultural Products and Increasing Transparency of Production Information

To improve the construction of databases that manage information throughout the production and processing stages of agricultural products, it is essential to record and manage quality and safety data for agricultural products before they enter the market. This effort aims to create a traceable data chain within the agricultural product supply chain, promoting the interoperability and sharing of traceability data. Additionally, efforts should be intensified to educate and promote awareness of agricultural product quality and safety traceability among grassroots rural farmers, effectively enhancing the public’s awareness and trust in the traceability system. This will guide consumers to strengthen their understanding and consciousness of food quality and safety, ultimately enhancing consumer confidence and food security.

6.3. Synchronously Enhancing Regulation of Banned Pesticides and Promoting Green Production

Through guidance and publicity by relevant governmental departments, the awareness and attitudes of farmers towards green agricultural production are enhanced [35], thereby increasing their inclination towards adopting green production methods. Simultaneously, government agencies intensify enforcement efforts, strengthening the regulation of pesticide production and sales. This includes further specifying the applicable scopes of various pesticides to maximize the safety of their use. Additionally, by publicizing the environmental benefits of green agricultural practices and highlighting the adverse effects of excessive use of fertilizers and pesticides, the role of green agricultural production bases as models is reinforced. This helps farmers tangibly perceive the economic and environmental benefits of green agricultural practices, reducing their risk perception associated with green production and increasing their willingness to adopt such methods.

6.4. Innovating Subsidy Approaches through Enhanced Transparency and Traceability

Government subsidies support farmers adopting sustainable and environmentally friendly production methods. This initiative encourages agricultural professionals to collaborate in promoting green production practices, aiming to elevate the industry’s overall standards for green production. A traceable subsidy system was established to ensure that all allocations and utilizations of subsidies are clearly documented, aligning with the WTO’s requirements for subsidy transparency and preventing international trade disputes due to unclear subsidy policies. Enhancing the transparency of subsidy policies includes the public disclosure of information such as subsidy amounts, beneficiaries, and procedural details, ensuring that all subsidy actions comply with the provisions of the SCM Agreement. Concurrently, regular audits by independent third-party entities ensure that the distribution and use of subsidies adhere to established policies specifically targeting green production, further affirming the commitment to sustainability and regulatory compliance.

6.5. Innovating Subsidy Methods through Enhanced Transparency and Traceability

Government subsidies [36,37] as well as green production risk compensation mechanisms [38] are provided to support farmers adopting sustainable and environmentally friendly production methods, encouraging agricultural practitioners to unite and collectively promote green production models, thus elevating the industry’s overall green production standards. Establishing a traceable subsidy system ensures that all subsidy flows and utilizations are clearly recorded, meeting the WTO’s requirements for subsidy transparency and averting international trade disputes due to ambiguous subsidy policies. Enhancing the transparency of subsidy policies, including the public disclosure of subsidy amounts, beneficiaries, and procedural details, ensures that all subsidy measures comply with the stipulations of the SCM Agreement. Concurrently, regular audits conducted by independent third-party institutions verify that the disbursement and utilization of subsidies adhere to the established policies for green production. This approach not only fosters transparency but also reinforces the credibility and efficacy of the subsidy framework in promoting environmental sustainability.

7. Conclusions

This study provides a comprehensive analysis of the actual attitudes of Chinese farmers, represented by major agricultural provinces, towards adopting green production technologies, along with the diverse influencing factors. It offers empirical evidence and policy recommendations for advancing China’s agricultural modernization and aligning with global agricultural sustainability goals. Through rigorous data analysis and valuable conclusions, the research serves as a crucial reference for optimizing strategies to promote green agricultural technologies in China, enhancing food safety standards, and fostering sustainable agricultural development. It contributes significantly to practical applications and advocacy for green policies.