Analysis of the Impact of Platform Economy on Residents’ Consumption of Green Agricultural Products

Abstract

The expansion of the consumption of green agricultural products is a vital direction for transforming and upgrading food consumption for China’s residents. The development of the platform economy, based on the development of electronic information technology, achieved through changing business models, economic form, and, especially, the consumption habits and channels of residents, further improves the consumption potential of green agricultural products. This paper uses micro-questionnaire survey data from 424 green agricultural product consumers nationwide and utilizes the propensity score matching (PSM) model to explore the impact of the platform economy on residents’ consumption of green agricultural products. This study found that the platform economy can significantly promote residents’ consumption of green agricultural products. In particular, the positive effect is more significant for urban residents with higher household incomes. In addition, improving residents’ health awareness and value perception is conducive to motivating residents’ green agricultural product consumption. Therefore, this paper proposes suggestions, such as focusing on strengthening the construction of platform infrastructure in rural areas, promoting the development of platform economy in urban areas through innovative application platform technologies, and cultivating residents’ basic literacy for effective participation in the platform economy, to provide new channels and ideas for expanding the consumption of green agricultural products in China.

1. Introduction

Green agricultural products refer to agricultural products that are pollution-free, safe, high-quality, and nutritious, produced according to specific production methods and following the principles of sustainable development, certified by specialized institutions, and permitted to use the green food logo. In China, green agricultural products are divided into Grade A and Grade AA. Grade A is the basic standard, meaning that the use of relatively safe fertilizers and pesticides is permitted during the growth process with restrictions on time, quantity, and variety. Grade AA, on the other hand, is a high-grade green agricultural product, equivalent to organic agricultural products in other countries. Therefore, the scope of green agricultural products discussed in this paper is broad and comprehensive, specifically including both green food and organic food.

As the national economy grows swiftly and health consciousness among residents enhances, the production and sales of green agricultural products grows daily. The year 2021 saw China achieve the production of 67.48 million tons of green agricultural products, marking a 2.8% annual growth, with sales hitting CNY 521.86 billion (about USD 80.80 billion). Since the 14th Five-Year Plan (2021–2025), China has certified 36,000 new green, organic, specialty, high-quality, and geographical indication agricultural products, an increase of 70% compared to the end of the 13th Five-Year Plan. The total annual supply of green and high-quality agricultural products exceeds 200 million tons, with green food sales reaching CNY 609.78 billion (about USD 86.13 billion) in 2024. Although the market size of green agricultural products is growing rapidly, the consumption market of green agricultural products accounts for less than 5% of the agricultural consumption market. Thus, there is lots of room and potential for development. To further support the development of the green agricultural product market, China has introduced a number of industrial support policies. The 2021 No. 1 Central Document from China’s Ministry of Agriculture and Rural Affairs emphasizes the need for the development of green, organic, and geographically specified agricultural products. Concurrently, China will introduce a pilot program for certifying consumable agricultural products, aiming to advance the establishment of national counties focused on the quality and safety of agricultural products. Additionally, the 14th Five-Year Plan underscores the importance of enhancing the certification processes for green products, organic farm products, and their geographical markers, bolstering the quality and safety oversight of agricultural products during production, and refining the tracking system. However, as a special kind of agricultural product, green agricultural products have a high market price and ascertaining their quality poses a challenge. In addition, the scarcity of consumer appreciation for green agricultural products hinders the realization of significant advancements in their consumption [1]. For this reason, in July 2025, the Ministry of Agriculture and Rural Affairs and ten other departments issued the Implementation Plan for Promoting Agricultural Product Consumption, which proposed key points such as optimizing the supply of green and high-quality products and leveraging the advantages of e-commerce platforms.

The platform economy is a new economic form that uses modern information technologies such as the Internet, the Internet of Things, and big data to build an industrial ecosystem of concentrated resources, convenient transactions, and efficiency improvements, and to promote the efficient integration and innovative development of commodity production, circulation, and supporting services. With the popularization of the Internet and the progress of digital technology, the platform economy has developed rapidly in recent years. In 2024, China’s online retail sales have reached CNY 15.5 trillion (about USD 2.21 trillion), increasing by 7.2% year on year, 3 percentage points faster than the growth rate of social retail sales. Platform economy development covers e-commerce, digital media, online payments, social communication, logistics, and finance. As a new economic form borne from digital technology, compared with the traditional economy, the development of the platform economy provides important support for the development of new high-quality productivity in terms of supply, with technological innovation and production mode innovation. In addition, the platform economy provides convenient and efficient transaction channels on the demand side to promote the expansion of the consumer market. In the Guidelines on Promoting the Standardized, Healthy, and Sustainable Development of Platform Economy issued by the National Development and Reform Commission and other departments in 2022, it was explicitly stated that “We should fully recognize the positive role of platform economy in boosting consumption, guide platforms to cultivate new growth points for consumption, and further stimulate market vitality and internal development momentum, thereby enhancing the platform’s capacity to create consumption”.

An in-depth analysis of the comprehensive impact of the platform economy on the improvement of residents’ consumption potential for green agricultural products will help guide the platform economy to cultivate new consumption growth points. It is of significance to optimize the market structure of agricultural products, enhance the consumption impetus, and promote the efficient development of the economy. Therefore, this paper will use the PSM model to systematically analyze the impact of the platform economy on residents’ consumption behavior regarding green agricultural products based on micro-survey data from 424 green agricultural product consumers across the country, and validate these findings through empirical estimation. This paper will provide strong theoretical support and a decision-making reference for effectively expanding the consumption of agricultural products, especially green agricultural products.

2. Literature Review and Theoretical Analysis

There are relatively few studies on green agricultural products characterized by Chinese traits [2]. Current studies can be broadly divided into three types: The first category of research focuses on the analysis of green agricultural product usage via surveys. Owing to the significant fluctuation in demand for green agricultural products, the consumers of these products are essentially influenced by their quality and pricing [3]. Particularly, individuals in their youth and middle years, those with advanced education, and office workers predominantly consume green agricultural products and show the highest interest in green vegetables [4]. Research in the second category delves into the challenges faced in the consumption of green agricultural products. Issues such as a pronounced inclination to use green agricultural products, which has not been successfully translated into their actual consumption patterns, uneven distribution of information throughout the consumption cycle, and a lack of adequate green agricultural products are noted [5,6]. The third category of research analyzes the influencing factors of green agricultural product consumption through empirical testing. These influencing factors include citizens’ income level, age, physical condition, personal education level, family size, e-commerce development, consumers’ attention to green agricultural product information, trust, and willingness to pay. Among them, the study of influencing factors such as e-commerce, live-streaming, and online platforms has become the focus of scholars’ research at this stage [7,8,9,10,11,12,13,14]. These factors all change residents’ consumption habits, broaden the channels for obtaining product information, and enrich the potential for consumption choices through Internet use, thereby alleviating the imbalance between product supply and demand, reducing costs for residents, and promoting consumption [15].

Current researchers have thoroughly investigated the link between Internet use and residents’ consumption, discovering a notable rise in the consumption displayed by residents’ families who use the Internet, which has a positive impact on consumption inequality [16,17,18]. Specifically, the use of the Internet will increase the scale of consumption and diversity of rural residents, and their consumption structure will also be optimized, especially in terms of the amount of online consumption [19,20]. In addition, the use of the Internet by elderly residents will also significantly increase their scale of consumption and improve their consumption structure [21]. In terms of green consumption, online platforms based on the Internet, such as online payments, e-commerce, and social media, will positively affect green consumption [22,23,24]. This online interaction between different subjects and subsequent information dissemination will have an important impact on consumers’ willingness to purchase green products, and green consumption intentions will transform into green consumption behaviors under the promotion of Internet use [25,26]. However, since the existing e-commerce model is not conducive to establishing trust with consumers, Peng et al. (2022) believe that e-commerce may have a significant negative impact on increasing green consumption [27]. Many existing studies focus on social media, which show that social media will impact consumers’ purchasing intentions and purchasing behavior in terms of green consumption, especially in younger millennials [28,29,30,31,32]. Social media, as an information channel, plays a key role in building consumers’ motivation for green consumption. That is, social media increases the environmental awareness of followers, thereby playing a positive role in environmental protection, such as green consumption [33,34]. In a study on five countries including Brazil, Egypt, India, South Africa, and Turkey, it is found that the impact of social media marketing activities on green consumption intention is as high as 89.9% [35]. However, some scholars have different views. Wang and Hao (2018) [36] believe that Internet use does not significantly impact individual sustainable consumption behavior. They believe that the increasing Internet penetration rate will increase energy and electronics consumption, but were limited by the sample size and sample source, which ultimately resulted in an insignificant impact on the research results.

The popularization of the Internet is conducive to achieving the Pareto optimal allocation of factors and to promoting changes in consumption patterns. With the continuous expansion of product sales channels and the increasing innovation of Internet technology, online consumption has gradually become one of the most important routes through which residents consume various products [37]. The platform economy is the core supporting carrier for the large-scale and diversified development of online consumption. The platform economy promotes the improvement of residents’ consumption potential by improving circulation efficiency, alleviating liquidity constraints, and mitigating income inequality [38,39]. The digital technologies and multilateral market architecture that the platform economy rely on can surpass the time and space limitations of traditional agricultural product circulation and consumption. By reducing transaction costs, alleviating information asymmetry, reconstructing supply and demand matching models, and empowering the upgrading of consumer awareness, it can profoundly influence residents’ consumption of green agricultural products. Specifically, the platform economy reduces costs through disintermediation (i.e., direct sales). The platform economy solves the trust problem through digital traceability, visualization, and credit evaluation systems. The platform economy promotes the transformation of green consumption from a “niche behavior” to a “mass trend” by leveraging the advantages of algorithm recommendation, precision marketing, and community fission.

Domestic and international research on the platform economy mainly focuses on its connotation, characteristics, monopoly, regulation, and influencing factors [40,41,42,43,44]. Regarding its connotation, while scholars have not reached a consensus on its definition, most agree that the platform economy is a core form of the digital economy. It relies on infrastructure such as cloud services, the Internet, and advanced computing chips, and utilizes intelligent matching systems to facilitate a series of economic interactions among user groups with mutual needs [45,46]. In terms of measurement, there are two main approaches: First, single indicators such as the number of Internet infrastructure facilities, Internet platform trade volume, per capita e-commerce trade volume, or the number of enterprises engaged in e-commerce transactions are used to measure the development level of the platform economy [47,48,49]. Second, comprehensive evaluation methods such as the entropy value method and principal component analysis are used to construct an evaluation index system for platform economic development [50,51,52]. In terms of characteristics, network externalities, lock-in effect, economies of scope effect, economies of scale effect, and long-tail effect are important characteristics of the platform economy [53,54]. Regarding influencing factors, the academic community mainly focuses on micro-level factors such as the degree of reliance on e-commerce, enterprise size and competitiveness, and website technology level, as well as macro-level factors such as regional institutional environment, regulatory methods, innovation and entrepreneurship atmosphere, transportation infrastructure, and international environment [55,56,57].

In summary, existing research related to this mainly focuses on analyzing the impact of Internet use on overall household consumption and green consumption. However, differences in measuring Internet use lead to a lack of unified conclusions regarding the relationship between the two. Research on the impact of platform economy on residents’ consumption is relatively limited and mostly focuses on macro-level theoretical discussions, which suggests that platform economy helps improve residents’ consumption levels. Since Internet use is not limited to platform economy scenarios, platform economy is a specific behavioral activity developed from the Internet. Considering the significant impact of the rapid development of platform economy on residents’ consumption, this paper will focus on analyzing its impact on residents’ purchase of green agricultural products from the perspective of online platform consumption, without considering the impact of factors such as consumers obtaining product information through Internet use. This will enrich the theoretical research on green agricultural product consumers’ platform purchasing behavior and provide a decision-making reference for expanding residents’ consumption of green agricultural products in China.

The possible marginal contributions of this paper include the following: First, this paper studies the impact of platform economy on consumption behavior for specific products, namely, green agricultural products. Existing research only focuses on the impact of platform economy on consumption and does not target specific products. However, according to the research conclusions on the impact of Internet use on green consumption, the greater the price elasticity of a commodity, the more significant the promoting effect of the Internet will be [58]. As products with high price elasticity and unique characteristics, the impact of the platform economy on the consumption of green agricultural products needs further exploration to verify whether the relationship between the two conforms to existing hypotheses. Second, this paper uses survey data to explore the impact of the platform economy on residents’ consumption of green agricultural products from a micro-perspective. Existing studies using survey samples to analyze the influencing factors of green agricultural product consumption tend to have concentrated sample sources, such as students at a specific university, users of a particular platform, or consumers in a specific province. Furthermore, existing studies analyzing the impact of the platform economy on residents’ consumption often use provincial or municipal macro-level data. These data selections may, to some extent, affect the accuracy and scientific rigor of the research conclusions. Therefore, this paper will take a micro-perspective of residents’ consumption, using nationwide, unrestricted sample data to empirically test the relationship between the development of the platform economy and residents’ consumption of green agricultural products, and ultimately propose corresponding policy recommendations. Third, this paper uses a score matching method (PSM) model to mitigate the self-selection problem. Using empirical methods such as benchmark regression and threshold models, which are mainly used in existing research, to explore the linear and non-linear impacts of the platform economy on residents’ consumption will ignore the potential self-selection problem in the data, potentially leading to biased empirical results. Therefore, this paper uses the PSM model to solve the self-selection problem of observable variables in order to obtain more robust research conclusions.

3. Data Source, Model Setting, and Variable Selection

3.1. Data Sources and Sample Characteristics

This paper utilizes a mix of on-site investigation and a web-based questionnaire to precisely understand the relationship between the platform economy and green agricultural product consumption. Between January and March 2023, a countrywide survey was conducted among green agricultural product consumers, unrestricted in terms of age, income, and educational attainment. Out of 471 gathered questionnaires, 424 were deemed valid post-exclusion of those with partial information and clear logical inconsistencies, resulting in a 90% effective recovery rate for the questionnaires. In terms of consumers’ familial traits, the sample comprises 236 women, accounting for 55.66%, and 188 men, making up 44.33%. A total of 78% of households are homes with children or the elderly, with 89.3% of those surveyed residing in urban areas. In other words, people living in urban areas primarily consume green agricultural products. Residents’ yearly household earnings predominantly fall under 50,000 and range from 50,000 to 150,000, making up 36.7% and 42.2% of the total, respectively.

3.2. Model Setting

The platform economy is a new economic model based on digital technology, supported by platforms and composed of network collaboration. The development of the platform economy will be affected by other factors, such as network infrastructure, so self-selection bias is prone to occur when selecting research objects, resulting in inaccurate estimation results. Using the propensity score matching method (PSM) can efficiently solve this problem. This method builds a counterfactual framework and performs sampling processing after matching the data so that the randomness and validity of the test data can be guaranteed to the greatest extent. This paper builds on the research of Yin et al. (2023), uses the PSM method to build an empirical model, and measures the impact of the platform economy on residents’ consumption of green agricultural products [58].

The PSM method divides green agricultural product consumers into two categories: platform consumers and non-platform consumers. Then, the average treatment effect of consumers of green agricultural products under the platform economy is shown in Formula (1):

$$ATT=E(\left.Y_{1i}\right|D_i=1)-E(\left.Y_{0i}\right|D_i=1)=E(\left.Y_{1i}-Y_{0i}\right|D_i=1)$$

(1)

Among them, Y_0i and Y_1i represent citizens’ non-platform consumption and platform consumption of green agricultural products, respectively. Based on this, we can determine the difference between residents who consume green agricultural products on the platform and those who do not. Using the PSM method to match residents who have not participated in the platform and do not use the Internet as a replacement sample $E(\left.Y_{0i}\right|D_i=1)$, the average treatment effect at this time can be expressed as

$$ATT=E\left\{E\left[Y_{1i}-\left.Y_{0i}\right|D_i=1,p(X_i)\right]\right\}$$

(2)

Among them, p(X_i) is the propensity score, which represents the conditional probability of consumer participation in the platform economy. The calculation formula is shown as Formula (3):

$$p(X)=\exp (\beta X_i)/\left[1+\exp (\beta X_i)\right]$$

(3)

After obtaining the propensity score, we use the nearest neighbor matching method, caliper matching method, radius matching method, and kernel matching method to carry out sample matching on the green agricultural products platform consumers, to match the samples of consumers with similar scores but who do not use the platform to purchase green agricultural products to form a control group.

3.3. Variable Selection

The Theory of Planned Behavior (TPB) is used to describe people’s decision-making and planning process before performing specific behaviors [59]. Therefore, this paper selects relevant variables based on the Theory of Planned Behavior to construct a model that affects the consumption behaviors regarding green agricultural products. This theory proposes that behavioral intention is the direct factor that determines behavior, and behavioral intention is affected by behavioral attitude, subjective norms, and perceived behavioral control. Among them, behavioral attitude refers to an individual’s evaluation of behavioral outcome, which will be affected by the attitude towards purchasing the product and the behavior itself, so it is measured using variables including value perception and health awareness. The subjective norm refers to the expectations of others for this behavior and the degree of individual attention to these expectations. It will be affected by the media, family, and consumer group, so it is measured using variables including attention to quality and safety information and the platform economy. Perceived behavioral control refers to the degree of difficulty an individual perceives when completing this behavior. It will be affected by factors such as economic conditions and purchase convenience, so it is measured using the variables purchasing convenience and income level.

Specifically, the explained variable is the purchasing behavior regarding green agricultural products. This study uses whether the respondents have consumed green agricultural products in the past 30 days to represent this variable to reflect the residents’ purchasing behavior regarding such products. The criterion is whether respondents had purchased agricultural products labeled as green or organic. The explanatory variable is Internet use. This is based on research by Yin et al. (2022), who used whether residents use smartphones to measure Internet use [60]. And on this basis, this is expanded to measure Internet use through whether residents use smartphones or computers to surf the Internet every month. Variables such as respondents’ gender, age, education level, family structure, income level, permanent residence area, health awareness, value perception, purchase convenience, and attention to quality and safety information are selected as variables that influence residents’ Internet use and green agricultural product purchasing behavior to fully consider their impact on the explained and explanatory variables. This can ensure that the measurement results of the PSM method are scientific and reasonable. The meanings and descriptive statistics of variables are shown in

Table 1. The meanings and descriptive statistics of variables.

Variable Classification	Variable Name	Variable Meaning and Assignment	Mean Value	Standard Deviation	Minimum Value	Maximum Value
Explained variable	Consumption of green agricultural products	Have you consumed green agricultural products in the past 30 days? Yes = 1, No = 0	0.788	0.409	0	1
Explanatory variables	Platform economy	Do you participate in the platform economy/use the Internet platform every day? (e.g., online shopping, group buying, ride-sharing, food delivery, and reviews) Yes = 1, No = 0	0.736	0.441	0	1
Control variable	Gender	Female = 1, Male = 2	1.443	0.497	1	2
	Age	Resident’s actual age (years)	38.32	10.660	25	62
	Education level	Resident’s years of education (years)	12.79	3.213	9	16
	Family members structure	Are there any children or older people in the family? Yes = 1, No = 0	0.733	0.443	0	1
	Income level	The annual income of the resident family: Less than 50,000 yuan (about 7000 US dollars) = 1, 50,000 yuan to 150,000 yuan (about 7000 US dollars to 21,000 US dollars) = 2, 150,000 yuan to 250,000 yuan (about 21,000 US dollars to 35,000 US dollars) = 3, more than 250,000 yuan (about 35,000 US dollars) = 4	1.894	0.851	1	4
	Resident area	The area where the resident resides: Rural = 1, urban = 2	1.894	0.308	1	2
	Health awareness	The degree of concern about one’s health: Not very concerned = 1, not concerned = 2, general = 3, concerned = 4, very concerned = 5	3.842	0.990	2	5
	Value perception	Do you agree that green agricultural products have higher nutritional value than ordinary ones? Strongly disagree = 1, disagree = 2, generally = 3, agree = 4, strongly agree = 5	3.788	1.200	1	5
	Purchasing convenience	Is it convenient to purchase green agricultural products? Very inconvenient = 1, inconvenient = 2, normal = 3, convenient = 4, very convenient = 5	4.099	0.915	2	5
	Attention to quality and safety information	Do you pay attention to green agricultural products’ quality and safety issues? Strongly indifferent = 1, indifferent = 2, generally = 3, concerned = 4, strongly concerned = 5	3.741	1.162	1	5

.

4. Empirical Results and Analyses

4.1. Benchmark Regression of the Influence of the Platform Economy on Residents’ Consumption Behavior of Green Agricultural Products

Initially, this paper examines the multicollinearity among different variables. Observations reveal that each variable’s variance inflation factor (VIF) falls below 10, signifying the absence of multicollinearity and a more scientific approach to model estimation.

Table 2. Benchmark regression results of the impact of the platform economy on residents’ consumption behavior regarding green agricultural products.

Variable	Regression Coefficients	Standard Deviation	VIF
Platform economy	0.4491 ***	0.0264	3.6
Gender	0.0064	0.0124	1
Age	−0.0115 ***	0.0014	5.59
Education level	0.0270 ***	0.0031	2.56
Family structure	−0.0094	0.0279	4.03
Income level	0.0336 ***	0.0125	3.01
Resident area	0.2645 ***	0.0379	3.62
Health awareness	0.0627 ***	0.0144	5.36
Value perception	0.0408 ***	0.0089	3.03
Purchasing convenience	0.0546 ***	0.0139	4.26
Attention to quality and safety information	0.0860 ***	0.0126	5.66
Constant	0.1708	0.1158
R2	0.9070

Note: *** indicates passing the significance test at 1%.

displays the outcomes of the benchmark regression analysis examining how the platform economy influences residents’ consumption behavior for green agricultural products. The platform economy’s regression coefficient stands at 0.4491, holding statistical significance at the 1% level. This indicates that the platform economy can markedly enhance residents’ consumption of green agricultural products. There is a notable inverse relationship between the age of residents and their consumption of green agricultural products, due to the older population’s lack of awareness about the nutritional benefits of these products and their reluctance to spend extra money on them, thereby deterring them from consuming green agricultural products. There is a notable positive link between the level of education and income and the consumption behavior of residents for green agricultural products, suggesting that enhanced educational attainment and increased disposable income among residents will boost their likelihood of consuming such products. Additionally, there will be a rise in the percentage of residents who consume green agricultural products in their overall agricultural consumption. The health awareness, value perception, purchasing convenience, and attention to quality and safety information among residents positively influence their purchasing behavior for green agricultural products, thereby encouraging their consumption of these products.

4.2. Estimation Results of the Decision Equation of Residents’ Participation in the Platform Economy

To effectively match the variables between residents who participate in the platform economy and those who do not, this study divides the residents into the treatment group (residents who participate in the platform economy) and the control group (residents who do not participate in the platform economy). It constructs the decision equation for residents’ participation in the platform economy based on the Logit model. The estimation results are shown in

Table 3. Estimation results of residents’ participation in the platform economy decision equation based on the Logit model.

Variable	Regression Coefficients	Standard Deviation
Gender	−0.0540	0.2803
Age	−0.0807 ***	0.0232
Education level	−0.0787	0.0648
Family structure	−0.2290	0.5543
Income level	0.9845 ***	0.2952
Resident area	1.1015 ***	0.4301
Health awareness	−0.5227	0.3184
Value perception	−0.0911	0.2172
Purchasing convenience	1.3137 ***	0.3267
Attention to quality and safety information	0.3132	0.2576
Constant	−2.7171	2.1963
Prob > chi2	0.0000
Pseudo-R2	0.4047

Note: *** indicates passing the significance test at 1%.

. The regression results indicate that the residents’ age and their participation in the platform economy show a significant negative correlation, especially for elderly residents. It is relatively difficult for them to learn and use the Internet. Therefore, it is difficult for them to take advantage of the convenience of the Internet platform. Income level and resident area have a significant positive correlation with residents’ participation in the platform economy, indicating that, compared to rural areas, the network infrastructure in urban areas is more complete, where, generally, there are the basic conditions for the application of the platform economy. As a result, urban residents are more likely to participate in the platform economy, and the higher their income, the more able they are to participate in the platform economy in their households.

4.3. Matching Test of the PSM Model

For verifying the precision and rationality of the propensity score matching method’s matching outcomes, an initial balance test is essential prior to examining how the platform economy influences the consumption behaviors of residents for green agricultural products. This study employs the technique of near-neighbor matching to match the data. If the standardized deviation post-sample matching falls below 20%, the outcomes achieved through the propensity score matching method prove effective [33].

Table 4. Balance test.

Variable	Before and After Matching	Mean Value		Standard Deviation
		Residents Participating in the Platform Economy	Residents Not Participating in the Platform Economy
Gender	Before matching	1.461	1.407	11
	After matching	1.470	1.302	13.9
Age	Before matching	33.799	46.406	−137.7
	After matching	35.545	32.971	18.1
Education level	Before matching	13.877	10.806	108.4
	After matching	13.530	13.784	−9
Family structure	Before matching	0.736	0.677	12.9
	After matching	0.876	1.000	−7.2
Income level	Before matching	2.193	1.336	118.6
	After matching	1.837	1.949	−15.5
Resident area	Before matching	1.922	1.581	85.7
	After matching	1.911	1.958	−11.8
Health awareness	Before matching	4.022	3.536	47.3
	After matching	4.045	4.041	0.3
Value perception	Before matching	3.896	3.613	24.8
	After matching	3.931	4.138	−18.1
Purchasing convenience	Before matching	4.398	3.555	97.6
	After matching	4.198	3.830	12.6
Attention to quality and safety information	Before matching	4.112	3.084	88.5
	After matching	4.059	4.774	−16.5

reveals a significant decrease in the standardized deviation post-sample matching, with all variables controlled within a 20% margin, signifying that the propensity score matching method diminishes sample bias and validates the matching outcomes.

In addition, this study calculates the fitting value of the conditional probability pi of resident i participation in the platform economy based on the estimation results of the residents’ participation in the platform economy decision equation. This value is the propensity score, which is used to further draw a kernel density map to more intuitively test the common support domain after sample matching. As shown in Figure 1, after matching (as shown Figure 1a), there is a large overlap in the propensity scores between the experimental group that participates in the platform economy and the control group that does not participate in the platform economy, and most of the observed values are in a common value range, which once again confirms that the matching results are valid.

4.4. Analysis of the Impact of the Platform Economy on Residents’ Consumption Behavior Regarding Green Agricultural Products

The results of the average treatment effect on the treated of the impact of the platform economy on residents’ consumption behavior regarding green agricultural products under the four matching methods of near-neighbor matching, caliper matching, radius matching, and kernel matching are shown in

Table 5. Average treatment effect on the treated (ATT) of the impact of the platform economy on the residents’ consumption behavior of green agricultural products.

Classification of Matching Methods	ATT	t Value
Near-neighbor matching	0.9356	−3.72
Caliper matching	0.9474	−1.76
Radius matching	0.9474	−1.76
Kernel matching	0.9278	−3.73
Mean value	0.9395	—

, which indicates that there are slight differences in the average treatment effect on the treated of the platform economy. Among them, the average treatment effect on the treated of the impact of using the caliper and radius matching methods is significant at the 10% level, while the average treatment effect on the treated of the impact of using the near-neighbor and kernel matching methods is significant at the 1% level. In summary, the mean value of the average treatment effect on the treated stands at 0.9395, suggesting a higher propensity among platform economy participants to consume green agricultural products compared to non-participants.

4.5. Heterogeneity Analysis of the Impact of the Platform Economy on Residents’ Consumption Behavior of Green Agricultural Products

4.5.1. Analysis of the Regional Heterogeneity

Under a similar degree of participation in the platform economy, the difference in residents’ areas will affect their consumption behavior of green agricultural products. To test this heterogeneous change, this study divides the sample into two groups of urban and rural residents according to the residents’ areas and conducts a regression analysis separately. According to the regression results in

Table 6. Heterogeneity regression results of the region.

Variable	Urban Residents		Rural Residents
	Regression Coefficients	Standard Deviation	Regression Coefficients	Standard Deviation
Platform economy	0.1417 ***	0.0410	0.0263 *	0.0150
Gender	−0.0245	0.0309	−0.0010	0.0099
Age	−0.0187 ***	0.0031	−0.0310 ***	0.0015
Education level	0.0143 **	0.0071	0.0923 ***	0.0045
Family structure	0.1749 ***	0.0553	0.4794 ***	0.0243
Income level	0.0050	0.0268	0.0289 ***	0.0107
Health awareness	0.1463 ***	0.0346	0.4097 ***	0.0133
Value perception	0.0938 ***	0.0206	0.2641 ***	0.0118
Purchasing convenience	0.0306	0.0363	0.3799 ***	0.0102
Attention to quality and safety information	0.0247	0.0288	0.0749 ***	0.0152
Constant	1.0491 ***	0.2727	1.3110 ***	0.1060
R2	0.4452		0.9952

Note: ***, **, and * indicate passing the significance test at 1%, 5%, and 10%, respectively.

, the platform economy positively affects urban and rural residents’ consumption behavior regarding green agricultural products. The regression coefficients are 0.1417 and 0.0263, respectively, indicating that, compared to rural residents, the positive impact of participation in the platform economy on urban residents’ consumption behavior regarding green agricultural products will be more significant. This may be because urban areas are more established in the development of online shopping, cold chain logistics, and other industries, so residents can purchase their favorite green agricultural products more conveniently through Internet platforms. However, the economic development of rural areas is relatively slow. The green agricultural product sales market is smaller and has failed to effectively utilize the Internet to connect with vegetable merchants. So, even if rural residents participate in the platform economy to consume green agricultural products, its effect is not as obvious as that on promoting urban residents.

4.5.2. Analysis of the Income Heterogeneity

Findings from the earlier benchmark regression and propensity score matching analysis indicate a significant influence of residents’ income levels on the impact of the platform economy on residents’ consumption behavior regarding green agricultural products. Essentially, as residents’ income levels increase, their ability to fully exploit the Internet platform channel for purchasing green agricultural products escalates. Conversely, with a low income among residents, despite frequent participation in the platform economy, the consumption behavior of green agricultural products remains challenging. Consequently, to delve deeper into the varied impacts of platform economy on the consumption behavior of green agricultural products among diverse resident income levels, this study categorizes the samples into two segments: those with low and middle incomes (earning less than CNY 150,000 annually) and those with higher incomes (earning over CNY 150,000 annually), with

Table 7. Heterogeneity regression results of population income.

Variable	Lower–Middle-Income Residents		Higher-Income Residents
	Regression Coefficients	Standard Deviation	Regression Coefficients	Standard Deviation
Platform economy	0.1962 ***	0.0395	0.2562 ***	0.0246
Gender	−0.0452	0.0284	−0.0043	0.0600
Age	−0.0193 ***	0.0025	0.0765	0.0674
Education level	0.0464 ***	0.0067	0.1690 ***	0.1176
Family structure	0.2681 ***	0.0500	0.0011	0.1693
Resident area	0.0027	0.0453	0.0597	0.1114
Health awareness	0.0837 ***	0.0314	−0.0789	0.1012
Value perception	0.1580 ***	0.0212	0.1173 *	0.0701
Purchasing convenience	0.1504 ***	0.0336	0.1536 *	0.0835
Attention to quality and safety information	0.1210 ***	0.0243	−0.0223	0.0724
Constant	1.0469 ***	0.2108	−4.4580	3.9268
R2	0.7130		0.5875

Note: *** and * indicate passing the significance test at 1% and 10%, respectively.

displaying the estimated outcomes for these segments. The platform economy’s regression coefficients stand at 0.1962 for low–middle-income individuals and 0.2562 for high-income ones, both statistically significant at the 1% level. This suggests that, in contrast to low–middle-income individuals, the platform economy plays a more pronounced role in encouraging consumption behavior regarding green agricultural products among the high-income population. The reason for this is the comparatively elevated market value of green agricultural products in contrast to ordinary agricultural products. High-income individuals become more capable of purchasing green agricultural products under fundamental circumstances when they understand the nutritional value, purchasing channels, and related data or information of these products through digital platforms. They possess a greater capacity to transform their consumption intentions into consumption actions.

4.6. Robustness Test

This paper uses the question “Do you participate in the platform economy or use the Internet platform every day?” as a measure of the platform economy. Although this can reflect the actual situation of residents’ participation in the platform economy to a certain extent, a single indicator such as “yes” or “no” may be insufficient in portraying the frequency of residents’ participation in the platform economy. This single-dimensional measurement can lead to excessively small data variance, failing to reflect the continuity, degree of difference, and complex characteristics of variables. Therefore, in order to ensure the robustness of the research conclusions, this paper adopts the logical approach of Liu (2022), who uses the weekly leisure time spent online to measure residents’ Internet use [19]. The total time residents spend using relevant platform software (mobile phone usage statistics) is used to measure residents’ participation in the platform economy. Based on this, this paper replaces the core explanatory variables to re-regress. The regression results in

Table 8. Regression results for robustness test.

Variable	Replacing the Core Variable		Changing Measurement Method
	Regression Coefficients	Standard Deviation	Regression Coefficients	Standard Deviation
Platform economy	0.0075 ***	0.0023	0.0165 ***	0.0012
Gender	−0.0364	0.0269	−0.0006	0.0008
Age	−0.0208 ***	0.0026	−0.0014 *	0.0008
Education level	0.0298 ***	0.0065	0.0083 **	0.0037
Family structure	0.2505 ***	0.0416	0.2081 ***	0.0551
Income level	0.0357 *	0.0206	0.0166 ***	0.0012
Resident area	0.0864 **	0.0425	0.0404 ***	0.0023
Health awareness	0.0611 **	0.0280	0.0132 ***	0.0013
Value perception	0.0725 ***	0.0183	0.0348 ***	0.0122
Purchasing convenience	0.0728 ***	0.0273	0.0399 ***	0.0023
Attention to quality and safety information	0.0700 ***	0.0229	0.0345 ***	0.0122
Constant	0.9741 ***	0.2169	1.0642 ***	0.1503
R2	0.6310

Note: ***, **, and * indicate passing the significance test at 1%, 5%, and 10%, respectively.

indicate that even after replacing the core explanatory variables, there is still a significant and positive impact of the platform economy on the consumption behavior of green agricultural products. The impact of control variables such as age and education level is also consistent with the findings presented in the previous section, which fully confirms the reliability and stability of the research results. That is, the research conclusions are robust. In addition, the treatment effects model (TEM) is used to test the robustness of the estimated results. The TEM, through structured model specification and estimation strategies, removes the interference of confounding variables on the treatment effect, thereby verifying the reliability of the causal effect of the platform economy on green agricultural product consumption and eliminating the random estimation bias caused by model specification. According to the results of the model estimation given in Table 8, the platform economy positively affects the consumption behavior of green agricultural products. This once again demonstrates the robustness of the research conclusions.

5. Discussion

This paper, based on nationwide micro-survey data and using the PSM model, studies whether the platform economy impacts residents’ consumption behavior of green agricultural products. The findings reveal that the platform economy can significantly increase residents’ consumption of green agricultural products. The platform economy can broaden residents’ channels for purchasing green agricultural products, stimulating their willingness to consume. This research conclusion further confirms that the platform economy can unleash residents’ consumption potential and promote consumption upgrading [38,50,61]. Developing the platform economy to drive residents’ consumption of green agricultural products is an important manifestation of residents’ agricultural product consumption upgrading. Currently, the development of the platform economy extensively involves multiple fields such as social networks, retail, tourism, payment, and information, all of which provide new channels for expanding residents’ consumption of green agricultural products and improve the accessibility of high-quality green agricultural products. Therefore, developing the platform economy not only meets the policy requirements of China’s Agricultural Product Consumption Promotion Implementation Plan, but is also crucial for promoting sustainable agricultural practices. Furthermore, the impact of the platform economy on urban residents’ consumption of green agricultural products is more significant, consistent with existing research findings. Whether the platform economy can be effective is closely related to the digital divide, characterized by disparities in Internet accessibility and usage. When residents cannot equally enjoy the benefits of digital technology, the platform infrastructure’s ability to stimulate consumer spending is ultimately limited [39]. Similarly, according to the absolute income consumption hypothesis, increases in residents’ income levels should boost their consumption. Specifically, regarding the relationship between the platform economy and the consumption of green agricultural products, residents with higher income levels are more likely to consider participating in the platform economy to purchase green agricultural products.

This paper has two main limitations: on the one hand, although the sample size is large and the coverage is broad (no age, income, or education level restrictions), and the questionnaire response rate is high, nearly 90% of the respondents are urban residents. The results primarily reflect the impact of the platform economy on urban residents’ consumption of green agricultural products, which to some extent weakens the general applicability of the findings to rural residents. Given that the data in this paper comes from a survey conducted from January to March 2023, supplementing the survey at this stage would severely impact the time-specificity of the data, potentially reducing data comparability and causing the results to lag behind actual changes, ultimately affecting the research conclusions. Therefore, the authors will conduct a targeted survey on the impact of the platform economy on rural residents’ consumption of green agricultural products, in order to fully explore the role of the platform economy in unleashing the consumption potential of rural residents against the backdrop of the continuous growth of new forms and models of rural digital economy. On the other hand, while this paper provides empirical evidence for the platform economy’s role in promoting the consumption of green agricultural products, it does not delve into the pathways and transmission channels between the two. Since the core objective of this paper is to verify whether the platform economy can significantly influence the consumption of green agricultural products, rather than how the effect occurs, and considering that the length of time spent surveying respondents will determine the accuracy of the sample data, questions involving the inherent logic between the two were not asked. Therefore, in subsequent research, the authors will revise the questionnaire, changing the questions corresponding to insignificant regression variables to those relevant to mechanism analysis and adding questions related to socioeconomic factors, thereby analyzing the potential mechanisms by which the platform economy influences the consumption of green agricultural products.

6. Conclusions and Suggestions

6.1. Conclusions

Utilizing data from a micro-questionnaire survey involving 424 nationwide green agricultural product consumers, this paper employs the PSM model to investigate how the platform economy influences the consumption behavior of residents in green agricultural products, leading to the following key conclusions: First, the platform economy can greatly enhance residents’ consumption of green agricultural products. Second, improving residents’ education, income level, health awareness, and value perception can effectively promote their consumption behavior for green agricultural products. But as the age of the inhabitants increases, their patterns of consumption and habits tend to become more single, thereby diminishing their inclination to consume green agricultural products. Third, the platform economy has heterogeneous effects on the consumption of green agricultural products. In contrast to those in rural areas, the impact of the platform economy on the consumption of urban dwellers regarding green agricultural products is more pronounced. In contrast to those with lower to middle incomes, individuals with higher incomes tend to consume green agricultural products more frequently after participating in the platform economy.

6.2. Suggestion

Promoting the consumption of green agricultural products plays a crucial role in upgrading the consumption of residential agricultural products. The market for developing green agricultural products holds immense potential. Utilizing the entire Internet to enhance the consumption of green agricultural products by residents will pave the way for the future evolution of the agricultural product market. Consequently, drawing from this paper’s conclusions, we propose the following recommendations: First, the emphasis ought to be on enhancing the development of platform infrastructure within rural regions. Relative to urban regions, rural areas suffer from insufficient network infrastructure, limiting rural residents’ access to consuming green agricultural products through the Internet. Consequently, it is necessary to focus on enhancing rural platform infrastructure construction and make up for the shortcomings of platform economy development. We will accelerate the planning and implementation of 4G and 5G base station projects in rural regions, improve network coverage and data transmission speed, and ensure that residents can enjoy green agricultural products and services provided by the platform through the Internet. Second, innovative technologies will be applied in the platform to promote the construction of a platform economy in urban regions. Urban regions boast a high rate of Internet penetration, yet the link between the Internet platform and the consumer segment of green agricultural products remains distant. Consequently, green agricultural product companies and related platform companies should take the network resources as the basis; use the Internet as the medium to open up the network marketing channels of green agricultural products; and use communities, social networks, and other platforms as the two-way users to provide different communication channels, strengthen the brand-building of high-quality green agricultural products, and unlock the possibilities of green agricultural product consumption in urban regions. Third, fundamental literacy should be cultivated in residents to effectively participate in the platform economy. The government should grasp the characteristics of the regional economy, education, marketization level, and service industry development, clarify the development direction of the platform economy, and formulate reasonable policies to guide the standardized operation and healthy development of the platform economy. At the same time, the education level and basic literacy of residents should be improved to reduce the digital divide in the process of platform economy development and improve the acceptance and recognition of platform consumption by residents.