Internet Use, Subjective Well-Being, and Environmentally Friendly Practices in Rural China: An Empirical Analysis

Abstract

Expanding internet connectivity to rural areas requires a comprehensive understanding of the influence path among internet usage, subjective well-being (SWB), and environmentally friendly agricultural practices (EAPs) of farmers. This study aimed to analyze the direct and indirect effects of internet use by employing structural equation modeling. Farmers with improved internet access adopted more EAPs, resulting in output loss (EAPOs) and increased time investment (EAPTs). However, increased internet function utilization negatively affected EAPs involving additional expenses (EAPEs). Although SWB increased EAP engagement, income satisfaction could decrease EAPEs and EAPTs. The impact of internet use on SWB initially increased but later declined with increased internet usage, indirectly affecting EAPs. This may strengthen the positive total effect or alleviate the negative total effect. Young individuals, higher-income households, educated individuals, and members of the Communist Party of China (CPC) were more inclined to adopt EAPs, as they helped mitigate the adverse influence of negative online information and enhance overall well-being. Consequently, it is imperative to provide internet usage education and training, utilize more internet features, including e-commerce or microcredit, to incentivize EAP adoption, address and alleviate negative emotions from the internet promptly, prioritize providing education to young rural residents, and empower CPC members in advocating for sustainable practices.

1. Introduction

Agriculture is a major contributor to global ecosystem service degradation [1]. China, as a highly populated and heavily agricultural country, faces severe sustainability challenges [2]. To address the issue of agricultural nonpoint source pollution in rural areas caused by unsustainable agricultural production methods [3], adopting environmentally friendly agricultural practices (EAPs) is crucial. EAPs involve farming practices that increase the provision of agricultural ecosystem services [4], fostering positive interaction between humans and nature; these practices include slow and controlled fertilizer release, conservation tillage, crop rotation, and returning crop straw to the field [2]. However, this adoption is not only influenced by green production policies but also depends on the consciousness and attitude of farmers. Therefore, sociologists and psychologists should acknowledge the motivating factors to evaluate EAP adoption.

Subjective well-being (SWB), which refers to the subjective evaluation of the integrated life of an individual, is crucial in understanding EAPs and the quality of life of households [5]. According to the National Bureau of Statistics of China, the per capita disposable income of rural residents is expected to reach approximately CNY 20,133 in 2022 [6], indicating a significant improvement in material living standards of rural residents in recent years, which has improved the SWB of farmers. The relationship between SWB and pro-environmental behavior has been investigated [7,8,9]; however, there is limited research on the impact of the SWB of farmers on EAPs. Moreover, some farmers have recognized the environmental consequences of traditional production methods [10,11]. This realization, coupled with a discernible shift in their environmental attitudes and a propensity to adopt EAPs, highlights the need for academic investigation into how SWB influences the EAPs undertaken by residents of rural areas.

The spread of the internet from urban to rural areas has become more prevalent with the proliferation of smartphones and improved rural network infrastructure. In late 2020, the internet proliferation rate in rural China reached 55.9% [12]. Internet usage has facilitated communication between farmers and the outside world, encouraging participation in various aspects of life [13]. Accordingly, more research has been diverted from the impact of internet use on urban residents to that on rural residents [14,15]. However, although research on the relationship between internet use and EAPs in the daily lives of farmers has been extensive, the impact of internet use on EAPs has been overlooked.

Several studies have explored the impact of internet use on SWB. The influence of internet usage on the well-being of residents is intricate and uncertain, as rapid and convenient access to vast amounts of information can have positive and negative effects [16,17,18]. Thus, methods to establish a positive interactive relationship between internet use and well-being have been explored. However, there have been few studies on internet use among rural farmers and the intermediary effects of SWB on internet use and EAPs.

To sum up, previous research has mainly focused on surface-level effects without scientifically explaining the underlying mechanisms and pathways between internet use, SWB, and EAPs. Therefore, in this study, we aimed to refine existing research by establishing a systematic and comprehensive analytical framework that explores the interrelationship between internet use, SWB, and EAPs from different dimensions in the agricultural activities of farmers in China. Moreover, we seek to answer the following questions: Under what circumstances will internet use positively or negatively impact the SWB and EAP adoption of farmers? How does internet use shape EAPs through SWB?

This study has significant theoretical and policy implications. First, to our knowledge, it is the first attempt to reveal the internal mechanisms among internet use, SWB, and EAPs, providing insights into the influence of the digital era on EAPs among farmers. Second, this study contributes to existing theories on negative bias and environmental psychology by highlighting the negative bias caused by internet use on EAPs. Third, this study identifies SWB as an important intermediary factor between internet use and EAPs. Finally, this study employed the nationally representative household-level data from the China Rural Revitalization Survey (CRRS) conducted by the Rural Development Institute of the Chinese Academy of Social Sciences, encompassing 10 provinces and 3833 rural households, to provide large-scale policy implications for government administrators. Here, rural households are defined as households living in rural areas, usually one family [19].

2. Theoretical Framework and Hypotheses

2.1. Internet Use on EAPs in Agriculture Activities

The rapid development of the internet in rural China has brought fundamental changes to information communication technology and the production activities of farmers. This has provided convenient conditions for farmers, changed information collection methods, and improved communication with the outside world, consequently affecting EAPs. Gong et al. [20] found that expanding internet access (IA) through environmental policies can encourage EAPs. Liu et al. [15] highlighted that, in contrast to traditional media, information on the internet is more open, complex, and accessible rather than solely positive and impartial. The extensive use of and potential addiction to the internet may have subtle impacts on the environmental attitudes and EAPs of individuals [21]. Based on this premise, two variables, including IA and internet function utilization (IFU), were selected to measure internet usage among farmers.

The EAPs adopted by farmers encompass various aspects, such as preserving natural habitats and biodiversity, conserving soil and water resources, preventing environmental pollution, and reducing pressure on land and energy resources [22,23]. Adopting EAPs often requires farmers to incur private costs [24], such as reduced output, additional expenses, or increased time investment. Moreover, it is important to consider the different influences of these private costs on the decision-making processes of farmers. Consequently, their respective impact mechanisms are evaluated individually.

Thus, we propose the following hypotheses as the first part of our research framework:

H1a. 

Farmers with improved IA adopt more EAPs resulting in output loss (EAPOs).

H1b. 

Farmers with improved IA adopt more EAPs involving additional expenses (EAPEs).

H1c. 

Farmers with improved IA adopt more EAPs requiring more time investment (EAPTs).

H2a. 

Farmers with greater IFU adopt more EAPOs.

H2b. 

Farmers with greater IFU adopt more EAPEs.

H2c. 

Farmers with greater IFU adopt more EAPTs.

2.2. SWB on EAPs in Agriculture Activities

Improved SWB is an important stimulus for EAP adoption [8,25,26]. SWB comprises evaluative, hedonic, and eudaimonic components [27]. Based on previous studies, we utilized general life satisfaction, income satisfaction, living environment satisfaction, and happiness as measures of SWB to identify the specific aspects of SWB that affect EAPs.

Thus, we propose the following hypotheses as the second part of our research framework:

H3a. 

Farmers with higher general life satisfaction adopt more EAPOs.

H3b. 

Farmers with higher general life satisfaction adopt more EAPEs.

H3c. 

Farmers with higher general life satisfaction adopt more EAPTs.

H4a. 

Farmers with higher income satisfaction adopt more EAPOs.

H4b. 

Farmers with higher income satisfaction adopt more EAPEs.

H4c. 

Farmers with higher income satisfaction adopt more EAPTs.

H5a. 

Farmers with higher living environment satisfaction adopt more EAPOs.

H5b. 

Farmers with higher living environment satisfaction adopt more EAPEs.

H5c. 

Farmers with higher living environment satisfaction adopt more EAPTs.

H6a. 

Farmers with higher levels of happiness adopt more EAPOs.

H6b. 

Farmers with higher levels of happiness adopt more EAPEs.

H6c. 

Farmers with higher levels of happiness adopt more EAPTs.

2.3. Internet Use on SWB and Intermediary Effect

Given the increasingly significant role of the internet in the daily lives and productivity of farmers, understanding its influence on their well-being is crucial. Internet use yields direct utility and economic returns, potentially increasing life satisfaction and subtly influencing lifestyle habits and values [15,20]. Nevertheless, problematic internet use might also have detrimental effects, including addiction, social isolation, and the negative impact of comparison psychology, resulting in an indirect negative effect on SWB [17,28]. Gollan et al. [29] highlighted the human tendency to focus more on negative news than on positive news. However, SWB is contingent upon the motivations behind internet use [30]. In addition, few studies have explored the intermediary effect of SWB on internet use and EAPs.

Thus, we propose the following hypotheses as the final part of our research framework.

H7a: 

Farmers with improved IA demonstrate higher general life satisfaction.

H7b: 

Farmers with improved IA demonstrate higher income satisfaction.

H7c: 

Farmers with improved IA demonstrate higher living environment satisfaction.

H7d: 

Farmers with improved IA demonstrate higher levels of happiness.

H8a: 

Farmers with more IFU demonstrate higher general life satisfaction.

H8b: 

Farmers with more IFU demonstrate higher income satisfaction.

H8c: 

Farmers with more IFU demonstrate higher living environment satisfaction.

H8d: 

Farmers with more IFU demonstrate higher levels of happiness.

H9: 

Farmer SWB has an intermediary effect between internet use and EAPs.

3. Data and Methods

3.1. Research Population and Sample

To our knowledge, several official Chinese surveys, including the Chinese General Social Survey (CGSS), China Family Panel Studies (CFPS), and CRRS, provide data on EAPs. However, only the CRRS encompasses the internet use and EAPs of farmers. CRRS is a nationwide, large-scale rural tracking survey conducted in 2020 by the Chinese Academy of Social Sciences, one of China’s most authoritative research institutions. It covers 10 provinces, 50 counties (cities), and 156 townships (towns), aiming to comprehensively, objectively, and accurately grasp the basic situation of rural areas and provide data support for academic and policy research on rural revitalization. The internet use, SWB, and agricultural production activities of farmers are closely related to rural revitalization; therefore, we selected the CRRS data to conduct our research.

In total, 3833 households were interviewed, covering population and labor force, agricultural production, household income/expenditure, and welfare. Considering data availability, economic development levels, and natural geographical location conditions, the samples were obtained using an equidistant random method. One-third of all provinces were randomly selected from the eastern, central, western, and northeastern regions of China. Subsequently, five counties were randomly selected from each province, followed by the random selection of three townships from each county, two villages from each township, and 14 households from each village (two of which were optional). Detailed information on sampling methods and data analysis has been provided by the institution [31]. The sample distribution is shown in

Table 1. Sample collection of rural households.

Region	Name Of Provinces	Number of Counties	Number of Townships	Number of Villages	Number of Households
Eastern	Guangdong, Zhejiang, and Shandong Province	15	49	92	1152
Central	Anhui and Henan Province	10	30	60	735
Western	Guizhou, Sichuan, Shaanxi Province, and Ningxia Hui autonomous region	20	61	125	1573
Northeastern	Heilongjiang Province	5	16	31	373
Total	10 provinces	50	156	308	3833

.

3.2. Measures

The CRRS uses questionnaires to obtain information on the internet use, SWBs, and EAPs of rural households. As the original data format of the database is unsuitable for establishing a connection between internet use, SWB, and EAPs, we made adjustments by improving the questions and options involved and reorganizing the data (

Table 2. Questionnaire design and data description.

Variable	Question	Option	Mean	SD	Min	Max
Internet access	What are your internet devices?	0 = none; 1 = mobile phone; 2 = mobile phone/tablet/computer and other devices	1.2113	0.5906	0	2
	How is your network?	0 = very poor, often disconnected; 1 = occasionally disconnected; 2 = good	1.3174	0.7253	0	2
	Do you have difficulty using a smartphone?	0 = very difficult; 1 = a little difficult; 2 = no difficulty	1.4074	0.6948	0	2
	Can you access information online when you need to?	0 = no; 1 = general; 2 = yes	1.2452	0.8324	0	2
	Your average online time per day	0 = within 1 h; 1 = 1 to 4 h; 2 = more than 4 h	0.8478	0.6865	0	2
Internet function utilization	Do you use the following internet functions?	□ Social (such as WeChat, Weibo, etc.) □Entertainment (such as games, live broadcasts, and short videos) □ Learning and education and official business □ Paid use of network software or app □Electronic commerce (such as online live sales) □ Internet finance (such as Huabei)	2.0737	1.3085	0	6
SWBs	Your general satisfaction with life	1 = very dissatisfied; 2 = relatively dissatisfied; 3 = general; 4 = quite satisfied; 5 = very satisfied	4.0681	0.8481	1	5
	Your satisfaction with family income		3.5516	1.0561	1	5
	Your satisfaction with the living environment		4.0898	0.8341	1	5
	Your happiness	1 = very unhappy; 2 = relatively unhappy; 3 = general; 4 = quite happy; 5 = very happy	4.1491	0.8357	1	5
EAPs	Do you adopt the following EAPs?	□ Pesticide reduction □ Land rotation/fallow □ Avoid groundwater irrigation □ Chemical fertilizer reduction □ Treat straw in an environmentally friendly way (used as fertilizer, feed, fuel, etc.) □ Recycling pesticide packaging	2.1328	0.8014	0	6

).

First, we designed five questions using a three-point Likert scale to assess network equipment, network conditions, smartphone usability challenges, access to online information, and time spent online. These questions were assigned 0, 1, and 2 point values corresponding to different degrees or levels. The average score was used to evaluate IA. The results revealed that most respondents were adept at using smartphones and obtaining information. Furthermore, the network equipment and conditions were deemed satisfactory. However, respondents did not allocate significant amounts of time to daily internet usage.

Second, we asked the respondents about their use of specific internet functions, such as socializing, entertainment, office/learning/education, software/app payment, e-commerce, and finance; 1 point was assigned for each function used, and 0 points were assigned if the function was not used. The total score was used to evaluate IFU. The results revealed that the respondents only used two functions on average; most of them only used basic functions, such as socializing and entertainment. Next, a five-point Likert scale was used to characterize the SWB of the respondents. Most respondents exhibited relatively higher levels of happiness and general satisfaction but lower satisfaction levels with income.

Finally, regarding EAPs, we inquired about the adoption of various practices, including pesticide reduction, land rotation/fallowing, fertilizer reduction, avoiding underground water irrigation, treating straw in an environmentally friendly manner, and recycling pesticide packaging. Each adopter received 1 point, and each non-adopter received 0 points. The first two practices were classified as EAPOs, as reducing cultivated area and using pesticides can result in reduced crop yields; the third and fourth practices were classified as EAPEs, as implementing irrigation measures and using organic fertilizer involve additional costs compared to groundwater irrigation and chemical fertilizers, respectively; the last two practices were classified as EAPTs, as recycling agricultural waste requires more time or labor force. The results revealed that the respondents only adopted two of these practices on average, demonstrating relatively low levels of EAPs.

3.3. Statistical Analysis

All direct and indirect paths of the theoretical model can be expressed through the following set of recursive equations:

$$\mathit{S}\mathit{W}{\mathit{B}}_{\mathit{i}}={\gamma }_0+{\gamma }_{1}I{A}_i+{\gamma }_{2}IF{U}_i+{\mathit{\gamma }}_{\mathbf{3}}{\mathit{C}}_{\mathit{i}}+e_i$$

$$\mathit{E}\mathit{A}{\mathit{P}}_{\mathit{i}}={\beta }_0+{\beta }_{1}I{A}_i+{\beta }_{2}IF{U}_i+{\mathit{\beta }}_{\mathit{3}}\mathit{S}\mathit{W}{\mathit{B}}_{\mathit{i}}+{\mathit{\beta }}_{\mathbf{4}}{\mathit{C}}_{\mathit{i}}+{\epsilon }_i$$

where $\mathit{S}\mathit{W}{\mathit{B}}_{\mathit{i}}$ represents general life satisfaction, income satisfaction, living environment satisfaction, and happiness level of farmers; $I{A}_i$, and $IF{U}_i$ represent the IA and IFU of farmers, respectively; $\mathit{E}\mathit{A}{\mathit{P}}_{\mathit{i}}$, represents EAPOs, EAPEs, and EAPTs; $e_i$ and ${\epsilon }_i$ are errors; and ${\mathit{C}}_{\mathit{i}}$ represents a series of control variables. Based on previous literature, the sex, age, education, marital status, and political status of the respondents and family income were selected [32,33].

Structural equation modeling (SEM), which can obtain the direct, indirect, or total effects of independent variables on dependent variables [34], was employed in this study. SEM provides a better fit between the estimation results and actual data than general methods. The entire path system and mediation effect were estimated via SEM syntax using the StataCorp Stata MP 16.0 software. For parameter estimation, classification variables were treated as continuous variables based on the Central Limit Theorem, considering large sample sizes. Maximum likelihood (ML) estimation, a progressive and effective approach [35], was also used. Although the questionnaire data had some missing values, the full information ML option in STATA was used for estimation. The observed information matrix method, which is based on the principle of asymptotic ML, was used to calculate the standard error. The method provides valid standard error when the error term follows an independent normal distribution. Additionally, the standard error obtained is robust when the distribution is symmetric or approximately normal.

4. Results and Discussion

4.1. Personal and Familial Characteristics

Given that in the decision-making process of farmers, the behavioral intention of the head of the household directly represents the whole family and agricultural activities are predominantly led by the head of the household, we selected the basic personal characteristics of the head of the household and household income to represent the typicality of the sample (

Table 3. Personal and familial characteristics.

Index	Option	Number	Proportion	Index	Option	Number	Proportion
Sex	Male	3577	93.32%	Age 1	18–30	9	0.24%
	Female	256	6.68%		31–40	331	8.77%
Marital Status	Married	3516	91.73%		41–50	961	25.47%
	Unmarried/Divorced/ Widowed	317	8.27%		51–60	1227	32.52%
Education1	Illiteracy	327	8.53%		>60	1245	33.00%
	Primary School	1179	30.77%	Household Income (yuan/year)	<20,000	960	25.05%
	Middle School	1744	45.51%		(20,000–40,000)	745	19.44%
	High School	433	11.30%		(40,000–60,000)	556	14.51%
	Bachelor’s Degree or above	149	3.89%		(60,000–80,000)	408	10.64%
Political Status1	Member of The CPC or Democratic Party	913	23.83%		(80,000–100,000)	296	7.72%
	Nonparty Personage	2918	76.17%		>100,000	868	22.65%

¹ Some indicator statistics are missing.

). The results showed that the heads of most farming households were male and married. Over 65% of them were older than 50 years, reflecting the prevalence of the aging rural population in China. Their education level was generally low, mainly around the junior and senior high school levels, and approximately one-tenth were illiterate. Furthermore, the income gap of rural households was relatively large: 25.05% and 22.65% had an annual income of CNY ˂ 20,000 and CNY ˃ 100,000, respectively. The basic living conditions of the sample households conform to the reality of rural families in China.

4.2. Path Analysis Results

The path analysis results showed that not every path had a significant effect. H1b, H2a, H2c, H3a, H3c, H5a, H5b, H6a and H6b could not be verified. Figure 1 depicts the paths with significant impacts and their normalized correlation coefficients.

The impact of internet use on EAPs was significant; however, its direction differed. Farmers with better IA adopted more EAPOs and EAPTs, validating H1a and H1c at significance levels of 5% and 1%, respectively. Improved rural network conditions prompted farmers to strengthen their communication with the outside world and embrace positive environmental protection concepts, thus encouraging them to make trade-offs regarding production output or spending more time adopting EAPs. However, farmers with greater IFU adopted decreased EAPEs; H2b was alternatively verified at a significance level of 10%. IA is crucial in facilitating information exchange between urban and rural areas. As farmers engage with more internet functions, their ability to use the internet and intensity of doing so both increase. In addition to government and official information, they may be exposed to negative information from the internet, such as socioeconomic disparities, developmental imbalances between urban and rural areas, and the imbalance between economically developed and ecological protection areas. Owing to the consumption of captivating information, negative news items tend to capture their attention easily. Consequently, this fosters a perception among rural internet users that they bear the financial burdens of rural environmental protection, whereas the ultimate beneficiaries are urban residents. This perception significantly reduces their willingness to engage in protective endeavors. Moreover, farmers that willingly engage in internet-based financing, e-commerce, learning, education, and other activities often exhibit larger scale and efficiency in agricultural production. Therefore, they become more cost-sensitive and less inclined to increase their expenditures on environmental protection.

SWB had a significant positive impact on EAPs; however, the influence on EAPs varied depending on the specific aspects of well-being. Farmers with higher income satisfaction adopted more EAPOs, validating H4a. Considering the lower returns on agricultural products in China, farmers satisfied with their income are more engaged in agricultural-scale operations and are highly dependent on land; thus, they are willing to trade off the output to adopt EAPs and protect cultivated land in the long term. Farmers with higher general life satisfaction adopted more EAPEs, validating H3b. Higher satisfaction with life and happiness infers a more optimistic attitude and a higher likelihood to accept the positive influences of society, fostering a willingness to contribute to improving the ecological welfare of the entire society. Moreover, the cost of adopting EAPs is relatively acceptable as it garners societal respect. Contrary to expectations, farmers with higher income satisfaction adopted fewer EAPEs, as H4b was alternatively validated at a 1% significance level. This outcome suggests that farmers whose livelihoods depend on agricultural activities are highly sensitive to production expenses, leading to reluctance to adopt higher-cost EAPs. Conversely, farmers with higher satisfaction regarding their living environment and higher happiness levels exhibited a stronger inclination towards adopting EAPTs, with H5c and H6c being validated at a significance level of 1%. Considering the seasonal nature of agricultural production, farmers typically have more flexible production schedules, resulting in a relatively lower opportunity cost of time. Consequently, farmers with a higher level of well-being are more likely to allocate time to adopting EAPs. Similarly, farmers with higher income satisfaction adopted fewer EAPTs, as H5b obtained alternative verification at a 5% significance level. This can be attributed to the substantial amount of labor input required by farmers engaged in large-scale operations, making them hesitant to allocate additional time for EAPs that would lead to escalated labor costs.

Internet use had a significant impact on SWB. Farmers with better IA demonstrated higher SWB; H7a, H7b, H7c, and H7d were validated at a significance level of 1%. Moreover, farmers with greater IFU demonstrated higher SWB; H8a, H8b, H8c, and H8d were validated at a significance level of 1%. The impact of internet use on SWB initially increased, followed by a subsequent decline as internet usage increased. Rural farmers have benefited from the expanding network infrastructure, experiencing increased convenience in production and lifestyle. The internet has provided them with efficient information access and reshaped their environmental values, thus improving their SWB regarding life satisfaction, income, and living environment, indicating a positive correlation between the internet and happiness. However, as the internet proficiency and usage of farmers grow, they transition from passive information recipients to active information seekers, which exposes them to the complexities of an unrestricted and open online environment where personal values are susceptible to uncertainties. Given the low level of education and limited media literacy, the negative information on the internet is amplified, potentially leading to feelings of depression, anxiety, and a negative worldview. While the internet can enhance social and material connections, it can also increase individual conflicts, consequently decreasing life satisfaction. Additionally, social comparison on the internet can contribute to feelings of depression among users. These factors may lead to a decline in SWB. Hence, it is crucial to recognize and address the negative effects of internet use.

4.3. Results of Indirect Effects

In SEM, the direct effect is the direct influence of one variable on another, whereas the indirect effect is the influence of one variable on another through an intermediary variable. If the direct effect is less than the indirect effect, the intermediary variable plays an important role and requires more consideration [34]. The indirect effects of the model were calculated with SEM syntax using the StataCorp Stata MP 16.0 software, and the results are presented in

Table 4. Direct and indirect effects in structural equation modeling.

Path	Direct Effects	Indirect Effects	Total Effects
IA → EAPOs	0.012 **	0.001	0.013 ***
IA → EAPEs	−0.002	−0.003 *	−0.005
IA → EAPTs	0.022 ***	0.006 ***	0.028 ***
IFU → EAPOs	0.008	−0.001	0.007
IFU → EAPEs	−0.017 *	0.003 **	−0.014
IFU → EAPTs	0.001	−0.004 **	−0.003

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

.

SWB had different intermediary effects on internet use and different types of EAPs.

IA had a significant positive and indirect effect on EAPTs at a 1% significance level, causing an increase in the total effect. While the direct and total effects on EAPOs were significant, the indirect effects were less conclusive. IA exerted a notable negative and indirect impact on EAPEs, causing an overall decrease in the effect. The lack of significance in both the direct and total effect can be attributed to the weak nature of the relationship, where the weak real effect may be perceived as a random error, making it untestable. Farmers generally invested less time; therefore, improving SWB through better IA could facilitate the adoption of EAPTs. However, regarding agricultural output and costs, the intermediary role of the well-being perception was unremarkable and even negative. This indicates that farmers accepted the information bias on the internet. The EAPs that are predominantly promoted online prioritize actions that emphasize communal benefits over additional financial expenses. Examples include water conservation and garbage classification. Therefore, although an improvement in well-being increases the motivation of farmers to adopt EAPs, they tend to contribute to environmental protection without incurring private costs.

The total effect of IFU on EAPEs and EAPTs was negative; however, the influence mechanism of the intermediary effect differed. Regarding EAPEs, SWB had a positive intermediary effect. Increased general satisfaction helped reduce the negative effects of the internet and encouraged farmers to contribute more, thus offsetting the direct effect to a certain extent. Regarding EAPTs, the negative impact of IFU on SWB, specifically on income satisfaction, indirectly resulted in a negative total effect. This suggests that extensive internet usage can indirectly encourage farmers with lower dependence on agricultural income to contribute more towards environmental protection by fostering a stronger sense of environmental responsibility and concern.

4.4. Influence of Control Variables

Drawing upon the knowledge and research findings previously mentioned in Section 3.3, the personal characteristics of the head of the household and the family income play a crucial role in decision-making related to EAPs (

Table 5. Path analysis results of structural equation modeling.

	Internet Use		SWB				EAPs
	Internet Access	Internet Function Utilization	General Life Satisfaction	Income Satisfaction	Living Environment Satisfaction	Happiness	Output Loss	Cost Money	Spend Time
Sex	0.013	−0.006	−0.018	−0.008	−0.020	0.002	−0.025	−0.012	−0.002
	(0.76)	(−0.38)	(−1.13)	(−0.51)	(−1.22)	(0.12)	(−1.5)	(−0.44)	(−0.07)
Age	−0.242 ***	−0.339 ***	0.124 ***	0.119 ***	0.065 ***	0.123 ***	0.016 ***	0.018	0.015
	(−13.81)	(−21.48)	(6.92)	(6.69)	(3.57)	(6.88)	(0.87)	(0.7)	(0.56)
Marital Status	0.000	0.006	−0.012	0.004	0.026	−0.039	−0.004	0.026	−0.001
	(0.01)	(0.38)	(−0.73)	(0.23)	(1.61)	(−2.36)	(−0.23)	(1.02)	(−0.03)
Education	0.164 ***	0.123 ***	0.015	−0.008	−0.022	0.028	0.024	0.010	0.031
	(9.49)	(7.24)	(0.87)	(−0.44)	(−1.27)	(1.64)	(1.31)	(0.37)	(1.15)
Political Status	0.078 ***	0.050 ***	0.061 ***	0.104 ***	0.019	0.053 ***	−0.013	−0.030	0.012
	(4.74)	(3.12)	(3.74)	(6.4)	(1.15)	(3.26)	(−0.74)	(−1.25)	(0.51)
Family Income	0.202 ***	0.175 ***	0.057 ***	0.118 ***	0.053 ***	0.075 ***	0.009	0.070 ***	0.014
	(13.08)	(11.38)	(3.46)	(7.29)	(3.22)	(4.59)	(0.55)	(2.56)	(0.52)
Constants	3.321	2.718	3.617	2.121	3.990	3.677	0.503	0.653	1.623
	(23.74)	(21.18)	(22.43)	(13.77)	(24.58)	(22.83)	(2.88)	(2.67)	(6.37)

*** indicate significance at the 1% levels.

).

The main factors affecting internet use included age, education, the political status of the head of the household, and household income, consistent with the academic consensus and the reality in rural China. Younger and more educated farmers who are members of the Communist Party of China (CPC) have more internet access and utilization. However, the elderly have gradually narrowed the gap in internet use with the younger generation, which could promote the adoption of EAPs. Additionally, the higher the household income, the more likely it was for internet usage to increase. Therefore, with the development of China’s rural revitalization and improvement in household income levels, the internet could also play a non-negligible role in production activities and daily life. Thus, it is crucial to recognize and address the negative impact of the internet on EAPs.

Factors affecting SWB included age, the political status of the head of the household, and family income. Unlike internet use, age had a positive impact on SWB, whereas education had no significant impact. Young people in rural areas experience significantly higher life pressures and have greater aspirations than the elderly, leading to a decline in their perception of SWB. Therefore, approaches to promoting the adoption of EAPs should focus on improving the perception of well-being among young people. People at all stages of education face different living conditions and have more complex emotional cognitions that are difficult to describe with linear causality. Income is a crucial indicator of family living standards, and those with higher incomes tend to have higher well-being. However, rural high-income families often have a higher proportion of non-agricultural employment, and the final impact of EAPs on agriculture requires further clarification.

Control variables had the least impact on EAPs, indicating that the production decisions of farmers are affected by multiple factors, thereby reducing the decisive impact of individual and family basic characteristics.

5. Conclusions

With the increasing use of the internet, the impact of internet use on EAP adoption was not entirely positive, emphasizing the importance of being mindful of potential negative impacts as farmers transition from passive recipients of information to active searchers. Prioritizing education and training on internet usage over superficial propaganda is crucial. Moreover, different types of EAPs were affected differently by internet usage. Farmers that are satisfied with their income may not be concerned about the output losses caused by EAPs, whereas those engaged with larger-scale agricultural production are cost-sensitive, prioritizing economic considerations over environmental protection. For the latter, leveraging internet tools, such as e-commerce or microcredit, may serve as incentives for their adoption of EAPs.

Beyond economic considerations, the subjective perception of farmers is crucial to their behavioral intentions and decision-making processes. The impact of internet use on SWB initially increased, followed by a subsequent decline as internet usage increased, indirectly affecting EAP adoption. Therefore, policymakers should not solely focus on improving internet infrastructure but also prioritize the happiness and gain of farmers and promptly identify and alleviate the negative influence of the internet.

Finally, with changes in the age and educational structure of the rural population, the new generation of farmers may experience important changes in their perception of the internet, well-being, and decision-making regarding EAPs. Improving income and education levels can mitigate the influence of negative internet information, enhance well-being, and foster greater adoption of EAPs. Additionally, it is crucial to empower members of the CPC to serve as vanguards in promoting sustainable practices.

This study had some limitations that should be addressed in future studies. For example, the indicators and data from public databases often overlook emotional cognition when measuring SWB. Our model achieved a perfect fit, as its goodness of fit (GFI), modified GFI index (AGFI), and Parsimony GFI all yielded values equal to 1, indicating no need for further modification. However, this was due to the lack of redundant degrees of freedom and does not imply that the model was perfect. Building a more robust model using continuous variables would be necessary to validate our results further. In addition, the data were obtained before the COVID-19 pandemic. Future research should investigate the impact of the COVID-19 pandemic on the relationship between internet use, SWB, and EAPs.