Valor Biopesticida
Valor Biopesticida
Valor Biopesticida
Environmental Research
and Public Health
Article
Willingness and Behaviors of Farmers’ Green
Disposal of Pesticide Packaging Waste in Henan,
China: A Perceived Value Formation
Mechanism Perspective
Mingyue Li, Jingjing Wang, Kai Chen * and Lianbei Wu
School of Economics and Management, Beijing Forestry University, Beijing 100083, China;
limingyue_2019@bjfu.edu.cn (M.L.); wangjingj97@bjfu.edu.cn (J.W.); wulianbei@bjfu.edu.cn (L.W.)
* Correspondence: chenkai3@bjfu.edu.cn; Tel.: +86-139-1038-5159
Received: 23 April 2020; Accepted: 22 May 2020; Published: 26 May 2020
Abstract: Environmental pollution as a result of the improper disposal of pesticide packaging wastes
(PPWs) has posed serious harm to groundwater, soil and public health. However, few studies focused
on PPWs green disposal willingness and behaviors of farmers from the perspective of perceived
value. Based on the first-hand data, collected from 635 farmers of grain-producing counties in Henan
province of China, through the questionnaire survey method, this paper adopted a structural equation
model (SEM) to empirically explore the formation mechanism of perceived value on PPWs green
disposal, and green disposal willingness and behaviors were further in-depth investigated. The results
showed that the action of farmers’ green disposal of PPWs followed the causal relationship, whereby
perceived value→behavioral willingness→behavioral performance, and farmers’ perceived value
came from the comprehensive tradeoff and comparison between perceived benefits and perceived
risks. Meanwhile, the perceived benefits and perceived risks could have significant effects on green
disposal willingness and behaviors directly and indirectly, among which perceived benefits (0.478)
had the greatest positive total effects on the willingness, and perceived risks (−0.362) had the greatest
negative total effects on the behaviors. Interestingly, there existed inconsistence between farmers’ green
disposal willingness and behaviors. When faced with the choice of PPWs green disposal, the farmers
were generally risk averse, which resulted in them being more inclined to take conservative behaviors
driven by the profit maximization, and even showed the “powerless” state with willingness but no
actual action.
Keywords: perceived benefits; perceived risks; green disposal willingness; green disposal behaviors;
structural equation model (SEM)
1. Introduction
Pesticides play a positive role in ensuring the increase of agricultural output and farmers’ income,
as well as the effective supply of agricultural products, and have an important inputs in agriculture [1,2].
China’s total agricultural output has increased, and pesticides have played an irreplaceable role in
maintaining its high yield [3]. Nonetheless, farmers tend to overuse pesticides to better control the crop
diseases and pests [4,5], which were accompanied by the improper disposal of amounts of pesticide
packaging waste (PPWs) resulting in environmental pollution that had become one of the outstanding
problems of agricultural non-point source pollution in China [6,7]. PPWs mainly refer to the packaging
materials directly in contact with pesticides discarded after use in the agriculture production, including
bottles, cans, barrels and bags made of plastic, glass, metal, paper and other materials [8]. In order
to control the pollution caused by PPWs, the State Council of China promulgated the newly revised
Int. J. Environ. Res. Public Health 2020, 17, 3753; doi:10.3390/ijerph17113753 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2020, 17, 3753 2 of 18
Regulations on Pesticide Management (RPM) in 2017 to clarify the responsibility subjects and important
obligations of recycling PPWs for the first time. Subsequently, pilot action of PPWs recycling was
carried out in Zhejiang, Shandong, Henan and other provinces in succession focusing on ecological
environment quality and agricultural product supply safety.
However, PPWs recycling mechanism in China is still in the exploration stage, and farmers
generally have a low awareness of recycling policies and measures, which weakens the implementation
effect of relevant policies to some extent [9]. Meanwhile, the deterioration of rural environment and
agricultural non-point source pollution were aggravated due to the lack of waste disposal facilities,
relatively weak environmental awareness of farmers and non-standard disposal methods and inherent
habits in the vast rural areas [10,11]. It was reported that China needs 10.4 billion pesticide packages
per year, and 3.2 billion of them are discarded randomly with a total weight of over 100,000 tons.
Nonetheless, residual pesticide in these packages caused irreversible harm to underground water, soil
structures, environmental organisms and human health [12–15].
The impact of PPWs on ecological environment was an important research hotspot, and it was
generally assumed that the green disposal of PPWs is not only related to the realization of agricultural
ecological value, but also affects the health of rural resident [16,17]. The “green disposal” refers to a
behavioral pattern of handling the pesticide packaging waste by adhering to the development concept
of “green”. In addition, ‘green’ in “green disposal” is quite similar to the concept of “sustainability” in
that it means reducing environmental pollution, saving resources and promoting health. Increasingly,
researchers have substantively addressed PPWs disposal behaviors, and basically concluded that
farmers often did not send PPWs to government recycling centers or pesticide supply and marketing
centers [18]. Instead, PPWs were usually thrown into the fields and ditches [19], or burned in the open,
buried in the farm [20] or placed the general rubbish [21], this kind of inappropriate disposal behaviors
commonly happened in the developing countries [14,22].
To explore the underlying causes of above behaviors, researchers have analyzed the key
factors affecting farmers’ behaviors of PPWs disposal. Researches showed that farmers’ behavior
decision-making of PPWs disposal was the results of multiple factors including individual characteristics
(age, education level, marital status) [23,24], family endowment (quantity of labor force, arable land
area, farming experience) [22], social responsibility (satisfaction degree of farmers on agricultural
activities) [25], geographical location (distance between village and the pesticides service center or the
city) [26], and cognition characteristics (knowledge of pesticides, awareness of risks) [27]. These have
all had a significant impact on PPWs disposal behaviors. In addition, Huang et al. [28] sorted out the
mature recycling models of PPWs, and believed that the government-led superfund system for pollution
control in the United States and the market-oriented green point waste recycling management system
in Germany were representative for now. Nevertheless, Li and Huang [8] explored the recycling and
utilization mechanism of PPWs from the perspective of reverse logistics, as well as the positive impacts
of the reverse logistics mechanisms on the ecological and social benefits. Geographic information
system (GIS) technology was used by the researcher to assess the generation of solid waste [29].
There are still some aspects of the above findings that need further investigation. First, the existing
research mainly discussed the factors influencing farmers’ PPWs disposal behaviors from the angle
of demographic characteristics [22,24], environmental cognitive characteristics [26,27], social system
characteristics [9,28], etc. However, few of them introduced psychological factors, such as perceived
value to investigate the psychological decision-making mechanism of farmers’ PPWs disposal.
According to relevant studies, attitude is the primary factor influencing their behavioral willingness [30],
while perceived value is the most direct reason for the formation of behavioral attitude [31].
Farmers’ green disposal behaviors of PPWs largely depend on their perceived value. Therefore,
the research explored farmers’ disposal willingness of PPWs from the perspective of perceived value,
which clarified the psychological mechanism and behavioral logic of the farmers’ green disposal of
PPWs, and this significant in standardizing farmers on the PPWs green disposal. Second, previous
studies mostly used discrete selection models such as Logit [26] or Probit [11] to analyze the direct
Int. J. Environ. Res. Public Health 2020, 17, 3753 3 of 18
influence of each independent explanatory variable on the farmers’ disposal behaviors of PPWs.
Nevertheless, few studies have adopted a structure equation model (SEM) to deeply explore the action
path and internal mechanism of each influencing factor. The SEM model [30,32,33] was used to study
the social psychological mechanism behind farmers’ willingness and behaviors to PPWs green disposal,
which not only identified the factors hindering farmers’ willingness, but also clarified the mechanism
involved in promoting farmers’ behaviors. In addition, there are few studies targeting the specific field
of farmers’ willingness and behaviors to PPWs green disposal.
In view of the above analysis, the main objective of this study was to draw lessons from theory of
perceived value, introduced the psychological variables, utilized the survey data of 635 farmers from the
major grain-producing counties of Henan province of China and adopted the SEM model to investigate
farmers’ willingness and behaviors of PPWs green disposal. The specific purposes of this study are to;
(i) validate the formation mechanism of farmers’ perceived value in the PPWs green disposal; (ii) further
investigate the effects of farmers’ perceived value on PPWs green disposal behaviors. These would
contribute to provide references for the government to identify the farmers’ PPWs green disposal
behavior characteristics and formulate relevant policies to control the agricultural pollution.
Figure1.1. Theoretical
Figure Theoretical Model.
Model.
Hypothesis 3 (H3). Perceived value has a significantly positive impact on farmers’ green disposal willingness.
Hypothesis 4 (H4). Perceived benefits have a significantly positive impact on farmers’ green disposal willingness.
Hypothesis 5 (H5). Perceived risks have a significantly negative impact on farmers’ green disposal willingness.
Hypothesis 6 (H6). Perceived value has a significantly positive impact on farmers’ green disposal behaviors.
Hypothesis 7 (H7). Perceived benefits have a significantly positive impact on farmers’ green disposal behaviors.
Hypothesis 8 (H8). Perceived risks have a significantly negative impact on farmers’ green disposal behaviors.
Hypothesis 9 (H9). Green disposal willingness has a significantly positive impact on farmers’ green disposal behaviors.
research areas, according to the super grain-producing counties, regular grain-producing counties,
and provincial grain-producing counties published by the financial department of Henan province
in December 2018 [57]. This study extracted six major grain-producing counties including two super
grain-producing counties, three regular grain-producing counties and one provincial grain-producing
county. Among them, the super major grain-producing counties were the Huaxian county and Xiayi
Int. J. Environ. Res. Public Health 2020, 17, x 6 of 18
county; regular major grain-producing counties were Lankao county, Luyi county and Weihui county;
provincial
and Weihuimajorcounty;
grain-producing
provincial majorcounty was Boai county.
grain-producing countyThe
wasselected counties
Boai county. can well
The selected reflect the
counties
situation
can wellof agricultural production,
reflect the situation ecologicalproduction,
of agricultural protectionecological
and others in Henan
protection andprovince.
others in The
Henansample
area is shown The
province. in Figure
sample2.area is shown in Figure 2.
Figure 2. Map
Figure ofof
2. Map sixsixmajor
majorgrain-producing countiesselected
grain-producing counties selected in this
in this study.
study.
According
Accordingto the sampling
to the sampling principle
principlestated
stated by Sharafietetal.al.(2018)
by Sharafi (2018)[58][58]
andand Sharifzadeh
Sharifzadeh et al. et al.
(2019)(2019)
[59],[59],
the multi-stage
the multi-stage sampling
samplingmethod
method was adoptedininorder
was adopted order to to ensure
ensure thethe survey
survey quality
quality as as
follows: Firstly, different sample townships (towns) were selected from
follows: Firstly, different sample townships (towns) were selected from each sample county according each sample county
to theaccording
economic to the economicand
conditions conditions and and
distance, distance, andof
a total a total of 12 townships
12 townships (towns)
(towns) were
were selected;Then,
selected;
different sample villages from each sample township (town) were further selected, anda atotal
Then, different sample villages from each sample township (town) were further selected, and total of
of 24 administrative villages were chosen (the sample size of selected villages was determined
24 administrative villages were chosen (the sample size of selected villages was determined according
according to the proportion of the total number of grain farmers in each village); Finally, 25–30
to the proportion of the total number of grain farmers in each village); Finally, 25–30 farmers were
farmers were selected from each sample village according to the number of households and
selected from each
population statussample village
(coordinate according
surveys to the
with each number
village of households
committee member to and ensurepopulation
that samplestatus
(coordinate surveys with each village committee
farmers were willing to participate in the project). member to ensure that sample farmers were willing
to participate in the project).
Considering the cultural level and cognitive ability of the farmers, semi-structured household
Considering
interview was the cultural
adopted level
as the andmode,
survey cognitive ability
and the of the farmers,
questionnaires semi-structured
were completed household
in the form of
“question
interview wasand answer”
adopted asbythethesurvey
investigators
mode,uniformly
and the trained by the research
questionnaires were group. A totalinofthe
completed 660form
questionnaires
of “question were issued,
and answer” by theand investigators
635 effective questionnaires were eventually
uniformly trained collected group.
by the research after deleting
A total of
questionnaires that were invalid and missing key variables. The effective questionnaires
660 questionnaires were issued, and 635 effective questionnaires were eventually collected after deleting was 96.21%.
questionnaires that were invalid and missing key variables. The effective questionnaires was 96.21%.
3.2. Measurement
3.2. Measurement
The scale consisting of 21 measurement items was developed based on the TPV, the design
concept of questionnaires in relevant fields [3,60], the semi-structured household interviews and the
The scale
actual consisting
situation of theofPPWs
21 measurement items
disposal in the was developed
research area. Likertbased on scale
5-point the TPV,
was the design
used concept
for each
of questionnaires in relevant fields [3,60], the semi-structured household interviews and the
measurement item, ranging from “strongly disagree” to “strongly agree” with values of 1, 2, 3, 4 and actual
5. In this research, a total of 5 variables were measured, all of which were latent variables and
measured by multi-item scales. Table 1 presents all the variables and measurement items.
Int. J. Environ. Res. Public Health 2020, 17, 3753 7 of 18
situation of the PPWs disposal in the research area. Likert 5-point scale was used for each measurement
item, ranging from “strongly disagree” to “strongly agree” with values of 1, 2, 3, 4 and 5. In this
research, a total of 5 variables were measured, all of which were latent variables and measured by
multi-item scales. Table 1 presents all the variables and measurement items.
It is worth noting that this study refined the measurement items of all variables in three steps
in order to improve the measurement accuracy of the scale. Firstly, an English version questionnaire
was developed and translated into Chinese, and then the measurement items of all variables were
slightly modified to adapt to the current research background in China. Secondly, three experts and
five graduate students from related research fields were invited to discuss each measurement item
several times to ensure the content validity of the questionnaire. After that, based on the feedback of
the subjects, some wording of the scale was adjusted in time to make it easier for the sample farmers
to read and understand. Finally, before the formal survey, we also conducted a pre-test to verify and
modify the measurement items of the questionnaire.
whoseThe theoretical
essence model constructed
is a Structural EquationinModeling
this research
(SEM)is to
asexplore
shown the causal path
in Figure 3 [32].and
In functional
the SEM,
relationship
the among abstract
21 measurement variables PR1-PR6,
items (PB1-PB6, in farmers’PV1-PV3,
PPWs green disposal behavior
GDW1-GDW3, decision-making,
GDB1-GDB3) were the
observable variables, and the 5 model variables (PB, PR, PV, GDW, GDB) were considered as the the
whose essence is a Structural Equation Modeling (SEM) as shown in Figure 3 [32]. In the SEM, 21
latent
measurement
variables. items (PB1-PB6,
The causal PR1-PR6,
path relationship of PV1-PV3, GDW1-GDW3,
the 5 latent GDB1-GDB3)
variables constituted the SEMwere the observable
structural model,
variables,
and and the 5 model
the relationship betweenvariables (PB, PR, PV,
latent variables andGDW, GDB) were considered
their corresponding observedasvariables
the latent variables.
constituted
TheSEM
the causal path relationship
measurement model.ofThe
theregression
5 latent variables
equationsconstituted the SEM
of each model structural
are as follows, model, and the
relationship between
Regression latent
equation variables model:
of structural and their corresponding observed variables constituted the
SEM measurement model. The regression equations of each model are as follows,
Regression equation of structural model: η2 = γξ + βη1 + ζ (1)
𝜂 𝛾𝜉 𝛽𝜂
Regression equation of the measurement 𝜁
model: (1)
Regression equation of the measurement model:
X = λx ξ + δ (2)
𝑋 𝜆 𝜉 𝛿 (2)
𝑌 𝜆 𝜂Y =𝜀λ y η + ε (3)
(3)
where, η𝜂 is
where, is the
the endogenous
endogenous latent variable, ξ𝜉 is
latent variable, is thethe exogenous
exogenous latent variable, γ𝛾 is
latent variable, is the
the estimated
estimated
parameter, β coefficient, X
parameter, 𝛽 is the regression coefficient, 𝑋 is the endogenous variable, namely the independent
is the regression is the endogenous variable, namely the independent
variable, Y𝑌isisthe
variable, theexogenous
exogenousvariable,
variable, namely
namely thethe dependent variable, λ𝜆x is
dependent variable, is the
the correlation
correlation coefficient
coefficient
matrix between the exogenous latent variable ( ξ ) , λ is the correlation
matrix between the exogenous latent variable 𝜉 ,y 𝜆 is the correlation coefficient coefficient matrix between
matrix the
between
endogenous
the endogenous latentlatent
variable (η), δ is𝜂 the
variable ,𝛿 ismeasurement
the measurement error of X variable,
error ε is the measurement
of 𝑋 variable, error of
𝜀 is the measurement
Y variable.
error of 𝑌 variable.
Based
Based onon the
the above
above considerations,
considerations, the the empirical
empirical research
research was
was conducted
conducted utilizing
utilizing the
the SEM
SEM and
and
the statistical software of AMOS 24.0 (SPSS, IBM, Armonk,
the statistical software of AMOS 24.0 (SPSS, IBM, Armonk, NY, USA) [32,33,49].NY, USA) [32,33,49].
Figure 3.
Figure 3. Structural
Structural equation
equation model
model of
of the
the constructed
constructed theoretical
theoretical model
model in
in this
this research.
research.
4.
4. Results
Results
4.1.
4.1. Demographic
Demographic Characteristics
Characteristics of
of the
the Sample
Sample Farmers
Farmers
As
As shown in Table 2, the proportion of men is
shown in Table 2, the proportion of men is significantly
significantly higher
higher than
than that
that of
of women,
women, reaching
reaching
73.39% in the sample farmers. In terms of age structure, the middle-aged and elderly are
73.39% in the sample farmers. In terms of age structure, the middle-aged and elderly are the majority, the majority,
and
and the
the farmers
farmers over
over 41
41 years
years old
old accounted
accounted forfor 71.50%,
71.50%, which
which indicated
indicated that
that current
current serious
serious situation
situation
of
of rural farming population aging and a large number of rural young adult migrant working. In
rural farming population aging and a large number of rural young adult migrant working. In terms
terms
of
of cultural
cultural structure,
structure, the
the education
education level
level of
of the
the sample
sample farmers
farmers was
was generally
generally low,
low, with
with 84.88%
84.88% ofof
them only having a middle school education or below. In addition, the sample families with the
labor force below 3 accounted for 68.03%, with arable land less than 0.67 hm2 accounting for 69.45%,
Int. J. Environ. Res. Public Health 2020, 17, 3753 9 of 18
farming experience more than 20 years accounting for 68.98%, the agricultural income accounted for
less than 40% of the annual family income accounting for 66.30%, which to some extent reflected the
rural agriculture labor shortage, the obvious degree of part-time employment, and the need to strength
the scale agriculture production.
In addition, the discriminant validity of each variable was tested. Liu et al. pointed out that the
discriminant validity referred to the comparative relationship between the common variance and AVES
value of each variable [64]. As shown in Table 4, the square root of each AVE value was higher than the
correlation coefficient of each variable, so the discriminant validity is supported [67]. In view of the
above test results, the questionnaire data were stable and reliable, with good convergent validity and
discriminant validity.
Goodness-of-Fit Index Statistical Test Index Model Estimate Judgement Standard Test Result
X2 /DF 3.915 <5 Accepted
Absolute fitness index GFI 0.907 >0.9 Accepted
RMSEA 0.068 <0.08 Accepted
NFI 0.913 >0.9 Accepted
Value-added IFI 0.934 >0.9 Accepted
fitness index TLI 0.920 >0.9 Accepted
CFI 0.934 >0.9 Accepted
PGFI 0.680 >0.5 Accepted
Simplified fitness index PNFI 0.752 >0.5 Accepted
the theoretical model is
1109.67 < 1721.787 smaller than both the
CAIC Accepted
1109.67 < 7981.686 saturation model and the
independent model
Note: X2 /DF, GFI, RMSEA, NFI, IFI, TLI, CFI, PGFI, PNFI and CAIC mean ratio of chi-square to degrees of
freedom, goodness-of-fit degree index, root mean square error of approximation, normed fit index, incremental
fit index, non-normed fit index, comparative fit index, parsimony goodness-of-fit index, parsimony-adjusted NFI,
consistent Akaike information criterion, respectively.
so H3, H4 and H5 were supported. The path coefficient between perceived value, perceived benefits,
perceived risks and the green disposal behaviors of PPWs was 0.358, 0.279 and −0.399 and were
significant at p < 0.01, p < 0.05, p < 0.001, respectively. This indicated that perceived value has a
significantly positive impact, perceived benefits have a significantly positive impact and perceived
risks have a significantly negative impact on the PPWs green disposal behaviors, thus, H6, H7 and H8
were verified. It was worth noting that the path coefficient between the green disposal willingness and
behaviors of PPWs was −0.190, which indicated that the green disposal willingness has a negative
impact on the behaviors, so H9 was not supported. This also showed that there existed inconsistence
between the famers’ PPWs green disposal willingness and behaviors.
Unstandardized Standardized
Hypothesis t-Values Results
Coefficients Coefficients
H1 Perceived Value <— Perceived Benefits 0.775 10.453 *** 0.633 supported
H2 (PV) <— Perceived Risks −0.273 −5.195 *** −0.279 supported
H3 Green Disposal <— Perceived Value 0.250 3.806 *** 0.334 supported
H4 Willingness <— Perceived Benefits 0.245 3.299 *** 0.267 supported
H5 (GDW) <— Perceived Risks −0.139 −3.172 ** −0.191 supported
H6 <— Perceived Value 0.358 2.958 ** 0.338 supported
H7 Green Disposal <— Perceived Benefits 0.279 2.053 * 0.215 supported
H8 Behaviors (GDB) <— Perceived Risks −0.399 −4.773 *** −0.384 supported
H9 <— Green Disposal Willingness −0.190 −1.783 −0.116 Not supported
Note: ***, ** and * mean significant at p < 0.001, p < 0.01, p < 0.05, respectively.
Table 7 showed the direct effect, indirect effect and total effect among each variable in the SEM.
Firstly, in terms of the perceived value, the impact from perceived benefits (0.633) was the highest,
while the impact from perceived risks (−0.279) was the lowest. Secondly, in terms of the green disposal
willingness, farmers’ perceived benefits and perceived risks could have important impacts on their
PPWs green disposal willingness directly and indirectly, where the total effects of perceived benefits
(0.478) were the highest followed by perceived value (0.334) and perceived risks (−0.284). Finally,
in terms of the green disposal behaviors, farmers perceived benefits and perceived risks could have
significant impacts on the green disposal behaviors of PPWs directly and indirectly, where the directly
negative effect (−0.384) of perceived risks was greater than the directly positive effect (0.215) of
perceived benefits. Moreover, the negative total effect of perceived risks (−0.362) on the green disposal
behaviors through the green disposal willingness was in turn greater than the positive total effect of
perceived value (0.299) on the green disposal behaviors through the green disposal willingness and
perceived benefits (0.184) on the green disposal behaviors through the green disposal willingness.
Notably, the overall effect (−0.116) of the green disposal willingness on the green disposal behaviors
was relatively minimal, which indicated that the farmers’ PPWs green disposal willingness to a large
extent could not be translated into the actual PPWs green disposal behaviors.
Table 7. Standardized direct effect, indirect effect and total effect between variables (N = 635).
5. Discussion
In the context of an increasingly severe pollution of PPWs, this study, based on the TPV, investigated
the impacts of farmers’ perceived value on their willingness and behaviors in the PPWs green disposal.
In the extant literature, targeted studies, focusing on the farmers’ PPWs green disposal willingness
and behaviors, have never been reported. This study found that the TPV was an effective theoretical
basis to explain the farmers’ PPWs green disposal willingness and behaviors, which made some
theoretical contributions. This provided a new insight to the promotion of grain farmers’ PPWs green
disposal willingness and behaviors in Henan province of China, and also a new idea for improving
and formulating relevant agricultural pollution prevention policies.
The present research showed that farmers’ perceived value was the result of their comprehensive
tradeoff and comparison of perceived benefits and perceived risks of PPWs green disposal. It was
further concluded that perceived benefits have more impacts on PPWs green disposal perceived value
than perceived risks, which was supported by existing research findings on crop straw and livestock
manure [31]. As “rational economic man”, farmers’ behavioral decisions were always based on the
prediction of the consequences of behavioral choices (such as the land investment behaviors and
the adoption of sustainable farming practices), and they make choices they believe can maximize
profits with the minimum risks [43,68]. Dessart et al. [42] pointed out that the financial risks perceived
by farmers in agricultural production activities, related to pest control and pesticide use, may be
one of the most important obstacles to their adoption actions. While Jin et al. [9] argued that PPWs
recycling program should be established through the institutional innovation utilizing the existing
economic structure, where all the stakeholders (including farmers) could get a return on the investment.
According to the above findings, improving farmers’ perceived benefits of PPWs green disposal and
reducing the perceived risks of PPWs green disposal is predicated to improve the perceived value of
PPWs green disposal.
The results also suggested that perceived value (0.334) was the most direct factor influencing
farmers’ PPWs green disposal willingness. Nonetheless, farmers’ perceived benefits (0.478) were the
most important factor influencing their PPWs green disposal willingness (Table 7). The possible
explanation was that farmers believed that green disposal of PPWs could increase economic
income, reduce environmental pollution and improve health, and this kind of perceived gains
could affect farmers’ PPWs green disposal willingness through the positive recognition of the value
perception. Research indicated that farmers’ expectations of economic benefits (such as labor saving,
high productivity and high returns) are more likely to promote their willingness to engage in
environmentally friendly activities [47]. Therefore, in agricultural production activities, improving
farmers’ perceived benefits and perceived value could promote the improvement of farmers’ PPWs
green disposal willingness, which was consistent with previous research conclusions on the impact of
information transfer on farmers’ uptake of innovative crop technologies [69]. Hurley and Mitchell [70]
also pointed out that only when farmers understood that the field returns and provides value, can they
be motivated to make economic disposal decisions regarding the neonicotinoid seed treatments.
In addition, the results showed that the farmers’ green disposal willingness has a negative impact
on the green disposal behaviors of PPWs, which was contrary to our theoretical expectations but
interesting to explain. Farmers were worried that PPWs green disposal could not be supported by more
policy subsidies, and they would have to invest some extra money, while PPWs green disposal could
only generate a little economic income. Therefore, farmers think that it would be better to earn money
by going out to work than to spend labor time on PPWs green disposal. Meanwhile, farmers were
prone to the inertial discarded behavior due to herd mentality [26], and they realized that PPWs green
disposal has significant positive externalities, such as ecological environment protection, safety and
health, etc., but they would not dispose PPWs in a green way driven by profit maximization. This was
consistent with the existing research results related to diversified agricultural system and farmers’
risk behavior [46,71]. Trujillo-Barrera et al. [47] indicated that the increase in farmers’ risk awareness
would not only directly reduce the opportunity to adopt sustainable practices, but also weaken the
Int. J. Environ. Res. Public Health 2020, 17, 3753 13 of 18
effect of expected economic returns brought by the adoption of sustainable practices. The empirical
results of this study showed that farmers’ perceived benefits showed the greatest total effect on their
PPWs green disposal willingness, while perceived risks showed the greatest total effect on their PPWs
green disposal behaviors. The findings were consistent with the conclusion of previous studies that
“farmers generally have risk aversion psychology when facing to the behavioral choices” [72,73]. To a
large extent, this hindered the transformation of farmers’ green disposal willingness into the actual
green disposal behaviors, and farmers often showed the “powerless” state in terms of PPWs green
disposal behaviors. Therefore, effective and sustainable practices have been adopted to improve farmers’
perceived benefits (especially economic income benefits) and reduce perceived risks (especially cost
input risks), which is conducive to the transformation of farmers’ willingness of PPWs green disposal
into practical actions.
It should also be pointed out that existing literature pointed out that most farmers usually
discarded agricultural waste, such as crop straw, livestock manure and so on, in their fields or
around arable land [11,31]. This was similar to the behavioral way the sample farmers disposed of
PPWs and its consequences in this study, that is, these inappropriate disposal behaviors seriously
threatened the agricultural ecological environment. However, previous studies have never explored
the specific behaviors of PPWs “green” disposal, and the impact of perceived value and its influencing
factors, namely perceived benefits and perceived risks, on farmers’ PPWs green disposal willingness
and behaviors have not been investigated. Marnasidis et al. [60] pointed out that the environmental
pollution caused by improper disposal of PPWs became increasingly serious, but the in-depth researches
from the micro level, including pesticide bottles were still absent [9]. Therefore, this paper explored
farmers’ green disposal willingness and behaviors of PPWs, in terms of the formation mechanism
of perceived value, which theoretically made up for the research deficiencies in the related field of
farmers’ behaviors. In addition, the conclusion of “the farmers’ inconsistence between PPWs green
disposal willingness and behaviors” extended the applicability of the extant behavior theory from a
new perspective.
6. Conclusions
Exploring the green disposal willingness from the formation mechanism of perceived value is
helpful for farmers to dispose PPWs in a green way. In this study, based on the first-hand data of
635 farmers in grain-producing counties in Henan province of China, we introduced the perceived
value and its influencing factors, namely perceived benefits and perceived risks, to investigate their
influence on the willingness and behaviors of farmers PPWs green disposal. The conclusions were
as follows:
(1) The theoretical model of this study based on TPV effectively explained the farmers’
green disposal willingness and behaviors of PPWs. This is because farmers’ green disposal action
logic followed the path pattern: perceived value→behavior willingness→behavior performance,
where farmers’ perceived value was the result of the tradeoff and comparison between the perceived
benefits and perceived risks. Moreover, it was further found that the perceived benefits have a greater
impact on the PPWs green disposal perceived value than the perceived risks.
(2) Farmers’ perceived benefits and perceived risks have significantly direct and indirect impacts
on their green disposal willingness and behaviors of PPWs, among which the perceived benefits have
the greatest positive total effect on farmers’ willingness and the perceived risks have the greatest
negative total effect on the behaviors. This indicated that farmers’ perceived risks was the most
important factor affecting their PPWs green disposal, and the perceived risks have greater influence
than the perceived benefits when farmers make real decisions in the PPWs green disposal.
(3) Inconsistence existed between the farmers’ green disposal willingness and behaviors of PPWs.
When faced with the choice of PPWs green disposal, farmers generally have the mentality of risk
aversion, which largely hindered the transformation of PPWs green disposal willingness into actual
green disposal behaviors. Furthermore, driven by the profit maximization, farmers were prone to
Int. J. Environ. Res. Public Health 2020, 17, 3753 14 of 18
conservative disposal behaviors and even showed the “powerless” state where they had willingness
but no actual action.
Author Contributions: M.L. and K.C. conceived and designed the conceptual model; M.L. and J.W. performed
the survey; K.C. and L.W. provided materials/analytical tools; M.L. and J.W. analyzed the data; M.L., J.W. and K.C.
wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by Major Projects of The National Social Science Fund of China, grant number
[20ZDA087].
Acknowledgments: We are indebted to the anonymous reviewers and editors.
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Int. J. Environ. Res. Public Health 2020, 17, 3753 15 of 18
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