DP2020-01

Valuing Rural Residentsʼ Attitude
Regarding Agri-environmental
Policy in China: A Best-worst
Scaling Analysis
Qinxin GUO
Junyi SHEN

January 14, 2020

Valuing rural residents’ attitude regarding agri-environmental policy in China:
A best-worst scaling analysis

Qinxin Guoa,*, Junyi Shenb,caGraduate School of Economics, Kobe University, Kobe University, 2-1 Rokkodai, Nada, Kobe
657-8501, Japan.
b Research Institute for Economics and Business Administration, Kobe University, 2-1 Rokkodai,
Nada, Kobe 657-8501, Japan.
c School of Economics, Shanghai University, 99 Shangda Road, Baoshan 200444, Shanghai,
China* Corresponding author. Email: guoguoxionggqx@gmail.com

Abstract

In this study, a stated choice survey was conducted in Anhui Province, China.
The best-worst scaling method, an alternative method to the discrete choice
experiment, was used to value rural residents’ attitude toward agri-environmental
policy. Using the multinomial logit and random parameter logit model, the results
showed that respondents thought the best policy included protecting undergroundwater quality as the objective, straw recycling as the method, technological support
provided by the government, a supervision level of 30% of farmers, and a 6,000 RMB
subsidy directly disbursed by the government. Conversely, respondents thought theworst policy included protecting biodiversity as the objective, purchasing pesticides
and fertilizers from the prescribed list as the method, no technological support
provided by the government, an increased supervision level of 50% of farmers, and a
4,500 RMB subsidy requiring a contract with the government. The results of the latentclass logit model suggested the respondents who are older, have fewer children under
middle school age, less agree with the rural environment will have a large impact on
agriculture production, have more knowledge of agricultural and environmental

1

protection would show more sensitivity to the attributes of agri-environmental
policies.

Keywords: Agri-environmental policy; Best-worst scaling; Latent class model;
Random parameter logit model; Multinomial logit model.

21. Introduction
When we examine environmental pollution, industrial pollution is usuallyconsidered to be the main topic of study. However, agricultural intensification, themain characteristic of modern agriculture, can also cause serious environmental
problems such as soil erosion, water pollution, and biodiversity loss in rural areas.
Agri-environmental public goods, which provide efficient ways to promoteagri-environmental protection, can be defined as environmental externalities fromagricultural activities that have characteristics of non-rivalry and non-excludability(Jones et al., 2015). There are nine targeted agri-environmental public goods,
including water quality, soil quality, and biodiversity. The provision ofagri-environmental public goods has positive externality which should be subsidized
by the government. Consequently, the common resolution of agri-environmental
problems is for the government to offer an additional subsidy to farmers (i.e., thesourceofagri-environmental

pollution)toencourage

them

to

provideagri-environmental public goods during the production process.
The research regarding agri-environmental policies mainly covers cases indeveloped countries like the US and member countries of the EU. Some research
focuses on the evaluation of policy and the comparison between different policies
implemented in different countries (Baylis et al., 2008; Dobbs and Pretty, 2008).
Regarding the various types of agri-environmental policies, Payments forEnvironmental Services (PES) is the most appealing, and quite a bit of research
focuses on its theory and practice (Engel et al., 2008; Wunder, 2015; Dedeurwaerdere
et al., 2015). Other research centers on specific topics concerning agri-environmental
policies. In one example, Brady et al. (2009) evaluated the long-term impact of the2003 EU reform on farm structure, landscape, and biodiversity. The result indicatedthat the reform may have had negative effects on the landscape by eliminating the link
between government support and production. In another example, Mettepenningen etal. (2011) defined factors influenced by public transaction costs of agriculturalenvironmental policies. The research showed that the factors perceived to be3

important included the frequency of information exchange with the farmers’
association, environmental managers trusting the farmers, and mitigating the adverse
effects of agriculture.
Instead of focusing on the top-down impact of agri-environmental policies, someresearch considered the attitudes of agri-environmental protection stakeholders inagri-environmental policies through stated preference methods such as the Discrete
Choice Experiment (DCE). Ruto and Garrod (2009) investigated the effect that
scheme design can have on encouraging participation. Farmers were found to require
greater financial incentives to join schemes with longer contracts, less flexibility, or
higher levels of paperwork. Broch and Vedel (2012) investigated preference
heterogeneity for agri-environmental contracts (e.g., afforestation contracts) among
farmers in Denmark, and found that having the option to cancel the contract decreased
farmers’ required compensation level, whereas monitoring increased it. Moran et al.
(2007) investigated the Scottish public’s preferences for future agri-environmental
reform. They suggest that the public has defined preferences and a willingness to pay(using general income taxation) to affect changes beyond the status quo.
Agri-environmental policies in China did not start until recently; thus, theresearch on this issue is scarce. There are two descriptive studies in the literature. In
one study, Zhu et al. (2018) conducted a comparative study of three agriculturalenvironmental policy models. The results indicated that agri-environmental schemesin China have significantly enhanced farmer enthusiasm toward farmland protectionand enhanced their satisfaction with the policy. In another study, Zhang et al. (2015)
investigated farmers’ attitudes towards agricultural infrastructure projects andperceptions of agri-environmental issues in Beijing and Changsha. The results
indicated that farmers were generally dissatisfied with the top-down implementation
process of agricultural infrastructure projects. However, these descriptive studies havenot investigated how and to what extent Chinese farmers evaluate the factors ofagri-environmental schemes in China. Therefore, in the current study, we would liketo fill this gap by providing empirical analysis on agri-environmental policies.
4

An alternative method called best-worst scaling (BWS) could elicit more
information than DCE (Guo and Shen, 2019). The multi-profile case in BWS includes
an extra question asking which profile respondents like least for each choice set of
DCE. Analysis of these results could provide additional ranking information of
attribute levels. For this reason, we use the BWS multi-profile case in the following
analysis to further analyze the attitude of rural residents on agri-environmental
policies in China.
The remainder of this paper is organized as follows: Section 2 describes thestatus of agri-environmental policy. Section 3 covers the methodology of BWS.
Survey design and data collection is presented in Section 4. Section 5 provides theresults of regressions. Finally, Section 6 offers discussions and conclusions.
2. Status of agri-environmental policy
2.1 Status of agri-environmental policy in developed countries
EU: The EU mainly targets what constitutes an agricultural externality (i.e., theagri-environmental public goods). Since the agri-environmental public goods arebeing supplied privately by farmers, EU member states consider it legitimate to offer
compensation in return for their provision (MAFF, 2000). Agri-environmental
schemes (AES) provide financial support for member states to design and implement
agri-environmental measures (AEM). The governments are inclined to offer
compensation to farmers who provide public goods if they commit to using
environmentally friendly agricultural inputs or technologies, regardless of whether
those techniques are used on specific land and how the technology will have an
impact on the environment (Baylis et al., 2008).

United States: The US administration tends to focus on the actual and potential
negative relationship between agricultural and environmental goals as well as
reducing agriculture’s negative externalities (Baylis et al., 2008). Farmers in the
United States are often paid specifically to return farmland to its native state. The US
5

Conservation Reserve Program uses the Environmental Benefits Index (EBI), which
requires a significant amount of information. It is based on data such as theenvironmental characteristics of applicants’ fields and the benefits produced by one ormore actions, such as only retiring the land versus retiring the land and planting native
grasses.

Japan: Eco-friendly agriculture in Japan is defined as a sustainable method of farming
to lower the environmental load by decreasing chemical fertilizers and pesticides
through improving soil quality while taking advantage of ’agriculture’s inherent
material recycling power, with consideration for harmonization with productivity(Yamada, 2011). Recent agri-environmental policies in Japan include the New Policy
for Food, Agriculture, and Rural Areas (New Policy), issued by the Ministry ofAgriculture, Forestry and Fisheries (MAFF) in 1992, and the Food, Agriculture andRural Areas Basic Act (New Basic Act), passed by the National Diet in 1999. The
New Policy defined the concept and direction of eco-friendly agriculture but did notinclude details on how to implement it. The New Basic Act, however, provided morecomprehensive coverage of agri-environmental issues based on the content put forthin the New Policy. The main concept of the New Basic Act is stated as “thesustainable development of agriculture by strengthening the natural recycling
functions and the realization of the multifunctionality of agriculture.” which addresses
the multifunctionality of agriculture rather than just traditional food production(Yamada, 2011).
2.2 Status of agri-environmental policy in China
The Bulletin of the national survey on soil pollution released on 17 April 2014
acknowledged that overall, 16.1% of soil in China was polluted, consisting of 19.4%
of farmland, 10.0% of forest land, 10.4% of grassland, and 11.4% of unused land
(Wan et al., 2018). The overuse of fertilizer and pesticide contributed to 70% of
farmland pollution and is the primary human cause of widespread soil pollution.
Figure 1 shows fertilizer consumption and pesticide use in China, a trend that had
6been increasing until 2014. While fertilizer consumption and pesticide use decreasedslightly after 2014, both levels were still elevated.

Figure 1. Fertilizer consumption and pesticide use in China
The agricultural policy in China focused mainly on agricultural production andfarmers’ income before 1978. In 1978, the government realized rural areas in China
had become pollution shelters and started to take action.

Gestation
stage

Initialstage

Acceleration
stage

Promotion
stage

1978-1994

1995-1999

2000-20162017-2019

Figure 2. Four stages of agri-environmental policy development in China
7

As presented in Figure 2, agri-environmental policies in China have experienced
four stages since 1978 (Han et al., 2019). In the gestation stage, from 1978 to 1994,
the pollution problem in rural areas came mostly from the transfer of industrial
pollution. However, the overuse of manure and pesticide began having serious
consequences during the transition from traditional agriculture to modern agriculture.
The State Council released “Opinions on developing ecological agriculture andstrengthening the protection of the agri-ecological environment” and “Decision onstrengthening environmental protection” to promote ecological agriculture (Han et al.,2019). Considerations regarding the prevention and control of agricultural non-point
source pollution were still at a nascent and insignificant level in the overall
environmental protection policy. There were only general targets and no specific ortargeted policy actions.
During the initial stage, from 1995 to 1999, the use of fertilizer and pesticides
increased rapidly, which had a huge impact on the environment in rural areas. For
example, excessive use of nitrogen fertilizer led to serious nitrate pollution in
groundwater (Liu, 1999). In 1998, rural environmental pollution began to exceed
environmental capacity, and rural areas started to show obvious signs of deterioration.
In 1999, the State Environmental Protection Administration released “Several
opinions of the state environmental protection administration on strengthening
ecological and environmental protection in rural areas,” which was the first policy
aimed at agri-environmental protection in China (Han and Jin, 2016). In this stage,
agricultural and environmental protection must be coordinated with economic
development. Meanwhile, strengthening agricultural pollution prevention and control
has become the overall policy goal of this stage.
The production and consumption of fertilizer and pesticides in China had
become the largest in the world during the acceleration stage covering 2000 to 2016,
while the use ratios of fertilizer and pesticides were so low that land fertility decreases
apparently. In 2008, the central government set up a special fund for rural
environmental protection to focus on improving the rural environment by “replacing
8

subsidies with awards” and “promoting governance with awards” in order to increase
the enthusiasm of local governments for improving the rural environment (Han andJin, 2016). Influenced by the State’s policies, major cities such as Chengdu, Suzhou,
Dongguan, Shanghai, Foshan, Guangzhou, Linhai, Haining, and Cixi have vigorously
conducted policy experiments. Table 1 presents the details of agri-environmental
policies in Suzhou and Shanghai. The basic contents of agri-environmental policies in
Suzhou and Shanghai are similar to AES in that farmers can be subsidized if they
complete assignments required by the government. In this study, the acceleration
stage will be the main period we focus on.

Table 1. The agri-environmental policies in Suzhou and Shanghai
PolicyDetails(i) Payment is based on farmland quality, location, andscale. Payment for prime farmland is 3,000 RMB per
Farmland eco-compensation

hectare per year where the area is between 66.667 and(Suzhou)

666.667 hm2, and 6,000 RMB per hectare per year where
the area is above 666.667 hm2. (ii) Participants receive theentire payment.
(i) The subjects of the subsidy are farmers, communities,

Agricultural

ecologyand and agriculture companies.

security subsidies (Shanghai)(ii) The subsidy consists of planting winter green manure,purchasing organic fertilizer, and straw recycling.

The last stage, or promotion stage, covers 2017-2019, a time period of increased
public concern about environmental protection, the safety of agricultural products,
and the quality of drinking water. However, some mandatory environmental actions
such as seed bans, livestock and poultry bans, and straw burning bans have also led toa certain degree of social controversy. It is critical to protect the environment while
also keeping the best interests of farmers in mind. The end goal is to help ’farmers
9become the real beneficiaries of environmental protection. In 2018, the strategic plan
for rural revitalization was released, defining specific action plans for implementing
the strategy (Han et al., 2019). The strategy of rural revitalization goes beyond any
single field of agricultural and rural development in the past. It covers many fields
including society, ecology, culture, and the economy. It is a comprehensive promotionof the concept of sustainable agricultural and rural development.
3 Methodology
3.1 Best-worst scaling
Since the discrete choice experiment (DCE) was developed in the early 1980s(Louviere and Woodworth, 1983), it has become a workhorse in economics literaturefor evaluating the stated preferences for various issues in areas such as transportation
economics, health economics, and environmental economics. The DCE was
implemented within the random utility theory (Thurstone, 1927), which depicted the
choice behavior from the perspective of economic theory relatively well. Hence, theDCE method surpassed other stated preference methods that were not derived from
economic theory and became a popular analysis tool in economics literature. However,
due to the absence of a ranking system of alternatives, DCE barely allowed us toknow the “best choice” for respondents (Guo and Shen, 2019). Thus, scholars sought
to further investigate and acquire more information about choice behavior.
There has been increasing interest in recent decades in an alternative method
called best-worst scaling (BWS) to elicit more information based on DCE. Finn andLouviere (1992) first proposed the BWS method for a food safety case in which aperson was asked to select both the best and worst items from a list of options interms of food safety. Since the pilot research was published, a number of applications
have been proposed and a complete theoretical system has been established. The
BWS method basket includes three types of cases: object case, profile case, andmulti-profile case (Flynn, 2010). In the object case, respondents choose the best andworst objects from a list of objects (or attributes without detailed levels). The profile
10

case involves only one profile or alternative in a normal DCE choice set andrespondents choose the best and worst levels from this profile. The multi-profile caseadds questions asking which profile respondents like most and which profile
respondents like least to each choice set of DCE. Namely, the respondents need tochoose the best and worst profiles from each choice set in the BWS multi-profile case.
In this study, we use the BWS multi-profile case to reveal the attitudes of rural
residents on agri-environmental policy. The BWS multi-profile case, or best-worst
DCE (BWDCE), is the closest method to DCE, since it is designed to ask the
respondent to choose the best and worst profiles in every choice set based on the DCE
choice set (Lancsar et al., 2013).

3.2 Econometric models in BWS multi-profile case
3.2.1 Multinomial logit model
The BWS multi-profile case choice model in this study is based on random utility
theory. The basic assumption in the random utility approach to choice modeling is thatdecision makers are utility maximizers; that is, given a set of alternatives, the decision
maker will choose the alternative that maximizes his/her utility (Shen, 2006).
However, the decision maker needs to choose not only the alternative that maximizes
his/her utility (i.e., the best alternative), but also choose the alternative that minimizes
his/her utility (i.e., the worst alternative) in a BWS multi-profile case. Since the utility
U of an alternative for an individual cannot be observed, it is assumed to consist of adeterministic component V and a random error term ε kk ′ for every alternative pairof kk.′
Formally, the utility difference of choosing the best alternative i and the worst
alternative i′ for individual q can be expressed as:

U iq − U i′q = Viq − Vi′q + ε ii′q

(1)

Hence the probability that individual q chooses alternative i as the bestalternative and alternative i′ ≠ i as the worst alternative from a particular set X canbe written as:
11

PBW (ii ′ | X ) = P (U iq - U i′q > U jq − U j ′q ; ∀j ≠ j′ ∈ X )= P (ε jj ′q < ε ii′q + (Viq − Vi′q ) − (V jq − V j ′q ); ∀(2)j ≠ j′ ∈ X )To transform the random utility model into a choice model, certain assumptions
about the joint distribution of the vector of random error terms are required. If therandom error terms are assumed to follow the extreme value type I distribution andare assumed to be independently and identically distributed (IID) across alternatives
and cases (or observations), the multinomial (or conditional) logit (MNL) model isobtained (McFadden, 1974). In the MNL model, the choice probability in Equation (2)is expressed as:
PBW (ii ′ | X ) = exp( µ (Viq − Vi′q )) /∑j , j ′∈ X exp( µ (V jq − V j ′q ))j ≠ j′(3)

If we make the further assumption that the deterministic component of utility islinear in its parameters, then Equation (3) can be given as:

PBW (ii ′ | X ) = exp( µβ ′( X iq − X i′q )) /∑j , j ′∈ X exp( µβ ′( X jq − X j ′q ))j ≠ j′(4)

where µ represents a scale parameter that determines the scale of the utilities which
is proportional to the inverse of the distribution of the error terms. Typically, it isnormalized to 1 in the MNL model. Viq and Vi′q are the explanatory variables of X iq
and X i′q , normally including alternative-specific constants (ASCs), the attributes ofalternative i and alternativei′ ,and the social-economic characteristics ofindividual q. β ′ is the parameter vector associated with vector X iq and X i′q .
In this study, we applied the sequential model as in the profile case and leveraged
both the opposite selection orders in the analysis. The sequential model is defined asfollows:

PBW (ii ′ | X ) =∑or
PWB (i ′i | X ) =∑exp( µβ ′X iq )j∈ X

exp( µβ ′X jq )×

exp(-µβ ′X i′q )j ′∈ Xexp(-µβ ′X j ′q )∑×

exp(-µβ ′X i′q )j ′∈ X \ i∑exp(-µβ ′X j ′q )exp( µβ ′X iq )j∈ X \ i ′exp( µβ ′X jq )(5)
The sequential model assumes that decision makers might abandon the best (resp.
the worst) option they initially chose from the alternatives and afterwards choose theworst (resp. the best) from the remaining alternatives.
12

It is well known that heterogeneity among individuals is extremely difficult to
examine in the MNL model (Shen, 2006; Louviere et al., 2000). This limitation can berelaxed to some extent by interaction terms between individual-specific characteristics
and the various choices. However, there is a limit to this method since it requires apriori selection of key individual characteristics and attributes and involves a limited
selection of individual-specific variables (Boxall et al., 2002).
3.2.2 Random parameter logit model
One approach that can account for individual heterogeneity is the Random
Parameter Logit (RPL) (or Mixed Logit) model, which allows model parameters tovary randomly through assumed distributions (normal, log-normal, triangular, etc.).
This model is a generalization of the MNL model and the form in BWS multi-profile
cases is summarized below:
PBW (ii ′ | X ) = exp(α ′ + β ′( X iqt − X i′qt ) + ϕ ′( Fiqt − Fi′qt )) /∑j , j∈ X exp(α ′ + β ′( X jqt − X j ′qt ) + ϕ ′( F jqt − F j ′qt ))j ≠ j′(6)
where α ′ is a vector of fixed or random alternative-specific constants (ASCs) inwhich one of the ASCs should be identified as 0. β ′ is a parameter vector that is
randomly distributed across individuals. ϕ ′ is a vector of non-random parameters. Xiqt
and Xi’qt are vectors of individual-specific characteristics and alternative-specificattributes at observation t and are estimated with random parameters. Fiqt and Fi’qt arevectors of individual-specific characteristics and alternative-specific attributes atobservation t and are estimated with fixed parameters.
In this specification, a subset or all of α ′ and the parameters in the β ′ vector canbe assumed to be randomly distributed across individuals. These random parameters
can also be defined as a function of the characteristics of individuals and/or otherattributes that are choice invariant. Based on these defined attributes, the mean andstandard deviations of the specified random parameters and contributions from thesechoice invariant attributes on random parameters are estimated by using the13

Maximum Simulated Likelihood (MSL) method. The RPL model is sufficiently
flexible to provide the modeler a tremendous range within which to specify individual
unobserved heterogeneity. To some extent, this flexibility offsets the specificity of the
distributional assumptions (Greene and Hensher, 2003).
3.2.3 Latent class logit model
The Latent Class Logit (LCL) model, unlike the RPL model that specifies therandom parameters to follow a continuous joint distribution, assumes that a discrete
number of classes are sufficient to describe the joint function of the parameters.
Therefore, the unobserved heterogeneity is captured by these latent classes in the
population, each of which is associated with a different parameter vector in thecorresponding utility. The LCL has often been used in marketing research instead of
the RPL model, while there are few studies in other fields such as transportation andenvironmental valuation.
The choice probability of individual q of Class s in a BWS multi-profile casecould be expressed as:s(ii ′ | X ) = exp( µβ ′( X iqs − X is′q )) /
PBW

∑

s
s
j , j ′∈ X exp( µβ ′( X jq − X j ′q ))j ≠ j′

s = 1, S(6)which is a simple MNL specification in class s . Additionally, one can construct aclassification model as a function of some individual-specific attributes to explain theheterogeneity across classes. The LCL model simultaneously estimates Equation (6)sfor S classes and predicts the probability H q as individual q in being in class s .

Therefore, the unconditional probability of choosing the best alternative i and theworst alternative i ′ for individual q can be expressed as:

PBW (ii ′ | X ) =S∑ P (ii′ | X ) H
s
BW

s =1

14

s
q

(7)

4. Survey design and data collection
4.1 Questionnaire
The questionnaire regarding agri-environmental policy that was used in thisstudy has two parts: In the first part, respondents are presented with the DCE choice
set plus an additional question (the worst choice question) to obtain the BWS
multi-profile data. In each choice set, we presented three unlabeled profiles oralternatives: Policy A, Policy B, and Policy C. As presented in Table 2, each profile
includes six attributes: policy objective, agri-environmental protection assignment,
whether the government provides free technical support, monitoring, form of
additional payment, and additional payment per hectare. Each attribute has threelevels except for whether the government provides free technical support and form of
additional payment, which have two levels. Most attributes were based on the studies
of Ruto and Garrod (2009), Broch and Vedel (2012), and the agri-environmental
policies being implemented in Suzhou and Shanghai. Moreover, we used
Design-Expert Version 9 to create twenty-four valid choice sets by employing the
D-optimal design. Since it would have been too cumbersome for respondents toanswer all the choice sets, we further divided these choice sets randomly into three
versions of questionnaires, and the respondents were only asked to answer the one
version that was randomly assigned to them. Table 3 presents an example of the BWS
multi-profile case choice sets in which the respondents would choose the policy they
think is the best and the policy they think is the worst for all eight choice sets.

Table 2. Attributes and their levels regarding agri-environmental policy
Agri-environmental Policy Attributes

Levels of Attributes
Protection of soil quality
Protection of underground water quality

Policy objective

Protection of biodiversity
Agri-environmental protection
assignment

Plant winter green manure
Purchase pesticides and fertilizers from the prescribed list
Straw recycling
Yes

Whether the government provides free15

technical support

No
10% of farmers will be supervised

Monitoring

30% of farmers will be supervised
50% of farmers will be supervised
Direct subsidy

Form of additional payment

Require contract with government
3,000 RMB

Additional payment per hectare

4,500 RMB
6,000 RMB

Table 3. An example of BWS multi-profile case choice sets
Policy A

Policy B

Policy C

Policy objective

Protection of soil
quality

Protection ofunderground water
quality

Protection of soil
qualityAgri-environmental
protection assignment

Plant winter green
manurePurchase pesticides andfertilizers from theprescribed list

Purchase pesticides
and fertilizers fromthe prescribed list

Whether thegovernment provides
free technical support

Yes

Yes

Yes

Monitoring

30% of farmers will
be supervised

30% of farmers will besupervised

10% of farmers will
be supervised

Form of additional
payment

Require contract
with government

Direct subsidy

Require contract
with government

Additional payment
per hectare

6,000 RMB

6,000 RMB

4,500 RMB

Please choose thepolicy you like the
most

□

□

□

Please choose thepolicy you like the
least

□

□

□

Questions in the second section of the questionnaire are related to demographic
characteristics such as gender, age, education level, household size, number ofchildren below middle school age, farmland area, household annual income, andhousehold annual income from agriculture production. We also asked respondents five
5-point Likert scale questions that are related to agri-environmental protection and16

agri-environmental policy. Table 4 presents the 5-point Likert scale questions. The
first two questions are related to satisfaction with the rural environment and theimpact of the rural environment on agricultural production. The next question isrelated to the responsibility of farmers for agri-environmental protection, and the lasttwo questions are related to knowledge of agri-environmental protection andagri-environmental policy.

Table 4. Questions regarding agri-environmental protection and policy
Question 1

How satisfied are you with the current environment in rural areas? (1=Very
dissatisfied; 5=Very satisfied)

Question 2

Do you agree that the rural environment will have a large impact on agricultural
production? (1=Totally disagree; 5=Totally agree)

Question 3

Do you agree that farmers should be responsible for the rural environment?
(1=Totally disagree; 5=Totally agree)

Question 4

How much knowledge do you have about agricultural and environmental
protection?

Question 5(1=Have no idea; 5=Know exactly)

How much do you know about current agricultural and environmental protection
policies?

(1=Have no idea; 5=Know exactly)

4.2 Data collection
We collected the data in Huainan City of Anhui Province by using paper andinternet questionnaires. One hundred seventy valid questionnaires were returned, 70%of which were paper questionnaires and 30% of which were internet questionnaires.
The demographic characteristics of the participants are presented in Table 5.
Respondents were 60% male and 40% female. The average age for respondents was
37, with 42.35% of respondents being 30 or younger. The proportion of respondents
who graduated from junior high school was 42.35%, and 39.41% of the respondents
have four people in the family. Respondents who have children under middle school
17

age account for 71.77%. Regarding farmland area, 82.94% of respondents have lessthan 0.667 hectares, and 70.57% of respondents have an annual household income
obtained from agricultural activity of less than 6,000 RMB.

Table 5. Demographic characteristics of the respondents (n=170)
Demographic characteristics% in sample

GenderMale
FemaleAge（mean=37）
30 or younger
Older than 30

60.00%
40.00%
42.35%
57.65%

Highest education level
Primary school
Junior high school
High school
Junior college
Bachelor’s degree or above
Household size
1
2
3
4
5
6 or above
Children under middle school age
0
1
2
5
7
Farmland area (Hectares)
Less than 0.667
0.667 and above
Annual household income (RMB)
Less than 30,000
30,000-50,000
50,000-70,000
70,000 and above
Annual household income obtained fromagriculture activity (RMB)
18

4.71%
42.35%
19.41%
9.43%
22.93%
1.18%
1.76%
25.28%
39.41%
22.37%
9.41%
27.65%
53.53%
16.47%
1.18%
0.59%
82.94%
17.06%
17.65%
42.37%
27.63%
12.35%

70.57%14.13%
7.65%
6.47%

Less than 6,000
6,000-8,000
8,000-10,000
10,000 and above

5. Results
5.1 Results of MNL and RPL regression
Table 6 presents the results of the MNL and RPL models. In both models,estimates are shown for both the best-worst order type (BW type) and the worst-bestorder type (WB type). The log-likelihood values in the MNL model in both types
were slightly lower than those in the RPL model, which suggests that the RPL modelis statistically superior. In addition, a number of the standard deviations of theassumed random parameters in the RPL model are significant, which provides
supporting evidence that taking unobserved individual heterogeneity into account isnecessary. In both the MNL and RPL regressions, all the policy attributes were treatedas discrete variables.
Table 6. Estimation results of MNL and RPL model
MNL
(BW)

MNL
(WB)

RPL
(BW)

S.D.

RPL
(WB)

S.D.

0.226***

0.199***

0.299***

0.336***

0.280***

0.402***(6.04)(5.38)(5.94)(5.13)(5.28)(6.01)-0.281***-0.323***-0.379***0.451***-0.446***

0.579***(-7.50)(-8.66)(-6.82)(7.29)(-7.06)(8.74)-0.262***-0.329***-0.322***0.300***-0.391***0.336***(-6.65)(-8.22)(-6.30)(4.48)(-7.26)(5.07)

Policy objective(Protection of soil
quality as base)Protection ofunderground water
quality
Protection of
biodiversity
Agri-environmental
protection assignment(Plant winter green
manure as base)
Purchase pesticides andfertilizers from theprescribed list

19

Straw recycling
Whether thegovernment provides
free technical support(No as base)
Yes
Monitoring bygovernment (10% of
farmers will besupervised as base)
30% of farmers will besupervised
50% of farmers will besupervised

0.568***

0.601***

0.694***

0.502***

0.746***

0.479***(12.54)(13.17)(10.37)(6.99)(11.09)(6.42)

0.171***

0.168***

0.188***

0.211***

0.197***

0.243***(5.84)(5.49)(5.11)(3.66)(4.94)(4.14)

0.185***

0.170***

0.194***

0.205**

0.181***

0.240***(4.34)(3.99)(3.87)(2.47)(3.42)(3.08)-0.102**-0.099**-0.114**0.210***-0.106**0.202**(-2.38)(-2.33)(-2.21)(2.63)(-2.04)(2.50)-0.149***-0.190***-0.178***0.015-0.217***-0.034(-5.13)(-6.48)(-5.34)(0.17)(-6.25)(-0.38)-0.077*-0.116***-0.116**-0.156***(-1.79)(-2.61)(-2.37)(-3.04)0.228***

0.282***

0.308***

0.390***(5.57)(6.77)(6.50)(7.76)

6,720

6,719

6,720

6,719

-2216.80-2178.08-2177.60-2125.94

Form of additional
payment (Direct subsidy
as base)Require contract withgovernment
Additional payment per
hectare (3,000 RMB as
base)
4,500 RMB
6,000 RMB
Observations
Log-likelihood

Notes: z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Results of the MNL model for both types appear in the second and the thirdcolumns of Table 6. As shown, all the estimated parameters are statisticallysignificant. Looking at policy objective, for example, the parameter of protection ofunderground water quality is statistically significant and positive compared to thebase (protection of soil quality), whereas the parameter of protection of biodiversity is
statistically significant and negative. This implies that respondents prefer a policy
aimed at protecting underground water quality the most and a policy aimed at
protecting biodiversity the least. Regarding the agri-environmental protection20

assignment, the parameter of assignment purchase pesticides and fertilizers from theprescribed list is statistically significant and negative, while the parameter of straw
recycling is statistically significant and positive. This indicates that respondents prefera policy with straw recycling as the assignment over the base level assignment (plant
winter green manure). However, it also indicates that respondents prefer a policy withthe base level assignment (plant winter green manure) over the assignment ofpurchase pesticides and fertilizers from the prescribed list. For the attribute on
government-provided free technical support, the parameter of the Yes parameter level(i.e., the government provides free technical support) is statistically significant andpositive, which implies that the respondents prefer a policy with government-provided
free technical support over a policy without it. Compared to the base level 10% of
farmers will be supervised, the parameter of 30% of farmers will be supervised isstatistically significant and positive, and the parameter of 50% of farmers will besupervised is statistically significant and negative. This result suggests that the policy
respondents prefer most calls for 30% of farmers being supervised, whereas the policy
respondents prefer least calls for 50% of the farmers being supervised. In addition, thesignificantly negative sign of require contract with government indicates that the
respondents prefer a policy with the direct subsidy provided by the government rather
than a policy requiring a contract with the government. The estimation of the last
attribute, additional payment per hectare, shows that the parameter of 4,500
RMB level is significantly negative, whereas the parameter of 6,000 RMB issignificantly positive. This implies that respondents prefer a policy offering theadditional subsidy of 6,000 RMB per hectare the most and a policy offering 4,500
RMB the least. The estimation results of this monetary attribute exhibit, to someextent, an unexpected U-shaped preference of respondents with regards to the subsidy.
We further checked the frequencies of choosing 3,000 RMB, 4,500 RMB, and 6,000
RMB among the choices of the best policy and found similar results that appeared (i.e.,
the proportion of choosing 3,000 RMB, 4,500 RMB and 6,000 RMB among the
choices of the best policy are 33.93%, 30.28%, and 35.79%, respectively). However,21the frequencies of choosing 3,000 RMB, 4,500 RMB, and 6,000 RMB among the
choices of the worst policy did not show this U-shaped result. Therefore, we infer thatthe unexpected estimation results of additional payment per hectare might beinfluenced by the respondents’ choices of the best policy 1.
In the RPL model, we assumed that the parameters of all attributes follow anormal distribution except for additional payment per hectare. As shown in the fourthand sixth columns of Table 6, there appears to be little difference between the meansof the parameters and the MNL estimates with respect to both signs and significance.
However, the estimated standard deviations of policy objectives, agri-environmental
protection

assignment,

whetherthegovernment

providesfree

technical

support, and monitoring by the government shown in the fifth and the seventh column
are statistically significant, indicating that there exists heterogeneity among
respondents in their preferences for these attributes.

5.2 Estimation results of latent class logit model
Table 7 and Table 8 present the results of class membership and the latent classlogit model. We use six individual characteristics (i.e., male, age, highest educationlevel, household size, number of children under middle school age, and farmland area)
and three questions regarding agri-environmental protection and policy to classify thelatent classes for the BW type and the WB type. The values of CAIC and BIC suggestthat the two classes are optimal and the results of class membership are shown in
Table 7. From the table, we can find that age, number of children under middle schoolage, farmland area, and the questions, “Do you agree that the rural environment will
have a large impact on agricultural production?” and, “How much do you know
about current agricultural and environmental protection policies?” are statisticallysignificant to determine the latent classes in both types. Therefore, Class 1 can be

1 We also implemented the DCE method (Additional payment per hectare is set as a continuous variable and its
quadratic term is also included) in the Appendix to check the results. The same U- shaped preference is also shownin the DCE estimation. In addition, the reason for this U-shaped preference on additional payment per hectare isambiguous and worthy of investigating more deeply in the future.

22

viewed as the respondents who are younger, have more children under middle schoolage, have more farmland area, agree that the rural environment will have a large
impact on agricultural production, and have less knowledge of current agricultural andenvironmental protection policies than Class 2.

Table 7. Results of class membership

Male
Age
Highest education level
Household size
Number of children under middle school age
Farmland area (Hectare)
Do you agree that the rural environment will
have a large impact on agricultural
production?
Do you agree that farmers should be
responsible for the rural environment?
How much do you know about current
agricultural and environmental protection
policies?
Constant

Latent class (BW)(Class 2 as base)

Latent class (WB)
(Class 2 as base)

Class 1

Class 1-0.477(-0.30)-0.196*(-1.80)1.905(1.53)-1.846(-1.06)2.343*(1.73)
2.439**(1.99)-0.593(-0.39)-0.191*(-1.82)
1.884(1.59)-1.915(-1.15)2.378*(1.82)
2.444**(2.07)

3.081**(2.10)

3.053**(2.20)-0.967(-0.91)-6.817*-0.983(-0.95)-6.823**(-2.04)(-1.94)22.689(1.51)

23.048(1.59)

Notes: z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 8 presents the results of the latent class logit model for the BW type andthe WB type. There is little difference in the significance and signs of parameters forboth types, but the magnitudes of parameters. For both types, Class 2 appears to be
more sensitive to the attributes of agri-environmental policy. In Class 1, theparameters of whether the government provides free technical support, form of23

additional payment, and additional payment per hectare present the same significanceand sign with the results of MNL and RPL, whereas other attributes show somedifferences. The parameters of protection of underground water quality and protectionof biodiversity are statistically insignificant compared with the base level protection ofsoil quality in Class 1, which means the respondents in Class 1 consider protection ofunderground water quality and protection of biodiversity to be essentially the sameas protection of soil quality. For the agri-environmental protection assignment
attribute, the parameter of straw recycling is statistically significant and has a positive
sign, which implies that the respondents in Class 1 prefer straw recycling over thebase level plant winter green manure. However, the parameter of purchase pesticides
and fertilizer from the prescribed list is insignificant, which suggests that the
respondents in Class 1 consider purchase pesticides and fertilizer from the prescribed
list to be essentially the same as plant winter green manure. The parameters of 30% of
farmers will be supervised and 50% of farmers will be supervised are both statisticallyinsignificant, which means both the above levels are considered by the respondents inClass 1 to be essentially the same as the base level 10% of farmers will besupervised. In Class 2, most of the parameters show the same significance and signwith the results of MNL and RPL except for the attribute additional payment per
hectare. The parameter of 4,500 RMB is still negatively significant, but the parameterof 6,000 RMB is statistically insignificant. The latter result implies that the
respondents in Class 2 regard receiving the subsidy of 6,000 RMB to be the same asreceiving 3,000 RMB.

Table 8. Estimation results of LCL model
Latent class clogit (BW)

Latent class clogit (WB)

Class1

Class2

Class1

Class2

Protection of underground water
quality

0.025(0.51)

1.082***(12.03)0.011(0.24)

1.055***(11.67)

Protection of biodiversity

0.074-1.355***0.053-1.489***

Policy objective(Protection of soil quality as base)24

Agri-environmental protection
assignment (Plant winter green
manure as base)
Purchase pesticides and fertilizer fromthe prescribed list
Straw recycling(1.55)(-14.25)(1.11)(-14.96)0.010(0.19)
0.135**(2.40)-1.196***(-11.74)
2.125***(15.23)-0.015(-0.30)0.125**(2.25)-1.240***(-11.61)
2.147***(15.22)0.300***(7.37)

0.221***(3.55)

0.359***(8.46)

0.123*(1.91)

0.059(1.07)-0.037(-0.67)0.551***(5.78)-0.375***(-3.81)0.030(0.54)-0.025(-0.47)0.543***(5.50)-0.356***(-3.60)-0.131***(-3.38)-0.219***(-3.48)-0.173***(-4.43)-0.242***(-3.64)-0.102*(-1.77)0.506***(9.09)
6,490
0.629-1878.76-0.209**(-2.39)-0.067(-0.75)
6,490
0.371

-0.144**(-2.46)
0.568***(10.10)6,490
0.630-1827.69-0.177*(-1.90)0.066(0.73)
6,490
0.370

Whether the government provides
free technical support (No as base)
Yes
Monitoring by government (10% of
farmers will be supervised as base)
30% of farmers will be supervised
50% of farmers will be supervised
Form of additional payment (Direct
subsidy as base)Require contract with government
Additional payment per hectare(3,000 RMB as base)4,500 RMB
6,000 RMB
Observations
Predicted percentage
Log-likelihood

Notes: z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1

6. Discussion and conclusion
In this study, we conducted a best-worst scaling method to investigate rural
residents’ attitudes toward agri-environmental policies in China and used MNL and
RPL models to analyze the data. We found that respondents thought the best policy
included protecting underground water quality as the objective, straw recycling as theassignment, technological support provided by the government, a supervision level of25

30%, and a 6,000 RMB subsidy with no contract requirement. Conversely,
respondents thought the worst policy included protecting biodiversity as the objective,
purchasing pesticides and fertilizer from the prescribed list as the assignment, notechnological support provided by the government, an increased supervision level of
50%, and a 4,500 RMB subsidy requiring a contract. The results of the latent classlogit model suggested the respondents who are older, have fewer children under
middle school age, less agree with the rural environment will have a large impact on
agriculture production, have more knowledge of agricultural and environmental
protection would show more sensitivity to the attributes of agri-environmental
policies.
Our results imply that rural residents in Huainan City consider the most urgent
agri-environmental protection to be protection of underground water quality, and thatstraw recycling should be the most efficient way to save the agri-environment. For thesake of encouraging farmers to contribute to a rural environment, the government
should provide free technological support as well as pay a direct subsidy without
requiring a contract. In addition, residents think a modest level of supervision isnecessary but prefer the government not be overly strict about it. As for which group
to target, promotion of this policy should begin with residents who are older and havemore knowledge of agricultural and environmental protection.
Finally, two issues remain for continued research. First, the preference of
respondents on the subsidy amount shows a U-shaped result. The reason is not clear
in this study, so this issue should be investigated further. Second, considering thewidespread variances among provinces in China, there may be significant regional
differences in opinions regarding agri-environmental policies. Thus, the attitudes ofrural residents in other provinces should also be investigated to determine the level ofheterogeneity among different regions in China.
Acknowledgements
Financial support from the Japanese Ministry of Education, Culture, Sports,
Science and Technology through Grants-in-aid for Scientific Research (C) 17885015,
19115990, and 19187135 are gratefully acknowledged. All the views expressed in thispaper and any errors are the sole responsibility of the authors.
26

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Appendix: Estimation results of MNL and RPL using DCE method

Policy objective(Protection of soil quality as base)Protection of underground water quality
Protection of biodiversity
Agri-environmental protection
assignment (Plant winter green manure
as base)Purchase pesticides and fertilizer from theprescribed list
Straw recycling
Whether the government provides freetechnical support (No as base)
Yes
Monitoring by government (10% of
farmers will be supervised as base)
30% of farmers will be supervised
50% of farmers will be supervised
Form of additional payment (Direct
subsidy as base)Require contract with government
Additional payment per hectare(thousands RMB)
Square of Additional payment per hectare
Observations
Log-likelihood

MNL

RPL

S.D.

0.036
(0.42)-0.615***(-6.74)0.087(0.72)-0.999***(-5.83)0.786***(4.48)
1.474***(8.43)-0.105(-1.13)
1.097***(11.50)-0.234*(-1.72)
1.490***(8.63)

0.928***(5.39)
1.377***(7.13)

0.471***(5.87)

0.627***(5.32)

0.776***(4.57)

0.141(1.45)-0.097(-1.03)0.113(0.92)-0.169(-1.43)0.123(0.30)-0.012(-0.04)-0.551***(-7.13)-1.250***-0.698***(-6.93)-1.673***0.075(0.24)(-3.21)0.148***(3.48)
4,032-1277.04(-3.32)0.203***(3.69)
4,032-1205.99

Notes: z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.130