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The Use of Choice Experiments to Value Public Preferences for Cultivated LandProtection in ChinaAuthor(s): Ma Aihui and Zhang JingjingSource: Journal of Resources and Ecology, 5(3):263-271. 2014.Published By: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy ofSciencesDOI: http://dx.doi.org/10.5814/j.issn.1674-764x.2014.03.009URL: http://www.bioone.org/doi/full/10.5814/j.issn.1674-764x.2014.03.009

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J. Resour. Ecol. 2014 5 (3) 263-271 DOI:10.5814/j.issn.1674-764x.2014.03.009www.jorae.cn

Sept., 2014 Journal of Resources and Ecology Vol.5 No.3

Report

Received: 2013-06-12 Accepted: 2014-07-21Foundation: National Social Science Foundation of China (12XGL011), the Youth Project of Ministry of Education (12YJC790136) and

the Central College Fund of Sichuan University (skqy201231).* Corresponding author: MA Aihui. Email: mah718@163.com.

The Use of Choice Experiments to Value Public Preferences for Cultivated Land Protection in China

MA Aihui1* and ZHANG Jingjing2

1 College of Public Administration, Sichuan University, Chengdu 610064, China;2 School of Economics, University of Sydney, NSW 2006 Australia

Abstract: Effective programs and policies for cultivated land resource protection are often the focus of government policy-makers and researchers. Here we use survey data from Wuhan City, Hubei, China to attempt to apply a choice experiment for assessing main stakeholder preferences for cultivated land resource protection based on the hypothesis of market and policy. According to the basic principle of choice experiments, the area of cultivated land, quality of cultivated land, cost of cultivated land protection and cultivated land ecological landscape were included as attributes in the experimental design. Surveys were undertaken on two main stakeholders groups (farmers and urban residents). Our results show strong divergence between farmers and urban residents regarding protecting attributes. We then analyzed and compared welfare changes affected by different attribute combination programs. The result of this study provides theoretical and decision-making support for farmland protection funds and agricultural subsidy systems.

Key words: choice experiments; cultivated land; preference; Wuhan

1 IntroductionChina’s recent accelerated industrialization and urbanization process has resulted in increasing demand for cultivated land resources. The scarring of cultivated land resources and serious degradation of cultivated land bring about a great challenge for cultivated land resource protection, such as the quantity and quality of food, national security, social stability and ecological security.

Effective policies designed for cultivated land protection have aroused the attention of researchers and government policy-makers. A cultivated land ecological compensation system has appeared as an economic incentive for environmental protection. The determination of compensation standards has become critical to the implementation of compensation. Ecological compensation is economic interest redistribution across stakeholders, so we need stakeholders to take part in defining the compensation standard and to consider the welfare of stakeholders. The grass-roots base and strength source of cultivated land protection, public willingness and preferences of cultivated land protection

will have an important influence on the formulation and implementation of cultivated land protection.

How to elicit consumer preferences and willingness? According to the micro-economic theory and the stated preference methods, respondents know their preferences, and their preferences are stable and coherent (Brouwer et al. 2010). This implies that individuals consistently know their preference of certain goods or services. That is to say, whether consumers are willing to buy or pay depends on characteristics or attributes such as price, quality, brand and so on. Here, we elicit the public cognition of different attributes of cultivated land and the willingness to pay from the perspective of public participation in and around the mega-city of Wuhan in central China. Our work will help governments design policies that are more useful for the sustainable development of cultivated land.

2 Choice experiments2.1 Theory and practice of choice experimentsIt has become important to estimate the monetary value of the environment with rising public environmental awareness.

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There are a variety of methods to estimate the monetary value of the environment, such as contingent valuation and travel cost models. These methods can calculate the monetary value of certain goods and services which cannot be traded or directly obtained in the current market. Choice experiments are a new preference technique to evaluate the non-market value of the environment that originated from the Lancasterian microeconomic approach (Lancaster 1966) in which individuals get utility from the attributes of the goods rather than directly from the goods themselves. Choice experiments transform a choice into a utility by using individuals’ stated behavior in a hypothetical setting. Based on surveys, individuals are given a hypothetical setting and choose their preferred alternatives among several alternatives in a choice set, and each alternative includes a number of attributes or characteristics (Alpizar et al. 2001). It is essential that there is a monetary price attribute among several attributes as only in this way can we calculate respondents’ marginal willingness to pay (WTP) for the other attributes. We can access individuals’ preferences and willingness when improving the current situation.

Choice experiments were first used in the sales and transport departments, and were not applied to the fields of environment, health policy and food quality until recently (Mørkbak et al. 2010). Adamowicz et al. (1994) was the first to evaluate non-market valuation by applying choice experiments. Hanley et al. (2006) used choice experiments to estimate a rivers’ value after ecological status improved. Later, choice experiments were used in health and food safety. For example, in order to improve the health and safety of the agricultural environment, Travisi and Nijkamp (2008) used choice experiments to assess economic value with reducing pesticide in Italy. Rambonilaza and Dachary-Bernard (2007) used choice experiments to calculate public preference values for the environmental landscape in land planning. When the effects of European agri-environmental schemes on agriculture were found to be limited, researchers (Espinosa et al. 2010) used choice experiments conducted in two regions (Aragón and Andalusia) and found reasons for farmers’ reluctance to participate. In China, there are also cases of choice experiments for environmental assessment: Jin and Wang (2005, 2006) applied choice experiments to assess solid waste management in Macao; Xu et al. (2003) evaluated ecosystem management in the Heihe River Basin by choice experiments; and Zhai et al. (2007) took China’s sloping land conversion program as a case study, being implemented under the Grain for Green Project.

2.2 The econometric model of choice experiments

Choice experiments have their roots in Lancaster’s characteristics theory of value, random utility theory and experimental design (Brouwer et al. 2010).

Choice experiments assume that utility depends on respondents’ choices of the available sets. Then individuals’

utility can be described as: Uij = Vij + εij = Vi (xj, Tj) + εij (1)

where, Uij is a total utility which individual i choose over some other options j; Vij is system observable components of utility function; εij is one random and unobservable components; x j represents specific at tr ibutes and characteristics which individual i choose alternative j; and Tj denotes the pay monetary attribute. In the choice set C, the probability can be expressed as:

Pij(j/C)=Pr(Uij>Uik; ∀k∈C)

=Pr(Vij − Vik ≠ ≥εik −εij; ∀k∈C) (2)Consumer behavior is the maximization of a utility

subject to a budget constraint.Maximum likelihood functions are as follows:

ln ln ijiji j

L = P ,d∑∑ (3)

where, dij is a dummy variable (if choice j is 1, otherwise is 0). Assuming the random error terms are independent from each other, and belong to the Gumbel distribution, then the selection probability Pij can be expressed by the Multinomial Logit Model (MLM):

(4)

here, σ is a scale parameter which is usually assumed to be equal to 1.

The utility in Multinomial Logit Model can be described as:

V(x,T)=P∑βpxp+βTT (5)

where , x p i s re la ted to the a t t r ibutes of se lected characteristics; T is monetary attribute characteristics; and βp, βT are the estimated coefficients of selection of attributes and economic characteristics, respectively.

Based on the conditions of maximization of individual utility, then Equation (5) can be written as:

(6)

Based on the estimation of model and the maximum utility level, when dV equals 0, each attribute value of environmental goods (WTP) can be expressed as:

(7)

The value of each attribute combination equals the different preference between the initial state and ultimate state, which can be written as:

(8)

where, CS means WTP; V0 is initial state utility; and V1 is ultimate state utility.

MA Aihui, et al.: The Use of Choice Experiments to Value Public Preferences for Cultivated Land Protection in China 265

3 Attributes and experimental design3.1 Identifying attributes for the choice designThe first step is to identify the relevant attributes. As a scarce, artificial ecological system, cultivated land has multi-dimensional functions. It provides not only food and fiber, which is of great importance to national food security, but also acts as an ecological barrier in protecting the environment and landscape. However, we tend to ignore the ecological functions of cultivated land during economic development.

With economic development and increasing population, the benefit of cultivated land is relatively low, leading to price discrimination between agricultural and industrial products which accelerates the reduction rate of cultivated land. This will also change the structure and function of the land ecological system. Chemical fertilizers and pesticides are overused in agriculture production, with serious negative influences on cultivated land productivity and natural ecological systems.

The decrease in the quantity and quality of cultivated land resources makes the ecosystem function more vulnerable and less independent. Under this situation, the central government of China is paying more attention to the ecological function of cultivated land. In the new round of land use planning the government stresses farmland as ecological barriers and claims that a larger area of basic farmland and more well-facilitated cultivated land will help to conserve beautiful landscapes and harmonious environments.

Hence, to optimize the cultivated land ecosystem function, we need to control the decreasing rate of cultivated land, curb direct or indirect activities that reduce the quality of cultivated land, and maintain the balance of cultivated land ecosystems.

Mazotta and Opaluch (1995) found when a choice set included more than four to five attributes it leads to a severe detriment in data quality due to complexity. So, we only have four attributes of cultivated land protection in this study: the quantity of cultivated land, quality of cultivated land, cultivated land surrounding landscape and ecological environment and the costs to protect cultivated land.

3.2 Level of attributes

The level of attributes is of great importance, so researchers must ensure that the attributes selected and their levels are consistent with the practical situation and easy to understand. It is necessary to include the status quo and optimal level when identifying attribute levels. Maybe the more attributes levels there are, the better the result

is, however, the respondents will face more choice. Many researchers have found that complexity of choices affects the preferences of respondents and model results (Adamowicz et al.1994; Mazotta and Opaluch 1995; Swait and Adamowicz 1996).

Since the quantity of cultivated land is declining seriously, we expect it will increase through cultivated land protection; and since the quality cultivated land is decreasing we expect i t can improve after the implementation of protection. It is same for the ecological landscape. Any change in the maximum price level had a statistically significant effect on both the general preference structures and WTP estimates. Hence, when defining the cost attribute, besides taking into account the range of cost attributes, we also consider the maximum level of the price attributes. The levels of cost attribute used here are based on the CVM interview in Wuhan and on information from a previous survey. The previous CVM survey showed that the price of 50 CNY appears highest probability for an open-ended survey; the highest frequency that the public are willing to accept is the choice between 101 and 120 CNY for payment card survey; and the second is between 151 and 200 CNY. So, 0, 50, 100, and 200 CNY are determined as the cost attributes for the comprehensive survey. Table 1 shows the attributes and their levels in choice experiments.

3.3 Defining choice sets

Each level of each attribute is combined with other levels of other attributes to obtain the number of alternative programs. We have 32 alternatives in which 3 factors 2 levels and 1 factor 4 levels generate a 23 × 4 combination. Respondents would be confronted with many choices, and experience pressure during the process of answering questions. Taking into account the costs of research, quality and possibility of completing the questionnaire, we expect to have less as well as maximum accuracy of the estimates. In a multiattribute multilevel context of choice the identification and efficiency of the estimates depends crucially on the choice of experimental design, i.e. how attributes and attributes levels are combined to create alternatives in the choice sets to be presented to respondents (Ferrini and Scarpa 2007). Recently, researchers have developed design techniques called orthogonal designs which can calculate the optimal combinations of attributes and attribute levels in the experiment; thus, we can use fewer experiments to achieve an efficient design. We used fractional factorial orthogonal design in our questionnaire and the unpractical alternatives are removed, only leaving the orthogonal term (Zhang et al. 2008).

A mixed orthogonal test table was used to implement

Table 1 Attributes and levels in choice experimental design.

Attributes Acreage of cultivated land ... Quality of cultivated land ... Cost Ecological landscapeLevel Reduction, keep unchanged Decline, improvement 0, 50, 100, 200 Decline, improvement

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experiments due to different attribute levels in this study. According to the formula (Xu et al. 2005), experimental number =∑(number of level – 1) + 1. To calculate and check the orthogonal test table, each respondent need answer at least eight questions (Table 2).

In this design, the orthogonal table determines the alternative combination, in which number 1 represents the baseline alternative (there is no protection action), and number 2 means every attribute reaches a rational and optimum state without paying any cost (which cannot happen according to the basic theory of economics).

According to the actual situation, we determined seven choice sets and each choice set include two alternatives. Alternative A represents the current cultivated land state in which no protection action was taken. That is to say, the changes we evaluate in Alternative B are the improved situation compared with the current situation. Table 3 is an example of a choice set.

4 Survey and dataGenerally, simple random data accuracy depends on the budget available for the survey. However, Scheaffer provides a formula to calculate the minimum sample size (Cai et al. 2008). According to the formula, statistic principle (4% error) and interest group, we required at least 625 samples. This study is drawn from two surveys based on personal interviews carried out in rural areas and cities. In total, 850 questionnaires were completed: 744 questionnaires are valid, including 383 from farmers and 361 from urban residents.

Data were obtained from face-to-face surveys undertaken in Wuhan region from September to October 2010. Most interviews took place in respondents’ houses or recreational areas and each interview took around 30 min.

Questionnaires sent to the respondents consisted of three parts. (i) Respondents’ cognition of cultivated land protection issues. (ii) The choice experiment sets about cultivated land protection. There are two choice experiment sets in our study, each set contains two programs, program A is the current state of cultivated land; each of the other programs is compared with program A. In this way, we have seven different choice sets among which respondents were asked to make their own choice. The choice task was repeated as many times as the number of choice sets built for the experimental design. In each case, the respondent

was asked to choose option A, option B or neither. And (iii) the third part of the questionnaire contained questions about socio-economic characteristics, such as income and education characteristics.

5 Econometric model and estimation 5.1 Defining model variables We analyzed the effect of attributes on the probability of choosing a given program using the main effect model, in which the coefficients of the attributes can be interpreted as marginal utilities. Without considering random error, the random utility model can be expressed in the form of linear functions of attribute vector (Z1, Z2, Z3 and Z4). In order to quantify the attribute value of farmland protection programs, we take the attributes as independent variables to construct the choice model. The attribute variables included four aspects, acreage of cultivated land, cultivated land quality, surrounding landscape and ecological environment, and payment for farmland protection. These can be used to analyze the factors and attributes that influence respondents’ selections. To explain individual choice, we use a linear utility model. The linear utility index for alternative i in choice j is defined as:

Vij=ASC+β1Z1,ij+β2Z2,ij+β3Z3,ij+β4Z4,ij (9)

where, ASC is the constant variable; β is parameter of attribute which affects the utility of the respondents; Z1 means the acreage of cultivated land; Z2 is quality and fertility of cultivated land; Z3 represents ecological landscape attribute; Z4 is the payment for farmland protection; and β4 is the marginal income coefficient, the model parameters have economic significance and marginal contribution.

People commonly choose the alternative that can maximize their utilities or well-being. In general, with increase in acreage, the improvement in the quality of the surrounding landscape, ecological environment of cultivated land, and public life will be more comfortable, have a stronger sense of happiness, and public utility will be higher. However, when the higher the public need to pay for cultivated land, the lower their utility will be. Therefore, the acreage of cultivated land, fertility and quality of cultivated land, surrounding landscape and ecological environment are positively related to indirect utility, while the attribute, the cost of protection cultivated land are negatively correlated

Table 3 A choice set from choice experiment.

Attribute Alternative A Alternative BAcreage of cultivated land. Reduction Keep unchangedQuality of cultivated land Decline DeclineEcological landscape Decline DeclinePer capita cultivated land cost

(CNY)0 50

I would select A ( ) I would select B ( ) Neither select A nor B ( )

Table 2 The design of orthogonal table (L8 41 × 23).

NumberColumn number The experimental

numberColumn number

1 2 3 4 1 2 3 41 1 1 1 1 5 3 1 2 22 1 2 2 2 6 3 2 1 13 2 1 1 2 7 4 1 2 14 2 2 2 1 8 4 2 1 2

MA Aihui, et al.: The Use of Choice Experiments to Value Public Preferences for Cultivated Land Protection in China 267

with choice utility, or the coefficient of Z4 is negative.When considering random error, the utility function can

be expressed as: Vij=ASC+β1Z1,ij+β2Z2,ij+β3Z3,ij+β4Z4,ij+βaS1+βbS2

+ ... +βmSk (10)where, Vij, ASC, Z1, Z2, Z3, Z4, β1, β2, β3 and β4 have the same meaning as Equation (9); S1, S2 … Sk are individual characteristics, which have effects on utility; and βa, βb … βm represent the coefficients of individual characteristics respectively.

Owing to the fact that stakeholders in this study are farmers and urban residents with different social and economic characteristics, the questionnaire is divided into two categories.

In order to simplify the econometric analysis we define the dependent and independent variables as below (Table 4).

With the aid of R software, we used two different multinomial logit models to analyze the econometrics. The dependent variable of model 1 is the probability of choice in each choice set. The independent variables of model 1 contain four attributes of cultivated land protection. The dependent variable in model 2 is the same as in model 1, but the independent variables not only contain the four attributes, but also include the socio-economic characteristics of respondents.

5.2 Econometric results

The final results of the econometric regressions are provided in Tables 5 and 6, which show that attributes affect utility value. The parameters of attributes of the utility function for farmers can be seen in Table 5, while that for urban residents’ can be seen in Table 6. In model 1, we only consider cultivated land protection levels, neglecting the variability of socio-economic characteristics. The acreage, the quality as well as the surrounding landscape and ecological environment of cultivated land have positive effects on public utility, but the cost of protecting cultivated land has a negative impact on public utility. The attribute’ coefficients are consistent with economic utility theory.

5.2.1 Estimates of farmers

Both model 1 and model 2 have passed the overall significant test, and all attributes (area, fertility and quality, the surrounding landscape and environment, pay cost) are significant at the level of 5% (Table 5). The results of the models are consistent with reality, indicating that the questionnaire design and the choice of the model are reasonable as well as scientific.

In the two different models all the attributes have statistically significant effects on their choices, and the models in Table 5 show attributes with positive effects on the probability of making a choice, however, the price has a negative relationship with the probability of making a

Table 4 Variable assignment in the choice model.

V names Variable assignmentDependent variable Alternatives scheme Alternative A or C=0, alternative B=1Independent variable of attributes Acreage of cultivated land Reduction=0, keep unchanged=1

Quality of cultivated land Reduction =0, improvement =1Surrounding landscape and ecological environment Reduction =0, improvement =1Payment for cultivated land protection cost 0, 50, 100, 200

Table 5 The results of farmers.

Note: “·”, “*”, “**” and “***” indicating significant at 10%, 5%,1% or 0.1% level.

Model 1 Model 2Estimate Std. Error t value Pr (>|t|) Estimate Std. Error t value Pr (>|t|)

ASC 0.3724 0.0631 5.9020 0.0275 * 0.39013 0.0098 39.9673 <2e–16***Z1 0.3591 0.0466 7.7141 0.0164 * 0.3428 0.0059 58.4341 <2e–16***Z2 0.5464 0.0466 11.7361 0.0072 ** 0.5283 0.0046 114.5866 <2e–16***Z3 0.6085 0.0466 13.0704 0.0058 ** 0.6211 0.0061 101.1471 <2e–16***Z4 –0.0138 0.0004 –38.7381 0.0007*** –0.0141 0.0043 –323.5390 <2e–16***The numbers need to be fed –0.0027 0.0016 1.7105 0.0876 ·Cultivated land scale 0.0011 0.0003 4.0904 4.6e–05***Environmental quality 0.0110 0.0024 4.5751 5e–06 ***Land fragmentation 0.0030 0.0006 5.0621 4.9e–07***Adjusted R2 0.9961 0.9912F statistical value 381.8 14690

Journal of Resources and Ecology Vol.5 No.3, 2014268

choice. The payment increases as the probability of choice decreases, as expected. The coefficients show marginal increase in the probability of choosing alternative programs when compared to base levels.

Through the results of continuous stepwise regression, only four variables of 15 indicators have significant impacts on utility, namely the number of family that need to be fed, cultivated land scale, and the quality of regional environmental and cultivated land fragmentation. The results show that farmer social status and living conditions directly influence the final choice of cultivated land protection. Although many respondents are concerned about the protection of cultivated land, they chose to keep the current situation. The reason is whether they have the economic ability to pay. However, after joining economic variables into utility, we found it is not significant; affordability is just one of the reasons but is not the main reason. In fact, the real reason is whether the respondents are really concerned about cultivated land protection (they doubt whether alternative programs can achieve any real effects). Consequently, the economic ability serves as a reason for the answer “neither”.

5.2.2 Estimates of urban residents

Both model 1 and model 2 have passed the overall significant test, all attributes are significant at the level of 5% (Table 6). Model 2 shows that only six variables of the 14 indicators are significant: the number family need to be fed, education lever, family income, regional environment quality and whether heard of the concept of ecological compensation or not. However, the variable of age is not significant.

Respondents with a high level of education tend to give

more support to farmland protection policy; the more family numbers that need to be fed the less attention they pay to farmland protection policies and the more they are inclined to maintain the status quo. The higher the economic level of the household, the more they payment. It seems that people in urban areas where the environmental quality is relative higher have not realized that the quality and area of cultivated land have improved their lives and they prefer to maintain the status quo. The variable of whether urban residents ever heard of ecological compensation has a negative correlation with choice. Perhaps these people do not understand or accept this economic mode or are looking forward to better policies to solve the problem of environment and social development.

5.2.3 Estimates attribute value

According to parameter results, assuming other attributes are unchanged, we can evaluate the marginal value of an attribute compared to baseline levels. The marginal value is the public’s willingness to pay, which means in order to improve the level of one element, how much the farmers (urban residents) are willing to pay equals the element’s implicit price.

According to Equation (7) and the results of Tables 5 and 6, we calculated the value of various attributes (Table 7). The relative importance of elements can be ranked as ecological environment, quality of cultivated land, and acreage of cultivated land. The results indicate that the ecological environment level is the most important characteristic when choosing a program of cultivated land protection policy. To the respondents, the marginal value of the three attributes is different, especially for urban residents. The value of ecological landscape and

Table 6 The results of urban residents.

Note: “·”, “*”, “**” and “***” indicating significant at 10%, 5%, 1% or 0.1% level.

Model 1 Model 2Estimate Std. Error t value Pr (>|t|) Estimate Std. Error t value Pr (>|t|)

ASC 0.0258 0.0374 0.690 0.5615 –0.0366 0.0132 –2.5460 0.011020 *Z1 0.1528 0.0276 5.537 0.0311* 0.1430 0.0039 36.7270 <2e–16 ***Z2 0.4243 0.0276 15.374 0.0042** 0.4585 0.0036 128.320 <2e–16***Z3 0.9004 0.0276 32.622 0.0009*** 0.9419 0.0040 234.056 <2e–16 ***Z4 –0.0061 0.02105 –29.071 0.0011** –0.0061 0.0029 –205.555 <2e–16***Age 0.0002 1.36E–4 1.425 0.1545Education level 0.0065 1.54E–3 4.231 2.5e–05***The family number need to be fed –0.0033 1.76E–3 –1.875 0.0609 · Family income 0.00002 8.00E–6 1.991 0.0467 * Environment quality –0.0069 2.04E–3 –3.366 0.0008 ***Whether heard concept of

ecological compensation–0.0052 2.13E–3 –2.456 0.0142 *

Adjusted R2 0.9851 0.9851F Statistical value 403.5000 8691

MA Aihui, et al.: The Use of Choice Experiments to Value Public Preferences for Cultivated Land Protection in China 269

environment is 147.11–154.41 CNY, which is about two times the value of fertility and quality of cultivated land (69.33–75.16 CNY) and six times the value of acreage of cultivated land (23.44–24.97 CNY).

5.2.4 Alternative scheme value

Based on Equation (8) as well as model results, we calculated alternative program values (Table 8). Equation (8) can be further re-written as (Ma et al. 2012; Ma and Zhang 2013; Xu et al. 2003):

CS=− (ASC +∆area·β1 +∆quality·β2

+∆environment·β3) (11)where, ∆β equals the coefficient of attributes multiplied by the subtraction between the attribute levels; and ASC is a constant variable. According to choice sets of the program, we have seven different options; welfare can be calculated using Equation (11). Indirect utility function implies compensating surplus, which means how much respondents are willing to pay when some attribute levels increase simultaneously.

No matter how farmers and urban residents make their choices, option 7 is the best option (Table 8) because all attribute levels are improved in option 7. The respondents expect no reduction in the acreage of cultivated land, improving fertility and quality of cultivated land, higher quality of the landscape and surrounding environment, and that their lives will be more pleasant. Farmers are looking forward to making all aspects of cultivated land better, and

their minimum willingness to pay is 133.50 CNY per year to achieve this kind of improvement; urban residents are willing to pay higher per year, 246.44–247.02 CNY, under the same circumstances.

6 Discussion and conclusions6.1 Differences between groups regarding the attributes

of cultivated land protectionUsing the choice experiment method we found differences in the preferences of different stakeholders (farmers and urban residents) and that calculating the value of protecting cultivated land at the attribute level can assess multi-attribute protection programs.

Combining the results of model 2 and Table 7, farmers’ estimated marginal value of the three attributes showed fewer differences. For the surrounding landscape and environment, the marginal value is 6.58 CNY more than fertility and quality of farmland, and 13.16 CNY more than the acreage of cultivated land. For urban residents, the difference is larger: the marginal value of surrounding landscape and environment is 79.25 CNY more than fertility and quality of farmland. The largest difference between preferences lays in changing the attribute of the ecological environment. Urban residents are willing to pay 154.41 CNY to improve the ecological environment, while farmers are willing to pay 44.05 CNY. For the attribute of fertility and quality, the willingness of urban residents is two times farmers’, however, their preferences for the acreage of cultivated land attribute is basically equal.

6.2 Preference heterogeneity among groups to conserve cultivated land is large

Calculating the value of protecting cultivated land resource at the attribute level can assess other multi-attribute protection programs. In a given area, after applying econometric modeling, the results reveal some interesting divergences on landscape preferences between different stakeholders. Preference heterogeneity is also affected by two socio-economic factors: income level and education level.

Table 8 The value of different alternative programs relative to the status quo.

Alternative scheme

Attribute Relative value (farmer) Relative value (urban residents)Acreage of

cultivated landQuality of

cultivated landLandscape and environment Model 1 Model 2 Model 1 Model 2

The status quo 0 0 0Alternative 1 0 0 1 71.08 71.72 151.83 148.41Alternative 2 1 1 0 92.60 89.45 98.84 92.61Alternative 3 0 1 1 110.67 109.19 221.39 223.57Alternative 4 1 0 0 53.01 51.98 29.28 17.44Alternative 5 0 1 0 66.58 65.14 73.79 69.16Alternative 6 1 0 1 97.10 96.03 176.88 171.85Alternative 7 1 1 1 136.70 133.50 246.44 247.02

Table 7 Attribute value of farmland resource protection.

AttributeFarmers Urban residents

Model 1 Model 2 Model 1 Model 2Acreage of cultivated land 26.02 24.31 24.97 23.44Quality of cultivated land 39.59 37.47 69.33 75.16Surrounding landscape and

Ecological environment44.09 44.05 147.11 154.41

Journal of Resources and Ecology Vol.5 No.3, 2014270

Among different cultivated land protection programs, all respondents regard option 7 as the best option. The reason is that the WTP for this option is the highest. There is a big difference between their WTP: urban residents are willing to pay an annual 247.02 CNY while farmers are willing to pay 133.50 CNY per year, implying that urban residents have higher income levels and higher environmental awareness than farmers. The choices of both farmers and urban residents are strongly correlated with regional environmental quality, but their directions are opposite. This can be explained because when compared to farmers, urban residents pay more attention to the improvement of the surrounding landscape and ecological environment in their daily lives resulting from cultivated land.

6.3 Limitations of the study

Despite the rapid development of choice experiment methods, the technique remains immature. It remains evidently difficult to draw precise conclusions considering WTP in choice experiments and further research is needed. In particular, the level of Chinese environmental awareness is not high and if respondents ignore the cost attribute, it is impossible to accurately estimate his or her marginal WTP for other attributes (Carlsson et al. 2010). This implies that studies that do not take into account respondents’ ignorance of the cost attribute are likely to give biased welfare estimates. However, the sensitivity of awareness to cultivated land and environment is a problem that is not easily solved and future research should aim at increasing our understanding of this problem.

In addition, our results may affect the underlying preferences of WTP because of cost levels. When the higher maximum price was presented to respondents they may prefer to pay more money to obtain the utility of goods, but in order to decrease the alternative choices we only set four price levels. Last, this evaluation was a trial, and its accuracy and authenticity need further research.

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中国居民耕地资源保护偏好研究：选择实验法的应用

马爱慧1 ，张晶晶2

1 四川大学公共管理学院，成都 610064；

2 悉尼大学经济学院，澳大利亚 悉尼 NSW2006

摘 要：耕地资源保护有效方案与政策一直是国内外政府决策者和学术界关注的热点。本文利用武汉市农民和市民实地调查数据，基于选择模型法构建假设的政策与交易市场，模拟在此假设市场下主要相关利益群体耕地资源保护偏好意愿。依据选择实验法的基本原理选取耕地面积、耕地质量、耕地周边景观与生态环境、支付耕地保护费用作为环境商品的属性。首先考查了主要相关利益群体的偏好，并获得了对于保护属性主要相关利益群体农民和市民不同偏好的实验证据；然后分析不同环境商品属性组合方案所可能引起的福利变化水平。本文研究所得出的结论，为中国的农地保护制度和目前实施的耕地保护基金、农业补贴制度提供理论上的支持和决策参考。

关键词：选择实验法；耕地；偏好；武汉