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Procedia Engineering 174 ( 2017 ) 835 – 842

13th Global Congress on Manufacturing and Management, GCMM 2016 The Analysis of Farmers’ Willingness to Accept and Its Influencing Factors for Ecological Compensation of Poyang Lake Wetland

Kai Xionga,b, Fanbin Kongb,c,*

aNanchang Institute of Technology, No.289, Tianxiang Avenue, High-Tech Industrial Development Zone, Nanchang 330099, China b Institute of Poyang Lake Eco-economics, Jiangxi University of Finance and Economics, Yuping Avenue, State-level Nanchang Economic & Technological Development Zone, Nanchang 330013, China cJiangxi Academy of Social Sciences, No.649, North Hongdu Avenue, Qingshanhu District, Nanchang 330077, China

Abstract

Based on the household-level survey data, the authors adopt the contingent valuation method (CVM) and Ordinal Logistic model to study the farmers’ willingness to accept and its influencing factors for ecological compensation of Poyang Lake Wetland. Results show that 87.80% of farmers are willing to accept ecological compensation, with an average price of $858.81/household per year. The influencing factors that significantly influence farmers’ WTA include household education years, number of family members, Source of income, residential location, emphasis on improvement of wetland resources, arable land area, and contracted water area. © 20172016 Published The Authors. by Elsevier Published Ltd. Thisby Elsevier is an open Ltd access. article under the CC BY-NC-ND license (Peerhttp://creativecommons.org/licenses/by-nc-nd/4.0/-review under responsibility of the organizing). committee of the 13th Global Congress on Manufacturing and Management. Peer-review under responsibility of the organizing committee of the 13th Global Congress on Manufacturing and Management Keywords: Poyang Lake Wetland; ecological compensation; willingness to accept; contingent valuation method; ordinal logistic model

1. Introduction

Wetland is known as the kidney of the earth [1], and it plays an extremely important role for alleviating environmental pollution. The wetland farmer is the natural owner and user of the Poyang Lake Wetland, however, protection measures implemented by the government on wetland had a huge influence on their income. According to the principle of equal rights and responsibilities of the ecological environment protection, the income loss of farmers should obtain proper economic compensation. Under the market economy condition, ecological compensation

* Corresponding author. Tel.: +86-791-8212-6285; fax: +86-791-8212-6285. E-mail address: [email protected]

1877-7058 © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the 13th Global Congress on Manufacturing and Management doi: 10.1016/j.proeng.2017.01.230 836 Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842

mechanism is very important and effective management approach on global ecological protection areas [2]. At present, the establishment of the ecological compensation mechanism of Poyang Lake Wetland has been officially included in the agenda of the central and local governments in China. However, the work is still faced with the difficulties that compensation object is difficult to determine. Based on research experience at home and abroad, exploring the rights and implementation issues of the direct interests impaired persons of wetland resources in the Ecological Compensation Mechanism, is not only the weak point of the wetland eco-compensation mechanism, but also is the difficulty which need to be overcome.

2. Literature Review

Contingent Valuation Method (CVM) is a typical stated preference valuation method [3], which investigate and inquiry WTA (Willingness to Accept) of the stakeholder on loss of the quality of environment and resource under the condition of the hypothetical market. CVM make the WTA of inquirers to estimate the economic value of environmental quality losses. This method does not need to establish explicit linkages between non-market commodity and the market price [4], but simply create a hypothetical market environment and get the value of the respondents of public goods [5]. The method is currently widely used in the research of forest [3], wetland [6], water [7] and so on. Furthermore, there is a lot literature about the assessment of influencing factors for farmers’ willingness to accept [8]. The tobit model [9], binary logistic model [10], multivariate logistic model, and log-lin model [11] were applied for the analysis which is about influencing factors of the WTA. However, the literature which is about the assessment of influencing factors for wetland farmers’ WTA is obviously deficient, and the binary logistic model is mainly applied for the study[10]. Moreover, the previous studies for wetland ecological compensation did not reflect the differences of region and household characteristics. The purpose of this research is to find the difference of farmers’ WTA, and propose ecological compensation scheme which is reflecting the differences of region and household characteristics. Based on the above considerations, the authors used contingent valuation method to analyze farmers’ WTA and its level of Poyang Lake wetland. Meanwhile, the authors established the ordinal logistic model to quantitatively analyze the main influencing factors for farmers’ WTA of the ecological compensation, whereby propose relevant policy suggestions. The study has important complementary value for perfecting farmers’ ecological compensation theory and method of the lake wetland of China.

3. Data and Methods

3.1. Area and Data

Poyang Lake wetland have been listed as a wetland of international importance in 1992, which is important for maintaining the security of region and country [12].The State Council has approved and established the Poyang Lake Ecological Economic Zone, which is divided into the lakeside controlled development area, efficient and intensive development area and core-protected area [11]. This study examines the lakeside controlled development and core protected areas.

Table 1. Classification of the study area. Type Region Ratio Area I Yugan, Duchang, Poyang >20% Large II Xingzi, Jinxian, Xinjian, Hukou, Yongxiu 10%–20% Medium III Dean, Gongqing cheng, Lushan, Nanchang <10% Small

Farmers of Poyang Lake wetland areas are mainly engaged in traditional primary industries, including cultivation, fish breeding, poultry raising and so on. In order to facilitate subsequent comparative studies, the study adopts the data from the “2012 Statistical Yearbook of Poyang Lake Ecological Economic Zone” and divides the 12 counties/areas into 3 types based on the ratio of the primary industry production value to the gross regional domestic Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842 837

product. The categories are demarcated as seen in Table 1, with small, medium, and large depicting ratios <10%, 10%–20%, and>20% [11], respectively. The data used in this study are sourced from the 2013, 2014 and 2015 household surveys designed to assess farmers’ WTA for ecological compensation of the Poyang Lake Wetland. The study conducted door-to door interviews for 384 respondents, of which 336 were found to provide reliable data. This number is more than the minimum required sample size, and thus the sample is statistically valid for deducing the total population. In order to ensure an effective and unbiased study, the sampling method adopts three-stage sampling method (Table 2).

Table 2. Sampling methods. Stage Sampling Unit Number Method 1 Town 24 Stratified sampling 2 Village 24 Probability proportionate to size sampling (PPS) 3 Household 384 Simple random sampling (SRS)

In the first stage, two towns are selected from each county type seen in Table 1 using the stratified sampling method (The sample frame is all of the towns of 13 counties which belongs to the lakeside controlled development and core protected areas.). In the second stage, the authors select one village from each of the selected towns using the probability proportionate to size sampling (PPS) method (The sample frame is all of the villages of selected towns in the first stage.). In the last step, the authors select households from these villages using the simple random sampling method, and then, the authors survey the households (The sample frame is all of the farmers of selected villages in the second stage.). In total, 336 (The total population is wetland farmers in the Poyang lake area.) of 384 questionnaires are found to be effective.

3.2. Research Methods

The CVM and Ordinal logistic regression model are used to quantitatively analyze the obtained household survey data. 3.2.1. Contingent Valuation Method In this study, the authors accurately assessed WTA using the valuation method. The authors used an open-ended WTA questionnaire so that responders would not be restricted by defined values (as in binary choice or closed- ended questions) [13]. The authors minimized missing responses and explained questions more clearly using face- to-face interviews [14]. Responses to open-ended questionnaires are likely to minimize standard error and lower estimates of central tendency [15], preventing bias [13]. In addition, the authors finalized the WTA questionnaires and the pre-testing process with experts to guarantee validity and make the questionnaire more clear to respondents. In addition, WTA values are calculated based on mathematical expectation (discrete variables), and the formula is expressed as below: ௡

ܧൌܹܶܣ ൌ෍ߛ௜ܲݎ௜ ሺͳሻ  ௜ୀଵ Where ߛ୨ stands for the amount farmer i is willing to accept, ܲݎ௜ represents the probability that farmer i will accept that amount, and n stands for the sample size of farmers. 3.2.2. Ordinal Logistic Regression Model Variables: Ten indicators/variables are designed [16, 17] to evaluate the changes in WTA of farmers in the study area (Table 3).

Table 3. Variables and description. Variable Unit Description

Gender (X1) Male = 1, Female = 0 These variables evaluate the possible impacts on

Age (X2) Years farmers’ WTA, using individual and household-level 838 Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842

Educated time (X3) Years information.

Number of family members (X4) Persons

Annual household income (X5) Dollar ($)

Source of income (X6) Aquaculture = 1, Cultivation = 2, Region III = 3

Residential location (X7) Region I = 1, Region II = 2, Region III = 3 This variable evaluates the impacts of the farmers’ Emphasis on improvement of Yes = 1, No = 0 concern and knowledge about environmental issues wetland resources (X8) pertaining to wetlands on WTA.

Arable land area (X9) Acres These variables examine whether the cultivation area

Contracted water area (X10) Acres and contracted water area impact farmers’ WTA.

Model selection: This study uses Ordinal logistic regression model to estimate the factors influencing WTA. Dependent variable is farmers’ WTA, and it has been divided into no WTA, low WTA, medium WTA, and high WTA. No WTA, low WTA, medium WTA, and high WTA are 0, 1, 2, and 3, respectively. Specifically, the model is expressed as follows: ᇱ ൅ߙூܺூ ൅ ߚሺʹሻڮ ൌߙ଴ ൅ߙଵܺଵ ൅ߙଶܺଶ ൅ߙଷܺଷ ܻ ᇱ ǡߙூڮIn the equation (2), ܻ is the latent variable, which represents the degree of farmers’ WTA. ߙ଴ǡߙଵǡߙଶǡߙଷǡ ǡܺூ are theڮ are coefficients that will be estimated while examining the factors affecting farmers’ WTA. ܺଵǡܺଶǡܺଷ explanatory variables, and ߚ is the residual term. ᇱ Ͳ݂ܼ݅୧ ൑ߝଵ ᇱ ۓ ͳ݂݅ߝଵ ൏ܼ୧ ൑ߝଶ ܻ௜ ൌ ᇱ ሺ͵ሻ ߝଶ ൏ܼ୧ ൑ߝଷ݂݅ʹ۔ ᇱ ୧ ൐ߝଷܼ݂݅͵ ە

In the equation (3), ܻ௜ represents the observed value of the level of the farmers’ WTA. ᇱ In order to make ܻ௜ and ܻ have strong correlation, ܻ௜ respectively take the probability of 0, 1, 2, or 3 as shown below: ܲ ሺܻ ൌͲȁܺ ǡ߲ǡߝሻ ൌܨሺߝ െ߲ܻ ሻ ఌ ௜ ௜ ଵ ௜ ௜ ۓ ܲ ሺܻ ൌͳȁܺ ǡ߲ǡߝሻ ൌܨሺߝ െ߲ܻ ሻ െܨሺߝ െ߲ܻ ሻ ఌ ௜ ௜ ଶ ௜ ௜ ଵ ௜ ௜ ሺͶሻ ሺߝଶ െ߲௜ܻ௜ሻܨሺߝଷ െ߲௜ܻ௜ሻ െܨఌሺܻ௜ ൌʹȁܺ௜ǡ߲ǡߝሻ ൌܲ۔ ሺߝଷ െ߲௜ܻ௜ሻܨఌሺܻ௜ ൌ͵ȁܺ௜ǡ߲ǡߝሻ ൌͳെܲ ە

In the equation (4), ܨ is the cumulative distribution function of ߚ௜. This study uses the ordinal logistic model, therefore, the authors assume that ܨ obey Logistic distribution. Specifically, the model is expressed as follows: ͳ ሺܻ௜ ൌͳȁܺ௜ሻ ൌ ሺͷሻ ͳ൅݁ିሺఉ೔ାడ೔௑೔ሻ The equation (5) takes the natural logarithm, and the final model is expressed as follows: ܲ௜ Ž ൬ ൰ൌȾ௜ ൅߲௜ܺ௜ሺ͸ሻ ͳെܲ௜

4. Empirical Study

4.1. WTA and Obtainment Levels

As shown in Table 4, 87.80% of farmers have positive WTA, while 12.20% of farmers do not.

Table 4. Frequency of willingness to accept. WTA Number Sample Size Proportion Yes 1 295 87.80% No 0 41 12.20% Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842 839

The authors use Equation (2) to estimate the obtainment levels in regions I, II, and III. The results appear as Equations (7)–(10) (see Figure 1). ௡

ܧሺܹܶܣூሻ ൌ෍ߙ௜ܲݎ௜ ൌ ͳ͵ͺ͸Ǥͷ͵ሺ͹ሻ  ௜ୀଵ ௡

ܧሺܹܶܣூூሻ ൌ෍ߙ௜ܲݎ௜ ൌ ͹͵ʹǤͺ͵ሺͺሻ  ௜ୀଵ ௡

ܧሺܹܶܣூூூሻ ൌ෍ߙ௜ܲݎ௜ ൌ ͶͳͲǤʹʹሺͻሻ  ௜ୀଵ ௡

ܧሺܹܶܣ݈݈ܽሻ ൌ෍ߙ௜ܲݎ௜ ൌ ͺͷͺǤͺͳሺͳͲሻ  ௜ୀଵ

Fig. 1. Distribution of farmers’ obtainment levels in the Poyang Lake Wetland area.

These values are expressed in US $ (The value measured as the Chinese currency (RMB Yuan) is converted into US $ value by the average exchange rate in 2014–2015 (RMB 6.1428 yuan to one dollar)) per household per year. The results in Equations (7)–(10) and Figure 1 show that the obtainment levels of all farmers toward the ecological compensation of Poyang Lake Wetland area is $858.81/household per year. The highest obtainment level, $1386.53/household per year, occurs in region I. The second-highest level of household obtainment, $732.83/household per year, occurs in region II. The lowest level of household obtainment, $410.22/household per year, is seen for region III. The results also indicate that the higher the regional agricultural production, the higher the obtainment levels of the household willing to accept for ecological compensation [18]. 840 Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842

4.2. Results of the Regressions

Ordinal logistic model is applied using SPSS16.0. The farmers’ WTA is used as the dependent variables, while household characteristics are used as the independent variables. The result is shown in Table 5, Table 6, Table 7 and Table 8.

Table 5. Model Fitting Information. Model -2 Log Likelihood Chi-Square df Sig. Intercept Only 888.258 - - - Final 499.192 390.066 12 .000

The results in Table 5 show that the model passed the test of significance.

Table 6. Goodness-of-Fit. Model Chi-Square df Sig. Pearson 751.584 801 .893 Deviance 495.844 801 1.000

Learn from Table 6, the authors find that the indexes of Pearson and Deviance are not statistically significant. Therefore, the results of indexes show that the goodness of fit of the model is good.

Table 7. Pseudo R-Square. Model R-Square Model R-Square Model R-Square Cox and Snell .687 Nagelkerke .738 McFadden .436

Learn from Table 7, the authors know that the independent variables have a high degree explanation on the dependent variable.

Table 8. Parameter Estimates. Variables Estimate Std. Error Wald df EXPሺߚሻ [X1=.00] -.383 .381 1.011 1 1.467 a [X1=1.00] 0 - - 0 - X2 -.008 .010 .654 1 1.008 X3 -.234*** .050 22.052 1 1.264*** X4 .478*** .087 30.323 1 1.613*** X5 -2.8E-006 2.0E-006 1.896 1 1.000 [X6=1.00] 2.429*** .417 33.979 1 11.348*** [X6=2.00] .792** .348 5.191 1 2.208*** a [X6=3.00] 0 - - 0 - [X7=1.00] 2.080*** .354 34.600 1 8.004*** [X7=2.00] 1.229*** .376 10.672 1 3.418*** a [X7=3.00] 0 - - 0 - [X8=.00] 1.882*** .304 38.324 1 6.567*** a [X8=1.00] 0 - - 0 - X9 .172*** .040 18.461 1 1.188*** X10 .059*** .019 9.447 1 1.061*** Note: ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

4.3. Factors Affecting WTA

The ordinal logistic model indicated in Table 8 shows that educated time (X3), number of family members (X4), source of income (X6), residential location (X7), emphasis on the improvement of wetland resources (X8), arable land area (X9), and contracted water area (X10) are significantly related to WTA, while gender (X1), age (X2), and household income (X3) do not show statistical significance. X3 is statistically significant with WTA, and the coefficient is negative, which means that farmers obtaining more years of education have lower WTA. It may be that when a farmer owning higher level of education, he can realize Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842 841 the protection for Poyang Lake wetland and the improvement on wetland environmental are likely to be more beneficial to his production and life, and therefore, such farmers are more willing to have lower WTA. X4 is statistically significant with WTA, and the coefficient is positive, indicating that peasant households who have more family members are more willing to accept compensation. It may be that the protection on wetland is more likely to decrease the income level of farmers. That is, the more the family members are, the higher it influences. Therefore, the peasant households who have more family members are more willing to have higher WTA. X6 is statistically significant with WTA, and the coefficient is positive, which means that farmers relying mainly on aquatic products and farming products for their incomes have stronger WTA. The WTA of the farmer whose income is from aquaculture is 11.35 times larger than the WTA of the farmer whose income is from otherwise. Meanwhile, the WTA of the farmer whose income is from cultivation is 2.21 times as larger as the WTA of the farmer whose income is from otherwise. It may be that when a farmer’s household income is sourced mainly from aquaculture, cultivation, and other traditional industries, the protection for wetland is likely to be more harmful to him, and therefore, such farmers are more willing to accept the compensation. X7 is statistically significant with WTA, and the coefficient is positive. The WTA of the farmer living in region I is 8.00 times higher than the farmer living in region II, and the WTA of the farmer living in region II is 3.42 times as high as the farmer living in region III, which means that farmers living in regions I and II have stronger WTA than those living in region III. This may be that the different levels of agricultural production in regions I, II, and III may affect farmers’ WTA; farmers living in regions I and II will earn more from their ecological environment, and therefore, they have a stronger WTA. X8 is statistically significant with WTA, and the coefficient is positive, which means that farmers who don’t pay close attention to wetland environmental improvements are more willing to accept compensation. The WTA of the farmer who don’t pay close attention to wetland environmental improvements is 6.57 times higher than the WTA of the farmer who pay attention to wetland environmental improvements. It may be that farmers who focus on the wetland environment are willing to improve the wetland environment, which enhances their willingness to reduce the compensation in order that the government has more money to improve the wetland environment. X9 is statistically significant with WTA, and the coefficient is positive. Thus, the higher the arable land, the stronger the WTA. It may be that farmers with higher arable land earn more revenue from farming. Given the relation between farming and the quality of the wetland environment, such farmers are willing to accept more compensation due to a series of improvement measures for wetland environment (e.g., reuse farmland for lake). X10 is statistically significant with WTA, and the coefficient is positive; the more contracted water area the farmers have, the stronger their WTA. It may be that farmers with more contracted water area earn well from fishing. Thus, if the government enhances the intensity of environment protection, their incomes from fishing would reduce. As a result, they are willing to accept much more compensation.

5. Implications

In order to effectively improve the efficiency level of the farmers’ WTA for the ecological compensation of Poyang Lake Wetland, it is necessary to promote the establishment and implementation of the Poyang Lake Wetland ecological compensation mechanism. The following policy implications would serve the purpose. First, the government should actively carry out publicity and education work in order to improve farmers’ emphasis on wetland environment. Experience has shown that farmers who pay close attention to wetland environmental are more willing to accept lower compensation. Therefore, the government should step up publicity and education work for strengthening communication and exchanges with wetlands farmers, so that farmers continue to increase awareness of wetland ecological and environmental protection. Then they will be aware of the importance of Poyang Lake wetland environment allowed, and realize the importance of Poyang Lake wetland environment protection and improvement, to reduce farmers WTA turn makes the government to put more money into the wetland environmental protection. Secondly, increase investment in education and improve teaching quality. The empirical results show that the education years and farmers’ WTA are significantly negative correlation. That is, the farmers who have higher level of education are more willing to accept lower compensation. Therefore, the government should increase education investment in the countryside of Poyang Lake wetland, to make more farmers especially school-age children get more education resources and opportunities. They will make its full knowledge 842 Kai Xiong and Fanbin Kong / Procedia Engineering 174 ( 2017 ) 835 – 842

and understanding the importance of the wetland environment, and continuously improve the environmental awareness of wetland, adding to the protection for Poyang Lake wetland. Third, establishing an ecological compensation system database to serve rural households around lakes should be the government’s priority. This exercise may be taken up as part of an annual census, wherein the relevant government departments would collate all the information related to ecological compensation, such as household income sources, arable land area, contracted water area, etc. Improved survey data would provide an important foundation for assessing farmers’ WTA and establishing a scientific ecological compensation system of the lake wetland. Fourth, the establishment of a discrepant ecological compensation standard, and do not pursue the “one size fits all”. According to the characteristics of farm households, the government who fully considering the heterogeneity of the characteristics of different farm households (i.e., source of income and residential location) could set out different ecological compensation disbursement criteria. In accordance with empirical results, the government should give the farmers who relying mainly on aquatic products and farming products for their incomes a higher compensation standards, meanwhile the farmers who living region I should acquire a higher compensation fund for ecological protection.

Acknowledgements

This research was funded by the Science and Technology Research Project of Education Department of Jiangxi Province (GJJ151098), Jiangxi Provincial Social Science "12th Five-Year" planning project (15YJ34).

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