Farmers' Heterogeneous Willingness to Pay for Farmland Non-Market Goods and Services on the Basis of a Mixed Logit Model-A Case Study of Wuhan, China

Int J Environ Res Public Health. 2019 Oct 12;16(20):3876. doi: 10.3390/ijerph16203876.

Abstract

The exploration of different stakeholders' heterogeneous willingness to pay for farmland ecological value is a fundamental part of understanding the total value of farmland protection and designing a scientific farmland protection policy. Unlike the homogenous assumption used in the previous studies, the mixed logit model of choice experiment method was applied to estimate respondents' heterogeneous willingness to pay for farmland non-market value (represented by farmland area, farmland fertility, water quality, air quality, species richness, and recreational value) in this study. Data came from a sample of 289 farmers in Wuhan, China who were face-to-face interviewed. Results showed that: (1) Farmers were unsatisfied with the status quo of the present farmland ecological environment and were willing to pay to preserve all the attributes of farmland non-market value. (2) Farmers had a heterogeneous preference for the status quo and recreational value-the error variances of these two attributes were both significant at the 1% level, and their willingness to pay for the farmland non-market value in Wuhan was 1141.88 Yuan/hm2. (3) Farmers' cognition degree of farmland importance and whether respondents bought medical insurance or not had significant impacts on their willingness to pay. The results can provide the basic foundation for accurate valuation of farmland non-market services, help farmland regulators make the right farmland conversion decisions, and improve the resource allocation efficiency of local financial expenditure during farmland protection in Wuhan.

Keywords: farmers; farmland non-market value; mixed logit; preference heterogeneity; willingness to pay.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Agriculture / economics*
  • Agriculture / statistics & numerical data*
  • China
  • Compensation and Redress*
  • Conservation of Natural Resources / economics*
  • Conservation of Natural Resources / statistics & numerical data*
  • Farmers / psychology*
  • Farmers / statistics & numerical data*
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged