Affiliation:
1. Shenyang Agricultural University, Shenyang, Liaoning 110866, China
2. School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
Abstract
In this paper, we broadly generalize the assignment auction algorithm to solve linear minimum cost network flow problems. It is significant to establish a market-based compensation mechanism by way of conservation auctions based on peasant households’ willingness, which can promote the innovation of ecocompensation policies, green development, and balanced growth. Using the survey data collected from 453 households within 3 national pilot counties in ecologically fragile regions in northwest Liaoning for the Sloping Land Conversion Programme, measuring peasant households’ willingness to accept ecocompensation through sealed auctions, we built a database through cloud computing to realize information collation and query and applied the Heckman’s Two-Step Model to study the impact of risk preference, social capital, cognitive preference, land parcel characteristics, and family endowments on farmers’ willingness to participate in protection auctions and their bid prices. The results reveal that the average bid price of peasant households in the ecologically fragile region in northwest Liaoning for the Sloping Land Conversion Programme is annually 274.5 yuan per mu and that risk preference and social capital have positive impacts on peasant households’ willingness to participate in conservation auctions and on their bid prices, cognitive preference has a positive impact on peasant households’ bid prices in conservation auctions, and land plot characteristics have a negative impact on peasant households’ bid prices in conservation auctions. It is suggested that ecocompensation policies should be optimized with such methods as lowering peasant households’ perception of high risks, setting role models for them to follow, and strengthening their perception of the environment, income, and property rights.
Funder
General Projects of National Social Science Fund
Subject
Computer Science Applications,Software
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