An evolutionary systematic framework to quantify short-term and long-term watershed ecological compensation standard and amount for promoting sustainability of livestock industry based on cost-benefit analysis, linear programming, WTA and WTP method

Environ Sci Pollut Res Int. 2021 Apr;28(14):18004-18020. doi: 10.1007/s11356-020-11769-x. Epub 2021 Jan 6.

Abstract

In order to achieve at better water quality of a given trans-boundary river mainly contributed by high-intensive and spatially dispersed pig farming at upstream area, an effective ecological compensation system is in urgent need. In this study, an evolutionary bottom-up framework of ecological compensation system was proposed to analyze the tradeoffs of behavior among the pig farmers, government of upstream area, and government of downstream area. Shutting down pig farms, upgrading traditional piggeries to elevated bed piggeries, and adopting centralized facilities for disposing wastes from small-scale pig farms are three effective measures to control pollution from pig farming and were considered into this study. The combined use of cost-benefit analysis, linear programming, willingness to accept and willingness to pay method, and its application to a typical case of Jiuzhou River, China, showed good performance to quantify short-term and long-term watershed ecological compensation standard and amount for promoting sustainability of livestock industry. Besides, we also proposed a framework of long-term reward and punishment compensation mechanism binding upon both sides for maintaining good water quality. The proposed systematic and feasible framework of methodology has important theoretical and application significance for other similar related researches and enriched the field in paying for good water quality.

Keywords: China; Compensation amount; Compensation standard; Livestock and poultry; Watershed ecological compensation.

MeSH terms

  • Animals
  • China
  • Cost-Benefit Analysis
  • Livestock*
  • Programming, Linear*
  • Reference Standards
  • Swine