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Assessing the opportunity costs of Chinese herder compliance with a payment for environmental services scheme

Karl Behrendt, Colin G. Brown, Guanghua Qiao, Bao Zhang

2021Ecological Economics13 citationsDOIOpen Access PDF

Abstract

Addressing serious grassland degradation without exacerbating already low herder incomes is a major challenge for the Chinese government. In response, the Grassland Ecosystem Subsidy and Award Scheme (GESAS) was introduced in 2011 where herders receive payments if they comply with specified stocking rates set at more sustainable levels. However, compliance with GESAS, as well as low herder incomes in some years, is an ongoing issue. Using a stochastic, dynamic bioeconomic model of representative herder households in the desert steppe grasslands of Inner Mongolia Autonomous Region, the opportunity costs for herders in meeting specified stocking rates under different states of nature are identified and compared with GESAS payments. In addition, the impacts on productivity and environmental services provided by herders operating within or outside of GESAS are identified. The results highlight states of nature under which no incentivising payments are needed for compliance or when the opportunity costs greatly exceed the GESAS payments, thereby increasing the risk of non-compliance. The study highlights the need to unbundle the environmental incentive and welfare components of GESAS if the twin objectives are to be achieved, and the need to define and understand the distribution of possible outcomes in designing grassland and ecocompensation policies.

Topics & Concepts

Ecosystem servicesOpportunity costPaymentIncentiveNatural resource economicsSubsidyBusinessPayment for ecosystem servicesGovernment (linguistics)WelfareEconomicsEnvironmental resource managementPublic economicsEcosystemFinanceEcologyLinguisticsMicroeconomicsBiologyPhilosophyMarket economyNeoclassical economicsRangeland Management and Livestock EcologyConservation, Biodiversity, and Resource ManagementLand Rights and Reforms