Exploratory analyses of local institutions for climate change adaptation in the Mongolian grasslands: An agent-based modeling approach

There has been a decrease in grazing mobility in the Mongolian grasslands over the past decades. Sedentary grazing with substantial external inputs has increased the cost of livestock production. As a result, the livelihoods of herders have become more vulnerable to climate variability and change. Sedentary grazing is the formal institutional arrangement in Inner Mongolia, China. However, this may not be an efficient institutional arrangement for climate change adaptation. Self-organized local institutions for climate change adaptation have emerged and are under development in the study area. In this study, we did exploratory analyses of multiple local institutions for climate change adaptation in the Mongolian grasslands, using an agent-based modeling approach. Empirical studies from literature and our field work show that sedentary grazing, pasture rental markets, and reciprocal pasture-use groups are three popular institutional arrangements in the study area. First, we modeled the social–ecological performance (i.e., livelihood benefits to herders and grassland quality) of these institutions and their combinations under different climate conditions. Second, we did exploratory analyses of multiple social mechanisms for facilitating and maintaining cooperative use of pastures among herders. The modeling results show that in certain value-ranges of some model parameters with assumed values, reciprocal pasture-use groups had better performance than pasture rental markets; and the comparative advantage of cooperative use of pastures over sedentary grazing without cooperation becomes more evident with the increase in drought probability. Agent diversity and social norms were effective for facilitating the development of reciprocal pasture-use groups. Kin selection and punishments on free-riders were useful for maintaining cooperation among herders. 2013 Elsevier Ltd. All rights reserved.