Authors: Ying Wang*, China University of Geosciences, Qi Zhang, The Frederick S. Pardee Center for the Study of the Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA 02215, USA , Conghe Song, Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA, Richard Bilsborrow, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
Topics: Human-Environment Geography
Keywords: Spatially Explicit Agent-Based Model; Social-Ecological Systems; Land Use; Labor Allocation; Agro-Environmental Policies
Session Type: Virtual Paper
Start / End Time: 4:40 PM / 5:55 PM
Room: Virtual 45
Presentation File: No File Uploaded
Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we develop a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households’ land and labor allocation decisions and investigate the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs reveal that the forest restoration program accelerates out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on parcels with good quality, but appears less effective for preventing marginal croplands from abandonment. The policy effects on labor allocation substantially differ between bounded rational and empirical knowledge rules defining household behavior, particularly for sending out-migrants and engaging in local non-agricultural jobs. The land use patterns show that the extent to which households maximize overall benefits by increasingly shrinking croplands is generally higher under bounded rationality than under empirical knowledge. Findings demonstrate that the social-ecological impacts of agro-environmental policies are nonlinear through time and can deviate from policy expectations due to complex interplays between households and land entities. This study suggests that spatial agent-based modeling is versatile in capturing key characteristics of social-ecological systems.