Using agent-based modeling to assess multiple strategy options and trade-offs for the sustainable urbanization of cultural landscapes: A case in Nansha, China
Jingyi Liu, Menghan Zhang, Xia Yu, Huisen Zheng, Chongxian Chen
Abstract
Many cultural landscapes are undergoing inevitable urbanization. For these landscapes to attain sustainability, participatory planning processes are often required. However, different stakeholders may prefer different strategies and goals, making decision-making extremely difficult. This paper explores how three urbanization strategy options (based on lay stakeholders’ willingness, historical landscape pattern, and land-use planning) and their possible trade-offs can affect cultural landscapes’ sustainability. Specifically, it examines 1) how these strategies’ varying adoption levels affect different landscape functions, and 2) how their different trade-offs affect sustainability perceived by different stakeholders. We conducted a case study in the Nansha District of Guangzhou, China. Based on interviews with lay and expert stakeholders, we developed an agent-based model to experiment with a range of scenarios reflecting different trade-offs of the three strategies. With 11 indicators and their stakeholder-weighted aggregation assessed in every simulation step, we analyzed the strategies’ effects and compared five typical scenarios’ sustainability on two timescales. The results showed that the scenario prioritizing lay stakeholders’ willingness outperformed other scenarios in the sustainability assessment. Following historical patterns performed well in the ecological aspect and in the near term, but it may fall short in intensive urbanization from non-experts’ perspectives. Land-use planning was plausible as a static destination but ineffective during the entire process. The findings suggest that sustainable rural development should prioritize local adaptations; landscape pattern preservation and land-use planning must consider place-specific processes based on stakeholder participation. They also suggest the usability of agent-based modeling and time-series analysis in cultural landscape and sustainability research.