How to promote residents' use of green space: An empirically grounded agent-based modeling approach

One focus of those responsible for making urban policies has been the improvement of green space effectiveness, including environmental plans and eco-city initiatives. In the evaluation of policy effectiveness, residents' needs, values and preferences are critical but often overlooked. This study proposes an agent-based model (ABM) for simulating the effectiveness of policy measures on residents' decision making with regard to the use of green space. Using a residential questionnaire survey conducted in Shanghai, China, we model individual decision making with artificial neural networks that account for the heterogeneous characteristics and imperfect rationality in the decision-making process, and compare three policy scenarios in local green space provision. The results of the model illustrate the unequal effectiveness of green space policies among different social groups and different types of green space (i.e., urban parks, neighborhood parks, and community gardens), and the sensitivity analysis suggests the key factors in different stages of green space provision. Based on the results, we argue that tailored policies are needed in order to meet residents' heterogeneous needs; in fact, relatively soft policies, particularly those that promote social interaction and participation, play a significant role in the appeal of green space use. Finally, policy suggestions are provided for the optimization of green space provision.

Automated summary

This study proposes an agent-based model to simulate the effectiveness of green space policy measures on residents' decision-making. The results illustrate the unequal effectiveness of different policy scenarios among different social groups and types of green space. Tailored policies are needed to meet residents' heterogeneous needs, and soft policies promoting social interaction and participation play a significant role in the appeal of green space use.