An agent-based decision tool to explore urban climate smart city possibilities

摘要

This paper investigates alternative ways to construct a decision tool intended to help the Delaware Valley Regional Planning Commission (DVRPC) region to meet or exceed the goal of 80% reduction in the emission of Green House Gas (GHG) by 2050. The goal is to explore and build several pre-prototypes to evaluate the value of the role for ABM, alternative data sources (Census, energy reports, DVRPC surveys, etc.), GIS modeling, and various social science theories of human behavior (land value theory, economic disparity theory, cognitive learning theory, etc.). Section 2 presents a model of the business as usual scenario that uses trend extrapolation to project energy consumption and GHG production until 2050. Section 3 then explains initial research on an Agent Based Model (ABM) with which users can investigate the role of attitude, information awareness, and economic disparities upon consumer choice of residence location and transportation mode. Finally, we conclude with some lessons learned and challenges for scaling.