13. Urban Form, Transportation Energy Consumption, and Environment Impact Integrated Simulation: A Multi-agent Model

More energy is being consumed as urbanization spreads. Extensive research has found that a dominant share of urban energy consumption belongs to transportation energy, which has a strong relationship with urban form in the intracity level. However, little attention has been paid to the relationship between urban form, transportation energy consumption, and its environmental impact in the inner-city level. This chapter aims to investigate the impact of urban form, namely, the land-use pattern, distribution of development density, and the number and distribution of job centers on the residential commuting energy consumption (RCEC). We developed a multi-agent model for the urban form, transportation energy consumption, and environmental impact integrated simulation (FEE-MAS). Numerous distinguishable urban forms were generated using the Monte Carlo approach in the hypothetical city. On the one hand, the RCEC for each urban form was calculated using the proposed FEE-MAS; on the other hand, we selected 14 indicators (e.g., Shape Index, Shannon’s Diversity Index, and Euclidean Nearest Neighbor Distance) to evaluate each generated urban form using the tool FRAGSTATS, which is loosely coupled with the FEE-MAS model. Afterward, the quantitative relationship between the urban form and RCEC was identified using the calculated 14 indicators and RCEC of all generated urban forms. Several conclusions were drawn from simulations conducted in the hypothetical city: (1) the RCEC may vary three times for the same space with various urban forms; (2) among the 14 indicators for evaluating urban form, the patch number of job parcels is the most significant variable for the RCEC; (3) the RCECs of all urban forms generated obey a normal distribution; and (4) the shape of an urban form also exerts an influence on the RCEC. In addition, we evaluated several typical urban forms—e.g., compact/sprawl, single center/multicenters, traffic-oriented development, and greenbelt—in terms of the RCEC indicator using our proposed model to quantify those conventional planning theories. We found that not all simulation results obey widely recognized existing theories. The FEE-MAS model can also be used for evaluating plan alternatives in terms of transportation energy consumption and environmental impact in planning practice.