Decision-Making Support System for Low-Carbon Urban Master Plan of New Town

Abstract

AbstractDue to urban and rural planning plays a strategical role in ecology, agricultural space protection, and urban layout, has become oriented toward attaining China’s goal of hitting peak emissions and achieving carbon neutrality. In this regard, we will build a platform with combined Constraint Cellular Automata (CCA) approach and Agent-Based Model (ABM) approach to simulate the carbon emission volume in urban area effected by urban master plan. We will take Xixian new town, one of the typical new towns in China as a case study area to employ the platform to identify the key planning factors that play effective roles on reducing carbon emission in urban master plan. Through scenario analysis, it is found that the effects of changing public transportation sharing rate and the degree of mixed land use on reducing carbon emission is not obvious. Instead, when the planning scheme is adjusted in terms of changing land use structure, the increase or decrease in emissions caused by different types of land use varies greatly. Meanwhile, the emission intensity of industrial land is the main influencing factor for low carbonization in New Towns, followed by the degree of mixed land use and the public transportation sharing rate.KeywordsLow-carbon cityCarbon neutralityUrban master planCellular automata (CCA)Agent-based model (ABM)Decision-making support system