China is currently the world's largest energy consumer. The rapid growth in energy consumption has resulted in many problems in this country. The Chinese government has realized the necessity of improving energy efficiency and reducing energy consumption. As a useful decision-support tool, simulation models can be used to examine the potential impacts of different plans on urban development and energy consumption. This study presents a model that integrates support vector regression (SVR) and cellular automata (CA) to simulate urban forms and to estimate the corresponding energy consumption in one of the most developed regions in China, the Pearl River Delta (PRD). SVR is used to predict energy consumption and to project future urban size. The logistic CA model simulates different urban forms to evaluate their effects on energy consumption. In this study, we simulated four scenarios to assess the impacts of different development strategies on urban forms and the related energy consumption. For each scenario, we used the model to predict land demand and energy consumption. The result indicates that land demand is more sensitive to changes of economic structure than is energy consumption. The comparison of different simulated scenarios suggests that promoting low-energy-consuming industries is the most effective strategy to balance economic development and energy and land consumption.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Earth-Surface Processes