As urbanization rapidly advances around the globe, there is a simultaneous increase in human welfare and an increase in resource and environmental burdens. In 2008, the world's urban population exceeded its rural population for the first time in history, and urban areas have entered the spotlight in various conflicts between humans and nature. Research into solving urban problems has thus become key to the reconciliation of many such conflicts. As the world's second-largest economy, China is a rapidly developing country, and it is affected by many kinds of urban problems; such problems are likely to become exacerbated and more complex. Statistics indicate that urbanization in China has rapidly increased from 17.92% at the beginning of its reform and opening-up in 1978 to 46.6% in 2009. In the process, urban settlements have undergone transformation in terms of large scale, fast pace of restructuring, and diversification. To study the changes in efficiency before and after this urban transformation took place has great implications for establishing sustainable human settlement in China. Urban metabolism studies, which are considered an important approach toward building sustainable cities and improving the urban operational efficiency, are a suitable means of investigating the effects of settlement transformation on urban metabolic efficiency. Through a literature review of settlements, settlement forms, and morphological analysis, typical urban settlement forms have been identified in this study as old neighborhood, traditional welfare neighborhood, average commercial neighborhood, modern commercial neighborhood, and peri-urban neighborhood. Using a functional classification method, an urban metabolic system classification framework was designed as the basis for developing a dynamic model for an urban metabolic system. Through model simulation, the changes in efficiency can be analyzed as one typical settlement form transforms into another. Xiamen in China was selected for the case study area, and a questionnaire interview was used to collect the data: 44 neighborhoods were chosen for the questionnaire interview, and 1482 responses were received. Emergy analysis was applied to calculate the efficiency of different settlement forms. Vensim software was used to monitor the effect of settlement transformation on the dynamic model for the urban metabolic efficiency system, and four scenarios were designed in connection with development patterns in China. Through scenario simulation, the following findings were made. Generally, all settlement-transformation processes increase welfare efficiency; however, in most cases negative trends in overall urban material metabolic efficiency appeared, with declines in transport efficiency and housing efficiency being the main reasons. The highest growth in welfare efficiency appeared in the scenario of an old neighborhood transforming into a traditional welfare neighborhood, but it was accompanied with the largest fall in overall urban metabolic efficiency. Although there was the least welfare-efficiency improvement in the scenario of an old neighborhood transforming into a peri-urban neighborhood, it was the only scenario in which the overall urban metabolic efficiency increased-and by a large margin. In the scenario of an average commercial neighborhood transforming into a modern commercial neighborhood, the decline in the overall urban metabolic efficiency and increase in welfare efficiency were both moderate. This was a typical transformation model whereby consumption was traded for welfare. Similarly, in the scenario of a peri-urban neighborhood transforming into a modern commercial neighborhood, there was an average increase in welfare efficiency and an average decrease in overall urban metabolic efficiency. The scenario of an old neighborhood transforming into a peri-urban neighborhood was the only scenario in which the overall urban material metabolic efficiency increased. In the scenario of a peri-urban neighborhood and average commercial neighborhood transforming into a modern commercial neighborhood, the increase in welfare efficiency and decrease in overall urban material metabolic efficiency were both moderate.