China has been experiencing an unprecedented urbanization process. In 2011, China’s urban population reached 691 million with an urbanization rate of 51.27%. Urbanization level is expected to increase to 70% in China in 2030, reflecting the projection that nearly 300 million people would migrate from rural areas to urban areas over this period. At the same time, the total fertility rate of China’s population is declining due to the combined effect of economic growth, environmental carrying capacity, and modern social consciousness. The Chinese government has loosened its “one-child policy” gradually by allowing childbearing couples to have the second child as long as either of them is from a one-child family. In such rapidly developing country, the natural growth and spatial migration will consistently reshape spatial pattern of population. An accurate prediction of the future spatial pattern of population and its evolution trend are critical to key policy-making processes and spatial planning in China including urbanization, land use development, ecological conservation and environmental protection. In this paper, a top-down method is developed to project the spatial distribution of China’s future population with considerations of both natural population growth at provincial level and the provincial migration from 2010 to 2050. Building on this, the spatial pattern and evolution trend of Chinese provincial population are analyzed. The results suggested that the overall spatial pattern of Chinese population will be unlikely changed in next four decades, with the east area having the highest population density and followed by central area, northeast and west area. Four provinces in the east, Shanghai, Beijing, Tianjin and Jiangsu, will remain the top in terms of population density in China, and Xinjiang, Qinghai and Tibet will continue to have the lowest density of population. We introduced an index system to classify the Chinese provinces into three categories in terms of provincial population densities: Fast Changing Populated Region (FCPR), Low Changing Populated Region (LCPR) and Inactive Populated Region (IPR). In the FCPR, China’s population is projected to continue to concentrate in net immigration leading type (NILT) area where receives nearly 99% of new accumulated floating population. Population densities of Shanghai, Beijing, Zhejiang will peak in 2030, while the population density in Guangdong will keep increasing until 2035. Net emigration leading type (NELT) area will account for 75% of emigration population, including Henan, Anhui, Chongqing and Hubei. Natural growth will play a dominant role in natural growth leading type area, such as Liaoning and Shandong, because there will be few emigration population. Due to the large amount of moving-out labors and gradually declining fertility rates, population density of the LCPR region exhibits a downward trend, except for Fujian and Hainan. The majority of the western provinces will be likely to remain relatively low population density, with an average value of no more than 100 persons per km2.
Chinese cities are plagued by the rise in resource and energy input and output over the last decade. At the same time, the scale and pace of economic development sweeping across Chinese cities have revived the debate about urban metabolisms, which could be simply seen as the ratio of output to resource and energy input in urban systems. In this study, an emergy (meaning the equivalent solar energy) accounting, sustainable indices of urban metabolisms, and an urban metabolic system dynamics model, are developed in support of the research task on Chinese cities ‘metabolisms and their related policies. The dynamic simulation model used in the paper is capable of synthesizing component-level knowledge into system behavior simulation at an integrated level, which is directly useful for simulating and evaluating a variety of decision actions and their dynamic consequences. For the study case, interactions among a number of Beijing’s urban emergy components within a time frame of 20 years (from 2010 to 2030) are examined dynamically. Six alternative policy scenarios are implemented into the system simulation. Our results indicate that Beijing’s current model of urban metabolism—tertiary industry oriented development mode—would deliver prosperity to the city. However, the analysis also shows that this mode of urban metabolism would weaken urban self-support capacity due primarily to the large share of imported and exported emergy in the urban metabolic system. The keys of improving the efficiency of urban metabolism include the priority on the renewable resource and energy, increase in environmental investment and encouragement on innovative technologies of resource and energy utilization, et al.
Urban systems, with the overall fluxes of energy, water, material, and wastes, can be modeled with a range of metabolic processes. To quantify the urban metabolism, we use the “emergy” assessment method (all materials and energy are transformed to solar energy equivalents) and then present a group of urban metabolic indicators, which quantify urban metabolic balance, capacity, and outputs to assess a city’s metabolic efficiencies. In this paper, we use 31 Chinese cities as a sample to illustrate how the model can be operated to evaluate the urban metabolism by emergy analysis. Our results indicate that metropolises and coastal cities were more metabolically efficient with higher metabolic balance, capacities, and outputs; but with more external dependency on imported resources. Central and western cities had lower metabolic efficiencies, with a high ratio of non-renewable emergy reliance. Policy implications highlight the need for renewable energy sources and improved management of imported services, goods, and fuels to achieve higher urban resilience and sustainability.
The Harvard-China Project adopted an Open Access policy in September 2017. Most journal articles published henceforth are available in the Harvard University open-access repository, DASH.