Spatiotemporal analysis and multiple scenarios prediction of sustainable ecosystem in China based on ecological footprint method

Dynamic changes of sustainable ecosystem in China have been assessed with long-term series from 1949 to 2006, and the ecosystem sustainability of 30 provinces in 1986, 1996, 2002 and 2006 are analyzed with ecological footprint index (EFI) and ecological footprint efficiency (EFE). The two indices are proposed based on ecological footprint (EF) method. Then, the fluctuant cycles of per capita EF and bicapacity (BC) in China 1949-2006 are decomposed and extracted based on EMD method, and series nonlinear dynamic predictive models are presented with the cycles. Three Forecasting scenarios are analyzed based on their predictive models according to three EF scenarios presented in Living Planet Report 2006 published by WWF et al. (2006). Over last 57 years, China’s EFI has reduced sharply with fluctuation. The change of EFE is very slowly before 1980, subsequently, is sharply increased. There are 6, 5, 12, and 7 provinces which are running ecological surplus in 1986, 1996, 2002, and 2006, respectively. There are 14, 16, 11, and 16 provinces which EFI are smaller than -100% in 1986, 1996, 2002, and 2006, respectively. The provinces with the highest EFE are Shanghai, Beijing, and Tianjin, and the lowest are Xinjiang, Guizhou, Hainan, Ningxia, et al. in 1986, 1996, 2002, and 2006, respectively. The obvious undulation cycles of per capita EF in China are 4.8 years and 10.9 years, and the periods of per capita BC are 3.03 years, 8.35 years, 14.25 years, and 28.15 years. The business-as-usual scenario looks at the consequence that per capita ED would be 11.200hm2 and EFI would be -1307.19% in China in 2050. The slow-shift scenario shows per capita ED would be 0.728hm2 and EFI would be -84.96% in 2050. The rapid-reduce scenario shows per capita ED in China would be 0.498hm2 in 2050 and 0.261hm2 in 2100, respectively. EFI of rapid-reduce scenario would be -58.11% in 2050. China could denote sustainability at the global level if slow-shift scenario and rapid-reduce scenario are implemented.