Desertification directly threatens more than 250 million people and one third of the earths land surface. Although it is well known that desertification could be reversed in most cases if the intensity of land use were reduced, there have been no studies on how to achieve this reversed desertification on a large scale. We conducted a case study in Hunshandak Sandland of North China, exploring how creation of a nature reserve might aid restoration of a degraded ecosystem. Experimental data indicated that desertified regions, if designated as a nature reserve, could be restored with conservation of biodiversity. The buffer zones in moderately desertified lands could serve as a base for forage production and/or ecotourism industry. The construction of ecologically designed towns (ecotowns) in transition zones could accommodate migrants moved from core zones so as to develop stock production, related industry, and ecotourism, enabling both economic and environmental development. Up to now, 5778 local inhabitants in the core zones of Zhenglan Banner (county) in the Hunshandak Sandland have been moved out of the severely degraded areas with the financial assistance of the central government. Those people have been moved into three eco-towns of the Banner with an objective of greatly enhancing the economic and social status while restoring the degraded sandlands. 相似文献
The ecological footprint value (abbreviated as EF) is the quantitative indicator on evaluating the sustainable development status of a region. How to simulate the EF’s trend with a long-time data series has been heatedly discussed. The economic development of Suzhou, one of the most developed cities in Yangtze Delta, China, has been accelerated in the past 20 years, and it is necessary to evaluate the influence of the socioeconomic growth on local natural resources. The EF values of Suzhou from 1999 to 2018 were calculated and simulated using both the ARIMA model and the GM(1,1) model. The ARIMA model has been used in the prediction of EF values in several cases. However, the EF data series of the city consisted of white noise and could not be fitted by the ARIMA model. The GM(1,1) model, an approach forecasting nonlinear data series, was not found in the studies of the EF simulation. Through the model precision test, the GM(1,1) model introduced fit the EF data series well and was considered to be appropriate to simulate the EF values for Suzhou. The fitting performance was accurate, and the EF values of the city could be forecasted by the model in short term. With the proposed model, the ecological sustainability status of the city was analyzed.
