长株潭地区生态可持续性

基于长株潭地区被批准为“全国资源节约型和环境友好型社会建设综合配套改革试验区”的背景,针对生态足迹方法的产量因子参数进行改进,利用区域产量因子代替全球产量因子,对长株潭地区1986-2005年生态足迹和生态承载力进行核算,在此基础上,着重采用两种预测方法对该地区2007-2015年生态足迹和生态容量进行预测.两种预测方法分别是二项式曲线预测模型和灰色GM(1,1)模型,对长株潭地区1986-2005年20a的人均生态足迹与时间关系进行了拟合,得出二项式曲线预测模型具有更高的预测精度；用两种预测模型预测了长株潭地区的人均生态容量,GM(1,1)模型的预测精度更高.选取精度最高的模型分别预测研究区未来10a人均生态足迹和生态容量.未来10a人均生态容量增长平缓(年平均增长率1.8％),人均生态足迹增长快(年平均增长率达16％),相应的人均生态赤字增长快.

Ecological sustainability in Chang-Zhu-Tan region:a prediction study

This study calculated the ecological footprint and ecological capacity in Chang-Zhu-Tan region from 1986 to 2005 through adjusting the yield factors' parameters of ecological footprint, which uses the global yield factor instead of the regional one. The results showed that the ecological footprint per capita generally increased, increasing dramatically by 15% each year from 2002 to 2005, and meanwhile the ecological capacity per capita grew gradually and slightly with an annual increase of 2.5%. Similar to the conditions of ecological footprint per capita, the ecological deficit per capita remained steady in the early stage but rose greatly from 2003 to 2005 with an annual increase of 48%. Overall, the ecological footprint expanded much faster than the ecological capacity, bringing out fast growth of ecological deficit. Based on this, we then forecasted the ecological footprint and capacity of Chang-Zhu-Tan region between 2007 and 2015 and fit the relationship between the ecological footprint and time there in the 20 years between 1986 and 2005 in this study. The two methods utilized in this process were the Binomial Curving Forecasting Model and the Grey GM (1.1) Model. When the Grey GM (1.1) Model was used, the average relative error rate of the predicted values of the ecological footprint per capita between 1996 and 2005 was 4.91% while it was 4.41% in the case of Binomial Curving Forecasting Model. Furthermore, the two models were also used in predicting the ecological capacity per capita there during the same time period. It was observed that the average absolute error rate and average relative error rate of the Binomial Curving Forecasting Model were 0.67% and 2.12%, respectively, while they were as 0.53% and 1.67%for the Grey GM (1.1) Model. Thus, it can be concluded that, in the prediction of ecological footprint of Chang-Zhu-Tan region, the Binomial Curving Forecasting Model performance much better compared to the Grey GM (1.1) Model. However, it was inverse when the two models were used to predict the ecological capacity per capita there. In addition, the two models were used to predict the ecological footprint per capita and the ecological capacity per capita in Chang-Zhu-Tan region from 1999 to 2005. It has to be noted here when the Binomial Curving Forecasting Model was used to predict ecological footprint, the the Grey GM (1.1) Model was used for calculating the ecological capacity per capita. The results showed that the ecological capacity per capita will grow gently with an annual increase of 1.8% while the ecological footprint per capital will grow much faster with an annual growth of 16%, producing rapid growth of ecological deficit per capita. Although the amount of ecological deficit per capita was initially low, it rose rapidly, equaling to that of ecological capacity per capita in 2009. And in 2015, the amount of ecological deficit per capita in Chang-Zhu-Tan region will be 1.67 times more than the ecological capacity per capita. The ecological footprint will exceed the ecological capacity and fall behind the demand, bringing about increasing serious ecological deficit, as well as restrain the regional development greatly. Therefore, the solution to this coming problem might be to import sufficient resources outside the region to make up for the ecological deficit and keep the ecology developing in a sustainable way.