Spatiotemporal analysis of ecological footprint and biological capacity of Gansu, China 1991–2015: Down from the environmental cliff

Spatial and Temporal Variations in the Ecological Footprints in Northwest China from 2005 to 2014

“…On spatial distribution, it is consistent with the results of Wu et al [40]. On temporal variation, it is also in line with the results of Li et al [21] who founded an increasing trend from 1990 to 2010 in Shanxi, Gansu, Ningxia and Xinjiang, and of Yue et al [41] who showed a steadily growth in Gansu from 1995 to 2003. In this paper, we used the equivalence factors on national scale based on Equation (1) from Gu et al [28].…”

Ecological footprint simulation and prediction by ARIMA model—A case study in Henan Province of China

“…(2) The inherent flaws of EF. For example, the global average yields (GAY) were an average value of agricultural outputs of different countries and regions in a certain year (Yue et al, 2006). So, for Henan Province, the annual GAY should be used to compute EF.…”

“…EF answers a specific research question: how much of the regenerative biological capacity of the planet is demanded in a given time by a given human activity which could be the consumption of resources, the production of goods, or the supply of a service (Kitzes and Wackernagel, 2009). However, EF itself is a static indicator, which means it cannot answer another specific research question: how to accurately estimate (simulate and predict) the EF's development trend in future according to the EF's development in the past in a given place in a long time series (Yue et al, 2006) and to provide reasonable policy recommendations for regional sustainable development based on the estimated EF's outcome in future. The focus of this paper is to answer this specific question by searching, selecting, comparing, and testing some noted data simulation and prediction methods.…”

“…These models' descriptive statistics (means and variances), goodness-of-fit (R 2 ), regression equation and forecasted values may be obtained easily by Ecological Indicators 10 (2010) 538-544 Microsoft Office Excel database or SPSS statistical software (Ediger et al, 2006). However, as the 'change rate' and 'scissors difference' (Yue et al, 2006), the errors of these methods will also become increasingly large with the increasing amount of data. The autoregressive integrated moving average (ARIMA) method, developed by Box and Jenkins (1976), is one of the most noted models for time series data prediction and often used in econometric research (Ediger et al, 2006;Gao, 1998).…”

“…His scenario analysis was after all a random method and had a large uncertainty. Taking the Gansu Province as an example in China, Yue et al (2006) introduced the 'change rate' and 'scissors difference' to quantitatively analyze the development trends of EF and ecological carrying capacity (EC) during past years and to predict them in future. However, the errors of 'change rate' and 'scissors difference' would become increasingly large with the increasing amount of data.…”