Spatio-temporal Evaluation And Forecasting Of Regional Carrying Capacity In Bohai-Bay Region

Regional carrying capacity (RCC) is an extension of the original concept ofcarrying capacity, which means that, within a confined space, the population and theirsocio-economic activities could be supported by the ecosystem with ensuring rationaldevelopment and without deteriorating the environment during a predictable timeperiod. An ecosystem consists of the socio-economic system and its naturalenvironment. From the complex system perspective, system's development byself-organization is unsustainable. For the sake of existence of human consciousness,ecosystem development can be kind of "by design". This "design" makes thesustainability of development of ecosystem possible. The study on RCC is a keyprocess for sustainable development.In this paper, a comprehensive evaluation system for RCC is constructed.Moreover, indexes are collected. On the basis of these works, basic database of theregion of interest – Bohai-Bay region is constructed. A cell file is used to discretizedthe spatial and non-spatial data.By comparison of various methods, some statistical methods, such as the PrincipalComponents Analysis (PCA), are selected to carry out the evaluation of elements ofRCC evaluating system, and synthetical indexes of these elements are gotten. Theseelements include Carrying Capacity, which comprising supportive capacity andassimilative capacity, Regulating Ability, Loading and Regional Carrying State (RCS).An Ecological-Footprint approach is used to analyze the biocapacity and ecologicaldeficit of the region of interest.To judge whether the Loading exceeds the Carrying Capacity or not, namely todetermine if the RCS is overloading, the low limit of overloading index must bedetermined. The histogram of annual Loading index values shows a bimodaldistribution characteristic. Statistically speaking, it means the compound of samplesbelong to two population with different density function. The Loading index values ofsamples without ecological deficit should belong to the non-overloading populationand its distribution function could be estimated. Then, the lower limit of overloadingpopulation is determined, and the overloading population and non-overloadingpopulation are separated.For grabbing the evolution trend of RCS, an Artificial-Neural-Networks-BasedCellular Automata (CA) Modal is use to forecast the spatio-temporal change of RCSin the near feature. The transition rule of CA is obtained by the training process ofANN based on historical data of 1996~2003. By this modal, the 2004~2020 RCS ofBohai-Bay region is forecasted. From forecasting result, we can see that the main partof Bohai-Bay region is deteriorating, and only a few districts in the north can sustainthe non-overloading state.On the basis the result of evaluation and forecasting, some instructive suggests areproposed in the end of this paper.