[Construction and application of economy-pollution-environment three-dimensional evaluation model for district]

For deep analysis on the regional environmental economic system, the paper analyzes the mutual relation of regional economy development, environmental quality, environmental pollution, and builds the theoretical basis. Then, the economy-pollution-environment quality three-dimensional coupling evaluation model for district is constructed. It includes economic development level index, environmental pollution index, and environmental quality index. The model is a cube, which has spatialization and visualization characteristics. The model includes 8 sub cubes, which expresses 8 types of state, e. g. low pollution-inferior quality-low level of economic development etc. The model can be used to evaluate the status of region, divide development phase, analyze evolution trend etc. It has two ways including relative meaning evaluation (RME) and absolute meaning evaluation (AME). Based on the model, Yinchuan City in the Ningxia Hui Autonomous Region is used as an example for the empirical study. Using RME, compared with Guangzhou city, The result shows that the Yinchuan City has been a high pollution-low quality-low level of economic development state for a long period during 1996-2010. After 2007, the state changed to a high pollution-high quality-low level of economic development. Now, the environmental quality of Yinchuan city gets better, but pollutant discharge pressure is high, and tends to be the break point of high environment quality and low environment. With AME, using national standard, the Yinchuan City remains a high pollution-low quality-low level of economic development state during 1996-2010. Empirical research verifies that different target reference areas and relevant national standards have different main parameters, the evaluating result has an flexible range. The dimensionless data enhances the coupling of index. The data position in model increases the visibility to the environmental management decisions. The model improves mismatches of calculated data size, time asymmetry of spatial data, verification of the former multi-target coupling model.