Scenario analysis of sustainable intensive land use based on SD model

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• 作者：Bing Geng^a ; ^{gengbing@cugb.edu.cn} ; ^{gengbing46@163.com} ; Xinqi Zheng^b ; ^{zxqsd@126.com} ; Meichen Fu^a ; ^{fumeichen@163.com}
• 关键词：Intensive land use ; SD model ; Diminishing returns ; Sustainable
• 刊名：Sustainable Cities and Society
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：29
• 期：Complete
• 页码：193-202
• 全文大小：2509 K
• 卷排序：29

文摘

The intensive and sustainable way of land use is one of the most important approaches to solve the issue of contradiction between human activities and land resource. In this paper, the system dynamics (SD) method was used to construct the sustainable intensive land use model. This model simulated the land use status of Beijing and analyzed variation of intensity under the different scenarios. The sustainable intensive land use model includes population sub-model, economy sub-model, land use sub-model and intensity sub-model. The model contains 8 variables of stocks, 135 variables, 29 constants and 173 mathematical equations. Through the causal relationship linking four sub-models, eventually build into a multivariate nonlinear intensive land use model. The results show that: (1) Variation of intensive degree follows the law of diminishing returns. The intensity will reach the optimal state in 2020–2050 under the current situation of Beijing social development. (2) Through analysis of intensity change at present, it has been found that the ideal population of Beijing should be around 28.77million and no more than 4144 km2 of constructive land use area is suitable for sustainable development under the present social development state. (3) The cycle time of intensity variation is around 20–30 years. Intensity can achieve continuous increase by the way of integrated adjustment of population, economy and land use on these periodic nodes. Sustainable intensive utilization of land can be achieved. (4) The model has passed validation test and its relation error controlled within ±5%. This research could provide scientific basis for further land supply and support government policy decisions.