Dynamic simulation of urbanization and eco-environment coupling: Current knowledge and future prospects

doi:10.1007/s11442-020-1731-x

Abstract

Abstract:

Urbanization and eco-environment coupling is a research hotspot. Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited. Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops. We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that: (1) The use of dynamic simulation is an increasing trend, the relevant theory is being developed, and modeling processes are being improved; (2) Dynamic simulation technology has become diversified, refined, intelligent and integrated; (3) Simulation is mainly performed for three aspects of the coupling, multiple regions and multiple elements, local coupling and telecoupling, and regional synergy. However, we also found some shortcomings: (1) Basic theories are inadequately developed and insufficiently integrated; (2) The methods of unifying systems and sharing data are behind the times; (3) Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified. Additionally, simulation of telecoupling does not quantify parameters and is not systemically unified, and therefore cannot be used to represent spatial synergy. In the future, we must promote communication between research networks, technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations. Finally, we must build decision support systems to plan and ensure regional sustainable urbanization.

Key words: urbanization and eco-environment coupling, dynamic simulation, theory, methods, applications

CUI Xuegang, FANG Chuanglin, LIU Haimeng, LIU Xiaofei, LI Yonghong. Dynamic simulation of urbanization and eco-environment coupling: Current knowledge and future prospects[J].Journal of Geographical Sciences, 2020, 30(2): 333-352.

Figures/Tables 2

Figure 1

Table 1

Comparison of techniques for dynamic simulation of urbanization and eco-environment coupling."

Name	Discipline	Advantages	Disadvantages	Application
System dynamics	Systems science and computer simulation	Modeling process is simple and can be combined with an index system to identify system boundary and related variables	Difficult to reflect the characteristics of adaptive and spatial change in the system, and the feedbacks are in part regression relationships	Urban system change, urban sustainable development and urbanization and eco-environment element coupling
Artificial neural network	Artificial intelligence	A typical human brain model with three advantages: self-learning, associative storage and high-speed optimization	Defective in learning, causal explanation and other aspects, especially in dealing with system uncertainty	Urban land expansion, environmental change, and resources demand
Bayesian networks	Artificial intelligence, probability theory, statistics and graph theory	Good at causal and diagnostic reasoning, as empirical data can be incomplete	Difficult to deal with the large number of nodes and the learning ability is less than for ANN	Identification of urban ecological vulnerability and demand for resources
CLUE-s	LUCC, systems science and computer simulation	Good at dealing with different spatial scales based on empirical data	Focus on local equilibrium analysis	Land use allocation on multiple spatial scales
Cellular automata	LUCC, systems science and computer simulation	Simplifies complex problems by bottom-up modeling and can simulate complex discrete systems	Difficult to solve the problem of spatial heterogeneity and lacks explanation of the mechanism	Urban sprawl and land use change
Multi-agent system	Artificial intelligence and complexity science	Compensates for the neglect of policy factors and explains land use change processes	Research space is abstracted as homogeneous and model validation is difficult	Policy-driven urban sprawl and land use change

Table 1

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