Evaluation of Airport Environmental Carrying Capacity: A Case Study in Guangzhou Baiyun International Airport, China

Airport environmental carrying capacity (AECC) provides the fundamental conditions for airport development and operation activities. The prediction of AECC is a necessary condition for planning an appropriate development mode for the airport. This paper studies the dynamic prediction method of the AECC to explore the development characteristics of AECC in different airports. Based on the driving force-pressure-state-response (DPSR) framework, the method selects 17 main variables from economic, social, environmental and operational dimensions, and then combines the drawing of causal loop diagrams and the establishment of system flow diagrams to construct the system dynamics (SD) model of AECC. The predicted values of AECC are obtained through SD model simulation and accelerated genetic algorithm projection pursuit (AGA-PP) model calculation. Considering sustainable development needs, different scenarios are set to analyze the appropriate development mode of the airport. The case study of the Pearl River Delta airports resulted in two main conclusions. First, in the same economic zone, different airports with similar aircraft movements have similar development characteristics of AECC. Second, the appropriate development modes for different airports are different, and the appropriate development modes for the airport in different periods are also different. The case study also proves that the AECC prediction based on SD model and AGA-PP model can realize short-term policy formulation and long-term planning for the airport development mode, and provide decision-making support for relevant departments of airport.

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Life Cycle Prediction of Airport Operation Based on System Dynamics

Wan,

Shan,

Jiang

et al. 2024

Sustainability

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Traditional airport development planning often overlooks an in-depth consideration of the airport operation life cycle, which frequently causes deviations from planned objectives during operation. This paper presents a framework for predicting and segmenting the airport operation life cycle by integrating the dynamic characteristics of the System Dynamics (SD) model with the static properties of Logistic modeling to examine the development trajectory of airport operations. The influencing factors in this model are selected across three levels: airport, city, and macro-environment. A system dynamics model of airport operation is constructed using causal loop diagrams and system flow diagrams. Using Guangzhou Baiyun International Airport (CAN) as a case study, the airport’s operational capacity from 2005 to 2035 is predicted through SD simulation. Subsequently, the airport operation life cycle from 2005 to 2050 is forecasted and segmented using Logistic modeling. The results indicate that, under the standard scenario, CAN’s operational capacity experiences two declines in 2016 and 2020, attributed to airport construction and emergencies. Logistic modeling identifies three distinct life cycle phases in the airport’s operation. Furthermore, by comparing various airport operation scenarios, the analysis reveals that fluctuations in the city economy significantly impact the airport’s operational system without altering its overall development trajectory. In contrast, the occurrence of emergencies can substantially modify the airport operation life cycle.

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Evaluation of Airport Environmental Carrying Capacity: A Case Study in Guangzhou Baiyun International Airport, China

Cited by 3 publications

References 32 publications

New method for diagnosing resilience of agricultural soil-water resource composite system: Projection pursuit model modified by sparrow search algorithm

New method for diagnosing resilience of agricultural soil-water resource composite system: Projection pursuit model modified by sparrow search algorithm

A System Dynamics Prediction Model of Airport Environmental Carrying Capacity: Airport Development Mode Planning and Case Study

Life Cycle Prediction of Airport Operation Based on System Dynamics

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