Evaluation of Land Use Efficiency in Tehran’s Expansion between 1986 and 2021: Developing an Assessment Framework Using DEMATEL and Interpretive Structural Modeling Methods

Abstract

This paper aims to reveal the shortcomings of the land use efficiency assessment formula presented in SDG 11.3.1 Indicator and develop a framework that can provide urban planners with a more accurate understanding of the variables influencing and/or influenced by urban expansion. Based on the mentioned formula, Tehran never experienced urban shrinkage between 1986 and 2021, as shown by the relationship between land consumption and population growth. However, the research findings indicate that land allocation patterns have not only decreased most urban services per capita, but have also undermined ecosystem services during this period. In this paper, we propose a new assessment framework by which a dual aspect of urban planning is addressed, namely providing sustainable urban services while protecting natural resources, and using ecosystem services sustainably to support cost–beneficial urbanization. For this purpose, a total of ten mainly repeated contributing variables were collected in the categories of environmental, physical-spatial, and economic–social effects of urban expansion. A questionnaire based on these variables was prepared, and 14 urban planning experts collaborated to classify the variables and identify causal relationships between them. In the following, data obtained from the questionnaires were analyzed using DEMATEL and Interpretive Structural Modeling (ISM) methods to determine which variables influence and/or are influenced by urban expansion (and to what extent). Third-level variables that directly influence urban expansion include transportation (A6), infill development (A7), and entrepreneurship (A10). Spatial justice (A8) and housing and population attraction (A9) were identified as middle-level variables that both affect and are affected by urban expansion. Finally, land surface temperature (A1), air pollution (A2), sewage and waste (A3), water resources (A4), and vegetation (A5) were identified as first-level variables that are mainly affected by urban expansion.

Assessing the Connectivity of Urban Green Spaces for Enhanced Environmental Justice and Ecosystem Service Flow: A Study of Tehran Using Graph Theory and Least-Cost Analysis

“…Tehran has experienced significant land use and population density changes, particularly over the past five decades. These changes have predominantly occurred in Tehran's western and southern parts, as physical constraints in the north and east limit expansion and land cover modifications [6].…”

“…This increase puts even more pressure on cities to develop housing, services, and infrastructure to meet this growth [1,2]. Such urban expansion, often driven by the conversion of natural landscapes into urban developments, not only places additional pressure on urban carrying capacity [3], but also exacerbates environmental injustice and inequality [4][5][6]. In response to these Urban Sci.…”

Assessing and Mapping Urban Ecological Resilience Using the Loss-Gain Approach: A Case Study of Tehran, Iran

“…The environmental thermal load indicator assesses the intensity of heat storage or emission in specific locations within urban areas. Factors influencing thermal load include the volume of buildings (which affects heat storage, limited sky view, and reduces nighttime cooling), topography, and accessibility to green spaces, which provide a cooling effect (Tayebi et al, 2023). Essentially, the thermal load considers how various factors can raise or lower the urban air temperature.…”

“…The urban populations growth and urban expansion have led to reduced land use efficiency and habitat degradation, placing pressure on natural resources and the environment's carrying capacity (Tayebi, Alavi, Esfandi, Meshkani, & Shamsipour, 2023). Urban expansion has a fragmenting, isolating, and destructive impact on natural habitats (Astiaso Garcia, Bruschi, Cinquepalmi, & Cumo, 2013;Marzluff, 2001;Potenza, Gerardi, Fascetti, & Rosati, 2022;Zambrano, Aronson, & Fernandez, 2019).…”

Solution for Convergence Problem in DEMATEL Method: DEMATEL of Finite Sum of Influences

“…It is especially useful in analyzing the causeand-effect relationships between factors of the system and also to find the critical factors of a system [10]. DEMATEL has received a great deal of attention, and it is a widely used method in various fields, such as engineering [11,12], management sciences [13,14], medicine [15], COVID-19 [16][17][18], energy [19,20], urbanization [21,22], ecology [23,24], and others. It is also one of suitable methods to deal with the degree of importance of evaluation factors and to determine the cause-effect relationships between factors [25].…”