A major component of urban management is studying and evaluating urban resilience in order to minimize the effects of natural hazards. This is because of the increasing number of natural hazards occurring worldwide. A spatial decision support system is presented for modeling urban resilience and selecting resilient zones in response to natural hazards. This system is implemented based on 22 criteria, grouped into three categories: demographics, infrastructure, and environmental. The criteria are then standardized using minimum and maximum methods, and their importance is determined by the analytical hierarchy process (AHP). The resilience maps in various scenarios are prepared using the ordered weighted average (OWA) method. Flow accumulation (distance from fault), vulnerable population density (vulnerable population density), and distance from road network (material type) were regarded as the most important criteria for flood resilience (earthquake resilience) from environmental, demographic, and infrastructure criteria, respectively. There are different areas that are considered to have very low resilience depending on the risk attitude. According a pessimistic scenario, 1% of Tehran’s area has very low resilience, while according to an optimistic scenario, 38% has very low resilience. This system can be used by urban planners and policymakers for the purpose of improving resilience to natural hazards in low-resilience areas.

Spatial evaluation of urban environment quality (UEQ) is a key prerequisite in urban planning and development. The main goal of this study is to present a scenario-based spatial multi-criteria decision-making system for evaluating UEQ. Therefore, stakeholder involvement was conducted and eight environmental criteria and six spatial-functional criteria were identified for five districts of Tehran. The weight of the effective criteria was calculated using the analytic hierarchy process (AHP) model. Then, the ordered weighted averaging (OWA) model was used to prepare UEQ maps in different scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic. Finally, the spatial distribution of the district population in different classes of UEQ was evaluated. Among the spatial-functional and environmental criteria, the sub-criteria of population density and air pollution, respectively, had the greatest impact on UEQ. In very pessimistic, intermediate, optimistic, and very optimistic scenarios, approximately 76.7, 51.8, 36.4, 23.7, and 9.8 km² of the studied area had unsuitable UEQ conditions, respectively. In the very pessimistic scenario, about 37,000 and 1,500,000 people lived in areas with suitable and unsuitable UEQ conditions, respectively. In the very optimistic scenario, the population increased to over 917,000 in areas with suitable UEQ and decreased to 336,000 in those with unsuitable UEQ conditions in terms of both environmental and spatial-functional criteria. The research results showed that a high percentage of the population in the study area live under unsuitable UEQ conditions, which indicates the need for attention to improving the current UEQ conditions. The proposed approach is timely to gain a better understanding of the adverse impact of climate change on human well-being in marginal societies and how climate-resilient urban planning can play a significant role.

Site selection and allocation of manufacturing and processing facilities are essential to sustainable economic productivity of a given product while preserving soil, the environment, and biodiversity. An essential criterion when evaluating various approaches to model land suitability for pistachio processing facilities is their adaptability to accommodate diverse perspectives and circumstances of managers and decision makers. Incorporating the concept of risk into the decision-making process stands as a significant research gap in modeling land suitability for pistachio processing facilities. This study presents a scenario-based multi-criteria decision-making system for modeling the land suitability of pistachio processing facilities. The model was implemented based on a stakeholder analysis as well as inclusion of a set of influential criteria and restrictions for an Iranian case study, which is among the top three producers. The weight of each criterion was determined based on the best-worst method (BWM) after the stakeholder analysis. Then, the ordered weighted averaging (OWA) model was used to prepare maps of spatial potential for building a pistachio processing factory in different decision-making scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic attitudes. Finally, the sensitivity analysis of very-high- and high-potential regions to changes in the weight of the effective criteria was evaluated and proved that the most important criteria were proximity to pistachio orchards, proximity to residential areas, proximity to the road network, and proximity to industrial areas. Overall, 327 km² of the study area was classified as restricted, meaning that they are not suitable locations for pistachio processing. The average estimated potential values based on the proposed model for very pessimistic, pessimistic, intermediate, optimistic, and very optimistic scenarios were 0.19, 0.47, 0.63, 0.78, and 0.97, respectively. The very-high-potential class covered 0, 0.41, 8.25, 39.64, and 99.78 percent of the study area based on these scenarios, respectively. The area of suitable regions for investment decreased by increasing risk aversion in decision making. The model was more sensitive to changes in the weights of proximity to residential areas, proximity to pistachio orchards, and proximity to transportation hubs. The proposed approach and the achieved findings could be of broader use to respective stakeholders and investors. Given the suitability of arid regions for planting pistachio and its relatively high profitability, the local authorities and decision makers can promote further expansion of the orchards, which can lead to better welfare of farmers and reducing rural-urban migration in the region.