University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Presenting a Model of Financial Challenges for Public Sector Participation in the Process of Urban Fabric Reconstruction using A Grounded Theory Approach Presenting a Model of Financial Challenges for Public Sector Participation in the Process of Urban Fabric Reconstruction using A Grounded Theory Approach 359 380 242443 10.22034/judpm.2026.573447.1087 FA Peyman Akbari Assistant Professor, Department of Public Management, Payame Noor University, Tehran, Iran 0000-0003-4302-1867 Reza Rostami Assistant Professor, Department of Business Management, Payame Noor University, Tehran, Iran 0000-0002-8618-0545 Journal Article 2026 01 28 This research aimed to present a model of the financial challenges of public sector participation in the reconstruction of dilapidated structures, using a case study of Tehran's District 1 Municipality. This is an applied and qualitative study with a data-based approach, and the data were collected through interviews with 20 managers and key employees and analyzed with MAXQDA software. The results showed that the causal factors include the diversity of services and resource constraints. The underlying factors include unforeseen costs, lack of resource sustainability, and financial dependence. Intervening factors include inflation, government indifference, and the cost-effectiveness of laws. The central category is the financial challenges of public participation. The proposed strategies include designing a financing program, reviewing laws, providing government resources, and encouraging private investment. The consequences of these measures include improving municipal performance, increasing citizen satisfaction, employment growth, and housing production. Finally, to overcome obstacles such as budget constraints and income instability, it is essential to rely on structural strategies such as government budget allocation, fiscal policy stability, and administrative process facilitation to improve the quality of life in target contexts. This research aimed to present a model of the financial challenges of public sector participation in the reconstruction of dilapidated structures, using a case study of Tehran's District 1 Municipality. This is an applied and qualitative study with a data-based approach, and the data were collected through interviews with 20 managers and key employees and analyzed with MAXQDA software. The results showed that the causal factors include the diversity of services and resource constraints. The underlying factors include unforeseen costs, lack of resource sustainability, and financial dependence. Intervening factors include inflation, government indifference, and the cost-effectiveness of laws. The central category is the financial challenges of public participation. The proposed strategies include designing a financing program, reviewing laws, providing government resources, and encouraging private investment. The consequences of these measures include improving municipal performance, increasing citizen satisfaction, employment growth, and housing production. Finally, to overcome obstacles such as budget constraints and income instability, it is essential to rely on structural strategies such as government budget allocation, fiscal policy stability, and administrative process facilitation to improve the quality of life in target contexts.

https://www.judpm.ir/article_242443_017826b49958308e89874312e9799e81.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Examining the Consequences of Moving the Capital to Makran with Hydropolitical Approaches and Hydro-Hegemonic Structures Examining the Consequences of Moving the Capital to Makran with Hydropolitical Approaches and Hydro-Hegemonic Structures 381 392 244609 10.22034/judpm.2026.581957.1105 FA Ramtin Tavoosi Rad Master Candidate, Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran 0009-0006-3284-2140 Mohammad Ansari Ghojghar Assistant Professor, Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran 0000-0003-0485-5551 Arash Malekian Professor, Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran 0000-0001-8174-6784 Journal Article 2026 02 27 This study examines the strategic implications and challenges of the proposed relocation of Iran’s capital from Tehran to the Makran region, with a particular focus on hydropolitical dimensions and water resource sustainability. Drawing on a comprehensive review of the literature and an analysis of responses collected through a researcher-designed questionnaire administered to experts in water resources management and hydropolitics, the study evaluates the multifaceted consequences of this proposed transition. The questionnaire consisted of three quantitative sections containing ten closed-ended items measured on a five-point Likert scale and was designed to provide an in-depth assessment across three key dimensions: water security, geopolitical implications, and national sustainability. The findings indicate that the current water resource management model in Tehran suffers from significant hydropolitical instability. The relocation of the capital could potentially contribute to a redistribution of power and a shift in the hydropolitical balance toward southeastern Iran. However, expert opinions reveal a critical gap between strategic necessity and infrastructural preparedness. In particular, the inadequacy of existing infrastructure in the Makran region was identified as the primary challenge, receiving agreement from 90% of respondents, while the risk of local conflicts over water resources was recognized as another major concern, supported by 40% of respondents. The study further emphasizes that, given the multidimensional nature of this decision and the strong consensus among respondents—90% of whom expressed support for the proposal—the successful implementation of the capital relocation plan requires a transition from a centralized management framework toward an integrated and coordinated governance model involving multiple sectors. Without substantial investment in water infrastructure and effective management of geopolitical risks, the project may exacerbate environmental and social challenges within the region. This study examines the strategic implications and challenges of the proposed relocation of Iran’s capital from Tehran to the Makran region, with a particular focus on hydropolitical dimensions and water resource sustainability. Drawing on a comprehensive review of the literature and an analysis of responses collected through a researcher-designed questionnaire administered to experts in water resources management and hydropolitics, the study evaluates the multifaceted consequences of this proposed transition. The questionnaire consisted of three quantitative sections containing ten closed-ended items measured on a five-point Likert scale and was designed to provide an in-depth assessment across three key dimensions: water security, geopolitical implications, and national sustainability. The findings indicate that the current water resource management model in Tehran suffers from significant hydropolitical instability. The relocation of the capital could potentially contribute to a redistribution of power and a shift in the hydropolitical balance toward southeastern Iran. However, expert opinions reveal a critical gap between strategic necessity and infrastructural preparedness. In particular, the inadequacy of existing infrastructure in the Makran region was identified as the primary challenge, receiving agreement from 90% of respondents, while the risk of local conflicts over water resources was recognized as another major concern, supported by 40% of respondents. The study further emphasizes that, given the multidimensional nature of this decision and the strong consensus among respondents—90% of whom expressed support for the proposal—the successful implementation of the capital relocation plan requires a transition from a centralized management framework toward an integrated and coordinated governance model involving multiple sectors. Without substantial investment in water infrastructure and effective management of geopolitical risks, the project may exacerbate environmental and social challenges within the region.

https://www.judpm.ir/article_244609_bff8f0b5cc710fea782704f6c61d1cde.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Property Valuation and Urban Revenue Collection within a Participatory Governance Framework: The Case of Isfahan Property Valuation and Urban Revenue Collection within a Participatory Governance Framework: The Case of Isfahan 393 409 244662 10.22034/judpm.2026.580224.1099 FA Reza Nasr Esfahani Associate Professor, Department of Economics & Entrepreneurship, Art University of Isfahan, Iran 0000-0002-8195-1339 Mostafa Nabati Nejad Ph.D. in Entrepreneurship, Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran Elham Agharian M.A. in Educational Sciences and Psychology, University of Isfahan, Isfahan, Iran Journal Article 2026 02 27 This study was conducted to improve the property valuation system of Isfahan City for the assessment of municipal charges by incorporating the participation of real estate consultants. The research adopted a descriptive–analytical approach and involved 914 participants, including municipal property valuation experts and members of the Isfahan Real Estate Consultants Association, who were selected through random sampling. Data were collected using structured questionnaires based on a detailed geographical zoning framework, with a precise distinction between main and secondary streets. The findings revealed that the average discrepancy between expert-assessed property values and market prices amounted to 60 million Iranian Rials in the residential sector and 130 million Iranian Rials in the commercial sector. In the residential sector, 22% of regional valuation codes exhibited low discrepancies (less than 10%), 31% showed moderate discrepancies (10–30%), and 47% required re-evaluation. The corresponding figures for the commercial sector were 9%, 19%, and 72%, respectively. A hybrid valuation model was developed based on minimizing estimation errors and empirically testing alternative weighting schemes. The optimal model assigned a weight of 70% to expert valuations and 30% to market-based valuations. The results demonstrated that this combination significantly reduced the gap between expert-assessed values and actual market prices. The findings confirm the effectiveness of the proposed hybrid model in urban property valuation systems. It is recommended that the Municipality of Isfahan employ this model for the periodic updating of benchmark property values. The application of the proposed approach can contribute to enhancing the efficiency of building charge assessment mechanisms and increasing transparency in urban governance and municipal management. This study was conducted to improve the property valuation system of Isfahan City for the assessment of municipal charges by incorporating the participation of real estate consultants. The research adopted a descriptive–analytical approach and involved 914 participants, including municipal property valuation experts and members of the Isfahan Real Estate Consultants Association, who were selected through random sampling. Data were collected using structured questionnaires based on a detailed geographical zoning framework, with a precise distinction between main and secondary streets. The findings revealed that the average discrepancy between expert-assessed property values and market prices amounted to 60 million Iranian Rials in the residential sector and 130 million Iranian Rials in the commercial sector. In the residential sector, 22% of regional valuation codes exhibited low discrepancies (less than 10%), 31% showed moderate discrepancies (10–30%), and 47% required re-evaluation. The corresponding figures for the commercial sector were 9%, 19%, and 72%, respectively. A hybrid valuation model was developed based on minimizing estimation errors and empirically testing alternative weighting schemes. The optimal model assigned a weight of 70% to expert valuations and 30% to market-based valuations. The results demonstrated that this combination significantly reduced the gap between expert-assessed values and actual market prices. The findings confirm the effectiveness of the proposed hybrid model in urban property valuation systems. It is recommended that the Municipality of Isfahan employ this model for the periodic updating of benchmark property values. The application of the proposed approach can contribute to enhancing the efficiency of building charge assessment mechanisms and increasing transparency in urban governance and municipal management.

https://www.judpm.ir/article_244662_c3146cd3c15c460f1081e2e5adb84fc3.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Intelligent Detection and Separation of Recyclable Urban Waste Using a Deep Learning-Based Computer Vision Model Intelligent Detection and Separation of Recyclable Urban Waste Using a Deep Learning-Based Computer Vision Model 411 431 244307 10.22034/judpm.2026.582800.1106 FA Ali Jelokhani School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran 0009-0005-6369-6265 Journal Article 2026 02 27 Urban waste management is one of the major environmental and economic challenges in modern cities. A considerable part of municipal solid waste consists of recyclable materials such as cardboard, glass, metal, paper, and plastic. Accurate detection and separation of these materials can improve recycling quality and reduce the environmental burden of urban waste. This study proposes a deep learning-based computer vision framework for intelligent detection and separation of recyclable urban waste. Unlike single-label image classification methods, the proposed framework performs multi-instance object detection and generates a bounding box, class label, and confidence score for each detected item. The architecture includes preprocessing and augmentation, lightweight feature extraction, a combined attention module, multi-level feature fusion, and a final detection stage. The dataset contains 4250 images and 6815 annotated waste instances. The proposed method achieved Precision, Recall, F1-score, mAP@0.5, and mAP@0.5:0.95 values of 0.934, 0.919, 0.926, 0.947, and 0.742, respectively. The results indicate that the proposed framework can support automated waste sorting systems, smart bins, robotic separators, and recycling facilities. Urban waste management is one of the major environmental and economic challenges in modern cities. A considerable part of municipal solid waste consists of recyclable materials such as cardboard, glass, metal, paper, and plastic. Accurate detection and separation of these materials can improve recycling quality and reduce the environmental burden of urban waste. This study proposes a deep learning-based computer vision framework for intelligent detection and separation of recyclable urban waste. Unlike single-label image classification methods, the proposed framework performs multi-instance object detection and generates a bounding box, class label, and confidence score for each detected item. The architecture includes preprocessing and augmentation, lightweight feature extraction, a combined attention module, multi-level feature fusion, and a final detection stage. The dataset contains 4250 images and 6815 annotated waste instances. The proposed method achieved Precision, Recall, F1-score, mAP@0.5, and mAP@0.5:0.95 values of 0.934, 0.919, 0.926, 0.947, and 0.742, respectively. The results indicate that the proposed framework can support automated waste sorting systems, smart bins, robotic separators, and recycling facilities.

https://www.judpm.ir/article_244307_38e16875073f820e36cfbc082ba557ba.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Forecasting Residential Electricity Demand under Climate Change Scenarios: A Python-based Model Incorporating Cooling and Heating Degree Days Forecasting Residential Electricity Demand under Climate Change Scenarios: A Python-based Model Incorporating Cooling and Heating Degree Days 433 453 243406 10.22034/judpm.2026.579252.1097 FA Mona Mirrazavi M.Sc. Student of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran 0009-0007-6038-5423 Younes Noorollahi Full Professor of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran Saba Amouzadeh M.Sc. Student of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran Mohanna Baba Hoseinpour M.Sc. Student of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran Hosein Yousefi Full Professor of Energy Systems Engineering, Faculty of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran 0000-0002-6372-5127 Journal Article 2026 01 28 This research aims to forecast the long-term household electricity demand under various climate change scenarios, including temperature increases of 1°C and 0.5°C per year, as well as decreases of 1°C and 0.5°C per year, utilizing CMIP6 climate projection models. With the rise in average temperatures and the intensification of heatwaves, cooling requirements in the residential sector have increased, leading to higher summer peak loads. To analyze this trend, a data-driven framework is proposed that employs a multivariate annual regression model incorporating Cooling Degree Days (CDD), Heating Degree Days (HDD), the number of subscribers, and temporal trends. Furthermore, daily temperature data are used to disaggregate annual consumption into daily and hourly scales, enabling the examination of peak load variations. Model evaluation indicates highly accurate performance, with coefficients of determination (R²) of 0.995 for the training set and 0.979 for the test set. The Root Mean Square Error (RMSE) is approximately 547.82 kWh, corresponding to less than one percent of annual consumption. Climate scenario analysis reveals that the CDD index is the primary driver of rising electricity use in Tehran expanding from roughly 780 units in the baseline year to 3,000 in the mild warming scenario and over 7,200 units in the severe one, representing growth of 285% to 820%. Conversely, the HDD index declines under warmer scenarios, contributing little to overall demand. Forecasts suggest that household electricity consumption will increase by 75–132% by 2050, with the difference between the warmest and coldest scenarios exceeding 77,000 kWh. Hourly analysis shows an evening peak load of approximately 21.5 kWh and an early-morning minimum of about 7 kWh. The proposed framework can serve as an effective tool for power system planning and load management under global warming conditions. This research aims to forecast the long-term household electricity demand under various climate change scenarios, including temperature increases of 1°C and 0.5°C per year, as well as decreases of 1°C and 0.5°C per year, utilizing CMIP6 climate projection models. With the rise in average temperatures and the intensification of heatwaves, cooling requirements in the residential sector have increased, leading to higher summer peak loads. To analyze this trend, a data-driven framework is proposed that employs a multivariate annual regression model incorporating Cooling Degree Days (CDD), Heating Degree Days (HDD), the number of subscribers, and temporal trends. Furthermore, daily temperature data are used to disaggregate annual consumption into daily and hourly scales, enabling the examination of peak load variations. Model evaluation indicates highly accurate performance, with coefficients of determination (R²) of 0.995 for the training set and 0.979 for the test set. The Root Mean Square Error (RMSE) is approximately 547.82 kWh, corresponding to less than one percent of annual consumption. Climate scenario analysis reveals that the CDD index is the primary driver of rising electricity use in Tehran expanding from roughly 780 units in the baseline year to 3,000 in the mild warming scenario and over 7,200 units in the severe one, representing growth of 285% to 820%. Conversely, the HDD index declines under warmer scenarios, contributing little to overall demand. Forecasts suggest that household electricity consumption will increase by 75–132% by 2050, with the difference between the warmest and coldest scenarios exceeding 77,000 kWh. Hourly analysis shows an evening peak load of approximately 21.5 kWh and an early-morning minimum of about 7 kWh. The proposed framework can serve as an effective tool for power system planning and load management under global warming conditions.

https://www.judpm.ir/article_243406_8267f94c8c785b3f23f8a5fc15dc3b54.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 The Application of Artificial Intelligence in Developing a Local Climate Change Adaptation Strategy for Qanats: Evaluating the Performance of XGBoost in Estimating the Discharge of Qaen Qanats The Application of Artificial Intelligence in Developing a Local Climate Change Adaptation Strategy for Qanats: Evaluating the Performance of XGBoost in Estimating the Discharge of Qaen Qanats 455 469 247073 10.22034/judpm.2026.579182.1096 FA Mohamad Fouladi Nasrabad Ph.D. Student in Water Resources, Department of Water Engineering, University of Birjand, Birjand, Iran Mohammad Reza Farzaneh Assistant Prof. at Research Group of Environmental Engineering and Pollution Monitoring, Research Center for Environment and Sustainable Development, RCESD, Tehran, Iran Sepideh Zeraati Neyshabouri Ph.D. in Water Resources, Department of Water Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran Journal Article 2026 02 27 Climate change, as a fundamental challenge of the present century, has made it inevitable for urban and regional policymakers to revise adaptation strategies and develop accurate prediction tools by affecting water resources in arid regions. Qanats, as strategic and vital infrastructures for water supply in settlements of Iran's arid regions, are currently facing threats due to declining groundwater levels, which doubles the need for applying artificial intelligence-based decision support systems. The present study aims to strengthen the foundations of water resources management planning by evaluating the performance of the XGBoost machine learning model in predicting the discharge of qanats in the Qaen Plain. To this end, 12-year data on qanat discharge (2007–2018), climatic variables (precipitation and temperature), and elevation were used. After preprocessing, the data were split into training (70%) and testing (30%) sets, and the model hyperparameters were tuned using RandomizedSearchCV with RMSE as the optimization objective. The evaluation results showed that XGBoost, with R² ≈ 0.88 and NS = 0.76 in the testing phase, has high reliability for use in hydrological analyses. Based on sensitivity analysis, the optimal combination of hyperparameters was n_estimators=500, max_depth=6, learning_rate=0.08, and min_child_weight=4. However, it was observed that the model suffers from poor performance in predicting discharges exceeding 20 L/s due to the RMSE loss function and data imbalance. The findings of this study, while confirming the effectiveness of machine learning models in smartening water management, emphasize the need for caution in policymaking based on extreme flow predictions and provide an operational framework for enhancing the resilience of traditional water infrastructures against climate change. Climate change, as a fundamental challenge of the present century, has made it inevitable for urban and regional policymakers to revise adaptation strategies and develop accurate prediction tools by affecting water resources in arid regions. Qanats, as strategic and vital infrastructures for water supply in settlements of Iran's arid regions, are currently facing threats due to declining groundwater levels, which doubles the need for applying artificial intelligence-based decision support systems. The present study aims to strengthen the foundations of water resources management planning by evaluating the performance of the XGBoost machine learning model in predicting the discharge of qanats in the Qaen Plain. To this end, 12-year data on qanat discharge (2007–2018), climatic variables (precipitation and temperature), and elevation were used. After preprocessing, the data were split into training (70%) and testing (30%) sets, and the model hyperparameters were tuned using RandomizedSearchCV with RMSE as the optimization objective. The evaluation results showed that XGBoost, with R² ≈ 0.88 and NS = 0.76 in the testing phase, has high reliability for use in hydrological analyses. Based on sensitivity analysis, the optimal combination of hyperparameters was n_estimators=500, max_depth=6, learning_rate=0.08, and min_child_weight=4. However, it was observed that the model suffers from poor performance in predicting discharges exceeding 20 L/s due to the RMSE loss function and data imbalance. The findings of this study, while confirming the effectiveness of machine learning models in smartening water management, emphasize the need for caution in policymaking based on extreme flow predictions and provide an operational framework for enhancing the resilience of traditional water infrastructures against climate change.

https://www.judpm.ir/article_247073_625d7d3ef781fdb16ffbe46809a5cd5b.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Analysis of Urban Policy-Making Paradigms in Tehran in the Context of National Urban Policy Analysis of Urban Policy-Making Paradigms in Tehran in the Context of National Urban Policy 471 494 251308 10.22034/judpm.2026.587096.1124 FA Gholamhossein Mohammadi PhD in Public Administration, Department of Public Administration, Alborz Campus, University of Tehran, Tehran, Iran 0009-0001-8335-6867 Rahmatollah Gholipor Souteh PhD in Public Administration, Department of Public Administration, Faculty of Management, University of Tehran,Tehran,Iran 0000-0001-9081-1576 Journal Article 2026 03 25 Urban policy-making in the metropolitan city of Tehran has undergone fundamental paradigmatic transformations in recent decades; transformations that, in the absence of a coherent National Urban Policy, have been largely reactive, fragmented, and lacking institutional coherence. This study aims to identify and analyze the paradigms of urban policy-making in Tehran in the light of the concept of National Urban Policy. The research approach is qualitative and based on a critical-interpretive paradigm, and data were collected and analyzed through documentary analysis, semi-structured interviews with 12 urban management experts, and thematic analysis. The findings indicate that Tehran's urban policy-making is transitioning from the first paradigm (physical-spatial) through the second paradigm (growth management) to the third paradigm (network governance), although this transition remains incomplete and contentious. The core of this tension is the conflict between two opposing regimes, "city-selling" versus "city-preservation", rooted in the lack of sustainable urban revenues and the weakness of National Urban Policy. Accordingly, a conceptual model of Tehran's urban policy-making space is presented, which provides an analytical framework for understanding the complexities of urban policy-making and practical guidance for addressing urban wicked problems. Urban policy-making in the metropolitan city of Tehran has undergone fundamental paradigmatic transformations in recent decades; transformations that, in the absence of a coherent National Urban Policy, have been largely reactive, fragmented, and lacking institutional coherence. This study aims to identify and analyze the paradigms of urban policy-making in Tehran in the light of the concept of National Urban Policy. The research approach is qualitative and based on a critical-interpretive paradigm, and data were collected and analyzed through documentary analysis, semi-structured interviews with 12 urban management experts, and thematic analysis. The findings indicate that Tehran's urban policy-making is transitioning from the first paradigm (physical-spatial) through the second paradigm (growth management) to the third paradigm (network governance), although this transition remains incomplete and contentious. The core of this tension is the conflict between two opposing regimes, "city-selling" versus "city-preservation", rooted in the lack of sustainable urban revenues and the weakness of National Urban Policy. Accordingly, a conceptual model of Tehran's urban policy-making space is presented, which provides an analytical framework for understanding the complexities of urban policy-making and practical guidance for addressing urban wicked problems.

https://www.judpm.ir/article_251308_abf3da1b40b91dfaa518ef01305bd80d.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Forecasting the budgeting system in Tehran Municipality based on designing a robust mathematical model under conditions of uncertainty with a multiple regression approach Forecasting the budgeting system in Tehran Municipality based on designing a robust mathematical model under conditions of uncertainty with a multiple regression approach 495 516 248099 10.22034/judpm.2026.587729.1126 FA Azam Gholami Zarezadeh Department of Industrial Management, NT.C., Islamic Azad University, Tehran, Iran 0009-0007-8927-1964 Sevan Sohraiee Department of Mathematics, NT. C., Islamic Azad University, Tehran, Iran 0000-0002-8817-9855 Maryam Khademi Department of Mathematics, ST.C., Islamic Azad University, Tehran, Iran 0000-0002-9741-6808 Ehsan Sadeh Department of Industrial Management, NT. C., Islamic Azad University, Tehran, Iran 0000-0002-9120-7984 Journal Article 2026 03 25 This research was conducted with the aim of designing a robust mathematical model for forecasting and budget planning at the Tehran Municipality under conditions of economic uncertainty. The main research gap was the lack of an integrated and localized model for converting historical data into optimal budget decisions, as well as the failure to localize indicators affecting revenue and expenditure for an Iranian metropolis. The novelty of this article lies in integrating three approaches within a single framework: forecasting with regularized regressions (Ridge, Lasso, Elastic Net), robust optimization using the Bertsimas-Sim approach, and multiple-choice goal programming (MCGP), which had not previously been applied to Tehran Municipality's budgeting. The robust model, with appropriate adjustment of the protection level, provided optimal solutions in which the transportation sector received the highest priority while the shares of other missions were maintained. Scenario analysis demonstrated that the model is resilient against economic fluctuations and can serve as an effective tool for budgetary decision-making under uncertainty. This research was conducted with the aim of designing a robust mathematical model for forecasting and budget planning at the Tehran Municipality under conditions of economic uncertainty. The main research gap was the lack of an integrated and localized model for converting historical data into optimal budget decisions, as well as the failure to localize indicators affecting revenue and expenditure for an Iranian metropolis. The novelty of this article lies in integrating three approaches within a single framework: forecasting with regularized regressions (Ridge, Lasso, Elastic Net), robust optimization using the Bertsimas-Sim approach, and multiple-choice goal programming (MCGP), which had not previously been applied to Tehran Municipality's budgeting. The robust model, with appropriate adjustment of the protection level, provided optimal solutions in which the transportation sector received the highest priority while the shares of other missions were maintained. Scenario analysis demonstrated that the model is resilient against economic fluctuations and can serve as an effective tool for budgetary decision-making under uncertainty.

https://www.judpm.ir/article_248099_615d979da471e74f70f4de44f55cd744.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Biophilic Landscape Design as a Strategy to Cope with the Water Crisis in Urban Spaces (Case Study: Stepped Landscape of Chamran Park, Karaj) Biophilic Landscape Design as a Strategy to Cope with the Water Crisis in Urban Spaces (Case Study: Stepped Landscape of Chamran Park, Karaj) 517 540 245083 10.22034/judpm.2026.582643.1107 FA Setayesh Zandi Babaei MSc Graduate, Landscape Architecture, Faculty of Agriculture, University of Tehran, Karaj, Iran 0009-0003-8289-692X Mohammad Mahdi Poorhasan Mehrzanjani MSc Graduate, Urban Watershed Management Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran 0009-0008-8042-4191 Journal Article 2026 03 28 This study aims to elucidate biophilic landscape design strategies as resilient ecological infrastructure to address the water crisis in urban spaces, focusing on the stepped landscape of Chamran Park in Karaj. The research is applied in purpose and descriptive–analytical in methodology. Data were collected through documentary studies and field evaluations. An integrated SWOT framework including the Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices, together with the Quantitative Strategic Planning Matrix (QSPM), was employed for data analysis and strategy formulation. The results indicate that the IFE score of 2.63 and the EFE score of 2.29 place the study area in a “Competitive (ST)” strategic position. Based on the QSPM analysis, the strategy of “developing a gravity-fed water landscape with phytoremediation” achieved the highest Total Attractiveness Score (TAS = 6.28). It was followed by “smart flood management using multi-functional infrastructures” (TAS = 5.09), “evaporation-reducing biophilic microclimate creation” (TAS = 4.98), and “enhancing the sense of belonging through participatory landscape” (TAS = 4.01). The findings suggest that achieving a resilient biophilic landscape requires a transition from purely aesthetic approaches toward pragmatic design that integrates environmental-ecological, physical-spatial, perceptual-psychological, and managerial-policy dimensions. Such an approach can transform urban landscapes into gravity-based, self-sustaining ecological systems that reduce water consumption and enhance ecological resilience. This study aims to elucidate biophilic landscape design strategies as resilient ecological infrastructure to address the water crisis in urban spaces, focusing on the stepped landscape of Chamran Park in Karaj. The research is applied in purpose and descriptive–analytical in methodology. Data were collected through documentary studies and field evaluations. An integrated SWOT framework including the Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices, together with the Quantitative Strategic Planning Matrix (QSPM), was employed for data analysis and strategy formulation. The results indicate that the IFE score of 2.63 and the EFE score of 2.29 place the study area in a “Competitive (ST)” strategic position. Based on the QSPM analysis, the strategy of “developing a gravity-fed water landscape with phytoremediation” achieved the highest Total Attractiveness Score (TAS = 6.28). It was followed by “smart flood management using multi-functional infrastructures” (TAS = 5.09), “evaporation-reducing biophilic microclimate creation” (TAS = 4.98), and “enhancing the sense of belonging through participatory landscape” (TAS = 4.01). The findings suggest that achieving a resilient biophilic landscape requires a transition from purely aesthetic approaches toward pragmatic design that integrates environmental-ecological, physical-spatial, perceptual-psychological, and managerial-policy dimensions. Such an approach can transform urban landscapes into gravity-based, self-sustaining ecological systems that reduce water consumption and enhance ecological resilience.

https://www.judpm.ir/article_245083_df840bbf4c61770e320a29ac9cdef59b.pdf

University of Applied Sciences and Technology of Tehran Municipality Urban Development Policy Making 3092-653X 3 3 2026 09 23 Designing a Native Model of an Empowering Entrepreneurial Ecosystem for Female-Headed Households in Tehran Metropolis Designing a Native Model of an Empowering Entrepreneurial Ecosystem for Female-Headed Households in Tehran Metropolis 541 556 251715 10.22034/judpm.2026.584861.1112 FA Mohammad Javad Lashkari Department of Entrepreneurship, KI.C., Islamic Azad University, Kish, Iran Mehran Maghsoudi Department of Entrepreneurship, KI.C., Islamic Azad University, Kish, Iran Shadi Shahverdiani Department of Entrepreneurship, KI.C., Islamic Azad University, Kish, Iran Journal Article 2026 05 20 This study aimed to design and validate a native model of an empowering entrepreneurial ecosystem for female-headed households in the 22 districts of Tehran metropolis. The research adopted a developmental-applied purpose with a sequential exploratory mixed-method design. In the qualitative phase, semi-structured interviews were conducted with 30 experts, including entrepreneurship management professors, Tehran municipality managers, activists from non-governmental organizations, and successful female entrepreneurs. In the quantitative phase, 380 female-headed households and female entrepreneurs residing in Tehran were selected through multi-stage cluster sampling from five selected districts (3, 6, 12, 15, and 22) and completed a researcher-developed questionnaire. Reliability was confirmed with Cronbach's alpha (0.86) and composite reliability (0.94). Data analysis was performed using Structural Equation Modeling with Partial Least Squares approach (PLS-SEM). The final model was organized into three levels: foundational/supportive (individual factors, social support, access to resources, institutional factors, market and opportunities), performance pathway (entrepreneurial intention, business creation, business performance), and empowerment outcomes (economic, psychological, and social empowerment). Findings revealed that institutional factors (β=0.74) were the strongest predictor of entrepreneurial intention, and market and opportunities (β=0.66) had the strongest impact on business performance. Model fit indices indicated good quality (SRMR=0.042, NFI=0.91, GoF=0.54). This model is the first localized entrepreneurial ecosystem model for empowering female-headed households at the urban district level in Tehran. This study aimed to design and validate a native model of an empowering entrepreneurial ecosystem for female-headed households in the 22 districts of Tehran metropolis. The research adopted a developmental-applied purpose with a sequential exploratory mixed-method design. In the qualitative phase, semi-structured interviews were conducted with 30 experts, including entrepreneurship management professors, Tehran municipality managers, activists from non-governmental organizations, and successful female entrepreneurs. In the quantitative phase, 380 female-headed households and female entrepreneurs residing in Tehran were selected through multi-stage cluster sampling from five selected districts (3, 6, 12, 15, and 22) and completed a researcher-developed questionnaire. Reliability was confirmed with Cronbach's alpha (0.86) and composite reliability (0.94). Data analysis was performed using Structural Equation Modeling with Partial Least Squares approach (PLS-SEM). The final model was organized into three levels: foundational/supportive (individual factors, social support, access to resources, institutional factors, market and opportunities), performance pathway (entrepreneurial intention, business creation, business performance), and empowerment outcomes (economic, psychological, and social empowerment). Findings revealed that institutional factors (β=0.74) were the strongest predictor of entrepreneurial intention, and market and opportunities (β=0.66) had the strongest impact on business performance. Model fit indices indicated good quality (SRMR=0.042, NFI=0.91, GoF=0.54). This model is the first localized entrepreneurial ecosystem model for empowering female-headed households at the urban district level in Tehran.

https://www.judpm.ir/article_251715_28ce21ea08bb92aae84b2dbb08600746.pdf