https://www.judpm.ir/ Urban Development Policy Making en daily 1 Wed, 23 Sep 2026 00:00:00 +0330 Wed, 23 Sep 2026 00:00:00 +0330 https://www.judpm.ir/article_242443.html 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_244609.html 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_244662.html 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_244307.html 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_240722.html This study aims to redesign Tehran’s urban water governance and integrate nature-based and participatory governance approaches to enhance socio-ecological resilience. It examines the hydrological resilience of Tehran metropolis to the combined stresses of climate change, population growth, and unsustainable water resource management. Using a mixed methodology including analysis of official documents and development plans, stakeholder interviews, and spatial analysis, the impact of strategies such as aqueduct rehabilitation, rainwater harvesting, and treated wastewater recycling was assessed. Findings indicate a 25% reduction in urban water consumption and a 40% increase in local participation under the influence of these strategies, which have significant potential to restore groundwater aquifers and improve the permeability of urban ecosystems. However, the scalability of the strategies is limited by deep structural barriers including policy fragmentation, legal vacuum, institutional inefficiency, and weak citizen participation. In response to these challenges, the study proposes the establishment of an integrated urban water governance system based on synergy between government institutions, NGOs, and local communities. This framework seeks to achieve both ecological resilience and social cohesion by establishing a “High Urban Water Council,” amending laws, implementing participatory pilot projects in underserved neighborhoods, making data transparent, and utilizing innovative solutions such as digital monitoring. Finally, by redefining water as a socio-ecological capital, this model provides an efficient model that can be generalized to other metropolises located in dry regions of the world. https://www.judpm.ir/article_243406.html This study aims to predict the long-term residential electricity demand under various climate change scenarios, including annual temperature increases or decreases of 1°C and 0.5°C, as well as CMIP6 climate models. Rising average temperatures and intensified heatwaves lead to higher cooling demand and increased summer peak loads. To analyze this trend, a data-driven framework based on an annual multivariate regression model is developed, incorporating cooling and heating degree days, the number of consumers, and temporal trends as input variables. Daily temperature data are also used to disaggregate annual consumption into daily and hourly scales, enabling detailed assessment of peak load variations. Model evaluation indicates high accuracy, with coefficients of determination of 0.995 for training data and 0.979 for testing data. The root mean square error is approximately 547.82 kWh less than one percent of annual consumption. The climate scenarios reveal that the cooling degree days (CDD) index is the main driver of increased electricity consumption in Tehran, rising from about 780 units in the base year to 3,000 in the mild warming scenario and over 7,200 in the severe one an increase of 285% to 820%. In contrast, the heating degree days (HDD) index decreases under warmer conditions and has minimal impact on demand. Projections suggest that residential electricity consumption will grow by 75% to 132% by 2050. This framework provides an effective tool for network planning and load management under global warming conditions. https://www.judpm.ir/article_245083.html 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_247073.html Climate change has fundamentally altered the hydrological balance in arid regions, necessitating robust predictive tools to inform urban adaptation strategies. Qanats, as vital strategic water infrastructures in Iran’s arid zones, face complex management challenges due to climate-induced groundwater depletion, highlighting the need for AI-driven decision-support systems. This study evaluates the performance of the XGBoost machine learning model in predicting qanat discharge in the Qaen Plain as a tool for evidence-based water resource management. The dataset comprised 12 years of discharge records (2007–2018), alongside climatic variables and topographical elevation. The data were partitioned into training (70%) and testing (30%) sets, with hyperparameters optimized via RandomizedSearchCV targeting RMSE minimization. The results demonstrate that the XGBoost model exhibits high reliability for policy-making, achieving an $R^2 \approx 0.88$ and a Nash-Sutcliffe efficiency of $NS \approx 0.76$. Sensitivity analysis identified the optimal configuration as n_estimators=500, max_depth=6, learning_rate=0.08, and min_child_weight=4. However, the model showed limitations in predicting peak discharge (exceeding 20 L/s), likely due to the RMSE loss function and data imbalance. These findings suggest that while machine learning significantly enhances the predictability of water resources for smart governance, policy frameworks must account for predictive uncertainties in extreme flow scenarios to ensure sustainable climate adaptation and infrastructural resilience. https://www.judpm.ir/article_248099.html This research aimed to design a robust mathematical model for forecasting and planning the budget of Tehran Municipality under conditions of economic uncertainty. Its main focus was on identifying indicators affecting revenue and cost and combining multivariate regression with multi-objective optimization. In this study, key indicators were first identified and localized through literature meta-synthesis and fuzzy Delphi method. Then, 9-year real data of revenue, cost and economic indicators of Tehran Municipality were collected and after pre-processing, they were analyzed with different regression models such as Ridge, Lasso, Elastic Net and Random Forest. The best models were selected with ten-way cross-validation and revenue and cost forecasts were presented with a 95% confidence interval. Next, a robust multi-objective optimization model was designed with the Bertsimas-Sim approach and the NSGA-III hybrid algorithm.The objectives of this model included maximizing net profit, reducing deviation from council goals, minimizing regret, and complying with legal constraints. The results showed that the Elastic Net and Lasso models performed very well in forecasting revenue and expenses with high accuracy and a difference of less than 0.3 percent compared to the 1404 supplementary budget. Also, the robust model, by adjusting the appropriate level of protection, provided optimal solutions for budget allocation, in which the transportation sector had the highest priority and the share of other missions was also maintained. Analysis of different scenarios also showed that this model is resistant to economic fluctuations and can be an effective tool for municipal budget decision-making under conditions of uncertainty