آینده پژوهی توسعه شهر چابک در راستای تاب آوری و پویایی شهری کلان شهر تهران (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
شهر چابک، پاسخی نوین به تحولات پیچیده و چالش های نوظهور است که با بهره گیری از فناوری های هوشمند و حکمرانی داده محور، زمینه تاب آوری و پویایی شهری را برای دستیابی به توسعه آینده شهری فراهم می کند. در این راستا، پژوهش با هدف آینده پژوهی توسعه شهر چابک در راستای تاب آوری و پویایی شهری کلان شهر تهران تدوین شده است. روش پژوهش رویکرد آینده پژوهی است که از لحاظ هدف کاربردی و به لحاظ نوع توصیفی-تحلیلی است. روش گردآوری داده ها کتابخانه ای و پیمایشی بر اساس روش دلفی بوده است. جامعه آماری خبرگان متخصص در مطالعات شهری می باشند که حجم نمونه به صورت هدفمند با روش نمونه گیری گلوله برفی 70 نفر انتخاب شده است. در بخش تجزیه و تحلیل برای ارزیابی، زمینه سازی و رتبه بندی شاخص ها از روش ماتریس اثرات متقابل (MICMAC) و برای تدوین سناریوهای نهایی از سناریو ویزارد (SCENARIOWIZARD) استفاده شده است. شاخص اتکا به منابع پاک بیشترین تأثیرگذاری و برنامه ریزی مشارکتی بیشترین تأثیرپذیری را دارند. همچنین، تصمیمات داده محور و بازیافت مواد به عنوان پیشران های کلیدی شناخته شدند، درحالی که شاخص های رفاه اجتماعی و فرهنگ سازی زیست محیطی بیشتر تأثیرپذیر و کمتر تأثیرگذار هستند. در تحلیل سناریو، پنج حوزه کلیدی توسعه شامل فناوری های نوین، تاب آوری اقلیمی، اقتصاد دیجیتال، آموزش مستمر و زیرساخت های هوشمند شناسایی شده است که هریک بر پایداری و کارآمدی کلان شهر تهران تأثیرات متنوعی دارند. توسعه شهر چابک مسیر تحقق مدیریت هوشمند، پویا و تاب آور را در کلان شهر تهران هموار ساخته و زمینه پاسخ گویی مؤثر به چالش های پیچیده شهری آینده را فراهم می کند.Foresight of Agile City Development for Urban Resilience and Dynamism in the Metropolitan City of Tehran
Introduction Cities are dynamic institutions continuously undergoing complex and unpredictable changes influenced by social, economic, environmental, and technological factors. Facing challenges such as climate change, pandemics, energy crises, and natural hazards, the need to reassess urban responsiveness capacities is more urgent than ever. These conditions have revealed the inefficiencies of traditional planning approaches and strengthened the trend toward innovative models like the "Agile City." An Agile City emphasizes smart governance, flexible infrastructures, efficient social services, and advanced technologies, pursuing goals beyond mere resilience to threats by intelligently redefining urban development pathways. This model integrates dynamism, resilience, data-driven decision-making, and civic participation, prioritizing environmental impact reduction, energy efficiency enhancement, and optimal resource management. Its realization requires a deep understanding of the interaction between space, citizens, and technology—a relationship where individual decisions across living, working, and recreational environments are intertwined with the city’s spatial and functional structures and evolve over time. Technologies such as the Internet of Things (IoT) and augmented reality enable real-time environmental data analysis, rapid decision-making, and improved efficiency in resource management, transportation, energy distribution, and pollution control. However, the gap between theoretical discourse and practical guidelines, alongside a shortage of operational research, poses significant barriers to actualizing the Agile City. Urban foresight with this approach is an indispensable necessity for achieving long-term resilience and sustainability. Within this framework, Tehran—a metropolis characterized by heterogeneous urban morphology, rapid population growth, and weak local governance—is highly vulnerable to environmental threats and requires an agile model more than ever; a model that identifies key indicators, develops developmental scenarios, and employs a qualitative-futures approach to enhance the city’s dynamism and resilience. Materials and Methods This research adopts a futures studies approach and is applied in purpose and descriptive-analytical in type. Data collection was conducted through library research, document analysis, and surveys utilizing the Delphi method. The Delphi panel was selected through purposive sampling based on criteria including theoretical mastery, practical experience, willingness and ability to participate, and accessibility. The expert population comprised university professors, urban planning managers from the municipality, IT experts, urban resilience researchers, and urban regeneration consultants. Due to the absence of a comprehensive database, snowball sampling was employed, resulting in a sample size of 70. Initially, nine components and 48 indicators were identified as drivers, then reviewed by experts, narrowing down to 30 final indicators for analysis. A semi-structured questionnaire was distributed among experts who evaluated variables’ influence and dependence using a cross-impact matrix with scores of 0 (no effect), 1 (weak), 2 (moderate), 3 (strong), and P (potential effect). Cross-impact analysis was performed using MICMAC software, and final scenarios were developed with Scenario Wizard to extract key indicators influencing the system’s future. Results Analysis of the direct impact matrix showed that out of 900 cells, 826 (91.78%) had non-zero values, with the highest frequency pertaining to strong and moderate influences, reflecting extensive and robust relationships among indicators. Among influential and dependent indicators, reliance on clean energy sources and energy consumption optimization had the highest direct influence, whereas climate impact assessment and mitigation had the lowest. Participatory planning showed the highest direct dependence, while digital business and platform expansion had the least. Regarding indirect effects, data-driven decisions had the greatest influence, and reliance on clean energy had the least. Direct and indirect impact diagrams illustrated complex interrelations of varying intensities among indicators, identifying key metrics for smart policymaking in Tehran’s urban governance. Indicators were categorized into five zones: Zone 1 (bilateral with high influence and dependence, e.g., material recycling and new technologies), Zone 2 (influential with high influence and low dependence, e.g., data-driven decisions and climate resilience), Zone 3 (dependent with low influence and high dependence), Zone 4 (independent with low influence and dependence), and Zone 5 (regulatory with balanced influence and dependence). Ranking showed reliance on clean energy and energy optimization leading in influence and dependence, and climate impact assessment ranking lowest. Scenario analysis via Scenario Wizard assessed these five zones under strong, plausible, and weak scenarios depicting various futures for Tehran’s technology, digital economy, infrastructure, and education, ultimately enhancing intelligent decision-making and effective urban governance policies. Discussion and Conclusion In today’s complex and high-risk world, futures studies have become essential tools for designing and managing resilient and agile cities. Tehran faces challenges including unbalanced urban expansion, infrastructure strain, air pollution, and climate change, requiring scenario-based and agile approaches in planning and governance. This study identified key drivers for Tehran’s agile city development, with data-driven decision-making as paramount. Without up-to-date data governance, anticipating and responding swiftly to urban threats is hindered, consistent with recent studies on smart city data reliance. Circular economy and resource recycling were highlighted as critical drivers, emphasizing that agile city development extends beyond technology to energy efficiency and resource recirculation. Reliance on clean energy also emerged as a vital driver reinforcing ecological resilience. Conversely, indicators such as participatory planning and institutional interaction showed highest dependence, underscoring social cohesion’s role in achieving urban agility. Intelligent traffic management and smart transportation function as regulatory indicators supporting system performance in crises. Analysis of three potential scenarios—strong, plausible, and weak—revealed Tehran’s future could range from sustainable to chaotic depending on institutional integration, technology, and data-driven governance. Ultimately, the research offers a practical model for enhancing Tehran’s resilience but faces limitations like incomplete stakeholder participation and weak dynamic data, necessitating further studies. Advanced analytical methods and local engagement are recommended to design more precise scenarios.
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