شناسایی عوامل مؤثر بر ایجاد نواحی نوآوری با تأکید بر بازآفرینی محلات ناکارآمد شهری
محورهای موضوعی : برنامه ریزی شهری
مریم مهدیزاده کیقباد
1
,
حمید رضا صارمی
2
*
,
مجتبی رفیعیان
3
1 - دانشجو کارشناسیارشد، گروه شهرسازی، هنر و معماری، تربیتمدرس، تهران، ایران.
2 - دانشیار، گروه شهرسازی، هنر و معماری، تربیتمدرس، تهران، ایران.
3 - استاد، گروه شهرسازی، هنر و معماری، تربیتمدرس، تهران، ایران.
کلید واژه: نواحی نوآوری, بازآفرینی, محلات ناکارآمد شهری, اعیانیسازی, دلفی,
چکیده مقاله :
نحوه مواجهه با بافتهای ناکارآمد شهری در ادوار مختلف بر اساس الگوهای مختلفی بوده است که متأسفانه نتیجه مطلوبی در جهت بهبود آنها به دست نیامده است، ایجاد مراکز نوآوری شهری سبب شکلگیری رویکردی نوین برای بازآفرینی بافتهای ناکارآمد شده است؛ اما مسئله این است که آنها نیز نتوانستهاند تأثیر چشمگیری در این بافتها بگذارند؛ زیرا نواحی نوآوری اعیانیسازی را تشویق میکنند و ممکن است موجب کاهش فرصتهای شغلی برای افراد کمسوادتر شوند، بدین ترتیب نیاز به سیاستها و اقداماتی مبتنی بر جامعه است که از اعیانیسازی جلوگیری کند؛ بنابراین هدف پژوهش شناسایی عوامل مؤثر بر ایجاد نواحی نوآوری با تأکید بر بازآفرینی محلات ناکارآمد شهری است. در این پژوهش ابتدا عوامل مؤثر از طریق مرور ادبیات نظری بهدستآمده و سپس برای تکمیل و غربالگری آن عوامل از روش دلفی در دو راند استفاده شده است، در نهایت ۲۸ عامل بهدستآمده است که در سه بعد کالبدی و محیطی، شبکهای و اجتماعی و اقتصادی قرار گرفتهاند.
One of the most significant challenges facing developing countries is the increasing expansion of inefficient urban fabrics. The approach to dealing with inefficient and dilapidated urban areas has varied across different periods, unfortunately yielding unsatisfactory results in terms of their improvement. Achieving innovations and creating creative urban innovation centers has led to a novel approach to the regeneration of these inefficient urban fabrics. However, the issue remains that these innovative and creative urban centers have also failed to make a significant impact on these urban areas. Innovation districts are emerging as place-based and knowledge-driven urban development strategies in various cities worldwide. However, they have faced criticism for top-down, non-participatory initiatives that encourage gentrification, which ultimately increases the gap between the rich and the poor. Evidence from the United States indicates that gentrification policies aimed at improving conditions for all classes have been ineffective. In fact, the effects of gentrification may reduce the survival chances of smaller manufacturing businesses that provide essential job opportunities for less-educated individuals. Therefore, there is an urgent need for community-based policies and actions that prevent the potential displacement of the most vulnerable economic stakeholders and resident groups. Consequently, to avoid gentrification, this research aims to identify the factors affecting the creation of innovation districts with a focus on the Urban Regeneration of inefficient neighborhoods. In this study, 22 influential factors were initially identified through a thorough literature review and content analysis. Subsequently, the Delphi method was employed in two rounds to screen and identify these factors comprehensively. The members of the Delphi panel were selected through non-probability sampling, utilizing a combination of purposive and snowball methods. This panel consisted of researchers or practitioners in the fields of urban innovation districts, urban Regeneration, or both. A semi-structured questionnaire was used for this research, with closed and open questions analyzed using SPSS and Atlas-Ti software, respectively. Based on the results obtained from SPSS, the factor of affordable housing was eliminated. From the open-ended coding results in Atlas-Ti, 21 codes were extracted, with 14 codes overlapping with factors in the closed questions, resulting in a total of 7 new factors being recognized. Ultimately, 28 factors were identified: 9 factors in the physical and environmental dimension, 12 factors in the social and network dimension, and 7 factors in the economic dimension. In the second round of Delphi, all factors received high scores, and none of the factors were eliminated. According to the results from this round, in the physical dimension, factors such as utilizing land and buildings with redevelopment potential and equitably distributing urban services and infrastructure received high scores. In the social and network dimension, factors like empowering local communities (especially vulnerable groups), fostering connections between local communities and newcomers, ensuring mutual benefits, and nurturing local talents scored highest. In the economic dimension, creating and attracting new financial resources and developing local economic development (LED) received the highest scores. These identified factors significantly impact inclusion and play an important role in preventing gentrification in urban settings