The Foundations of a Knowledge-Based Urban Development Framework for Achieving a Low-Carbon City: A Case Study of Qazvin City
محورهای موضوعی : Sustainable DevelopmentFattaneh Haji Norouzi 1 , Seyyed Mohammad Reza Khatibi 2 , Maryam khastou 3
1 - PhD student, Department of Urban Planning, Qa. C., Islamic Azad University, Qazvin, Iran
2 - Associate Professor Department of Urban Planning, Qa. C., Islamic Azad University, Qazvin, Iran
3 - Associate Professor Department of Urban Planning, Qa. C., Islamic Azad University, Qazvin, Iran
کلید واژه: Low-Carbon City, Knowledge-Based Urban Development (KBUD), Qazvin City, Conceptual Model,
چکیده مقاله :
BACKGROUND AND OBJECTIVES: Urban areas are major sources of carbon emissions, and rapid urban growth in developing countries poses sustainability challenges. Qazvin, a medium-sized industrial city in Iran, faces rising energy use, urban sprawl, and increasing CO₂ emissions. Current urban policies lack an integrated low-carbon transition strategy. This study proposes a localized Knowledge-Based Urban Development framework to support low-carbon transformation in Qazvin.
METHODS: The results identified six key dimensions shaping low-carbon urban development in Qazvin. Governance and knowledge–innovation infrastructure exert the strongest causal influence on the transition process. This underscores the importance of institutional learning and integrated policy frameworks in implementing low-carbon strategies.
FINDINGS: The findings identified six key dimensions influencing low-carbon urban development in Qazvin: governance and policy integration, sustainable energy management, renewable energy deployment, smart transportation, social participation and equity, and knowledge and innovation infrastructure. Governance and knowledge infrastructure showed the strongest causal impacts, highlighting the critical role of institutional learning and integrated policies in advancing low-carbon strategies.
CONCLUSION: The study proposes a causal KBUD-based framework that emphasizes institutional transformation and knowledge-driven planning as prerequisites for emission reduction. The model provides a practical foundation for designing coherent low-carbon policies in Qazvin and can be adapted for similar developing cities. Future research is recommended to validate the model quantitatively using multi-criteria decision-making or structural modeling techniques.
BACKGROUND AND OBJECTIVES: Urban areas are major sources of carbon emissions, and rapid urban growth in developing countries poses sustainability challenges. Qazvin, a medium-sized industrial city in Iran, faces rising energy use, urban sprawl, and increasing CO₂ emissions. Current urban policies lack an integrated low-carbon transition strategy. This study proposes a localized Knowledge-Based Urban Development framework to support low-carbon transformation in Qazvin.
METHODS: The results identified six key dimensions shaping low-carbon urban development in Qazvin. Governance and knowledge–innovation infrastructure exert the strongest causal influence on the transition process. This underscores the importance of institutional learning and integrated policy frameworks in implementing low-carbon strategies.
FINDINGS: The findings identified six key dimensions influencing low-carbon urban development in Qazvin: governance and policy integration, sustainable energy management, renewable energy deployment, smart transportation, social participation and equity, and knowledge and innovation infrastructure. Governance and knowledge infrastructure showed the strongest causal impacts, highlighting the critical role of institutional learning and integrated policies in advancing low-carbon strategies.
CONCLUSION: The study proposes a causal KBUD-based framework that emphasizes institutional transformation and knowledge-driven planning as prerequisites for emission reduction. The model provides a practical foundation for designing coherent low-carbon policies in Qazvin and can be adapted for similar developing cities. Future research is recommended to validate the model quantitatively using multi-criteria decision-making or structural modeling techniques.
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