Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Smart cities, smart growth : paving the way to urban regeneration
As the global urban population continues to grow, expanded urbanization is inevitable. The massive, rapid, uncontrolled, and unplanned urbanization has negative socio-economic and environmental consequences that highly affect the life quality and public health. On the other hand, urbanization can benefit urban sustainability transitions when adequately planned and managed under a holistic, systematic approach. Urban systems represent nonlinear, dynamical, and interconnected urban processes and functions that require better design and management of their complexity to be able to tackle the current urban challenges such as intensive demographic growth, economic and social stagnation to resources salvation, and climate changes threats. Therefore, to benefit from urbanization and reduce the environmental impacts while maximizing the socio-economic benefits, it is essential to understand, measure, assess, and predict the complexity of urban dynamics, including an ecological perspective in the process. For doing so, we use the urban metabolism framework to study and assess to couple natural and human systems in an integrated interdisciplinary approach engaging social and ecological science. This thesis dissertation follows a two-fold methodology. Initially, we conducted a systematic literature review to study the evolution of the emerging concepts on sustainable urban development, shedding light on the state of the art of smart and regenerative urban design under the urban metabolism framework. Having identified the literature gaps, we propose a novel conceptual framework; smart and regenerative urban places (SRUP), able to tackle the urban complexity challenges. For the second part of the methodology of this dissertation thesis, we propose an original multidimensional systems-based methodology, coupling Life Cycle Thinking and Machine Learning under the perspective of urban ecosystem services. We apply the proposed methodology measuring the smart and regenerative urban metabolism of the urban core of the functional ...
Smart cities, smart growth : paving the way to urban regeneration
As the global urban population continues to grow, expanded urbanization is inevitable. The massive, rapid, uncontrolled, and unplanned urbanization has negative socio-economic and environmental consequences that highly affect the life quality and public health. On the other hand, urbanization can benefit urban sustainability transitions when adequately planned and managed under a holistic, systematic approach. Urban systems represent nonlinear, dynamical, and interconnected urban processes and functions that require better design and management of their complexity to be able to tackle the current urban challenges such as intensive demographic growth, economic and social stagnation to resources salvation, and climate changes threats. Therefore, to benefit from urbanization and reduce the environmental impacts while maximizing the socio-economic benefits, it is essential to understand, measure, assess, and predict the complexity of urban dynamics, including an ecological perspective in the process. For doing so, we use the urban metabolism framework to study and assess to couple natural and human systems in an integrated interdisciplinary approach engaging social and ecological science. This thesis dissertation follows a two-fold methodology. Initially, we conducted a systematic literature review to study the evolution of the emerging concepts on sustainable urban development, shedding light on the state of the art of smart and regenerative urban design under the urban metabolism framework. Having identified the literature gaps, we propose a novel conceptual framework; smart and regenerative urban places (SRUP), able to tackle the urban complexity challenges. For the second part of the methodology of this dissertation thesis, we propose an original multidimensional systems-based methodology, coupling Life Cycle Thinking and Machine Learning under the perspective of urban ecosystem services. We apply the proposed methodology measuring the smart and regenerative urban metabolism of the urban core of the functional ...
Smart cities, smart growth : paving the way to urban regeneration
Peponi, Angeliki (Autor:in) / Morgado, Paulo / Kumble, Peter
01.05.2022
101626983
Hochschulschrift
Elektronische Ressource
Englisch
DDC:
710
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