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Smart cities, smart growth : paving the way to urban regeneration
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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 urban area of Lisbon, predict the metabolic changes for the year 2025 in terms of purchasing power per capita, and identify the key drivers for these metabolic changes. Using GIS, we mapped the predicted metabolic changes within the study area. The results showed that the urban processes related to employment and unemployment rates (17%), energy systems (10%), sewage and waste management/treatment/recycling, demography and migration, hard/soft cultural assets, and air pollution (7%), education and training, welfare, cultural participation, and habitat-ecosystems (5%), urban safety, water systems, economy, housing quality, urban void, urban fabric, and health services and infrastructure (2%), consists the prominent drivers for the urban metabolic changes. Conceptualizing SRUP, we contribute to urban and environmental planning and design, providing a framework to improve the quality of life in the built environment, highlighting the need to use ICT, Big Data, and ubiquitous technologies along with ecological principles. Smart and regenerative urban places are planned and designed to be safe and secure, attractive, liveable, healthier, and more creative. Appling SRUP using the proposed novel methodological protocol, we contribute to urban design, planning, and development, addressing gaps and research needs of previously applied methodologies for assessing urban metabolism, providing a tool for decision support systems and policymaking, ensuring sustainability enhancing system resilience.