Data-Driven Smart Eco-Cities of the Future: An Empirically Informed Integrated Model for Strategic Sustainable Urban Development

Abstract

The eco-city is seenas a major force in the overall transition toward a more sustainable urban development paradigm, acting as a set of exemplary techno-environmental solutions for social transformation. However, the scale and rate of urbanization fundamentally disrupt the challenges that eco-cities must contend with. This is coupled with the increased need to understand, plan, and manage eco-cities in new and innovative ways. On this account, transformative processes and practices within eco-cities are being highly responsive to a form of data-driven urbanism as a more effective way to address and overcome the kinds of challenges that eco-cities are designed to solve. That is, eco-cities are increasingly embracing and leveraging what smart cities have to offer in terms of data-driven technologies and applied solutions in an effort to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart eco-cities.” The aim of this paper is to develop an integrated model for strategic sustainable urban development based on empirical research in the form of case studies. This model combines and integrates the leading global paradigms of urbanism—namely eco-cities, data–driven smart cities, and environmentally data-driven smart sustainable cities—in terms of their strategies and solutions. The main contribution of this study lies in providing an innovative way of building future models for sustainable urban development, as well as practical insights into developing strategic planning processes of transformative change toward sustainability based on integrated approaches. The proposed model serves to facilitate progress toward achieving the long-term goals of sustainability for those cities that are badging or regenerating themselves as eco-cities, or manifestly planning to be or become smart eco-cities in the era of big data.