An entity-relationship model of the flow of waste and resources in city-regions: Improving knowledge management for the circular economy

Waste and resources management is one of the domains where urban and regional planning can transition towards a Circular Economy, thus slowing environmental degradation. Improving waste and resources management in cities requires an adequate understanding of multiple systems and how they interact. New technologies contribute to improve waste management and resource efficiency, but knowledge silos hinder the possibility of delivering sound holistic solutions. Furthermore, lack of compatibility between data formats and diverse definitions of the same concept reduces information exchange across different urban domains. This paper addresses the challenge of organising and standardising information about waste and resources management in city regions. Given the amount and variety of data constantly captured, data models and standards are a crucial element of Industry 4.0. The paper proposes an Entity-Relationship Model to harmonise definitions and integrate information on waste and resources management. Furthermore, it helps to formalise the components of the system and their relationships. Semi-structured interviews with government officials, mobile app developers and academics provided insights into the specific system and endorsed the model. Finally, the paper illustrates the translation of the ERM into a relational database schema and instantiates Waste Management and industrial Symbiosis cases in Buenos Aires (ARG) and Helsingborg (SWE) to validate its general applicability. The data model for the Circular Flow of Waste and Resources presented here enhances traditional waste management perspectives by introducing Circular Economy strategies and spatial variables in the model. Thus, this research represents a step towards unlocking the true potential of Industry 4.0

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Unlocking system transitions for municipal solid waste infrastructure:A model for mapping interdependencies in a local context

Rapid global urbanization, urban renewal and changes in people's lifestyles have led to both an increase in waste generation and more complex waste types. In response to these changes, many local governments have invested in municipal solid waste infrastructure (MSWI) to implement circular strategies. However, matching and bridging the costly and logistically complex MSWI with the dynamic social context is a central challenge. In this paper we aim to explore the interdependencies between MSWI and the local social system, and then conceptualize and empirically validate the systemic nature of MSWI. We first review the current MSW treatment methods, corresponding infrastructure, and the challenges facing them. Then, we interrogate system-oriented concepts and use two key insights to set up a conceptual model for mapping the interdependencies in a MSWI system (MSWIS). Finally, a case study of the Dutch city of Almere is used to empirically validate the MSWIS model and identify the social systems that contribute to the development of the MSWIS. The analysis reveals that the development of MSWIS is beyond the municipality's control: efficient resource recovery facilities established by businesses under market rules and waste reuse facilities constructed by social organizations/individuals based on their own needs are key pieces of the puzzle to complete the MSWIS. This highlights the ability of the framework to capture interdependencies that go further than just the formal municipal sphere of influence.</p

Citizen empowerment and innovation in the data-rich city

This book analyzes the ongoing transformation in the “smart city” paradigm and explores the possibilities that technological innovations offer for the effective involvement of ordinary citizens in collective knowledge production and decision-making processes within the context of urban planning and management. To so, it pursues an interdisciplinary approach, with contributions from a range of experts including city managers, public policy makers, Information and Communication Technology (ICT) specialists, and researchers. The first two parts of the book focus on the generation and use of data by citizens, with or without institutional support, and the professional management of data in city governance, highlighting the social connectivity and livability aspects essential to vibrant and healthy urban environments. In turn, the third part presents inspiring case studies that illustrate how data-driven solutions can empower people and improve urban environments, including enhanced sustainability. The book will appeal to all those who are interested in the required transformation in the planning, management, and operations of data-rich cities and the ways in which such cities can employ the latest technologies to use data efficiently, promoting data access, data sharing, and interoperability

Assessing the current landscape of AI and sustainability literature:Identifying key trends, addressing gaps and challenges

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.</p

Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems