Digital transformation refers to the unprecedented disruptions in society, industry, and organizations stimulated by advances in digital technologies such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT). Presently, there is a lack of studies to map digital transformation in the environmental sustainability domain. This paper identifies the disruptions driven by digital transformation in the environmental sustainability domain through a systematic literature review. The results present a framework that outlines the transformations in four key areas: pollution control, waste management, sustainable production, and urban sustainability. The transformations in each key area are divided into further sub-categories. This study proposes an agenda for future research in terms of organizational capabilities, performance, and digital transformation strategy regarding environmental sustainability.

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Digital technologies are considered to be an essential enabler of the circular economy in various industries. However, to date, very few studies have investigated which digital technologies could enable the circular economy in the built environment. This study specifically focuses on the built environment as one of the largest, most energy- and material-intensive industries globally, and investigates the following question: which digital technologies potentially enable a circular economy in the built environment, and in what ways? The research uses an iterative stepwise method: (1) framework development based on regenerating, narrowing, slowing and closing resource loop principles; (2) expert workshops to understand the usage of digital technologies in a circular built environment; (3) a literature and practice review to further populate the emerging framework with relevant digital technologies; and (4) the final mapping of digital technologies onto the framework. This study develops a novel Circular Digital Built Environment framework. It identifies and maps ten enabling digital technologies to facilitate a circular economy in the built environment. These include: (1) additive/robotic manufacturing, (2) artificial intelligence, (3) big data and analytics, (4) blockchain technology, (5) building information modelling, (6) digital platforms/marketplaces, (7) digital twins, (8) the geographical information system, (9) material passports/databanks, and (10) the internet of things. The framework provides a fruitful starting point for the novel research avenue at the intersection of circular economy, digital technology and the built environment, and gives practitioners inspiration for sustainable innovation in the sector.

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Hyunjoo Han

Papers

A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment