With the growth of data, data-intensive approaches for sustainability are becoming widespread and have been endorsed by various stakeholders. To understand their implications, in this paper, data-intensive approaches for sustainability will be explored by conducting an extensive review. The current data-intensive approaches are defined as an amalgamation of traditional data-collection methods, such as surveys and data from monitoring networks, with new data-collection methods that involve new information communication technology. Based on a comprehensive review of the current data-intensive approaches for sustainability, key challenges are identified: the lack of data availability, diverse indicators developed from a narrowly viewed base, diverse definitions and values, skewed global representation, and the lack of social and economic information collected, especially among the business indicators. To clarify the implications of these trends, four major research assumptions regarding data-intensive approaches are elaborated: the methodology, epistemology, normativity, and ontology. Caution is required when data-intensive approaches are masked as “objective”. Overcoming this issue requires interdisciplinary and community-based approaches that can offer ways to address the subjectivities of data-intensive approaches. The current challenges to interdisciplinarity and community-based approaches are also identified, and possible solutions are explored, so that researchers can employ them to make the best use of data-intensive approaches.
A review of data-intensive approaches for sustainability: methodology, epistemology, normativity, and ontology
Asokan, Vivek Anand, Masaru Yarime, Motoharu Onuki. 2019. A review of data-intensive approaches to sustainability: Methodology, epistemology, normativity, and ontology. Sustainability Science (online first).