Holistic Framework to Data-Driven Sustainability Assessment

Paulo Peças (), Lenin John, Inês Ribeiro, António J. Baptista, Sara M. Pinto, Rui Dias, Juan Henriques, Marco Estrela, André Pilastri and Fernando Cunha
Additional contact information
Paulo Peças: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Lenin John: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Inês Ribeiro: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
António J. Baptista: LAETA—Associate Laboratory for Energy, Transports and Aerospace, 4200-465 Porto, Portugal
Sara M. Pinto: LAETA—Associate Laboratory for Energy, Transports and Aerospace, 4200-465 Porto, Portugal
Rui Dias: Low Carbon & Resource Efficiency, R&Di, Instituto de Soldadura e Qualidade, 4415-491 Grijó, Portugal
Juan Henriques: Low Carbon & Resource Efficiency, R&Di, Instituto de Soldadura e Qualidade, 4415-491 Grijó, Portugal
Marco Estrela: Low Carbon & Resource Efficiency, R&Di, Instituto de Soldadura e Qualidade, Taguspark, 2740-120 Oeiras, Portugal
André Pilastri: EPMQ-IT Engineering Maturity and Quality Lab, CCG ZGDV Institute, 4800-058 Guimarães, Portugal
Fernando Cunha: Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, 2914-508 Setúbal, Portugal

Sustainability, 2023, vol. 15, issue 4, 1-21

Abstract: In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven approaches with sustainability assessment approaches, since overcoming this challenge will foster more agile sustainable development. Without disregarding the effort of academics and practitioners in the development of sustainability assessment approaches, the authors consider the need for holistic frameworks that also encourage continuous improvement in sustainable development. The main objective of this research is to propose a holistic framework that supports companies to assess sustainability performance effectively and more easily, supported by digital capabilities and data-driven concepts, while integrating improvement procedures and methodologies. To achieve this objective, the research is based on the analysis of published approaches, with special emphasis on the data-driven concepts supporting sustainability assessment and Lean Thinking methods. From these results, we identified and extracted the metrics, scopes, boundaries, and kinds of output for decision-making. A new holistic framework is described, and we have included a guide with the steps necessary for its adoption in a given company, thus helping to enhance sustainability while using data availability and data-analytics tools.

Keywords: Industry 4.0; decarbonizing; data-driven sustainability; holistic framework; continuous improvement; sustainability assessment; lean thinking; data analytics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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