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Advanced Sustainability Assessment Frameworks Using Digital Tools

Author(s) Joop de Kraker
Country Netherlands
Abstract Sustainability assessment has become increasingly important for evaluating the environmental, economic, and social consequences of development activities, technological systems, organizational practices, and resource-management decisions. Traditional assessment approaches can face limitations related to fragmented datasets, delayed reporting, limited spatial coverage, and difficulties in integrating multiple sustainability dimensions. Digital tools provide opportunities to improve sustainability assessment through artificial intelligence, machine learning, Internet of Things systems, remote sensing, geographic information systems, big-data analytics, cloud platforms, digital twins, and automated reporting technologies. This study examines advanced sustainability assessment frameworks using digital tools through a conceptual and descriptive research approach. The study focuses on sustainability indicators, data integration, lifecycle assessment, environmental monitoring, resource efficiency, predictive analytics, scenario modelling, stakeholder participation, and sustainability reporting. An integrated framework is developed that connects indicator selection, digital data acquisition, data integration, analytical processing, sustainability evaluation, scenario analysis, decision support, reporting, and continuous improvement.
The analysis indicates that digital tools can enhance the timeliness, spatial coverage, analytical depth, and transparency of sustainability assessments. IoT systems can provide continuous environmental and operational data, while remote sensing can support large-scale monitoring. AI and machine learning can identify patterns, predict sustainability risks, and support scenario analysis. GIS can integrate spatial indicators, while digital twins can simulate sustainability outcomes under alternative development conditions. Nevertheless, data quality, interoperability, cybersecurity, digital infrastructure, technical capabilities, assessment complexity, and unequal access remain significant challenges. Effective digital sustainability assessment therefore requires standardized indicators, transparent methodologies, lifecycle thinking, institutional coordination, stakeholder participation, and appropriate governance mechanisms. Future research should examine AI-enabled sustainability assessment, real-time sustainability dashboards, digital twins, automated lifecycle assessment, integrated ESG analytics, climate-risk modelling, and participatory digital assessment platforms. The proposed framework provides a structured foundation for using digital tools to improve comprehensive, evidence-based, and continuous sustainability assessment.
Keywords Sustainability Assessment, Digital Tools, Artificial Intelligence, Machine Learning, IoT, Remote Sensing, GIS, Digital Twins, Lifecycle Assessment, Sustainability Indicators.
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-29
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