The growing concern that climate change has represented in recent decades prompted the United Nations to develop a broad agenda designed to achieve Sustainable Development Goals (SDGs). The response to this agenda has included strong commitments in the form of economic, environmental, and social initiatives designed to address the challenges of sustainability and assure a sustainable future. The implementation of such efforts demands that the discourse on sustainability be converted into action. Rational decision-making plays a key role in promoting sustainable development. In recent decades, sensing technology has increasingly evolved into a powerful tool that contributes to the improvement of sustainability in decision-making, particularly by promoting the data availability and accessibility for a more transparent and efficient decision-making process.
Big data application is embodied within data engineering and data science. The former focuses on how to expand the data sources, such as the deployment of ground Internet of Things, satellite remote sensing, social media, unmanned aerial vehicles monitoring, etc., to record the information regarding economic, environmental, and social perspectives. The latter gives emphasis on data mining and data fusion, aiming to provide decision support for sustainable development, in terms of better policy-making However, existing environmental management based on big data is mostly limited to terminal control, which has not covered all the key dimensions (i.e., economic, environmental and social) regarding sustainable development. With the rapid development of sensing technology, the increase of multi-source heterogeneous data makes data fusion more difficult. This Research Topic welcomes Original Research, Commentaries, Reviews, and Perspectives on advances in data engineering or data science applications for sustainable development, providing a needed shift in the approaches to address the challenges of sustainability.
Potential topics include but are not limited to the following:
• Monitoring of human behaviors or perceptions towards sustainable development (e.g., measurement on electroencephalography (EEG), heart rate, or eye tracker to understand people’s conscious and subconscious behaviors in response to climate change, environmental protection, etc.);
• Multi-source heterogeneous data applications (e.g., high-resolution remote sensing, UAV photogrammetry, Internet of Things, and others for decision-making on environmental management, including water, air, and soil management);
• Data mining and data fusion for decision-making on sustainability (e.g., data mining from economic, environmental, and social perspectives to address environmental impacts and conflicts);
• Development of decision-making approaches for sustainable development (e.g., conventional multi-criteria decision-making approaches hybrid with data-driven for various application scenarios of sustainable development, including carbon emissions reduction, waste management, etc.).
The growing concern that climate change has represented in recent decades prompted the United Nations to develop a broad agenda designed to achieve Sustainable Development Goals (SDGs). The response to this agenda has included strong commitments in the form of economic, environmental, and social initiatives designed to address the challenges of sustainability and assure a sustainable future. The implementation of such efforts demands that the discourse on sustainability be converted into action. Rational decision-making plays a key role in promoting sustainable development. In recent decades, sensing technology has increasingly evolved into a powerful tool that contributes to the improvement of sustainability in decision-making, particularly by promoting the data availability and accessibility for a more transparent and efficient decision-making process.
Big data application is embodied within data engineering and data science. The former focuses on how to expand the data sources, such as the deployment of ground Internet of Things, satellite remote sensing, social media, unmanned aerial vehicles monitoring, etc., to record the information regarding economic, environmental, and social perspectives. The latter gives emphasis on data mining and data fusion, aiming to provide decision support for sustainable development, in terms of better policy-making However, existing environmental management based on big data is mostly limited to terminal control, which has not covered all the key dimensions (i.e., economic, environmental and social) regarding sustainable development. With the rapid development of sensing technology, the increase of multi-source heterogeneous data makes data fusion more difficult. This Research Topic welcomes Original Research, Commentaries, Reviews, and Perspectives on advances in data engineering or data science applications for sustainable development, providing a needed shift in the approaches to address the challenges of sustainability.
Potential topics include but are not limited to the following:
• Monitoring of human behaviors or perceptions towards sustainable development (e.g., measurement on electroencephalography (EEG), heart rate, or eye tracker to understand people’s conscious and subconscious behaviors in response to climate change, environmental protection, etc.);
• Multi-source heterogeneous data applications (e.g., high-resolution remote sensing, UAV photogrammetry, Internet of Things, and others for decision-making on environmental management, including water, air, and soil management);
• Data mining and data fusion for decision-making on sustainability (e.g., data mining from economic, environmental, and social perspectives to address environmental impacts and conflicts);
• Development of decision-making approaches for sustainable development (e.g., conventional multi-criteria decision-making approaches hybrid with data-driven for various application scenarios of sustainable development, including carbon emissions reduction, waste management, etc.).
