Sustainability is the way to a better future. The global community is seeking a way to protect the society and the environment while maintaining our economic prosperity, embodied by the UN Sustainable Development Goals (SDGs). Regrettably, however, progress toward the sustainable future lacked before the pandemic; worse, there are some early signs that governments are even less financing for attaining the SDGs in the post-COVID era.
Data may hold the key to change this tide. Today, researchers have access to an unprecedented amount of data in every aspect of sustainability. The recent developments in data science gave academics and practitioners the ability to analyze the progress of sustainable development more holistically and swiftly than ever before -- a critical ability in a rapidly changing post-COVID landscape. Artificial intelligence is already proving useful in assessing the environmental impact from remote sensing data. Machine learning algorithms are being deployed to track poverty in real-time from alternative data including remittance. Further, existing quantitative methodologies, such as life cycle assessment (LCA), input-output analysis, material flow analysis, techno-economic analysis, and system dynamics, are also benefiting greatly from enhanced access to data.
Therefore, data science has tremendous potential in the field of sustainability today: we can urge immediate actions for the global community by objectively analyzing the environmental, social, and economic progress towards SDGs -- to open a new way to a sustainable future.
It is time for us, researchers and practitioners, to reinvigorate global strides toward a sustainable future through coordinated efforts to utilize data in analyzing every aspect of sustainability. What is particularly needed today is the quantitative analysis of actual phenomena based on the empirical development of sustainability theory and observation. With this approach – which we named sustainametrics – academics may be able to present a path to a sustainable future.
There are still challenges to overcome toward this objective. Some practitioners are still skeptical of the usefulness of data science in the actual world due to the incompatibility, uncertainty, and gaps in real-life data; others have suspicions about the capabilities of simple analytical models on understanding the inherently indeterminate real-life consequences. This is why the Topic Editors see the need to bring together the wisdom on how data science and other quantitative methodologies can push the sustainability development forward now more than ever.
This Research Topic aims to create a collection of the latest academic researches that showcase the usefulness of data science and other quantitative methodologies in the field of sustainability, to demonstrate the practicality of data approach in giving empirical content to sustainable development.
Expected paper themes include, but not limited to:
• Application of AI and Machine Learning Technologies to Real-world Data - e.g., use of remote sensing and other alternative data.
• Sustainability Indices and Metrics – e.g., measurement of SDGs, ESG evaluation, assessment of development beyond GDP, holistic analysis of sustainability.
• Quantitative Assessment on Sustainable Policy – e.g., environmental input-output analysis, socio-technical and socio-economic assessment, text mining.
• Life Cycle Assessment – e.g., environmental LCA, social LCA, organizational LCA.
• System Dynamics
• Climate Modeling and Climate Change Scenarios
• Environmental Modeling and Assessment
We invite Original Research and Review articles (including Systematic Review, Mini Review, and Policy and Practice Reviews) analyzing the state-of-art of sustainable development with data-oriented approaches.
Topic Editors may accept Perspective or Opinion articles that contribute to the advancement of sustainametrics, pending editorial review. If you wish to contribute a Perspective or Opinion article, please contact the corresponding Topic Editor, Prof. Shutaro Takeda, before submission.
Sustainability is the way to a better future. The global community is seeking a way to protect the society and the environment while maintaining our economic prosperity, embodied by the UN Sustainable Development Goals (SDGs). Regrettably, however, progress toward the sustainable future lacked before the pandemic; worse, there are some early signs that governments are even less financing for attaining the SDGs in the post-COVID era.
Data may hold the key to change this tide. Today, researchers have access to an unprecedented amount of data in every aspect of sustainability. The recent developments in data science gave academics and practitioners the ability to analyze the progress of sustainable development more holistically and swiftly than ever before -- a critical ability in a rapidly changing post-COVID landscape. Artificial intelligence is already proving useful in assessing the environmental impact from remote sensing data. Machine learning algorithms are being deployed to track poverty in real-time from alternative data including remittance. Further, existing quantitative methodologies, such as life cycle assessment (LCA), input-output analysis, material flow analysis, techno-economic analysis, and system dynamics, are also benefiting greatly from enhanced access to data.
Therefore, data science has tremendous potential in the field of sustainability today: we can urge immediate actions for the global community by objectively analyzing the environmental, social, and economic progress towards SDGs -- to open a new way to a sustainable future.
It is time for us, researchers and practitioners, to reinvigorate global strides toward a sustainable future through coordinated efforts to utilize data in analyzing every aspect of sustainability. What is particularly needed today is the quantitative analysis of actual phenomena based on the empirical development of sustainability theory and observation. With this approach – which we named sustainametrics – academics may be able to present a path to a sustainable future.
There are still challenges to overcome toward this objective. Some practitioners are still skeptical of the usefulness of data science in the actual world due to the incompatibility, uncertainty, and gaps in real-life data; others have suspicions about the capabilities of simple analytical models on understanding the inherently indeterminate real-life consequences. This is why the Topic Editors see the need to bring together the wisdom on how data science and other quantitative methodologies can push the sustainability development forward now more than ever.
This Research Topic aims to create a collection of the latest academic researches that showcase the usefulness of data science and other quantitative methodologies in the field of sustainability, to demonstrate the practicality of data approach in giving empirical content to sustainable development.
Expected paper themes include, but not limited to:
• Application of AI and Machine Learning Technologies to Real-world Data - e.g., use of remote sensing and other alternative data.
• Sustainability Indices and Metrics – e.g., measurement of SDGs, ESG evaluation, assessment of development beyond GDP, holistic analysis of sustainability.
• Quantitative Assessment on Sustainable Policy – e.g., environmental input-output analysis, socio-technical and socio-economic assessment, text mining.
• Life Cycle Assessment – e.g., environmental LCA, social LCA, organizational LCA.
• System Dynamics
• Climate Modeling and Climate Change Scenarios
• Environmental Modeling and Assessment
We invite Original Research and Review articles (including Systematic Review, Mini Review, and Policy and Practice Reviews) analyzing the state-of-art of sustainable development with data-oriented approaches.
Topic Editors may accept Perspective or Opinion articles that contribute to the advancement of sustainametrics, pending editorial review. If you wish to contribute a Perspective or Opinion article, please contact the corresponding Topic Editor, Prof. Shutaro Takeda, before submission.