About this Research Topic
Sustainability and climate change present grand, urgent challenges for science and society for the 21st Century. Sustaining our marine and terrestrial ecosystems to deliver what people need and value, under future multi-scale and multi-type risks/threats/hazards will require a complex set of mitigation and adaptation strategies and actions. Such complex decision-making, to be reliable and effective, will require a broad array of different science-based indicators, metrics, frameworks and modeling systems that will enhance our capability to make more informed decisions.
Knowing how we make decisions is as important as knowing clearly what decisions to make – and we must confront better the complex interplay involved in translating scientific evidence into real-world operational or actionable solutions. Real-world uncertainties may be exceedingly difficult to quantify, because it is often highly fragmented in space and highly dynamic in time. Often, their dynamic behaviour precludes any simple, (e.g., deterministic) identification of a fixed set of driving factors or causal linkages. Sustainability assessment pathways will, increasingly, be facilitated by predictive systems (e.g., those that integrate artificial intelligence (AI) and machine-learning algorithms) capable of rapidly integrating and synthesizing mixed-type data obtained across a broad range of observational and monitoring platforms (e.g., fixed/mobile sensor arrays and ground stations, unmanned aerial vehicles (UAVs)/drones, Earth Observational Satellites). Domain-specific knowledge and applied expertise is crucial for improving the structure, performance and operational implementation of quantitative predictive systems. Operational methods that are capable of integrating highly complex interactions from causal factors (natural, social or economic) and increasing amounts of sensory data (Big Data) will be needed in the future. Insights in this arena require an understanding of multi-hazard decision-making, and how a diverse set of competing, viable solutions may be traded-off when addressing different types of impacts and risks in space and time. In this way, real-world sustainability solutions must strive to encapsulate regional needs and priorities.
We must continue to strengthen our ‘adaptive capacity’, seeking solutions that provide a high degree of methodological flexibility to ensure they incorporate case-study application-based learning to identify latent factors, unforeseen risks, and changing levels of exposure and vulnerability. Bridges between islands of fragmented knowledge still need to be built across a spectrum of disciplines from hypothesis, theory, methods and applied insights. As the use of data becomes more integrated and operational methodologies become more sophisticated in how they utilize and couple human and machine intelligence, greater transparency will be required. Such transparency will ensure the ‘social licence’ across cultural groups, by more clearly elucidating feasible sets of trade-offs that are identified by operational, impact assessment and predictive systems. This will require the private and public sectors to work more cohesively together so as to enable citizens/community groups/society to gain a sufficient understanding of the scientific support behind increasingly complex policy (e.g., water conservation, extreme weather, energy sector transition to renewables, agricultural insurance/reinsurance, disaster risk reduction, disease and air quality forecasting, ecosystem and human health).
This Research Topic aims to showcase research, development and technology (RDT) work towards devising and delivering integrated, operational solutions to globally-relevant, complex, real-world natural resource management (water, energy, food) problems. ‘Integrative solutions’ include: improved indicators of sustainability and metrics of risk, more reliable pathways for impact assessment and more accurate methods and tools for incorporating into broader sustainability decision-making, disciplines and quantitative predictive systems in time and space, and actionable knowledge to support real-world sustainability decision-making.
We welcome articles of all types (e.g., original research (particularly those that detail novel statistical/computational/artificial intelligence/machine-learning approaches), review, opinion, perspective). Refer to the Frontiers Author Guidelines: http://home.frontiersin.org/about/author-guidelines).
Knowing how we make decisions is as important as knowing clearly what decisions to make – and we must confront better the complex interplay involved in translating scientific evidence into real-world operational or actionable solutions. Real-world uncertainties may be exceedingly difficult to quantify, because it is often highly fragmented in space and highly dynamic in time. Often, their dynamic behaviour precludes any simple, (e.g., deterministic) identification of a fixed set of driving factors or causal linkages. Sustainability assessment pathways will, increasingly, be facilitated by predictive systems (e.g., those that integrate artificial intelligence (AI) and machine-learning algorithms) capable of rapidly integrating and synthesizing mixed-type data obtained across a broad range of observational and monitoring platforms (e.g., fixed/mobile sensor arrays and ground stations, unmanned aerial vehicles (UAVs)/drones, Earth Observational Satellites). Domain-specific knowledge and applied expertise is crucial for improving the structure, performance and operational implementation of quantitative predictive systems. Operational methods that are capable of integrating highly complex interactions from causal factors (natural, social or economic) and increasing amounts of sensory data (Big Data) will be needed in the future. Insights in this arena require an understanding of multi-hazard decision-making, and how a diverse set of competing, viable solutions may be traded-off when addressing different types of impacts and risks in space and time. In this way, real-world sustainability solutions must strive to encapsulate regional needs and priorities.
We must continue to strengthen our ‘adaptive capacity’, seeking solutions that provide a high degree of methodological flexibility to ensure they incorporate case-study application-based learning to identify latent factors, unforeseen risks, and changing levels of exposure and vulnerability. Bridges between islands of fragmented knowledge still need to be built across a spectrum of disciplines from hypothesis, theory, methods and applied insights. As the use of data becomes more integrated and operational methodologies become more sophisticated in how they utilize and couple human and machine intelligence, greater transparency will be required. Such transparency will ensure the ‘social licence’ across cultural groups, by more clearly elucidating feasible sets of trade-offs that are identified by operational, impact assessment and predictive systems. This will require the private and public sectors to work more cohesively together so as to enable citizens/community groups/society to gain a sufficient understanding of the scientific support behind increasingly complex policy (e.g., water conservation, extreme weather, energy sector transition to renewables, agricultural insurance/reinsurance, disaster risk reduction, disease and air quality forecasting, ecosystem and human health).
This Research Topic aims to showcase research, development and technology (RDT) work towards devising and delivering integrated, operational solutions to globally-relevant, complex, real-world natural resource management (water, energy, food) problems. ‘Integrative solutions’ include: improved indicators of sustainability and metrics of risk, more reliable pathways for impact assessment and more accurate methods and tools for incorporating into broader sustainability decision-making, disciplines and quantitative predictive systems in time and space, and actionable knowledge to support real-world sustainability decision-making.
We welcome articles of all types (e.g., original research (particularly those that detail novel statistical/computational/artificial intelligence/machine-learning approaches), review, opinion, perspective). Refer to the Frontiers Author Guidelines: http://home.frontiersin.org/about/author-guidelines).
Keywords: Adaptation, Ecosystems, Informatics, Intelligence, Sustainability
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
