The global environmental agenda urgently needs a semantic web of knowledge

Abstract

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Fire risk: an integrated modelling approach

“…Models informing environmental decisions are usually developed in isolation, self-contained and with results mostly accessible to code owners and their collaborators. However, in a globalized world with increasingly complex and intertwined problems, it is key to connect knowledge and develop methods that can identify integrated solutions (Balbi et al, 2022). The application of appropriate and reliable risk assessment techniques is key to understanding and potentially preventing future damage, but so is making this knowledge accessible to stakeholders.…”

The future of ecosystem assessments is automation, collaboration, and artificial intelligence

“…Although these challenges are not specific to ecosystem assessments, they are amplified by the often multidisciplinary nature of the evaluations. Programs working towards using AI to couple data and models following our understanding of complex human-natural systems include the Artificial Intelligence for Environment & Sustainability platform [21]. Initiatives like this show how AI can make environmental modeling more accessible by creating networks that grow as scientific collaborators contribute to them.…”

Exploring the nexus link of environmental technology innovation, urbanization, financial development, and energy consumption on environmental pollution: Evidence from 27 emerging economies

“…This study categorizes the purpose of choosing these countries and their respective data for analysis as twofold: (1) African nations' governments and individual stakeholders have extensively espoused environmental resilience and economic prosperity issues to be eco-friendly, which merits in-depth investigation because of its excessive use of non-renewable resources which leads to ENVP. Thus, the coherent coordination and interaction of the economy and environment culminated in the decline of the ENVP, which is critical to the aims of global environmental sustainability and development agenda [ [110] , [111] , [112] ]. (2) African nations are recently experiencing tremendous FID, ENC, ECG, and TROP growth, culminating in a higher ENVP [ 51 , 52 , 85 ].…”