Data and Knowledge: State and Future

Current science justifies policy action now, but more detailed knowledge can enable more refined and preemptive policy. Existing knowledge is already sufficient to mobilize action today. New knowledge, including disaggregated data from Earth Observation, in situ data, citizen science, socially-disaggregated data, ground-truthing and indigenous and local knowledge, needs to be brought into national policy and accounting more broadly. There are major benefits in accounting systems that register the details about who causes damage to the environment, how and why; what is the extent of nature's contributions to humans and the loss of ecosystem goods and services; and who is affected. Statistics and accounting systems also need to recognize the realities of predominantly poor people in the informal economy, who are often particularly dependent on nature's contributions to people and hence more vulnerable to environmental degradation. ﹛Co-Chairs’ Message﹜

Meeting environmental challenges will require advances in environmental indicators and analysis, particularly analysis that addresses interlinkages across different environmental domains and among the environment, society and the economy (well established). Recent advances in collecting official statistics related to the environment, including geospatial ones, contribute to environmental monitoring and permit representations of environmental flows (Figure 6.1). However, there are still methodological gaps in measuring many aspects of the environment; there is very limited information linking social and environmental processes; and there are significant capacity gaps in countries attempting to build their environmental information and natural capital accounting systems. ﹛Chapter 3, ExecSum﹜

Meeting the SDGs will require a new commitment to environmental data collection. (established but incomplete) Global commitments to the SDGs increase global recognition that monitoring the environmental dimension of development requires regular, standardized data collection, including time series statistics and indicators. Monitoring progress on the SDGs requires shifts in data collection, analysis and dissemination, including the use of environmental statistics, geospatial data, earth observation and new data sources (i.e. citizen science, big data, traditional knowledge). The number of Tier 2 and Tier 3 environmental indicators relating to the SDGs (Figure 6.2 and Figure 6.3) shows that implementing the SDGs calls for a data revolution, incorporating disaggregated data, to ensure that no one is left behind, and for reporting at all levels of the 17 goals. New and innovative approaches to data and knowledge systems, with an aligned focus on evidence-based information gathering, are essential for achieving the ambitious SDG framework. ﹛25.4.8﹜