From COVID-19 to Green Recovery with natural capital accounting.

The COVID-19 pandemic and related social and economic emergencies induced massive public spending and increased global debt. Economic recovery is now an opportunity to rebuild natural capital alongside financial, physical, social and human capital, for long-term societal benefit. Yet, current decision-making is dominated by economic imperatives and information systems that do not consider society's dependence on natural capital and the ecosystem services it provides. New international standards for natural capital accounting (NCA) are now available to integrate environmental information into government decision-making. By revealing the effects of policies that influence natural capital, NCA supports identification, implementation and monitoring of Green Recovery pathways, including where environment and economy are most positively interlinked.

A methodology for adaptable and robust ecosystem services assessment.

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant "one model fits all" paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES--both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.