Abstract

Most emission scenarios consistent with the Paris Agreement target of limiting global warming to 1.5°C include net negative CO₂ emissions in the second half of this century, i.e. CO₂ removal (CDR) from the atmosphere exceeds CO₂ emissions. These pathways differ significantly with regards to their: a) CDR efficiency — the net CO₂ removal; b) timing — the potential for net CO₂ removal occurring at the right time to meet the net-zero targets; and c) permanence — the net CO₂ removal from the atmosphere for a sufficiently long length of time.

Here, we adapted the MONET framework to compare the CDR efficiency, timing, and permanence of a non-exhaustive portfolio of archetypal CDR pathways representing afforestation/reforestation (AR), biochar, bioenergy with carbon capture and storage (BECCS), direct air capture of CO₂ with storage (DACCS) and enhanced weathering (EW) (see Fig. 1). We showed that, in the case of BECCS, the carbon footprint of biomass feedstocks contribute to up to 26% reduction in CDR efficiencies, especially when high-moisture biomass feedstock is adopted. Upstream activities, such as biomass cultivation and processing, are responsible for the largest share of CO₂ emissions. By contrast, biomass supply chain emissions have a mild impact on the overall CDR efficiency of biochar, which is mainly affected by the overall C yield of pyrolysis processes, by almost 50%. AR is subject to a range of catastrophic events, specifically wildfires, which risk can be assessed through their frequency and severity. Ongoing forestry management could help reduce this risk, thus contributing to increase the overall CO₂ removal potential of this CDR pathway. The CDR efficiency of AR declines by more than half in warm and dry climates (i.e., subtropical and tropical), whereas it remains unchanged in cold and humid climates (i.e., boreal). Consequently, AR’s permanence is overall very likely to decrease significantly over time, and to become very low. Finally, the CDR efficiency of DACCS and EW is affected by the carbon intensity of the energy used in the CO₂ capture process and in the grinding of the rock, respectively. We also observed a trade-off between the rock size adopted in EW processes, with smaller rock leading to higher CDR removals, and the higher energy consumption associated with rock grinding, leading to lower CDR removals.

Plain-language Summary

CDR options differs in term of CO₂ removal efficiency. Importantly, the CO₂ removal efficiency of all CDR options is intertwined with their timing and permanence, but comparative quantitative analyses remain a lacuna in the literature. As CDR is expected to be deployed at a commercial-scale, and the service that gets remunerated is the permanent removal of CO₂ from the atmosphere, there is a need to understand how impactful removal of CO₂ is, now and over time. This study addresses this knowledge gap by identify and quantify their key sources of CO₂ leakages, and discuss the impact of time, both in terms of timing and permanence, on the CO₂ removal efficiency of these CDR methods.

Abstract: Energy-economic and coupled human-natural system models are often used to explore potential energy futures and their implications for climate. There are many uncertain assumptions in the human system models that drive those futures, and in previous work we used a traditional Monte Carlo approach to explore socio-economic uncertainties in a multi-sector, multi-region energy-economic-emissions model of the global economy and generate probabilistic ensembles. The amount of data and information generated from these large ensembles is immense and it can be difficult to sort through and extract relevant insights. The goal of this work is to apply a variety of scenario discovery techniques to the probabilistic ensembles in order to extract insights related to energy futures, with a particular focus on the penetration of renewable energy. We apply Classification and Regression Trees (CART) with Random Forest Classifier (RFC) and Time Series Clustering (TSC) to explore key input drivers of the share of renewable generation, how those drivers can vary over time and across regions, different types of pathways for renewables, and relationships among model outputs. We find that the key drivers of renewables can vary significantly based on the policy scenario, region and time period. In particular, the time series clustering revealed interesting dynamics that are missed by looking at individual years. Through this work we demonstrate the value of scenario discovery techniques in drawing insights from large ensembles of energy-projecting models by facilitating the identification of drivers, relationships among variables and areas of the uncertainty space that are particularly interesting or relevant.

Abstract: This Perspective evaluates recent progress in modeling nature–society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice—defining purpose, selecting components, analyzing interactions, and assessing interventions—we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature–society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.