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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Microbial abundances and carbon use under ambient temperature or experimental warming in a southern boreal peatland

Abstract Organic peat soils occupy relatively little of the global land surface area but store vast amounts of soil carbon in northern latitudes where climate is warming at a rapid pace. Warming may result in strong positive feedbacks of carbon loss and global climate change driven by microbial processes if warming alters the balance between primary productivity and decomposition. To elucidate effects of warming on the microbial communities mediating peat carbon dynamics, we explored the abundance of broad microbial groups and their source of carbon (i.e. old carbon versus more recently fixed photosynthate) using microbial lipid analysis (δ 13 C PLFA) of peat samples under ambient temperatures and before/after initiation of experimental peat warming (+ 2.25, + 4.5, + 6.75, and + 9 °C). This analysis occurred over a profile to 2 m depth in an undrained, ombrotrophic peat bog in northern Minnesota. We found that the total microbial biomass and individual indicator lipid abundances were stratified by depth and strongly correlated to temperature under ambient conditions. However, under experimental warming, statistically significant effects of temperature on the microbial community were sporadic and inconsistent. For example, 3 months after experimental warming the relative abundance of Gram-negative bacterial indicators across depth combined and > 50 cm depth and Gram-positive bacterial indicators at 20–50 cm depth showed significant positive relationships to temperature. At that same timepoint, however, the relative abundance of Actinobacterial indicators across depth showed a significant negative relationship to temperature. After 10 months of experimental warming, the relative abundance of fungal biomarkers was positively related to temperature in all depths combined, and the absolute abundance of anaerobic bacteria declined with increasing temperature in the 20–50 cm depth interval. The lack of observed response in the broader microbial community may suggest that at least initially, microbial community structure with peat depth in these peatlands is driven more by bulk density and soil water content than temperature. Alternatively, the lack of broad microbial community response may simply represent a lag period, with more change to come in the future. The long-term trajectory of microbial response to warming in this ecosystem then could either be direct, after this initial lag time, or indirect through other physical or biogeochemical changes in the peat profile. These initial results provide an important baseline against which to measure long-term microbial community and carbon-cycling responses to warming and elevated CO 2 .

Felice, Mark↗
DOE OSTI2025

Environmental DNA as a tool for hydropower impact assessments: current status, special considerations, and future integration

Globally there is an urgent need to find sustainable solutions to balance energy production with the protection of vulnerable species and conservation of biodiversity. This is particularly critical for freshwater ecosystems, habitats, and species that may be impacted by hydropower development and operations needed to meet energy grid demands. Reliable and accurate environmental impact assessments (EIAs) that identify the biological, physical, or social impacts of hydropower are key to ensure biodiversity, ecosystem, and societal sustainability. The analysis of environmental DNA (eDNA) has the potential to transform hydropower EIAs, management and mitigation planning, and decision-making procedures. Further, the incorporation of eDNA surveys into EIAs during both hydropower planning and continued operations may streamline regulatory processes by improving our understanding of potentially impacted biota and habitats and evaluating environmental impacts mitigation. Here, we: (i) highlight current understanding and use of eDNA in freshwater environments; (ii) examine critical considerations for eDNA integration into hydropower EIAs and biological monitoring; (iii) identify knowledge gaps in eDNA analysis and applications unique to hydropower-regulated systems; and (iv) discuss future opportunities to bolster the incorporation of eDNA into hydropower research including regulatory acceptance and public engagement. While we acknowledge that there are several factors that may complicate the broad adoption of eDNA as a tool for assessing the impacts of hydropower, we anticipate that growing confidence in eDNA through hydropower-specific protocols, calibrations, and validations will overcome these inherent uncertainties.

aquatic biodiversity↗
DOE OSTI2022

Assessing the performance of global thermostat adjustment in commercial buildings for load shifting demand response

Abstract Efficiently leveraging new sources of flexibility is critical to mitigating load balancing challenges posed by variable renewable resources. The thermal inertia of commercial buildings allows us to shift their power consumption on minute to hourly timescales to provide demand response to the grid while maintaining occupant comfort. Global thermostat adjustment (GTA) provides a readily available and scalable approach for implementing load shifting demand response using commercial heating, ventilation, and air conditioning (HVAC) systems, since it leverages the inherent sophistication of modern building automation systems. However, there is an incomplete understanding of GTA’s performance for this purpose and its impact on building systems and occupant comfort. In this paper, we explore the performance of GTA by analyzing results from nearly nine hundred experiments on eight university campus buildings in Michigan and North Carolina. Using GTA, we manipulate each building’s thermostat setpoints causing the building to shift its power consumption with respect to its baseline. We quantify the magnitude of HVAC power response, energy use of HVAC subsystems, and impact on occupant comfort. Finally, we connect our experimental results with power system operation using an optimization model that coordinates GTA actions across a large collection of grid-interactive efficient buildings (GEBs) to reduce high ramp rates on the grid and mitigate renewable energy curtailment. Overall, our work finds that the impacts on HVAC subsystems are often complex, and may result in additional energy being consumed by fans and terminal reheat. These effects must be considered when using GTA for load shifting. Additionally, we demonstrate that occupant comfort, as assessed by indoor temperature and humidity, can be maintained during GTA events. From a societal perspective, our modeling work finds that the additional renewable energy that can be integrated through the use of GTA strategies eclipses any additional energy consumed by buildings.

Keskar, Aditya (ORCID:0000000244617980)↗
DOE OSTI2021

Hidden Killing fields, geometric symmetries and black hole mergers

Highlights: • Construction of geometric models with a variable degree of symmetry. • Formulation of Energy and Momentum Balance Laws. • Description of backreactions on spacetime geometries caused by dissipative effects. • Analytic description of accretion and/or merger processes in GR. • Exact model for the merger of two extremal Reissner–Nordström black holes. In the present work, using the recently introduced framework of local geometric deformations, special types of vector fields – so-called hidden Killing vector fields – are constructed, which solve the Killing equation not globally, but only locally, i.e. in local subregions of spacetime. Taking advantage of the fact that the vector fields coincide locally with Killing fields and therefore allow the consideration of integral laws that convert into exact physical conservation laws on local scales, balance laws in dynamical systems without global Killing symmetries are derived that mimic as closely as possible the conservation laws for energy and angular momentum of highly symmetric models. The utility of said balance laws is demonstrated by a concrete geometric example, namely a toy model for the binary merger of two extremal Reissner–Nordström black holes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Grand challenges of wind energy science – meeting the needs and services of the power system

The share of wind power in power systems is increasing dramatically, and this is happening in parallel with increased penetration of solar photovoltaics, storage, other inverter-based technologies, and electrification of other sectors. Recognising the fundamental objective of power systems, maintaining supply–demand balance reliably at the lowest cost, and integrating all these technologies are significant research challenges that are driving radical changes to planning and operations of power systems globally. In this changing environment, wind power can maximise its long-term value to the power system by balancing the needs it imposes on the power system with its contribution to addressing these needs with services. A needs and services paradigm is adopted here to highlight these research challenges, which should also be guided by a balanced approach, concentrating on its advantages over competitors. The research challenges within the wind technology itself are many and varied, with control and coordination internally being a focal point in parallel with a strong recommendation for a holistic approach targeted at where wind has an advantage over its competitors and in coordination with research into other technologies such as storage, power electronics, and power systems.

17 WIND ENERGY↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Triple-junction solar cells with 39.5% terrestrial and 34.2% space efficiency enabled by thick quantum well superlattices

Multijunction solar cell design is guided by both the theoretical optimal bandgap combination as well as the realistic limitations to materials with these bandgaps. For instance, triple-junction III-V multijunction solar cells commonly use GaAs as a middle cell because of its near-perfect material quality, despite its bandgap being higher than optimal for the global spectrum. Here, we modify the middle cell bandgap using thick GaInAs/GaAsP strain-balanced quantum well (QW) solar cells with excellent voltage and absorption. Additionally, these high-performance QWs are incorporated into a triple-junction inverted metamorphic multijunction device consisting of a GaInP top cell, GaInAs/GaAsP QW middle cell, and lattice-mismatched GaInAs bottom cell, each of which has been highly optimized. We demonstrate triple-junction efficiencies of 39.5% and 34.2% under the AM1.5 global and AM0 space spectra, respectively, and the global efficiency is higher than previous record six-junction devices.

14 SOLAR ENERGY↗
DOE OSTI2023

High‐Latitude Stratospheric Aerosol Injection to Preserve the Arctic

Abstract Stratospheric aerosol injection (SAI) has been shown in climate models to reduce some impacts of global warming in the Arctic, including the loss of sea ice, permafrost thaw, and reduction of Greenland Ice Sheet (GrIS) mass; SAI at high latitudes could preferentially target these impacts. In this study, we use the Community Earth System Model to simulate two Arctic‐focused SAI strategies, which inject at 60°N latitude each spring with injection rates adjusted to either maintain September Arctic sea ice at 2030 levels (“Arctic Low”) or restore it to 2010 levels (“Arctic High”). Both simulations maintain or restore September sea ice to within 10% of their respective targets, reduce permafrost thaw, and increase GrIS surface mass balance by reducing runoff. Arctic High reduces these impacts more effectively than a globally focused SAI strategy that injects similar quantities of SO 2 at lower latitudes. However, Arctic‐focused SAI is not merely a “reset button” for the Arctic climate, but brings about a novel climate state, including changes to the seasonal cycles of Northern Hemisphere temperature and sea ice and less high‐latitude carbon uptake relative to SSP2‐4.5. Additionally, while Arctic‐focused SAI produces the most cooling near the pole, its effects are not confined to the Arctic, including detectable cooling throughout most of the northern hemisphere for both simulations, increased mid‐latitude sulfur deposition, and a southward shift of the location of the Intertropical Convergence Zone. For these reasons, it would be incorrect to consider Arctic‐focused SAI as “local” geoengineering, even when compared to a globally focused strategy.

Lee, Walker Raymond↗
DOE OSTI2025

Earth's Energy Imbalance More Than Doubled in Recent Decades

Abstract Global warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.

Mauritsen, Thorsten [Department of Meteorology and↗
DOE OSTI2025

Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant

Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Further, our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Differential effects of drought on nonstructural carbohydrate storage in seedlings and mature trees of four species in a subtropical forest

Nonstructural carbohydrates (NSC) play important roles in forest vulnerability to climate change, especially under increasing drought intensity and frequency. Understanding NSC dynamics is essential for accurately predicting the resistance and resilience of forests in response to drought. However, our knowledge of NSC re-sponses to drought is still limited due to the lack of research in trees of di?erent life stages. In this study, we conducted a throughfall exclusion experiment (TFE) with four subtropical evergreen tree species to examine drought e?ects on NSC in mature trees and understory seedlings. Our results showed the differential effects of drought on NSC dynamics of understory seedlings and mature trees. In the TFE experiment, mature trees of all four species were relatively homoeostatic with the insigni?cantly changed NSC pools, photosynthesis, and growth under the drought treatment compared to the control. In contrast, understory seedlings displayed sig-ni?cant decreases in total NSC and soluble sugars (-14.70 ± 3.66% and -16.93 ± 3.85%, respectively) with the exception of Lithocarpus glaber (L. glaber). The seedlings of L. glaber with the highest hydraulic resistance maintained or slightly increased NSC and its components in response to drought. Our study highlights the im-portance of life stage in assessing drought e?ects of trees on NSC storage and then forest C cycling, which could be incorporated into the dynamic global vegetation models (DGVMs) to better understand drought effects on forest C balance in the future.

Zhang, Peipei↗
DOE OSTI2022

Embodied energy and carbon from the manufacture of cadmium telluride and silicon photovoltaics

Here, looking beyond the traditional cost and efficiency metrics of photovoltaics (PV), this work evaluates the impact of embodied energy, embodied carbon, and energy payback time of two dominant technologies (CdTe and Si) on global decarbonization goals. The relative effects of PV technology type, technological advances, energy grid mix, and recycling are evaluated in terms of fostering decarbonization goals. If the highest carbon-intensity scenarios are realized, 2%-14% of the remaining estimated global carbon budget might be consumed to manufacture modules without including their balance of systems. Applying a carbon cost indicates that CdTe might have an additional value of $\$$0.02-$\$$0.04/W relative to Si PV manufactured with the same energy mix. Due to the scale of the challenge, any actions leading to an increased deployment of thin-film PV and/or a significant decrease in the deployed PV's embodied carbon through changing the manufacturing grid mix have demonstrable value in helping the world stay within its remaining estimated carbon budget.

14 SOLAR ENERGY↗
DOE OSTI2024

Experimental warming and drying increase older carbon contributions to soil respiration in lowland tropical forests

Tropical forests account for over 50% of the global terrestrial carbon sink, but climate change threatens to alter the carbon balance of these ecosystems. We show that warming and drying of tropical forest soils may increase soil carbon vulnerability, by increasing degradation of older carbon. In situ whole-profile heating by 4 °C and 50% throughfall exclusion each increased the average radiocarbon age of soil CO 2 efflux by ~2–3 years, but the mechanisms underlying this shift differed. Warming accelerated decomposition of older carbon as increased CO 2 emissions depleted newer carbon. Drying suppressed decomposition of newer carbon inputs and decreased soil CO 2 emissions, thereby increasing contributions of older carbon to CO 2 efflux. These findings imply that both warming and drying, by accelerating the loss of older soil carbon or reducing the incorporation of fresh carbon inputs, will exacerbate soil carbon losses and negatively impact carbon storage in tropical forests under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Scalable and Secure Power Outage Data Reporting: A Hexagonal Geospatial Approach

Power outages disrupt critical infrastructure and cause billions of dollars in economic losses annually in the United States. Accurate and granular outage reporting is vital for effective restoration and mitigation. This paper examines the integration of the Hexagonal Hierarchical Geospatial Indexing System (H3) to enhance power outage reporting, leveraging its uniform grid structure, scalable resolutions, and support for privacy-preserving analysis. Using high-resolution LandScan Global population data and K-anonymization techniques, this work achieves a balance between data granularity and privacy. Results show that lower privacy thresholds (e.g., K-anonymity = 2) enable higher resolution, while stricter thresholds (e.g., >15 people per hex) reduce granularity, potentially affecting localized responses. State-and county-level resolution case studies demonstrate H3’s adaptability and the trade-offs between precision and privacy. The proposed H3-based framework offers a scalable and efficient solution for geospatial data integration within the energy sector, such as outage data, aiding utilities and regulators in improving resilience and response efforts, particularly in disaster-prone regions.

Ahmad, Nasir [ORNL] (ORCID:0000000150677368)↗
DOE OSTI2022

SNICAR-ADv4: a physically based radiative transfer model to represent the spectral albedo of glacier ice

Abstract. Accurate modeling of cryospheric surface albedo is essential for our understanding of climate change as snow and ice surfaces regulate the global radiative budget and sea-level through their albedo and mass balance. Although significant progress has been made using physical principles to represent the dynamic albedo of snow, models of glacier ice albedo tend to be heavily parameterized and not explicitly connected with physical properties that govern albedo, such as the number and size of air bubbles, specific surface area (SSA), presence of abiotic and biotic light absorbing constituents (LACs), and characteristics of any overlying snow. Here, we introduce SNICAR-ADv4, an extension of the multi-layer two-stream delta-Eddington radiative transfer model with the adding–doubling solver that has been previously applied to represent snow and sea-ice spectral albedo. SNICAR-ADv4 treats spectrally resolved Fresnel reflectance and transmittance between overlying snow and higher-density glacier ice, scattering by air bubbles of varying sizes, and numerous types of LACs. SNICAR-ADv4 simulates a wide range of clean snow and ice broadband albedo (BBA), ranging from 0.88 for (30 µm) fine-grain snow to 0.03 for bare and bubble-free ice under direct light. Our results indicate that representing ice with a density of 650 kg m−3 as snow with no refractive Fresnel layer, as done previously, generally overestimates the BBA by an average of 0.058. However, because most naturally occurring ice surfaces are roughened “white ice”, we recommend modeling a thin snow layer over bare ice simulations. We find optimal agreement with measurements by representing cryospheric media with densities less than 650 kg m−3 as snow and larger-density media as bubbly ice with a Fresnel layer. SNICAR-ADv4 also simulates the non-linear albedo impacts from LACs with changing ice SSA, with peak impact per unit mass of LACs near SSAs of 0.1–0.01 m2 kg−1. For bare, bubble-free ice, LACs actually increase the albedo. SNICAR-ADv4 represents smooth transitions between snow, firn, and ice surfaces and accurately reproduces measured spectral albedos of a variety of glacier surfaces. This work paves the way for adapting SNICAR-ADv4 to be used in land ice model components of Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On the role of ion temperature gradient turbulence in driving ion thermal transport in neutral beam injection-heated L-mode plasmas in a superconducting tokamak

In this paper, we report a detailed experimental study of the role of ion-temperature-gradient driven turbulence in driving ion thermal transport in neutral beam injection (NBI)-heated L-mode plasmas in Experimental Advanced Superconducting Tokamak (EAST) (Wan et al 2000 Nucl. Fusion 40 1057) coupled with linear and nonlinear gyrokinetic simulations. Significant ion-scale turbulence k ⟂ < cm −1 (k ⟂ < 1.5, where k ⟂ where is the perpendicular wavenumber and ρ s is the ion gyroradius calculated using local electron temperature T e ), measured with a microwave reflectometer, is observed in the plasma core at for r/a ≈ 0.265 and 0.5 (where r is half the diameter of the closed flux surface at a given radial position, and a is half the diameter of the last closed flux surface). Local linear stability analysis with experimental equilibrium quantities at these two radial locations using the GS2 gyrokinetic code shows that the most unstable ion-scale micro-instability is the ion temperature gradient (ITG) mode. Since the computed maximum ion-scale linear growth rates are larger than the local Waltz-Miller E × B shearing rate (Waltz and Miller 1999 Phys. Plasmas 6 4265), the E × B shear is unable to suppress ion-scale turbulence, consistent with the experimental observation of ion-scale turbulence. Experimental ion and electron thermal transports, calculated with power balance analysis, are both anomalous with ion thermal transport being the dominant channel. Nonlinear gyrokinetic simulations using the electrostatic global particle-in-cell gyrokinetic tokamak simulation (GTS) code (Weixing Wang et al 2010 Phys. Plasmas 17 072511) show good quantitative agreement between predicted ion thermal transport and that from the power balance analysis. These results demonstrate for the first time the important role of ITG turbulence in driving thermal transport in NBI-heated L-mode plasmas in EAST, showing the effectiveness of GTS code in simulating electrostatic turbulence in EAST and the need of suppressing ITG turbulence to improve energy confinement in EAST.

Sun, P. J. [Chinese Academy of Sciences (CAS), Hef↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Critical adjustment of land mitigation pathways for assessing countries' climate progress

Collective progress towards the Paris Agreement’s temperature targets will be assessed by the Agreement’s Global Stocktake process. Global emission pathways by Integrated Assessment Models (IAMs) are a likely benchmark against which aggregated countries’ greenhouse gas mitigation pledges will be assessed. However, this approach requires comparability, which is currently hampered by a 5 GtCO2yr-1 mismatch between the estimated global land anthropogenic flux of IAMs and countries’ GHG inventories (GHGIs) for 2005-2015. We show that this gap is mostly the result of differences in how the anthropogenic forest sink is defined. Countries define managed forest more broadly than IAMs, and on this larger area typically consider anthropogenic also fluxes due to human-induced environmental change, which IAMs do not include their pathways. Using five IAMs and a dynamic global vegetation model (DGVM), we present and apply a novel method that adjusts IAM results to the countries’ definition, closing the gap with GHGIs at global and regional scale. When expressed in a way compatible with GHGIs, adjusted 1.5°C and well-below 2°C pathways have a cumulative CO2 balance until carbon neutrality that is 110–176 GtCO2 lower than the original IAMs pathways. The use of these country-compatible emission pathways under the Global Stocktake would therefore be essential for an accurate assessment of the collective progress towards the Paris Agreement’s climate goals.

Grassi, Giacomo↗