Related papers: Quantile estimation of CO2 marginal abatement cost…
Global warming from carbon dioxide (CO2) is known to depend on cumulative CO2 emissions. We introduce a model of global expenditures on limiting cumulative CO2 emissions, taking into account effects of decarbonization and rising global…
We developed an emulator for Integrated Assessment Models (emIAM) based on a marginal abatement cost (MAC) curve approach. Using the output of IAMs in the ENGAGE Scenario Explorer and the GET model, we derived a large set of MAC curves: ten…
Accurate estimation of Marginal Emission Factors (MEFs) is critical for evaluating the decarbonization potential of low-carbon technologies and demand-side management. However, canonical methodologies, predominantly relying on linear…
The fossil-fuel induced contribution to further warming over the 21st century will be determined largely by integrated CO2 emissions over time rather than the precise timing of the emissions, with a relation of near-proportionality between…
Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional…
The mitigation of climate change requires a fundamental transition of the energy system. Affordability, reliability and the reduction of greenhouse gas emissions constitute central but often conflicting targets for this energy transition.…
Numerous mitigation methods exist for quantum noise suppression, making it challenging to identify the optimum approach for a specific application; especially as ongoing advances in hardware tuning and error correction are expected to…
Global warming is caused by increasing concentrations of greenhouse gases, particularly carbon dioxide (CO2). A metric used to quantify the change in CO2 emissions is the marginal emission factor, defined as the marginal change in CO2…
We study the problem of a profit maximizing electricity producer who has to pay carbon taxes and who decides on investments into technologies for the abatement of carbon emissions in an environment where carbon tax policy is random and…
Accurate estimation of greenhouse gas (GHG) is essential to meet carbon neutrality targets, particularly through the calculation of direct CO2 emissions from electricity generation. This work reviews and compares emission factor-based…
Numerous quantum error-mitigation protocols have been proposed, motivated by the critical need to suppress noise effects on intermediate-scale quantum devices. Yet, their general potential and limitations remain elusive. In particular, to…
Assessing the costs of climate change is essential to finding efficient pathways for the transition to a net-zero emissions economy, which is necessary to stabilise global temperatures at any level. In evaluating the benefits and costs of…
In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and…
Effective usage of approximate circuits for various performance trade-offs requires accurate computation of error. MCAC is a novel model counting framework for exact computation of several average and worst-case error metrics that are used…
Climate mitigation decisions today affect future generations, raising questions of intergenerational equity. Integrated assessment models (IAMs) rely on discounting to evaluate long-term policy costs and benefits. Using the DICE model, we…
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes, many still view it as valuable when optimizing for grid…
Faults on power lines and other electric equipment are known to cause wildfire ignitions. To mitigate the threat of wildfire ignitions from electric power infrastructure, many utilities preemptively de-energize power lines, which may result…
Carbon matching aims to improve corporate carbon accounting by tracking emissions rather than energy consumption and production. We present a mathematical derivation of carbon matching using marginal emission rates, where the unit of…
Carbon dioxide will likely need to be removed from the atmosphere to avoid significant future warming and climate change. Technologies are being developed to remove large quantities of carbon from the atmosphere. Enhanced rock weathering…
This study employs the Causal Machine Learning (CausalML) statistical method to analyze the influence of electricity pricing policies on carbon dioxide (CO2) levels in the household sector. Investigating the causality between potential…