Reducing Global Greenhouse Gas Emissions to Meet Climate Targets : A Comprehensive Quantification and Reasonable Options

A model is presented which covers the global greenhouse gas emissions (GHG emissions) and the energy consumption (fuels, electricity) in five sectors of end users, industry, transport, buildings, agriculture, and fugitive emissions. The electricity sector is also considered, but the associated GHG emissions are reallocated to the five end users. Different GHG reduction measures were calculated ranging from substitution of coal for electricity generation by renewables, electrification of road transport and buildings, restructuring of the sector industry to finally a 50% reduction of both food waste and meat consumption. To elucidate the consequences of global warming, future emission scenarios were also incorporated. One major conclusion is that the world can only reach the 2-degree climate target if electricity is only produced by renewables, and if transportation, buildings, and the industry are completely electrified by 2050. Compared to today, the electricity production by renewables will then rise by a factor of 11, and the total electricity demand by a factor of 2.4

Numerical and semi-analytic core mass distributions in supersonic isothermal turbulence

We investigate the influence of the turbulence forcing on the mass distributions of gravitationally unstable cores by postprocessing data from simulations of non-selfgravitating isothermal supersonic turbulence with varying resolution. In one set of simulations solenoidal forcing is applied, while the second set uses purely compressive forcing to excite turbulent motions. From the resulting density field, we compute the mass distribution of gravitationally unstable cores by means of a clump-finding algorithm. Using the time-averaged probability density functions of the mass density, semi-analytic mass distributions are calculated from analytical theories. We apply stability criteria that are based on the Bonnor-Ebert mass resulting from the thermal pressure and from the sum of thermal and turbulent pressure. Although there are uncertainties in the application of the clump-finding algorithm, we find systematic differences in the mass distributions obtained from solenoidal and compressive forcing. Compressive forcing produces a shallower slope in the high-mass power-law regime compared to solenoidal forcing. The mass distributions also depend on the Jeans length resulting from the choice of the mass in the computational box, which is freely scalable for non-selfgravitating isothermal turbulence. Provided that all cores are numerically resolved and most cores are small compared to the length scale of the forcing, the normalised core mass distributions are found to be close to the semi-analytic models. Especially for the high-mass tails, the Hennebelle-Chabrier theory implies that the additional support due to turbulent pressure is important.Comment: 15 pages, 7 figures, submitted to A&

On the Modelling of an Agent's Epistemic State and its Dynamic Changes

Given a set of unquantified conditionals considered as default rules or a set of quantified conditionals such as probabilistic rules, an agent can build up its internal epistemic state from such a knowledge base by inductive reasoning techniques. Besides certain (logical) knowledge, epistemic states are supposed to allow the representation of preferences, beliefs, assumptions etc. of an intelligent agent. If the agent lives in a dynamic environment, it has to adapt its epistemic state constantly to changes in the surrounding world in order to be able to react adequately to new demands. In this paper, we present a high-level specification of the Condor system that provides powerful methods and tools for managing knowledge represented by conditionals and the corresponding epistemic states of an agent. Thereby, we are able to elaborate and formalize crucial interdependencies between different aspects of knowledge representation, knowledge discovery, and belief revision. Moreover, this specification, using Gurevich's Abstract State Machines, provides the basis for a stepwise refinement development process of the Condor system based on the ASM methodology

Uncertainty-aware predictive modeling for fair data-driven decisions

Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explainability are often addressed, the safety of systems is typically not sufficiently taken into account. By viewing data-driven decision systems as socio-technical systems, we draw on the uncertainty in ML literature to show how fairML systems can also be safeML systems. We posit that a fair model needs to be an uncertainty-aware model, e.g. by drawing on distributional regression. For fair decisions, we argue that a safe fail option should be used for individuals with uncertain categorization. We introduce semi-structured deep distributional regression as a modeling framework which addresses multiple concerns brought against standard ML models and show its use in a real-world example of algorithmic profiling of job seekers

Synergetic Utilization of Renewable and Fossil Fuels: Dual Fluidized Bed Steam Co-gasification of Coal and Wood

AbstractGasification of biomass and coal is an attractive technology for combined heat and power production, as well as for synthesis processes such as the production of liquid and gaseous biofuels. The allothermal steam blown gasification process yields a high calorific product gas, practically free of nitrogen. Originally designed for wood chips, the system can also handle a large number of alternative fuels. To demonstrate the influence on the system performance of fuels that have a different origin, wood pellets, as the designated feedstock, and hard coal as an example fossil fuel were fed into the DFB gasifier with a fuel blend ratio of 20% coal in terms of energy. A fuel power of 78kW and a steam to fuel ratio of 1.0kg/kgdb were achieved. The system was operated at gasification temperatures between 830 and 870°C. This paper points out the influence of the temperature on the system

Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis