Impact of short-lived non-CO2 mitigation on carbon budgets for stabilizing global warming

Limiting global warming to any level requires limiting the total amount of CO2 emissions, or staying within a CO2 budget. Here we assess how emissions from short-lived non-CO2 species like methane, hydrofluorocarbons (HFCs), black-carbon, and sulphates influence these CO2 budgets. Our default case, which assumes mitigation in all sectors and of all gases, results in a CO2 budget between 2011-2100 of 340 PgC for a >66% chance of staying below 2 degrees C, consistent with the assessment of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Extreme variations of air-pollutant emission from black-carbon and sulphates influence this budget by about +/-5%. In the hypothetical case of no methane or HFCs mitigation - which is unlikely when CO2 is stringently reduced - the budgets would be much smaller (40% or up to 60%, respectively). However, assuming very stringent CH4 mitigation as a sensitivity case, CO2 budgets could be 25% higher. A limit on cumulative CO2 emissions remains critical for temperature targets. Even a 25% higher CO2 budget still means peaking global emissions in the next two decades, and achieving net zero CO2 emissions during the third quarter of the 21st century. The leverage we have to affect the CO2 budget by targeting non-CO2 diminishes strongly along with CO2 mitigation, because these are partly linked through economic and technological factors

The model selection methods for sparse biological networks

It is still crucial problem to estimate high dimensional graphical models and to choose the regularization parameter in dependent data. There are several classical methods such as Akaike’s information criterion and Bayesian Information criterion to solve this problem, but also more recent methods have been proposed such as stability selection and stability approach to regularization selection method (StARS) and some extensions of AIC and BIC which are more appropriate for high dimensional datasets. In this review, we give some overview about these methods and also give their consistency properties for graphical lasso. Then, we evaluate the performance of these approaches in real datasets. Finally, we propose the theoretical background of our proposal model selection criterion that is based on the KL-divergence and the bootstrapping computation, and is particularly suggested for the sparse biological networks