Biofuels, Climate Policy and the European Vehicle Fleet

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We examine the effect of biofuels mandates and climate policy on the European vehicle fleet, considering the prospects for diesel and gasoline vehicles. We use the MIT Emissions Prediction and Policy Analysis (EPPA) model, which is a general equilibrium model of the world economy. We expand this model by explicitly introducing current generation biofuels, by accounting for stock turnover of the vehicle fleets and by disaggregating gasoline and diesel cars. We find that biofuels mandates alone do not substantially change the share of diesel cars in the total fleet given the current structure of fuel taxes and tariffs in Europe that favors diesel vehicles. Jointly implemented changes in fiscal policy, however, can reverse the trend toward more diesel vehicles. We find that harmonizing fuel taxes reduces the welfare cost associated with renewable fuel policy and lowers the share of diesel vehicles in the total fleet to 21% by 2030 compared to 25% in 2010. We also find that eliminating tariffs on biofuel imports, which under the existing regime favor biodiesel and impede sugar ethanol imports, is welfare-enhancing and brings about further substantial reductions in CO2 emissions.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Future Yield Growth: What Evidence from Historical Data?

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).The potential future role of biofuels has become an important topic in energy legislation as it is seen as a potential low carbon alternative to conventional fuels. Hence, future yield growth is an important topic from many perspectives, and given the extensions of the period over which data are available a re-evaluation of yields trends is in order. Our approach is to focus on time series analysis, and to improve upon past work by investigating yields of many major crops in many parts of the world. We also apply time series techniques that allow us to test for the persistence of a plateau pattern that has worried analysts, and that provide a better estimate of forecast uncertainty. The general conclusion from this time series analysis of yields is that casual observation or simple linear regression can lead to overconfidence in projections because of the failure to consider the likelihood of structural breaks.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to \textbf{super-resolve} low-resolution magnetic field images and \textbf{translate} between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram

Fair Representations by Compression

Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data. We propose a novel unsupervised approach to map data into a compressed binary representation independent of sensitive attributes. We show that in an information bottleneck framework, a parsimonious representation should filter out information related to sensitive attributes if they are provided directly to the decoder. Empirical results show that the method achieves state-of-the-art accuracy-fairness trade-off and that explicit control of the entropy of the representation bit stream allows the user to move smoothly and simultaneously along both rate-distortion and rate-fairness curves

Super-Resolution Maps of the Solar Magnetic Field Covering 40 Years of Space Weather Events

As modern society becomes increasingly dependent on technology, space weather events will have a farther-reaching impact than ever before. For nearly 10 years, NASA's Solar Dynamics Observatory (SDO) has continuously monitored the Sun, however, the SDO-era coincides with the weakest solar cycle of the last century: over the last 40 years, there have been nearly 500 X-class solar flares—around 10 times the number of events observed by SDO alone. It is also clear that there is no single observational survey with sufficient time coverage to enable an effective deep learning space weather forecasting application. Crucially, over the past 40 years, numerous observatories have monitored the Sun's magnetic field. However, cross calibrating magnetograms is a complex and non-trivial endeavour as the relationship between observed pixels is strongly affected by a wide range of systematics. Here we present a deep learning application that can convert magnetograms to a target survey while preserving the features and systematics of the target survey. We will first present our approach for upscaling and cross-calibrating images obtained by the Michelson Doppler Imager (MDI; on-board the Solar and Heliospheric Observatory, SOHO), to the resolution of the Helioseismic and Magnetic Imager (SDO/HMI). We will discuss the physics-based metrics, deep learning architectures, and the lessons learned along the way. This work was performed at NASA’s Frontier Development Laboratory (FDL), a public-private partnership to apply AI techniques to accelerate space science discovery and exploration