Investor attention and carbon return: evidence from the EU-ETS

This paper firstly puts forward to employ investor attention obtained from Google trends to explain and forecast carbon futures return in the European Union-Emission Trading Scheme (EU-ETS). Our empirical results show that investor attention is a granger cause to changes in carbon return. Furthermore, investor attention generates both linear and non-linear effects on carbon return. The results demonstrate that investor attention shows excellent explanatory power on carbon return. Moreover, we conduct several out-of-sample forecasts to explore the predictive power of investor attention. The results indicate that incorporating investor attention indeed improve the accuracy of out-of-sample forecasts both in short and long horizons and can generate significant economic values. All results demonstrate that investor attention is a non-negligible pricing factor in carbon market

Basis projection for linear transform approximation in real-time applications

Abstract This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion, in linear multimedia transforms, in an optimal manner. The problem is important in real-time systems where the computational resources available are time-dependent. We solve the real-time adaptation problem by developing an approximate transform framework. There are three key contributions of this paper -(a) a fast basis approximation framework that allows us to store signal independent partial transform results to be used in real-time, (b) estimating the complexity distortion curve for the linear transform using a basis set and (c) determining optimal operating points and a meta-data embedding algorithm for images that allows for real-time adaptation. We have applied this approach on the FFT transform with excellent results

\textsc{DeFault}: Deep-learning-based Fault Delineation

The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection serve as indispensable tools, offering vital insights into subsurface structures and fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high-quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, DeFault, specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain-adaptation, DeFault allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using DeFault, the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of DeFault on a field case study involving CO2 injection related microseismic data from the Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and aided in the prevention of potential geological hazards. Our results highlight the potential of DeFault as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning's capacity to refine these methods. Ultimately, our work bear significant implications for CCUS technology deployment, an essential strategy in combating climate change