European carbon storage resource requirements of climate change mitigation targets

As a part of climate change mitigation plans in Europe, CO2 storage scenarios have been reported for the United Kingdom and the European Union with injection rates reaching 75 – 330 MtCO2 yr-1 by 2050. However, these plans are not constrained by geological properties or growth rates with precedent in the hydrocarbon industry. We use logistic models to identify growth trajectories and the associated storage resource base consistent with European targets. All of the targets represent ambitious growth, requiring average annual growth in injection rates of 9% – 15% from 2030-2050. Modelled plans are not constrained by CO2 storage availability and can be accommodated by the resources of offshore UK or Norway alone. Only if the resource base is significantly less, around 10% of current estimates, does storage availability limit mitigation plans. We further demonstrate the use of the models to define 2050 rate targets within conservative bounds of both growth rate and storage resource needs.

Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network

Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively.

Geologic carbon storage resource requirements of climate change mitigation targets in Europe

<p>To limit global warming to well below 2<sup>o</sup>C, integrated assessment models have projected that gigaton-per-year-scale carbon capture and storage is needed by c. 2050. These scenarios are unconstrained by limiting growth rates or historical data due to the limited existing deployment of the technology. A new approach using logistic growth models identifies a coupling between storage resource base (pore space underground) and minimum growth rates necessary to meet global climate change mitigation targets (Zahasky & Krevor, 2020). However, viable growth trajectories consistent with carbon storage targets remain unexplored at the regional level. Here, we show the application of logistic modelling constrained by climate change targets and assessed storage resources for the European Union (EU), the United Kingdom (UK), and Norway. This allows us to identify plausible growth trajectories of CCS development and the associated discovered storage resource base requirement in these regions. We find that the EU storage resource base is sufficient to meet storage targets of 80 MtCO<sub>2</sub>/year and 92 MtCO<sub>2</sub>/year suggested in the European Commission climate change mitigation strategy to 2050, ‘A Clean Planet for All’. However, the more ambitious goals of 298 MtCO<sub>2</sub>/year and 330 MtCO<sub>2</sub>/year are likely to require additional storage resources based predominantly in the North Sea. Results for the UK indicate that all anticipated storage targets to achieve net-zero economy are achievable, requiring no more than 42 Gt of the storage resource base for the most ambitious target. Furthermore, the UK and the Norwegian North Sea may be able to serve as a regional CO<sub>2</sub> storage hub. There are sufficient storage resources to support combined storage targets from the EU and the UK. The tools used here demonstrate a practical approach for regional stakeholders to monitor carbon storage progress towards future stated carbon abatements goals, as well as to evaluate future storage resource needs.</p><p>Zahasky, C., & Krevor, S. (2020). Global geologic carbon storage requirements of climate change mitigation scenarios. Energy & Environmental Science. https://doi.org/10.1039/D0EE00674B</p>

Research of vehicular cloud storage resource management based on trust mechanism

This article focuses on how to effectively make full use of the storage resources in vehicular cloud. A trust mechanism called DI-Trust (Trust Model Based on Dynamic Incentive Mechanism) is proposed to schedule vehicular cloud storage resource. The discussion is under the scenario of the parking lot where vehicle nodes are in static state. The model can reasonably arrange the suitable scheduling algorithm according to the attribute characteristics of different kind of service requirements. The trust value of a vehicle is updated according to the model to fully utilize the vehicle idle storage resources. The simulation experiment results show that the model can work effectively. It can objectively evaluate trust values of vehicle nodes, and construct the effective resource schedule of the vehicular cloud storage resource to meet needs from users.