Suchergebnisse

To achieve the carbon goals, the Chinese government initially implemented the carbon ETS in 2013 in 7 pilot provinces and cities. Using the firm-level financial and management data of the A-share listed companies in the 30 provinces of mainland China from 2008 to 2020, this paper examines the ETS impact on corporate financialization by constructing a DID model. The result supports the "crowd-out" effect that the implementation of ETS decreases corporate financialization and this negative impact is weaker on the state-owned firms, located in the eastern region of China, and are not in the manufacturing industry. These findings imply that other than the original target to reduce carbon emissions, the ETS, by its market-based nature, is effective in reducing the risk of over-financialization.

Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep learning has been explored in a wide variety of fields and produced unparalleled performance results. It plays a vital role in daily applications and is also subtly changing the rules, habits, and behaviors of society. However, inevitably, data-based learning strategies are bound to cause potential security and privacy threats, and arouse public as well as government concerns about its promotion to the real world. In this article, we mainly focus on data security issues in deep learning. We first investigate the potential threats of deep learning in this area, and then present the latest countermeasures based on various underlying technologies, where the challenges and research opportunities on offense and defense are also discussed. Then, we propose SecureNet, the first verifiable and privacy-preserving prediction protocol to protect model integrity and user privacy in DNNs. It can significantly resist various security and privacy threats during the prediction process. We simulate SecureNet under a real dataset, and the experimental results show the superior performance of SecureNet for detecting various integrity attacks against DNN models.