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• The total spillover intensity among Chinese carbon bond market, carbon stock market and energy stock market is transitional. • The role of carbon stock market and carbon bond market as net transmitter and receiver of shocks, respectively, are persistent. • We find strong information transmit intensity from carbon stock market to energy stock market and strong information transmit intensity from energy stock market to carbon bond market. In this paper, we employ the time domain dynamic connectedness method of Antonakakis et al. (2020) and the frequency domain dynamic connectedness method of Barunik and Ellington (2020) to explore the complex linkages among Chinese carbon bond market, carbon stock market and energy stock market. We find firstly that the spillover effects among Chinese carbon bond market, carbon stock market and energy stock market are dynamic and mainly transitional. Secondly, carbon stock market and carbon bond market (especially the carbon neutral bond market) play the role of the major net transmitter and receiver of shocks, respectively. And, their roles as net transmitter and receiver of shocks are persistent. Finally, the risks of companies that have achieved effective results in the field of carbon neutrality may bring greater pressure to the energy stock market, but the carbon bond market can alleviate this pressure to a certain extent. Meanwhile, the impact of carbon stock market on energy stock market and the ability of carbon bond market (especially the carbon neutral bond market) to mitigate the risks of the energy stock market is persistent. [ABSTRACT FROM AUTHOR]

With the emergence of mobile edge computing (MEC), the edge cloud with certain computing power is deployed closer to the mobile device, which can well solve the computing and delay requirements of the mobile device. In 5G ultra-dense heterogeneous networks, where the macro base station (MBS) and multiple dense small base stations (SBS) are deployed in the region, the offloading decision faces multiple choices. In order to solve the problem of computing offloading and resource allocation in 5G ultra-dense heterogeneous networks, we propose a collaborative optimization strategy based on multi-agent deep reinforcement learning (MADRL). At each time, the mobile device only needs to make the optimal offloading decision according to its own historical offloading decision, the allocated bandwidth and computing resources at the past time, as well as the service response delay and energy consumption at the past time, without knowing other user information and dynamic network environment information. Simulation results show that the proposed collaborative optimization strategy is better than the other three baseline schemes in terms of service response delay and energy consumption performance. [Display omitted] • A joint optimization strategy of task offloading and resource allocation for 5G ultra-dense networks is proposed. • The offloading decision of mobile devices is made based on MAPPO. • The heterogeneous edge clouds realize the allocation of bandwidth and computing resources based on convex optimization. [ABSTRACT FROM AUTHOR]