The low-carbon transformation has turned out to be a challenging task faced by government agencies, enterprises, and society because of the global warming. Endorsing the expansion of the low-carbon revolution is considered as an essential measure for low-carbon alteration and advancement. Therefore, articulating realistic environmental control strategies intended to enhance the motivation level of low-carbon innovation, though outward foreign direct investment (OFDI) can produce direct and indirect influences on the growth of low-carbon innovation. According to the data of 30 provinces of China from 2004 to 2017, the relationship among environmental regulation, OFDI, and low-carbon innovation was analyzed using the spatial econometric model. Based on the analyzed data, the following conclusions were drawn. (i) From the national and regional perspectives, China’s low-carbon innovation takes understandable agglomeration features in the longitudinal dimension. In addition, environmental regulation plays a key role in promoting low-carbon innovation and regional heterogeneity. (ii) Environmental regulation might force enterprises outward foreign direct investment efficiently and increase the level of OFDI that will be capable of promoting low-carbon innovation. (iii) OFDI acts as an intermediary in the relationship between environmental regulation and low-carbon innovation, and this role has regional heterogeneity. (iv) There are significant spatial spillover effects of environmental regulation and OFDI on low-carbon innovation, environmental regulation on OFDI, and the intermediary effect of OFDI on environmental regulation and low-carbon innovation. This study supplements our understanding of the relationship between environmental regulation and OFDI, in addition to low-carbon innovation, which provides illumination for enterprise practice, as well as decision-makers.
The stability of the financial system plays a crucial role in the sustainable economic development. Hence, to identify systemically important banks and firms, we take lending relationships with different loan terms and common asset relationships with different investment cycles into consideration to present a multilayer DebtRank model of the bank-firm system. In the light of simulation research, we can obtain the following results. First, the bank-firm system constructed displays a significant core-periphery structure, which exists in the actual financial system. Then, only very few banks and firms show systemically important characteristics, where “important” subjects hold very high net assets and profits, while "fragile" subjects possess negative net assets and serious losses. Furthermore, the bank-firm multilayer DebtRank model presents a great stability to a certain extent. Overall, the multilayer DebtRank model constructed in this paper has certain theoretical reference value for the supervisory authorities to extract the internal characteristics of systemically important banks and firms and identify them effectively.
Blockchain technology applied to cryptocurrencies is the dominant factor in maintaining the security of cryptocurrencies. This article reviews the technological implementation of cryptocurrency and the security and stability of cryptocurrency and analyzes the security support from blockchain technology and its platforms based on empirical case studies. Our results show that the security support from blockchain technology platforms is significantly insufficient and immature. In addition, we further Zyskind and Nathan (2015) and Choi (2019) and find that the top ten platforms play critical roles in security support and have significant advantages in terms of funds, duration, and human resources. Moreover, these platforms provide computational resources and benefits to the consensus algorithm selection for blockchain practitioners. Second, encryption ensures the security of cryptocurrencies. On the one hand, the digital signatures identify the identity of the signatory and the transaction. However, the principle of the hash algorithm (SHA256) confirms ownership. Meanwhile, SHA256 is infeasible to compute in the reverse direction and is difficult to attack. Furthermore, the records in the blockchain can be queried by every participant, making the system information transparent and open reliable. Third, compared to the study of Fu and Fang 2016, we find that the blockchain structure is composed of security components and basic components of six layers that are independent and cannot be extended completely and have a certain coupling among them. Fourth, the underlying ledger structures of Bitcoin and DAG are highly correlated to their security. Specifically, we follow Sompolinsky et al. (2016) and detect that the structure of SPECTRE ensures network security and robustness from its block production, conflict resolution, and generated trusted transaction sets. Meanwhile, the voting algorithm of SPECTRE makes resolving conflicting transactions by calculating votes and ensuring the transaction information that is virtually unable to be tampered with possible. In particular, the security calculation power of SPECTRE can reach 51% and resist “double-spend attacks” and “censorship attacks” effectively. In addition, the RDL framework of SPECTRE achieves security confirmation of transferring funds. Moreover, PHANTOM identifies evil blocks by employing block connectivity analysis and ensures its security. Eventually, we also expand the studies of (Sompolinsky et al., 2016 and Sompolinsky et al., 2017) and compare the basic characteristics of the protocols of Bitcoin, SPECTRE, and PHANTOM and find that protocols play imperative roles throughout the implementation process of cryptocurrency. In addition, the underlying ledger structure and consensus mechanism make up a blockchain while the confirmation time, throughput limit, and ordering are prerequisites for smart contracts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.