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Subnational input–output (IO) tables capture industry- and region-specific production, consumption, and trade of commodities and serve as a common basis for regional and multi-regional economic impact analysis. However, subnational IO tables are not made available by national statistical offices, especially in the United States (US), nor have they been estimated with transparent methods for reproducibility or updated regularly for public availability. In this article, we describe a robust StateIO modeling framework to develop state and two-region IO models for all 50 states in the US using national IO tables and state industry and trade data from reliable public sources such as the US Bureau of Economic Analysis. We develop 2012-2017 state IO models and two-region IO models at the BEA summary level. The two regions are state of interest and rest of the US. All models are validated by a series of rigorous checks to ensure the results are balanced at state and national levels. We then use these models to calculate a 2012–2017 time series of macro economic indicators and highlight results for 11 states that have distinct economies with respect to size, geography, and industry structure. We also compare selected indicators to state IO models created by popular licensed and open-source software. Our StateIO modeling framework is consolidated in an open-source R package, stateior, to ensure transparency and reproducibility. Our StateIO models are US-focused, which may not be transferrable to international accounts, and form the economic base of state versions of the US environmentally-extended IO models.

The construction of information infrastructure as well as the transformation and upgrading of the industrial structure are among the major challenges facing China's economy nowadays. Therefore, it is of great significance to explore how information infrastructure affects the upgrading of industrial structure. Based on the panel data of 31 provinces in China from 2013 to 2020, mediating effect model and non-parametric percentile bootstrap method are used to carry out empirical research, by creating an information infrastructure construction level and industrial structure upgrading indicators. The results show that, in addition to the direct effect of information infrastructure on industrial structure upgrading, information infrastructure can also work indirectly through three paths: (1) information infrastructure acts on industrial structure upgrading by enhancing urbanization level; (2) information infrastructure affects industrial structure upgrading by boosting technological progress; (3) information infrastructure first enhances urbanization level, then acts on technological progress, and finally promotes industrial structure upgrading. In addition, the intermediary effect of technological progress is stronger than urbanization. In general, this study acknowledges that urbanization and technological progress are partial mediators in the process of information infrastructure affecting industrial structure upgrading, notwithstanding other potential impact pathways being studied further.

In practice, carbon tax is an effective supplement to emissions trading system, and is also a key to achieving China's emission reduction targets. Although much research has been done to study the internal mechanism and macro impact of carbon tax, prior literature paid little attention on whether carbon tax affect the strategy and performance of low-carbon technology sharing among enterprise. A differential game model is developed in this paper, which assumed that enterprise low-carbon technology stock is jointly determined by both the efforts of the enterprise low-carbon technology innovation and the external enterprise low-carbon technology sharing. This paper constructs dynamic models under decentralized decision-making with cost-sharing, without cost-sharing, and centralized decision-making respectively, obtain enterprise optimal feedback equilibrium strategies, low-carbon technology stocks and the trajectory of optimal value function of benefits. The results demonstrate that carbon tax can promote enterprise low-carbon technology innovation and sharing to some extent. Meanwhile, this paper found that under the scenario of centralized decision-making, the more the efforts of enterprise low-carbon technology innovation or sharing, the higher the system emission reduction benefits than non-cooperation scenarios. Some factors, such as cost coefficient, cost sharing ratio, etc., will affect the enterprise's decision-making and emission reduction benefits. Finally, the validity of the model is verified through numerical simulation and the sensitivity of the relevant parameters in the scenario of cooperative innovation are analyzed. The results may provide important policy implications for promoting low-carbon technology sharing among enterprises, so as to faciliate the achievement of China's emission reduction targets.

With the progressive deterioration of cyber threats, collecting cyber threat intelligence (CTI) from open-source threat intelligence publishing platforms (OSTIPs) can help information security personnel grasp public opinions with specific pertinence, handle emergency events, and even confront the advanced persistent threats. However, due to the explosive growth of information shared on multi-type OSTIPs, manually collecting the CTI has had low efficiency. Articles published on the OSTIPs are unstructured, leading to an imperative challenge to automatically gather CTI records only through natural language processing (NLP) methods. To remedy these limitations, this paper proposes an automatic approach to generate the CTI records based on multi-type OSTIPs (GCO), combing the NLP method, machine learning method, and cybersecurity threat intelligence knowledge. The experiment results demonstrate that the proposed GCO outperformed some state-of-the-art approaches on article classification and cybersecurity intelligence details (CSIs) extraction, with accuracy, precision, and recall all over 93%, finally, the generated records in the Neo4j-based CTI database can help reveal malicious threat groups.