No abstract

From the context of China's "peak carbon dioxide emissions" and "carbon neutral" goals, this study determines whether digital development, as an important path of China's modern development, contributes to the achievement of China's "carbon neutral" goals. In addition, this paper examines whether digital development as an important path of China's modern development also contributes to realizing China's "carbon neutral" goal, while taking into account the internal logic and theoretical mechanism. Consequently, panel data of 267 prefecture-level cities and above nationwide for the time period ranging from 2003 to 2017 is constructed in this study, and used the two-way fixed effects model and instrumental variable estimation method to verify the intrinsic mechanism of digital development affecting carbon intensity. The study findings indicate that: (1) digital development exerts a significant inhibitory effect on carbon intensity, and overall, it demonstrates an "inverted U-shaped" relationship of promotion in the first place and inhibition in the subsequent phase. Moreover, the proposed inhibitory effect represents the marginally increasing trend; (2) promoting technological innovation serves as a significant mechanism for digital development to inhibit the carbon intensity; (3) most cities in China are still below the inflection point of digitalization; (4) optimizing the regional innovation environment effectively advances the inflection point of digitalization; thereby, implying that the government should pay due attention to the inhibiting effect of digitalization on carbon intensity. Besides, there is also a need to further optimize the environment of regional innovation, increase innovation investment, and smoothen the influence mechanism of technological innovation in order to quickly cross the "emission increase" period of digitalization development.

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.