Impact of digital technology on carbon emissions: Evidence from Chinese cities

IntroductionPromoting the development of digital technology is an important step in meeting the challenge of global climate change and achieving carbon peaking and carbon neutrality goals.MethodsBased on panel data of Chinese cities from 2006 to 2020, this paper used econometrics to investigate the impact and mechanism of digital technology on carbon emissions.ResultsThe results showed that digital technology can significantly reduce carbon emission intensity and improve carbon emission efficiency. These results remained robust after changing the estimation method, adding policy omission variables, replacing core variables, and solving the endogeneity problem. Digital technology can indirectly reduce carbon emissions by promoting green technological innovation and reducing energy intensity, and it plays a significant role in the carbon emission reduction practices of carbon emission trading policies and comprehensive national big data pilot zones. The replicability, non-exclusivity, and high mobility of digital technology help to accelerate the spread of knowledge and information between different cities, which leads to a spillover effect on carbon emission reductions. Our unconditional quantile regression model results showed that digital technology’s carbon emission reduction effect continuously decreases with increases in carbon dioxide emissions.DiscussionThe results of this paper provide evidence for the potential use of digital technology in achieving the goal of carbon neutrality, which is of great significance for achieving high-quality innovation and promoting the green transformation of the economy and society

R&D subsidies, executive background and innovation of Chinese listed companies

Enterprises are the mainstay of national innovation. The improvement of enterprise productivity and the sustained and rapid development of the economy are inseparable from the research and development (R&D) and innovation of enterprises. Most previous research studies have focused on the economic system and scale of enterprises to study the impact of government–enterprise relations on corporate innovation. This article takes the heterogeneity background characteristics of executives as the starting point, discusses the internal mechanism of R&D subsidies, the background characteristics of corporate executives and the innovative behaviour of Chinese listed companies, and draws two conclusions: (1) The current Chinese government’s innovation subsidy allocation process still has some preferences, such as the company’s executives have more R&D subsidies for listed companies with a background in government, technical background and high academic background; and (2) Although the three types of background characteristics of executives are beneficial to the company’s R&D subsidies, the government background of executives has not significantly promoted the innovation of listed companies, while the background of technology R&D and high academic background of executives have significantly promoted corporate innovation

The impact of two-way FDI on total factor productivity in China and countries of the belt and road initiative

This study utilizes the DEA-Malmquist index method to measure the total factor productivity of 36 Belt and Road countries and establish a dynamic panel model. This study carries out an empirical analysis of whether two-way investment in China and the Belt and Road Initiative can improve total factor productivity. First, the technology spillover of the home country has a significant effect on improving total factor productivity and the technical efficiency index of countries along the route, while the technology spillover of host countries has no significant effect on total factor productivity. Second, in Asia, the technology spillover of host countries has a significant effect on total factor productivity, while the technology spillover of the home country has no significant effect on total factor productivity. Finally, in Europe, the spillover effect of technology in the home country is beneficial to the improvement of resource allocation. Meanwhile, the spillover effect of technology in host countries is beneficial to the improvement of total factor productivity and the technical efficiency index. Therefore, China should continue to increase its investment in Belt and Road countries

Connecting Researchers with Companies for University-Industry Collaboration

Nowadays, companies are spending more time and money to enhance their innovation ability to respond to the increasing market competition. The pressure makes companies seek help from external knowledge, especially those from academia. Unfortunately, there is a gap between knowledge seekers (companies) and suppliers (researchers) due to the scattered and asymmetric information. To facilitate shared economy, various platforms are designed to connect the two parties. In this context, we design a researcher recommendation system to promote their collaboration (e.g. patent license, collaborative research, contract research and consultancy) based on a research social network with complete information about both researchers and companies. In the recommendation system, we evaluate researchers from three aspects, including expertise relevance, quality and trustworthiness. The experiment result shows that our system performs well in recommending suitable researchers for companies. The recommendation system has been implemented on an innovation platform, InnoCity.

Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target ad. In particular, we explore auxiliary ads from two viewpoints: one is from the spatial domain, where we consider the contextual ads shown above the target ad on the same page; the other is from the temporal domain, where we consider historically clicked and unclicked ads of the user. The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent. In order to effectively utilize these auxiliary data, we propose the Deep Spatio-Temporal neural Networks (DSTNs) for CTR prediction. Our model is able to learn the interactions between each type of auxiliary data and the target ad, to emphasize more important hidden information, and to fuse heterogeneous data in a unified framework. Offline experiments on one public dataset and two industrial datasets show that DSTNs outperform several state-of-the-art methods for CTR prediction. We have deployed the best-performing DSTN in Shenma Search, which is the second largest search engine in China. The A/B test results show that the online CTR is also significantly improved compared to our last serving model.Comment: Accepted by KDD 201

Autotoxin affects the rhizosphere microbial community structure by influencing the secretory characteristics of grapevine roots

Autotoxins secreted by roots into the soil can trigger rhizosphere microecological imbalances and affect root secretory properties resulting in conditions such as replanting disease. However, information on the effect of autotoxins on root secretion characteristics and regulation of the composition of rhizosphere microorganisms by altered root exudates is limited. In this study, autotoxin ρ-hydroxybenzoic acid (4-HBA) was added to the soil of potted grapevine seedlings, CO2 pulse-labeling, and DNA stable isotope probing were used to track the rhizosphere microbiome that assimilates root exudates. Bacterial and fungal microbiomes that assimilated plant-derived carbon were identified by high-throughput sequencing. Results showed that 4-HBA treatment altered bacterial and fungal communities in 13C-labeled organisms, with a lower abundance of beneficial bacteria (e.g., Gemmatimonas, Streptomyces, and Bacillus) and a higher abundance of potential pathogen fungi (e.g., Fusarium, Neocosmospora, Gibberella, and Fusicolla) by changing the composition of root exudates. The exogenous addition of upregulated compound mixtures of root exudates reduced the abundance of beneficial bacterial Bacillus and increased the abundance of potential pathogen fungi Gibberella. These results suggest that 4-HBA can alter root secretion properties and altered root exudates may enrich certain potential pathogens and reduce certain beneficial bacteria, thereby unbalancing the structure of the rhizosphere microbial community