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As the most active field in China's economic development, the digital economy is crucial to the high-quality development of enterprises. Taking Chinese-listed agricultural enterprises as research samples, this paper discusses the effect of the digital economy on promoting the high-quality development of agricultural enterprises and the mechanism behind it from the perspective of total factor productivity of enterprises. The study found that the digital economy significantly promoted the improvement of total factor productivity of agricultural enterprises and enabled the high-quality development of agricultural enterprises, and this conclusion remained valid after controlling for endogeneity and conducting robustness tests. In terms of action mechanisms, the digital economy is conducive to promoting green innovation and promoting the high-quality development of agricultural enterprises; However, entrepreneurial activity has a masking effect. Further analysis shows that the digital economy plays a more significant role in promoting non-resource-intensive agricultural enterprises located in northern China. At the same time, the higher the industrial structure and financial development level of the region, the more positive impact the digital economy can play. This study verifies the impact mechanism of the digital economy on the high-quality development of agricultural enterprises and provides enlightenment for China's the digital economy policy-making and enterprises' the high-quality development strategy decision-making. [ABSTRACT FROM AUTHOR]

The digital economy is integral in driving economic growth, particularly of high-quality standards. Against the backdrop of the "Double Carbon" project, exploring the impact of the digital economy on green agricultural development bears tremendous practical significance. Thus, this study aims to investigate how the digital economy affects agricultural green total factor productivity (GTFP). The entropy-TOPSIS method and SBM-GML index are used to measure core variables quantitatively. In addition, two-way fixed effects panel data models, Tobit, and intermediary effect models are implemented. Three research findings emerge. First, China's agricultural GTFP level generally experiences an upward trend, and the development of the digital economy has a significant positive effect on increasing agricultural GTFP. Second, the digital economy mainly promotes agricultural technology innovation that boosts agricultural GTFP. Third, Western China experiences a more significant positive effect of the digital economy on agricultural GTFP than Central and Eastern China. Finally, based on the findings, this paper proposes relevant policy recommendations to promote green and sustainable agriculture development. [ABSTRACT FROM AUTHOR]

The digital economy is developing rapidly worldwide, which is of great strategic significance in leading and driving the process of high-quality and high-efficiency development of the economy. It has gradually become a new engine to promote China's economic growth. Therefore, it is vital to establish a digital economy indicator system. In this context, according to the connotation of digital economy, comprehensively consider the four dimensions of digital foundation, digital application, digital innovation and digital benefit, and build a full-scale digital economy indicator system. However, some previous literature proposed operating the K-means clustering algorithm in optimizing the index system. The algorithm, susceptible to the initial selection of cluster center, generates completely different clustering results with other random seed points and is thus unsuitable for optimizing the index system. Based on this, the advantages of GRA, SA, and K-means algorithms are combined to propose a K-means-SA algorithm that can obtain the global optimal solution. Then, by combining the K-means SA algorithm and the rough set algorithm, the constructed index system is further optimized. Ultimately, it establishes a set of all-round, multi-system and multi-dimensional digital economy index systems, which is of great reference significance for formulating relevant policies, provides scientific index support for the subsequent extension of digital economy theory, and promotes the benign growth of the worldwide economy. Plain language summary: The importance of constructing and optimizing the digital economy indicator system In today's rapid development of the digital economy, a set of scientific, feasible, and practical application values of the digital economy evaluation index system is constructed to evaluate better and promote the development of the digital economy. Based on the four dimensions of digital foundation, digital application, digital innovation, and digital benefit, this paper constructs a comprehensive evaluation index system for the development level of the digital economy. In addition, by combining the advantages of GRA, SA, and K-means algorithms, a K-means SA algorithm can obtain the global optimal solution proposed. Then, the K-mean-SA algorithm is combined with the rough set algorithm to optimize the constructed index system further. Finally, a comprehensive, multisystem, and multi-dimensional evaluation index system for the development level of the digital economy is established, which provides scientific support for the development of digital economy theory and promotes the healthy development of the digital economy. Although this paper collects indicators related to the digital economy as comprehensively as possible based on existing studies, there are still some limitations in the selection of needles due to the strong integration of the digital economy. [ABSTRACT FROM AUTHOR]

The digital economy is an important driving force for China's economic transformation and upgrading, while "carbon peaking and carbon neutrality" is the goal to be achieved in China. Based on the panel data of 30 Chinese provinces (cities and districts) from 2012 to 2019, the article empirically investigates the impact of the digital economy on regional carbon emissions and its mechanism of action by using a two-way fixed-effects model and mediated-effects model. The study shows that, firstly, the digital economy has a significant carbon emission reduction effect, and the conclusion still holds after robustness testing. Secondly, the emission reduction effect of industry digitization in the digital economy dimension is the most obvious. Again, the carbon emission reduction effect of digital economy development on different regions varies, with the strongest carbon emission reduction effect on western regions and regions with high carbon emission levels. Finally, the analysis of mediating effects shows that the energy structure is an important mechanism for the digital economy to suppress regional carbon emissions. The findings of the study provide a feasible path and policy reference for the digital economy to contribute to the goal of "carbon peaking and carbon neutrality". [ABSTRACT FROM AUTHOR]

To study the impact of the digital economy on industrial structure upgrading, this paper constructs the dynamic spatial Durbin model, dynamic spatial intermediary model, and spatial threshold model based on the panel data of 155 prefecturelevel cities in China from 2011 to 2021. It is found that: (a) the improvement of the digital economy in the region can directly promote the upgrading of local industrial structure, but there is a significant "siphon effect," which makes the spatial spillover effect negative and significant. (b) The digital transformation of enterprises plays a mediating role in the path of digital economy-driven industrial structure upgrading, with a short-term mediating effect size of about 17.18%. (c) The digital economy has a threshold effect on the path of enterprise digital transformation for industrial structure upgrading. These findings provide suggestions for policy. [ABSTRACT FROM AUTHOR]

Internet-based companies such as Amazon, Facebook, and Tencent hold an enormous amount of consumer data that are utilized to create business value via big data analytics. Although some companies use big data to provide professional services, such as targeted advertising and product recommendations, according to the current CAS, IFRS, and U.S. GAAP accounting standards, these assets are not recognized as assets since they are generated internally. This paper starts with a discussion of how Amazon, Facebook, Tencent, and Walmart use big data to create value for their businesses and then argues why it makes sense to recognize big data as intangible assets. Possible methods of data asset evaluation and their implications for business managers are also explored. [ABSTRACT FROM AUTHOR]