Energy vulnerability is gaining attention in light of global economic, social, and political conflicts. The digital economy has the potential to become a vital tool in addressing current issues. This paper explores the relationship, mechanisms, and heterogeneity between the digital economy and energy vulnerability for 110 economies by using a generalized method of moments (GMM) model. Our key findings are as follows: (1) The digital economy effectively reduces energy vulnerability, with digital infrastructure and social impact as the main contributors to this effect. (2) The digital economy helps upgrade the industrial structure and the level of financial development, thereby reducing energy vulnerability. (3) Heterogeneity results show that the negative impact of the digital economy on energy vulnerability is more significant in regions with higher income levels, particularly Europe and North America. (4) The level of economic development acts as a practical threshold variable, with the digital economy having a more pronounced negative impact on energy vulnerability at higher threshold levels. These findings provide new development ideas for improving global energy security and energy efficiency, and reducing energy vulnerability. 

Is the digital economy an effective tool for decreasing energy vulnerability? A global case

Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure. 

Will artificial intelligence make energy cleaner? Evidence of nonlinearity

The rapid development of China’s digital economy has promoted the digital transformation of Chinese society and become a new driving force for China’s social development. Furthermore, increasing farmers’ income is the central task of China’s “three rural issues” project, and the integrated development of primary, secondary, and tertiary rural industries is an important way to revitalize rural industries and broaden farmers’ income channels, so it is very important to promote the integrated development of rural industries. In the context of the rapid development of China’s digital economy, it is of great significance to study how the digital economy promotes the integration of rural industries. Therefore, this study analyzed how China’s digital economy promotes the integration of rural industries by using the panel data and regression models of 30 provinces (municipalities and districts) in China from 2011 to 2021. The results show that (1) there is a significant positive relationship between the development level of the digital economy and the level of rural–industrial integration and that the development of the digital economy promotes the process of rural–industrial integration; (2) intermediary effect analysis shows that as the digital economy promotes rural–industrial integration, scientific and technological innovation levels and rural human capital are important intermediary channels; and (3) spatial analysis shows that the development of the digital economy can not only promote the integration of rural industries in the province but also have a positive spatial spillover effect on neighboring provinces. Therefore, it is necessary to create a good atmosphere for the development of the digital economy, constantly promote the development of the digital economy, pay attention to scientific and technological innovation and rural talent training, and promote the integration and coordinated development of the digital economy and rural industries between regions in order to improve the level of rural–industrial integration and contribute to the rural revitalization strategy.

How Can the Digital Economy Promote the Integration of Rural Industries—Taking China as an Example

<h2>Abstract</h2> Based on 2013–2019 panel data covering 31 provinces and cities in China, this paper uses the entropy weight technique for order of preference by similarity to ideal solution (TOPSIS) method to measure rural common prosperity (CP). Based on the global and local Moran's I methods, we analyze the dynamic evolutionary characteristics of China's rural CP and the regional differences. Additionally, we use the necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) methods to explore how six antecedents at the technology-organization-environment (TOE) level interact to affect CP. This research finds that, first, China's rural CP showed a fluctuating upward trend, with the highest level of CP in rural areas in the eastern region, followed by the central and western regions. Additionally, the gap between the three gradually narrowed. Second, China's rural CP had "high-high" and "low-low" agglomeration characteristics, with positive spatial autocorrelation, no transition changes, and strong spatial stability. Third, individual digital economic elements and organizational and environmental elements were not necessary conditions for promoting rural CP. Fourth, the multiple concurrent factors of the digital economy, organizations and the environment constituted three diversified configurations of rural CP, showing that the driving path of rural CP was characterized by "different paths that lead to the same goal". Moreover, "perfect digital facilities" and "high entrepreneurial activity" had a universal role in promoting rural CP. The conclusions of this research hold important theoretical and practical significance for improving China's rural CP. 

Research on the evaluation and configuration path of China's rural common Prosperity—NCA and fsQCA based on provincial panel data

The potential for the development of digital data and their infrastructure creates new opportunities for economic growth. The purpose of this study was to develop an approach to identify a set of indicators to quantify the data economy and model its impact on economic growth. The cumulative index and Gini coefficient indicated differentiation and disparity in the digital data infrastructure of 85 regions for 2016–2021. In the presence of a positive spatial correlation, digital development does not indicate clear spatial clubs. Selected according to the calculation of Lagrange multipliers and likelihood ratios, panel econometric models with spatial lags, using SAR, SEM and SAC, showed a short-term negative effect and a long-term positive effect of the digital data economy on economic growth, confirmed by the calculation of marginal effects. During the pandemic, the data economy had a positive impact on regional economic growth. The positive spatial effect of interactions between regions detected by the models in the framework of economic growth indicates the synergistic nature of digitalization. The main conclusions of this study provide evidence-based support for the digital transformation of regions and can help create information infrastructure and accumulate human capital to eliminate disparities in the digital development of regions. 

Modeling the Spatial Effects of Digital Data Economy on Regional Economic Growth: SAR, SEM and SAC Models

The digital economy, which was born during the late third technological revolution, has caused significant economic and societal changes. Amid sluggish global economic growth, China's economy is facing upgrades and transformations. The sample selection for this study was conducted from 2013 to 2020. Data related to the digital economy and servitization of the industrial structure of 30 Chinese provinces, municipalities, and autonomous regions were collected. This study presents the human capital variable, based on which an econometric analysis was conducted, and examines its moderating effect. The findings indicate that even after the replacement variable indicator's robustness test, the relationship between the digital economy and the servitization of industrial structures remained unchanged. This study demonstrated that the quality of human capital plays a positive role in this effect. Finally, a heterogeneity test demonstrated that there are different pathways for the impact of the digital economy on the servitization of industrial structures in the eastern, central, and western regions. This study provides evidence to help researchers understand the moderating utility of human capital. 

The impact of the digital economy on the servitization of industrial structures: The moderating effect of human capital

. Based on the rise of green economy to the national strategic level, the development of green economy has become an important means to improve the social environment, and it is imperative to achieve green and low-carbon development of energy. In this regard, state-owned energy enterprises have carried out preliminary exploration of green trading in the building of e-commerce platforms, andshouldfurtherdeepenthebuildingofgreensupplychainbasedone-commerce tradinginthefuture.Therefore,wecanrelyonPDCAcycletobuildastrategy modelfordeepeningthegreensupplychainsystemofenergystate-ownedenter-prisesintoe-commerce,andpromotethedeepeningofthegreensupplychain systemthroughtheformulationofgreenprocurementstandards,greentransaction standards,greentransactionevaluation,andscientiﬁcrewardsandpunishments, so as to realize the development of green economy in the energy industry and effectively protect the environment.

Strategy Research on Deepening Green Supply Chain System of Energy State-Owned Enterprises from the Perspective of Green Economy

With big data, Artificial Intelligence(AI) and Internet of Things(IoT) as emerging digital applications, the development of the digital economy has become an important opportunity for the economic take-off of developing countries. We estimate the influence of digital economic development on the marketization level of each province by constructing panel data of each province in China and using the fixed effect model. The result shows that every change in standard unit of digital economic development is positively promoting the marketization level by about 0.5%. We believe that the digitalization process is a process of promoting marketization process and releasing economic growth vitality in developing countries. Policy makers can reinforce the construction of digital infrastructure for big data and cloud computing, support the application of artificial intelligence and blockchain in traditional industrial sectors, and promote the overall development of the digital economy, so as to improve the marketization level and stimulate the vitality of the whole market and economic development potential.

The Digital Economy driving marketization in China: based on Big data, Al and IoT

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The impact of the digital economy on the servitization of industrial structures: The moderating effect of human capital

Strategy Research on Deepening Green Supply Chain System of Energy State-Owned Enterprises from the Perspective of Green Economy

The Digital Economy driving marketization in China: based on Big data, Al and IoT