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China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China's sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China's sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China's sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them. [ABSTRACT FROM AUTHOR]

This study estimates the effects of the rapid expansion of digital infrastructure on rural employment and income. We use a triple‐difference framework and exploit the geographic variation of the recent universal telecommunication service in China. Empirical results reveal increased broadband adoption after the implementation of the program with governmental subsidy. The universal telecommunication service led to an increase in rural residents' income and their employment in the non‐agricultural sector, especially salaried work. The findings suggest that digital infrastructure promotes the transformation of the rural economy in emerging markets. [ABSTRACT FROM AUTHOR]

This paper investigates the short‐term response of the Saudi stock market (Tadawul) to the COVID‐19 outbreak. Event study methodology applied to data derived from the 21 industry groups that constitute the Saudi stock market to calculate abnormal returns for the trading days after the announcement of the COVID‐19 in both China and Saudi Arabia. The results indicate that the estimated CARs for the industry groups and their sum on the event day were not statistically significant. Furthermore, the formal announcement of the first case of the COVID‐19 in China had a negative but not significant impact on the Saudi stock market. In contrast, in the first 9‐days event window, the announcement of the first confirmed case in Saudi Arabia had a negative and significant effect. Moreover, the most negatively affected industry groups were banks, consumer services, capital goods, transportation and commercial services, whereas telecommunication services and food and beverage were positively affected at the event window (+1, +9). In general, the Saudi stock market's response had become weaker in the event windows come after (+1, +9), and different industry groups were found to have different responses to the COVID‐19 outbreak. [ABSTRACT FROM AUTHOR]

In today's economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the "Specification for Design of Data Center" in GB50174-2017 of China: economic factors, natural climate environment factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China. [ABSTRACT FROM AUTHOR]

This paper uses event study based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to study the impact of the COVID-19 outbreak on China's financial market. It finds that the pandemic had an overall significant and negative impact on the stock prices of firms listed on SSE, SZSE and ChiNext. However, such impact appeared to be heterogeneous across industries, affecting listed firms in industries such as pharmaceutical and telecommunications positively, but those in services industries such as accommodation, catering, and commercial services negatively. Apparently, a crisis for some had been an opportunity for others. In addition, this paper seeks to understand the micro mechanism behind the heterogeneity of pandemic shock from the perspective of firms' financial position. It finds that listed firms with higher debt level were hit harder, whereas those with more net cash flow had displayed higher resilience against the blow of the pandemic. However, the opposite pattern is found among those listed on ChiNext and in industries severely devastated by the pandemic. These findings have policy implications in terms of preventing systemic financial risks and facilitating recovery during pandemic-induced economic downturns. It also helps investor adjust investment strategies, hedge against risks, and secure gains when the market conditions in general are unfavorable. [ABSTRACT FROM AUTHOR]

By calculating the mutual information of stock indexes of 10 primary industry sectors in China, this paper analyzes the dependence relationship among Chinese stock sectors during the COVID-19 and the dynamic evolution of the relationship by using the sliding window method. According to the actual situation of the development of COVID-19 in China, the samples were divided into three stages, namely, calm period, pandemic period, and post-pandemic period. The results show that the dependence relationship among Chinese stock sectors is significantly enhanced in the pandemic period, but it decreases in the post-pandemic period and the dependence structure is similar to that in the calm period. The industrials sector is most closely connected with other sectors in the pandemic period. The information technology sector and telecommunication services sector maintain strong dependence in the three periods and share little contact with other sectors. In the pandemic period, the dependence between the consumer staples sector and other sectors is significantly enhanced, and consumer staples sector and health care sector maintain a strong dependence. From the results of the sliding window, the Chinese stock market is sensitive to the impact of COVID-19, but the duration of the impact on the dependence among the stock sectors is not long. [ABSTRACT FROM AUTHOR]

This study examines the dynamic volatility connectedness between different types of policy uncertainty and sectoral stock markets in both the time and frequency domains in China. Using the time–frequency connectedness index approach, we find extremely high connectedness between policy uncertainty and Chinese sectoral stock markets mainly in the medium and long terms. In particular, uncertainty regarding monetary policies makes the weakest contribution to spillovers among the four policy uncertainties, while uncertainty regarding trade policies contributes prominently. The energy, financial, IT, telecommunication services and utilities sectors are the most vulnerable to policy uncertainties. [ABSTRACT FROM AUTHOR]

Urbanization in China has dramatically increased from 39.10 in 2002 to 58.52% in 2017. Studies have discussed the impacts of urbanization and its corresponding changes in consumption patterns on carbon dioxide emissions; however, little is known about their impacts on black carbon (BC). Therefore, we collected data on the BC emissions of various sectors to calculate the consumption-based BC emissions in China, and we used an input–output analysis (IOA) and structural decomposition analysis (SDA) to explore the impacts of urbanization and changes in consumption patterns on BC emissions from 2002 to 2017, focusing on sectoral BC emissions. The total BC emissions of various sectors first increased and then decreased. BC emissions increased from 1083.47 in 2002 to 2550.83 Gg in 2012. They were then reduced to 2478.63 Gg in 2017. Additionally, with the rise in the urbanization rate, household consumption BC emissions increased from 446.18 in 2002 to 1080.12 Gg in 2017. Urban consumption, rural consumption, and BC emission intensity were the three main contributing factors to household consumption BC emission changes. Transport, storage, postal, and telecommunications services (TSP); farming, forestry, animal husbandry, and fishery (FFA); and residential and other industries (RES) contributed the most to the urbanization-related BC emission increase. In particular, the TSP sector contributed the most to the BC emission increase because of the increasing TSP needs related to urbanization. Therefore, it is necessary to formulate mitigation policies for the TSP sector. [ABSTRACT FROM AUTHOR]

The role of Information and Communication Technology (ICT) in mitigating climate change is increasingly gaining attention, yet its environmental performance remains uncertain. This ambiguity serves as the motivation for examining how universal telecommunication service (UTS) impacts CO2 emissions. Utilizing a staggered difference-in-difference (DID) specification, our empirical investigation reveals that UTS exacerbates CO2 emissions. A series of robustness checks corroborate these findings. The study uncovers the mechanisms underlying this environmental degradation, pinpointing increased energy consumption, the promotion of secondary industries, and population migration as the key contributing factors. Importantly, the analysis reveals that the negative environmental effects are particularly pronounced in cities with low-income and relatively lower levels of market integration. However, the study finds that well-designed environmental regulations can offset the negative impact of UTS on the environment. Given the urgent need for climate action, these findings provide critical insights for policymakers to balance the societal benefits of universal connectivity with environmental sustainability. [Display omitted] • Universal telecommunication service (UTS) significantly aggravates CO2 emissions in China. • UTS aggravates CO2 emissions by raising energy consumption, promoting secondary industry, and boosting population migration. • The effect of UTS is heterogeneous due to cities' income and market integration levels. • Environmental regulations can offset the negative environmental influence of UTS. [ABSTRACT FROM AUTHOR]

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Although the digital industry (DI) has brought lasting impetus to the Chinese economy, it has also raised concerns about the environmental impacts. Consequently, by employing the input-output method and structural decomposition analysis (SDA) method, we decompose the production structure factors of the DI on China's embodied carbon emissions into a direct structural effect and indirect structural effect of DI between 2002 and 2018. We also analyze the direct and indirect structural effects of various DIs and their influence on various sectors. The main results indicate that: (1) In generally, production structure factors related to the DI have significant negative effect on embodied carbon emissions of China from 2002 to 2017; (2) among them, the direct structural effects of DI factor exert a negative effect on China's embodied carbon emissions, but the indirect structural effects of DI contribute an increase in embodied carbon emissions of China from 2007 to 2018; (3) the direct structural effects of Electronic components factor reduce the most emissions by 246.66 Mt between 2007 and 2012; (4) the indirect structural effects of Telecommunication services factor are conducive to reducing carbon emissions from 2002 to 2018, and the digital service of non-DI is more conducive to China's carbon emission reduction; and (5) high-energy-consuming sectors are most affected by the changes of production structure factors of DI. We also propose some policy implications. • The input-output method and structural decomposition analysis method is applied. • We explore the CO 2 caused by digital industries (DI) in China. • The direct structural effect of the DI decrease China's CO 2 between 2002 and 2018. • The indirect structural effect of Telecommunication services is conducive to reducing CO 2. [ABSTRACT FROM AUTHOR]