Exploring the impact of China-Europe Railway Express on the urban-rural income gap.

The China-Europe Railway Express (CRexpress) has established a new land transportation route between Asia and Europe as part of China's westward expansion. The resulting trade promotion effect has the potential to improve industrial development and factor flows, ultimately leading to a reduction in the income disparity between urban and rural areas in cities that use the CRexpress. The impact of the CRexpress on income disparities between urban and rural areas in cities that use the service is of particular interest, as the empirical evidence on the relationship between international trade and these disparities is inconsistent. Using a difference-in-differences model and macro panel data, this study found that the CRexpress significantly narrowed the urban-rural income gap in cities where it was operational, and that this effect had a spillover effect on nearby cities. However, the magnitude of this effect decreased with distance. The mechanism analysis indicated that the CRexpress narrowed the income gap by promoting secondary industry development, but this effect varied significantly by region, with pronounced effects in eastern coastal cities and less pronounced effects in inland cities in the central and western region. The study suggests that local governments in these regions should focus on improving the institutional environment and providing industrial support to promote industrial transfer in order to narrow the urban-rural income gap and promote overall economic development.

Mechanism and Impact of Digital Economy on Urban Economic Resilience under the Carbon Emission Scenarios: Evidence from China's Urban Development.

China is currently experiencing a phase of high-quality development, and fostering the resilience of the urban economy is key to promoting this development. The growth of the digital economy is seen as critical to achieving this goal. Therefore, it is necessary to study the mechanism by which the digital economy affects urban economic resilience and the impact of carbon emissions. To this end, this paper empirically analyzes the mechanisms and impacts of the digital economy on urban economic resilience using panel data from 258 prefecture-level cities in China between 2004 and 2017. The study employs a two-way fixed effect model and a moderated mediation model. The results show that: (1) The development of the digital economy can significantly improve the resilience of the urban economy in different periods and different city sizes; (2) The development of the digital economy promotes the economic resilience of developed cities and eastern cities more significantly; (3) In the context of carbon emissions, the digital economy positively contributes to urban economic resilience through population quality and industrial structure but negatively contributes to urban economic resilience through above-scale enterprises; (4) Carbon emissions have a positive moderation effect on the historical path of the industrial structure, above-scale enterprises, and the front path of population quality in the mechanism of the role of the digital economy on the economic resilience of cities, and a negative moderation effect on the front path of above-scale enterprises. Based on these findings this paper proposes several suggestions, such as revolutionizing the digital development of cities, optimizing regional industrial collaboration, accelerating the training of digital talents, and preventing the disorderly expansion of capital.

Can agricultural mechanization services narrow the income gap in rural China?

This paper examines the impact of agricultural mechanization services (AMS) on rural household income and income gap, utilizing the recentered influence function regression method and publicly available data collected through the China Labor-force Dynamics Survey. The results of this study shed light on various impacts of AMS. First, agricultural mechanization services can significantly increase rural household income, but there is heterogeneity in the impact on rural household income in different quantiles. The effect of income growth on medium-income and low-income groups is greater. Second, agricultural mechanization services help to narrow the income gap between rural households and alleviate income inequality in rural areas. Third, the effect of agricultural mechanization services on reducing the income gap between rural households in the eastern and western regions is significantly stronger than that in the central region. Finally, further analysis based on income source structure reveals that agricultural mechanization services can significantly reduce the non-agricultural income gap of rural households, but the impact on the agricultural income gap is negligible. Our findings highlight the importance of government's efforts in promoting the development of agricultural mechanization service market in order to improve the income inequality in rural areas.

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach.

Bitcoin (BTC)-the first cryptocurrency-is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research's innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.