Weiwei Zhang

The land factor in China still has many distortions which are the key constraints to economic development. The government intervention in land prices is the root cause of distortions in land factor. So, how to achieve the optimization land utilize through policy guidance is one of the most important issues. Using China’s city panel data from 2010 to 2020, this study assesses the economic impacts of the inter-provincial transfer of construction land indicators by synthetic difference-in-differences method and estimates the growth effect and equilibrium development effect of the policy, respectively. The results show that first, the differentiation of the land policy will give more land indicators to the backward regions, so that the local governments can obtain more land concession opportunities; thus, a more obvious “land finance” is formed and local economic growth is promoted; second, the differentiation of the land policy fails to promote the balanced development between regions, further expanding the development differences between regions. The findings of this study not only provide countermeasure suggestions for the area in China but can also be applied to many developing countries, especially those which are rapidly developing, to help the development of poor regions through targeted fiscal transfer policies.

As countries worldwide are pursuing green development, assessing whether the digital economy as a new economic engine can help us achieve new breakthroughs is of great research value. China, being the largest resource consumer in the world but with a rapidly developing digital economy, can offer us a special view on this question. Using China’s provincial panel data from 2010 to 2020, this study comprehensively measures the development of the digital economy from four dimensions and empirically examines the impact of digital economy development on the green economy based on the super efficiency SBM-GML model. The results show that: first, digital economy development has a significant positive effect on promoting a green economy; second, there are regional differences in both the digital economy and the green economy in China, with the development in the southern region better than that in the northern region; third, the environmental regulation has a double-threshold effect on the relationship that we assessed. The findings in this study highlight the importance of digital economic development in driving the growth of the real economy and are of value to the development of a green economy in the new period.

The Chinese bond market has achieved rapid development over the years. However, since the “rigid payment” in China was broken in 2014, the number and size of bond defaults have climbed up promptly and caused huge volatility in bond and even stock markets. To better manage and control the risk of the Chinese corporate bonds market, deep learning can be used as a helpful tool to predict the corporate default risk. This paper constructs a security warning model based on deep neural networks after a reasonable selection of characteristic indicators. By comparing Multi-Layer Perceptron (MLP) with the logistic regression method, it is found that MLP is more suitable in the security warning model to predict the default risk. And, hyper-parameter analyses and ablation study are conducted to explore the performance and accuracy of the best MLP settings. The experimental results show that the deep learning method accommodating the widely chosen factors in the security warning model is effective in predicting corporate bond defaults in China.

China has committed to reaching carbon peak before 2030. To realize the carbon peak goal, financial development plays an essential role in developing a green economy. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper explores the impact of financial development on carbon intensity both theoretically and empirically. A financial development index system is constructed and computed using the entropy method. A spatial lag panel data model is employed to empirically test the interaction effect of financial development on carbon intensity. Moreover, the mediating effects of industrial upgrading and technological innovation are further investigated. The results show that: first, carbon intensity generates strong spatial spillover effects between provinces in China. Second, financial development significantly reduces carbon intensity, and is most pronounced in central China, followed by western and eastern China. Third, industrial upgrading and technological innovation are important channels to assist financial development in cutting down carbon intensity, and both produce positive spatial spillover effects. These findings suggest that inter-regional cooperation and coordination on financial development, industrial upgrading, and technological innovation are conducive to achieving low-carbon development targets. This research not only has practical significance to China, but also provides global reference value to other countries.

For interpretation of China’s economy, total factor productivity is considered as one of the crucial aspects which is generally dependent on innovation in technologies especially those driven by both scientific research and efficiency of the methodology or process which is dedicated for the allocation of numerous resources available, among enterprises. It is important to note that various factors, which are either directly or indirectly involved, to cause misallocation of the resources to the enterprises, are overly complex. Therefore, an affective mechanism is needed to be realized which is capable of resolving these issues with the available resources and infrastructures. In this paper, we have focused on the construction or development of an artificial neutral network (ANN) based evaluation model to study the impact of resource misallocation on total factor productivity. Likewise, we have conducted a counterfactual experiment, i.e., simulation only, to thoroughly examine the relationship between two very important factors, that is, (i) resource misallocation and (ii) total factor productivity. To do this, we are aiming at investigating the growth potential of total factor productivity when there is no resource misallocation. After comparing 8 industries in different regions, we conclude that the contribution of capital and labor distortion to total factor productivity is the highest in the eastern region of China with −0.036 and 0.065, respectively, followed by the northeast, central, and western regions. In the experiment, China’s total factor productivity growth potential could reach 1.1296, if there is no resource misallocation. The results in this paper would shed some lights on the paths to improve resource allocation efficiency and total factor productivity.

This article examines the impact of China’s New Budget Law implementation on the pricing of Local Government Bonds (LGBs). We collect 5,442 province-year LGBs observations from 2009 to 2019 and estimate the Simultaneous Equation Models that consider the endogeneity of fiscal transparency. Our results show that the enforcement of the New Budget Law unexpectedly leads to a rise in the interest rates of LGBs overall while enhancing the fiscal transparency of local governments. The findings in this paper suggest that the marketization level of LGBs’ pricing in China needs to be further improved.