This paper constructs an open economy dynamic stochastic general equilibrium (DSGE) model with oil to investigate the transmission mechanism and impact effects of oil price fluctuations driven by different factors on China’s macroeconomy using quarterly data from 1996 to 2019. The results show that the international crude oil supply-driven oil price decline promotes positive output growth in the short run through the positive cost effect of the supply channel, and the production regulation cost will dampen the incentive to invest in the new energy sector in the long run. Domestic economic development demand-driven oil price increases act on the demand channel, driving output and oil prices to fluctuate in the same direction, generating a negative real balance effect on the economy through the interest rate channel. The oil-specific demand driven by foreign nominal interest rate shocks is transmitted through the exchange rate channel, triggering imported inflation, lower aggregate demand, and lower output. Different sources of oil price fluctuations have different transmission mechanisms and thus differential effects. For this reason, based on the root causes of oil price fluctuations, policy recommendations to deal with international oil price fluctuations in the new situation are proposed at the supply level, demand level, and international level.

Driven by innovation, the implementation of a strategy for developing a quality workforce is the key to promoting the high-quality development of China’s economy. Based on the panel data of 31 provinces on the Chinese mainland from 2013 to 2020, a spatial econometric model is used to explore the impact of the regional environment, regional heterogeneity and its spatial effect on the gathering of technological talent. The results show that: (1) The improvement of the regional innovation environment can significantly promote the gathering of technological talent in a region; (2) The regional innovation environment has an obvious regional effect on the gathering of technological talent, which is manifested in the strong promotion of the eastern and western regions, and an obvious siphon effect in the eastern region, but it is not the key factor affecting the gathering of technological talent in the central region; (3) The gathering of technological talent has a significant spatial effect among neighboring provinces. Therefore, improving the regional innovation environment, adapting measures to local conditions in different regions and strengthening economic cooperation among provinces have become the key to the rational allocation of technology talent resources.

Economic resilience represents a nation’s capacity to withstand external shocks, quicken economic recovery, and attain sustainable development. Can Pilot Free Trade Zones (PFTZs), as testing fields for China’s institutional openness, boost the economic resilience of host cities? This study empirically investigates the impact and mechanisms of establishing PFTZs on urban economic resilience. It does so by building overlapping Difference-in-Differences (DID), Propensity Score Matching DID (PSM-DID), and spatial DID models using panel data across 284 cities in China from 2007 to 2021. It is found that establishing PFTZs significantly promotes urban economic resilience, and PFTZs largely achieve this by increasing population density, consumer demand, and economic growth in host cities. Spatial heterogeneity analysis reveals that PFTZs in North, East, Central, and South China notably enhance urban economic resilience, whereas those in Northeast, Southwest, and Northwest China do not. Regarding spatial spillover effects, the establishment of PFTZs has a beneficial impact on the economic resilience of nearby cities within a radius of 100 km to 400 km. The impacts become stronger as the distance grows, peaking at a radius of 400 km. This research offers important policy implications for promoting the establishment of PFTZs, unlocking the benefits of institutional openness, and strengthening urban economic resilience.

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are infiltrating the field of finance. Digital finance (DF) is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally-friendly enterprises, thereby promoting green low-carbon circular development (GLCD). However, few studies have explored the coupling mechanism between DF and GLCD. To fill this gap, this paper explores the effect of DF on GLCD, and established a mediating effect model to investigate the mechanism of DF in promoting GLCD. Additionally, this paper established a random forest model and a CatBoost model based on machine learning to examine the relative importance of DF and the factors affecting GLCD. The results show that DF has significant positive effects on GLCD, and technological innovation plays a key role in the effect of DF on GLCD; meanwhile, the effect of DF on GLCD shows nonlinear features with an increasing “marginal effect”; moreover, both DF and conventional factors have significant impacts on GLCD. Our study highlights the effect of DF on GLCD and underscores the importance of developing policies for DF and GLCD. This study provides an empirical basis and path reference for DF to achieve “carbon peak, carbon neutralization” in China.

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are penetrating the field of finance. Digital finance is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally friendly enterprises, thereby promoting green and low-carbon development. This paper investigates the effects of digital finance on green and low-carbon cyclic development and the mechanism. First, the level of green and low carbon cyclic development in China is estimated from multiple aspects based on the panel data of 31 provinces by using the spatio-temporal range entropy weight method. Then, the working mechanism of digital finance on green and low carbon cyclic development is revealed by performing empirical tests based on panel regression models, mediating effect models, and threshold models. Finally, the effects of digital finance and conventional variables on green and low-carbon cyclic development are investigated by using a random forest model and a CatBoost model in based on the machine learning. The results show that digital finance has significant positive effects on green and low-carbon cyclic development, and technological innovation plays a key role in the effects of digital finance on green and low-carbon cyclic development; meanwhile, the driving effect of digital finance on green and low carbon cyclic development shows nonlinear features with increasing “marginal effect”; besides, both digital finance and conventional factors have significant impacts on green and low carbon cyclic development. This study provides an empirical basis and path reference for digital finance to achieve “carbon peak, carbon neutralization” in China.