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.

IMPACT China’s central government has taken a more active and resolute attitude to solve the hidden debt problem, which has achieved positive results. However, the transformation of local government financing platforms is full of uncertainty, therefore the challenges of future implicit debt resolution have still to be met. As many developing countries are facing an increasing debt burden, China’s exploration and experience in defusing local government debt risk may be of use to debt management and debt transparency in 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.