In the face of resource and environmental problems caused by extensive economic development, China has put forward a green development strategy. Scientific measurement and analysis of green total factor productivity (GTFP) is of great significance for achieving high-quality economic development. By introducing the human capital composition, including education, health, scientific research, and training, this paper study adopts the Slack Based Measure-Global Malmquist-Luenberger (SBM-GML) index to re-measure the GTFP and its decomposition of 30 provinces, municipalities, and autonomous regions in China from 2000 to 2019. The results show that: (1) China’s GTFP has a fluctuating growth trend, with an average annual growth rate of 2.31%. (2) In terms of its decomposition, technical progress is the main force driving GTFP growth, with a contribution rate of 1.59%; the improvement of technical efficiency is a secondary driving force, with a contribution rate of 0.71%. (3) The measurement results of GTFP, considering the human capital composition, are generally higher than those without consideration, and the GTFP growth under the two modes shows a trend of “high in the east and low in the west”. The conclusions have enlightening significance for improving GTFP and the growth potential of the economy in China.

The time-varying factor share runs through the entire process of the Chinese economic miracle, unlike the ‘Kaldor Facts’ in developed countries. Following the new structural economics theory, we construct a time-varying elastic production function model that characterises the structural changes of China’s economic element, and decompose the driving force of economic growth to measure the contribution of factor structure. We found that, from 1978–2017, the average contribution of capital, labour, technological progress, and factor structure change to the GDP was 67.01%, 10.38%, 23.08%, and −0.47%, respectively. The measurement results can aptly portray the impact of policy changes in China’s unique gradual reform process, such as the economic market reforms in 1992, the global financial crisis in 2008, and the policy changes of the new economic normal in 2014. Meanwhile, the results reveal that improving factor allocation can accelerate the total factor productivity and promote high-quality development of China’s economy.

Purpose: The purpose of this article is to explore the impact of the firm's entrepreneurship for the transformation of circular economy (CE). The role of entrepreneurship is thought to be important for the process of four Rs in the CE, and the authors have tried to study the role and impact path of entrepreneurship in CE.Design/methodology/approach: Empirical data from Chinese listed firms are collected, and a measure of digital technology is constructed by text mining method. Mediation analysis method is used to test the proposed hypothesis.Findings: The results show that the innovation entrepreneurship has a significant positive impact upon the CE and digital technology is playing a mediating role in the impact path. However, the business entrepreneurship is negatively affecting the CE adoption. Also, the proportion of shares hold by the institution has a heterogenous influence for the innovation entrepreneurship.Practical implications: This study guides policy makers about the role of entrepreneurship and the mediating effect of digital technology and to encourage the adoption of CE for firms.Originality/value: This study reveals the mediation effect of digital technology in the impact of entrepreneurship on CE in the emerging market. The heterogeneity of the proportion of shares hold by the institutions is also analyzed in the empirical study.

Previous studies have assumed that the volatility of exogenous shocks is constant, which can only measure the level effects of uncertain shocks. This article introduces the time-varying volatility model into a Dynamic Stochastic General Equilibrium (D.S.G.E.) model and uses the third-order perturbation method to identify and decompose the level and volatility effects of uncertainty shocks. Based on the results of empirical research in China, the effect of volatility shocks is different from that of level shocks: the effect of level shocks is direct and positive, and its impact is larger, while the effect of volatility shocks is indirect and negative, and its impact is smaller. This article also finds that the impact of uncertainty shocks will lead to economic stagnation, inflation, and the stagflation effect.