Since total factor productivity growth plays an essential role in China’s economic growth, the source of this growth has been a critical issue over the past decades. Hence, this paper applies an input slack-based productivity (ISP) index to investigate the contributors (i.e., labor and capital inputs) to China’s total factor productivity growth. The ISP index, combining the features of the directional distance function and Luenberger productivity index, can calculate the productivity change of each input factor under the total factor framework. According to the decomposition analyses, we find that China is confronting a dual challenge in total factor productivity growth: first, capital productivity growth exhibits a remarkable slowdown after the mid-1990s; second, although labor productivity continually expands, the relative labor efficiency among provinces has deteriorated since the 2000s. The results imply that the government should not only advocate upgrading industrial structure, but also consider balanced regional development policies for China’s sustainable growth.

Although economic growth brings abundant material wealth, it is also associated with serious PM_2.5 pollution. Decoupling PM_2.5 emissions from economic development is important for China’s long-term sustainable development. In this paper, the generalized Divisia index method (GDIM) is extended by introducing innovation indicators to investigate the main drivers of PM_2.5 pollution in China and its four subregions from 2008 to 2017. Afterwards, a GDIM-based decoupling index is developed to examine the decoupling states between PM_2.5 emissions and economic growth and to identify the main factors leading to decoupling. The obtained results show that: (1) Innovation input scale and GDP are the main drivers for increases in PM_2.5 emissions, while innovation input PM_2.5 intensity, emission intensity, and emission coefficient are the main reasons for reductions in PM_2.5 pollution. (2) China and its four subregions show general upward trends in the decoupling index, and their decoupling states turn from weak decoupling to strong decoupling. (3) Innovation input PM_2.5 intensity, emission intensity, and emission coefficient contribute largely to the decoupling of PM_2.5 emissions. Overall, this paper provides valuable information for mitigating haze pollution.

The purpose of this paper is to reveal the characteristics, roles and sizes of the risk types (systematic risk and idiosyncratic risk) of different financial markets in cross-market contagion in China. Therefore, this paper first constructs the financial stress index (FSI) of the banking sector, the stock market, the bond market and the exchange rate market. Secondly, principal component analysis (PCA) is used to decompose the financial stress index of four financial markets into the systematic risk and the idiosyncratic risk. Finally, we use the generalized variance decomposition method of Diebold and Yilmaz (2012) to study the dynamic spillover effect among the systematic risk and the idiosyncratic risk of the four financial markets. The innovative findings of this paper are as follows: (1) If the interaction and strong contagion of the systematic risk and the idiosyncratic risk are not taken into account, the degree of cross-market risk contagion is seriously underestimated. (2) Although the systematic risk contagion network and the idiosyncratic risk contagion network of the four markets are relatively “self-contained”, we can still identify that bank systematic risk plays an important role in connecting the two networks and is an important connecting node in the whole system. (3) In the systematic risk network, there are significant two-way spillovers between the bond market and the bank, between the stock market and the bond market, and between the stock market the foreign exchange market, which are the most important “connecting lines” in the systematic risk spillover network. The systematic risk of the bond market is a significant net sender of risk and the systematic risk of the bank is a significant net receiver of risk. (4) Compared with the systematic risk network, the contagion network relationship between the idiosyncratic risks of the four markets is much closer, in which the idiosyncratic risk of the bond market is the significant net risk sender, while the idiosyncratic risk of the exchange market is the net risk receiver. (5) In the face of various financial shocks, such as the subprime mortgage and European debt crisis, the liquidity crisis in 2013, the stock market crash and the “811” exchange rate reform in 2015, the high risk of shadow banking in 2017, and the outbreak of Sino-US trade frictions in 2018, risk spillovers and contagion have increased, and different markets (and different types of risks play a “systemically important financial market (SIFMs)” role in the face of different financial shocks. (6) Compared with financial shocks, major public health shocks (such as COVID-19) are more significant, and the spillover network relationship between financial markets presents a more complex and intertwined network structure, and the idiosyncratic risk of each market plays an important spillover role. Therefore, it is very important to distinguish different risk types, judging the important connecting lines in the risk spillover network, and identifying the SIFMs in different periods for crisis prevention and crisis management.