The relationship between financial development and green economic growth has received much attention in recent years. Research on the relationship between financial development and green total factor productivity (GTFP) is of great importance to China and other countries. This study has attempted to reveal the spatial distribution of China’s provincial GTFP and impact of financial development on GTFP by using the method of GML index based on SBM-DDF and the spatial Durbin model (SDM) during the period 1996–2015. Innovation is added to the SDM to reflect the influencing mechanism of financial development on GTFP. The empirical results show the following: (1) The mean of China’s provincial GTFP showed a U-shaped curve in 1996–2015. (2) China’s provincial financial development promotes the growth of GTFP through innovation channel. The reason is that financial development boosts eco-friendly innovation and the introduction of energy saving technology, leading to a decrease in energy consumption and pollutant emissions. (3) Increasing the level of financial development in the surrounding areas will restrain local GTFP. Our results provide new evidence that China’s regional financial development has a spatial spillover effect. (4) China’s provincial GTFP has a significant spatial positive correlation. Finally, several policy implications can be summarized to China’s 30 provinces.

The COVID-19 pandemic has highlighted the importance of the digital economy in restoring economic and social development, creating more jobs and improving people's well-being. To inform policy makers about changes to digital strategies, measuring the digital economy is a prerequisite. This study aimed to compile an index of digital economy at the provincial (municipalities, autonomous regions, collectively referred to as "provinces") level to present an accurate and in-depth depiction of how it has developed in China. Our sample covers 31 provinces in China, over the period 2010–2020. This paper firstly constructs the digital economy index system from the four dimensions of digital users, digital platforms, digital industries and digital innovation, and then adopts a combination of entropy weighting method and grey target theory to measure the digital economy index. This paper study revealed that China's digital economy has been on an upward trend from 2010 to 2019 and has a decline in 2020, and the digital innovation is an important driving force for the growth of the digital economy index. The convergence of China's digital economy is decreasing, indicating that the gap in digital economy development between provinces is increasing. The proposed index in this study can be used as a screening tool, decision making tool, benchmarking tool and guidance of high-quality digital economy development.

The digital economy plays a dual role in the process of global carbon emissions decoupling; for this reason, its overall impact direction and mechanism are worth discussing. This paper attempts to answer the question of the role of the digital economy, based on a review of the existing literature. By constructing a panel smooth transition regression (PSTR) model, this paper empirically tests the effect of the digital economy on carbon emissions decoupling, based on panel data from 30 provinces in China from 2010 to 2019. In order to study the impact mechanism of the digital economy on carbon emissions decoupling, the mediating effect of industrial structure optimization is analyzed through a mediating effect model; the moderating effect is also explored by analyzing the network centrality characteristics of the digital economy. The core-periphery analysis method is adopted to group the samples to test the impact heterogeneity of the digital economy on carbon emissions decoupling. Based on this empirical analysis, the following conclusions are drawn. First, the digital economy has a promoting effect on carbon emissions decoupling, but this effect gradually weakens with the development of the digital economy. Second, the digital economy can promote carbon emissions decoupling through industrial structure optimization, and network centrality has a positive moderating effect on this mechanism. Third, heterogeneity exists in the promoting effect of the digital economy on carbon emissions decoupling, which is reflected in the different intensities of the promotion effect between the core nodes and the peripheral nodes in the network; the attenuation range of the promotion effect is also different when the regime switches.

The difficulty of balance between environment and energy consumption makes countries and enterprises face a dilemma, and improving energy efficiency has become one of the ways to solve this dilemma. Based on the data of 158 countries from 1980 to 2018, the dynamic TFP of different countries is calculated by means of the Super-SBM-GML model. The TFP is decomposed into indexes of EC (Technical Efficiency Change), TC (Technological Change) and EC has been extended to PEC (Pure Efficiency Change) and SEC (Scale Efficiency Change). Then the fixed effect model and the fixed effect panel quantile model are used to analyze the moderating effect and the exogenous effect of energy efficiency on PM2.5 concentration on the basis of verifying that energy efficiency can reduce PM2.5 concentration. We conclude, first, the global energy efficiency has been continuously improved during the sample period, and both technological progress and technical efficiency have been improved. Second, the impact of energy efficiency on PM2.5 is heterogeneous which is reflected in the various elements of energy efficiency decomposition. The increase in energy efficiency can inhibit PM2.5 concentration and the inhibition effect mainly comes from TC and PEC, but SEC promotes PM2.5 emission. Third, energy investment plays a moderating role in the environmental protection effect of energy efficiency. Fourth, the impact of energy efficiency on PM2.5 concentration is heterogeneous in terms of national attribute, which is embodied in the differences of national development, science and technology development level, new energy utilization ratio, and the role of international energy trade.

Enterprise digital transformation involves leveraging digital technologies to optimize and improve business operations. Not only does it augment operational efficiency, but it also establishes favorable conditions for bolstering ESG. To investigate the impact of digital transformation on ESG performance, this study employs a fixed effects model. The analysis utilizes data from a sample of 1422 publicly listed companies in China, spanning the period of 2012 to 2021. This paper further explores the mechanism and heterogeneity behind this impact. The research findings indicate that digital transformation has a positive impact on the ESG performance of companies. It remains robust even after conducting robustness tests, which include omitted variable and endogeneity tests. Furthermore, the study identifies variations in the influence of digital transformation on different dimensions of ESG performance. Through a mechanism analysis, it is revealed that digital transformation positively affects ESG performance by optimizing the structure of human capital, enhancing operational efficiency, and promoting green innovation. Additionally, heterogeneity analysis indicates that the positive effect of digital transformation on ESG performance is particularly significant in capital-intensive industries, high-tech companies, and companies with low carbon emissions.

Due to multiple properties, the international crude oil price is influenced by various and complex interrelated factors from different determinants in different periods. However, the previous studies on crude oil price fluctuation with economic policy uncertainty (EPU) haven’t taken a wider range of volatility sources into their analysis frameworks. In this paper, the time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is introduced in order to avoid important information loss, as well as capture the time-varying impact on crude oil price fluctuation by EPU. Furthermore, the differences on crude oil fluctuations from net-oil exporting and net-oil importing country’s EPU are also elaborated. Here are three findings as follows. First, the impacts of global EPU on the crude oil price volatility show time-varying characteristics both in time duration and time-points. Second, the instantaneous impacts of global EPU on the price volatility of crude oil are directly relevant to major events, and the impacts are different in event types as well. Third, the time-varying characteristics depicting the impacts of EPU in countries who are net-oil exporter and net-oil importer on price volatility of crude oil show heterogeneity in fluctuation range, fluctuation intensity, and stage.