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Amid the Fourth Industrial Revolution and the 2030 Sustainable Development Goals (SDGs), China’s digital village initiative has emerged as a localized implementation for achieving multidimensional sustainability. However, the progress of digital villages in China remains uneven, posing challenges to achieving sustainable rural transformation. This study develops a multidimensional index system at four levels: rural digital infrastructure, the digital development environment in rural areas, the digital industry in rural areas, and agricultural production digitalization. Entropy weighting was used to evaluate digital village progress across 30 Chinese provinces (2013–2022). Kernel density estimation, the Dagum Gini coefficient, and the obstacle degree model were used to study China’s spatiotemporal dynamics, regional disparities, and digital village development barriers. The results show that between 2013 and 2022, digital villages in China advanced (the average annual growth rate: 9.43%), with a spatial distribution pattern of “east superior, west inferior, south prosperous, and north declining”. National and regional digital villages have advanced yearly, with absolute and relative disparities increasing, extensibility increasing, and multi-polarizing rising. Digital village development is becoming increasingly imbalanced, with inter-regional differences driving “east, central, and west” disparity and intra-regional disparities driving North–South disparity. Ranking the average hurdle levels: the digital industry in rural areas (45.94%) > the digital development environment in rural areas (24.83%) > rural digital infrastructure (21.85%) > agricultural production digitalization (7.38%). Taobao villages are a major restraint on China’s digital village development.

The linkages among carbon, renewable energy, and electricity markets are gradually strengthening. In order to prevent risk transmission among markets, this paper uses the TVP-VAR-DY (Time-Varying Parameter–Vector Auto Regression–Dynamic) model to analyze the dynamic risk spillover effects and network structure of risk transmission among carbon, renewable energy, and electricity markets. The empirical results show that there are significant asymmetric spillover effects among carbon, renewable energy, and electricity markets. The total spillover index shows that spillover effects among carbon, renewable energy, and electricity markets are time-varying, especially during unexpected events. Besides, the net spillover index indicates that the spillover effects are bidirectional, asymmetric, and time-varying. Finally, under the influence of unexpected events, the network structures of risk transmission among carbon, renewable energy, and electricity markets are heterogeneous. Compared to the Russia-Ukraine conflict, the COVID-19 pandemic has a more significant impact on these markets.

Power technology innovation has been positioned as an effective way to contribute to China’s carbon productivity. However, limited empirical evidence exists on the impact of power technology innovation on carbon productivity. Thus, based on the annual panel dataset of 30 China’s provinces from 2001 to 2019, this study explored whether and how power technology innovation promotes or impedes the improvement of carbon productivity. First, carbon productivity in the framework of total factor was calculated based on the metafrontier Malmquist-Luenberger productivity index. Second, the effect of power technology innovation on carbon productivity was investigated using the spatial Durbin model. And we also examined whether heterogeneous power technology innovations have a synergistic effect on carbon productivity. Third, influence mechanism of power technology innovation affecting carbon productivity was identified. Results show that (1) there are notable differences in China’s provincial carbon productivity, which is characterized by the spatial correlation. (2) Local power technology innovation has a promotion effect on carbon productivity in both local and neighboring provinces. Moreover, the promotion effect of breakthrough power technology innovation is stronger than that of incremental power technology innovation. (3) Catching-up Effect and Innovation Effect are important transmission channels through which power technology innovation improves carbon productivity. Finally, policy recommendations are provided.

To investigate the variations in price volatility spillover effects on different time scales across the LME, SMM, and Chinese spot nickel markets. This study uses data from the London Metal Exchange (LME), Shanghai Nonferrous Metals (SMM) nickel daily closing prices, and Chinese nickel daily market prices from April 1, 2015, to September 30, 2022, to categorize the above three markets’ yield series into four-time scales: very short, short, medium, and long term. The multivariate BEKK-GARCH approach is then used to analyze the spillover effects at different time scales. The study’s findings reveal that (1) the LME, SMM, and spot nickel prices are all impacted by external shocks and inherent volatility at all time scales; (2) at the very short time scale, there is a two-way volatility spillover effect between the SMM nickel and the other two markets, in addition, a one-way volatility spillover effect between the LME and spot nickel price; (3) at the short time scale, the volatility spillover effect between the LME and spot nickel price disappears; (4) at the medium-term time scale, there is no volatility spillover effect between the LME, SMM, and spot nickel price; (5) at the long-term time scale, the SMM has a volatility spillover effect on spot nickel price.

PurposeThis paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.Design/methodology/approachWith daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.FindingsThe empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.Originality/valueThe MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.