The disparities in green transition have led to the call for a ‘just transition’. However, the large differences in energy efficiency across different regions have been identified as a primary hazard to the just transition. This study examines whether transferring low-carbon technology can improve the efficiency of energy, enhancing the overall energy efficiency, and marketing a sustainable and equitable energy future. In this paper, we utilize the Undesirable-SE-SBM model to estimate the energy efficiency of China's 30 provinces during 2012 to 2022, and empirically tested the impact of low-carbon technology transfer on the convergence of total-factor energy efficiency by convergence analysis. The results showed that: (1) There is evidence of σ convergence and absolute β convergence in the eastern and western regions, but not in the central region. (2) Low-carbon technology transfer can accelerate the convergence of total factor energy efficiency. Lagging regions that adopt low-carbon technologies can catch up with the advanced regions' level of total-factor energy efficiency. (3) There is regional heterogeneity in the effect of low-carbon technology transfer on the accelerating convergence of total factor energy efficiency. The western region experiences the most significant acceleration, followed by the eastern and central regions.

Digital economy promotes the modernization of industrial structure by influencing the rationalization and upgrading of industrial structure through technical level and factor level; while excessive credit expansion hinders the modernization of industrial structure. This paper uses panel data from 31 jurisdictions in China to conduct empirical analysis, and finds that digital economy development shows a year-on-year rising trend, and there is a large gap between different regions. The conclusion still holds after the robustness test and regional heterogeneity analysis, thus enriching the understanding of mechanisms and regional differentiation of digital economy, credit expansion on industrial structure modernization.

As the problem of global climate change becomes more severe, countries have proposed the goals of carbon capping and carbon neutrality. Green finance is an essential capacity support for achieving carbon peaking and carbon neutrality, and it can guide and stimulate social capital to invest in low-carbon industries and initiatives via marketbased mechanisms. Based on the panel data of Chinese prefecture-level cities from 2006 to 2020, this paper empirically examines the impact of green finance on carbon emission efficiency using a two-way fixed-effects model, conducts a regional heterogeneity analysis, and examines the threshold effect of economic development level and the mediating role of regional innovation. The results indicate that, first, green finance contributes significantly to the improvement of carbon emission efficiency, and second, the level of regional economic development has a double threshold effect on the contribution of green finance to the improvement of carbon emission efficiency. Third, regional innovation is an important green finance channel for influencing carbon emission efficacy. The sensitivity of carbon emission efficiency to the green finance index demonstrates an inverted U-shaped trend. Fifth, the importance of green finance sub-dimensions in relation to carbon emission efficacy is as follows: green support, green credit, green insurance, green investment, green equity, green bond, and green fund. These findings provide theoretical support for green finance's role in promoting co-carbon efficiency and are valuable for policy formulation.

China’s emissions trading system applies a two-stage emissions intensity-based compliance quota allocation scheme different from the cap-and-trade systems prevalent in developed economies. It was designed to accommodate the country’s socioeconomic complexities and implemented following a learning-by-doing approach. Compliance firms significantly expanded green investment and production workforce. Their climate decisions are influenced by state ownership and regional heterogeneity. State-owned enterprises (SOEs) and less liberal market firms increased hiring, but not investment; non-SOEs and more liberal market firms grew investment. There are mixed welfare effects: compliance firms maintained productivity and efficiency; however, ordinary workers’ real wages were reduced, more prominent in SOEs.

We develop a heterogeneous-agent model with local spatial markets to study the relationships among bank expansion, growth, and inequality. In the model, households choose their occupations, consumption, and holdings of loans and portfolio assets that vary by liquidity. Banks choose the locations of new branches, which affect the financial frictions facing households across regions. We calibrate the model using a geographic information system to evaluate the rapid bank expansion in Thailand between 1986-1996. The model quantifies the sources of growth and inequality over time and a cross space and the potential role of digital banking in substantially reducing regional heterogeneity.