Government intervention and structural transformation play an important role in both the economy and carbon emissions. Based on provincial panel data from China from 2003 to 2020, this paper employs econometric models to investigate the impact of government intervention and structural transformation on carbon emissions. In particular, structural transformation is divided into two indicators: The rationalization of the industrial structure and the upgrading of the industrial structure. According to the research findings, government intervention has significantly promoted carbon emissions and structural transformation has had dual effects on carbon emissions; meanwhile, the rationalization of the industrial structure has significantly increased carbon emissions, while the upgrading of the industrial structure has slowed down carbon emissions, with these findings passing the corresponding robustness test. The relationship between government intervention, structural transformation, and carbon emissions varies significantly over time and across regions. Further investigations revealed that government intervention and structural transformation have a significant impact on carbon emissions in various panel quantiles. Finally, the paper makes policy recommendations in order to provide empirical support for promoting China’s high-quality economic development and achieving the “double carbon” goal.

The digital economy, closely linked to agricultural progress, plays a key role in reducing agricultural carbon emissions. By utilizing panel data between 2011 and 2021 from 30 Chinese provincial-level regions, the present work empirically assesses the direct impact and nonlinear spatial spillover effects of the digital economy on agricultural carbon emissions, where methodologies including fixed effects, threshold models, and spatial econometrics are employed. The results were as follows: (1) The digital economy led to remarkable inhibition of agricultural carbon emissions, which was consistent across various robustness checks. (2) The carbon reduction efficiency of the digital economy presented obvious spatial variation, which had a greater negative effect in eastern regions and major grain-producing regions. (3) The digital economy had a nonlinear impact on agricultural carbon emissions, and technological innovation played a threshold effect, showing an “inverted U-shaped” characteristic of increasing first and then decreasing overall. (4) This emission abatement effect also showed a significant spatial spillover aspect, meaning that the digital economy development in one area prominently abates agricultural carbon emissions in adjacent provinces. Our findings might provide a theoretical and empirical foundation for comprehending and addressing agricultural carbon emissions abatement in China from the digital economy perspective.

Purpose: Supply chain finance (SCF) is a promising financing solution for small and medium enterprises (SMEs). The study aims to highlight the determinants of the adoption of SCF and the theoretical implications for SCF in SMEs.Design/methodology/approach: Drawing on the theory of planned behaviour (TPB), the authors develop a model and test hypotheses about the factors (1) SE, (2) attitude toward SCF, (3) social influence (SI), (4) adoption intention (AI) and (5) actual adoption (AA) of SCF. Data collected from a survey of 211 managers in SMEs in China were used to conduct a partial least squares (PLS) estimation.Findings: The empirical results indicate that attitude toward SCF and SI positively affect AI towards SCF, whereas AI positively affects the AA of SCF. Specifically, the authors find that AI plays a mediating role in the relationship between SE and AA of SCF, whereas attitude toward SCF plays a mediating role between SI and AI.Originality/value: First, the authors use the TPB to study the adoption of SCF in SMEs. Second, the authors apply PLS to clarify the influence mechanism of behavioural factors on the adoption of SCF. The authors provide a useful approach for practitioners in examining the adoption of SCF by SMEs.

Identifying the specific scale status by evaluating scale efficiency of each DMU (decision making unit) is critical for policy makers to make a correct decision on inefficiency improvement. Although some traditional DEA (Data Envelopment Analysis) models can evaluate different scale efficiencies and identify the specific status of each DMU, they are unable to deal with the evaluation indicators with imprecise observation. Therefore, we propose two different uncertain DEA models with imprecise inputs and outputs to recognize two scale status of increasing returns to scale (IRS) and decreasing returns to scale (DRS), which can better help policy makers make a correct decision on inefficiency improvement without accurate data. Moreover, we analyse the stability of these uncertain DEA models to make effective suggestions for decision makers to further improve the inefficient DMU _o or to adjust the inputs and outputs on the premise of remaining the new DMU _o efficient.