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.

Digital technologies are dramatically reshaping the maritime industry and transforming how ports operate in a global transport system. To remain competitive and improve its productivity, a port needs to build its digital capability and become a ‘smart’ port. Despite its increasing importance, port digitalisation is largely under-researched. Our research explores how information integration afforded by the recent development of Port Centric ICT systems (PCIS) may impact port performance. A survey was conducted in Qingdao Port, the seventh-largest comprehensive port in the world. We adopted the partial least squares structural equation modelling (PLS-SEM) technique for analysis. Our model focuses on testing the mechanism of information integration on port performance among the port community members; that is, both internal information integration and external information integration and their direct and indirect impact on port performance. Our main contribution lies in that our study identifies two clear pathways for developing a port’s digitalisation processes to achieve competitive advantages: a) the mediated path, where port performance will improve if information integration is coupled with port community operational capability (PCOC); b) the direct path, where the innovative use of PCIS leads to radical ideas, new business models and transformative changes.

Data envelopment analysis (DEA) is widely used to estimate efficiency, particularly with respect to firm performance in economics. When the observed data are imprecise, as in the cases involving carbon emissions or social benefits, an uncertain DEA model can provide a reasonable solution. However, current uncertain DEA models only consider technical efficiency and not scale efficiency. This leads to information scarcity during decision making processes. Therefore, in this paper, we attempt to introduce an uncertain DEA model for scale efficiency evaluations using imprecise inputs and outputs. Additionally, we provide a sensitivity and stability analysis of the uncertain DEA model for scale efficiency, and document a numerical example of the uncertain DEA model for scale efficiency.