Undesirable outputs, such as carbon emissions and loss of property due to traffic accidents, hold great significance for the sustainable development of the transport industry. In this study, we applied a super-efficiency data envelopment analysis model with a slack-based measure (Super-SBM DEA) considering undesirable outputs to measure the integrated transport efficiency (ITE) of 31 provinces in China during the period of 2009–2016. Following this, we used a spatial autocorrelation model to test and verify the spatial autocorrelation of the ITEs at the level of the 31 provinces, and further to explore the aggregating features. Finally, considering the spatial effects that emerged, we constructed a β-convergence model to analyze the convergence characteristics of China’s ITEs and investigate its conditional factors. The research results show that the average ITE demonstrated a linear growth trend; the effective decision-making units (the ITE value was greater than 1) are only 11 provinces, accounting for about 35% by 2016. The mean of ITEs was also found to present a law of decreasing order of Eastern, Central and Western Zones. However, the Central Zone and Western Zone have a better efficiency improvement trend compared to the Eastern Zone. The Moran’s I index was bigger than zero, indicating that the ITEs formed a spatial autocorrelation phenomenon. The Moran scatter plots further showed that the provincial ITEs mainly followed the patterns of high–high, high–low and low–low aggregation. The ITE of the 31 provinces was found to have a clear absolute β-convergence and conditional β-convergence characteristics. Moreover, the level of economic development, household per capita traffic consumption, transport industry scale, technology advancement and transport intensity were all seen to have an important impact on the convergence of integrated transport efficiency. It is hoped that the findings of this study may contribute further insights and practical knowledge to effectively measuring the development level of China’s integrated transport efficiency, and to understanding future changes in the ITE gap among Chinese provinces.

Low-carbon and green development is important to promote the sustainable economic and social development of countries along the One Belt and One Road. These countries have distinct differences in their ability to withstand carbon emission pressures and their driving factors, due to different stages of development and economic characteristics. This paper presents a model of ecological pressure of the carbon footprint in energy consumption (EP_cfec), defined by three states: light, balanced, and heavy pressure. The EP_cfec was calculated from data on 56 countries along the One Belt and One Road from 1994–2014, and analysis of the data’s temporal and spatial evolutionary rules was conducted. Furthermore, we used the LMDI method to extract the driving forces of EP_cfec and evaluated the contribution of these factors to the overall region and seven sub-regions. The results showed that EP_cfec growth slowed over time, with the value of EP_cfec reaching 3190.51 in 2014. Resource-rich countries have a greater value of EP_cfec and are mainly distributed in parts of West Asia, North Africa, and Southeast Asia. The per capita export of goods and services, and the population density on productive land contribute to ecological pressure on the carbon footprint. Energy structure, the influence of international trade on GDP, and energy intensity exerted an inhibitory effect on the ecological pressure of the carbon footprint. This paper proposes mitigation measures for optimizing energy structure, improving energy efficiency, developing low energy consumption, and promoting green international trade. Our results provide support for countries along the One Belt and One Road to mitigate ecological pressures resulting from their carbon footprint.