The fast development of the transport sector has resulted in high energy consumption and carbon dioxide (CO₂) emissions in China. Though existing studies are concerned with the factors influencing transport sector CO₂ emissions at the national level (or in megacities), little attention has been paid to the comprehensive impact of socio-economic, urban form, and transportation development on transport sector carbon emissions and emissions efficiency in central China. This paper examines the comprehensive impact of the transport sector’s carbon emissions from six provinces in central China, during the period from 2005 to 2016, based on the panel data model. The dynamic change of CO₂ emissions efficiency is then analyzed using the Global Malmquist Luenberger Index. The results indicate that, firstly, economic growth, road density, the number of private vehicles, and the number of public vehicles have caused greater CO₂ emissions during the study period, while the freight turnover, urbanization level, and urban population density had repressing effects on CO₂ emissions. Secondly, an uneven distribution of CO₂ emissions and CO₂ emissions efficiency was found among different provinces in central China. Thirdly, changes in CO₂ emissions efficiency were mainly due to technical changes. Finally, we present some policy suggestions to mitigate transport sector CO₂ emissions in central China.

Supplier selection is a multi-criteria problem including various factors. In order to select the best suppliers, it is necessary to make a trade off among these factors when some of them conflict, where few existing systems work well. In this paper a supplier selection model based on support vector machine (SVM) is firstly developed. The supplier selection criteria and quantitative methods using fuzzy and pairwise comparison are presented. Simulations show that the proposed supplier selection model is a more useful additional tool than fuzzy synthetical evaluation for supplier management.

The increasing volume of spatial-textual data generated from check-ins and reviews facilitates many practical applications. In these applications, similarity joins are the basic operation which further supports a wide range of similarity queries. However, this compute-intensive join operation calls for more efficient and effective algorithm to support real-time queries. Exploiting general purpose GPUs (GPGPUs) is one natural solution. Nevertheless, existing join algorithms utilizing GPGPU lack considerations on unique features of spatial-textual trajectories, especially the textual domain, thus resulting in poor performance. In this paper, we first propose a baseline spatial-textual trajectory similarity join algorithm, which is essentially based on a traditional algorithm for relational joins on GPGPUs. Then, by analyzing the bottleneck of this baseline, we further develop a fine-grained join algorithm which consists of memory access techniques and optimizations. We propose a strategy of flip to ensure coalesced memory accesses, and a batch scheduler for dividing the whole task into batches to fit the global memory while alleviating imbalanced throughput. Extensive experiments have been conducted over real-life datasets and the results show that our fine-grained algorithm performs the best and reduces the latency effectively.