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This paper constructs an additive two-stage network DEA (data envelopment analysis) model with consideration of undesirable outputs and shared inputs. Following the triple bottom line standard of sustainability, we calculate both the overall and sub-stage sustainability performance of railway transportation in China from 2002 to 2013, from the aspects of economy, environment and society. The results show that the overall sustainability of China's railway presents a character of first declining, then rising and declining again. Moreover, the railway sustainability of China's eastern areas are much better than that of the western and central areas, and the gap has become much larger since 2009. As for the sub-stage efficiency, neither the production stage nor the service stage of the railway is efficient in sustainability, and the efficiency of the production stage is lower than that of service stage and plays a greater impact on the overall sustainability. Therefore, in order to improve the overall sustainability of China's railways, it is essential to improve the level of railway engineering construction and develop technological innovations in railway production.

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This paper divides the typical social scientific research process into two stages: the research development stage and the research innovation stage, with personnel as a shared input between them. We construct an input-shared multi-period two-stage DEA (data envelopment analysis) model as measurement of sustainability capacities of social scientific research, and then measure both the overall efficiencies and the stage efficiencies of scientific research of 18 HEIs (higher education institutions) in Jiangsu province of China from 2010 to 2014. There are some revelatory traits among the overall efficiencies and stage efficiencies of HEIs. As for the HEIs, the efficiencies of the first stage of certain HEIs that are better in social science are higher than those which have advantage in natural science. The results are the opposite in the second stage. Additionally, in virtue of common-weight global MPI (Malmquist Productivity Index), we figure out the dynamic efficiencies within and between HEIs. Finally, we appeal to administration departments to establish academic sharing systems and construct interdisciplinary ecosystems among HEIs, which aims to pool the knowledge, experience, and capacities of the region's academic actors and improve the sustainable development and innovation abilities of social scientific research. In addition, collaborating with industrial organizations is also a strategic avenue to improve their research abilities, which can push the boundaries of sustainable development and also bring vigor in social scientific research.