Impact of R&D and innovation in Chinese road transportation sustainability performance: A novel trigonometric envelopment analysis for ideal solutions (TEA-IS)

Previous road transportation sustainability studies not only neglected the epistemic uncertainty that surrounds the impact of innovation and Research and Development (R&D) expenditure on pollutant emissions performance but also failed in designing and simultaneously exploring the strengths of alternative MCDM (Multiple-Criteria Decision-Making) approaches to better discriminate performance scores. This paper focuses on these two gaps by presenting a road transportation sustainability performance assessment of 29 Chinese provinces for a 14-year period in light of relevant socioeconomic and demographic variables. First, a novel TEA-IS model is developed to assess road transportation sustainability performance. Besides possessing the beneficial features of each model, this hybrid DEA-TOPSIS can analyze the sustainability performance from the perspective of the synergistic effects among the criteria. From the socioeconomic and demographic perspectives, we use machine learning techniques for predicting high-low performance and synergistic Chinese provinces. Results suggest that the discriminatory power of TEA-IS is good and there is high synergy in Chinese provinces in terms of sustainable road transportation. We further find that there is a high level of heterogeneity in road transportation sustainability among different geographical locations in China. The results further suggest that higher levels of synergy are strongly associated with medium and high-performing provinces. Finally, we find that innovation index, foreign direct investment, and non-coastal cities have both higher performance and synergy levels. We recommend that Chinese government should further provide favorable policies to enhance innovation and efforts should also be made to provide better environment for attracting more foreign direct investment. Finally, instead of road transportation, other modes of transportation, such as air and sea, are recommended to be further developed and utilized.

Automated summary

This study uses a novel TEA-IS model to assess the road transportation sustainability performance of 29 Chinese provinces over a 14-year period, and employs machine learning techniques to predict high-low performance and synergistic provinces. The results indicate that TEA-IS has good discriminatory power and there is high synergy in sustainable road transportation in China. Additionally, there is a high level of heterogeneity in road transportation sustainability among different geographical locations in China. Innovation index, foreign direct investment, and non-coastal cities are associated with higher performance and synergy levels. The Chinese government is recommended to provide favorable policies to enhance innovation and attract more foreign direct investment, and to further develop and utilize other modes of transportation such as air and sea.