Digital transformation, smart technologies, and eco-innovation are paving the way toward sustainable supply chain performance.

The world has limited resources and resource depletion has been a serious concern for many years. To ensure that ecological balance is maintained, the United Nations has developed 17 Sustainable Development Goals (SDGs). This study attempts to meet the 12th SDG of "responsible production and consumption" and provide a guideline to manufacturing organizations in Pakistan. Many scholars have examined the role of different technologies on sustainable performance. However, research using eco-innovation (EI), digital transformation (DT), and smart technologies (ST) is still scarce. The current study develops the conceptual model based on the resource-based view (RBV) and technology, organization, and environment (TOE) theories, and using a simple random sampling technique, valid data from 375 professionals in the manufacturing industry is collected to test the relationship between sustainable development strategy (SDS), EI, DT, ST, and sustainable supply chain performance (SSCP). The results indicate strong positive relationships between SDS, EI, DT, ST, and SSCP. Results indicate that even if organizations are executing SDS and EI, without DT and ST, organizations cannot achieve SSCP. Therefore, it is recommended to the managers that they must apply DT and ST to guarantee their pursuit of achieving sustainable performance and contribution toward SDGs.

Study on environmental performance evaluation of different linkage development types of the logistics and manufacturing industries considering the unexpected output.

According to the different organizational forms of logistics in manufacturing, the types of linkage development between the logistics and manufacturing industries include two different logistics service modes: logistics outsourcing and logistics self-operation. This study considers two manufacturing clusters for comparison: the northeast region in China, mainly state-owned and self-operated, and the Yangtze River Delta region in China, mainly focusing on logistics outsourcing services. First, we construct a parallel network system considering heterogeneity and an unexpected super-efficiency slacks-based measure: the Grey Relational Analysis model. Second, considering the unexpected output, this study evaluates the environmental performance of the linkage development of the logistics and manufacturing industries and explains the types of linkage development between them. Finally, the spatial and temporal characteristics of linkage efficiency under different linkage modes are studied using an exploratory spatial data analysis method. The research results show that the efficiency of the two-industry linkage in different linkage types that consider the unexpected output presents different development characteristics. Regarding time characteristics and trends, we compare the development efficiency of the two-industry linkage at the overall level of the northeast and the Yangtze River Delta regions from 2009 to 2016. The Yangtze River Delta region showed a higher state of linkage while the northeast region showed a medium linkage state. Regarding spatial pattern evolution, the effect of the technical efficiency of the linkage between the two industries in the three northeastern provinces showed continuous evolution from the medium linkage stage to the weak linkage stage, and the Yangtze River Delta showed continuous evolution from the medium linkage to the strong linkage stage.Implications: Due to data availability, references to the relevant achievements of the research on the linkage between the manufacturing and logistics industries, the transportation industry, warehousing, and postal and telecommunications industry data are generally used as logistics industry data. Manufacturing data are replaced by industrial data. The manufacturing industry is the core industry of industrial systems. Its output value accounts for more than 90% of the industrial output. Thus, it can reflect the development trend of the manufacturing industry in general.

Evaluation of linkage efficiency between manufacturing industry and logistics industry considering the output of unexpected pollutants.

Applied a non-radical non-oriented Slack Based Measure (SBM) model of Data Envelopment Analysis (DEA) to measure the linkage efficiency between the two sectors from 2009 to 2016. The paper analyzes the current situation of manufacturing and logistics efficiency, presents a way to improve the linkage efficiency of manufacturing and logistics industry, and using Tobit regression to analyze the environmental factors that affect the linkage efficiency. The results show that: Totality, the two industries in the three northeast provinces show a steady development trend in the period from 2009 to 2016, but the development of the two industries in northeast China is still insufficient. Unexpected pollutant output is the main reason for the decline of manufacturing efficiency in the regions. The redundancy of input factors is the main reason that affects the efficiency decline of the logistics industry. The results of Tobit regression analysis show that the investment of science and technology and opening to the outside world have a positive influence on the efficiency of the two industries, and government consumption has a negative effect on the efficiency of the two industries. This is to correctly understand and grasp the status of two industry linkage development in three northeast provinces. And it provides a certain basis for the development policy of the two-industry linkage development.Implications: Due to the availability of data, reference to the relevant achievements of the research on the linkage between manufacturing industry and logistics industry. The transportation industry, warehousing, and postal and telecommunications industry data are generally used as logistics industry data. Manufacturing data replaced by industrial data. The manufacturing industry is the core industry in the industrial system. Its output value accounts for more than 90% of industrial output value, so it can reflect the development trend of the manufacturing industry in general.