Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis

Abstract

Green innovation is imperative for the high-quality and sustainable development of the modern logistics industry. It is also key for achieving the goals of peak emissions and carbon neutrality. This study provides a way of thinking about the evaluation of the green innovation level of the logistics industry. The variance inflation factor-variance coefficient method was employed to construct an evaluation index system of the regional logistics green innovation level (RLGIL) from three dimensions. Empirical data were collected from statistical yearbooks covering 30 provinces in China from 2013 to 2017. Thereafter, the combination weighting-based GRA-TOPSIS method was applied to evaluate the RLGIL, and the spatial distribution differences and spatiotemporal evolution characteristics of inter-provincial green innovation levels were analyzed. The RLGILs in the 30 provinces were found to be generally unbalanced, and the differences between the eastern and western regions were significant. Guangdong, Jiangsu, and Zhejiang had stronger RLGILs, whereas most other provinces did not reach the average level. The RLGIL of the 30 provinces had a high positive spatial correlation and spatial aggregating effect. From a national perspective, the values for the RLGIL were generally higher in the eastern and southern regions and lower in the western and northern regions. Although significant differences were found in the RLGIL of these provinces, the overall development trend was stable.

Low dose tacrolimus exposure and early steroid withdrawal with strict body weight control can improve post kidney transplant glucose tolerance in Japanese patients

“…after KT. This suggested that precise evaluation of pre-KT glucose tolerance might be an important factor for predicting PTDM [ 9 , 18 , 19 ].…”

“…We performed univariate or multivariate logistic regression analysis and built a calibration plot by using the R packages “rms”, “cor”, “Hmisc”, “boot”,. Variance inflation factors of each factor was checked for multicollinearity [ 9 ]. The Hosmer-Lemeshow goodness of fit (GOF) test was performed to evaluate the certainty of the prediction model.…”