The logistics industry is one of the major fossil energy consumers and CO₂ emitters in China, which plays an important role in achieving sustainable development as well as China’s emission reduction targets. To identify the key influencing factors regarding the logistics of CO₂ reductions and ensure that the development of China’s logistics industry becomes less dependent on CO₂ emissions, this paper built an extended log-mean Divisia index model (LMDI) to decompose the logistics of CO₂ changes between 1985 and 2015. Then, we introduced a decoupling model that combined the decomposition results to analyze the decoupling state and identify the main factors that influenced the decoupling relationship. The results show the following. (1) The urbanization effect was the decisive factor in CO₂ emissions increases, followed by structural adjustment effects, while technological progress effects played a major role in inhibiting CO₂ emissions. Particularly, the energy structure showed great potential for CO₂ emissions reduction in China. (2) Highways appeared to have dominant promoting roles in increasing CO₂ emissions regarding transportation structure effects; highways and aviation proved to have the largest impact on CO₂ emission reduction. (3) There has been an increase in the number of expansive negative decoupling states between 2005 and 2015, which implies that the development of the logistics industry has become more dependent on CO₂ emissions. Finally, this paper puts forward some policy implications for CO₂ emission reductions in China’s logistics industry.

The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency.

Realizing the decoupling development between the economic expansion and carbon dioxide emissions of the transport sector is of great importance if the Yellow River basin is to achieve green and low-carbon development. In this paper, we adopt the Tapio decoupling index to examine the decoupling relationship within the transport sector in the Yellow River basin, and then introduce the standard deviational ellipse to dynamically analyze the spatial heterogeneity of carbon emissions and economic growth at the provincial level. Furthermore, based on the decoupling method, we expand the traditional logarithmic mean Divisia index decomposition (LMDI) model to decompose the decoupling index into eight sub-indices, and we identify the impact of each factor on the decoupling relationship. The results indicate that the carbon emissions of the transport sector in the Yellow River basin show the non-equilibrium characteristics of “upstream region < midstream region < downstream region”. The decoupling state of the transport sector shows obvious spatial differences. The less-developed regions are more likely to present non-ideal decoupling states. The growth rate of carbon emissions in Sichuan, Qinghai, and Shandong provinces is relatively fast, and the azimuth of the transport sector’s carbon emissions shows a clockwise trend. Moreover, the inhibitory effects of urbanization on decoupling in the Yellow River basin are much greater than the non-urbanization factors. In addition to the effect of urbanization, the transport structure has a major negative effect on decoupling development in the upstream and midstream regions, while energy intensity and energy structure are key to realizing a decoupled status in the downstream region. Finally, we propose some differentiated policy recommendations.

In order to realize the optimization of urban spatial patterns in the Yellow River Basin, a study on the inefficient use of urban land in the Yellow River Basin was carried out. In this study, Dali County and Hancheng County in Weinan City are selected as the research areas. Firstly, the analytic hierarchy process is used to build a comprehensive evaluation system for the identification of inefficient land in stock; secondly, the standard deviation ellipse method and spatial kernel density estimation method are used to quantitatively analyze the spatial distribution characteristics of inefficient land. Thirdly, the contribution model is used to analyze the influencing factors of inefficient land use. Finally, corresponding redevelopment suggestions are given for each type of inefficient land. The results show that Dali had the smallest area of inefficient land; second is Xincheng Street in Hancheng City; and Longmen Town, Hancheng City has the largest area. The distribution of inefficient land in Dali and Longmen Town in Hancheng City is relatively balanced, while the distribution of all kinds of inefficient land in Xincheng Street in Hancheng City is not concentrated. The density of the road network is the most important contributing factor to inefficient land use in the study area. This paper comprehensively uses the methods of economics and geography to study inefficient land use, quantifies the spatial-temporal characteristics and influencing factors of land use units, explores the spatial patterns of land use and enriches the research into relevant theories.

The ecological efficiency of industrial linkage considering unexpected output is an important indicator to measure the coordinated development of industrial linkage, resources, and the environment. It is an important basis for realizing the sustainable development of industry linkage. Taking the composite index of carbon emissions of the logistics industry and pollution emissions of the manufacturing industry as the unexpected output, we used the unexpected SBM model to evaluate the ecological efficiency of industrial linkage between the logistics industry and the manufacturing industry in Northeast China from 2011 to 2019, and used the spatial autocorrelation analysis method to analyze the spatial differences in industrial linkage efficiency. The results show that (1) considering the unexpected output, in Northeast China, the ecological efficiency cannot reach a high level of linkage development stage. (2) The results of the spatial correlation show that there are spatial differences between H-H agglomeration and L-L agglomeration in the linkage ecological efficiency of the two industries, and the spatial agglomeration attribute is relatively stable. (3) The analysis results of spatial agglomeration characteristics show that the spatial agglomeration of the two industries has a spatial evolution process from the southern coastal area to the central region. (4) Spatial trend analysis shows that in Northeast China, the western region is slightly higher than the eastern region, while the southern region is higher than the northern region. (5) From the development trend of linkage ecological efficiency, the linkage ecological efficiency of the study area will be improved in the future, but in the short term, the linkage ecological development level is not high and may still be at the primary linkage level.