The logistics industry is one of the major fossil energy consumers and CO 2 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 CO 2 reductions and ensure that the development of China's logistics industry becomes less dependent on CO 2 emissions, this paper built an extended log-mean Divisia index model (LMDI) to decompose the logistics of CO 2 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 CO 2 emissions increases, followed by structural adjustment effects, while technological progress effects played a major role in inhibiting CO 2 emissions. Particularly, the energy structure showed great potential for CO 2 emissions reduction in China.(2) Highways appeared to have dominant promoting roles in increasing CO 2 emissions regarding transportation structure effects; highways and aviation proved to have the largest impact on CO 2 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 CO 2 emissions. Finally, this paper puts forward some policy implications for CO 2 emission reductions in China's logistics industry.

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Improving green total factor productivity (GTFP) is an important theme. Whether collaborative agglomeration between logistics industry and manufacturing (LMCA) can effectively promote GTFP is worth further research. Based on the panel data of 284 cities in China from 2005 to 2018, GTFP is calculated by using the Biennial Malmquist-Luenberger productivity index (BMLPI), and this research investigates the impact of LMCA on GTFP by adopting the spatial Durbin model (SDM) and threshold regressive model (TRM). First, LMCA plays a significant role in promoting the improvement of GTFP in the local and surrounding areas through the knowledge spillover effect, scale economy effect, resource allocation effect and symbiotic economic effect, and the spillover effect is greater than the local effect. Second, the positive direct effect of LMCA on GTFP comes mainly from technological progress, and the positive indirect effect of LMCA on GTFP comes mainly from the positive spillover effect of technological progress and technical efficiency improvement. Finally, the Williamson hypothesis exists significantly in the collaborative agglomeration scenario of the logistics industry and manufacturing of China. With the improvement of the level of economic development, the impact of LMCA on GTFP changes from insignificant to promoting. However, when it is further improved, the promoting effect turns into an inhibiting effect, and this change is dominated mainly by the impact of LMCA on technical change.

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.