Agriculture is becoming a substantial contributor to carbon emissions in China, and the efficient reduction of agricultural carbon emissions (ACEs) has become a universal concern. Currently, the digital transformation of agriculture (DTA) provides a new way to reduce carbon emissions. However, questions regarding whether and how DTA suppresses ACEs remain the focus of both academic and practical attention; thus, they serve as the objectives of this study. Based on theoretical analyzes, the carbon emission reduction effect of DTA is empirically analyzed using panel data from 30 Chinese provinces between 2015 and 2021 with a benchmark regression model, mediating effect model and threshold effect model. The results show the following: (1) DTA significantly reduces ACEs. (2) DTA reduces ACEs through three paths: expanding the agricultural production scale, optimizing the agricultural industrial structure, and promoting agricultural technological progress. The agricultural industrial structure has the most considerable mediating effect, while the agricultural production scale expansion has the smallest effect. (3) The inhibitory effect of DTA on ACEs shows very distinct heterogeneity. DTA primarily reduces ACEs in eastern and nongrain production areas yet has an insignificant impact on other areas. In addition, the DTA has a significant inhibitory effect on ACEs from both fertilizers and diesel oil. (4) The inhibitory effect of DTA on ACEs is nonlinear rather than linear. There is a single threshold effect for the effect of DTA on ACEs, which is insignificant below the threshold and significantly negative above the threshold. Therefore, China’s central and local governments need to increase the level of DTA, encourage farmers to moderately expand the scale of their operations, emphasize the agricultural service industry, and enhance agricultural technological innovation to ultimately reduce ACEs.

The rapid development of China’s logistics industry is accompanied by the deterioration of the ecological environment and excessive energy consumption. Therefore, how to effectively measure and improve the green total factor productivity (GTFP) of the logistics industry is an important guarantee for achieving the coordination of the logistics industry development and the ecological environment protection in the high-quality development stage. This study evaluated the logistics industry’s GTFP of 30 provinces in China from 2004 to 2017 using the Epsilon-based measure model (EBM) and global Malmquist-Luenberger index (GML). Then, this paper applied the geographically and temporally weighted regression (GTWR) to analyze the spatiotemporal non-stationarity of influences of driving factors on GTFP. There are three main conclusions drawn in this paper. Firstly, the GTFP of the logistics industry has significant spatial and temporal differences. From a temporal perspective, the GTFP has undergone a process of alternating changes in ascent and descent. From a spatial perspective, the GTFP has an obvious “east-central-west” gradient decreasing trend. Secondly, compared with the ordinary least squares (OLS) and the geographically weighted regression (GWR), GTWR performs best in terms of goodness of fit. Thirdly, the regression results of GTWR indicate that the influences of factors have different directions and intensities on GTFP in the logistics industry at different times and regions, showing obvious characteristics of spatiotemporal non-stationarity. Finally, some practical recommendations are put forward in this paper.

As the global problems of environmental pollution and ecological degradation are becoming important obstacles to the realisation of sustainable development, green technological innovation (GTI) has received wide attention in the academic and practical communities worldwide. Commonly, the GTI process can be divided into two stages: green technology R&D and green achievement transformation. The contribution of GTI to economic development and environmental protection depends to a large extent on green innovation efficiency (GIE). Based on the panel data on inputs and outputs of 30 provinces in China from 2007 to 2021, this study applied the super efficiency SBM model considering undesirable outputs to evaluate green technology R&D efficiency (GTRDE) and green achievement transformation efficiency (GATE). Additionally, this study adopted the global and local Moran’s I index for spatial autocorrelation analysis. First, GTRDE showed a trend of “eastern > western > central > northeastern”, while GATE showed a trend of “eastern > central > western > northeastern”. Second, although GATE was higher than GTRDE in most provinces, the differences between provinces were significantly larger for the former than for the latter. Third, global spatial autocorrelation in GIE across provinces was significant only in a few years, while local spatial autocorrelation existed only in a few provinces. Based on the two-stage value chain perspective, the green innovation process is divided into green technology R&D and green achievement transformation. This paper also introduces a super-efficiency SBM model that considers undesirable outputs when calculating GIE. This is in line with the basic laws of GTI and development in reality. Thus, to enhance the efficiency of green innovation, governments and enterprises should raise awareness of GTI, enhance inter-regional exchanges and collaboration, and take a variety of measures to narrow the gap between regions.

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Green innovation efficiency (GIE), which reflects the relationship between inputs and outputs that consider environmental impacts, is critical to China’s realization of green and sustainable development. In view of the differences in economic development and resource endowment across provinces, it is necessary to measure and evaluate the GIE in a rational manner. In this paper, we study the evaluation, dynamic evolutionary characteristics, and influencing factors of GIE for 30 provinces in China over the period 2011-2022, using the super-SBM undesirable model, the spatial Markov chain model, and the geographically and temporally weighted regression model, respectively. Results show that the eastern region has a significantly higher GIE than the other regions, followed by the central region. Also, the state of provincial GIE in China is affected by the level of neighboring regions. GDP per capita, the marketization index, and industrial structure promoted GIE, while R&D expenditure, import and export trade, and the digital financial inclusion index showed negative results. There is also spatiotemporal heterogeneity in the effects of individual factors on GIE. Thus, a one-size-fits-all policy for GIE is highly unsuitable for the realities of individual provinces in China. We propose a number of targeted and differentiated policy recommendations that can be implemented.