Understanding potential drought characteristics under climate change is essential for reducing vulnerability and establishing adaptation strategies, especially in North China, a major grain production area. In this study, the key growth period of winter wheat was taken as the entry point. We comprehensively utilized data from meteorological stations and circulation factors and employed methods such as the modified Mann–Kendall test, run theory, wavelet analysis, and cluster analysis. We quantitatively assessed the drought conditions during the winter wheat growth stages using the Standardized Precipitation Evapotranspiration Index at a 1-month timescale (SPEI-1). We analyzed the spatiotemporal evolution characteristics of drought and explored the inherent correlation between drought and atmospheric circulation factors. Results indicate that the SPEI-1 index increased significantly during the entire growth period, the overwintering-jointing stage, and the heading-maturity stage at rates of 0.0058, 0.0044, and 0.0140 per year, respectively, showing a wetting trend. Higher drought frequency values were observed in northern Shanxi, northern Hebei, western Henan, and southern Shandong during the entire growth period, with the drought frequency of the overwintering-jointing stage approximately twice that of the emergence-tillering and heading-maturity stages. Furthermore, drought frequency values in southern Hebei and southern Henan decreased from high to low frequency during the heading-maturity stage compared to the overwintering-jointing period. The SPEI-1 is predominantly influenced by the Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO), varying by growth stages.

Improving the resilience of the logistics industry to enhance its ability to withstand a variety of uncertain risks is critical to its development. However, the spatio-temporal evolution of resilience in the logistics industry has not been adequately characterized in the literature. And the factors influencing it remain unclear. This study analyzes the patterns of spatio-temporal evolution of the resilience of the logistics industry in China’s 31 provinces from 2012 to 2021 to identify the factors influencing it. This research found that the overall resilience of the logistics industry has been increasing with a pattern in which the east regions are high, the west regions are low, the south regions are high, and the north regions are low, while the coastal levels are generally higher. However, the levels of resilience and adaptability of the logistics industry are both low and fragmented and need to be improved over the short term. This study emphasizes promoting projects to improve the logistics infrastructure and increasing fixed-asset investment in the logistics industry to improve the resilience of the logistics industry in China.

In China, haze days is occurring more and more frequently. By remote sensing, haze area can be monitored quickly over large region. In this paper, from level 1B data of Moderate Resolution Imaging Spectrometer (MODIS) and meteorologic data, the haze distribution is monitored by the threshold of apparent reflectance and brightness temperature in eight bands, and haze optical depth (HOD) are monitored by deep blue algorithm. From October 2 to 14 in the year of 2011, we monitor haze occurrence from MODIS data over North China Plain. The results show: (1) the areas with dense industries, large population, and a mass of vehicles have the most haze days; (2) when the haze occurs, PM10 will increase obvious.

Understanding the efficiency of green innovation in the logistics industry can drive sustainable development in China. However, there is a lack of evolutionary patterns and drivers of green innovation efficiency in the logistics industry. We examine the evolutionary patterns of green innovation efficiency in the logistics industry and identify its drivers across 30 provinces and cities in China from 2012-2021. This study examines the evolutionary patterns of green innovation efficiency in the logistics industry and identifies its drivers across 30 provinces and cities in China. It was discovered that there has been a consistent upward trend in the logistics sector's degree of green innovation efficiency. The eastern region exhibits the greatest average values across three indices of green innovation efficiency, with significant regional differentiation that decreases gradually from east to west. The overall spatial network displays an uneven pattern, with the central east showing higher efficiency and the west showing lower efficiency. Drivers such as spatial proximity, industrial structure, scientific and technological levels, and energy utilization significantly affect green innovation efficiency (P < 0.05). We underscore the importance of promoting regional cooperation and exchange, strengthening industrial restructuring and upgrading, and enhancing science, technology, and energy utilization to unleash the logistics industry's potential for green innovation.

In bimatrix games with symmetric triangular fuzzy payoffs, our work defines an (α, β)-risk aversion Nash equilibrium ((α, β)-RANE) and presents its sufficient and necessary condition. Our work also discusses the relationships between the (α, β)-RANE and a mixed-strategy Nash equilibrium (MSNE) in a bimatrix game with a risk-averse player 2 and certain payoffs. Finally, considering 2 × 2 bimatrix games with STFPs, we find the conditions where the increase in player 2’s risk-aversion level hurts or benefits himself/herself.

While existing research has focused on logistics carbon emissions, understanding spatiotemporal emission cost dynamics and drivers remains limited. This study bridges three gaps through methodological advances: (1) Applying the Non-Radial Directional Distance Function (NDDF) to measure Marginal Carbon Abatement Costs (MCAC), overcoming traditional Data Envelopment Analysis (DEA) model’s proportional adjustment constraints for provincial heterogeneity; (2) Pioneering dual-dimensional MCAC analysis integrating temporal trends (2013–2022) with spatial autocorrelation; and (3) Developing a spatial Durbin error model with time-fixed effects capturing direct/indirect impacts of innovation and infrastructure. Based on provincial data from 2013–2022, our findings demonstrate a U-shaped temporal trajectory of MCAC with the index fluctuating between 0.3483 and 0.4655, alongside significant spatial heterogeneity following an Eastern > Central > Northeastern > Western pattern. The identification of persistent high-high/low-low clusters through local Moran’s I analysis provides new evidence of spatial dependence in emission reduction costs, with these polarized clusters consistently comprising 70% of Chinese cities throughout the study period. Notably, the spatial econometric results reveal that foreign investment and logistics infrastructure exert competitive spillover effects, paradoxically increasing neighboring regions’ MCAC, a previously undocumented phenomenon in sustainability literature. These methodological advancements and empirical insights establish a novel framework for spatial cost allocation in emission reduction planning.