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Simple Summary: Black-billed capercaillie (Tetrao parvirostris) were listed as least concern (LC) class by the International Union for Conservation of Nature (IUCN). However, black-billed capercaillie was an endangered species in China and classified as first-class state protection animal (category I). Only a few studies have focused on the feeding habits of black-billed capercaillie and there is less research content at the molecular level, such as genomics, microbiome, and molecular markers. To improve our understanding of black-billed capercaillie, the fecal microbiome was characterized. This study provides reasonable scientific data for the understanding of black-billed capercaillie and may provide important insights for protecting this endangered species in China. Black-billed capercaillie (Tetrao parvirostris) was listed as a first-class state-protected animal because it was endangered in China (Category I). This study is the first to examine the diversity and composition of T. parvirostris gut microbiome in the wild. We collected fecal samples from five black-billed capercaillie flock roosting sites (each 20 km apart) in one day. Thirty fecal samples were sequenced with 16S rRNA gene amplicons on the Illumina HiSeq platform. This study is the first to analyze the fecal microbiome composition and diversity of black-billed capercaillie in the wild. At the phylum level, Camplyobacterota, Bacillota, Cyanobacteria, Actinomycetota, and Bacteroidota were the most abundant in the fecal microbiome of black-billed capercaillie. At the genus level, unidentified Chloroplast, Escherichia−Shigella, Faecalitalea, Bifidobacterium, and Halomonas were the dominant genera. Based on alpha and beta diversity analyses, we found no significant differences in the fecal microbiome between five flocks of black-billed capercaillie. Protein families: genetic information processing; protein families: signaling and cellular processes, carbohydrate metabolism; protein families: metabolism and energy metabolism are the main predicted functions of the black-billed capercaillie gut microbiome through the PICRUSt2 method. This study reveals the composition and structure of the fecal microbiome of the black-billed capercaillie under wild survival conditions, and this study provides scientific data for the comprehensive conservation of the black-billed capercaillie. [ABSTRACT FROM AUTHOR]

Based on the EBM-DEA (Explainable Boosting Machine-Data Envelopment Analysis) model, this paper constructs an evaluation model of urban industrial carbon efficiency (UICE), measures and analyzes the spatial evolution characteristics of China's UICE from 2003 to 2016, and analyzes the influencing factors of UICE using the Tobit model. The research draws the following conclusions: (1) China's UICE improved from 2003 to 2016, and the distribution showed a spatial pattern decreasing from the east, central, west, and northeast regions. (2) The UICE, by region, was at an initial low stable level in 2003 and was in the process of moving towards a highly-efficient stable state up until 2016. The differences between regions have been the main aspect which affects the overall variation in UICE in China. (3) There is a logistic curve relationship between the economic development level and UICE. (4) Nationally, the factors that are significantly and positively correlated with UICE are: industrial agglomeration, local fiscal decentralisation, level of economic development, technological progress, industrial enterprises' average size, and industrial diversification. Factors that are significantly negatively correlated with UICE are the level of industrialization, the share of output value of state-owned enterprises in total output value, industrial openness, and environmental regulation. The factors influencing UICE differ depending on the stage of industrialization. [ABSTRACT FROM AUTHOR]

It is challenging to use traditional remote sensing techniques to accurately determine the extent and thickness of ice in the Bohai Sea, on account of the presence of sea impurities (i.e. mud, salt bubbles and sand) and shape irregularities. Accordingly, we performed a series of reflectance spectra experiments to empirically link remote measurements of surface reflectance with in situ sea ice thickness measurements in the Bohai Sea. Two years of Thematic Mapper (TM) band 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) band 4 data were used to distinguish between the following sea ice types, using spectral reflectance thresholds of 6.4, 9.6, 10.3 and 12.1%: (a) clean nilas ice (a thin elastic crust of ice up to 10 cm thick that, under pressure, may deform by finger rafting; (b) nilas ice and pancake ice (roughly circular accumulations of frazil ice, usually less than about 3 m in diameter, with raised rims caused by collisions); (c) grey and grey-white ice; and (d) cumulative ice (<30 cm). By establishing a relationship between sea ice type and ice thickness, a novel, practical and low-cost remote sensing technique is introduced to estimate the extent and distribution of sea ice thickness over a large spatial scale. The results obtained by remote sensing are validated with in situ ice shape measurements. The MODIS and TM data are used to distinguish between three ice thickness grades (6-9, 10-20 and 20-30 cm). [ABSTRACT FROM AUTHOR]

Sample series of clean regular‐shaped ice representative of thermal interaction processes only were gathered in a controlled experiment on ice floes in China's Bohai Sea. Based on these samples, spectral characteristics for one‐year‐old sea ice were measured and analyzed. The same experiments were then performed for ice samples with two normal impurities and the results compared against those of clean samples. Along with increasing thickness, the reflectance of clean regular‐shaped ice rises in the form of a ladder within the visible light range. While flat regular‐shaped ice is covered by thaws snow and/or mixed with mud and sand, the trend of increasing reflection with thickness of clean regular‐shaped ice is disordered. [ABSTRACT FROM AUTHOR]

In order to verify how environmental regulation affects the improvement of urban industrial carbon emission efficiency, this study first measures the balanced panel data of industrial carbon emission efficiency of 282 cities in China from 2003 to 2019, and evaluates the direct and regulating impact of environmental regulation on China's urban industrial carbon emission efficiency. Meanwhile, in order to investigate the potential heterogeneity and asymmetry, the panel quantile regression method is used. The empirical results show that (1) during the period 2003-2016, China's overall industrial carbon emission efficiency showed a upward trend, with a decreasing spatial pattern from the east-central-west-northeast region. At the urban scale in China, environmental regulation has a significant direct effect on industrial carbon emission efficiency, which is lagged and heterogeneous. At the low quantiles, a one-period lag in environmental regulation has a negative effect on the improvement of industrial carbon emission efficiency. At the middle and high quantiles, a one-period lag in environmental regulation has a positive effect on the improvement of industrial carbon emission efficiency. Environmental regulation has a moderating effect on industrial carbon efficiency. With increasing industrial emission efficiency, the positive moderating effect of environmental regulation on the relationship between technological progress and industrial carbon emission efficiency shows a pattern of diminishing marginal benefits. The main contribution of this study is the systematic analysis of the potential heterogeneity and asymmetry of the direct and moderating effects of environmental regulation on the industrial carbon emission efficiency at the city scale in China by using panel quantile regression method., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China's urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China's urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China's urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China's urban industrial carbon emissions was "low carbon-horizontal expansion" dominated by positive growth, and in 2016, it was "low carbon-vertical expansion" dominated by scale growth; (4) China's urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.