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Accurately identifying changes in carbon trading prices can provide reasonable reference indicators for a government's macrocontrol and can also help companies more effectively avoid risks brought by carbon emissions and increase the income of carbon assets. Based on the Prophet model, LSTM neural network model, and backpropagation (BP), this paper proposes a method to predict carbon trading prices using the ensemble learning model and uses the Hubei carbon trading market data to predict carbon trading prices. Results show that in terms of accuracy, the Prophet-LSTM-BP ensemble learning model achieves better predictive ability than existing models; its RMSE, MAE, and MAPE are 1.479, 0.951, and 2.135, respectively, which are markedly smaller than the Prophet model's 5.631, 4.471, and 9.661, and the LSTM model's 3.352, 3.105, and 6.880, respectively. Compared with the traditional time series ARIMA model, the MAPE of ARIMA reaches 12.933, which is nearly 1.5 times that of the Prophet model, nearly 2 times that of the LSTM model, and nearly 7 times that of the ensemble learning model. In terms of applicability, when the model is applied to the national carbon trading market, the difference in MAPE compared with the Hubei carbon trading market is only 0.6%, and the other parameters are not more than 16%. The model improves the relevant research on carbon trading price predicting, and concurrently, this method provides ideas for carbon trading price predicting in other carbon trading markets and promotes the sustainable development of the national carbon trading market. [ABSTRACT FROM AUTHOR]

The tomb of Marquis Yi of Zeng was unearthed in 1978 at Leiguudun in Suizhou City, Hubei Province, China. This is the largest wooden coffin in rock pit and vertical cave in the Warring States period in China. The large wooden coffin components provide reliable material data for the study of the feudal burial system in the pre-Qin period and have high value. In this paper, the wooden coffin excavated from the tomb of Marquis Yi of Zeng was studied. By understanding the composition of wood, studying the weight gain rate, shrinkage rate, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and compression resistance, the wood reinforced by rosin and epoxy resin was compared, and the reinforcement effect and principle of the two reinforcement methods were discussed. The study found that the wood properties of rosin reinforced group were better than those of epoxy reinforced group in the aspects of compressive strength, weight gain rate and volume shrinkage rate, and the mechanical properties of wood were significantly improved. In addition, methanol or ethanol can be used to dissolve the rosin shellac in this strengthening process, which can achieve reversible strengthening treatment. The results provide valuable examples and research ideas for the selection of different conservation techniques of large wooden cultural relics unearthed in the middle and lower reaches of the Yangtze River during the Warring States Period. [ABSTRACT FROM AUTHOR]

Based on the panel data of 16 cities in Hubei Province from 2009 to 2019 (excluding the data during Covid-19 pandemic period), we construct multi-temporal difference-in-differences model (DID) and Spatial Durbin model (SDM) to explore the impact of the High-speed Railway (HSR) construction on the regional economic development in terms of direct economic effect, indirect economic effect and spatial spillover effect. The main conclusions of this paper are as follows: firstly, HSR construction promotes the economic development of cities in Hubei Province in a long time and in a large spatial scope. And the impacts on the economic development are significantly heterogeneous among different types of cities. Also, HSR construction has widened the economic development gap among cities to some extent. Secondly, the upgrading of an industrial structure and the improvement of urbanization level both play mediating roles in the economic impact of HSR construction. Thirdly, the HSR construction has significant spatial spillover effect on economic development, but the spillover effect becomes insignificant with the increase of distance. [ABSTRACT FROM AUTHOR]

The preservation and transmission of traditional villages is crucial to the prosperity and development of ethnic cultures. However, current traditional village surveys usually require a large number of experts and scholars to conduct field research, which is an expensive and time-consuming method, especially for large-scale tasks. Therefore, this study proposes an automatic classification method based on deep learning (DL) for the identification of traditional village heritage value elements (TVHVE). The study evaluates four selected convolutional neural network (CNN) frames using traditional villages in Hubei Province as a sample dataset. The results show that Residual Network152 (ResNet152) is the most suitable CNN frame for identifying TVHVE in Hubei. The stability and consistency of various TVHVE present in the ResNet152 model were evaluated using Area Under Curve (AUC) and Precision Recall Curve (PRC), which indicated satisfactory prediction performance for most elements, except for specific elements such as tombstones and stone carvings, which showed lower accuracy. In addition, the study sheds light on the areas of concern of the model with respect to different TVHVE images and elucidates the reasons behind the confusion between elements through semantic clustering based on image classification and interpretability analysis using the Gradient-Weighted Class Activation Mapping (Grad-CAM) heat map. By using an automated classification method based on DL, this study significantly reduces the cost and effort associated with traditional surveys. At the same time, insight into areas of concern and confusion in the model improves guidance for conservation efforts and provides valuable references for subsequent research. [ABSTRACT FROM AUTHOR]

With the deep integration of the digital economy and agricultural development, agricultural digital transformation promotes agricultural production, and industrial upgrading and broadens sales channels to achieve the strategic goal of rural revitalization in China. To explore whether agricultural digitization can help farmers increase their income and what path can be achieved, this study is based on the theoretical framework of rural revitalization theory and digitization. Through a questionnaire survey of 1500 farmers in Hubei Province, the impact mechanism of agricultural digitization transformation on farmers' income is empirically studied. The empirical results show that the digital transformation of agriculture can promote the increase in farmers' income, and promote the increase in farmers' income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. At the same time, both production efficiency and sales channels form a chain double intermediary path with the upgrading of agricultural structure. Under the background of rural revitalization, this study provides theoretical references and guidance for further promoting agricultural digital transformation to increase farmers' income. The marginal contribution of this study is to construct a theoretical model of agricultural digitization to promote farmers' income increase, which has important theoretical reference and guiding significance for guiding the development direction of agricultural digitization and promoting farmers' income increase. [ABSTRACT FROM AUTHOR]

Global warming is caused by rising carbon dioxide concentrations, while ecological and environmental issues are becoming increasingly evident. Many governments' main objective in addressing climate challenges is to achieve carbon balance. Exploring the distribution and changing trends of carbon balance can contribute to carbon balance strategies. This study takes the Yangtze River Economic Belt (YREB) as the study area, analyzing the spatial and temporal dynamic changes in carbon emissions and carbon sequestration in the YREB from 2000 to 2017. The county-level supply–demand ratios and accessibility are calculated based on the enhanced two-step floating catchment method according to the dynamic flow radius. According to our experimental results, carbon emissions can achieve different degrees of carbon balance according to the flow radius. After carbon emissions flow between counties within the province, some provinces can realize the transition from carbon surplus to carbon balance. With the expansion of the flow radius and the increase in internal mobility, more counties can participate in carbon sequestration, and some provinces can achieve carbon neutrality after carbon emissions transfer across provinces. Hubei, Chongqing, Zhejiang, Hunan and other provinces with special carbon sequestration capacity are very sensitive to changes in the radius of carbon emission flow. In 2017, when the carbon emission flow radius is 50 km, the net carbon emission of the flow can be reduced by 3%. If the radius is increased to 200 km, the YREB can achieve carbon balance. This study can provide decision-making information for carbon emission ecosystem management in the YREB. [ABSTRACT FROM AUTHOR]

Liver cancer, a chronic non-communicable disease, represents a serious public health problem. Long-term trends in the burden of liver cancer disease are heterogeneous across regions. Incidence and mortality of liver cancer, based on the Global Burden of Disease, were collected from the Chinese Centre for Disease Control and Prevention. Age–period–cohort model was utilized to reveal the secular trends and estimate the age, period and cohort effects on primary liver cancer due to specific etiologies. Both the age-standardized incidence and mortality rate of liver cancer in Hubei province were on the rise, although there were discrepancies between gender groups. From age–period–cohort analysis, both incidence and mortality of liver cancer due to Hepatitis B virus were the highest in all age groups. The incidence of all liver cancer groups increased with time period in males, while this upward trend was observed in females only in liver cancer due to alcohol use group. Cohort effects indicated the disease burden of liver cancer decreased with birth cohorts. Local drifts showed that the incidence of liver cancer due to specific etiologies was increasing in the age group of males between 40 and 75 years old. The impact of an aging population will continue in Hubei Province. the disease burden of liver cancer will continue to increase, and personalized prevention policies must be adopted to address these changes. [ABSTRACT FROM AUTHOR]

Using untreated livestock manure adversely affects the environment and human health. Although transforming untreated livestock manure into organic fertilizer can alleviate these adverse effects, the adoption rate of this technology is relatively low in rural areas. This study investigates whether information technology adoption, focusing on Internet use, increases farmers' willingness to transform untreated livestock manure into organic fertilizer using data collected from pig breeding farmers in the Hubei province of China. A recursive bivariate probit model addresses the sample selection bias issues. Findings show that Internet use increases the probability of pig breeding farmers' willingness to transform untreated manure by around 35–56 percentage points. Gender, farm size, credit availability, extension contact, environmental protection attitude and governmental regulation are also important factors driving pig breeding farmers' willingness to transform untreated manure. Moreover, the impact of Internet use on farmers' willingness to transform untreated pig manure tends to be higher for pig breeding farmers with a larger breeding size and longer breeding years. Our findings have significant policy implications that help to improve livestock manure management, environmental quality, and the well-being and health of livestock breeding farmers. [ABSTRACT FROM AUTHOR]

The abnormity of climate change poses environmental concerns and indirectly promotes the development of low-carbon economic growth pattern. Climate change mitigation technology innovations contribute significantly to achieve environmental goals in China, and thus, technological cooperation has become a strategic choice for the Chinese provinces to achieve technological breakthroughs. Based on 3, 280, 808 patents, this study uses social network analysis (SNA) method and similarity measurement to discuss the evolution process and structural features of the spatial correlation network to analyze the patent data from 1985 to 2020. The process for the research is as follows. First, a form solution of the correlation problem to calculate the minimum Euclidean distance is available in the field of social network. Second, the European distance is used to measure the similarity between provinces, and then, SNA is used to construct the spatial correlation network. Finally, the similarity of growth trend of provincial patent applications in different years from a nonlinear perspective is described, so as to calculate the potential provincial linkage strength. The results demonstrate that low-carbon technology innovation in China takes the form of a network structure. The network exhibits significant characteristics of regional agglomeration, and several central provinces exert the role of mediation and transmission, such as Tianjin, Hubei, and Henan. The study contributes by illustrating the current situation of green technology innovation and provides a critical theoretical foundation for the government when formulating future low-carbon economy development policies. [ABSTRACT FROM AUTHOR]

Livestock and poultry manure is usually recycled to the nearby cropland as it is rich in elements required for crop growth. It is necessary to measure the pollution risk caused by recycled manure. In this study, a risk assessment model based 2-tuple linguistic is constructed to evaluate the pollution risk. Firstly, based on pollution source factors and migration factors, the index system is established, which includes nominal indexes and quantitative indexes. The 2-tuple linguistic method converts two attribute indexes into one form. Meanwhile, the firework algorithm is adopted to make the weights of indicators objective. Then, livestock and poultry farms in Xinzhou District, Wuhan City, Hubei Province, are taken as the empirical objects to get the spatial distribution of farms' risk. Through the index ablation experiment and sensitivity analysis, it is found that the soil resistance factor, manure removal method and the river channel distance are the main influencing indexes. Targeted improvement of these three influencing indicators can reduce the pollution risk by manure. [ABSTRACT FROM AUTHOR]