Wei Wang

Accurate forecasting of agricultural product prices is vital for stabilizing market dynamics and informing effective policy decisions. However, the strong nonlinearity and complexity of influencing factors often limit the performance of traditional models. To address these challenges, this study proposes a hybrid forecasting framework—GRU-IHEOA-N-BEATS-BP—targeted at apple and crucian carp price prediction. The model first utilizes STL decomposition to separate the time series into seasonal, trend, and residual components. Pearson correlation analysis is applied to identify key influencing factors for each component. A GRU network is then constructed to model sequential dependencies, with its parameters optimized by an improved Human Evolutionary Optimization Algorithm (IHEOA) to enhance prediction accuracy. Concurrently, an N-BEATS model captures high-order nonlinear patterns from the decomposed components. Finally, a BP neural network aggregates outputs from all forecasting submodels to generate the final prediction. Experimental results validate the effectiveness of the proposed model. For crucian carp, the value R ² improves from 64. 67% (with N-BEATS standalone) to 97. 95\%, while MAE and MSE decrease by 81.5% and 93.6%, respectively. These results demonstrate the model’s superior ability to capture complex temporal and nonlinear characteristics. In summary, this hybrid approach significantly enhances the accuracy and robustness of forecasting. It provides a valuable tool for intelligent agricultural management, contributing to food security, higher farmer income, and more precise market regulation.

Land transfer and its socio-economic impact are key areas of research interest. Such an examination can help to enhance the sustainability of farming livelihoods, maximise livelihood strategies, and achieve sustainable development. This paper establishes a sustainable livelihood evaluation index for rural households based on sustainable livelihood theory. It measures the degree of sustainability in the livelihoods of farmers based on field research data from 650 rural households in Hubei Province, China, and analyses the impact of land transfer using a multiple linear regression model. A number of control variables were identified and introduced into the analysis. It also uses the regression decomposition approach to investigate the impact of each factor on the sustainable livelihoods of rural households. The findings revealed that (1) land transfer can significantly increase the sustainability of rural households’ livelihoods; (2) livelihood sustainability increases with the size of the land transfer area; and (3) the primary elements determining the ability of rural households to maintain sustainable living are land transfers, the amount of land transferred, and the size of the family. Based on these findings, this study argues for the need to improve land transfer management, accelerate agricultural industrialisation and promote the transfer of land contract management rights to improve the livelihoods of rural households.

By introducing network game theory, this paper analyzes the internal relationship and interaction mechanism among environmental governance, local government competition, and agricultural carbon peak level. On the basis of theoretical analysis, a spatial panel model is constructed using panel data from 30 provincial-level regions in China for empirical analysis. The research finds that local governments have positive competition with respect to the agricultural carbon peak, they adopt complementary carbon peak competition strategies, and they are more inclined to take geographical distance to adjacent regions as the yardstick in the competition with respect to the agricultural carbon peak strategy. That is, when the carbon peak level of surrounding provinces increases, the carbon peak level of the region will also increase. Thus, there is a phenomenon of mutual imitation and convergence between neighboring provinces. Environmental governance has a significant positive direct effect and a positive spatial spillover effect. From the perspective of coefficient, its direct effect is significantly greater than the spatial spillover effect. Therefore, more attention should be paid to local environmental governance to promote the improvement of the agricultural carbon peak level. Furthermore, the agricultural industrial structure, fiscal decentralization, agricultural public investment, regional industrial structure, and the proportion of the rural population have significant spatial spillover effects. The agricultural industrial structure and fiscal decentralization are significantly positively correlated with the peak level of agricultural carbon while the proportion of the rural population is significantly negatively correlated with the peak level of agricultural carbon. The research results have important theoretical value for expanding the research in the field of agricultural carbon emissions and provide important practical reference for China to successfully achieve the goal of agricultural carbon peak and promote the high-quality development of agriculture

With the successful completion of the battle against poverty, after 2020, the focus and difficulty of China’s poverty governance will change from solving absolute poverty to alleviating relative poverty. Analyzing and studying the alleviation of relative poverty from the perspective of public health services is in line with the current needs of consolidating and expanding poverty alleviation in China, and it is also of great significance to building a long-term solution mechanism for relative poverty. In this study, basic panel data were constructed by using the data of five CFPS surveys in 2010, 2012, 2014, 2016, and 2018 and matched with the macro data. The correlation between public health services and rural households’ relative poverty was also analyzed by using logit regression analysis and the KHB mediation effect decomposition method. The results show that (1) public health services play a significant role in promoting the accumulation of health human capital, improving individual feasible ability, and alleviating the relative poverty of rural families; (2) the improvement of public health services is conducive to the alleviation of the relative poverty of rural families; (3) we should continue to increase investment in public health care in underdeveloped areas and strive to promote the balanced development of public health services, so as to further consolidate and expand the achievements of poverty eradication.

Hubei Province, located in the middle reaches of the Yangtze River, is a complex area of fragile ecological environment and traditional agricultural production in China. With the further intensification of the impact of global warming, flood disasters have brought a more severe threat to the sustainable development of farmers’ livelihoods. This paper therefore examines the livelihood resilience of farmers with different livelihood strategies in the region by constructing a livelihood resilience evaluation system based on three target levels: buffering capacity, Adaptation and restoration, and using a contribution model to identify the main contributing factors affecting the livelihood resilience of fa rmers. The following three conclusions were found: (1). The overall level of livelihood resilience of farmers in flood-affected areas in Hubei Province is not high, and the difference in livelihood resilience indices between farmers with different livelihood strategies is large; (2). Farming-led farmers and part-time balanced farmers can better adapt to external shocks brought about by floods; (3). The main contributing factors affecting the livelihood resilience of various types of farmers have Convergence.

With the rising temperature, uneven precipitation and frequent occurrence of extreme weather caused by global climate change, agricultural production is facing more severe challenges. Based on the sustainable livelihoods framework, this paper measures the index of farmer households’ livelihood strategy adaptability and analyzes the benefits of farmer households’ environmental risk management on livelihood strategy adaptability by using microscopic research data of 970 farmer households’ livelihoods in Hubei Province, China, in 2020. This paper found that the farmer households’ environmental risk management variables have a significant impact on the adaptability of farmer households’ livelihood strategies, with the stronger the farmer households’ environmental risk management capacity, the more adaptable its livelihood strategy. The impact of farmer households’ environmental risk management variables on livelihood strategies varies for farmer households with different income levels. Therefore, this paper proposes that we should improve farmers’ perception of climate change, promote the diffusion of adaptive technologies, improve agricultural insurance policies, give full play to the collective role of village collectives, companies and cooperatives, and promote the transformation and upgrading of livelihood approaches to further improve the adaptability of livelihood strategies.

Rural family differentiation is an important perspective to analyze farmers’ behavior and poverty. Based on the data of 1673 farm households from rural field survey in 2019 in Hubei Province of China, this paper examines the main influencing factors of farm household differentiation on farm household poverty vulnerability from the perspective of the sustainable livelihoods of farm households. On this basis, the contribution of each influencing factor to farm household poverty vulnerability is analysed using the regression decomposition method. The results of the study show that the variables of farm household differentiation have a significant impact on poverty vulnerability, and the net household income per capita, which reflect the vertical differentiation of farm households, and the proportion of non-farm labor, which reflects the horizontal differentiation of farm households. Both have a significant negative impact on the poverty vulnerability of farm households. The regression decomposition method shows that the proportion of non-farm labor force, which reflects the horizontal differentiation of farm households, has the highest contribution to the poverty vulnerability of farm households. Human capital, natural capital, social capital, and physical capital also influence the poverty vulnerability of farm households to a certain extent.

Disaster preparation can reduce the impact of an earthquake on residents. Residents are more likely to undertake disaster preparedness if they perceive it to be effective. However, few studies have analyzed the influence of trust on this perception. This study surveyed 327 households in areas stricken by the Wenchuan and Lushan earthquakes to explore these issues. Trust was divided into government trust, emotional trust, and social trust, while the efficacy of disaster preparedness was divided into self-efficacy and response efficacy. A partial least squares structural equation model was used to explore the influence of trust on perceptions of the efficacy of disaster preparedness. The results show that: (1) government trust can directly increase perceived efficacy and indirectly increase self-efficacy via emotional trust; (2) emotional trust can directly increase self-efficacy; (3) social trust can directly reduce self-efficacy while indirectly increasing it by increasing emotional trust. This study deepens our understanding of the relationship between trust and perceptions of the efficacy of disaster preparedness. This study can provide inspiration to improve risk communication and construct systems of community-based disaster-prevention.

The Sustainable Development Goals call for taking urgent action to combat climate change and reduce inequalities. However, the related actions have not been effective. Global CO2 emissions in 2021 are projected to rebound to approaching the 2018–2019 peak, and wealth inequality has been increasing at the very top of the distribution resulting from the COVID-19 pandemic. To test whether a trade-off exists between social and environmental benefits, this study calculates county-level wealth inequality with the Gini coefficient and consumption-based household carbon emissions with the emissions coefficient method and input–output modeling. Data are collected from the China Family Panel Studies, the Visible Infrared Imaging Radiometer Suite, the Chinese National Bureau of Statistics and Carbon Emission Account and Datasets in 2014, 2016 and 2018. In addition, a high-dimensional fixed-effects model, an instrumental variable model and causal mediation analysis are adopted to empirically test how wealth inequality influences household carbon emissions and explore the underlying mechanisms. The results show that county-level wealth inequality has a positive impact on household carbon emissions per capita. This means that policies designed to narrow the wealth gap can help reduce carbon emissions, making progress toward multiple SDGs. Moreover, the study reveals that the social norms of the Veblen effect and short-termism play an important role in mediating the relationship between wealth inequality and consumption-based household carbon emissions. This finding provides a new perspective to understand the mechanism behind wealth inequality and household carbon emissions related to climate change.