Spatio-Temporal Variation and Decomposition Analysis of Livelihood Resilience of Rural Residents in China

Re-measurement of agricultural carbon emission rates: Drivers, regional differences and dynamic evolution

This research paper introduces a novel approach by combining a Backpropagation (BP) neural network with a non-angular and non-radial directional distance function to construct a BPNN-DDF model. This innovative model evaluates, decomposes, and analyzes China's agricultural sector's carbon emission rate across nine key subregions between 2010 and 2021. The key findings of this study are that China's agricultural carbon emission rate is decreasing, primarily due to technological advancements rather than technological efficiency. Subregions with robust economies and stable climates exhibit higher carbon emission efficiency, whereas those with underdeveloped economies, low agricultural technology, and volatile climates show relatively lower efficiency. The Dagum Gini coefficient analysis reveals a widening disparity in carbon emission rates among agricultural subregions, escalating from 0.174 in 2010 to 0.425 in 2021, indicating a growing gap between subregions that demands immediate attention. The kernel density distribution demonstrates an overall upward trend in China's carbon emission efficiency but also highlights an increasing divergence among subregions, particularly between the South China Area, the Huang-Huai-Hai Plain, and other regions. Therefore, this paper posits that strategies focusing on technological progress, sustainable agricultural development, regional development initiatives, and addressing inter-subregional imbalances will be crucial pathways for China's future low-carbon agricultural development.

Potential factors associated with resilience among older adults in rural China: a multilevel analysis

BACKGROUND

Resilience is crucial for older adults who experience adversities, but research on the issue in rural China remains limited. This study aims to examine factors associated with resilience among older adults in rural China, as related to different types of resilience, and under different levels of adversity.

METHODS

Data were taken from the eight-wave (2001-2021) Longitudinal Study of Older Adults in Anhui Province, China. We used data from the eighth wave (2021) for the outcome variables and lagged predictors (2018) to avoid reverse causal effects. The study sample included individuals 60 years and above, excluding new participants from 2021, those without any adverse events, and any respondents with incomplete analytic data. Resilience was operationalized as residuals of the regressions of life satisfaction (Life Satisfaction Scale) and depressive symptoms (CES-D) on adversity, referred to as Type-1 and Type-2 resilience respectively. These two types of resilience were then treated as the outcome variables in subsequent multilevel regressions, with the predictors focusing on individual, social, and environmental characteristics and resources. This study adheres to STROBE guidelines.

RESULTS

43% of rural older adults exhibited both Type-1 and Type-2 resilience, whereas 18% exhibited only Type-1 resilience and 7% exhibited only Type-2 resilience. Common factors associated with both types of resilience included self-rated health, satisfaction with one's own financial situation, and the prestigiousness of social networks. Predictors for higher levels of Type-1 resilience included higher levels of financial and emotional support and more options for places of leisure. Predictors for higher levels of Type-2 resilience included greater access to medical care. The prestigiousness of social networks, higher levels of emotional support and instrumental support, access to medical care, and more options of places of leisure were positively associated with resilience in the low-adversity group (first tertile of adversity), only satisfaction with financial situation was positively correlated with the resilience of the middle-adversity group (second tertile), while better self-rated health, satisfaction with financial situation, and financial support yielded greater resilience in the high-adversity group (third tertile).

CONCLUSIONS

We examined two types of resilience among older adults in rural China, and found that they have shared and unique associated factors. In addition, the potential factors influencing resilience varied with the level of adversity.