Investigating the impact of rural digitalization on agricultural carbon emissions contributes to achieving carbon neutrality goals and facilitates the green transformation of agriculture with enhanced efficiency. Based on panel data from 31 provincial-level regions in China spanning 2005 to 2022, this study employs a dynamic panel model to analyze the influence of rural digitalization on agricultural carbon emission intensity. Heterogeneity analysis, mechanism testing, and spatial effect examination are also conducted. The main findings are fourfold. (1) Rural digitalization effectively promotes the reduction of agricultural carbon emissions. (2) Heterogeneity analysis revealed that the effect of rural digitalization on lowering agricultural carbon emission intensity is particularly significant in production-marketing balanced regions. (3) The carbon emission reduction effect of rural digitalization is primarily realized through the scaling of agricultural operations, the accumulation of human capital, and the improvement of total factor productivity. (4) A positive spatial correlation exists in the agricultural carbon emission intensity across provinces, and the inhibitory effect of rural digitalization on agricultural carbon emission intensity exhibits spatial spillover effects. Therefore, to accelerate rural digitalization and advance agricultural carbon emission reduction, it will be essential to promote the scaling of agricultural operations, guide farmers in adopting advanced technologies, and enhance their ability to utilize digital tools.

The rapid development of the rural digital economy has emerged as a global phenomenon affecting both developed and developing countries, and China is no exception. Based on field survey data from 432 citrus family farms in Jiangxi Province in 2023, the mediating effect test and heterogeneity analysis are employed in this study to assess the impact of digital applications (DA) on family farm income (FFI). The results show that DA significantly increases FFI, with its enabling effect spanning the entire production chain from pre-production information acquisition to mid-production management and post-production marketing. Mechanism analysis indicates that DA enhances economic returns through three synergistic pathways: improving policy resource acquisition efficiency, promoting resource-efficient technology adoption, and expanding market sales channels. Heterogeneity analysis further shows that the income-enhancing effect of DA is more pronounced among farms with smaller scales, lower incomes, weaker social capital, and poorer infrastructure. These findings reflect inclusive “catch-up” and “substitution” effects, rather than the emergence of a digital divide. This study enriches the theoretical framework of digital agricultural empowerment and provides policy-relevant evidence for the formulation of targeted digital agriculture policies.

Climate variability is considered one of the major challenges facing agricultural productivity in ecologically fragile mountain regions such as the Garhwal Himalaya. A study on this topic examines whether temperature and rainfall are Granger-causing each other, considering crop yields; it applies a Granger Causality Test and ARIMA- based time series modelling. A time series dataset of climate, from 1951 to 2023, including maximum temperature (T-max), minimum temperature (T-min), and rainfall, was collected to determine casual relationships and further predict future trends. In addition, the results indicate that T-max has a significant influence on the pattern of rainfall; hence, temperature changes cause a change in precipitation, whereas the latter is not found to be causally related to the former. Lastly, T-max and T-min were strongly interdependent, meaning that the fluctuation in temperature played a central role in influencing agricultural outcomes. The ARIMA model reveals a minor cooling trend in T-max from 2024 to 2030; thus, this might benefit heat-sensitive crops like wheat and potatoes but possibly be a concern for warm temperature crops like rice and mango. Such outcomes, therefore, demand the significance of climate-responsive agricultural planning, especially in rain-fed farming systems that are vulnerable to irregularity in precipitation. With the implementation of predictive climate models and adaptive farming strategies, policymakers and farmers can develop more resilient agricultural systems. The study, therefore, calls for enhanced water resource management, climate-smart crop selection, and policy intervention to mitigate climate risks in the Garhwal Himalaya. Future research studies should include extra climatic and agronomic variables by using high-level machine learning models to better enhance the forecast accuracy and resilience of agriculture with ongoing climate change.

Forest ecosystem enhances environmental resilience by maintaining ecosystem stability and supporting natural processes. In Nepal, the rising temperature has exerted immense pressure on people’s livelihoods and ecosystems. In a forest, soil characteristics and tree diversity are the key components that enhance its resilience in response to various disturbances such as drought, fire, erosion, and landslides. However, the role of forest management in improving soil quality, fostering tree diversity, and building resilience is less investigated in Nepal. The present study aims to assess the role of forest management in soil quality, tree diversity, and building resilience. For this purpose, the soil quality was determined and soil quality rating (SQR) was computed across three management zones of the Panchase protected forest, using a semi-quantitative equation model. The observed tree richness was obtained from transect walk and tree counts around soil sample points. The community resilience adjoining the forest was assessed through participatory approach employing a scoring method. The results showed that SQR was higher in the Protected Zone (0.82) followed by the Intensive Management Zone (0.77) and the Impact Zone (0.69). The highly significant differences in SQR among the three management zones (P<0.001) and the highly significant difference in mean SQR between the Protected Zone and the Impact Zone (P<0.001) highlighted the role of forest management in fostering soil quality. The Protected Zone exhibited higher tree richness compared to the Intensive Management and the Impact Zones, suggesting the need for soil quality enhancement through management measures that also promote tree diversity. Furthermore, the community residing near the forest, which encompasses larger forest area demonstrated higher resilience score of 3.94 than the community residing relatively far, scoring 3.53. This suggests the contribution of forest ecosystem in building community resilience and recommends to strengthen agricultural diversity, agriculture innovation, and biodiversity-based livelihoods in community with low resilience score.