In recent years, social vulnerability has gained much importance in academic studies. However, social indices are rarely combined and validated with exposure and resilience components. This study provides an integrated analysis of the flood exposure and social vulnerability of rural households in a case area of Charsadda District, Khyber Pakhtunkhwa, Pakistan. A conceptual framework was designed (based on the MOVE framework) as a guideline and key indicators were identified. For the exposure component, parameters such as elevation, flooded locations, and distance from the river were endorsed to understand flood mechanisms. For populating socioeconomic variables, questionnaire-based interviews were conducted with 210 households. The results were presented through ArcGIS-generated maps. The most significant indicators interplaying with high vulnerability were exposure-related indicators. The findings showed that the southern areas, including Agra, Daulat Pura, and Hisar Yasinzai were highly vulnerable due to having the highest number of flood locations, lowest elevations, and shortest distances from rivers, as well as larger household sizes, more elderly, children and women, illiteracy rates, and weak financial capacity. Understanding such dominant indicators and areas where high social vulnerability and high exposure converge can inform the authorities in mitigating both social and physical flood vulnerability.

Agricultural land, as an important carbon source, has produced about 20% of carbon dioxide globally. The calculation and spatial-temporal distribution of carbon emissions resulting from agricultural land utilization (ALU) has attracted a great deal of attention from scholars. Most of the existing literature widely agrees that China’s carbon emissions from ALU showed significant regional discrepancies, but rarely pays attention to the evolutionary characteristics of the discrepancies. This study calculated the total carbon emissions from ALU based on six kinds of carbon emissions sources in the 31 provinces of mainland China, which showed obviously different characteristics in terms of their abundances of agricultural land resources, relative scarcities of production factors, levels of science and technology and economic prosperity. We then analyzed the evolutionary process and characteristics of regional discrepancies in carbon emissions from ALU at the national level and regional level with the method of kernel density estimation. The key results demonstrated the following: (1) The carbon emissions from ALU in the whole country and the eastern, central and western regions of China have increased sharply during the study period. From 2000 to 2015, the carbon emissions from ALU in the whole of China, the eastern region, central region, and western region were increased by 2626.11 (10⁴ tons), 441.32 (10⁴ tons), 1054.45 (10⁴ tons), and 1130.3 (10⁴ tons), respectively, with an average annual growth rate of 2.75%, 1.29%, 3%, and 4.35%, respectively; (2) The scale of carbon emissions from ALU showed significant spatial disparities at the regional and inter-provincial levels. From 2000 to 2015, the central region had the highest carbon emissions from ALU, while the eastern and western regions had the second and third highest carbon emissions; (3) The distribution curves of carbon emissions from ALU in the whole country and each region all moved in the right direction gradually during the study period, and the width of the curves increased, indicating the regional discrepancies of carbon emissions from ALU was expanding at different spatial scales. Distribution curves of carbon emissions from ALU in the eastern, central and western regions all showed a “multi-polar” differentiation phenomenon in 2000, while presented a “tri-polar”, “bipolar” and “multi-polar” division in 2015, respectively.

Farm-scale expansion is widely viewed as a means of improving agricultural efficiency and linking smallholders to modern agriculture. Yet whether it improves the conditions under which smallholders participate in grain production remains unclear. Using panel data from the China Rural Revitalization Survey (CRRS) for 2020 and 2022, this study examines how village-level farm-scale expansion affects smallholder grain production. The results show that a 0.1 increase in village-level farm-scale expansion intensity is associated with a 0.81-percentage-point higher grain-sown share, but without corresponding improvements in production conditions. Farm-scale expansion is also associated with lower mechanization, a lower share of spending on purchased agricultural services, greater reliance on household-owned machinery, and higher family labor input. We describe this pattern as constrained grain-oriented adjustment: an increase in grain-sown share without corresponding improvements in mechanization or external service support, leaving production more dependent on household-based resources. Cooperative membership is associated with less severe mechanization and cost pressures. Overall, a higher grain-sown share under farm-scale expansion does not necessarily imply improved conditions for smallholder grain production. To promote inclusive agricultural modernization, policy efforts should focus not only on farm-scale operations, but also on strengthening smallholders’ access to mechanized, service-based, and organizational support.