| This SW studied the relationship between extreme weather and agricultural defaults at the county level in the United States. By using the IDW method to interpolate weather data (including temperature, precipitation, and wind speed), we obtained relatively complete weather data for a total of 11 years from 2012 to 2022; At the same time, bank data with less than 3 branches in these 11 years were screened out, and regression analysis was performed combining weather and financial data. The results showed that extreme low temperature will increase the degree of agricultural default, and the impact of high temperature and precipitation on agricultural default is not significant, with most of them showing negative correlation results. For weather conditions, the spatial distribution is relatively consistent during 11-year-period, and there are periodic changes. However, the results for climate change are not significant enough, possibly because the time scale is not broad enough. The results of this article provide new directions and means for future policy formulation. |
