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Forest Farm Carbon Sink (FFCS) projects are one of the effective ways to achieve carbon neutrality and mitigate global warming. Though the existing literature has widely discussed the effect of FFCS on the allocation of agricultural factors, such as land, labor employment structure and income structure, little is known about whether FFCS projects could have an effect on agricultural development. Based on the panel data of 140 counties in Sichuan province, China, from 2002 to 2018, we examined the causal effect on agricultural total factor productivity (TFP), and revealed their dynamic effect and underlying mechanisms. Propensity score matching and the difference in difference (PSM-DID) method were used to address the endogeneity problem of FFCS implementation. Results showed that FFCS projects increased agricultural TFP by 1.7%–2.4%. Health, saving and industrial structure were the important channels through which FFCS projects affect agricultural TFP. Our findings suggest that policies promoting FFCS projects can increase agricultural TFP while achieving environmental goals—a win-win situation. • The causal effect of FFCS projects on agricultural development was examined. • A theoretical framework was constructed to quantify FFCS projects and agricultural development. • FFCS projects significantly increased agricultural TFP by 1.7%–4.9%. • FFCS projects affect agricultural TFP in short term but not in the medium and long term. • Health, saving and industrial structure were the important channels underlying the effect of FFCS projects. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the most common female malignant tumor. Previous studies have suggested a big incidence disparity among different cities in China. The present work selected a typical city, Hangzhou, to study BC incidence disparity within the city. Methods: Totally, 8784 female breast cancer cases were obtained from the Hangzhou Center for Disease Control and Prevention during the period 2008–2012. Analysis of Variance and Poisson Regression were the statistical tools implemented to compare incidence disparity in the space-time domain (reference group: township residents during 2008, area: subdistrict, town, and township, time frame: 2008–2012), space-time scan statistics was employed to detect significant spatiotemporal clusters of BC compared to the null hypothesis that the probability of cases diagnosed at a particular location was equal to the probability of cases diagnosed in the whole study area. Geographical Information System (GIS) was used to generate BC spatial distribution and cluster maps at the township level. Results: The subdistrict populations were found to have the highest and most stable BC incidence. Although town and township populations had a relatively low incidence, it displayed a significant increasing trend from 2008 to 2012. The BC incidence distribution was spatially heterogeneous and clustered with a trend-surface from the southwest low area to the northeast high area. High clusters were located in the northeastern Hangzhou area, whereas low clusters were observed in the southwestern area during the time considered. Conclusions: Better healthcare service and lifestyle changes may be responsible for the increasing BC incidence observed in towns and townships. One high incidence cluster (Linping subdistrict) and two low incidence clusters (middle Hangzhou) were detected. The low clusters may be attributable mainly to developmental level disparity, whereas the high cluster could be associated with other risk factors, such as environmental pollution. [ABSTRACT FROM AUTHOR]