Spatial differentiation of carbon emissions reduction potential for construction and demolition waste recycling

Identifying the regional differences and drivers for carbon reduction of construction and demolition waste (C&DW) recycling is essential to combat climate change. This study aims to calculate the carbon reduction potential for C&DW recycling from 2006 to 2021 in China and investigates the spatial differences and driving factors of carbon reduction potential for C&DW waste by combining the Theil index, Gini coefficient, and geographic detector methods. The carbon reduction potential for C&DW recycling in China was "high in the east and low in the west" overall level, with an average annual growth rate of 6.27%. The overall differences in carbon reduction potential for C&DW recycling are decreasing, mainly due to intraregional differences and inter-provincial differences in Northeast China. The population size, urbanization rate, and technological effect are the key factors influencing carbon reduction potential for C&DW recycling. There are two types of interactions between influencing factor pairs: nonlinear enhancement and two-factor enhancement. This study's results can guide policymakers to devise relevant, regionally specific policies.

Risk transboundary transmission areas and driving factors of brucellosis along the borders between China and Mongolia

OBJECTIVE

Brucellosis is a common and neglected zoonotic infectious disease worldwide caused by Brucella. However, transboundary transmissions among countries, particularly those with high incidences, are seldom investigated. In the present study, by taking China and Mongolia as examples, we aim to identify transboundary transmission risk and driving factors of brucellosis along borders.

METHODS

167 brucellosis outbreak locations along the border between China and Mongolia were collected. Wildlife distribution and cross-border activities were mapped. Maximum entropy approach modeling was conducted to predict the potential risk of prevalence of brucellosis with meteorological factors, geographical environment, economic development, living habits et al. The accuracy of the models was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), Kappa test, and correctly classified instances (CCI).

RESULTS

The spatial model performed excellent predictive performance with the predictor variables of soils, pastures, goat density, mean precipitation of the wettest month, temperature seasonality, and population density, which with the contribution and permutation important in 27.2 %, 31.9; 23.3 %, 6.8; 18.0 %, 17.2; 11.2 %, 18.1; 10. 3 %, 15.2; 10.0 %, 10.8. The calculated AUC, SD, Kappa, and CCI are 0.870, 0.001, 0.882, and 0.883, respectively. The distribution map of brucellosis showed high-risk areas along the borders.

CONCLUSIONS

Our study identified high-risk areas and the driving effect of brucellosis along the borders between China and Mongolia. Moreover, there is the possibility of cross-border wildlife activities in high-risk areas, which increases the risk of cross-border brucellosis transmission. The funding provides clues for cooperative prevention and control of brucellosis by reducing transboundary transmission.