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The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis. Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences. The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance. [Display omitted] • E. coli 's resistance rates change with climate and Hu line. • Region-specific health strategies against antibiotic resistance is needed. • The model has a higher explanatory power. [ABSTRACT FROM AUTHOR]

Data center is a very important infrastructure to support the development of information technology, and its development and increment are very remarkable. However, with the rapid and large-scale development of data centers, the problem of energy consumption turns to be also very prominent. Under the background of global carbon peak and carbon neutrality, developing green and low-carbon data centers has become an inevitable trend. This paper reviews and analyzes the policies and their roles in promoting China's green development of data centers in the past 10 years, summarizes the current situation of the implementation of green data center projects in China and gives the changes of PUE limits of data centers under the policy constraints. Application of green technologies is an important measure for energy-saving and low-carbon development of data centers, so encouraging innovation and application of green technologies in data center is also a priority task in relevant policies. This paper points out the green and low-carbon technology system of data centers, further summarizes energy-saving and carbon-reducing technologies in IT equipment, cooling system, power supply and distribution system, lighting, intelligent operation and maintenance, and provides an outlook on the future green development of data centers. • China's green data center policy in recent 10 years has played an important role in promoting the development of green data centers. • Summarizes the status of green data centers under policy guidance. • Provides energy saving and carbon reduction technology path for data centers. • Prospects for the future development of green data centers. [ABSTRACT FROM AUTHOR]