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The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide. • An integrated framework combining DBN, SD, PLUS and InVEST models was proposed. • Impact of natural and socioeconomic factors on ESs was determined under climate scenarios. • The spatial pattern of ESs was optimized and partitioned using DBNs. • ESs in arid oasis urban agglomerations are mainly affected by natural factors. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, the paper uses Super Efficiency Data Envelopment Analysis (DEA) to evaluate the level of regional energy efficiency in China, then selects Theil index to analyze the difference and change of regional energy efficiency. The results are as follows: (1) The overall level of energy efficiency in China is relatively low, and the average efficiency level from 2007 to 2019 is 0.568. Among them, Beijing is the highest, at 1.261, and Xinjiang is the lowest, at only 0.205. The energy efficiency of the eastern region is the highest in China: the average is 0.812. The central region is lower than the national average, with the value of 0.534. The lowest energy efficiency is in the western region, which is only 0.349. It can be seen that there are significant differences in regional energy efficiency, and from west to east has increased. (2) The Theil coefficient increased before 2012 and began to show a downward trend in 2013. The difference in the eastern region maintained the smallest downward trend among the three regions. The difference in the central region fluctuated and increased before 2011, and dropped sharply after 2012. In the western region, the coefficient is relatively stable and has not changed significantly. (3) Differences within and between regions in China show an overall downward trend. From the perspective of contribution rate, the difference between regions contributes the most to the overall difference. • We use super-efficiency DEA to measure China's regional energy efficiency. • We use Theil Index to illustrate regional differences in energy efficiency. • We have reached a conclusion that can provide a theoretical reference for policy making. • We compared the magnitude and trend of energy efficiency in various regions. [ABSTRACT FROM AUTHOR]