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In the healthcare sector, sense of calling has raised increasing attention for its crucial role in fostering nurses' motivation and dedication to their work. Despite the fact that individuals with a sense of calling are more likely to go beyond their duties, the potential influence of this calling on their innovative behaviour remains largely unexplored. Drawing on work as a calling and self‐determination theories, this study aims to establish a link between a sense of calling and innovative work behaviour in the healthcare sector and investigate the underlying mechanisms and boundary conditions. Questionnaires were distributed through field and online surveys to collect the responses of 350 nurses in China. Structural equation modelling was adopted to test the research hypotheses. The results showed that sense of calling had a positive effect on nurses' innovative work behaviour and the relationship was mediated through meaning of work and employee resilience. Moreover, psychological safety was found to strengthen the positive impact of sense of calling on nurses' innovative work behaviour. Theoretical and practical implications were discussed along with the direction for future research. [ABSTRACT FROM AUTHOR]

In order to achieve sustainable development, low-carbon economic efficiency (LCEE) is particularly important in China. Therefore, this study uses SBM-DEA model to evaluate the LCEE of 30 provinces in China from 2008 to 2017. Based on the uncoordinated coupling model, this study discusses the interaction between China’s provincial LCEE and scientific and technological development level (STDL), and uses the panel VAR model to consider the interactive response relationship between China’s provincial LCEE and STDL. The research shows that the uncoordinated coupling degree (UCCD) between the STDL and LCEE in 30 provinces showed a decreasing trend as a whole during the research period. In terms of spatial distribution, the provinces with UCCD less than 0.5 mainly concentrated in the eastern and southern provinces, gradually spread to the north, and showed positive spatial autocorrelation, with significant spatial accumulation effect. From the perspective of influencing factors, patents, urbanization level, traffic level and financial development have significant positive effects on promoting the coordinated development of STDL and LCEE. From the relationship between them, the STDL has a positive promoting effect on LCEE, but the mechanism of the two is not obvious enough. Therefore, it is necessary to emphasize the coordinated development of low-carbon economy and science and technology, and promote the development of low-carbon economy through scientific innovation.

"The Belt and Road Initiative" (BRI) seeks to attain high‐quality development and sustainability. This study examines the green total factor energy efficiency (GTFEE) of 58 BRI countries from 2005 to 2019 and its influence factors using the Super slacked‐based measurement and Tobit models. The findings indicate that: 1) Singapore and the other seven countries have GTFEEs above 0.9, which is better than other countries; seven out of 58 countries have GTFEEs below 0.5. 2) Due to regional disparities, Southeast Asia, Central, and Eastern Europe have the greatest GTFEE, followed by South Asia, West Asia, and the Commonwealth of Independent States, and the lowest is in Central Asia, China, and Mongolia. 3) Compared to developing economies, developed economies have a higher GTFEE. 4) Between 2005 and 2019, the overall BRI energy efficiency dropped from 0.75 to 0.56. 5) According to Tobit results, for every 1% increase in economic development and energy structure, GTFEE increases by 0.08% and 0.31% points, respectively, and for every 1% increase in trade and land area, GTFEE decreases by 0.04 and 0.05, respectively. Therefore, BRI countries should strengthen international cooperation, develop advanced technologies, and increase the proportion of renewable energy utilization. [ABSTRACT FROM AUTHOR]

The aim of the present study was to assess differences in the conditioning factors and the performance of landslide susceptibility mapping (LSM), employing the SHapley Additive exPlanations (SHAP) model to gain profound insights into the intrinsic decision‐making mechanism of LSM in diverse landforms. Two typical karst erosion landforms were selected as the research areas. Based on 15 conditioning factors, LSMs for the two areas were developed using the Bayesian optimization random forest (RF) and eXtreme Gradient Boosting (XGBoost). The SHAP model was used to explore the landslide formation mechanisms from both global and local perspectives. The results show that the area under the curve (AUC) values of the XGBoost models were 0.791 and 0.761, and the AUC values of the RF models were 0.844 and 0.817, in the two different landform areas, respectively. The RF model's accuracy was higher than that of the XGBoost model in both regions. In the low‐elevation hills area, the primary three conditioning factors were identified as slope, topographic relief and distance from the river. Conversely, in the microrelief and mesorelief low mountain area, the predominant conditioning factors were elevation, distance from the river and distance from the road. Both karst landform areas exhibited a high sensitivity to the distance from the river, indicating its significant interaction with other factors contributing to landslide occurrences. Notably, the RF model demonstrated superior performance compared to the XGBoost model, rendering it a more suitable choice for conducting landslide susceptibility mapping research in karst erosion landform areas. In the present study, a comprehensive explanatory framework based on the RF‐SHAP model was proposed, which enables both global and local interpretation of landslides in various karst landscapes. Such an approach explores the intrinsic decision‐making mechanism of the model, enhancing the transparency and realism of landslide susceptibility prediction results. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered "black box" models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possesses the capability to automatically select and scale data features. In this study, Fengjie County in China was selected as the research area, with slope units serving as evaluation units. A geospatial database was constructed using 12 conditioning factors, including elevation, slope, and annual average rainfall. LSM models were conducted using both the QLattice and random forest (RF) algorithms. The findings demonstrate that the QLattice model achieved an area under curve value of 0.868, while the RF model attained an area under curve value of 0.849 for the test datasets. These results highlight the superior predictive ability and stability of the QLattice model compared with RF. Furthermore, QLattice can explicate and clarify the change processes of conditioning factors, thereby revealing the internal decision‐making mechanism and causes behind the LSM model's decisions. The innovative QLattice‐based model provides new ideas and methodologies for LSM research. [ABSTRACT FROM AUTHOR]

The article analyzes the comprehensive development levels of the three subsystems of Chongqing's tourism, urbanization and ecological environment from 2000 to 2017, and the temporal evolution characteristics of the coordinated development of the three. The results show that: (1) The comprehensive evaluation index of Chongqing's tourism industry system and urbanization system is on the rise; the comprehensive ecological environment index showed an upward trend before 2008, and fluctuated greatly after 2008. (2) The degree of coupling and coordination of the three systems in Chongqing City generally shows a transformational trend of maladjustment recession-intermediate transition-coordination improvement. In the future, Chongqing should continue to deepen institutional reforms and continue to develop new tourism markets; improve transportation, promote enterprise transformation and upgrading, implement policies for talent introduction; strengthen the construction of environmental protection talents and capital investment, and vigorously develop ecological industries

Scientific descriptions and simulations of the ecological risks in mountainous areas can promote the sustainable use of land resources in these areas and improve the reliability of decision-making for ecological risk management. Taking Chongqing, China, as an example, we constructed a landscape ecological risk (LER) evaluation model based on land use data from 1995 to 2020 and analysed the spatiotemporal evolution characteristics of the LER pattern. Moreover, we coupled the patch-generating land use simulation (PLUS) model and multi-objective programming (MOP) method and input multiple scenarios (inertial development, ID; economic priority development, ED; ecological priority development, PD; and sustainable development, SD) to simulate the ecological risk pattern in 2030. The model coupling the "top-down" and "bottom-up" processes obtained optimal land use patterns in different contexts, and it was used to perform a spatially explicit examination of LER evolutionary trends in different contexts. The results showed that LER evolution in Chongqing has had obvious stage characteristics. The high-risk area decreased significantly under various constraints, including topographic, economic, and other constraints, and the distribution showed a trend of high in the west and low in the east. The LER spatial clustering characteristics were highly coupled with the risk level pattern. The ED scenario presented the most severe risk, the PD scenario presented a moderate risk, and the SD scenario balanced the land demand for economic and ecological development and had a better land use structure and LER compared with the other scenarios. The coupled model proposed in this study helps to obtain the optimal land use structure and mitigate ecological risks, thus providing a scientific basis for future urban development. [ABSTRACT FROM AUTHOR]