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China's carbon emissions trading (CET) policy aims to force relevant enterprises to implement low‐carbon technology innovation and address environmental challenges through marketization means. However, how China's CET policy may affect enterprise technology innovation and whether this effect may differ in industries remain to be further investigated. Therefore, based on the panel data of listed enterprises covered by the CET policy in China during 2009–2017, this paper employs the difference‐in‐difference (DID) and DID‐based propensity score matching models to evaluate the effect of CET on technology innovation. The empirical results indicate that the effect of China's CET on the technology innovation of related enterprises is generally not significant during the sample period, but this effect presents evident industrial heterogeneity. Specifically, among the eight CET‐covered industries, the CET policy helps to improve technology innovation for power and aviation enterprises but not in the other six industries (i.e., steel, chemical, building material, petrochemical, nonferrous metals, and paper), which implies that China's CET policy still has great potential for promoting the technology innovation of related enterprises. In addition, the central findings remain robust when the system generalized method of moment and DID‐based coarsened exact matching models are applied to consider the influence of omitted variables, unobservable confounders, and different matching methods. [ABSTRACT FROM AUTHOR]

Ghostwriting has been reported as a particular problem in China, and this article aimed to discover the extent of ghostwriting activity and attitudes by surveying Chinese authors and editors. Three questionnaires were created and distributed via WeChat: an author questionnaire for healthcare professionals, an editor questionnaire for medical journal editors, and a questionnaire for ghostwriters. A total of 1,073 healthcare professionals, 78 medical editors, and 23 ghostwriters responded; 33 (3.075%, 95% confidence interval: 2.041–4.110%) authors reported hiring ghostwriters, most of whom were clinicians (5.06%) or nurses (7.86%) and were in the 31–50 years old group. There was a negative correlation with the level of education. Editors reported few papers identified as ghostwritten, and most of them would immediately reject such articles. The majority of ghostwriters reported working part time as a ghostwriter for less than 1 year and earning little money from the work. The majority of respondents recognized ghostwriting as unethical, but only on the edge of law and ethics, and with few repercussions for the authors. [ABSTRACT FROM AUTHOR]

Objective: To explore the development of hip arthroscopy in China through reviewing the change of the application of hip arthroscopy operation on treating femoracetabular impingement (FAI). Method: Papers were retrieved from January 1, 2005 to November 1, 2019, from databases CNKI, Wanfang Data, VIP, PubMed, and Embase. The keywords are Hip Impingement, Femoroacetabular impingement, Hip arthroscopy, Arthroscopic operation, Hip Arthroscopy operation, and Arthroscope, etc. The quality of papers was assessed through MINORS , and statistics and meta‐analysis were performed by Word, Excel, and Revman 5.3 Zurich, Switzerland. Results: From a total of 8,953 papers, 46 review articles without data and 48 articles with data were involved, and 25 papers were included in the Meta‐analysis. The twenty‐five papers were selected from 48 papers with data, of which 41 were reported in Chinese, 11 were missing complete Harris scores, five did not mention the number of patients who had lost follow‐up, three had minors quality scores below 7, one did not have enough FAI cases, and three did not have standard deviations in Harris scores. Overall, in China, the application of hip arthroscopy regarding FAI has flourished while maintaining a high level of treatment and has reached its peak in the past 2 years. Conclusion: With the rapid development of hip arthroscopy in China, hip operation is widely recognized, many reports on its application on FAI have emerged successively, and the scope of application and technical level have been improved. [ABSTRACT FROM AUTHOR]

With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM. [ABSTRACT FROM AUTHOR]

Drought is an important meteorological event in China and can cause severe damage to both livelihoods and socio‐ecological systems, but current seasonal prediction skill for drought is far from successful. This study used convolutional neural network (CNN) to build an effective seasonal forecast model for the summer consecutive dry days (CDD) over China. The principal components (PC) of the six leading empirical orthogonal function modes of CDD anomaly were predicted by CNN, using the previous winter precipitation, 2‐m temperature and 500 hPa geopotential height as predictors. These predicted PCs were then projected onto the observed spatial patterns to obtain the final predicted summer CDD anomaly over China. In the independent hindcast period of 2007–2018, the interannual variabilities of first six PCs were significantly predicted by CNN. The spatial patterns of CDD were then skillfully predicted with anomaly correlation coefficient skills generally higher than 0.2. The interannual variability of summer CDD over the middle and lower Yangtze River valley, northwestern China and northern China were significant predicted by our CNN model three months in advance. CNN identified that the previous winter precipitation was the important predictor for PC1–PC3, whereas the previous winter 2‐m temperature and 500 hPa geopotential height were important for the prediction of PC4–PC6. This research provides a new and effective method for the seasonal prediction of summer drought. Plain Language Summary: Drought can cause serious agricultural and ecosystem disasters, so its forecast is valuable for preventing and mitigating related natural disasters and regional socioeconomic sustainability. However, current prediction skill for drought is far from successful since its extreme feature. The gradually emerging deep learning methods offer new possibilities, but how to effectively apply deep learning models in climate prediction with a small sample size remains an open question. In this paper, we build seasonal prediction convolutional neural network model for summer consecutive dry days over China using previous winter predictors. This model achieves significant prediction skill three months in advance. The empirical orthogonal function decomposition is used to reduce the dimensionality of consecutive dry days data in our model. Our research provides a new perspective for drought prediction, and it is expected that such method will be also useful for other seasonal climate prediction problems. Key Points: Convolutional neural network (CNN) skillfully predicts summer consecutive dry days (CDD) over China three months in advanceThe principal components of CDD are predicted by CNN and then projected on the observed spatial patternsPrevious winter 2‐m temperature, geopotential height at 500 hPa and precipitation are the essential predictors in CNN [ABSTRACT FROM AUTHOR]

From core observations of shales of the Niutitang formation in the northern part of Guizhou Province, China, calcite was often found to act as a natural fracture filler and affect the extension of fractures in hydraulic fracturing. Therefore, it is crucial to understand the tensile mechanical behavior of shales by calcite veins. In this paper, by computed tomography scanning of shales containing calcite veins of different dip angles from the Niutitang formation, a three‐dimensional numerical model reflecting the internal fine structure of the shale was constructed. Direct tensile numerical simulations were carried out to investigate the effect of calcite veins at different angles on the fine‐scale damage process and mechanical properties of shale. The experimental results show that the tensile capacity of the shale increases with the increase in calcite veins. Depending on the dip angle of the calcite vein, the damage pattern of the shale is divided into three types of damage: pull‐off damage along the calcite vein, jagged damage, and horizontal damage perpendicular to the loading method. At high dip angles, the shale damage is more intense and the fracture network more complex. The temporal and spatial characteristics of the acoustic emissions provide a good indication of the microscopic behavior of the shale specimens during the damage process. The box dimension method was used to calculate the fractal dimension of the shale specimens at the final damage, and it was found that the damage was more intense and the fracture network was more complex at high dip angles, and there was an angular threshold. [ABSTRACT FROM AUTHOR]

This paper examines the impact of institutional pressure (IP), top management support (TMS), green supply chain management practices (GSCM), and supply chain competitive advantage (SCCA) on corporate environmental performance (EP). We also analyze the mediation effect of GSCM on the interplay between TMS and EP. Additionally, the paper also provides an analysis of the moderating role of SCCA between IP and EP. To attain the objective of this research, we assembled data from 710 business entities within the Shaanxi province of China utilizing a survey design approach. The structural equation model (SEM) was applied to test and assess the hypothetical outline. The study outcomes empirically show that TMS, GSCM, and SCCA positively and significantly impact EP. Interestingly, our study found an insignificant association between IP and EP. The study's results also demonstrate that IP directly relates to top management support. Moreover, the study's empirical findings reveal that GSCM positively mediates IP and EP. The study findings show that SCCA shapes IP and EP's connection. Accordingly, the practical implications of our study's findings suggest that business managers, investors, and government agencies must know the importance of adopting sustainable practices within the supply chain. Business managers must take action to integrate environmental criteria into supplier selection, evaluate suppliers' EP, and collaborate with eco‐friendly suppliers. Hence, government agencies, stakeholders, and business managers can use this information to shape regulations and policies that encourage businesses to adopt sustainable supply chain practices. Offering incentives such as tax benefits or grants for sustainability initiatives can also promote adoption. The study recommends that a business culture that targets improving EP due to IP and top management support is essential in achieving GSCM practices, thereby promising competitive advantage. [ABSTRACT FROM AUTHOR]

The platform economy is growing fast in the world. Its expansion, exemplified by industry giants like Amazon, Airbnb, and Uber, is reshaping societal lifestyles and commercial businesses. However, its environmental impacts are uncertain. Currently, the relationship between electronic commerce (e‐commerce) activities and environmental pollution has attracted a lot of attention. This paper attempts to construct a comprehensive theoretical model to investigate the nexus between e‐commerce and regional environmental pollution, with a particular focus on the effects of air pollution and waste pollution. Fixed effect models and China's provincial data during the period from 2008 to 2018 are employed to examine the roles of energy efficiency and resource consumption in such a nexus. The findings suggest that the development of e‐commerce is linked to a reduction in regional air pollution through the channel of improving transportation energy efficiency. The results also show that e‐commerce is associated with an increase in regional waste pollution due to the mechanism of resource consumption in the express packaging industry. As a result, the relationship between e‐commerce and environmental pollution is quite complex at the regional level, transcending a binary distinction of solely positive or negative impacts. This paper contributes to a better understanding of the complex environmental impacts of e‐commerce and helps governments design more efficient and targeted policies to reduce regional pollution. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the impact of government environmental concerns on green innovation and whether this has a "leverage effect" or a "crowding‐out effect." This study employs a two‐way fixed‐effects model to conduct an empirical test using panel data from 2010 to 2020 on Chinese firms listed in heavy‐polluting industries. The results suggest that an increase in government environmental concerns can promote corporate green innovation. However, government environmental concerns primarily stimulate strategic innovation rather than substantive innovation. After a series of robustness and endogeneity tests, the conclusions of this paper still hold. Corporate green innovation induced by government environmental concerns is not the "leverage effect" superimposed on existing innovation activities but the "crowding‐out effect" of other technological innovation. The heterogeneity tests in this paper indicate that the impact is more pronounced for firms with poorer environmental conditions, stronger political connections, and richer executive backgrounds. [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

Nudging the amelioration of enterprise environmental performance is the micro‐foundation for effectively enhancing resource utilization and realizing sustainable development. Based on the upper echelons theory, the characteristics and previous experiences of managers determine their strategic choices and then affect the behavior of enterprises at the whole social layer. Nevertheless, the literature concerning the effect of CEO green experience on enterprise environmental performance is void. To bridge this gap, this paper uses the data of China's A‐share listed enterprises from 2010 to 2021 to empirically investigate the direct impact, influence mechanisms, and heterogeneities of CEO green experience on enterprise environmental performance. The study finds that CEO green experience can significantly promote the improvement of enterprise environmental performance. Based on the main effect, to exploring potential mechanisms behind the conclusion, we figure that environmental ethics commitment, environmental protection investment, and green technology innovation are the core mechanism approaches during the process of CEO green experience affects environmental performance. Heterogeneity analysis of CEO characteristics demonstrates that the green experience of female CEOs, older CEOs, and high educated CEOs both have a pronounced effect on ameliorating enterprise environmental performance. Furthermore, the positive effect of CEO green experience on environmental performance is more prominent in non‐state‐owned enterprises, heavily polluting industries, and regions with high environmental protection pressure. Besides providing an effective supplement for the research on managers and environmental performance, this paper also generates micro‐empirical support for government departments to formulate and optimize environmental policies. [ABSTRACT FROM AUTHOR]

A PDE‐constrained model predictive control (MPC) algorithm for open‐channel systems based on the Saint‐Vevant(S‐V) equations is investigated in this paper. The S‐V equations, which precisely model the dynamics of open‐channel systems, are quasi‐linear hyperbolic partial differential equations (PDEs) without analytical solutions. Directly applying the S‐V equations to an MPC controller design becomes sophisticated. In this work, the calculus of variation is used to obtain the adjoint equations and the adjoint analysis method is utilized to deduce the gradients of the MPC optimization problem. Particularly, the physical constraints involving both the state and control variables are also considered. A gradient‐based optimization algorithm in combination with the numerical computation of Preissmann implicit scheme is proposed to solve the constrained MPC optimization problem. The control performances of the developed PDE‐constrained MPC algorithm with respect to the controlled water levels and gate openings are compared with those of the MPC controller designed for the linearized model. All the simulation tests are carried out on an aqueduct reach in Yehe Irrigation District in Hebei Province, China. The results show that the proposed PDE‐constrained MPC algorithm is a promising method in dealing with the constraints in terms of hyperbolic PDEs, control variables and state variables simultaneously. [ABSTRACT FROM AUTHOR]

This paper explores the impact of the political hierarchies of cities in China from different perspectives. First, we examine the economic disparities between prefectural cities and municipalities. Furthermore, this paper draws upon a quasi‐ experiment to analyze the impact of upgrading Chongqing to a municipality in 1997 using the synthetic control method. The city‐upgrading policy significantly increased Chongqing's gross domestic product (GDP) in the following 4 years. Finally, we find that the policy increased GDP in treated cities within 1200 km of Chongqing by about 10%–13% relative to the control cities. [ABSTRACT FROM AUTHOR]

The Shanghai Soft X-ray Free-Electron Laser (SXFEL) is the first X-ray freeelectron laser facility in China. The SASE beamline, which consists of a pinkbeam branch and a mono-beam branch, is one of the two beamlines in the Phase-I construction. The pink-beam branch opened for users in 2023 after successful first-round beamline commissioning. In this paper, the design of the beamline is presented and the performance of the pink-beam branch is reported. The measured energy-resolving power of the online spectrometer is over 6000 @ 400 eV. The focusing spot size of the pink beam is less than 3 μm in both the horizontal and vertical at the endstation. [ABSTRACT FROM AUTHOR]

To systematically assess the effect of negative pressure wound therapy (NPWT) on postoperative surgical wound infection, length of hospital stay and postoperative complications after spinal surgery. Relevant studies on the application of NPWT in spinal surgery were conducted via a computerised database search, including PubMed, EMBASE, Web of Science, MEDLINE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wanfang, from inception to June 2023. The identified literature was rigorously screened and data extraction was performed by two investigators independently. The quality of the relevant studies was evaluated using the Newcastle–Ottawa scale (NOS). The effect size for count data was determined by the odds ratio (OR), while the impact size for measurement data was expressed as the standardised mean difference (SMD). The 95% confidence interval (CI) was calculated for each effect magnitude. Stata 17.0 software was used for the meta‐analysis. Ten papers, totalling 1448 patients, were finally included. This study demonstrated that NPWT led to a statistically significant reduction in the occurrence of postoperative surgical wound infections (OR: 0.377, 95% CI: 0.238–0.598, p < 0.001), fewer postoperative complications (OR: 0.526, 95% CI: 0.360–0.770, p = 0.001) and a shortened hospital stay (SMD: −0.678, 95%CI: −1.324 to −0.031, p = 0.040) after spinal surgery compared with the control group. When compared with other treatment approaches, NPWT also demonstrated a substantial reduction in surgical wound infections and postoperative complications, as well as a shorter duration of hospitalisation after spinal surgery. [ABSTRACT FROM AUTHOR]

This paper analyses the integration of the Sustainable Development Goals (SDGs) in the revisions of China's primary, secondary and high school curriculum standards. Employing a word frequency analysis of SDGs‐related keywords across ten disciplines, the study reveals an increasing trend in SDGs integration, particularly in Physical Education and Health, and Biology, but notes a lack of growth in the Arts discipline. Key findings include the widespread presence of SDG3 (Good Health and Well‐being) and SDG11 (Sustainable Cities and Communities), with a notable absence of SDG5 (Gender Equality) and SDG12 (Responsible Consumption and Production). This study suggests enhancing SDGs coverage in Arts education, broadening the scope to include underrepresented SDGs and promoting a balanced integration of all SDGs across disciplines. It emphasizes the critical role of Geography Education in addressing global challenges and advocates for interdisciplinary teaching methods. The study highlights the importance of regular assessment in curriculum standards to ensure effective SDGs integration, aiming to align China's educational framework with global developmental objectives for a sustainable future. Education emerges as a potent agent for promoting Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world‐class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans‐North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) is a comprehensive demand response (DR) technology to aggregate various power supply and consumption resources to help stabilise power grid fluctuations and reduce CO2 emissions. Fast‐charging electric vehicles (EVs) with higher charging power and shorter charging time are the most potential high‐quality DR resources of VPP in the future. In this paper, a new 'spatial‐temporal' bi‐layer optimal control strategy is proposed to solve the daily real‐time DR dispatching problem of large‐scale EVs including fast‐charging ones in a multi‐area VPP oriented to China's dual carbon target. This proposed bi‐layer strategy has two main contributions in both spatial and temporal domains. One is that, compared with the limited geographical area in traditional VPP, the upper layer of the proposed strategy considers the DR problem of EVs in a VPP with multiple geographical areas and establishes a spatial optimisation model of EVs DR considering both energy and carbon transaction costs of the overall VPP. The other is that rather than traditional slow‐charging EVs DR control with 1‐h temporal interval, the lower layer of the proposed strategy adds fast‐charging EVs in the real‐time orderly charging and discharging DR control by a shorter temporal interval of 15 min and designs two different charging and discharging price stimulation scenarios. The simulation results in the last section of this paper show that the proposed 'spatial‐temporal' bi‐layer optimal control strategy can effectively guide the charging and discharging behaviours of EVs in both spatial and temporal domains of a multi‐area VPP, achieving lower energy and carbon transaction cost and smaller load fluctuations. [ABSTRACT FROM AUTHOR]

In the circumstances of "Carbon Peak," it is imminent to implement green construction technology in the construction industry as it is a major source of CO2 emissions. Nevertheless, little study has been done on the impact of self‐face awareness (SA) on green building project performance (GP). On the basis of a large‐scale questionnaire survey in China and an in‐depth analysis of the four categories (cognition, behavior, satisfaction, and performance), this paper has added an SA dimension, and proposed a new impacting path. Then, based on the improved conceptual model for measuring the impact of green construction behavior on GP, this paper has conducted empirical tests by using a large sample of questionnaire data and partial least square‐structural equation modeling method. The results show that: (1) green construction behavior has a positive impact on GP, and the impact of perceived behavioral control of green construction technology is most significant for promoting green construction in the industry, and SA has a significant positive effect on green construction in the industry; (2) environmental perception and perceived behavioral control for green construction technology play a mediating role; and (3) significant differences indeed exist in the multigroup analysis, and the biggest difference is working experiences in the engineering field. Finally, this paper has proposed corresponding management implications based on the above findings. The main contribution of this study is that this study has developed a new dimension (SA) to the model considering Chinese national and cultural differences, in an attempt to reveal the impact of green construction technology on GP in the Chinese context. In addition, the promotion of green construction (PGC) in the construction industry was also added based on the sustainability perspective, which is the first attempt to explore the role of the PGC. [ABSTRACT FROM AUTHOR]

The intermittent access of renewable energy sources leads to a significant increase in fault short‐circuit currents in power grid. DC superconducting fault current limiters (SFCL) can limit short‐circuit currents in flexible DC transmission systems, which work in combination with direct current circuit breaker (DCCBs) to protect the grid system effectively. However, for the parameter design of DC superconducting fault current limiter (DCSFCL), it is difficult to balance the economics of the device and its adaptability to different fault situations. In this paper, a cross‐mapping design methodology for the DCSFCL is proposed and applied in the Nan'ao flexible DC transmission project, China. A cross‐mapping method is designed combining the critical parameters of DCSFCL and the system parameters. A three‐terminal simulation model of the flexible DC transmission system is constructed through MATLAB/Simulink and the design process of DCSFCL parameters is introduced. A cyclic calculation approach is proposed for the design of DCSFCL parameters through three fault conditions of the power system. Meanwhile, impact current short‐circuits test and artificial ground short‐circuit test were conducted on the prototype. Test results show that the designed DCSFCL meets the requirements of practical applications. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We estimate the relationship between high‐temperature exposure and land transaction using daily transaction data from 2013 to 2018 in China. Standard hedonic price method is used to perform multidimensional fixed effects regression on 458,564 transaction samples. This study shows that each additional day of the extremely high temperature increases the average bid price by 0.6%, which is equivalent to an additional annual increase of 15.018 billion yuan in land transaction costs. The effect of high temperature on land prices is considerably stronger for residential and commercial lands. We further find that high‐temperature‐induced changes of mood and cognition play a role. Extreme gradient boosting (XGBoost) algorithm is used to calculate price deviation, which overcome missing eigenvalues and reduce nonlinear measurement error. Our findings indicate that heat can make investors more aggressive, which occurs when bidders continue to raise prices to win. The number of bidding behavior will not drop due to the hidden costs of land transactions. Specially, bidding experience offsets the cognitive output bias. This study contributes to the behavioral finance and decision‐making literature, helps investors make better investment management decisions, and alleviates the negative effect of land transaction premium on the real estate market. Plain Language Summary: Extreme temperatures are becoming more frequent due to climate change. Extreme temperature can affect people's cognitive performance. Using large‐scale land transaction data and a fixed‐effects model, this paper analyzes the impact of extreme temperature on decision‐making. Results show that each additional day of extremely high temperature increases the average bid price by 0.6%, which is equivalent to an additional annual increase of 15.018 billion yuan in land transaction costs. This paper analyzes the impact of extreme temperature on human cognitive performance from the perspective of e climate change and uses land transaction behavior as a measure of decision‐making to reveal the mechanism of climate change affecting cognitive performance. Key Points: Each additional day of the extremely high temperature increases the average bid price by 0.6%Heat can make investors more aggressive, which occurs when bidders continue to raise prices to winBidding experience offsets the cognitive output bias [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

Challenges brought on by the rapid rate of economic growth include the depletion of natural resources (NAR) and environmental deterioration. Due to its abundant mineral resources and rapid economic growth, China has gained attention from both the developing and developed worlds. Using data from 1970/Q1 to 2020/Q4, this paper evaluates the time‐frequency nexus between CO2 emissions and natural resources, coal consumption, and economic growth. The study employs the Wavelet Local Multiple Correlation (WLMC) to explore this nexus. This approach enhances the capacity to comprehend the fundamental relationships between these indicators at different frequencies. To the best of the investigator' knowledge, this is the first empirical investigation to explore the time‐frequency nexus between natural resource, economic growth, coal use, and CO2 emissions in China, thus filling a gap in the literature. Furthermore, wavelet coherence is employed as a robustness check. The WLMC bivariate cases revealed that economic growth, coal consumption, and natural resources drive CO2 emissions positively at all frequencies, that is, in the short and long‐term, thus leading to environmental degradation. Furthermore, the wavelet coherence results support the outcomes of the WLMC bivariate cases. This study offers significant implications for policy and insights that are supported by the findings. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the performances of the wavelet, Hodrick–Prescott (HP), and Baxter–King (BK) filters in extracting cyclical information and to use an appropriate method to analyze China's business cycles. First, we use a second‐order autoregression (AR (2)) and random walk, based on Monte Carlo simulation experiments, to generate the data‐generating processes (DGPs) with different frequency characteristics. Second, the HP, BK, and wavelet filters are applied to extract the cyclical components of the respective DGPs. Third, the filtering abilities of the three methods are statistically compared. The results show the following: (1) Under the condition that the DGP is low frequency (long cycle) and trend dominated, the filtering performance of the three methods will remain unsatisfactory. (2) If the DGP is high frequency (short cycle), all three methods can serve as effective methods regardless of whether they are trend dominated or cycle dominated. However, it can be seen that the BK and wavelet filters present better performance than the HP filter. (3) In other cases, better filtering performances can be observed in the wavelet. Finally, the three methods are applied to estimate China's business cycles. In conclusion, this paper argues that the wavelet can effectively replace HP and BK filters to extract cyclical components. [ABSTRACT FROM AUTHOR]

This special issue is on active tectonics and palaeeo‐enviroment in West China. It comprises 19 original research papers under the following three themes: (a) Late Cenozoic intracontinental deformation in West China, especially in the northeast and north margins of the Tibetan Plateau (nine papers), (b) Geomorphic evolution (four papers), and (c) Palaeo‐environmental evolution and Cenozoic strata (six papers). [ABSTRACT FROM AUTHOR]

Bloody Mahjong is a kind of mahjong. It is very popular in China in recent years. It not only has the characteristics of mahjong's conventional state space, huge hidden information, complicated rules, and large randomness of hand cards but also has special rules such as Change three, Hu must lack at least one suit, and Continue playing after Hu. These rules increase the difficulty of research. These special rules are used as the input of the deep learning DenseNet model. DenseNet is used to extract the Mahjong situation features. The learned features are used as the input of the classification algorithm XGBoost, and then the XGBoost algorithm is used to derive the card strategy. Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of high‐dimensional sparse features. In the case of fewer training rounds, accuracy of the model can still reach 83%. In the games against real people, it plays like human. [ABSTRACT FROM AUTHOR]

Employing a time‐varying difference‐in‐differences model and a data set collected from 2,735 counties in China from 2003 to 2017, this study examines the causal relationship between Chinese government transfer payments to resource‐exhausted cities (TPREC) and carbon emissions for the first time. Our results show that TPREC could significantly reduce carbon emissions of resource‐exhausted cities in China. A variety of robustness tests supports our findings. Our further mechanism analysis shows that TPREC mainly affects carbon emissions through the following three channels: (a) promoting technological progress and industrial upgrades; (b) increasing vegetation carbon sequestration; (c) strengthening government environmental regulations. Finally, this paper analyzes the spatial heterogeneity of the carbon emission reduction effect of TPREC and finds that the most significant reduction effect of TPREC is in the western region and coal‐exhausted cities. This paper provides new empirical evidence for the role of transfer payments in promoting low‐carbon development using a unique set of data. Our findings provide new insights for decision‐makers to facilitate the low‐carbon development of resource‐exhausted cities. Plain Language Summary: By employing a time‐varying difference‐in‐differences model, we examine the causal relationship between central government transfer payments to resource‐exhausted cities and carbon emissions for the first time. We find China's TPREC can significantly reduce carbon emissions by promoting technological progress and industrial upgrades, increasing vegetation carbon sequestration, and strengthening government environmental regulations. Key Points: China's transfer payments to resource‐exhausted cities (TPREC) could significantly reduce carbon emissions, and this conclusion has also been supported by multiple robustness testsThe TPREC could achieve carbon emission reduction by promoting technological progress, industrial upgrading, and economic efficiencyThe most significant carbon emission reduction effect of TPREC is in the western region and coal‐exhausted cities [ABSTRACT FROM AUTHOR]

With the continuous advancement of urbanization, the contradiction between urban development and environmental resources has become increasingly prominent, and environmental pollution has become increasingly serious. To fundamentally solve the problems of environment, energy, and low carbon, we must rely on the intelligence of energy. This paper aims to study the sustainable development of China's intelligent energy industry based on artificial intelligence and low‐carbon economy. In view of the problems existing in the optimization of power generation industry, this paper uses the annual load, electricity price, weather, and climate data of a southern power grid, uses the statistical variation particle swarm optimization algorithm, uses the historical runoff and rainfall data to optimize it, and studies the analysis methods, characteristics and laws of short‐term load, electricity price and runoff, as well as the uncertain factors affecting their changes. The experimental results show that the predicted price is close to the actual price, and the median error of each period is <1% in statistical analysis, so the forecast value can be used to replace the actual value for scheduling. This method makes full use of the adaptive mutation in the late stage of particle optimization, and introduces the mechanism of particle size selection, which fully ensures the diversity of particles and improves the search ability of particles. [ABSTRACT FROM AUTHOR]

Food production is one of the main contributors to greenhouse gas emissions and climate change. China, as a rapidly developing economy, contributes to an unsustainable food system as its consumption of animal products and meat has continued to grow in recent decades. Using the extended theory of planned behavior as the conceptual framework, this paper examines factors influencing consumers' intention to purchase sustainable food in China. To this end, a population‐based face‐to‐face survey was conducted with 2422 respondents in five provinces spanning the north and south of China. The results showed that the traditional constructs of behavioral attitude, subjective norms, perceived behavioral control, and the additional construct of perceived quality are significant in inducing such intentions. This paper suggests that to enhance consumers' willingness to shift to sustainable food consumption, appropriate regulation and monitoring framework is needed to increase consumers' trust toward sustainable food. The government can also cooperate with the media, experts, and social media opinion leaders to ensure that messages on sustainable development are promoted in effective ways. [ABSTRACT FROM AUTHOR]

A comprehensive and accurate estimation of water quality in lakes and reservoirs is vital for the protection of the aquatic biota. Research on the spatiotemporal variations of nitrogen (N) and phosphorus (P) concentrations in lacustrine systems is typically plagued, however, by a lack of long‐term, spatially continuous monitoring data. This paper assembled a 30‐year (1989–2018) data set of water quality in 586 lakes and reservoirs in China, along with basin characteristics and climate conditions, forming the comprehensive data set available. These data were then used in a stacking model (based on random forest, support vector regression, and K‐nearest neighbor models) to identify the relationships between nutrient concentrations and their influencing factors, including net anthropogenic N/P inputs, geographical position, climate, land use pattern, and soil type. The stacking models were developed using data collected over multiple time scales (annual, seasonal, and monthly), which were then applied to reconstruct TN and TP concentrations during the periods of 1980–2018 and 2020s–2050s under the climate scenarios of RCP 4.5 and RCP 8.5. The accuracy of the stacking models was 99.1% and 98.3% for TN and TP concentrations using ensembled data, respectively. The interannual variations in TN and TP contents in the 586 lakes and reservoirs during 1980–2018 exhibited a non‐monotonic pattern with a peak of 1.12 and 0.049 mg/L in 2007, respectively. This study demonstrates that stacking machine learning models represent a new effective approach for estimating nutrient concentrations in unmonitored lakes and reservoirs across broad spatiotemporal scales. Plain Language Summary: The current research assembled a 30‐year (1989–2018) data set of 586 lake and reservoir water quality as well as watershed characteristics and climate conditions at a national scale to build the stacking models. As far as we know, this is the most comprehensive data set of Chinese lake and reservoir water quality. Our study first developed models take into account more indicators of the terrestrial ecosystem and consider the impact of spatial attributes and natural background conditions of lakes and reservoirs on TN and TP concentrations. We then applied the stacking model combined the results of various base machine learning algorithms to reduce the variations of the prediction and assimilating the advantages of the base models. Through containing precipitation and temperature variables with various time scales, the models have the ability to estimate and predict the annual, seasonal, and monthly variations in TN and TP concentrations during the period of 1980–2018 and 2020–2050s under different future climate scenarios. The primary target of this paper is to probe the potential benefits of using a stacking model approach to more accurately describe and reconstruct TN and TP concentrations in lakes and reservoirs throughout China over a range of spatiotemporal scales. Key Points: λ $\lambda $ This study provides spatiotemporal estimates of nutrient concentrations in 586 lakes and reservoirs of Chinaλ $\lambda $ Spatiotemporal differences of nutrient concentrations and associated drivers have been revealedλ $\lambda $ Future changes in TN and TP concentrations were estimated for selected climate scenarios [ABSTRACT FROM AUTHOR]

Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises. [ABSTRACT FROM AUTHOR]

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment. [ABSTRACT FROM AUTHOR]

The accurate evaluation of the relationship between nature reserves and poverty is highly significant for the harmonious coexistence between human and nature. It is widely recognized that the establishment of nature reserves is of great importance to the income poverty of farmers, but less attention has been paid to the impact of different reserves on the multidimensional relative poverty of farmers. Based on the survey data of Panda Nature Reserves in China, we analyze the influence of reserve regulation (or not) and regulation intensity on the multidimensional relative poverty of farmers and its mechanism. Results show that farmers in reserves are more likely to fall into multidimensional relative poverty than those outside the reserves, and there is a U‐shaped relationship between regulation intensity and multidimensional relative poverty. Further, the mechanism analysis show that, on average, the establishment of reserves has no significant impact on farmers' resource utilization capability, but too high or too low regulation intensity will affect farmers' resource utilization capacity, and aggravate their multidimensional relative poverty. The conclusions of this paper are not only conducive to the expansion of theoretical research on regulation and poverty, but also provide policy implications for realizing the coordinated development between biodiversity conservation of nature reserves and rural livelihood. Recommendations for Resource Managers: The effects of the regulation of nature reserves on the relative poverty of farmers is analyzed in this paper. We find that there is a "quantity" problem of the impact of reserve regulation on the farmers' relative poverty. The following implications could be realized based on the observations: •When identifying the relative poverty of farmers, it is necessary to make an in‐depth analysis from the perspectives of economic poverty and multidimensional poverty, and provide targeted and accurate assistance policies for different types of farmers.•The formulation and implementation of the reserve regulations should consider the difficulty of farmers' livelihood transformation to balance the relationship between the community development and ecological protection. [ABSTRACT FROM AUTHOR]

Aim: To improve the level of hospital workers' safety performance in response to emergencies (e.g. COVID‐19), this paper examines the relationship between hospital workers' job control on safety performance, and the mediating role of hospital safety climate and the moderating role of social support. Design: In this cross‐sectional questionnaire survey, a convenience sampling of hospital workers from three hospitals that have COVID‐19 cases from Beijing and Shandong Province in China. Methods: These questionnaires were used to obtain self‐reported data on hospital workers' job control, hospital safety climate, social support and safety performance. Mplus software was used to calculate CFA. SPSS25.0 software was used to calculate mean values, standard deviations, correlations and regression analyses. Results: The participants were 241 hospital workers from three hospitals in China (male = 55.2%, female = 44.8%; age range <30 to >45; physician = 58%, nurse = 22%, other hospital worker = 20%). A moderated mediation model among job control, hospital safety climate, social support and safety performance was supported. Moderated mediation analysis indicates hospital workers' job control effectively improves the level of safety performance; hospital safety climate plays a partially mediating role in the process of job control affecting hospital workers' safety performance; social support moderates the effect of work control on medical workers' safety climate. Hence, it is important to increase job control and hospital safety climate. Further, social support for hospital workers should be encouraged, advocated and supported. [ABSTRACT FROM AUTHOR]

Despite its invaluable contribution to the field of comparative economics, the socialist calculation debate has focused on the narrow topic of the impossibility of the rational economic calculation under socialism. The literature on new institutional economics suggests that economic development is determined by economic and political institutions which are far more complex than the issue of economic calculation. To bridge the gap between the calculation debate and new institutional economics, this paper utilizes the historical case studies of Perestroika and Deng's China to demonstrate relationship between state capacity and economic calculation. We argue that rational economic calculation requires the state's institutional ability to make a credible commitment to constraints inhibiting public predation. [ABSTRACT FROM AUTHOR]

Building resilient cities has become a hot topic in urban planning and risk management. Adopting the perspective of the social‐ecological system (SES), this paper incorporated economy–ecology–society–infrastructure into an integrated research framework and established a system for assessing the urban resilience development level. The entropy weight method was combined with a cloud model to systematically calculate and classify the resilience of 31 provinces (cities) in China. The evaluation results show that (1) the overall level of urban resilience in China was relatively low; the regional analysis showed that (2) the urban resilience of the eastern region was higher than the national average and that of the central and western regions, and from the perspective of the four subsystems, (3) the comprehensive development of each subsystem was the key to improving urban resilience. Finally, five improvement strategies were proposed to provide ideas for enhancing urban SES ability. [ABSTRACT FROM AUTHOR]

As ecological civilization construction receives growing attention in China, the eco‐friendliness of the power supply structure is becoming increasingly important. Thermal power and hydropower are the two major power sources in China. Hydropower has both positive and negative effects on the ecosystem. However, there are few studies on the comprehensive impact of hydropower on the ecosystem. Therefore, the main innovation of this article is to integrally and comprehensively study the impact of hydropower to propose recommendations for hydropower development. The study of hydropower's effects on the overall ecosystem including resources, the environment, and economic society reflects the integrity, and the simultaneous study of hydropower's positive and negative effects reflects the comprehensiveness. First, this paper constructs a system dynamics (SD) model that can analyze the impact of replacing thermal power with hydropower on the ecosystem and proposes an improved coupling coordination degree (CCD) model. Then, this paper takes the province of Fujian as an example for SD scenario simulation and proposes recommendations for each indicator in the ecology subsystem. Finally, this paper performs CCD analysis on subsystems of the ecology subsystem in different policy scenarios and provides suggestions for future hydropower development in Fujian. Results show that hydropower technology improvement and efficiency increase (HTIEI) is more conducive to the development of the overall ecosystem. The scientificity of the current hydropower policy in Fujian is proven through theoretical analysis, and further hydropower development proposals are proposed. This paper provides a basis for the formulation of power generation policies and promotes ecological civilization construction for Fujian. The proposed methods in this paper can also provide tools for policy research on clean energy power generation. [ABSTRACT FROM AUTHOR]

An economic growth target is a declaration by policy authorities of commitment to that target and the corresponding allocation of resources. The constraints created by economic growth target are an important economic management method in China, which has helped China's economy to achieve remarkable achievements. However, it has also brought about serious environmental problems, threatening China's sustainable development. Using the data about economic growth targets in the work reports of 30 Chinese provincial governments from 2006 to 2017, this paper constructs several spatial measurement methods, such as the spatial Durbin model, to examine the constraints created by economic growth target's impacts on air pollution. The main conclusions are as follows. First, a significant "U‐shaped" relationship exists between the constraints created by economic growth targets and air pollution. Second, the spatial Durbin model analysis revealed that PM2.5 across China's provinces display significant positive spatial spillover effects and spatial agglomeration characteristics. Third, the direct, indirect, and total effects of constraints created by economic growth targets on air pollution are all statistically significant and depict a "U shape." Finally, the constraints created by economic growth targets have an apparent threshold effect on air pollution, and the inhibiting effects increase with human capital and industrial restructuring. However, with the increase in foreign direct investments, constraints created by economic growth targets may increase air pollution. The conclusions of this paper are of great significance for improving the management of local government economic growth targets and sustainable development. [ABSTRACT FROM AUTHOR]

Background: Thoracic aortic aneurysm and dissection (TAAD) is a life‐threatening pathology that remains a challenge worldwide. Up to 40% of TAAD cases are hereditary with complex heterogeneous genetic backgrounds. The purposes of this study were to determine the diagnostic rate of patients with TAAD, investigate the molecular pathologic spectrum of TAAD by next‐generation sequencing (NGS), and explore the future preclinical prospects of genetic diagnosis in patients at high ‐risk of study. Methods: NGS was used to screen 15 genes associated with genetic TAAD in 212 patients from northwestern China. Clinical data of patients were gathered by electrocardiography, transthoracic echocardiography, and computed tomography. Results: Of the 212 patients, 67 (31.60%) tested positive for a (likely) pathogenic variant, 42 (19.81%) had a variant of uncertain significance (VUS), and 103 (48.58%) had no variant (likely benign/benign/negative). A total of 135 reportable variants were detected in our test, among which 77 (57.04%) are first reported in this paper. A genotype–phenotype correlation of FBN1 was assessed, and the data showed that the patients with truncating and splicing mutations are more prone to developing severe aortic dissection than those with missense mutations, especially frameshift mutations (82.76% vs. 42.86%). In this study, 43 (20.28%) patients had a family history of sudden death or TAAD, whereas 132 (62.26%) did not (the remaining 37 were not available), and the positive rate of genetic testing was higher in TAAD patients with family history than in those without (76.74% vs. 18.94%). Conclusion: Our study concludes that genetic variation is an important consideration in the risk stratification of individualized prediction and disease diagnosis. [ABSTRACT FROM AUTHOR]

Railway transportation system is one of the most important components of the integrated transportation system. A functional and efficient railway transportation system can be conducive to sustainable development of society and economy, thus highlighting the importance of studies on railway transportation efficiency. Therefore, this research proposes the cross‐efficiency evaluation method based on the relative entropy evaluation method (CERE) to evaluate railway transportation efficiency in China. Firstly, the research constructs an evaluation indicators system for railway transportation efficiency from the perspective of input‐output. Secondly, the paper obtains cross‐efficiency matrix by using the cross‐efficiency evaluation method (CE). Then, the study takes the obtained the cross‐efficiency matrix as the decision matrix of multiple criteria decision‐making (MCDM) problem, and applies the relative entropy evaluation method to evaluate the efficiency of railway transportation. Finally, the empirical analysis of the efficiency evaluation results for 18 railway transportation companies shows that, in 2019, Taiyuan Railway Transportation Company has the highest efficiency score, owing probably to a greater coal railway transportation demand and an advanced railway heavy haul transportation technology; railway transportation companies in the Eastern China typically have the highest efficiency scores, owing probably to a greater regional railway transportation demand. [ABSTRACT FROM AUTHOR]

This paper presents a study of the late Cenozoic Chifeng basalts (CBs) of NE China, including their olivine He isotopic compositions, whole‐rock major‐ and trace‐element contents, and whole‐rock Sr–Nd–Hf–Pb–Mo isotopic compositions, with the aim of constraining their mantle source. Results show that the basalts have high MgO, low CaO contents, and high FeOT/MnO values, which indicate that their mantle lithology was most likely pyroxenite. The CBs also exhibit ocean‐island‐basalt‐like trace‐element patterns (e.g., enrichment in light rare earth elements and high‐field‐strength elements) and have depleted Sr–Nd–Hf and relatively radiogenic Pb isotopic compositions, requiring both depleted and enriched components in their mantle. The low olivine He (3He/4He = 0.8–5.5 Ra) and whole‐rock Mo (δ98/95Mo = −0.71‰ to −0.18‰) isotopic values of the CBs, together with geophysical evidence, indicate that the rocks were derived from a depleted MORB mantle (DMM) enriched by recycled oceanic crust that was sourced from the mantle transition zone (MTZ). During the late Cenozoic, ascending wet mantle plumes triggered by dehydration of a stagnant Pacific oceanic slab are inferred to have transported preexisting recycled Pacific oceanic crust from the MTZ into the overlying asthenosphere mantle. The upwelling Pacific oceanic crust reacted with asthenospheric mantle peridotite (i.e., DMM) to produce mantle pyroxenite, whose partial melting at shallow depths generated the CBs. Considering the low δ98/95Mo values of both the CBs and coeval potassic basalts from NE China, we speculate that there may be a low δ98/95Mo reservoir in the MTZ beneath NE China. Key Points: The Chifeng basalts (CBs) exhibit relatively low 3He/4He ratios ranging from 0.8 to 5.5 RaThe mantle source end‐members of CBs were the depleted mid‐ocean‐ridge basalt mantle and recycled oceanic crustA low δ98/95Mo reservoir is inferred in the mantle transition zone beneath NE China [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic interactions of the cross‐section distribution of sectoral price changes and the output growth in the Chinese economy. We compare in depth the results of Granger causality tests, Impulse Response, and Forecast Error Variance Decompositions from Mixed Sampling Frequency Vector Autoregression (MFVAR) with those from common frequency vector autoregression (VAR). It shows that potential causalities for inflation, relative price variability, relative price skewness, and output growth can be successfully detected by the MFVAR. The cross‐section distribution of sectoral price changes stands to be a fundamental determinant of fluctuations in the aggregate economy, not only in the short run but also in the long run. Moreover, the empirical results are robust to the identification restrictions imposed as well as to alternative measures for model variables. Our findings are in line with the predictions of a standard sticky‐price model, and thus pricing frictions are important factors behind the short‐run nonneutrality of nominal shocks. We highlight the primacy of the information contained in the higher‐order moments of cross‐section distribution of sectoral price changes. We propose that policy authorities should make proper use of all of the valuable information available, particularly those embodied in the distribution of sectoral prices. [ABSTRACT FROM AUTHOR]

A large‐scale AC/DC hybrid power network has recently been constructed in China because of the fast development of high voltage direct current (HVDC) technology. The receiving AC system must have sufficient system strength to guarantee the safe operation of the DC side. The paradox is that raising the AC side system's strength will increase the short‐circuit current (SCC) level, endangering the safety of the system. In order to resolve the contradiction between the system strength of multi‐infeed DC receiving‐end power networks and SCC, this paper proposes an optimal transmission switching strategy. Firstly, the system strength is characterised by the multi‐infeed short‐circuit ratio (MISCR), followed by constructing a comprehensive constraint set that considers the MISCR and SCC simultaneously. Secondly, to optimize SCC with MISCR and N−1 safety constraints, a mixed integer linear programming model is developed. Finally, to enhance the computing performance for large power systems, a new connectivity constraint set and decision space reduction techniques are provided. The proposed method is tested for effectiveness using the IEEE 30‐bus and IEEE 118‐bus systems. [ABSTRACT FROM AUTHOR]

A model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO. [ABSTRACT FROM AUTHOR]

Storm‐flood‐dominated deltas are sedimentary systems in which a complex interplay of hydrodynamic processes occurs during storms (e.g. tropical cyclones) due to the coeval action of continental and oceanic processes. This paper reports on a superbly exposed, 135.5 m thick stratigraphic succession of the Pleistocene Cholan Formation exposed along the Da'an River, Taiwan. The sedimentary succession comprises alternating mudstone and sandstone, is mostly fine‐grained, and exhibits multiple event beds that record deposition during tropical cyclones and post‐depositional deformation features produced during earthquakes. Detailed facies analyses reveal that deposition towards the base of the succession occurred in the palaeo‐Taiwan Strait in storm‐flood‐dominated prodelta and delta‐front environments passing upwards into delta‐plain environments. Tropical cyclone beds are encountered throughout the subaqueous storm‐flood delta successions, and are identified by (i) trough cross‐stratified sandstone bedsets with erosive bases that contain both mud clasts and mudstone beds, (ii) sandstone with aggrading wave ripples and (iii) hummocky cross‐stratified sandstone with rare gutter casts filled with coal fragments and shell remains. Tropical cyclone deposits are either top‐down burrowed or capped by massive or laminated mudstone. Seismites are rare and are mainly recognised through soft‐sediment deformation of beds; they do not show evidence of slope failure. Compared to storm‐flood delta successions described elsewhere, the Cholan Formation shows significantly fewer oscillatory‐generated sedimentary structures and gutter casts. This difference is attributed to the Cholan Formation being deposited in and along the margin of a strait characterised by strong shore‐parallel currents and relatively small storm waves due to its position between Taiwan and mainland China. This study refines depositional process interpretations of the Cholan Formation, provides criteria for recognising storm‐flood delta deposits in tectonically active straits with multiple sediment sources fed by steep drainages and short river catchments, and provides additional criteria for recognising tropical cyclone deposits in shallow‐marine settings. [ABSTRACT FROM AUTHOR]

In this study, CMIP6 and single model initial‐condition large ensemble (SMILE) simulations were applied to reveal possible changes in the spatial coverage (RSC) of summer extreme precipitation (≥99th percentile) in China. Four different approaches were applied to provide a comprehensive assessment of RSC changes. Results indicate that the trend of RSC in summer for the period 1961–2020 in China is underestimated by raw simulations of CMIP6 multi‐model ensembles. Further analyses suggest that the confidence in model simulations reflecting the observed change can be improved based on the two observation constraint approaches and SMILE‐based approach. In addition, the reliability of the spatial distribution can also be improved. Projection results indicate that the RSC of summer extreme precipitation increases consistently with the increment of global warming across the different approaches. Among them, the results based on observational constraints and the SMILE‐based approach show enhanced reliability and present larger RSC changes than by directly using CMIP6 ensembles. In conclusion, extreme precipitation in summer is expected to become more widespread in China as the level of global warming increases. Plain Language Summary: Extreme precipitation causes severe impacts on agriculture, economy, and human health. However, it is projected by models to change in China but with low consistency. Thus, this paper provides an assessment of the projected characteristics of the ratio of spatial coverage (RSC) for extreme precipitation in China combined with multiple evidence and approaches. CMIP6 and single model initial‐condition large ensemble (SMILE) simulations are applied. Four approaches based on the statistics principle and physical interpretability are conducted to comprehensively assess change in RSC. Results reveal that extreme precipitation will become more widespread in China. Among them, results based on observation constraint approaches and SMILE‐based approach, which show enhanced accuracy and reliability, present larger RSC than directly using CMIP6 ensembles. It indicates that extreme precipitation will be more widespread in China combined with multiple evidence and methods. This study provides some scientific foundation for climate mitigation and adaptation. Key Points: Observed trend in RSC in China increased significantly in summer for the period 1961–2020 but is underestimated by CMIP6 multi‐model ensemblesConfidence is improved based on observation constraint and SMILE‐based approaches to raw model simulationsExtreme precipitation in summer will become more widespread, as supported by multiple evidence lines and approaches [ABSTRACT FROM AUTHOR]

As a group of ectoparasites, chiggers (larvae of chigger mites) are the exclusive vector of scrub typhus (tsutsugamushi disease). Rodents are the most important hosts of chiggers. The Anderson's niviventer rat, Niviventer andersoni, is an endemic species of rodent in China. However, few studies have involved this endemic rodent species and its ectoparasites including chiggers. According to the field investigation in five provincial regions of southwest China between 2001 and 2019, this paper retrospectively analysed the infestation and distribution of chiggers on the body surface of N. andersoni in southwest China for the first time. From 77 Anderson's niviventer rats captured, a total of 527 chiggers were collected and they were identified as 39 species and nine genera in two subfamilies of family Trombiculidae. Of 39 chigger species identified, Leptotrombidium deliense and L. scutellare are the most important vectors of scrub typhus in China. The overall infestation indexes were PM = 29.87%, MA = 6.84 and MI = 22.91, and the indexes of chigger mite community were Mf = 39, H' = 2.60, E = 0.71 and D = 0.12. The dominant chigger species are L. wenense, L. xiaguanense and L. fujianense with a total Cr = 51.04%, among which L. wenense is one of the six main vectors of scrub typhus in China. The dominant chigger species are of aggregated distribution among different individuals of the rats. [ABSTRACT FROM AUTHOR]

The term laminopathies refers to a group of congenital diseases characterized by accelerated degeneration of human tissues. Mutations in LMNA, LMNB, ZMPSTE24, and other genes lead to structural and functional abnormalities associated with lamins. One subtype of laminopathy is the generalized lipodystrophy‐associated progeroid syndrome (GLPS), which occurs in patients with heterozygous mutations of the LMNA gene c.29C>T(p.T10I). This paper reports the first case of GLPS in China and compares the clinical features of other GLPS patients with literature reports. A 16‐year‐old male patient was treated for diabetic ketoacidosis, presenting with premature aging appearance, systemic lipodystrophy, severe fatty liver, and decreased bone density. After peripheral blood DNA extraction and second‐generation sequencing, a heterozygous mutation of exon 1 of the LMNA gene c.29C>T(p.T10I) was detected. This case of GLPS may provide a diagnostic and therapeutic basis for potential patients. [ABSTRACT FROM AUTHOR]