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The psychology of sustainability in terms of sustainable development for work–family conflict (WFC) in organizations has become increasingly relevant in sustainable human resource management research, and the pursuit of sustainable creative performance is an important aspect of corporate sustainable development. Taking a regulatory focus perspective, this study integrates a moderated-mediation model to examine the relationship between work–family conflict and sustainable creative performance. Data were collected from 203 supervisor–subordinate dyads from two branches of a high-end manufacturing enterprise in Shanghai, China. The results reveal that work–family conflict has a positive effect on sustainable creative performance through job crafting, and the effect is stronger when individuals show higher promotion regulatory focus. We discuss the theoretical and practical implications of the study, along with potential future research directions.

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.

Based on the samples of 207 prefecture-level cities in China from 2002 to 2010, this study uses the exogenous shock of China’s first incorporation of environmental regulations into the assessment of local officials as a quasi-natural experiment, and applies the continuous difference-in-differences (DID) method to examine the impact of environmental regulation assessment pressure on local fiscal revenue. We find that the target pressure of environmental regulations for local officials has contributed to the growth of local fiscal revenue, and for each 0.01 increase in the targets of pollution emission reduction, local fiscal revenue increases by 0.204%. This result demonstrates a strong robustness. Our mechanism analysis further confirms that local governments employ various strategies to alleviate the financial burden induced by environmental regulations. These strategies include (1) not only adopting the “grabbing hand” approach, which involves extracting fiscal revenues from the market by reducing the fixed asset investment of local governments and enhancing the collection of pollution fees from enterprises, (2) but also utilizing the “helping hand” approach to augment financial resources, such as improving tax administration efficiency by cracking down on profit under-reporting and income tax evasion among enterprises. Moreover, the heterogeneity analysis suggests that the impact of environmental regulations on fiscal revenue is contingent upon the level of local fiscal self-sufficiency. This article offers empirical evidence to assist governments in devising effective environmental policies that aim to achieve a harmonious balance between economic growth and environmental protection.

China’s steel industry is an energy-intensive sector. Synergistic reduction of emissions of CO2 and air pollutants (SO2, NOx, and PM2.5) in the steel industry has an important practical significance for climate change and air pollution control. According to the CO2 emission reduction intensity targets (CERO) and air pollutant emission targets (PERO) for 2020 and 2030, 28 types of energy-saving and emission reduction technologies (20 types of carbon reduction technology and eight types of air pollution end-of-pipe technology) were selected for examination, and a two-stage dynamic optimization model with collaborative implementation of PERO and CERO was built to assess the near future (2015–2020) and long-term (2020–2030) implementation plans for synergistic emissions reduction of CO2 and air pollutants. The results show that in the near future, the implementation of PERO will have a greater synergistic effect on CO2 emission reduction. CO2 emission reduction under PERO in 2020 will be 97 million tons (Mt) higher than that of CERO, an increase of nearly 26%. However, the effects of implementing CERO are better in the long run. Under CERO, the emission reductions of SO2, NOx, and PM2.5 in 2030 are 2.44 Mt, 1.47 Mt, and 0.86 Mt, respectively, and 7%, 4%, and 5% higher than the implementation of PERO. As far as marginal abatement cost is concerned, in the near future, the marginal abatement costs of CO2 and air pollutant equivalents are 1.06 yuan/kgCO2 and 133 yuan/kg pollution equivalent (pe) under PERO, which are 23% and 11% lower than that of CERO, while in the long run, the marginal abatement costs of CO2 and pollutant equivalents under CERO are 0.025 yuan/kgCO2 and 2.73 yuan/kgpe, about 96% and 95% lower than that of PERO.

In today’s increasingly competitive coffee industry, the point of running a good coffee shop is no longer to run a coffee and beverage shop simply, but to focus on the quality of service and the value that customers feel as a result. Previous studies have mainly discussed the customer satisfaction and behavioral intention of restaurants, while few studies have explored the influencing factors of customer satisfaction and behavioral intention of chain coffee shops. Given that the perceived service quality theory and DINESERV model can effectively predict customer satisfaction and behavioral intention, this study took 385 consumers in the first Starbucks Reserve flagship store in China as survey objects. SmartPLS 3.0 software was used to explore the relationship among respondents’ perceived service quality, customer perceived value, satisfaction, and behavioral intention. The results show that service quality has a partially significant positive effect on perceived value. Perceived service quality and customers’ perceived value both have significant positive effects on satisfaction. Customers’ satisfaction has a significant positive effect on their behavioral intention. These results indicate that enterprises should strengthen the emotional bond between consumers and enterprises and improve the reliability, assurance, and empathy of perceived service quality to create a better emotional resonance between consumers and the Starbucks brand to improve customer satisfaction. At the same time, it should also promote the symbolic perceived value of Chinese consumers to Starbucks to realize the sustainable development of coffee-shop operation and consumer repurchase. This study expands the research on the service quality, perceived value, and behavioral intention of coffee chain enterprises in the context of non-habitual coffee-drinking countries. Moreover, it provides case support for the operation and research of regional cultural consumption habits of international catering chain enterprises.

“Low expectation of success” is proposed as a factor in relation to problem behavior in the comprehensive theoretical model of problem behavior (CTMPB). Based on the framework of CTMPB, this study aimed to further examine the association between confidence in the future (including parental confidence in the adolescent future and adolescent self-confidence in the future in this study) and problem behavior. A nationwide representative sample data from the China Education Panel Survey (CEPS) were used in this study. A total of 8328 middle school students and their parents were included. Among the students, 4081 (49.0%) were boys, the mean age was 14.53 years (SD = 0.70); among the parents, 3908 (46.9%) were male, the mean age was 41.15 years (SD = 5.14). The results reveal that parental confidence in adolescent future (PCAF) can play a role in adolescent problem behavior through adolescent perceived parental confidence in adolescent future (APPCAF) and adolescent self-confidence in the future (ASF). Low levels of both PCAF and ASF are vulnerability risk factors in adolescent problem behavior. Lowering PCAF and ASF might increase the likelihood of engaging in problem behavior. However, this study was based on cross-sectional data only and is required to be supported by further experimental or longitudinal studies.

China’s air transportation system is evolving with its own unique mechanism. In particular, the structural features of the Chinese air passenger network (CAPN) are of interest. This paper aims to analyze the CAPN from holistic and microcosmic perspectives. Considering that the topological structure and the capacity (i.e., available passenger-seats) flow are important to the air network’s performance, the CAPN structure features from non-weighted and weighted perspectives are analyzed. Subnets extracted by time-scale constraints of one day or every two-hours are used to find the temporal features. This paper provides some valuable conclusions about the structural characteristics and temporal features of the CAPN. The results indicate that the CAPN has a small-world and scale-free structure. The cumulative degree distribution of the CAPN follows a two-regime power-law distribution. The CAPN tends to be disassortative. Some important airports, including national air-hubs and local air-hubs, remarkably affect the CAPN. About 90% of large capacities exist between airports with large degrees. The properties of CAPN subnets extracted by taking two hours as the time-scale interval shed light on the air network performance and the changing rule more accurately and microcosmically. The method of the spectral destiny estimation is used to find the implicit periodicity mathematically. For most indicators, a one-day cycle, two-day cycle, and/or three-day cycle can be found.

The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection.