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Under "dual-carbon" goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately. [ABSTRACT FROM AUTHOR]

Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward. [ABSTRACT FROM AUTHOR]

Using 2009 to 2020 data for Chinese A-share listed firms in Shanghai and Shenzhen, we examine how much state ownership in firms is most conducive to improving corporate innovation quantity and quality. Our results are as follows: First, state ownership enhances corporate innovation by alleviating financing constraints (resource availability) but inhibits corporate innovation by weakening risk-taking (innovation willingness) and increasing agency costs (resource efficiency), leading to an inverted U-shaped relationship between state ownership and corporate innovation. Second, the percentage of state ownership that maximises innovation quality differs from that of maximising innovation quantity. State-owned enterprises (SOEs) can choose an appropriate state-ownership structure according to the actual situation in the mixed-ownership reform process. Third, non-state-owned shareholders' control right negatively moderates the inverted U-shaped relationship between state ownership and innovation quantity but do not significantly moderate the inverted U-shaped relationship between state ownership and innovation quality. Fourth, foreign direct investment (FDI) negatively moderates the inverted U-shaped relationship between state ownership and innovation quantity and quality. Our findings reveal the impact of state ownership on corporate innovation and its transmission path, providing empirical evidence for optimising the ownership structure based on a corporate innovation perspective in the process of SOE mixed-ownership reform. [ABSTRACT FROM AUTHOR]