Innovation is a driving force of wealth distribution. To explore its time-varying effect on income inequality, we propose a nonparametric model using the local linear dummy variable estimation ...(LLDVE) method. Based on province-level panel data from China spanning from 2006 to 2020, we find that innovation initially reduces income disparity until 2009, then exacerbates it from 2013 to 2016, and alleviates inequality again over 2018–2020. We further verify that financial permeation serves as a catalyst in the inequitable income distribution driven by innovation. However, this moderating effect reverses the relationship between green innovation and income inequality. This suggests that we should enhance the financial service towards all aspects of innovation beyond its support of green innovation.
Separating induction motor noise sources can provide an important reference basis for induction motor condition detection, noise reduction treatment, and fault diagnosis. Induction motors have ...different types of noise sources that partially overlap, and most radiate outward through the housing, so it is difficult to separate these noise sources. Therefore, a single-channel induction motor noise source separation and identification method, based on adaptive scale-space modal extraction (ASSME) is proposed. Firstly, the adaptive scale-space mode extraction method is proposed by constructing the electromagnetic feature scale space and the adaptive penalty factor. The simulation results show that this method solves over-decomposition problems in the classical scale-space variational mode decomposition and the difficulty in balancing the harmonic and shock modes. Secondly, motor noise experiments are conducted to construct blind source separation multi-channel inputs using the adaptive scale-space modal extraction method, judging the validity of the modal components using correlation and the variance contribution rate. Finally, robust independent component analysis (RobustICA) is used to extract independent noise components and identify these noise sources by power spectral density and envelope analysis. The results show that the multi-channel input signals obtained by the proposed method are more accurate and practical than those obtained by other methods. The independent components extracted through this noise source separation method are: electromagnetic noise of different orders, aerodynamic noise, and switching frequency noise.
Enhancing corporate environmental performance is crucial to addressing the global climate crisis. The extant research has explored the factors influencing environmental performance at the ...macroeconomic level. In contrast, this study focuses on micro-firms and selects a sample of Chinese-listed companies from 2011 to 2021 to investigate how the peer effect of digital transformation impact corporate environmental performance. Machine learning and textual analysis are adopted to measure digital transformation. Environmental performance is measured by corporate carbon emission reduction and the environmental score in the environmental, social, and governance (ESG) rating. This paper confirms that the industry and regional peer effects of digital transformation contribute to environmental performance. The industry peer effect of digital transformation can improve corporate environmental performance by promoting innovation, while the regional peer effect of digital transformation can alleviate corporate financing constraints, improving its environmental performance. This paper can serve as a reference for companies to explore sustainable development and for governments to formulate environmental protection policies.
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•This study empirically verifies the peer effects of digital transformation.•The peer effects of digitization contribute to environmental performance.•The moderating roles of innovation and financing constraints are demonstrated.•Industry peer effects and regional peer effects are explored in detail.
To investigate the time-varying net environmental impact of Information and Communication Technology (ICT), we apply the local linear dummy variable estimation (LLDVE) method using a panel data ...consisting of 63 countries for the period 1995–2017. Our analysis reveals that ICT increases CO2 emissions until 2004, while reducing them after 2008, regardless of the national income level. We further uncover that the positive environmental impact of ICT on high-income countries is about 10 times greater than that on middle-income countries over time. These findings indicate that the development of ICT should be encouraged to alleviate carbon emissions on a global scale, especially for middle-income countries, given the benefits of an improved technology absorption rate on the mitigation effect in high income countries.
•Examine the environmental impact of information and communication technology (ICT).•Propose non-parametric panel models for high-income and middle-income countries.•ICT has time-varying impact on carbon emissions.•ICT increases carbon emissions from 1995–2004, after which the trend reverses..•ICT has a greater reduction impact in high-income countries.
To empirically gauge the efficacy of energy policies, we propose a non-parametric method to investigate the relationship between economic growth and energy consumption from both time and space ...perspectives. Specifically, we rely on the local linear dummy variable estimation (LLDVE) method to explore the time-varying province-specific trends, the common trend, and the coefficients based on panel data from 26 provinces in China from 1995 to 2017. We find that the promotion effect of energy consumption on economic growth changes over time, as evidenced by the inverted U shape of the relationship. Moreover, the non-parametric model captures such an effect better than the parametric model. With the dual goals of sustainable economic growth and carbon emissions reduction in mind, we classify the sample according to the degree of carbon intensity, which indicates that energy efficiency should be improved in high-carbon development areas, while more attention should be paid to investment and innovations in low-carbon development areas.
•Investigate the economic growth-energy consumption relationship.•Propose a non-parametric panel data model to this topic.•Energy consumption has time-varying impacts on economic growth.•Energy consumption promotes the economy differently across provinces in China.•Heterogeneous energy policy for industrial sectors with different carbon intensities.
This paper aims to model the extreme risk spillovers between crude oil and Chinese energy futures markets to assess the effect of excessive oil price volatility on Chinese energy sectors. To this ...end, we set up a Generalized Autoregressive Conditional Heteroskedasticity - Extreme Value Theory Value-at-Risk specification (or GARCH-EVT-VaR hereafter) to flexibly model extreme risks. Moreover, we focus on two international crude oil futures markets and ten Chinese energy futures markets to measure the extreme risk spillovers. Our findings point to two main results. First, we find significant evidence of extreme risk spillovers from the two international crude oil markets to Chinese energy futures markets, which are asymmetric. More specifically, the spillover effects across extreme risks are more significant than those measured with the return series. Second, some Chinese energy future markets also exhibit internal extreme risk spillovers from the petrochemical sector to the coal sector. These findings reveal the potential vulnerability of Chinese energy sectors and call for active risk management policies to better hedge Chinese energy futures markets against extreme events.
•We model the extreme risk spillovers between oil and Chinese energy futures markets.•We show risk spillovers from the two international oil markets to Chinese energy futures markets.•Some Chinese energy future markets also exhibit intra extreme risk spillovers.
During engine operation, the crankshaft journals are prone to wear, resulting in a decrease in surface matching accuracy and abnormal vibration and noise. Therefore, it is important to strengthen the ...surface and improve the wear resistance. Laser cladding, which involves multiple disciplines, such as automation control, optics, fluid mechanics, heat transfer, etc., is an effective method. In this paper, based on the finite element method, a numerical model of the laser cladding for the crankshaft journal was established. A disk laser was used to laser clad Fe45 powder on the ASTM1045 substrate. The multi-physics field coupling evolution mechanism of the laser cladding process was quantitatively revealed. The cladding temperature, liquid metal flow rate, and stress are positively correlated with the laser power. Combined with the cellular automata method, the microstructure evolution model during the solidification was established, and the morphology and size distribution of the grains in the cladding layer was obtained. In the middle area of the cladding layer, the grains are mostly columnar and equiaxed crystals. At the same time, the change of material physical parameters with temperature was taken into account in the model, which makes the numerical simulation results more accurate. The numerical simulation results were compared with the experimental results through the microscopic characterization experiment, and the accuracy of the numerical model was verified. Finally, through a friction-wear test, the wear resistance between the cladding layer and the substrate is compared. The average wear amount of the substrate is 1.86mm3 and the average wear amount of the cladding layer is 0.64mm3. The experiment shows that the wear resistance of the cladding layer is higher than that of the substrate, and the wear rate is reduced by 65.59%.
•Laser cladding on the crankshaft can improve the crankshaft wear resistance.•A coupled multi-physics field model is established to reveal the evolution law.•The microstructure evolution model is established by the cellular automata method.•Theoretical model is validated by microscopic characterization experiment.•The wear resistance of crankshaft is proven by white-light interference experiment.
In reality, investors are uncertain about the dynamics of the risky asset returns (e.g., the expected returns and the correlation between the returns of two risky assets). Consequently, investors ...make robust investment decisions with special concerns on the expected returns and correlations. In this paper, we propose a hierarchical rule for robust investment between two risky assets: select the relatively safe asset first and then decide how much to invest in the relatively risky asset to hedge the ambiguity embedded in the relatively safe asset. After introducing criteria for relative riskiness and cross-hedging for investors with a constant relative risk averse (CRRA) utility, we find that a typical investor would equally invest in the two risky assets regardless of their correlation when they are indistinguishable from the riskiness perspective. Furthermore, the investor will take a long or short position on the relatively risky asset if it can work as the cross-hedging instrument due to their correlation; otherwise, it will not be traded at all. These results provide a unified explanation for the observed “under-diversification”, “home bias”, and “portfolioinertia” in financial markets from the cross-hedging point of view.
•A two-stage sound-vibration signal fusion method effectively improved the fault diagnosis effect of rolling bearings.•The two-stage sound-vibration signal fusion algorithm enriched the fault ...characteristic information and improved SNR significantly.•Increasing the number of sound measuring points can improve the effect of fault feature extraction;•The combination of gray B-type correlation degree and empirical mode decomposition reduced the influence of background noise.
Sound-vibration signal fusion methods are widely applied in fault diagnosis, but the acquisition of the sound signal is obviously affected by the position of the measurement points, and it is difficult to detect the weak fault characteristics under by strong background noise. In this paper, a two-stage sound-vibration signal fusion algorithm is proposed, which enriches the states information of the bearing system and reduces the influence of background noise. In the first stage, the fault features of sound signals at multiple measuring points are combined and weighted by gray B-type correlation degree, and then the features of signals are extracted by empirical mode decomposition and kurtosis superposition; In the second stage, the sound fusion signal and vibration signal are fused again by sampling frequency unification, and the weak fault detection of rolling bearings are realized by combining the fault characteristics of sound and vibration signals. Experimental results show that the two-stage signal fusion improves the fault feature detection accuracy significantly, and the signal-to-noise ratios of the fault features are enhanced obviously. This research provides a new fusion method for fault diagnosis of rolling bearings, which is helpful for the status monitoring of bearing systems.
