Abstract:A spatial approach that incorporates three economic components and one environmental factor has been developed to evaluate the dynamic behaviours of the rural areas at a provincial level. An artificial fish swarm algorithm with variable population size (AFSAVP) is proposed for the spatial problem. A functional region affecting index (Θ) is employed as a fitness function for the AFSAVP driven optimisation, in which a gross domestic product (GDP) based method is utilised to estimate the CO 2 emission of all prov… Show more
“…The difference in green finance development level is related to the country’s economic development level. The difference in carbon dioxide emission intensity is related to economic scale, energy structure technology, industrial structure, foreign trade, etc., [ 26 ].…”
This paper uses the entropy method to estimate China’s green financial development from four aspects, namely, green credit, green securities, green insurance, and green investment, based on the provincial-level panel data from 2008 to 2019. The spatial Durbin model (SDM) is adopted to estimate the spatial effect of green finance on carbon emissions. We then compare the heterogeneous effect in the South and North of China. The results show that China’s green financial development can significantly reduce carbon emissions, and regional heterogeneities are obvious. In the South of China, this effect from local and adjacent regions is not significant, while on the whole, green finance can significantly reduce carbon emissions; but for Northern China, this effect is not significant; nationally, the development of green finance and carbon emissions in adjacent areas showed an inverted U-shaped relationship. China’s green financial development and carbon emissions also showed an inverted U-shaped relationship. These results suggest that the effect of green finance development on carbon emissions exhibits substantial regional heterogeneity in China. Our paper provides some concrete empirical evidence for policymakers to formulate green financial policies to achieve the double carbon goal in China.
“…The difference in green finance development level is related to the country’s economic development level. The difference in carbon dioxide emission intensity is related to economic scale, energy structure technology, industrial structure, foreign trade, etc., [ 26 ].…”
This paper uses the entropy method to estimate China’s green financial development from four aspects, namely, green credit, green securities, green insurance, and green investment, based on the provincial-level panel data from 2008 to 2019. The spatial Durbin model (SDM) is adopted to estimate the spatial effect of green finance on carbon emissions. We then compare the heterogeneous effect in the South and North of China. The results show that China’s green financial development can significantly reduce carbon emissions, and regional heterogeneities are obvious. In the South of China, this effect from local and adjacent regions is not significant, while on the whole, green finance can significantly reduce carbon emissions; but for Northern China, this effect is not significant; nationally, the development of green finance and carbon emissions in adjacent areas showed an inverted U-shaped relationship. China’s green financial development and carbon emissions also showed an inverted U-shaped relationship. These results suggest that the effect of green finance development on carbon emissions exhibits substantial regional heterogeneity in China. Our paper provides some concrete empirical evidence for policymakers to formulate green financial policies to achieve the double carbon goal in China.
“…A "max-generation" is the trial number of an AF school searching for food under given initial conditions, which is one of the widely used criteria for terminating the AFSAVP simulation [21]. Table 1 summarises the batteries test conditions; in this test, the test batteries have a graphite anode and a lithium cobalt oxide cathode verified by electron dispersive spectroscopy, with a rated 0.9 Ah capacity.…”
Section: Swarm Fish Algorithm With Variable Populationmentioning
confidence: 99%
“…highly flexible ability of parametric representation and the newly developed high efficient AFSA method with a variable population size (AFSAVP) [20,21]. This paper is organised as follows.…”
Batteries are one of the most important components in many mechatronics systems, as they supply power to the systems and their failures may lead to reduced performance or even catastrophic results. Therefore, the prediction analysis of remaining useful life (RUL) of batteries is very important. This paper develops a quantitative approach for battery RUL prediction using an adaptive bathtub-shaped function (ABF). ABF has been utilised to model the normalised battery cycle capacity prognostic curves, which attempt to predict the remaining battery capacity with given historical test data. An artificial fish swarm algorithm method with a variable population size (AFSAVP) is employed as the optimiser for the parameter determination of the ABF curves, in which the fitness function is defined in the form of a coefficient of determination (R2). A 4 x 2 cross-validation (CV) has been devised, and the results show that the method can work valuably for battery health management and battery life prediction
“…A ‘max-generation’ is the trial number of an AF school searching for food under given initial conditions, which is one of the widely used criteria for terminating the simulation. 27,28 By borrowing this idea, in this paper, the AFSA is employed to optimize the signal residual to determine the adaptive prediction operator. Figure 5.The schematic diagram of AFSA.…”
The rolling element bearing is one of the most extensively used components in various rotating machinery, and it is therefore critical to develop a suitable online rolling element bearing fault-diagnostic framework to improve a rolling element bearing system’s failure protection during conditional operations. In this paper, a hybrid fault-feature extraction method by detecting localized defects and analyzing vibration signals of rolling element bearings via customized multi-wavelet packet transform is proposed, in which the swarm fish algorithm has been utilized for the minimization of signal residual to determine the adaptive prediction operator. With good properties of concurrent symmetry, orthogonality, short support and high-order vanishing moment, the multiple wavelet functions and scaling functions are defined for the hybrid fault-feature extraction, which match the diverse characteristics of hybrid fault and extract coupling features, and the proposed lifting scheme-based multi-wavelet packet transform is highly effective. Then, the proposed method is validated by rolling element bearing experimental results, which show that this approach can effectively extract the hybrid fault features of inner race and rolling element.
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