Bitcoin is one of the most successful cryptocurrencies, and research on price predictions is receiving more attention. To predict Bitcoin price fluctuations better and more effectively, it is necessary to establish a more abundant index system and prediction model with a better prediction effect. In this study, a combined prediction model with twin support vector regression was used as the main model. Twenty-seven factors related to Bitcoin prices were collected. Some of the factors that have the greatest impact on Bitcoin prices were selected by using the XGBoost algorithm and random forest algorithm. The combined prediction model with support vector regression (SVR), least-squares support vector regression (LSSVR), and twin support vector regression (TWSVR) was used to predict the Bitcoin price. Since the model’s hyperparameters have a great impact on prediction accuracy and algorithm performance, we used the whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) to optimize the hyperparameters of the model. The experimental results show that the combined model, XGBoost-WOA-TWSVR, has the best prediction effect, and the EVS score of this model is significantly better than that of the traditional statistical model. In addition, our study verifies that twin support vector regression has advantages in both prediction effect and computation speed.

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress.

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively.

Based on the principal component analysis and k-means cluster analysis, the paper classified and analyzed the 310 stations of the Beijing subway. According to data such as the number of people entering and leaving each station of the Beijing Metro, the line to which the station belongs, and whether it is a transfer station by period. We selected a total of 52-time variables to participate in the PCA analysis and finally got two principal components and named them principal component 1 and principal component 2. Subsequently, we clustered the Principal Component 1 and Principal Component 2 scores of 310 sites and the latitude and longitude of the sites in k-means clustering. According to the elbow method and the contour coefficient, the final clustering results into five clusters are classified. The five clusters of stations have strong regional characteristics, and the stations of each type are more concentrated. The results of principal component analysis and cluster analysis have passed the test and have good convincing power. Finally, we conduct an in-depth analysis of the two categories with the most stations. Cluster 4 contains the largest number of stations, located in the core area of Beijing's urban area, and the passenger flow is significantly higher than other areas. Cluster 1 contains the second largest number of stations, mainly including Fengtai District. The region's economy is underdeveloped, the passenger flow is small, and there are peak and trough periods. This paper performs cluster analysis on high-dimensional data. The principal component analysis is used to reduce the dimension of high-dimensional data and retain the time and space dimensions required for clustering. The final result was in line with expectations. It is improved the shortcomings of k-means clustering for high-dimensional data. This article analyzes the daily passenger flow in Beijing, and the analysis results may be random. In the subsequent analysis, more data can be added to make the analysis more accurate.