The analysis and application of a new hybrid pollutants forecasting model using modified Kolmogorov-Zurbenko filter

Comparing the EU and Chinese carbon trading market operations and their spillover effects

A carbon trading market (CTM) policy for trading carbon dioxide emission rights as a commodity was created to reduce greenhouse gas emissions. CTMs operate differently in different countries and regions, and their interactions deserve an in-depth study. This study focused on the world's largest CTM, the European Union (EU), and the CTM of China, largest carbon-emitting country. First, we evaluate the liquidity and volatility of the two CTMs. Subsequently, the VAR model is used to explore the mean spillover effect between the two markets and the BEKK-GARCH model is used to explore the volatility spillover effect between the two markets. The study concludes that: (1) The liquidity of China's CTM is better than that of the EU's CTM. (2) Both the EU and Chinese CTMs are unstable, but the volatility of the Chinese CTM is lower than that of the EU CTM. (3) Price changes in the EU and Hubei CTMs have a mutual influence. (4) There are interactions between the market fluctuations of the EU CTM and the Shanghai CTM and those of the EU CTM and the Hubei CTM. The results of this study have implications for the construction and development of CTMs in the EU and China.

Challenges to environmental governance arising from the Russo-Ukrainian conflict: Evidence from carbon emissions

The destruction of ecosystems, increase in carbon emissions, and volatility of energy prices following the outbreak of the Russo-Ukrainian conflict constitute a complex situation that environmental managers must cope with. In response, this study aims to explore the impact of the Russo-Ukrainian conflict on carbon emissions in the European Union (EU) and associated heterogeneity factors. This study utilized stacked data from 2021 to 2022 on daily carbon emissions and used the differences-in-differences (DID) model as its methodological framework. This study also provides additional analyses for the United States (US), the United Kingdom (UK), and Russia. The full-blown Russo-Ukrainian conflict led to a significant increase in carbon emissions in the EU, averaging 0.092 MtCO2. Further investigations showed that the conflict led to a significant increase in energy prices and that changes in the prices of different energy sources had a heterogeneous effect on carbon emissions. Specifically, an increase in natural gas prices drove a rise in carbon emissions, whereas an increase in oil prices led to a decrease in carbon emissions in the EU. Third, the conflict also affected countries outside the EU, including the US and the UK, which experienced significant increases in carbon emissions in contrast to Russia, which underwent a decline. Finally, the study identified four sectors - international aviation, industry, power, and residential - as the primary contributors to elevated carbon emissions in the EU. This study provides a novel perspective for exploring the interplay between conflicts and carbon emissions and offers valuable insights into shaping effective environmental management policies and measures.

Can digital economy development contribute to carbon emission reduction? Evidence from China

With the rapid growth of the digital economy, it is essential to understand its impact on carbon emissions reduction. This study uses provincial panel data from China during 2011-2019 to construct a moderating mediating effect model and a spatial panel Durbin model to examine the relationship between the digital economy and carbon emissions reduction. This study analyzes the mediating effect of the energy structure on the digital economy's impact on carbon emission reduction, and the spatial effect and regional heterogeneity of the digital economy's impact on carbon emission reduction. The findings indicate that the development of the digital economy can effectively promote regional carbon emission reductions, both directly and indirectly, with a significant spatial spillover effect. Second, the energy structure plays a significant mediating role in promoting carbon emission reduction in the digital economy, and the industrial structure has a positive moderating effect. Third, the impact of the digital economy on carbon emissions reduction has significant regional heterogeneity, and the inhibitory effect of the digital economy is more effective in the central and western provinces. This study provides a theoretical reference for achieving high-quality development of the digital economy while promoting carbon emissions reduction.

Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model

The ongoing Russia-Ukraine conflict has led to significant upheaval in the worldwide natural gas sector. Accurate natural gas price forecasting, as an essential tool for mitigating market uncertainty, plays a crucial role in commodity trading and regulatory decision-making. This study aims to develop a hybrid forecasting model, the FS-GA-SVR model, which integrates feature selection (FS), genetic algorithm (GA), and support vector regression (SVR) to investigate Henry Hub natural gas price prediction amidst the Russia-Ukraine conflict. The results show that: (1) The feature selection automates model input variable selection, decreasing the time required while improving the model's accuracy. (2) The use of genetic algorithm for selecting support vector regression hyperparameters significantly improves the accuracy of natural gas price predictions. The algorithm leads to a decrease of approximately 70% in measurement indicators. (3) During the Russia-Ukraine conflict, the FS-GA-SVR hybrid model demonstrates more consistent and accurate predictions for natural gas spot prices than the base SVR model. This study serves as a valuable theoretical reference for energy policymakers and natural gas market investors worldwide, supporting their ability to anticipate fluctuations in natural gas prices.

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

Spatiotemporal Patterns and Regional Transport of Ground-Level Ozone in Major Urban Agglomerations in China

Ground-level ozone (O3) pollution has become a serious environmental issue in major urban agglomerations in China. To investigate the spatiotemporal patterns and regional transports of O3 in Beijing–Tianjin–Hebei (BTH-UA), the Yangtze River Delta (YRD-UA), the Triangle of Central China (TC-UA), Chengdu–Chongqing (CY-UA), and the Pearl River Delta urban agglomeration (PRD-UA), multiple transdisciplinary methods were employed to analyze the O3-concentration data that were collected from national air quality monitoring networks operated by the China National Environmental Monitoring Center (CNEMC). It was found that although ozone concentrations have decreased in recent years, ozone pollution is still a serious issue in China. O3 exhibited different spatiotemporal patterns in the five urban agglomerations. In terms of monthly variations, O3 had a unimodal structure in BTH-UA but a bimodal structure in the other urban agglomerations. The maximum O3 concentration was in autumn in PRD-UA, but in summer in the other urban agglomerations. In spatial distribution, the main distribution of O3 concentration was aligned in northeast–southwest direction for BTH-UA and CY-UA, but in northwest–southeast direction for YRD-UA, TC-UA, and PRD-UA. O3 concentrations exhibited positive spatial autocorrelations in BTH-UA, YRD-UA, and TC-UA, but negative spatial autocorrelations in CY-UA and PRD-UA. Variations in O3 concentration were more affected by weather fluctuations in coastal cities while the variations were more affected by seasonal changes in inland cities. O3 transport in the center cities of the five urban agglomerations was examined by backward trajectory and potential source analyses. Local areas mainly contributed to the O3 concentrations in the five cities, but regional transport also played a significant role. Our findings suggest joint efforts across cities and regions will be necessary to reduce O3 pollution in major urban agglomerations in China.

Double decomposition and optimal combination ensemble learning approach for interval-valued AQI forecasting using streaming data

To forecast possible future environmental risks, numerous models are developed to predict the hourly values or daily averages of air pollutant concentrations using streaming data (a kind of big data collected from the Internet). On the one hand, real-time hourly data is massive and redundant, making it difficult to process. On the other hand, daily averages cannot reflect the fluctuations of air pollutant concentrations throughout the day. Therefore, a double decomposition and optimal combination ensemble learning approach is proposed for interval-valued AQI (air quality index) forecasting in this paper. In the first decomposition, considering the strong seasonal representation of AQI, the original data of each year is decomposed into four seasonal subseries on the basis of the Chinese calendar. Subsequently, we reconstruct the data of the same season in different years to get a new seasonal series to reduce the interference of seasonal changes on AQI forecasting. In the second decomposition, due to the nonlinearity and irregularity of interval-valued AQI time series, BEMD (bivariate empirical mode decomposition) is employed to decompose the interval-valued signals into a finite number of complex-valued IMF (intrinsic mode function) components and one complex-valued residue component with different frequencies to reduce the complexity of interval times series. Interval multilayer perceptron (iMLP) is utilized to model the lower bound and the upper bound simultaneously of the total components to obtain the corresponding forecasting results, which are merged to produce the final interval-valued output by an optimal combination ensemble method. Empirical study results show that the proposed model with different datasets and different forecasting horizons is significantly better than other considered models for its superior forecasting performances.

Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.

Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China

A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM_2.5) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO₂) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM_2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R²) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m³. For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of the stacked ensemble model.