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In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensively assess the ESG performance of the solid waste treatment industry and identify company types, this study constructs a targeted ESG evaluation index system based on existing literature, SASB industry standards, and company reports and utilizes a random forest approach combined with K-means clustering to determine indicator weights. Based on this index system, the paper evaluates the ESG performance of 71 solid waste disposal companies (SWDCs) from 2013 to 2021 and identifies their ESG types from static and dynamic perspectives. In the static view, company types are determined based on annual ESG performance, while the dynamic view considers time-series changes to observe the evolution of company ESG types. The results show that the overall ESG performance of SWDCs falls within the 2-8-point range, indicating a noticeable high-low imbalance. Key initiatives to improve ESG performance in this industry include enhancing waste management measures, developing emergency plans, and reinforcing ESG disclosure. From a static perspective, this paper can identify companies into three categories: delayed development, single-wheel-driven, and coordinated development. Finally, from a dynamic perspective considering the time factor, companies are further subdivided into five types: continual leading, growth catch-up, slow progress, fluctuating change, and retrogressive inertia. This study not only provides targeted recommendations for different types of ESG companies but also helps various sectors of society better understand the ESG conditions of this high environmental risk industry, thereby enhancing the regulation and support for its sustainable development. [Display omitted] • ESG evaluation index system for solid waste disposal companies is proposed. • A random forest method based on k-means clustering is used to construct the index. • Comparing E, S, and G scores with industry averages to identify static ESG types. • Analyzing trends in annual static ESG types to identify dynamic ESG types. • Identify companies as three static ESG types and five dynamic ESG types. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as 'dimensional curse' and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading. • We investigate the nexus between transportation facilities and green development. • We employ the double machine learning model to perform quasi-merge regression. • We affirm this positive effect from the micro enterprise level. • We verify the mediation channels of service industry agglomeration and green innovation level. [ABSTRACT FROM AUTHOR]

A significant milestone in China's carbon market was reached with the official launch and operation of the National Carbon Emission Trading Market. The accurate prediction of the carbon price in this market is crucial for the government to formulate scientific policies regarding the carbon market and for companies to participate effectively. Nevertheless, it remains challenging to accurately predict price fluctuations in the carbon market because of the volatility and instability caused by several complex factors. This paper proposes a new carbon price forecasting framework that considers the potential factors influencing national carbon prices, including data decomposition and reconstruction techniques, feature selection techniques, machine learning forecasting techniques for intelligent optimisation, and research on model interpretability. This comprehensive framework aims to improve the accuracy and understandability of carbon price projections to respond better to the complexity and uncertainty of carbon markets. The results indicate that (1) the hybrid forecasting framework is highly accurate in forecasting national carbon market prices and far superior to other comparative models; (2) the factors driving national carbon prices vary according to the time scale. High-frequency series are sensitive to short-term economic and energy market indicators. Medium- and low-frequency series are more susceptible to financial markets and long-term economic conditions than high-frequency series. This study provides insights into the factors affecting China's national carbon market price and serves as a reference for companies and governments to develop carbon price forecasting tools. • A hybrid carbon price prediction model named FS-CEEMDAN-VMD-GWO-LightGBM is proposed. • Accurately predicted the national carbon price in China using multi-influence factor time series data. • Using a data decomposition and reconstruction strategy, the model's accuracy was improved. • The SHAP algorithm is used to assess the impact of various influencing factors on the fluctuations of carbon prices. [ABSTRACT FROM AUTHOR]

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict. • We propose a novel probability forecast model QTCN for carbon prices. • The impact of economic policy uncertainty and geopolitical risks are considered. • The prediction accuracy of QTCN outperforms other competing models. • QTCN-based trading strategies yield higher profits than a point forecast-based one. [ABSTRACT FROM AUTHOR]

China pushed for the unification of markets for natural resources and emissions, wherein initial allocation mechanisms have a significant impact on how well the market functions. To identify how the market works well, this paper builds an integrated framework, comprised of permits initialization and reallocation processes. Additionally, we simulate variables by 2030 through scenario analysis and machine learning methods. At the unified energy-carbon market, GDP could increase by more than 49%, while the total energy consumption and the total carbon emission increase by around 410 million tons and 711 million tons, respectively, in China. This illustrates how joint trading markets favor economic growth and emission reduction simultaneously. Besides, most of China's provinces obtain improved energy consumption and carbon emission intensities, which proves the effectiveness and importance of the unified market. However, two rebound effects are found, one is that the increased investment is not always promoting positive effects, and the other is that provinces with higher GDPs have less incentive to cut their energy use. Further, the accumulated per capita initialization type favors energy conservation, while per GDP initialization benefits economic growth and carbon reduction. It's needed that simultaneously constructing an economic and efficient incentive mechanism while effectively playing up the government's role. [Display omitted] • Energy and carbon joint markets have economic and environmental co-benefits. • A novel framework is designed enabling the exchange of energy and carbon permits. • The influence of socio-economic and climate change scenarios in the future is considered. • The influence of initial permits allocation modes on market performance is identified. • Promotion measures are needed especially for provinces with higher economic outputs. [ABSTRACT FROM AUTHOR]

Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavelet's layers in PM10 forecasting, an innovative coupled model based on WT, long short-term memory (LSTM), and SAE (stacked autoencoder) are proposed. This study designs a crossover experiment with 960 high- and low-frequency components by wavelet decomposition and predicts each component with SAE-LSTM based on 12 samples from different regions. The results indicate that the developed model outperforms other BiLSTM (Biredictional LSTM) and LSTM based on some error evaluation indicators (i.e. Nash-Sutcliffe efficiency coefficient (NSEC)), and compared with other steps, the accuracy of two-step prediction is the highest in view of root mean squares error (RMSE). In addition, for 12 samples, the prediction accuracy by using high layers is higher than that by adopting low layers for decomposing them. This paper fixes the optimal wavelet' layers in PM10 prediction, which provides a meaningful reference in other prediction scenarios based on the application of WT. • A novel WT-SAE-LSTM is proposed to forecast PM10 based on twelve samples in China. • Compared with the advanced machine learning algorithms (e.g., BiLSTM), SAE-LSTM has better forecasting performance. • The optimal wavelet's layers are determined for different samples. [ABSTRACT FROM AUTHOR]