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This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system. • Machine learning techniques were employed to predict green purchase intention (GPI). • Green Self-identification & Environmental Consciousness were the crucial factors in influencing GPI. • Social and health consciousness had less of an impact on influencing GPI. • Perceived personal inconvenience was reported to have no significant impact on GPI. [ABSTRACT FROM AUTHOR]

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

The relationship between government and enterprise has attracted extensive attention. Under the increasingly severe requirement of sustainable development, there is still a lack of research on the economic effect of political acuity from the perspective of enterprise's positive party value creation. We built a dictionary of political acuity and demonstrated reliability through machine learning algorithms such as K-Nearest Neighbor (KNN). Then, based on the annual reports of China's listed companies from 2013 to 2020, the relationship between political acuity, ESG atmosphere, and corporate performance is empirically discussed. Examination through the formal institutional perspective of informal institutionalization shows that the positive acuity of the Communist Party of China(CPC) and the democratic parties(DPC) will improve corporate performance, while the negative acuity will have an early warning effect on performance. Furthermore, the stronger ESG atmosphere synergized this relationship. The CPC's negative acuity has supervision effect on non-state-owned companies, and this supervisory effect still exists in the ESG atmosphere. However, whether the moderating effect is considered or not, the positive political acuity of state-owned enterprises doesn't significantly improve performance. We found that the CPC's embedding in corporate governance will lead to both economic and political benefits, and have thresholds. We constructed a political acuity dictionary and introduced text analysis and nonparametric estimation method in this paper, which provided a basis and reference for future research on political acuity at the enterprise level. Our findings also enrich the institutional theory and the embeddedness theory. Meanwhile, our research broadened the boundary of political parties, ESG, and enterprises, the integration of ideological values and formal institutions, and had practical implications for the green transformation of firms and the precise positioning of political party functions. • We build a dictionary of political acuity for the first time. This dictionary provides a new measurement perspective for the study of corporate political sentiment and sustainable development. • From the perspective of positive value creation, we examine the impact path of a company's party acuity, ESG atmosphere, and financial performance. This paper helps to expand the governance boundaries of ideology and formal institutions, political concepts, and corporate behavior. • We explored the accounting consequences of political acuity for different types of firms, and measured the economic and political effects thresholds. • We introduced machine learning, python crawler, text analysis, and nonparametric estimation methods into this paper, which helped to expand the technical approach to corporate governance and green transformation. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to the strategic planning of the supply chain for lime cultivation. The uncertainty associated with lime production due to factors such as transportation, crime, and water availability, among others, represents different challenges. To address these challenges, the authors propose the use of machine learning tools coupled with deterministic optimization to obtain optimal solutions for the supply chain for future years. This paper highlights the use of justice schemes to ensure an equitable distribution of lime production between farmers and learning machines to quantify the insecurity impact on lime production in Michoacan. The problem can be solved using a Mixed-Integer Linear Programming (MILP) model that focuses on maximizing economic benefits, minimizing the use of irrigation water in crops, and minimizing Carbon Dioxide Equivalent (CO 2eq) emissions within the supply chain. The results demonstrate that the proposed approach can effectively address the challenges facing the lime production supply chain, resulting in an optimized and equitable lime production and distribution between farmers using the Social Welfare Justice scheme. Scenario A presents a utopian viewpoint with maximum benefits and minimum CO 2eq generated emissions, corresponding to $ 4.08 × 109 and 1.94 × 106 tons of CO 2eq , respectively. This paper contributes to the growing body of literature on the use of Artificial Intelligence (AI) in supply chain management, specifically in the optimization of supply chain processes. [Display omitted] [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality, China must grasp the intricate factors driving coal market, as it addresses coal consumption and reduces the carbon footprint. While previous studies have identified certain factors, they have largely overlooked the impact of the overall industry sector on the coal market. In this paper, a hybrid causal machine learning model was proposed that combine with least absolute shrinkage and selection operator (LASSO), multiple linear regression (MLR), peter and clark momentary conditional independence (PCMCI) and interpretable machine learning. The approach employed a complex network perspective to identify 10 key factors in China's coal market from three dimensions: industrial sector, foreign trade and energy market. The hierarchical pattern of these factors is classified into two levels: direct factors, primarily encompassing crude oil futures, exchange rates and architectural decoration, and intermediary factors, mainly including the chemical industry and building material. Interestingly, this paper reaffirms the roles of crude oil futures and exchange rates in the coal market, as identified in prior studies. Additionally, it uncovers new influential factors primarily driven by the chemical industry and architectural decoration, previously unexplored. Furthermore, this paper found a significant contemporaneous relationship between crude oil futures and coal price index (0.151) as well as coal consumption (0.102), and the strong relationship between coal consumption and the Indonesian Rupiah to Chinese Yuan exchange rate (0.063) was also confirmed. Among the emerging dominant factors, the coal price index and the chemical industry (0.145), along with coal consumption and building materials (0.250), also exhibit a strong contemporaneous relationship. These findings highlight the importance of considering the entire industry sector while analyzing the coal market and promoting a cleaner transformation of the industry in China, and the paradigm presented not only applicable to the coal market, but can be extended to other industries. [Display omitted] • Better grasping key factors in China's coal market critical to carbon neutrality. • A comprehensive study encompassing industrial sector, foreign trade and energy market. • A novel hybrid machine learning models and industry-wide features are introduced. • Identification and categorization of direct and intermediary factors. • Findings offer a new paradigm for decision-making, driving cleaner production reforms. [ABSTRACT FROM AUTHOR]

Recycling and utilization of plastic waste are receiving more and more attention, and the combination of spectroscopic techniques and machine learning is expected to solve the problem of efficiently identifying and classifying plastic waste at the front end of high-value recycling. Currently, the spectroscopic techniques used for plastic waste classification include near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy (LIBS), X-ray fluorescence (XRF) spectroscopy, terahertz (THz) spectroscopy, etc., and the machine methods combined with them include traditional machine methods and deep learning methods. This paper mainly summarizes the research progress in the application of spectroscopic techniques combined with machine learning in the rapid identification of plastic waste in the past five years, focusing on the innovative research of machine learning methods in plastic identification, the relative advantages and disadvantages of various spectroscopic techniques, and the influencing factors of spectroscopic techniques in plastic identification. In addition, this paper describes the application of spectroscopic instrumentation in the plastic waste recycling industry. In the end, the paper presents an outlook on the future trajectory and potential of this field and proposes recommendations for its advancement in three key dimensions: spectroscopy, machine learning algorithms, and practical engineering applications. [Display omitted] [ABSTRACT FROM AUTHOR]

The need for high productivity in the construction field is becoming increasingly urgent, and the efficiency of loading and unloading materials by construction vehicles is one of the main influencing factors. The fill factor, which refers to the percentage of the volume of materials in the shovel bucket to the capacity of the shovel bucket, can significantly affect the productivity of construction vehicles, and thereby affect the environmental pollution caused by these vehicles. Therefore, an effective estimation of the fill factor is a prerequisite for improving productivity and suppressing pollution in construction vehicles. However, previous studies have been conducted under ideal environments, the actual harsh environments of the construction sites are not in the purview. Therefore, in this paper, the fill factor of a loader is estimated in nine construction environments, including six ideal environments and three harsh environments. First, the corresponding image processing pipelines are proposed for a variety of environmental characteristics. Then, a stereo segmentation map is constructed based on the loader bucket trajectory and the knowledge of different color space characteristics, which not only addresses the environmental limitations but also significantly improves the accuracy. Finally, an improved CNN is combined with probabilistic knowledge for the fill factor estimation, and transfer learning is also applied to improve the training speed and accuracy. The results show that a satisfactory accuracy of 96.89% is achieved for the abovementioned construction environments, which results in a more efficient and cleaner production of construction vehicles. • In this paper, the bucket fill factor estimation method in harsh construction environments is studied. • An image processing method based on environmental features is proposed. • Stereo segmentation maps are constructed by integrating the bucket trajectory and color spaces. • The proposed bucket fill factor estimation method is generalized and validated with a different-size loader. [ABSTRACT FROM AUTHOR]

Solar energy has been rapidly utilized in urban environments owing to its significant potential to fulfill the energy demand. The precise forecasting of solar energy, including solar radiation and photovoltaic power forecasting, is crucial for effective energy utilization in cities. Currently, artificial intelligence algorithms, including machine learning (ML) and deep learning (DL) algorithms, have been widely utilized in the research of solar energy forecasting and have shown excellent performance. The different research scales lead to variations in the influencing factors and forecasting approaches used in solar energy prediction. Therefore, this paper provides a comprehensive review of solar radiation and photovoltaic power forecasting research using ML and DL algorithms from a multi-scale perspective, including the meso-scale, micro-scale, and building-scale forecasting in urban environments. The aim is to summarize the state-of-the-art progress and evaluate the solar forecasting effectiveness in diverse research scenarios. The characteristic analyses of ML- and DL-based solar forecasting approaches, prediction models, forecasting horizons, inputs and outputs were conducted for different research scales. This paper discusses the existing issues and future research directions for multiscale solar energy forecasting in cities. The analyses can help researchers and engineers in identifying suitable prediction algorithms and approaches. Consequently, this exploration can contribute to the efficient utilization of solar radiation and photovoltaic power generation in multi-scale urban environments. [Display omitted] • Machine learning and deep learning-based solar radiation and photovoltaic power forecasting research in urban environment are classified from a multi-scale perspective. • The characteristics and key points of solar radiation and photovoltaic power forecasting at the meso-scale, micro-scale, and building-scale in cities are analyzed. • Solar radiation and photovoltaic power forecasting approaches, prediction models, input and output values, and forecasting horizons are discussed. • Future research directions of solar radiation and photovoltaic power forecasting in urban environment are presented. [ABSTRACT FROM AUTHOR]

Addressing the urgent need for accurate carbon emissions forecasting to support global emissions reduction goals, this paper introduces a novel approach for carbon emissions prediction that dynamically considers the spatial–temporal correlation of carbon emissions across forecasting targets. Traditional statistical and machine learning models have limitations, such as oversimplification and inefficiency in capturing evolving dynamics among multiple forecasting targets, alongside a focus on longer forecasting horizons like monthly or yearly. To tackle these, we propose an innovative Dynamic Spatial–Temporal Graph Convolutional Recurrent Network (DSTGCRN), a blend of graph convolutional and recurrent neural network structures customized for up-to-date multi-regional spatial–temporal predictions at a daily level. Comprehensive empirical analyses on datasets from China, the US, and the EU confirm DSTGCRN's superior performance and robustness across diverse geographical contexts. It outperforms the second-best among 10 baseline models, achieving 40.6%, 24.6%, and 38.5% improvements in MAE, RMSE, and MAPE, respectively, across various horizons. The incorporation of environmental data, including temperature and Air Quality Index, as supplementary predictors in the DSTGCRN model, has demonstrated its efficacy in the context of the China dataset. The DSTGCRN model's improved accuracy and daily multi-regional forecasting deepen emission dynamics understanding, supporting the development of informed and region-specific environmental policies that can respond promptly to changes. [ABSTRACT FROM AUTHOR]

Environmental managers attempt to increasingly incorporate precautionary principles into decision making. The literature lacks Machine Learning -based approaches for forewarning lifecycle environmental impacts. This paper proposes a method to support electric vehicle design. The main innovation of the work lays in merging Life Cycle Assessment (LCA) and Machine Learning foundations to provide support and awareness to designers. The present approach overcomes the present literature because it provides a method for the design phase, is based on a well-established methodology (LCA) and provides quantitative results from little inputs. The approach exploits machine Learning Methods to develop models with the design features of a generic electric vehicle (such as vehicle mass and distance traveled) in six phases (Problem definition; Data collection; Data Preparation; Modeling; Model evaluation; Model interpretation). Differently from existing environmental analyses, all stages of the product life cycle have been considered in building the database; moreover, the model provides quantitative results. Regression models and supervised algorithms were used. The obtained model can be used by product engineers, as well as those not experts on LCA. Moreover, the model guarantees the database and hypothesis's uniqueness, ensuring the results coherence and comparability. The level of accuracy obtained in the case study (error or 17%) is comparable with studies handling full environmental analysis (that should be more accurate), and outstanding, as the present case is for the design phase. Future works will focus on additional significative indicators, similar electric vehicle design and integration with prospective LCA approaches. [ABSTRACT FROM AUTHOR]

Abstract The research on the preparation of activated carbon co-activated by hydrothermal carbonization and pyrolysis was uncommon and less experiments used ultrasonic assisted impregnation. The synergistic effect of two catalysts had certain research value. There are still just a few studies on the performance prediction of activated carbon. In this paper, wheat straw, corn straw and sorghum straw were used as the raw materials. ZnCl 2 and H 3 PO 4 were used as the catalysts for the synergetic catalysis. Hydrothermal carbonization combined with pyrolysis was used to co-activate with the ultrasonic auxiliary impregnation method in order to prepare straw activated carbon. The straw activated carbon was characterized by different means and the principle of the method was analyzed. Then the performance prediction models of straw pyrolytic activated carbon using methylene blue number and iodine number as the main evaluation index based on Linear Regression, Support Vector Regression, Random Forest Regression were proposed and compared. The results indicated the three kinds of straw showed similar characteristics in the preparation of straw pyrolytic activated carbon whose specific surface area reached 1258.3927 m2/g, 1101.8430 m2/g and 1060.9723 m2/g respectively. The Random Forest Regression model was the most suitable. The n_estimators was set to 10. The evaluation indexes of the model were all good. It demonstrated the three kinds of straw were highly efficient precursor for the preparation of activated carbon used to remove dyes from wastewater. The preparation method in this paper combines the advantages of physical and chemical activation. The prediction model will accelerate the utilization of straw resources, realize the controllable and clean preparation of straw activated carbon and reduce environmental pollution. Highlights • Hydrothermal carbonization combined with pyrolysis was used to co-activation. • Ultrasonic was used to assisted impregnation and the principle was analyzed. • Machine learning was used to predict the performance of SAC. • The prediction models based on LR, SVR and RFR were proposed and compared. • It proved RFR showed a good fit for the SAC performance prediction. [ABSTRACT FROM AUTHOR]

An excellent energy management strategy is paramount to the new energy vehicle safety, durability, and reliability, which invariably affects the driving experience. This paper proposes a novel parallel electric-hydraulic hybrid electric vehicle (PEHHEV), which has the characteristics of multiple working modes and power sources. Aiming at the defects of outmoded control strategies, this paper applies the long short-term memory (LSTM) neural network to the proximal policy optimization (PPO) algorithm. A PPO-LSTM-based energy management strategy for PEHHEV to achieve optimal working mode switching is established. To avoid paying too much attention to the economy and neglecting other performance parameters, we design a local sample Shannon entropy to realize dynamic evaluation for performance parameters. Through offline training, online testing, and entropy evaluation, the energy consumption rate under WLTC and NEDC increased by 18.51% and 15.74% respectively. It is indicated that PEHHEV can maintain dynamic performance and obtain lower energy consumption under the PPO-LSTM-based energy management strategy, which verifies its feasibility and robustness. The investigation in this paper can improve energy management performance and fill the literature gap, which has more preponderance than other strategies. This is the first of its kind to apply PPO-LSTM and entropy evaluation to developing control strategies for modern vehicles. • Parallel electric-hydraulic hybrid electric vehicle is designed to improve the EV endurance. • Deep reinforcement learning algorithm is used to achieve optimal vehicle working mode switching. • Concept of inserting LSTM into proximal policy optimization algorithm is proposed. • Neoteric PPO-LSTM-based energy management strategy optimizes the energy consumption rate. • Application of entropy evaluation fills the literature gap of vehicle EMS design. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Plastic waste pollution is a challenging and complex issue caused mainly by high consumption of single-use plastics and the linear economy of "extract-make-use-throw". Improvements in recycling efficiency, behaviour changes, circular business models, and a more precise waste management system are essential to reduce the volume of plastic waste. This paper proposes a simplified conceptual model for a smart plastic waste separation system based on sensor technology and deep learning (DL) to facilitate recovery and recycling. The proposed system could be applied either at the source (in a smart waste bins) or in a centralised sorting facility. Two smart separation systems have been investigated: i) the one utilising 6 sensors (near-infrared (NIR), humidity, temperature, CO 2 , CH 4 , and a laser profile sensor) and ii) the one with an RGB camera to separate packaging materials based on their composition, size, cleanliness, and appearance. Simulations with a case study showed that for a camera-based sorting, Inception-v3, a DL model based on convolution neural networks (CNN), achieved the best overall accuracy (78%) compared to ResNet-50, MobileNet-v2, and DenseNet-201. In addition, the separation resulted in a higher number of misclassified items in bins, as it focused solely on appearance rather than material composition. Sensor-based sorting faced limitations, particularly with dark colouration and organic matter entrapment. Combining the information from sensors and cameras could potentially mitigate the limitations of each individual method, thus resulting in higher purity of the separated fractions. [Display omitted] • Plastic packaging waste is separated using sensors and deep learning algorithms. • The Separation is based on plastic type and cleanliness. • Inseption-V3 yielded the best classification results (78.34 %). • Sensors resulted in a better identification of dirty packaging items then camera. • Both methods minimised the number of items in the mixed waste bin. [ABSTRACT FROM AUTHOR]

This paper focuses on addressing a critical challenge in 3D printing: managing energy consumption. 3D printing has emerged as a transformative technology in manufacturing, but its energy demands are a key concern for cost-effectiveness and sustainability. To tackle this challenge, an energy management system is proposed to leverage manifold model morphing and semi-supervised machine learning, to reduce production costs for 3D printing. The manifold model is a mathematical representation of the 3D object to be printed, and the refinement process involves optimizing the morphing parameters of the manifold model to achieve desired printing outcomes. To enable flexibility in the grasping space, the semi-supervised machine learning is then incorporated to leverage unlabeled data, enhancing the accuracy and robustness of the energy management system. The proposed system addresses the challenges of limited labeled data and complex morphologies of biological products in layered additive manufacturing. The energy management system is more applicable to soft robotics and biomedical products. The performance of the proposed system is evaluated through extensive experiments that demonstrate its effectiveness and efficacy in predicting and managing energy consumption in 3D printing. Our system offers a practical solution for estimating energy consumption in the design stage, and thus guides the designers to adopt the optimal part geometry and process planning for cleaner production. The results highlight that the proposed system is capable of optimizing energy usage in 3D printing, contributing to the advancement of green and sustainable industrial practices. • A manifold morphing method is proposed to generate multiple family-products. • A kinematics model of the self-grasping manifold is constructed for validation. • A semi-supervised deep network is designed to leverage generated family-products. • The system addresses the challenges of limited sample data in energy management. [ABSTRACT FROM AUTHOR]

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed. [ABSTRACT FROM AUTHOR]

With industrial informatization, rich data frameworks provide the possibility of efficient design and global optimization of methane to hydrogen processes. However, the high coupling between mixed variables leads to the difficulty of first-principles modeling (FPM) to capture the optimal Pareto front in infeasible domains. In this paper, a machine learning proxy (MLP) method is proposed to minimize economic, exergy destruction, CO 2 emissions, and maximize hydrogen production. First, process simulation as FPM is built using physical properties, energy balance, and reaction kinetics to analyze single variable effects and objective trade-offs. Then, datasets consisting of feed, heat transfer, and design variables are built by sampling the FPM in design and extended spaces. The eXtreme Gradient Boosting (XGBoost) with hyperparameter optimization is built as a proxy FPM, capturing the complex mapping between variables and objectives. Finally, the Pareto front is searched in design and extended spaces by combining the proxy model and genetic algorithm. The proposed method benefits 20.2%, 37.2%, 46.7%, and 2.3% in economic, exergy destruction, CO 2 emissions and hydrogen production respectively compared to the heuristic design. XGBoost not only can effectively proxy FPM to capture the complex behavior of reforming techniques, but also can perform better in infeasible domains that are difficult to explore with FPM. [Display omitted] • A MLP optimization method is proposed to explore the trade-offs for four objectives. • The R2 score of XGBoost is above 98.4% for four objective values. • Proxy model exhibits effective exploration of the space far beyond the FPM boundaries. • TAC, exergy destruction, CO 2 emissions, and H 2 production benefits 20.2%, 37.2%, 46.7% and 2.3%. [ABSTRACT FROM AUTHOR]

Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence. • We present a review of ML techniques for water resources management (WRM). • ML proved to be efficient in identifying patterns within nonlinear datasets. • Transformer architectures can be used to simplify reinforcement learning. • Generative Pre-trained Transformers open further opportunities to WRM. [ABSTRACT FROM AUTHOR]

Pavement life cycle assessments (LCAs) enable decision-makers to evaluate the environmental impact of alternative maintenance, rehabilitation, and reconstruction strategies. This paper explores the viability of deep reinforcement learning (DRL), a framework that enables agents to learn optimal actions within a given situation, to identify environmentally benign pavement management strategies. More specifically, this study utilizes proximal-policy optimization (PPO), a subtype of DRL algorithms, to identify a management strategy that minimizes the expected global warming impact of a pavement facility over its lifecycle. Through an urban Interstate case study, this paper shows that the proposed PPO algorithm identifies management strategies that are anticipated to reduce the expected global warming impact of a pavement facility over its planning horizon by 16 percent relative to traditional practice. Furthermore, the PPO algorithm is able to identify this management strategy in only 25 learning iterations, which is in stark comparison to Q -learning, a common reinforcement learning algorithm, that requires 70,000 learning iterations. The results of this work highlight the viability of DRL to integrate within complex LCA models to determine environmentally sustainable pavement management strategies. • Deep reinforcement learning (DRL) supports the identification of environmentally benign pavement management strategies. • Probabilistic LCA integrates DRL to minimize a pavement's global warming impact. • Proximal policy optimization (PPO) converges to a near-optimal management strategy. • PPO identifies a management strategy where expected GWP is 16% lower than traditional practice. [ABSTRACT FROM AUTHOR]

As an important means to reduce CO 2 emissions from coal-fired power plants, CCUS project has received more and more attention. In this paper, the comprehensive benefits of CCUS project in coal-fired power plants are defined as economic benefits, environmental benefits and energy benefits, and comprehensive evaluation and prediction analysis of CCUS project in coal-fired power plants are made from these three aspects. The goodness-of-fit of the constructed comprehensive evaluation model can reach 0.99, and the ratio of economic, environmental and energy benefits is about 5: 9: 1. Among them, the best scenario of policy influence, economic development and technological progress can promote CCUS project with benefits of 6.51%, 6.56% and 13.07% respectively. Through case analysis, it can be calculated that when the service life of CCUS project reaches 21.7 years, the investment cost can be recovered from the economic benefits. In the prediction model, the goodness of fit of the economic benefit prediction model is up to 0.95, in which the carbon capture cost of CCUS technology, operation and maintenance cost and carbon price fluctuation play a major role. The goodness of fit of the environmental benefit prediction model can reach 0.90, in which the carbon capture capacity of CCUS project and the world environmental situation are the main influencing factors, and the goodness of fit of the energy benefit prediction model can reach up to 0.95, which is mainly affected by the energy consumption level of CCUS project. The research results show that CCUS project of coal-fired power plants at home and abroad has a good development prospect and remarkable comprehensive benefits. This paper puts forward relevant policy suggestions that are beneficial to the development of CCUS project in coal-fired power plants, with a view to providing empirical evidence for the evaluation and decision-making of CCUS project in coal-fired power plants. • A comprehensive evaluation and prediction system of CCUS projects in coal-fired power plants is established. • In this paper, neural network is used for benefit accounting and evaluation. Meanwhile, machine learning is used for benefit prediction, with a good degree of fitting and high accuracy of the model.Neural network and machine learning are used to forecast and estimate the benefits. • The analysis method is universal and can be used to study CCUS projects in other industries. [ABSTRACT FROM AUTHOR]

The construction sector is one of the main contributors to carbon dioxide (CO 2) emission and causes of global warming. CO 2 mitigation solutions are vital. New technologies can facilitate and improve these efforts. Thus, the paper reviews how new technologies of artificial intelligence and machine learning have contributed to CO 2 emissions reduction in construction and what techniques have been applied in the literature to provide significant information that will be beneficial for the construction sector design and management. The paper provides the results of a content review, including their contributions and gaps. A total of 78 papers were identified to develop the dataset. The method was a combination of systematic reviews, including co-occurrence analytical map development of the main keywords, co-authorship network analyses, publication source analyses, and content analysis, including theme identification and review of the selected papers, which were divided into five conceptual clusters based on their scopes: (1) sustainable materials and components design/production, (2) on-site vehicles and equipment, (3) energy and life cycle assessment, (4) optimization, decision-making and solution-based platforms, and (5) real-world monitoring. The content of each cluster of papers was also reviewed, and the potential gaps were identified and discussed. A set of directions for future research investigations were presented that can be a valuable source for researchers in their future research. This paper contributes to the current knowledge base by presenting insights into intelligent techniques in the construction industry to mitigate CO 2 emissions. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The development of machine learning-based ensemble models for the prediction of complex processes with non-linear nature (such as adsorption) has been remarkably advanced over recent years. As a result, having an informative vision of these models' progression, appears to be critical for better understanding and using them in applications such as adsorption modeling. This paper systematically and critically reviews 38 articles in the field of application of ensemble models for the prediction of adsorption process efficiency for pollutants' removal from aquatic solutions. Two aspects, including the adsorption process and ensemble models' characteristics, are discussed in details. The type of adsorbate and adsorbent, as well as the system operation mode, are explored from the first point of view. The type of ensemble technique, software, input and output variables, dataset size and partitioning method, and performance metrics are all investigated in the ensemble model section. Based on discussed aspects and outcomes acquired from reviewed papers, some future research perspectives, including choosing model input variables from adsorbate properties, adsorbent characteristics, and adsorption condition parameters to increase the reliability of model predictions and also increasing dataset size to augment the accuracy of the ensemble models, are recommended for promoting next investigations. [Display omitted] • Prediction of adsorption efficiency using ensemble models was thoroughly reviewed. • 38 studies that used ensemble models for adsorptive pollutant removal were reviewed. • Three ensemble techniques such as Bagging, Boosting, and Stacking were explored. • The survey was carried out from both adsorption process and ensemble models aspects. • Based on the survey achievements, numerous future directions were recommended. [ABSTRACT FROM AUTHOR]

Air pollution has a significant impact on human health and influences housing choices. Existing studies on determinant factors affecting property prices, including air pollution, mainly employed hedonic and spatial regression models that have limitations in capturing non-linear relationships and local interactions. Neglecting the non-linear relationships can lead to incomplete estimation impacts between explainable features and prices. To fill these gaps, this study used machine learning algorithms and SHAP (SHapley Additive exPlanations) to identify the relative importance of air pollutant variables on land values and their non-linear mechanism. The results showed that the Extreme Gradient Boosting (XGBoost) outperformed Ordinary Least Squares (OLS), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Extra Tree Regression (ETR) models in price prediction and capturing the non-linear relationship between air pollution and land values. Notably, Ozone (O 3) and Nitric Oxide (NO) concentrations contribute 1.64% and 1.47% to land value variation, respectively. The results confirmed the non-linear relationship and identified that the threshold effect between air pollution and land values is at mean concentration values. Furthermore, we observed that the negative influence of O 3 and NO on land values appears as their concentration level is higher than 29.3 and 13 ppb, respectively. This paper contributes valuable insights by advancing our understanding of the non-linear mechanism impact of air pollution on house and land values, informing environmental policymaking, and shedding light on housing decisions. [ABSTRACT FROM AUTHOR]

This paper focuses on a comprehensive solution for dry municipal solid waste (MSW) management with describes the scenario of the gasification process. For this purpose, the potential of implementing the MSW gasification process in Southeast Asia (Iran) with three agents of air, steam, and air-steam is investigated. In order to manage the MSW gasification process as accurately as possible in the study area, a thermodynamic, exergy, economic, environmental, carbon footprint, water footprint, ecological emergy, and risk assessment for the three mentioned gasification agents were carried out in the most populated cities of Iran. In the capital of Iran (Tehran) with the largest population and as a result of largest MSW production, the potential of the gasification process has been investigated at different times (10 consecutive years and 12 months of a year). Finally, by combining artificial neural networks and genetic programming, comprehensive relationships have been established to predict the thermodynamic conditions of gasification with an air-steam agent. The general results of the survey indicate that gasification with air agents has good potential for most regions. The use of the steam agent has created worse economic, environmental, and risk conditions than the other two agents. Investigating the potential of the gasification process in Tehran shows that this city has a good potential to implement this process compared to other cities in Iran, and the most suitable location for establishing the gasification process is the 4th region of this city. [Display omitted] • Potential of municipal solid waste (MSW) gasification for all Iranian cities. • Comparison of using air, steam, and air-steam agents for MSW gasification. • The payback period of air agent MSW gasification is lower than other agents. • Machine Learning used for prediction of the behavior of MSW gasification. [ABSTRACT FROM AUTHOR]

The efficient distribution and timely vaccine delivery to the masses during any pandemic situation require reliable and efficient vaccine cold chain management (VCCM). Therefore, the present study is an effort to explore the factors of VCCM to develop a sustainable and resilient healthcare delivery system in any health outbreak. The factors of VCCM are scrutinized using Fuzzy-Delphi method and then prioritized adopting Fuzzy-Analytical Hierarchy Process (F-AHP) approach while considering the stakeholders' inputs. Finally, factors are categorized into clusters by implementing a machine learning tool ' k -means clustering' algorithm. The results of the study are further validated by conducting extended robustness tests. The findings of the study highlight the factors, namely, cold chain infrastructure, vaccine storage guidelines and availability of skilled professionals as the most important for VCCM. The ′ k -means clustering' algorithm analysis classifies all the 16 factors into three clusters: continuous process improvement (including eight factors), stakeholders' coordination (five factors) and maximum resource utility (three factors). The study can be significantly useful to VCCM decision-makers to implement the prioritized factors by ensuring the advanced cold chain infrastructure and issuing special storage and distribution guidelines to fight against health outbreaks like COVID-19. This is one of the few papers to discuss the factors of VCCM for developing a sustainable and resilient healthcare delivery system in the context of pandemics and stakeholders can address the factors in different clusters at a time. [Display omitted] • Vaccine cold chain management (VCCM) to develop a resilient healthcare delivery system during health outbreaks. • Prioritization of factors of VCCM using Fuzzy-Analytic Hierarchy Process approach. • Categorization of factors of VCCM by implementing a machine learning tool of K − m e a n s clustering algorithm. • Storage and distribution guidelines to fight against the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Determining the rural domestic water consumption (RDWS, L/person-day) and rural sewage production (RSP, L/person-day) of rural residents at the county scale can provide decision support for rural domestic sewage treatment. However, methods for predicting county-scale RDWS and RSP based on geographical, social, and governance factors are rarely reported. This paper proposes an RDWS and RSP prediction method based on Automated Machine Learning (AutoML) for the Inner Mongolia Autonomous Region. The RDWS and RSP in the Inner Mongolia Autonomous Region were successfully predicted from multi-source data. The AutoML model accurately predicted RDWS (mean absolute error = 1.7072, R2 = 0.7759) and RSP (mean absolute error = 0.6580, R2 = 0.65107). Average years of education (AYOS), proportion of resident population, and proportion of elderly and children (POEAC) were key driving factors related to RDWS prediction. The most important factors associated with RSP prediction were AYOS, POEAC, and annual average precipitation (AAP). An explanatory analysis further revealed dependence between the input variables, RDWS, and RSP. The framework was demonstrated to be a practical tool for qualitative and quantitative analyses of rural domestic water consumption and domestic sewage production in the Inner Mongolia Autonomous Region. Overall, this study provides a solution for predicting county-level RDWS and RSP, providing a reference for the design of rural domestic sewage treatment. [Display omitted] • Less than 24% of rural domestic sewage is currently treated in Inner Mongolia. • Planning needs county-scale domestic water consumption and sewage production data. • Best of many models based on data from multiple sources were selected automatically. • Nonlinear relationships between input output variables were quickly revealed. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) provides a pathway for efficiently utilizing the battery storage in a microgrid. However, traditional value-based RL algorithms used in battery management focus on formulating the policies based on the reward expectation rather than its probability distribution. Hence the scheduling strategy is solely based on the expectation of the rewards rather than the distribution. This paper focuses on scheduling strategy based on probability distribution of the rewards which optimally reflects the uncertainties in the incoming dataset. Furthermore, the prioritized experience replay samples of the training experience are used to enhance the quality of the learning by reducing bias. The results are obtained with different variants of distributional RL algorithms like C51, Quantile Regression Deep Q -Network (QR-DQN), Fully Quantizable Function (FQF), Implicit Quantile Networks (IQN) and rainbow. Moreover, the results are compared with the traditional deep Q -learning algorithm with prioritized experienced replay. The convergence results on the training dataset are further analyzed by varying the action spaces, using randomized experience replay and without including the tariff-based action while enforcing the penalties for violating battery SoC limits. The best trained Q -network is tested with different load and PV profiles to obtain the battery operation and costs. The performance of the distributional RL algorithms is analyzed under different schemes of Time of Use (ToU) tariff. QR-DQN with prioritized experience replay has been found to be the best performing algorithm in terms of convergence on the training dataset, with least fluctuation in validation dataset and battery operations during different tariff regimes during the day. [Display omitted] • Distributional reinforcement learning algorithm is utilized with prioritized experience samples. • Performances of a wide variety of distributional reinforcement learning algorithms are benchmarked. • Characteristics include variations in discrete action spaces and penalty on exceeding SoC limits. • The system is trained on one week dataset while tested on the 3-days dataset. • Battery technology implications have been discussed with respect to various algorithms. [ABSTRACT FROM AUTHOR]

Assessing and comprehending the social impact of firms at global and local level is a pressing concern for both researchers and policy-makers. To address this concern, our paper contributes to the stream of literature that studies the content of Corporate Social Responsibility (CSR) reports (which are also referred to as non-financial statements, sustainability reports or parts of annual reports) using text mining methods. We present a novel approach called Standard-based Impact Classification method (SBIC method), which employs natural language processing (NLP) and supervised machine learning techniques to identify the types of social impacts reflected in CSR reports. We deploy a Random Forest model which we train on reports adhering to Global Reporting Initiative (GRI) framework, enabling the identification of social impact in the majority of CSR reports that do not conform to this standard. Our proposed SBIC method serves as a valuable tool for comparing the social impacts generated by firms, industries, or countries. We showcase an application of our approach by examining the relationship between a company's social impact and its innovation capacity. Our findings support the existing literature consensus that CSR activities generally exhibit a positive correlation with a firm's ability to innovate. Furthermore, we reveal that specific types of social impacts have a more pronounced influence on innovation capacity. [ABSTRACT FROM AUTHOR]

The intelligent management of weed clearing can improve wheat yield and reduce the use of pesticides. In this paper, our contribution is to propose a method for wheat and weed recognition based on multispectral imaging and Membrane Search Algorithm Support Vector Machine (MSA-SVM), which is the basis for intelligent weed clearing. A spectral data acquisition system is set up in the laboratory. Then, a total of 700 groups of wheat and weed spectral data are collected. The classical SVM based on spectral data is applied to identify weeds to study the classification performance of wheat and weeds in different bands. The Membrane Search Algorithm (MSA) algorithm proposed by us was used for SVM parameters optimization, and the dimension reduction algorithm was adopted to reduce the interference of redundant information. After local linear embedding (LLE) dimensionality reduction of spectral data, MSA-SVM has the highest average recognition accuracy for weeds and wheat, which is 91.00%. The experimental results show that our research can distinguish wheat from weeds, which is of great significance for the development of smart agriculture. [Display omitted] • Construct a multispectral data collection system for wheat and weeds. • A weed recognition method based on spectral imaging and SVM is proposed. • Dimension reduction of spectral data based on LLE was studied. • The MSA algorithm we proposed is used for SVM parameters optimization. • The provided method can distinguish between wheats and weeds. [ABSTRACT FROM AUTHOR]

Promoting low-carbon development in the iron and steel industry (ISI) is essential for China to achieve the carbon neutrality targets. This paper separately adopts the Logarithmic Mean Divisia Index (LMDI) technique and the Mean Impact Value (MIV) method to analyze the impact of driving factors on the CO 2 emissions of ISI in the historical and future dimensions. Furthermore, this paper establishes the extreme learning machine model optimized by the bat algorithm (BA-BP) to explore the carbon emission reduction pathways of ISI in the business-as-usual (BAU) scenario, the low-speed, medium-speed and high-speed development scenarios considering the constraints of carbon neutrality targets. The results reveal that: (1) Production capacity and energy efficiency are important drivers of CO 2 emissions in ISI; (2) The emission reduction situation is not optimistic under the BAU scenario, and it is difficult to accomplish the carbon neutrality goals by 2060; (3) Under the most ideal emission reduction pathway (corresponding to the high-speed development scenario), ISI will reach its peak in 2022 with the peak value of 2143.42 MtCO 2. Compared to the peak year, the CO 2 emissions will be reduced by 654.69 MtCO 2 and 1558.61 MtCO 2 in 2030 and 2050, respectively. Moreover, the achievement of short-term and long-term emission reduction targets depends on production capacity decline and technological progress, respectively. The optimal emission reduction pathway provides a reference for ISI to formulate periodic emission reduction targets. • The improved extreme learning machine is employed to predict the carbon emissions. • The emission reduction pathways are explored under the carbon neutrality target. • Controlling production capacity is a crucial reduction strategy. • The energy rebound effect is supposed to be weakened. • The application of advanced technologies will greatly promote emission reduction. [ABSTRACT FROM AUTHOR]

This paper investigates how institutional pressures affect the development of Circular Economy (CE) in firms. Using Institutional Entrepreneurship as a theoretical framework, this paper considers three different levels of institutional pressures (coercive, normative, and mimetic) to examine the effect of each pressure and their interactions on the development of CE. Seeking to clarify the debate on the effect of institutional pressures, this paper considers that the main limitation arises from the fact that previous research has analysed the relationship between institutional pressures without considering the interaction between them and the non-linearity of the processes. Deviating from previous papers, our analysis combines regression methods with Machine learning (i.e. Artificial Neural Networks), and employs data from the EU survey on Public Consultation on the Circular Economy. This research finds that while coercive pressures have a compulsory effect on the development of CE, mimetic and normative pressures do not have an effect by themselves, but only in interaction with coercive pressures. Moreover, this paper shows that the application of machine learning tools has an important contribution in solving interaction problems. From the perspective of environmental policy, this means that a comprehensive policy is required, which implies the coexistence or interaction of the three types of pressures. [Display omitted] • Discrepancies on the effect of institutional pressures are due to methodology. • Coercive pressure has a compulsory effect on the development of Circular Economy. • Mimetic & Normative pressures only have effect in interaction with coercive pressure. • Artificial Neural Networks contribute to solving interaction problems. • Comprehensive environmental policy that considers these interactions is required. [ABSTRACT FROM AUTHOR]

The rapidly increasing demand for electrical energy and the depletion of fossil fuels ignited research in renewable energy generation and management. Thermoelectric generation (TEG) systems as a green energy resource are designed to recover waste heat recovery. Concentrated solar tackles the disadvantages of low power generation efficiency. This paper investigates the feasibility of using machine learning (ML) based MPPT technique, to harvest maximum power of a centralized TEG system under various operating conditions. The environment-dependent issues such as the non-constant distribution of heat, loss to the heat transfer coating between sinks and sources, and mechanical faults create non-uniform current generation and impedance mismatch causing power loss. In this article, we have proposed a feed-forward neural network (FNN) trained by a novel flow direction algorithm (FDA) with a tuned PID controller to harvest the energy under non-uniform temperature gradient conditions. The non-uniformity of physical conditions generates multiple local maxima's (LMs) in electrical characteristics of TEG systems. Common gradient-based MPPT techniques are unlikely to track true GMPP. As a solution to this non-linear problem, novel FNN-FDA is tested under non uniformly distributed temperature (NTUD) over heat source surfaces under varying load and temperature conditions. In this study, certain contributions to the field of TEG systems were made by tackling the issues such as GMPP tracking, low efficiency, and oscillations around GMPP. The results are compared to the particle swarm optimization (PSO), Barnacle mating optimization (BMO), and grey wolf optimization (GWO) algorithm. Five experiments under different weather conditions are performed. Results are validated and proved with experiments and MATLAB/SIMULINK. The experimental results demonstrated that FNN-FDA performs significantly better when compared with other control techniques. • This paper presents Flow Direction Optimization (FDO) algorithm trained using Feed Forward Neural Network (FDA-FNN) based novel intelligent techniques for Maximum Power Point Tracking (MPPT) control of a centralized TEG systems. • FDA-FNN Results are compared against PSO, BMO and GWO based MPPT Techniques. • 99.9% power efficiency and 6% more energy under dynaic operating conditions is achieved. • Varying stochastic temperature, varying load, Nicholas-Zigler trained PID and experimental validation cases are studied. [ABSTRACT FROM AUTHOR]

Recent problems faced by industrial players commonly relates to global warming and depletion of resources. This situation highlights the importance of improvement solutions for industrial operations and environmental performances. Based on interviews and literature studies, manpower, machine, material, money and environment are known as the foundation resources to fulfil the facility's operation. The most critical and common challenge that is being faced by the industrialists is to perform continuous improvement effectively. The needs to develop a systematic framework to assist and guide the industrialist to achieve lean and green is growing rapidly. In this paper, a novel development of an adaptive analytic model for lean and green operation and processing is presented. The development of lean and green index will act as a benchmarking tool for the industrialist. This work uses the analytic hierarchy process to obtain experts opinion in determining the priority of the lean and green components and indicators. The application of backpropagation optimisation method will further enhance the lean and green model in guiding the industrialist for continuous improvement. An actual industry case study (combine heat and power plant) will be presented with the proposed lean and green model. The model is expected to enhance processing plant performance in a systematic lean and green manner. Image 105947 • Development of adaptive lean and green model in processing industries. • Adaptive analytical approach to 4M1E in lean and green approach. • Continuous process improvement strategy with backpropagation algorithm. • Adaptive model can direct industrialist to improve Lean and Green indicators. • More data will enhance the stability and prediction of the adaptive model. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO 2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO 2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US. • Integrating engine characteristics with the MOVES model can improve model performance. • Integrating machine learning with MOVES performs well in prediction of vehicle CO 2 and NO emission. • The proposed emission model provides a possibility for calculation rather than costly an experiment in countries outside the USA. [ABSTRACT FROM AUTHOR]

This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM 1 , PM 2.5 , PM 10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (σ). RMSE ranged between 0.65 and 1 μg/m3, while σ has values between 2.75 and 4.1 μg/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square - Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and σ vary between 8 and 9.1 μg/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 μg/m3, while the σ ranged between 4.44 and 6.26 μg/m3, further validating the precision of our forecasting model. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO 2 -footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development. • Innovative approach to designing sustainable building materials using sequential learning. • Provides clear guidance for design process, can advance sustainable material development immediately. • Challenges data-driven materials dev. practices by prioritizing practical design frameworks over accurate models. • Significant reduction in dev. time, lower research costs, and straightforward predictions through sequential learning. • Large data sets of different types of alkali-activated building materials used for evaluation. [ABSTRACT FROM AUTHOR]

How far the de-linking and morphological and hydrological degradation of tie channels connecting river to the wetland are caused for the areal and hydrological transformation of a wetland was not received enough attention in previous literature. This paper tried to explain this about the confluence reach of the Dwarka and Brahmni rivers in Moribund deltaic India. In order to explain the linkages 16 tie channels' change depicting variables and seven wetland area and hydrological transformation (consistency, hydro-period, water depth, water richness etc.) related variables were taken. Ordinary least square (OLS) regression was applied for explain the linkage. Machine learning approaches were applied for water richness mapping. Tie channel evolution was digitized from the Survey of India (SOI) toposheet (1974) and historical Google Earth images. The result revealed that, the tie channel witnessed morphological and hydrological degradations like channel constriction, channel clogging, flow lowering etc. and these were further identified as some vectors of areal shrinkage and growing hydrological insecurity in the linked wetlands. The total wetland area declined from 44.89 km2 to 16.86 km2 from 1991 to 2021. The rate of areal loss, shallowing WD, growing inconsistency of water presence, narrowing HP, and weakening WR was found high in recent times due to de-linking and degradations of tie channels. This approach of explanation of hydrological insecurity of wetland in relation to tie channel degradations, de-linking and findings are quite unique. So, re-linking and morphological correction of the tie channels would be a good policy for wetland restoration. • The floodplain wetland area was reduced by 37.55% between 1991 and 2021. • Tie channels were either lost or degraded losing sufficient water-carrying capacity. • Consistency, hydro-period, wetland depth declined in relation to tie channel loss. • De-linking of tie channel deteriorated water richness zone in wetlands. • Re-linking of tie channel could improve the hydrological condition of the wetland. [ABSTRACT FROM AUTHOR]

Energy conservation is an important strategy for low-carbon development. With utilizing stage recognition and control strategies, engineering machinery can achieve energy savings, and reduce carbon emissions. The traditional machine learning approach requires a large amount of data to train models. In addition to many cases where the available data is limited, even if the data volume is sufficient, it often means that a large amount of testing is required, which is difficult for users who are not professionals and may cause further waste, it is necessary to develop methods that can achieve high accuracy even with limited data. This paper proposes an intelligent recognition method based on library for support vector machine (LIBSVM), which uses the relationship between working stages and pressure waveforms of working cycle. Initially, the pressure waveforms are carefully extracted and subjected to a robust filtering process to effectively eliminate noise artifacts. Subsequently, relevant features are extracted from the waveforms, which include important characteristics such as the amplitude and frequency of the pressure waves, then the extracted features are normalized using appropriate techniques. The LIBSVM model is carefully trained using the aforementioned extracted features and label data. Finally, to further enhance the accuracy of the proposed method, an intelligent calibration system is utilized. This system plays a crucial role in ensuring that the results obtained from the machine learning algorithm are effectively calibrated, thereby improving the overall accuracy of the method. A series of experiments have verified the accuracy of the proposed identification method, and the effectiveness of using this method for energy-saving design has been verified through real machine validation. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) and deep eutectic solvents (DESs) are regarded as the next generation solvents for carbon capture which consist of cations and anions. Thousands of combinations of cations and anions can lead to varied properties of ILs/DESs, which makes it difficult to screen such ILs/DESs for CO 2 in experiments. Computer-aided molecular design (CAMD) saves time and cost by reversing the search for the structure of ILs that are suitable for carbon capture. Compared with other thermodynamic models, machine learning (ML) models have the advantages of efficiency and accuracy in CAMD; hence, the number of studies on the application of ML models in the field of CAMD is growing each year. In this paper, a concise review of the application of ML to ILs/DESs-based CO 2 capture technology is provided. The development process of ML models in (1) the prediction of the properties of ILs/DESs using their structure; and (2) the prediction of the carbon capture effect using process parameters is discussed. Perspectives on future research directions are proposed and key challenges are identified for screening suitable ILs/DESs using the capture effectiveness of a specific carbon capture process as an evaluation criterion. • The application of ML to the reverse design of ILs and DESs is discussed. • A new standard evaluating ILs/DESs based on the whole CO 2 capture process is proposed. • Integration of ILs/DESs design and process simulation by ML models is proposed. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology with wearable devices provides a promising solution that enables con-tinuous monitoring of health parameters. Non-invasive sensors enable remote health monitoring and improve many aspects of life quality. The study explores advances in IoT and Wearable Devices through a conceptualization of health monitoring observer networks. The paper proposes an architecture inspired by a formal description of human perception, which has been successfully employed in environmental monitoring and can be adapted to healthcare for better integration and comprehension of patient health data. The proposed architecture involves a three-layer ap-proach Sensors, Integration and Association and Application and Diagnostic, similar to the architecture used in forest fire monitoring. The Sensors layer describes state-of-the-art cutting-edge equipment and related sensors for gathering information. The second layer, Integration and Association propose an ontology and a potential solution for integrat-ing sensor information from the device to the cloud. Furthermore, the third layer, Application and Diagnostic gives a review of used machine learning algorithms for detecting and diagnosing various diseases. Finally, a case study of elderly health monitoring using wearable devices is presented, discussing the methodology for economic value calcu-lation to decrease the cost of efficient health management and improve quality of life. The use of a health monitoring observer network not only improves quality of life, but also reduces the cost of health monitoring, making it more sustainable for the general community. • An overview of wearable technologies along with their associated characteristics. • Using IoT ontologies is vital in healthcare and medicine to integrate and associate multiple sensors. • Application and diagnostic layer include the ML for predicting and early detecting diseases. • Economic sustainability through the implementation of wearable devices in healthcare. [ABSTRACT FROM AUTHOR]

Modern polygeneration systems have increased the flexibility of production in energy systems. In this paper, a polygeneration system for producing power, freshwater, and methanol using solar and waste resources is defined. By using waste heat recovery in Brayton, Steam Rankine, organic Rankine, and Kalina systems, power has been produced in the proposed system. Methanol is also provided by using CO 2 capture from exhaust flue gases and hydrogen produced by Proton Exchange Membrane electrolyzer. The integration of microbial fuel cells, multi-effect distillation, and reverse osmosis desalination has been used for muddy seawater recovery and freshwater production. The proposed system has been analyzed using energy, exergy, exergeoeconomic, and exergoenvironmental (4E) analyses for three biomass fuels. The Multi-Objective Dragonfly Algorithm, Multi-Objective Thermal Exchange Optimization, Multi-objective Salp Swarm Algorithm, and Multi-Objective Water Cycle Algorithm have been used for the optimization of the whole system. In the end, the studied system has been modeled hourly. In this research, machine learning has been used to facilitate modeling the desalination sector, CO 2 capture sector, and methanol production sector. Also, it has played a significant role in reducing optimization run time. The modeling results show that Municipal solid waste is the most economical fuel for the study system with a payback of 4.28 years. Date palm waste has also caused fewer environmental impacts than other fuels. The comparison of the results of optimization indicates that Multi-objective Salp Swarm Algorithm is the fastest method for system optimization and can lead to 4.04% and 7.16% improvement in the system cost and environmental impact rate. In general, the energy and exergy efficiencies of the system in the optimal state are calculated as 29.25%, and 23.59%, respectively. The total optimal production of electricity, methanol, and freshwater in the whole system is calculated at 62.86 MW, 1820.78 kg/h, and 63.88 kg/s, respectively. The dynamic analysis of the proposed system also showed that the production of methanol throughout the year will be higher using Municipal solid waste fuel than other fuels. [Display omitted] • A Novel Solar-Biomass Polygeneration system has been proposed. • Simultaneous production of power, methanol, and freshwater. • Combination of microbial fuel cells with thermal desalination units. • Employing deep learning to model CO2 capture, methanol unit, and desalination process. • Application of four meta-heuristic algorithms to optimization. [ABSTRACT FROM AUTHOR]

Abstract The crucial role of customers' positive experience and their subsequent word-of-mouth have been highlighted by both scholars and practitioners for all industry sectors. In response to an increasing concern of environmental sustainability and sensitivity of consumers for deteriorating environment, eco-friendly (green) products and services gained tremendous attention. TripAdvisor is increasingly known as one of the most popular e-tourism platforms. Understanding and predicting the traveler' preferences by advanced big data analytics technology is an important task that the recommendation engine of this platform does. In this paper, we aim to develop a new soft computing method with the aid of machine learning techniques in order to find the best matching eco-friendly hotels based on the several quality factors in TripAdvisor. We develop the method using dimensionality reduction and prediction machine learning techniques to improve the scalability of prediction from the large number of users' ratings. The proposed soft computing method is evaluated on a large dataset discovered from the TripAdvisor platform. The results show that the combination of dimensionality reduction and prediction machine learning techniques is robust in processing the large number of the ratings provided by users on the features of eco-friendly hotels and predicting travelers' choice preferences of eco-friendly hotels in TripAdvisor. [ABSTRACT FROM AUTHOR]

Abstract With the frequent occurrence of extreme weather, power transmission line icing, a general phenomenon during winter which may bring serious economic losses to the electric power system, has attracted increasing attention. However, owing to the complexity nature of wire ice covering, it is essential to establish an icing thickness forecasting model with high-accuracy for guaranteeing the security and stability of the power grid. Hence, this paper proposes a hybrid model that combines wavelet transform (WT) with extreme learning machine optimized by bat algorithm (BA-ELM). The original icing data containing icing thickness and meteorological factors are first denoised by WT and then divided into several stages based on the characteristics of icing period. Bivariate correlation analysis and partial auto correlation function (PACF) are used to select the inputs of different stages. Subsequently, ELM whose input weights and bias threshold were optimized BA is built to forecast icing thickness. To verify the developed model, icing data from two power transmission lines located in Hunan province are applied for experiments. The simulation results demonstrate that not only the proposed model shows a better performance but also the staged modeling can highly improve icing thickness prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract There is an increasing concern in manufacturing systems to reduce electricity costs and lower carbon footprints. There are usually two ways of reducing manufacturing electricity costs. One consists of reducing the total electricity demand by using more efficient equipment and processes, and the other focuses on scheduling algorithms to determine the best response to pricing policies put in place by energy companies. In parts of the United States these pricing policies consist of both a consumption charge, which is related to the total amount of energy consumed, and a demand charge for the highest average power consumed over a given time window. Consideration for both time-dependent consumption charges and demand charges when planning a production schedule (e.g. parallel machine scenario) can reduce the total electricity costs. In this paper, a mathematical optimization model is proposed to schedule jobs in which both demand charges and consumption charges are considered for a facility with parallel machines. Results show that total electricity costs can be reduced significantly when demand charges are included in scheduling decisions versus considering only consumption charges. Results further indicate a significant reduction in peak demands for the facility, which provides a benefit for both the facility and the utility. Results also indicate that considering both demand and consumption charges does not increase the total electricity consumed. This study further analyzes the implications of changing model parameters such as the number of machines, machine utilization, and the price of the demand charge. The results provide a better understanding of how demand charges can affect the schedule of a manufacturing facility if total energy costs are to be minimized. Highlights • Considering demand charges in production schedules can lower costs. • Considering demand charges does not increase the total electricity consumed. • Considering demand charges significantly reduces peak loads. • Greater system flexibility increases the benefits of including demand charges. [ABSTRACT FROM AUTHOR]

Abstract In this paper, an incremental unsupervised neural network algorithm namely memory self-organizing incremental neural network (M-SOINN) is proposed to first predict the output power and subsequently detect the occurrence of power fluctuation events in a photovoltaic microgrid system. The M-SOINN uses clustering technique to form the data map, identifies the most similar patterns to predict the photovoltaic output power and then detects power fluctuation events. A novel memory layer is incorporated to establish the time-series learning. By using real life environment data, the proposed M-SOINN based real-time prediction engine detects power fluctuation events with a high detection rate of 92.69% and it outperforms the conventional self-organizing map (SOM), k -nearest neighbour (KNN), focused time delay neural network (FTDNN), and nonlinear autoregressive with exogenous input (NARX) networks. The system is simulated in the PSCAD environment and later experimentally. The proposed M-SOINN is then integrated into a power management system to mitigate power fluctuation events of the photovoltaic grid-tied system. Results show that the proposed power management system reduces 79.62% of power fluctuation events with an energy loss of 2.16% and battery state-of-charge maintains within 30%–100%. The proposed system outperforms hourly rule-based controller and the ramp rate controller by 44.02% and 27.57%, respectively in terms of the mitigated power fluctuation events. Highlights • Use a novel memory layer to establish time-series learning. • Requires no historical data to train the AI and it is life-long learning. • The dynamic stress of the battery are greatly reduced. • Detects 92.69% of power fluctuation events in advanced. • Mitigates 79.60% power fluctuation events with energy loss of 2.16%. [ABSTRACT FROM AUTHOR]

Abstract Predictive analytics play an important role in the management of decentralised energy systems. Prediction models of uncontrolled variables (e.g., renewable energy sources generation, building energy consumption) are required to optimally manage electrical and thermal grids, making informed decisions and for fault detection and diagnosis. The paper presents a comprehensive study to compare tree-based ensemble machine learning models (random forest – RF and extra trees – ET), decision trees (DT) and support vector regression (SVR) to predict the useful hourly energy from a solar thermal collector system. The developed models were compared based on their generalisation ability (stability), accuracy and computational cost. It was found that RF and ET have comparable predictive power and are equally applicable for predicting useful solar thermal energy (USTE), with root mean square error (RMSE) values of 6.86 and 7.12 on the testing dataset, respectively. Amongst the studied algorithms, DT is the most computationally efficient method as it requires significantly less training time. However, it is less accurate (RMSE = 8.76) than RF and ET. The training time of SVR was 1287.80 ms, which was approximately three times higher than the ET training time. Highlights • Developed predictive modelling techniques for solar energy systems. • Compared performance of SVR with random forest, extra trees and decision trees. • Training time of SVR was three times higher than the ET (extra trees) training time. • RF and ET models can be used to as a variable selection tools. [ABSTRACT FROM AUTHOR]

Solar still (SS), being one of the convenient solar desalination systems, has been widely studied in terms of their productivity, efficiency, and economics during the last decades. Recently, to overcome the bottleneck of conventional SS and improve its performance, optimizing the solar evaporation process based on micro/nano-materials has been proposed as a promising strategy. In this review, the recent developments for achieving high-performance SS and solar evaporation by micro/nano-materials were discussed. The reviewed studies have indicated that the enhancement of productivity and efficiency depends on many factors, such as particles concentration, materials type, systems configurations, solar intensity, and so on. It has been found that thermal conductivity and solar absorptivity of the utilized micro/nano-particles are the most important factors. On the other hand, porous structure, surface property, and thermal design are the key factors for porous materials. The paper also discussed the potential of using machine learning to predict the SS performance and analyze the importance of different factors of systems and materials. Moreover, several important points for challenges of SS and solar evaporation investigations were also discussed. These challenges included: the erosion and corrosion of components by micro/nano-particles, complexity-productivity trade-off, salt rejecting, the durability of porous materials, mechanism of water phase change, and so on. The objective of this paper was achieved by highlighting the materials and systems design for the effective use of micro/nano-materials in SS. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting for the district heating network (DHN) is vital for energy companies to develop a stable and dependable generation capacity. Many machine learning methods have been developed recently, and several of them are now employed in DHN for heat demand forecasting. Despite the rapid growth of machine learning methods, developing accurate forecasting techniques for sparse DHN data remains a difficult task. As a result, hybrid solutions have been introduced, to overcome the issue. This paper develops a hybrid model to accurately determine DHN heat load utilization based on the LightGBM and FB-Prophet models. To get accurate output results from the FB-Prophet model, it is embedded with the positional encoding layers. The LightGBM model is used to train the non-linear components obtained from the output of the FB-Prophet model. The optimal parameters for the LightGBM model are evaluated, and three hyperparameter approaches are compared, namely grid search, random search, and sequential model-based optimization, and the results indicate that the grid search approach is the most accurate. The proposed model's effectiveness is compared to the performance of other 10 machine learning algorithms. The proposed hybrid model's forecasting performance is superior, with MAE of 30.88, RMSE of 50.50, and R2 of 0.92; therefore, it can perform an efficient guiding role in DHN energy distribution. • Hybrid model is built for heat load forecasting using LightGBM and FB-Prophet model. • Positional encoding layers are added to improve the output of the FB-Prophet model. • The LightGBM model is used to train the non-linear components. • Proposed hybrid model is compared with another ten machine learning models. • MAE of 30.88, RMSE of 50.50, and R2 of 0.92 proved the effectiveness of proposed model. [ABSTRACT FROM AUTHOR]