AI and IoT for Promoting Green Operation and Sustainable Environment

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Systems systems	2.3	2.8	2013	19.6 Days	CHF 2400

18 pages, 10764 KiB

Open AccessArticle

Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management

by Mirna Castro-Bello, Dominic Brian Roman-Padilla, Cornelio Morales-Morales, Wilfrido Campos-Francisco, Carlos Virgilio Marmolejo-Vega, Carlos Marmolejo-Duarte, Yanet Evangelista-Alcocer and Diego Esteban Gutiérrez-Valencia

Sustainability 2025, 17(8), 3523; https://doi.org/10.3390/su17083523 - 14 Apr 2025

Viewed by 368

Abstract

Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer [...] Read more.

Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by delimiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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24 pages, 873 KiB

Open AccessReview

A Review of New Methods for Extracting Oil from Plants to Enhance the Efficiency and Physicochemical Properties of the Extracted Oils

by Hamid Bakhshabadi, Mohammad Ganje, Mehdi Gharekhani, Toktam Mohammadi-Moghaddam, Cristina Aulestia and Afsaneh Morshedi

Processes 2025, 13(4), 1124; https://doi.org/10.3390/pr13041124 - 9 Apr 2025

Viewed by 473

Abstract

In general, there are three methods for extracting oil from various sources: mechanical, solvent, and pre-press-solvent. Each of these methods has its own advantages and disadvantages, with extraction efficiency depending on key factors such as the extraction technique, the properties of the plant [...] Read more.

In general, there are three methods for extracting oil from various sources: mechanical, solvent, and pre-press-solvent. Each of these methods has its own advantages and disadvantages, with extraction efficiency depending on key factors such as the extraction technique, the properties of the plant component matrix, and the solvent used. Factors like temperature, pressure, and time also play a role. Researchers have consistently sought to replace or complement these methods to reduce residual oil in products. This study introduces new oil extraction methods that have gained attention in recent years, including the microwave, pulsed electric field, ultrasound, supercritical fluid, enzymatic, ohmic, and combined methods to enhance efficiency. The research demonstrates that these methods increase oil extraction efficiency and bioactive compound extraction from plant sources, resulting in improved oil quality. Most methods also reduce extraction time, offering researchers and industrialists a variety of options for their oil extraction needs. However, the study notes contradictions in the results, such as varying acidity levels in the oil, which may be attributed to raw materials and study conditions. In the end, it was determined that ultrasound, pulsed electric field, and enzyme methods can be used industrially to extract oil from olives, while supercritical fluid can be used to extract oil from certain seeds. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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18 pages, 4080 KiB

Open AccessArticle

Predicting Fuel Consumption and Emissions Using GPS-Based Machine Learning Models for Gasoline and Diesel Vehicles

by Fahd Alazemi, Asmaa Alazmi, Mubarak Alrumaidhi and Nick Molden

Sustainability 2025, 17(6), 2395; https://doi.org/10.3390/su17062395 - 9 Mar 2025

Viewed by 701

Abstract

The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation [...] Read more.

The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions (

{C O}_{2}

and

{N O}_{x}

). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of

{C O}_{2}

emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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27 pages, 6820 KiB

Open AccessArticle

PCF-RWKV: Large Language Model for Product Carbon Footprint Estimation

by Zhen Li, Peihao Tang, Xuanlin Wang, Xueping Liu and Peng Mou

Sustainability 2025, 17(3), 1321; https://doi.org/10.3390/su17031321 - 6 Feb 2025

Cited by 1 | Viewed by 1089

Abstract

As global climate change intensifies, assessing product carbon footprints serves as a foundational step for quantifying greenhouse gas emissions throughout a product’s lifecycle, forming the basis for achieving sustainability and emission reduction goals. Traditional lifecycle assessment methods face challenges such as subjective boundary [...] Read more.

As global climate change intensifies, assessing product carbon footprints serves as a foundational step for quantifying greenhouse gas emissions throughout a product’s lifecycle, forming the basis for achieving sustainability and emission reduction goals. Traditional lifecycle assessment methods face challenges such as subjective boundary definitions and time-consuming inventory construction. This study introduces PCF-RWKV, a novel model based on the RWKV architecture with task-specialized low-rank adaptations (LoRAs). Trained on carbon footprint datasets, the model minimizes memory use and data interference, enabling efficient deployment on consumer-grade GPUs without relying on cloud computing. By integrating multi-agent technology, PCF-RWKV automates the creation of lifecycle inventories and aligns production processes with emission factors to calculate carbon footprints. This approach significantly improves the efficiency and security of corporate carbon footprint assessments, providing a potential alternative to traditional methods. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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4 pages, 168 KiB

Open AccessEditorial

Introduction to the Topic of AI and IoT for Promoting Green Operation and Sustainable Environment

by Nan Zhang, Wei Liu and Chia-Huei Wu

Systems 2025, 13(1), 62; https://doi.org/10.3390/systems13010062 - 20 Jan 2025

Viewed by 665

Abstract

In the current era, green operation has become a key strategic direction for firm development, with its core being the realization of a win–win situation for economic and environmental benefits through environmentally friendly production processes [...] Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

24 pages, 7349 KiB

Open AccessArticle

Research on a Novel Unsupervised-Learning-Based Pipeline Leak Detection Method Based on Temporal Kolmogorov–Arnold Network with Autoencoder Integration

by Hengyu Wu, Zhu Jiang, Xiang Zhang and Jian Cheng

Sensors 2025, 25(2), 384; https://doi.org/10.3390/s25020384 - 10 Jan 2025

Cited by 1 | Viewed by 1068

Abstract

Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the [...] Read more.

Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years. In this study, we propose a novel leak detector, which includes a new model and a sequence labeling method that integrates prior knowledge with traditional reconstruction error theory. The proposed model combines the Kolmogorov–Arnold Network (KAN) with an autoencoder (AE). This model combines the Kolmogorov–Arnold Network (KAN) with an autoencoder (AE), forming a hybrid framework that effectively captures complex temporal dependencies in the data while exhibiting strong pattern modeling and reconstruction capabilities. To improve leak detection, we developed a novel unsupervised anomaly sequence labeling method based on traditional reconstruction error theory, which incorporates an in-depth analysis of the reconstruction error curve along with prior knowledge. This method significantly enhances the interpretability and accuracy of the detection process. Field experiments were conducted on real urban water supply pipelines, and a benchmark dataset was established to evaluate the proposed model and method against commonly used models and methods. The experimental results demonstrate that the proposed model and method achieved a high segment-wise precision of 93.1%. Overall, this study presents a transparent and robust solution for automated pipeline leak detection, facilitating the large-scale, cost-effective development of digital twin systems for urban pipeline leak emergency management. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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32 pages, 8102 KiB

Open AccessReview

Fault Diagnosis Techniques for Electrical Distribution Network Based on Artificial Intelligence and Signal Processing: A Review

by Yunyu Cao, Jinrui Tang, Shaohui Shi, Defu Cai, Li Zhang and Ping Xiong

Processes 2025, 13(1), 48; https://doi.org/10.3390/pr13010048 - 28 Dec 2024

Cited by 1 | Viewed by 1560

Abstract

This paper provides a comprehensive and systematic review of fault diagnosis methods based on artificial intelligence (AI) in smart distribution networks described in the literature. For the first time, it systematically combs through the main fault diagnosis objectives and corresponding fault diagnosis methods [...] Read more.

This paper provides a comprehensive and systematic review of fault diagnosis methods based on artificial intelligence (AI) in smart distribution networks described in the literature. For the first time, it systematically combs through the main fault diagnosis objectives and corresponding fault diagnosis methods for a smart distribution network from the perspective of combined signal processing and artificial intelligence algorithms. The paper provides an in-depth analysis of the advantages and disadvantages of various signal processing techniques and intelligent algorithms in different fault diagnosis tasks, focusing on the impact of different data dimensions on the effect of fault diagnosis. This paper points out that data security issues and the question of how to combine expert domain knowledge with artificial intelligence technology are essential directions for the future development of fault diagnosis in smart distribution network. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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24 pages, 9407 KiB

Open AccessArticle

Discharge Voltage Prediction Model of Batteries in Different Degradation States Based on IWOA-ATCN

by Jingwei Yang, Yitong Chen, Qiang Huang, Guilong Wu, Lin Liu, Zhimin Yang and Yu Huang

Processes 2025, 13(1), 46; https://doi.org/10.3390/pr13010046 - 28 Dec 2024

Viewed by 787

Abstract

As a key technology for future decarbonization, storage batteries are widely used in areas such as electric vehicles and power systems. However, battery aging inevitably limits their broader application. To address the low accuracy in predicting discharge voltage under different aging states, this [...] Read more.

As a key technology for future decarbonization, storage batteries are widely used in areas such as electric vehicles and power systems. However, battery aging inevitably limits their broader application. To address the low accuracy in predicting discharge voltage under different aging states, this paper proposes the IWOA-ATCN method based on a TCN model with a sliding window mechanism. First, the improved whale optimization algorithm (IWOA) is employed to optimize the hyperparameters in the TCN model, including window size and sliding step, to obtain the optimal sample structure. Then, the temporal attention mechanism is introduced into the TCN model to accurately capture the temporal correlation of discharge voltage, thereby improving the prediction accuracy of long time series data. Finally, the model is tested on the NASA dataset with an RMSE of 0.0072, MAE of 0.0046, and R² of 0.9984. The test results on the PL Sample dataset showed RMSE of 0.0081, MAE of 0.0040, and R² of 0.9983. It is indicated that the prediction accuracy and stability of the IWOA-ATCN model are higher than other models, such as BP, RNN, and LSTM. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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15 pages, 3582 KiB

Open AccessArticle

Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications

by Md Minhazur Rahman, Md Ibne Joha, Md Shahriar Nazim and Yeong Min Jang

Appl. Sci. 2024, 14(24), 11970; https://doi.org/10.3390/app142411970 - 20 Dec 2024

Viewed by 1252

Abstract

The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study [...] Read more.

The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum

R^{2}

score of 82.04%, the showing proposed model’s superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT’s reliability in real-time environmental and power forecasting. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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23 pages, 7869 KiB

Open AccessArticle

Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for Sustainable Water Management in the Murat River Basin

by Ibrahim A. Hasan and Mehmet Ishak Yuce

Sustainability 2024, 16(24), 11077; https://doi.org/10.3390/su162411077 - 17 Dec 2024

Viewed by 1017

Abstract

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This study presents a detailed sustainable [...] Read more.

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This study presents a detailed sustainable model for predicting PET using the Thornthwaite equation, which requires only mean monthly temperature (Tmean) and latitude, with calculations performed using R-Studio. A geographic information system (GIS) was employed to interpolate meteorological data, ensuring coverage of all sub-basins within the Murat River basin, the study area. Additionally, Python libraries were utilized to implement artificial intelligence-driven models, incorporating both machine learning and deep learning techniques. The study harnesses the power of artificial intelligence (AI), applying deep learning through a convolutional neural network (CNN) and machine learning techniques, including support vector machine (SVM) and random forest (RF). The results demonstrate promising performance across the models. For CNN, the coefficient of determination (R²) varied from 96.2 to 98.7%, the mean squared error (MSE) ranged from 0.287 to 0.408, and the root mean squared error (RMSE) was between 0.541 and 0.649. For SVM, the R² varied from 94.5 to 95.6%, MSE ranged between 0.981 and 1.013, and RMSE ranged from 0.990 to 1.014. RF showed the best performance, achieving an R² of 100%, MSE values of 0.326 and 0.640, and corresponding RMSE values of 0.571 and 0.800. The climate and topography data used for all algorithms were consistent, and the results indicate that the RF model outperforms the others. Consequently, The RF model’s superior accuracy highlights its potential as a reliable tool for sustainable PET prediction, supporting informed decision-making in water resource planning. By leveraging GIS, AI, and machine learning, this study enhances PET modeling methodologies, addressing critical water management challenges and promoting sustainable hydrological practices in the face of climate change and resource limitations. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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29 pages, 3537 KiB

Open AccessArticle

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

by Jingrui Wang, Yi Li, Zhongwei Zhang, Zhaoyun Wu, Lihui Wu, Shun Jia and Tao Peng

Processes 2024, 12(11), 2423; https://doi.org/10.3390/pr12112423 - 2 Nov 2024

Cited by 2 | Viewed by 1957

Abstract

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated [...] Read more.

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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25 pages, 1206 KiB

Open AccessArticle

Does Government Digital Transformation Drive High-Quality Urban Economic Development? Evidence from E-Government Platform Construction

by Li Xiong, Xiaoyu Wang, Zijie Liu and Xiaoliang Long

Systems 2024, 12(9), 372; https://doi.org/10.3390/systems12090372 - 15 Sep 2024

Viewed by 1919

Abstract

Digitalization represents a pivotal global development trend and serves as a significant force propelling economic and social transformation. This manuscript uses the global Malmquist–Luenberger (GML) model to estimate green total factor productivity (GTFP) across 284 Chinese cities from 2003 to 2018, taking the [...] Read more.

Digitalization represents a pivotal global development trend and serves as a significant force propelling economic and social transformation. This manuscript uses the global Malmquist–Luenberger (GML) model to estimate green total factor productivity (GTFP) across 284 Chinese cities from 2003 to 2018, taking the pilot policy of “construction and application of e-government public platforms based on cloud computing” as an example to assess the impact of government digital transformation on the qualitative development of the economy by using a difference-in-differences model to explore the path of its role and driving mechanism. The results reveal that government digital transformation promotes the qualitative improvement of the city’s economic development, and its driving effect shows a marginal incremental law. Moreover, government digital transformation can contribute to the formation of a “latecomer advantage” in the lagging regions, which creates a “catch-up effect” on the regions with favorable development foundations, excellent geographical conditions, high urban ranking, and high education quality. Additionally, government digital transformation boosts economic and social development quality through both innovation spillover and structural optimization. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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27 pages, 10941 KiB

Open AccessArticle

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

by Yanyan Huang, Yi Yang, Hangyi Ren, Lanxin Ye and Qinhan Liu

Sustainability 2024, 16(16), 7193; https://doi.org/10.3390/su16167193 - 21 Aug 2024

Cited by 3 | Viewed by 2393

Abstract

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable [...] Read more.

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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16 pages, 3649 KiB

Open AccessArticle

Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach

by Narayanamoorthi Rajamanickam, Dominic Savio Abraham, Roobaea Alroobaea and Waleed Mohammed Abdelfattah

Processes 2024, 12(8), 1574; https://doi.org/10.3390/pr12081574 - 27 Jul 2024

Cited by 1 | Viewed by 1264

Abstract

Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause [...] Read more.

Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause problems, including decreased charging efficiency, safety risks, charging system damage, communication issues, and health risks. To address this problem, this paper proposes the deep learning object detection network approach of using YOLOv4 (You Only Look Once), which is a single-shot detector. Additionally, for real-time implementation, YOLOv4-Tiny is suggested, which is a compressed version of YOLOv4 designed for devices with low computational power. YOLOv4-Tiny enables faster inferences and facilitates the deployment of FOD detectors on edge devices. The algorithm is trained using the FOD dataset, consisting of images of common debris on runways or taxiways. Furthermore, utilizing the concept of transfer learning, the last few layers of the pre-trained YOLOv4 model are modified using the COCO (Common Objects in Context) dataset to transfer features to the new network and retrain the model on the FOD dataset. The results obtained using this YOLOv4 model yielded a precision rate of 99.05%, while the results from YOLOv4-Tiny achieved a precision rate of 97.74%, with an average inference time of 150 ms under the ambient light and weather conditions. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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32 pages, 3480 KiB

Open AccessArticle

Strategic Dimensions of Eco-Innovation Adoption in Manufacturing SMEs in the Context of Mexico City

by Andrés David Barragán-Hernández and Mario Aguilar-Fernández

Systems 2024, 12(7), 246; https://doi.org/10.3390/systems12070246 - 9 Jul 2024

Cited by 2 | Viewed by 2583

Abstract

This paper investigates the strategic dimensions influencing the adoption of environmental innovations (EIs) in small and medium-sized manufacturing enterprises (SMEs) in Mexico City. The study aims to understand the fundamental strategic dimensions defining eco-innovation and examine how SMEs manage the complexities of incorporating [...] Read more.

This paper investigates the strategic dimensions influencing the adoption of environmental innovations (EIs) in small and medium-sized manufacturing enterprises (SMEs) in Mexico City. The study aims to understand the fundamental strategic dimensions defining eco-innovation and examine how SMEs manage the complexities of incorporating these practices. Employing a mixed-methods approach with documentary analysis and expert interviews, the study identifies key factors influencing EI adoption through causal maps and the viable system model. Findings highlight the significance of external and internal factors such as competitive pressure, economic constraints, technological capabilities, political support, and social influence. This integrative approach emphasizes the importance of adaptability and responsiveness, presenting eco-innovation as a source of sustainable competitive advantage for SMEs. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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18 pages, 1135 KiB

Open AccessArticle

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

by Md. Faiyaz Abdullah Sayeedi, Jannatul Ferdous Deepti, Anas Mohammad Ishfaqul Muktadir Osmani, Taimur Rahman, Safrin Sanzida Islam and Md. Motaharul Islam

Appl. Sci. 2024, 14(13), 5459; https://doi.org/10.3390/app14135459 - 24 Jun 2024

Viewed by 1928

Abstract

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We [...] Read more.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML tasks: image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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20 pages, 4883 KiB

Open AccessArticle

Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia

by Juan Carlos Vesga Ferreira, Faver Adrian Amorocho Sepulveda and Harold Esneider Perez Waltero

Sustainability 2024, 16(13), 5300; https://doi.org/10.3390/su16135300 - 21 Jun 2024

Cited by 1 | Viewed by 1904

Abstract

Around the world, managing and classifying solid waste is one of the most important challenges to sustaining economic growth and preserving the environment. The objective of this paper is to propose the use of Smart Ecological Points as a strategy to address the [...] Read more.

Around the world, managing and classifying solid waste is one of the most important challenges to sustaining economic growth and preserving the environment. The objective of this paper is to propose the use of Smart Ecological Points as a strategy to address the problem of solid waste management systems at the source, which has become one of the biggest problems globally, and Colombia is no exception. This article describes the current state of the problem in the country and presents a prototype of a low-cost Smart Ecological Point supported by the use of an experimental capacitive sensor and machine learning algorithms, which will reduce the time necessary for the classification of recyclable and non-recyclable waste, increasing the percentage of waste that can be reused and minimizing health risks by reducing the probability of being contaminated at the source, an aspect that is very common when waste is sorted manually. According to the results obtained, it is evident that the proposed prototype made an adequate classification of waste, generating the possibility of it being manufactured with existing technology in order to promote adequate waste classification at the source. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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21 pages, 3509 KiB

Open AccessArticle

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

by Pemila Mani, Pongiannan Rakkiya Goundar Komarasamy, Narayanamoorthi Rajamanickam, Roobaea Alroobaea, Majed Alsafyani and Abdulkareem Afandi

Processes 2024, 12(6), 1251; https://doi.org/10.3390/pr12061251 - 18 Jun 2024

Cited by 2 | Viewed by 1238

Abstract

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is [...] Read more.

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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15 pages, 3697 KiB

Open AccessArticle

Investigating the Physical and Operational Characteristics of Manufacturing Processes for MFI-Type Zeolite Membranes for Ethanol/Water Separation via Principal Component Analysis

by Hamdi Chaouk, Emil Obeid, Jalal Halwani, Wiem Abdelbaki, Hanna Dib, Omar Mouhtady, Eddie Gazo Hanna, Célio Fernandes and Khaled Younes

Processes 2024, 12(6), 1145; https://doi.org/10.3390/pr12061145 - 1 Jun 2024

Viewed by 1009

Abstract

In this study, Principal Component Analysis (PCA) was applied to discern the underlying trends for 31 distinct MFI (Mobil No. 5)-zeolite membranes of 11 textural, chemical, and operational factors related to manufacturing processes. Initially, a comprehensive PCA approach was employed for the entire [...] Read more.

In this study, Principal Component Analysis (PCA) was applied to discern the underlying trends for 31 distinct MFI (Mobil No. 5)-zeolite membranes of 11 textural, chemical, and operational factors related to manufacturing processes. Initially, a comprehensive PCA approach was employed for the entire dataset, revealing a moderate influence of the first two principal components (PCs), which collectively accounted for around 38% of the variance. Membrane samples exhibited close proximity, which prevented the formation of any clusters. To address this limitation, a subset acquisition strategy was followed, based on the findings of the PCA for the entire dataset. This resulted in an enhanced overall contribution and the revelation of diverse patterns among the membranes and the considered manufacturing factors (total variance between 55% and 77%). The segmentation of the data unveiled a robust correlation between silica (SiO₂) concentration and pervaporation conditions. Additionally, a notable clustering of the chemical compositions of the preparation solutions underscored their significant influence on the operational efficacy of MFI zeolite membranes. On the other hand, an exclusive chemical composition of the preparation solution was noticed. This highlighted the high influence of the chemical composition on the operational efficiency of MFI zeolite membranes. The coupling of PCA with experimental results can provide a data-driven enhancement strategy for the manufacturing of MFI-type zeolite membranes used for ethanol/water separation. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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34 pages, 3932 KiB

Open AccessArticle

A Review of Eco-Product Value Realization and Eco-Industry with Enlightenment toward Village Ecosystem Service in the Karst Desertification Control

by Ziyi Zhao, Kangning Xiong, Bin Ying, Rong Zhao, Jiawang Yan and Huiqiong Huang

Sustainability 2024, 16(11), 4671; https://doi.org/10.3390/su16114671 - 30 May 2024

Cited by 1 | Viewed by 1388

Abstract

In the context of achieving certain results in rural revitalization and karst desertification control, promoting the realization of eco-product value to promote the rural revitalization strategy has become a hot topic in China. The importance of eco-product value has been raised to an [...] Read more.

In the context of achieving certain results in rural revitalization and karst desertification control, promoting the realization of eco-product value to promote the rural revitalization strategy has become a hot topic in China. The importance of eco-product value has been raised to an unprecedented level, confirming that the realization of eco-product value is an important way to promote rural revitalization and ecological civilization construction in desertification areas. This study reviewed 489 relevant documents on the realization of eco-product value and conducted a statistical analysis. The analysis results show that (1) the number of published related documents is generally on the rise, indicating that after the policy was proposed, research in this field has received high attention from the academic community; (2) the research content mainly focuses on five aspects: eco-product value accounting, eco-product value realization, eco-industry, and theoretical attributes of eco-products; (3) the research area is concentrated in the demonstration pilot areas of China’s eco-product value realization; and (4) combined with the above results, this paper summarizes the concept and connotation of eco-products, the value accounting of ecological products, the mechanism and path of the value realization of eco-products, the ecological industry, the landmark achievements of home and abroad, and the scientific problems to be solved, so as to better serve the karst area and promote the revitalization of rural areas. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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28 pages, 782 KiB

Open AccessArticle

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

by Amine Bouaouda, Karim Afdel and Rachida Abounacer

Sustainability 2024, 16(11), 4438; https://doi.org/10.3390/su16114438 - 23 May 2024

Cited by 3 | Viewed by 1646

Abstract

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments [...] Read more.

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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18 pages, 560 KiB

Open AccessArticle

The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector

by Jiawen Yun and Shanyue Jin

Systems 2024, 12(4), 135; https://doi.org/10.3390/systems12040135 - 17 Apr 2024

Cited by 1 | Viewed by 2844

Abstract

Green finance, an essential tool for high-quality economic development, is valued by policymakers and researchers in line with the growing global concern for environmental protection, climate change, and sustainable development. The banking sector, as a major part of China’s green financial system, undertakes [...] Read more.

Green finance, an essential tool for high-quality economic development, is valued by policymakers and researchers in line with the growing global concern for environmental protection, climate change, and sustainable development. The banking sector, as a major part of China’s green financial system, undertakes significant responsibility for green finance while also confronting the opportunities and requirements of digital transformation. Big data technology is a major driver of digital transformation in the banking sector and can improve the green operational capability of the banking sector. The purpose of this study is to explore the ways in which the extent of big data technology transformation in the banking sector in China affects its ability to operate in a green manner and to analyze the moderating role of green credits, funds, and bonds. For this reason, this study selected A-share listed banks in China from 2015 to 2022 as research subjects and adopted a panel data regression method to study the impact of the degree of big data technology transformation on green operations. The results demonstrate that the degree of big data technology transformation in the banking sector positively influenced green operations. Green credit, funds, and bonds played a moderating role, meaning that financial products strengthened the role of the degree of big data technology transformation in green operations. This study examined the effect of big data technology transformation in the banking sector and enriches research on green finance. This study also provides practical insights for investors and regulators concerned with green development in the banking sector. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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22 pages, 475 KiB

Open AccessArticle

The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy

by Xiangyi Li, Qing Wang and Ying Tang

Sustainability 2024, 16(8), 3200; https://doi.org/10.3390/su16083200 - 11 Apr 2024

Cited by 13 | Viewed by 3631

Abstract

China’s economy is stepping into a new stage of high-quality development. The shift not only marks the optimization and upgrading of the economic structure, but also reflects the in-depth implementation of the concept of sustainable development. In this context, the development of AI [...] Read more.

China’s economy is stepping into a new stage of high-quality development. The shift not only marks the optimization and upgrading of the economic structure, but also reflects the in-depth implementation of the concept of sustainable development. In this context, the development of AI technology is playing an important role in balancing economic growth and ecological protection with its unique advantages. This paper empirically studied the impact of AI development on urban energy efficiency by constructing panel data for 282 prefecture-level cities from 2006 to 2019 and then using the super-efficiency SBM model based on non-expected outputs to evaluate the urban energy efficiency indicators of prefecture-level cities. It was discovered that the development of AI had a key influence on increasing urban energy efficiency and the optimization of the energy structure by speeding up green technology innovation and digital economy development, which in turn improved urban energy efficiency. In terms of heterogeneity analysis, AI development had a greater impact on urban energy efficiency in the eastern region, which has higher levels of human capital, financial independence, and government intervention. This study combined the smart city pilot policy with a multi-period DID model, based on the treatment of endogeneity issues, in order to perform a parallel trend test and investigate further the effects of policy implementation on the advancement of AI in the context of improving urban energy efficiency. Accordingly, to achieve green and sustainable urban development, the relevant government departments should increase funding for AI research and development, pay attention to the introduction and cultivation of professionals, establish a platform for international exchanges and cooperation between AI and energy management, and continue to advocate for the pilot development of smart cities to increase urban energy efficiency. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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15 pages, 16359 KiB

Open AccessArticle

Analysis and Simulation of Land Use Changes and Their Impact on Carbon Stocks in the Haihe River Basin by Combining LSTM with the InVEST Model

by Yanzhen Lin, Lei Chen, Ying Ma and Tingting Yang

Sustainability 2024, 16(6), 2310; https://doi.org/10.3390/su16062310 - 11 Mar 2024

Cited by 5 | Viewed by 1409

Abstract

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data [...] Read more.

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data in the Haihe River Basin from 2000 to 2020 was studied via Mann–Kendall (MK) trend analysis, the transfer matrix, and land use dynamic attitude. Through integrating the models of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and the Long Short-Term Memory (LSTM), the results of the spatial distribution of land use and carbon stock were obtained and compared with Cellular Automation (CA-Markov), and then applied to predict the spatial distribution in 2025. The results show the following: (1) The land use and land cover (LULC) changes in the Haihe River Basin primarily involve an exchange between cultivated land, forest, and grassland, as well as the conversion of cultivated land to built-up land. This transformation contributes to the overall decrease in carbon storage in the basin, which declined by approximately 1.20% from 2000 to 2020. (2) The LULC prediction accuracy of LSTM is nearly 2.00% higher than that of CA-Markov, reaching 95.01%. (3) In 2025, the area of grassland in Haihe River Basin will increase the most, while the area of cultivated land will decrease the most. The spatial distribution of carbon stocks is higher in the northwest and lower in the southeast, and the changing areas are scattered throughout the study area. However, due to the substantial growth of grassland and forest, the carbon stocks in the Haihe River Basin in 2025 will increase by about 10 times compared with 2020. The research results can provide a theoretical basis and reference for watershed land use planning, ecological restoration, and management. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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21 pages, 4426 KiB

Open AccessArticle

Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

by Mingxing Guo, Ran Lv, Zexing Miao, Fei Fei, Zhixin Fu, Enqi Wu, Li Lan and Min Wang

Processes 2024, 12(3), 523; https://doi.org/10.3390/pr12030523 - 5 Mar 2024

Cited by 3 | Viewed by 1688

Abstract

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the [...] Read more.

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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17 pages, 3144 KiB

Open AccessArticle

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

by Jie Yang, Guihong Ren, Yaxin Wang, Qi Liu, Jiamin Zhang, Wenqi Wang, Lingzhi Li and Wuping Zhang

Sustainability 2024, 16(5), 2021; https://doi.org/10.3390/su16052021 - 29 Feb 2024

Cited by 4 | Viewed by 1714

Abstract

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps [...] Read more.

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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24 pages, 14473 KiB

Open AccessArticle

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

by Wei Guo, Li Xu, Tian Wang, Danyang Zhao and Xujing Tang

Sensors 2024, 24(5), 1593; https://doi.org/10.3390/s24051593 - 29 Feb 2024

Cited by 7 | Viewed by 2449

Abstract

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning [...] Read more.

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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23 pages, 3150 KiB

Open AccessArticle

How Does Artificial Intelligence Impact Green Development? Evidence from China

by Mingyue Chen, Shuting Wang and Xiaowen Wang

Sustainability 2024, 16(3), 1260; https://doi.org/10.3390/su16031260 - 2 Feb 2024

Cited by 7 | Viewed by 5555

Abstract

Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an [...] Read more.

Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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16 pages, 2952 KiB

Open AccessArticle

Research on Service Design of Garbage Classification Driven by Artificial Intelligence

by Jingsong Zhang, Hai Yang and Xinguo Xu

Sustainability 2023, 15(23), 16454; https://doi.org/10.3390/su152316454 - 30 Nov 2023

Viewed by 4471

Abstract

This paper proposes a framework for AI-driven municipal solid waste classification service design and management, with an emphasis on advancing sustainable urban development. This study uses narrative research and case study methods to delve into the benefits of AI technology in waste classification [...] Read more.

This paper proposes a framework for AI-driven municipal solid waste classification service design and management, with an emphasis on advancing sustainable urban development. This study uses narrative research and case study methods to delve into the benefits of AI technology in waste classification systems. The framework includes intelligent recognition, management strategies, AI-based waste classification technologies, service reforms, and AI-powered customer involvement and education. Our research indicates that AI technology can improve accuracy, efficiency, and cost-effectiveness in waste classification, contributing to environmental sustainability and public health. However, the effectiveness of AI applications in diverse city contexts requires further verification. The framework holds theoretical and practical significance, offering insights for future service designs of waste management and promoting broader goals of sustainable urban development. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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16 pages, 1113 KiB

Open AccessArticle

Green Financial Supervision Information System Based on Genetic Algorithm Optimization under Carbon Peaking and Carbon Neutrality Goals

by Wangfangyu Wan

Sustainability 2023, 15(22), 15866; https://doi.org/10.3390/su152215866 - 12 Nov 2023

Cited by 1 | Viewed by 1389

Abstract

Under the guidance of “carbon peaking and carbon neutrality”, “ecological priority” and “green development” have become the popular consensus, and the financial regulatory level continuously guides financial institutions to increase investment in green and low carbon projects. In the field of green financial [...] Read more.

Under the guidance of “carbon peaking and carbon neutrality”, “ecological priority” and “green development” have become the popular consensus, and the financial regulatory level continuously guides financial institutions to increase investment in green and low carbon projects. In the field of green financial supervision in China, due to imperfect systems and poor adaptability, financial risks are often difficult to control within a reasonable range, which has had a significant impact on financial supervision and management. This article aimed to optimize the green financial regulatory information system under the carbon peaking and carbon neutrality goals. Firstly, this article analyzed the concept and background of green finance regulation; then, an investigation was conducted on the construction of the green finance service information system, and a green finance information system supervision plan was established. Finally, data collection and analysis were conducted, and the supervision of the green finance information system was carried out using a standard genetic algorithm based on a fuzzy evaluation matrix. This article used a genetic algorithm to optimize the green financial regulatory information system, and selected 500 people to evaluate the use of the system before and after the optimization. The proportion of very satisfied people increased from 11.2% to 19.2%; the proportion of satisfied people increased from 17.2% to 37.6%; the proportion of people who were very dissatisfied decreased from 14.4% to 3.6%. The experiment in this article showed that the optimized system could operate more stably, and the process was more reasonable. The statistical analysis ability was significantly enhanced, and the functions were more comprehensive. This suggests that the system could better regulate the development of green finance. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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10 pages, 251 KiB

Open AccessCase Report

Shortening the Supply Chain through Smart Manufacturing and Green Technology

by Pandwe Aletha Gibson

Sustainability 2023, 15(22), 15735; https://doi.org/10.3390/su152215735 - 8 Nov 2023

Cited by 3 | Viewed by 2993

Abstract

Correcting inefficiencies in the supply chain requires us to reimagine manufacturing by recapturing processes—particularly material sourcing and end-use recycling, which create vast amounts of waste. Inefficiencies in the supply chain create massive waste and stifle innovation in manufacturing, both well-established concerns for the [...] Read more.

Correcting inefficiencies in the supply chain requires us to reimagine manufacturing by recapturing processes—particularly material sourcing and end-use recycling, which create vast amounts of waste. Inefficiencies in the supply chain create massive waste and stifle innovation in manufacturing, both well-established concerns for the environment. Carbon-based fuels and products are detrimental to the land, air, and sea. Single-use products made from toxic materials flood the food and medical supply chains. Businesses are increasingly moving toward the single purchasing platform model (for example, Uber and Airbnb). Following that model, this paper proposes a platform as a service (PaaS) manufacturing sharing service that matches small- to mid-size manufacturers with production capacity as a solution to obtaining ethically sourced products at a competitive price while offering access to last-mile delivery locally on a single purchasing platform. The development of an Internet of Things (IoT) platform can achieve these four things: (1) provide better coordination of the sourcing and supply of materials, (2) ensure effective provisions of eco-friendly and recycled inputs, (3) provide efficient distribution of equipment and manufacturing resources, and (4) shorten the supply chain by centralizing and coordinating last-mile delivery. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

14 pages, 1891 KiB

Open AccessEditor’s ChoiceArticle

Development of a DNN Predictive Model for the Optimal Operation of an Ambient Air Vaporizer of LNG

by Jong-Ho Shin, Seung-Kil Lim, Jae-Gon Kim, Geun-Cheol Lee and June-Young Bang

Processes 2023, 11(11), 3143; https://doi.org/10.3390/pr11113143 - 3 Nov 2023

Cited by 3 | Viewed by 1786

Abstract

In this study, we conducted preliminary research with the objective of leveraging artificial intelligence to optimize the efficiency and safety of the entire Ambient Air Vaporizer (AAV) system for LNG (Liquid Natural Gas). By analyzing a year-long dataset of real operational data, we [...] Read more.

In this study, we conducted preliminary research with the objective of leveraging artificial intelligence to optimize the efficiency and safety of the entire Ambient Air Vaporizer (AAV) system for LNG (Liquid Natural Gas). By analyzing a year-long dataset of real operational data, we identified key variables that significantly influence the outlet temperature of Natural Gas (NG). Based on these insights, a Deep Neural Network (DNN) prediction model was developed to forecast the NG outlet temperature. The endeavor to create an effective prediction model faced specific challenges, primarily due to the narrow operational range of fan speeds and safety-focused guidelines. To surmount these obstacles, various learning algorithms were evaluated under multiple conditions. Ultimately, a DNN model exhibiting lower values of both absolute mean error (MAE) and mean square error (MSE) was successfully established. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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33 pages, 2969 KiB

Open AccessReview

Multi-Objective Optimization for High-Performance Building Facade Design: A Systematic Literature Review

by Rudai Shan and Lars Junghans

Sustainability 2023, 15(21), 15596; https://doi.org/10.3390/su152115596 - 3 Nov 2023

Cited by 10 | Viewed by 4840

Abstract

Building facade design plays an essential role in enhancing energy efficiency and reducing environmental impact in high-performance building design. Balancing the conflicts among various building facade design variables to satisfy different optimization objectives constitutes a highly complex optimization problem. The rapidly increasing number [...] Read more.

Building facade design plays an essential role in enhancing energy efficiency and reducing environmental impact in high-performance building design. Balancing the conflicts among various building facade design variables to satisfy different optimization objectives constitutes a highly complex optimization problem. The rapidly increasing number of studies demonstrates a significant interest in implementing multi-objective optimization methods to tackle building facade optimization problems. This study conducts a systematic review of optimization methods for building facade optimization (BFO). The optimization objectives and design variables are categorized based on their characteristics. The efficiency and effectiveness of optimization algorithms in addressing BFO problems are compared. Building optimization techniques and tools are showcased, along with their functions and limitations. Key findings highlight the robust feasibility and effectiveness of optimization algorithms, methods, and techniques in resolving a diverse range of BFO challenges. The limitations, challenges, and future potential of these methods are summarized and proposed. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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14 pages, 2573 KiB

Open AccessArticle

Application of Physics-Informed Neural Networks to River Silting Simulation

by Perizat Omarova, Yedilkhan Amirgaliyev, Ainur Kozbakova and Aisulyu Ataniyazova

Appl. Sci. 2023, 13(21), 11983; https://doi.org/10.3390/app132111983 - 2 Nov 2023

Cited by 3 | Viewed by 2768

Abstract

Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of [...] Read more.

Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study’s methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the physics-informed neural network (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANNs) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived using the PINN method and those obtained using conventional numerical approaches with the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and a significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assume paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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15 pages, 1470 KiB

Open AccessArticle

What Indicators Are Shaping China’s National World-Class High-Tech Zones? Constructing a Feature Indicator System Based on Machine Learning

by Sida Feng, Hyunseok Park and Fang Han

Appl. Sci. 2023, 13(19), 10690; https://doi.org/10.3390/app131910690 - 26 Sep 2023

Viewed by 1607

Abstract

China’s high-tech parks have significant effects on driving national ecological innovation. Among them, ten world-class high-tech parks represent the highest level of development in China’s high-tech industry. Understanding the development characteristics of national world-class high-tech parks is of great significance for guiding the [...] Read more.

China’s high-tech parks have significant effects on driving national ecological innovation. Among them, ten world-class high-tech parks represent the highest level of development in China’s high-tech industry. Understanding the development characteristics of national world-class high-tech parks is of great significance for guiding the construction of other parks and achieving the high-quality development of parks. Based on the evaluation data of over 200 indicators of national high-tech parks from 2013 to 2017, this study used the XGBoost classic machine learning algorithm to select the characteristic indicators of national world-class high-tech parks and establish an evaluation indicator system, and it identified four primary indicators of the world-class high-tech parks, including innovation development, enterprise development, international development, and economic development. The indicators cover 30 important sub-indicators and highlight the importance of innovation resource input indicators, such as “use of technology activity funding from government departments”, “full-time equivalent of R&D personnel”, and “financial technology expenditure in high-tech parks”. Compared to the expert analysis, the application of the machine learning method in the evaluation of national high-tech parks improves the efficiency of selecting important indicators and makes the selection results more objective. The results of this research provide a reference value for guiding and promoting national high-tech parks to become world-class parks. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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16 pages, 903 KiB

Open AccessArticle

How the Digital Economy Enables Regional Sustainable Development Using Big Data Analytics

by Ruohan Wang, Qingjin Wang, Renbo Shi, Kaiyun Zhang and Xueling Wang

Sustainability 2023, 15(18), 13610; https://doi.org/10.3390/su151813610 - 12 Sep 2023

Cited by 7 | Viewed by 1753

Abstract

The development of the cultural industry cannot be isolated from the efficient integration with the digital economy and digital technology at the current stage of the technological and industrial revolution. This paper constructs an indicator system to measure the sustainable development of the [...] Read more.

The development of the cultural industry cannot be isolated from the efficient integration with the digital economy and digital technology at the current stage of the technological and industrial revolution. This paper constructs an indicator system to measure the sustainable development of the cultural industry and tests the relationship between the digital economy and the sustainable development of the cultural industry using an OLS model based on China’s provincial panel data from 2011 to 2021. The findings of this study suggest that the digital economy can significantly aid in the long-term growth of cultural companies. The process of promoting sustainable development of the cultural industry through the digital economy has also advanced thanks to the government’s strong support. This report also suggests governmental recommendations based on these findings for the sustainable development of China’s cultural industry in the age of the digital economy. This paper theoretically elucidates the mechanism of the role of the digital economy on the sustainable development of the cultural industry, constructs a system of indicators to measure the sustainable development of the cultural industry, and tests the impact of the digital economy on the sustainable development of the cultural industry. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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17 pages, 548 KiB

Open AccessEditor’s ChoiceArticle

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

by Yixuan Chen and Shanyue Jin

Processes 2023, 11(9), 2705; https://doi.org/10.3390/pr11092705 - 10 Sep 2023

Cited by 35 | Viewed by 8437

Abstract

Carbon emissions have gained worldwide attention in the industrial era. As a key carbon-emitting industry, achieving net-zero carbon emissions in the manufacturing sector is vital to mitigating the negative effects of climate change and achieving sustainable development. The rise of intelligent technologies has [...] Read more.

Carbon emissions have gained worldwide attention in the industrial era. As a key carbon-emitting industry, achieving net-zero carbon emissions in the manufacturing sector is vital to mitigating the negative effects of climate change and achieving sustainable development. The rise of intelligent technologies has driven industrial structural transformations that may help achieve carbon reduction. Artificial intelligence (AI) technology is an important part of digitalization, providing new technological tools and directions for the low carbon development of enterprises. This study selects Chinese A-share listed companies in the manufacturing industry from 2012 to 2021 as the research objects and uses a fixed-effects regression model to study the relationship between AI and carbon emissions. This study clarifies the significance of enterprise AI technology applications in realizing carbon emissions reduction and explores the regulatory mechanism from the perspective of the innovation effect. The results show that the application of enterprise AI technology positively impacts carbon emissions reduction. Simultaneously, green technological innovation, green management innovation, and green product innovation play moderating roles; in other words, enterprise green innovation strengthens the effect of AI on carbon emissions reduction. This study clarifies the necessity of intelligent manufacturing and enriches theories related to AI technology and carbon emissions. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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27 pages, 1139 KiB

Open AccessArticle

Analysis and Empirical Study of Factors Influencing Urban Residents’ Acceptance of Routine Drone Deliveries

by Zhao Zhang, Chun-Yan Xiao and Zhi-Guo Zhang

Sustainability 2023, 15(18), 13335; https://doi.org/10.3390/su151813335 - 6 Sep 2023

Cited by 9 | Viewed by 2308

Abstract

The usage of drone delivery couriers has multiple benefits over conventional methods, and it is expected to play a big role in the development of urban intelligent logistics. Many courier companies are currently attempting to deliver express delivery using drones in the hopes [...] Read more.

The usage of drone delivery couriers has multiple benefits over conventional methods, and it is expected to play a big role in the development of urban intelligent logistics. Many courier companies are currently attempting to deliver express delivery using drones in the hopes that this new type of tool used for delivery tasks will become the norm as soon as possible. However, most urban residents are currently unwilling to accept the use of drones to deliver express delivery as normal. This study aims to find out the reasons for the low acceptance of the normalization of drone delivery by urban residents and formulate a more reasonable management plan for drone delivery so that the normalization of drone delivery can be realized as soon as possible. A research questionnaire was scientifically formulated which received effective feedback from 231 urban residents in Jinjiang District, Chengdu City. A binary logistic model was used to determine the factors that can significantly influence the acceptance of residents. In addition, the fuzzy interpretive structural model(Fuzzy-ISM) was used to find out the logical relationship between the subfactors inherent to these influencing factors. It was concluded that when the infrastructure is adequate, increasing public awareness and education, enhancing the emergency plan, lowering delivery costs, enhancing delivery efficiency and network coverage, and bolstering the level of safety management can significantly raise resident acceptance of unmanned aerial vehicle(UAV) delivery. Given the positional characteristics of the subfactors in the interpretive structural model(ISM) and matrices impacts croises-multiplication appliance classemen(MICMAC) in this study, we should first make sure that the drone delivery activities can be carried out in a safe and sustainable environment with all the necessary equipment, instead of focusing on increasing the residents’ acceptance right away, in the future work of regularized drone urban delivery has not yet started the construction phase. There should be more effort put into building the links that will enable acceptance to be improved with higher efficiency, which will be helpful to the early realization of the normalization of drone urban delivery if there is already a certain construction foundation in the case where the drone delivery environment is up to standard and hardware conditions are abundant. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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13 pages, 1598 KiB

Open AccessArticle

Analysis of Greenhouse Gas Emissions Characteristics and Emissions Reduction Measures of Animal Husbandry in Inner Mongolia

by Yingnan Cao, Xiaoxia Yang, Fang Yang, Ying Sun, Qianqian Wang, Futian Ren, Lei Nie, Aodemu and Weiying Feng

Processes 2023, 11(8), 2335; https://doi.org/10.3390/pr11082335 - 3 Aug 2023

Cited by 2 | Viewed by 1898

Abstract

Global warming has had a profound impact on human life, with animal husbandry being a significant contributor to greenhouse gas emissions and playing a crucial role in the global greenhouse gas budget. Inner Mongolia is a major contributor to these emissions, making it [...] Read more.

Global warming has had a profound impact on human life, with animal husbandry being a significant contributor to greenhouse gas emissions and playing a crucial role in the global greenhouse gas budget. Inner Mongolia is a major contributor to these emissions, making it vital to study the link between greenhouse gas emissions and animal husbandry in this region for the purpose of reducing emissions. In this study, the emissions of greenhouse gases (CH₄, N₂O, and CO₂) from livestock and poultry breeding from 2010 to 2020 and the emissions of each city from 2020 were estimated, the emissions characteristics were analysed, and the low carbon emissions reduction technical measures were proposed. The results show that (1) the overall greenhouse gas emissions from 2010 to 2020 in Inner Mongolia showed a fluctuating trend; the main emissions sources were gastrointestinal fermentation and faecal management. The annual average CH₄ emissions were 994,400 ta⁻¹, and the annual average N₂O emissions were 35,100 ta⁻¹. (2) In 2020, the total emissions of each league city were 38.05 million t equivalent of CO₂, and the emissions gradually decreased from east to west, with a significant emissions reduction potential. Based on these findings, this study also proposed technical measures for reducing carbon emissions, offering theoretical support to drive the industrial transformation and upgrading of the livestock industry, and promoting green economic development in Inner Mongolia as part of its carbon peaking and neutrality goals. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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18 pages, 2981 KiB

Open AccessArticle

Environmental Impact Assessment of the Dismantled Battery: Case Study of a Power Lead–Acid Battery Factory in China

by Zhiguo Wang, Jie Yang, Renxiu Qu and Gongwei Xiao

Processes 2023, 11(7), 2119; https://doi.org/10.3390/pr11072119 - 16 Jul 2023

Cited by 4 | Viewed by 2294

Abstract

With the increase in battery usage and the decommissioning of waste power batteries (WPBs), WPB treatment has become increasingly important. However, there is little knowledge of systems and norms regarding the performance of WPB dismantling treatments, although such facilities and factories are being [...] Read more.

With the increase in battery usage and the decommissioning of waste power batteries (WPBs), WPB treatment has become increasingly important. However, there is little knowledge of systems and norms regarding the performance of WPB dismantling treatments, although such facilities and factories are being built across the globe. In this paper, environmental performance is investigated quantitively using life cycle assessment (LCA) methodology for a dismantled WPB manufacturing process in Tongliao city of Inner Mongolia Province, China. The functional unit was selected to be one metric ton of processed WPB, and the average data of 2021 were used. The results indicated that WPB dismantling treatments are generally sustainable in their environmental impacts, because the life cycle environmental effects can be neutralized by the substitution of virgin products with recycled counterparts. Of all the processes of dismantlement, Crude Lead Making, Refining, and Preliminary Desulfurization, were the top three contributors to the total environmental burden. The results of the sensitivity analysis showed that increasing photovoltaic power, wind power, and natural gas usage may significantly reduce the burden on the environment. On the basis of our findings, some suggestions are put forward for a policy to promote environmental green growth of WPB treatment. Although this paper is aimed at the power lead–acid battery, the research method is also of significance for the power lithium-ion battery, and we will conduct relevant research on the disassembly process of the power lithium-ion battery in the future. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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17 pages, 684 KiB

Open AccessArticle

Artificial Intelligence and Green Total Factor Productivity: The Moderating Effect of Slack Resources

by Ying Ying, Xiaoyan Cui and Shanyue Jin

Systems 2023, 11(7), 356; https://doi.org/10.3390/systems11070356 - 11 Jul 2023

Cited by 14 | Viewed by 3962

Abstract

With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises. Green total factor productivity is a key indicator of green sustainable development. While traditional total factor productivity does not consider the [...] Read more.

With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises. Green total factor productivity is a key indicator of green sustainable development. While traditional total factor productivity does not consider the constraints of natural resources and the environment, green total factor productivity remedies this deficiency by incorporating environmental protection indicators, such as pollutant emissions, into the accounting system. To further clarify the relationship between AI technology and corporate green total factor productivity, this study uses a two-way fixed effects model to examine the impact of AI technology on the corporate green total factor productivity of A-share listed companies in China from 2013 to 2020 while examining how corporate slack resources affect the relationship between the two. The results show that the AI application positively contributes to the green total factor productivity of enterprises. Meanwhile, firms’ absorbed, unabsorbed, and potential slack resources all positively moderate the positive impact of AI technology on firms’ green total factor productivity. This study offers a theoretical basis for a comprehensive understanding of digital technology and enterprises’ green development. It also contributes practical insights for the government to formulate relevant policies and for enterprises to use digital technology to attain green and sustainable development. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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31 pages, 13616 KiB

Open AccessArticle

Research on Coupling Adsorption Experiments for Wall–Climbing Robots in Coal Mine Shafts

by Ying Xu and Wenjun Fu

Processes 2023, 11(7), 2016; https://doi.org/10.3390/pr11072016 - 5 Jul 2023

Cited by 2 | Viewed by 1564

Abstract

Based on the composite shaft lining structure, the research on the electromagnetic and negative pressure coupling adsorption technology of wall–climbing robots is of great significance to improve the level of safety monitoring during the construction and service of coal mine shafts. On the [...] Read more.

Based on the composite shaft lining structure, the research on the electromagnetic and negative pressure coupling adsorption technology of wall–climbing robots is of great significance to improve the level of safety monitoring during the construction and service of coal mine shafts. On the basis of theoretical research and computational data, the numerical simulation and simulation experiments of the coupled adsorption system of a wall–climbing robot are conducted in this research. In the ANSA software environment, of experimental models and experimental environments of electromagnetic and negative pressure adsorption devices are constructed to investigate, parameters such as air flow and the law behavior of fan pressure under different system conditions, including negative pressure and varying fan speeds. The intensity distribution of the magnetic flux inside the electromagnetic circuit under different working conditions and the law of change in the direction of movement are explored. Furthermore, the power consumption and power increment of the electromagnetic and negative pressure adsorption system under the same adsorption force output are compared and analyzed. Based on the experimental results, a series of conclusions are verified; firstly the negative pressure of the system should be formed under certain basic specific fundamental conditions; secondly, the main velocity of the negative pressure adsorption system and the full pressure of the fan are determined by the internal and external pressure difference and the fan speed, respectively; lastly, the adsorption efficiency of electromagnetic adsorption is significantly higher than that of negative pressure adsorption. These research findings are expected to introduce a new technical means approach for the safety monitoring of vertical shafts and shafts in coal mines, thereby demonstrating the theoretical significance and practical value of the application and development of an underground multi–scenario robot automation system in coal mines. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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22 pages, 5736 KiB

Open AccessArticle

Green Supply Chain Circular Economy Evaluation System Based on Industrial Internet of Things and Blockchain Technology under ESG Concept

by Cheng Qian, Yuying Gao and Lifeng Chen

Processes 2023, 11(7), 1999; https://doi.org/10.3390/pr11071999 - 3 Jul 2023

Cited by 20 | Viewed by 4862

Abstract

A green supply chain economy considering environmental, social, and governance (ESG) factors improves the chances of functional growth through minimal risk factors. The implication of sophisticated technologies such as the Industrial Internet of Things (IIoT) and the blockchain improves the optimization [...] Read more.

A green supply chain economy considering environmental, social, and governance (ESG) factors improves the chances of functional growth through minimal risk factors. The implication of sophisticated technologies such as the Industrial Internet of Things (IIoT) and the blockchain improves the optimization and evaluation of ESG performance. An IIoT-Blockchain-based Supply Chain Economy Evaluation (IB-SCEE) model is introduced to identify and reduce functional growth risk factors. The proposed model uses green blockchain technology to identify distinct transactions’ economic demands and supply distribution. The flaws and demands in the circular economy process are validated using the IIoT forecast systems relying on ESG convenience. The minimal and maximum risks are identified based on economic and distribution outcomes. The present investigation highlights the significance of ongoing ESG-conceptualized research into blockchain-based supply chain economics. Companies who recognize the blockchain’s potential can improve corporate governance, environmental impact, and social good by increasing transparency, traceability, and accountability. A more sustainable and responsible future for global supply chains can be shaped through further research and development in this field, which will make a substantial contribution to the scientific world. This information is individually held in the green blockchain for individual risk factor analysis. The proposed model improves the recommendation and evaluation rate and reduces the risk factors with controlled evaluation time. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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13 pages, 1170 KiB

Open AccessArticle

Research on Innovation Management of Enterprise Supply Chain Digital Platform Based on Blockchain Technology

by Lu Xiang and Renyong Hou

Sustainability 2023, 15(13), 10198; https://doi.org/10.3390/su151310198 - 27 Jun 2023

Cited by 8 | Viewed by 2062

Abstract

Purpose: In response to the problems of high production costs, weak comprehensive competitiveness, and incomplete capital chain in the development of enterprises in the international market, this article proposes to apply blockchain technology to the digital platform of the supply chain. By scientifically [...] Read more.

Purpose: In response to the problems of high production costs, weak comprehensive competitiveness, and incomplete capital chain in the development of enterprises in the international market, this article proposes to apply blockchain technology to the digital platform of the supply chain. By scientifically managing the digital platform, enterprises can reduce production costs and improve their capital chain. Design/methodology/approach: In this long-term economic life, theoretical science and technology are still developing, gradually forming a blockchain-based enterprise supply chain development model. The continuous research on the blockchain theory can provide more convenient services for the supply chain digital platform innovation management. Findings: This article studies the entire supply chain management process through blockchain algorithms and innovative management methods, which can enable enterprises to further develop on the digital platform of supply chain, promote the digital construction of supply chain, and ensure the sustainable development of enterprises. Originality/value: This can reduce the supply chain digital platform innovation management risk of enterprises by more than 60%, as well as reduce the cost of enterprises, which has a more far-reaching impact on the sustainable development of enterprises. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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28 pages, 7760 KiB

Open AccessArticle

Research on Electromagnetic Adsorption Technology Based on Composite Shaft Lining Structure

by Ying Xu and Wenjun Fu

Processes 2023, 11(6), 1752; https://doi.org/10.3390/pr11061752 - 8 Jun 2023

Viewed by 1415

Abstract

The working conditions and environment of coal mine shafts are intricate and special. Currently, manual inspections or fixed-point monitoring is generally applied for daily safety monitoring, and intelligent and automated inspection equipment and its supporting technologies are not available. Starting from the technical [...] Read more.

The working conditions and environment of coal mine shafts are intricate and special. Currently, manual inspections or fixed-point monitoring is generally applied for daily safety monitoring, and intelligent and automated inspection equipment and its supporting technologies are not available. Starting from the technical requirements of the electromagnetic adsorption device of the wall-climbing robot for safety monitoring of the coal mine shaft, based on the structural characteristics and chemical composition of the composite shaft lining of the coal mine, the fundamental structure of the electromagnetic array and the electromagnetic unit are clarified, and a multi-layer matrix simulation point overlap mapping analysis method is proposed. Based on the system modeling and simulation calculations in MATLAB software, the number and distribution law of effective mapping points between the endpoints of the electromagnetic array and the reinforced frame in the shaft lining are inferred, which leads to the establishment of a calculation model of the equivalent adsorption area. The NSGA-II algorithm, a non-dominant elite strategy based on a genetic algorithm, is used to calculate the optimum combination scheme of various genetic parameters of individual electromagnetic units. Through the statistical analysis of the optimal individual data of each generation in the iterative process, the accuracy of the algorithm process and constraints, as well as the fitness function, are verified. Based on the research results of this paper, the electromagnetic adsorption issue of the mine shaft wall-climbing robot on the composite shaft lining structure has been effectively solved, which has theoretical significance and practical value for improving the autonomous ability and monitoring level of coal mine shaft safety monitoring. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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23 pages, 9095 KiB

Open AccessArticle

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

by Hairui Wang, Xin Ye, Yuanbo Li and Guifu Zhu

Sustainability 2023, 15(12), 9176; https://doi.org/10.3390/su15129176 - 6 Jun 2023

Cited by 6 | Viewed by 2315

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance [...] Read more.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R²) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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19 pages, 4750 KiB

Open AccessArticle

Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions

by Alper Sen and Kutalmis Gumus

Systems 2023, 11(5), 261; https://doi.org/10.3390/systems11050261 - 19 May 2023

Cited by 2 | Viewed by 2310

Abstract

Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. [...] Read more.

Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. The use of machine learning methods, such as artificial neural networks for spatial interpolation, contributes to spatial interpolation with more accuracy. In this study, the performances of FBNN interpolation based on different parameters such as the number of hidden layers and neurons, epoch number, processing time, and training functions (gradient optimization algorithms) were compared, and the differences were evaluated statistically using an analysis of variance (ANOVA) test. This research offers significant insights into the optimization of neural network gradients, with a particular focus on spatial interpolation. The accuracy of the Levenberg–Marquardt training function was the best, whereas the most significantly different training functions, gradient descent backpropagation and gradient descent with momentum and adaptive learning rule backpropagation, were the worst. Thus, this study contributes to the investigation of parameter selection of ANN for spatial interpolation in DEM height estimation for different terrain types and point distributions. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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13 pages, 2055 KiB

Open AccessArticle

Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction

by Xiaoyuan Feng, Yue Chen, Hongbo Li, Tian Ma and Yilong Ren

Sustainability 2023, 15(9), 7696; https://doi.org/10.3390/su15097696 - 8 May 2023

Cited by 3 | Viewed by 2341

Abstract

Traffic flow prediction is an important function of intelligent transportation systems. Accurate prediction results facilitate traffic management to issue early congestion warnings so that drivers can avoid congested roads, thus directly reducing the average driving time of vehicles, which means less greenhouse gas [...] Read more.

Traffic flow prediction is an important function of intelligent transportation systems. Accurate prediction results facilitate traffic management to issue early congestion warnings so that drivers can avoid congested roads, thus directly reducing the average driving time of vehicles, which means less greenhouse gas emissions. However, traffic flow data has complex spatial and temporal correlations, which makes it challenging to predict traffic flow accurately. A Gated Recurrent Graph Convolutional Attention Network (GRGCAN) for traffic flow prediction is proposed to solve this problem. The model consists of three components with the same structure, each of which contains one temporal feature extractor and one spatial feature extractor. The temporal feature extractor first introduces a gated recurrent unit (GRU) and uses the hidden states of the GRU combined with an attention mechanism to adaptively assign weights to each time step. In the spatial feature extractor, a node attention mechanism is constructed to dynamically assigns weights to each sensor node, and it is fused with the graph convolution operation. In addition, a residual connection is introduced into the network to reduce the loss of features in the deep network. Experimental results of 1-h traffic flow prediction on two real-world datasets (PeMSD4 and PeMSD8) show that the mean absolute percentage error (MAPE) of the GRGCAN model is as low as 15.97% and 12.13%, and the prediction accuracy and computational efficiency are better than the baselines. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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20 pages, 1641 KiB

Open AccessArticle

Analysing Multiple Paths of Urban Low-Carbon Governance: A Fuzzy-Set Qualitative Comparative Analysis Method Based on 35 Key Cities in China

by You-Dong Li, Chen-Li Yan, Yun-Hui Zhao and Jia-Qi Bai

Sustainability 2023, 15(9), 7613; https://doi.org/10.3390/su15097613 - 5 May 2023

Cited by 5 | Viewed by 2324

Abstract

The city is a crucial space carrier for the country to carry out low-carbon construction and solve sustainable–development problems. However, existing research lacks an in-depth discussion of the complex mechanisms and governance paths of urban low-carbon transformation. Therefore, this study explores multiple paths [...] Read more.

The city is a crucial space carrier for the country to carry out low-carbon construction and solve sustainable–development problems. However, existing research lacks an in-depth discussion of the complex mechanisms and governance paths of urban low-carbon transformation. Therefore, this study explores multiple paths of urban low-carbon governance (ULCG). This study constructs a theoretical model of ULCG based on the technology–organisation–environment (TOE) framework. It uses fuzzy-set qualitative comparative analysis (fsQCA) to analyse the overall and sub-regional paths of 35 key cities in China to explore various ULCG approaches. The following three conclusions are drawn. First, a single antecedent condition is not a necessary condition for ULCG. Second, five differentiated paths have been formed under the joint action of the TOE conditions to improve ULCG. It can be divided into three types: the ULCG model dominated by ‘big data + market’, ‘big data’, and ‘market’. Third, apparent differences exist in the ULCG paths in China’s eastern, central and western regions. The study deepens the rational understanding of multiple factors interacting in the complex mechanism behind urban low-carbon transformation and provides differentiated ULCG paths, enabling cities in eastern, central, and western China to choose low-carbon governance paths tailored to their local conditions based on both a comprehensive perspective and a regional perspective. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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17 pages, 3176 KiB

Open AccessArticle

The Application of Hyperspectral Images in the Classification of Fresh Leaves’ Maturity for Flue-Curing Tobacco

by Xiaochong Lu, Chen Zhao, Yanqing Qin, Liangwen Xie, Tao Wang, Zhiyong Wu and Zicheng Xu

Processes 2023, 11(4), 1249; https://doi.org/10.3390/pr11041249 - 18 Apr 2023

Cited by 11 | Viewed by 2164

Abstract

The maturity of tobacco leaves directly affects their curing quality. However, no effective method has been developed for determining their maturity during production. Assessment of tobacco maturity for flue curing has long depended on production experience, leading to considerable variation. In this study, [...] Read more.

The maturity of tobacco leaves directly affects their curing quality. However, no effective method has been developed for determining their maturity during production. Assessment of tobacco maturity for flue curing has long depended on production experience, leading to considerable variation. In this study, hyperspectral imaging combined with a novel algorithm was used to develop a classification model that could accurately determine the maturity of tobacco leaves. First, tobacco leaves of different maturity levels (unripe, under-ripe, ripe, and over-ripe) were collected. ENVI software was used to remove the hyperspectral imaging (HSI) background, and 11 groups of filtered images were obtained using Python 3.7. Finally, a full-band-based partial least-squares discriminant analysis (PLS-DA) classification model was established to identify the maturity of the tobacco leaves. In the calibration set, the model accuracy of the original spectrum was 88.57%, and the accuracy of the de-trending, multiple scattering correction (MSC), and standard normalization variable (SNV) treatments was 91.89%, 95.27%, and 92.57%, respectively. In the prediction set, the model accuracy of the de-trending, MSC, and SNV treatments was 93.85%, 96.92%, and 93.85%, respectively. The experimental results indicate that a higher model accuracy was obtained with the filtered images than with the original spectrum. Because of the higher accuracy, de-trending, MSC, and SNV treatments were selected as the candidate characteristic spectral bands, and a successive projection algorithm (SPA), competitive adaptive reweighted sampling (CASR), and particle swarm optimization (PSO) were used as the screening methods. Finally, a genetic algorithm (GA), PLS-DA, line support vector machine (LSVM), and back-propagation neural network (BPNN) classification and discrimination models were established. The combination SNV-SPA-PLS-DA model provided the best accuracy in the calibration and prediction sets (99.32% and 98.46%, respectively). Our findings highlight the efficacy of using visible/near-infrared (ViS/NIR) hyperspectral imaging for detecting the maturity of tobacco leaves, providing a theoretical basis for improving tobacco production. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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14 pages, 4557 KiB

Open AccessArticle

K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics

by Jie Meng, Ziyang Yu, Yuxin Cai and Xiuling Wang

Appl. Sci. 2023, 13(8), 4736; https://doi.org/10.3390/app13084736 - 9 Apr 2023

Cited by 5 | Viewed by 2471

Abstract

We used statistical methods to study the classification of high-potassium glass and lead–barium glass and analyzed the correlation between the chemical composition of different types of glass samples. We investigated the categorization methodology of glass cultural relics, conducted a principal component analysis on [...] Read more.

We used statistical methods to study the classification of high-potassium glass and lead–barium glass and analyzed the correlation between the chemical composition of different types of glass samples. We investigated the categorization methodology of glass cultural relics, conducted a principal component analysis on the chemical composition data of the glass, and developed a case-specific clustering algorithm (K-Means++) to further categorize the glass cultural relics. K-Means++ was developed to reduce the sensitivity of a traditional K-Means clustering algorithm, by choosing the next clustering center with probability inversely proportional to the distance from the current clustering center. Then we verified the validity of the six subcategories we defined by inertia and silhouette score and evaluated the sensitivity of the clustering algorithm. We obtained a robustness ratio that maintained over 0.9 in the random noise test and a silhouette score of 0.525 in the clustering, which illustrated significant divergence among different clusters and showed the result is reasonable. With our proposed algorithm and classification result, a more comprehensive understanding of glass relics can be gained. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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17 pages, 2134 KiB

Open AccessArticle

A Blockchain-Based Recycling Platform Using Image Processing, QR Codes, and IoT System

by Emin Borandag

Sustainability 2023, 15(7), 6116; https://doi.org/10.3390/su15076116 - 1 Apr 2023

Cited by 6 | Viewed by 4173

Abstract

The climate crisis is one of the most significant challenges of the twenty-first century. The primary cause of high carbon emissions is industrial production that relies on carbon-based energy sources such as fuel oil, paraffin, coal, and natural gas. One of the effective [...] Read more.

The climate crisis is one of the most significant challenges of the twenty-first century. The primary cause of high carbon emissions is industrial production that relies on carbon-based energy sources such as fuel oil, paraffin, coal, and natural gas. One of the effective methods to minimize carbon emissions originating from the use of energy resources is using recycling systems. A blockchain-based recycling platform was developed in this regard, adhering to the basic principles of Industry 4.0, which Robert Bosch GmbH and Henning Kagermann’s working group described as an industrial strategy plan at the Hannover Fair in 2013. Concurrently, the recycling platform has set up an infrastructure that combines blockchain, AI, and IoT technologies for recycling objects. An IoT-based smart device was developed to collect recyclable objects. Thanks to the embedded artificial intelligence software and QR code sensor on the device, recyclable objects can be collected in different hoppers. In the laboratory studies, correct object recognition success was achieved at a rate of 98.2%. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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20 pages, 1889 KiB

Open AccessArticle

The Impact of Green M&A Listed Companies’ Size on the Rural Ecological Environment—Digitalization as Moderating Effect

by Lingling Zhou, Wenqi Li, Brian Sheng-Xian Teo and Siti Khalidah Md Yusoff

Sustainability 2023, 15(7), 6068; https://doi.org/10.3390/su15076068 - 31 Mar 2023

Cited by 4 | Viewed by 3845

Abstract

In promoting high-quality economic development, environmental protection has become an essential responsibility for the sustainable development of listed companies. This research constructs and measures the level of rural ecological environment in China based on panel data on the rural ecological environment in Chinese [...] Read more.

In promoting high-quality economic development, environmental protection has become an essential responsibility for the sustainable development of listed companies. This research constructs and measures the level of rural ecological environment in China based on panel data on the rural ecological environment in Chinese inland provinces. Further, the impact of the size of green M&A listed companies on the rural ecological environment and its moderating effect is analyzed. This study uses the entropy method to measure the Rural Ecosystem Index (REI) and STATA software to conduct OLS, 2SLS, IV-GMM regressions, and regressions on moderating variables. This research aims to analyze the impact of listed companies on the environment and explore the role of the digitalization level’s moderating effect. The results show that the size of green M&A listed companies has a negative effect on the development of the rural ecological environment, and the digitalization level positively moderates the relationship between them. The following conclusions are drawn: (1) The average value of the rural ecological index for the 22 provinces in China ranged from 17.32 to 65.17. The index value is higher in the southeastern coastal region, with the highest values in Jiangsu, Guangdong, Zhejiang, and Fujian provinces. (2) From 2010–2020, green M&A listed companies were divided into 14 sectors. The industries with the most extensive green M&A are the raw chemical, non-metallic, rubber, and plastic industries. (3) During 2010–2016, the quantity of green M&A listed companies in China showed an upward trend with prominent regional non-equilibrium characteristics, then gradually declined in 2017–2020. It shows that the number is higher in the eastern coastal areas and lower in the inland regions. (4) The size of green M&A listed companies has a negative impact on the rural ecological environment. This negative impact has prominent heterogeneous characteristics, and the higher the index of the rural ecological environment is, the more significant its negative impact is. (5) The digitalization level positively moderates the size of green M&A listed companies and the rural ecological environment. The positive influence of the size of green M&A listed companies on the development level of the rural ecological environment is more significant in the regions with a higher degree of rural digitalization. In other words, the increase in the level of rural digitalization can improve the negative effect of the size of green M&A listed companies on the ecological environment. Based on the above findings, this paper puts forward corresponding countermeasure suggestions. Full article

(This article belongs to the Topic AI and IoT for Promoting Green Operation and Sustainable Environment)

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