Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e29825 - e29825

Published: April 25, 2024

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine precision and effectiveness prediction model, gravitational algorithm is introduced optimize parameter selection in support vector model. A nationwide model for economic development efficiency meticulously constructed leveraging public economic, environmental, demographic data. comprehensive empirical analysis follows, revealing noteworthy reduction mean squared error root with increasing iterations, reaching minimum 0.007 0.103, respectively—figures that are lowest among all considered models. Moreover, absolute percentage value remarkably low at 0.0923. The data illustrate gradual decline average standard deviation throughout optimization process, indicative both convergence heightened accuracy. These results underscore significant potential technology optimizing management. provides valuable insights decision-makers researchers navigating landscape sustainable development.

Language: Английский

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Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 4111 - 4111

Published: May 14, 2024

This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining role artificial intelligence-enhanced multi-energy optimization in rural energy planning within broader context a sustainable economy. By proposing an innovative framework that accounts for geographical economic disparities across regions, this study specifically targets systems X County Yantai City, Y Luoyang Z Lanzhou City. Furthermore, it establishes foundation integrating these localized approaches into national carbon-neutral efforts assessments green total factor productivity. The comparative analysis demand, conservation, efficiency, metrics among counties underscores potential tailored solutions significantly advance low-carbon practices agriculture, urban development, industry. Additionally, insights derived from offer deeper understanding dynamics between government enterprise governance, empirically supporting Porter hypothesis, which postulates stringent policies can foster innovation competitiveness. coal-coupled biomass power generation model introduced work represents convergence economy principles financial systems, serving as valuable guide decision-making decisions aimed at consumption production. Moreover, importance resilient adaptable pathway evaluating emission trading markets promoting recovery strategies align with sustainability goals.

Language: Английский

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Intelligentization helps the green and energy-saving transformation of power industry-evidence from substation engineering in China

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 15, 2024

Abstract The coordinated development of intelligence and greening is an intrinsic demand for high-quality economic social development. Intelligentization are the leading directions sustainable power industry. This paper directs empirically analyzes effect mechanism on green environmental friendliness electric substations by using a panel fixed-effects model instrumental variable regression, substation engineering data from China southern grid during 2013–2022. It found that level significantly promotes performance projects, this conclusion still holds after series robustness tests. Intelligence can reduce material waste pollutant emissions improving monitoring capability refinement resource control, thus project. research in helps to promote integrated intelligent engineering, better achieve goals.

Language: Английский

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The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: April 22, 2024

Introduction This paper aims to explore the intersection of corporate social responsibility (CSR) and public health within context digital platforms. Specifically, explores impact platforms on sustainable development practices enterprises, seeking comprehend how these influence implementation environmental protection policies, resource management, initiatives. Methods To assess behavior, we conducted a questionnaire survey targeting employees in private enterprises. aimed evaluate relationship between adoption policies practices. Results Analysis responses revealed significant positive correlation use behavior enterprises ( r=0.523;p<0.001 ), Moreover, presence innovative technologies was found positively enforcement with calculated ratio id="M2">a∗b/c=55.31% ). An intermediary analysis highlighted that innovation technology plays mediating role this process. Additionally, adjustment showed various sizes industries respond differently platforms, indicating need for tailored Discussion These findings underscore pivotal enhancing CSR efforts by fostering improved among corporations. The effect suggests not only facilitate direct actions but also enhance efficiency effectiveness such initiatives through technological advances. variability response different points importance customizable strategies policy formulation. By offering empirical evidence platforms’ potential advance initiatives, contributes ongoing dialogue goals. It provides practical insights implications governments striving craft more effective strategies.

Language: Английский

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Enhancing Crossdocking for a Green Supply Chain Based on IoT and AI

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 601, P. 00072 - 00072

Published: Jan. 1, 2025

In the realm of supply chain management, integration sustainable practices alongside competitive efficiency is increasingly crucial. This study explores convergence cross-docking methodologies with advanced technologies such as IoT and AI to enhance sustainability chains. Cross-docking, known for its direct transfer approach, minimizes storage duration operational costs, potentially reducing environmental footprints associated traditional logistics. Concurrently, enables real-time monitoring goods parameters, while AI-driven analytics optimize logistics operations precision. integrated approach not only enhances but also underscores pivotal role technological innovation in fostering practices. Insights from this contribute advancing strategies tailored contemporary imperatives industrial competitiveness.

Language: Английский

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The evolution of internal audit in anti-corruption activities: leveraging data analytics and it technology

EDPACS, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 7

Published: Jan. 30, 2025

The article explores the transformative role of internal audit in anti-corruption efforts, emphasizing how technological advancements, particularly data analytics and IT tools, have redefined traditional practices. Advanced enables comprehensive transaction reviews, detecting anomalies forecasting risks. Machine learning algorithms refine corruption detection by adapting to historical data, while network analysis tools uncover hidden connections within organizations. Practical applications such as real-time monitoring, behavioral analytics, integrated risk management bolstered strategies. However, successful implementation these technologies requires robust governance, skilled personnel, ethical considerations regarding privacy. underscores that technology enhances, rather than replaces, critical human auditors interpreting complex insights making decisions. Looking ahead, emerging like blockchain predictive modeling promise further advance mechanisms, ensuring a proactive effective approach.

Language: Английский

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Demand Forecasting and Allocation Optimization of Green Power Grid Supply Chain Based on Machine Learning Algorithm: A Study Based on the Whole-Process Data of Power Grid Materials

Sustainability, Journal Year: 2025, Volume and Issue: 17(3), P. 1247 - 1247

Published: Feb. 4, 2025

The efficient management of the green power grid supply chain is great significance in addressing global energy transformation and achieving sustainable development goals. However, traditional methods struggle to effectively cope with complexity dynamics demand forecasting multi-objective optimization problems material allocation. In response this challenge, paper proposes a machine-learning-based allocation method, aiming improve efficiency reduce environmental impacts. First, based on whole-process data materials, multi-model fusion strategy adopted for forecasting. By combining machine learning models such as long short-term memory (LSTM), extreme gradient boosting (XGBoost), random forest, prediction accuracy generalization ability model are significantly improved. Moreover, “distributed collaborative algorithm” proposed. decomposing regions, optimizes transportation routes inventory management, comprehensively reduces transportation, inventory, protection costs while taking into account real-time requirements complex environment. Finally, an empirical analysis carried out combination optimized plan, verifying practical effectiveness proposed method. results indicate that scheme outperforms method terms total cost, efficiency, carbon emissions. Specifically, achieves 13% reduction costs, 10% decrease 25% cut expenses. Additionally, it decreases transportation-related emissions by approximately 250 tons. has obvious economic advantages chain. integrating various algorithms, enhances stability substantially reducing operating This line goals development. provides framework value managing industry.

Language: Английский

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Enhancing the decision optimization of interaction design in sustainable healthcare with improved artificial bee colony algorithm and generative artificial intelligence

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317488 - e0317488

Published: Feb. 25, 2025

With the development of digital health, enhancing decision-making effectiveness has become a critical task. This study proposes an improved Artificial Bee Colony (ABC) algorithm aimed at optimizing models in field health. The draws inspiration from dual-layer evolutionary space cultural algorithms, combining normative knowledge credibility to dynamically adjust search range, thereby improving both convergence speed and exploration capabilities. Additionally, population dispersion strategy is introduced maintain diversity, effectively balancing global with local exploitation. Experimental results show that ABC exhibits 96% probability when approaching optimal solution, significantly efficiency accuracy medical resource optimization, particularly complex environments. Integrating this Chat Generative Pre-trained Transformer (ChatGPT) decision system can intelligently generate personalized recommendations leverage natural language processing technologies better understand respond user needs. provides effective tool for scientific healthcare offers technical support analyzing large-scale data.

Language: Английский

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Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

Water, Journal Year: 2024, Volume and Issue: 16(11), P. 1544 - 1544

Published: May 27, 2024

This study investigates the prioritization and resource allocation strategies adopted by coastal local governments of Qingdao, Dalian, Xiamen in context marine regulatory reform aimed at enhancing efficiency. Data on relevant opinions, departmental requirements, existing allocations were collected through a questionnaire survey. A backpropagation (BP) neural network was then applied to analyze survey data, prioritize tasks, propose schemes. The findings demonstrate that integrating machine learning into regulation can significantly improve utilization efficiency, optimize task execution sequences, enhance scientific refined nature work. BP model exhibited strong predictive capabilities training set demonstrated good generalization abilities test set. performance varied slightly across different management levels. For level, accuracy, precision, recall rates 85%, 88%, 82%, respectively. supervisory these metrics 81%, 83%, 78%, At employee 79%, 76%, These results indicate provide differentiated recommendations based needs Additionally, model’s assessed employees’ years experience. employees with 0–5 experience, 84%, those 5–10 86%, 80%, over 10 data further confirm applicability effectiveness experience groups. Thus, adoption technologies for optimizing resources holds significant practical value, aiding enhancement capacity within governments.

Language: Английский

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Grasshopper platform-assisted design optimization of fujian rural earthen buildings considering low-carbon emissions reduction

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 6, 2024

This work aims to explore optimization methods for the design of earthen buildings in rural Fujian achieve low-carbon emissions and improve structural stability buildings. First, parametric modeling algorithms are employed through Grasshopper platform. An intelligent building is created by combining factors such as structure buildings, materials, orientation. Then, a comparison made with unoptimized, energy-efficient, carbon emission reduction designs. Finally, concludes that proposed significantly optimizes total emissions, energy consumption, stability, economic aspects. The scheme achieves highest effect, rate 34.64%. exhibits lower maximum stress higher minimum safety factor terms compared other scenarios, along smaller displacement. It also performs well initial investment, annual operating costs, construction period. significance this lies providing scientific guidance promoting organic integration development initiatives. indicates use feasible can yield positive results practice.

Language: Английский

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