An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China DOI
Lingling Guo, Miao Cui, Ying Qu

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123834 - 123834

Published: Dec. 31, 2024

Language: Английский

Monitoring high-carbon industry enterprise emission in carbon market: A multi-trusted approach using externally available big data DOI
Bixuan Gao,

Xiangyu Kong,

Gaohua Liu

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 466, P. 142729 - 142729

Published: May 30, 2024

Language: Английский

Citations

9
Improving forecasting of concrete strength using advanced machine learning methods DOI Creative Commons

Prabakaran Ellappan,

Lakshmi Keshav,

Kalyana Chakravarthy Polichetty Raja

et al.

Matéria (Rio de Janeiro), Journal Year: 2025, Volume and Issue: 30

Published: Jan. 1, 2025

Abstract This study presents an improved technique that uses many machine-learning models to estimate the compressive strength of concrete. The goal project is increase precision predictions based on age and composition concrete mixes. Cement, fly ash, water, superplasticizer, coarse fine aggregate, sample are among materials. Megapascals (MPa) used quantify strength. To determine connections between mix proportions, age, strength, a variety blends were examined. Machine learning techniques including Random Forest, XGBoost, AdaBoost, Bagging, Support Vector Regression, Linear Regression used. efficiency model was assessed using performance indicators such as accuracy, R-squared (R2), Mean Absolute Error (MAE), Squared (MSE). With MAE 2.2, MSE 10.5, R2 0.94, MAPE 8.5, RMSE 3.25, accuracy 0.92, XGBoost (optimized) performed best. noticeably better than others, highlighting how machine may improve optimize concrete, thus promoting fields materials science civil engineering.

Language: Английский

Citations

0
Investigating the effects of industrial transformation and agglomeration on industrial eco-efficiency for green development: Evidence from enterprises in the Yangtze River Economic Belt DOI
Peng Lu, Zhihui Li, Haowei Wu

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 479, P. 143949 - 143949

Published: Oct. 11, 2024

Language: Английский

Citations

3
Spatiotemporal evolution and influencing mechanisms of carbon pressure at the county scale: A case study of central-south Liaoning urban agglomeration, China DOI Creative Commons
Xinrui Liu, Rongfei Guo, Yabing Zhang

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112900 - 112900

Published: Nov. 30, 2024

Language: Английский

Citations

1
Optimizing concrete compressive strength prediction with a deep forest model: an advanced machine learning approach DOI Creative Commons

Rajanandhini Vadivel Muthurathinam,

Nuha Alruwais,

Alanoud Al Mazroa

et al.

Matéria (Rio de Janeiro), Journal Year: 2024, Volume and Issue: 29(4)

Published: Jan. 1, 2024

Language: Английский

Citations

1
Forecasting Green Technology Diffusion in OECD Economies Through Machine Learning Analysis DOI Creative Commons
Büşra Ağan

Ekonomi Politika ve Finans Arastirmalari Dergisi, Journal Year: 2024, Volume and Issue: 9(3), P. 484 - 502

Published: Sept. 30, 2024

An accelerating global shift towards sustainable development has made the diffusion of green technologies a critical area focus, particularly within OECD economies. This study aims to use machine-learning approach explore future technology across countries. It provides detailed forecasts from 2023 2037, highlighting varying rates (GTD) among different nations. To achieve this, Autoregressive Integrated Moving Average (ARIMA) model is employed offer new evidence on how progress can be predicted. Based empirical data, categorizes countries into high, moderate, and low GTD growth. The findings suggest that Japan, Germany, USA will experience significant growth in GTD, while like Australia, Canada, Mexico see moderate increases. Conversely, some nations, including Ireland Iceland, face challenges with or negative values. concludes applying this valuable insights predictions for policymakers aiming enhance adoption their respective

Language: Английский

Citations

0
An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China DOI
Lingling Guo, Miao Cui, Ying Qu

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123834 - 123834

Published: Dec. 31, 2024

Language: Английский

Citations

0