AI-Driven Circular Economy of Enhancing Sustainability and Efficiency in Industrial Operations DOI Open Access
Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10358 - 10358

Published: Nov. 27, 2024

This study investigates integrating circular economy principles—such as closed-loop systems and economic decoupling—into industrial sectors, including refining, clean energy, electric vehicles. The primary objective is to quantify the impact of practices on resource efficiency environmental sustainability. A mixed-methods approach combines qualitative case studies with quantitative modelling using Brazilian Land-Use Model for Energy Scenarios (BLUES) Autoregressive Integrated Moving Average (ARIMA). These models project long-term trends in emissions reduction optimization. Significant findings include a 20–25% waste production an improvement recycling from 50% 83% over decade. Predictive demonstrated high accuracy, less than 5% deviation actual performance metrics, supported by error metrics such Mean Absolute Percentage Error (MAPE) Root Square (RMSE). Statistical validations confirm reliability these forecasts. highlights potential reduce reliance virgin materials lower carbon while emphasizing critical role policy support technological innovation. integrated offers actionable insights industries seeking sustainable growth, providing robust framework future management applications.

Language: Английский

Exploring public perspectives on solar energy adoption in Mexico DOI Creative Commons

Ana Sofia Andrade-Arias,

Golam Kabir, Seyedmehdi Mirmohammadsadeghi

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 212, P. 115410 - 115410

Published: Jan. 27, 2025

Language: Английский

Citations

0
Climate Sustainability through AI-Crypto Synergies and Energy Transition in the Digital Landscape to Cut 0.7 GtCO2e by 2030 DOI
Apoorv Lal, Fengqi You

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

The rapid expansion of artificial intelligence (AI)-enabled systems and cryptocurrency mining poses significant challenges to climate sustainability due energy-intensive operations relying on fossil-powered grids. This work investigates the strategic coupling AI data centers through shared energy infrastructure including colocated renewable power installations, battery storage, green hydrogen infrastructure, carbon offsetting measures achieve cost-effective climate-neutral operations. Employing a novel modeling framework, it explores synergistic AI-crypto with detailed scenario design along an optimization framework assess decarbonization potential economic implications, enabling transformative shift in digital landscape. results indicate that synergizing while achieving net-zero targets can avoid up 0.7 Gt CO2-equiv 2030. Moreover, reaching these strategies globally requires 90.7 GW solar 119.3 wind capacity. findings advocate for robust policy facilitate credit schemes tailored sector, incentives efficiency improvements, international collaborations bridge disparities. Future research should focus refining interventions across different geopolitical contexts enhance global applicability.

Language: Английский

Citations

0
AI-Driven Circular Economy of Enhancing Sustainability and Efficiency in Industrial Operations DOI Open Access
Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10358 - 10358

Published: Nov. 27, 2024

This study investigates integrating circular economy principles—such as closed-loop systems and economic decoupling—into industrial sectors, including refining, clean energy, electric vehicles. The primary objective is to quantify the impact of practices on resource efficiency environmental sustainability. A mixed-methods approach combines qualitative case studies with quantitative modelling using Brazilian Land-Use Model for Energy Scenarios (BLUES) Autoregressive Integrated Moving Average (ARIMA). These models project long-term trends in emissions reduction optimization. Significant findings include a 20–25% waste production an improvement recycling from 50% 83% over decade. Predictive demonstrated high accuracy, less than 5% deviation actual performance metrics, supported by error metrics such Mean Absolute Percentage Error (MAPE) Root Square (RMSE). Statistical validations confirm reliability these forecasts. highlights potential reduce reliance virgin materials lower carbon while emphasizing critical role policy support technological innovation. integrated offers actionable insights industries seeking sustainable growth, providing robust framework future management applications.

Language: Английский

Citations

0