Carbon dynamics in agricultural greenhouse gas emissions and removals: a comprehensive review

Kamyab, Hesam; SaberiKamarposhti, Morteza; Hashim, Haslenda; Yusuf, Mohammad

doi:10.1007/s42823-023-00647-4

Cited by 36 publications

(1 citation statement)

References 79 publications

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“…Kamyab et al [38] examined greenhouse gas emissions in the agriculture industry, revealing the complex range of obstacles facing this sector in regard to its emission reduction pursuits and also investigated new approaches to address them. The authors concluded that the adoption of sophisticated technologies, including carbon capture techniques and AI-based monitoring, is a cornerstone of numerous emission reduction strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

Doran,

Badareu,

Doran

et al. 2024

Sustainability

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This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.

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Section: Literature Reviewmentioning

confidence: 99%