Economic Growth and Environmental Sustainability: Quantitative Methods and Machine Learning Applications
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".
Deadline for manuscript submissions: 10 January 2026 | Viewed by 85
Special Issue Editors
Interests: data science; management; grey systems theory; machine learning; economic growth
Special Issue Information
Dear Colleagues,
The relationship between economic growth and environmental sustainability remains a crucial challenge in contemporary research, as nations seek to balance development with ecological responsibility. Traditional economic models have provided valuable insights into this relationship, yet the increasing availability of big data, advanced quantitative methods, and machine learning (ML) techniques offers new opportunities for deeper, more accurate analysis. This Special Issue aims to explore data-driven approaches to understanding and modeling the interplay between economic expansion and sustainability goals.
The focus of this issue is on innovative quantitative methodologies, including statistical modeling, econometric techniques, artificial intelligence, and ML algorithms, that contribute to sustainability analysis. We welcome research that applies these methods to topics such as carbon emission forecasting, green finance, circular economy modeling, climate change mitigation strategies, and policy evaluation. The goal is to enhance predictive capabilities, optimize decision making, and provide policymakers with evidence-based strategies for sustainable economic development.
This Special Issue will contribute to the existing literature by integrating state-of-the-art data analytics and machine learning techniques with sustainability studies, offering novel insights which complement traditional theoretical and empirical approaches. It aligns with the journal’s mission by addressing socio-economic and scientific perspectives on sustainability, advancing methods to measure, monitor, and enhance sustainable development through computational tools, policy modeling, and interdisciplinary research.
We invite researchers from economics, environmental science, data science, and related disciplines to contribute original research, case studies, and methodological advancements that bridge the gap between economic growth, sustainability, and cutting-edge quantitative analysis.
We look forward to your contributions!
Dr. Marcin Nowak
Dr. Marta Pawłowska-Nowak
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- economic growth
- environmental sustainability
- machine learning for sustainability
- quantitative methods for sustainability
- sustainable development
- green finance
- eco-efficiency and innovation
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