The digital transformation and AI for sustainability: challenges, solutions, and financing the ecological transition

The necessity of sustainable economic and environmental models has grown increasingly important as world environmental problems get more severe. Particularly in fields including resource optimization, energy efficiency, and climate risk management, this paper investigates how digital transformation and artificial intelligence (AI) are changing strategies to sustainability. Data-driven techniques available from technologies including artificial intelligence, machine learning, and the Internet of Things (IoT) help to enable proactive environmental initiatives. Their implementation suffers major obstacles, too, including low infrastructure, ethical and privacy issues, large implementation costs, and organizational opposition to change. This work emphasizes creative uses such smart city projects meant to lower carbon emissions, blockchain for green financing transparency, and artificial intelligence-powered environmental monitoring. It also offers a thorough study of sustainable finance tools including green bonds, public-private alliances, and foreign funding sources, therefore providing useful ideas on overcoming structural and financial limitations. By means of a mix of theoretical research and vivid case studies, this paper advances a better knowledge of how digital technologies could promote a balanced ecological transformation. Policymakers, business executives, and academics dedicated to matching technical development with environmental objectives find strategic reference in it.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-02
DOI
https://doi.org/10.5281/zenodo.15328316
Primary Topic
Sustainable Finance and Green Bonds
Type
article
Field-Weighted Citation Impact
0.00
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article

The digital transformation and AI for sustainability: challenges, solutions, and financing the ecological transition

Zenodo (CERN European Organization for Nuclear Research)
Sustainable Finance and Green Bonds
article

The digital transformation and AI for sustainability: challenges, solutions, and financing the ecological transition

article en

Abstract

The necessity of sustainable economic and environmental models has grown increasingly important as world environmental problems get more severe. Particularly in fields including resource optimization, energy efficiency, and climate risk management, this paper investigates how digital transformation and artificial intelligence (AI) are changing strategies to sustainability. Data-driven techniques available from technologies including artificial intelligence, machine learning, and the Internet of Things (IoT) help to enable proactive environmental initiatives. Their implementation suffers major obstacles, too, including low infrastructure, ethical and privacy issues, large implementation costs, and organizational opposition to change. This work emphasizes creative uses such smart city projects meant to lower carbon emissions, blockchain for green financing transparency, and artificial intelligence-powered environmental monitoring. It also offers a thorough study of sustainable finance tools including green bonds, public-private alliances, and foreign funding sources, therefore providing useful ideas on overcoming structural and financial limitations. By means of a mix of theoretical research and vivid case studies, this paper advances a better knowledge of how digital technologies could promote a balanced ecological transformation. Policymakers, business executives, and academics dedicated to matching technical development with environmental objectives find strategic reference in it.

Zenodo (CERN European Organization for Nuclear Research)
National School of Business and Management in Settat (MA)
Openalex Percentile: Top 46%
Sustainable Finance and Green Bonds
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The digital transformation and AI for sustainability: challenges, solutions, and financing the ecological transition · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS