This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Artificial intelligence could help evaluate climate protection projects more effectively and channel funding more precisely to where it is needed. Nevertheless, banks and insurance companies in Ghana have so far made very little use of the technology.
A new study conducted at the Brandenburg University of Technology Cottbus-Senftenberg (BTU) in collaboration with the Durban University of Technology in South Africa investigated the reasons behind this. The study is published in Discover Sustainability. Enormous sums are now being channeled into climate protection worldwide.
In 2024, the figure stood at around $2 trillion. The problem is that almost none of this money reaches sub-Saharan Africa, even though the region is particularly hard hit by climate change and is in urgent need of funding. One reason is that banks and investors often lack reliable data and effective tools to assess which climate projects are genuinely worthwhile and secure.
Professor Herwig Winkler, chair of Production Administration and Management and co-author of the study, sees a solution. "This is precisely where AI could help, for example, by automatically assessing risks or analyzing sustainability data. So the possibilities are there.
It's just that hardly anyone has made use of them so far." To find out why AI adoption is so low, the researchers surveyed 317 employees at Ghanaian banks, insurance companies and investment firms. "The key factor is whether the respondents see a clear benefit in AI and whether they trust it," Winkler adds. "Whether a piece of software is easy to use, on the other hand, plays a much smaller role." Consequently, if a company is well-positioned—that is, has clear rules, modern technology and trained staff—easy-to-use AI software has a significantly more positive impact on acceptance than in less well-positioned firms.
In terms of perceived benefits alone, however, this made hardly any difference. When asked about the biggest obstacles, respondents cited three main factors: a lack of technical infrastructure, insufficient in-house expertise and poor data quality. Uncertainty regarding legal regulations and high costs followed as secondary concerns.
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