AI Development Probe into Decarbonization
DOI:
https://doi.org/10.70301/SBS.MONO.2025.1.7Keywords:
artificial intelligence; net-zero carbon emissions; science mapping approach; AI-powered learning platforms;Abstract
The rise of artificial intelligence (AI) brings with it a broad range of challenges and opportunities for organizations. As we are aware that AI is a collection of tools, an intelligent toolbox. This article will explain why this toolbox is so valuable in helping humanity overcome key bottlenecks that currently hinder sustainability progress. Each passing year brings new record -high temperatures, larger catastrophic wildfires, and more frequent devasting floods. In response, international agreements have set ambitious global sustainability targets. the United Nations (UN) urges that net zero commitments require credible action for a living climate. Net zero refers to reducing carbon emissions to a chump change of residual emissions. Although there are numerous applications of AI, there is no leading-edge review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. By mid-century (2050), carbon dioxide emissions must reach net zero and by 2030, the loss of biodiversity must be suspended and reversed. The encouraging news is that significant progress is being made toward these goals. More than 9,000 companies across more than 140 countries have joined the Race to Zero, a coalition that pledges to take immediate action to halve global emissions by 2030. But change is simply not happening fast enough. The scale and speed of changes that are needed to meet global sustainability goals is daunting. Consider a few examples, such as Global renewable power generation must triple in less than a decade – International Energy Agency (IEA) and methane emissions from fossil fuel operations must be reduced by 75%. Over the next couple of decades, food production must increase by 50%, and we will need to develop 1,000 times more durable carbon removal capacity than exists today. A total of 124 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. AI-powered learning platforms can help by providing:
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