CEST2025

Traditionally, Environmental Impact Assessments (EIAs) depended on data compilation and review done by people, even though detailed, this often-meant drawn-out timelines and issues scaling up. At the same time the rising incorporation of Artificial Intelligence (AI) across different fields has prompted a notable alteration in approaches to gauging environmental consequences, though notable hurdles are still present. EIA in general have seen some pretty big changes lately, mainly thanks to AI. AI tools can bring new ways to make environmental evaluations more accurate, faster and transparent through numbers of KPIs such as legislative targets, circular economy principles, national priorities and many ithers. This is very important because environmental problems often made worse by human activity and are getting more complicated. Using AI in EIAs gives us a way to handle the huge amounts of data that come with modern environmental decision-making. AI can help with things like predicting what might happen, figuring out risks as well as running simulations. Moreover, regular assessment methods often can’t keep up with the complex ways ecosystems work and how they react to different pressures. This research setup a new AI tool to compare and make assessment of the EIA studies that are been submitted to the Department of Environment of Cyprus Government. In addition, the results indicate that the AI tool speeds up the process, making quick decisions in project planning and environmental management as well as makes the whole process easier to get everyone involved in the EIA The idea that questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) has never been addressed directly, points to a key gap in current research. Additionally, when deciding if AI should be used to get those involved in the EIA process, more engaged and make it easier to monitor the environment in real-time with smart technologies, the risks of making existing inequalities worse must be taken into account. As people increasingly rely on AI for decision-making, there is an important need for clear and inclusive assessment processes. This is further shown in visual representations of the data-driven approach to EIA, where many metrics are utilized to convey impacts visually and intuitively as can be seen in tools like the Environmental Impact Assessment dashboard. This cutting-edge platform have the potential to change how those involved participate, as they must also address problems like data security and biases in algorithms that could weaken public confidence