. 2025 Apr 4;32(17):10705–10724. doi: 10.1007/s11356-025-36356-w

Table 4.

SWOT analysis of the AI application in climate change modeling

Strengths	Weaknesses
• These technologies can analyze large, intricate datasets while considering several aspects simultaneously. This all-encompassing method enables a more thorough evaluation of ecosystems’ interconnectedness. It aids researchers and decision-makers in comprehending how modifications to one component may impact the entire system • It assists in making better emergency or disaster recovery decisions • Scientists, policymakers, and communities can simulate and forecast future climatic scenarios using predictive models essential to studying climate change • Climate management is aided by optimization and sequential decision-making algorithms to provide a sustainable environment • Predictive AI can be used remotely to support developing and disadvantaged nations against climate phenomena	• Emergency agencies find it challenging to defend choices against disasters using black-box AI algorithms • Climate forecasting requires real-time, accurate information, which is not always affordable • One major issue in the study of climate change is still the interpretability of AI models • Insufficient information to quantify climate change in developing nations • Economic expense and political opposition to using large-scale AI systems to predict urban pollution emissions
Opportunities	Threats
• Early forecasting of natural disasters from climate allows for rapid response by the authorities and minimizes losses • Rainfall forecasting in desert regions enables a better understanding of desertification trends • AI-powered early warning systems to provide a quick and life-saving response in cases of severe famine • Predicting traffic and energy requirements with AI reduces pollutants with a significant ecological impact • AI tools assist in government decision-making in the fight against climate change	• In production, sustainability and cost reduction are frequently mutually exclusive objectives. Communities, scientists, and policymakers may find the expense of incorporating AI into climate change action intolerable • The diversity of AI approaches makes it difficult to select the best methods, especially considering the dearth of personnel with joint expertise in AI and climate change • Unexpected events that impact energy use could make demand forecasts less accurate • The computing cost of AI in terms of climate change impacts is fundamentally high and energy intensive • AI models that use data to predict natural disasters may become outdated due to climate change

Strengths

Weaknesses

• These technologies can analyze large, intricate datasets while considering several aspects simultaneously. This all-encompassing method enables a more thorough evaluation of ecosystems’ interconnectedness. It aids researchers and decision-makers in comprehending how modifications to one component may impact the entire system

• It assists in making better emergency or disaster recovery decisions

• Scientists, policymakers, and communities can simulate and forecast future climatic scenarios using predictive models essential to studying climate change

• Climate management is aided by optimization and sequential decision-making algorithms to provide a sustainable environment

• Predictive AI can be used remotely to support developing and disadvantaged nations against climate phenomena

• Emergency agencies find it challenging to defend choices against disasters using black-box AI algorithms

• Climate forecasting requires real-time, accurate information, which is not always affordable

• One major issue in the study of climate change is still the interpretability of AI models

• Insufficient information to quantify climate change in developing nations

• Economic expense and political opposition to using large-scale AI systems to predict urban pollution emissions

Opportunities

Threats

• Early forecasting of natural disasters from climate allows for rapid response by the authorities and minimizes losses

• Rainfall forecasting in desert regions enables a better understanding of desertification trends

• AI-powered early warning systems to provide a quick and life-saving response in cases of severe famine

• Predicting traffic and energy requirements with AI reduces pollutants with a significant ecological impact

• AI tools assist in government decision-making in the fight against climate change

• In production, sustainability and cost reduction are frequently mutually exclusive objectives. Communities, scientists, and policymakers may find the expense of incorporating AI into climate change action intolerable

• The diversity of AI approaches makes it difficult to select the best methods, especially considering the dearth of personnel with joint expertise in AI and climate change

• Unexpected events that impact energy use could make demand forecasts less accurate

• The computing cost of AI in terms of climate change impacts is fundamentally high and energy intensive

• AI models that use data to predict natural disasters may become outdated due to climate change