Climate change

Exploring AI solutions for climate change

AI has already had a substantial positive influence in the fight against global warming. However, quantifying its importance and characterizing its effects remain open issues.

This article provides an overview of initiatives and projects leveraging AI to understand and combat climate change, highlights existing research documenting the potential positive impact of AI on climate change, and identifies a set of barriers to overcome to ensure that such use of AI is both practical and ethical.

Climate change problem

Climate change will profoundly affect environments, societies and economies. Many of its environmental impacts, from prolonged droughts to more devastating storms, are already visible.

According to a 2022 study, 87% of CEOs in business and the public sector have decision-making power in AI and climate. BCG Climate AI Survey report, believe that AI is a crucial tool in the fight against climate change. In the same survey, public and private sector leaders ranked mitigation (reduction) as the most critical business benefit of advanced climate-related analytics and AI, with mitigation (measured emissions) reaching 57%. The remaining percentages are distributed as follows:

  • 44% in adaptation to climate change (prediction of hazards)
  • 42% in climate change adaptation (vulnerability and exposure management)
  • 37% in climate change mitigation (removal of emissions)
  • 28% in fundamentals (facilitating climate research, climate finance and education)

Existing solution

To get a good idea of ​​how urban areas are affected by climate change, we need to look at them from several angles. Some are ground and water surface temperatures, weather events, and the number of plants and ground ice. Using these parameters, many climate models can determine what the weather will be like in a region. Earth System Models (ESM) and Global Climate Models (GCM) are two of the most important models that are often used in this field (GCM). ESMs have all the same parts as GCMs, but they also simulate the carbon cycle and other chemical and biological cycles that are important in determining the amount of greenhouse gases in the air in the future. Moreover, ESM models simulate environmental indicators in large computational domains, so we can use them to predict large areas.

  • Researchers from Finland, Sweden and Canada ran higher resolution ESM simulations. They found flux-blocking effects that were highly relevant to global climate behavior and were absent from the coarse cases.
  • American researchers also talked about this problem and pointed out that global models (or large grid models) produce minimal accurate results when applied in a smaller area.
  • According to scholars from University of Washington Seattle and other universities.
  • Research conducted in Ireland looked at different types of data we should collect in smart cities to better understand the causes and impacts of human activity on the environment.
  • UK researchers suggested that we could use the ISO (International Organization for Standardization) smart city indicators (ISO 37122:2019 Sustainable cities and communities, Indicators for smart cities) to determine what metrics should be collected to track the sustainable development of cities.
  • Italian researchers demonstrated that it is possible to estimate and monitor the evolution of the urban heat island effect by measuring the surface heat stress of cities using satellite sensors.
  • German researchers proposed using remote sensing photos to measure water quality and levels, soil and water surface temperatures, biomass, carbon, and air quality.
  • Researchers from Spain, Sweden and the Netherlands demonstrated that we can use coarse data in conjunction with high fidelity turbulent flow simulations. This study builds confidence in the future potential of high-fidelity flow and climate prediction using remote sensing imagery and AI models.


The technology is already being used to create greener smart cities in China, monitor deforestation in the Amazon, and send natural disaster alerts in Japan. Additionally, AI applications could optimize the deployment of renewable energy sources by supplying solar and wind power to the power grid as needed, increasing energy storage, and designing more energy-efficient structures. energy. On a smaller scale, it could help families reduce their energy consumption by automatically turning off lights when not in use or redirecting electricity from electric cars to the grid to meet projected demand.