AI aids in forecasting climate change trends and pinpointing potential impact zones. In this chart we show the droughts, floods, extreme temperatures (% of population, average 1990-2009).

This perspective offers a real-time view of the worldwide news on climate change-related topics, providing us with a media perspective on the progress at the different fronts of SDG 13.

 

Developed in collaboration with 

A SDG Live Report is what it is all about! Here, you can have a real-time perspective of the state of the world in regard to AI and sustainability, tracking some of the main issues to address in the near future.

AI has significantly influenced the progress of Sustainable Development Goal 13 (SDG 13), which focuses on taking urgent action to combat climate change and its impacts. Between 2015 and 2023, there have been 1,421,980 scientific publications exploring the role of AI in addressing climate change. These studies highlight AI’s potential to enhance climate modeling, optimize renewable energy systems, and improve resource management. AI technologies can analyze vast datasets to predict climate patterns, assess environmental impacts, and develop mitigation strategies. Media exposure, with 124,045 news articles, reflects substantial public interest in the role of AI in combating climate change. Additionally, the development of 469 AI policies targeting SDG 13 indicates a growing commitment from policymakers to integrate AI into climate action plans, focusing on reducing greenhouse gas emissions, enhancing climate resilience, and promoting sustainable development.

Looking ahead, the next 5 to 10 years are expected to witness further advancements in AI applications for climate action. AI-driven climate models will become more sophisticated, providing more accurate predictions and enabling better-informed decision-making. AI will play a crucial role in optimizing the integration of renewable energy sources, such as wind and solar power, into the energy grid, enhancing efficiency and reducing carbon emissions. Moreover, AI can support the development of innovative solutions for carbon capture and storage, helping to mitigate the impacts of climate change. As awareness of AI’s potential in climate action grows, collaboration between governments, research institutions, and the private sector will be essential to ensure that AI-driven innovations are implemented effectively and ethically. This collaborative effort will accelerate progress towards SDG 13, fostering a more resilient and sustainable future in the face of climate change.

 

Developed in collaboration with the European Commission project 

Here is your window to the contribution of AI to reduce the impact of the climate change. This dashboard is configurable and can be integrated into other systems to bring actionable technology into the systems of research institutions, governments and enterprises that are willing to make a difference.

 

 

Developed in collaboration with the European Commission project 

Avoiding data bias in AI systems is crucial to ensure fair, accurate, and equitable outcomes, preventing the reinforcement of existing inequalities and enabling more trustworthy and inclusive technologies. Here you will see a dashboard analysing the bias related to the data ingested in this observatory for SDG 13.

For analysis we use OECD AI Policy documents. Some of those documents are very large, and we split each document into smaller parts (so called “chunks”), which can contain multiple paragraphs. The reason for this is to prepare data for easier analysis with large language models, so called Retrieval-Augmented Generation (RAG). RAG is an advanced technique that combines retrieval-based methods with generative models to improve the performance of tasks such as question answering, text generation, and other natural language processing (NLP) applications. For each chunk then the sentiment is computed based on VADER (Valence Aware Dictionary and sEntiment Reasoner) methodology. Since VADER is known to have weak multilingual capabilities, all the documents were machine translated into English first.

While the results of this procedure are reliant not only upon the accuracy of the sentiment analysis tool, but also upon the accuracy of machine translation, it is important to stress that sentiment analysis is less sensitive to common machine translation problems than other usages, because sentiment analysis usually focuses on identifying the polarity (positive, negative, neutral) of a text rather than understanding its full semantic content. Also, sentiments in text are often expressed redundantly, which can help mitigate the impact of translation errors. As a result, minor translation errors that do not alter the overall sentiment and do not significantly impact the sentiment analysis is possible.

For the purpose of this analysis, they computed the average sentiment of (chunks of) AI policy documents for each country. We are presenting the visualisation of average sentiment of countries’ AI policy documents on the map. Since AI policy documents are mostly documents of legal nature (acts, policies, regulatory and governance frameworks), the sentiment should be mostly neutral, however, the analysis shows that there are country differences.

VADER computes positive, negative and neutral sentiment. Each of those values are between 0 and 1. The score indicates the proportion of text that is considered positive, negative and neutral. The sum of negative, positive, and neutral sentiment scores always equals 1, however in practice the sum of three sentiment scores can sometimes slightly exceed or fall below 1 due to floating-point precision errors or rounding issues that occur during computation.

 

Developed in collaboration with the European Commission project