By analyzing energy consumption and production data, AI can aid in optimizing energy utilization and minimizing waste. In this chart we see a worldwide perspective on renewable energy consumption (% of total final energy consumption).

This perspective offers a real-time view of the worldwide news on energy-related topics, providing us with a media perspective on the progress at the different fronts of SDG 7.

 

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 been instrumental in advancing Sustainable Development Goal 7 (SDG 7), which aims to ensure access to affordable, reliable, sustainable, and modern energy for all. Between 2015 and 2023, there have been 127,849 scientific publications examining the impact of AI on energy systems. These studies underscore AI’s potential to optimize energy production, enhance grid management, and improve energy efficiency. AI algorithms can analyze large datasets to predict energy demand, optimize the integration of renewable energy sources, and reduce energy wastage. Media exposure, with 24,425 news articles, reflects a growing public interest in the role of AI in transforming energy systems and supporting sustainable energy solutions. Furthermore, the development of 460 AI policies targeting SDG 7 highlights a commitment from policymakers to harness AI technologies to promote energy sustainability, focusing on issues such as smart grids, energy storage, and renewable energy integration.

Looking ahead, the next 5 to 10 years are likely to witness significant advancements in the application of AI to energy systems. AI-driven smart grids will become more prevalent, allowing for better management of electricity distribution and reducing energy losses. These smart grids will enable real-time monitoring and dynamic response to fluctuations in energy demand and supply, leading to more efficient and reliable energy systems. AI will also play a crucial role in advancing renewable energy technologies, optimizing the placement and operation of solar panels and wind turbines to maximize energy production. Additionally, AI can help in the development of advanced energy storage solutions, ensuring a stable supply of renewable energy even when production is variable. As AI technologies continue to evolve, collaboration between governments, energy providers, and technology companies will be essential to ensure that AI-driven innovations contribute to the achievement of SDG 7, promoting sustainable and equitable access to energy for all.

 

Developed in collaboration with the European Commission project 

Here is your window to the current global status of the world’s energy efficiency. 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 7.

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