Fact Checking Climate Change An Analysis Of Claims And Verification

npj Climate Action volume 4, Article number: 17 (2025) Cite this article Accurate identification of true versus false climate information in the digital age is critical. Misinformation can significantly affect public understanding and policymaking. Automated fact-checking seeks to validate claims against trustworthy factual data. This study tackles the challenge of fact-checking climate claims by leveraging the currently most capable Large Language Models (LLMs). To this end, we introduce Climinator, an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning.

It significantly boosts the performance of automated fact-checking by integrating authoritative, up-to-date sources within a novel debating framework. This framework provides a trustworthy and context-aware analysis incorporating multiple scientific viewpoints. Climinator helps identify misinformation in real time and facilitates informed dialog on climate change, highlighting AI’s role in environmental discussions and policy with reliable data. In the era of digital information abundance, the endeavor to counter climate misinformation has found a promising ally in artificial intelligence (AI). Research shows that engaging with an AI chatbot on climate change can significantly align public perception with scientific consensus1, highlighting the importance of ensuring that the large language models (LLMs) underpinning these systems are... Therefore, we ask how well we can embed scientific consensus into automated fact-checking.

To this end, we developed Climinator—an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning. Climinator evaluates the veracity of climate statements and improves its verdicts with evidence-based and scientifically credible reasoning and references to relevant literature. Our vision is to use AI to catalyze a well-informed global climate dialog, enrich public discourse with scientific insights, and foster a more informed society ready to engage with climate challenges. Climinator serves as a first step in this direction. Platforms like Climate Feedback and Skeptical Science have made commendable efforts to involve climate scientists in volunteering their expertise and providing an essential service in addressing climate misinformation. These scientists voluntarily dedicate their time to giving concise science-based evaluations, including references, and delivering a final verdict on disputed claims.

Despite their valuable contributions, these efforts face significant challenges, including scalability and actuality. Hence, their impact is limited by the sheer volume of misinformation and skepticism in digital media, worsened by misinformation spreading more rapidly and widely than factual information2. As a response, automated fact-checking3,4 aims to debunk misinformation at scale using natural language processing methods. While automated fact-checking tools have improved, they struggle with complex claims due to a lack of detailed reasoning5,6,7, particularly in the domain of climate change8. To address this problem, we introduce an advanced framework that overcomes these limitations by integrating LLMs within a Mediator-Advocate model. Although recent work has explored the aggregation of different viewpoints using LLMs to build a general consensus9, we address real-world claim complexities and evidence controversies in a novel way10,11,12.

In particular, we introduce separate “Advocates,” each drawing on a distinct text corpus to represent a specific viewpoint, while a “Mediator” either asks follow-up questions or synthesizes these perspectives into a cohesive and balanced... In this era of rampant misinformation, the role of fact-checkers is becoming increasingly important. Yet, the practice is relatively understudied. Research from the University of Kansas has analyzed how fact-checkers in four countries practice the craft in relation to the contentious issue of climate change. In addition to finding that fact-checkers across the world have different approaches, the researchers suggest the most effective ways to approach the practice to provide accurate, reliable and easy to understand information to the... The KU research team analyzed nearly 500 examples of fact-checking on climate change information from the United States, United Kingdom, Germany and Australia conducted between 2015 and 2019.

They found the fact-checks mostly focused on four aspects of climate change: existence, causes, impacts and solutions. The most effective examples provided visual information, cited sources and provided concise information for the public, researchers found. “Fact-checking is seen as a way to clear up any doubt on climate change information. Since about 2016, it has taken root in many countries as we have seen rampant misinformation on the topic,” said Hong Tien Vu, associate professor of journalism & mass communications at KU, the study’s... “People largely associate fact-checking with the news media. Because we don’t often have good guidelines about how to practice the profession, looking at how it’s practiced in different countries is important.”

The study, co-written with Annalise Baines and Nhung Nguyen, doctoral candidates in journalism & mass communications at KU, was published in the journal Journalism & Mass Communication Quarterly. The analysis showed that among the four aspects of climate change fact-checking, in the United States, most instances assessed claims about whether climate change really existed. Australian instances most often were fact-checking claims about solutions, In the United Kingdom, most instances regarded impact. Overall, about one-fourth of claims fact-checked were about its existence, again with most of those coming from the United States, and about 22% were regarding climate change effects. As part of our series on Project 2025, we look at some instances where President Donald Trump and Project 2025 agree on climate change and fossil fuels — beginning with the rollback of climate... In an hourlong address before the U.N.

General Assembly on Sept. 23, President Donald Trump lashed out at those promoting “the green energy agenda.” Along the way, he made numerous false and misleading claims about renewable energy and climate change, many of which we’ve fact-checked... The Environmental Protection Agency is holding public hearings this week on its effort to undo the legal foundation for its regulation of greenhouse gases, the heat-trapping gases that cause climate change. In a major policy shift announced in late July, the Trump administration EPA said it would rescind the so-called “endangerment finding,” which allows the agency to regulate such emissions, arguing that legal and scientific... Unfounded rumors linking an extreme weather event to human attempts at weather modification are again spreading on social media. It is not plausible that available weather modification techniques caused or influenced the July 4 flash flooding along the Guadalupe River in Texas.

We explain what’s known about how the catastrophic L.A. wildfires started and the factors that scientists do — and don’t — think contributed. In the last 20 years, we’ve seen an unprecedented increase in extreme weather events. How do we know this? The data proves it. Until 2005, there were less than 5 billion dollar weather events per year in the US.

Now, the US averages 23 per year (NOAA). This is because human-caused climate change warms the air and oceans. Warmer air holds more moisture, and warmer oceans provide more energy—fueling the production of more frequent and intense weather extremes like tornados, tropical cyclones, heat waves, and polar vortex disruptions. The past ten years 2015-2024 are the ten warmest years on record (WMO). That’s pretty unbelievable. Better believe it, though.

Climate+Tech launches an open source AI fact-checking system that combines multiple AI models to evaluate climate claims against scientific sources, achieving 85% agreement with expert consensus. The spread of climate misinformation poses a significant challenge in our digital age. Recent research highlights the scale of this problem: To address this challenge, we’re excited to announce the launch of Climate+Tech FactChecker, an open source AI-powered system designed to fact-check climate claims at scale while maintaining scientific rigor and transparency. Related Solution: This tool is part of our broader initiative on AI tools for society. Learn more about our complete ecosystem of fact-checking and democratic discourse tools in “AI Tools for Society: Fact-Checking, Democracy & Climate Communication”.

Our system’s unique approach mirrors how expert panels evaluate scientific claims. Multiple AI “advocates” examine evidence independently from different scientific sources, provide structured reasoning with citations, and a “mediator” AI reconciles their findings into a final verdict. This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed by authoritative sources like the IPCC reports and peer-reviewed scientific literature, Climinator employs an innovative Mediator-Advocate framework. This design allows Climinator to effectively synthesize varying scientific perspectives, leading to robust, evidence-based evaluations. Our model demonstrates remarkable accuracy when testing claims collected from Climate Feedback and Skeptical Science.

Notably, when integrating an advocate with a climate science denial perspective in our framework, Climinator’s iterative debate process reliably converges towards scientific consensus, underscoring its adeptness at reconciling diverse viewpoints into science-based, factual conclusions. While our research is subject to certain limitations and necessitates careful interpretation, our approach holds significant potential. We hope to stimulate further research and encourage exploring its applicability in other contexts, including political fact-checking and legal domains. In the ongoing debate on climate change, the truthfulness of public statements is regularly called into question, emphasizing the critical need for swift and reliable fact-checking. A case in point is the recent claim made by Sultan Al Jaber, the president of COP28 and chief executive of the United Arab Emirates’ state oil company Adnoc. On November 21, 2023, Al Jaber controversially asserted that “There is no science out there, or no scenario out there, that says that the phase-out of fossil fuel is what’s going to achieve 1.5C.”...

Recognizing this challenge, our paper introduces Climinator, a novel framework designed to assess climate-related claims, leveraging advancements in LLMs. Climinator -– an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning -– not only evaluates the accuracy of statements but also enhances its verdicts with evidence-based reasoning and relevant literature references. In an era where information proliferates at an unprecedented pace, the task of manually reviewing claims for accuracy becomes increasingly resource-intensive and challenging. Over a decade ago, scholars warned that the exponential growth of online content would eventually overwhelm journalistic fact-checkers, diminishing news quality and contributing to societal harms like diminished government accountability (Cohen et al. 2011). This concern has given rise to a new strand of research in Natural Language Processing (NLP), namely automated fact-checking (Cohen et al.

2011; Vlachos and Riedel 2014a; Hassan et al. 2017; Graves 2018; Guo, Schlichtkrull, and Vlachos 2022). With misinformation spreading faster and deeper than factual news (Vosoughi, Roy, and Aral 2018), there is a pressing need for sophisticated tools capable of effective and real-time fact-checking. While early automated fact-checking tools, such as those based on the FEVER dataset (Thorne et al. 2018) and climate-focused datasets like climateFEVER (Diggelmann et al. 2020), have made significant progress, they often fall short in providing the nuanced reasoning necessary for a comprehensive understanding of complex claims.

This is where generative AI models, specifically LLMs, come into play. LLMs can offer holistic evaluations rooted in an extensive scientific knowledge base. They can provide the necessary context, reasoning, and argumentation essential for reaching well-informed verdicts on complex climate-related claims. Building on this premise, Climinator leverages the capabilities of LLMs to evaluate climate-related claims based on empirical evidence and scientific consensus. The aim is to provide a comprehensive, transparent, and objective assessment of claims that is not limited to countering polarized views but rather enables a more nuanced understanding of climate issues. Figure 1 depicts the operational flow of the Climinator framework, where the initial claim is parsed by an LLM into subclaims, enhancing the specificity and efficiency of the evaluation process.

Specialized LLMs, henceforth referred to as advocates, are pivotal in the next phase. Each advocate examines the claim against a curated corpus of texts. The general GPT-4 model (OpenAI 2023) serves as one Advocate, while other advocates consist of retrieval-augmented generation (RAG) systems. These systems, which ensure LLM responses are grounded in credible sources, draw from diverse scientific and trusted repositories: the Intergovernmental Panel on Climate Change (IPCC) AR6 reports, World Meteorological Organization (WMO) sources, and two... We describe the data in Section A. Each of the advocates is grounded on one particular text corpus and delivers a verdict informed by its respective data sources, prompted to provide evidence-backed rationales.

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