ChatNetZero is a climate-specific large language model (LLM) chatbot and question-answering platform designed to interpret the complicated nuances of “net zero” and evaluate the credibility of decarbonization plans set by businesses, governments, and financial institutions.

The world must reach net-zero emissions by mid-century if we are to avoid the most dangerous climate change impacts. But what does “net zero” actually mean, and how do we decipher who is taking credible action to get there? General LLMs, such as ChatGPT , are not attuned to the nuances of net-zero targets and often fabricate convincing but incorrect statements when queried for specific details.

ChatNetZero is being upgraded for the next release cycle with agentic retrieval, more advanced analytical capabilities, and estimated energy consumption per query. The goal is to make climate AI more trustworthy, transparent, and useful for researchers, practitioners, and organizations.

Despite the rapid advancement of generative AI such as ChatGPT , the technology still has two major limitations that prevent much of the climate community from relying on it:

• Black box answers: users cannot easily verify whether an answer came from a credible climate source.
• Hallucination: general-purpose models may produce fabricated but plausible claims.

ChatNetZero is being built to address those bottlenecks and add climate-specific capabilities that general chatbots usually lack:

• Answers are grounded in verified climate documents and data, including material curated with the Net Zero Tracker.
• We use hallucination-reduction and traceability checks to improve answer reliability.
• The next release adds agentic retrieval, native climate analytics, and estimated energy consumption per query to improve both answer quality and system transparency.

ChatNetZero 3.0 and the releases after it are focused on a broader upgrade of the platform rather than just incremental UI changes. Current roadmap items include:

• Agentic retrieval to reduce unnecessary compute while improving retrieval precision.
• Estimated energy consumption for each query, including step-by-step breakdowns.
• Climate analytics for counting, comparisons, and industry-level breakdowns.
• More dynamic data updates across company reports, research PDFs, and Net Zero Tracker snapshots.
• Organization-specific deployment pathways for teams that want their own branded climate AI frontend.

ChatNetZero 3.0 is a backend and product upgrade centered on three ideas: stronger retrieval, stronger analysis, and stronger transparency.

• Stronger retrieval: we are moving from a more classical retrieval pattern toward an agentic retrieval approach.
• Stronger analysis: we are adding native analytical capabilities for climate-specific questions that involve aggregation, comparisons, and structured evidence.
• Stronger transparency: we will report estimated energy consumption per query so researchers can better understand the cost of AI workflows.

The release is being rolled out gradually, so some features may appear first in preview, demo, or staged form before they are fully integrated into the main product.

Agentic retrieval is the next retrieval architecture we are developing for ChatNetZero. Instead of loading broad retrieval context in the same way for every question, the system first interprets the question and the document landscape, then identifies the most relevant sources and retrieval path for that specific task.

The goal is to keep answer quality high while reducing wasted compute, making updates easier, and supporting a larger knowledge base over time. This matters especially for climate research, where users often need grounded, multi-document answers rather than generic summaries.

We believe climate AI tools should also be transparent about their own energy footprint. ChatNetZero is being designed to report estimated energy consumption for each query so users can benchmark more efficient AI workflows and better understand the trade-offs behind retrieval and inference.

This work builds on our research and methods including The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products and the How Hungry is AI paper.

Yes. We are actively looking for collaboration opportunities to improve the platform and expand the knowledge base. Please contact us via the Contact page if you would like to discuss adding documents, reports, or a specialized dataset.

We are also exploring organization-specific deployments in which a curated knowledge base can power a dedicated, branded climate AI frontend.

Yes. ChatNetZero is designed with a modular infrastructure so it can support customized deployment for organizations that want their own climate or sustainability AI interface.

We are increasingly positioning ChatNetZero as both a public-facing product and a benchmark/template for teams that want to deploy their own branded AI frontend on top of trusted climate workflows and domain-specific knowledge.

Please contact us via the Contact page if you are interested in this use case.

ChatNetZero is still evolving, and some capabilities are in staged release rather than fully complete. We are continuously improving the platform, but current limitations still include:

• Some advanced retrieval and analytical features are still being rolled out gradually.
• Follow-up conversations and more interactive workflows are still improving.
• Accuracy may drop on very complex multi-entity questions or tasks that require broad aggregation across many actors.
• Speed and compute efficiency are active optimization areas as we transition to newer retrieval architecture.
• Multilingual support and more advanced table-heavy reasoning remain ongoing work.

It is the definitive register of net zero targets set by countries, states and regions, cities and large companies. It shines a light on the existence (or lack thereof) of pledges and parameters of integrity — for example, whether an entity has:

• set interim targets
• published a plan outlining how it will reach net zero
• committed to report its progress annually
• been clear about the scopes of emissions contained in its target, and planned use of offsets

The Net Zero Tracker complements the work of organizations such as CDP, Climate Action Tracker and SBTi, performing a different function for the climate community.

It is an essential transparency tool through which stakeholders of all kinds can hold governments and companies to account — rigorous enough for analysts and decision-makers, accessible enough for journalists and campaigners.

ChatNetZero is still in active development, and like all large language models there are questions it answers better than others. In some cases, the best next step is to inspect the cited sources, compare the answer with your own domain knowledge, or reframe the question more narrowly.

For example, you may want to know whether Company X has set a “good” net zero target. ChatNetZero can help surface evidence, but subjective integrity judgments may still require external standards and expert interpretation. We recommend the following resources:

• UN High Level Expert Group Recommendations on Net Zero Integrity
• Oxford Principles for Making a Net Zero Commitment

If you think a response reveals a product gap, we would also love to hear from you via the Contact page.