Drawdown Georgia Launches Solutions Tracker To Accelerate Equitable Climate Action Across the State
April 15, 2025 /3BL/ - Drawdown Georgia, the state's premier science-based climate initiative, has launched the new Drawdown Georgia Solutions Tracker, an interactive online platform designed to help community leaders, civic officials, policy makers, and businesses better understand where climate solutions are taking root in Georgia—and where infrastructure and equity considerations may call for additional investments.
Grounded in the work of researchers at the Climate and Energy Policy Lab at Georgia Tech's School of Public Policy, the Solutions Tracker is part of the broader Drawdown Georgia initiative, which is focused on identifying and scaling the most effective carbon-reducing solutions for the state. The tracker offers users data-driven insights into how 16 of the 20 Drawdown Georgia solutions, like rooftop solar, recycling, composting, and energy-efficient transportation, are being adopted across Georgia's 159 counties.
'Georgia has the opportunity to lead the South in scaling climate solutions that not only reduce emissions, but also improve lives and livelihoods,' said Dr. Marilyn A. Brown, Regents' Professor and Brook Byers Professor of Sustainable Systems in the School of Public Policy at Georgia Tech and leader of the research team behind Drawdown Georgia. 'The Solutions Tracker shows where climate progress is happening—and how it can be expanded to bring more benefits to more people.'
The Solutions Tracker includes side-by-side state maps. The first map allows users to explore the prevalence of the chosen solution by county, often with multiple variables to choose from. For example, in the 'Retrofitting' solution, data is available on the percentage of homes that contain electric water heaters, heat pumps, LED lighting, and more. The second map offers data on a comparison variable chosen by the Drawdown Georgia research team for its relevance to the uptake of the selected solution, such as median household income or percentage of urban area.
The Solutions Tracker was designed by Dr. Brown to complement the Drawdown Georgia Emissions Tracker, designed by William Drummond, Associate Professor of City and Regional Planning at Georgia Tech. 'Together the Trackers are a powerful combination with the capacity to identify locations where solutions are most needed, and where supporting infrastructure is available,' explained Dr. Brown. 'Together, they allow anyone—citizens, city planners, business leaders—to see what's working where and identify where investments can do the most good."Bright Spots and Scaling SuccessThe Tracker highlights several top-performing Drawdown Georgia climate solutions that are scaling quickly across Georgia, including:
Communities like Atlanta, Fulton County, Fort Benning, Avondale Estates, and Floyd County are featured as local leaders demonstrating the positive impact of these solutions—from improved air quality and healthier food systems to job creation and cleaner energy.
To learn where your county stands, use the Drawdown Georgia Solutions Tracker.About Drawdown GeorgiaDrawdown Georgia is a statewide research-based initiative launched in 2020 that was born from a multi-university collaboration, funded by the Ray C. Anderson Foundation. Taking inspiration from Project Drawdown®, the world's leading resource for taking action on climate change, Drawdown Georgia localized that work by identifying the 20 highest-impact solutions for reducing greenhouse gas emissions in our state over the next decade.
This framework focuses on climate solutions in five sectors: transportation, buildings & materials, food & agriculture, electricity, and land sinks. It considers how these solutions can reduce emissions and advance 'beyond carbon' priorities, including equity, economic development, public health, and nurturing the larger environment.
Drawdown Georgia has grown into a 'leader-full' movement, bringing together many organizations, universities, companies, leaders, and funders who are working to advance climate solutions in Georgia, including Drawdown Georgia Research, the Drawdown Georgia Business Compact, Drawdown Georgia Congregations, and Drawdown Georgia Higher Education. Learn more at drawdownga.org.
GEORGIA CLIMATE SOLUTIONS REPORT
See this report that offers a snapshot of Georgia's best-performing climate solutions and the communities where they are scaling.
Visit 3BL Media to see more multimedia and stories from Ray C. Anderson Foundation
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Open-source models let anyone see how the model was built, and they're typically available for anyone to customize and build one. Open-weights models give us some insight into how the model weighs specific characteristics when making decisions. Meta AI vs. ChatGPT: AI Chatbots Compared Meta AI vs. ChatGPT: AI Chatbots Compared Click to unmute Video Player is loading. Play Video Pause Skip Backward Skip Forward Next playlist item Unmute Current Time 0:04 / Duration 0:06 Loaded : 0.00% 0:04 Stream Type LIVE Seek to live, currently behind live LIVE Remaining Time - 0:02 Share Fullscreen This is a modal window. This video is either unavailable or not supported in this browser Error Code: MEDIA_ERR_SRC_NOT_SUPPORTED The media could not be loaded, either because the server or network failed or because the format is not supported. Technical details : Session ID: 2025-05-31:c79bda8fcb89fbafa9a86f4a Player Element ID: vjs_video_3 OK Close Modal Dialog Beginning of dialog window. Escape will cancel and close the window. Text Color White Black Red Green Blue Yellow Magenta Cyan Opacity Opaque Semi-Transparent Text Background Color Black White Red Green Blue Yellow Magenta Cyan Opacity Opaque Semi-Transparent Transparent Caption Area Background Color Black White Red Green Blue Yellow Magenta Cyan Opacity Transparent Semi-Transparent Opaque Font Size 50% 75% 100% 125% 150% 175% 200% 300% 400% Text Edge Style None Raised Depressed Uniform Drop shadow Font Family Proportional Sans-Serif Monospace Sans-Serif Proportional Serif Monospace Serif Casual Script Small Caps Reset Done Close Modal Dialog End of dialog window. Close Modal Dialog This is a modal window. This modal can be closed by pressing the Escape key or activating the close button. Close Modal Dialog This is a modal window. This modal can be closed by pressing the Escape key or activating the close button. Meta AI vs. ChatGPT: AI Chatbots Compared What do large language models do really well? LLMs are very good at figuring out the connection between words and producing text that sounds natural. "They take an input, which can often be a set of instructions, like 'Do this for me,' or 'Tell me about this,' or 'Summarize this,' and are able to extract those patterns out of the input and produce a long string of fluid response," Riedl said. But they have several weaknesses. Where do large language models struggle? First, they're not good at telling the truth. In fact, they sometimes just make stuff up that sounds true, like when ChatGPT cited six fake court cases in a legal brief or when Google's Bard (the predecessor to Gemini) mistakenly credited the James Webb Space Telescope with taking the first pictures of a planet outside of our solar system. Those are known as hallucinations. "They are extremely unreliable in the sense that they confabulate and make up things a lot," Sap said. "They're not trained or designed by any means to spit out anything truthful." They also struggle with queries that are fundamentally different from anything they've encountered before. That's because they're focused on finding and responding to patterns. A good example is a math problem with a unique set of numbers. "It may not be able to do that calculation correctly because it's not really solving math," Riedl said. "It is trying to relate your math question to previous examples of math questions that it has seen before." While they excel at predicting words, they're not good at predicting the future, which includes planning and decision-making. "The idea of doing planning in the way that humans do it with … thinking about the different contingencies and alternatives and making choices, this seems to be a really hard roadblock for our current large language models right now," Riedl said. Finally, they struggle with current events because their training data typically only goes up to a certain point in time and anything that happens after that isn't part of their knowledge base. Because they don't have the capacity to distinguish between what is factually true and what is likely, they can confidently provide incorrect information about current events. They also don't interact with the world the way we do. "This makes it difficult for them to grasp the nuances and complexities of current events that often require an understanding of context, social dynamics and real-world consequences," Snyder said. How are LLMs integrated with search engines? We're seeing retrieval capabilities evolve beyond what the models have been trained on, including connecting with search engines like Google so the models can conduct web searches and then feed those results into the LLM. This means they could better understand queries and provide responses that are more timely. "This helps our linkage models stay current and up-to-date because they can actually look at new information on the internet and bring that in," Riedl said. That was the goal, for instance, a while back with AI-powered Bing. Instead of tapping into search engines to enhance its responses, Microsoft looked to AI to improve its own search engine, in part by better understanding the true meaning behind consumer queries and better ranking the results for said queries. Last November, OpenAI introduced ChatGPT Search, with access to information from some news publishers. But there are catches. Web search could make hallucinations worse without adequate fact-checking mechanisms in place. And LLMs would need to learn how to assess the reliability of web sources before citing them. Google learned that the hard way with the error-prone debut of its AI Overviews search results. The search company subsequently refined its AI Overviews results to reduce misleading or potentially dangerous summaries. But even recent reports have found that AI Overviews can't consistently tell you what year it is. For more, check out our experts' list of AI essentials and the best chatbots for 2025.
