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Welcome to TianGong Initiative - a beacon in sustainable development. This non-profit, research-driven project harnesses the power of data and AI to advance environmental sustainability, all through the lens of Life Cycle Assessment. Join our journey towards a greener, more sustainable future.
TianGongLCA的外部链接
CN,Beijing
We believe this work plays a crucial role in advancing the application of AI for sustainability.
Large Language Models (LLMs) are developing so fast. There are also many prospective applications in the environment and sustainability fields. However, there lacks a standard benchmark to test various LLMs and LLM applications for their performance in environment and sustainability fields. We, the TianGongLCA Team at Tsinghua University, are excited to invite you to work with us to put together a benchmark Q&A dataset to evaluate the applications of LLMs in the environment and sustainability fields. Your contributions would be greatly appreciated as we work together to improve AI’s role in environmental management. Examples of the Q&A pair: Q: If the primary pollutant in Beijing's air quality on that day is PM2.5, with a 24-hour average concentration of 100 µg/m³, how would you calculate the Air Quality Index (AQI) for that day? A: According to the Technical Regulation on Ambient Air Quality Index (HJ 633-2012) issued by China's Ministry of Environmental Protection, we can calculate the Air Quality Index (AQI) based on the 24-hour average concentration of PM2.5. First, we identify the PM2.5 concentration range. According to the regulation, we find that 100 μg/m³ falls within the third level range of 75-115 μg/m³. The formula for calculating the Individual Air Quality Index (IAQI) for PM2.5 is: IAQI = (IAQIH - IAQIL) / (BPH - BPL) × (C - BPL) + IAQIL, where IAQIH is the upper limit of AQI for the concentration range, IAQIL is the lower limit of AQI for the concentration range, BPH is the upper limit of concentration range. So here is how you can help in three simple steps: 1. Download the “Q&A Template and Instructions.docx”; 2. Fill out the Q&A pairs and save your document; 3. Click the link to submit your responses: Submit Your Q&A Pairs Thank you for your support!
TianGongLCA转发了
It’s a super nice trip to come to Berlin for the OpenLCA conference and I am so excited to study a lot from all of the presentations and discussions. I am also excited to share TianGong Initiative TianGongLCA , with LCA database and AI tools to all of the experts in this wonderful conference. TianGong initiative, a beacon in sustainable development. This non-profit, research-driven project harnesses the power of data and AI to advance environmental sustainability, all through the lens of Life Cycle Assessment (LCA). I am also excited to announce a major new verersion of the TianGong Database, now compatible with the latest version of the EF 3.1 reference package. The dataset is openness and free to use. You can download it here: https://www.tiangong.earth Thanks for the efforts of all staffs in the Greendelta. It’s really a wonderful experience. Andreas Ciroth Conrad Spindler Julia Cilleruelo Palomero Looking forward to seeing you all again #lifecycleassessment #lifecycledatabase #openlca #tiangonginitiative
On January 10th, UN Environment Programme's Global LCA Data Access network (GLAD) announced the breakthrough of surpassing 100,000 datasets and expressed sincere gratitude for the contributions of the TianGong Initiative. TianGongLCA is currently the third largest database on the GLAD platform and the largest open-access database available. In celebration of GLAD reaching the major milestone of 100,000 datasets, Professor Ming Xu, the Baofeng Chair Professor of Carbon Neutrality at Tsinghua University (China) and founder of the TianGong Initiative, has been interviewed by the UN Environment Programme. Professor Xu provided an in-depth account of how the TianGong Initiative has supported GLAD in this impressive achievement, as well as their collaborative efforts to advance global environmental sustainability research. For more information, please visit the official GLAD website: https://lnkd.in/gcb7z2g4. 1月10日,联合国环境署全球LCA数据网络 (GLAD)宣布突破10万数据集大关,并特别对天工计划的贡献表示了诚挚感谢。天工数据(TianGong)目前是GLAD平台全球第三大数据库、是最大的公开免费数据库。 在GLAD达到10万个数据集的重要里程碑之际,清华大学碳中和讲席教授及天工计划发起人徐明教授接受联合国环境署采访,详细讲述天工计划如何协助GLAD实现这一壮举,以及他们如何共同推动全球环境可持续性研究的发展。更多信息,请访问GLAD官方网站:https://lnkd.in/g5g6HHGn
The TianGong AI team from the School of Environment at Tsinghua University, along with Kaiwu has contributed the Chat Model API component from the ZHIPU AI open platform to LangChain. The contributed code has been officially incorporated into the LangChain v0.0.354 version. For detailed instructions on how to use it, please visit the official LangChain documentation: https://lnkd.in/giphKWtX. Your use and feedback are welcome. 清华大学环境学院天工AI团队联合Kaiwu,向LangChain贡献了智谱AI(ZHIPU AI)开放平台的Chat Model API组件,所贡献的代码已在LangChain v0.0.354版本被官方合并。具体应用方法可访问LangChain官方文档:https://lnkd.in/gC_6jVva