Core Insights - The 2025 Sustainable Global Leaders Conference will be held from October 16 to 18 in Shanghai, focusing on sustainable development and global governance [1][4] - Professor Losoncz Miklós emphasized the importance of integrating regional energy advantages and clean energy technologies in AI energy solutions during his keynote speech [3] Group 1: Conference Overview - The conference is co-hosted by the World Green Design Organization (WGDO) and Sina Group, with support from the Shanghai Huangpu District Government [4] - Key topics discussed include creating a sustainable development model in Shanghai and exploring new growth paradigms for the city's five major centers [4] Group 2: AI and Sustainability - Professor Miklós highlighted the need for a comprehensive approach to address energy issues in high-energy consumption scenarios like AI data centers, citing examples from China and Hungary [3] - He called for the scientific community to establish measurement standards for AI performance and for higher education to adapt to new AI scenarios by cultivating skilled talent [3]

来源：上海证券报·中国证券网 "呼应报告的重要观察，台达将致力以科技创新协助可持续AI的推进，包括打造智慧微电网以及为边缘计算设计的AI数据中心等。"台达首席品牌官郭珊珊对 上海证券报记者表示。 在本届工博会上，台达以"AI赋新 GI永续"为主题亮相，通过AI数字化工厂、智慧工业场域、智慧微电网、AI数据中心等场景，集中展示创新研发的绿色产 品及解决方案。其中，旋盖数字化产线、液-液数据中心解决方案、固态变压器（SST）智慧电源等展品备受关注。 宋薇萍摄 首次亮相的台达固态变压器（SST）以新一代电力电子核心技术脱颖而出，荣获"CIIF工业自动化奖"，成为展区里的焦点展品。展台工作人员对记者介绍， 该展品通过碳化硅(SiC)高频电能转换替代传统电磁变换，实现10kV中压电网直降800V (可调范围200-1000Vdc)直流输出，电源效率突破性提升能效至 98%+，设备体积缩小50%以上，其高模组化、易部署设计可以轻松扩展和配置，以满足不同的电力需求，使维护、更换升级和针对特定应用的适应变得更 加容易。 上证报中国证券网讯（记者 宋薇萍）正在举行的第二十五届中国国际工业博览会(简称"工博会")上，台达通过呈现 ...

Group 1 - Microsoft's "landfill" plan involves collaborating with Vaulted Deep to collect human and animal waste, converting it into bio-sludge for deep underground storage to prevent greenhouse gas emissions [1] - The project aims to process over 4 million tons of carbon equivalent by 2038, with an estimated cost of $1.7 billion, allowing Microsoft to gain carbon credits and tax incentives under the U.S. 45Q tax credit mechanism [1] - The initiative reflects the broader trend of tech companies exploring unconventional carbon offset methods due to the increasing carbon footprint associated with AI development [2][6] Group 2 - Various innovative carbon offset methods are emerging globally, such as Iceland's Carbfix project that converts CO2 into rock and Switzerland's Climeworks capturing atmospheric CO2 for beverage production [2] - The "whale carbon credit" initiative recognizes whales as significant carbon sinks, allowing companies to purchase credits for whale conservation, thus promoting marine carbon absorption [3] - China's unique approaches to carbon reduction include seed paper for event credentials that can grow into plants and bamboo carbon trading that turns environmental protection into economic benefits [4][5] Group 3 - The "wind-solar-fish" integrated project in Jiangsu combines wind power, solar panels, and aquaculture for efficient resource utilization [5] - The "Carbon Benefit Tianfu" mechanism in Chengdu incentivizes low-carbon behaviors among citizens through rewards, making carbon reduction a part of daily life [5] - Critics argue that effective carbon reduction should focus on reducing emissions at the source rather than relying on purchasing carbon credits, highlighting the challenges faced by the AI industry in achieving carbon neutrality [6][7] Group 4 - China's AI development is characterized by proactive measures, such as the DeepSeek model that significantly reduces computational demands compared to traditional models, showcasing a commitment to sustainable practices [8] - The integration of green principles into technological innovation is essential for achieving a balance between development and environmental responsibility, positioning sustainability as a competitive advantage in the tech industry [8]

Core Viewpoint - The article discusses the hidden resource consumption associated with AI interactions, particularly focusing on energy and water usage, and the social implications of human-AI interactions. Group 1: AI Resource Consumption - AI interactions, such as saying "thank you," may contribute to significant resource consumption, with estimates suggesting that OpenAI could incur millions in electricity costs due to user interactions [4][6]. - A typical AI query consumes approximately 0.3Wh of electricity, and the cumulative energy usage from global interactions is substantial, with AI data centers consuming as much electricity as tens of thousands of households [9][11]. - The International Energy Agency (IEA) projects that global data center electricity consumption will rise from 415 TWh in 2024 to over 1300 TWh by 2035, surpassing Japan's current total electricity consumption [14]. Group 2: Water Usage in AI Operations - AI systems not only consume electricity but also require significant water resources for cooling high-performance servers, with estimates indicating that training models like GPT-3 could require water equivalent to that needed for cooling a nuclear reactor [19]. - Each interaction with models like ChatGPT can consume water equivalent to a 500ml bottle, highlighting the extensive water usage associated with AI operations [19]. Group 3: Human-AI Interaction Dynamics - The article explores the psychological aspects of human interactions with AI, noting that users often anthropomorphize AI, treating it as a conscious entity despite its lack of understanding or emotions [25][29]. - Research indicates that polite language can influence AI responses, with users reporting that more courteous interactions yield more comprehensive and human-like answers from AI [34][37]. - The phenomenon of users expressing gratitude towards AI, despite its inability to comprehend such gestures, reflects a deeper human tendency to maintain social niceties even in non-human interactions [48].