Rethinking Higher Education/Chapter 11/en-zh
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Chapter 11: Green Digital Education
Martin Woesler
| English (Source) | 中文 (Target) |
|---|---|
| == Green Digital Education: Sustainability, Digital Sobriety, and Environmental Awareness in EU and Chinese Universities == | == 绿色数字教育：欧盟和中国大学中的可持续发展、数字节制与环境意识 == |
| Martin Woesler | Martin Woesler |
| Hunan Normal University | 湖南师范大学 |
| Abstract | 摘要 |
| The digital transformation of higher education carries a hidden environmental cost that is rarely acknowledged in educational policy discourse. Data centres consumed 415 terawatt-hours of electricity in 2024 — 1.5 percent of global electricity demand — and are projected to reach 945 TWh by 2030, growing four times faster than any other sector. Training a single large language model such as GPT-3 produces approximately 552 metric tons of CO2 equivalent, comparable to the annual emissions of 121 average American households. Annual digital content consumption generates 229 kg of CO2 equivalent per user, approximately 3–4 percent of per capita anthropogenic greenhouse gas emissions. Yet the educational technology sector has largely escaped scrutiny for its environmental footprint, even as universities expand their digital infrastructure under the banners of innovation and accessibility. This article examines the tension between digital education and environmental sustainability through a systematic comparison of European Union and Chinese approaches. The EU has developed the GreenComp framework for sustainability competences and is beginning to address „digital sobriety„ — the principle of minimizing unnecessary digital consumption — as an educational goal. China has pursued „ecological civilization education“ as a framework that integrates environmental awareness with broader civilizational goals, while simultaneously undertaking the world’s largest expansion of digital educational infrastructure. We argue that both systems face an „AI energy paradox„ — the deployment of artificial intelligence in education simultaneously promises to enhance sustainability awareness and contributes substantially to environmental degradation — and that neither has yet developed an adequate response. | 高等教育的数字化转型带有一种在教育政策话语中很少被承认的隐性环境成本。2024年数据中心消耗了415太瓦时的电力——占全球电力需求的1.5%——预计到2030年将达到945太瓦时，增长速度是任何其他行业的四倍。训练单个大型语言模型（如GPT-3）产生约552公吨二氧化碳当量，相当于121个美国普通家庭的年排放量。每位用户的年数字内容消费产生229公斤二氧化碳当量，约占人均人为温室气体排放量的3—4%。然而，教育技术行业在很大程度上未受到其环境足迹的审视，即使大学在创新和可及性的旗帜下扩大其数字基础设施。本文通过对欧盟和中国方法的系统比较，审视了数字教育与环境可持续性之间的张力。欧盟开发了GreenComp可持续发展能力框架，并开始将"数字节制"——最小化不必要数字消费的原则——作为教育目标来对待。中国追求"生态文明教育"作为将环境意识与更广泛文明目标相结合的框架，同时进行了世界上最大规模的数字教育基础设施扩张。我们认为，两个体系都面临"人工智能能源悖论"——人工智能在教育中的部署同时承诺增强可持续发展意识并对环境退化做出重大贡献——而且两者都尚未制定出充分的应对措施。 |
| Keywords: green digital education, sustainability, digital sobriety, ecological civilization, carbon footprint, data centres, AI energy paradox, GreenComp, higher education, EU-China comparison | 关键词：绿色数字教育、可持续发展、数字节制、人工智能能源悖论、GreenComp、生态文明、碳足迹、数据中心能耗、欧中比较 |
| 1. Introduction | 1. 引言 |
| The environmental sustainability of digital education is a topic that most educational technologists would prefer not to discuss. The digital transformation of higher education has been driven by powerful narratives of progress: AI-personalized learning, immersive virtual reality, global connectivity, and institutional efficiency. These narratives are not wrong — the companion chapters in this anthology document genuine educational benefits from digital technologies (Woesler, this volume). But they are incomplete, because they systematically ignore the material basis of digital education: the servers, networks, devices, and energy systems that make it possible, and the environmental consequences of operating them at scale. | 数字教育的环境可持续性是大多数教育技术专家不愿讨论的话题。高等教育的数字化转型由强大的进步叙事所驱动：人工智能个性化学习、沉浸式虚拟现实、全球互联互通和机构效率。这些叙事并非错误——本论文集的配套章节记录了数字技术真正的教育效益（Woesler, 本卷）。但它们是不完整的，因为它们系统性地忽视了数字教育的物质基础：使其成为可能的服务器、网络、设备和能源系统，以及大规模运营它们的环境后果。 |
| The numbers are sobering. The International Energy Agency reports that data centres consumed 415 terawatt-hours of electricity in 2024, representing 1.5 percent of global electricity demand. This figure is projected to reach 945 TWh by 2030 — more than Japan’s total electricity consumption — with growth rates of approximately 15 percent per year, four times faster than all other sectors combined (IEA 2025). The carbon emissions of the major technology companies have surged in parallel: Google’s greenhouse gas emissions rose 48 percent between 2019 and 2024, while Microsoft’s grew 29 percent since 2020, with data centre energy consumption identified as the primary driver (NPR 2024). An analysis of corporate sustainability reports suggests that actual data centre emissions may be 7.62 times higher than reported, due to accounting practices that count renewable energy certificates as equivalent to actual renewable energy consumption (Le Goff 2025). | 数据令人警醒。国际能源署报告称，2024年数据中心消耗了415太瓦时的电力，占全球电力需求的1.5%。这一数字预计到2030年将达到945太瓦时——超过日本的总电力消耗——年增长率约15%，是所有其他行业总和的四倍（IEA 2025）。主要科技公司的碳排放量同步激增：谷歌的温室气体排放在2019年至2024年间增长了48%，微软自2020年以来增长了29%，数据中心能源消耗被确定为主要驱动因素（NPR 2024）。一项对企业可持续性报告的分析表明，由于将可再生能源证书等同于实际可再生能源消费的会计实践，实际数据中心排放可能比报告数据高7.62倍（Le Goff 2025）。 |
| For higher education, these figures have direct relevance. Universities are among the largest institutional consumers of digital infrastructure, operating learning management systems, research computing clusters, videoconferencing platforms, and — increasingly — AI-powered educational tools. Yet the environmental footprint of this digital infrastructure is almost never included in university sustainability assessments. Williamson, Hogan, and Selwyn (2025), in a chapter for a Springer volume on critical EdTech studies, argue that the environmental impact of educational technology platforms is „persistently overlooked“ in university carbon calculations, drawing attention to the globally distributed, energy-intensive IT infrastructure in which universities are enmeshed through their EdTech partnerships. | 对于高等教育，这些数字具有直接相关性。大学是数字基础设施最大的机构消费者之一，运营学习管理系统、研究计算集群、视频会议平台以及越来越多的人工智能教育工具。然而，这种数字基础设施的环境足迹几乎从未被纳入大学可持续性评估中。Williamson、Hogan和Selwyn（2025）在Springer批判性教育技术研究论文集的一章中指出，教育技术平台的环境影响在大学碳核算中被"持续忽视"，他们着重揭示了大学通过其教育技术合作关系所嵌入的全球分布式、高能耗IT基础设施。 |
| This article examines how the European Union and China — the two largest higher education systems by enrollment — are addressing (or failing to address) the environmental dimension of their digital education strategies. We compare the EU’s emerging framework of sustainability competences and digital sobriety with China’s ecological civilization education, assessing each for its capacity to confront the environmental costs of the digital infrastructure on which modern education increasingly depends. | 本文考察了欧盟和中国——按注册人数计全球两大高等教育体系——如何应对（或未能应对）其数字教育战略中的环境维度。我们将欧盟新兴的可持续发展能力与数字节制框架同中国的生态文明教育进行比较，评估各自应对现代教育日益依赖的数字基础设施环境成本的能力。 |
| 2. The Carbon Footprint of Educational Technology | 2. 教育技术的碳足迹 |
| 2.1 Data Centres and AI Training | 2.1 数据中心与人工智能训练 |
| The energy consumption of digital infrastructure has two main components: the operational energy of data centres (including cooling, which can account for 30–40 percent of total energy use) and the embodied energy of hardware production and disposal. For AI systems specifically, a third component — the energy consumed during model training — has become increasingly significant. | 数字基础设施的能源消耗有两个主要组成部分：数据中心的运营能耗（包括可占总能耗30—40%的冷却系统）和硬件生产及处置的隐含能量。对于人工智能系统而言，第三个组成部分——模型训练过程中消耗的能量——已变得日益重要。 |
| Patterson and colleagues (2021), in a study by researchers at Google and UC Berkeley, estimated that training GPT-3 produced approximately 552 metric tons of CO2 equivalent and consumed 1,287 megawatt-hours of electricity. Strubell, Ganesh, and McCallum (2019), in the paper that first drew wide attention to the carbon cost of training large language models, demonstrated that training a single large NLP model can emit as much carbon as five automobiles over their entire lifetimes. These figures have grown substantially with the development of larger models: GPT-4 and its successors consume orders of magnitude more energy, though precise figures are not publicly disclosed. | Patterson等人（2021）在谷歌和加州大学伯克利分校研究人员的一项研究中估计，训练GPT-3产生了约552公吨二氧化碳当量，消耗了1,287兆瓦时电力。Strubell、Ganesh和McCallum（2019）在首次引起广泛关注的关于大型语言模型训练碳成本的论文中证明，训练单个大型NLP模型可以排放相当于五辆汽车整个使用寿命的碳排放量。随着更大模型的开发，这些数字已大幅增长：GPT-4及其后续版本消耗的能量高出数个数量级，尽管具体数据未被公开披露。 |
| The water footprint of AI is equally concerning. Li and colleagues (2023), in a study published in Communications of the ACM, estimate that training GPT-3 in Microsoft’s US data centres directly evaporated 700,000 litres of freshwater. Global AI water demand is projected to reach 4.2–6.6 billion cubic metres by 2027 — more than the total annual water withdrawal of four to six Denmarks. De Vries (2025), writing in the Cell Press journal Patterns, estimates the AI industry’s 2025 carbon footprint at 32.6–79.7 million metric tons of CO2, comparable to the total emissions of New York City, with a water footprint of 312.5–764.6 billion litres. | 人工智能的水足迹同样令人担忧。Li等人（2023）在发表于Communications of the ACM的研究中估计，在微软美国数据中心训练GPT-3直接蒸发了70万升淡水。全球人工智能用水需求预计到2027年将达到42亿至66亿立方米——超过四至六个丹麦的年度总取水量。De Vries（2025）在Cell Press旗下期刊Patterns上撰文，估计人工智能行业2025年的碳足迹为3,260万至7,970万公吨二氧化碳，与纽约市的总排放量相当，水足迹为3,125亿至7,646亿升。 |
| For universities, these aggregate figures translate into institutional responsibility. Every time a student uses a cloud-based AI writing assistant, submits work to an AI-powered plagiarism detector, or engages with an AI tutoring system, the university’s digital infrastructure generates emissions that are invisible to the user but cumulatively significant. Xiao and colleagues (2025), writing in Nature Sustainability, argue that the US AI industry is unlikely to meet net-zero targets by 2030 without substantial reliance on „highly uncertain carbon offset and water restoration mechanisms.“ | 对于大学而言，这些总体数据转化为机构层面的责任。每当学生使用基于云端的人工智能写作助手、向人工智能驱动的抄袭检测系统提交作业或使用人工智能辅导系统时，大学的数字基础设施就在产生对用户不可见但累积起来相当可观的排放。Xiao等人（2025）在Nature Sustainability上撰文指出，美国人工智能行业在不大量依赖"高度不确定的碳抵消和水资源修复机制"的情况下，不太可能在2030年前实现净零目标。 |
| 2.2 Digital Content Consumption | 2.2 数字内容消费 |
| Beyond AI training, the routine digital activities of education carry their own environmental costs. Istrate and colleagues (2024), in a study published in Nature Communications, estimate that annual global average digital content consumption — web browsing, social media, video and music streaming, and videoconferencing — generates 229 kg of CO2 equivalent per user per year, approximately 3–4 percent of per capita anthropogenic greenhouse gas emissions. Under a 1.5 degrees Celsius warming scenario, this could account for approximately 40 percent of the per capita carbon budget. | 除人工智能训练外，教育中的日常数字活动也承载着自身的环境成本。Istrate等人（2024）在发表于Nature Communications的研究中估计，年均全球数字内容消费——网页浏览、社交媒体、视频和音乐流媒体以及视频会议——每用户每年产生229公斤二氧化碳当量，约占人均人为温室气体排放量的3—4%。在1.5摄氏度升温情景下，这可能占到人均碳预算的约40%。 |
| For universities, the implications are significant. A single semester of online course delivery for thousands of students involves substantial video streaming, file sharing, and platform interaction. Caird and Lane (2024), writing in the Future Healthcare Journal, note that while digital learning generally has a lower carbon footprint than face-to-face instruction when travel is factored in — travel to in-person conferences can produce 1,000 times more CO2 than virtual alternatives — the comparison is less favorable when the full lifecycle costs of digital infrastructure are included. | 对于大学而言，其影响不容忽视。单个学期为数千名学生提供在线课程涉及大量的视频流、文件共享和平台交互。Caird和Lane（2024）在Future Healthcare Journal上指出，当考虑出行因素时，数字学习的碳足迹通常低于面授教学——参加现场会议产生的二氧化碳可能是虚拟替代方案的1,000倍——但当将数字基础设施的全生命周期成本纳入考量时，这种比较就不那么有利了。 |
| 2.3 E-Waste and the Hardware Lifecycle | 2.3 电子废弃物与硬件生命周期 |
| The environmental cost of digital education extends beyond energy consumption to include the physical devices on which it depends. The accelerating pace of hardware replacement in educational institutions — driven by software requirements, institutional procurement cycles, and the planned obsolescence of consumer electronics — generates a growing stream of electronic waste that is rarely included in discussions of sustainable education. | 数字教育的环境成本超出能源消耗范畴，还包括其所依赖的物理设备。教育机构中硬件更换的加速——受软件需求、机构采购周期和消费电子产品计划性淘汰的驱动——产生了日益增长的电子废弃物流，而这在可持续教育的讨论中很少被提及。 |
| Valai Ganesh and colleagues (2025), in a study published in Scientific Reports, examined 452 electrical products across academic institutions in India and found that 32.1 percent were older than five years and 34.1 percent needed repair or replacement. Their proposed sustainable e-waste management framework demonstrated that on-site recycling can achieve a 90 percent material recovery rate — but such frameworks require institutional investment and commitment that most universities have not yet made. Thao, Hanh, and Huy (2025), in a study of Ho Chi Minh City University of Technology published in the International Journal of Environmental Science and Technology, project that e-waste at a single university campus will increase 1.5 times from 16,792 kg in 2024 to 25,230 kg in 2034, reflecting the growing hardware intensity of digital education. | Valai Ganesh等人（2025）在发表于Scientific Reports的一项研究中，考察了印度学术机构的452件电子产品，发现32.1%的设备使用超过五年，34.1%需要维修或更换。他们提出的可持续电子废弃物管理框架表明，现场回收可以实现90%的材料回收率——但此类框架需要大多数大学尚未进行的机构投资和承诺。Thao、Hanh和Huy（2025）在发表于International Journal of Environmental Science and Technology的一项对胡志明市理工大学的研究中预测，单个大学校园的电子废弃物将从2024年的16,792公斤增长1.5倍至2034年的25,230公斤，反映了数字教育日益增长的硬件密集度。 |
| For Chinese and European universities alike, the e-waste problem is compounded by the trend toward institutional tablet and laptop programs, one-to-one device initiatives, and the regular replacement of smart classroom equipment. When a university deploys thousands of tablets for a digital learning initiative, the educational benefit may be genuine — but the environmental cost of manufacturing, operating, and eventually disposing of those devices is rarely calculated. The concept of digital sobriety, discussed in the following section, offers a framework for addressing this gap. | 对于中国和欧洲的大学而言，电子废弃物问题因机构平板电脑和笔记本电脑项目、一对一设备倡议以及智慧教室设备定期更换的趋势而更加严峻。当一所大学为数字学习项目部署数千台平板电脑时，教育效益可能是真实的——但制造、运行和最终处置这些设备的环境成本却很少被计算。下一节讨论的数字节制概念为解决这一缺口提供了框架。 |
| 3. Digital Sobriety as Educational Goal | 3. 数字节制作为教育目标 |
| 3.1 Origins and Definition | 3.1 起源与定义 |
| The concept of „digital sobriety„ (sobriété numérique) originated with the French think tank The Shift Project, whose 2019 report „Lean ICT: Towards Digital Sobriety“ defined it as the principle of buying the least powerful equipment possible, changing devices as rarely as possible, and reducing unnecessary energy-intensive digital uses. The report estimated that ICT energy consumption was increasing at 9 percent annually and argued that a sobriety approach could limit growth to 1.5 percent (The Shift Project 2019). | "数字节制"（sobriete numerique）的概念源于法国智库The Shift Project，其2019年报告"精益ICT：走向数字节制"将其定义为购买尽可能低功耗的设备、尽可能少地更换设备以及减少不必要的高能耗数字使用的原则。该报告估计ICT能源消耗以每年9%的速度增长，并提出节制方法可将增长率限制在1.5%（The Shift Project 2019）。 |
| In education, digital sobriety has gained recognition through UNESCO’s 2024 decision to award the King Hamad Bin Isa Al-Khalifa Prize for ICT in Education to the Belgian EducoNetImpact initiative, which promotes sustainable digital practices in schools. Approximately 1,000 teachers now use its materials (UNESCO 2024). This recognition signals that the international educational community is beginning to acknowledge the environmental dimension of educational technology — though the scale of the response remains modest relative to the scale of the problem. | 在教育领域，数字节制通过联合国教科文组织2024年将哈马德·本·伊萨·阿勒哈利法国王信息通信技术教育奖授予比利时EducoNetImpact倡议而获得认可，该倡议在学校中推广可持续的数字实践。目前约有1,000名教师使用其材料（UNESCO 2024）。这一认可表明国际教育界开始承认教育技术的环境维度——尽管相对于问题的规模，应对措施仍然有限。 |
| 3.2 The EU Framework: DigComp and GreenComp | 3.2 欧盟框架：DigComp与GreenComp |
| The EU’s approach to sustainability in digital education draws on two complementary frameworks. The DigComp 2.2 framework (Vuorikari, Kluzer, and Punie 2022) incorporates sustainability-related examples within its five competence areas, addressing the environmental implications of digital technology use. However, digital sobriety is not explicitly named as a competence dimension within DigComp 2.2 — a gap that suggests the framework’s development has not fully caught up with the emerging environmental concerns. | 欧盟在可持续数字教育方面的方法依赖两个互补框架。DigComp 2.2框架（Vuorikari, Kluzer & Punie 2022）在其五个能力领域中纳入了与可持续性相关的示例，涉及数字技术使用的环境影响。然而，数字节制并未被明确列为DigComp 2.2的能力维度——这一缺口表明该框架的发展尚未完全跟上新兴的环境关切。 |
| The GreenComp framework (Bianchi, Pisiotis, and Cabrera Giraldez 2022), published by the EU Joint Research Centre, provides a complementary framework with four competence areas: embodying sustainability values, embracing complexity in sustainability, envisioning sustainable futures, and acting for sustainability. Its 12 competences are designed to be non-prescriptive reference points for learning schemes across formal and informal education. | GreenComp框架（Bianchi, Pisiotis & Cabrera Giraldez 2022）由欧盟联合研究中心发布，提供了包含四个能力领域的互补框架：体现可持续发展价值观、拥抱可持续发展的复杂性、展望可持续未来和为可持续发展采取行动。其12项能力被设计为正式和非正式教育学习方案的非规定性参考点。 |
| The GreenSCENT project (Horizon 2020, 2021–2024) has tested the practical application of Green Deal topics in approximately 45 schools and universities across the EU, creating the ECCEL — a European „driving licence“ for climate and environmental competences (European Commission 2021–2024). McDonagh, Caforio, and Pollini’s (2024) edited volume, „The European Green Deal in Education,“ published by Routledge, provides case studies from the project, documenting the first published applications of Green Deal topics in classroom settings. | GreenSCENT项目（地平线2020，2021—2024年）在欧盟约45所学校和大学中测试了绿色协议主题的实际应用，创建了ECCEL——一种欧洲气候与环境能力"驾照"（European Commission 2021-2024）。McDonagh、Caforio和Pollini（2024）编辑出版的Routledge论文集"欧洲绿色协议与教育"提供了该项目的案例研究，记录了绿色协议主题在课堂场景中的首批公开应用。 |
| Calis and colleagues (2025), in a study of 896 pre-service teachers published in Humanities and Social Sciences Communications, found only a „moderate level“ of digital carbon footprint awareness. Participants showed stronger understanding of electronic device impacts than of data transmission impacts — that is, they understood that manufacturing a laptop has environmental costs but were less aware that streaming a video lecture or using a cloud-based AI tool also generates emissions. Female participants had significantly higher awareness levels than males. This finding is particularly concerning: if teachers themselves are unaware of the environmental costs of digital technology, they cannot be expected to cultivate that awareness in their students. | Calis等人（2025）在发表于Humanities and Social Sciences Communications的一项对896名职前教师的研究中发现，其数字碳足迹意识仅为"中等水平"。参与者对电子设备影响的理解强于对数据传输影响的理解——即他们了解制造笔记本电脑的环境成本，但较少意识到流媒体视频讲座或使用基于云端的人工智能工具也会产生排放。女性参与者的意识水平显著高于男性。这一发现尤为令人担忧：如果教师本身不了解数字技术的环境成本，就不能期望他们在学生中培养这种意识。 |
| At the institutional level, the European University Association’s 2023 report „A Green Deal Roadmap for Universities,“ based on a survey of nearly 400 institutions from 56 higher education systems, found that a large majority of European universities have either incorporated sustainability into their main institutional strategy or developed specific sustainability strategies. However, the majority of institutions called for enhanced funding and more peer-learning opportunities (EUA 2023). The report’s recommendations span public engagement, research, teaching, and campus operations — but the environmental cost of digital infrastructure receives no dedicated treatment, suggesting that even sustainability-committed institutions have not yet integrated digital sobriety into their environmental strategies. | 在机构层面，欧洲大学协会2023年的报告"大学绿色协议路线图"基于对来自56个高等教育体系近400所院校的调查，发现绝大多数欧洲大学已将可持续性纳入其主要机构战略或制定了专门的可持续性战略。然而，大多数机构呼吁加强资金支持和更多的同行学习机会（EUA 2023）。该报告的建议涵盖公众参与、研究、教学和校园运营——但数字基础设施的环境成本没有得到专门处理，表明即使是致力于可持续发展的机构也尚未将数字节制纳入其环境战略。 |
| The growing importance of sustainability reporting in higher education is reflected in the expansion of the Times Higher Education Impact Rankings, which measured universities’ contributions to the UN Sustainable Development Goals. Urbano and colleagues (2025), in an analysis published in the Journal of Cleaner Production, found that the 2024 rankings saw 1,963 participating institutions — a 23 percent increase over the previous year — demonstrating growing institutional commitment to sustainability reporting. However, the rankings’ methodology does not include specific metrics for digital infrastructure emissions, creating a significant blind spot in an otherwise comprehensive assessment framework. | 可持续性报告在高等教育中的日益重要反映在泰晤士高等教育影响力排名的扩展中，该排名衡量大学对联合国可持续发展目标的贡献。Urbano等人（2025）在发表于Journal of Cleaner Production的分析中发现，2024年排名有1,963所参与机构——比上一年增加了23%——显示出机构对可持续性报告的承诺在不断增长。然而，该排名的方法论不包含数字基础设施排放的具体指标，在其他方面全面的评估框架中造成了一个显著的盲点。 |
| 4. China‘s Approach: Ecological Civilization Education | 4. 中国的方法：生态文明教育 |
| 4.1 Ecological Civilization as Educational Framework | 4.1 生态文明作为教育框架 |
| China‘s approach to environmental education is framed not through „sustainability„ in the Western sense but through the concept of „ecological civilization„ (生态文明, shengtai wenming) — a comprehensive framework that integrates environmental protection with economic development, social governance, and civilizational identity. Wang and colleagues (2025), in a chapter for the Springer Handbook of Ecological Civilization, trace the evolution of ecological civilization education as a key project for China’s sustainable development, noting its integration into educational policy at all levels. | 中国的环境教育方法不是通过西方意义上的"可持续发展"来框定的，而是通过"生态文明"（生态文明，shengtai wenming）概念——一个将环境保护与经济发展、社会治理和文明认同整合在一起的综合框架。Wang等人（2025）在Springer《生态文明手册》的一章中追溯了生态文明教育作为中国可持续发展关键项目的演进，指出其在各级教育政策中的整合。 |
| Ecological civilization education differs from European sustainability education in several important respects. First, it is explicitly political: the concept was enshrined in the Chinese Communist Party’s constitution in 2012 and incorporated into the national constitution in 2018, giving it a juridical status that European sustainability frameworks lack. Second, it is comprehensive in scope: ecological civilization encompasses not merely environmental protection but a transformation of the relationship between human civilization and the natural world. Third, it is state-led rather than citizen-oriented: whereas GreenComp empowers individual citizens to make sustainable choices, ecological civilization education positions individuals within a collective project of national transformation. | 生态文明教育在若干重要方面不同于欧洲的可持续发展教育。首先，它是明确的政治性的：该概念于2012年写入中国共产党党章，2018年纳入国家宪法，赋予其欧洲可持续发展框架所缺乏的法律地位。其次，它在范围上是全面的：生态文明不仅涵盖环境保护，而是人类文明与自然世界关系的全面转型。第三，它是国家主导而非公民导向的：GreenComp赋权公民做出可持续选择，而生态文明教育则将个人定位于国家转型的集体项目之中。 |
| Zhou (2024), in a study published in Social Inclusion, examines the transition from Education for Sustainable Development (ESD) to ecological civilization in China through a climate justice framework. The analysis finds that ecological civilization is heavily political, limited primarily to environmental sustainability (neglecting social and economic dimensions), and that education stakeholders are underrepresented in decision-making processes. These findings suggest that while the framework is ambitious in scope, its top-down implementation may limit its capacity to foster the kind of critical, participatory environmental engagement that the GreenComp framework envisions. | Zhou（2024）在发表于Social Inclusion的研究中，通过气候正义框架审视了中国从可持续发展教育（ESD）向生态文明的过渡。分析发现，生态文明具有浓重的政治色彩，主要局限于环境可持续性（忽视了社会和经济维度），且教育利益相关者在决策过程中代表性不足。这些发现表明，尽管该框架在范围上雄心勃勃，但其自上而下的实施可能限制了其培养GreenComp框架所设想的那种批判性、参与式环境意识的能力。 |
| Tian and colleagues (2024), in a bibliometric review published in Humanities and Social Sciences Communications analyzing 25 years of Chinese ESD research through LDA topic modelling and social network analysis, identify a „trending-declining“ publication pattern with ample space for expansion. The shift from internationally aligned sustainability goals to a localized, politicized framework under Xi Jinping’s ecological civilization concept is identified as a defining characteristic of Chinese ESD — a trajectory that has both strengths (political commitment, institutional backing) and limitations (reduced critical engagement, limited international comparability). | Tian等人（2024）在发表于Humanities and Social Sciences Communications的一项文献计量综述中，通过LDA主题建模和社会网络分析考察了25年来中国可持续发展教育研究，确定了一种"趋势—衰落"的发表模式，仍有充分的拓展空间。从国际对齐的可持续发展目标向习近平生态文明概念下的本土化、政治化框架的转变被认为是中国ESD的决定性特征——这一轨迹兼具优势（政治承诺、制度支持）和局限（批判性参与减少、国际可比性有限）。 |
| 4.2 China‘s Dual Carbon Goals and Higher Education | 4.2 中国的双碳目标与高等教育 |
| A distinctive feature of China‘s approach is the direct integration of national climate targets into higher education policy. In 2022, the Ministry of Education issued a „Work Program for Building a Strong Carbon Peak Carbon Neutral Higher Education Talent Training System“ (加强碳达峰碳中和高等教育人才培养体系建设工作方案), mandating universities to establish new faculties, courses, and vocational programs aligned with China’s dual carbon goals of reaching peak emissions by 2030 and carbon neutrality by 2060. As of 2022, 21 undergraduate programs were directly related to dual-carbon pledges, covering new energy, smart grids, carbon storage, hydrogen energy, and big data for environmental resources (Ministry of Education 2022). | 中国方法的一个显著特征是将国家气候目标直接整合到高等教育政策中。2022年，教育部发布了"加强碳达峰碳中和高等教育人才培养体系建设工作方案"，要求大学建立与中国双碳目标——2030年碳达峰、2060年碳中和——相匹配的新院系、课程和职业项目。截至2022年，已有21个本科专业直接与双碳目标相关，涵盖新能源、智能电网、碳储存、氢能和环境资源大数据（教育部 2022）。 |
| This policy represents a more direct intervention in curriculum design than anything attempted in the EU, where sustainability education remains largely voluntary and institution-led. The dual carbon education mandate reflects China‘s broader governance philosophy: when the state identifies a strategic priority, universities are expected to align their programs accordingly. Whether this top-down approach produces deeper environmental engagement than the EU’s bottom-up framework of competence development remains an open empirical question. | 这一政策代表了比欧盟所尝试的任何做法都更直接的课程设计干预——在欧盟，可持续发展教育在很大程度上仍是自愿的和机构主导的。双碳教育规定反映了中国更广泛的治理理念：当国家确定一项战略优先事项时，大学被期望相应调整其项目。这种自上而下的方法是否能比欧盟自下而上的能力发展框架产生更深层的环境参与，仍是一个开放的实证问题。 |
| Wang and colleagues (2023), in a study published in the Journal of Cleaner Production, developed a novel LEAP-LCA hybrid methodology for assessing the carbon footprint of a medium-sized Chinese university campus. They found that electricity consumption caused 77 percent of total campus carbon emissions and that proposed carbon reduction measures — photovoltaics, energy efficiency improvements, electrification — could reduce emissions by 97 percent by 2060, with electricity decarbonization alone contributing 64.7 percent of the reduction. These findings suggest that Chinese universities have significant potential for carbon reduction, but that realizing this potential requires infrastructure investment and institutional commitment that cannot be achieved through curriculum reform alone. | Wang等人（2023）在发表于Journal of Cleaner Production的研究中，开发了一种新型LEAP-LCA混合方法来评估一所中等规模中国大学校园的碳足迹。他们发现电力消费占校园总碳排放的77%，所提议的碳减排措施——光伏、能效改进、电气化——可在2060年前减少97%的排放，其中仅电力脱碳就贡献了64.7%的减排量。这些发现表明，中国大学在碳减排方面具有巨大潜力，但实现这一潜力需要仅凭课程改革无法达成的基础设施投资和机构承诺。 |
| 4.3 Green Campus Initiatives | 4.3 绿色校园倡议 |
| China‘s approach to green digital education embodies a distinctive tension. On one hand, the government is pursuing the world’s largest expansion of digital educational infrastructure — the National Smart Education Platform serving 293 million students, near-universal school broadband, mandatory AI education from September 2025. On the other hand, it is simultaneously promoting ecological civilization education and green campus initiatives. | 中国的绿色数字教育方法体现了一种独特的张力。一方面，政府正在追求世界上最大规模的数字教育基础设施扩张——服务2.93亿学生的国家智慧教育平台、近乎普及的校园宽带、自2025年9月起的强制人工智能教育。另一方面，它同时在推进生态文明教育和绿色校园倡议。 |
| Yuan and colleagues (2024), in a study published in the International Journal of Chinese Education, examine Beijing’s Green School Program through a study of 98 primary and secondary schools, documenting the program’s role as an ESD tool for SDG achievement. Zou and colleagues (2024), writing in the Journal of Cleaner Production, propose a four-dimensional framework — green education, green research, green campus, and green life — for the digitalization of green university initiatives, arguing that digital technologies can facilitate community engagement, support green innovation, reduce campus carbon footprints, and cultivate sustainability awareness. | Yuan等人（2024）在发表于International Journal of Chinese Education的研究中，通过对98所小学和中学的调查，考察了北京绿色学校计划，记录了该计划作为实现可持续发展目标的ESD工具的作用。Zou等人（2024）在Journal of Cleaner Production上提出了一个四维框架——绿色教育、绿色研究、绿色校园和绿色生活——用于绿色大学倡议的数字化，认为数字技术可以促进社区参与、支持绿色创新、减少校园碳足迹并培育可持续发展意识。 |
| These initiatives are real and valuable, but they do not directly address the environmental cost of the digital infrastructure itself. The tension between digital expansion and environmental sustainability is acknowledged in Chinese policy discourse but not yet resolved in practice. Over 200 Chinese universities have implemented Campus Energy Management Systems, reflecting a growing institutional awareness of energy consumption — but these systems typically do not include the energy consumed by cloud-based educational platforms, which is generated in data centres that may be located thousands of kilometres from the campus. | 这些倡议是真实而有价值的，但它们并未直接解决数字基础设施本身的环境成本。数字扩张与环境可持续性之间的张力在中国政策话语中得到了承认，但在实践中尚未得到解决。超过200所中国大学已实施了校园能源管理系统，反映出日益增长的机构能耗意识——但这些系统通常不包括基于云端的教育平台所消耗的能源，而这些能源是在可能距离校园数千公里的数据中心产生的。 |
| 5. The AI Energy Paradox | 5. 人工智能能源悖论 |
| The most acute tension in green digital education is what we term the „AI energy paradox.“ Artificial intelligence is simultaneously the most energy-intensive component of digital education and the technology most frequently invoked as a solution to environmental challenges. AI systems promise to optimize energy consumption, model climate change, personalize sustainability education, and identify patterns in environmental data that human analysis cannot detect. Yet the energy required to train and operate these systems is growing at a rate that threatens to overwhelm the efficiency gains they produce. | 绿色数字教育中最尖锐的张力是我们所称的"人工智能能源悖论"。人工智能同时是数字教育中最耗能的组成部分和最常被援引为环境挑战解决方案的技术。人工智能系统有望优化能源消耗、模拟气候变化、个性化可持续发展教育，以及识别人工分析无法发现的环境数据模式。然而，训练和运行这些系统所需的能源正以威胁到压倒其效率收益的速度增长。 |
| This paradox manifests in a form recognized in economics as a „rebound effect“ or Jevons Paradox: efficiency improvements lead to increased consumption rather than reduced resource use. A 2025 study in Frontiers in Energy Research systematically reviews 150 articles on the rebound effect in AI-driven sustainable development, finding that AI-driven efficiency reduces energy per unit of output but often leads to higher overall consumption, potentially negating environmental benefits. | 这一悖论以经济学中被称为"回弹效应"或杰文斯悖论的形式表现：效率改进导致消费增加而非资源使用减少。2025年发表于Frontiers in Energy Research的一项研究系统综述了150篇关于人工智能驱动可持续发展中回弹效应的文章，发现人工智能驱动的效率降低了单位产出能耗，但往往导致总体消费增加，可能抵消环境效益。 |
| For universities, the paradox is immediate. Deploying an AI-powered adaptive learning system may improve educational outcomes (as documented in the companion chapters on AI in language learning and the university of the future), but it also increases the university’s digital energy consumption. The European Commission’s sustainability frameworks and China‘s ecological civilization education both lack mechanisms for weighing these trade-offs: the environmental costs of educational technology are simply not part of the calculation. | 对于大学来说，这一悖论是直接的。部署人工智能自适应学习系统可能改善教育成果（如本论文集中关于语言学习中的人工智能和未来大学的配套章节所记录的），但也增加了大学的数字能源消费。欧盟委员会的可持续性框架和中国的生态文明教育都缺乏权衡这些取舍的机制：教育技术的环境成本根本不在计算之中。 |
| Selwyn (2021), in what remains the most direct academic treatment of this issue, proposes an „Ed-Tech Within Limits“ approach that requires fundamental shifts in thinking about educational technology. Rather than asking how technology can enhance education, Selwyn argues, we should ask what level of technology is compatible with environmental sustainability — and accept that the answer may involve less, rather than more, digital infrastructure. | Selwyn（2021）在迄今为止对这一问题最直接的学术论述中提出了"有限度的教育技术"方法，要求从根本上转变对教育技术的思维方式。Selwyn认为，我们不应该问技术如何增强教育，而应该问什么水平的技术与环境可持续性相容——并接受答案可能涉及更少而非更多的数字基础设施。 |
| 5.1 Green AI as Partial Response | 5.1 绿色人工智能作为部分应对 |
| The emerging field of „Green AI“ offers technical approaches to reducing the environmental cost of artificial intelligence, though it cannot eliminate the paradox entirely. Tabbakh and colleagues (2024), in a comprehensive framework published in Discover Sustainability, review techniques including model pruning, quantization, and knowledge distillation that can substantially reduce the energy consumption of AI inference — the ongoing operational cost of running trained models. They also review tools such as CodeCarbon and Carbontracker that enable researchers to measure and report the carbon footprint of their AI experiments. | 新兴的"绿色人工智能"领域提供了降低人工智能环境成本的技术方法，尽管它无法完全消除这一悖论。Tabbakh等人（2024）在发表于Discover Sustainability的综合框架中，综述了包括模型剪枝、量化和知识蒸馏在内的技术，这些技术可以大幅降低人工智能推理——即运行已训练模型的持续运营成本——的能源消耗。他们还综述了CodeCarbon和Carbontracker等工具，使研究人员能够测量和报告其人工智能实验的碳足迹。 |
| Paula and colleagues (2025), in a comparative analysis published in Scientific Reports, demonstrate that applying model compression techniques to transformer-based models can achieve a 32 percent reduction in energy consumption for models like BERT. Compressed large models can match or approach the efficiency of purpose-built small models, suggesting that educational AI applications need not rely on the most resource-intensive architectures. For universities deploying AI tutoring systems or automated assessment tools, these findings indicate that choosing efficient model architectures — or insisting that vendors demonstrate the energy efficiency of their products — could meaningfully reduce the environmental footprint of AI-powered education. | Paula等人（2025）在发表于Scientific Reports的比较分析中证明，将模型压缩技术应用于基于Transformer的模型可以实现BERT等模型32%的能源消耗降低。经压缩的大型模型可以达到或接近专门构建的小型模型的效率，这表明教育人工智能应用不必依赖最高能耗的架构。对于部署人工智能辅导系统或自动评估工具的大学来说，这些发现表明，选择高效的模型架构——或要求供应商证明其产品的能效——可以切实减少人工智能驱动教育的环境足迹。 |
| However, Green AI techniques address only the efficiency of individual systems, not the aggregate growth in AI deployment. If every AI application becomes 32 percent more efficient but the number of AI applications doubles, total energy consumption still increases — a textbook illustration of the Jevons Paradox. The technical solutions of Green AI are necessary but not sufficient; they must be combined with the institutional discipline of digital sobriety. | 然而，绿色人工智能技术只解决了单个系统的效率问题，而非人工智能部署的总体增长。如果每个人工智能应用变得高效32%，但人工智能应用的数量翻倍，总能耗仍然增加——这是杰文斯悖论的典型例证。绿色人工智能的技术方案是必要的但不充分的；它们必须与数字节制的机构自律相结合。 |
| 6. Comparative Analysis | 6. 比较分析 |
| The EU and Chinese approaches to green digital education reflect their broader governance philosophies and reveal complementary strengths and weaknesses that merit systematic comparison. | 欧盟和中国的绿色数字教育方法反映了它们更广泛的治理理念，揭示了值得系统比较的互补性优势和弱点。 |
| 6.1 Governance and Implementation | 6.1 治理与实施 |
| The EU’s framework-based approach — GreenComp, DigComp 2.2, the Green Deal, the GreenSCENT project, the EUA Green Deal Roadmap — provides conceptual clarity and citizen empowerment but struggles with implementation. Digital sobriety is recognized as a concept but not yet integrated into educational practice at scale. The environmental costs of EdTech platforms are acknowledged in academic literature but not in policy frameworks or procurement decisions. The EU approach is bottom-up: it empowers institutions and individuals to make sustainable choices but cannot compel them to do so. | 欧盟基于框架的方法——GreenComp、DigComp 2.2、绿色协议、GreenSCENT项目、EUA绿色协议路线图——提供了概念清晰度和公民赋权，但在实施方面挣扎。数字节制作为概念已被认可，但尚未在教育实践中大规模整合。教育技术平台的环境成本在学术文献中得到承认，但在政策框架或采购决策中尚未体现。欧盟的方法是自下而上的：它赋权机构和个人做出可持续选择，但无法强制他们这样做。 |
| China‘s state-led approach achieves rapid deployment of both digital infrastructure and ecological civilization education, but the two streams operate largely in parallel. The National Smart Education Platform and the Green School Program coexist without a framework for addressing their potential contradictions. The 2022 Ministry of Education dual carbon work program demonstrates the capacity for rapid, system-wide curriculum reform — 21 new undergraduate programs created in a single policy cycle — but it focuses on training students for the green economy, not on reducing the environmental footprint of the educational system itself. The emphasis on ecological civilization as a comprehensive worldview provides a philosophical resource that European frameworks lack — the language of civilizational transformation — but its top-down implementation limits bottom-up innovation and critical engagement. | 中国的国家主导方法实现了数字基础设施和生态文明教育的快速部署，但两条路线在很大程度上并行运作。国家智慧教育平台和绿色学校计划共存，但缺乏解决其潜在矛盾的框架。2022年教育部双碳工作方案展示了快速、全系统课程改革的能力——在单个政策周期内创建了21个新的本科专业——但其重点是为绿色经济培养学生，而非减少教育体系本身的环境足迹。将生态文明作为综合世界观的强调提供了欧洲框架所缺乏的哲学资源——文明转型的话语——但其自上而下的实施限制了自下而上的创新和批判性参与。 |
| 6.2 The Sustainability Paradox | 6.2 可持续性悖论 |
| A 2026 study in Humanities and Social Sciences Communications identifies a „sustainability paradox“ in digital education: environmentally, digital education can reduce travel and material impacts but increases energy demand; socially, it can widen access but deepen inequalities. This two-dimensional paradox is present in both European and Chinese contexts, though manifested differently in each. | 2026年发表于Humanities and Social Sciences Communications的一项研究确定了数字教育中的"可持续性悖论"：在环境方面，数字教育可以减少出行和材料影响，但增加能源需求；在社会方面，它可以扩大准入，但加深不平等。这一双维度悖论在欧洲和中国的语境中都存在，尽管在各自的表现形式不同。 |
| In the EU, the paradox manifests primarily as a tension between green aspirations and market realities. European universities increasingly adopt sustainability strategies, but their EdTech procurement decisions are driven by functionality and cost rather than environmental impact. The EUA survey found broad commitment to sustainability in principle, but specific mechanisms for reducing digital environmental footprints — energy-efficient procurement criteria, carbon budgets for cloud services, institutional policies on AI deployment — remain rare. | 在欧盟，这一悖论主要表现为绿色愿望与市场现实之间的张力。欧洲大学日益采纳可持续性战略，但其教育技术采购决策受功能性和成本而非环境影响驱动。EUA调查发现了原则层面对可持续性的广泛承诺，但减少数字环境足迹的具体机制——节能采购标准、云服务碳预算、人工智能部署机构政策——仍然罕见。 |
| In China, the paradox manifests as a tension between the state’s simultaneous commitments to digital expansion and ecological civilization. The ambition to build the world’s most digitally advanced education system is in direct tension with the ambition to achieve carbon neutrality by 2060. Wang and colleagues’ (2023) finding that electricity accounts for 77 percent of campus carbon emissions underscores the scale of this challenge: as digital infrastructure expands, so does the electricity demand that drives campus emissions. | 在中国，这一悖论表现为国家同时承诺数字扩张和生态文明之间的张力。建设世界上最先进的数字教育体系的雄心与到2060年实现碳中和的雄心直接冲突。Wang等人（2023）发现电力占校园碳排放77%的结论凸显了这一挑战的规模：随着数字基础设施的扩张，驱动校园排放的电力需求也随之增长。 |
| 6.3 Research Trajectories | 6.3 研究轨迹 |
| The research landscapes in both regions reflect these governance differences. Tian and colleagues’ (2024) bibliometric analysis of Chinese ESD research reveals a field increasingly shaped by domestic political frameworks rather than international sustainability discourse. European research, by contrast, remains more internationally connected but less politically integrated — producing sophisticated analyses that may not translate into policy change. Neither research tradition has yet produced a comprehensive framework for integrating digital sobriety with broader sustainability goals in higher education. | 两个地区的研究格局反映了这些治理差异。Tian等人（2024）对中国ESD研究的文献计量分析揭示了一个日益受国内政治框架而非国际可持续发展话语塑造的领域。欧洲研究则在国际联系上保持更强，但在政治整合方面较弱——产出精密的分析却可能无法转化为政策变革。两种研究传统都尚未产生一个将数字节制与高等教育更广泛可持续发展目标相整合的综合框架。 |
| 7. Recommendations | 7. 建议 |
| Based on our comparative analysis, we propose seven recommendations for universities seeking to integrate environmental sustainability into their digital education strategies. | 基于我们的比较分析，我们为寻求将环境可持续性整合到数字教育战略中的大学提出七项建议。 |
| First, include digital infrastructure in institutional carbon accounting. The environmental cost of cloud computing, AI services, and platform subscriptions should be calculated and reported alongside traditional energy consumption metrics. The THE Impact Rankings and similar assessment frameworks should develop specific indicators for digital infrastructure emissions. Urbano and colleagues’ (2025) finding that 1,963 institutions now participate in impact rankings demonstrates the institutional willingness to engage with sustainability metrics — but the metrics themselves must be expanded to include the digital dimension. | 第一，将数字基础设施纳入机构碳核算。云计算、人工智能服务和平台订阅的环境成本应与传统能源消耗指标一起计算和报告。THE影响力排名和类似评估框架应开发数字基础设施排放的具体指标。Urbano等人（2025）发现1,963所机构现已参与影响力排名的事实证明了机构参与可持续性指标的意愿——但指标本身必须扩展以涵盖数字维度。 |
| Second, adopt digital sobriety as a design principle for educational technology. Procurement decisions should include environmental impact assessments alongside functionality and cost. Unnecessary digital consumption — mandatory video-on policies during lectures, excessive cloud storage allocation, redundant platform subscriptions, and the routine deployment of AI tools for tasks that do not require them — should be identified and reduced. The Shift Project’s original recommendation to „buy the least powerful equipment possible and change devices as rarely as possible“ applies directly to educational technology procurement. | 第二，将数字节制作为教育技术的设计原则。采购决策应在功能性和成本之外纳入环境影响评估。不必要的数字消费——讲座期间的强制开摄像头政策、过度的云存储分配、冗余的平台订阅以及对不需要人工智能的任务常规部署人工智能工具——应予以识别和减少。The Shift Project最初"购买尽可能低功耗的设备并尽可能少地更换设备"的建议直接适用于教育技术采购。 |
| Third, integrate environmental awareness into digital literacy education. The environmental costs of digital activities should be part of the digital competence curriculum, not a separate sustainability module. Calis and colleagues’ (2025) finding that pre-service teachers have only moderate awareness of digital carbon footprints suggests that teacher training programs urgently need to incorporate this dimension. Students who learn about AI should also learn about AI’s energy and water consumption; students who use cloud-based learning platforms should understand the infrastructure that makes them possible. | 第三，将环境意识融入数字素养教育。数字活动的环境成本应成为数字能力课程的组成部分，而非独立的可持续发展模块。Calis等人（2025）发现职前教师对数字碳足迹的意识仅为中等水平，这表明教师培训项目急需纳入这一维度。学习人工智能的学生也应了解人工智能的能源和用水消耗；使用基于云端学习平台的学生应理解使其成为可能的基础设施。 |
| Fourth, develop institutional metrics for the AI energy paradox. Universities deploying AI in education should be required to demonstrate that the educational benefits justify the environmental costs — or at minimum, that the environmental costs have been calculated and minimized. Green AI techniques such as model compression (Paula et al. 2025) and efficient architectures (Tabbakh et al. 2024) should be criteria in AI procurement decisions, not afterthoughts. | 第四，为人工智能能源悖论开发机构指标。在教育中部署人工智能的大学应被要求证明教育效益可以证明环境成本的合理性——或至少证明环境成本已被计算并已尽量减低。绿色人工智能技术如模型压缩（Paula等 2025）和高效架构（Tabbakh等 2024）应成为人工智能采购决策的标准，而非事后考量。 |
| Fifth, address e-waste through institutional lifecycle management. The growing hardware intensity of digital education generates e-waste that is typically invisible in sustainability assessments. Valai Ganesh and colleagues’ (2025) demonstration that on-site recycling can achieve 90 percent material recovery rates suggests that universities could significantly reduce their e-waste footprint with relatively modest institutional investment. Extending device lifecycles through repair programs and choosing durable, upgradeable hardware would further reduce environmental impact. | 第五，通过机构生命周期管理应对电子废弃物。数字教育日益增长的硬件密集度产生了在可持续性评估中通常不可见的电子废弃物。Valai Ganesh等人（2025）证明现场回收可以实现90%的材料回收率，这表明大学可以通过相对适度的机构投资显著减少电子废弃物足迹。通过维修项目延长设备使用寿命以及选择耐用、可升级的硬件可进一步减少环境影响。 |
| Sixth, support research on sustainable educational technology. The academic community should invest in research on low-energy learning technologies, efficient AI architectures for educational applications, and pedagogical approaches that achieve equivalent outcomes with less digital infrastructure. Wang and colleagues’ (2023) LEAP-LCA methodology for campus carbon assessment could be adapted to include digital infrastructure emissions, providing universities with a comprehensive tool for environmental accounting. | 第六，支持可持续教育技术研究。学术界应投资于低能耗学习技术、教育应用的高效人工智能架构以及以更少数字基础设施达到同等成效的教学方法研究。Wang等人（2023）的LEAP-LCA校园碳评估方法可以被改编以纳入数字基础设施排放，为大学提供全面的环境核算工具。 |
| Seventh, create EU-China dialogue on green digital education. The EU’s conceptual frameworks (GreenComp, digital sobriety) and China’s implementation capacity (dual carbon mandates, rapid curriculum reform) are complementary strengths. A structured dialogue — potentially within the framework of the Jean Monnet Centre of Excellence — could accelerate the development of practical approaches to the environmental challenges that both systems face. China’s experience with mandatory curriculum reform for the dual carbon goals could inform European efforts to scale sustainability education, while the EU’s digital sobriety framework could help China address the environmental costs of its digital education expansion. | 第七，建立欧中绿色数字教育对话。欧盟的概念框架（GreenComp、数字节制）和中国的实施能力（双碳规定、快速课程改革）是互补的优势。结构化对话——可能在让·莫内卓越中心的框架内——可以加速制定应对两个体系共同面临的环境挑战的实际方法。中国在双碳目标方面的强制课程改革经验可以为欧洲扩大可持续发展教育的努力提供借鉴，而欧盟的数字节制框架可以帮助中国解决其数字教育扩张的环境成本。 |
| 8. Conclusion | 8. 结论 |
| The digital transformation of higher education is not environmentally neutral. Data centres consume 415 TWh of electricity annually and growing. AI training generates hundreds of metric tons of CO2 and evaporates hundreds of thousands of litres of freshwater. Digital content consumption accounts for 3–4 percent of per capita emissions. E-waste from educational technology is projected to grow by 50 percent within a decade. These facts are not arguments against digital education — the benefits documented in this anthology are real and significant. But they are arguments for environmental honesty: for acknowledging the costs alongside the benefits, and for designing educational technology systems that minimize environmental harm rather than ignoring it. | 高等教育的数字化转型在环境上并非中性。数据中心每年消耗415太瓦时电力且持续增长。人工智能训练产生数百公吨的二氧化碳并蒸发数十万升淡水。数字内容消费占人均排放的3—4%。教育技术的电子废弃物预计十年内增长50%。这些事实不是反对数字教育的论据——本论文集记录的效益是真实而重大的。但它们是关于环境诚实的论据：承认成本与收益并存，设计最小化环境危害而非忽视它的教育技术系统。 |
| The EU and China bring different resources to this challenge. The EU has developed sophisticated conceptual frameworks — GreenComp, DigComp 2.2, digital sobriety — that provide a language for discussing the environmental costs of digital education, and its Green Deal integration into education through projects like GreenSCENT represents a genuine, if still modest, step toward practice. China has demonstrated the capacity for rapid, system-wide curriculum reform through its dual carbon education mandate and has embedded ecological civilization in its constitutional and educational frameworks — providing a depth of political commitment that European voluntary approaches cannot match. | 欧盟和中国为这一挑战带来了不同的资源。欧盟开发了精密的概念框架——GreenComp、DigComp 2.2、数字节制——提供了讨论数字教育环境成本的话语，其通过GreenSCENT等项目将绿色协议融入教育代表了一个真实的、尽管仍然有限的实践步骤。中国展示了通过其双碳教育规定实现快速、全系统课程改革的能力，并将生态文明嵌入其宪法和教育框架——提供了欧洲自愿性方法无法比拟的政治承诺深度。 |
| Yet neither system has developed an adequate response to the AI energy paradox — the uncomfortable reality that the most powerful educational technologies are also the most environmentally costly. Green AI techniques offer partial mitigation, but the Jevons Paradox suggests that efficiency gains will be consumed by growing demand unless institutional discipline constrains deployment. The pre-service teachers surveyed by Calis and colleagues (2025) — tomorrow’s educators — have only moderate awareness of digital carbon footprints, suggesting that the problem will persist without deliberate curricular intervention. | 然而，两个体系都没有对人工智能能源悖论——最强大的教育技术也是环境成本最高的这一令人不适的现实——制定出充分的应对。绿色人工智能技术提供了部分缓解，但杰文斯悖论表明，除非机构自律约束部署，否则效率收益将被不断增长的需求所消耗。Calis等人（2025）调查的职前教师——明天的教育工作者——对数字碳足迹的意识仅为中等水平，表明如果没有刻意的课程干预，这一问题将持续存在。 |
| Developing an adequate response to these challenges is among the most important tasks facing higher education in the coming decade. It requires not less technology but smarter technology — and the institutional willingness to ask, before every digital deployment, whether the educational benefit justifies the environmental cost. The EU-China comparison suggests that the answer will require both conceptual sophistication and implementation capacity — the strengths that each system can contribute to a shared global challenge. | 应对这些挑战是高等教育在未来十年面临的最重要任务之一。它不需要更少的技术，而是更智慧的技术——以及在每次数字部署之前愿意追问教育收益是否证明环境成本合理的机构意愿。欧中比较表明，答案将需要概念的精密性和实施的能力——每个体系能够贡献于这一共同全球挑战的优势。 |
| Acknowledgments | 致谢 |
| This research was conducted within the framework of the Jean Monnet Centre of Excellence „EUSC-DEC“ (EU Grant 101126782, 2023–2026). The author thanks the members of Research Groups 1 and 5 for their contributions to the comparative analysis of sustainability and digital education policy. | 本研究在让·莫内卓越中心"欧盟研究中心：中欧数字化"（EUSC-DEC）框架内进行（欧盟资助 101126782，2023—2026年）。作者感谢第1和第5研究组成员对可持续发展与数字教育政策比较分析的贡献。 |
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| Index | |
| A | |
| academic integrity 7, 14, 15, 71, 173, 175 | |
| AI energy paradox 183, 184, 193, 197, 199 | |
| AI ethics 6, 37, 69, 75, 87, 144, 160, 166 | |
| AI in education 7, 10, 14, 18, 39, 48, 58, 59, 75, 78, 79, 98, 174, 182, 197 | |
| AI in higher education 18, 19, 20, 77, 165, 181, 182 | |
| AI labour market 115 | |
| AI literacy 6, 7, 9, 11, 17, 18, 37, 65, 69, 70, 76, 123, 124, 143, 147, 148, 149, 151, 152, 153, 157, 158, 159, 160, 161, 162, 176, 178, 180 | |
| AI-assisted language learning 59, 75, 78, 79, 84, 90, 170 | |
| alternative education 115, 125, 129 | |
| B | |
| Brussels Effect 7, 16, 17, 19, 20, 44, 56 | |
| C | |
| carbon footprint 184, 186, 189, 191, 192, 194, 197, 199, 200 | |
| ChatGPT 36, 58, 61, 64, 66, 67, 70, 71, 76, 77, 79, 80, 81, 99, 100, 106, 108, 112, 172, 173, 176 | |
| China 1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 38, 39, 42, 43, 44, 46, 48, 49, 50, 53, 54, 55, 56, 59, 61, 65, 70, 71, 74, 75, 76, 78, 79, 81, 104, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 127, 128, 129, 130, 133, 134,135, 136, 138, 139, 140, 141, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 165, 166, 167, 168, 169, 171, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 185, 190, 191, 192, 193, 195, 196, 198, 199, 200, 201, 202 | |
| China AI governance 6 | |
| China digital education 148 | |
| China education technology 134 | |
| comparative education 90, 116, 148 | |
| comparative study 20, 77, 78, 79, 182 | |
| competency-based education 114, 115, 180 | |
| complementarity thesis 79 | |
| cross-border data flows 39, 52, 55 | |
| D | |
| data centres 127, 184, 185, 186, 193 | |
| DeepL 58, 60, 61, 66, 70, 76, 77 | |
| DigComp 2.2 142, 147, 148, 149, 156, 158, 159, 162, 188, 195, 199, 201 | |
| digital competence 30, 147, 148, 149, 153, 154, 157, 159, 160, 171, 197 | |
| digital divide 128, 142, 148, 149, 153, 154, 158, 161 | |
| digital education 54, 78, 79, 115, 126, 135, 136, 137, 138, 147, 150, 153, 165, 183, 184, 185, 187, 188, 192, 193, 195, 196, 197, 198, 199 | |
| digital literacy 128, 140, 144, 147, 148, 150, 151, 153, 155, 156, 157, 158, 159, 160, 162, 163, 180, 197 | |
| digital natives 148 | |
| digital sobriety 183, 184, 185, 187, 188, 189, 194, 196, 197, 198, 199 | |
| E | |
| ecological civilization 183, 184, 185, 190, 191, 192, 193, 195, 196, 199 | |
| Edu-Metaverse 133, 134, 135, 136, 144, 146 | |
| EU AI Act 6, 8, 11, 14, 17, 18, 48, 53, 69, 151, 158, 177, 178 | |
| EU-China comparison 18, 39, 58, 165, 184, 199 | |
| European digital skills 148 | |
| European Union 2, 6, 7, 9, 19, 38, 39, 55, 76, 79, 96, 115, 116, 126, 130, 131, 134, 147, 149, 151, 161, 162, 181, 183, 185, 200, 201 | |
| European universities 16, 40, 48, 50, 69, 119, 120, 133, 134, 167, 177, 187, 189, 195 | |
| European-Chinese comparison 115, 118 | |
| F | |
| foreign language education 78 | |
| G | |
| GDPR 3, 20, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 156, 170, 178, 179, 182 | |
| Gen Z 148 | |
| generative AI policy 7, 165 | |
| green digital education 184, 192, 193, 194, 198 | |
| GreenComp 183, 184, 188, 190, 191, 195, 198, 199, 200 | |
| H | |
| higher education 6, 7, 11, 13, 14, 15, 17, 19, 20, 38, 39, 40, 41, 47, 49, 51, 53, 55, 75, 99, 112, 116, 117, 121, 122, 133, 134, 135, 137, 138, 145, 146, 151, 167, 171, 172, 173, 174, 177, 181, 182, 183, 184, 185, 189, 191, 196, 198, 199, 201 | |
| human-AI interaction 78, 88 | |
| hybrid learning 39, 165, 166, 171, 172, 181 | |
| I | |
| immersive learning 134 | |
| L | |
| language education 58, 76, 78, 79, 87, 92, 93, 95, 96, 162 | |
| learning analytics 38, 39, 40, 46, 47, 49, 52, 53, 54, 56, 167, 178, 179 | |
| lifelong learning 115, 120, 122, 129, 131 | |
| M | |
| machine translation 58, 59, 60, 63, 64, 65, 66, 67, 69, 73, 74, 75, 76 | |
| micro-credentials 114, 115, 116, 128, 131, 180 | |
| P | |
| PIPL 3, 20, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 156, 179, 182 | |
| post-editing 58, 59, 62, 64, 67, 68, 69, 70, 72, 74, 76 | |
| privacy 13, 15, 18, 19, 39, 41, 43, 44, 46, 47, 50, 52, 53, 55, 56, 124 | |
| proctoring 6, 7, 8, 9, 14, 15, 20, 38, 39, 41, 47, 48, 54 | |
| S | |
| sensory modalities 78 | |
| smart campus 165, 167, 168, 178, 180, 182 | |
| smart classrooms 134 | |
| smart education platform 134 | |
| student attitudes 78 | |
| student data protection 39 | |
| sustainability 27, 30, 157, 166, 183, 184, 185, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202 | |
| T | |
| translation literacy 58, 65, 74 | |
| U | |
| university transformation 165, 166, 181 | |
| V | |
| virtual reality 22, 133, 134, 145, 146, 166, 184 | |
| VR effectiveness 134 | |
| W | |
| workforce transformation 115 | |
| X | |
| XR 133, 134, 136, 137, 145 |