Rethinking Higher Education/Chapter 11/en-zh

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Green Digital Education: Sustainability, Digital Sobriety, and Environmental Awareness in EU and Chinese Universities	(zu übersetzen)
Martin Woesler	(zu übersetzen)
'Abstract'	(zu übersetzen)
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.	(zu übersetzen)
Keywords: green digital education, sustainability, digital sobriety, ecological civilization, carbon footprint, data centres, AI energy paradox, GreenComp, higher education, EU-China comparison	(zu übersetzen)
'1. Introduction'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'2. The Carbon Footprint of Educational Technology'	(zu übersetzen)
'2.1 Data Centres and AI Training'	(zu übersetzen)
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.	(zu übersetzen)
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.“	(zu übersetzen)
'2.2 Digital Content Consumption'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'2.3 E-Waste and the Hardware Lifecycle'	(zu übersetzen)
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.	(zu übersetzen)
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.	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。
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.	(zu übersetzen)
'3. Digital Sobriety as Educational Goal'	(zu übersetzen)
'3.1 Origins and Definition'	(zu übersetzen)
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).	(zu übersetzen)
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.	(zu übersetzen)
'3.2 The EU Framework: DigComp and GreenComp'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'4. China‘s Approach: Ecological Civilization Education'	(zu übersetzen)
'4.1 Ecological Civilization as Educational Framework'	(zu übersetzen)
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.	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
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.	(zu übersetzen)
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.	(zu übersetzen)
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).	(zu übersetzen)
'4.2 China‘s Dual Carbon Goals and Higher Education'	(zu übersetzen)
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).	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。
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.	(zu übersetzen)
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.	(zu übersetzen)
'4.3 Green Campus Initiatives'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'5. The AI Energy Paradox'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'5.1 Green AI as Partial Response'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'6. Comparative Analysis'	(zu übersetzen)
The EU and Chinese approaches to green digital education reflect their broader governance philosophies and reveal complementary strengths and weaknesses that merit systematic comparison.	(zu übersetzen)
'6.1 Governance and Implementation'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'6.2 The Sustainability Paradox'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'6.3 Research Trajectories'	(zu übersetzen)
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.	(zu übersetzen)
'7. Recommendations'	(zu übersetzen)
Based on our comparative analysis, we propose seven recommendations for universities seeking to integrate environmental sustainability into their digital education strategies.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'8. Conclusion'	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
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.	(zu übersetzen)
'Acknowledgments'	(zu übersetzen)
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.	(zu übersetzen)
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'Index'	(zu übersetzen)
A	(zu übersetzen)
academic integrity 7, 14, 15, 71, 173, 175	(zu übersetzen)
AI energy paradox 183, 184, 193, 197, 199	(zu übersetzen)
AI ethics 6, 37, 69, 75, 87, 144, 160, 166	(zu übersetzen)
AI in education 7, 10, 14, 18, 39, 48, 58, 59, 75, 78, 79, 98, 174, 182, 197	(zu übersetzen)
AI in higher education 18, 19, 20, 77, 165, 181, 182	(zu übersetzen)
AI labour market 115	(zu übersetzen)
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	(zu übersetzen)
AI-assisted language learning 59, 75, 78, 79, 84, 90, 170	(zu übersetzen)
alternative education 115, 125, 129	(zu übersetzen)
B	(zu übersetzen)
Brussels Effect 7, 16, 17, 19, 20, 44, 56	(zu übersetzen)
C	(zu übersetzen)
carbon footprint 184, 186, 189, 191, 192, 194, 197, 199, 200	(zu übersetzen)
ChatGPT 36, 58, 61, 64, 66, 67, 70, 71, 76, 77, 79, 80, 81, 99, 100, 106, 108, 112, 172, 173, 176	(zu übersetzen)
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	(zu übersetzen)
China AI governance 6	(zu übersetzen)
China digital education 148	(zu übersetzen)
China education technology 134	(zu übersetzen)
comparative education 90, 116, 148	(zu übersetzen)
comparative study 20, 77, 78, 79, 182	(zu übersetzen)
competency-based education 114, 115, 180	(zu übersetzen)
complementarity thesis 79	(zu übersetzen)
cross-border data flows 39, 52, 55	(zu übersetzen)
D	(zu übersetzen)
data centres 127, 184, 185, 186, 193	(zu übersetzen)
DeepL 58, 60, 61, 66, 70, 76, 77	(zu übersetzen)
DigComp 2.2 142, 147, 148, 149, 156, 158, 159, 162, 188, 195, 199, 201	(zu übersetzen)
digital competence 30, 147, 148, 149, 153, 154, 157, 159, 160, 171, 197	(zu übersetzen)
digital divide 128, 142, 148, 149, 153, 154, 158, 161	(zu übersetzen)
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	(zu übersetzen)
digital literacy 128, 140, 144, 147, 148, 150, 151, 153, 155, 156, 157, 158, 159, 160, 162, 163, 180, 197	(zu übersetzen)
digital natives 148	(zu übersetzen)
digital sobriety 183, 184, 185, 187, 188, 189, 194, 196, 197, 198, 199	(zu übersetzen)
E	(zu übersetzen)
ecological civilization 183, 184, 185, 190, 191, 192, 193, 195, 196, 199	(zu übersetzen)
Edu-Metaverse 133, 134, 135, 136, 144, 146	(zu übersetzen)
EU AI Act 6, 8, 11, 14, 17, 18, 48, 53, 69, 151, 158, 177, 178	(zu übersetzen)
EU-China comparison 18, 39, 58, 165, 184, 199	(zu übersetzen)
European digital skills 148	(zu übersetzen)
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	(zu übersetzen)
European universities 16, 40, 48, 50, 69, 119, 120, 133, 134, 167, 177, 187, 189, 195	(zu übersetzen)
European-Chinese comparison 115, 118	(zu übersetzen)
F	(zu übersetzen)
foreign language education 78	(zu übersetzen)
G	(zu übersetzen)
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	(zu übersetzen)
Gen Z 148	(zu übersetzen)
generative AI policy 7, 165	(zu übersetzen)
green digital education 184, 192, 193, 194, 198	(zu übersetzen)
GreenComp 183, 184, 188, 190, 191, 195, 198, 199, 200	(zu übersetzen)
H	(zu übersetzen)
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	(zu übersetzen)
human-AI interaction 78, 88	(zu übersetzen)
hybrid learning 39, 165, 166, 171, 172, 181	(zu übersetzen)
I	(zu übersetzen)
immersive learning 134	(zu übersetzen)
L	(zu übersetzen)
language education 58, 76, 78, 79, 87, 92, 93, 95, 96, 162	(zu übersetzen)
learning analytics 38, 39, 40, 46, 47, 49, 52, 53, 54, 56, 167, 178, 179	(zu übersetzen)
lifelong learning 115, 120, 122, 129, 131	(zu übersetzen)
M	(zu übersetzen)
machine translation 58, 59, 60, 63, 64, 65, 66, 67, 69, 73, 74, 75, 76	(zu übersetzen)
micro-credentials 114, 115, 116, 128, 131, 180	(zu übersetzen)
P	(zu übersetzen)
PIPL 3, 20, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 156, 179, 182	(zu übersetzen)
post-editing 58, 59, 62, 64, 67, 68, 69, 70, 72, 74, 76	(zu übersetzen)
privacy 13, 15, 18, 19, 39, 41, 43, 44, 46, 47, 50, 52, 53, 55, 56, 124	(zu übersetzen)
proctoring 6, 7, 8, 9, 14, 15, 20, 38, 39, 41, 47, 48, 54	(zu übersetzen)
S	(zu übersetzen)
sensory modalities 78	(zu übersetzen)
smart campus 165, 167, 168, 178, 180, 182	(zu übersetzen)
smart classrooms 134	(zu übersetzen)
smart education platform 134	(zu übersetzen)
student attitudes 78	(zu übersetzen)
student data protection 39	(zu übersetzen)
sustainability 27, 30, 157, 166, 183, 184, 185, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202	(zu übersetzen)
T	(zu übersetzen)
translation literacy 58, 65, 74	(zu übersetzen)
U	(zu übersetzen)
university transformation 165, 166, 181	(zu übersetzen)
V	(zu übersetzen)
virtual reality 22, 133, 134, 145, 146, 166, 184	(zu übersetzen)
VR effectiveness 134	(zu übersetzen)
W	(zu übersetzen)
workforce transformation 115	(zu übersetzen)
X	(zu übersetzen)
XR 133, 134, 136, 137, 145	(zu übersetzen)
	(zu übersetzen)