The survey found that 61% of U.S. adults somewhat or strongly oppose the construction of new data centers in their communities. That figure is 12 percentage points higher than the 49% recorded in an earlier APPC survey conducted in February and March. Support fell during the same period, from 21% to 14%. The change comes as communities across the country debate the environmental, economic, and social consequences of rapidly expanding computing infrastructure used to train and operate advanced AI systems.

Data centers are specialized facilities filled with servers, networking equipment, cooling systems, backup power supplies, and security infrastructure. AI applications can require enormous amounts of computational power because they process vast datasets and perform calculations across billions of model parameters. Training a large AI model may require thousands of processors operating simultaneously, while everyday use also depends on data centers to generate responses, analyze images, translate languages, and carry out other tasks. These facilities can consume substantial amounts of electricity and water, making their construction a highly visible local issue even when the software they support is used remotely.

Opposition to local data centers crosses political boundaries. Majorities of Democrats, Republicans, and independents said they opposed new facilities in their areas, although the proportions differed: 69% of Democrats, 54% of Republicans, and 53% of independents expressed opposition. Resistance was strongest among adults younger than 30, reaching 70%, and declined to 57% among people 65 and older. The pattern is notable because younger adults are often assumed to be more comfortable with emerging technologies and are more likely to use AI-powered services in education, work, entertainment, and communication.

Despite the shift in views about local infrastructure, broader opinions about AI have remained stable. Thirty-nine percent of respondents said they expected AI’s impact on the United States over the next decade to be somewhat or very negative, compared with 18% who expected a positive impact. The result was statistically unchanged from the spring survey. In addition, 68% said the federal government has done “too little” to regulate AI, a view shared by majorities of Democrats, Republicans, and independents. The results suggest that concerns about AI regulation have become persistent rather than being driven solely by short-term news events.

Respondents distinguished sharply between areas where AI might produce public benefits and areas where it could create risks. Among 13 sectors examined, medical research and scientific discovery was the only category receiving a net-positive assessment, with anticipated benefits exceeding expected harms by 41 percentage points. AI systems are increasingly being used to identify patterns in biological data, predict molecular structures, assist with medical imaging, and help researchers screen potential treatments. These applications may accelerate work that would otherwise require years of laboratory experiments, although they still depend on human validation and carefully controlled clinical studies.

The most negative assessments involved personal privacy and data security, which registered a net score of minus 63 points. Children’s online safety followed at minus 50 points, while employment and jobs reached minus 46 points. These concerns reflect the technical and social consequences of deploying AI at scale. Models may be trained on or exposed to sensitive information, automated systems can make decisions that are difficult to explain, and workplace applications may alter or eliminate certain tasks. The survey does not measure whether these risks will materialize in specific industries, but it indicates that many Americans view them as more immediate than AI’s possible benefits outside medicine.

Personal experience with AI was associated with more favorable opinions about its national impact. Among respondents who said they had never used AI during the previous month, 54% expected its effect on the country to be negative. That proportion fell to 40% among light users and to 29% among those who used AI many times or almost every day. However, frequent use did not make people more supportive of data centers. Opposition to building new facilities near respondents’ homes remained essentially flat across different levels of AI use. This distinction suggests that familiarity may influence perceptions of digital tools, while concerns about electricity demand, water consumption, noise, traffic, land use, and local utility costs may remain regardless of personal experience with AI software.

“The people who use AI the most are the most optimistic about what it will do for the country, and that has been consistent across our surveys,” said Matt Levendusky, a University of Pennsylvania political science professor and the Stephen and Mary Baran Chair in the Institutions of Democracy at APPC. He said that optimism had clear limits, particularly when questions involved privacy, employment, or the construction of a data center nearby. The survey was conducted by SSRS for APPC’s Institutions of Democracy division among 1,320 U.S. adult citizens from June 16 through July 19, 2026. Respondents were surveyed primarily online, with a small telephone sample, and weighted to match population benchmarks. The margin of error for the full sample was plus or minus 3.5 percentage points, with larger margins for subgroups. Together, the findings indicate that the next phase of the AI debate may be shaped less by abstract promises about intelligent software than by negotiations over the highly physical systems that make it possible.

Subject of Research: People

Article Title: Opposition to Local Data Centers Rises Sharply as Views on AI Hold Steady

Web References: Annenberg Public Policy Center news release; Reuters; The New York Times; Associated Press

References: Annenberg Public Policy Center Institutions of Democracy national survey, June 16–July 19, 2026; APPC AI topline survey data; APPC survey methodology

Image Credits: Annenberg Public Policy Center

Keywords: Artificial intelligence, data centers, public opinion, AI regulation, medical research, privacy, data security, employment, social surveys, political science

Data centers, the backbone of our increasingly digital world, operate hundreds of thousands of servers housed within climate-controlled environments. This colossal energy consumption inevitably produces vast quantities of waste heat, traditionally regarded as an ancillary issue but now recognized as a critical urban thermal hazard. According to new experimental measurements conducted by researchers at Arizona State University (ASU), this waste heat elevates air temperatures in downstream neighborhoods by up to 4 degrees Fahrenheit, intensifying the urban heat island effect.

The ASU team, led by Professor David Sailor, embarked on an innovative field study using high-precision, rapid-response temperature sensors mounted on vehicles. These mobile sensors traversed the Phoenix metropolitan area around four major data centers, capturing real-time temperature data both upwind and downwind. This empirical approach marked a significant departure from prior studies that relied predominantly on satellite remote sensing, providing ground-truth evidence of how data center emissions translate into tangible atmospheric changes.

Their findings revealed that air discharged by data centers, primarily heated by air-cooled condenser systems, can reach temperatures 14 to 25 degrees Fahrenheit above the ambient air at the facility’s perimeter. This heated air moves horizontally as a thermal plume, dispersing heat over several city blocks. Specifically, measurable temperature increases of 1.3 to 1.6 degrees Fahrenheit were typical immediately downwind, with occasional spikes reaching 4 degrees Fahrenheit warmer than areas upwind and unaffected by data center emissions. Notably, the heat effect extended approximately one-third of a mile from the data center boundary.

The implications of these results extend beyond mere thermal discomfort. Even marginal increases in air temperature can exacerbate energy demand, as residents and businesses rely more heavily on air conditioning to maintain indoor comfort levels. This feedback loop not only drives electricity consumption higher but also pushes additional waste heat back into the urban atmosphere, creating a compounding cycle of heat amplification within cities already vulnerable to extreme temperatures. In Phoenix—a city notorious for its blistering summer heat—this phenomenon could deepen public health risks, strain power grids, and elevate heat-related morbidity.

The scale of the issue is underscored by the vast capacity of modern data centers. The waste heat released by a single large facility can exceed the thermal output generated by upwards of 40,000 residential households. As data infrastructure continues to expand in response to escalating digital demands, the cumulative impact of these centers on regional climate may become a defining environmental challenge in the coming decade. Projections suggest U.S. data center capacity may more than double by the year 2030, potentially magnifying this heat hazard if left unmitigated.

Recognizing the urgency, the ASU researchers aim to develop advanced atmospheric models incorporating their empirical data, enabling the simulation and evaluation of mitigation strategies. Future research will broaden temporal and meteorological conditions to better understand variability and optimize responses. Potential interventions include design modifications to cooling systems that maximize thermal efficiency, the integration of green infrastructure to absorb and dissipate waste heat, and urban planning policies that enforce siting guidelines minimizing community exposure.

“The challenge is not to impede data center growth, but to innovate solutions that balance technological progress with environmental stewardship,” Sailor explains. He emphasizes collaboration with data center operators, policymakers, and urban planners to foster resilient, sustainable infrastructure that prevents localized temperature spikes without compromising operational integrity.

This study, published in the Journal of Engineering for Sustainable Buildings and Cities, marks the first time neighborhood-scale, in-situ temperature impacts of data centers have been documented and analyzed. It bridges a critical knowledge gap, revealing a previously underappreciated urban heat source and spurring a call to action for the technology and environmental sectors alike. The research was supported by the U.S. Department of Energy’s Office of Science, underscoring the strategic importance of tackling heat pollution in cities adapting to the digital age.

By integrating experimental field data with atmospheric modeling, these findings pave the way for holistic urban climate solutions. Data centers, often situated in areas already vulnerable to heat stress, can no longer be considered benign in their environmental effects. Addressing their thermal footprint will demand interdisciplinary innovation, combining engineering, environmental science, urban design, and public policy.

The results also stimulate a broader discourse on energy sustainability and climate resilience. As cities worldwide grapple with rising temperatures linked to anthropogenic climate change, the additive role of infrastructure-based heat emissions must be accounted for in climate models and adaptation strategies. This emerging awareness has the potential to inspire new standards for energy-intensive facilities, turning them from urban heat culprits into exemplars of green building and operational excellence.

Ultimately, the ASU study illuminates a crucial dimension of urban environmental dynamics, connecting the dots between digital infrastructure, energy consumption, and the lived experiences of city residents. It prompts a reevaluation of how we build and manage our information economy in harmony with the planet’s climatic systems—a vital frontier for science and society.

Subject of Research: Not applicable

Article Title: Data center waste heat as an emerging urban thermal hazard: First field measurements of neighborhood-scale air temperature impacts

News Publication Date: 12-May-2026

Web References:

• DOI: 10.1115/1.4071922

Image Credits: Wikimedia Commons

Keywords: Environmental sciences, Heat, Energy transfer, Heat transmission, Information infrastructure, Environmental issues, Pollution control, Climate change mitigation, Climate change

The notion of offloading data processing to outer space may sound like a sci-fi fantasy, but advancements in technology and resource sustainability have made this idea feasible. By leveraging the abundant solar energy available in space and utilizing innovative cooling solutions, we can devise a model for data centers that not only meets our growing computational needs but does so in an environmentally responsible manner. The sunlight in space is extraordinarily intense and can be harnessed using high-efficiency solar cells, making energy generation in orbit more effective than conventional ground-based methods. Furthermore, the vast and cold expanse of space provides an unparalleled medium for dissipating heat generated through data processing, even allowing for spontaneous absorption of waste heat.

To fully realize the potential of space-based computing, we propose the establishment of orbital edge data centers designed specifically for carbon-neutral data processing at the source. By positioning data processing capabilities in proximity to satellite-borne sensors and AI accelerators, we minimize latency and maximize the efficiency of data handling. This proximity allows for real-time data processing, significantly reducing the bandwidth requirements for data transmission back to Earth. With intelligent systems capable of managing and processing data locally in orbit, we can enhance our responsiveness to dynamic conditions in space, such as variations in satellite functionality or unexpected environmental changes.

Moreover, we envision the creation of an orbital cloud data center paradigm characterized by a constellation of computational satellites. These satellites would be equipped with robust servers capable of handling significant computational loads and maintaining broadband connectivity for both in-space operations and terrestrial outsourcing. The result would be a seamless network of interconnected data centers in orbit, capable of efficiently distributing computational tasks and resources where they are most needed, whether in support of space missions or ground-based applications.

An essential aspect of our framework is a systematic approach to evaluate the lifecycle carbon usage effectiveness of these orbital cloud data centers. It becomes imperative to establish metrics that quantify the overall environmental impact of establishing and maintaining such facilities in space. This involves assessing the carbon footprint produced during the manufacturing, launching, and operational phases of these satellites relative to the carbon savings gained through their efficiencies. By measuring sustainability from inception to operation, we can ensure that these pioneering technologies genuinely contribute to global carbon neutrality.

The transition to space-based data centers also posits significant implications for the advancement of artificial intelligence and machine learning. Currently, AI technologies typically demand vast amounts of processing power, often leading to increased energy consumption and subsequent carbon footprints. However, with orbital data centers utilizing solar energy and deep space cooling, we can create a sustainable model for AI processes. This shift could allow for more sophisticated algorithms, capable of addressing complex problems in real time, without exacerbating environmental concerns.

As we explore these advances, we must also consider potential obstacles. The challenges associated with launching equipment into space, including cost, logistical complexity, and regulatory concerns, cannot be understated. Development timelines for space hardware can be unpredictable, and each mission entails substantial risk. Building a reliable supply chain for the necessary technology tailored specifically for space applications will thus be crucial in ensuring the feasibility of carbon-neutral data centers.

Another critical consideration is the role of international collaboration in facilitating the success of space-based data centers. As multiple nations and private entities begin to pursue satellite deployments and emerging technologies in space, fostering cooperative efforts will be vital for pooling resources, expertise, and establishing common guidelines for efficient and sustainable operations. This collaborative approach can extend beyond technological sharing to include policy development aimed at protecting space resources and minimizing conflict over orbital zones.

Moreover, a strong emphasis must be placed on public perception and acceptance of space-based data centers. Given the relatively nascent stage of this concept, public understanding and support will be essential for garnering the necessary funding and fostering a favorable political climate. Initiatives aimed at educating the public about the benefits of sustainable data processing in space can enhance acceptance, and dispelling myths or misconceptions will be important to creating a shared vision of a sustainable future in space.

In conclusion, the push for carbon-neutral data centers in space represents a unique intersection of technological advancement and environmental responsibility. By marrying the capabilities of orbital computing with sustainability, we stand before a transformative opportunity to address the dual challenges posed by our expanding data needs and the imperative to combat climate change. As research progresses and frameworks solidify, it becomes imperative that we collaboratively embrace this paradigm shift, pioneer the technological innovations required, and catalyze a revolution in how we think about data processing—not merely in the context of Earth, but across the cosmos.

The road ahead may be fraught with challenges, but the prospect of space-based, carbon-neutral data centers offers a compelling vision for a sustainable future. With abundant solar energy and deep space’s natural cooling properties, the potential benefits are manifold. In a world increasingly aware of its carbon impact, transitioning our data centers to the final frontier in the name of sustainability could transform not only our approach to technology but our relationship with the environment itself.

Subject of Research: The development of carbon-neutral data centres in space.

Article Title: The development of carbon-neutral data centres in space.

Article References:

Aili, A., Choi, J., Ong, Y.S. et al. The development of carbon-neutral data centres in space.
Nat Electron (2025). https://doi.org/10.1038/s41928-025-01476-1

Image Credits: AI Generated

DOI:

Keywords: Carbon-neutral data centers, orbital computing, artificial intelligence, sustainability, solar energy.

The tech sector’s environmental impact is increasingly significant, driven by the vast energy demands of burgeoning AI applications and growing digital infrastructure. Data centers, the backbone of AI development, experienced a striking 12% per annum increase in electricity consumption between 2017 and 2023, a rate quadruple the pace of global electricity growth over the same period. This disproportionate rise underlines how fast-evolving digital innovations intensify power requirements, creating a critical tension between technological advancement and climate responsibility.

The report highlights that four major AI-centric companies alone have seen their operational carbon emissions surge by an average of 150% since 2020. These emissions, comprising Scope 1 (direct emissions from owned or controlled sources) and Scope 2 (indirect emissions from purchased energy), underscore the acute need for transparency and proactive management of energy consumption within AI operations. The aggregate greenhouse gas (GHG) emissions reported by 166 digital firms account for roughly 0.8% of all global energy-related emissions, a non-negligible contribution demanding immediate industry attention.

Electricity consumption figures further emphasize the sector’s role in global energy use. Among the 164 digital companies disclosing electricity data, an estimated 581 terawatt-hours (TWh) were consumed in 2023 alone, representing 2.1% of worldwide electricity demand. Significantly, just ten companies are responsible for half of this consumption, indicating a concentrated footprint within a handful of industry giants. This concentration presents both challenges and opportunities for targeted decarbonization strategies in the digital domain.

Despite these growing impacts, the report acknowledges meaningful strides toward sustainability within the tech landscape. Transparency and accountability around climate commitments have notably strengthened. Eight companies surpassed 90% in the report’s climate commitment index—a marked improvement compared to only three achieving this threshold the previous year. These advances signify a growing recognition among leading firms of the necessity to rigorously measure and report environmental performance.

For the first time, the research features data assessing companies’ progress in meeting their climate goals and advancing net-zero pledges. Nearly half of the surveyed organizations have declared ambitions to achieve net-zero emissions, with 41 targeting the year 2050 and 51 committing to more accelerated timelines. This momentum is buttressed by increased adoption of robust disclosure practices and independent verification, elements essential to credible climate action in highly complex digital operations.

Renewable energy integration emerges as a cornerstone of this progress. In 2023, 23 companies reported operating entirely on renewable energy sources, up from 16 in 2022. This significant increase illustrates the sector’s pivot towards cleaner electricity procurement, although it remains only a fraction of the overall industry. Enhanced renewable adoption is critical to curbing Scope 2 emissions, directly tied to electricity consumption by data centers and other digital assets.

Complementing this shift, the proliferation of dedicated climate reports—released standalone rather than embedded within broader sustainability disclosures—reflects an industry-wide trend toward heightened transparency and rigorous environmental accountability. With 49 companies issuing such reports, stakeholders now have improved access to detailed insights into emission profiles, reduction strategies, and investment in cleaner technologies.

An emerging focus on Scope 3 emissions adds further depth to companies’ climate efforts. These indirect emissions, spanning the entire value chain including supply operations and product lifecycle usage, have historically posed measurement challenges. Encouragingly, the number of firms setting targets related to Scope 3 emissions rose sharply from 73 to 110, suggesting an expanding awareness of the full environmental implications of digital products and services.

The report calls for bold, concerted action to arrest—and ultimately reverse—the tech sector’s rising emissions trajectory. Recommendations emphasize enhancing the rigor of data verification processes, elevating target ambition, and disclosing the environmental footprint of AI operations in full. Such transparency is pivotal for enabling stakeholders to evaluate progress and hold companies accountable.

Furthermore, the report advocates for multi-stakeholder collaboration, proposing alliances among technology firms, energy providers, policymakers, and environmental advocates to accelerate digital decarbonization. Given the sector’s outsized role in shaping future digital economies, fostering collaborative ecosystems will be indispensable to achieving scalable, systemic climate solutions.

Accelerating renewable energy adoption remains a central tenet of sustainable tech growth. The report stresses continued investment in clean power procurement and innovative energy efficiency technologies within data centers and network infrastructures. These efforts are critical in offsetting the inherently high energy intensity of AI systems and expanding digital services.

ITU’s Telecommunication Development Bureau, alongside regulators and domain experts, is actively advancing methodologies to track and improve GHG emissions data quality within the digital sector. Their work supports national and international mechanisms for measurement, reporting, and verification aligned with global climate goals, underpinning data-driven policy and corporate action.

As the international community approaches the COP30 climate summit, ITU’s Green Digital Action initiative seeks to ensure that digital technologies’ environmental effects are comprehensively integrated into updated climate pledges and adaptation frameworks. This includes embedding digital sustainability considerations into broader global climate dialogues to align technology development with planetary boundaries.

The Greening Digital Companies 2025 report serves as a vital tool to illuminate the complex and evolving relationship between the tech sector’s rapid growth and environmental sustainability. While emissions continue their upward climb, the industry demonstrates improved disclosure and emerging pathways to mitigate its environmental impact. Maintaining and accelerating this momentum will be essential if digital innovation is to become a driver of positive climate action rather than a contributor to global emissions.

Subject of Research: Not applicable
Article Title: Tech Industry’s Carbon Footprint Soars Amid AI Boom, But Green Progress Emerges
News Publication Date: 5 June 2025
Web References:

• https://www.itu.int/en/ITU-D/Environment/Pages/Publications/GDC-25.aspx
• https://www.itu.int/en/ITU-D/Environment/Pages/Events/2025/GDC-report-2025-launch.aspx
• https://www.itu.int/initiatives/green-digital-action/
  Image Credits: International Telecommunication Union (ITU)
  Keywords: Technology, Artificial intelligence, Greenhouse gases, Sustainability, Environmental issues, Environmental impact assessments, Climate change, Corporations

The urgency of sustainable data centre development stems from their outsized carbon footprint. Estimates suggest that data centres account for roughly 1% of global electricity use, a proportion poised to rise sharply as 5G, IoT, and edge computing proliferate. Traditional data centres rely heavily on fossil fuel-based energy grids to maintain round-the-clock operation and ensure the ultra-low latency users demand. This dependency not only inflates operational costs but also ties digital progress intrinsically to emissions, threatening urban environmental targets.

Researchers led by Liu, F.H.M., Lai, K.P.Y., and Seah, B. examine this intersection of technology, urban infrastructure, and ecology with remarkable sophistication. Their study explores how data centres can pivot toward greener operational models, decreasing carbon intensity without sacrificing performance. Embedded within a broader vision of urban sustainability, the research advocates for integrating data centre planning and energy policy, reframing these digital behemoths as catalysts for eco-innovation rather than climate liabilities.

Central to their argument is a comprehensive analysis of the energy consumption profile of data centres in urban areas. The study maps not only direct electricity use but also indirect emissions, such as those from cooling systems traditionally reliant on hydrofluorocarbon refrigerants, which have a high global warming potential. By conducting this granular examination, the authors highlight key leverage points where technological and policy interventions can yield meaningful impact. For example, transitioning to renewable energy sources and innovating in cooling technology emerge as prime avenues for decarbonisation.

One of the study’s technical breakthroughs is the detailed assessment of modular cooling solutions. Unlike conventional chilled water systems, these scalable modules leverage ambient conditions, liquid cooling, and intelligent airflow management, greatly improving energy efficiency. The research quantifies how such systems can reduce cooling energy consumption by up to 40%, a transformative shift given that cooling can constitute nearly half of a data centre’s total energy demand. By coupling advanced cooling with AI-driven energy management, operators can dynamically optimize performance, reacting instantly to fluctuations in workload and climatic variables.

Renewable energy integration stands as another cornerstone of the proposed sustainability framework. The study emphasizes synergistic co-location of data centres with renewable energy generation—solar farms, wind turbines, and emerging technologies like green hydrogen. Through smart grid technologies and energy storage solutions, data centres can mitigate intermittency challenges commonly associated with renewables. This not only reduces reliance on fossil fuels but also stabilizes urban electrical grids challenged by variable demand and supply, promoting resilience and energy equity.

In discussing urban planning, the researchers reveal how data centres often become islands of energy consumption divorced from the surrounding city fabric. By embedding these facilities within multi-use urban developments, the digital infrastructure can be harnessed for district energy sharing, waste heat recovery, and community microgrids. Such integration promotes circular resource flows, where excess thermal energy from servers warms nearby buildings or powers local greenhouses, closing feedback loops and generating new economic opportunities.

Policy considerations permeate the study’s recommendations, highlighting the necessity of cross-sector collaboration. Effective decarbonisation requires municipal governments to set ambitious carbon targets specifically for data infrastructure while incentivizing private-sector innovation through subsidies and carbon pricing mechanisms. Transparent reporting standards and certifications for “green data centres” can drive competitive advantage, nudging the industry toward best practices centered on both energy efficiency and social responsibility.

Addressing the rapidly evolving regulatory environment, the study underscores the importance of anticipatory governance. Urban policymakers must foresee and manage emerging risks posed by digital infrastructure expansion, ensuring that electrification and decarbonisation efforts do not get stymied by fragmented policy frameworks. Streamlined permitting processes and multi-stakeholder partnerships, including utilities, technology firms, and community groups, are critical enablers for scalable sustainable data centre deployment.

On the technology frontier, artificial intelligence and machine learning are leveraged within the research to optimize energy consumption patterns in real time. By forecasting computational loads and ambient temperature variability, AI systems can orchestrate server utilization, cooling intensity, and power sourcing with unprecedented precision. This digital intelligence layer acts as a force multiplier, allowing existing hardware to operate more sustainably without necessitating costly physical overhauls.

The study also critically examines barriers to decarbonisation. Legacy infrastructure, high capital costs, and the complexity of retrofitting existing facilities pose significant challenges. Furthermore, disparities between global regions—where developing economies may have limited access to green energy solutions—highlight the need for tailored approaches that couple technology deployment with capacity building and financing innovations.

Socio-environmental equity is another vital dimension addressed. As data centres proliferate in urban zones, communities vulnerable to pollution and energy insecurity risk disproportionate impacts unless sustainability is woven into infrastructural design from inception. The authors argue for inclusive planning processes that prioritize transparency and community benefits, ensuring that green digital infrastructure contributes holistically to urban wellbeing.

Looking ahead, the research envisions a future where zero-carbon digital infrastructure is not merely an ideal but a practical reality. Integrated urban ecosystems could host sensory networks, smart transportation hubs, and digital services powered by sustainable data centres that are silent partners in city life. This vision aligns with the United Nations’ Sustainable Development Goals, positioning decarbonised digital infrastructure as a key enabler for climate action and resilient urban growth.

Crucially, the paper’s innovative modeling framework provides policymakers and industry leaders with quantitative tools to evaluate different decarbonisation pathways under variable urban contexts. This adaptability is vital as cities grapple with unique geographic, climatic, and economic conditions. Digital twins and scenario analysis embedded in the framework can elucidate trade-offs and synergistic opportunities, enabling data-driven decision-making.

In conclusion, the work of Liu, Lai, Seah, and colleagues sets a new benchmark for understanding and addressing the carbon footprint of digital infrastructure. Their interdisciplinary approach—marrying engineering, urban planning, energy policy, and social sciences—offers a roadmap toward harmonizing the digital revolution with planetary boundaries. As climate urgency escalates, this research not only highlights pressing challenges but importantly charts actionable solutions essential for a sustainable digital future.

Subject of Research: Decarbonisation of digital infrastructure with a focus on data centres and their role in urban sustainability.

Article Title: Decarbonising digital infrastructure and urban sustainability in the case of data centres.

Article References:
Liu, F.H.M., Lai, K.P.Y., Seah, B. et al. Decarbonising digital infrastructure and urban sustainability in the case of data centres. npj Urban Sustain 5, 15 (2025). https://doi.org/10.1038/s42949-025-00203-1

Image Credits: AI Generated

Modern data centers are the backbone of the digital era, orchestrating vast volumes of computations every second. However, a persistent bottleneck has hindered the raw potential of processors: the difficulty in accurately forecasting the sequence of instructions that a processor must execute next. This unpredictability has led to delays in data flow, causing slower responses in applications ranging from search engines to real-time analytics. Skia addresses this critical challenge by improving the processor’s foresight, essentially teaching it to better “predict” ahead, thus accelerating instruction throughput.

The technology centers on an improved understanding and handling of instruction streams—the detailed series of steps processors undertake to complete tasks. Traditionally, processors utilize mechanisms like the Branch Target Buffer (BTB) and Branch Prediction Units to anticipate which paths the code will follow next. However, as workloads grow increasingly complex and voluminous, these traditional systems often falter, especially when dealing with what the researchers term “shadow branches”—sections of instructions present in cache memory but not actively decoded or used by current execution sequences. These shadow branches have remained largely untapped, representing a hidden reserve of predictive information.

Skia innovatively captures these shadow branches and decodes them, storing this enriched dataset within a newly designed Shadow Branch Buffer. This auxiliary structure operates alongside the conventional BTB, enabling the processor to utilize a broader and more nuanced set of predictive data. By leveraging information already present but previously ignored, Skia amplifies instruction prediction accuracy without the need for extensive additional hardware resources. The result is a substantial increase in throughput—measured as the number of instructions executed per unit of time—thereby speeding up computational workflows.

Professor Paul V. Gratz of Texas A&M’s Department of Electrical and Computer Engineering highlights the significance of this advancement: “Processing instructions efficiently is one of the primary challenges in modern CPU design. Skia allows us to alleviate this bottleneck, enhancing both speed and power efficiency.” The project also benefits from the contributions of experts such as Dr. Daniel A. Jiménez and graduate student Chrysanthos Pepi, whose technical insights have been instrumental in refining the architecture and algorithms behind Skia.

Current mainstream processors employ Fetch Directed Instruction Prefetching (FDIP), a sophisticated system that attempts to fetch instructions ahead of time by predicting future execution paths through branch prediction. Yet, FDIP’s reliance on the precision of the Branch Target Buffer introduces vulnerability. When the BTB encounters faults or inaccuracies, it can trigger incorrect predictions, leading to cache pollution—where unnecessary or erroneous data clutters the cache, undermining performance rather than enhancing it. Skia mitigates these issues by tapping into the shadow branches, effectively turning previously “dark” areas of cache into sources of predictive insight.

The impact of improved throughput extends well beyond raw computational acceleration. Efficiency gains translate directly into reduced power consumption, a critical factor in the operation of data centers, which are notorious for their significant energy demands. Dr. Gratz elaborates, “Achieving even a 10% improvement in processor efficiency could mean that a company requires 10 fewer data centers nationwide, saving millions of dollars and cutting the equivalent power consumption of an entire plant. That’s a transformative step for the industry both economically and environmentally.”

This development arrives at an opportune moment, as data centers face mounting pressure to scale sustainably amidst escalating energy costs and environmental concerns. The growing demand for cloud computing, big data analytics, and artificial intelligence training only intensifies the need to optimize every element of processor architecture. Skia’s ability to deliver almost twice the performance improvement, compared to simply enlarging existing caching hardware, presents a compelling path forward.

The interdisciplinary collaboration underscores the strength of combining academic rigor and industrial expertise. Beyond Texas A&M, the project integrates knowledge from Princeton’s Department of Computer Science, where Professor David I. August has lent his profound expertise, as well as contributions from Intel and AheadComputing’s CPU architects. This synergy has been home to not only technical breakthroughs but also the successful dissemination of results on an international stage, with the team presenting their findings at the prestigious ACM International Conference on Architectural Support for Programming Languages and Operating Systems in the Netherlands.

Technically speaking, the ingenuity of Skia lies in its subtle yet powerful approach to instruction prediction. Instead of overhauling processor cores or dramatically increasing cache sizes—both costly and complex endeavors—the solution harnesses latent data already residing within the cache’s shadowed regions. This design choice minimizes hardware overhead, ensuring that data centers can adopt Skia’s benefits without prohibitive infrastructural changes. Graduate researcher Chrysanthos Pepi emphasizes this: “Our approach achieves remarkable gains in efficiency with only a minimal hardware budget, which is critical for scalable deployment.”

Furthermore, Skia’s ability to decode and employ shadow branches reduces the rate of mispredictions, cutting down wasted cycles where the processor executes unnecessary operations or waits idle. This increases not only processing speed but also improves the overall consistency and reliability of data center workloads, which increasingly run complex and interdependent tasks in massive parallel configurations.

Looking forward, the implications of Skia’s insights could extend beyond data centers into other domains reliant on efficient instruction prediction, such as embedded systems, high-performance computing clusters, and even emerging quantum-classical hybrid processors. As software workloads continue to diversify and grow more intricate, having robust predictive mechanisms becomes indispensable.

The publication of “Skia: Exposing Shadow Branches” in the ACM International Conference proceedings marks a significant milestone in computational research, offering a roadmap for future architectural innovations aimed at maximizing processing capabilities while controlling power consumption. As the digital infrastructure that supports billions of users worldwide continues to expand, breakthroughs like Skia represent the forefront of turning theoretical computer science into practical, impactful solutions.

In essence, Skia exemplifies how meticulous analysis of underutilized data, combined with strategic hardware design, can unlock new performance horizons. This marriage of innovation and pragmatism carries the promise of reshaping how data centers operate—propelling them into a more efficient, sustainable, and swift digital future.

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Subject of Research: Computer processor instruction prediction and throughput improvement for data center workloads.

Article Title: Skia: Exposing Shadow Branches

News Publication Date: 30-Mar-2025

Web References:
https://doi.org/10.1145/3676641.3716273

Image Credits: Hayden Schonhoeft/Texas A&M Engineering

Keywords: Computer processing, Computer science, Computational creativity, Computer architecture, Computers, Computer hardware, Computer memory, Computer networking, Computational science, Systems analysis, Energy resources conservation, Power industry