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Towards a Systematic Survey for Carbon Neutral Data Centers

Zhiwei Cao, Xin Zhou, Han Hu, Zhi Wang, Yonggang Wen

arXiv:2110.09284v3cs.CY

TL;DR

Data centers’ growing energy use creates a need for better-investigated technological and policy routes to carbon neutrality. This survey synthesizes carbon accounting, carbon markets, renewable energy, efficiency, waste-heat recovery, and a digital-twin industrial AI framework. It presents a multi-pronged roadmap combining emissions reduction with carbon offsetting.

  • Problem

    Technological and policy instruments for reducing or neutralizing data-center carbon emissions have not been thoroughly investigated despite the sector’s carbon intensity.

  • Method

    The survey reviews carbon markets and technologies for renewable-energy management, energy efficiency, waste-heat recycling, and digital-twin industrial AI integration.

  • Results

    The paper proposes a multi-pronged roadmap that combines renewable energy, energy efficiency, energy circulation, and carbon offsetting for carbon-neutral data centers.

  • Takeaways & Limitations

    Carbon neutrality requires simultaneous emissions reduction and offsetting supported by coordinated policy, infrastructure, optimization, and AI-based management.

Abstract

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Data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8\% of global carbon emissions by 2030. However, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efficient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artificial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential.

I. INTRODUCTION

Data centers’ rising energy use drives urgent concern over carbon emissions, while carbon-neutrality efforts combine policy instruments with renewable energy, efficiency, and energy-recovery technologies.

  • I. INTRODUCTION: Data center carbon emissions are escalating, with electricity consumption identified as a major climate concern requiring urgent reduction efforts.The paper attributes emissions growth to carbon-intensive electricity generation and depicts the trend from 2018 to 2030.
  • I. INTRODUCTION: Carbon-neutrality efforts by major providers include renewable-energy commitments, carbon-free power targets, and carbon-negative pledges.Examples include Chindata’s 2030 renewable-energy roadmap, Google’s 2030 carbon-free-power goal, and Microsoft’s carbon-negative target.
  • I. INTRODUCTION: The survey covers carbon markets, renewable-energy management, energy-efficiency improvement, and waste-heat recycling as complementary routes toward carbon-neutral data centers.It also reviews carbon footprints, efficiency metrics, and industrial efforts.
  • I. INTRODUCTION: Data-center carbon flows span embodied emissions from manufacturing, assembly, transport, and disposal and operational emissions from electricity use and infrastructure refreshment.Fossil-fuel combustion supplies the energy underlying these lifecycle emissions.
  • I. INTRODUCTION: Operational systems include IT, cooling, power distribution, and waste-heat recovery, with surplus renewable energy and recovered heat supporting emissions reduction or carbon-credit generation.The operational flow also includes mixed green and brown electricity supplies.

B. Categorical Evaluation of Data Center Carbon Emissions

The paper categorizes data-center emissions by scope and lifecycle role, then emphasizes operational emissions—especially purchased electricity—as the most actionable focus for efficient carbon neutrality.

  • B. Categorical Evaluation of Data Center Carbon Emissions: Carbon emissions are categorized into Scope 1 direct fuel emissions, Scope 2 purchased-energy emissions, and Scope 3 other indirect emissions.The categories cover diesel generation, purchased electricity or heat, and indirect sources such as transmission and distribution losses.
  • B. Categorical Evaluation of Data Center Carbon Emissions: The paper recommends prioritizing Scope 2 reductions and offsets because purchased electricity is the most efficient target for achieving data-center carbon neutrality.This recommendation follows the reported emissions distribution across scopes.
  • B. Categorical Evaluation of Data Center Carbon Emissions: Carbon neutrality is defined as offsetting lifecycle emissions through atmospheric carbon removal, while this survey focuses on carbon-neutral technologies for data centers.The survey also includes offsetting through carbon markets, waste heat, and excess renewable energy.
  • B. Categorical Evaluation of Data Center Carbon Emissions: The survey evaluates carbon efficiency using metrics designed to reflect a data center’s total carbon footprint and summarizes them in Table II.The metrics support benchmarking carbon-efficiency performance.

1) Carbon Usage Effectiveness:

The survey presents CUE, CFE Score, and Avoided Emission as metrics for evaluating data-center carbon performance, while noting limitations of grid-only accounting.

  • 1) Carbon Usage Effectiveness:: CUE measures carbon emissions from data-center electricity consumption using grid carbon intensity and the facility’s energy-efficiency ratio.CUE equals β·PUE, where β is grid carbon intensity and PUE is the ratio of total to IT energy consumption.
  • 1) Carbon Usage Effectiveness:: CUE can unfairly evaluate data centers because it excludes off-site renewable energy procured through PPAs or RECs.
  • 1) Carbon Usage Effectiveness:: CFE Score accounts for contracted carbon-free energy and grid carbon-free energy, with 100% required every hour for round-the-clock carbon-free operation.The grid component reflects the local renewable-energy penetration rate, while contracted energy is capped by total data-center load.
  • 1) Carbon Usage Effectiveness:: CFE Score can conceal differences in actual emissions when sites have identical CFE ratios but use fuels with different carbon intensities.The survey contrasts coal-generated electricity with natural-gas-generated electricity to motivate an emission-related metric.
  • 1) Carbon Usage Effectiveness:: Avoided Emission compares carbon emissions without and with contracted carbon-free energy, highlighting renewable projects in regions with higher grid carbon intensity.

C. Industrial Efforts Towards Carbon-Neutral Data Centers

Major cloud providers pursue carbon neutrality through renewable-energy deployment, grid decarbonization, energy optimization, carbon-aware computing, and carbon-removal commitments.

  • C. Industrial Efforts Towards Carbon-Neutral Data Centers: Google had achieved carbon neutrality by 2007 and matched its electricity consumption with purchased renewable energy from 2017.
  • C. Industrial Efforts Towards Carbon-Neutral Data Centers: Google prioritizes electrical-grid decarbonization, energy-portfolio optimization, and carbon-aware scheduling to support round-the-clock carbon neutrality.Carbon-aware platforms schedule non-critical jobs spatially and temporally to improve renewable-energy utilization.
  • C. Industrial Efforts Towards Carbon-Neutral Data Centers: Microsoft combines renewable-energy matching, internal carbon fees, and carbon-removal investment in plans to become carbon negative by 2030.Its stated targets include fully matching daily operational electricity with renewable energy in 2025 and removing its historical footprint by 2050.
  • C. Industrial Efforts Towards Carbon-Neutral Data Centers: Facebook targets carbon neutrality for Scope 1 and 2 emissions in 2020 and net-zero Scope 1, 2, and 3 emissions in 2030.The company constructed more than 5,400 MW of new solar and wind capacity, associated with reducing GHG emissions by more than 2.6 million metric tons.
  • C. Industrial Efforts Towards Carbon-Neutral Data Centers: Amazon expands utility-scale wind and solar projects while using intelligent direct evaporative cooling to improve AWS infrastructure efficiency.

2) Efforts from Cloud Providers in Asia:

Asian and European data-center companies combine renewable-energy adoption, energy-efficiency improvements, carbon markets, circularity, and broader energy-and-material circulation in their neutrality efforts.

  • 2) Efforts from Cloud Providers in Asia:: Huawei’s sustainability efforts include data-driven energy management, intelligent cooling, and renewable-energy use, with iCooling reducing refrigeration-station power consumption by 8%-10%.The reported electricity saving is 3.85 million kWh, described as equivalent to planting 79,500 trees.
  • 2) Efforts from Cloud Providers in Asia:: The proposed roadmap categorizes carbon-neutral data-center approaches into carbon-market policy instruments and technological methods spanning energy supply, utilization, and circulation.
  • 2) Efforts from Cloud Providers in Asia:: European operators pursue renewable-energy PPAs, hardware-life extension, refurbished servers, industrial ecodesign, and zero-waste goals.OVHCloud targets 100% renewable-energy supply by 2025 and 0% waste to landfill by 2025.
  • 2) Efforts from Cloud Providers in Asia:: The survey identifies carbon markets, renewable energy, energy efficiency, and energy-and-material circulation as widely acknowledged routes toward carbon-neutral data centers.

A. Cap-and-trade

Cap-and-trade limits total emissions while enabling permit trading, and the survey contrasts its cost efficiency with concerns that low carbon prices may discourage technological reductions.

  • A. Cap-and-trade: Cap-and-trade combines an annually declining emissions cap with permits that data-center operators can use, save, or trade during compliance periods.Each permit represents one metric ton of carbon emissions.
  • A. Cap-and-trade: Data center A purchases permits from technology-innovating data center B because permits cost less than emission-reduction investment or penalties.A meets its cap at minimum cost while B receives cash revenue, illustrating the system’s cost-efficiency rationale.
  • A. Cap-and-trade: Critics argue that low carbon prices may encourage emitters to buy permits rather than invest in emission-reduction technologies.
  • A. Cap-and-trade: Carbon taxes charge operators for annual emissions without the permit trading and emissions cap used in cap-and-trade.
  • A. Cap-and-trade: Carbon credits can be issued by an acknowledged third party for verified emission reductions and then used for offsetting or sold for cash.

1) On-site Renewable Energy:

On-site renewable generation can supply data-center operations, but storage, disposal, siting, and accounting constraints complicate cost-efficient deployment.

  • On-site Renewable Energy:: On-site wind and solar can supply all or part of a data center’s daily electricity when integrated into its microgrid.Operators remain responsible for managing, maintaining, and operating the renewable plant.
  • On-site Renewable Energy:: Energy storage may require prohibitive space, respond poorly to workload variation, experience shortened lifetime, and create disposal concerns.The cited limitations include low battery turnaround efficiency, frequent charge–discharge cycling, and harmful chemical components.
  • On-site Renewable Energy:: On-site generation may be cost-inefficient when a data center’s location is poorly suited to renewable production.The paper therefore distinguishes site-specific feasibility from the general availability of renewable technologies.
  • On-site Renewable Energy:: Off-site renewable procurement shifts generation and grid management to utilities, but requires mechanisms to record project contributions in the data center’s bill.Power purchase agreements and renewable energy certificates provide distinct procurement and accounting mechanisms.
  • On-site Renewable Energy:: Renewable-energy data-center optimization targets brown-energy reduction, operating-cost reduction, profit maximization, and renewable-energy utilization.These objectives overlap because better renewable matching can reduce grid procurement and operating costs.

C. Grid Electricity Procurement Minimization

Grid-procurement studies use spatial, temporal, predictive, and online optimization to reduce brown electricity or operating costs while respecting workload and service constraints.

  • C. Grid Electricity Procurement Minimization: Geo-distributed scheduling can perfectly match jobs with renewable supply across sites, reducing grid electricity procurement.The cited example assigns jobs to different data centers according to complementary renewable-generation profiles.
  • C. Grid Electricity Procurement Minimization: 30.58% and 30.82% average cost savings were reported when electricity price, bandwidth price, and renewable availability were jointly considered.The comparison was against methods prioritizing cheapest electricity or renewable-energy utilization.
  • C. Grid Electricity Procurement Minimization: 24% brown-energy savings were achieved with a 10% increase in total cost through multi-timescale power-mix and workload-distribution optimization.The framework used ARIMA workload prediction and simulated annealing for request distribution.
  • C. Grid Electricity Procurement Minimization: Prediction-based optimization depends on highly accurate renewable-energy forecasts, motivating online algorithms that operate without future production information.Lyapunov Optimization transforms time-averaged constraints into per-slot problems using virtual queues.
  • C. Grid Electricity Procurement Minimization: MultiGreen combines long-term and real-time grid procurement with battery charging and discharging, achieving near-offline-optimal performance in simulation.It is a two-stage online algorithm based on Lyapunov Optimization and outperforms a single-stage solution.

E. Operational Profit Maximization

Operational-profit studies extend renewable-energy management beyond bill reduction by combining service revenues, energy trading, ancillary services, and renewable-utilization objectives.

  • E. Operational Profit Maximization: Renewable-integrated data centers can earn revenue from SLA fulfillment, excess-energy trading, and ancillary services for the grid.Energy storage supports supplying electricity during shortages and reserving it during grid surpluses.
  • E. Operational Profit Maximization: Geo-distributed wind-powered data centers outperformed a non-geographical-load-balancing baseline in profit maximization, with profit increasing as wind penetration increased.The study modeled SLA-violation probability using a G/D/1 queue.
  • E. Operational Profit Maximization: Renewable-energy utilization research explicitly targets carbon-emission reduction rather than only optimizing bills, costs, or profits.The surveyed works formulate renewable utilization as a distinct optimization objective.
  • E. Operational Profit Maximization: GreenWare uses geographical workload scheduling and renewable-energy management to maximize renewable utilization under a limited budget.It considers on-site wind and solar generation and formulates the problem as linear fractional programming.
  • E. Operational Profit Maximization: Renewable-utilization methods include temporal scheduling of delay-tolerant jobs, supply–demand cooperative VM scheduling, and deep reinforcement learning.The surveyed approaches use prediction, wind-generation categorization, and PPO-based multi-cloud scheduling while avoiding task-deadline violations.

VI. CARBON-NEUTRAL DATA CENTER: FROM ENERGY UTILIZATION PERSPECTIVE

Energy-utilization decarbonization focuses on the IT, cooling, power-distribution, and waste-heat systems, emphasizing joint optimization because daily operations dominate data-center carbon footprints.

  • VI. CARBON-NEUTRAL DATA CENTER: FROM ENERGY UTILIZATION PERSPECTIVE: Daily operational energy use accounts for 97% of total data-center carbon footprint, while ICT and cooling consume approximately 86% of total energy.These figures motivate prioritizing IT and cooling efficiency.
  • VI. CARBON-NEUTRAL DATA CENTER: FROM ENERGY UTILIZATION PERSPECTIVE: IT and cooling systems are thermally coupled, so joint optimization is preferred over treating them independently.IT management determines electricity consumption and heat generation, while cooling rejects heat to maintain safe operating conditions.
  • VI. CARBON-NEUTRAL DATA CENTER: FROM ENERGY UTILIZATION PERSPECTIVE: IT-efficiency techniques span server, rack, and data-center levels, including DVFS, dynamic provisioning, workload dispatching, VM migration, and geo-distributed job placement.Dynamic provisioning can reduce energy by consolidating workloads, but switching costs may shorten server lifetime and increase failure rates.
  • VI. CARBON-NEUTRAL DATA CENTER: FROM ENERGY UTILIZATION PERSPECTIVE: Cooling control must balance overheating risk against wasted electricity from excessive cooling.Fan speed reduces server temperature and leakage power, but fan consumption grows cubically with speed.
  • VI. CARBON-NEUTRAL DATA CENTER: FROM ENERGY UTILIZATION PERSPECTIVE: Cooling efficiency can improve through static layouts that prevent hot-air recirculation and dynamic control of CRAC units or chiller plants.Hot-aisle containment, row-level cooling, in-rack cooling, and economizers are among the surveyed techniques.

B. Energy-Efficient Computing

The survey organizes energy-efficient computing methods by their control knobs, including CPU frequency and voltage scaling, multicore coordination, and virtualized-server control. Reported studies reduce energy while maintaining service performance, with gains reaching 49% for CPU energy and 17% for multicore scheduling.

  • Energy-Efficient Computing: The reviewed methods frame energy-efficient computing as control of processor frequency, voltage, core state, task assignment, and virtual-machine resources.These control knobs are summarized in the survey’s energy-efficient-computing literature table.
  • Chip Level Optimization: 49% maximum CPU-energy reduction was reported for a real-time DVFS scheduler that lowers frequency during non-CPU-intensive task phases.The scheduler forecasts CPU intensity and selects a weighted voltage–frequency pair while maintaining service performance.
  • Chip Level Optimization: 17% energy savings were achieved over an always-on baseline by jointly controlling CPU core activation, DVFS, and task assignment while maintaining throughput.The three decisions operate at different time granularities and use greedy search, PI control, and dynamic programming.
  • Virtualized Server Optimization: Virtualized-server control combines load balancing and resource allocation to reduce energy relative to open-loop control while stabilizing virtual-machine response time.The approach uses a two-layer hierarchy with system identification, LQR control, and PI control.

2) Server Power Mode Optimization:

Server power-mode and distributed-management studies use predictive, adaptive, and geographically aware control to reduce energy, electricity cost, and carbon emissions under service constraints. Reported improvements include 26% energy savings, 30.15% cost reduction, and 21% lower carbon emissions versus specified baselines.

  • Server Power Mode Optimization: Server power-mode methods regulate transitions among active, idle, and sleep states using workload queues and timing thresholds to limit service-quality degradation.PowerSleep introduces idle-period, sleep-period, and procrastination-period thresholds.
  • Cluster Level Optimization: 26% energy savings were reported for predictive cluster provisioning that selects active servers and virtual machines at coarse timescales and distributes workload at fine timescales.The method uses Limited Lookahead Control and Kalman-filter workload forecasting while satisfying QoS constraints.
  • Cluster Level Optimization: Deep reinforcement learning provides an online alternative to prediction-based management when real-world workload patterns are difficult to forecast.A global resource-allocation tier and local distributed power-management tier are evaluated against control-theoretical and round-robin baselines.
  • Geographical Distributed Data Centers Optimization: 30.15% electricity-cost reduction was reported by exploiting spatial and temporal price variation when assigning workloads and active servers across data centers.The framework combines workload distribution with active-server selection under request-delay modeling.
  • Geographical Distributed Data Centers Optimization: 21% lower total carbon emissions were achieved than a round-robin baseline through joint workload distribution, server activation, and speed scaling across data centers.The optimization accounts for electricity costs and carbon emissions in geographical workload balancing.
  • Server Power Mode Optimization: The survey summarizes server fan-control studies alongside power-management and distributed optimization methods, including constraints, control knobs, formulations, algorithms, and results.The fan-control literature is organized in a dedicated summary table.

2) Data Hall Configuration Optimization:

Data-hall and chiller optimization studies control server placement, airflow, CRACs, pumps, temperatures, and cooling loads, increasingly coordinating IT and cooling systems. Reported results include around 40% cooling-energy savings and over 30% chiller-plant energy savings, while static and model-based methods face practical limits.

  • Data Hall Configuration Optimization: Around 40% cooling-energy savings were possible through thermal-aware placement of heterogeneous servers to reduce heat recirculation.The study compares greedy, integer-linear-programming, and stochastic-programming approaches for server layout planning.
  • Data Hall Configuration Optimization: Static server placement reduces cooling power but cannot accommodate changing workloads and periodic server upgrades across a data center’s lifecycle.This limitation motivates dynamic control of CRAC speed, air-supply temperature, and water flow.
  • Data Hall Configuration Optimization: The survey’s data-hall configuration literature compares constraints, control knobs, formulations, algorithms, and achieved results across existing studies.These dimensions are summarized in Table X.
  • Data Hall Configuration Optimization: Coordinating multiple CRAC controllers can improve thermal-zone operation, while prototype evaluation shows reduced rack-inlet-temperature variance and potential energy savings.The cooperative scheme exchanges information among nearby thermal zones and aligns air-supply temperatures to prevent load imbalance.
  • Chiller Plant Optimization: Model-based chiller control formulates cooling-energy optimization from physical component models, whereas model-free methods address inaccuracies caused by weather dynamics and equipment aging.The survey discusses model-based control, extremum seeking, and data-driven approaches for chiller plants.
  • Chiller Plant Optimization: Over 30% chiller-plant energy savings were reported by data-driven modeling and optimization that allocates cooling load according to predicted coefficient of performance and demand.The controller satisfies cooling demand while minimizing electricity consumption.
  • Joint IT and Cooling Optimization: Joint IT–cooling optimization is preferable because workload-driven heat generation and cooling rejection are nonlinearly coupled, making near-real-time CFD control impractical.Simplified linear thermal relationships derived from offline CFD simulations are introduced to support tractable control.

VII. CARBON-NEUTRAL DATA CENTER: FROM ENERGY CIRCULATION PERSPECTIVE

Data center waste heat can be recycled through cooling-system-specific recovery points, transferring otherwise dissipated energy to external applications and supporting carbon neutrality.

  • VII. Carbon-Neutral Data Center: From Energy Circulation Perspective: Energy recycling is motivated by the conservation law that energy is transformed or transferred rather than destroyed.The section frames waste heat recovery as a way to reduce data-center energy consumption and carbon emissions.
  • VII. Carbon-Neutral Data Center: From Energy Circulation Perspective: Waste heat can be supplied to nearby power plants and seawater desalination plants, reducing customers’ emissions and generating carbon credits for offsetting data-center emissions.The recovery section reviews waste-heat sources, reuse techniques, and applications together with their merits and drawbacks.
  • A. Waste Heat Sources Within a Data Center: Waste heat recovery depends on the cooling system, with air-cooled centers offering 50–60 °C heat at CRAC returns and liquid-cooled centers reaching 85 °C at server exits.The liquid-cooled outlet is identified as a suitable harvesting location, while two-phase cooling is excluded because of practical recycling difficulties.

B. Existing Techniques for Data Center Waste Heat Reusing

Existing waste-heat reuse techniques apply data-center heat to district heating, power plants, biomass processing, absorption cooling, desalination, and electricity generation, but their deployment depends strongly on temperature and location.

  • 1) District Heating: District heating commonly recycles low-grade waste heat, but a heat pump is often needed to raise it to network temperatures of approximately 90 °C supply and 70 °C return.Heat may be recovered from the hot aisle or the chiller condenser.
  • 2) Power Plant Co-location: A co-located power plant can use data-center condenser heat to preheat feed water, improving plant efficiency by 2.2% and saving up to $46,000,000 annually for both facilities.For a 32.5 MW data center, reported savings include up to $45,000,000 for the data center and $1,000,000 for the power plant.
  • 2) Power Plant Co-location: Power-plant co-location requires heat pumps for standard air-cooled centers and close proximity because heat quality degrades with distance.These constraints can make deployment infeasible despite substantial cost savings.
  • 3) Biomass Processing: Biomass processing can use data-center waste heat for drying materials or warming anaerobic digestion reactors while reducing moisture content.The reviewed approach connects waste-heat reuse with on-site biomass renewable-energy production.
  • 4) Absorption Cooling: Absorption cooling is impractical for traditional air-cooled centers when the available waste heat is below the required temperature without a heat pump.The cited system achieved lower efficiency at 70 °C, but that temperature remained infeasible for traditional air cooling.

5) Desalination:

Waste heat can support desalination and Organic Rankine Cycle electricity generation, but temperature, geography, efficiency, and cost constrain practical deployment.

  • 5) Desalination: Multiple Effect Distillation uses 75 °C waste heat to boil seawater, with successive stages producing vapor at progressively lower temperatures.The described process generates steam from data-center heat and reuses vapor as the heating medium for later stages.
  • 5) Desalination: Desalination can produce clean water and potentially eliminate chiller-plant needs, but it requires suitable coastal locations and heat-transfer conditions.Air-cooled centers generally require a heat pump, while inland regions cannot directly use seawater desalination.
  • 6) Organic Rankine Cycle: Organic Rankine Cycle systems can use data-center waste heat as low as 32 °C with reduced efficiency, and a lab-scale testbed estimated 4%–8% power savings at 90 °C.The cycle vaporizes a low-boiling-point organic fluid to drive a turbine and generate electricity.
  • 6) Organic Rankine Cycle: Organic Rankine Cycle systems generate on-site supplementary electricity and avoid transport losses, but current thermal efficiency remains only 1.9%–4.6%.The paper notes that their economic viability requires further investigation.
  • C. Summary: Waste-heat reuse can reduce data-center emissions through renewable energy or cooling and can also reduce emissions from customers in other sectors.Both pathways are presented as beneficial for achieving carbon neutrality.
  • C. Summary: Most waste-heat techniques target high-temperature heat from liquid- or two-phase-cooled centers rather than low-grade heat from prevailing air-cooled centers.Heat pumps, geographic constraints, low efficiency, and high cost remain barriers to large-scale deployment.
  • VIII. Digital Twin-Assist Industrial AI Framework for Carbon-Neutral Data Centers: The proposed digital-twin approach combines carbon-emission modeling and optimization with the paper’s renewable-energy, efficiency, and energy-circulation elements.It is presented as a framework for future carbon-neutral data-center development.

A. Multi-pronged Solution for Carbon Neutrality

The paper envisions carbon neutrality through simultaneous energy-efficiency enhancement, renewable-energy integration, and circular-economy practices, supported by a digital twin-assisted industrial AI framework.

  • Multi-pronged solution: The multi-pronged solution combines energy-efficiency enhancement, renewable-energy integration, and circular-economy practices to pursue carbon neutrality.The proposed circular practices include reusing LNG cold energy, data-center waste heat, and electrical equipment.
  • Energy Efficiency Enhancement: Data centers should coordinate IT and physical infrastructure to improve efficiency while accounting for their sophisticated coupling.This coordination is intended to support more aggressive power usage effectiveness targets.
  • Renewable Energy Integration: Renewable-energy integration targets electricity demand and carbon usage effectiveness through judicious use of clean energy.
  • Circular Economy: Circular-economy practices can reuse LNG cold energy, data-center waste heat, and electrical equipment, while surplus energy or heat may support other sectors.
  • Digital twin-assisted industrial AI framework: The proposed framework combines physical systems, digital twins, and AI engines to model, train, and control data-center operations.The AI engine can use real and synthetic data, while digital twins can predict future system states and support risk prevention.

C. Inherent Scientific Problems

The framework faces scientific and deployment challenges involving cyber–physical data quality, safe transfer of learned controls, heterogeneous subsystem dynamics, and practical application constraints.

  • Data quality and retrieval: Synthetic cyber data may be noisy, so data-retrieval policies must balance massive digital-twin data against accurate but costly physical data.Synthetic-data quality affects the AI engine’s generalization performance.
  • Cyber–physical control transfer: Controls learned in the cyber world must be mapped safely to physical systems because they may otherwise violate operating constraints.The paper gives server temperature exceeding its safe range as an example of this risk.
  • Heterogeneous system dynamics: Different subsystems have substantially different dynamics and response times, complicating coordinated training across their digital twins.The cooling-system and IT-system control response times may differ by an order of magnitude.
  • Applications: Potential applications include carbon-aware computing, renewable-energy forecasting for proactive workload and cooling control, and waste-heat utilization.Carbon-aware computing can shift non-emergent jobs to times or locations with adequate carbon-free energy.
  • Applications: Large-scale cold-energy integration remains constrained by practical transport challenges, especially moving offshore LNG cold energy to inland data centers with low thermal loss.
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