{"content_id":"xavttcqi03","slug":"ai-data-center-power-grid-tariffs-carbon","locale":"en","schema_type":"Report","category":"report","category_name":"Report","title":"Why AI Data Center Power Demand Is Putting Pressure on the Power Grid, Electricity Rates, and Carbon Reduction Targets All at Once","summary":"AI data centers place simultaneous strain on power grids, electricity costs, and corporate carbon targets due to the high-density GPU and accelerator computations and cooling demands they require. This article examines grid bottlenecks, cost allocation, sustainability metrics, and alternatives for power-flexible AI operations from a data-driven perspective.","author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["Power demand at AI data centers is growing rapidly in regions that serve as cloud hubs, in areas with a high concentration of semiconductor and network infrastructure, and in regions where affordable or clean power is available.","Power grid bottlenecks are caused not only by a shortage of power generation but also by a complex interplay of transmission and distribution capacity, substation expansion, waiting times for grid connection, and local permitting issues.","A key issue in electricity rate policy is how the costs of data center power infrastructure should be shared among operators, power companies, general consumers, and local communities.","When reading a corporate sustainability report, it is important to consider total electricity consumption, renewable energy procurement, greenhouse gas emissions, water consumption, and cooling efficiency together.","While “grid-flexible AI factories”—which adjust AI training and inference tasks based on grid conditions—show great promise, it is important to consider the difference between tasks that can tolerate latency and real-time services."],"content_markdown":"## At a Glance\n\nAI data centers are not merely server buildings; they are major consumers of electricity and cooling, users of local infrastructure, and key factors in corporate carbon accounting. As generative AI and large-scale model training and inference expand, data center electricity demand is simultaneously driving up grid connectivity, electricity rate design, renewable energy procurement, water usage, and local permitting issues.\n\nThe IEA’s energy and AI analyses, data center power infrastructure studies, and major Big Tech sustainability reports all raise the same question: **While the benefits of AI infrastructure are spread globally, the burdens on the power grid, land, water, and utility rates may be concentrated in specific regions.**\n\n## Key Concept Definitions\n\n| Term | Meaning | Why It Matters |\n|---|---|---|\n| AI Data Center | A data center that performs AI training and inference using GPUs, TPUs, accelerators, high-speed networks, and high-capacity storage | Has higher power density per rack and greater cooling requirements than general office IT |\n| Grid Connection | The process by which power plants and consumers connect to the transmission or distribution grid | Large data centers may require connection capacities ranging from tens to hundreds of MW, leading to wait times |\n| Power Grid Bottleneck | A phenomenon where power supply is delayed due to insufficient capacity in any one segment of the power generation, transmission, substation, or distribution system | Even if power is available, it may not be supplied to the required location at the required time |\n| Scope 1, 2, and 3 Emissions | Direct emissions, emissions related to purchased electricity, and other indirect emissions such as those from the supply chain and product use | These are the basic metrics used to assess whether data center operators are meeting their carbon reduction targets |\n| Power Flexibility | The ability to adjust the timing and intensity of power usage in response to grid conditions, prices, and renewable energy output | A key means of operating AI computations in a grid-friendly manner |\n| Water Use Efficiency (WUE) | A metric that correlates the amount of water a data center uses for cooling and other purposes with its IT load | In water-scarce regions, this is a factor in permitting and local conflicts that is just as important as electricity |\n\n## Where Is Demand for AI Data Centers Growing Rapidly?\n\nWhile the expansion of AI data centers is occurring worldwide, it is not happening at the same pace in every region. Areas experiencing rapid demand growth generally meet the following conditions:\n\n1. **Regions with existing cloud regions and network hubs** \n AI services require large-scale data transfer, low latency, and global service connectivity. Regions where cloud providers and telecommunications networks are already concentrated have an advantage as sites for new AI data centers.\n\n2. **Regions capable of securing large-scale power supplies** \n AI training clusters require high power density. Electricity costs, transmission grid capacity, substation capacity, and access to renewable energy are key variables in site selection.\n\n3. **Regions Close to Semiconductor and Server Supply Chains and Operational Staff** \n The availability of GPU servers, cooling equipment, power equipment, and specialized operational staff directly impacts the speed of data center expansion.\n\n4. **Regions with Clear Policy Incentives and Permitting Processes**  \n   Regions with clear tax benefits, land use regulations, power contract systems, and environmental review standards offer greater predictability for operators.\n\n5. **Regions where renewable energy power purchase agreements (PPAs) are possible** \n Big Tech companies emphasize 24-hour carbon-free power, renewable energy procurement, and long-term power purchase agreements. However, the carbon intensity of contractually procured renewable energy may differ from that of actual hourly electricity consumption.\n\n## Why Do Grid Connection Bottlenecks Occur?\n\nThe power challenges facing AI data centers cannot be explained simply by asking, “Is there a power shortage?” Bottlenecks typically occur at four stages.\n\n### 1. The Discrepancy Between Generation Capacity and Actual Availability\n\nSolar and wind power generation fluctuates depending on the time of day and weather conditions. A stable supply requires operation in conjunction with other resources, such as nuclear, gas, hydroelectric, and energy storage systems. Since data centers often operate 24 hours a day, power shortages during specific time periods pose a significant risk.\n\n### 2. Physical Limitations of Transmission Grids and Substations\n\nElectricity must travel from power plants to data centers. Even if generation capacity is sufficient, connection delays occur if the capacity of transmission lines, substations, or distribution networks is insufficient. For this reason, plans for new data centers are reviewed in conjunction with plans to expand the power grid.\n\n### 3. Grid Connection Wait Times and Equipment Procurement Delays\n\nWhen large consumers and renewable energy generation projects increase simultaneously, the wait time for grid connection lengthens. Delivery times for critical equipment—such as transformers, circuit breakers, and power electronic devices—can also create bottlenecks.\n\n### 4. Local Permits and Community Acceptance\n\nData centers affect not only electricity but also land use, water, noise, scenery, tax revenue, employment, and local electricity rates. Residents may oppose such projects if they perceive the infrastructure burden to outweigh the local benefits.\n\n## Electricity Rates and Cost Allocation: Who Bears the Cost of the AI Power Grid?\n\nThe expansion of AI data centers poses difficult questions for power companies and regulatory agencies. When transmission lines and substations are expanded for a single large customer, who should bear the cost?\n\n| Cost Item | Cause | Issue |\n|---|---|---|\n| Grid Connection Construction Costs | Expansion of substations and transmission/distribution facilities to connect data centers | Should the operator bear the cost, or should it be reflected in rates for all consumers? |\n| Peak Power Response Costs | Ensuring supply stability during periods of highest electricity demand | Should customers responsible for creating the peak be charged higher rates? |\n| Reserve Capacity and Backup Power Costs | Preventing power outages and maintaining grid stability | How should the data center’s requirement for 24-hour, high-reliability power be priced? |\n| Renewable Energy and Energy Storage Costs | Investments to simultaneously achieve carbon reduction targets and power stability | Who will bear the costs of long-term power purchase agreements, energy storage systems, and the transmission grid? |\n| Local Environmental Costs | Water usage, heat emissions, land use, noise, etc. | Are local compensation and permitting conditions required beyond electricity rates? |\n\nFrom a policy perspective, the following approaches could be discussed.\n\n- **Polluter Pays Principle**: The operator bears a larger share of the expansion costs required due to a specific data center’s connection.\n- **Time-of-Use Rates**: Electricity rates are raised during times when the grid is congested or carbon intensity is high.\n- **Demand Response Contracts**: Provide compensation to data centers that reduce some computational operations or relocate to other regions during power shortages.\n- **Long-Term Minimum Fees or Demand Charges**: Customers who reserve large-scale power infrastructure pay a fixed fee regardless of actual usage.\n- **Local Benefit Conditions**: Include conditions such as tax revenue, job creation, waste heat utilization, water usage restrictions, and investment in the local power grid as part of the licensing requirements.\n\n## Key Metrics to Look for in Corporate Sustainability Reports\n\nEnvironmental and sustainability reports from major tech companies like Google and Amazon are important resources for understanding the actual environmental impact of AI infrastructure. However, since calculation methods, fiscal years, treatment of renewable energy certificates, and the distinction between data centers and overall business operations vary by company, the figures should not be compared directly.\n\n### Key Indicator Checklist\n\n| Indicator | What to Check | Points to Note When Interpreting |\n|---|---|---|\n| Total Electricity Consumption | Trends in electricity consumption across the entire company and data centers | AI-specific consumption may not be disclosed separately |\n| Renewable Energy Procurement | PPAs, certificates, on-site generation, carbon-free electricity targets | Annual matching and time-of-use matching have different implications |\n| Scope 2 Emissions | Greenhouse gas emissions from purchased electricity | Differences between market-based and region-based calculation methods must be verified |\n| Scope 3 Emissions | Supply chain emissions from servers, semiconductors, construction, logistics, etc. | Emissions from equipment manufacturing may increase as AI infrastructure expands |\n| Water Usage | Cooling, facility operations, regional water stress | Water usage must be considered in conjunction with local water resource conditions |\n| PUE | Ratio of IT equipment power consumption to total facility power consumption | Even with a low PUE, total power consumption can increase sharply if overall power usage surges |\n| WUE | Water usage per unit of IT load | Varies significantly depending on cooling methods and climate conditions |\n| Carbon Removal and Offsetting | Approaches to addressing residual emissions | A distinction must be made between reduction and offsetting |\n\n### Key Points for Data Interpretation\n\n- **Efficiency improvements do not automatically offset increases in total consumption.** Even if server and cooling efficiency improves, total electricity and water consumption may still rise if AI usage grows faster.\n- **Purchasing renewable energy does not immediately resolve grid congestion.** Even with a power purchase agreement, the transmission grid must have sufficient capacity at the required times and locations.\n- **Carbon targets must account for Scope 3 emissions as well as Scope 2.** This is because supply chain emissions from AI accelerators and data center construction can be significant.\n\n## The Potential and Limitations of the “Power-Flexible AI Factory”\n\nA power-flexible AI factory is an operational model that adjusts AI computations based on the state of the power grid. The core idea is that not all AI tasks have the same level of urgency.\n\n### Possible Approaches\n\n1. **Time Shifting of Training Tasks** \n Training large models or batch processing tasks can be rescheduled in units of hours or days. These tasks can be shifted to times when electricity prices are low or renewable energy output is high.\n\n2. **Inter-regional Workload Migration** \n By leveraging global cloud infrastructure, tasks from regions with congested power grids can be moved to other regions. However, data sovereignty, latency, and network costs pose constraints.\n\n3. **Prioritizing Inference Workloads** \n By distinguishing between services requiring real-time responses and analytical tasks that can tolerate latency, certain tasks can be reclassified as low priority during power shortages.\n\n4. **Integration of Batteries, Thermal Storage, and Backup Resources**  \n   Storage devices and cooling systems within data centers can be utilized for grid demand response.\n\n### Limitations\n\n- Tasks with low latency tolerance—such as real-time search, customer support, financial transactions, and healthcare and security services—have limited room for adjustment.\n- Workload migration may conflict with data protection regulations, regional cloud contracts, and latency requirements.\n- For power flexibility to translate into actual carbon reductions, data on the carbon intensity of electricity by time of day is required.\n- For operators to provide power flexibility, electricity rates and demand response compensation mechanisms must be sufficiently clear.\n\n## Regional Water Resources and Cooling Challenges\n\nThe environmental impact of AI data centers extends beyond electricity alone. High-performance servers generate significant heat, and water usage can increase depending on the cooling method.\n\n| Cooling Method | Advantages | Considerations |\n|---|---|---|\n| Air-cooling | Relatively simple structure and may require less water | May have limitations for high-density AI racks |\n| Evaporative cooling | Can help reduce electricity consumption | Water usage may increase |\n| Liquid cooling | Suitable for high-density GPU servers | May involve high system complexity and initial investment costs |\n| Hybrid cooling | Can be combined based on climate and load | Operational optimization is complex |\n\nIn regions with high water stress, data center water usage may compete with agriculture, domestic water supplies, and ecosystems. Therefore, local governments must review water consumption, the use of recycled water, drought response plans, and the potential for waste heat utilization during the permitting process.\n\n## Data Items Required for Policy and Regulatory Design\n\nFor AI data center policies, data is more important than declarations. The following items must be jointly managed by local governments, power companies, and regulatory agencies.\n\n| Data Item | Provider | Purpose |\n|---|---|---|\n| Requested power capacity, phased expansion plans | Data center operator | Assessment of grid reinforcement needs |\n| Estimated load by time period | Operator, power company | Peak demand and rate design |\n| Scope of power flexibility | Operator | Demand response contracts and emergency operation plans |\n| Cooling method and estimated water consumption | Operator | Water resource impact assessment |\n| Renewable energy procurement plan | Operator | Carbon targets and regional power grid impact assessment |\n| Transmission and substation reinforcement costs | Electric utility | Cost allocation and rate approval |\n| Local Employment, Tax Revenue, and Waste Heat Utilization Plans | Operators·Local Governments | Assessment of Community Acceptance |\n| Emissions Calculation Methodology | Operators | Verification of Sustainability Reports |\n\n## Conclusion\n\nThe increase in power demand from AI data centers is not just an issue for the technology industry. It is a public infrastructure issue that simultaneously involves grid investment, electricity rates, local permits, water resources, and carbon targets. The core of the policy is not to hinder AI innovation, but to transparently measure and fairly allocate the costs that large-scale electricity demand imposes on local communities and the power grid.\n\nThe most realistic approach is to combine three elements. First, data center operators must disclose more granular data on electricity, water, and emissions. Second, power companies and regulatory agencies must develop rate structures that reflect the grid costs associated with large consumers. Third, AI computing operations should be flexibly adjusted to grid conditions to the extent possible.","content_html":"\u003ch2\u003e\u003ca href=\"#at-a-glance\" class=\"anchor\" id=\"at-a-glance\"\u003e\u003c/a\u003eAt a Glance\u003c/h2\u003e\n\u003cp\u003eAI data centers are not merely server buildings; they are major consumers of electricity and cooling, users of local infrastructure, and key factors in corporate carbon accounting. As generative AI and large-scale model training and inference expand, data center electricity demand is simultaneously driving up grid connectivity, electricity rate design, renewable energy procurement, water usage, and local permitting issues.\u003c/p\u003e\n\u003cp\u003eThe IEA’s energy and AI analyses, data center power infrastructure studies, and major Big Tech sustainability reports all raise the same question: \u003cstrong\u003eWhile the benefits of AI infrastructure are spread globally, the burdens on the power grid, land, water, and utility rates may be concentrated in specific regions.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#key-concept-definitions\" class=\"anchor\" id=\"key-concept-definitions\"\u003e\u003c/a\u003eKey Concept Definitions\u003c/h2\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTerm\u003c/th\u003e\n\u003cth\u003eMeaning\u003c/th\u003e\n\u003cth\u003eWhy It Matters\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Data Center\u003c/td\u003e\n\u003ctd\u003eA data center that performs AI training and inference using GPUs, TPUs, accelerators, high-speed networks, and high-capacity storage\u003c/td\u003e\n\u003ctd\u003eHas higher power density per rack and greater cooling requirements than general office IT\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGrid Connection\u003c/td\u003e\n\u003ctd\u003eThe process by which power plants and consumers connect to the transmission or distribution grid\u003c/td\u003e\n\u003ctd\u003eLarge data centers may require connection capacities ranging from tens to hundreds of MW, leading to wait times\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Grid Bottleneck\u003c/td\u003e\n\u003ctd\u003eA phenomenon where power supply is delayed due to insufficient capacity in any one segment of the power generation, transmission, substation, or distribution system\u003c/td\u003e\n\u003ctd\u003eEven if power is available, it may not be supplied to the required location at the required time\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eScope 1, 2, and 3 Emissions\u003c/td\u003e\n\u003ctd\u003eDirect emissions, emissions related to purchased electricity, and other indirect emissions such as those from the supply chain and product use\u003c/td\u003e\n\u003ctd\u003eThese are the basic metrics used to assess whether data center operators are meeting their carbon reduction targets\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Flexibility\u003c/td\u003e\n\u003ctd\u003eThe ability to adjust the timing and intensity of power usage in response to grid conditions, prices, and renewable energy output\u003c/td\u003e\n\u003ctd\u003eA key means of operating AI computations in a grid-friendly manner\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWater Use Efficiency (WUE)\u003c/td\u003e\n\u003ctd\u003eA metric that correlates the amount of water a data center uses for cooling and other purposes with its IT load\u003c/td\u003e\n\u003ctd\u003eIn water-scarce regions, this is a factor in permitting and local conflicts that is just as important as electricity\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca href=\"#where-is-demand-for-ai-data-centers-growing-rapidly\" class=\"anchor\" id=\"where-is-demand-for-ai-data-centers-growing-rapidly\"\u003e\u003c/a\u003eWhere Is Demand for AI Data Centers Growing Rapidly?\u003c/h2\u003e\n\u003cp\u003eWhile the expansion of AI data centers is occurring worldwide, it is not happening at the same pace in every region. Areas experiencing rapid demand growth generally meet the following conditions:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRegions with existing cloud regions and network hubs\u003c/strong\u003e\nAI services require large-scale data transfer, low latency, and global service connectivity. Regions where cloud providers and telecommunications networks are already concentrated have an advantage as sites for new AI data centers.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRegions capable of securing large-scale power supplies\u003c/strong\u003e\nAI training clusters require high power density. Electricity costs, transmission grid capacity, substation capacity, and access to renewable energy are key variables in site selection.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRegions Close to Semiconductor and Server Supply Chains and Operational Staff\u003c/strong\u003e\nThe availability of GPU servers, cooling equipment, power equipment, and specialized operational staff directly impacts the speed of data center expansion.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRegions with Clear Policy Incentives and Permitting Processes\u003c/strong\u003e\u003cbr\u003e\nRegions with clear tax benefits, land use regulations, power contract systems, and environmental review standards offer greater predictability for operators.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRegions where renewable energy power purchase agreements (PPAs) are possible\u003c/strong\u003e\nBig Tech companies emphasize 24-hour carbon-free power, renewable energy procurement, and long-term power purchase agreements. However, the carbon intensity of contractually procured renewable energy may differ from that of actual hourly electricity consumption.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\u003ca href=\"#why-do-grid-connection-bottlenecks-occur\" class=\"anchor\" id=\"why-do-grid-connection-bottlenecks-occur\"\u003e\u003c/a\u003eWhy Do Grid Connection Bottlenecks Occur?\u003c/h2\u003e\n\u003cp\u003eThe power challenges facing AI data centers cannot be explained simply by asking, “Is there a power shortage?” Bottlenecks typically occur at four stages.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#1-the-discrepancy-between-generation-capacity-and-actual-availability\" class=\"anchor\" id=\"1-the-discrepancy-between-generation-capacity-and-actual-availability\"\u003e\u003c/a\u003e1. The Discrepancy Between Generation Capacity and Actual Availability\u003c/h3\u003e\n\u003cp\u003eSolar and wind power generation fluctuates depending on the time of day and weather conditions. A stable supply requires operation in conjunction with other resources, such as nuclear, gas, hydroelectric, and energy storage systems. Since data centers often operate 24 hours a day, power shortages during specific time periods pose a significant risk.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#2-physical-limitations-of-transmission-grids-and-substations\" class=\"anchor\" id=\"2-physical-limitations-of-transmission-grids-and-substations\"\u003e\u003c/a\u003e2. Physical Limitations of Transmission Grids and Substations\u003c/h3\u003e\n\u003cp\u003eElectricity must travel from power plants to data centers. Even if generation capacity is sufficient, connection delays occur if the capacity of transmission lines, substations, or distribution networks is insufficient. For this reason, plans for new data centers are reviewed in conjunction with plans to expand the power grid.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#3-grid-connection-wait-times-and-equipment-procurement-delays\" class=\"anchor\" id=\"3-grid-connection-wait-times-and-equipment-procurement-delays\"\u003e\u003c/a\u003e3. Grid Connection Wait Times and Equipment Procurement Delays\u003c/h3\u003e\n\u003cp\u003eWhen large consumers and renewable energy generation projects increase simultaneously, the wait time for grid connection lengthens. Delivery times for critical equipment—such as transformers, circuit breakers, and power electronic devices—can also create bottlenecks.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#4-local-permits-and-community-acceptance\" class=\"anchor\" id=\"4-local-permits-and-community-acceptance\"\u003e\u003c/a\u003e4. Local Permits and Community Acceptance\u003c/h3\u003e\n\u003cp\u003eData centers affect not only electricity but also land use, water, noise, scenery, tax revenue, employment, and local electricity rates. Residents may oppose such projects if they perceive the infrastructure burden to outweigh the local benefits.\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#electricity-rates-and-cost-allocation-who-bears-the-cost-of-the-ai-power-grid\" class=\"anchor\" id=\"electricity-rates-and-cost-allocation-who-bears-the-cost-of-the-ai-power-grid\"\u003e\u003c/a\u003eElectricity Rates and Cost Allocation: Who Bears the Cost of the AI Power Grid?\u003c/h2\u003e\n\u003cp\u003eThe expansion of AI data centers poses difficult questions for power companies and regulatory agencies. When transmission lines and substations are expanded for a single large customer, who should bear the cost?\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCost Item\u003c/th\u003e\n\u003cth\u003eCause\u003c/th\u003e\n\u003cth\u003eIssue\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGrid Connection Construction Costs\u003c/td\u003e\n\u003ctd\u003eExpansion of substations and transmission/distribution facilities to connect data centers\u003c/td\u003e\n\u003ctd\u003eShould the operator bear the cost, or should it be reflected in rates for all consumers?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePeak Power Response Costs\u003c/td\u003e\n\u003ctd\u003eEnsuring supply stability during periods of highest electricity demand\u003c/td\u003e\n\u003ctd\u003eShould customers responsible for creating the peak be charged higher rates?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReserve Capacity and Backup Power Costs\u003c/td\u003e\n\u003ctd\u003ePreventing power outages and maintaining grid stability\u003c/td\u003e\n\u003ctd\u003eHow should the data center’s requirement for 24-hour, high-reliability power be priced?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRenewable Energy and Energy Storage Costs\u003c/td\u003e\n\u003ctd\u003eInvestments to simultaneously achieve carbon reduction targets and power stability\u003c/td\u003e\n\u003ctd\u003eWho will bear the costs of long-term power purchase agreements, energy storage systems, and the transmission grid?\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLocal Environmental Costs\u003c/td\u003e\n\u003ctd\u003eWater usage, heat emissions, land use, noise, etc.\u003c/td\u003e\n\u003ctd\u003eAre local compensation and permitting conditions required beyond electricity rates?\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eFrom a policy perspective, the following approaches could be discussed.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003ePolluter Pays Principle\u003c/strong\u003e: The operator bears a larger share of the expansion costs required due to a specific data center’s connection.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTime-of-Use Rates\u003c/strong\u003e: Electricity rates are raised during times when the grid is congested or carbon intensity is high.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDemand Response Contracts\u003c/strong\u003e: Provide compensation to data centers that reduce some computational operations or relocate to other regions during power shortages.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLong-Term Minimum Fees or Demand Charges\u003c/strong\u003e: Customers who reserve large-scale power infrastructure pay a fixed fee regardless of actual usage.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLocal Benefit Conditions\u003c/strong\u003e: Include conditions such as tax revenue, job creation, waste heat utilization, water usage restrictions, and investment in the local power grid as part of the licensing requirements.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca href=\"#key-metrics-to-look-for-in-corporate-sustainability-reports\" class=\"anchor\" id=\"key-metrics-to-look-for-in-corporate-sustainability-reports\"\u003e\u003c/a\u003eKey Metrics to Look for in Corporate Sustainability Reports\u003c/h2\u003e\n\u003cp\u003eEnvironmental and sustainability reports from major tech companies like Google and Amazon are important resources for understanding the actual environmental impact of AI infrastructure. However, since calculation methods, fiscal years, treatment of renewable energy certificates, and the distinction between data centers and overall business operations vary by company, the figures should not be compared directly.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#key-indicator-checklist\" class=\"anchor\" id=\"key-indicator-checklist\"\u003e\u003c/a\u003eKey Indicator Checklist\u003c/h3\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eIndicator\u003c/th\u003e\n\u003cth\u003eWhat to Check\u003c/th\u003e\n\u003cth\u003ePoints to Note When Interpreting\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal Electricity Consumption\u003c/td\u003e\n\u003ctd\u003eTrends in electricity consumption across the entire company and data centers\u003c/td\u003e\n\u003ctd\u003eAI-specific consumption may not be disclosed separately\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRenewable Energy Procurement\u003c/td\u003e\n\u003ctd\u003ePPAs, certificates, on-site generation, carbon-free electricity targets\u003c/td\u003e\n\u003ctd\u003eAnnual matching and time-of-use matching have different implications\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eScope 2 Emissions\u003c/td\u003e\n\u003ctd\u003eGreenhouse gas emissions from purchased electricity\u003c/td\u003e\n\u003ctd\u003eDifferences between market-based and region-based calculation methods must be verified\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eScope 3 Emissions\u003c/td\u003e\n\u003ctd\u003eSupply chain emissions from servers, semiconductors, construction, logistics, etc.\u003c/td\u003e\n\u003ctd\u003eEmissions from equipment manufacturing may increase as AI infrastructure expands\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWater Usage\u003c/td\u003e\n\u003ctd\u003eCooling, facility operations, regional water stress\u003c/td\u003e\n\u003ctd\u003eWater usage must be considered in conjunction with local water resource conditions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePUE\u003c/td\u003e\n\u003ctd\u003eRatio of IT equipment power consumption to total facility power consumption\u003c/td\u003e\n\u003ctd\u003eEven with a low PUE, total power consumption can increase sharply if overall power usage surges\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWUE\u003c/td\u003e\n\u003ctd\u003eWater usage per unit of IT load\u003c/td\u003e\n\u003ctd\u003eVaries significantly depending on cooling methods and climate conditions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCarbon Removal and Offsetting\u003c/td\u003e\n\u003ctd\u003eApproaches to addressing residual emissions\u003c/td\u003e\n\u003ctd\u003eA distinction must be made between reduction and offsetting\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch3\u003e\u003ca href=\"#key-points-for-data-interpretation\" class=\"anchor\" id=\"key-points-for-data-interpretation\"\u003e\u003c/a\u003eKey Points for Data Interpretation\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eEfficiency improvements do not automatically offset increases in total consumption.\u003c/strong\u003e Even if server and cooling efficiency improves, total electricity and water consumption may still rise if AI usage grows faster.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePurchasing renewable energy does not immediately resolve grid congestion.\u003c/strong\u003e Even with a power purchase agreement, the transmission grid must have sufficient capacity at the required times and locations.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCarbon targets must account for Scope 3 emissions as well as Scope 2.\u003c/strong\u003e This is because supply chain emissions from AI accelerators and data center construction can be significant.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca href=\"#the-potential-and-limitations-of-the-power-flexible-ai-factory\" class=\"anchor\" id=\"the-potential-and-limitations-of-the-power-flexible-ai-factory\"\u003e\u003c/a\u003eThe Potential and Limitations of the “Power-Flexible AI Factory”\u003c/h2\u003e\n\u003cp\u003eA power-flexible AI factory is an operational model that adjusts AI computations based on the state of the power grid. The core idea is that not all AI tasks have the same level of urgency.\u003c/p\u003e\n\u003ch3\u003e\u003ca href=\"#possible-approaches\" class=\"anchor\" id=\"possible-approaches\"\u003e\u003c/a\u003ePossible Approaches\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTime Shifting of Training Tasks\u003c/strong\u003e\nTraining large models or batch processing tasks can be rescheduled in units of hours or days. These tasks can be shifted to times when electricity prices are low or renewable energy output is high.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInter-regional Workload Migration\u003c/strong\u003e\nBy leveraging global cloud infrastructure, tasks from regions with congested power grids can be moved to other regions. However, data sovereignty, latency, and network costs pose constraints.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrioritizing Inference Workloads\u003c/strong\u003e\nBy distinguishing between services requiring real-time responses and analytical tasks that can tolerate latency, certain tasks can be reclassified as low priority during power shortages.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of Batteries, Thermal Storage, and Backup Resources\u003c/strong\u003e\u003cbr\u003e\nStorage devices and cooling systems within data centers can be utilized for grid demand response.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003ca href=\"#limitations\" class=\"anchor\" id=\"limitations\"\u003e\u003c/a\u003eLimitations\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eTasks with low latency tolerance—such as real-time search, customer support, financial transactions, and healthcare and security services—have limited room for adjustment.\u003c/li\u003e\n\u003cli\u003eWorkload migration may conflict with data protection regulations, regional cloud contracts, and latency requirements.\u003c/li\u003e\n\u003cli\u003eFor power flexibility to translate into actual carbon reductions, data on the carbon intensity of electricity by time of day is required.\u003c/li\u003e\n\u003cli\u003eFor operators to provide power flexibility, electricity rates and demand response compensation mechanisms must be sufficiently clear.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003ca href=\"#regional-water-resources-and-cooling-challenges\" class=\"anchor\" id=\"regional-water-resources-and-cooling-challenges\"\u003e\u003c/a\u003eRegional Water Resources and Cooling Challenges\u003c/h2\u003e\n\u003cp\u003eThe environmental impact of AI data centers extends beyond electricity alone. High-performance servers generate significant heat, and water usage can increase depending on the cooling method.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eCooling Method\u003c/th\u003e\n\u003cth\u003eAdvantages\u003c/th\u003e\n\u003cth\u003eConsiderations\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAir-cooling\u003c/td\u003e\n\u003ctd\u003eRelatively simple structure and may require less water\u003c/td\u003e\n\u003ctd\u003eMay have limitations for high-density AI racks\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEvaporative cooling\u003c/td\u003e\n\u003ctd\u003eCan help reduce electricity consumption\u003c/td\u003e\n\u003ctd\u003eWater usage may increase\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLiquid cooling\u003c/td\u003e\n\u003ctd\u003eSuitable for high-density GPU servers\u003c/td\u003e\n\u003ctd\u003eMay involve high system complexity and initial investment costs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHybrid cooling\u003c/td\u003e\n\u003ctd\u003eCan be combined based on climate and load\u003c/td\u003e\n\u003ctd\u003eOperational optimization is complex\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eIn regions with high water stress, data center water usage may compete with agriculture, domestic water supplies, and ecosystems. Therefore, local governments must review water consumption, the use of recycled water, drought response plans, and the potential for waste heat utilization during the permitting process.\u003c/p\u003e\n\u003ch2\u003e\u003ca href=\"#data-items-required-for-policy-and-regulatory-design\" class=\"anchor\" id=\"data-items-required-for-policy-and-regulatory-design\"\u003e\u003c/a\u003eData Items Required for Policy and Regulatory Design\u003c/h2\u003e\n\u003cp\u003eFor AI data center policies, data is more important than declarations. The following items must be jointly managed by local governments, power companies, and regulatory agencies.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eData Item\u003c/th\u003e\n\u003cth\u003eProvider\u003c/th\u003e\n\u003cth\u003ePurpose\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRequested power capacity, phased expansion plans\u003c/td\u003e\n\u003ctd\u003eData center operator\u003c/td\u003e\n\u003ctd\u003eAssessment of grid reinforcement needs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEstimated load by time period\u003c/td\u003e\n\u003ctd\u003eOperator, power company\u003c/td\u003e\n\u003ctd\u003ePeak demand and rate design\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eScope of power flexibility\u003c/td\u003e\n\u003ctd\u003eOperator\u003c/td\u003e\n\u003ctd\u003eDemand response contracts and emergency operation plans\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling method and estimated water consumption\u003c/td\u003e\n\u003ctd\u003eOperator\u003c/td\u003e\n\u003ctd\u003eWater resource impact assessment\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRenewable energy procurement plan\u003c/td\u003e\n\u003ctd\u003eOperator\u003c/td\u003e\n\u003ctd\u003eCarbon targets and regional power grid impact assessment\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransmission and substation reinforcement costs\u003c/td\u003e\n\u003ctd\u003eElectric utility\u003c/td\u003e\n\u003ctd\u003eCost allocation and rate approval\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLocal Employment, Tax Revenue, and Waste Heat Utilization Plans\u003c/td\u003e\n\u003ctd\u003eOperators·Local Governments\u003c/td\u003e\n\u003ctd\u003eAssessment of Community Acceptance\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEmissions Calculation Methodology\u003c/td\u003e\n\u003ctd\u003eOperators\u003c/td\u003e\n\u003ctd\u003eVerification of Sustainability Reports\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\u003ca href=\"#conclusion\" class=\"anchor\" id=\"conclusion\"\u003e\u003c/a\u003eConclusion\u003c/h2\u003e\n\u003cp\u003eThe increase in power demand from AI data centers is not just an issue for the technology industry. It is a public infrastructure issue that simultaneously involves grid investment, electricity rates, local permits, water resources, and carbon targets. The core of the policy is not to hinder AI innovation, but to transparently measure and fairly allocate the costs that large-scale electricity demand imposes on local communities and the power grid.\u003c/p\u003e\n\u003cp\u003eThe most realistic approach is to combine three elements. First, data center operators must disclose more granular data on electricity, water, and emissions. Second, power companies and regulatory agencies must develop rate structures that reflect the grid costs associated with large consumers. Third, AI computing operations should be flexibly adjusted to grid conditions to the extent possible.\u003c/p\u003e\n","tags":["AI Data Center","Power grid","Electricity rates","Carbon emissions","Sustainability"],"faqs":[{"question":"Why do AI data centers consume more power than regular data centers?","answer":"AI training and inference rely heavily on high-performance accelerators such as GPUs and TPUs, resulting in high power density per server rack. The equipment also generates a significant amount of heat, which increases the power required for cooling and, in some cases, water consumption as well."},{"question":"Are power grid bottlenecks caused solely by a shortage of power plants?","answer":"No. Even if there is sufficient power generation capacity, the data center may not receive the necessary power on time if there are shortages in transmission lines, substations, distribution networks, grid connection procedures, equipment delivery schedules, or local permits."},{"question":"Could AI data centers cause electricity rates for ordinary consumers to rise?","answer":"It is possible. Since investment in the power grid is required to connect large data centers, and if those costs are reflected in overall rates, ordinary consumers may also bear the burden. That is why design elements such as the “polluter pays” principle, demand-based rates, and time-of-use rates are important."},{"question":"Will signing a renewable energy power purchase agreement solve the data center's carbon problem?","answer":"While this may help address some of the issues, it may not be sufficient. Even if the annual volume of renewable energy purchased equals consumption, the carbon intensity of the electricity may vary depending on the actual time of consumption and the region, and transmission grid bottlenecks remain a separate issue."},{"question":"What is the first metric you should look at in a corporate sustainability report?","answer":"It is important to consider total energy consumption, renewable energy procurement methods, Scope 2 emissions, Scope 3 emissions, water consumption, PUE, and WUE together. Focusing on a single efficiency metric alone may cause you to overlook increases in total consumption or emissions in the supply chain."},{"question":"Does a low PUE mean a data center is eco-friendly?","answer":"While PUE is an important indicator of a facility’s energy efficiency, it is not a sufficient condition. Total power consumption, the carbon intensity of electricity, water usage, emissions from equipment manufacturing, and the impact on the local power grid must also be considered."},{"question":"What is a power-flexible AI factory?","answer":"This is an operational approach that adjusts the timing, location, and priority of AI training and inference tasks based on grid congestion, electricity prices, and renewable energy output. While it is advantageous for training tasks that can tolerate delays, its applicability to real-time services is limited."},{"question":"What should local governments check when issuing permits for AI data centers?","answer":"The following factors must be reviewed together: the requested power capacity, the phased expansion plan, the costs of reinforcing the transmission and distribution networks, water consumption, cooling methods, noise and heat impacts, local employment and tax revenue, waste heat utilization, and the emergency power operation plan."},{"question":"Why has water usage in data centers become a policy issue?","answer":"Some cooling methods can consume significant amounts of water, and in regions with high water stress, they may compete with domestic water use, agriculture, and ecosystems. Therefore, water consumption, along with electricity demand, is a key factor in local permitting decisions."},{"question":"Can the spread of AI coexist with carbon neutrality goals?","answer":"While these goals can coexist, they will not happen automatically. They require a combination of high-efficiency equipment, carbon-free power procurement by time of day, power flexibility, investment in the transmission grid, and transparent disclosure of emissions."}],"sources":[{"url":"https://www.iea.org/reports/key-questions-on-energy-and-ai","title":"IEA - Key Questions on Energy and AI","type":"source"},{"url":"https://www.capgemini.com/wp-content/uploads/2026/06/Final-Infographic-Data-Centers.pdf","title":"Capgemini - Data Centers Power Infrastructure Infographic","type":"source"},{"url":"https://sustainability.google/google-2026-environmental-report/","title":"Google 2026 Environmental Report","type":"source"},{"url":"https://sustainability.aboutamazon.com/2025-amazon-sustainability-report.pdf","title":"Amazon 2025 Sustainability Report","type":"source"},{"url":"https://www.weforum.org/stories/2026/06/is-ai-the-next-great-energy-technology/","title":"World Economic Forum - Is AI the Next Great Energy Technology?","type":"source"}],"images":[{"id":116,"url":"https://injoys.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MTEwOSwicHVyIjoiYmxvYl9pZCJ9fQ==--24e2c37e44ae23294770b11187b669b242e5e656/ai-66d8a04c.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"데이터센터와 전력망, 공장 배출가스, 주거지역, 물과 비용을 함께 보여주는 일러스트","caption":"AI 데이터센터의 전력 수요가 전력망, 배출, 물 사용, 전기요금 부담으로 이어지는 모습을 나타낸다.","description":null},"en":{"alt":"Illustration of a data center linked to power grids, emissions, homes, water use, and costs","caption":"The scene shows AI data center demand straining grids, emissions goals, water resources, and electricity bills.","description":null},"ja":{"alt":"データセンターと送電網、排出ガス、住宅、水資源、費用を描いたイラスト","caption":"AIデータセンターの電力需要が送電網、排出量、水利用、電気料金に圧力をかける様子を示している。","description":null},"es":{"alt":"Ilustración de un centro de datos conectado a redes eléctricas, emisiones, viviendas, agua y costos","caption":"La escena muestra cómo la demanda eléctrica de los centros de datos de IA presiona la red, las emisiones, el agua y las tarifas.","description":null},"id":{"alt":"Ilustrasi pusat data terhubung ke jaringan listrik, emisi, permukiman, air, dan biaya","caption":"Gambar ini menunjukkan kebutuhan listrik pusat data AI yang menekan jaringan, emisi, air, dan tagihan listrik.","description":null},"pt":{"alt":"Ilustração de um data center ligado à rede elétrica, emissões, casas, água e custos","caption":"A cena mostra a demanda de energia de data centers de IA pressionando redes, emissões, água e tarifas.","description":null},"zh-hant":{"alt":"資料中心連接電網、排放、住宅、用水與成本的插圖","caption":"畫面呈現 AI 資料中心用電需求對電網、排放、用水與電費造成壓力。","description":null}}},{"id":117,"url":"https://injoys.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MTExNSwicHVyIjoiYmxvYl9pZCJ9fQ==--92979d3eec1c9893780bdbd43bff5db85b674c7d/ai-c2ab6b65.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"전력망과 냉각수 배관으로 연결된 데이터센터, 태양광·풍력 설비와 전력 아이콘","caption":"데이터센터들이 전력망, 냉각수, 재생에너지 설비와 연결된 모습을 보여준다.","description":null},"en":{"alt":"Data centers linked to power lines and cooling pipes, with solar panels, wind turbines, and energy icons","caption":"The illustration shows data centers drawing power and water while connected to renewable energy and the grid.","description":null},"ja":{"alt":"送電線と冷却水配管でつながるデータセンター、太陽光・風力設備と電力アイコン","caption":"データセンターが電力網や冷却水、再生可能エネルギー設備と結び付く様子を示している。","description":null},"es":{"alt":"Centros de datos conectados a líneas eléctricas y tuberías de agua, con paneles solares y turbinas eólicas","caption":"La ilustración muestra centros de datos conectados a la red, al agua de enfriamiento y a energías renovables.","description":null},"id":{"alt":"Pusat data terhubung ke jaringan listrik dan pipa pendingin, dengan panel surya, turbin angin, dan ikon energi","caption":"Ilustrasi ini menunjukkan pusat data yang terhubung ke listrik, air pendingin, dan energi terbarukan.","description":null},"pt":{"alt":"Centros de dados conectados à rede elétrica e a tubos de resfriamento, com solar e eólica","caption":"A ilustração mostra centros de dados ligados à rede, à água de resfriamento e a fontes renováveis.","description":null},"zh-hant":{"alt":"資料中心連接電網與冷卻水管，周圍有太陽能板、風力發電機與電力圖示","caption":"插圖呈現資料中心與電網、冷卻用水和再生能源設施相互連結。","description":null}}}],"published_at":"2026-07-09T23:13:39+09:00","updated_at":"2026-07-09T23:13:39+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant"],"url":"https://injoys.com/en/articles/ai-data-center-power-grid-tariffs-carbon"}