Responsible Use of AI to Minimize Carbon Footprint
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Responsible Use of AI to Minimize Carbon Footprint
Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, revolutionizing industries ranging from healthcare to finance, transportation, and beyond. As AI continues to evolve, it promises to unlock unprecedented efficiencies, drive innovation, and create new opportunities across the global economy. However, the rapid development and deployment of AI technologies have also raised significant concerns regarding their environmental impact, particularly in terms of their carbon footprint and contribution to global warming.
The energy consumption associated with AI is considerable, especially when training large-scale models that require vast computational resources. This energy usage, in turn, contributes to greenhouse gas (GHG) emissions, which exacerbate climate change. To address these challenges, it is crucial to adopt responsible practices that minimize the environmental footprint of AI. This newsletter explores the responsible use of AI to reduce its carbon footprint, focusing on optimizing energy efficiency, leveraging renewable energy, promoting transparency, and integrating sustainability throughout the AI lifecycle.
Understanding AI's Environmental Impact
1. The Role of AI in Carbon Footprint
AI technologies, particularly those that rely on deep learning and large-scale models, are inherently energy-intensive. Training a state-of-the-art AI model often involves running computations on thousands of processors for days, weeks, or even months. For example, OpenAI's GPT-3, one of the largest language models ever created, required thousands of petaflop/s-days (a measure of computational power) to train. This level of computational intensity translates into significant energy consumption.
According to a study by the University of Massachusetts Amherst, training a large AI model can emit as much carbon as five cars over their lifetimes (626,000 pounds of CO2). The energy used during the training phase of AI models primarily comes from data centers, which are responsible for around 1% of global electricity demand, a figure that could increase to 2.6% by 2030. Data centers are expected to consume approximately 200 to 250 Terawatt hours (TWh) of electricity annually, which is equivalent to the energy consumption of some entire countries.
In addition to electricity consumption, the environmental impact of AI also includes emissions from natural gas, diesel fuel, and refrigerant leakage used in data center cooling systems. For instance, refrigerant leakage from cooling systems in data centers contributes to global warming due to the high Global Warming Potential (GWP) of substances like hydrofluorocarbons (HFCs). HFCs can be thousands of times more potent than CO2 in terms of their warming effect.
2. Global Warming Potential (GWP) and AI
Global Warming Potential (GWP) is a crucial metric for understanding the environmental impact of various greenhouse gases emitted by AI operations. GWP measures the amount of heat a greenhouse gas traps in the atmosphere over a specific time period, usually 20, 100, or 500 years, relative to carbon dioxide (CO2), which has a GWP of 1.
Different greenhouse gases have different GWPs. For example:
AI systems contribute to GWP through the emissions associated with the energy consumption of data centers, as well as through the leakage of refrigerants used in cooling systems. Therefore, it is essential to consider GWP when assessing the carbon footprint of AI operations.
Strategies for Reducing AI's Carbon Footprint
1. Optimizing Energy Efficiency
Algorithm Efficiency
The energy consumption of AI models can be significantly reduced by optimizing the algorithms used in their development and deployment. One approach is to use sparse models, which have fewer parameters and require less computation, thereby reducing energy use without compromising performance. Research has shown that sparse models can reduce energy consumption by up to 90% compared to dense models, making them a more sustainable choice for AI development.
Another effective strategy is model pruning, where unnecessary weights and connections in neural networks are removed. This reduces the complexity of the model, leading to lower computational requirements and energy consumption. For instance, pruning a neural network can reduce its size by up to 90%, resulting in significant energy savings during both training and inference.
Specialized Hardware
The choice of hardware also plays a critical role in determining the energy efficiency of AI systems. Using processors specifically designed for AI workloads, such as Tensor Processing Units (TPUs) or Graphics Processing Units (GPUs), can lead to substantial energy savings. These specialized chips are more efficient than general-purpose processors, reducing the overall energy required for AI training and inference.
For example, Google's TPU v4, which is optimized for deep learning tasks, offers a 2x increase in performance per watt compared to its predecessor. This means that AI models trained on TPUs can achieve the same level of performance with half the energy consumption, significantly reducing their carbon footprint.
Efficient Data Centers
Data centers are at the heart of AI operations, and optimizing their energy use is critical to minimizing the carbon footprint of AI. One approach is to choose cloud-based data centers that operate in regions with cleaner energy sources, such as hydropower or wind energy. A study by the Lawrence Berkeley National Laboratory found that cloud data centers can be up to 80% more energy-efficient than traditional on-premises data centers, primarily due to their ability to aggregate workloads and optimize resource utilization.
In addition to choosing energy-efficient data centers, companies can also invest in AI-driven optimization techniques to further reduce energy consumption. For example, Google's DeepMind AI was able to reduce the energy consumption for cooling its data centers by 40% using machine learning algorithms that optimize cooling processes. This reduction was achieved by analyzing data from thousands of sensors to adjust cooling systems in real-time, demonstrating the potential of AI to enhance energy efficiency in data center operations.
2. Leveraging Renewable Energy
Renewable Energy Integration
Shifting to renewable energy sources is one of the most effective ways to reduce the carbon footprint of AI systems. Renewable energy, such as wind, solar, or hydropower, generates electricity without producing greenhouse gas emissions, making it a cleaner alternative to fossil fuels. Companies like Amazon, Microsoft, and Google have made significant investments in renewable energy to power their data centers, setting a precedent for the industry.
For instance, Amazon has committed to achieving 100% renewable energy across its operations by 2025, with plans to power its data centers with wind and solar energy. Microsoft has also pledged to be carbon negative by 2030, meaning it will remove more carbon from the environment than it emits. By 2020, Microsoft had already achieved a 60% reduction in its carbon footprint by transitioning to renewable energy sources.
Green Power Purchase Agreements (PPAs)
Green Power Purchase Agreements (PPAs) are contracts between a company and a renewable energy provider to purchase electricity generated from renewable sources. By entering into these agreements, AI companies can ensure that their operations are powered by clean energy, even if their data centers are located in regions with a higher reliance on fossil fuels.
For example, in 2019, Google signed a series of PPAs to purchase 1,600 megawatts (MW) of renewable energy, making it the largest corporate buyer of renewable energy in the world at the time. These agreements allowed Google to match its global energy consumption with 100% renewable energy, significantly reducing its carbon footprint.
On-Site Renewable Generation
In addition to purchasing renewable energy through PPAs, some companies are investing in on-site renewable energy generation. This involves installing solar panels, wind turbines, or other renewable energy systems at data center locations to directly power their operations.
Google has implemented on-site solar installations at several of its data centers, further reducing its carbon footprint. For example, its data center in Belgium is partially powered by on-site solar panels, which generate 2.1 MW of electricity, enough to power approximately 1,000 homes. By generating renewable energy on-site, companies can reduce their reliance on the grid and lower their overall carbon emissions.
3. Promoting Transparency and Accountability
Energy and Emissions Tracking
Transparent reporting of energy consumption and emissions is critical for understanding the environmental impact of AI systems. Companies need to accurately measure and report the energy use and associated emissions of their AI models to identify areas for improvement and to demonstrate their commitment to sustainability.
Tools such as the Greenhouse Gas Protocol provide methodologies for tracking and reporting emissions. These tools help companies calculate their carbon footprint, taking into account the GWP of different greenhouse gases. By reporting this data publicly, companies can hold themselves accountable and encourage industry-wide transparency.
For example, Microsoft has developed a Carbon Calculator tool that allows customers to track the carbon emissions associated with their use of Azure cloud services. The tool provides insights into energy consumption, emissions, and the carbon intensity of different Azure regions, helping customers make informed decisions about where to run their AI workloads.
Lifecycle Assessment
A comprehensive lifecycle assessment (LCA) considers the environmental impact of AI systems at every stage, from development to deployment and disposal. This approach helps companies make informed decisions about sustainability, reducing emissions across the entire lifecycle of AI models.
For instance, an LCA of AI models might consider the energy consumption and emissions associated with the manufacturing of hardware, the training and deployment of models, and the disposal or recycling of obsolete equipment. By identifying the most carbon-intensive stages of the lifecycle, companies can implement targeted strategies to reduce their environmental impact.
Regulatory Compliance
Compliance with regulations that require disclosure of environmental impact is essential for accountability. The European Union, for example, imposes penalties on companies that fail to comply with regulations related to high-GWP substances, ensuring that businesses take their environmental responsibilities seriously.
The European Union has recently updated its regulations on hydrofluorocarbons (HFCs) with the adoption of Regulation (EU) 2024/573, which introduces more stringent measures to phase down the use of these potent greenhouse gases. The new regulation, effective from 2024, sets a more aggressive timeline, aiming for a 98% reduction in HFCs by 2048 compared to 2015 levels. Significant reductions will occur between 2027 and 2029, with stricter bans on HFC use in various air conditioning and refrigeration systems starting in 2025. This regulation is part of the EU's broader strategy to meet its climate goals under the European Green Deal and to transition towards natural refrigerants with lower global warming potential (GWP)
Case Studies: AI's Role in Reducing Carbon Footprints
1. Google's DeepMind and Data Centers
Google's DeepMind AI has been instrumental in reducing the energy consumption of its data centers. By optimizing cooling processes, DeepMind achieved a 40% reduction in energy use, translating into a 15% reduction in the overall energy consumption of the data center. This significant decrease in energy usage demonstrates the potential of AI to enhance energy efficiency and reduce the carbon footprint of large-scale operations.
The success of DeepMind's AI-driven optimization in data centers has led Google to explore other applications of AI in sustainability. For example, Google is now using AI to optimize the energy consumption of its office buildings, with early results showing a 10% reduction in energy use.
2. Smart Grids and Renewable Energy Integration
In Denmark, AI is being used to manage the country's wind energy resources, which account for over 40% of its electricity generation. The AI-powered wind farm management system optimizes turbine operation based on real-time weather forecasts and grid demand. By adjusting the turbines to maximize energy production when the wind is strong and reduce output during periods of low demand, the system increased energy output by 15% and reduced maintenance costs.
This case study highlights the potential of AI to enhance the integration of renewable energy sources into the grid. By optimizing the use of wind and solar energy, AI can help reduce the reliance on fossil fuels, lower carbon emissions, and increase the overall sustainability of the energy system.
3. European Bank's HVAC and Lighting Systems
A European bank with over 3,000 branches implemented an AI-powered system to regulate its HVAC (heating, ventilation, and air conditioning) and lighting systems. The AI system optimizes energy use by analyzing data from thousands of sensors to adjust heating, cooling, and lighting based on real-time occupancy and environmental conditions.
As a result, the bank achieved a 30% reduction in energy costs and a significant decrease in carbon emissions. This case study demonstrates how AI can be used to optimize energy consumption in large networks, leading to substantial environmental benefits and cost savings.
Tools for Calculating Carbon Footprints Using GWP
1. GHG Protocol Tools
The Greenhouse Gas Protocol (GHG Protocol) is the most widely used international accounting tool for government and business leaders to understand, quantify, and manage greenhouse gas emissions. The GHG Protocol provides a comprehensive suite of tools for calculating carbon footprints, allowing companies to develop reliable inventories of their GHG emissions.
These tools include cross-sector tools, which can be applied across different industries, and sector-specific tools, which are tailored to specific industries such as energy, manufacturing, and transportation. By using these tools, companies can accurately calculate their carbon footprint, taking into account the GWP of different greenhouse gases.
2. One Click LCA
One Click LCA is a life-cycle assessment (LCA) software designed to help manufacturers calculate the carbon footprint of their products. The software complies with international standards such as ISO 14067 and provides a streamlined process for conducting LCAs.
One Click LCA allows companies to assess the environmental impact of their products throughout their lifecycle, from raw material extraction to manufacturing, distribution, use, and disposal. By providing detailed insights into the carbon footprint of their products, One Click LCA helps companies identify opportunities for reducing emissions and improving sustainability.
3. Carbon Footprint Calculators
Various online carbon footprint calculators are available for individuals, households, and small businesses. These tools estimate the total GHG emissions produced by an individual, organization, or activity, based on factors such as energy consumption, transportation habits, and lifestyle choices.
For example, the U.S. Environmental Protection Agency (EPA) offers a Carbon Footprint Calculator that guides users through the process of estimating their emissions. The tool provides actionable recommendations for reducing emissions, such as improving energy efficiency, reducing waste, and switching to renewable energy sources.
The WWF Footprint Calculator is another popular tool that assesses personal emissions by considering factors like energy use, transportation, and diet. It offers insights into how individuals can reduce their carbon footprint by making more sustainable choices.
Calculating Carbon Footprint in Data Centers and Eco-Resorts
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1. Carbon Footprint Calculation in Data Centers
Data centers are critical infrastructure for AI operations, providing the computational power required to run complex algorithms, process vast amounts of data, and store information securely. However, they are also significant consumers of energy and contributors to greenhouse gas emissions. To calculate the carbon footprint of a data center, several key elements must be considered:
a. Electricity Consumption:
Electricity is the primary driver of carbon emissions in data centers. The energy used to power servers, networking equipment, and storage systems contributes directly to the facility’s carbon footprint. As of 2020, data centers were responsible for approximately 200 to 250 Terawatt hours (TWh) of electricity annually, accounting for about 1% of global electricity demand.
To calculate the carbon footprint associated with electricity consumption, the energy mix used to generate this electricity must be considered. For example, if a data center is powered by coal, the carbon emissions will be significantly higher compared to a data center powered by renewable energy sources like wind or solar. The carbon intensity of electricity generation, often measured in kilograms of CO2 per kilowatt-hour (kg CO2/kWh), is used to estimate the emissions from electricity consumption.
b. Cooling Systems and Refrigerant Leakage:
Cooling systems are essential to maintain optimal operating temperatures for data center equipment. However, these systems can contribute significantly to a data center's carbon footprint, both through energy consumption and refrigerant leakage.
Refrigerants used in cooling systems, such as hydrofluorocarbons (HFCs), have high Global Warming Potential (GWP). For instance, R-134a, a common HFC refrigerant, has a GWP of 1,430, meaning it is 1,430 times more potent than CO2 in terms of its impact on global warming. Leakage of refrigerants during maintenance or through system wear can contribute significantly to the overall carbon footprint.
To mitigate this, data centers can use alternative cooling technologies, such as free-air cooling, which leverages ambient air to cool equipment without the need for energy-intensive refrigeration. Additionally, using refrigerants with lower GWP or implementing stricter maintenance protocols to prevent leaks can reduce the carbon footprint.
c. Diesel Generators and Backup Power:
Data centers often rely on diesel generators as a backup power source to ensure continuous operation during power outages. Diesel fuel combustion emits significant amounts of CO2, contributing to the overall carbon footprint. The carbon intensity of diesel fuel is approximately 2.68 kg CO2 per liter.
To calculate the emissions from diesel generators, it is necessary to estimate the amount of diesel fuel consumed during testing, maintenance, and actual power outages. This data, combined with the carbon intensity of diesel, provides an estimate of the emissions associated with backup power systems.
d. Water Usage for Cooling
Some data centers use water-cooled systems, where water is circulated to absorb heat from the servers and then cooled through evaporation or heat exchange. The energy required to treat and pump water, as well as the emissions associated with water consumption, must be included in the carbon footprint calculation.
For example, in the United States, water-related energy use accounts for about 13% of the nation’s electricity consumption. The carbon footprint of water usage can be calculated based on the volume of water consumed and the energy required for water treatment and distribution.
e. Carbon Offsetting and Renewable Energy Certificates (RECs):
Many data centers offset their carbon emissions by purchasing Renewable Energy Certificates (RECs) or investing in carbon offset projects. RECs represent proof that one megawatt-hour (MWh) of electricity was generated from a renewable energy resource. By purchasing RECs, data centers can effectively reduce their net carbon footprint, even if they are not directly powered by renewable energy.
The effectiveness of carbon offsetting depends on the quality of the offset projects and the accuracy of the carbon footprint calculation. High-quality carbon offset projects, such as reforestation or renewable energy development, can provide meaningful reductions in global greenhouse gas emissions.
2. Carbon Footprint Calculation in Eco-Resorts
Eco-resorts are designed to minimize their environmental impact by incorporating sustainable practices in energy use, water management, waste reduction, and resource conservation. Calculating the carbon footprint of an eco-resort involves assessing various elements that contribute to emissions throughout the resort’s operations:
a. Electricity and Energy Use:
Like data centers, electricity consumption is a significant component of an eco-resort’s carbon footprint. However, eco-resorts often prioritize renewable energy sources to minimize their environmental impact. Solar panels, wind turbines, and geothermal energy are commonly used to power resort operations, including lighting, heating, cooling, and other amenities.
For example, a luxury eco-resort in Costa Rica, which is powered by 98% renewable energy, has a carbon footprint of approximately 4.7 kg CO2e per room per night, significantly lower than traditional resorts powered by fossil fuels. The carbon footprint calculation for electricity use in an eco-resort includes the carbon intensity of the energy sources and the total energy consumption.
b. Water Consumption and Treatment:
Water usage in eco-resorts includes consumption for guest amenities, landscaping, and on-site agriculture. The carbon footprint of water consumption is calculated based on the energy required for extraction, treatment, and distribution. In regions where water is scarce, energy-intensive desalination processes may be used, increasing the carbon footprint.
To reduce their water-related carbon footprint, eco-resorts often implement water conservation measures, such as low-flow fixtures, greywater recycling, and rainwater harvesting. The energy savings from these practices can be quantified and included in the overall carbon footprint calculation.
c. Waste Management and Recycling:
Waste generation and disposal contribute to the carbon footprint of eco-resorts, particularly through methane emissions from organic waste decomposition in landfills. Methane has a GWP of 28-30 over 100 years, making it a potent contributor to global warming.
Eco-resorts typically implement waste reduction strategies, such as composting organic waste, recycling, and reducing single-use plastics. The effectiveness of these strategies in reducing the carbon footprint can be assessed by measuring the volume of waste diverted from landfills and the associated reduction in methane emissions.
d. Guest Transportation:
Guest transportation to and from the resort is another significant contributor to the carbon footprint. Air travel, in particular, has a high carbon intensity, with long-haul flights emitting approximately 0.2 kg CO2e per passenger-kilometer. To calculate the carbon footprint of guest transportation, eco-resorts can estimate the average distance traveled by guests and the mode of transportation used.
Some eco-resorts offer carbon offset programs for guest travel, where the emissions from flights or car travel are offset through projects such as reforestation or renewable energy development. These offsets can help reduce the overall carbon footprint of the resort.
e. Building Materials and Construction:
The carbon footprint of an eco-resort also includes the embodied carbon of building materials and construction activities. Embodied carbon refers to the greenhouse gas emissions associated with the production, transportation, and installation of building materials.
Eco-resorts often use sustainable building materials, such as locally sourced timber, bamboo, or recycled materials, to reduce their embodied carbon. The carbon footprint calculation for construction includes the emissions from material production, transportation, and on-site construction activities.
f. Carbon Sequestration and Conservation Projects:
Many eco-resorts engage in carbon sequestration projects, such as reforestation, to offset their carbon emissions. These projects involve planting trees or restoring ecosystems that capture and store CO2 from the atmosphere. The carbon sequestration potential of these projects can be calculated and subtracted from the overall carbon footprint of the resort.
For example, a reforestation project may sequester 10 metric tons of CO2 per hectare per year. If an eco-resort manages 100 hectares of reforested land, this could offset 1,000 metric tons of CO2 annually, significantly reducing the resort’s net carbon footprint.
The Complexities of Carbon Footprint Calculation
The calculation of carbon footprints in data centers and eco-resorts illustrates the complexity and the various factors that must be considered to accurately assess environmental impact. By taking into account elements such as electricity consumption, cooling systems, water usage, waste management, transportation, and construction, organizations can develop a comprehensive understanding of their carbon emissions and identify opportunities for reduction.
For data centers, the focus on energy efficiency, refrigerant management, and the integration of renewable energy can lead to substantial reductions in carbon footprint. For eco-resorts, sustainable practices in energy use, water management, waste reduction, and carbon sequestration are key to minimizing environmental impact.
As organizations strive to achieve sustainability goals, tools and methodologies for calculating carbon footprints, such as those provided by the Greenhouse Gas Protocol and other lifecycle assessment platforms, will play a crucial role. By accurately measuring and reporting emissions, companies can hold themselves accountable, set meaningful reduction targets, and contribute positively to global efforts to combat climate change.
The examples of data centers and eco-resorts demonstrate that while calculating carbon footprints can be complex, it is a necessary step in the journey toward sustainability. Through careful analysis and the adoption of best practices, organizations can minimize their environmental impact and help pave the way for a more sustainable future.
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The Role of Global Warming Potential in Carbon Footprint Calculations
1. Understanding GWP
Global Warming Potential (GWP) is a critical metric for comparing the climate impact of different greenhouse gases. GWP provides a standardized way to assess the relative contribution of various gases to global warming, allowing for the aggregation of emissions from different sources into a single metric, expressed as carbon dioxide equivalents (CO2e).
For example, methane (CH4) has a GWP of 28-30 over 100 years, meaning that one tonne of methane emissions is equivalent to 28-30 tonnes of CO2 in terms of its impact on global warming. This makes methane a high-priority target for emission reduction strategies, even though its atmospheric concentration is lower than that of CO2.
2. Strategic Implications
Understanding the GWP of different gases is essential for developing effective emission reduction strategies. For instance, reducing emissions of high-GWP gases like methane and HFCs can have a significant impact on the overall carbon footprint of an organization or activity.
In addition to targeting high-GWP gases, companies can use GWP to prioritize emission reduction efforts across different stages of the AI lifecycle. For example, an LCA might reveal that the manufacturing of hardware has a higher GWP than the energy consumption of AI models during operation. In this case, the company might focus on sourcing materials with lower GWP or improving the energy efficiency of the manufacturing process.
Responsible AI Design for Minimizing Carbon Footprint
1. Energy-Efficient AI Design
Algorithm Optimization
Designing AI systems with energy-efficient algorithms is crucial for minimizing their carbon footprint. Techniques such as quantization, pruning, and energy-aware training can reduce the computational load of AI models, leading to lower energy consumption without sacrificing performance.
For example, a study by MIT researchers found that by applying quantization and pruning techniques to a deep learning model, they were able to reduce its energy consumption by up to 94% while maintaining the same level of accuracy. This demonstrates the potential of algorithm optimization to significantly reduce the environmental impact of AI.
Sustainable Hardware
In addition to optimizing algorithms, designing AI systems with sustainable hardware can further reduce their carbon footprint. Using specialized AI chips, such as TPUs or FPGAs, can improve energy efficiency and reduce the overall environmental impact of AI operations.
Collaborative design efforts that align hardware and software for maximum efficiency are essential for sustainable AI development. For example, companies like NVIDIA and Google are working on hardware-software co-design projects to optimize AI performance while minimizing energy use.
2. AI for Environmental Benefits
Energy Management
AI can be used to optimize energy consumption in real-time, adjusting power usage based on workload and demand. This improves the overall efficiency of data centers and AI systems, contributing to a lower carbon footprint.
For example, AI-driven energy management systems can dynamically adjust the power settings of servers and cooling systems based on real-time demand, reducing energy consumption during periods of low activity. Studies have shown that AI-powered energy management can reduce data center energy consumption by up to 30%.
Predictive Maintenance
AI-driven predictive maintenance enhances equipment efficiency and reduces downtime, lowering energy use and extending the life of infrastructure. This approach not only saves costs but also reduces the environmental impact of energy-intensive operations.
For example, Shell uses AI for predictive maintenance in its oil and gas operations, analyzing data from sensors on pipelines and drilling equipment to identify potential issues before they lead to failures. By proactively addressing maintenance needs, Shell has reduced unplanned downtime and extended the life of its equipment, contributing to more efficient and sustainable operations.
Conclusion
The responsible use of AI to minimize its carbon footprint is critical for ensuring that the benefits of this transformative technology are realized without compromising the environment. By optimizing energy efficiency, leveraging renewable energy, promoting transparency, and integrating sustainability into AI development, the technology industry can play a pivotal role in reducing global greenhouse gas emissions. As AI continues to evolve, it is essential that these practices are adopted and scaled to ensure a sustainable future.
The strategies, tools, and case studies presented in this article demonstrate that it is possible to harness the power of AI while minimizing its environmental impact. By adopting a comprehensive approach to sustainability, AI developers, companies, and policymakers can ensure that AI contributes positively to global efforts to combat climate change. The integration of AI into various sectors holds the promise of not only enhancing operational efficiencies but also driving significant reductions in carbon footprints, making AI a crucial tool in the fight against climate change.
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