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Tools Towards the Sustainability and Circularity of Data Centers

• Original Paper
• Published: 01 July 2022
• Volume 3 , pages 173–197, ( 2023 )

Cite this article

• Mohamed Sameer Hoosain 1 ,
• Babu Sena Paul 1 ,
• Susanna Kass 2 &
• Seeram Ramakrishna 3  

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We are living in an age when data centers are expanding, require abundant spaces, and are an integral part in the urban communities, using massive amounts of environmental resources, and remains in the foreseeable future as the primary driver of the global energy consumption. This demand is disruptive and at times of both peril and opportunity due to impacts such as the COVID-19 pandemic, which is altering the demand of digital infrastructure around the world. With the global call for zero carbon emissions, there needs to be solutions put in place for the de-carbonization of data centers. New innovations are made available, which will have an economic, social, and environmental impact on data centers. Concepts such as circular economy and fourth industrial revolution technologies are useful procedural tools that can be used to systematically analyze data centers, control their mining and critical raw materials, can be utilized in the transition towards a sustainable and circular data center, by objectively assessing the environmental and economic impacts, and evaluating alternative options. In this paper, we will look at the current research and practice, the impact on the United Nations Sustainable Development goals, and look at future strides being taken towards more sustainable and circular data centers. We had discovered that decreasing the environmental effect and energy consumption of data centers is not sufficient. When it comes to data center architecture, both embodied and operational emissions are critical. Data centers also have a vital societal role in our daily lives, enabling us to share data and freely communicate via social media, transacting on the blockchain with cryptocurrencies, free online education, and job creation. As a result, sustainability and efficiency measures have expanded in a variety of ways, including circularity and its associated tools, as well as newer technologies.

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Introduction

The increasing demand for data center sustainability and efficiency is directly proportional to the cost-effectiveness and daily demand of the environmental resources with regard to the manufacturing of data center products, construction, operation of the data centers, and reduction in the environmental impacts such as the carbon emissions and waste. They must also keep up with external factors, including driving forces of the likes of Industry 4.0 or also known as the fourth industrial revolution (4IR).

We have seen a growth in technical developments since the commencement of the industrial revolution. Factories were first powered by water and steam engines in the nineteenth century, then by electricity in the twentieth century, and ultimately by automation in the 1970s. We are presently on the verge of a revolutionary digital industrial technology. Cyber physical systems can communicate with one another in this fourth technology wave in business by employing artificial intelligence (AI), machine learning (ML), Big Data, and the Internet of Things (IoT), among other technologies. Japan proposed the intelligent manufacturing system (IMS) in the 1980s, and the US followed suit with the cyber physical system (CPS). Germany recently offered Industry 4.0, while China advocated China Manufacturing 2025. Productivity and growth will surely improve as a result of Industry 4.0.

If we are to succeed with circular economics in the data center industry, we must deliberately depart from the linear practices, transition to circular thinking, utilize newer digital technologies, measure our progress throughout the lifecycle for a data center, and facilitate these changes for those who design, build, manage, and maintain them. Therefore, the entire life cycle needs to be considered. Circular economy tools can assist in the transition from a linear economy to a circular economy in a data center, which in turn can lead to sustainability, efficiency, and optimization [ 1 ].

Like industrial systems, data centers require significant amounts of natural resources with a growing demand for energy and materials. It has a certain significance, and it is regarded as the dedicated space used for inventory management of computer hardware, require sufficient space, environmental control (air conditioned cooling, fire suppression, water usage), data connectivity, and power supply. Therefore, the large-scale data center industry is responsible for substantial environmental degradation and is now on the radar of growing environmental concern due to huge amounts of annual carbon dioxide emission. We need to rethink how not to, dig up materials, turn them into data center products, generate large volumes of carbon emissions, waste heat in the data center operation, and discard the electronic waste (e-waste) in the environment. We need to adopt circular thinking to create new sustainable products, as the circular economy synchronizes with each phase of the data center life cycle keeping materials in circulation. Several solutions and research outputs have been put in place for data centers to create sustainability and circularity.

Think of data centers as ever-evolving to support the surge of 2.7 billion online meeting minutes per day in the month of March 2020, the on-demand data analytics in zettabytes caused by tele-medicine and tele-diagnostic applications, and the sensors in the 40 billion IoT constantly gathering and correlating data. The Google Sustainability post reports 54% of the world’s population lives in the urban areas which account for 75% of natural resource consumption, 50% of the global waste production, and 60–80% of greenhouse gas emissions (GHGE). According to the United Nations Migration report, 68% of the global population is expected to migrate and reside in urban communities, giving rise to 10 more megacities each with over 10 million residents. More data centers will be built in these urban communities; there is an opportunity now to develop “circular data center cities” to adapt to the digital transformation changes. The data center life span depends on use, design, build, and operation. Just as you would care for a human being, the same is needed for data centers in the form of new technology adoption, maintenance, and upgrade of the physical and digital infrastructure [ 2 ].

Circular thinking is not just for data centers; they range from general construction to any electrical, mechanical, manufacturing, safety, and other systems. By using circular economy tools, stakeholders gain better insight into their buildings and businesses, while at the same time achieving sustainability and circularity [ 3 ].

While the data center sector continues to expand and its efficiency is being scrutinized more closely, it is important to raise awareness about where its highest economic, social, and environmental effects are. Examining the factors that affect this impact can help guide policy makers and decision makers, as well as promote the development of sustainable and circular data centers. It is time to adopt sustainability practices within data centers and simultaneously maximize their life cycle [ 4 ].

Data Centers and Information and Communication Technology

Since the late 1950s, when American Airlines and IBM collaborated to create a passenger reservation system provided by Sabre, the idea of data centers has existed, automating one of its main business areas.

Data centers are information-based repositories which include server farms and networking equipment that store, process, and transmit enormous amounts of data. They offer digital services like cloud computing, virtualization, high availability, and unlimited compute, storage of data processing, data analytics, and data storage. A conventional data center comprises software digital application and physical infrastructure of:

Information technology (IT) systems (server units, storage units, network, and communication equipment);

Mechanical systems (compressors, heat rejection fans, pumps, cooling technologies);

Electrical systems (transformers, uninterrupted power supply (UPS), generators, power distribution units (PDU), switches, rack mounted UPS, lighting);

Physical infrastructure (building, physical facility, modular IT container).

Similar to two computers linked in a local network, internet servers transmit information through network connections to any internet addressable devices. The data center is the physical repository of the cloud, providing instantaneous access to digital services and cloud applications with unlimited compute, network and data scalability, and storage. Internet, cloud computing, and data centers make the internet possible. All the machines on the internet either provide the cloud and digital services to other machines (servers) or are used to access data of social media, digital services, and cloud applications (clients) using Internet protocol (IP) addresses. IP addresses are uniquely allocated to a computer or server that links to the internet. They assist in command mediation, addressing, and processing of data. The conversion of IP addresses into domain names (e.g.,.com,.co,.za,.org, or.gov) radically altered the essence of the internet to deliver equal and ubiquitous access.

Tiers are used by Uptime Institute to identify the redundancy representation of the data center components at the infrastructure level to compare the uptime of different data centers, namely:

Tier 1 — a single path for power and cooling and few, if any, redundant and backup components.

Tier 2 — same as tier 1, but with less down time annually.

Tier 3 — has multiple paths for power and cooling and systems in place to update and maintain it without taking it offline.

Tier 4 — built to be completely fault tolerant with redundancy for every component.

Most large communications companies have dedicated backbones of their own which link different regions. In each area, the company has a point of presence (POP), a location where local customers can reach the company’s network either through a local telephone number or a dedicated line.

It is estimated that data centers use 200 terawatt hours (TWh) each year, which surpasses that of some countries. Data centers contribute about 0.3% of total global carbon emissions, whereas information and communications technology (ICT) accounts for more than 2% of global emissions. This is predicted to increase to 20% by 2030, which in turn rises the carbon footprint of economies, according to the International Energy Agency in 2017. We look at the key estimates of digital evolution between 2018 and 2023 by Cisco which is re-modeled in Fig.  1 [ 5 ]. Contrary to common opinion, as the number of data centers increase every year, their energy consumption is constant and even slowly declining.

Estimated digital evolution percentage increase from 2018 to 2023 elaborated from [ 5 ]

Standards and regulations need to be adhered to when planning, constructing, and running a data center. These codes are for basic security standards to guarantee protection of life and energy conservation. The major industry standards developed and are most often applied for the data center are shown below in Table 1 [ 6 ][ 7 ].

Data centers range in size from a single server room to clusters of buildings spread over many locations, but they are all common in regard to being business assets. We list some of the common types of data centers below:

Enterprise — built and utilized by a single organization for its own internal objectives. These are frequent among technology giants.

Colocation — functions as a type of rental property in which the space and resources of a data center are made available to those who wish to rent them.

Hyper Scale — Cloud Computing has proven to optimize energy resources that private data center as a viable option by providers like Amazon Web Services, Google, and Microsoft Azure, and others, that are constructing new data centers in the form of Hyper Scale Data Centers. They offer scalable apps and storage portfolio services for businesses and developers.

Edge — these are newer types of data centers and are smaller facilities that supply cloud computing resources and cached information to end users and are placed near to the populations they serve. They are often linked to a bigger central data center or a network of data centers.

Circular Economy

Circular economy ideas gained popularity in the late 1970s. Through literature, there is often a connection between sustainability and circular economy, and it has gained popularity among researchers and practitioners. Internationally, organizations such as the Ellen Macarthur Foundation have been promoting the term in a variety of fields. Critics claim that it can be defined differently to different people. According to research, the circular economy is most commonly described as a combination of reducing, reusing, and recycling activities, whereas it is frequently overlooked that circular thinking necessitates systemic reform [ 8 ].

Establishing a circular economy is essential for overcoming growing waste obstacles, which are vital for both resource management and environmental protection, and will lead to a prosperous and competitive digital economy. It can lead to a reduction in waste such as e-waste, socio impacts such as water, supply chains, and maximize the re-use of recycled and second-hand products. Not forgetting the reduction in GHGE and job creation [ 9 ]. We depict a typical data center circular economy approach example of our own in Fig.  2 . The diagram represents the steady movement of technical materials through the value chain.

A typical circular economy approach example

Using current evaluation methodologies and methods is part of the Circular Economy toolbox. There are a variety of resources available, and the use of new technologies has made them more accessible. Online libraries and databases, software templates, online calculators, and algorithms are examples of emerging technologies. We look at some tools in Table 2 that can be used for the transition to a circular economy [ 10 ] [ 11 ].

Life cycle assessment (LCA) monitors environmental effect, life cycle costing (LCC) measures economic impact, and social life cycle assessment (S-LCA), which is a relatively young and expanding subject, assesses social impact. To measure sustainability, all the three techniques are currently being merged into a new statistic called life cycle sustainability assessment (LCSA).

To investigate similarities and gather useful evidence, a literature review was conducted. This article’s analysis process was accompanied by a snowballing technique for an in-depth evaluation. We looked for and uncovered a variety of literature based on circular economy and sustainability of data centers from around the world. The research centered on scientific papers (e.g., journal articles, conference papers, and dissertations) as well as non-academic papers (e.g., government publications, surveys, reports, newspaper articles, white papers by data center organizations) [ 12 ].

In this section, we will look at circular economy in the context of data centers, as well as the current applications and research being done towards circularity and sustainability. We will further asses 4IR digital technologies being used to assist towards circularity. Finally, we look at the impact on the UN-SDGs.

Circular Economy in the Context of Data Centers

Circular data centers redefine the longevity of data center infrastructure by incorporating primary and secondary business applications research into a common system, with transformative results. We can further assess how well data centers are performing in the transition from a linear to a circular economy by measuring MCI; these can be done by using the online platforms and calculators provided in Table 5 . The calculation for MCI is quite complex; therefore, there are online digital calculators such as; Circular economy toolkit (CET), Circularity calculator, Ellen MacArthur’s Material circular indicator (MCI) tool, Flex 4.0 by Delft University, and RELi 2.0 by USGBC, to name a few.

Tracking materials using materials passports is another solution towards circularity, with the result of bringing back residual value back to the data center market. Currently there are online databases for materials passports such as MADASTER and Buildings as Material Banks (BAMB) [ 10 ] [ 11 ].

There are a number of short-term and long-term goals and benefits for a transition towards a circular economy in data centers. Some of these are summarized below [ 13 ]:

Stakeholders become knowledgeable about all the costs and environmental impact.

Efficiency is increased.

Energy efficiency and greater sustainability.

A reduction in the carbon footprint.

Investors are able to compare designs based on short-term and long-term yields.

Compliance and regulation requirements are met.

Public safety.

Maximizes the life cycle of a data center.

Barriers and Solutions

There are still a number of barriers for the data center transition towards a circular economy. These are in the form of uncertain global markets, efficiency and sustainability, and competitiveness within the market, to name a few. We look at some barriers in Table 3 [ 14 ].

Despite the list of barriers above, we list some suggested solutions to help curb this:

Government interventions in the form of funding. Government should leverage its resources to change existing practices.

Education to show the usefulness and worthiness, to improve the efficiency and sustainability.

Finally, there needs to be an adoption of newer digital technologies. Some examples are described below:

Smart Apps, sensors, and robots may be used to do sophisticated collection, sorting, and recycling of electronic waste.

Machine learning and artificial intelligence can be used to process materials more efficiently.

Additive manufacturing 3-dimensional (3D) printing may be used to help design better for circularity.

Interactive platforms can be developed using online databases and IoT.

Blockchain technology may be utilized to enhance circular production procedures, corporate operations, and financial and environmental performance.

While operational energy and carbon assessment are important when it comes to data centers, embodied energy and carbon play a pivotal role with regard to sustainability and circularity of data centers. This includes emissions from resource extraction, production, and transportation, as well as emissions from the installation of materials and components needed to construct the built environment. It also covers lifetime emissions from continued usage, such as maintenance, repair, and replacement, as well as end-of-life activities such as deconstruction, transportation, trash processing, and disposal. These lifecycle emissions must be taken into account in order to calculate the overall embedded carbon cost. However, because embodied carbon is largely paid up front as the facility is built, there is a strong rationale to incorporate it in all assessments and data center design decisions. A whole life carbon strategy that incorporates embodied and operational emissions gives the possibility to positively contribute to SDG’s goal to decrease greenhouse gas emissions, while saving money. More information on embodied carbon in data centers can be found in i3 Solutions white paper [ 15 ], as well as the use of life cycle assessment when examining the effect and utility of design decisions on a cradle-to-cradle basis, taking the circularity of the facility and its components into consideration.

The control of hazardous and unethical mining and materials processing for data centers are of utmost importance, particularly the effect this has on the SDGs. Mining, whether large-scale industrial mining or small-scale artisanal mining, is a hazardous industry. The majority of data center equipment is made up of ordinary metals, polymers, and key critical raw materials (CRM). Many of these and other commodities are extracted using hazardous chemicals, and because much of their mining is uncontrolled and/or illegal, the accompanying negative environmental and social repercussions are severe. Many of the components’ functionality is dependent on CRM. Twenty-three of the 30 CRMs are found in server, storage, and networking equipment. This implies that the resources we rely on for the world’s data centers are in low supply or are politically unpredictable. CRMs are finite materials provided by the European Commission that are in short supply globally; examples of some of these materials are titanium, lithium, germanium, etc. Because these raw materials are in low supply globally, there are no viable substitutes; hence, the answer to the CRM problem is a circular approach. This can be done by minimizing mining of these raw materials from the earth and re-use our existing resources. Choosing used equipment has several environmental, financial, and performance advantages. Selling obsolete IT equipment from data centers is another excellent approach to keep technology alive. We can get a return on undesired equipment while assuring that all data is unrecoverable. As a result, by implementing circular economy waste reduction measures, such as product life extension through recycling, reuse, and remanufacture, could prove fruitful for the mining sector. Furthermore, facilities can be designed for these circular processes while at the same time develop ethical, well-paying opportunities that allow workers to work in ecologically safe, non-hazardous settings. Not forgetting, to have a positive social impact, mining projects should be controlled globally, and they must find better methods to connect with local populations, such as via active engagement in social development, increased respect for human rights, reduction in pollution, and assistance in overcoming poverty.

Current applications and research towards circular and sustainable data centers

Global business has a vital role to play in switching from a take-make-dispose model-based economy to a restorative and regenerative system-based one. This circular thinking allows for efficiency and sustainability and a reduction in the environmental footprint of data centers. Given this need, there are a number of countries and organizations that have made significant strides towards sustainable and circular data centers; these applications and initiatives are listed in Table 4 .

Data Centers and 4IR Technologies

With the introduction of the 4IR, which makes up a variety of fields such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine learning (ML), Big Data, Block chain, 3D technologies, Quantum computing, and Robotics, we find further solutions to a circular and sustainable data center in the form of digital technologies [ 29 ]. These technologies are capable methods of accelerating circularity, dematerializing, and making us less dependent on primary materials. Research has been done to show the impact of 4IR digital technologies together with CE on the UN-SDGs [ 10 ] [ 11 ].

AI and ML can be used for automating and enhancing operational performance, including materials upgrade, and CFE use. This is done with the use of historical performance data, AI algorithms, day ahead weather conditions which in turn can predict renewable energy generation and calculate 7 × 24 match with the energy consumption usage in the data center. With the use of IoT-based sensor networks and AI, we can monitor and predict server utilization and operation performances of any data center, thus allowing early warning notifications of faulty hardware, excessive heat from servers, as well as preventive maintenance to alleviate system downtime. Sustainable data centers for the hyper scale cloud providers such as Google have utilized renewable energy for its annual global consumption of over 3 GW in 2018. On Earth Day in 2020, Google announced it would source clean energy at its operations for 24 × 7 match across 21 sites, 22 cloud regions, and 200 countries; that is always and anywhere. In addition to using carbon free energy, naturally cold temperature outside air and submersion liquid cooling with AI provide benefits, such as reducing energy use and eliminating the use of diesel generators and air conditioning machines [ 30 ].

Researchers in the United States (US) also created a new energy-based benchmark for the quantum advantage to prove that noisy intermediate-scale quantum (NISQ) computers consume less energy than the most efficient supercomputer in the world when conducting a single function. A test to prove this theory was carried out between a supercomputer and NISQ computers with amazing results. The researchers found that Electra supercomputer at Ames required 97 MWh to solve a particular problem and 21 MWh on the Summit supercomputer (the world’s most powerful supercomputer) at Oak Ridge, whereas the problem could be solved by a NISQ using only 4.2 × 10 − 4 MWh [ 31 ]. Research has been done recently with the use of two AI methods, namely, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), for predicting life cycle environmental impacts and energy efficiency [ 32 ] [ 33 ].

While 4IR technologies have major benefits within data centers and their sustainability, they can function as a catalyst for a shift to a circular economy while also hastening the process of addressing some of humanity’s most pressing issues, such as the UN-SDGs. Individuals’ abilities are supplemented, and their capability is increased. It enables individuals to learn more quickly from reviews, cope with uncertainty more successfully, and gain a deeper understanding of large amounts of data [ 10 ]. E-waste is the world’s fastest increasing waste source, particularly within data centers. It includes important resources, including those that are limited and critical. Mishandling of e-waste causes unnecessary pollution and greenhouse gas emissions. Newer digital technologies can help with the circular management of e-waste, including prevention, collection, and treatment, by improving information sharing, streamlining operations, and linking the key players across the value chain. AI can enhance information collecting and processing, allowing for the circular design of electronics. It can also help to enhance sorting and law enforcement. Digital product passports (DPPs), like materials passports, can allow producers and other value chain participants to track and trace electronics. Robots and IoT sensors can help with e-waste sorting and disposal. 3D printing may offer spare components for electronics, prolonging their lifespan [ 34 ].

Digital Tools

Many data center operators promote their energy efficiency, and some even publish their carbon footprint. However, there is a need for easy tools to assist operators in better understanding and quantifying the embedded impact, as well as informing green procurement. Focusing only on energy efficiency may result in a burden shift, for example, by replacing less efficient equipment with more efficient equipment while increasing the embodied impact. The overall environmental effect may remain the same or rise, but with the perception of a “greener” data center [ 35 ].

Circular economy tools will surely provide for safety and reliability, efficiency, circularity, and sustainability in data centers. Together with these techniques, there are innovative software and prototype options available particularly for the purpose of circularity which are listed and explained in Table 5 .

The Impact on the UN-SDGs

In 2015, the United Nations Member States created a shared blueprint for peace and prosperity for people and the planet. The 2030 agenda features 17 United Nations Sustainable Development Goals shown in Fig.  3 , which are an urgent call for action by all countries in global partnership.

The 17 sustainable development goals, elaborated from ( https://sdgs.un.org/goals )

The following UN-SDGs are particularly relevant to the next-generation sustainable data centers:

Goal #6: Clean water and sanitation

Goal #7: Affordable and clean energy

Goal #9: Industry, innovation, and infrastructure

Goal #11: Sustainable cities and communities

Goal #12: Responsible consumption and production.

Goal #13: Climate action

While data centers’ energy efficiency has been widely examined, their water footprint has gotten little to no attention. Water consumption is an important factor in data centers, especially because it is tied to the SDGs. Data centers are predicted to need between 1047 and 151,061 m 3 /TJ of water. Data center outbound data traffic has a water factor of 1–205 l/gigabyte [ 36 ]. According to the Organization for Economic Cooperation and Development (OECD), worldwide water consumption for industrial industries would rise 400% between 2000 and 2050. As a result, in order to achieve Inclusive and Sustainable Industrial Development (ISID), water consumption efficiency must increase. United Nations Industrial Development Organization (UNIDO) describes water stewardship as, the use of water in a socially fair, environmentally sustainable, and economically profitable manner. This is accomplished through a multi-stakeholder process that includes site and catchment-based activities [ 37 ]. Water is used to cool servers in data centers, which have an influence on local water resources. As a result, water stewardship efforts in data centers should concentrate on lowering water demands. Water stewardship initiatives should include efforts targeted at replacing water consumed and enhancing overall watershed health, in addition to water consumption reduction. To get the most sustainable solution for data center cooling, power and water use must be balanced.

What we learned from previous economic dislocations, such as the dotcom bust and the 2008 financial crisis, is that data center providers adapt, emerge, and stay resilient. The acceleration of new business models, the data center hyper-scalers’ pledge to climate change and the leading sustainability leaders to rebuild the economy to a more sustainable future. The data center sector is the beneficiary of the shift to a socially distanced, contactless, work-anywhere new normal. Furthermore, some environmental, social, and governance (ESG) fund managers are outperforming the broader market during the pandemic, meaning new capitals invested in ICT and data center infrastructure have been more resilient. ESG, being the three main factors towards measuring sustainability and socio impacts of an investment in companies, is a data center in this case. ESG reporting towards sustainability has become compulsory in some organizations and countries around the world.

With the global call for zero carbon emissions, there needs to be solutions put in place for the de-carbonization of data centers; these include: low carbon materials ; embodied carbon , which is the carbon emitted in the manufacture process and transport of building materials; and operational carbon , which is the carbon load generated by the heating and power consumption of the building.

The cleanest data centers are the ones that are not built at all. Digital transformation is a necessity to keep society running, especially in times of mass shutdowns to slow down the burden to our society of the transmission of COVID-19 pandemic. The goal is to promote digital services and sustainable cost of ownership throughout the data center life cycle to improve human, social, and environmental welfare. The data center materials must comprise a circular nature, not harmful to the environment, simultaneously prolong the longevity of nature and human progress, and zero carbon emissions and waste. A circular economy enabled sustainable data center is designed for disassembly, where each connection of the data center can be taken apart and each material component can be refurbished, reused, recycled with zero waste and remake into a new material to give rise to a circular economic growth.

In Fig.  4 , we put theory to practice by mapping the circular economy for a data center lifecycle. Sustainable materials that are both friendly to the environment and people can have a perpetual life of use throughout the data center lifecycle. We align with the UN-SDGs in the data center design to protect the planet and ensure all people enjoy peace and prosperity. We select data center sites to locations with affordable renewable energy and utilize CFE generation for net-zero energy consumption. We build data center products and facilities with sustainable materials. We operate and maintain data center with a circular thinking to keep materials in circulation for multiple uses.

The circular economy approach embraces end to end sustainability throughout the lifecycle for a data center and facilitates these changes for those who design, build, operate, and maintain them [ 38 ]

In the last decade, significant progress has been made in data center efficiency and sustainability. Access to clean, renewable energy is vitally necessary. It is mandatory to be environmentally friendly when making decisions about using carbon free energy. Another critical component of an adaptive data center is advanced cooling technology that can result in 80% less energy and 85% less water. Global sustainability and data center leaders have made 100% clean energy pledge to reduce greenhouse gas emissions, promote carbon free energy, and adopt a de-carbonization pathway of fossil fuel for data centers. SustainInfra is the new infrastructure standard to use naturally clean energy generation for data center sites with 99.999% uptime, achieved naturally by location design of data centers at the source of renewable energy grids powered by hydroelectric, wind, or solar. Sustainable data centers are measured by zero carbon, zero emission, and zero waste. They use naturally generated renewable energy, increased resource-use efficiency, environmentally sound technologies such as outside air cooling and circular economy processes to reuse, recycle, and remanufacture waste. All countries will take action to adopt a clean energy infrastructure for the ICT sector.

CFE design enables a data center located anywhere to achieve the same net zero results; use of hydrogen, renewable gas with fuel cells — a technology to convert clean electric power generation on site — offers a good alternative. The use of fuel cells for powering data center racks is a reality today and can be a cornerstone of the data center industry establishing itself as a global energy leader. Powering data centers with fuel cells is a departure from the status quo model of purchasing power from a third-party utility provider who generates and delivers power. Instead, in a design technique called rack level fuel cells (RLFC), renewable power is generated onsite with fuel cells installed at the server rack level. When paired with renewable natural gas (RNG) as a fuel source, fuel cells become a carbon negative power generation choice, therefore allowing a data center to be independent from the electric grid’s cost, reliability, capacity, and carbon footprint.

Keppel Corporation is a Singaporean conglomerate who has made sustainability an important part of their business. Keppel data centers partnered with NUS engineering department to develop an energy-efficient and cost-effective cooling system for data centers. Floating data centers are one solution to save energy and water; the IT equipment is cooled using the natural temperature of the water that data center floats on, thereby reducing water consumption.

While environmental, energy, and carbon impacts are important with regard to sustainable data centers and the SDGs, social impacts play an important role as well. Data centers are hubs for major social media platforms globally; this allows for freedom of speech online, advertising, business opportunities, and social communication. The increase in sustainable data centers can lead to an increase to access to the internet to many more people around the world; this will in turn lead to more education opportunities for the less fortunate. Google, being a good example, allows people access to many forms of hyperlinks. They have enabled students to incorporate material for research projects, individuals to keep track of the stock market, and consumers to take advantage of unusual chances. With this increase in accessibility and sustainable data centers, more jobs are created. These are all important factors that are directly linked to the UN-SDGs.

Cryptocurrencies and crypto mining is presently disproportionately hurting the most vulnerable, worsening social, and environmental difficulties for people already suffering from numerous forms of deprivation. For the crypto blockchain to exist, the mining process necessitates enormous processing power and a great deal of energy, and has a significant impact on data centers. The social impact of some cryptocurrencies’ unsustainable trajectory disproportionately affects poor people, and vulnerable communities, where miners and other actors profit from economic instabilities, weak regulations, and access to cheap energy, and other resources. Therefore, sustainable data centers need to implement stricter policies in order to curb these negative impacts left by crypto mining. One solution would be to decrease CO 2 emissions for blockchain and cryptofinance, by implementing low-carbon technologies and tools such as circular economy. Policies need to be also put in place by governments in order to support the sustainable data center, to limit the number of miners in a particular area. We need to also take note of the positive impacts crypto and the blockchain have on the UN-SDGs; (SDG 1) — cryptocurrencies and other blockchain-based tokens enable the world’s 2 billion UN-banked people to trade and transact, (SDG 3, 12, 14, 15) —sharing of important data securely and efficiently, it has the ability to allow for a more circular economy by ensuring excellent provenance [ 39 ].

We promote sustainability, circularity, combined with the UN-SDG goals in this paper for the data center. We came to understand the latest research and practice, as well as looked at future strides being taken with the use of 4IR digital technologies. What does this mean for the future data center? The circular economy is based on the concepts of waste and emission control, the conservation of goods and services in use, and the restoration of natural resources. We should shift towards sustainable and circular data centers by implementing circularity, 24· × 7 × 365 resiliency, triple zero (carbon, pollution, waste) measures, reduce water usage, refine e-waste strategies, social cost of ownership, capital usage, materials passports, lifecycle maintenance strategies, and 24 × 7 reporting per each kilowatt hour used; this future concept is shown in Fig.  5 .

Future concept of a data center

In order to achieve the efficient, sustainable, and circular data center of the future, we will need to incorporate the concepts such as SustainInfra and SustainTech, as well as companies such as Google and Microsoft. We need to reduce data center environment degradation by reducing carbon emissions. Resilience and 99.999% uptime are the primary goals of data center design and build. The next-generation data center should also include clean energy infrastructure, carbon free energy use, and net zero management; certain practices that were deemed groundbreaking a few years ago have now become common best practices. Every year, 50 million tons of e-waste composed of CRMs that may be reused, reconditioned, or repurposed are thrown to landfill. This quantity is expected to more than treble to 110 million tons by 2050. Therefore, by implementing circular approaches for future sustainable data centers would surely prove fruitful, particularly by controlling hazardous and unethical mining and materials processing. Finally, by following the ground rules set out by the UN-SDGs, we can manage engineering and innovations of data centers for a better environment and a better world for all.

Data centers are ever-growing and complex facilities; therefore, sustainability is challenging in an unpredictable world and unexpected circumstances such as the current COVID-19 pandemic, which will affect our choices. We can conclude that while reducing the impact on the environment and energy consumption of data centers is important, it is not enough. Embodied and the operational emissions are both important when it comes to the design of data centers. Data centers have an important social role in how our daily lives function. Social media allows individuals to freely interact and share data with those they trust. Similarly, data centers make education available to everybody through a variety of online platforms and smart apps, and the ability to complete business transactions and others, using the blockchain and cryptocurrencies through responsible crypto mining. Most critically, there has been significant employment creation. Therefore, sustainability and efficiency measures have grown using a number of methods; these are in the form of circularity and its relevant tools as well as newer 4IR digital technologies.

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Data for this study was extracted from scholarly publications, government reports, and statistical reports.

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Hoosain, M.S., Paul, B.S., Kass, S. et al. Tools Towards the Sustainability and Circularity of Data Centers. Circ.Econ.Sust. 3 , 173–197 (2023). https://doi.org/10.1007/s43615-022-00191-9

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The environmental footprint of data centers in the United States

Md Abu Bakar Siddik 1 , Arman Shehabi 2 and Landon Marston 3,1

Published 21 May 2021 • © 2021 The Author(s). Published by IOP Publishing Ltd Environmental Research Letters , Volume 16 , Number 6 Citation Md Abu Bakar Siddik et al 2021 Environ. Res. Lett. 16 064017 DOI 10.1088/1748-9326/abfba1

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1 Department of Civil & Environmental Engineering, Virginia Tech, Blacksburg, VA, United States of America

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Arman Shehabi https://orcid.org/0000-0002-1735-6973

Landon Marston https://orcid.org/0000-0001-9116-1691

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Much of the world's data are stored, managed, and distributed by data centers. Data centers require a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center's water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint.

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1. Introduction

Data centers underpin our digital lives. Though relatively obscure just a couple of decades prior, data centers are now critical to nearly every business, university, and government, as well as those that rely on these organizations. Data centers support servers, digital storage equipment, and network infrastructure for the purpose of large-scale data processing and data storage [ 1 ]. Increasing demand for data creation, processing, and storage from existing and emerging technologies, such as online platforms/social media, video streaming, smart and connected infrastructure, autonomous vehicles, and artificial intelligence, has led to exponential growth in data center workloads and compute instances [ 2 ].

The global electricity demand of data centers was 205 TWh in 2018, which represents about 1% of total global electricity demand [ 3 ]. The United States houses nearly 30% of data center servers, more than any other country [ 3 – 5 ]. In 2014, 1.8% of US electricity consumption was attributable to data centers, roughly equivalent to the electricity consumption of New Jersey [ 1 ]. Previous studies found power densities per floor area of traditional data centers almost 15–100 times as large as those of typical commercial buildings [ 6 ], and data center power density has increased with the proliferation of compute-intensive workloads [ 7 ]. Though the amount of data center computing workloads has increased nearly 550% between 2010 and 2018, data center electricity consumption has only risen by 6% due to dramatic improvements in energy efficiency and storage-drive density across the industry [ 1 , 3 ]. However, it is unclear whether energy efficiency improvements can continue to offset the energy demand of data centers as the industry is expected to continue its rapid expansion over the next decade [ 8 ].

The growing energy demand of data centers has attracted the attention of researchers and policymakers not only due to scale of the industry's energy use but because the implications the industry's energy consumption has on greenhouse gas (GHG) emissions and water use. Data centers directly and indirectly consume water and emit GHG in their operation. Most data centers' energy demands are supplied by the electricity grid, which distributes electricity from connected power plants. Electricity generation is the second largest water consumer [ 9 ] and the second largest emitter of GHGs in the US [ 10 ]. These environmental externalities can be attributed to the place of energy demand using several existing approaches [ 11 , 12 ].

In addition to the electricity consumed directly by data centers, electricity is used to supply treated water to data centers and treat the wastewater discharged by data centers. Like data centers, water and wastewater facilities are major electricity consumers, responsible for almost 1.8% of total electricity consumption in the US in 2013 [ 13 ]. The electricity required in the provisioning and treatment of water and treatment of discharged wastewater also emits GHGs that can be attributed to data centers. Likewise, water used to generate the electricity used by water and wastewater utilities in their service of data centers contributes to the water footprint of these data centers. Water is also used directly within a data center to dissipate the immense amount of heat that is produced during its operation.

The geographic location [ 14 , 15 ] and the local electricity mix [ 16 ] are strong determinants of a data center's carbon footprint, though these spatial details are often excluded in data center studies. A preliminary water footprint assessment of data centers by Ristic et al [ 17 ] provided a range of water footprints associated with data center operation. Although Ristic et al provided general estimates based on global average water intensity factors, their study highlights the importance of considering both direct and indirect water consumption associated with data center operation. Moreover, Ristic et al highlights the importance of considering the type of power plants supplying electricity to a data center and the type/size of a data center, as each of these factors can significantly impact energy use and indirect water footprint estimates.

In this study we utilize spatially-detailed records of data center operations to provide the first sub-national estimates of data center water and carbon footprints. Here, water footprint is defined as the consumptive blue water use (i.e. surface water and groundwater). The carbon footprint of a data center, expressed as equivalent CO 2 , is used to represent its global warming potential. Our assessment focuses on the operational environmental footprint of data centers (figure 1 ), which includes the power plant(s), water supplier, and wastewater treatment plant servicing the data center. The non-operational stages of a data center's life cycle (e.g. manufacturing of servers) consume relatively much less energy [ 18 ] and are excluded in this study. The spatial detail afforded by our approach enables more accurate estimates of water consumption and GHG emissions associated with data centers than previous studies. Moreover, we evaluate the impact of data center operation on the local water balance and identify data centers located in, or indirectly reliant upon, already water stressed watersheds. We investigate the following questions: (i) What is the direct and indirect operational water footprint of US data centers? (ii) Which watersheds support each data center's water demand and what portion of these watersheds are water stressed? (iii) How much GHG emissions are associated with the operation of data centers? (iv) To what degree can strategic placement of future data centers within the US reduce the industry's operational water and carbon footprints?

Figure 1.  The system boundaries and interlinkages defining the operational water and carbon footprints of data centers. Specific power plants, water utilities, and wastewater treatment (WWT) utilities are connected to each data center through their provisioning of electricity and water. Power plants emit GHGs and consume water in the production of electricity. These environmental impacts are attributed to data centers in proportion to how much electricity the data center uses (red and blue dashed lines connecting facilities). The GHG emissions and water consumption associated with the provisioning of treated water and disposal of wastewater, including the GHGs and water consumed in the generation of the electricity supplied to these facilities, are also attributed to data centers in proportion to their use of these utilities. Data centers do not directly emit GHGs but they do directly consume water to dissipate heat. All these facilities work together to keep data centers operational and contribute to the water and carbon footprint of data centers.

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We utilize spatially detailed records on data centers, electricity generation, GHG emissions, and water consumption to determine the carbon footprint and water footprint of data centers in the US. Our approach connects specific power plants, water utilities, and wastewater treatment plants to each data center within the US. All data used in this study are for the year 2018, the most recent year where all data are publicly available. A visual summary of our methods is shown in supplementary figure S1 (available online at stacks.iop.org/ERL/16/064017/mmedia ).

2.1. Data center location and energy use

Information availability on data center location and size varies depending on its type and owner. Ganeshalingam et al [ 4 ] reports likely locations of in-house small and midsize data centers, which house approximately 40% of US servers. Detailed information on colocation and hyperscale data centers is derived from commercial compilations [ 19 – 21 ] that get direct support and input from data center service providers.

Table 1.  Combined direct and indirect water consumption and GHG emissions (carbon equivalence) by data center type. Water intensity and carbon intensity are reported per MWh of electricity used and per computing workload. Better energy utilization, more efficient cooling systems, and increased workloads per deployed server has increased the water efficiency of larger data centers. Computing workloads in hyperscale data centers are almost six times more water efficient compared to internal data centers. Workload estimates are based on traditional and cloud workloads from [ 2 , 3 ].

where PUE s is the power usage effectiveness of space type s , and A is the floor area of data center in ft 2 . We account for potential overstatement of data center capacity [ 4 ], a lack of distinction between gross and raised floor area, and unfilled rack capacity by scaling our server counts to match the 2018 estimate of servers by data center type [ 3 ], as shown in table 1 and figure S2. Scaled server estimates are then spatially distributed in proportion to the current spatial distribution of installed server bases. The number of servers by state is shown in figure S2.

Power usage effectiveness (PUE) is a key metric of data center energy efficiency [ 23 ]. A value of 1.0 is ideal as it indicates all energy consumed by a data center is used to power computing devices. Energy used for non-computing components, such as lighting and cooling, increases the PUE above 1.0 (see equation ( 2 )). Generally, a data center's PUE is inversely proportionate to its size since larger data centers are better able to optimize their energy usage. Average PUE values and energy use by data center type were taken from Masanet et al [ 3 ] and shown in table 1 and table S1.

2.2. Electricity generation, water consumption, and GHG emissions

Power plant-specific electricity generation and water consumption data come from the US Energy Information Administration (EIA) [ 24 ]. Of the approximately 9000 US power plants, the EIA requires nearly all power plants report electricity generation. However, only power plants with generation capacity greater than 100 MW (representing three-fourths of total generation) must report water consumption. We assigned national average values of water consumption per unit of electricity generation by fuel type (i.e. water intensity; m 3 MW h −1 ) to all power plants with unspecified water consumption. Operational water footprints of solar and wind power were taken from Macknick et al [ 25 ]. Following Grubert [ 26 ], we assign all reservoir evaporation to the dam's primary purpose (e.g. hydropower). We connected hydroelectric dams with their respective power plants using data from Grubert [ 27 ]. Reservoir specific evaporation comes from Reitz et al [ 28 ].

The U.S. Environmental Protection Agency's eGRID database [ 29 ] provided GHG emissions associated with each power plant. GHG emissions are converted to an equivalent amount of carbon dioxide (CO 2 )-eq with the same global warming potential so to derive a single carbon footprint metric [ 30 ]. Direct GHG emission during the operation of data centers are negligible [ 18 ] and therefore not considered in this study.

Data centers, water suppliers, and wastewater treatment plants typically utilize electricity generated from a mix of power plants connected to the electrical grid. Within the electrical grid, electricity supply matches electricity demand by balancing electricity generation within and transferred into/out of a power control area (PCA). Though it is infeasible to trace an electron generated by a particular power plant to the final electricity consumer, there are several approaches to relate electricity generation to electricity consumption (Siddik et al [ 31 ] summarizes the most common approaches).

Here, we primarily rely on the approach used by Colett et al [ 32 ] and Chini et al [ 33 ] to identify the generative source of electricity supplied to any given data center. This approach assesses electricity generation and distribution at the PCA level where it is primarily managed. PCA boundaries are derived from the Homeland Infrastructure Foundation level data [ 34 ] and crosschecked against Form EIA-861 [ 35 ], which identifies the PCAs operating in each state. Annual inter-PCA electricity transfers reported by the Federal Energy Regulatory Commission [ 36 ] are also represented within this approach. A data center (as well as water and wastewater utilities) draws on electricity produced within its PCA, unless the total demand of all energy consumers within the PCA exceeds local generation, in which case electricity imports from other PCAs are utilized. If a PCA's electricity production equals or exceeds the PCA's electricity demand, it is assumed all electricity imports pass through the PCA and are re-exported for utilization in other PCAs. Siddik et al [ 31 ] notes that water and carbon footprints are sensitive to the attribution method used to connect power plants to energy consumers. Therefore, we conduct a sensitivity analysis (see the supporting information for additional details) to test the degree to which our electricity attribution method affects our results. Additionally, we also test different assumptions regarding the water footprint of hydropower generation, as this too is a key source of uncertainty.

We focus on the annual temporal resolution and assume an average electricity mix proportional to the relative annual generation of each contributing power plant. Though the electricity mix within a PCA can fluctuate hourly depending on balancing measures, these intra-annual variations will not significantly impact our annual-level results. While it is infeasible to determine the precise amount of electricity each power plant provides to each data center, water utility, and wastewater treatment plant, our approach will enable us to estimate where each facility is most likely to draw its electricity. The dependency of a data center on local and imported electricity from other PCAs was calculated using equations ( 3 ) and ( 4 ).

where Import con is defined as the electricity from a linked PCA i that was consumed within PCA p . Any imported electricity not consumed with PCA p is re-exported.

Adjusted electricity consumption from the PCAs were assigned to the power plants using equation ( 5 ).

2.3. Water consumption and GHG emissions associated with data centers

The indirect water and carbon footprint of each data center consists of water consumption or GHG emissions associated with the generation of (i) electricity utilized during data center operation, (ii) electricity used by water treatment plants for treatment and supply of cooling water to data centers, and (iii) electricity used by wastewater treatment plants to treat the wastewater generated by a data center. The GHG emissions or water consumption of a power plant supplying electricity to a data center is attributed to the data center as follows:

Although the IPCC does not consider water treatment a notable emitter of GHGs [ 37 ], wastewater treatment plants are a major source of GHG emission [ 38 , 39 ]. In 2017, total GHG gas emission from wastewater treatment plants was estimated to be 20 million metric tons, with a direct emission rate of 0.3 kg CO 2 -eq/y per m 3 of wastewater treated [ 38 , 39 ]. In absence of facility specific emission data, we have used the average emission rate for treating wastewater for all wastewater generated from data center operation [ 39 ]. No direct GHG emissions are assumed to be associated with data center operation at the facility [ 18 ].

The EPA Safe Drinking Water Information System contains information on the location, system type, and source of water for each public water and wastewater utility [ 40 , 41 ]. We assumed the nearest non-transient water treatment plant and wastewater treatment plant services a data center's water demand and wastewater management, respectively. After calculating the water supply requirement of a data center (discussed later in this section), the electricity needed for treatment and distribution of cooling water can be calculated using the data from Pabi et al [ 13 ] (see table S2). Water and wastewater treatment plants were linked to power plants (as described previously) to estimate the indirect water footprint associated with electricity required to distribute and treat water and wastewater used by a data center. We then sum the water consumed by each power plant to directly or indirectly service a data center to determine the total indirect water footprint of that data center. The indirect water footprint associated with each power plant was also aggregated within watershed boundaries to determine which water sources each data center was reliant upon.

Direct water consumption of a data center can be estimated from the heat generation capacity of a data center [ 42 ], which is related to the amount of electricity used [ 43 ]. Estimates of data center specific electricity demand were multiplied by the typical water cooling requirement [ 1 ]—1.8 m 3 MWh −1 —to estimate the direct water footprint of each data center. The direct water consumption is assigned to the watershed where the water utility supplying the data center withdraws its water.

Data center wastewater is largely comprised of blowdown; that is, the portion of cooling water removed from circulation and replaced with freshwater to prevent excessive concentration of undesirable components [ 44 ]. We assume all data centers utilize potable water supplies and cycle this water until the concentration of dissolved solids is roughly five times the supplied water [ 44 ]. We calculate blowdown from data center cooling towers using the following commonly employed approach [ 45 ]:

2.4. Water scarcity footprint

The water scarcity footprint ( WSF ; as defined by ISO 14046 and Boulay et al [ 46 ]) indicates the pressure exerted by consumptive water use on available freshwater within a river basin and determines the potential to deprive other societal and environmental water users from meeting their water demands. We quantified the WSF of data centers using the AWARE method set forth by Boulay et al [ 46 ] (see the Supportive Information for more details). Other societal and environmental water use data, as well as data on natural water availability within each US watershed, come from [ 47 – 49 ].

3.1. The water footprint of data centers

The total annual operational water footprint of US data centers in 2018 is estimated at 5.13 × 10 8 m 3 . Data center water consumption is comprised of three components: (i) water consumed directly by the data center for cooling and other purposes (figure 2 (A)), (ii) water consumed indirectly through electricity generation (figure 2 (B)), and (iii) water consumed indirectly via the water embedded with the electricity consumption of water and wastewater utilities servicing the data center (figure 2 (C)). The data center industry directly or indirectly draws water from 90% of US watersheds, as shown in figure 3 (A).

Figure 2.  The blue water footprint (m 3 ) of US data centers in 2018, resolved to each subbasin (8-digit Hydrologic Unit Code). (A) Direct water footprint of data centers, (B) indirect water footprints associated with electricity utilization by data center equipment, and (C) indirect water footprints associated with treatment of supplied cooling water and treatment of generated wastewater.

Figure 3.  The subbasin or state of direct and indirect environmental impact associated with data center operation. (A) Water footprint (m 3 ). (B) WSF (m 3 US-eq water). (C) Carbon footprint (tons CO 2 -eq/y).

Roughly three-fourths of US data centers' operational water footprint is from indirect water dependencies. The indirect water footprint of data centers in 2018 due to their electricity demands is 3.83 × 10 8 m 3 , while the indirect water footprint attributed to water and wastewater utilities serving data centers is several orders of magnitude smaller (4.50 × 10 5 m 3 ). Nationally, we estimate that 1 MWh of energy consumption by a data center requires 7.1 m 3 of water. However, this national average masks the large spatial variation (range 1.8–105.9 m 3 ) in water demand associated with a data center's energy consumption. Data centers are indirectly dependent on water from every state in the contiguous US, much of which is sourced from power plants drawing water from subbasins in the eastern and western coastal states. Less than one-fifth of the industry's total electricity demand is from data centers in the West and Southwest US (regions as defined by NOAA [ 50 ]; see outlined areas in figures 2 – 5 , and figure S4 for region identification), yet nearly one-third of the industry's indirect water footprint is attributed to data centers in these regions. Indirect water consumption associated with energy production in Southwest subbasins is particularly high, despite relatively low electricity supplied from this region, due to the disproportionate amount of electricity from water-intensive hydroelectricity facilities and the high evaporative potential in this arid region. Conversely, the Southeastern region consumes one-quarter of the electricity used by the industry but only one-fifth of the indirect water since data centers in this region source their electricity from less water-intensive sources.

On-site, direct water consumption of US data centers in 2018 is estimated at 1.30 × 10 8 m 3 . Collectively, data centers are among the top-ten water consuming industrial or commercial industries in the US [ 47 ]. Approximately 1.70 × 10 7 m 3 of water directly consumed by data centers are sourced from a different subbasin than the location of the installed servers. Large direct water consumption in the Northeast, Southeast, and Southwest regions indicate clustering of servers in these regions. Combined direct and indirect water and carbon intensities are broken down by data center type in table 1 .

3.2. Reliance of data centers on scarce water supplies

The WSF of data centers in 2018 is 1.29 × 10 9 m 3 of US equivalent water consumption, which is more than twice that of the volumetric water footprint reported in the previous section. The WSF (including both direct and indirect water requirements) per unit of energy consumption is 17.9 m 3 US-eq water MWh −1 , more than double the nationally averaged water intensity (7.1 m 3 MWh −1 ) that does not account for water scarcity. WSFs that are larger than volumetric water footprints suggest that data centers disproportionately utilize water resources from watersheds experiencing greater water scarcity than average.

Only one-fourth of the volumetric water footprint of data centers resulted from onsite water use. Yet, more than 40% of the WSF is attributed to direct water consumption. This indicates that direct water consumption of data centers, which occurs close to where the data center is located, is skewed toward water stressed subbasins compared to its indirect water consumption, which is distributed more broadly geographically. We find that most of the watersheds that data centers draw from, particularly those in the Eastern US, face little to no water stress on average. In contrast, many of the watersheds in the Western US exhibit high levels of water stress, which is exacerbated by data centers direct and indirect water demands. Combined, the West and Southwestern watersheds supply only 20% of direct water and and 30% indirect water to data centers, while hosting approximately 20% of the nation's servers. Yet, 70% of the overall WSF occurs in these two regions (figure 3 (B)), which indicates a disproportionate dependency on scarce waters in the western US.

3.3. GHG emissions attributed to data centers

Total GHG emissions attributed to data centers in 2018 was 3.15 × 10 7 tons CO 2 -eq, which is almost 0.5% of total GHG emissions in the US [ 10 ]. A little over half (52%) of the total emissions of data center operations are attributed to the Northeast, Southeast, and Central US, which have a high concentration of thermoelectric power plants, along with large number of data centers (figure 3 (C)). Almost 30% of the data center industry's emissions occur within the Central US, which relies heavily on coal and natural gas to meet its electricity demand. Yet, only 10% the industry's energy demand comes from the Central US, and just 9% of the water consumption associated with data centers operation occurs in this region. Moreover, the Central region is a net exporter of electricity to other regions, providing electricity for data centers located in the Northeast and Southeast regions, which houses almost one-third of servers. Yet, the generation of less carbon intensive electricity in the Northeast (hydroelectricity) and Southeast (wind/solar) regions means that while their electricity consumption comprises 34% of data centers' national electricity demand, these regions only constitute 23% of the industry's GHG emissions. The GHG emissions from treating the wastewater generated from data centers is around 550 tons/y (0.002% of total GHG emissions associated with data centers).

3.4. Where to locate data centers to minimize water and carbon footprints

Our results indicate significant variability of environmental impacts depending on where a data center is located. Here we explore how the geographic placement of a data center can lead to improved environmental outcomes. We find that the total water intensity of a data center can range from 1.8–106 m 3 MWh −1 , the water scarcity intensity from 0.5 to 305 m 3 US-eq MWh −1 , and the carbon intensity from 0.02 to 1 ton CO 2 -eq MWh −1 depending on where the data center is placed (figure 4 ). Data center placement decisions are complicated by the electricity grid, which displaces environmental impacts from the physical location of a data center.

Figure 4.  A data center's environmental footprint is highly contingent on where it is located. The (A) water intensity (m 3 MWh −1 ), (B) water scarcity intensity (m 3 US-eq MWh −1 ), and (C) GHG emissions intensity (tons CO 2 -eq MWh −1 ) of a hypothetical 1 MW data center placed in each of the 2110 subbasins of the continental United States.

Figure 5 depicts subbasins in the top quartile of environmental performance as it relates to water footprint ( 5 (A)), WSF ( 5 (B)), and carbon footprint ( 5 (C)) per MWh of electricity used by a hypothetical data center located within each subbasin. Less than 5% of subbasins are in the top quartile of environmental performance for both WSF and carbon footprint (hatched areas in figures 5 (B) and (C), meaning that 40% of subbasins will require making a trade-off between reducing WSFs and carbon footprints. The remaining 55% of subbasins (white areas shared by figures 5 (B) and (C) are not among the best locations to place a data center for either water or GHG reduction. Though the water footprint and WSF are related concepts, we show that nearly one-fifth of subbasins that were in the top quartile with respect to the water footprint are in the bottom quartile for WSF. In other words, a data center placed in these basins would use less water than 75% of potential sites, but it would draw that water from subbasins facing higher levels of water scarcity. In general, locating a data center within the Northeast, Northwest, and Southwest will reduce the facilities carbon footprint, while locating a data center in the Midwest and portions of the Southeast, Northeast, and Northwest will reduce its WSF.

Figure 5.  The (A) water footprint, (B) WSF, and (C) carbon footprint of data centers can be reduced by placing them in subbasins with the smallest footprint (top quartile of all subbasins), as denoted by the shaded subbasins in each panel. The bar graphs represent the percent reduction/increase of each environmental footprint within the shaded subbains compared to the national average data center environmental footprint. Hatched areas indicate subbasin that are among the most (top quartile) environmentally favorable locations for both water scarcity and GHG emissions.

In the coming years, cloud and hyperscale data centers will replace many smaller data centers [ 3 ]. This shift will lower the environmental footprint in some instances but introduce new environmental stress in other areas. Assuming added servers employ similar technology as existing servers and are placed in cloud and hyperscale data centers in proportion to the current spatial distribution of data centers (i.e. business-as-usual scenario), these new data center servers will have a collective water footprint of 77.77 × 10 6 m 3 (15% of the current industry total), WSF of 170.56 × 10 6 m 3 US-eq (9%), and 4.36 × 10 6 tons CO 2 -eq (14%). However, if these new servers are strategically placed in areas identified to have a lower environmental footprint, their water and carbon burden could be significantly reduced.

The WSF and carbon footprint of new data centers can be reduced by 153.00 × 10 6 m 3 US-eq (90% less than business-as-usual expansion) and 2.34 × 10 6 tons CO 2 -eq (55%), respectively (figure 6 (A)) if they are placed in areas with the lowest carbon and WSFs (hatched areas in figure 5 ). However, placing all new data centers within a small area may strain local energy and water infrastructure due to their collective water and energy demands. Data centers can be dispersed more broadly in areas that are favorable with respect to water footprint (figure 5 (A)), WSF (figure 5 (B)), or carbon footprint (figure 5 (C)). However, only considering one environmental characteristic can lead to environmental trade-offs (figure 6 ).

Figure 6.  Percent change in environmental footprints associated with new data center servers compared to the 'business-as-usual' scenario. While the business-as-usual scenario assumes new servers will be placed in proportion to historical server locations, alternative scenarios explicitly consider the environmental implications of data center placement. Scenario A places data center servers in subbasins within the top quartile of all subbasins in environmental performance for both carbon (CF) and water scarcity (WSF) footprints. Scenario B represents server placement within subbasins in the top quartile for carbon footprints, while scenario C and D represent the best (top 25%) subbasins to place data center servers with respect to minimizing WSFs and water footprints (WF), respectively.

4. Discussion and conclusion

The amount of data created and stored globally is expected to reach 175 Zettabytes by 2025, representing nearly a six-fold increase from 2018 [ 51 ]. The role of data centers in storing, managing, and distributing data has remained largely out of view of those dependent on their services. Similarly, the environmental implications of data centers have been obscured from public view. Here, for the first time, we estimate the water and carbon footprints of the US data center industry using infrastructure and facility-level data. Data centers heavy reliance on water scarce basins to supply their direct and indirect water requirements not only highlight the industry's role in local water scarcity, but also exposes potential risk since water stress is expected to increase in many watersheds due to increases in water demands and more intense, prolonged droughts due to climate change [ 52 – 54 ]. For these reasons, environmental considerations may warrant attention alongside typical infrastructure, regulatory, workforce, customer/client proximity, economic, and tax considerations when locating new data centers.

The data center industry can take several measures to reduce its environmental footprint, as well as minimize its water scarcity risks. First, the industry can continue its energy efficiency improvements. The ongoing shift to more efficient hyperscale and co-location data centers will lower the energy requirements per compute instance. Software and hardware advances, as well as further PUE improvements, can continue to reduce energy requirements, and thus environmental externalities. For instance, quarterly PUE of as low as 1.07 has been reported by Google for some of their data centers [ 55 ]. Liquid immersion cooling technologies show promise of further reductions in PUE, with one study reporting a PUE below 1.04 [ 56 ]. The prospect of recovering low-grade heat (i.e. low temperature or unstable source of heat) from data centers for space or water heating is limited; however, approaches such as absorption cooling and organic Rankine cycle are promising technologies for generating electricity from waste heat [ 57 ].

Second, the data center industry can make investments in solar and wind energy. Directly connecting data center facilities to wind and solar energy sources ensures that water and carbon footprints are minimized. Purchasing renewable energy certificates from electricity providers does not necessarily reduce the water or carbon footprints of a data center. However, these investments gradually shift the electrical grid toward renewable energy sources, thus lowering the overall environmental impact of all energy users. Data center workloads can be migrated between data centers to align with the portion of the grid where renewable electricity supplies exceed instantaneous demand [ 58 ].

Third, as we show in this study, strategically locating new data centers can significantly reduce their environmental footprint. Climatic factors can make some areas more favorable due to lower ambient temperatures, thereby reducing cooling requirements. Lower cooling requirements reduces both direct and indirect water consumption, as well as GHG emissions, associated with data center operation. Since most data centers meet their electricity demands from the grid, the composition of power plants supplying electricity to a data center plays a significant role in a data center's environmental footprint. For an industry that is centered on technological innovation, we show that real estate decisions may play a similar role as technological advances in reducing the environmental footprint of data centers.

Acknowledgments

L M acknowledges support by the National Science Foundation Grant No. ACI-1639529 (INFEWS/T1: Mesoscale Data Fusion to Map and Model the US Food, Energy, and Water (FEW) system). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Lawrence Berkeley National Laboratory is supported by the Office of Science of the United States Department of Energy and operated under Contract Grant No. DE-AC02-05CH11231.

Data availability statement

Data center locations come from [ 4 , 19 – 21 ]. Power plant electricity generation, water consumption, and GHG emission data come from [ 35 , 59 , 60 ]. Location of public water utility and wastewater treatment data comes from [ 40 , 41 ]. Study data and code can be found in the Supporting Information, as well as at https://doi.org/10.7294/14504913 . The DOI contains relevant shapefiles, tabular data, and scripts to help replicate and extend our work. All data that support the findings of this study are included within the article (and any supplementary files).

Author contributions

L M conceived and designed the study. M A B S conducted the analysis. A S provided data and fundamental concepts regarding the analysis. All authors contributed to the writing of the manuscript.

Conflict of interest

The authors declare that they have no competing financial interests.

Supplementary data

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Title: second-order adiabatic expansions of heat and charge currents with nonequilibrium green's functions.

Abstract: Due to technological needs, nanoscale heat management, energy conversion and quantum thermodynamics have become key areas of research, putting heat pumps and nanomotors center stage. The treatment of these particular systems often requires the use of adiabatic expansions in terms of the frequency of the external driving or the velocity of some classical degree of freedom. However, due to the difficulty of getting the expressions, most works have only explored first-order terms. Despite this, adiabatic expansions have allowed the study of intriguing phenomena such as adiabatic quantum pumps and motors, or electronic friction. Here, we use nonequilibrium Green's functions, within a Schwinger-Keldysh approach, to develop second-order expressions for the energy, heat, and charge currents. We illustrate, through two simple models, how the obtained formulas produce physically consistent results, and allow for the thermodynamic study of unexplored phenomena, such as second-order monoparametric pumping.

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Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

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We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

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Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

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The Current Territorial Differentiation of the Industry of Irkutsk Oblast

N. a. ippolitova.

1 Sochava Institute of Geography, Siberian Branch, Russian Academy of Sciences, 664033 Irkutsk, Russia

2 Irkutsk State University, 664003 Irkutsk, Russia

M. A. Grigoryeva

This article discusses recent changes in the development of industrial production in Irkutsk oblast from 2010 to 2019. Industry is the basic component in the economic complex; it provides about half of the region’s gross added value and is characterized by a multi-sectoral structure formed primarily on the basis of using natural resources and cheap electricity. It is pointed out that in the last decade, a significant change in the structure of industry has led to a structural simplification of its sectoral composition with a significant shift toward the raw materials sector. Cities remain the leading centers of concentration of the manufacturing industry. The grouping of municipalities according to the level of industrial development was carried out according to the available statistical data: the calculated share of the employed in industry and the volume of shipped products of large and medium-sized organizations. On the basis of their ratio, groups of regions with intensive development of the extractive industries, primarily the oil and gas sector, as well as territories in which the industrial profile was formed in Soviet times but underwent transformations under the influence of changes in the market, were identified. A group of regions with very low industrial development has been identified, in which economic activities are mainly related to agriculture, logging, transport, and tourism. It is shown that some of the municipalities have changed their position in the groups when compared to 2010. The rest of the composition is relatively stable. It was found that in the first and fourth groups a change in priority in the development of types of economic activity occurred, whereas the second and third groups show a change in their proportions. Large business contributes to the extremely uneven distribution of investments across the oblast in the implementation of investment projects.

INTRODUCTION

Irkutsk oblast, one of the key industrial regions of Siberia, has great industrial and natural resource potential, which, together with its competitive advantages, make it possible to occupy a leading position among other regions of the country. Research by N.N. Klyuev [ 1 ] shows that Irkutsk oblast is one of the ten Russian regions that maximized the volume of industrial production from 1990 to 2017.

The modern industrial structure of Irkutsk oblast is made up of several basic industries, including the electric power industry, mining and timber processing complexes, nonferrous metallurgy, chemical and petrochemical industries, as well as mechanical engineering and metalworking. With the start of oil and gas production, the oil and gas industry has developed.

Currently, the spatial development of Irkutsk oblast is based on large territorial production centers located in Irkutsk, Bratsk, Shelekhov, Angarsk, Sayansk, Ust-Ilimsk, Zheleznogorsk-Ilimsk, Taishet, Ust-Kut, and Bodaibo, where over 55% of the region’s population lives. These territories account for more than 85% of the added value produced in the region, and about 60% of investments [ 2 ].

The development of industrial production and its territorial features have been widely considered by domestic geographers at different times. It is worth noting the works devoted to the period of industrialization of the eastern territories [ 3 ], economic development [ 4 ], and issues of the location and development of certain industries [ 5 , 6 ]. In recent years, the main attention has been paid to the study of industry in the sectoral context [ 7 – 11 ], as well as using the theory of territorial production complexes [ 12 , 13 ]. The use of an integrated approach makes it possible to determine structural changes in the industry of the regions [ 14 ].

At the regional level, there are many methods and approaches to the construction of typologies and groupings for the socioeconomic development of territories, and in particular industrial development. Consideration of the intraregional level of industrial development in the scientific literature is less common, for example [ 15 – 18 ], which increases the relevance of this research, which is of an applied nature.

MATERIALS AND METHODS

The information base of the study, which covers 2010–2019, was the materials of the Federal State Statistics Service, including databases of indicators of municipalities and official sites of local governments (analytical and forecast reports).

It is assumed in this work that at present industrial production includes the following sections of OKVED-2: Extraction of minerals (B); Manufacturing industries (C); Provision of electricity, gas and steam; air conditioning (D); Water supply; sewerage, waste collection, and disposal, and pollution elimination activities (E). According to OKVED, in 2010 industrial production consisted of the following types of activities: Extraction of minerals (C); Manufacturing (D); Production and distribution of electricity, gas, and water (E). We note that the work did not take into account the subsection Forestry and logging, which is included in the section Agriculture, forestry, hunting, fishing, and fish farming (A), although logging is a specialization of individual municipalities of the region.

The statistical data used at the municipal level (shipped goods of its own production, performed works and services on its own; the average number of employees of organizations by type of economic activity; investments in fixed assets) are given by Rosstat for large and medium-sized organizations, excluding small businesses. For example, the difference between the volume of products shipped for large and medium-sized organizations and for the full range of organizations is 5.6%, and for those employed in industrial production it is about 15%.

Due to the fact that according to the indicator called shipped goods of our own production, performed works and services on our own (without subjects of municipalities), information on certain types of economic activity is not published for 29 out of 42 municipalities of Irkutsk oblast in order to ensure the confidentiality of primary statistical data [ 19 ], the materials posted on the official websites of the corresponding municipalities were taken into account.

This work used comparative geographical and statistical research methods.

RESULTS AND DISCUSSION

In the structure of gross value added in Irkutsk oblast, industry accounted for 31.7% in 2010, and 44.8% in 2018. The specific weight of the volume of shipped products of the region in Russia increased from 1.4% in 2010 to 1.7% in 2019, due to the fact that the volume of mining operations increased nine times. The average annual number of workers employed in the industrial sector decreased by 3.6%.

In 2010–2019, the production index in Irkutsk oblast, based on the results of its retrospective recalculation by Rosstat, did not fall below 100%, reaching its maximum value in 2010, 113.3%, and the minimum value in 2019, 100.4% ( Fig. 1 ). Growth rates of the industrial production index in 2010–2012, were due to significant volumes of mining (especially hydrocarbons). The drop in production volumes in 2019 is associated with a decrease in the production of crude oil, metal ores, and due to the current federal emergency in the region in the summer of 2019 (flooding of settlements) and coal. The trend continued in 2020 under the influence of external and internal factors (Russian participation in the agreement with the OPEC + countries and, accordingly, the restriction on oil production, as well as restrictions on the part of Russian Railways in accepting coal for export). Against the background of this situation, the manufacturing industry in 2020, in contrast, showed an increase in production (in particular, the contribution was made by Pharmasynthez, which began to produce medicines for the treatment of coronavirus infection).

The production indices of Irkutsk oblast, %. Types of economic activity: 1 , industrial production; 2 , mining; 3 , manufacturing; 4 , supply of electricity, gas and steam; air conditioning.

Transformational processes, which differ in intensity and direction in different periods, have formed the modern structure of the region’s industry, whose leading industries are: extraction of crude oil and natural gas, which accounts for 35.2% of the volume of shipped products; energetics , 10.3; metallurgical production, 9.1; production of paper and paper products, 4.7; wood processing, 4.5%. In 2010, the leading positions were occupied by the energy sector, 18.3%; production of machinery, equipment, vehicles, 16.3; metallurgical production, 15.3; extraction of fuel and energy minerals, 8.9; and chemical production, 7.6%.

The average number of employees of organizations (excluding small businesses) and the volume of goods, works, and services shipped by large and medium-sized organizations were used as indicators that characterize the level of development of industrial production in 42 regional municipalities in 2010 and 2019.

The ratio of these indicators made it possible to distinguish four groups of municipalities by the level of industrial development (high, medium, low, and very low) in 2010 and 2019. ( Figs. 2, 3 ).

The distribution of the share of people employed in industry and the volume of industrial production of large and medium-sized organizations in Irkutsk oblast in 2010

The distribution of the share of employed in industry and the volume of industrial production of large and medium-sized organizations in Irkutsk oblast in 2019

In 2019, compared to 2010, there was a transition of a number of territories of the region from one group to another. In three municipalities (Ust-Kutsky, Katangsky, and Alarsky districts) there was an increase in the level of industrial development, and in the other three municipalities (Angarsk, Shelekhovsky district, and Usolye-Sibirskoe) a decrease occurred.

The Highly developed group (Katangsky, Ust-Kutsky regions, and Bratsk) is characterized by high values of the share of people employed in industrial production and the volume of shipped products. It accounts for 21.2% of those employed in industrial organizations of the region (rotation work is also used) and 48.4% of investments in fixed assets. The northern regions continue to increase their concentration of the volume of shipped products in the region by the type of economic activity mining (2010, 43.8%; 2019, 73.6%). Oil and gas condensate production increased by 5.4 times, from 3.3 million tons in 2010 to 17.9 million tons in 2019. Oil produced from fields in the north of the region is fed to the Eastern Siberia–Pacific Ocean (ESPO) pipeline system; it is delivered to the Far East and then exported to China and other countries of the Asia–Pacific region. The main companies represented on the territory of these municipalities are Verkhnechonskneftegaz, Dulisma, and the Irkutsk Oil Company. The latter is the largest taxpayer to the regional budget (in 2018, the share of its contributions was 12.5%). Generally, oil producing enterprises provided 46% of all income tax revenues in 2018 in the structure of tax revenues of the consolidated budget of the region.

Industrial production in Bratsk is associated with the activities of such processing enterprises as RUSAL Bratsk (in 2019 it provided 38% of the aluminum production in Russia), the Ilim Group in Bratsk, and the Bratsk Ferroalloy Plant, which form the industrial image of the city. During the period an increase in the volume of shipped products in the manufacturing sector was noted in Bratsk (2010, 22.6%; 2019, 29.9%), which allows it to remain a large industrial hub of the region.

Group with an average level of development (the Angarsk, Svirsk, Sayansk, Ust-Ilimsk, Irkutsk, Bodaibinsky, Shelekhovsky, Nizhneilimsky, and Tulunsky districts) is distinguished by a high share of those employed in industry and an average volume of industrial production. About half of the region’s population lives in these municipalities, they produce 40.6% of industrial production, and concentrate 61.5% of those employed in the industrial sector, as well as 38.4% of investments. The group includes almost all major industrial centers in the region. Unlike the previous case, the sectoral composition of this group is more diverse and is represented by enterprises of nonferrous metallurgy, mechanical engineering, chemical and petrochemical, pulp and paper, nuclear, pharmaceutical, and food industries (Irkutsk Aluminum Plant, Irkutsk Aviation Plant, Angarsk Petrochemical Company, Angarsk Polymer Plant, Sayanskkhimplast, Ilim Group in Ust-Ilimsk, Angarsk Electrolysis Chemical Plant, Pharmasintez, etc.). The production profile of these territories was formed back in the Soviet era, but at the present stage enterprises continue to play a significant role in the socioeconomic development of the region, especially for export-oriented industries.

The mining sector is represented by gold mining at ore and alluvial deposits (Polyus Verninskoe, Vysochaishy, Druza, Lenzoloto, etc., which provided more than 9% of the gold mining in Russia), iron ore (Korshunovsky GOK), and coal (Tulunugol open pit).

A separate place in this group is occupied by Irkutsk, the administrative center of the region with a diversified industry, which is the center of the emerging agglomeration of the same name. For Irkutsk, there is a significant increase in shipped products by the type of economic activity supply of electricity, gas, and steam, and air conditioning (in 2010, 27.9%, and in 2019, 87.4%). This increase is explained by a peculiarity of statistical accounting: most of the products produced on the territory of the region for this type of economic activity are attributed to the city. In reality, Irkutsk produces 25 times less of them. We note that almost all large energy companies, except for Vitimenergo, are registered in the regional center.

The low development group (Usolye-Sibirskoe, Winter, Tulun, Cheremkhovo, Kirensky, Zhigalovsky, Nizhneudinsky, Usolsky, Mamsko-Chuisky, Zalarinsky, Kazachinsko-Lensky, Taishetsky, Ust-Ilimsky, Bratsky, Nukutsky, Alarsky, Slyudyansky, Chunsky, Chunkhovsky, and Irkutsky) is the most numerous and heterogeneous in its composition. It is characterized by a small share of those employed in the industrial production of the region and a low volume of goods shipped. This group accounts for 8.8% of the volume of shipped goods, works, and services of the region, 16.8% of those employed in industry, and 12.7% of investments. In more than half of the municipalities, the leading type of economic activity is manufacturing, which is represented by medium-sized and large companies: a branch of the Ilim Group in the Bratsk District, Knauf Gips Baikal, Rusforest Magistralny, Usolye Salt Extraction and Processing Shop (part of Russol), and others.

Mining predominates in six municipalities (Gazprom Dobycha Irkutsk, IOC, Nedra mining company (GPK), Tyretsky salt mine, Cheremkhovugol open pit, etc.); in Tulunsky district, it is power engineering, and in Mamsko-Chuysky, it is water supply. At the end of 2022, gas is planned to be supplied from the Kovykta field (Irkutsk gas production center) to the Power of Siberia gas trunkline, which is oriented to external consumption (China).

The group includes territories both with industrial enterprises closed in the post-Soviet period and with new industrial facilities that have just begun to function. Single-industry towns (Usolye-Sibirskoye, Cheremkhovo, and Tulun) were given the status of a territory of advanced socioeconomic development to support the economy.

The very low development group (Osinsky, Kuytunsky, Bayandaevsky, Olkhonsky, Balagansky, Kachugsky, Bokhansky, Ekhirit-Bulagatsky, Ust-Udinsky, and Ziminsky districts). This accounts for only 0.1% of the volume of products produced in the region, 0.5% of those employed in industrial organizations of the region, 0.5% of investments in fixed assets. The industry is mainly represented by food. The districts specialize in agriculture, logging, and recreational activities. There are no large companies; small business prevails.

In the first and fourth groups, the priorities in the development of types of economic activity changed, and in the second and third groups, their proportions changed ( Fig. 4 ).

The change in the structure of types of economic activities by groups of municipalities of Irkutsk oblast in 2010 and 2019, %. Types of economic activity: 1 , mining; 2 , manufacturing industries; 3 , supply of electricity, gas and steam; air conditioning; 4 , water supply, sewerage, waste collection and disposal, activities to eliminate pollution.

From 2010 to 2019, the volume of shipped products and investments increased by 3.4 and 3.8 times, respectively (on average per one municipal district) (see Table 1 ), with a decrease in the population and employed in the industrial sector. The greatest change in these indicators is noted in the first and second groups.

Industrial development indicators by groups of municipalities of Irkutsk oblast in 2010 and 2019 (on average for one municipality)

The regional industry is dominated by local organizations of various sizes, a quarter of the large and medium-sized companies are controlled by holding companies such as Gazprom, Rosneft, Polyus, Ilim Group, Rosatom, Rostekh, Mechel, Renova, En+ Group, and RUSAL.

In recent years, as a result of the active development of oil and gas resources, the process of complex formation has begun 1 : for example, IOC is building a polymer plant in Ust-Kut (commissioning is planned in 2024) and is building the Ust-Kutsk gas processing plant for the supply of raw materials (to be launched in 2021).

RUSAL invested and attracted large investments in the construction of the Taishet aluminum plant (the launch was postponed to 2021), as well as the Taishet anode factory, which will meet the plant’s needs for baked anodes. The Ilim Group will build a pulp and cardboard mill in Ust-Ilimsk by 2023, which will increase the production of unbleached packaging materials. These and other projects, which were initiated by big business and are in an active stage, attract investments to the municipalities of the region ( Fig. 5 ).

The share of investments in fixed assets of large and medium-sized organizations of Irkutsk oblast, %. Municipalities: 1 , Irkutsk; 2 , Katangsky district; 3 , Bratsk; 4 , Angarsk; 5 , Usolye-Sibirskoye; 6 , Ust-Kutsky district; 7 , Taishetsky district; 8 , others.

In 2010 the share of investments of the five leading municipalities in the region was 71.2%, while by 2019 the concentration increased to 78.8%. In 2017–2019 investment growth rates increased, on average, in most municipalities (85.7%), especially in municipalities of the third group, the Tulun, Ust-Ilimsky, Kuytunsky, and Cheremkhovsky districts. Per capita investment rates are the highest for municipalities of the first and second groups, Katangsky, Ust-Kutsky, and Bodaibinsky northern regions, which is explained by the large volumes of investments made by large companies in the development of natural resources and the low population density.

CONCLUSIONS

In the last decade, an increase in the share of the raw materials sector (by four times) with a significant decrease in the share of mechanical engineering (by four times), chemical production (by almost two times), energy, and metallurgy determines the structural shifts in the region’s economy. The shift towards the extractive sector, which is more focused on the export of raw materials, structurally simplifies the sectoral composition of industry.

The existing main territories for gold and iron ore mining (Bodaibinsky, Nizhneilimsky regions), as well as peripheral northern regions (Katangsky, Ust-Kutsky), areas for the development of oil and gas resources, have increased their importance and increased concentration in industrial production. In 2019, they accounted for the largest volume of shipped products in the extraction of minerals, 89.1% (2010, 50.6%). This is also facilitated by the pipeline system, the main ESPO oil pipeline and the Power of Siberia gas pipeline (its section under construction in the region), as well as the increased demand for hydrocarbons in the markets of the Asia–Pacific region. The development of the oil and gas industry attracted labor resources from other regions of the country (Western Siberia, the Republic of Tatarstan, etc.). In 2019, the number of workers on a rotational basis exceeded 25 000 people per quarter, of which more than 30% are residents of the region.

The cities, the leading industrial centers of the region (Bratsk, Irkutsk, Angarsk, Shelekhov, Ust-Ilimsk, and Sayansk), whose large enterprises were created in Soviet times, have adapted to changing conditions and still retain their stability. In 2019, they formed 87% of the shipped products of the manufacturing industry (in 2010, 90.5%). Other cities (Tulun, Zima, and Usolye-Sibirskoye) lost their importance as a result of the closure of city-forming enterprises in the post-Soviet period; in 2013, Baikalsk was added to them. As a regional center, Irkutsk is statistically attributed to a significant volume of shipped goods, works, and services in the energy sector (2019, 87.4%), which complicates the territorial analysis of this industry.

To identify intraregional differentiation of the level of industrial development in 2010 and 2019 four groups of medical organizations were identified, which are different in composition depending on the distribution of quantitative criteria (the share of people employed in industrial production and the volume of shipped products of large and medium-sized organizations). Six MOs changed their position in the groups, while the rest retained their positions,

Over the past 10 years, only five municipalities (Irkutsk, Bratsk, Angarsk, Katangsky, and Ust-Kutsky districts) have concentrated more than two-thirds of their investments in fixed assets, which indicates the extreme unevenness of their distribution. Basically, the resource advantages of the region in the implementation of large investment projects in gas chemistry, nonferrous metallurgy, timber processing, pulp and paper production, and mining are used by large businesses that control significant enterprises. However, investment activity has little effect on improving socioeconomic conditions, which has been noted by other researchers [ 14 , 21 ].

The work was carried out at the expense of the state assignment (АААА-А21-121012190019-9).

1 According to P.Ya. Baklanov, the processes of the initial formation and subsequent development of territorial combinations of nodal elements, various enterprises (or territorial-production complexes) are complex formation [ 20 , p. 213].

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Facility for Rare Isotope Beams

At michigan state university, frib researchers lead team to merge nuclear physics experiments and astronomical observations to advance equation-of-state research, world-class particle-accelerator facilities and recent advances in neutron-star observation give physicists a new toolkit for describing nuclear interactions at a wide range of densities..

For most stars, neutron stars and black holes are their final resting places. When a supergiant star runs out of fuel, it expands and then rapidly collapses on itself. This act creates a neutron star—an object denser than our sun crammed into a space 13 to  18 miles wide. In such a heavily condensed stellar environment, most electrons combine with protons to make neutrons, resulting in a dense ball of matter consisting mainly of neutrons. Researchers try to understand the forces that control this process by creating dense matter in the laboratory through colliding neutron-rich nuclei and taking detailed measurements.

A research team—led by William Lynch and Betty Tsang at FRIB—is focused on learning about neutrons in dense environments. Lynch, Tsang, and their collaborators used 20 years of experimental data from accelerator facilities and neutron-star observations to understand how particles interact in nuclear matter under a wide range of densities and pressures. The team wanted to determine how the ratio of neutrons to protons influences nuclear forces in a system. The team recently published its findings in Nature Astronomy .

“In nuclear physics, we are often confined to studying small systems, but we know exactly what particles are in our nuclear systems. Stars provide us an unbelievable opportunity, because they are large systems where nuclear physics plays a vital role, but we do not know for sure what particles are in their interiors,” said Lynch, professor of nuclear physics at FRIB and in the Michigan State University (MSU) Department of Physics and Astronomy. “They are interesting because the density varies greatly within such large systems.  Nuclear forces play a dominant role within them, yet we know comparatively little about that role.” 

When a star with a mass that is 20-30 times that of the sun exhausts its fuel, it cools, collapses, and explodes in a supernova. After this explosion, only the matter in the deepest part of the star’s interior coalesces to form a neutron star. This neutron star has no fuel to burn and over time, it radiates its remaining heat into the surrounding space. Scientists expect that matter in the outer core of a cold neutron star is roughly similar to the matter in atomic nuclei but with three differences: neutron stars are much larger, they are denser in their interiors, and a larger fraction of their nucleons are neutrons. Deep within the inner core of a neutron star, the composition of neutron star matter remains a mystery. 

  “If experiments could provide more guidance about the forces that act in their interiors, we could make better predictions of their interior composition and of phase transitions within them. Neutron stars present a great research opportunity to combine these disciplines,” said Lynch.

Accelerator facilities like FRIB help physicists study how subatomic particles interact under exotic conditions that are more common in neutron stars. When researchers compare these experiments to neutron-star observations, they can calculate the equation of state (EOS) of particles interacting in low-temperature, dense environments. The EOS describes matter in specific conditions, and how its properties change with density. Solving EOS for a wide range of settings helps researchers understand the strong nuclear force’s effects within dense objects, like neutron stars, in the cosmos. It also helps us learn more about neutron stars as they cool.

“This is the first time that we pulled together such a wealth of experimental data to explain the equation of state under these conditions, and this is important,” said Tsang, professor of nuclear science at FRIB. “Previous efforts have used theory to explain the low-density and low-energy end of nuclear matter. We wanted to use all the data we had available to us from our previous experiences with accelerators to obtain a comprehensive equation of state.”   

Researchers seeking the EOS often calculate it at higher temperatures or lower densities. They then draw conclusions for the system across a wider range of conditions. However, physicists have come to understand in recent years that an EOS obtained from an experiment is only relevant for a specific range of densities. As a result, the team needed to pull together data from a variety of accelerator experiments that used different measurements of colliding nuclei to replace those assumptions with data. “In this work, we asked two questions,” said Lynch. “For a given measurement, what density does that measurement probe? After that, we asked what that measurement tells us about the equation of state at that density.”   

In its recent paper, the team combined its own experiments from accelerator facilities in the United States and Japan. It pulled together data from 12 different experimental constraints and three neutron-star observations. The researchers focused on determining the EOS for nuclear matter ranging from half to three times a nuclei’s saturation density—the density found at the core of all stable nuclei. By producing this comprehensive EOS, the team provided new benchmarks for the larger nuclear physics and astrophysics communities to more accurately model interactions of nuclear matter.

The team improved its measurements at intermediate densities that neutron star observations do not provide through experiments at the GSI Helmholtz Centre for Heavy Ion Research in Germany, the RIKEN Nishina Center for Accelerator-Based Science in Japan, and the National Superconducting Cyclotron Laboratory (FRIB’s predecessor). To enable key measurements discussed in this article, their experiments helped fund technical advances in data acquisition for active targets and time projection chambers that are being employed in many other experiments world-wide.   

In running these experiments at FRIB, Tsang and Lynch can continue to interact with MSU students who help advance the research with their own input and innovation. MSU operates FRIB as a scientific user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. FRIB is the only accelerator-based user facility on a university campus as one of 28 DOE-SC user facilities .  Chun Yen Tsang, the first author on the Nature Astronomy  paper, was a graduate student under Betty Tsang during this research and is now a researcher working jointly at Brookhaven National Laboratory and Kent State University. 

“Projects like this one are essential for attracting the brightest students, which ultimately makes these discoveries possible, and provides a steady pipeline to the U.S. workforce in nuclear science,” Tsang said.

The proposed FRIB energy upgrade ( FRIB400 ), supported by the scientific user community in the 2023 Nuclear Science Advisory Committee Long Range Plan , will allow the team to probe at even higher densities in the years to come. FRIB400 will double the reach of FRIB along the neutron dripline into a region relevant for neutron-star crusts and to allow study of extreme, neutron-rich nuclei such as calcium-68. 

Eric Gedenk is a freelance science writer.

Michigan State University operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.