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Energy Efficiency

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29-03-2019 | Original Article

How do data centers make energy efficiency investment decisions? Qualitative evidence from focus groups and interviews

Authors: Heather Klemick, Elizabeth Kopits, Ann Wolverton

Published in: Energy Efficiency | Issue 5/2019

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Abstract

The data center industry is one of the fastest growing energy users in the USA. While the industry has improved its energy efficiency over the past decade, engineering analyses suggest that ample opportunities remain to reduce energy use that would save firms money. This study explores whether and why data centers might limit investment in energy efficiency. Given the scarcity of empirical data in this context, we conducted focus groups and interviews with data center managers to elicit information about factors affecting their investments and used content analysis to qualitatively evaluate the results. Split incentives between departments within companies and between colocation data centers and their tenants, imperfect information about the performance of new technologies, and tradeoffs with data center reliability were the most pervasive factors discussed by participants. While we find some evidence that market failure explanations such as split incentives and imperfect information had a limited role in slowing adoption for participants, rival explanations such as the cost of acquiring context-specific information, and opportunity costs associated with alternate uses of funds or highly valued attributes played a larger role in slowing investment in energy efficiency.

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For a discussion of trends in data center energy efficiency in Europe, see Avgerinou et al. (2017).

A 2013 California regulation mandates the use of specific energy-efficient cooling, airflow, and fan technologies in data centers (California Building Standards Commission 2013). Energy consumption for the rest of the industry remains unregulated in the USA, despite a few federal initiatives to promote best practices.

The presence of unpriced negative externalities, such as greenhouse gases and other air pollutants, also leads to market failures, but externalities are not expected to be factored into firms’ private profit maximization decisions so we do not consider them here.

A profit-maximizing firm invests until the additional benefit from the investment just equals the additional cost.

The hypotheses we discuss are consistent with Sorrell et al. (2011) and Thollander et al. (2010).

Conversely, it is possible that there are synergies between energy efficiency and other attributes, meaning that improvements in energy efficiency could actually lead to greater reliability or product quality in some cases. See “Tradeoffs between energy efficiency and other factors” section for a discussion in the context of data centers.

A semi-structured approach uses a moderator’s guide, which lists the questions and topics to be discussed across interviews and focus groups. This approach allows the interviewer or moderator some discretion regarding the order in which the questions are asked and provides prompts to indicate when a topic is worthy of follow-up to facilitate a deeper understanding of a given topic as it relates to the hypotheses of interest.

The questionnaire and moderator’s guide are available in an online appendix.

Given the technical nature of the discussions and the fact that participants did not have access to questions ahead of time, we interpreted filler words—“um” or “err” or pauses—as indicative of a participant trying to gather his or her thoughts. These types of words were eliminated in the speech-to-text conversation process; we did not omit other informal speech mannerisms that might conceivably convey meaning.

This form of coding is sometimes referred to as hypothesis coding and is a common approach to content analysis when attempting to assess researcher-generated hypotheses (Miles et al. 2014).

We also used sub-codes. For instance, a discussion that was coded as “tradeoffs with other attributes” and “widely viewed as a slowing adoption” would also be tagged with “reliability” if it discussed how a technology affects performance, redundancy, uptime, or reliability.

Focus groups (FG) and interviews (INT) are labeled numerically throughout the document.

Four focus groups were held at Data Center Dynamics conferences in Dallas and New York, and two focus groups were held at an AFCOM conference in Boston.

Excluding this sector could potentially bias our sample. However, the proportion of US servers in federal government data centers fell from roughly 5–10% in 2007 to 1–2% in 2012–2014 (Shehabi et al. 2016). Our sample does include private companies that provided computing services to federal government users.

EPA and DOE’s Energy STAR buildings program recognizes top performers in energy efficiency through certification of individual facilities as well as portfolios of buildings or plants.

The degree to which a data center operated as a colocation facility varied from less than 10 to over 90% of racks. More than half of the sample also leased space from others, though 25% leased a relatively small portion (i.e., 20% or less). In addition, most data centers occasionally outsourced facility management.

Barriers that prevent mixing of incoming cold air and hot exhaust air (cold- or hot-aisle containment), tiles that guide cool air to servers (directional floor tiles, blanking panels), air conditioning unit devices that allow air flow to vary as cooling demand fluctuates (variable fan or speed drives), and higher temperature set points are frequently described as paying back in less than 2 years. Company statements and research indicate that investments in cooling method often have higher upfront costs, particularly retrofits, but can still yield large energy savings in some circumstances. See Van Geet (2017), Pacific West Air Conditioning (2014), Green Grid (2011), Wathaifi (2009), and https://www.energystar.gov/products/low_carbon_it_campaign/12_ways_save_energy_data_center/.

This finding is consistent with Cooremans (2012) regarding the importance of investment characteristics in decision-making.

A PUE of one indicates that a data center uses no additional power for lighting, cooling, power distribution, or facility operation beyond what is drawn by the IT equipment; a PUE of two indicates that for every unit of power consumed by IT equipment, another unit is used for facility operation. Participants reported a median PUE of 1.7 for localized facilities, 1.55 for enterprise facilities, and 1.4 for mega/utility-scale data centers.

Howard and Holmes (2012) also found evidence of split incentives between the facilities and IT departments for the data centers they interviewed. They found that having one person responsible for both facilities and IT equipment decisions was more common among companies whose core business was data center services.

While we do not know exactly how many participants rely on internal vs. external financing, discussion suggested they use a mix. Cooremans (2012) and Klemick et al. (2017) found that internal financing is prevalent in other sectors; as here, energy efficiency often competes with non-energy investments for internal capital.

For an overview of incentive and rebate programs offered by utilities, see Howard and Holmes (2012).

Mills et al. (2008) also pointed out that energy efficiency improvements in high-tech industries such as data centers often compete internally with other capital investments that pay off very quickly.

These statements are consistent with Mills et al. (2008): risks of power failure are often overestimated, resulting in “overdesign” of backup systems and “strict emphasis on proven reliable technologies.” Howard and Holmes (2012) noted that a major challenge to improving data center energy efficiency is an “extreme focus on reliability.”

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Title: How do data centers make energy efficiency investment decisions? Qualitative evidence from focus groups and interviews
Authors: Heather Klemick
Elizabeth Kopits
Ann Wolverton
Publication date: 29-03-2019
Publisher: Springer Netherlands
Published in: Energy Efficiency / Issue 5/2019
Print ISSN: 1570-646X
Electronic ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-019-09782-2

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