Modeling the energy retrofit decision in commercial office buildings

Energy and Buildings 131 (2016) 1–20 Contents lists available at ScienceDirect Energy and Buildings journal homepage: www.elsevier.com/locate/enbuil...

Download PDF

3MB Sizes 0 Downloads 109 Views

Report

Full Text

Energy and Buildings 131 (2016) 1–20

Contents lists available at ScienceDirect

Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild

Modeling the energy retroﬁt decision in commercial ofﬁce buildings Constantine E. Kontokosta Assistant Professor of Urban Informatics; Head, Quantiﬁed Community Research Lab; Deputy Director for Academics, Center for Urban Science and Progress & Tandon School of Engineering, New York University

a r t i c l e

i n f o

Article history: Received 6 March 2016 Received in revised form 9 June 2016 Accepted 22 August 2016 Available online 30 August 2016 Keywords: Energy retroﬁt Commercial building sector Energy investment Energy decision-making Building energy efﬁciency Energy data

a b s t r a c t Retroﬁtting existing buildings has emerged as a primary strategy for reducing energy use and carbon emissions, both nationally and in cities. Despite the increasing awareness of retroﬁtting opportunities and a growing portfolio of successful case studies, little is known about the decision-making processes of building owners and asset managers with respect to energy efﬁciency investments. Speciﬁcally, the research presented here examines the effects of ownership type, tenant demand, and real estate market location on building energy retroﬁt decisions in the commercial ofﬁce sector. This paper uses an original, detailed survey of asset managers of 763 ofﬁce buildings in nineteen cities sampled from the CBRE, Inc. portfolio. Controlling for various building characteristics, the results demonstrate that ownership type and local market do, in fact, inﬂuence the retroﬁt decision. Overall, this analysis provides new evidence for the importance of understanding ownership type and the varying motivations of differing types of owners in building energy efﬁciency investment decisions. The ﬁndings of both the survey analysis and the predictive model demonstrate additional support for the targeting of energy efﬁciency incentives and outreach based on ownership entity, local market conditions, and speciﬁc physical building characteristics. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Ambitious building energy efﬁciency goals across local, state, and federal governments have directed signiﬁcant attention to the potential to retroﬁt existing buildings to improve their energy performance, while generating new investment and job creation opportunities. According to the U.S. Department of Energy (DOE), the U.S. buildings sector accounts for 40% of greenhouse gas (GHG) and energy use, a sizeable ﬁgure that could be reduced by as much as 30% using existing technologies and energy conservation measures (ECMs) (Brown et al. 2008) [33]. This is especially true for large buildings in cities; in New York City, for instance, buildings account for 79% of all GHG emissions and energy use and fully half of that is attributable to buildings over 50,000 square feet, even though buildings of this size represent only 2% of the total number of buildings in the City [11,12,13]. Similarly, the building sector in Chicago accounts for fully 71% of its total urban GHG emissions [10]. As a result, major cities have adopted signiﬁcant building energy efﬁciency and GHG reduction plans to not only address issues of climate change and sustainability, but also to stimulate economic

E-mail address: [email protected] http://dx.doi.org/10.1016/j.enbuild.2016.08.062 0378-7788/© 2016 Elsevier B.V. All rights reserved.

growth, encourage technology innovation, and mitigate potentially negative economic, environmental, and public health impacts. Although the need and potential beneﬁts of energy retroﬁts have been well-documented, the pace of adoption of energy efﬁcient practices and technologies has been slow, and signiﬁcant barriers – both perceived and actual – persist to limit building energy investments [39,44]. New policy initiatives have been introduced to address some of these barriers and catalyze market transformation around the beneﬁts of energy performance improvements [7,29]. Energy disclosure laws now enacted in over a dozen U.S. cities provide a new stream of building energy data and peer-group benchmarking to overcome marketplace information asymmetries and knowledge gaps in building sustainability best practices [26,28]. Energy audit and retro-commissioning requirements have also begun to emerge, providing owners, tenants, and policymakers with detailed accounting of building systems and energy end-use, as well as the energy savings and cost savings potential of the implementation of speciﬁc ECMs. These requirements have come in several forms, from policies similar to New York City’s Local Law 87 (LL87) that obligate large buildings to conduct an audit every ten years, to energy audit requirements by lenders at time of sale or re-ﬁnance. Despite the increasing knowledge base around retroﬁtting opportunities and a growing portfolio of successful case studies, little is known about the decision-making processes of build-

2

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

ing owners and asset managers with respect to energy efﬁciency investments. Speciﬁcally, the research presented here examines the effects of ownership type, tenant demand, and market competitiveness on building energy retroﬁt decisions in the commercial ofﬁce sector. This paper uses an original, detailed survey of asset managers of 763 ofﬁce buildings in nineteen cities as part of the CB Richard Ellis portfolio and a machine learning prediction model to quantify the factors associated with energy retroﬁt activity. The survey was designed to collect energy performance, space use, and retroﬁt/ECM data, as well as information on the ownership structure and motivations and constraints to implementing ECMs. This analysis provides new evidence on the factors that inﬂuence the decision to pursue – or avoid – building energy improvements, as well as to classify buildings by design components, ownership type, and energy proﬁle to predict the likelihood of energy retroﬁts in other similar buildings. The paper begins with a discussion of the recent literature, then follows with a description of the survey and empirical methodology and ﬁndings. The results of the classiﬁcation and prediction model are presented together with implications of the ﬁndings for advancing the pace of adoption of energy efﬁciency investments in ofﬁce buildings.

2. Literature review There is an extensive body of research on the opportunity that energy retroﬁtting of commercial buildings creates to reduce national energy use and carbon emissions [8,31,32]. Given that commercial buildings account for approximately 20% of national energy use, ambitious, but potentially achievable, 30–50% efﬁciency gains in existing commercial buildings through retroﬁtting would yield a 6–10% reduction in energy consumption for the U.S. as a whole [5]. This would equate to a reduction of approximately 3000 to 5000 trillion Btu per year, based on 2015 consumption estimates (U.S. Energy Information Administration, Monthly Energy Review, Table 2.1). In addition to the energy use implications, improving energy efﬁciency in the commercial building sector has been estimated to be a sizeable catalyst for capital investment and driver of economic activity. Studies by the Rockefeller Foundation, Deutsche Bank, and McKinsey Consulting suggest commercial building energy retroﬁts could be an estimated $72 billion investment opportunity, one that could generate as many as 857,000 job-years over a ten year period [40]. Energy retroﬁts have also been shown to produce both direct and indirect beneﬁts for building occupants and owners. In addition to possible energy savings and associated lower operating costs, beneﬁts include reductions in equipment maintenance, improved air quality, improved rental rates, higher tenant retention, and higher occupancy rates [2,15,18,27]. Despite what appear to be signiﬁcant positive outcomes from energy retroﬁts in the commercial building sector, the adoption of energy efﬁciency projects and practices continues to be slow [42,43]. Barriers to the adoption of energy retroﬁt measures include information asymmetries between stakeholders, uncertainty over future savings, lack of knowledge in energy technologies, and economic dis-incentives including the “split-incentive” problem between owners and tenants and the decreasing cost of oil and natural gas [22,25]. These perceived and actual barriers have been exacerbated by a case-study approach to retroﬁt strategies, often due to the lack of comprehensive, robust data and large-scale pre/post studies of consumption following ECM installation. Several studies have attempted to better understand optimal ECM strategies for commercial buildings. Doukas et al. [14] introduce an intelligent operations management software that accounts for building operational data and external factors to identify energy efﬁciency opportunities. Asadi et al. [3] utilize a multi-objective

mathematical model to simultaneously evaluate a range of ECMs applicable to single-family homes. Focusing on material and building construction factors, the model identiﬁes trade-offs between cost and energy savings across multiple alternatives. A similar study by Verbeeck and Hens [45] looks at the existing housing stock in Belgium to identify optimal ECMs through the use of building simulation models. The authors conclude by presenting a hierarchy of ECMs in this context. Beyond physical and technological features of energy retroﬁts, Menassa [34] uses cost-beneﬁt analysis and option pricing theory to provide decision-makers with guidance for sequencing individual ECMs over time. The study examines singlestage and multi-stage investment scenarios to develop alternatives to net present value decision criteria. Marasco and Kontokosta [48] utilize actual energy audit report data for more than 2,000 buildings in New York City to predict the likelihood of ECM recommendations for a particular building. Using a machine learning classifer, ¨ ¨ the authors develop an automated auditprocedure to estimate ECM eligibility given a speciﬁc set of building and systems characteristics.Additional studies have focused on modeling and optimization of potential energy retroﬁt measures under differing degrees of uncertainty [24,41]. Few studies have looked comprehensively at the decision and motivations to implement an energy retroﬁt in commercial buildings. In a study of homeowners in Canada, the decision to move from energy audit to retroﬁt investment was inﬂuenced by the projected energy savings, the up-front cost, and incentives available, as well as building age and householder demographics [21]. In a study of retroﬁt activity of homeowners in Germany, Achtnicht and Madlener [1] ﬁnd similar results as the Canadian study, with ﬁnancial capacity, favorable payback periods, and timing of system replacement shown to increase the likelihood of retroﬁt implementation. In an examination of retroﬁt adoption by ECM type in manufacturing facilities, Anderson and Newell [46] ﬁnd that ﬁrms are more sensitive to initial costs rather than expected future savings. As expected, ﬁrms are found to be more likely to implement ECMs that have lower costs, shorter payback periods, and relatively higher energy savings. While all important ﬁndings, these studies address the residential and manufacturing sectors, respectively, and the generalizability of the ﬁndings to the commercial sector may be limited due to the varying regulatory, ownership, and ﬁnancial structures of ofﬁce buildings in the U.S.

3. Theoretical framework Within the context of the expected beneﬁts and perceived constraints of energy efﬁciency investments, the decision by a commercial building owner to invest in energy improvements and implement energy conservation measures is driven by a range of endogenous and exogenous factors. These include regulatory context and compliance risk, increased resilience and business continuity, knowledge and awareness, and tenant and occupant demand for energy efﬁcient space. The relationship and interaction of these factors (as shown in Fig. 1) ultimately impacts the retroﬁt decision through economic/ﬁnancial considerations and technical feasibility, potentially mediated by social and moral inﬂuences. Below, each is discussed in turn and a series of propositions to be tested in the empirical model are outlined. Factors in Retroﬁt Decision-making- Increasingly, ﬁrms require certain energy or sustainability certiﬁcations (LEED, Energy Star, etc.) as a prerequisite for leasing space in particular building [35]. For instance, the General Services Administration will only lease space in buildings that have achieved an Energy Star label (U.S. General Services Administration Realty Services Letter, RSL-201002). In the private sector, ﬁrms are adopting corporate and social responsibility policies that encourage or require leasing in build-

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Regulatory environment and compliance risk

3

Tenant/occupant demand for EE Knowledge, awareness, PR

Resilience and Business Connuity Obsolescence Risk

Social and moral consideraons

Economic and ﬁnancial consideraons

Technical feasibility

Direct eﬀect Indirect eﬀect

Retroﬁt decision Fig. 1. Factors that inﬂuence the retroﬁt decision.

ings that have earned an eco-label certiﬁcations, including LEED and Energy Star. Tenants may also demand more efﬁcient systems and space utilization as a way to control operating expenses, and, in some cases, provide for a healthier and more productive environment for its employees [16,36]. In this case, tenants may be willing to pay more for more energy efﬁcient space, and/or be more likely to continue to lease such space, thus reduce tenant turnover rates [18]. The challenge here for building owners is effectively measuring and communicating to potential tenants the actual energy performance of the space and other ancillary beneﬁts relating to indoor air quality and environmental quality. Regulatory environment and compliance risk- As cities continue to develop and enact sustainability plans to reduce operational costs, carbon emissions, and improve quality of life, regulations and policies focusing on more efﬁcient building design and operations are becoming more widespread [38]. These include more stringent building codes (such as San Francisco’s Chapter 13C Green Building Ordinance), requirements for new construction and substantial rehabilitation to meet LEED standards, and energy disclosure and audit mandates for existing buildings [29,30]. The increasing regulatory attention on energy efﬁciency could lead to the potential for greater efﬁciency standards for new and existing buildings in the future, thus exposing owners of less efﬁcient buildings to both the economic and technical risk of meeting any new, more stringent requirements. Economic and ﬁnancial considerations- Fundamentally, a building energy retroﬁt is a ﬁnancial investment in a capital asset. This requires sufﬁcient up-front or ﬁrst-cost capital to purchase upgraded equipment and systems and a viable risk-adjusted rate of return on the capital invested, given the risk and uncertainty of quantifying future energy savings. Numerous ﬁnancial products and public incentives have been introduced to account for capital limitations and address information asymmetries that may inﬂate the perceived risk of energy investments [6,29]. However, owners may be inﬂuenced by alternative investments and the opportunity cost of energy efﬁciency improvements. For instance, upgrading and re-modeling an ofﬁce building lobby has what is often considered to be deﬁnitively measurable returns – the impact on rents, competitive positioning, and tenant retention are deemed to be

clear. This may be driven by both extensive examples of the impact of lobby quality on building status and by the very tangible nature of such a cosmetic upgrade, when compared to an energy retroﬁt. Resilience and business continuity- The link between sustainability and resilience in buildings continues to gain attention, and the complementarity of approaches designed to increase efﬁciency are being recognized for their potential to maximize building resilience and the ability to operate through (or recover faster from) disasters and emergencies. The decision to conduct an energy retroﬁt, then, may also be inﬂuenced by occupant/tenant demand to maximize business continuity or by demand from owners and investors to reduce the risk of building system damage and loss of rental income. Knowledge and awareness- It is to be expected that there exist varying degrees of knowledge and awareness about sustainable design practices and energy efﬁcient building operations. A greater understanding of green building practices should be reﬂected in reduced uncertainty around energy retroﬁtting, and thus lower risk-adjusted return hurdles for owners and investors. Green knowledge should be greater among larger portfolio owners/managers, potentially due to demand from investors and the presence of operational efﬁciencies at scale. At the building scale, larger buildings should also beneﬁt from cost efﬁciencies of scale, as well as have more highly trained building operators and managers who would be able to carry out, or at least oversee, an energy retroﬁt project. Social and moral considerations- Social pressures and the local culture around environmental conservation can inﬂuence a building owner’s decision within particular submarkets and cities. In California, for instance, public perception may inﬂuence efforts to promote environmental responsibility in building design and operations. Similarly, owners will have differing personal and corporate values about sustainability and environmental responsibility. These values may minimize concerns about ﬁnancial returns and increase willingness to pay for more energy efﬁcient buildings, thus leading to a higher propensity for implementing energy retroﬁts and ECMs. However, the strength of inﬂuence of personal values will be mediated by the type of ownership; for example, it can be expected that private owners’ values will have a more significant impact on energy decision-making, while REITs will be more

4

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

affected by shareholders values than its own corporate leadership [23]. Technical feasibility- Ultimately, the impact of implementing ECMs will depend on the existing systems and construction type of a particular building, as the range of appropriate ECMs and the potential energy and cost savings are a function of the current condition of systems and design. Factors such as a building’s age, primary fuel type, or construction type will therefore impact the feasibility and economic viability of an energy retroﬁt. 3.2. Propositions Given the theoretical framework established above, this paper seeks to empirically test three hypotheses relating to the decisionmaking process of ofﬁce building owners when considering an energy retroﬁt, derived from the propositions set forth below. Proposition 1. The type of ownership structure will impact the likelihood of conducting an energy retroﬁt. It is expected that the type of ownership will inﬂuence the retroﬁt decision in several ways. First, different owner types may face differing regulations and requirements relating to energy efﬁciency. For instance, REITs are subject to differing tax and regulatory reporting requirements than individual private owners. Second, owner type may impact the role of social/moral considerations in decision-making and the inﬂuence of such issues from investors. Third, owner type may affect investment time horizons, required rates of return, risk tolerance, and other factors that shift the economic calculus of the retroﬁt decision. Institutional owners, for example, tend to have a lower risk tolerance and longer investment hold period for portfolio assets [9]. Proposition 2. If tenant demand for more efﬁcient space is made explicit as part of the leasing process, then the probability of an energy retroﬁt increases. It is proposed here that tenant demand for more efﬁcient space will inﬂuence the retroﬁt decision through the expectation of price/occupancy premiums for more efﬁcient space (or discounts for less efﬁcient properties). Therefore, it can expected that where tenants express a preference for energy efﬁciency, either through explicit (stated need or requirement for leasing) or implicit (previous location decisions) channels, owners will be more likely to undergo an energy retroﬁt on an energy inefﬁcient or underperforming asset. Proposition 3. The probability of a property undergoing an energy retroﬁt – after controlling for physical, design, and energy use characteristics of the building – will be impacted by the region and speciﬁc market location of the building. It can be assumed that the spatial location of a property will impact the retroﬁt decision for that property. This inﬂuence could reveal itself in three ways: (1) through differences in climate that drive heating and cooling loads, (2) through the pricing of electricity/fuel, and (3) through regulations for energy efﬁciency at the local level. It is expected that this will be observed through retroﬁt behavior in buildings across geographically disperse cities.

Table 1 Energy use intensity summary statistics. Statistic

Value (kwh/m2)

Mean Standard Deviation Minimum, Maximum 25th, 50th, 75th percentiles

632.02 (200.35 kbtu/sf) 272.56 (86.40 kbtu/sf) 26.04, 1897.30 (8.37, 601.44 kbtu/sf) 479.18, 610.73, 774.42 (151.90, 193.60, 245.49 kbtu/sf)

by CB Richard Ellis (CBRE), focusing on nineteen cities. The survey instrument was designed to collect data on building design, use, and occupancy characteristics; energy use; construction type; building systems; retroﬁt projects and ECMs installed; and the motivations and barriers to implementing energy improvements. The full survey is included in Appendix A. The survey was distributed via email using NYU’s Qualtracs service in March 2014, and responses were collected through the end of May 2014. In total, the survey was distributed to the respective asset managers for 763 commercial ofﬁce properties. 4.2. Data collection and processing The initial survey response data set contained 859 observations provided for 763 unique commercial properties. Preliminary data cleaning removed incomplete and duplicate entries, resulting in 635 post-cleaning observations (buildings) for analysis. The next step in the data cleaning process removed observations that, after cross-referencing with data downloaded from the EPA Energy Star Portfolio Manager database, had missing gross ﬂoor area information, energy use values reported for greater than monthly periods, or only annual electricity usage values reported, as this prevented weather normalization of energy use. For responses with incomplete (fewer than twelve months) energy consumption data, an extrapolation was conducted to account for missing monthly values based on variations in monthly energy use in buildings in a similar climate (measured here by being located within the same MSA as the subject building). Properties with the highest and lowest one percent energy use intensity (EUI) values were considered outliers and removed the analysis. The ﬁnal data set used for this analysis consists of 393 complete records. Summary statistics for EUI are shown in Table 1 and a geographic breakdown by MSA, indicating the count of buildings, median EUI, and total gross ﬂoor area is provided in Table 2. Please note that all EUI values used in this analysis are weather-normalized.1 4.3. Descriptive statistics—building characteristics In order to better understand the characteristics of the buildings included in the dataset, various building attributes are described. Fig. 2 shows the sample distribution by building gross ﬂoor area and median EUI by size. Approximately two-thirds of the sample consists of buildings having a gross ﬂoor area of less than 200,000 square feet, although a number of large buildings in excess of 1,000,000 square feet were represented in the survey sample. Median EUI ﬁgures are found to be more than 10% lower for larger buildings than small- and mid-sized buildings, using a threshold size of 200,000 square feet.

4. Data description and survey methodology 4.1. Survey intent and design The data used for this analysis are part of a broader research project to develop an ofﬁce tenant energy performance benchmarking system. The initial step, which is the source of the data described here, was to develop and administer a survey to asset managers across the U.S. portfolio of ofﬁce properties managed

1 Weather normalization of energy use accounts for year-to-year differences in weather and allows for the analysis of building energy use over time and across different climate regions. For each building, a linear regression model was constructed using reported monthly energy values as the dependent variable and monthly average temperatures from the National Oceanic and Atmospheric Administration (NOAA) as the independent variable. Thirty-year monthly temperature averages from NOAA were then used in the model to generate weather normalized energy usage for building energy analysis.

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

5

Table 2 Number of buildings, median EUI, and total gross ﬂoor area by Metropolitan Statistical Area. MSA

Full Name

Number of Buildings

Median EUI – kwh/m2 (kBtu/sqft)

Gross Floor Area (sqft)

ATL BOS CHI COL DEN DFW HOU LAX MIN NYC ORL PHI PHX SAC SDG SEA SFO SLC WAS

Atlanta, GA Boston, MA Chicago, IL Columbus, OH Denver, CO Dallas, TX Houston, TX Los Angeles, CA Minneapolis, MN New York City, NY Orlando, FL Philadelphia, PA Phoenix, AZ Sacramento, CA San Diego, CA Seattle, WA San Francisco, CA Salt Lake City, UT Washington DC

19 4 30 3 45 16 21 40 14 11 17 10 27 7 5 43 24 11 46

569.72 (180.60) 453.03 (143.61) 577.54 (183.08) 801.27 (254.00) 597.04 (189.26) 688.49 (218.25) 525.33 (166.53) 546.44 (173.22) 617.13 (195.63) 632.18 (200.40) 670.19 (212.45) 784.14 (248.57) 576.62 (182.79) 540.38 (171.30) 439.62 (139.36) 660.07 (209.24) 590.10 (187.06) 739.34 (234.37) 673.32 (213.44)

5,559,962 1,329,746 7,863,088 277,114 5,519,541 5,382,262 17,981,298 11,763,098 3,198,912 6,650,496 2,348,073 2,777,227 4,619,658 1,024,321 1,512,290 6,774,919 4,872,853 2,536,090 9,550,407

Fig. 3. Proportion of data set gross ﬂoor area by owner entity type.

Fig. 2. Count of Buildings and Median EUI by Size.

As shown in Fig. 3, institutional-owned buildings represent 35.6% of the total gross ﬂoor area of the dataset, Real Estate Investment Trust (REIT) and privately-owned buildings represent approximately one-quarter each, and the remainder are owned by corporations, governments, and non-proﬁts. The majority of the

analyzed buildings were built from the 1980s and onward, along with a sizable amount of privately owned buildings built in the 1970s, as can be seen in Fig. 4. Fig. 5 shows the number of buildings by size for each owner type. 4.4. Energy use analysis Here, the calculated EUI values are analyzed with respect to building and ownership characteristics in an effort to better under-

Fig. 4. Number of buildings by owner entity type and decade built.

6

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20 Table 3 Median EUI by Owner Type.

Fig. 5. Number of buildings by owner entity type and gross ﬂoor area.

stand energy use patterns and build on research that examines the drivers of commercial building energy usage intensity [28,37]. The energy use data reported in the survey are given as site energy consumption, which has then been converted to source energy for analysis (site energy is the energy use associated with utility bills and source energy represents the total amount of energy sent from the plant to sufﬁciently provide the site energy) [47].

Owner Type

Median EUI (kwh/m2)

Real Estate Investment Trust (REIT) Institutional Corporate/Owner Occupied Government Nonproﬁt Private Owner

588.96 (186.70 kbtu/sf) 612.53 (194.17 kbtu/sf) 618.62 (196.10 kbtu/sf) 584.36 (185.24 kbtu/sf) 738.58 (234.13 kbtu/sf) 622.84 (197.44 kbtu/sf)

Fig. 6 displays how median EUI differs geographically, where each circle represents one of the nineteen MSAs from this study and the color of the circle indicates the median EUI of that MSA, green indicating lower values (less energy intense) and red or warmer colors indicating higher values (higher energy intensity). West coast MSAs exhibit a lower median EUI and most MSAs in the Midwest and Mountain Time zone (with the exception of Chicago and Houston) reveal a higher median EUI. East coast MSAs display high median EUI as well, with the exception of Boston. Of course, this is a cursory spatial analysis that does not account for differences in subsample size or building characteristics, so the observed differences provide only a guide for deeper analysis as presented below. Fig. 7 provides a visual representation of median EUI in each MSA, while simultaneously incorporating the number of buildings and gross ﬂoor area represented by that MSA. Fig. 7 shows that Houston, which has comparatively larger buildings, performs relatively well in terms of total energy consumption and energy intensity. Houston has the largest proportion of building area analyzed (18.8%), followed by Washington DC (10%) and Los Angeles (9.2%). Looking at owner type by EUI, Table 3 shows that nonproﬁt buildings have the highest median EUI at 234.13 kbtu/sf, although it should be noted that there are only three nonproﬁt-owned build-

Fig. 6. Geographic variance in median EUI.

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Fig. 7. Median EUI by number of properties and MSA.

7

(Total area of the circles represents gross ﬂoor area [square feet] represented in that MSA.)

Fig. 9. Number of buildings by heating method used. Fig. 8. Number of buildings by cooling method used.

ings in the sample. Government owned buildings have the lowest median EUI at 584.36 kWh/m2 (185.24 kbtu/sf). This may be a consequence of the higher regulations and standards placed on government ofﬁces and buildings, such as the energy management goals established for federal government buildings through the Energy Independence and Security Act of 2007, although further investigation is warranted. Energy intensity, particularly measured in terms of source energy use, is impacted by the type of fuel/energy source used for heating and cooling. Similarly, the nature of a building’s fuel source should impact the decision to pursue an energy retroﬁt. In some cases, this may be a function of regulatory requirements (e.g. NYC fuel oil conversion), infrastructure constraints (e.g. lack of natural gas distribution system adjacent or near a particular building), and economics. Localized energy prices should play a signiﬁcant role in the decision process to implement energy efﬁciency investments. The vast majority of buildings from this study are heated with electricity, natural gas, or a combination of both (Fig. 8), while most buildings are cooled by cooling towers, electric drive chillers, direct expansion package units, or combinations of these (Fig. 9).

Fig. 10. Histogram of Energy Star scores.

In the ﬁnal component of this descriptive analysis, building Energy Star scores and LEED certiﬁcation status are analyzed. Fig. 10

8

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

directly known; this value is modeled as a binary variable, denoted as r, equal to 1 if a retroﬁt is implemented, and 0 otherwise.. The value of r is given by:

Fig. 11. Scatterplot with linear ﬁt of EUI and Energy Star score.

rij = 1

if E[X]ij > 0

rij = 0

if E[X]ij ≤ 0

The retroﬁt decision is predicted here using a logistic regression model with a binary output variable Y to account for a series of predictors (x1 . . . xj ) that include building characteristics ﬁxed effects, ownership type, tenant drivers, and primary market. The model is used to understand the factors that inﬂuence the likelihood of implementing an energy retroﬁt across the surveyed building portfolio. To support targeting of high-probability, high-ROI buildings for energy retroﬁts, and identify the shared set of building characteristics of buildings that implemented energy investments, a predictive model of energy retroﬁt activity in commercial ofﬁce buildings is developed. The model is given by in log-odds form:

p (r) =˛+ ˇk ∗ xk 1 − p (r) n

log

k=1

Fig. 12. Distribution of LEED Certiﬁed Buildings, by Certiﬁcation Level.

demonstrates that the majority of buildings in the sample have an Energy Star score above 75 with the median at 85. To assess the correlation between Energy Star score and energy efﬁciency (as measured by EUI), a scatterplot of EUI by Energy Star score was generated (Fig. 11). As expected, the linear regression best-ﬁt line shows a slight trend where EUI decreases with higher Energy Star scores. Fig. 12 displays the distribution of buildings in the sample that have received LEED certiﬁcation. Most buildings with a LEED certiﬁcation achieved the Gold level. 5. Methodology—retroﬁt decision analysis It can be expected that an owner of a particular building will implement ECMs if the expected net beneﬁt of the retroﬁt are positive given the building’s characteristics, its ownership proﬁle, and location. Following Anderson and Newell [46], the expected net beneﬁt is deﬁned as E[X] for building i and owner j according to the following function: E[X]ij = f I, N, X, S + ε where I is the initial, or up-front, cost of the ECM or retroﬁt package, N is the expected net beneﬁt over the lifecycle of the ECM, X is a vector of building and systems characteristics of the given building, and S is a group of spatial factors, including sub-market and weather. The uncertainty of the future cost savings of the retroﬁt is given by the error term, ␧. Since the expected net beneﬁt is not

where, ␣ is the constant to be estimated xk are the predictors ˇk are coefﬁcients, estimated using the maximum likelihood method. n is the count of unique predictors in the model Predictors in the model include factors that are expected to inﬂuence the decision to conduct an energy retroﬁt on an existing ofﬁce building derived from the theoretical framework presented above. These factors are characterized into four groups: building design and systems, fuel type and consumption patterns (primary fuel type and weather normalized EUI), ownership type and tenant demand, and spatial/market controls. The building design factors act as controls in the model to account for physical characteristics of the building (prior to retroﬁt, if implemented) that can inﬂuence the technical and economic feasibility of an energy retroﬁt. These include variables for gross ﬂoor area, number of ﬂoors, building age, and construction type. To empirically test the propositions stated above, the variables of interest are owner type, tenant demand, and city. Owner type consists of a series of binary categorical variables representing the nature of ownership entity for a speciﬁc building. This includes Real Estate Investment Trusts (REITs), Corporate (owner-occupant), Institutional (includes pension funds and insurance companies), Government (owned by municipal, state, or federal government entity), Non-Proﬁt, and Private (privately-held company or individual). The tenant demand variable is operationalized in the survey by determining whether tenants asked for the building’s Energy Star score prior to signing a lease. This question was associated with the time of lease signing and thus the model accounts only for the responses indicated prior to the retroﬁt activity (if a retroﬁt was implemented). The city variable accounts for the speciﬁc city, based on political boundaries, in which a building is located. We begin by dividing our dataset in a training set and test set, covering 80 percent and 20 percent of the sample, respectively. Of the 393 observations in the full survey sample, this split results in 314 buildings in the training set and 79 in the test set.

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Fig. 13. Reported Motivating Factors for an Energy Retroﬁt.

Fig. 14. Reported Barriers to Energy Retroﬁts.

6. Results and discussion 6.1. Retroﬁt survey analysis—motivations and constraints The survey queried building managers as to both their motivations for, and deterrents to, performing an energy retroﬁt and implementing ECMs. These motivating factors and barriers are summarized in Figs. 13 and 14, respectively. The most frequently cited motivating factors for implementing ECMs were those related

9

to economic beneﬁts, speciﬁcally the reduction in energy costs and potentially attractive returns to energy investments. The second most reported motivating factor, though, was tied to necessary repairs to buildings systems. Opportunities to implement ECMs are thus tied to the timing for repairs to critical building components. Interestingly, the most signiﬁcant barriers for managers to implementing ECMs related to cost and ﬁnancing, both in terms of payback periods and lacking the necessary capital to support ﬁrstcosts of energy upgrades. Survey responses also indicated that the payback period for implementing such measures is too long and the initial cost too high. Figs. 13 and 14 reveal consistent, although in some senses contradictory, evidence on the motivations and barriers to energy retroﬁts for ofﬁce building owners. While those that did carry out an energy retroﬁt cite reduced energy bills and attractive returns to energy investments, those that avoided retroﬁtting ﬁnd capital barriers and long payback periods as confounding factors in the owners’ decision-making. When we examine these motivations and barriers by owner type, we again ﬁnd payback period as a concern across ownership entities, but lack of capital and physical (building) constraints are found to be perceived barriers, as well. Interestingly, almost 20% of corporate owners cited a “lack of interest” in energy efﬁciency improvements. Also, challenging the rather extensive discussions around the split incentive problem, it is reported to be an issue in only a small minority (less 10%) of responses. Buildings that implemented ECMs as part of an energy retroﬁt account for 26.7% of the sample, with the highest proportion in Corporate and Government-owned buildings (approximately 40%), followed by Institutional owners (31%), REITs (23.6%), and, ﬁnally, private owners (20%).2 Fig. 15 shows the proportion of buildings by owner type that implemented certain ECMs, and Fig. 16 shows this proportion by building size. The survey speciﬁed twenty different measures and we group these into nine categories (described in Fig. 17) for purposes of the analysis and visualization. From Fig. 15, we ﬁnd that Corporate and Institutional owners have relatively high rates of implementation across the ECM categories. Notably, we ﬁnd more than half of the Corporate/Owner-occupied buildings adopted operational and behavior change energy conversation strategies, as well as improvements to HVAC controls and sensors. REITs appear to have the lowest rate of adoption of energy improvements, with the highest proportion falling in the operations and education category. It should be noted that there were only three non-proﬁt buildings in the dataset, only one of which implemented an energy conservation measure by modifying an energy system. Fig. 15 shows the proportion of buildings in each building size bin that implemented the different ECMs. In almost every ECM category, buildings greater than 400,000 total square feet appear to have a higher propensity to implement energy improvements. This may be due to economies of scale, greater capital resources available to implement improvements, and more sophisticated building operations and management than might be found in smaller buildings. The nature of tenant types in larger buildings may also drive energy improvements in the building. Interestingly, a number of low-cost opportunities seem to emerge in smaller (less than 200,000 square feet) buildings, including education and awareness, lighting retroﬁts, and installation of energy management systems. The challenge posed by smaller buildings also emerges in the results. While more than half of all large buildings (greater than 400,000 square feet) in the sample implemented ECMs in each of the categories shown in Fig. 15, the highest proportion of smaller buildings (less than 200,000 sq ft) that adopted ECMs is 33%, with

2 There are only three buildings in the sample owned by non-proﬁt entities, and none of these reported any signiﬁcant energy retroﬁt activity.

10

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Fig. 15. Proportion of buildings that implemented by conservation measure and owner type (can be part of original building design rather than through retroﬁt).

Fig. 16. Proportion of buildings that implemented by conservation measure and owner type (can be part of original building design rather than through retroﬁt).

lighting improvements being the most popular ECM implemented. If smaller buildings implement retroﬁt measures at the same rate as larger buildings, the number of small buildings with ECMs installed would double. We also see that signiﬁcant opportunities remain for the adoption and diffusion of relatively low-cost ECMs. On average, only about one-third of the surveyed buildings adopted each of the small upfront-cost and/or rapid payback ECMs, including operational changes and education, lighting upgrades, and energy usage monitoring. The implementation of these measures is lower in buildings owned by REITs and those Privately held, which suggest opportunities for targeting these ownership types as way to increase the proliferation of low-costs approaches to energy savings. Energy efﬁciency awareness and education campaigns targeted to different ownership types may represent an approach to more efﬁciently allocate public resources and outreach. 6.2. Results of the predictive model Fig. 18 presents the results of the logistic prediction model, with coefﬁcients reported as odds ratios. Note that a total of 373 properties with complete data were included in the logistic regression model. The model correctly classiﬁes 81.2% of the retroﬁt activity in the sample, with a pseudo r2 of 0.27 a ROC AUC value of 0.85, plotted in Fig. 19. The results provide useful insight to developing city-level policies to support energy and carbon reduction goals through the mechanism of building energy retroﬁts. The output reinforces the

Fig. 17. ECMs by Category.

opportunity to use data analytics to segment buildings and target policies, incentives, and regulations that can be tailored to the varying motivations, barriers, and propensity to implement an energy retroﬁt. Control variables are found to be of expected sign and signiﬁcance. The likelihood of a retroﬁt increases with building age, although we see only a modest effect for buildings built prior to 1950. Buildings of masonry construction are found to have a higher likelihood of undergoing an energy retroﬁt. Building size does not appear to be correlated with the probability of an energy retroﬁt, a result that is robust to alternate speciﬁcations of building size in the model. Similarly, the energy intensity of a building does not seem to impact the retroﬁt decision, although this may be expected as EUI is an unreliable measure of energy efﬁciency.

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Number of Observaons LR chi2 (35) Prob ?> chi2 Log likelihood Pseudo R2

11

373 118.97 0.000 -158.360 0.273 Odds Rao

Ownership Type REIT Instuonal Government Private Market Atlanta Boston Chicago Denver Dallas Houston Los Angeles Minneapolis Orland Philadelphia Phoenix Sacramento San Diego Seale San Francisco Salt Lake City Washington DC Tenant Demand Tenant Asked for Energy Star Score (binary) Construcon Type Masonry Reinforced Concre Building Size Gross Floor Area ('000s) Number of Floors Energy Proﬁle Weather Normalized Source EUI (log) Heang Type - Fuel Oil HeangType - Steam Heang Type - Electric Heang Type - Other (does not include natural gas) Building Age Built Before 1950 Built 1950 to 1969 Built 1970 to 1989 Built 1990 to 1999 Constant

Std. Err.

0.249 0.419 0.922 0.172

0.164 0.258 0.871 0.109

2.149 8.153 0.631 0.405 1.563 0.584 1.461 0.058 0.069 0.321 0.706 3.190 0.637 0.929 0.883 1.044 0.194

2.535 13.582 0.742 0.464 1.968 0.700 1.651 0.082 0.106 0.482 0.842 4.390 0.992 1.040 0.989 1.377 0.231

1.790

0.739

2.692 1.535

1.433 0.554

0.999 1.028

0.001 0.027

0.827 4.200 1.515 2.349 0.217

0.220 7.418 1.542 0.989 0.210

4.812 37.008 19.639 4.804 0.184

4.534 31.137 10.956 2.966 0.345

**

***

** *

*

**

* *** *** **

0.00

0.25

Sensitivity 0.50

0.75

1.00

Fig. 18. Results of the logistic regression model (dependent variable = energy retroﬁt implemented yes/no).

0.00

0.25

0.50 1 - Specificity

0.75

1.00

Area under ROC curve = 0.8497

Fig. 19. Receiver operating characteristic (ROC) curve for the model as speciﬁed.

An important ﬁnding relates to the varying likelihood and appetite for investing in energy improvements across different ownership entity types. In particular, we ﬁnd that REITs and Private owners are the least likely to carry out an energy retroﬁt of their respective buildings. A REIT-owned building is only 25% as likely to undergo a retroﬁt as a Corporately-owned building, and a Privatelyowned building is only 17% as likely. These are signiﬁcantly lower probabilities, not accounted for by differences in building characteristics of the respective portfolios. It is possible that this can be explained by the tax status of REITs, which do not beneﬁt from tax incentives available for energy efﬁciency improvements. Similarly, this lack of participation by REITs may also be explained by the absence of shareholder demand for a more efﬁcient portfolio. Certainly further research is warranted. The explanation for Privately-owned buildings may be more difﬁcult to discern. It is conceivable that such buildings are more inﬂuenced by owners’ personal opinions on sustainability. Capital constraints may be more of a concern for Private owners, and risk

12

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

tolerance may be lower. Awareness and knowledge of the beneﬁts of an energy retroﬁt and the incentives available may also be less than other types of ownership entities, which may have more resources to assess, manage, and implement ECMs. In addition, we ﬁnd several signiﬁcant differences in the likelihood of a building having undergone an energy retroﬁt across cities. Buildings in Minneapolis and Orlando are far less likely than buildings in New York City (the reference case) to have ECMs installed. This could represent, in part, the less competitive nature of the respective markets, as one would expect from local climate conditions that energy efﬁciency improvements would be justiﬁed by heating loads in the Midwest and cooling loads in the Southeast regions. In addition, the regulatory environment of New York City is notably more restrictive than several others cities included in the model. New York City adopted one of the ﬁrst energy disclosure policies in the U.S. and has implemented revisions to its building code to require more energy efﬁcient design for new and substantially renovated buildings [29]. It should be noted that both Orlando and Minneapolis have recently adopted energy disclosure policies; it will be interesting to track whether the implementation of energy reporting changes the pace of energy retroﬁts in these areas as these policies mature. The tenant demand variable is positive, but not statistically signiﬁcant. This suggests that tenant demand for energy efﬁcient properties may not be sufﬁcient to drive the retroﬁt decision. However, several limitations must be noted with respect to this conclusion. First, it is possible that tenants are self-selecting more efﬁcient properties by using eco-labels as indicators of energy efﬁciency. Thus, building owners are not explicitly aware of a tenant’s preferences as the tenant might only pursue potential leasing opportunities in buildings that have already secured an Energy Star or LEED certiﬁcation. Second, tenants that may show an interest in the energy proﬁle of a building may not use that as a determining factor in the leasing decision. Finally, not all buildings where tenants asked to learn more about energy use may be suitable candidates for, or in need of, an energy retroﬁt. Although tenant demand is not shown to be a statistically significant factor in the decision model, the survey results indicate that tenant-related factors impact the retroﬁt decision. There is a need to more fully capture tenant beneﬁts of building energy efﬁciency, and the more widespread adoption of tenant sub-metering and other tools to motivate tenant energy efﬁciency can be particularly effective. Of the 393 buildings included in the sample with data on sub-metering, 209 (53.3%) are sub-metered. In multi-tenant ofﬁce buildings, approximately 60% of energy use is driven by tenant consumption patterns and systems and design choices implemented in tenant spaces. Universal sub-metering would create opportunities for energy use benchmarking at the tenant space level and would help to overcome potential split-incentive issues by quantifying energy savings in relation to individual tenant energy use. It is important to note the possible limitations of the analysis. First, there is the potential for selection bias in the survey sample that could limit the generalizability of the ﬁndings. It is conceivable that properties managed by CBRE evaluate retroﬁt opportunities differently than those managed by other entities, or those self-managed, and thus the likelihood of ECM implementation is fundamentally different in-sample. However, CBRE manages a wide-range of properties and the consistency of the asset manager in this case actually helps to control for the potential inﬂuence of the asset manager in the retroﬁt decision. Consequently, the robustness of the results is improved by isolating the impact of the variables of interest. Second, we have limited information about pre-ECM energy use intensity (EUI). Thus, it is not possible to evaluate the relative energy intensity or performance of the building as factor in the retroﬁt decision. On the other hand, the building characteristics

included in the model can act as a general proxy for the expected energy intensity of the property. 7. Conclusions and policy implications Retroﬁtting existing buildings has emerged as a primary strategy for reducing energy use and carbon emissions, both nationally and in cities. However, retroﬁt activity has been hampered by actual and perceived barriers that have limited the widespread adoption of ECMs. Ofﬁce buildings represent a signiﬁcant component of national energy use (approximately 20%) and, in dense commercial centers like New York City, account for a signiﬁcant proportion of total energy use and GHG emissions. Understanding the factors that positively inﬂuence the decision to implement building energy efﬁciency improvements can help to design targeted programs – regulations, incentives, and ﬁnancing mechanisms – that will have the potential to catalyze greater retroﬁt activity and increase the likelihood of achieving urban sustainability goals. The analysis presented here examines three propositions relating to the decision to implement an energy retroﬁt in an ofﬁce building. Overall, the model correctly classiﬁes 81.2% of the retroﬁt activity in the sample, with a pseudo r2 of 0.27 a ROC AUC value of 0.85. The analysis focused on the effect of three variables of interest: type of ownership, tenant demand, and spatial factors. Controlling for various building characteristics, the results demonstrate that ownership type and local market do, in fact, inﬂuence the retroﬁt decision. The ﬁndings indicate that REIT-owned and Privately-owned ofﬁce buildings are only 25% and 17% as likely to undergo an energy retroﬁt than a Corporate-owned building. Less evident is the direct effect of tenant demand, although this may manifest in ways not fully observable and thus difﬁcult to operationalize into a predicative model. Overall, this analysis provides new evidence for the importance of understanding ownership type and the varying motivations of differing types of owners in building energy efﬁciency investment decisions. The ﬁndings of both the survey analysis and the predictive model demonstrate additional support for the targeting of energy efﬁciency incentives and outreach based on ownership entity, local market conditions, and speciﬁc physical building characteristics. The outcomes reinforce the importance of local regulations and market conditions, and introduce the need to consider and account for ownership type in design and implementing building energy efﬁciency policy. The results also reveal persistent opportunities to implement relatively low-cost ECMs through market segmentation and highlight the need to approach energy efﬁciency and ECM opportunities in small- and mid-size ofﬁce buildings differently than in large commercial buildings. Acknowledgements The author would like to thank Dave Pogue at CBRE for his assistance in reﬁning and administering the survey, as well as Yerina Mugica of the Natural Resources Defense Council for input on the survey design. This research is funded, in part, by a CBRE Real Green Research Challenge Grant. The author would also like to thank Anjali Mehta and Eduardo Franco for their data cleaning work as graduate student research assistants at NYU’s Center for Urban Science and Progress. All errors remain the author’s. Appendix A. Survey instrument Q1 Survey Description Q2 What is the property address? Number (1) Street (2)

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

City (3) State (4) Zip code (5) Q3 What type of entity owns the building? • • • • • •

Real Estate Investment Trust (REIT) (1) Institutional (2) Corporate/Owner Occupied (3) Government (4) Nonproﬁt (5) Private Owner (6)

Masonry (1) Reinforced concrete (2) Structural steel/Metal clad (3) Frame construction (usually wood) (4) Other (5)

Q15 What type of windows does the building have? (Check all that apply)

Q4 What year was the building built? Please slide to correct year (1) Q5 What is the building’s gross square footage? Q6 What is the building’s rentable square footage? Q7 How many ﬂoors does the building have? Please slide to correct number (1) Q8 How many elevators does the building have? Please slide to correct number (1) Q9 Has the building undergone any major renovations in the following areas within the past 10 years? (A major renovation is deﬁned as greater than 2× annual operating expense for the property)

Common Areas/Lobby (1) Tenant Spaces (2) Building Systems (3)

13

Renovation?

When was the most recent renovation completed?

Yes (1)

No (2)

(Please enter the year below) (1)

Answer If Has the building undergone any renovations in the following areas? Building Systems − Renovation? − Yes Is Selected Q10 Were the following Building Systems upgraded (i.e. replaced to increase efﬁciency)? 䊏 HVAC system, please indicate year of most recent renovation: (1) 䊏 Building Envelope, please indicate year of most recent renovation: (2) 䊏 Major Lighting Retroﬁt, please indicate year of most recent renovation: (3) 䊏 Energy Management System (EMS), please indicate year of most recent renovation: (4) Answer If Has the building undergone any renovations in the following areas? Building Systems − Renovation? − Yes Is Selected Q11 Have you applied for and/or received Federal, State, Municipal or Utility incentives for a Building Systems upgrade? Yes (9) No (10) Answer If Has the building undergone any major renovations in the following areas within the past 10 years? Building Systems − Renovation? − Yes Is Selected Q12 Was external ﬁnancing used for Building Systems renovations?

䊏䊏䊏䊏䊏䊏

Single pane (1) Double pane (2) Energy efﬁcient (i.e. low U value) (3) Other (4) Fixed (5) Operable (6)

Q16 What are the roof attributes? (Check all that apply) Green roof − a vegetative layer grown on a rooftop. White/cool roof − roof with high solar reﬂectance or albedo. This helps to reﬂect sunlight and heat away from a building. 䊏䊏䊏䊏䊏

Green roof (1) White/Cool roof (2) PV installed on roof (3) Other (4) None of the above (5)

Q17 What is the building’s weekly hours of operation? Please indicate the number of hours per week. Weekly hours of operation (1) Q18 How many days per year is building heated and cooled? Annual heating days (1) Annual cooling days (2) Q19 Is the building sub-metered? Yes (1) No (2) If No Is Selected, Then Skip To Is the building LEED Certiﬁed? Q20 What is the level of sub-metering? (check all that apply) 䊏䊏䊏䊏

Sub-metering of entire tenant space (2) Sub-metering of tenant data center/server room only (3) Sub-metering by energy use (4) Using technology other than direct sub-metering to break out loads, please specify: (5) Q21 Are cooling loads included in sub-metering?

Yes (1) No (2) Q22 Are plug-loads included in sub-metering? Yes (1) No (2) Q23 Is the building LEED Certiﬁed?

Yes (18) No (19) Q13 What is the annualized occupancy rate for 2013 (%)? Please slide to correct percent (1) Q14 What is the primary construction type of the building?

Yes (1) No (2) If No Is Selected, Then Skip To Energy Star Labeled? Q24 What is the LEED certiﬁcation level?

14

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Certiﬁed (1) Silver (2) Gold (3) Platinum (4)

Q25 What is your LEED score? LEED score (1) Q26 In what year(s) was the building LEED certiﬁed? (Hold CTRL to select all that apply) 䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏

1998 (1) 1999 (2) 2000 (3) 2001 (4) 2002 (5) 2003 (6) 2004 (7) 2005 (8) 2006 (9) 2007 (10) 2008 (11) 2009 (12) 2010 (13) 2011 (14) 2012 (15) 2013 (16) 2014 (17)

Q27 Under which version of the LEED rating system was the building certiﬁed? 䊏䊏䊏䊏䊏䊏

V1.0 (1) V2.0 (2) V3.0 (3) Other (4) Don’t know (5) V4.0 (6) Q28 What is the LEED Certiﬁcation type?

䊏䊏䊏䊏

New Construction (1) Core & Shell (2) Existing Buildings Operation & Maintenance (EBOM) (3) Other (4)

Q29 Does the building currently have an Energy Star label or rating? Yes (1) No (2) If No Is Selected, Then Skip To Has a building energy analysis report. . . Q30 What is the Energy Star score of the building? Please slide to the correct score (1) Q31 In what year(s) was the building Energy Star labeled? (Hold CTRL to select all that apply) 䊏䊏䊏䊏䊏䊏䊏

1999 (7) 2000 (8) 2001 (9) 2002 (10) 2003 (11) 2004 (12) 2005 (13)

䊏䊏䊏䊏䊏䊏䊏䊏䊏

2006 (14) 2007 (15) 2008 (16) 2009 (18) 2010 (19) 2011 (20) 2012 (21) 2013 (22) 2014 (23)

Answer If Is the building LEED Certiﬁed? No Is Selected And Is the building currently Energy Star labeled? No Is Selected Q32 Has a building energy analysis report been completed for the building in the past 10 years? Yes (9) No (10) Q33 Have you or the building manager received specialized training or certiﬁcation in energy management? (Please select all that apply) 䊏䊏䊏䊏䊏䊏䊏

LEED GA (1) LEED AP (2) BOMI (3) Certiﬁed Energy Manager (CEM) (4) BPI (5) Other (6) None of the above (7) Q34 Does the building have on-site electricity/energy supply?

Yes (1) No (2) If No Is Selected, Then Skip To Do you currently have a contract to p. . . Q35 What percentage do you estimate on-site electricity/energy supply contributes to total energy use?

0–2.5% (1) 2.6%–7.5% (2) 7.6%–12.5% (3) 12.6%+ (4)

Q36 What type of on-site electricity/energy supply does the building have?On-site renewable − renewable energy on building site (e.g. Solar/PV, Wind, Geothermal)On-site CO-GEN − on-site co-generation capabilities (e.g. Combined-Heat-Power) 䊏䊏䊏䊏䊏

On-site renewable − Solar/PV (1) On-site CO-GEN (2) Other (3) On-site renewable − Wind (4) On-site renewable − Geothermal (5)

Q37 Do you currently have a contract to purchase renewable energy from your utility? Yes (18) No (19) Answer If Do you currently have a contract to purchase renewable energy from your utility? Yes Is Selected

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Q38 What percentage of your total energy use comes from this contract?

0–2.5% (1) 2.6%–7.5% (2) 7.6%–12.5% (3) 12.6%+ (4)

Q39 Do you use passive solar energy for heat (i.e. designed to collect/store/transfer sunlight into heating)? Yes (4) Don’t know (5) No (6) Q40 Does your building have any solar easements (rights to sunlight built into the deed)? Yes (4) Don’t know (5) No (6) Q41 How is the building heated? 䊏䊏䊏䊏䊏

Natural Gas (1) Heating Oil (2) Steam (3) Electricity (4) Other (5) Q42 How is the building cooled?

䊏䊏䊏䊏䊏䊏

Electric drive chillers (1) Steam absorbers (2) Steam turbines (3) Direct Expansion (DX) package units (4) Cooling towers (5) Other (6) Q43 What is your monthly electricity consumption (in kWh)? Jan 2013 (1) Feb 2013 (2) Mar 2013 (3) April 2013 (4) May 2013 (5) June 2013 (6) July 2013 (7) Aug 2013 (8) Sept 2013 (9) Oct 2013 (10) Nov 2013 (11) Dec 2013 (12) I do not have monthly data, my annual consumption is: (13) Q44 What is your monthly electricity costs (in $)? Jan 2013 (1) Feb 2013 (2) Mar 2013 (3) April 2013 (4) May 2013 (5) June 2013 (6) July 2013 (7) Aug 2013 (8) Sept 2013 (9) Oct 2013 (10) Nov 2013 (11) Dec 2013 (12)

15

I do not have monthly data, my annual electricity costs are: (13) Q45 Is the building’s electricity provided by a third party via a power purchase agreement? Yes (18) No (19) Answer If How is the building heated? Natural Gas Is Selected Q46 What units would you like to enter your Natural Gas consumption in? BTUs (1) Therms (2) Answer If How is the building heated? Natural Gas Is Selected Q47 What is your monthly Natural Gas consumption? Jan 2013 (1) Feb 2013 (2) Mar 2013 (3) April 2013 (4) May 2013 (5) June 2013 (6) July 2013 (7) Aug 2013 (8) Sept 2013 (9) Oct 2013 (10) Nov 2013 (11) Dec 2013 (12) I do not have monthly data, my annual Natural Gas consumption is: (13) Answer If How is the building heated? Heating Oil Is Selected Q48 What is your 2013 monthly Heating Oil consumption (in gallons)? Jan 2013 (1) Feb 2013 (2) Mar 2013 (3) April 2013 (4) May 2013 (5) June 2013 (6) July 2013 (7) Aug 2013 (8) Sept 2013 (9) Oct 2013 (10) Nov 2013 (11) Dec 2013 (12) I do not have monthly data, my annual Heating Oil consumption is: (13) Answer If How is the building heated? Steam Is Selected Q49 What is your 2013 monthly Steam consumption (in cubic feet)? Jan 2013 (1) Feb 2013 (2) Mar 2013 (3) April 2013 (4) May 2013 (5) June 2013 (6) July 2013 (7) Aug 2013 (8) Sept 2013 (9) Oct 2013 (10) Nov 2013 (11) Dec 2013 (12) I do not have monthly data, my annual Steam consumption is: (13) Q50 What does the reported energy data cover? (Check all that apply)

16

Electricity (1) Natural gas (3) Heating oil (4) Steam (5)

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Common areas (1)

Tenant spaces (2)

Outdoor space, parking (3)

䊐䊐䊐䊐

䊐䊐䊐䊐

䊐䊐䊐䊐

Q51 What types of Energy Conservation Measures are you aware of that have been installed in the building? (Check all that apply)

Behavior Change/Employee Education (1) Operations and Management (2) Conveying Systems (3) Cooling System (4) Data Centers and Server Rooms (5) Distribution System (6) Domestic Hot Water (7) Energy Management System (8) Envelope (9) Fuel Switching (10) Heating System (11) HVAC Controls and Sensors (12) Lighting (13) Motors (14) On-Site Generation (15) Process and Plug Loads (16) Space (17) Submetering (18) Ventilation (19) None (20)

Within the last 10 years (1)

Within the last 2 years (2)

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Behavior Change/Employee Education – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Behavior Change/Employee Education – Within the last 2 years Is Selected Q52 What Behavior Change measures were implemented? (Check all that apply)

• Occupant Engagement (1) • Occupant Training (2) • Other (3)

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Conveying Systems – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Conveying Systems – Within the last 2 years Is Selected Q53 What Conveying Systems measures were implemented? (Check all that apply)

Elevator Regenerative Drives (1) Upgraded Controls (2) Upgraded Motors (3) Other (4)

As part of a standard retroﬁt (1)

As part of a tenant ﬁt out (2)

As part of new construction (3)

䊐䊐䊐䊐

䊐䊐䊐䊐

䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Cooling System – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Cooling System – Within the last 2 years Is Selected Q54 What Cooling Systems measures were implemented? (Check all that apply)

Economizer Cycle (1) Added or Upgraded Cooling Tower (2) Free Night Cooling (3) Heat Recovery − Cooling only (4) Installed VSD on Electric Centrifugal Chillers (5) Radiant Cooling Systems (6) Replaced Chiller (7) Replaced Packaged Units (8) Upgraded Chiller (9) Upgraded Packaged Units (10) Other (11)

As part of a standard retroﬁt (1)

As part of a As part of new tenant ﬁt out construction (2) (3)

䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Distribution System – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Distribution System – Within the last 2 years Is Selected Q55 What Distribution Systems measures were implemented? (Check all that apply) As part of a standard retroﬁt (1) Captured and Return Condensate (1) 䊐 Installed or Upgraded Master Venting 䊐 (2) 䊐 Insulated Ducts (3) 䊐 Insulated Pipes (4) Repaired Leaks (5) 䊐 Replaced or Repaired Main Steam Trap 䊐 (6) Replaced or Repaired Steam Traps (7) 䊐 Replaced or Repaired Vacuum Pump 䊐 (8) 䊐 Replaced PTACs (Packaged Terminal Air Conditioner) (9) Upgraded PTACs (Packaged Terminal 䊐 Air Conditioner) (10) Sealed Ducts (11) 䊐䊐 Upgraded Pumps (12) Other (13) 䊐

As part of a As part of new tenant ﬁt out construction (2) (3) 䊐䊐

䊐䊐

䊐䊐䊐䊐

䊐䊐䊐䊐

䊐䊐

䊐䊐

䊐

䊐

䊐

䊐

䊐䊐䊐

䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Domestic Hot Water – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Domestic Hot Water – Within the last 2 years Is Selected Q56 What Domestic Hot Water (DHW) measures were implemented? (Check all that apply)

Decreased DHW Temperature (1) Installed DHW Controls (2) Installed Low-Flow Aerators (3) Installed Solar Thermal DHW (4) Installed Water Pressure Booster (5) Insulated DHW Piping (6) Insulated DHW Tank (7) Replaced Piping (8) Replaced Tankless Coil (9) Separated DHW from Heating (10) Upgraded DHW Boiler (11) Other (12)

As part of a standard retroﬁt (1)

As part of a As part of new tenant ﬁt out construction (2) (3)

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Energy Management System – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

apply) Energy Management System – Within the last 2 years Is Selected Q57 What Energy Management Systems measures were implemented? (Check all that apply) As part of a As part of a As part of new standard tenant ﬁt outconstruction retroﬁt (1) (2) (3) Installed Energy Management System (1)䊐䊐 Other (2)

䊐䊐

䊐䊐

Answer If What Energy Management Systems measures were implemented? (Check all that apply) Installed Energy Management System − As part of a standard retroﬁt Is Selected Or What Energy Management Systems measures were implemented? (Check all that apply) Installed Energy Management System − As part of a tenant ﬁt out Is Selected Q58 Do the personnel who manage the Energy Management System have any of the following certiﬁcations? 䊏䊏䊏䊏䊏䊏䊏

LEED GA (1) LEED AP (2) BOMI (3) Certiﬁed Energy Manager (4) BPI (5) Other (6) None of the above (7)

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Envelope – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Envelope – Within the last 2 years Is Selected Q59 What Envelope measures were implemented? (Check all that apply)

Added Window Films (1) Exterior Door Replacement (2) External Shading (3) Increased Insulation − Basement/Crawl Space (4) Increased Insulation − Floor (5) Increased Insulation − Roof (6) Increased Insulation − Wall (7) Installed Cool or Green Roof (8) Installed Radiant Barrier (9) Replaced Curtain/Window Wall (10) Replaced Glazing and Frames (11) Replaced Windows (12) Sealing − Door (13) Sealing − Roof Penetrations (14) Sealing − Room A/C (15) Sealing − Vertical Shafts (16) Sealing − Windows (17) Utilization of Thermal Mass (18) Other (19)

As part of a standard retroﬁt (1)

As part of a As part of new tenant ﬁt out construction (2) (3)

䊐䊐䊐䊐

䊐䊐䊐䊐

䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Fuel Switching – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Fuel Switching – Within the last 2 years Is Selected Q60 What Fuel Switching measures were implemented? (Check all that apply) 䊏 #2 Oil to Natural Gas (1) 䊏 #6 Oil or #4 Oil to #2 Oil (2) 䊏 #6 Oil or #4 Oil to Natural Gas (3)

䊏䊏䊏䊏

17

#6 to Dual Fuel (4) District Steam to On-site Generation (5) Utility Steam to On-site Generation (6) Other (7)

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Heating System – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Heating System – Within the last 2 years Is Selected Q61 What Heating Systems measures were implemented? (Check all that apply) As part of a standard retroﬁt (1) Added Static Pressure Control (1) 䊐 Cleaned & Tuned Boiler/Furnace (2) 䊐 Electric to Gas Conversion (3) 䊐 Electric to Hydronic Conversion (4) 䊐䊐 Electric to Steam Conversion (5) 䊐 Heat recovery − Heating only (6) Heat recovery from Utility Steam (7) 䊐 Installed Barometric Damper (8) 䊐䊐 Installed Bleeders (9) Installed Circulators (10) 䊐 Installed Thermostatic Damper (11) 䊐 Insulated Condensate Tank (12) 䊐 Replaced Boiler (13) 䊐䊐 Upgraded Boiler (14) Replaced Boiler Jacket (15) 䊐 Replaced Burner (16) 䊐 Upgraded Burner (17) 䊐䊐 Replaced Furnace (18) 䊐 Upgraded Furnace (19) Replaced Steam Control Valves (20) 䊐 Replaced Steam Header (21) 䊐 Steamed to Hydronic Conversion (22) 䊐䊐 Other (23)

As part of a As part of new tenant ﬁt out construction (3) (2) 䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) HVAC Controls and Sensors – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) HVAC Controls and Sensors – Within the last 2 years Is Selected Q62 What HVAC Controls & Sensors measures were implemented? (Check all that apply) 䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏䊏

Changed Set Points/Setbacks – Cooling (1) Changed Set Points/Setbacks – Heating (2) Implemented Static Pressure Reset (3) Installed Indoor Sensors (4) Installed or Upgraded Energy Mgmt or Building Mgmt System (5) Installed Programmable Thermostats (6) Installed Temperature Regulation Valves (TRVs) (7) Pneumatic to Direct Digital Controls (DDC) conversion (8) Replaced Outdoor Reset (9) Zone Control Upgrades (10) Other (11)

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Lighting – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Lighting – Within the last 2 years Is Selected

18

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Q63 What Lighting Systems measures were implemented? (Check all that apply) As part of a As part of a As part of new standard tenant ﬁt out construction retroﬁt (1) (2) (3) Automatic Lighting Control (1) 䊐 Delamping (2) 䊐䊐 Installed Bi-level Lighting (3) 䊐 Installed Light Shelves (4) Installed Occupancy/Vacancy Sensors (5)䊐 Installed Photocell Control (6) 䊐 Installed Shades or Blinds (7) 䊐䊐 Installed Timers (8) 䊐 Replaced Diffusers (9) 䊐 Upgraded Exit Signs to LED (10) Upgraded Exterior Lighting (11) 䊐 Upgraded to Fluorescent (12) 䊐 Upgraded to LED (13) 䊐䊐 Other (14)

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Motors – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Motors – Within the last 2 years Is Selected Q64 What Motor measures were implemented? (Check all that apply)

Installed VFDs (1) Removed Motors (2) Upgraded Motors (3) Other (4)

As part of a standard retroﬁt (1)

As part of a tenant ﬁt out (2)

As part of new construction (3)

䊐䊐䊐䊐

䊐䊐䊐䊐

䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) On-Site Generation – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) On-Site Generation – Within the last 2 years Is Selected Q65 What on-site Generation measures were implemented? (Check all that apply) As part of a standard retroﬁt (1) Installed Cogeneration Plant (1) 䊐 Installed Ground Source Heat Pump (2)䊐䊐 Installed Solar/PV (3) 䊐 Other (4)

As part of a As part of new tenant ﬁt out construction (2) (3) 䊐䊐䊐䊐

As part of a standard retroﬁt (1) 䊐 Converted Constant Volume (CV) system to Variable Air Volume (VAV) system (1) 䊐 Installed Displacement Ventilation System (2) Installed CAR Dampers (3) 䊐 Installed Demand Control Ventilation 䊐 (4) 䊐 Installed Exhaust Fan Timers (5) Reset Supply Air Temperature Based on䊐 Season (6) 䊐 Upgraded Exhaust Fans (7) 䊐 Upgraded Fan/Air Handlers (8) 䊐 Other (9)

As part of a standard retroﬁt (1)

As part of a tenant ﬁt out (2)

As part of new construction (3)

䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Ventilation – Within the last 10 years Is Selected Or What

As part of a As part of new tenant ﬁt out construction (2) (3) 䊐

䊐

䊐

䊐

䊐䊐

䊐䊐

䊐䊐

䊐䊐

䊐䊐䊐

䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Space – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Space – Within the last 2 years Is Selected Q68 What Space measures were implemented? (Check all that apply)

Densiﬁcation (1) Space Layout Optimization (2) Other (3)

As part of a standard retroﬁt (1)

As part of a tenant ﬁt out (2)

As part of new construction (3)

䊐䊐䊐

䊐䊐䊐

䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Data Centers and Server Rooms – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Data Centers and Server Rooms – Within the last 2 years Is Selected Q69 What Data Centers and Server Rooms measures were implemented? (Check all that apply)

䊐䊐䊐䊐

Answer If What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Operations and Management – Within the last 10 years Is Selected Or What types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Operations and Management – Within the last 2 years Is Selected Q66 What Operations and Maintenance measures were implemented? (Check all that apply)

Facilities Staff Training (1) Monitoring (2) Ongoing Commissioning (3) Retro-Commissioning (4) Other (5) Energy Audit (6)

types of Energy Conservation Measures are you aware that have been installed in the building? (Check all that apply) Ventilation – Within the last 2 years Is Selected Q67 What Ventilation measures were implemented? (Check all that apply)

Decommissioning Servers (1) Hot Aisle/Cold Aisle Design (2) Eliminated Redundancies (3) Server Virtualization (4) Upgraded Servers (5) Other (6)

As part of a standard retroﬁt (1)

As part of a tenant ﬁt out (2)

As part of new construction (3)

䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

䊐䊐䊐䊐䊐䊐

Q70 Do you have detectors installed for any of the following Air Quality Measures? 䊏䊏䊏䊏䊏䊏

Volatile Organic Compounds (VOCs) (1) Carbon Monoxide (2) Radon (3) Particulate matter (4) Bioaersols (5) Other (6) Q71 Does your building have a waste recycling program?

Yes (4) No (6)

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

Answer If Does your building have a waste recycling program? Yes Is Selected Q72 What does your building recycle? (Check all that apply) 䊏䊏䊏䊏䊏䊏

Paper (1) Plastic (2) Aluminum (3) Cardboard (4) Compost (5) Other (6)

Q73 Does any tenant lease in this building include a tenant cost recovery clause that can be used for energy efﬁciency related capital improvements? (This typically means that the list of operating expenses is expanded to include capital expenses intended to save energy, with the annual pass-through amount most often determined either by an amortization schedule or projected savings.) Yes (9) No (10) Answer If Does any tenant lease in this building include a tenant cost recovery clause that can be used for energy efﬁciency related capital improvements? (This typically means that the list of opera. . . Yes Is Selected Q74 If yes, has the tenant cost recovery clause been exercised for any energy efﬁciency related capital improvements? Yes (1) No (2) Q75 Do any tenant leases in this building include minimum standards or tenant improvement speciﬁcations for energy efﬁciency? Yes (9) No (10) Q76 Does any tenant lease in this building include sustainable operations and maintenance rules and regulations? (Lease language would cover restricted HVAC weekend operating hours, janitorial services provided during daytime hours, tenants not allowed to bring in space heaters). Yes (9) No (10) Q77 Do you incorporate energy management best practices into building operations, such as regular benchmarking, energy audits, or commissioning of building systems? Yes (9) No (10) Q78 Have any tenants requested that the landlord share the ENERGY STAR score of the building and/or other energy and waste usage information on a regular basis? Yes (9) No (10) Q79 Do any tenant leases in this building have site selection language focused on leasing space that has met the requirements of third-party certiﬁcation (such as ENERGY STAR, LEED, or other green building certiﬁcation)?

19

Yes (9) No (10) Q80 Has any tenant in this building encouraged energy efﬁciency improvements to be implemented in the space and/or building? (This could cover a range of lease language from agreeing to cost recovery clauses for capital improvement to agreeing to share the costs of LEED certiﬁcation or retro-commissioning of the building). Yes (1) Not (2) Q81 When approaching the lease renewal process, are any tenants in this building willing to incorporate energy performance into: (Check all that apply) 䊏 Lease negotiations (1) 䊏 Tenant build out (2) Q82 Which of the following motivating factors inﬂuence the building owner to undertake energy efﬁciency projects? (Check all that apply) 䊏 Energy efﬁciency investments provide attractive returns (1) 䊏 Repair broken or outdated equipment that needs repair or replacement (2) 䊏 Environmental beneﬁts linked with energy efﬁciency investments (3) 䊏 A peer building made a similar investment and had a positive outcome (4) 䊏 Reduce building energy bills and/or operating costs (5) 䊏 Increase tenants comfort level (6) 䊏 Increase marketability of the building (7) 䊏 Offer more competitive rents due to lower operating costs (8) 䊏 Offer higher rents due to increased efﬁciency of the building (9) 䊏 Take advantage of a government or utility incentive for energy efﬁciency (10) 䊏 To achieve a market recognition award − Energy Star, LEED, etc. (11) 䊏 Other, please specify: (12) Q83 Which of the following are barriers for the building owner to undertake energy efﬁciency projects? (Check all that apply) 䊏 Lack of ﬁnancial resources required for the initial investment (1) 䊏 Limited access to appropriate or cost effective ﬁnancing (2) 䊏 Physical limits on the type of energy efﬁciency technologies that can be installed (3) 䊏 Payback period is too long (4) 䊏 Building management is weary of disrupting tenants/day-to-day operations (5) 䊏 Split economic incentive between building owners and tenants (6) 䊏 Too much uncertainty about the actual energy efﬁciency savings (7) 䊏 Lack of interest regarding energy efﬁciency (8) 䊏 Other, please specify: (9) References [1] M. Achtnicht, R. Madlener, Factors inﬂuencing German house owners’ preferences on energy retroﬁts, Energy Policy 68 (2014) 254–263. [2] F. Ardente, M. Beccali, M. Cellura, M. Mistretta, Energy and environmental beneﬁts in public buildings as a result of retroﬁt actions, Renew. Sustain. Energy Rev. 15 (1) (2011) 460–470.

20

C.E. Kontokosta / Energy and Buildings 131 (2016) 1–20

[3] E. Asadi, M.G. Da Silva, C.H. Antunes, L. Dias, Multi-objective optimization for building retroﬁt strategies: a model and an application, Energy Build. 44 (2012) 81–87. [5] E. Azar, C.C. Menassa, A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks, Energy Policy 67 (2014) 459–472. [6] S. Bird, D. Hernández, Policy options for the split incentive: increasing energy efﬁciency for low-income renters, Energy Policy 48 (2012) 506–514. [7] A.C. Burr, C. Keicher, J. Lawrence, Commercial Building Energy Rating and Disclosure Policies, Institute for Market Transformation, Washington DC, 2013. [8] S.E. Chidiac, E.J.C. Catania, E. Morofsky, S. Foo, Effectiveness of single and multiple energy retroﬁt measures on the energy consumption of ofﬁce buildings, Energy 36 (8) (2011) 5037–5052. [9] G.H. Chun, J. Sa-Aadu, J.D. Shilling, The role of real estate in an institutional investor’s portfolio revisited, J. Real Estate Finance Econ. 29 (3) (2004) 295–320. [10] City of Chicago, 2014. City of Chicago Energy Benchmarking Report, 2014. [11] City of New York, New York City Local Law 84 Benchmarking Report, September 2014, Mayor’s Ofﬁce of Sustainability, New York, 2014. [12] City of New York, New York City Local Law 84 Benchmarking Report, September 2013, Mayor’s Ofﬁce of Sustainability, New York, 2013. [13] City of New York, New York City Local Law 84 Benchmarking Report, August 2012, Mayor’s Ofﬁce of Sustainability, New York, 2012. [14] H. Doukas, C. Nychtis, J. Psarras, Assessing energy-saving measures in buildings through an intelligent decision support model, Build. Environ. 44 (2) (2009) 290–298. [15] P. Eichholtz, N. Kok, J.M. Quigley, Doing well by doing good? Green ofﬁce buildings, Am. Econ. Rev. (2010) 2492–2509. [16] W.J. Fisk, Health and productivity gains from better indoor environments and their relationship with building energy efﬁciency, Annu. Rev. Energy Environ. 25 (1) (2000) 537–566. [18] F. Fuerst, P. McAllister, An investigation of the effect of eco-labeling on ofﬁce occupancy rates, J. Sustain. Real Estate 1 (1) (2009) 49–64. [21] S.F. Gamtessa, An explanation of residential energy-efﬁciency retroﬁt behavior in Canada, Energy Build. 57 (2013) 155–164. [22] T. Häkkinen, K. Belloni, Barriers and drivers for sustainable building, Build. Res. Inform. 39 (3) (2011) 239–255. [23] J.C. Hartzell, L. Sun, S. Titman, The effect of corporate governance on investment: evidence from real estate investment trusts, Real Estate Econ. 34 (3) (2006) 343–376. [24] Y. Heo, R. Choudhary, G.A. Augenbroe, Calibration of building energy models for retroﬁt analysis under uncertainty, Energy Build. 47 (2012) 550–560. [25] A.J. Hoffman, R. Henn, Overcoming the social and psychological barriers to green building, Organ. Environ. 21 (4) (2008) 390–419. [26] D. Hsu, How much information disclosure of building energy performance is necessary? Energy Policy 64 (2014) 263–272. [27] R.K. Jain, J.M. Moura, C.E. Kontokosta, Big data+ big cities: graph signals of urban air pollution [Exploratory SP] signal processing magazine, IEEE 31 (5) (2014) 130–136.

[28] C.E. Kontokosta, A market-speciﬁc methodology for a commercial building energy performance index, J. Real Estate Finance Econ. 51 (2015) 288–316. [29] C.E. Kontokosta, Energy disclosure, market behavior, and the building data ecosystem, Ann. N. Y. Acad. Sci. 1295 (1) (2013) 34–43. [30] C.E. Kontokosta, Greening the regulatory landscape: the spatial and temporal diffusion of green building policies in U.S. cities, J. Sustain. Real Estate 3 (2011) 68–90. [31] J.G. Koomey, N.C. Martin, M. Brown, L.K. Price, M.D. Levine, Costs of reducing carbon emissions: US building sector scenarios, Energy Policy 26 (5) (1998) 433–440. [32] Z. Ma, P. Cooper, D. Daly, L. Ledo, Existing building retroﬁts: methodology and state-of-the-art, Energy Build. 55 (2012) 889–902. [33] McKinsey, Co, Unlocking Energy Efﬁciency in the U.S. Economy, McKinsey & Co., New York, NY, 2009. [34] C.C. Menassa, Evaluating sustainable retroﬁts in existing buildings under uncertainty, Energy Build. 43 (12) (2011) 3576–3583. [35] E. Miller, L. Buys, Retroﬁtting commercial ofﬁce buildings for sustainability: tenants’ perspectives, J. Property Invest. Finance 26 (6) (2008) 552–561. [36] N. Miller, D. Pogue, Q. Gough, S. Davis, Green buildings and productivity, J. Sustain. Real Estate 1 (1) (2009) 65–89. [37] L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy Build. 40 (3) (2008) 394–398. [38] A. Peterman, A. Kourula, R. Levitt, A roadmap for navigating voluntary and mandated programs for building energy efﬁciency, Energy Policy 43 (2012) 415–426. [39] S. Reddy, J.P. Painuly, Diffusion of renewable energy technologies—barriers and stakeholders’ perspectives, Renew. Energy 29 (9) (2004) 1431–1447. [40] Rockefeller Foundation DB Climate Change Advisors, United State Building Energy Efﬁciency Retroﬁts: Market Sizing and Financial Models, Deutsche Bank AG, Frankfurt am Main, 2012. [41] A.M. Rysanek, R. Choudhary, Optimum building energy retroﬁts under technical and economic uncertainty, Energy Build. 57 (2013) 324–337. [42] M. Ryghaug, K.H. Sørensen, How energy efﬁciency fails in the building industry, Energy Policy 37 (3) (2009) 984–991. [43] R.J. Sutherland, Market barriers to energy-efﬁciency investments, Energy J. (1991) 15–34. [44] P. Tuominen, K. Klobut, A. Tolman, A. Adjei, M. de Best-Waldhober, Energy savings potential in buildings and overcoming market barriers in member states of the European Union, Energy Build. 51 (2012) 48–55. [45] G. Verbeeck, H. Hens, Energy savings in retroﬁtted dwellings: economically viable? EnergyBuild. 37 (7) (2005) 747–754. [46] S.T. Anderson, R.G. Newell, Information programs for technology adoption: the case of energy-efﬁciency audits, Resour. Energy Econ. 26 (1) (2004) 27–50. [47] M. Deru, P. Torcellini, National Renewable Energy Laboratory, Sour. Energy Emission Factors Energy Use Build. (2008). [48] D.E. Marasco, C.E. Kontokosta, Applications of machine learning methods to identifying and predicting building retroﬁt opportunities, Energy Build. 128 (2016) 431–441.

Modeling the energy retrofit decision in commercial office buildings

Modeling the energy retrofit decision in commercial office buildings

Recommend Documents