Factors influencing commercial buildingsto obtain green certificates
Yueming (Lucy) Qiu, Arizona State University, (623)209-4725,
Xin Su, Renmin University of China, (480)512-1108,
Overview
The energy consumption in residential and commercial buildings constitutes 40% of the total U.S. energy consumption in 2013 (EIA 2014).[1] Specifically, commercial buildings, which consume a large amount of energy in heating/ventilation/air conditioning (HVAC), lighting and other end-uses, are responsible for approximately 20% of all energy consumption in the United States.[2] Navigant Research predicts that global revenue benefit from energy efficiency in commercial buildings will increase from $68.2 billion in 2014 to $127.5 billion in 2023.[3] In general, Energy efficiency and lowering carbon emissions from commercial buildings in U.S. can play an important role in the reduction of global consumption of fossil-fuel resources.Even though increasing number of buildings have obtained voluntary green certificates such as Energy Star or LEED certificatesin recent years, there still remain amply unexploited room for commercial buildings to reduce energy consumption.Since obtaining a green building certificate from third-party rating systems is completely voluntary, it is quite important to find out the key factors that may have influence on obtaining green certificates.
In this paper, binary logit, multinomial logit and nested logit models are used to identify thevariables that have influence on obtaining green certificates and quantify the impacts of these variablesfor commercial buildings in New Yorkstate.There is a significant correlation between the characteristics of commercial buildings such as square footage, lot size and years built and the choices to get green certificates. The studyalso finds the opposite of principal-agent problem between building owners and tenants in the process of obtaining a Energy Star certificate: if a commercial building is occupied by its owner, there exists a smaller likelihood for the building owner to obtain a Energy Star certificate. This interesting finding is opposite to what we previously thought to be.
Methods
We analyze all commercial buildings with square footage over 500 square feet and lot size over 100 square feet in New York State (NY) at the building level. The dataset is compiled from three primary data sources: U.S. Green Building Council, Energy Star program and ProspectNow.com. For each commercial building in NY state, we gathered the information of building’s name and address as well as other detailed information such as lotsize, square footage, property value, owner occupancy, etc. Even though there are incresing number of commercial buildings obtaining green certifications in recent years, the share of green buildings is still pretty low. In our study, the number of green buildings with Energy Star, LEED and both certifications is 100, 40 and 193, respectively. However, these energy-saving certified buildings account for only 0.4% of all commercial buildings.
Logit models are used to determine which factors are related to the commercial buildings’ choices to go green. In thebinary logistic regression ,we transfered our dependent variable Z into different values, with 1 represent for green building and 0 represent for non-green building. Positive coefficient means an increase in the possibility of the variable Z occuring, which indicates that the independent variable have a positive effect on commercial building to go green. Then we use multinomial logit model to examine the possibility that a commercial building obtaining any type of green certification: Energy Star certification only, LEED certification only or with both certifications. The nested logit or hierarchical logit model is then conducted further to analyse commercial buildings’ decision processes. In our two levels nested logit model, commercial buildings first decide whether to get a green certificate or not; thenif they decide to get a green certificate, they decidewhich certification they will choose.
Results
In both binary and multinomial logit models, there are no significant relationship between dependent variables including unit market value, number of buildings, establishments and the independent variable Z, which represents the choice of a green certificate. The influences of square footage and lot size were found to have a positive effect (p<0.01) on the decision to become certified. Besides, years built shows a negative relationship with the green choice, which means the longer a building has been built, the less likely it will go green. The coefficient of owner type appears to be positively significant in almost all models. In other words, a commercial building is more likely to obtain a green certificate if it is owned by a company rather than an individual.Since our research is conducted for commercial buildings, it is quite meaningful to discuss the possible split-incentive problem between building owners and tenantsbased on our large sample dataset. An unexpected regression outcome occurred when we try to analyze the existence of principal-agent problem. We are surprised to find out it is the opposite of principal-agent problem. Owner occupied buildings only present an obvious negative likelihood to get Energy Star certificates. We propose several possible explanationsto the opposite of principal-agent problem, such as opportunity costs, bounded rationality, path dependence of building owners and the energy efficiency gap.
Conclusions
Commercial buildings with larger square footage and lot size are more likely to be green-certified and those with longer built years are less likely to get certified. Unit market value of commercial buildings and number of buildings in a architectural complex do not have significant relationship with green choices in our study as in previous researches. Our study strengthens the importance of building scale and ownership in going for green certificates, although more specific research is needed to address how this works.
In particular, we also find out some results that are quite different from previous research, namely the existence of the opposite of principal-agent problem. This surprising finding suggests that if a commercial building is occupied by its owner, it may be reluctant to obtain a Energy Star certificate, perhaps because of several reasons, such as opportunity costs, bounded rationality and the energy efficiency gap. Finally, in looking at property uses, we also find that commercial buildings used as offices, department stores, hotels and shopping centers are more willing to equiped with energy-saving facilities, while restaurants and gas stations are less likely to obtain green certificates. This also demonstrates that commercial building and its industry will have a great impact on the choice of going green.
There are also some restrictions in our paper. First, if we could obtain more physical information of the commercial buildings, we could conduct a more detailed descriptive analysis and find out more possible influencing factors. Second, we could confirm our guesses about potential reasons that result in the opposite of principal-agent problem if we are able to access more related financial data, such as the actual energy savings and economic gains benefited from being certified. Finally, due to availability of dataset, we only studied the situation of green certification in NY state. Further studies could conduct a wider scope of research in more states and make comparisons between different states.
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[1]U.S. Energy Information Administration,How much energy is consumed in residential and commercial buildings in the United States?June 18, 2014
[2]U.S. Department of Energy, Better Building 2013 Annual Report, May 7, 2014
[3]Navigant Research, Commercial Building Energy Efficiency Retrofits Will Surpass $127 Billion in Annual Market Value by 2023, April 3,2014