The purpose of this paper is to present new empirical data on leases, energy management, and energy meters in the UK, with a particular focus on small and medium enterprises (SMEs) and other “minor” players. The paper develops a new segmentation model that identifies six different combinations of energy and organizational conditions.
The authors surveyed participants in an online energy management and data analytics service. A 30-question online survey gathered data from 31 respondents on three kinds of infrastructure – legal, organizational, and technical.
SMEs and other minor players are generally “data poor,” lack energy managers, and have legacy meters that are read only annually or quarterly; some rent via leases that inhibit permanent alterations to the premises, including the meter.
The research is exploratory and subject to self-selection bias. Further research is needed into: lease language, governance structures, social practices to facilitate cooperation between tenants and landlords; the scope for energy management positions in small organizations; low-cost “smart-er” meters that can be reversibly retrofitted onto existing energy meters; and the combination of these areas.
Organizations may need to augment a combination of legal, organizational, and technical infrastructures to enable better energy management.
SMEs and other “minor” energy users are important to society and the economy, yet they are often overlooked by government programs. This developing data set can help policymakers include these groups in their programs.
This paper presents a new conceptual framework for future research and new empirical data on understudied groups.
Portions of this work have been supported by the second phase of the UK Energy Research Centre (UKERC) under its Demand Theme (www.ukerc.ac.uk). UKERC is funded by the UK Research Councils’ Energy Programme through Grant No. NE/G007748/1. A version was presented at the 2014 Improving Energy Efficiency in Commercial Buildings Conference (April 1-3, 2014).
B. Janda, K., Bottrill, C. and Layberry, R. (2014), "Learning from the “data poor”: energy management in understudied organizations", Journal of Property Investment & Finance, Vol. 32 No. 4, pp. 424-442. https://doi.org/10.1108/JPIF-03-2014-0018Download as .RIS
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