In the US commercial real estate sector, the path to green energy transition is strong, but higher energy efficiency and lower demand for competing brown sources makes them relatively more cost-effective over time, which acts as a counteracting force to the path of transition to green sources.
Investing in energy-efficient technologies and infrastructure is increasingly portrayed as a major driver of market value for commercial real estate properties. Despite a long-standing debate, assessing the value that US commercial real estate operators attach to energy efficiency is still elusive.
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Note: This article was originally published in January 2025 on the blog of the Georgetown University McDonough School of Business: “Clark Quick Take: How Does U.S. Commercial Real Estate Fare on Green Energy Adoption?“
On top of the baseline value of energy efficiency, an overlooked aspect is that when the demand for green energy sources spikes, the demand for brown sources drops, which makes brown sources relatively cheaper and more attractive economically over time.
How can we quantify these second-order effects on the relative value of green vs. brown energy sources?
THE RESEARCH
A recent academic paper* proposes the first steps to tackle this question, benchmarking the paths of adoption of energy efficiency in commercial real estate to those in the manufacturing sector—a natural benchmark given that both sectors faced a push to convert to green energy sources over the last two decades.
To assess the potential second-order effects of changes in the demand for green sources on the economic viability of brown sources, the paper employs newly available data collected from a set of US sources on the split of usage of renewable sources and carbon fossil energy sources by sector and over time.
The paper develops a statistical model called a “UCTT multivariate diffusion model” to examine and compare the diffusion of coal, gas, and biomass as sources of the energy used in the US commercial real estate and manufacturing sectors over time. This model assesses the dynamics of adoption of each energy source and the direct and indirect effects across energy sources and sectors.
Exhibit 1 reports the raw data and modeled paths of energy-source adoption across sectors over the last two decades.
In the US commercial real estate sector, the path to green energy transition is strong —biomass adoption, in particular, grows at sustained rates. At the same time, the higher efficiency and lower demand for competing brown sources (gas and coal) makes them relatively more cost-effective over time, which acts as a counteracting force to the path of transition to green sources.
For manufacturing, the higher viability of natural gas has crowded out the adoption of green sources over the last decade, leading to a flat or even declining path of adoption for green sources that has no parallel in commercial real estate.
Ultimately, the existing data and predictive model suggest a sustained adoption of green sources in US commercial real estate largely because of the lack of a crowding out effect, which instead is strong in manufacturing.
WHAT CAN WE CONCLUDE FROM THIS RESEARCH?
Subsidizing green energy sources makes brown sources relatively cheaper, which pulls the break rather than accelerating the race toward sustainability and energy efficiency. Policies and programs that support the adoption of renewable energy sources cannot be effective unless they consider this quantitatively important and unintended counter-effect.
Modeling Energy Transition in US Commercial Real Estate: A Diffusion Comparison with the Industrial Sector
(The following is a modified version of a paper originally published for Engineering Proceedings for ITISE 2023: Savio, A. (2023). “Modeling Energy Transition in US Commercial Real Estate: A Diffusion Comparison with the Industrial Sector.” View the original at mdpi.com/2673-4591/39/1/15.)
An extensive structural transformation in energy systems is denoted by the term energy transition. This transition is often referred to as the decarbonization of the energy sector and aims to shift the system to renewable energy technologies (RETs), implying a change from centralized to decentralized energy production.[i]
According to the International Renewable Energy Agency,[ii] the use of appropriate technology and regulations in all sectors, including real estate, may potentially reduce carbon emissions from the energy sector by 90%. In recent years, many studies have been conducted on the causal relationships between green energy consumption and economic growth in the US,[iii] emphasizing how institutional and political policies have impacted on the diffusion of US renewable energy and the decrease in fossil fuels.[iv]
In this context, the US commercial real estate industry has made significant strides in energy efficiency[v] and sustainability[vi] using green energy sources. Many policies and initiatives have been put in place to encourage and facilitate the adoption of more environmentally friendly practices in this sector.[vii] In this context, the Energy Performance of Buildings Directive requires all new real estate construction beginning after 2021 to adhere to the “virtually zero-energy buildings” standard in order to combat the property industry’s GHG emissions’ slow decline.[viii]
Incentives, technological advancements, and cost reductions all contribute to reducing barriers that hinder renewable energy development,[ix] paving the way to continue expanding the use of green energy in the future.[x] Energy efficiency and sustainability are becoming increasingly valued in the commercial sector not only for the environmental benefit aspects but also for investing in energy-efficient technologies, and infrastructure is becoming a sliding door for constructors and property managers to increase the market value of their properties.[xi]
Based on these premises, this project aims to analyze in depth how the energy transition path is developing in the commercial real estate sector. This study compares this green energy diffusion scenario with the one of another exemplary and significant US sector, the industrial one, focusing on examining the relationships between renewable and carbon fossil energy diffusion.
Energy policies for the commercial and industrial sectors in the US are similar in many ways, as both sectors are subject to the same national- and state-level policies regarding energy efficiency and sustainability.[xii] However, the specifics of these policies and regulations may differ based on several factors, such as the scale, energy intensity, and operational differences between the two sectors.[xiii]
In the literature, diffusion models have been extremely valuable for defining and forecasting the development of an energy source, considering it as a technology that must be accepted in a market.[xiv] This well-established area of study[xv] allows for analysis of the temporal dynamics of energy sources in order to comprehend the intricate dynamics of energy systems.
Understanding how products or technologies compete or collaborate is essential for describing the trend of diffusion processes. Depending on the situation, the presence of competition can act both as a barrier to the growth of the innovation under consideration and as a stimulus for its development.[xvi]
From this perspective, this project studies and compares commercial and industrial sectors’ energy transitions in order to comprehend the intricate dynamics of energy systems through a refinement of the UCTT multivariate diffusion model presented in Forecasting.[xvii]
This paper examines the temporal diffusion of coal, gas, and biomass in the two sectors to identify peculiarities, similarities, and differences that characterize each energy system since the development and diffusion among different sectors can play a role in establishing technological innovation systems.[xviii]
View the full presentation: https://www.mdpi.com/2673-4591/39/1/15#B6-engproc-39-00015
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NOTES
[i] Jäger, J.; O’Riordan, T. The history of climate change science and politics. In Politics of Climate Change; Routledge: London, UK, 2019; pp. 1–31.
[ii] Energy Transition. Available online: https://www.irena.org/energytransition (accessed on 8 February 2023).
[iii] Alola, A.A.; Yildirim, H. The renewable energy consumption by sectors and household income growth in the United States. Int. J. Green Energy 2019, 16, 1414–1421; Carmona, M.; Feria, J.; Golpe, A.A.; Iglesias, J. Energy consumption in the US reconsidered. Evidence across sources and economic sectors. Renew. Sustain. Energy Rev. 2017, 77, 1055–1068; Omri, A. An international literature survey on energy-economic growth nexus: Evidence from country-specific studies. Renew. Sustain. Energy Rev. 2014, 38, 951–959; Payne, J.E. On biomass energy consumption and real output in the US. Energy Sources Part B Econ. Plan. Policy 2011, 6, 47–52.
[iv] Kilinc-Ata, N. The evaluation of renewable energy policies across EU countries and US states: An econometric approach. Energy Sustain. Dev. 2016, 31, 83–90.
[v] Ionescu, C.; Baracu, T.; Vlad, G.E.; Necula, H.; Badea, A. The historical evolution of the energy efficient buildings. Renew. Sustain. Energy Rev. 2015, 49, 243–253.
[vi] Starr, C.W.; Saginor, J.; Worzala, E. The rise of PropTech: Emerging industrial technologies and their impact on real estate. J. Prop. Invest. Financ. 2021, 39, 157–169.
[vii] Robinson, S.; McIntosh, M.G. A Literature Review of Environmental, Social, and Governance (ESG) in Commercial Real Estate. J. Real Estate Lit. 2022, 30, 54–67; Basher, S.A.; Masini, A.; Aflaki, S. Time series properties of the renewable energy diffusion process: Implications for energy policy design and assessment. Renew. Sustain. Energy Rev. 2015, 52, 1680–1692; Popp, D.; Hascic, I.; Medhi, N. Technology and the diffusion of renewable energy. Energy Econ. 2011, 33, 648–662.
[viii] Hirsch, J.; Spanner, M.; Bienert, S. The carbon risk real estate monitor—Developing a framework for science-based decarbonizing and reducing stranding risks within the commercial real estate sector. J. Sustain. Real Estate 2019, 11, 174–190.
[ix] Eleftheriadis, I.M.; Anagnostopoulou, E.G. Identifying barriers in the diffusion of renewable energy sources. Energy Policy 2015, 80, 153–164
[x] Norberg-Bohm, V. Creating incentives for environmentally enhancing technological change: Lessons from 30 years of US energy technology policy. Technol. Forecast. Soc. Chang. 2000, 65, 125–148.
[xi] Leskinen, N.; Vimpari, J.; Junnila, S. The impact of renewable on-site energy production on property values. J. Eur. Real Estate Res. 2020, 13, 337–356.
[xii] Bagheri, M.; Delbari, S.H.; Pakzadmanesh, M.; Kennedy, C.A. City-integrated renewable energy design for low-carbon and climate-resilient communities. Appl. Energy 2019, 239, 1212–1225.
[xiii] Kiliccote, S.; Olsen, D.; Sohn, M.D.; Piette, M.A. Characterization of demand response in the commercial, industrial, and residential sectors in the United States. Adv. Energy Syst. Large-Scale Renew. Energy Integr. Chall. 2019, 425–443.
[xiv] Bunea, A.M.; Guidolin, M.; Manfredi, P.; Della Posta, P. Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns. Forecasting 2022, 4, 456–476
[xv] Guidolin, M.; Manfredi, P. Innovation Diffusion Processes: Concepts, Models, and Predictions. Annu. Rev. Stat. Its Appl. 2023, 10; Bessi, A.; Guidolin, M.; Manfredi, P. Diffusion of Renewable Energy for Electricity: An Analysis for Leading Countries. In Theory and Applications of Time Series Analysis and Forecasting: Selected Contributions from ITISE 2021; Springer International Publishing: Cham, Switzerland, 2022; pp. 291–305; Savio, A.; Ferrari, G.; Marinello, F.; Pezzuolo, A.; Lavagnolo, M.C.; Guidolin, M. Developments in Bioelectricity and Perspectives in Italy: An Analysis of Regional Production Patterns. Sustainability 2022, 14, 15030; Bessi, A.; Guidolin, M.; Manfredi, P. The role of gas on future perspectives of renewable energy diffusion: Bridging technology or lock-in? Renew. Sustain. Energy Rev. 2021, 152, 111673.
[xvi] Guidolin, M.; Alpcan, T. Transition to sustainable energy generation in Australia: Interplay between coal, gas and renewables. Renew. Energy 2019, 139, 359–367.
[xvii] Savio, A.; De Giovanni, L.; Guidolin, M. Modelling energy transition in Germany: An analysis through Ordinary Differential Equations and System Dynamics. Forecasting 2022, 4, 438–455.
[xviii] Malhotra, A.; Schmidt, T.S.; Huenteler, J. The role of inter-sectoral learning in knowledge development and diffusion: Case studies on three clean energy technologies. Technol. Forecast. Soc. Chang. 2019, 146, 464–487.
ABOUT THE AUTHOR
Andrea Savio was a 2022-2023 Steers Research Fellow at Georgetown University and a current Econometrics Specialist at Cerved.