Quantifying the financial value of building decarbonization technology under uncertainty: Integrating energy modeling and investment analysis

Abstract

The built environment accounts for approximately 40% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency.

Introduction

Globally, buildings consume approximately 35 percent of total energy and account for 38 percent of CO2 emissions [1]. As such, the building sector represents a key target for decarbonization. As of 2022, more than 300 U.S. cities1 have released, or are in the process of developing, climate action plans in order to outline their commitments to reach low levels of emissions over the next few decades. In addition, many large cities, including New York, have set a goal of reaching net zero emissions by 2050 or earlier. To meet these targets, private sector decision makers such as building owners, building developers, real estate investors, and tenants, are becoming increasingly pressured to invest in energy efficiency and electrification measures through monetary penalties, such as Local Law 97 in New York City2, the Building Energy Reporting and Disclosure 2.0 act (“BERDO 2.0”) in Boston3, or the Building Energy Performance Standard (“BEPS”) in Washington, D.C.4

In order to comply with these plans and to meet the increasing consumer demand for reducing carbon emissions, decision makers need to invest in decarbonization technologies. These investment decisions involve a trade-off between the costs of implementing an investment and the future benefits obtained from the investment [2]. The multi-decade investment horizon of this type of investments, together with the changing policy environment and market conditions in energy and real estate markets create all kinds of uncertainties in the estimated future costs and benefits of the investment decision. To properly assess the decision to adopt such technologies, rigorous financial and energy modeling are required along with the integration of future uncertainties [3]. Uncertainties that affect the valuation of an investment in a technology can include microeconomic factors such as capital and operating costs, as well as macroeconomic factors such as energy prices, consumer demand for decarbonization technologies, and other factors such as changes in (local or national) policy with regards to penalties associated with emissions, subsidies or changes in the energy infrastructure (e.g., energy mix in the sector). Against this background, the building can be exposed to a wide range of scenarios that affect the profitability, and thus financial feasibility, of the decarbonizing investments as a result of changes in energy costs, greenhouse gas emissions or the appreciation of this characteristic of the building in the real estate market.

This paper describes a framework that integrates tools from three different disciplines (energy, finance, and uncertainty analysis) to support comprehensive assessments of adopting decarbonizing technologies. Traditional energy models do not provide an explicit assessment of uncertainty around economic and policy parameters of the model (e.g., energy prices, rent premiums, penalties etc.), and instead most design outcomes of such models are based on deterministic values (for an extensive discussion of the role of uncertainty in studies in the field of energy performance see [4]).

Traditionally, models and frameworks aiming to combine robust energy modeling with financial modeling rely on fixed assumptions on parameters such as a fixed budget [5], focusing primarily on reducing payback periods [6], and do not incorporate uncertainty in energy prices, market conditions and regulatory frameworks (i.e., taxes or penalties). A notable exception is Burhenne et al., who incorporated Monte Carlo (“MC”) methods into a cost-benefit model to evaluate the investment of a solar thermal system in a building. MC simulations varied economic parameters such as interest rates, inflation rates, gas prices, as well as technical parameters such as the hot water mass flow and air change rates. The results found that there was a 9% probability that the investment would yield a positive net present value (“NPV”) [4]. While investors are facing numerous uncertainties (e.g., regulation, prices for fossil fuels, etc.) around the energy efficiency of buildings in their portfolios, there is a shortage of studies providing decision making frameworks for building owners.

Our paper builds on this existing literature and proposes a method that integrates building energy modeling, real estate financial analysis, and uncertainty analysis in one decision-making tool that supports building decarbonization investments. In integrating these three key aspects, the tool provides a way for decision-makers to quantitatively characterize the risks of specific investments in decarbonization technologies under uncertainty in technological development, macroeconomic factors, and regulations. To demonstrate its value, the paper applies it to the assessment of a relevant case study: the electrification of a new office building development in NYC.

Section snippets

Methodology

The tool described in this paper integrates a building energy model and a financial model to analyze the costs and benefits of an energy investment under future uncertainty. The building energy model outputs a range of values for the building energy use, which are used to calculate the CO2 emissions and the operating costs of a building. Other costs including regulatory costs (which can be dependent on CO2 emissions or building energy use, depending on the regulation) and capital costs, as well

Overview

To demonstrate the framework, we compare the risks and benefits of two primary building design options in New York City: (A) constructing a new office building with natural-gas powered water and space heating, and (B) constructing a fully electrified office building (i.e., uses air-source heat pumps (ASHPs) for water and air heating.) After comparing these two design options, we also assess the value of a third design option, (C) which involves constructing a new building with a natural-gas

Conclusion and discussion

In this paper, we develop a decision-making framework to analyze the financial viability of decarbonizing investments in buildings. This framework integrates building performance simulations, real estate financial analysis techniques, and uncertainty quantification of a variety of influential parameters. By doing so, it overcomes the limitations of each individual analysis method, available in practice to one party of the development process (architects, engineers, and real estate…

Fuente: Sciencedirect