Abstract
US residential and commercial buildings were responsible for about 41 exajoules (EJ) of primary energy use per year in 2002, accounting for approximately 9% of the world fossil-fuel related anthropogenic carbon (C) emissions of 6.7 Gt that contribute to climate change. US Government-sponsored building energy efficiency research and implementation programs are focused on reducing energy consumption in US residential and commercial buildings and reducing these carbon (C) emissions. Although not specifically intended for adaptation to a warmer climate and less effective than under today’s cooler climate, these programs also could help reduce energy demand in a future warmer world. Warming scenarios projected by the United Nations Intergovernmental Panel on Climate Change (IPCC) in 2001 imply net overall decreases in both site energy and primary energy consumption in US residential and commercial buildings, largely because of the reduced need for heating. However, there would be as much as a 25% increase in building space cooling demand and a significant part of the increase could be offset by energy-efficiency improvements in buildings. Overall, in the US, buildings-related energy efficiency programs would reduce site energy consumption in buildings in the US by more than 2 EJ in 2020 and primary energy by more than 3.5 EJ, more than enough to offset the projected growth in cooling energy consumption due to climate change and growth in the US building stock. The savings would have an estimated annual net value at 2005 energy prices of between $45.0 and $47.3 billion to consumers.
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Notes
Transient climate simulations are based on a complete operation of the global climate models in which the climate is allowed to evolve over time. They contain realistic lagged climate responses to the composition of the atmosphere. As such, they are more realistic than are scenarios based on a simple addition of 1 or 2 degrees of warming to current temperatures or the equilibrium temperature response to a given input of greenhouse gases.
Although some climate change projections predict increased variability, we were not able to make use of that information in this paper to systematically shift temperature profiles for individual day types in individual cities. It may be argued that greater daily extremes in temperatures, combined with heat island effects may interact with the non-linear cooling responses of buildings to cause the cooling energy consumption calculated in this paper to be underestimated. However, most recent IPCC analysis suggests that the diurnal temperature range has not shifted. We were not able to take into account greater probability of heat waves.
Lighting interacts with the heating and cooling system to decrease heating loads and increase cooling loads. Those interaction effects would be expected to increase with warmer temperatures. Water heating likely would be affected by climate warming and is kept here for completeness, but scenarios of the impacts of climate on water temperatures and energy loads for water heating were not available and were not analyzed in this study.
The North is climate zones 1 through 3 (i.e., zones with >4,000 heating-degree days) as defined for the Energy Information Administration’s Commercial Buildings Energy Consumption Survey. It is roughly the region encompassed by the following Census Divisions: New England, Mid-Atlantic, East North Central, West North Central, Mountain (minus southern New Mexico and Arizona) and Pacific (minus most of California).
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Acknowledgments
The authors would like to acknowledge the assistance of the anonymous reviewers. Any remaining errors remain our responsibility. The work reported in this paper was partially funded by the US Department of Energy (DOE) at Pacific Northwest National Laboratory, operated by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views expressed in this paper are those of the authors and do not necessarily reflect those of the U.S. Department of Energy or Battelle Memorial Institute.
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Appendix: Example of detailed computations on energy impacts
Appendix: Example of detailed computations on energy impacts
This appendix demonstrates the interaction of the FEDS and BEAMS models in estimating climate effects on energy consumption in US residential and commercial buildings. Throughout this appendix, the modeling interactions and calculations are demonstrated by following the step-by-step process that is used to determine the changes in energy usage under different climate scenarios for cooling loads in newly constructed education buildings in the New England and Mid-Atlantic US climate regions in the year 2020. Fig. A.1 illustrates how energy consumption estimates are calculated by working through the step-by-step process using the example sub-set (Mid-Atlantic/New England new education cooling) of the data. As shown in the main text Table 2, the climate of Providence, Rhode Island, represents the New England and Mid-Atlantic climate regions. The example concentrates on space cooling only. Similar calculations are performed for space heating and interactive effects with lighting. First, it is necessary to apply a climate change scenario to modify the existing climate regime. As outlined in the main text, for the year 2020 this was done by increasing the seasonal average temperature forecasts in 2020 by the values shown in Table 1.Footnote 4 These seasonal increases in temperature values are added to the hourly temperature in FEDS for each hour of the day for three types of days (weekday, Saturday, and Sunday) during each month of the year in 2020, and the corresponding building energy loads for cooling and heating computed for each type of building, since different sizes, vintages, and types of buildings have different climate sensitivities. Table A.1 shows a typical hourly load calculation for cooling load for a new, occupied 4,000 square foot education building in Providence in January, April, July, and October at 2 p.m. This building represents 3.2% of the new education building square footage for the climate zones (New England and Mid Atlantic) represented by Providence. The same calculations are performed for other size new education buildings.
Steps in constructing an estimate of cooling energy consumption and savings
Table A.2 shows the overall computations for deriving changes in cooling load in new education buildings of all sizes represented by the Providence climate for both the seasonal minimum and maximum temperature increases for 2020 reported by Ruosteenoja et al. (2003). As described in Elliott et al. (2004), the six prototype education building sizes that represent new education buildings have their hourly cooling loads estimated for baseline average temperatures in Providence for three types of day for each month of the year. These loads are aggregated to monthly values and then annual values for each building type, and weights reflecting square footage by building type are applied to yield weighted annual cooling energy consumption for new education buildings. The same computation is then done for the low and high temperature increases, and the annual aggregate cooling energy requirements calculated in watt-hours and kilojoules (KJ). Note that larger buildings have smaller cooling loads per square foot than smaller buildings because the ratio of external wall area to internal volume is less for larger buildings than for smaller ones. Larger buildings’ cooling requirements thus are more likely to be influenced by internal loads than are smaller buildings, which are relatively more sensitive to their external climate. Larger buildings also dominate the total square footage to be cooled, and the weighted average energy requirement per square foot is closer to that of the larger buildings than to that of the smaller ones. When temperature increases, the per-square-foot cooling loads of the larger buildings also increase less than do those in smaller buildings. (The opposite is true on a percentage basis, because the difference in cooling load increases between large and small buildings is smaller on a square footage basis than is the initial spread in cooling loads between them, leading to a larger percentage change in cooling requirements for the larger buildings, with their smaller initial loads per square foot—see Table A.2, columns 6 and 7).
Table A.3 shows the mix of equipment used to provide cooling in new education buildings, as well as each type’s rated efficiency in Coefficient of Performance (COP) or Seasonal Coefficient of Performance (SCOP), and the cooling energy required to address the cooling loads in Table A.2. Cooling equipment with a COP of 3.5 is able to remove 3.5 kWh of heat for every kWh of electricity consumed. SCOP is the COP averaged over the cooling season. These ratios are stated directly for heat pumps and are translated from energy efficiency ratios (EERs) for the other types of cooling equipment. The inverse of COP is, in effect, the amount of energy consumed per unit of cooling, and the lower the efficiency (lower the COP), the more energy that is required to address a given cooling load. The increase in the weighted average cooling energy requirement per square foot in new education buildings in Table A.3 is non-linear. For example, the low temperature increase of about 0.6°C results in an 8.9% increase in cooling energy consumption or 14.8% (and 1,130 KJ) per 1°C over the base case, while at 2.5°C in the high temperature case the increase in cooling is 44.6%, or 17.8% (4,415 KJ) per 1°C. Finally, since the climate and load estimates from Providence are used in FEDS and BEAMS to represent 36.7% of the new education building stock in the North,Footnote 5 the square footage energy use rates calculated for Providence are multiplied by the estimated 2020 stock of new education buildings in the North times the 36.7% represented by Providence to obtain an overall estimate of cooling energy requirements at the expected energy efficiencies prevailing in the year 2020 in new education buildings in New England and the Mid-Atlantic region. To this must be added the impacts in portions of the 2020 building stock represented by other locations.
Table A.4 shows the impact of a specific DOE program (Electrochromic Windows) on cooling energy consumption in the context of the climate warming in 2020 in new education buildings in New England and the Mid-Atlantic region (represented by Providence, Rhode Island). As with all of the programs, the specifics of the program were estimated in a previous project via a dialog process with the responsible DOE project managers. Project managers articulate a market segment target, the market penetration rate, and technical performance goals for their programs, modified by technical peer reviews and technical studies to assess the feasibility of the program’s goals. The goals, thus, are not ‘maximum technical performance’ or ‘maximum market penetration,’ but expected based on these market and technical assumptions. The performance target of the Electrochromic Windows program in 2005 was to reduce unwanted heat gains and losses and perimeter lighting by replacing conventional double-glazed, low-emissivity windows currently in the marketplace. The overall market was assumed to be both new and commercial buildings. Market introduction was projected to begin in 2010, and the program was projected to accelerate market introduction of the technology by 10 years (relative to normal private industry market introduction). Based on simulations of energy performance of the building shell in the FEDS model, Release 5.0, these windows were expected to average a specific performance target of 38.54% reduction in cooling in new education buildings in the North. The program managers projected market penetration by the year 2020 to be about 20% of new sales for commercial buildings.
To actually perform the program savings calculations for new education buildings required two steps, as follows.
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1.
Apply the market penetration rate in each year from 2005 to 2020 times the new units sold in that year and accumulate the resulting number of new units with electrochromic windows from 2005 to 2020. That would give us the total and percentage of new education buildings in the North in 2020 that would have electrochromic windows. That figure, multiplied by 36.7%, would equal the number of new education buildings in New England and the Mid-Atlantic region (represented by Providence, RI).
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2.
To calculate program savings for new education buildings represented by Providence, RI, we take baseline, low, temperature, and high temperature total cooling energy consumption without electrochromic windows from Table A.3 and multiply times the weighted average savings weight as follows: (Percent of 2005–2020 new cumulative stock with electrochromic windows × 38.54% savings + percent of 2005–2020 new cumulative stock without electrochromic windows × 0 savings) × total cooling energy consumption in new education buildings represented by Providence, RI.
As shown in Table A.4, the increase in temperature increases the cooling energy savings associated with a program like Electrochromic Windows. At the same time, warmer winters reduce the need for heating and reduce the heating energy savings. However, even though the change in energy consumption for heat due to climate in new Providence education buildings is three times larger than is the change in cooling energy consumption, the percentage energy savings from electrochromic windows is seven times larger (38.54%) for cooling than for heating (4.9%). On balance, electrochromic windows save more energy under a warmer climate than under a baseline climate in 2020 in new Northern education buildings represented by Providence, R.I. However, when all the energy efficiency programs are taken together, with all types of buildings and all U.S. baseline climates, the opposite is true. The energy programs in aggregate save more energy in the 2020 baseline climate than if the climate were to get warmer. Even so, as stated in the main text, the energy efficiency programs still offset some of the impact of climate warming and therefore have adaptive value.
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Scott, M.J., Dirks, J.A. & Cort, K.A. The value of energy efficiency programs for US residential and commercial buildings in a warmer world. Mitig Adapt Strateg Glob Change 13, 307–339 (2008). https://doi.org/10.1007/s11027-007-9115-4
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DOI: https://doi.org/10.1007/s11027-007-9115-4