Skip to main content

Advertisement

SpringerLink
Log in

Using marginal emission factors to improve estimates of emission benefits from appliance efficiency upgrades

  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

This work uses marginal emission factors to analyze avoided emissions from household energy efficiency improvements in air conditioning and lighting. This approach considers CO2, SO2, and NOx emissions avoided from the marginal power plant that would have been dispatched to meet demand, and compares the results to the more commonly used average emission factor approach as well as time-averaged marginal emission factors. Results from the lighting analysis indicate that, depending on location, a household can save $50–$430/year on electricity bills and avoid between 600 and 1300 kg of CO2/year by switching from a mix of compact fluorescent and incandescent to LED bulbs. For air conditioning, efficiency upgrades save between zero and $600/year in electricity cost and avoid zero to 300 kg of CO2/year. When comparing the marginal approach to the traditional average emission method, the average approach can underestimate CO2, SO2, and NOx emissions by 50% or overestimate by 100%, illustrating the relevance of the marginal emission approach to the study of efficiency-induced emission reductions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. The efficiency of an air conditioner is described by its SEER value, which represents the amount of cooling energy (BTU) per unit of electricity consumed (kWh). The greater the seasonal energy efficiency ratio (SEER), the more efficient the air conditioning unit.

  2. The MEF data are freely available for download at https://cedm.shinyapps.io/MarginalFactors/.

References

  • Asaee, S., Ugursal, V., & Beausoleil-Morrison, I. (2015). An investigation of the techno-economic impact of internal combustion engine based cogeneration systems on the energy requirements and greenhouse gas emissions of the Canadian housing stock. Applied Thermal Engineering, 87, 505–518.

    Article  Google Scholar 

  • Borg, S., & Kelly, N. (2011). The effect of appliance energy efficiency improvements on domestic electric loads in European households. Energy and Buildings, 43(9), 2240–2250.

    Article  Google Scholar 

  • Brecha, R., Mitchell, A., Hallinan, K., & Kissock, K. (2011). Prioritizing investment in residential energy efficiency and renewable energy—a case study for the U.S. Midwest. Energy Policy, 39(5), 2982–2992.

    Article  Google Scholar 

  • Dandres, T., Moghaddam, R., Nguyen, K., Lemieux, Y., Samson, R., & Cheriet, M. (2017). Consideration of marginal electricity in real-time minimization of distributed data centre emissions. Journal of Cleaner Production, 143, 116–124.

    Article  Google Scholar 

  • Gilbraith, N., & Powers, S. E. (2013). Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City. Energy Policy, 59, 459–469.

    Article  Google Scholar 

  • Graff-Zivin J., Kotchen M., & Mansur E. (2014). Spatial and temporal heterogeneity of marginal emissions: implications for electric cars and other electricity-shifting policies. National Bureau of Economic Research, no. Working Paper 18462, pp. 1–49.

  • Gregg M. (2011) How many light bulbs are in the average American household?, EzineArticles, 10 August 2011. [Online]. Available: http://ezinearticles.com/?How-Many-Light-Bulbs-Are-In-The-Average-American-Household?&id=6486896. [Accessed 16 May 2016].

  • Hittinger, E. S., & Azevedo, I. M. L. (2015). Bulk energy storage increases United States electricity system emissions. Environmental Science and Technology, 5(49), 3203–3210.

    Article  Google Scholar 

  • Holladay, J., & LaRiviere, J. (2017). The impact of cheap natural gas on marginal emissions from electricity generation and implications for energy policy. Journal of Environmental Economics and Management, 85(C), 205–227.

    Article  Google Scholar 

  • Horner N. (2016). Powering the information age: metrics, social cost optimization strategies, and indirect effects related to data center energy use.

  • Horner N., Azevedo I., Sicker D., & Agarwal Y. (2016). Dynamic data center load response to variability in private and public electricity costs. 2016 I.E. International Conference on Smart Grid Communications (SmartGridComm).

  • Ilic, D., & Trygg, L. (2014). Economic and environmental benefits of converting industrial processes to district heating. Energy Conversion and Management, 87, 305–317.

    Article  Google Scholar 

  • International Energy Agency (2007). Energy efficiency in the North American existing building stock. [Online]. Available: https://www.iea.org/publications/freepublications/publication/NAM_Building_Stock.pdf. [Accessed Dec 2017].

  • National Academy of Sciences (2010). Real prospects for energy efficiency in the United States, National Academies Press.

  • National Renewable Energy Laboratory. National Solar Radiation Data Base. National Renewable Energy Laboratory, [Online]. Available: http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/. [Accessed 30 Mar 2016].

  • Novan, K. (2015). Valuing the wind: renewable energy policies and air pollution avoided. American Economic Journal: Economic Policy, 7(3), 291–326.

    Google Scholar 

  • OpenEI. US Utility Rate Database. July 2014. [Online]. Available: http://en.openei.org/wiki/Utility_Rate_Database. [Accessed Sept 2015].

  • Parker D. S., Mazzara M. D., & Sherwin J. R. (1996). Monitored energy use patterns in low-income housing in a hot and humid climate. Tenth Symposium on Improving Building Systems in Hot Humid Climates, p. 316.

  • Hendron R., & Engebrecht. (2010). Building America House Simulation Protocols. [Online]. Available: http://www.nrel.gov/docs/fy11osti/49246.pdf.

  • Sadineni, S. B., France, T. M., & Boehm, R. F. (2011). Economic feasibility of energy efficiency measures in residential buildings. Renewable Energy, 36(11), 2925–2931.

    Article  Google Scholar 

  • Schwartz L., Wei M., Morrow W., Deason J., Schiller S., Leventis G., Smith S., Leow W., Levin T., Plotkin S., Zhou Y., & Teng J. (2017). Electricity end uses, energy efficiency, and distributed energy resources baseline. [Online]. Available: https://emp.lbl.gov/sites/all/files/lbnl-1006983.pdf. [Accessed Nov 2017].

  • Siler-Evans, K., Azevedo, I. L., & Morgan, M. (2012). Marginal emissions factors for the U.S. electricity system. Environmental Science and Technology, 9(46), 4742–4748.

    Article  Google Scholar 

  • Siler-Evans, K., Azevedo, I. L., Morgan, M. G., & Apt, J. (2013). Regional variations in the health, environmental, and climate benefits of wind and solar generation. Proceedings of the National Academy of Sciences of the United States of America, 110(29), 11768–11773.

    Article  Google Scholar 

  • U.S. Census Bureau. QuickFacts United States, U.S. Department of Commerce, 1 July 2014. [Online]. Available: https://www.census.gov/quickfacts/table/PST045215/00. [Accessed 16 May 2016].

  • U.S. Department of Energy. Report of the Building Energy Efficiency Subcommittee to the Secretary of Energy Advisory Board. November 2012. [Online]. Available: https://energy.gov/sites/prod/files/Building%20Efficiency%20Report.pdf. [Accessed Nov 2017].

  • U.S. Department of Energy. Commercial and residential hourly load profiles for all TMY3 locations in the United States. July 2013. [Online]. Available: http://en.openei.org/doe-opendata/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states. [Accessed Sept 2015].

  • U.S. Department of Energy. Building characteristics for residential hourly load data. OpenEI, [Online]. Available: http://en.openei.org/doe-opendata/dataset/eadfbd10-67a2-4f64-a394-3176c7b686c1/resource/cd6704ba-3f53-4632-8d08-c9597842fde3/download/buildingcharacteristicsforresidentialhourlyloaddata.pdf. [Accessed 5 Apr 2016].

  • U.S. Energy Information Administration. Electric generator dispatch depends on system demand and the relative cost of operation. 17 August 2012. [Online]. Available: http://www.eia.gov/todayinenergy/detail.cfm?id=7590. [Accessed 21 Mar 2016].

  • U.S. Energy Information Administration. Table 5. Electric power industry generation by primary energy source, 1990–2014, 24 March 2016. [Online]. Available: http://www.eia.gov/electricity/state/unitedstates/index.cfm. [Accessed 2 May 2016].

  • U.S. Energy Information Administration. How is electricity used in U.S. homes? February 2017. [Online]. Available: https://www.eia.gov/tools/faqs/faq.php?id=96&t=3. [Accessed Nov 2017].

  • U.S. Energy Information Administration. Residential Energy Consumption Survey (RECS). February 2017. [Online]. Available: https://www.eia.gov/consumption/residential/data/2015/hc/php/hc5.1.php. [Accessed November 2017].

  • U.S. Environmental Protection Agency. eGRID2012 Version 1.0 Year 2009 Summary Tables. April 2012. [Online]. Available: https://www.epa.gov/sites/production/files/2015-02/documents/egrid2012v1_0_year09_summarytables.pdf. [Accessed 16 April 2016].

  • U.S. Environmental Protection Agency. eGRID, U.S. Environmental Protection Agency, [Online]. Available: https://www.epa.gov/energy/egrid. [Accessed 16 April 2016].

  • U.S. Environmental Protection Agency. The social cost of carbon, U.S. Environmental Protection Agency, [Online]. Available: https://www3.epa.gov/climatechange/EPAactivities/economics/scc.html. [Accessed 16 May 2016].

  • U.S. Environmental Protection Agency. Sector-based PM2.5 benefit per ton estimates, U.S. Environmental Protection Agency, [Online]. Available: https://www.epa.gov/benmap/sector-based-pm25-benefit-ton-estimates. [Accessed 16 May 2016].

  • Ürge-Vorsatz, D., & Novikova, A. (2008). Potentials and costs of carbon dioxide mitigation in the world's buildings. Energy Policy, 36(2), 642–661.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Public Policy, Rochester Institute of Technology, Rochester, NY, 14623, USA

    Courtney N. Smith & Eric Hittinger

Authors
  1. Courtney N. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric Hittinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric Hittinger.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

ESM 1

(PDF 2168 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smith, C.N., Hittinger, E. Using marginal emission factors to improve estimates of emission benefits from appliance efficiency upgrades. Energy Efficiency 12, 585–600 (2019). https://doi.org/10.1007/s12053-018-9654-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-018-9654-4

Keywords

Search

Navigation