Energy, emissions and emergency medical services: Policy matters

Energy Policy 46 (2012) 585–593

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Energy, emissions and emergency medical services: Policy matters Lawrence H. Brown a,n,1, Ian E. Blanchardb,1 a b

Anton Breinl Centre for Public Health and Tropical Medicine, James Cook University, Townsville, QLD 4811, Australia Alberta Health Services Emergency Medical Services, Calgary, AB, Canada T1Y 6C2

a r t i c l e i n f o

abstract

Article history: Received 8 March 2011 Accepted 11 April 2012 Available online 5 May 2012

Understanding the energy consumption and emissions associated with health services is important for minimizing their environmental impact and guiding their adaptation to a low-carbon economy. In this post-hoc analysis, we characterize the energy burden of North American emergency medical services (EMS) agencies and estimate the potential marginal damage costs arising from their emissions as an example of how and why health services matter in environmental and energy policy, and how and why environmental and energy policy matter to health services. We demonstrate EMS systems are energy intensive, and that vehicle fuels represent 80% of their energy burden while electricity and natural gas represent 20%. We also demonstrate that emissions from EMS operations represent only a small fraction of estimated health sector emissions, but for EMS systems in the United States the associated marginal damage costs are likely between $2.7 million and $9.7 million annually. Significant changes in the supply or price of energy, including changes that arise from environmental and energy policy initiatives designed to constrain fossil fuel consumption, could potentially affect EMS agencies and other health services. We encourage cross disciplinary research to proactively facilitate the health system’s adaptation to a low-carbon economy. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Health services Greenhouse gas emissions Adaptation

1. Introduction 1.1. Climate change and health policy The predominant scientific and political consensus is that human activity, rising atmospheric carbon dioxide (CO2) levels and steadily increasing surface and sea temperatures are all interrelated (Allen et al., 2009; Hansen et al., 1998; Matthews et al., 2009; Ramanathan and Feng, 2008; Solomon et al., 2007). All but five of the 193 nations participating in the 15th Conference of the Parties of the United Nations Framework Convention on Climate Change (UNFCCC), held in Copenhagen, Denmark in December 2009, supported the ‘‘Copenhagen Accord.’’ The opening paragraphs of that accord state, ‘‘We underline that climate change is one of the greatest challenges of our time’’ and ‘‘We agree that deep cuts in global emissions are requiredy.’’ (Ramanathan and Xu, 2010). A lack of agreement on how to mitigate climate change is not a lack of agreement about the relationships between anthropogenic greenhouse gases, rising atmospheric CO2 levels, and global warming.

n

Corresponding author. Tel.: þ61 7 4781 4390; fax: þ61 7 4781 5254. E-mail address: [email protected] (L.H. Brown). 1 For the North American emergency medical services emissions study group. Contributing members of the North American emergency medical services emissions study group are listed in the Appendix. 0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.04.019

Climate change and global warming have relevance beyond environmental policy. They are also relevant to health policy. Nearly twenty years ago, Sir Richard Doll, one of the foremost epidemiologists of the 20th century, wrote about the profound linkages between our environment and human health (Doll, 1992). He identified food shortages, expanding favorable habitats for parasitic diseases, and increasing migration as some of the potential health effects of global warming. A number of authors have echoed those concerns (Frumkin et al., 2009; Patz et al., 2000; Sellman and Hamilton, 2007; Wilkinson, 2008a) identifying greater distribution and potency of allergens (Beggs, 2004), injury (Roberts and Hillman, 2005), water scarcity (Barnett et al., 2005), increasing adverse weather events (Greenough et al. 2001; Haines et al., 2006; McMichael et al., 2006; Patz et al., 2000; Sellman and Hamilton, 2007; van Aalst, 2006), and rising incidence and virulence of infectious diseases (Greer et al., 2008; Haines et al., 2006; McMichael et al., 2006; Patz et al., 2000; Sellman and Hamilton, 2007) as additional potential effects. Many of these concerns are theoretical and without empirical basis. For example, while the potential spread of vector-borne diseases like malaria is an oft-cited risk of climate change, Reiter (2000) eloquently describes how control of that disease has been accomplished through straight-forward public health activities. The current distribution of malaria is primarily related to economic well-being, not climate. This, however, emphasizes another important concept: the health impacts of climate change are

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likely to be most heavily borne by economically disadvantaged places and people—even though they contribute least to greenhouse gas emissions and global warming (Patz et al., 2008; Wilkinson et al., 2007; Wilkinson, 2008a). This might be particularly true for indigenous people, even within wealthier societies (Ford et al., 2010; Petheram et al., 2010). Not all of the health impacts of climate change are conjecture. Rice harvests in India are significantly reduced by dryer growing seasons and rising temperatures (Auffhammer et al., 2006). Anthropogenic climate forcing contributes significantly to European summertime temperatures and likely increases the risk of heat waves (Stott et al., 2004), and increasing temperature variability is strongly linked to heat-related deaths (Gosling et al., 2009). There are also clear links between air pollution and health, and these might be further exacerbated by climate change (Bernard et al., 2001; Chen et al. 2007; Cifuentes et al., 2001; Gallagher et al. 2010; Hall et al., 2010; Kunzli 2002; Pope et al., 2009; Samet et al., 2000; Spadaro and Rabl, 2001; Wang, 2010; Wilkinson et al., 2009). 1.2. Energy scarcity, energy costs and health policy Sheikh Zaki Yamani, the Saudi Arabian oil minister between 1962 and 1986, is quoted as saying, ‘‘The Stone Age did not end for lack of stone, and the Oil Age will end long before the world runs out of oil’’ (Wilkinson, 2008b). It was a prediction about relative – not absolute – scarcity; that eventually we would consume oil faster than we could cost-efficiently pump it out of wells. The concept of ‘‘peak oil’’ was actually first described by M. King Hubbert in the 1950s, when he correctly predicted the United States’ (U.S.) oil production would peak in the 1970s, and then rapidly decline. In 1970 U.S. oil production peaked at approximately 3.5 billion barrels per year; it has declined ever since and by 2007 it had dropped to less than 2 billion barrels per year (Verbruggen and Al Marchohi, 2010). The focus now is on global peak oil, when it will occur, or whether it already has (Aleklett et al., 2010; Sorrell et al., 2010; Verbruggen and Al Marchohi, 2010). And oil is not the only energy source at risk of relative scarcity; more recently authors have raised the specter of impending ‘‘peak coal’’ (Heinberg and Fridley, 2010; Mason et al., 2011; Patzek and Croft, 2010). Energy scarcity and energy costs also have implications that reach beyond energy policy and into health policy. A number of authors have made projections about the potential impact of peak oil on public health (Frumkin et al., 2007, 2009; Hanlon and McCartney, 2008; Wilkinson, 2008b). For example, food production and distribution is energy dependant (Frumkin et al., 2007), and the food supply could be threatened as croplands are converted for growing bio-fuels (Hanlon and McCartney, 2008). Also, some aspects of both public health and health care are dependent on vehicles (and thus fossil fuels) for transportation (Frumkin et al., 2007). At the same time some authors, indeed some of these same authors, have recognized the potential health benefits of conversion to a low-carbon society, including increased active transport such as walking and bicycling as well as a likely reduction in motor vehicle crashes and crash-related injuries and deaths (Frumkin et al., 2007; Hanlon and McCartney, 2008; Schnoor, 2007; Wilkinson, 2008b). No published studies identified to date have explicitly evaluated the associations between energy costs and public health, but energy costs do impact health services. Nandha and Faff (2008) included health sector stocks in an analysis of the impact of oil prices on global stock market returns, and health stocks were as adversely affected as other sectors. A recent analysis from the U.S. demonstrated a correlation between energy price rises

and subsequent health care price inflation (Hess et al., 2011). There are numerous reports in the lay media describing the adverse effects of fuel price hikes on ambulance services, also known as emergency medical services (EMS) (Anonymous, 2011; Fallon, 2011; Harlin, 2009; Odell 2011; Penner, 2008; Stock and Siceloff, 2008).

1.3. The environmental and energy burden of health services Two studies have calculated complete life cycle emissions from entire health sectors: in England, the National Health Service emits approximately 21 million tons of CO2e annually, and is responsible for 3% of all of England’s greenhouse gas emissions (Sustainable Development Commission-Stockholm Environment Institute (SDCSEI), 2008); in the United States, the healthcare sector emits approximately 545 million tons of CO2e annually (254 million Scope 1 and Scope 2; 291 million Scope 3), representing 8% of that nation’s total greenhouse gas emissions (Chung and Meltzer, 2009). Hospitals, which consume energy to provide lighting, power medical equipment, heat water, and to supply heating and air conditioning (Bujak, 2010; Chirarattananon et al., 2010; Daschner and Dettenkofer, 1997; Gaglia et al., 2007; Renedo et al., 2006; Saidur et al., 2010) consume more energy per square meter of floor space compared to other non-residential buildings, due in part to their continuous 24-h operations (Gaglia et al., 2007). Several studies have individually addressed the environmental impact and/or energy burden of laparoscopic surgery (Gilliam et al., 2008), cataract surgery (Somner et al., 2009), surgical scrubbing (Jones, 2009; Somner et al., 2008), peri-operative patient warming devices (Bayazit and Sparrow, 2010), anesthetic gases (Ryan and Nielsen, 2010), reflux control (Gatenby, 2011) and EMS operations (Blanchard and Brown, 2011). Emissions associated with clinical trials (Burnett et al., 2007; Subaiya et al., 2011) and attendance at medical conferences (Callister and Griffiths, 2007; Crane and Caldwell, 2006; Hudson, 2008; Roberts and Godlee, 2007) have also been scrutinized. In all of these cases, per-patient or per-event energy consumption and emissions are modest. For example, our recent exploration of the Scope 1 and Scope 2 emissions of North American EMS agencies found median emissions of 36.6 kg of CO2e per ambulance response, or 3.5 kg CO2e per capita. Even when extrapolated to the entire U.S. population of approximately 309 million (U.S. Census Bureau, 2010), the 660,000 to 1.6 million metric tons of annual EMS-related Scope 1 and Scope 2 CO2e emissions would constitute less than 1% of all U.S. health sector Scope 1 and Scope 2 emissions (Blanchard and Brown, 2011).

1.4. Purpose Since it is estimated that the health sector in general is responsible for between 3% to 8% of emissions in England and the U.S. (and probably no more than that in other developed countries), and individual healthcare services, activities, disciplines and subspecialties would be responsible for exceedingly small fractions of the health sector’s energy consumption and emissions, are the health sector and health services too small to be of interest in – or significantly affected by – environmental and energy policy decisions? In this post-hoc analysis we characterize the total direct and purchased energy consumption of North American EMS operations using a standard energy metric, and estimate the emissions-related marginal damage costs arising from that energy consumption, as an example of how and why health services matter in environmental and energy policy, and how and why environmental and energy policy matter to health services.

L.H. Brown, I.E. Blanchard / Energy Policy 46 (2012) 585–593

2. Methodology 2.1. Original data collection The original study methodology and results have been fully reported elsewhere (Blanchard and Brown, 2011). Briefly, energy consumption data were collected from a convenience sample of fifteen EMS systems located in geographically and climatically diverse regions of North America. Collectively they served urban, suburban and rural settings and provided both ground ambulance and air-medical services. During the one-year period of data collection, the participating agencies served a population of 6.3 million and performed 554,040 ambulance responses; the median call volume of the services was 77.3 (IQR: 66–113) responses per 1000 population. A data collection tool that had been pilot-tested in an earlier proof of concept study (Blanchard and Brown, 2009) was distributed to operational managers of the participating systems. The data collection tool asked participants to report their agencies’ energy consumption including fuel for ambulances and other vehicles, on-site energy consumption such as natural gas and electricity usage at stations and offices, aviation fuel use, and employee work-related commercial air travel for either fiscal year or calendar year 2008, depending on record availability. For liquid fuels and natural gas, carbon emissions per unit of consumption were calculated using conversion factors from the U.S. Energy Information Administration (EIA), the U.S. Environmental Protection Agency (EPA), and local sources. Emissions from electricity consumption were calculated using state-, province-, or cityspecific conversion factors for each participating agency. Aggregate emissions for the participating EMS agencies were then calculated, along with per response and per capita emissions. Fourteen (93%) of the fifteen participating agencies reported full-year Scope 1 consumption of diesel fuel and gasoline, producing 14,345 metric tons of CO2e, with a median of 31.7 (IQR: 22.8– 43.8) kg CO2e per ambulance response and 2.5 (IQR: 1.9–3.7) kg CO2e per capita. Ten systems (66%), performing 409,446 ambulance responses, reported complete full-year data on Scope 1 and Scope 2 energy consumed to support ground ambulance operations, with the exception of commercial air travel which was tracked by only three systems. The Scope 1 and Scope 2 carbon footprint of these ten systems was 13,890 metric tons of CO2e, or 36.6 (IQR 29.5–48.3) kg CO2e per ambulance response and 3.5 (IQR 2.1–5.1) kg CO2e per capita. As shown in Fig. 1, vehicle fuels were the leading sources of emissions (71.6%), followed by electricity (19.5%) and natural gas (8.7%) (Blanchard and Brown, 2011).

Gasoline 7% Natural Gas 9%

To characterize the aggregate Scope 1 and Scope 2 energy burden of North American EMS agencies, conversion factors from the EIA (U.S. Energy Information Administration, 2010a, 2010b) were used to calculate the approximate heat content of the energy consumption of the ten participating agencies that reported complete full-year Scope 1 and Scope 2 data, as shown in Eq. 1. ! 10 n X X Etot ðMJÞ ¼ F i  kBTU=F i 1:055 ð1Þ j¼1

i¼1

j

where Etot is the total energy consumption in mega-joules (MJ); Fi is the energy consumption for each fuel source, ‘‘i’’; kBTU/ Fi is the heat content per unit of energy consumption for each fuel source ‘‘i’’; ‘‘j’’ represents the participating agency; and 1.055 is a constant multiplier representing the ratio of MJ to kBTU.

LP 0%

Air Travel CNG 0% 0%

Electricity 19% Diesel 65%

Fig. 1. Sources of emissions for 10 systems with complete Scope 1 and Scope 2 data.

From this aggregate energy consumption measure, both aggregate energy consumption per ambulance response and aggregate energy consumption per capita were calculated. 2.3. Calculation of marginal damage costs A previous systematic review and meta-analysis of studies exploring the marginal damage costs of CO2 emissions has reported median marginal damage costs of $14/t of carbon emitted, and mean marginal damage costs of $50/t of carbon emitted, when incorporating only the peer reviewed literature. When including the gray literature, median marginal damage costs did not change but the mean increased to $93/t of carbon emitted (Tol, 2005). To estimate the marginal damage costs of EMS activities, the previously determined EMS-related CO2e emissions were first converted into elemental carbon equivalents; the marginal damage costs of these emissions were then calculated using the median ($14/t) and mean ($50/t) marginal damage costs estimated from meta-analysis of the peer-reviewed literature (Tol, 2005) as shown in Eq. (2) and Eq. (3). MCðX 50 Þ ¼ CO2 e MCðXÞ ¼ CO2 e

2.2. Calculation of raw energy consumption

587

12 $14 44

12 $50 44

ð2Þ

ð3Þ

where MC (X50) and MC (X ) denote estimated marginal damage costs from carbon emissions based respectively on the reported median ($14) and mean ($50) marginal damage costs of one metric ton of carbon; CO2e is the total greenhouse gas emissions from the participating EMS agencies; and 12=44 is a constant multiplier for converting CO2e to carbon equivalents. 3. Results and discussion 3.1. Raw energy consumption The direct and purchased energy consumption of the ten agencies reporting both Scope 1 and Scope 2 data totaled 180,809,674 MJ (0.18 PJ), which translates into 441.7 MJ per ambulance response and 36.7 MJ per capita. Diesel fuel and gasoline accounted for 79.5% of raw energy consumption, followed by natural gas (13.3%) and

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electricity (7.2%). Extrapolating these figures to the 21 million annual ambulance responses in the U.S. (Nawar et al., 2007) and the U.S. population of approximately 309 million (U.S. Census Bureau, 2010) indicates that EMS services in that country use approximately 9.3 to 11.3 PJ of Scope 1 and Scope 2 energy each year. 3.2. Carbon emissions and marginal costs The 13,890 metric tons of CO2e emitted as a result of the Scope 1 and Scope 2 energy consumption of the ten participating agencies equates to 3788 t of elemental carbon equivalents. At the median literature-based value of $14/t, the marginal damage costs of the greenhouse gas emissions arising from these ten EMS operations would total $53,032, or 13 cents per ambulance response. At the mean literature-based value of $50/t, the marginal damage costs of emissions would be $189,400, or 46 cents per ambulance response. 3.3. The relevance of EMS to energy policy Consistent with reports from both the lay media (Anonymous, 2011; Fallon, 2011; Harlin, 2009; Odell 2011; Penner, 2008; Stock and Siceloff, 2008) and the peer-reviewed literature (Hess and Greenberg, 2011), these data highlight the energy dependence of EMS operations in North America, and particularly their dependence on fossil-based liquid fuels. Changes in either the supply or price of energy could have a measurable impact on EMS operations. Energy policy is relevant to EMS agencies. For example, without commenting on the effectiveness or appropriateness of such policies, these estimates demonstrate that efforts to reduce emissions through a ‘‘carbon tax’’ structure that economically penalizes the consumption of fossil-based energy could have unintended consequences for health services (and other public services), particularly for energy-intense services such as EMS. Such consequences should not be an argument against policy efforts to constrain emissions, but they should be identified and addressed proactively. But what do these data say about the relevance of EMS operations to energy policy? The EIA reports total energy consumption in the U.S. for 2009 at 94 EJ (or 94,000 PJ) (U.S. Energy Information Administration, 2010c); thus the estimated Scope 1 and Scope 2 energy consumption of ground EMS activities would be on the order of 1/10,000 of total national energy consumption. Yet, size is relative. The Hoover Dam on the Nevada–Arizona border produces approximately 14.4 PJ of energy each year (U.S. Department of the Interior, 2011): thus U.S. EMS activities consume the equivalent of between 65% and 78% of all of the energy produced by the Hoover Dam. The relevance of EMS operations (or any health sector activity) to energy policy is a matter of perspective: a 10% improvement in the energy efficiency of U.S. EMS operations would equate to a savings of 1/100,000 of all U.S. energy consumption; it would also approximately equate to a 6% to 8% increase in energy production from the Hoover Dam. 3.4. The relevance of EMS to environmental policy The marginal damage cost figures also seem modest from one perspective. Few patients, no matter how minor their illness or injury, would quibble over a half-dollar if it meant lifesaving care arrived at their home more quickly, or they were delivered to an emergency department even just a few minutes faster. But as with the energy consumption data, when these data are extrapolated to the whole of the U.S. they become more meaningful. At 21 million annual ambulance responses (Nawar et al., 2007) and marginal damage costs of $0.13 to $0.46 per response, the

marginal damage costs of Scope 1 and Scope 2 greenhouse gas emissions from U.S. EMS operations are likely between $2.7 million and $9.7 million every year. While $3 million, or even $10 million, is a small fraction of the $2.5 trillion in total health expenditures for the U.S. (Organization for Economic Cooperation and Development (OECD), 2010), these are not trivial amounts. As previously discussed, these marginal damage costs are likely not borne, or not entirely borne, by the patients and communities that benefit from the EMS agencies’ activities; they are likely borne by people or societies that are already economically disadvantaged, and for whom $3 million to $10 million is indeed significant.

3.5. Potential energy and emission reduction strategies for EMS systems There are a number of strategies EMS agencies could adopt to reduce their energy consumption and greenhouse gas emissions. Generally these can be described in the context of the IPAT equation; that is, addressing the amount of service delivered (production), the structure of the EMS system (affluence) and technological innovations (technology) (Waggoner and Ausubel, 2002; York et al., 2003). For example, reducing unnecessary ambulance responses and ambulance transports would be a production-related strategy. One difficulty with such a strategy is that medical necessity is not well defined (Cone et al., 2004; Hauswald and Jambrosic, 2004; Patterson et al., 2006), not easily determined by dispatch center call takers based on reports from patients, family members or bystanders (Schmidt et al., 2004; Shah et al., 2005), and often difficult to determine even for paramedics attending the patient (Brown et al., 2009; Snooks et al., 2004). Further, in most developed countries there is a societal expectation of service on demand that would need to be addressed. Limiting the use of air-medical resources, whether rotor-wing or fixed-wing, to only those situations in which they are demonstrated to provide some clinical benefit is another production-related strategy that could produce substantial reductions in energy consumption and emissions. One example of a structure-related strategy would be to change the way EMS systems respond to emergency calls. Presently, most EMS systems must meet rigid response time standards for all unscheduled calls (Bailey and Sweeney, 2003; Myers et al., 2008), which requires considerable resources in the form of vehicles, stations, and personnel. In fact, there are very few emergencies for which an association between rapid EMS response and decreased mortality has been demonstrated (Blackwell and Kaufman, 2002; Blanchard et al., 2012; De Maio, et al., 2003; Pons and Markovchick, 2002; Pons et al., 2005). In the last decade, many EMS leaders have called for a reconsideration of rapidly responding to every emergency call, citing this lack of established benefit, the financial costs of meeting response time standards, and the risk to the general public and EMS personnel from high speed driving (Eckstein, 2004; Salvucci, et al., 2004; Swor and Cone, 2002). Developing strategies and policies that support responding to specific situations in the optimal amount of time, instead of responding to all situations in a uniformly short amount of time, has the potential to greatly reduce EMS resource requirements (Eisenberg et al., 1979; Salvucci et al., 2004) and thus EMS-related energy consumption and emissions. Reducing driving speeds when transporting stable patients without life-threatening conditions to hospital might also reduce energy consumption and emissions; indeed, previous research has established that driving with the flow of traffic, without warning lights and sirens, only marginally increases ambulance transport times (Brown et al., 2000; Hunt et al., 1995).

Another strategy would be reduced ambulance idling at emergency scenes and hospitals. Many EMS agencies are reluctant to shut down ambulances at emergency scenes, as the ambulance warning lights provide some measure of safety. Reducing idling at receiving hospitals, however, might be effective in reducing energy consumption and emissions given the extended off-load times confronting many EMS systems as a result of emergency department over-crowding (Eckstein et al., 2005; Vandeventer et al., 2011). A structural aspect of EMS systems in particular need of research is the relative energy consumption and emissions profiles of fixed-station versus dynamic deployment staging strategies (the two predominant types of deployment strategy used in EMS). Fixed-station systems have the added energy burden of stationhouses and usually longer response distances; dynamic deployment systems have the added fuel consumption of ambulances being constantly relocated to street-corner posts in order to minimize response distances and response times. Technology-related strategies would include the use of hybrid vehicles for administrative and support vehicle fleets. Hybrid vehicles might also be appropriate for non-transporting emergency response vehicles. Hawkins (2008) has demonstrated that a gasoline-electric hybrid sports utility vehicle (SUV) can perform as well as a traditional heavy duty SUV as an EMS quick response vehicle. Another potential technology related strategy would be to use bio-diesel in the ambulances, although bio-fuels are not universally carbon-neutral (Searchinger et al., 2008; Solomon, 2010). That a tank of bio-diesel produces less energy (and thus, shorter travel distances) than a tank of regular diesel fuel might be an additional limitation on such a strategy, particularly in rural areas with long response and transport distances. Expanded utilization of telemedicine technology might also be able to reduce the need to transport some patients (Patterson, 2005; Smith et al., 2007a, 2007b; Yellowlees et al., 2010). There are certainly other potential strategies for reducing EMS-related energy consumption and emissions. Importantly, however, none of these proposed strategies have been empirically evaluated; their actual impact on energy consumption, emissions and patient outcomes, as well as their social acceptability, remain to be determined. It would be important to conduct a complete life cycle analysis of the effectiveness of any proposed strategies (Matthews et al., 2008). 3.6. Additional benefits of EMS sustainability efforts Whatever strategies are employed, improved EMS operational efficiency can also strategically contribute to larger sustainability efforts in health care. EMS systems are a highly visible, generally admired element of the health system. Advocacy for and efforts toward economic, environmental and social sustainability among EMS systems could positively influence the perceptions of the lay public, other health professionals and broader aspects of the health system. There are also health co-benefits to improved EMS system efficiency, which might accrue more quickly and be more compelling than less apparent, longer term economic and environmental benefits. For example, ambulance transport is not without risk (Kahn et al., 2001): reducing the need for transport also reduces the risk of crash-related injury and death.

4. Uncertainties 4.1. Uncertainty in the energy consumption and emissions estimates Our energy consumption and emissions estimates are based solely on the reported Scope 1 and Scope 2 energy consumption of

Thousand Metric Tons of CO2 e

L.H. Brown, I.E. Blanchard / Energy Policy 46 (2012) 585–593

10 9 8 7 6 5 4 3 2 1 0

589

Explicitly measured

Diesel Electricity Natural Gasoline Gas

LP

Estimated

Air Travel

CNG

Fig. 2. Scope 1 and Scope 2 emissions arising from measured and estimated energy consumption.

the participating EMS systems. Some of that energy consumption was estimated, rather than explicitly measured, and that may have resulted in overestimation or underestimation of energy consumption, emissions and marginal damage costs. Of the 13,890 metric tons of CO2e produced by the ten EMS systems, 96.5% arose from measured energy consumption, 1.8% arose from estimated energy consumption, and the nature of the source data could not be determined for 1.8% of the reported energy consumption. Fig. 2 demonstrates that the emission sources that contributed most to the Scope 1 and Scope 2 carbon footprint of these EMS agencies were also the sources with the highest proportion of explicit measurement, including diesel fuel (99.5% measured), natural gas (98.7% measured), gasoline (90.1% measured), and electricity (88.4% measured). Liquefied petroleum (LP) (31.3% measured), business air travel (23.5% measured), and compressed natural gas (CNG) (0.0% measured) were the most frequently estimated types of energy consumption, but these accounted for less than 1% of the calculated CO2e emissions. As a high proportion of the direct and purchased energy consumption data incorporated into the reported Scope 1 and Scope 2 carbon footprint of North American EMS systems were explicitly measured rather than estimated, any bias in our calculations more likely resulted in under-representation, rather than over-representation, of estimated greenhouse gas emissions. That is, the estimated data are too small a proportion of the total data to have artificially inflated the calculated carbon footprint. An analysis that incorporates a greater proportion of explicitly measured data, and more importantly an analysis that incorporates complete life cycle energy consumption and emissions, would result in even larger estimates of energy consumption, emissions and marginal damage costs associated with EMS operations. 4.2. Uncertainty in the extrapolations Our extrapolations are based on various emission measures, including the median, 25th and 75th percentiles for the data reported by our convenience sample of North American EMS systems, which might not be representative of all EMS systems in North America generally, or the U.S. specifically. Similarly, our calculations of the marginal damage costs of emissions are based on the mean and median costs that Tol (2005) estimated through meta-analysis of the peer reviewed literature. Using the 10th or 90th percentiles for per response or per capita EMS-related emissions from our original data, using the mode or the 95th percentile estimates of marginal damage costs from the peer reviewed literature, or using estimates of marginal damage costs that incorporate reports from the gray literature all produce very different emission and marginal damage costs estimates for U.S. EMS systems. These estimates range from a low of around 23 kg of CO2e per response, 126,000 t of carbon equivalents annually

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Table 1 Uncertainty of estimated annual emissions and marginal damage costs. Scope 1 & 2 emissions (kg CO2e)

Estimated annual marginal damage cost for U.S. EMS systems ($ million) Peer-reviewed literature only

Gray literature included

Measure

Per response

Per capita

Mode ($5/tC)

Median ($14/tC)

Mean ($50/tC)

90th Percentile ($125/tC)

Mode ($1.5/tC)

Mean ($93/tC)

90th Percentile ($165/tC)

10th Percentile 25th Percentile Median Mean 75th Percentile 90th Percentile

22.8 29.5 36.6 45.5 48.3 122.2

1.5 2.1 3.5 3.7 5.1 7.3

0.6–0.7 0.8–0.9 1.0–1.5 1.3–1.6 1.4–2.1 3.1–3.5

1.8 2.4–2.5 2.9–4.1 3.6–4.4 3.9–6.0 8.6–9.8

6.3–6.5 8.4–8.8 10.5–14.7 13.0–15.6 13.8–21.5 30.8–35.0

15.8–16.3 21.1–22.1 26.2–36.9 32.6–39.0 34.6–53.7 76.9–87.5

0.2 0.3 0.3–0.4 0.4–0.5 0.4–0.6 0.9–1.0

11.8–12.1 15.7–16.5 19.5–27.4 24.2–29.0 25.7–40.0 65.1–57.2

20.9–21.5 27.9–29.2 34.6–48.7 43.0–51.4 45.6–70.9 101.5–115.5

and marginal damage costs of $200,000 to a high of 122 kg CO2e per response, 700,000 t of carbon equivalents annually and marginal damage costs of $155.5 million (Table 1). The process for estimating the current value of the long-term marginal damage costs of emissions is not without controversy, so these estimates might still be under-estimates. In a more recent (but not peer-reviewed) working paper summarizing 311 published estimates, Tol (2011) reports a median estimate of the social cost of carbon of $116/t of carbon, and a 95th percentile estimate of $749/t carbon, when incorporating both peerreviewed and gray literature and using a 0% pure rate of time preference. Using that extreme estimate, the marginal damage costs from U.S. EMS activities could be as high as $7 per response, or $147 million annually.

4.3. Uncertainty about the effects of policy initiatives A final area of uncertainty is the success and final impact of any policy efforts—whether energy policy, environmental policy, or health system policy. There could be rebound or even backfire effects from strategies intended to reduce EMS system energy consumption and emissions. For example, a system responding to 60,000 calls each year that reduced driving speeds, and thus increased unit utilization time by an average of two minutes per call, would need to add at least two full-time staff, at least one additional ambulance, and all of the required medical equipment. Additionally, the health benefits of reduced EMS-related emissions might or might not offset any detrimental effects of reduced service delivery. There could, of course, also be indirect benefits from some policies: a carbon tax designed to constrain fossil fuel consumption might initially drive up energy prices, but also spur developments and innovations in alternative fuels and energy efficiency that eventually lead to a reduction in energy costs. The interactions between health systems, the environment and energy policy are both complex and dynamic, and significant research is needed to understand those relationships.

5. Summary and conclusions

marginal damage costs associated with emissions from U.S EMS operations. Although not inconsequential, estimated emissions from EMS operations are only a small fraction of the estimated emissions from the entire health care system. While any organization or sector should be encouraged to reduce its carbon footprint, EMS services do not appear to pose a significant threat to the environment when compared with other economic sectors. Still, even marginal reductions in the emissions associated with each individual EMS response, when aggregated across the millions of responses occurring each year, could result in substantial emission reductions. We estimate the marginal damage costs of Scope 1 and Scope 2 greenhouse gas emissions from U.S. EMS services likely total between $2.7 million and $9.7 million annually, but could be as high as $155 million. As these costs will likely be most heavily borne by people or societies that are already economically disadvantaged, we argue they represent a substantial burden. We are equally concerned with how mounting energy scarcity, increasing energy costs, and societal pressures to reduce emissions might actually pose a threat to EMS systems—or all health services. Our estimation of the energy intensity of North American EMS systems reveals that this is more than a theoretical concern. We have demonstrated that vehicle fuels are the primary energy burden for these agencies, but electricity and natural gas represent 20% of their energy burden. We warn that significant changes in the supply or price of any of these energy sources, including supply and price pressures resulting from policy efforts to reduce fossil-based energy consumption, could potentially affect the delivery of health services. 5.2. Future research We believe that proactively identifying the energy consumption and emissions associated with health services, including EMS, is important to both minimize the environmental impact of those services and to facilitate their adaptation to a low-carbon economy. This post hoc analysis is just one study in a series of studies intended to contribute to that process. We encourage other researchers from the health services, energy policy, environmental, and economics disciplines to consider similar efforts, and to explore opportunities for cross-disciplinary research.

5.1. Summary We have previously demonstrated that North American EMS systems emit a meaningful amount of Scope 1 and Scope 2 greenhouse gases; for the U.S., on the order of 660,000 to 1.6 million metric tons of CO2e each year. In this study, we have characterized the varied energy consumption of North American EMS agencies using a common metric, and used multipliers from a previously published meta-analysis to estimate the potential

Appendix Contributing members of the North American EMS Emissions Study Group Mark A. Barrier, AAS, NREMTP, CCEMTP, EMD (Burke County Emergency Services, Morganton, NC, USA)

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Lea H. Becker, MT (ASCP) (University of Virginia, Charlottesville, VA, USA) Ian E. Blanchard, MSc, EMT-P (Alberta Health Services Emergency Medical Services, Calgary, AB, Canada) Jane H. Brice, MD, MPH (University of North Carolina, Chapel Hill, NC, USA) Lawrence H. Brown, MPH&TM (James Cook University, Townsville QLD, Australia) Alix J. E. Carter, MD, MPH (Emergency Health Services Nova Scotia, Halifax, NS, Canada) Thomas Dobson, ACP, BA (Emergency Health Services Nova Scotia, Halifax, NS, Canada) Seth C. Hawkins, MD (Western Carolina University, Cullowhee, NC, USA) Jeff Howard, EMT-P (American Medical Response—Sonoma, Santa Rosa, CA, USA) Chris Koppenhafer (American Medical Response—Oregon, Portland, OR, USA) Randy Lauer, EMT-P (American Medical Response—Oregon, Portland, OR, USA) Brian J. Leahey, A-EMCA, PCP (F) (County of Renfrew Paramedic Service, Pembroke, ON, Canada) Lauri McFadden (American Medical Response—Alameda, San Leandro, CA, USA) Michael J. Nolan, MA, CCP (F) (County of Renfrew Paramedic Service, Pembroke, ON, Canada) Esequiel R. Ornelas, NREMTP (American Medical Response, Stanislaus County, CA, USA) Clint Osborn, MHA (Orange County Emergency Services, Hillsborough, NC, USA) Terri A Schmidt, MD, MS (Oregon Health & Science University, Portland, OR, USA) Michael Taigman (American Medical Response—Alameda, San Leandro, CA, USA) Matthew J. Trowbridge, MD, MPH (University of Virginia, Charlottesville, VA, USA) Chris Weinress (American Medical Response—Monterey, Santa Cruz, CA, USA)

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