Transportation Research Part D 12 (2007) 195–207

www.elsevier.com/locate/trd

Telecommuting and environmental policy:Lessons from the ecommute program

Peter Nelson, Elena Safirova *, Margaret Walls

Resources for the Future, 1616 P St NW, Washington, DC 20036, USA

Abstract

In 1999 the National Air Quality and Telecommuting Act established pilot telecommuting programs (ecommute) in fivemajor US metropolitan areas. The major goal of the ecommute program was to examine whether a particular type of eco-nomic incentive, tradable emissions credits from telecommuting, represents a viable strategy for reducing vehicle milestraveled and improving air quality. A context is established for evaluating whether the envisioned trading scheme repre-sents a feasible approach to reducing mobile source emissions and promoting telecommuting and a review of the limitedexperience with mobile source emissions trading programs is provided. Using two-and-one-half years of data collected inthe ecommute program, telecommuting frequency, mode choice, and emissions reductions are examined. It is found thatfrom a regulatory perspective, the most substantial drawback to such a program is its questionable environmental integ-rity, resulting from difficulties in designing sufficiently rigorous quantification protocols to accurately measure the emis-sions reductions from telecommuting. Such a program is not likely to be cost-effective because the emissions reductionsfrom a single telecommuter are very small.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Telecommuting; Emissions; Emissions trading; Mode choice; Air quality

1. Introduction

In an effort to tackle the twin problems of congestion and air pollution, many metropolitan planning orga-nizations (MPOs) in the US are searching for reliable methods of getting cars off the roads. These methods arewide-ranging and include improvements in public transit, carpooling programs, and incentives for employersto develop transportation management plans for their employees. In developing emission forecasts for theirstate implementation plans, required documents for all states with areas that do not attain national air qualitystandards, MPOs generally assume that these measures will achieve some specific reduction in emissions.However, the extent to which these demand-side management methods really work is an open question.

One of the most speculative means of achieving such reductions is via telecommuting. Many MPOs assumethat some fraction of employees in their area will work from home a certain number of days per week, thus

1361-9209/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.trd.2007.01.011

* Corresponding author.E-mail address: safirova@rff.org (E. Safirova).

196 P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207

reducing the number of work trips and amount of emissions. But accounts of the current scale of teleworknationwide are quite low, ranging from only 1.0% to 2.1% of the workforce telecommuting on a given work-day (Walls and Safirova, 2004). This calls into question the validity of MPO forecasts that sometimes havedaily estimates of work from home rising as high as 8% in the near future.1

Even though telecommuting growth may be much slower than predicted, reducing work-related travel canprovide social benefits in the form of reduced traffic congestion and emissions.2 In recognition of this, somestates and the federal government have come up with various kinds of telework incentives. In the 1980s andearly 1990s, state governments provided telecommuting education and training to companies. Also, severalstates began offering telecommuting programs for state employees that were supposed to serve as examplesto be followed by other employers in the states. Actual financial incentives for telecommuting are providedby only a few states. The Oregon Department of Energy offers tax credits to firms that employ telecommuters.Georgia, New Jersey and Virginia are following suit by also providing, or seriously considering, financialincentives to employers.3

One of the biggest efforts by the federal government came in 1999 when Congress passed the ‘‘NationalTelecommuting and Air Quality Act’’. This law initiated a 5-city pilot program to encourage telecommutingfor emissions reduction purposes. This ‘‘ecommute’’ program was to develop and evaluate methods for calcu-lating reductions in emissions due to telecommuting. Then, as a part of the pilot project, employers whoencouraged their workers to telecommute, were supposed to receive credits reflecting the amount of achievedemissions reductions. The credits could be traded with firms that needed emissions reductions for purposes ofcompliance with the US Clean Air Act. The goal of the pilot program was to evaluate whether the emissionscredit mechanism could increase the level of telecommuting.

An obvious advantage of this program compared to explicit financial incentives – tax credits, subsidies, andso forth – is that the government does not need to allocate funds to reward employers encouraging telecommut-ing; employers are supposed to be able to trade emissions credits in the private marketplace. The major purposeof the pilot program was to evaluate and to sharpen the mechanisms that could be used later on a national scale.

2. Regulatory and institutional framework for mobile source emissions trading

2.1. Introduction to emissions trading

In theory, emissions trading programs allow polluters to trade emissions allowances or permits with oneanother while meeting some type of overall industry pollution standard. In the US, two basic types of interfirmtrading regimes are currently used in air pollution policy: allowance-based cap-and-trade and emissions reduc-tion credit trading.

In a cap-and-trade system, an overall limit is placed on aggregate emissions during a specified complianceperiod. Emissions allowances that entitle the holder to emit a preset amount of a certain pollutant are allo-cated to firms, with the number of allowances set equal to the cap.4

Unlike cap-and-trade programs, in which the emissions cap establishes the level of pollution allowed to thepolluting industry, emissions reduction credit programs rely on other regulatory policies, such as rate-basedemissions standards, to establish a baseline level of pollution. Credits are generated when firms reduce emis-sions below this baseline. These credits can then be used for other compliance purposes, such as offsetting pol-lution from a new source, dealing with penalties, or avoiding requirements to install ‘‘reasonably availablecontrol technology (RACT)’’.

1 The South Coast Air Quality Management District forecasts that 7.9% of the workforce will be telecommuting each day in the year2010 (South Coast Air Quality Management District, 2003). This forecast contributes to their projected reduction in on-road emissionsfrom a variety of transportation control measures.

2 Although some literature suggested that availability of telecommuting in the long run can lead to even more congestion (Safirova,2002) in a manner similar to the ‘‘induced demand’’ argument, we do not consider this possibility.

3 University of South Florida Telecommuting Clearinghouse, www.nctr.usf.edu/clearinghouse/statestatutes.htm.4 The number of allowances equals the emissions cap if 1 allowance is equal to 1 ton of emissions, as is often the case (as in the SO2

program), but any fixed ratio between emissions and allowances is acceptable.

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The EPA has produced a series of guidance documents outlining a set of criteria that have to be met toconvert emissions reductions into credits in these programs. Specifically, the reductions must be surplus, per-manent, quantifiable, and enforceable (US EPA, 1986). In other words, the reductions must avoid doublecounting (surplus), must occur as long as they are relied on in the state implementation plans (permanent),can be easily measured (quantifiable), while violations can be identified and liability can be determined(enforceable). If these criteria are met, the emissions reductions are said to have ‘‘integrity’’ – that is, theydo not result in emissions above those that would occur in the absence of a trading program.

2.2. Emissions trading with mobile source credits

Clearly, any mobile source program falls under emissions reduction credit programs. Although most emis-sions reduction credits are created by stationary sources, trades involving mobile sources are permitted underfederal law. EPA has allowed mobile source emissions reductions to be a source of tradable credits since 1986.

Mobile emissions reductions credits (MERCs) can be generated from a range of actions, including vehiclescrappage programs, modifications of vehicle fleets (such as conversion to natural gas or other clean fuel),inspection and maintenance programs in areas where they are not required, alternative fuel programs, andauto trip reduction measures such as encouraging car-pooling and improvements in public transit.

In practice, MERC trading has been very limited. Although some states have provisions for includingmobile sources in their offset programs, only a few – Connecticut, New Jersey, Michigan, and three air districtsin California – have approved the creation of mobile emissions reduction credits. In those states, MERCtrades have accounted for a tiny fraction of the emissions trades that have taken place. For example, accordingto Haites and Haider’s (1998) review, MERCs have been involved in less than 1% of the offset trades inCalifornia.5 Several states allow credits to be created through trip reduction activities, but no MERCs haveyet been created this way.

There are several possible reasons for the limited use of MERCs. The first is the inherent difficulty of apply-ing emissions trading to a sector with large numbers of relatively low-emitting sources; the costs of monitoringand certifying individual emissions reductions may dwarf the potential value of the credits generated. Anotherimportant reason is a mismatch between the nature of mobile source credits and the needs of credit buyers. Bytheir nature, mobile source emissions reductions are temporary. For this reason, states and air districts haverequired that MERCs be considered temporary. Firms requiring emissions offsets, on the other hand, musttake steps to reduce emissions indefinitely. Finally, one more reason lies in problems of demonstrating thatMERCs meet EPA’s criteria for integrity. Of the integrity criteria, the requirements that reductions be quan-tifiable and surplus present the biggest challenge for mobile source reductions. There exists no monitoringregime for automobiles, and MERC programs frequently rely on emissions factor approaches rather thanon monitored emissions.6

Given these regulatory problems, it is not surprising that none of the cities participating in the ecommuteprogram had a mechanism in place that would allow participating firms to actually sell their emissions credits.Consequently, the ecommute program was incapable of testing the extent to which telecommuting activitymight be responsive to economic incentives arising from emissions credit trading opportunities.

3. Ecommute program

It was into this milieu that the ecommute program was launched in 1999. The original five metropolitanareas in the pilot program were Chicago, Washington, DC, Houston, Los Angeles, and Philadelphia. Chicagodropped out of the program in 2000 and was replaced by Denver. In each city, a local planning organizationwould take the lead promoting the program to businesses that would sign up and then in turn, enroll their

5 The most significant MERC project in California created offsets for PG&E National Energy Group’s Otay Mesa Generating Project inSan Diego County. The offsets were generated from the conversion of diesel-fueled trash trucks to compressed natural gas, and theconversion of ferries to clean diesel technology (Diesel Technology Forum, 2003).

6 EPA’s MOBILE emissions factor model and California’s EMFAC model are often used.

198 P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207

employees. The ecommute program commenced in June 2001 and by March 2004, 49 companies with 535employees had participated.

The collection of data on teleworking activity in the ecommute program, as well as information on othercommuting modes, was done using a web-based commute-tracking software designed specifically to track theemission benefits from telecommuting and other ‘‘green’’ commute alternatives (such as carpooling and bik-ing). Participating employees filled out a form at the end of each week indicating for each day whether or notthey went to work and their means of travel.7

Upon entering the program, each employee provided information about their vehicle and their commute.Information about the employee’s vehicle included the year, make, and model of the vehicle, the engine type,and whether or not it had passed an emissions test. Commute information included the distance of the journeyto work, as well as the time that the employee leaves home, arrives at work, and leaves work, arrives at home.The employee reported his mode choice for each day, including driving alone to work, carpooling, using pub-lic transit, walking, biking, telecommuting at home, or telecommuting at a telework center. The employee alsoreported if (s)he did not work on that particular day.8

The data gathered in the ecommute program has some distinctive features that can shed light on importantquestions concerning teleworking activity. The weekly commute log combined with the commute profileenables relatively accurate estimates of the emission benefits from telecommuting and other ‘‘green’’ commuteoptions. Because information on commute-length and vehicle type is specific to each participant, there is noneed to rely on default averages, which might overstate or understate the emissions savings from individualtelecommuting behavior. Moreover, because trip activity is recorded contemporaneously, the reported levelof telecommuting may be more accurate than estimates obtained from one-time surveys. An additional advan-tage of the data is that individuals are followed over time. The data allows for a detailed examination of howprogram participation and telecommuting behavior change over time. This dataset provides one of the fewsources of such information.

At the same time, some other aspects of Teletrips data collection leave much to be desired. Most impor-tantly, it is not a full-fledged trip diary, and therefore it does not capture the complete picture of travel behav-ior of individual telecommuters. As a consequence, it cannot address whether telecommuters drive more orless on their telecommuting days than on their commuting days.

Another factor limiting the scope of conclusions that can be drawn from the data is the self-selected natureof the sample, since both firms and workers had to demonstrate their interest in telecommuting to be included.While some degree of self-selection is present in all studies of telecommuting, unfortunately, this data set doesnot provide a suitable ‘‘control’’ group that would allow more general questions about telecommuting behav-ior to be answered.

Finally, there was no formal mechanism in place to validate the integrity of the reporting. Employees wereunder the honor system to report their telecommuting activity accurately For this program, however, the lackof monetary rewards meant the incentives for falsification were very low.

4. Program results

4.1. Number of firms, employees, drop-out rates, and telecommuting frequencies

Table 1 shows the number of companies and number of employees enrolled in the program from June 2001through February 2004. The 535 employees enrolled across all cities; this number includes all employees in thesystem as of February 2004, as well as those who were in the system at one time and then, for one reason oranother, stopped reporting.

Denver has had the most active ecommute program of the five cities, with 252 employees enrolled across 13companies. Los Angeles had as many companies signed up as Denver but far fewer employees per company.Likewise, Washington, DC, had several companies but not very many employees in each.

7 The software was developed by the Canadian firm Teletrips Inc.8 The software interface allowed only one choice of commute mode per day. In the case when two or more modes were involved, it was

ultimately up to the employee to choose the primary mode and enter it into the system.

Table 1Participation in the ecommute program, by city

Number ofcompanies

Number ofemployees

Number ofworkdays

Employee reportingratea

Employee dropoutrateb

Washington,DC

11 52 5259 72.7 71.2

Denver 13 252 49,645 76.5 62.3Houston 7 108 16,182 51.7 70.4Los Angeles 13 31 1127 82.5 41.9Philadelphia 5 92 15,072 74.0 72.8

Total 49 535 87,285 71.0 65.4

a Average number of days that employees fill out a commute log as a percent of days in the program.b Percentage of registered employees who have not reported to Teletrips in the most recent month of data, February 2004.

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On average, across all five cities, employees filled out their commute logs 71% of the time. This means that29% of the time they failed to report. This non-reporting is highest in Houston, where employees fill out theirlogs just a little over half the time, and is lowest in Los Angeles.

Because the ecommute program provided the unique opportunity to track telecommuters over time, we areable to investigate whether workers seem to continue to telecommute over a long period of time or whetherthey do so for a period of time and then stop. Obviously, this is a critical concern for any emissions tradingprogram, which must rely on permanence of the emissions reductions it achieves. The last column of Table 1shows the percentage of employees enrolled in the ecommute program over the entire program period whohave not reported to Teletrips within the last month for which we have data, February 2004. Overall, about65% of the sample have dropped out, or at least have not reported to Teletrips during that month.9 The per-centages vary across the cities from only 42% in LA, the city with the most recently enrolled companies, up to73% in Philadelphia. Another way to look at drop-outs is to compute the percentage of participants who wereoriginally signed up but who, after particular periods of time, are no longer reporting to the system. This givesus a glimpse into how participation changes over time. We find that the percentage of participants no longerreporting after three months is 21%, after six months is 32%, and after a year, 49%.

Neither these percentages nor the figures in the last column of Table 1 tell us for sure that a person hasstopped telecommuting; it is possible that (s)he is still teleworking, even though (s)he is no longer fillingout the logs.10 Nonetheless, it is a concern that the frequency percentages are so high.11

Table 2 shows the number of employees enrolled less than 3 months, 3–6 months, 6–12 months, and a yearor more. These figures are generated by counting the number of days between the first date and the last datethe employee reported to the system. Thus, an employee may have gaps in his commute log, but we do notconsider that dropping out of the program. Over half of the 535 employees in the system, 276 people, havebeen enrolled for at least a year. The numbers vary significantly across the cities, however. Denver has beenactive in registering companies and encouraging employee participation since the program began, thus 134people in Denver have been in the program a year or more. Denver also continued to sign up new participants,

9 The ecommute program did not have a well-defined procedure to terminate participation in the program. The companies did not seemto provide workers with any incentives to fill out their weekly logs and therefore it is hard to separate workers who no longertelecommuted from those who still telecommuted, but did not fill out weekly logs.10 Many people in the sample routinely fill out their commute logs but do very little telecommuting – even, in a few cases, no

telecommuting. The correlation between the days telecommuted as a proportion of either workdays or all days reported and the daysreported as a proportion of all days in the program (using first and last days reported to calculate that number) is quite low, only 0.115.This seems to suggest that employees in this program were not more likely to fill out the logs on telecommuting days than other days. Wealso cannot observe a change in telecommuting behavior over time for individual employees; this contrasts with findings in other studies,such as Mokhtarian and Meenakshisundaram (2002) and Mokhtarian et al. (2004), where telecommuting frequency declines over time.11 Varma et al. (1998) look at the duration of telecommuting for employees who use telework centers; they report that 50% of all

telecommuters quit within the first 9 months. These authors estimate a survival function with their data, an exercise we do not undertakehere because of data limitations. For one thing, companies in ecommute signed up to the program at different times. Consequently, someemployees did not have an opportunity to stay in the program for a very long time. Also, we only know about reporting to the system andnot whether someone continued to telecommute after they stopped reporting.

Table 3Average number of days employees in the ecommute program used each commute mode, as percentage of work days

Drove alonea Used public transit Teleworkedb

Washington, DC 62.3 3.4 21.9Denver 47.2 3.4 38.6Houston 63.5 1.4 17.9Los Angeles 45.2 12.5 32.4Philadelphia 33.9 11.4 50.8

All cities 49.2 4.9 35.0

a Drove alone option includes motorcycles.b Teleworked includes telework centers, as well as working at home.

Table 2Length of time employees have been enrolled in the ecommute program, by city

Number of employees enrolled

<3 months 3–6 months 6–12 months >12 months Total

Washington, DC 18 13 4 17 52Denver 63 24 31 134 252Houston 13 8 6 81 108Los Angeles 23 3 4 1 31Philadelphia 15 27 7 43 92

Total 132 75 52 276 535

200 P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207

as can been seen in the other time period categories in the table. Virtually all participants in Los Angeles, onthe other hand, have been in less than 3 months. Sixty percent of employees in the DC area have been in 6months or less.

For each day of the week, employees reported not only their telecommuting activity, but also their mode forcommuting to work when they did not telework and/or whether they did not work at all. In Table 3, we sum-marize the ecommute mode choice and telework data. We calculate the proportion of days that each employeetelecommutes, drives alone to work, and so forth, and then report averages across employees. Even thoughemployees have different numbers of days reported, we make employees comparable by looking at the propor-tion of their days in the system that they undertake each activity.12 In Table 3, we show only the drove alone,transit, and telecommuting options. On average, workers have telecommuted 35% of the days that they haveworked and reported to the system.13 The rest of the time, employees reported most often that they drovealone to work; this option accounts for 49.2% of all workdays. The figures vary in some rather surprising waysacross the cities. Los Angeles, the land of the automobile, has the highest percentage using public transit –12.5%. Houston has the lowest telework frequency of less than one day a week. That city also has the highest‘‘drove alone’’ percentage, and the lowest rate of transit use.

4.2. Estimates of emissions reductions from the ecommute program

Because participants in the ecommute program reported the age, make, and model of the vehicle in whichthey commuted each day, as well as the distance traveled to and from work, a fairly reliable estimate of the

12 In Walls and Nelson (2004), on which this article is partly based, telecommuting frequencies are calculated in two ways: one as reportedhere and the other based on simply adding up all days reported, across all employees, and dividing by number of days reported. Thesecond method treats reporting days as equivalent regardless of who is reporting, a person who has been enrolled only a few days or oneenrolled for a few years. Using this latter methodology, the percentage of workdays telecommuted was 36.4%.13 This percentage appears to be roughly in line with telecommuting frequencies reported in studies covering baseline telecommuting in

entire metropolitan areas. Safirova and Walls (2004) report 49% (excluding home-based business owners) using data from SouthernCalifornia. Computations based on Theriault et al. (2005), using data from Quebec, show a 23% frequency if full-time home-based workersare excluded from the calculation and 45% if they are included.

Table 4Distribution of one-way distance commuted by participants in the ecommute program, by city (miles)

Mean Median Std. Dev. Minimum Maximum

Washington, DC 24.1 20.0 21.2 3 115Denver 19.7 15.0 14.7 1 85Houston 22.3 16.5 19.5 1 146Los Angeles 33.6 30.0 20.6 5 80Philadelphia 25.3 20.0 19.2 1 100

All cities 22.4 17.0 17.9 1 146

P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207 201

emissions avoided by telecommuting can be computed on an individual employee basis using emissions factorsby vehicle age and type for each individual city. The emissions factors, in grams per mile, are obtained fromruns of EPA’s MOBILE6 model for four of the five cities and from runs of California’s EMFAC2002 modelfor Los Angeles. Both models have very detailed emission factors, specific to both vehicle type (passenger car,motorcycle, large and small trucks, etc.) and vehicle age. Vehicle age is a crucial piece of information for esti-mating emissions benefits from avoided trips. Because technological improvements have dramatically reducedthe emission rates of new cars over the past 20 years, the emissions savings from averting a vehicle trip of anew car are much lower than the savings of averting a trip using an old car.

Table 4 begins by showing the average one-way distances commuted from home to the office. The averageone-way distance traveled by employees in the ecommute program is just over 22 miles. Not surprisingly, LosAngeles has the highest average commute length at nearly 34 miles; Denver’s is the lowest at approximately 20miles. In each of the cities, however, the range is wide. Some employees report that they commute only a mileeach way, while the longest one-way commute is 146 miles.14 As shown in Table 4, the mean is greater than themedian in each of the cities because of these few participants with very long commutes.

The average one-way distance of 22 miles appears somewhat high. In a report on telecommuting and emis-sions trading and the potential of the ecommute program by the National Environmental Policy Institute(NEPI, 2000), estimates of average distance to and from work, net of miles traveled for non-work trips duringthe day, were given for each of the five cities. These averages are as follows: 19 miles (DC), 15 miles (Denver),27.5 (Houston), 16.5 (Los Angeles), and 12.5 (Philadelphia). With the exception of Houston, these numbersare all below the averages in the first column of Table 4. The median distances reported in Table 4 are actuallycloser to the averages reported by NEPI, at least the median distances in Denver and Washington, DC, matchup well. Again, the relatively few participants in the ecommute program who live a long way from their work-place appear to be driving up the means in Table 4.

Tables 5 and 6 unveil the distribution of vehicle types and ages among the 535 employees in the ecommuteprogram. Table 5 shows that most employees, 67%, own light-duty gasoline vehicles. The next highest percent-age is mid-size trucks at nearly 18%, followed by full-size trucks (>8500 lbs) at 7.5%. Table 6 indicates thatmost of the employees in the ecommute program own relatively new vehicles. Only 5.6% of the vehicles inuse by ecommuters are pre-1990 vehicles. Fully 41%, on the other hand, are model year 2000 or newer.The average vehicle is 5.4-years-old.15 According to the Federal Highway Administration (FHWA), the aver-age car on the road in 2000 was 9 years old, more than 31/2 years older than the vehicles owned by partici-pants in the ecommute program (US Federal Highway Administration, 2003). This is an important finding – iftelecommuters tend to own newer and thus cleaner vehicles, as suggested by this pilot program data, the emis-sions benefits of telecommuting may be lower than expected.

To generate specific emission factors for NOx and VOC for each city from MOBILE6, we use the assump-tions employed by local planners for their regulatory demonstrations. This means that temperatures, traffic

14 The distances were missing or erroneously reported as zero for a few employees in the dataset. When we had the zip codes available,which we did in most cases, we looked up on the Internet distances between zip codes.15 This an overestimate of vehicle age. The figure was calculated by subtracting the vehicle model year for each employee from 2003 and

then computing the average age. However, some participants were in the program prior to 2003 and dropped out (or stopped reporting),thus their vehicles were newer in those years than they were in 2003. We do not adjust for this.

Table 6Distribution of ages of vehicles owned by employees in ecommute program

Vehicle Model year Percentage of employees in ecommute program

1984–1989 5.61990–1994 15.31995–1999 37.82000 and 2001 27.12002 and 2003 14.0

Table 5Distribution of types of vehicles owned by employees in ecommute program

Vehicle type Percentage of employees in ecommute program

Light-duty gasoline vehicles (i.e., passenger cars) 67.5Light-duty gasoline trucks (6000–8500 lbs gross vehicle weight) 17.8Light-duty gasoline trucks (>8500 lbs gross vehicle weight) 7.5Light-duty gasoline trucks (<6000 lbs gross vehicle weight) 5.2Motorcycles 0.9Light-duty diesel vehicles (i.e., passenger cars) 0.6Light-duty diesel trucks (under 8500 lbs gross vehicle weight) 0.6

202 P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207

congestion, inspection and maintenance programs, and the like are consistent with those used by the cities forpurposes of demonstrating transportation conformity and other regulatory uses.

Two important issues emerge for calculating emissions reductions: (1) whether we assume that the employeewould have driven alone to work had they not telecommuted, and (2) whether we assume that there is anychange in non-work trips as a result of working at home. Since we do not have any travel information onthe individuals in the program other than their journeys to work, we cannot speak to (2). We simply assumethere is no change in non-work travel as a result of telecommuting.16 With respect to (1), we show emissionsresults under two scenarios. In the first scenario, we assume that everyone who telecommuted would otherwisehave driven alone to and from work in the vehicle they report that they own. The emissions reduction fromtelecommuting is then the emissions factor, in grams/mile, multiplied by the reported round-trip commutemileage from home to office.17 In the second scenario, we use the information on mode choices that employeesreport to Teletrips and adjust this figure downward. Specifically, for each employee the emissions reduction isthe emissions factor, in grams per mile, multiplied by round-trip mileage multiplied by the percentage attrib-utable to the ‘‘drove alone’’ option. This second scenario thus assumes that some of the telecommuting work-ers would otherwise have used public transit, walked or biked, or ridden in a car- or vanpool.18

Table 7 shows VOC and NOx emissions reductions for each city under the two mode choice assumptions.Emissions reductions are greater under Scenario 1 because credit is taken for the full mileage from home tooffice (or telework center to office) on every day that the employee reports that (s)he teleworks. Thus emissionsbenefits shown in the first two columns are approximately 18% greater than those shown in the latter two col-umns. Under both scenarios, Denver shows the greatest emissions benefits and Los Angeles the least. These

16 Alternatively, we could have used a relevant scaling factor from the earlier literature (Kitamura et al., 1991; Gould and Golob, 1997;Mokhtarian and Varma, 1998), but the literature disagrees on an appropriate percentage. Mokhtarian (1998) suggests that non-worktravel can be construed as constituting random noise around a zero mean.17 When the employee uses a telework center and reports the distance to the telework center, we account for that in our mileage

calculations. If the distance is unreported, which it was for approximately 50 employees, the reported home-to-office distance is multipliedby the average ratio of the home-to-telework-center distance to home-to-office distance, across employees, to obtain an estimate of thehome-to-telework center distance. This represents a slight overestimate of emission reductions because the approach does not account forthe fixed emissions associated with cold starts.18 For those few employees who report that they telecommute every day, there is no information on what their mode choice would have

been had they not telecommuted. In these cases, the average ‘‘drove-alone’’ percentage for the rest of the sample, 77% (Table 2) is used, toadjust their emissions reductions.

Table 8Emissions reductions from ecommute program, per telecommuting day June 1, 2001–February 29, 2004 (in pounds per day)

Scenario 1a Scenario 2a

VOCs NOx VOCs NOx

Washington, DC 0.139 0.126 0.116 0.108Denver 0.157 0.122 0.134 0.104Houston 0.107 0.097 0.077 0.076Los Angeles 0.123 0.062 0.100 0.048Philadelphia 0.120 0.140 0.105 0.118

All cities 0.142 0.124 0.120 0.105

a Scenarios as defined in Table 7.

Table 7Emissions reductions from ecommute program: June 1, 2001–February 29, 2004 (in pounds)

Scenario 1a Scenario 2b

VOCs NOx VOCs NOx

Washington, DC 260 237 218 203Denver 2992 2319 2539 1981Houston 316 289 228 226Los Angeles 49 24 39 19Philadelphia 907 1055 794 892

All cities 4524 3925 3818 3321

a Under Scenario 1, it is assumed that employees would have driven alone to work every telecommuting day had they not telecommuted.b Under Scenario 2, it is assumed that employees would have driven alone to work some fraction of the time based on reported mode

choices.

P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207 203

findings are primarily a result of Denver participants having been in the program longer while those from LosAngeles joined more recently. Emissions reductions across all cities since inception of the program throughFebruary 2004 approximately 2 tons each of VOC and NOx.

Table 8 provides more useful information by showing emissions reductions per telecommuting day – inother words, the numbers in Table 7 are divided by the number of person-days of telecommuting for each city.

Some interesting results show up in a comparison of Tables 7 and 8. The emissions reductions in Table 7 arestrongly determined by the overall number of participants in the program. Thus, the emissions reductions inPhiladelphia, in scenario 1, are more than 18 times the reductions in Los Angeles. On a per telecommuting daybasis, however, Table 8 shows that the two cities yield approximately the same emissions reduction. This resultis driven by the relatively high number of telecommuting days in Philadelphia, which is caused, in turn, by thefact that the average participant in the ecommute program in Philadelphia telecommutes a large percentage ofhis workdays. Denver, which has the largest emissions reductions (Table 7) because of the large number ofpeople enrolled, also has the largest reductions on a per telecommuting day basis as well (Table 8). Thisresult appears to primarily stem from emissions factors. The overall average emissions factors for VOCand NOx across all five cities are 1.308 g/mi and 1.109 g/mi, respectively. In Denver, the averages are higher:1.530 g/mi (VOC) and 1.242 g/mi (NOx). Thus getting a car off the road in Denver, for the same mileage,reduces emissions by more than in the other cities.19

In general, a combination of factors comes into play in Tables 7 and 8: number of participants in the pro-gram, number of days each participant telecommutes, emissions factors of the vehicles owned by those par-ticipants, and distances traveled to work. Although we can identify some of the more important factors, itis impossible to sort out all of the conflicting influences on emissions reductions and emission reductions

19 Because the vehicles of participants in the Denver pilot program emitted more per mile than those for the other cities, the per-mileemission reductions from the program were greater for Denver than for the other cities.

Table 9Teleworkers needed to reduce 25 tons of VOCs per yeara

Number of Teleworkers

Assuming % of days spent teleworking as in Table 2 Assuming 35% of days spent teleworking

Washington, DC 6591 4124Denver 3290 3628Houston 10,486 5363Los Angeles 5012 4640Philadelphia 3268 4744

a Using Scenario 1 – see Table 7 – and figures in Table 4 and assuming 250 workdays in a year.

204 P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207

per telecommuting day. Overall, across all the pilot programs, an average of slightly more than one tenth of apound each of VOC and NOx is reduced per day of teleworking.

We can use these figures in Table 8 to calculate the number of teleworkers that would be needed to achievea hypothetical target annual emissions reduction from telecommuting. Table 9 shows the results of this calcu-lation, assuming an annual target of 25 tons of VOCs.20 This amounts to approximately 0.1 tons per workday.As a point of comparison, the 2005 maximum allowable VOC emissions for the on-road mobile sector in thePhiladelphia metropolitan area was 70 tons per workday. This makes our hypothetical target seem small.However, in the world of transportation planning, where a fraction of a ton can determine eligibility for fed-eral transportation funding, a tenth of a ton can be significant. Results in the first column are obtained assum-ing that each worker telecommutes the percentage of days given in Table 9. These percentages range from17.9% for Houston up to over 50% for Philadelphia, or slightly less than 1 day per week up to 2 1/2 daysper week. Column 2 results are obtained using the average of 35% that holds across all cities. We also assumethat there are 250 workdays in a year and that each worker would otherwise have driven alone to work on allof the days that he telecommuted (Scenario 1 in Tables 7 and 8).

To achieve the 25-ton target, Philadelphia would need 3268 people to telecommute, assuming that each onetelecommutes, on average, 50.8% of the time (see Table 9) – i.e., about 2 1

2days per week. Denver’s number is

roughly the same – 3290 – but those people would be telecommuting only 38.6% of the time (see Table 9). If weuse the overall 5-city average of 35% for Philadelphia rather than the relatively high 50.8%, we find that manymore people are needed to reach the target, approximately 4700. In Houston, if telecommuters work at homeonly 18% of the time, as Table 9 showed, then nearly 10,500 telecommuters are needed to reach the VOC tar-get. If each telecommuter works at home 35% of the time, however, only about half as many people areneeded. The two columns of the table simply highlight the trade-off that exists, for emissions reductions pur-poses, in number of people telecommuting and the frequency with which they do so.

Using US Bureau of Labor Statistics figures on employment during the 2001–2003 period for the fivemetropolitan areas in the ecommute program, the figures in the first column of Table 9 represent the followingpercentage of (non-farm) employment: 0.24% (Washington, DC), 0.30% (Denver), 0.50% (Houston), 0.13%(Los Angeles), and 0.14% (Philadelphia).21 So assuming that each teleworker works at home anywhere fromslightly less than 1 day per week up to 2 1

2days per week and assuming the average distances commuted and

types of vehicles owned correspond to the figures in Tables 4–6 – i.e., relatively long distances and relativelyclean vehicles – between 0.14% and 0.50% of the employed non-farm workforce in these metro areas must tele-commute to generate a 25 ton per year reduction in VOC.

This does not seem like an unreasonable telework participation rate to achieve a non-trivial decrease inannual emissions. The unanswered question, however, is how permanent these emissions reductions wouldbe in comparison with alternative approaches. The ecommute program data seems to suggest that drop-outsoccur in telecommuting, thus more information is needed to see if participation can be sustained over time.

20 We cannot simultaneously target both NOx and VOC so we just look at a scenario with a VOC target. Some NOx reductions will beachieved as well.21 Customized table on non-farm employment created on BLS website. See http://www.bls.gov/sae/home.htm.

Table 10Estimated value of emissions reduction credits per teleworker per year ($)

Scenario NOx credit prices Revenue per teleworker per year

Low price High price Low revenue High revenue

NOx allowance prices 1000 7500 5.00 35.00DER prices 300 1300 1.50 6.50Mobile source reductions 7200 33,700 36.00 168.00Rideshare programs 8000 19,000 40.00 95.00RECLAIM 15,000 75.00Other telework programs 16,250 81.26

Based on assumption of 10 lbs of NOx reduced per teleworker per year.

P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207 205

5. Credit values

To calculate the emissions credit incentive from telecommuting, the calculated emissions reduction esti-mates and an estimate of the value of the credit are needed. The value will equal the opportunity cost of reduc-ing emissions by other means. Estimates of this opportunity cost for NOx reductions are taken from sixsources: current and predicted NOx allowance prices in regional cap-and-trade programs; current and histor-ical prices of discrete (one-time) emissions reduction, or DERs, in various state programs; costs of other on-road mobile source reductions such as programs targeting diesel engines; costs of MERCs used by firms tocomply with southern California’s rideshare regulations; the price of NOx allowances in California’s emissionstrading program during the California electricity crisis, which represents a likely upper bound for the value ofcredits;22 and the cost-effectiveness of other telecommuting programs estimated by some metropolitan plan-ning organizations.23

Given figures for the yearly emissions saving from telecommuting and the prices of credits from these sixsources, it is possible to estimate the revenue that a firm could generate per year per telecommuter. Table 10displays revenue estimates based on the range of NOx credit prices from the above scenarios and an emissionsreduction of 10 pounds of NOx per year per telecommuter (consistent with Table 8). Revenue can be as low as$1.50 per telecommuter per year, based on the low prices for DERs, or as high as $168 per telecommuter peryear based on some relatively high-cost mobile source reduction programs. The range of revenue is mainlybetween $40 and $95 per telecommuter per year.

The question, then, is whether the estimated emission reduction credit revenue from telecommuting isenough to induce firms to offer such programs to their employees. This is a difficult question, and the answerwill obviously vary across businesses. However, some insights were offered in discussions with business leadersparticipating in the ecommute Business Roundtable.24 These discussions produced estimates of about $500 to$1000 per teleworker per year as an amount needed to prompt firms to implement formal telework pro-grams.25 Similarly, a recently proposed telecommuting subsidy in the US would give employers a tax creditof $500 for each employee participating in an employer-sponsored telework program.26 If $500 per employeeper year is indeed the incentive necessary to get major increases in telecommuting activity, revenue from emis-sions trading is likely to fall well short of providing this incentive, even using high-end estimates for the futurevalue of credits.

The discussion here has focused on US markets for criteria pollutant credits. It can be argued that green-house gases represent a more hospitable setting for trading telecommuting credits. However, the issues of ver-ification and transaction costs still apply. Even ignoring these issues, reasonable assumptions about credit

22 The program is known as the Regional Clean Air Incentives Market, or RECLAIM, and covers all facilities in southern California thatemit at least 4 tons of NOx or SOx per year. See South Coast Air Quality Management District (2004) for more information.23 See Nelson (2004) for more details on the six hypothetical scenarios and for references on emission reduction values obtained for each.24 http://www.ecommute.net/spotlight/index.cfm?Page=1&NewsID=26353 (accessed 20.03.06).25 The discussants implied that this amount is to be provided to the firms beyond possible internal benefits from telecommuting such as

workforce retention, productivity improvements, absenteeism reduction, etc.26 Teleworking Advancement Act, S. 1856, 107th Congress, December 18, 2001.

206 P. Nelson et al. / Transportation Research Part D 12 (2007) 195–207

prices translate into just pennies per gallon of gasoline, again not likely to have significant impact on com-muter and firm behavior.

6. Conclusions

The ecommute program provides an interesting dataset on teleworking because vehicles, commute patterns,and mode choices are directly linked to the individual employee; it is not necessary to rely on default averageemissions factors and distances. Moreover, because telecommuting behavior in the program has been moni-tored over time, this is one of the few datasets that track employees weekly rather than asking them a questionin a survey once a year.

It is found that, although employees are tracked over time, many drop out or have reporting gaps, and thusthe data are not as complete as one would like. In addition, although participants in some of the cities wereenrolled for more than a year, other cities were active in signing up companies and their workers for only afraction of the study time. Nonetheless, we are able to summarize several aspects of the program, includinghow much the participants telecommuted, what transportation modes they used to get to work, how far theytraveled to and from work, what types of vehicles they own, and finally, what reductions in VOC and NOx

emissions could be attributed to the program. The emissions reductions were relatively modest – Denver,which has by far the most telecommuters of the five cities, prevented only 1.0–1.5 tons of VOCs and NOx

between June 2001 and March 2004.Mobile source emissions come from a large number of vehicles, each emitting a relatively small amount, and

as a result the costs of monitoring and verifying the emissions will cut into the efficiency benefits of an emissionstrading regime. Moreover, the US EPA requires that any emissions trades demonstrate environmental integrityby showing that the reductions are surplus, permanent, quantifiable, and enforceable. In practice, the surplusrequirement brings emissions trading of telecommuting credits into conflict with other claims on telecommut-ing, notably from local planning organizations and state environmental agencies seeking compliance in their airquality plans. Emissions reductions from telecommuting may also be considered temporary rather than perma-nent, and results from the ecommute program seem to suggest that participation may drop over time.

The results do not imply that efforts to encourage telecommuting should be abandoned. Vehicle miles trav-eled everywhere continue to rise due to demographic factors, such as population growth, rising incomes, and alarger share of licensed drivers in the population. State transportation agencies have a strong interest in delay-ing or averting costly road construction, and strategies like telecommuting may lessen the pressure for theinvestments by reducing vehicle trips. Therefore, transportation agencies and metropolitan planning organi-zations are likely to continue to pursue telecommuting, as one strategy among many, to reduce both trafficcongestion and air pollution.

Acknowledgement

This work was a part of a project funded by the US Environmental Protection Agency through a subcon-tract with the Global Environment and Technology Foundation.

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