Can we save GHG emissions by working from home?

A disease, first identified in Wuhan, the capital of China's Hubei province, in December 2019, has expanded throughout the world, which is now experiencing a pandemic, the first in over 100 years. The disease and the virus causing it are so new that they were only named by the World Health Organization (WHO) on 11 February 2020 (WHO 2020a). The disease was named COVID-19 (COronaVIrus Disease 2019), and the virus, SARS-CoV-2 (Severe Acute Respiratory Syndrome CoronaVirus 2). Although the majority of those infected with SARS-CoV-2 experience no or mild symptoms, some escalate to pneumonia, multi-organ failure, and even death (WHO 2020b). At the time of writing this paper, over 5 million deaths have been attributed to COVID-19 worldwide (Johns Hopkins University 2021).

Although in December 2020, a number of vaccines were approved and rolled out in several countries, at the beginning of the pandemic, there were no vaccines and no proven treatment either. The protective measures recommended by the WHO included (and still include) hand-washing and physical distancing (WHO 2020b). In order to facilitate this, many countries, including the UK, implemented emergency protocols, typically in the form of lockdowns. Whilst these were implemented to different degrees, they all entailed asking most of the population to stay at home, except for essential trips and work. Many countries also closed schools, colleges and universities, non-essential shops, hotels, restaurants, cinemas, theatres, and sports facilities around March to July 2020, and later again, over October 2020 to February 2021 and April 2021.

These extreme (but necessary) measures negatively impacted national economies and the global economy. Global GDP experienced negative growth of 3.2% in 2020, and the UK economy contracted by 9.8% (International Monetary Fund, IMF 2021, p. 6, table 1). The question, however, is whether any valuable lessons can be learnt from the 2020 lockdown, which essentially forced a social experiment. In particular, we concentrate on homeworking, and what impact this can have on GHG emissions from commuting trips, and GHG emissions linked to residential and non-residential energy use. Focusing on England and Wales, we estimate the changes in GHG emissions likely to occur if those who can work from home do so instead of commuting. We contribute to the literature on three fronts: (a) we estimate the GHG emissions savings that can be achieved by working from home under a range of scenarios, something that, to the best of our knowledge, has not been done for England and Wales using the lessons learnt from the long 2020 lockdown combined with data pre-pandemic; (b) we present a clear methodology for estimating GHG emissions savings, which can be used for other countries and regions; (c) we propose policy recommendations, based on our findings.

The paper proceeds as follows. Section 2 reviews the literature. Section 3 concentrates on the share of the workforce that can work from home in England and Wales. Section 4 presents the changes in GHG emissions that would result from an increase in energy consumption from dwellings and a decrease in energy consumption from non-residential buildings. Section 5 focuses on GHG emissions that a reduction in commuting trips can save. Section 6 combines and discusses the results from the previous two sections. Section 7 concludes and proposes policy recommendations.

2.1. Working from home

2.2. GHG emissions and working from home

2.3. What can we learn from the literature?

The first question we need to tackle is the potential for homeworking in the UK, or in other words, the percentage of jobs that can be carried out from home, i.e., remotely.

Based on information from the US-based O-Net classification and description of just under 1,000 occupations, Boeri et al (2020) argue that the share of jobs that can be done remotely, which are mainly service sector jobs, ranges from 32% in the UK, to 28% in France and Germany, to 24% in Italy. On the basis of surveys describing the experience of workers in the US in just under 1,000 occupations, Dingel and Neiman (2020) find that the share of jobs that can be done entirely from home in the US is 37%.

Some jobs can be done entirely from home, and some jobs can be done partly from home, with workers still needing to be physically present in the workplace one or more days a week. In 2019, before the COVID-19 pandemic, an ONS survey found that 5.1% of respondents worked mainly from home, 9.1% worked on the same grounds or buildings as their home, or used their home as a base, ⁴ 12.4% had worked from home in the week prior to being interviewed, and 26.7% had ever worked from home (ONS 2020a, table 1). These were mainly knowledge workers in managerial and/or professional occupations, who are also at the highest-earning end, suggesting that higher-paid workers tend to be more likely to work from home (ONS 2020a). This is indeed in line with findings for other countries, like those reported by Boeri et al (2020) and Dingel and Neiman (2020).

By late May 2020, two months after the lockdown had been implemented in the UK, almost 25% of employees, or 8 million jobs, had been furloughed under the Coronavirus Job Retention Scheme, ⁵ and 2 million self-employed had claimed income support under the Self-Employed Income Support Scheme ⁶ (HM Treasury 2020). This confirms that working from home is not compatible with all jobs, and some businesses cannot be run from home (such as those in the hospitality industry). In addition, many key workers, such as, for example, those working in health and social care, key public services, food and essential goods, public safety and national security, continued to physically go to work during the pandemic.

However, one striking fact is that from all adults still in employment in the UK, 49.2% were working from home between 3 and 13 April 2020 (ONS 2020b). There is a sharp contrast between 49.2% of homeworkers in April 2020 and the 26.7% of workers that reported having ever worked from home in 2019 (ONS 2020a, table 1). In 2019 there was no need to work from home, other than convenience or flexibility both for employers and employees. In 2020, under lockdown, the situation was very different.

A careful inspection of employment by occupation (with over 400 different occupations), covering the period January to December 2019, as published by ONS (2020c), allowed us to allocate the type of building workers work in (offices, educational establishments, health care settings, and other). These were matched with the percentages of workers under each main type of occupation (regularly work from home, had worked from home in the week prior to the ONS survey, or had ever worked from home) as reported by ONS (2020a, figure 4).

One point that becomes clear from the discussion above is that many workers whose jobs were compatible with homeworking pre-pandemic did not work from home. Table 1 summarises the shares of workers that work or could potentially work from home. Table 2 shows the number of people in employment in England and Wales and the number and shares of employees engaged in jobs compatible with homeworking.

There are 27.6 million households in the UK (ONS 2019a), of which 20.9 million have at least one member of working age (16–64 years) (ONS 2019b). Therefore, we assume that 75% of all households have at least one member aged between 16 and 64. Since there is approximately the same number of households as of dwellings, and 75% of households have at least one person of working age, we can assume that 75% of the dwelling stock in the UK (and in each of the four nations, including England and Wales) has one person of working age.

There are 25.8 million dwellings in England and Wales (Welsh Government 2020, Ministry of Housing, Communities and Local Government 2020). There are also 335,000 office premises and 39,000 education premises, plus another 1,055,000 non-residential buildings, excluding factories, but including shops, health centres and hospitals, hospitality venues, arts, community and leisure buildings, amongst others (Department for Business, Energy and Industrial Strategy, BEIS 2021a).

Table 4 shows the annual energy use and CO₂e emissions for offices and education premises. These act as workplaces for just over 30% of the employed population in England and Wales, as advanced in table 2. Scenarios 4 and 5 necessitate more workers to work from home. Those workers would be from various sectors, which makes it difficult to allocate them to one specific type of non-residential building (i.e., workplace). For this reason, we created an additional non-residential building, which we call 'Other', that acts as a 'representative' workplace in England and Wales. The annual energy use and CO₂e emissions for this 'Other' building category were estimated as the weighted average of the annual energy use and CO₂e emissions from all non-residential buildings ⁷ in England and Wales, excluding factories. Factories were excluded because of their energy-intensive processes, which would have skewed the annual energy use and CO₂e emissions of this 'representative' non-residential building. In addition, factory jobs are not compatible with homeworking. The numbers in table 4 for annual energy use were taken from BEIS (2021a) and only cover Natural Gas and Electricity.

The energy use data for dwellings was taken from Table U1 of the End Uses Data Tables of 'Energy consumption in the UK' (BEIS 2021b), but it is only available for the whole of the UK, so it was scaled down based on the number of dwellings in England and Wales, which is 88% of that in the UK.

4.1. Domestic energy use patterns

4.2. Scenarios and homeworking

In order to estimate CO₂e emissions from buildings under the five scenarios introduced in table 3, the total number of workers that work from home was increased from the 14% baseline to a total of 50%. We started with workers engaged in occupations compatible with teleworking (Scenarios 1 to 3) and then added workers engaged in occupations somewhat, although not necessarily fully, compatible with teleworking (Scenarios 4 and 5). Essentially, the first bunch of workers to move to telework will be office workers, as these jobs are the most amenable to working from home. There are enough office workers in England and Wales to increase the share of homeworkers to 30%. Education workers come in Scenario 4, and finally, other workers from various occupations join in Scenario 5. Scenario 5 is an unlikely scenario, but one that became a reality during the 2020 lengthy lockdown in the UK. Figure 1 illustrates the additional number of workers that work from home under each scenario.

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4.3. Change in CO₂e emissions from changes in energy consumption in dwellings

Given that, as mentioned above, 25% of households do not have any member of working age, and that 14% of workers already work from home or on the same grounds or buildings as their home, we can assume that 39% of dwellings do not change their energy consumption under any scenario. ⁸ However, these households use more energy on average than households with working-age members due to being occupied most of the time. We, therefore, have two types of households:

• (a)  
  households that do not have any member of working age or that have workers already working from home or on the same grounds or buildings as their home, which do not change under any scenario; and
• (b)  
  households with at least one member of working age that is away from home roughly 7 h per day, some of which may be affected under at least one scenario with one family member switching to teleworking.

When all households are considered together, we have a national average energy consumption, with associated national average CO₂e emissions, which we normalise at 100. Compared to this national average of 100 based on the average number of active hours in table 6 between the two groups (4,955 h), households in group (a) have a weighted average energy consumption of 116, and households in group (b) have a weighted average energy consumption of 84. These numbers were computed as follows. The energy use during sleeping hours does not change in either group and, as explained above, was assumed to be 10% of the national average energy use. The remaining 90% of the energy use is affected by the number of hours dwellings are occupied. For group (a), the number of active hours per year is higher than the average and is 118% (computed as 5840/4955). For group (b), the number of active hours per year is lower than the average and is 82% (computed as 4069/4955). These figures were used to weigh energy use during active hours for both groups, as follows:

$$\begin{eqnarray*}&&{\rm{Group}}\,\left({\rm{a}}\right){\rm{average}}\,{\rm{energy}}\,{\rm{use}}=\left(10 \% \times {\rm{national}}\,{\rm{average}}\right)+\left(90 \% \,\times \,{\rm{national}}\,{\rm{average}}\right)\times 118 \% \end{eqnarray*}$$

$$\begin{eqnarray*}&&{\rm{Group}}\,\left({\rm{b}}\right){\rm{average}}\,{\rm{energy}}\,{\rm{use}}=\left(10 \% \times {\rm{national}}\,{\rm{average}}\right)+\left(90 \% \,\times \,{\rm{national}}\,{\rm{average}}\right)\times 82 \% \end{eqnarray*}$$

The final step was to get the Energy use factor (${\boldsymbol{Fa}}$) between the average energy use per group and the national average energy use, as follows:

$$\begin{eqnarray*}&&\,{\boldsymbol{Fa}}\,{\rm{Group}}\left({\rm{a}}\right)={\rm{Group}}\left({\rm{a}}\right)\,{\rm{average}}\,{\rm{energy}}\,{\rm{use}}/{\rm{national}}\,{\rm{average}}=1.16\end{eqnarray*}$$

$$\begin{eqnarray*}&&{\boldsymbol{Fb}}\,{\rm{Group}}\left({\rm{b}}\right)={\rm{Group}}\left({\rm{b}}\right){\rm{average}}\,{\rm{energy}}\,{\rm{use}}/{\rm{national}}\,{\rm{average}}=0.84\end{eqnarray*}$$

Thus, the portion of energy consumption 'affected' or subject to change was estimated using equation (1):

$$\begin{eqnarray}&&{\boldsymbol{Ea}}={\boldsymbol{Te}}\times \left(1-\left({\boldsymbol{Fo}}+{\boldsymbol{Fh}}\right)\right)\times {\boldsymbol{Fa}}\end{eqnarray} \tag{ 1 }$$

where:

${\boldsymbol{Ea}}:$ Energy consumption affected (Natural Gas and Electricity after excluding the unaffected portions)

${\boldsymbol{Te}}:$ Total energy consumption (Natural Gas and Electricity)

${\boldsymbol{Fo}}:$ 25% of households do not have any member of working age

${\boldsymbol{Fh}}:$ 14% of workers already work from home or on the same grounds or buildings as their home

${\boldsymbol{Fa}}:$ Energy use factor for households with at least one member of working age (with ${\boldsymbol{Fa}}$ assumed equal to 0.84)

Increasing the number of workers who work from home will cause the 'away' hours to be converted into 'active' hours. The ratio of away hours to active hours per year is ${\boldsymbol{AA}}$ = 43.52%. ⁹ Also, as explained above, we assume that 90% of energy consumption occurs during active hours and 10% during sleeping hours. This is because when people are sleeping, the use of electricity is minimal (mainly baseload appliances like fridge/freezer and router), and the heating is typically switched off, although heating and hot water may be switched on just before people wake up. With that in mind, it can be assumed that 90% of gas and electricity (${\boldsymbol{Feg}}$) consumption occurs during active hours.

To estimate the change in energy consumption, we used equation (2):

$$\begin{eqnarray}&&{\boldsymbol{Ec}}={\boldsymbol{Ea}}\times \left({\boldsymbol{Feg}}\right)\times {\boldsymbol{AA}}\,\times {\boldsymbol{S}}\end{eqnarray} \tag{ 2 }$$

where:

${\boldsymbol{Ec}}:$ Estimated energy consumption change (Natural Gas and Electricity)

${\boldsymbol{Ea}}:$ Energy consumption affected (Natural Gas and Electricity after excluding the unaffected portions)

${\boldsymbol{Feg}}:$ Natural Gas use factor during active hours (0.9)

${\boldsymbol{AA}}:$ Percentage of away hours from active hours (0.4352)

${\boldsymbol{S}}:$ Scenarios (0.2, 0.25, 0.3, 0.4, 0.5)

4.4. Change in CO₂e emissions from changes in energy consumption in non-residential buildings

The number of workers in non-residential buildings progressively decreases as the number of homeworkers increases through Scenarios 1 to 5, as shown in figure 1. To estimate changes in energy consumption and CO₂e emissions, we make two important assumptions: (a) the workers that become teleworkers do so for the entire week, and (b) the 'space' previously occupied by the now homeworkers is 'closed down'. This essentially means that all office space in England and Wales is closed by the time we reach Scenario 4. Although this is an unrealistic assumption, it is a very useful one because it gives the maximum saving that can be achieved with homeworking. If this maximum is relatively low, we can safely conclude that under more realistic assumptions of some workers remaining in the office or working from the office part of the week, the savings will be lower and may not be worth the effort. Table 7 presents how non-residential buildings are progressively closed down under the five scenarios.

Table 7. Reduction in non-residential space under the five scenarios.

Scenario	Additional share of workers working from home	Total share of workers working from home	Reduction in non-residential buildings space
Baseline	0%	14 %	No change
Scenario 1	6%	20%	Office space is reduced by 23.8%
Scenario 2	11%	25%	Office space is reduced by 43.7%
Scenario 3	16%	30%	Office space is reduced by 63.5%
Scenario 4	26%	40%	Office space is reduced by 100% Education space is reduced by 12.5%
Scenario 5	36%	50%	Office space is reduced by 100%
			Education space is reduced by 100%
			'Other' representative non-residential building space is reduced by 28.3% a

^a This does not mean that 28.3% of all other residential buildings are closed. Recalling how we created the 'Other' building category, this type of building acts as a 'representative' workplace in England and Wales excluding Factories, originally hosting the additional 10% of workers who eventually switch to teleworking under Scenario 5, which is an unrealistic, non-sustainable scenario, only likely under extreme circumstances, such as a national lockdown.

To estimate the share of employers affected by working from home, we used equation (3):

$$\begin{eqnarray}&&{\boldsymbol{W}}={\boldsymbol{Te}}\times \displaystyle \frac{\left({\boldsymbol{S}}-{\boldsymbol{Fh}}\right)}{{\boldsymbol{N}}\,}\end{eqnarray} \tag{ 3 }$$

where:

${\boldsymbol{W}}:$ Percentage of workers affected by working from home

${\boldsymbol{Te}}:$ Total people in employment

${\boldsymbol{S}}:$ Scenarios (0.2, 0.25, 0.3, 0.4, 0.5)

${\boldsymbol{Fh}}:$ 0.14 workers who already work from home or on the same grounds or buildings as their home

${\boldsymbol{N}}:$ Number of workers (office, education, 'other')

The estimated energy change from non-domestic buildings due to working from home was calculated using the percentage of workers affected by working from home in each sector, as shown by equation (4):

$$\begin{eqnarray}&&{\boldsymbol{Ec}}={\boldsymbol{Te}}\times {\boldsymbol{W}}\end{eqnarray} \tag{ 4 }$$

where:

${\boldsymbol{Ec}}:$ Estimated Energy change (Natural Gas and Electricity)

${\boldsymbol{Te}}:$ Total actual energy (Natural Gas and Electricity)

${\boldsymbol{W}}:$ Percentage of workers affected by working from home

4.5. Results for dwellings and non-residential buildings

Table 8 presents GHG emissions by building type in 2019 in absolute and relative terms.

Table 8. Data on absolute and relative CO₂e emissions by building type in England and Wales in the Baseline ^a .

Building type	Total gas and electricity CO2e emissions (million tonnes of CO2e)	CO2e emissions as % of CO2e emissions from all buildings in England and Wales b	CO2e emissions as % of total source CO2e emissions in England and Wales c
Dwellings	71.2	54%	19%
Offices	7.1	5%	2%
Education	3.5	3%	1%
Other d	29.5	22%	8%
Total	111.3	85%	30%

^a Data for the Baseline are data for 2019. ^b Total CO₂e emissions from all buildings, including factories: 131.43 million tonnes. These were calculated as follows. The total energy use by the domestic sector was obtained from Table C1 of the Consumption Data Tables of 'Energy consumption in the UK' (BEIS 2021b). The total energy use by non-residential buildings was obtained from the Non-domestic National Energy Efficiency Data-Framework 2020: Supporting Data Tables (BEIS 2021a). Energy consumption was translated into CO₂e emissions using the 2019 conversion factors from BEIS (2019). These were 0.18385 kgCO₂e/kWh for natural gas and 0.2556 kgCO₂e/kWh for electricity. ^c Total CO₂e emissions from all sources: 372.55 million tonnes (Thistlethwaite et al 2021). ^d 'Other' is a 'representative non-residential building' as defined in the text.Source: As explained in the table footnotes.

Table 9 and figure 2 present the changes in CO₂e emissions for each scenario and building type. The increase in CO₂e emissions resulting from an increase in energy consumption in dwellings is offset by the reduction in CO₂e emissions resulting from a reduction in energy consumption in non-residential buildings under all five scenarios. However, the key assumption is, as already explained, that the 'space' occupied by workers who previously commuted to work is closed once they switch to homeworking.

Table 9. Estimated annual CO₂e emissions and annual change in CO₂e emissions for each building type, expressed in million tonnes, and CO₂e emissions change relative to total CO₂e emissions from all buildings and to total CO₂e emissions from all sources in England and Wales.

	Million tonnes of CO2e from energy consumption					Annual change in million tonnes of CO2e b	CO2e emissions change relative to total CO2e emissions from all buildings in England and Wales c	CO2e emissions change relative to total CO2e emissions from all sources in England and Wales d
Scenario	Domestic	Office	Education	Other a	Total
Scenario 1	72.1	5.4	3.5	29.5	110.5	−0.79	−0.61%	−0.21%
Scenario 2	72.9	4.0	3.5	29.5	109.9	−1.46	−1.11%	−0.39%
Scenario 3	73.6	2.6	3.5	29.5	109.2	−2.12	−1.62%	−0.57%
Scenario 4	75.1	0.0	3.1	29.5	107.7	−3.67	−2.79%	−0.98%
Scenario 5	76.6	0.0	0.0	21.1	97.7	−13.6	−10.35%	−3.65%

^a 'Other' is a 'representative non-residential building' as defined in the text. ^b Computed relative to the baseline of 111.34 million tonnes of CO₂e for all buildings (table 8). ^c Total CO₂e emissions from all buildings, including factories: 131.43 million tonnes (computed as explained in table 8 footnotes). ^d Total CO₂e emissions from all sources: 372.55 million tonnes (Thistlethwaite et al 2021).Source: Own calculations as explained in the text.

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4.6. Buildings kept in use and repurposing buildings

Commuting trips accounted for 14.71% of all trips made in England in 2019 (DfT 2020b, Table NTS0409a). There are no equivalent data for Wales, so we assume that the share of commuting trips in Wales was the same as in England. Table 11 shows the mode share and the number of commuting trips by mode in England and Wales. The car is the most used mode of transport for commuting trips, with 67.1% of all commuting trips being made by car in England in 2019 and 80.3% in Wales (DfT 2020c, Table TSGB0108). ¹⁰ The car is also the main contributor to GHG emissions from commuting transport. For that reason, we assumed that all those switching to homeworking under each scenario used to commute by car previously. This assumption means that the estimated CO₂e emissions savings from reduced commuting under each scenario are the maximum that could be achieved and thus represent an upper bound.

Table 12 presents the number of commuting trips made under each scenario (using table 3 as the starting point). Since the reduction in commuting trips is allocated to cars in each scenario, the number of commuting trips by all other modes remains constant, and the shares of commuting trips by car and other modes progressively decrease and increase, respectively. The share of commuting trips, which is 14.71% in the Baseline, also goes down, as we assume that all non-commuting trips remain unchanged, as shown in table 13. This assumption is later relaxed to allow for rebound effects in section 5.1.

In order to estimate the changes in CO₂e emissions, both in absolute and relative terms, data on absolute and relative CO₂e emissions from selected modes of transport in England and Wales for 2019 was needed. This data was sourced from the National Atmospheric Emissions Inventory report, hosted by BEIS and prepared by Thistlethwaite et al (2021). Table 14 shows the data of interest for the present study.

Table 15 mirrors table 14 but focuses on commuting trips only. The CO₂e emissions from commuting trips in England and Wales in the Baseline were estimated for each mode of transport using equation (5):

$$\begin{eqnarray}&&{\boldsymbol{Hc}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}={{\boldsymbol{M}}}_{{\boldsymbol{j}}}\times {\boldsymbol{C}}\times {\boldsymbol{Ha}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}\end{eqnarray} \tag{ 5 }$$

where:

${\boldsymbol{Hc}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}:$ CO₂e emissions from commuting trips for each mode, with j = Car, Motorcycle, Bus, Rail, Bicycle and Walk

${{\boldsymbol{M}}}_{{\boldsymbol{j}}}:$ share of commuting trips by each mode, with j as above (table 11)

${\boldsymbol{C}}:$ share of commuting trips relative to all trips (which is 14.7% in the Baseline, as shown in table 13)

${\boldsymbol{Ha}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}:$ CO₂e emissions from all trips for each mode in England and Wales, with j as above (table 14)

The share of CO₂e emissions from commuting trips for each mode relative to total CO₂e emissions from domestic transport in England and Wales in the Baseline was computed using equation (6):

$$\begin{eqnarray}&&{\boldsymbol{Pc}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}={{\boldsymbol{M}}}_{{\boldsymbol{j}}}\times {\boldsymbol{C}}\times {\boldsymbol{Pa}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}\end{eqnarray} \tag{ 6 }$$

where:

${\boldsymbol{Pc}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}:$ share of CO₂e emissions from commuting trips for each mode relative to total CO₂e emissions from domestic transport in England and Wales, with j = Car, Motorcycle, Bus, Rail, Bicycle and Walk

${{\boldsymbol{M}}}_{{\boldsymbol{j}}}:$ as defined for equation (5)

${\boldsymbol{C}}:$ as defined for equation (5)

${\boldsymbol{Pa}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}:$ share of CO₂e emissions from all trips for each mode relative to total CO₂e emissions from domestic transport in England and Wales, with j as above (table 14)

The share of CO₂e emissions from commuting trips for each mode relative to total CO₂e emissions from all sources in England and Wales in the Baseline was computed using equation (7):

$$\begin{eqnarray}&&{\boldsymbol{Gc}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}={{\boldsymbol{M}}}_{{\boldsymbol{j}}}\times {\boldsymbol{C}}\times {\boldsymbol{Ga}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}\end{eqnarray} \tag{ 7 }$$

where:

${\boldsymbol{Gc}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}:$ share of CO₂e emissions from commuting trips for each mode relative to total CO₂e emissions in England and Wales, with j = Car, Motorcycle, Bus, Rail, Bicycle and Walk

${{\boldsymbol{M}}}_{{\boldsymbol{j}}}:$ as defined for equation (5)

${\boldsymbol{C}}:$ as defined for equation (5)

${\boldsymbol{Ga}}{{\boldsymbol{t}}}_{{\boldsymbol{j}}}:$ share of CO₂e emissions from all trips for each mode relative to total CO₂e emissions from all sources in England and Wales, with j as above (table 14)

As stated above, we assume that all emissions savings from commuting trips result from a reduction in the number of commuting trips by car, with the number of commuting trips by all other modes staying constant. One important point for emissions savings calculations for each scenario is car occupancy. The actual number of car trips for commuting purposes is lower than the number of people commuting by car, as shown in table 11. It can therefore be reasonably assumed that under each scenario, car occupancy for commuting trips remains constant at 1.13. It can also be assumed that when the number of car trips is reduced, emissions are reduced in the same proportion. Thus, combining the number of car trips for commuting purposes (assumed equal to the number of commuting trips by car as the driver) from table 12, and the CO₂e emissions from commuting trips by car in the Baseline from table 15, the direct rule of three can be applied to estimate CO₂e emissions from commuting trips by car under each scenario. These are shown in table 16.

5.1. Rebound effects and high share of commuting trips by public transport

Table 18 combines the results presented in tables 9 and 16. There are emissions savings under all scenarios. However, these are small relative to total CO₂e emissions from all sources in England and Wales. The most plausible scenarios (Scenarios 1 to 3), which assume a share of homeworkers of 20% to 30%, yield emissions savings of only 0.38% to 1.01% relative to total annual CO₂e emissions. The extreme, unlikely scenario of 50% of homeworkers yields a more sizeable reduction of 4.64% relative to total annual CO₂e emissions.

Relative to total CO₂e emissions from the transport and building sectors combined, the savings in Scenarios 1 to 3 range from 0.61% to 1.63%. In Scenarios 4 and 5, the savings are 2.74% and 7.49%, respectively.

The problem with these results is that they rest on two unrealistic assumptions: (a) buildings not occupied by workers any longer close down, and (b) there are no rebound trips. The assumption of non-residential buildings closing down is unlikely to materialise in the real world, as most employers may choose to downsize rather than close down their premises. Even if all non-residential buildings were closed down, they would be probably repurposed. Once converted to another use, they would continue to contribute to CO₂e emissions, as discussed in section 4.6 and entertained in table 10. The assumption of no rebound trips is also unlikely to be verified in practice. As explained in sections 2 and 5.1, teleworkers may move further away from their workplace and end up travelling longer distances when they do commute. Also, these teleworkers or other family members may make new non-commuting car journeys previously not made. Examples of the potential impact of these rebound effects are presented in table 17.

Table 19 combines the results from tables 10 and 17 and shows the results when the assumptions of buildings closing down and no rebound trips are relaxed. As it can be seen, the net result is an increase rather than a decrease in CO₂e emissions. This increase, however, is always under 1% of total emissions from domestic transport and buildings, and never above 0.6% of all emissions from all sources.

Another caveat that should be borne in mind is that we assumed all suppressed commuting trips were trips made by car. If those switching to homeworking used a mode of transport other than the car before switching to homeworking, the reductions in CO₂e emissions from reduced commuting would be very small, as explored in section 5.1.

In addition, as the car fleet is progressively electrified in the UK, the emissions savings from any reduced commuting are likely to be eroded.

Using data from official government publications in the UK, we estimate the change in CO₂e emissions that would result from an increase in the number of homeworkers in England and Wales. Around 14% of those currently employed already work from home or on the same grounds or buildings as their home, and they did so pre-pandemic. We assume five different scenarios under which 20%, 25%, 30%, 40% and 50% of those employed work from home.

Emissions associated with commuting trips and energy consumption in workplaces would decrease, and emissions associated with energy consumption in dwellings would increase. Assuming former workplaces close down and there are no rebound trips, the net change would be a reduction in CO₂e emissions in England and Wales. This reduction would range from 0.61% to 7.49% relative to CO₂e emissions from the transport and building sectors and from 0.38% to 4.64% relative to CO₂e emissions from all sources. The upper end of the estimates corresponds to the extreme assumption of 50% of the workforce working from home.

The assumptions of former workplaces closing down permanently and no rebound trips are unrealistic. Allowing for repurposing of buildings and rebound trips yields an increase rather than a decrease in CO₂e emissions. The increase, however, is very small and ranges from 0.18% to 0.97% relative to emissions from the buildings and transport sectors combined and from 0.11% to 0.60% relative to emissions from all sources.

The findings of this study provide valuable benchmarks as policymakers attempt to learn lessons from the COVID-19 lockdown. As discussed in section 2, working from home increases job satisfaction, workers' well-being and organisational commitment and may also increase productivity. Downsizing can potentially save employers rent and bills. All these are good reasons to move towards homeworking for those engaged in jobs compatible with working from home. The answer to the question of whether we can save CO₂e emissions by working from home (in a new normal) is probably no. The net result is actually likely to be a small increase in CO₂e emissions. However, this increase in CO₂e emissions is likely to disappear as dwellings switch from gas to electricity for heating, in line with the Energy White Paper (BEIS 2020).

Policies such as road transport electrification, building insulation, and switching to electricity for heating are much more likely to reduce GHG emissions than working from home. Another policy that has traditionally been perceived as the cornerstone of sustainable transport has been the increase in the mode share of public transport, as explored in section 5.1. Public transport use, however, became controversial during the COVID-19 pandemic, with public transport users stating that they were not intending to return to public transport post-pandemic (Autotrader 2020), which would inevitably increase the number of journeys made by car, along with fuel consumption and GHG emissions (Crow and Milliot 2020). This poses a new challenge for public transport agencies and local government, but the problem falls outside the remit of the present study.

The findings reported here apply to England and Wales and may not be transferable to other countries, for the following reasons: (a) The share of jobs compatible with homeworking may be different, especially in developing countries; (b) The energy generation mix, which in turn determines conversion factors to translate energy consumption into GHG emissions, may be different; (c) The share of gas and electricity in energy consumption may be different; and (d) The mode share of commuting trips may be different.

What needs to be noted, however, and this applies to any country, is that (a) Working from home is mainly compatible with managerial and/or professional occupations, and so, unless a sizeable share of the labour force falls under that description, working from home may not be viable; (b) The energy mix plays an essential role in GHG emissions, and so if electricity is produced using carbon-intensive technologies, then a change in the energy mix is likely to be more effective than any shift to homeworking; (c) The higher the reliance of residential and non-residential buildings on electricity and the higher the share of clean electricity, the lower the Baseline GHG emissions from the buildings sector are likely to be; and (d) The lower the share of the car as a commuting mode, the less likely that reduced commuting will result in any significant reduction in GHG emissions.

The authors are grateful to Philip Steadman, Alex Summerfield, Ian Hamilton, Rob Liddiard and Daniel Godoy Shimizu for clarifications on energy efficiency, energy consumption and CO₂e emissions from buildings, and to Rodrigo Santos and María Emma Santos for help with the transport emissions calculations. All errors are the authors' own.

This work was supported by the Engineering and Physical Sciences Research Council [EP/S032053/1].

All data that support the findings of this study are secondary data available from the sources we cite within the article.