Visualising the Environmental Effects of Working near Home: Remote Working Hubs and Co-Working Spaces in England and Wales

Abstract

Background: The pressure on the transport sector to decarbonise intensifies the need to look beyond the usual recommendations (e.g., walking, cycling, technological innovations). Therefore, strategies to avoid or modify commutes to places of work have long been seen as an option to decarbonise. Recognised for achieving an optimal balance between working from home and working in an office, co-working spaces may also minimise the length of commutes and therefore reduce emissions, traffic congestion, road maintenance, stress experienced by drivers, and other negative externalities of traffic. Methods: This study quantifies the above using a digital model of England and Wales. Two distributions of co-working spaces have been compared in this paper (i.e., one co-working space (i) in each Middle-layer Super Output Area or (ii) at the nearest train station). Results: The overall reduction in travel time and distance exceeds 70% if everyone who commutes by car outside their home MSOA drives to a co-working space. Despite a change in the place of work having no impact on the cold start emissions, substantial emission savings can still be achieved. These range from 35.8% to 92.1% depending on the pollutant, scenario, and distribution of co-working spaces.

1. Introduction

Centralised working has historically been the widely accepted standard as business enterprise gravitates towards the urbanised metropolis, satisfying the demand for qualified personnel [1]. While over time this development has offered numerous advantages, the downside has become an increasingly visible source of criticism [1]: from housing shortages in cities to crowded public transport systems compounded with rush hour commutes [1], the associated impact on the environment, and increased road maintenance [2] as well as living costs, takes its inevitable toll on people residing or working in a city. A 10 min increase in commuting time has been shown to decrease job satisfaction proportionally comparable to a 19% reduction in gross income (sample of 26,000 employees in England [3]). Considering all other negative impacts of long commutes (e.g., increased strain on mental welfare and reduced satisfaction in the quality of life [3]), it is important not only from an environmental viewpoint but for personal contentment to minimise the time spent commuting. Decentralised working is often seen as an opportunity to solve the diverse challenges of centralised working [1,4].

Teleworking and home office—at least for part of the week—has become the new norm for many working sectors and occupational profiles [5]. There is no unified definition for teleworking [6], but the term is generally understood as performing duties (i.e., employment activities) outside of the company’s main office building, such as in shared telecommuting centres, at home, or any other space, like a cafe [7]. Over the last few years, various new terminologies have emerged, such as, remote working hubs [1,8], decentralised hubs [1], neighbourhood telecommuting centres [9], local or neighbourhood work centre [10], digital work hubs [10], smart working centres [11], shared office networks [2], and smart work hubs [10]. A new variation of these centres has gained momentum, namely, co-working spaces. Emerging in the mid-2000s [4], these spaces allow freelancers and entrepreneurs to rent workplaces on a daily, weekly, or monthly basis [12] and seek to establish a broader sense of community [10]. Although the target audience for co-working spaces might differ from the typical neighbourhood telecommuting centre, both share the potential to reduce the length of commutes [10].

While co-working spaces are prevalent in urban centres, they are spilling over into suburban and rural areas [4,5,13], and contribute to the development of the smart countryside [13]. Also, remote working hubs are said to revive rural regions by bringing job opportunities [8,13] and much-needed innovation to rural regions [5]. Thus, they reduce the need for long commutes and combat the outmigration of well-paid employees [13]. Even the tiniest co-working spaces (e.g., prefabricated movable buildings for max. six people), have been praised in the academic literature, as an option to socially and economically revitalise deprived villages [14]. Co-working spaces are not only an option for rural areas but also for those so-called ‘left-behind places’ in disadvantaged communities [4].

These new forms of teleworking not only affect workflows but also change our commuting habits [15] and therefore positively influence congestion [16] and the carbon footprint [17]. This solution can therefore support the transport sector to reduce its contribution to global warming and climate change [18] and diminish the adverse health effects caused by prolonged exposure to harmful pollutants [19,20], especially for children [21]. With the increasing need for the transport sector to cut emissions [22], many academics and policymakers are looking beyond the usual strategies, such as a shift towards more climate-friendly modes of transport (i.e., walking, cycling [23]), urban planning [24], or technological innovation (e.g., electric cars [25], alternative fuels [26]). Strategies to avoid journeys [7], or to modify the temporal and spatial patterns of commutes, have long been seen as an option to decarbonise [8]. Some governments, including Ireland’s, also see the expansion of remote and homeworking as a way to reduce carbon emissions as part of their climate action plan [1]. Many academics and policymakers show a keen interest in teleworking’s potential in reducing the environmental impact of rush hour traffic [10] with its associated emissions [16] and alleviating undue pressure on the road network through changing the commute time, frequency, or length [16]. The idea of adjusting work arrangements to influence commuting patterns is not new and has been studied for decades [10]. The focus, prior to the year 2000, was mostly in a North American and European context [10], such as Henderson et al. [27]. Evidence synthesised in a 2021 literature review showed that the bulk of academic studies is still geographically centred in Europe, North America, and Australia [28]. In recent years, new scholarly works originating from Latin America or Asia have started to emerge. For example, Nakano et al. [29] explored knowledge sharing practices in co-working spaces in São Paulo, Brazil, and Luo et al. [30] investigated the role of these spaces in supporting female entrepreneurs in China.

While the influence of telecommuting on the environment has been extensively studied since the COVID-19 pandemic, the academic publications focus heavily on working from home [10], with limited consideration for neighbourhood telecommunication centres or remote working hubs [10]. The studies on co-working spaces tend to prioritise other aspects than the commuting reductions, or the associated environmental benefits [10]. Despite the lack of clear and exhaustive evidence, remote working hubs promise to offer a perfect balance between working from home and at the employer’s premises [8], by reducing commuting times while avoiding the social isolation associated with staying at home [10]. Co-working and other remote working hubs are said to integrate well with the goal of the 15 min city notion [5]. With the rise in dual-career couples, finding progression in the same geographic area for both partners remains a challenge, with some engaging in a commuter partnership [31], or they sacrifice one partner’s career [32]. Co-working spaces and remote working hubs could offer a mitigation strategy, by allowing both partners to work for their preferred employer within the vicinity of each other.

This paper contributes to the limited research on the influence of co-working spaces and other remote working hubs on the commute distance and associated environmental impacts. Many of these studies addressing this research question rely on surveying participants, and some of these are not representative of the whole population, according to the authors [8]. This study overcomes this limitation by creating a large-scale digital model of the entire landmass of England and Wales to answer the following research question:

RQ1: How does the commute change (i.e., distance, duration, emissions) if employees who drive by car choose a nearby remote working hub instead of their usual location?

While co-working spaces do exist in England and Wales, these are not yet as widespread as would be required for the purposes of this study. Therefore, the research question has been answered for two different theoretical placement strategies of co-working spaces.

The remainder of the paper is structured as follows: first, the limited background literature is presented. Next, the creation of the digital model, as well as the simulated scenarios, are explained. Then, the results are presented and discussed. Finally, the conclusions are drawn and the implications for policy and academic relevance are highlighted.

For simplicity, the term co-working spaces is used as a synonym for any type of remote working hub in this paper.

2. Background

A few authors have investigated the influence of co-working spaces and remote working hubs on the commute distance and/or emissions. Surveys or trip diaries are a common choice to answer this question, followed by simulations. One of the earliest publications on the environmental benefits of remote working hubs was Henderson et al. [27]. They evaluated the impact of centre-based telecommuting on commutes and emissions using travel diary data. The centre-based telecommuters in that study worked at the Washington State Telework Centre in North Seattle. While they observed that the number of trips did not change, it was recorded that their length reduced. They therefore concluded that even though the cold start emissions did not change, the overall pollution reduced significantly [27]. Based on a survey of 249 co-working space users in Switzerland, Ohnmacht et al. [33] concluded that a 10% emission reduction would be possible if their participants worked only at co-working spaces. Their results showed higher CO₂ emissions for those using rural co-working centres than those using urban options, due to the higher mode share for cars. The distance between the home and co-working location was not a factor in that urban/rural comparison—according to Ohnmacht et al. [33]. Caulfield et al. [8] also utilised an online survey of 514 existing remote working hub users—this time in the periphery of the city centre in Dublin, Ireland. They concluded that each year, 1.126 tonnes of CO₂ could be saved if employees worked for 3 days a week at a remote working hub. However, as they only surveyed existing co-working space users, they stated that their study was not representative of the whole population in Ireland. Vaddadi et al. [9] conducted a living lab with 67 participants. They observed minimal effects on the sustainability of the commute. Only a small subset of participants, who lived close to the neighbourhood telecommuting centre, opted for low-emission transportation modes. The majority worked from these centres as opposed to their home and therefore were not reducing any emissions. The sustainability effects were further limited by participants who usually travelled by train to their office now commuting by car to the neighbourhood telecommuting centre.

In terms of simulation studies, Mastio et al. [2] optimised the location of shared offices in the region around Toulouse, France to minimise the travel distance. They concluded that they could reduce the total car commuting distance by 23%. They used a French population census dataset that provides details of the home and work location, as well as the mode of transport used. Baynes et al. [11] created a traffic simulation of a key arterial road in Sydney to test the influence of smart work centres located in residential areas on AM peak flows. With just eight smart work centres being used by only 200 people, they already saw a reduction in morning peak travel time of one minute for all commuters on that road.

This brief literature review highlights the following gap in the academic research: solely relying on surveys of existing co-working space users is not representative of the whole population [8]. Therefore, a large-scale simulation, as developed in this study, could substantially bridge that knowledge gap.

3. Materials and Methods

3.1. Overview

This study applies a similar methodology as in Kelly et al. [1] and Schnieder [34]. Kelly et al. [1] evaluated the environmental and financial benefits as well as potential time savings of working from anywhere. Similarly to the present study, they used a dataset (in their case POWSCAR), that provides details of work and home locations. However, they only calculated the straight line-distance between both locations, and therefore, in their own words, underestimated the travel distance [1]. They also assumed an average speed for all trips (i.e., 35.3 kph), instead of calculating the travel duration for each trip specifically, as this study does. The average speed will certainly differ, as longer commutes are more likely to include distances travelled on motorways, while shorter commutes may be within the same city. Schnieder [34] created a digital model of England and Wales to compare different parcel delivery strategies, namely, carrier consolidation and alternative delivery locations (i.e., workplace instead of home). The present study uses the same population data and work/home locations as Schnieder [34] to create a digital model of England and Wales.

3.2. Population Data

The dataset titled ‘location of usual residence and place of work by method of travel to work’ was used in this study. The data were provided by the UK Data Service (https://statistics.ukdataservice.ac.uk/dataset/wu03ew-2011-msoamsoa-location-usual-residence-and-place-work-method-travel-work, accessed on 16 December 2023). It gives, for each combination of Middle Layer Super Output Areas (MSOAs), the number of people who commute between both areas, split according to the mode of transport utilised. An MSOA serves as a medium-sized geographical unit and is made up of four to five Lower Layer Super Output Areas (LSOAs). Each of the over 7000 MSOAs in England and Wales usually represents a population of between 2000 and 6000 households (https://www.ons.gov.uk/methodology/geography/ukgeographies/statisticalgeographies, accessed on 4 October 2025). The dataset from 2011 was chosen, as the share of people working from home was only 10.3% in 2011 [35], compared to the over 38% working hybrid or from home in April 2025 [36]. Using the 2011 data ensures that the model captures the geographical distribution of employees’ residences if employed in a traditional office setting, to then contrast this with a hypothetical world structured around co-working spaces. The use of workplace commuting data from the following census, conducted a decade later, is problematic in light of the disruptions caused by the COVID-19 pandemic.

Only those who drove a car were included, as shortening their commute was the main priority. Also, in a small-scale study (67 participants), Vaddadi et al. [9] observed that some participants, who usually opted for train travel to their office, commuted by car to the neighbourhood telecommuting centre. To avoid that effect, only those that currently drive themselves by car were included in this study. Those that (i) mainly work at home, (ii) mainly work at offshore installations, (iii) do not have a fixed place of work, or (iv) work outside of the UK were removed. Those who work and live in the same MSOA were excluded, as providing a co-working space to someone who already lives near their office is not sensible. The shapefile of the census boundaries (MSOA) that corresponds to the data was sourced also from the UK Data Service (https://statistics.ukdataservice.ac.uk/dataset/2011-census-geography-boundaries-middle-layer-super-output-areas-and-intermediate-zones, accessed on 16 December 2023). The data from the Humanitarian Data Exchange have been used to estimate a plausible work and home location for each car commuter (https://data.humdata.org/dataset/united-kingdom-high-resolution-population-density-maps-demographic-estimates, accessed on 16 December 2023). The population density statistics in that dataset are aggregated to arc-second blocks. After overlaying the MSOA boundaries and the population data, the following process was applied to create a dataset of the coordinates of car commuters’ homes and places of work. The algorithm follows the same process for each combination of MSOAs (i.e., all car commuters that travel between a specific combination of MSOAs). A selection of all arc-second blocks located within the home MSOA has been randomly chosen using the spatial population distribution for each arc-second block as its weight. The number of arc-second blocks was equal to the number of commuters who travel between that specific combination of MSOAs. Duplicate selection of the same arc-second block was possible. After that, the algorithm moves to the next combination of MSOAs. The same process was repeated to select one office location and one co-working space location per MSOA, also using the arc-second block population density data as a weight. The simulation was implemented in Python 3.11 using a variety of libraries, including pandas [37], geopandas [38], matplotlib [39], seaborn [40], and NumPy [41].

3.3. Location of Co-Working Spaces

While co-working spaces exist in the UK, their network has not yet expanded to the density required to be an effective network for the purpose of this study. To understand the bias caused by the placement of co-working spaces, two different strategies have been applied. First, it was assumed that each MSOA has one co-working space. Its location was randomly chosen based on the population density distribution within each MSOA (i.e., the same way the location of people’s residences was chosen (see Section 3.2)). The area covered by each MSOA varies, as their size is defined so that a similar number of people are grouped within the same MSOA. Therefore, this method is ideal to create a situation where the co-working space network density is set according to the population distribution and therefore balancing the need to offer co-working spaces in rural areas while still having enough people in its vicinity to utilise it. For the second co-working space network, the centroid for each MSOA was determined and its nearest train station (Haversine distance) was calculated using geopandas [38]. The dataset of all train stations in the UK was sourced (https://github.com/trainline-eu/stations, accessed on 8 July 2025). It may not always be possible to build railway stations at locations that service the needs of passengers best, due to constraints caused by, for example, design criteria and land availability [42]. However, train stations are also seen as a catalyst sparking economic activity in the surrounding area—when the conditions are right [43]. Castaldo et al. [44] suggested that railway stations are an attractive location for co-working spaces as they are well integrated into the public transport network. To substantiate this position, they mentioned the company Regus, which offered shared or temporary workplaces within train stations in France in 2007.

In short, the first co-working space network is guided by the population density, and therefore the network covers rural regions appropriately. In the second scenario, the co-working spaces are located at train stations in towns and cities. While the second network proposed may therefore be less favourable for rural inhabitants, it is probably a more realistic option.

3.4. Routing

The travel distances and duration were calculated using a locally hosted Open Source Routing Machine [45] and the free-flow street network from OpenStreetMap [46]. The shortest round-trip from the home to the place of work was calculated using the car profile. No trip could be calculated for 3.31 × 10⁻⁵% of the combinations of (i) co-working spaces and homes or (ii) homes and workplaces. These employees have been removed. The round-trip distance between 0.13% of the co-working spaces and homes is 0 m. This is due to (i) the randomness of both locations, (ii) the population data being aggregated to arc-second blocks, (iii) the usage of the same population distribution as a weight for home and co-working space locations, and (iv) the small size of some MSOAs combined with a high number of employees. Given that the proportion of these trips is negligible (i.e., well below 1%), the results remain essentially unchanged, regardless of whether the affected employees are included or excluded. Hence, they have not been removed.

3.5. Scenarios

For both types of co-working space locations (i.e., home MSOA and train stations), three scenarios have been calculated.

In scenario (a), those who live closer (time-wise or distance-wise) to their employer’s site than their proposed co-working space were removed. This can happen in larger home MSOAs, given that the randomly chosen office location in the neighbouring MSOA may be closer to their home than the randomly chosen co-working space location in the home MSOA, as shown in Figure 1. Obviously, the placement of co-working spaces near train stations leads to certain individuals’ residences being closer to their current workplace than their proposed co-working space/train station. Scenario (a) is designed to exclude those for whom commuting to a co-working space is not sensible.

In scenario (b), all employees who currently commute less than 5 km were also removed. While a reduction from a 5 km to a 1 km commute is—percentage-wise speaking—a major reduction, in absolute values, the savings are minimal. Considering the limited benefits for the employees, it is unlikely that a company would encourage the use of a co-working space so close to their main office. Consequently, this scenario only includes those employees for whom commuting to a co-working space would provide a tangible advantage.

Scenario (c) includes all employees who commute by car outside of their home MSOA. They are travelling to whichever office is closer (i.e., the employer’s premises or co-working space). This scenario retains all employees removed in scenarios (a) or (b). Consequently, it provides a realistic assessment of individuals’ commutes—when all employees are considered.

3.6. Emissions

Datasets from the UK National Atmospheric Emissions Inventory (NAEI) were used to estimate the emissions (https://naei.energysecurity.gov.uk/emission-factors/emission-factors-transport, accessed 28 July 2025). These have been widely used in the UK [47], for example, in [48,49]. To simplify the calculations, the ‘average road transport emission factors for UK fleet in 2023’ was used, which provides emission factors weighted for the UK fleet considering hot exhaust, cold start exhaust, and non-exhaust emissions. The emission factors are available, for example, for the UK fleet of petrol cars, the fleet of diesel light goods vehicles (LGVs) and rigid heavy goods vehicles (HGVs). To calculate the total emissions per employee, it was assumed that up to 8 km per round-trip were driven within an urban environment while the remainder—if any—is driven on rural roads or motorways (i.e., using the average of both emission factors). The 8 km stems from the conservative assumption that it would take up to 2 km one way to reach the nearest motorway/rural road from an employee’s home or work. The cold start emissions were counted twice per round-trip as the car would—most likely—be parked for a few hours at the place of work.

The CO₂e emissions were sourced from the dataset ‘UK Government GHG Conversion Factors for Company Reporting’ (https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024, accessed on 28 July 2025) published by the Department for Energy Security and Net Zero. The cold start emissions were not considered separately for CO₂e. The data for an ‘average’ petrol, diesel, hybrid, CNG, LPG, and plug-in hybrid electric vehicle, as defined in the dataset, has been used.

3.7. Sensitivity Analysis

As previously stated, the exact location of people’s homes and offices has a degree of randomness. Therefore, the process was repeated five times to assess whether the results are due to random chance or follow a consistent pattern.

3.8. Limitation

The digital model created as part of this study does not provide a fully realistic assessment of the extent to which co-working spaces can be utilised in the real world. The study merely estimates the theoretical potential of co-working spaces to reduce commuting needs. The model is a large-scale simulation using real data (e.g., population density, commutes, street network) and circumvents some of the limitations of other studies (e.g., using straight line distances and one average speed, as in [1]). Although the model is very detailed, the assumption that every car commuter could work at a co-working space is rather idealistic or aspirational. Hence, the results of this study should be seen as an encouragement to investigate how co-working spaces could be incorporated into the modern world of work and not as an easy achievable target.

Positions suitable for working from home are typically found in highly digitalised sectors and are characterised by cognitive, non-manual tasks often performed using computers [50]. Several authors have estimated the share of employees that could work from home, which may serve as an indicator of those who could work at a co-working space. Half of the US workforce worked from home during the COVID-19 pandemic [51]. A similar pattern could also be observed in, for example, Belgium, Ireland, and Italy [52]. While the share of employees who can work from home varies greatly between industries, Dingel et al. [53] estimated that 37% of jobs can be completed from home in the USA. Alipour et al. [50] estimated that 56% could work remotely in Germany, although alternative estimates in the literature range from 17% to 42%. As of April 2025, over 38% of employees are working hybrid or from home in April 2025 in the UK [36]. In short, these figures suggest that a meaningful share of employees could potentially work, at least partially, at a co-working space in England and Wales.

4. Results

4.1. Co-Working Spaces Within Home MSOAs

If the co-working spaces are located within the home MSOA, the overall time spent commuting would be reduced by between 82.0% and 83.5%, as shown in

Table 1. Reduction in the overall travelled distance and duration (co-working spaces are located within home MSOAs).

Scenario	Duration (%)	Distance (%)	Share of People Removed (%)
(a) only those living closer to the co-working space than their current place of work	83.3	90.0	4.8
(b) like (a) but those living less than 5 km from their current place of work have been removed	83.5	90.1	8.4
(c) everyone travels to the workplace that is closest to them (i.e., the current place of work or co-working space)	82.0	89.0	0.0

. These enormous reductions are caused by the close proximity of the proposed co-working spaces to people’s homes. The decrease in duration is slightly less pronounced than the reduction in the distance, owing to the significantly shorter commutes to co-working spaces taking place predominantly in an urban setting at a lower average speed.

Figure 2 illustrates the reductions in various pollutants. Both diesel and petrol are listed, as it is unknown which type of vehicle was or would be driven. The reductions vary depending on the pollutant, given that commuting to a co-working space instead of the usual office does not affect the cold start emissions. As the CO₂e and SO₂ emissions do not include any cold start component, the values are rather similar to the values in Table 1. However, they are not the same either, as the shorter travel distance to co-working spaces means that a higher proportion of the trip is driven in urban environments instead of rural roads or motorways, which changes the emission factor. In short, the values presented in Figure 2 are not proportional to the travel distances (Table 1), since cold start emissions are independent of the distance driven. For those pollutants without cold start emissions, the values still differ, as emission factors are influenced by the share of distance driven on rural roads vs. urban roads. The considerable reduction in commuting distance leads to a shift in that share, which consequently affects the emission factor.

The CO₂e emissions are reduced by around 89% to 90% when hybrid, CNG, LPG, or plug-in hybrid electric vehicles have been used. No cold start emissions or road types were considered for these vehicle types.

Figure 3 illustrates the commuting time reduction in percent for each MSOA (i.e., the results are aggregated for each home MSOA). As explained in the methodology section, an MSOA contains between 2000 and 6000 households (https://www.ons.gov.uk/methodology/geography/ukgeographies/statisticalgeographies, accessed on 4 October 2025). Accordingly, the size of an MSOA reflects the density of its population. Only those who commute to their employer’s premises within or adjacent to the M25 (London Orbital Motorway), but do not live there, were included. Some may expect the colours to resemble concentric isoclines, with areas closer to London experiencing the smallest reductions in percent, while areas further away see larger reductions. Although this assumption holds to a degree, it is apparent that a smaller MSOA attains the greatest reductions, as the commute to the co-working space is exceptionally brief. When scrutinised in detail, it is noticeable that a few MSOAs, despite being similar in size and distance to London, are depicted in slightly different colours in Figure 3. The explanation lies in the limited number of commuters to the Greater London Area from these MSOAs, which amplifies the influence of outliers. Since the exact location of the co-working spaces and homes was randomly selected according to the population distribution, the distance between both may vary, even for similarly sized MSOAs. Even if the distance to the employer’s office in London is broadly comparable, the heterogeneity of the average distance to the co-working space leads to variations in the magnitude of the percentage reductions. This issue ceases to exist the more people commute to the Greater London Area from the same MSOA, as outliers are smoothed through averaging.

Figure 4 displays the commuting distance reduction in percent for each home MSOA. Despite their considerable distance to employment hubs, rural areas tend to show lower to medium reductions. This arises from the greater separation between co-working spaces and homes in the larger rural MSOAs compared to the compact MSOAs in city centres. While this effect is solely caused by the allocation of co-working spaces, as explained before, the solution, providing the same density of co-working spaces in rural areas and in cities, is unrealistic.

Looking at an individual employee level, Figure 5 visualises distributions of the percentual reductions in terms of travelled distance and duration for each scenario. The distributions are markedly right-skewed, indicating that the majority of commuters can benefit from a substantial reduction in both distance and duration. The reduction in travel distance exhibits an even more pronounced skewness than the duration due to the reduced average speed associated with the predominantly intra-city travel to co-working spaces. The differences between each scenario are rather negligible. For example, the medians of the reductions in travel distance are 87.5%, 88.1%, and 86.4%, respectively, for each scenario. The small minority in scenario (c) that do not benefit from any reductions are those that do not live closer to their co-working space than their usual place of work, and therefore still commute to the latter (i.e., do not change their commute). The lower tail is relatively thin for all distributions, with reductions below 30% occurring for only a few commuters (i.e., 2.8% to 9.8%).

While it may be obvious, these values are not the same as in Table 1. Table 1 calculates the overall reduction in percent, while Figure 5 depicts the first quartile, median, and third quartile of the reductions (in percent) experienced by individual people.

4.2. Co-Working Spaces at Train Stations

While the overall reduction in distance and duration travelled, depicted in

Table 2. Reduction in the overall travelled distance and duration (co-working spaces are located at train stations).

Scenario	Duration (%)	Distance (%)	Share of People Removed (%)
(a) only those living closer to the co-working space than their current place of work	74.5	83.1	16.1
(b) like (a) but those living less than 5 km from their current place of work have been removed	74.6	83.2	17.8
(c) everyone travels to the workplace that is closest to them (i.e., the current place of work or co-working space)	70.0	79.5	0.0

, is certainly more than noteworthy, it is around 7 pp to 12 pp (percentage points) smaller than in the previous example, where each MSOA has its own co-working space (Table 1). Although the results for scenarios (a) and (b) remain similar to scenario (c), the divergence is more substantial compared to co-working spaces being located within MSOAs (1 to 2 pp vs. 4 to 5 pp). This is mostly caused by a much larger share of people living closer to their employer’s premises than to a train station. Around 16.1% will not benefit from co-working spaces located within or near train stations.

Figure 6 depicts the percentage decrease in selected pollutants. The results exhibit a similar trend to Figure 2, though the difference between scenario (c) and the other two is further amplified. This can be expected, as a greater portion of individuals do not live closer to a co-working space at a train station than their employer’s office. The CO₂e emissions are reduced by around 80% to 83% when hybrid, CNG, LPG, or plug-in hybrid electric vehicles have been used.

As anticipated, Figure 7 indicates somewhat marginally lower reductions compared to Figure 4. The general trend remains similar, especially in urban and metropolitan regions with a high density of train stations. Although only a limited number of predominantly rural areas in England and Wales lack train stations, these regions are somewhat visible on the maps—indicated by darker colours.

Although the general shape of the violins in Figure 8 may be similar to those in Figure 5, the first quartiles (Q1) are markedly lower, and the medians are also reduced for all three scenarios. This is especially visible for scenario (c), where the Q1 for the reduction in the travel distance is only 30.2%, compared to 68.4% in Figure 5. In short, when co-working spaces are located at train stations instead of the home MSOA, a comparably higher number of commuters will not benefit much from co-working spaces. This is partially caused by the 16.1% of the commuters who live closer to their current on-site workplace than their nearest train station. The interquartile range (IQR) is also much wider for scenario (c) than in scenarios (a) or (b).

4.3. Sensitivity Analysis

Given that there is some degree of randomness in the allocation of people’s homes and the location of co-working spaces or offices, a sensitivity analysis has been conducted. As shown in Figure 9, the differences between each round of evaluation are well below 1 pp. The large scale of the digital model used in this study mitigates the impact of any outliers. Hence, the overall results stay remarkably similar.

While the overall results (e.g., distance, duration, emissions) are stable, a small degree of sensitivity becomes apparent when the results are aggregated for each home MSOA. Based on the five simulation runs (A to E), the difference between the highest and lowest of the percentage reduction for each MSOA has been calculated. The median difference is around 3 pp, and the 99-percentile is 23 pp. In short, the digital model is robust when the results are aggregated for England and Wales. When the results are aggregated for each home MSOA, the results are slightly more sensitive to the random placement of the co-working spaces.

To assess the effect of rural and urban emission factors, a sensitivity analysis was conducted in which the maximum urban travel distance was adjusted to 4 km and 12 km per day. The baseline assumption was that each round-trip would involve a maximum of 8 km driven in urban environments, based on a 2 km distance to the nearest motorway to/from home or work. Reducing the threshold to 4 km results in changes of no more than 3 pp for any pollutant or scenario and at 12 km the largest change is 2 pp.

4.4. Further (Environmental) Considerations of Co-Working Spaces

Although co-working spaces tend to have a favourable impact on transportation systems, this positive effect may not extend to the building sector. The life cycle of office buildings—from constructing and furnishing to maintaining and operating (e.g., heating, lights, power and cooling), as well as decommissioning—is linked to substantial adverse environmental impacts [54]. As such, the building industry remains one of the largest consumers of natural resources and contributors to (hazardous) waste [55]. In the short term, the emergence of co-working spaces could lead to a net increase in office space [54]. New sites may be established in residential neighbourhoods, whereas offices at employers’ premises may remain empty pending reallocation or conversion. This spatial shift would reduce the efficiency of the existing office stock, and the presence of unused office buildings can diminish urban aesthetics. A possible solution—converting offices into residential units and vice versa—is an environmentally intensive process.

In terms of co-benefits, co-working spaces are said to act as a catalyst for local economic and cultural development while also constituting to a revitalisation of rural communities and small towns [56]. Some facilities extend their use to the broader community, offering affordable event venues or study spaces for pupils [56]. The social interaction fostered by co-working spaces is reported to spark collaborations and new business ventures [56]. However, this openness may raise concerns if employees interact and socialise with competitors.

At an individual level, co-working spaces are found to enhance people’s life satisfaction by allowing them to live in their preferred location, free from the constraint of residing near their employer’s premises [56]. This flexibility reduces the need for commuter partnerships [31] or career sacrifices within dual-income households [32]. Compared to home-based work, employees in co-working spaces report higher levels of concentration and productivity, alongside a clearer separation between professional and private lives [57].

5. Discussion

Based on a digital model of the commutes to work in England and Wales, this study estimates the potential savings (i.e., time, distance, emissions) if everyone chose to drive to a co-working space near their home instead of their employer’s premises. This study only considers those who commute by car or van to a location outside their home MSOA.

Regardless of whether co-working spaces are located at train stations or within each MSOA, the potential reductions in travel distance, time, and emissions are substantial: the overall time spent commuting by car in England and Wales can be reduced by at least 70% if everyone who usually commutes outside of their home MSOA works at a nearby co-working space instead. The duration reductions are approximately 9 pp to 12 pp higher if a co-working space is located within each MSOA instead of a train station. Given that the cold start emissions are unaffected by a change in workplace location, the potential emission reductions (i.e., petrol and diesel passenger car) vary greatly, ranging from around 40% for VOC and Benzene to around 90% for NH₃. In terms of CO₂e, the emissions would reduce by around 90% if a co-working space was located within each MSOA.

For both distributions of co-working spaces, urban and metropolitan regions benefit the most from co-working spaces. Rural areas experience less favourable results, as the distance to a co-working space would be substantially larger compared to densely populated areas. However, needless to say that the reductions are still more than noteworthy, especially when a co-working space is located within each MSOA. This study also indicates that it is not just people commuting from rural areas to their nearest city, many also commute from one urban area to another. While it is essentially irrelevant for densely populated urban regions whether a co-working space is located in each MSOA or at the nearest train station, the same cannot be said for some rural regions. In particular, those with a lack of train stations would certainly benefit from having a co-working space within each MSOA.

On an individual level, the distributions of individual travel time/distance reductions are heavily right-skewed, with the majority of people experiencing large reductions. However, when co-working spaces are located at train stations, the share of people who would not benefit from co-working spaces is 16.1%.

While there are countless possible reasons for someone to not live close to their place of work (i.e., dual-career couple, temporary job, preferences for a specific region, schools for children), co-working spaces could be a solution to allow employees to work for their preferred employer without a long commute or the need to work from home. In sum, the results of this study highlight that co-working spaces are not only a solution for rural areas but can also support those employees who commute between different cities.

The results of this study are somewhat aligned with the conclusions of previous studies that were working with participants. Henderson et al. [27], Ohnmacht et al. [33], and Caulfield et al. [8] observed some level of reductions in emissions if people used co-working spaces, albeit smaller than in this study. Hence, this study contributes to the growing body of academic evidence that can assist policymakers in evaluating both the advantages and potential risks associated with co-working spaces [58].

While it might be expected that shorter commuting distances to co-working spaces would promote active modes of transport, some authors have presented evidence contradicting this expectation (e.g., [9]). Also, the limited infrastructure for accessing rural co-working spaces via active modes of transport reduces the feasibility of cycling or using public transport, often resulting in a reliance on private cars for commuting [56]. Although public transport may be appealing for long commutes as secondary tasks can be completed while travelling, this is less applicable for short trips [54]. Furthermore, potential rebound effects should be considered, where the time saved from shorter commutes is spent on other environmentally impactful activities [54].

6. Conclusions

With centralised working becoming less predominant [5] and with a few drawbacks of working from home becoming increasingly apparent [10], remote working hubs or co-working spaces may be an alternative solution. Praised for offering the perfect balance between working from home and working in an office [8], co-working spaces may also minimise the length of commutes and therefore reduce emissions, traffic congestion, road maintenance, stress experienced by drivers, and other negative effects of traffic and driving.

This study creates a digital model of England and Wales to highlight the potential savings (i.e., time, distance, emissions) if everyone chose to work closer to their home. Utilising that digital model, the status quo of driving to the employer’s premises has been compared with the commute to two different networks of co-working spaces. When each MSOA has its own co-working space, the overall reductions in travel distance and duration can be reduced by up to 83.5% and 90.1%, respectively. Depending on the type of pollutant, the emissions would then reduce between 41.8% and 92.2%. If everyone commutes to a co-working space at their nearest train station, these reductions are lower, but still noteworthy. These reductions are relatively high, as only people who commute by car outside or of their home MSOA are considered in this study. Hence, only those who currently have a relatively long commute were included. However, this is not a limitation, as offering a co-working space to someone who already lives near their employer’s premises would not be sensible.

While it certainly will not be possible for everyone to work at a co-working space, the extreme savings illustrated in this paper indicate that moderate savings can still be achieved, even if only a fraction of employees work at co-working spaces. Therefore, this study provides strong evidence for using co-working spaces as part of a climate action plan.

Future work should focus on combining the findings of this study with people’s willingness to use more sustainable modes of transport, as well as their ability to fulfil their job role at a co-working space. It was assumed in this study that everyone will continue to drive a car, even if the commuting distances become small enough to use more sustainable modes of transport. As highlighted previously, studies have observed an increase in car mode share when commuting the short distance to co-working spaces [9] and have highlighted potential re-bound effects [54]. Future research should therefore develop practical strategies to encourage a shift towards active modes of transport, for example, by improving the accessibility of active modes of transport—especially in rural regions [56]. On the other hand, an accurate estimation of the share of people who are able and willing to work at a co-working space would further strengthen the model. While one study (i.e., [2]) already focused on identifying the optimal location of co-working spaces for one city, future research could apply this to an entire country. Subsequent studies should investigate the economic sustainability of co-working spaces, especially in rural regions with a low population density. As suggested by Hölzel et al. [58], any subsidies aimed at supporting (rural) co-working spaces should be accompanied by clearly defined objectives and key performance indicators to assess the initiative’s success.