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The Impact of Road Infrastructure Development Projects on Local Communities in Peri-Urban Areas: the Case of Kisumu, Kenya and Accra, Ghana

• Original Research Article
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• Published: 22 September 2020
• Volume 4 , pages 33–53, ( 2021 )

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• Risper Sarah Khanani   ORCID: orcid.org/0000-0002-9999-2411 1 ,
• Emmanuel Junior Adugbila   ORCID: orcid.org/0000-0003-1768-8078 1 ,
• Javier A. Martinez   ORCID: orcid.org/0000-0001-9634-3849 1 &
• Karin Pfeffer   ORCID: orcid.org/0000-0002-6080-1323 1  

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Large-scale spatial planning and urban development projects have gained popularity in cities of the Global South. Such projects are being used to improve connectivity, scale up cities’ competitiveness, and in return, attract investments. However, while road development changes peri-urban environments in the Global South cities, little attention is given to the consequences of road infrastructure in those areas. The objective of this paper is to investigate how the implementation of road infrastructure projects is transforming the socio-spatial landscapes and economic development, and how they affect social groups within the peri-urban areas of Kisumu (Kenya) and Accra (Ghana) cities, focusing on effects at the community level. The research employed a case study approach, using qualitative, quantitative and spatial methods to examine these socio-spatial and economic development dynamics. The findings show that, on the one hand, road infrastructure projects scaled up residential development, both in Kisumu and Accra, as the roads contributed to housing rents and land prices to increase and rendered peri-urban communities along them as attractive zones for real estate developers. Furthermore, accessibility to facilities and services improved. Also, in both cities, the road improvements fuelled employment opportunities. Conversely, in both cities, the road infrastructure projects led to gentrification and therefore to the displacement of poor residents into the hinterlands, which changed the social fibre and integration to a certain degree. The road infrastructure projects benefitted the rich, who own land at the expense of the poor. The findings that the impacts of road infrastructure appear to differ in locational context and class of individuals within peri-urban areas make us suggest that place-based and people-based policies need to be combined to address the consequences of road infrastructure projects.

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Introduction

Large-scale spatial planning and urban development projects are increasingly becoming popular in transforming landscapes in rapidly urbanizing cities of the Global South. Most governments in the Global South are using such projects to scale up their cities’ competitiveness, requiring structuring and re-structuring of their road networks, and in return, attract investments. These large-scale projects, for instance road infrastructure projects, touch on multiple stakeholders, possibly leading to all kinds of changes in adjacent areas (Erkul et al. 2016 ; Oviedo Hernandez and Dávila 2016 ). However, the attempt of scaling- up the city and making it competitive sometimes fails to consider the needs of the affected citizens during the infrastructural development (Brussel et al. 2019 ; Aoun 2016 ). Construction of roads, whether new or upgrading of existing ones, is presumed to have a range of impacts on the population, urban form, economic status and environment (Mackett and Edwards 1998 ). The impacts may be both positive and negative depending on the situated social, spatial, economic and environmental context.

Studies have shown that due to availability of land at low cost and nearness to jobs in cities, peri-urban areas have become the destination for infrastructural development (Ravetz et al. 2013 ; Simon et al. 2004 ; Thuo 2010 ). Such infrastructural development has been found to improve mobility enhancing accessibility to jobs, social facilities and services such as schools and hospitals as well as an increased market for agricultural goods and services (Pradhan and Bagchi 2013 ; Gibson and Rozelle 2003 ). It has also been presumed that new roads lead to improved living conditions of those living near such projects through possibilities for social development, economic opportunities and enhancement of the welfare of communities. They were also seen to have the power to promote interactions among residents, especially those who are engaged in commerce (Doan and Oduro 2012 ). However, road infrastructure projects are also endowed with negative socio-economic changes in certain communities or for specific social groups, increasing income disparity as they offer limited benefits to the poor or further disintegrating the urban form into urban fragments (Porter 2011 ; Wiegand et al. 2017 ). This suggests that road infrastructure projects tend to benefit the rich, who own land and are further able to access additional land due to increase in land values, at the expense of the poor (Porter 2011 ; Leinbach 2000 ).

Additionally, road infrastructure projects also tend to lead to active land-use changes in many Global North and South cities, specifically, the breaking up of the urban environment into smaller areas with limited or no interaction between them, and with no relationship that permits cohesion and coherence (Balbo and Navez-Bouchanine 1995 ; Landman 2011 ; Al Shawish 2015 ; Bocarejo et al. 2015 ). Therefore, both social and spatial differentiations among social groups may emerge following road developments. This is likely to aggravate segregation, gentrification and polarisation enhancing existing inequalities (Manderscheid and Bergman 2008 ; Bocarejo et al. 2015 ). Road improvements in peri-urban areas of most Global South cities are also seen as a recipe for the displacement of the poor due to gentrification processes (Briggs and Mwamfupe 2000 ). Empirical evidence shows gentrification in the Global South as direct and indirect displacement of low-income groups by the rich as a result of an increase in property rentals and urbanization (Kombe 2005 ; Davidson and Lees 2005 ; López-Morales 2015 ; Krijnen 2018 ).

Studies on peri-urban areas have given little attention to the consequences of road infrastructure projects in local communities of Global South countries. Road infrastructure projects in such countries are mainly critiqued for their impacts on urban sustainability at the city level (Kennedy 2015 ). Thus, there is a need for further empirical studies on socio-economic and spatial ramifications of road infrastructural expansions in peri-urban areas (Doan and Oduro 2012 ), and especially at the community level. Therefore, the main question addressed in this paper is how road infrastructure projects shape the urban fabric and associated processes, and how they affect social groups within the peri-urban areas of Kisumu (Kenya) and Accra (Ghana) cities, focusing on effects at the community level. By looking at three dimensions derived from the literature related to the urban fabric and its associated processes such as social changes, spatial (residential) dynamics and economic processes for different social groups, the paper provides empirical insights on potential consequences of road infrastructure projects in these two Global South cities. Global South countries in this paper refer to low to middle-income countries.

In the subsequent sections, the paper first provides historical background on transport infrastructures and describes the theories on which the conceptual framework is anchored. This is followed by the methodology, case study areas, results and discussion of the results. In the conclusion section, this paper summarizes the main impacts of road infrastructure projects in transforming the socio-spatial landscapes and economic development and their effects on local communities in the peri-urban areas and reflects on how empirical insights can inform policy formulation.

Background and Related Work

Historical background.

Road infrastructure projects have massive investments since they have been used to achieve economic prosperity through haulage of goods and services from one place to another (Jedwab and Moradi 2016 ). In Sub-Sahara Africa, road infrastructure presently remains the means of conveying about 75% of freights and passengers (Beuran et al. 2015 ). Considering that about 50% of the roads in the Sub-Sahara region are yet to be constructed implies that road infrastructure development remains on the top list of physical infrastructure developments in such cities, potentially impacting the socio-economic and physical environment of the cities and their peri-urban areas (Gachassin et al. 2010 ; Cobbinah et al. 2015 ).

From a historical point of view of the Global South countries, especially Kenya and Ghana, transportation infrastructures show some contrasting outcomes though with a few similar scenarios. In both countries, road infrastructure projects are on the increase resulting in social and spatial heterogeneities, particularly within the peri-urban areas. These include growing inequalities, exclusion, housing, safety and security (UN-Habitat 2016 ). This has also led to competition between newcomers and old inhabitants for land and other natural resources, thus affecting living conditions, especially of the very poor (Yankson and Gough 1999 ; ASIRT 2014 ).

According to Jedwab et al. ( 2017 ), the Kenya – Uganda, railroad, “the lunatic line”, was built between 1896 and 1901 to connect landlocked and relatively prosperous Uganda to the port of Mombasa in Kenya. Kenya was a transit territory and the railroad followed the route that minimized construction costs. Thus, the railroad traversed sparsely settled areas that had no freight to transport. However, plots near the railroad were offered to European settlers to create an agricultural export industry. This also established the general urban patterns in Kenya, where urban centres sprouted along the main railroad route. The contemporary spatial arrangement of economic activity depends on these earlier infrastructure investments (Jedwab et al. 2017 ). The Kenya-Uganda railroad collapsed between the 1960s and 1970s and paved way to other forms of transport like road infrastructure especially away from the railroad (Jedwab et al. 2017 ). In Kenya, a contemporary emphasis has been given to the implementation of the Northern Corridor Transport Improvement Project (NCTIP). The transport corridor aims at connecting Kenya nationally and transnationally. This would also correct the region’s history of socio-economic marginalization that was experienced during colonialism.

In Ghana, before the 1920s, the railroad was the primary mode of transportation of goods by the colonial authorities from the hinterlands to the coastal zones. Road infrastructure became the first complementary transport mode in the 1920s serving as feeders to the railway system (Jedwab and Moradi 2016 ). Road infrastructure came to the fore with the implementation of the Tarmet Program to aid in the transportation of cocoa for export. The British colonial administration consolidated its dominance in trade and military through transportation infrastructures. The aim was to link the hinterlands of the country to the coastal areas, to move goods and to quickly dispatch troops to its resistance areas (Jedwab and Moradi 2016 ). Post-colonial governments have continued to invest in road infrastructure to further open up the country, such as the Accra-Kasoa road that links Greater Accra and Central regions to boost trading activities by reducing travel time (Jedwab and Moradi 2016 ). Colonialists planned railroads in the two cities to link the hinterlands to the coastal areas. In Kenya, the railroad was majorly for transportation of goods and raw materials but in Ghana it was substantially used to transport troops to quash local resistance.

Related Work / Conceptual Approach

In order to answer the empirical question of how road infrastructure shapes the urban fabric and the associated processes, and how it impacts on different social groups at the community level in Kisumu and Accra, the study employed a conceptual approach based on analytical categories of social, spatial and economic aspects (Fig.  1 ). This conceptual framework is anchored in theories of urbanization and the theory of socio-spatial integration, building on arguments by Bodo ( 2019 ), Woltjer ( 2014 ), Ruiz-Tagle ( 2013 ) and Allen ( 2003 ). According to Woltjer ( 2014 ), peri-urban areas have been characterized by their changing local economic and employment patterns from rising land values and dynamic land uses. These kinds of changes have been observed in many peri-urban and urban areas of Global South countries resulting from rapid urbanization, and they may be positive or negative (Bodo 2019 ; Woltjer 2014 ). The spatial changes that emerge during urbanization encompass an increase in residential development, land cover conversions or changes in land uses. Social changes likely to emerge include social disintegration. Economic ones include a shift from agriculturally-based economic activities to those of higher productivity and infrastructures like roads, with the possibility of employment opportunities (Allen 2003 ; Woltjer 2014 ). Ruiz-Tagle ( 2013 ) argued that socio-spatial integration is characterized by four dimensions, namely physical, functional, relational and symbolic which are about physical proximity, access to opportunities, social groups’ interaction and identity. The dimensions are likely to help in enhancing the integration of residents between and within communities. Accordingly, in this study we considered social, spatial and economic aspects of development.

Conceptual approach (Source: Authors’ construct, 2019, based on literature review)

Based on the arguments from the literature on these theories, we developed a conceptual framework where certain dimensions, relevant for the context of our case studies, and their relations are depicted (see Fig. 1 ). The dimensions were used to measure the possible impacts of road infrastructure projects in the studied communities which may inform community wellbeing of different social groups.

Methods and Materials Used

The paper uses a mixed-methods and a comparative case study approach. It integrates qualitative, quantitative and spatial methods where the data is collected and analyzed in a concurrent way to reach completeness and a more comprehensive account (Bryman 2006 ). We selected two case studies (Kisumu in Kenya and Accra in Ghana) based on their geographic locations regarding road infrastructure projects and contrasting characteristics concerning social, spatial and economic dynamics. In Kisumu city, Kenya, the study analyzed Tom Mboya and Obunga communities, adjacent to a bypass road which was the main focus for the assessment of interaction among residents and subjective quality of life, Footnote 1 reflecting on their satisfaction with domains in life and experiences within the socio-economic and physical environment that they live in. In Accra city, Ghana, the study focused on Tuba and Mataheko communities lying opposite each other. The analytical dimensions and associated indicators are presented in Table 1 .

The primary data collection took place between September and November 2018 and included Key informant Interviews (KIIs), Focus Group Discussions (FGDs), questionnaires, walking interviews, and field observations. Nine KIIs were conducted in Kisumu and seven in Accra, to obtain opinions from academia, residents, County, government ministry officials, experts and agencies on road infrastructure and residential development. Additionally, in Kisumu city, eight walking interviews with residents (four men and four women selected randomly) were conducted to assist in an in-depth understanding of the perceived change in the studied communities. In contrast, in Accra city, one FGD was held in each community comprising members who had lived in the communities and witnessed the road construction considering the road was constructed over ten years. The FGDs (six men and four women) were selected by contacting the assembly members Footnote 2 of the communities to reach out to opinion and community leaders who had adequate knowledge about the Accra-Kasoa road expansion. Questionnaires were administered to household heads, above 18 years, both men and women who had lived in the communities for at least five years or longer during the road construction, for the Kisumu case, and at least ten years or longer for the Accra case (as the road projects started in 2013 and 2008 respectively).

Random sampling was used to select questionnaire respondents. In Kisumu, respondents were selected from each administrative unit for Obunga and each estate for Tom Mboya community. A total of 239 questionnaires were administered (111 in Tom Mboya, 128 in Obunga). In Accra, a total of 310 questionnaires were administered in the household survey, 155 respondents each in Tuba and Mataheko to obtain their perceptions of the road expansion on their livelihoods. The household questionnaires were collected using Open Data Kit (ODK) Collect for Kisumu case and KoBoCollect for Accra case. The mobile applications choice was based on ease of use and the familiarisation by the data collectors. The variables adopted for analyzing the residents’ perceptions were selected based on the local conditions of the studied communities. This study used household heads as a unit of data collection and community as a unit of analysis in determining interaction/integration, accessibility to facilities, land-use changes, employment opportunities and land value dynamics between different social groups within the studied communities (Table 2 ).

The secondary sources were aerial images/satellite imagery (sourced from google maps, 2018 (Kisumu and Accra)), population data (from Kenya National Bureau of Statistics and Pamoja Trust (Kisumu) and Ghana Statistical Service (GSS)), road data (shapefiles from Kisumu Physical Planning office and Accra Planning Authority), and boundary data of the communities (shapefile derived from ArcGIS online for both Kisumu and Accra) as well as grey-literature and reports from relevant departments (for literature review). The aerial images were used to classify the studied communities as “better-off” and “worse-off” in terms of the spatial characteristics with field validation in both cities and to analyze the two moments, “before and after” of the road projects.

Data were analyzed concurrently employing qualitative, quantitative and spatial tools to arrive at a complete and comprehensive understanding of the impacts of road expansion in the local communities (Bryman 2012 ). The qualitative data were coded and analyzed using ATLAS.ti software to understand the social and economic impacts which may have emerged in the communities over time due to the road projects. The quantitative data were analyzed using descriptive statistics in Software Package for Social Science (SPSS), for better understanding of the perceived impacts. The spatial data analysis was done in ArcGIS for visualization (maps) of physical land-use changes which had emerged overtime on studied communities as identified through the visual image interpretation, interviews and field observations.

Case Study Areas

Rapid urbanization in Kenya has made Kisumu city to continue attracting investments and large-scale projects, with the most recent ones being road infrastructure projects to ease transport. In Ghana, Accra city is transforming in terms of spatial and socio-economic dynamics. The Kisumu road infrastructure project (also known as Nyamasaria-Airport Bypass, constructed between 2013 and 2016) is a sub-section of the Northern Corridor Transport Improvement Project (NCTIP), an international road to transit goods being implemented in Kenya (Fig.  2a ). The NCTIP is a mega road project that links the Democratic Republic of Congo, Burundi, Rwanda and Uganda, and starts from Mombasa in Kenya and covers more than 2000 km (Gichaga 2017 ). In the case of Accra, the road infrastructure is a toll road highway expanded in 2008 by the Government of Ghana with donor financial support to ease traffic from Greater Accra to Central regions (Fig. 2b ) and to facilitate the movement of goods and people between the regions and beyond as it also connects Ghana to its neighbouring country, Ivory Coast (Ardayfio-Schandorf et al. 2012 ) .

a Contextual location of Kisumu communities. b Contextual location of Accra communities. Source: World ocean base & Google Earth, 2018 and Author, 2018. Boundary data: ArcGIS online (Kisumu ward boundaries, 2016)

When the railway line arrived in Kisumu in 1901 many people migrated into the town. In 1908, Kisumu was struck by the bubonic plague, which resulted in the zoning of residential areas (CRDC 2016 ). According to Anderson ( 2016 ), the British colonial township board zoned the residential areas of the city into blocks; European and Asians were zoned in one block, which included Tom Mboya and Milimani communities. The other blocks were left for Africans and Arabs, and they included Obunga and Nyalenda, among others. The social divisions of the city during colonialism were reflected in the racial location of residential quarters and this is still evident in some estates.

Accra became an urban centre in 1877 when the British colonial administration relocated their capital from Cape Coast to Accra. This was due to varied reasons, including health-related and geographical factors. Cape Coast was perceived to have native-born diseases which could affect colonial staff and had a shallow ocean berth hindering vessels used for transporting raw materials for export to Europe and beyond from docking (Grant and Yankson 2003 ). Since then, Accra grew to be an economic hub and a cosmopolitan city attracting a fishing community to the cosmopolitan type, which later saw infrastructural growth and expansion over its peri-urban areas (Grant and Yankson 2003 ). This physical growth of Accra has led to the emergence of peri-urban towns like Dodowa, Aburi and Kasoa, which are functionally connected to Accra Central infrastructurally.

The two cases studied four communities with different degree of living conditions (quality of life). For the case of Kisumu, one of the communities is Tom Mboya (planned Footnote 3 ) and the other is Obunga (unplanned) (See Fig. 2a ). Considering the prevailing socio-economic and physical conditions, Tom Mboya and Obunga can also be categorized as a “better-off” and a “worse-off” Footnote 4 community respectively. In the case of Accra, both communities, Tuba and Mataheko, are unplanned, since they are in peri-urban Accra, in a transition zone that does not have planning schemes (Fig. 2b ) and they are also categorized as a “better-off” and a “worse-off” community respectively. The social, spatial and economic dimensions identified from the literature were used in measuring the impacts of the road infrastructure on different social groups across the communities in both cases.

Characteristics of the Respondents

The study comprised of 549 respondents, of which 239 were from Kisumu, Kenya while 310 were from Accra, Ghana. For the Kisumu case, most of the respondents in the better-off community (Tom Mboya) were men (53%). Contrary, most of the respondents in the worse-off community (Obunga) were women (60%). For the Accra case, 52% of the respondents in the better-off community (Tuba) were men while in the worse-off community (Mataheko), 57% were women (See Table 3 ).

In Obunga community, 58% of the respondents had attained primary school level of education. However, for Tom Mboya community, those with university level of education registered the highest percentage (32%). In Accra city, 41% and 39% of the respondents in Tuba and Mataheko had education up to secondary and post-secondary level respectively, 39% in Tuba and 41% of them in Mataheko had primary level of education and the same percentage (20%) of the respondents in Tuba and Mataheko with the highest education level of post-graduate degree (Table 3 ). The analysis gives a general overview of the literacy levels of the residents in both communities, which surmises that the studied communities likely had lower to middle –income residents. Considering age cohorts, the majority of the respondents in Kisumu were between 35 and 54 years (61%) in Tom Mboya, and between 18 and 34 years (56%) in Obunga. While in Accra, majority of the respondents were between 35 and 54 years in both communities (65% in Tuba and 73% in Mataheko).

Impacts of Road Infrastructure Development Projects on Selected Communities in Kisumu and Accra

This paper premises its results on the before and after situation concerning social, spatial and economic impacts of the road infrastructure projects between and within the studied communities. The identified potential impacts were grouped into positive and negative impacts, depending on how beneficial they were to the interaction and integration processes, and subjective quality of life of the residents in the studied communities as summarised in Table 5 .

Social Impact

The road infrastructure projects have different impacts on social integration in the two cities. The dimension of social impact dwelt on social interaction and integration between residents and within communities, being measured by social networks and residents’ perception. Social networks in the selected communities of Kisumu city entailed respondents feeling at home in their neighbourhoods, receiving support from friends, relatives and neighbours (Table 1 ). In Kisumu, the road enhanced social interactions amongst residents of the researched communities and opened up connectivity between and within communities as also expressed by an interviewee from Maseno University in Kisumu: “ … the Bypass has also led to the opening up of some streets within Obunga like Pamba road which again encourages some residents from Tom Mboya to go through Obunga and join Kakamega road.” However, in Accra, residents in the studied communities perceived a decline in social integration due to the springing up of gated communities replacing non-gated ones after the road expansion. A member of an FGD in Mataheko in Accra city mentioned that: “ Before the road was expanded we could organize outdoor games like football games with other communities and we play together and go our various ways without any troubles. After this road expansion, all the residential buildings are self-contained types which keep households from interacting easily with one another as it was the case before the expansion with the predominantly compound types Footnote 5 of buildings”. Some residents within Mataheko perceive that with the road expansion a lot of middle-income people came to settle in the community because of reduction in traffic to and out of the community to Accra central, resulting in less interaction.

Additionally, in both Kisumu communities, insecurity reported beneath overpasses of the bypass road to non-motorized users especially at night, may be attributed to the bypass design and absence of lights beneath the overpasses as stated by resident’s association official from Obunga: “… the bypass road has also contributed to insecurity, there is a flyover (overpass) where thieves and muggers hide at night and attack people. The place is a bit dark at night, so thieves take advantage” (Table 5 ) . Similarly, in Tuba and Mataheko in Accra, residents perceived that the road project led to an increase in crime rate. Activities such as pickpocketing surfaced in Tuba where most of the rich live. Although in Mataheko, cases of snatching of bags from women by criminals on motorbikes were also reported. A member of the FGD in Mataheko in Accra city expressed that: “ Women in this community had the bags snatched by criminals on motorbikes after the road expansion but before the road expansion this was not present in our community like that. Before the expansion of the road, these kinds of things were not present in this town”. FGDs in Tuba and Mataheko alluded to these criminal activities being present in their communities after the road expansion.

Spatial Impact

The spatial dynamics which emerged in the studied communities in Kisumu and Accra due to the expansion of the roads as expressed in the interviews of experts and agencies included: high degree of residential development, changes in access to facilities and services, change in land uses, and informal developments. Interviews and FGDs in Kisumu and Accra cities revealed that the road expansion led primarily to improved access to facilities like education and health facilities, recreational areas, religious and cultural institutions, water and electricity. Respondents in Tom Mboya and Obunga in Kisumu perceived low accessibility to facilities before, and high accessibility after road expansion as shown through mean scores in Table 4 , for instance, high accessibility to education facilities after road expansion, with mean scores of 3.59 and 3.78 respectively. The increase in perceived accessibility after road expansion was registered across the five dimensions in both communities in Kisumu (Table 4 ). Most of the interviews agreed with the perception of the respondents about improved accessibility in both communities with an interviewee from Maseno University in Kisumu stating that; “The opening up of the Bypass has promoted accessibility in that you can easily go through Obunga or Tom Mboya and access the various activity zones that one may want to visit in that area. So, we can say that the Bypass has promoted accessibility…”.

Similarly, respondents in Tuba and Mataheko in Accra city also perceived high access to primary schools after the road expansion, with mean scores of 4.16 and 4.57 respectively (Table 4 ). The Kisumu bypass improved not only accessibility to basic services and facilities within both communities, but also opened up their connectivity to other neighbouring communities, even beyond those that were studied.

The Accra city road project turned the small town of Kasoa into a cosmopolitan town “overnight” as it served as an impetus for the influx of both high and middle-income people into the peri-urban communities, and this changed the urban form of residential development patterns. According to Ardayfio-Schandorf et al. ( 2012 ), and as also given by the interviewee from the University of Ghana, “…in 1970 and 1984, the population of Kasoa was 863 and 2,597 respectively, but it reached 34,719 in 2000 and 69,834 in 2010”. This meant that Kasoa was growing leaps and bands and the road project was considered one of the reasons; however, this may be due to other reasons such as population growth. Interviews and FGDs pointed to an upscale and improvement of the physical development of the studied communities after the road expansion.

It was observed that changes were on-going within the residential communities; both social and physical changes were visible, after the road expansion in Kisumu city. In Tom Mboya, demand for land increased, leading to changes in residential densities and sub-divisions of land parcels. An emerging trend was also that some residents were leasing out their residential houses for commercial use and relocated to peri-urban areas (Fig.  3a ). According to the Kisumu city Physical Planner, “… these areas were originally zoned for single-family residential, low density as per the 1984 Structure Plan of the Kisumu city, the changes may be partly attributed to road project. The scarcity for office space within the Central Business District (CBD) largely influenced the leasing out of the residential houses for commercial use which include; hotels, health facilities, learning institutions and offices for Non-Governmental Organizations”. Similarly, the quality of housing in Obunga community in Kisumu city was also changing with middle standard buildings springing up as was observed during the walking interviews (Fig. 3b ) .

a Change in land use in Tom Mboya community from residential (left) to commercial-hotel (right). b Change in quality of houses in Obunga community, Kisumu. Source: Authors, 2018, based on field observation

In Accra city the conversion of land uses emerged in the studied communities after the road project. Interviews and FGDs revealed that agricultural lands were converted into residential and commercial uses over time. Residential uses, mainly those closer to the road, were converted into commercial uses such as stores, shops, stalls and fuel stations among others by investors. Survey respondents, FGD participants and interviewees all stressed that the road drove in more residents from Accra central into the studied communities. After the road expansion, some residents from Accra central started moving into the communities to live there. Hence some parcels of farmlands were being acquired and converted into residential uses by real estate developers (Fig.  4 ). The Accra city road project encouraged the emergence of gated and informal settlements as revealed during the FGDs and key informant interviews.

Farmlands (left) transformed into residential uses (right) in Mataheko after road expansion (Source: Google Earth, 2008 and 2018)

Economic Impact

The Kisumu and Accra cities road infrastructure projects brought employment opportunities to residents of the studied communities. In Kisumu, not only did businesses improve after the implementation of the bypass road but also the operation of motorcycles to ferry people from one place to another increased. Small-scale businesses like welding, vegetable vending, among others, sprung up along the Bypass. In Accra, women in the communities gained employment through the sale of food to road construction workers not only during the road construction but even after, some of them are earning income for their livelihoods through small businesses by the roadside. According to the Physical City Planner of Kasoa in Accra city, confirms that “… the road expansion served as a means for movement of goods and services and security within the town, it therefore brought direct and indirect employable avenues to the residents of the studied communities ”.

Contrary, the road projects also led to the displacement of some residents in the studied communities. The Kisumu bypass interfered with residents’ businesses that were being operated along the road reserve and displaced people during the construction phase, especially in Obunga community. An interview with one of the Obunga residents confirmed an increase in house rents and property values following the bypass construction (Fig. 3b ). Further, a replacement with housing typical of higher income groups than those present in the area was observed. This led to the displacement of some of the low-income residents and adversely affected the already established social networks. A resident in Obunga confirmed this; “…The construction of the Bypass has influenced housing. The property value of plots in Obunga has risen because people are seeing the opportunities now and if you check on the designs and quality of the upcoming houses, they are different from what used to be associated with Obunga. People doing this may not be necessarily the Obunga residents …” However, in Tom Mboya community, the increase in property value was realized to have a positive impact because the residents were leasing out their dwellings for commercial use to earn cash. Therefore, the impacts were twofold, on the one hand, the residents could sell and lease land, but on the other, people were being displaced.

In peri-urban Accra, after the road expansion, the property market became vibrant with rents increasing beyond the reach of some residents forcing them to migrate to interior parts. Sale of land or property rentals within the communities was subjected to the market forces, causing the displacement of the poor as property owners were asking higher rents. Also, the chiefs as the traditional authority and original landholders in the communities took advantage of the high land prices to confiscate land parcels granted to the peasant farmers to sell to property developers. Although this benefitted the local authority through tax payment from business setups by the rich in the communities, it affected to some extent the social composition within the communities as the poor were displaced by the rich. Kisumu bypass in the initial development plan of the city existed as a narrow road and so people settled on the road reserves, subdivided land and erected business premises. This revealed that people had encroached on the land earmarked for the road development and this brought conflict during implementation as confirmed by the Obunga residents’ association official: “There was conflict between people and the government because people were not paid before construction started. Again there was conflict between people themselves, those who bought land didn’t transfer land ownership, this delayed compensation process and some were compensated and never relocated.”

With the road expansion, travel time to and from Accra central was reduced. Hence, Kasoa became the residential destination for most high and middle-income earners, and this led to land management problems due to increase in demand for land with several multiple sales of land by some landowners which are hindering development in the area negatively (Table 5 ). Key informants highlighted that communities along the expanded Accra-Kasoa road were inundated with land disputes over plots of land which adversely scared away potential developers and investors due to activities of land guards.

To understand how road infrastructure projects impact peri-urban areas in Global South cities, this paper relates its findings from the four studied communities in Kisumu and Accra cities to the existing body of literature’s perspectives on road infrastructure impacts. The paper has analyzed the impacts of road infrastructure projects in the four communities in the peri-urban areas of both cities, by comparing changes and processes due to the projects concerning social, spatial and economic aspects.

The findings on social interaction measured by social networks of residents varied across the two cities. Findings from Tom Mboya and Obunga communities of Kisumu revealed that intra-neighbourhood interactions improved after road expansion. This aligns with the findings of Lusher et al. ( 2010 ); Hoogerbrugge and Burger ( 2018 ) and Farahani ( 2016 ) on social networks which are fond of augmenting interaction between residents and making one feel at home. In contrast, in Accra, residents in the studied communities perceived a decline in the social integration due to the springing up of gated communities replacing non-gated ones.

In both Kisumu and Accra, improved accessibility to basic facilities and services after implementation of the road projects was noted in the studied communities. In Kisumu, residents of Tom Mboya and Obunga experienced improved access to facilities and services, which may be due to enhanced intra neighbourhood accessibility to facilities, partly due to implementation of the Bypass. In Accra, the impact was mainly an increase in access to potable water due to the laying of water pipelines and drilling of about 20 boreholes in and around Accra. This conforms with what is found in other studies on roads as a means of enhancing socio-economic needs of societies by providing access to basic facilities and services (Pradhan and Bagchi 2013 ; Gibson and Rozelle 2003 ).

The findings on the spatial aspect underscore the fact that peri-urban towns in the Global South have become the destination for residential developments, this has been observed to be due to road developments and serves as a vehicle for the increase in land consumption mainly for residential development (Ravetz et al. 2013 ; Simon et al. 2004 ; Woltjer 2014 ) as noted explicitly in Tuba and Mataheko in Accra city. Such road developments are changing the urban forms of communities near the roads in a distinct mosaic way. In Tom Mboya in Kisumu, the Bypass led to increase in residential densities and conversion of residential to commercial use, whereas in Obunga, improved quality of houses was noted as middle-income residents moved in because of the Bypass, which may be attributed to gentrification. Just as highlighted by Davidson and Lees ( 2005 ) on their writing on contemporary gentrification in the Global South, our cases show characteristics of gentrification to include direct and indirect displacement of low-income groups, and social upgrading of locale by incoming high-income groups.

Concerning the economic aspects, this study found that the Kisumu and Accra cities road infrastructure projects led to employment opportunities for residents. The study revealed that more job opportunities were noted during and after the construction phase of the road projects. The Kisumu bypass sparked off the use of motorcycles as commercial transport services and increased commerce, while in Accra, small-scale businesses often operated by female residents along the road improved. However, across the four communities, poor residents were displaced due to an increase in land prices and house rents.

Gentrification in the Global South often happens due to the displacement of low-income people (poor residents) by the rich with an increase in the rental market forces and urbanization caused by infrastructure developments (López-Morales 2015 ; Krijnen 2018 ; Briggs and Mwamfupe 2000 ). Also our study reveals unregulated conversion of land uses from residential areas to commercial uses, especially in Tom Mboya in Kisumu city. In Accra, however, agricultural lands were converted to residential uses causing the displacement of some households who depended on farming and this adversely affected integration in the studied communities, after the expansion of the roads. Besides, the study revealed the disruption of businesses operating along the road reserve during its construction phase in Kisumu.

To summarise, considering the analysis of the impacts accruing from the sub-sections of the Kisumu and Accra road infrastructure projects in their peri-urban areas, both positive and negative impacts were noted along the social, spatial and economic dimensions. Findings of the four studied communities indicated that road infrastructure projects led to improved access to facilities and services. Positive effects of the roads on the socio-economic aspects included more job opportunities and improved commerce in all the communities. Also, reduction in travel time in the four communities, with improved connectivity to other communities was observed only in the two communities in Kisumu, but not those in Accra. The road infrastructure projects also brought with them adverse effects. Displacement and insecurity were felt across the four communities; in Tom Mboya and Obunga in Kisumu, specific sections of the overpasses posed a danger to residents especially at night. While in Accra, robbery and pickpocketing were being experienced by residents of Tuba and Mataheko.

The study aimed to investigate how road infrastructure projects impact peri-urban areas in Kisumu, Kenya and Accra, Ghana. In order to do so, it operationalized its main objective across the studied communities in both cases. It produced empirical findings and conclusions on how road infrastructure projects change the urban fabric and associated processes and how they impact different social groups of peri-urban areas in the Global South. As also found in earlier studies, this study has shown that road infrastructure projects come with both positive and negative impacts on different social groups where they are being implemented. In both cities, the completion of the road projects created some common developments in the studied communities: improvement in accessibility, increase in commerce, more employment opportunities and reduction in travel time. However, the road projects brought out some contrasting developments across them as well. The Kisumu road project led to economic and spatial dynamics in Obunga, including disruption of business activities and displacement of people on the road reserves during the construction phase. The Accra road project caused spatial and social impacts, including informal settlements and gated communities to emerge and led to a loss of farmlands in Tuba and Mataheko and also negatively affected integration. The study reveals that road infrastructure projects led to changes in the social fabric, economic processes and spatial differences, meaning that road infrastructure projects have a range of impacts on both the population and urban fabric.

The study has certain limitations. The cases investigated two different moments in time of before and after the construction of road infrastructure projects and the respondents were asked to give responses to both moments. However, evaluation of memory sometimes may lead to a bias of recall, and respondents are likely to remember specific questions and recent memories but not the past. The study analyzed the effects of road infrastructure projects using a case study approach. It is therefore important to carry out further research on more communities adjacent to the road infrastructure projects and assess if the results found in this study are unique or can be generalized.

As the impacts of road infrastructure appear to differ in locational context and class of individuals within peri-urban areas, our research suggests that place-based and people-based policies be combined to address the consequences of road infrastructure projects. More specifically, policies to protect land rights and enforce strategic spatial planning and those that deal with the emerging impacts of road infrastructure projects on different social groups. This study reveals that road infrastructure projects in peri-urban areas have positive implications. However, when looking at community wellbeing, it has a negative impact on livelihoods too. Thus they have the potential to cause both social and functional decompositions in peri-urban areas such as inequality, conflicts, segregation and unplanned developments for policymakers to deal with. Both cases show an indication that the environmental dimension is also an important aspect and may play a role when considering impacts accruing from road infrastructure projects. However, it was not considered in this study, hence the paper recommends this for future research.

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We would like to thank all those who took part in this study, especially those who accepted to be interviewed and Dr. Alexander Follmann for reviewing the manuscript.

This study was funded by the Faculty of Geo-Information Science and Earth Observation of the University of Twente.

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Khanani, R.S., Adugbila, E.J., Martinez, J.A. et al. The Impact of Road Infrastructure Development Projects on Local Communities in Peri-Urban Areas: the Case of Kisumu, Kenya and Accra, Ghana. Int. Journal of Com. WB 4 , 33–53 (2021). https://doi.org/10.1007/s42413-020-00077-4

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Received : 30 March 2020

Accepted : 04 September 2020

Published : 22 September 2020

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Global patterns of current and future road infrastructure

Johan R Meijer 1,3 , Mark A J Huijbregts 2 , Kees C G J Schotten 1 and Aafke M Schipper 1,2

Published 23 May 2018 • © 2018 The Author(s). Published by IOP Publishing Ltd Environmental Research Letters , Volume 13 , Number 6 Citation Johan R Meijer et al 2018 Environ. Res. Lett. 13 064006 DOI 10.1088/1748-9326/aabd42

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1 PBL Netherlands Environmental Assessment Agency, PO Box 30314, 2500 GH The Hague, The Netherlands

2 Department of Environmental Science, Institute for Water and Wetland Research, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands

3 Author to whom any correspondence should be addressed.

Johan R Meijer https://orcid.org/0000-0002-1219-7694

• Received 20 June 2017
• Accepted 11 April 2018
• Published 23 May 2018

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Method : Single-anonymous Revisions: 2 Screened for originality? Yes

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Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R 2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

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Introduction

Roads are important for socio-economic development by providing access to resources, jobs and markets [ 1 – 5 ], but they also bring about various direct and indirect environmental impacts. For example, road construction and use lead to increased emissions of greenhouse gasses and air pollutants, including carbon dioxide, nitrogen oxides and fine particulate matter, which in turn lead to climate change as well as adverse health effects [ 6 – 8 ]. Ecosystems and wildlife are affected mainly because roads provide access to otherwise undisturbed areas. This results in habitat fragmentation, deforestation, and reduced wildlife abundance though disturbance, mortality (road kills) and overhunting, particularly in tropical regions [ 9 – 13 ]. Further, roads may exacerbate societal inequities. For example, only farmers with sufficiently high output might be able to afford and benefit from bulk transportation services, which gives them a competitive advantage over smaller farmers [ 14 ].

To adequately quantify the benefits as well as the impacts of roads, accurate and up-to-date georeferenced information on the global road network is essential [ 15 ]. Possible applications of such data include global-scale estimations of travel time and accessibility, quantification of road transport and associated emissions, biodiversity impact assessments, and explorations of options to reduce impacts [ 5 , 8 , 11 , 15 , 16 ]. Global road data is typically retrieved from the Vector Map Level 0 (VMAP0) dataset from the United States National Imagery and Mapping Agency [ 17 ]. VMAP0 (covering the period 1979–1997) comprises approximately 7.4 million km of road, which is only a minor share of the total road length as reported in national statistics [ 15 , 18 ]. The gRoads initiative [ 19 ] substantially increased the road coverage for specific countries, but it still remains largely based on VMAP0 data. This also applies to commercial products, such as the ADC worldmap and Global Discovery dataset [ 20 , 21 ]. Commercial high-resolution datasets intended for navigation have also been produced [ 22 , 23 ], but these are not freely available. Moreover, the spatial coverage of these commercial datasets is biased towards Europe, North America and large urban areas. This limitation also holds for the crowdsourced information available from the OpenStreetMap project [ 24 , 25 ], due to the opportunistic approach to data collection. This means that current public-domain global road maps are either outdated or that their coverage is strongly geographically biased.

Over the last decade, digital georeferenced data on roads are becoming increasingly available online, and the manifestation of numerous thematic and national spatial data infrastructures (SDIs) facilitates the discovery of these road network datasets [ 26 , 27 ]. Driven by the need to improve the coverage of georeferenced global road data as well as the increasing availability of road data in the public domain, we initiated the Global Roads Inventory Project (GRIP). The primary aim of GRIP was to gather and integrate existing publicly available georeferenced roads datasets into a consistent global roads dataset. In addition, we aimed to explore and quantify possible relationships between road construction and ultimate socio-economic drivers, which may serve to obtain estimates of future infrastructural expansion.

To create the GRIP dataset, we selected, combined and harmonized publicly available national and supra-national vector datasets from governments, research institutes, NGOs and crowdsource initiatives into one global dataset. Apart from the vector dataset, we also compiled gridded layers for road length (km of road per cell) and road density (meters of road per km 2 land area per cell) on a 5  ×  5 arcminute resolution (approximately 8  ×  8 km at the equator). In order to quantify possible relationships between road construction and socio-economic drivers, we then performed a multiple linear regression analysis where we related the total road length per country, as retrieved from the GRIP dataset, to four explanatory variables: the countries' total land surface area, human population density, gross domestic product per capita (GDP; in international PPP US$) and OECD membership (yes or no) [ 28 , 29 ]. Thus, we cover both human population and affluence, which are generally considered two main ultimate drivers of environmental impact [ 30 ]. Finally, we applied our regression models to obtain country-level estimates of the total additional road length for the year 2050, based on projections of GDP and population density according to the so-called Shared Socioeconomic Pathway (SSP) scenarios [ 31 ], i.e. a coherent set of scenarios describing five alternative socio-economic futures.

Data and methods

Data gathering and selection.

To compile the GRIP dataset we combined the best available (supra-)national geospatial road data. We searched for data from organizations and activities that are responsible for or have an interest in spatial data on road infrastructure. This included United Nations (UN) organizations like the Logistics Cluster of the World Food Programme, the Food and Agriculture Organization, the Organization for the Coordination of Humanitarian Affairs and the High Commission on Refugees. We also searched for data via international research organizations and non-governmental organizations (NGOs). Crowdsourced data initiatives were searched for to cover gaps in the (supra-)national datasets.

For the (supra-)national datasets, the following selection criteria were defined for inclusion in GRIP:

• The selected dataset has a supra-national or national coverage, in order to prevent the sub-national bias in network coverage that characterizes existing global roads datasets due to fragmented digitization of map tiles, varying intensity of crowdsourcing activities and the occasional inclusion of detailed sub-national fragments of data [ 5 , 15 , 32 ]. If more than one data source was identified for a country, they were compared and combined to achieve the best national road network coverage.
• The error in the spatial positional accuracy is maximum 500 meters, as derived from meta-data or determined based on visual comparison with the VMAP data, digital and paper atlases, official topographic maps and remote sensing images from Google Maps, ESRI Basemaps and other available sources, using ArcGIS.
• The attribute information should include an indication of road type, in order to facilitate classification into one of five commonly applied functional road types [ 19 , 33 , 34 ]: highways, primary roads, secondary roads, tertiary roads and local roads.
• The scale should range between 1:100 000 and 1:500 000, in order to reduce spatial bias in road coverage induced by large differences in scale.
• The temporal coverage of the dataset should be as recent as possible and at least exceed the VMAP0 dataset (>1997).
• The dataset is publicly available in order to ensure that the GRIP database can be easily shared with others.

Crowdsourced OpenStreetMap data were used to cover Europe, as best available seamless dataset, and China, because of a lack of publicly available data from government or research institutes. OpenStreetMap data were further used for 200 cities worldwide with a population of more than 1 million people. In total 35% of the global road length in GRIP is derived from OpenStreetMap. An overview of all data sources used in GRIP is provided in tables S1 and S2 available at stacks.iop.org/ERL/13/064006/mmedia .

Data model for harmonization

To harmonize the road attribute information among the different source datasets, we applied the UNSDI-Transportation data model. The UNSDI-Transportation data model is a globally applicable transportation network attribute description designed by the United Nations Logistics Cluster [ 34 ]. It defines and categorizes various relevant attributes of roads, such as road type, pavement type and seasonality. Its description of road characteristics is commonly used in UN operations globally and other data initiatives that harmonize transportation information across countries [ 19 , 33 , 35 , 36 ]. Following this data model, we classified each road segment into one of five distinguished functional road type categories: highways, primary roads, secondary roads, tertiary roads and local roads. We included surface pavement attributes because paved highways typically have much larger environmental impacts than unpaved roads, especially in wet environments where unpaved roads can become seasonally impassable [ 37 ], which in turn could also affect humanitarian operations. In case information on seasonality was lacking, unpaved rural secondary/tertiary roads in tropical regions were classified as having seasonal availability.

Data handling and integration

All datasets were re-projected to the WGS84 geographic coordinate system. If the original projection information of the dataset was unknown, the details were derived from the source, national mapping agencies or manually defined. To achieve coverage of all defined road types, for some countries multiple data sources were combined to create the best representation of the national road network (see table S2 for data sources used per road type per country). Then, in order to clean the dataset and reduce the number of line segments to optimize performance for analyses, a single features 'dissolve' operation on the road type attribute was performed. Next all remaining attributes were added and classified based on the available source information. Finally, a global coverage dataset was created by first merging country datasets into continental datasets and subsequently merging those into one global dataset. Highway and primary road network connections across country borders were visually inspected and discontinuities were repaired if needed. We did this by manually reconnecting the line elements for discontinuities larger than 50 m and using the 'extend line' tool in ArcGIS to repair smaller discontinuities, whereby we verified the locations of the road using ESRI satellite imagery basemaps in ArcGIS. All data handling and integration was performed in ArcGIS, with version 9.1 through 10.3.1 [ 38 ].

Raster datasets

In addition to the vector dataset, we produced global road density raster layers (road length per unit of area) at a resolution of 5 arcminutes (approximately 8  ×  8 km at the equator). To that end we overlaid the road vector dataset with a global 5 arcminute 'fishnet' vector dataset with unique cell identifiers and assigned all road vector elements within a given cell the corresponding cell ID. We then calculated the length (in meters) of each individual road vector element in ArcGIS, accounting for the distance distortion in the WGS84 coordinate system, and summed the lengths per cell ID for each of the individual road types. The resulting table was joined to the fishnet vector dataset, which was then converted to 5 arcminute raster datasets using the summed road length per road type. Finally, the 5 arcminute road length rasters were divided by a matching 5 arcminute resolution area (km 2 per cell) raster [ 39 ] to derive road densities (in m per km 2 ).

Figure 1.  Road network length included in GRIP as compared to the geo-referenced global road datasets VMAP0 and gRoads and the country-level non-spatial data in the World Road Statistics (WRS) database, ( a ) per region and ( b ) per country. The comparison per country is based on the log 10 -transformed total road length (km) with +1 added prior to the log-transformation. WRS data represent 10 year averages over 2005–2014.

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Regression analysis

After establishing the dataset, we performed a multiple linear regression analysis where we related road length to four explanatory variables: area, human population density, gross domestic product (GDP) per capita and OECD membership. We did this analysis on a country level, because this matched best with both the scope of the road data collection in GRIP and the availability of current and future explanatory variable data. We retrieved country-specific data on land surface area (km 2 ), population size ( n ) and GDP per capita (purchasing power parity (PPP) corrected, in current international US$) from the World Bank and obtained population density by dividing the population size by the land surface area. For countries not included in the World Bank database, we retrieved data from additional sources (mainly the CIA World Fact Book or Wikipedia; see table S3). For both population size and GDP per capita, we used values from the most recent year with data available (mostly 2015; table S3). We then took the countries with data available for all response and explanatory variables ( n  = 219) and log 10 -transformed all continuous variables because of the skewed data distribution (table S4). As zero values cannot be log-transformed, we added +1 to all road length values prior to the transformation. Variance inflation factors (VIFs) of the four explanatory variables were well below 2 (country area = 1.62, human population density = 1.40, GDP = 1.37, and OECD = 1.33), indicating limited multicollinearity [ 40 , 41 ]. To account for possible levelling off of the effects of affluence or population density on road length, we added the squared terms of these explanatory variables to our regression models. To prevent overfitting, we performed a step-wise variable selection procedure to identify the most parsimonious model, using the Bayesian Information Criterion (BIC) as selection criterion, and allowing a quadratic term only if the corresponding linear term was also included. We evaluated the resulting regression models based on Cook's distance values and residual plots. Cook's distances were all well below 1, indicating that none of the observations had an undue influence on the regression coefficients [ 42 ]. The residuals were randomly distributed and outliers (absolute standardized residuals > 3) were restricted to cases where zero road length was observed (figure S3). Finally, to test the predictive ability of our models, we performed a three-fold cross-validation, using 146 countries for model training and 73 for testing, such that each country was within the test set once. All statistical analyses were performed in the R environment [ 43 ].

Projecting future road length

For the future projections we used the regression model for total road length only. This model performed equally well on the training and independent test data (cross-validated mean R 2 of 90%), whereas the cross-validation results showed considerably lower and more variable performance of the regression models for specific road types (table S5). We applied our regression model to obtain estimates of the total additional road length for the year 2050, based on projections of GDP and population density according to the so-called shared socio-economic pathway (SSP) scenarios, i.e. a coherent set of scenarios describing five alternative socio-economic futures. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The three main socio-economic drivers of the scenarios are population, urbanization and gross domestic product [ 31 ]. For our projections of future road length, we retrieved country-specific projections of population growth and GDP from the IIASA SSP portal [ 44 ]. Data were available for 165 of the 222 countries included in GRIP (representing 97% of the present-day total road length). In the projections we assumed that the road network in a given country would not shrink, i.e. if future population size and per-capita GDP resulted in projected road lengths smaller than the present-day estimate, the present-day estimate was retained (as presented in table S6).

Coverage of the global road network

The GRIP dataset comprises more than 21.6 million km of roads in total. This is a considerable improvement over the VMAP0 and the gRoads datasets, which cover 7.4 and 9.1 million km, respectively (figure 1 ). Yet, the total global road length in GRIP is mostly smaller than suggested by the World Road Statistics (WRS) database, which contains non-spatial country-level road length estimates [ 18 ]. Over 50% of the road length included in GRIP is from data sources published in 2010 or later. A further 23% is from data sources published between 2006 and 2010 and 14% was published between 2000 and 2005. Only 8% is still based on the VMAP0 data (table S1). Improved coverage compared to the earlier geo-referenced datasets was especially apparent for North America, South and Central America, Europe, and South and East Asia (figure 1 ( a )). For regions were GRIP relies mainly on VMAP0 data, such as the Russian Federation and several other Central Asian countries, only a slight increase in road length coverage was obtained.

Figure 2.  ( a ) Distribution of road length over the different road types in the GRIP dataset compared to VMAP0 and gRoads, and ( b ) road length plotted against rank on a log 10 transformed scale, with road types ranked starting from highways (type 1).

The more recent gRoads initiative (2013) mainly focused on finding data sources that helped improve coverage in Africa [ 19 ]. These data sources were also included in GRIP, which explains the relatively small difference in total road length between the two global datasets for this region. Yet, for 186 countries worldwide, GRIP covers a larger part of the road network compared to VMAP0 and gRoads (figure 1 ( b )). In a few cases the total road length in GRIP was (slightly) lower compared to VMAP0 or gRoads. These included small (island) states like Bermuda, Cook Islands, Niue, Palau and Wallis and Futuna, which have very small road networks, hence an uncertainty of just a few kilometers may result in a relatively large deviation between the different datasets (figure 1 ( b )).

Road type distribution

On a global scale, the distribution of the total length of the road types 1, 2, 3 and 4 (figure 2 ( a )) resembles a pattern that is commonly found when roads are hierarchically classified into roads that enable fast long-distance mobility as opposed to roads that increase local accessibility. This typically results in relatively few kilometers of highways and primary roads and a larger length for secondary and tertiary roads [ 45 , 46 ]. In the VMAP0 and gRoads datasets, a similar pattern was observed for road types 1–3, but not for type 4 (figure 2 ( a )).

In the GRIP data, the distribution of the total length across the first four road types appeared to follow a power law, with the total length per road type nearly doubling with each increase in rank from the highways (rank 1) to the tertiary roads (rank 4) (figure 2 ( b )). Power law distributions between frequency of occurrence or size on the one hand and rank on the other are commonly observed in a wide variety of phenomena, including the frequency of words in texts, population sizes of cities, and size distributions of earthquakes, although the power law does not always hold over the entire range [ 47 , 48 ]. The total length of the local roads (type 5) clearly deviated from the power-law distribution. This suggests that local roads in particular may be underrepresented in the GRIP dataset, likely due to geographical bias in data availability. Many of the country-level datasets incorporated in GRIP have only limited coverage of local roads. The majority of the local roads in GRIP (almost 60% of the total length) have been derived from OpenStreetMap, thus including its bias in coverage towards more developed regions and large urban areas [ 25 ].

Global patterns in road density and quality

The global GRIP road maps show clear spatial variability in road density (figures 3 and 4 ). In general, the highest road densities were observed in Northwest Europe and parts of South and East Asia. Roads are rare or even absent in the northern parts of Canada and the Russian Federation, the Sahara desert and large parts of the Amazon forest. The maps confirm a spatial bias in coverage for the local roads (type 5; figure 3 ). For various countries in South America, Africa and Asia, the coverage of local roads is less dense compared to other regions and other road types, and mainly limited to larger urban areas (see also table S3). Road surface type and accessibility information was available for 75% of the total road length covered in GRIP (see figure S1). Globally we found that 35% of the roads are paved and 50% of the roads have all year accessibility. Most paved roads are found in North America and Europe, whereas most unpaved and only seasonally accessible roads are found in Central and South America and Africa.

Figure 3.  The GRIP global road maps, displaying the detailed and harmonized coverage over world regions and the coverage per individual road type.

Figure 4.  GRIP global road density map on 5 arcminute resolution (approximately 8  × 8 km at the equator), representing the densities summed across the five road types.

Figure 5.  Total road length per country ( n  = 222) as obtained with multiple linear regression models (table 1 ) compared to the values observed for ( a ) all roads combined and ( b ) per sub-type. Values represent log 10 -transformed total road length (km) with +1 added prior to the log-transformation. Main roads include highways and primary roads.

According to our road length regression models, total road length in a country is strongly and positively related to its total land surface area, human population density, gross domestic product and OECD membership (table 1 ; figure 5 ). The variation explained by the regression models ranged from 63% for the local roads to 90% for all road types combined (figure 5 ), which is larger than existing country-level regression models [ 28 , 29 , 49 ]. The positive relationships of road length with GDP, OECD membership and population density reflects that road densities are higher in developed countries with higher GDP, like those in Northwest Europe, as well as more densely populated countries like India, Bangladesh and Rwanda (figure 3 ; figure 4 ).

a Main roads include highways and primary roads. b See table S3 for per-country data specification.

Road length scaled with land surface area with a coefficient close to 1, while generally smaller regression coefficients were found for the socio-economic variables. This may reflect that increases in population density and GDP result not only in the construction of new roads, but also an increased use of existing roads [ 28 , 50 ]. For local road length, however, relatively large regression coefficients were observed for population density, GDP and OECD membership (table 1 ). This might reflect bias in the availability of crowdsourced data on local roads towards more densely populated and prosperous regions.

SSP projections for GDP and population density were available for 165 of the 222 countries included in GRIP, together accounting for 97% of both the total land surface area and total road length of the countries in GRIP. The total additional road length projected for 2050 ranged from 3.0 million km for SSP scenario 4 to 4.7 million km for SSP scenario 5 (table 2 ). These differences were driven mainly by differences in economic growth, as SSP4 combines moderate global population growth with relatively low increases in per-capita GDP, whereas SSP5 combines relatively low population growth with very rapid economic development that converges among countries [ 31 ]. When averaged over the five SSP scenarios, most additional road kilometers are expected in Africa, South and East Asia and South America (figure S2). On a country level, the largest absolute increases in road length were observed for the USA, India, Australia, Canada and China (table S6). Considerable increases were also found for developing nations in some of the world's last remaining wilderness areas, such as the Democratic republic of Congo (+81%), Nigeria (+52%), Papua New Guinea (+50%) and Brazil (+10%). This reflects that these countries are projected to undergo relatively large increases in population density and/or GDP according to the SSP scenarios.

Table 2.  Global road length estimates (in million km and percent change) for 2050 for each of the five SSP scenarios, obtained by applying the road length regression models (table 1 ) to country-specific projections of GDP and population size as available for 165 countries. Country-specific road length estimates are provided in table S6.

a For comparability, present-day totals were calculated over the same 165 countries for which future projections could be made.

The Global Road Inventory Project has resulted in a globally harmonized road network database covering a larger part of the road network than the currently available country-based global road datasets VMAP0 and gRoads. Differences in road patterns between GRIP and the earlier global road datasets reflect not only the construction of new roads, but also an increase in data coverage (i.e. GRIP filling gaps in earlier maps). Moreover, information on the year of construction was not available in the data sources used to construct GRIP. Hence, the datasets cannot be directly used in order to quantify historic road expansion. The total road length in GRIP (21.6 million km) is smaller than suggested by the World Road Statistics (WRS) database [ 18 ], which reports a global total of 32 million km of roads as the sum of country data averaged over the period 2005–2014. It should be noted that the WRS dataset is characterized by considerable differences in road length estimates and road type definitions per country over the years reported. Also between countries the methodologies for measuring road length differ, for example, according to the limited WRS metadata, some countries include dual carriageways, sidewalks, non-public farm roads or measure road length by traffic directions. Without further detail, this implies that the reliability of the WRS information and the usability for comparison is limited. Yet, the difference in total road length between GRIP and WRS indicates that the coverage of GRIP can be further improved (figure 1 ). On a global scale, 68% of the road network length averaged over 2005–2014 reported by WRS is classified as type 'other roads'. In GRIP the corresponding local roads class constitutes 23% of the total road network. This confirms our finding that future improvements in GRIP should focus in particular on the representation of the local roads (figure 2 ). Moreover, given that a large proportion (34%) of the GRIP data originates from official governmental sources (table S1), unofficial roads are currently likely underrepresented. Unofficial or unplanned roads may make up an increasing share of the road network particularly in relatively pristine areas, such as the Amazon and the Congo basins, driven by the private sector that constructs roads without government permission in order to exploit natural resources [ 37 , 51 , 52 ].

Until recently, geospatial information was typically compiled by national mapping agencies or private sector firms with sufficient financial and technological resources available. Over the past decade, additional road data collection methods and sources have become available, including (semi)-automated extraction from remote sensing imagery, GPS tracking, and crowdsourcing [ 53 , 54 ]. In GPS tracking, large quantities of GPS tracks, for example from recreational traveling, are merged to produce mapped road segments. Currently, both GPS tracking and (semi-)automated extraction of road data from remote sensing imagery are still in the experimental phase, with approaches being developed to clean the data from positional inaccuracies and false or missing road segments [ 53 , 54 ]. Hence, these methods could not yet be routinely applied at the global scale during the construction of the GRIP dataset, but they might come in useful for future improvements of GRIP, particularly to fill the gaps for unofficial roads in pristine areas [ 51 ]. Crowdsourcing is a workflow to gather data that might be collected through multiple methods. OpenStreetMap (OSM) is a well-known crowdsourced collaborative project and considered a prominent example of volunteered geographic information collection. OSM constituted an important data source for GRIP in the European Union, where its high precision and wide coverage make it the best available seamless dataset [ 25 , 55 – 57 ]. Beside technical issues of importing OSM datasets in ArcGIS, the main challenges for using national-coverage OSM data in GRIP were identified as the validation of the data coverage, the use of multiple parallel line elements to represent segregated roads (e.g. motorways and highways), and the clear differences in coverage between urban and rural areas [ 32 ]. All these issues can largely be attributed to the volunteered and piecemeal data gathering approach of OSM [ 5 , 11 , 32 ], which currently results in a high coverage in densely populated developed regions (notably urban areas in Europe and North America) but varying in developing regions.

Various factors may promote road expansion, including pursuits for natural resources (timber, ores) and further agricultural development [ 37 , 58 ] as well as policies to stimulate economic development [ 1 , 4 , 59 ]. In our regression models we accounted for two ultimate drivers of road development only (population and affluence), which are not necessarily representative of all relevant proximate factors underlying road expansion. Moreover, in absence of reliable time series data, we had to use a cross-sectional rather than longitudinal regression modelling approach ('space-for-time' substitution). Given the limitations of the regression model, our projections should be considered as first-tier estimates of future road expansion at the country level. Future research may focus on more spatially explicit modelling of future road network changes. According to our tentative projections, road length will increase by 3.0 to 4.7 million km in 2050, which represents an increase of 14%–23% compared to the present-day estimate (table 2 ). In comparison, Dulac [ 60 ] estimated future road length based on projected future vehicle travel (km) and arrived at an estimate of 14.8–25.3 additional million km of paved-lane road length by 2050, which represents an increase of about 35%–60% compared to the approximately 43 million km of paved lane length as estimated for 2010. Although the estimates are difficult to compare due to differences in methodology and underlying data, this suggests that our estimates, focusing on roads instead of paved lanes, are relatively conservative.

Against the expected future increase in roads, the effectiveness of land use planning is seen as a crucial element in successfully dealing with various tradeoffs involved in road construction [ 37 , 58 ]. This is even more so given that the largest increases in road length are foreseen for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, Congo basin and New Guinea (table S6). To inform and support global policy analysis and spatial modelling of future road developments and their related impacts, up-to-date and accurate data on roads are needed. With GRIP, we have provided a major step towards a more representative, consistent and harmonized global road map.

Acknowledgments

We would like to thank Kees Klein Goldewijk for his long term encouragement to create GRIP, the UN Logistics Cluster for assisting us in the use of the UNSDI-T data model, Eddy Scheper, Anke Keuren and Eiso Zanstra for their help with data collection, harmonization and integration, Alex de Sherbinin for organizing interest in this challenge via the CODATA Global Roads Task Group, the OpenStreetMap foundation for access to their data and Ana Benítez López and Lex Bouwman for proof-reading the manuscript. We also thank five anonymous reviewers for their valuable and constructive comments, which greatly helped us to improve the manuscript.

The GRIP dataset (vector data and 5 arcminute raster layers) can be downloaded from www.globio.info/download-grip-dataset

Supplementary data (1.38 MB, PDF)

An Open Access Journal

• Original Paper
• Open access
• Published: 03 April 2020

A systematic review of indicators to assess the sustainability of road infrastructure projects

• Gede B. Suprayoga   ORCID: orcid.org/0000-0002-9409-7207 1 , 2 ,
• Martha Bakker 3 ,
• Patrick Witte 2 &
• Tejo Spit 2  

European Transport Research Review volume  12 , Article number:  19 ( 2020 ) Cite this article

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Metrics details

This study aims to examine to what extent sustainability has been incorporated into assessments of road infrastructure projects. It identifies promising approaches that include indicators reflecting core sustainability criteria, determines criteria that were insufficiently covered as indicators, and develops an integrated indicator set covering all criteria.

A systematic review was performed to obtain all related papers/reports in two academic databases: Scopus and Web of Sciences. The indicators extracted from papers/reports were first coded, then evaluated by using quantitative and qualitative content analysis.

The project appraisal methods for decision-making is found to be a promising approach, covering more extensive criteria than others. Two criteria – namely adaptation and precaution and intergenerational equity – were hardly ever adopted as indicators. Ten main groups of indicators were extracted to construct an integrated set incorporating all core criteria.

Conclusions

Some criteria appear to have become mainstream, while others deserve attention. The safest choice is to combine methods/tools or to adopt the integrated set developed for exhaustive criteria inclusion.

1 Introduction

Since the late 1980s, the emergence of the sustainable development concept has sparked interest from academia, government agencies, business organizations, and civic communities in developing a tool to help decision-making towards sustainability, called sustainability assessment (SA). SA refers to “a methodology that can help decision-makers and policy-makers decide what actions they should take in an attempt to make society more sustainable” ([ 15 ], p. 9). The main aim of SA is to ensure that plans and activities make an optimal contribution to sustainable development [ 82 ]. SA has increasingly become a common practice in various areas, such as product, policy, and institutional appraisals [ 70 ], as well as in project evaluations [ 8 ]. As a concept, sustainability generally denotes a balance of economic, social, and environmental goals with a long-term (intergenerational) concern [ 34 , 70 ].

In transportation projects, SA is applied to evaluate whether a project “contributes to favor economic development and fulfill the transportation needs of the society in a manner consistent with ecological and human values” ([ 8 ], p. 642). SA is an advanced methodology to ensure that decision-making is comprehensive and inclusive, meaning that it covers all three dimensions/pillars of sustainable development (i.e., environmental, social, and economic dimensions), including the indirect effects [ 37 , 70 ]. Political ambition can play a huge part in the planning of road projects. Such projects have vital roles in enhancing regional growth and economic competitiveness, especially in developing countries [ 17 ]. However, environmental aspects are relatively neglected and frequently only incorporated later on. Traditional impact assessment tools are often solely concerned with the environmental dimension, while the social and economic dimensions are less often considered (see Fischer [ 23 ] for strategic environmental assessment of transportation projects). This paper focuses on road infrastructure projects because of their impacts on the environment and society [ 30 , 50 , 83 ]. These projects are often key drivers of landscape transformations, habitat fragmentation, and societal change on both global and local scales [ 26 , 67 ], with impacts lasting for long periods (e.g., [ 22 , 72 ]) and producing intergenerational consequences (e.g., [ 32 , 52 ]). Therefore, a better inclusion of sustainability dimensions is needed.

Bueno et al. [ 8 ] categorized methods and tools for the SA of transportation projects into three distinct approaches: (1) project appraisal methods for decision-making, (2) techniques for impact assessment, and (3) sustainability assessment methodologies. These approaches often adopt generic indicators that allow for uniform application in different situations. The purpose of these indicators is to identify trends, predict problems, set targets, evaluate solutions, and measure progress [ 51 ]. The indicators also serve as a compass for desirable development paths and communicate knowledge through the use of specific variables. The investigation of indicators in the SA of road infrastructure projects can provide general insights into whether a project and its components are contributing to sustainability. First, these approaches differ in their application of the indicators with regard to focus, number of attributes, and the methodological concepts (and frameworks) used [ 34 ]. Second, the interpretation of indicators varies concerning what sustainability means and which indicators to include [ 5 ].

After years of deliberation and experimentation, “it is not difficult to discern a limited number of common themes and broadly accepted general positions” ([ 29 ], p. 95) to interpret sustainability. Gibson et al. [ 29 ] developed eight basic requirements to attain greater sustainability that highlight the main criteria/aspects in SA. Based on these criteria/ aspects, this study examined the indicators for the SA of road projects in academic papers. Bond and Morrison-Saunders [ 5 ] suggest that, at present, SA seems prone to manipulation to suit particular discourses. This paper therefore provides a starting point for the development of an inclusive and balanced use of indicators. Such an effort can avoid the tendency to promote a specific frame of outcomes (such as economic growth instead of societal wellbeing) in the SA of road infrastructure projects. The primary research question (RQ) was: To what extent have sustainability criteria/aspects been incorporated as indicators to assess road infrastructure projects? Three sub-RQs were also formulated:

Which sustainability criteria have the papers already included as indicators to assess road infrastructure projects? Is there a robust assessment approach based on the inclusion of these criteria?

Which sustainability criteria are sufficiently or insufficiently covered as indicators in the examined papers?

How can an integrated indicator set be developed and be further implemented to assess the sustainability of road infrastructure projects?

In order to answer the RQs, both quantitative and qualitative research methods were employed. A systematic literature search was conducted in two databases, Scopus and Web of Sciences. The following section outlines the theoretical framework. This is followed by the research methods.

1.1 Theoretical framework

1.1.1 approaches to the sustainability assessment of road infrastructure projects.

Scholars distinguish SA approaches differently. Sala et al. [ 70 ] categorize them according to the level of integrated-ness, ranging from a general method for decision support (such as multicriteria analysis and fuzzy analysis) to a more integrated tool/method (such as a genuine progress indicator or lifecycle sustainability assessment). De Ridder et al. [ 13 ] divide the approaches based on their potential role in the assessment phases: (i) participatory tools, (ii) scenario tools, (iii) multicriteria tools, and (iv) accounting and model tools. Bueno et al. [ 8 ] classify the methods and tools in the SA of transportation projects into three distinct approaches: (i) project appraisal methods for decision-making, (ii) techniques for assessing impacts, and (iii) sustainability assessment methodologies. This study adopted Bueno’s classifications to investigate how SA is applied to guide decision-making in different project stages (i.e., planning, construction, usage) in order to capture various sustainability elements of road infrastructure throughout its lifecycle. The first approach has been extensively used by decision-makers to plan road projects. Some of the tools in the third approach, such as the rating system tool, have now become popular [ 33 ].

Project appraisal methods for decision-making

This approach is employed as an ex-ante evaluation to compare and select alternatives once it has been decided to implement a road project. Two methods are included in this approach. First, cost-benefit analysis (CBA), which supports sustainability by providing a “tangible and rational” judgment of the benefits and costs associated with alternative versions of a project [ 12 ]. CBA is based on the monetary values of user benefits (e.g., travel time savings) and other “negative” effects (e.g., energy consumption, resource use, and CO 2 emissions). The second method is multi-criteria analysis (MCA). By using this method, several criteria – including those that are difficult to monetize and quantify – can be considered simultaneously [ 4 ]. MCA can cover project impacts comprehensively (i.e., the environmental, social, and economic impacts) and enable the involvement of stakeholders through the inclusion of their subjective judgments [ 64 ].

Techniques for assessing environmental/social impacts

The second approach is aimed at quantifying the environmental efficiency of road projects [ 76 ]. Life-cycle assessment (LCA) is a technique used to assess the environmental impacts of a product, activity, or process (mostly in construction and operation stages). It is deployed to evaluate the sustainability performance of the whole project cycle, from cradle to grave (material extraction, manufacturing, transportation and distribution, utilization and maintenance, energy consumption, and waste handling) [ 73 ]. Second, social LCA (SLCA) was developed to incorporate social impacts in LCA [ 41 ]. This method – which is often called a social impact assessment – quantifies the social and distributional effects of projects throughout their lifecycles. Bueno et al. [ 8 ] argue that SLCA uses a broad definition of social impacts, but still lacks a specific framework to guide implementation.

Sustainability assessment methodologies

The sustainability assessment methodologies approach is an ex-post project evaluation, aimed at assessing full accounts of project effects based on best practices. Bueno et al. [ 8 ] elaborate it into (i) rating systems and certification, and (ii) frameworks, models, and guidelines. First, the rating system and certification contain a collection of best practices to incorporate sustainability into road projects [ 49 ]. This tool is associated with a standard metric of points or credits that are used to evaluate and compare the sustainability elements of projects (e.g., pollutant loading in stormwater runoff, pavement design life, recycled material uses, pedestrian access). The rating system often comprises a self-evaluation mechanism developed for civil infrastructure projects, e.g., Greenroads, GreenLites, I-LAST, INVEST, and BE2ST-In-Highways. The second category has a much broader scope and includes software tools for modelling and forecasting. Some of the tools have already been extensively applied, such as the UK Department of Transport Analysis (Web TAG) and Scottish transport appraisal guidance (STAG). These tools are deployed to (i) to represent best practices, (ii) provide expert advice for transportation projects, and (iii) establish criteria for assessing options. Therefore, tools in this approach use criteria to provide information about best practices and procedures related to ideal road projects and to improve road sustainability performance based on the assigned criteria [ 8 ].

1.1.2 Core sustainability criteria for evaluating indicators

Numerous sustainability criteria can be extracted from literature to examine indicators in SA. The literature provides criteria for extensive areas of practice, including agricultural undertakings [ 2 ], urban development [ 14 ], nature conservation [ 35 ], and spatial planning [ 63 ]. However, none of the criteria found is explicitly used for assessing road infrastructure projects. The criteria of Gibson et al. [ 29 ] are used here to develop what they refer to as “a minimal set of core [sustainability] requirements” (p. 95) and “key changes needed for progress towards sustainability” (p. 115). The criteria are elaborated into (i) socio-ecological system integrity, (ii) livelihood sufficiency and opportunity, (iii) intragenerational equity, (iv) intergenerational equity, (v) resource maintenance and efficiency, (vi) socio-ecological civility and democratic governance, (vii) precaution and adaptation, and (viii) immediate and long-term integration. Based on these criteria, this paper gauges whether approaches and indicators in the SA of road infrastructure projects have already considered sustainability in implementation.

Gudmundsson et al. [ 34 ] distinguish three aspects of indicators for transportation development, namely (i) dimension, (ii) comprehension, and (iii) staging/position. The “dimension” refers to the movement of the indicators (in space and time) to illustrate the importance of contexts in SA. The comprehension of indicators conveys an inclusion of information about what needs to be measured, for example, sustainability pillars (i.e., economic, social, and environmental). The staging presents activities at different stages (i.e., design, planning, construction, usage) that the indicators support to achieve the sustainability of projects. The sustainability criteria and aspects of indicators are listed in Table  1 . The criterion immediate and long-term integration is omitted from the list because it includes cross-cutting criteria that should be evaluated at once.

Table 1 was also used to extract detailed indicators from the reviewed papers. The following section explains the research methods used to investigate the approaches, the criteria, and the indicators.

2.1 Selection and categorization of papers

A systematic literature search was conducted by using two academic databases – Scopus and the Web of Sciences – on June 24 and 25, 2019. The search strategy was initiated by identifying diverse terms that may refer to SA in the databases, such as “sustainability appraisal,” “sustainability impact assessment,” “sustainability evaluation,” and “integrated assessment” (e.g., [ 28 , 37 , 63 ]) (Table  2 ). Both the singular and the plural form of these terms were searched for.

In this first selection, 490 papers were extracted by using the key search terms in Table 2 . Papers representing assessments of other infrastructure projects, such as waterways, energy, and railways, were excluded from our selection. We also excluded papers on small or fragmented elements of road infrastructure (e.g., pavements, roadside facilities) and technological assessments (e.g., innovative construction materials, intelligent systems) to concentrate on the road project scope. Next, we filtered out papers identified as similar reports. Finally, a dataset consisting of 31 papers was analyzed (Fig.  1 ). The papers in the dataset originated from the disciplines of engineering, ecology, environmental sciences, geography, and social sciences. Most (15) papers concern European countries, namely Germany, UK, Spain, France, Denmark, Croatia, Poland, and Hungary. North American countries constituted cases in eight papers. Seven papers originated from Asian countries and one from an African country.

The search process

We extracted all indicators found in the examined papers. We categorized the indicators into core sustainability criteria and elaborated on the criteria based on the descriptions given in Table 1 . The number of criteria applied was also noted.

2.2 Analysis methods

Both a quantitative and a qualitative method were used to examine the paper set. To answer the first sub-RQ, we grouped papers by using a cluster analysis, based on the coded description from “a” to “j” in Table 1 . For the second sub-RQ (Which criteria are sufficiently or insufficiently covered as indicators in the examined papers?), we counted the number of papers using the criteria in indicators. The third sub-RQ was based on qualitative content analysis.

2.2.1 Quantitative content analysis

The clusters were formed using a complete-linkage technique, namely an agglomerative hierarchical clustering technique that is appropriate for the analysis of a relatively small sample size [ 59 ]. Papers with similar characteristics were combined into a cluster [ 58 ]. The application of this technique has more flexibility because no predefined number of clusters should be set. It allows a more intuitive way to define the number [ 75 ] by exploring the similarity of the characteristics of the dataset in detail based on the criteria included. The cluster set was represented in a tree diagram (a “dendrogram”). There is no exact rule about defining the sample size [ 59 ]. Dolnicar [ 18 ] observes this size ranging from 10 to 20,000 elements and, by using Pearson’s and Spearman’s correlation, concludes that “even very small sample sizes are used for clustering in very high dimensional attribute space” (p. 2). The size may be less relevant to consider since the analysis works with an unknown structure (see [ 19 ]).

We used the descriptions in Table 1 to establish the coverage of the criteria. A descriptive statistic was applied to represent mode and the percentage of the criteria. Next, categorical principal component analysis (CatPCA) was performed to evaluate the correlation between the criteria. We used the Varimax rotation method to examine the correlation between the criteria and visually present their proximity so that they could be grouped into smaller criteria. The method maximizes the sum of the variances of the squared loadings (or squared correlations) within fewer dimensions [ 56 ]. The result was a bi-plot informing the dimensions of correlated criteria.

2.2.2 Qualitative content analysis

We started the analysis by extracting all indicators found in the examined papers. All indicators were grouped by using a configurative method [ 31 ] to develop an integrated set. If an indicator did not match a specific group, a new group was added as complementary to the set. To avoid redundancies, we also investigated whether the extracted indicators addressed specific criteria. Lastly, we compared the findings with the result of the quantitative content analysis.

3.1 Results 1: sustainability criteria in the SA of road infrastructure projects

Figure  2 presents the outcome of the cluster analysis. Four major clusters were identified. Cluster 1 contains the largest number of papers ( n  = 21) with no more than seven criteria adopted in each paper. This cluster can be divided into two smaller groups (sub-clusters 1a and 1b). Sub-cluster 1a contains all papers using the criteria socio-ecological system integrity (a1) and livelihood security and opportunity (b). Sub-cluster 1b contains papers that include indicators that apply the criteria socio-ecological system integrity (a1), resource maintenance and efficiency (e3) , and comprehension of pillars (i).

The resulting clusters and grouping of the examined papers [ 1 , 7 , 9 , 10 , 11 , 16 , 21 , 24 , 25 , 36 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 47 , 48 , 53 , 55 , 57 , 60 , 61 , 66 , 69 , 71 , 78 , 79 , 81 , 84 ]

Only three papers were found in cluster 2, and only two in cluster 3 . These clusters included indicators with more exhaustive and diverse criteria than the other clusters. Finally, cluster 4 comprises five papers with indicators that adopt three similar criteria: socio-ecological civility and democratic governance (f1 and f3) and comprehension of pillars (i).

In Fig.  2 , the clusters represent the diverse approaches deployed. Cluster 1 contains papers applying all three approaches. One sub-sub-cluster (cluster 1b.1) mainly comprises papers deploying “techniques for impact assessment.” All papers in sub-cluster 2 and cluster 3 apply “project appraisal methods.” In cluster 4, all papers deploy “sustainability assessment methodologies.” Considering that clusters 2 and 3 adopt more criteria as indicators, the approach deployed can be considered more comprehensive than the others. Papers in clusters 2 and 4 successfully adopt the criterion comprehension of pillars (i).

The bar plot in Fig.  3 shows the number of papers that adopt the criteria in Table 1 as indicators. Criteria a1 and b are the most used criteria, adopted in 29 of the 31 papers (93.5% of the papers). The criterion socio-ecological system integrity (a1 and a2) is used in 28 and 18 papers, respectively. The least adopted criteria are precaution and adaptation (g3) and intergenerational equity (d) (each appear in only one paper). On average, seven criteria are adopted as indicators in the examined papers.

Bar plot showing the sustainability criteria addressed as indicators in the papers reviewed

Figure  4 depicts two principal components (PCs) that position the proximity between the sustainability criteria/aspects and the approaches. The line direction (vector) visualizes the correlation of the criteria/aspects with the PCs. A strong correlation is shown by the vector proximity that corresponds to the PCs. PC1 and PC2 represent 19.6% and 16.2% of the total variance, respectively. Four criteria strongly correlate with PC1, namely: socio-ecological system integrity (a2), resource maintenance and efficiency (e2), comprehension of pillars (i), and dimension (j). This implies that the criteria/aspects can be grouped into fewer criteria. However, these criteria are in a negative correlation with “techniques for impact assessment,” meaning that they are hardly included as indicators in this approach.

The bi-plot of CatPCA derived from the coded descriptions in Table 1

Figure 4 shows that the criteria intergenerational equity (f2) and precaution and adaptation (g4) and the “sustainability assessment methodologies” approach strongly correlate with PC2. Both the criteria and the approach are in negative correlation, meaning that the criteria are less adopted as indicators in the approach. The other criteria are more independent than previously mentioned, so they are grouped into a much smaller number of criteria. The figure also shows that the “project appraisal methods” approach has a similar direction to the criterion ‘precaution and adaptation (g4), implying that the approach consistently adopts the criterion. Another finding is that the “techniques for impact assessment approach” has a closer relationship with the aspect of complete staging (h).

3.2 Results 2: sustainability indicators extracted from the examined literature

The qualitative content analysis revealed 10 major groups of indicators in the examined papers (see Appendix 1 for details). These groups categorized the assessment indicators into: (1) Mitigation of species habitat fragmentation and land use management, (2) Mobility and accessibility improvement, (3) Pollution (soil, water, air, light, noise) prevention, (4) Climate change adaptation and resilient infrastructure, (5) Community livability improvement, (6) Resource efficiency, (7) Societal wellbeing and equity (both intrageneration and intergeneration), (8) Integrative planning and decision-making, (9) Technological utilization for impact mitigation, and (10) Context-sensitive development.

The findings show that the indicators adopted are not limited to environmental protection aspects (mitigation of habitat fragmentation, land use management, pollution prevention, and resource efficiency), but also cover socioeconomic aspects (community livability, societal wellbeing, and equity) – thus revealing the importance of integrative decision-making to achieve sustainability goals. Two distinct groups of indicators were found concerning the utilization of technology for impact mitigation and context-sensitive development. The finding implies that both process and context are vital in the SA of road projects. The results show that road projects are assessed against various indicators and that some indicators are used more often than others. Without considering the adoption of the sustainability criteria in Table 1 , the SA of road infrastructure projects may serve specific discourses, such as the mitigation of ecological impacts. The following section discusses this matter.

4 Discussion

Based on the results, this section discusses i) the robust SA approach to road infrastructure projects, ii) the criteria sufficiently or insufficiently covered, and iii) the development and operationalization of an integrated indicator set.

4.1 Finding a robust approach to assess road infrastructure projects

This paper shows that although considerable efforts have been made to include sustainability criteria in SA approaches to road infrastructure projects, none of the approaches includes all criteria/aspects. This finding substantiates the conclusion drawn by Bueno et al. [ 8 ] that “none of the [existing] methods and tools can be used to carry out a holistic appraisal.” Fig. 2 shows that two clusters (clusters 2 and 3) use a more exhaustive set of criteria than the others. Both clusters contain papers applying “project appraisal methods” that consistently adopt more than eight criteria. MCA, in particular, identifies criteria, evaluates alternatives, assigns weighting coefficients to the criteria, and finally evaluates sustainability criteria by ranking the alternatives [ 4 ]. The method allows decision-makers to account for complex problems within biophysical and socioeconomic systems through the inclusion of multiple elements using the criteria (see [ 46 ]). Pope and Morrison-Saunders [ 64 ] also argue that MCA allows many considerations to be incorporated into the decisions and enables diverse stakeholder perspectives to consider transparently. The “project appraisal methods” approach therefore has the potential to enhance project performance, as the chance of incorporating sustainability improves in the early part of the project lifecycle [ 68 ].

Both the “project appraisal methods” and the “sustainability assessment methodologies” approach have become useful to incorporate all pillars of sustainability, as found in clusters 1b and 3. The rating system tool is mostly applied to “rank and score projects against sustainability performance by putting economic, environmental, and social aspects together” ([ 8 ], p. 632). The “techniques for impact assessment” approach tends to include the criterion complete staging (h). The bi-plot result (Fig.  4 ) indicates that the approach and the criterion are closely correlated. This finding substantiates that LCA is better deployed to assess project sustainability performance with regards to the efficient use of material and energy throughout the lifecycle (reuse, recycling, recovery, and final waste handling). As few papers apply it, this finding is just a weak indication.

The cluster analysis also shows some problems with the deployment of the approaches. First is the lack of coherence use of criteria to develop indicators in the assessments. The selection of these indicators tends to be arbitrary. Gibson [ 28 ] suggests a sustainability test by using the core criteria set to distinguish whether the assessments are genuinely aimed at achieving sustainability. Second, none of the approaches can successfully include indicators based on the criteria/aspects in Table 1 . The realistic way to include all criteria is to combine diverse approaches/methods, such as the combination of LCA and CBA. LCA can better assess the inter-temporal aggregation of impacts (intergenerational equity), while CBA covers thoroughly the sustainability pillars as the basis for identifying the project effects in monetary terms (e.g., [ 54 ]).

4.2 Sustainability criteria fully covered/uncovered as indicators

This paper demonstrates that the sustainability criteria have been varyingly incorporated as indicators. The two most frequently used criteria are socio-ecological system integrity and livelihood security and opportunity . The criterion socio-ecological system integrity is often used to develop indicators that refer to project effects across scales from climate change and ozone layer depletion at a global scale [ 11 , 24 , 48 , 55 , 71 ], to soil and local water quality at a fine spatial scale [ 45 , 60 , 81 ]. The criterion socio-ecological system integrity is associated with the indicators concerning the mitigation of species habitat fragmentation, land use management, and pollution prevention. The criterion livelihood security and opportunity is adopted to construct indicators related to the socioeconomic effects of projects. These indicators can be grouped into mobility and accessibility improvement, community livability [ 9 , 16 , 24 , 45 , 48 , 55 , 57 , 84 ], and societal wellbeing and equity [ 10 , 21 , 36 , 42 , 71 ]. Several indicators concern intergenerational equity (e.g., direct and indirect effects on employment), and transportation costs are also derived from the criterion [ 42 , 71 ].

Two criteria are the least covered as indicators in the examined papers. One is precaution and adaptation , which is aimed at evaluating whether irreversible damage and risks to people and the environment have been taken into account in projects (UNCED, 1992). A group of indicators reflects this criterion: resilient infrastructure and climate change adaptation. By using the criterion, indicators are developed to assess the ability of road infrastructure to withstand shocks and unpredicted events (e.g., climate disaster, earthquakes) [ 42 ]. Gibson et al. [ 29 ] identify the possible barriers to their incorporation: (i) unawareness of the assessor, (ii) cognitive uncertainty regarding the condition being assessed, and (iii) methodological difficulties. Salling and Pryn [ 71 ] suggest a certainty analysis in CBA to estimate future costs and possible changes in the value of benefit and cost ratios. Bueno and Magro [ 7 ] also recommend the application of sensitivity analysis in MCA to identify to what extent the geographical context of the projects has varied, resulting in different risks (and uncertainty) to consider in the assessments.

The second least adopted criterion is intergenerational equity . The criterion is used to evaluate the cross-generational effects of projects through indicators concerning societal wellbeing and intergenerational equity (e.g., long-term employment opportunities). The inherent methodological limitation is often blamed for the lack of inclusion. Gasparatos et al. [ 27 ] argue that most SA methods/tools focus only on economic efficiency, and not on equity. Bueno et al. [ 8 ] state that the “traditional” assessment methods/tools only identify impacts for limited time-horizons, most of which are intangible. Joumard and Nicolas [ 42 ] express the criticism that the typical linear accounting method (such as CBA) imposes a much lower present impact valuation, which is critical for future generations. Therefore, the components of the discount rate need to be reframed in such a way that the intergenerational inequity concerns of the projects can be included and evaluated, such as concerns about agricultural land losses and community disruptions. These findings show that pragmatism might play a role in the inclusion of the indicators. Therefore, a robust SA approach to road infrastructure projects based on the criteria included is still a long way off.

4.3 Developing an integrated indicator set

This study categorized assessment indicators in the examined papers into 10 main groups. These groups show that sustainable road infrastructure projects are reflected not only in the mitigation of environmental impacts, but also in the improvement of societal wellbeing and community livability. Some papers included indicators about processes to ensure that sustainability is achieved. Consequently, a group of indicators concerning integrative planning and decision-making was added to the set.

Two criteria – namely intergenerational equity and precaution and adaptation – were identified in one cluster, and the two are closely correlated (see Fig. 2 ). However, both are infrequently adopted as indicators, but can be incorporated in the SA of road projects by applying scenarios, adaptive management plans, and socio-environmental risk estimations [ 42 ]. The criteria are further elaborated in two groups of indicators, that is, “ resilient infrastructure and climate change adaptation” and “technological utilization for impact mitigation.”

Three criteria – resource maintenance and efficiency , socio-ecological civility and democratic governance , and comprehension of pillars – were identified in a similar dimension and are highly correlated in the bi-plot (see Fig. 4 ). On the one hand, administrative and market arrangements (standards, regulations, and carbon markets) can enforce efficient uses of energy and materials in road construction and operation. On the other hand, efficiency can be achieved if these arrangements are available and used to guide decision-making if no conflicts are found between the arrangements and the actual implementation [ 24 ]. However, Bond and Morrison-Saunders [ 6 ] doubt that on their own, the arrangements will ensure effective implementation.

Better inclusion of the aspect of comprehension of the pillars can be made possible if inclusive decision-making is carried out [ 60 ]. This finding underlines that sustainability is not only about outcomes, but also about processes, such as stakeholder involvement, the coordination of responsible agencies, and sustainable funding mechanisms [ 34 , 65 ]. Therefore, integrative planning and decision-making are included as one distinct group of indicators.

Sustainability needs to take into account the aspect of dimensions (space and time) so that the assessment can differ according to the place and the social conditions [ 8 ]. The qualitative content analysis explored a group indicator that includes options and actions to harmonize road development with the surroundings; for example, roads are designed to suit local contexts (e.g., safe streets for school zones) and to meet local regulations and standards. In the examined papers, road infrastructure projects already take into account aesthetic, environmental, and art/culture/community values [ 51 , 60 ].

4.4 Operationalizing the indicator set

The integrated indicator set provides a guideline on which indicators should be included in the SA of road projects or whether sustainability has already been considered. The full application of the set may be difficult because resource availability (e.g., money, funding, and data) and the complexity of the decision-making process can act as barriers. How should the indicators be chosen in actual assessments?

Some scholars suggest that a framework is needed as a constraining factor when choosing the appropriate indicators [ 20 , 34 ]. This framework maintains the link between the sustainability objective and the indicators applied to monitor progress. Svarstad et al. [ 77 ] show that frameworks tend to favor the particular discourses of the organizations that construct them. For example, the DSPIR (driver–state–pressure–impact–response) framework tends to focus on the pressure indicators (e.g., mobility improvement in congested regions) rather than the state or impact indicators (e.g., species habitat fragmentation and community disruption) [ 80 ]. Bell and Morse [ 3 ] suggest that the participation of affected stakeholders can obviate the selection bias and increase opportunities to incorporate multiple discourses in the indicators.

Still, the sustainability outcomes of road projects will depend on the tested alternatives and the baseline against which the individual indicators are applied. For example, if the aim of a proposed road passing through a protected forest is to connect isolated communities, an alternative policy may entail the construction of the road away from the forest, but lead to much longer travel times. Another alternative is to adopt indicators with regard to the mitigation of species habitat fragmentation. But this option may not be so beneficial to people’s mobility and areal accessibility, or to intra-generational equity and societal wellbeing (improved access of community members to public services). Irrespective of the indicators chosen, the choice often depends on the decision makers offering contextually sensitive solutions that respect the local environmental and community values, and applying technologies that make the project less harmful to the surrounding area.

This study suggests that the SA of road infrastructure projects should prioritize the inclusion of indicators that can secure natural capital and manage its long-term changing state. Most of the examined papers acknowledge that negative impacts are inevitable and use indicators to illustrate these impacts (e.g., pollution prevention and technological utilization for mitigation). But the assessments are applied without testing whether any critical natural capital is lost or secured (see [ 80 ]). As a consequence, the criteria precaution and adaptation and intergenerational equity – both of which are less considered in the examined papers (Fig. 3 ) – need to be incorporated as indicators. By integrating these criteria, SA can identify those who are affected by the change of critical resource/capital and in what ways road infrastructure projects cause less damage to the environment.

5 Conclusion

This paper examined the extent to which the assessment of road infrastructure projects has considered sustainability through the inclusion of indicators closely associated with sustainability criteria in the literature. Some criteria appear to have become mainstream indicators, while others deserve attention. None of the reviewed papers considers all criteria, probably for feasibility reasons, but also sometimes out of pragmatism. Special attention should be paid to the criteria precaution and adaptation and intergenerational equity . Both criteria are either tricky or inconvenient to elaborate as indicators. We therefore suggests that these criteria should be included as indicators more often in future applications. The safest choice is to follow the “methodological pluralism” argument (i.e., the combination of multiple methods/tools) [ 27 ] for an exhaustive criteria inclusion. Without considering the core sustainability criteria in Table 1 , the development and implementation of indicators can become arbitrary and tend to serve particular discourses of outcomes [ 5 ]. The integrated indicator set presented here provides the full account of the discourses.

The advantage of using a systematic review is evidential with regard to transparency [ 62 ]. However, there are also drawbacks. First, in our case, relatively few papers were evaluated, raising the question whether the studied sample was sufficiently representative. Only a small selection of instances of the SA of road infrastructure projects are published in peer-reviewed scientific journals, and we have to bear in mind that these somehow deviate from the majority, which are published in the grey literature. For a paper to be accepted in a scientific journal, it needs to contain some innovative elements, such as the use of an innovative method or a new set of indicators. If that is indeed the bias of our sample, it suggests that the broader body of the literature is likely to be more “on the beaten track” than the papers evaluated here. This issue means that specific indicators are probably even more pronounced in the grey literature.

Future research should be able to elaborate further on the integrated indicator set. The set needs to be completed so that all sustainability criteria can be fully incorporated. The criteria intergenerational equity and precaution and adaptation require further elaboration, as do the ways in which frameworks can be constructed to better incorporate the criteria. Another research avenue is the investigation of distinct perspectives on sustainable development, namely the comprehensive and the sectoral view [ 34 ], which may influence the selection of these indicators. The use of the indicators also differs according to the scale of the assessments in which they are applied (e.g., global, regional, local, or neighborhood level). Context-specificity may determine how the indicators are selected. The present study shows that the SA of road infrastructure projects is not only a matter of technical deployment of the approaches, but also an integrated decision-making process [ 74 ]. Therefore, to improve effectiveness, not only must the approaches be advanced, but also the process and contextual barriers must be identified.

Availability of data and materials

The list of datasets (or the list of papers) used and analyzed during the study are available from the corresponding authors on reasonable requests.

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Acknowledgements

The authors would like to thank Professor Leonie B. Janssen-Jansen, who died in 2018, for commenting on the conceptual design of the manuscript that initiated a thorough examination of sustainability assessment of road infrastructure projects.

This work was supported through the Indonesia Endowment Fund for Education (LPDP) grant (Nr. 20160222015432). The Ministry of Finance, Republic of Indonesia, granted the funding. The findings, interpretations, and conclusions presented in this article are entirely those of authors and should not be attributed in any way to the ministry.

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Gede B. Suprayoga

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Gede B. Suprayoga, Patrick Witte & Tejo Spit

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GBS designed the study, analyzed both the quantitative and qualitative data, interpreted the data based on the sustainability criteria found in the literature, and wrote the whole manuscript sections. MB contributed to the interpretation of the quantitative analysis and drafted the discussion section about the sustainability criteria. PW interpreted the results of the qualitative analysis. TS conceptualized the manuscript design and contributed to synthesize the findings of the report. All authors read and approved the final manuscript.

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The integrated indicator set to assess the sustainability of road infrastructure projects.

• Source: Own elaboration based on the examined papers. Note: All groups of indicators are extracted based on the indicators included in the papers reviewed. The detailed indicators are collected and compared. If indicators were found to be more or less the same, they were treated as the same indicator. Units of these indicators have been left out to allow the merging of the indicators without omitting the exact meanings.

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Suprayoga, G.B., Bakker, M., Witte, P. et al. A systematic review of indicators to assess the sustainability of road infrastructure projects. Eur. Transp. Res. Rev. 12 , 19 (2020). https://doi.org/10.1186/s12544-020-0400-6

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Received : 05 August 2019

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DOI : https://doi.org/10.1186/s12544-020-0400-6

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