Incorporating Socioeconomic Metrics in Civil Engineering Projects: The Resilience Perspective

The world increasingly exhibits characteristics of a volatile, uncertain, complex, and ambiguous (VUCA) state (Millar et al. 2018). Four of the five most expensive years on record for weather- and climate-related disasters in the US occurred in the last four years, causing more than US$59 billion in direct losses (NOAA 2021). These losses largely consist of the required repair of damaged infrastructure, and increase substantially when considering the cascading impacts of the need to rehouse disaster victims and of lost business revenue. The VUCA state is defined by a combination of shocks and stressors, such as climate change, increasing occurrence and intensity of extreme events, resource scarcity, increasing wealth inequality, political conflicts, social justice, an aging population, pandemics, housing shortages, population growth, traffic congestion, poor air and water quality, the displacement of labor forces through automation, and dysfunctional political governance (Scovronick et al. 2017; Wetzstein 2017; Glasser 2019). Together, this instability challenges the fixed notions of performance, operations and maintenance, and reliability that historically have defined civil engineering projects (Field and Look 2018).

In response to this increasing uncertainty, and the fact that the complexity and interdependence of our infrastructure systems and projects (Ouyang 2014; Choi et al. 2019) are not reflected in current codes, the Infrastructure Resilience Division (IRD) of the ASCE was established in 2014. It had become apparent to the membership of the ASCE that an overarching effort to work across subdisciplines within civil engineering was needed to better articulate and facilitate common goals and objectives that consider the broader context of connected infrastructure systems. To address this issue, a number of technical committees were formed, including the Civil Infrastructure and Lifeline Systems Committee; the Disaster Response and Recovery Committee; the Emerging Technologies Committee; the Risk and Resilience Measurements Committee; and the Social Science, Policy, Economics, Education, and Decision Committee (SPEED). The SPEED committee focuses on integrating social science, policy, and economics into the planning, design, and management decisions surrounding physical infrastructure projects. The SPEED committee works across disciplines and consists not only of engineers, such as civil engineers, but also of sociologists and economists.

This paper describes one timely project within the SPEED committee that focuses on identifying metrics that allow for quantification of socially driven outcomes into civil engineering projects within the discourse of resilience. Specifically, we aim to reemphasize that investing in resilience in the context of infrastructure systems delivers value by reducing disruption and speeding recovery, connecting our communities, supporting our way of life, delivering productivity gains and economic growth, reducing environmental impact, and providing enhanced protection. In this way, resilience in the context of infrastructure systems is an essential component of national well-being, and the development and maintenance of a national competitive advantage (Chang and Shinozuka 2004; Callaghan and Colton 2008; Gilbert and Ayyub 2016; Markhvida et al. 2020). The embeddedness of infrastructure systems within communities is signified further in the context of resilience, because merely withstanding disruptions is not sufficient for an infrastructure system when it cannot maintain its role in the community (Naderpajouh et al. 2018). For this purpose, we synthesized a range of socioeconomic metrics to establish further their incorporation into civil engineering projects, and discussed them in three case studies.

Fundamental Canon 1 of the ASCE Code of Ethics states that “Engineers should seek opportunities to be of constructive service in civic affairs and work for the advancement of the safety, health and well-being of their communities, and the protection of the environment through the practice of sustainable development.” Standard engineering design prioritizes safety, but largely has left health and well-being out of the equation. The latter is evident from an examination of recent disaster events in the US, in which nonstructural damage (e.g., the 2019 Ridgecrest earthquake) and functionality failure (e.g., 2017 Hurricane Irma) caused widespread disruption and the majority of disaster losses and deaths. A change to the current approach, one that is based on resilience to include long-term socioeconomic considerations, profoundly is needed.

The concept of resilience has been discussed extensively across domains, including engineering, disaster, ecological, socioecological, urban, and community variants (Davidson et al. 2016; Meerow et al. 2016). Application of resilience with domain-specific conceptual, analytical, and empirical knowledge range from descriptive single-equilibrum processes to the normative development of future stable states shaped by collective social learning. In practice, these different conceptualizations of resilience have been applied to phenomena of varying scales and complexity, ranging from designed components of enginseered public infrastructure to participartory models for community engagement (Keenan 2017).

Engineering resilience has been internalized with varying degrees of analytical prowess with a focus on technical systems examining everything from systems engineering and software design to electrical engineering and microgrid design (Hosseini et al. 2016). Cross-disciplinary disaster researchers have associated disaster resilience with the addition of critical social dimensions and methodologies, namely those associated with social learning, the socialization of risk and responsibility, and human and public health (Cai et al. 2018; Tiernan et al. 2019). Bridging the material realm of engineering resilience with the social dimensions of disaster resilience, emerging scholarship and practice in community resilience has arisen as a means to understand methodologically the relationships between designed engineered systems and a range of users, managers, beneficiaries, and community stakeholders (Masoomi and van de Lindt 2019; Koliou et al. 2020). Although progress has been made in internalizing community resilience methods and knowledge into practice, there still is a pressing need to formalize and integrate the social and human dimensions into the analysis, design and management of technical systems (Sutley et al. 2017).

ASCE Policy Statement 518 states that “[r]esilience refers to the capability to mitigate against significant all-hazards risks and incidents and to expeditiously recover and reconstitute critical services with minimum damage to public safety and health, the economy, and national security.” The SPEED committee believes that the engineering resilience memorialized by the ASCE Policy Statement 518 should be contextualized by an epistemologically diverse field of different types of resilience, including ecological resilience and community resilience. To this end, it is critical that engineers not only engage the elasticity for single-equilibrium recovery associated with engineering resilience, but they also must understand and utilize a wide range of metrics associated with multiple potential future states associated with community and ecological resilience. In synthesizing engineering, ecological and community resilience, there is an opportunity to productively engage engineered systems and social systems with the adaptive capacity to accommodate a wide range of costs, benefits, and adaptive outcomes.

To date, metrics associated with standardized civil engineering procedures and guidelines often relate to physical properties of the materials used and the function of the finished built environment componenet. For example, engineers evaluate bridges in terms of the number and width of lanes (i.e., capacity) and the loads that they can carry. Similarly, they evaluate buildings in terms of compliance with applicable building codes, fire ratings, and zoning regulations. Although these metrics may be obvious to engineers, it is difficult for taxpayers or developers to understand the linkage to life, safety, health, and general welfare outcomes, particularly at the scale of a community. This failure to create linkages undermines a popular awareness of the important role that infrastructure plays in our daily lives.

It is not enough for engineers to provide a design that functions; they also must contribute to sustainable development as stewards of the natural environment (Levitt 2007) while also giving consideration to the distributional equity and procedural justice considerations of the communities that they serve and support (Doorn et al. 2019). To manage professional liability adequately, civil engineers will need to incorporate a variety of metrics and data, including those from social and natural sciences, and to understand fully the spectrum of considerations defining risk and minimally competent practices (ASCE 2007). This provides the impetus for the present work, which introduces new socioeconomic metrics that often are essential to professional practice.

Civil infrastructure projects are the network of human-made systems that function together to provide essential goods and services needed to maintain the functions of communities and societies and the broader environment (Marsh et al. 1997). Shocks and stressors may result in failure or compromise of the services of the interdependent systems that enable everyday life (Davis et al. 2018). These disturbances often disrupt the continuity of services to society (Choi et al. 2019). In particular, severe shocks tend to be uneven in their impact and associated costs—often disportionally impacting the most disadvantaged and vulnerable (Chang 2016; Hamideh and Rongerude 2018; Mitsova et al. 2018; Ye and Aldrich 2020).

An example of such an impact is flooding damage in the city of Lumberton, North Carolina, which is a diverse community with median income far below the US average. Lumberton suffered extensive flooding following Hurricane Matthew in 2016, and again following Hurricane Florence in 2018. A recent study showed that population stability (i.e., dislocation of households) is a function not only of flood damage but of race and ethnicity (van de Lindt et al. 2018, 2020; Sutley et al. 2021). Another example is the impact of the 2019 bushfires on the city of Braidwood, New South Wales, Australia, which relied heavily on the business of travelers from King Highway in Canberra. The extensive bushfires in Australia in 2019 resulted in the closure of King Highway, which heavily disrupted the businesses and lives of the residents of Braidwood (Canberra Times 2019). These examples highlight the increasing need to integrate impacts to persons and vulnerable populations into civil infrastructure projects, which requires convergence in research and practice (Lakhina et al. 2021), and a perspective change that includes accounting for how a project supports broader community outcomes and its ability to deliver multiple benefits (i.e., social, environmental, and economic).

The application of measuring broader social and economic outcomes is gaining traction, particularly on large projects that have both positive and negative impacts on communities. In addition, the measurement of the social value of projects often is required, particularly in Europe, for government-funded projects. For example, in the United Kingdom, the Public Services (Social Value) Act came into force in 2013, and requires people who commission public services to think about how they also can secure wider social, economic, and environmental benefits. However, this is not performed routinely on nongovernment projects, and there is not a consistent approach or set of metrics that currently is widely used.

In the US, the NIST formulated a six-step process for communities to adopt for setting and achieving resilience goals. In this context, community is defined across sociological and geographic boundaries to include a diverse array of people who have shared values, characteristics, and histories within specific geographic and networked relationships (Blokland 2017), which essentially is a scale at which decision-making is possible. This process (Fig. 1) (NIST 2015) can be translated easily to infrastructure projects striving for resilience. The first step is to form a collaborative team. In infrastructure projects, this should include representatives from the engineering team, the architectural team, individuals with expertise in economics and the social sciences, and relevant stakeholders from the community. The stakeholders engaged in this process must understand and represent the diverse community values, culture, and needs, and may include (1) representatives from the local government, such as community development, public works, and building departments; (2) public and private developers; (3) owners and operators of buildings and infrastructure systems; (4) local business and industry representatives; (5) representatives of community organizations, nongovernmental organizations, and health and educational institutions; and (6) other stakeholders or interested community groups, such as residents of public housing (NIST 2015). The second step is to understand the situation, or get everyone on the same page with the local context. Third, the team should determine goals and objectives for the project, including identifying which socioeconomic metrics should be adopted at each stage of the project. Fourth, a plan for meeting each metric should be established. Fifth and sixth, the plan should be reviewed, approved, and maintained by the entire project team. As discussed in the following sections, not every metric is needed for every project, and after a collaborative, diverse team is formed, other similar metrics also may be developed and incorporated for tailored resilience goals and assessment. In other words, the authors propose the need to include a broader set of metrics that can be tailored based on contextual needs, rather than a standardized global set of metrics. However, the proposed set of metrics can be used as a foundation to determine the list of metrics for each project.

In line with the seven capitals inherent in every functioning community, i.e., built, social, human, cultural, financial, political, and natural (Ritchie and Gill 2011), socioeconomic metrics are presented in Tables 1–6, categorized as measuring (1) health and well-being, (2) community cohesion and inclusivity, (3) social equity, (4) mobility and accessibility, (5) safety, and (6) prosperity, respectively. All six socioeconomic metric categories are critical for civil engineering projects being incorporated into communities in a way that improves resilience, rather than exacerbating socioeconomic vulnerabilities. All six socioeconomic metric categories include subcategories, and interacting metrics appropriately applied in either the infrastructure design phase, in quantifying the project’s role in the community, or in the finished project (Tables 1–6). The initial categories and design interventions were based on and extended from UKGBC (2018). The extension included metric adoption and development by the authors through focus group discussions and input from both literature and industry practices. Citations are provided in Tables 1–6 as appropriate for the adopted metrics. Each table and category is discussed briefly; this section concludes with supporting context for how to use the metrics.

Metrics supporting health and well-being are presented in Table 1, and include coverage of green community space, healthy air quality, good physical health, and rapid recovery postdisruption, thus capturing both normal and crisis periods. Specific focus is given to quantification of health and well-being associated with the broader range of project stakeholders, including infrastructure users, employees, customers, and the surrounding community or indigenous communities.

Community cohesion and inclusivity metrics are presented in Table 2, and include metrics for quantifying strong local ownership of the project/development/local risks, thriving social networks, strong sense of community culture and heritage, diverse and integrated community, and diverse project team. As in Table 1, emphasis is put on the broader range of project stakeholders, by emphasizing how the project supports the larger community’s goals of cohesion and inclusivity, as well as on the project users. Furthermore, there is an emphasis on how the broader socioeconomic metrics can be instigteed through diversity in teams that are involved in projects.

Two subcategories for social equity are presented in Table 3: fair and just approach; and engaged community with diverse representation in project teams and decision makers, such as designers and planners. Cohesion and inclusivity focus on making spaces available that can strengthen the fabric of the community, whereas social equity focuses on measuring whether those spaces are accessed and are meeting the disparate needs of stakholders, and how access to the decision-making process within project teams can facilitate this process.

Table 4 presents metrics for mobility and accessibility that specifically gauge community connectivity through access to different transportation options, including physical distance and transportation options for access of communities to a range of services. Table 5 presents metrics for safety that promote safe communities through metrics specific to projects, as well as across communities at large. Lastly, Table 6 shifts to economics to present metrics for prosperity, including four subcategories of thriving local businesses, job opportunities for local people, increasing productivity, and economic diversity. These metrics aim to capture the economic prosperity that is achieved by the project directly and through future growth stimulated by the project’s location.

What often has been excluded from the set of metrics used as proxies to evaluate public and private infrastructure are explicit factors that address those needs and preferences tied directly to life, safety, health, and well-being of the public. Such factors are accounted for by social and economic metrics. Metrics used within a resilience assessment for a community or project often are proxies of different dimensions of resilience (Burton 2015), which are developed as quantitative in nature, but qualitative assessments and inputs can enter into the decision-making process by providing additional information upon which to judge and weigh a dashboard of metrics (Linkov et al. 2013). Each metric may have a different level of importance depending on the community and context of the problem. To ensure that a variety of metrics are used, it is important that a diverse array of stakeholders is involved, as discussed previously, and that the project team devises a selection procedure that allows tailoring of metrics to specific projects and locales (Curt and Tacnet 2018). In developing the procedure, it is critical to understand, and most importantly for all parties to agree, that ‘one-size does not fit all.’ Therefore, metrics will vary depending on exposure, vulnerability and local conditions. After candidate metrics have been identified, the final selection and incorporation into project phases should be performed by selecting and giving weight to the final set of metrics so as to integrate them into the technical design.

Engineering, ecological, and community resilience should be considered at each project phase, with unique implications for the stakeholders, including the community. Therefore, there needs to be an understanding of the level of resilience that is designed into the system in order to address a wide spectrum of community and projects goals and trade-offs. Furthermore, the expected levels of disruption and plans for response and recovery following the inevitable future shocks and stressors must be understood and integrated.

Measures and metrics will be determined prior to commencement of the project. These can be used to compare the wider potential benefits of design options during the project; a hypothetical example of such scoring is presented in Table 7. Each metric can be scored on a Likert scale of 1–5 in terms of how well it would be achieved through that option. Subcategories and metrics within subcategories can be weighted and summed to provide a total for each category. Some of the activities associated with the categories will be implemented at different stages of the project. All the categories should start with a benchmark at the feasibility and planning stages of the project, and then should be measured constantly after the completion of the project to provide a quantified framework for the actual benefit delivered. These categories need clear definition of the context and type of the project to provide a formalized process of clarifying the meaning of Likert scales 1–5 among the project participants. These scales can be developed by local government or project developers with detailed explanation of the judgements, and can be refined through application within the projects. Furthermore, the use of metrics may result in a range of issues, including the reductionist approach to complex dynamics (Naderpajouh et al. 2016), paralysis by analysis (Langley 1995), misuse of metrics (Muller 2018; Saltelli 2019), or shortcomings in reflecting the transformative nature of resilience (Copeland et al. 2020). Therefore, there is a need to complement the use of metrics with rich local contextual data from the range of stakeholders.

Some real-world case studies are presented in this section to illustrate the outcome of accounting for key socioeconomic considerations in infrastructure project planning and design and the co-benefits that can be delivered. These case studies discuss how real-world projects previously have adopted socioeconomic considerations; a selection of metrics proposed in Tables 1–6 was added to the examples for illustrative purposes.

It is well-known that social dynamics impact the output of civil engineering projects to a large extent, despite the comprehensiveness of technical and engineering design. The inclusion of socioeconomic factors that significantly impact the communities hosting civil engineering projects is critical to ensure engineering, ecological, and community resilience performance in addressing the shocks and stressors of natural hazards, climate change, and global change. This can be accounted for by incorporating diverse socioeconomic metrics, such as those identified and catalogued in this paper. It is imperative that use of metrics is coupled with inclusion of disiplinary expertise and local context expertise, so that metrics are not applied in a way that has harmful or otherwise unintended consequences. Communities should choose appropriate metrics based on their needs and given the context of the challenges they face. In this sense, there is a need for qualitative and narrative support to the development and use of metrics, as well as an understanding of the assumptions and biases that come with any given metric. The inclusion of natural and social scientists as part of the design team may be required to meet these new project requirements so that the totality of the challenges can be recognized and successfully accommodated or accounted for in the civil engineering planning, design, and management processes.

There are major challenges to addressing the systemic impacts to society and specific populations that result from disruptions to civil infrastructure systems and associated services. For example, governments and private investors have finite budgets for long-term infrastructure investments, ongoing service provision (Vugrin et al. 2014), and resilience planning (Keenan 2018). Within an ever more competitive environment, maximizing the impact of capital and operating expenditures to ensure stability and reliability in the short- and long-term presents a complex design and management challenge (Ouyang 2017). A comprehensive approach to engineering, ecological, and community resilience can help prioritize how funds best should be invested to provide both optimal investment performance and the most robust set of performance outcomes (Field et al. 2017). This requires preciseness in engineering economics when specifying the specific system boundary, performance outputs, time horizons, and investment beneficiaries; in essence, not attempting to monetize everything within a resilience problem, but rather presenting multifaceted alternatives to decision-makers (Cutter 2016).

A comprehensive list of socioeconomic metrics is provided based on the identification of a broad range of issues, and were discussed within the context of real word examples. These examples represent a first step toward demonstrating how socioeconomic considerations can be integrated into project decision-making and planning. Future potential steps include richer case studies that need to be studied with sufficient detail and through comparative analysis. In this stream, the next steps for the SPEED committee include the development of a detailed project with quantification of several key socioeconomic resilience metrics to demonstrate how these metrics can, would, or should be integrated into a large civil infrastructure project. That case study will include the planning, design, and operation phases of a large public project. Furthermore, the committee aims to study the limitations of the use of socioeconomic factors in civil infrastructure projects through these case studies.

No data were generated as part of this paper.

The authors acknowledge all ASCE Infrastructure Resilience Division (IRD) Social Science, Policy, Economics, Education, and Decision (SPEED) committee members for their contribution through committee discussions on the manuscript and presented metrics. This work was performed as part of a project sponsored by the ASCE IRD awarded to the SPEED Committee to establish socioeconomic metrics for civil engineering projects. The views expressed are those of the authors and may not represent the official position of ASCE or IRD.