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https://doi.org/10.1177/1078087419884650
Urban Affairs Review 2021, Vol. 57(2) 583 –608
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Article
The Perfect Amount of Help: An Examination of the Relationship Between Capacity and Collaboration in Urban Energy and Climate Initiatives
Rachel M. Krause1 , Christopher V. Hawkins2, and Angela Y. S. Park3
Abstract Many municipalities are taking meaningful action in pursuit of climate, environmental, and energy objectives. These issues are complex and transboundary and thus provide fertile ground for collaboration, particularly in metropolitan regions. However, despite the many benefits that can result from collaboration, it also entails transaction risk. As a result, cities have incentive to be selective about who they collaborate with. In some cases, cities, particularly those with considerable internal resources and capacities, might find it easier to “go it alone.” We pull from the literature on collaboration risk, transaction cost economics, and organizational capacity to develop hypotheses about the relationship between capacity and collaboration in urban sustainability initiatives. Our analysis finds that the extent to which cities collaborate with external organizations on climate
1The University of Kansas, Lawrence, KS, USA 2University of Central Florida, Orlando, FL, USA 3Kansas State University, Manhattan, KS, USA
Corresponding Author: Rachel M. Krause, School of Public Affairs & Administration, The University of Kansas, 4060 Wescoe Hall, 1445 Jayhawk Blvd., Lawrence, KS 66045, USA. Email: [email protected]
884650 UARXXX10.1177/1078087419884650Urban Affairs ReviewKrause et al. research-article2019
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and energy issues is shaped by local administrative capacity; however, the relationship is nonlinear.
Keywords administrative capacity, urban sustainability, cities, climate protection, collaboration
Introduction
Collaborative governance—and questions of why it is pursued, what makes it effective, and what it offers in terms of public outcomes—is a topic of con- siderable saliency in policy, governance, and public administration research. Moreover, because it is a key mechanism for overcoming institutional collec- tive action dilemmas, which particularly afflict metropolitan regions, collab- orative governance is also a focus of urbanists (Feiock 2009). Multiple traditional local government responsibilities—such as land use planning, transportation, and economic development—have an extended history of being examined through a collaborative governance lens; the regional exter- nalities associated with these issues are readily visible and the importance of extra-jurisdictional cooperation to the achievement of policy objectives has long been recognized (Berman, Smith, and Bauer 2005; Feiock et al. 2009; Gerber, Henry, and Lubell 2013).
More recently, a similar collaborative governance lens is being employed to examine local leadership on sustainability issues including climate change and energy use (Gollagher and Hartz-Karp 2013; Hawkins et al. 2018; A. Park, Krause, and Feiock 2019). A substantial portion of greenhouse gas emissions in the United States are attributable to buildings and land uses that are regulated by local governments (Nolon 2013). Recognizing the signifi- cance of their role, a large number of cities have made progress in advancing policy and institutional changes able to facilitate climate protection objec- tives (Hughes 2017). However, it is broadly recognized that local govern- ments cannot generate the necessary scale of changes alone (van der Heijden et al. 2019). Along these lines, urban scholars have noted the importance of cities’ ability to manage transboundary policy initiatives in contexts charac- terized by political fragmentation and divergent stakeholder preferences toward policy solutions (Feiock 2009). Notably, research on the ability of local governments to develop environmental policy proposals and subse- quently implement them across city government departments and the larger
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community have often centered on cities’ professional human capacity (Swann 2017; Terman and Feiock 2015).
Our research enters this crowded space and examines the relationship between cities’ capacity and the extent to which they collaborate with exter- nal actors to achieve climate and energy objectives. Although assessments of the effect that human capacity has on collaboration are not particularly novel, most existing studies hypothesize and find evidence supporting a simple pos- itive linear relationship between them (Hawkins et al. 2016; Swann 2017). We pull from literature on collaboration risk and transaction cost economics and hypothesize a more complicated relationship. Specifically, we look at the factors that influence city governments’ decisions to work with a variety of governmental and private entities on energy efficiency and climate protection and find evidence of a nonlinear relationship between cities’ overall human capacity and their degree of external collaboration. The resulting curvilinear relationship suggests that at lower levels of capacity, increases lead to greater collaboration. However, at some point that effect diminishes and eventually turns negative, suggesting that, at high levels of internal capacity, additional collaborative partnerships are relatively less advantageous.
Mutual advantage, either real or perceived, is a necessary but insufficient condition for organizational collaboration. Put simply, potential partners would opt not to engage in voluntary joint action unless they both expect to benefit. Organizations, generally speaking, do not engage in collaboration purely out of altruism (Emerson and Nabatchi 2015). However, even when beneficial, collaboration comes at a price, typically in the form of reduced autonomy and a variety of transaction costs. If the costs of working together outweigh the benefits of doing so, collaboration will not occur. These simple postulates, which are standard expectations of behavior for “rational actors,” open the door to a wide range of research questions, including those about the types of benefits generated by collaboration and the conditions under which they are maximized (A. Park, Krause, and Feiock 2019; Scott 2015, 2016); factors that influence the costs of collaboration (Berardo and Scholz 2010; Feiock 2009, 2013); and characteristics of the organization or problems that drive its pursuit (Andrew 2009; Shrestha and Feiock 2011).
Our research builds from this base and uses a collaboration risk lens to gain insight into the relationship between administrative capacity and the extent of an organization’s collaborative partnerships. We start from the premise that, despite the many benefits that can result from collaboration, it also entails risk (e.g., will partners follow through as promised?) and cost (e.g., information gathering, negotiation, monitoring). As a result, organiza- tions have incentive to be selective about who they collaborate with. In the subsequent pages of this article, we develop and test hypotheses related to
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how organizational capacity shapes actors’ need to collaborate with others to accomplish their objectives. We also consider that, at some point, organiza- tions with considerable internal resources might find it easier to “go it alone,” leading us to hypothesize a nonlinear relationship whereby the extent of col- laboration begins to decline at high levels of capacity. The results have impli- cations for understanding the degree to which cities are able to pursue potential partnerships and implement sustainability initiatives.
We utilize climate and energy challenges as the issue settings in which to examine city governments’ collaboration with a variety of governmental and nongovernmental organizations. Like many other challenges that local gov- ernments face, they do not adhere to jurisdictional boundaries and may require levels of resources and expertise that cities do not possess internally. As such, and particularly in metropolitan regions, it is a policy area ripe for collaboration. The article proceeds as follows: The next section reviews the extant literature on the benefits and costs of collaboration, both in general and as related to the environment and sustainability. It then reviews the literature on collaboration risk and administrative capacity and presents hypotheses about their relationship. This is followed by a description of data and meth- ods, a presentation of results, and a discussion of their implications on local sustainability partnerships.
Collaboration Benefits and Costs
Collaboration tends to be presented as normatively good and, often, as neces- sary to effectively address challenges that cut across organizational or juris- dictional boundaries. Although there are legitimate critiques about the extent to which the literature has uncritically characterized collaboration as a pana- cea to complex public problems (Bryson, Crosby, and Stone 2006; Huxham 2003; McGuire 2006), an increasing number of empirical studies demon- strate its positive effects. They have, for example, found collaboration as effective in building trust (Lubell 2005), promoting policy learning (Leach et al. 2013) and contributing positively to perceived organizational outcomes (Gazley 2010). Looking more narrowly in the realm of urban energy and the environment, Swann (2017) finds that interlocal collaboration increases the number of green practices implemented both in city government operations and the community at large and A. Park, Krause, and Feiock (2019) find that having a larger collaborative network increases the efficiency of cities’ energy retrofit initiatives. Looking at outcome rather than output or process, Scott (2015) finds that collaboration in watershed management improves water quality and Kalesnikaite (2019) concludes collaborative activity leads
Krause et al. 587
to improved flood management in U.S. cities. Failure to collaborate risks cit- ies missing out on these potential benefits.
Despite these benefits, collaboration can impose significant costs on part- ners. Decision-making becomes more time-consuming when it requires agreements be reached by multiple actors (Mitchell, O’Leary, and Gerard 2015) and participation in collaborative activities can shift employees’ focus away from their primary responsibilities and routine tasks (Emerson and Nabatchi 2015; Mitchell, O’Leary, and Gerard 2015). The institutional col- lective action framework identifies three types of transaction costs—infor- mation costs, negotiation costs, and enforcement costs—that emerge when cities engage in collaborative activities (Feiock 2009, 2013). Information costs accrue during the search for potential partners, technical advice, best practices, and strategies to inform decision-making. Negotiation costs are those involved in the bargaining process, where potential partners work to align objectives and form agreements on the allocation of responsibilities, contributions, and benefits. Enforcement and monitoring costs are associated with ensuring follow-through on the part of collaborative partners. In addi- tion, collective action inevitably involves the loss of some degree of organi- zational autonomy as actors consider joint interests instead of solely individual actions.
Given these costs, the perceived advantage of collaborative action must be sufficiently large for it to be pursued. The size of this advantage is shaped not only by the nature of the problem but also by its severity. As problems become more severe, growing demand for effective solutions may overwhelm trans- action cost-related barriers (Krutilla and Krause 2011). Moreover, the repeated interactions and trust that can accrue between actors as a result of jointly addressing long-term problems may reduce the costs of collaborating on related newer issues. For example, a large number of U.S. urban areas have faced air quality nonattainment conditions under the Clean Air Act, which requires cities and regional players to work together to find solutions or risk the loss of federal highway monies. Many of the same players that have been active around regional air quality are also involved in local energy and climate initiatives and their history of previous interactions may reduce transaction costs and make collaboration on these newer objectives more likely.
In addition, Feiock (2009, 2013) identifies a variety of mechanisms to achieve institutional collective action that vary in their degree of complexity and imposed authority. Cooperative networks and partnerships—which are the focus of this study—are the least demanding on both these dimensions and are typically associated with the lowest transaction costs. Compared with other mechanisms, they enable members to maintain a high degree of
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autonomy, and that which is given up is done so informally and voluntarily (Feiock 2009; Heclo 1978). As such, local actors tend to prefer these arrange- ments to facilitate coordination and resolve “low-risk” collective action situ- ations (Feiock 2013). When actors share a common goal, and coordination is focused on relatively routine activities, collaboration can be a straightforward endeavor with low transaction costs (Berardo and Scholz 2010; McAllister, Taylor, and Harman 2015). However, when tasks become more complex and success depends on the interconnectedness of a large number of actors, a greater degree of authority and/or significant investments in trust-building may be necessary for collective action.
The multilevel context in which collaboration takes place also shapes the relative costs and benefits associated with collaboration around energy and climate-related objectives. State and federal agencies can act as direct col- laborative partners with cities and, indirectly, their policies may influence the ease with which cities engage with other entities. For example, certain state policies—such as California’s SB 375 which sets regional greenhouse gas emissions targets and requires cities and counties to develop plans to achieve them—may increase the likelihood of collaboration between various actors within that state by ensuring the issue is on their mutual radar screens. In comparison, Federal policy and agency engagement is also expected to influ- ence overall collaboration, but in a manner that is relatively consistent across all U.S. cities. For example, the Department of Energy (DOE) considered cities’ “partnership structure and capabilities” as part of its Energy Efficiency Conservation Block Grant (EECBG) application review (U.S. DOE 2009). It is reasonable to expect that this attachment of a tangible reward for collabora- tion increased its overall frequency.
Administrative Capacity, Resource Dependency, and Collaboration Risk
According to resource dependency theory, policy actors search for and accrue resources that are both material (e.g., financial resources, informa- tion and knowledge, and personnel) and nonmaterial (e.g., organizational legitimacy, symbolic power, and prestige) in nature to carry out their mis- sion and achieve their goals (Alter and Hage 1993; Pfeffer and Salancik 1978; Weible 2005). The underlying assumption of this theory is that orga- nizations operate within uncertain and fluctuating environments that are shaped by scarcity, unpredictability, functional specialization, and lack of control over critical resources. As a result, organizations develop strategies, including coordinating with external entities, to reduce the uncertainty of
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obtaining difficult-to-acquire resources that are essential for organizational survival (H. H. Park and Rethemeyer 2012).
By providing opportunities for innovation and learning through the shar- ing of information, collaboration can enable administrators to leverage scarce resources (Isett et al. 2011; McGuire 2006; Milward and Provan 2006; Provan and Lemaire 2012). Engaging in collaborative networks also represents one strategy for cities to manage external dependencies and uncertainties and buf- fer against turbulent conditions in the resource environment (Pfeffer and Salancik 1978). Cities that lack resources have incentive to expand their col- laborative network to better ensure that necessary resources can be obtained (Berardo 2009; Hawkins et al. 2018). Thus, from a resource dependence per- spective, lower capacity cities have a greater need for collaboration.
Nonetheless, despite the greater need for collaboration cities with less capacity may be less adept at achieving it. Low-capacity cities face the dual challenge of having fewer resources to devote to pursue collaborations while also being viewed more cautiously by potential partners. Specifically, they may be seen as not bringing much to the table, having little ability to recipro- cate, and as being high risk for potentially not following through on agree- ments. Furthermore, while organizations can more easily reach their goals when they engage other actors and benefit from shared resources, the value of those relationships diminish as the organization’s ability to process these resources reaches its limit (Berardo 2009). More information is preferable to less, but this is true only as long as the recipient is not overwhelmed by its availability. The amount of information an organization can effectively pro- cess at any given time is directly tied to its human capacity (Zandt 2004). These dynamics lead to the general expectation that greater administrative capacity has a positive effect on the extent of cities’ collaboration (Hawkins et al. 2018; Swann 2017). Specifically, we hypothesize the following:
Hypothesis 1: Cities with greater capacity are engaged in more extensive collaboration with a larger number of external partners.
However, this effect may diminish after a point. Cities with particularly high administrative capacity often make significant internal investments to obtain asset-specific goods, including expertise in specialized functional areas (Shrestha and Feiock 2011; Williamson 1991). In the current context, asset-specific investments might include the completion of a local green- house gas emissions inventory, energy audit, or climate action plan. They also may include the creation of new staff positions characterized by well- specified responsibilities and requiring particular skill sets. Cities that have made investments in relevant administrative capacity have greater expertise
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to pursue their intended climate and energy initiatives “in-house” and can avoid the additional transaction costs that accompany building and maintain- ing an extensive collaborative network.
Decision makers weigh the anticipated gains—both in terms of efficiency and likely outcome—of utilizing an expansive collaborative network versus “going it alone” or only with a small number of select partners. It is reason- able to expect that, relative to other cities, those with particularly high capac- ity are less dependent on external partners for benefits and gain fewer resources from expanding their numbers. Thus, these high-capacity cities may engage in collaborative networks less extensively, resulting in a plateau or possibly a concave relationship between capacity and collaboration extent. With a few exceptions, this hypothesized nonlinear relationship has not received close attention (Graddy and Chen 2006).1 Thus, we hypothesize the following:
Hypothesis 2: For high-capacity cities, increases in administrative capac- ity yield diminishing returns on collaboration. Eventually, additional capacity reduces the extent to which they engage with external partners.
Sample, Data and Methods
The relationship between cities’ organizational capacity and the extent to which they collaborate with external actors on issues related to climate and energy is examined using a combination of data gathered from the Integrated City Sustainability Database (ICSD; Feiock et al. 2014) and a variety of archival sources. The ICSD contains harmonized data compiled from seven different surveys, each covering a nation-wide sample of U.S. cities over an 18-month period in 2010 and 2011. Although independently administered, the surveys focused on the same issues (local climate, energy, and sustain- ability policies), contained many of the same questions, and were sent to many of the same cities.
As the sample for this research, we consider the 1,038 U.S. cities with populations over 30,000, according to the 2000 Census. We draw primarily from one of the ICSD surveys: the 2011 EECBG Grantee Implementation survey, which was sent to the designated EECBG liaison in all of the city gov- ernments eligible for formula-based EECBG awards, including nearly all cit- ies with more than 30,000 residents. The survey was administered electronically with hard copies mailed as follow-up to nonrespondents. Useable responses were received from 687, or 66%, of the cities in our sample. The dependent variable and three independent variables used in the analysis are obtained
Krause et al. 591
from this survey. Archival sources—specifically the Annual Survey of Public Employment, the U.S. Economic Census, Environmental Protection Agency (EPA) Air Quality Index Report, International City/County Management Association form of government listing, and the U.S. Census American Community Survey—are also drawn from to operationalize the independent variables. After merging these data sets, 428 city observations remain, which is 41% of the original sample frame. Missing data from the Annual Survey of Public Employment, which provides a key independent variable, is the pri- mary reason for the drop in observations leading to the final sample.
Table 1 provides a basic comparison of the cities in the full sample frame, that is, U.S. cities with more than 30,000 residents, with the final sample used in the analysis. The two groups of cities have similar mean populations, median household incomes, and rates of educational attainment. The most notable variation can be seen in the distribution of cities across population categories, whereby a greater portion of cities in the final sample have popu- lations above 100,000. Slight variation across regions is also observed.
Dependent Variables
We characterize the dependent variable in this study as “the extent to which” a city collaborates with external partners on climate and energy issues. This
Table 1. Comparison of Sample Cities to All.
U.S. Cities above 30,000 (n = 1,038)
Cities in Final Sample (n = 428)
Population (2000) M 112,205 115,453 Percent below 50,000 42.2% 27.3% Percent 50,000–100,000 34.8% 42.5% Percent 100,000–500,000 20.1% 25.2% Percent above 500,000 2.7% 4.9% Regiona
Northeast 14.5% 10.5% Midwest 23.5% 27.6% South 29.9% 32.7% West 32.1% 29.2% Median Household Income (2000) $44,790 $42,635 Bachelor degree+ (2000) 26.8 27.5
a.Per the U.S. Census Bureau’s designation of regions.
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includes and combines two component measures: the number of different types of collaborative partners a city engages with, sometimes referred to as the scope of its collaborative network (Hawkins et al. 2018), and the degree of collaboration with these partners. Specifically, with regard to the former, the dependent variable indicates the extent that a city works cooperatively with seven types of external organizations: other cities in the region or metro area, regional organizations, state government agencies, federal government agencies, utilities, private businesses, and universities. The degree of collab- oration with each of these is measured on a scale of 0 (not at all) to 4 (a great extent), as indicated by the survey respondent. The full dependent variable has a 28-point range, calculated as each potential partner type weighted by the specified collaboration extent. For example, a city that works coopera- tively to “a great extent” with two of the seven organization types noted above and “not at all” with any others would have a collaboration value of eight. We also break down the full collaboration variable into three sub-com- ponents, based on partner type: nearby cities and regional organizations (hor- izontal collaboration); state and federal agencies (vertical collaboration); and businesses, utilities, and universities (nongovernmental collaboration). The dependent variable for each subcomponent is measured using the same method as the full collaboration variable.
There are numerous different ways to operationalize and measure collabo- ration, none of which are fully satisfactory. Although not immune from criti- cism, indices similar to the one employed here have been used in network and collaboration studies in the larger literature (such as Agranoff and McGuire 2003) as well as in studies focused specifically on cities’ involvement in col- laboration on energy, environment, climate, and sustainability issues (see, for example, Kalesnikaite 2019; A. Park, Krause, and Feiock 2019; Swann 2017).
Independent Variables
A total of five independent variables are used to operationalize organizational capacity. Key among these is the total number of full-time equivalent (FTE) employees working in city departments broadly related to sustainability and the square of this number. The inclusion of the quadratic term in the model enables us to test the potential nonlinear relationship that exists between human capacity and the dependent variable. The U.S. Census’ Annual Survey of Public Employment contains the total number of employees in cities across the country broken into 31 sectors. We construct our focal independent vari- able Sustainability-related FTEs, by summing the number of FTE employees across 10 of these sectors, specifically: Health, Housing and Community
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Development, Natural Resources, Parks and Recreation, Public Welfare, Sewage, Solid Waste Management, Transit, Water Supply, Water Transport. Although climate and energy issues may not be their primary function, all of the sectors included in the independent variable intersect with and potentially influence cities’ ability to implement relevant projects or achieve goals. Climate and energy objectives transcend the functions of multiple units across city government (Krause, Feiock, and Hawkins 2016). For example, although the responsibilities of a city’s Public Welfare department may not explicitly include energy and climate, it is still relevant if it oversees a com- munity’s public housing facilities and shapes whether and what energy effi- ciency improvements are made to them. The professional staff capacities of these “indirect” units may be particularly influential in shaping whether cities need the expertise of outside partners to pursue energy and climate goals.
Certain sectors were excluded from this independent variable because they are not widely shared as city government functions. For example, air transportation, hospitals, and elementary and secondary education are not a part of all city governments and their inclusion would make total FTE num- bers incomparable across cities. We also exclude from the count employees in public safety jobs, that is, police and fire. Although a core local govern- ment service, public safety employees tend to interact with the city govern- ment in ways that are qualitatively different than those in other line departments. Finally, we exclude general administrative FTEs from the count to keep variable targeted on human capacity in sustainability-related func- tional sectors. As a robustness check, a variable was constructed that also included counts for employees working in general or financial administra- tion. The use of this broader variable did not meaningfully change model results.
The cities in the sample had between zero (Centennial, CO and Elsmere, DE) and 4,050 (Denver, CO) FTE staff employed in one of the 10 sustainabil- ity-related sectors, with an average of 369 employees. The number of sustain- ability-related staff correlates with the cities’ total staff at 0.87. In the empirical model, the Sustainability-related FTEs variable is expressed in terms of hundreds of employees and is centered on its mean prior to squaring to reduce collinearity with its quadratic counterpart. The centering does not affect coefficient interpretation. To our knowledge, a count of sustainability- related FTEs has not been used in empirical examinations of local sustainability.
We include in the model several additional variables that are frequently employed in the empirical literature on local climate and sustainability as indicators of local capacity. Dichotomous variables indicating whether a city has a dedicated sustainability budget and dedicated staff are among the most
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consistently significant capacity measures found in these literatures (Krause 2012; Swann and Deslatte 2019; Wang et al. 2012). Cities’ own source reve- nue, that is, the funds collected via local taxes and fees that are not tied to intergovernmental transfers, serves as a common proxy for cities’ general fiscal health (Hawkins et al. 2018). The impact that these indicators have on the extent of cities’ collaboration is substantively meaningful; however, in the current context, they are used to help isolate the relationship that exists between cities’ overall human capacity and collaboration.
Another factor that may shape the extent to which a city collaborates on energy and climate protection includes the local saliency of these issues. If they are considered important by city residents, governments may be more likely to take action and engage with others in an attempt to resolve them (Portney 2005; Portney and Berry 2010). In addition, cities that have previ- ously faced challenges on related issues that required a collaborative response may already have some relationships formed and thus have an easier time developing partnerships to pursue energy and climate objectives. Along these lines, a measure of past local air quality is included in the model, with the expectation that cities that have faced a greater number of “unhealthy” air days may have already established connections with relevant actors, provid- ing a base for a larger collaborative network.
Local institutions, particularly cities’ form of government have been shown to influence the nature of the energy and climate activities that they pursue, with council-manager forms having more efforts focused on govern- mental operations and mayor-council forms increasing community-orien- tated actions (Bae and Feiock 2013). Finally, residents’ educational attainment and cities’ overall population size are included in the model. Past research indicates that highly educated individuals are more likely to hold pro-envi- ronmental views and use their political power to advance quality of life issues (O’Connell 2009; Saha 2009; Wang, Hawkins, and Berman 2014). Larger cities have likewise been found to engage in more sustainability-related activities (Krause 2012), which may have implications for collaboration. Table 2 describes all the variables and their sources and provides their means and standard deviation. Given the 29-point range of the dependent variable and its relatively normal distribution, we use ordinary least squares regres- sion to estimate the model. Robust standard errors are clustered by state.
Results
Table 3 presents the results of the full model on the factors that impact the extent to which cities collaborate with outside actors on energy and climate initiatives. Three of the five independent variables representing city
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government capacity are significant at α = .05, including the variables of primary interest: the number of FTE city employees and the square of that number. The significant positive relationship that the number of FTE employ- ees and the presence of a dedicated sustainability budget have with the extent of collaboration provides support for the first hypothesis, which posits that increased capacity is associated with increased collaboration. The significant and negative coefficient for the FTE quadratic term indicates that the rela- tionship between the number of city employees and collaboration is not lin- ear, which supports the second hypothesis. A positive relationship between FTE and the scope of collaboration exists up to a certain point, after which, additional increases in staff have the effect of reducing collaboration. More specifically, the results in Table 3 can be interpreted as indicating that, all else equal, when a city has no sustainability-related staff, the addition of 100 such staff increases its collaboration score by 0.462 on a scale of 0 to 28. However, the significant negative quadratic term suggests that this effect is not constant and decreases slightly (by 0.014) with every additional 100 FTE. The effect turns negative and begins to reduce the scope of cities’ collaboration at just over 1,600 sustainability-related staff. Figure 1 shows this curvilinear relationship.
All else equal, cities with budget lines dedicated to sustainability initia- tives have collaboration scores that are 2.7 points higher than those which do not. However, neither cities’ levels of per capita own source revenue nor hav- ing staff explicitly dedicated to sustainability—as opposed to working
Table 3. The Factors that Influence Cities’ Collaboration with External Partners on Climate and Energy Initiatives.
City FTE employees 0.462*** (0.12) City FTE employees (squared) −0.014*** (0.00) Sustainability budget 2.704*** (0.88) Sustainability staff 0.568 (0.76) Per capita own source revenue 0.001 (0.00) Issue importance 0.990*** (0.23) Air quality −0.058** (0.02) Mayor-council form of government 0.459 (0.62) Education 0.012 (0.02) Population (1,000) 0.003 (0.00) Constant 9.754*** (2.37)
Note. N = 389, R2 = .245, F = 21.32 (0.000). Ordinary least squares with robust SEs clustered by state. FTE = full-time equivalent. *p < .1. **p < .05. ***p < .01.
598 Urban Affairs Review 57(2)
in generally related units, as the FTE variables indicate—are significantly associated with the extent of cities’ collaboration. Of the independent vari- ables not related to capacity, issue importance and local air quality signifi- cantly influence the extent to which cities’ collaborate with outside entities on climate and energy issues. Cities whose local officials perceive that these issues are more important to residents also engage in greater collaboration, as do those cities that experienced more unhealthy air days the prior year.
Table 4 shows the results of the regression models with the dependent variable broken down by collaborative partner type. Three independent vari- ables are significantly associated with an increase in collaboration around energy and climate issues across all partner types: regular and squared FTE terms and energy and climate being considered priority issues for the com- munity. However, although a curvilinear impact is evidenced between FTEs and collaboration across all three partner types, the relationship is both sub- stantively and significantly larger with nongovernmental actors (see Figure 2). Other differences are likewise notable, including that having a dedicated sus- tainability budget is significantly associated with more extensive collabora- tion with nongovernmental partners and state and federal agencies, but not local and regional governments. Collaboration with local and regional
5 10
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Figure 1. Relationship between the number of FTE employees and the extent of cities’ collaboration with external partners. Note. FTE = full-time equivalent; CI = confidence interval.
Krause et al. 599
governments, however, is modestly associated with the presence of dedicated sustainability staff and with fiscal health as represented by a greater own source revenue. Perhaps more interestingly, local air quality is associated with increased collaboration only with other cities and regional organiza- tions. The significance of this association appears to be driving the α = .05 significance seen in the full model (Table 2). The negative coefficient sign is interpreted as indicating that better air quality reduces the extent of regional collaboration around energy and climate issues. Alternatively, and perhaps more helpfully in this context, cities that have faced greater air quality chal- lenges in the recent past are more engaged with other governments in the region on climate and energy issues. Ambient air quality issues, which require a regional response, may incentivize local governments to work together on related issues.
Discussion
The results of the regression analysis identify factors associated with the extent to which cities collaborate with a variety of external entities on energy
Table 4. The Factors that Influence Cities’ Collaboration with Different Types of External Partners on Climate and Energy Initiatives.
Local and Regional Governments
(DV Range 0–8)
State and Federal Government Agencies
(DV Range 0–8)
Businesses, Utilities and Universities
(DV Range 0–12)
City FTE employees 0.098* (0.055) 0.129** (0.051) 0.222*** (0.068) City FTE employees
(squared) −0.004*** (0.001) −0.003** (0.001) −0.007*** (0.002)
Sustainability budget 0.506 (0.342) 0.653** (0.309) 1.493*** (0.413) Sustainability staff 0.488* (0.290) 0.063 (0.300) 0.175 (0.407) Per capita own 0.001* 0.000 0.000 Source revenue (0.000) (0.000) (0.000) Issue importance 0.306*** (0.084) 0.319*** (0.076) 0.416*** (0.115) Air quality −0.060*** (0.007) −0.005 (0.008) 0.007 (0.010) Mayor-council form
of government −0.094 (0.243) 0.301 (0.227) 0.194 (0.255)
Education 0.011 (0.006) −0.001 (0.008) 0.001 (0.010) Population (1,000) 0.002 (0.002) −0.000 (0.001) 0.002 (0.002) Constant N 405 399 393 F 15.99 (0.000) 13.59 (0.000) 18.94 (0.000) R2 .187 .147 .244
Note. Ordinary least squares with robust SEs clustered by state. DV = dependent variable; FTE = full-time equivalent. *p < .1. **p < .05. ***p < .01.
600 Urban Affairs Review 57(2)
and climate initiatives. The key finding revolves around the significant and nonlinear impact that professional capacity has on collaboration. Past research has indicated that increases in local government capacity, particu- larly professional capacity, lead to additional activity and greater collabora- tion around environmental issues (Hawkins et al. 2018; Swann 2017). However, the prevailing conclusion that the capacity-collaboration relation- ship is positive and linear may, in part, be an artifact of the measurements and data utilized. Operationalizing human capacity is tricky and the extant empir- ical literature on urban sustainability, which relies heavily on survey data, has generally done so using dichotomous variables that indicate, for example, whether a city has a dedicated sustainability office (Cruz 2018), dedicated sustainability staff (Homsy and Warner 2015; Pitt 2010), or a sustainability coordinator (Krause 2012). In attempt to be somewhat more comprehensive, Hawkins et al. (2018) add several of these dichotomous measures together to form a 4-point “capacity index.” However, neither dichotomous nor ordinal measures of capacity allow for the possibility of a nonlinear relationship to be meaningfully examined. A continuous variable is necessary.
Figure 2. Relationship between the number of FTE employees and the extent of cities’ collaboration with different types of external partners. Note. FTE = full-time equivalent; CI = confidence interval.
Krause et al. 601
While retaining traditional measures as controls, this article offers a new approach to operationalize city governments’ human capacity around sustain- ability by counting the number of FTE staff they employ across 10 different sustainability-related functions. Although not all of the employees in this variable count are directly involved in energy and climate issues, they work in city units that intersect with and often help implement relevant initiatives. The square of this broad measure of capacity enables the presence of a non- linear relationship to be assessed. The results generated by our empirical models caution against assumptions that additional investments in adminis- trative capacity have an indefinite positive effect on the scope of the collabo- ration. Rather, the results also support the second hypothesis that increases in capacity yield diminishing returns on collaboration, which eventually turns negative and results in a curvilinear relationship.
Broad engagement with different types of organizations facilitates access to a range of information and resources, which in turn can lead to greater efficiency and improved environmental outcomes (A. Park, Krause, and Feiock 2019; Scott 2015). However, cities are not equally equipped to take advantage of these benefits. Pursuing collaboration takes time and effort and cities with greater administrative capacity may have greater ability to seek out partners and manage subsequent relationships. Moreover, because public organizations select their partners strategically and target those best able to help them achieve their organizational goals (Agranoff and McGuire 2003), high-capacity cities are more likely to be viewed as attractive partners. The empirical results presented in Table 3 suggest that for many cities, greater administrative capacity translates into a more expansive collaborative net- work. Eventually, however, additional investments in administrative capacity loses its positive effect on collaboration. As the results demonstrate, above a certain point—shown here to be roughly 1,600 full-time employees in sus- tainability-relevant sectors—the addition of more staff has a negative impact on the extent of cities’ collaborative networks around climate protection and energy initiatives. The presence of considerable resources and in-house expertise reduces cities’ reliance on external collaborative partners to achieve goals. This relative independence, in turn, may reduce the amount of transac- tion costs they are willing to accept and ultimately lead them to engage with fewer partners.
An examination of the different types of partner organizations considered in this study provides additional insight into the relationship between capac- ity and collaboration. In all three models presented in Table 4 and Figure 2, the hypothesized nonlinear relationship between professional capacity and collaboration is observed; yet the strongest effect—both substantively and statistically—is found in cities’ collaboration with nongovernmental
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partners. The particularly strong curvilinear relationship exhibited may be driven by cities’ initial need for context-specific information as well as the larger transaction costs assumed by working with these organizations. More specifically, because energy and climate-related initiatives are often technical and situationally specific, there is a premium for context-driven research and knowledge. It is possible that the nongovernmental organizations considered here (e.g., businesses, universities, utilities) are best suited to provide cities with tailored information able to meet local needs. Existing research also suggests that, in comparison to other actor types, cities in collaboration with nongovernmental actors is more likely formal. Whereas, collaboration between different government actors around sustainability may be based on personal relationships or frequent interactions through professional affilia- tions, collaborations with nongovernmental organizations often involve con- tracts and financial exchanges (Gazley 2008). As a result of their higher transaction costs, they may be among the first to be let go as internal capacity grows.
Also of interest in Table 4 is the observed relationship between the pres- ence of a dedicated sustainability budget and the expansiveness of collabora- tion. Effectively managing urban problems through collaborative partnerships often requires public officials to have adequate financial capacity (Turrini et al. 2010). Dedicated budget resources provide a stabilizing mechanism for collaborative activities and also signal a credible commitment for following through on individual tasks. The results suggest that, depending on the type of partner, these resources vary in their importance for explaining cities’ col- laborative networks around climate protection and energy initiatives. For example, cities with a dedicated sustainability budget may be able to better engage with organizations (e.g., business, utilities, and universities) that seek a formal contract or payment for services. The effect of having a dedicated sustainability budget on collaboration with state and federal agencies is like- wise positive and significant. Higher-level government partners often engage urban areas as grantors and may require matching funds or are otherwise concerned with the financial commitments and ability of cities to dedicate resources to agreed upon projects (Terman and Feiock 2015). The fact that cities’ sustainability budget status has no significant impact on collaboration with other local or regional governments may reflect the relative informality of these interactions, as proposed earlier.
Conclusion
Research on administrative capacity, as it relates to local sustainability initia- tives, has often focused on explanations for why cities commit staff and fiscal
Krause et al. 603
resources to address energy and climate issues (Hawkins et al. 2016; Krause, Feiock, and Hawkins 2016). However, there is little research that examines the implications of these resource commitments on the characteristics of col- laborative mechanisms established by cities to promote sustainability. This study identifies the factors that explain the extent of cities’ collaboration on energy and climate issues. Collaboration is important in the context of urban sustainability because, given the cross-boundary nature of the challenge, cit- ies’ individual policy initiatives may have limited impact without collabora- tion with other governing units and sectors. Coordination problems, however, can inhibit collective action and impede the development of larger scale activities. Based on the institutional collective action framework, we view collaborative networks as a mechanism to reduce information costs and miti- gate coordination problems. In addition, from a resource dependency per- spective, collaborative networks enable cities to acquire the resources that are needed to achieve their sustainability objectives.
Our empirical analysis identifies factors that influence the scope of col- laborative network that cities have established. Committing at least a thresh- old amount of resources in the form of staff and budget are important steps in enabling more extensive collaborative networks. Collaborative networks are an important mechanism to promote integrated solutions and mitigate coordi- nation problems across regions. As their resources increase, cities’ ability to manage collaborations improves and they become more attractive partners with whom others seek to engage. However, above a certain point, the effect of additional resources reverses and they begin to reduce the scope of the col- laborative network. Collaborations require transaction costs as well as risk, and high-capacity cities may determine it is easier to operate on their own in some areas. Specifically, the ability to produce information through greater investments in internal capacity may enable cities to more selectively work with organizations that complement available resource commitments.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publi- cation of this article.
ORCID iD
Rachel M. Krause https://orcid.org/0000-0003-1490-1996
604 Urban Affairs Review 57(2)
Note
1. Graddy and Chen (2006) tested for but did not report a statistically significant inverse U-shaped relationship between institutional capacity and collaboration. Several reasons may explain the lack of supportive findings, including their rela- tively small sample size, multicollinearity, and model specification.
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Author Biographies
Rachel M. Krause is associate professor in the School of Public Affairs and Administration at the University of Kansas and director of its Masters of Public Administration (MPA) program. Her research focuses on local governance, urban sustainability policy, and municipal climate protection initiatives.
Christopher V. Hawkins is associate professor in the School of Public Administration at the University of Central Florida. His research focuses on urban politics, metropoli- tan governance, and urban sustainability policy.
Angela Y. S. Park is assistant professor in the Department of Political Science at Kansas State University. Her research focuses on understanding the key challenges facing local governments in delivering sustainability services and programs and what enables them to overcome these challenges. She is particularly interested in the effects of institutional arrangements in dealing with the issues of interagency coordination and performance management.
