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Nonprofit Sector Size and the Breadth of Local Government Climate Actions: 
Exploring the Moderating Role of Collaboration 
Yuan (Daniel) Cheng, University of Minnesota 
Angela Park, Kansas State University 
Rachel Krause, University of Kansas 
 
Accepted by Nonprofit and Voluntary Sector Quarterly 
DOI: https://doi.org/10.1177/08997640221146967  
 
Abstract: Existing studies often use the association between sector sizes to test the 
supplementary and complementary models of government-nonprofit relations, assuming that one 
mode of government-nonprofit relations dominates a policy subsector. We challenge this 
assumption and propose that the relationship between nonprofit sector size and the breadth of 
local government policy actions depends on their level of collaboration. Situated in the context 
of urban climate governance and drawing information from a national survey of U.S. local 
government climate actions, we test this modified model and find a statistically significant 
moderation effect of collaboration. However, contrary to our proposed hypotheses, our findings 
suggest that a positive association between the number of environmental nonprofits and 
governmental climate actions exists when the level of government-nonprofit collaboration is low 
to moderate. We posit that the adversarial lens of government-nonprofit relations and the cost of 
collaboration are key to understanding these surprising findings.  
Keywords: government-nonprofit relations, climate change, environmental nonprofits, 
collaboration 
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Interactions and collaborations between the government and nonprofit sectors have been a key 
concern in nonprofit studies and public management. Built on Weisbrod’s (1977) government 
failure theory and Salamon’s (1987) interdependence theory, Young’s (2000) supplementary and 
complementary modes of government-nonprofit relations are among the most tested frameworks 
by recent empirical studies. The supplementary mode predicts a negative association between the 
sizes of the government and nonprofit sectors while the complementary mode posits a positive 
association. While many empirical studies have followed Young’s initial framework, there is 
mixed evidence about whether the supplementary or complementary mode dominates 
government-nonprofit relations. According to a recent meta-analysis of the existing literature (Lu 
& Xu, 2018), there is generally a weak and sometimes statistically insignificant positive 
association between the sizes of the two sectors. In other words, the field still lacks consistent 
evidence regarding these two competing models. Moreover, the supplementary and 
complementary modes tend to weigh differently in various policy subsectors (Cheng, 2019a; 
Grasse et al., 2021; Lecy & Van Slyke, 2013; Matsunaga et al., 2004).   
 
In this article, we propose a more accurate reading of the supplementary and complementary 
modes of government-nonprofit relations as a means to reconcile competing evidence. Instead of 
using the association of sector sizes to directly test the two models, we propose that the 
association between the sizes of the two sectors depends on how collaborative the government 
and nonprofit sectors are in any respective policy subsector. We define collaboration as the 
process of creating multiorganizational or multisectoral arrangements to solve problems that are 
not easily solved by a single organization or sector (McGuire, 2003). By merging data on the size 
of the local environmental nonprofit sector with data from a national survey of city 
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administrators, we empirically test this contingency model of government-nonprofit relations in 
the context of municipal climate actions where environmental nonprofits and city governments 
may choose to collaborate or to work independently on these critical issues.   
 
This article makes several theoretical and empirical contributions to the existing literature. First, 
Despite the significance of the climate crisis and critical roles nonprofits play in urban climate 
governance, there are few empirical studies that explicitly examine the relationship between the 
nonprofit sector and what governments do to tackle climate change (Gazley, 2019; Kagan & 
Dodge, 2022). Moreover, as climate change becomes increasingly politicized at the US federal 
level, thus challenging the prospect of unified national or international governmental actions 
(Jaffe, 2017), local governments and nonprofit organizations are increasingly highlighted as key 
actors who offer a ground-up approach to addressing the climate problem (Bulkeley, 2013; 
Hughes, 2020). Our study begins to fill these gaps of knowledge by adapting one of the most 
widely used frameworks in voluntary and nonprofit studies to the understanding of urban climate 
governance. 
 
Second, there is an assumption in existing studies that the mere presence of environmental 
nonprofits in a community may automatically increase local governments’ climate actions 
(Portney & Cuttler, 2010; Sharp et al., 2011). In other words, the nonprofit sector is often 
regarded as part of the civic capacity necessary for the implementation of climate policies 
(Brandtner, 2022). By developing a contingent model of local governments’ climate actions 
based on the nature of the relationship between the government and nonprofits, we contribute to 
4 
 
a better understanding of how their interactions may stimulate or suppress local governments’ 
climate protection initiatives.  
 
Finally, while Young’s (2000) supplementary and complementary models of government-
nonprofit relations have been used extensively by scholars and policymakers, the common 
association of certain policy subsectors with particular modes of government-nonprofit relations 
can pose a serious empirical limitation. For example, human services are often examined via the 
complementary lens (Salamon & Toepler, 2015) while arts and culture nonprofits are often 
assessed via the supplementary lens (Kim, 2015). By focusing on the policy field of urban 
climate governance, for which there is little expectation about the dominant form of government-
nonprofit interaction (Bies et al., 2013), we are able to integrate the nature of the relationship 
into Young’s framework based on the nature of the relationship, without being hampered by a 
priori sector-wide assumptions about the type of relationship likely to dominate. 
 
A Brief Review of the Classic Supplementary-Complementary Models of Government-
Nonprofit Relations 
Understanding the diverse ways that nonprofits and the government interact is a key theme in 
nonprofit and voluntary studies. For example, Najam (2000) uses the differences in the 
combinations of policy strategies and goals to characterize government-nonprofit relations 
according to the four Cs: cooperation, co-optation, complementarity, and confrontation. 
Brinkerhoff (2002) employs organizational identity and mutuality to distinguish different forms 
of government-nonprofit partnerships. While these frameworks are widely used in public and 
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nonprofit management research, Young’s (2000) supplementary-complementary-adversarial 
framework is still regarded as the most comprehensive and widely used framework to understand 
the dynamics of government-nonprofit interactions. Compared with the aforementioned 
frameworks, Young’s has several unique advantages. First, it is easier to test empirically than 
Najam’s (2000) framework, which depends on an assessment of whether the goals of public and 
nonprofit actors are similar or different. Compared to the model offered by Brikerhoff (2002), 
Young’s framework does not assume that the government and nonprofits only work in 
partnerships, and therefore captures a wider range of government-nonprofit relations. Given 
these advantages, we have selected Young’s (2000) supplementary-complementary models of 
government-nonprofit relations as a basis for this article. 
 
In the classic supplementary model, governments and nonprofits are expected to operate in 
different market niches (Smith & Grønbjerg, 2006). The demand heterogeneity thesis (Weisbrod, 
1977) sets the foundation for the supplementary model. According to this thesis, government 
services are driven by the demand of the median voter, whereas nonprofits are created to serve 
the demand that is not being met by the government. Therefore, as the government takes on more 
responsibility in one service area, nonprofits would take less, and vice versa. Empirically, we 
would expect that a larger nonprofit sector is associated with fewer governmental activities in a 
policy subsector. There is some empirical support for the supplementary model. For example, 
Cheng (2019a) found that as park-supporting charities increase their spending in a city, the level 
of public spending on parks and recreation services decreases over time.  
 
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In contrast, the complementary model of government-nonprofit relations suggests that the 
nonprofit sector operates in close partnership with the government in service provision and 
delivery. Salamon (1987; 1995) pioneered this line of inquiry by discovering an enduring pattern 
of government-nonprofit collaboration around welfare services. According to the complementary 
view, the government provides funding to nonprofits to deliver public services. Therefore, we 
would expect a positive correlation between governmental and nonprofit activities in a service 
subsector. Multiple empirical studies have validated this prediction from the complementary 
model. As one example, Lecy and Van Lyke (2013) found that compared to private donations, 
government funding is a stronger predictor of nonprofit density.  
 
While the supplementary and complementary models of government-nonprofit relations offer 
alternative explanations for how the two sectors interact, in certain circumstances, a third model 
is also relevant. Young’s (2000) adversarial model points to nonprofits as using policy advocacy 
and grassroots organizing to push the government to take action. This perspective is most aligned 
with the understanding of the role of the nonprofit sector as a key civil society institution 
(Eikenberry & Kluver, 2004). While the adversarial model captures a key function of the 
nonprofit sector, this model does not specifically predict the association between the size of 
government and nonprofit sectors (Young & Casey, 2017). Nonprofits can either advocate for 
more, less, or different governmental actions in a policy subsector. Because of the ambiguity and 
difficulty in deriving testable hypotheses from the adversarial model, there are few empirical 
studies quantitatively testing the adversarial model. The supplementary-complementary models 
of government-nonprofit relations thus remain the most tested framework based on Young’s 
initial conceptualization, particularly in empirical studies using quantitative analysis.   
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While the supplementary model suggests a negative association between the size of the nonprofit 
sector and the breadth of government policy actions, the complementary model suggests a 
positive association. In their meta-analysis of 30 empirical studies examining the relationship 
between government size and nonprofit sector size, Lu and Xu (2018) found an overall positive 
yet generally weak association between government size and nonprofit sector size, offering 
overall limited support to the complementary model. Although Young (2000) recognized that 
these different modes of government-nonprofit relations may exist simultaneously in a policy 
subsector, we can conclude from this brief overview that empirical tests of his framework often 
frame them as competing models of how governments and nonprofits may interact with each 
other. The classic hypotheses corresponding to each model are as follows:  
 
Classic supplementary hypothesis (H1a): there is a negative association between nonprofit 
sector size and the breadth of government policy actions. 
Classic complementary hypothesis (H1b): there is a positive association between nonprofit 
sector size and the breadth of government policy actions. 
 
Collaboration as a Moderator in the Relationship between Nonprofit Sector Size and the 
Breadth of Government Policy actions 
As reviewed in the previous section, current research often assumes that one model of 
government-nonprofit relations dominates a policy subsector. Therefore, existing empirical 
8 
 
studies generally test these models in the aggregate to determine the dominant mode of 
government-nonprofit relations in a policy subsector via the association between nonprofit sector 
size and the breadth of governmental policy actions (Carroll & Calabrese, 2017; Cheng, 2019a; 
Grasse et al., 2021). These approaches, while efficient in establishing testable hypotheses (i.e., 
the classic supplementary and complementary hypotheses as we previously described), are 
inconsistent with Young’s speculation in his original article. He states: “the three perspectives 
are by no means mutually exclusive. Nonprofits may simultaneously finance and deliver services 
where government does not, deliver services that are financed or otherwise assisted by 
government, advocate for changes in government policies and practices and be affected by 
governmental pressure and oversight” (Young 2000, p.151). However, the standard practice of 
examining the association between the size of the nonprofit sector and the breadth of 
governmental policy actions in a policy subsector is insufficient to incorporate this view.  
 
Going back to the essence of the supplementary-complementary models of government-nonprofit 
relations, we suggest that existing studies overlook or assume away the nature of the relationship 
in their empirical analysis. The classic supplementary model is built on the assumption that 
nonprofit activities and governmental activities occur in different market niches, and they may 
compete with each other to fill in different niches (Cheng, 2019a; Smith & Grønbjerg, 2016). 
Therefore, the supplementary model rests on the premise that there is little collaboration between 
nonprofits and the government. The complementary model, on the contrary, emphasizes the 
prevalent collaborative and contractual relationship between the government and the nonprofit 
sector (Salamon, 1987; Salamon & Toepler, 2015). In other words, each model of government-
nonprofit relations assumes a certain level of collaboration between the two sectors.  
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Based on this rationale, we propose that those classic hypotheses regarding the supplementary 
and complementary models of government-nonprofit relations are incomplete and do not offer a 
direct test of Young’s framework. The relationship between nonprofit sector size and the breadth 
of governmental policy actions in a policy subsector depends on the level of collaboration 
between the two sectors. In particular, the classic supplementary hypothesis–which posits a 
negative association between nonprofit sector size and the breadth of governmental policy 
actions–will be supported when the level of collaboration between the two sectors is low. The 
classic complementary hypothesis, on the contrary, will be supported when the level of 
collaboration is high. Figure 1 presents a visual presentation of the moderating role of 
collaboration. We proposed the following modified hypotheses based on the supplementary and 
complementary models of government-nonprofit relations. What needs to be noted is that the 
following two hypotheses are on the flip side of each other. If one is true, the other is also true. 
Therefore, based on our conceptualization, when the level of collaboration between the 
government and nonprofit sectors is taken into consideration, the complementary and 
supplementary models of government-nonprofit relations become a unified framework.  
 
[Figure 1 here] 
 
Modified Supplementary Hypothesis (H2a): When the level of government-nonprofit 
collaboration is low, there is a negative association between the size of the nonprofit sector and 
the breadth of government policy actions.  
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Modified Complementary Hypothesis (H2b): When the level of government-nonprofit 
collaboration is high, there is a positive association between the size of the nonprofit sector and 
the breadth of government policy actions.  
 
Research Context: Applying the Modified Model of Government-Nonprofit Relations to 
Urban Climate Governance 
The field of urban climate governance offers a unique opportunity to test this modified model of 
government-nonprofit relations. Because of its complexity and the huge collective action 
problem involved in its mitigation and adaptations, many scholars and policymakers have 
advocated for a ground-up and multistakeholder approach to respond to this challenge (Cole, 
2015; Ostrom, 2012; Dahe & Stocker, 2014). Environmental nonprofit organizations are 
considered important actors pushing forward the sustainability movement and climate initiatives 
among U.S. cities (Bies et al., 2013; Portney and Berry 2010; 2015). Although they share a 
general aim of improving environmental quality, local environmental nonprofits vary widely in 
their focus and approach. As a result, they are likely to develop a range of relationships with 
their local governments, reflecting various modes of government-nonprofit relations. For 
example, a segment of environmental nonprofits focuses largely on stewardship and have a 
relatively independent relationship with their local governments. Local Keep America Beautiful 
chapters or various “friends of the park” and water bodies groups often organize member events 
to pick up trash, plant trees, and offer public education programs to raise awareness of 
environmental issues in their communities (Cheng, 2019b; Gazley et al., 2018). As these 
organizations often organize their events on public lands, they need to work with local 
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governments and get their approval. However, the working relationship is more independent as 
these organizations make their own programming decisions and strategic priorities.  
 
Other types of environmental nonprofits are likely to work more closely with local governments 
in the form of collaborative partnerships. These organizations may assist in policy development, 
implementation, and/or capacity building, enabling the government to take on a larger set of 
activities. One such example is the Clean Air Partnership (CAP), a registered public charity in 
Canada, which works in partnership with their local governments to reduce greenhouse gas 
emissions and improve air quality in their communities. In 2020, the CAP published a report to 
guide how local governments can work with local nonprofit organizations to advance their 
climate actions (Behan, 2021). The recent “30 by 30” conservation plan initiated by the Biden 
Administration also explicitly recognizes the importance of and encourages collaboration with 
local conservation groups and private landowners.  
 
Given the varied and important roles that nonprofits play in the urban climate policy landscape 
and the leeway local governments have in their climate actions (Cheng et al., 2021), this policy 
area offers an ideal context to further refine and test the modified models of government-
nonprofit relations we previously identified. In particular, according to the modified 
supplementary model of government-nonprofit relations, we expect that when the level of 
government-nonprofit collaboration in urban climate governance is low, there is a negative 
association between the size of the environmental nonprofit sector and the number of climate 
actions enacted by a city government. Based on the modified complementary model, we expect 
12 
 
that when the level of government-nonprofit collaboration in urban climate governance is high, 
there is a positive association between the size of the environmental nonprofit sector and the 
number of local climate actions. In the next section, we will discuss our data, measurements, and 
empirical strategy. 
 
Data and Methodology 
Data Source and Sample 
Data for this analysis comes from two primary sources. First, the dependent variable and several 
of the independent variables were obtained from the 2015 Smart and Sustainable Cities Survey.  
Survey invitations were sent directly to the city government employee pre-identified as “most in 
charge of sustainability” in every US city with a population over 20,000 (n=1282). The identity 
of the survey recipient was obtained via systematic searches of city websites.  Phone calls were 
made to city offices when either the appropriate person or their contact information was not able 
to be found online. The survey was administered online using Qualtrics survey software and was 
followed by two emailed reminders. Individuals who did not respond to the electronic invitations 
were mailed a paper copy of the survey.  In total, 507 cities completed the survey yielding a 
response rate of 39.4%. Another 86 provided partial responses. Completed responses were 
received from cities in 48 states and Washington D.C. As shown in Table 1, the respondent cities 
are similar to non-respondents on basic demographic indicators and the number of environmental 
nonprofits in their residing counties. The one metric on which there is a statistically significant 
(α=0.05) difference is average educational attainment. However, the difference is modest with 
2.5 percent more adults in the responding cities having attained a Bachelor’s degree or higher. 
 
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[Table 1 Here] 
 
The second main sources of data are the National Center for Charitable Statistics (NCCS) IRS 
Business Master Files (BMF), which contains the most comprehensive and timely information 
on active registered nonprofits in the U.S. (McKeever, 2018). We used the National Taxonomy 
of Exempt Entities (NTEE) Code C to identify and count the number of private nonprofit 
organizations with the primary purpose of protecting and conserving the environment in a 
locality. The 2015 American Community Survey (ACS) and the MIT Election Lab serve as 
sources for our control variables to account for city/county level socioeconomic and political 
characteristics.  
 
Variables and Measurement 
Our Dependent Variable is local government climate actions, an additive index of various 
initiatives and programs taken by municipalities to address climate issues and advance 
environmental sustainability. The Smart and Sustainable Cities survey asks a total of 17 
questions about cities’ climate-relevant actions, including around specific issues like water 
conservation, green space preservation, energy conservation, renewable energy generation, and 
waste reduction. Importantly, despite not being explicitly about climate, each of these actions has 
the potential to either directly or indirectly influence local greenhouse gas emissions. The 
dependent variable is essentially a count of how many of these actions cities have implemented.  
Each action is weighted equally, which places the focus on the number of distinct actions 
14 
 
undertaken or the breadth of local government climate actions, rather than on estimates of their 
difficulty or depth.  Appendix 1 provides a full list of included actions. 
 
As explained in the hypotheses section, our primary goal is to understand the nature of nonprofit-
government relationships in urban climate governance. To that end, we employ key two 
independent variables in our study: the number of environmental nonprofits and the extent of 
government-nonprofit collaboration. Both variables are measured at the city level. Following 
Sharp (2011), we estimated the number of environmental nonprofits within each city by 
multiplying the total county-level number of environmental nonprofits by the proportion of the 
city’s population to the overall county population. This approach is used over matching zip codes 
to cities because zip code boundaries do not always match city limits. The government-nonprofit 
collaboration variable is generated from the Smart and Sustainable Cities survey data and 
measures the extent to which a city government “works collaboratively on energy, sustainability, 
and climate issues” with nonprofit organizations. Respondents were presented with a five-scale 
response ranging from (1) “not at all” to (5) “very much”, which we collapsed into three 
categories to make sure that there is a sufficient number of responses under each category. A 
total of 254 cities (50.3%) reported little to no collaboration while 116 cities (23%) indicated 
moderate collaboration. 135 cities (26.7%) reported they collaborate with nonprofits on climate 
issues to a significant degree. 
 
The model also includes variables that extant research finds to be key predictors of local climate 
protection efforts. Human capacity and financial capacity are binary variables that respectively 
15 
 
indicate if each city has dedicated staffing and a dedicated line in the city budget for 
sustainability. Climate Priority captures the extent to which a city prioritizes climate change 
adaptation, ranging from 1(Not at all priority) to 5 (Very high priority). Given the significance of 
community influence on municipalities’ political agenda, two additional variables are included 
that measure the level of support for the city’s climate actions from key community stakeholder 
groups. The extent to which the general public and chamber of commerce and business 
associations support the city’s climate action efforts were asked on a five-point Likert scale 
ranging from 1 (Strongly Opposed) to 5(Strongly Support). These factors have been documented 
as key factors in shaping U.S. cities’ climate adaptation efforts (e.g., Shi, Chu, & Debats 2015) 
 
We also control for several community characteristics that shape local climate actions in 
important ways, including the city’s population size, median income, the proportion of the city’s 
population that is non-Hispanic white1, the percent of voters who voted for the Democratic 
presidential candidate, and whether a city is a member of the organization ICLEI-Cities for 
Sustainability. All of the information is available at the city level, except the Democratic voting 
data, which is at the county level. All continuous IVs are standardized to facilitate the 
comparison of coefficients. Table 2 offers the descriptive statistics of variables.  
 
Because the number of environmental nonprofits varies considerably across cities, we have taken 
several measures to deal with the presence of outliers and its possible influence on our estimates. 
First, by visually inspecting a box plot, we removed 2 outliers from our analysis sample. We then 
followed up with an additional model that further excluded 63 observations identified as outliers 
by Interquartile Range (IQR) measure as a robustness check. Results of key variables 
16 
 
consistently hold across models and significance slightly improves with the exclusion of outliers. 
This yields strong evidence for the reliability of our findings. In what follows, we present the 
estimates of our first model with the larger sample size and offer the results of the additional 
model in appendix 3. The frequency histograms of nonprofit counts for both samples are also 
provided in appendix 2.   
 
[Table 2 Here] 
Empirical Strategy 
A fair amount of variation was observed in the range of actions respondent cities reported they 
were involved in. The additive index ranges from 0 to 16, indicating none of the respondent 
cities were undertaking all of the 17 actions asked in the survey. Because the dependent variable 
is a count of climate-relevant actions, we use a Poisson regression model, which allows a lower 
bound at 0 and accounts for the discrete nature of the outcome data. Poisson regression makes 
several key assumptions that must be met to validate its use. Specifically, the dependent variable 
should neither display excessive zeros nor be over-dispersed, meaning the variance of the 
distribution should not be greater than the conditional mean. Violating these assumptions 
necessitates alternative modeling techniques, including Negative Binomial regression that 
accounts for overdispersion in Poisson models. Excessive zeros are not a concern in our model, 
as less than 1% of our sample (4 cities) reported zero involvement in the identified climate-
relevant actions. Half of our respondent cities reported at least 6 or more climate programs and 
activities while 25% of the sample indicates 10 or more programs and activities. Figure 2 plots 
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the histogram of our outcome variable with the normal curve overlaid and shows no indication of 
overdispersion.  
 
[Figure 2 Here] 
 
The results of two goodness-of-fit tests and a likelihood ratio test further support the use of the 
Poisson regression. Deviance goodness-of-fit and Pearson-goodness-of-fit for both models are 
found to be highly insignificant, which indicates a good model fit. We estimate two models: one 
with main effects only and the other with interactive effects. The first model, which only tests the 
main effects of our key variables, shows that the current model fits the data well with a deviance 
of 405.818 on 409 degrees of freedom and a corresponding P-value of 0.535. Pearson goodness-
of-fit for the same model is also highly insignificant with a P-value of 0.801.  Our second model 
likewise proves to be well-fitting with a highly insignificant P-value at 0.617 (a deviance of 
397.92 and 407 degrees of freedom) and Pearson goodness-of-fit at a P-value of 0.850. Lastly, a 
likelihood ratio test was conducted to compare with the Negative Binomial model and the 
Poisson model provided a significantly better fit, thereby further validating our model fit. 
  
 
 
Empirical Findings and Results 
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We first run a model with main effects only to test the classic hypotheses developed from the 
supplementary and complementary models of government-nonprofit relations. We then proceed 
with a second model that includes an interaction term to test contingent relationships based on 
our refined hypotheses. Both models are estimated with state-fixed effects. Table 3 offers 
regression results for both models.  
 
[Table 3 Here] 
 
The main effects model shows some interesting dynamics shaping local climate protection 
efforts. First, the overall number of local environmental nonprofits is positively associated with 
an increased level of cities’ climate efforts (p < 0.1), which provides modest support for the 
classic complementary hypothesis. As the number of environmental nonprofits increases by one 
standard deviation in a city, the log count of cities’ climate programs increases by 0.059. This 
equates to roughly 1.146 of the 17 actions included in the index. 
 
Second, the reported extent of collaboration between a local government and environment 
nonprofits is overall significantly associated with the number of climate-related actions a city has 
undertaken. The expected difference in log count between cities that moderately collaborate with 
environmental nonprofits on climate issues and cities that rarely collaborate is 0.132 (roughly 
1.355 actions). When cities and nonprofits collaborate significantly on climate issues, the 
expected difference is even larger and increases by 0.178 (roughly 1.507 actions). Therefore, our 
results suggest that cities engaged in collaborative relationships with local environmental 
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nonprofits are undertaking a larger number of climate-relevant actions. The positive effects of 
nonprofit-government collaboration, together with the number of environmental nonprofits in a 
city, further support the complementary relationships between local governments and 
environmental nonprofits in our sample cities.  
 
Several other variables are also found to be statistically significant. As expected, cities that place 
a greater priority on climate mitigation are engaged in greater municipal climate efforts. The 
support from the business community shapes cities’ climate protection efforts positively and 
quite significantly (p<.000), whereas the support from the public at large does not. Consistent 
with previous research showing that membership in ICLEI modestly increases cities’ climate 
protection activities (Krause, 2012), our results also show that all else equal, ICLEI participants 
have a greater number of climate-relevant programs. ICLEI is itself an environmental nonprofit 
organization, and while a city’s membership in it represents a type of collaboration with this 
sector, it is international in scope and not a part of any city’s local nonprofit sector considered in 
the focal independent variable. Neither having a budget nor staff dedicated to sustainability 
appears to be a significant predictor of cities’ climate actions. This may be a result of the binary 
nature of the variables, thus needing improvement in future research. No demographic variables 
are found to influence climate protection efforts.  
 
The second model in Table 3 includes an interaction term to see whether the level of 
government-nonprofit collaboration moderates the relationship between the number of 
environmental nonprofits and the local governments’ climate actions. As described in our 
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hypotheses, we are primarily interested in understanding how the impact of a larger local 
nonprofit system is moderated by the overall nature of the relationship that a city government has 
with the organizations that comprise it. Specifically, the modified supplementary hypothesis 
suggests a negative association between the number of environmental nonprofits and local 
governments’ climate actions when the level of government-nonprofit collaboration is low, while 
the modified complementary hypothesis suggests a positive association between the two when 
the level of collaboration is high.    
 
According to the results of Model 2 in Table 3, the positive impact on the number of climate-
relevant actions observed diminishes as the number of local nonprofits increases when there is 
significant collaboration. The main effect of the number of environmental nonprofits without the 
interaction term is 0.059 (Model 1). With the interaction, the total effect of the environmental 
nonprofit sector size is 0.223 – 0.235 = -0.012. This shows that when the collaboration between 
government and nonprofits is high, more environmental nonprofits actually have a negative 
impact (the effect size is quite small though). Figure 3 shows the same story with a more 
intuitive graphical presentation. For the blue and green lines (indicating the level of government-
nonprofit collaboration is little or moderate), we observe an upward-sloping curve for the 
relationship between local governments’ climate programs and the number of environmental 
nonprofits. However, when the level of government-nonprofit collaboration is significant (the 
red line), there is a slightly negative slope. In other words, when the level of government-
nonprofit collaboration is little to moderate, there is a positive association between 
environmental nonprofit sector size and the breadth of local governments’ climate programs. 
When the level of collaboration is high or significant, there is a negative association. Neither the 
21 
 
modified supplementary hypothesis (H2a) nor the modified complementary hypothesis (H2b) are 
supported by the interaction model results.  
 
[Figure 3 Here] 
 
Moreover, Figure 3 shows the decreasing marginal effects of collaboration. Those three lines 
cross with one another, showing a cross-over interaction. The fewer environmental nonprofits 
cities have, significant collaboration is associated with a greater number of climate programs 
when compared with moderate collaboration. However, the red and green lines cross over and 
beyond that point, city governments that moderately collaborate with nonprofit partners start to 
surpass the cities that significantly collaborate with nonprofits in their number of climate 
programs. In other words, having a significant collaborative effort with a larger number of 
nonprofits does not necessarily benefit municipal climate efforts. This is further supported by 
looking at cities that rarely collaborate with nonprofits. The blue line also starts to surpass the 
other two lines and beyond the cross-over points, a blue line is always above the other two lines 
and draws a more rapidly increasing trend. Given that the majority of our sample cities have a 
moderate size of the nonprofit sector (below the crossover point), we can conclude that 
nonprofit-government collaboration generally brings a positive force to city climate efforts; that 
is, in most cases, cities that collaborate with nonprofits on their climate protection efforts do 
more than those who do not. Nonetheless, the effects of nonprofit size are not always linear and 
consistent across different levels of nonprofit-government collaboration while the positive effects 
of collaboration are also not uniform across different nonprofit sizes. Rather, once the number of 
22 
 
environmental nonprofits becomes large enough, cities with little collaboration tend to be 
engaged in more extensive climate action efforts. Government-nonprofit relations operate 
beyond a dichotomous and linear pattern - either positively or negatively associated.  
 
Discussion 
In this article, we propose a modification to the traditional complementary and supplementary 
models of government-nonprofit relations by incorporating the level of collaboration as a 
moderator of the relationship between nonprofit sector size and the breadth of government policy 
actions. While our findings support the moderating role of collaboration, they deviate from our 
initial hypotheses drawn from the modified complementary and supplementary models. We find 
that when there is little to moderate level of collaboration, the number of environmental 
nonprofits in a city is positively associated with the breadth of local government climate actions. 
However, when the level of collaboration is high, this positive association goes away. In this 
section, we provide additional interpretation and discussion about how our findings contribute to 
the existing literature around government-nonprofit relations and urban sustainability.  
 
Consistent with recent research finding that service subsectors and the level of government 
funding (federal, state, or local) together determine whether public funding crowds out or 
increases private donations (Grasse et al., 2021), we find that underlying relationship parameters 
shape how governments and nonprofits interact with each other across different subsectors. In 
particular, the level of collaboration between those two sectors is a strong predictor of policy 
activity than the issue-based identity of the particular subsector or the size of the local nonprofit 
23 
 
sector itself. Moreover, our study speaks to the importance of further refining and modifying 
classic theories of government-nonprofit relations in the field by exploring possible moderating 
or mediating relationships (Cheng & Wu, 2021; Gazley & Guo, 2020; Paarlberg & Zuhlke, 
2019). 
 
Our findings also confirm some conclusions from the urban sustainability literature which 
suggests that the presence of a large environmental nonprofit sector is an important source of the 
local civic capacity necessary for carrying out climate actions (Brandtner, 2022; Brandtner & 
Suárez, 2021; Portney & Cutter, 2010). In terms of our findings of the strong positive correlation 
between government-nonprofit collaboration and city climate action, they are consistent with the 
existing literature’s emphasis on finding collaborative solutions to address climate change (Cole, 
2015; Park et al., 2021). Given the transboundary nature of climate issues, collaborative 
strategies are now widely recognized as a preferred, if not inevitable, approach to addressing 
them. Research well documents empirical evidence for inter-jurisdictional and -sectoral climate 
networks of various sizes and purposes (Lee & Dodge, 2019) while several studies find positive 
effects of such collaborative efforts on achieving greater climate and sustainability outputs and 
outcomes (Scott, 2015). 
 
Our study departs from and contributes to the existing literature by incorporating the moderating 
role of collaboration. While our findings support the role of collaboration in moderating the 
relationship between environmental nonprofit sector size and governmental urban climate 
actions, they do not operate in the way we initially hypothesized based on the modified models 
24 
 
of government-nonprofit relations. We find that when the level of collaboration between 
nonprofits and local governments is low, there is a stronger positive association between the 
number of environmental nonprofits and governmental climate actions. When the level of 
collaboration is high, as it is for the majority of our sample, governments in cities with a large 
nonprofit sector take on fewer climate-relevant activities (see Figure 3).  
 
Several dynamics may explain this finding; however, due to data limitations, we are only able to 
capture the level of a collaborative relationship between nonprofits and local governments. When 
the collaboration level is relatively low, one of two dynamics may be at play. First, it may mean 
that environmental nonprofits are operating independently as supplement to government. 
Alternatively, the low collaboration level may indicate that nonprofits and local governments are 
involved in an adversarial relationship. In the current context, an adversarial relationship may be 
characterized by nonprofits pushing local governments to undertake more, less, or different 
climate actions. Given our data, we are unable to identify which dynamic is at play. However, 
the observed direction of our results (i.e. when the environmental nonprofit sector is large and 
collaboration is low, city governments are engaged in a larger number of climate actions) 
suggests an adversarial dynamic where a robust environmental nonprofit sector is pushing the 
government to do more. 
 
Although somewhat speculative, this interpretation of results is consistent with other recent 
studies that have discussed the diminishing marginal returns of collaboration around local 
sustainability initiatives, given the various costs of forming and maintaining collaborative 
25 
 
partnerships. Specifically, when cities have higher capacity, they may have less need for 
additional collaborative partners (Krause, Hawkins, &Park, 2021). Our results similarly suggest 
that while working with nonprofits is conducive to municipal climate efforts, having more 
nonprofit partners does not necessarily bring more benefits. The overall diminishing marginal 
returns of government-nonprofit collaboration shown in figure 3 suggests that costs increase 
along with the number of nonprofits. At some point, the costs of investment in collaboration start 
to outweigh the benefits. These conclusions must be accompanied by a caveat, however, as the 
number of observations in our sample with a “particularly large” nonprofit ecosystem is rather 
small. 
 
Conclusion 
Climate change is one of the biggest collective action problems facing human society (Cole, 
2015). There is an imperative to pay closer attention to how the nonprofit sector and the 
government interact with each other to respond to this huge challenge, particularly because of the 
polarized political environment which has stymied unified governmental action. Research has 
recognized the increasingly important role nonprofits play in shaping climate governance 
agendas (Bulkeley & Bestill, 2013; Gruby et al., 2021; Hughes, 2020). Studying government-
nonprofit relations in urban climate governance is not only of substantive significance but also 
theoretical significance. As an emerging policy area where the government and nonprofits are 
still negotiating and testing their proper roles in urban climate governance, it provides a unique 
opportunity for us to test and refine our classic theories of government-nonprofit relations.  
 
26 
 
Focusing on the complementary and supplementary models of government-nonprofit relations, 
we contribute to the existing nonprofit literature by problematizing the existing empirical 
approach of associating certain policy subsectors with a particular model of government-
nonprofit relations. By introducing the moderating role of collaboration, we show that multiple 
models of government-nonprofit relations can coexist in a policy subsector and the nature of the 
relationship seems to be key to understanding the dynamics of government-nonprofit 
interactions. Situated in the context of urban climate governance, we show that the relationship 
between nonprofit sector size and governmental climate actions depends on their level of 
collaboration. In particular, when collaboration between the two sectors is low, there is a 
stronger positive association between the number of environmental nonprofits and governmental 
climate actions. While this finding does not support either of our proposed modified hypotheses, 
it does reinforce the importance of taking the nature of cross-sectoral relationships seriously, 
both theoretically and empirically. The relationship between nonprofit sector size and the breadth 
of governmental policy actions appears more complex than stated. A highly collaborative 
relationship between the two sectors may sometimes be counterproductive in creating the 
desirable governmental efforts in responding to climate change. It is important to keep the 
adversarial and advocacy roles of environmental nonprofits in urban climate governance (Kagan 
& Dodge, 2022).  
 
Our study has limitations and creates several promising avenues for future research. Although a 
comprehensive effort has been made to merge a unique national survey of U.S. local 
governments’ climate actions and environmental nonprofits, our study only takes a snapshot of 
the complex relationship between the government and nonprofits in urban climate governance. 
27 
 
The dynamics of government-nonprofit interactions are likely to change over time as public 
opinions towards climate change evolve and as local governments further build or erode their 
administrative capacity in tackling climate change. Longitudinal data collection and study design 
are needed to better capture the temporal relationship between the government and nonprofits in 
urban climate governance.  
 
Another limitation is that our study only focuses on one type of relationship between the two 
sectors – collaboration. As we have stated in the discussion section, the lack of collaboration 
does not always fall into the supplementary model. Future studies should develop a better 
measure to fully distinguish those three types of relationships between the government and 
nonprofits – collaboration, competition, and conflict (Ostrom et al., 1961), or complementary, 
supplementary, and adversarial (Young, 2000). Future research should also examine other policy 
subsectors to further test the modified complementary and supplementary models of 
government-nonprofit relations. We expect that the classic complementary and supplementary 
hypotheses would be supported in policy subsectors where there is one predominant form of 
government-nonprofit interactions. However, for policy subsectors where nonprofits interact 
with local governments in diverse ways (for example, urban climate governance in our research 
context), the modified model has a stronger explanatory power. 
 
Finally, our study focuses on city-level government-nonprofit interactions in urban climate 
governance, therefore not being able to account for within-city variations in the size of individual 
nonprofits or collaboration heterogeneity (e.g., some nonprofits in a city may collaborate more 
28 
 
closely with the local government than other nonprofits). Future studies should zoom in on 
individual cities to fully capture these dynamics and understand how nonprofits use different 
strategies to exert influence on the local government decision-making process (Thompson, 
2022).  
 
In conclusion, our research points to the importance of reorienting our understanding of 
government-nonprofit relations based on the nature of the relationship, instead of solely focusing 
on the association between the size of the two sectors. We offer unique data and empirical 
approaches to test the supplementary-complementary models of government nonprofit relations. 
This widely used model of government nonprofit relations will be more theoretically and 
empirically sound if the size of the two sectors and the nature of the relationship can be 
simultaneously incorporated into model development and testing. 
 
Notes: 
1. Many recent studies suggest the linkage between whiteness and urban sustainability (e,g, 
Connolly & Anguelovski, 2021) and the lack of diversity in environmental organizations 
across governmental and nonprofit organizations (Taylor, 2014). 
 
 
 
References 
Acuto, M. (2016). Give cities a seat at the top table. Nature, 537(7622), 611– 613. 
29 
 
Behan, K. (2021). Using Nonprofits to Deliver Municipal Climate Action.  Accessed July 26th, 
2021. https://www.cleanairpartnership.org/wp-content/uploads/2020/12/Report-7_Using-
nonprofits-to-deliver-municipal-climate-action.pdf  
Bies, A. L., Lee, D. G., Lindsey, C., Stoutenborough, J. W., & Vedlitz, A. (2013, May). Citizens, 
nonprofits and climate change policy. In Nonprofit Policy Forum (Vol. 4, No. 1, pp. 5-28). De 
Gruyter. 
Brandtner, C. (2022). Green American city: Civic capacity and the distributed adoption of urban 
innovation. Forthcoming at American Journal of Sociology.  
Brandtner, C., & Suárez, D. (2021). The structure of city action: Institutional embeddedness and 
sustainability practices in US cities. The American Review of Public Administration, 51(2), 121-
138. 
Carroll, D. A., & Calabrese, T. D. (2017). Intersecting sectors? The connection between 
nonprofit charities and government spending. Journal of Public and Nonprofit Affairs, 3(3), 247-
271. 
Cheng, Y. (2019a). Nonprofit spending and government provision of public services: Testing 
theories of government–nonprofit relationships. Journal of Public Administration Research and 
Theory, 29(2), 238-254. 
Cheng, Y. (2019b). Exploring the role of nonprofits in public service provision: Moving from 
coproduction to cogovernance. Public Administration Review, 79(2), 203-214. 
Cheng, Y. D., Farmer, J. R., Dickinson, S. L., Robeson, S. M., Fischer, B. C., & Reynolds, H. L. 
(2021). Climate change impacts and urban green space adaptation efforts: Evidence from US 
30 
 
municipal parks and recreation departments. Urban Climate. 
https://doi.org/10.1016/j.uclim.2021.100962 
Cheng, Y., & Wu, Z. (2021). The contingent value of political connections on donations to 
Chinese foundations: Exploring the moderating role of transparency. Administration & 
Society, 53(1), 36-63. 
Cole, D. H. (2015). Advantages of a polycentric approach to climate change policy. Nature 
Climate Change, 5(2), 114-118. 
Connolly, J. J., & Anguelovski, I. (2021). Three histories of greening and whiteness in American 
cities. Frontiers in Ecology and Evolution, 9, 621783.  
Dahe, Q., & Stocker, T. (2014). Highlights of the IPCC working group I fifth assessment 
report. Advances in Climate Change Research, 10(1), 1. 
De Wit, A., & Bekkers, R. (2017). Government support and charitable donations: A meta-
analysis of the crowding-out hypothesis. Journal of Public Administration Research and 
Theory, 27(2), 301-319. 
Gazley, B. (2019). Who’s (Not) Talking About Climate Change? A Systematic, Comprehensive 
Review of the Nonprofit Scholarship. Paper presented at the 48th ARNOVA Annual Conference.  
Gazley, B., Cheng, Y. D., & Lafontant, C. (2018). Charitable support for US national and state 
parks through the lens of coproduction and government failure theories. In Nonprofit Policy 
Forum (Vol. 9, No. 4). de Gruyter. 
Gazley, B., & Guo, C. (2020). What do we know about nonprofit collaboration? A systematic 
review of the literature. Nonprofit Management and Leadership, 31(2), 211-232. 
31 
 
Grasse, N. J., Searing, E. A., & Neely, D. G. (2021). Finding Your Crowd: The Role of 
Government Level and Charity Type in Revenue Crowd-Out. Journal of Public Administration 
Research and Theory. 
Gruby, R. L., Enrici, A., Betsill, M., Le Cornu, E., Basurto, X., & Co-Designers, R. (2021). 
Opening the black box of conservation philanthropy: A co-produced research agenda on private 
foundations in marine conservation. Marine Policy, 132, 104645. 
Hughes, Sara. 2020. Repowering Cities. Cornell University Press: Ithaca, New York. 
Jaffe, C. (2017). Melting the polarization around climate change politics. Geo. Envtl. L. Rev., 30, 
455. 
Kagan, J. A., & Dodge, J. (2022). The Third Sector and Climate Change: A Literature Review 
and Agenda for Future Research and Action. Online First at Nonprofit and Voluntary Sector 
Quarterly,  
Kim, M. (2015). Socioeconomic diversity, political engagement, and the density of nonprofit 
organizations in US counties. The American Review of Public Administration, 45(4), 402-416. 
Krause, R. M. (2012). An assessment of the impact that participation in local climate networks 
has on cities’ implementation of climate, energy, and transportation policies. Review of Policy 
Research, 29(5), 585-604. 
Krause, R. M., Hawkins, C. V., & Park, A. Y. (2021). The perfect amount of help: An 
examination of the relationship between capacity and collaboration in urban energy and climate 
initiatives. Urban Affairs Review, 57(2), 583-60 
32 
 
Lee, T. (2014). Global cities and climate change: The trans-local relations of environmental 
governance. Routledge. 
Lee, J., & Dodge, J. (2019). Keeping Your Enemies Close: The Role of Distrust in Structuring a 
Local Hydraulic Fracturing Policy Network in New York. Journal of Public Administration 
Research And Theory, 29(2), 175-192. 
Lecy, J. D., & Van Slyke, D. M. (2013). Nonprofit sector growth and density: Testing theories of 
government support. Journal of Public Administration Research and Theory, 23(1), 189-214.8. 
Lu, J., & Xu, C. (2018). Complementary or supplementary? The relationship between 
government size and nonprofit sector size. VOLUNTAS: International Journal of Voluntary and 
Nonprofit Organizations, 29(3), 454-469. 
McGuire, M. (2006). Collaborative public management: Assessing what we know and how we 
know it. Public Administration Review, 66(s1), 33-43. 
McKeever, B. (2018). Beginner's Guide to Using NCCS Data. Urban Institute. 
https://nccs.urban.org/sites/default/files/2018-12/Guide%20to%20Using%20NCCS%20Data.pdf 
Accessed on Oct. 10, 2021.  
Moser, S. C., & Ekstrom, J. A. (2010). A framework to diagnose barriers to climate change 
adaptation. Proceedings of the National Academy of Sciences, 107(51), 22026-22031. 
Nguyen Long, L. A., & Krause, R. M. (2021). Managing policy‐making in the local climate 
governance landscape: The role of network administrative organizations and member cities. 
Public administration, 99(1), 23-39. 
33 
 
Ostrom, E. (2012). Nested externalities and polycentric institutions: must we wait for global 
solutions to climate change before taking actions at other scales?. Economic theory, 49(2), 353-
369. 
Ostrom, V., Tiebout, C. M., & Warren, R. (1961). The organization of government in 
metropolitan areas: a theoretical inquiry. American political science review, 55(4), 831-842. 
Park, A. Y., Krause, R. M., & Hawkins, C. V. (2021). Institutional mechanisms for local 
sustainability collaboration: Assessing the duality of formal and informal mechanisms in 
promoting collaborative processes. Journal of Public Administration Research and 
Theory, 31(2), 434-450. 
Portney, K. (2005). Civic engagement and sustainable cities in the United States. Public 
Administration Review, 65(5), 579-591. 
Portney, K. E., & Berry, J. M. (2010). Participation and the pursuit of sustainability in US cities. 
Urban Affairs Review, 46(1), 119-139. 
Portney, K. E., & Berry, J. M. (2016). The impact of local environmental advocacy groups on 
city sustainability policies and programs. Policy Studies Journal, 44(2), 196-214. 
Portney, K. E., & Cuttler, Z. (2010). The local nonprofit sector and the pursuit of sustainability 
in American cities: A preliminary exploration. Local Environment, 15(4), 323-339. 
Rousseau, H. E., Berrone, P., & Gelabert, L. (2019). Localizing sustainable development goals: 
Nonprofit density and city sustainability. Academy of Management Discoveries, 5(4), 487-513. 
34 
 
Salamon, L. M., & Toepler, S. (2015). Government–nonprofit cooperation: Anomaly or 
necessity?. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 26(6), 
2155-2177. 
Scott, T. (2015). Does collaboration make any difference? Linking collaborative governance to 
environmental outcomes. Journal of Policy Analysis and Management, 34(3), 537-566. 
Sharp, E. B., Daley, D. M., & Lynch, M. S. (2011). Understanding local adoption and 
implementation of climate change mitigation policy. Urban Affairs Review, 47(3), 433-457. 
Shi, L., Chu, E., & Debats, J. (2015). Explaining progress in climate adaptation planning across 
156 US municipalities. Journal of the American Planning Association, 81(3), 191-202. 
Shi, L., & Moser, S. (2021). Transformative climate adaptation in the United States: Trends and 
prospects. Science. 372(6549), doi:10.1126/science.abc8054. 
Smith, S. R., & Grønbjerg, K. A. (2006). 10. Scope and Theory of Government-Nonprofit 
Relations. In The nonprofit sector (pp. 221-242). Yale University Press. 
Taylor DE.(2014). The state of diversity in environmental organizations. Green 2.0, Washington, 
D.C. 
Thomson, D. E. (2022). Foundations of Influence: Intervention Pathways of Foundation 
Influence on City Governance and Policy. Nonprofit and Voluntary Sector Quarterly, 
https://doi.org/10.1177/08997640221115653.  
Young, D. R. (2000). Alternative models of government-nonprofit sector relations: Theoretical 
and international perspectives. Nonprofit and Voluntary Sector Quarterly, 29(1), 149-172. 
35 
 
 
Table 1. Comparison of Survey Respondents to Non-Respondents 
  
  Respondents 
(n=507) 
Non-
respondents 
(n=775) 
Significantly 
different 
(α=0.05) 
Population (2010) 107,220 102,036 No 
Median Household Income 54,661 55,046 No 
Percent in Poverty 10.82 10.71 No 
Education (Bachelors +) 31.71 29.21 Yes 
Number of Environmental 
Nonprofits in their Residing 
County 
38.77 52.48 No 
 
 
 
 
 
 
 
 
 
 
 
36 
 
Table 2. Descriptive Statistics of Variables 
  N Mean Std Dev Min Max 
Number of Local Government 
Climate Actions  
507 6.765 2.994 1 16 
Number of Local Environmental 
Nonprofits   
593 9.429 21.919 0 355 
Level of Nonprofit-Government 
Collaboration 
505 1.764 .846 1 3 
Climate Priority  586 2.699 1.127 1 5 
Human Capacity 523 .514 .500 0 1 
Financial Capacity 524 .225 .418 0 1 
Public Support 492 3.496 .892 1 5 
Business Community Support 490 3.227 .804 1 5 
Population (log) 593 11.141 .820 9.885 15.177 
Median Income (log) 593 10.875 .346 9.896 11.901 
Demographics-White (%) 593 .712 .174 .127 .965 
ICLEI Membership 593 .258 .438 0 1 
Democrats (%) 593 53.645 14.300 5.77 90.948 
 
 
 
 
 
 
37 
 
Table 3. Poisson Regression Results for Main and Interaction Effects 
 Models 1  
(Main Effects Only) 
Model 2  
(Incl. Interactions) 
 Coefficients p-value Coefficients p-value 
Number of Local Environmental 
Nonprofits 
.059* 
(.035) 
.090 .223* 
(.118) 
.058 
Moderate Level of Government-
Nonprofit Collaboration 
.132*** 
(.049) 
.008 .094 
(.053) 
.077 
Significant Level of 
Government-Nonprofit 
Collaboration 
 
.178*** 
(.056) 
.001 .152*** 
(.058) 
.009 
Number of Local Environmental 
Nonprofits *Moderate 
Collaboration 
- - -.123 
(.118) 
.296 
Number of Local Environmental 
Nonprofits *Significant 
Collaboration 
- - - .235** 
(.117) 
.045 
Climate Priority .054** 
(.022) 
.014 .055** 
(.022) 
.011 
Staff Capacity .054 
(.046) 
.235 .048 
(.046) 
.299 
Financial Capacity .035 
(.050) 
.476 .047 
(.050) 
.344 
Public Support -.033 
(.031) 
.278 -.032 
(.031) 
.287 
Business Community Support .116*** 
(.028) 
.000 .114*** 
(.028) 
.000 
Population -.001 
(.034) 
.970 .018 
(.036) 
.609 
Median Household Income -.019 
(.021) 
.386 -.021 
(.022) 
.325 
Demographics-White .002 
(.002) 
.285 .002 
(.002) 
.266 
ICLEI Membership .106** 
(.047) 
.024 .102** 
(.047) 
.030 
Democrats (%)  .002 
(.002) 
.355 -.001 
(.002) 
.465 
State Fixed Effects Yes Yes Yes Yes 
Constant 1.641*** 
(.269) 
.000 1.669*** 
(.271) 
.000 
LR chi2 183.26 (59) 191.16 (61) 
Prob > chi2 .000 .000 
Pseudo 𝑅2 .079 .083 
N 469 469 
Standard errors in parentheses. *** p<0.01 ** p<0.05, * p<0.1 
38 
 
Figure 1. Collaboration as a Moderator in the Relationship between Nonprofit Sector Size and 
the Breadth of Government Policy Actions 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
39 
 
Figure 2. Histogram of Outcome Variable 
 
 
 
 
 
 
 
 
 
 
 
 
40 
 
Figure 3. Predicted Margins of the Number of Local Environmental Nonprofits  
 
 
 
 
 
 
 
 
 
 
 
 
 
41 
 
Appendix 1. List of Local Government Climate Actions  
 
1)            City/town works with existing businesses, employers, or business associations 
to identify opportunities to “green” operations or services 
2)            City/town provides financial incentives specifically targeted to support the 
renewable energy sector 
3)            City/town implemented “Buy Local” campaigns 
4)            City/town Provides property tax credits to any commercial building that 
achieved LEED certification 
5)            City/town created a demand for green products through public procurement 
policies 
6)            City/town Supports a local farmer’s market 
7)            City/town requires sidewalks in new development 
City/town unitizes regulations or financial incentives to encourage 
8)            Water conservation by city residents 
9)            Greenspace or open space preservation 
10)        Mixed-use development 
11)        Brownfield site repurposing 
12)        Reducing the use of plastic bags by grocery/retail stores 
To reduce energy consumption, city/town offers residents or businesses financial incentives 
to take the following actions: 
13)        Retrofit existing buildings 
14)        Purchase energy-efficient appliances 
15)        Upgrade heating or air-conditioning systems 
16)        Install renewable energy infrastructure (e.g., solar panels) 
17)        Conduct energy audits 
  
 
 
42 
 
Appendix 2-1. Frequency Histogram of Cities by the Nonprofit Sector Size  
 
Appendix 2-2. Frequency Histogram of Cities by the Nonprofit Sector Size (Excluding outliers) 
 
 
 
43 
 
Appendix 3. Poisson Regression Results for Main and Interaction Effects (Excluding outliers) 
 Models 1  
(Main Effects Only) 
Model 2  
(Incl. Interactions) 
 Coefficients p-value Coefficients p-value 
Number of Local Environmental 
Nonprofits 
.057 
(.035) 
.111 .233* 
(.121) 
.053 
Moderate Level of Government-
Nonprofit Collaboration 
.115** 
(.052) 
.028 .074 
(.056) 
.187 
Significant Level of 
Government-Nonprofit 
Collaboration 
 
.172*** 
(.059) 
.004 .144** 
(.061) 
.018 
Number of Local Environmental 
Nonprofits *Moderate 
Collaboration 
- - -.135 
(.120) 
.263 
Number of Local Environmental 
Nonprofits *Significant 
Collaboration 
- - - .251** 
(.120) 
.037 
Climate Priority .061* 
(.023) 
.008 .062***  
(.023) 
.006 
Staff Capacity .053 
(.049) 
.282 .047 
(.050) 
.346 
Financial Capacity .034 
(.053) 
.524 .045 
(.053) 
.399 
Public Support -.021 
(.032) 
.514 -.020 
(.032) 
.533 
Business Community Support .094*** 
(.030) 
.002 .092*** 
(.030) 
.003 
Population .001 
(.034) 
.988 .021 
(.036) 
.560 
Median Household Income -.026 
(.024) 
.290 -.027 
(.024) 
.267 
Demographics-White .002 
(.002) 
.304 .002 
(.002) 
.279 
ICLEI Membership .074 
(.051) 
.146 .071 
(.051) 
.160 
Democrats (%)  .002 
(.002) 
.391 -.001 
(.002) 
.471 
Constant 1.689*** 
(.276) 
.000 1.719*** 
(.278) 
.000 
LR chi2 152.61 (58) 160.88 (60) 
Prob > chi2 .000 .000 
Pseudo 𝑅2 .074 .078 
N 418 418 
Standard errors in parentheses. *** p<0.01 ** p<0.05, * p<0.1  
44 
 
Appendix 4.  Predicted Margins of the Number of Local Environmental Nonprofits with 
Confidence Intervals