POLICY INNOVATION, INTERGOVERNMENTAL RELATIONS, AND THE ADOPTION OF CLIMATE PROTECTION INITIATIVES BY U.S. CITIES RACHEL M. KRAUSE Indiana University
ABSTRACT: In the absence of federal requirements, how do state- and municipal-level character-
istics impact the probability of local policy innovation? This article provides insight by examining the adoption of sub-national climate change mitigation initiatives in the United States. Drawing from literature on policy innovation, a multilevel model is developed to examine the factors influencing over 900 U.S. cities to eschew free-rider tendencies and formally commit to greenhouse gas reduction. Multilevel analysis recognizes the nested structure of cities within states and accounts for the shared economic, political, and policy environments experienced by cities within the same state. The level of initiative state governments have taken toward climate protection varies considerably, and the influence of different state policies on related local decisions is empirically examined. Results are consistent with hypotheses derived from the innovation literature and suggest local-level characteristics are the dominant drivers of cities’ decisions to commit to climate protection.
Metropolitan areas are key drivers of climate change while also being uniquely vulnerable to its effects. An estimated 30 to 40%1 of global anthropogenic greenhouse gas (GHG) emissions emanate from within cities’ boundaries (Intergovernmental Panel on Climate Change, 2008; Satterthwaite, 2008) and due to their higher population densities and extensive land cover modification, a majority of cities are expected to experience more dramatic climate-induced changes than surrounding areas (Grimmond, 2007). As with most global challenges, however, climate change is generally considered an issue best addressed at national and international levels. Carbon dioxide dissipates globally so the environmental benefits of its reduced atmospheric concentration are the same no matter where in the world emissions are reduced. Unlike many traditional air pollutants, where the costs and benefits of abatement are felt locally, the reduction of GHG emissions results in nonexcludable global benefits. Each abating entity receives only minimal direct benefits from its efforts, yet bears the full burden of the cost. As such, in a noncoercive environment, the incentive to free-ride would theoretically block the involvement of subnational governments in efforts to produce public goods such as increased climate protection (Olson, 1965). In the face of federal inaction and in apparent defiance of this logic, a significant number of U.S. state and local governments have taken initiative to reduce the GHG emissions coming from
Direct correspondence to: Rachel M. Krause, Indiana University, School of Public and Environmental Affairs, 1315 E 10th St, Bloomington, IN 47405. E-mail: rmkrause@indiana.edu. JOURNAL OF URBAN AFFAIRS, Volume 33, Number 1, pages 45–60. C 2010 Urban Affairs Association Copyright All rights of reproduction in any form reserved. ISSN: 0735-2166. DOI: 10.1111/j.1467-9906.2010.00510.x
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their jurisdictions. Even if the federal government increases its involvement in climate protection, state and local governments will remain important units of analysis, in part because they have authority over key climate-relevant policy areas often including transportation, land-use, building codes, electricity production and transmission, and waste management (Coenen & Menkveld, 2002). Further, subnational governments provide a great deal of variation against which theories of policy innovation and governmental decision making can be tested. This article investigates the factors that influence cities’ decisions to engage in climate policy innovation, while explicitly recognizing the nested political, economic, and policy structure of cities within states. BACKGROUND ON SUBNATIONAL CLIMATE PROTECTION INITIATIVES Scholars frequently point to state governments as the climate protection leaders in the United States (Lustey & Sperling, 2008; Pew Center, 2009; Rabe, 2004). Either on their own or as part of regional groups, a majority of state governments have implemented policies aimed at greening energy production. These frequently include requirements for GHG emissions reporting and regional portfolio standards, which require utilities to produce a minimum amount of renewable energy. Climate action plans, which identify state-specific opportunities to decrease GHG emissions, have been developed by 36 states (Pew Center, 2009). More recently, states have begun to adopt GHG targets, most of which are nonbinding, but set formal reduction goals for state efforts. Organized local-level involvement in the United States on climate change can be traced back to 1991, when six municipalities began working with ICLEI (then known as the International Council for Local Environmental Initiatives) to develop comprehensive strategies to reduce their GHG emissions2 (Betsill, 2001). Membership in ICLEI’s climate protection program increased slowly for its first decade and a half, until experiencing a notable acceleration in 2005 following the launch of the U.S. Mayors’ Climate Protection Agreement (MCPA).3 Sponsored by the U.S. Conference of Mayors, the MCPA reflects the commitment of signatory cities to reduce their GHG emissions by 7% below their 1990 levels, that is, the Kyoto Protocol’s goal amount for the United States. Most local climate change activity falls under the umbrella of these two affiliated organizations. As of July 2009, 960 municipalities had signed the MCPA and nearly 550 had joined ICLEI. This equates to approximately 5% of all U.S. municipalities, covering nearly 30% of the population, with some type of formal involvement in GHG mitigation. Although network membership and the adoption of reduction goals clearly do not equate to effective climate policy, they are an explicit acknowledgment of local ability and responsibility to help mitigate climate change. Much of the research that has been conducted on municipal climate protection efforts thus far is qualitative in nature. Initial work utilized case study methodologies to examine the motivations and approaches of select cities that had adopted explicit climate protection goals (Bulkeley & Betsill, 2003), or to observe the functioning of a specific climate protection network, such as ICLEI (Betsill, 2001; Bulkeley & Betsill, 2003; Lindseth, 2004). A second generation of qualitative studies evaluates, via interviews and document analysis, the ways in which small samples of (typically) best practice cities implement their climate protection plans (Aall, Groven, & Lindseth, 2007; Wheeler, 2008). Conclusions emanating from these lines of research suggest that municipalities often frame climate protection initiatives so to emphasize the localized cobenefits of efforts, such as economic development and local environmental improvements (Bulkeley & Betsill, 2003; Lindseth, 2004). The presence of a committed policy “champion” or “entrepreneur” within the local government has been found as key to both the adoption and implementation of climate protection initiatives (Bulkeley & Betsill, 2003). Finally, findings suggest that municipalities often limit their implementation of climate protection initiatives to activities that target “low hanging fruits” and are generally reluctant to invest their own funds into policy changes
I Policy Innovation, Intergovernmental Relations, and the Adoption of Climate Protection I 47
(Aall et al., 2007; Wheeler, 2008). These studies provide valuable insight to the climate protection dynamic that exists within specific municipalities; however, as with all case study and small sample research, the ability to generalize findings is limited. In a series of quantitative papers, Zahran and colleagues use an environmental stress, civic capacity, and vulnerability lens to address the question of why local governments join climate protection networks. They find that metropolitan areas characterized by GHG intensive operations/behaviors are significantly less likely to engage in voluntary climate protection activities (Zahran, Grover, Brody, & Vedlitz, 2008). On the other hand, cities with greater levels of human capital, as measured by demographic statistics such as income, education, and environmental group activity, are significantly more likely to participate in such networks (Zahran, Brody, Vedlitz, Grover, & Miller, 2008; Zahran, Grover et al., 2008). Their findings are less clear regarding local vulnerability to climate change-related risk, which is expected to differ considerably across the country. In some cases, cities at greater risk of experiencing negative impacts are significantly more likely to increase participation in climate protection efforts (Zahran, Brody et al., 2008). In other cases, this relationship appears to be insignificant (Zahran, Grover et al., 2008). A fundamental inconsistency exists within the vulnerability causal story. Because carbon dioxide dissipates globally, a city can eliminate 100% of its emissions and have virtually no impact on its climate-related risks. Thus, the idea that higher vulnerability leads to an increased likelihood of engagement in GHG mitigation is flawed, unless it is premised upon an assumed misunderstanding by the public and/or decision makers regarding how climate change works. Although this is not an altogether unreasonable assumption, the vulnerability causal story fits better with adaptation rather than mitigation efforts. This article takes a new angle and examines local climate protection decisions through a policy innovation and intergovernmental relations lens. Municipal climate protection initiatives are often viewed as a result of local factors and independent from state-level actions (Selin & VanDeveer, 2007). However, the rate of local participation in the MCPA varies significantly between states, indicating that state-level policies and/or characteristics might influence the propensity of the cities within their borders to join. The sample of local governments considered by this study is limited to incorporated places with populations greater than 25,000. Of these, approximately 40% are members of the MCPA. However, in a “typical� state, the probability of each city being a member is 0.582.4 Ninety percent of states have probabilities of local participation that fall between 0.185 and 0.894. This distribution illustrates that the probability of local MCPA membership is uneven across states. The reason for such state-to-state variation is either because (1) the internal characteristics of cities relevant to adoption tend to differ by state, or (2) something about the state economic, political, policy, or social environment influences cities’ propensity to adopt. POLICY INNOVATION AND RESEARCH HYPOTHESES Policy innovation, as distinct from policy invention, is described in the literature as the adoption of a policy or program by a government entity that had never before utilized it; that is, it is new to the government adopting it, but is not necessarily an altogether new idea (Berry & Berry, 1999). Studies of policy innovation often seek to explain why some governments adopt a particular policy while others do not (Berry & Berry, 1990; Feiock & West, 1993; Glick & Hays, 1991) or to explain why some entities are generally more innovative than others (Savage, 1978; Walker, 1969). The majority of empirical research on policy innovation has been conducted on the state level. However, a growing number of studies have considered the local adoption of regulatory policies, including gun control laws (Godwin & Schroedel, 2000) and smoking bans
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(Skeer, George, Hamilton, Cheng, & Siegel, 2004), and the provision of new services, such as curb-side recycling (Feiock & West, 1993). The process by which policy innovation occurs can be characterized as being either “acute” or “incubated” (Deyle, Meo, & James, 1994; Polsby, 1984). Acute innovation occurs over a short period of time at the behest of a small group of decision-makers, often in response to a crisis. Incubated innovation results in policy change developed as part of a longer process addressing a chronic problem or environmental condition. It is more likely to be influenced by scientific and technical information and stakeholder negotiations. The decision-making process associated with municipal GHG mitigation commitments can be characterized as incubated innovation. Innovations of this type often face barriers to securing a position on the policy-making agenda and policy entrepreneurs therefore play a considerable strategic role in pushing the issue forward (Deyle et al., 1994). This is consistent with findings from qualitative research on local climate protection, which often single out the influence of policy entrepreneurs and networks of technical experts (such as those within ICLEI), who often frame GHG mitigation policy in ways that are relevant to local concerns (Bulkeley & Betsill, 2003; Lindseth, 2004). In a seminal piece on innovation in organizations, Mohr (1969) finds evidence supporting his hypothesis that innovation is “directly related to the motivation to innovate, inversely related to the strength of obstacles to innovation, and directly related to the resources available for overcoming such obstacles” (p. 114). Berry and Berry (1990, 1999) identify two distinct perspectives from which policy innovation is typically studied, which they stress are not mutually exclusive under Mohr’s theory. The first perspective considers the internal determinants of the adopting government. According to this view, the factors encouraging or restricting innovation are the political, economic, and/or social characteristics of the potentially adopting government. The second perspective, tested via diffusion models, holds that innovation is the result of the emulation of policies previously adopted by other governmental units, which have been communicated via intergovernmental channels. Although traditionally considered separately, Berry and Berry (1999) posit that more realistic empirical models consider both simultaneously, and recommend event history analysis as a means of estimation. This article adds to the existing literature in two primary ways. First, both the unit of analysis and the type of policy it considers are relatively underexamined in innovation studies. The article considers the adoption, by local governments, of an entrepreneurial policy, that is, one whose benefits are dispersed but whose costs are concentrated locally (Wilson, 1980). Second, it utilizes multilevel modeling, a methodology not commonly seen in adoption studies, but one that is called for when the adopting units of government are embedded within larger ones, such as cities within states. This model examines how the characteristics internal to cities and states impact the likelihood of local innovation. It also allows us to see if previously enacted state policies influence the likelihood that cities within their borders will adopt related policies. This can be seen as a form of vertical diffusion. The first set of hypotheses presented in this article builds from Shipan and Volden (2006) whose work examines the vertical diffusion of smoking regulations from local to state governments. They posit that initiatives taken at one level of government may exert either a complementary or weakening influence to similar initiatives taken at another level. Expanding from their work, this article proposes two opposing hypotheses regarding the potential influence of state-level climate change policies on the local adoption of related ones. The first suggests that state-level climate policies will enable and/or encourage the adoption of related local policies (i.e. they will create a “snowball effect,” as termed by Shipan and Volden). On the other hand, the second posits that state-level policies will reduce the probability of local initiatives because they lead to the perception that sufficient action is already being taken and additional local action is therefore not necessary (i.e., a “pressure valve effect”) (Shipan & Volden, 2006).
I Policy Innovation, Intergovernmental Relations, and the Adoption of Climate Protection I 49
The article’s second set of hypotheses considers the impact of cities’ and states’ internal characteristics and mirror those suggested by Mohr (1969): namely, the likelihood of a city’s membership in the MCPA is hypothesized to be determined by the relative strengths of its motivations and obstacles to environmental action, modified by the resources it has available to overcome those obstacles. The next section describes the social, economic, and political independent variables used to model cities’ decisions to commit to climate protection and categorizes them according to Mohr’s hypothesis. DATA AND METHODS Sample and Variables The sample of level-1 observations considered in this article draws from the 1,078 incorporated places in the United States with populations greater than 25,000, per the 2000 County and City Databook. Of these, 1,026 or approximately 95%, have sufficient data to be included in the analysis. The 50 U.S. states make up the level-2 observations. Of interest is the probability that a city will become involved in climate change protection through the adoption of local initiatives. A dichotomous dummy variable representing participation of the U.S. Mayor’s Climate Protection Agreement is used as this article’s dependent variable (1 if participant; 0 if not). Signatories to the Agreement commit to taking actions that will reduce their cities’ GHG emissions by 7% below their 1990 levels, that is, the amount specified for the United States in the Kyoto Protocol. Independent variables are introduced at both the state and local levels (See Table 1 for the description and source of each and Table 2 for summary statistics). Following Mohr’s hypothesis, the independent variables can be viewed as resources, motivations, or obstacles either supporting or inhibiting innovation. The level-1 independent variables that are indicative of the resources a city has available to pursue innovative policies include its population size and per capita general revenue. A longstanding consensus in the literature suggests that large entities with greater resources are more likely to adopt policy innovations (Walker, 1969). It follows that large cities are more likely to have the, sometimes considerable, administrative capacity necessary to coordinate climate protection programs (Betsill, 2001). Ownership of a municipal utility may also indicate higher city income and the increased ability to adopt and follow-through on GHG reduction pledges. The experiences of similar or nearby entities with a new policy can serve as an informational resource and facilitate adoption. Horizontal diffusion studies, often conducted at the state level, suggest that the adoption of policies by neighboring states increases the likelihood of a given state to similarly adopt (Berry & Berry, 1999). The number of neighboring cities each city has that are members of the MCPA is also included as an independent “resource” variable. Certain demographic statistics such as a city’s median household income and average educational attainment can be viewed as motivations to innovation, as both environmental concern and civic engagement are correlated with these attributes (Rothenberg, 2002; Verba, Schlozman, Brady, & Nie, 1993). Climate change initiatives in the United States, particularly those that involve emission caps, have been characterized by partisanship. Democrats are generally more in favor of such efforts, whereas Republicans have more often been opposed. As such, an independent variable indicating local political leanings estimates the level of political support or opposition that may accompany the adoption of a local climate change initiative. The presence of a MayorCouncil form of local government is another possible feature motivating the adoption of climate change initiatives, as this political form has the effect of making local government more overtly political. Along these lines, Clingermayer (1990) finds that the adoption of symbolic policies and credit-claiming is more common in municipalities with Mayor-Council government types.
50 I JOURNAL OF URBAN AFFAIRS I Vol. 33/No. 1/2011 TABLE 1 Variables Description and Source
Dependent Variable Mayors’ Climate Protection Agreement
Level 1 (Local) Variables Population Education Income Percent Democrat
Per capita general revenue
Type of city government
Number of unhealthy air days
Municipal utilities
Participating “neighbors”
Value added by manufacturing
Level 2 (State) Variables Climate action plan
GHG target
Government ideology
Manufacturing
A dummy variable indicating whether or not each city’s mayor had signed the Mayors’ Climate Protection Agreement by May 2008, committing the city to achieve what would have been the U.S. GHG reduction goal under the Kyoto Protocol. Source: U.S. Conference of Mayors, Mayors Climate Protection Center. Logged population of each city in 1999. Source: County and City Data Book: 2000 CD-Rom. Percentage of population over the age of 25 with a BA or higher. Source: U.S. Census Bureau 2000, SF-3. Median household income in 1999 in $1,000s. Source: U.S. Census Bureau 2000, SF-3. The percentage of each county’s total votes that supported the Democratic candidate in the 2000 presidential election. Source: CQ Voting and Elections Collection. Per capita general revenue for each city in 1997 in $100s. Source: County and City Data Book 2000, Department of Housing and Urban Development’s State of Cities data source. A dummy variable indicating if a city has a mayor-council form of government(1) or a different form(0). Source: The Municipal Year Book 2000, International City/Council Management Association. The number of days in 2000 that each city’s county had “unhealthy” air quality as determined by the EPA’s Air Quality Index. Source: Environmental Protection Agency, Air Quality Standard. A dummy variable indicating the municipal ownership of an electricity producing or distributing utility. Source: U.S. Energy Information Administration. The number of “neighbor cities” within a 50-mile radius of each city that is has joined the Mayor’s Agreement. Source: Constructed with GIS using data from the U.S. Conference of Mayors and the U.S. Census Bureau. Value (in $100,000s) added by the manufacturing sector to each city’s county or metro/micropolitan statistical area in 2002. Source: U.S. Census Bureau, 2002 Economic Census, NAICS 31-33. A dummy variable indicating whether or not a state had completed a climate action plan specifying ways GHG emissions can be reduced statewide prior to the 2005 start of the Mayors’ Climate Change Agreement. Source: Pew Center on Global Climate Change, Knigge, and Bausch (2006). A dummy variable indicating whether or not a state adopted a greenhouse gas reduction target (either binding or nonbinding prior to the 2005 start of the Mayors’ Climate Change Agreement. Source: Environmental Protection Agency. A measure of the ideological position of state governments in 2005. Higher scores are more liberal, lower are conservative. Source: Revised 1960–2006 citizen ideology series, updated Berry, Ringquist, Fording, and Hansen 1998. Percentage of each state’s domestic product that came from manufacturing in 2005. Source: U.S. Bureau of Economic Analysis.
I Policy Innovation, Intergovernmental Relations, and the Adoption of Climate Protection I 51 TABLE 2 Descriptive Statistics Mean
Std. Dev
N
Level 1: City Characteristics Mayor’s Agreement Signatory Resources Population (logged) Gen rev (per cap, 100s) Municipal utility Participating neighbors Motivations Income (1,000’s) Education Government type Unhealthy air quality days Percent vote Democrat Obstacle Manufacturing value added ($100,000’s)
0.40 11.11 10.37 0.12 6.67 43.62 26.50 0.33 15.46 49.82 86.29
0.49 0.78 6.45 0.32 7.79 15.25 13.55 0.47 27.31 11.79 137.26
1026 1026 1026 1026 1026 1026 1026 1026 1026 1026 1026
Level 2: State Characteristics Unclear Greenhouse gas target Climate action plan Obstacle Pcnt. GSP manufacturing Motivation Liberal government ideology
0.16 0.58 0.12 49.94
0.37 0.50 0.06 26.74
50 50 50 50
Studies frequently show that municipalities emphasize the local cobenefits of climate protection, including the public health and visibility improvements associated with decreased local air pollution (Betsill, 2001; Bulkeley & Betsill, 2003). In certain cities, leadership in climate protection and alternative energy has been directly tied to community air quality concerns (Portney, 2004). As such, having poor urban air quality may provide cities with additional motivation to adopt climate change initiatives. The decline of manufacturing in the United States has been pointed to as a factor enabling local governments to become engaged in planning efforts that explicitly include sustainability as an objective (Portney, 2003; Zahran, Grover et al., 2008). Despite a potential conflict with the previously described assumption that poor air quality may motivate climate protection initiatives, cities where manufacturing remains economically important may be less inclined to adopt GHG reducing initiatives than those that have a service or technology-based economy. The amount of economic value added by manufacturing is considered the best measure of the sector’s relative importance to specific geographic areas and is included in the model as an independent variable hypothesized to act as an obstacle to adoption (U.S. Census Bureau, 2002) Like local variables, state-level variables can provide motivation, resources, and obstacles to local policy adoption. For example, Feiock and West (1993) find that most state-level efforts to encourage municipalities to provide recycling services significantly increase the probability that this will occur. Under the climate change scenario, the state-level encouragement is less direct. For example, as of the time of this study, no states mandate that cities reduce their GHGs, while some do mandate recycling. Still, the presence of state climate policies, such as climate action plans (CAPs), may serve as both a resource and a motivation for cities to adopt their own related initiatives. CAPs describe sources of GHG emissions within a state and how they can be reduced. This information may prove helpful to cities in the development of their own plans and the adoption of a CAP may send a signal that the state considers climate change protection important and is likely to pursue additional policies in that area. On the other hand, the presence of a CAP might provide a “pressure valve effect” and reduce local motivation (Shipan & Volden, 2006).
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State GHG reduction targets, whether binding or not, may similarly influence local propensity to adopt. Other potentially relevant state-level characteristics include the amount that manufacturing contributes to a state’s economy (potential obstacle) and the ideology of the state government on a liberal–conservative continuum (potential obstacle, as it moves right).
Method of Analysis Multiple cities are clustered within each state and are thus subject to the same state-level political, policy, and economic conditions. As a result of this nested structure, the assumption that each city represents an independent observation is likely incorrect and statistical methods that assume independently and identically distributed errors are thus inappropriate (Primo, Jacobsmeier, & Milyo, 2007). Standard statistical methods, including logit and probit models, are likely to yield results that underestimate the standard errors associated with coefficient estimates, particularly group-level coefficients (Snijders & Bosker, 1999). As such, this article uses multilevel modeling, which allows independent variables at different levels to be analyzed simultaneously and accounts for the likelihood that observations of cities within the same state are not completely independent of each other.5 The idea underlying multilevel modeling is that the dependent value, Y, is influenced both by individual level-1 (local) and group level-2 (state) variables (Snijders & Bosker, 1999). The general structural form of a random intercept equation is Yij = β0j + β1j X1ij + rij where β0j = γ00 + γ01 Z1ij + u0j β1j = γ10 , where i represents individuals, j represents groups and β0J is the intercept that varies according to the value of level-2 independent variable Z1ij and the error term u0j . Although multilevel models can also allow for the possibility that group-level forces influence the value of the coefficients of level-1 variables (β1j ), in random intercept models they are held constant (note the absence of an error term and level-2 variables in β1j = γ10 ). The question of what factors influence local climate initiative adoption is explored via a random intercept logistic multilevel model and is estimated with the statistical program HLM6.
RESULTS The multilevel model in this article is developed following the process described by Hox (1995). Notably, only city-level variables are statistically significant. The final Bernoulli model predicting the likelihood of cities participating in the U.S. MCPA is represented by the following structural equations: Level-1: Structural Equation η = β0 + β1 (logpop) + β2 (income) + β3 (education) + β4 (democrat) + β5 (govtype) + β6 (gen rev) + β7 (municipal util) + β8 (air quality) + β9 (neighbor) + β10 (man val added)
I Policy Innovation, Intergovernmental Relations, and the Adoption of Climate Protection I 53
Level-2: Structural Equation β0 = γ00 + u0 β1 = γ10 β2 = γ20 β3 = γ30 β4 = γ40 β5 = γ50 β6 = γ60 β7 = γ70 β8 = γ80 β9 = γ90 β10 = γ10 As determined by deviance tests, the final model is not statistically different from the full model that has state-level variables added to the intercept term (see Table 3). Findings at the State Level None of the state-level characteristics were found to significantly influence the propensity of local governments to adopt climate protection policies. Thus, neither the “snowball” nor “pressure valve” hypotheses described above receive empirical support: the presence of state GHG reduction targets and/or climate action plans fail to impact local adoption in either direction. This is different from previous findings that show support for the snowball-effect hypothesis, that is, that policies adopted by one level of government increase the likelihood that other levels will adopt similar policies (Feiock & West, 1993; Shipan & Volden, 2006). A key policy implication of this finding is that if state climate policy goals include having local governments follow suit by adopting complementary initiatives, more explicit encouragement is necessary. States “leading by example” is not effective in this context. When compared to Feiock and West’s (1993) study, which found that supportive state-level policies significantly increase the probability that local governments will adopt curb-side recycling programs, two factors stand out that may explain the different outcomes. First, several of the state policies they examine encourage local policy adoption directly. Mandates for the provision of some kind of local recycling (although not necessarily curb-side service) and incentive programs indeed may have had a greater effect on local actions than do the more indirect climate action plans and GHG reduction goals. A second explanation that may account for the difference in outcome significance concerns the statistical methods employed. Feiock and West use a standard probit analysis. The use of standard models, which assume observations are fully independent of each other, can result in standard errors for level-2 variables being underestimated when observations are clustered into groups. The statistical significance of state-level policies on the adoption of complimentary local ones may be less than is generally thought because standard analysis leads to their overstatement. Findings at the Local Level All but one of the city-level variables included in the analysis are significant, and most have the effect predicted by Mohr’s motivation-resource-obstacle hypothesis. As expected, having
54 I JOURNAL OF URBAN AFFAIRS I Vol. 33/No. 1/2011 TABLE 3 Impact of State and Local Characteristics on City’s Decision to Commit to Greenhouse Gas Emission Reduction by Signing the Mayor’s Climate Protection Agreement
Variable Intercept City-Level Variables Fixed Effects Population (logged) Income Education Percent Democrat Government type General revenue Municipal utility Unhealthy air days Participating neighbors Manufacturing value added State-Level Variable Effects on the Intercept GHG reduction target Climate action plan Government ideology GSP from manufacturing Model Characteristics Reliability Final variance component Deviance (parameters)
Model 1: Local Characteristics
Model 2: Local + State Characteristics
−0.531∗∗∗ (0.152)
−0.669∗∗∗ (0.247)
0.932∗∗∗ (0.113) −0.038∗∗∗ (0.008) 0.067∗∗∗ (0.008) 0.031∗∗∗ (0.009) 0.353∗ (0.189) 0.046∗∗∗ (0.015) −0.490∗∗ (0.241) −0.002 (0.004) 0.040∗∗ (0.015) −0.002∗∗∗ (0.001)
0.939∗∗∗ (0.114) −0.038∗∗∗ (0.008) 0.068∗∗∗ (0.008) 0.031∗∗∗ (0.009) 0.347∗ (0.194) 0.045∗∗∗ (0.015) −0.498∗∗ (0.241) −0.003 (0.005) 0.038∗∗ (0.016) −0.002∗∗∗ (0.001)
0.207 (0.383) 0.142 (0.313) −0.003 (0.005) 0.992 (2.379) 0.385 0.267 2985.981 (12)
0.385 0.268 2985.106 (16)
∗
p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01, Standard errors in parenthesis. Level-1 n = 1026, Level-2 n = 50. Note: Continuous variables are grand mean centered, dichotomous variables are uncentered; population average models outcome are shown; full maximum likelihood used.
a mayor-council government type and citizens with higher levels of education and democratic political leanings appear to be significant motivations for local climate protection innovation. Larger populations and higher levels of per capita general revenue are significant enabling resources. Evidence of horizontal diffusion is also present, with results suggesting that cities with larger numbers of participating neighbors are themselves more likely to commit to climate protection by joining the MCPA. This may be a result of communication and information sharing that occurs between officials in neighboring cities or of increased public pressure resulting from heightened regional awareness about the initiative. In terms of substantive significance, a city’s size, the level of its residents’ educational attainment, and the number of its neighbors that are MCPA participants have the largest impact on the likelihood of climate protection commitment (see Table 4). Results also support the stated hypothesis that a higher reliance on manufacturing in a local economy decreases the probability that a city will commit to climate protection. This is consistent with the concept of incubated innovation, in which the adoption of new policy is heavily influenced by the ability to achieve consensus among stakeholders (Deyle et al., 1994). A strong manufacturing interest likely decreases the ability to form consensus around local climate protection. These results also reflect findings from Zahran, Grover et al., (2008), who conclude that
I Policy Innovation, Intergovernmental Relations, and the Adoption of Climate Protection I 55 TABLE 4 Substantive Effect Variables on Joining the Mayor’s Climate Protection Agreement Independent Variable Logged population (1 std. deviation increase) Income (1 std. dev. increase) Education (1 std. dev. increase) Democratic voters (1 std. dev. increase) Mayor Council Gov. (0 to 1) Per cap general rev. (1 std. dev. increase) Municipal utility (0 to 1) Unhealthy air (1 std. dev. increase) Participating neighbors (1 std. dev. increase) Manufacturing value added (1 std. dev. increase) ∗
Change in the Likelihood of Joining 0.180∗∗∗ −0.127∗∗∗ 0.223∗∗∗ 0.091∗∗∗ 0.086∗ 0.073∗∗∗ −0.119∗∗ −0.06 0.151∗∗ −0.006∗∗∗
p < 0.1, ∗ ∗ p < 0.05, ∗∗∗ p < 0.01.
the amount of stress a locale exerts on the environment, in terms of high levels of engagement in carbon-intensive activities, is negatively associated with joining a climate protection network. The real and perceived costs of GHG reduction, both in terms of financial investment and lifestyle disruption, act as a statistically significant barrier. However, and perhaps surprisingly, the magnitude of this barrier effect is quite small. Cities’ likelihood of MPCA participation decreases by less than 1% as the total amount of money manufacturing adds to a local economy increases by a standard deviation. Unexpected results include the negative signs associated with median household income and city ownership of a municipal electric utility. Referring back to Mohr’s hypothesis, having a higher median household income was originally classified as a factor motivating innovation in city governance. Environmental protection is typically seen as a normal good, the demand for which increases along with prosperity and economic well-being (Rothenberg, 2002). This makes the resulting negative sign for income appear counterintuitive. However, the significant negative value for income is present only when controlling for education. When the variable for education is removed from the model, the impact of income loses all significance.
A Closer Look at the Influence of Municipally Owned Utilities The ownership of a municipal electric utility was originally considered a resource able to provide support to local GHG emission reduction efforts. However, the results of the analysis suggest that it instead acts as an obstacle. All else equal, cities with a municipally owned utility are almost 12% less likely to join the MCPA than those without one. This runs counter to observations made in several prominent cases where municipal utilities have assumed leadership roles in the promotion of local GHG reduction by providing staff and funding for climate protection as well as making changes to their own energy production activities.6 Municipal utilities are nonprofit organizations, free from shareholder demands. This organizational structure could conceivably make them more responsive to local climate protection initiatives. On the other hand, many municipal utilities act only as energy distributors and those that do generate their own electricity are on average more carbon-intensive than investor-owned ones (Wilson, Plummer, Fischlein, & Smith, 2008). Further, because they are often small, municipally owned utilities are regularly exempt from state and federal regulations, including requirements for demand-side management (DSM) programs (Wilson et al., 2008).
56 I JOURNAL OF URBAN AFFAIRS I Vol. 33/No. 1/2011 TABLE 5 Impact of Municipal Utility Characteristics on Joining MCAP Parameter Estimate Income Education Percent Democrat Government type General revenue Unhealthy air days Participating neighbors Manufacturing value added Sales (TWh)a Nameplate capacity (MW)b Constant
−0.074∗∗ 0.101∗∗∗ 0.022 0.477 −0.009 0.021 0.130∗∗ −0.001∗ 0.211∗∗ 0.003∗∗∗ −2.860∗∗
Standard Error 0.031 0.027 0.234 0.518 0.042 0.014 0.059 0.000 0.086 0.001 1.563
Logit analysis. ∗ p < 0.10, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01. x 2 45.39 Prob x 2 0.000 n = 123. a Utilities’ total electricity sales in terawatt hours, Energy Information Administration, 2005. b Nameplate capacity in megawatts; a generator’s maximum output. Fifty-eight of the utilities have a nameplate capacity of zero, meaning they produce no electricity inhouse, but distribute power purchased from other generators.
One hundred twenty-three cities from the original sample own electric utilities. A subsequent analysis of this subsample suggests that utility size and generator characteristics are significant determinants of a city’s MCPA membership. Small, distribution-only utilities act as the main inhibitors. Cities that own larger utilities, both in terms of total sales and generating capacity, are more likely to make reduction commitments than their small-utility counterparts (see Table 5). This may be because larger operations are subject to more regulations and thus may already be cleaner. Also, when compared to small utilities, larger ones likely have higher capacity, both in terms of their ability to change their own power-generating operations and to allocate staff and financial resources to follow through on GHG reduction commitments. Finally, efficiency innovations undertaken by utilities, such as DSM, often result in short-term revenue loss and yield savings only if they defer the construction of new facilities (Hirst, 1994). Such savings are unlikely to be realized by distribution-only utilities and by small municipal generators not facing increasing demand. DISCUSSION AND POLICY IMPLICATIONS This article’s first major finding is that state-level characteristics and climate-related policies have an insignificant effect on the likelihood that their cities are active in climate protection. Of the state climate plans and GHG reduction targets that have been adopted, few have any regulatory teeth. California is the only state with legislation that enables enforcement for failure to adhere to emission caps (Pew Center, 2009). Information and vague goals emanating from state offices are insufficient to motivate municipal decision making. State government’s “leading by example” fails to yield results in this case. If this is an objective of state climate policy, more explicit positive incentives (i.e., intergovernmental grants) or negative incentives (i.e., enforcement threats) appear necessary. Whereas state-level variables fail to have significant impacts, local-level characteristics largely do. Demographic characteristics, including city size, education rate, median income, and political leaning, are significant determinants of city membership in the MCPA, as are the form of municipal government, the amount of per capita of general revenue collected, and the importance
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of manufacturing in the local economy. While these findings are interesting in and of themselves, their implications for policy are minimal as little can be done to change them for the sake of increasing attention to climate protection. On the other hand, the findings regarding the presence of horizontal diffusion and the impact of municipally owned utilities may be of practical use to policymakers seeking to increase local involvement in climate protection. As previously discussed, this article’s results show that, all else equal, cities that are surrounded by higher numbers of MCPA members are themselves more likely to join. The diffusion of local policy to reduce GHGs is no doubt influenced by the activities of climate protection networks (Bulkeley & Betsill, 2003). The regionally based diffusion patterns may reflect areas of focus in network activity or may reflect intergovernmental communication channels, which are strongest between nearby cities. Either way, the observed patterns of horizontal diffusion could be leveraged by policymakers with the creation and/or support of formal geographic networks designed as forums of communication about innovative local policy. The finding that municipally owned utilities, particularly small ones, tend to act as obstacles to climate protection initiatives is similarly important. First, it tempers assumptions that municipal utilities are showing widespread leadership on this issue. Second, the U.S. Environmental Protection Agency, the Department of Energy, and many state regulators are actively interested in removing the many contradictory incentives that exist, which limit utilities’ engagement in energy conservation/efficiency measures (U.S. EPA & U.S. DOE, 2006). In the case of municipal utilities and climate protection, it appears that obstacles extend beyond the utilities’ direct operations and continue to influence the decision-making of the cities that own them. It is likely that over the next several years regulations for utility operations will be revised considerably and the proposed changes would benefit from additional study exploring this dynamic. LIMITATIONS AND FUTURE RESEARCH Two limitations to this study deserve note and offer direction for future research. First, as with all adoption models that use dichotomous dependent variables, joining the MCPA does not distinguish between “deep” and “superficial” commitment (Berry & Berry, 1999; Glick & Hays, 1991). Two cities may adopt the same policy label or goal, but put different amounts of effort into achieving it. The Mayor’s Climate Protection Agreement has no enforcement mechanism or monitoring system, making it possible for some cities to sign on and take minimal followthrough action, while others make significant policy changes to mitigate their GHG emissions. Currently, there is not a single comparable measure that adequately indicates the extent of local GHG reduction efforts taking places within a large number of municipalities, and a lack of reliable GHG emissions data for units below the state level makes generalizable evaluation of local policy impact virtually impossible. To make progress in this area, researchers must acquire comparable data on the specific efforts that a large number of local governments are undertaking to reduce their GHG emissions (Krause, 2009), arrive at a consistent methodology by which to estimate local GHG emissions (Dodman, 2009), and develop a set of criteria to evaluate the implementation of climate change programs, perhaps by building off assessments conducted at the state level (Feldman & Wilt, 1996; Lustey & Sperling, 2008). Second, the model employed in the study is static, but is looking at a process that is somewhat dynamic. Panel data and event history analysis are thus often recommended as the most appropriate methodology for adoption models (Berry & Berry, 1999). However, the time frame in this study is three years, much shorter than most, so the use of models that account for changes over time is less necessary. As the relevant time frame grows, dynamic models may offer important additional insights. Examining when a city joins, as opposed to simply if it does, may draw a clearer picture about the factors that lead to this decision. Dynamic extensions of the model presented in this
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article may prove particularly useful in examining potential ripple effects in policy across different levels of government, once a comprehensive federal climate protection policy is adopted. CONCLUSION Subnational governments have become the de facto leaders of the U.S. climate protection efforts. Direct municipal involvement in the issue has accelerated since the 2005 formation of the Mayor’s Climate Protection Agreement. Mayoral signatories to the Agreement commit to take actions to reduce GHG emissions in their cities. Significant state-by-state differences exist in the rate of city participation, which raises the question about whether this is influenced by differences in the state-level policy or economic environment. The results of the multilevel model run here suggest that this is not the case. Rather, the primary determinants influencing the propensity of municipalities to make GHG reduction commitments are local. Several demographic, economic, and city government characteristics emerge as significant determinants of local involvement in climate protection and generally support Mohr’s motivation-resources-obstacles hypothesis. Findings of horizontal diffusion and the barriers posed by municipally owned electric utilities have potential to assist the efforts of policymakers interested in encouraging local involvement in climate protection. ENDNOTES 1 Considerable debate exists over the proper accounting framework to use when assessing local GHG emissions, including how to determine appropriate urban boundaries and whether to use a production- or consumptionbased emissions methodology (Dodman, 2009; Larsen, 2009). Satterthwaite (2008) utilizes IPCC data and a production-based methodology and likely produces a lower-end estimate. 2 The six U.S. participants included: Chula Vista, CA; Dade County, FL; Denver, CO; Minneapolis, MN; Portland, OR; and St. Paul, MN. 3 Membership in ICLEI increased an average of less than 10 U.S. cities a year for its first decade (Betsill, 2001; ICLEI, 1997). Membership in 2008 is increasing by up to four cities a week (Susan Ode, Outreach director, ICLEI, personal communication, July 2008). 4 This is calculated by running an unconditional two-level model, that is, one with no explanatory variables at either level. The resulting log odds of participation is −0.3312, making the corresponding probability 0.582. Raudenbush and Bryk (2002) recommend this procedure as part of examining the magnitude of level-2 variation in models with binary dependent variables. 5 The number of level-1 observations (cities) varies widely from group to group. The range extends from one sample city in Vermont to 217 cities in California. The result is less efficient estimates than would otherwise result. 6 Examples of municipally owned utilities that are particularly active in climate protection include Austin Energy, Los Angeles Water and Power, and Seattle City Light.
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