Abstract
Climate action at the local level represents an important and unique complement to global and national-level policies. This study provides one of the first systematic analyses of local climate actions in the State of California by comparing cities’ adoption of alternative policies and statistical modeling of local choices of climate actions. The pattern of adopting different climate actions incrementally suggests that cities prefer certain actions than others. Coastal location, instead of the usual predictors of local mitigation actions, is found to affect cities’ adaptation actions. Whether a city is more likely to keep its commitment to mitigate climate change depends on the nature of the commitment.
Introduction
In the absence of a national climate change policy in the United States, local government actions have proliferated during the past decade as part of a voluntary subnational effort to cope with climate change. Climate action at the local level represents an important and unique complement to global and national-level policies in a multilevel policy system (see, for example, Bulkeley and Betsill 2003; Corfee-Morlot et al. 2009). Various international and domestic local policy alliances, such as the International Council for Local Government Initiatives (ICLEI) Cities for Climate Protection (CCP) program and the U.S. Mayors Climate Protection Agreement (MCPA), have been formed to promote local mitigation of greenhouse gas (GHG) emissions. It is now widely recognized that local governments play a key role in infrastructure provision, traffic management, land use, and utilities and waste management—all important determinants of local carbon footprint. In fact, climate change policies made at the state level, such as California’s Senate Bill 375, signed into law in 2008 with the expressed goal of reducing GHG emissions through land-use measures, can hardly succeed without local political will to implement them. Research has also confirmed the significant potential of carbon reduction and ancillary benefits by local climate actions such as cost reduction and air quality and traffic improvements (e.g., Climate Action Team 2006).
This article contributes to the literature on local climate actions by providing systematical evidence on California cities’ mitigation commitments and actions, as well as their adaptation policy. The following section “Literature Review” presents the relevant literature, followed by the third section “Research Question.” The fourth section explains the “Data and Method” used. The fifth (“The Incremental Adoption of Different Climate Actions”), sixth (“Mitigation Versus Adaptation”), and seventh (“Promises Versus Actions”) sections describe and explain cities’ adoption of different climate actions and the relationship between commitments and actions. The eighth section (“Conclusion and Discussions”) concludes the article and identifies future research needs.
Literature Review
There is a rapidly-growing literature on local climate actions, of which many studies focus on motivations and barriers for making commitments and taking actions, processes of decision-making, and policy implementation (e.g., Betsill 2001; Betsill and Bulkeley 2006; Engel and Orbach 2008; Gore and Robinson 2009; Kousky and Schneider 2003; Lindseth 2004; Robinson and Gore 2005; Schreurs 2008; Selin and VanDeveer 2007). For example, Betsill (2001) reviews the ICLEI-CCP experience and concludes that localizing the global climate issue is key to political support. Through interviewing local officials, Kousky and Schneider (2003) highlight the importance of perceived local co-benefits of controlling GHG emissions such as energy saving, reuse of wastes, and reduction of traffic congestion and emissions. Several studies (e.g., Betsill 2001; Bulkeley and Betsill 2003; Bulkeley and Kern 2006) indicate that many local authorities fail to adopt a systematic and structured strategy to tackle climate change. Instead, they focus on win-win measures, especially in the field of energy saving. Literature reviews by Betsill and Bulkeley (2007), Sippel and Jenssen (2009), and Bulkeley (2010) provide extensive evaluations of the history, practices, motivators, and challenges to urban climate governance and actions. Generally, case studies suggest the importance of motives (e.g., local environmentalism; fiscal, economic, and/or environmental co-benefits; perceived risks; and peer pressure), means (e.g., financial resource and technical expertise), and leadership (e.g., political champion within local authority).
A small but growing number of studies statistically quantify the relationship between the characteristics of local jurisdictions and their political will to take local actions. Zahran, Grover, et al. (2008) analyze the correlates of climate action commitment by U.S. metropolitan statistical areas (MSAs) through examining aggregate measures of local climate change risks, emission intensities and local sociodemographic characteristics. In the similar fashion, Zahran, Brody, et al. (2008) study the relationship between cities’ membership in the ICLEI-CCP program and local characteristics, suggesting the importance of civic capacity and existing level of GHG emissions. Krause (2011a) employs a multilevel analysis to examine cities’ adoption of the MCPA, suggesting that local demographic, government, and economic characteristics, rather than state policies, significantly correlate with cities’ adoption of the MCPA. Later on, a small number of studies have gone beyond political commitment (memberships in the ICLEI-CCP, the MCPA, etc.) to analyze cities’ adoption of substantial climate actions. By constructing an index that quantifies the GHG-reduction policies implemented by local governments in the State of Indiana, Krause (2011b) evaluates the extent and type of follow-through made on municipal climate-protection commitments (i.e., the MCPA). Findings point to the lack of linkage to actual implementation of the MCPA, the important role of policy entrepreneurs, and the lack of significant effect of staff professionalism (measured by the level of education held by the individual in charge of a municipality’s environmental programs). This study probably should, however, emphasize that the existence of policy entrepreneurs is endogenous. Using a random sample of 122 U.S. cities, Sharp, Daley, and Lynch (2011) analyze the impact of interest group pressure, political institutions, and problem severity on a city’s decision to develop and implement climate protection programs measured by the implementation of ICLEI-CCP milestones. Their results suggest that organized interests influence both local adoption and implementation of the ICLEI-CCP. But such an effect is significant only in cities with mayoral as opposed to city manager forms of governments. Nonetheless, analyzing mayoral and city manager cities separately seems unusual given their identical model structures.
Compared with climate change mitigation, adaptation has attracted much less attention by academics. As Sippel and Jenssen (2009) point out, the number of cities formally engaged in adaptation planning remains quite small. The same case exists in California according to Hanak et al. (2008)’s survey of 310 cities and counties. Even among cities that have announced climate action plans, Wheeler (2008) finds that most plans do not address adaptation to a changing climate. Bulkeley et al. (2009) suggest multiple barriers for local adaptation actions, including availability of data and information about local impacts from climate change, access to financial and human resources, and coordination of policies and measures across both local agencies and levels of government. Both studies indicate that climate change mitigation and adaptation are subject to different sets of motivators and barriers. Unfortunately, given the limited information on local adoption of adaptation actions, more systematic and/or quantitative studies on local adaptation are rare.
Overall, existing knowledge on local climate policy covers a range of theoretical explanations and empirical evidence regarding local adoption of climate actions such as motives, capacity, barriers, and leadership. However, one may still consider our understanding is somewhat fragmented because of the lack of comparison between mitigation and adaptation and between promise and implementation, perhaps largely because of the lack of systematic information of what has been done and how policies vary across locations. Combining the current knowledge and a systematic set of data of California cities, this study aims at addressing the gap in our knowledge by a more systematic examination of how and why local climate policies are adopted.
Research Question
California is one of the largest and most diversified states in the United States, boasting 480 cities that vary widely in size, economy, partisanship majority, and natural geography. Local governments across California have undertaken a range of actions to address and respond to climate change, such as measuring their own GHG emissions (inventory), setting quantitative targets to reduce these emissions (target setting), adopting various sectorial policies to reduce emissions, and taking steps to adapt to potential effects of climate change. This research focuses on understanding what climate actions California cities have adopted and why from three interrelated aspects: policy priority in mitigation, mitigation versus adaptation, and promise versus action.
The first is on the pattern of adopting various actions to mitigate GHG emissions. Which type(s) of climate actions (policies, programs, etc.) are more or less frequently adopted among local adopters? Is there an understandable pattern? Studies such as Betsill (2001), Bulkeley and Betsill (2003), and Bulkeley and Kern (2006) all suggest that local authorities often focus on win-win measures and fail to adopt a systematic and structured strategy to tackle climate change. We are interested in how California cities have selected local climate actions from a range of measures supposedly varying in their potential of local co-benefits, financial costs, and institutional barriers.
The second aspect is the determinants of local adaptation versus mitigation actions. Are adaptation and mitigation actions predicted by similar local characteristics or not? For local residents, mitigating GHG emissions contributes to a global public good (sometimes with local co-benefits), whereas taking collective measures to adapt to climate change is a local public good. Although certain factors, such as financial resources, are often required to implement both kinds of policies, one may expect some difference between the communities adopting mitigation actions and those adopting adaptation policies, as suggested by Sippel and Jenssen (2009) and Bulkeley et al. (2009).
The third and final aspect is the linkage between local political commitments to mitigation and the adoption of substantial actions. All else being equal, do political commitments predict more actions? Does it vary with different commitments? If a political commitment is merely rhetorical, it may have little or no relationship to the actions taken (Krause 2011b), perhaps because breaking the promise does not cost much more than the political reputation of the politicians involved. However, if a commitment entails resource investment and/or contracted activities, one would expect it to be more likely to increase the willingness and ability of local communities to pursue real actions.
Data and Method
This study utilizes a data set comprising systematic observations of the various climate actions taken by local governments in California. The source of the data set is the California Governor’s Office of Planning and Research’s 2008 and 2009 Local Government Annual Planning Surveys (APS08 and APS09), which contain extensive lists of questions on cities’ climate actions, with many questions asked in both years. 1 The surveys were completed by each local jurisdiction and represent its current, adopted policies and/or programs.
The survey response rates of APS08 and APS09 are 69% and 70%, respectively. Of the 480 California cities queried, 72 (15%) did not respond to either survey, 220 (31%) responded to one of the surveys, and 260 (54%) responded to both. Cities responding to the surveys are positively correlated in the two years, with a statistically significant coefficient of 0.27. There is, however, no statistically significant relationship between cities’ response behavior and their average household income level, population size, or total or per capita number of planners. Thus, both samples are considered random.
The probit regression technique is commonly used to analyze discrete choices and is employed here to statistically analyze the association between cities’ climate actions and their characteristics and commitments. The dependent variables are dichotomous discrete dummies (1 = yes; 0 = no) indicating cities’ adoption of each of the climate actions including the mitigation actions listed in Table 2 (detailed discussion provided in the section “The Incremental Adoption of Different Climate Actions”) and an adaptation action—requiring California Environmental Quality Act (CEQA) analysis of the impact of climate change on proposed projects. The independent variables include three sets of local characteristics—sociodemographics, staff capacity, and geographic and environmental characteristics. Cities’ climate actions are presumably caused by or associated with their characteristics (described below), which are assumed to be determined by factors outside of the model (exogenous). The existence of multicollinearity among explanatory variables is tested, and they are free of the multicollinearity concern.
Local sociodemographics include population size, average household income, and percentages of registered Democrats and Green Party voters. Population size indicates a city’s overall administrative capacity. All else being equal, a more resourceful city government is certainly more capable of addressing the issue of climate change. A larger city may have dedicated personnel for climate and sustainability issues, whereas a smaller but wealthier city may not. Income level indicates a community’s ability to afford to substitute environmentally-harmful industries and behaviors with those less so. Poorer communities are often constrained by their budgets, and have fewer choices (Betsill 2001). In addition, in Kahn (2006)’s comparison between smart growth cities and “brown” cities, people with higher income levels tend to care more about the quality of life issues, and wealthier communities are more capable of creating strategies and implementing them. Local voter preference, represented by Democrat and Green Party voter shares, is obviously important. Vocal individual environmentalists and their organizations, as well as those who tend to believe in human-induced climate change may influence local policy decisions and are predominantly Democratic, as suggested by a recent Brookings survey (Borick, Lachapelle, and Rabe 2011).
A city’s per capita number of planning professionals, who are often the key technical staff involved in land-use, transportation, and environmental decisions, serves as a proxy for the level of technical capacity available to plan and implement climate policy and actions. Given the same level of wealth, a city with more resources allocated toward planning-related staff is likely to have a higher administrative capacity to design and implement climate policies.
An indicator of local air quality is used to measure the potential of local co-benefits of reducing GHG emissions. We document whether a city is located within a nonattainment county/air basin, as designated by the U.S. Environmental Protection Agency (EPA) to describe the air quality in a given area for any of the six common air pollutants known as criteria pollutants. 2 Local climate variables on precipitation and temperature are used to reflect multiple characteristics of a city. Climate directly affects the energy-use patterns of a city, mainly by affecting indoor climate control and water use. However, local climate characteristics also reflect local vulnerabilities to climate change risks such as flooding and wildfire in California. Finally, we create the indicator of whether part of a city’s jurisdiction includes the coastal shoreline to proxy the potential vulnerability to the most direct and perceivable consequences of sea level rise.
Collectively, the variables on local characteristics are able to at least partially capture the relevant information on both the demand (e.g., voter preferences, potential for perceived local co-benefits, and perceived vulnerability to climate change) and supply (e.g., financial capacity and technical expertise) sides of local climate policy adoption. Descriptions of these variables and their sources are listed in Table 1. In addition, information on cities’ promises to mitigate GHG emissions is collected by identifying those who signed the MCPA and those who joined the ICLEI-CCP before 2009 to match the local climate action data we have.
Description of Variables on Local Characteristics.
Note: EPA = Environmental Protection Agency.
The exceptional large observations are from the small cities such as Vernon, which has three planners but a small population size of 95 in 2008. The 95 percentile value is 6, and the 99 percentile value is 35.
The Incremental Adoption of Different Climate Actions
Cities can take a range of actions to reduce their GHG emissions to mitigate their impacts on the climate. Table 2 lists the adoption rates of six types of local mitigation actions adopted by California cities. With the progress of state climate policies and the switch of federal political leadership, the rates in 2009 are understandably higher than those in 2008 for the four actions surveyed in both years.
Adoption Rates of Local Mitigation Actions.
Note: GHG = greenhouse gas; CEQA = California Environmental Quality Act.
Table 2 clearly depicts the great deal of variation in California cities’ adoption of different mitigation actions. The interesting finding, however, is that patterns of how cities’ adoption rates vary among different policies are consistent in the two years for the four actions surveyed in both years, indicating a rank of popularity among different types of actions.
In 2008, the policies that were most widely adopted are those at the individual project level within the current regulatory framework. Of the surveyed cities, 71% applied CEQA mitigation measures or strategies to mitigate GHG emissions from major projects, 3 and 44% had formally required that a CEQA analysis of proposed project impacts on the climate be included in their environmental impact analysis (EIA). Compared with project-level actions, actions that occurred at the policy/program level were less commonly adopted. 4 In 2008, 37% (increased to 72% in 2009) of surveyed cities had adopted at least one policy/program to address climate change, whereas only 19% (increased to 29% in 2009) of the cities included formal language related to climate change and GHG reduction in their general or master plans. 5 The least commonly adopted climate actions seemed to be those representing systematic efforts to measure GHG emission inventory and to set reduction targets for the whole jurisdiction. In 2008, only 15% (increased to 27% in 2009) of the surveyed cities calculated community-wide GHG emissions baselines. A total of 9% (increased to 14% in 2009) went further to formally set community-wide GHG emission reduction targets. For both years, cross-tabulations of cities’ adoption of the above climate actions find that if a city adopted a less commonly adopted action, such as calculating GHG emissions baseline, it is highly likely that it also implemented the more commonly adopted policies, such as having an individual climate change mitigation program or addressing specific projects’ GHG emissions using CEQA mitigation measures. This indicates a pattern of incremental adoption of policies.
Table 3 provides a detailed look into the major CEQA mitigation measures or strategies applied by cities at the project level in 2008 (there is no corresponding 2009 data). There exists a noticeable pattern of differential adoption, which seems consistent with the pattern found in Table 2. Energy/resource efficiency measures were about twice as frequently adopted as sequestration measures, and more than 20 times more often adopted than the purchase of offsets. Similarly, cross-tabulations reveal that the majority of cities adopting the less commonly taken measures also adopted the ones that were adopted more often, but not vice versa, again indicating a pattern of incremental adoption. For example, among cities adopting offset measures, 88% adopted building-efficiency measures and vehicle-miles traveled (VMT) reduction measures, and 68% adopted waste reduction/recycling measures and sequestration measures. Similarly, among cities adopting sequestration measures, about 80% adopted waste reduction/recycling measures, 77% adopted building-efficiency measures, and 65% adopted VMT reduction measures.
CEQA Mitigation Measures/Strategies Applied to Projects in 2008.
Note: GHG = greenhouse gas; CEQA = California Environmental Quality Act.
The fact that cities adopting less commonly taken actions were more prone to adopt the ones more commonly adopted suggests that the variation in local choice of climate policies is not just because of randomness or heterogeneous preference. Instead, it suggests the incremental levels of effort or cost-benefit ratios associated with different actions. For instance, mitigating climate impact of individual projects is perhaps easier to adopt than establishing formal mitigation policies/programs because the former can easily fit into the existing regulatory framework (CEQA). Individual mitigation policies/programs may in general be less resource demanding than conducting a systematic inventory and setting a formal emission target. Similarly, at the project level, the incremental adoption of different CEQA mitigation measures also seems to reflect the net costs of mitigation measures/strategies. For example, while reducing GHG emissions, the efficiency measures also save energy and resource consumption, which provides a direct financial incentive, termed as “win-win measures” by Bulkeley and Betsill (2003) and “(measures) producing immediate results” by Bassett and Shandas (2010). In fact, such measures may even be implemented simply for the cost-saving purpose, independent of the motivation to mitigate climate change. On the other hand, sequestration measures are less attractive because they require direct expenses, although there may be environmental and social co-benefits from planting trees. Purchasing offsets is understandably the least favorable because it increases costs without any tangible local benefits.
The above observations indicate that some actions were adopted first and more frequently, whereas other actions, probably because of their higher costs or institutional barriers, were mainly adopted by a subgroup of those who adopted the “easier” actions. This incremental pattern of adoption supports the existing literature’s finding that cities tend to focus on win-win measures but fail to adopt a systematic and structured strategy to tackle climate change (Betsill 2001; Bulkeley and Betsill 2003; Bulkeley and Kern 2006).
Mitigation Versus Adaptation
Can the characteristics of cities predict their likelihoods of adopting adaptation as well as mitigation actions? What are the differences in the driving forces of adaptation and mitigation actions? Table 4 presents the marginal effect estimates of probit models. In general, the local characteristics, especially sociodemographics and air quality, lead to higher likelihoods of adopting mitigation actions with the exception of the adoption of climate change or GHG-related language in general plans.
Determinants of Local Mitigation and Adaptation Actions.
Note: CEQA = California Environmental Quality Act; GHG = greenhouse gas. Robust standard errors in parentheses.
Significance at 10%. **Significance at 5%. ***Significance at 1%.
Among the different local characteristics, sociodemographics (population size, income level, and political preference) are more consistent predictors of local mitigation actions. The likelihood to adopt mitigation actions is higher for cities with the following characteristics: larger size, higher average income, and higher shares of Democratic and Green Party voters. Such effects are more statistically significant for the more systematic efforts, such as inventory and target setting, although results on city size and income level are more consistent across years than those on political preference. Air quality also shows expected negative relationship with cities’ adoption of mitigation actions—Attainment designations by the EPA (meaning good air quality) is associated with lower likelihood of local mitigation actions, although this effect is not consistent across all types of actions. Other variables—per capita number of planners, coastal location, and local climate—are generally insignificant factors for predicting local mitigation actions. Overall, these results echo some findings (i.e., sociodemographics, staff professionalism) in previous studies (e.g., Krause 2011a, 2011b; Wang, forthcoming) on the determinants of local climate policy commitment.
Do the same factors predict local adaptation actions? A pair of matched survey questions on mitigation and adaptation enables us to compare the adopters of the two types of climate actions. Among the 331 cities surveyed in 2008, 44% required CEQA analysis of the climate change impacts of proposed projects (mitigation), whereas only 16% required CEQA analysis of the climate change impacts of proposed projects (adaptation). There was a relatively weak positive correlation (with a statistically significant coefficient of 0.26) between adopters of the two policies. Among the cities requiring adaptation analysis, 74% required mitigation analysis, whereas the reverse ratio is only 27%.
Overall, it does not seem that the adopters of adaptation policies are the same as those of mitigation policies. Unlike mitigation, local adaptation actions are expected to be directly linked to climate-change-related risks perceived by local residents and decision makers. This is supported by comparing results in the last data column to those in the second data column in Table 4. The usual sociodemographic predictors of cities’ mitigation actions have little power in explaining whether a city requires a CEQA analysis of the impacts of climate change on a proposed project. 6 The only significant factor turns out to be whether a city is coastal or not. Holding all other variables constant, the correlation between adaptation policy and the coastal dummy is 0.18. This is in contrast to the nonsignificant correlation coefficient of 0.091 between the respective CEQA mitigation analysis requirement and the coastal dummy, shown in the second data column. Because of our limited understanding of local adaptation policies, the test here mainly suggests what is not correlated to local adaptation, rather than what are the real motivators and barriers, such as a lack of clear strategies and stabilized knowledge suggested by Corfee-Morlot et al. (2009).
Promises Versus Actions
An interesting question about local voluntary climate actions is whether cities take real actions after making commitments to mitigate climate change. Perhaps the two most common commitments to mitigate climate change by U.S. cities are their decisions of joining the MCPA and the ICLEI-CCP. By the end of 2008, 27% of the 480 California cities, or 35% of the 235 cities with populations of 30,000 or more, joined the MCPA (The U.S. Mayors Conference is the official nonpartisan organization of cities with populations of 30,000 or more). At the same time, 28% of the California cities were members of the ICLEI-CCP. California cities’ MCPA and ICLEI-CCP memberships are correlated, with statistically significant coefficients of 0.44 among all cities and 0.47 among cities with populations of 30,000 or more.
Using the same regressors for local mitigation actions, the first three data columns in Table 5 present the marginal effects of cities’ characteristics on their likelihood of making promises by 2008. Consistent with results on the mitigation actions, population size, income, and political preferences show statistically significant positive impact on a city’s likelihood to join the MCPA or the ICLEI-CCP. Air quality attainment status shows statistically significant negative effects on joining the ICLEI-CCP but not the MCPA.
Determinants of Local Mitigation Commitments and Mitigation Actions.
Note: MCPA = U.S. Mayors Climate Protection Agreement; ICLEI-CCP = International Council for Local Government Initiatives Cities for Climate Protection; GHG = greenhouse gas. Robust standard errors in parentheses.
Sample includes cities with 30,000 or more residents because of the fact that the U.S. Mayors’ Conference is the official organization of cities with populations of 30,000 or larger.
Sample includes all cities with available data.
Significance at 10%. **Significance at 5%. ***Significance at 1%.
How do promises made by 2008 affect actions of the cities in 2009? In the rest of the data columns of Table 5, dummy variables indicating California cities’ MCPA and ICLEI-CCP memberships before 2009 are added to the independent variables to predict cities’ climate actions in 2009. Similar to the results in the section “Mitigation Versus Adaptation,” sociodemographic predictors are more powerful in predicting systematic climate actions. By adding the commitment dummy variables, statistical significance levels of those variables have been understandably weakened. It is important to note that the results here on local commitments do not necessarily imply causal relationship even after controlling for local characteristics. They can only show that all else being equal, the ICLEI-CCP membership is a stronger predictor of actions than the MCPA. It is possible that unobserved variables cause both promises and actions.
Furthermore, compared with the effect of being a MCPA signatory, the ICLEI-CCP membership is a much stronger and more consistent predictor of a city’s climate actions. Signing the MCPA may predict a city’s claim that they have some policy or programs, but it is not statistically associated with the more systematic efforts, that is, inventory or target setting. This result points to the limits of policy networks relying primarily on information and communication (Gore 2010; Kern and Bulkeley 2009) and echoes the finding on the relationship between the MCPA membership and the municipal climate protection index of Krause (2011b). The MCPA is a voluntary agreement representing mayors’ commitments to reduce local GHG emissions and lobby for state and federal climate change actions—There is no measurement or enforcement on specific actions to be taken or goals to be reached. In contrast, the ICLEI-CCP is a paid membership with technical assistance provided by the ICLEI staff to help cities with a “milestone” approach, in which members agree to (1) conduct an emissions inventory and projection, (2) set a target for controlling emissions, (3) create a local action plan for achieving that target, (4) implement policies from the plan, and (5) monitor and report on their progress. Thus, a formal working relationship between the member local governments and ICLEI had been established, and the incremental goal-oriented commitment may likely result in more substantial climate actions. It seems that by formally committing actual resource input (at least a small amount initially), the ICLEI-CCP have better facilitated California participant cities to adopt serious climate actions compared with the MCPA. 7
Conclusion and Discussions
Understanding why local governments adopt certain climate actions or not is important. Our ability to more effectively motivate and assist with the progress of local climate actions is directly impaired by the lack of both accurate information on actions taken by local governments and explanations of the great variation in local climate policy adoption.
This study contributes to the literature on local climate actions by providing recent and innovative empirical evidence from California on the motivators of local climate actions from three aspects: policy priority, mitigation versus adaptation, and promise versus action. It conducts one of the earliest quantitative analyses on local priority among different mitigation actions and on the motivators of local adaptation actions compared with mitigation actions. It also shows the extent to which various commitments are linked to substantial actions.
This study finds some local climate policies are more widely adopted than others in California. The pattern of incremental policy adoption indicates that cities adopt individual actions (often within the existing regulatory framework such as EIA required by CEQA) more often than systematic efforts, and adopt “win-win” policies more often than policies that simply incur additional costs. This seems consistent with the popular wisdom that lower-hanging fruits get picked first. This study also tests whether the same local characteristics predict mitigation and adaptation. Similar to the findings of the existing literature, city size, income level, and political preference are important predictors of a city’s adoption of mitigation actions. Unlike mitigation, adaptation policies are adopted by a smaller number of California cities, with a significant difference in cities’ motivations to adapt versus to mitigate. The location on the Pacific coast was confirmed as the only variable to correlate with adaptation actions, perhaps indicating the importance of sea level rise as a perceived climate-related risk in California. Comparing the MCPA and the ICLEI-CCP, this study asks the question of whether cities actually move from promise to action. It is found that committing to climate change mitigation may significantly increase a city’s likelihood to adopt climate actions when it involves financial investment and technical assistance, rather than political rhetoric only. This has important implications for policy makers to encourage local jurisdictions to make commitments that are more likely to be effective.
For federal and state policy makers, the findings of this study may help them design policies and direct technical assistance more effectively with an improved understanding of how and why local climate efforts vary across jurisdictions. For example, federal and state policies may direct help to local climate actions that are cost-effective but of lower local priority (e.g., certain sequestration programs) and assist local governments technically to create more systematic efforts for mitigating local GHG emissions (e.g., inventory or baseline estimate). Regional adaptation plans can also greatly help communities to reduce their vulnerability to potential climate change effects. Creating policy networks that go beyond political rhetoric may better facilitate local actions.
This study’s power of explanation is limited by the data available to the author. City size, income level, and political preference of residents explain a limited portion of the variation in cities’ likelihood to adopt mitigation actions. Variables such as more complete and better measured co-benefits, local economic characteristics, and vulnerabilities to climate change, if included in statistical analyses, may help explain the remaining variations. Similarly, being a coastal city only explains part of the variation in local adaptation actions. There could be other incentives to adapt to climate change such as the frequency and intensity of natural hazards (McCarney 2009). In addition, although the major advantage of focusing on the single state of California is the avoidance of dealing with different state-level policies, a national-level study would form a more complete picture of local climate actions in the United States. Finally, it is important to study not only the adoption of local climate actions but also the outcomes of these policies, for example, the actual emission reductions achieved. For future research on local climate policies, richer empirical evidence needs to be combined with further theoretical development for more rigorous hypothesis testing.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the UCLA Ziman Center for Real Estate.