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ABSTRACT
This article focuses on the organizational factors of environmental sustainability implementation in local government. We investigate the interactions, as well as direct and indirect impacts, of the framing of environmental sustainability and organizational culture and structure on implementation outcomes. We use a survey of 217 city/county planners and managers in 146 randomly selected American mid-sized cities and counties. The survey was specifically designed to tease out organizational features and their impacts. We model these impacts using structural equation modeling. We find that horizontally and vertically integrated organizational structure supports two essential dimensions of organizational culture: innovation adoption and consensus building. These cultural traits positively impact the framing of environmental sustainability at the core of organizations’ logic, which in turn significantly supports implementation outcomes. These findings provide important insights into city/county managers seeking to promote sustainability, and provide a base for future studies of the organizational factors of implementation.
Funding
Directorate for Social, Behavioral, and Economic Sciences, #1122730.
Notes
1. Sustainability encompasses economic, socio-cultural and environmental goals. In this study, we focus strictly on its environmental dimension. We conceptualize environmental sustainability to include land development patterns and the management of habitat and open space, transportation and air quality, waste, water, energy, natural hazards, climate, and food systems.
2. We constituted an ad-hoc expert panel that includes 23 academics and senior practitioners from the US, Canada, New Zealand, Great Britain, Germany, The Netherlands, and Sweden. They were identified based on recognized publications, reputations, as well as their leadership in cities and organizations working in the field of sustainability (e.g., Portland, Oregon, and Sustainable Cities International). They reviewed the conceptual framework and the questionnaire, which we revised in response to their input.
3. To generate a regionally-balanced sample, we randomly sampled 63 mid-sized cities and 63 mid-sized counties in each of the four US Census Bureau regions (Northeast, South, Midwest, West), for a total sample of 252 cities and 252 counties. In each region, we underrepresented localities with populations 10,000–20,000 (they make up 43% of all localities but 31% in the sample) and oversampled localities with populations 20–50,000 (they make up 35% of localities and 46% in the sample). This sampling strategy was adopted to obtain a more varied sample in terms of population size and because very small localities often did not have websites with staff contact information, making the identification of respondents difficult. We retained population proportions for localities 50–100,000 and 100–200,000 (15% and 7%, respectively).
4. This response rate is fairly low, but on par with other surveys of local government officials. Staff working in localities most interested or successful in implemented sustainability might have disproportionately chosen to answer the survey (due to personal interest and/or interest in presenting their agency in a positive light). This could bias results toward an overly positive assessment of sustainability implementation. However, the low implementation scores observed indicate that this bias is highly unlikely.
5. When analyzing inter-coder implementation scores, we found only five localities with standard deviations across coders above 0.5, and two of them have no clear outliers. Intra-class correlations show that variance between LGOs is greater than within LGOs for implementation and key concept scores. Hausman regression models predicting implementation based on key explanatory factors perform as well with fixed as with random effects. We ran all descriptive statistics, including concept scores, at the individual and agency level. All results are within 0 to 5% of each other, and within a 10% range for questions scored on 1–4 scales. Therefore, multiple responses are consistent and reliable. We present organizational-level data. For agencies with several respondents, we average individual responses’ into an agency score.
6. The questionnaire is available upon request to the authors.
7. Nationwide, median household income in 2010 was about $53,000 (slightly less than in our sample) and about 28% of the US population was non-white, more than in our sample. Our sampling strategy excludes large cities and counties, which are typically more diverse than smaller ones.
8. The questionnaire also included six items on climate planning, renewable energies and natural hazard mitigation. However, best practice implementation in these domains is so rare that factor loadings were very weak and we excluded these practices altogether.
9. In contrast, Wang et al. (Citation2012) find high implementation scores for a series of government actions. Some are unsurprisingly high because they are national mandates (e.g., monitoring water quality) while others are very common (e.g., planting trees). Among other high results, they also found that three quarters of local governments purchase alternative fuel vehicles and 85% promote bike use. These higher scores can be explained by the fact that they consider larger cities (population 50,000 or more) and exclude counties. They can also be explained by a different measurement strategy: respondents answered whether or not cities adopt certain practices, not the actual implementation stage. Thus, a respondent may state that their city has a policy on alternative fuel vehicle purchase even though no vehicle has been purchased yet, or that their city has “joined a sustainability group” although no tangible outcome may have resulted from this action.