| dc.description.abstract | The lack of a national comprehensive climate change policy in the United States has prompted cities to take the lead on urban sustainability actions. Extent research has explored various political, socio-economic and institutional factors to explain why some cities pursue sustainability actions and others do not. The role of organized interest groups – particularly business interest groups – is unclear as to whether their involvement correlates with more or less likelihood of sustainability policy adoption. The pluralist nature of the American political system suggests that various organized interests compete to advance their policy positions, and business interest groups have generally been theorized as economically rational profit-maximizers who would presumably oppose environmental regulation. The overall rise in environmental awareness (Yale University and George Mason University, 2017) raises the possibility that business interest groups will support urban sustainability policies, as firm can be profitable while also limiting environmental impacts. This dissertation explores how various types of business interest groups effect the adoption of select urban sustainability policies that regulate the environmental impacts of buildings. My rationale for studying buildings is that urban sustainability is too broad of a concept to get at the nuances of interest group activity occurring in each sector, and distinctive business interest groups participate in urban policy processes depending on what sector is being regulated as many firms only work in one sector (e.g., buildings, transportation, water). Further, urban sustainability research commonly operationalizes business interest groups as one group which assumes a singular profit interest, but not all businesses respond to urban sustainability in the same way. I segment the business interest groups in an attempt to measure the effects of distinctive organized interests within a single industry – the construction industry. I generate sector-specific business interest group data rather than relying on survey data or general proxies for business interests which are more common approaches in urban sustainability research. This work overcomes the issue with obtaining business interest group data in cities by using an algorithmic approach to data collection using the Python programming language for text mining industry association websites and member directories. Using various regression methods, my findings suggest that this approach to operationalizing interest groups has merit. The segmented business interest groups have divergent effects on the energy efficiency and green building policies with traditional construction interest groups having a negative effect on policy adoption while ‘green’ construction groups have a positive effect (Chapter 3 and Chapter 4). In Chapter 5, I explore the effects of organized interests on reported energy savings in published studies using a regression-based meta-analysis approach. My results suggest that organized interests have an effect on reported energy savings, supporting a theory of advocacy bias in information sharing. On a theoretical level, this research contributes to understanding business interest groups in local urban policymaking in a low salience policy setting. It provides the insight that some segments of business interest groups are likely to have a positive effect on urban sustainability and environmental policy adoption while other segments are likely to have a negative effect, so it is important to segment business interest groups rather than treating them as one group with the same motivations. Also regarding theory, this work supposes that the buildings policy domain is low salience but it does attract political participants, albeit a narrowly focused group of technical professionals, which is divergent from some extent literature that suggests that low salience policy issues do not attract interest groups. Considering other urban sustainability sectors as low salience may be appropriate, as other areas may also attract groups of technical experts more so than citizen groups. Methodologically, this research promotes algorithmic data collection as a way to overcome difficulties in collecting city-level data. | |
