DOI: 10.5061/dryad.prr4xgxts
title: Semi-structured interviews about sustainable and equitable development in the USA

description: We conducted semi-structured interviews with 23 individuals working in the
 broad field of sustainable and equitable urban development in the U.S.A.
 between August and September 2022. We used purposive and snowball
 sampling, targeting people with experience working in this field across
 multiple sectors, states, and types of organizations. Participants were
 informed and consented and interviews were audio-recorded and transcribed.
 Participants reported working in the following professions: Built
 environment, Transport & Mobility, Health and social care,
 Housing, Culture, Parks and Recreation, Water, Education, Energy,
 Workforce support, Sanitation, Waste, and Others. Participants'
 organization types included: local government, non-profit organizations,
 private sector, and others. Participants lived in various U.S. states.
description: We conducted semi-structured interviews with 23 individuals
 working in the broad field of sustainable and equitable urban development
 in the U.S.A. between August and September 2022. There were 21 interviews,
 in which two participants attended two interviews. Participants were
 informed and consented (following research ethics approval from University
 College London's Bartlett School of Energy, Environment and Resources
 low-risk procedure) and interviews were audio-recorded and transcribed.
 Interviews were conducted virtually. Transcripts were generated through
 Zoom's computer-generated audio transcription (n=8) or using Rev.com
 (n=13). Transcripts were anonymized using best practices for sharing human
 subject data. We removed data that could allow a person to be identified,
 including direct and indirect identifiers (such as descriptions of their
 employer, geographical location, and other data that would be unique to a
 specific place). Generic terms were replaced with identifiable data using
 brackets. For example, a participant's name would be replaced with
 [name]. In cases where a long description was not possible to de-identify,
 the relevant text was deleted and this was indicated in the transcription
 with a notation, such as: [details deleted]. The
 interview guide included the following questions:  
 
 <strong>1</strong> 
 Can you start by briefly telling me about your role in INSERT
 ORGANIZATION? 
 <strong>2</strong> 
 We have arranged this interview to learn about your experiences
 working on INSERT X SUSTAINABLE AND EQUITABLE DEVELOPMENT project. Can you
 talk me through the background of this project and your role in
 it? <em>Prompts: How did this project come
 about?  </em> 
 <strong>3</strong> 
 What problem or set of problems was this project
 trying to solve?  <em>Prompts: Who was
 experiencing these problems and how did your organization come to know
 about them?  </em> 
 <strong>4</strong>
 <strong> </strong>
 <strong> </strong> 
 How did considerations of equity feature in this
 project?  <em>Prompts: Who brought this up and
 when?  </em> 
 <strong>5</strong> 
 Who were the key players in this project?
 <em>Prompts: Did the project require collaboration with
 multiple departments/agencies? </em> 
 
 <strong>6</strong> 
 How were members of the community involved in this
 project? <em>Prompts: Which community groups or
 individual residents were involved and how?</em>
 
 <strong>7</strong> 
 On a project of this scale, there must have been tensions,
 setbacks, or challenges. Can you talk me through the toughest challenges
 and how you dealt with them? <em>Prompts: How did
 different people perceive the challenge? What strategies did you use to
 overcome the problem?</em> 
 <strong>8</strong>
 What kinds of compromises or negotiated
 solutions were required for this project, if any?
 <em>Prompts: What compromises were made (i.e.
 sustainability, equity, financial, other) such as increasing project
 budgets or making exceptions for innovations not allowed through local
 regulations? How did these compromises work for your
 organization?</em> 
 <strong>9</strong> 
 Looking back on your work on this project, what
 moments felt like big ‘wins’ that contributed to the project’s successful
 completion? <em>Prompts: Would you describe these
 as innovative? Who was involved? How were these ‘wins’
 achieved? </em> 
 <strong>10</strong> 
 In reflecting on your work in this area, what
 knowledge, skills, or capacities could make the work more
 successful? <em>Prompts: Is there anything that
 you think you might learn from peers across the globe about how to do this
 work?</em>
description: # Semi-structured interviews about sustainable and equitable development
 in the USA
 [https://doi.org/10.5061/dryad.prr4xgxts](https://doi.org/10.5061/dryad.prr4xgxts) This dataset contains 21 transcripts of semi-structured interviews with 23 individuals working in the broad field of sustainable and equitable urban development in the U.S.A. ## Description of the data and file structure The interview transcripts are verbatim transcriptions of participants' audio, including pause fillers (such as 'um') and errors. The data do not include the researcher's observations, such as the participants' expressions or body language. Transcripts were generated through Zoom's computer-generated audio transcription (n=8) or using Rev.com (n=13). Transcripts were anonymized using best practices for sharing human subject data. We removed data that could allow a person to be identified, including direct and indirect identifiers (such as descriptions of their employer, geographical location, and other data that would be unique to a specific place). Generic terms were replaced with identifiable data using brackets. For example, a participant's name would be replaced with [name]. In cases where a long description was not possible to de-identify, the relevant text was deleted and this was indicated in the transcription with a notation, such as: [details deleted]. Abbreviations within the data were not always defined by the participants because they were also known by the researchers. Abbreviations are often related to federal government funding sources, such as ARPA (American Rescue Plan Act of 2021). These data are suitable for analysis with qualitative data analysis methods, including software. ## Sharing/Access information A manuscript that uses this data is available here: [https://doi.org/10.12688/wellcomeopenres.21180.1](https://doi.org/10.12688/wellcomeopenres.21180.1) ## Code/Software N/A