Speaker
Description
Worldwide, there is an unprecedented rise in demand for space dedicated to production, transportation, and storage of goods. Urban theorist Neil Brenner (2019) proposed the overarching term operational landscapes to capture the dynamic nature of these spaces and their increasing role in defining urban and peri-urban areas. Studies on the spatial (land) transformation entailed by operational landscapes is a part of a broader ‘turn’ of urban studies which go beyond the distinction between urban and non-urban. Development of operational landscapes has been described as a result of coalitions of governmental and nongovernmental actors with a common interest in economic growth (Nefs and Daamen, 2022). These actors work together across multiple scales, creating ‘soft spaces’ by using various planning instruments and governance mechanisms (Allmendinger and Haughton, 2009). This study contributes to debates on how governments, particularly embedded in multi-level governance systems, influence the development of operational landscapes.
Using Romania as a study area, we determine if and how the development of operational landscapes is driven by planning at local, county and regional level. Our focus is on 32 cases of operational landscapes developed after 2009. Generative artificial intelligence, specifically ChatGPT, is used to analyse strategic planning documents adopted at the three up mentioned planning levels. Recent developments in generative artificial intelligence (AI) have simplified its use for a range of users, including document analysis. We developed a two-steps procedure to a) analyse the documents using a combined use of GhatGPT and coding of the prompts by the authors and b) validate the results to ensure reliability. Considering the size of the 81 analysed documents, i.e., each having between 100 and 500 pages of text, tables and visualizations, the procedure allows for a fast analysis of large amounts of data.
Results reveal broadly two approaches used by governments in development of operational landscapes. First, which we call “strategic approach”, includes cases where governments, alone or in coalitions with private actors, proactively develop operational landscapes, setting planning goals in this regard and collaborating across levels from local to regional. Second, titled the “opportunistic approach”, refers to situations where governments react to initiatives of private investors.
The contribution of our study is twofold: it provides empirical evidence on the role of governments in development of operational landscapes, and it illustrates how generative artificial intelligence can be used to enhance our understanding of spatial processes.
References
Brenner, N. (2019). New Urban Spaces: Urban Theory and the Scale Question.
Allmendinger, P., & Haughton, G. (2009). Soft spaces, fuzzy boundaries, and metagovernance: the new spatial planning in the Thames Gateway. Environment and planning A, 41(3), 617-633.
Nefs, M., & Daamen, T. (2023). Behind the Big Box: understanding the planning-development dialectic of large distribution centres in Europe. European Planning Studies, 31(5), 1007-1028.
| Keywords | logistics, strategic planning, ChatGPT, artificial intelligence |
|---|---|
| Best Congress Paper Award | Yes |
