Understanding the role of technological complexity in sustainability transitions using stochastic, bi-level optimization

Nathan T. Boyd () and Steven A. Gabriel ()
Additional contact information
Nathan T. Boyd: University of Maryland, College Park
Steven A. Gabriel: University of Maryland, College Park

Computational Management Science, 2025, vol. 22, issue 1, No 6, 36 pages

Abstract: Abstract Practically unlimited natural resources, such as solar energy and advanced seawater desalination, are potential solutions to sustainable resource consumption. However, accessing these natural resources depends on complex technologies that require further research and development. This technological complexity introduces significant uncertainty in the actions required to transition societies to more sustainable levels of resource consumption. Such uncertainty has important implications in terms of risk. The short-term depletion of limited natural resources, such as fossil fuels and freshwater, can pay off if these sustainable technologies mature in the long term. However, this short-term, resource-depletion policy carries the risk that these sustainable technologies will not materialize. In such a case, economic decline, population decline, or both are possible undesirable outcomes. To address this challenge, a stochastic, bi-level optimization problem is developed for sustainability transitions in natural-resource contexts. This model is formulated as a mathematical program with equilibrium constraints and is solved as a mixed-integer, non-linear program. This model is applied to an illustrative water-resources problem with two lower-level players where a policymaker manages freshwater in conjunction with a new water-treatment technology. Overall, this model demonstrates how policies for sustainable resource management can be quantified in terms of risk aversion to adopting new technologies.

Keywords: Sustainability transitions; Mathematical programs with equilibrium constraints; Emerging technologies; Energy; natural resources; and the environment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10287-025-00534-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:22:y:2025:i:1:d:10.1007_s10287-025-00534-5

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-025-00534-5

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().