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The objective of this study is to propose a stochastic programming model to optimize the deployment of infrastructure, specifically pipelines and ships, to transport\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\text {CO}_{2}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    from industrial sources to sequestration sites. The proposed model accounts for uncertainties in the capture (or supply) of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\text {CO}_{2}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and is tested using an illustrative case study of the Humber cluster in the UK. The constructed scenario trees incorporate the risk of potential closure and reactivation of the capture facilities, allowing decision makers to make more robust first stage decisions about the type and capacity of transport infrastructure to construct. The experiments in this paper examine changes in optimal investment decision for a range of uncertainties. For example, the trade-off between investment in pipelines and ships is influenced by assumptions about the potential for ships to relocate to other regions if CCS projects close and key policy decisions (e.g. availability of upfront funding for infrastructure investment). For our illustrative case study, pipelines are preferred in cases with lower probabilities of closure in later periods when sufficient budget is available. When a higher probability of project closure is considered or discount rate is increased to typical commercial rates, our model indicates that investment in ships will dominate.\n                  <\/jats:p>","DOI":"10.1007\/s10287-026-00555-8","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T02:50:40Z","timestamp":1771555840000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A stochastic programming model for planning CO2\u00a0transport infrastructure with uncertainty"],"prefix":"10.1007","volume":"23","author":[{"given":"Lihan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel F.","family":"Anjos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Chalmers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"555_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.120605","volume":"338","author":"B Abdoli","year":"2023","unstructured":"Abdoli B, Hooshmand F, MirHassani S (2023) A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem. 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