Stochastic Forecasting of CNG Demand: A GBM Approach for Tanzania

Stochastic Forecasting of CNG Demand: A GBM Approach for Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stochastic Forecasting of CNG Demand: A GBM Approach for Tanzania Justine Kira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9611507/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The transition to compressed natural gas (CNG) as a transport fuel in Tanzania offers significant potential for energy security, emissions reduction, and cost savings. However, demand evolution is subject to substantial uncertainty arising from fuel price volatility, limited refuelling infrastructure, variable adoption rates, and policy shifts. This study develops a hybrid stochastic–econometric modelling framework. The model is specified by the Itô stochastic differential equation dD(t) = µ(X(t))D(t) dt + σD(t) dW (t), where the drift µ is a linear function of observable drivers (fuel price differential P (t), converted vehicles V (t), and infrastructure index I(t)), and W (t) is a Wiener process. Probabilistic forecasts are generated via Monte Carlo simulation of 10,000 discretised paths using the Euler–Maruyama scheme. The framework is parameterised using publicly available official data from the Energy and Water Utilities Regulatory Authority (EWURA) and Tanzania Petroleum Development Corporation (TPDC). Key observed figures include approximately 7,000 CNG vehicles and 5,662 metric tonnes of vehicle consumption in FY 2023/24 (75% growth from the previous year), with refuelling stations expanding to around 9. Simulation results project continued mean demand growth through 2026, ac- companied by widening 95% confidence intervals reflective of cumulative volatility (Var[ln D(t)] = σ 2 t). Sensitivity analysis highlights the dominant influence of in- frastructure expansion on drift and price differentials on both mean and volatility. Model validation against available actuals yields low MAPE (≈ 0.3%–4%). These findings provide policymakers and investors with distribution-valued fore- casts suitable for risk-aware planning. The study contributes to the literature by demonstrating a rigorous, context-specific application of stochastic calculus and Monte Carlo methods to CNG demand modelling in an emerging African energy market. Applied Mathematics Stochastic modelling Geometric Brownian Motion CNG demand fore- casting Uncertainty quantification Monte Carlo simulation Energy transition Tanzania Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9611507","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636397015,"identity":"3fd32637-6d99-42dc-8c56-c7b175ece116","order_by":0,"name":"Justine Kira","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Transport","correspondingAuthor":true,"prefix":"","firstName":"Justine","middleName":"","lastName":"Kira","suffix":""}],"badges":[],"createdAt":"2026-05-04 19:09:39","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9611507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9611507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108998647,"identity":"059c0328-a915-41c9-8773-d516c50c2b8a","added_by":"auto","created_at":"2026-05-11 14:33:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":200075,"visible":true,"origin":"","legend":"","description":"","filename":"cngpapernmrd07052026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9611507/v1_covered_2632ba92-8cff-45b7-ba84-3397c0cd4552.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eStochastic Forecasting of CNG Demand: A GBM Approach for Tanzania\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Institute of Transport","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stochastic modelling, Geometric Brownian Motion, CNG demand fore- casting, Uncertainty quantification, Monte Carlo simulation, Energy transition, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-9611507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9611507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe transition to compressed natural gas (CNG) as a transport fuel in Tanzania offers significant potential for energy security, emissions reduction, and cost savings. 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