public disclosure authorized - world bank...figure 2.5. ranges of aadf flows by mode.....49 figure...
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AddressingClimateChangeinTransport
Volume2:PathwaytoResilientTransport
VietnamTransportKnowledgeSeriesSupportedbyAUSTRALIA–WORLDBANKGROUPSTRATEGICPARTNERSHIPINVIETNAMandNDCPARTNERSHIPSUPPORTFACILITY
AddressingClimateChangeinTransport
Volume2:PathwaytoResilientTransport
JungEunOh,XavierEspinetAlegre,RaghavPant,ElcoE.Koks,
TomRussell,RoaldSchoenmakers,andJimW.Hall
FINALREPORT
September2019
©2019TheWorldBank
1818HStreetNW,WashingtonDC20433
Telephone:202-473-1000;Internet:www.worldbank.org
This work is a product of the staff of The World Bank with external contributions. The findings,
interpretations and conclusions expressed in thiswork do not necessarily reflect the views of TheWorldBankanditsBoardofExecutiveDirectors.TheWorldBankdonotguaranteetheaccuracyofthedataincluedinthiswork.
Theboundaries,colors,denominationsandotherinformationshownonanymapinthisworkdonotimplyanyjudgementonthepartofTheWorldBankconcerningthelegalstatusofanyterritoryorthe
endorsementoracceptanceofsuchboundaries.
Nothinghereinshallconstituteorbeconsideredtobea limitationuponorwaiverof theprivileges
andimmunitiesofTheWorldBank,allofwhicharespecificallyreserved.
AllqueriesonrightsandlicensesshoudbeaddressedtothePublishingandKnowledgeDivision,TheWorld Bank, 1818 H Street NW, Washington DC 20433, USA; fax: 202-522-2625; email:[email protected].
Cover photo: A motorcycle crosses over a flooded road in rural Vietnam. Hanoi Photography –stock.adobe.com.
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Contents
FiguresandTables ................................................................................................................ vii
Foreword ............................................................................................................................... xi
Acknowledgments ................................................................................................................xiii
AbbreviationsandAcronyms...............................................................................................xvii
ExecutiveSummary .............................................................................................................. 21
Chapter1:Introduction ........................................................................................................ 29BackgroundandObjectives ........................................................................................................................ 29ScopeoftheStudy ...................................................................................................................................... 31StudyContributionsandLimitations .......................................................................................................... 33ReportStructure ......................................................................................................................................... 36
Chapter2:VietnamProfile:TransportNetworkandExposuretoNaturalHazard................. 39TransportNetworks .................................................................................................................................... 39
National-scaleroadinfrastructurenetworks ................................................................................... 39Province-scaleroadsnetworks......................................................................................................... 40Railwayinfrastructurenetwork........................................................................................................ 41Inlandwaterwayinfrastructurenetwork ......................................................................................... 41Maritimeinfrastructurenetwork ..................................................................................................... 42Airtransportinfrastructurenetwork................................................................................................ 42
ModelingFreightFlowonTransportNetworks ......................................................................................... 42Resultsoffreightflowmappingatnationalscale............................................................................ 42Resultsofprovince-scaleflowmapping ........................................................................................... 45Uncertaintiesinfreightflowmapping ............................................................................................. 48
NaturalHazardExposureofTransportNetworks ...................................................................................... 50National-scaleroadandrailwayshazardexposures........................................................................ 51Province-scalehazardexposureofroads ......................................................................................... 57
Chapter3:Vulnerability,Criticality,andRiskAssessment..................................................... 65VulnerabilityandCriticalityAssessment .................................................................................................... 65National-ScaleRoadNetworkCriticality..................................................................................................... 66National-ScaleRailwayCriticality ............................................................................................................... 69Province-ScaleRoadCriticality ................................................................................................................... 71
LaoCaiprovinceroadsdisruptions................................................................................................... 71BinhDinhprovinceroadsdisruptions............................................................................................... 72ThanhHoaprovinceroadsdisruptions............................................................................................. 73
RiskAssessment.......................................................................................................................................... 74National-scalenetworks................................................................................................................... 75Province-scaleanalysis..................................................................................................................... 81
UncertaintiesinCriticalitiesandRisks........................................................................................................ 86CriticalitiesandVulnerabilitiesofPorts...................................................................................................... 90
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Chapter4:AdaptationStrategyandAnalysis........................................................................ 93ScopeandPurposeofAdaptationAnalysis ................................................................................................ 93IdentifyingFailuresbyClimateChangeDrivenHazardThreats ................................................................. 94AdaptationOptionsandCosts.................................................................................................................... 95ResultsofAdaptationAnalysis ................................................................................................................... 97
National-scaleroadnetworkadaptationcostsandbenefits ........................................................... 98Province-scaleroadnetworkadaptationcostsandbenefits ......................................................... 104Decision-MakingunderUncertainty:SensitivityofCostsofAdaptation........................................ 111Rangesanduncertaintiesinadaptationestimates........................................................................ 112
Chapter5:ExploringMultimodalOptionsforNational-ScaleTransport.............................. 117IncludingMultimodalLinksandCosts ...................................................................................................... 118ResultsofPartialMultimodalShiftsinRoadFailureAnalysis .................................................................. 119RailFailureAnalysisResults ...................................................................................................................... 122
Chapter6:ConclusionsandPolicyRecommendations ........................................................ 127IncreasingVulnerabilitiesDuetoClimateChange ................................................................................... 127PrioritizingTransportNetworkResilienceforSystemicEconomicBenefit ............................................. 127MakingtheCaseforInvestmentinAdaptationtoBuildTransportNetworkResilience......................... 128StrongCaseforImprovingMultimodalTransportLinkagesandEnhancingEfficiencyacrossModes....129ImprovingExistingDataGapsforFurtherStudies ................................................................................... 129
AppendixA:MethodologyforTransportRiskandAdaptationAssessment ........................ 131MethodologicalFrameworkandImplementationStructure................................................................... 131
Keydefinitions ................................................................................................................................ 132Estimationofadaptationoptions................................................................................................... 135Uncertaintyanalysis ....................................................................................................................... 136
AppendixB:OverviewofDatasets...................................................................................... 141Naturalhazardsdataset................................................................................................................. 142
AppendixC:VulnerabilityResultsforPorts......................................................................... 145
AppendixD.CommunesandDistrictsbyExposureofRoadNetworkstoNaturalHazards . 151
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FiguresandTables
FIGURES
Figure E.1.Maximum Economic Losses and Benefit-Cost Ratios of National RoadNetworks Due to
TransportDisruptions ...................................................................................................................... 24
Figure1.1.VietnameseProvincesandNeighboringCountriesConsideredintheStudy ...................... 32
Figure2.1.MaximumAADFfortheNational-ScaleTransportNetwork ............................................... 45
Figure2.2.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenueson
LaoCaiProvincialRoads................................................................................................................... 46
Figure2.3.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenueson
BinhDinhProvincialRoads............................................................................................................... 47Figure2.4.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenueson
ThanhHoaProvincialRoads............................................................................................................. 48
Figure2.5.RangesofAADFFlowsbyMode .......................................................................................... 49
Figure2.6.RangesofNetRevenueFlowsAssignedtoRoadNetworksinLaoCai,BinhDinh,andThanh
Hoa ................................................................................................................................................... 50
Figure2.7.HazardExposureofNational-ScaleRoadsNetworkinVietnam.......................................... 54
Figure2.8.HazardExposureofNational-ScaleRailwayNetworkinVietnam ....................................... 55
Figure 2.9. Percentage Change in Exposure of National-Scale Road Network to 1,000-Year River
Floodingby2030.............................................................................................................................. 56
Figure 2.10. Percentage Change in Exposure of National-Scale Rail Network to 1,000-Year River
Floodingby2030.............................................................................................................................. 57
Figure2.11.HazardExposureofProvinceRoadsNetworkinLaoCai ................................................... 59
Figure2.12.PercentageChangeinExposureofLaoCaiRoadNetworkLinkstoLandslidesby2050 ... 60Figure2.13.HazardExposureofProvinceRoadsNetworkinBinhDinh............................................... 60
Figure 2.14. Percentage Change in Exposure of Binh Dinh Road Network Links to 1,000-year River
Floodingby2030.............................................................................................................................. 61
Figure2.15.HazardExposureofProvinceRoadsNetworkinThanhHoa ............................................. 62
Figure2.16.PercentageChange inExposureof ThanhHoaRoadNetwork Links to1,000-YearRiver
Floodingby2030.............................................................................................................................. 63
Figure3.1.National-ScaleRoadsCriticalityResults .............................................................................. 68
Figure3.2.National-ScaleRailwayCriticalityResults............................................................................ 70
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Figure3.3.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossforLaoCai
ProvinceRoadNetwork ................................................................................................................... 72
Figure3.4.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossfortheBinh
DinhProvinceRoadNetwork ........................................................................................................... 73
Figure 3.5. Maximum Estimated Disruption in Net Revenue andMaximum Economic Loss for the
ThanhHoaProvinceRoadNetwork ................................................................................................. 74
Figure3.6.MaximumEstimatedRisksforNational-ScaleRoadNetworkLinks .................................... 76Figure 3.7. PercentageChange inMaximumFailureRisks ofNational-Scale RoadNetwork Links for
Future2030RiverFloodingunderClimateScenarios ...................................................................... 77
Figure3.8.MaximumEstimatedRisksforNational-ScaleRailwayNetwork......................................... 79
Figure3.9. PercentageChange inMaximumFailureRisksofNational-ScaleRailNetwork for Future
2030RiverFloodingunderClimateScenarios.................................................................................. 80
Figure3.10.MaximumRisksofLaoCaiProvince-ScaleRoadNetwork................................................. 81
Figure 3.11. PercentageChange inMaximumFailureRisks of LaoCai Province-ScaleRoadNetwork
LinksforFuture2050LandslideSusceptibilityunderClimateScenarios ......................................... 82
Figure3.12.MaximumRisksofBinhDinhProvince-ScaleRoadNetwork............................................. 83
Figure3.13.PercentageChangeinFailureRisksofBinhDinhProvince-ScaleRoadNetworkforFuture
2030RiverFloodingunderClimateScenarios.................................................................................. 84
Figure3.14.MaximumEstimatedRisksforThanhHoaProvince-ScaleRoadNetwork ........................ 85
Figure3.15.PercentageChangeinFailureRisksofThanhHoaProvince-ScaleRoadNetworkforFuture
2030RiverFloodingunderClimateScenarios.................................................................................. 86
Figure 3.16. Estimated Ranges of Daily Economic Losses for Individual Network Link Failures in
Vietnam’sNational-ScaleRoadandRailNetworks .......................................................................... 87
Figure 3.17. EstimatedRanges ofMinimumandMaximumRisks for Individual Link FailuresDue to
CurrentandFutureRiverFloodingonNational-ScaleNetworks ..................................................... 88
Figure3.18.EstimatedRangesofDailyEconomicLossesandRisksDue toCurrentandFutureRiver
FloodingofIndividualLinkFailuresontheLaoCaiRoadNetwork .................................................. 89
Figure3.19.EstimatedRangesofDailyEconomicLossesandRisksDue toCurrentandFutureRiver
FloodingofIndividualLinkFailuresontheBinhDinhRoadNetwork .............................................. 89
Figure3.20.EstimatedRangesofDailyEconomicLossesandRisksDue toCurrentandFutureRiver
FloodingofIndividualLinkFailuresontheThanhHoaRoadNetwork ............................................ 90
Figure4.1.MaximumTotalInvestmentover35YearsontheClimateAdaptationforIdentifiedLinksin
theNational-ScaleRoadNetworkinVietnam.................................................................................. 99Figure4.2.EstimatedMaximumBenefitsover35YearsandMaximumBCRsofAdaptationOptionsfor
IdentifiedLinksintheNational-ScaleRoadNetworkinVietnam .................................................. 100
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Figure 4.3. Comparisons of BCRs of Adaptation Options for National-Scale Road Network Links in
Vietnam.......................................................................................................................................... 102
Figure4.4.Investments,Benefits,andAdaptationBCROptionsforIdentifiedLinksintheLaoCaiRoad
Network ......................................................................................................................................... 105
Figure4.5.Investments,Benefits,andBCRsofAdaptationOptionsforIdentifiedLinksintheBinhDinh
RoadNetwork ................................................................................................................................ 107
Figure4.6.Investments,Benefits,andAdaptationBCRsOptionsforIdentifiedLinksintheThanhHoa
RoadNetwork ................................................................................................................................ 109
Figure4.7.ParameterSensitivityforNational-andProvince-ScaleRoads ......................................... 111
Figure4.8.AdaptationAnalysisResultsfortheNational-ScaleRoadNetworkinVietnam ................ 113
Figure4.9.RangesofAdaptationBCRsfortheNational-ScaleRoadNetworkinVietnam,Evaluatedfor
CurrentandFutureExtremeRiverFloodingScenarios .................................................................. 113
Figure4.10.AdaptationBCRsforProvince-ScaleRoadNetworks ...................................................... 115
Figure5.1.MultimodalLinksCreatedintheNetworkModel ............................................................. 118
Figure 5.2. Total Economic Impacts of National-Scale Road Links Failures When Considering 90
PercentFlowReroutingonRoadsand10PercentReroutingalongOtherNetworksviaMultimodal
Links ............................................................................................................................................... 120
Figure5.3.Redistributionof10PercentofMaximumRoadTonnageFlowsasAddedTonnagesonto
RailandWaterwayNetworksfollowingRoadNetworkLinkDisruptions ...................................... 122
Figure5.4. Total Economic Impacts of Rail Link FailureswhenConsideringReroutingOptions along
National-ScaleRoads,InlandWaterwaysandMaritimeNetworksviaMultimodalLinks ............. 123
Figure5.5.RedistributionofMaximumTonnageFlowsasAddedTonnagesontoNational-ScaleRoads
andWaterway(InlandandMaritime)NetworksfollowingRailNetworkLinkDisruptions ........... 124
Figure A.1. Graphical Representation of Infrastructure System-of-Systems Risk Assessment
Framework ..................................................................................................................................... 132
TABLES
TableE.1.ClimateAdaptationAnalysisforProvince-ScaleRoadNetworks.......................................... 25
Table2.1.RoadCategoriesbyAverageDailyVehicleTrafficCounts .................................................... 39
Table2.2.EstimatedTotalLengthsbyRoadCategoryintheNational-ScaleRoadNetworkModelfor
Vietnam............................................................................................................................................ 40
Table 2.3. Estimated Lengths by Road Category in the Province-Scale Road Network Model for
Vietnam............................................................................................................................................ 40
Table 2.4. Estimated Total Link Lengths and Percentages of Rail Routes Identified in the National-
ScaleRailNetworkModelforVietnam ............................................................................................ 41
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Table2.5.DailyTrafficVolumesbyCommodityandTransportMode.................................................. 44
Table2.6.LengthsofNationalRoadsandRailwayNetworksExposedtoDifferentTypesofHazards . 52
Table2.7.ExposureofProvinceRoadsNetworkstoHazards ............................................................... 58
Table3.1.SelectedNumbersofSingleLinkFailureScenariosforDifferentTransportNetworks........ 66
Table4.1.PotentialFailureTypesDuetoIncreasedHazardThreatsDrivenbyAdverseClimateChange
......................................................................................................................................................... 95
Table 4.2. Cost Summary of Initial Adaptation Investment for Building Prototype Climate Resilient
RoadsinVietnam ............................................................................................................................. 97
Table 4.3. Numbers of Single-Link Failure and Hazard Exposure/Adaptation Options Scenarios
EvaluatedforTransportNetworksinVietnam................................................................................. 98
Table4.4.TwentyNational-ScaleRoadsRanked inDescendingOrderofMaximumAdaptationBCRs
....................................................................................................................................................... 103
Table4.5.Top20LaoCaiRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs. 106
Table4.6.Top20BinhDinhRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs
....................................................................................................................................................... 108
Table4.7.Top20ThanhHoaRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs
....................................................................................................................................................... 110
Table B.1. Raw Datasets Collected from Different Data Sources for the Transport Risks Analysis
ModelinginVietnam...................................................................................................................... 141
TableB.2.NaturalHazardDatasetsAssembledfortheStudyNotes .................................................. 143TableC.1.AirportswithAADFFlowsandHazardExposureScenarioResults ..................................... 145
TableC.2.MajorMaritimePortswithAADFFlowsandHazardExposureScenarioResults ............... 146
TableC.3.MajorInlandWaterwayPortswithAADFFlowsandHazardExposureScenarioResults... 147
TableD.1.TwentyCommunesMostExposedtoFloodinginLaoCaiProvince................................... 151
TableD.2.TwentyCommunesMostExposedtoFloodinginBinhDinhProvince............................... 152
TableD.3.TwentyCommunesMostExposedtoFloodinginThanhHoaProvince............................. 153
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Foreword
ClimatechangeissettohaveprofoundeffectsonVietnam’sdevelopment.Withnearly60percentofits land area and 70 percent of population at risk ofmultiple natural hazards, Vietnam globally isamongthemostvulnerablecountries tobothchronicandextremeevents.Over thepast25years,
extremeweather events have resulted in 0.4 to 1.7 percent of GDP loss, which climate change ispredicted to steeply rise by 2050. At the same time, as Vietnam’s economy grows, the country isbecomingasignificantemitterofgreenhousegases.WhileVietnam’sabsolutevolumeofemissionsis
still small compared to that of larger and richer countries, emissions are growing rapidly anddisproportionate to its economy size. Vietnam is the 13thmost carbon intensive economy in theworld, measured in terms of emissions per GDP, and 4th among the low- and middle-income
countriesinEastAsia.
Thetransportsectorplaysacriticalroleintheserecenttrends.Asteepriseinincomeandeconomic
growthhasledtorapidmotorization:Thecountryofaround96millionpeopleisalsohometonearly40 million vehicles, including 35 million motorbikes. While car ownership is still relatively low inVietnam,asincomerisescarsarequicklyreplacingmotorbikes,especiallyinthelargestcities.Public
transportmodalshareremainspersistentlylow,partlyduetothelowlevelofnetworkdevelopmentand partly to the convenience and affordability of two-wheeler-based mobility. Thanks to itseconomicsuccessandrapidintegrationwiththeinternationaltrade,cargotransportationinVietnam
has seen remarkable growth in the recent years.Vietnam’s long coastal lines andextensive inlandwaterway network have been extensively used for themovement of goods; however, theirmodalsharevis-à-visroadtransportisdeclining.
Vietnam’stransportnetwork,whichhasseenanimpressiveexpansionoverthepasttwodecades,isincreasinglyvulnerabletotheintensifyingclimatehazards.Today,Vietnam’sroadnetworkextendsto
over 400,000 km,much ofwhichwas not built towithstand extreme hazard scenarios,which areexpectedtobecomemorefrequentduetoclimatechange.Withouteffortstoimprovetheresilienceofthebuiltnetwork,Vietnam’sachievements inprovidinguniversalaccessto itsruralcommunities
may be undermined. Moreover, resilience of connectivity is critical to the continued success ofVietnam’s economy, which heavily relies on external trade and would increasingly depend onseamlessrural-urbanlinkages.
Inthisanalyticalwork,AddressingClimateChangeinTransportforVietnam,carriedoutbytheWorldBankandseveralotherpartnerswithsupportfromtheMinistryofTransportofVietnam,thestudy
aimstosetoutavisionandstrategyforclimate-smarttransport,inordertominimizethecarbonfootprintofthesectorwhileensuringitsresilienceagainstfuturerisks.The
analytical findings and recommendations are presented in two
volumes of the report: Volume 1—Pathway to Low CarbonTransport and Volume 2—Pathway to ResilientTransport. The first volume provides how Vietnam
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canreduceitscarbonemissionbyemployingamixofdiversepoliciesandinvestments,undervaryinglevels of ambition and resources. The second volume provides a methodological framework to
analyzenetworkcriticalityandvulnerability,andtoprioritizeinvestmentstoenhanceresilience.
Thesetworeportvolumeshavebeenpreparedatacriticaltime,wheretheGovernmentofVietnam
isworkingtoupdateitsNationallyDeterminedContributionandsetoutitsnextmedium-termpublicinvestment plan for the period of 2021 to 2025.We hope that these findings can provide usefulinsightsandspecific recommendations towards thesecriticaldocuments, contributing toVietnam’s
achievementindevelopingalow-carbonandresilienttransportsector.
GuangzheChen OusmaneDioneGlobalDirector CountryDirector
TransportGlobalPractice Vietnam
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Acknowledgments
ThisreportwaspreparedbytheTransportGlobalPracticeandtheEastAsiaandPacificRegionoftheWorldBank.
ThepreparationofthereportwasledbyDr.JungEunOh(SeniorTransportEconomist)andDr.XavierEspinet Alegre (Transport Specialist) at The World Bank, along with Dr. Raghav Pant at OxfordInfrastructureAnalytics(OIA).TheteamincludesMs.MariaCordeiro,Dr.JasperCook,Mr.DucCong
Phan,Dr.NguyenThanhLong,Mr.ChiKienNguyenattheWorldBank,andDr.ElcoE.Koks,Mr.TomRussell, Mr. Roald Schoenmakers and Prof. Jim W. Hall at OIA. All crop production outputs aregratefullyacknowledgedfromDr.JawooKoo,Dr.ZheGuo,Dr.UlrikeWood-Sichra,andDr.YatingRu
atInternationalFoodPolicyResearchInstitute.
The team extends its appreciation for the guidance of Guangzhe Chen (Global Director, Transport
Practice), Franz R. Drees-Gross, (Director, Transport Practice), Ousmane Dione (Vietnam CountryDirector), AlmudWeitz (Transport Practice Manager, Southeast Asia and the Pacific), Achim Fock(VietnamOperationsManager),andMadhuRaghunath(VietnamInfrastructureProgramLeader).
TheteamconductedthestudyincollaborationwiththeGovernmentofVietnam,andappreciatesthestrongsupportandadvicegenerouslyprovidedbyMr.LeDinhTho,ViceMinisterofTransport,and
Mr.TranAnhDuong(DirectorGeneral),Ms.NguyenThiThuHang(DeputyDirector),Mr.VuHaiLuu(official),Ms. Doan Thi Hong Tham (official) andMr.Mai VanHien (official) at theDepartment ofEnvironment,MinistryofTransport (MoT). Several governmententitiesandorganizationsprovided
vital inputs: Directorate for Roads of Vietnam (DRVN); Vietnam InlandWaterways Administration(VIWA);VietnamMaritimeAdministration(VINAMARINE);CivilAviationAuthorityofVietnam(CAAV);VietnamRailwayAuthority(VRA);andVietnamRegister(VR).
Theworkbenefited from the adviceprovidedby the followingpeer reviewers: Stephen Ling (LeadEnvironmental Specialist), Cecilia M. Briceno-Garmendia (Lead Economist), Neha Mukhi (Senior
ClimateChangeSpecialist),andIanHalvdanRossHawkesworth(SeniorGovernanceSpecialist)attheWorldBank,andRobinBednall(FirstSecretary)andDuc-CongVu(SeniorInfrastructureManager)atthe Government of Australia. Kara Watkins (World Bank Communications Consultant) thoroughly
copyedited the report for consistency and readability, and Chi Kien Nguyen (Transport Specialist)reviewedtheVietnamesetranslationforaccuracy,forwhichtheteamisgrateful.
TheteamalsoappreciatestheexcellentproductionsupportprovidedbyNguyenThanhHang,NguyenMaiTrang,IraChairaniTriasdewi(administration),DangThiQuynhNga(operations),andNguyenHongNgan (communications). The team also appreciates the excellent production support provided by
Nguyen Thanh Hang, Nguyen Mai Trang, Ira Chairani Triasdewi (administration), Dang ThiQuynhNga(operations),andNguyenHongNgan(communications).
TheteamwouldliketothanktheGovernmentofAustraliaandNDCPartnership Support Facility for their generous supportand funding toward this analyticalwork andpublication.
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AbouttheAuthors
JungEun“Jen”OhisaseniortransporteconomistattheWorldBank.Withabackgroundintransport
engineeringandeconomics,Jenhasledanumberofinvestmentprojectsandtechnicalassistanceforvarious client countries inSoutheastAsia,CentralAsia, Sub-SaharanAfrica, andEurope, coveringabroad range of transport sector issues. Jen served as a transport cluster leader for Vietnam from
2016 to 2019, overseeing and coordinating a large portfolio of World Bank-financed projects inVietnam’s transport sector, and led several knowledge pieces on climate change in transport,transportconnectivity,urbanmobility,and infrastructure financing. JenholdsanMSc ineconomics
and a PhD in transportation systems, and has authored several articles, including a chapter in thebook,TheUrbanTransportCrisisinEmergingEconomies(2017),publishedbySpringerInternational.
XavierEspinetAlegre,atransportspecialistforthechiefeconomistforsustainabledevelopmentatthe World Bank, has extended expertise in transport planning and economics, climate changeadaptation, disaster riskmanagement, anddecision-making science.Hehasworked formore than
five years developing interdisciplinary and cross-sectoral solutions, as the Decision-Making underDeep Uncertainties approach, to challenges posed by climate change to transport planning inMozambique,SierraLeone,Albania,Vietnam,Cambodia,andSaintLucia,amongothers.Priortothe
WorldBank,heco-foundedResilientAnalytics,servingasatechnicallead,andworkedasaresearchassociateat the InstituteofClimateandCivil Systems.XavierholdsaPhD incivil systems fromtheUniversityofColoradoatBoulderandacivilengineeringdegreefromtheUniversitatPolitecnicade
CatalunyainSpain.
RaghavPantservesasaseniorpostdoctoralresearcherattheEnvironmentalChangeInstitute(ECI)at the University of Oxford, and is a founder and director of Oxford Infrastructure Analytics Ltd.
Raghav has more than 12 years of academic and professional experience on infrastructure andeconomic network risks modeling, and leads the resilience analysis research within the UKInfrastructure Transitions Research Consortium (ITRC). He has worked with the World Bank on
ongoing projects on transport risk analysis in Tanzania and Argentina. His research paper onvulnerabilityassessmentofGreatBritain’srailwayinfrastructurehasbeenawardedthe2016LloydsScienceofRiskPrizeinSystemsModeling.
Elco E. Koks works as a postdoctoral researcher in global network analysis at ECI andassistantprofessorat the Institute forEnvironmentalStudiesatVrijeUniversiteitAmsterdam.Elcohasmorethansevenyearsofexperienceworkingonseveraltopicsrelatedtodisasterimpactmodeling,climate
change, and water economics in the context of national (Knowledge for Climate) and EUresearchprojects (ENHANCEandTURAS).Hiskey researchdomain is themodeling
oftheeconomy-wideconsequencesofnaturaldisastersonbotharegionaland
interregional level, with a specific focus on industrial areasandcriticalinfrastructure.
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Tom Russell, a research software engineer at the Environmental Change Institute, University ofOxford,hasmorethansevenyearsofexperience inresearchsoftwaredevelopment,particularly in
spatial network analysis and data-driven design and interaction. Tom is creating the infrastructuresystems modeling integration framework for the Infrastructure Transitions Research Consortium(ITRC) and developing open-source tools for infrastructure risk analysis through the Oxford
Infrastructure Analytics–World Bank projects on transport risk analysis in Tanzania, Vietnam andArgentina.
Roald Schoenmakers works as a research software developer, actively collaborating with domainexpertstofacilitateintheirneedofsoftwaredevelopment.Heisalsoinvolvedinmaintaininglegacysoftware,toensureavailabilityofthemodelingplatformtotheITRCanditsindustrialpartners.Roald
has a background in engineering, with several years of experience in professional softwaredevelopmentforindustrialcontrolsystemsandembeddedapplications.
JimW.Hall,theprofessorofclimateandenvironmentalrisksattheUniversityofOxford,isafounderand director of Oxford Infrastructure Analytics Ltd. Jim leads the UKInfrastructure TransitionsResearch Consortium, which has developed the world’s first national infrastructure simulation
modelsforappraisalofnationalinfrastructureinvestmentandrisks.Hisbook,TheFutureofNationalInfrastructure:ASystemofSystemsApproach,waspublishedbyCambridgeUniversityPressin2016.He sits on the Expert Advisory Group for theNational Infrastructure Commissionand Chairs
theDAFNIDataandAnalyticsFacilityforNationalInfrastructure.HeisafellowoftheRoyalAcademyofEngineeringandforthelasttenyearshehasbeenamemberoftheUKindependentCommitteeonClimateChangeAdaptation.
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AbbreviationsandAcronyms
AADF AverageAnnualDailyFreight
AADT AverageAnnualDailyTraffic
BCR Benefit-CostRatio
CAAV CivilAviationAdministrationofVietnam
CBA Cost-BenefitAnalysis
CGE ComputationalGeneralEquilibrium
CI CapitalInvestments
CMIP5 CoupledModelIntercomparisonProjectPhase5
CVTS CommercialVehicleTrackingSystem
DMDU Decision-MakingUnderDeepUncertainty
DRVN DirectorateofRoadsforVietnam
EAD ExpectedAnnualDamages
EAEL ExpectedAnnualEconomicLosses
GCM GlobalClimateModel
GDP GrossDomesticProduct
GIS GeospatialInformationSystems
GLOFRIS GlobalFloodRiskwithImageScenarios
GSO GeneralStatisticsOfficeofVietnam
GVA GrossValueAdded
IFPRI InternationalFoodPolicyResearchInstitute
IMF InternationalMonetaryFund
IMHEN VietnamInstituteofMeteorology,HydrologyandClimateChange
IO Input-Output
JICA JapanInternationalCooperationAgency
km Kilometers
m Meters
MapSPAM SpatialProductionAllocationModel(MapSPAM)
MARD MinistryofAgricultureandRuralDevelopmentinVietnam
Page|xviii
MoNRE MinistryofNaturalResourcesandEnvironmentinVietnam
MoT MinistryofTransportinVietnam
MRIA Multi-RegionalImpactAssessment
MRIO Multi-RegionalInput-Outputdatasets
MT MetricTons
NPV NetPresentValue
OD Origin-Destination
OIA OxfordInfrastructureAnalytics
OSM OpenStreetMap
PDoT ProvincialDepartmentsofTransport
RCP RepresentativeConcentrationPathways
TDSI TransportandStrategyDevelopmentInstitute
TEU Twenty-FootEquivalentUnit
UN UnitedNations
UNCTAD UNConferenceonTradeandDevelopment
UNCOMTRADE UnitedNationsCommodityTradestatisticsdatabases
US$ UnitedStatesDollars
VINAMARINE VietnamMaritimeAdministration
VITRANSSIITheComprehensiveStudyontheSustainableDevelopmentofTransportSystemInVietnam
VIWA VietnamInlandWaterwayAdministration
VND VietnameseDong
VRAMS VietnamRoadAssetManagementSystem
WHO WorldHealthOrganization
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CURRENCYMEASURE
Currencyunit—US$
WEIGHTANDMEASURES
Metricsystem
BASELINEDATAYEAR
Commodityfreights—2009,2016
Macroeconomicdata—2012
Censusdata—2012
BusinessSurveydata—2012
EXCHANGERATES
US$1in2016=22,500VND
US$1in2012=22,000VND
US$1in1999=14,000VND
CURRENCYINFLATION
US$1in2016=US$1.5in1999
US$1in2016=US$1.05in2012
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ExecutiveSummary
Vietnam is one of the world’s fastest growing economies—with Gross Domestic Product (GDP)growthabove7percentinthefirstquarterof2018—andstrongforecastedgrowthfortheremainingyear(WorldBank2018).Vietnamisalsoprojectedtobeamongthefastestgrowingeconomiesover
the2016to2050period,capableofsustainingapotential5percentannualGDPgrowthrate (PwC2017).However,thisrapidgrowthisthreatenedbyextremeweathereventssuchasstorms,floods,typhoons,andlandslides.Vietnamrankshighasanaturaldisasterhotspot;twoormoremultihazard
eventspotentiallyexpose60percentofthelandareaand71percentofthepopulationtorisk(Dilleyetal2005)—whichcouldresultinannualaverageassetlossesamountingto1.5percentofGDP,andconsumptionlossesamountingto2percentofGDP(Hallegatteetal2016).
Climatechangewillexacerbatetheseextremehazards.TheMinistryofTransport(MoT) inVietnamaimstodevelopanationalstrategyforclimateresilienttransportandplans—aspartofthetransportsector’scontributiontotheNationallyDeterminedContributions(NDCs)—tomeettheParisClimate
Agreement targets. To this end, this report intends to support MoT on the development ofmultimodalnetwork-levelcriticalityandvulnerabilityanalysis,aswellasmethodsandtoolstoinformprioritization of investments in transport asset management to ensure integration of climate and
naturalriskconsiderations.
This report presents the results from theTransportMulti-Hazard Risk Analysis for Vietnam at twoscales—national and provincial—and draws upon policy recommendation to enhance climate
resilienceofthetransportsector.Thescopeofthenationalanalysiscoverskeynational-scaleroads,railways,civilaviation,inlandwaterways,andmaritimesystemsthatmakeupVietnam’smultimodaltransportationinfrastructure.Atthenationalscale,thefocusistounderstandtheeconomicimpacts
ofdisruptionstomultimodalfreighttransportationduetoinfrastructurefailures.
The scope of the province-scale analysis covers three specific provinces—Lao Cai, Binh Dinh, andThanh Hoa—and their road networks only, which are represented in greater granularity than the
national-scaleanalysis.Theroadnetworks includenational,provincial,district,andcommuneroadsandotherassets suchasbridgesandculverts,amongothers.At theprovince scale, the focus is tounderstand how road failures impact access to key locations within communes, thereby affecting
economicoutputgeneration.
Atbothnationalandprovincescales, twotypesofnaturalhazard—floodingand landslides—inducetransportfailures.Thefloodinghazardsconsidered inthisstudy includeriverflooding(i.e., flooding
causedby riversovertopping theirbanks), surfacewater flooding (i.e., flooding causedbyextremerainfall—also known as “pluvial” or “flash” flooding), and tidal flooding (i.e., flooding caused bytyphoon-induced storm surges). Besides looking at the present situation (the year 2016), we use
climatechangescenario-drivenmodeloutputsrepresentingfuturefloodingintheyears2025,2030,and 2050. The landslide hazards information includes model outputs for landslide susceptibilitypresented for current conditions (the year 2016) and future climate change scenarios in 2025 and
2050.
Theframeworkdevelopedforthisstudyoutlinesasystem-of-systemsmethodology.Eachtransport
mode(road,railways,inlandwaterways,maritime,andairlines)consideredinthisstudyistreatedas
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an infrastructure system, which is the collection and interconnection of all physical facilities andhumansystemsthatoperateinacoordinatedwaytoprovideinfrastructureservice(Halletal2016).
The infrastructure or transport service refers to the mobility for freight and passengers betweenlocations.Themultimodal transport infrastructure is thendefinedasa system-of-systems,which isthecollectionandinterconnectionofindividualtransportsystems.
The frameworkpresents the following typesof system-of-systemsassessmentsuseful fordecision-making:
• Criticalityassessmentasthemeasureofatransportlink’simportanceanddisruptiveimpactontherestofthetransportinfrastructure(Pantetal2015).
• Vulnerabilityassessmentasthemeasureofthenegativeconsequencescausedbyfailuresoftransportlinksfromexternalshockevents(Pantetal2016).
• Risk assessment as the product of the probability of a hazard and the consequences oftransportlinkfailures.
• Adaptationplanningasengineeringmeasurestakentoreducerisks.Inthecontextofclimatechange, the planned adaptation seeks to capitalize on the opportunities associated withclimatechange(Füssel2007).
Criticalities for the national-scale roads and railway networks are measured in terms of the total
economic impact metric, measured in United States dollars (US$) per day as the sum of the
macroeconomiclossesandtheincreasedfreightredistributioncostsincurredduetotheirfailures.
At the province scale, the study estimates road network criticalities in terms ofeconomic impactsestimated as the sum of the economic losses (net revenue losses) in US$ per day, due to lack of
accessible routes toward the commune centers, and the increases in rerouting costs where it ispossibletomaintainaccesstothecommunecentersafterthedisruptions.
Thestudyestimatestherisksofextremehazardfailuresbycombiningknowledgeofthehazardsand
impactsintoonemetric.ThenetworkrisksareestimatedintermsofanExpectedAnnualEconomicLoss (EAEL) metric. The losses signify the daily economic impacts, estimated in the criticalityassessment,multiplied by certain duration of disruption duringwhich the affected network link is
assumedtobeoutofoperation.
Theadaptationanalysisexploresthemeasuresintendedtoimprovethestructurereliabilityofroadassets to make them more resistant to climate change impacts. The adaptation options for a
particularroadassetarequantifiedintermsoftheircostsandbenefits.Thecostsincludetheinitialcostofinvestmenttoimplementtheadaptationoptions,alongwiththeroutineandperiodiccostsofmaintainingtheclimateresilienceoftheroadasset.Thebenefitsincludetheavoidedlosses,interms
of the damage (or rehabilitation) costs and the network-wide economic impacts of transport flowdisruptionsduetotheroadassets’ failure,weretheadaptationoptionnot implemented.Basedontheanalysis,thekeyfindingsandrecommendationsaresummarizedasfollows.
Vietnam’stransportsectorneedstoprepareforextremehazardsofincreasingintensitiesandwithincreasing frequency due to climate change. Our analysis shows that under climate changescenarios, exposures to extreme hazards would substantially increase for all transport sectors in
Vietnam.Theoverallmaximumkilometers(km)ofnationalroadsandrailwaysalongwithprovince-scaleroadnetworksexposedtoextremehazardlevelsincreasesunderclimatechangescenarios.Forexample,inLaoCai,roadlengthexposurestoextremelandslideschangefrom142kminthecurrent
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scenarios to 210 km in the future high-emission scenario for 2050, shown in RepresentativeConcentration Pathway (RCP) 8.5, or RCP8.5. In this scenario, extreme levels of flooding seen in
1,000-year events could start occurring in five-year events, making major inland, maritime, andairportsvulnerabletoriverfloodingwithincreasingfrequency,translatingtoincreasedriskexposure.
Systemic understanding of locations whose failures create increasing economic risks presents a
strongeconomiccaseforinvestinginbuildingclimateresilienceofVietnam’stransportnetworks.Themodelandanalysisresultshighlightsystemiccriticalitiesandhazard-specific,high-risk locationsin thenetworks,which canbeprioritized for furtherdetailed investigations into climate resilience.
ThemodelresultsinfigureE.1showlocationsofroadnetworkswhosefailurescanresultinveryhighdaily losses of up to US$1.9million per day, while railway failures can result in losses as high asUS$2.6millionperday.Whenfactoringsuchlosses,thewiderimplicationsofmacroeconomiclosses
due to transport disruptions become quite crucial. Generally, such effects are often ignored intransportanalysis,whichresultsinunderestimatingtheimpactsoftransportdisruptions.
ComparisonsofcurrentandfuturehazardrisksprominentlyhighlightthestrongcaseforVietnamto
invest in building climate-resilient national-scale roads and railways. Comparisons also stronglyindicate that access to economicopportunities in several provincial areaswill be severely affectedwithout investment in building climate-resilient roads. The expected annual economic losses by
2030—ameasureofrisks—duetotransportfailuresfromfutureclimatechange-drivenriverfloodingsignificantly increasebymorethan100percentatseveral locationsacrossnationalroads,railways,andprovinceroadnetworks.Suchincreasesarerecordedatallnetworklinkswithhighimpacts.The
analysisalsoshowsthethreeVietnameseprovinceswillexperienceadramaticincreaseinriskduetoclimate change scenarios,up to400percent (LaoCai), 900percent (BinhDinh), and4,000percent
(ThanhHoa)onroad linkswiththehighest losses.Additionally,severalnew instancesofsignificantlossesalsoemergeinfuturefloodingscenariosforthethreeprovinces.
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FigureE.1.MaximumEconomicLossesandBenefit-CostRatiosofNationalRoadNetworksDuetoTransportDisruptions
Vietnam’s roadnetworks require investments tooverhaulexisting roadassets tohigher climate-
resilientdesign standards. Though such investments couldbe costly, theirbenefitsoutweigh thecosts for priority network assets, making adaptation investments viable options for roads. Theanalysis has demonstrated that the national-scale and province-scale road networks all need
adaptation planning to protect against future climate-related hazards. The analysis shows theBenefit-CostRatios(BCR)ofadaptationinvestmentsintonational-scaleroadsaremostlygreaterthanone.Theanalysissuggeststhat,forsomenational-scaleroads,upgradingtoclimate-resilientdesigns
could cost up toUS$3.4million per kilometer—very high and comparable to costs of constructinghigh-standardnewhighways.But thehigheconomicbenefitsof such investments justify suchhighcosts.Whenthetop20roadlinkswithhighestmaximumBCRsareselected,theanalysisshowsthat
cumulativeclimateadaptationinvestmentsamounttoapproximatelyUS$95millioninitially,andover
A.Maximumtotaleconomicloss B.MaximumBCRofadaptationovertime
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35 years total approximately US$153 million. The cumulative benefits over 35 years of suchinvestments—estimated by adding the benefits from individual links—are substantial, ranging
betweenUS$651millionandUS$3.66billion.Whenlookingatadaptationtoflooding,thenumberoflinkswithBCR>1doubleswhenconsideringclimatechangeprojections.
Similarly, forprovince-scaleroadnetworksarelativelysmall,butsignificant,numberofassetshave
BCRs > 1.Many of these are bridges and local roads, which are crucial for accessing locations ofeconomic activities. For all road networks, the increasing BCRs under future climate-hazardsscenariosstrengthenthecaseforinvestinginclimateresiliencetoprotectagainfutureclimaterisks.
TableE.1summarizestheresultsoftheadaptationanalysisattheprovinciallevel.
TableE.1.ClimateAdaptationAnalysisforProvince-ScaleRoadNetworks
Vietnam’stransportnetworkscanbecomemoreresilientiftheyfunctionasintegratedmultimodal
systems, achieved by improving existing and creating new multimodal linkages. This study hasshowneconomicimpactsofdisruptionscanbereducedbybuildingadditionaltransportmultimodalconnectivity.Oftenknownasincreasinganetwork’s“redundancy,”thisphrasemightbeinterpreted
asbeingwastefuland inefficient.However,providingextraconnectivityandcapacitywillassist theeverydayflowofgoodsandpeople,aswellasprovidingnewopportunitiesforreroutingwhenmajordisruptionsoccur.
Thisstudyshowsaveryclearbenefitof redistributing flows fromtheroadnetworkto therailwaysandinlandandmaritimenetworks.Evena10percentshiftofroadfreights,especiallytorailways,can
lead to a 20 to 25 percent reduction of economic impacts. In addition, enhancing railway andwaterwayefficiencycanreducerisksforalreadycongestedroads.Moreover,existingunusedcapacity
intherailwaysaccommodatestheadditionalmodalshiftfromroads.Thepotentialrailwaylossesarealsosignificantlyreducedthroughtheavailabilityofmultimodaloptions,whichshouldbeconsideredinthefuture.
Itwouldbe important to invest in improvingdata gaps in future studies inorder tomakemorerobust economic case for investment in the transport network. Vietnam’s transport resilience
planning can benefit from a system-of-systems transport risk analysis. Importantly, creating cross-sectorandcross-organizationcollaborationshelpsincreasecapacitytoundertakefurtherstudiesandpursue continued efforts to improve the underlying datasets. The study has dealt with severely
Roadnetworkassets LaoCai BinhDinh ThanhHoa
PercentofassetswithBCR>1 15 2–3 2–3
NumberofassetswithBCR>1 190 220–330 530–800
AdaptationInvestmentover35years(US$millions) 12.9 1.6 2.3
AdaptationBenefitsover35years(US$millions) 16.4–22.5 14.2–31.4 7.8–22.3
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limited data, highlighted throughout the report. Some of these limitations exist in assembling andstandardizing:
• Topologicalrepresentationsofinfrastructurenetworkswithproperattributes
• Transportflowsthatrepresentlatestconditionsandtrends
• Transportationcostassignmentsreflectiveofexistingconditions
• Economicflowsdatashowingcurrentstructureofthecountryandregionaleconomies
• Probabilistic hazards at similar spatial resolutions and with Vietnam-specific climatescenarios
• Seasonalityinformationofextremelevelsofhazardsandtransportnetworkflows
• Informationondisruptiondurationsandresponsebehaviors
• CostsofvariousadaptationoptionsmostrelevanttoVietnam.
Next steps: Creating a cross-sector and cross-organization collaboration involving various
governmentagenciesisextremelyimportantforincreasingcapacitytostandardizeandsharedata.While the model developed in this study can be readily adapted to accommodate furtherimprovements, this study addresses only some of the economic and social functions of transport
infrastructure.Notably,ithasnotexploredtheroleoftransportinfrastructureinenablingpassengertravelforworkorotherpurposes,orforlabormarketparticipation.Thatshouldbeincludedinfuturestudies, alongside other wider economic and social benefits of transport infrastructure. In these
recommendations we have already identified that prioritizing investments to improve transportnetwork resilience would require consideration of the costs and effectiveness of alternativeinterventions. Building on this study of transport network vulnerability and risks, we recommend
studyingthecostsandeffectivenessofalternativeinterventionsasthenextstep.
References
Dilley,Maxx,RobertS.Chen,UweDeichmann,ArthurL.Lerner-Lam,MargaretArnold,JonathanAgwe,PietBuys,OddvarKjevstad,BradfieldLyon,andGregoryYetman.2005.NaturalDisasterHotspots:A
GlobalRiskAnalysis.DisasterRiskAnalysisSeries.Washington,DC:WorldBank.http://documents.worldbank.org/curated/en/621711468175150317.
Füssel,Hans-Martin.2007.“AdaptationPlanningforClimateChange:Concepts,AssessmentApproaches,andKeyLessons.”SustainabilityScience2(2):265–75.doi:10.1007/s11625-007-0032-y.
Hall,JimW.,MartinoTran,Adrian.J.Hickford,andRobertJ.Nicholls,eds.2016.TheFutureofNationalInfrastructure:ASystem-of-SystemsApproach.Cambridge,UK:CambridgeUniversity
Press.
Hallegatte,Stephane,AdrienVogt-Schilb,MookBangalore,andJulieRozenberg.2016.Unbreakable:
BuildingtheResilienceofthePoorintheFaceofNaturalDisasters.Washington,DC:WorldBank.https://openknowledge.worldbank.org/handle/10986/25335.
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Pant,Raghav,JimW.Hall,andSimonP.Blainey.2016.“VulnerabilityAssessmentFrameworkforInterdependentCriticalInfrastructures:CaseStudyforGreatBritain’sRailNetwork.”European
JournalofTransportandInfrastructureResearch16(1):174–94.https://eprints.soton.ac.uk/385442/.
Pant,Raghav,JimW.Hall,Simon.P.Blainey,andJohnM.Preston.2015.“AssessingSinglePointCriticalityofMulti-ModalTransportNetworksattheNational-Scale.”Paperpresentedatthe25thEuropeanSafetyandReliabilityConference,Zurich,Switzerland,September2015.
https://eprints.soton.ac.uk/385456/.
PwC(PricewaterhouseCoopers).2017.TheWorldin2050—TheLongView:HowWilltheGlobal
EconomicOrderChangeby2050.PwCEconomics&PolicyTeamreport.London:PricewaterhouseCoopersLLP.https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world-in-2050-full-report-feb-2017.pdf.
WorldBank.2018.“Vietnam’seconomicprospectimprovesfurtherwithGDPprojectedtoexpandby68percentin2018.”PressRelease,June14.https://www.worldbank.org/en/news/press-
release/2018/06/14/vietnams-economic-prospect-improves-further-with-gdp-projected-to-expand-by-68-percent-in-2018.
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Chapter1:Introduction
BackgroundandObjectives
TheSocialistRepublicofVietnam (henceforth referred toasVietnam) isoneof theworld’s fastest
growingeconomieswithGrossDomesticProduct(GDP)growthabove7percentinthefirstquarterof2018,withstrongforecastedgrowthfortheremainingyear(WorldBank2018).Thisfollowsfromaconsistent average GDP growth of 6.4 percent since the 2000s.1 Vietnam is also projected to be
amongthefastestgrowingeconomiesoverthe2016to2050period,capableofsustainingapotential5percentannualGDPgrowthrate(PwC2017).
However, such rapid growth is threatened by extremeweather hazards (storms, floods, typhoons,
landslides).PreviousreportssuggestVietnamalreadyrankshighasanaturaldisasterhotspotoftwoormoremulti-hazardevents,potentiallyexposing60percentlandareaand71percentpopulationtorisk(Dilleyetal2005)—whichcouldresultinannualaverageassetlossesamountingto1.5percentof
GDPandconsumptionlossesamountingto2percentofGDP(Hallegatteetal2016).Itispossiblethatclimate change will exacerbate these extreme hazards, even after factoring uncertainties indownscaled global climate model predictions (MoNRE 2009; World Bank 2011; Irish Aid 2017).
Recognizingthatthecountry’svulnerabilitytoimpactsofclimatechangecanimpedeitsgrowth,andas part of its Nationally Determined Contributions (NDCs) to meet the Paris Climate Agreementtargets, theMinistry of Transport (MoT) inVietnamaims to establish national transport strategies
andplansaspartof the transport sector’s contribution todeliveronNDC implementation. To thisend, this report supports MoT in the development of multimodal network level criticality andvulnerability analysis, as well as methods and tools to inform prioritization of investments in
transportassetmanagementtoensureintegrationofclimateandnaturalriskconsiderations.
In delivering the multimodal transport network criticality and vulnerability analysis to prioritizemaintenance, rehabilitation, and infrastructure upgrading, this study answers some key questions
relevanttotransportplanners,investors,andrelevantstakeholders,namely:
1. Whereandwhatare thekey transportnetwork locationsexposed todifferent typesof
extremenaturalhazards?
2. In which areas in the country are transport assets particularly exposed to hazards, as
predictedbymodeloutputsofcurrentandfutureclimatechange-drivennaturalhazard
scenarios?
3. Whatarethewidermacroeconomiclossesatthenationalscalewhenfreighttransportis
disruptedduetotransportfailures?
4. Whataretheimpactsonthelocalcommune-scaleeconomiesduetolackofaccesstokey
locationsofeconomicactivity,becauseoflocalroadfailures?
5. Whataretheimpactsoftransportdisruptionsintermsofincreasedtransportationcosts
ofreroutingtrafficformaintainingservicecontinuity?
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6. How resilient are the multimodal transport networks in providing continuous service
when individual transport modes are damaged or disrupted due to external natural
hazardshocks?
7. When considering the key climate resilience investments and strategies evaluated for
currentandfutureclimatescenarios,whatarethenetbenefitsofadaptation?
8. Whereandwhatarethekeytransportnetworklocationsprioritizedaccordingtohighest
netbenefitsofclimateadaptationmeasures?
9. What are the most robust climate resilience interventions or policies to reduce the
vulnerabilityofcriticalsegmentstofutureclimatechangeimpacts,takingaccountofthe
uncertaintiesandsensitivities?
In answering the above questions, some specific objectives satisfied in this report include the
following:
1. Creating multi-scale geospatial network flow models to represent the multimodaltransportinfrastructuresinVietnam,bothinthepresentandfuture
2. Modelingtrafficflowdisruptionsandflowreallocationswhennetworkassetsfailduetonaturaldisasters
3. Evaluating the potential economic and social impacts when the multimodal transportnetworksaredisruptedbynaturaldisasters
4. Creatingnetworkcriticalitymetricstosystemicallymeasureandidentifycriticalsegments
of the multimodal transport networks that lack or have built-in resilience and/orredundancy
5. Performingexhaustive scenario-based simulations to incorporatemultipleuncertaintiesin underlying model assumptions. This aligns with the decision making under deepuncertainty(DMDU)approaches(Espinetetal2015)
6. Incorporatingvariousclimate-resilienttransportinvestmentsandpoliciestoevaluatetheadaptation options and identify robust interventions or policies to reduce thevulnerabilityofcriticalsegmentstofutureclimatechangeimpacts,takingaccountoftheuncertaintiesandsensitivities.
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ScopeoftheStudy
Thisreportfocusesitsrecommendationsattwoscales:nationalandprovincial.Thefocusareaofthis
studyisthemainlandofVietnam,2whichonthewestandnorthshareslandborderswithneighboringcountries(LaoPDR,Cambodia,andChina)accessiblebyroad,and issurroundedbycoastal linesontheeastandsouth.Forthenational-scaleanalysis, inputdata isderivedfromprovincial,district,or
commune-level statistics where appropriate. The province-scale analysis focuses on three specificprovinces—LaoCai,BinhDinh,andThanhHoa—where information isderived fromcommune-levelstatistics.
Figure1.1providesarepresentationoftheregionalandinternationalboundariesofVietnamrelevanttothisstudy.ThethreeprovincesofLaoCai,BinhDinh,andThanhHoa,forwhichthedetailedroadnetworkanalysisisconducted,arehighlightedindarkgray.
The scope of the national analysis covers the key national-scale roads, railways, airline, inlandwaterways,andmaritimesystemsthatmakeupVietnam’smultimodaltransportationinfrastructure.At this scale the study focuses on understanding the economic impacts of disruptions to freight
transportationduetotransportfailures.
The scope of the provincial analysis is confined to road networks only, which are represented ingreater granularity than the national-scale roads and include national, provincial, district, and
communeroadsandotherassetssuchasbridgesandculverts,amongothers.Atthisscale,thestudyfocusesonunderstandinghowroadfailuresimpactaccesstokeylocationswithincommunes,therebyaffectingeconomicoutputgeneration.
Specifically, for all road networks considered in this study, adetailed adaptation analysismodel iscreated to quantify the benefits and costs of adaptation, identify the road network assets whereclimate-resilient investments should be prioritized, and provide estimates of the scales of
investmentsrequired.AnoverviewofallinfrastructurerelateddatasetsusedinthisstudyisgiveninappendixBofthisreport.
Atbothnationalandprovincialscales,transportfailuresareinducedbytwotypesofnaturalhazards:
floodingand landslides. The floodhazards considered in this study are river flooding (i.e., floodingcausedby riversovertopping theirbanks), surfacewater flooding (i.e., flooding causedbyextremerainfall—also known as “pluvial” or “flash” flooding) and tidal flooding (i.e., flooding caused by
typhoon-induced storm surges). Besides looking at the present situation (the year 2016), we useclimatechangescenario-drivenmodeloutputsrepresentingfuturefloodingintheyears2025,2030,and 2050. The landslide hazards information includes model outputs for subsets of landslides
quantified in termsof landslide susceptibility presented for current conditions (the year 2016) andfuture climatechange scenarios in2025and2050.Chapter2, section “NaturalHazardExposureofTransportNetworks”andappendixBprovidefurtherdetailsforfloodhazarddata.3
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Figure1.1.VietnameseProvincesandNeighboringCountriesConsideredintheStudy
.
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StudyContributionsandLimitations
Themaincontributionofthisstudyistoprovidedetailedspatialevidenceofsystemicvulnerabilities
and risks on the multimodal transport systems of Vietnam. Several study contributions would bevaluable to stakeholders in Vietnam such asMinistry of Transport (MoT),Directorate of Roads forVietnam (DRVN), Provincial Departments of Transport (PDoT), Civil Aviation Administration of
Vietnam (CAAV), Vietnam Maritime Administration (VINAMARINE), Vietnam Inland WaterwayAdministration(VIWA),TransportDevelopmentandStrategyInstitute(TDSI),MinistryofAgricultureandRuralDevelopment(MARD),andMinistryofNaturalResourcesandEnvironment(MoNRE).
Thisstudyincludesthefollowingspecificusefulcontributions:
1) Creatinguniquedatasetsandmodelingresources
a. First-of-a-kindrepresentationsoftopologicallyconnectedgeospatialnational-scaleroads,railways, inland waterways, and maritime multimodal transport networks with flowassignmentsforVietnam
b. Detailedagriculturecropsandcommodityor industrylevelnetworkflowsmappedonto
themultimodaltransportnetworks increaseunderstandingofthedomesticfreightflowpatternsinthecountry
c. Detailedtransportcostestimatesincreaseunderstandingofrealisticcriteriafortransportfreightflowassignments
d. Long-termgrowthforecastsincorporatedontothegeospatialnetworks
e. The study’s codebase, developed in Python programming language as an open-sourceresource,isavailabletothepublicathttps://github.com/oi-analytics/vietnam-transport.
Themethodologybehind the codehasbeendocumented indetail through this report,anda separateuserdocumenton thecompilationandcreationofunderlyingdataandcode is also available publicly at: https://vietnam-transport-risk-analysis.readthedocs.io/en/latest/.
f. Testing of the underlying codes to perform billions of computations of network flow
assignmentsandfailurescenarioswithoptimalperformance,afundamentalrequirementforastudyofthisscale.
2) Prioritizingcriticality-basednetworks
a. The key metrics created and analyzed in this study answer the previously highlightedquestionsrelevanttotransportstakeholders,whowanttoknowtheeffectsoftransportdisruptionsoncontinuedprovisionofserviceandtransportcosts.
b. Thedetailednetworkassessment conducted in this studyhelps identify locationsmost
important for thecontinuityof thetransportservice in thecountry,andwhose failurescanpotentiallyleadtolarge-scalesocio-economicconsequences.
c. The study’s ranking of critical assets, based on their disruptive potential, provides ameanstoprioritizeandsequenceriskreductionactivities.
d. Ranking of critical assets further supports the rationale for prioritizing investments toreduce vulnerability and build systemic resilience, forming the basis for long-termadaptationplanning(Thackeretal2018).
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3) Increasingtheunderstandingofhazardvulnerability
a. Thestudyspatiallydepictstheexposureoftransportnetworkstovariousnaturalhazardsat detailed spatial scales, enabling understanding of the potential severity of differentthreatstoinfrastructurenetworks.
b. Study results presented at national and provincial scales highlight areaswhere several
transportassetsaremorepronetobeexposedtohazards in thecurrentscenariosandhowthismightchangeinthefutureduetoclimatechangedrivenscenarios.
c. Thestudy’sspatial informationonextremehazardexposuresoftransportnetworkscaninformnationalandprovincialtransportplanningdecisionstoallocateadequatebudgetforshort-termandlong-termtransportriskreductions.
4) Developingaroadmapforadaptationplanning
a. Akeycontributionofthisstudyistoprovidethetoolstoundertakeadaptationanalysis
whereby the benefits and costs of different strategies designed to build transportstructuralresiliencecanbeassessedundervarioushazardscenarios.
b. Thestudypresentsvariousadaptationoptionsat thescaleof individual roadassets,aswell as the cost-benefit analyses metrics to deliver a detailed understanding ofinvestmentoptionsforeachassetunderdifferenthazardscenarios.
c. Thestudyperformskeyanalysistounderstandwhetherasset leveladaptationplanningshouldbedoneproactivelytoprotectagainstfutureclimatehazards.
d. The study catalogues and presents locations of all road assets with high benefits of
adaptationtoprovidedecision-makerswithinformationthatwillhelpprioritizeresourcestowardthemostcriticallyimportantassetsinthenetworks.
5) Assessingmultimodality
a. Thestudy’spresentationofkeymethodsandanalysesquantifiestheeffectsoftransportfailures,ifmultimodaloptionswereavailable.
b. As one of its key aims, the study’s multimodality assessment provides evidencesupportingtheneedforenhancedmultimodalconnectivityasanadaptationstrategy.
c. Understanding the benefits of multimodality at detailed geospatial network scales
provides a means to target network locations where multimodal options could bestrengthenedthroughmoreinvestments.
6) Assessinguncertainty
a. Themodels and analyses presented in this report havemany sources of uncertaintiescomingfromlackofproperdataonphysicaltransportnetworkstructuresandtheirusagestatistics, among others. These uncertainties have been accounted for throughout theanalysis.
b. Thestudyfollowsarobustdecision-makingapproach(Lempertetal2013)wherebythe
analysis quantifies the extreme ranges (minimum and maximum) of potential flows,criticalitiesandadaptation.Thisapproachprovidesadecision-makingtoolthataccountsforrobustperformanceofdifferentadaptationoptionsunderdifferentscenarios.
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Theanalysispresented in this reportoffersahigh-level indicativeassessmentof transport systemsand their natural hazard risks, providing a first-order screeningwhereby locations and assetswith
highriskscanbenarroweddownfor further investigation.Hence,certainconsiderationsshouldbemade in interpreting thiswork.Nevertheless,mostof these limitationsexist due to lackof properdata; with improvement in input data the underlying model is fully capable of correcting several
limitationsunderlinedbelow:
1) Physicalinfrastructurenetworkdataandrepresentations
a. Thetransportdatacreatedinthemodelservesasanetworkpresentationoffunctionalconnectivityof thetransportsystem,ratherthanadetailedmasterplanrepresentation
ofeachlocation’sdetailedspatiallayoutandattributes.Forexample,inthisanalysis,HoChiMinhportisrepresentedasapointinspace,whileinrealityitexistsoveralargearea.
b. Similarly, the represented road, railways, airports, ports, and multimodal links showgeneral travel routes. Any details provided for the physical width, number of lanes orlines,ornavigationalchannelsofthesesystemshavebeenestimated.
c. The representationof thenetworks’built-in functionalandphysical connectivity isalso
heavily contingent of the availability or lack of data. For example, if the available datadoes not show a road or rail connection that exists, the model will not be able torepresentthatconnection.
d. Themultimodallinksinthisanalysisrepresentderivedconnectivity,withasmanylinksaspossibleverifiedthroughsatelliteimagery.
2) Networkflowassignments
a. As explained later in the report, themodel estimates network flow assignments from
several sources anddatasets. Several assumptions have been taken in interpreting theflows.
b. Representationoffutureflowsandlossesareestimatedbasedonhigh-levelforecastsofgrowthforthewholecountry.
c. Hence,theflowandfailurevaluesintheanalysisshowthehigh-leveltrends,ratherthantheexactestimatesofactualflows.
3) Failureandhazardvulnerabilityanalysis
a. Griddeddatasetsshowhazardsatdifferentspatialresolutions,whicharesometimesverycoarse. Thus, the hazard information should not be used for detailed site-specificanalysis.
b. Derivedfromtheanalysisof transportnetworkexposurestohazards, themain insightsprovideusefuloverviewsofthelikelyhazardsaroundspecificlocations.
4) Adaptationanalysis
a. The adaptation analysis presented in this report reflects the underlying data of cost
estimates for different options under consideration. Therefore, the cost estimatesderivedfromdatasetsrepresentbestestimates.
b. The main insights from the adaptation analyses provide a means to understand howdifferentoptionscanbeevaluatedandwhichscenariosshouldbeconsidered.
Page|36
ReportStructure
Followingthisintroductorychapter,theremainderofthereportisstructuredasfollows:
Chapter 2 — Vietnam Profile: Transport Network and Exposure to Natural Hazard: Chapter 2describes the underlying transport network information assembled and created in this study, thetypes of hazards considered—with their source—climate change scenarios (if considered), spatial
resolutions, and spatial coverage. The chapter infers some information regarding the state of theinfrastructure from the underlying network data, which is compared with relevant literaturewhereverpossible.
Chapter3—Vulnerability,CriticalityandRiskAssessment: Chapter3presents the results for thetransport criticality assessmentof freight flowdisruptionson the assembled transport networks inVietnam.Thecriticalityassessmentconsidersanexhaustivesetof individualnetwork linkscenarios
exposedtohazardsforfailure.Inaddition,theassessmentconsidersimpactsintermsofmetricsthatgivea senseof thenetwork locationswith themost socio-economic impacts.A risk assessment toinferthehazardsthatcausetheimpactsfollows.Therangesofdisruptiveimpactsacrosseachhazard
arequantified foreachnetwork tobetterunderstandwhetherhazard impactswill increasedue tofutureclimatescenarios.
Chapter 4 — Adaptation Strategy and Analysis: Chapter 4 presents the adaptation options
considered for national-scale and province-scale road network assets, with a detailed benefit-costanalysisperformedforeachassetunderdifferenthazardscenarios.Inaddition,thechapterevaluatestherangesofadaptationoptionsandtheirbenefits,exploringtheadvantagesofadaptingtofuture
climatescenarios.
Chapter5—ExploringMultimodalOptionsforNational-ScaleTransport:Thischapterexplainsthedevelopmentof thestudy’smultimodal transportsystem.Following this, thechapterevaluates the
effectofmultimodalityonfailuresbyexploringmodalshiftsfromroadandrailnetworks.
Chapter6—ConclusionsandPolicyRecommendations: Chapter6presents recommendations forbuildingandenhancingtransportresiliencetoclimatechangeimpacts.
Notes
1.SeetheWorldBank’sonlinecountrypageforVietnam:http://www.worldbank.org/en/country/vietnam/overview.
2.Forthepurposesofthisstudy,theanalysiswasconfinedwithinthemainlandofVietnam,consideringthedataavailabilityandsignificanceoftrafficflows.
3.Thenaturalhazarddatasetsusedinthisstudyhavebeenobtainedfromthirdpartysources—eitherfromtheGovernmentofVietnamorthroughtheWorldBankpartners—andhavenotbeenalteredinanywaybytheauthors.
Page|37
ReferencesDilley,Maxx,RobertS.Chen,UweDeichmann,ArthurL.Lerner-Lam,MargaretArnold,JonathanAgwe,
PietBuys,OddvarKjevstad,BradfieldLyon,andGregoryYetman.2005.NaturalDisasterHotspots:A
GlobalRiskAnalysis.DisasterRiskAnalysisSeries.Washington,DC:WorldBank.http://documents.worldbank.org/curated/en/621711468175150317.
Espinet,Xavier,AmySchweikert,andPaulChinowsky.2015.“RobustPrioritizationFrameworkforTransport.”ASCE-ASMEJournalofRiskandUncertaintyinEngineeringSystems,PartA:CivilEngineering3(1).doi:10.1061/AJRUA6.0000852.
Hallegatte,Stephane,AdrienVogt-Schilb,MookBangalore,andJulieRozenberg.2016.Unbreakable:BuildingtheResilienceofthePoorintheFaceofNaturalDisasters.Washington,DC:WorldBank.
https://openknowledge.worldbank.org/handle/10986/25335.
IrishAid.2017.VietnamClimateActionReportfor2016.IrishAidResilienceandEconomicInclusion
Teamreport,Limerick,Ireland:IrishAid.https://www.irishaid.ie/media/irishaid/allwebsitemedia/30whatwedo/climatechange/Vietnam-Country-Climate-Action-Reports-2016.pdf.
Lempert,Robert,NidhiKalra,SuzannePeyraud,ZhiminMao,SinhBachTan,DeanCira,andAlexanderLotsch.2013.“EnsuringRobustFloodRiskManagementinHoChiMinhCity.”PolicyResearch
WorkingPaper6465,WorldBank,Washington,DC.http://documents.worldbank.org/curated/en/749751468322130916.
MoNRE(MinistryofNaturalResourcesandEnvironment).2009.ClimateChange,SeaLevelRisescenariosforVietnam.Hanoi:MinistryofNaturalResourcesandEnvironment,Vietnam.https://www.preventionweb.net/files/11348_ClimateChangeSeaLevelScenariosforVi.pdf.
PwC(PricewaterhouseCoopers).2017.TheWorldin2050—TheLongView:HowWilltheGlobalEconomicOrderChangeby2050.PwCEconomics&PolicyTeamreport.London:
PricewaterhouseCoopersLLP.https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world-in-2050-full-report-feb-2017.pdf.
Thacker,Scott,ScottKelly,RaghavPant,andJimW.Hall.2018.“EvaluatingtheBenefitsofAdaptationofCriticalInfrastructurestoHydrometeorologicalRisks.”RiskAnalysis38(1):134–50.doi:10.1111/risa.12839.
WorldBank.2011.ClimateResilientDevelopmentinVietnam:StrategicDirectionsforTheWorldBank.Report.WorldBankSustainableDevelopmentDepartment,VietnamCountryOffice,
Vietnam.http://documents.worldbank.org/curated/en/348491468128389806.
WorldBank.2018.“Vietnam’seconomicprospectimprovesfurtherwithGDPprojectedtoexpandby
68percentin2018.”PressRelease,June14.https://www.worldbank.org/en/news/press-release/2018/06/14/vietnams-economic-prospect-improves-further-with-gdp-projected-to-expand-by-68-percent-in-2018.
Page|39
Chapter2:VietnamProfile:TransportNetworkandExposuretoNaturalHazard
TransportNetworks
Thissectiondescribesthecurrentnetworkswithfreightflowvolumesandcostsacrossnational-scale
roads, railways, inland waterways, maritime ports, and airline networks. Similarly, flows are alsoidentifiedonprovinceroadsnetworksintermsoftheaccesstoimportantcommunecenterswithinprovinces, uncertainties of flows due to rice-crop seasonality, and network cost assignments are
considered.
National-scaleroadinfrastructurenetworks
Thenational-scalenetworkmodelcreatedforthestudyextendstoabout30,900kmandconsistsof2,130nodesand2,512links.Ofthe30,900kmofthenetworkanalyzed,30,032kmor97.2percent,is
paved;amere868kmremainsunpaved.Roadshavebeenclassifiedinscaleof1to6,withagreatertraffic volume associated with higher road class (table 2.1). Table 2.2 presents the estimatedclassification.
Table2.1.RoadCategoriesbyAverageDailyVehicleTrafficCounts
Source:MoTVietnam2000.
Roadtrafficcount Roadcategory
>6000 1
3000–6000 2
1,000–3000 3
300–1,000 4
50–300 5
<=50 6
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Table2.2.EstimatedTotalLengthsbyRoadCategoryintheNational-ScaleRoadNetworkModelforVietnam
Source:Authors’estimationusingCVTSGPSdatamapping
Province-scaleroadsnetworks
Giventheunderlyinggeospatialdatasetshadthesameattributes,theprovince-scalenetworkmodelsused similar types of assumptions across all provinces. Table 2.3 shows the estimated lengths ofpaved and unpaved roads of different levels in each of the province road network models. The
estimatesindicatetheseprovincescontainmostlyunpavedroads.Table2.3.EstimatedLengthsbyRoadCategoryintheProvince-ScaleRoadNetworkModelforVietnam
Roadclass Length(km) Length(%)
1 1,656 5.35%
2 2,331 7.54%
3 4,951 16.02%
4 6,449 20.87%
5 8,561 27.70%
6 6,952 22.70%
Province Nodes Links RoadlevelPaved(km)
Unpaved(km)
Paved(%)
Unpaved(%)
National 579 0 14.71% 0.00%
Provincial 435 0 11.06% 0.00%
Local(district/commune)
65 1,342 1.65% 34.11%
Other 0 1,514 0.00% 38.48%
LaoCai 3,744 4,697
Total 1,079 2,857 27.42% 72.58%
National 341 0 6.31% 0.00%
Provincial 321 0 5.93% 0.00%
Local(district/commune)
226 446 4.17% 8.25%
Other 0 4,075 0.00% 75.34%
BinhDinh 21,686 26,213
Total 887 4,522 16.41% 83.59%
National 921 0 4.58% 0.00%
Provincial 457 0 2.27% 0.00%
Local(district/commune)
394 2,312 1.96% 11.49%
Other 5 16,025 0.03% 79.67%
ThanhHoa 66,863 91,304
Total 1,777 18,337 8.83% 91.17%
Page|41
Railwayinfrastructurenetwork
Therail infrastructurenetworkmodelwasderivedfromdataonstationpointlocationsandrail line
geometries provided from the Ministry of Transport (MoT) dataset, including the VITRANSS IIdatasetsfrom2009(JICAandMoT2010).Theresultingnetworkcontains313nodes,ofwhich234arestationsand324arelinks.Themodeledlengthoftherailnetworkisapproximately2,662kilometers.
Table 2.4 reports the lengths of somemain routes. Themain hub of the rail network lies aroundHanoi,withtheHanoitoHoChiMinhrouteasthelongestlineofthenetworkbyfar.
Due to an aging railway infrastructure largely comprised of a single-track system, railway usage isvery limited inVietnam,which limits train lengths in thedifficult andnarrow terrainsalong routes(Systra2018).
Table2.4.EstimatedTotalLinkLengthsandPercentagesofRailRoutesIdentifiedintheNational-ScaleRailNetworkModelforVietnam
Inlandwaterwayinfrastructurenetwork
The study identified inlandwaterwaynetworkmodel nodes fromdataobtained from theVietnamInlandWaterwayAdministration(VIWA)1andtheVITRANSSIIstudy.Theresultingnetworkcontains
131inlandwaterwayportnodesand502links.Themodeledlengthoftheinlandwaterwaynetworkis approximately 5,407 kilometers, concentrated along theMekongDelta in the south, or the Red
RiverDeltainthenorth,withaverysmallsectioninQuangBinh.
Railroutecode Routedescription Length(km) Length(%)
DSTN Hanoi–HoChiMinh 1,719 64.56%
YV-LC Hanoi–LaoCai 297 11.16%
HN-DD Hanoi–LangSon 171 6.42%
GL-HP Hanoi–HaiPhong 106 3.98%
K-HL BacGiang–QuangNinh 104 3.90%
DA-QT Hanoi–ThaiNguyen 58 2.16%
K-LX BacGiang–ThaiNguyen 55 2.07%
BH-VD Hanoi 39 1.47%
MP-LD LangSon 31 1.17%
CG-ND NgheAn 30 1.12%
CL-PL HaiDuong 17 0.65%
MM-PT BinhThuan 12 0.45%
PL-KK HaNam 9 0.34%
DT-QN BinhDinh 9 0.34%
L-TM Unknown 6 0.22%
Page|42
Maritimeinfrastructurenetwork
Thestudyidentifiedmaritimenetworkmodelnodesfromgeospatialportlocationdataobtainedfrom
the MoT, and maritime routes published by the Ministry of Natural Resources and Environment(MoNRE).Theresultingnetworkcontained45maritimeportsand206 links.Themodeled lengthofthemaritimenetworkisapproximately5,254kilometers,fordomestictransportationusesonly.The
VietnamMaritimeAdministration (VINAMARINE) classifies international hubs classified as Class 1Aports,anddomestichubsasClass1ports(MoT2015).
Airtransportinfrastructurenetwork
The study identified air transport infrastructure network model nodes from geospatial airport
locationdataobtainedfromtheMoT.BasedonapreviousMoTstudyoftheVITRANSSIImodel(JICAandMoT2010),thestudyidentifiedmainairportswithanyfreightflowsbetweenthemandcreatedstraight-line link geometries to join the paired airports. The resulting network contains 23 airport
nodes, of which 8 are connected via 10 links. While the share of air transport of total cargomovementinVietnamisincreasingrapidly,detaileddataforairfreightvolumeswerenotavailableatthe time of this study. Given this limitation, the study analyzed the air transport sector as a
standalonesector,ratherthanasamultimodaltransportoptioninVietnam.
ModelingFreightFlowonTransportNetworks
Thisstudymodeledandestimatedthefollowingtransportflowmetrics:
• Averageannualdailyfreightvolumes(AADF):Theestimatedvolumeoffreightintonsperdayonanaveragedaybetweendifferentnetworklocations;and
• Net revenue measures: The estimated daily net revenue of goods and services withaccesstoan important locationviaatransportnetwork.Thesemeasuresareestimatedtounderstandlocalroadaccesstoimportantlocationsatprovincescale.
Trafficflowswereconstructedbasedonavailableexistingdata,includinginformationreceivedfrom
relevantministries anddepartments inVietnam,data compiled in previous studies and reports on
transport planning and development in Vietnam, and open-source data and online information oftransport estimates. The study relied on previous origin-destination (OD)matrix provided byMoT,CVTSdatafrom2017, input-output(IO)matrix,trafficcountdatafromDRVN,cropproductiondata
from IFPRI (Koo et al 2018), and other socio-economic statistics, among others. The process ofassigningfreighttransportflowsisdescribedindetailinappendixA.
Resultsoffreightflowmappingatnationalscale
Theresultsoftheflowassignmentsprovideinsightsintothefollowingimportantquestions:
• Wherearethelocationsonthenetworklinkswiththemostflowconcentrations?
• Whichlinksandrouteshavethelargestconcentrationsofeconomicvalues?
First the results of the national-scaleODmatrix aremapped in figure 2.1 and shown in table 2.5,
whichpresentsthedifferentdailytonnagesforeachcommodityconsideredinthestudyandsplitofthesedaily tonnagesbyeach transportmode (road, railway, air, inlandwaterways, andmaritime).
Foreachcommodity,themodewiththehighestpercentageshareishighlighted(ingray)intable2.5.Theresultshowsthefollowing:
Page|43
• The fluctuations in the rice volumes show a significant change in the daily tonnagevolumes on the transport systems,which can have huge implications on the values offailureestimates.
• Inland waterways and roads are the two most dominant transport modes for thesecommodities.Dependinguponthefluctuationsinthericetransporttonnages,themodesharesbetweenthesetwotransportmodesvariesbetween44and48percent.
• Maritime transport is the next significant mode, with around 4 to 5 percent modalshares, followedbyrailways,witha1.9 to2.1percentmodalshare.Airlinetransport isalmostinsignificant.
• For commodities such as cement, coal, construction materials, fertilizer, fishery, andsugar, inland waterways represent the most dominant mode. Roads rank as thedominant mode for manufacturing, steel, meat, petroleum, and all crop commoditiesusedinthisstudy.
The tonnage volumes andmodal shares presented are a subset of the estimated annual tonnages
that focusonmajornational inter-provincial commodity flowsandarebasedon thebestavailable
origin-destinationdataofsomecommodities.ThisresultsinmodalsharefiguresthataresomewhatdifferentfromthosefromthestatisticsoftheGeneralStatisticsOffice(GSO)ofVietnam.2AccordingtotheGSOfiguresfor2015,roughly76.5percentoftradeflowsoccurredonroad,17.5percenton
inland waterways, 5.3 percent on maritime, 0.6 percent on railways, and 0.02 percent on airlinenetworks.Hence, theestimatedmodeshares in thisstudyunder-representthedominanceof roads,whileover-representingthecontributionsofinlandwaterwaysandrailways.
Themaps in figure 2.1 show the how daily transport flows in Vietnam happen at long distances,connectingtheregionofeconomicactivitiesinthesouth(MekongDelta)tothoseinthenorth(RedRiver Delta). For the national-scale road network, the high-volume freight routes run through the
nationalhighwaysandexpresswaysontheeasterncoast.Therailway flowsaremoreconcentratedtowardthenorthernsections.The inlandwaterwayshavekeyhigh-volumeroutes inthenorthandsouth,whilethecross-countrydomesticmaritimearethebusiestroutes.Basedontheavailabledata
for airlines, the connectivity between the Hanoi and Ho Chi Minh airports is most important forfreighttransport.
Page|44
Table2.5.DailyTrafficVolumesbyCommodityandTransportMode
Co
mm
od
ity
Ro
ad
Ra
ilw
ay
Air
Inla
nd
wa
te
rw
ay
sM
ariti
me
To
ta
l
To
ns/d
ay
Pe
rce
nt
sh
are
To
ns/d
ay
Pe
rce
nt
sh
are
To
ns/d
ay
Pe
rce
nt
sh
are
To
ns/d
ay
Pe
rce
nt
sh
are
To
ns/d
ay
Pe
rce
nt
sh
are
Ce
me
nt
34,534
30.93%
3,58
13.21
%1
0.00
%60
,626
54.31%
12,898
11.55%
11
1,6
40
Co
al
14,963
13.49%
2,02
21.82
%0
0.00
%83
,917
75.68%
9,97
79.00
%1
10
,87
9
Cons
truc
tion
ma
te
ria
ls10
8,72
123
.37%
8,42
31.81
%5
0.00
%34
6,18
974
.41%
1,90
40.41
%4
65
,24
1
Ferti
lizer
10,940
26.58%
1,97
34.79
%3
0.01
%27
,058
65.74%
1,18
52.88
%4
1,1
58
Fis
he
ry
7,13
836
.96%
300.16
%1
0.00
%11
,912
61.67%
234
1.21
%1
9,3
15
Ma
nu
factu
rin
g15
3,12
182
.40%
4,70
22.53
%15
20.08
%13
,933
7.50
%13
,921
7.49
%1
85
,82
8
Coffe
e A
rabi
ca57
99.61%
00.37
%0
0.00
%0
0.00
%0
0.03
%5
7
Ca
sh
ew
2,99
699
.61%
10.03
%0
0.00
%2
0.07
%9
0.29
%3
,00
8
Ca
ssa
va
19,416
92.85%
436
2.09
%1
0.00
%14
60.70
%91
24.36
%2
0,9
11
Mai
ze5,35
483
.90%
199
3.11
%12
0.18
%29
94.68
%51
88.12
%6
,38
2
Pe
pp
er
404
97.94%
30.71
%0
0.00
%4
1.07
%1
0.29
%4
13
Coffe
e ro
bust
a2,03
399
.64%
70.33
%0
0.00
%0
0.00
%1
0.03
%2
,04
0
Rubb
er2,57
098
.57%
200.76
%0
0.00
%2
0.07
%16
0.60
%2
,60
8
Sw
ee
t p
ota
to
es
1,03
459
.22%
874.98
%6
0.35
%38
722
.15%
232
13.31%
1,7
46
Te
as
162
77.75%
136.20
%0
0.03
%0
0.01
%33
16.02%
20
8
Me
at
50,940
72.79%
555
0.79
%48
0.07
%14
,019
20.03%
4,42
56.32
%6
9,9
87
Pe
tro
leu
m32
,532
71.49%
384
0.84
%8
0.02
%5,30
111
.65%
7,28
416
.01%
45
,50
9
Ste
el
33,446
77.08%
2,35
05.42
%2
0.01
%6,84
015
.76%
751
1.73
%4
3,3
89
Su
ga
r3,06
437
.98%
130.16
%0
0.00
%4,84
460
.06%
145
1.80
%8
,06
6
Wo
od
10,748
46.51%
325
1.40
%7
0.03
%11
,211
48.51%
820
3.55
%2
3,1
10
Ric
e (
min
)73
,557
70.96%
1,21
01.17
%9
0.01
%25
,762
24.85%
3,11
43.00
%1
03
,65
3
Ric
e (
ma
x)
361,47
859
.64%
8,04
31.33
%43
0.01
%20
4,78
133
.78%
31,799
5.25
%6
06
,14
4
To
ta
l to
ns (
min
)5
67
,72
94
4.8
7%
26
,33
22
.08
%2
54
0.0
2%
61
2,4
52
48
.41
%5
8,3
80
4.6
1%
1,2
65
,14
7
To
ta
l to
ns (
ma
x)
85
5,6
50
48
.41
%3
3,1
65
1.8
8%
28
70
.02
%7
91
,47
14
4.7
8%
87
,06
54
.93
%1
,76
7,6
38
Page|45
Figure2.1.MaximumAADFfortheNational-ScaleTransportNetwork
A. Total tonnage (Road) B. Total tonnage (Inland waterway)
C. Total tonnage (Maritime)
D. Total tonnage (Railway)
E. Total tonnage (Air)
Page|46
Resultsofprovince-scaleflowmapping
Trafficflowsandrevenuesonprovince-scaleroadnetworkshavebeencalculatedbasedontheIFPRIcropsdata (Kooetal2018),andcommune-level revenuestatistics fromGSO.3Figure2.2 (LaoCai),
figure 2.3 (Binh Dinh), and figure 2.4 (ThanhHoa) show heatmaps of flow concentrations due tomaximumdailycroptonnages(panelA)andmaximumdailynetrevenues(panelB),inUS$thousandsperday)beingdirectedtowardcommunecentersoftherespectiveprovinces.
Theresultspredominantlyhighlighttheeffectofroaddensityintheprovinces.ForLaoCai,wheretheroad network is not very dense, some road clusters (e.g., in the Bao Yen district) have moresignificantusagethanothers.Ontheotherhand,forThanhHoatheroadnetworkisverydense,with
manyrouteoptions,makingtheflowspatternsmoredistributivewithfewclustersofverysignificantroads. The roadnetwork is sparse in parts of BinhDinh,which createsmore significant clusters inlocationssuchasLaoCai,whilefewerclustersexistinareaswherethenetworkisdense,asinThanh
Hoa.
Theanalysisbycommodityhighlightsthenetrevenue’sdependenceonagricultureproductivity:
• ForLaoCai,thenon-agriculturefirmrevenuesdominatetheminimumandmaximumnetrevenuedistributionsonthenetwork,whichresultinlittlevariationsinflowpatterns.
• In Binh Dinh, the dependence on agriculture production results in some differencesbetweentheminimumandmaximumnetrevenueallocations.
• InThanhHoa,again,theamountofagricultureproductionaffectsthenetrevenue.
Figure2.2.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenuesonLaoCaiProvincialRoads
A.MaximumtonsofcropflowsinLaoCai
B.MaximumnetrevenueofcropflowsinLaoCai
Page|47
Figure2.3.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenuesonBinhDinhProvincialRoads
A.MaximumtonsofcropflowsinBinhDinh
B.MaximumnetrevenueofcropflowsinBinhDinh
Page|48
Figure2.4.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenuesonThanhHoaProvincialRoads
Uncertaintiesinfreightflowmapping
Thenational-scaleresultsshowthemaximumestimatesofflowunderthefollowingassumptions:
• Estimatedfromthehigh-levelODmatrices,dailyODtonnagesrepresentthehighestdailytonnagesallocatedonthetransportlinks.
• Basedonthehighestgeneralizedcostsoftheflows,flowassignmentsdependontravelspeedsandtransporttariffs.
Considering the range of OD tonnage, this report captures and presents the uncertainties in
estimated flows. Figures 2.5A through 2.5D show the ranges of daily freight flow estimates alongnetwork links for the national road, railway, inland waterway, and maritime sectors. The results
illustrate the flows, ranked from lowest to highest, based on themaximumestimated flows alonglinks.Accordingly,thex-axis is labeledas“percentilerank,”wherethepercentilerankof80impliesthat80percentoflinkshaveflowslowerthanthatlink.
Each of these figures gives a sense of the uncertainties of flow estimates along links. Theseuncertaintiesaredrivenmainlybytwofactors:a)fluctuations inODestimates,whichdependuponthe seasonality of production and transport; and b) changes in route choice due to fluctuating
generalized transport costs, which depend upon speeds and transport tariffs. The relativecontributions of these factors toward flow uncertainties can be measured by comparing theminimum and maximum flow assignment routes for the same OD pairs. If the minimum and
maximum flow assignment routes are the same, then flow assignments are only sensitive to thevolumesofODtonnages.Iftheminimumandmaximumflowassignmentroutesdiffer,thentheflowassignmentsaresensitivetothegeneralizedcostsoftransports.
A.MaximumtonsofcropflowsinThanhHoa
B.MaximumnetrevenueofcropflowsinThanhHoa
Page|49
The analysis for the national-scale road network shows a 28 percent difference between theminimumandmaximumflowsforthesameODpairs,suggesting28percentoftheflowissensitiveto
the transport costs. In figure 2.5A, some links show significant variability between minimum andmaximumaverageannualdailyfreight(AADF)flows,resultinginsomenoticeablespikesintheplot.Analysis shows small differences in the railway, inland waterway, and maritime network flows,
signifying that the flow uncertainties in figures 2.5B through 2.5D are driven by changing ODtonnagesratherthanchangesinthetransportcostsassignedtothesenetworks.Thisisexplainedbythegreater variability in attributesof the roadnetwork, suchas roads types, terrains, speeds, and
tariffs,whichleadstogreatersensitiveoftrafficflows.
Figure2.5.RangesofAADFFlowsbyMode
Similar to the national-scale network, flow assignments on the province-scale networks are alsouncertain due to changing values of net revenues generatedwithin communes, and the changing
transportcostsonroadsusedtogainaccesstothenearestcommunecenters.Figures2.6Athrough2.6C show the ranges of rankednet revenue flowestimated alongnetwork links for the province-scaleroadnetworksofLaoCai,BinhDinh,andThanhHoa.Whencomparedtoothernetworklinks,
most road links in each province produce lower values of assigned net revenue flows, due to theconcentrationofeconomicactivitiesnearahandfulofcommuneswithlimitedaccesstoroadroutes.
Theanalysisshowssmalldifferencesbetweenthechosenroutesintheminimumandmaximumnetrevenueflowassignmentsbetweenvillages,cropproductionsites,andthecommunecenters.ForLaoCai,only1.6percentoftheminimumandmaximumflowassignmentroutesdiffer,witha2.6percent
differenceforBinhDinh,andadifferenceof3.3percentforThanhHoa.Thus,thechangingtransportcostshaveverylittleinfluenceonthetransportpatternsandtheresultinguncertaintiesinthevalues
ofnet revenue flowsalong links,duemainly tomostcommutesbetweenorigins (villagesandcropproduction sites) and destinations (communes) covering small distances along local networkswithfewalternativeroutes.
A. Range of AADF flows on road links B. Range of AADF flows on railway links
C. Range of AADF flows on inland waterway links D. Range of AADF flows on maritime links
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Figure2.6.RangesofNetRevenueFlowsAssignedtoRoadNetworksinLaoCai,BinhDinh,andThanhHoa
NaturalHazardExposureofTransportNetworks
Thestudyassembledfourmaintypesofnaturalhazarddatasets:
• Landslide susceptibility zonation maps include classification of the types, areas, and
spatialdistributionofexistingandfuturelandslides.Thesemapsshowtherelativespatiallikelihoodfortheoccurrenceoflandslidesbytypeandvolume—orarea(Felletal2008).
• Flashfloodsusceptibilityzonationmapssimilarlyshowtherelativespatiallikelihoodthataheavyraineventwillbecomestationaryandprotractedoveranarea(Hapuarachchietal2011).
• Typhoon induced floodmaps estimate the increases in storm surgeheights inducedbypressuregradientscreatedbytyphoonsalongcoastalareas,whichtranslatetoover-landinundations(Lapidezetal2015).
• Fluvialfloodmapsshowinundationcausedbyriversovertoppingtheirbanks.
Of the above hazards, only the fluvial flood map data offered a country wide spatial coverage,whereas all other types of hazard map data covered only certain parts of the country. The study
assumedallhazardmapswithoutany future climatechange scenarios representhazardevents forthepresentrisks intheyear2016.Fromtheunderlyingdatasets, thestudyselectedspatialextentsandareaswherehazardlevelsexceededthethresholdneededtobeconsideredcatastrophicenough
tocausetransportfailures.
A. Net revenue flows on links in Lao Cai B. Net revenue flows on links in Binh Dinh
C. Net revenue flows on links in Thanh Hoa
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The study accessed some of the natural hazardsmaps with future climate changemodel outputs
developed by MoNRE, based on the internationally recognized Representative ConcentrationPathways(RCP)4.5and8.5scenarios(Meinshausenetal2011).RCP4.5scenariosassumethatglobalemissionspeakaroundtheyear2040andthendecline,whileRCP8.5scenariosassumenodeclinein
globalemissionsthroughthe21stcentury.
In addition, the study looked at flashflood and landslide susceptibility maps for fifteen northernprovinces generated for RCP4.5 and RCP8.5 scenarios in 2025 and 2050. The study also accessed
fluvialfloodmapsforthewholecountryshowingcurrentfloodingandfuturefloodingunderRCP4.5and RCP8.5 scenarios in 2030, from the GLOFRIS (Winsemius et al 2013) model. See appendix B,includingtableB.2,foradetaileddescriptionofthehazarddatasets.
Per the IPCC definition (IPCC 2014), the study conducted a hazard exposure analysis as the firstcomponentofavulnerabilityassessment.Onlyareasofhighandveryhigh landslideandflashfloodsusceptibilityaswellasareasofflooddepthsgreaterthanorequalto1meterwereconsideredinthe
study’s extreme hazard scenarios. The analysis aimed to identify the types of hazards to whichtransportassetsaremostexposedtoandthechangesinthehazardexposuresduetovariousclimatechangescenarios.
National-scaleroadandrailwayshazardexposures
Table2.6showstheoverallminimumandmaximumtotalkilometersofthenational-scaleroadandrailwaynetworks inVietnamthatareexposed todifferentextremehazard levels.Foreveryhazardwhere future climate change scenarios are considered, the lengths of road and railway networks
exposedtoextremehazardswouldincreaseduetoclimatechange.Forinstance,whileapproximately720kmto1,163kmofthenationalroadnetworkisexposedtoextremeriverfloodinginthecurrent
flooding scenarios, thenetwork lengthswould increase tobetween786kmand1,180kmunderafutureRCP4.5scenario.Inthecaseofriverflooding,theminimumexposurevaluescorrespondtothelowest return periods (two or five years), while themaximum exposure values correspond to the
1,000-yearreturnperiodfloodmaps.4Whiletheroadnetworksaremostexposedtoriverflooding,railwaysaremostexposedtolandslides,primarilyinthecoastalprovinces.
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Table2.6.LengthsofNationalRoadsandRailwayNetworksExposedtoDifferentTypesofHazards
Note:a.Theflashfloodsusceptibilitydatacoveronly15northernprovinces.b.Thelandslidesusceptibilitydataforcurrentscenarioscover8centraland15northernprovinces.Thefutureclimatescenarioscoveronly15northernprovinces.c.Thetyphoonfloodingdatacoveronly28coastalprovinces.
Nationalroadsexposures(kilometers)
Nationalrailexposures(kilometers)
HazardtypeClimatescenario
Year
Min. Max. Min. Max.
— 2016 188 188 3 3
2025 197 197 3 3RCP4.5
2050 222 222 3 3
2025 211 211 3 3
Flashfloodsusceptibilitya
RCP8.5
2050 235 235 3 3
— 2016 720 1,163 88 117
RCP4.5 2030 786 1,180 97 121Riverflooding
RCP8.5 2030 785 1,174 97 119
— 2016 896 896 189 189
2025 276 276 7 7RCP4.5
2050 320 320 9 9
2025 289 289 7 7
Landslidesusceptibilityb
RCP8.5
2050 334 334 10 10
Typhoonfloodingc — 2016 783 783 123 123
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By examining the spatial patterns of the hazard exposures presented in figure 2.7 and figure 2.8,additionalinsightscanbederivedbyexaminingthespatialpatternsofthehazardexposuresforthe
national-scale road and railway networks respectively.5 Each of the figures shows (at the district-level) thepercentagenetwork kilometers exposed to theworst-casesof current (2016) levels. Thespatialpatternsofextremehazardexposuresrevealthedistrictswithveryhighpercentages(greater
than40percent)of networksexposed toextremehazards, especially for landslides, river flooding,and typhoon flooding. Based on the above findings, the study analysis indicates districts withelevatedpercentagesofexposurefacehighpotentialfortransportfailures.
The study next considered the effects shown in climate change scenarios, examining the spatialchangestonational-scaleroadandrailwaynetworkscausedbyexposurestoextremeriverflooding.
Figure2.9 shows the change inpercentage kilometerswith road links exposures to the1,000-yearreturnperiodriverflooding.PanelAshowstheeffectsin2030undertheRCP4.5emissionscenarioand panel B illustrates the RCP 8.5 emission scenario. These changes compare to the figure 2.7b
result. Though theworst-case scenarios of river flooding in general showa very slight decrease inrisk,theresultssharedinfigure2.9and2.10providean importanttakeaway:climatechangecouldcausesomeregionstofaceasubstantialincreaseinnetworkflooding.Inconsiderationofadvanced
planningforclimatechange,suchlocationsshouldbeexaminedfurtherforpotentialfailureimpacts.
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Figure2.7.HazardExposureofNational-ScaleRoadsNetworkinVietnam
Note:PanelA=landslides,wherelandslidesusceptibilityishighandveryhigh;panelB=riverflooding,wherefloodingdepthexceeds1meterfora1,000-yearevent;panelC=typhoonflooding,wherefloodingdepthexceeds1meter;andpanelD=flashfloods,whereflashfloodsusceptibilityishighandveryhigh.
A. Extreme landslide susceptibility exposures B. Extreme 1,000-year river flooding exposures
C. Extreme typhoon flooding exposure D. Extreme flashflood susceptibility exposure
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Figure2.8.HazardExposureofNational-ScaleRailwayNetworkinVietnam
A. Extreme landslide susceptibility exposure
B. Extreme 1,00-year river flooding exposure
C. Extreme typhoon flooding exposure D. Extreme flashflood susceptibility exposure
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Figure2.9.PercentageChangeinExposureofNational-ScaleRoadNetworkto1,000-YearRiverFloodingby2030
A.Percentagechangeforfluvialflooding(RCP4.52030)
B.Percentagechangeforfluvialflooding(RCP8.52030)
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Figure2.10.PercentageChangeinExposureofNational-ScaleRailNetworkto1,000-YearRiverFloodingby2030
Province-scalehazardexposureofroads
Thestudyconductedsimilaranalysisfortheprovince-scaleroads.Table2.7showstheminimumand
maximumkilometersoftheprovince-scaleroadnetworksexposedtovariousextremehazardlevels,asspecifiedbytheunderlyinghazarddata.TheresultsshowthatLaoCaiandBinhDinhexperiencethehighestexposures toextreme landslides,while inThanhHoa, river flooding causes thehighest
exposures. Generally, the current and future climate change scenarios indicate minor differencesbetweentheworst-casehazardexposures.
A.Percentagechangeforfluvialflooding(RCP4.52030)
B.Percentagechangeforfluvialflooding(RCP8.52030)
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Table2.7.ExposureofProvinceRoadsNetworkstoHazards
Geospatial analyses highlight the locations where much higher percentages of road networkkilometers are exposed to extreme hazards. Figure 2.11 shows the commune-level percentages of
roads exposed to hazards in Lao Cai, where the spatial landslides are more prevalent morecommunesarepronetoflashfloodsandriverflooding.Asshowninfigure2.13,exposuresofroadstolandslidesare farmorepronounced throughoutBinhDinhandsimilarly inThanhHoa,as shown in
figure2.15. From thesemaps, the study identified thecommunes facing increasedmultiplehazardthreatsduetothehighpercentagesofroadsexposedoneormorehazards(seeappendixD).
Thestudyalsolookedatclimatechange-inducedexposurescenariosforhazardsaffectingprovincial
road networks. In Lao Cai, figure 2.12 shows changes in exposures to extreme landslides in 2050underRCP4.5andRCP8.5scenarios,whichresults inagreaternumberofcommunesexperiencingincreased percentages of roads exposed to extreme landslides, with amore pronounced increase
under the RCP 8.5 climate scenario. In figure 2.14, similar comparisons are seen in Binh Dinh forextreme1,000-year river floodingunder climate scenarios,with an increasedexposureof roads toextremeriverflooding.TheresultsforThanhHoa,showninfigure2.16,highlightsubstantialclusters
of communes where climate change causes a potentially significant (greater than 40 percent)increaseofroadsexposedtoextreme1,000-yearriverflooding(seeappendixD).
Roadhazardexposures(km.)
LaoCai BinhDinh ThanhHoaHazardtypeClimate
scenarioYear
Min. Max. Min. Max. Min. Max.
— 2016 88 88
2025 88 88 RCP4.5
2050 93 93
2025 90 90
Flashflood
susceptibility
RCP8.52050 132 132
— 2016 57 57 534 539 1,803 1,841
RCP4.5 2030 57 57 535 543 1,875 1,910Riverflooding
RCP8.5 2030 55 57 534 539 1,864 1,889
— 2016 142 142 801 801 921 921
2025 145 145 RCP4.5
2050 180 180
2025 163 163
Landslidesusceptibility
RCP8.52050 210 210
Typhoonflooding — 2016 315 315 592 592
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Figure2.11.HazardExposureofProvinceRoadsNetworkinLaoCai
A. Extreme landslide susceptibility exposures B. Extreme flashflood susceptibility exposures
C. Extreme river flooding
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Figure2.12.PercentageChangeinExposureofLaoCaiRoadNetworkLinkstoLandslidesby2050
Figure2.13.HazardExposureofProvinceRoadsNetworkinBinhDinh
A.RCP4.52050 B.RCP8.52050
A.Extremelandslidesusceptibilityexposures
B.Extremeriverfloodingexposures
C.Extremetyphoonfloodingexposures
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Figure2.14.PercentageChangeinExposureofBinhDinhRoadNetworkLinksto1,000-yearRiverFloodingby2030
A.RCP4.52030 B.RCP8.52030
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Figure2.15.HazardExposureofProvinceRoadsNetworkinThanhHoa
A.Extremelandslidesusceptibilityexposures B.Extremeflashfloodsusceptibilityexposures
C.Extremetyphoonfloodingexposures
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Figure2.16.PercentageChangeinExposureofThanhHoaRoadNetworkLinksto1,000-YearRiverFloodingby2030
Notes1.Dataaccessedat:http://cangben.viwa.gov.vn.
2.GSOtransportstatistics,availableat:https://www.gso.gov.vn/default_en.aspx?tabid=781.
3.GSOtransportstatistics,availableat:https://www.gso.gov.vn/default_en.aspx?tabid=778.
4.Novariationbetweentheminimumandmaximumvaluesresultsfromtheunderlyinghazarddata
havingonerealization.
5.Themapsinfigure3.9andfigure3.10provideasenseofthespatialextentsanddisaggregationof
the underlying hazard data, where flashflood exposures are clearly confined to northern areasbecausetheunderlyinghazarddataonlyexistedforthoseregions.Similarly,landslidesexposuresarelimited by the spatial coverages of the underlying data. As stated previously, only river flooding
datasetsofferedwhole-nationcoverage.
ReferencesIPCC(IntergovernmentalPanelonClimateChange).2014:ClimateChange2014:Impacts,Adaptation,
andVulnerability.PartB:RegionalAspects.WorkingGroupIIContributiontotheFifthAssessmentReportoftheIntergovernmentalPanelonClimateChange.EditedbyV.R.Barros,C.B.Field,D.J.Dokken,M.D.Mastrandrea,K.J.Mach,T.E.Bilir,M.Chatterjee,K.L.Ebi,Y.O.
Estrada,R.C.Genova,B.Girma,E.S.Kissel,A.N.Levy,S.MacCracken,P.R.Mastrandrea,andL.L.White.LondonandNewYork:CambridgeUniversityPress.https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-PartB_FINAL.pdf.
A.RCP4.52030 B.RCP8.52030
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Fell,Robert,JordiCorominas,ChristopheBonnard,LeonardoCascini,EricLeroi,andWilliamZ.Savage.2008.“GuidelinesforLandslideSusceptibility,HazardandRiskZoningforLandUse
Planning.”EngineeringGeology102(3):85–98.doi:10.1016/j.enggeo.2008.03.014.
Hapuarachchi,H.A.P.,Q.J.Wang,andT.C.Pagano.2011.“AReviewofAdvancesinFlashFlood
Forecasting.”HydrologicalProcesses25(18):2771–84.doi:10.1002/hyp.8040.
JICA(JapanInternationalCooperationAgency),MoT(MinistryofTransport,SocialistRepublicof
Vietnam),andTDSI(TransportDevelopmentandStrategyInstitute,Vietnam).2000.TheStudyontheNationalTransportDevelopmentStrategyIntheSocialistRepublicOfVietnam(VITRANSS)—TechnicalReportNo.3:TransportCostAndPricingInVietnam.Tokyo,Japan:JICA,ALMEC
Corporation,andPacificConsultantsInternational.http://open_jicareport.jica.go.jp/pdf/11596814.pdf.
Koo,J.,Z.Guo,U.Wood-Sichra,andY.Ru.2018(unpublished).SpatialDisaggregationof2015Sub-NationalCropProductionStatisticsinVietnam.InternaldataprovidedbyIFPRIforthestudy.Washington,DC:InternationalFoodPolicyResearchInstitute.
Lapidez,J.P.,J.Tablazon,L.Dasallas,L.A.Gonzalo,K.M.Cabacaba,M.M.A.Ramos,J.K.Suarez,J.Santiago,A.M.F.Lagmay,andV.Malano.2015.“IdentificationofStormSurgeVulnerableAreas
inthePhilippinesthroughtheSimulationofTyphoonHaiyan-inducedStormSurgeLevelsoverHistoricalStormTracks.NatHazardsEarthSystSci3(2):1473–81.doi:10.5194/nhess-15-1473-2015.
Meinshausen,M.,S.J.Smith,K.Calvin,J.S.Daniel,M.L.T.Kainuma,J-F.Lamarque,K.Matsumoto,S.A.Montzka,S.C.B.Raper,K.Riahi,A.Thomson,G.J.M.Velders,D.P.P.vanVuuren.2011.“The
RCPGreenhouseGasConcentrationsandtheirExtensionsfrom1765to2300.ClimaticChange109(1-2):213–41.doi:10.1007/s10584-011-0156-z.
JICA(JapanInternationalCooperationAgency)andMoT(MinistryofTransport,Vietnam).2010.TheComprehensiveStudyontheSustainableDevelopmentofTransportSysteminVietnam(VITRANSS
II).Tokyo:JapanInternationalCooperationAgency(JICA)andALMECCorporation.http://open_jicareport.jica.go.jp/700/700/700_123_11999943.html.
MoT(MinistryofTransport,Vietnam).2015.SeaportClassification.Hanoi:VINAMARINE(VietnamMaritimeAdministration),MinistryofTransport,Vietnam.http://www.vinamarine.gov.vn/Index.aspx?page=reportshipdetail&id=13.
Systra. 2018 (unpublished). Strengthening of the Railway Sector in Vietnam. Report 3.2: RailwayDemandAnalysis—Freight.ReportprovidedbySystraforthestudy.
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Chapter3:Vulnerability,Criticality,andRiskAssessment
This chapter brings the results of the analyses presented in Chapter 2, to analyze vulnerability,criticalityandriskassessmentofflowdisruptionsontheassembledtransportnetworksforVietnam.
Here,theanalysisconsidersanexhaustivesetofexposurescenariosforfailure,assessingthesocio-economic impacts through a variety ofmetrics to identify the network locationswith the greatestimpacts. For each network, the analysis quantifies the ranges of disruptive impacts across each
hazardtounderstandwhetherhazardimpactswillincreaseinfutureclimatescenarios.
VulnerabilityandCriticalityAssessment
After identifying the network links exposed to different extreme levels of hazards, a vulnerability
assessment can be conducted. Vulnerability assessments, completed within the context of thehazards, result in understanding the relative impacts of hazards on the continued transportavailability.Oncethevulnerabilityassessmentisdone,acriticalityassessmentcanprovideresultsin
ranking network elements, based on their relative impacts on the serviceability of the transportnetworks(ArgaJafino2017).
The criticality analysis performs an exhaustive analysis of the individual network links’ “what if
failure”scenarios.Havingbeenexposedtotheextremelevelsofhazards,theselinksexistinpotentialfailurelocations.Thecriticalityanalysisservesasusefulasafirststepinexploringandevaluatingthesystemicperformanceinageneralizedcontext, impartialtothenatureoftheexternalshockevent.
Theassessmentcanidentifythemostcritical links inthenetworks,whichcanthenbeusedtohelpselectasmallersetoflinksforfurtherhazardriskanalysis.Suchexploratoryanalysiscanbeusedtoexaminewhetherthemostcriticallinksidentifiedhavealsobeenexposedtohazards,thusproviding
astrongcasefordetailedsite-specificanalysis.
Thecriticalityassessmentaimstoanswerthefollowingquestions:
• Whichlinksandroutesalongthenetworksaremostimportanttothecontinuedserviceofnetworkflows?
• Whichindividuallinksfailuresresultinmacroeconomicdisruptiveimpacts,andwhatarethemagnitudesofthosedisruptiveimpacts?
• Whichindividuallinksfailureshavemostimpactsonthecostsofreroutingflows?
Inresponse,thestudycreatedandestimatedthefollowingcriticalitymetrics:
• Average annual daily freight (AADF) disruptions: The potential daily tonnage of freightflow disrupted by the failure of individual network links exposed to any hazardconsideredinthestudy.
• Totalmacroeconomic loss: The sumof thedirectand indirectmacroeconomic losses inUS$ per day, due to the losses of commodity flows that create economic supply and
demandimbalancesintheeconomy.Suchlossesarisefromindividuallinkswhosefailurecausestripisolations,wheretheonlyavailablerouteoptionalongtheorigin-destination(OD)routebecomesphysicallyinaccessiblethroughthefailededge.
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• Freight redistributioncostmetric:The totaldifferencebetween thepost-disruptionandpre-disruption generalized cost estimates of allOD flows rerouteddue to edge failure.
The freight redistribution costs are assigned to the edge whose failure causes thoseredistributions.Thesevaluesshowthemostcost-effectivelinksinthenetwork.
• Total economic impact metric: The overall economic criticality of network links ismeasuredasthesumoftheirmacroeconomiclossesandthefreightredistributioncostsincurredduetofailures.
Forthevulnerabilityandcriticalityassessments,theanalysisselectedindividualnetworklinksbased
onthespatialintersectionofnetworksandthehazardslistedintable3.1.Theselectionwaslimited
tothelandslideandflashfloodsusceptibilityareaswithhighandveryhighlikelihoodvaluesaswellasriverandtyphoonfloodingareaswithflooddepths≥1meter.Eachselectedtransportnetwork’slinkswasintersectedwithallselectedhazardstoidentifytheexhaustivesetofalluniquenodesandlinks
subjected to a hazard. The subsequent sections present and discuss only the following networkresults:
Table3.1.SelectedNumbersofSingleLinkFailureScenariosforDifferentTransportNetworks
Transportnetworkselected Uniquesinglelinkfailurescenarios
National-scaleroads 968
National-scalerailways 164
LaoCaiprovinceroads 1,299
BinhDinhprovinceroads 11,042
ThanhHoaprovinceroads 26,852
National-ScaleRoadNetworkCriticality
Figure 3.1 shows the national-scale road network criticality results, including themaximumAADFdisruption values in figure 3.1A. The result highlights the network locations with very high flows,where there are threats of systemic disruptions due to exposures to extreme hazards. The results
showaclusterofroadsaroundHoChiMinhandlargesectionsoftheQL1Atrans-Vietnamhighwaywith high AADF flows that could be disrupted by hazards. The highest ranges of potential AADFdisruptionssubstantial,between124,000to156,000tonsperday.
Figure 3.1B shows themaximum totalmacroeconomic losses inUS$millionper day, basedon theidentified cases, where some of the AADF disruptions reported in figure 3.1A result in immediatemacroeconomic losses. These trip isolations, where the commodities comprising the AADF mix
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cannot be transported, create supply and demand imbalances in themacroeconomic system. Theprocess of estimating thesemacroeconomic losses is explained in appendixA.This process can be
treatedasaworst-caseassumption;realisticallyadisruptionlastingadaywillprobablynotproducemacroeconomic losses. Therefore, the results can be interpreted to highlight the macroeconomicimportance of network links. Quite significantly, the results show that disruption of themajor link
towardHoChiMinh(DT743)couldpotentiallycreateanestimatedUS$0.074toUS$0.44millionperday in macroeconomic losses. If this link is disrupted for a significant time, such losses couldrealistically materialize. The macroeconomic loss results also, importantly, highlight that large
disruptions in tonnagemight not necessarily result in significant economic losses in every case. Insomecases,economicgainshaveresultedfromregionssubstitutingforproductioncapacitylossesinotherregions,leadingtoreducedmacroeconomicimpactsandevennetmacroeconomicgainspost-
disruption.
WherethedisruptedAADFflowscanbereroutedalongalternativeroutes,freightredistributioncostincreases along disrupted links create significant failure impacts on the national road network.
However, this comes at the price of increased transportation costs (figure 3.1c), where the valuesshowthesystemicfreightredistributioncostsonthewholenetworkreportedattheindividualfailedlinks. The highest rerouting costs can range between US$0.67 million per day (minimum flow
disruptionscenario) toUS$1.90millionperday (maximumflowdisruptionscenario).Mostof thesearethroughoutsectionsoftheQL1Atrans-Vietnamhighway,averysignificantrouteinthecountry.Inparticular, thesectionsof thishighwaythroughNgheAhtoThanhHoashowhighest impactson
reroutingcosts.
Theeconomicimpactsofroaddisruptionsareshowninfigure3.1D,whichcombinestheresultsfrom
figures3.1Band3.1C.Asdiscussed, the significant economic impacts are createdby redistributioncost increases, ratherthanmacroeconomic losses fromtrip isolations—witha fewexceptions,suchastheDT743link.
Basedon themodel results, it couldbeworthconsideringwhether thehighcostsof reroutingwillresultincompanies’non-useoftheroadswhilewaitingfordisruptedroadstobefixed,orpartialuseofthereroutingoptionsduringroutereconstruction.Thisbehaviorhasnotbeenexploredhere,but
thisanalysisprovidesameanstoconsidersuchpossibilities.
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Figure3.1.National-ScaleRoadsCriticalityResults
A.Maximumdailytonsdisrupted B.Maximummacroeconomiclosses
C.Maximumreroutingloss D.Maximumtotaleconomicloss
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National-ScaleRailwayCriticality
Figure3.2showstheresultsfortherailwaynetwork.Giventhesingleroutestructurealongmostof
the railwaynetwork, link failures impact significant routeswith veryhigh flows, resulting inworst-caseAADFdisruptionsbetween8,000to10,000tonsperday(figure3.2A).
FollowingthecasesofAADFdisruptions,figure3.2Bshowsmaximumtotalmacroeconomiclossesin
US$million per day, based on the assumption that the AADF losses reported result in immediateeconomicimpacts.Quitesignificantly,theresultsshowthatrailwaydisruptionscanpotentiallyresultinveryhigheconomiclosses,witheconomiclossesonthebusiestroutesrangingfromUS$2.3to2.6
million per day. These results show that though the overall railway network usage is much lesscompared to roads, use is significant in the regions vulnerable to disruptions. The high impactscausedbyrailwaydistributionscouldberesponsiblefordecliningrailwayusageinVietnam
Figure3.2Chighlightsthefreightredistributioncosts,withafewlinkscausingonlyminorimpactsonredistribution cost increases along the railway network; the highest rerouting costs, at mostUS$9,000perday,occuraroundtheHanoihub.
Theeconomic impactsof railwaydisruption (figure3.2D)arecreatedby themacroeconomic lossesresulting from trip isolation redistribution cost increases, rather than redistribution cost increasesalone.Basedonthemodelresults,therailwaynetworkshowsverylittleredundancyincopingwith
disruptions.
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Figure3.2.National-ScaleRailwayCriticalityResults
A.Maximumdailytonsdisrupted B.Maximummacroeconomiclosses
C.Maximumreroutingloss D.Maximumtotaleconomicloss
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Province-ScaleRoadCriticality
Attheprovincescale,thestudyestimatesroadnetworkcriticalitiesintermsof:
• Net revenue flow disruptions, which potentially arise from hazard induced disruptionsalongaccessibleroutestowardthecommunecenters.
• Economic impacts estimates,which represent the sumof theeconomic losses (thenetrevenue disrupted) due to the lack of accessible routes toward the commune centers,andtheincreasesinreroutingcosts,wherepost-disruptionaccesstocommunecentersismaintained.
Theresultsthatfollowhighlight,throughthenetrevenueflowdisruptionmaps,potentialdisruptionsto the network links most important in providing access to commune centers. Also, the maps
highlight any rerouting options around each disrupted network link. Taking such rerouting intoaccount, theresultsunderlinetheeconomic impactsofdisruptions.Crucially, theresultsshowthatthe existence of rerouting options decreases the impacts of flow disruptions. The economic loss
resultsprominently reveal thenetwork linksusedmostheavily fornet revenuegeneration,whosefailureswouldresultincompletelackofaccess.
To give a senseof theworst-case scenariosof failures, the sub-sections that follow showonly the
maximumnetrevenueflowdisruptionsandmaximumeconomicimpacts.
LaoCaiprovinceroadsdisruptions
Theheat-mapforLaoCaiprovince in figure3.3Ashowsthemaximumnetrevenueflowdisruptions
due to hazard-driven road failures. The spare concentration of roads in Lao—especially in theBaoYen,BatXat,andVanBandistricts—focuses flowdisruptionsalong fewerroutes.Potentially, thesenetrevenueflowdisruptionscancauseuptoUS$27,000perdayineconomiclosses.
Theeconomic impacts shown in figure3.3B reflecthow theexistenceof reroutingoptionsabsorbsmostofthepotentialdisruptionsinnetrevenueflows.However,somelinkfailurescausecompletelossofaccesstocommunecenters,resultinginworst-caseeconomiclossesofuptoUS$26,000perday.
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Figure3.3.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossforLaoCaiProvinceRoadNetwork
A.Maximumnetrevenuedisruption
inLaoCaiprovince
B.Maximumeconomicloss
inLaoCaiprovince
BinhDinhprovinceroadsdisruptions
In figure 3.4, heat-maps for Binh Dinh province show the maximum net revenue flow disruptions
(panelA)andmaximumeconomic impacts (panelB)causedbyhazard-drivenroadfailures.Mostofthe network is very redundant, leading to insignificant impacts due to disruptions. However, asignificant cluster in the Quy Nhon district, which has very high disruptive impacts, could see
potentialeconomicimpactsofUS$56,000perday.
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Figure3.4.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossfortheBinhDinhProvinceRoadNetwork
A.MaximumnetrevenuedisruptioninBinhDinhprovince
B.MaximumeconomiclossinBinhDinhprovince
ThanhHoaprovinceroadsdisruptions
In figure 3.5, heat-maps for ThanhHoa province show themaximumnet revenue flow disruptions(panelA)andmaximumeconomicimpacts(panelB)resultingfromhazard-drivenroadfailures.Hereagain,most of the network is very redundant, leading to insignificant disruption-induced impacts.
However, significant clusters in the Bim Son districtwith the highest disruptive impacts could seepotentialeconomicimpactsofUS$67,000perday.
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Figure3.5.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossfortheThanhHoaProvinceRoadNetwork
A.MaximumnetrevenuedisruptioninThanhHoaprovince
B.MaximumeconomiclossinThanhHoaProvince
RiskAssessment
Following the assessment of network link criticalities, the risk of extreme hazard failures can beestimatedbycombiningtheknowledgeofthehazardsandimpactsintoonemetric.Themainaimoftheriskestimationistoidentify:
• Whichtypesofhazardshavethemostimpactsontransportlinks?
• Whatarethechangesinthehazardimpactsduetodifferentclimatechangescenarios?
The losses here signify the daily economic impacts, estimated in the criticality assessment, and
multipliedbycertaindurationofdisruption—duringwhichtheaffectednetworklinkisassumedtobeoutofoperation.Todifferentiatebetween the severitiesofeventswithdifferentprobabilities, the
analysisassumedthatthedurationofdisruptionwilldependuponwhatpercentageofthenetworklink’slengthisexposedtotheextremehazardforthatevent.
Limitation:The riskcalculation resultspresented in thesubsequentsectionsshowveryhighvalues
forthelandslideandtyphoonfloodingevents—andtosomeextenttheflashfloodevents—basedonthe assumption that these hazards have a probability equal to one. The analysis made theassumption in the absence of any probabilistic information for these hazards. Because only the
underlying river floodinghazards information isprobabilistic,only the results for the river floodingriskscapturethetruenatureofriskestimation.
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National-scalenetworks
Theresultsthatfollowforthenational-scaleroadandrailwaynetworkassumeamaximumduration
ofdisruptionof 10days,with a500m threshold lengthof exposureof anetwork link.Hence, if anetwork link length greater than 500mwas exposed to the chosen extreme hazard scenario, theresultsassumeda10-daydisruption.Belowthatlength,thedisruptiondurationdependeduponthe
fractionofnetworklengthexposedtothehazard.
National-scaleroadnetworkrisks
Figure3.6 shows themaximumvalueofeconomic riskson thenational-scale roadnetworkdue tocurrent 2016 hazards of, respectively, (A) landslide susceptibility, (B) river flooding, (C) typhoonflooding, and (D) flashflood susceptibility.The results aim to highlight the hazard-specific high-risk
locations on the network, which can be prioritized for further detailed investigations into climateresilience. Theanalysis shows that risksalongkey sectionsof theQL1A trans-Vietnamhighwayaremainly driven by landslides and typhoon flooding,while river flooding affects links around Ho Chi
Minh and Thua Then Hue, and flashfloods affect somemountain provinces due to the underlyinghazardsconcentratedonlyinthoseregions.
To understand the changes in the hazard impacts due to climate change scenarios, the analysiscompares road network risks due to future climate hazards with those due to the current dayhazards.Suchacomparisonispossibleonlyforriverfloodingbecauseitistheonlyhazardthatgives
fullnationalcoverageofdifferentprobabilistichazardsunderclimatechangescenarios.Bothclimatechangescenariosforriverfloodingseeasubstantialincreaseinthevalueofsystemicrisksoffuturefailure of national-scale road network links (figure 3.7). On almost all affected network links, the
potential risks of failure due to river flooding increase by at least 40 percent in the future 2030hazard scenarios, a substantial increase in risks that highlight a strong case to invest in buildingnetworksresilienttofutureclimatehazards.
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Figure3.6.MaximumEstimatedRisksforNational-ScaleRoadNetworkLinks
A.Landslidesusceptibilitymaximumrisks B.Riverfloodingmaximumrisks
C.Typhoonfloodingmaximumrisks D.Flashfloodsusceptibilitymaximumrisks
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Figure3.7.PercentageChangeinMaximumFailureRisksofNational-ScaleRoadNetworkLinksforFuture2030RiverFloodingunderClimateScenarios
A.RCP4.52030 B.RCP8.52030
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National-scalerailwaynetworkrisks
Similartotheresultsoftheprevioussection,figure3.8showsthemaximumvalueofeconomicriskson the national-scale rail network links due to current 2016 hazards of, respectively: (A) landslide
susceptibility, (B) river flooding, (C) typhoon flooding,and (D) flashfloodsusceptibility.Theanalysisshowsthat largesectionsoftherailwaysareatriskmainlydueto landslidesandtyphoonflooding,while river flooding affects a small section of links around Quang Nam and Thua Then Hue, and
flashfloodsaffectsomemountainprovincesduetotheunderlyinghazardsconcentratedonlyinthoseregions.
Fortherailwaynetworkaswell,therisksduetofutureclimatechangescenario-drivenriverflooding
hazardsin2030comparewiththoseduetothecurrentdayriverfloodinghazardsin2016(figure3.9).Underbothclimate change scenariosof river flooding, systemic risksofnational-scale railnetworkfailureincreasessubstantiallyinthefuture.Similartothenational-scaleroadnetworklinks,atalmost
allaffectednetworklinksthepotentialrisksoffailureduetoriverfloodingfaceasubstantialincreaseofat least40percent in the future2030hazardscenarios,highlightingastrongcase inVietnamtoinvestinbuildingnationalrailwaysresilienttofutureclimatehazards.
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Figure3.8.MaximumEstimatedRisksforNational-ScaleRailwayNetwork
A.Landslidesusceptibilitymaximumrisks B.Riverfloodingmaximumrisks
C.Typhoonfloodingmaximumrisks D.Flashfloodssusceptibilitymaximumrisks
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Figure3.9.PercentageChangeinMaximumFailureRisksofNational-ScaleRailNetworkforFuture2030RiverFloodingunderClimateScenarios
A.RCP4.52030 B.RCP8.52030
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Province-scaleanalysis
Theresultsfortheprovince-scaleroadnetworksassumeamaximumdisruptiondurationof10days,witha100mexposurethresholdlengthofanetworklink.Hence,ifanetworklinkgreaterthan100mwas exposed to the chosen extreme hazard scenario, the results assumed a 10-day disruption.
Belowthat length,thedurationofdisruptiondependeduponthefractionofthe lengthexposedtothehazard.
LaoCaiprovince-scaleroadsrisks
TheanalysishighlightskeysectionsofthenetworklinksintheVanBanandSaPadistrictsaffectedbylandslides,flashfloods,andriverflooding(figure3.10),whosefailuresresultsinhighrisksduetolack
ofaccesstotheirnearestcommunecenters.
Figure3.10.MaximumRisksofLaoCaiProvince-ScaleRoadNetwork
A.Landslidesusceptibilitymaximumrisks B.Flashfloodsusceptibilitymaximumrisks
C.Riverfloodingmaximumrisks
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TheanalysisshowsthatalongseveralroadsintheBatXat,LaoCai,VanBan,andBaoYendistrictsthefuture landslide-driven failure risks of large continuous sections of the road network increase
substantiallybyatleast40percent,withmoreseverechangesunderRCP8.5scenarios(figure3.11).Hence,strongly indicatingthat futureaccess toeconomicopportunities in thispartof theprovincewillbeseverelyaffectedwithoutinvestmentinbuildingclimate-resilientroads.
Figure3.11.PercentageChangeinMaximumFailureRisksofLaoCaiProvince-ScaleRoadNetworkLinksforFuture2050LandslideSusceptibilityunderClimateScenarios
A.RCP4.52050 B.RCP8.52050
BinhDinhprovince-scaleroadsrisks
TheanalysishighlightskeysectionsoftheroadnetworklinksintheTuyPhuocandQuyNhondistrictsaffectedbylandslides,typhoonflooding,andriverflooding(figure3.12),whosefailuresresultinhighrisksduetolargeareasofeconomicactivityexperiencingalackofaccesstotheirnearestcommune
centers.
ThechangingpercentagesofmaximumrisksduetoclimatechangeintheBinhDinhprovinceclearlyindicateincreasingrisksduetoroadlinkfailuresfromfutureclimatechangedrivenhazardscenarios,
withexpectedannuallossesinalmosteverycasesubstantiallyincreasedbyatleast40percent(figure3.13).Again,thereisastrongindicationthatinthefuture,accesstoeconomicopportunitieswillbeseverelyaffectedwithoutinvestmentinbuildingclimate-resilientroads.
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Figure3.12.MaximumRisksofBinhDinhProvince-ScaleRoadNetwork
A.Typhoonfloodingmaximumrisks B.Landslidesusceptibilitymaximumrisks
C.Riverfloodingmaximumrisks
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Figure3.13.PercentageChangeinFailureRisksofBinhDinhProvince-ScaleRoadNetworkforFuture2030RiverFloodingunderClimateScenarios
A.RCP4.52030 B.RCP8.52030
ThanhHoaprovince-scaleroadsrisks
TheanalysishighlightskeysectionsofnetworklinksinBimSondistrictaffectedbylandslides,whose
failuresresults inhighrisksdueto largeareasofeconomicactivityexperiencinga lackofaccesstotheirnearestcommunecenters(figure3.14).
As shown in figure3.15, thechangingmaximumrisksdue toclimatechange in theThanhHoa isa
clearindicationofincreasingrisksduetoroadlinkfailuresfromfutureclimatechange-drivenhazardscenarios, with expected annual losses in almost every case substantially increased by at least 40percent. Again, there is a strong indication that access to economic opportunitiesmay be severely
affectedwithoutinvestmentinclimateresilience.
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Figure3.14.MaximumEstimatedRisksforThanhHoaProvince-ScaleRoadNetwork
A.Typhoonfloodingmaximumrisks B.Landslidesusceptibilitymaximumrisks
C.Riverfloodingmaximumrisks
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Figure3.15.PercentageChangeinFailureRisksofThanhHoaProvince-ScaleRoadNetworkforFuture2030RiverFloodingunderClimateScenarios
A.RCP4.52030 B.RCP8.52030
UncertaintiesinCriticalitiesandRisks
Uncertaintiesareanaturalpartofestimatingthecriticalitiesandrisksof failuresoftransport links.
The estimated network link criticality uncertainties are sensitive to the modeled ranges of flowassignmentsandreroutingonthenetworksaswellastotheparametersinthemacroeconomiclossmodel.
The result shown in figure3.16Ahighlights thehighvariability in theestimated road losses,wherethelargestlossesvarybetweenUS$0.67andUS$1.9millionperday.Theselosses,mainlydrivenbythe increases intransportcosts fromreroutingofnetworkflows, follow individual link failures.The
resulting increased transport costs are primarily influenced by the volumes of rerouted freight.Hence,theuncertaintiesinestimatedlossesaremainlysensitivetothevolumesofdisruptedflows.
As discussed in the section “Rail Failure Analysis Results” in Chapter 5, the economic losses for
railways aremainly driven bymacroeconomic losses. Themacroeconomic loss estimates take intoaccount theabilityof themultiregionaleconomy to substitute for lost freight ina region,which inturndependsonthepercentageof regionaleconomicproductiondependentuponthe lost freight.
Substitutionsforahighfailurepercentagewillnotbesufficienttooffsetmacroeconomiclossesdueto freight disruptions; however, a low failure percentage allows a multiregional economy tosubstitute and offset macroeconomic losses. The large fluctuations in economic losses shown in
figure3.16Baremainlysensitivetothesesubstitutioneffects inthemacroeconomicmodel.Hence,losses for several links range between very small values (~0)—when the volumes of disruptedtonnagesdonotaffecttheregionaleconomies—andveryhighvalues(inexcessofUS$2millionper
day),becausethevolumesofdisruptedtonnagessubstantiallydisrupttheregionaleconomies’abilitytosubstitute.
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Figure3.16.EstimatedRangesofDailyEconomicLossesforIndividualNetworkLinkFailuresinVietnam’sNational-ScaleRoadandRailNetworks
A.Roads:
Rangeofeconomiclossesduetolinkfailures
B.Railways:
Rangeofeconomiclossesduetolinkfailures
The estimated risks are sensitive to the type of hazards, in addition to factors influencing thecriticalities. Thus, comparing ranges of hazard-specific risks across different scenarios of the same
hazardtypeshowshowthehazarduncertaintiesinfluencenetworkrisks.Suchacomparisonismadefor the case of national-scale road and railway network risks due to river flooding in current andfutureconditionsundertheRCP4.5andRCP8.5scenarios.
Figures3.17Aand3.17Bshowtherangesofcurrent(2016)andfuture(2030)climatechange-drivenriverfloodingrisks,calculatedastheExpectedAnnualEconomicLosses(EAEL)inUS$millionsfor10-
daymaximumdisruptionofindividualnetworklinks, inthenational-scaleroadandrailwaynetworkrespectively. The results show the uncertainties of failure risks due to climate change significantlyincrease under the RCP4.5 and RCP8.5 scenarios. Also, both climate scenarios show significant
increases in the maximum estimates of risks, due to increasing severity and frequency of futureextremeriverflooding.AnalysisfortheroadnetworkshowsthehighestEAELincreasesfromUS$0.44toUS$0.88million,anincreaseof100percent.Fortherailwaynetwork,thehighestEAELincreases
fromUS$1.4toUS$2.8million,asignificantincreaseof100percentinhighestlosses.
Figures 3.18 to 3.20 present the uncertainties in estimating criticalities and risks for the threeprovince-scalenetworks(LaoCai,BinhDinh,andThanhHoa).Theplotontheleftpanel(panelA)of
each figure shows the rangeofdailyeconomic impacts forall impacted links, rankedbymaximumdaily impacts.Theplotontherightpanel (panelB)showstheEAELforall linksaffectedbycurrentandfutureextremeriverflooding.
Eachcriticalityresultindicatesthedominanceofarelativelysmallpercentileoflinksineachprovince.HighlossesinLaoCaiareconcentratedinapproximatelythetop20percentoflinks,inBinhDinhtheyareconcentratedinapproximatelythetop10percentoflinks,andinThanhHoainapproximatelythe
top5percentoflinks.Thehighrangesoflossesoccurmainlyforthoselinkswhosefailuresresultincompletelackofaccesstocommunecenters.Themagnitudesofsuchlossesaremainlysensitivetothe changing net revenues assigned to these links, which in turn are sensitive to the changing
tonnagesofcrop(rice)productionsassumedintheflowestimationmodel.Theanalysisinfigure3.18showsthatforLaoCai,thehighestdailyeconomicimpactsrangefromUS$23,000toUS$26,000perday,forBinhDinh(figure3.19)thehighestdailylossesrangefromUS$47,000toUS$56,000perday,
andforThanhHoa(figure3.20),thehighestdailylossesrangefromUS$64,000toUS$67,000perday.
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Figure3.17.EstimatedRangesofMinimumandMaximumRisksforIndividualLinkFailuresDuetoCurrentandFutureRiverFloodingonNational-ScaleNetworks
A.Roads:Riverfloodingrisks
B.Railways:Riverfloodingrisks
However,theserangesofhighestlossesmightnotnecessarilyberecordedforthesamelinks,astheunderlyingdistributionsofnetrevenueallocatedtolinksvarywidely.
Theriskresults, inthecontextofriverflooding,showsignificantly increaseduncertaintiesoffailure
risksdue toclimatechange,underbothRCP4.5andRCP8.5 scenarios. Similar to thenational-scaleanalysis, both climate scenarios show significant increases in themaximum risk estimates, due toincreasing severity and frequency of extreme river flooding in the future. In both future climate
scenarios for LaoCai roadnetwork (figure3.18), the linkwith thehighestmaximumEAEL (aroundUS$20,000) showed a maximum EAEL of around US$4,000 in the current scenario—reflecting anincreaseofmorethan400percent.AnalysisfortheBinhDinhroadnetwork(figure3.19)showsthe
linkwiththehighestmaximumEAELofaroundUS$4,500inthefutureRCP4.5climatescenariohadamaximumEAEL inthecurrentscenario,approximatelyUS$450, indicatingan increaseofmorethan900percent. In the ThanhHoa roadnetwork, analysis shows the linkwith highestmaximumEAEL
(approximately US$11,900) in the future RCP 8.5 climate scenario had amaximum EAEL of aboutUS$300 in the current scenario, an increaseofmore than3,800percent.All threeprovinces showsubstantialincreasesinrisksduetoclimatechange-drivenfloodingforindividuallinks.
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Figure3.18.EstimatedRangesofDailyEconomicLossesandRisksDuetoCurrentandFutureRiverFloodingofIndividualLinkFailuresontheLaoCaiRoadNetwork
A.DailyeconomiclossesinLaoCai
B.RiverfloodingrisksinLaoCai
Figure3.19.EstimatedRangesofDailyEconomicLossesandRisksDuetoCurrentandFutureRiverFloodingofIndividualLinkFailuresontheBinhDinhRoadNetwork
A.DailyeconomiclossesinBinhDinh
B.RiverfloodingrisksinBinhDinh
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Figure3.20.EstimatedRangesofDailyEconomicLossesandRisksDuetoCurrentandFutureRiverFloodingofIndividualLinkFailuresontheThanhHoaRoadNetwork
A.DailyeconomiclossesinThanhHoa
B.RiverfloodingrisksinThanhHoa
CriticalitiesandVulnerabilitiesofPorts
The following section presents criticality and vulnerability assessment results for the air transport,
inlandwaterways,andmaritimesectorsatthemajorportsscales,asthesearethemost importantassetsonthesenetworks.
Theresultsaimtoprovideevidenceon:
• Themainportsinthreesectorsidentifiedwithknownexposurestoextremehazards.
• Theeffectofclimatechangeonthechangingrisksofexposure.
The resultsdemonstrate that, foreach sector, the risksof climate change-drivenhazardexposureswill increase.1 For example, themaximum hazard probability of flooding of the Ho ChiMinh port
increasesto0.2(1in5-yearflooding)undertheRCP4.5andRCP8.5scenarios, incomparisontothe0.04(1in25-yearflooding)probability.Fromthis,theanalysisconcludestheHoChiMinhportwillbeabout 5 timesmore prone to frequent flooding in the future, and observed similar trends for all
majorportsineverysector.
Basedonriskestimatesforairportsproducedwitholderstatistics,theanalysisrevealsthatonlyDa
Nang airport shows significant AADF flows exposed to extreme hazard. Since 2006 developmentaroundDaNanghas increased,andatpresentDaNangservesasamajorpassengerhub incentralVietnam.2Becauseofthis,impactsontheairlinesectorshouldbeinterpretedintermsofpassenger
usage,amuchmoresubstantialmetricthancargotransport.
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Resultsshowexposuretoextremehazards inthemajormaritimeportsofHoChiMinh,HaiPhong,and Can Tho, with analysis for the Ho ChiMinh and Can Tho ports clearly indicating that climate
change will increase the frequency of extreme hazards. The significance of these ports to theeconomy of Vietnam is immense, with the analysis showing that under worst-case scenariosapproximately 68,000 to 106,000 tons of cargo flows per day could be affected. Even though this
studyincludeddomesticflows,theseportshavefargreatersignificanceasmajorimportandexporthubsforVietnam.
In terms of the country’s inlandwaterway ports (a total of around 41), results suggest thatmajorinland hubs in An Giang, Hai Phong, Thai Binh, Quang Ninh, and Ho Chi Minh are identified asexposedtoextremehazards,withthefrequencyofhazardexposuresforAnGiangincreasinginthe
futureduetoclimatechange.Theimplicationsofdisruptionstotheseportscouldbeimmense,withanalysis indicating that under worst-case scenarios approximately 25,000 to 55,000 tons of cargoflowsperdaycouldbeaffected.
Notes1. Appendix C shows detailed results of vulnerability assessment of the airports, major identifiedmaritime ports, and major identified inland waterway ports. The analysis showed the 44 inlandwaterwayportsand10maritimeportswith significant commodity flowswereexposed toextreme
hazards,mostlytyphoonfloodingandriverflooding.
2.Source(inVietnamese):https://web.archive.org/web/20130729200548/http://www.mt.gov.vn/PrintView.aspx?ArticleID=10663.Reference
ArgaJafino,Bramka.2017.MeasuringFreightTransportNetworkCriticality:ACaseStudyinBangladesh.Delft,Netherlands:TUDelft(DelftUniversityofTechnology).
http://resolver.tudelft.nl/uuid:0905337b-cdf7-4f6e-9cf6-ac26e4252580.
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Chapter4:AdaptationStrategyandAnalysis
Thischapterpresentstheadaptationoptionsconsideredfornational-scaleandprovinces-scaleroadnetworkassets,evaluatingeachassettherangesofadaptationoptionsandtheassociatedcostsandbenefits. Following this, the chapter discusses results of a detailed cost-benefit analysis for each
networkassetunderdifferenthazardscenarios.
ScopeandPurposeofAdaptationAnalysis
Adaptationplanningisamuchlargerprocessthandiscussedinthisstudy.Ideally,adaptationoptionsshouldinvolveawiderangeofmeasuresincluding,amongothers,structural,institutional,andsocial
changes.Also,thetypesofadaptationoptionsdependuponthecontextofthespecificcountryandthe financing objectives, which govern the types of objectives outlined by different multidonor-funded development agencies (Ebinger and Vandycke 2015). This study recommends several
proposedguidelineson the steps required fordetailed site-specific adaptationoptions inVietnam,neighboring Lao PDR, and other countries, based on an understanding of local conditions (ICEM
2017a and 2017b; MoPWT Laos 2008; CSIR et al 2011). The reader can refer to these studies tounderstandthestep-by-stepguidelinesintheadaptationplanningandimplementationprocess.
Adaptation options explored in this study look specifically at measures intended to improve the
structurereliabilityofroadassetstomakethemmoreresilienttoclimatechangeimpacts.Thesecanalsobecalledclimateresiliencestrategies.Thefocushereisspecificallyonevaluatingthebenefitsofstructural engineeringoptions tomake the roadassets structurally “climateproof.” Therefore, the
approachadoptedhereconsistsofexploringengineeringoptionsofadaptationtargetedtoimproveor create climate-resilient engineering standards of design for various aspects of road assets—subsurface conditions, upgrading material specifications, cross section and standard dimensions,
drainage and erosion, and protective engineering structures (ADB 2011). The levels of hazardexposuresofroadassetsselectedherearethemostextremebasedontheir identifiedmagnitudes:selectedlandslidesandflashfloodssusceptibilitiesarehighandveryhigh;selectedriverandtyphoon
floodingaregreaterthan1meter.Hence,thestudyassumestheselevelsofhazardswillpotentiallycausecatastrophic failurestoroadassets, i.e.,physically, theroadassetswillbedamagedand losetheir ability to provide service and will require rehabilitation. This assumption implies that the
purposeoftheadaptationoptionistomakeroadassetsresilienttocatastrophicfailuresinordertoavoidrehabilitationandthusmaintainthecontinuityofserviceflows.
The generalized evaluation of adaptation options presented here creates a high-level indicative
assessment of transport systems and their climate resilience strategies; evaluation of adaptationoptionsisaverysite-specificproblem,anddependsonthedetailedinvestigationof,amongothers,local terrains, structuraldimensionsofassets,conditionsofassets,andspecific failuremechanisms
duetohazards.Estimatingadaptationcostsisaverysite-specificproblemandinparticulardifferenttypes of engineering-based adaptation options require detailed site investigation and structuraldesign calculations of road asset design standards. At best, this analysis presents a first-order
screening whereby the efficacy of different types of adaptation options can be compared andlocations and assets with high (or low) adaptation benefits can be narrowed down for furtherinvestigation.
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As discussed later, in Vietnam the vulnerability to slope stability of roads due to landslides andflashfloods is amajor issue.Unfortunately, the underlying data in this study does not provide any
information on road slopes inclines and areas, which would have been used to fully quantifyadaptationoptionstobuildclimateresiliencetolandslidesandflashfloods.
IdentifyingFailuresbyClimateChangeDrivenHazardThreats
Listedbelowaresomeofthetypesoffailuresofroadassetsduetohazards—landslidesandflooding(river,flash,andtyphoon)—inVietnam.Thisisnotanexhaustivelist,butratheralistofmaincauses
offailuresthataffectthecontinuityofroadusage.Inseveralinstancestwoormoreofthesefailuretypescouldmanifestsimultaneously:
• Erosionoftheroadpavement;
• Embankmentfailures:slopeerosion;
• Cutslopefailure;
• Drainagefailures:a)pavementdrainagefailure,b)crossdrainagefailureaffectingdams,culvertsandspillways,andc)riverbankerosion;
• Structural failures: Collapse of any of the road asset, especially critical assets such asbridgesandculverts.
While the several hazardmechanisms could trigger failures, the analysis identifies intense rainfall-
inducedlandslidesandfloodingastheprimarycauseofroadfailuresinVietnam.Forexample,istheanalysis recognizes that in Vietnam water is the single biggest trigger of slope movements, since
saturation following heavy rain reduces pore suction, adds to the weight of the slope mass, andreducesbothcohesionandfriction(GITEC2018).
Climatechangecanamplify theseverityof failuresdue to increased levelsofhazard threats.Table
4.1listssomefailuretypesthatcanintensifyduetoclimatechange.
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Table4.1.PotentialFailureTypesDuetoIncreasedHazardThreatsDrivenbyAdverseClimateChange
ClimateChange Impacts
Increaseinrainfall
andintensity
• Damagetoroads,under-groundandsurfacedrainagesystemsduetoflooding
• Increaseinscouringofroads,bridges,culverts,andsupportstructures• Erosionofembankmentsandroadinfrastructureduetoflooding• Triggeringofroadsideslopefailuresduetolandslides• Overloadingofdrainagesystemsduetoincreasedsedimentationfor
flooding• Deteriorationofstructuralintegrityofroadsandbridgesduetoincrease
insoilmoisturelevels• Washoutofgravelandearthroadsduetoflooding
Sealevelrise
• Generalovertoppingofroads,embankmentsandbridges• Damagetolow-lyingstructures,andbridgesduetoflooding,inundation
incoastalareas,andcoastalerosion• Damagetoembankmentsfromlandsubsidenceandlandslides• Scouringofroads,bridges,andbridgesupports
Highwinds• Damagetoroadpavementsandstructuresfromtreesandotherdebris• Damagetobridgedecksandsuspensions• Impactbywinddrivenwaveactiononembankments,bridgeabutments
Increaseintyphoonfrequency
• Directimpacterosionofroads,slopes,andembankmentsfromflashfloods
• Allrisksassociatedwithincreaseinrainfallintensityduetoincreaselandfall
Sources:JasperCook,ReCAP1;ADB2011;EbingerandVandycke2015;WorldBank2017.
AdaptationOptionsandCosts
IntheVietnam-specificcontext,therelevantadaptationinterventionscanbebroadlydescribedasacombinationofthefollowing:
1. Pavement strengthening: Unpaved roads made of earth and gravel are prone to rapiddeterioration due to soil moisture saturation from excess water. Hence, the pavementstrengtheningoptionaimstocreateamoreclimate-resilientsurfacebyinvestinginbitumen
sealingorconcretepaving,which increases thestrengthandprotectionof subsurface roadconditionstopreventsoilsaturation.Periodicmaintenanceofthepavementtopreserve itsstrengtheningcharacteristics shouldbeperformed toprevent soilmoisture saturation.Thisinterventionprotectsroadsagainstfloodingdisruptions.
2. Improvedpavementdrainage:Toavoiddisruptivepoolingofwaterandsedimentsonroads(or even bridges), this investment option is aimed at improving the road surface crossfall,puttingmoresidedrains,turn-outdrains,andscourchecks.Periodicmaintenanceclearsthedrainageandpreventscour.Thisinterventionprotectsroadsagainstfloodingdisruptions.
3. Earthwork protection: Toprevent structural collapseof slopedearthworks, this investment
optionworksbyadoptingdifferentclimate-resilientslopestrengtheningmeasures,includingimproved masonry standards (ADB 2011) to enhance embankments and cut road slopes.
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Periodic maintenance of the slopes should be performed to remove loose materials. Thisinterventionprotectstheroadsagainstlandslidedisruptions.
4. Slopeprotection:Aimedspecificallyatenhancingclimate-resilienceofroadslopestructures,
thisoptionincludesinstallinggabions,blockfacing,bio-engineeringofplantedvegetationtostrengthen slopes (ICEM 2017b), and improving face drainage against overtopping due toriver flooding. Investment in slope strengthening can be undertaken with periodicmaintenance.Thisinterventionprotectstheroadsagainstlandslidedisruptions.
5. Improved cross drainage (culverts): This investment option builds more climate-resilient
culvertsand/or increasesthesizeandprotectionstandardsofexistingculverts (streamandrelief), reflecting the increases in flood intensities due to climate change. Periodicmaintenance of the culverts is performed to remove debris and sediments to prevent
scouring.FloodvulnerabilityofculvertsinVietnamisamajorissue,withseveralinstancesofculvert and small bridge failures recordedduringmajor floodevents (CSCNDPC2017).Thisinterventionprotectstheroadsagainstfloodingdisruptions.
Implementationof theaboveoptionscreatesaclimate-resilient roadprotectedagainstall typesofnatural hazards risks. Hence, the study suggests a climate-resilient road design, which includes
combinations of the five options as described above. In simplistic terms, this study suggests thatbuildingaclimate-resilient road inVietnamrequires implementingoneormorepavementupgradeoptions, enhancing the pavement drainage, constructing embankments and cut-slopes, increasing
slope stability by investing in different measures, and improving cross-drainage by building moreculverts.
Forthepurposesofthisstudy,theanalysisgeneralizesasetoffixedadaptationoptionsacrossroads,dependingon:
• Roadclass,eithertothestandardofaclimate-resilientnationalroadortothestandardofaclimate-resilientdistrictroad.
• Road terrain, where a sample road is designed either for a flat terrain or amountainterrain.
• Bridge,whereasamplebridgeisdesignedtorepresentanytypeofbridgeinVietnam.2
In order to enhance a road’s climate resilience, the study selected four climate-resilient roadprototype designs: national mountain, national flat, district mountain, district flat, and bridge,
identifyingeveryat-riskroadassociatedwithoneoftheseprototypedesigns.
For each prototype road, the study designed a sample climate-resilient, 100-meter section bycreatingabillofquantities,whichspecifiestheamountsandunitcostsofquantitiesneededtouplift
the climate resilience of existing roads. The results of the study’s climate-resilient designassumptions, summarized in table 4.2, arrive at unit costs of investment (CI) in US$ millions perlengthforupgradingexistingnationalanddistrictroadstoprototypeclimate-resilientroadstandards.
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Table4.2.CostSummaryofInitialAdaptationInvestmentforBuildingPrototypeClimateResilientRoadsinVietnam
Prototyperoad TerrainCostofadaptationinvestment(CI)
(US$/km)
Flat 1,535,000Nationalroad• Two-lane,22.5meterswide Mountain 1,828,500
Flat 808,000Districtroad• One-lane,6.5meterswide Mountain 1,439,000
Bridge All 10,179,000
Source:WorldBankcalculations,withinputsprovidedbyDr.JasperCook,ResearchforCommunityAccessPartnership(ReCAP):http://research4cap.org.
ResultsofAdaptationAnalysis
In estimating the parameters of the adaptation net present value (NPV) calculations, the studyassumedthefollowing:
• All climate hazard scenarios models estimated hazard severity until the year 2050.3
Accordingly,thestudyconsidersa35-yeartimelineofanyadaptationintervention,from2016to2050.
• The study assumed a discount rate of 12 percent, based on the Vietnam Road AssetManagementSystem(VRAMS)cost-benefitanalysistoolassumption.
• The study assumed the adaptation options were implemented over the length of thenetworklinksaffectedbyhazards.Forexample,if10percentofaroadnetworklinkwasaffectedbyahazard,thenthecostswereestimatedforthis10percentsection.
• Thestudyassumedthefreightflowvolumesacrossthenetworkswouldgrowasperthe
International Monetary Fund (IMF) forecasted 6.5 percent GDP growth for Vietnam.4Thus, thestudyassumedeconomic impactswouldgrowby6.5percenteveryyearoverthetimelineoftheadaptationoption.
For each roadnetwork under consideration, the study performed adaptation analysis on each link
subjectedtoeachhazardmodelassembledforthestudy.Thus,ifasingleroadnetworklinkisatrisk
for typhoon flooding, river flooding, and landslides, the study estimates the costs and benefits ofadaptation options for each type of hazard risk under each model scenario (current, or climatechangeRCP4.5andRCP8.5,withthegiventime-horizon).Forexample,ifa1kmnationalroadlinkis
exposedtoextremeriverfloodingover100to500mof its length,dependinguponfloodingreturnperiods,thenthestudyestimatesthecostsandbenefitsofadaptationforachosenclimate-resilientdesignfor500mofthisroad.Ifthesameroadisexposedtoextremetyphoonfloodingover100to
200m of its length, depending upon typhoon intensities, then the study estimates another set ofcostsandbenefitsforbuildingclimateresilienceover200moftheroad.Thus,thestudyestimatesmultiple climate-resilient designs for the same roads, depending upon their hazard exposures and
risks.Table4.3liststheoverallnumbersofuniquehazardexposureandadaptationoptionsscenarioscalculatedforeachtypeofroadnetwork.
Page|98
Table4.3.NumbersofSingle-LinkFailureandHazardExposure/AdaptationOptionsScenariosEvaluatedforTransportNetworksinVietnam
Selectedtransportnetwork Uniquesingle-linkfailurescenariosUniquehazardexposure/adaptation
optionsscenarios
National-scaleroads 968 6,010
LaoCaiprovinceroads 1,299 5,049
BinhDinhprovinceroads 11,042 14,397
ThanhHoaprovinceroads 26,852 38,449
National-scaleroadnetworkadaptationcostsandbenefits
This section presents a number of results at the national-scale road network level, discussing theadaptationmetricsdescribedinearliersections.
Figure4.1Ashowsthemaximuminitialinvestments(inUS$millions)requiredforindividualroadlinkstomake them resilient to theirworst levels of climate hazards. These estimates dependupon thelengthandwidthof theroad linksexposedtotheextremehazards,which implies thatsomeroads
requirelargerinvestmentsbecauseoftheirverylargehazardexposures.TheresultsshowparticularroadslinksaroundHoChiMinhmightrequirementashighasanUS$55millioninvestmenttomakethem climate resilient. By adding themaintenance costs to the initial adaptation investments, the
resultsalsohighlightparticularroadlinksaroundHoChiMinhthatmightrequireashighinvestmentsasUS$105millionover35yearstomaintainclimateresilience.
Figure 4.1B shows the adaptation investment costs per kilometers, which gives a sense of the
comparativelevelsofinvestmentsrequiredtoupliftroadsfromcurrentstandardstoclimate-resilientstandards.Thespatialanalysisshowsthecostsofinvestmentsperkilometerofnationalroads,whichcan reachashighasUS$3.4million for initial investmentsandUS$6.4million for investmentsover
time.Duetothestudy’sassumedchosenoptionsforveryhighdesignstandardsmeanttoavoidanyfailures,theseinvestmentamountscomparetotheconstructionofanewexpressway,
Page|99
Figure4.1.MaximumTotalInvestmentover35YearsontheClimateAdaptationforIdentifiedLinksintheNational-ScaleRoadNetworkinVietnam
Figure 4.2A shows themaximumvalues of the estimatedbenefits over 35 years of investments inclimateresilienceofindividualroadlinksinthenational-scaleroadnetwork.Thesebenefitsrepresentthesumoftheavoidedannualexpectedrehabilitationcostsovertimeforindividualroadlinksaswell
as the systemic expected annual economic losses (EAEL) over time from 10-day disruptions oftransport freight flows caused by failures of individual road links. The results highlight the largebenefits of investing into building climate resilience, particularly along the expressway sections
towardtheeasterncoastline.
Figure4.2Bshowsthemaximumbenefit-costratios(BCRs)ofadaptationforallidentifiedroadlinks,
derived from the results of figures 4.1A and 4.2A. The BCRs highlights where climate resilienceinvestmentscouldbeprioritized—forroadlinkswithhighBCRs,muchgreaterthanone.Theresultshere show that the expressway link betweenNghe An and ThanhHoa has the highest BCR (197),
whichmakesitahighprioritylinkforclimateresilienceinvestments.
A.Maximuminvestmentovertimepersection B.Maximumtotalinvestmentperkmovertime
Page|100
Figure4.2.EstimatedMaximumBenefitsover35YearsandMaximumBCRsofAdaptationOptionsforIdentifiedLinksintheNational-ScaleRoadNetworkinVietnam
A.Maximumbenefitsovertime B.MaximumBenefit-CostRatio(BCR)
Page|101
Another key insight from the adaptation analysis considers howBCRsmight change under currentand future climate hazard scenarios. This helps make a case for proactively investing in climate
resilience for future levels of extreme hazards, rather than planning for current extreme hazardlevels. For the national-scale roadnetwork links, such a comparison ismadebetween current andfuture (RCP4.5andRCP8.5) scenariosof river flooding. Figure4.3Ashows themaximumBCRs for
currentlevelsofriverflooding,whileFigures4.3Band4.3CshowthemaximumBCRsforfuturelevelsofriverfloodingundertheRCP4.5andRCP8.5scenariosrespectively.AsubstantialupliftintheBCRsin the futurehazard scenariosmakes a strong case for investing intobuilding climate resilience to
future hazards in Vietnam. The increase in BCRs is duemainly to the increase in the future riverfloodingrisksunderbothemissionscenarios,showingthegreaterbenefitsfrominvestinginclimateresiliencetoprotectagainstpotentialfuturehigherlosses.
Table 4.4 provides the list of specific national road network links identified by their BCRs ofadaptation.Theselinksrepresentthetop20assetsrankedbymaximumBCRvalues,andalsoreport
theminimumBCRs.IfboththeminimumandmaximumBCRvaluesaregreaterthan1,thenthecaseforinvestingintosuchassetsisquiterobust,sinceeveryhazardscenarioillustratesthatinvestinginclimate resilience ismore beneficial than paying the adaptation costs. All 20 assets shown have a
robust case for investing in climate resilience. The analysis shows that for these 20 assets acumulativeclimateadaptationinvestmentamountstoapproximatelyUS$95millioninitially;over35years is the investmentswouldamount toapproximatelyUS$153million.However, thecumulative
benefits of such investments over 35 years—estimated by adding together the benefits fromindividual links—arequitesubstantial, rangingbetweenUS$651andUS$3,656million.Notably, thecombined benefits of investing in all roads concurrently will not necessarily equal the sum of
individualbenefitsbecausetheavoidedeconomiclossesarenotadditive,butratherdependontheestimationofsystemicnetworkimpacts.
Page|102
Figure4.3.ComparisonsofBCRsofAdaptationOptionsforNational-ScaleRoadNetworkLinksinVietnam
A.Current2016riverflooding
B.Future2030riverfloodingunder
RCP4.5emissionscenarios
C.Future2030riverfloodingunder
RCP8.5emissionscenarios
Page|103
Table4.4.TwentyNational-ScaleRoadsRankedinDescendingOrderofMaximumAdaptationBCRs
Nati
onal
highway
Rout
e na
me
Road
cl
ass
Max
imum
haz
ard
expo
sure
leng
th
(met
ers)
Initi
al in
vest
men
t (U
S$ m
illio
ns)
Tota
l inv
estm
ent
(US$
mill
ions
)
Bene
fit (U
S$ m
illio
ns)
BCR
Min
.M
ax.
Min
.M
ax.
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
21,
285
2.49
4.04
32.5
731
5.87
819
7
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
31,
943
3.00
4.73
36.3
332
0.16
814
9
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
24,
178
8.07
13.1
085
.70
333.
837
128
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
21,
414
2.74
4.45
50.3
020
3.75
1111
9
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
179
61.
792.
9829
.07
319.
6110
107
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
32,
303
3.56
5.61
11.2
113
7.31
210
4
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
33,
189
4.93
7.77
12.8
420
6.74
294
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
22,
688
5.19
8.43
23.4
417
1.94
385
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
31,
019
1.59
2.50
71.8
219
2.87
2977
1TT
H-D
NG
bor
der
- KH
A-N
TN b
orde
r2
1,44
52.
804.
5444
.28
133.
1310
76
DBV
BĐ
ường
bộ
ven
biển
2
3,72
17.
1811
.67
23.7
716
9.72
275
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
29,
609
18.5
330
.12
82.2
018
9.73
372
1TT
H-D
NG
bor
der
- KH
A-N
TN b
orde
r2
1,29
82.
524.
0816
.15
131.
754
70
1TT
H-D
NG
bor
der
- KH
A-N
TN b
orde
r2
1,84
93.
575.
809.
3312
7.84
262
1TT
H-D
NG
bor
der
- KH
A-N
TN b
orde
r2
2,74
65.
308.
6123
.68
170.
443
62
1TT
H-D
NG
bor
der
- KH
A-N
TN b
orde
r3
6,41
49.
9015
.62
35.7
811
3.90
261
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
155
0.15
0.23
6.50
13.6
828
60
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
21,
080
2.10
3.40
11.0
215
9.09
357
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
24,
055
7.84
12.7
231
.62
134.
682
55
1N
BH-T
HA
bor
der
to T
TH-D
NG
bor
der
21,
052
2.05
3.32
14.1
411
0.81
455
Tota
l95
.30
153.
7265
1.76
3,65
6.85
Page|104
Province-scaleroadnetworkadaptationcostsandbenefits
Thestudyalsoperformedanadaptationanalysisforeachofthethreeprovince-scaleroadnetworks.
Inallmapfiguresthatfollow,anadaptationanalysiswasperformedforeachofthelinkshighlightedin black or shades of red—signifying an exposure to hazard. As indicated in the maps, blackhighlighting represents a failed linkwith no alternative routes to nearby commune centers,which
impairs economic activity. Red highlighting indicates failed links with alternative routes, whichmaintainaccesstothenearestcommunecenters.
Ontheprovince-scale,identifyingsomespecificassetswithhighBCRswouldhelpprovideasenseof
which kinds of assets—national roads, provincial roads, local (commune or district) roads, orbridges—are key to the accessibility province-scale economic activities. The following sectionspresentlistsspecificassets—showingtheassettypes,thecommuneanddistrictstheyarelocatedin,
theirmaximum lengths of hazard exposures, the initial and total investments required to achieveclimateresilience,andtheminimumandmaximumbenefitsandBCRsfrominvestinginthoseassets.
LaoCaiprovince-scaleroadsnetwork
Figures 4.4A through 4.4D show, respectively, the maximum initial investment, the maximum
investmentsover35years,maximumvaluesoftheestimatedbenefitsover35years,andmaximumBCRsofadaptation forall identifiedroad linksexposedtoextremehazard.Thekeyhighlights fromthese results identify several road links in the province—prominent candidates for adaptation
investments—whose failures cut off access to commune centers and hence economic activities.Figure4.4DshowsseveralsuchlinksinthesouthoftheprovincehaveBCR>1,andincludesignificantpointsinthenetwork(possiblybridges)wheretheBCRsarehighest.
Table4.5givesthelistofthespecificroadnetworkassetsidentifiedbytheiradaptationofBCRs.Thetableranksthetop20assetsbymaximumBCRvalues,whilealsoreportingtheirminimumBCRs.Theresults indicate that some local (communeordistrict) roadsandbridges in theprovinceshave the
highest BCRs,making themmost significant to the provision of economic activities; consequently,building their climate resilience should be prioritized. In addition, several road links in Lao Caiimportant for gaining access to economic activities also emerge as priority assets for climate
adaptationinvestments.Theanalysisshowsthatforthese20assetsacumulativeclimateadaptationinvestmentamountstoapproximatelyUS$9millioninitially,withatotalinvestmentover35yearsofapproximatelyUS$12.9million.Thecumulativebenefitsofsuchinvestmentsover35years,estimated
byaddingthebenefitsfromindividuallinks,rangebetweenUS$16.4andUS$22.5million.
Page|105
Figure4.4.Investments,Benefits,andAdaptationBCROptionsforIdentifiedLinksintheLaoCaiRoadNetwork
A.Maximuminitialinvestment B.Maximuminvestmentovertime
C.Maximumbenefitovertime D.MaximumBCR
Page|106
Table4.5.Top20LaoCaiRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs
Ass
et t
ype
Com
mun
e na
me
Dis
tric
t na
me
Max
imum
haz
ard
expo
sure
leng
th
(met
ers)
Initi
al in
vest
men
t (U
S$)
Tota
l inv
estm
ent
(US$
)
Ben
efit
(US$
)B
CR
Min
.M
ax.
Min
.M
ax.
Bri
dge
Nam
Pun
gB
at X
at15
50,1
2259
,824
524,
009
565,
302
8.8
9.4
Loca
l roa
dG
ia P
huB
ao T
hang
9316
1,24
122
2,33
739
2,40
61,
758,
052
1.8
7.9
Bri
dge
Tan
An
Van
Ban
2890
,263
110,
696
543,
361
712,
690
4.9
6.4
Loca
l roa
dD
an T
hang
Van
Ban
3260
,139
81,3
8887
,584
431,
644
1.1
5.3
Expr
essw
ayTa
n A
nVa
n B
an10
036
5,27
757
0,97
21,
156,
642
1,34
2,00
82.
02.
4
Loca
l roa
dG
ia P
huB
ao T
hang
146
249,
353
345,
176
231,
463
766,
128
0.7
2.2
Loca
l roa
dVa
n H
oaLa
o Ca
i59
399
9,20
31,
388,
596
654,
402
2,82
7,63
00.
52.
0
Nati
onal
roa
dSo
n Th
uyVa
n B
an65
395
7,33
91,
387,
381
1,81
5,88
12,
668,
361
1.3
1.9
Loca
l roa
dKh
anh
Yen
Ha
Van
Ban
9015
5,69
721
4,60
913
9,48
937
5,26
80.
61.
7
Bri
dge
Ban
Lie
nB
ac H
a24
77,2
3592
,928
48,9
0514
0,40
30.
51.
5
Bri
dge
Nam
Pun
gB
at X
at15
50,8
4760
,709
55,8
7180
,054
0.9
1.3
Nati
onal
roa
dH
oa M
acVa
n B
an48
371
0,31
21,
028,
267
1,32
6,06
71,
337,
024
1.3
1.3
Nati
onal
roa
dM
inh
Luon
gVa
n B
an56
682
7,09
11,
199,
299
1,55
1,40
11,
555,
555
1.3
1.3
Nati
onal
roa
dTh
uong
Ha
Bao
Yen
553
809,
385
1,17
3,36
71,
516,
086
1,51
8,54
81.
31.
3
Nati
onal
roa
dPh
o R
ang
Bao
Yen
530
776,
177
1,12
4,73
11,
451,
507
1,45
1,82
51.
31.
3
Nati
onal
roa
dM
uong
Khu
ong
Muo
ng K
huon
g15
122
8,83
232
8,45
342
1,72
242
3,48
31.
31.
3
Nati
onal
roa
dXu
an H
oaB
ao Y
en39
457
8,96
683
8,57
61,
081,
134
1,08
1,18
51.
31.
3
Nati
onal
roa
dN
am X
eVa
n B
an39
257
5,96
083
4,17
41,
075,
350
1,07
5,37
61.
31.
3
Nati
onal
roa
dTh
anh
Bin
hM
uong
Khu
ong
493
724,
721
1,04
9,37
01,
352,
037
1,35
2,10
31.
31.
3
Nati
onal
roa
dM
uong
Khu
ong
Muo
ng K
huon
g36
854
1,06
578
3,06
71,
007,
730
1,00
7,73
51.
31.
3
Tota
l8,
989,
225
12,8
93,9
2116
,433
,046
22,4
70,3
73
Tabl
e 4.
5. T
op 2
0 La
o Ca
i Roa
d A
sset
s R
anke
d in
Des
cend
ing
Ord
er o
f Max
imum
Ada
ptati
on B
CRs
Page|107
BinhDinhprovinceroads
Theanalysisshowsthatforthese20assetsacumulativeclimateadaptationinvestmentamountstoapproximatelyUS$1.2millioninitially,andover35yearsreachesapproximatelyUS$1.6million(figure
4.5).Thecumulativebenefitsover35years,estimatedbyaddingthebenefitsfromindividuallinks,ofsuchinvestmentsrangebetweenUS$14.2andUS$31.4million(table4.6).
Figure4.5.Investments,Benefits,andBCRsofAdaptationOptionsforIdentifiedLinksintheBinhDinhRoadNetwork
A.Maximuminitialinvestment B.Maximuminvestmentovertime
C.Maximumbenefitovertime D.Benefit-costratio
Page|108
Table4.6.Top20BinhDinhRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs
Tabl
e 4.
6. T
op 2
0 Bi
nh D
inh
Road
Ass
ets
Ran
ked
in D
esce
ndin
g O
rder
of M
axim
um A
dapt
ation
BCR
s
Ass
et t
ype
Com
mun
e na
me
Dis
tric
t na
me
Max
imum
haz
ard
expo
sure
leng
th
(met
ers)
Initi
al in
vest
men
t (U
S$)
Tota
l inv
estm
ent
(US$
)
Bene
fit (U
S$)
BCR
Min
.M
ax.
Min
.M
ax.
Loca
l roa
dPh
uoc
My
Quy
Nho
n10
811
4,59
417
9,47
02,
670,
602
9,41
3,24
315
52
Nati
onal
road
Ghe
nh R
ang
Quy
Nho
n8
8,91
710
,436
124,
418
428,
047
1241
Brid
geA
n Ta
nA
n La
o8
29,1
6334
,232
683,
481
1,20
3,71
720
35
Loca
l roa
dTu
y Ph
uoc
Tuy
Phuo
c63
69,5
4210
7,44
83,
496,
164
3,62
9,34
233
34
Loca
l roa
dA
n N
ghia
Hoa
i An
122
128,
485
201,
678
1,27
1,25
34,
114,
864
620
Loca
l roa
dN
hon
Hau
An
Nho
n7
13,5
5517
,944
95,9
9332
5,04
15
18
Brid
geH
oai S
onH
oai N
hon
1139
,739
47,1
4523
2,84
777
1,42
25
16
Brid
geH
oai T
anH
oai N
hon
4513
9,97
116
9,52
82,
487,
608
2,75
3,17
715
16
Brid
gePh
uoc
My
Quy
Nho
n20
66,1
6579
,412
368,
444
1,26
0,27
85
16
Brid
geTa
y Xu
anTa
y So
n11
38,4
5645
,579
203,
829
640,
231
414
Brid
geA
n H
ao T
ayH
oai A
n13
45,4
8054
,155
383,
299
708,
861
713
Brid
geN
hon
Hau
An
Nho
n5
22,4
8326
,076
98,7
7232
7,81
94
13
Loca
l roa
dTa
y Xu
anTa
y So
n31
37,5
1656
,250
204,
282
640,
684
411
Brid
gePh
uoc
Qua
ngTu
y Ph
uoc
624
,850
28,9
6797
,273
324,
475
311
Nati
onal
road
Ghe
nh R
ang
Quy
Nho
n81
40,8
5056
,983
188,
705
523,
325
39
Loca
l roa
dTa
y A
nTa
y So
n16
817
3,69
727
3,95
591
8,27
22,
462,
332
39
Brid
geTa
y G
iang
Tay
Son
1860
,388
73,5
5728
8,15
960
9,25
24
8
Loca
l roa
dCa
t Cha
nhPh
u Ca
t56
62,0
4595
,463
222,
854
708,
369
27
Brid
geBi
nh N
ghi
Tay
Son
521
,950
25,4
2555
,223
168,
167
27
Loca
l roa
dPh
uoc
Thua
nTu
y Ph
uoc
3642
,295
63,8
9013
9,77
641
6,99
32
7
Tota
l1,
180,
139
1,64
7,59
414
,231
,253
31,4
29,6
40
Page|109
ThanhHoaprovinceroads
Figure 4.6 illustrates and table 4.7 lists the top 20 specific road network assets identified by theirBCRsofadaptation.AllassetshaveminimumandmaximumBCRs>1,whichmakesarobustcasefor
investingintheirclimateresilience.Forthese20assets,acumulativeclimateadaptationinvestmentamounts to approximatelyUS$1.5million initially,witha total ofUS$2.3millionover35 years. Thecumulative benefits of such investments over 35 years, estimated by adding the benefits from
individuallinks,rangebetweenUS$7.8andUS$23.4million.
Figure4.6.Investments,Benefits,andAdaptationBCRsOptionsforIdentifiedLinksintheThanhHoaRoadNetwork
A.Maximuminitialinvestment B.Maximuminvestmentovertime
C.Maximumbenefitovertime D.BCR
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Table4.7.Top20ThanhHoaRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs
Tabl
e 4.
7. T
op 2
0 Th
anh
Hoa
Roa
d A
sset
s R
anke
d in
Des
cend
ing
Ord
er o
f Max
imum
Ada
ptati
on B
CRs
Ass
et t
ype
Com
mun
e na
me
Dis
tric
t na
me
Max
imum
haz
ard
expo
sure
leng
th
(met
ers)
Initi
al in
vest
men
t (U
S$)
Tota
l inv
estm
ent
(US$
)
Ben
efit
(US$
)B
CR
Min
.M
ax.
Min
.M
ax.
Loca
l roa
dPu
Nhi
Muo
ng L
at44
50,0
6276
,306
771,
381
2,67
8,80
910
35
Brid
geQ
uang
Chi
euM
uong
Lat
212
,265
13,6
0013
5,06
645
1,06
810
33
Brid
geXu
an C
aoTh
uong
Xua
n6
23,8
4927
,744
162,
858
576,
843
621
Brid
gePu
Nhi
Muo
ng L
at2
12,1
8213
,499
74,4
0225
7,19
46
19
Brid
geQ
uang
Chi
euM
uong
Lat
623
,790
27,6
7313
1,30
144
4,39
85
16
Loca
l roa
dN
ga D
ien
Nga
Son
2631
,724
46,9
9016
4,64
255
9,08
24
12
Brid
geM
uong
Ly
Muo
ng L
at7
28,2
9033
,167
176,
400
364,
716
511
Loca
l roa
dQ
uang
Chi
euM
uong
Lat
2732
,997
49,0
2615
9,50
351
4,01
53
10
Loca
l roa
dBa
c So
nBi
m S
on71
272
4,51
21,
150,
046
3,98
1,35
311
,480
,906
310
Brid
geQ
uang
Chi
euM
uong
Lat
522
,011
25,5
0075
,491
251,
168
310
Brid
geH
ai V
anN
hu T
hanh
2991
,084
109,
837
261,
960
864,
780
28
Loca
l roa
dN
ga D
ien
Nga
Son
4248
,101
73,1
7217
0,46
756
4,90
62
8
Brid
geQ
uang
Chi
euM
uong
Lat
5416
5,71
520
0,96
148
3,48
61,
501,
439
27
Urb
an
road
Trun
g So
nQ
uan
Hoa
2051
,807
81,2
4739
3,96
552
2,84
75
6
Loca
l roa
dXu
an K
hang
Nhu
Tha
nh37
42,8
5464
,783
133,
467
411,
658
26
Loca
l roa
dLu
an T
hanh
Thuo
ng X
uan
3237
,903
56,8
6910
5,02
435
5,64
62
6
Brid
geQ
uang
Chi
euM
uong
Lat
933
,924
40,0
4676
,539
244,
625
26
Loca
l roa
dTh
anh
Tan
Nhu
Tha
nh52
57,8
9788
,832
154,
620
502,
973
26
Loca
l roa
dXu
an K
hang
Nhu
Tha
nh11
16,9
4723
,368
38,2
5512
2,98
82
5
Loca
l roa
dH
ai V
anN
hu T
hanh
4046
,325
70,3
3210
9,88
235
6,36
92
5
Tota
l1,
554,
239
2,27
2,99
97,
760,
062
23,0
26,4
29
Page|111
Decision-MakingunderUncertainty:SensitivityofCostsofAdaptation
The global sensitivity analysis shown in figure 4.7 demonstrates that the variations in initial
investmentcostestimates fornational roadsaremostsensitivetothecostsofpavementupgradesand improvements in slope stability, accomplished by installing embankment-retaining gabions(figure 4.7A). The initial climate resilience investment costs in themountainous terrain of Lao Cai
(figure4.7B)aremostsensitivetothe investmentcostsofupgradingandprotectingembankments,whileBinhDinh(figure4.7C)andThanhHoa(figure4.7D)aremostsensitivetotheinvestmentcostsof improving slope stability through road embankments, building embankments, and upgrading
pavements. The numbers in figure 4.7 showwhat percentages of costs are driven by variations indifferenttypesofinvestments.
Figure4.7.ParameterSensitivityforNational-andProvince-ScaleRoads
A.Parametersensitivityfornationalroads
B.ParametersensitivityforLaoCai
C.ParametersensitivityforBinhDinh
D.ParametersensitivityforThanhHoa
Code Item Code Item Code Item
Gravel DT LineDrainL&R SS3 Concrete/blockFace
Sealed EW1 EmbankmentConstruction SS4 Riverbank
Concrete EW2 CutslopeFormation
P
ConcreteFW SS1 CutslopeToeRetainingGabions
SS2 EmbankmentRetainingGabions SS6 Bioengineering
Page|112
Rangesanduncertaintiesinadaptationestimates
National-scaleroads
Thissectiondiscussestherangesanduncertaintiesintheestimationofadaptationcostsandbenefits.Figures 4.8A through 4.8D show for individual national-scale road network links the ranges of: (a)
initial investmentcosts, (b) theNPVoftotal investmentsoverthe35-yearplanninghorizon; (c) theNPV of adaptation benefits due to losses avoided over the 35-year planning horizon, and (d) theBCRs.
The variations in cost estimates for links in figures 4.8A and 4.8B are evident because of theirexposure to multiple hazards; thus, the analysis estimates costs depending upon the extent ofexposuretoeachhazard.Asdiscussedintheprevioussection,thevariationsininitialinvestmentcost
estimatesaremostsensitivetothecostsofpavementupgradesandimprovementsinslopestabilitythrough the installation of embankment-retaining gabions. Total investments costs over time aremost sensitive to the increases in pavement maintenance costs; in the model, most pavement
maintenance costs are far more than other maintenance costs. The results show the highestestimated initial investments for building climate-resilient national-scale road network range fromUS$52toUS$55million,withtheNPVoftotalinvestmentovertimegoingupfromUS$87toUS$105
million.However,theseinvestmentsmightnotnecessarilybeforthesameroadlinks.
Variations in NPVs of the adaptation benefits shown in figure 4.8C are sensitive generally to theavoided economic losses, and to some extent the costs of rehabilitation. For links with very high
NPVs, thebenefits aredrivenmainlyby thevaluesof avoidedeconomic losses. The importanceofthese links to the wider network makes the case for investing in climate resilience. The analysisshows that the highest NPVs of adaptation benefits range from US$118 to US$334 million,
substantiallylargerthantheNPVsoftotalinvestments.Asshowninfigure4.8D,forseverallinksthisleadstogenerallyhighbenefit-costratios(BCR>>1).Significantly,theanalysisshowsthatfromthe60th percentile onwards, theminimum andmaximumBCRs of several links are both greater than
one,indicatingthatundereveryextremeclimatehazardscenario,investingintheclimateresilienceofasignificantproportionofnational-scaleroadswouldbebeneficial.
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Figure4.8.AdaptationAnalysisResultsfortheNational-ScaleRoadNetworkinVietnam
A.Rangeofinitialadaptationinvestments B.Rangeoftotaladaptationinvestments
C.Rangeofadaptationbenefits D.RangeofadaptationBCRs
Comparinghazard-specificadaptationBCRsacrossdifferentscenariosofthesamehazardtypeshowshowthehazarduncertaintiesinfluencetheadaptationanalysisoutcomes.Suchacomparisonismade
for thecaseofnational-scaleroadnetworkrisksduetoriver flooding incurrentconditionsandforthefutureunderRCP4.5andRCP8.5emissionscenarios,asshown infigure4.9.TheresultsshowasignificantupliftofBCRsofadaptationwhenclimateresilienceinvestmentsaremadetoavoidfuture
levels of extreme river flooding driven risks. The analysis mostly does not show any differencebetween BCRs of adaptationwhen evaluated for RCP4.5 and RCP8.5 climate scenario driven riverfloodingrisks,whichisreflectedthroughthecompleteoverlapofthecurvesforthesetwoscenarios.
Significantly, in comparison to the current scenarios, large percentiles of road links have BCRs > 1whenadaptingtofutureclimatescenarios.ThisresultprovidesaverycompellingcaseforaneedinVietnam to plan climate resilience investments in anticipation of future climate change driven
hazards,whentherisksandthereforebenefitsofavoidingthoseriskswillbemuchhigher.
Figure4.9.RangesofAdaptationBCRsfortheNational-ScaleRoadNetworkinVietnam,EvaluatedforCurrentandFutureExtremeRiverFloodingScenarios
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Province-scaleroads
Theprovince-scaleanalysesshow,incomparisontomostroadassets,arelativelysmallpercentileofroad assets have significantly higher adaptation costs. Initial climate-resilience investments for
individual road assets reach in excess of US$2 million for 1) approximately the top 10 percentileassetsinLaoCai;and2)approximatelythetop2to3percentileassetsinBinhDinhandThanhHoa.Someoftheseveryhighvalueassetsmightpotentiallyhavehighbenefits;hence,theircostsmightbe
justified.TheanalysesshowverylittlevariationsofinitialandtotalinvestmentcostsinbothLaoCaiandBinhDinh,whilethevariabilityismoreapparentinThanhHoa.ThelackofvariationsinLaoCaiandBinhDinhstemsfromtheroadnetworkassetsinbothprovinceshavingsimilarexposurelengths
tothevarioushazardsscenarios;therefore,sincethecosts,whichdependuponthelengthsofroadassets exposed to hazards, are also similar. As discussed in the previous section, the sensitivity ofinitialclimateresilienceinvestmentcostsineachprovinceareasfollows:a)LaoCaiismostsensitive
totheinvestmentcostsforupgradingandprotectingembankments,duetoitsmountainousterrain;and b) Both BinhDinh and ThanhHoa aremost sensitive to investment costs for improving slopestabilityofroadsembankments,buildingembankments,andupgradingpavements.
Therangesofadaptationbenefitsinthethreeprovince-scaleanalysesalsoshowthatforarelativelysmallpercentileofroadassets, thebenefitsofadaptationaremuchhigherthanfortherestof the
networkassets. Initialclimateresilience investments for individual roadassetsexceedUS$2millionfor:a)approximatelythetop5percentileassetsinLaoCai;andb)aroundthetop2to3percentileassetsinBinhDinhandThanhHoa.Thevariationsintherangesofbenefitsofadaptationinthethree
province-scaleanalysesarisearemainlysensitivetothechangesintheavoidedeconomicimpactsofdisruptions. For such assets the case for investing in climate resilience is compelling, because thebenefitsoutweighthecosts,asisevidentfromtheBCRplotforthethreeprovinces.
Figure4.10DshowsthatinLaoCaiapproximatelythetop15%percentileofassets(roughly190intheLaoCainetwork)atriskofhazardhavemaximumBCR>1.InBinhDinh(figure4.10C)againassetsin
thetop2to3percentilehavemaximumBCRs>1,whichamounttoroughly220to330assetsinBinhDinh and530 to 800 assets in ThanhHoa. Thougha relatively small proportion, thesenumbers ofassets are significantwhen it comes to prioritizing climate investments. The next section provides
more evidence of where and which assets could be further prioritized, based on identifying thelocationsandtypesofspecificassets.
TheresultsshowaclearcaseofincreasedBCRsinLaoCaiwhencomparingcurrentandfutureriverfloodingscenarios,asshowninfigure4.10B,wheremaximumBCRsbecomegreaterthanoneonlyfor
future extreme river flooding scenarios. In Binh Dinh and Thanh Hoa, the number of assets withmaximumBCRs>1inthefutureriverfloodingscenariosalsoshowanincrease.Aspresentedearlierinthereport,allincreasesarisefromincreasesinfutureriverfloodingrisks,whichavoidedeconomic
loss. Landslide susceptibility in LaoCai is includedhere as a prominenthazard in theprovince, forwhich theunderlyinghazarddataoffered some climate change scenario-based information. In theabsenceofanyprobabilistichazard informationon the landslide susceptibilitymuchof thecurrent
andfutureadaptationoptionsshowsimilaroutcomes.But,significantly,severalassetswithBCR>1createacompellingcaseforinvestinginclimateresilience.
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Figure4.10.AdaptationBCRsforProvince-ScaleRoadNetworks
A.RangeofBCRadaptationtolandsliderisks(LaoCai)
B.RangeofBCRadaptationtoriverfloodingrisks(LaoCai)
C.RangeofBCRadaptationtoriverfloodingrisks(BinhDinh)
D.RangeofBCRadaptationtoriverfloodingrisks(ThanhHoa)
Notes1.Dr.JasperCook,acivilengineer,servesasinfrastructureresearchmanagerfortheAfrica
CommunityAccessPartnership(AfCAP)andtheAsiaCommunityAccessParternship(ASCAP),researchprogramsundertheumbrellaoftheUKAid-fundedResearchforCommunityAccessPartnership(ReCAP).Formoreinformation,visit:http://research4cap.org.
2.Costsestimatesforbridgesarehighlyuncertainanddependheavilyontheparticularbridgebeingstudied.Hencenoclaimismadeherethatthebridgecostsconsideredinthisstudywillapplytoeach
andeverybridgeinVietnam.Thecostsaremoreanindicativeoftheordertoinvestmentthatmightberequiredintobridges.
3.TheGLOFRISmodelsestimatetheaveragemaximumflooddepthsfrom2010to2049,whichisusedasarepresentativeofa2030event.However,thesemodelsessentiallyestimatehazardscenariosto2049.
4.DataaccessedviatheInternationalMonetaryFundDataMapper:http://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLD/VNM.
References
ADB(AsianDevelopmentBank).2011.GuidelinesforClimateProofingInvestmentintheTransportSector:RoadInfrastructureProjects.MandaluyongCity,Philippines:AsianDevelopmentBank.
https://www.adb.org/documents/guidelines-climate-proofing-investment-transport-sector-road-infrastructure-projects.
Page|116
CSCNDPC(CentralSteeringCommitteeforNaturalDisasterPreventionandControl).2017(unpublished).NaturalDisastersinVietnamin2017.InternalreportfundedbyUNDP.
CSIR(CouncilforScientificandIndustrialResearch),Paige-GreenConsulting(Pty)Ltd,andStHelensConsultingLtd.2018.ClimateAdaptation:RiskManagementandResilienceOptimisationfor
VulnerableRoadAccessinAfrica,ClimateAdaptationHandbook.AfricaCommunityAccessPartnership(AfCAP)ProjectGEN2014C.London:ResearchforCommunityAccessPartnership(ReCAP)forDepartmentforInternationalDevelopment(DFID).
http://research4cap.org/Library/CSIR-Consortium-2018-ClimateAdaptation-Handbook-AfCAP-GEN2014C-181130.pdf.
Ebinger,JaneOlgaandNancyVandycke.2015.“MovingTowardClimate-ResilientTransport:TheWorldBank’sExperiencefromBuildingAdaptationintoPrograms.”WorldBank,Washington,DC.https://openknowledge.worldbank.org/handle/10986/23685.
ICEM(InternationalCentreforEnvironmentalManagement).2017a.TechnicalReport14:DemonstrationEffectivenessAudit.PromotingClimateResilientRuralInfrastructureinNorthern
VietNamReportSeries.PreparedforMinistryofAgricultureandRuralDevelopmentandAsianDevelopmentBank,Hanoi,Vietnam.http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern-vietnam-report-series/
———.2017b.TechnicalReport18:SlopeProtectionDesignsandSpecifications.PromotingClimateResilientRuralInfrastructureinNorthernVietNamReportSeries.PreparedforMinistryof
AgricultureandRuralDevelopmentandAsianDevelopmentBank,Hanoi,Vietnam.http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern-vietnam-report-series/
———.2017c.TechnicalReport17:Bioengineering–FieldGuidelinesforSlopeProtection.Promoting
ClimateResilientRuralInfrastructureinNorthernVietnamReportSeries.PreparedforMinistryofAgricultureandRuralDevelopmentandAsianDevelopmentBank,Hanoi,Vietnam.http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern-
vietnam-report-series/
MPWT(MinistryofPublicWorksandTransport,LaoPDR).2008.(unpublished).SlopeStability
Manual.Vientiane:GovernmentofLaoPDR.
GITEC.2018.ProjectPreparationStudy(PPS)forRuralInfrastructureProgrammePhaseVI(PPSRIP6):
GuidelineinClimateAdaptationforRIPRuralRoads,LaoPDR.https://gitec-consult.eu/index.php/en/projects?view=project&id=69.
Henning,TheunsFrederickPhillip,SusanTighe,IanDouglasGreenwood,andChristopherR.Bennett.2017.IntegratingClimateChangeintoRoadAssetManagement:TechnicalReport2017.Washington,DC:WorldBank.
http://documents.worldbank.org/curated/en/981831493278252684/Technical-report-2017.
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Chapter5:ExploringMultimodalOptionsforNational-ScaleTransport
Thischapterexplainshowthemultimodaltransportsystemwascreatedforthisstudy.Followingthis,thechapterevaluatestheeffectofmultimodalityonfailuresbyexploringmodalshiftsfromroadandrailnetworks.
In the analysis presented to this point, eachmode of transport has been assessed individually tounderstandtheimpactsoffailures.Inreality,failuresmayresultinexploringmultimodaloptionsformaintainingtransportservicecontinuity.Basedonthestudy’sunderstanding,multimodalswitching
optionshavenotbeenwellexploredinVietnam.Hence,thischapterexploresthepossiblechangesinnetwork failure effects if multimodal options were always available to accommodate for failures
alongindividualmodes.
Thechapterexploresthefollowingkeyquestions:
• What are themultimodal transport networksoptions available inproviding continuous
servicewhen individual transportmodesaredamagedordisruptedbyexternalnaturalhazardshocks?
• Arethereanygainsintermsofdecreasinglossesduetomultimodaloptions?
Switchingfromonemodetoanotherduringfailure,viaamultimodalconnection,presentsapotentialadaptationoption,andwheremodalswitchingisapreferredoption,itsbenefitsshouldbecompared
withthecostsinvolvedinfacilitatingandimprovingmultimodallinksandupgradingtransportmodes.Theanalysispresentedhereexploresthebenefitsofmultimodalityinmakinginformeddecisions.
Theanalysisquantifiesimpactsofmultimodalitythroughtwometrics:
• Totaleconomicimpactmetric:Theoveralleconomiccriticalityofnetworklinksmeasuredas the sumof theirmacroeconomic losses and the freight redistribution costs incurredduetotheirfailures
• TheAADFtonnageshift:Theoverallsystemicshiftoftonnagefromadisruptedmodetoothermodesduetomultimodaloptions
Thekeystepsinundertakingamultimodalanalysisinvolve:
• Identifying and creating links at all locations where multimodal transfers might beinferred
• Estimatingthecostsofmultimodalshifts
• Performingthefailureanalysis,aspreviouslyoutlinedinthecriticalityanalysis,withthe
change,nowthattheentiremultimodaltransportnetwork-of-networksisconsideredareroutingoption
• Estimatingthemultimodalmetrics
Themultimodalanalysispresentedhereconsideredonlythefailuresscenariosforthenational-scale
roadandtherailwaynetworks.
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IncludingMultimodalLinksandCosts
Thestudycreatedthephysicaltopologyofmultimodalnetworkforthenational-scaleanalysis,using
thenational-scaleroad,railway,inlandwaterway,andmaritimenetworkmodels.Thestudyassumedthefollowingwhilecreatingthemultimodalnetworklinks:
• Tocreatetheport-roadandport-rail links,all identifiedinlandandmaritimeportswereselectedasmultimodalpointsandjoinedtotheirnearestroadandrailnodes.
• To create the road-rail multimodal links, all rail stations were selected as multimodalpointsandjoinedtothenearestroadnodes.
• Tocreateamorerealisticspatialmapping,onlythosemultimodal linkswithlengths<3kilometerswereselected.
The multimodal network links represent a notional connectivity between modes, and at best
representthenearestpossibleaccessibilitypointfromonetransportmodetoanother.Asampleofthenetwork,zoomedinataselectedlocation,isshowninfigure5.1.
Figure5.1.MultimodalLinksCreatedintheNetworkModel
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ResultsofPartialMultimodalShiftsinRoadFailureAnalysis
The968casesof individual road failures identified in theroadcriticalityanalysiswereagain tested
with the multimodal networks. For each failure scenario, the analysis estimated the AADFdisruptions,themacroeconomiclosses,thereroutinglosses,andtheoveralleconomicimpacts.Theresultsshowedquiteevidentlythatbecauseofmultimodality,themaineffectwasseenintermsof
the changing redistribution costs, as most cases of single-point failure were eliminated. Hence,understandingtheneteconomicimpacts,duetochangingreroutingcosts,representsthemainresulthere.
Toassumeacompleteshiftofroadfreighttowardothermodesviamultimodallinkagesisunrealistic,because networks such as railways have no capacity to accommodate the large volumes of roadtonnages.Amore feasibleandrealisticanalysiswouldbe tounderstandcases thatexploreoptions
forreroutingasmallpercentageofthedisruptedflowsfromtheroadtomultimodalnetworks,whilererouting the remaining freight to undamaged roads. In discussing this option here, the studyassumesthatforeachroadfailurescenario,90percentoffreightisreroutedalongtheroadnetwork,
with the remaining 10 percent rerouted to the least-costmultimodal option—which could be theroadnetwork.
The result in figure 5.2 shows that even a 10 percent modal shift can help reduce the economic
impacts of road failure. For example, key sections of roads showgains, attributed tomodal shifts,ranging from US$0.18 to US$0.47 million per day. Also, across the minimum and maximum flowscenarios,thehighestlossesalongthehighestflowroutesgetreducedtoUS$0.53toUS$1.43million
perday, incomparisontonomultimodal reroutingoption lossvaluesofUS$0.67toUS$1.9millionper day. Notably, this 10 percent shift from road to other modes reduces economic losses byapproximately20to25percent.
Page|120
Figure5.2.TotalEconomicImpactsofNational-ScaleRoadLinksFailuresWhenConsidering90PercentFlowReroutingonRoadsand10PercentReroutingalongOtherNetworksviaMultimodalLinks
A.Minimumeconomicimpact B.Maximumeconomicimpact
Thechapternextpresentsthecausaleffectsoftheeconomicgainsandlossesduetomultimodalityindicate where the systemic modal shifts take place. Figure 5.3 shows the combined effects ofredistributionof10percentof flows in termsofAADF tonnagesshifts fromnational-scale roads to
othermodes—railways,inlandwaterways,andmaritime.Ineachfigure,panelAshowsresultsfortherailwaysandpanelBshowsthecombinedinlandwaterwaysandmaritime.Theresultsrevealthatapredominantnumberofdisruptedroadfailuresprefertobe,reroutedviarailwaysbecauseitisoften
the cheapest and closest option to high-flow road links. The original VITRANSS study estimatedrailwaytransportcostsinVietnamtobelowerthancostsforroadtransport(JICAetal2000),andthisfinding is reflected in this study. Consequently, if transportation assignments were based only on
transportation costs, railwayswould be preferred over roads. For theminimum tomaximum flow
Page|121
scenarios,therailwayswitnessanaddedmaximumofapproximately8,500to10,600tonsperdayinsystemic flowrerouting.Thiscan leadtoanalmostdoublingofrail tonnagevolumes,basedonthe
estimated tonnages in this study.According toexistinganalysis (Systra2018),mostof the railwayscurrentlyoperateundercapacity,forexample:
• AlongtheHanoi–HoChiMinhline,freightusesonly35to41percentofnetworkcapacity,
with an estimated leftover capacity between0 and 29 percent,with 0 percent aroundSaigon.
• Alongtheremaininglinesinthenorth(radiatingfromHanoi),freightusesonly11to30percent of network capacity, with an estimated leftover capacity between 48 and 85percent.
Basedontheaboveitwouldbequitefeasibletoredistributeroadfreighttowardrailways,especially
along the northern routes.Moreover, the figure 5.2 result indicates gains in transferring even 10
percentofdisruptedfreightsfromroadtorailalongtheHanoitoLaoCairoute.
Betweenrailwaysandwaterways,thepreferenceforrailwaysoutweighsthatofwaterways,exceptinasmallareainthenorthandapredominantpreferenceinthesouthernareasthathavenorailway
networks.Giventhehighvolumestransportedonsouthernwaterways,thetonnagesshownherecanbeaccommodatedbytheirpreferredmodeswithoutexceedingcapacities.
Page|122
Figure5.3.Redistributionof10PercentofMaximumRoadTonnageFlowsasAddedTonnagesontoRailandWaterwayNetworksfollowingRoadNetworkLinkDisruptions
A.Maximumtotaltonnageofroadtransfers
(Railways)
B.Maximumtotaltonnageofroadtransfers
(WaterwaysandMaritime)
RailFailureAnalysisResults
The164casesof individualrailfailuresidentifiedintherailcriticalityanalysiswerealsotested,andforeachfailurescenario,thestudyestimatedtheAADFdisruptions,themacroeconomic losses,thererouting losses, and the overall economic impacts. Again, multimodality quite evidently exerted
influenceintermsofthechangingredistributioncosts,asmostlycasesofsingle-pointsfailurewereeliminated.Themodeldidnotfindanysubstantialcasesofmacroeconomiclosses.Accordingly,theanalysisdemonstratestheneteconomicimpacts,mainlyduetochangingreroutingcosts.
Figure 5.4 shows the economic impacts of individual railway link failures corresponding to theminimum and maximum tonnages flows and generalized costs scenarios, where the economicimpacts fora linkarerepresentedonthatspecific link.Thebiggestchangehere—incomparisonto
theresultinfigure3.2—isthattheeliminationofallsinglefailurescenariossignificantlyreducestheeconomiclosses.Also,largesectionsoftherailwaynetworkclosertothesouthernendofHanoi–HoChi Minh line demonstrate several instances where multimodal options result in economic gains,
shownasnegativevaluesand in thecolorblueon themaps. Inparticular, theeconomicgainscan
Page|123
reach as high as US$0.02 to US$0.06 million per day, based on the minimum to maximum flowscenarios. In addition, the highest economic losses fall to US$0.05 to US$0.10 million per day,
contrastingwiththefigure3.2result,wheretheselinksshowedeconomiclossesrangingfromUS$2.3toUS$2.6millionperday for thesame failure scenarios.However, inagreementwith the result infigure5.2,asacheaperoptionthanroads,themultimodalshiftalongtheHanoi–LaoCairailwayline
showsaloss.
Figure5.4.TotalEconomicImpactsofRailLinkFailureswhenConsideringReroutingOptionsalongNational-ScaleRoads,InlandWaterwaysandMaritimeNetworksviaMultimodalLinks
A.Minimumeconomicimpact B.Maximumeconomicimpact
Page|124
Figure5.5showsthecombinedeffectsofflowredistributionintermsofAADFtonnagesshiftsfromrailwaysand roads toothermodes—national-scale roads, inlandwaterways,andmaritime. Ineach
figure,panelAshowstheresultsforthenational-scaleroadsandpanelBshowsthecombinedinlandwaterwaysandmaritime.Theresultsdemonstratethatapredominantnumberofdisruptedrailwayfailureswouldprefertobereroutedvianational-scaleroadsastheclosestoptionaroundhigh-flow
railway links.Thenational-scaleroadswitnessanaddedmaximumofapproximately8,300to9,900tonsperdayinsystemicflowreroutingfortheminimumtomaximumflowscenarios,largelyduetotransfers along the Hanoi–Lao Cai route. The waterway networks witness an added maximum of
approximately3,200to4,300tonsperdayinsystemicflowreroutingfortheminimumtomaximumflowscenarios,mainlyalongasmallnorthernsection.
Since railway flows aremuch lower when compared to the roads, these transfer volumes can beaccommodatedbytheothernetworks.Eventhoughthemodalshiftsfromrailwaystoothermodesreduce losses, the frequent traffic congestion along alternative road networks could cause costly
delays. The road network failure analysis presents a strong case for the benefits of investing in areliablerailwayandstrongermultimodaloptionsratherthanacontinuedrelianceonroadsalone.
Figure5.5.RedistributionofMaximumTonnageFlowsasAddedTonnagesontoNational-ScaleRoadsandWaterway(InlandandMaritime)NetworksfollowingRailNetworkLinkDisruptions
A.Maximumtotaltonnage(National-ScaleRoads)
B.Maximumtotaltonnage(InlandWaterwaysandMaritime)
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ReferencesJICA(JapanInternationalCooperationAgency),MoT(MinistryofTransport,SocialistRepublicof
Vietnam),TDSI(TransportDevelopmentandStrategyInstitute,Vietnam).2000.TheStudyontheNationalTransportDevelopmentStrategyIntheSocialistRepublicOfVietnam(VITRANSS)—TechnicalReportNo.3:TransportCostAndPricingInVietnam.Tokyo,Japan:JICA,ALMEC
Corporation,andPacificConsultantsInternational.http://open_jicareport.jica.go.jp/pdf/11596814.pdf.
Systra. 2018 (unpublished). Strengthening of the Railway Sector in Vietnam. Report 3.2: RailwayDemandAnalysis—Freight.ReportprovidedbySystraforthestudy.
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Chapter6:ConclusionsandPolicyRecommendations
Aspreviouslystated,thisreportaimstocreateamethodologicalandcomputational frameworkfortransportriskanalysisinVietnam.Throughthesuccessfuldemonstrationofthisframework,thestudy
deliveredanovelsystem-of-systemstoolforanalyzingandprioritizingtransportresiliencebasedonspatial criticalities, risks, and adaptation benefits. Hence, for Vietnam stakeholders, the report’sprimary recommendation is to recognize the investment value in a detailed spatial risk analysis
approachandbuildingontheexistingandnewknowledgecompiledandcreatedthroughthisproject.
Based on the analysis results and insights of this project, several other recommendations forstakeholdersemergefromthisstudy,asdiscussedbelow:
IncreasingVulnerabilitiesDuetoClimateChange
Recommendation:Vietnam’stransportsectorsneedtobepreparedfortheincreasingintensityandfrequencyofextremehazardsduetoclimatechange.
The analysis demonstrates that for all transport sectors in Vietnam, extreme hazard exposures tosimilar hazards over the same spatial scales increase under climate change scenarios. The overall
maximumkilometersofnational-scaleroads,railways,andprovince-scaleroadnetworksexposedtoextremehazardlevelsincreaseunderallclimatescenarios.Allmajorinland,coastal,andairportsarevulnerabletoriverfloodingwithincreasingfrequency,whenextremelevelsoffloodingseenin1,000-
yeareventscouldstartoccurringinfive-yearevents.
PrioritizingTransportNetworkResilienceforSystemicEconomicBenefit
Recommendation: The systemic understanding of locations whose failures create increasing
economic risks presents a strong economic case for investing in building climate resilience ofVietnam’stransportnetworks.
Themodelandanalysisresultsmostusefullyhighlightsystemiccriticalitiesandhazard-specific,high-risk network locations, which can be prioritized for further detailed investigations into climateresilience.Themodelresultsshowlocationsofroadnetworkswhosefailurescanresultinveryhigh
daily losses of up to US$1.9million per day, while railway failures can result in losses as high asUS$2.6millionperday.Thewiderimplicationsofmacroeconomiclossesduetotransportdisruptionsarequitecrucialwhenfactoringsuchlosses.Generally,transportanalysisignoressucheffects,which
resultinunderestimatingtheimpactsoftransportdisruptions.
ComparisonsofcurrentandfuturehazardrisksprominentlyhighlightthestrongcaseinVietnamtoinvest in building climate-resilient national-scale roads and railways. In addition, the comparisons
stronglyindicatethataccesstoeconomicopportunitiesinseveralareasofprovinceswillbeseverelyaffected without investment in building climate-resilient roads. The expected annual economiclosses—a measure of risks due to transport failures from future climate change-driven river
flooding—allsignificantlyincreasebyatleast100percentacrossnational-scaleroads,railways,andprovince-scaleroadnetworks.Allnetworklinkswithhighimpactsshowtheseincreases.
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MakingtheCaseforInvestmentinAdaptationtoBuildTransportNetworkResilience
Recommendation:Vietnam’s roadnetworks require investments tooverhaulexisting roadassets
to higher climate-resilient design standards. Though such investments could be costly, theirbenefits outweigh the costs for priority network assets, making adaptation investments viableoptionsforroads.
The analysis has demonstrated that the national-scale and province-scale road networks all needadaptation planning to protect against future climate-related hazards. In the Vietnam specific
context,therelevantadaptationinterventionsforroadsconsideredinthisstudyinclude:
• Pavementstrengthening;
• Improvingpavementdrainage;
• Earthworkprotection;
• Slopeprotection;and
• Improved cross drainage (culverts). To build a ‘climate proof’ road, a climate-resilient
roaddesignissuggestedinthisstudy,whichincludescombinationsofalloptions.
Prioritization of investments in transport network resilience should be based on evaluation of thecostsofinvestmentsinthetransportnetworkandthebenefitsyieldedbytheseinvestmentsthroughavoideddisruptions.Analysisofcostsandbenefitsof interventions inthetransportnetworkshould
berisk-based,i.e.,shouldconsiderboththeprobabilityandtheconsequencesoftransportnetworkfailure. This study has provided evidence of all these steps, providing essential evidence forprioritizinginterventions.
TheanalysisshowsthattheBenefit-CostRatios (BCR)ofadaptation investments intonational-scaleroads are mostly greater than one. The analysis suggests that, for some national-scale roads,
upgradingtoclimate-resilientdesignscouldcostuptoUS$3.4millionperkilometer,comparedtotheUS$1.0million per kilometer costs of building roads to existing standards. But the high economicbenefits of such investments justify the high costs. Similarly, for province-scale road networks a
relatively small—but significant—number of assets have BCRs > 1.Many of these are bridges andlocal roads,crucial foraccessing locationsofeconomicactivities.For roadnetworks, the increasingBCRsunderfutureclimate-hazardsscenariosstrengthenthecaseforinvestinginclimateresilienceto
protectagainfutureclimaterisks.
However, in some cases, the selected and analyzed adaptation optionsmight not be the best for
climatechangeadaptation, suggestinganeed to investigateseveralothersuitable technologies forincreasingtheresistanceoftransportinfrastructurestohazards.Tosomeextent,optionsarelocationspecific.Forexample,twootheradaptationoptionsworthconsideringcouldincludethefollowing:
1. Scourprotectionofbridgefoundationsandabutments,and
2. Forecasting and warning systems, which help to avoid vehicle accidents and train-
derailingincidentsthatexacerbatedisruptions.
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Giventhatthisreporthasdemonstratedhowadaptationoptionscanbeconsideredandhasshownindetailhowtoevaluatetheirbenefits,asimilarprocesscanbefollowedforothertypesofoptionsnot
assessedinthisstudy.Forexample,beyonddirectinvestmentinsuggestandnewclimateresiliencetechnologies,adoptionofappropriateoperationregimesiscentraltoensuringtheireffectivenessinthe long-term.Thecostsofoperationsshouldbe incorporatedwithinthe lifetimecostoftheasset,
ensuringrobustcost-benefitadaptationdecisionappraisal.
StrongCaseforImprovingMultimodalTransportLinkagesandEnhancingEfficiencyacrossModes
Recommendation: Vietnam’s transport networks need to function as integrated multimodal
systems,whichcanbeachievedbyimprovingexistingmultimodallinkagesandcreatingnewones.
Thisstudyhasshownthattheeconomicimpactsofdisruptionscanbereducedbybuildingadditional
transport multimodal connectivity. Often known as increasing “redundancy” in the network, thisphrase might be interpreted as being wasteful and inefficient, when in fact, providing extra
connectivityandcapacitywillassisttheeverydayflowofgoodsandpeopleaswellasprovidingnewreroutingopportunitieswhenmajordisruptionsoccur.
Thisstudyshowsaveryclearbenefitof redistributing flows fromtheroadnetworkto therailways
andinlandandmaritimenetworks.Evena10percentshiftofroadfreights,especiallytorailways,canreduceeconomic impactsby20to25percent. Improvements inenhancingrailwaysandwaterwaysefficiencycanreducerisksforalreadycongestedroadsandtakeadvantageofunusedcapacityinthe
railways to accommodate the additional modal shift from roads. The availability of multimodaloptions would significantly reduce potential losses on the railways, and such options should beconsideredinthefuture.
ImprovingExistingDataGapsforFurtherStudies
Recommendation: Under this study, Vietnam’s transport resilience planning benefits from asystem-of-systems transport risk analysis. The study recognizes the extreme importance ofincreasing capacity through cross-sector, cross-organization collaborations,whichwill enable the
pursuitoffurtherstudiesandcontinuedeffortstoimprovetheunderlyingdatasets.
Ashighlightedthroughoutthereport,thestudyhascopedwithseverelylimiteddata.Someofthese
limitationsexistinassemblingandstandardizingthefollowing:
• Topologicalrepresentationsofinfrastructurenetworkswithproperattributes
• Transportflowsthatrepresentthelatestconditionsandtrends
• Transportationcostassignmentsreflectiveofexistingconditions
• Economicflowsdatashowingcurrentstructureofthecountryandregionaleconomies
• Probabilistic hazards at similar spatial resolutions and with Vietnam-specific climatescenarios
• Seasonalityinformationofextremehazardslevelsandtransportnetworkflows
• Informationondisruptiondurationsandresponsebehaviors
• CostsofvariousadaptationoptionsmostrelevanttoVietnam
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Thestudyrecommendsthateffortisinvestedinimprovingthesedatagapsinordertoimprovefuturestudiesandmorerobustlymaketheeconomiccaseforinvestmentinthetransportnetwork.
For increasing capacity to standardize and share data, the study notes the extreme importance orcreatingacross-sectorandcross-organizationcollaboration,whichinvolvesMoT,DRVN,PDoT,CAAV,VINAMARINE,VIWA,TDSI,MARD,MoNRE,GeneralStatisticsOffice,amongotherdepartments.
Thestudyteamhassetouttheprocessofimprovinggapsbyprovidingthestudymodelasanopen-source resource (https://github.com/oi-analytics/vietnam-transport), and creating auserdocumentthat shows the standardized data requirements (https://vietnam-transport-risk-
analysis.readthedocs.io/en/latest/).GainingthemostbenefitfromtheseinnovativenewcapabilitieswillrequiretrainedpersonnelinVietnam.
Themodeldeveloped in thisstudycanbereadilyadaptedtoaccommodate further improvements.
Thestudyhasonlyaddressedsomeoftheeconomicandsocialfunctionsoftransportinfrastructure.Notably,thestudyhasnotexploredtheroleoftransportinfrastructureinenablingpassengertravelfor work or other purposes, or its role in labormarket participation. These should be included in
future studies, alongside other wider economic and social benefits of transport infrastructure. Intheserecommendationsthestudyhasalreadyidentifiedthattheinvestmentprioritizationneededtoimprove resilience of the transport network would require consideration of the costs and
effectivenessofalternativeinterventions,andrecommendthesestudiesserveasthenextstepafterthisstudyoftransportnetworkvulnerabilityandrisks.
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AppendixA:MethodologyforTransportRiskandAdaptationAssessment
MethodologicalFrameworkandImplementationStructure
FigureA.1providesagraphicaloverviewof thesystem-of-systemsmethodologicalapproach,which
consists of the following components explained below. The report chapters present details on thedataandmodelsforthefollowingcomponents:
A. Hazardassembly:(A-1)Assemblingcurrentandfuturehazarddatasetswiththespatialextent,
magnitudes, and return periods (or probabilities), and extracting those hazardswith values abovecertainthresholds.
B. Multimodal transport networks assembly: The aim is to assemble the multimodal transport
system-of-systems. The steps toward creating this system-of-systems include (B-1) CollectingGeospatial InformationSystems(GIS)dataandcreatingconnectednetworkmodelsfromsuchdata;(B-2)Identifyinglocationsonthenetworksandassigningthemattributes(e.g.,roadconditions,type
ofport,railstation,etc.);(B-3)Identifyingkeynetworknodeswherefreighttransportstarts(origins)andends(destination);(B-4)Collectingfreightdataandintegratingitwiththenetworklocations;(B-5)Assemblinginformationonmodalsplitoptionsandgeneralizedcostbasedperformancemeasures
forthemultimodalnetworks;(B-6)AssigningODflowsonthenetworksbasedonaleastgeneralizedcostcriteriatocreateaverageannualdailyfreight(AADF)estimates;and(B-7)Attheprovincialscalesassigningareasofpopulationconcentrationstolocationsofinterestviatheroadnetwork.
C. Failure and flow disruption analysis: Following from A and B, the flow disruption analysisinvolves:(C-1)Intersectingthehazardswiththenetworkstoinitiatenetworkedgefailureconditions;(C-2)Findingallexistingdisruptedorigin-destination(OD)routes;(C-3)Findingreroutingoptionsand
redirecting flows toward alternative routes; (C-4) Estimating flow disruptions in terms of freighttonnage lost or passenger counts lost when there are no rerouting options; and (C-5) Estimatingchanges in performance measures such as generalized cost changes for freight transport or for
changingaccessfortoimportantlocationinprovinces.
D. Macroeconomiclossanalysis:FollowingfromC,macroeconomiclossinvolves(D-1)Assembling
the regional input-output (IO) datasets to map the trading relationship between provinces; (D-2)ConvertingthefreighttonnagelossestoeconomicflowlossesasUS$perdaydirecteconomicsupplylosses anddirect economicdemand losses; and (D-3) Estimating the indirect economic lossesover
themulti-regionalsystem.
E. Adaptation options analysis: Estimation of Net Present Values (NPVs) of adaptation, whichfollowsD,involves(E-1)Selectingalistofinvestmentstrategiesforadaptation;(E-2)Assemblingdata
on the costs of damages of assets, the one-time costs and periodmaintenance costs of differentadaptation options; (E-3) Assembling estimates of the discount rates and timelines of adaptationplanning;and(E-4)CalculatingtheNPVs(BCRs)givenbyEquations(3)through(6).
F. Creation of future scenarios: To understand future transport failures and losses, the stepsinclude (F-1) Assembling statistics on future OD growth scenarios based on projected national orregional Gross Domestic Product (GDP) growth; (F-2) Incorporating structural changes to the
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networks(ifpossible)intermsofchangingconditionsoflinks(e.g.,increaseinpavedroads,upgradedrail lines); (F-3) Assembling statistics and estimating the changes in performance measures that
determine generalized cost functions in the future; (F-4) Creating modal options for new flowassignments.Tounderstand the systemicnatureofdisruptionsand losses, theanalysis is repeatedseveraltimesfordifferenthazardevents.Itcanalsobeupdatedforcurrentandfutureinfrastructure
network configurations and climate change driven hazard events to perform several vulnerability,risk,andadaptationassessments.
The above methodological framework is consistent with those previously applied to The United
Republic of Tanzania (Pant et al 2018b) and United Kingdom (Thacker et al 2017a), to informinfrastructure vulnerability and extreme hazard risk assessment at regional (Pant et al 2018b) andnational(Thackeretal2017b)scales.
FigureA.1.GraphicalRepresentationofInfrastructureSystem-of-SystemsRiskAssessmentFramework
Keydefinitions
The framework presents different types of system-of-systems assessments useful for decision-making:
• Criticality assessment: Criticality here is defined as a measure of the transport link’simportanceanddisruptive impacton the restof the transport infrastructure (Pantetal
2015). Criticality assessment results in ranking network assets based on their relativeimpactsontheserviceabilityofthetransportnetworks(ArgaJafino2017).
• Vulnerability assessment: Vulnerability is defined as the measure of the negativeconsequences due to failures of transport links from external shock events (Pant et al2016).Vulnerabilityassessment isdone in thecontextofnaturalhazardsandresults inunderstandingtherelativeimpactsofhazardsonthecontinuedtransportavailability.
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• Risk assessment:Risk is definedas the product of the probability of a hazard and theconsequences of transport link failures. Risk assessment results in understanding the
comparative impactsondifferenthazardfrequenciesandassigningcompositescorestomostdisruptedtransportlinks.
• Adaptationplanning:Plannedadaptationreferstomeasurestakentoreducerisks.Inthecontextofclimatechangetheplannedadaptationseekstocapitalizeontheopportunities
associatedwithclimatechange(Füssel2007).Fortransportsystems,adaptationplanningseeks to identify the assets and locations that could be prioritized for targetedinvestmentstoprovidemaximumbenefitsinreducingrisks.
Alltheaboveassessmentsaredonewiththeaimofunderstandingtheimpactsoftransportnetwork
service flow disruptions due to natural hazards. Network service flow is defined as the generalmeasureof volumesofmobilitybetweennetwork locationsovera suitably chosen time frame. The
locationsbetweenwhichserviceflowsaremeasuredarecalledorigin-destination(OD)pairs,withtheflowbeingdirectedfromanorigintowardadestination.Inthisstudynetworkflowsaremodeledandmeasuredintermsof:
• Averageannualdaily freight (AADF)volumes:Theestimated tonnageof freightmovingon an average day between different network locations. These measures are derivedfromamodel.
• Average annual vehicle traffic (AADT) counts: The estimated numbers of commercialvehiclesonanaveragedaybetweendifferent roadnetwork locations. Thesemeasuresarederivedfromobserveddata.1
• Net revenue measures: The estimated daily net revenue of goods and services withaccesstoan important locationviaatransportnetwork.Thesemeasuresareestimatedforunderstandinglocalroadaccesstoimportantlocationsattheprovincescale.
Theestimatesfornetworkflowsarebasedonperformancemeasuresthatquantifythecriteriaused
forassigningflowsonthenetworks.Inthisstudy,keynetworkperformancemeasuresforfreightandnetrevenueflowassignmentdependon:
• Identificationofkeylocationsandknowledgeofspatialpatternsoffreighttransport,forexample,mappingandtrackingoffreightroutesbetweenprovinces;
• Modalsplitsthatindicatehowfreighttransportmodechoicesaremade;and
• Generalizedcost,whichisacompositemeasureofthetransportcosts(inUS$),expressedasafunctionoftraveltime,transportprices,andvariabilityofservice.
Theestimationofnetworkflowsdisruptionsisdoneviafailureanalysis.Failurehereisdefinedasthe
conditionwhereanetworknodeorlinkhaslostcompletefunctionality.Thisstudydoesnotconsiderthecaseswherefailednetworknodesorlinksmightfunctionpartially.Therationaleforconsideringcomplete loss of functionality is that: a) The main goal is to understand how important the
uninterruptedfunctionalityofindividuallinksistotherestofthenetworks;andb)Theinteresthereisinsimulatingcatastrophiceventconditionswherethereareinstancesofcompletelossofnetworkfunctionalityalongaffectednodesorlinks.
Thisstudyperformedfailureanalysesbasedonspatiallyintersectinghazardswithnetworknodesandlinks,toinferhazardexposures.Ahazardsignifiesanexternalshockeventthatinitiatesfailureinthetransport networks. Inferring the asset failures criteria due to hazard exposure requires detailed
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physical process-based models of fragility curves2 of assets for given hazard magnitudes, whichgenerally is not possible. For example, the flood fragility of a road will depend upon its physical
dimensions, construction standards, material types, scour to flooding, etc. To select sets of failednodes and links, simplified failure criteria are chosen, where hazard levels above a threshold arecatastrophicenoughtocausefailures.
The characterization of failures within the transportation networks leads to characterizing thedisruptions to network flows. Disruptions refer to the change in network flows and performancemeasures due to the failures of network links. The disruption estimation consists of the following
steps:a)EachODroutethroughfailedlinksisassumedtobenolongeravailable;b)AflowreroutingoptionforthedisruptedODrouteischosenbasedonthenextbestrouteoptionthatgivestheleastgeneralizedcostestimate;c)Theredistributedflowsandperformancemeasuresarecomparedwith
thepre-disruptionvalues.
Insomeinstances,noflow-reroutingoptionsexistbecausephysicallytheODroutethroughthefailedlinksistheonlyavailableoption.Suchinstancesarecalledcompletefailurescenarios,andindividual
linkscausingsuchscenariosarecalledsinglepointsof failure.Thegeneral termapointof failure isdefinedtosignifya locationonthetransportrouteswhosefailuresignificantlythreatenstheflowofessentialservices. Inthisstudy,pointsoffailures’disruptiveimpactsaremeasuredas:a)AADFflow
losses;andb)dailylossesofnetrevenue.
Ininstanceswhereflow-reroutingoptionsareavailable,disruptiveimpactsaremeasuredintermsofthe freight redistribution costs, or the difference between the post-disruption and pre-disruption
generalizedcostestimatesofanODflow.Thestudyassumesthereroutingoptionsarefullyutilized,irrespectiveoftheincreaseintraveldistance,time,andcostofrerouting.
For thenational-scaleanalysis, thepointsof failure scenariosmay further result inmacroeconomicflowlosses,duetotheAADFflowlosses.Thesemacroeconomiclossesareestimatedthroughamulti-regionaleconomicinput-output(MRIO)datasetfordisasterlossestimations(KoksandThissen2016).
By tracking the flow typeand industry typeofeachdisruptedOD route, theeconomic losses fromAADFflowlossesresultin:
• Macroeconomic supply losses occur when the production capacities of industries arediminishedduetounavailabilityofcommoditiesrequiredforproduction.
• Macroeconomicdemandlossesoccurwhenthefinaldemandsforgoodsofthedisruptedindustriesarediminished,astheycannotreachthemarkets.
Theaboveeconomicflows lossesareconsidereddirect(output) losses thatcanbedescribedastheeconomicflowlosseswhichoccurtoindustrysectorsdirectlyaffectedbythedisruptionoftransport
flows.Intheeconomicinput-output(IO)systemtheseindustrysectorsaretradingwithseveralotherindustry sectors across regions,which togethermake up amulti-regional economic system. Thesetraderelationshipscreatebackward(supply)and/orforward(demand)linkages,whichgetdisturbed
duetodirectlosses.Thisresultsinindirect(output)lossesthatcanbedescribedasthesystem-wideeffects to other firms and industries via backward and/or forward linkages, proceeding tobecometherippleeffectoftheoriginaldirect loss(OkuyamaandSantos2014). Inthisstudy,thedirectand
indirecteconomiclossespointsoffailurescenariosareestimatedinUS$perdayvalues,toaccountfortheimpactofanAADFtonnageworthoffreightflowlosses.
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Estimationofadaptationoptions
Adaptationoptionsaredefinedasthearrayofstrategiesandmeasuresavailableandappropriatefor
addressingadaptationneeds(CSIRetal2016).Ideally,weshouldconsiderseveraltypesofadaptationmeasures, including structural, institutional, and social changes, among others. In this study,adaptationoptionsinvolvemakinginvestmentstoimprovethestructuralreliabilityoftransportlinks.
Theeffectivenessofdifferentadaptationoptions isevaluatedandcomparedthroughacost-benefitanalysis (CBA), which is a well-established technique to compare the costs of an option with itsbenefits (OECD2006).When the adaptationoptions involve investments to improve the structural
reliabilityofthetransportlinks,then:
1. ThebenefitsofadaptationarethesumoftheavoidedExpectedAnnualDamages(EAD)and
theExpectedAnnualEconomicLosses (EAEL).EADsignify thedirectdamagecosts incurreddue to physical collapse of transport links, while EAEL signify the costs incurred due totransportflowdisruptionsasdescribedintheprevioussection.
2. The costs of adaptation are the sum of the capital costs, CI, of an initial investment to
implementachosenoption,andfurthermaintenancecosts,CP,topreservethequalityoftheassetoveritslifetime.
3. Thevalueoftheadaptationoptionsisestimatedasthedifferencebetweenthebenefitsandthecosts.
4. Thebenefitsofadaptationareassumedtolastoveralongtimeperiod;thus,theactualvalueofanadaptationoptionisestimatedoveranannualtime-scalet0,…,tT,startingatthetimet0whentheoptionisimplementedandcontinuesoveritsplannedtimehorizonT.ThisleadstotheNetPresentValue(NPV)ofadaptationgivenas:
NPV=j=0j=TEADtj+EAELtj–CPtj1+rj–CIt0 (3)
WhereEADtj is the expected annual damages for year tj,EAELtj, is the expected annualeconomiclossforyeartj,CIt0,istheadaptationinvestmentmadeatthestartyeart0,CPtj,isthecostofperiodicinvestmentneeded,3risthediscountrate,andjisthecountfortheyearsoverwhichthevalueofadaptationisevaluated.
5. Themetricofbenefit-costratio(BCR)cansimilarlybeestimatedtoevaluatetheeffectivenessoftheadaptionoptions:
BCR=j=0j=TEADtj+EAELtj1+rjj=TCPtj1+rj+CIt0 (4)
ThepracticalstepsinestimatingtheNetPresentValue(NPV)ofadaptationinvolve:
• Havingasampleofprobabilistichazardevents;
• Gettingdataonthecostsofassetdamages,theone-timecostsoftheadaptationoption,andtheperiodiccostsofmaintenance;and
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• Estimating the economic losses due to transport link failures in two ways, dependingupon the scale of the analysis. In both cases the economic loss estimates are dailyestimates,whicharemultipliedbyanassumeddurationofdisruption:
a. Forthenational-scalenetworkanalysis,theeconomiclossesaregivenassumofthedailymacroeconomiclossesandthetotalcostsofrerouting.
b. Fortheprovince-scalenetworkanalysis,theeconomiclossesaregivenassumofthelossofdailynetrevenueandthetotalcostsofrerouting.
TheprocessofestimatingtheNPV(orBCR)ofanadaptationoption isundertakenforall the failed
transportlinks,followingwhichallNPV(andBCR)estimatescanberanked.ThelinkswiththehighestBCR values should be prioritized for investments, as they provide the highest benefits towardmaintainingthenetworkreliability.
A similar process can be repeated for a set of adaptation options, as there are potentially severaldifferentadaptationoptionsworthconsidering.Hence,theNPVscouldbecomparedacrossallassetsand all options to select the ones with high benefits for the system. When considering climate
change, theeffectivenessof thesamesetofadaptationoptionscanbe furthercomparedbetweencurrenthazardscenariosandfutureclimatechange-drivenscenarios.
Uncertaintyanalysis
Previous sections outline several sources of uncertainty throughout the whole process of the
criticality,vulnerability,riskandadaptationassessments.Someofthese,amongothers,include:
• Uncertainties associated with the estimation of the projected natural hazards under
differentclimatechangescenarios;
• Aleatory uncertainties (Bae et al 2004) in the networkmodel structures due to lack of
propertopologicalnetworkdata;
• Epistemic uncertainties4 in the transport flow estimation models, due to imprecise
informationonthenetworkspeeds,costs,andtonnages;and
• Uncertainties in the estimation of the costs of asset damages, costs of the adaptation
optionandtheperiodiccostsofmaintenance.
Toovercomethechallengesofaccountingforthedifferenttypesofuncertainties,thisstudyadoptsarobustdecision-makingapproachbysimulatinghundredstothousandsofdifferentsetsoftransport
link failures under different current and future hazards to describe how adaptation optionsmightperformundermanyplausiblefailurescenarios(Lempertetal2013).Ateverystepthesensitivityofthe model outputs to input parameters are analyzed to understand which parameters might
influence the estimates the most. This approach aligns with the Decision Making Under Deep-Uncertainty(DMDU)methods,whicharegainingpopularityamongdecision-makers(Hallegatteetal2012;Espinetetal2018).
TherobustestimatesoftheNPVs(orBCRs)ofdifferentadaptationoptionsfordifferenttransportlinkfailuresarecreatedbycalculatingtheminimumandmaximumNPVforeachtypeofoptionforeachtypeoffailurescenario.
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Toarriveattheseestimates,thestudyassumesthat:
• For every adaptation option, the costs of adaptation and maintenance are given orestimatedoverarange;
• Thedamagecostsofassetsaregivenorestimatedoverarange;and
• The economic losses are estimated by accounting for the range of possible tonnages,speeds,andcostsalongthenetworks.
The study concludes that transport links for which adaptation options give both NPVmin>0 (or
BCRmin>1𝐵𝐵𝐶𝑅m) and𝑁𝑃𝑉max>0NPVmax>0 (orBCRmax>1𝐵𝐶𝑅max>1), should be treated asno-regretoptionsandprioritizedforinvestments.Furtherprioritizationofsuchtransportlinkscanbe
donebyrankingBCRsandselectingthosewiththehighestvaluesasthesegivethehighestbenefitintermsofimprovingtransportreliability.
Notes
1.NoAADTmeasuresareavailableforothermodesoftransport.
2.Fragilitycurvesquantifytheconditionalprobabilityoffailureofanassetforagivenlevelofhazard.
Fragility curves that are derived from statistical analysis of historical failure data that record theconditionofanasset,itslevelofhazardexposure.Theycanalsobecreatedbysimulatingthephysicalproperties of a system based on its design standards and stress testing it under different hazard
levels.
3.Maintenancecouldbeconsideredtobepartofthebenefitsaswell,ifwewereconsideringpartial
failures. However, used here, maintenance represents a cost option undertaken to prevent thecatastrophicfailureofanasset.
4.Seenoteno.3.
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WorkingPaper6465,WorldBank,Washington,DC.http://documents.worldbank.org/curated/en/749751468322130916.
CSIR(CouncilofScientificandIndustrialResearch),Paige-GreenConsulting(Pty)Ltd,andSt.HelensConsulting,Ltd.2016.ClimateAdaptation:RiskManagementandResilienceOptimisationforVulnerableRoadAccessinAfrica—ClimateThreatsReport.AfricaCommunityAccessPartnership
(AfCAP)ProjectGEN2014C.London:ResearchforCommunityAccessPartnership(ReCAP)forDepartmentforInternationalDevelopment(DFID).http://research4cap.org/Library/CSIR-Consortium-2016-ClimateChangeAdaptation-ClimateThreatsReport-AfCAP-GEN2014C-
160908.compressed.pdf.
Okuyama,YasuhideandJoostR.Santos.2014.“DisasterImpactandInput–OutputAnalysis.”
EconomicSystemsResearch26(1):1–12.doi:10.1080/09535314.2013.871505.
Pant,Raghav,JimW.Hall,Simon.P.Blainey,andJohnM.Preston.2015.“AssessingSinglePointCriticalityofMulti-ModalTransportNetworksattheNational-Scale.”Paperpresentedatthe25thEuropeanSafetyandReliabilityConference,Zurich,Switzerland,September2015.
https://eprints.soton.ac.uk/385456.
Pant,Raghav,JimW.Hall,andSimonP.Blainey.2016.“VulnerabilityAssessmentFrameworkfor
InterdependentCriticalInfrastructures:CaseStudyforGreatBritain’sRailNetwork.”EuropeanJournalofTransportandInfrastructureResearch16(1):174-194.https://eprints.soton.ac.uk/385442/.
Pant,Raghav,ElcoE.Koks,TomRussell,andJimW.Hall.2018a.TransportRisksAnalysisforTheUnitedRepublicofTanzania:SystemicVulnerabilityAssessmentofMulti-ModalTransport
Networks.FinalReportDraft.OxfordInfrastructureAnalyticsLtd.,Oxford,UK.doi:10.13140/RG.2.2.25497.26722
Pant,Raghav,ScottThacker,JimW.Hall,DavidAlderson,andStuartBarr.2018b.“CriticalInfrastructureImpactAssessmentDuetoFloodExposure.”JournalofFloodRiskManagement11(1):22–33doi:10.1111/jfr3.12288.
OECD(OrganisationforEconomicCo-operationandDevelopment).2006.Cost-BenefitAnalysisandtheEnvironment:RecentDevelopments.Paris:OECD.doi:10.1787/9789264010055-en
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Thacker,Scott,RaghavPant,andJimW.Hall.2017a.“System-of-SystemsFormulationandDisruptionAnalysisforMulti-ScaleCriticalNationalInfrastructures.”ReliabilityEngineeringandSystems
Safety167:30–41.doi:10.1016/j.ress.2017.04.023.
Thacker,Scott,StuartBarr,RaghavPant,JimW.Hall,andDavidAlderson.2017b.“Geographic
HotspotsofCriticalNationalInfrastructure.”RiskAnalysis37(12):2490–2505,doi:10.1111/risa.12840.
Page|141
AppendixB:OverviewofDatasets
Each transportmode in this study first represented as anetwork,which is the collection of nodes
joinedtogetherbyacollectionof links.Thephysicalnetworktopologydefinedasthestructurethatencodesthephysicalplacementofnodesandtheirconnectinglinksdescribesthenetworkstructure.Theexistenceofphysicallinkswithinformationabouttheirconnectingnodelocationsisanecessary
andessentialconditionforthecreationofthetransportnetworkmodels,becauseofthegeospatialnature of the transport systems. In the absence of such information the underlying infrastructuredatahastobeprocessedfurtherbyeitheraddingnewlinkgeometriestoconnectnodesorbyfilling
gapsinmissingphysicallinkgeometriestoconnectnodesandtherebycompletethephysicalnetworktopology.
Allnetworkmodelswerecreatedfromgeospatialdatathatneededpost-processingtofillgapsinthe
underlying rawdatasets (table B.1).Depending upon the quality of the received rawdatasets, thenetwork models represent the physical properties such as the lengths, conditions, and widths oftransportnetworkassetsasapproximationsoftheequivalentreal-worldsystems.
TableB.1.RawDatasetsCollectedfromDifferentDataSourcesfortheTransportRisksAnalysisModelinginVietnam
Dataset Datascope Source Year
Administrativeboundaryandstatistics
Commune,districtandprovincelevels
GSO,WHO 2016
Economicinput-outputdata Nationalscale GSO 2012
Nationalroads TDSI 2018
Provinceroads Vietbando 2018
Rail VITRANSSIIandMoT 2009–2016
Inlandwaterway VIWA 2018
Maritime Ports–VINAMARINERoutes–WHO
2018
Airports
National,province(roadsonly)
Airports–CAARoutes–OIAmodeled
2018
Inter-provinceODs VITRANSSII 2009ODflows
Cropproductionlocations IFPRI 2016
TransportCosts
NationalVITRANSSIIreports
(projectedto2016values)2009–2016
Adaptationcosts Roads JasperCook,VRAMS,PMU6 2018
Page|142
Naturalhazardsdataset
TableB.2provides the listofallassemblednaturalhazarddatasetsused in this study.From left to
right,thetablegivesthe:
• Namesofthehazardtypes;
• Shorthandmodelnames,eithergiveninthedatasourceorcreatedforthestudy;
• Assumedyearofhazardrepresentedbythemodel;
• Climatescenarios;
• Hazardprobabilities(ifany);
• Selectedbandedvaluesforbandedhazarddatasetsforsamplinghazardextremes;
• Selectedflood-depththresholdforallflooddatasetsforsamplingextremefloodingscenarios;
• Spatialresolutionintermsofthegridsizeunitareasinthemodel;
• Provincescoveredbythehazardmaps;and
• Sourceofthedatasets.
Ideally,foranaturalhazardriskassessmentthehazarddatasetsshouldcontainsatleastthefollowing
information for each type of natural hazard: a) spatial extent; b) magnitude of severity; and c)probability.Unfortunately,onlythefluvialfloodmapsprovidedforthisstudycontainedallthreeofthe above attributes. The typhoon-induced flood maps contained only the spatial extent and
magnitudes (flood depths) of flooding, and no probabilities. In the flashflood and landslidesusceptibility maps, likelihood banded-scores (representing high or very high susceptibility) overareaswereusedasproxiesforserveflashfloodandlandslideeventsrespectively,whiletherewasno
probabilisticinformation.
Some of the natural hazards further maps were also provided with future climate change model
outputs based on the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios(Meinshausenetal2011).RCP4.5scenariosassumethatglobalemissionspeakaround2040andthendecline, while RCP8.5 scenarios assume no decline in global emissions through the 21st century.
Flashfloodand landslidesusceptibilitymaps foreightnorthernprovincesgenerated forRCP4.5andRCP8.5 scenarios in 2025 and 2050were available for the study. Fluvial floodmaps for thewholecountry were also provided for current flooding and future flooding under RCP4.5 and RCP8.5
scenariosin2030,fromtheGLOFRIS(Winsemiusetal2013)model.Inaddition,thestudylookedatabaselinemodelpresentingcurrentfloodingconditions,calledEUWATCH.Also,thestudyconsideredflooding outcomes under different climate change scenarios, using bias-corrected future
meteorologicaldata foranensembleof fiveGlobalClimateModels (GCMs) in theCMIP5project–GFDL-ESM2M,1HadGEM2-ES,2IPSL-CM5A-LR,3MIROC-ESM-CHEM,4andNorESM1-M.5Allthecurrentand future floodmapswere derived by downscaling global distributed hydrologicalmodelswith a
resolutionof0.5°×0.5° (approximately50m×50kmattheequator)to1/120°×1/120°(900m×900maroundtheequator) togivecountryscalemaps.Using thesemodels, the future floodmapsshowed average maximum flood depths from 2010 to 2049, which in this study are assumed to
represent flood scenarios for the year 2030. For eachmodel output, nine different flood intensitymapswereproducedtorepresentfluvialfloodsthatcouldoccurevery:2,5,10,25,50,100,250,500,and1,000years.
Page|143
TableB.2.NaturalHazardDatasetsAssembledfortheStudyNotesH
azar
d ty
peM
odel
nam
eYe
arCl
imat
e sc
enar
ios
Prob
abili
ties
Band
ed v
alue
sFl
ood
dept
h (m
eter
)Sp
atial
re
solu
tion
Spati
al
cove
rage
Sour
ce
Land
slid
e su
scep
tibili
ty
Mo
NR
E2
01
6
—
—
3-h
igh
,
4-v
ery
hig
h
—3
0m
×3
0m
8 C
en
tra
l
pro
vin
ce
saV
IGM
R-M
ON
RE
20
14
b
20
16
4-h
igh
,
5-v
ery
hig
h
Th
an
h H
oa
c
IMH
EN
20
16
15
No
rth
ern
pro
vin
ce
sdM
AR
D 2
01
7e
20
25
RC
P4
.5,
RC
P8
.52
05
0
Flas
h flo
od
susc
eptib
ility
20
16
—
20
25
RC
P4
.5,
RC
P8
.52
05
0
Typh
oon
flood
ing
MA
RD
Le
ve
l 1
3 –
16
f2
01
6—
—>
= 1
m
10
0m
×1
00
m2
8 C
oa
sta
l
pro
vin
ce
sgM
AR
D 2
01
6
Fluv
ial fl
oodi
ng
MA
RD
20
16
—0
.01
, 0
.05
, 0
.1,
0.2
30
m×
30
mT
ha
nh
Ho
aM
AR
D 2
01
2h
WA
TC
H2
01
6—
0.0
01
, 0
.00
2,
0.0
04
, 0
.01
,
0.0
2,
0.0
4,
0.1
,
0.2
, 0
.5
90
0m
×9
00
mW
ho
le c
ou
ntr
yG
LO
FR
ISi
MIR
OC
-ES
M-C
HE
M
20
30
RC
P4
.5,
RC
P8
.5
GF
DL-E
SM
2M
No
rES
M1
-M
IPS
L-C
M5
A-L
R
Ha
dG
EM
2-E
S
Notes
:
a.
Pro
vin
ce
s o
f P
hu
Ye
n,
Bin
h D
inh
, Q
un
ag
Na
m,
Da
Na
ng
, T
hu
a T
hie
n H
ue
, Q
ua
ng
Tri
, a
nd
Qu
an
g B
inh
.
b. L
ands
lide
inve
stiga
tion,
ass
essm
ent a
nd zo
natio
n fo
r mou
ntai
nous
are
as in
Vie
tnam
for d
isas
ter m
itiga
tion
cond
ucte
d in
201
4 by
the
Viet
nam
Insti
tute
of G
eosc
ienc
es a
nd M
iner
al R
esou
rces
, Min
istry
of N
atur
al R
esou
rces
a
nd
En
vir
on
me
nt.
c.
Th
e o
rig
ina
l d
ata
set
co
nta
ine
d c
olo
r-co
de
d b
an
ds,
wh
ich
we
re t
ran
sla
ted
to
in
teg
er
va
lue
s, b
ase
d o
n f
oll
ow
ing
ru
les:
(1
) R
ed
= B
an
d 5
; a
nd
(2
) O
ran
ge
= B
an
d 4
.
d.
Pro
vin
ce
s o
f La
o C
ai,
La
i C
ha
u,
Die
n B
ien
, Ye
n B
ai,
So
n L
a,
Ho
a B
inh
, P
hu
Th
o,
Tu
ye
n Q
ua
ng
, H
a G
ian
o,
Ca
o B
an
g,
Ba
c K
an
, T
ha
Ny
ug
en
, B
ac G
ian
g,
an
d L
an
g S
on
.
e. P
rom
oting
Clim
ate
Resi
lient
Rur
al In
fras
truc
ture
in N
orth
ern
Viet
Nam
pro
ject
fund
ed b
y U
ND
P an
d AD
B an
d co
mpl
eted
in 2
017
by th
e M
inist
ry o
f Agr
icul
ture
and
Rur
al D
evel
opm
ent,
Viet
nam
.f.
Typh
oon
leve
ls a
re d
eter
min
ed b
y w
ind
spee
ds b
ased
on
the
Beau
fort
cla
ssifi
catio
n, w
ith fo
llow
ing
win
ds s
peed
s fo
r diff
eren
t lev
els:
(1) 1
3: 1
34–1
49 k
m p
er h
our;
(2) 1
4: 1
50–1
66 k
m p
er h
our;
(3) 1
5: 1
67–1
83 k
m p
er h
our;
an
d (4
) 16:
184
–201
km
per
hou
r. g
. A
ll p
rovin
ce
s fr
om
Nin
h T
hu
an
to
Qu
an
g N
inh
to
uch
ing
th
e E
ast
ern
co
ast
lin
e o
f th
e c
ou
ntr
y.
h. In
tegr
ated
Dis
aste
r Ris
k M
anag
emen
t Pro
ject
fund
ed b
y th
e W
orld
Ban
k an
d co
mpl
eted
in 2
012
by th
e Ce
ntra
l Pro
ject
Offi
ce, M
inist
ry o
f Agr
icul
ture
and
Rur
al D
evel
opm
ent,
Viet
nam
.i.
Win
sem
ius
et
al
20
13
.
Tabl
e B.
2. N
atur
al H
azar
d D
atas
ets
Asse
mbl
ed fo
r the
Stu
dy
Page|144
Notes
1.FromNOAAinUS:https://www.gfdl.noaa.gov/earth-system-model/.
2.FromUKMetOffice:https://portal.enes.org/models/earthsystem-models/metoffice-hadley-centre/hadgem2-es.
3.FromIPSLinFrance:http://icmc.ipsl.fr/index.php/icmc-projects/icmc-international-
projects/international-project-cmip5.
4.FromJapan:Watanabe,Mashahiro,TatsuoSuzuki,RyoutaO’ishi,YoshikiKomuro,ShingoWatanabe,SeitaEmori,ToshihikoTakemura,MinoruChikira,TomooOgura,MihoSekiguchi,Kumiko
Takata,DaiYamazaki,TokutaYokohata,ToruNozawa,HiroyasuHasumi,HiroakiTatebe,andMasahideKimoto.2010.“ImprovedClimateSimulationbyMIROC5:MeanStates,Variability,andClimateSensitivity.”JournalofClimate23:6312–35.doi:10.1175/2010JCLI3679.1.
5.FromNorway:Bentsen,M.,I.Bethke,J.B.Debernard,T.Iversen,A.Kirkevåg,Ø.Seland,H.Drange,C.Roelandt,I.A.Seierstad,C.Hoose,andJ.E.Kristjánsson.2013.“TheNorwegianEarthSystemModel,NorESM1-M—Part1:DescriptionandBasicEvaluationofthePhysicalClimate.”GeosciModel
Dev6(3):687–720.doi:10.5194/gmd-6-687-2013
References
Meinshausen,M.,S.J.Smith,K.Calvin,J.S.Daniel,M.L.T.Kainuma,J-F.Lamarque,K.Matsumoto,S.A.Montzka,S.C.B.Raper,K.Riahi,A.Thomson,G.J.M.Velders,D.P.P.vanVuuren.2011.“TheRCPGreenhouseGasConcentrationsandtheirExtensionsfrom1765to2300.Climatic
Change109(1-2):213–241.doi:10.1007/s10584-011-0156-z.
Winsemius,H.C.,L.P.H.VanBeek,B.Jongman,P.J.Ward,andA.Bouwman.2013.“AFramework
forGlobalRiverFloodRiskAssessments.”HydrologyandEarthSystemSciences17(5):1871–92.doi:10.5194/hess-17-1871-2013.
Page|145
AppendixC:VulnerabilityResultsforPorts
For the air transport, inland waterway, and maritime sectors, the report presents results of thecriticality andvulnerability assessmentperformedat themajorports scales, as theseare themostimportant assets on these networks. Here, the following section presents the summary tables for
eachsector showing thenameand locationofaport, theminimumandmaximumaverageannualdailyfreight(AADF)dailytonnageattheportthatcouldpotentiallybevulnerabletodisruptions,thetype of hazard that could cause said disruption, the climate scenario for the hazard, and the
minimumandmaximumprobabilitiesofhazardexposureoftheport.
Foreachsector,therisksofhazardexposuresduetoclimatechangewillincrease.Everytableshowsthat wherever the identified hazard has climate scenarios with probabilities, the maximum
probabilities of exposure under the climate scenario is greater than the current probability ofexposure.Forexample,inTableC.1themaximumhazardprobabilityoffloodinginHoChiMinhportincreasesto0.2(1infive-yearflooding)undertheRCP4.5andRCP8.5scenarios,incomparisontothe
0.04(1 in25-yearflooding)probabilityatpresent.ThismeansthattheHoChiMinhport isabout5timesmorepronetofrequentfloodinginthefuturethanatpresent.Similartrendsareobservedforallthemajorportsineverysector,includingmaritime(tableC.2)andinlandwaterways(tableC.3).
TableC.1.AirportswithAADFFlowsandHazardExposureScenarioResults
AADF
(tons/day)Probability
Name Commune District ProvinceMin Max
HazardtypeClimatescenario
Min Max
Landslide — — —
ĐàNẵng HoaThuanTay
HaiChau
DaNang 26.0 28.3 TyphoonFlooding
— — —
CátBi ThanhTo HaiAnHai
Phong5.6 5.8
TyphoonFlooding
— — —
— 0.001 0.002
RCP4.5 0.001 0.01River
Flooding
RCP8.5 0.001 0.01
Landslide — — —
PhúBài PhuBaiHuongThuy
ThuaThienHue
2.8 2.8
TyphoonFlooding
— — —
Page|146
TableC.2.MajorMaritimePortswithAADFFlowsandHazardExposureScenarioResults
AADF(tons/day) ProbabilityName Commune District Province
Min Max
Hazardtype
Climatescenario Min Max
— 0.001 0.04
RCP4.5 0.001 0.2TP.HồChíMinh
CatLai Quan2HoChiMinh
84,050.0 106,425.6River
Flooding
RCP8.5 0.001 0.2
HảiPhòng
MayToNgo
QuyenHaiPhong 80,688.2 101,739.6
TyphoonFlooding
— — —
— 0.001 0.04
RCP4.5 0.001 0.2CầnThơ BinhThuyBinhThuy
CanTho 68,863.8 86,686.3River
Flooding
RCP8.5 0.001 0.1
— 0.001 0.001ĐồngNai
LongBinhTan
BienHoa DongNai 27,534.1 29,102.7River
FloodingRCP4.5 0.1 0.1
NghiSơn NghiSon TinhGiaThanhHoa
18,140.3 18,822.4TyphoonFlooding
— — —
QuyNhơn
HaiCangQuyNhon
BinhDinh 4,917.8 5,376.7 Landslide — — —
Landslide — — —ThuậnAn
ThuanAnPhuVang
ThuaThienHue
1,105.6 2,893.8TyphoonFlooding
— — —
CửaLò NghiThuy CuaLo NgheAn 1,428.6 2,021.0TyphoonFlooding
— — —
BếnThủy NghiHoa CuaLo NgheAn 1,131.2 1,655.5TyphoonFlooding
— — —
— 0.001 0.04
RCP4.5 0.001 0.1CáiMépPhuocHoa
TanThanh
BaRia—VungTau
416.8 416.8River
Flooding
RCP8.5 0.001 0.1
Page|147
TableC.3.MajorInlandWaterwayPortswithAADFFlowsandHazardExposureScenarioResults
Name Commune District ProvinceAADF (tons per day) Hazard
typeClimate scenario
Probability
Min Max Min Max
Cảng Bình Long
Binh Long Chau Phu An Giang 24,681.4 55,018.2River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.1
Cảng Thủy nội địa Hà Hùng Anh
Gia DucThuy
NguyenHai Phong 32,265.7 40,662.7
Typhoon Flooding
— — —
Cảng Nhiệt Điện Thái Bình 2
My Loc Thai Thuy Thai Binh 36,441.7 37,206.7Typhoon Flooding
— — —
Cảng TNĐ Liên hiệp Khoa học Công nghệ Tài nguyên khoáng sản, môi trường và năng lượng
Dien Cong Uong Bi Quang Ninh 33,948.1 34,822.8Typhoon Flooding
— — —
CẢNG LONG BÌNH
Long Binh Quan 9Ho Chi Minh
25,214.8 31,927.5River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
Cảng Khuyến Lương
Yen So Hoang Mai Ha Noi 18,291.7 18,552.1River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng Bốc xếp hàng hóa An Giang
Vinh Thanh Trung
Chau Phu An Giang 6,174.6 14,637.2River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.1
Cảng Thủy nội địa Long Đức
Long Duc Tra Vinh Tra Vinh 3,257.6 14,515.1River
Flooding
— 0.001 0.004
RCP 4.5 0.001 0.04
RCP 8.5 0.001 0.04
An Hòa An Hong An Duong Hai Phong 8,064.4 10,160.4Typhoon Flooding
— — —
CẢNG TNĐ KHO VẬN MIỀN NAM
Truong Tho
Thu DucHo Chi Minh
7,553.0 9,560.9River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
Phuong 5 Vinh Long Vinh Long 8,051.3 9,080.0River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.04
RCP 8.5 0.001 0.04
CẢNG TNĐ TÍN NGHĨA
Long Tan Nhon Trach Dong Nai 8,320.2 9,022.3River
Flooding
— 0.001 0.02
RCP 4.5 0.001 0.04
RCP 8.5 0.001 0.1
Table continues on next page
Page|148
TableC.3continued
XÓA TÊN KHỎI DS (CẢNG TNĐ VẬN TẢI SONADEZI)
An Binh Bien Hoa Dong Nai 8,261.4 8,747.6River
Flooding
— 0.001 0.001
RCP 4.5 0.1 0.1
Cảng Phượng Hoàng
Cam Thuong
Hai Duong Hai Duong 6,186.8 6,226.3River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
CẢNG HÙNG TÀI
Thien Tan Vinh Cuu Dong Nai 5,526.7 5,949.2River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
CẢNG TNĐ XĂNG DẦU LONG BÌNH TÂN
Long Binh Tan
Bien Hoa Dong Nai 5,516.1 5,879.8River
Flooding
— 0.001 0.001
RCP 4.5 0.1 0.1
Cảng Hồng Vân
Hong Van Thuong Tin Ha Noi 4,585.8 4,690.7River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
CẢNG TNĐ AJINOMOTO
An Binh Bien Hoa Dong Nai 4,402.2 4,651.0River
Flooding
— 0.001 0.001
RCP 4.5 0.1 0.1
Cảng thủy nội địa Vissai Gia Tân
Gia Tan Gia Vien Ninh Binh 3,392.6 3,762.9River
Flooding
NONE 0.001 0.001
RCP 4.5 0.001 0.002
RCP 8.5 0.001 0.004
Cảng thủy nội địa CuBi
Kim Xuyen Kim Thanh Hai Duong 3,705.2 3,715.1River
Flooding
— 0.001 0.001
RCP 8.5 0.001 0.001
CẢNG TNĐ HOÀNG LONG
Thien Tan Vinh Cuu Dong Nai 3,301.7 3,488.7River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
CẢNG TNĐ BÊ TÔNG LY TÂM LA PHƯƠNG NAM
Luong Binh
Ben Luc Long An 2,237.7 2,673.5River
Flooding
— 0.001 0.02
RCP 4.5 0.001 0.1
RCP 8.5 0.001 0.1
Cảng Binh Đoàn 11
Thanh Tri Hoang Mai Ha Noi 2,282.2 2,310.8River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng thủy nội địa xi măng Xuân Thành
Thanh Nghi
Thanh Liem Ha Nam 1,905.3 1,910.0River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng Thủy Nội Địa Cống Câu
Ngoc Son Tu Ky Hai Duong 1,855.1 1,870.9River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
XÓA TÊN KHỎI DS (CẢNG TNĐ VẬN TẢI SONADEZI)
An Binh Bien Hoa Dong Nai 8,261.4 8,747.6River
Flooding
— 0.001 0.001
RCP 4.5 0.1 0.1
Cảng Phượng Hoàng
Cam Thuong
Hai Duong Hai Duong 6,186.8 6,226.3River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
CẢNG HÙNG TÀI
Thien Tan Vinh Cuu Dong Nai 5,526.7 5,949.2River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
CẢNG TNĐ XĂNG DẦU LONG BÌNH TÂN
Long Binh Tan
Bien Hoa Dong Nai 5,516.1 5,879.8River
Flooding
— 0.001 0.001
RCP 4.5 0.1 0.1
Cảng Hồng Vân
Hong Van Thuong Tin Ha Noi 4,585.8 4,690.7River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
CẢNG TNĐ AJINOMOTO
An Binh Bien Hoa Dong Nai 4,402.2 4,651.0River
Flooding
— 0.001 0.001
RCP 4.5 0.1 0.1
Cảng thủy nội địa Vissai Gia Tân
Gia Tan Gia Vien Ninh Binh 3,392.6 3,762.9River
Flooding
NONE 0.001 0.001
RCP 4.5 0.001 0.002
RCP 8.5 0.001 0.004
Cảng thủy nội địa CuBi
Kim Xuyen Kim Thanh Hai Duong 3,705.2 3,715.1River
Flooding
— 0.001 0.001
RCP 8.5 0.001 0.001
CẢNG TNĐ HOÀNG LONG
Thien Tan Vinh Cuu Dong Nai 3,301.7 3,488.7River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
CẢNG TNĐ BÊ TÔNG LY TÂM LA PHƯƠNG NAM
Luong Binh
Ben Luc Long An 2,237.7 2,673.5River
Flooding
— 0.001 0.02
RCP 4.5 0.001 0.1
RCP 8.5 0.001 0.1
Cảng Binh Đoàn 11
Thanh Tri Hoang Mai Ha Noi 2,282.2 2,310.8River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng thủy nội địa xi măng Xuân Thành
Thanh Nghi
Thanh Liem Ha Nam 1,905.3 1,910.0River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng Thủy Nội Địa Cống Câu
Ngoc Son Tu Ky Hai Duong 1,855.1 1,870.9River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Name Commune District ProvinceAADF (tons per day) Hazard
typeClimate scenario
Probability
Min Max Min Max
Page|149
TableC.3continued
Name Commune District ProvinceAADF (tons per day) Hazard
typeClimate scenario
Probability
Min Max Min Max
Cảng thủy nội địa Vissai Hà Nam
Thanh Thuy
Thanh Liem Ha Nam 1,435.2 1,456.7River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng xi măng Vicem Bút Sơn
Chau Son Phu Ly Ha Nam 1,429.5 1,435.6River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
CẢNG TNĐ VIỆT HÓA NÔNG
Phuoc Dong
Can Duoc Long An 978.3 1,214.8River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.1
RCP 8.5 0.001 0.1
Cảng hàng hóa Thành Hưng
My Khanh Phong Dien Can Tho 801.1 1,088.8River
Flooding
— 0.001 0.02
RCP 4.5 0.001 0.04
RCP 8.5 0.001 0.04
Cảng Trường Nguyên
Gia MinhThuy
NguyenHai Phong 797.0 1,000.3
Typhoon Flooding
— — —
Cảng thủy nội địa Nhà máy xi măng Thành Thắng
Thanh Nghi
Thanh Liem Ha Nam 859.0 866.0River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Cảng thủy nội địa xi măng Hoàng Long
Thanh Nghi
Thanh Liem Ha Nam 856.4 856.6River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
CẢNG TNĐ XI MĂNG SÀI GÒN
Long Binh Quan 9Ho Chi Minh
660.9 833.0River
Flooding
— 0.001 0.04
RCP 4.5 0.001 0.2
RCP 8.5 0.001 0.2
Cảng xuất sét xi măng Vicem Hải Phòng
Song Khoai
Quang Yen Quang Ninh 682.0 719.4Typhoon Flooding
— — —
Cảng Hoàng Gia
Kim Luong Kim Thanh Hai Duong 614.2 617.5Typhoon Flooding
— — —
Cảng Hải Nam
Luu KyThuy
NguyenHai Phong 416.0 552.3
Typhoon Flooding
— — —
Minh ĐứcDong Hai 1
Hai An Hai Phong 388.8 513.4Typhoon Flooding
— — —
Page|150
TableC.3continued
Name Commune District ProvinceAADF (tons per day) Hazard
typeClimate scenario
Probability
Min Max Min Max
Cảng thủy nội địa Châu Sơn
Chau Son Phu Ly Ha Nam 303.9 353.6River
Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
NGỪNG HOẠT ĐỘNG (CẢNG NHÀ MÁY KHÍ ĐIỆN NHƠN TRẠCH 2)
Phuoc Khanh
Nhon Trach Dong Nai 62.3 141.3River
Flooding
— 0.001 0.02
RCP 4.5 0.001 0.1
RCP 8.5 0.001 0.1
Cảng Bến Kiền
An Hong An Duong Hai Phong 10.9 38.6Typhoon Flooding
— — —
Cảng Kho trung chuyển xăng dầu Thái Bình
Hoa Binh Vu Thu Thai Binh 10.2 30.8
River Flooding
— 0.001 0.01
RCP 4.5 0.001 0.02
RCP 8.5 0.001 0.02
Typhoon Flooding
— — —
Page|151
AppendixD.CommunesandDistrictsbyExposureofRoadNetworkstoNaturalHazards
TableD.1.TwentyCommunesMostExposedtoFloodinginLaoCaiProvince
Flashfloodsusceptibility Riverflooding Landslidesusceptibility
Commune District Totalkm Current
(2016)RCP4.5(2025)
RCP8.5(2025)
Current(2016)
RCP4.5(2030)
RCP8.5(2030)
Current(2016)
RCP4.5(2025)
RCP8.5(2025)
TongSanh BatXat 9.2 46.0 46.0 20.4 41.6 41.6 41.6 7.8 7.8 19.7
TrungLengHo
BatXat 3.7 30.7 30.7 0.0 0.0 0.0 0.0 1.8 1.8 1.8
NamSai SaPa 6.6 21.2 21.2 21.2 0.0 0.0 0.0 9.3 9.3 16.3
MuongVi BatXat 13.8 19.0 19.0 19.0 19.5 19.5 19.5 1.6 1.6 9.2
NamKhanh BacHa 15.5 17.6 17.6 17.6 0.0 0.0 0.0 0.6 0.6 0.6
CocSan BatXat 16.2 17.0 17.0 2.4 10.2 10.2 10.2 7.2 7.2 16.2
SaPa SaPa 32.3 15.2 15.2 15.2 0.0 0.0 0.0 11.2 11.2 11.2
BanCai BacHa 23.5 14.2 14.2 14.2 0.0 0.0 0.0 3.2 3.2 3.2
NamXeVanBan
21.8 14.0 14.0 14.0 0.0 0.0 0.0 14.9 14.9 14.9
DenSang BatXat 20.8 12.4 12.4 12.4 0.0 0.0 0.0 4.2 4.2 3.3
BanLien BacHa 49.3 10.9 10.9 10.9 0.0 0.0 0.0 1.8 1.8 1.8
ThamDuongVanBan
18.9 10.4 10.4 10.4 0.0 0.0 0.0 4.7 4.6 6.1
YTy BatXat 23.4 9.6 9.6 11.2 0.0 0.0 0.0 3.4 3.4 3.4
NamRangVanBan
15.2 9.2 9.2 9.2 0.0 0.0 0.0 10.0 10.0 10.0
DuongQuyVanBan
30.8 9.1 9.1 9.1 0.0 0.0 0.0 2.1 2.1 2.1
TaPhoi LaoCai 35.7 8.4 8.4 7.0 16.5 16.5 16.5 11.1 8.6 8.7
NamCang SaPa 6.2 8.3 8.3 8.3 0.0 0.0 0.0 1.8 1.8 4.2
LaoChai SaPa 11.6 8.3 8.3 8.3 0.0 0.0 0.0 20.8 20.8 20.8
LangGiangVanBan
18.9 7.7 8.0 8.0 0.0 0.0 0.0 5.0 5.3 5.3
TaPhin SaPa 20.2 7.2 7.2 7.2 0.0 0.0 0.0 14.3 14.3 14.3
Page|152
TableD.2.TwentyCommunesMostExposedtoFloodinginBinhDinhProvince
RiverfloodingLandslide
susceptibilityTyphoonflooding
Commune District Totalkm Current
(2016)RCP4.5(2030)
RCP8.5(2030)
Current(2016)
Current(2016)
ThiNai QuyNhon 11.8 87.2 87.2 87.2 0.0 43.2
NhonHoa AnNhon 89.4 76.4 80.4 80.4 8.0 15.2
LyThuongKiet QuyNhon 15.7 78.9 78.9 78.9 0.0 0.0
LeHongPhong QuyNhon 16.1 71.6 71.6 71.6 0.0 0.0
TayXuan TaySon 34.7 67.2 67.2 67.2 9.7 0.0
PhuocNghia TuyPhuoc 33.5 55.0 55.0 55.0 9.8 14.2
TranPhu QuyNhon 6.5 60.4 60.4 60.4 0.0 0.0
PhuocHiep TuyPhuoc 92.3 45.1 45.1 45.1 5.6 14.3
NhonBinh QuyNhon 68.6 41.2 41.2 41.2 0.0 20.9
AnTuongDong HoaiAn 38.5 33.9 33.9 33.9 37.3 0.0
PhuocThanh TuyPhuoc 100.8 41.0 41.4 41.4 8.4 0.2
TayPhu TaySon 32.4 37.0 37.0 37.0 13.9 0.0
CanhLien VanCanh 6.3 24.2 24.2 24.2 45.8 0.0
NhonTan AnNhon 38.6 35.2 35.2 35.2 8.0 0.0
NhonTho AnNhon 41.8 32.0 32.0 32.0 10.3 2.8
PhuocAn TuyPhuoc 108.5 27.9 29.3 29.3 16.1 0.1
VinhAn TaySon 5.0 25.8 25.8 25.8 18.5 0.0
TranHungDao QuyNhon 6.4 27.6 27.6 27.6 0.0 3.3
BinhNghi TaySon 89.6 24.3 24.3 24.3 12.7 0.0
AnDung AnLao 8.6 0.0 0.0 0.0 81.8 0.0
Page|153
TableD.3.TwentyCommunesMostExposedtoFloodinginThanhHoaProvince
RiverfloodingLandslide
susceptibilityTyphoonflooding
Commune District Totalkm Current
(2016)RCP4.5(2030)
RCP8.5(2030)
Current(2016)
Current(2016)
DongSon BimSon 47.7 96.9 96.9 96.9 1.7 0.0
DongXuan DongSon 8.3 96.1 96.1 96.1 0.0 0.0
ThinhLoc HauLoc 12.5 94.3 94.3 94.3 0.0 0.0
HoangLong ThanhHoa 10.4 91.8 91.8 91.8 0.0 0.0
NgaThach NgaSon 37.5 87.0 87.0 87.0 0.0 3.1
HoaLoc HauLoc 34.7 87.7 87.7 87.7 0.0 0.0
DongTien TrieuSon 39.3 84.7 84.7 84.7 0.0 0.0
RungThong DongSon 5.7 82.5 82.5 82.5 0.0 0.0
DinhTang YenDinh 51.5 81.3 81.3 81.3 0.3 0.0
LamSon BimSon 22.3 79.7 79.7 79.7 2.2 0.0
DongLoi TrieuSon 37.4 78.7 78.7 78.7 2.8 0.0
HauLoc HauLoc 16.2 79.6 79.6 79.6 0.0 0.0
XuanLam ThoXuan 22.7 76.9 76.9 76.9 0.5 0.0
LienLoc HauLoc 28.4 76.5 76.5 76.5 0.0 0.0
HaVinh HaTrung 57.4 73.1 73.1 73.1 1.3 0.0
HungLoc HauLoc 36.6 71.5 71.5 71.5 0.0 3.3
LocTan HauLoc 35.5 72.1 72.1 72.1 0.0 0.0
NgaNhan NgaSon 19.3 72.0 72.0 72.0 0.0 0.0
XuanVinh ThoXuan 42.2 72.0 72.0 72.0 0.0 0.0
YenNinh YenDinh 17.1 70.8 70.8 70.8 0.0 0.0