Natural　gas　pipeline　construction　is　developing　rapidly　worldwide　to　meet　the　needs　of　international　and　domestic　energy　transportation.　Meanwhile,　leakage　accidents　occur　to　natural　gas　pipelines　frequently　due　to　mechanical　failure,　personal　operation　errors,　etc.,　and　induce　huge　economic　property　loss,　environmental　damages,　and　even　casualties.　However,　few　models　have　been　developed　to　describe　the　evolution　process　of　natural　gas　pipeline　leakage　accidents　(NGPLA)　and　assess　their　corresponding　consequences　and　influencing　factors　quantitatively.　Therefore,　this　study　aims　to　propose　a　comprehensive　risk　analysis　model,　named　EDIB　(ET-DEMATEL-ISM-BN)　model,　which　can　be　employed　to　analyze　the　accident　evolution　process　of　NGPLA　and　conduct　probabilistic　risk　assessments　of　NGPLA　with　the　consideration　of　multiple　influencing　factors.　In　the　proposed　integrated　model,　event　tree　analysis　(ET)　is　employed　to　analyze　the　evolution　process　of　NGPLA　before　the　influencing　factors　of　accident　evolution　can　be　identified　with　the　help　of　accident　reports.　Then,　the　combination　of　DEMATEL　(Decision-making　Trial　and　Evaluation　Laboratory)　and　ISM　(Interpretative　Structural　Modeling)　is　used　to　determine　the　relationship　among　accident　evolution　events　of　NGPLA　and　obtain　a　hierarchical　network,　which　can　be　employed　to　support　the　construction　of　a　Bayesian　network　(BN)　model.　The　prior　conditional　probabilities　of　the　BN　model　were　determined　based　on　the　data　analysis　of　773　accident　reports　or　expert　judgment　with　the　help　of　the　Dempster-Shafer　evidence　theory.　Finally,　the　developed　BN　model　was　used　to　conduct　accident　evolution　scenario　analysis　and　influencing　factor　sensitivity　analysis　with　respect　to　secondary　accidents　(fire,　vapor　cloud　explosion,　and　asphyxia　or　poisoning).　The　results　show　that　ignition　is　the　most　critical　influencing　factor　leading　to　secondary　accidents.　The　occurrence　time　and　occurrence　location　of　NGPLA　mainly　affect　the　efficiency　of　emergency　response　and　further　influence　the　accident　consequence.　Meanwhile,　the　weight　ranking　of　economic　loss,　environmental　influence,　and　casualties　on　social　influence　is　determined　with　respect　to　NGPLAs.　©　2023　Elsevier　Ltd