Geopolitical Rupture and Inflationary Contagion: Sectoral Vulnerabilities in Asia Following the 2026 Iran War

Geopolitical Rupture and Inflationary Contagion: Sectoral Vulnerabilities in Asia Following the 2026 Iran War | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Geopolitical Rupture and Inflationary Contagion: Sectoral Vulnerabilities in Asia Following the 2026 Iran War Shiau Ping Chew, Chen Chen Yong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9291437/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The 2026 Iran War triggered significant geopolitical disruptions, particularly impacting Asian economies that rely on energy transit through the Strait of Hormuz. Such disruptions can generate inflationary pressures that extend beyond the energy sector into production, distribution, and food systems. This study examines how these war-related cost pressures are transmitted across sectors and into national inflation in ten selected Asian economies. Using an input-output (I-O) framework, this study analyzes how energy price shocks and supply chain disruptions spread inflationary pressures across energy-intensive sectors in ten selected Asian economies. The findings show clear differences across selected economies. Pakistan and Bangladesh emerge as the most vulnerable, with the highest crisis-adjusted inflation and stronger price effects in agriculture and food-related sectors. In contrast, Thailand and Sri Lanka show relatively smaller effects. Sectoral transmission is broad, with real estate, transport services, and trade consistently among the most affected sectors. Overall, the results indicate that geopolitical energy disruptions can intensify inflation through production and distribution networks, with important implications for food affordability and household welfare. 2026 Iran War Inflationary Pressure Asian Economies Agriculture Food Affordability Introduction The 2026 Iran War created a major geopolitical shock, significantly impacting global economic stability. The Strait of Hormuz is a key maritime chokepoint through which approximately 20% of global crude oil and 25% of liquefied natural gas (LNG) transits (World Economic Forum 2026 ; Herrero 2026 ). The disruption of nearly 20 million barrels of oil per day raised global energy prices and increased concerns about supply-chain security and energy market volatility (World Economic Forum 2026 ). These effects extend well beyond the immediate conflict zone, generating wider pressures across interconnected economies. Asian markets are particularly vulnerable to this shock due to their high dependence on energy flows through the Strait of Hormuz. In 2024, Asia received 84% of the oil and 83% of the LNG transiting the Strait, making the region especially vulnerable to external energy disruptions (Hedley and Chin 2026; Jeyaretnam 2026 ). This dependence creates a direct transmission channel through which maritime instability can be converted into domestic inflationary pressure. This research is urgent because of the scale and nature of the 2026 shock, which sharply increased energy prices and severely disrupted supply (Varma and Toh 2026). Unlike earlier energy crises that were driven mainly by price increases, the 2026 Iran War also disrupted key shipping routes and damaged critical energy infrastructure, including Saudi Arabia’s largest refinery (Cushman and Wakefield 2026). Consequently, the crisis produced a dual shock: Brent crude rose to nearly USD 120 per barrel, and gas and fuel rationing became a major concern in countries such as India, Pakistan, and Bangladesh (Cushman and Wakefield 2026; Herrero 2026 ). Despite extensive literature on energy price transmission, empirical evidence on sectoral vulnerability and inflationary pass-through in Asia, particularly during major choke-point supply disruptions, remains limited (Varma and Toh 2026). This gap is critical, as it hinders granular policy responses to complex, multi-faceted crises, such as the 2026 Iran War. Existing studies often focus on price elasticity and aggregate macroeconomic effects, while giving less attention to the structural interdependencies captured by input-output (I-O) linkages in an era of high LNG dependence (Zhuang et al. 2025 ). To address this gap, this study investigates the following research questions: (a) How do national-level inflation rates in the selected ten Asian countries respond to the 2026 Iran War shock? (b) Which specific sectors exhibit the highest vulnerability based on input-output linkages? (c) What is the transmission mechanism of these shocks to agriculture and food? By quantifying these impacts, this research provides critical evidence to inform policy responses to inflationary and supply-side pressures stemming from such geopolitical conflicts. The study aims to provide evidence for policymakers to develop proactive strategies and robust responses to inflationary and supply-side pressures generated by the conflict. Research Methodology This study employs an input-output (I-O) price model to trace the transmission of war-related cost pressures arising from the 2026 Iran War across sectors in selected Asian economies. The I-O framework is appropriate because it captures the structural interdependence of production activities and allows both direct and indirect price effects to be identified through interindustry linkages. In contrast to the standard demand-driven Leontief quantity model, this study adopts a price-based specification to examine inflation transmission. Since the Iran War was expected to intensify energy-related production costs, the study proxied the shock with a 1% increase in the value-added input cost component. The sectoral price system is specified as, \(\:p={A}^{{\prime\:}}\:p+v,\) where \(\:{A}^{{\prime\:}}\) denotes the transpose of the domestic technical coefficient matrix and v represents value-added costs. Equilibrium prices are obtained from, \(\:{p=(I-A}^{{\prime\:}}{)}^{-1}v,\) whereas the effect of the simulated shock is estimated as ∆ \(\:p={(I-A}^{{\prime\:}}{)}^{-1}\varDelta\:v.\:\) Here, ∆ v captures the exogenous 1% increase in value-added input costs. The analysis draws on 2024 country-level input–output tables from the Asian Development Bank (ADB) for ten Asian economies: Bangladesh, Hong Kong (China), India, Japan, the Republic of Korea, Pakistan, the Philippines, Sri Lanka, Taipei (China), and Thailand. To relate the sectoral simulation results to national inflation outcomes, the study uses baseline inflation data from the International Monetary Fund (IMF) database, specifically the 2024 observed inflation rate and the 2026 forecast inflation rate for each economy. The study then incorporate the simulated price shock into these benchmark inflation rates to derive the crisis-adjusted inflation measure for 2024 and the forecast energy shock inflation measure for 2026, as Table 1 reports. The model is further used to estimate sectoral output price changes and identify the sectors most vulnerable to inflation pass-through, particularly those linked to agriculture and food systems. While the static I-O framework provides valuable insights into immediate, direct, and indirect shock transmission, it inherently represents a short-run impact. The absence of substitution effects, dynamic adjustments, and policy interventions means the reported inflationary pressures likely represent an upper bound, as economic agents and governments would adapt over time. Future research could integrate dynamic macroeconomic models to capture these adaptive responses. Results This section presents the main findings of the input–output analysis, focusing on the simulated inflationary effects of the 2026 Iran War across selected Asian economies. The analysis first reports crisis-adjusted inflation rates at the national level and subsequently identifies the most vulnerable sectors across economies to trace the principal transmission channels of war-related cost shocks. It then examines sectoral vulnerabilities in greater detail, with particular emphasis on agriculture and food-related industries. Table 1 Comparison of Crisis Adjusted Inflation, Forecasted Inflation, and Energy Shock Inflation Rates in Selected Asian Economies Country *2024 Crisis-Adjusted Inflation Rate (%) *2026 Forecasted Inflation Rate (%) 2026 Forecasted Energy Shock Inflation Rate (%) Bangladesh 10.42 8.75 8.94 Hong Kong, China 2.09 2.10 2.17 India 5.15 4.05 4.21 Japan 3.26 2.14 2.35 Korea, Republic of 2.77 1.85 2.00 Pakistan 24.09 6.04 6.20 Philippines 3.76 2.55 2.65 Sri Lanka 1.83 n.a. n.a. Taipei, China 2.70 1.56 1.67 Thailand 0.83 0.71 0.86 Note: * Value obtained from the IMF database. Table 1 shows that the inflationary effects of the simulated shock are uneven across the selected Asian economies. Pakistan recorded the highest 2024 crisis-adjusted inflation rate at 24.09%, followed by Bangladesh at 10.42%, indicating that these economies are more exposed to war-related cost pressure. This high exposure is further underscored by Pakistan also reporting the highest 2026 simulated energy shock inflation rate of 6.20%, suggesting that additional cost increases could significantly worsen domestic inflation conditions. In contrast, Thailand and Sri Lanka displayed relatively low crisis-adjusted inflation rates at 0.83% and 1.83%, respectively, although no 2026 forecast values are available for Sri Lanka. These differences imply that the inflationary transmission of shocks is shaped by varying degrees of structural vulnerability across the economies. A comparison between the baseline 2026 forecast inflation rates and the simulated energy shock inflation rates further shows that the shock adds upward pressure to inflation in all reported cases. For example, Bangladesh’s inflation rate rises from a baseline forecast of 8.75% to 8.94% under the shock scenario, while Pakistan’s increases from 6.04% to 6.20%. Similar, but smaller, increases are observed in Hong Kong (China), Japan, the Republic of Korea, and Thailand, where the gap between baseline and shock-adjusted inflation remains modest. This suggests that although the simulated shock affects all economies, its magnitude is more pronounced in economies that are already facing elevated inflation or greater exposure to external cost pressures. Overall, the findings indicate that war-related energy disruptions can intensify existing inflationary pressures; however, the scale of the effect differs substantially across Asian economies. Table 2 Top Five Most Vulnerable Sectoral Output Price Changes Resulting from Energy Shocks in Selected Asian Countries and Territories Country Top 1 Sector (%) Top 2 Sector (%) Top 3 Sector (%) Top 4 Sector (%) Top 5 Sector (%) Bangladesh Mining (0.98) Real Estate (0.96) Retail Trade (0.92) Other Manufacturing (0.92) Wood Products (0.91) Hong Kong, China Transport Support Services (0.88) Real Estate (0.81) Construction (0.78) Inland Transport (0.74) Retail Trade (0.70) India Real Estate (0.95) Agriculture (0.92) Motor Vehicle Trade (0.85) Retail Trade (0.85) Wholesale Trade (0.85) Japan Real Estate (0.85) Motor Vehicle Trade (0.83) Water Transport (0.82) Transport Support Services (0.79) Wood Products (0.78) Korea, Republic of Real Estate (0.78) Wholesale Trade (0.76) Motor Vehicle Trade (0.72) Agriculture (0.71) Inland Transport (0.69) Pakistan Real Estate (0.96) Wholesale Trade (0.94) Agriculture (0.93) Mining (0.89) Food Manufacturing (0.88) Philippines Retail Trade (0.87) Real Estate (0.87) Wholesale Trade (0.86) Agriculture (0.84) Motor Vehicle Trade (0.79) Sri Lanka Real Estate (0.93) Agriculture (0.89) Mining (0.87) Transport Support Services (0.84) Retail Trade (0.83) Taipei, China Retail Trade (0.84) Real Estate (0.82) Wholesale Trade (0.82) Transport Support Services (0.81) Agriculture (0.77) Thailand Agriculture (0.80) Paper products (0.78) Real Estate (0.74) Retail Trade (0.73) Wholesale Trade (0.72) Note: Values represent the estimated sectoral output changes under a simulated 1% energy price shock, derived from the input–output model. The sector names are shortened versions of the original industry classification. Table 2 illustrates the top five changes in sectoral output price resulting from a simulated 1% energy shock scenario in ten selected Asian economies. The findings indicate that the shock’s impact is not uniformly distributed across sectors, with several consistent patterns emerging across different economies. Real estate appears among the most affected sectors in nearly all cases and often ranks first or second, indicating a strong sensitivity to rising production and operating costs. Transport-related activities, including transport support services, inland transport, and water transport, also feature prominently, reflecting their close dependence on energy input. In addition, trade-related sectors, such as wholesale and retail trade, frequently appear among the most vulnerable, suggesting that the shock is transmitted beyond directly energy-dependent industries to broader distribution and commercial networks. Agriculture has also emerged as a vulnerable sector in several economies, including India, Pakistan, Sri Lanka, Taipei (China), and Thailand, although it is not among the top five sectors in Bangladesh and Hong Kong (China). The impact of the simulated shock on inflation appears to differ based on each economy's production structure and reliance on sectoral inputs. Notably, sectors like mining, manufacturing, and motor vehicle trade have experienced significant price hikes in certain economies, underscoring the widespread pass-through effects of the shock. The findings reveal that energy-related cost pressures are disseminated through various channels, with particularly pronounced impacts on real estate, transport, trade, and specific agriculture-related activities. These outcomes emphasize the necessity for sector-specific policy measures, particularly in economies where key production and distribution sectors are more vulnerable to inflation transmission. Table 3 Sectoral Inflation Under the Crisis Scenario in Selected Asian Economies: Agriculture and Food-Related Sectors (2024 Baseline) Country Agriculture, hunting, forestry, and fishing Food, beverages, and tobacco Actual Inflation Rate (2024, %) Estimated Inflation Rate (%) Actual Inflation Rate (2024, %) Estimated Inflation Rate (%) Bangladesh 5.40 6.25 10.70 11.53 Hong Kong, China 2.90 3.34 -0.30 -0.06 India 2.30 3.22 7.50 8.28 Japan 2.30 3.06 4.80 5.54 Korea, Republic of 1.70 2.41 3.90 4.49 Pakistan 20.20 21.13 20.80 21.68 Philippines -0.70 0.14 4.40 5.17 Sri Lanka 0.00 0.89 1.60 2.36 Taipei, China 1.40 2.17 4.40 5.06 Thailand 1.70 2.50 0.80 1.34 Note: Baseline inflation rates are derived from IMF national inflation data, as sector-specific inflation data are not available. The estimated inflation rates are based on a simulated crisis scenario. Table 3 reports the actual and estimated inflation rates for the agriculture, hunting, forestry, and fishing sector and the food, beverages, and tobacco sector across selected Asian economies under the crisis scenario. The results indicate substantial cross-country variation in sectoral inflation exposure. Pakistan recorded the highest inflation rates in both sectors, with agriculture rising from 20.20% to an estimated 21.13%, and food, beverages, and tobacco increasing from 20.80% to 21.68%. Bangladesh also shows relatively high estimated inflation, reaching 6.25% in agriculture and 11.53% in food, beverages, and tobacco. These findings suggest that economies facing elevated inflation are likely to experience further pressure in essential food-related sectors under a simulated shock scenario. In contrast, Hong Kong (China) and the Philippines display relatively low baseline inflation in agriculture, although the estimated rates still move upward under the crisis scenario. Similar upward adjustments are observed in India, Japan, the Republic of Korea, Taipei (China), and Thailand, where the estimated inflation rates consistently exceed the corresponding baseline values in both sectors. This pattern indicates that the simulated shock amplifies inflationary pressure not only in highly vulnerable economies but also in those with more moderate initial inflation conditions. Overall, the results suggest that agriculture and food-related sectors are particularly sensitive to war-related cost transmission, with potentially important implications for food affordability and broader inflation dynamics across Asian economies. Discussion The findings reveal that the simulated 2026 Iranian war shock had uneven inflationary effects across the selected Asian economies (Jeyaretnam 2026 ; Stirling and Golle 2026 ). Pakistan and Bangladesh recorded the strongest inflationary responses (Bahceli and Jones 2026 ), with 2024 crisis-adjusted inflation rates of 24.09% and 10.42%, respectively. At both the aggregate level and in key food-related sectors, this suggests that economies with greater dependence on imported energy and weaker macroeconomic buffers are more vulnerable to external supply-side disruptions. In contrast, Japan, the Republic of Korea, and Thailand showed smaller inflationary deviations under the shock scenario, although the results still indicated meaningful upward pressure on prices through higher energy-related production costs and wider cost pass-through effects (Kihara 2026 ; Reuters 2026a ; Reuters 2026b ; Reuters 2026c ). The sectoral results further show that the transmission of the shock is broad, rather than directly confined to energy-dependent industries. Real estate appears repeatedly among the most vulnerable sectors, while transport-related services and trade sectors are prominent across economies. Agriculture has emerged as another key vulnerable sector, especially when considered together with the results for food, beverages, and tobacco. The observed pattern indicates that the financial pressures associated with war permeate various production and distribution pathways, leading to increased costs not only in the initial stages of production but also in commercial, logistics, and consumer pricing. Table 3 supports this interpretation by demonstrating that countries with already high inflation rates, such as Pakistan and Bangladesh, experience the most significant estimated inflation hikes in the agriculture and food sectors. Conversely, regions with lower initial inflation, like Hong Kong (China) and the Philippines, show smaller estimated increases, although the trend remains upward. Overall, the findings imply that the simulated shock exacerbates existing inflationary pressures rather than causing a uniform impact across all economies. The findings have significant implications for policy. In economies that are heavily impacted, urgent actions might need to prioritize stabilizing the prices of essential commodities and ensuring food security, especially by offering targeted assistance to agriculture and food-related sectors. Since the study also highlights real estate, transport, and trade-related industries as key transmission channels, short-term strategies may be necessary to mitigate cost pass-through in production, logistics, and distribution networks. These strategies could involve temporary and focused support for at-risk producers, enhancing distribution efficiency, and safeguarding low-income households that are most vulnerable to increasing food and transport expenses. In the medium to long term, the findings highlight the significance of enhancing resilience to energy-related disruptions through structural adjustments. This involves boosting energy efficiency, diversifying energy sources, and minimizing excessive reliance on imported fuel in vulnerable sectors. Economies like Hong Kong (China), Japan, and the Republic of Korea, which seem less impacted in the simulation, might gain from proactive strategies to mitigate future pass-through effects. Overall, the varied nature of the results indicates that a standardized regional policy response is unlikely to be effective. Instead, policy design should reflect differences in economic structures, energy dependence, and sectoral exposure across economies. These findings should also be interpreted with caution, as the input–output framework is static and does not capture substitution effects, behavioral adjustments, or policy responses over time. Conclusion The 2026 Iran War has redefined the parameters of global economic stability by exposing the profound structural dependencies that tie Asian prosperity to a single, narrow maritime channel. Through the application of a Leontief price model within an input-output framework, this study has demonstrated that a geopolitical energy shock is not an isolated sector event but a systemic contagion. The extreme vulnerability observed in Pakistan and Bangladesh serves as a warning for other emerging economies with high energy import dependence and fragile fiscal positions (The Financial Express 2026 ). Meanwhile, the "stagflationary" pressures faced by Japan and South Korea highlight that even the most advanced industrial powers are susceptible to the physical and economic disruption of energy flows (The Star 2026 ). The prominence of sectors such as real estate, retail trade, and food processing among the most vulnerable underscores that energy shocks strike at the heart of household welfare and urban stability. To sum up, the crisis scenario of 2026 necessitates a profound transformation in economic and energy strategies. The dependence on inexpensive and uninterrupted maritime energy supplies has been disrupted by geopolitical tensions. To build a sustainable future, it is essential to move past merely responding to inflation and instead focus on proactively designing economic systems that are diverse, efficient, and significantly less reliant on the uncertainties of global maritime chokepoints. Only through such comprehensive, long-term strategies can Asian economies aspire to maintain enduring macroeconomic stability in an increasingly unpredictable global environment. 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Systems 13(10):870. https://doi.org/10.3390/systems13100870 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9291437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":615908693,"identity":"a6acec25-0042-4e8b-bb52-8e22b0d6db37","order_by":0,"name":"Shiau Ping Chew","email":"","orcid":"","institution":"Tunku Abdul Rahman University of Management and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shiau","middleName":"Ping","lastName":"Chew","suffix":""},{"id":615908694,"identity":"e97be25a-15e0-4ae0-b223-6443bfa5d9c6","order_by":1,"name":"Chen Chen Yong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYPACGzApQYqWNNK1HCZBC3/7GcPHBb/OJ65tYD54m4dhW2IDIS0SZ3KMjWf23U7cdoAt2ZqH4TZhLQYSPGbSvD0gLUAGKVrOAbXwfyNBC8+PAyBb2IjTInEmrdiYtyHZeNthNmPLOQa3jQlq4W8/vPExzx872W3Hmx/eeFNxW5agFgYGDgMGxjYgzQx2J4MjEVrYHzAw/EFw7QnrGAWjYBSMgpEGAFyOPE+VhjtcAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1022-6615","institution":"Universiti Malaya","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"Chen","lastName":"Yong","suffix":""}],"badges":[],"createdAt":"2026-04-01 11:46:03","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9291437/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9291437/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093598,"identity":"3ec3e5f1-e827-4356-8faf-07f19df25197","added_by":"auto","created_at":"2026-04-03 11:38:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":536848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9291437/v1/958f545b-276f-4cec-8770-01e679ec3345.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGeopolitical Rupture and Inflationary Contagion: Sectoral Vulnerabilities in Asia Following the 2026 Iran War\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 2026 Iran War created a major geopolitical shock, significantly impacting global economic stability. The Strait of Hormuz is a key maritime chokepoint through which approximately 20% of global crude oil and 25% of liquefied natural gas (LNG) transits (World Economic Forum \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Herrero \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The disruption of nearly 20\u0026nbsp;million barrels of oil per day raised global energy prices and increased concerns about supply-chain security and energy market volatility (World Economic Forum \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). These effects extend well beyond the immediate conflict zone, generating wider pressures across interconnected economies. Asian markets are particularly vulnerable to this shock due to their high dependence on energy flows through the Strait of Hormuz. In 2024, Asia received 84% of the oil and 83% of the LNG transiting the Strait, making the region especially vulnerable to external energy disruptions (Hedley and Chin 2026; Jeyaretnam \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This dependence creates a direct transmission channel through which maritime instability can be converted into domestic inflationary pressure.\u003c/p\u003e \u003cp\u003eThis research is urgent because of the scale and nature of the 2026 shock, which sharply increased energy prices and severely disrupted supply (Varma and Toh 2026). Unlike earlier energy crises that were driven mainly by price increases, the 2026 Iran War also disrupted key shipping routes and damaged critical energy infrastructure, including Saudi Arabia\u0026rsquo;s largest refinery (Cushman and Wakefield 2026). Consequently, the crisis produced a dual shock: Brent crude rose to nearly USD 120 per barrel, and gas and fuel rationing became a major concern in countries such as India, Pakistan, and Bangladesh (Cushman and Wakefield 2026; Herrero \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite extensive literature on energy price transmission, empirical evidence on sectoral vulnerability and inflationary pass-through in Asia, particularly during major choke-point supply disruptions, remains limited (Varma and Toh 2026). This gap is critical, as it hinders granular policy responses to complex, multi-faceted crises, such as the 2026 Iran War. Existing studies often focus on price elasticity and aggregate macroeconomic effects, while giving less attention to the structural interdependencies captured by input-output (I-O) linkages in an era of high LNG dependence (Zhuang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To address this gap, this study investigates the following research questions: (a) How do national-level inflation rates in the selected ten Asian countries respond to the 2026 Iran War shock? (b) Which specific sectors exhibit the highest vulnerability based on input-output linkages? (c) What is the transmission mechanism of these shocks to agriculture and food? By quantifying these impacts, this research provides critical evidence to inform policy responses to inflationary and supply-side pressures stemming from such geopolitical conflicts. The study aims to provide evidence for policymakers to develop proactive strategies and robust responses to inflationary and supply-side pressures generated by the conflict.\u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cp\u003eThis study employs an input-output (I-O) price model to trace the transmission of war-related cost pressures arising from the 2026 Iran War across sectors in selected Asian economies. The I-O framework is appropriate because it captures the structural interdependence of production activities and allows both direct and indirect price effects to be identified through interindustry linkages. In contrast to the standard demand-driven Leontief quantity model, this study adopts a price-based specification to examine inflation transmission. Since the Iran War was expected to intensify energy-related production costs, the study proxied the shock with a 1% increase in the value-added input cost component.\u003c/p\u003e \u003cp\u003eThe sectoral price system is specified as, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p={A}^{{\\prime\\:}}\\:p+v,\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e denotes the transpose of the domestic technical coefficient matrix and \u003cem\u003ev\u003c/em\u003e represents value-added costs. Equilibrium prices are obtained from, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p=(I-A}^{{\\prime\\:}}{)}^{-1}v,\\)\u003c/span\u003e\u003c/span\u003e whereas the effect of the simulated shock is estimated as ∆\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p={(I-A}^{{\\prime\\:}}{)}^{-1}\\varDelta\\:v.\\:\\)\u003c/span\u003e\u003c/span\u003eHere, ∆\u003cem\u003ev\u003c/em\u003e captures the exogenous 1% increase in value-added input costs. The analysis draws on 2024 country-level input\u0026ndash;output tables from the Asian Development Bank (ADB) for ten Asian economies: Bangladesh, Hong Kong (China), India, Japan, the Republic of Korea, Pakistan, the Philippines, Sri Lanka, Taipei (China), and Thailand. To relate the sectoral simulation results to national inflation outcomes, the study uses baseline inflation data from the International Monetary Fund (IMF) database, specifically the 2024 observed inflation rate and the 2026 forecast inflation rate for each economy. The study then incorporate the simulated price shock into these benchmark inflation rates to derive the crisis-adjusted inflation measure for 2024 and the forecast energy shock inflation measure for 2026, as Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports. The model is further used to estimate sectoral output price changes and identify the sectors most vulnerable to inflation pass-through, particularly those linked to agriculture and food systems. While the static I-O framework provides valuable insights into immediate, direct, and indirect shock transmission, it inherently represents a short-run impact. The absence of substitution effects, dynamic adjustments, and policy interventions means the reported inflationary pressures likely represent an upper bound, as economic agents and governments would adapt over time. Future research could integrate dynamic macroeconomic models to capture these adaptive responses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section presents the main findings of the input\u0026ndash;output analysis, focusing on the simulated inflationary effects of the 2026 Iran War across selected Asian economies. The analysis first reports crisis-adjusted inflation rates at the national level and subsequently identifies the most vulnerable sectors across economies to trace the principal transmission channels of war-related cost shocks. It then examines sectoral vulnerabilities in greater detail, with particular emphasis on agriculture and food-related industries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Crisis Adjusted Inflation, Forecasted Inflation, and Energy Shock Inflation Rates in Selected Asian Economies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*2024 Crisis-Adjusted Inflation Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*2026 Forecasted Inflation Rate\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2026 Forecasted Energy Shock\u003c/p\u003e \u003cp\u003eInflation Rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKorea, Republic of\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaipei, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: * Value obtained from the IMF database.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the inflationary effects of the simulated shock are uneven across the selected Asian economies. Pakistan recorded the highest 2024 crisis-adjusted inflation rate at 24.09%, followed by Bangladesh at 10.42%, indicating that these economies are more exposed to war-related cost pressure. This high exposure is further underscored by Pakistan also reporting the highest 2026 simulated energy shock inflation rate of 6.20%, suggesting that additional cost increases could significantly worsen domestic inflation conditions. In contrast, Thailand and Sri Lanka displayed relatively low crisis-adjusted inflation rates at 0.83% and 1.83%, respectively, although no 2026 forecast values are available for Sri Lanka. These differences imply that the inflationary transmission of shocks is shaped by varying degrees of structural vulnerability across the economies.\u003c/p\u003e \u003cp\u003eA comparison between the baseline 2026 forecast inflation rates and the simulated energy shock inflation rates further shows that the shock adds upward pressure to inflation in all reported cases. For example, Bangladesh\u0026rsquo;s inflation rate rises from a baseline forecast of 8.75% to 8.94% under the shock scenario, while Pakistan\u0026rsquo;s increases from 6.04% to 6.20%. Similar, but smaller, increases are observed in Hong Kong (China), Japan, the Republic of Korea, and Thailand, where the gap between baseline and shock-adjusted inflation remains modest. This suggests that although the simulated shock affects all economies, its magnitude is more pronounced in economies that are already facing elevated inflation or greater exposure to external cost pressures. Overall, the findings indicate that war-related energy disruptions can intensify existing inflationary pressures; however, the scale of the effect differs substantially across Asian economies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop Five Most Vulnerable Sectoral Output Price Changes Resulting from Energy Shocks in Selected Asian Countries and Territories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTop 1 Sector (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTop 2 Sector (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTop 3\u003c/p\u003e \u003cp\u003eSector (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTop 4\u003c/p\u003e \u003cp\u003eSector (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTop 5\u003c/p\u003e \u003cp\u003eSector (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMining\u003c/p\u003e \u003cp\u003e(0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal Estate (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetail Trade (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther Manufacturing (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWood Products (0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransport Support Services\u003c/p\u003e \u003cp\u003e(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal Estate (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConstruction (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInland Transport (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRetail Trade (0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReal Estate (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgriculture (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMotor Vehicle Trade\u003c/p\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetail Trade (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWholesale Trade (0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReal Estate (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMotor Vehicle Trade\u003c/p\u003e \u003cp\u003e(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater Transport (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransport Support Services (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWood Products (0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKorea, Republic of\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReal Estate (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWholesale Trade\u003c/p\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMotor Vehicle Trade\u003c/p\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgriculture (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInland Transport (0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReal Estate (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWholesale Trade\u003c/p\u003e \u003cp\u003e(0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003cp\u003e(0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMining\u003c/p\u003e \u003cp\u003e(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFood Manufacturing (0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetail Trade (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal Estate (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWholesale Trade (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgriculture (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMotor Vehicle Trade\u003c/p\u003e \u003cp\u003e(0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReal Estate (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgriculture (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMining\u003c/p\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransport Support Services (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRetail Trade (0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaipei, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetail Trade (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReal Estate (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWholesale Trade (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransport Support Services (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAgriculture (0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePaper products (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReal Estate\u003c/p\u003e \u003cp\u003e(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRetail Trade (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWholesale Trade\u003c/p\u003e \u003cp\u003e(0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Values represent the estimated sectoral output changes under a simulated 1% energy price shock, derived from the input\u0026ndash;output model. The sector names are shortened versions of the original industry classification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the top five changes in sectoral output price resulting from a simulated 1% energy shock scenario in ten selected Asian economies. The findings indicate that the shock\u0026rsquo;s impact is not uniformly distributed across sectors, with several consistent patterns emerging across different economies. Real estate appears among the most affected sectors in nearly all cases and often ranks first or second, indicating a strong sensitivity to rising production and operating costs. Transport-related activities, including transport support services, inland transport, and water transport, also feature prominently, reflecting their close dependence on energy input. In addition, trade-related sectors, such as wholesale and retail trade, frequently appear among the most vulnerable, suggesting that the shock is transmitted beyond directly energy-dependent industries to broader distribution and commercial networks.\u003c/p\u003e \u003cp\u003eAgriculture has also emerged as a vulnerable sector in several economies, including India, Pakistan, Sri Lanka, Taipei (China), and Thailand, although it is not among the top five sectors in Bangladesh and Hong Kong (China). The impact of the simulated shock on inflation appears to differ based on each economy's production structure and reliance on sectoral inputs. Notably, sectors like mining, manufacturing, and motor vehicle trade have experienced significant price hikes in certain economies, underscoring the widespread pass-through effects of the shock. The findings reveal that energy-related cost pressures are disseminated through various channels, with particularly pronounced impacts on real estate, transport, trade, and specific agriculture-related activities. These outcomes emphasize the necessity for sector-specific policy measures, particularly in economies where key production and distribution sectors are more vulnerable to inflation transmission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSectoral Inflation Under the Crisis Scenario in Selected Asian Economies: Agriculture and Food-Related Sectors (2024 Baseline)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture, hunting, forestry, and fishing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFood, beverages, and tobacco\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActual Inflation\u003c/p\u003e \u003cp\u003eRate (2024, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated Inflation Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActual Inflation Rate (2024, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimated Inflation Rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKorea, Republic of\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhilippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaipei, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Baseline inflation rates are derived from IMF national inflation data, as sector-specific inflation data are not available. The estimated inflation rates are based on a simulated crisis scenario.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the actual and estimated inflation rates for the agriculture, hunting, forestry, and fishing sector and the food, beverages, and tobacco sector across selected Asian economies under the crisis scenario. The results indicate substantial cross-country variation in sectoral inflation exposure. Pakistan recorded the highest inflation rates in both sectors, with agriculture rising from 20.20% to an estimated 21.13%, and food, beverages, and tobacco increasing from 20.80% to 21.68%. Bangladesh also shows relatively high estimated inflation, reaching 6.25% in agriculture and 11.53% in food, beverages, and tobacco. These findings suggest that economies facing elevated inflation are likely to experience further pressure in essential food-related sectors under a simulated shock scenario.\u003c/p\u003e \u003cp\u003eIn contrast, Hong Kong (China) and the Philippines display relatively low baseline inflation in agriculture, although the estimated rates still move upward under the crisis scenario. Similar upward adjustments are observed in India, Japan, the Republic of Korea, Taipei (China), and Thailand, where the estimated inflation rates consistently exceed the corresponding baseline values in both sectors. This pattern indicates that the simulated shock amplifies inflationary pressure not only in highly vulnerable economies but also in those with more moderate initial inflation conditions. Overall, the results suggest that agriculture and food-related sectors are particularly sensitive to war-related cost transmission, with potentially important implications for food affordability and broader inflation dynamics across Asian economies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings reveal that the simulated 2026 Iranian war shock had uneven inflationary effects across the selected Asian economies (Jeyaretnam \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Stirling and Golle \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Pakistan and Bangladesh recorded the strongest inflationary responses (Bahceli and Jones \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), with 2024 crisis-adjusted inflation rates of 24.09% and 10.42%, respectively. At both the aggregate level and in key food-related sectors, this suggests that economies with greater dependence on imported energy and weaker macroeconomic buffers are more vulnerable to external supply-side disruptions. In contrast, Japan, the Republic of Korea, and Thailand showed smaller inflationary deviations under the shock scenario, although the results still indicated meaningful upward pressure on prices through higher energy-related production costs and wider cost pass-through effects (Kihara \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Reuters \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2026a\u003c/span\u003e; Reuters \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2026b\u003c/span\u003e; Reuters \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2026c\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sectoral results further show that the transmission of the shock is broad, rather than directly confined to energy-dependent industries. Real estate appears repeatedly among the most vulnerable sectors, while transport-related services and trade sectors are prominent across economies. Agriculture has emerged as another key vulnerable sector, especially when considered together with the results for food, beverages, and tobacco. The observed pattern indicates that the financial pressures associated with war permeate various production and distribution pathways, leading to increased costs not only in the initial stages of production but also in commercial, logistics, and consumer pricing. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e supports this interpretation by demonstrating that countries with already high inflation rates, such as Pakistan and Bangladesh, experience the most significant estimated inflation hikes in the agriculture and food sectors. Conversely, regions with lower initial inflation, like Hong Kong (China) and the Philippines, show smaller estimated increases, although the trend remains upward. Overall, the findings imply that the simulated shock exacerbates existing inflationary pressures rather than causing a uniform impact across all economies.\u003c/p\u003e \u003cp\u003eThe findings have significant implications for policy. In economies that are heavily impacted, urgent actions might need to prioritize stabilizing the prices of essential commodities and ensuring food security, especially by offering targeted assistance to agriculture and food-related sectors. Since the study also highlights real estate, transport, and trade-related industries as key transmission channels, short-term strategies may be necessary to mitigate cost pass-through in production, logistics, and distribution networks. These strategies could involve temporary and focused support for at-risk producers, enhancing distribution efficiency, and safeguarding low-income households that are most vulnerable to increasing food and transport expenses.\u003c/p\u003e \u003cp\u003eIn the medium to long term, the findings highlight the significance of enhancing resilience to energy-related disruptions through structural adjustments. This involves boosting energy efficiency, diversifying energy sources, and minimizing excessive reliance on imported fuel in vulnerable sectors. Economies like Hong Kong (China), Japan, and the Republic of Korea, which seem less impacted in the simulation, might gain from proactive strategies to mitigate future pass-through effects. Overall, the varied nature of the results indicates that a standardized regional policy response is unlikely to be effective. Instead, policy design should reflect differences in economic structures, energy dependence, and sectoral exposure across economies. These findings should also be interpreted with caution, as the input\u0026ndash;output framework is static and does not capture substitution effects, behavioral adjustments, or policy responses over time.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe 2026 Iran War has redefined the parameters of global economic stability by exposing the profound structural dependencies that tie Asian prosperity to a single, narrow maritime channel. Through the application of a Leontief price model within an input-output framework, this study has demonstrated that a geopolitical energy shock is not an isolated sector event but a systemic contagion. The extreme vulnerability observed in Pakistan and Bangladesh serves as a warning for other emerging economies with high energy import dependence and fragile fiscal positions (The Financial Express \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Meanwhile, the \"stagflationary\" pressures faced by Japan and South Korea highlight that even the most advanced industrial powers are susceptible to the physical and economic disruption of energy flows (The Star \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The prominence of sectors such as real estate, retail trade, and food processing among the most vulnerable underscores that energy shocks strike at the heart of household welfare and urban stability.\u003c/p\u003e \u003cp\u003eTo sum up, the crisis scenario of 2026 necessitates a profound transformation in economic and energy strategies. The dependence on inexpensive and uninterrupted maritime energy supplies has been disrupted by geopolitical tensions. To build a sustainable future, it is essential to move past merely responding to inflation and instead focus on proactively designing economic systems that are diverse, efficient, and significantly less reliant on the uncertainties of global maritime chokepoints. Only through such comprehensive, long-term strategies can Asian economies aspire to maintain enduring macroeconomic stability in an increasingly unpredictable global environment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsian Development Bank (2024) ADB multiregional input-output tables at current prices. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kidb.adb.org/globalization\u003c/span\u003e\u003cspan address=\"https://kidb.adb.org/globalization\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahceli Y, Jones M (2026) Which economies will hurt most from the Iran war? 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Reuters. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.reuters.com/world/asia-pacific/thailand-seeking-new-energy-sources-promote-subsidised-biodiesel-2026-03-09/?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://www.reuters.com/world/asia-pacific/thailand-seeking-new-energy-sources-promote-subsidised-biodiesel-2026-03-09/?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuters (2026c) South Korea FM Cho requests Iran to ensure safety of vessels inside Strait of Hormuz. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.reuters.com/business/energy/south-korea-fm-cho-requests-iran-ensure-safety-vessels-inside-strait-hormuz-2026-03-23/?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://www.reuters.com/business/energy/south-korea-fm-cho-requests-iran-ensure-safety-vessels-inside-strait-hormuz-2026-03-23/?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStirling C, Golle V (2026) Shockwave of chaotic Middle East war is rippling through the global economy. 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The Star. :text=South%20Korea%20and%20Japan%20have,bespoke%20solutions%20at%20Aberdeen%20Investments. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thestar.com.my/aseanplus/aseanplus-news/2026/03/18/south-korea-and-japan-bear-brunt-of-global-stock-sell-offs-amid-oil-shock#:~\u003c/span\u003e\u003cspan address=\"https://www.thestar.com.my/aseanplus/aseanplus-news/2026/03/18/south-korea-and-japan-bear-brunt-of-global-stock-sell-offs-amid-oil-shock#:~\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarma S, Toh SY. Asia Economic Monthly: Asia's Hormuz Exposure Scorecard., Nomura (2026) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nomuraconnects.com/focused-thinking-posts/asia-economic-monthly-asias-hormuz-exposure-scorecard/\u003c/span\u003e\u003cspan address=\"https://www.nomuraconnects.com/focused-thinking-posts/asia-economic-monthly-asias-hormuz-exposure-scorecard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood Mackenzie (2026) Strait of Hormuz closure threatens South Asia LNG supply. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.woodmac.com/press-releases/strait-of-hormuz-closure-threatens-south-asia-lng-supply/\u003c/span\u003e\u003cspan address=\"https://www.woodmac.com/press-releases/strait-of-hormuz-closure-threatens-south-asia-lng-supply/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Economic Forum (2026) The global price tag of war in the Middle East. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.weforum.org/stories/2026/03/the-global-price-tag-of-war-in-the-middle-east/\u003c/span\u003e\u003cspan address=\"https://www.weforum.org/stories/2026/03/the-global-price-tag-of-war-in-the-middle-east/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang X, Wang S, Tang Z, Fu Z, and Dong. B (2025) Food Security Under Energy Shock: Research on the Transmission Mechanism of the Effect of International Crude Oil Prices on Chinese and U.S. Grain Prices. Systems 13(10):870. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/systems13100870\u003c/span\u003e\u003cspan address=\"10.3390/systems13100870\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universiti Malaya","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"2026 Iran War, Inflationary Pressure, Asian Economies, Agriculture, Food Affordability","lastPublishedDoi":"10.21203/rs.3.rs-9291437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9291437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 2026 Iran War triggered significant geopolitical disruptions, particularly impacting Asian economies that rely on energy transit through the Strait of Hormuz. Such disruptions can generate inflationary pressures that extend beyond the energy sector into production, distribution, and food systems. This study examines how these war-related cost pressures are transmitted across sectors and into national inflation in ten selected Asian economies. Using an input-output (I-O) framework, this study analyzes how energy price shocks and supply chain disruptions spread inflationary pressures across energy-intensive sectors in ten selected Asian economies. The findings show clear differences across selected economies. Pakistan and Bangladesh emerge as the most vulnerable, with the highest crisis-adjusted inflation and stronger price effects in agriculture and food-related sectors. In contrast, Thailand and Sri Lanka show relatively smaller effects. Sectoral transmission is broad, with real estate, transport services, and trade consistently among the most affected sectors. Overall, the results indicate that geopolitical energy disruptions can intensify inflation through production and distribution networks, with important implications for food affordability and household welfare.\u003c/p\u003e","manuscriptTitle":"Geopolitical Rupture and Inflationary Contagion: Sectoral Vulnerabilities in Asia Following the 2026 Iran War","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:06:26","doi":"10.21203/rs.3.rs-9291437/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb72e634-fdc9-4872-90aa-1cf5378398ad","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T07:06:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:06:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9291437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9291437","identity":"rs-9291437","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}