Effects of Compensation Measures on Post-earthquake Building Reconstruction by Taking Reconstruction Factors as Mediating Variables: Evidence from China

Abstract

Post-earthquake building reconstruction usually faces challenges such as inadequate preparation and lack of resources, while compensation measures can help alleviate these difficulties. Although various compensation measures have been proposed for accelerating post-earthquake recovery, their effects on building reconstruction are still unclear. This study aimed to analyze and evaluate the effects of compensation measures on post-earthquake building reconstruction. To achieve this objective, this study first identified the post-earthquake building reconstruction goals, 21 reconstruction factors across five categories, and four compensation measures, and employed the methods of structural equation modeling, paired-samples t-test, and repeated measures analysis of variance to analyze the effects of reconstruction factors on the reconstruction goals and the effects of compensation measures on the reconstruction factors, respectively. A questionnaire was designed to investigate the post-earthquake building reconstruction projects in China, with 251 respondents participated. The results show that apart from project-specific condition, the other four categories of reconstruction factors, including owner condition, technology condition, resource availability, and contractor management capability, have significant effects on the reconstruction goals. The study also evaluated the extent of the above effects. On the other hand, the results indicate that the affecting extents of various compensation measures on reconstruction factors are significantly different. These findings can provide support and guidance for government management of post-earthquake building reconstruction and the development of compensation measures. They also provide a basis for future research on optimizing compensation measures for post-earthquake building reconstruction.

Keywords

Building reconstruction / China / Compensation measures / Post-earthquake / Reconstruction factors

References

[1] AakreS, BanaszakI, MechlerR, RübbelkeD, WrefordA, KaliraiH. Financial adaptation to disaster risk in the European Union. Identifying roles for the public sector. Mitigation and Adaptation Strategies for Global Change, 2010, 157721-736.

[2] Abd El-RazekME, BassioniHA, MobarakAM. Causes of delay in building construction projects in Egypt. Journal of Construction Engineering and Management, 2008, 13411831-841.

[3] Alonso-Dos-SantosM, Llanos-ContrerasO. Family business performance in a post-disaster scenario: The influence of socioemotional wealth importance and entrepreneurial orientation. Journal of Business Research, 2019, 101: 492-498.

[4] AnilkumarS, BanerjiH. An inquiry into success factors for post-disaster housing reconstruction projects: A case of Kerala, south India. International Journal of Disaster Risk Science, 2021, 12124-39.

[5] AoYB, ZhongJL, ZhangZJ, HanLL, WangY, ChenYF, WangT. Determinants of villagers’ satisfaction with post-disaster reconstruction: Evidence from surveys ten years after the Wenchuan Earthquake. Frontiers in Environmental Science, 2022, 10: Article 952700.

[6] BaarimahAO, AlaloulWS, LiewMS, KartikaW, Al-SharafiMA, MusaratMA, AlawagAM, QureshiAH. A bibliometric snalysis and review of building information modelling for post-disaster reconstruction. Sustainability, 2022, 141Article 393.

[7] BahmaniH, ZhangW. Application of system thinking and factors interrelationship analysis to identify primary success factors of post-natural disaster recovery projects. Sustainability, 2021, 136Article 3392.

[8] BahmaniH, ZhangW. Why do communities recover differently after socio-natural disasters? Pathways to comprehensive success of recovery projects based on Bam’s (Iran) neighborhoods’ perspective. International Journal of Environmental Research and Public Health, 2022, 192Article 678.

[9] Bevere, L., and F. Remondi. 2022. Natural catastrophes in 2021: The floodgates are open. Zurich, Switzerland: Swiss Re Institute.

[10] BhattaraiS, MaycockB, AlfonsoH, ReidA. Development of an integrated pathways model of factors influencing the progress of recovery after a disaster. Asia-Pacific Journal of Public Health, 2020, 325226-234.

[11] BilauA, WittE, LillI. Practice framework for the management of post-disaster housing reconstruction programmes. Sustainability, 2018, 1011Article 3929.

[12] BonnI, Rundle-ThieleS. Do or die – Strategic decision-making following a shock event. Tourism Management, 2007, 282615-620.

[13] BrownC, SevilleE, VargoJ. Efficacy of insurance for organisational disaster recovery: Case study of the 2010 and 2011 Canterbury earthquakes. Disasters, 2017, 412388-408.

[14] BrownC, StevensonJ, GiovinazziS, SevilleE, VargoJ. Factors influencing impacts on and recovery trends of organisations: Evidence from the 2010/2011 Canterbury earthquakes. International Journal of Disaster Risk Reduction, 2015, 14: 56-72.

[15] BurtonHV, DeierleinG, LallemantD, LinT. Framework for incorporating probabilistic building performance in the assessment of community seismic resilience. Journal of Structural Engineering, 2016, 1428Article C4015007.

[16] ChangY, WilkinsonS, PotangaroaR, SevilleE. Donor-driven resource procurement for post-disaster reconstruction: Constraints and actions. Habitat International, 2011, 352199-205.

[17] ChangY, WilkinsonS, SevilleE, PotangaroaR. Changes in resource need for post-disaster reconstruction: A longitudinal study in China. Building Research and Information, 2012, 403327-336.

[18] ChangY, WilkinsonS, PotangaroaR, SevilleE. Resourcing for post-disaster reconstruction: A comparative study of Indonesia and China. Disaster Prevention and Management, 2012, 2117-21.

[19] Chang-RichardsY, WilkinsonS, SevilleE, BrunsdonD. Effects of a major disaster on skills shortages in the construction industry: Lessons learned from New Zealand. Engineering Construction and Architectural Management, 2017, 2412-20.

[20] Cui, L. 2008. The direct economic loss of the Wenchuan Earthquake was 845.1 billion yuan. http://www.npc.gov.cn/zgrdw/npc/zt/2008-09/05/content_1448390.htm. Accessed Jan 30 2024.

[21] DikmenN, Elias-OzkanST, DavidsonC. Comparison of post-disaster housing procurement methods in rural areas of Turkey. Open House International, 2012, 37128-39.

[22] El HageJ, ShahrourI, ChehadeFH, FarrajFA. A comprehensive assessment of buildings for post-disaster sustainable reconstruction: A case study of Beirut Port. Sustainability, 2023, 1518Article 13433.

[23] EnshassiA, ChatatT, von MedingJ, ForinoG. Factors influencing post-disaster reconstruction project management for housing provision in the Gaza Strip, Occupied Palestinian Territories. International Journal of Disaster Risk Science, 2017, 84402-414.

[24] EstiriM, Heidary DahooieJ, SkareM. COVID-19 crisis and resilience of tourism SME’s: A focus on policy responses. Economic Research-Ekonomska Istraživanja, 2022, 3515556-5580.

[25] FornellC, LarckerDF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 1981, 18139-50.

[26] Goretzko, D., K. Siemund, and P. Sterner. 2023. Evaluating model fit of measurement models in confirmatory factor analysis. Educational and Psychological Measurement 84: Article 2112648234.

[27] GuoY, HouY. Multi-factor model based on the gray model for the cost control of post-earthquake reconstruction projects. China Earthquake Engineering Journal, 2019, 411215-220

[28] HaynesGW, DanesSM, SchrankHL, LeeY. Survival and success of family-owned small businesses after hurricane Katrina: Impact of disaster assistance and adaptive capacity. Journal of Contingencies and Crisis Management, 2019, 272130-144.

[29] IslamE, Abd WahabH, BensonOG. Structural and operational factors as determinant of meaningful community participation in sustainable disaster recovery programs: The case of Bangladesh. International Journal of Disaster Risk Reduction, 2020, 50: Article 101710.

[30] KamariM, HamY. AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning. Automation in Construction, 2022, 134: Article 104091.

[31] KarunarathneAY, GressDR. The role of organizational networks in ameliorating flood disaster impacts: A case study of flood inundated rural and urban areas in Sri Lanka. International Journal of Disaster Risk Reduction, 2022, 71: Article 102819.

[32] KatzR, Attal-JuncquaA, FischerJE. Funding public health emergency preparedness in the United States. American Journal of Public Health, 2017, 107S2S148-S152.

[33] KhatibBA, PohYS, El-ShafieA. Delay factors management and ranking for reconstruction and rehabilitation projects based on the relative importance index (RII). Sustainability, 2020, 1215Article 6171.

[34] KotaniH, HondaR. Effective combinations of information content and channels for the post-disaster reconstruction of rural housing: A case study of the 2015 Gorkha Nepal earthquake. International Journal of Disaster Risk Reduction, 2019, 39: Article 101118.

[35] KotaniH, HondaR, ImotoS, ShakyaL, ShresthaBK. Transition of post-disaster housing of rural households: A case study of the 2015 Gorkha earthquake in Nepal. International Journal of Disaster Risk Reduction, 2020, 44: Article 101443.

[36] KouskyC. The role of natural disaster insurance in recovery and risk reduction. Annual Review of Resource Economics, 2019, 11: 399-418.

[37] LiY, FengXF. Influence of housing resettlement on the subjective well-being of disaster-forced migrants: An empirical study in Yancheng City. Sustainability, 2021, 1315Article 8171.

[38] LiF, WangL, JinZ, HuangL, XiaB. Key factors affecting sustained business operations after an earthquake: A case study from New Beichuan, China, 2013–2017. Natural Hazards, 2020, 1041101-121.

[39] LuY, XuJ. NGO collaboration in community post-disaster reconstruction: Field research following the 2008 Wenchuan Earthquake in China. Disasters, 2015, 392258-278.

[40] MarshallMI, SchrankHL. Sink or swim? Impacts of management strategies on small business survival and recovery. Sustainability, 2020, 1215Article 6229.

[41] MehdizadehR, DeckO, PottierN, Péné-AnnetteA. Post-disaster reconstruction of residential buildings: Evolution of structural vulnerability on Caribbean Island of Saint Martin after Hurricane Irma. Sustainability, 2023, 1517Article 12788.

[42] MiaoQ, HouY, AbrigoM. Measuring the financial shocks of natural disasters: A panel study of U.S. States. National Tax Journal, 2018, 71111-44.

[43] NakashimaT, OkadaS. Financial imbalances in regional disaster recovery following earthquakes –Case study concerning housing-cost expenditures in Japan. Sustainability, 2018, 109Article 3225.

[44] OpabolaEA, GalassoC. A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries. Reliability Engineering & System Safety, 2024, 242: Article 109679.

[45] PaleariS. Disaster risk insurance: A comparison of national schemes in the EU-28. International Journal of Disaster Risk Reduction, 2019, 35: Article 101059.

[46] PasupuletiRS. Designing culturally responsive built environments in post disaster contexts: Tsunami affected fishing settlements in Tamilnadu, India. International Journal of Disaster Risk Reduction, 2013, 6: 28-39.

[47] PengL, WuF. Building up alliances and breaking down the state monopoly: The rise of non-governmental disaster relief in China. The China Quarterly, 2018, 234: 463-485.

[48] PengJ, PengC, WangM, HuK, WuD. Research on the factors of extremely short construction period under the sufficient resources based on Grey-DEMATEL-ISM. PLoS One, 2022, 173Article e0265087.

[49] PengY, ShenLY, TanC, TanDL, WangH. Critical determinant factors (CDFs) for developing concentrated rural settlement in post-disaster reconstruction: A China study. Natural Hazards, 2013, 662355-373.

[50] People’s Goverment of Sichuan Province. 2022. Recovery and reconstruction support policy measures after 9·5 Luding Earthquake. https://www.sc.gov.cn/10462/zfwjts/2023/1/5/69466eec10df48bca7b87e8256d2a7df.shtml. Accessed 30 Jan 2022 (in Chinese).

[51] PezzicaC, CutiniV, de SouzaCB, AloiniD. The making of cities after disasters: Strategic planning and the central Italy temporary housing process. Cities, 2022, 131: Article 104053.

[52] Rodríguez-EspíndolaO. Two-stage stochastic formulation for relief operations with multiple agencies in simultaneous disasters. Or Spectrum, 2023, 452477-523.

[53] RoseA, HuyckCK. Improving catastrophe modeling for business interruption insurance needs. Risk Analysis, 2016, 36101896-1915.

[54] SafapourE, KermanshachiS, PamidimukkalaA. Post-disaster recovery in urban and rural communities: Challenges and strategies. International Journal of Disaster Risk Reduction, 2021, 64: Article 10535.

[55] SambasivanM, SoonYW. Causes and effects of delays in Malaysian construction industry. International Journal of Project Management, 2007, 255517-526.

[56] Serrao-NeumannS, CrickF, Low ChoyD. Post-disaster social recovery: Disaster governance lessons learnt from Tropical Cyclone Yasi. Natural Hazards, 2018, 9331163-1180.

[57] SinesilassieEG, TabishSZS, JhaKN. Critical factors affecting cost performance: A case of Ethiopian public construction projects. International Journal of Construction Management, 2017, 182108-119.

[58] SteigerJH. Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 1990, 252173-180.

[59] SweisG, SweisR, Abu HammadA, ShboulA. Delays in construction projects: The case of Jordan. International Journal of Project Management, 2008, 266665-674.

[60] ThirawatN, UdompolS, PonjanP. Disaster risk reduction and international catastrophe risk insurance facility. Mitigation and Adaptation Strategies for Global Change, 2017, 2271021-1039.

[61] TiefenbachT, KohlbacherF. Disasters, donations, and tax law changes: Disentangling effects on subjective well-being by exploiting a natural experiment. Journal of Economic Psychology, 2015, 50: 94-112.

[62] TiwariP, ShuklaJ. Measuring housing well-being of disaster affected persons in Chennai (India). Housing Studies, 2023, 39: 2425-2454.

[63] TranTA. Post-disaster housing reconstruction as a significant opportunity to building disaster resilience: A case in Vietnam. Natural Hazards, 2015, 79161-79.

[64] TsaiSL, OchiaiC, TsengMH, DengCZ. A framework for NGO-led post-disaster reconstruction programs in the indigenous community: A case study of the 2009 Typhoon Morakot in Taiwan. International Journal of Disaster Risk Reduction, 2022, 73: Article 10879.

[65] UllmanJB, BentlerPMWeinerI, SchinkaJ, VelicerW. Structural equation modeling. Handbook of psychology, 20122Hoboken, NJWiley661-690

[66] VahanvatiM, MulliganM. A new model for effective post-disaster housing reconstruction: Lessons from Gujarat and Bihar in India. International Journal of Project Management, 2017, 355802-817.

[67] VarolgünesFK. Post-disaster permanent housing: The case of the 2003 Bingol earthquake in Turkey. Disaster Prevention and Management, 2021, 30277-93.

[68] VaseyMW, ThayerJF. The continuing problem of false positives in repeated measures ANOVA in psychophysiology: A multivariate solution. Psychophysiology, 1987, 244479-486.

[69] VilventhanA, KalidindiSN. Interrelationships of factors causing delays in the relocation of utilities: A cognitive mapping approach. Engineering Construction and Architectural Management, 2016, 233349-368.

[70] WatsonM. The role of SBA loans in small business survival after disaster events. Journal of Planning Education and Research, 2021.

[71] XiJ. Relationship between the organizational structure in implementing post-disaster housing reconstruction and outcome characteristics: A study on urban Dujiangyan after the Wenchuan Earthquake. International Journal of Disaster Risk Reduction, 2022, 80: Article 103221.

[72] XieW, LiN, WuJD, HaoXL. Disaster risk decision: A dynamic computable general equilibrium analysis of regional mitigation investment. Human and Ecological Risk Assessment, 2015, 21181-99.

[73] XuJ, SunC. Analysis of the factors affecting durations of post-Wenchuan Earthquake reconstruction. World Sci-Tech R & D, 2008, 304509-515

[74] YiF, TuY. An evaluation of the paired assistance to disaster-affected areas program in disaster recovery: The case of the Wenchuan Earthquake. Sustainability, 2018, 1012Article 4483.

[75] ZhaoJ, LeeJY, LiY, YinY. Effect of catastrophe insurance on disaster-impacted community: Quantitative framework and case studies. International Journal of Disaster Risk Reduction, 2020, 43: Article 101387.

[76] ZhaoY, LeeJ, YuM. Catastrophe risk, reinsurance and securitized risk-transfer solutions: A review. China Finance Review International, 2021, 114449-473.

[77] ZhouZ, IrizarryJ. Integrated framework of modified accident energy release model and network theory to explore the full complexity of the Hangzhou subway construction collapse. Journal of Management in Engineering, 2016, 325Article 5016013.

[78] ZimmermanDW. Teacher’s corner: A note on interpretation of the paired-samples t-test. Journal of Educational and Behavioral Statistics, 1997, 223349-360

[79] ZouM, YuanY. China’s comprehensive disaster reduction. International Journal of Disaster Risk Science, 2010, 1124-32

RIGHTS & PERMISSIONS

The Author(s)

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.