Underground Space-Subsidence and Economic Impact Planning Model in Shanghai: Cause-Effect and Spatiotemporal Regression Analyses for Year 1960-2020

Underground Space-Subsidence and Economic Impact Planning Model in Shanghai: Cause-Effect and Spatiotemporal Regression Analyses for Year 1960-2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Underground Space-Subsidence and Economic Impact Planning Model in Shanghai: Cause-Effect and Spatiotemporal Regression Analyses for Year 1960-2020 Muhammad Akmal Hakim bin Hishammnuddin, Jianxiu Wang, Muhammad Azizol Ismail, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849481/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 Shanghai is continuously threatened with land subsidence with impact on its built environment e.g. building, land, properties, infrastructure and socio-economic activities. This research studies the mutiregression and spatiotemporal of the identified causing factors; surface load, groundwater-induced, socio-economic growth, underground space development (UUS), climate change and presents the relations of the multifactor in 1960–2020. Data are collected secondarily from open sourced databases. Results determine rhe causing factors with high correlation coefficients > 0.90 R squared values are; UUS development induced-subsidence, tunneling leakage and weak spatial modelling. Economic impact factors include; building price, reconstruction area, arable land, GDP by district and metro tunnel settlement. Spatiotemporal patterns depict the population and land subsidence is growing in spatial autocorrelation with the ratio of 0.89:1.00 indirect-negative economic impact from Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. These results can be referred as preparation for further adaptive and resilient scenario spatial planning and modelling. Land subsidence cause-effect spatiotemporal economic impact underground space spatial planning model Shanghai Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Climate change influences natural hazards e.g. accelerated sea level rise, storm surge, land subsidence and tectonic movement (Mimura, 2013 ). This sets hurdles in low laying, coastal urban areas with rapid and unconscious socio-economic development, agglomeration, population growth which increase demand for space and resources (e.g. excessive groundwater extraction, minerals, oil, gas, coal mining), surface development load, water demand and carbon emissions (Qin et al., 2014 ; Avtar et al., 2019 ). Taking land subsidence as a silent killer, underrated issue and influence direct and indirect economic impact in coastal megacity, the cause-effects are diverse from the excessive groundwater extraction, mining, natural subsidence: tectonic movements to urbanisation induced subsidence e.g. population growth; surface development load and currently, dangerous or unconscious urban underground space (UUS) development (refer Fig. 1 ). Despite having the land subsidence successfully controlled since 1960s through the banning of groundwater extraction in the urban and built-up area (refer Fig. 2), Shanghai as a developed coastal megacity and the center of national economic agglomeration region still anticipate for continuous possible challenges in the future (Zhang et al. 2018 ; Peng et al. 2021 ; Yuan et al. 2017 ; Wu et al. 2017 ; Wu et al. 2019 ; Hishammuddin et al. 2021 ). The International Geoscience Programme (IGCP) Plan by the United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2017 has continuously funding research projects towards investigating the control and monitoring of land subsidence and impacts especially in coastal megacities like Shanghai. With continuous land subsidence rate of 6-100mm per year, increasing sea water level and flood risks created further vast damage to the urban economic include factors e.g. buildings, properties, infrastructures and land. Theoretically, different megacities possess different anthropogenic activities that represents different load or impact on land subsidence (Ortega-Guerrero & Carrillo-Rivera, 2012 ). The cause-effect relationships in urban settings are complex due to many independent factors and undesirable impacts (Komeily and Srinivasan, 2015 ). The UUS exploration in a condensed Shanghai megacity may become of the factor. With the physical offers of three-dimensional (3D) development and the targeted vision of ‘big, deep, long, fast and dense’ (Huang et al., 2015 ), it is expected to grow significantly by twenty years to come. However, researches on resilient urban spatial planning with the concern of UUS-subsidence-economic integration for coastal megacity is still lacking. Coastal megacity itself is complex and there is still limited research on the potential of such integrated model. There currently are many disconnected assessment frameworks on underground space (Zhang et al. 2018 ; Peng et al., 2021 ) and land subsidence-economic impact (Abidin et al., 2015 ; Shi et al., 2018 ; Erkens et al., 2015 ; He et al., 2019 ; Lyu et al., 2020 ; Kok and Costa, 2021 ], hence, this research attempts to merge as much as possible, the existing frameworks towards instituting a comprehensive connected model in relation with the urban planning. This paper presents the understanding of development cause-effect extent concerning however not limited to the UUS-subsidence-economic via cause-effect analysis and spatiotemporal. It is imperative to study the ever-complex multidisciplinary spectrum by the investigation of the past and current situation and potentially provide basis for future scenario modelling. 2. Materials and Methods There are two main steps presented (refer Fig. 4), namely; (1) the cause-effect analysis with the aim of understanding multifactor’s relations, (2) Learning of previous spatiotemporal growth pattern circa 1960–2020 towards possible scenario spatial modelling by targeted year. The process of deriving and determining the inclusive main causing and economic impact factors consist of collating key existing framework in bulk via online search engine, simplified and validated by closed-group discussion with experts in the research group grounded on data availability, simplicity, viability, importance, redundancy avoidance and relevance. Existing frameworks include land subsidence-economic impact (Abidin et al., 2016 ; Kok and Costa, 2021 ), digital land price model (DLPM) (Zhang et al. 2018 ), property values (Yoo and Perrings, 2017 ), UUS planning and evaluation database (Wade et al., 2018 ; Zhou et al. 2019 ), infrastructure vulnerabilities (Wang et al., 2014 ; Lyu et al., 2020 ), as well as key policy content from the Shanghai Masterplan 2017–2035 (SM 2035) and Sustainable Development Goals (SDGs) 2030. 1960 is selected as the base year due to the start of groundwater banning and land subsidence control policies implemented in Shanghai. Data are gathered secondarily from various open sourced online databases e.g. scientific journal articles, news articles, websites, statistics and governmental reports reviews. Detailed data sources e.g. article, document titles, websites and links can be procured from the corresponding author. 2.1. Step 1: Multi-Regression Analysis of the Cause-Effect Multifactor Regression for the cause-effect multifactor (consists of the causing and economic impact factors e.g. land, building, properties, UUS development and infrastructures) can be considered as a complex method [39]. However, it is crucial in determining the correlations significance of the multifactor. The cause-effect multifactor is preliminary organised in the form of ‘Ishikawa fish-bone’ diagram [40] to provide a clear outline (refer Fig. 5 ). There are four major causing factors commonly determined in the literatures focusing on UUS-subsidence-economic impact planning; (1) rapid socio-economic development, (2) excessive groundwater extraction, (3) rapid population growth and (4) natural climate change risks, intertwined with the sustainable development spectrum of economic, social and environment. Moreover, economic impact factors consist of; land, buildings, properties and infrastructures. Data are entered in Excel sheets with each factor has varied type of measurement unit from exact measure (e.g. kilometer, millimeter) and scoring (e.g. Full (F) = 100%, Intermediate (I) = 60% or Low (L) = 40%) by year. The causing (independent variables (x)) and economic impact factors (dependent variable ( y )) are then analysed via multiple regression to predict the value of single dependent value by following equation: Y = β 0 + β 1 X 1 + β 2 X 2 … + β i Xi (1) Y is dependent variable; β 0 is intercept, β i is slope for Xi and X is independent variable. The main causing factors cover: increasing surface load; UUS development, groundwater extraction and natural disaster probability. Whilst, direct-indirect economic impact factors include: land, underground, infrastructure, buildings/properties, socio-economic activities and further land subsidence. The detailed causing ( x ) and economic impact factors ( y ) are enlisted in the following Table 1 and Table 2 . Table 1 Causing factors. Main Causing Factor Factors Unit *Data sources 1.0 Increasing surface load 1.1 Building floor area 10 6 m 2 Scientific journal article, Open online database 1.2 Residential buildings investment/profit value Bill. yuan/year Open online database 1.3 Special building codes in land subsidence prone areas Fully (F) (100) /Intermediate (I) (60)/Limited (L) (40) Policies review 1.4 Continuous financial and technical assistance for buildings-subsidence characteristics F/I/L Policies review 1.5 Total length highways km/year Open online database 1.6 Total metro system length in operation km/year Scientific journal article, Open online database 1.7 Quality, reliable, sustainable and resilient infrastructure layout planning and development for subsidence prevention and control F/I/L Policies review 1.8 Percentage of land conversion to built-up areas F/I/L 1.9 Land subsidence sensitive urban spatial development planning and control characteristic in subsidence prone areas F/I/L 1.10 Experts’ knowledge, awareness and availability of data and technologies Yes (100)/no (0) 1.11 Population density in a medium to very high risk of subsidence prone area No. of people/km 2 Open online database 2.0 UUS pre-construction-operation-post failures/development 2.1 Underground tunnel leakage L/m 2 /day Scientific journal article 2.2 Cumulative UUS development hectare Scientific journal article 2.3 Construction of tunnel for transportation km Open online database 2.4 Area of tunnel settlement/UUS-induced land subsidence expansion zones hectare Scientific journal article, Open online database 2.5 Historical and potential/probability UUS-induced subsidence rate mm/year 3.0 Groundwater pumping/extraction 3.1 Total groundwater withdrawal/pumping rate/exploitation (historical and potential) within urban/city center/downtown region 10 6 m 3 /year Scientific journal article, Open online database 3.2 Groundwater replenishment/artificial recharge for supply resources from surrounding to subsidence prone/urban region %, million m 3 3.3 Total groundwater withdrawal in suburban/transition region 10 6 m 3 /year 3.4 Groundwater replenishment/artificial recharge for supply resources from surrounding to subsidence prone/suburban region %, million m 3 3.5 Lack of groundwater extraction limit/groundwater replenishment/ prohibition in subsidence/pumping prone areas F/I/L Policies review 3.6 Weak policies, mitigation, adaptation strategies (need for continuous updates) Yes (100)/no (0) 3.7 Lack of subsidence awareness, management, policy enforcement F/I/L 3.8 Poor groundwater spatial control, planning and lack or discontinuous monitoring of subsidence characteristics F/I/L 3.9 Lack or discontinues monitoring of subsidence characteristics F/I/L 3.10 Unstrict groundwater banning implementation F/I/L 3.11 Historical and potential cumulative/average subsidence/compression due to pumping rate urban and suburban overall cumulative mm/year Scientific journal article, Open online database 4.0 Natural disaster probability 4.1 Tectonic movement speed/activities/rate/active fault mm/year Open online database 4.2 Seawater level rise mm/year 4.3 Subsidence-adaptive and resilient urban development and spatial planning F/I/L Policies review 4.4 Continuous monitoring of subsidence characteristics F/I/L 4.5 Storm surge induced/flood events/inundation probability/ prone area/zone/designation/subsidence hazards risk expansion vulnerability > 500mm:km 2 Open online database 4.6 Experts’ awareness/knowledge/data/ technologies on disaster adaptive urban spatial planning F/I/L Policies review * Detailed data sources e.g. article, document titles, websites and links can be procured from the corresponding author. Table 2 Economic impact factors. Main Economic Impact Factor Factors Unit *Data Sources Type of impact 1.0 Land 1.1 Average benchmarked land price $ /m 2 Open online database Indirect 1.2 Loss of land resources/arable/cultivated land/area/elevation > 500mm:km 2 Indirect 1.3 Decrease/changes/ (+-) in land and property values/price %. $ /RMB Indirect 1.4 Land area developed m 2 Indirect 2.0 Underground 2.1 Underground structure damage/deformation $ /m 2 /mm/year Scientific journal article Direct 2.2 Tunnel /metro settlement mm Indirect 3.0 Infrastructures 3.1 Reconstruction area ratio (housing) Billion yuan/year, million m 2 Open online database Indirect 4.0 Buildings/properties 4.1 Average building/property/real estate values/prices – residential, commercial, industrial, etc. (Selling prices) $ /m 2 Scientific journal article, Open online database Indirect 4.2 Demand in building and reconstruction sector (+) (value of completed buildings residential) Yuan/m 2 /year Indirect 4.3 Percentage of newly-built buildings will reach the standards for green building. E.g. 'Gold' LEED certified, carbon neutrality zero energy buildings (ZEB) by 2060 China %/No Indirect 5.0 Socio-economic activities 5.1 Disruption to economic activities and governance F/I/L Policies review Indirect 5.2 Production effect for companies Gross Domestic Product (GDP)/RMB per year Open online database Indirect 5.3 Government revenue $ /district/year Scientific journal article, Open online database Indirect Indirect 5.4 Industry's share of unemployment rate % Open online database Indirect 5.5 Number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations Number of populations in urban area/ No. (mills) Scientific journal article, Open online database Direct 5.6 Climate change-adaptation related policies measures in developed-developing countries cooperation spectrum (effectiveness) F/I/L Policies review Indirect 6.0 Further land subsidence 6.1 Subsidence hazards intensity rate (historical and potential/probability) mm/cm/year Scientific journal article Indirect 6.2 Increased inundated flood hazards and coastal flooding expansion areas and infrastructures > 500mm:km 2 Scientific journal article, Open online database Indirect 6.3 Quality of environment condition F/I/L Policies review Indirect * Detailed data sources e.g. article, document titles, websites and links can be procured from the corresponding author. 2.2. Step 2: Spatiotemporal Analysis with ArcGIS Pro Factors with high R -squared values (depicts high regression relationships) from 0.90 and above are selected for spatiotemporal visualisation in ArcGIS for year 1960–2020. Assumptions formed based on the considerations of existing raster and vector data on UUS development, land subsidence, previous-current land use spatial planning, interdependence of land use and human activity changes in coastal areas, economic impact and socio-economic factors for year 1960–2020. Basically, this step prepares for further potential scenario modelling. 3. Results & Discussions 3.1. Relations of Causing-Economic Impact Factors Regressions are conducted for each group of main causing and economic impact factors based on Business as Usual (BaU) scenario. The high correlation coefficients causing factors with more than 0.90 R squared are; 1.0 Increasing surface load and 2.0 UUS development to economic impact factors; 1.0 Land, 2.0 Underground, 4.0 Buildings/properties and 5.0 Socio-economic activities. The sole main causing factor which is not listed as high regression is groundwater pumping/extraction and the economic impact factor is infrastructures. This means, for a developed coastal megacity like Shanghai, groundwater-induced subsidence is a controlled issue and the impact of infrastructures is the least. However, Shanghai may need to control the increasing surface load and UUS construction issues which mostly impact the indirect economic factors e.g. buildings properties, land, socioeconomic activities and underground settlement. Hence, policymakers can effectively put forward to control the land subsidence in Shanghai by increasing more experts’ awareness, control and monitoring technologies, arranging resilient built environment load as well as safer UUS construction with control measures. Table 3 catalogues the regression results from highest to lowest values. Table 3 Regression results of causing and economic impact factors. Main Causing Factors Factors Main Economic Factors Factors R squared values 1.0 Increasing surface load Lack of experts’ awareness on USEM 4.0 Buildings/properties 4.1 Average building/property/real estate values/prices – residential, commercial, industrial, etc. 1.00 1.0 Increasing surface load Lack of awareness/experts’ knowledge 4.0 Buildings/properties 4.2 Demand in building and reconstruction sector (+) (value of completed buildings residential, housing) 0.98 1.0 Increasing surface load Population density in medium to high risk subsidence prone area 1.0 Land 1.1 Average benchmarked land price 0.97 2.0 UUS pre-construction-operation-post failures/development Cumulative UUS development 1.0 Land 1.2 Loss of land resources/arable/cultivated land/area/elevation 0.96 2.0 UUS pre-construction-operation-post failures/development Cumulative UUS-subsidence 5.0 Socio-economic activities 5.3 Government revenues 0.95 2.0 UUS pre-construction-operation-post failures/development Historical and potential UUS induced-subsidence rate 4.0 Buildings/properties 4.3 Number/percentage of newly-built buildings with standards for green building. E.g. 'Gold' LEED certified, carbon neutrality ZEB by 2060 China 0.93 2.0 UUS pre-construction-operation-post failures/development Underground tunnel leakage 2.0 Underground 2.2 Metro tunnel settlement 0.92 2.0 UUS pre-construction-operation-post failures/development Cumulative UUS development 5.0 Socio-economic activities 5.5 Number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations – Number of death/people affected, decrease of GDP by water-related disasters, poor people in vulnerable areas (government revenue). 0.92 3.3. Spatiotemporal Analysis between 1960–2020 3.3.1. Causing Factors Based on the high correlation coefficients, the factors are then conveyed for spatiotemporal analysis. In 1960, there was no underground tunnel leakage and metro tunnels identified whereby UUS were merely for utilities and infrastructures particularly in the Urban City Centre and Pudong New Area. Underground tunnel leakage starts to be logged with high concentration in the areas when metro lines were actively constructed and operated starting in 1990. The prominent causing factors grow spatially correlated and worsen by 2020 especially in the Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. UUS development was not started until 1990 and was mostly concentrated at the central business district (CBD) and expanded to the encircling districts. Figure 7 depicts the autocorrelated spatiotemporal growth pattern of causing factors. 3.3.2 Economic Impact Factors The economic impact factors have similarly shown spatiotemporal autocorrelation equivalence with the causing factors concentrated in the Urban City Centre, developed to Pudong New Area, Minhang, Baoshan and Songjiang districts. Building price has positive spatiotemporal relationship with the causing factors e.g. reconstruction area and GDP growth, whilst average land price is influenced by the proximity to the Urban City Centre. However, arable land demonstrates a negative decreasing pattern to the increment pattern of UUS development, tunnel settlement and leakage. Eventually, by 2020, the patterns are growing further with the decrease of arable land from 89% to barely 46%. Having thoroughly examine the spatial distribution of economic impact factors, there are two major findings; (1) positive impact (may boosts valuable monetary potential to the megacity), and (2) negative impact (highly correlated however require cautious control due to potential of monetary deficit). 3.4. Monetary Potential The following causing factors; increasing population, UUS-induced subsidence, development and tunnel leakage highly correlated with the positive economic impact e.g. building price, land price and GDP by district. They contribute to as much as 75% in positive economic impact encompassing areas around 4,655 square kilometers. The increment of the economic factors is astonishingly noteworthy despite the adverse increase of land subsidence, tunnel leakage and settlement. 3.5. Monetary Deficit On the downside, the growth of tunneling leakage, reconstruction area, and the decrease of arable land are deemed as negative impact. The affected areas need to be controlled further in terms of land subsidence monitoring and careful UUS exploration to avoid further tunnel leakage, settlement, uneconomical buildings-properties-infrastructure reconstructions, affected population and arable land diminish. These worsening situation covers as much as 85% spatial areas of Shanghai (about 5,289 square kilometer). 5. Conclusions With ongoing UUS exploration and increase of tunnel leakage occurrences due to the megacity’s coastal region nature and heavy development load, many ad-hoc efforts have been pursued however, the awareness and knowledge for resilient spatial planning is still lack and needed for implementation to avoid future costlier damages. This paper presents an attempt in synthesising the current various existing framework related to UUS-land subsidence-economic impact to emerge a comprehensive resilient megacity scale assessment for urban planning in both statistical and spatial method. The study of multifactor and spatiotemporal is important to validate each factor’s impact by preventing redundant, uneconomical assessment and prepare for accurate approximation for resilient planning control measures particularly in large coastal megacity. Nonetheless, there are a few suggestions which can be concluded for further research. 5.1. Preparation for Step 3 and Countermeasures (CM) Policies Formulation The spatiotemporal analysis in the developed megacity of Shanghai from 1960–2020 have shown the high correlation coeeficients of causing factors which are mainly from the increasing surface load, UUS construction and no longer groundwater-induced. Furthermore, negative spatial autocorrelation of economic impact is by far the most influential in Shanghai with ratio of positive to negative indirect economic impact of 4,655km 2 /5,289km2 (0.8:1) in statistical significance. These BaU results of regression and spatiotemporal analyses can be used for further adaptive and resilient scenario spatial planning model by targeted year (2030 or 2050) and referred by policymakers to improve future urban adaptive and resilient policies in Shanghai e.g. control spatial land for mitigating land subsidence and its negative economic impact. Policies may be focused on sectors e.g. buildings code, UUS construction, spatial land planning and further expert’s awareness programmes. In addition, accounting and monitoring land subsidence impact economically may deemed straightforward due to its visibility, monetary and quantifiable form and may help the local decision makers for feasible, sustainable and cost-saving policies formulation. However, it commands good quality, reliable databases in obtaining results, continuous research collaboration and further validation among experts (academia and professionals). 5.2. Global megacities’ comparisons The causing-economic impact factors selection and results may depict different situation in other developing megacities in comparison to the developed Shanghai megacity. These research may opens up for further collaboration between developed and developing megacities in improving land subsidence sensitive urban planning via concrete scientific evidence and analysis both statistically and spatially. The methods can be applied in other coastal megacities in the world with the same purpose of determining disaster risks, formulating scenario spatial model by possible countermeasures’ identification, planning options, land use changes and consequence mitigation strategies. Declarations Funding Declaration This research is sponsored by the Shanghai Government Scholarship (SGS); Shanghai Municipal Science and Technology Project (18DZ1201301; 19DZ1200900); Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources of the People’s Republic of China (No. KLLSMP202101), and International Geoscience Programme (IGCP) Project (663-Land subsidence in coastal cities). Competing Interest Declaration The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The authors declare no conflict of interest. Author Contribution The draft and overall paper write is prepared and managed by Muhammad Akmal Hakim bin Hishammuddin 1, , research supervision is by Jianxiu Wang 1 , research method suggestions by Muhammad Azizol bin Ismail 2 , funding and administrative by Tianliang Yang and Xinlei Huang, cooperative foreign input, comments and research supervision suggestions by Hasanuddin Zainal Abidin, Chin Siong Ho, Kasturi Devi Kanniah and Keng Yinn Wong. Availabaility of Data and Materials Declaration Data and materials in this manuscript are available upon request from the corresponding author References Abidin, H.Z., Andreas, H., Gumilar, I., & Brinkman, J.J. (2015). Study on the risk and impacts of land subsidence in Jakarta. Proceedings of the International Association of Hydrological Sciences , 372 , 115–120. https://doi.org/10.5194/piahs-372-115-2015 Abidin, Z.H., Andreas, H., Gumilar, I. (2016). Disaster risk reduction of land subsidence in Jakarta land subsidence in Indonesian cities land subsidence in Java, Conference: The 2nd International Workshop on Coastal Subsidence , Venice, Italy, 10.13140/RG.2.1.4448.9206 Avtar, R., Tripathi, S., Aggarwat, A.K., & Kumar P. (2019). Population-urbanisation-energy nexus: A review. Resources , 8(3), 136. https://doi.org/10.3390/resources8030136 Climate impacts on coastal cities . (2022, January 31). United States Environmental Protection Agency. https://19january2017snapshot.epa.gov/climate-impacts/climate-impacts-coastal-areas.html Erkens, G., Bucx, T., Dam, R., de Lange, G., & Lambert, J. (2015). Sinking coastal cities, Proceedings of the International Association of Hydrological Sciences , Netherlands, 372:189-198. 10.5194/piahs-372-189-2015 Project: UNESCO Working Group on Land Subsidence. Fishbone diagram . (2021, March 27). American Society for Quality. https://asq.org/quality-resources/fishbone He, X. C., Yang, T. L., Shen, S. L., Xu, Y. S., & Arulrajah, A. (2019). Land subsidence control zone and policy for the environmental protection of Shanghai. International Journal of Environmental Research and Public Health , 16(15) , 1–13. https://doi.org/10.3390/ijerph16152729 Hishammuddin, M.A.H., Wang, J.X., Bin Ismail, M.A., Abidin, H.Z., Sabri, S., Ho, C.S., & Wong, K.Y. (2021). Adaptive, resilient urban underground space (UUS)-subsidence-economic impact spatial planning model (USEM): The extent development impact via cause-effect analysis circa 1980-2050 in Shanghai megacity. Preprints , 2021120505. Doi:10.20944/preprints2021.0505.v1) Hishammuddin, M.A.H., & Wang J.X., (2021). Towards sustainable, resilient and adaptive urban underground space (UUS) exploration, land subsidence and economic impact spatial model (USEM) in shanghai, p.r. china: systematic reviews, model framework, initial results and pre-determined challenges. IOP Conference Series: Earth Environmental Science , 861 , 072-33, 1-8. Available online: 10.1088/1755-1315/861/7/072033 Huang, Y., Bao, Y. J., & Wang, Y. (2015). Analysis of environmental hazards in urban underground space development in Shanghai. Natural Hazards , 75 , 2067-79. DOI 10.1007/s11069-014-1414-y. Kok, S., & Costa, A.L. (2021). Framework for economic cost assessment of land subsidence. Natural Hazards , 106, 1931–1949. https://doi.org/10.1007/s11069-021-04520-3. Komeily, A., & Srinivasan, R. S. (2015). A need for balanced approach to neighborhood sustainability assessments: A critical review and analysis. Sustainable Cities and Society , 18 , 32-43. http://dx.doi.org/10.1016/j.scs.2015.05.004. Land subsidence is one of the world’s underrated problems . (2023, April 17). Deltares. https://www.deltares.nl/en/topdossiers/subsidence/ . Liu, J., Wang, H., & Yan, X. (2015). Risk evaluation of land subsidence and its application to metro safety operation in Shanghai. Proccedings of the International Association of Hydrological Sciences (PIAHS), 372, 543-553. Doi:10.5194/piahs-372-543-2015. Li, M.G., Chen, J.J., Xu, Y.S., Tong, D.G., Cao, W.W., & Shi, Y.J. (2021). Effect of groundwater exploitation and recharge on land subsidence and infrastructure settlement patterns in Shanghai. Engineering Geology , 282, 105995 . https://doi.org/10.1016/j.enggeo.2021.105995. Lyu, H. M., Shen, S. L., Zhou, A., & Yang, J. (2020). Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP. Data in Brief , 28 , 105007. https://doi.org/10.1016/j.dib.2019.105007. Mimura, N. (2013). Review: Sea-level rise caused by climate change and its implications for society, Proceedings of the Japan Academy, Series B , Japan, 2013; doi: 10.2183/pjab.89.281 The Japan Academy. Multiple regression . (2021, July 25). Science Direct. https://www.sciencedirect.com/topics/social-sciences/multiple-regression. Ortega-Guerrero M.A., & Carrillo-Rivera J.J. (2012). Land subsidence in urban environment, in: Meyers R.A. (eds). Encyclopedia of Sustainability Science and Technology , Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_440 Peng, F.L., Qiao, Y.K., Sabri, S., Atazadeh, B., & Rajabifard, A. (2021). A collaborative approach for urban underground space development toward sustainable development goals: Critical dimensions and future direction. Frontiers of Structural and Civil Engineering , 15, 20–45. https://doi.org/10.1007/s11709-021-0716-x. Qin, H.P., Su, Q., Khu, S.T., & Tang, N.V. (2014). Water quality changes during rapid urbanisation in the Shenzhen river catchment: An integrated view of socio economic and infrastrcutre development. Sustainability , 6 , 7433-7451. doi: 10.3390/su6107433. Sea level rise and implications for low-lying islands, coasts and communities . (2022, January 31). Intergovernmental Panel on Climate Change. https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/ . Shanghai Masterplan 2017-2035 . (2021, June 18). Shanghai Municipal Government. http://english.shanghai.gov.cn/newshanghai/xxgkfj/2035004.pdf Shi, Y., Shi, D., & Cao, X. (2018). Impacting factors and temporal and spatial differentiation of land subsidence in Shanghai. Sustainability , 10(9) , 3146. https://doi.org/10.3390/su10093146 Sinking cities II – example cases . (2022, July 24). Deltares. https://www.deltares.nl/app/uploads/2015/09/Sinking-cities.pdf The 17 Sustainable Development Goals . (2021, July 11). United Nations. https://sdgs.un.org/goals UNESCO Annual Report 2020 . (2020, October 9). International Geosciences Programme (IGCP). https://en.unesco.org/sites/default/files/international_geosciences_programme_annual_report_final_3.pdf. Urbanization and water quality . (2022, January 28). United States Geological Survey. https://www.usgs.gov/special-topics/water-science-school/science/urbanization-and-water-quality . Wade, C. M., Cobourn, K. M., Amacher, G. S., & Hester, E. T. (2018). Policy targeting to reduce economic damages from land subsidence. water resources research, Water Resources Research , 54 (7), 4401–4416. https://doi.org/10.1029/2017WR022133. Wang, D., Guo, X., & Jia. Y. (2014). Analysis of land subsidence deformation caused by urban shallow-burred tunnel construction. The Open Civil Engineering Journal , 8 , 219-224. Available online: https://opencivilengineeringjournal.com/contents/volumes/V8/TOCIEJ-8-219/TOCIEJ-8-219.pdf. Wu, Y.X., Yang, T.L., Li, P.C., & Lin, J.X. (2019). Investigation of groundwater withdrawal and recharge affecting underground structures in the Shanghai urban area. Sustainability , 11(24 ), 7162. https://doi.org/10.3390/su11247162. Wu, H.N., Shen, S.L., & Wu, J.Y. (2017). Identification of tunnel settlement caused by land subsidence in soft deposit of Shanghai . Journal of Performance of Constructed Facilities , 31(6) . DOI: 10.1061/(ASCE)CF.1943-5509.0001082. Xu, Y.S., Shen, S.L., Ren, D.J., & Wu, H.N. (2016). Analysis of factors in land subsidence in Shanghai: a view based on strategic environmental assessment. Sustainability, 8(6), 573. https://doi.org/10.3390/su8060573 Yuan, Y., Xu, Y.S., & Arulrajah., A. (2017). Sustainable measures for mitigation of flooding hazards: A case study in Shanghai, China. Water, 9, 310. doi:10.3390/w9050310 Yoo, J., & Perrings, C. (2017). An externality of groundwater depletion: land subsidence and residential property prices in Phoenix, Arizona. Journal of Environmental Economics and Policy , 6(2), 121–133. https://doi.org/10.1080/21606544.2016.1226198. Zhang, W.J., Zhang, L.M., Wu, X.G., Liu, Y. (2018). Risk Assessment of leakage water of tunnel construction in coastal soft soils. Advances in Economics, Business and Management Research (AEBMR) , 54 , 110-115. https://dx.doi.org/10.2991/msmi-18.2018.20 Zhang, J., Fu, M., Chen, J., Chu, P., & Zhang, C., (2018). Variations in mine subsidence-disturbed residential land price: Case study of critical determinants and spatial relationships in the Nanhu ecoregion of Tangshan, China. Journal of Urban Planning and Development , 144(3), 05018012. DOI: 10.1061/(ASCE)UP.1943-5444.0000457. Zhou, D., Li, X., Wang, Qi., Wang, R., Wang, T., Gu, Q., & Xin, Y. (2019). GIS-based urban underground space resources evaluation toward three-dimensional land planning: A case study in Nantong China. Tunnelling and Underground Space Technology (TUST), 84, 1-19. https://doi.org/10.1016/j.tust.2018.10.017. Additional Declarations No competing interests reported. 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-3849481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267576637,"identity":"f1750c43-29b2-43fd-a398-50816c22821c","order_by":0,"name":"Muhammad Akmal Hakim bin Hishammnuddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACHiB+wHDAgIG9gYGZeC0JIC08B0jWIpFApBb+ntOJHxIY7hjrznxj+LmgwoaBv707Aa8WibO9m4HmPzMzu51jLD3jTBqDxJmzG/Bbc553A1DLYRugFgNp3rbDDAYSufi1yJ/n3fwDrOXmGePfRGkxONu7DWSLmdkNHjPibDE8c3abBVCLsdmZtDJrnjNpPAT9Incmd/ONDwyHDbcdP7z5Nk+FjRx/ey8B74MA4z8QyWEAInkIK0cA9gekqB4Fo2AUjIIRBADpbkogExjjnwAAAABJRU5ErkJggg==","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Akmal Hakim bin","lastName":"Hishammnuddin","suffix":""},{"id":267576638,"identity":"407d0ead-b59a-4c93-b26b-19b908da2de8","order_by":1,"name":"Jianxiu Wang","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Jianxiu","middleName":"","lastName":"Wang","suffix":""},{"id":267576639,"identity":"b11eceb9-643a-4bfc-9540-7a5e7ff90bdf","order_by":2,"name":"Muhammad Azizol Ismail","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Azizol","lastName":"Ismail","suffix":""},{"id":267576640,"identity":"af48c488-6878-4b2f-99a8-127072e06f62","order_by":3,"name":"Tianliang Yang","email":"","orcid":"","institution":"Shanghai Institute of Geological Survey","correspondingAuthor":false,"prefix":"","firstName":"Tianliang","middleName":"","lastName":"Yang","suffix":""},{"id":267576641,"identity":"d4537c79-3c8e-41f5-8553-9758cac339c6","order_by":4,"name":"Xinlei Huang","email":"","orcid":"","institution":"Shanghai Institute of Geological Survey","correspondingAuthor":false,"prefix":"","firstName":"Xinlei","middleName":"","lastName":"Huang","suffix":""},{"id":267576642,"identity":"c75a4464-6905-4d89-b883-e3bd6c550b7b","order_by":5,"name":"Hasanuddin Zainal Abidin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hasanuddin","middleName":"Zainal","lastName":"Abidin","suffix":""},{"id":267576643,"identity":"832b80aa-4ed6-43b3-82be-2d359219e689","order_by":6,"name":"Chin Siong Ho","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Chin","middleName":"Siong","lastName":"Ho","suffix":""},{"id":267576644,"identity":"bcb5d66b-6611-4f5c-8999-f4b15642fa05","order_by":7,"name":"Kasturi Devi Kanniah","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Kasturi","middleName":"Devi","lastName":"Kanniah","suffix":""},{"id":267576645,"identity":"bad44038-e1d5-465c-a6b4-8f630f3a60df","order_by":8,"name":"Keng Yinn Wong","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Keng","middleName":"Yinn","lastName":"Wong","suffix":""}],"badges":[],"createdAt":"2024-01-10 05:44:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49826276,"identity":"9fd26d3e-8f31-4d0f-9a65-851227721743","added_by":"auto","created_at":"2024-01-18 15:46:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239436,"visible":true,"origin":"","legend":"\u003cp\u003eNatural-anthropogenic multifactor of causes-effect regarding land subsidence in major coastal megacities worldwide. Adapted and modified from (Abidin et al., 2015; Shi et al., 2018; Erkens et al., 2015; Lyu et al., 2020).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/430ea486b03fd9883d6dba31.png"},{"id":49826716,"identity":"da9d64cd-cd81-4c4a-b17c-f8055ee878e2","added_by":"auto","created_at":"2024-01-18 15:54:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245160,"visible":true,"origin":"","legend":"\u003cp\u003eShanghai megacity’s current land subsidence monitoring stations, control zones and district maps: (\u003cstrong\u003ea\u003c/strong\u003e) Groundwater monitoring well and land subsidence monitoring points distribution map; (\u003cstrong\u003eb\u003c/strong\u003e) Comprehensive division map of land subsidence control zones in Shanghai. Modified from (Het al., 2019; Liu et al., 2015; Li et al., 2021). Note: Maps are not to scale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/e362c9c4dc3b91fd79353d4c.png"},{"id":49826717,"identity":"b8d55abd-ebfd-4e4e-acfd-643f35820269","added_by":"auto","created_at":"2024-01-18 15:54:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130144,"visible":true,"origin":"","legend":"\u003cp\u003eThe uncertain phase of land subsidence in Shanghai megacity: (\u003cstrong\u003ea\u003c/strong\u003e) Accelerated (1920-1960), controlled (1960-1990) and continuous by 1990-2000 due to the rapid urbanisation (Xu et al., 2016); (\u003cstrong\u003eb\u003c/strong\u003e) Shanghai’s cumulative subsidence by year to UUS development from 1920-2010, note that in 1990 depicts rapid increment (Hishammuddin \u0026amp; Wang, 2021).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/f98ff8e0623f1a1b56cc64c3.png"},{"id":49826271,"identity":"80fe2dd4-8ea2-42b5-a7b3-f77d4f5182ad","added_by":"auto","created_at":"2024-01-18 15:46:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7500,"visible":true,"origin":"","legend":"\u003cp\u003eMethod framework.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/a876f763152a36bcab267ff5.png"},{"id":49826274,"identity":"6fef2073-de8a-4ebb-b0c3-d26f19b178fd","added_by":"auto","created_at":"2024-01-18 15:46:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":182476,"visible":true,"origin":"","legend":"\u003cp\u003eThe determined main causing-economic impact factors with spatial planning, technological advancement and experts’ awareness as equally similar factors highlighted in light red dotted boxes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/e663480ae848087ddea669b6.png"},{"id":49826270,"identity":"8a961725-fe3e-4884-81b4-7277845a1763","added_by":"auto","created_at":"2024-01-18 15:46:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25868,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/383524abdddfc020a2d5b56c.png"},{"id":49826275,"identity":"ae4d8d92-92a0-4bf9-a900-b8c5f2e74c9c","added_by":"auto","created_at":"2024-01-18 15:46:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":180615,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal for population density, cumulative land subsidence, underground tunnel leakage, cumulative UUS development and metro construction in Shanghai megacity between 1960, 1990 and 2020. Note: data for tunnel leakage and metro in operation by 1960 is not available hence, no maps are shown. The maps are not to scale.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/9440e633d2309702997fe2ce.png"},{"id":49826277,"identity":"7bb239b1-425b-428c-ad14-341d75bf0d19","added_by":"auto","created_at":"2024-01-18 15:46:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":240220,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal of economic impact factors: average building price, reconstruction area, average land price, arable land, GDP by district and metro tunnel settlement in Shanghai megacity between 1960, 1990 and 2020. Not to scale.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/98f142f08c73b092438b1782.png"},{"id":50195065,"identity":"4483ca83-d55f-4e1e-9b34-ee2545a0d7ee","added_by":"auto","created_at":"2024-01-26 03:07:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1656256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849481/v1/4197fb6c-4495-45b8-8f20-280bc001340f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Underground Space-Subsidence and Economic Impact Planning Model in Shanghai: Cause-Effect and Spatiotemporal Regression Analyses for Year 1960-2020","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eClimate change influences natural hazards e.g. accelerated sea level rise, storm surge, land subsidence and tectonic movement (Mimura, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This sets hurdles in low laying, coastal urban areas with rapid and unconscious socio-economic development, agglomeration, population growth which increase demand for space and resources (e.g. excessive groundwater extraction, minerals, oil, gas, coal mining), surface development load, water demand and carbon emissions (Qin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Avtar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Taking land subsidence as a silent killer, underrated issue and influence direct and indirect economic impact in coastal megacity, the cause-effects are diverse from the excessive groundwater extraction, mining, natural subsidence: tectonic movements to urbanisation induced subsidence e.g. population growth; surface development load and currently, dangerous or unconscious urban underground space (UUS) development (refer Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite having the land subsidence successfully controlled since 1960s through the banning of groundwater extraction in the urban and built-up area (refer Fig.\u0026nbsp;2), Shanghai as a developed coastal megacity and the center of national economic agglomeration region still anticipate for continuous possible challenges in the future (Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yuan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hishammuddin et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The International Geoscience Programme (IGCP) Plan by the United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2017 has continuously funding research projects towards investigating the control and monitoring of land subsidence and impacts especially in coastal megacities like Shanghai. With continuous land subsidence rate of 6-100mm per year, increasing sea water level and flood risks created further vast damage to the urban economic include factors e.g. buildings, properties, infrastructures and land.\u003c/p\u003e \u003cp\u003eTheoretically, different megacities possess different anthropogenic activities that represents different load or impact on land subsidence (Ortega-Guerrero \u0026amp; Carrillo-Rivera, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The cause-effect relationships in urban settings are complex due to many independent factors and undesirable impacts (Komeily and Srinivasan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The UUS exploration in a condensed Shanghai megacity may become of the factor. With the physical offers of three-dimensional (3D) development and the targeted vision of \u0026lsquo;big, deep, long, fast and dense\u0026rsquo; (Huang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), it is expected to grow significantly by twenty years to come. However, researches on resilient urban spatial planning with the concern of UUS-subsidence-economic integration for coastal megacity is still lacking. Coastal megacity itself is complex and there is still limited research on the potential of such integrated model. There currently are many disconnected assessment frameworks on underground space (Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and land subsidence-economic impact (Abidin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Erkens et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lyu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kok and Costa, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], hence, this research attempts to merge as much as possible, the existing frameworks towards instituting a comprehensive connected model in relation with the urban planning. This paper presents the understanding of development cause-effect extent concerning however not limited to the UUS-subsidence-economic via cause-effect analysis and spatiotemporal. It is imperative to study the ever-complex multidisciplinary spectrum by the investigation of the past and current situation and potentially provide basis for future scenario modelling.\u003c/p\u003e "},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThere are two main steps presented (refer Fig.\u0026nbsp;4), namely; (1) the cause-effect analysis with the aim of understanding multifactor\u0026rsquo;s relations, (2) Learning of previous spatiotemporal growth pattern circa 1960\u0026ndash;2020 towards possible scenario spatial modelling by targeted year. The process of deriving and determining the inclusive main causing and economic impact factors consist of collating key existing framework in bulk via online search engine, simplified and validated by closed-group discussion with experts in the research group grounded on data availability, simplicity, viability, importance, redundancy avoidance and relevance. Existing frameworks include land subsidence-economic impact (Abidin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kok and Costa, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), digital land price model (DLPM) (Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), property values (Yoo and Perrings, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), UUS planning and evaluation database (Wade et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), infrastructure vulnerabilities (Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lyu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as key policy content from the Shanghai Masterplan 2017\u0026ndash;2035 (SM 2035) and Sustainable Development Goals (SDGs) 2030. 1960 is selected as the base year due to the start of groundwater banning and land subsidence control policies implemented in Shanghai. Data are gathered secondarily from various open sourced online databases e.g. scientific journal articles, news articles, websites, statistics and governmental reports reviews. Detailed data sources e.g. article, document titles, websites and links can be procured from the corresponding author.\u003c/p\u003e \u003ch2\u003e2.1. Step 1: Multi-Regression Analysis of the Cause-Effect Multifactor\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRegression for the cause-effect multifactor (consists of the causing and economic impact factors e.g. land, building, properties, UUS development and infrastructures) can be considered as a complex method [39]. However, it is crucial in determining the correlations significance of the multifactor. The cause-effect multifactor is preliminary organised in the form of \u0026lsquo;Ishikawa fish-bone\u0026rsquo; diagram [40] to provide a clear outline (refer Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e). There are four major causing factors commonly determined in the literatures focusing on UUS-subsidence-economic impact planning; (1) rapid socio-economic development, (2) excessive groundwater extraction, (3) rapid population growth and (4) natural climate change risks, intertwined with the sustainable development spectrum of economic, social and environment. Moreover, economic impact factors consist of; land, buildings, properties and infrastructures.\u003c/p\u003e \u003cp\u003eData are entered in Excel sheets with each factor has varied type of measurement unit from exact measure (e.g. kilometer, millimeter) and scoring (e.g. Full (F)\u0026thinsp;=\u0026thinsp;100%, Intermediate (I)\u0026thinsp;=\u0026thinsp;60% or Low (L)\u0026thinsp;=\u0026thinsp;40%) by year. The causing (independent variables (x)) and economic impact factors (dependent variable (\u003cem\u003ey\u003c/em\u003e)) are then analysed via multiple regression to predict the value of single dependent value by following equation:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eY\u0026thinsp;=\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026hellip; + β\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eXi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e is dependent variable; \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is intercept, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is slope for \u003cem\u003eXi\u003c/em\u003e and \u003cem\u003eX\u003c/em\u003e is independent variable. The main causing factors cover: increasing surface load; UUS development, groundwater extraction and natural disaster probability. Whilst, direct-indirect economic impact factors include: land, underground, infrastructure, buildings/properties, socio-economic activities and further land subsidence. The detailed causing (\u003cem\u003ex\u003c/em\u003e) and economic impact factors (\u003cem\u003ey\u003c/em\u003e) are enlisted in the following Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eCausing factors.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Causing Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*Data sources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e1.0 Increasing surface load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 Building floor area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 Residential buildings investment/profit value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBill. yuan/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 Special building codes in land subsidence prone areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFully (F) (100) /Intermediate (I) (60)/Limited (L) (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 Continuous financial and technical assistance for buildings-subsidence characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 Total length highways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekm/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 Total metro system length in operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekm/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7 Quality, reliable, sustainable and resilient infrastructure layout planning and development for subsidence prevention and control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 Percentage of land conversion to built-up areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 Land subsidence sensitive urban spatial development planning and control characteristic in subsidence prone areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 Experts\u0026rsquo; knowledge, awareness and availability of data and technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes (100)/no (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 Population density in a medium to very high risk of subsidence prone area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of people/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2.0 UUS pre-construction-operation-post failures/development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 Underground tunnel leakage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL/m\u003csup\u003e2\u003c/sup\u003e/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 Cumulative UUS development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehectare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 Construction of tunnel for transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 Area of tunnel settlement/UUS-induced land subsidence expansion zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehectare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 Historical and potential/probability UUS-induced subsidence rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e3.0 Groundwater pumping/extraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 Total groundwater withdrawal/pumping rate/exploitation (historical and potential) within urban/city center/downtown region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 Groundwater replenishment/artificial recharge for supply resources from surrounding to subsidence prone/urban region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%, million m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3 Total groundwater withdrawal in suburban/transition region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e6\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4 Groundwater replenishment/artificial recharge for supply resources from surrounding to subsidence prone/suburban region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%, million m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 Lack of groundwater extraction limit/groundwater replenishment/ prohibition in subsidence/pumping prone areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6 Weak policies, mitigation, adaptation strategies (need for continuous updates)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes (100)/no (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7 Lack of subsidence awareness, management, policy enforcement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 Poor groundwater spatial control, planning and lack or discontinuous monitoring of subsidence characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9 Lack or discontinues monitoring of subsidence characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 Unstrict groundwater banning implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.11 Historical and potential cumulative/average subsidence/compression due to pumping rate urban and suburban overall cumulative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e4.0 Natural disaster probability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 Tectonic movement speed/activities/rate/active fault\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 Seawater level rise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 Subsidence-adaptive and resilient urban development and spatial planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4 Continuous monitoring of subsidence characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 Storm surge induced/flood events/inundation probability/ prone area/zone/designation/subsidence hazards risk expansion vulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500mm:km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 Experts\u0026rsquo; awareness/knowledge/data/ technologies on disaster adaptive urban spatial planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003e* Detailed data sources e.g. article, document titles, websites and links can be procured from the corresponding author.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \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\u003eEconomic impact factors.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Economic Impact Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*Data Sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eType of impact\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.0 Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 Average benchmarked land price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/m\u003csup\u003e2\u003c/sup\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 Loss of land resources/arable/cultivated land/area/elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500mm:km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 Decrease/changes/ (+-) in land and property values/price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%. \u003cspan\u003e$\u003c/span\u003e/RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 Land area developed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003em\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.0 Underground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 Underground structure damage/deformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/m\u003csup\u003e2\u003c/sup\u003e/mm/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScientific journal article\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 Tunnel /metro settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.0 Infrastructures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 Reconstruction area ratio (housing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBillion yuan/year, million m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.0 Buildings/properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 Average building/property/real estate values/prices \u0026ndash; residential, commercial, industrial, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Selling prices) \u003cspan\u003e$\u003c/span\u003e/m\u003csup\u003e2\u003c/sup\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 Demand in building and reconstruction sector (+) (value of completed buildings residential)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYuan/m\u003csup\u003e2\u003c/sup\u003e/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 Percentage of newly-built buildings will reach the standards for green building. E.g. 'Gold' LEED certified, carbon neutrality zero energy buildings (ZEB) by 2060 China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e5.0 Socio-economic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1 Disruption to economic activities and governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2 Production effect for companies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross Domestic Product (GDP)/RMB per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.3 Government revenue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/district/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4 Industry's share of unemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5 Number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of populations in urban area/ No. (mills)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6 Climate change-adaptation related policies measures in developed-developing countries cooperation spectrum (effectiveness)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.0 Further land subsidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1 Subsidence hazards intensity rate (historical and potential/probability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm/cm/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2 Increased inundated flood hazards and coastal flooding expansion areas and infrastructures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;500mm:km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScientific journal article, Open online database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3 Quality of environment condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF/I/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003e* Detailed data sources e.g. article, document titles, websites and links can be procured from the corresponding author.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Step 2: Spatiotemporal Analysis with ArcGIS Pro\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFactors with high \u003cem\u003eR\u003c/em\u003e-squared values (depicts high regression relationships) from 0.90 and above are selected for spatiotemporal visualisation in ArcGIS for year 1960\u0026ndash;2020. Assumptions formed based on the considerations of existing raster and vector data on UUS development, land subsidence, previous-current land use spatial planning, interdependence of land use and human activity changes in coastal areas, economic impact and socio-economic factors for year 1960\u0026ndash;2020. Basically, this step prepares for further potential scenario modelling.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results \u0026 Discussions","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Relations of Causing-Economic Impact Factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRegressions are conducted for each group of main causing and economic impact factors based on Business as Usual (BaU) scenario. The high correlation coefficients causing factors with more than 0.90 \u003cem\u003eR\u003c/em\u003e squared are; 1.0 Increasing surface load and 2.0 UUS development to economic impact factors; 1.0 Land, 2.0 Underground, 4.0 Buildings/properties and 5.0 Socio-economic activities. The sole main causing factor which is not listed as high regression is groundwater pumping/extraction and the economic impact factor is infrastructures. This means, for a developed coastal megacity like Shanghai, groundwater-induced subsidence is a controlled issue and the impact of infrastructures is the least. However, Shanghai may need to control the increasing surface load and UUS construction issues which mostly impact the indirect economic factors e.g. buildings properties, land, socioeconomic activities and underground settlement. Hence, policymakers can effectively put forward to control the land subsidence in Shanghai by increasing more experts\u0026rsquo; awareness, control and monitoring technologies, arranging resilient built environment load as well as safer UUS construction with control measures. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e catalogues the regression results from highest to lowest values.\u003c/p\u003e \u003c/div\u003e \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\u003eRegression results of causing and economic impact factors.\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Causing Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain Economic Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR squared values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0 Increasing surface load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of experts\u0026rsquo; awareness on USEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 Buildings/properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1 Average building/property/real estate values/prices \u0026ndash; residential, commercial, industrial, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0 Increasing surface load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of awareness/experts\u0026rsquo; knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 Buildings/properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2 Demand in building and reconstruction sector (+) (value of completed buildings residential, housing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0 Increasing surface load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density in medium to high risk subsidence prone area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 Average benchmarked land price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0 UUS pre-construction-operation-post failures/development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative UUS development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 Loss of land resources/arable/cultivated land/area/elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0 UUS pre-construction-operation-post failures/development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative UUS-subsidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 Socio-economic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3 Government revenues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0 UUS pre-construction-operation-post failures/development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistorical and potential UUS induced-subsidence rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 Buildings/properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 Number/percentage of newly-built buildings with standards for green building. E.g. 'Gold' LEED certified, carbon neutrality ZEB by 2060 China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0 UUS pre-construction-operation-post failures/development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderground tunnel leakage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 Underground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 Metro tunnel settlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0 UUS pre-construction-operation-post failures/development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCumulative UUS development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 Socio-economic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5 Number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations\u003c/p\u003e \u003cp\u003e\u0026ndash; Number of death/people affected, decrease of GDP by water-related disasters, poor people in vulnerable areas (government revenue).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Spatiotemporal Analysis between 1960\u0026ndash;2020\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Causing Factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on the high correlation coefficients, the factors are then conveyed for spatiotemporal analysis. In 1960, there was no underground tunnel leakage and metro tunnels identified whereby UUS were merely for utilities and infrastructures particularly in the Urban City Centre and Pudong New Area. Underground tunnel leakage starts to be logged with high concentration in the areas when metro lines were actively constructed and operated starting in 1990. The prominent causing factors grow spatially correlated and worsen by 2020 especially in the Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. UUS development was not started until 1990 and was mostly concentrated at the central business district (CBD) and expanded to the encircling districts. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the autocorrelated spatiotemporal growth pattern of causing factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Economic Impact Factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe economic impact factors have similarly shown spatiotemporal autocorrelation equivalence with the causing factors concentrated in the Urban City Centre, developed to Pudong New Area, Minhang, Baoshan and Songjiang districts. Building price has positive spatiotemporal relationship with the causing factors e.g. reconstruction area and GDP growth, whilst average land price is influenced by the proximity to the Urban City Centre. However, arable land demonstrates a negative decreasing pattern to the increment pattern of UUS development, tunnel settlement and leakage. Eventually, by 2020, the patterns are growing further with the decrease of arable land from 89% to barely 46%.\u003c/p\u003e \u003cp\u003eHaving thoroughly examine the spatial distribution of economic impact factors, there are two major findings; (1) positive impact (may boosts valuable monetary potential to the megacity), and (2) negative impact (highly correlated however require cautious control due to potential of monetary deficit).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Monetary Potential\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe following causing factors; increasing population, UUS-induced subsidence, development and tunnel leakage highly correlated with the positive economic impact e.g. building price, land price and GDP by district. They contribute to as much as 75% in positive economic impact encompassing areas around 4,655 square kilometers. The increment of the economic factors is astonishingly noteworthy despite the adverse increase of land subsidence, tunnel leakage and settlement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Monetary Deficit\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOn the downside, the growth of tunneling leakage, reconstruction area, and the decrease of arable land are deemed as negative impact. The affected areas need to be controlled further in terms of land subsidence monitoring and careful UUS exploration to avoid further tunnel leakage, settlement, uneconomical buildings-properties-infrastructure reconstructions, affected population and arable land diminish. These worsening situation covers as much as 85% spatial areas of Shanghai (about 5,289 square kilometer).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith ongoing UUS exploration and increase of tunnel leakage occurrences due to the megacity\u0026rsquo;s coastal region nature and heavy development load, many ad-hoc efforts have been pursued however, the awareness and knowledge for resilient spatial planning is still lack and needed for implementation to avoid future costlier damages. This paper presents an attempt in synthesising the current various existing framework related to UUS-land subsidence-economic impact to emerge a comprehensive resilient megacity scale assessment for urban planning in both statistical and spatial method. The study of multifactor and spatiotemporal is important to validate each factor\u0026rsquo;s impact by preventing redundant, uneconomical assessment and prepare for accurate approximation for resilient planning control measures particularly in large coastal megacity. Nonetheless, there are a few suggestions which can be concluded for further research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Preparation for Step 3 and Countermeasures (CM) Policies Formulation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatiotemporal analysis in the developed megacity of Shanghai from 1960\u0026ndash;2020 have shown the high correlation coeeficients of causing factors which are mainly from the increasing surface load, UUS construction and no longer groundwater-induced. Furthermore, negative spatial autocorrelation of economic impact is by far the most influential in Shanghai with ratio of positive to negative indirect economic impact of 4,655km\u003csup\u003e2\u003c/sup\u003e/5,289km2 (0.8:1) in statistical significance. These BaU results of regression and spatiotemporal analyses can be used for further adaptive and resilient scenario spatial planning model by targeted year (2030 or 2050) and referred by policymakers to improve future urban adaptive and resilient policies in Shanghai e.g. control spatial land for mitigating land subsidence and its negative economic impact. Policies may be focused on sectors e.g. buildings code, UUS construction, spatial land planning and further expert\u0026rsquo;s awareness programmes. In addition, accounting and monitoring land subsidence impact economically may deemed straightforward due to its visibility, monetary and quantifiable form and may help the local decision makers for feasible, sustainable and cost-saving policies formulation. However, it commands good quality, reliable databases in obtaining results, continuous research collaboration and further validation among experts (academia and professionals).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Global megacities\u0026rsquo; comparisons\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe causing-economic impact factors selection and results may depict different situation in other developing megacities in comparison to the developed Shanghai megacity. These research may opens up for further collaboration between developed and developing megacities in improving land subsidence sensitive urban planning via concrete scientific evidence and analysis both statistically and spatially. The methods can be applied in other coastal megacities in the world with the same purpose of determining disaster risks, formulating scenario spatial model by possible countermeasures\u0026rsquo; identification, planning options, land use changes and consequence mitigation strategies.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is sponsored by the Shanghai Government Scholarship (SGS); Shanghai Municipal Science and Technology Project (18DZ1201301; 19DZ1200900); Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources of the People’s Republic of China (No. KLLSMP202101), and International Geoscience Programme (IGCP) Project (663-Land subsidence in coastal cities).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe draft and overall paper write is prepared and managed by Muhammad Akmal Hakim bin Hishammuddin \u003csup\u003e1,\u003c/sup\u003e, research supervision is by Jianxiu Wang \u003csup\u003e1\u003c/sup\u003e, research method suggestions by Muhammad Azizol bin Ismail \u003csup\u003e2\u003c/sup\u003e, funding and administrative by Tianliang Yang and Xinlei Huang, cooperative foreign input, comments and research supervision suggestions by Hasanuddin Zainal Abidin, Chin Siong Ho, Kasturi Devi Kanniah and Keng Yinn Wong.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailabaility of Data and Materials Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and materials in this manuscript are available upon request from the corresponding author\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbidin, H.Z., Andreas, H., Gumilar, I., \u0026amp; Brinkman, J.J. (2015). Study on the risk and impacts of land subsidence in Jakarta. \u003cem\u003eProceedings of the International Association of Hydrological Sciences\u003c/em\u003e, \u003cem\u003e372\u003c/em\u003e, 115\u0026ndash;120. https://doi.org/10.5194/piahs-372-115-2015\u003c/li\u003e\n\u003cli\u003eAbidin, Z.H., Andreas, H., Gumilar, I. (2016). Disaster risk reduction of land subsidence in Jakarta land subsidence in Indonesian cities land subsidence in Java, \u003cem\u003eConference: The 2nd International Workshop on Coastal Subsidence\u003c/em\u003e, Venice, Italy, 10.13140/RG.2.1.4448.9206\u003c/li\u003e\n\u003cli\u003eAvtar, R., Tripathi, S., Aggarwat, A.K., \u0026amp; Kumar P. (2019). Population-urbanisation-energy nexus: A review. \u003cem\u003eResources\u003c/em\u003e, \u003cem\u003e8(3),\u003c/em\u003e 136. https://doi.org/10.3390/resources8030136\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eClimate impacts on coastal cities\u003c/em\u003e. (2022, January 31). United States Environmental Protection Agency. https://19january2017snapshot.epa.gov/climate-impacts/climate-impacts-coastal-areas.html\u003c/li\u003e\n\u003cli\u003eErkens, G., Bucx, T., Dam, R., de Lange, G., \u0026amp; Lambert, J. (2015). Sinking coastal cities, \u003cem\u003eProceedings of the International Association of Hydrological Sciences\u003c/em\u003e, Netherlands, 372:189-198. 10.5194/piahs-372-189-2015 Project: UNESCO Working Group on Land Subsidence.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eFishbone diagram\u003c/em\u003e. (2021, March 27). American Society for Quality. https://asq.org/quality-resources/fishbone\u003c/li\u003e\n\u003cli\u003eHe, X. C., Yang, T. L., Shen, S. L., Xu, Y. S., \u0026amp; Arulrajah, A. (2019). Land subsidence control zone and policy for the environmental protection of Shanghai. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e16(15)\u003c/em\u003e, 1\u0026ndash;13. https://doi.org/10.3390/ijerph16152729\u003c/li\u003e\n\u003cli\u003eHishammuddin, M.A.H., Wang, J.X., Bin Ismail, M.A., Abidin, H.Z., Sabri, S., Ho, C.S., \u0026amp; Wong, K.Y. (2021). Adaptive, resilient urban underground space (UUS)-subsidence-economic impact spatial planning model (USEM): The extent development impact via cause-effect analysis circa 1980-2050 in Shanghai megacity. \u003cem\u003ePreprints\u003c/em\u003e, 2021120505. Doi:10.20944/preprints2021.0505.v1)\u003c/li\u003e\n\u003cli\u003eHishammuddin, M.A.H., \u0026amp; Wang J.X., (2021). Towards sustainable, resilient and adaptive urban underground space (UUS) exploration, land subsidence and economic impact spatial model (USEM) in shanghai, p.r. china: systematic reviews, model framework, initial results and pre-determined challenges. \u003cem\u003eIOP Conference Series: Earth Environmental Science\u003c/em\u003e, \u003cem\u003e861\u003c/em\u003e, 072-33, 1-8. Available online: 10.1088/1755-1315/861/7/072033\u003c/li\u003e\n\u003cli\u003eHuang, Y., Bao, Y. J., \u0026amp; Wang, Y. (2015). Analysis of environmental hazards in urban underground space development in Shanghai. \u003cem\u003eNatural Hazards\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e75\u003c/em\u003e, 2067-79. DOI 10.1007/s11069-014-1414-y.\u003c/li\u003e\n\u003cli\u003eKok, S., \u0026amp; Costa, A.L. (2021). Framework for economic cost assessment of land subsidence. \u003cem\u003eNatural Hazards\u003c/em\u003e, \u003cem\u003e106,\u003c/em\u003e 1931\u0026ndash;1949. https://doi.org/10.1007/s11069-021-04520-3.\u003c/li\u003e\n\u003cli\u003eKomeily, A., \u0026amp; Srinivasan, R. S. (2015). A need for balanced approach to neighborhood sustainability assessments: A critical review and analysis. \u003cem\u003eSustainable Cities and Society\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e, 32-43. http://dx.doi.org/10.1016/j.scs.2015.05.004. \u003c/li\u003e\n\u003cli\u003e\u003cem\u003eLand subsidence is one of the world\u0026rsquo;s underrated problems\u003c/em\u003e. (2023, April 17). Deltares. https://www.deltares.nl/en/topdossiers/subsidence/\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eLiu, J., Wang, H., \u0026amp; Yan, X. (2015). Risk evaluation of land subsidence and its application to metro safety operation in Shanghai. \u003cem\u003eProccedings of the International Association of Hydrological Sciences\u003c/em\u003e (PIAHS), \u003cem\u003e372, \u003c/em\u003e543-553. Doi:10.5194/piahs-372-543-2015. \u003c/li\u003e\n\u003cli\u003eLi, M.G., Chen, J.J., Xu, Y.S., Tong, D.G., Cao, W.W., \u0026amp; Shi, Y.J. (2021). Effect of groundwater exploitation and recharge on land subsidence and infrastructure settlement patterns in Shanghai. \u003cem\u003eEngineering Geology\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e282,\u003c/em\u003e 105995\u003cem\u003e.\u003c/em\u003ehttps://doi.org/10.1016/j.enggeo.2021.105995.\u003c/li\u003e\n\u003cli\u003eLyu, H. M., Shen, S. L., Zhou, A., \u0026amp; Yang, J. (2020). Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP. \u003cem\u003eData in Brief\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, 105007. https://doi.org/10.1016/j.dib.2019.105007.\u003c/li\u003e\n\u003cli\u003eMimura, N. (2013). Review: Sea-level rise caused by climate change and its implications for society, \u003cem\u003eProceedings of the Japan Academy, Series B\u003c/em\u003e, Japan, 2013; doi: 10.2183/pjab.89.281 The Japan Academy. \u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMultiple regression\u003c/em\u003e. (2021, July 25). Science Direct. https://www.sciencedirect.com/topics/social-sciences/multiple-regression. \u003c/li\u003e\n\u003cli\u003eOrtega-Guerrero M.A., \u0026amp; Carrillo-Rivera J.J. (2012). Land subsidence in urban environment, in: Meyers R.A. (eds). \u003cem\u003eEncyclopedia of Sustainability Science and Technology\u003c/em\u003e, Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_440\u003c/li\u003e\n\u003cli\u003ePeng, F.L., Qiao, Y.K., Sabri, S., Atazadeh, B., \u0026amp; Rajabifard, A. (2021). A collaborative approach for urban underground space development toward sustainable development goals: Critical dimensions and future direction. \u003cem\u003eFrontiers of Structural and Civil Engineering\u003c/em\u003e, \u003cem\u003e15,\u003c/em\u003e 20\u0026ndash;45. https://doi.org/10.1007/s11709-021-0716-x. \u003c/li\u003e\n\u003cli\u003eQin, H.P., Su, Q., Khu, S.T., \u0026amp; Tang, N.V. (2014). Water quality changes during rapid urbanisation in the Shenzhen river catchment: An integrated view of socio economic and infrastrcutre development. \u003cem\u003eSustainability\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e, 7433-7451. doi: 10.3390/su6107433. \u003c/li\u003e\n\u003cli\u003e\u003cem\u003eSea level rise and implications for low-lying islands, coasts and communities\u003c/em\u003e. (2022, January 31). Intergovernmental Panel on Climate Change. https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eShanghai Masterplan 2017-2035\u003c/em\u003e. (2021, June 18). Shanghai Municipal Government. http://english.shanghai.gov.cn/newshanghai/xxgkfj/2035004.pdf \u003c/li\u003e\n\u003cli\u003eShi, Y., Shi, D., \u0026amp; Cao, X. (2018). Impacting factors and temporal and spatial differentiation of land subsidence in Shanghai. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e10(9)\u003c/em\u003e, 3146. https://doi.org/10.3390/su10093146\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eSinking cities II \u0026ndash; example cases\u003c/em\u003e. (2022, July 24). Deltares. https://www.deltares.nl/app/uploads/2015/09/Sinking-cities.pdf\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eThe 17 Sustainable Development Goals\u003c/em\u003e. (2021, July 11). United Nations. https://sdgs.un.org/goals\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eUNESCO Annual Report 2020\u003c/em\u003e. (2020, October 9). International Geosciences Programme (IGCP). https://en.unesco.org/sites/default/files/international_geosciences_programme_annual_report_final_3.pdf.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eUrbanization and water quality\u003c/em\u003e. (2022, January 28). United States Geological Survey. https://www.usgs.gov/special-topics/water-science-school/science/urbanization-and-water-quality\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eWade, C. M., Cobourn, K. M., Amacher, G. S., \u0026amp; Hester, E. T. (2018). Policy targeting to reduce economic damages from land subsidence. water resources research, \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e54 (7),\u003c/em\u003e 4401\u0026ndash;4416. https://doi.org/10.1029/2017WR022133.\u003c/li\u003e\n\u003cli\u003eWang, D., Guo, X., \u0026amp; Jia. Y. (2014). Analysis of land subsidence deformation caused by urban shallow-burred tunnel construction. \u003cem\u003eThe Open Civil Engineering Journal\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 219-224. Available online: https://opencivilengineeringjournal.com/contents/volumes/V8/TOCIEJ-8-219/TOCIEJ-8-219.pdf. \u003c/li\u003e\n\u003cli\u003eWu, Y.X., Yang, T.L., Li, P.C., \u0026amp; Lin, J.X. (2019). Investigation of groundwater withdrawal and recharge affecting underground structures in the Shanghai urban area. \u003cem\u003eSustainability\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e11(24\u003c/em\u003e), 7162. https://doi.org/10.3390/su11247162. \u003c/li\u003e\n\u003cli\u003eWu, H.N., Shen, S.L., \u0026amp; Wu, J.Y. (2017). Identification of tunnel settlement caused by land subsidence in soft deposit of Shanghai\u003cem\u003e. Journal of Performance of Constructed Facilities\u003c/em\u003e, \u003cem\u003e31(6)\u003c/em\u003e. DOI: 10.1061/(ASCE)CF.1943-5509.0001082. \u003c/li\u003e\n\u003cli\u003eXu, Y.S., Shen, S.L., Ren, D.J., \u0026amp; Wu, H.N. (2016). Analysis of factors in land subsidence in Shanghai: a view based on strategic environmental assessment. \u003cem\u003eSustainability,\u003c/em\u003e\u003cem\u003e8(6),\u003c/em\u003e 573. https://doi.org/10.3390/su8060573\u003c/li\u003e\n\u003cli\u003eYuan, Y., Xu, Y.S., \u0026amp; Arulrajah., A. (2017). Sustainable measures for mitigation of flooding hazards: A case study in Shanghai, China. \u003cem\u003eWater,\u003c/em\u003e\u003cem\u003e9, \u003c/em\u003e310. doi:10.3390/w9050310 \u003c/li\u003e\n\u003cli\u003eYoo, J., \u0026amp; Perrings, C. (2017). An externality of groundwater depletion: land subsidence and residential property prices in Phoenix, Arizona. \u003cem\u003eJournal of Environmental Economics and Policy\u003c/em\u003e, 6(2), 121\u0026ndash;133. https://doi.org/10.1080/21606544.2016.1226198.\u003c/li\u003e\n\u003cli\u003eZhang, W.J., Zhang, L.M., Wu, X.G., Liu, Y. (2018). Risk Assessment of leakage water of tunnel construction in coastal soft soils. \u003cem\u003eAdvances in Economics, Business and Management Research (AEBMR)\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 110-115. https://dx.doi.org/10.2991/msmi-18.2018.20 \u003c/li\u003e\n\u003cli\u003eZhang, J., Fu, M., Chen, J., Chu, P., \u0026amp; Zhang, C., (2018). Variations in mine subsidence-disturbed residential land price: Case study of critical determinants and spatial relationships in the Nanhu ecoregion of Tangshan, China. \u003cem\u003eJournal of Urban Planning and Development\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e144(3), \u003c/em\u003e05018012. DOI: 10.1061/(ASCE)UP.1943-5444.0000457.\u003c/li\u003e\n\u003cli\u003eZhou, D., Li, X., Wang, Qi., Wang, R., Wang, T., Gu, Q., \u0026amp; Xin, Y. (2019). GIS-based urban underground space resources evaluation toward three-dimensional land planning: A case study in Nantong China. \u003cem\u003eTunnelling and Underground Space Technology (TUST),\u003c/em\u003e\u003cem\u003e84, \u003c/em\u003e1-19. https://doi.org/10.1016/j.tust.2018.10.017. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Land subsidence, cause-effect, spatiotemporal, economic impact, underground space, spatial planning model, Shanghai","lastPublishedDoi":"10.21203/rs.3.rs-3849481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShanghai is continuously threatened with land subsidence with impact on its built environment e.g. building, land, properties, infrastructure and socio-economic activities. This research studies the mutiregression and spatiotemporal of the identified causing factors; surface load, groundwater-induced, socio-economic growth, underground space development (UUS), climate change and presents the relations of the multifactor in 1960\u0026ndash;2020. Data are collected secondarily from open sourced databases. Results determine rhe causing factors with high correlation coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.90 R squared values are; UUS development induced-subsidence, tunneling leakage and weak spatial modelling. Economic impact factors include; building price, reconstruction area, arable land, GDP by district and metro tunnel settlement. Spatiotemporal patterns depict the population and land subsidence is growing in spatial autocorrelation with the ratio of 0.89:1.00 indirect-negative economic impact from Urban City Centre, Pudong New Area, Minhang, Baoshan and Songjiang districts. These results can be referred as preparation for further adaptive and resilient scenario spatial planning and modelling.\u003c/p\u003e","manuscriptTitle":"Underground Space-Subsidence and Economic Impact Planning Model in Shanghai: Cause-Effect and Spatiotemporal Regression Analyses for Year 1960-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:46:32","doi":"10.21203/rs.3.rs-3849481/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":"8247af68-c901-43f5-b84f-4d9128987257","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-06T13:59:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 15:46:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3849481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849481","identity":"rs-3849481","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}