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Sustainability Science

, Volume 14, Issue 1, pp 53–75 | Cite as

Scenario analysis of land-use and ecosystem services of social-ecological landscapes: implications of alternative development pathways under declining population in the Noto Peninsula, Japan

  • Shizuka HashimotoEmail author
  • Rajarshi DasGupta
  • Kei Kabaya
  • Takanori Matsui
  • Chihiro Haga
  • Osamu Saito
  • Kazuhiko Takeuchi
Special Feature: Original Article Future Scenarios for Socio-Ecological Production Landscape and Seascape
  • 276 Downloads
Part of the following topical collections:
  1. Special Feature: Future Scenarios for Socio-Ecological Production Landscape and Seascape

Abstract

Population-decline and subsequent underuse of social-ecological landscapes are increasingly being recognized as one of the crucial drivers behind the loss and deterioration of biodiversity and ecosystem services. In line with this, the study aimed to explore how alternative development pathways influence future land-use patterns, biodiversity and ecosystem services against a rapidly declining population in the Noto peninsula of Japan. By combining land-use simulation and evaluation of ecosystem services, the study formulated four exploratory scenarios for 2050, assuming a contrasting level of the society’s reliance on domestic natural capital and different demographic patterns. At first, we analyzeds historical land-use changes between 1997 and 2007 and thereby simulated four plausible alternative scenarios using the Multi-Layer Perceptron Neural Network model. These scenarios were further used to quantify ecosystem services and landscape heterogeneity (as biodiversity indicator). The scenario analysis demonstrated that future land-use pattern could vary drastically depending on how the society utilize local natural capital even under the severe depopulation trend, whereas demographic patterns, in general, did not make discernible differences in land-use, biodiversity and ecosystem services. Nevertheless, a land-use change made considerable differences in the level of ecosystem services and landscape heterogeneity with varying degrees. Our analysis suggested that ecosystem services such as food production and nitrogen retention as well as landscape heterogeneity would decrease considerably by 2050 under the scenarios where the utilization of local natural capital decline and a significant amount of farmland are abandoned. Our findings highlight the vital role of land-use and agricultural policy in shaping the future availability of ecosystem services and biodiversity in this area.

Keywords

Ecosystem services Natural capital Underuse Scenario analysis Land use change Social-egological production landscapes 

Notes

Acknowledgements

This research was supported by the Environment Research and Technology Development Fund (S-15 Predicting and Assessing Natural Capital and Ecosystem Services (PANCES)) of the Ministry of the Environment, Japan, JSPS KAKENHI Grant Number 17KT0076 and ‘Research and Social Implementation of Ecosystem-based Disaster Risk Reduction as Climate Change Adaptation in Shrinking Societies’ of the Research Institute for Humanity and Nature, Japan.

References

  1. Ala-Hulkko T, Kotavaara O, Alahuhta J et al (2016) Introducing accessibility analysis in mapping cultural ecosystem services. Ecol Indic 66:416–427.  https://doi.org/10.1016/j.ecolind.2016.02.013 CrossRefGoogle Scholar
  2. Alcamo J (2008) Environmental futures : the practice of environmental scenario analysis. Elsevier, OxfordGoogle Scholar
  3. Bagan H, Yamagata Y (2015) Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. GIScience Remote Anal.  https://doi.org/10.1080/15481603.2015.1072400 Google Scholar
  4. Beilin R, Lindborg R, Stenseke M et al (2014) Analysing how drivers of agricultural land abandonment affect biodiversity and cultural landscapes using case studies from Scandinavia, Iberia and Oceania. Land Use Policy 36:60–72.  https://doi.org/10.1016/J.LANDUSEPOL.2013.07.003 CrossRefGoogle Scholar
  5. Castillo CR, Güneralp İI, Güneralp B (2014) Influence of changes in developed land and precipitation on hydrology of a coastal Texas watershed. Appl Geogr 47:154–167.  https://doi.org/10.1016/j.apgeog.2013.12.009 CrossRefGoogle Scholar
  6. Costanza R, d’Arge R, de Groot R et al (1997) The value of the world’s ecosystem services and natural capital. Nature 387:253–260.  https://doi.org/10.1038/387253a0 CrossRefGoogle Scholar
  7. Daily GC, Polasky S, Goldstein J et al (2009) Ecosystem services in decision making: time to deliver. Front Ecol Environ 7:21–28.  https://doi.org/10.1890/080025 CrossRefGoogle Scholar
  8. Díaz S, Demissew S, Carabias J et al (2015) The IPBES conceptual framework—connecting nature and people. Curr Opin Environ Sustain.  https://doi.org/10.1016/j.cosust.2014.11.002 Google Scholar
  9. Duraiappah AK, Nakamura K, Takeuchi K et al (2012) Satoyama-Satoumi ecosystems and human well-being: socio-ecological production landscapes of Japan. UNU, TokyoGoogle Scholar
  10. Eastman JR (2016) TerrSet manual. Clark University, WorcesterGoogle Scholar
  11. Eastman JR, Jiang H, Toledano J (1998) Multi-criteria and multi-objective decision making for land allocation using GIS. Springer, Dordrecht, pp 227–251Google Scholar
  12. Economic Planning Agency (1974) Soil map of fundamental land classification survey for Ishikawa. Economic Planning Agency, JapanGoogle Scholar
  13. Egarter Vigl L, Depellegrin D, Pereira P et al (2017) Mapping the ecosystem service delivery chain: capacity, flow, and demand pertaining to aesthetic experiences in mountain landscapes. Sci Total Environ 574:422–436.  https://doi.org/10.1016/J.SCITOTENV.2016.08.209 CrossRefGoogle Scholar
  14. Egoh B, Rouget M, Reyers B et al (2007) Integrating ecosystem services into conservation assessments: a review. Ecol Econ 63:714–721.  https://doi.org/10.1016/J.ECOLECON.2007.04.007 CrossRefGoogle Scholar
  15. Estoque RC, Murayama Y (2012) Examining the potential impact of land use/cover changes on the ecosystem services of Baguio city, the Philippines: a scenario-based analysis. Appl Geogr 35:316–326.  https://doi.org/10.1016/j.apgeog.2012.08.006 CrossRefGoogle Scholar
  16. Fu Q, Li B, Hou Y et al (2017) Effects of land use and climate change on ecosystem services in Central Asia’s arid regions: a case study in Altay Prefecture, China. Sci Total Environ 607–608:633–646.  https://doi.org/10.1016/J.SCITOTENV.2017.06.241 CrossRefGoogle Scholar
  17. Furumai H (2011) Final report of the study on load estimation of non-point source pollution. http://jswe-nonpoint.com/h23suishin/pdf/h23report_00all2.pdf. Accessed 28 Aug 2018 (in Japanese)
  18. Gao J, Li F, Gao H et al (2017) The impact of land-use change on water-related ecosystem services: a study of the Guishui River Basin, Beijing, China. J Clean Prod 163:S148–S155.  https://doi.org/10.1016/j.jclepro.2016.01.049 CrossRefGoogle Scholar
  19. Green RE, Cornell SJ, Scharlemann JPW, Balmford A (2005) Farming and the fate of wild nature. Science (80-) 307:550–555.  https://doi.org/10.1126/science.1106049 CrossRefGoogle Scholar
  20. Greenhouse Gas Inventory Office of Japan (2017) National greenhouse gas inventory report of Japan. https://www.env.go.jp/earth/ondanka/ghg-mrv/unfccc/material/NIR-JPN-2017_E.pdf. Accessed 28 Aug 2018
  21. Gu H, Subramanian SM (2014) Drivers of change in socio-ecological production landscapes: implications for better management. Ecol Soc 19:41.  https://doi.org/10.5751/es-06283-190141 CrossRefGoogle Scholar
  22. Hamon WR (1961) Estimating potential evapotranspiration. J Irrig Drain Div 108:107–120Google Scholar
  23. Hashimoto S, Nakamura S, Saito O et al (2015) Mapping and characterizing ecosystem services of social–ecological production landscapes: case study of Noto, Japan. Sustain Sci 10:257–273.  https://doi.org/10.1007/s11625-014-0285-1 CrossRefGoogle Scholar
  24. Hou Y, Li B, Müller F, Chen W (2016) Ecosystem services of human-dominated watersheds and land use influences: a case study from the Dianchi Lake watershed in China. Environ Monit Assess 188:652.  https://doi.org/10.1007/s10661-016-5629-0 CrossRefGoogle Scholar
  25. Institute for Agro-Environmental Sciences (2017) Soil inventory of Japan. https://soil-inventory.dc.affrc.go.jp/. Accessed 28 Aug 2018
  26. IPBES (2016) Scenarios and models of biodiversity and ecosystem services—summary for policymakers. https://www.ipbes.net/system/tdf/downloads/pdf/spm_deliverable_3c_scenarios_20161124.pdf?file=1&type=node&id=15245. Accessed 28 Aug 2018
  27. Jacob AL, Vaccaro I, Sengupta R, Chapman CA (2008) Integrating landscapes that have experienced rural depopulation and ecological homogenization into tropical conservation planning. Science (80-) 1:307–320Google Scholar
  28. Japan Meteorological Agency (2010) Climatological normals (in Japanese) Google Scholar
  29. Japan Soil Association (2010) Database of soil survey for soil fertility conservation. Japan Soil Association, [CD-ROM]Google Scholar
  30. Joorabian Shooshtari S, Shayesteh K, Gholamalifard M et al (2017) Impacts of future land cover and climate change on the water balance in northern Iran. Hydrol Sci J 62:2655–2673.  https://doi.org/10.1080/02626667.2017.1403028 CrossRefGoogle Scholar
  31. Kabaya K (2014) Quantitative assessment of the nitrogen removal service in Japan using InVEST (in Japanese). Rev Environ Econ Policy Stud 7:37–49.  https://doi.org/10.14927/reeps.7.2_37 Google Scholar
  32. Kadoya T, Washitani I (2011) The Satoyama Index: a biodiversity indicator for agricultural landscapes. Agric Ecosyst Environ 140:20–26.  https://doi.org/10.1016/j.agee.2010.11.007 CrossRefGoogle Scholar
  33. Katayama N, Osawa T, Amano T, Kusumoto Y (2015) Are both agricultural intensification and farmland abandonment threats to biodiversity? A test with bird communities in paddy-dominated landscapes. Agric Ecosyst Environ 214:21–30.  https://doi.org/10.1016/J.AGEE.2015.08.014 CrossRefGoogle Scholar
  34. Kim J, Choi J, Choi C, Park S (2013) Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Sci Total Environ 452–453:181–195.  https://doi.org/10.1016/j.scitotenv.2013.02.005 CrossRefGoogle Scholar
  35. Kishioka T, Hashimoto S, Nishi M et al (2017) Fostering cooperation between farmers and public and private actors to expand environmentally friendly rice cultivation: intermediary functions and farmers’ perspectives. Int J Agric Sustain.  https://doi.org/10.1080/14735903.2017.1374321 Google Scholar
  36. Klein S (2015) Young urban migrants in the Japanese countryside between self-realization and slow life? In: Assmann S (ed) Sustainability in contemporary rural Japan : challenges and opportunities. Routledge, Abingdon, p 198Google Scholar
  37. Koh LP, Ghazoul J (2010) Spatially explicit scenario analysis for reconciling agricultural expansion, forest protection, and carbon conservation in Indonesia. Proc Natl Acad Sci USA 107:11140–11144.  https://doi.org/10.1073/pnas.1012681107 CrossRefGoogle Scholar
  38. Kok MTJ, Kok K, Peterson GD et al (2017) Biodiversity and ecosystem services require IPBES to take novel approach to scenarios. Sustain Sci 12:177–181.  https://doi.org/10.1007/s11625-016-0354-8 CrossRefGoogle Scholar
  39. Leh MDK, Matlock MD, Cummings EC, Nalley LL (2013) Quantifying and mapping multiple ecosystem services change in West Africa. Agric Ecosyst Environ 165:6–18.  https://doi.org/10.1016/j.agee.2012.12.001 CrossRefGoogle Scholar
  40. Lin Y-P, Chu H-J, Wu C-F, Verburg PH (2011) Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling—a case study. Int J Geogr Inf Sci 25:65–87.  https://doi.org/10.1080/13658811003752332 CrossRefGoogle Scholar
  41. Luck GW (2007) A review of the relationships between human population density and biodiversity. Biol Rev 82:607–645.  https://doi.org/10.1111/j.1469-185X.2007.00028.x CrossRefGoogle Scholar
  42. MA (2005) Ecosystems and human well-being : synthesis. Island, Washington, DCGoogle Scholar
  43. Matsui T, Haga C, Saito O, Hashimoto S (2018) Spatially explicit residential and working population assumptions for projecting and assessing natural capital and ecosystem services in Japan. Sustain Sci.  https://doi.org/10.1007/s11625-018-0605-y Google Scholar
  44. Mauerhofer V, Ichinose T, Blackwell BDD et al (2018) Underuse of social-ecological systems: a research agenda for addressing challenges to biocultural diversity. Land Use Policy 72:57–64.  https://doi.org/10.1016/j.landusepol.2017.12.003 CrossRefGoogle Scholar
  45. Ministry of Agriculture, Forestry and Fisheries (2010) 2010 Census of agriculture and forestry in Japan. Ministry of Agriculture, Forestry and Fisheries, TokyoGoogle Scholar
  46. Ministry of Agriculture, Forestry and Fisheries (2011) Basic policy and action plan for the revitalization of our country’s food and agriculture. Forestry and Fishery Industries, Ministry of Agriculture, Forestry and Fisheries. https://www.cas.go.jp/jp/seisaku/npu/policy05/pdf/20120815/20120815_en.pdf. Accessed 28 Aug 2018
  47. Ministry of Environment (2010) Report of comprehensive assessment of biodiversity in Japan (Japan Biodiversity Outlook)Google Scholar
  48. Ministry of Land, Infrastructure, Transport and Tourism (2017) Population Projections for individual 1km mesh (National Spatial Planning and Regional Policy Bureau estimates for 2017) (Shape format data). http://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-G02.html. Accessed 28 Aug 2018
  49. Ministry of Land, Infrastructure, Transport and Tourism (2018) National land numerical information land use fragmented mesh details. http://nlftp.mlit.go.jp/ksj-e/gml/datalist/KsjTmplt-L03-b.html. Accessed 10 Apr 2018Google Scholar
  50. Ministry of the Environment (2010) Report of Comprehensive Assessment of Biodiversity in Japan (Japan Biodiversity Outlook). http://www.biodic.go.jp/biodiversity/activity/policy/jbo/jbo/files/JBO_E.pdf. Accessed 28 Aug 2018
  51. Ministry of the Environment (2014) Japan biodiversity outlook 2. http://www.env.go.jp/nature/biodic/jbo2/pamph01_full.pdf. Accessed 28 Aug 2018 (in Japanese)
  52. Mishra VN, Rai PK (2016) A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9:249.  https://doi.org/10.1007/s12517-015-2138-3 CrossRefGoogle Scholar
  53. Mitsuda Y, Ito S (2011) A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landsc Ecol Eng 7:117–125.  https://doi.org/10.1007/s11355-010-0113-4 CrossRefGoogle Scholar
  54. Mouflis GD, Gitas IZ, Iliadou S, Mitri GH (2008) Assessment of the visual impact of marble quarry expansion (1984–2000) on the landscape of Thasos island, NE Greece. Landsc Urban Plan 86:92–102.  https://doi.org/10.1016/J.LANDURBPLAN.2007.12.009 CrossRefGoogle Scholar
  55. Murakami K, Gilroy R, Atterton J (2009) Planning for the ageing countryside in Japan: the potential impact of multi-habitation. Plan Pract Res 24:285–299.  https://doi.org/10.1080/02697450903020734 CrossRefGoogle Scholar
  56. Naidoo R, Balmford A, Costanza R et al (2008) Global mapping of ecosystem services and conservation priorities. Proc Natl Acad Sci USA 105:9495–9500.  https://doi.org/10.1073/pnas.0707823105 CrossRefGoogle Scholar
  57. NASA (2017) ASTER Global digital elevation map. https://asterweb.jpl.nasa.gov/gdem.asp. Accessed 10 Apr 2018
  58. National Institute of Population and Social Security Research (2017) Population projections for Japan (2017): 2016 to 2065 (in Japanese) Google Scholar
  59. Nelson E, Mendoza G, Regetz J et al (2009) Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front Ecol Environ 7:4–11.  https://doi.org/10.1890/080023 CrossRefGoogle Scholar
  60. Paracchini ML, Zulian G, Kopperoinen L et al (2014) Mapping cultural ecosystem services: a framework to assess the potential for outdoor recreation across the EU. Ecol Indic 45:371–385.  https://doi.org/10.1016/j.ecolind.2014.04.018 CrossRefGoogle Scholar
  61. Partidário MR, Sheate WR, Bina O et al (2009) Sustainability assessment for agriculture scenarios in Europe’s mountain areas: lessons from six study areas. Environ Manage 43:144–165.  https://doi.org/10.1007/s00267-008-9206-3 CrossRefGoogle Scholar
  62. Pontius RG (2000) Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens 66:1011–1016Google Scholar
  63. Roy HG, Fox DM, Emsellem K (2014) Predicting land cover change in a Mediterranean catchment at different time scales. Springer, Cham, pp 315–330Google Scholar
  64. Saito O, Kamiyama C, Hashimoto S et al (2018) Co-design of national-scale future scenarios in Japan to predict and assess natural capital and ecosystem services. Sustain Sci.  https://doi.org/10.1007/s11625-018-0587-9 Google Scholar
  65. Sangermano F, Toledano J, Eastman JR (2012) Land cover change in the Bolivian Amazon and its implications for REDD + and endemic biodiversity. Landsc Ecol 27:571–584.  https://doi.org/10.1007/s10980-012-9710-y CrossRefGoogle Scholar
  66. Sasaki H (2016) Do Japanese citizens move to rural areas seeking a slower life? Differences between rural and urban areas in subjective well-being. In: 5th Italian association of agricultural and applied economics conference—the changing role of regulation in the bio-based economy, p 17Google Scholar
  67. Sato Y, Higuchi A, Takami A et al (2016) Regional variability in the impacts of future land use on summertime temperatures in Kanto region, the Japanese megacity. Urban For Urban Green 20:43–55.  https://doi.org/10.1016/j.ufug.2016.07.012 CrossRefGoogle Scholar
  68. Schaldach R, Priess JA, Alcamo J (2011) Simulating the impact of biofuel development on country-wide land-use change in India. Biomass Bioenerg 35:2401–2410.  https://doi.org/10.1016/j.biombioe.2010.08.048 CrossRefGoogle Scholar
  69. Sharp R, Chaplin-Kramer R, Wood S, et al (2015) InVEST 3.2.0 User’s guide. http://data.naturalcapitalproject.org/invest-releases/documentation/3_2_0/. Accessed 28 Aug 2018
  70. Silva TS, Tagliani PRA (2012) Environmental planning in the medium littoral of the Rio Grande do Sul coastal plain—Southern Brazil: elements for coastal management. Ocean Coast Manag 59:20–30.  https://doi.org/10.1016/j.ocecoaman.2011.12.014 CrossRefGoogle Scholar
  71. Silva RFB, Batistella M, Moran EF (2016) Drivers of land change: Human-environment interactions and the Atlantic forest transition in the Paraíba Valley, Brazil. Land Use Policy 58:133–144.  https://doi.org/10.1016/j.landusepol.2016.07.021 CrossRefGoogle Scholar
  72. Statistics Bureau (2017) Housing and land survey. http://www.stat.go.jp/data/jyutaku/index.html. Accessed 28 Aug 2018 (in Japanese)
  73. Su C, Fu B (2013) Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. Glob Planet Change 101:119–128.  https://doi.org/10.1016/j.gloplacha.2012.12.014 CrossRefGoogle Scholar
  74. Swart R, Raskin P, Robinson J (2004) The problem of the future: sustainability science and scenario analysis. Glob Environ Chang 14:137–146.  https://doi.org/10.1016/J.GLOENVCHA.2003.10.002 CrossRefGoogle Scholar
  75. Takada T, Miyamoto A, Hasegawa SF (2010) Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landsc Ecol 25:561–572.  https://doi.org/10.1007/s10980-009-9433-x CrossRefGoogle Scholar
  76. Tallis H, Polasky S (2009) Mapping and valuing ecosystem services as an approach for conservation and natural-resource management. Ann N Y Acad Sci 1162:265–283.  https://doi.org/10.1111/j.1749-6632.2009.04152.x CrossRefGoogle Scholar
  77. Van Ty T, Sunada K, Ichikawa Y, Oishi S (2012) Scenario-based impact assessment of land use/cover and climate changes on water resources and demand: a case study in the Srepok river basin, Vietnam-Cambodia. Water Resour Manag 26:1387–1407.  https://doi.org/10.1007/s11269-011-9964-1 CrossRefGoogle Scholar
  78. van Vliet J, de Groot HLF, Rietveld P, Verburg PH (2015) Manifestations and underlying drivers of agricultural land use change in Europe. Landsc Urban Plan 133:24–36.  https://doi.org/10.1016/J.LANDURBPLAN.2014.09.001 CrossRefGoogle Scholar
  79. Vojtech V (2010) Policy measures addressing agri-environmental issues. In: OECD Food, Agric Fish Pap 0_1,1,5-41.  https://doi.org/10.1787/5kmjrzg08vvb-en
  80. Wang Z, Mao D, Li L et al (2015) Quantifying changes in multiple ecosystem services during 1992–2012 in the Sanjiang Plain of China. Sci Total Environ 514:119–130.  https://doi.org/10.1016/J.SCITOTENV.2015.01.007 CrossRefGoogle Scholar
  81. Weber JL (2007) Implementation of land and ecosystem accounts at the European Environment Agency. Ecol Econ 61:695–707.  https://doi.org/10.1016/j.ecolecon.2006.05.023 CrossRefGoogle Scholar
  82. Westhoek HJJ, van den Berg M, Bakkes JAA (2006) Scenario development to explore the future of Europe’s rural areas. Agric Ecosyst Environ 114:7–20.  https://doi.org/10.1016/j.agee.2005.11.005 CrossRefGoogle Scholar
  83. Wheatley D (1995) Cumulative Viewshed Analysis: a GIS-based method for investigating intervisibility, and its archaeological application. In: Lock GR, Gary R, Stančič Z (eds) Archaeology and geographical information systems : a European perspective. Taylor & Francis, New York, pp 171–186Google Scholar
  84. Whitehead PG, Barbour E, Futter MN et al (2015) Impacts of climate change and socio-economic scenarios on flow and water quality of the Ganges, Brahmaputra and Meghna (GBM) river systems: low flow and flood statistics. Environ Sci Process Impacts 17:1057–1069.  https://doi.org/10.1039/C4EM00619D CrossRefGoogle Scholar
  85. Yu S, Xu Z, Wu W, Zuo D (2016) Effect of land use types on stream water quality under seasonal variation and topographic characteristics in the Wei River basin, China. Ecol Indic 60:202–212.  https://doi.org/10.1016/J.ECOLIND.2015.06.029 CrossRefGoogle Scholar
  86. Zhang L, Hickel K, Dawes WR et al (2004) A rational function approach for estimating mean annual evapotranspiration. Water Resour Res.  https://doi.org/10.1029/2003WR002710 Google Scholar

Copyright information

© Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Agriculture and Life SciencesThe University of TokyoTokyoJapan
  2. 2.Integrated Research System for Sustainability ScienceThe University of TokyoTokyoJapan
  3. 3.Graduate School of EngineeringOsaka UniversitySuitaJapan
  4. 4.United Nations University Institute for the Advanced Study of SustainabilityTokyoJapan
  5. 5.Institute for Global Environmental StrategiesHayamaJapan

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