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Sustainability Science

, Volume 14, Issue 1, pp 23–37 | Cite as

Spatially explicit residential and working population assumptions for projecting and assessing natural capital and ecosystem services in Japan

  • Takanori MatsuiEmail author
  • Chihiro Haga
  • Osamu Saito
  • Shizuka Hashimoto
Special Feature: Technical Report Future Scenarios for Socio-Ecological Production Landscape and Seascape
First Online:
Part of the following topical collections:
  1. Special Feature: Future Scenarios for Socio-Ecological Production Landscape and Seascape

Abstract

In scenario studies of biodiversity and ecosystem services, the population distribution is one of the key driving forces. In this study, we developed a coupling method for narrative scenarios and spatially explicit residential and working population designs for all of Japan as a common dataset for ecosystem scenario analysis implemented by 5-year project entitled “Predicting and Assessing Natural Capital and Ecosystem Services (PANCES)”. Four narrative scenarios were proposed by the PANCES project using two axes as major uncertainties: the population distribution and the capital preference. The residential population and the working population in primary industries were calculated using a gravity-based allocation algorithm in a manner consistent with the storylines of the PANCES scenarios. Using the population distribution assumption by scenario, the population was overlaid with the natural capital and the supply potential of ecosystem services. The results supported to understand the gaps between natural capital and maintainability, and the potential of ecosystem services and realizability. The spatially explicit population distribution data products are expected to help design the nature conservation strategy and governance option in terms of both social system and ecological system.

Keywords

Ecosystem services Natural capital Scenario analysis Population distribution Spatially explicit 
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Notes

Acknowledgements

This research was funded by the Environment Research and Technology Development Fund [S-15 ‘‘Predicting and Assessing Natural Capital and Ecosystem Services’’ (PANCES), Ministry of the Environment, Japan] and Grant-in-Aid for JSPS Research Fellow (number 18J20266) from Japan Society for the Promotion of Science (JSPS). We specially appreciate the project members and respondents who contributed to this survey design and implementation.

References

  1. Barbieri C, Mahoney E (2009) Why is diversification an attractive farm adjustment strategy? Insights from Texas farmers and ranchers. J Rural Stud 25:58–66.  https://doi.org/10.1016/j.jrurstud.2008.06.001 CrossRefGoogle Scholar
  2. Cabinet Office, Government of Japan (CAO) (2012) Tourism nation promotion basic plan. http://www.mlit.go.jp/common/000234920.pdf. Accessed 27 Apr 2018
  3. Cabinet Office, Government of Japan (CAO) (2016) A 2016 declining birthrate white paper. http://www8.cao.go.jp/shoushi/shoushika/whitepaper/measures/english/w-2016/index.html. Accessed 27 Apr 2018
  4. Dalkey N, Helmer O (1963) An experimental application of the Delphi method to the use of experts. Manage Sci 9(3):458–467.  https://doi.org/10.1287/mnsc.9.3.458 CrossRefGoogle Scholar
  5. Food and Agricultural Organization of the United Nations (FAO) (2018) Globally important agricultural heritage systems (GIAHS) Noto’s Satoyama and Satoumi. http://www.fao.org/giahs/giahsaroundtheworld/designated-sites/asia-and-the-pacific/notos-satoyama-and-satoumi/en/. Accessed 27 Apr 2018
  6. Grübler A, O’Neill B, Riahi K, Chirkov V, Goujon A, Kolp P, Prommer I, Scherbov S, Slentoe E (2007) Regional, national, and spatially explicit scenarios of demographic and economic change based on SRES. Technol Forecast Soc Change Greenh Gases Integr Assess 74:980–1029.  https://doi.org/10.1016/j.techfore.2006.05.023 CrossRefGoogle Scholar
  7. Hokkaido prefecture (2018) The forestry statistics (in Japanese). http://www.pref.hokkaido.lg.jp/sr/sum/kcs/rin-toukei/rin-toukei-index.htm. Accessed 27 Apr 2018
  8. Intergovernmental Panel on Climate Change (IPCC) (2000). http://www.ipcc.ch/. Accessed 27 Apr 2018
  9. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (2016) The methodological assessment report on scenarios and models of biodiversity and ecosystem services. https://www.ipbes.net/sites/default/files/downloads/pdf/2016.methodological_assessment_report_scenarios_models.pdf. Accessed 27 Apr 2018
  10. Japan Biodiversity Outlook Science Committee (2010) Report of comprehensive assessment of biodiversity in Japan (Japan Biodiversity Outlook). Global Biodiversity Strategy Office, Biodiversity Policy Division, Nature Conservation Bureau, Ministry of the Environment, TokyoGoogle Scholar
  11. Japan Biodiversity Outlook Science Committee (2016) Report of comprehensive assessment of biodiversity and ecosystem services in Japan (Japan Biodiversity Outlook 2). Global Biodiversity Strategy Office, Biodiversity Policy Division, Nature Conservation Bureau, Ministry of the Environment, TokyoGoogle Scholar
  12. Japan Satoyama Satoumi Assessment (JSSA) (2010) Satoyama-satoumi ecosystems and human wellbeing: socio-ecological production landscapes of Japan (summary for decision makers). United Nations University, TokyoGoogle Scholar
  13. Jones B, O’Neill BC (2013) Historically grounded spatial population projections for the continental United States. Environ Res Lett 8:044021.  https://doi.org/10.1088/1748-9326/8/4/044021 CrossRefGoogle Scholar
  14. Jones B, O’Neill BC (2016) Spatially explicit global population scenarios consistent with the shared socioeconomic pathways. Environ Res Lett 11:084003.  https://doi.org/10.1088/1748-9326/11/8/084003 CrossRefGoogle Scholar
  15. Jones L, Norton L, Austin Z, Browne AL, Donovan D, Emmett BA, Grabowski ZJ, Howard DC, Jones JPG, Kenter JO, Manley W, Morris C, Robinson DA, Short C, Siriwardena GM, Stevens CJ, Storkey J, Waters RD, Willis GF (2016) Stocks and flows of natural and human-derived capital in ecosystem services. Land Use Policy 52:151–162.  https://doi.org/10.1016/j.landusepol.2015.12.014 CrossRefGoogle Scholar
  16. Kamiyama C, Hashimoto S, Kohsaka R, Saito O (2016) Non-market food provisioning services via homegardens and communal sharing in Satoyama socio-ecological production landscapes on Japan’s Noto peninsula. Ecosyst Serv 17:185–196.  https://doi.org/10.1016/j.ecoser.2016.01.002 CrossRefGoogle Scholar
  17. Kindermann G, McCallum I, Fritz S, Obersteiner M (2008) A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fenn 42:387–396.  https://doi.org/10.14214/sf.244 CrossRefGoogle Scholar
  18. Millennium Ecosystem Assessment (MA) (2005a). https://www.millenniumassessment.org/. Accessed 27 Apr 2018
  19. Millennium Ecosystem Assessment (MA) (2005b) Sub-global assessments. https://www.millenniumassessment.org/en/Multiscale.html. Accessed 27 Apr 2018
  20. Ministry of Agriculture, Forestry and Fisheries (MAFF) (2017) FY2016 Summary of the annual report on food, agriculture and rural areas in Japan. http://www.maff.go.jp/j/wpaper/w_maff/h28/attach/pdf/index-29.pdf. Accessed 27 Apr 2018
  21. Ministry of Economy, Trade and Industry (METI) (2009) White paper on international economy and trade 2009. http://www.meti.go.jp/english/report/data/gWT2009fe.html. Accessed 27 Apr 2018
  22. Ministry of Economy, Trade and Industry (METI) (2012) 2012 white paper on small and medium enterprises in Japan. http://www.chusho.meti.go.jp/pamflet/hakusyo/H24/download/2012hakusho_eng.pdf. Accessed 27 Apr 2018
  23. Ministry of Environment (MOE) (2012) The National Biodiversity Strategy of Japan 2012–2020 roadmap towards the establishment of an Enriching Society in harmony with nature. www.env.go.jp/press/files/en/528.pdf. Accessed 27 Apr 2018
  24. Ministry of Internal Affairs and Communications (MIAC) (2018) Labor force survey. http://www.stat.go.jp/english/data/roudou/index.html. Accessed 27 Apr 2018
  25. Ministry of Land, Infrastructure, Transport and Tourism (MLIT) (2010) National land numerical information download service, accommodation capacity data. http://nlftp.mlit.go.jp/ksj/gmlold/meta/suikei.html. Accessed 27 Apr 2018
  26. Ministry of Land, Infrastructure, Transport and Tourism (MLIT) (2014a) National land numerical information download service, population projections for individual 1 km mesh. http://nlftp.mlit.go.jp/ksj/gmlold/meta/suikei.html. Accessed 27 Apr 2018
  27. Ministry of Land, Infrastructure, Transport and Tourism (MLIT) (2014b) National land numerical information download service, land use tertiary mesh data. http://nlftp.mlit.go.jp/ksj/gmlold/meta/suikei.html. Accessed 27 Apr 2018
  28. Ministry of Land, Infrastructure, Transport and Tourism (MLIT) (2014c) National land numerical information download service, Tourism Resource Data. http://nlftp.mlit.go.jp/ksj/gmlold/meta/suikei.html. Accessed 27 Apr 2018
  29. Ministry of Land, Infrastructure, Transport and Tourism (MLIT) (2017) National land numerical information download service, population projections for individual 500 m mesh. http://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-mesh500.html. Accessed 27 Apr 2018
  30. Muramatsu N, Akiyama H (2011) Japan: super-aging society preparing for the future. Gerontologist 51(4):425–432.  https://doi.org/10.1093/geront/gnr067 CrossRefGoogle Scholar
  31. National Institute of Population and Social Security Research (NIPSSR) (2014) Household projections by prefecture in Japan: 2010–2035, outline of results and methods. http://www.ipss.go.jp/pp-ajsetai/e/hhprjpref2014/t-page_e.asp. Accessed 27 Apr 2018
  32. National Institute of Population and Social Security Research (NIPSSR) (2018) Regional population projections for Japan: 2015–2045, outline of results and methods (in Japanese). http://www.ipss.go.jp/pp-shicyoson/j/shicyoson18/t-page.asp. Accessed 27 Apr 2018
  33. Nayak S, Dairaku K, Takayabu I, Suzuki-Parker A, Ishizaki NN (2017) Extreme precipitation linked to temperature over Japan: current evaluation and projected changes with multi-model ensemble downscaling. Clim Dyn.  https://doi.org/10.1007/s00382-017-3866-8 Google Scholar
  34. Predicting and Assessing natural Capital and Ecosystem Services (PANCES) (2016) http://pances.net/eng/. Accessed 27 Apr 2018
  35. Reimann L, Merkens JL, Vafeidis AT (2018) Regionalized shared socioeconomic pathways: narratives and spatial population projections for the Mediterranean coastal zone. Reg Environ Change 18:235–245.  https://doi.org/10.1007/s10113-017-1189-2 CrossRefGoogle Scholar
  36. Saito O, Kamiyama C, Hashimoto S, Matsui T, Shoyama K, Kabaya K, Uetake T, Taki H, Ishikawa Y, Matsushita K, Yamane F, Hori J, Ariga T, Takeuchi K (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
  37. Samal NR, Wollheim WM, Zuidema S, Stewart RJ, Zhou Z, Mineau MM, Borsuk ME, Gardner KH, Glidden S, Huang T, Lutz DA, Mavrommati G, Thorn AM, Wake CP, Huber M (2017) A coupled terrestrial and aquatic biogeophysical model of the Upper Merrimack River watershed, New Hampshire, to inform ecosystem services evaluation and management under climate and land-cover change. Ecol Soc 22(4):18.  https://doi.org/10.5751/ES-09662-220418 CrossRefGoogle Scholar
  38. Schoemaker PJH (1993) Multiple scenario development: its conceptual and behavioral foundation. Strat Manage J 14:193–213.  https://doi.org/10.1002/smj.4250140304 CrossRefGoogle Scholar
  39. Statistics Bureau of Japan (SBJ) (2010) The population census in Japan 2010. http://www.stat.go.jp/english/data/kokusei/index.html. Accessed 27 Apr 2018
  40. Takemi T, Okada Y, Ito R, Ishikawa H, Nakakita E (2016) Assessing the impacts of global warming on meteorological hazards and risks in Japan: philosophy and achievements of the SOUSEI program. Hydrol Res Lett 10:119–125.  https://doi.org/10.3178/hrl.10.119 CrossRefGoogle Scholar
  41. Takeuchi K, Brown RD, Washitani I, Tsunekawa A, Yokohari M (eds) (2006) Satoyama—the traditional rural landscape of Japan. Springer, Tokyo, p 229Google Scholar
  42. Tamura K, Masuda N (2017) Effects of the distant population density on spatial patterns of demographic dynamics. R Soc Open Sci 4:170391.  https://doi.org/10.1098/rsos.170391 CrossRefGoogle Scholar
  43. Tatebayashi K, Kamiyama C, Matsui T, Saito O, Machimura T (2018) Accounting shadow benefits of non-market food through food-sharing networks on Hachijo Island, Japan. Sustain Sci.  https://doi.org/10.1007/s11625-018-0580-3 Google Scholar
  44. Thorn AM, Wake CP, Grimm CD, Mitchell CR, Mineau MM, Ollinger SV (2017) Development of scenarios for land cover, population density, impervious cover, and conservation in New Hampshire, 2010–2100. Ecol Soc 22(4):19.  https://doi.org/10.5751/ES-09733-220419 CrossRefGoogle Scholar
  45. United Nations Educational, Scientific and Cultural Organization (UNESCO) (2018a) Biosphere Reserves, Mount Hakusan. http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/asia-and-the-pacific/japan/mount-hakusan/. Accessed 27 Apr 2018
  46. United Nations Educational, Scientific and Cultural Organization (UNESCO) (2018b) World Heritage List. https://whc.unesco.org/en/list/. Accessed 27 Apr 2018
  47. van’t Klooster SA, van Asselt MBA (2006) Practising the scenario axes technique. Futures 38:15–30.  https://doi.org/10.1016/j.futures.2005.04.019 CrossRefGoogle Scholar
  48. Volkery A, Ribeiro T, Henrichs T, Hoogeveen Y (2008) Your vision or my model? Lessons from participatory land use scenario development on a European scale. Syst Pract Action Res 21:459–477.  https://doi.org/10.1007/s11213-008-9104-x CrossRefGoogle Scholar
  49. Westhoek HJ, van den Berg M, Bakkes JA (2006) Scenario development to explore the future of Europe’s rural areas. Agr Ecosyst Environ 114:7–20.  https://doi.org/10.1016/j.agee.2005.11.005 CrossRefGoogle Scholar
  50. Zenrin Geo Intelligence (ZGI) (2018) The population census regional mesh statistics. On sale via http://www.zgi.co.jp/database/statistics/h22kokucho.html. Accessed 27 Apr 2018

Copyright information

© Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Sustainable Energy and Environmental Engineering, Graduate School of EngineeringOsaka UniversitySuitaJapan
  2. 2.United Nations University Institute for the Advanced Study of Sustainability (UNU-IAS)TokyoJapan
  3. 3.Graduate School of Agricultural and Life SciencesThe University of TokyoTokyoJapan

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