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Exploring forests vulnerable to over-logging to supply woody biomass to power plants in Mie, Central Japan

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Abstract

The rapid increase of woody biomass power plants has given rise to concerns about the balance of supply and demand. The purpose of this study was to explore forests vulnerable to over-logging and show them visually in Mie Prefecture, central Japan when supplying woody biomass to power plants based on transportation distance and the time using a non-commercial road network. The destinations were the three biomass power plants and the origins were artificial forests divided by watersheds. Transportation distances and time between destinations and origins were estimated using the route-search function in Google Maps. Forests vulnerable to over-logging were explored based on two thresholds: a one-way distance of 50 km and a travel time of 2.5 h. Our results show that many of the artificial forests in Mie Prefecture might be subject to high harvesting competition. In all, 55.07% of the forest plantations in Mie Prefecture were within 50 km of two or three biomass power plants and 87.11% were within 2.5 h one-way. It might be necessary to supply woody biomass from southern Mie Prefecture. The stakeholder should share logging plans and monitor over-logging while planning for the efficient use of woody biomass in the southern part of Mie Prefecture.

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References

  1. Goldemberg, J., & Coelho, S. T. (2004). Renewable energy: Traditional biomass vs. modern biomass. Energy Policy, 32(6), 711–714.

    Article  Google Scholar 

  2. Berndes, G., Hoogwijk, M., & Van den Broek, R. (2003). The contribution of biomass in the future global energy supply: A review of 17 studies. Biomass and Bioenergy, 25(1), 1–28.

    Article  Google Scholar 

  3. Offermann, R., Seidenberger, T., Thrän, D., Kaltschmitt, M., Zinoviev, S., & Miertus, S. (2011). Assessment of global bioenergy potentials. Mitigation and Adaptation Strategies for Global Change, 16(1), 103–115.

    Article  Google Scholar 

  4. Lauri, P., Havlík, P., Kindermann, G., Forsell, N., Böttcher, H., & Obersteiner, M. (2014). Woody biomass energy potential in 2050. Energy Policy, 66, 19–31.

    Article  Google Scholar 

  5. Janowiak, M. K., & Webster, C. R. (2010). Promoting ecological sustainability in woody biomass harvesting. Journal of Forestry, 108(1), 16–23.

    Google Scholar 

  6. Aruga, K., Murakami, A., Nakahata, C., Yamaguchi, R., Saito, M., & Yoshioka, T. (2014). Estimating annual available amounts of forest biomass resources with total revenues and costs during the 60-year rotation in a mountainous region in Japan. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 35(2), 125–138.

    Google Scholar 

  7. Japan Woody Bioenergy Association (2018). Wood biomass energy data book 2018. Retrieved July 16, 2019, from https://www.jwba.or.jp/app/download/13159696992/Data+book+2018+%28English+version%29.pdf?t=1523342683.

  8. Shimizu, N. (2018). Restoration of forests on degraded hillslopes of Rokko Mountain. Water Science, 61(6), 114–129. (in Japanese).

    Google Scholar 

  9. Kamimura, K., Kuboyama, H., & Yamamoto, K. (2009). Estimation of spatial distribution on wood biomass supply for three prefectures in the northern Tohoku Region. Journal of the Japanese Forestry Society, 88, 877–883. (in Japanese with English summary).

    Google Scholar 

  10. Ranta, T. (2005). Logging residues from regeneration fellings for biofuel production–a GIS-based availability analysis in Finland. Biomass and Bioenergy, 28(2), 171–182.

    Article  Google Scholar 

  11. Moriguchi, K., Suzuki, Y., Gotou, J., Inatsuki, H., Yamaguchi, T., Shiraishi, Y., et al. (2004). Cost of comminution and transportation in the case of using logging residue as woody biofuel. Journal of the Japanese Forestry Society, 86(2), 121–128. (in Japanese with English summary).

    Google Scholar 

  12. Sosa, A., Acuna, M., McDonnell, K., & Devlin, G. (2015). Managing the moisture content of wood biomass for the optimisation of Ireland’s transport supply strategy to bioenergy markets and competing industries. Energy, 86, 354–368.

    Article  Google Scholar 

  13. Nurminen, T., & Heinonen, J. (2007). Characteristics and time consumption of timber trucking in Finland. Silva Fennica, 41(3), 471–487.

    Article  Google Scholar 

  14. Han, S. K., & Murphy, G. E. (2012). Solving a woody biomass truck scheduling problem for a transport company in Western Oregon, USA. Biomass and Bioenergy, 44, 47–55.

    Article  Google Scholar 

  15. Kunii, D. & Hayashi, T. (2016). Resource competition among wood biomass power plants: A case in Iwate prefecture. In Proceedings of the 53th annual meeting of the japan section of the RSAI, Japan. Japan Section of the Regional Science Association. (in Japanese with English summary).

  16. Sakai, S., Tsuda, T., & Yasaka, M. (2017). Calculating the supply potential of unused wood for woody biomass power generation plants in Hokkaido, Japan. Journal of the Japanese Forest Society, 99, 233–240. (in Japanese with English abstract).

    Article  Google Scholar 

  17. Forestry Agency. (2015). The report and manual for rural development. Retrieved May 31, 2019, from http://www.rinya.maff.go.jp/j/sanson/kassei/kenyukai.html. (in Japanese).

  18. Goel, A. (2012). The minimum duration truck driver scheduling problem. EURO Journal on Transportation and Logistics, 1(4), 285–306.

    Article  Google Scholar 

  19. Davidović, J., Pešić, J., & Antić, B. (2018). Professional drivers’ fatigue as a problem of the modern era. Transportation Research Part F: Traffic Psychology and Behaviour, 55, 199–209.

    Article  Google Scholar 

  20. Ministry of Internal Affairs and Communications. (2020). Labour force survey basic tabulation whole japan monthly. Retrieved January 30, 2019, from https://www.e-stat.go.jp/en.

  21. Nagashima, K., & Kaneko, Y. (1992). Problems of land transportation company: job and job consciousness of truck driver. Departmental Bulletin Paper, 20, 1–54.

    Google Scholar 

  22. Mahachandra, M., & Sutalaksana, I. Z. (2015). Fatigue evaluation of fuel truck drivers. Procedia Manufacturing, 4, 352–358.

    Article  Google Scholar 

  23. Narciso, F. V., & Mello, M. T. D. (2017). Safety and health of professional drivers who drive on Brazilian highways. Revista de Saude Publica, 51, 26.

    Article  Google Scholar 

  24. Sasaki, S., Tadano, O., Higashino, T., Fukazawa, H., & Ogasawara, K. (2005). Trial calculation of wood chip supply cost for fuel. Journal of the Japan Forest Engineering Society, 19(4), 319–322. (in Japanese).

    Google Scholar 

  25. Shirasawa, H., Hasegawa, H., & Umegaki, H. (2013). Cost-reducing effectiveness of selecting the type of transportation vehicle in a roundwood supply chain: A case study in Hyogo prefecture. Journal of the Japan Forest Engineering Society, 28(1), 7–15. (in Japanese with English abstract).

    Google Scholar 

  26. Oyama, H., Suzuki, K., & Sakai, K. (2011). Research on the working conditions, fatigue, and sleep of truck drivers: The problems of small and medium-sized transport enterprises analyzed by using a questionnaire survey. Journal of Science of Labour, 87(2), 41–55. (in Japanese with English summary).

    Google Scholar 

  27. Japan Meteorological Agency. (2018). Meteorological data. Retrieved February 17, 2020, from http://www.data.jma.go.jp/obd/stats/etrn/index.php?prec_no=53&block_no=47651&year=&month=&day=&view=. (in Japanese).

  28. Mie Prefecture. (2018). 2018 Forest and forestry statistics book. Retrieved February 28, 2020, from http://www.pref.mie.lg.jp/common/content/000779122.pdf. (in Japanese).

  29. Nakata, C. & Itaya, A. (2017). Developing accessibility measurement tool between forests and woody biomass plants using Google Maps API. In Proceedings of joint regional meeting of IUFRO RG3.03.00 and RG3.06.00 in Asia, Japan. Productivity and Safety of Final Cutting on Mountain Forests (pp. 84–89).

  30. Zhou, X., & Kim, J. (2013). Social disparities in tree canopy and park accessibility: A case study of six cities in Illinois using GIS and remote sensing. Urban Forestry & Urban Greening, 12(1), 88–97.

    Article  Google Scholar 

  31. Gu, W., Wang, X., & McGregor, S. E. (2010). Optimization of preventive health care facility locations. International Journal of Health Geographic, 9(1), 17.

    Article  Google Scholar 

  32. Nair, D. J., Gilles, F., Chand, S., Saxena, N., & Dixit, V. (2019). Characterizing multicity urban traffic conditions using crowdsourced data. PLoS ONE. https://doi.org/10.1371/journal.pone.0212845.

    Article  Google Scholar 

  33. Wang, F., & Xu, Y. (2011). Estimating O-D travel time matrix by Google Maps API: Implementation, advantages, and implications. Annals of GIS, 17(4), 199–209.

    Article  Google Scholar 

  34. Dumbliauskas, V., Grigonis, V., & Barauskas, A. (2017). Application of Google-based data for travel time analysis: Kaunas city case study. Promet: Traffic & Transportation, 29(6), 613–621.

    Article  Google Scholar 

  35. Santos, L., Coutinho-Rodrigues, J., & Antunes, C. H. (2011). A web spatial decision support system for vehicle routing using Google Maps. Decision Support Systems, 51(1), 1–9.

    Article  Google Scholar 

  36. Kolcsár, R. A., & Szilassi, P. (2018). Assessing accessibility of urban green spaces based on isochrone maps and street resolution population data through the example of Zalaegerszeg, Hungary. Carpathian Journal of Earth and Environmental Sciences, 13(1), 31–36.

    Article  Google Scholar 

  37. Ohkawabata, O. (1988). Studies on the planning of forest roads for cable logging. Bulletin of the Forestry and Forest Products Research Institute, 351, 1–79. (in Japanese).

    Google Scholar 

  38. Forestry Agency. (2011). Forest Road Regulation. Retrieved February 17, 2020, from https://www.rinya.maff.go.jp/j/seibi/sagyoudo/pdf/kitei.pdf. (in Japanese).

  39. Takahashi, K., Tanaka, N., & Matsui, M. (2017). Stand structure dynamics in Shirasaka small watershed of Ecohydrology Research Institute: Results of inventories in 1954, 2007, 2014. Chubu Forestry Research, 65, 101–104. (in Japanese).

    Google Scholar 

  40. Takanashi, K., & Ooshima, Tomoo. (2015). The reviving forests of devastated land in Ashio forest conservation projects in Ashio. Water Science, 59(1), 126–144. (in Japanese).

    Google Scholar 

  41. Forestry Agency. (2019). Overview of woody biomass power generation business. Retrieved February 17, 2019, from https://www.rinya.maff.go.jp/j/sanson/kassei/pdf/shishin_s2-2_1~2.pdf. (in Japanese).

  42. Japan Wood Energy Co. Ltd. (2019). The map of woody biomass power plant in Japan. Retrieved December 27, 2019, from http://www.mori-energy.jp/hatsuden1.html. (in Japanese).

  43. Goel, A., Archetti, C., & Savelsbergh, M. (2012). Truck driver scheduling in Australia. Computers & Operations Research, 39(5), 1122–1132.

    Article  Google Scholar 

  44. Cole, N. B., Barrett, S. M., Bolding, M. C., & Aust, W. M. (2019). An analysis of fatal log truck crashes in the United States from 2011 through 2015. International Journal of Forest Engineering, 30(2), 121–131.

    Article  Google Scholar 

  45. Joseph, L., & Conrad, I. V. (2019). Analysis of timber transportation accident frequency, location, and contributing factors in Georgia, USA 2006–2016. International Journal of Forest Engineering, 30(2), 109–120.

    Article  Google Scholar 

  46. Ministry of Land, Infrastructure, and Tourism. (2016). Current situation and issues in the truck industry such as lack of drivers. Retrieved January 30, 2020, from http://wwwtb.mlit.go.jp/chubu/jidosya/tekiseitorihiki/img10/10shiryou1.pdf. (in Japanese).

  47. Forestry and Timber Manufacturing Safety & Health Association. (2020). List of forestry work accidents. Retrieved February 6, 2020, from http://www.rinsaibou.or.jp/cont03/03_frm.html. (in Japanese).

  48. Vijay, V., Pimm, S. L., Jenkins, C. N., & Smith, S. J. (2016). The impacts of oil palm on recent deforestation and biodiversity loss. PloS one, 11(7), e0159668.

    Article  Google Scholar 

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Acknowledgements

We extend our deepest appreciation to Prof. Ishikawa for offering continuing support and constant encouragement. We also thank our colleagues in the Forest Engineering Laboratory at Mie University, who provided invaluable comments and warm encouragement. This work was supported by JSPS KAKENHI Grant Number JP15K07479.

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Correspondence to Akemi Itaya.

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Nakata, C., Itaya, A. Exploring forests vulnerable to over-logging to supply woody biomass to power plants in Mie, Central Japan. Spat. Inf. Res. 29, 569–576 (2021). https://doi.org/10.1007/s41324-020-00365-3

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