Geospatial distribution of ecosystem services and biomass energy potential in eastern Japan

Accepted Manuscript Geospatial distribution of ecosystem services and biomass energy potential in eastern Japan Makoto Ooba, Minoru Fujii, Kiichiro Hayashi PII: S0959-6526(16)00100-1 DOI: 10.1016/j.jclepro.2016.01.065 Reference: JCLP 6653 To appear in: Journal of Cleaner Production Received Date: April 2015 Revised Date: 20 January 2016 Accepted Date: 24 January 2016 Please cite this article as: Ooba M, Fujii M, Hayashi K, Geospatial distribution of ecosystem services and biomass energy potential in eastern Japan, Journal of Cleaner Production (2016), doi: 10.1016/ j.jclepro.2016.01.065 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain ACCEPTED MANUSCRIPT Geospatial distribution of ecosystem services and biomass energy potential in eastern Japan Makoto Ooba*1, Minoru Fujii1, Kiichiro Hayashi2 National Institute for Environmental Studies, Tsukuba, Japan EcoTopia Science Institute, Nagoya University, Nagoya, Japan *Corresponding Author: ooba.makoto@nies.go.jp Abstract SC RI PT Detailed assessments of the effects of biomass production on ecosystems were carried out in the 11 eastern region of Japan using geographical statistics and statistical methods Ecosystems that 12 might be used as a source of energy-related biomass already provide a variety of goods and 13 services for humans widely known as ecosystem services Various indices were mapped to 14 describe the potential supply of biomass energy and the proxy variables for ecosystem services 15 provided in the region These indices were analyzed using a multivariate statistical technique to 16 identify specific key factors for the use of biomass and ecosystem services Finally, using 17 zoning software, priority areas of potential supply of biomass energy and ecosystem services 18 were indicated and the conflict between them analyzed Biomass energy was clearly 19 distinguished into three axes, suggesting that biomass is strongly related to the location and 20 ecosystem, while the distribution of the types of ecosystem services in the studied areas was not 21 separated clearly The relative priority ranks of bioenergy and ecosystem services were 22 complementarily distributed; however, parts of the studied area had high-ranking areas The results 23 suggested that a more detailed zoning information is needed for promoting energy-related 24 biomass production considering the high supply of ecosystem services TE D EP AC C 25 M AN U 10 26 Keywords: Biomass, Eastern Japan, Ecosystem services, Geographic information system, 27 Spatial analysis, Zonation 28 ACCEPTED MANUSCRIPT 1 Introduction Biomass is a crucial energy resource for creating a sustainable society because of its renewability, low or no carbon emission, and low environmental impact However, the use of biomass has many disadvantages, as other renewable resources such as solar and wind power Transport of the biomass is relatively difficult due to its high moisture content, and its gross heating value is lower than that of other energy resources Additionally, intensive production of biomass can lead to competition with forest conservation and cultivation for food supply Harvesting the biomass from forests and agricultural ecosystems has also some effects on these and the neighboring ecosystems, and the growth of biomass requires the use of land for this SC 10 RI PT purpose over relatively long intervals Surveys of potential biomass were conducted from the regional to global level Hoogwijk et 12 al (2005) estimated the timeline of the production and consumption of biomass and land use at 13 the global level using their model (IMAGE mode, Hoogwijk et al., 2003) and considering 14 several Intergovernmental Panel for Climate Change (IPCC) scenarios Moreover, Hoogwijk et 15 al (2009), applying the economic cost-supply analysis, indicated a region at global level that is 16 of interest for its low production cost and high potential of biomass energy Ericsson and 17 Nilsson (2006) analyzed the potential biomass supply in 15 EU countries Henry (2010) 18 discussed a possibility for replacement of fossil fuel by biofuel using high-yielding crop and 19 biotechnology at a global level TE D M AN U 11 Previous studies have also suggested that the analysis of biomass supply may be conducted 21 at a small scale as well as at a country level using geospatial analysis This is because 22 management and production costs affecting the potential (or available) amount of target biomass 23 also depend on geospatial conditions including ecosystem distribution, access roads, distance to 24 the production factory, and location of demand for the biomass; the cost should also include the 25 disadvantages of using the biomass as energy resource Sacchelli et al (2014) analyzed the 26 socio-economic and environmental effects of multiple factors on wood residue energy, including 27 geographical conditions on a local scale They concluded that both the implementation of 28 advanced technology and environmental parameters related to allocation of sources and 29 demands were important Delivand et al (2015) also carried out geographical analysis, and the 30 effects of logistics costs and greenhouse gas emissions were discussed The availability of land AC C EP 20 ACCEPTED MANUSCRIPT for bioenergy crops in Mozambique, in the timeframe 2005–2030, was modeled by van der Hilst (2012) From geographical detailed analysis, the most suitable locations for bioenergy production were determined based on agro-ecological suitability and accessibility and partly based on the most suitable locations for current agricultural practices Ooba et al (2012, 2015) described the relationship between the cost of woody biomass production and the geographical location of forests in two different regions in Japan RI PT Detailed assessments of the effects of biomass production and consumption on ecosystems and social systems, taking into account ecological processes and regional characteristics, have not yet been conducted in Japan After the great earthquake and nuclear accident in 2011, 10 renewable energy received more attention compared to the period before these disasters The 11 Japanese government developed a new policy to promote the use of biomass (Ministry of 12 Economy, Trade and Industry of Japan 2014), and local governments, especially in the areas 13 damaged by the earthquake, began planning the development of biomass boilers and electric 14 generators (Kaji et al., 2013) Japan introduced a feed-in tariff (FIT) scheme for renewable 15 energy in 2012 to promote the use of these energy sources; hence, the demand for biomass is 16 now higher than it was before the earthquake Under such conditions, more changes in land use 17 (e.g., conversion from forest to cropland) and development (e.g., conversion from natural forest 18 to plantation forest) may occur to enhance biomass production TE D M AN U SC Ecosystems that might be used as a source of energy-related biomass already provide a 20 variety of goods and services to humans widely known as ecosystem services (ES; Millennium 21 Ecosystem Assessment, 2005) Many studies have stressed the negative effects on ecosystems 22 due to production of biomass without considering the ecosystem services and biodiversity 23 Several studies have indicated that the assessment of environmental impact of biomass production 24 for energy must consider the existing ecosystems and biodiversity of the potential production areas 25 Hanafiah et al (2012) found that inclusion of the impacts on biodiversity is needed for calculating 26 the production footprint by comparing the ecological footprint and biodiversity footprint 27 Myllyviita et al (2012) mentioned less impact of imported biomass compared to local biomass 28 production in Finland, as inferred from the life cycle assessment and the multi-criteria decision 29 analysis Cao et al (2015) performed an impact assessment of land use based on economic values 30 and ecosystem services at country level The distribution of ecosystems appropriate for AC C EP 19 ACCEPTED MANUSCRIPT production of biomass is neither uniform nor coherent with the current land use Ecological impact assessment is also needed in relation to the development of biomass production, as already pointed out in previous studies on biomass potential (e.g., Hoogwijk et al., 2005) To suggest a conservation or development in specific areas, geospatial analysis may be needed at a local scale Many geographical software programs have been used in conservation planning of the biodiversity in ecosystems under particular socioeconomic constraints (e.g., Moilanen et al., 2012) They can indicate hot spots and cold spots under certain conditions and constraints (e.g., management cost, cost effectiveness, and subjective weight of various services) These tools are also used to determine geographical priority in terms of biomass SC 10 RI PT development and to resolve conflicts between development and conservation The objective of this study was to assess the impact of biomass production on ecosystem 12 services in the eastern region of Japan using geographical information system (GIS) Data sets 13 were collected and indices created by which to estimate the geographical distribution of 14 energy-related biomass and the current state of ecosystem services Various indices were used to 15 map potential supplies of biomass energy and proxy variables for ecosystem services provided 16 in the region These indices were analyzed using a multivariate statistical technique to identify 17 specific key factors for the use of biomass and ecosystem services To detect potential hot spots 18 of these resources and areas of conflict with the current ecosystem, the potential supplies of 19 biomass energy and ecosystem services were assigned a rank using Zonation software 20 (Moilanen et al., 2012) A comparison of ranks with or without the FIT weighted prices was also 21 carried out to estimate the effect of the FIT system on ecosystem services The results provided 22 useful planning and zoning information for promoting the production of biomass and 23 conservation of ecosystem AC C EP TE D M AN U 11 24 In this study, the potential for biomass energy was evaluated for two energy-producing 25 processes: direct combustion of biomass, and fermentation of biomass to produce methane 26 These methods are not the latest technology (Naik, et al., 2010), but they are relatively common 27 in Japan 28 29 Models and study site 30 2.1 Study area ACCEPTED MANUSCRIPT The eastern part of Japan that was selected as the study area includes Kanoto, Tohoku, and Jouetsu regions (14 prefectures; area: 110,000 km2) The islands located far from the Tokyo metropolitan area were omitted from this study because of the difficulty in transporting the biomass produced on these islands RI PT 2.2 Data sources Biomass data: A comprehensive biomass dataset from 2011 used in this study was provided by the New Energy and Industrial Technology Development Organization (NEDO) This dataset was initially developed by Iuchi (2003) for 15 types of biomass Table data at the municipality 10 level published by the NEDO were downloaded and subdivided as follows (Table 1): wood 11 residual from forest (wf, two types); wood residual from other ecosystems (we, two types); 12 agricultural residual (aa, four types); grassland residual (ae, two types); livestock manure (ma, 13 five types); sludge (sl, three types); and food processing waste (fw, three types) The biomass 14 dataset provided the annual maximum potential, available amount (dry weight), and heat energy 15 (GJ/y) In this study, the available heat energy of the biomass was used for realistic estimation 16 For livestock manure, sludge, and food processing waste, heat energy was generated by methane 17 fermentation, and for other types of biomass, heat energy was calculated using their lower 18 calorific value TE D M AN U SC These biomass data were represented in thermal units, on the assumption that they would be 20 used for combustion in boilers and in methane fermentation (NEDO, 2011) Woody and 21 agricultural biomass was combusted in biomass or multi-fuel combustion boilers with 22 combustion efficiency set to 1.0 Manure and food processing waste were consumed in a 23 methane fermentation plant; the detailed conditions of the fermentation are given in Table 24 The energy of biomass-derived methane was used as heat These assumptions were not fully 25 realistic, but they were effective for estimating the maximum potential amount of biomass in the 26 local areas considered 27 28 AC C EP 19 Data on natural and social parameters about the study area were also obtained and used as variables in calculations of biomass energy and ecosystem services (Table 2) 29 Biological data (Table 2): Vegetation maps (Vg) and data on the occurrence of mammalian 30 species (Sp) were obtained from the Biodiversity Center of Japan (2014) The distribution of the ACCEPTED MANUSCRIPT plant community and the degree of artificial disturbance (10 levels) were indicated on the Vg Sp data indicated the distribution of eight mammal species (Macaca fuscata, Cervus nippon, Capricornis crispus, Ursus thibetanus, Sus scrofa, Vulpes vulpes japonica, Nyctereutes procyonoides, and Meles meles) Climatic data: Annual precipitation (Cp) and mean temperature (Ct) with a 1-km mesh RI PT (30-year averages) were used (Ministry of Land, Infrastructure, Transport and Tourism, 2014) Agricultural data: Areas of agricultural land use (Aa) and annual gross agricultural production (Ag) were obtained from the World Census of Agriculture and Forestry in Japan and from the Statistics of Agricultural Production and Income (Ministry of Internal Affairs and 10 SC Communications, 2014), respectively Ag statistics data were collected at prefectural levels Social data: Population data at a municipal level were obtained from the Population Census 12 (Statistics Bureau, Population Census 2014) Tourism spots (Tr) were listed according to the 13 Ministry of Land, Infrastructure, Transport and Tourism (2014), and domestic tourism statistics 14 (Tp) data were obtained from the Domestic Tourism Consumption Trend Survey for Tourism 15 (Japan Tourism Agency, 2014) Tr indicated locations of both natural spots and leisure facilities M AN U 11 TE D 16 17 2.3 Data processing 18 2.3.1 Target year and processing software The above-mentioned data for natural and social parameters were converted into raster (cell) 20 data with a 5-km mesh The sampling year selected was 2010 because after the 2011 earthquake 21 in the northeast region of the country, the population and land use data were not as well 22 developed as before the earthquake Some of the data were older than 2010 because of data 23 availability constraints AC C EP 19 24 The data were analyzed using Microsoft Excel 2013 statistical software (Excel statistics, 25 Social Survey Research Information, Japan) Geographical processing was carried out using 26 ArcGIS 10.2 (ESRI Japan) 27 28 2.3.2 Ecosystem services 29 The following proxy variables, which were categorized based on the MEA (2005) and The 30 Economics of Ecosystems and Biodiversity (TEEB 2010) methodologies, were selected for ACCEPTED MANUSCRIPT estimating potential supply of ecosystem services (Table 3) using the methods of Ooba et al (2014) For the purposes of this work, conservation of both habitat and biodiversity was considered a supporting service Provisioning services: Annual economic output from the gross agricultural production (Ap, JPY/y), including rice, other vegetables, and orchard tree fruits, was used as a proxy for the provisioning services of an agricultural ecosystem Data source Ag was divided into municipal-level values according to agricultural areas in the municipalities (Aa) and converted to a 5-km mesh Precipitation that occurs in urban areas does not infiltrate into the soil Therefore, annual potential water resources (Wr, mm/y) in an area were estimated using annual 10 rainfall (Cr, mm/y) and the ratio of non-urban areas to total area The non-urban area was 11 estimated from the index for degree of artificial disturbance given in Vg M AN U SC RI PT 12 Regulation services: A simpler method was used in this study to estimate carbon 13 sequestration rate (Sc) according to ecosystem type (Vg) and climate condition (Ct) This 14 method has been outlined in the forest monitoring research (Hirata et al., 2008) and described in 15 detail in the Appendix Supporting services: Ecosystem continuity (Vc) was calculated using focal statistics in 17 ArcGIS (with radius set at 10 km) to assess fragmentation of vegetation, which disturbs 18 biological and ecosystem processes A natural ecosystem was assumed to be an ecosystem 19 without urban and agricultural land uses, while biodiversity was assumed to be represented by 20 the number of species indicated in the data from a survey of domestic mammals (Sp) EP TE D 16 Cultural services: Ecosystems provide cultural services for human psychological and 22 recreational activities Indirect values (option value, the value of maintaining ecosystems for 23 future generations, and existence value) are also important services for humans, but they are not 24 easy to measure Thus, herein, we used the value of recreation services from ecosystems as a 25 more direct value The number of individuals visiting a natural ecosystem for sightseeing (Pd) 26 was used as a proxy of cultural services The method used to estimate Pd from tourism locations 27 and statistics (Tr and Tp) is described in the Appendix AC C 21 28 These proxy variables have units different from the physical units (Sc, Mg-C/(ha y)) to a 29 social unit (person-day/y) For the statistical and zoning analyses, proxies were converted to 30 relative values using maximum and minimum values, due to differences in units ACCEPTED MANUSCRIPT 2.4 Statistical analysis The variables for ES and biomass were analyzed using principal component analysis (PCA) to identify potential factors and to classify the distribution of these variables The principal components (PCs) represent potential factors, and PCs with high order have relatively strong descriptive power in relation to the given dataset Scatter plots representing the scores of high-order PCs indicate variance of the multi-dimensional dataset in low (e.g., two) dimensions The distribution of points in the scatter plot was divided into sub-classes to clarify key factors SC RI PT 10 2.5 Hotspot and conflict analysis M AN U 11 12 The conservation planning software Zonation (Moilanen et al., 2012) provides several 13 algorithms to determine conservation priorities (e.g., core-area zonation) when calculating the 14 rank of potential supplies of ES and biomass energy Version 4.0.0b26 was used in this study Zonation software can calculate conservation priority as value order according to an 16 evaluation function For example, Moilanen et al (2011) researched the competing land uses (in 17 terms of their biodiversity, carbon storage, agricultural production, and urban area) in Great 18 Britain using Zonation It is likely that the application of Zonation for biomass supply may 19 reveal valuable areas from geospatial analysis TE D 15 The authors chose to use the simplest algorithm, the additive benefit function, which 21 calculates the sum of all calculated values of ecosystem services for each mesh cell and 22 produces a mesh map of the sums In this study, the input values were the absolute values of 23 biomass energy and the relative values of ecosystem services AC C EP 20 24 The additive benefit function was used with equal weights (1.0) for every proxy of the 25 ecosystem services The priority was also calculated for biomass energy (absolute values) using 26 two methods for weighting—equal weights (as was used for ecosystem services) and weighting 27 biomass types—according to their corresponding prices (in 2015) in the FIT scheme for renewable 28 energy in Japan (Table 1) For each variable, the default value (1.0) was assumed to be the cost 29 30 Results and discussion ACCEPTED MANUSCRIPT 3.1 Geographical distribution of biomass energy and ecosystem services Various maps of the geographical distribution of the biomass potential and ecosystem services before 2010 are shown in Figs and These maps show a 5-km mesh grid in the eastern part of Japan The potentials of biomass energy are indicated as absolute values (TJ/(y 5-km mesh), Fig 2) High potentials were observed for mountainous and agricultural areas in cases of forest-origin biomass (wf and we; Table 1, Nomenclature) and biomass originated from agricultural land (aa, ae, and ma) The distributions of these two biomass types were relatively distinct, possibly owing to land use patterns Urban-origin biomass (ww and fw) was widely distributed and related to the SC 10 RI PT population distribution (Fig 1c) Proxy variables for the potential supply of ecosystem services are indicated as relative values 12 that were transformed to fall within the range to for the minimum and maximum values, 13 respectively, due to the difficulty in assigning values to ecosystem services (Fig 3) Provisioning 14 services classified as agricultural production (Ap) used proxies that were different from the 15 agricultural biomass (aa), because Ap represented the economic value of agricultural products from 16 agricultural fields (ecosystem) and had a weak relationship to the amount of agricultural products 17 The highest carbon sequestration rates were indicated in mountainous areas, and this distribution 18 resembled that of supporting services (Sp and Vc) In the western region of eastern Japan, Wr was 19 relatively high This was explained by the difference between the annual precipitation on the 20 coastline of the Sea of Japan and that of the Pacific Ocean The Kanto Region, inhabited by 21 approximately 66% of the population of eastern Japan, and the areas around the concentrated cities 22 are generally used for agriculture and are partly mountainous Kanto Region provides substantial 23 cultural services (Pd), which may reflect the accessibility of these sites from areas of high 24 population concentration TE D EP AC C 25 M AN U 11 26 3.2 Geospatial analysis 27 3.2.1 Biomass energy 28 The correlation matrix was calculated for biomass energy variables (data not shown) High 29 correlation coefficients were detected between the following biomass resources: wf and we (0.87), 30 aa and ae (0.99), and ww and fw (0.69) ACCEPTED MANUSCRIPT where a, b, c, and RE0 are constants (0.97 Mg-C/(ha y °C), 8.4 Mg-C/(ha y), 207.8 °C, 14.47 Mg-C/(ha y)) T is defined as follows: T = 1/(Tk + Tref − T0) − 1/(Tk + Ta − T0) (A2), RI PT where Tk, Tref, and T0 are constants (273.15 °C, 10 °C, 227.13 °C) For forest ecosystems, Sc = GPP − RE (A3) SC Agricultural and other ecosystems and urban areas were assumed to provide no carbon 11 sequestration (Sc = 0) Finally, the spatial average, Sc (Mg-C/(ha y)), was calculated for each 12 municipality M AN U 10 13 Number of visitors to a natural ecosystem for sightseeing (Pd): The number of tourism spots 15 (for natural ecosystems only), n, was determined from data of tourism spots (Tp) that received 16 cultural services from the natural ecosystem The rate of n to total tourism spots (for both 17 natural and urban spots) for each prefecture was obtained (r) and ranged from 0.11 (Chiba 18 Prefecture) to 0.84 (Iwate Prefecture) 19 Domestic tourism statistics (Td) included the annual number of tourists, the purpose of visit 20 (sightseeing or not), and destination (at prefecture level) The total stay (in days) per year (PD1) 21 in a given prefecture for sightseeing purposes was estimated from the sum of the values of 22 domestic one-day trips (d1) and domestic overnight trips (d2), 24 25 EP AC C 23 TE D 14 PD1 = d1 + m d2 (A4), 26 where m is the mean length of an overnight domestic trip (= 2.3, according to tourism statistics 27 from 2010) The total number of days of stay per year (PD2) for sightseeing within a natural 28 ecosystem inside a prefecture were estimated from PD1 and r 29 30 PD2 = r PD1 (A5) 16 ACCEPTED MANUSCRIPT Finally, a number of person-days (Pd, person-days/y) for sightseeing a natural ecosystem at the municipality level was estimated from PD2 and the ratio of the area of natural ecosystems in the municipality (Amun) to the area of natural ecosystems in the prefecture(Apref), which was calculated from Vg, RI PT Pd = PD2 (Amun/Apref) (A6) 11 12 SC 10 References 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partial 30 equilibrium model for a case study in Alpine area J Clean Prod 66, 431-442 19 ACCEPTED MANUSCRIPT Schmidt, J, H., Weidema, B, P., Brandão, M., 2015 A framework for modelling indirect land use changes in Life Cycle Assessment J Clean Prod 99, 230-238 Statistics Bureau, 2014 Population Census of Japan Available at: http://www.stat.go.jp/english/data/kokusei/index.htm TEEB, The Economics of Ecosystems and Biodiversity, 2014 Mainstreaming the Economics of RI PT Nature: A Synthesis of the Approach, Conclusions and Recommendations of TEEB Available at:http://www.teebweb.org/our-publications/teeb-study-reports/synthesis-report/ AC C EP TE D M AN U SC 20 ACCEPTED MANUSCRIPT Table Biomass data set Parameters for biomass use Type and detail of biomass Weight b Wood residual from forest wf Combustion, Lower Heating (Harvest and thinning of plantation Value(LHV): 18.1 GJ/t 0.202 we RI PT forest) Wood residual from other ecosystems Combustion, LHV: 11.5-12.5 (Orchard forest, and bamboo) GJ/t Agricultural residual Combustion, LHV: 13.6-14.2 0.121 GJ/t (for crop straw), Methane (Rice husk and straw, and wheat husk ) fermentation a: VS/TS=0.75, SC aa 0.121 GR=400 m /t (for other Grassland residual ae (Bamboo grass and Japanese silver grass) Wood waste fw a construction waste) and 11.5 GJ/t demolition debris, construction debris, (for pruned residual from park and pruning branch from public parks) forest) Livestock manure Methane fermentation a (Dairy cattle, beef cattle, swine, layer VS/TS=0.8-0.83, VS=0.4, GR=500-650 m /t Sludge Methane fermentation a (Two types of sewage sludge, and VS/TS=0.75-0.77, VS=0.46-0.52, human waste sludge) GR=620-780 0.066 0.197 Food-processing waste Methane fermentation a 0.086 m3/t VS/TS=0.2, VS=0.80, GR=500 (Waste from food-processing factory, (for food processing waste), kitchen, food vendor waste) VS/TS=0.84, VS=0.84, GR=808 m3/t 0.086 (for other) Lower Heating Value (LHV) for methane: 0.036 GJ/m3, VS/TS: Ratio of volatile solid to total solid, VS: decomposed rate of volatile solid, GR: gas production rate b 0.121 chicken, and broiler chicken) AC C sl (Residual of lumber sawing, TE D ma Combustion, LHV: 13.6 GJ/t Combustion, LHV: 18.1 GJ/t (for EP ww M AN U agricultural residual) FIT weight, see sections 2.5 and 3.3, and Fig for the zoning analysis 21 ACCEPTED MANUSCRIPT Table Data sources for assessment of biomass energy and ecosystem services Name Cp, Ct Animal distribution survey map a Climate map b World census of Aa agriculture and forestry in Plant community, Degree of disturbance Occurrence of mammals Annual precipitation and mean air temperature Area of agricultural land use Japan c km mesh Municipality level table Gross agricultural Prefecture production and income c production level table Population number and Municipality density level table Po Population census d Tr Map of tourism spots d Tp km mesh Statistics of agricultural Consumption trend survey for tourism e M AN U Ag 1/50,000 map Point map Number of people and Municipality days for domestic tourism level table Biodiversity Canter of Japan, 2014 b Ministry of Land, Infrastructure, Transport and Tourism, 2014 c Ministry of Internal Affairs and Communications, 2014 d Statistics Bureau, 2014 e Japan Tourism Agency, 2014 AC C EP TE D a 22 Year 1979– 1998 2000– RI PT Sp Vegetation survey map a Data Type SC Vg Details 2004 1982 2010 2010 2010 1999 2010 ACCEPTED MANUSCRIPT Table Proxy variables for ecosystem services Category Unit Sources Wr Effective precipitation mm/y Vg, Cp Ap Economic gross agricultural production JPY/y Ag, Aa Regulation Sc Carbon sequestration rate Mg-C/(ha y) Vg, Ct Supporting Vc Index of continuity of natural ecosystem Sp Species number Pd Tourists in natural ecosystems Vg Sp person-day/y AC C EP TE D M AN U SC Cultural RI PT Provisioning Proxy variable 23 Vg, Tr, Tp ACCEPTED MANUSCRIPT Figure Captions RI PT Figure Areas studied (Tohoku, Kanto, and Jouetsu regions) (a) Location of study area, (b) Altitude distribution (m), (c) Population density distribution (people/km2) Figure Potential supply distribution of biomass energy (TJ/y in 5-km grid squares) within eastern Japan: (a) wf: Wood residual from forest, (b) we: Wood residual from other SC ecosystems, (c) aa: Agricultural residual, (d) ae: Grassland residual, (e) ww: Wood waste, (f) ma: Livestock manure, (g) sl: Sludge, (h) fw: Food-processing waste Figure Potential supply distribution of ecosystem services (relative values): (a) Wr: M AN U Effective precipitation, (b) Ap: Economic gross agricultural production, (c) Sc: Carbon sequestration rate, (d) Vc: Index of continuity of natural ecosystem, (e) Sp: Species number, (f) Pd: Tourists in natural ecosystems Figure Loading vectors and scatter plots of the principle component scores (2nd and 3rd principle components) from the principle component analysis (PCA) for variables of respectively TE D biomass energy, (a) and (c), and variables of ecosystem services, (b) and (d), Figure Classification for (a) biomass energy and (b) ecosystem services (a) shows the classification of urban (red), agricultural (green), and forest biomass (blue) Brightness EP indicates the relative amount of the potential supply (1st principle component, PC, see text) (b) shows the relative values of the second and third PCs in green (related to AC C provisioning services) and blue (related to other services) Grid cells in (a) white and (b) red represented high-ranking areas for both biomass and ecosystem services Figure Relative ranking of priority areas for biomass energy and ecosystem services (a) Rank of biomass energy with equal weighting, (b) Rank of ecosystem services, (c) Rank of biomass energy with the FIT weighting (see Table 1), and (d) Difference between (a) and (c) 50ºN (b) M AN U (a) m TE D Kanto 30ºN 135ºE 140ºE AC C 20ºN Tohoku EP 40ºN Jouetsu (Part of Chubu) SC RI PT ACCEPTED MANUSCRIPT Figure (c) people/km2 ACCEPTED MANUSCRIPT (b) we (e) ww (f) ma (c) aa (d) ae M AN U SC RI PT (a) wf AC C EP TE D (g) sl Figure (h) fw ACCEPTED MANUSCRIPT (b) Ap (d) Vc (e) Sp (c) Sc M AN U SC RI PT (a) Wr AC C EP TE D (f) Pd Relative Value Figure Loading Vectors (a) (b) ww uw ae SC 3rd PC Wr ma aa -1 Ap M AN U -1 Pd sl RI PT ACCEPTED MANUSCRIPT Vc Sp Sc we wf -1 -1 20 EP AC C 3rd PC (c) TE D Principle Component Score (d) -5 -20 -20 2nd PC 20 Figure -5 2nd PC (a) RI PT ACCEPTED MANUSCRIPT (b) PC3 (Forest) SC Urban Agric M AN U Forest AC C EP TE D PC2 (Agric.) Figure ACCEPTED MANUSCRIPT (a) Rbio (b) Reco TE D (d) Difference Rbio AC C EP (c) Rbio (FIT weight) M AN U SC RI PT (equal weight) Rank High Low Figure Diff 0.26 -0.26 [...]... curves of biomass energy from short‐rotation crops at abandoned cropland 10 and rest land under four IPCC SRES land‐use scenarios Biomass Bioenergy, 33, 26-43 Iuchi, M., 2004 Development of system that supporting use planning of biomass energy: M AN U 11 SC 8 12 modeling of storage database and cost of collecting Central Research Institute of Electric 13 Power Industry, Research Report Y03023, 1-26 (In Japanese... amount of the potential supply (1st principle component, PC, see text) (b) shows the relative values of the second and third PCs in green (related to AC C provisioning services) and blue (related to other services) Grid cells in (a) white and (b) red represented high-ranking areas for both biomass and ecosystem services Figure 6 Relative ranking of priority areas for biomass energy and ecosystem services. .. discussion on biomass use and design of the FIT in Japan, the total potential 25 amount of biomass in Japan, the conflicts between biomass and other renewable energy (solar 26 power, wind power, geothermal), the coordination of grid-based power, and the project cost and 27 profit analyses of renewable energy were central issues In addition to these issues, it has been 28 suggested that geographical distribution. .. (agricultural supporting services and other services) M AN U 11 Considering the ranks, the potential distribution of biomass and ecosystem services were 15 complementary However, both ranks were high in the area surrounding the Kanto Region and 16 the middle of the Tohoku Region, and intensive biomass production in these areas affected the 17 regional ecosystems that provide ecosystem services with high... http://nlftp.mlit.go.jp/ksj-e/index.html (In Japanese) Ministry of Internal Affairs and Communications, 2014 Portal Site of Official Statistics of Japan Available at: http://www.e-stat.go.jp/SG1/estat/eStatTopPortalE.do (In Japanese) Ministry of Economy, Trade and Industry of Japan, 2014 Cabinet Decision on the New 29 Strategic Energy Plan Available at: 30 http://www.meti.go.jp/english/press/2014/0411_02.html (In Japanese) 18... applicable to all land-use types and 18 indicated the dynamics of land use caused by economic reasons This may be helpful for 19 considering long-term temporal analysis by these modeling approaches TE D M AN U SC 7 In addition, an understanding of the demands for biomass energy and ecosystem services is also 21 important to match the supply from ecosystems Biomass energy and heat generated from biomass 22... component scores (2nd and 3rd principle components) from the principle component analysis (PCA) for 8 variables of respectively TE D biomass energy, (a) and (c), and 6 variables of ecosystem services, (b) and (d), Figure 5 Classification for (a) biomass energy and (b) ecosystem services (a) shows the classification of urban (red), agricultural (green), and forest biomass (blue) Brightness EP indicates the... economic and ecological aspects, rough mapping of conflicting areas between biomass production 3 and ecosystem services may be useful An accurate and comprehensive assessment of the supply of 4 ecosystem services provides policy recommendations about a more eco-healthy biomass 5 production RI PT 1 6 7 4 Conclusions This study determined the spatial distributions of the potential supply of ecosystem services. .. Region [Pd] and middle area of the Tohoku Region [Ap]) In general, Rbio and Reco were 26 complementary; however, this was not true for all regions High-ranking areas in which both Rbio 27 and Reco were larger than 0.7 are indicated in the classified maps (Fig 5) These indicate a potential 28 area of conflict with a high supply of ecosystem services and biomass supply potential Potential 29 areas of conflict... were distributed in the areas surrounding the Kanoto Region and on the border of 30 the prefectures of the Tohoku and Jouetsu regions In these areas, intensive biomass production AC C 22 11 ACCEPTED MANUSCRIPT 1 may cause ecosystem degradation and a related decrease in ecosystem services After weighting the woody biomass according to the actual Japanese FIT condition, the rank 3 decreased in urban areas,