Ontology type: schema:ScholarlyArticle Open Access: True
2018-01-24
AUTHORS ABSTRACTIn COP21 followed by the Paris Agreement, the world is now seriously planning actions to mitigate greenhouse gas emissions toward a “below 2 °C above preindustrial levels” future. Currently, we are still far from identifying the emission pathways to achieve this target because of the various uncertainties in both climate science and the human behavior. As a part of the ICA-RUS project, conducted by Dr. Seita Emori of the National Institute for Environmental Studies we have studied how these uncertainties are eliminated by the accumulation of scientific knowledge and the decision-making processes. We consider the following three questions: first, when and how will the uncertainty range on the global temperature rise be eliminated, second which global emission pathway should be chosen before we get the perfect information, and third how much expenditure is justified in reducing the climate uncertainties. The first question has been investigated by one of the authors. Shiogama et al. (Sci Rep 6:18903, 2016) developed the Allen–Stott–Kettleborough (ASK) method further to estimate how quickly and in what way the uncertainties in future global mean temperature changes can decline when the current observation network of surface air temperature is maintained. Fourteen global climate model results in CMIP5 (CMIP http://cmip-pcmdi.llnl.gov/, 2017) are used as virtual observations of surface air temperature. The purpose of this study is to answer the remaining two questions. Based on the ASK research outcomes, we apply the multi stage decision-making known as Act Then Learn (ATL) process to an integrated assessment model MARIA which includes energy technologies, economic activities, land use changes and a simple climate model block. We reveal how accumulating observations helps to mitigate economic losses by expanding the existing ATL method to deal with the uncertainty eliminating process by ASK. The primary findings are as follows. First, the value of information largely increases as the climate target policy is more stringent. Second, even if the uncertainties in the equilibrium climate sensitivity are not fully resolved, scientific knowledge is still valuable. In other words, the expenditure for scientific researches is rationalized when we really concern the global climate changes. More... »
PAGES351-368
http://scigraph.springernature.com/pub.10.1007/s11625-018-0528-7
DOIhttp://dx.doi.org/10.1007/s11625-018-0528-7
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1100617668
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30147785
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Earth Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0401",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Atmospheric Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Tokyo University of Science, Yamasaki 2641, 278-8510, Noda-shi, Chiba, Japan",
"id": "http://www.grid.ac/institutes/grid.143643.7",
"name": [
"Tokyo University of Science, Yamasaki 2641, 278-8510, Noda-shi, Chiba, Japan"
],
"type": "Organization"
},
"familyName": "Mori",
"givenName": "Shunsuke",
"id": "sg:person.011602002215.72",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011602002215.72"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba-City, Ibaraki, Japan",
"id": "http://www.grid.ac/institutes/grid.140139.e",
"name": [
"National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba-City, Ibaraki, Japan"
],
"type": "Organization"
},
"familyName": "Shiogama",
"givenName": "Hideo",
"id": "sg:person.011356656533.12",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s10584-014-1082-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032148748",
"https://doi.org/10.1007/s10584-014-1082-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/35036559",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016303772",
"https://doi.org/10.1038/35036559"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-014-1223-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051870262",
"https://doi.org/10.1007/s10584-014-1223-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-011-0148-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021241034",
"https://doi.org/10.1007/s10584-011-0148-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-008-9406-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050947087",
"https://doi.org/10.1007/s10584-008-9406-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11625-017-0521-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100241766",
"https://doi.org/10.1007/s11625-017-0521-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/srep18903",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027872095",
"https://doi.org/10.1038/srep18903"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/416723a",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022821020",
"https://doi.org/10.1038/416723a"
],
"type": "CreativeWork"
}
],
"datePublished": "2018-01-24",
"datePublishedReg": "2018-01-24",
"description": "In COP21 followed by the Paris Agreement, the world is now seriously planning actions to mitigate greenhouse gas emissions toward a \u201cbelow 2\u00a0\u00b0C above preindustrial levels\u201d future. Currently, we are still far from identifying the emission pathways to achieve this target because of the various uncertainties in both climate science and the human behavior. As a part of the ICA-RUS project, conducted by Dr. Seita Emori of the National Institute for Environmental Studies we have studied how these uncertainties are eliminated by the accumulation of scientific knowledge and the decision-making processes. We consider the following three questions: first, when and how will the uncertainty range on the global temperature rise be eliminated, second which global emission pathway should be chosen before we get the perfect information, and third how much expenditure is justified in reducing the climate uncertainties. The first question has been investigated by one of the authors. Shiogama et al. (Sci Rep 6:18903, 2016) developed the Allen\u2013Stott\u2013Kettleborough (ASK) method further to estimate how quickly and in what way the uncertainties in future global mean temperature changes can decline when the current observation network of surface air temperature is maintained. Fourteen global climate model results in CMIP5 (CMIP http://cmip-pcmdi.llnl.gov/, 2017) are used as virtual observations of surface air temperature. The purpose of this study is to answer the remaining two questions. Based on the ASK research outcomes, we apply the multi stage decision-making known as Act Then Learn (ATL) process to an integrated assessment model MARIA which includes energy technologies, economic activities, land use changes and a simple climate model block. We reveal how accumulating observations helps to mitigate economic losses by expanding the existing ATL method to deal with the uncertainty eliminating process by ASK. The primary findings are as follows. First, the value of information largely increases as the climate target policy is more stringent. Second, even if the uncertainties in the equilibrium climate sensitivity are not fully resolved, scientific knowledge is still valuable. In other words, the expenditure for scientific researches is rationalized when we really concern the global climate changes.",
"genre": "article",
"id": "sg:pub.10.1007/s11625-018-0528-7",
"inLanguage": "en",
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1136009",
"issn": [
"1862-4065",
"1862-4057"
],
"name": "Sustainability Science",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "13"
}
],
"keywords": [
"surface air temperature",
"global climate model results",
"emission pathways",
"global mean temperature change",
"air temperature",
"climate model results",
"equilibrium climate sensitivity",
"mean temperature change",
"current observation network",
"global temperature rise",
"climate sensitivity uncertainty",
"global climate change",
"land use change",
"preindustrial levels",
"climate sensitivity",
"observation network",
"uncertainty range",
"climate uncertainty",
"model results",
"climate change",
"climate science",
"use change",
"environmental studies",
"temperature changes",
"global emission pathway",
"virtual observations",
"Paris Agreement",
"greenhouse gas emissions",
"uncertainty",
"gas emissions",
"model blocks",
"CMIP5",
"temperature rise",
"changes",
"Maria",
"accumulation",
"temperature",
"economic losses",
"process",
"rise",
"emission",
"part",
"scientific knowledge",
"economic activity",
"block",
"stage",
"values",
"agreement",
"information",
"scientific research",
"range",
"value of information",
"COP21",
"future",
"primary findings",
"project",
"comparison",
"study",
"world",
"results",
"Institute",
"knowledge",
"activity",
"single stage",
"loss",
"sensitivity",
"levels",
"questions",
"research outcomes",
"science",
"method",
"network",
"pathway",
"decision-making process",
"multi stage",
"human behavior",
"first question",
"research",
"behavior",
"National Institute",
"way",
"purpose",
"perfect information",
"acts",
"authors",
"energy technologies",
"observations",
"findings",
"policy",
"action",
"decisions",
"target",
"technology",
"knowledge accumulation",
"expenditure",
"words",
"outcomes",
"ASK",
"ATL method",
"target policy"
],
"name": "The value of knowledge accumulation on climate sensitivity uncertainty: comparison between perfect information, single stage and act then learn decisions",
"pagination": "351-368",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1100617668"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11625-018-0528-7"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30147785"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11625-018-0528-7",
"https://app.dimensions.ai/details/publication/pub.1100617668"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:34",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_784.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s11625-018-0528-7"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11625-018-0528-7'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11625-018-0528-7'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11625-018-0528-7'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11625-018-0528-7'
This table displays all metadata directly associated to this object as RDF triples.
204 TRIPLES
22 PREDICATES
134 URIs
118 LITERALS
7 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s11625-018-0528-7 | schema:about | anzsrc-for:04 |
2 | ″ | ″ | anzsrc-for:0401 |
3 | ″ | schema:author | Nbc61fc31c0c44232aac1dc2be9425e57 |
4 | ″ | schema:citation | sg:pub.10.1007/s10584-008-9406-0 |
5 | ″ | ″ | sg:pub.10.1007/s10584-011-0148-z |
6 | ″ | ″ | sg:pub.10.1007/s10584-014-1082-7 |
7 | ″ | ″ | sg:pub.10.1007/s10584-014-1223-z |
8 | ″ | ″ | sg:pub.10.1007/s11625-017-0521-6 |
9 | ″ | ″ | sg:pub.10.1038/35036559 |
10 | ″ | ″ | sg:pub.10.1038/416723a |
11 | ″ | ″ | sg:pub.10.1038/srep18903 |
12 | ″ | schema:datePublished | 2018-01-24 |
13 | ″ | schema:datePublishedReg | 2018-01-24 |
14 | ″ | schema:description | In COP21 followed by the Paris Agreement, the world is now seriously planning actions to mitigate greenhouse gas emissions toward a “below 2 °C above preindustrial levels” future. Currently, we are still far from identifying the emission pathways to achieve this target because of the various uncertainties in both climate science and the human behavior. As a part of the ICA-RUS project, conducted by Dr. Seita Emori of the National Institute for Environmental Studies we have studied how these uncertainties are eliminated by the accumulation of scientific knowledge and the decision-making processes. We consider the following three questions: first, when and how will the uncertainty range on the global temperature rise be eliminated, second which global emission pathway should be chosen before we get the perfect information, and third how much expenditure is justified in reducing the climate uncertainties. The first question has been investigated by one of the authors. Shiogama et al. (Sci Rep 6:18903, 2016) developed the Allen–Stott–Kettleborough (ASK) method further to estimate how quickly and in what way the uncertainties in future global mean temperature changes can decline when the current observation network of surface air temperature is maintained. Fourteen global climate model results in CMIP5 (CMIP http://cmip-pcmdi.llnl.gov/, 2017) are used as virtual observations of surface air temperature. The purpose of this study is to answer the remaining two questions. Based on the ASK research outcomes, we apply the multi stage decision-making known as Act Then Learn (ATL) process to an integrated assessment model MARIA which includes energy technologies, economic activities, land use changes and a simple climate model block. We reveal how accumulating observations helps to mitigate economic losses by expanding the existing ATL method to deal with the uncertainty eliminating process by ASK. The primary findings are as follows. First, the value of information largely increases as the climate target policy is more stringent. Second, even if the uncertainties in the equilibrium climate sensitivity are not fully resolved, scientific knowledge is still valuable. In other words, the expenditure for scientific researches is rationalized when we really concern the global climate changes. |
15 | ″ | schema:genre | article |
16 | ″ | schema:inLanguage | en |
17 | ″ | schema:isAccessibleForFree | true |
18 | ″ | schema:isPartOf | N76e937d764f843479b9cfb315fec31b1 |
19 | ″ | ″ | N7f8d59e340c84aa18e6b74c7d6eae42b |
20 | ″ | ″ | sg:journal.1136009 |
21 | ″ | schema:keywords | ASK |
22 | ″ | ″ | ATL method |
23 | ″ | ″ | CMIP5 |
24 | ″ | ″ | COP21 |
25 | ″ | ″ | Institute |
26 | ″ | ″ | Maria |
27 | ″ | ″ | National Institute |
28 | ″ | ″ | Paris Agreement |
29 | ″ | ″ | accumulation |
30 | ″ | ″ | action |
31 | ″ | ″ | activity |
32 | ″ | ″ | acts |
33 | ″ | ″ | agreement |
34 | ″ | ″ | air temperature |
35 | ″ | ″ | authors |
36 | ″ | ″ | behavior |
37 | ″ | ″ | block |
38 | ″ | ″ | changes |
39 | ″ | ″ | climate change |
40 | ″ | ″ | climate model results |
41 | ″ | ″ | climate science |
42 | ″ | ″ | climate sensitivity |
43 | ″ | ″ | climate sensitivity uncertainty |
44 | ″ | ″ | climate uncertainty |
45 | ″ | ″ | comparison |
46 | ″ | ″ | current observation network |
47 | ″ | ″ | decision-making process |
48 | ″ | ″ | decisions |
49 | ″ | ″ | economic activity |
50 | ″ | ″ | economic losses |
51 | ″ | ″ | emission |
52 | ″ | ″ | emission pathways |
53 | ″ | ″ | energy technologies |
54 | ″ | ″ | environmental studies |
55 | ″ | ″ | equilibrium climate sensitivity |
56 | ″ | ″ | expenditure |
57 | ″ | ″ | findings |
58 | ″ | ″ | first question |
59 | ″ | ″ | future |
60 | ″ | ″ | gas emissions |
61 | ″ | ″ | global climate change |
62 | ″ | ″ | global climate model results |
63 | ″ | ″ | global emission pathway |
64 | ″ | ″ | global mean temperature change |
65 | ″ | ″ | global temperature rise |
66 | ″ | ″ | greenhouse gas emissions |
67 | ″ | ″ | human behavior |
68 | ″ | ″ | information |
69 | ″ | ″ | knowledge |
70 | ″ | ″ | knowledge accumulation |
71 | ″ | ″ | land use change |
72 | ″ | ″ | levels |
73 | ″ | ″ | loss |
74 | ″ | ″ | mean temperature change |
75 | ″ | ″ | method |
76 | ″ | ″ | model blocks |
77 | ″ | ″ | model results |
78 | ″ | ″ | multi stage |
79 | ″ | ″ | network |
80 | ″ | ″ | observation network |
81 | ″ | ″ | observations |
82 | ″ | ″ | outcomes |
83 | ″ | ″ | part |
84 | ″ | ″ | pathway |
85 | ″ | ″ | perfect information |
86 | ″ | ″ | policy |
87 | ″ | ″ | preindustrial levels |
88 | ″ | ″ | primary findings |
89 | ″ | ″ | process |
90 | ″ | ″ | project |
91 | ″ | ″ | purpose |
92 | ″ | ″ | questions |
93 | ″ | ″ | range |
94 | ″ | ″ | research |
95 | ″ | ″ | research outcomes |
96 | ″ | ″ | results |
97 | ″ | ″ | rise |
98 | ″ | ″ | science |
99 | ″ | ″ | scientific knowledge |
100 | ″ | ″ | scientific research |
101 | ″ | ″ | sensitivity |
102 | ″ | ″ | single stage |
103 | ″ | ″ | stage |
104 | ″ | ″ | study |
105 | ″ | ″ | surface air temperature |
106 | ″ | ″ | target |
107 | ″ | ″ | target policy |
108 | ″ | ″ | technology |
109 | ″ | ″ | temperature |
110 | ″ | ″ | temperature changes |
111 | ″ | ″ | temperature rise |
112 | ″ | ″ | uncertainty |
113 | ″ | ″ | uncertainty range |
114 | ″ | ″ | use change |
115 | ″ | ″ | value of information |
116 | ″ | ″ | values |
117 | ″ | ″ | virtual observations |
118 | ″ | ″ | way |
119 | ″ | ″ | words |
120 | ″ | ″ | world |
121 | ″ | schema:name | The value of knowledge accumulation on climate sensitivity uncertainty: comparison between perfect information, single stage and act then learn decisions |
122 | ″ | schema:pagination | 351-368 |
123 | ″ | schema:productId | N032434d0826f46eab268ac2bbda61635 |
124 | ″ | ″ | N26ae406d5dc34334812a0caea7b8a7f8 |
125 | ″ | ″ | Nd99659accec74fd791ed89b2c923cfa0 |
126 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1100617668 |
127 | ″ | ″ | https://doi.org/10.1007/s11625-018-0528-7 |
128 | ″ | schema:sdDatePublished | 2022-05-20T07:34 |
129 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
130 | ″ | schema:sdPublisher | N4b84dbf7af4449e0a2eaefa533f5751a |
131 | ″ | schema:url | https://doi.org/10.1007/s11625-018-0528-7 |
132 | ″ | sgo:license | sg:explorer/license/ |
133 | ″ | sgo:sdDataset | articles |
134 | ″ | rdf:type | schema:ScholarlyArticle |
135 | N032434d0826f46eab268ac2bbda61635 | schema:name | doi |
136 | ″ | schema:value | 10.1007/s11625-018-0528-7 |
137 | ″ | rdf:type | schema:PropertyValue |
138 | N26ae406d5dc34334812a0caea7b8a7f8 | schema:name | dimensions_id |
139 | ″ | schema:value | pub.1100617668 |
140 | ″ | rdf:type | schema:PropertyValue |
141 | N4b84dbf7af4449e0a2eaefa533f5751a | schema:name | Springer Nature - SN SciGraph project |
142 | ″ | rdf:type | schema:Organization |
143 | N76e937d764f843479b9cfb315fec31b1 | schema:issueNumber | 2 |
144 | ″ | rdf:type | schema:PublicationIssue |
145 | N7f8d59e340c84aa18e6b74c7d6eae42b | schema:volumeNumber | 13 |
146 | ″ | rdf:type | schema:PublicationVolume |
147 | Nbc61fc31c0c44232aac1dc2be9425e57 | rdf:first | sg:person.011602002215.72 |
148 | ″ | rdf:rest | Nc7338c798574469aa91c3b366644ae71 |
149 | Nc7338c798574469aa91c3b366644ae71 | rdf:first | sg:person.011356656533.12 |
150 | ″ | rdf:rest | rdf:nil |
151 | Nd99659accec74fd791ed89b2c923cfa0 | schema:name | pubmed_id |
152 | ″ | schema:value | 30147785 |
153 | ″ | rdf:type | schema:PropertyValue |
154 | anzsrc-for:04 | schema:inDefinedTermSet | anzsrc-for: |
155 | ″ | schema:name | Earth Sciences |
156 | ″ | rdf:type | schema:DefinedTerm |
157 | anzsrc-for:0401 | schema:inDefinedTermSet | anzsrc-for: |
158 | ″ | schema:name | Atmospheric Sciences |
159 | ″ | rdf:type | schema:DefinedTerm |
160 | sg:journal.1136009 | schema:issn | 1862-4057 |
161 | ″ | ″ | 1862-4065 |
162 | ″ | schema:name | Sustainability Science |
163 | ″ | schema:publisher | Springer Nature |
164 | ″ | rdf:type | schema:Periodical |
165 | sg:person.011356656533.12 | schema:affiliation | grid-institutes:grid.140139.e |
166 | ″ | schema:familyName | Shiogama |
167 | ″ | schema:givenName | Hideo |
168 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12 |
169 | ″ | rdf:type | schema:Person |
170 | sg:person.011602002215.72 | schema:affiliation | grid-institutes:grid.143643.7 |
171 | ″ | schema:familyName | Mori |
172 | ″ | schema:givenName | Shunsuke |
173 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011602002215.72 |
174 | ″ | rdf:type | schema:Person |
175 | sg:pub.10.1007/s10584-008-9406-0 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1050947087 |
176 | ″ | ″ | https://doi.org/10.1007/s10584-008-9406-0 |
177 | ″ | rdf:type | schema:CreativeWork |
178 | sg:pub.10.1007/s10584-011-0148-z | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1021241034 |
179 | ″ | ″ | https://doi.org/10.1007/s10584-011-0148-z |
180 | ″ | rdf:type | schema:CreativeWork |
181 | sg:pub.10.1007/s10584-014-1082-7 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1032148748 |
182 | ″ | ″ | https://doi.org/10.1007/s10584-014-1082-7 |
183 | ″ | rdf:type | schema:CreativeWork |
184 | sg:pub.10.1007/s10584-014-1223-z | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1051870262 |
185 | ″ | ″ | https://doi.org/10.1007/s10584-014-1223-z |
186 | ″ | rdf:type | schema:CreativeWork |
187 | sg:pub.10.1007/s11625-017-0521-6 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1100241766 |
188 | ″ | ″ | https://doi.org/10.1007/s11625-017-0521-6 |
189 | ″ | rdf:type | schema:CreativeWork |
190 | sg:pub.10.1038/35036559 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1016303772 |
191 | ″ | ″ | https://doi.org/10.1038/35036559 |
192 | ″ | rdf:type | schema:CreativeWork |
193 | sg:pub.10.1038/416723a | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1022821020 |
194 | ″ | ″ | https://doi.org/10.1038/416723a |
195 | ″ | rdf:type | schema:CreativeWork |
196 | sg:pub.10.1038/srep18903 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1027872095 |
197 | ″ | ″ | https://doi.org/10.1038/srep18903 |
198 | ″ | rdf:type | schema:CreativeWork |
199 | grid-institutes:grid.140139.e | schema:alternateName | National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba-City, Ibaraki, Japan |
200 | ″ | schema:name | National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba-City, Ibaraki, Japan |
201 | ″ | rdf:type | schema:Organization |
202 | grid-institutes:grid.143643.7 | schema:alternateName | Tokyo University of Science, Yamasaki 2641, 278-8510, Noda-shi, Chiba, Japan |
203 | ″ | schema:name | Tokyo University of Science, Yamasaki 2641, 278-8510, Noda-shi, Chiba, Japan |
204 | ″ | rdf:type | schema:Organization |