An Integrated Assessment of Climate Change and the Accelerated Introduction of Advanced Energy Technologies - An Application of MiniCAM 1.0 View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1997-03

AUTHORS

Jae Edmonds, Marshall Wise, Hugh Pitcher, Richard Richels, Tom Wigley, Chris MacCracken

ABSTRACT

We report results from the application of an integrated assessment model, MiniCAM 1.0. The model is employed to explore the full range of climate change implications of the successful development of cost effective, advanced, energy technologies. These technologies are shown to have a profound effect on the future magnitude and rate of anthropogenic climate change. We find that the introduction of assumptions developed by a group of ‘bottom-up’ modelers for the LEESS scenarios into a ‘top-down’ model, the Edmonds-Reilly-Barns Model, leads to ‘top down’ emissions trajectories similar to those of the LEESS. The cumulative effect of advanced energy technologies is to reduce annual emissions from fossil fuel use to levels which stabilize atmospheric concentrations below 550 ppmv. While all energy technologies play roles, the introduction of advanced biomass energy production technology is particularly important. The consideration of all greenhouse related anthropogenic emissions, and in particular sulfur dioxide, is found to be important. We find that the consideration of sulfur dioxide emissions coupled to rapid reductions in carbon dioxide emissions leads to higher global mean temperatures prior to 2050 than in the reference case. This result is due to the short-term cooling impact of sulfate aerosols, which dominates the long-term warming impact of CO2 and CH4 in the years prior to 2050. We also show that damage calculations which use only mean global temperature and income may be underestimating damages by up to a factor of five. Disaggregating income reduces this to a factor of two, still a major error. Finally, the role of the discount rate is shown to be extraordinarily important to technology preference. More... »

PAGES

311-339

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:miti.0000027386.34214.60

DOI

http://dx.doi.org/10.1023/b:miti.0000027386.34214.60

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051224556


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/05", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0501", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Ecological Applications", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0502", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Science and Management", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA", 
          "id": "http://www.grid.ac/institutes/grid.451303.0", 
          "name": [
            "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Edmonds", 
        "givenName": "Jae", 
        "id": "sg:person.011274425577.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011274425577.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA", 
          "id": "http://www.grid.ac/institutes/grid.451303.0", 
          "name": [
            "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wise", 
        "givenName": "Marshall", 
        "id": "sg:person.013430506420.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013430506420.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA", 
          "id": "http://www.grid.ac/institutes/grid.451303.0", 
          "name": [
            "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pitcher", 
        "givenName": "Hugh", 
        "id": "sg:person.014035341167.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014035341167.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Electric Power Research Institute, USA", 
          "id": "http://www.grid.ac/institutes/grid.418781.3", 
          "name": [
            "Electric Power Research Institute, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Richels", 
        "givenName": "Richard", 
        "id": "sg:person.015167673341.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015167673341.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Corporation for Atmospheric Research, USA", 
          "id": "http://www.grid.ac/institutes/grid.413455.2", 
          "name": [
            "University Corporation for Atmospheric Research, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wigley", 
        "givenName": "Tom", 
        "id": "sg:person.016171504677.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171504677.21"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA", 
          "id": "http://www.grid.ac/institutes/grid.451303.0", 
          "name": [
            "Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "MacCracken", 
        "givenName": "Chris", 
        "id": "sg:person.015476415367.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015476415367.37"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00198619", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010820131", 
          "https://doi.org/10.1007/bf00198619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01054491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040451933", 
          "https://doi.org/10.1007/bf01054491"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/341132a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021765591", 
          "https://doi.org/10.1038/341132a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00209163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030448021", 
          "https://doi.org/10.1007/bf00209163"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/349503a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041655521", 
          "https://doi.org/10.1038/349503a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/330127a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016835893", 
          "https://doi.org/10.1038/330127a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/357293a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027555370", 
          "https://doi.org/10.1038/357293a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01098378", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007993517", 
          "https://doi.org/10.1007/bf01098378"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1997-03", 
    "datePublishedReg": "1997-03-01", 
    "description": "We report results from the application of an integrated assessment model, MiniCAM 1.0. The model is employed to explore the full range of climate change implications of the successful development of cost effective, advanced, energy technologies. These technologies are shown to have a profound effect on the future magnitude and rate of anthropogenic climate change. We find that the introduction of assumptions developed by a group of \u2018bottom-up\u2019 modelers for the LEESS scenarios into a \u2018top-down\u2019 model, the Edmonds-Reilly-Barns Model, leads to \u2018top down\u2019 emissions trajectories similar to those of the LEESS. The cumulative effect of advanced energy technologies is to reduce annual emissions from fossil fuel use to levels which stabilize atmospheric concentrations below 550 ppmv. While all energy technologies play roles, the introduction of advanced biomass energy production technology is particularly important. The consideration of all greenhouse related anthropogenic emissions, and in particular sulfur dioxide, is found to be important. We find that the consideration of sulfur dioxide emissions coupled to rapid reductions in carbon dioxide emissions leads to higher global mean temperatures prior to 2050 than in the reference case. This result is due to the short-term cooling impact of sulfate aerosols, which dominates the long-term warming impact of CO2 and CH4 in the years prior to 2050. We also show that damage calculations which use only mean global temperature and income may be underestimating damages by up to a factor of five. Disaggregating income reduces this to a factor of two, still a major error. Finally, the role of the discount rate is shown to be extraordinarily important to technology preference.", 
    "genre": "article", 
    "id": "sg:pub.10.1023/b:miti.0000027386.34214.60", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135912", 
        "issn": [
          "1381-2386", 
          "1573-1596"
        ], 
        "name": "Mitigation and Adaptation Strategies for Global Change", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "1"
      }
    ], 
    "keywords": [
      "advanced energy technologies", 
      "energy technologies", 
      "energy production technologies", 
      "dioxide emissions", 
      "carbon dioxide emissions", 
      "introduction of assumptions", 
      "damage calculations", 
      "fossil fuel use", 
      "reference case", 
      "sulfur dioxide emissions", 
      "warming impact", 
      "fuel use", 
      "production technology", 
      "annual emissions", 
      "emission", 
      "technology", 
      "temperature", 
      "sulfur dioxide", 
      "applications", 
      "anthropogenic emissions", 
      "particular sulphur dioxide", 
      "CH4", 
      "model", 
      "successful development", 
      "ppmv", 
      "dioxide", 
      "CO2", 
      "integrated assessment model", 
      "assessment model", 
      "full range", 
      "mean temperature", 
      "accelerated introduction", 
      "integrated assessment", 
      "climate change implications", 
      "results", 
      "error", 
      "cost", 
      "consideration", 
      "atmospheric concentrations", 
      "range", 
      "rapid reduction", 
      "calculations", 
      "effect", 
      "emissions trajectories", 
      "aerosols", 
      "magnitude", 
      "rate", 
      "climate change", 
      "sulfate aerosols", 
      "scenarios", 
      "introduction", 
      "reduction", 
      "trajectories", 
      "impact", 
      "change implications", 
      "modelers", 
      "damage", 
      "profound effect", 
      "concentration", 
      "major errors", 
      "global temperature", 
      "assumption", 
      "use", 
      "changes", 
      "cumulative effect", 
      "discount rate", 
      "factors", 
      "development", 
      "assessment", 
      "cases", 
      "anthropogenic climate change", 
      "global mean temperature", 
      "greenhouse", 
      "technology preferences", 
      "future magnitude", 
      "levels", 
      "role", 
      "years", 
      "LEES", 
      "implications", 
      "group", 
      "preferences", 
      "income"
    ], 
    "name": "An Integrated Assessment of Climate Change and the Accelerated Introduction of Advanced Energy Technologies - An Application of MiniCAM 1.0", 
    "pagination": "311-339", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1051224556"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/b:miti.0000027386.34214.60"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/b:miti.0000027386.34214.60", 
      "https://app.dimensions.ai/details/publication/pub.1051224556"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_291.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1023/b:miti.0000027386.34214.60"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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.10.1023/b:miti.0000027386.34214.60'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/b:miti.0000027386.34214.60'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/b:miti.0000027386.34214.60'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/b:miti.0000027386.34214.60'


 

This table displays all metadata directly associated to this object as RDF triples.

217 TRIPLES      21 PREDICATES      117 URIs      100 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/b:miti.0000027386.34214.60 schema:about anzsrc-for:05
2 anzsrc-for:0501
3 anzsrc-for:0502
4 schema:author N39e549a9066646e0a57e487cc33a9bad
5 schema:citation sg:pub.10.1007/bf00198619
6 sg:pub.10.1007/bf00209163
7 sg:pub.10.1007/bf01054491
8 sg:pub.10.1007/bf01098378
9 sg:pub.10.1038/330127a0
10 sg:pub.10.1038/341132a0
11 sg:pub.10.1038/349503a0
12 sg:pub.10.1038/357293a0
13 schema:datePublished 1997-03
14 schema:datePublishedReg 1997-03-01
15 schema:description We report results from the application of an integrated assessment model, MiniCAM 1.0. The model is employed to explore the full range of climate change implications of the successful development of cost effective, advanced, energy technologies. These technologies are shown to have a profound effect on the future magnitude and rate of anthropogenic climate change. We find that the introduction of assumptions developed by a group of ‘bottom-up’ modelers for the LEESS scenarios into a ‘top-down’ model, the Edmonds-Reilly-Barns Model, leads to ‘top down’ emissions trajectories similar to those of the LEESS. The cumulative effect of advanced energy technologies is to reduce annual emissions from fossil fuel use to levels which stabilize atmospheric concentrations below 550 ppmv. While all energy technologies play roles, the introduction of advanced biomass energy production technology is particularly important. The consideration of all greenhouse related anthropogenic emissions, and in particular sulfur dioxide, is found to be important. We find that the consideration of sulfur dioxide emissions coupled to rapid reductions in carbon dioxide emissions leads to higher global mean temperatures prior to 2050 than in the reference case. This result is due to the short-term cooling impact of sulfate aerosols, which dominates the long-term warming impact of CO2 and CH4 in the years prior to 2050. We also show that damage calculations which use only mean global temperature and income may be underestimating damages by up to a factor of five. Disaggregating income reduces this to a factor of two, still a major error. Finally, the role of the discount rate is shown to be extraordinarily important to technology preference.
16 schema:genre article
17 schema:isAccessibleForFree false
18 schema:isPartOf N061ee1abf3624111bd5827de6bd5d14c
19 N955c861ebb78477b9dcde868b19b64f7
20 sg:journal.1135912
21 schema:keywords CH4
22 CO2
23 LEES
24 accelerated introduction
25 advanced energy technologies
26 aerosols
27 annual emissions
28 anthropogenic climate change
29 anthropogenic emissions
30 applications
31 assessment
32 assessment model
33 assumption
34 atmospheric concentrations
35 calculations
36 carbon dioxide emissions
37 cases
38 change implications
39 changes
40 climate change
41 climate change implications
42 concentration
43 consideration
44 cost
45 cumulative effect
46 damage
47 damage calculations
48 development
49 dioxide
50 dioxide emissions
51 discount rate
52 effect
53 emission
54 emissions trajectories
55 energy production technologies
56 energy technologies
57 error
58 factors
59 fossil fuel use
60 fuel use
61 full range
62 future magnitude
63 global mean temperature
64 global temperature
65 greenhouse
66 group
67 impact
68 implications
69 income
70 integrated assessment
71 integrated assessment model
72 introduction
73 introduction of assumptions
74 levels
75 magnitude
76 major errors
77 mean temperature
78 model
79 modelers
80 particular sulphur dioxide
81 ppmv
82 preferences
83 production technology
84 profound effect
85 range
86 rapid reduction
87 rate
88 reduction
89 reference case
90 results
91 role
92 scenarios
93 successful development
94 sulfate aerosols
95 sulfur dioxide
96 sulfur dioxide emissions
97 technology
98 technology preferences
99 temperature
100 trajectories
101 use
102 warming impact
103 years
104 schema:name An Integrated Assessment of Climate Change and the Accelerated Introduction of Advanced Energy Technologies - An Application of MiniCAM 1.0
105 schema:pagination 311-339
106 schema:productId N01d01792eae14f9fa44e27b454121a83
107 Neb23c05a30e34163b2fecf4b0b6e6a7d
108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051224556
109 https://doi.org/10.1023/b:miti.0000027386.34214.60
110 schema:sdDatePublished 2022-12-01T06:21
111 schema:sdLicense https://scigraph.springernature.com/explorer/license/
112 schema:sdPublisher N5e0e65dbbbfb48c8a97ac1da0091dc95
113 schema:url https://doi.org/10.1023/b:miti.0000027386.34214.60
114 sgo:license sg:explorer/license/
115 sgo:sdDataset articles
116 rdf:type schema:ScholarlyArticle
117 N01d01792eae14f9fa44e27b454121a83 schema:name doi
118 schema:value 10.1023/b:miti.0000027386.34214.60
119 rdf:type schema:PropertyValue
120 N061ee1abf3624111bd5827de6bd5d14c schema:issueNumber 4
121 rdf:type schema:PublicationIssue
122 N32df429812714fcb92e62e807d60f01c rdf:first sg:person.015476415367.37
123 rdf:rest rdf:nil
124 N39e549a9066646e0a57e487cc33a9bad rdf:first sg:person.011274425577.79
125 rdf:rest Ne6d7236ab4d64513af3c434114f39d06
126 N5e0e65dbbbfb48c8a97ac1da0091dc95 schema:name Springer Nature - SN SciGraph project
127 rdf:type schema:Organization
128 N7201442157434d21beb66142c0b0b7d5 rdf:first sg:person.015167673341.53
129 rdf:rest N97ea5bb03a004763898a122e7ae98ece
130 N955c861ebb78477b9dcde868b19b64f7 schema:volumeNumber 1
131 rdf:type schema:PublicationVolume
132 N957f966db10a4e378820ee5be30876dd rdf:first sg:person.014035341167.17
133 rdf:rest N7201442157434d21beb66142c0b0b7d5
134 N97ea5bb03a004763898a122e7ae98ece rdf:first sg:person.016171504677.21
135 rdf:rest N32df429812714fcb92e62e807d60f01c
136 Ne6d7236ab4d64513af3c434114f39d06 rdf:first sg:person.013430506420.60
137 rdf:rest N957f966db10a4e378820ee5be30876dd
138 Neb23c05a30e34163b2fecf4b0b6e6a7d schema:name dimensions_id
139 schema:value pub.1051224556
140 rdf:type schema:PropertyValue
141 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
142 schema:name Environmental Sciences
143 rdf:type schema:DefinedTerm
144 anzsrc-for:0501 schema:inDefinedTermSet anzsrc-for:
145 schema:name Ecological Applications
146 rdf:type schema:DefinedTerm
147 anzsrc-for:0502 schema:inDefinedTermSet anzsrc-for:
148 schema:name Environmental Science and Management
149 rdf:type schema:DefinedTerm
150 sg:journal.1135912 schema:issn 1381-2386
151 1573-1596
152 schema:name Mitigation and Adaptation Strategies for Global Change
153 schema:publisher Springer Nature
154 rdf:type schema:Periodical
155 sg:person.011274425577.79 schema:affiliation grid-institutes:grid.451303.0
156 schema:familyName Edmonds
157 schema:givenName Jae
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011274425577.79
159 rdf:type schema:Person
160 sg:person.013430506420.60 schema:affiliation grid-institutes:grid.451303.0
161 schema:familyName Wise
162 schema:givenName Marshall
163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013430506420.60
164 rdf:type schema:Person
165 sg:person.014035341167.17 schema:affiliation grid-institutes:grid.451303.0
166 schema:familyName Pitcher
167 schema:givenName Hugh
168 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014035341167.17
169 rdf:type schema:Person
170 sg:person.015167673341.53 schema:affiliation grid-institutes:grid.418781.3
171 schema:familyName Richels
172 schema:givenName Richard
173 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015167673341.53
174 rdf:type schema:Person
175 sg:person.015476415367.37 schema:affiliation grid-institutes:grid.451303.0
176 schema:familyName MacCracken
177 schema:givenName Chris
178 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015476415367.37
179 rdf:type schema:Person
180 sg:person.016171504677.21 schema:affiliation grid-institutes:grid.413455.2
181 schema:familyName Wigley
182 schema:givenName Tom
183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171504677.21
184 rdf:type schema:Person
185 sg:pub.10.1007/bf00198619 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010820131
186 https://doi.org/10.1007/bf00198619
187 rdf:type schema:CreativeWork
188 sg:pub.10.1007/bf00209163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030448021
189 https://doi.org/10.1007/bf00209163
190 rdf:type schema:CreativeWork
191 sg:pub.10.1007/bf01054491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040451933
192 https://doi.org/10.1007/bf01054491
193 rdf:type schema:CreativeWork
194 sg:pub.10.1007/bf01098378 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007993517
195 https://doi.org/10.1007/bf01098378
196 rdf:type schema:CreativeWork
197 sg:pub.10.1038/330127a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016835893
198 https://doi.org/10.1038/330127a0
199 rdf:type schema:CreativeWork
200 sg:pub.10.1038/341132a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021765591
201 https://doi.org/10.1038/341132a0
202 rdf:type schema:CreativeWork
203 sg:pub.10.1038/349503a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041655521
204 https://doi.org/10.1038/349503a0
205 rdf:type schema:CreativeWork
206 sg:pub.10.1038/357293a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027555370
207 https://doi.org/10.1038/357293a0
208 rdf:type schema:CreativeWork
209 grid-institutes:grid.413455.2 schema:alternateName University Corporation for Atmospheric Research, USA
210 schema:name University Corporation for Atmospheric Research, USA
211 rdf:type schema:Organization
212 grid-institutes:grid.418781.3 schema:alternateName Electric Power Research Institute, USA
213 schema:name Electric Power Research Institute, USA
214 rdf:type schema:Organization
215 grid-institutes:grid.451303.0 schema:alternateName Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA
216 schema:name Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, DC 20024-2115, Washington, USA
217 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...