When we don't know the costs or the benefits: Adaptive strategies for abating climate change View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1996-06

AUTHORS

Robert J. Lempert, Michael E. Schlesinger, Steve C. Bankes

ABSTRACT

Most quantitative studies of climate-change policy attempt to predict the greenhouse-gas reduction plan that will have the optimum balance of long-term costs and benefits. We find that the large uncertainties associated with the climate-change problem can make the policy prescriptions of this traditional approach unreliable. In this study, we construct a large uncertainty space that includes the possibility of large and/or abrupt climate changes and/or of technology breakthroughs that radically reduce projected abatement costs. We use computational experiments on a linked system of climate and economic models to compare the performance of a simple adaptive strategy - one that can make midcourse corrections based on observations of the climate and economic systems - and two commonly advocated ‘best-estimate’ policies based on different expectations about the longterm consequences of climate change. We find that the ‘Do-a-Little’ and ‘Emissions-Stabilization’ best-estimate policies perform well in the respective regions of the uncertainty space where their estimates are valid, but can fail severely in those regions where their estimates are wrong. In contrast, the adaptive strategy can make midcourse corrections and avoid significant errors. While its success is no surprise, the adaptive-strategy approach provides an analytic framework to examine important policy and research issues that will likely arise as society adapts to climate change, which cannot be easily addressed in studies using best-estimate approaches. More... »

PAGES

235-274

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00140248

DOI

http://dx.doi.org/10.1007/bf00140248

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025061398


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/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1402", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "RAND, 1700 Main St., 90407, Santa Monica, CA, U.S.A.", 
          "id": "http://www.grid.ac/institutes/grid.34474.30", 
          "name": [
            "RAND, 1700 Main St., 90407, Santa Monica, CA, U.S.A."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lempert", 
        "givenName": "Robert J.", 
        "id": "sg:person.01214111434.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01214111434.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Atmospheric Sciences, University of IllinoisUrbana-Champaign, 105 South Gregory Ave., 61801, Urbana, IL, U.S.A.", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Atmospheric Sciences, University of IllinoisUrbana-Champaign, 105 South Gregory Ave., 61801, Urbana, IL, U.S.A."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schlesinger", 
        "givenName": "Michael E.", 
        "id": "sg:person.015755327722.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015755327722.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "RAND, 1700 Main St., 90407, Santa Monica, CA, U.S.A.", 
          "id": "http://www.grid.ac/institutes/grid.34474.30", 
          "name": [
            "RAND, 1700 Main St., 90407, Santa Monica, CA, U.S.A."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bankes", 
        "givenName": "Steve C.", 
        "id": "sg:person.014006402517.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014006402517.94"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-94-009-0691-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012750287", 
          "https://doi.org/10.1007/978-94-009-0691-4"
        ], 
        "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.10.1038/350219a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011203009", 
          "https://doi.org/10.1038/350219a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/357315a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011758178", 
          "https://doi.org/10.1038/357315a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01094402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006954065", 
          "https://doi.org/10.1007/bf01094402"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/scientificamerican0990-64", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056600963", 
          "https://doi.org/10.1038/scientificamerican0990-64"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1996-06", 
    "datePublishedReg": "1996-06-01", 
    "description": "Most quantitative studies of climate-change policy attempt to predict the greenhouse-gas reduction plan that will have the optimum balance of long-term costs and benefits. We find that the large uncertainties associated with the climate-change problem can make the policy prescriptions of this traditional approach unreliable. In this study, we construct a large uncertainty space that includes the possibility of large and/or abrupt climate changes and/or of technology breakthroughs that radically reduce projected abatement costs. We use computational experiments on a linked system of climate and economic models to compare the performance of a simple adaptive strategy - one that can make midcourse corrections based on observations of the climate and economic systems - and two commonly advocated \u2018best-estimate\u2019 policies based on different expectations about the longterm consequences of climate change. We find that the \u2018Do-a-Little\u2019 and \u2018Emissions-Stabilization\u2019 best-estimate policies perform well in the respective regions of the uncertainty space where their estimates are valid, but can fail severely in those regions where their estimates are wrong. In contrast, the adaptive strategy can make midcourse corrections and avoid significant errors. While its success is no surprise, the adaptive-strategy approach provides an analytic framework to examine important policy and research issues that will likely arise as society adapts to climate change, which cannot be easily addressed in studies using best-estimate approaches.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/bf00140248", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1028211", 
        "issn": [
          "0165-0009", 
          "1573-1480"
        ], 
        "name": "Climatic Change", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "33"
      }
    ], 
    "keywords": [
      "system of climate", 
      "long-term costs", 
      "economic model", 
      "policy prescriptions", 
      "abatement costs", 
      "economic system", 
      "midcourse corrections", 
      "policy attempts", 
      "important policy", 
      "climate change problem", 
      "policy", 
      "climate change", 
      "cost", 
      "different expectations", 
      "reduction plan", 
      "respective regions", 
      "uncertainty space", 
      "analytic framework", 
      "longterm consequences", 
      "best estimate approach", 
      "estimates", 
      "benefits", 
      "traditional approaches", 
      "technology breakthroughs", 
      "uncertainty", 
      "surprise", 
      "expectations", 
      "research issues", 
      "framework", 
      "strategies", 
      "computational experiments", 
      "approach", 
      "changes", 
      "society", 
      "issues", 
      "plan", 
      "model", 
      "consequences", 
      "balance", 
      "optimum balance", 
      "quantitative study", 
      "adaptive strategies", 
      "success", 
      "attempt", 
      "large uncertainties", 
      "climate", 
      "region", 
      "study", 
      "problem", 
      "abrupt climate change", 
      "performance", 
      "prescription", 
      "possibility", 
      "system", 
      "error", 
      "contrast", 
      "breakthrough", 
      "significant errors", 
      "space", 
      "experiments", 
      "observations", 
      "correction"
    ], 
    "name": "When we don't know the costs or the benefits: Adaptive strategies for abating climate change", 
    "pagination": "235-274", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025061398"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00140248"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00140248", 
      "https://app.dimensions.ai/details/publication/pub.1025061398"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:02", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_276.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/bf00140248"
  }
]
 

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.10.1007/bf00140248'

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/bf00140248'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00140248'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00140248'


 

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

161 TRIPLES      22 PREDICATES      94 URIs      80 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00140248 schema:about anzsrc-for:14
2 anzsrc-for:1402
3 schema:author Nd946f55b23b94375a0a2f333925b1591
4 schema:citation sg:pub.10.1007/978-94-009-0691-4
5 sg:pub.10.1007/bf01054491
6 sg:pub.10.1007/bf01094402
7 sg:pub.10.1038/350219a0
8 sg:pub.10.1038/357315a0
9 sg:pub.10.1038/scientificamerican0990-64
10 schema:datePublished 1996-06
11 schema:datePublishedReg 1996-06-01
12 schema:description Most quantitative studies of climate-change policy attempt to predict the greenhouse-gas reduction plan that will have the optimum balance of long-term costs and benefits. We find that the large uncertainties associated with the climate-change problem can make the policy prescriptions of this traditional approach unreliable. In this study, we construct a large uncertainty space that includes the possibility of large and/or abrupt climate changes and/or of technology breakthroughs that radically reduce projected abatement costs. We use computational experiments on a linked system of climate and economic models to compare the performance of a simple adaptive strategy - one that can make midcourse corrections based on observations of the climate and economic systems - and two commonly advocated ‘best-estimate’ policies based on different expectations about the longterm consequences of climate change. We find that the ‘Do-a-Little’ and ‘Emissions-Stabilization’ best-estimate policies perform well in the respective regions of the uncertainty space where their estimates are valid, but can fail severely in those regions where their estimates are wrong. In contrast, the adaptive strategy can make midcourse corrections and avoid significant errors. While its success is no surprise, the adaptive-strategy approach provides an analytic framework to examine important policy and research issues that will likely arise as society adapts to climate change, which cannot be easily addressed in studies using best-estimate approaches.
13 schema:genre article
14 schema:inLanguage en
15 schema:isAccessibleForFree false
16 schema:isPartOf N335416bac38c4232945cc594549713e0
17 N59496f3c163342d09bdef3eb8f3894b6
18 sg:journal.1028211
19 schema:keywords abatement costs
20 abrupt climate change
21 adaptive strategies
22 analytic framework
23 approach
24 attempt
25 balance
26 benefits
27 best estimate approach
28 breakthrough
29 changes
30 climate
31 climate change
32 climate change problem
33 computational experiments
34 consequences
35 contrast
36 correction
37 cost
38 different expectations
39 economic model
40 economic system
41 error
42 estimates
43 expectations
44 experiments
45 framework
46 important policy
47 issues
48 large uncertainties
49 long-term costs
50 longterm consequences
51 midcourse corrections
52 model
53 observations
54 optimum balance
55 performance
56 plan
57 policy
58 policy attempts
59 policy prescriptions
60 possibility
61 prescription
62 problem
63 quantitative study
64 reduction plan
65 region
66 research issues
67 respective regions
68 significant errors
69 society
70 space
71 strategies
72 study
73 success
74 surprise
75 system
76 system of climate
77 technology breakthroughs
78 traditional approaches
79 uncertainty
80 uncertainty space
81 schema:name When we don't know the costs or the benefits: Adaptive strategies for abating climate change
82 schema:pagination 235-274
83 schema:productId N4b998c5850474f1c8eee86bad361bcb9
84 Na3cca6d45ab74f6f888fdda05376ff32
85 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025061398
86 https://doi.org/10.1007/bf00140248
87 schema:sdDatePublished 2022-06-01T22:02
88 schema:sdLicense https://scigraph.springernature.com/explorer/license/
89 schema:sdPublisher N00f9549dc5464f148f5bb93d3b624c97
90 schema:url https://doi.org/10.1007/bf00140248
91 sgo:license sg:explorer/license/
92 sgo:sdDataset articles
93 rdf:type schema:ScholarlyArticle
94 N00f9549dc5464f148f5bb93d3b624c97 schema:name Springer Nature - SN SciGraph project
95 rdf:type schema:Organization
96 N17b1ee0ad8044e2588df45145d22bed2 rdf:first sg:person.015755327722.56
97 rdf:rest N587e6edd16be444586f8795bc9535696
98 N335416bac38c4232945cc594549713e0 schema:volumeNumber 33
99 rdf:type schema:PublicationVolume
100 N4b998c5850474f1c8eee86bad361bcb9 schema:name doi
101 schema:value 10.1007/bf00140248
102 rdf:type schema:PropertyValue
103 N587e6edd16be444586f8795bc9535696 rdf:first sg:person.014006402517.94
104 rdf:rest rdf:nil
105 N59496f3c163342d09bdef3eb8f3894b6 schema:issueNumber 2
106 rdf:type schema:PublicationIssue
107 Na3cca6d45ab74f6f888fdda05376ff32 schema:name dimensions_id
108 schema:value pub.1025061398
109 rdf:type schema:PropertyValue
110 Nd946f55b23b94375a0a2f333925b1591 rdf:first sg:person.01214111434.56
111 rdf:rest N17b1ee0ad8044e2588df45145d22bed2
112 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
113 schema:name Economics
114 rdf:type schema:DefinedTerm
115 anzsrc-for:1402 schema:inDefinedTermSet anzsrc-for:
116 schema:name Applied Economics
117 rdf:type schema:DefinedTerm
118 sg:journal.1028211 schema:issn 0165-0009
119 1573-1480
120 schema:name Climatic Change
121 schema:publisher Springer Nature
122 rdf:type schema:Periodical
123 sg:person.01214111434.56 schema:affiliation grid-institutes:grid.34474.30
124 schema:familyName Lempert
125 schema:givenName Robert J.
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01214111434.56
127 rdf:type schema:Person
128 sg:person.014006402517.94 schema:affiliation grid-institutes:grid.34474.30
129 schema:familyName Bankes
130 schema:givenName Steve C.
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014006402517.94
132 rdf:type schema:Person
133 sg:person.015755327722.56 schema:affiliation grid-institutes:None
134 schema:familyName Schlesinger
135 schema:givenName Michael E.
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015755327722.56
137 rdf:type schema:Person
138 sg:pub.10.1007/978-94-009-0691-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012750287
139 https://doi.org/10.1007/978-94-009-0691-4
140 rdf:type schema:CreativeWork
141 sg:pub.10.1007/bf01054491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040451933
142 https://doi.org/10.1007/bf01054491
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/bf01094402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006954065
145 https://doi.org/10.1007/bf01094402
146 rdf:type schema:CreativeWork
147 sg:pub.10.1038/350219a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011203009
148 https://doi.org/10.1038/350219a0
149 rdf:type schema:CreativeWork
150 sg:pub.10.1038/357315a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011758178
151 https://doi.org/10.1038/357315a0
152 rdf:type schema:CreativeWork
153 sg:pub.10.1038/scientificamerican0990-64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056600963
154 https://doi.org/10.1038/scientificamerican0990-64
155 rdf:type schema:CreativeWork
156 grid-institutes:None schema:alternateName Department of Atmospheric Sciences, University of IllinoisUrbana-Champaign, 105 South Gregory Ave., 61801, Urbana, IL, U.S.A.
157 schema:name Department of Atmospheric Sciences, University of IllinoisUrbana-Champaign, 105 South Gregory Ave., 61801, Urbana, IL, U.S.A.
158 rdf:type schema:Organization
159 grid-institutes:grid.34474.30 schema:alternateName RAND, 1700 Main St., 90407, Santa Monica, CA, U.S.A.
160 schema:name RAND, 1700 Main St., 90407, Santa Monica, CA, U.S.A.
161 rdf:type schema:Organization
 




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


...