Sustainable Governance of Natural Resources: Uncovering Success Patterns with Machine Learning
A comprehensive theoretical synthesis of the various success factors required to successfully and sustainably manage natural resources, Sustainable Governance of Natural Resources offers a quantitative model to predict the success of natural resource management
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Oxford
Oxford University Press, Incorporated
2020
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| Subjects: | |
| Summary: | A comprehensive theoretical synthesis of the various success factors required to successfully and sustainably manage natural resources, Sustainable Governance of Natural Resources offers a quantitative model to predict the success of natural resource management |
| Physical Description: | 1 Online-Ressource (335 Seiten) |
| ISBN: | 9780197502228 9780197502211 |
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| 505 | 8 | |a Cover -- Sustainable Governance of Natural Resources -- Copyright -- Dedication -- Contents -- Acknowledgments -- 1. Introduction -- 1.1 The high importance of natural resources -- 1.1.1 Natural resources in danger of depletion -- 1.1.2 Social-ecological systems -- 1.2 Research question and goals -- 1.2.1 Goals and benefits -- 1.2.2 Gaps and obstacles -- 1.2.3 Hypotheses -- 2. State of Research -- 2.1 What are fundamental biological mechanisms of cooperation? -- 2.1.1 The tragedy of the commons exists for many different species -- 2.1.2 Some possibilities for preventing resource overuse -- 2.2 What drives cooperation in laboratory experiments? -- 2.2.1 Bias and intercultural comparisons -- 2.2.2 Field experiments -- 2.2.3 Behavioral experiments: Conclusion -- 2.3 Common-pool resources -- 2.3.1 Characterization of common-pool resource problems -- 2.3.2 The common-pool problem structure-exemplified -- 2.4 A primer on social-ecological systems -- 2.4.1 Three very different ways to manage common-pool resources -- 2.4.2 Institutional analysis of social-ecological systems by Elinor Ostrom -- 2.4.2.1 Some conceptual clarifications -- 2.4.2.2 A short historical account of different frameworks analyzing social-ecological systems -- 2.4.3 Other related research approaches -- 2.4.3.1 Resilience of social-ecological systems -- 2.4.3.2 (Adaptive) co-management of social-ecological systems -- 2.5 Potential success factors for sustainable management of social-ecological systems -- 2.5.1 Design principles -- 2.5.2 Overview about success factor syntheses -- 2.5.2.1 Synthesis 1 (synthesis of success factors) -- 2.5.2.2 Synthesis 2 (fisheries in Asia) -- 2.5.2.3 Synthesis 3 (meta-analysis forestry worldwide) -- 2.5.2.4 Synthesis 4 (small-scale forest management in Germany) -- 2.5.2.5 Synthesis 5 (meta-analysis of local communities worldwide) | |
| 505 | 8 | |a 2.5.2.6 Synthesis 6 (irrigation systems in India) -- 2.5.2.7 Synthesis 7 (meta-analysis fisheries, worldwide) -- 2.5.2.8 Synthesis 8 (fisheries in Kenya, Tanzania, Madagascar, Indonesia, and Papua-New Guinea) -- 2.5.2.9 Synthesis 9 (nature conservation projects, worldwide) -- 2.5.2.10 Synthesis 10 (synthesis of success factors, social-ecological systems framework) -- 2.5.3 Summary of syntheses for social-ecological systems -- 3. Data -- 3.1 Common-pool resource database -- 3.2 Nepal irrigation institution study database -- 3.3 International forestry resources and institutions database -- 3.4 Comparability of databases -- 3.5 Data preparation -- 3.5.1 Check of raw data -- 3.5.1.1 Check for data correctness-step 1: Data collection -- 3.5.1.2 Verification of the correctness of the data-step 2: Data entry -- 3.5.1.3 Verification of the correctness of the data-step 3: Databases -- 3.5.2 Selection of data -- 3.5.3 Recoding of variables -- 3.5.3.1 Recoding of variables-step 1: Aggregation -- 3.5.3.2 Recoding of variables-step 2: Text variables -- 3.5.3.3 Recoding of variables-step 3: Multiple use of variables -- 3.5.3.4 Recoding of variables-step 4: Imputation -- 3.5.4 Weighting of variables and indicators -- 3.5.4.1 Weighting of the variables and indicators 2: Indicator weighting -- 3.5.4.2 Weighting of variables and indicators-step 2: Weighting of indicators -- 3.5.4.3 Weighting of variables and indicators-step 3: Selection of the Top 3 variables -- 3.5.5 Split of data sets in training and test sets -- 3.5.6 Preparation of the analysis results -- 3.5.7 Summary of methodology and data -- 4. Methods -- 4.1 Introducing the three statistical methods used -- 4.1.1 Multivariate linear regressions -- 4.1.2 Random forests -- 4.1.3 Artificial neural networks -- 4.1.3.1 Construction of artificial neural networks | |
| 505 | 8 | |a 4.1.3.2 Learning and generalization -- 4.1.3.3 Design -- 4.1.3.4 Extraction of the relevance of factors -- 4.2 Operationalizing the success factors via a new indicator system -- 4.2.1 Why do we need a new indicator system? -- 4.2.2 How to develop and validate an indicator system -- 4.2.3 Overview about the indicators used -- 4.2.4 Assigning variables to indicators -- 4.2.5 A difficult task-operationalizing ecological success (part 1-theory) -- 4.2.6 A difficult task-operationalizing ecological success (part 2-implementation) -- 5. Results and Discussion -- 5.1 Synthesis of success factors -- 5.1.1 Justifying the choice of success factors -- 5.1.2 A comprehensive synthesis (which is too unwieldy) -- 5.1.3 A minimal synthesis (which is about right) -- 5.1.4 Which success factors are excluded? -- 5.1.5 Why these success factors should be highly relevant -- 5.1.5.1 Resource system -- 5.1.5.2 Resource units -- 5.1.5.3 Actors -- 5.1.5.4 Governance systems -- 5.1.5.5 External influences -- 5.2 Results for the common-pool resource data -- 5.2.1 Descriptive statistics -- 5.2.2 Correlations -- 5.2.3 Multivariate linear regressions -- 5.2.4 Random forests -- 5.2.5 Neural networks -- 5.2.6 Discussion -- 5.3 Results for the Nepal irrigation institution study data -- 5.3.1 Descriptive statistics -- 5.3.2 Correlations -- 5.3.3 Multivariate linear regressions -- 5.3.4 Random forests -- 5.3.5 Neural networks -- 5.3.6 Discussion -- 5.4 Results for the international forestry resources and institutions data -- 5.4.1 Descriptive statistics -- 5.4.2 Correlations -- 5.4.3 Multivariate linear regressions -- 5.4.4 Random forests -- 5.4.5 Neural networks -- 5.4.6 Discussion -- 5.5 Results for a combined full model -- 5.5.1 Descriptive statistics -- 5.5.2 Correlations -- 5.5.3 Multivariate linear regressions -- 5.5.4 Random forests -- 5.5.5 Neural networks | |
| 505 | 8 | |a 5.5.6 Discussion -- 5.6 Robustness and sensitivity analyses -- 5.6.1 Common-pool resource data -- 5.6.1.1 Multivariate linear regressions -- 5.6.1.2 Random forests -- 5.6.1.3 Neural networks -- 5.6.2 Nepal irrigation institution study data -- 5.6.2.1 Multivariate linear regressions -- 5.6.2.2 Random forests -- 5.6.2.3 Neural networks -- 5.6.3 International forestry resources and institutions data -- 5.6.3.1 Multivariate linear regressions -- 5.6.3.2 Random forests -- 5.6.3.3 Neural networks -- 6. Discussion and Conclusion -- 6.1 Final assessment -- 6.2 New findings -- 6.3 Summary -- 6.4 Outlook -- 7. Appendix -- References | |
| 520 | 3 | |a A comprehensive theoretical synthesis of the various success factors required to successfully and sustainably manage natural resources, Sustainable Governance of Natural Resources offers a quantitative model to predict the success of natural resource management | |
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| contents | Cover -- Sustainable Governance of Natural Resources -- Copyright -- Dedication -- Contents -- Acknowledgments -- 1. Introduction -- 1.1 The high importance of natural resources -- 1.1.1 Natural resources in danger of depletion -- 1.1.2 Social-ecological systems -- 1.2 Research question and goals -- 1.2.1 Goals and benefits -- 1.2.2 Gaps and obstacles -- 1.2.3 Hypotheses -- 2. State of Research -- 2.1 What are fundamental biological mechanisms of cooperation? -- 2.1.1 The tragedy of the commons exists for many different species -- 2.1.2 Some possibilities for preventing resource overuse -- 2.2 What drives cooperation in laboratory experiments? -- 2.2.1 Bias and intercultural comparisons -- 2.2.2 Field experiments -- 2.2.3 Behavioral experiments: Conclusion -- 2.3 Common-pool resources -- 2.3.1 Characterization of common-pool resource problems -- 2.3.2 The common-pool problem structure-exemplified -- 2.4 A primer on social-ecological systems -- 2.4.1 Three very different ways to manage common-pool resources -- 2.4.2 Institutional analysis of social-ecological systems by Elinor Ostrom -- 2.4.2.1 Some conceptual clarifications -- 2.4.2.2 A short historical account of different frameworks analyzing social-ecological systems -- 2.4.3 Other related research approaches -- 2.4.3.1 Resilience of social-ecological systems -- 2.4.3.2 (Adaptive) co-management of social-ecological systems -- 2.5 Potential success factors for sustainable management of social-ecological systems -- 2.5.1 Design principles -- 2.5.2 Overview about success factor syntheses -- 2.5.2.1 Synthesis 1 (synthesis of success factors) -- 2.5.2.2 Synthesis 2 (fisheries in Asia) -- 2.5.2.3 Synthesis 3 (meta-analysis forestry worldwide) -- 2.5.2.4 Synthesis 4 (small-scale forest management in Germany) -- 2.5.2.5 Synthesis 5 (meta-analysis of local communities worldwide) 2.5.2.6 Synthesis 6 (irrigation systems in India) -- 2.5.2.7 Synthesis 7 (meta-analysis fisheries, worldwide) -- 2.5.2.8 Synthesis 8 (fisheries in Kenya, Tanzania, Madagascar, Indonesia, and Papua-New Guinea) -- 2.5.2.9 Synthesis 9 (nature conservation projects, worldwide) -- 2.5.2.10 Synthesis 10 (synthesis of success factors, social-ecological systems framework) -- 2.5.3 Summary of syntheses for social-ecological systems -- 3. Data -- 3.1 Common-pool resource database -- 3.2 Nepal irrigation institution study database -- 3.3 International forestry resources and institutions database -- 3.4 Comparability of databases -- 3.5 Data preparation -- 3.5.1 Check of raw data -- 3.5.1.1 Check for data correctness-step 1: Data collection -- 3.5.1.2 Verification of the correctness of the data-step 2: Data entry -- 3.5.1.3 Verification of the correctness of the data-step 3: Databases -- 3.5.2 Selection of data -- 3.5.3 Recoding of variables -- 3.5.3.1 Recoding of variables-step 1: Aggregation -- 3.5.3.2 Recoding of variables-step 2: Text variables -- 3.5.3.3 Recoding of variables-step 3: Multiple use of variables -- 3.5.3.4 Recoding of variables-step 4: Imputation -- 3.5.4 Weighting of variables and indicators -- 3.5.4.1 Weighting of the variables and indicators 2: Indicator weighting -- 3.5.4.2 Weighting of variables and indicators-step 2: Weighting of indicators -- 3.5.4.3 Weighting of variables and indicators-step 3: Selection of the Top 3 variables -- 3.5.5 Split of data sets in training and test sets -- 3.5.6 Preparation of the analysis results -- 3.5.7 Summary of methodology and data -- 4. Methods -- 4.1 Introducing the three statistical methods used -- 4.1.1 Multivariate linear regressions -- 4.1.2 Random forests -- 4.1.3 Artificial neural networks -- 4.1.3.1 Construction of artificial neural networks 4.1.3.2 Learning and generalization -- 4.1.3.3 Design -- 4.1.3.4 Extraction of the relevance of factors -- 4.2 Operationalizing the success factors via a new indicator system -- 4.2.1 Why do we need a new indicator system? -- 4.2.2 How to develop and validate an indicator system -- 4.2.3 Overview about the indicators used -- 4.2.4 Assigning variables to indicators -- 4.2.5 A difficult task-operationalizing ecological success (part 1-theory) -- 4.2.6 A difficult task-operationalizing ecological success (part 2-implementation) -- 5. Results and Discussion -- 5.1 Synthesis of success factors -- 5.1.1 Justifying the choice of success factors -- 5.1.2 A comprehensive synthesis (which is too unwieldy) -- 5.1.3 A minimal synthesis (which is about right) -- 5.1.4 Which success factors are excluded? -- 5.1.5 Why these success factors should be highly relevant -- 5.1.5.1 Resource system -- 5.1.5.2 Resource units -- 5.1.5.3 Actors -- 5.1.5.4 Governance systems -- 5.1.5.5 External influences -- 5.2 Results for the common-pool resource data -- 5.2.1 Descriptive statistics -- 5.2.2 Correlations -- 5.2.3 Multivariate linear regressions -- 5.2.4 Random forests -- 5.2.5 Neural networks -- 5.2.6 Discussion -- 5.3 Results for the Nepal irrigation institution study data -- 5.3.1 Descriptive statistics -- 5.3.2 Correlations -- 5.3.3 Multivariate linear regressions -- 5.3.4 Random forests -- 5.3.5 Neural networks -- 5.3.6 Discussion -- 5.4 Results for the international forestry resources and institutions data -- 5.4.1 Descriptive statistics -- 5.4.2 Correlations -- 5.4.3 Multivariate linear regressions -- 5.4.4 Random forests -- 5.4.5 Neural networks -- 5.4.6 Discussion -- 5.5 Results for a combined full model -- 5.5.1 Descriptive statistics -- 5.5.2 Correlations -- 5.5.3 Multivariate linear regressions -- 5.5.4 Random forests -- 5.5.5 Neural networks 5.5.6 Discussion -- 5.6 Robustness and sensitivity analyses -- 5.6.1 Common-pool resource data -- 5.6.1.1 Multivariate linear regressions -- 5.6.1.2 Random forests -- 5.6.1.3 Neural networks -- 5.6.2 Nepal irrigation institution study data -- 5.6.2.1 Multivariate linear regressions -- 5.6.2.2 Random forests -- 5.6.2.3 Neural networks -- 5.6.3 International forestry resources and institutions data -- 5.6.3.1 Multivariate linear regressions -- 5.6.3.2 Random forests -- 5.6.3.3 Neural networks -- 6. Discussion and Conclusion -- 6.1 Final assessment -- 6.2 New findings -- 6.3 Summary -- 6.4 Outlook -- 7. Appendix -- References |
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| discipline | Biologie Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau |
| discipline_str_mv | Biologie Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau |
| format | Electronic eBook |
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Introduction -- 1.1 The high importance of natural resources -- 1.1.1 Natural resources in danger of depletion -- 1.1.2 Social-ecological systems -- 1.2 Research question and goals -- 1.2.1 Goals and benefits -- 1.2.2 Gaps and obstacles -- 1.2.3 Hypotheses -- 2. State of Research -- 2.1 What are fundamental biological mechanisms of cooperation? -- 2.1.1 The tragedy of the commons exists for many different species -- 2.1.2 Some possibilities for preventing resource overuse -- 2.2 What drives cooperation in laboratory experiments? -- 2.2.1 Bias and intercultural comparisons -- 2.2.2 Field experiments -- 2.2.3 Behavioral experiments: Conclusion -- 2.3 Common-pool resources -- 2.3.1 Characterization of common-pool resource problems -- 2.3.2 The common-pool problem structure-exemplified -- 2.4 A primer on social-ecological systems -- 2.4.1 Three very different ways to manage common-pool resources -- 2.4.2 Institutional analysis of social-ecological systems by Elinor Ostrom -- 2.4.2.1 Some conceptual clarifications -- 2.4.2.2 A short historical account of different frameworks analyzing social-ecological systems -- 2.4.3 Other related research approaches -- 2.4.3.1 Resilience of social-ecological systems -- 2.4.3.2 (Adaptive) co-management of social-ecological systems -- 2.5 Potential success factors for sustainable management of social-ecological systems -- 2.5.1 Design principles -- 2.5.2 Overview about success factor syntheses -- 2.5.2.1 Synthesis 1 (synthesis of success factors) -- 2.5.2.2 Synthesis 2 (fisheries in Asia) -- 2.5.2.3 Synthesis 3 (meta-analysis forestry worldwide) -- 2.5.2.4 Synthesis 4 (small-scale forest management in Germany) -- 2.5.2.5 Synthesis 5 (meta-analysis of local communities worldwide)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.5.2.6 Synthesis 6 (irrigation systems in India) -- 2.5.2.7 Synthesis 7 (meta-analysis fisheries, worldwide) -- 2.5.2.8 Synthesis 8 (fisheries in Kenya, Tanzania, Madagascar, Indonesia, and Papua-New Guinea) -- 2.5.2.9 Synthesis 9 (nature conservation projects, worldwide) -- 2.5.2.10 Synthesis 10 (synthesis of success factors, social-ecological systems framework) -- 2.5.3 Summary of syntheses for social-ecological systems -- 3. Data -- 3.1 Common-pool resource database -- 3.2 Nepal irrigation institution study database -- 3.3 International forestry resources and institutions database -- 3.4 Comparability of databases -- 3.5 Data preparation -- 3.5.1 Check of raw data -- 3.5.1.1 Check for data correctness-step 1: Data collection -- 3.5.1.2 Verification of the correctness of the data-step 2: Data entry -- 3.5.1.3 Verification of the correctness of the data-step 3: Databases -- 3.5.2 Selection of data -- 3.5.3 Recoding of variables -- 3.5.3.1 Recoding of variables-step 1: Aggregation -- 3.5.3.2 Recoding of variables-step 2: Text variables -- 3.5.3.3 Recoding of variables-step 3: Multiple use of variables -- 3.5.3.4 Recoding of variables-step 4: Imputation -- 3.5.4 Weighting of variables and indicators -- 3.5.4.1 Weighting of the variables and indicators 2: Indicator weighting -- 3.5.4.2 Weighting of variables and indicators-step 2: Weighting of indicators -- 3.5.4.3 Weighting of variables and indicators-step 3: Selection of the Top 3 variables -- 3.5.5 Split of data sets in training and test sets -- 3.5.6 Preparation of the analysis results -- 3.5.7 Summary of methodology and data -- 4. Methods -- 4.1 Introducing the three statistical methods used -- 4.1.1 Multivariate linear regressions -- 4.1.2 Random forests -- 4.1.3 Artificial neural networks -- 4.1.3.1 Construction of artificial neural networks</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.1.3.2 Learning and generalization -- 4.1.3.3 Design -- 4.1.3.4 Extraction of the relevance of factors -- 4.2 Operationalizing the success factors via a new indicator system -- 4.2.1 Why do we need a new indicator system? -- 4.2.2 How to develop and validate an indicator system -- 4.2.3 Overview about the indicators used -- 4.2.4 Assigning variables to indicators -- 4.2.5 A difficult task-operationalizing ecological success (part 1-theory) -- 4.2.6 A difficult task-operationalizing ecological success (part 2-implementation) -- 5. Results and Discussion -- 5.1 Synthesis of success factors -- 5.1.1 Justifying the choice of success factors -- 5.1.2 A comprehensive synthesis (which is too unwieldy) -- 5.1.3 A minimal synthesis (which is about right) -- 5.1.4 Which success factors are excluded? -- 5.1.5 Why these success factors should be highly relevant -- 5.1.5.1 Resource system -- 5.1.5.2 Resource units -- 5.1.5.3 Actors -- 5.1.5.4 Governance systems -- 5.1.5.5 External influences -- 5.2 Results for the common-pool resource data -- 5.2.1 Descriptive statistics -- 5.2.2 Correlations -- 5.2.3 Multivariate linear regressions -- 5.2.4 Random forests -- 5.2.5 Neural networks -- 5.2.6 Discussion -- 5.3 Results for the Nepal irrigation institution study data -- 5.3.1 Descriptive statistics -- 5.3.2 Correlations -- 5.3.3 Multivariate linear regressions -- 5.3.4 Random forests -- 5.3.5 Neural networks -- 5.3.6 Discussion -- 5.4 Results for the international forestry resources and institutions data -- 5.4.1 Descriptive statistics -- 5.4.2 Correlations -- 5.4.3 Multivariate linear regressions -- 5.4.4 Random forests -- 5.4.5 Neural networks -- 5.4.6 Discussion -- 5.5 Results for a combined full model -- 5.5.1 Descriptive statistics -- 5.5.2 Correlations -- 5.5.3 Multivariate linear regressions -- 5.5.4 Random forests -- 5.5.5 Neural networks</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.5.6 Discussion -- 5.6 Robustness and sensitivity analyses -- 5.6.1 Common-pool resource data -- 5.6.1.1 Multivariate linear regressions -- 5.6.1.2 Random forests -- 5.6.1.3 Neural networks -- 5.6.2 Nepal irrigation institution study data -- 5.6.2.1 Multivariate linear regressions -- 5.6.2.2 Random forests -- 5.6.2.3 Neural networks -- 5.6.3 International forestry resources and institutions data -- 5.6.3.1 Multivariate linear regressions -- 5.6.3.2 Random forests -- 5.6.3.3 Neural networks -- 6. Discussion and Conclusion -- 6.1 Final assessment -- 6.2 New findings -- 6.3 Summary -- 6.4 Outlook -- 7. Appendix -- References</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">A comprehensive theoretical synthesis of the various success factors required to successfully and sustainably manage natural resources, Sustainable Governance of Natural Resources offers a quantitative model to predict the success of natural resource management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Natural resources-Management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sustainable development</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Umweltökonomie</subfield><subfield code="0">(DE-588)4061638-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Rohstoffverbrauch</subfield><subfield code="0">(DE-588)4178391-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Kooperatives Spiel</subfield><subfield code="0">(DE-588)4120603-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Nachhaltigkeit</subfield><subfield code="0">(DE-588)4326464-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="6"><subfield code="a">Electronic books</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Rohstoffverbrauch</subfield><subfield code="0">(DE-588)4178391-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Nachhaltigkeit</subfield><subfield code="0">(DE-588)4326464-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Kooperatives Spiel</subfield><subfield code="0">(DE-588)4120603-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Umweltökonomie</subfield><subfield code="0">(DE-588)4061638-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Frey, Ulrich</subfield><subfield code="t">Sustainable Governance of Natural Resources</subfield><subfield code="d">Oxford : Oxford University Press, Incorporated,c2020</subfield><subfield code="z">9780197502211</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034006440</subfield></datafield></record></collection> |
| id | DE-604.BV048631420 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:16:05Z |
| indexdate | 2024-07-10T09:44:31Z |
| institution | BVB |
| isbn | 9780197502228 9780197502211 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034006440 |
| oclc_num | 1190726869 |
| open_access_boolean | |
| physical | 1 Online-Ressource (335 Seiten) |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Oxford University Press, Incorporated |
| record_format | marc |
| spelling | Frey, Ulrich Verfasser aut Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning Oxford Oxford University Press, Incorporated 2020 ©2020 1 Online-Ressource (335 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Sustainable Governance of Natural Resources -- Copyright -- Dedication -- Contents -- Acknowledgments -- 1. Introduction -- 1.1 The high importance of natural resources -- 1.1.1 Natural resources in danger of depletion -- 1.1.2 Social-ecological systems -- 1.2 Research question and goals -- 1.2.1 Goals and benefits -- 1.2.2 Gaps and obstacles -- 1.2.3 Hypotheses -- 2. State of Research -- 2.1 What are fundamental biological mechanisms of cooperation? -- 2.1.1 The tragedy of the commons exists for many different species -- 2.1.2 Some possibilities for preventing resource overuse -- 2.2 What drives cooperation in laboratory experiments? -- 2.2.1 Bias and intercultural comparisons -- 2.2.2 Field experiments -- 2.2.3 Behavioral experiments: Conclusion -- 2.3 Common-pool resources -- 2.3.1 Characterization of common-pool resource problems -- 2.3.2 The common-pool problem structure-exemplified -- 2.4 A primer on social-ecological systems -- 2.4.1 Three very different ways to manage common-pool resources -- 2.4.2 Institutional analysis of social-ecological systems by Elinor Ostrom -- 2.4.2.1 Some conceptual clarifications -- 2.4.2.2 A short historical account of different frameworks analyzing social-ecological systems -- 2.4.3 Other related research approaches -- 2.4.3.1 Resilience of social-ecological systems -- 2.4.3.2 (Adaptive) co-management of social-ecological systems -- 2.5 Potential success factors for sustainable management of social-ecological systems -- 2.5.1 Design principles -- 2.5.2 Overview about success factor syntheses -- 2.5.2.1 Synthesis 1 (synthesis of success factors) -- 2.5.2.2 Synthesis 2 (fisheries in Asia) -- 2.5.2.3 Synthesis 3 (meta-analysis forestry worldwide) -- 2.5.2.4 Synthesis 4 (small-scale forest management in Germany) -- 2.5.2.5 Synthesis 5 (meta-analysis of local communities worldwide) 2.5.2.6 Synthesis 6 (irrigation systems in India) -- 2.5.2.7 Synthesis 7 (meta-analysis fisheries, worldwide) -- 2.5.2.8 Synthesis 8 (fisheries in Kenya, Tanzania, Madagascar, Indonesia, and Papua-New Guinea) -- 2.5.2.9 Synthesis 9 (nature conservation projects, worldwide) -- 2.5.2.10 Synthesis 10 (synthesis of success factors, social-ecological systems framework) -- 2.5.3 Summary of syntheses for social-ecological systems -- 3. Data -- 3.1 Common-pool resource database -- 3.2 Nepal irrigation institution study database -- 3.3 International forestry resources and institutions database -- 3.4 Comparability of databases -- 3.5 Data preparation -- 3.5.1 Check of raw data -- 3.5.1.1 Check for data correctness-step 1: Data collection -- 3.5.1.2 Verification of the correctness of the data-step 2: Data entry -- 3.5.1.3 Verification of the correctness of the data-step 3: Databases -- 3.5.2 Selection of data -- 3.5.3 Recoding of variables -- 3.5.3.1 Recoding of variables-step 1: Aggregation -- 3.5.3.2 Recoding of variables-step 2: Text variables -- 3.5.3.3 Recoding of variables-step 3: Multiple use of variables -- 3.5.3.4 Recoding of variables-step 4: Imputation -- 3.5.4 Weighting of variables and indicators -- 3.5.4.1 Weighting of the variables and indicators 2: Indicator weighting -- 3.5.4.2 Weighting of variables and indicators-step 2: Weighting of indicators -- 3.5.4.3 Weighting of variables and indicators-step 3: Selection of the Top 3 variables -- 3.5.5 Split of data sets in training and test sets -- 3.5.6 Preparation of the analysis results -- 3.5.7 Summary of methodology and data -- 4. Methods -- 4.1 Introducing the three statistical methods used -- 4.1.1 Multivariate linear regressions -- 4.1.2 Random forests -- 4.1.3 Artificial neural networks -- 4.1.3.1 Construction of artificial neural networks 4.1.3.2 Learning and generalization -- 4.1.3.3 Design -- 4.1.3.4 Extraction of the relevance of factors -- 4.2 Operationalizing the success factors via a new indicator system -- 4.2.1 Why do we need a new indicator system? -- 4.2.2 How to develop and validate an indicator system -- 4.2.3 Overview about the indicators used -- 4.2.4 Assigning variables to indicators -- 4.2.5 A difficult task-operationalizing ecological success (part 1-theory) -- 4.2.6 A difficult task-operationalizing ecological success (part 2-implementation) -- 5. Results and Discussion -- 5.1 Synthesis of success factors -- 5.1.1 Justifying the choice of success factors -- 5.1.2 A comprehensive synthesis (which is too unwieldy) -- 5.1.3 A minimal synthesis (which is about right) -- 5.1.4 Which success factors are excluded? -- 5.1.5 Why these success factors should be highly relevant -- 5.1.5.1 Resource system -- 5.1.5.2 Resource units -- 5.1.5.3 Actors -- 5.1.5.4 Governance systems -- 5.1.5.5 External influences -- 5.2 Results for the common-pool resource data -- 5.2.1 Descriptive statistics -- 5.2.2 Correlations -- 5.2.3 Multivariate linear regressions -- 5.2.4 Random forests -- 5.2.5 Neural networks -- 5.2.6 Discussion -- 5.3 Results for the Nepal irrigation institution study data -- 5.3.1 Descriptive statistics -- 5.3.2 Correlations -- 5.3.3 Multivariate linear regressions -- 5.3.4 Random forests -- 5.3.5 Neural networks -- 5.3.6 Discussion -- 5.4 Results for the international forestry resources and institutions data -- 5.4.1 Descriptive statistics -- 5.4.2 Correlations -- 5.4.3 Multivariate linear regressions -- 5.4.4 Random forests -- 5.4.5 Neural networks -- 5.4.6 Discussion -- 5.5 Results for a combined full model -- 5.5.1 Descriptive statistics -- 5.5.2 Correlations -- 5.5.3 Multivariate linear regressions -- 5.5.4 Random forests -- 5.5.5 Neural networks 5.5.6 Discussion -- 5.6 Robustness and sensitivity analyses -- 5.6.1 Common-pool resource data -- 5.6.1.1 Multivariate linear regressions -- 5.6.1.2 Random forests -- 5.6.1.3 Neural networks -- 5.6.2 Nepal irrigation institution study data -- 5.6.2.1 Multivariate linear regressions -- 5.6.2.2 Random forests -- 5.6.2.3 Neural networks -- 5.6.3 International forestry resources and institutions data -- 5.6.3.1 Multivariate linear regressions -- 5.6.3.2 Random forests -- 5.6.3.3 Neural networks -- 6. Discussion and Conclusion -- 6.1 Final assessment -- 6.2 New findings -- 6.3 Summary -- 6.4 Outlook -- 7. Appendix -- References A comprehensive theoretical synthesis of the various success factors required to successfully and sustainably manage natural resources, Sustainable Governance of Natural Resources offers a quantitative model to predict the success of natural resource management Natural resources-Management Sustainable development Umweltökonomie (DE-588)4061638-1 gnd rswk-swf Rohstoffverbrauch (DE-588)4178391-8 gnd rswk-swf Kooperatives Spiel (DE-588)4120603-4 gnd rswk-swf Nachhaltigkeit (DE-588)4326464-5 gnd rswk-swf Electronic books Rohstoffverbrauch (DE-588)4178391-8 s Nachhaltigkeit (DE-588)4326464-5 s Kooperatives Spiel (DE-588)4120603-4 s Umweltökonomie (DE-588)4061638-1 s DE-604 Erscheint auch als Druck-Ausgabe Frey, Ulrich Sustainable Governance of Natural Resources Oxford : Oxford University Press, Incorporated,c2020 9780197502211 |
| spellingShingle | Frey, Ulrich Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning Cover -- Sustainable Governance of Natural Resources -- Copyright -- Dedication -- Contents -- Acknowledgments -- 1. Introduction -- 1.1 The high importance of natural resources -- 1.1.1 Natural resources in danger of depletion -- 1.1.2 Social-ecological systems -- 1.2 Research question and goals -- 1.2.1 Goals and benefits -- 1.2.2 Gaps and obstacles -- 1.2.3 Hypotheses -- 2. State of Research -- 2.1 What are fundamental biological mechanisms of cooperation? -- 2.1.1 The tragedy of the commons exists for many different species -- 2.1.2 Some possibilities for preventing resource overuse -- 2.2 What drives cooperation in laboratory experiments? -- 2.2.1 Bias and intercultural comparisons -- 2.2.2 Field experiments -- 2.2.3 Behavioral experiments: Conclusion -- 2.3 Common-pool resources -- 2.3.1 Characterization of common-pool resource problems -- 2.3.2 The common-pool problem structure-exemplified -- 2.4 A primer on social-ecological systems -- 2.4.1 Three very different ways to manage common-pool resources -- 2.4.2 Institutional analysis of social-ecological systems by Elinor Ostrom -- 2.4.2.1 Some conceptual clarifications -- 2.4.2.2 A short historical account of different frameworks analyzing social-ecological systems -- 2.4.3 Other related research approaches -- 2.4.3.1 Resilience of social-ecological systems -- 2.4.3.2 (Adaptive) co-management of social-ecological systems -- 2.5 Potential success factors for sustainable management of social-ecological systems -- 2.5.1 Design principles -- 2.5.2 Overview about success factor syntheses -- 2.5.2.1 Synthesis 1 (synthesis of success factors) -- 2.5.2.2 Synthesis 2 (fisheries in Asia) -- 2.5.2.3 Synthesis 3 (meta-analysis forestry worldwide) -- 2.5.2.4 Synthesis 4 (small-scale forest management in Germany) -- 2.5.2.5 Synthesis 5 (meta-analysis of local communities worldwide) 2.5.2.6 Synthesis 6 (irrigation systems in India) -- 2.5.2.7 Synthesis 7 (meta-analysis fisheries, worldwide) -- 2.5.2.8 Synthesis 8 (fisheries in Kenya, Tanzania, Madagascar, Indonesia, and Papua-New Guinea) -- 2.5.2.9 Synthesis 9 (nature conservation projects, worldwide) -- 2.5.2.10 Synthesis 10 (synthesis of success factors, social-ecological systems framework) -- 2.5.3 Summary of syntheses for social-ecological systems -- 3. Data -- 3.1 Common-pool resource database -- 3.2 Nepal irrigation institution study database -- 3.3 International forestry resources and institutions database -- 3.4 Comparability of databases -- 3.5 Data preparation -- 3.5.1 Check of raw data -- 3.5.1.1 Check for data correctness-step 1: Data collection -- 3.5.1.2 Verification of the correctness of the data-step 2: Data entry -- 3.5.1.3 Verification of the correctness of the data-step 3: Databases -- 3.5.2 Selection of data -- 3.5.3 Recoding of variables -- 3.5.3.1 Recoding of variables-step 1: Aggregation -- 3.5.3.2 Recoding of variables-step 2: Text variables -- 3.5.3.3 Recoding of variables-step 3: Multiple use of variables -- 3.5.3.4 Recoding of variables-step 4: Imputation -- 3.5.4 Weighting of variables and indicators -- 3.5.4.1 Weighting of the variables and indicators 2: Indicator weighting -- 3.5.4.2 Weighting of variables and indicators-step 2: Weighting of indicators -- 3.5.4.3 Weighting of variables and indicators-step 3: Selection of the Top 3 variables -- 3.5.5 Split of data sets in training and test sets -- 3.5.6 Preparation of the analysis results -- 3.5.7 Summary of methodology and data -- 4. Methods -- 4.1 Introducing the three statistical methods used -- 4.1.1 Multivariate linear regressions -- 4.1.2 Random forests -- 4.1.3 Artificial neural networks -- 4.1.3.1 Construction of artificial neural networks 4.1.3.2 Learning and generalization -- 4.1.3.3 Design -- 4.1.3.4 Extraction of the relevance of factors -- 4.2 Operationalizing the success factors via a new indicator system -- 4.2.1 Why do we need a new indicator system? -- 4.2.2 How to develop and validate an indicator system -- 4.2.3 Overview about the indicators used -- 4.2.4 Assigning variables to indicators -- 4.2.5 A difficult task-operationalizing ecological success (part 1-theory) -- 4.2.6 A difficult task-operationalizing ecological success (part 2-implementation) -- 5. Results and Discussion -- 5.1 Synthesis of success factors -- 5.1.1 Justifying the choice of success factors -- 5.1.2 A comprehensive synthesis (which is too unwieldy) -- 5.1.3 A minimal synthesis (which is about right) -- 5.1.4 Which success factors are excluded? -- 5.1.5 Why these success factors should be highly relevant -- 5.1.5.1 Resource system -- 5.1.5.2 Resource units -- 5.1.5.3 Actors -- 5.1.5.4 Governance systems -- 5.1.5.5 External influences -- 5.2 Results for the common-pool resource data -- 5.2.1 Descriptive statistics -- 5.2.2 Correlations -- 5.2.3 Multivariate linear regressions -- 5.2.4 Random forests -- 5.2.5 Neural networks -- 5.2.6 Discussion -- 5.3 Results for the Nepal irrigation institution study data -- 5.3.1 Descriptive statistics -- 5.3.2 Correlations -- 5.3.3 Multivariate linear regressions -- 5.3.4 Random forests -- 5.3.5 Neural networks -- 5.3.6 Discussion -- 5.4 Results for the international forestry resources and institutions data -- 5.4.1 Descriptive statistics -- 5.4.2 Correlations -- 5.4.3 Multivariate linear regressions -- 5.4.4 Random forests -- 5.4.5 Neural networks -- 5.4.6 Discussion -- 5.5 Results for a combined full model -- 5.5.1 Descriptive statistics -- 5.5.2 Correlations -- 5.5.3 Multivariate linear regressions -- 5.5.4 Random forests -- 5.5.5 Neural networks 5.5.6 Discussion -- 5.6 Robustness and sensitivity analyses -- 5.6.1 Common-pool resource data -- 5.6.1.1 Multivariate linear regressions -- 5.6.1.2 Random forests -- 5.6.1.3 Neural networks -- 5.6.2 Nepal irrigation institution study data -- 5.6.2.1 Multivariate linear regressions -- 5.6.2.2 Random forests -- 5.6.2.3 Neural networks -- 5.6.3 International forestry resources and institutions data -- 5.6.3.1 Multivariate linear regressions -- 5.6.3.2 Random forests -- 5.6.3.3 Neural networks -- 6. Discussion and Conclusion -- 6.1 Final assessment -- 6.2 New findings -- 6.3 Summary -- 6.4 Outlook -- 7. Appendix -- References Natural resources-Management Sustainable development Umweltökonomie (DE-588)4061638-1 gnd Rohstoffverbrauch (DE-588)4178391-8 gnd Kooperatives Spiel (DE-588)4120603-4 gnd Nachhaltigkeit (DE-588)4326464-5 gnd |
| subject_GND | (DE-588)4061638-1 (DE-588)4178391-8 (DE-588)4120603-4 (DE-588)4326464-5 |
| title | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_auth | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_exact_search | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_exact_search_txtP | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_full | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_fullStr | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_full_unstemmed | Sustainable Governance of Natural Resources Uncovering Success Patterns with Machine Learning |
| title_short | Sustainable Governance of Natural Resources |
| title_sort | sustainable governance of natural resources uncovering success patterns with machine learning |
| title_sub | Uncovering Success Patterns with Machine Learning |
| topic | Natural resources-Management Sustainable development Umweltökonomie (DE-588)4061638-1 gnd Rohstoffverbrauch (DE-588)4178391-8 gnd Kooperatives Spiel (DE-588)4120603-4 gnd Nachhaltigkeit (DE-588)4326464-5 gnd |
| topic_facet | Natural resources-Management Sustainable development Umweltökonomie Rohstoffverbrauch Kooperatives Spiel Nachhaltigkeit |
| work_keys_str_mv | AT freyulrich sustainablegovernanceofnaturalresourcesuncoveringsuccesspatternswithmachinelearning |