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Investigating key uncertainties across global and national decarbonisation pathways to 2050; a novel clustering and classification approach
Ellie Smallwood
University College London
Research output
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Thesis
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Master's Thesis
Overview
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Dive into the research topics of 'Investigating key uncertainties across global and national decarbonisation pathways to 2050; a novel clustering and classification approach'. Together they form a unique fingerprint.
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Engineering
Climate Change
100%
Energy Engineering
100%
Input Parameter
100%
Model Structure
100%
Clustering Algorithm
100%
Attainment
100%
Energy Transition
100%
Model Input
100%
Classification Algorithm
100%
Random Forest
100%
Predictive Ability
100%
Learning System
100%
Climatic Change
100%
Earth and Planetary Sciences
Uncertainty Analysis
100%
Clustering
100%
Climate Change
50%
Machine Learning
50%
Net Zero
50%
Nutrition Policy
50%
Biochemistry, Genetics and Molecular Biology
Mental Capacity
100%
Random Forest
100%
Classification Algorithm
100%
Economics, Econometrics and Finance
Decarbonization
100%
Macroeconomics
33%
Energy Transition
33%
Net Zero
33%
Nutrition Policy
33%
Machine Learning
33%