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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2018 | Supplement | Buchkapitel

4. Assessing the Effectiveness of Climate Finance: Composite Indicators and Quantile Regression

verfasst von : Antonio A. Romano, Giuseppe Scandurra, Alfonso Carfora, Monica Ronghi

Erschienen in: Climate Finance as an Instrument to Promote the Green Growth in Developing Countries

Verlag: Springer International Publishing

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Abstract

In this chapter we briefly explain methods proposed to assess the effectiveness of the climate funds to reduce the greenhouse gas emissions.
Greenhouse gas emissions are constituted by Carbon Dioxide (CO2), Methane (CH4), Nitrous oxide (N2O) and Chlorofluorocarbons (CFCs), hydro chlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6), together called F-gases.
Considering the multidimensional concept and the complexity of the topic we first explain the composite indicator as useful tools to summarize in one indicator the individual components of the greenhouse gas emissions and to compare performances of the countries. Second, we describe the quantile regression, focusing on the theory and possible applications in research fields of interest. Quantile regression, in fact, is one of the tools used to assess the effects of economic policies on some of the target variables.

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Vorheriges Kapitel Climate Finance
Nächstes Kapitel Empirical Study of Climate Finance
Fußnoten
1
Furthermore, composite indices have been criticized because, in a way, they re-introduce unidimensionality. For the pros and cons of using CIs see, e.g., Nardo et al. (2004) and OECD (2008).
 
2
There is a wide range of normalization methods (see, e.g., OECD 2008) and the choice depends on the type of data and on weighting and aggregation (Hudrilikova 2013).
 
3
Extreme values (outliers) are identified but not adjusted for, at least not in this first version of the composite. Nor has it been considered necessary at this stage to make any non-linear transformations of any of the underlying component indicators.
 
4
PCA is usually employed in explanatory data analysis frameworks as well as for developing predictive models and a wide range of applications concerned with economic, social and environmental aspects.
 
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Metadaten
Titel
Assessing the Effectiveness of Climate Finance: Composite Indicators and Quantile Regression
verfasst von
Antonio A. Romano
Giuseppe Scandurra
Alfonso Carfora
Monica Ronghi
Copyright-Jahr
2018
Buch
Climate Finance as an Instrument to Promote the Green Growth in Developing Countries

Print ISBN: 978-3-319-60710-8

Electronic ISBN: 978-3-319-60711-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-60711-5_4