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High-quality data is crucial in evaluations. The IEU invests in gathering both quantitative and qualitative data from various sources through its DataLab. The IEU DataLab provides high-quality data to support the IEU in its rigorous, evidence-based evaluations and continually develops and maintains databases through extracting and updating quantitative and qualitative information from the GCF as well as external sources. 

 

The IEU DataLab has the following objectives.
  • Lead data-related work in rigorous, evidence-based evaluations;
  • Gather, manage, analyze, and share portfolio data; 
  • Utilizing geospatial techniques and developing spatially aware approaches in evaluation;
  • Carry out cutting-edge research in the field of climate change evaluation;
  • Support impact evaluation data generated through the IEU's Learning-Oriented Real-Time Impact Assessment (LORTA) program.  

What types of data does the IEU Datalab collect?
The IEU extracts raw data from a variety of sources. These include both internal data from within GCF and external datasets from other organizations, such as:
  • Funding proposals, concept notes, readiness proposals, project preparation proposals
  • Country programme, entity working programmes, NAPs, NDCs
  • Annual performance reports, funded activities agreement, term sheets
  • Secretariat and iTAP reviews of funding proposals
  • Gender assessment and Gender action plans


Financial instruments deployed in the GCF portfolio
Performance Review of the GCF (FPR), 2019

*Hover your cursor over the chart to explore the data
 


DataLab is also relying on the existing credible and reliable publicly available data sources in the climate change space. The following are some of the sources used in the IEU evaluations. 
  • ND-GAIN Index (University of Notre Dame)
  • NDC (NDC Partnerships)
  • Climate Fund Update (Heinrich Boll Stiftung)
  • Debt ratio (IMF)
  • Migration (United Nations Population Division)
  • Remittance Inflow and Outflow (World Bank)
  • Global Landscape of Climate Finance (Climate Policy Initiative)

After the data has been processed and systematically reviewed for relevance, bias, completeness and consistency, the IEU uses the resulting databases and datasets for updates, analyses, and sharing. The IEU DataLab produces and replicates data, develops protocols and meta-data, and analyses data.
 
Quantitative data are measures of values, typically numbers. They are essential and easy to manipulate for statistical analyses. The IEU DataLab provides insightful analyses by exploring the relationships between quantitative variables, identifying relevant trends, and creating data visualizations from internal and external sources.
 
Qualitative data can complement quantitative data by addressing the “why” behind the insights and findings generated from quantitative analyses. The DataLab categorizes large volumes of qualitative information, such as interviews and mission reports, into patterns and themes suitable for evaluations and assessments. Qualitative data can further strengthen the rationale behind the trends emerging from quantitative analyses. 



Median processing time of RPSP grants by year of initial submission
Readiness Evaluation (RPSP), 2018

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The IEU’s LORTA program uses a mixed-methods approach to measure the quality of implementation and impact in GCF projects. The DataLab helps to produce and analyze baseline, midline, and endline data to measure causal changes attributable to GCF projects. LORTA’s real-time framework and qualitative data system aim to help project teams to measure progress at the early stages of implementation.
 
Geospatial data (GIS data) is also collected and analyzed by the IEU DataLab. GIS has the potential to strengthen and improve evaluations by allowing the IEU to analyze data spatially and immediately collect and validate information from the ground and the impact of projects beyond the information that accredited entities give to the GCF. The DataLab conducts geospatial analyses on the GCF’s projects at the country and sub-country levels. The analyses examine environmental and socio-economic factors relevant to the GCF’s objectives and employ a broad spectrum of methodologies such as hotspot analysis, a spatial analysis and mapping technique, inverse distance weighting interpolation which estimates unknown values with specifying search distance, closest points, power setting and barriers and spatial autocorrelation. 



Overall it takes 1364 days for the entities to get the first GCF dollar.
Independent Assessment of the GCF's Country Ownership Approach, 2019


Source: Secretariat (Accreditation team), as of March 12th, 2020, analyzed by the IEU DataLab.


Precipitation deviation from long-term trend (April-June, 2017 in comparison to 1984-2014)
Performance Review of the GCF (FPR), 2019


Source: Willmott, C. J. and K.Matsuura (2001), Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1900 - 2017)


Most GCF Funds are waiting for post-approval process (legal effectiveness)
Performance Review of the GCF (FPR), 2019

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