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An indirect estimation approach for disaggregating SDG indicators using survey data

Case study based on SDG Indicator 2.1.2









FAO. 2022­. An indirect estimation approach for disaggregating SDG indicators using survey data - Case study based on SDG Indicator 2.1.2. Rome. 





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    The Sustainable Development Goals (SDG) indicator framework represents a major challenge and a unique opportunity for the advancement of the global statistical system, both in terms of methodological development and governance. Over the past three years, the Inter-Agency and Expert Group on SDG indicators (IAEG-SDG) has gradually developed a number of documents providing criteria and guidelines for regulating data flows between countries and custodian agencies needed to inform the global SDG reporting process. The validation of methods and data for SDG indicators, while apparently consisting of two completely separate matters, have been closely linked in the SDG process. When validating country data, National Statistics Offices (NSOs) are effectively also certifying the specific methodology used by the custodian agency for the compilation of the indicator, in particular the data source used and the adjustments made to harmonize national definitions and classifications. This article highlights some of the main challenges in the practical implementation of the guidelines on data flows, identifies areas in need of further guidance from the IAEG-SDG and provides some proposals aimed at improving the global SDG reporting process.
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    Integrating surveys with geospatial data through small area estimation to disaggregate SDG indicators at subnational level
    Case study on SDG Indicators 2.3.1 and 2.3.2
    2023
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    The present technical report illustrates a case study on the adoption of small area estimation techniques to produce granular sub-national estimates of SDG Indicators 2.3.1 and 2.3.2, by integrating survey microdata with auxiliary information retrieved from various trustworthy geospatial information systems. The technical report provides practical guidance to national statistical offices and other institutions wanting to implement small area estimation techniques on SDG Indicators 2.3.1 and 2.3.2 or similar indicators based on surveys microdata.
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    Article
    Using Standardized Time Series Land Cover Maps to Monitor the SDG Indicator “Mountain Green Cover Index” and Assess Its Sensitivity to Vegetation Dynamics 2021
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    SDG indicators are instrumental for the monitoring of countries’ progress towards sustainability goals as set out by the UN Agenda 2030. Earth observation data can facilitate such monitoring and reporting processes, thanks to their intrinsic characteristics of spatial extensive coverage, high spatial, spectral, and temporal resolution, and low costs. EO data can hence be used to regularly assess specific SDG indicators over very large areas, and to extract statistics at any given subnational level. The Food and Agriculture Organization of the United Nations (FAO) is the custodian agency for 21 out of the 231 SDG indicators. To fulfill this responsibility, it has invested in EO data from the outset, among others, by developing a new SDG indicator directly monitored with EO data: SDG indicator 15.4.2, the Mountain Green Cover Index (MGCI), for which the FAO produced initial baseline estimates in 2017. The MGCI is a very important indicator, allowing the monitoring of the health of mountain ecosystems. The initial FAO methodology involved visual interpretation of land cover types at sample locations defined by a global regular grid that was superimposed on satellite images. While this solution allowed the FAO to establish a first global MGCI baseline and produce MGCI estimates for the large majority of countries, several reporting countries raised concerns regarding: (i) the objectivity of the method; (ii) the difficulty in validating FAO estimates; (iii) the limited involvement of countries in estimating the MGCI; and (iv) the indicator’s limited capacity to account for forest encroachment due to agricultural expansion as well as the undesired expansion of green vegetation in mountain areas, resulting from the effect of global warming. To address such concerns, in 2020, the FAO introduced a new data collection approach that directly measures the indicator through a quantitative analysis of standardized land cover maps (European Space Agency Climate Change Initiative Land Cover maps—ESA CCI-LC). In so doing, this new approach addresses the first three of the four issues, while it also provides stronger grounds to develop a solution for the fourth issue—a solution that the FAO plans to present to the Interagency and Expert Group on SDG Indicators (IAEG-SDG) at its autumn 2021 session.

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