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As part of the evidence-based lessons learning for the Midterm Review of the Strategic Plan for 2018-2020, UNDP conducted a performance factor analysis (PFA), an advanced statistical analysis to identify key factors and approaches that contribute to the achievement of higher-level development results. (...) All analyses are performed using STATA software version 15.1. Performance Factor Analysis - Findings A number of independent variables were found to be statistically significantly[footnoteRef:2] associated with the dependent variable of country performance over two years. [2: “Statistical significance” suggests that the changes in performance are indeed associated with changes in these factors, not due to randomness. (...) Conclusion UNDP found the PFA a valuable tool to help the organisation identify key factors that contribute to the attainment of higher-level development results.
Language:English
Score: 820381.5 - https://www.undp.org/sites/g/f...ession/dp2020-8_Annex%202.docx
Data Source: un
Land Water Carbon link FIGURE 13 Relative contributions of the main food groups to overall food loss and waste and their carbon, blue-water and land footprints Cereals and pulses Fruits and vegetables Roots, tubers and oil-bearing crops Meat and animal products Note: The environmental footprints are calculated by multiplying the amount of food lost and wasted by its environmental impact factors. (...) The carbon impact factor expresses tonnes of CO2 equivalent emitted, the land impact factor indicates hectares of land used, and the blue-water impact factor indicates cubic metres of water used, all per tonne of food lost or wasted. (...) The estimations of food loss and waste differ from the ones presented in Figure 4 with respect to the inclusion of the retail level, the share of food loss and waste being measured in terms of quantity (rather than economic value), and the use of loss and waste data for only those commodities for which an impact factor was available. Thus, food products that do not belong to any of the groups included in the figure (e.g. coffee beans) are excluded from the graph due to the lack of data for impact factors, despite contributing around 20 percent to food loss and waste.
Language:English
Score: 819516 - https://www.fao.org/state-of-food-agriculture/2019/en/
Data Source: un
Land Water Carbon link FIGURE 13 Relative contributions of the main food groups to overall food loss and waste and their carbon, blue-water and land footprints Cereals and pulses Fruits and vegetables Roots, tubers and oil-bearing crops Meat and animal products Note: The environmental footprints are calculated by multiplying the amount of food lost and wasted by its environmental impact factors. (...) The carbon impact factor expresses tonnes of CO2 equivalent emitted, the land impact factor indicates hectares of land used, and the blue-water impact factor indicates cubic metres of water used, all per tonne of food lost or wasted. (...) The estimations of food loss and waste differ from the ones presented in Figure 4 with respect to the inclusion of the retail level, the share of food loss and waste being measured in terms of quantity (rather than economic value), and the use of loss and waste data for only those commodities for which an impact factor was available. Thus, food products that do not belong to any of the groups included in the figure (e.g. coffee beans) are excluded from the graph due to the lack of data for impact factors, despite contributing around 20 percent to food loss and waste.
Language:English
Score: 819516 - https://www.fao.org/state-of-food-agriculture/2019/fr_blank
Data Source: un
Land Water Carbon link FIGURE 13 Relative contributions of the main food groups to overall food loss and waste and their carbon, blue-water and land footprints Cereals and pulses Fruits and vegetables Roots, tubers and oil-bearing crops Meat and animal products Note: The environmental footprints are calculated by multiplying the amount of food lost and wasted by its environmental impact factors. (...) The carbon impact factor expresses tonnes of CO2 equivalent emitted, the land impact factor indicates hectares of land used, and the blue-water impact factor indicates cubic metres of water used, all per tonne of food lost or wasted. (...) The estimations of food loss and waste differ from the ones presented in Figure 4 with respect to the inclusion of the retail level, the share of food loss and waste being measured in terms of quantity (rather than economic value), and the use of loss and waste data for only those commodities for which an impact factor was available. Thus, food products that do not belong to any of the groups included in the figure (e.g. coffee beans) are excluded from the graph due to the lack of data for impact factors, despite contributing around 20 percent to food loss and waste.
Language:English
Score: 819516 - https://www.fao.org/state-of-food-agriculture/2019/en
Data Source: un
The S4x4 values are the scaling factors from Clause 9.5.2, multiplied by 4 to simplify the description in the ABT section. Since QP factors below QPnew = = 12 induce a violation of the 16 bit constraint for transform coefficients of the 8x8 blocks, ABT is not used below QPnew = 12. Table 14-1 – ABT scaling mantissa values k S8x8 S8x4,4x8 S4x4 0 15 9 11 40 64 52 1 17 10 12 44 72 56 2 19 11 14 52 80 64 3 22 12 16 56 92 72 4 24 14 17 64 100 80 5 27 15 20 72 116 92 The application of these new scaling factors results in very minor changes of the coding performance with a slight improvement when using the new scaling factors.
Language:English
Score: 819512.3 - https://www.itu.int/wftp3/av-a...002_07_Klagenfurt/JVT-D053.doc
Data Source: un
Knowledge about the high performing countries is also important to inform the development of related goals and measures of progress, including for the post-2015 development agenda. To contribute to ongoing efforts to understand the factors that influence reductions in preventable maternal and child mortality, the ‘Success Factors’ studies, a three-year collaborative effort, seeks to answer two key questions: - What factors statistically distinguish high-performing countries from countries that did not perform as well in reducing maternal and child mortality, given similar levels of economic development? (...) Objectives of the country multi-stakeholder review: • To review and finalise the country policy analysis “Success Factors in Women’s and Children’s Health – Mapping Pathways to Progress: [Country]” • To validate the draft findings for the overall study and to propose recommendations for the post-2015 development agenda. • To contribute to a publication on Success Factors for Women’s and Children’s Health, to be launched at the Partner’s forum in June 2014. 3. (...) (review of DHS, MICS and other population- based surveys; review of international data sources (WHO, UNICEF, World Bank); review of peer reviewed journal studies and articles – review has been done and articles are available). • Review success factors in major health sector and multi-sector areas; ensure that data support the inclusion of these factors with an emphasis on establishing how selected factors contributed to improved intervention coverage and mortality decline; add additional success factors if necessary; obtain additional data to support existing or additional factors as needed (consult with staff involved with the first draft of the analysis; arrange one-on-one meetings with key stakeholders in the MOH, development partners and NGOs to review content).
Language:English
Score: 818363.1 - https://www.who.int/pmnch/know...ons/success_factors_review.pdf
Data Source: un
Finally, increases in single factor-productivity – with labour productivity providing the most popular example and also the one included in the SIDI – give a first hint to the contribution of technological change to output growth. More importantly, however, attempts can be made to single out the technology portion of growth enhancement by determining so-called total factor-productivity (TFP) growth. The method of growth accounting normally used for this purpose relates output growth to the growth of major factor inputs simultaneously in order to filter out the contribution made by technological change and related developments. (...) The last one of these three labels proves particularly useful for examining a ‘mushrooms pattern’ of the sources of growth, since it yields convenient conditions for adding up the contributions of the various industries on the one hand and points out the fact that in addition to a most important technological factor a host of other things are likely to be involved in increases of total factor productivity.
Language:English
Score: 818303.1 - https://www.unido.org/sites/de...iles/2006-10/23idbevent2_0.pdf
Data Source: un
Market development in selected countries III. FACTORS AFFECTING DEMAND AND SUPPLY A. Impact of demand side factors on the global tea economy B. Impact of supply side factors affecting the global tea economy C. Factors driving the growth of the smallholder tea sub-sector IV. (...) Document CCP:TE 14/2 analyses the contribution of global macroeconomic factors to changes in the international tea prices and discusses the implications for consumption, using retail prices, and tea production. 6.
Language:English
Score: 817979.9 - https://www.fao.org/fileadmin/...tings/IGGtea21/14-1-Agenda.pdf
Data Source: un
The extent to which these factors contributed to changes in tea prices and their impact on the tea market is the subject of this document. 1 Gilbert, C. (...) This supported the claim that common demand-side factors such as per capita GDP and USD exchange rates, contributed to price changes in tea. (...) However, the analysis showed the need to look beyond those factors inherent in the tea market itself in order to isolate the full range of factors contributing to price movements.
Language:English
Score: 817797.2 - https://www.fao.org/fileadmin/...tea21/14-2-ImpactMacro__2_.pdf
Data Source: un
SPECTRUM UTILIZATION EVALUATION SPECTRUM UTILIZATION EVALUATION Presented by : Gp 03 ITU-R SM.1046 SPECTRUM UTILISATION FACTOR DEPENDS ON BANDWITH, SPACE , TIME U=B*S*T B-bandwidth S-space T-time 1. (...) (divide areas) Differentiate which systems depends similar factors divide factor related groups for all services. (...) In use SM results and before use SM formulating a close loop SM Prepare KPI for specific area for all similar systems (SUE) Evaluate existing assigned systems Check and analyze its factors B,S,T Make reassignment decision Make assumption before make assumption Make reassignment And whether the in-use SM results can contribute to the before –use SM in a proper way, formulating a close loop management.
Language:English
Score: 816562.4 - https://www.itu.int/en/ITU-D/R...B/Presentations/Group%203.pptx
Data Source: un