SE4All excom global tracking preview

Editor's Notes

• #9: Non solid-fuels include (i) liquid fuels (e.g., kerosene, ethanol or other biofuels), (ii) gaseous fuels (e.g. natural gas, liquefied petroleum gas (LPG), biogas), and (iii) electricity. Solid fuels include both (i) traditional biomass (e.g. wood, coal, charcoal, agricultural residues, forestry residues, dung, etc), and (ii) processed biomass (e.g. pellets, briquettes, etc.).Electrified population rose by 200 million over population and non-solid fuel using population just kept pace with population.Global electrification rate increased from 74 to 83 percent and access to non-solid fuels rose from 47 to 59 percent, driven largely by increase in rural access rateMost dramatic improvement was in East Asia and South Asia regions.
• #10: Population with access to electricity rose more than twice in urban areasPopulation with access to non-solid fuels rose about four times in urban areasSouth Asia and East Asia have registered dramatic growth in population with access to modern energy services
• #14: Access to energy should be measured as a continuum of improvement Addresses deficiencies in current definition such as quality of services, affordability, informalityHousehold surveys needs to be expanded to capture more detailed information and piloting of questionnaires and consensus building
• #18: Hydro contributes to over 80% of RE electricity production
• #19: RE consumption grew from 40 EJ in 1990 to 59 EJ in 2010, this increase is equivalent to what India and Malaysia combined consumed in 2010Today’s RE consumption is equivalent to total final energy consumption of a country like the US or ChinaMost of the incremental increase in consumption came from: tradbiomass –6.6 EJ, Modern – 3.8 Ej, hydro 3.7 EJ, Biofules – 2.2 Ej, wind, solar and others – 2.77 EJ (total is 19EJ)TFEC grew at 1.5%, RE grew at 2.0 %
• #28: Regional Definitions:NAm - Northern AmericaEU - EuropeEE - Eastern EuropeCCA - Caucasian and Central AsiaWA - Western Asia (Middle East)EA - Eastern AsiaSEA - South Eastern AsiaSA - Southern AsiaOceania - OceaniaLAC - Latin America and CaribbeanNAf - Northern AfricaSSA - Sub-Saharan Africa
• #29: Low and lower middle income countries show uniformly levels of energy consumption per capita that are uniformly well below the global average. However, they vary hugely in energy intensity spanning the full range from the lowest to highest energy intensity ranges observed globally High income countries, on the other hand, show uniformly low levels of energy intensity, but vary hugely in their energy consumption per capitaThe upper middle income countries, by contrast, tend to present either both high energy intensity and consumption per capita as in Iran and a number of CIS countries, or both low energy intensity and consumption per capita, as in Turkey and a number of Latin American countries.
• #30: Ukraine is the only country that appears on both lists
• #33: The first chart plots the compound annual growth rate of energy intensity during the period 1990-2010 against initial energy intensity in 1990, and then for comparison purposes against final energy intensity in 2010. The anticipated negative relationship between the starting point and the annual rate of change is clearly evident in the chart. The country that most clearly stands out is China that started in 1990 with one of the highest levels of energy intensity among the top 20 energy consumers, and despite the huge expansion in its industrial sector that took place over the same period, experienced the steepest decline in energy intensity. Indeed, by 2010, China had undergone a major reduction in energy intensity reaching a level of energy comparable to that of other large middle income emerging economies.
• #34: It should be reminded however that China’s energy intensity went down from 30.5 in 1990 to 11.7 in 2010, while US’s intensity went down from 10.0 to 7.1 (MJ/$2005) and Europe’s from 6.5 to 5.0 (and Japan from 5.6 to 5.3) So China started from really high levels and still have not reached the best existing practices (Japan, UK, Germany, ect).
• #45: The primary source of data that have been used to develop the model includes household survey (DHS, LSMS, MICS and WHS), national censuses and other nationally developed and implemented surveys, covering years 1990-2010. A total of 212 countries have been grouped in 10 regions following the UN regional classification. Among them, 40 countries do not have any data point in the selected time period.  A mixed model has been preferred to estimate values for countries with at least one data point. Firstly, we used a natural cubic spline transformation cantered in 2000, the median date of the surveys data collected, and we specified 5 knots over the entire time period. Therefore, data has been converted in logit function as the outcome variable of interest is a percentage of people with access over total population.  The mixed model includes fixed effects for the time variable and the regional aggregation and it defines hierarchical random effects by regions and country and for time at country level. The covariance model was chosen to be unstructured.  Mixed models contain both fixed effects and random effects. The fixed effects are analogous to standard regression coefficients and are estimated directly. The random effects are summarized according to their estimated variances and covariances. A fitted option has been used to predict the fixed portion plus contributions based on predicted random effects in countries with at least one data point. Values in countries without any data point are estimated by using the linear predictor for the fixed portion of the model based on the regional average value.