2015/43 95787 k nKonw A A weldegdeg e ol n oNtoet e s eSrei r e ise s f ofro r p r&a c t hteh e nEenregryg y Etx itcrea c t i v e s G l o b a l P r a c t i c e The bottom line Integrating Climate Model Data into Power System Planning When pondering long-term investments in power systems, has already been detected in some parts of the world. The average choices about the mix of Why is this issue important? wind speed in China deteriorated by 25 percent between 1969 and renewables and their geographic Climate model outputs can enhance our 2000, according to Xu and others (2006). There has been a 40 percent distribution must take into understanding of the long-term variability of reduction in surface wind speed in India over the past four decades account seasonal, multiyear, renewable resources, an essential component of (Padmakumari, Jaswal, and Goswami 2013). and multidecade variability, Good-quality data generated by climate models—both historical information derivable from data power system planning and projected over decades—are available for all countries at little generated by a suite of climate Significant multiyear and multidecade variations in intermittent or no cost. Such data can and should form part of power system models that are available renewable resources hold major implications for power system planning, complementing more detailed, but expensive, renewable at almost no cost. To date, investments. Hydropower planners in New Zealand, Brazil, Norway, energy resource mapping and actual observations and measure- policy makers, planners, and and elsewhere know this from long experience. They have been ments of wind, solar, and hydro power. To date, however, policy investors in renewable-energy using extensive hydrology data for many years to represent hydro- makers, planners, and investors in renewable-energy fields have fields have generally not made logical risks in their planning (see for instance, Meridian Energy 2011). generally not used climate model data, favoring other forms of data extensive use of such data. The variability is even more pronounced for solar and wind (Hoste, and analytics, including site-specific measurements and renewable Climate modelers and power Dvorak, and Jacobson 2010). Often, however, policy-driven invest- resource maps. system planners should work ments barely consider such risks. Although the potential effects of The universe of such data includes outputs from a range of together so that the outputs and long-term variability were flagged a decade ago (see, for example, climate-analysis models such as global circulation models (GCM), scenarios from climate models IEA 2005), efforts are still largely focused on fine-tuning short-term which can run long-term climate scenarios and generate simulated can be interpreted carefully and wind and solar forecasting systems. historical data to complement or supplant missing or low-quality used in power system planning. Climate model data are particularly suited for the assessment original data. The data from most such models—including the of longer-term variability. A good grasp of seasonal, multiyear, and experimental CMIP5 (Coupled Model Inter-Comparison Project multidecade trends is essential in assessing the economic merits of version 5, http://cmip-pcmdi.llnl.gov/cmip5/) used in the Fourth and Debabrata investments in renewable resources and the extent to which such Fifth Assessment Reports of the Intergovernmental Panel on Climate Chattopadhyay resources can complement one other or may need to be backed up Change (Meehl and Stocker 2007)—are available for free. is a senior energy specialist in the World by further investments in nonrenewable sources. For instance, plan- That these datasets have not yet been put to use in power Bank’s Energy and ners of hydro-dominated systems have learned to use risk-based system planning is surprising given their potential value and their Extractives Global Practice. criteria such as so-called 1-in-50-year drought coverage to deal with applications in related areas. Data from climate modeling exercises Rhonda L. Jordan the risk posed by extremely dry years. have made important contributions to infrastructure planning since is an energy specialist That climate models can provide scenarios over several decades the 1990s—most notably, in the area of urban planning (Carmin, in the same practice. makes them equally applicable to wind and solar planning. For Nadkarni, and Rhie 2012; Füssel 2007), where the increased fre- example, a significant deterioration in the quality of wind resources quency of natural hazards, temperature extremes, changes in rainfall 2 I n t e g r a ti n g C l i m a t e M o d e l D a t a i n t o P o w e r S y st e m P l a n n i n g patterns, and the rise in sea levels are routinely analyzed through and fuel choices, have been studied (see, for example, Mansur, down-scaled climate models despite data limitations. In urban Mendelsohn, and Morrison 2007). The same cannot be said about planning, climate model data are usefully applied even in relatively supply-side analysis of generation and resource availability. Yet the coarse form—that is, at resolutions of 50 km x 50 km. Low spatial variability of wind, solar, and hydro resources in different times- resolution implies examination of average climate conditions (such cales—over the years, seasonal, daily—matters a great deal for as temperature, wind speed, solar radiation, and precipitation) over a power systems. Climate data help us understand all three types of “That climate model significant area (of 2,500 km2 in this case), with gaps in accuracy for variability, particularly the first two. datasets have not yet been specific locations within that area. Renewable resource maps have already proved their use in put to use in power system understanding variability, though not for the long time horizons on planning is surprising given What is the key challenge? which climate models operate. The Renewable Resource Data Center their potential value and at the NREL (http://www.nrel.gov/rredc/) and the Global Atlas of the Attention has focused on renewable resource International Renewable Energy Agency (IRENA, http://globalatlas. their applications in related maps, to the neglect of longer-term data generated irena.org/) are two well-known repositories of such maps and data, areas.” by climate models the production and generation of which are co-funded by the Energy Sector Management Assistance Program (ESMAP), a global, multido- Despite the clear utility of understanding the variability of renewable nor technical assistance trust fund administered by the World Bank resources over the longer term, the models and data that can and cosponsored by 13 official bilateral donors (http://www.esmap. deliver that understanding remain underexploited in power system org/RE_Mapping). Regional units of the World Bank have undertaken planning and operation (IRENA 2013). Studies from the U.S. National comprehensive resource mapping and geospatial planning under Renewable Energy Laboratory (NREL) and other research institutions ESMAP-funded projects. Other major data sources include the U.S. integrate some form of meteorological models, but their datasets are National Aeronautics and Space Administration, the Department of not long and rich enough to plot and predict the impact of climate Wind Energy at the Technical University of Denmark, the German variability over a term that is long enough to be optimally useful for Aerospace Center (DLR), and Spain’s National Renewable Energy planning investments in energy assets. For example, the NREL’s 2010 Center (CENER). study on integrating wind and solar energy into the power mix in the Donor agencies, including the World Bank, have been actively western United States (GE Energy 2010) used high-resolution data promoting the compilation of such maps in developing nations. from a numerical weather prediction model called 3TIER data but Combined with direct measurements and simulated data, the maps over a period of just three years (2004–06).1 have been useful in shaping renewable-energy investments and in Climate modeling data can be useful to power system planners analyzing their impacts on power system operations. not only because the climate exerts direct effects on demand for However, each resource-mapping exercise is expensive, and power (for example, the rising frequency of high-temperature days maps, like actual observations, are limited because they are either and other extreme weather events affect peak demand and electric- static or do not cover enough time to capture variability over periods ity consumption generally), but also because such data can provide of years. Integrating renewable-energy data into power system plan- rich information on the long-term availability of wind and hydro ning requires that all aspects of variability be taken into consideration. resources, information that is crucial for planning. To ensure reliable supplies of power, planning must take into account The effect of climate change on demand, as evidenced by the availability or unavailability of a given resource over a given period climate modeling data, and the implications of change for generation of time so that back-up generation capacity or other alternatives 1 (such as storage and demand-side responses) can be put in place. The study’s inattention to variability beyond an annual time horizon has been typical, but wind and solar forecasting tools using real-time data and sophisticated analytical models such as 3TIER (or the Australian Bureau of Meteorology’s ACCESS model, http://www.bom.gov.au/ australia/charts/about/about_access.shtml) are in increasingly wide use. 3 I n t e g r a ti n g C l i m a t e M o d e l D a t a i n t o P o w e r S y st e m P l a n n i n g What’s the solution? Box 1. Data from the following GCMs are freely available Accurate power system planning requires an integrated solution • ECHAM5 (Germany, Roeckner and others 2003) Even though the GCMs do not yet meet all of the needs of long-term • GFDL’s CM2 Global Coupled Climate Models (United States, Delworth and others 2007) “Integrating renewable- power system planning, they can already enhance planning—and at a very low cost (box 1). Planners can afford to err to some extent on • Hadley Centre Global Environment Model Version 1 (United energy data into power Kingdom, Hadley Center 2006) resolution with respect to site-specific resources (and can compen- system planning requires • CSIRO Mk 3.5 (Australia, Gordon and others 2002) sate for the GCMs’ low resolution through other means, as discussed), that all aspects of variability but they cannot afford to ignore the longer-term variability that GCMs • K-1 Coupled GCM (MIROC) (Japan, Hasumi and Emori 2004). be taken into consideration. track and reveal. A reassessment by Lawrence Berkeley National Applicable datasets are available for several decades and forecast periods at a granularity of 4–6 hour blocks within each day and at To ensure reliable supplies Laboratory of wind energy potential in China and India increased ear- resolutions of up to 10 km x 10 km. They are generally adequate for lier estimates by a massive amount. The 2012 study for India (Phadke, analyses at the country and regional levels but not for site-specific of power, planning must Bharvirkar, and Khangura 2012) raised the original estimate of 48 GW wind and solar analysis. With respect to long-term power system take into account the that had been prepared by the Indian government to more than 2,000 planning, which typically extends over 10–50 years, a relatively coarse dataset may be sufficient to augment short-term data of finer availability or unavailability GW (a 42-fold increase!) based on a limited set of actual observations. resolution. Such analysis is typically used to provide broad guidance of a given resource But the Berkeley analysis did not consider the interannual variability on factors such as fuel mix, prices, and volume of investment, rather of wind data—a serious deficiency because a significant part of the than for planning site-specific projects. over a given period of proposed development would take place in the southern states of The authors of this note are compiling a sample set of climate model time so that back-up India (including Tamil Nadu and Karnataka) that exhibit significant data of potential use in power system planning. Using the World Bank’s Spatial Agent mobile application (https://itunes.apple.com/us/app/ generation capacity or seasonal and interannual variability, as discussed further on. spatial-agent/id890565166?mt=8) they aim to ensure that this data will other alternatives (such as Debate over the degree of accuracy needed to capture the be readily available. variability of wind and solar resources helps explain why the long term storage and demand-side has received relatively little attention. IRENA’s Global Atlas project responses) can be put in proposal, for example, noted that the wind energy resource was place.” underestimated because “a large part of the wind resource [is] not To illustrate the potential application of GCM data, we draw from being captured in the analysis” for lack of data on small-scale variabil- a recent research paper that uses power system planning results ity of wind (IRENA 2011). With averaging, it is easy to miss promising for India based on data from a 21-year reanalysis (1980–2000) of the sites. As one moves from the country and regional level to sites within interannual variability of solar and wind resources. Chattopadhyay a 10 km x 10 km area, the potential for error grows and can reach an (2014) provides insights into the nature of variability over time as well unacceptable degree. There is obviously a trade-off between creating as across states and subregions. Figure 1 shows geographical and resource maps at high granularity and doing it over many years. seasonal variability in solar irradiance for 1980. Seasonal variation Other challenges will need to be overcome on the climate is significant in all states, especially during the transition from modeling front. Because the GCMs are not initialized with actual winter (December–January) to the pre-monsoon period (April–May). observations—which are important for predicting the timing of However, as the figure makes clear, the geographic spread is also specific events, especially in the short term—they are not ideally very significant. This geographic and temporal variability poses a suited to forecast the precise timing of a major phenomenon, such problem, given that power generation capacity is often inadequate to as an El Nino event. Improving the ability of GCMs to produce better meet peak demand even when all resources are available. forecasts is an area of active research. 4 I n t e g r a ti n g C l i m a t e M o d e l D a t a i n t o P o w e r S y st e m P l a n n i n g Figure 2 shows wind-power density (Watts/sq.m.) for wind-rich but, as noted, did not consider longer-term variability. Addressing Tamil Nadu in southern India from 1980 to 2000. Electricity demand that issue may be the key to attracting investments that could exploit in the state is at its highest in April–June, just before the monsoon the state’s wind potential and solve its power problems. And the arrives, a time when the availability of hydropower is also typically necessary data are freely available! low. To be noted in the figure are how much wind density varies The same reasoning applies to the solar potential of states such across the seasons and over the years for the same season. as Gujarat (Chattopadhyay and Chattopadhyay 2012; CEA 2013b). “With respect to long-term Although there is a broad trend of high wind availability around mid- Renewable resources may complement one other, or they may power system planning, year, the variability across 21 years of data is significant. overlap to a significant degree, creating seasonal cycles of over- and which typically extends Tamil Nadu has no more than 7 GW of installed wind capacity undersupply, as has happened in India. Climate model data can over 10–50 years, a and, since 2000, has made almost no investment in generating help planners understand such relationships and assemble a more relatively coarse dataset capacity to meet base load since 2000. The state ended 2012–13 balanced set of resources, as well as necessary back-up measures. with a 29.6 percent peak deficit (CEA 2013a; Chattopadhyay and A proper exploration of climate data and its integration into power may be sufficient to Chattopadhyay 2012), recalling the days of rampant load shedding in system planning would lead to more informed policies with regard augment short-term data the 1960s and 1970s. So the state’s need is great, and its potential for to renewable resources, and, in conjunction with resource maps of finer resolution. Such wind energy may be equally great. Phadke, Bharvirkar, and Khangura and actual observations, a more judicious selection of renewable analysis is typically used (2012) found up to 65 GW of economic wind potential in the state projects. to provide broad guidance on factors such as fuel Figure 1. Geographical and seasonal variation of solar irradiance for key Indian states in 1980 mix, prices, and volume 400 of investment, rather than High end: Rajasthan, Gujarat for planning site-specific 350 Andhra Pradesh projects.” 300 Delhi Solar irradiance (Watts/sq m) Gujarat 250 Haryana Karnataka 200 Orissa Madhya Pradesh 150 Low end: Tamil Nadu, Orissa Maharashtra Punjab Rajasthan 100 Tamil Nadu Uttar Pradesh 50 0 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sept. Oct. Nov. Dec. Source: Chattopadhyay (2014). 5 I n t e g r a ti n g C l i m a t e M o d e l D a t a i n t o P o w e r S y st e m P l a n n i n g What have we learned? Figure 2. Variability in wind-power density by month and year, 1980–2000 Climate modeling data should be a 250 mainstream resource in power system planning, complementing detailed resource 200 maps and site-specific observations Wind power density (Watts/sq m) “Researchers found up to 65 GW of economic wind Our understanding of renewable-based power generation 150 potential in Tamil Nadu but has come a long way over the past decade. The reposi- did not consider longer- tory of renewable resource maps, observations, and ana- lytical tools that are available to policy makers, planners, 100 term variability. Addressing and investors is already impressive, and growing. that issue may be the key Short-term intermittency of renewable resources is to attracting investments an important issue, and determining the level of integra- 50 that could exploit the tion of such resources that is right for individual power systems requires detailed data and analysis. But concern state’s wind potential and 0 for accuracy in assessments of the short-term potential Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec solve its power problems. of specific wind and solar sites has drawn attention 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 And the necessary data are away from the even weightier issue of long-term variabil- 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 AVG freely available!” ity of these resources, variability that holds profound implications for the viability of investments. Source: Chattopadhyay and Chattopadhyay 2012. When pondering long-term investments in power systems, choices about the mix of renewables and their geographic distribution must take into account seasonal, multiyear, and multidecade variability of the sort derivable from data generated scenarios, and the need for backup measures including interconnec- by a suite of well-established GCMs that are available at almost no tion to other systems. cost. Intelligence from these models—used, for example, to simulate power system plans for alternative climate-change scenarios or to reanalyze other data—can help policy makers, planners, and References investors understand the optimal mix of capacity and generation for Carmin, J., N. Nadkarni and C. Rhie. 2012. “Progress and Challenges a given power system at various points in time. in Urban Climate Adaption Planning: Results of a Global Survey.” Climate modelers and power system planners should work Department of Urban Studies and Planning, Massachusetts together so that the outputs and scenarios from GCMs can be Institute of Technology, Cambridge, Massachusetts, USA. http:// interpreted carefully and fed into planning models. Power system web.mit.edu/jcarmin/www/urbanadapt/Urban%20Adaptation%20 planners can also use data from climate models to guide decisions Report%20FINAL.pdf on the geographic spread of investments, taking into account the CEA (Central Electricity Authority). 2013a. Load Generation Balance complementarity of their seasonal distribution, the long-term poten- Report 2012–13. Ministry of Power, Government of India. http:// tial of given renewable resources under alternative climate-change www.cea.nic.in/archives/god/lgbr/1213.pdf 6 I n t e g r a ti n g C l i m a t e M o d e l D a t a i n t o P o w e r S y st e m P l a n n i n g CEA. 2013b. Large Scale Grid Integration of Renewable Energy Hoste, G., M. J. Dvorak, and M. Z. Jacobson. 2010. “Matching Hourly Resources—Way Forward. Ministry of Power, Government of and Peak Demand by Combining Different Renewable Energy India. November. http://www.cea.nic.in/reports/powersystems/ Sources: A Case Study for California in 2020.” Atmosphere large_scale_grid_integ.pdf. and Energy Program, Stanford University, Palo Alto, California. Chattopadhyay, D. 2014. Modeling Renewable Energy Impact on the https://web.stanford.edu/group/efmh/jacobson/Articles/I/ Electricity Market in India, 2014, Available online: http://www. CombiningRenew/HosteFinalDraft “Concern for accuracy in academia.edu/2647279/Modelling_Renewable_Energy_Impact_ IEA (International Energy Agency). 2005. “Variability of Wind Power assessments of the short- on_the_Electricity_Market_in_India_forthcoming_in_Renewable_ and Other Renewables: Management Options and Strategies.” term potential of specific and_Sustainable_Energy_Reviews_ Paris. http://www.uwig.org/iea_report_on_variability.pdf. wind and solar sites has Chattopadhyay, D., and M. Chattopadhyay. 2012. “Climate-Aware IRENA (International Renewable Energy Agency). 2013. “IRENA’s Work drawn attention away from Generation Planning: A Case Study of the Tamil Nadu Power on Technology Integration Planning. Presentation at Climate System in India.” Electricity Journal 25(6): 62–78. Change Impacts and Integrated Assessments XIX, July 22–August the even weightier issue Delworth, T. L., A. J. Broccoli, A. Rosati, and many others. 2007. 2, Snowmass, Colorado, USA. https://emf.stanford.edu/projects/ of long-term variability of GFDL’s CM2 Global Coupled Climate Models. Part 1: Formulation snowmass-workshops-climate-change-impacts-and-integrat- these resources, variability and Simulation Characteristics. Journal of Climate—Special ed-assessment-cciia. that holds profound Section 19: 643–674. http://citeseerx.ist.psu.edu/viewdoc/ IRENA. 2011. “Global Atlas for Solar and Wind Energy: Proposal download?doi=10.1.1.147.4729&rep=rep1&type=pdf for Implementation, Summary Paper. http://globalatlas. implications for the viability Füssel, H. M. 2007. Adaptation Planning for Climate Change: irena.org/UserFiles/Publication/Global%20Atlas%20%20 of investments.” Concepts, Assessment Approaches, and Key Lessons.” Implementation%20strategy.pdf. Sustainability Science (2): 265–275. 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Cambridge and New York: Hadley Centre for Climate Prediction and Research. 2006. “Met Office Cambridge University Press. http://www.ipcc.ch/pdf/assess- Hadley Centre Global Environment Model Version 1 (HadGEM1) ment-report/ar4/wg1/ar4-wg1-chapter10.pdf. Data.” NCAS British Atmospheric Data Centre, http://catalogue. Meridian Energy. 2011. “Managing Hydrology Risk.” https://www. ceda.ac.uk/uuid/e18c24b402fbaed061ab2f63e4f22669. meridianenergy.co.nz/assets/PDF/Company/Investors/ Hasumi, H., and S. Emori (eds). 2004. “K-1 Coupled Model (MIROC) Reports-and-presentations/Investor-presentations/ Description.” Tech. Rep. 1, Center for Climate System Research, AnalystpresentationManagingHydrologyRisk170811.pdf University of Tokyo. http://ccsr.aori.u-tokyo.ac.jp/~hasumi/ miroc_description.pdf. 7 I n t e g r a ti n g C l i m a t e M o d e l D a t a i n t o P o w e r S y st e m P l a n n i n g Padmakumari, B., A. K. Jaswal, and B. N. Goswami. 2013. Roeckner, E., G. Bäuml, L. Bonaventura, R. 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The authors gratefully acknowledge the comments received from Stéphane Live Wire 2014/17. Hallegate and Sebastian Wienges on an earlier draft of this paper. They also “Incorporating Energy from thank Vivien Foster and Morgan Bazilian for reviewing the paper and pro- Renewable Resources into viding helpful comments. Substantial revisions made to the original draft by Steven B. Kennedy to improve clarity and readability are greatly appreciated. Power System Planning,” by Marcelino Madrigal and Rhonda Lenai Jordan. Live Wire 2015/38. “Integrating Variable Renewable Energy into Power System Operations,” by Thomas Nikolakakis and Debabrata Chattopadhyay. Get Connected to Live Wire Live Wires are designed for easy reading on the screen and for downloading The Live Wire series of online knowledge notes is an initiative of the World Bank Group’s Energy and self-printing in color or “Live Wire is designed and Extractives Global Practice, reflecting the emphasis on knowledge management and solu- black and white. tions-oriented knowledge that is emerging from the ongoing change process within the Bank for practitioners inside Group. For World Bank employees: and outside the Bank. 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Once a year, the Energy and Extractives Global Practice takes stock of all notes that appeared, reviewing their quality and identifying priority areas to be covered in the following year’s pipeline. Please visit our Live Wire web page for updates: http://www.worldbank.org/energy/livewire e Pa c i f i c 2014/28 ainable energy for all in easT asia and Th 1 Tracking Progress Toward Providing susT TIVES GLOBAL PRACTICE A KNOWLEDGE NOTE SERIES FOR THE ENERGY & EXTRAC THE BOTTOM LINE Tracking Progress Toward Providing Sustainable Energy where does the region stand on the quest for sustainable for All in East Asia and the Pacific 2014/29 and cenTral asia energy for all? in 2010, eaP easTern euroPe sT ainable en ergy for all in databases—technical measures. This note is based on that frame- g su v i d i n had an electrification rate of Why is this important? ess Toward Pro work (World Bank 2014). SE4ALL will publish an updated version of 1 Tracking Progr 95 percent, and 52 percent of the population had access Tracking regional trends is critical to monitoring the GTF in 2015. to nonsolid fuel for cooking. the progress of the Sustainable Energy for All The primary indicators and data sources that the GTF uses to track progress toward the three SE4ALL goals are summarized below. consumption of renewable (SE4ALL) initiative C T I V E S G L O B A L P R A C T I C E ENERGY & EXTRA • Energy access. Access to modern energy services is measured T E S E R I E S F O R T H EIn declaring 2012 the “International Year of Sustainable Energy for energy decreased overall A KNO W L E D G E N Oand 2010, though by the percentage of the population with an electricity between 1990 All,” the UN General Assembly established three objectives to be connection and the percentage of the population with access Energy modern forms grew rapidly. d Providing Sustainable accomplished by 2030: to ensure universal access to modern energy energy intensity levels are high to nonsolid fuels.2 These data are collected using household Tracking Progress Towar services,1 to double the 2010 share of renewable energy in the global surveys and reported in the World Bank’s Global Electrification but declining rapidly. overall THE BOTTOM LINE energy mix, and to double the global rate of improvement in energy e and Central Asia trends are positive, but bold Database and the World Health Organization’s Household Energy for All in Eastern Europ efficiency relative to the period 1990–2010 (SE4ALL 2012). stand policy measures will be required where does the region setting Database. The SE4ALL objectives are global, with individual countries on that frame- on the quest for sustainable to sustain progress. is based share of renewable energy in the their own national targets databases— technical in a measures. way that is Thisconsistent with the overall of • Renewable energy. The note version energy for all? The region SE4ALL will publish an updated their ability energy mix is measured by the percentage of total final energy to Why is this important ? spirit of the work initiative. (World Bank Because2014). countries differ greatly in has near-universal access consumption that is derived from renewable energy resources. of trends is critical to monitoring to pursue thetheGTF in 2015. three objectives, some will make more rapid progress GTF uses to Data used to calculate this indicator are obtained from energy electricity, and 93 percent Tracking regional othersindicators primary will excel and data sources that elsewhere, depending on their the while the population has access le Energy for All in one areaThe goals are summarized below. balances published by the International Energy Agency and the the progress of the Sustainab respective track starting progress pointstowardand the three SE4ALL comparative advantages as well as on services is measured to nonsolid fuel for cooking. access. Accessthat they modern to are able to energy marshal. United Nations. despite relatively abundant (SE4ALL) initiative the resources and support Energy with an electricity connection Elisa Portale is an l Year of Sustainable Energy for To sustain percentage of by the momentum forthe the population achievement of the SE4ALL 2• Energy efficiency. The rate of improvement of energy efficiency hydropower, the share In declaring 2012 the “Internationa energy economist in with access to nonsolid fuels. three global objectives objectives, andathe means of charting percentage of the population global progress to 2030 is needed. is approximated by the compound annual growth rate (CAGR) of renewables in energy All,” the UN General Assembly established the Energy Sector surveys and reported access to modern universalAssistance The World TheseBank and data are the collected International using household Energy Agency led a consor- of energy intensity, where energy intensity is the ratio of total consumption has remained to be accomplished by 2030: to ensure Management Database and the World of theenergy intium of 15 renewable international in the World Bank’s Global agencies toElectrification establish the SE4ALL Global primary energy consumption to gross domestic product (GDP) energy the 2010 share of Program (ESMAP) relatively low. very high energy services, to double Database. measured in purchasing power parity (PPP) terms. Data used to 1 t ’s Household provides Energy a system for regular World Bank’s Energy the global rate of improvemen and Extractives Tracking Framework Health (GTF), which Organization in the energy intensity levels have come and to double the global energy mix, Global Practice. (SE4ALL 2012). based on energy. of renewable The sharepractical, rigorous—yet energy given available calculate energy intensity are obtained from energy balances to the period 1990–2010 global reporting, Renewable down rapidly. The big questions in energy efficiency relative setting by the percentage of total final energy consumption published by the International Energy Agency and the United evolve Joeri withde Wit is an countries individual mix is measured Data used to are how renewables will The SE4ALL objectives are global, economist in with the overall from renewable energy when every resources. person on the planet has access Nations. picks up a way energy that is consistent 1 The universal derived that isaccess goal will be achieved balances published when energy demand in from energy their own national targets through electricity, clean cooking fuels, clean heating fuels, rates the Bank’s Energy and countries differ greatly in their ability calculate this indicator are obtained to modern energy services provided productive use and community services. The term “modern solutions” cookingNations. again and whether recent spirit of the initiative. Because Extractives Global rapid progress and energy for Energy Agency and the United liquefied petroleum gas), 2 Solid fuels are defined to include both traditional biomass (wood, charcoal, agricultural will make more by the refers to solutions International that involve electricity or gaseous fuels (including is pellets and briquettes), and of decline in energy intensity some t of those of efficiency energy and forest residues, dung, and so on), processed biomass (such as to pursue the three objectives, Practice. depending on their or solid/liquid fuels paired with Energy efficiency. The rate stoves exhibiting of overall improvemen emissions rates at or near other solid fuels (such as coal and lignite). will excel elsewhere, rate (CAGR) of energy will continue. in one area while others liquefied petroleum gas (www.sustainableenergyforall.org). annual growth as well as on approximated by the compound and comparative advantages is the ratio of total primary energy respective starting points marshal. where energy intensity that they are able to intensity, measured in purchas- the resources and support domestic product (GDP) for the achievement of the SE4ALL consumption to gross calculate energy intensity Elisa Portale is an To sustain momentum terms. Data used to charting global progress to 2030 is needed. ing power parity (PPP) the International energy economist in objectives, a means of balances published by the Energy Sector International Energy Agency led a consor- are obtained from energy The World Bank and the SE4ALL Global Energy Agency and the United Nations. Management Assistance agencies to establish the the GTF to provide a regional and tium of 15 international for regular This note uses data from Program (ESMAP) of the which provides a system for Eastern Tracking Framework (GTF), the three pillars of SE4ALL World Bank’s Energy and Extractives on rigorous—yet practical, given available country perspective on Global Practice. global reporting, based has access Joeri de Wit is an will be achieved when every person on the planet The universal access goal heating fuels, clean cooking fuels, clean energy economist in 1 agricultural provided through electricity, biomass (wood, charcoal, to modern energy services The term “modern cooking solutions” to include both traditional and briquettes), and Solid fuels are defined the Bank’s Energy and use and community services. biomass (such as pellets 2 and energy for productive petroleum gas), and so on), processed fuels (including liquefied and forest residues, dung, involve electricity or gaseous at or near those of Extractives Global refers to solutions that overall emissions rates other solid fuels (such as coal and lignite). with stoves exhibiting Practice. or solid/liquid fuels paired (www.sustainableenergyforall.org). liquefied petroleum gas