The boTTom line

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, information derivable from data generated by a suite of climate models that are available at almost no cost. To date, policy makers, planners, and investors in renewable-energy fields have generally not made extensive use of such data. Climate modelers and power system planners should work together so that the outputs and scenarios from climate models can be interpreted carefully and used in power system planning.

integrating Climate model Data into Power System Planning

Why is this issue important?

Climate model outputs can enhance our understanding of the long-term variability of renewable resources, an essential component of power system planning

Significant multiyear and multidecade variations in intermittent renewable resources hold major implications for power system investments. Hydropower planners in New Zealand, Brazil, Norway, and elsewhere know this from long experience. They have been using extensive hydrology data for many years to represent hydro-logical risks in their planning (see for instance, Meridian Energy 2011). The variability is even more pronounced for solar and wind (Hoste, Dvorak, and Jacobson 2010). Often, however, policy-driven invest-ments barely consider such risks. Although the potential effects of long-term variability were flagged a decade ago (see, for example, IEA 2005), efforts are still largely focused on fine-tuning short-term wind and solar forecasting systems.

Climate model data are particularly suited for the assessment of longer-term variability. A good grasp of seasonal, multiyear, and multidecade trends is essential in assessing the economic merits of investments in renewable resources and the extent to which such resources can complement one other or may need to be backed up by further investments in nonrenewable sources. For instance, plan-ners of hydro-dominated systems have learned to use risk-based criteria such as so-called 1-in-50-year drought coverage to deal with the risk posed by extremely dry years.

That climate models can provide scenarios over several decades makes them equally applicable to wind and solar planning. For example, a significant deterioration in the quality of wind resources

has already been detected in some parts of the world. The average wind speed in China deteriorated by 25 percent between 1969 and 2000, according to Xu and others (2006). There has been a 40 percent reduction in surface wind speed in India over the past four decades (Padmakumari, Jaswal, and Goswami 2013).

Good-quality data generated by climate models—both historical and projected over decades—are available for all countries at little or no cost. Such data can and should form part of power system planning, complementing more detailed, but expensive, renewable energy resource mapping and actual observations and measure-ments of wind, solar, and hydro power. To date, however, policy makers, planners, and investors in renewable-energy fields have generally not used climate model data, favoring other forms of data and analytics, including site-specific measurements and renewable resource maps.

The universe of such data includes outputs from a range of climate-analysis models such as global circulation models (GCM), which can run long-term climate scenarios and generate simulated historical data to complement or supplant missing or low-quality original data. The data from most such models—including the experimental CMIP5 (Coupled Model Inter-Comparison Project version 5, http://cmip-pcmdi.llnl.gov/cmip5/) used in the Fourth and Fifth Assessment Reports of the Intergovernmental Panel on Climate Change (Meehl and Stocker 2007)—are available for free.

That these datasets have not yet been put to use in power system planning is surprising given their potential value and their applications in related areas. Data from climate modeling exercises have made important contributions to infrastructure planning since the 1990s—most notably, in the area of urban planning (Carmin, Nadkarni, and Rhie 2012; Füssel 2007), where the increased fre-quency of natural hazards, temperature extremes, changes in rainfall

Debabrata Chattopadhyay is a senior energy specialist in the World Bank’s Energy and

Extractives Global Practice.

Rhonda L. Jordan is an energy specialist in the same practice.

A k n o w l e d g e n o t e s e r i e s f o r t h e e n e r g y p r A c t i c e

2015/43

A k n o w l e d g e n o t e s e r i e s f o r t h e e n e r g y & e x t r A c t i v e s g l o b A l p r A c t i c e

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2 I n T e g r a T I 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 S T e m P l a n n I n g

“That climate model

datasets have not yet been

put to use in power system

planning is surprising given

their potential value and

their applications in related

areas.”

patterns, and the rise in sea levels are routinely analyzed through down-scaled climate models despite data limitations. In urban planning, climate model data are usefully applied even in relatively coarse form—that is, at resolutions of 50 km x 50 km. Low spatial resolution implies examination of average climate conditions (such as temperature, wind speed, solar radiation, and precipitation) over a significant area (of 2,500 km2 in this case), with gaps in accuracy for specific locations within that area.

What is the key challenge?

Attention has focused on renewable resource maps, to the neglect of longer-term data generated by climate models

Despite the clear utility of understanding the variability of renewable resources over the longer term, the models and data that can deliver that understanding remain underexploited in power system planning and operation (IRENA 2013). Studies from the U.S. National Renewable Energy Laboratory (NREL) and other research institutions integrate some form of meteorological models, but their datasets are not long and rich enough to plot and predict the impact of climate variability over a term that is long enough to be optimally useful for planning investments in energy assets. For example, the NREL’s 2010 study on integrating wind and solar energy into the power mix in the western United States (GE Energy 2010) used high-resolution data from a numerical weather prediction model called 3TIER data but over a period of just three years (2004–06).1

Climate modeling data can be useful to power system planners not only because the climate exerts direct effects on demand for power (for example, the rising frequency of high-temperature days and other extreme weather events affect peak demand and electric-ity consumption generally), but also because such data can provide rich information on the long-term availability of wind and hydro resources, information that is crucial for planning.

The effect of climate change on demand, as evidenced by climate modeling data, and the implications of change for generation

1 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.

and fuel choices, have been studied (see, for example, Mansur, Mendelsohn, and Morrison 2007). The same cannot be said about supply-side analysis of generation and resource availability. Yet the variability of wind, solar, and hydro resources in different times-cales—over the years, seasonal, daily—matters a great deal for power systems. Climate data help us understand all three types of variability, particularly the first two.

Renewable resource maps have already proved their use in understanding variability, though not for the long time horizons on which climate models operate. The Renewable Resource Data Center at the NREL (http://www.nrel.gov/rredc/) and the Global Atlas of the International Renewable Energy Agency (IRENA, http://globalatlas.irena.org/) are two well-known repositories of such maps and data, the production and generation of which are co-funded by the Energy Sector Management Assistance Program (ESMAP), a global, multido-nor technical assistance trust fund administered by the World Bank and cosponsored by 13 official bilateral donors (http://www.esmap.org/RE_Mapping). Regional units of the World Bank have undertaken comprehensive resource mapping and geospatial planning under ESMAP-funded projects. Other major data sources include the U.S. National Aeronautics and Space Administration, the Department of Wind Energy at the Technical University of Denmark, the German Aerospace Center (DLR), and Spain’s National Renewable Energy Center (CENER).

Donor agencies, including the World Bank, have been actively promoting the compilation of such maps in developing nations. Combined with direct measurements and simulated data, the maps have been useful in shaping renewable-energy investments and in analyzing their impacts on power system operations.

However, each resource-mapping exercise is expensive, and maps, like actual observations, are limited because they are either static or do not cover enough time to capture variability over periods of years. Integrating renewable-energy data into power system plan-ning requires that all aspects of variability be taken into consideration. To ensure reliable supplies of power, planning must take into account the availability or unavailability of a given resource over a given period of time so that back-up generation capacity or other alternatives (such as storage and demand-side responses) can be put in place.

3 I n T e g r a T I 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 S T e m P l a n n I n g

“Integrating renewable-

energy data into power

system planning requires

that all aspects of variability

be taken into consideration.

To ensure reliable supplies

of power, planning must

take into account the

availability or unavailability

of a given resource

over a given period of

time so that back-up

generation capacity or

other alternatives (such as

storage and demand-side

responses) can be put in

place.”

What’s the solution?

Accurate power system planning requires an integrated solution

Even though the GCMs do not yet meet all of the needs of long-term 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 resolution with respect to site-specific resources (and can compen-sate for the GCMs’ low resolution through other means, as discussed), but they cannot afford to ignore the longer-term variability that GCMs track and reveal. A reassessment by Lawrence Berkeley National Laboratory of wind energy potential in China and India increased ear-lier estimates by a massive amount. The 2012 study for India (Phadke, Bharvirkar, and Khangura 2012) raised the original estimate of 48 GW that had been prepared by the Indian government to more than 2,000 GW (a 42-fold increase!) based on a limited set of actual observations. But the Berkeley analysis did not consider the interannual variability of wind data—a serious deficiency because a significant part of the proposed development would take place in the southern states of India (including Tamil Nadu and Karnataka) that exhibit significant seasonal and interannual variability, as discussed further on.

Debate over the degree of accuracy needed to capture the variability of wind and solar resources helps explain why the long term has received relatively little attention. IRENA’s Global Atlas project proposal, for example, noted that the wind energy resource was underestimated because “a large part of the wind resource [is] not being captured in the analysis” for lack of data on small-scale variabil-ity of wind (IRENA 2011). With averaging, it is easy to miss promising sites. As one moves from the country and regional level to sites within a 10 km x 10 km area, the potential for error grows and can reach an unacceptable degree. There is obviously a trade-off between creating resource maps at high granularity and doing it over many years.

Other challenges will need to be overcome on the climate modeling front. Because the GCMs are not initialized with actual observations—which are important for predicting the timing of specific events, especially in the short term—they are not ideally suited to forecast the precise timing of a major phenomenon, such as an El Nino event. Improving the ability of GCMs to produce better forecasts is an area of active research.

To illustrate the potential application of GCM data, we draw from a recent research paper that uses power system planning results for India based on data from a 21-year reanalysis (1980–2000) of the interannual variability of solar and wind resources. Chattopadhyay (2014) provides insights into the nature of variability over time as well as across states and subregions. Figure 1 shows geographical and seasonal variability in solar irradiance for 1980. Seasonal variation is significant in all states, especially during the transition from winter (December–January) to the pre-monsoon period (April–May). However, as the figure makes clear, the geographic spread is also very significant. This geographic and temporal variability poses a problem, given that power generation capacity is often inadequate to meet peak demand even when all resources are available.

Box 1. data from the following gCms are freely available

• ECHAM5 (Germany, Roeckner and others 2003)

•GFDL’s CM2 Global Coupled Climate Models (United States, Delworth and others 2007)

•Hadley Centre Global Environment Model Version 1 (United Kingdom, Hadley Center 2006)

•CSIRO Mk 3.5 (Australia, Gordon and others 2002)

• K-1 Coupled GCM (MIROC) (Japan, Hasumi and Emori 2004).

Applicable datasets are available for several decades and forecast periods at a granularity of 4–6 hour blocks within each day and at resolutions of up to 10 km x 10 km. They are generally adequate for analyses at the country and regional levels but not for site-specific wind and solar analysis. With respect to long-term power system planning, which typically extends over 10–50 years, a relatively coarse dataset may be sufficient to augment short-term data of finer resolution. Such analysis is typically used to provide broad guidance on factors such as fuel mix, prices, and volume of investment, rather than for planning site-specific projects.

The authors of this note are compiling a sample set of climate model data of potential use in power system planning. Using the World Bank’s Spatial Agent mobile application (https://itunes.apple.com/us/app/spatial-agent/id890565166?mt=8) they aim to ensure that this data will be readily available.

4 I n T e g r a T I 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 S T e m P l a n n I n g

“With respect to long-term

power system planning,

which typically extends

over 10–50 years, a

relatively coarse dataset

may be sufficient to

augment short-term data

of finer resolution. Such

analysis is typically used

to provide broad guidance

on factors such as fuel

mix, prices, and volume

of investment, rather than

for planning site-specific

projects.”

Figure 2 shows wind-power density (Watts/sq.m.) for wind-rich Tamil Nadu in southern India from 1980 to 2000. Electricity demand in the state is at its highest in April–June, just before the monsoon arrives, a time when the availability of hydropower is also typically low. To be noted in the figure are how much wind density varies across the seasons and over the years for the same season. Although there is a broad trend of high wind availability around mid-year, the variability across 21 years of data is significant.

Tamil Nadu has no more than 7 GW of installed wind capacity and, since 2000, has made almost no investment in generating capacity to meet base load since 2000. The state ended 2012–13 with a 29.6 percent peak deficit (CEA 2013a; Chattopadhyay and Chattopadhyay 2012), recalling the days of rampant load shedding in the 1960s and 1970s. So the state’s need is great, and its potential for wind energy may be equally great. Phadke, Bharvirkar, and Khangura (2012) found up to 65 GW of economic wind potential in the state

but, as noted, did not consider longer-term variability. Addressing that issue may be the key to attracting investments that could exploit the state’s wind potential and solve its power problems. And the necessary data are freely available!

The same reasoning applies to the solar potential of states such as Gujarat (Chattopadhyay and Chattopadhyay 2012; CEA 2013b).

Renewable resources may complement one other, or they may overlap to a significant degree, creating seasonal cycles of over- and undersupply, as has happened in India. Climate model data can help planners understand such relationships and assemble a more balanced set of resources, as well as necessary back-up measures. A proper exploration of climate data and its integration into power system planning would lead to more informed policies with regard to renewable resources, and, in conjunction with resource maps and actual observations, a more judicious selection of renewable projects.

Figure 1. geographical and seasonal variation of solar irradiance for key Indian states in 1980

Source: Chattopadhyay (2014).

Andhra Pradesh

Gujarat

Karnataka

Orissa

Punjab

Haryana

Tamil Nadu

Uttar Pradesh

Madhya Pradesh

Maharashtra

Delhi

Rajasthan So

lar

irra

dia

nce

(W

atts

/sq

m)

High end: Rajasthan, Gujarat

Low end: Tamil Nadu, Orissa

Jan. Nov.Oct.Sept.Aug.Jul.Jun.May.Apr.Mar.Feb. Dec.

400

350

300

250

200

150

100

50

0

5 I n T e g r a T I 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 S T e m P l a n n I n g

“Researchers found up to

65 GW of economic wind

potential in Tamil Nadu but

did not consider longer-

term variability. Addressing

that issue may be the key

to attracting investments

that could exploit the

state’s wind potential and

solve its power problems.

And the necessary data are

freely available!”

What have we learned?

Climate modeling data should be a mainstream resource in power system planning, complementing detailed resource maps and site-specific observations

Our understanding of renewable-based power generation has come a long way over the past decade. The reposi-tory of renewable resource maps, observations, and ana-lytical tools that are available to policy makers, planners, and investors is already impressive, and growing.

Short-term intermittency of renewable resources is an important issue, and determining the level of integra-tion of such resources that is right for individual power systems requires detailed data and analysis. But concern for accuracy in assessments of the short-term potential of specific wind and solar sites has drawn attention away from the even weightier issue of long-term variabil-ity of these resources, variability that holds profound implications for the viability of investments.

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 by a suite of well-established GCMs that are available at almost no 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 investors understand the optimal mix of capacity and generation for a given power system at various points in time.

Climate modelers and power system planners should work together so that the outputs and scenarios from GCMs can be interpreted carefully and fed into planning models. Power system planners can also use data from climate models to guide decisions on the geographic spread of investments, taking into account the complementarity of their seasonal distribution, the long-term poten-tial of given renewable resources under alternative climate-change

scenarios, and the need for backup measures including interconnec-tion to other systems.

References

Carmin, J., N. Nadkarni and C. Rhie. 2012. “Progress and Challenges in Urban Climate Adaption Planning: Results of a Global Survey.” Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. http://web.mit.edu/jcarmin/www/urbanadapt/Urban%20Adaptation%20Report%20FINAL.pdf

CEA (Central Electricity Authority). 2013a. Load Generation Balance Report 2012–13. Ministry of Power, Government of India. http://www.cea.nic.in/archives/god/lgbr/1213.pdf

Figure 2. Variability in wind-power density by month and year, 1980–2000

Source: Chattopadhyay and Chattopadhyay 2012.

0

50

100

150

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250

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d po

wer

den

sity

(Wat

ts/s

q m

)

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1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 AVG

6 I n T e g r a T I 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 S T e m P l a n n I n g

“Concern for accuracy in

assessments of the short-

term potential of specific

wind and solar sites has

drawn attention away from

the even weightier issue

of long-term variability of

these resources, variability

that holds profound

implications for the viability

of investments.”

CEA. 2013b. Large Scale Grid Integration of Renewable Energy Resources—Way Forward. Ministry of Power, Government of India. November. http://www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf.

Chattopadhyay, D. 2014. Modeling Renewable Energy Impact on the Electricity Market in India, 2014, Available online: http://www.academia.edu/2647279/Modelling_Renewable_Energy_Impact_on_the_Electricity_Market_in_India_forthcoming_in_Renewable_and_Sustainable_Energy_Reviews_

Chattopadhyay, D., and M. Chattopadhyay. 2012. “Climate-Aware Generation Planning: A Case Study of the Tamil Nadu Power System in India.” Electricity Journal 25(6): 62–78.

Delworth, T. L., A. J. Broccoli, A. Rosati, and many others. 2007. GFDL’s CM2 Global Coupled Climate Models. Part 1: Formulation and Simulation Characteristics. Journal of Climate—Special Section 19: 643–674. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.4729&rep=rep1&type=pdf

Füssel, H. M. 2007. Adaptation Planning for Climate Change: Concepts, Assessment Approaches, and Key Lessons.” Sustainability Science (2): 265–275.

GE Energy. 2010. Western Wind and Solar Integration Study. Prepared for the National Renewable Energy Laboratory, U.S. Department of Energy. May. http://www.nrel.gov/docs/fy10osti/47434.pdf.

Gordon, H. B., L. D. Rotstayn, J. L. McGregor, and many others. 2002. The CSIRO Mk3 Climate System Model. Technical Report 60, CSIRO Atmospheric Research, Aspendale, Victoria, Australia. https://publications.csiro.au/rpr/download?pid=procite:ff94d-b7e-ad41-40bf-b6be-2ab1ad07805c&dsid=DS1.

Hadley Centre for Climate Prediction and Research. 2006. “Met Office Hadley Centre Global Environment Model Version 1 (HadGEM1) Data.” NCAS British Atmospheric Data Centre, http://catalogue.ceda.ac.uk/uuid/e18c24b402fbaed061ab2f63e4f22669.

Hasumi, H., and S. Emori (eds). 2004. “K-1 Coupled Model (MIROC) Description.” Tech. Rep. 1, Center for Climate System Research, University of Tokyo. http://ccsr.aori.u-tokyo.ac.jp/~hasumi/miroc_description.pdf.

Hoste, G., M. J. Dvorak, and M. Z. Jacobson. 2010. “Matching Hourly and Peak Demand by Combining Different Renewable Energy Sources: A Case Study for California in 2020.” Atmosphere and Energy Program, Stanford University, Palo Alto, California. https://web.stanford.edu/group/efmh/jacobson/Articles/I/CombiningRenew/HosteFinalDraft

IEA (International Energy Agency). 2005. “Variability of Wind Power and Other Renewables: Management Options and Strategies.” Paris. http://www.uwig.org/iea_report_on_variability.pdf.

IRENA (International Renewable Energy Agency). 2013. “IRENA’s Work on Technology Integration Planning. Presentation at Climate Change Impacts and Integrated Assessments XIX, July 22–August 2, Snowmass, Colorado, USA. https://emf.stanford.edu/projects/snowmass-workshops-climate-change-impacts-and-integrat-ed-assessment-cciia.

IRENA. 2011. “Global Atlas for Solar and Wind Energy: Proposal for Implementation, Summary Paper. http://globalatlas.irena.org/UserFiles/Publication/Global%20Atlas%20%20Implementation%20strategy.pdf.

Mansur, E. T., R. Mendelsohn, W. Morrison. 2007. “Climate Change Adaptation: A Study of Fuel Choice and Consumption in the U.S. Energy Sector.” Journal of Environmental and Economic Management 55(2): 175–193.

Meehl, G. A., and T. F. Stocker. 2007. “Global Climate Projections.” In Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, ed. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller (chapter 10). Cambridge and New York: Cambridge University Press. http://www.ipcc.ch/pdf/assess-ment-report/ar4/wg1/ar4-wg1-chapter10.pdf.

Meridian Energy. 2011. “Managing Hydrology Risk.” https://www.meridianenergy.co.nz/assets/PDF/Company/Investors/Reports-and-presentations/Investor-presentations/AnalystpresentationManagingHydrologyRisk170811.pdf

7 I n T e g r a T I 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 S T e m P l a n n I n g

Padmakumari, B., A. K. Jaswal, and B. N. Goswami. 2013. Decrease in Evaporation over the Indian Monsoon Region: Implication on Regional Hydrological Cycle.” Climatic Change 121: 787–799. http://link.springer.com/article/10.1007%2Fs10584-013-0957-3#page-1.

Phadke, A., R. Bharvirkar, and J. Khangura. 2012. “Reassessing Wind Potential Estimates for India: Economic and Policy Implications (Revision 1).” Report 5077E, Lawrence Berkeley National Laboratory, Berkeley, California, USA. March.

Roeckner, E., G. Bäuml, L. Bonaventura, R. Brokopf, and many others. 2003. “The Atmospheric General Circulation Model ECHAM5. Part 1: Model description.” Report 349, Max-Planck-Institut für Meteorologie, Hamburg, Germany.

Xu, M., P.-C. Chang, C. Fu, Y. Qi, A. Robock, D. Robinson, and H. Zhang. 2006. “Steady Decline of East Asian Monsoon Winds, 1969–2000: Evidence from Direct Ground Measurements of Wind Speed.” Journal of Geophysical Research 111: D24111.

The authors gratefully acknowledge the comments received from Stéphane Hallegate and Sebastian Wienges on an earlier draft of this paper. They also thank Vivien Foster and Morgan Bazilian for reviewing the paper and pro-viding helpful comments. Substantial revisions made to the original draft by Steven B. Kennedy to improve clarity and readability are greatly appreciated.

mAke fuRTheR ConneCTionS

live Wire 2014/10. “Implementing onshore Wind Power Projects,” by gabriela elizondo azuela and rafael Ben.

live Wire 2014/17. “Incorporating energy from renewable resources into 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.

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1 T r a c k i n g P r o g r e s s T o w a r d P r o v i d i n g s u s T a i n a b l e e n e r g y f o r a l l i n e a s T a s i a a n d T h e Pa c i f i c

THE BOTTOM LINE

where does the region stand

on the quest for sustainable

energy for all? in 2010, eaP

had an electrification rate of

95 percent, and 52 percent

of the population had access

to nonsolid fuel for cooking.

consumption of renewable

energy decreased overall

between 1990 and 2010, though

modern forms grew rapidly.

energy intensity levels are high

but declining rapidly. overall

trends are positive, but bold

policy measures will be required

to sustain progress.

2014/28

Elisa Portale is an

energy economist in

the Energy Sector

Management Assistance

Program (ESMAP) of the

World Bank’s Energy and Extractives

Global Practice.

Joeri de Wit is an

energy economist in

the Bank’s Energy and

Extractives Global

Practice.

A K N O W L E D G E N O T E S E R I E S F O R T H E E N E R G Y & E X T R A C T I V E S G L O B A L P R A C T I C E

Tracking Progress Toward Providing Sustainable Energy

for All in East Asia and the Pacific

Why is this important?

Tracking regional trends is critical to monitoring

the progress of the Sustainable Energy for All

(SE4ALL) initiative

In declaring 2012 the “International Year of Sustainable Energy for

All,” the UN General Assembly established three objectives to be

accomplished by 2030: to ensure universal access to modern energy

services,1 to double the 2010 share of renewable energy in the global

energy mix, and to double the global rate of improvement in energy

efficiency relative to the period 1990–2010 (SE4ALL 2012).

The SE4ALL objectives are global, with individual countries setting

their own national targets in a way that is consistent with the overall

spirit of the initiative. Because countries differ greatly in their ability

to pursue the three objectives, some will make more rapid progress

in one area while others will excel elsewhere, depending on their

respective starting points and comparative advantages as well as on

the resources and support that they are able to marshal.

To sustain momentum for the achievement of the SE4ALL

objectives, a means of charting global progress to 2030 is needed.

The World Bank and the International Energy Agency led a consor-

tium of 15 international agencies to establish the SE4ALL Global

Tracking Framework (GTF), which provides a system for regular

global reporting, based on rigorous—yet practical, given available

1 The universal access goal will be achieved when every person on the planet has access

to modern energy services provided through electricity, clean cooking fuels, clean heating fuels,

and energy for productive use and community services. The term “modern cooking solutions”

refers to solutions that involve electricity or gaseous fuels (including liquefied petroleum gas),

or solid/liquid fuels paired with stoves exhibiting overall emissions rates at or near those of

liquefied petroleum gas (www.sustainableenergyforall.org).

databases—technical measures. This note is based on that frame-

work (World Bank 2014). SE4ALL will publish an updated version of

the GTF in 2015.

The primary indicators and data sources that the GTF uses to

track progress toward the three SE4ALL goals are summarized below.

• Energy access. Access to modern energy services is measured

by the percentage of the population with an electricity

connection and the percentage of the population with access

to nonsolid fuels.2 These data are collected using household

surveys and reported in the World Bank’s Global Electrification

Database and the World Health Organization’s Household Energy

Database.

• Renewable energy. The share of renewable energy in the

energy mix is measured by the percentage of total final energy

consumption that is derived from renewable energy resources.

Data used to calculate this indicator are obtained from energy

balances published by the International Energy Agency and the

United Nations.

• Energy efficiency. The rate of improvement of energy efficiency

is approximated by the compound annual growth rate (CAGR)

of energy intensity, where energy intensity is the ratio of total

primary energy consumption to gross domestic product (GDP)

measured in purchasing power parity (PPP) terms. Data used to

calculate energy intensity are obtained from energy balances

published by the International Energy Agency and the United

Nations.

2 Solid fuels are defined to include both traditional biomass (wood, charcoal, agricultural

and forest residues, dung, and so on), processed biomass (such as pellets and briquettes), and

other solid fuels (such as coal and lignite).

1 T r a c k i n g P r o g r e s s To wa r d P r o v i d i n g s u s Ta i n a b l e e n e r g y f o r a l l i n e a s T e r n e u r o P e a n d c e n T r a l a s i a

THE BOTTOM LINE

where does the region stand

on the quest for sustainable

energy for all? The region

has near-universal access to

electricity, and 93 percent of

the population has access

to nonsolid fuel for cooking.

despite relatively abundant

hydropower, the share

of renewables in energy

consumption has remained

relatively low. very high energy

intensity levels have come

down rapidly. The big questions

are how renewables will evolve

when energy demand picks up

again and whether recent rates

of decline in energy intensity

will continue.

2014/29

Elisa Portale is an

energy economist in

the Energy Sector

Management Assistance

Program (ESMAP) of the

World Bank’s Energy and Extractives

Global Practice.

Joeri de Wit is an

energy economist in

the Bank’s Energy and

Extractives Global

Practice.

A K N O W L E D G E N O T E S E R I E S F O R T H E E N E R G Y & E X T R A C T I V E S G L O B A L P R A C T I C E

Tracking Progress Toward Providing Sustainable Energy

for All in Eastern Europe and Central Asia

Why is this important?

Tracking regional trends is critical to monitoring

the progress of the Sustainable Energy for All

(SE4ALL) initiative

In declaring 2012 the “International Year of Sustainable Energy for

All,” the UN General Assembly established three global objectives

to be accomplished by 2030: to ensure universal access to modern

energy services,1 to double the 2010 share of renewable energy in

the global energy mix, and to double the global rate of improvement

in energy efficiency relative to the period 1990–2010 (SE4ALL 2012).

The SE4ALL objectives are global, with individual countries setting

their own national targets in a way that is consistent with the overall

spirit of the initiative. Because countries differ greatly in their ability

to pursue the three objectives, some will make more rapid progress

in one area while others will excel elsewhere, depending on their

respective starting points and comparative advantages as well as on

the resources and support that they are able to marshal.

To sustain momentum for the achievement of the SE4ALL

objectives, a means of charting global progress to 2030 is needed.

The World Bank and the International Energy Agency led a consor-

tium of 15 international agencies to establish the SE4ALL Global

Tracking Framework (GTF), which provides a system for regular

global reporting, based on rigorous—yet practical, given available

1 The universal access goal will be achieved when every person on the planet has access

to modern energy services provided through electricity, clean cooking fuels, clean heating fuels,

and energy for productive use and community services. The term “modern cooking solutions”

refers to solutions that involve electricity or gaseous fuels (including liquefied petroleum gas),

or solid/liquid fuels paired with stoves exhibiting overall emissions rates at or near those of

liquefied petroleum gas (www.sustainableenergyforall.org).

databases—technical measures. This note is based on that frame-

work (World Bank 2014). SE4ALL will publish an updated version of

the GTF in 2015.

The primary indicators and data sources that the GTF uses to

track progress toward the three SE4ALL goals are summarized below.

Energy access. Access to modern energy services is measured

by the percentage of the population with an electricity connection

and the percentage of the population with access to nonsolid fuels.2

These data are collected using household surveys and reported

in the World Bank’s Global Electrification Database and the World

Health Organization’s Household Energy Database.

Renewable energy. The share of renewable energy in the energy

mix is measured by the percentage of total final energy consumption

that is derived from renewable energy resources. Data used to

calculate this indicator are obtained from energy balances published

by the International Energy Agency and the United Nations.

Energy efficiency. The rate of improvement of energy efficiency is

approximated by the compound annual growth rate (CAGR) of energy

intensity, where energy intensity is the ratio of total primary energy

consumption to gross domestic product (GDP) measured in purchas-

ing power parity (PPP) terms. Data used to calculate energy intensity

are obtained from energy balances published by the International

Energy Agency and the United Nations.

This note uses data from the GTF to provide a regional and

country perspective on the three pillars of SE4ALL for Eastern

2 Solid fuels are defined to include both traditional biomass (wood, charcoal, agricultural

and forest residues, dung, and so on), processed biomass (such as pellets and briquettes), and

other solid fuels (such as coal and lignite).

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