3. SE4ALL Objectives for 2030
• Energy access
– Ensure universal access to modern energy services
• Renewable energy
– Double the share of renewable energy in the global energy mix
• Energy efficiency
– Double the global rate of improvement in energy efficiency
3
4. A phased and differentiated approach
Immediate
Medium term
Global
tracking
Which indicator is ready to go
for global tracking with all
data needs (past, present, and
future) already fully met?
Which indicator is highly desirable
for global tracking, but would
require a feasible incremental
investment in global energy data
systems over the next five years?
Country
level
tracking
Na.
Which indicator is ideal for
tracking, and although too
ambitious for global tracking,
could be very suitable for country
level tracking under SE4ALL?
4
5. Available data allows coverage of over 180 countries
Data Sources
Country Coverage
(% global popn.)
Electrification
Global omnibus and national household surveys
plus some censuses
170 (97%)
Cooking
Household surveys Global omnibus and national
household surveys plus some censuses
128 (80%)
Renewable
Energy
IEA (plus UN) for Energy Balances
REN 21, IRENA, BNEF for complementary indicators
181 (98%)
Energy
Efficiency
IEA (plus UN) for Energy Balances
WDI for GDP and sectoral value added
181 (98%)
5
7. Methodological challenges immediate resolution
Measurement of Access
•Household survey are the most common source of information on
primary cooking fuel and electricity connections
• Surveys are carried out every 3-4 years
• A modeling approach has been adopted to allow data estimation for
all countries annually
Definition of Access
• For electricity, availability of an electricity connection at home or use
of electricity as a primary energy for lighting is considered access
• For cooking, primary use of various non-solid fuels is considered as
access
Global Tracking Framework| Energy Access
Chapter
7
8. Access rate to modern energy rose driven by increase in rural
access rate and growth in South Asia and East Asia regions
Global
Regional
100
Urban
90
Total
70
Rural
60
50
40
30
Access rate (% )
Electricity
Access
Access rate (% )
80
20
10
0
2000
CCA
DEV
EA
LAC
NA
Oceania
SA
SEA
SSA
WA
1990
2010
2000
2010
WORLD
100
100
90
80
70
60
50
40
30
20
10
0
Urban
Total
Rural
CCA
90
DE
V
80
Access rate (%)
Non-solid
fuel
Access
Access rate (%)
1990
100
90
80
70
60
50
40
30
20
10
0
E
A
70
LAC
60
NA
50
Oceania
40
SA
30
SE
A
20
SSA
10
WA
0
1990
2000
2010
1990
Global Tracking Framework| Energy Access Chapter
2000
2010
WOR
LD
8
9. Most of the absolute growth took place in urban areas and in
South Asia, East Asia, and South East Asia regions
Global
Rural
1772
Urban
Electricity
Access
596
2122
Total
SA
EA
Population with access in 1990
DEV
SSA
Incremental access in 1990-2010
SEA
Population without access in 2010
LAC
WA
NA
1809
1151
CCA
Oceania
969
1220
200
3919
0
2000
Regional
4000
6000
Population with access in 1990
Incremental access in 1990-2010
Population without access in 2010
0
8000
500
Population (million)
Rural
Non-solid
fuel
Access
776 382
Urban
1732
Total
2179
1215
2503
0
568
1573
2000
SA
EA
Population with access in 1990
DEV
SSA
Incremental access in 1990-2010
SEA
Population without access in 2010
LAC
WA
NA
2777
CCA
Oceania
4000
1000
1500
2000
Population (Million)
6000
8000
Population with access in 1990
Incremental access in 1990-2010
Population without access in 2010
0
500
Population (million)
Global Tracking Framework| Energy Access Chapter
1000
1500
2000
Population (Million)
9
10. Still, 1.2 billion people live without electricity and
2.8 billion cook with solid fuels
Global
Electricity
Access
Non-solid fuel
Access
Global Tracking Framework| Energy Access Chapter
10
11. More than three quarters of global access deficit
concentrated in some 20 high impact countries
Top 20 access deficit countries
Electricity
Access
India
Nigeria
E
thiopia
Bangladesh
Congo, DR
Tanzania
Kenya
Sudan
Uganda
Myanmar
Mozambique
Korea, DR
Afghanistan
Madagascar
Philippines
Burkina Faso
Yemen
Niger
Malawi
Angola
Top 20 lowest access rate countries
330.4
79.5
65.4
62.3
54.0
38.6
31.4
30.9
29.7
24.1
20.0
17.7
17.6
17.0
15.7
14.3
14.1
14.0
13.6
11.6
902 Million People
without access
Population (million)
Non-solid
fuel
Access
India
China
Bangladesh
Indonesia
Nigeria
Pakistan
E
thiopia
Congo, DR
Vietnam
Philippines
Myanmar
Tanzania
Sudan
Kenya
Uganda
Afghanistan
Nepal
Mozambique
Korea, DR
Ghana
705.0
612.8
134.9
131.2
117.8
110.8
81.1
61.3
49.4
46.2
44.0
42.3
34.6
32.6
32.2
26.7
24.6
22.2
22.2
20.4
2,352 Million People
without access
Population (million)
Kenya
E
thiopia
Mauritania
Congo, DR
Madagascar
Zambia
Lesotho
Mozambique
Tanzania
CAR
Burkina Faso
Sierra Leone
Uganda
Niger
R
wanda
Malawi
Burundi
Chad
Liberia
South Sudan
Timor-Leste
Congo, DR
Togo
Tanzania
Mozambique
Somalia
Burundi
Niger
CAR
Lao PDR
Uganda
Malawi
Guinea
Guinea-Bissau
E
thiopia
Liberia
Sierra Leone
R
wanda
Mali
Madagascar
22.6
21.2
18.3
18.1
17.9
17.6
16.8
14.7
14.0
13.7
13.4
364 Million People
13.1
11.1
9.8
9.0
8.8
without access
5.5
5.3
3.1
1.6
Access rate (% )
8.0
7.0
5.6
5.6
5.0
4.7
4.4
4.0
3.8
3.7
3.6
3.4
3.2
369 Million People
without access
2.4
2.2
2.2
2.0
2.0
2.0
2.0
Access rate (%)
Global Tracking Framework| Energy Access Chapter
11
12. Fastest moving countries have succeeded in providing
access to 3-4% of their populations annually
Annual incremental access and population
Population (Million)
Electricity
Access
20
incremental access
incremental total population
10
5
Annual incremental access (%)
4%
25
15
Annual change in incremental access
3%
2%
1%
World annual
average= 1.3%
0%
0
Population (Million)
20
15
Non-solid
fuel
Access
incremental access
incremental total population
10
5
0
Annual incremental access (%)
4%
3%
2%
1%
World annual
average= 1.1%
0%
Global Tracking Framework| Energy Access Chapter
12
13. Methodological challenges in medium term
Measuring
Household access
to electricity
•Access to Electricity Supply defined by increasing levels of supply
attributes including quantity, duration, evening supply, affordability,
legality, and quality
•Electricity Services defined by the use of number of key electricity
services. The use is measured through ownership of appliances,
which are categorized by tier following the equivalent tier of
electricity supply needed for their adequate operation.
Measuring access
to modern cooking
solutions
•Technical performance is done firstly by categorizing cookstoves
into low, medium or high grade, based on direct observation.
Secondly, the manufactured cookstove is assessed based on
whether it is certified or not
•Conformity, convenience and adequacy (CCA) are the three
attributes that are included in addition to the technical performance
of the cooking solution to obtain an integral measurement of access
to cooking.
13
14. Multi-tier access index can be approximated using data on
average residential electricity consumption
Tier-0
-
Tier-1
Radio, Cellphone
Charging, Task Light
Tier-2
General
Lighting
AND Television
AND
Fan
Tier-3
Tier-2
AND
any
low-power
appliances
Tier-4
Tier-3
AND
any medium-power
appliances
Tier-5
Tier-4
AND
any
high-power
appliances
<3
3-66
66-321
321-1,317
1,317-2,120
>2,120
Indicative Electricity
Services
Consumption (kWh)
per hh per year
Index of Access to Electricity Services = ∑(PT x T)
with PT = Proportion of households at the Tth tier
T=Tier number {0,1,2,3,4,5}
Thousands
Average residential electricity consumption per
household (1,000 Kwh) - (IEA 2010)
Simplified energy access index
based on average consumption
14.4
15
13.3
2000
2010
6
2000
2010
5
10
4.9
4.5
5
1.5
1.0
1.71.5
1.31.6
2.1
1.7
2.7
1.9
2.52.8
3.7
2.8
Index (0-5)
Kwh
4.2
4.8
4.4
3.4
2.9
4
3.1
3
2.7
2.2
2
1
3.3
3.0
3.5
3.0
3.8
3.1
3.2
2.6
2.1
1.5
1.0
0.5
0
0
SA
CCA
SSA
LAC
EA
NA
SEA
DEV
WA
World
SSA
SA
SEA
LAC
Global Tracking Framework| Energy Access Chapter
WA
CCA
EA
NA
DEV
14
16. Methodological Challenges
Challenge
Issues
Proposed Approach
Energy
Accounting
Method
Primary energy accounting under-estimates useful energy
produced by renewable sources, multiple methods exist
for estimating final energy consumption
Measure the share of renewable
energy in total final energy
consumption terms using direct
equivalent method
Measuring
Sustainability
Despiteprogress, no internationally agreed criteria and
assessment methodologies for each of the renewable
energy technologies
Create a framework for measuring
sustainability in the medium term
Classifying
Biomass
Available data repositories do not distinguish between
traditional and modern uses of biomass
Improve capability to separately
track different categories of bioenergy in medium term
Data Gaps
Some aspects of renewable energy not fully captured in
data (small distributed grid-connected generation, direct
production of heat, waste fuels, heat pumps, etc.)
Develop methods for accounting
these categories and including them
in data collection efforts / surveys in
medium term (link to access)
16
17. Traditional biomass accounts for over half of total
renewable energy used mainly for heating and cooking
Global Share of Renewable Energy in TFEC, 2010
Renewable Energy Applications
1%
17%
17
Electricity
21%
82%
Source: IEA
Transport
4%
Heating
75%
1990
2010
Hydro
accounts
for 75% of
renewable
electricity
Biomass
accounts
for 97% of
heating
needs
18. Share of renewables in global energy mix hardly increased
since 1990 (despite absolute growth)
Renewable Energy Consumption (EJ) vs. Share of RE
Incremental Growth, EJ 1990-2010
Traditional Biomass
6.6
Modern Biomass
70
16.6%
17.2%
18.0%
17.4%
+1.4 %
17.0%
3.8
Hydro
3.7
Biofuels
60
2.2
Wind
RE Share
Other RE
Solar
+19 EJ
50
Hydro
0.6
Biogas
0.6
Geothermal
Waste
40
1.0
Modern
Biomass
Marine
0.3
0.2
0.0
Total: 19 EJ
30
Share Change in TFEC, 1990-2010
Traditional Biomass
20
Traditional
Biomass
-0.7%
Modern Biomass
0.2%
Hydro
0.5%
Biofuels
10
0.6%
Wind
0.3%
Solar
1990
1995
2000
2005
2010
0.2%
Biogas
0.2%
Geothermal
0.1%
Waste
0.0%
Marine
0.0%
Total: 1.4 %
Source: IEA
18
19. Non-conventional renewables experienced double digit
growth reflecting surge in policy incentives and finance
Compound Annual Growth Rate (%) of RE Consumption, 1990-2010
25.0%
16.7%
11.1%
5.1%
0.0%
Marine
1.2%
1.9%
Traditional Modern
Biomass
Biomass
11.4%
Biofuels
Solar
6.6%
2.3%
Hydro
Geothermal
Waste
Number of Countries Introducing RE Policies and Targets
12
Wind
Investments in Renewable Energy
About 25
developing
countries have a
FITP today
10
Biogas
$bn
300
258
250
220
8
200
But auctions are
also becoming a
key policy choice
6
Low Income
Upper Middle Income
High Income
India
133
97
Lower Middle Income
4
Brazil
167 161
150
100
50
2
China
61
United States
40
9
15
14
Rest of World
19
Europe
0
2000
0
1990-2000
2001-2005
2006-2012
FITP
Source: IEA, REN 21, BNEF
1990-2000
2001-2005
2002
2004
2006
2008
2010
2006-2012
RPS / Auctions
19
20. Less developed regions show higher (though declining)
renewable energy shares – and vice versa
Regional and Country Share of Renewables in TFEC(%)
Share of RE in Each Region / Country
1990
2000
2010
Europe
7.8
9.4
13.7
North America
6.0
7.1
9.0
Former Soviet Union
3.2
3.8
4.0
Middle East
1.2
0.7
0.9
Latin America
32.3
28.2
29.0
Africa
62.1
63.0
61.6
Asia (excl. China and India)
21.8
18.9
18.8
China
33.5
29.2
19.4
India
57.5
52.6
42.4
World
16.6
17.4
18.0
Regional Contribution to the Global RE Share, 2010
Middle East
0.2%
Region/Country
FSU
2%
N. America
10%
Africa
21%
L. America
11%
Europe
12%
China
19%
India
13%
Other Asia
14%
20
21. Two thirds of expansion of modern renewable energy
during last 20 years concentrated in just five countries
Top 20 Countries by RE consumption increase, 1990-2010 (PJ)
2,804
United States
2,274
1,719
Germany
730
546
Italy
Sweden
(4)
France
(1)
(0)
Poland
Austria
United Kingdom
Chile
Venezuela
(500)
Waste
340
297
221
214
209
204
198
159
139
138
142
- 118
112
95
94
-
500
Modern Biomass
Biogas
Biofuels
Hydro
Geothermal
Solar
Wind
1,000
1,500
2,000
2,500
3,000
RE excluding traditional biomass
Source: IEA
21
22. Countries most heavily dependent on renewable energy
have reached penetration levels of around one third
RE excluding traditional biomass and hydro
Source: IEA
22
24. Methodological challenges
Challenge
Proposed Approach
Multi-dimensionality
Track global performance
onenergyintensitycomplementedbyenergyintensity of
majoreconomicsectors and efficiency of energyindustry
Movetowardsbetter tracking of
targets, policies, institutions, investments
Intensity vs. Efficiency
Trackenergyintensityforcountries and
majorregions/blocks, wherefeasiblecomplementwithefficiencydecom
positiontostripoutstructuraleffects
Market Exchange Rate vs.
PurchasingPowerParity
Trackpurchasingpowerparity
Primary vs. finalenergy
Track global energyintensity in terms of primaryenergydemand
Tracksectoralenergyintensity in terms of final energyconsumption
Volatility
Track a fiveyearmovingaveragetrend
24
25. Last decade shows slowing rates of improvement
in energy intensity (higher when adjusted)
CAGR Energy Intensity (PPP)
1990-2000
2000-2010
1990-2010
Adjusted CAGR Energy Intensity
1990-2000
-0.99%
1990-2010
-1.21%
-1.30%
-1.61%
2000-2010
-1.49%
-1.77%
Source: IEA, WDI
25
26. Service sector contributed the most to energy savings
during last 20 years
Energy Intensity Trends by Sector
Share of Cumulative Savings by Sector
MJ/$2005 PPP
Industry
Agriculture
Services
0%
10
Industry
40%
5
-1.4%
-1.4%
Service
56%
Agriculture
4%
-2.2%
-3%
CAGR 1990-2010 (left)
EI in 1990 (right)
0
EI in 2010 (right)
Note: Services include services, transport, and residential
Source: IEA, WDI
26
27. East Asia accounted for the lion’s share of energy
saved, even as Middle Eastern energy intensity
deteriorated
Share of Cumulative Savings by
Region, 1990-2010
Energy Intensity Trends by Region
CAGR 1990-2010 (left)
EI in 1990 (right)
EI in 2010 (right)
MJ/$2005 PPP
40
2%
0.8%
30
0%
-0.1%
-0.5%
-0.5%
-1.1%
-1.3%
-2%
-1.5%
-1.7%
20
-1.1%
-1.3%
10
-2.3%
EA (58%)
NAm (17%)
EU (10%)
EE (6%)
SA (4%)
CCA (2%)
LAC (1%)
SSA (1%)
Oceania ( <1%)
SEA (<1%)
-3.2%
-4%
0
NAm
EU
EE
CCA
WA
EA
SEA
SA
Oceania
LAC
NAf
SSA
Source: IEA, WDI
27
28. Energy consumption patterns differ by income group
Energy Consumption/GDP (PPP)
HICs
UMICs
LMICs
LICs
Energy
Consumption/Capita
Source: IEA, WDI
Bubble size represents volume of
primary energy consumption in 2010
28
29. Top 20 (2) consumers accounting for 80% (40%) of
global energy demand
Largest Primary Energy Consumers, 2010 (EJ)
China
USA
Russia
India
Japan
Germany
Brazil
France
Canada
South Korea
Iran
Indonesia
UK
Mexico
Italy
Saudi Arabia
South Africa
Ukraine
Spain
Australia
107.4
92.8
29.4
29.0
20.8
13.7
11.1
11.0
10.5
10.5
8.7
8.7
8.5
7.5
7.1
7.1
5.7
5.5
5.3
5.2
Most Energy Intensive Countries, 2010 (MJ/$2005 PPP)
Liberia
Congo, DRC
Burundi
Trinidad & T.
Sierra Leone
Turkmenistan
Uzbekistan
Guinea
Mozambique
Iceland
Togo
Ukraine
Zambia
Uganda
Ethiopia
Kazakhstan
Sao Tome & P.
Guyana
Bhutan
Swaziland
59.8
47.6
33.3
28.8
26.7
23.8
23.3
22.2
22.2
21.6
20.8
19.8
18.8
18.2
18.0
17.6
16.3
16.3
16.0
15.9
Countries with Highest Level of Energy Intensity Among 20 Largest Energy Consumers, 2010
1
2
3
4
5
All Sectors
Ukraine
Russia
Saudi Arabia
South Africa
China
Industry
Ukraine
Russia
Canada
Brazil
South Africa
Services
Iran
Ukraine
Saudi Arabia
Indonesia
Russia
Agriculture
Canada
South Africa
Russia
United States
Brazil
Source: IEA, WDI
29
30. Fast moving countries typically register improvements
in the range of 4-8% annually
CAGR Energy Intensity, 1990-2010
Bosnia-Herz.
Estonia
Azerbaijan
Armenia
Afghanistan
East Timor
Sao Tome & P.
Belarus
Georgia
China
Lithuania
Kyrgyzstan
Albania
Bhutan
Laos
Eritrea
Romania
Turkmenistan
Moldova
Uganda
Countries with Lowest Level of Energy Intensity (MJ/$2005 PPP)
St. Lucia
Botswana
Ireland
Bahamas
Switzerland
Malta
Grenada
Kiribati
Panama
Albania
Colombia
Antigua & Barb.
Peru
Solomon Isl.
St. Vincent
Afghanistan
Vanuatu
Dominica
Hong Kong
Macau
11.9%
8.4%
7.9%
7.3%
6.8%
6.3%
5.9%
5.3%
4.9%
4.7%
4.6%
4.5%
4.4%
4.3%
4.2%
4.1%
4.0%
4.0%
3.9%
3.9%
3.9
3.8
3.7
3.7
3.7
3.7
3.6
3.6
3.6
3.5
3.4
3.4
3.3
3.0
2.9
2.9
2.7
2.6
2.0
1.0
Countries with Lowest Level of Energy Intensity Among 20 Largest Energy Consumers, 2010
1
2
3
4
5
All Sectors
UK
Spain
Italy
Germany
Japan
Industry
Japan
Germany
UK
Spain
Italy
Services
Japan
UK
Spain
Italy
Germany
Agriculture
Saudi Arabia
Indonesia
India
Germany
China
Source: IEA, WDI
30
31. Structural and activity effects partially mask extent of
energy efficiency efforts among top 20 energy consumers
3%
CAGR 1990-2010 (left)
Intensity in 1990
Intensity in 2010 (right)
35
MJ/$2005
30
CAGR for Intensity
Components, 1990-2010
0%
25
20
-3%
15
10
-6%
5
-9%
0
9%
CAGR for Activity, Structure
and Intensity
Components, 1990-2010
4%
-1%
-6%
Total Energy
Activity component
Structure component
Intensity component
31
32. China stands out in terms of greatest improvement
seen among top energy consuming nations
Energy Intensity in 1990
Energy Intensity in 2010
CAGR 1990-2010
3%
Saudi Arabia
0%
-
Japan
10
Germany
UK
-3%
Energy Intensity, 1990
0%
S. Africa
20
Russia
Indonesia
30
40
- Japan
50
Germany
UK
Kazakhstan
India
Nigeria
Uzbekistan
Iraq
-3%
Poland
Thailand
Venezuela
Brazil
Ukraine
Canada
United States
10
Energy Intensity, 2010
S. Africa
20
Indonesia
30
40
50
Ukraine
Canada Russia
Kazakhstan
India Nigeria
United States
Iraq
Uzbekistan
Poland
China
-6%
Iran
UAE
Thailand
Venezuela
HICs
UMICs
LMICs
Saudi Arabia
Iran
UAE
Brazil
CAGR 1990-2010
3%
HICs
UMICs
LMICs
China
-6%
Bubble size represents volume of
primary energy consumption in 2010
Source: IEA, WDI
Global Tracking Framework| Energy Efficiency
Chapter
32
33. Through energy intensity improvements China saved
about as much energy as it consumed over last 20 years
Largest Energy Consumers, Cumulative 1990-2010(EJ)
USA
Europe
China
Russia
Japan
India
Germany
France
Canada
UK
Brazil
Korea
Italy
Ukraine
Indonesia
Mexico
Iran
Spain
S. Africa
S. Arabia
Australia
1,904
1,346
1,269
595
435
413
297
221
214
190
168
155
146
138
134
131
118
103
101
99
93
Largest Energy Savers, Cumulative 1990-2010 (EJ)
China
USA
Europe
India
Germany
UK
Poland
Bosnia-Herz.
Russia
Iraq
Canada
Belarus
Romania
Estonia
Mexico
France
Australia
Kazakhstan
Argentina
Nigeria
Czech Rep.
1,320
369
223
114
69
47
46
38
35
24
23
18
18
16
14
14
13
12
11
11
10
Source: IEA, WDI
33
35. SE4ALL starting point in perspective
Percentages
Universal access to
modern energy
Electrification Cooking
Renewable
Rate of
energy share improvement of
in global
energy intensity
energy mix CAGR 1990-2010
(%)
(%)
Historic reference
1990
74
47
16.6
Starting point 2010
83
59
18.0
Objective for 2030
-1.3
-2.6
100
100
36.0
35
36. Large absolute achievements of last 20 years diluted by
surging population and energy demand
Absolute achievements
Relative achievements
-
1.8 bn. connected to electricity
1.6 bn. gained access to primary
non-solid fuel use
- Electrification increases at 1.3% pa
- Non-solid fuel use increases at 1.1% pa
-
20 EJ of energy provided
through renewable sources
2,216 EJ of energy saved
through reductions in energy
intensity
- Compound growth rate of renewable
energy consumption of 2% pa
- Compound growth rate of energy
intensity only -1.3% pa
-
Global population grew at 1.3% per year
Global primary energy demand grew at 2.0% per year
Global GDP grew at 3.2% per year
36
37. Projections from IEA and IIASA illustrate scale of
challenge entailed by SE4ALL objectives
Percentage in 2030
IEA SCENARIOS
Current Policies
New Policies
Efficient World
450 PPM (20C)
GEA SCENARIOS
SE4ALL
20C
Universal access to
modern energy
Electrification Cooking
Renewable
20 year rate of
energy share improvement of
in global mix energy efficiency
-
-
18
-2.0
88
88
-
69
69
-
20
22
27
-2.3
-2.8
-2.9
-
-
34 to 41
23 to 41
-3.0 to -3.2
-1.8 to -3.2
37
39. Launch and dissemination calendar
• Consultation
– First round methodological consultation, November 2012
– Second round full consultation, February 2013
• Previews
–
–
–
–
ESMAP CG, Washington DC, March 1st
SE4ALL EXCOM, Washington DC, March 11th
Energy Thematic Consultation, Oslo, April 9th
SE4ALL Advisory Board, Washington DC, April 19th
• Launch
–
–
–
–
Vienna Energy Forum, Vienna, May 28th-30th
Briefing to EU Development Ministers, Brussels
Briefing to UN Ambassadors, New York
Other opportunities?
39
40. Plans for future global tracking
• Details are still being discussed and funding unclear
• Individual partners commit to on-going tracking work
–
–
–
–
Electrification: WB/ESMAP via STEAR?
Cooking: WHO?
Renewable energy: various possibilities?
Energy efficiency: IEA and WB/ESMAP?
• Bi-annual unified report timed around Vienna Energy Forum
40
41. Global energy data improvement agenda
Recommended targeting of effort over next five years
Energy
access
a) Work to improve energy questionnaires for Global Omnibus Surveys
b) Pilot country level surveys for multi-tier framework
Renewable
energy
a)
b)
c)
d)
Energy
efficiency
a) Integrate data systems on energy consumption and associated
output measures
b) Strengthen country systems and capability to collect data on sectoral
intensities (and ideally sub-sectoral process efficiency )
c) Improve data on physical activity drivers (traffic volumes, number of
households and floor space, etc. )
d) Improve data on energy efficiency targets, policies and investments
Improve data and definitions for bio-energy and sustainability
Capture renewable energy in distributed generation
Capture renewable energy in off-grid (including micro-grids)
Promote a more harmonized approach to target-setting
41
43. Definition of renewable energy
Energy from natural sources that are replenished at a faster rate
than they are consumed, including the following
–
–
–
–
–
–
–
Hydro
Bio-energy
Geothermal
Aero-thermal
Solar
Wind
Ocean
43
44. Estimation model
Mixed model to estimate values for countries with at least one data
point: 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
FEATURES OF THE MODEL:
• Natural cubic spline transformation cantered in 2000, the median date of
the surveys data collected, and 5 knots over the entire time period.
• Data has been converted in logit function
• 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.
44
45. Candidate Multi-tier frameworks
Measuring Household access to electricity
Measuring access to modern cooking solutions
Supply side: Tiers based on six attributes of electricity supply
ATTRIBUTES
Technical Performance: Grades based on type of cookstove, fuel
used and certification
Low Grade
Tier-0
Tier-1
Tier-2
Tier-3
Tier-4
Tier-5
Peak Available Capacity (Weq)
-
>1
>20
>200
>2000 >2000
Duration (Hrs)
-
≥4
≥4
≥8
≥16
≥22
-
≥2
≥2
≥2
≥4
≥4
Affordability
-
-
√
√
√
√
Indoor
Pollution
Formality
-
-
-
√
√
√
Overall
Pollution
Quality (Voltage)
-
-
-
√
√
√
Safety
High Grade
Efficiency
Evening Supply (Hrs)
Medium Grade
Tier-1
Tier-2
Task Lighting
General
AND
Lighting
Phone
AND Television
Charging
AND
Fan
Grade-E
Grade-D
Self-made
cookstove
Grade-C
Grade-B
Grade-A
Non-BLEN certified cookstoves
Uncertified
non BLEN
cookstove
BLEN
cookstove
Practicality: Tiers based on CCA attributes (conformity,
convenience and adequacy)
Service side: Tiers based on regular use of appliances
Tier-0
-
Attributes
Tier-3
Tier-4
Tier-5
Tier-2
AND
any
low-power
appliances
Tier-3
AND
any mediumpower
appliances
Tier-4
AND
any
high-power
appliances
Tier-0
Tier -1
Tier -2
Tier -3
Tier -4
Tier -5
Grade-A
w/o CCA
w/ CCA
Grade-B
w/o CCA
w/ CCA
Grade-C
w/o CCA
w CCA
Grade-D
w/o CCA
w/ CCA
Grade-E
w/o CCA
w/ CCA
Index of Access = ∑(PT x T)
PT= Proportion of households at the Tthtier
T = Tier number {0,1,2,3,4,5}
45
46. Tracking Access to Energy
Opt-in countries: The further development of the multi-tier metric can be substantially
strengthened by rigorous piloting of questionnaires, certification, and consensus building
Global Tracking: a simplified three-tier measurement condensing the six-tiers in the multi-tier
candidate proposal is suggested, requiring only marginal improvement in data collection
The metric is flexible and allows for country specific targets to be set to adequately account for
varying energy challenges among countries.
Immediate Tracking
Tracking
Access
to
Electricity
Medium
Term
Tracking
Global
Tracking
Country Level
Tracking
Immediate Tracking
Tracking
Access
to Cooking
Solutions
Medium
Term
Tracking
No Access
No Electricity
Access
Electricity Connection or Electricity for Lighting
No Access
Basic Access
Solar
No Electricity
Lantern
Tier-0
Tier-1
No Access
Cooking with
Solid Fuels
Global
Tracking
Tier-0
Tier-2
Tier-3
Tier-4
Tier-5
Access
Cooking with Non-Solid Fuels
No Access
Self-made
cookstove
Country Level
Tracking
Advanced Access
Home System or
Grid Connection
Basic Access
Manufactured
cookstove
Tier-1
Tier-2
Advanced Access
BLEN
cookstove
Tier-3
Tier-4
Tier-5
46
47. Achieving objectives calls for substantial financing
as well as major policy commitments
Average
annual
US$ billion
2010-2030
Actual for
2010
Additional
from WEO
Additional
from GEA
Universal access
Electricity Cooking
Renewable
energy share
in global mix
20 year rate of
improvement of
energy
efficiency
Total
9.0
0.1
220
180
409.1
45.0
4.4
>>174
393
>>616.4
60.0
19.0
158
207
1,081.6
Both WEO and GEA coincide on need for phasing out fossil fuel subsidies
and providing carbon pricing measures in order to meet objectives
47
Notas do Editor
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.
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
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
Hydro contributes to over 80% of RE electricity production
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 %
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
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.
Ukraine is the only country that appears on both lists
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.
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).
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.