D B J R e s e a rc h C e n t er o n G l o b a l Wa r m in g
D is c u s s i o n Pa p er S e r i e s N o . 7 0 ( 3 / 2 0 2 4 )Post-Pandemic Surges of Real Unit En ergy Costs
in Eight Industrialized Countries

K o j i N om u r a
S h o I n a ba

The aim of this discussion paper ser ies is to stimulat e discussion an dcomment from academics and policymakers through a preliminar y draft
form. This paper is included by permission of the author(s) as a tentative
material before publication in peer-reviewed journals or books. Copyrightand all related rights are maintained by the author(s). Views expressed inthis paper are those of the author(s) and do not reflect the views of the
Research Institute of Capital Formation or Development Bank of Japan.

Post-Pandemic Surges of Real Unit Energy Costs
in Eight Industrialized Countries
Koji Nomura and Sho Inaba†

March 2024
Abstract
This paper develops a high-frequency indicator to assess real price and cost differentials for energy use
across eight industrialized countries: China, France, Germany, Italy, Japan, South Korea, the UK, and the
U.S. The study assesses the overall energy cost burden using the real price level index (PLI) and the realunit energy cost (RUEC). The real PLI, unaffected by exchange rate fluctuations, provides a stable measureof real energy price differentials, while the RUEC indicates the challenges facing the energy transition. An
analysis of RUEC trends from the first quarter of 2015 to the fourth quarter of 2023 highlights a substantial
post-pandemic surge, particularly evident in Germany and Italy, where levels spiked by about 80%
compared to pre-pandemic periods. This surge in Germany has coincided with a 20% decline in output
within energy-intensive manufacturing by the end of 2023. Asian countries, on the other hand, managed tocurb the post-pandemic RUEC surge to less than half of this level through energy subsidies and government
interventions. Nonetheless, the higher RUEC levels in China and South Korea underscore the formidable
challenges they encounter in propelling their energy transition initiatives forward. In Japan, about half ofthe limitations on the RUEC surge are attributed to reduced energy consumption resulting from thehollowing out of its industrial sector. Without a fundamental reevaluation of energy policies to ensure
economic growth, the current path of the energy transition remains precarious.

Keywords: Real unit energy cost (RUEC); Real energy prices; Price level index (PLI); Real PLI; Energy
productivity; Energy-intensive trade-exposed (EITE) industries
JEL classification: C81, E31, O44, Q41† Koji Nomura is a professor at Keio Economic Observatory (KEO), Keio University, Tokyo, and Sho Inaba is a

research trainee at KEO (a student at the Graduate School of Business and Commerce, Keio University.). This project
was granted by the Keidanren (Japan Business Federation) Foundation for Environmental Protection Measures. The
authors thank Mansaku Yoshida, Department of Business and Commerce, Keio University, for his patient research
assistance. The figures reported here are preliminary estimates as of March 2024 and will be revised as more statistics
become available. Please see the website (https://www.ruec.world/) for the latest estimates. Any errors that remain are
our sole responsibility. Correspondent author: Koji Nomura (nomura@sanken.keio.ac.jp).

1Contents
1

Introduction..................................................................................................................... 3

2

Framework....................................................................................................................... 5

2.1 Unit Energy Costs............................................................................................................ 6
2.2 Price Differentials ............................................................................................................ 8
3

Measurement ................................................................................................................... 9

3.1 Base Year Estimates ......................................................................................................... 9
3.2 Monthly Estimates .......................................................................................................... 11
3.3 Annual Benchmark......................................................................................................... 14
3.4 Forecasts ........................................................................................................................ 15
4

Estimated Results ........................................................................................................... 15

4.1 Energy Prices ................................................................................................................. 15
4.2 Real PLI ......................................................................................................................... 20
4.3 RUEC ............................................................................................................................ 22
4.3.1 Post-Pandemic RUEC Surge ...................................................................................... 22
4.3.2 Economic Impacts of RUEC Surge ........................................................................... 24
4.3.3 Sources of RUEC Recovery ....................................................................................... 27
5Conclusion ..................................................................................................................... 29

References ............................................................................................................................ 30
Appendix .............................................................................................................................. 32
A Nominal PLI .................................................................................................................. 32
B

Volume Balance Assumption.......................................................................................... 33

C

Verification .................................................................................................................... 34

D Supplementary Figures ................................................................................................... 38
D.1 Energy Prices, Volumes, and Costs ................................................................................. 38
D.2 Output Prices, Volumes, and Values ............................................................................... 44
D.3 PLI ................................................................................................................................ 45

2

1

Introduction

The global drive to achieve carbon neutrality by mid-century is expected to unlock growth
opportunities for companies with advantages in green technologies. While this demand-side effect of
the energy transition seems to offer promising prospects to some sectors, it is critical to consider therisks on the supply side―higher energy costs for energy-intensive sectors from low-carbon policies
may hurt price competitiveness relative to suppliers in countries with low or no carbon prices. The
global economy experienced fossil fuel price surges, starting in late 2020 because of the post-COVID19 pandemic demand recovery and further exacerbated by the war in Ukraine starting in February
2022. While the spikes began to reverse in mid-2022, they remain significantly elevated relative to prepandemic levels. The outlook for fossil fuel prices remains uncertain. Energy transition policies indeveloped countries aiming for carbon neutrality may lead to an anticipated long-term decline in fossil
fuel prices.1 On the other hand, concerns persist that sustained fossil fuel price surges could occur
due to investment constraints imposed on fossil fuel suppliers or increased demand in developing
countries.2
This study aims to develop a high-frequency indicator that effectively measures real energy costdifferentials across eight industrialized countries: China, France, Germany, Italy, Japan, South Korea,
the UK, and the U.S. The challenge in constructing data for this purpose is to develop monthly figureson energy costs that are as consistent as possible with economic statistics, such as national accounts
and Supply and Use Tables/Input-Output Tables (SUT/IOT). 3 The overall energy cost burden
relative to output (value added) is evaluated in this paper based on the real Price Level Index (PLI) for
quality-adjusted final energy use and the Real Unit Energy Cost (RUEC). This allows us to closely
monitor the potential of supply-side impacts on the long-term trajectory of the energy transition.
The nominal PLI for energy is the ratio of the Purchasing Power Parity (PPP) for energy to themarket exchange rate. Using the U.S. as a reference country, the PLI represents the energy pricedifferential index relative to the U.S. The fact that the nominal PLI is affected by exchange rate
fluctuations leads to a phenomenon that may seem somewhat odd as a measure of international pricedifferentials. Suppose the currency of a country facing higher energy prices becomes weaker against
the U.S. dollar, assuming that electricity prices in the domestic currency are stable. In that case, the1 The Net Zero Emissions by 2050 Scenario (NZE) in IEA (2023b) projects a 74% drop in oil prices from $98/bbl in 2022 to $25/bbl

in 2050. Under this scenario, natural gas prices in the U.S. are projected to fall 62% and common coal prices 57% between 2022 and2050. Even in the Stated Policies Scenario (STEPS), which assumes manufacturing capacity for currently planned or implemented
policies and clean energy technologies, between 2022 and 2050, the prices of crude oil and natural gas are projected to fall by 16%
and coal by 23%.
2 The High Zero-Carbon Technology Cost (HighZTC) scenario in the U.S. EIA (2023) assumes no learning cost reductions in thecapital costs of zero-emission power generation facilities, while the Low Zero-Carbon Technology Cost (LowZTC) scenario assumes
a 40% lower cost level in 2050 (compared to the reference case). Crude oil prices are projected to be about the same in 2050 regardlessof these two scenarios, rising to about 1.9 times the 2022 level. Natural gas price is also expected to increase after falling to pre-war
levels in Ukraine until 2027 and is projected to increase by about 1.2 times in the HighZTC and remain about the same in the LowZTC
compared to 2022.
3 Energy statistics are characterized by their promptness in reporting, focusing mainly on energy volume balances. However, themonthly data on energy consumption costs, comparable with nominal GDP, have yet to be established universally, with notable
exceptions in countries such as the U.S., the UK, and France. Even in the UK and France, they are regarded as insufficiently consistent
with the economic statistics. In contrast, economic statistics incorporating energy consumption data exhibit a less timely reporting
pattern, often published with a lag of one to several years. Through the establishment of comprehensive monthly data on energy costs,
appropriate aggregate energy prices are constructed, and the factors contributing to their fluctuations are also decomposed by energy
type or sector in Section 4.1.

3

energy price differential in dollar terms is understood to be rather smaller. The real PLI, which is thefocus of this paper, is defined as the ratio of the nominal PLI for energy divided by the nominal PLI
for output (real GDP). This measure is unaffected by exchange rate fluctuations and can provide amore stable real energy price differential.
The RUEC is an indicator that includes adaptation due to changes in real energy prices and canbe seen as an indicator of economic vulnerability to energy price increases.4 The change in RUEC isdefined as the growth of real energy price (REP) minus the growth of average energy productivity(AEP). An increase in the RUEC means that the economy has become more vulnerable to higher
energy prices, as the increase in REP outweighs the moderating effect of improved AEP. If theincrease in energy use prices could be fully passed through to output prices, the rise in REP would be
reduced, and the real burden would be lessened. And if the increase in REP could improve AEP, thereal burden would be reduced. Such AEP changes may be limited in the short term. Even in the longrun, improvements in AEP may be limited in countries that have long faced energy prices more than
twice as high as those in the U.S.5
In the energy transition context, the RUEC can indicate the challenges a country faces in
implementing ambitious green policies. Countries with higher nominal energy prices, lower valueadded prices for their outputs, more energy-intensive industries, and lower energy productivity tend tohave higher RUECs. These countries may struggle to manage the high-cost burden associated with theenergy transition. The post-pandemic surge in energy prices provided an unexpected lesson in these
dynamics.
This paper evaluates the monthly energy costs using various energy and economic statistics ineach country and the quarterly estimates of real PLI and RUEC, corresponding to the official quarterly
GDP in current and constant prices published by the national statistical offices in each country. While
this measurement covers January 2015 to December 2023, the recent monthly energy cost estimates
are preliminary due to the available energy price and volume data limitations. Therefore, they are
updated with the latest annual statistics on volumes from energy statistics and nominal values from
economic statistics as soon as they are available, which serve as annual benchmarks for 2019–2022.6
The data developed in this paper are called multilateral energy cost monitoring (ECM). 7 In
addition to assessing price and cost aspects like the real energy cost, the ECM tries to monitor output
changes in energy-intensive trade-exposed (EITE) industries. This paper primarily concentrates on
4 A similar indicator, real unit labor cost, is well known and has many examples of measurement, but there are few examples of RUEC

measurement. The European Commission (2014) indicated a gradual increase in RUEC in the manufacturing sector of major
developed countries between 1995 and 2009. Nomura (2023, Chapter 3) analyzes the long-term RUEC changes and their industrial
origins from 1955 to 2019 in the Japanese economy.
5 Japan’s PLI for energy use has always been quite high, ranging from 1.5 to 3.0 times that of the U.S. from 1955 to 2019. As energy
prices rose in the 2000s, Japan’s advantage in AEP over the U.S. shrank since the U.S., with its relatively lower energy prices, improved
its AEP at a faster pace (Nomura 2023, p. 93).
6 In ECM_202403, the yearly totals of energy use volumes are benchmarked with the IEA’s World Energy Balances in 2021. The annual
totals of nominal energy costs are benchmarked with the available SUT/IOT or national accounts in 2019 for South Korea, 2020 for
China, Germany, and Italy, and 2022 for the rest. The limitations of the paper’s approach, such as estimating nominal values fromprice and volume estimates, should be recognized. In particular, the large price fluctuations in 2022–2023 could lead to significant
revisions in future annual benchmarks.
7 The ECM for Japan began development in January 2022 to construct a monthly RUEC for the Japanese economy (Nomura andInaba 2023). In parallel with this project, monthly estimates of production-side GDP (output price and volume), named JMGDP, arebeing developed for each of the 36 industries in the Japanese economy. For developing the multilateral ECM in this paper, the Japanese
ECM was revised for better comparability based on the common sectors in Table 2 in Section 2.1. (Before the revision, the Japanese
ECM separated only two non-transforming sectors.)

4

international comparisons of the former while utilizing Germany as a case study to illustrate the latter.
The latest ECM (i.e., ECM_202403) also provides a short-term outlook, calculated using the U.S.
Energy Information Administration’s (EIA) outlook for energy prices and the OECD’s forecast foreconomic growth up to December 2024.
This paper is structured as follows. Section 2 presents the framework for measuring energy costs
and energy price differentials. Section 3 describes the measurement processes. Section 4 analyzes thesources of monthly energy price changes in each country and provides the quarterly estimates of real
PLI and the RUEC from the first quarter of 2015 to the fourth quarter of 2023, with forecasts up to
the fourth quarter of 2024. Germany had the largest post-pandemic surge in RUEC among the eight
countries covered. Section 4.3.2 assesses the impact of industrial hollowing out in Germany. Section
5 concludes. Appendix A discusses the nominal PLIs measured monthly in ECM with nominal
exchange rate fluctuations. Our methodology for measuring monthly energy costs can be applied tohistorical data to help verify its accuracy. Some verification works are provided in Appendix C. Finally,
the supplementary figures are presented in Appendix D.
2

Framework

The ECM elementary level of final energy consumption (FEC) is defined as the 𝑖-product and 𝑗energy-using sector. We define the following variables in ECM:
𝐸𝑖𝑗

FEC at the elementary level, assessed in terms of calorific value,

𝑃𝑖𝑗𝐸
𝑉𝑖𝑗𝐸
𝐸

Energy unit price at purchaser’s price8 in the elementary level, measured at LCU,

𝐸

Energy use at the aggregate level, defined in Eq. (4),

𝑉
𝑃
𝐸̅

𝐸

Energy costs at the elementary level（= 𝑃𝑖𝑗𝐸 𝐸𝑖𝑗 ）,Energy costs at the aggregate level, defined as ∑𝑖𝑗 𝑉𝑖𝑗𝐸 ,Energy use price at the aggregate level, implicitly defined as 𝑉 𝐸 ⁄𝐸,

̅𝐸𝑃

FEC at the aggregate level, expressed as a simple aggregation（∑𝑖𝑗 𝐸𝑖𝑗 ）,Average energy price at the aggregate level, defined as 𝑉 𝐸 ⁄𝐸̅,𝑋

Aggregate output (real GDP),

𝑉𝑋
𝑃

𝑋

Aggregate output value (GDP at current prices), andAggregate output price (GDP deflator) measured at LCU, implicitly defined as 𝑉 𝑋 ⁄𝑋.

Note that all variables are defined by country (𝑐) and period (𝑡), i.e., monthly for energy uses andquarterly for outputs, but they are omitted here for simplicity. Unit prices and costs are assessed ateach country’s local currency units. Table 1 presents the ECM product classification and its
concordance with the IEA World Energy Balances product (IEA 2020). The ECM defines six major
product categories (1. Coal products, 2. Natural gas, 3. Oil products, 4. Electricity, 5. Heat, and 6.
Others) and 29 sub-categories.

8 The ECM defines the energy prices at the purchaser’s price (including trade margins and transportation costs) for international

comparison. This corresponds to the end-use price in the IEA’s Energy Prices and Taxes.

5Table 1: Product Classification
ECM products
1. Coal products
101. Coal

IEA World Energy Balances products

102. Coal coke103. Coal gas

104. Peat and peat products
105. Oil shale and oil sands
2. Natural gas
3. Oil products
301. Crude,NGL and feedstocks

Hard coal (if no detail)Brown coal (if no detail)
Anthracite
Coking coal
Other bituminous coalSub-bituminous coalLignitePatent fuel
BKB
Coke oven cokeGas coke
Coal tar
Gas works gas
Coke oven gas
Blast furnace gas
Other recovered gases
Peat
Peat products
Oil shale and oil sands
Natural gas

Crude/NGL/feedstocks (if no detail)Crude oilRefinery feedstocks
Additives/blending components
Other hydrocarbons
Natural gas liquids
302. Liquefied petroleum gases
Liquefied petroleum gases (LPG)
303. Motor gasoline excl. biofuels Motor gasoline excl. biofuels304. Jet fuelAviation gasoline
Gasoline type jet fuelKerosene type jet fuel excl. biofuels305. Kerosene
Other kerosene
306. Gas/diesel oil
Gas/diesel oil excl. biofuels

ECM products
307. Fuel oil
308. Naphtha309. Lubricants
310. Other oil product4. Electricity
401. Electricity
402. Autoproducer electricity
5. Heat
6. Others
601. Waste

602. Biofuels603. Nuclear
604. Hydro
605. Geothermal
606. Solar photovoltaics
607. Solar thermal
608. Tide, wave and ocean
609. Wind
610. Other sources

IEA World Energy Balances products
Fuel oil
Naphtha
Lubricants
Refinery gas
Petroleum coke
EthaneWhite spirit & SBP
BitumenParaffin waxes
Other oil productsElectricity
Elec/heat output from non-specified manufactured gases
Heat
Heat output from non-specified combustible fuels
Industrial waste
Municipal waste (renewable)
Municipal waste (non-renewable)
Primary solid biofuels
Biogases
Biogasoline
Biodiesels
Bio jet kerosene
Other liquid biofuels
Non-specified primary biofuels and waste
Charcoal
Nuclear
Hydro
Geothermal
Solar photovoltaics
Solar thermal
Tide, wave and ocean
Wind
Other sources

Sources: ECM_202403 and IEA (2020). Note: Each product is defined separately for domestically produced and imported products,
as shown in Table 3.

2.1 Unit Energy Costs
Based on these variables defined above, the nominal unit energy cost (NUEC) at LCU at the aggregate
level is defined as the nominal FEC cost per unit of real GDP as follows:(1)

𝑁𝑈𝐸𝐶 = 𝑉 𝐸 ⁄𝑋.

The real unit energy cost (RUEC) is a measure of the NUEC deflated by aggregate output price (𝑃 𝑋 ).
(2)

𝑅𝑈𝐸𝐶 = 𝑁𝑈𝐸𝐶 ⁄𝑃 𝑋 = 𝑉 𝐸 ⁄𝑉 𝑋 = 𝑅𝐸𝑃⁄𝐴𝐸𝑃,

where 𝑅𝐸𝑃 is the real energy price and 𝐴𝐸𝑃 is the (gross) average energy productivity, defined as:(3)

𝑅𝐸𝑃 = 𝑃𝐸 ⁄𝑃 𝑋 and 𝐴𝐸𝑃 = 𝑋⁄𝐸 .

As shown in Eq. (2), the RUEC is recognized as the ratio of nominal energy costs to nominal GDP(𝑉 𝐸 ⁄𝑉 𝑋 ) and as an index of REP divided by AEP. The RUEC rises when the improvement in AEP
cannot cover the increase in REP, indicating that the country is vulnerable to higher energy prices.
The energy use (𝐸) in 𝐴𝐸𝑃 is defined as the quality-adjusted measure using the Translog index:(4)∆ln 𝐸 = ∑𝑖𝑗 𝑣̅𝑖𝑗 ∆ln 𝐸𝑖𝑗 ,where ∆ is the difference between two consecutive periods and ∆ln 𝐸𝑖𝑗 is the growth rate of each
FEC by product (𝑖) and sector (𝑗). The above equation aggregates the growth rates weighted by thetwo-period average cost share of each product and sector (𝑣̅𝑖𝑗 ) in total energy use (∑𝑖𝑗 𝑣̅𝑖𝑗 =1). The
price per unit of calorific value varies depending on the quality of the product. The price of qualityadjusted energy use (𝑃𝐸 ) is implicitly defined by 𝑉 𝐸 ⁄𝐸.
An index calculated from the quantity of energy use (𝐸) and FEC (𝐸̅ ) is defined as 𝑞:6(5)𝑞 = 𝐸 ⁄𝐸̅ = 𝑃̅𝐸 ⁄𝑃𝐸 .

We call 𝑞 the energy quality index.9 Electricity has a higher price per unit of calorific value thanutility gas. Thus, when there is an energy shift from utility gas to electricity in FEC, 𝑞 increases, even
if 𝐸̅ remains constant. This quality index is recognized from a price side as a relative price of 𝑃̅𝐸 ⁄𝑃𝐸
in Eq. (5).
Table 2 presents the ECM sector classification. There, it is broadly divided into the 1.
Transformation sector and 2. Non-transformation sector. The transformation sector comprises five
two-digit categories (11. Electricity, 12. Heat, 13. Coke and refinery petroleum products, 14. Mining
and quarrying, and 15. Biofuel) and the non-transformation sector has five industries (21. Industries,
22. Transport, 23. Residential, 24. Commercial and public services, and 25. Agriculture, forestry, andfishing). Among 21. Industries, 211. EITE industries and 211. Non-EITE industries are separated. For
correspondence with economic statistics, the transport sector is divided into consumption byhouseholds (2201) and non-households (2202). In ECM, “household” is defined as the sum of 2201.
Transport activities by households and 23. Residential and “industry” as the sum of 21. Industries,
2202. Transport activities by non-households, 24. Commercial and public services, and 25. Agriculture,
forestry, and fishing. This sector classification allows annual benchmarking (Section 3.3) with economic
statistics such as the SUT/IOT.
Table 2: Sector Classification
ECM sectors
1 Transformation sector
11 Electricity
1101 Electricity–Coal
1102 Electricity–Oil
1103 Electricity–Natural gas1104 Electricity–Combustible renewables
1105 Electricity–Other combustible non-renewables
1106 Electricity–Nuclear
1107 Electricity–Hydro
1108 Electricity–Wind
1109 Electricity–Solar
1110 Electricity–Geothermal
1111 Electricity–Other renewables
1112 Electricity–Others
1113 Autoproducer electricity–Coal
1114 Autoproducer electricity–Oil
1115 Autoproducer electricity–Natural gas
1116 Autoproducer electricity–Combustible renewables
1117 Autoproducer electricity–Other combustible non-renewables
1118 Autoproducer electricity–Nuclear
1119 Autoproducer electricity–Hydro
1120 Autoproducer electricity–Wind
1121 Autoproducer electricity–Solar
1122 Autoproducer electricity–Geothermal
1123 Autoproducer electricity–Other renewables
1124 Autoproducer electricity–Others

ECM sectors
12 Heat13 Coke and refinary petroleum products
1301 Oil
1302 Coal coke
1303 Coal gas
14 Mining and quarrying
1401 Coal
1402 Crude, NGL, and feedstocks1403 Natural gas
15 Biofuel
2 Non–transformation sector
21 Industries
2101 EITE industries
21011 Iron and steel
21012 Chemical and petrochemical
21013 Non-ferrous metals
21014 Non-metallic minerals
21015 Paper, pulp, and print
2102 Non–EITE industries
22 Transport
2201 Transport activities by households
2202 Transport activities by non–households
23 Residential
24 Commercial and public services
25 Agriculture, forestry, and fishing

Sources: ECM_202403. Note: Household is defined as the sum of 2201 and 23, and industry is as the sum of 21, 2202, 24, and 25.

The ECM constructs the monthly estimates of an energy use matrix by product and sector. Table
3 provides the structure of the ECM’s energy use table. Each product consumption is estimatedseparately for energy transformation (𝐸𝑖𝑗′ defined in the upper block) and final consumption (𝐸𝑖𝑗
9 A more complete picture of the relationship between primary energy consumption (PEC), FEC, and (quality-adjusted) energy use

is provided in Nomura (2023, Chapter 2).

7

defined in the lower block), which are further divided into domestic products (D) and imported′

products (M), respectively. The energy use tables are measured at a monetary term (𝑉𝑖𝑗𝐸 and 𝑉𝑖𝑗𝐸 ) anda calorific value (𝐸𝑖𝑗′ and 𝐸𝑖𝑗 ). This paper’s price/cost differentials are measured based on the final
energy use table, shown in the lower block of Table 3.
Table 3: Energy Use Table
ECM sector (j)11.Electricty

1. T ransofrmation sector
12. Heat
13. Coke and refinary
petroleum products

21. Industries
・・・(Unit：LCU)
Domestic products (D)2. Non–transformation sector
22. T ransport 23. Residential 24.
Commercial
and public
services

25. Agriculture,
forestry, andfishing

Domestic
consumption

101. Coal
102. Coal coke・・・PE ij,D E'ij,DPE i,D E'i,D

T ransformation
610. Other sources
processes(Intermediate Imported products (M)consumption)
101. Coal0

ECM products (i)102. Coal coke・・PE ij,M E'ij,Mp E i,M E'i,M・
610. Other sources
Domestic products (D)101. Coal
102. Coal coke・PE ij,D Eij,DPE i,D Ei,DPE ij,M Eij,M

PE i,M Ei,M・・
Final energy
consumption

610. Other sources

0

Imported products (M)101. Coal
102. Coal coke・・・
610. Other sources

Sources: ECM_202403. Notes: See Table 1 for product classification and Table 2 for sector classification in ECM. The ECM energy
use tables are measured monthly at a monetary term and a calorific value.

2.2 Price Differentials
Nominal PLI measures the energy price differential between countries for FEC, a PPP ratio to themarket exchange rate (e𝑡 ). The PPP for energy use between the country-𝑐1 and the country-𝑐2 isdefined as the Fisher index.
(6)

E(F)

E(L)

E(P)

PPP𝑐1 𝑐2 = √PPP𝑐1 𝑐2 PPP𝑐1 𝑐2 ,
E(L)

E(P)

where PPP𝑐1𝑐2 and PPP𝑐1𝑐2 are the PPPs based on the Laspeyres and Paasche PPP indices between
the country-𝑐1 and the country-𝑐2 , which are defined as, respectively:
(7)

E(L)

𝐸
𝑃𝑖𝑗,𝑐
𝐸𝑖𝑗,𝑐1

PPP𝑐1 𝑐2 = ∑𝑖𝑗 𝐸 2

𝑃𝑖𝑗,𝑐1 𝐸𝑖𝑗,𝑐1

𝐸
𝑃𝑖𝑗,𝑐𝐸𝑖𝑗,𝑐2E(P)and PPP𝑐1𝑐2 = ∑𝑖𝑗 𝐸 2

𝑃𝑖𝑗,𝑐1 𝐸𝑖𝑗,𝑐2.

Since the Fisher index in Eq. (6) does not satisfy the transitivity test in multilateral comparison,
we measure the PPP for energy uses based on the EKS (Éltető-Köves-Szulc) method as:
(8)

E(F)

E(F) 1/N

PPP𝑐E1 𝑐2 = ∏𝑐3 (PPP𝑐1 𝑐3 PPP𝑐3𝑐2 ),where N is the number of countries this paper covers (i.e., eight). The EKS-PPP for energy use ismeasured in this paper for the base year (𝑇=2015), in which this paper attempts to capture the mostaccurate unit price information by product and sector as discussed in Section 3.1. The times-series
8

𝐸
PPP estimates are extrapolated using the energy price indices measured in each county (𝑃𝑐,𝑡), which

are normalized as 1.0 at 𝑇.
(9)

𝑃𝑐𝐸 ,𝑡

PPP𝑐E1 𝑐2 ,𝑡 = PPP𝑐E1 𝑐2 ,𝑇 𝑃𝐸1 .
𝑐2 ,𝑡These time series PPPs for energy use are measured using the monthly energy use tables described inTable 3. Based on this EKS-PPP for energy use (PPP𝑐E1 𝑐2 ,𝑡 ) and the average exchange rate (e𝑐1 𝑐2 ,𝑡 ), thenominal PLI for energy use is defined as(10)

PLI𝑐E1𝑐2 ,𝑡 = PPP𝑐E1 𝑐2,𝑡 ⁄e𝑐1 𝑐2 ,𝑡 .

The real PLI is measured every quarter. On the PPP for output (PPP𝑐X1 𝑐2 ,𝑇 ) at the base year
(𝑇=2017), we follow the estimate in the 2017 International Comparisons Program (ICP) round (WorldBank 2020). And the time series estimates of the PPP for output (PPP𝑐X1𝑐2 ,𝑡 ) are measured using theGDP prices measured in the quarterly national accounts in each country, in a similar way of Eq. (9).
The real PLI is defined as:(11)

Real PLI𝑐E1 𝑐2 ,𝑡 = PLI𝑐E1 𝑐2 ,𝑡 ⁄PLI𝑐X1 𝑐2 ,𝑡 = PPP𝑐E1 𝑐2 ,𝑡 ⁄PPP𝑐X1 𝑐2,𝑡 .

This real price differential measure is not affected by exchange rate fluctuations.
3

Measurement

The measurement of multilateral ECM consists of the following four processes:
1) Establish volume and value balances of annual energy use tables in Table 3 for the base year
(i.e., 2015 in ECM_202403) and split it into monthly energy use tables (i.e., from January to
December 2015), using monthly energy volume/value data (explained in the 2nd process),2) Update the monthly energy use table up to the most recent month (i.e., December 2023), using
monthly data on energy prices and volumes, based on the monthly table for December 2015
(or for December after the most recent annual benchmark carried out in the next 3rd process),3) Benchmark monthly estimates (updated in the 2nd process) against available recent annual dataon energy use volume and value estimates (i.e., the 2016 value, …, 2021 value), and
4) Forecast 6–12 months ahead of the most recent observation period (i.e., from January 2024 to
December 2024).
Section 3.1 describes the development of the annual energy use table for the base year, described in
the first process. Section 3.2 presents the methodology for developing monthly estimates in the second
process (and the information required for the division into monthly tables in the first process). Sections
3.3 and 3.4 describe the third and fourth processes, respectively.
3.1 Base Year Estimates
The year 2015 is the base year of ECM_202403, providing the initial annual energy use table. The unit
prices of a given product can differ considerably by sector in the energy use value table defined inTable 3, depending on the differences in energy kind, quality, consumption size, contract, and so on.
In the base year, unit price differentials among sectors are considered in each product as much as
9possible, while the monthly estimates discussed in Section 3.2 basically capture only product-specificprice changes. Table 4 provides the data for annual estimates for nominal values (𝑃𝐸 ) and volume
measure (𝐸). The first block from the top of Table 4 organizes data commonly used for several
countries, including the data by the IEA, Eurostat, and OECD, identified using the data code (D01–
D06) in the second column.
Table 4: Data Used for Annual Estimates
Country
International

Data code Variables Data
D01
E
D02
E
D03
E
D04*
PE
D05*
E
D06*
E
D01, D05, D06, and
CHN-D01 VE
CHN-D02 P E
CHN-D03 P E

China

CHN-D04 P E
Japan

Korea

U.S.

FranceGermany

Italy

UK

Organization

World Energy Balances
Physical Energy Flow Accounts
Energy Statistics
Energy Prices and Taxes
Quarterly National Accounts
Economic Outlook

IEA
EurostatEurostat
IEA
OECD
OECD

全国投入产出表 (Input-Output Table)
全国居民消费价格指数 (Consumer Price Indices by Category)
全国电力价格情况监管通报 (National Electricity Price
Supervision Report)
中国石油天然气股份有限公司2015 年度报告 (Petro China
Company Limited 2015 Annual Report)

National Bereau of Statistics (NBS)
China Statistical Press
Nationl Energy Administration (NEA)
PetroChina Company Limited (CNPC)

D01, D05, D06, and
JPN-D01 VE, E
産業連関表 (Input-Output Table)
Ministry of Internal Affairs and Communications (MIC)
JPN-D02 VE, P E
国民経済計算年報 (JSNA)
Economic and Social Research Institute (ESRI)
JPN-D03 VE, E
KEO Database
Keio Economic Observatory (KEO)
JPN-D04 E
総合エネルギー統計 (General Energy Statistics)
Ministry of Economy, Trade and Industry (METI)
D01, D04, D05, D06, and
KOR-D01 VE
Bank of Korea (BOK)산업연관표 (Input-Output Tables)
KOR-D02 E
Korea Energy Statistical Information System (KESIS)확장 밸런스 (Extended Energy Balance)
KOR-D03 E상용자가발전업체조사 (Survey of Commercial Self-Generators) Korea Energy Statistical Information System (KESIS)
D01, D04, D05, D06, and
USA-D01 VE
Input-Output Accounts Data
U.S Bureau of Economic Analysis (BEA)
USA-D02 VE, E
State Energy Data System
Energy Information Administration (EIA)
USA-D03 VE, E
Electricity Power Annual
Energy Information Administration (EIA)
USA-D04* VE, E
Quarterly Coal Report
Energy Information Administration (EIA)
D01, D02, D03, D04, D05, D06, and
FRA-D01 VE
Tableau des Entrées-Sorties (Input-Output Table)
The French National Institute of Statistics and Economic Studies(INSEE)
FRA-D02 VE
Bilan Énergétique de la France (France's Energy Balance)
The French National Institute of Statistics and Economic Studies(INSEE)
D01, D02, D03, D04, D05, D06, and
DEU-D01 VE
Verwendungstabelle (Use Table)
Federal Statistical Office Germany (Destatis)
DEU-D02 VE, P E
Volkswirtschaftlichen Gesamtrechnungen (VGR)
Federal Statistical Office Germany (Destatis)
DEU-D03 VE
Kostenstrukturerhebung im Verarb. Gewerbe, Bergbau
Federal Statistical Office Germany (Destatis)(Cost Structure Survey in Manufacturing, Mining and Quarrying)
DEU-D04 E
Energieverwendung der Betriebe im Verarb. Gewerbe
Federal Statistical Office Germany (Destatis)(Energy Use of Companies in the Manufacturing Sector)
D01, D02, D03, D04, D05, D06, and
ITA-D01 VE
Le Tavole Delle Risorse E Degli Impieghi (the Supply and Use
The National Institute for Statistics (Istat)
Table)ITA-D02 VE
Conti Economici NazionaI (National Accounts)
The National Institute for Statistics (Istat)
D01, D02, D03, D04, D05, D06, and
GBR-D01 VE
Supply and Use Tables
Office of National Statistics (ONS)
GBR-D02 VE, E
Digest of UK Energy Statistics (DUKES)
Department for Energy Security and Net Zero
GBR-D03* P E
Prices of Fuels Purchased by Manufacturing Industry
Department for Energy Security and Net Zero

Source: ECM_202403. Note: * are quarterly data.

The ECM mainly follows the IEA’s Energy Prices and Taxes (data code: D04) for unit prices byproduct and broad sector (i.e., electricity, industry, and residential). For China, which D04 does notcover, and for products for which data is unavailable in D04, some country-specific annual data
presented from the second block in Table 4 or monthly data provided in Table 5 (Section 3.2) are used.
For example, for Japan, the 2015 Benchmark-year IOT (JPN-D01) and the energy account in the KEO
Database (JPN-D03) provide detailed unit price data of energy uses. For the U.S., the EIA’s Monthly
Energy Review (data code is USA-D05 in Table 5) provides high-quality data on energy use volumes andunit prices by product and broad sector. In other countries, unit price data are supplemented by China
10

(CHN-D01, D03–D04, D12, and D13 in Table 5), South Korea (KOR-D01 and D04), France (FRAD02 and D03), Italy (ITA-D01, D02, and D04), and the UK (GBR-D03, D06, and D07).
When some unit cost data are not available at the detailed level of energy use tables, we assumed
the differential unit price ratio among products, measured as the Jevons index, aggregating the pricedifferential indices observed in other countries. For example, in Germany, if the unit price data for103. Coal gas is unavailable, we set 101. Coal as a reference product. The unit price for 103 is estimatedby multiplying the unit price for 101 observed in Germany by the Jevons index of 103 to 101. 10
Finally, the unit prices in base year 2015 are adjusted so that base year annual consumption values,
defined as yearly aggregates of the product of monthly energy use volume and its unit price,correspond to nominal values recorded in the SUT/IOT and the system of national accounts.11 These
adjustments in terms of nominal values are conducted based on the classifications available in the
SUT/IOT to maintain consistency at a more aggregated level of product (e.g., the first-digit ECM
product as 1. Coal product or 3. Oil product) than the energy use tables (based on the three-digit ECM
product). On the other hand, the above energy statistics do not give unit cost differentials of a productfor industrial use. Still, more detailed differentials across industries are available from the SUT/IOT.
Such industry-specific unit costs can be reflected in the finalized base year estimates, for example, in
the EITE industries breakdown in the ECM (sector 21011–21015).
A similar process of aligning monthly estimates with annual nominal values, such as IOT, is also
applied to the annual benchmark of nominal values for non-base years in Section 3.3. However, in thebase year, the 2015 benchmark IOTs are available in Japan and South Korea and reflect more detailed
information than those used for the annual benchmarks.
3.2 Monthly Estimates
Table 5 provides the data for monthly estimates of energy prices and volumes in ECM to develop themonthly energy use tables (Table 3). The first block from the top of Table 5 organizes data commonlyused for several countries, including the U.S. EIA, Eurostat, and IEA data, identified using the datacode (D04–D11) in the second column. The second through ninth blocks of Table 5 provide a list of
country-specific data with data codes (e.g., CHN-D05, JPN-D05, and so on) for each of the eight
countries. The data used to estimate energy use volumes is denoted by 𝐸 and the energy price databy 𝑃𝐸 in the third column.
The monthly energy use table establishes the volume balance by product between domestic
demand, which consists of transformation use (𝐸𝑖𝑗′ ) and final use (𝐸𝑖𝑗 ), and domestic supply (𝐸𝑖 ),which is defined as domestic shipment plus net imports. In the US, the EIA publishes complete
monthly data on energy demand and supply for almost all products in the Monthly Energy Review (USAD05), while in other countries, we must fill in missing volumes in the respective data on domestic
demand and/or domestic supply by product.
10 There are some cases where such an application is difficult, for example, in 601. Waste, consumption prices are highly subsidized,

so the disparity rates observed in other countries may not provide a good approximation. The lowest unit price (per calorific value)
among products is assumed for such products.
11 The annual value data are China (CHN-D01), Japan (JPN-D01 and D02), South Korea (KOR-D01), the U.S. (USA-D01), France(FRA-D02), Germany (DEU-D01 and D02), Italy (ITA-D01 and D02), and the UK (GBR-D01).

11

Table 5: Data Used for Monthly Estimates
Country
Data code Variables
International
D07
E
D08
E
D09
E
D10
PE
D11
PE
D12
E
D13
VE, E
D14
P E, E
China
D12, D14, and
CHN-D05 E
CHN-D06 E
CHN-D07 E
CHN-D08 P E
CHN-D09 P E
CHN-D10 P E
CHN-D11 P E
CHN-D12 P E
CHN-D13 P E

CHN-D14 P E
CHN-D15 VE, E
Japan

Organization

Energy Statistics
Production in Industry
Production in Services
Harmonaised Indices of Consumer Prices
Energy Prices and Costs in Europe
Monthly Electricity Statistics
International Trade in Goods
Short-term Energy Outlook

EurostatEurostatEurostatEurostat
European Commission (EC)International Energy Agency (IEA)
Eurostat
Energy Information Administration (EIA)

能源主要产品产量 (Output of Energy Products)
工业主要产品产量 (Output of Major Industrial Products)
服务业生产指数 (Index of Service Production)
工业生产者出厂价格指数 (Producer Price Indices）
居民消费价格分类月度环比指数 (Month-to-Month Consumer
Price Index by Category)

National Bureau of Statistics of China (NBS)
National Bureau of Statistics of China (NBS)
National Bureau of Statistics of China (NBS)
National Bureau of Statistics of China (NBS)
China Statistical Press

居民消费价格指数 (Consumer Price Indices by Category)
商品零售价格指数 (Retail Price Indicies)

National Bureau of Statistics of China (NBS)
National Bureau of Statistics of China (NBS)
National Bureau of Statistics of China (NBS)

流通领域重要生产资料市场价格变动情况 (Market Price ofImportant Means of Production in Circulation)
各省区市和中心城市汽、柴油最高零售价格表 (List of
Maximum Retail Prices of Gasoline and Diesel in Various Provinces,
Autonomous Regions, Municipalities and Central Cities)
Energy Prices in the EU and Main Trading Partners

National Development and Reform Commision (NDRC)

European Commission (EC)General Administration of Customs of the People's Republic of
China海关统计 (Customs Statistics)

D14 and
JPN-D05
JPN-D06
JPN-D07
JPN-D08

E
E
E
E

電力調査統計 (Electric Power Investigation Statistics)
鉱工業指数 (Indices of Industrial Production)
第3次産業活動指数 (Indices of Terciary Industry Activity)
石油等消費動態統計 (Monthly Report of the Current Survey ofEnergy Consumption)

JPN-D09

E

資源・エネルギー統計月報 (Monthly Report of Mineral Resources Ministry of Economy, Trade and Industry (METI)
and Petroleum Products Statistics)

JPN-D10E

電力需要実績 (Actual Electricity Demand)

Organization for Cross-regional Coordination of Transmission
Operators (OCCTO)

消費者物価指数 (Consumer Price Index)
企業物価指数 (Producer Price Index)
企業向けサービス価格指数 (Service Producer Price Index)
貿易統計 (Trade Statistics of Japan)

Ministry of Internal Affairs and Communications (MIC)Bank of Japan (BOJ)
Bank of Japan (BOJ)
Ministry of Finance (MOF)

JPN-D11
JPN-D12
JPN-D13
JPN-D14

Korea

Data Name

PE
PE
PE
VE, E
D12, D14, and
KOR-D04 P E, E
KOR-D05 E
KOR-D06 E
KOR-D07 P E
KOR-D08 P E
KOR-D09 P E
KOR-D10 VE, E

Ministry of Economy, Trade and Industry (METI)
Ministry of Economy, Trade and Industry (METI)
Ministry of Economy, Trade and Industry (METI)
Ministry of Economy, Trade and Industry (METI)

Korea Energy Economics Institute (KEEI)에너지통계월보 (Monthly Energy Statistics)
Korea Energy Statistical Information System (KESIS)간이 밸런스 (Simple Energy Balances)광업제조업동향조사 (Monthly Survey of Mininig and Manufacturing) Korean Statistical Information Service (KOSIS)
Bank of Korea (BOK)생산자물가지수 (Producer Price Indices)Bank of Korea (BOK)소비자물가지수 (Consumer Price Indices)Bank of Korea (BOK)수입물가지수 (Import Price Indices)
Korea International Trade Association (KITA)
해외무역통계 (Foreign Trade Statistics)

Source: ECM_202403.

For domestic demand (𝐸𝑖𝑗′ and 𝐸𝑖𝑗 ), in countries where there are no monthly energy use data(such as Japan) or where there are statistics but no breakdown of demand for energy transformation(such as South Korea), provisional estimates are developed by multiplying the output of each sector(e.g., IP (Index of production/Industrial production/Indices of industrial production) and ISP (Indexof services/Services production/Indices of tertiary production)) by the energy use coefficient foreach product of the corresponding sector. 12 Supplemented by these output-based estimates,
provisional estimates of the energy use matrix (𝐸̂𝑖𝑗′ and 𝐸̂𝑖𝑗 ) are obtained.
For domestic supply (𝐸𝑖 ), in countries where monthly data on domestic supply by energy type

12 If

an energy productivity improvement (EPI) is measured in terms of changes in the input coefficient for the relevant energy
product in the sector concerned, an average rate of EPI based on the past trend is assumed to estimate energy consumption volumes.
The household sector’s output indicator is based on real household consumption in OECD’s Quarterly National Accounts (D05 in Table4) as a crude approximation in this paper, except for China.

12are not available (such as Japan), domestic supply is determined from shipments of the energy productconcerned (e.g., JPN-D06) and import data from trade statistics (e.g., JPN-D14). In countries where
shipment data by energy type are unavailable (such as China), it is estimated using output volumes of
IP/ISP (e.g., CHN-D05, D06, and D07), assuming a proportional relationship with shipments without
adjusting for inventory changes.13 Supplemented by these estimates, a provisional estimate of the row
total of the energy use table (𝐸̂𝑖 ) is obtained.
Table 5: Data Used for Monthly Estimates (Cont’d)Country
U.S.

FranceData code Variables
D12, D14, and
USA-D05 P E, E
USA-D06 P E
USA-D07 P E
USA-D08 P E
USA-D09 E
USA-D10 E
USA-D11 E
USA-D12 VE, E

Data Name

Organization

Monthly Energy Review
U.S. Bioenergy Statistics
Producer Price Indexes
Consumer Price Index
Industrial Production and Capacity Utilization
US Monthly GDP (MGDP) Index
Electricity Power MonthlyInternational Trade Data

Energy Information Administration (EIA)
U.S Department of Agriculture (USDA)
U.S Bureau of Labor Statistics (BLS)
U.S Bureau of Labor Statistics (BLS)
Federal Reserve Board (FRB)
S&P Global
Energy Information Administration (EIA)
United States Census Bureau

D07, D08, D09, D10, D11, D12, D13, D14, and
FRA-D03 P E, E
Conjoncture Mensuelle de l'énergie (Monthly Energy Review)
Ministry of Ecology Transition and Territorial Cohesion
FRA-D04 P E
Indice de Prix de Production de l'industrie Française pour le Marché
The French National Institute of Statistics and Economic Studies
Français (Producer Price Index in Industrial Production Sold in France)

Germany D07, D08, D09, D10, D11, D12, D13, D14, and
DEU-D05 P E
Index der Erzeugerpreise Gewerblicher Produkte (Producer Price Index Federal Statistical Office Germany (Destatis)for Industrial Products)
DEU-D06 P E
DEU-D07 P E

Italy

UK

Index der Einfuhrpreise (Index of Import Prices)
Gesamtausgabe der Energiedaten (Energy Data: Complete Edition)

DEU-D08 VE, E
Außenhandel (Foreign Trade)
D07, D08, D10, D11, D12, D13, D14, and
ITA-D03 P E
Prezzi alla Produzione dell'industria (Industrial Producer Price Index)
ITA-D04 P E, E
Statistiche Energetiche e Minerarie (Energy and Mining Statistics)
ITA-D05 E
Indice delle Vendite del Commercio al Dettaglio (Index of Retail Trade
Sales)
D07, D11, D12, D14, and
GBR-D04 P E
Producer Price Inflation
GBR-D05 P EConsumer Price Inflation
GBR-D06 P E
Domestic Energy Price Indices
GBR-D07 P E
Monthly and Annual Prices of Road Fuels and Petroleum Products
GBR-D08 E
Index of Production
GBR-D09 E
Index of Services
GBR-D10 E
Energy Trends
GBR-D11 VE, E
Trade Data

Federal Statistical Office Germany (Destatis)
Federal Ministry for Economic Affairs and Climate Action(BMWK)
Federal Statistical Office Germany (Destatis)
The National Institute for Statistics (Istat)
Ministry of Environment and Energy Security
The National Institute for Statistics (Istat)

Office for National Statistics (ONS)
Office for National Statistics (ONS)
Department for Energy Security and Net Zero
Department for Energy Security and Net Zero
Office for National Statistics (ONS)
Office for National Statistics (ONS)
Department for Energy Security and Net Zero
HM Revenue and Customs

Source: ECM_202403.

The volume balance for each product in each country is maintained according to our evaluationof the data accuracy in one of three assumptions:a) with 𝐸̂𝑖 as a constraint, the estimate is split into 𝑗-sectors using 𝐸̂𝑖𝑗′ and 𝐸̂𝑖𝑗 ,b)c)with 𝐸̂𝑖𝑗′ and 𝐸̂𝑖𝑗 as constraints, 𝐸̂𝑖 is defined as ∑𝑗(𝐸̂𝑖𝑗′ + 𝐸̂𝑖𝑗 ), andwith 𝐸̂𝑖 and 𝐸̂𝑖𝑗 as constraints, the row sum (∑𝑗 𝐸̂𝑖𝑗′ ) is split into 𝑗-sectors using 𝐸̂𝑖𝑗′ .

The assumptions by product and country are shown in Table 6 in Appendix B.
The monthly preliminary estimates of energy prices by product are extended using available
energy price statistics for Korea, the U.S., France, Germany, Italy, and the UK.14 Due to differencesin how each data set classifies energy sectors or uses, an effort has been made to closely match the
13 The changes in energy inventory stocks are considered in our annual benchmark process described in Section 3.3.

14 The monthly energy statistics of these countries are KOR-D04, USA-D05 and D06, FRA-D03, DEU-D07, ITA-D04, and GBR-

D05 in Table 5. However, for products for which these statistics are unavailable or for the most recent months for which the latest
estimates are unavailable, expanded estimates were made using PPI and CPI.

13

ECM sector classification (see Table 2). When such data are unavailable for China, Japan, and Germany,
monthly estimates are extrapolated using the most detailed Producer Price Index (PPI) and Consumer
Price Index (CPI).15 There may be significant problems with viewing the available price indices as
indicative of average price changes relative to total use by product, including price differences by sector,
type of use, and time of day. Because of these problems, the preliminary monthly estimates will bebenchmarked on a value basis in the year the SUT/IOT becomes available (see Section 3.3). The PPI
and CPI estimates are subject to revision due to changes in the base year, and the ECM is revised
retrospectively when the latest data becomes available.16
The purpose of the ECM is to evaluate the energy costs relative relationship to output (GDP).
In the estimated results presented in Section 4, real PLI represents the relative price between energy
use and output, while RUEC signifies the relative value between energy use and output. To incorporatesignificant trends in these measures, output is typically defined as the seasonally adjusted price andvolume of GDP. Consequently, although energy statistics are often not seasonally adjusted, thecorresponding energy use prices and volumes in ECM are also seasonally adjusted. For monthly data
for which seasonally adjusted series, such as IP/ISP and CPI/PPI, are available, these are used, while
those not seasonally adjusted, such as energy statistics, trade statistics, and some IP in China, areseasonally adjusted in ECM using the X-13ARIMA-SEATS (U.S. Census Bureau 2023).
3.3 Annual Benchmark
Annual benchmarking is conducted at several aggregate levels, based on detailed —but preliminary—monthly estimates of ECM’s energy use tables developed in Section 3.2. The World Energy Balances(D01 in Table 4) published by IEA (2023a) provides the annual benchmark estimates for FEC
volumes.17 For example, the IEA published the latest data for the 2021 estimates in September 2023;
the ECM monthly estimates from January to December 2021 are adjusted to the IEA’s 2021 estimatesby product as an annual total. Based on the revised estimates in December 2021, the recent estimates
are revised from January 2022 to the latest date (i.e., December 2023 in ECM_202403). A similar
process will be repeated next year when the new data of D01 is available.
After the annual benchmarking on a volume basis, annual benchmarking on a value basis is carriedout. This process is similar to the base year estimation described in Section 3.1. Applying the annual
benchmarking to the past would allow us to verify the accuracy of the ECM monthly preliminary
estimates in Section 3.2 (before annual benchmarks). Some verification works from 2015 are provided
for the German cases in Appendix C. Based on historical data, the monthly cost estimates derived
from the current methodology provide a reliable approximation, with average error rates of about 2%
in volume and 4% in value for Germany. While the accuracy is understandable during periods of lower
price volatility, it’s important to recognize that the error rate could be even higher during the critical
15 The PPI and CPI are D10 for EU countries, CHN-D08–D12, JPN-D11–D14, KOR D07–D09, USA-D07–D08, FRA-D04, DEU-

D05–06, ITA-D03, GBR D04–D05 in Table 5.
16 Even in energy products, the impact of base year revision is not small. For example, in the German PPI for natural gas (352227100:
Erdgas, bei Abgabe an Wiederverkäufer (Natural gas, when sold to resellers)), the 2015 base PPI shows an increase to 4.5 times fromJanuary 2021 to the peak in September 2022 but was revised to 3.6 times in the 2021 base PPI published in March 2024.
17 In D01, time-series defects sometimes occur due to changes in statistical concepts. In this case, the ECM adjusted the past data to
conform as closely as possible to the revised concept.

14

period in the 2022–2023 RUEC surge.
Finally, deviations from the annual national accounts are also adjusted in real and nominal termsof output. The ECM output measures rely on quarterly GDP estimates in each country. Quarterly
GDP estimates do not necessarily coincide with the GDP estimates in the annual national accounts
for each country, which are published with a time lag of more than one year. If adjusted quarterly
GDP estimates are published with the annual national accounts, the ECM quarterly outputs are
replaced, but if it is not published, the ECM output in nominal and real values are benchmarked against
the annual GDP.
3.4 Forecasts
In the current ECM, our efforts continue to improve the accuracy of monthly preliminary estimates
while continuing to verify them against past annual estimates. The role of forecasts is, therefore, limited.
Although fairly simplified, a short-term outlook, which is 6–12 months ahead of the period of thelatest observation, is constructed based on the available information in the U.S. EIA’s Short-Term Energy
Outlook (D14 in Table 5) for energy prices and the OECD’s Economic Outlook (D06 in Table 4) for
output growths. In ECM_202403, the forecast values are from January to December 2024 in themonthly estimates. In the estimation results in Section 4, dotted lines represent forecasted values.
4

Estimated Results

4.1 Energy Prices
Figure 1 provides the estimated monthly quality-adjusted and seasonally adjusted price changes of the
final energy use at local currency units in each country, with the average price in 2015 set at 1.0. Sincelate 2020, high fossil fuel prices have significantly impacted the global economy due to the demand
recovery following the COVID-19 pandemic. This situation has been further exacerbated by Russia’s
invasion of Ukraine in February 2022. Subsequently, energy prices in the U.S. started to decrease earlyin the second quarter of 2022. In contrast, Germany, Italy, and the UK experienced ongoing increases,
reaching their peaks that were more than double that of 2015 in 2022. Although prices began to decline
after that, they remained at 1.2 to 1.7 times the pre-pandemic levels (average price in 2015–2019) asof December 2023.
The factors contributing to energy price surges vary significantly among countries. Figure 2
provides the product and sector contributions in each country. The main difference between the U.S.
and Germany, Italy, and the UK is the notable impact of electricity price spikes and those of naturalgas. In the U.S., which relies on the domestic supply of natural gas, price increases were limited frommid-2022 compared to Europe,18 which was forced to find alternatives to imports from Russia, as
shown in Figure 29 in Appendix D.1. These increases were passed on to electricity prices. With theadded impact of Germany’s nuclear phase-out in 2023, electricity prices remained high. Anothernotable feature of the U.S. energy price increase is that price increases in the EITE industry have been
18 Most energy price increases in the U.S. are attributable to petroleum products, with the transportation sector, particularly household

gasoline consumption, making the largest contribution on a sectoral basis, as shown in the U.S. left chart of Figure 2.

15

very limited compared to other countries, as shown in Figure 2, the U.S. right chart. The strength of
the U.S. industry supports households’ energy consumption burdens through higher incomes.
2.4（2015=1.0）

Germany

2.2

Italy

2.0

UK

1.8

FranceSouth Korea

1.6

1.4
U.S.

China

1.2
Japan
1.0

0.8

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Unit: Index (average price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2024. Source:
ECM_202403. Notes: The dotted line represents forecasts for January–December 2024. Quality-adjusted energy price (𝑃 𝐸 ) is definedas the implicit Translog index in Eq. (4). The prices are seasonally adjusted and include taxes and subsidies.

With its heavy reliance on nuclear power, France was expected to be an exception to the postpandemic energy price surges. Still, in 2022, half of its 56 reactors were temporarily shut down forinspection and repair. 19 To meet demand, Electricite de France (EDF) was forced to purchaseelectricity on the European market during a period of very high prices, costing the group an estimatedEUR 29 billion and contributing to a record net loss (EBITDA) of EUR 17.9 billion for 2022
(MacLachlan 2023).20 To pass on such price increases, EDF raised electricity prices in stages from the
beginning of 2022 until 2023 (see Figure 2, left-hand chart for France). In Figure 2, the contributionof price increases due to electricity is divided into the impact of domestic and imported electricity.
Germany and Italy have also seen electricity import prices contribute significantly to energy priceincreases. This indicates that they are forced to purchase higher-priced electricity during limited
renewable energy generation and electricity shortages.
Figure 1 suggests that Asian countries have successfully mitigated energy price increases.
However, examining the product-specific contributions in Figure 2 shows little cause for optimism
regarding Japan, Korea, and China’s ability to curb these price hikes. In essence, the detrimental effects
are merely being deferred.
Japan relies primarily on LNG imports, many of which are based on long-term contracts, which
has helped mitigate the impact of higher spot prices relative to the European countries. However, it is
19 MacLachlan (2023) indicates that inspection and repair outages due to generic stress corrosion cracking (SCC), first discovered in

October 2021, reduced the output of EDF’s nuclear fleet by 81.7 TWh in 2022.
20 In 2023, EDF returned to profitability (EUR 10.0 billion) after the French government completed the nationalization of the group,in a year marked by higher nuclear power output in the country, as reported by The Wall Street Journal (Orru 2024).

16

important to note that this energy price comparison is based on prices suppressed by subsidies. The
impact of subsidies and governmental interventions to curb energy price surges appears to be
substantial. According to Japan’s ECM, which breaks down the impact of subsidies on energy prices
by product, the analysis shows that in 2022, gasoline subsidies lowered the overall energy price byapproximately 5%. Moreover, in 2023, the combined effect of gasoline, electricity, and gas subsidies
reduced overall energy prices by about 9%.21 These subsidies reached about 5 trillion yen (about 36
billion USD) in 2023 and have been criticized. But under Japan’s current policy, the subsidies aresupposed to last until April 2024, with further extensions being considered.22 The forecasts shown in
Figure 1 by the dotted line tend to increase from April 2024 due to the impact of the increase in the
FIT surcharge for renewable energy and the phased subsidy reductions.
In addition to the impact of subsidies, the decline in electricity prices in Japan, particularly in
2022, includes the effects of insufficient price pass-through due to rising fossil fuel prices. Despite
formal market liberalization, government intervention has consistently slowed the escalation ofelectricity prices. Following the Fukushima Daiichi nuclear power plant accident in 2011, Japan’s largest
electric power company, Tokyo Electric Power Company (TEPCO), is effectively under government
control. Implementing an increase in residential electricity prices in 2022 was particularly difficult, withall major power companies recording large losses in the same year. While such domestic electricity
price suppression measures are desirable for electricity consumers, there are concerns that they may
jeopardize the stable supply of electricity in the long run.
Similar trends are observed in South Korea. The state-owned enterprise, Korea Electric Power
Corporation (KEPCO), which monopolizes the entire process of power generation, transmission, anddistribution, incurred a substantial deficit of 32.6 trillion won (about 30 billion USD) in 2022 due tothe inability to pass on the soaring fuel prices. In 2023, despite a reduction in the deficit throughgovernment-approved electricity tariff increases (see Figure 2, South Korea’s left chart), KEPCO
continued to record a deficit for the third consecutive period from 2021.
China restructured its national power sector, splitting the State Power Corporation into separate
entities for power generation and transmission/distribution in late 2002. This resulted in the creationof two transmission and distribution companies and five major power generation companies. Despite
the surges in coal and LNG prices, China has effectively contained the rise in electricity prices (Figure
2, China’s left chart), making it the only one of the eight industrialized countries with no significant
price increases in the post-pandemic period. This achievement can be attributed to the Chinese
government’s efforts to curb household burdens (see Figure 2, China’s right chart) and policies topromote a significant uptake of electric vehicles (EVs). However, substantial concerns exist about thesustainability of the current suppressed electricity prices.

21 The impact of electricity and gas subsidy addition is shown as the fault from January 2023 in Figure 2, Japan’s right chart (sector

decomposition).
22 The Japanese government has stated that it will extend subsidies on fuel oil prices beyond May. Figure 1 assumes that subsidies forelectricity and gas are halved in May and zero after June and that subsidies for fuel oil are halved from May to September and zeroafter October.

17

1.40
1.35
1.30
1.25
1.20

1.40

（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)

1.15

0.350

1.35

（January 2015=1.0）

EITE industries (2101)

35%

Non–EITE industries (2102)
1.30

0.250

1.25

Transport activities by non–households (2202)
Commercial and public services (24)25%

Agriculture, forestry, and fishing (25)

1.20Transport activities by households (2201)

0.150

1.15

15%

Residential (23)

Industry

1.10

1.10
1.05

0.050

1.05

1.00

1.00

0.95

-0.050 0.95

0.90

0.85

-0.150 0.85

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201501
1.5

1.4

1.3

1.2

5%
Household

-5%

-15%

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201501

0.90(China)Energy price (pE)

Product decomposition

Sector decomposition
0.500（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)

1.5

50%

（January 2015=1.0）

EITE industries (2101)

0.400

1.4

Non–EITE industries (2102)

40%

Transport activities by non–households (2202)

0.300

Commercial and public services (24)

1.3

30%

Agriculture, forestry, and fishing (25)0.200Transport activities by households (2201)

1.2

20%

Residential (23)Energy price (pE)

Industry

0.100

1.1

10%

1.0

0.000

1.0

Household0%0.9-0.100

0.9

-10%

0.8

-0.200

0.8

-20%

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201501121007
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201501

1.1(Japan)1.6

1.5

Product decomposition（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)Sector decomposition

0.600

1.6

0.500

1.5

60%

（January 2015=1.0）

EITE industries (2101)
Non–EITE industries (2102)

50%

Transport activities by non–households (2202)
0.400

1.4

0.300

1.3

1.2

0.200

1.2

1.1

0.100

1.1

10%

1.0

0.000

1.0

0%

0.9

-0.100

0.9

-10%

0.8

-0.200

0.8

-20%

1.4

1.3

Commercial and public services (24)40%

Agriculture, forestry, and fishing (25)Transport activities by households (2201)

30%
Industry

Residential (23)Energy price (pE)Household

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201501(South Korea) Product decomposition
1.8

20%

Sector decomposition
0.800

1.8

0.700

1.7

0.600

1.6

0.500

1.5

0.400

1.4

1.3

0.300

1.3

1.2

0.200

1.2

20%

1.1

0.100

1.1

Household
10%

1.0

0.000

1.0

0%

0.9

-0.100

0.9

-10%

0.8

-0.200

0.8

-20%

1.7
1.6
1.5
1.4

（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)80%

（January 2015=1.0）

EITE industries (2101)

70%

Non–EITE industries (2102)
Transport activities by non–households (2202)
Commercial and public services (24)

60%
50%

Agriculture, forestry, and fishing (25)Transport activities by households (2201)

40%

Residential (23)Energy price (pE)30%

Industry

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20190110
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201501(the U.S.) Product decomposition

Sector decomposition

Figure 2: Sources of Energy Price Changes

Unit: Index (price at local currency unit in January 2015=1.0 in each country). Period: January 2015–December 2023. Source:
ECM_202403. Note: The prices are seasonally adjusted and include taxes and subsidies.

180.800

1.8

0.700

1.7

0.600

1.6

0.500

1.5

1.4

0.400

1.4

1.3

0.300

1.3

1.2

0.200

1.2

20%

1.1

0.100

1.1

10%

1.0

0.000

1.0

0%

0.9

-0.100

0.9

-10%

1.8

1.7

1.6

1.5

（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)80%

（January 2015=1.0）

EITE industries (2101)

70%

Non–EITE industries (2102)
Transport activities by non–households (2202)

60%

Commercial and public services (24)Agriculture, forestry, and fishing (25)

Industry
50%

Transport activities by households (2201)
40%

Residential (23)Energy price (pE)30%Household

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201501(France)

Product decomposition

Sector decomposition
1.600

2.6

1.400

2.4

1.200

2.2

1.000

2.0

0.800

1.8

0.600

1.6

0.400

1.4

Industry
40%

0.200

1.2

20%

1.0

0.000

1.0

0%

0.8

-0.200

0.8

-20%

2.6

2.4

2.2

2.0

（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)

1.8

1.6

1.4

1.2

160%

（January 2015=1.0）

EITE industries (2101)

140%

Non–EITE industries (2102)
Transport activities by non–households (2202)

120%

Commercial and public services (24)Agriculture, forestry, and fishing (25)100%Transport activities by households (2201)
80%

Residential (23)Energy price (pE)60%

Household

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201501(Germany) Product decomposition

2.4

2.2

（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)Sector decomposition

1.400

2.4

1.200

2.2

140%

（January 2015=1.0）

EITE industries (2101)
Non–EITE industries (2102)

120%

Transport activities by non–households (2202)
1.000

2.0

0.800

1.8

1.6

0.600

1.6

1.4

0.400

1.4

40%

1.2

0.200

1.2

20%

1.0

0.000

1.0

0%

0.8

-0.200

0.8

-20%

2.0

1.8

Commercial and public services (24)100%

Agriculture, forestry, and fishing (25)Transport activities by households (2201)

80%

Residential (23)Energy price (pE)60%

Industry

Household

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201501(Italy)
2.2

2.0

1.8

1.6

Product decomposition（January 2015=1.0）

1.Coal products
2.Gas
3.Oil products
4.Electricity (Domestic)
4.Electricity (Imported)
5.Heat6.Others
Energy price (pE)Sector decomposition
1.200

2.2

1.000

2.0

120%

（January 2015=1.0）

EITE industries (2101)
Non–EITE industries (2102)

100%

Transport activities by non–households (2202)

0.800

1.8

Commercial and public services (24)80%

Agriculture, forestry, and fishing (25)

0.600

1.6

Transport activities by households (2201)

60%

Residential (23)Energy price (pE)

Industry

1.4

0.400

1.4

40%

1.2

0.200

1.2

20%

1.0

0.000

1.0

0%

0.8

-0.2000.8-20%Household

12
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201501(the UK) Product decomposition

Sector decomposition

Figure 2: Sources of Energy Price Changes (Cont’d)

Unit: Index (prices in each country in January 2015=1.0). Period: January 2015– December 2023. Source: ECM_202403. Note: The
prices are seasonally adjusted and include taxes and subsidies.

194.2 Real PLI
As a real energy cost burden relative to output price, this section provides the quarterly estimates of
the real PLI, and Section 4.3 discusses the RUEC.23 The real PLIs for overall final energy use andindustry electricity use are shown in Figure 3 and Figure 4, respectively, with the U.S. level set at 1.0
each quarter from 2015 to 2023. The real PLI provides a highly stable trend independent of exchange
rate fluctuations and indicates international price differentials in real terms. A notable feature is theextremely advantageous position of the U.S. among major industrialized countries in terms of pricesof final energy use (Figure 3) and industry electricity (Figure 4).
While China’s nominal PLI for final energy use is comparable to that of the U.S. until the prepandemic period, post-pandemic measures to contain price increases have allowed China to achieve
similar or lower prices than the U.S. after 2022, shown in Figure 12 for energy use and Figure 13 forindustry electricity in Appendix A. However, in real terms, the output prices generated by productionin China are much cheaper than in the U.S., as shown in Figure 48 in Appendix D.3. When interpreted
as real energy prices, China bears an energy price burden about twice that of the U.S. in Figure 3 as
one unit of output still generates only a smaller value. The substantial burden seen in the real PLI
indicates that China is also facing difficulties in bearing additional energy costs on its energy transition
path.
（U.S.=1.0）

Italy

4.5

4.0

3.5

Germany

3.0South Korea

UK

2.5

Japan
2.0

China

France
1.5

1.0

U.S.

0.5

Q4
Q3
Q2
Q1 2024Q4Q3Q2
Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015

Figure 3: Real PLI for Energy Use
Unit: Index (the U.S. REP in each period=1.0). Period: Q1 2015–Q4 2024. Source: ECM_202403. Notes: The dotted line represents
forecasts for Q1–Q4 in 2024. The quality-adjusted price of final energy use is defined in Eq. (4), and the real PLI is defined in Eq.
(11). The nominal PLI for final energy use is provided in Figure 12 in Appendix A, and that for output is in Figure 48 in Appendix
D.3. The prices are seasonally adjusted and include taxes and subsidies.

The widening gap in real PLI relative to nominal PLI for final energy use is similar in South
Korea. The nominal PLI is at the same level as in Japan (Figure 12), but the real PLI is higher, reflecting

23 The nominal PLI measured monthly in ECM is discussed in Appendix A, with nominal exchange rate fluctuations in our observation

period. In addition, the time-series changes in prices and volumes in each country and the nominal PLI by product are provided inAppendix D.1 and D.3, respectively.

20

a lower nominal PLI for output (Figure 48). Conversely, in the UK, the gap in real PLI for final energy
use is smaller than in nominal PLI and the same level as in Japan, due to higher nominal PLI for output,
indicating a higher price pass-through capacity.
Figure 4 compares real PLI, focusing on industrial electricity usage.24 The overall trend mirrorsthat of Figure 3 for overall final energy use. However, noteworthy changes include the elevatedposition of the UK, attributed to suppressed electricity prices in Japan during the post-pandemic
period. In South Korea and China, particularly in the post-pandemic period in China, industrial
electricity price spikes are restrained, and the gap with the U.S. remains largely within a factor of two.
6.0

（U.S.=1.0）

Italy
5.5
5.0

Germany

4.5
4.0
3.5
UK

3.0

France

2.5

Japan

2.0
1.5

China
South Korea

1.0

U.S.

0.5

Q4
Q3
Q2
Q1 2024Q4Q3Q2
Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015

Figure 4: Real PLI for Industry Electricity Use

Unit: Index (the U.S. REP in each period=1.0). Period: Q1 2015–Q4 2024. Source: ECM_202403. Notes: The dotted line represents
forecasts for Q1–Q4 in 2024. See footnote 24 for industry definition. Electricity includes 402. Autoproducer electricity. The prices areseasonally adjusted and include taxes and subsidies.

Industry electricity price spikes in Germany were considerably influenced by the domestic postpandemic correction of the electricity price burden. Figure 5 shows the HI-ratio, which indicates thedifference between household and industrial average electricity prices, independently from theexchange rate fluctuations. Countries with a HI-ratio greater than 1.0 indicate that the average
household-use electricity price is higher than the average industry electricity price, meaning that theindustrial electricity burden is relatively reduced.
Before the pandemic, this was the case in all countries except China and South Korea, as shownin Figure 5. Electricity policies are such that industry is burdened less, and households bear more of
the cost, as in Germany, France, and Japan, where the HI ratio was above 1.8. However, after late 2021,
the inclined burden by households is unacceptable, reflecting high electricity prices, and the industrial
burden is also expanding in these countries.

24 The industry is defined roughly corresponding to economic statistics in Figure 4 and Figure 5. As shown in Table 2, it is defined as

the aggregate sector of 21. Industries, 2202. Transport activities by non-households, 24. Commercial and public services, and 25.
Agriculture, forestry, and fishing.

21

2.5

（U.S.=1.0）

Germany
Italy
2.0France
Japan

1.5
U.S.
UK

South Korea
1.0

China

0.5

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 5: HI-Ratio of Electricity Price

Unit: Index (industry electricity use price in each period=1.0). Period: Q1 2015–Q4 2023. Source: ECM_202403. Notes: See footnote
24 for industry definition. Electricity is defined includes 402. Autoproducer electricity. The prices are seasonally adjusted and include
taxes and subsidies.

4.3 RUEC
4.3.1 Post-Pandemic RUEC Surge
The RUEC is the share of the total final energy cost to GDP at current prices, as defined in Eq. (2) in
Section 2.1. Figure 6 compares RUEC disparities among eight countries, suggesting different
difficulties in advancing the energy transition initiatives. In particular, the post-pandemic RUEC surge
and its recovery are illustrated in Figure 7 as a change from the pre-pandemic RUEC average.
Compared to the real PLI in Figure 3 in Section 4.2, the RUEC is a measure of overall costburden that further reflects differences in industrial structure and (gross) energy productivity acrosscountries. Countries with a higher output share of EITE manufacturing and lower energy productivity
have a higher RUEC rank than the real PLI. The first observation from the RUEC comparison, China
and South Korea have ranked significantly higher in the pre-pandemic period. The higher RUECs in
China and South Korea suggest that their economies are more vulnerable to rising energy prices thanother industrialized countries. In other words, even on a long-term energy transition path, it is moredifficult to take steps that would raise electricity and energy prices, including the cost of intermittent
renewable backup sources.
On the other hand, the UK, which has the lowest GDP share of manufacturing, 25 has reduced
its RUEC to a level close to that of the U.S. and France, compared to a higher gap in real PLI in Figure
3. In the UK, despite ranking relatively low in RUEC, navigating the energy transition remains
challenging amid macroeconomic and fiscal constraints. Hughes (2024) advocates for a pragmatic
approach, stating, “Rather than pretense and muddle, it would be better to extend the period and pace
25 According to OECD’s Annual National Accounts and official national accounts in each country, the manufacturing GDP share in the

UK is 9.3% in 2022, compared to 10.3% in the U.S., 10.7% in France, 19.4% in Japan, 20.4% in Germany, South Korea in 28.0% in
the same period and 27.5% in China in 2021.

22

of the energy transition to match the resources that can realistically be afforded.”
16%（Nominal GDP=100%）

Italy
Germany
14%

12%
China

Japan

South Korea
10%

UK
8%

France
U.S.
6%

4%

Q4
Q3
Q2
Q1 2024Q4Q3Q2
Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015

Figure 6: RUEC

Unit: Share (GDP at current market prices in each period=100% in each country). Period: Q1 2015–Q4 2024. Source: ECM_202403.
Notes: The dotted line represents forecasts for Q1–Q4 in 2024. The prices are seasonally adjusted and include taxes and subsidies.
The volumes are seasonally adjusted.

The second observation from the RUEC comparison is that all countries except China
experienced a sharp increase in RUEC between 2021 and 2022 and a recovery from late 2022 to 2023.
The numbers after the country name in Figure 7 indicate the peaks of the post-pandemic RUEC surges
and the recovered level as of the fourth quarter of 2023 relative to the pre-pandemic level (averages
in 2015–2019). The post-pandemic RUEC surge has greatly increased the vulnerability of national
economies to energy price volatility. Section 4.3.2. discusses the economic consequences. By the fourth
quarter of 2023, RUEC had been successfully contained in all countries to within 5% to 19% of prepandemic levels (except for the U.S., which is rather below). The sources of RUEC recovery are
discussed in Section 4.3.3.
The third observation from the post-pandemic RUEC surges is the notable increases in Germany
and Italy. After the pandemic, Italy experienced a noteworthy rise in RUEC starting in early 2021,
surpassing South Korea in the fourth quarter of 2021 and China in the first quarter of 2022, as shownin Figure 6. Meanwhile, Germany outpaced China and approached South Korea’s level in the third
quarter of 2022. The RUECs in Germany and Italy peaked in the third quarter of 2022 and gradually
declined through the end of 2023. As a comparison of peak levels of RUEC surges, Germany and
Italy have risen to 1.79 and 1.76 times the pre-pandemic level, respectively, as shown in Figure 7, whileother countries except China are from 1.24 to 1.34 times their pre-pandemic levels.

23

1.8

Germany (1.79 in Q3 2022, 1.15)
Italy (1.76 in Q3 2022, 1.19)（Average RUEC in 2015–2019=1.0）

1.7
1.6
1.5

Japan (1.34 in Q3 2022, 1.07)
UK (1.34 in Q4 2022, 1.16)
South Korea (1.32 in Q3 2022, 1.14)

1.4
1.3

U.S. (1.26 in Q2 2022, 0.96)
France (1.24 in Q2 2022, 1.05)

1.2
China (1.12 in Q2 2022, 1.09)

1.1
1.0
0.9
0.8
0.7

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4 2019

Figure 7: Post-Pandemic RUEC Surge

Unit: Index (average RUEC in 2015–2019=1.0 in each country). Period: Q4 2019–Q4 2023. Source: ECM_202403. Notes: The counts
after the country name indicate the peaks in RUEC surges and the recovered level as of Q4 2023 relative to the pre-pandemic level.
The prices are seasonally adjusted and include taxes and subsidies. The volumes are seasonally adjusted.

4.3.2 Economic Impacts of RUEC Surge
This section discusses the economic impact of the post-pandemic RUEC surges. Figure 8 compareschanges in RUEC and economic growth between the pre-pandemic and the RUEC-surge periods. The
x-axis in this figure represents the RUEC, starting from the 2015–2019 average and concluding at the2022–2023 average. The y-axis depicts economic growth rates, beginning with the average annual
growth rate from 2015–2019 and concluding with the growth rate from 2022–2023.
Various factors influence the economic growth rate, so comparing RUEC and economic growth
rates provides only a crude tendency. For example, in Germany, Italy, and the UK, the RUEC increased
significantly from the pre-pandemic to the post-pandemic (indicated by the arrows to the right inFigure 8). At the same time, the economic growth rates in Germany and the UK experienced asignificant decline (pointing downward). Conversely, although RUEC increased significantly in Italy,
economic growth only slowed modestly compared to pre-pandemic levels. Italy implemented policies
such as a substantial expansion of fiscal spending to bridge the demand-supply gap during the
pandemic,26 making it difficult to discern the impact of RUEC on observed economic growth. It is
understood that adverse effects may be deferred to the future.
In Asia, the pronounced slowdown of the Chinese economy against the backdrop of the U.S.China decoupling was evident, perhaps more so than was reflected in GDP statistics. Despite thelimited increase in China’s RUEC, growth was trending down. South Korea experienced a slowdown
26 The exceptionally strong performance of Italy can be largely attributed to the ‘super bonus,’ a generous tax break for improving theenergy efficiency of the housing stock introduced in 2020 under then-Prime Minister Giuseppe Conte. To grasp the magnitude ofthis measure, Financial Times (Romei 2024) indicates that Italian investment, including housing, has surged by 30% compared to prepandemic levels (the fourth quarter of 2019), marking the fastest pace of growth since comparable records began in 2000. Eurostat
data further illustrates that while construction output in December 2023 experienced a 13% decline in Spain, a 7% decrease in Germany,and remained unchanged in France, Italy witnessed a remarkable 40% increase compared to the same month in 2019. Prime Minister
Giorgia Meloni, who has led the country since October 2022, suspended this system completely at the end of 2023.

24

in growth because of its RUEC surge associated with China’s great economic slowdown. While Japan
experienced the RUEC surge, the continued negative interest rate policy from January 2016 to March2024 and the substantial yen depreciation (Figure 14) contributed to the modest recovery of economic
growth from pre-pandemic levels.27 As noted in Section 4.2, the real PLI for final energy use in Asian
countries was accompanied by policy measures to contain price increases, particularly in electricity
prices. This helped to mitigate the RUEC surge and minimized the short-term impact on economic
growth. However, despite their temporary effectiveness, they postponed the fundamental challenge ofachieving a more substantial reduction in energy prices.
（Output growth）(11.6%, 6.5%)

6%Pre-pandemic

RUEC surges

China(12.7%, 5.1%)

5%

4%

3%

South Korea(10.7%, 2.8%)

U.S.
(6.8%, 2.5%)(6.3%, 2.4%)

Germany
France(6.8%, 1.8%) (7.8%, 1.7%)2%

1%(8.0%, 0.7%)(6.3%, 1.9%)

UK( 7.8%, 0.1%)

0%(9.6%, 1.9%)(12.9%, 1.3%)
Italy(8.8%, 1.1%)(12.9%, 0.9%)Japan(8.0%, 0.7%)（RUECs）(11.1%, -0.3%)

-1%

6%

7%

8%

9%

10%

11%

12%

13%

Figure 8: Post-Pandemic RUEC Surge and Economic Growth
Unit: %. Period: Q1 2015–Q4 2023. Sources: ECM_202403 for RUEC and the official annual national accounts for economic growth.
Note: The x-axis is the RUEC share, with the starting point being the 2015–2019 average and the end point being the 2022–2023
average; the y-axis is the economic growth rate, with the starting point being the average annual growth rate from 2015 to 2019 andthe endpoint being the growth rate from 2022 to 2023.

The impact of RUEC peaks can be observed more directly at the industry level. Here, we focus
on Germany, where the RUEC has recorded the most rapid rise in Figure 7. Figure 9 presents theimport and output volume indices for EITE and non-EITE manufacturing in Germany.28 While thenon-EITE manufacturing shows a slight decline to levels comparable to pre-pandemic production in
the right chart of Figure 9, the EITE manufacturing experiences an accelerated decline in its output
with an import spike from early 2022 after the initial recovery from the pandemic outbreak in the left
chart of Figure 9. By the end of 2023, this decline led to stagnation, exceeding the level equivalent to
the downturn caused by the pandemic outbreak in 2020.

27 However, it’s important to note that Japan’s economic growth rate was the lowest among the eight industrialized countries before

the pandemic, averaging 0.8% from 2015 to 2019. Nomura (2023, Chapters 2 and 5) indicates that since the 2010s, policies aimed atconserving energy consumption and reducing CO2 emissions have accelerated the hollowing out of production in energy-intensive
goods.
28 Statistisches Bundesamt (2024) discussed the importance of energy-intensive industries in Germany in the context of declining
production in those industries, as discussed in this paper. In 2021, these industries required around 77% of the total amount of energy
used in industry, generated 17% of GDP at factor costs and employed around 15% of the workforce in industry.

25

(January 2015=1.0)(January 2015=1.0)

1.8

1.8

1.6

1.6

1.4

1.4

1.2

1.2

1.0

1.0

0.8

0.8
Import (M)Import (M)0.6

Output (X)0.6

Output (X)
M/X

M/X
0.4

0.4

12
07
04
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20190110
07
042018011007
042017011007
042016011007
04
2015011207
042023011007042022011007042021011007
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20190110
07
042018011007
042017011007
042016011007
04
201501(EITE Manufacturing)(Non-EITE Manufacturing)

Figure 9: Import-Output Ratio in EITE and Non-EITE Manufacturing in Germany

Unit: Index (import and output volumes in January 2015=1.0). Period: January 2015–December 2023. Source: Our computation isbased on Statistisches Bundesamt (Destatis) German National Accounts (DEU-D02), Cost Structure Survey in Manufacturing, Mining and
Quarrying (DEU-D03), Indices of Production in Manufacturing (D08 in Table 5), Index of import prices (DEU-D06), Foreign Trade (DEU-D08).

Figure 10 shows the industry contributions in the production volume change in the German
EITE manufacturing. The iron and steel manufacturing has hesitated to recover domestic productionin the post-pandemic. From the mid-2022 peak of RUEC (Figure 7), chemical manufacturing, acornerstone of the German economy, has declined its production significantly.29 This decline can be
attributed to the rapid increase in natural gas prices in Germany resulting from the loss of affordablegas supply from Russia,30 exacerbated by the economic slowdown in China, a key trading partner. The
paper and paper products manufacturing also witnessed substantial declines in production from mid2022. Furthermore, production reductions extended to the glass and glass products and cement
manufacturing sectors from mid-2023 onwards.
Germany’s RUEC also declined considerably to about 15% above the pre-pandemic level in the
fourth quarter of 2023, as shown in Figure 7. However, this has not led to a recovery in EITE
manufacturing production by the fourth quarter of 2023 in Figure 10.31

29 The largest chemical company in Germany, BASF, is investing €10 billion to construct a factory in China. According to the news

release on January 18 (BASF 2024), BASF plans to power the entire Zhanjiang Verbund site with 100% renewable energy by 2025.
The new Verbund site will be BASF’s largest investment, with around €10 billion upon completion. It will be operated under the sole
responsibility of BASF and will be the company’s third-largest Verbund site worldwide, following Ludwigshafen, Germany, andAntwerp, Belgium.
30 Holger Schmieding, chief economist at Berenberg Bank, asserts, “Germany is paying the price for its energy policies,” highlightingthat “the perception of Germany’s underlying strength may also have contributed to the misguided decisions to exit nuclear energy,
ban fracking for natural gas and bet on ample natural gas supplies from Russia.” (Mchugh 2023).
31 Cable (2024) reports that the manufacturing PMI (Hamburg Commercial Bank’s preliminary composite Purchasing Managers’
Index) dropped to a three-month low of 45.7 in March 2024, down from 46.5 in February. This figure fell well below the expectations
outlined in the Reuters poll, which had forecasted an increase to 47.0. Cyrus de la Rubia, chief economist at Hamburg Commercial
Bank, said, “If you were hoping for a recovery in the manufacturing sector in the first quarter, it’s time to throw in the towel.”26

1.10 (January 2015=1.0)

10%

1.05

5%

1.00

0%

0.95

-5%
a. Paper and paper products
b. Chemicals and chemical products

0.90

-10%

c. Glass and glass products
d. Cement, lime and plastere. Non-metallic mineral products n.e.c.
0.85

-15%

f. Iron and steel
g. Non-ferrous metal
Production (EITE)

0.80

-20%

12
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07
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20190110
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201501Figure 10: Decline in EITE Manufacturing Output in Germany

Unit: Index (output in January 2015=1.0). Period: January 2015–December 2023. Source: Our computation is based on Statistisches
Bundesamt (Destatis) German National Accounts (DEU-D02), Cost Structure Survey in Manufacturing, Mining and Quarrying (DEU-D03),
and Indices of Production in Manufacturing (D08 in Table 5). Note: The industry contribution is based on the Translog index using theprevious year’s output value share.

4.3.3 Sources of RUEC Recovery
RUEC was generally returned to within 19% of pre-pandemic levels by the end of 2023 in Figure 7,
but this rebound leaves traces of structural challenges. Figure 11 decomposes the changes in RUEC,
with the level in the first quarter of 2015 set as 1.0, into contributions from energy price and volume
and output price and volume. A notable aspect is that about half of Japan’s RUEC recovery by the
fourth quarter of 2023 is attributed to a decrease in energy use. However, this reduction in energy use
is not driven by a desirable improvement in energy productivity but rather by an undesirable decreasein output through deindustrialization.32 Consequently, Japan’s real GDP growth has remained stagnant
since the pre-pandemic period (see Figure 36 in Appendix D.2), and industrial hollowing out hascontributed to deflationary pressures, which have suppressed the rise of the GDP deflator. The
reduction effect of RUEC due to the expansion of nominal output has been significantly limited inJapan.
A similar, albeit more moderate than in Japan, downward trend in final energy use is observed in
the European countries from 2022 in Figure 11. The cause of the decrease in these countries is more
likely due to deindustrialization, as observed in Germany in Section 4.3.2 than to the desired technicalenergy productivity gains. Further measurements to analyze the causes of the decrease in energy use
associated with industrial hollowing out is another future task in the multilateral ECM.

32 The Japan Business Federation (Keidanren 2023) analyzes three factors that contribute to changes in CO2 emissions by industries.

These factors are a) changes in output volumes, b) changes in CO2 emission coefficients (transition to low-carbon energy), and c)changes in energy productivity improvement. The factor decomposition shows that, of the CO2 emission reductions in industries
from fiscal 2013 to 2022, a substantial 76 percentage points is attributable to the decline in output (a). The low-carbonization of energy
through the expansion of renewable energy and the restart of nuclear power plants (b) accounts for 19 percentage points. In
comparison, the effects of energy productivity improvement (c) contribute only five percentage points.

27

1.8

1.6

1.4

（Q1 2015=1.0）

Energy Price (pE)

0.800

1.5

0.600

1.4

0.500

（Q1 2015=1.0）

Energy Price (pE)

Output Price (pX)

Output Price (pX)

Output Volume (X)Output Volume (X)

0.400

0.400

1.3

0.200

1.2

1.0

0.000

1.1

0.100

0.8

-0.200

1.0

0.000

0.6

-0.4000.9-0.1000.4-0.6000.8-0.2000.2-0.8000.7-0.300

Energy Volume (E)
1.2

Q4
Q3
Q2
Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015(China)(Japan)1.6（Q1 2015=1.0）

1.5

Energy Price (pE)1.4

Output Price (pX)

0.500

（Q1 2015=1.0）

1.5

Energy Price (pE)1.4

Output Price (pX)

0.300

1.3

0.300

Energy Volume (E)Energy Volume (E)
1.2

RUEC

1.1

0.100

RUEC

1.1

0.100

1.0

1.0
0.9

-0.1000.9-0.100

0.8

0.8
0.7

-0.3000.7-0.300

0.6

0.6
0.5

-0.5000.5-0.500Q4Q3Q2Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015Q4Q3Q2Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015(South Korea)(the U.S.)

0.600

Energy Price (pE)

2.0Energy Price (pE)

Output Volume (X)0.400

Output Volume (X)0.700

Energy Volume (E)Energy Volume (E)
1.2

0.900

Output Price (pX)

Output Price (pX)
1.4

1.100

（Q1 2015=1.0）（Q1 2015=1.0）

1.6

0.500

Output Volume (X)Output Volume (X)
1.3

1.2

0.200

RUEC

Q4
Q3
Q2
Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 20151.6

0.300

Energy Volume (E)

RUEC

RUEC

RUEC

0.200

1.5

0.500

0.300
1.0

0.000

0.8

-0.2000.6-0.4000.100
1.0-0.100-0.600-0.500Q4Q3Q2Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015Q4Q3Q2Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 20150.4-0.3000.5(France)（Q1 2015=1.0）

2.0(Germany)
1.100

1.8

0.900

1.6

（Q1 2015=1.0）

Energy Price (pE)

Output Price (pX)

Output Price (pX)

Output Volume (X)

1.4

Energy Volume (E)

RUEC
1.5

0.600

Output Volume (X)0.700

Energy Volume (E)0.800Energy Price (pE)

0.400

RUEC

0.500

1.2

0.200

1.0

0.000

0.8

-0.2000.6-0.4000.4-0.600

0.300

0.100
1.0
-0.100-0.3000.5-0.500Q4Q3Q2Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015Q4Q3Q2Q1 2023
Q4Q3Q2
Q1 2022Q4Q3Q2Q1 2021Q4Q3Q2
Q1 2020Q4Q3Q2Q1 2019
Q4Q3Q2
Q1 2018
Q4Q3Q2Q1 2017Q4Q3Q2Q1 2016Q4Q3Q2
Q1 2015(Italy)(the UK)

Figure 11: Sources of RUEC Changes
Unit: Index (RUEC in Q1 2015=1.0). Period: Q1 2015–Q4 2023. Source: ECM_202403. Notes: The prices are seasonally adjusted
and include taxes and subsidies. The volumes are seasonally adjusted.

28

5Conclusion

The situation in each country in the long-term energy transition is fluid, and it is unclear to what extent
the public will accept the direct burden of higher energy costs and the indirect burden of stagnating
economic growth and incomes. This paper developed the multilateral ECM (Energy Cost Monitoring)to capture high-frequency indicators of the real price and cost differentials for final energy use acrosseight industrialized countries. This initial estimate serves as a valuable starting point, providing insights
into international real energy cost differentials within 1–2 months of the release of quarterly GDP
figures for each country. While the estimates will be revised as more statistics become available andthe current measurement framework is refined, the findings of this paper can be summarized in thefollowing three points.
Firstly, international comparisons of the real Price Level Index (PLI) for overall final energy use
and the Real Unit Energy Cost (RUEC) reveal that China and South Korea exhibit greater vulnerability
to energy price fluctuations than other industrialized countries. Policy suppression of nominal energy
prices, such as electricity, in both countries is sometimes understood to provide sufficient room toabsorb the additional costs of the energy transition. However, higher RUECs indicate that thesecountries face formidable challenges in advancing the energy transition. Even if other industrialized
nations shoulder a significant burden to decrease the CO2 emission intensity of electricity, China andSouth Korea may be constrained to implementing cost-effective measures. Consequently, this dynamic
could promote the growth of energy-intensive industries in these countries, exacerbating carbon
leakage.
Second, there were significant increases in real energy prices in Germany, Italy, and the UK after
the pandemic. Germany’s RUEC spiked to 1.79 times pre-pandemic levels at its peak in the third
quarter of 2022, exceeding the high RUEC in China. This rapid increase in RUEC has coincided with
a 20% reduction in output in Germany’s energy-intensive and trade-exposed (EITE) manufacturing
by the fourth quarter of 2023. Although RUEC has gradually declined from the fourth quarter of2022 to 15% above pre-pandemic levels as of the fourth quarter of 2023, the downward trend inGerman EITE manufacturing output has not diminished. This sustained production decline poses along-term challenge to German economic growth, even though the RUEC surge has eased.
Third, in Japan and other Asian countries, explicit energy subsidies and government interventions
have effectively mitigated the post-pandemic RUEC surges, contrasting the notable increases in RUEC
observed in Germany. However, unlike China and South Korea, where power companies arenationalized, Japan has simultaneously pursued electricity liberalization, posing challenges toinvestments in new nuclear and thermal power facilities. This has sparked concerns about power supply
stability. Instead of the visible trajectory of soaring energy prices and RUECs and the hollowing out
of EITE manufacturing seen in Germany, Japan has promoted hollowing out through policy
interventions to curtail production in various sectors to reduce CO2 emissions since before thepandemic. The increased risk of a disrupted stable electricity supply casts an even deeper shadow overfuture production. The current energy transition path cannot be secured without a review of energy
policy to ensure that economic growth is not sacrificed.
29

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31

Appendix
A

Nominal PLI

This paper considered the disparity in energy prices as real compared to output prices. One of thereasons for doing so is that understanding the trend of disparities based on nominal energy prices
becomes difficult due to fluctuations in exchange rates. Figure 12 and Figure 13 present the monthly
nominal PLIs for (quality-adjusted) final energy use and industry electricity use, respectively, with the
U.S. level set at 1.0 each month from January 2015 to December 2023. These nominal PLIs arecomparable to the real PLIs in Figure 3 and Figure 4, respectively, discussed in Section 4.2.
（U.S.=1.0）

3.0

Italy
Japan

2.5

South Korea

Germany

2.0

UKFrance

1.5
China

1.0

U.S.

0.5

12
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07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 12: Nominal PLI for Energy Use

Unit: Index (the U.S. prices in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The nominal
PLI for final energy use is measured based on Eq. (10). The real PLI for final energy use is provided in Figure 3 in Section 4.2. The
nominal PLIs for energy uses for industry and household uses are presented in Figure 38 and Figure 39, respectively, in Appendix D.3.
The prices are seasonally adjusted and include taxes and subsidies.
（U.S.=1.0）

Italy
4.0

3.5

Germany
UK

3.0

2.5

2.0

France
Japan

1.5

U.S.

1.0

South Korea

China

0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 13: Nominal PLI for Industry Electricity Use

Unit: Index (the U.S. prices in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The nominal
PLI is measured based on Eq. (10). Electricity includes 402. Autoproducer electricity. The real PLI for industry electricity is provided
in Figure 4 in Section 4.2. The nominal PLIs for electricity for the whole economy and household are presented in Figure 42 andFigure 43, respectively, in Appendix D.3. The prices are seasonally adjusted and include taxes and subsidies.

32

The nominal PLIs are sensitive to changes in market exchange rates. If the home country’s
exchange rate depreciates, it leads to an increase in imported fossil fuel prices. However, the increasein domestic electricity prices can remain more moderate, reflecting the stable prices of domestic energy
sources such as nuclear and renewables. Therefore, domestic electricity prices are evaluated as
becoming cheaper relative to a reference product (measured as the market exchange rate), thus thenominal PLI for electricity decreased. As depicted in Figure 14, the euro experienced a significant
depreciation against the US dollar from early 2021 through October 2022. Despite the impact of
currency depreciation, the European countries’ PLIs for energy use increased by approximately 30%
in Figure 12, and the PLIs for industrial electricity use doubled from pre-pandemic levels in Figure 13.
It’s worth noting that these price increases were underestimated due to the weakened euro.
（2015=1.0）

UK
1.3

1.2

China
1.1
FranceGermany

Italy

1.0

U.S.

South Korea
0.9
Japan

0.8

12
10

07

04

10

2023010704102022010704

1020210107

04

1020200107

04

10

20190107

04

1020180107

04

1020170107

04

1020160107

04

201501Figure 14: Nominal Exchange Rate

Unit: Index (averages in 2015=1.0 in each country). Period: January 2015–December 2023. Source: IMF International Financial Statistics.

During the same period from early 2021 to October 2022, Asian countries also faced similar
currency depreciation. From December 2022 to January 2023, while European and Asian countries
started to correct their currency’s depreciation against the U.S. dollar, Asian countries have again
experienced sharp currency depreciation since early 2023, shown in Figure 14. As a result, the nominal
PLIs for energy use in Figure 12 and for industry electricity use in Figure 13 have been declining in
Asian countries, especially in Japan, against the U.S. These trends differ significantly from real PLIs in
Section 4.2. To avoid the complexities arising from exchange rate fluctuations and to account for the
price pass-through in output price, a comparison using real PLIs would be more appropriate.
B

Volume Balance Assumption

As noted in Section 3.2, the volume balancing by product in each country is based on the following
assumptions:a)b)with 𝐸̂𝑖 as a constraint, the estimate is split into 𝑗-sectors using 𝐸̂𝑖𝑗′ and 𝐸̂𝑖𝑗 ,with 𝐸̂𝑖𝑗′ and 𝐸̂𝑖𝑗 as constraints, 𝐸̂𝑖 is defined as ∑𝑗(𝐸̂𝑖𝑗′ + 𝐸̂𝑖𝑗 ), and

33

c)

with 𝐸̂𝑖 and 𝐸̂𝑖𝑗 as constraints, the row sum (∑𝑗 𝐸̂𝑖𝑗′ ) is split into 𝑗-sectors using 𝐸̂𝑖𝑗′ .33

Table 6 provides the assumptions by product (domestic production and imports separately) in eachcountry in ECM_202403. When monthly data with balanced domestic supply and demand are available,
as in the U.S. EIA (USA-D05 in Table 5), they are evaluated as “ab” in this table. As discussed in
Section 3.3, since preliminary figures based on monthly statistics are subject to annual benchmarking
corrections, the data accumulation and our examination (as conducted in Appendix C) are to improve
the accuracy of future estimates.
Table 6: Balancing Assumptions by Product and Country
Domestic products
ECM products
CHN JPN
101. Coalaa102. Coal cokeab103. Coal gas(a+b)/2 b
104. Peat and peat products
105. Oil shale and oil sands
200. Natural gasaa301. Crude,NGL and feedstocksaa302. Liquefied petroleum gasesaa303. Motor gasoline excl. biofuelsaa304. Jet fuelba305. Keroseneaa306. Gas/diesel oilaa307. Fuel oilaa308. Naphthaab309. Lubricantsba310. Other oil productab401. Electricityaa402. Autoproducer electricityba500. Heatba601. Wastebb602. Biofuelsbb603. Nuclearbb604. Hydrobb605. Geothermalbb606. Solar photovoltaicsbb607. Solar thermalbb608. T ide, wave and oceanb609. Windbb
610. Other sources

KOR

USA

c
c
b

ab
a
a

c
b
a
(a+b)/2
a
a
a
c
c
a
c
c
b
(a+b)/2
b
b
b
b
b
b
b
b
b
b

ab
a
ab
ab
ab
ab
ab
ab
ab
ab
ab
ab
ab
a
ab
ab
b
ab
ab
ab
ab
ab

Imported products

FRA

DEU

IT A

GBR

CHN

JPN

KOR

USA

FRA

DEU

IT A

GBR

a
a

a
a
b

a
a
b

a
b
a

a
a

b
b

c
a

ab
a

a
a

a
a

a
a

c
c

a
b
a
a
a
b
a
a
a
b
a
a
b
a
b
a
b
b
b
b
b

a
a
a
a
a
a
a
a
a
a
a
a
b
b
b
a

a
a
a
a
a
a
a
a
a
a
a
a

b
a
a
a
a
a
a
b
a
a

a
c
a

ab
a

b
a
c
c
a
a

ab
ab
ab
ab
ab
ab
ab
ab
ab

a
a
a
(a+b)/2aaaaaa(a+b)/2
a

a
a
a
a
a
a
a
a
a
a
a
a

a
a
a
a
a
a
a
a
a
b
a
a

c
a
a
b
a
a
a
c
a
a
c
a

b

a

ab

a

a

a

a

b

b

a
a
a
b
a
a
a
c
a
a
c
a
b
b
b
a
b
b
b
b
b
b
b

a
a
(a+b)/2a(a+b)/2aa(a+b)/2
a
b
b
b
b
b
b
b
b
b
b
b

b
b
b
b

Source: ECM_202403. Note: The ‘ab’ indicates data where domestic supply and demand are balanced, and ‘(a+b)/2’ means that the
average of a and b was used as the constraint.

C

Verification

ECM_202403 provides monthly estimates from January 2021 to December 2023, while the IEA’s World
Energy Balances (D01 in Table 4), which is the latest currently available, is used as a benchmark tomaintain consistency for the estimated annual energy use volume in 2021. The data after January 2022,
connected after the 2021 annual benchmark, are called preliminary estimates in the ECM. The accuracyof the ECM measurement framework can be confirmed by applying the methodology used to obtainpreliminary estimates to historical benchmark estimates (D01 from 2015 to 2020) and comparing themto subsequently published benchmark estimates (D01 from 2016 to 2021).
Figure 15 shows the errors with the annual volume benchmarks from 2016 to 2021 for the past
preliminary estimates for the German case. The numbers in parentheses in the left panel are the mean
absolute percentage error (MAPE); the first series is the MAPE for the first year of the preliminary

33 If the row sum as a residual is negative, the assumption-a is used.

34

estimates (average from 2016 to 2021), and the second series is the MAPE for the second year of thepreliminary estimates (average from 2017 to 2021). Regarding the aggregate FEC, the MAPE for the
first year is 1.8%. For the second year, it is 2.6% with a slight expansion, confirming that the
preliminary estimates in volumes show a certain degree of accuracy in Germany.
After benchmarking on a volume basis, the ECM conducts a benchmark on a value basis against
available economic statistics, i.e., SUT/IOT, for some available products. Figure 16 presents the errors
with the annual value benchmarks, Statistisches Bundesamt Use Table (DEU-D01 in Table 4), from2016 to 2020 in Germany. As shown in the left panel of Figure 16, the MAPE is 3.8% in the first year
and 3.7% in the second year, as the sum of the products benchmarked in value terms. This is after
benchmarking on a volume basis, which may include the effects of divergence in statistical concepts
between energy and economic statistics, along with energy price estimation errors. While the accuracy
is understandable during periods of lower price volatility, it’s important to recognize that the error
could be even higher during the critical period in the 2022–2023 RUEC surge.
There may be significant problems in viewing the available price indexes as an indication of
average price changes relative to total use by product, such as price differences by sector and time ofday. In addition, the timing of subsidies may not be appropriate, such as when they begin or end. Thereis a 2–3-year time lag between the release of SUT/IOT (the 2021 estimates of DEU-D01 arescheduled to be released in Summer 2024). To compensate for this, the ECM also adjusts for somespecific products, such as household electricity use, using the German national accounts for 2021–
2022 (DEU-D02).

35

FEC
10,100

900

(PJ)

9,900

(PJ)

850

9,700
800

9,500

750

9,300
9,100

700

8,900
8,700

Annual benchmark data

8,500
2015

1.

2016

2017

Coal products
650

650

2018

(1.8%, 2.6%)
2019

2020

2021

2022

After–benchmarking estimates
Preliminary estimates
12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501After–benchmarking estimates
Preliminary estimates

2023
55

(PJ)

(PJ)

50

600

45

550

40

500

35
450

30

400

2016

2017

2018

2019

2020

2021

2022

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501

2015

2.

25

(4.1%, 8.6%)

350

Natural gas

2,600

(PJ)

240

2,500
2,400

200

2,300

180

2,200

160

2,100

140

2,000

2015

2016

2017

2018

2019

2020

2021

2022

120

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501(5.7%, 3.6%)

1,900

3.

(PJ)

220

Oil products

4,200

(PJ)

4,000
3,800
3,600
3,400

2015

4.

2016

2017

2018

2019

2020

2021

2022

2023

(PJ)

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501(1.5%, 2.3%)

3,200

380
360
340
320
300
280
260
240
220

Electricity

2,100

180

(PJ)

(PJ)

170

2,000

160

1,900

150

1,800

140
1,700

130

1,600

2015

5.

2016

2017

2018

2019

2020

2021

2022

120

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501(2.6%, 4.4%)

1,500

Heat
440

400
380
360

(0.9%, 1.1%)

340

2015

6.

2016

2017

2018

2019

2020

2021

2022

2023

(PJ)

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501

41
39
37
35
33
31
29
27
25

(PJ)

420

Others
800

80

(PJ)

(PJ)

70
750
60

700

50
40

650

2015

2016

2017

2018

2019

2020

2021

2022

2023

Figure 15: Annual Volume Benchmark in Germany

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
20150130(5.8%, 5.5%)

600

Unit: PJ. Period: 2015–2023. Source: ECM_202403. Note: The figures in parentheses in the left panel are the mean absolute percentage
error (MAPE); the first series is the MAPE for the first year of the preliminary estimates (six-year average of 2016–2021), and thesecond series is the MAPE for the second year of the preliminary estimates (five-year average of 2017–2021).

36

The sum of 1–5
450

45(Billion EUR)

400

After–benchmarking estimates
Preliminary estimates40

Annual benchmark data

35

(Billion EUR)

After–benchmarking estimates
Preliminary estimates

30

350

25

300
20
250

2015

101. Coal
2.8

2016

2017

2018

2019

2020

2021

2022

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
20150115(3.8%, 3.7%)

200

2023

(Billion EUR)

0.26

2.5

(Billion EUR)

0.23
0.20

2.2

0.17
1.9

0.14
0.11

1.6

0.08
1.3

2015

2016

2017

2018

2019

2020

2021

2022

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501

0.05(10.8%, 21.5%)

1.0102. Coal coke and 103. Coal gas
2.6(Billion EUR)

0.23

2.4

(Billion EUR)

0.20

2.2

0.17

2.0

0.14

1.8
1.6

0.11

1.4

0.08

(7.2%, 9.7%)

1.0
2015

2016

2017

2018

2019

2020

2021

2022

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
2015010.05

1.2

2. Natural gas and 301. Crude, NGL, and feedstocks
120(Billion EUR)

14(Billion EUR)

12
100

10
8

80

6
4

60

2
40

2015

2016

2017

2018

2019

2020

2021

2022

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
2015010(9.3%, 6.7%)20

3. Oil products (ex. 301. Crude, NGL and feedstocks)
180(Billion EUR)

16

170

160

14

150

12

140

(Billion EUR)

10

130
8

120

(2.7%, 3.2%)

100
2015

2016

2017

2018

2019

2020

2021

2022

2023

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501

6

110

4. Electricity and 5. Heat
160

(Billion EUR)

16

150
140

(Billion EUR)

14

130

12

120

110

10

100

8

90

(4.1%, 5.2%)

70
2015

2016

2017

2018

2019

2020

2021

2022

2023

Figure 16: Annual Value Benchmark in Germany

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501

6

80

Unit: Billion EUR. Period: 2015–2023. Source: ECM_202403. Note: The figures in parentheses in the left panel are the mean absolute
percentage error (MAPE); the first series is the MAPE for the first year of the preliminary estimates (five-year average of 2016–2020),
and the second series is the MAPE for the second year of the preliminary estimates (four-year average of 2017–2020).

37

D

Supplementary Figures

D.1 Energy Prices, Volumes, and Costs（2015=1.0）

Germany
3.4

3.0
Italy
2.6

2.2
UK
South Korea

1.8

France

1.4
China
U.S.
1.0
Japan

0.6

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 17: Industry Energy Prices

Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.
（2015=1.0）

2.0

Italy

1.8
UK
1.6

1.4
U.S.

GermanySouth Korea

1.2
France
China

Japan

1.0

0.8

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 18: Household Energy Prices

Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.
（2015=1.0）

1.5
1.4
1.3
China

1.2
Germany

1.1South Korea

UK
U.S.

1.0
0.9
Italy

Japan
France

0.8
0.7

0.6

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 19: Energy Use Volumes
Unit: Index (quality-adjusted final energy use in 2015 =1.0 in each country). Period: January 2015–December 2023. Source:
ECM_202403. Notes: The volume is defined as the Translog index. The volumes are seasonally adjusted.

38

（2015=1.0）

Germany
2.2
Italy
2.0

1.8
South Korea

UK

1.6
China
1.4

Japan
1.2

France

U.S.

1.0

0.8

0.6

12
10

07

04

10

07

20230104100720220104100720210104

100720200104

10

07

20190104

10

20180107

04

1020170107

04

1020160107

04

201501Figure 20: Energy Use Costs

Unit: Index (cost at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The costs are seasonally adjusted and include taxes and subsidies.

（2015=1.0）

2.6
UK
2.4

Italy
2.2
2.0

FranceGermany

1.8
1.6South Korea
1.4
Japan

U.S.

1.2

China

1.0
0.8

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 21: Electricity Prices
Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Notes: Energy price is defined as the implicit Translog index. The prices are seasonally adjusted and include taxes and subsidies.

（2015=1.0）

3.5

Germany

3.0

UK
2.5

2.0
Italy

France
1.5
Japan
U.S.
1.0
China

South Korea

0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 22: Industry Electricity Prices

Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.

39

（2015=1.0）

3.2
Italy

3.0
2.8
2.6
2.4

UK
2.2
2.0
1.8

1.6
France1.4

Germany

1.2

U.S.

Japan

1.0
China

South Korea

0.8

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 23: Household Electricity Prices
Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.

（2015=1.0）

1.8
1.7

China

1.6

1.5
1.4
1.3
1.2

Japan

U.S.

Italy

South Korea

1.1

1.0
0.9

UK
Germany

0.8France
0.7

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 24: Electricity Use Volumes

Unit: Index (volume in 2015 =1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403. Notes: The volume
is defined as the Translog index. The volumes are seasonally adjusted.

2.8（2015=1.0）

Italy

2.6
Germany
2.4
2.2

UK

2.0
1.8

1.6
China

1.4

U.S.
1.2

France1.0South Korea
Japan

0.8
0.6

12
10

07

04

10

07

20230104100720220104

1020210107

04

1020200107

04

10

20190107

04

1020180107

04

1020170107

04

1020160107

04

201501Figure 25: Electricity Use Costs

Unit: Index (cost at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.

40

（2015=1.0）

France
3.5

Italy
Japan

3.0

2.5

UK

China

2.0

GermanySouth Korea

1.5

U.S.

1.0

0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 26: Coal Products Prices
Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Notes: The price is defined as the implicit Translog index. The prices are seasonally adjusted and include taxes and subsidies.

1.3（2015=1.0）

1.2South Korea

1.1
France
1.0

Japan

0.9

China

0.8

U.S.

0.7
Germany
0.6
UK
0.5
Italy
0.4
0.3

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 27: Coal Products Use Volumes
Unit: Index (volume in 2015 =1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403. Notes: The volume
is defined as the Translog index. The volumes are seasonally adjusted.
（2015=1.0）

3.0
France

2.8
2.6

Japan
2.4

2.2
2.0

China

South Korea
1.8
Italy
1.6
1.4
1.2
1.0

U.S.

UK

0.8
Germany
0.6
0.4

12
10

07

04

10

07

20230104100720220104

1020210107

04

1020200107

04

10

201901

07

04

10

07

20180104

100720170104

1020160107

04

201501Figure 28: Coal Products Use Costs

Unit: Index (cost at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.

41

（2015=1.0）

5.0

Germany

4.5

4.0

3.5

3.0
Italy
2.5
UK
France

2.0

1.5

U.S.
China

1.0

Japan

South Korea
0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 29: Gas Prices

Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Notes: The price is defined as the implicit Translog index. The prices are seasonally adjusted and include taxes and subsidies.

（2015=1.0）

2.2
China

2.0

1.8

1.6

1.4
UK

South Korea

Germany

1.2

U.S.
1.0

0.8

Italy

Japan

France
0.6

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 30: Gas Use Volumes
Unit: Index (volume in 2015 =1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403. Notes: The volume
is defined as the Translog index. The volumes are seasonally adjusted.
（2015=1.0）

5.0
Germany
4.5
4.0
3.5
3.0

2.5
Italy
2.0

China

UK
U.S.

1.5
1.0

FranceJapan

South Korea

0.5

12
10

07

04

10

202301070410202201

07

04

10

07

20210104

1020200107

04

10

201901

07

04

10

07

20180104

100720170104

20160107

1004201501Figure 31: Gas Use Costs

Unit: Index (cost at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.

42

（2015=1.0）

2.0

1.8
UK
1.6
China
U.S.

1.4

South Korea

Japan

1.2
France1.0Germany

Italy

0.8

0.6

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 32: Oil Products Prices
Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Notes: The price is defined as the implicit Translog index. The prices are seasonally adjusted and include taxes and subsidies.

（2015=1.0）

1.6

China
1.4

1.2
GermanySouth Korea
UK

U.S.

1.0

Japan

0.8

France

0.6
Italy
0.4

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 33: Oil Products Use Volumes
Unit: Index (volume in 2015 =1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403. Notes: The volume
is defined as the Translog index. The volumes are seasonally adjusted.

2.2（2015=1.0）

China
2.0
South Korea

1.8

1.6
Germany

1.4

France
Japan

1.2

1.0

0.8

Italy

0.6

UK
0.4

12
10

07

04

10

07

20230104100720220104

1020210107

04

1020200107

04

10

20190107

04

1020180107

04

1020170107

04

1020160107

04

201501Figure 34: Oil Products Use Costs
Unit: Index (cost at local currency unit in 2015=1.0 in each country). Period: January 2015–December 2023. Source: ECM_202403.
Note: The prices are seasonally adjusted and include taxes and subsidies.

43

D.2 Output Prices, Volumes, and Values
1.30（2015=1.0）

1.25
U.S.

Germany

1.20
UK
Italy

1.15
FranceChina

1.10

South Korea

1.05

Japan

1.00

0.95

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 35: Output Prices

Unit: Index (price at local currency unit in 2015=1.0 in each country). Period: Q1 2015–Q4 2023. Sources: Quarterly national accounts
in each country. Note: The prices are seasonally adjusted.

1.60（2015=1.0）

China
1.50

1.40

1.30
South Korea
1.20
U.S.
UK

1.10
Germany

Italy
Japan

1.00
France
0.90

0.80

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 36: Output Volumes
Unit: Index (volume in 2015 =1.0 in each country). Period: Q1 2015–Q4 2023. Source: Quarterly national accounts in each country.
Note: The volumes are seasonally adjusted.

（2015=1.0）

1.80

China

1.70
1.60
U.S.

1.50

1.40

UK

1.30
FranceGermany

1.20

Italy

South Korea
1.10
Japan

1.00
0.90

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 37: Nominal Outputs

Unit: Index (nominal value at local currency unit in 2015=1.0 in each country). Period: Q1 2015–Q4 2023. Source: Quarterly national
accounts in each country. Note: The values are seasonally adjusted.

44

D.3 PLI
4.0（U.S.=1.0）

3.5

3.0
Japan

Italy
UK

2.5

2.0

South Korea

Germany
China

1.5

France

1.0

U.S.

0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 38: Nominal PLI for Industry Energy Use

Unit: Index (the U.S. price in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The PLI is
defined as the implicit Translog price index of the energy prices for each product by industry. The prices are seasonally adjusted andinclude taxes and subsidies.
（U.S.=1.0）

Italy

2.5
Japan

2.0

Germany

UK

France1.5South Korea

1.0

U.S.

China
0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 39: Nominal PLI for Household Energy Use

Unit: Index (the U.S. price in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The PLI is
defined as the implicit Translog price index of the energy prices for each product by household. The prices are seasonally adjusted
and include taxes and subsidies.

6.0（U.S.=1.0）

5.5
Italy

5.0
Germany

4.5
4.0
3.5

South Korea

Japan

3.0
2.5
China
2.0

UK
France

1.5
1.0

U.S.

0.5

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 40: Real PLI for Industry Energy Use

Unit: Index (the U.S. REP in each period=1.0). Period: Q1 2015–Q4 2023. Source: ECM_202403. Notes: The quality-adjusted energy
use is defined in Eq. (4), and real PLI is defined in Eq. (11). The prices are seasonally adjusted and include taxes and subsidies.

45

4.0

（U.S.=1.0）

3.5

3.0
Italy
Germany

2.5South Korea
Japan

2.0

France
UK

1.5
China

1.0

U.S.

0.5

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 41: Real PLI for Household Energy Use

Unit: Index (the U.S. REP in each period=1.0). Period: Q1 2015–Q4 2023. Source: ECM_202403. Notes: The quality-adjusted energy
use is defined in Eq. (4), and real PLI is defined in Eq. (11). The prices are seasonally adjusted and include taxes and subsidies.

4.0（U.S.=1.0）

Italy
3.5

UK
3.0

2.5
Germany
2.0

Japan

1.5

FranceSouth Korea
U.S.

1.0

China
0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 42: Nominal PLI for Electricity Use

Unit: Index (the U.S. price in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Note: The prices areseasonally adjusted and include taxes and subsidies.
（U.S.=1.0）

4.5

4.0

Italy

3.5

3.0
UK
Germany

2.5

2.0

Japan

1.5

FranceSouth Korea

U.S.

1.0
China
0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 43: Nominal PLI for Household Electricity Use
Unit: Index (the U.S. price in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The prices areseasonally adjusted and include taxes and subsidies.

46

（U.S.=1.0）

5.5
Italy

5.0
4.5
4.0
3.5

UK
3.0

Germany
2.5

Japan

2.0

FranceSouth Korea

1.5

China

1.0

U.S.

0.5

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 44: Real PLI for Electricity Use

Unit: Index (the U.S. REP in each period=1.0). Period: Q1 2015–Q4 2023. Source: ECM_202403. Notes: Real PLI is measured basedon Eq. (11). The prices are seasonally adjusted and include taxes and subsidies.

7.5（U.S.=1.0）

7.0
6.5

6.0
5.5
5.0
4.5
Italy
4.0
3.5
3.0

Germany

2.5
JapanFrance

2.0

UK

1.5

South Korea

China

1.0
U.S.
0.5

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 45: Real PLI for Household Electricity Use

Unit: Index (the U.S. REP in each period=1.0). Period: Q1 2015–Q4 2023. Source: ECM_202403. Notes: The prices are seasonallyadjusted and include taxes and subsidies.

3.5（U.S.=1.0）

Italy
3.0
South Korea
2.5

UK
2.0
China

Japan
Germany

1.5
France

1.0

U.S.

0.5

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 46: Nominal PLI for Industry Oil Products Use
Unit: Index (the U.S. price in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The PLI is
defined as the implicit Translog price index of the energy prices for each product. The prices are seasonally adjusted and include taxes
and subsidies.

47

（U.S.=1.0）

7.0

6.0

Italy
5.0

South Korea

France

4.0
Japan

China
3.0

2.0

Germany
UK

1.0

U.S.

0.0

12
10
07
04
2023011007042022011007042021011007
0420200110
07
04
20190110
07
042018011007
042017011007
042016011007
04
201501Figure 47: Nominal PLI for Industry Coal Products Use

Unit: Index (the U.S. price in each period=1.0). Period: January 2015–December 2023. Source: ECM_202403. Notes: The PLI forenergy use is defined as the implicit Translog price index of the energy prices for each product of FEC. The prices are seasonallyadjusted and include taxes and subsidies.

1.1（U.S.=1.0）

U.S.

1.0
Japan
0.9

UK
Germany

0.8FranceSouth Korea

0.7

Italy
China
0.6

0.5

Q4

Q3

Q2

Q1 2023

Q4

Q3

Q2

Q1 2022

Q4

Q3

Q2

Q1 2021

Q4

Q3

Q2

Q1 2020

Q4

Q3

Q2

Q1 2019

Q4

Q3

Q2

Q1 2018

Q4

Q3

Q2

Q1 2017

Q4

Q3

Q2

Q1 2016

Q4

Q3

Q2

Q1 2015

Figure 48: Nominal PLI for Output

Unit: Index (the U.S. price in each period=1.0). Period: Q1 2015–Q4 2023. Sources: the ICP’s 2017 round (World Bank 2020) and theextended quarterly estimates in ECM_202403.

48