DOI: 10.3303/CET24114123
Paper Received: 9 July 2024; Revised: 11 August 2024; Accepted: 8 December 2024
Please cite this article as: Koppány K., 2024, The Construction and Application of the Economy-Electricity-Emissions Input-Output (IO-E3)
Table for Hungary, Chemical Engineering Transactions, 114, 733-738 DOI:10.3303/CET24114123
CHEMICAL ENGINEERING TRANSACTIONS
VOL. 114, 2024
A publication of
The Italian Association
of Chemical Engineering
Online at www.cetjournal.it
Guest Editors: Petar S. Varbanov, Min Zeng, Yee Van Fan, Xuechao Wang
Copyright © 2024, AIDIC Servizi S.r.l.
ISBN 979-12-81206-12-0; ISSN 2283-9216
The Construction and Application of the Economy-Electricity-
Emissions Input-Output (IO-E3) Table for Hungary
Krisztián Koppány
Széchenyi István University, Department of International and Applied Economics, Egyetem tér 1 Győr, Hungary
koppanyk@sze.hu
This paper presents the steps and methods of producing the IO-E3 Economy-Electricity-Emissions input-output
table for Hungary, which contains 28 industries, 8 sub-industries of electric power plants, and 5 final demand
categories. Simulations performed with the model show that the ongoing 120 % expansion of nuclear capacity
can result in a 55.2 %, 35.1 %, and 30.1 % increase in electricity production, value-added, and greenhouse gas
emissions if the structure of final demand and technology are not changed. Smart use of the predicted
17.48 TWh of electricity surplus, however, must precisely aim these changes to best serve Hungary's
sustainability transition through harnessing technological developments and by changing economic structures.
1. Introduction
The examination of sustainable development issues requires an increasingly integrated approach: simultaneous
thinking in several socio-economic and physical-ecological fields. Decision support models must link these
subsystems. E3-type (Economy-Energy-Environment) input-output (IO) tables (like those of the World Input-
Output Database, Dietzenbacher et al., 2013, Timmer et al., 2015) and models (like those of the Cambridge
Econometrics, 2024) precisely serve this purpose. With their help, not only can the flow of products between
productive and end-user sectors and the value-added (income) generation of industries be examined, but also
the energy needs and environmental pollution of production and use, and even their indirect (multiplicative)
effects on other sectors and the whole economy.
This study presents the steps and methods of producing an IO table (IOT) of this type using the Dissemination
Database and STADAT tables of the Hungarian Central Statistical Office (HCSO, 2023), the statistics of the
Hungarian Energy and Public Utility Regulatory Authority, financial reports of energy companies, the Electricity
Maps web portal, and the information from interviewed industry experts, which will be reviewed in Section 2.
Integrating and harmonizing the latest Hungarian official statistics and public financial data to construct an
Economy-Electricity-Emissions IOT is a pioneering project. IO-E3 HUN 2020 table presents the examined
subsystems in more detail than other currently available databases, albeit with a narrower interpretation of the
acronym E3. It focuses on electricity in terms of energy production and use, and air pollution as one the most
important environmental impacts. Applications in Section 3 show some input-output indicators calculated using
IO-E3 to analyse the 2020 status of Hungary in an economy-electricity-emissions context. For illustration,
Section 3 presents the result of a simulation investigating the economic, environmental, and energy supply
consequences of a nuclear power plant capacity expansion. Section 4 concludes and sets possible directions
for future research.
2. Data and methods
To compile the IO-E3 HUN table, the starting point was the official input-output table of Hungary by the
Hungarian Central Statistical Office (HCSO, 2023), the latest available edition of which is for 2020. Other data
and the supply and use tables available would have allowed an IOT for 2021 to be compiled (based on own
calculations). However, both individual financial reports and sectoral aggregates reflected confusing post-
COVID and war turbulences in energy markets in 2021-2022 (significant losses, negative output value of
electricity trade, etc.). This confirmed the decision in favour of the reference year 2020.
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The air pollution database of the HCSO uses the same 65-sector breakdown as the official IO data. Although
an environmentally augmented IOT is not available in Hungary, it was relatively easy to create it by combining
the two databases. Serious challenges arose, however, during the production of the energy block, which
required collecting and harmonising data from various sources, as well as applying expert estimates beyond
official data. The most important task was the decomposition of the D35 Electricity, gas, steam and air
conditioning supply (undivided both in official IOTs and air emission statistics) into D3511 Electricity production;
D3512-14 Electricity transmission, distribution, and trade; D352 Gas supply; and D353 Steam (heat) and air
conditioning supply, based on the production and sales data of industrial sectors by the Dissemination Database
(technical identifier: ID403_W). The distribution ratios have been confirmed by the financial reports (bought from
D&B Hungary) of companies from the D35 sector (approximately 1,500 organisations). Other input side
categories of the four subsectors above, such as value-added and intermediate use by supplier industries, were
also determined based on company reports.
The output of D351 Electricity production, estimated at 598,214 M HUF, was first broken down by user sectors
on the basis of natural indicators (TJ, GWh) by the annual Eurostat-type energy balance of the Hungarian
Energy and Public Utility Regulatory Authority (HEPURA) (MEKH, 2023), which presents a detailed report of
primary production, exports, imports, change in stocks, transformation input and output, own use of the energy
sector, distribution losses, and consumption of industries and households. Similarly to electricity, data on heat
and natural gas production and consumption have also been processed and assigned to industries and sectors
included in the IOT. The matching was obvious in most cases. However, some industries had to be merged
according to the list presented in the first column of Table A1 in the Online appendix (Koppány, 2014).
To determine the output breakdown (i.e., sales to intermediate and final users) of the energy sector (i.e., row
values of industries D3511, 3512-14, 352, and 353 in the IOT), natural rates of industrial, household, and export
use were applied. Transmission and trading fees, i.e. revenues from different sectors in D3512-14 and 352,
were initially assumed to be proportional to the consumption of the basic product, i.e. electricity and natural gas.
Such a resolution assumes that everyone has access to electricity, heat, and related distribution services based
on natural usage proportions and at the same prices; this, however, is not true. Companies from various sectors,
households, and export partners obtain energy at different prices depending on the level of consumption and
contractual terms. Sectoral export shares based on monetary values could be determined using ID403_W, but
similar superior information is not available for industries and households. Therefore, excluding the export ratio,
the remaining monetary row values were first allocated based on the proportions of usage measured in TJ. Then
the formation of price differences was entrusted to a bi-proportional balancing algorithm, the RAS method widely
used in IO analysis (Miller and Blair, 2009), that adjusted the cells of the initial block matrix to the D35 row
values in the official IOT (as required column sums) and estimated output values of D3511, D3512-14, D352,
and D353, respectively (as required row sums).
The next step was a further decomposition of the D3511 according to the primary energy sources: nuclear, gas
and oil, coal, biomass, hydro, wind, solar, and other. The annual amount of electricity produced by different
power plant subsectors is provided by the 6.1.1.9 STADAT table (HCSO, 2023) and the HEPURA (MEKH, 2023)
energy balance. For the monetary values, financial report data have been exploited. The classification of power
plants into the most characteristic subsector of electricity production was performed using the tables of
HEPURA-licensed electricity producers. Approximately 89 % of the estimated output value of the electricity
production sector could be classified this way. In Hungary, the nuclear power plant category includes a single
company for which both output and value-added are clearly given. In the case of the other power plant
disciplines, the previously defined ratios have been applied to the output value of total electricity production
calculated without the nuclear power plant. Value-added ratios based on the financial data were employed for
the output values of all D35 sub-branches (including D3511 by primary energy source, 3512-14, 352, and 353).
These raw estimations were then adjusted to sum up to the total value added of D35 in the official IOT.
The HCSO does not provide data on air pollution, particularly caused by electricity production; only the
emissions of the entire D35 sector are published. Direct greenhouse gas (GHG) emissions associated with
different power plant sectors and primary energy sources have been estimated based on the available data on
electricity and heat production from the HEPURA, the intensity and efficiency parameters of plants, and the
specific g CO2eq/kWh coefficients reported by the interactive Electricity Maps (2023) website.
Intermediate consumption of each power plant subsector, as well as electricity transmission, distribution and
trade, gas, steam, and air conditioning supply, have been valued using direct input coefficients. A representative
company was selected for each sector (in some cases, more than one), and their cost elements were assigned
to the respective IO-E3 supplier industries. Input coefficients were determined as the ratios of inputs from each
supplier industry and the company’s output value. These proportions were then applied to the entire power plant
sector. Row and column sums of intermediate use resulting from the product of the input coefficients and
subsectors’ output must be in line with the total sales and purchases either known from the official IOT or given
by previous estimations. Naturally, these conditions were not met at first. A multi-step balancing procedure was
734
applied: first horizontally, using various methods, and then with the RAS iteration again, starting with a column-
wise adjustment.
The multi-month process of data collection and harmonisation, consultations with the related organisations (see
Acknowledgements), and calculations resulted in an IOT with 28 industries (for the full list see Table A1 in the
Online appendix (Koppány, 2024), 8 electric power plant subindustries, and 5 final demand categories, which
was named IO-E3 HUN 2020 (Economy-Electricity-Emissions Input-Output Table of Hungary) and is shown (in
an aggregated form) in Table A2 in the Online appendix (Koppány, 2024).
Fuel expenses related to personal vehicle (car) usage have been separated from other household consumption
to prepare for future modelling exercises. In the case of e-vehicles, for the entire population, these expenses
are still low in 2020 but are expected to increase significantly in the future. In the IO-E3 table, along with
production and consumption data, the annual electricity generation capacity is also displayed in two separate
rows with two different approaches. The first one can be referred to as theoretical electricity production potential,
which represents the theoretical annual production under continuous operation and corresponds to the installed
gross capacity. The second one is the practical electricity generation potential, which is the maximum annual
production achievable, considering maintenance requirements, technical downtime, and local weather
conditions (such as hours of sunshine and wind). The values found here were derived based on data and
information available on the website of the Hungarian Electricity Industry Transmission System Operator
(MAVIR, 2023), in addition to the HEPURA and HCSO statistics referred to above.
3. Applications and discussion of simulation results
Figure 1 shows the big picture of the economic, energy, and environmental structures of Hungary in 2020, which
can be drawn using IO-E3 data directly. Two-thirds of the gross value added (GVA, a.k.a. GDP at basic prices)
belongs to the Services (highest blue column). Hungarian economic policy, however, focuses on Manufacturing,
which has a share of appr. 20 % in GVA and the highest (32.4 %) in electricity consumption (light orange).
Electricity production (dark orange) does not cover domestic needs. Consequently, Hungary must import
electricity in a significant amount, more than half of the domestic production. Together with a significant but
much lower export level of excess electricity in peak-production periods, this results in a negative trade balance.
After households, the energy sector has the second highest share of direct GHG emissions (grey). The share
of manufacturing in total is not outstanding; however, there are significant deviations between different
subsectors.
Figure 1: GVA, electricity and GHG emissions by productive industry and final use sector in Hungary, 2020
(No bars on the chart means that the category is not applicable or practically zero)
3.1 Input-output analyses of the economy-electricity-emissions status in 2020
With an input-output analysis of IO-E3 data, one can reveal more than direct shares. Figure A1 in the Online
appendix (Koppány, 2024) compares direct and total (direct + indirect) value-added, electricity use, and
emissions generated by the final demand for the products of indicated industry groups. Total backward linkages
were calculated using the Leontief demand pull IO model and the product of final use of industries and GVA,
electricity use, and GHG emission multipliers (Koppány, 2017) as well as see Table A1 in the Online appendix
(Koppány, 2024). The results indicate that considering the whole domestic upstream value chain, manufacturing
has a greater impact on Hungarian GVA, electricity use, and GHG emissions than its direct numbers show.
(Despite the aggregated presentation, the calculations were performed with the detailed 28+8-industry IO-E3
table.)
0%
10%
20%
30%
40%
50%
60%
70%
A-B Agriculture and
mining
CA10-CM32
Manufacturing
D Electricity, gas,
steam and air
contitioning supply
F Construction H Transportation CM33, E, G-S Other
services
Households:
transportation
Households: other Other domestic Electricity export
and import
Industries Final use
Value Added, Electricity, and Greenhous Gas Emission Structures in Hungary
by Productive Industry and Final Use Sector, 2020
Value added (billion HUFs) Eletricity use (TWh) Electricity production and imports (TWh) Greenhouse gas emissions (Mt CO2eq)
735
Figure 2a highlights that exports, nearly 70 % of which are made up of manufacturing, with their total linkages,
give 38 % of Hungarian value added (left bar, red part), comprise half the total electricity use (middle bar, red
part), and are responsible for 35 % of GHG emissions. Households generate half the total emissions (right bar,
green part). Figure 2b shows Hungarian industries from an E3-dimensional aspect, where the size of the bubbles
indicates the total GVA belonging to the entire upstream value chains of industries, and horizontal and vertical
coordinates measure the GHG and electricity content of one (or thousand) HUF(s) of GVA. The manufacturing
of motor vehicles (CL29) and electronic products (CI26), being the focus of industrial policy, might have the
lowest GVA multipliers, and yet, due to huge amounts of exports, their value-added impacts are still significant.
The specific electricity and GHG content of the GVA they induce is also among the lowest. Table A1 in the
Online appendix (Koppány, 2024) presents all the indicators that have been used for the IO calculations.
Figure 2: Share of final users in value-added, electricity use, and GHG emissions in Hungary, 2020 (a);
Hungary's industries from an E3-dimensional (economy, energy, emissions) aspect, 2020 (b)
3.2 Electricity production: mix and carbon intensity
Electricity production, with its 10.9 Mt of direct GHG (see Figure 3a), has a 14.2 % share in total emissions; and
in the 35 TWh of total annual electricity produced by the sector, the nuclear power plant of Paks1, with its 16
TWh output, makes the highest (46 %) contribution (see Figure 3b). Figure 3b also shows that its carbon
intensity is minimal (5 gCO2eq/kWh); 90 % of emissions from electricity production are caused by gas and coal
power plants. Table 1 presents the carbon intensities of different power plants in detail. The literature generally
reports direct production intensities (UNECE, 2022; Ritchie et al., 2024); however, carbon statistics for ready-
to-use (socket) electricity must contain transmission, distribution and trading services, and involve losses of
transmission and distribution, own use of power plants, and several indirect activities in the upstream supply
chains of suppliers, as well, which in turn cause additional emissions. IO analysis detects that in Hungary, all
these effects triple the carbon intensity of nuclear electricity.
Figure 3: Electricity use, production, and GHG emissions in the whole D35 energy sector (a) and in the D3511
Electricity production industry (b) in Hungary, 2020
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Value added Electricity use Greenhouse
gas emissions
Households indirect Households direct
Other domestic indirect Other domestic direct
Exports indirect Exports direct
A01-02
A03
B05-09
CA10-12
CB13-15
CC16
CC17
CC18
CD19
CE20
CF21
CG22
CG23CH24
CH25
CI26
CJ27
CK28
CL29-30
CM31-32
D352
F41-43
H49-51
CM33, E, G-S
0
1
2
3
4
5
0 1 2 3 4 5 6 7
E
le
c
tr
ic
it
y
u
s
e
/G
V
A
(
k
W
h
/t
h
o
u
s
a
n
d
H
U
F
)
GHG/GVA (gCO2eq/HUF)
Households' consumption
Other domesitc final use
Exports
0
1
2
3
4
5
6
7
8
9
10
11
12
0
3
6
9
12
15
18
21
24
27
30
33
36
D3511 Production of
electricity
D3512-14
Transmission,
distribution and trade of
electiricity
D352 Manufacture of
gas; distribution of
gaseous fuels through
mains
D353 Steam and air
conditioning supply
M
t
C
O
2
e
q
T
W
h
Eletricity use (TWh) Electricity production (TWh) Greenhouse gas emissions (Mt CO2eq) (right scale)
0
1
2
3
4
5
6
0
3
6
9
12
15
18
nuclear gas and oil coal biomass hydro wind solar other
D3511 Production of electricity
M
t
C
O
2
e
q
T
W
h
Eletricity use (TWh) Electricity production (TWh) Greenhouse gas emissions (Mt CO2eq) (right scale)
a b
a b
736
Table 1: Direct, indirect, and total carbon intensity of electricity (gCO2eq/kWh) in Hungary 2020
Nuclear Gas
and oil
Coal Biomass Hydro Wind Solar Other D3511
Direct Production 5 578 1,158 230 11 13 31 700 312
Transmission, distribution,
and trade
5 5 5 5 5 5 5 5 5
Ready-to-use (socket)
electricity
10 583 1,163 235 16 18 36 705 317
Indirect Production 2 13 26 50 4 4 1 42 11
Transmission, distribution,
and trade
3 10 19 9 3 3 6 99 9
Total Production 7 591 1,184 280 15 17 32 742 323
Transmission, distribution,
and trade
8 15 24 14 8 8 12 104 14
Ready-to-use (socket)
electricity
15 606 1,208 294 24 26 44 846 337
3.3 Simulation of a nuclear capacity increase
Despite the higher total carbon intensity, the ongoing project aspiring to a 120 % increase in Hungary’s nuclear
production capacities (Paks2) can also be justified from an economic, energy and environmental policy point of
view. Nuclear electricity is still the cleanest and cheapest option. Paks1 and Paks2 together can increase the
share of nuclear power in the electricity mix to over 65 % (see Figure 4a) and decrease the overall direct carbon
intensity of Hungarian-produced socket electricity from 317 to 208 gCO2eq/kWh. Developing and extending the
production of preferred manufacturing branches, electrification of transportation, heating, and many other fields,
together with expected technology changes of the net zero transition, all mean a huge electricity demand
increase in the future. Simulations performed using IO-E3 data predict a 55.2 % increase in electricity production
(see Figure 4b). For this extra electricity to be ready to use, a 36 % growth in the output of industry D3512-14
Electricity transmission, distribution and trade is needed. Maintaining the current level of exports and imports,
direct own use of plants and network losses alone cause a 3.2 % increase in electricity use. Once the indirect
effects are included, this rises to 3.3 %, leaving a total of 17.48 TWh of electricity for other purposes.
Obviously, 17.48 TWh of electricity surplus cannot replace 19.18 TWh of imports. It is also advisable to maintain
imports and exports because they can balance domestic production and consumption, and it is still conceivable
that in certain periods, Hungary can get cheaper and cleaner electricity from imports than production.
A 0.7 % and 0.4 % increase in GVA and GHG emissions occur even if Hungary does nothing with its 17.48 TWh
of extra electricity and simply exports it. Simulation#2 investigates a hypothetical business-as-usual scenario
when only the size of the total final use increases, and its structure remains unchanged at the 2020 levels. In
this case, assuming no other constraints, domestic use of total electricity surplus can result in a 35.1 % GVA
and 30.1 % GHG emission growth (see Figure 4b). The GHG content of one unit of GVA decreases by 3.7 %,
from 577 to 556 g/USD, which is not significant and is still above the global average (Román et al., 2019). This
scenario encounters several input constraints (e.g. the required increase in employment and other energy use)
that are impossible to resolve. In any case, an unchanged economic structure and technology are rather unlikely
assumptions. Accordingly, the use of expected electricity surplus energy must be defined in a way that best
serves Hungary's sustainability transition by harnessing technological developments and changing the
economic structures. Further simulations using the IO-E3 model can help to assess the alternatives.
Figure 4: Impact analysis of a nuclear capacity expansion in Hungary: the change in the electricity mix (a); and
simulation results (b)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Baseline
2020
Simulation:
nuclear
+120%
Other
Solar
Wind
Hydro
Biomass
Coal
Gas and oil
Nuclear
Sim#2: nuclear +120%; max growth by energy
surplus with fixed final use structure and technology
Direct effects
34.9 54.2 54.2 54.2
55.2% 55.2% 55.2%
54.1 55.8 55.9 73.4
3.2% 3.3% 35.6%
17.52 17.48 0
40,861 41,083 41,159 55,187
0.5% 0.7% 35.1%
76.6 76.8 76.9 99.6
0.3% 0.4% 30.1%
1 HUF 1.875 1.869 1.868 1.805
1 USD 577 576 575 556
-0.3% -0.4% -3.7%
Baseline 2020
Simulation#1:
nuclear +120%
Direct and indirect effects
Electricity production (TWh)
% change to the baseline
% change to the baseline
GHG content of value added
(GHG / GVA, gCO2eq/ HUF)
% change to the baseline
Electricity use (TWh)
% change to the baseline
Electricity surplus (TWh)
GVA (billion HUF)
% change to the baseline
GHG emissions (Mt CO2eq)
a b
737
4. Conclusions and further research
Input-output tables serve as a primary database for multisectoral macro models describing the direct and indirect
linkages between industries, final users, output, and value-added. Extended by special blocks, they can be used
for more extensive and integrated analyses involving not only economic but also energy production and use and
environmental issues. This paper presented such a database, the IO-E3 Economy-Electricity-Emissions IOT for
Hungary, which contains the examined subsystems in more detail than any other currently available database.
With its special focus, it allows for previously impossible model calculations to be performed related to the
changes in the electricity mix. The export-oriented, high-volume-manufacturing-focused industrial policy of
Hungary, together with efforts to decrease GHG emissions, requires cleaner and cheaper electricity. Simulations
performed using IO-E3 showed that a 120 % nuclear capacity expansion could result in a 55.2 %, 35.1 %, and
30.1 % increase in electricity production, GVA, and GHG emissions, assuming no change in the structure of
final demand and technology. Smart use of the predicted 17.48 TWh of electricity surplus, however, must
precisely aim these changes to best serve Hungary's sustainability goals. Integrated E3 assessment and impact
analyses of other planned modifications in the energy mix (e.g. new gas turbine power plants, greening heat
supply) and alternatives for industrial and household use of the excess electricity (e.g. green hydrogen, e-
mobility) are subjects for further research which hopefully support decisions of policymakers.
Acknowledgments
This research was supported by the HUMDA Hungarian Motorsport and Green Mobility Development Agency.
The author thanks László Winkler (HEPURA), Zoltán Mayer (HFC Hungary), Tamás Bándy (Messer
Hungaroház Kft.), Barna Hanula and Géza Köteles (Széchenyi István University) for the consultations, and
Natasha Bailey-Borbély (Széchenyi István University) for the proofreading.
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