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Report of the Inter-Ministerial Committee on
Energy Data Managemen
Energy Data Management
Report of the Inter-Ministerial
Committee on
Under Sustainable Growth Pillar of India-US Strategic
Clean Energy Partnership COMPOSITION OF THE
INTER-MINISTERIAL
COMMITTEE
Composition of the Inter-Ministerial Committee on Energy Data
Management
Sl. No.Name and DesignationPosition
1 Sh. Rajnath Ram, Adviser (Energy), NITI Aayog Chairman
2 Ms Santosh, DDG, Ministry of CoalMember
3 Ms Anshu Singh, DDG (Stats), Ministry of
Environment, Forest and Climate Change
Member
4 Ms. Harmeet Minhas Kumar, Director, Ministry of
Statistics and Programme Implementation
Member
5 Sh. Arijit Sengupta, Director, Bureau of Energy
Efficiency
Member
6 Sh. Prahlad, Chief Engineer, Central Electricity
Authority
Member
7 Sh. VP Singh, Director, Coal Controller’s OrganisationMember
8 Dr Pankaj Sharma, Additional Director, Petroleum
Planning and Analysis Cell
Member
9 Sh. Vikram Dhaka, Scientist C, Ministry of New and
Renewable Energy
Member
10 Ms Avinash Kumari, Assistant Director, Ministry of
Petroleum and Natural Gas
Member
11 Prof. Venkat Ramadesigan, IIT Bombay Member
12 Prof Ankush Sharma, IIT Kanpur Member
13 Sh. Srihari Dukkipati, Prayas (Energy Group) Member
14 Ms. Apurva Chaturvedi, Senior Clean Energy
Sepcialist, USAID
Member
15 Ms. Meredydd Evans, Scientist, Pacific Northwest
National Laboratory
Member
16 Mr. Samson Adeshiyan, Director, Office of Statistical
Methods and. Research, EIA
Member
iiiReport of the Inter-Ministerial Committee on Energy Data Management Composition of the Inter-Ministerial CommitteeReport of the Inter-Ministerial Committee on Energy Data Management
iv
Sl. No.Name and DesignationPosition
17 Mr. Jon Weers, Lead Technologist and Data Systems
Architect. National Renewable Energy Laboratory
Member
18 Sh. Kamil KPS Bhullar, Research Officer, NITI Aayog
(Member Convener)
Member
Terms of reference
i. Standardising the definitions, terminologies and calculation methodology
of all the key parameters in the energy sector so that reporting of data is
uniform.
ii. Arrive at uniform values of standard data including Gross Calorific Value,
operation hours, etc.
iii. Standardize methodologies for data collection, data quality, validation,
survey design methodologies, etc. and their reporting
iv. Based on the findings of 8 Sub-group reports on energy data management
for the various demand and supply sectors, build on the identified energy
data gaps and ensure collection and maintenance of the required data.
v. Study and make suggestions for the setting up of a centralised data agency
in India
vi. Suggest measures for enriching of India Energy Dashboards
vii. Publish an Energy Statistics Manual/ Handbook, which will include, e.g.:
- Definitions and concepts of all the key parameters in the energy
sector
- Formats and methodologies for data collection and reporting
- Standardised data (GCV, operation hours, etc.)
- Energy Balances and energy accounting
viii Organising stakeholder consultations, if required
iv Report of the Inter-Ministerial Committee on Energy Data Management FOREWORD
vReport of the Inter-Ministerial Committee on Energy Data Management PREFACE
viiReport of the Inter-Ministerial Committee on Energy Data Management ACKNOWLEDGEMENTS
ixReport of the Inter-Ministerial Committee on Energy Data Management
The report of the committee could not have been completed without the active
intellectual support provided by individuals representing various ministries/
departments, agencies and think tanks. The committee would like to thank
Ms Santosh, DDG, Ministry of Coal, Ms Anshu Singh, DDG (Stats), Ministry of
Environment, Forest and Climate Change, Sh. Arijit Sengupta, Director, Bureau
of Energy Efficiency, Sh. Prahlad, Chief Engineer, Central Electricity Authority,
Sh. VP Singh, Director, Coal Controller’s Organisation, Dr Pankaj Sharma, Additional
Director, Petroleum Planning and Analysis Cell, Sh. Vikram Dhaka, Scientist C,
Ministry of New and Renewable Energy, Ms Avinash Kumari, Assistant Director,
Ministry of Petroleum and Natural Gas, Prof Venkat Ramadesigan, IIT Bombay,
and Prof Ankush Sharma, IIT Kanpur for their valuable inputs.
The committee would like to acknowledge the support and inputs provided
by Ms Meredydd Evans, Scientist, Pacific Northwest National Laboratory and
Mr Samson Adeshiyan, Director, US Energy Information Administration. In particular
the committee would like to thank Sh. Srihari Dukkipati, Research Fellow, Prayas
(Energy Group) who provided support in terms of the initial paper for discussion.
A special thanks to Dr Rakesh Sarwal (Additional Secretary, NITI Aayog when the
committee was constituted) and Sh. Navin Kumar Vidyarthi, Director (Energy),
NITI Aayog for their efforts towards taking the work of the committee to its
logical conclusion. CONTENTS Composition of the Inter-Ministerial Committee iii
Foreword v
preface vii
Acknowledgements ix
Background 1
1. Introduction 2
2. Standardization of Data 4
Units 4
Calorific values 5
Energy flows 8
Classification of economic/statistical units 9
Classification of energy products 11
Classification of energy resources 13
Miscellaneous classifications 13
Classification by geographical area 13
Energy and commodity balances 14
Metadata 14
3. Data collection and compilation 15
4. Centralized Energy Data Management Agency 16
5. Requirement of an Energy Data Manual 17
6. The Way Forward 18
Annexure I 22
Classification of statistical units 22
Energy industry classification 23
Energy consumer classification 24
Single Integrated Metadata Structure (SIMS) 25
Conversion Factors 26
Annexure II 28
Annexure III 35 BACKGROUND
1Report of the Inter-Ministerial Committee on Energy Data Management
Energy data (resource assessment, extraction, conversion, transmission, distribution,
and consumption) for India is published by a number of state and national
agencies. However, much of the data available is dispersed and difficult to collate
due to differences in the organization of data, use of incompatible formats and
standard definitions. Moreover, there are significant data gaps.
The mandate for Energy Data Management (EDM) in India is fairly decentralized,
with the earlier Act being “The Collection of Statistics Act, 2008”. Individual
Ministries have issued mandates for specific data under their respective domain,
however a legal framework needs to be evolved in view of data collection being
carried out by through decentralized agencies under various ministries besides
MoSPI. Lack of standardized formats make it difficult to automate the process
of data sharing and publication on dashboards. There exists a data gap in
consumption and demand, no regular surveys are conducted at the household
level to determine consumption data of interest. Consumption data also needs to
be accessed from four areas in particular – agriculture, building (both residential
and commercial), industry and transport.
The shape of the data architecture varies across countries. The local conditions
of each country have to be considered while analysing the system best suited to
it. Data management systems range from the highly centralized ones (Canada)
to highly decentralized (Germany).
There is a need to standardise the definitions, terminologies and calculation
methodology of all the key parameters in the energy sector (e.g. import
dependency of crude oil, natural gas, etc.) so that reporting of data is uniform.
It is also required that we arrive at uniform values of standard data (Gross Calorific
Value, operation hours, etc.) and standardize methodologies for data collection,
data quality, validation, survey design methodologies, etc. and reporting. 1. INTRODUCTION
2Report of the Inter-Ministerial Committee on Energy Data Management
1.1 A committee on energy data management was formed by NITI Aayog on
30
th
July 2021 under the Sustainable Growth Pillar established under the
India-US Strategic Partnership. During the meeting of joint working group
held on 23
rd
August, a sub-group was formed on Energy Data Management
to identify the parameters which need to be standardized across various
energy statistics products in India. The inter-ministerial committee deliberated
twice on the subject.
1.2 While preparing the report, the following documents have been referred to:
International Recommendations for Energy Statistics (IRES) and the
associated Energy Statistics Compiler Manual (ESCM) published by the
UN Statistics Division
System of Environmental-Economic Accounting for Energy (SEEA-
Energy) published by the UN Statistics Division
Documents published by MoSPI for industry and products classification,
i.e., NIC-2008 and NPCMS-2011 respectively
Latest annual Indian energy statistics publications, i.e., Coal Directory
2019-20, Petroleum and Natural Gas Statistics 2019-20, All India
Electricity Statistics-General Review 2020, Energy Statistics 2021
1.3 During the deliberations of the committee, it was observed that data lacks
data definitions and standard classification due to which it is difficult to
compile energy balance. This issue was prevalent across ministries. The
use of standard classifications is important in the collection, compilation
and dissemination of statistics. Standard classifications provide a clear
definition, with a unique structure, of the objects that are being measured
and collected. They facilitate the compilation of data as the classifications
define relationships between concepts and objects. Finally, they allow for
better integration of data collected across different statistical domains
such as, for example, energy, environment and economic statistics. (ESCM,
Section 3.1) 1. IntroductionReport of the Inter-Ministerial Committee on Energy Data Management
3
1.4 The committee felt that the following aspects need standardisation in
energy statistics as per IRES of UN.
1. Measurement units
2. Energy flows
3. Economic/statistical units
4. Energy products
5. Energy resources
6. Various other classification not mentioned in the IRES
7. Energy and commodity balances
8. Metadata
1.5 The above topics are covered in detail in the subsequent sections of the
report, along with some examples where standardisation is needed. 2. STANDARDIZATION
OF DATA
4Report of the Inter-Ministerial Committee on Energy Data Management
Units
2.1 The only unit for energy in the SI system is the joule and is the common
energy unit recommended to be used in energy statistics.
2.2 While data may be collected in units that are most suitable for national
circumstances, it is desired that the data is disseminated in units that
are standardised internationally. Table 1 (reproduced below) in the IRES
provides the recommended units for dissemination of energy data and Table
2 provides the dimensions and price units used in India. This table has been
reproduced below.
Table 1: Reproduced Table 4.4 of IRES
Energy productsDimensionUnit
Solid fossil fuels Mass Thousand metric tons
Liquid fossil fuels (crude
oil, petroleum products,
condensate)
Mass Thousand metric tons
(Liquid) Biofuels Mass/Volume Thousand metric tons/
Thousand cubic metres
Gases Energy Terajoules
Wastes Energy Terajoules
Fuelwood Volume/Energy Thousand cubic metres/
Terajoules
Charcoal Mass Thousand metric tons
Electricity Energy GWh
Heat Energy Terajoules
Common unit (e.g., balances) Energy Terajoules
Electricity installed capacity Power MW
Refinery capacity Mass/time Thousand metric tons/year 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
5
Table 2: Units used in India
Energy ProductUnit (Quantity)Unit (Price)
Crude Oil and Condensate Thousand metric tonsUSD per barrel
Natural GasMillion metric standard cubic metresUSD per mmbtu
Petroleum products Million metric tonsINR per litre
Biofuels (ethanol) Crore litresINR per litre
Electricity installed capacityMWINR per MW
Electrical energy GWhINR per kWh
CoalMillion tonsINR per ton
BiomassMillion metric tonsINR per ton
2.3 The units followed in Indian energy statistics are more or less similar
although there are some minor differences, e.g., coal quantities are reported
in million metric tons whereas thousand metric tons is recommended.
2.4 Units for products such as biomass and waste which vary widely in energy,
moisture and ash content are recommended to be reported in energy
content rather than physical quantities such as mass or volume.
Calorific values
2.5 When expressing energy content of energy products in common energy
units, e.g., when building energy balance tables, net calorific values (NCVs)
are preferred over gross calorific values (GCVs). The following tables provide
the calorific values in use in India.
Table 3: Average International Calorific Values (reproduced from PNG Statistics)
Average International Calorific Value of different fuels
ProductsK Cals/Kg BTUs/Kg
Oil Equivalence
Average Crude Oil/NGLS 1030040870
United Nations1017540375
Oil Products
NGL1213548150
Motor Gasoline1113544190
Kerosene1063842210
Jet Fuel1179046790 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
6
Average International Calorific Value of different fuels
ProductsK Cals/Kg BTUs/Kg
Gas Oil1079042820
Fuel Oil1044041430
Coal Equivalences
Average Hard Coal 700027775
India
Hard Coal500019840
Lignite Brown Coal2310916
Firewood475018848
Charcoal690027379
Natural Gas Production (Average) India
8000-9480
Electricity
Output Basis860 kcal/kwH
Fuel Input Basis25002700
Table 4: Non-coking coal - gradation based on GCV
GCV BAND (K.Cal./Kg.)
Exceeding 7000
Exceeding 6700 and not exceeding 7000
Exceeding 6400 and not exceeding 6700
Exceeding 6100 and not exceeding 6400
Exceeding 5800 and not exceeding 6100
Exceeding 5500 and not exceeding 5800
Exceeding 5200 and not exceeding 5500
Exceeding 4900 and not exceeding 5200
Exceeding 4600 and not exceeding 4900
Exceeding 4300 and not exceeding 4600
Exceeding 4000 and not exceeding 4300
Exceeding 3700 and not exceeding 4000
Exceeding 3400 and not exceeding 3700
Exceeding 3100 and not exceeding 3400
Exceeding 2800 and not exceeding 3100
Exceeding 2500 and not exceeding 2800
Exceeding 2200 and not exceeding 2500 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
7
Table 5: Gradation of coking coal - based on ash content
GradeAsh Content
Steel Grade - INot exceeding 15%
Steel Grade - lIExceeding 15% but not exceeding 18%
Washery Grade - IExceeding 18% but not exceeding 21%
Washery Grade - IlExceeding 21% but not exceeding 24%
Washery Grade - IllExceeding 24% but not exceeding 28%
Washery Grade - IVExceeding 28% but not exceeding 35%
Washery Grade - VExceeding 35% but not exceeding 42%
Washery Grade - VIExceeding 42% but not exceeding 49%
2.6 In order for better accuracy in energy balances, it is recommended that
specific calorific values are used where possible through the entire chain
of flow for each fuel since the calorific values may vary to meet various
market requirements, e.g., ethanol blending. The calorific value of ethanol
being much lower than Motor spirit and further with its blending %age in MS
on the increase, a separate product classification in NPCMS-2011 is required,
blending more than 10%. The same is essential in view of the introduction
of flex fuel engines and the proposed sale of 100% ethanol as a fuel.
As per NPCMS-2011, under Division 34 (Basic Chemicals), Group 341 (Basic
Organic chemicals), Class 3413 (Alcohols, phenols, phenol-alcohols, and their
halogenated, sulphonated, nitrated or nitrosated derivatives; industrial fatty
alcohols) & Subclass 34110 (Ethyl alcohol and other spirits, denatured, of any
strength), product codes listed are 3413101 (Ethyl alcohol) & 3413102 (Methyl
alcohol (methanol)). In view of the usage as a fuel, the product classification
be need to be modified accordingly. Similarly, products obtained from
gasification of the heavier petroleum compounds need to be classified
under the Group 334. In addition, a particular energy product may have
different energy content over different batches of production, for imports
and exports and over time, such as in the case of coal. In such cases, the
specific calorific values for each of these quantities should be used, and
weighted average calorific value of the energy product should be used when
aggregating. All the methods used in arriving at the calorific value should
be documented to ensure transparency, clarity and comparability. Default
calorific values should be avoided as much as possible. Where needed,
default net calorific values provided in Table 4.1 of IRES for various energy
products should be used. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
8
Publication DescriptionRemarks
Energy
Statistics 2021
Chapter 7: Energy Balance
and Sankey Diagram
While the methodology for converting
physical quantities to energy units is
described, the underlying calorific value
data is not reported.
PNG Statistics
2019-20
AppendicesLot of detailed conversion tables (volume
to weight, unit versions, impact of
temperature, calorific value etc.) which
are very useful.
Density for a fuel may vary as per
different batches of production, imports
and exports and ethanol blending over
time etc. Similarly, energy content may
also vary.
Therefore, it is essential that weighted
average for volume-mass conversion &
calorific value of the energy product
be used for calculation purpose. Global
conversion factor or energy content may
be taken as a standard reference only.
It will also entail statistical differences &
inaccurate conclusions.
2.7 In case of bio-energy, it is recommended that a detailed assessment of
availability of different bio-energy resources and their potential needs to
be undertaken. Previous discussions with Ministry of Coal have yielded
proposals for a using grade wise calorific values approach whereby the
mid-point of the range of calorific values against each grade are used. The
note on this methodology is provided in Annexure II.
Energy flows
2.8 As per Energy Statistics 2022, In 2020-21 (P), Primary Energy Supply added
up to 8,88,523 Kilo Tonnes of Oil equivalent (KToE)
Coal, which accounted for 64.93% of the total, and Crude Oil, which
accounted for 26.29% were two major contributors to the total energy
supply in the country
Final Energy Consumption (End Use) was 5,53,971ktoe in 2020-21 (P).
The industrial sector was the largest consumer of energy in the country,
using more than half, i.e., 56.22% of the total final energy consumption.
The most energy intensive industries were iron and steel, accounting
for 15.37% of the industrial energy use followed by Chemicals and
petrochemicals 4.43% and construction 1.96%. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
9
The consumption of the residential, agriculture, commercial & public
sectors, non-energy purpose and other sectors represented 34.96%
of the total final consumption in the country, and, transport sector
accounted for 8.82% of Total Final Consumption.
Data on energy flows needs to be defined for all of the following categories,
as per the definitions in Chapter 5 of the IRES:
Production, including primary and secondary production
Imports
Exports
International marine bunkers
International aviation bunkers
Stock changes
Transfers
Transformation
Losses
Energy industries own use
Non-energy use
Final consumption
2.9 Data on International/coastal bunkers are already available in Table V.5 of the
IPNG publication released by MoPNG, the ESD may too start reporting the
data in their publication. It may be noted that not all the quantity items listed
above are reported in the annual energy statistics, e.g. storage capacity and
stock changes pertaining to Petroleum and Natural Gas. However, the Stock
change data is being reported in the JODI by the Ministry of Petroleum &
Natural Gas. . In the case of coal, pit-head stocks are reported, but without
data regarding calorific value.
Classification of economic/statistical units
2.10 Classification of statistical units is done according to the type of economic
activity, and is a useful way to classifying entities associated with the energy
sector. Since there is wide variation in how economic units are organised,
there are multiple ways of categorising statistical units depending on the
context and application (see Annexure).
2.11 For the purposes of aggregate annual energy statistics, statistical units
may be grouped into energy industries, other energy producers, and
energy consumers, representing different stages along the energy flows.
(IRES, para 5.8) 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
10
2.12 Energy industries are defined as those economic units whose principal activity
is the primary energy production, transformation of energy or distribution of
energy. This includes primary energy producers such as coal mining companies,
oil and gas extraction companies;energy transformation companies such as
coal washeries, electricity generating plants and refineries; energy storage
companies; and energy transportation and distribution companies. In order
to improve cross-country comparability of statistics on energy production
by energy industries, IRES recommends that countries identify the energy
industries listed in the left column of the Table 5.1 of IRES (reproduced in the
Annexure). The table also provides information on the ISIC Rev. 4 division/
group/class in which the different energy industries are included.
2.13 Other energy producers include those entities who have some other principal
activity, but for whom energy supply is a secondary activity. This includes
activities such as bagasse co-generation, captive electricity generation, and
rooftop solar generation. This information is not readily available due to
self-consumption etc. and also since these entities is dispersed and large
in number. These entities are not classified as energy suppliers, but based
on their primary activity.
2.14 Energy consumers are economic units that are final users of energy that
may use energy products for energy use or for non-energy use. Table 5.3 in
IRES lists groups of energy consumers along with correspondence between
the identified groups of energy consumers and the relevant categories
of ISIC Rev. 4. This table is reproduced in the Annexure. Share of the
non-energy sector in Oil & Gas consumption is projected to increase in
future with all the global agencies projecting it as the most resilient sector.
In view of the projected increase in share of the non-energy sector and in
line with the global practices, the same needs to be classified separately
with sub-classification basis the current & foreseeable uses. Although the
endeavor in the NIC-2008 & NPCMS/NPCS classification is to include all
the possible options, however having a long list may come as a constraint
in normal usage and based on user interpretation. A concise list based on
the current dynamics can make the Energy data management easier and
avoid statistical imbalances.
2.15 The IRES recommends that national energy statistics should use a classification
compatible with the International Standard Industrial Classification of
All Economic Activities (ISIC) Rev. 4 in order to facilitate cross-country
comparisons. However, the ISIC classification is broad in scope, since it is
a generic classification applicable to all nations globally. Hence countries
are encouraged to add sufficient detail needed to reflect their specific
circumstances. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
11
2.16 The National Industrial Classification (NIC-2008) published by MoSPI is
derived from ISIC Rev 4, and the structure of NIC-2008 is identical to the
structure of ISIC Rev. 4 up to 4-digit level ‘class’, and subsequently divided
into 5-digit ‘sub classes’ according to national requirements. NIC-2008
is used by various government agencies and industry associations and
researchers for economic data. For example, NIC-2008 is used to classify the
surveyed units in the Annual Survey of Industries (ASI) conducted by MoSPI.
However, the annual energy statistics published by energy data agencies in
the country currently do not classify industries using the NIC-2008 codes.
Some examples (not comprehensive) have been listed in the following table.
Publication DescriptionRemarks
Coal Directory
2019-20
Section IV: Despatch and
Off-take, Section VIII: Coal
Consumption, Section IX:
Captive Coal and Lignite
Blocks
Consuming sectors, companies owning
coal washeries and captive coal and
lignite producers need to be identified
using the NIC-2008 codes
PNG Statistics
2019-20
Table II.18 and Section VConsuming sectors in Tables II.18, V.4
to V.12 need to be identified using the
NIC-2008 codes
Energy
Statistics 2021
Chapter 7: Energy Balance
and Sankey Diagram
Consuming sectors listed in the energy
balances can be categorised using the
NIC-2008 codes
Classification of energy products
2.17 Classification of energy products facilitates standardised data collection and
enables integration of data collected by different agencies.
2.18 Energy products are classified in the IRES using the Standard International
Energy Product Classification (SIEC), which represents the first internationally
agreed classification of energy products. SIEC was the result of a
harmonization process of definitions used by international, regional and
supranational organizations involved in the collection and compilation of
energy statistics. A classification structure with a coding system was then
developed based on the harmonized definitions. SIEC went through a wide
consultation process with countries as well as consultation with experts on
statistical standard classifications. (ESCM, para 3.32)
2.19 The Central Product Classification (CPC) is a complete product classification
covering all goods and services, and provides a framework for international
comparison and promotes harmonization of various types of statistics. It
serves as an international standard for assembling and tabulating all kinds
of data requiring product detail, including statistics on industrial production, 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
12
domestic and foreign commodity trade, international trade in services,
balance of payments, consumption and price statistics and other data used
within the national accounts. The latest version of CPC is v2.1.
2.20 National Product Classification for Manufacturing Sector (NPCMS-2011)
and National Product Classification for Services Sector (NPCS) developed
by CSO, MoSPI to classify products in India are based on the CPC. The
NPCMS-2011 is a 7-digit classification with the first 5 digits equal to the CPC
and the next 2 digits to cover additional details to suit Indian requirements.
The NPCS is an 8-digit classification starting with “99” following by the
5-digit CPC code, followed by 1 digit for Indian requirements.
2.21 Indian Trade Classification, used for classification of imports and exports,
is based on the Harmonized Commodity Description and Coding System
(HS) – an exhaustive nomenclature of internationally traded commodities
(goods) classified according to the following criteria: (a) raw or basic
material; (b) degree of processing; (c) use or function; and (d) economic
activities. The HS system is used by the DGCI&S to report all export/import
data.
2.22 SEEA-Energy uses SIEC in the physical measurement of energy products.
Monetary flows of energy products, on the other hand, are often classified
using the Central Product Classification (CPC). (SEEA-Energy para 1.72)
2.23 The SIEC along with its correspondence with various versions of the CPC
and the HS is provided in Table 3.1 of the IRES and Annex 3A of the ESCM.
Even so, SEEA-Energy para 1.72 states the following: “As a one-to-one
relationship does not exist between SIEC and Central Product Classification
categories, a correspondence between these classifications will be needed
for detailed analysis of combined physical and monetary data sets.” This
needs further investigation. An institutional mechanism must exist to take
in record the updates in the concordances as mentioned below.
2.24 Following are some examples where energy products can be better
classified by using the NPCMS-2011 code.
Publication DescriptionRemarks
Energy
Statistics 2021
Chapter 3: Production of
Energy Resources
Products need to be identified using
NPCMS-2011 code
PNG Statistics
2019-20
Table V.1: Products MoPNG/PPAC comment: MoPNG will
explore shifting of products identification
using the NPCMS 2011 code. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
13
Classification of energy resources
2.25 The United Nations Framework Classification for Fossil Energy and Mineral
Reserves and Resources (UNFC-2009) is the international standard for
the assessment of the status of different mineral and energy resources.
It provides a scheme for classifying and evaluating these resources according
to three dimensions, namely, their economic and social viability, the field
project status and feasibility, and the geological knowledge about these
resources. Table 5.1 of the SEEA-Energy groups the detailed categories
of UNFC into three aggregated classes characterizing the commercial
recoverability of the resources as follows:
Class A Commercially recoverable resources
Class B Potentially commercially recoverable resources
Class C Non-commercial and other known deposits
2.26 Following are examples of non-standardised reporting of coal, oil and gas
resources.
Publication DescriptionRemarks
PNG Statistics
2019-20
Table II.9 and II.13: Balance
Recoverable Reserves of Crude
Oil and Natural Gas
Proved and indicated balance
recoverable reserves are reported,
but not by UNFC classification or
the aggregate classes as defined
above.
Miscellaneous classifications
2.27 This section consists of some additional classifications that are used in
energy statistics publications in India. Standardisation of these classifications
would help comparability across various energy statistics publications.
Classification by geographical area
2.28 Where energy statistics are reported at a sub-national geographical area,
a standardised classification is needed. This includes reporting the data by
states, etc.
Publication DescriptionRemarks
PNG Statistics
2019-20
Table III.32: PNG Status Connections across multiple states
and cities are combined. In some
cases, CGD companies are listed
under “GA/Cities Covered”
PNG Statistics
2019-20
Table II.13: Balance Recoverable
Reserves of Natural Gas;
Table II.17: Gross and Net
Production of Natural Gas
Offshore needs to be classified by
basin 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
14
Energy and commodity balances
2.29 The IRES publishes the scope and general principles for compiling energy
balances. The energy balance published in the Energy Statistics is in line with
these principles. However, since the source data that is used to compile the
balance is not available at the necessary granularity, there are gaps in the
balance especially on the consumption side and large statistical differences
in some instances. These could probably be improved by following some
of the recommendations listed above, i.e., uniform reporting of calorific
values of energy products, and better classification of energy consumers
and energy products. In addition, much of the end-use data is as reported
by the supply agencies. This needs to be reconciled with data collected at
the consumer end for better accuracy.
2.30 The IRES recommends that commodity-wise balances also be published
along the same principles as energy balances. The Coal Directory publishes
a simplified commodity balance for domestic coal, lignite and imported coal.
The PNG Statistics does not publish a commodity balance for oil and gas.
Metadata
2.31 It is recommended that national energy statistics are accompanied by
adequate metadata (“data about data”), which is “a specific form of
documentation that defines and describes data so that users can locate and
understand them, make an informed assessment of their strengths, limitations,
usefulness and relevance, and use and share them”. (IRES para 9.33)
IRES defines two types of metadata:
Structural metadata are identifiers and descriptors of the data that are
essential for discovering, organizing, retrieving and processing statistical
datasets. This type of metadata includes labels such as names of the
table columns, units of measurement, time period, commodity code,
etc., and needs to be published along with the actual data.
Reference metadata describe the content and quality of the statistical
data.
2.32 IRES recommends that development of metadata be provided high priority,
and dissemination of up-to-date metadata along with the data should be
an integral part of the dissemination of energy statistics. Recommended
metadata structure is based on Single Integrated Metadata Structure (SIMS)
published by the European Commission (reproduced in the Annexure). Use
of web technology and SDMX standards can reduce the reporting burden. 3. DATA COLLECTION
AND COMPILATION
15Report of the Inter-Ministerial Committee on Energy Data Management
3.1 The legal framework underpinning data collection and compilation is very
important. Responsibilities of collection, compilation and dissemination
should be clearly identified. An efficient institutional arrangement will be
cost effective and will avoid duplication. A mechanism for coordination
among different agencies involved in data collection and dissemination is
required and should have authority to implement its decisions. Experience
from other countries shows that a successful energy data management
system can be either centralized or decentralized. What is important is that
there should be a clear definition of rights and responsibilities of all agencies
involved. Formal and informal interaction between various agencies need
to be organised for smooth coordination.
3.2 In the Indian context, there is a need to evaluate the benefits of an integrated
approach to data collection. Questions related to energy consumption can
be integrated in the periodic surveys. For example, household surveys
conducted by the National Statistical Office (NSO) can integrate energy
related questions in the questionnaire. Similarly, seeking more information
on energy use can be incorporated in enterprise surveys.
3.3 MoSPI receives all the required information on Energy related matters
directly from different source Ministries; no specific survey gets conducted
exclusively for this purpose. Based on the requirement of the different
national/international stakeholders, which can be best identified by the
concerned administrative Ministries, the ‘demand side’ data on Energy can
be captured by ‘reporting agencies’.
3.4 Recommendation – Stakeholders may co-ordinate with MoSPI with respect
to the energy specific questions which could be integrated in the periodic
surveys. Demand side data on energy which is best captured by reporting
agencies themselves may also be identified. Data thus collected should be
collated and published in useful formats, and reconciled with other official
published data.
NITI Aayog can work together with MoSPI in order to work out the details on
the specifics and the kind of questions which can be incorporated. 4. CENTRALIZED ENERGY
DATA MANAGEMENT
AGENCY
16Report of the Inter-Ministerial Committee on Energy Data Management
4.1 Energy Statistics Division (ESD) of MoSPI does collect and compile all the
key information on Energy Statistics (collected directly from different Energy
Ministries) of India, on annual basis. ESD also computes and publishes
different Energy Indicators like Energy Balance, Sankey Diagram (Energy
flow diagram), Per-Capita Energy Consumption (PCEC), Energy Intensity etc.
as per IRES (International Recommendation on Energy Statistics) guidelines.
4.2 Recommendation - ESD, MoSPI ought to be the natural centralised Energy
Data Management Agency of India. A suitable strengthening of the present
Energy Statistics Unit of ESD can always deliver the task upto the desired
level. However, as per Allocation of Business Rules the expected deliverables
of MoSPI does not cover a wide range of activities which are included in
the ambit of energy data management agencies internationally. NITI Aayog
can support MoSPI for capacity building, training etc in this regard. 5. REQUIREMENT OF AN
ENERGY DATA MANUAL
17Report of the Inter-Ministerial Committee on Energy Data Management
5.1 A compilation of definitions, calorific values, metadata etc are published
in the Energy Statistics publication of MoSPI. There is a need to further
enrich the Energy Statistics by incorporating the suggestions made for
standardisation and classification as highlighted in this report. Further
improvements, in consultation with the concerned ministries, may also be
incorporated pertaining to;
Definitions and concepts of all the key parameters in the energy sector
Formats and methodologies for data collection and reporting
Standardised data (GCV, operation hours, etc.)
Energy Balances and energy accounting
Standard data reporting formats for easy consumption to humans as
well as computers
5.2 Recommendation – ESD, MoSPI does maintain a compendium containing
the concepts and definitions used by different source agencies and based
on which energy statistics is compiled. The same can always be updated
based on the specific updations/additions from concerned energy ministries/
report. 6. THE WAY FORWARD
18Report of the Inter-Ministerial Committee on Energy Data Management
6.1 The recommendations of the committee have been compiled in this section.
Calorific Values
PublicationDescription Remarks
Coal Directory 2019-20 Table 4.15, 4.16Along with grade-wise despatches,
weighted average net calorific value
within each grade should be provided
for coking coal. In case of unavailability
of net calorific value, gross calorific
values can continue being used as per
IRES recommendations
Energy Statistics 2021 Chapter 7:
Energy Balance
and Sankey
Diagram
While the methodology for converting
physical quantities to energy units is
described, the underlying calorific value
data is not reported.
PNG Statistics 2019-20 Appendices Lot of detailed conversion tables (volume
to weight, unit versions, impact of
temperature, calorific value etc.) which
are very useful.
Density for a fuel may vary as per
different batches of production, imports
and exports and ethanol blending over
time etc. Similarly, energy content may
also vary.
Therefore, it is essential that weighted
average for volume-mass conversion &
calorific value of the energy product
be used for calculation purpose. Global
conversion factor or energy content may
be taken as a standard reference only.
It will also entail statistical differences &
inaccurate conclusions. 6. The Way ForwardReport of the Inter-Ministerial Committee on Energy Data Management
19
PublicationDescription Remarks
MNRE estimates of
bioenergy potential on
its website
Units for products such as biomass
and waste which vary widely in
energy, moisture and ash content are
recommended to be reported in energy
content rather than physical quantities
such as mass or volume.
6.2 Classification of economic and statistical units
Publication DescriptionRemarks
Coal
Directory
2019-20
Section IV: Despatch and
Off-take, Section VIII: Coal
Consumption, Section IX:
Captive Coal and Lignite
Blocks
Consuming sectors, companies owning
coal washeries and captive coal and
lignite producers need to be identified
using the NIC-2008 codes
PNG
Statistics
2019-20
Table II.18 and Section V Consuming sectors in Tables II.18, V.4
to V.12 need to be identified using the
NIC-2008 codes
Energy
Statistics
2021
Chapter 7: Energy Balance
and Sankey Diagram
Consuming sectors listed in the energy
balances can be categorised using the
NIC-2008 codes
6.3 Classification of energy products
Publication DescriptionRemarks
Energy
Statistics
2021
Chapter 3: Production of
Energy Resources
Products need to be identified using
NPCMS-2011 code
PNG
Statistics
2019-20
Table V.1: Products MoPNG/PPAC comment: MoPNG
will explore shifting of products
identification using the NPCMS 2011
code.
6.4 Classification of energy resources
Publication DescriptionRemarks
Coal
Directory
2019-20
Section II: Resources and Exporation Total resources are
reported, but not balance
recoverable reserves. In
the meeting it was stated
that Central Mine Planning
and Design Institute has
data pertaining to such
reserves and this data can
be shared with Ministry of
Statistics and Programme
Implementation 6. The Way ForwardReport of the Inter-Ministerial Committee on Energy Data Management
20
Publication DescriptionRemarks
PNG
Statistics
2019-20
Table II.9 and II.13: Balance Recoverable
Reserves of Crude Oil and Natural Gas
Proved and indicated
balance recoverable
reserves are reported, but
not by UNFC classification
or the aggregate classes as
defined above.
6.5 Classification by geographical area
Publication DescriptionRemarks
PNG
Statistics
2019-20
Table III.32: PNG StatusConnections across multiple
states and cities are
combined. In some cases,
CGD companies are listed
under “GA/Cities Covered”
PNG
Statistics
2019-20
Table II.13: Balance Recoverable
Reserves of Natural Gas;
Table II.17: Gross and Net Production
of Natural Gas
Offshore needs to be
classified by basin
6.6 Energy and Commodity Balances
The IRES recommends that commodity-wise balances also be published
along the same principles as energy balances. The Coal Directory publishes
a simplified commodity balance for domestic coal, lignite and imported coal.
The PNG Statistics does not publish a commodity balance for oil and gas.
6.7 Data Collection and compilation
Recommendation – Stakeholders may co-ordinate with MoSPI with respect
to the energy specific questions which could be integrated in the periodic
surveys. Demand side data on energy which is best captured by reporting
agencies themselves may also be identified. Data thus collected should be
collated and published in useful formats, and reconciled with other official
published data.
NITI Aayog can work together with MoSPI in order to work out the details
on the specifics and the kind of questions which can be incorporated.
6.8 Centralized Energy Data Management Agency
Recommendation - ESD, MoSPI ought to be the natural centralised Energy
Data Management Agency of India. A suitable strengthening of the present
Energy Statistics Unit of ESD can always deliver the task upto the desired
level. However, as per Allocation of Business Rules the expected deliverables
of MoSPI does not cover a wide range of activities which are included in 6. The Way ForwardReport of the Inter-Ministerial Committee on Energy Data Management
21
the ambit of energy data management agencies internationally. NITI Aayog
can support MoSPI for capacity building, training etc.
6.9 Requirement of an Energy Data Manual
Recommendation – ESD, MoSPI does maintain a compendium containing
the concepts and definitions used by different source agencies and based
on which energy statistics is compiled. The same can always be updated
based on the specific updations/additions from concerned energy ministries/
report. ANNEXURE I
22Report of the Inter-Ministerial Committee on Energy Data Management
Classification of statistical units
The IRES divides statistical units into two categories (IRES, para 6.5):
a. Observation units—identifiable legal/organizational or physical units which
are able, actually or potentially, to report data about their activities; and
b. Analytical units—units created by statisticians, often by splitting or
combining observation units, in order to compile more detailed and
homogeneous statistics than is possible by using data on observation
units
Even though analytical units are not able to reported data themselves about
their activities, the use of analytical units is recommended since it may improve
the accuracy of energy statistics in cases where complex economic entities are
active in both energy production and other economic activities.
SEEA-Energy classifies economic units as enterprises, establishments and
industries. (SEEA-Energy para 2.4.2).
a. An enterprise is the view of an institutional unit as a producer of goods
and services. Enterprises undertake production in a range of ways
including as profit-making businesses, as a part of household activity
or as part of the function of government. Importantly, an enterprise can
own assets and acquire liabilities and has the capacity to engage in
transactions and other economic activities with other economic units. An
enterprise may comprise one or more establishments and hence may be
situated across multiple locations within a single economy. An enterprise
may also undertake ancillary production. In most cases, the production
of these services is not recorded as a separate set of outputs; rather,
the relevant inputs are recorded as constituting part of the overall inputs
to the production of the enterprise’s primary and secondary products.
b. An establishment is a unit situated in a single location at which either a
single type of productive activity is carried out, or a single productive Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
23
activity (the primary activity) accounts for the majority of the value
added. If more than one productive activity is carried out by an
establishment, activities other than the primary activity are considered
secondary activities.
c. The groupings of establishments that undertake similar types of productive
activity are referred to as industries. Within SEEA-Energy, establishments
are classified within industries using ISIC.
Energy industry classification
IRES Table 5.1: Energy industries with reference to the relevant ISIC category
Energy industryISIC Rev. 4
Electricity, CHP and heat
plants
Division 35 – Electricity, gas, steam and air conditioning
supply
Pumped storage plants
Coal mines Division 05 – Mining of coal and lignite
Coke ovens Group 191 – Manufacture of coke oven products
Coal liquefaction plants Group 192 – Manufacture of refined petroleum
products
Patent fuel plants Group 192 – Manufacture of refined petroleum
products
Brown coal briquette plants Group 192 – Manufacture of refined petroleum
products
Gas works (and other
conversion to gases)
Group 352 – Manufacture of gas; distribution of
gaseous fuels through mains
Gas separation plants Division 06 – Extraction of crude petroleum and
natural gas
Gas-to-liquids (GTL) plants Group 192 – Manufacture of refined petroleum
products
LNG plants/regasification
plants
Group 091 – Support activities for petroleum and
natural gas extraction
Class 5221 – Service activities incidental to land
transportation
Blast furnaces Group 241 – Manufacture of basic iron and steel
Oil and gas extraction Division 06 – Extraction of crude petroleum and
natural gas
Group 091 – Support activities for petroleum and
natural gas
Oil refineries Division 19 – Manufacture of coke and refined
products Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
24
Energy industryISIC Rev. 4
Charcoal plantsClass 2011 – Manufacture of basic chemicals
Biogas production plants Group 352 – Manufacture of gas; distribution of
gaseous fuels through mains
Nuclear fuel extraction and
fuel processing
Class 0721 – Mining of uranium and thorium ores
Class 2011 – Manufacture of basic chemicals
Other energy industry not
elsewhere specified
Class 0892 – Extraction of peat
…
Energy consumer classification
IRES Table 5.3: Main categories of energy consumers (Manufacturing, construction
and non-fuel mining industries)
Energy consumersISIC Rev. 4
Iron and steel ISIC Group 241 and Class 2431. Note that the consumption
of energy products in coke ovens and blast furnaces
is excluded, as these plants are considered part of the
energy industries.
Chemical and petrochemical ISIC Divisions 20 and 21. Note that the consumption
of energy products by plants manufacturing charcoal
or carrying out the enrichment/production of nuclear
fuels (both classified in ISIC 2011) is excluded, as these
plants are considered part of the energy industries.
Non-ferrous metals ISIC Group 242 and Class 2432
Non-metallic minerals ISIC Division 23
Transport equipment ISIC Divisions 29 and 30
Machinery ISIC Divisions 25, 26, 27 and 28
Mining and quarrying ISIC Divisions 07 and 08, and Group 099, excluding
the mining of uranium and thorium ores (Class 0721)
and the extraction of peat (Class 0892).
Food and tobacco ISIC Divisions 10, 11 and 12
Paper, pulp and print ISIC Divisions 17 and 18
Wood and wood products
(other than pulp and paper)
ISIC Division 16
Textile and leather ISIC Divisions 13, 14 and 15
Construction ISIC Divisions 41, 42 and 43
Industries not elsewhere
specified
ISIC Divisions 22, 31 and 32 Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
25
Energy consumersISIC Rev. 4
Household ISIC Divisions 97 and 98
Commerce and public
services
ISIC Divisions 33, 36–39, 45–96 and 99, excluding
ISIC 8422
Agriculture, forestry ISIC Divisions 01 and 02
Fishing ISIC Division 03
Not elsewhere specified
(including defence activities)
ISIC Class 8422
Single Integrated Metadata Structure (SIMS)
Reproduced from IRESBox 9.3: Metadata items for statistical releases
S.1 Contact (organization, contact person, address, email, phone, fax)
S.2 Introduction
S.3 Metadata update (last certified, last posted and last update)
S.4 Statistical presentation
S.4.1 Data description
S.4.2 Classification system
S.4.3 Sector coverage
S.4.4 Statistical concepts and definitions
S.4.5 Statistical unit
S.4.6 Statistical population
S.4.7 Reference area
S.4.8 Time coverage
S.4.9 Base period
S.5 Unit of measure
S.6 Reference period
S.7 Institutional mandate (legal acts and other agreements, data sharing)
S.8 Confidentiality (policy, data treatment)
S.9 Release policy (release calendar, calendar access, user access)
S.10 Frequency of dissemination Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
26
S.11 Dissemination format, accessibility and clarity (News release, publications,
online database, micro-data access, other),
S.12 Accessibility of documentation (documentation on methodology, quality
documentation)
S.13 Quality management (quality assurance, quality assessment)
S.14 Relevance (user needs, user satisfaction, completeness)
S.15 Accuracy and reliability (overall accuracy, sampling error, non-sampling
error (coverage errors, measurement errors, non-response errors, processing
errors, model assumption errors))
S.16 Timeliness (time lag to final results) and punctuality (delivery and publication)
S.17 Comparability (geographical, over time)
S.18 Coherence (cross-domain, internal)
S.19 Cost and burden
S.20 Data revision (policy, practice)
S.21 Statistical processing
S.21.1 Source data
S.21.2 Frequency of data collection
S.21.3 Data collection
S.21.4 Data validation
S.21.5 Data compilation
S.21.6 Adjustments
S.21.61 Seasonal adjustment
Conversion Factors
1 kilogram=2.2046 pounds
1 Pound=454 gm.
1 Cubic metres=35.3 cubic feet (gas)
1 Metric ton=1 Tonne =1000 kilogram
1 Joule=0.23884 calories
1 Mega Joule=10^6 joules = 238.84 x 10^3 calories
1 Giga Joule=10^9 joules = 238.84 x 10^6 calories Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
27
1 Tera Joule=10^12 joules = 238.84 x 10^9 calories
1 Peta Joule=10^15 joules = 238.84 x 10^12 calories
One million tonnes of coal=15.13 petajoules of energy
One million tonnes of oil equivalent
(MTOE)
=41.87 petajoules of energy
One billion cubic meter of natural gas=38.52 petajoules of energy
One million cubic meter of natural gas=38.52 terajoules of energy
=0.03852 petajoules of energy
One billion kilowatt hour of electricity=3.60 petajoules of energy ANNEXURE II
28Report of the Inter-Ministerial Committee on Energy Data Management
GCV of Coal and the Total Primary Energy Supply (TPES)
Background
The National Statistical Office (NSO) under Ministry of Statistics and Program
Implementation (MoSPI) publishes one Annual publication under the heading of
Energy Statistics, in which the diverse aspects on energy related issues are being
analysed and published. Since Coal is a major source of energy in India, thus the
energy which gets generated from Coal and supplied to different sectors are one
of the key components of this publication. Coal Controller’s Org. under MoC is
the nodal office which provides all the required information to MoSPI for doing
necessary calculation at their end and finally incorporating the same to the latest
edition of Energy Statistics.
Present scenario
In the minutes of meeting held on 22th July-2020, under the Chairmanship of
Shri Rajnath Ram, Adviser, Niti Aaoyoge; CCO, MoC has been directed to come
up with some ‘representative unique value of GCV’, which will be used for the
purpose of calculation of TPES by MoSPI in their publication of Energy Statistics.
It has been observed that, though Coal(Non Coking) is a mineral having a wide
range of GCV (ranging from as low as 2200 Kcl against G17 to as high as above
7000Kcl against G1) there are several different agencies who uses ‘different
unique representative GCV values’ to represent the ‘average calorific value of
Coal’. A summary of which is given below: Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
29
GRADE
of Non
Coking
Coal
GCV BAND
(K.Cal./Kg.)
Representative GCV (Kcal./Kg.) Used by
MoSPI
BP
Statistics
IESS
2047
BEE IEA
Prayas
Group
G-1Exceeding 7000
3614 4009 39984000
3400-
4600
Weighted
average
of all the
GCV’s
G-2
Exceeding 6700 and
not exceeding 7000
G-3
Exceeding 6400 and
not exceeding 6700
G-4
Exceeding 6100 and
not exceeding 6400
G-5
Exceeding 5800 and
not exceeding 6100
G-6
Exceeding 5500 and
not exceeding 5800
G-7
Exceeding 5200 and
not exceeding 5500
G-8
Exceeding 4900 and
not exceeding 5200
G-9
Exceeding 4600 and
not exceeding 4900
G-10
Exceeding 4300 and
not exceeding 4600
G-12
Exceeding 3700 and
not exceeding 4000
G-13
Exceeding 3400 and
not exceeding 3700
G-14
Exceeding 3100 and
not exceeding 3400
G-15
Exceeding 2800 and
not exceeding 3100
G-16
Exceeding 2500 and
not exceeding 2800
G-17
Exceeding 2200 and
not exceeding 2500
Since there is a wide range of variation of GCV in Coal, representing the GCV
of Coal by means of a ‘unique GCV figure’ may not be the ideal scenario. But
before CCO can suggest any modification/improvement, in term of proposing
‘more than one representative values of GCV’, it is indispensable to understand
that the same can be incorporated in the ‘methodology used for calculation of
TPES (Total Primary Energy Statistics) from Coal’. Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
30
In this regards, after having a discussion with Niti Aayoge and MoSPI and going
through the latest publication of Energy Statistics 2020 (which has data upto
FY: 2018-19(P)), it has been understood that, the basic formula which is used to
derive the Energy is,
Energy (in KToe) = Quantity of Commodity * Conversion Factor
Where,
Conversion factor = [Net Calorific Value (NCV)]/Mega joules per ton of
oil equivalent
where NCV is in kj per kg
Net Calorific Value (NCV) = Gross calorific value (GCV) – (% Moisture
Content) [1NCV = 0.9 GCV]
That is case of Coal:
Energy (in KToe) derived from Coal = Quantity of Coal * f(GCV).
(a function of GCV).
Thus, there is provision for using more than one GCV values in order to derive
the final Energy which came out of Coal in a particular year.
Limitation of the present practice
It has been observed that there are few limitations of the present system which
are given below:
i. The Coal is primarily classified into 2 categories Non Coking (measured
in term of GCV values) and Coking (measured in terms of ash-content),
while considering the ‘representative value 3614 Kcal/kg of coal’ both the
categories are combined and treated as a single class which is not ideal;
ii. Since there is a wide range of GCV, thus a ‘unique/single GCV value
representation against the entire class of Coal’ is also not ideal (as it has
been already stated before);
iii. In case of Non-Coking Coal, the grade-wise (i.e. GCV wise) production
and dispatch quantity coal varies over the year, thus even though ‘the
total quantity is same for two years, since the Grade-wise composition
of production/dispatch is different’, thus there will be ‘2 different volume
of Energy’ that will be attributed against those 2 years, which doesn’t
get reflected under the present practice; Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
31
Example:
Considering G1-G6 as Top Grade, G7-G14 as Middle and G15-G17 as
Bottom let us consider the following hypothetical case of ‘grade-wise
dispatch’ of Non Coking Coal of 2 years,
Grades of NC Coal Year X Year Y Difference (X – Y)
Top
G110 5
G230 10
G317 10
G440 10
G530 26
G624 21
Total 151 8269
Middle
G745 90
G850 80
G950 24
G1065 25
G1145 28
G1210 31
G135 30
G1435 34
Total 305 342-37
Bottom
G1524 20
G1610 35
G1710 21
Total 44 76-32
Grand Total 500 5000
i.e. though in the year X, more coal having Top Grades have been
dispatched but since the grand total dispatch of Coal remains same in
both the year, thus using the present methodology, the ‘energy supplied
from Coal in both years’ will remain same.
iv. It has been observed that the ‘TPES (Total Primary Energy Supply) from
Coal’ which gets calculated using the ‘representative value of GCV as
3614Kcal’ is a gross underestimation of the Energy Generated from Coal.
If we consider the grade-wise dispatch of coal during FY : 2018-19, we
find that. Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
32
Grade-wise Dispatch of Coal during FY : 2018-19
Grade Mid Range GCV
Qty
(Million Tonnes)
Cumulative
Qty
Percentage
G17 2350-G17 3.356 3.356 0.49%
G16 2650-G16 3.083 6.439 0.94%
G15 2950-G15 6.783 13.222 1.92%
G14 3250-G14 50.650 63.872 9.30%
G13 3550-G13 99.528 163.400 23.79%
G12 3850-G12 69.116 232.516 33.85%
G11 4150-G11 178.854 411.370 59.88%
G10 4450-G10 105.981 517.351 75.31%
G9 4750-G931.130 548.481 79.84%
G8 5050-G8 56.671 605.152 88.09%
G7 5350-G7 42.694 647.846 94.31%
G6 5650-G68.802 656.648 95.59%
G5 5950-G511.726 668.374 97.30%
G4 6250-G4 14.702 683.076 99.44%
G3 6550-G33.332 686.408 99.92%
G2 6850-G2 0.430 686.838 99.99%
G1 7150-G1 0.099 686.937 100.00%
Observations:
1. The representative GCV of 3614 Kcal/Kg, which is used by for calculation
of TPES from Coal comes under the group G13.
2. During FY : 2018-19 around 24% of Coal got dispatched having grades
G13 or less (i.e having GCV 3614 or less).
3. More than 75% of dispatched NC Coal are having GCV over 3614 Kcal/kg,
which have been treated as Coal having GCV of 3614 Kcl.
The graphical representation is given below:
0%
25%
50%
75%
100%
0
2350-G17
2650-G16
2950-G15
3250-G14
3550-G13
3850-G12
4150-G11
4450-G10
4750-G9
5050-G8
5350-G7
5650-G6
5950-G5
6250-G4
6550-G3
6850-G2
7150-G1
20
40
60
80
100
120
140
160
180
200% of Coal dispatched
Tonnes of Coal
Mid Range GCV of Each Grades
Grade wise dispacth of Coal during FY : 2018-19
Qty (Million Tonnes) Percentage Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
33
Similarly for FY : 2017-18, 2016-17, 2015-15 and 2014-15 also is has been
found that, the percentage of Coal, having GCV of 3614 Kcal or less,
which got dispatched were, around 23%, 18%, 14% and 13% respectively
(please refer the excel sheet “Calculation_TPES” for details) and most of
the coal got dispatched were having GCV much higher than 3614 Kcal.
Thus, we can see that the figure of TPES generated from Coal using
representative value of 3614 Kcal/Kgs grossly underestimates the true
energy generated and supplied from Coal.
v. Again, since there are more than one representative values of GCVs have
been considered by different agencies, the ‘Final results’ (of generation
of Energy) will always vary and which will leads to confusion;
Proposed methodology
As we know that in India the total available Coal in India is from 2 sources,
i) Domestic and ii) Import.
Domestic Coal:
a. Non Coking Coal:
In the schedules of Coal Directory, CCO captures information on
‘sectorwise-gradewise’ dispatch of Coal (i.e. each company has to submit
the different grades of Coal he has dispatched for different end uses like
Power, Iron & Steel, Fertilizer etc. during a particular Financial Year). Since
the methodology used for calculation of TPES uses the simple ‘quantity
X f(GCV)’ formula, thus instead of using ‘single representative figure of
GCV against Coal’, we can make use of ‘Mid-Level GCV’ (which is the
‘average of the two class boundaries against of a particular grade) of all
the Grades of Non-Coking coal. The assumption will be made is that the
GCV against a particular grade of coal is distributed uniformly among
the entire range of it.
An example is given in this regard has been given in the Excel sheet
“Calculation_TPES”.
In this method we will be able to make use of the maximum information
available with CCO, MoC.
b. Coking Coal:
In case of Coking Coal also CCO is having the sector-wise dispatch
quantity of Coal. Here the classifications are made in term of Ash
content and not GCV ranges. Thus, determination of Energy generated
from Coking coal may not be as simple as in case of Non Coking coal. Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
34
If CMPDIL can provide some approximate ‘representative’ figures against
each Coking Coal grades, then using the similar methodology, the Energy
supplied to different sectors from Coking Coal can be determined.
Imported Coal:
The coal which are imported are generally having much higher GCV than 3614 Kcal
and used mainly for the purpose of blending. In absence of proper information
about the GCVs of Imported coal, we can make use of the following assumptions,
which MoC has already made while calculating the National Coal Index (NCI) and
Representative Price/Base Bid Prices of Coal, which are,
i. Coal Imported from South Africa belongs to Top Grades (i.e. having GCV
range from 5501 Kcal/kg and onwards);
ii. Coal Imported from Indonesia belongs to Middle Grades (i.e. having GCV
ranges from 3101Kcal/kg to 5500 Kcal/kg.);
iii. No Coal gets Imported to India which belongs to Bottom grades
(i.e. having GCV less than 3100Kcal/kg.);
iv. Coal Imported from Australia are primarily Coking Coal and are of top
quality;
For Imports other than these countries, using the assumption (iii) above we may
use the ‘average of mid-level GCV of Middle Grades Coal’, which comes out to
be 4,300 Kcal/kg.
Benefits of the proposed methodologies:
i. There will be maximum utilisation of the information/data available;
ii. Any changes in the ‘dispatch grades’ will be captured;
iii. Some classification on Imported coal has already been derived by ISI
(during their formulation of NCI and RP), we are making use of those
findings to come up with some ‘calorific estimates’ of Imported Coal. ANNEXURE III
35Report of the Inter-Ministerial Committee on Energy Data Management Annexure IIIReport of the Inter-Ministerial Committee on Energy Data Management
36 Annexure IIIReport of the Inter-Ministerial Committee on Energy Data Management
37 www.niti.gov.in
Report of the Inter-Ministerial Committee on
Energy Data Managemen
Energy Data Management
Report of the Inter-Ministerial
Committee on
Under Sustainable Growth Pillar of India-US Strategic
Clean Energy Partnership
Report of the Inter-Ministerial Committee on
Energy Data Managemen
Energy Data Management
Report of the Inter-Ministerial
Committee on
Under Sustainable Growth Pillar of India-US Strategic
Clean Energy Partnership COMPOSITION OF THE
INTER-MINISTERIAL
COMMITTEE
Composition of the Inter-Ministerial Committee on Energy Data
Management
Sl. No.Name and DesignationPosition
1 Sh. Rajnath Ram, Adviser (Energy), NITI Aayog Chairman
2 Ms Santosh, DDG, Ministry of CoalMember
3 Ms Anshu Singh, DDG (Stats), Ministry of
Environment, Forest and Climate Change
Member
4 Ms. Harmeet Minhas Kumar, Director, Ministry of
Statistics and Programme Implementation
Member
5 Sh. Arijit Sengupta, Director, Bureau of Energy
Efficiency
Member
6 Sh. Prahlad, Chief Engineer, Central Electricity
Authority
Member
7 Sh. VP Singh, Director, Coal Controller’s OrganisationMember
8 Dr Pankaj Sharma, Additional Director, Petroleum
Planning and Analysis Cell
Member
9 Sh. Vikram Dhaka, Scientist C, Ministry of New and
Renewable Energy
Member
10 Ms Avinash Kumari, Assistant Director, Ministry of
Petroleum and Natural Gas
Member
11 Prof. Venkat Ramadesigan, IIT Bombay Member
12 Prof Ankush Sharma, IIT Kanpur Member
13 Sh. Srihari Dukkipati, Prayas (Energy Group) Member
14 Ms. Apurva Chaturvedi, Senior Clean Energy
Sepcialist, USAID
Member
15 Ms. Meredydd Evans, Scientist, Pacific Northwest
National Laboratory
Member
16 Mr. Samson Adeshiyan, Director, Office of Statistical
Methods and. Research, EIA
Member
iiiReport of the Inter-Ministerial Committee on Energy Data Management Composition of the Inter-Ministerial CommitteeReport of the Inter-Ministerial Committee on Energy Data Management
iv
Sl. No.Name and DesignationPosition
17 Mr. Jon Weers, Lead Technologist and Data Systems
Architect. National Renewable Energy Laboratory
Member
18 Sh. Kamil KPS Bhullar, Research Officer, NITI Aayog
(Member Convener)
Member
Terms of reference
i. Standardising the definitions, terminologies and calculation methodology
of all the key parameters in the energy sector so that reporting of data is
uniform.
ii. Arrive at uniform values of standard data including Gross Calorific Value,
operation hours, etc.
iii. Standardize methodologies for data collection, data quality, validation,
survey design methodologies, etc. and their reporting
iv. Based on the findings of 8 Sub-group reports on energy data management
for the various demand and supply sectors, build on the identified energy
data gaps and ensure collection and maintenance of the required data.
v. Study and make suggestions for the setting up of a centralised data agency
in India
vi. Suggest measures for enriching of India Energy Dashboards
vii. Publish an Energy Statistics Manual/ Handbook, which will include, e.g.:
- Definitions and concepts of all the key parameters in the energy
sector
- Formats and methodologies for data collection and reporting
- Standardised data (GCV, operation hours, etc.)
- Energy Balances and energy accounting
viii Organising stakeholder consultations, if required
iv Report of the Inter-Ministerial Committee on Energy Data Management FOREWORD
vReport of the Inter-Ministerial Committee on Energy Data Management PREFACE
viiReport of the Inter-Ministerial Committee on Energy Data Management ACKNOWLEDGEMENTS
ixReport of the Inter-Ministerial Committee on Energy Data Management
The report of the committee could not have been completed without the active
intellectual support provided by individuals representing various ministries/
departments, agencies and think tanks. The committee would like to thank
Ms Santosh, DDG, Ministry of Coal, Ms Anshu Singh, DDG (Stats), Ministry of
Environment, Forest and Climate Change, Sh. Arijit Sengupta, Director, Bureau
of Energy Efficiency, Sh. Prahlad, Chief Engineer, Central Electricity Authority,
Sh. VP Singh, Director, Coal Controller’s Organisation, Dr Pankaj Sharma, Additional
Director, Petroleum Planning and Analysis Cell, Sh. Vikram Dhaka, Scientist C,
Ministry of New and Renewable Energy, Ms Avinash Kumari, Assistant Director,
Ministry of Petroleum and Natural Gas, Prof Venkat Ramadesigan, IIT Bombay,
and Prof Ankush Sharma, IIT Kanpur for their valuable inputs.
The committee would like to acknowledge the support and inputs provided
by Ms Meredydd Evans, Scientist, Pacific Northwest National Laboratory and
Mr Samson Adeshiyan, Director, US Energy Information Administration. In particular
the committee would like to thank Sh. Srihari Dukkipati, Research Fellow, Prayas
(Energy Group) who provided support in terms of the initial paper for discussion.
A special thanks to Dr Rakesh Sarwal (Additional Secretary, NITI Aayog when the
committee was constituted) and Sh. Navin Kumar Vidyarthi, Director (Energy),
NITI Aayog for their efforts towards taking the work of the committee to its
logical conclusion. CONTENTS Composition of the Inter-Ministerial Committee iii
Foreword v
preface vii
Acknowledgements ix
Background 1
1. Introduction 2
2. Standardization of Data 4
Units 4
Calorific values 5
Energy flows 8
Classification of economic/statistical units 9
Classification of energy products 11
Classification of energy resources 13
Miscellaneous classifications 13
Classification by geographical area 13
Energy and commodity balances 14
Metadata 14
3. Data collection and compilation 15
4. Centralized Energy Data Management Agency 16
5. Requirement of an Energy Data Manual 17
6. The Way Forward 18
Annexure I 22
Classification of statistical units 22
Energy industry classification 23
Energy consumer classification 24
Single Integrated Metadata Structure (SIMS) 25
Conversion Factors 26
Annexure II 28
Annexure III 35 BACKGROUND
1Report of the Inter-Ministerial Committee on Energy Data Management
Energy data (resource assessment, extraction, conversion, transmission, distribution,
and consumption) for India is published by a number of state and national
agencies. However, much of the data available is dispersed and difficult to collate
due to differences in the organization of data, use of incompatible formats and
standard definitions. Moreover, there are significant data gaps.
The mandate for Energy Data Management (EDM) in India is fairly decentralized,
with the earlier Act being “The Collection of Statistics Act, 2008”. Individual
Ministries have issued mandates for specific data under their respective domain,
however a legal framework needs to be evolved in view of data collection being
carried out by through decentralized agencies under various ministries besides
MoSPI. Lack of standardized formats make it difficult to automate the process
of data sharing and publication on dashboards. There exists a data gap in
consumption and demand, no regular surveys are conducted at the household
level to determine consumption data of interest. Consumption data also needs to
be accessed from four areas in particular – agriculture, building (both residential
and commercial), industry and transport.
The shape of the data architecture varies across countries. The local conditions
of each country have to be considered while analysing the system best suited to
it. Data management systems range from the highly centralized ones (Canada)
to highly decentralized (Germany).
There is a need to standardise the definitions, terminologies and calculation
methodology of all the key parameters in the energy sector (e.g. import
dependency of crude oil, natural gas, etc.) so that reporting of data is uniform.
It is also required that we arrive at uniform values of standard data (Gross Calorific
Value, operation hours, etc.) and standardize methodologies for data collection,
data quality, validation, survey design methodologies, etc. and reporting. 1. INTRODUCTION
2Report of the Inter-Ministerial Committee on Energy Data Management
1.1 A committee on energy data management was formed by NITI Aayog on
30
th
July 2021 under the Sustainable Growth Pillar established under the
India-US Strategic Partnership. During the meeting of joint working group
held on 23
rd
August, a sub-group was formed on Energy Data Management
to identify the parameters which need to be standardized across various
energy statistics products in India. The inter-ministerial committee deliberated
twice on the subject.
1.2 While preparing the report, the following documents have been referred to:
International Recommendations for Energy Statistics (IRES) and the
associated Energy Statistics Compiler Manual (ESCM) published by the
UN Statistics Division
System of Environmental-Economic Accounting for Energy (SEEA-
Energy) published by the UN Statistics Division
Documents published by MoSPI for industry and products classification,
i.e., NIC-2008 and NPCMS-2011 respectively
Latest annual Indian energy statistics publications, i.e., Coal Directory
2019-20, Petroleum and Natural Gas Statistics 2019-20, All India
Electricity Statistics-General Review 2020, Energy Statistics 2021
1.3 During the deliberations of the committee, it was observed that data lacks
data definitions and standard classification due to which it is difficult to
compile energy balance. This issue was prevalent across ministries. The
use of standard classifications is important in the collection, compilation
and dissemination of statistics. Standard classifications provide a clear
definition, with a unique structure, of the objects that are being measured
and collected. They facilitate the compilation of data as the classifications
define relationships between concepts and objects. Finally, they allow for
better integration of data collected across different statistical domains
such as, for example, energy, environment and economic statistics. (ESCM,
Section 3.1) 1. IntroductionReport of the Inter-Ministerial Committee on Energy Data Management
3
1.4 The committee felt that the following aspects need standardisation in
energy statistics as per IRES of UN.
1. Measurement units
2. Energy flows
3. Economic/statistical units
4. Energy products
5. Energy resources
6. Various other classification not mentioned in the IRES
7. Energy and commodity balances
8. Metadata
1.5 The above topics are covered in detail in the subsequent sections of the
report, along with some examples where standardisation is needed. 2. STANDARDIZATION
OF DATA
4Report of the Inter-Ministerial Committee on Energy Data Management
Units
2.1 The only unit for energy in the SI system is the joule and is the common
energy unit recommended to be used in energy statistics.
2.2 While data may be collected in units that are most suitable for national
circumstances, it is desired that the data is disseminated in units that
are standardised internationally. Table 1 (reproduced below) in the IRES
provides the recommended units for dissemination of energy data and Table
2 provides the dimensions and price units used in India. This table has been
reproduced below.
Table 1: Reproduced Table 4.4 of IRES
Energy productsDimensionUnit
Solid fossil fuels Mass Thousand metric tons
Liquid fossil fuels (crude
oil, petroleum products,
condensate)
Mass Thousand metric tons
(Liquid) Biofuels Mass/Volume Thousand metric tons/
Thousand cubic metres
Gases Energy Terajoules
Wastes Energy Terajoules
Fuelwood Volume/Energy Thousand cubic metres/
Terajoules
Charcoal Mass Thousand metric tons
Electricity Energy GWh
Heat Energy Terajoules
Common unit (e.g., balances) Energy Terajoules
Electricity installed capacity Power MW
Refinery capacity Mass/time Thousand metric tons/year 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
5
Table 2: Units used in India
Energy ProductUnit (Quantity)Unit (Price)
Crude Oil and Condensate Thousand metric tonsUSD per barrel
Natural GasMillion metric standard cubic metresUSD per mmbtu
Petroleum products Million metric tonsINR per litre
Biofuels (ethanol) Crore litresINR per litre
Electricity installed capacityMWINR per MW
Electrical energy GWhINR per kWh
CoalMillion tonsINR per ton
BiomassMillion metric tonsINR per ton
2.3 The units followed in Indian energy statistics are more or less similar
although there are some minor differences, e.g., coal quantities are reported
in million metric tons whereas thousand metric tons is recommended.
2.4 Units for products such as biomass and waste which vary widely in energy,
moisture and ash content are recommended to be reported in energy
content rather than physical quantities such as mass or volume.
Calorific values
2.5 When expressing energy content of energy products in common energy
units, e.g., when building energy balance tables, net calorific values (NCVs)
are preferred over gross calorific values (GCVs). The following tables provide
the calorific values in use in India.
Table 3: Average International Calorific Values (reproduced from PNG Statistics)
Average International Calorific Value of different fuels
ProductsK Cals/Kg BTUs/Kg
Oil Equivalence
Average Crude Oil/NGLS 1030040870
United Nations1017540375
Oil Products
NGL1213548150
Motor Gasoline1113544190
Kerosene1063842210
Jet Fuel1179046790 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
6
Average International Calorific Value of different fuels
ProductsK Cals/Kg BTUs/Kg
Gas Oil1079042820
Fuel Oil1044041430
Coal Equivalences
Average Hard Coal 700027775
India
Hard Coal500019840
Lignite Brown Coal2310916
Firewood475018848
Charcoal690027379
Natural Gas Production (Average) India
8000-9480
Electricity
Output Basis860 kcal/kwH
Fuel Input Basis25002700
Table 4: Non-coking coal - gradation based on GCV
GCV BAND (K.Cal./Kg.)
Exceeding 7000
Exceeding 6700 and not exceeding 7000
Exceeding 6400 and not exceeding 6700
Exceeding 6100 and not exceeding 6400
Exceeding 5800 and not exceeding 6100
Exceeding 5500 and not exceeding 5800
Exceeding 5200 and not exceeding 5500
Exceeding 4900 and not exceeding 5200
Exceeding 4600 and not exceeding 4900
Exceeding 4300 and not exceeding 4600
Exceeding 4000 and not exceeding 4300
Exceeding 3700 and not exceeding 4000
Exceeding 3400 and not exceeding 3700
Exceeding 3100 and not exceeding 3400
Exceeding 2800 and not exceeding 3100
Exceeding 2500 and not exceeding 2800
Exceeding 2200 and not exceeding 2500 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
7
Table 5: Gradation of coking coal - based on ash content
GradeAsh Content
Steel Grade - INot exceeding 15%
Steel Grade - lIExceeding 15% but not exceeding 18%
Washery Grade - IExceeding 18% but not exceeding 21%
Washery Grade - IlExceeding 21% but not exceeding 24%
Washery Grade - IllExceeding 24% but not exceeding 28%
Washery Grade - IVExceeding 28% but not exceeding 35%
Washery Grade - VExceeding 35% but not exceeding 42%
Washery Grade - VIExceeding 42% but not exceeding 49%
2.6 In order for better accuracy in energy balances, it is recommended that
specific calorific values are used where possible through the entire chain
of flow for each fuel since the calorific values may vary to meet various
market requirements, e.g., ethanol blending. The calorific value of ethanol
being much lower than Motor spirit and further with its blending %age in MS
on the increase, a separate product classification in NPCMS-2011 is required,
blending more than 10%. The same is essential in view of the introduction
of flex fuel engines and the proposed sale of 100% ethanol as a fuel.
As per NPCMS-2011, under Division 34 (Basic Chemicals), Group 341 (Basic
Organic chemicals), Class 3413 (Alcohols, phenols, phenol-alcohols, and their
halogenated, sulphonated, nitrated or nitrosated derivatives; industrial fatty
alcohols) & Subclass 34110 (Ethyl alcohol and other spirits, denatured, of any
strength), product codes listed are 3413101 (Ethyl alcohol) & 3413102 (Methyl
alcohol (methanol)). In view of the usage as a fuel, the product classification
be need to be modified accordingly. Similarly, products obtained from
gasification of the heavier petroleum compounds need to be classified
under the Group 334. In addition, a particular energy product may have
different energy content over different batches of production, for imports
and exports and over time, such as in the case of coal. In such cases, the
specific calorific values for each of these quantities should be used, and
weighted average calorific value of the energy product should be used when
aggregating. All the methods used in arriving at the calorific value should
be documented to ensure transparency, clarity and comparability. Default
calorific values should be avoided as much as possible. Where needed,
default net calorific values provided in Table 4.1 of IRES for various energy
products should be used. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
8
Publication DescriptionRemarks
Energy
Statistics 2021
Chapter 7: Energy Balance
and Sankey Diagram
While the methodology for converting
physical quantities to energy units is
described, the underlying calorific value
data is not reported.
PNG Statistics
2019-20
AppendicesLot of detailed conversion tables (volume
to weight, unit versions, impact of
temperature, calorific value etc.) which
are very useful.
Density for a fuel may vary as per
different batches of production, imports
and exports and ethanol blending over
time etc. Similarly, energy content may
also vary.
Therefore, it is essential that weighted
average for volume-mass conversion &
calorific value of the energy product
be used for calculation purpose. Global
conversion factor or energy content may
be taken as a standard reference only.
It will also entail statistical differences &
inaccurate conclusions.
2.7 In case of bio-energy, it is recommended that a detailed assessment of
availability of different bio-energy resources and their potential needs to
be undertaken. Previous discussions with Ministry of Coal have yielded
proposals for a using grade wise calorific values approach whereby the
mid-point of the range of calorific values against each grade are used. The
note on this methodology is provided in Annexure II.
Energy flows
2.8 As per Energy Statistics 2022, In 2020-21 (P), Primary Energy Supply added
up to 8,88,523 Kilo Tonnes of Oil equivalent (KToE)
Coal, which accounted for 64.93% of the total, and Crude Oil, which
accounted for 26.29% were two major contributors to the total energy
supply in the country
Final Energy Consumption (End Use) was 5,53,971ktoe in 2020-21 (P).
The industrial sector was the largest consumer of energy in the country,
using more than half, i.e., 56.22% of the total final energy consumption.
The most energy intensive industries were iron and steel, accounting
for 15.37% of the industrial energy use followed by Chemicals and
petrochemicals 4.43% and construction 1.96%. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
9
The consumption of the residential, agriculture, commercial & public
sectors, non-energy purpose and other sectors represented 34.96%
of the total final consumption in the country, and, transport sector
accounted for 8.82% of Total Final Consumption.
Data on energy flows needs to be defined for all of the following categories,
as per the definitions in Chapter 5 of the IRES:
Production, including primary and secondary production
Imports
Exports
International marine bunkers
International aviation bunkers
Stock changes
Transfers
Transformation
Losses
Energy industries own use
Non-energy use
Final consumption
2.9 Data on International/coastal bunkers are already available in Table V.5 of the
IPNG publication released by MoPNG, the ESD may too start reporting the
data in their publication. It may be noted that not all the quantity items listed
above are reported in the annual energy statistics, e.g. storage capacity and
stock changes pertaining to Petroleum and Natural Gas. However, the Stock
change data is being reported in the JODI by the Ministry of Petroleum &
Natural Gas. . In the case of coal, pit-head stocks are reported, but without
data regarding calorific value.
Classification of economic/statistical units
2.10 Classification of statistical units is done according to the type of economic
activity, and is a useful way to classifying entities associated with the energy
sector. Since there is wide variation in how economic units are organised,
there are multiple ways of categorising statistical units depending on the
context and application (see Annexure).
2.11 For the purposes of aggregate annual energy statistics, statistical units
may be grouped into energy industries, other energy producers, and
energy consumers, representing different stages along the energy flows.
(IRES, para 5.8) 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
10
2.12 Energy industries are defined as those economic units whose principal activity
is the primary energy production, transformation of energy or distribution of
energy. This includes primary energy producers such as coal mining companies,
oil and gas extraction companies;energy transformation companies such as
coal washeries, electricity generating plants and refineries; energy storage
companies; and energy transportation and distribution companies. In order
to improve cross-country comparability of statistics on energy production
by energy industries, IRES recommends that countries identify the energy
industries listed in the left column of the Table 5.1 of IRES (reproduced in the
Annexure). The table also provides information on the ISIC Rev. 4 division/
group/class in which the different energy industries are included.
2.13 Other energy producers include those entities who have some other principal
activity, but for whom energy supply is a secondary activity. This includes
activities such as bagasse co-generation, captive electricity generation, and
rooftop solar generation. This information is not readily available due to
self-consumption etc. and also since these entities is dispersed and large
in number. These entities are not classified as energy suppliers, but based
on their primary activity.
2.14 Energy consumers are economic units that are final users of energy that
may use energy products for energy use or for non-energy use. Table 5.3 in
IRES lists groups of energy consumers along with correspondence between
the identified groups of energy consumers and the relevant categories
of ISIC Rev. 4. This table is reproduced in the Annexure. Share of the
non-energy sector in Oil & Gas consumption is projected to increase in
future with all the global agencies projecting it as the most resilient sector.
In view of the projected increase in share of the non-energy sector and in
line with the global practices, the same needs to be classified separately
with sub-classification basis the current & foreseeable uses. Although the
endeavor in the NIC-2008 & NPCMS/NPCS classification is to include all
the possible options, however having a long list may come as a constraint
in normal usage and based on user interpretation. A concise list based on
the current dynamics can make the Energy data management easier and
avoid statistical imbalances.
2.15 The IRES recommends that national energy statistics should use a classification
compatible with the International Standard Industrial Classification of
All Economic Activities (ISIC) Rev. 4 in order to facilitate cross-country
comparisons. However, the ISIC classification is broad in scope, since it is
a generic classification applicable to all nations globally. Hence countries
are encouraged to add sufficient detail needed to reflect their specific
circumstances. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
11
2.16 The National Industrial Classification (NIC-2008) published by MoSPI is
derived from ISIC Rev 4, and the structure of NIC-2008 is identical to the
structure of ISIC Rev. 4 up to 4-digit level ‘class’, and subsequently divided
into 5-digit ‘sub classes’ according to national requirements. NIC-2008
is used by various government agencies and industry associations and
researchers for economic data. For example, NIC-2008 is used to classify the
surveyed units in the Annual Survey of Industries (ASI) conducted by MoSPI.
However, the annual energy statistics published by energy data agencies in
the country currently do not classify industries using the NIC-2008 codes.
Some examples (not comprehensive) have been listed in the following table.
Publication DescriptionRemarks
Coal Directory
2019-20
Section IV: Despatch and
Off-take, Section VIII: Coal
Consumption, Section IX:
Captive Coal and Lignite
Blocks
Consuming sectors, companies owning
coal washeries and captive coal and
lignite producers need to be identified
using the NIC-2008 codes
PNG Statistics
2019-20
Table II.18 and Section VConsuming sectors in Tables II.18, V.4
to V.12 need to be identified using the
NIC-2008 codes
Energy
Statistics 2021
Chapter 7: Energy Balance
and Sankey Diagram
Consuming sectors listed in the energy
balances can be categorised using the
NIC-2008 codes
Classification of energy products
2.17 Classification of energy products facilitates standardised data collection and
enables integration of data collected by different agencies.
2.18 Energy products are classified in the IRES using the Standard International
Energy Product Classification (SIEC), which represents the first internationally
agreed classification of energy products. SIEC was the result of a
harmonization process of definitions used by international, regional and
supranational organizations involved in the collection and compilation of
energy statistics. A classification structure with a coding system was then
developed based on the harmonized definitions. SIEC went through a wide
consultation process with countries as well as consultation with experts on
statistical standard classifications. (ESCM, para 3.32)
2.19 The Central Product Classification (CPC) is a complete product classification
covering all goods and services, and provides a framework for international
comparison and promotes harmonization of various types of statistics. It
serves as an international standard for assembling and tabulating all kinds
of data requiring product detail, including statistics on industrial production, 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
12
domestic and foreign commodity trade, international trade in services,
balance of payments, consumption and price statistics and other data used
within the national accounts. The latest version of CPC is v2.1.
2.20 National Product Classification for Manufacturing Sector (NPCMS-2011)
and National Product Classification for Services Sector (NPCS) developed
by CSO, MoSPI to classify products in India are based on the CPC. The
NPCMS-2011 is a 7-digit classification with the first 5 digits equal to the CPC
and the next 2 digits to cover additional details to suit Indian requirements.
The NPCS is an 8-digit classification starting with “99” following by the
5-digit CPC code, followed by 1 digit for Indian requirements.
2.21 Indian Trade Classification, used for classification of imports and exports,
is based on the Harmonized Commodity Description and Coding System
(HS) – an exhaustive nomenclature of internationally traded commodities
(goods) classified according to the following criteria: (a) raw or basic
material; (b) degree of processing; (c) use or function; and (d) economic
activities. The HS system is used by the DGCI&S to report all export/import
data.
2.22 SEEA-Energy uses SIEC in the physical measurement of energy products.
Monetary flows of energy products, on the other hand, are often classified
using the Central Product Classification (CPC). (SEEA-Energy para 1.72)
2.23 The SIEC along with its correspondence with various versions of the CPC
and the HS is provided in Table 3.1 of the IRES and Annex 3A of the ESCM.
Even so, SEEA-Energy para 1.72 states the following: “As a one-to-one
relationship does not exist between SIEC and Central Product Classification
categories, a correspondence between these classifications will be needed
for detailed analysis of combined physical and monetary data sets.” This
needs further investigation. An institutional mechanism must exist to take
in record the updates in the concordances as mentioned below.
2.24 Following are some examples where energy products can be better
classified by using the NPCMS-2011 code.
Publication DescriptionRemarks
Energy
Statistics 2021
Chapter 3: Production of
Energy Resources
Products need to be identified using
NPCMS-2011 code
PNG Statistics
2019-20
Table V.1: Products MoPNG/PPAC comment: MoPNG will
explore shifting of products identification
using the NPCMS 2011 code. 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
13
Classification of energy resources
2.25 The United Nations Framework Classification for Fossil Energy and Mineral
Reserves and Resources (UNFC-2009) is the international standard for
the assessment of the status of different mineral and energy resources.
It provides a scheme for classifying and evaluating these resources according
to three dimensions, namely, their economic and social viability, the field
project status and feasibility, and the geological knowledge about these
resources. Table 5.1 of the SEEA-Energy groups the detailed categories
of UNFC into three aggregated classes characterizing the commercial
recoverability of the resources as follows:
Class A Commercially recoverable resources
Class B Potentially commercially recoverable resources
Class C Non-commercial and other known deposits
2.26 Following are examples of non-standardised reporting of coal, oil and gas
resources.
Publication DescriptionRemarks
PNG Statistics
2019-20
Table II.9 and II.13: Balance
Recoverable Reserves of Crude
Oil and Natural Gas
Proved and indicated balance
recoverable reserves are reported,
but not by UNFC classification or
the aggregate classes as defined
above.
Miscellaneous classifications
2.27 This section consists of some additional classifications that are used in
energy statistics publications in India. Standardisation of these classifications
would help comparability across various energy statistics publications.
Classification by geographical area
2.28 Where energy statistics are reported at a sub-national geographical area,
a standardised classification is needed. This includes reporting the data by
states, etc.
Publication DescriptionRemarks
PNG Statistics
2019-20
Table III.32: PNG Status Connections across multiple states
and cities are combined. In some
cases, CGD companies are listed
under “GA/Cities Covered”
PNG Statistics
2019-20
Table II.13: Balance Recoverable
Reserves of Natural Gas;
Table II.17: Gross and Net
Production of Natural Gas
Offshore needs to be classified by
basin 2. Standardization of DataReport of the Inter-Ministerial Committee on Energy Data Management
14
Energy and commodity balances
2.29 The IRES publishes the scope and general principles for compiling energy
balances. The energy balance published in the Energy Statistics is in line with
these principles. However, since the source data that is used to compile the
balance is not available at the necessary granularity, there are gaps in the
balance especially on the consumption side and large statistical differences
in some instances. These could probably be improved by following some
of the recommendations listed above, i.e., uniform reporting of calorific
values of energy products, and better classification of energy consumers
and energy products. In addition, much of the end-use data is as reported
by the supply agencies. This needs to be reconciled with data collected at
the consumer end for better accuracy.
2.30 The IRES recommends that commodity-wise balances also be published
along the same principles as energy balances. The Coal Directory publishes
a simplified commodity balance for domestic coal, lignite and imported coal.
The PNG Statistics does not publish a commodity balance for oil and gas.
Metadata
2.31 It is recommended that national energy statistics are accompanied by
adequate metadata (“data about data”), which is “a specific form of
documentation that defines and describes data so that users can locate and
understand them, make an informed assessment of their strengths, limitations,
usefulness and relevance, and use and share them”. (IRES para 9.33)
IRES defines two types of metadata:
Structural metadata are identifiers and descriptors of the data that are
essential for discovering, organizing, retrieving and processing statistical
datasets. This type of metadata includes labels such as names of the
table columns, units of measurement, time period, commodity code,
etc., and needs to be published along with the actual data.
Reference metadata describe the content and quality of the statistical
data.
2.32 IRES recommends that development of metadata be provided high priority,
and dissemination of up-to-date metadata along with the data should be
an integral part of the dissemination of energy statistics. Recommended
metadata structure is based on Single Integrated Metadata Structure (SIMS)
published by the European Commission (reproduced in the Annexure). Use
of web technology and SDMX standards can reduce the reporting burden. 3. DATA COLLECTION
AND COMPILATION
15Report of the Inter-Ministerial Committee on Energy Data Management
3.1 The legal framework underpinning data collection and compilation is very
important. Responsibilities of collection, compilation and dissemination
should be clearly identified. An efficient institutional arrangement will be
cost effective and will avoid duplication. A mechanism for coordination
among different agencies involved in data collection and dissemination is
required and should have authority to implement its decisions. Experience
from other countries shows that a successful energy data management
system can be either centralized or decentralized. What is important is that
there should be a clear definition of rights and responsibilities of all agencies
involved. Formal and informal interaction between various agencies need
to be organised for smooth coordination.
3.2 In the Indian context, there is a need to evaluate the benefits of an integrated
approach to data collection. Questions related to energy consumption can
be integrated in the periodic surveys. For example, household surveys
conducted by the National Statistical Office (NSO) can integrate energy
related questions in the questionnaire. Similarly, seeking more information
on energy use can be incorporated in enterprise surveys.
3.3 MoSPI receives all the required information on Energy related matters
directly from different source Ministries; no specific survey gets conducted
exclusively for this purpose. Based on the requirement of the different
national/international stakeholders, which can be best identified by the
concerned administrative Ministries, the ‘demand side’ data on Energy can
be captured by ‘reporting agencies’.
3.4 Recommendation – Stakeholders may co-ordinate with MoSPI with respect
to the energy specific questions which could be integrated in the periodic
surveys. Demand side data on energy which is best captured by reporting
agencies themselves may also be identified. Data thus collected should be
collated and published in useful formats, and reconciled with other official
published data.
NITI Aayog can work together with MoSPI in order to work out the details on
the specifics and the kind of questions which can be incorporated. 4. CENTRALIZED ENERGY
DATA MANAGEMENT
AGENCY
16Report of the Inter-Ministerial Committee on Energy Data Management
4.1 Energy Statistics Division (ESD) of MoSPI does collect and compile all the
key information on Energy Statistics (collected directly from different Energy
Ministries) of India, on annual basis. ESD also computes and publishes
different Energy Indicators like Energy Balance, Sankey Diagram (Energy
flow diagram), Per-Capita Energy Consumption (PCEC), Energy Intensity etc.
as per IRES (International Recommendation on Energy Statistics) guidelines.
4.2 Recommendation - ESD, MoSPI ought to be the natural centralised Energy
Data Management Agency of India. A suitable strengthening of the present
Energy Statistics Unit of ESD can always deliver the task upto the desired
level. However, as per Allocation of Business Rules the expected deliverables
of MoSPI does not cover a wide range of activities which are included in
the ambit of energy data management agencies internationally. NITI Aayog
can support MoSPI for capacity building, training etc in this regard. 5. REQUIREMENT OF AN
ENERGY DATA MANUAL
17Report of the Inter-Ministerial Committee on Energy Data Management
5.1 A compilation of definitions, calorific values, metadata etc are published
in the Energy Statistics publication of MoSPI. There is a need to further
enrich the Energy Statistics by incorporating the suggestions made for
standardisation and classification as highlighted in this report. Further
improvements, in consultation with the concerned ministries, may also be
incorporated pertaining to;
Definitions and concepts of all the key parameters in the energy sector
Formats and methodologies for data collection and reporting
Standardised data (GCV, operation hours, etc.)
Energy Balances and energy accounting
Standard data reporting formats for easy consumption to humans as
well as computers
5.2 Recommendation – ESD, MoSPI does maintain a compendium containing
the concepts and definitions used by different source agencies and based
on which energy statistics is compiled. The same can always be updated
based on the specific updations/additions from concerned energy ministries/
report. 6. THE WAY FORWARD
18Report of the Inter-Ministerial Committee on Energy Data Management
6.1 The recommendations of the committee have been compiled in this section.
Calorific Values
PublicationDescription Remarks
Coal Directory 2019-20 Table 4.15, 4.16Along with grade-wise despatches,
weighted average net calorific value
within each grade should be provided
for coking coal. In case of unavailability
of net calorific value, gross calorific
values can continue being used as per
IRES recommendations
Energy Statistics 2021 Chapter 7:
Energy Balance
and Sankey
Diagram
While the methodology for converting
physical quantities to energy units is
described, the underlying calorific value
data is not reported.
PNG Statistics 2019-20 Appendices Lot of detailed conversion tables (volume
to weight, unit versions, impact of
temperature, calorific value etc.) which
are very useful.
Density for a fuel may vary as per
different batches of production, imports
and exports and ethanol blending over
time etc. Similarly, energy content may
also vary.
Therefore, it is essential that weighted
average for volume-mass conversion &
calorific value of the energy product
be used for calculation purpose. Global
conversion factor or energy content may
be taken as a standard reference only.
It will also entail statistical differences &
inaccurate conclusions. 6. The Way ForwardReport of the Inter-Ministerial Committee on Energy Data Management
19
PublicationDescription Remarks
MNRE estimates of
bioenergy potential on
its website
Units for products such as biomass
and waste which vary widely in
energy, moisture and ash content are
recommended to be reported in energy
content rather than physical quantities
such as mass or volume.
6.2 Classification of economic and statistical units
Publication DescriptionRemarks
Coal
Directory
2019-20
Section IV: Despatch and
Off-take, Section VIII: Coal
Consumption, Section IX:
Captive Coal and Lignite
Blocks
Consuming sectors, companies owning
coal washeries and captive coal and
lignite producers need to be identified
using the NIC-2008 codes
PNG
Statistics
2019-20
Table II.18 and Section V Consuming sectors in Tables II.18, V.4
to V.12 need to be identified using the
NIC-2008 codes
Energy
Statistics
2021
Chapter 7: Energy Balance
and Sankey Diagram
Consuming sectors listed in the energy
balances can be categorised using the
NIC-2008 codes
6.3 Classification of energy products
Publication DescriptionRemarks
Energy
Statistics
2021
Chapter 3: Production of
Energy Resources
Products need to be identified using
NPCMS-2011 code
PNG
Statistics
2019-20
Table V.1: Products MoPNG/PPAC comment: MoPNG
will explore shifting of products
identification using the NPCMS 2011
code.
6.4 Classification of energy resources
Publication DescriptionRemarks
Coal
Directory
2019-20
Section II: Resources and Exporation Total resources are
reported, but not balance
recoverable reserves. In
the meeting it was stated
that Central Mine Planning
and Design Institute has
data pertaining to such
reserves and this data can
be shared with Ministry of
Statistics and Programme
Implementation 6. The Way ForwardReport of the Inter-Ministerial Committee on Energy Data Management
20
Publication DescriptionRemarks
PNG
Statistics
2019-20
Table II.9 and II.13: Balance Recoverable
Reserves of Crude Oil and Natural Gas
Proved and indicated
balance recoverable
reserves are reported, but
not by UNFC classification
or the aggregate classes as
defined above.
6.5 Classification by geographical area
Publication DescriptionRemarks
PNG
Statistics
2019-20
Table III.32: PNG StatusConnections across multiple
states and cities are
combined. In some cases,
CGD companies are listed
under “GA/Cities Covered”
PNG
Statistics
2019-20
Table II.13: Balance Recoverable
Reserves of Natural Gas;
Table II.17: Gross and Net Production
of Natural Gas
Offshore needs to be
classified by basin
6.6 Energy and Commodity Balances
The IRES recommends that commodity-wise balances also be published
along the same principles as energy balances. The Coal Directory publishes
a simplified commodity balance for domestic coal, lignite and imported coal.
The PNG Statistics does not publish a commodity balance for oil and gas.
6.7 Data Collection and compilation
Recommendation – Stakeholders may co-ordinate with MoSPI with respect
to the energy specific questions which could be integrated in the periodic
surveys. Demand side data on energy which is best captured by reporting
agencies themselves may also be identified. Data thus collected should be
collated and published in useful formats, and reconciled with other official
published data.
NITI Aayog can work together with MoSPI in order to work out the details
on the specifics and the kind of questions which can be incorporated.
6.8 Centralized Energy Data Management Agency
Recommendation - ESD, MoSPI ought to be the natural centralised Energy
Data Management Agency of India. A suitable strengthening of the present
Energy Statistics Unit of ESD can always deliver the task upto the desired
level. However, as per Allocation of Business Rules the expected deliverables
of MoSPI does not cover a wide range of activities which are included in 6. The Way ForwardReport of the Inter-Ministerial Committee on Energy Data Management
21
the ambit of energy data management agencies internationally. NITI Aayog
can support MoSPI for capacity building, training etc.
6.9 Requirement of an Energy Data Manual
Recommendation – ESD, MoSPI does maintain a compendium containing
the concepts and definitions used by different source agencies and based
on which energy statistics is compiled. The same can always be updated
based on the specific updations/additions from concerned energy ministries/
report. ANNEXURE I
22Report of the Inter-Ministerial Committee on Energy Data Management
Classification of statistical units
The IRES divides statistical units into two categories (IRES, para 6.5):
a. Observation units—identifiable legal/organizational or physical units which
are able, actually or potentially, to report data about their activities; and
b. Analytical units—units created by statisticians, often by splitting or
combining observation units, in order to compile more detailed and
homogeneous statistics than is possible by using data on observation
units
Even though analytical units are not able to reported data themselves about
their activities, the use of analytical units is recommended since it may improve
the accuracy of energy statistics in cases where complex economic entities are
active in both energy production and other economic activities.
SEEA-Energy classifies economic units as enterprises, establishments and
industries. (SEEA-Energy para 2.4.2).
a. An enterprise is the view of an institutional unit as a producer of goods
and services. Enterprises undertake production in a range of ways
including as profit-making businesses, as a part of household activity
or as part of the function of government. Importantly, an enterprise can
own assets and acquire liabilities and has the capacity to engage in
transactions and other economic activities with other economic units. An
enterprise may comprise one or more establishments and hence may be
situated across multiple locations within a single economy. An enterprise
may also undertake ancillary production. In most cases, the production
of these services is not recorded as a separate set of outputs; rather,
the relevant inputs are recorded as constituting part of the overall inputs
to the production of the enterprise’s primary and secondary products.
b. An establishment is a unit situated in a single location at which either a
single type of productive activity is carried out, or a single productive Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
23
activity (the primary activity) accounts for the majority of the value
added. If more than one productive activity is carried out by an
establishment, activities other than the primary activity are considered
secondary activities.
c. The groupings of establishments that undertake similar types of productive
activity are referred to as industries. Within SEEA-Energy, establishments
are classified within industries using ISIC.
Energy industry classification
IRES Table 5.1: Energy industries with reference to the relevant ISIC category
Energy industryISIC Rev. 4
Electricity, CHP and heat
plants
Division 35 – Electricity, gas, steam and air conditioning
supply
Pumped storage plants
Coal mines Division 05 – Mining of coal and lignite
Coke ovens Group 191 – Manufacture of coke oven products
Coal liquefaction plants Group 192 – Manufacture of refined petroleum
products
Patent fuel plants Group 192 – Manufacture of refined petroleum
products
Brown coal briquette plants Group 192 – Manufacture of refined petroleum
products
Gas works (and other
conversion to gases)
Group 352 – Manufacture of gas; distribution of
gaseous fuels through mains
Gas separation plants Division 06 – Extraction of crude petroleum and
natural gas
Gas-to-liquids (GTL) plants Group 192 – Manufacture of refined petroleum
products
LNG plants/regasification
plants
Group 091 – Support activities for petroleum and
natural gas extraction
Class 5221 – Service activities incidental to land
transportation
Blast furnaces Group 241 – Manufacture of basic iron and steel
Oil and gas extraction Division 06 – Extraction of crude petroleum and
natural gas
Group 091 – Support activities for petroleum and
natural gas
Oil refineries Division 19 – Manufacture of coke and refined
products Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
24
Energy industryISIC Rev. 4
Charcoal plantsClass 2011 – Manufacture of basic chemicals
Biogas production plants Group 352 – Manufacture of gas; distribution of
gaseous fuels through mains
Nuclear fuel extraction and
fuel processing
Class 0721 – Mining of uranium and thorium ores
Class 2011 – Manufacture of basic chemicals
Other energy industry not
elsewhere specified
Class 0892 – Extraction of peat
…
Energy consumer classification
IRES Table 5.3: Main categories of energy consumers (Manufacturing, construction
and non-fuel mining industries)
Energy consumersISIC Rev. 4
Iron and steel ISIC Group 241 and Class 2431. Note that the consumption
of energy products in coke ovens and blast furnaces
is excluded, as these plants are considered part of the
energy industries.
Chemical and petrochemical ISIC Divisions 20 and 21. Note that the consumption
of energy products by plants manufacturing charcoal
or carrying out the enrichment/production of nuclear
fuels (both classified in ISIC 2011) is excluded, as these
plants are considered part of the energy industries.
Non-ferrous metals ISIC Group 242 and Class 2432
Non-metallic minerals ISIC Division 23
Transport equipment ISIC Divisions 29 and 30
Machinery ISIC Divisions 25, 26, 27 and 28
Mining and quarrying ISIC Divisions 07 and 08, and Group 099, excluding
the mining of uranium and thorium ores (Class 0721)
and the extraction of peat (Class 0892).
Food and tobacco ISIC Divisions 10, 11 and 12
Paper, pulp and print ISIC Divisions 17 and 18
Wood and wood products
(other than pulp and paper)
ISIC Division 16
Textile and leather ISIC Divisions 13, 14 and 15
Construction ISIC Divisions 41, 42 and 43
Industries not elsewhere
specified
ISIC Divisions 22, 31 and 32 Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
25
Energy consumersISIC Rev. 4
Household ISIC Divisions 97 and 98
Commerce and public
services
ISIC Divisions 33, 36–39, 45–96 and 99, excluding
ISIC 8422
Agriculture, forestry ISIC Divisions 01 and 02
Fishing ISIC Division 03
Not elsewhere specified
(including defence activities)
ISIC Class 8422
Single Integrated Metadata Structure (SIMS)
Reproduced from IRESBox 9.3: Metadata items for statistical releases
S.1 Contact (organization, contact person, address, email, phone, fax)
S.2 Introduction
S.3 Metadata update (last certified, last posted and last update)
S.4 Statistical presentation
S.4.1 Data description
S.4.2 Classification system
S.4.3 Sector coverage
S.4.4 Statistical concepts and definitions
S.4.5 Statistical unit
S.4.6 Statistical population
S.4.7 Reference area
S.4.8 Time coverage
S.4.9 Base period
S.5 Unit of measure
S.6 Reference period
S.7 Institutional mandate (legal acts and other agreements, data sharing)
S.8 Confidentiality (policy, data treatment)
S.9 Release policy (release calendar, calendar access, user access)
S.10 Frequency of dissemination Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
26
S.11 Dissemination format, accessibility and clarity (News release, publications,
online database, micro-data access, other),
S.12 Accessibility of documentation (documentation on methodology, quality
documentation)
S.13 Quality management (quality assurance, quality assessment)
S.14 Relevance (user needs, user satisfaction, completeness)
S.15 Accuracy and reliability (overall accuracy, sampling error, non-sampling
error (coverage errors, measurement errors, non-response errors, processing
errors, model assumption errors))
S.16 Timeliness (time lag to final results) and punctuality (delivery and publication)
S.17 Comparability (geographical, over time)
S.18 Coherence (cross-domain, internal)
S.19 Cost and burden
S.20 Data revision (policy, practice)
S.21 Statistical processing
S.21.1 Source data
S.21.2 Frequency of data collection
S.21.3 Data collection
S.21.4 Data validation
S.21.5 Data compilation
S.21.6 Adjustments
S.21.61 Seasonal adjustment
Conversion Factors
1 kilogram=2.2046 pounds
1 Pound=454 gm.
1 Cubic metres=35.3 cubic feet (gas)
1 Metric ton=1 Tonne =1000 kilogram
1 Joule=0.23884 calories
1 Mega Joule=10^6 joules = 238.84 x 10^3 calories
1 Giga Joule=10^9 joules = 238.84 x 10^6 calories Annexure IReport of the Inter-Ministerial Committee on Energy Data Management
27
1 Tera Joule=10^12 joules = 238.84 x 10^9 calories
1 Peta Joule=10^15 joules = 238.84 x 10^12 calories
One million tonnes of coal=15.13 petajoules of energy
One million tonnes of oil equivalent
(MTOE)
=41.87 petajoules of energy
One billion cubic meter of natural gas=38.52 petajoules of energy
One million cubic meter of natural gas=38.52 terajoules of energy
=0.03852 petajoules of energy
One billion kilowatt hour of electricity=3.60 petajoules of energy ANNEXURE II
28Report of the Inter-Ministerial Committee on Energy Data Management
GCV of Coal and the Total Primary Energy Supply (TPES)
Background
The National Statistical Office (NSO) under Ministry of Statistics and Program
Implementation (MoSPI) publishes one Annual publication under the heading of
Energy Statistics, in which the diverse aspects on energy related issues are being
analysed and published. Since Coal is a major source of energy in India, thus the
energy which gets generated from Coal and supplied to different sectors are one
of the key components of this publication. Coal Controller’s Org. under MoC is
the nodal office which provides all the required information to MoSPI for doing
necessary calculation at their end and finally incorporating the same to the latest
edition of Energy Statistics.
Present scenario
In the minutes of meeting held on 22th July-2020, under the Chairmanship of
Shri Rajnath Ram, Adviser, Niti Aaoyoge; CCO, MoC has been directed to come
up with some ‘representative unique value of GCV’, which will be used for the
purpose of calculation of TPES by MoSPI in their publication of Energy Statistics.
It has been observed that, though Coal(Non Coking) is a mineral having a wide
range of GCV (ranging from as low as 2200 Kcl against G17 to as high as above
7000Kcl against G1) there are several different agencies who uses ‘different
unique representative GCV values’ to represent the ‘average calorific value of
Coal’. A summary of which is given below: Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
29
GRADE
of Non
Coking
Coal
GCV BAND
(K.Cal./Kg.)
Representative GCV (Kcal./Kg.) Used by
MoSPI
BP
Statistics
IESS
2047
BEE IEA
Prayas
Group
G-1Exceeding 7000
3614 4009 39984000
3400-
4600
Weighted
average
of all the
GCV’s
G-2
Exceeding 6700 and
not exceeding 7000
G-3
Exceeding 6400 and
not exceeding 6700
G-4
Exceeding 6100 and
not exceeding 6400
G-5
Exceeding 5800 and
not exceeding 6100
G-6
Exceeding 5500 and
not exceeding 5800
G-7
Exceeding 5200 and
not exceeding 5500
G-8
Exceeding 4900 and
not exceeding 5200
G-9
Exceeding 4600 and
not exceeding 4900
G-10
Exceeding 4300 and
not exceeding 4600
G-12
Exceeding 3700 and
not exceeding 4000
G-13
Exceeding 3400 and
not exceeding 3700
G-14
Exceeding 3100 and
not exceeding 3400
G-15
Exceeding 2800 and
not exceeding 3100
G-16
Exceeding 2500 and
not exceeding 2800
G-17
Exceeding 2200 and
not exceeding 2500
Since there is a wide range of variation of GCV in Coal, representing the GCV
of Coal by means of a ‘unique GCV figure’ may not be the ideal scenario. But
before CCO can suggest any modification/improvement, in term of proposing
‘more than one representative values of GCV’, it is indispensable to understand
that the same can be incorporated in the ‘methodology used for calculation of
TPES (Total Primary Energy Statistics) from Coal’. Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
30
In this regards, after having a discussion with Niti Aayoge and MoSPI and going
through the latest publication of Energy Statistics 2020 (which has data upto
FY: 2018-19(P)), it has been understood that, the basic formula which is used to
derive the Energy is,
Energy (in KToe) = Quantity of Commodity * Conversion Factor
Where,
Conversion factor = [Net Calorific Value (NCV)]/Mega joules per ton of
oil equivalent
where NCV is in kj per kg
Net Calorific Value (NCV) = Gross calorific value (GCV) – (% Moisture
Content) [1NCV = 0.9 GCV]
That is case of Coal:
Energy (in KToe) derived from Coal = Quantity of Coal * f(GCV).
(a function of GCV).
Thus, there is provision for using more than one GCV values in order to derive
the final Energy which came out of Coal in a particular year.
Limitation of the present practice
It has been observed that there are few limitations of the present system which
are given below:
i. The Coal is primarily classified into 2 categories Non Coking (measured
in term of GCV values) and Coking (measured in terms of ash-content),
while considering the ‘representative value 3614 Kcal/kg of coal’ both the
categories are combined and treated as a single class which is not ideal;
ii. Since there is a wide range of GCV, thus a ‘unique/single GCV value
representation against the entire class of Coal’ is also not ideal (as it has
been already stated before);
iii. In case of Non-Coking Coal, the grade-wise (i.e. GCV wise) production
and dispatch quantity coal varies over the year, thus even though ‘the
total quantity is same for two years, since the Grade-wise composition
of production/dispatch is different’, thus there will be ‘2 different volume
of Energy’ that will be attributed against those 2 years, which doesn’t
get reflected under the present practice; Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
31
Example:
Considering G1-G6 as Top Grade, G7-G14 as Middle and G15-G17 as
Bottom let us consider the following hypothetical case of ‘grade-wise
dispatch’ of Non Coking Coal of 2 years,
Grades of NC Coal Year X Year Y Difference (X – Y)
Top
G110 5
G230 10
G317 10
G440 10
G530 26
G624 21
Total 151 8269
Middle
G745 90
G850 80
G950 24
G1065 25
G1145 28
G1210 31
G135 30
G1435 34
Total 305 342-37
Bottom
G1524 20
G1610 35
G1710 21
Total 44 76-32
Grand Total 500 5000
i.e. though in the year X, more coal having Top Grades have been
dispatched but since the grand total dispatch of Coal remains same in
both the year, thus using the present methodology, the ‘energy supplied
from Coal in both years’ will remain same.
iv. It has been observed that the ‘TPES (Total Primary Energy Supply) from
Coal’ which gets calculated using the ‘representative value of GCV as
3614Kcal’ is a gross underestimation of the Energy Generated from Coal.
If we consider the grade-wise dispatch of coal during FY : 2018-19, we
find that. Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
32
Grade-wise Dispatch of Coal during FY : 2018-19
Grade Mid Range GCV
Qty
(Million Tonnes)
Cumulative
Qty
Percentage
G17 2350-G17 3.356 3.356 0.49%
G16 2650-G16 3.083 6.439 0.94%
G15 2950-G15 6.783 13.222 1.92%
G14 3250-G14 50.650 63.872 9.30%
G13 3550-G13 99.528 163.400 23.79%
G12 3850-G12 69.116 232.516 33.85%
G11 4150-G11 178.854 411.370 59.88%
G10 4450-G10 105.981 517.351 75.31%
G9 4750-G931.130 548.481 79.84%
G8 5050-G8 56.671 605.152 88.09%
G7 5350-G7 42.694 647.846 94.31%
G6 5650-G68.802 656.648 95.59%
G5 5950-G511.726 668.374 97.30%
G4 6250-G4 14.702 683.076 99.44%
G3 6550-G33.332 686.408 99.92%
G2 6850-G2 0.430 686.838 99.99%
G1 7150-G1 0.099 686.937 100.00%
Observations:
1. The representative GCV of 3614 Kcal/Kg, which is used by for calculation
of TPES from Coal comes under the group G13.
2. During FY : 2018-19 around 24% of Coal got dispatched having grades
G13 or less (i.e having GCV 3614 or less).
3. More than 75% of dispatched NC Coal are having GCV over 3614 Kcal/kg,
which have been treated as Coal having GCV of 3614 Kcl.
The graphical representation is given below:
0%
25%
50%
75%
100%
0
2350-G17
2650-G16
2950-G15
3250-G14
3550-G13
3850-G12
4150-G11
4450-G10
4750-G9
5050-G8
5350-G7
5650-G6
5950-G5
6250-G4
6550-G3
6850-G2
7150-G1
20
40
60
80
100
120
140
160
180
200% of Coal dispatched
Tonnes of Coal
Mid Range GCV of Each Grades
Grade wise dispacth of Coal during FY : 2018-19
Qty (Million Tonnes) Percentage Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
33
Similarly for FY : 2017-18, 2016-17, 2015-15 and 2014-15 also is has been
found that, the percentage of Coal, having GCV of 3614 Kcal or less,
which got dispatched were, around 23%, 18%, 14% and 13% respectively
(please refer the excel sheet “Calculation_TPES” for details) and most of
the coal got dispatched were having GCV much higher than 3614 Kcal.
Thus, we can see that the figure of TPES generated from Coal using
representative value of 3614 Kcal/Kgs grossly underestimates the true
energy generated and supplied from Coal.
v. Again, since there are more than one representative values of GCVs have
been considered by different agencies, the ‘Final results’ (of generation
of Energy) will always vary and which will leads to confusion;
Proposed methodology
As we know that in India the total available Coal in India is from 2 sources,
i) Domestic and ii) Import.
Domestic Coal:
a. Non Coking Coal:
In the schedules of Coal Directory, CCO captures information on
‘sectorwise-gradewise’ dispatch of Coal (i.e. each company has to submit
the different grades of Coal he has dispatched for different end uses like
Power, Iron & Steel, Fertilizer etc. during a particular Financial Year). Since
the methodology used for calculation of TPES uses the simple ‘quantity
X f(GCV)’ formula, thus instead of using ‘single representative figure of
GCV against Coal’, we can make use of ‘Mid-Level GCV’ (which is the
‘average of the two class boundaries against of a particular grade) of all
the Grades of Non-Coking coal. The assumption will be made is that the
GCV against a particular grade of coal is distributed uniformly among
the entire range of it.
An example is given in this regard has been given in the Excel sheet
“Calculation_TPES”.
In this method we will be able to make use of the maximum information
available with CCO, MoC.
b. Coking Coal:
In case of Coking Coal also CCO is having the sector-wise dispatch
quantity of Coal. Here the classifications are made in term of Ash
content and not GCV ranges. Thus, determination of Energy generated
from Coking coal may not be as simple as in case of Non Coking coal. Annexure IIReport of the Inter-Ministerial Committee on Energy Data Management
34
If CMPDIL can provide some approximate ‘representative’ figures against
each Coking Coal grades, then using the similar methodology, the Energy
supplied to different sectors from Coking Coal can be determined.
Imported Coal:
The coal which are imported are generally having much higher GCV than 3614 Kcal
and used mainly for the purpose of blending. In absence of proper information
about the GCVs of Imported coal, we can make use of the following assumptions,
which MoC has already made while calculating the National Coal Index (NCI) and
Representative Price/Base Bid Prices of Coal, which are,
i. Coal Imported from South Africa belongs to Top Grades (i.e. having GCV
range from 5501 Kcal/kg and onwards);
ii. Coal Imported from Indonesia belongs to Middle Grades (i.e. having GCV
ranges from 3101Kcal/kg to 5500 Kcal/kg.);
iii. No Coal gets Imported to India which belongs to Bottom grades
(i.e. having GCV less than 3100Kcal/kg.);
iv. Coal Imported from Australia are primarily Coking Coal and are of top
quality;
For Imports other than these countries, using the assumption (iii) above we may
use the ‘average of mid-level GCV of Middle Grades Coal’, which comes out to
be 4,300 Kcal/kg.
Benefits of the proposed methodologies:
i. There will be maximum utilisation of the information/data available;
ii. Any changes in the ‘dispatch grades’ will be captured;
iii. Some classification on Imported coal has already been derived by ISI
(during their formulation of NCI and RP), we are making use of those
findings to come up with some ‘calorific estimates’ of Imported Coal. ANNEXURE III
35Report of the Inter-Ministerial Committee on Energy Data Management Annexure IIIReport of the Inter-Ministerial Committee on Energy Data Management
36 Annexure IIIReport of the Inter-Ministerial Committee on Energy Data Management
37 www.niti.gov.in
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