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| id | Vol-3194/paper30 |
| wikidataid | →Q117344935 |
| title | Shared Metrics of Sustainability: a Knowledge Graph Approach |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper30.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/DiamantiniKPS22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Shared Metrics of Sustainability: a Knowledge Graph
Approach
Claudia Diamantini, Tarique Khan, Domenico Potena and Emanuele Storti
DII, Polytechnic University of Marche, Ancona, Italy
Abstract
Environmental, Social, and Corporate Governance (ESG) criteria allow to evaluate business’s overall
awareness and attention to social and environmental aspects. They are fundamental tools to direct
investments towards sustainable projects and activities. However, each ESG rating agency has its own
definitions, criteria and methodologies for the selection of the sustainable business portfolio, each not
directly comparable with the other, which includes a set of indicators capable to measure sustainability.
A shared understanding of what “sustainability” means is compelling. As a contribution in this direction,
in the present paper we report ongoing work on the definition of a Knowledge Graph of some of the
ESG indicators existing in the Literature. Links in the Knowledge Graph express semantically rich
information including mathematical relations among indicators. We discuss the advantages for sharing,
communication and comparison of this approach as well as open research issues.
Keywords
performance indicators, ESG, sustainability, knowledge graph, ontology
1. Introduction
Sustainability and resilience are central to the European political agenda. The Next Generation
EU financial instrument places issues such as equitable climate and digital transitions and the
fight against climate change at the centre of the European recovery strategy. Among recent
research topics, that of Industry 5.0 is guided by the vision by which “in order to remain the
engine of prosperity, industry must lead the digital and green transitions” fostering “a vision of
industry that aims beyond efficiency and productivity as the sole goals, and reinforces the role
and the contribution of industry to society” in the belief that “Industries can play an active role in
providing solutions to challenges society including the preservation of resources, climate change
and social stability”1 . Public funds cannot by themselves support an economic revolution that is
estimated in about 180 billion euros per year2 , and the role of finance is crucial to direct economic
activities towards, and sustain the financial risk of, a green transition. However a shared notion
of what it means to be “sustainable” and how to measure “sustainability” is necessary. Quoting
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ c.diamantini@univpm.it (C. Diamantini); t.khan@pm.univpm.it (T. Khan); d.potena@univpm.it (D. Potena);
e.storti@univpm.it (E. Storti)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://ec.europa.eu/info/research-and-innovation/research-area/industrial-research-and-
innovation/industry-50_en
2
https://www.consilium.europa.eu/en/press/press-releases/2020/04/15/sustainable-finance-council-adopts-a-
unified-eu-classification-system
�from EU: “In order to meet the EU’s climate and energy targets for 2030 and reach the objectives
of the European green deal, it is vital that we direct investments towards sustainable projects and
activities. [...] To achieve this, a common language and a clear definition of what is sustainable
is needed. This is why the action plan on financing sustainable growth called for the creation
of a common classification system for sustainable economic activities, or an EU taxonomy 3 ”.
The taxonomy was to come into effect in December 2021, but at present the process is still
ongoing, slowed down by the lack of agreement among the different stakeholders. In the quest
of shared criteria, Environmental, Social, and Corporate Governance (ESG) indicators have been
introduced by organizations like the German Investment Professional Association (DVFA)4 as
early as 2008, to quantitatively measure sustainability. They received the endorsement of the
European Federation of Financial Analysts Societies (EFFAS)5 , thus gaining the status of an
official EFFAS Standard [1]. The Global Reporting Initiative (GRI)6 has developed a set of widely
accepted standards for sustainability reporting [2]. Other frameworks and standards have been
developed by SASB7 , TCFD8 , CDSB9 among others, each not easily comparable with the other,
and efforts are underway towards homogenization and harmonization [3, 4]
In the present paper, we report on an ongoing work contributing to a common understanding
of “sustainability” and integration of ESG indicators into a unique Knowledge Graph that can act
as a semantic substrate. Links in the Knowledge Graph express semantically rich information
including mathematical relations among indicators, that can be exploited to support their
sharing, communication and comparison. In Section 2 we report relevant related work on
semantic models for indicators and frameworks for ESG indicators. We describe the preliminary
survey and analysis of ESG standards [1, 2] in Section 3, and their modeling as a Linked Open
Dataset in Section 4. In Section 5 we then discuss some insights from the work done and discuss
open research issues in Section 6.
2. Related work
Semantic modeling of indicators through shared vocabularies or ontologies emerged in recent
years as an effective way to define measures through a precise, unambiguous representation.
A variety of approaches for semantic representations of indicators have been proposed in
the Literature, for documentation of indicators and their sharing, or with the goal to simplify
and support design and analysis of multidimensional data cubes or statistical datasets. In some
cases, indicators are directly used to design a monitoring framework like in [5]. In such a
work, an ontology is proposed for definition of Process Performance Indicators (PPIs) making
the relationship between an indicator and a process explicit and thus supporting design-time
analysis of process performance.
3
https://ec.europa.eu/info/business-economy-euro/banking-and-finance/sustainable-finance/eu-taxonomy-
sustainable-activities_en
4
https://www.dvfa.de/dvfa.html
5
https://effas.com
6
https://globalreporting.org
7
https://www.sasb.org
8
https://www.fsb-tcfd.org
9
https://www.cdsb.net/
� Most work focuses on definition of atomic indicators, with little or no representation of their
calculation formulas. Some exceptions include a formula representation with limited support to
manipulation, that is mostly performed through ad-hoc software modules [6],[7].
Few contributions include a formal notion of dependency among indicators, e.g. in [8] where
relations among indicators are represented by logical predicates. In such a work, organization
goals are tied to performance indicators and reasoning is performed to check consistency of
goal structures when designing a new organization structure. These approaches are often
exploited also to support the organisation as well as the reuse, exchange and alignment of
business knowledge on indicators. A more recent example is [9] where an OWL ontology and
SWRL rules are developed for reasoning on KPIs, with the goal to support selection and analysis
of indicators and detect inconsistencies.
For what concerns semantic-based formats and standards for representation of indicators,
the Statistical Core Vocabulary (SCOVO) was one of the first proposals for the publication of
statistical datasets on the web as RDF. It included a minimal set of classes that however could
not fully support multidimensional modeling. Such a vocabulary was then superseded in 2013
by the Data Cube vocabulary (QB), a W3C Recommendation allowing to publish statistical data
following the Linked Data principles, and capable to model the schema of a cube as a set of
dimensions, attributes, and measures. QB enables the definition and the sharing of a statistical
dataset as a multidimensional cube, but a number of issues remained open, in that it does not
provide means to represent hierarchical relations between dimension levels and dimension
members, and lacks a controlled vocabulary for measures. Some proposals elaborated on this
standard to include further functionalities, e.g. to support OLAP operations on QB (e.g. [10],
[11]) and fully represent OLAP cubes in RDF as in QB4OLAP [12]. Our previous work on the
KPIOnto ontology, as discussed in the next section, was on the other hand driven by the goal to
extend Data Cube towards a formal representation of calculation expressions for indicators,
that is required to guarantee meaningful comparisons.
We report also on recent proposals for semantic frameworks aimed to represent ESG in-
dicators. The SDG Interface Ontology (SDGIO) [13], focuses on the formal specification and
representation of Sustainable Development Goals (SDGs) defined by the United Nations. The
framework represents key entities involved in the SDG process, linking them with the goals,
targets, and indicators. It integrates several existing ontologies, applying best practices in
ontology development from mature work of the Open Biological and Biomedical Ontology
(OBO) Foundry and Library. The SDG KOS [14] defines a formal knowledge organization system,
based on SKOS and developed for representing the SDGs, with the purpose to enable data pub-
lishing using common terminologies. Unlike our proposal, the framework is focused on United
Nations indicators only, without considering multiple standards and their integration, and for a
different purpose and abstraction level, namely supporting global measuring of sustainability
goals. Furthermore, with respect to our proposals, indicators in SDG include neither a formal
representation of the calculation formula nor a detailed characterization, e.g. in terms of unit of
measurement, dimensions of analysis, aggregation functions.
�3. Analysis of ESG Standards
ESG has become synonym of sustainability, and criteria aimed at measuring and controlling
the sustainability of business’s activities can be decomposed along these three dimensions.
Environmental criteria focus on the impact of the business on environment. Social criteria
assess the impact on the community with which a business interacts, like people, employees,
other companies in the value chain. Finally, criteria related to governance aim at assessing
how much ethics and good practices drive the management of business, considering equity
or transparency of decisions among others. In this work we started the analysis of two of the
existing standards, the DFVA-EFFAS standard [1] and the GRI standard. [2]. The former has
been one the first standardization attempts and has laid down the basis to develop standard
procedures and criteria for the integration of ESG data into business reporting, while the latter
is one of the most widely adopted standards.
The DFVA-EFFAS Standard
One of the first attempts to define a guideline for the inclusion of ESG criteria in company
evaluation is the work done by DVFA, subsequently adopted by EFFAS and described in [1].
The document points out a minimum set of metrics, or Performance Indicators (PI or KPI), that
should be adopted by companies. At the core of PI definition, there are some principles that can
be synthesized as follows: (1) correlation between metrics and companies risk/success factors;
(2) significance for investment decisions; (3) quantifiability and comparability.
Environment, Social and Governance are considered as different segments of the general
sustainability problem. In addition, a fourth area called LongTerm Viability (identified by
the letter V) is defined, that represents the ability of a company to produce long-term profits
without sacrificing resources or skills in the short term. Indicators used to quantify and report
on companies sustainability are organized according to an ESGV taxonomy that organizes KPIs
in General (i.e. applicable to any industrial sector) and sector-specific ESGs topics for each area.
Table 1 shows an excerpt of General indicators.
For each indicator a specification is given, shown in Table 2 for indicators ESG 2-1 and ESG
2-2. The specification remains at a very abstract and informal level. Note that indicator ESG 2-2
is in fact a set of specific indicators differing in practice for the normalization factor. Sometimes
the normalization procedure is reported, e.g. for the industrial specific ESG 11 NO, SO Emissions,
the Unit/Calculation is specified as “Total NO,SO Emissions/total passenger-km” but in general
calculation rules are not reported, not even in a descriptive way. Furthermore, it is unclear the
relation existing between ESG 2-2 and ESG 11 (i.e., are NO and SO greenhouse gases?)
The standard has undergone through several revisions up to 3.0 [15], including changes to
KPI identifiers, organization of KPIs in a taxonomy, introduction of new KPIs.
The GRI Standard
The GRI Standard is a system of interrelated documents organized into three series: GRI
Universal Standards, GRI Sector Standards, and GRI Topic Standards for a total of 37 documents
(2021 edition). The first two contain guidelines on how to report information about the impact
of a company on the economy, environment, and people, including human rights. The Topic
� Area ESG Indicator
E ESG 1 Energy efficiency ESG 1-1 Energy conpsumption, total
ESG 1-2 Energy consumption, specific (intensity)
ESG 2 GHG emissions ESG 2-1 GHG emissions, total
ESG 2-2 GHG emissions, specific
S ESG 3 Staff Turnover ESG 3-1 Percentage of employees leaving p.a./total employees
ESG 4 Training & qualification ESG 4-1 Percentage of trained employees p.a./total employees
ESG 4-2 Average expenses on training per employee p.a
ESG 5 Maturity of workforce ESG 5-1 Age structure/distribution (number of employees per age group, 10
year intervals)
ESG 5-2 Percentage of workforce to retire in next 5 years
ESG 6 Absenteeism rate ESG 6-1 Number of mandays lost per employee p.a.
G ESG 7 Litigation risks ESG 7-1 Expenses and fines on filings, law suits related to anti-competitive
behavior, anti-trust and monopoly practices
ESG 7-2 Reserves on preventive measurements against anti-competitive be-
haviour, anti-trust and monopoly practices
ESG 7-3 (other) litigation payments, total
ESG 7-4 (other) litigation payments, reserves
ESG 8 Corruption ESG 8-1 Percentage of revenues in regions with TI corruption index below 6.0
V ESG 9 Revenues from new products ESG 9-1 Percentage of revenues from products at end of life-cycle
ESG 9-2 Percentage of new products or modified products introduced less than
12 months ago
Table 1
DVSA-EFFRA: Overview of General ESGs and their PIs.
ESG ESG 2 GHG emissions
Description Greenhouse gas emissions are the main cause of climate change. This Indicator can be used to explain
targets for regulations or trading systems at international or national levels. It also provides insights into
the potential cost implications of taxation or trading systems for reporting companies.
KPI ESG 2-1 ESG 2-2 ESG 2-2
Description GHG emissions Total GHG emissions per unit of produc- GHG emissions per employee
tion volume
Shortname GHG.emiss.total GHG.emiss.production GHG.emiss.employee
Unit/Calc. Total GHG Emissions in Million Total GHG Emissions in Million Total GHG Emissions in Million
tons tons tons
Format xxx,xxx,xxx.xx MtGHG xxx,xxx,xxx.xx MtGHG xxx,xxx,xxx.xx MtGHG
Sector All All All
Table 2
DVFA-EFFAS: Specification of PI ESG 2-1.
standards are those of interest for the purpose of this work, containing each a set of indicators
for a specific so-called material topic, ranging from economic and financial performance, to the
usage and production of materials and resources. An explicit taxonomy does not exists, although
the subdivision in documents reflect some form of organization of indicators, as well as reference
to general ESG areas. For instance, the standard GRI 305 - Emissions provides 7 indicators:
Direct GHG emissions; Energy indirect GHG emissions; Other indirect GHG emissions; GHG
emissions intensity; Reduction of GHG emissions; Emissions of Ozone-Depleting Substances
(ODS); Nitrogen oxides (NOx), sulfur oxides (SOx), and other significant air emissions. For each
indicator a guideline for the calculus is provided by a mathematical expression or, more often,
as a textual description. For instance, in the case of GHG emissions intensity:
Calculate the ratio by dividing the absolute GHG emissions (the numerator) by the
organization-specific metric (the denominator).
� Gases included in the calculation; whether 𝐶𝑂2 , 𝐶𝐻4 , 𝑁2 𝑂, 𝐻𝐹 𝐶𝑠, 𝑃 𝐹 𝐶𝑠, 𝑆𝐹6 , 𝑁 𝐹3 ,
or all.
The list of organization-specific metrics include: units of product; size, number of
full-time employees, monetary units.
The standard also provides other recommendations for the calculation, like the breakdown of
the indicator by business, country or type of activity, or the unit of measure.
From this brief analysis it should be clear the difficulty to precisely make sense of these
informal specifications and translate them in an actual procedure for the calculation of the
indicators. As a result different companies, or the same company in different times, may easily
interpret and implement them differently, with outcomes hardly comparable.
4. Ontology model for ESG
The Knowledge Graph has been defined by referring to the KPIOnto ontology [16], which
provides terminology for definition of an indicator and its properties, including a description
(KPIDescription), the unit of measurement (unitOfMeasure), its objective (hasBusObj), the ag-
gregation function (aggrType), the compatible dimensions of analysis (hasDimension), and its
computation formula if available (hasFormula).10 The ontology has been extended with a further
set of domain-oriented object properties, classes and instances, to specify the link between
an indicator and the ESG class it refers to (forESGClass), the standard in which it is defined
(inESGStandard) and the official name used in such a standard (hasESGCodeName).
To make an example, the following triples define the indicator “ESG 2-2 GHG emissions,
total”. It is an Environmental KPI, measured in million of tons, that can be aggregated through
sum and can be analysed along the Time dimension. The prefix kpi refers to KPIOnto, while
prefix esgo refers to the ESG extension and esg stands is used for definition of indicators.
esg:GHG_Emiss_Tot a kpi:Indicator;
kpi:KPIDescription "GHG Emissions, total"@en;
esgo:hasESGDescription [
esgo:forESGClass esgo:GHG_Emissions;
esgo:inESGStandard esgo:DVFA_EFFAS;
esgo:hasESGCodename "ESG 2-1" ], [
esgo:forESGClass esgo:GHG_Emissions;
esgo:inESGStandard esgo:GRI;
esgo:hasESGCodename "GRI 305-1" ];
kpi:unitOfMeasure esgo:MLNtons;
kpi:hasBusObj esgo:Environmental;
kpi:aggrType kpi:Sum;
kpi:hasDimension esgo:Time;
kpi:hasFormula [
kpi:hasFunction om:plus;
kpi:hasArgument [
kpi:hasArgumentPosition "1"^^xsd:int ; kpi:hasArgumentName "addend" ;
kpi:hasArgumentValue esg:Tot_CO2 ],
.........].
10
Ontology specification is available at https://kdmg.dii.univpm.it/kpionto/specification/
�Figure 1: Graph representing some relevant classes and properties describing a formula for
GHG_Emiss_VolProd indicator. The direct links among indicators (blue nodes) correspond to inferred
relations of type dependsOn.
Whenever a computation formula is available, it is represented as a mathematical expression
including arguments and operator(s). The formula can be derived from the GRI Standard as the
summation of several pollutants, and is only partially reported due to the lack of space. The
following triples define the complete formula for indicator “ESG 2-2 GHG emission per unit of
production volume”, that can be calculated by dividing the total GHG emissions by the total
volume of production. The fragment of the Knowledge Graph representing the above-defined
formulas is shown in Figure 1.
esg:GHG_Emiss_VolProd kpi:hasFormula [
kpi:hasFunction om:divide;
kpi:hasArgument
[ kpi:hasArgumentPosition "1"^^xsd:int ; kpi:hasArgumentName "dividend" ;
kpi:hasArgumentValue esg:GHG_Emiss_Tot],
[ kpi:hasArgumentPosition "2"^^xsd:int ; kpi:hasArgumentName "divisor" ;
kpi:hasArgumentValue esg:Tot_VolProd ]
].
The Knowledge Graph contains the definition of 150 indicators, including all 125 indicators
from DVSA_EFFRA standard, along with a selected set of indicators from GRI. A number of 57
indicators are compound and their calculation formulas have been defined. It was implemented
�Figure 2: A subset of the Knowledge Graph of ESG indicators (red), with units of measurement (yellow),
dimensions (green), aggregation functions (pink) and objectives (violet).
as a RDF graph in GraphDB, which provides standard SPARQL-based access mechanisms. A
fragment of the resulting Knowledge Graph is shown in Figure 2.
5. Reasoning-based Applications
The Knowledge Graph representation brings a number of advantages: first of all it provides
a formal description of the informal specifications contained in the standards. This often
requires to make the meaning of those specifications explicit, and thus should be checked
against the original intended meaning of author’s standards, but again the formal representation
can simplify the browsing and verification of definitions. Browsing can support also final users
when they need to get acquainted with standards, e.g. by supporting to analyse formula-based
relations among indicators, as well as by applying formula drill-down, since simple unfolding
mechanisms allow to decompose a KPI into its components [17]. It can be adopted as the
enterprise reference vocabulary in a supply chain to deal with interoperability of their ESG data
and reporting. Ultimately, if the Knowledge Graph is certified by standardization bodies, it can
become an invaluable tool for companies and financial institutions to certificate and compare
ESG disclosures.
Besides advantages simply related to the existence of a reference model, reasoning mecha-
�nisms can be developed on the Knowledge Graph to define flexible and powerful tools for inter-
operable ESG management. They rely on a set of logic-based functionalities, which also includes
math-aware services in Logic Programming to provide non-standard reasoning capabilities[16]:
• Automated calculation of Performance Indicators is provided by reasoning services.
Composition of indicators in formulas, based on the semantics of mathematical op-
erators, in fact enables their automatic manipulation through symbolic resolution of
equations. Such services are capable to derive all alternative formulas for a given indica-
tor (get_formulas), to evaluate the common set of dependencies among a set of indicators
(get_common_dependencies), or to derive what indicators can be computed starting from
those already available (get_computable_indicators).
• On top of the reasoning services, advanced functionalities can be defined with the purpose
to support monitoring of indicators across multiple organizations. This may be particularly
useful for cooperating organizations to assess the status of shared Business Processes.
The set of KPIs produced by the organizations is compared in order to derive, through
the exploitation of get_common_dependencies and get_computable_indicators, common
indicators among them. They include not only indicators that are mapped to the same
ontological concepts, but also indicators that can be calculated from those available.
• Management of the graph, including the creation of new indicators or the maintenance
of their definitions rely on consistency check mechanisms. They are capable to verify
that a new indicator is neither equivalent to nor incoherent with already defined ones.
While equivalence_check may help to perform de-duplication of indicators to minimize
redundancy, the guarantee given by coherence_check is critical to keep the graph consistent
by avoiding contradictory definitions [16].
6. Discussion
From the analysis of standards it emerges a profound heterogeneity in the format, structure,
terminology adopted in the definition of indicators, and in the approach to their categorization.
This is widely recognized as a limit to the development of actions towards a green transition, as
discussed in the Introduction. In this respect, semantic technologies can and should provide
support to efforts towards homogenization and harmonization. The present paper provides
a contribution in this direction, starting from the construction of a Knowledge Graph for
the modeling of ESG indicators. This sometimes required a hard work of interpretation of
underspecified definitions, that could be not correct or shared by standardization bodies. We
believe that the formal representation produced would ease a possible verification and dialogue
with stakeholders.
The inherent modularity and flexibility of a graph structure, combined with the ability to
represent and reason on the compound nature of indicators, has demonstrated to be suitable to
tackle the challenge in an incremental way, checking time by time the coherence of the overall
graph.
Still, several open issues exist that are not covered in this preliminary proposal and represent
interesting research directions:
� • while the expressivity of the model has demonstrated itself sufficient to represent the
ESG indicators structure (formula), some information contained in the standards still
remains uncovered. It is mainly related to the semantics of atomic indicators that, despite
being atomic, often have an underline calculation procedure not falling in the formula
category. As an example, “processed orders” can be defined as a subclass of orders, but
besides requiring to resort to domain ontologies, which is certainly possible, it is purely
terminological and does not solve the ultimate challenge of providing the interpretation.
A step towards the implementation of effective tools for the calculation and certification
of indicators would require a data integration system and GAV/LAV mappings as a means
to directly refer to data in business’s information systems that correspond to an atomic
indicator.
• There is evidence of specifications at different abstraction levels in the standards. For
sure the standard cannot cover the lowest enterprise level. This suggest that the model
should be extended to explicitly deal with and support abstraction management.
• Localization issues: there is evidence of different implementations of the standards, for
instance the GRI standard is provided in several languages, with different names for
indicators. This issue can be addressed by referring to best practices for multilingual
ontologies, ranging from simple design patterns such as referring to textual labels for
the indicator name/description with an attached language tag, to more complex patterns
such as language content negotiation. However, other more tricky localization issues
exist, related to the definition of the indicator’s formula, for instance due to differences in
country’s constraints and regulations (e.g. the definition of employee and its categories,
the components of a salary)
• Standards are “living bodies” subject to revision. Hence the model should be endowed
with the capability to support changes and versioning.
• The graph model simplifies the integration of different standards, however instead of
adopting this view, an alternative approach towards interoperability of standards can
be pursued. This implies the modeling of each standard as a distinct Knowledge Graph
with its own categorizations and definitions, building relations between graphs’ concepts.
Interoperability would correspond to more flexibility and simplicity in the management of
standards than a design-time integration of the different views provided by the standard.
Organizations may disclose their ESG indicators following the preferred standards, and
suitable reasoning over the Knowledge Graphs would still provide proper translations
and correspondences. In this direction, the model easily supports the derivation of the
equivalence of compound indicators provided that “same-as“relations are established
among atomic indicators in different Knowledge Graphs. This reduces the cost of a manual
alignment or of generating large shared vocabularies of indicators terms, at the same
time improving the quality with respect to simple terminological similarity techniques.
Acknowledgements
This work was partially funded by Veeco Srl. Authors would like to thank Miriam D’Aloia for
her contribution in the development of the Knowledge Graph.
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