Using Automated Text Mining
to Align Investments with the
Sustainable Development Goals:
A Case Study Analyzing
World Bank Projects
Authors: Efraim Hernández-Orozco (SEI),
Mario Cárdenas-Vélez (SEI), Colleen Keenan(World Bank), Zoe Russo (World Bank)2© 2022 International Bank for Reconstruction and Development / The World Bank
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3

Table of Contents
Acknowledgments
List of Abbreviations

4
6

Executive Summary
Introduction

7
9

Section 1. An Overview of Approaches to Map Policies & Projects to the SDGs
Section 2. Methodology

1116

Phase I. Creating the Controlled Vocabularyo Data Preparation

17
17

o Frequency List of Words
o Key Word in Context Concordance Analysis

18
19

o Co-occurrence network
Phase II. Creating the Coding Rules

20
22o Writing Syntaxis
Phase III. Mapping

22
24

o Crosstab of Codeso Validation of Results

24
24

Section 3. Results
SDG Mapping Results in the Impact Report 2020

26
27

Section 4. Discussion
Conclusion

35
38

Appendixeso Appendix 1. Full Results of the Mapping Exercise, Project to Target

40
40

o Appendix 2. Full Results of the Mapping Exercise, Project to Goalo Appendix 3.1. Validation Exercise

44
45

o Appendix 3.2. Validation Exercise
o Appendix 4. Results Graph Network: Project to Goal, Full Results

46
47

o Appendix 5. Results Graph Network: Project to Goal, Results without SDG 17
Endnotes

48
49

References50

4

Acknowledgments
This paper was prepared by a team of colleagues from the Stockholm Environment Institute (SEI) Latin
America Centre and the World Bank—Efraim Hernández-Orozco and Mario Cárdenas-Vélez are from SEI,
and Colleen Keenan and Zoe Russo are from the World Bank Capital Markets and Investments Department.
The paper was prepared under the direction of Ivonne Lobos, Senior Expert and Leader of the Sustainable
Development Goals (SDGs) Line of Inquiry at SEI Latin America, and Heike Reichelt, Head of Investor
Relations and Sustainable Finance from the Capital Markets and Investments Department at the World Bank.
The authors would like to acknowledge the World Bank Group SDG Partnership Fund for supporting analyticalresearch to raise awareness for the SDGs and the potential role they can play in building sustainable capital
markets. This support is what made the partnership with SEI to develop the SDG mapping methodology andthis paper possible. Our thanks go especially to Juliana Knapp and Lisa Castro at the SDG Partnership Fund.
We would like to thank those who participated in the literature review of the paper and the validation exercise,Daniela Maestre-Másmela and Juan Betancur-Jaramillo from SEI. We also would like to thank those who
provided comments on the paper during the peer review process. They include, from the World Bank, Frank
Wissing Madsen in Operations Policy and Country Services, Brian William Stacy in the Development Data
Group, and James Seward in the Capital Markets and Investments Department, and from the International
Finance Corporation, Atiyah Curmally and Blaise Wendwaoga Sandwidi. We are also grateful for feedback
from others including Judith Moore, Alya Kayal and Stuart Kinnersley from ImpactArc, and Otilia Ciotau in theoffice of the World Bank Group Chief Finance Officer, who provided helpful input to the content of the paper.
Additionally, we wish to thank David Purkey, Director of the SEI Latin America Centre; Aaron Maltais, SeniorResearch Fellow and Director of the Stockholm Sustainable Finance Centre; Adis Dzebo, Research Fellow
and Co-leader of SEI’s Initiative on Global Finance; and Georgia Savvidou, Research Associate at SEI
Headquarters, for providing their expertise and advising the development of the research.
Finally, we would also like to recognize George Richardson, Director of Capital Markets and Investments atthe World Bank, for his support to the project.
The paper has been funded by the World Bank Group SDG Partnership Fund. The views expressed herein
are solely the authors’ and should not be attributed to SEI or the World Bank or its stakeholders.

5

About the World Bank
The World Bank (International Bank for Reconstruction and Development, IBRD) is aninternational organization. Created in 1944, it is the original member of the World Bank
Group and operates as a global development cooperative owned by 189 nations. The WorldBank provides loans, guarantees, risk management products, and advisory services to
middle-income and other creditworthy countries to support the Sustainable Development
Goals and to end extreme poverty and promote shared prosperity. The World Bank has
been issuing sustainable development bonds in the international capital markets for over 70
years to fund programs and activities that achieve a positive impact.

About the Stockholm Environment Institute
The Stockholm Environment Institute (SEI) is an international nonprofit research andpolicy organization that tackles environment and development challenges. SEI connects
science and decision-making to develop solutions for a sustainable future for all. SEI’s
approach is highly collaborative: stakeholder involvement is at the heart of its efforts tobuild capacity, strengthen institutions, and equip partners for the long term. SEI’s workspans climate, water, air, and land-use issues, and integrates evidence and perspectives
on governance, the economy, gender, and human health. Across eight centers in Europe,Asia, Africa, and the Americas, SEI engages with policy processes, development action,and business practices throughout the world.

6

List of Abbreviations
AUN

Aurora Universities Network

DIE
FY

Deutsches Institut für Entwicklungspolitik
fiscal year

IAEG-SDGs
IBRD
KWIC
NLP

Inter-agency and Expert Group on SDG Indicators
International Bank for Reconstruction and Development
key word in context
natural language processing

OECD
Organization for Economic Co-operation and Development
OECD DAC Organization for Economic Co-operation and Development’s Development Assistance Committee
PPV
SDB

positive prediction value
Sustainable Development Bond

SDG
SEI

Sustainable Development Goal
Stockholm Environment InstituteUN
UN DESA

United Nations
United Nations Department for Economic and Social Affairs

7

Executive Summary
Investors, issuers, and other capital market participants are using the Sustainable Development Goals(SDGs) as a reference framework to show the impacts of investments on global sustainable development. But
assessing the impacts requires the development of methodologies that can help investors examine how theiractivities are aligning with and contributing to the SDGs.
This paper offers a case study on linking investments, which in this case are a group of 100 World Bank –financed projects, to the SDGs to understand the projects’ connections to the SDGs. The paper focuses on acollaboration between the World Bank (specifically, the International Bank for Reconstruction andDevelopment, IBRD), a pioneer in sustainable finance and a leader in promoting models for transparency anddisclosure to build sustainable capital markets, and the Stockholm Environment Institute (SEI), aninternational nonprofit research and policy organization that connects science and decision-making to develop
solutions and policies for a sustainable future.
This work aims to develop a methodology that provides a lens through which investors can see indicative
linkages between their investments and the SDGs, in support of investment reporting and potentially aiding
other capital markets participants in connecting project results or investments to the SDGs. The paper also
adds to ongoing global efforts to map the contributions of development projects, policies, and research to the
SDGs and aims to fill gaps in the existing mapping methodologies. Greater transparency around impact canhelp investors develop insights and inform decision-making on sustainable investment to help channel financeto sustainable purposes.
The methodology described in this paper involved the creation of a text mining protocol using Boolean
queries1 based on a controlled vocabulary of the language used by the World Bank. This included formulating
keywords and phrases that can identify SDGs and their targets in the text of World Bank project
documentation. For its test case, SEI applied the methodology to the 100 new IBRD-funded projects that wereadded to the World Bank Sustainable Development Bond (SDB) project portfolio in fiscal 2020 (July 1, 2019 –June 30, 2020) — a subset of the roughly 600 active IBRD projects in total.
In general terms, the exercise successfully highlighted which SDGs appeared the most, according to thismethodology, in the subset of the fiscal 2020 projects. For instance, SDGs 17 (Partnerships for the Goals), 9(Industry, Innovation, and Infrastructure), and 3 (Good Health and Well-Being) are those that appear mostoften in the mapping results. At the target level, target 17.9 (Enhancing capacity for the SDGs) appears themost frequently (in 50 projects); aside from SDG 17, targets 3.8 (Achieve universal health coverage) and 3.c(Increase health financing) appear the most, with 35 and 26 mapped projects, respectively. The results werethen validated by manually mapping 20 randomly chosen projects and comparing the manual results to thetext mining ones.
We note, however, that the interpretation of results needs to consider the data set used, the limitations of the8methodology, the intended use and purpose of the mapping, and the interconnected nature of the SDGs
when making conclusions. For instance, the SDGs include topics that cut across the entire 2030 Agenda for
Sustainable Development (hereafter, 2030 Agenda), such as climate change and gender equality. Therefore,
we recommend using a holistic view to interpret the results to avoid focusing on individual goals, which maylead to inaccurate interpretations. For example, the results show how World Bank projects are connected tomultiple SDGs, including looking specifically at those projects connected to gender equality and climatechange and environmental issues. In looking at this grouping, the results show that 66 percent of the mappedprojects relate to environmental and climate change SDGs, 79 percent are related to gender- sensitive SDGs,
and 49 percent are related to both. In addition, because of the prevalence of SDG 17 targets in the mapping
results, which is due to the World Bank’s focus on capacity building and economic development in member
countries, the analysis controls for the mapping to these targets.
This paper also discusses the limitations of the proposed approach and similar methodologies. In particular,
we note that this mapping methodology represents one of the many solutions available for indicative mappingof investments to the SDGs and that different approaches can produce distinct results. Moreover, thismethodology and other approaches examined do not account for the differential and weighted impact that
individual projects can have on the SDGs. Not all projects related to the same goal or target will have thesame degree of impact. This limitation remains an important consideration and provides a direction forfurther research.
Finally, the proposed methodology may be adapted to the broader sustainable finance space. If scaled up
and further developed, through artificial intelligence, for example, it can support capital markets’ participantsin meeting the growing disclosure requirements from new national and regional regulations and taxonomies
setting definitions and standards around sustainable activities by assessing the alignment of investments tothese emerging frameworks.

9Introduction
The SDGs, which consist of a set of 17 goals and 169 targets, were adopted by the United Nations (UN)General Assembly and the international community in 2015 to address social, economic, and environmental
global challenges in an integrated manner and to reach sustainable development globally by 2030 (UN 2016).
However, the UN has estimated a financing gap of US$2.5 trillion annually for developing countries to achieve
the SDGs by 2030 (UNCTAD 2014). Sustainable finance plays a pivotal role in filling this gap by unlocking
private capital as a complement to public money, but investors need to be able to examine and understandhow they are contributing to more sustainable and inclusive economic growth. Many capital markets
participants, such as issuers and investors, use the SDGs as a reference framework to show links between
investments and sustainable development. Disclosures on the links between the SDGs and investments canimprove transparency on the impacts of sustainable development investments and potentially help channel
funds to sustainable purposes.
This paper develops a methodology to efficiently and accurately map investments to the SDGs, applies themethodology to World Bank–financed projects, and shares the results. It was produced through acollaboration between the World Bank (namely the IBRD), a pioneer in sustainable finance and a leader inpromoting models for transparency and disclosure to build sustainable capital markets, and SEI, aninternational nonprofit research and policy organization that connects science and decision-making to develop
solutions for a sustainable future. The expectation is that the methodology will support investors and issuersin connecting project results or investments to the SDGs. This paper also contributes to ongoing efforts tomap development projects’ contributions by reviewing the most common approaches for SDG mapping andproviding recommendations to fill gaps in their methodologies.
The UN system has endorsed the use of the Inter-agency and Expert Group on SDG Indicators (IAEG- SDGs)
as a framework to monitor progress for the SDGs; however, most of those indicators are generated only at thecountry level, which complicates their application outside governmental and national-level reporting(Hernández-Orozco et al. 2021). Efforts to map activities and projects from different types of institutions to the
SDGs are growing, including among governments (Hawai’i Green Growth Local2030 HUB 2020), enterprises(for example, Horne et al. 2020), scientific and academic institutions (Asatani et al. 2020; Bordignon 2021;
Mistry et al. 2020; Sullivan, Thomas, and Rosano 2018), and development and investment banks(Dangelmaier 2019; Natixis 2018; World Bank 2021). This paper identifies the two main trends in themethodologies used to produce these mappings: conceptual mapping of themes and categories to the SDGs
(Dangelmaier 2019; also, for example, Government of Mexico City 2019; Hawai’i Green Growth Local2030
HUB 2020; NYC Government 2019) and methodologies that analyze large volumes of text to automaticallyfind SDG- related themes and concepts, such as text mining (for example, Bordignon 2021; Sullivan, Thomas,
and Rosano 2018; World Bank 2021), content analysis (Uehara and Sakurai 2021), natural language
processing (NLP) (Asatani et al. 2020), and machine learning (Pincet, Okabe, and Pawelcyzk 2019).

10

The methodology developed in this exercise corresponds to the second trend. It involved the creation of a
Boolean queries protocol based on a controlled vocabulary of the language used by the World Bank in its
project documentation for projects featured in the Impact Report 2020 on IBRD SDBs and Green Bonds andthe “World Bank Theme Taxonomy and Definitions” (World Bank 2016). Then, using KH Coder software(Higuchi 2021), SEI recorded the occurrence of SDG keywords in World Bank project details—if one or more
SDG keywords appeared in the text, that project was mapped to the related SDG target. SEI developed thismethodology based on its NDC-SDG Connections Tool, which is developed with the Deutsches Institut für
Entwicklungspolitik (DIE), that visualizes potential connections between the SDGs and countries’ climate
actions toward their nationally determined contributions (NDC) (DIE and SEI 2017).
As a test case, SEI applied the resulting methodology to the 100 IBRD projects that were added to the WorldBank SDB project portfolio in fiscal 2020 (July 1, 2019–June 30, 2020),2 which is a subset of the roughly 600
World Bank projects active in fiscal 2020 (World Bank 2021). The results were then validated by manually
mapping 20 projects to verify the automated results. A positive predictive value (PPV), which assesses thelikelihood of the Boolean queries producing a correct mapping result, of 75 percent was obtained.
This paper reports on this exercise and discusses the limitations of and considerations for the proposed
approach and similar methodologies. It is useful to note that this mapping methodology represents one ofseveral solutions available for indicative mapping of investments to the SDGs, and different approaches can
produce distinct results. Moreover, the research reveals that this methodology and most other approaches
examined do not account for the differential and weighted impacts that individual projects can have on the
SDGs. This limitation remains an important consideration and provides direction for further research.
Finally, when interpreting the mapping results from this exercise, and other similar analyses, it is important toconsider that the SDGs are inherently interconnected and include transversal topics that cut across the entire
2030 Agenda, such as climate change and gender equality. Therefore, a focus on mapping results toindividual goals may lead to inaccurate interpretations. A more holistic view is recommended, and this paper
models that by highlighting some World Bank projects that are connected to gender equality and climatechange and environmental issues rather than either SDG 5 or SDG 13.
The paper is structured as follows: Section 1 presents a literature review of the existing methods to map
different development activities to the SDGs. Section 2 explains the creation of the keyword protocol and its
application in KH Coder to map World Bank–financed projects to the SDGs. Section 3 presents the results ofthe exercise. Section 4 discusses the results and suggests how to improve SDG mapping methodologies
based on the lessons learned from this mapping exercise. The last section concludes.

11

SECTION 1:
An Overview of Approaches
to Map Policies & Projects
to the SDGs

12

Section 1. An Overview of Approaches to Map Policies & Projects
to the SDGs
The SDGs are being used as a framework to link the impact of investment activities and their contribution
toward sustainable development, and making that link requires a deployment of approaches that can map
investments, projects, and activities to the SDGs. Conventionally, to produce such an assessment, governing
bodies have aimed to measure progress using the list of indicators from IAEG-SDGs (2016). However, mostof these indicators are generated only at the country level, which complicates their application outside
national-level reporting (Hernández- Orozco et al. 2021).
Furthermore, when assessing the contribution of various types of institutions to the SDGs, the use of the
IAEG-SDGs indicators can be challenging because the concepts and terminology used by other institutions
often differ from the concepts used in the SDGs and their indicators. For instance, the World Bank uses its
own taxonomy of themes3 and sectors along with Organization for Economic Co-operation andDevelopment’s Development Assistance Committee (OECD DAC) purpose codes to classify the activities ofWorld Bank–supported projects (World Bank 2016). The themes are aligned with the SDGs but do not matchthem exactly, and the themes can be subjectively mapped to more than one target; for example, the WorldBank theme “Adolescent health” can be thematically mapped to SDG targets 3.7 (Sexual and reproductive
health), 3.8 (Achieve universal health coverage), and 5.3 (Eliminate all harmful practices against children
and women).
The implication is that to know the link to or contribution of various development activities to the SDGs,
researchers and practitioners need to deploy methods to map different concepts and categories to those usedin the SDGs. In this regard, this paper identifies different SDG mapping methodologies that are grouped into
two main trends: 1) conceptual mapping of indicators and categories4 to the SDGs and 2) automatic detectionof SDG topics in texts and documents. The first approach consists of a manual review and classification ofindicators and categories conducted by researchers, policy makers, institutions, and other stakeholders, whothen examine how the concepts present in their organizational frameworks overlap with the SDGs. Thisapproach allows the rapid mapping of any project tabulated under a certain category to its corresponding
SDGs. The second approach uses computer assistance to automatically detect words and phrases in texts
related to the SDGs, mapping their thematic meaning (or topics) to corresponding SDGs and eliminating theneed to directly review the contents of each project. This type of approach is mainly used for large data setsof text.
It is important to point out that both approaches require the researcher to subjectively map the identified topics
or keywords to SDG themes, either at the category level or by assigning meaning to text mining queries. It
can be argued that the subjectivity behind this process occurs because of the ambiguous nature of the SDGs:
given that the SDGs relate to politically sensitive topics, the vision of what sustainable development entails
and how to achieve it can depend on the vision of the implementing actors (see Mair et al. 2018). This
ambiguity plus the possibility for words or phrases to have many meanings (or polysemy, see Bordignon
2021) can produce various mapping results that correspond to the different understandings of the SDGs.

13

Category mapping is mostly used by governmental institutions. For instance, some regional governments
have used the Sustainable Development Report methodology (Sachs et al. 2021), which includes criteria to
map indicators from distinct sources when the IAEG-SDGs indicators are missing. Examples of this approach
include Índice de Desenvolvimiento Sustentável das Cidades – Brasil (Instituto Cidades Sustentáveis 2021)
and Los Objetivos de Desarrollo en 100 ciudades españolas (Sánchez de Madariaga et al. 2020), which
report SDG progress in Brazilian municipalities and Spanish cities, respectively. Other governments havemapped the categories of their government programs to the SDGs; any project, plan, or indicator assigned toa specific category is then directly mapped to its corresponding SDG. For instance, the State of Hawaii in the
United States has its own sustainability framework distinct from the SDGs, called the “Aloha+ Challenge,”
outlining six priority areas for the state’s sustainable development; in this framework, the “Local Food” priority
area and any project under it are mapped to goals 1 (No Poverty), 2 (Zero Hunger), 3 (Good Health and WellBeing), 12 (Responsible Consumption and Production), 13 (Climate Action), and 17 (Partnerships for
the Goals).
Comparatively fewer nongovernmental organizations than governmental institutions have used category
mapping to report on SDG alignment. One organization that uses it is KfW, the German development bank,which describes in its SDGs mapping methodology document how it has assigned “markers” and “objectives”
to the SDGs, and it then assigns any activity organized under specific categories to the related SDGs
(Dangelmaier 2019). For instance, projects categorized with the “gender marker” are mapped to SDGs 5(Gender Equality) and 10 (Reduced Inequalities). KfW also uses this mapping to report contributions to each
SDG based on the financial resources spent on development activities. KfW does this to measure the impact
of its development portfolio on the SDGs, using financial resources as a weighting factor so that all theactivities mapped under a specific SDG and their corresponding budget are noted as contributing to that
SDG. For instance, KfW reports that its 2018 portfolio contributed €33.4 billion to SDG 8 (Decent Work andEconomic Growth), which is the SDG with the highest investment by KfW’s development activities that year
(Dangelmaier 2019).
The mapping of SDG categories can be convenient in terms of technical capacity and the use of time andfinancial resources since it does not require the examination of each project individually. Instead, the mapping
is performed at the category level, and any project tabulated under a certain category is rapidly mapped to thecorresponding SDG. However, the categories’ definitions and concepts can have thematic differences fromthe SDGs, so they do not always map exactly to the goals or targets. For instance, when reviewing the “WorldBank Theme Taxonomy and Definitions” (World Bank 2016), theme 436, “State-Owned Enterprise Reformand Privatization,” is challenging to map to the SDGs because privatization is not explicitly covered in the
SDGs. Therefore, it is necessary to define the details of the privatization process to assess SDG alignment.
The implication is that it is more effective to examine each project individually to improve the accuracy of the
mapping. Nonetheless, manual examination of single documents can become time- and resource-consuming,
especially when analyzing large documents or text data sets. For this reason, institutions are increasingly
opting to mechanize this process, using text mining and other automated text analysis techniques, which are
the second family of SDG mapping methodologies identified in this paper.

14

Overall, these automated document evaluation approaches predefine search queries related to the SDGs,
formulating keywords and phrases that can identify SDG goals and targets in text documents. For instance,
Boolean queries can be designed to detect singular words, phrases, and sentences using operators such as“OR,” “AND,” and “NOT.” For example, applying the query “climate AND change” will look for the phrase“climate change” in a text—if there are true values, the results can then be assigned to SDG 13 (Climate
Action), mapping the text to the goal.
Text mining methods have been used to map texts to the SDGs in various contexts. Still, its use is more
prominent in academia, where researchers have sought to assess academic research and publications’contributions to the SDGs (for example, Armitage et al. 2020; AUN 2021; Bordignon 2021; Elsevier 2020;
Hassan, Haddawy, and Zhu 2014; Jayabalasingham et al. 2019; Mistry et al. 2020; Olawumi and Chan 2018;
Vanderfeesten, Otten, and Spielberg 2020). According to Bordignon (2021), the academic use of text mining
for SDG mapping has had mixed results, as some queries can be too constrained because they use theoriginal UN texts for the SDGs, while others may be too broad because of the polysemy of words, producing
false positives. Nonetheless, some approaches have had promising results, such as AUN’s SDG mapping ofresearch publications that reports a “precision” of 70 percent, which was validated by the authors of the
publications mapped in the analysis (AUN 2021).
In addition, text mining, namely semi-automated content analysis, was used in Germany to map SDG-related
activity in 193 entrepreneurial venture competitions (Horne et al. 2020). It was also used to map media
content, scientific publications, and patents to SDG 13 (Climate Action) in South Korea (Hwang et al. 2021);
find the most discussed SDGs in government-issued Voluntary National Reviews (Sebestyén, Domokos, andAbonyi 2020); and align policy making in the European Union with the SDGs (JRC and INTPA n.d.).
Other text analysis approaches use artificial intelligence techniques that automatically create queries (withoutmanual programming), learning from the data sets used as inputs to build controlled vocabularies or thematic
categories. The artificial intelligence methods that have been used for SDG mapping include machinelearning, NLP, and Bidirectional Encoder Representations from Transformers. The OECD (Pincet, Okabe,and Pawelcyzk 2019) used machine learning to find which SDGs are most affected by worldwide
development and aid projects, and the UN Department of Economic and Social Affairs (UN DESA) used it tocreate topic models aligned with the SDGs for UN publications. Asatani et al. (2020) used NLP, which createsclusters of the SDG topics emerging in a data set, to find the contributions to the SDGs of over 300,000
publications. And Guisiano and Chiky (2021) used Bidirectional Encoder Representations from Transformers
to label texts related to the SDGs from the OnePlanet network parentship.
Text analysis approaches can be used in a wide variety of documents to assess SDG mapping, and eachapproach developed can potentially be used on any text data set in English. However, the applicability ofthese approaches is not universal, as Bordignon (2021) notes, because of polysemy and the risk that the
restrictive vocabularies used in queries will produce less accurate results. To make mapping more accurate,
text mining queries have to be adapted and tailored using the specific language (also called a “controlled

15

vocabulary”) found in the analyzed texts (for example, Krallinger et al. 2012; Spasić et al. 2008). For example,
for this paper, a controlled vocabulary was created using KH Coder software to find the most used words andphrases that appear in the subset of 100 World Bank projects. This analysis was then used to create Boolean
queries tailored for World Bank– specific vocabulary to map SDGs at the target level. This process is
described in the next section.

16

SECTION 2:
Methodology

17

Section 2. Methodology
To map the subset of 100 projects financed by the World Bank during fiscal 2020 (World Bank 2021) to SDGs
at the target level, the publicly available text mining methodologies described above could potentially havebeen used. But because of polysemy (Bordignon 2021), there was a risk that the queries they designed would
be too general, which generates false positives, or be too narrow, which reduces the number of positive
results.
To avoid this, SEI proposed the use of a controlled vocabulary developed from the type of language present
in World Bank project documents and Boolean queries created from the text of the fiscal 2020 projects andthe “World Bank Theme Taxonomy and Definitions” (World Bank 2016). KH Coder 3.Beta.02f version wasthen used to analyze the texts’ vocabulary to identify the most common words and phrases in each documentthat can refer to SDG topics. Next, each topic identified was assigned to the SDGs at the target level. By
creating queries from the vocabulary of the SDG targets, it was possible to account for other words andphrases that the KH Coder analysis did not reveal. The version of KH Coder used for this paper and newversions are free and downloadable (see Higuchi 2022), and SEI used instructions from the publicly available
KH Coder manuals to build this approach (see Higuchi 2016b, 2017).
The KH Coder mapping process involves three phases. The first is a quantitative content analysis approachthat automatically extracts words from the texts to analyze them statistically (Higuchi 2016a, 2017). The
second is the creation of coding rules for the Boolean queries. The final phase involves intersecting thecoding rules with the project documents to obtain the number of SDGs mapped per project. It is important tonote that the developed approach does not account for the differential and weighted impact that individual
development actions may have on the SDGs.

Phase I. Creating the Controlled Vocabulary
Data Preparation
The World Bank collected and supplied text extracts of the 100 projects’ documentation, in most cases the
Project Appraisal Document (to review the World Bank’s project database, see the website in World Bankn.d.). These excerpts included the project development objective, project components, and target results
indicators for projects currently under implementation. The information was then inserted into KH Coder’sFrequency List tool to obtain the list of words used most frequently in the texts.
The next step in the data preparation was to identify all the stop words in the analysis, which are the words
that frequently appear in the texts but that do not have a specific meaning, such as conjugations of the verb
“to be” or prepositions. KH Coder comes preloaded to eliminate common stop words in English like “do” and“is.” However, it was necessary to include other stop words that appear in the World Bank documents and do

18

not indicate SDGs—most referred to places, nationalities, and abbreviations. Such stop words can beappropriately removed from an analysis to improve it (Rani and Lobiyal 2020). The stop words were inserted
in a TXT document, which was then linked to the KH Coder in the software’s settings.

Frequency List of Words
SEI created another words frequency list, again excluding the stop words, to obtain the number of times aword appeared in the complete text data set. The output, to an Excel document, is shown in table 1. It is
important to note that KH Coder also isolated and counted every word by considering its root form. For
instance, the word/concept “say” would include the words “say,” “says,” “saying,” and “said” (Higuchi 2016a).

Table 1: Frequency List of Words for All 100 Projects

Words

Frequency

Service198

Development

193

Public179

Management

169

Improve

169

Percentage

169

Support165

Health159

Increase

153

Water

149

Strengthen

137

Access132

Capacity

110

COVID-19

108

Program100

Beneficiary99

Social96

Education

92

Provide92

Sector85

Female83

Quality80

Source:An elaboration by SEIusing KHcoder software.
Note: The list shows the words that appear 80 times or more in the 100 analyzed texts without
considering the stop words.

19

Key Word in Context Concordance Analysis
Subsequently, the Key Word In Context (KWIC) Concordance tool in KH Coder was applied to the data set toanalyze and identify how a specific word appeared in a sentence and show how other words related to it
grammatically. The KWIC method performs a match analysis for each concept (word) within a frequency list
based on the word’s positional criteria (Luhn, 1960). The model assesses and identifies the pattern of words(word associations) that accompanies those primary words, or “node words” (Higuchi 2016a; Luhn 1960). The
node words have more connections and centrality within the data set, which the software considers to be
more critical because of their position and semantic meaning.

Figure 1: KWIC Concordance Analysis on “Health”

Source: An elaboration by SEI using KH coder software.
Note: The figure lists the 14 most common words appearing together with “health” in the 100
projects in order of frequency.

Next, KH Coder’s “Broader Context” option was used to observe the context of the node words. Thus, words were
sorted in ascending order, utilizing the frequency value of the words that appear before or after a node word. The“frequency” value calculation for the KWIC method is shown in Equation (1),20$

𝑙! + 𝑟!
,𝑓 𝑤 =$𝑖!"#
Where the frequency ′l1′ is the frequency of a particular word that appears just before the node word; ′l2′ is its
frequency, two terms before the node word; ′r1′ is its frequency just after the node word; and ′r2′ is its
frequency, two terms after the node word (Higuchi, 2016a, 2017).

The higher the frequency value of the particular word, w, that appears before or after the node word, thehigher the value of f(w). In calculating f(w), the frequencies are divided and weighted according to theirdistance from the node word (Scott 2001). Thus, words that appear closer to the node word (for example, one
or two words before or after the node word) have a higher weight than those located five words before or after
it. SEI was therefore able to indicate the most common associations of the node words (figure 1).

Co-occurrence network
Additionally, the KH Coder Co-occurrence Network tool was applied to graphically display the associations
between the most frequent words. This technique shows the node words in a chart that reveals the relative
position of common words relative to others (Chen et al., 2016; Ozgur et al., 2008). Groupings of highly
connected communities of words are joined by lines (or “edges”) and linked in a cluster with the words thatfrequently appear close to each other (figure 2). This use of lines to connect words to show a strong cooccurrence in the analyzed texts was developed by Fruchterman and Reingold (1991) and Kamada and
Kawai (1989).

21

Figure 2: Co-occurrence Network of the 100 Projects

Source: An elaboration by SEI using KH coder software.
Note: Each circle represents words and terms that frequently appear in the 100 analyzed projects;
the words are shown exactly as they appear in the database. The size of the circle shows thenumber of times the word appears in the texts. The circles are grouped in clusters or communities
of words that frequently appear together, suggesting association. Each community is
depicted in a different color.

Once the most frequent topics had been generated, SEI compared their meanings to the topics in each of the
169 SDG targets and assigned the queries to their corresponding targets. Because the themes in the WorldBank documents did not exactly match the topics covered by the SDGs, a single World Bank theme could bemapped to various SDGs topics. The SEI team mapped the words and phrases from the controlled

22

vocabulary to every appropriate SDG target. It is important to restate that this process was performed
subjectively, so others doing the mapping with a different understanding of the SDGs and World Bank themes
could yield different results.
In addition, SEI created Boolean queries from the official UN text that appears in the SDG targets (UN 2016)
to account for the words and phrases that could have escaped the concordance and co- occurrence analyses,
which show only the most frequent words and associations from the 100 projects. Each coding rule iscomposed of a series of Boolean queries of words or phrases that can thematically refer to the SDGs—every
time the Boolean query appears in a text, the matching SDG is mapped to the project. This allows for multiple
SDG mappings from a single project. The design of the code rules is described in the next section.

Phase II. Creating the Coding Rules
Writing Syntaxis
A TXT document was developed for each SDG coding rule. Coding rules are created using Boolean queries
programming: before each code name, an “*” (asterisk) is added so that KH Coder recognizes it as the nameof a code. In the following line, the condition is specified for that code and the kind of situation it should beapplied to. Using this format, KH Coder allows as many codes as needed for text analysis.
A robust coding protocol was designed to avoid searching for and targeting single words, such as“agriculture,” when appropriate. Instead, a combination of coding rules was applied, using rules such as AND,OR, NOT, “-,” and “+,” among others (Higuchi 2016a).
The coding used is explained below.
Single Phrase: This rule allows the specification of a phrase rather than a single word, such as“Word1+Word2+Word3+ ....“ For instance, a code for a phrase including “agricultural productivity” can be
written as follows:
1 *Agricultural_productivity
2

Agricultural+productivity

Nearby Words: This rule facilitates defining a code with specific words in an adjacent sentence structure
or expression.
1 *climate_change_resilience2

near (climate-change-resilience)23

Arithmetic Operators: As shown in table 2, there is also an option to use arithmetic operators to give a code
based on how often a word occurs in a sentence structure (Higuchi, 2017).

Table 2: Standard Arithmetic Operators for the Coding Rules Document

Operators

Alternative

|

Or

&

And

!

Not

&!

And Not|!

Or NotSource: Obtained from Higuchi 2016b.
Note: These operators, or their alternatives, specify which words and phrases KH Coder is to use
or exclude while mapping texts; in other words, they define the conditions for codes to be
assigned to words or groups of words occurring in text. The operator “|” and its alternative “Or” areused to specify that a code corresponds to various individual words and that the KH coder will finda match if any of the words occurs in the text; the operator “&” and its alternative “And” are used to
specify that multiple words must appear at the same time in the text; the “!” operator and its
alternative “Not” are used to specify that, for a code to be mapped, one or more words most notappear in the text. Some of these operators can be combined to form complex operators, such as“&!” (And not), which is used to create the condition for mapping a code if a specific word appears
in text and a second one does not; and “|!” (Or not) which is used to create the condition formapping a code if either one word appears in text or a second one does not. Finally, operators canalso be used in conjunction with single phrases and nearby words to specify
mapping conditions for phrases.

Lastly, multiple conditions were used to define whether a word appears in the data set and the number oftimes it appears and identify synonyms. Thus, symbols such as “AND” (to determine when two words appear
in the same sentence or paragraph), “OR” (to define synonyms), and “&!” (“and not” operator) were used. For
instance, “TB or tuberculosis” means that “TB” and “Tuberculosis” are synonyms and should be counted as
the same word when appearing in the text. Figure 3 shows the coding protocol designed for SDG 3.3.

24

Figure 3: Coding Protocol for SDG 3.3*SDG 3.3
HIV | AIDS | TB or tuberculosis | near(health-deficiency) | immune | virus | near(sexuallytransmitted) | disease | epidemics or epidemic | COVID | Covid-19 | Sars-cov-2 | COVID19 | Coronavirus | TB or tuberculosis | malaria | near(tropical-diseases) | hepatitis |near(communicable-diseases) | near(water- borne) | near(aids-epidemic) | near(end-epidemics)| miasma | contagion | plague | pest | near(neglected- tropical-diseases) | near(combatdiseases) | near(combat-hepatitis) | near(combat-communicable- diseases) | near(humaninfection) | near(infection-control) | near(infectious-disease)

Source: An elaboration by SEI.
Note: These are the Boolean queries designed for SDG 3.3 based on the World Bank
controlled vocabulary.

Phase III. Mapping
Crosstab of Codes
The final phase of the mapping process consisted in applying the coding rule file to the texts of the 100
projects. This was achieved by linking the TXT file with the Boolean queries to the KH Coder file using the
Crosstab tool, with the SDG target specified as the “coding rule file” and the World Bank projects as the“coding units.” Then, a cross-tabulation was run to show the frequency of documents to which each code wasapplied, and KH Coder generated a table showing the positive or negative results for each project in terms of
the SDG targets. The results from this mapping are described in section 3.

Validation of Results
To validate the results, 20 projects were randomly selected from the subset of 100 projects. To calculate theconfidence interval of the validation sample, SEI applied equation (2), from Rodríguez del Águila and
González-Ramírez (2014):

25

𝑍% & ∗ 𝑝 ∗ 𝑞 ∗ 𝑁
𝑆𝑎𝑚𝑝𝑙𝑒 𝑠𝑖𝑧𝑒 =&∗ 𝑒& +𝑁−1

𝑍% & ∗ 𝑝 ∗ 𝑞&,

Where:
Z! ": Z-score for a 90 percent confidence interval = 1.65,"

𝑝: probability of success classifying the project = 0.5,
𝑞: probability of failure classifying the project = 0.5,
𝑁: population size = 100,
𝑒: margin of error = 0.165.

Using this equation, a sample size of 20 projects allows for validation with a confidence interval of 90 percent
and a margin of error of 16.5 percent.
Next, the 20 projects were manually mapped to the SDG targets to obtain a conceptual mapping of theselection. Presumably, the results from the manual mapping would be similar to the ones from themethodology described above. Because the team of technicians that developed the Boolean queries
conducted the manual mapping, the same understanding of the SDGs and World Bank themes used increating the methodology was applied.
Then, the PPV was obtained to quantitively assess similarities between the manual and automatic mappings(see Chu 1999; Saito and Rehmsmeier 2015). This value is obtained by dividing the number of true positives(TP) in the data set (the SDG targets that were mapped in both the manual and automatic mappings) by the
number of TP plus false positives (FP) (the mapping results that were obtained in the automatic mapping but
not in the manual mapping). The PPV directly assesses the likelihood of the Boolean queries yielding acorrect mapping result, as verified by the manual exercise. To see the full validation calculation, please see
Appendix 3. Validation Exercise.
The PPV from the data set is calculated as:

𝑃𝑃𝑉 =

67 .
67897

Running this equation with the manual and automatic mapping samples produced a PPV of 75 percent.

26

SECTION 3:
Results

27

Section 3. Results
In interpreting the results on how investments in World Bank–financed projects contribute to the SDGs, it isimportant to remember that different methodologies are likely to yield different outcomes. This paper focuseson only a subset of World Bank–financed projects, the 100 projects added to the World Bank’s project
portfolio in fiscal 2020. It does not offer a complete representation of the full range of development activities
performed by the World Bank across different regions and sectors or account for the differential and weighted
impact that individual projects can have on the SDGs.
Additionally, given the interconnected nature of the SDGs, focusing on the results for individual SDGs maylead to inaccurate interpretations; a more holistic view of the results is recommended. For example, theresults show the number of projects related to cross-cutting SDG topics such as gender equality and climatechange and environmental issues. Each of the 100 projects was mapped to the SDGs at the target level, andthe use of Boolean queries allowed us to identify more than one SDG target per project when applicable.

SDG Mapping Results in the Impact Report 2020
Impact reporting is part of the World Bank’s efforts to build models for transparency and disclosure that help
to develop sustainable capital markets. The Impact Report 2020, which reports on the use of proceeds fromWorld Bank SDBs and Green Bonds, shows the results of the mapping exercise described in this paper.
Table 3 shows one of the SDG mapping visualizations included in the impact report.

Table 3: Project Mapping per SDG

28

Source: World Bank 2021.
Note: The table shows a portion of the results from the 100 analyzed texts. Each row corresponds
to the World Bank project number in the far-left column. The Sustainable Development Goal
(SDG) columns represent the number of times each project referred to the SDG. The darkness ofthe color corresponds to the number of targets mapped under that SDG, so the darker the tone,
the greater number of SDG targets mapped to the project. To review the full results of this table forthe 100 projects, please see “Appendix 2. Full Results of the Mapping Exercise, Project to Goal.”

The results also show that every project is mapped to at least two SDG targets. On average, each project inthe data set is mapped to 10 targets, and the mode is eight mapped targets per project (figure 4). Project
P167455 has the most mapped targets (27), followed by P157575 with 23 targets, and then P158502,
P165129, and P157245, with 20 mapped targets each. In total, the projects in the data set mapped to 109
SDG targets. This means that from the 169 Boolean queries protocols developed (corresponding to the 169
SDG targets), a sum of 109 protocols generated positive results at least in one of the 100 analyzed projects.

Figure 4: Distribution of SDG Targets Mapped per Project

Mean number oftargets per project
Distribution of thenumber of targets

29

Source: An elaboration by SEI.
Note: FY20 = fiscal year 2020; SDG = Sustainable Development Goal. On average for the 100
projects, a single project was mapped to 10 targets, while the mode is 8 targets per project. The
highest number of targets per project is 27, and the lowest is 2.

For the subset of 100 projects analyzed, all 17 SDGs are matched to at least one project (figure 5). SDG 17(Partnerships for the Goals) is the most represented, appearing in 82 projects, followed by SDG 9 (Industry,Innovation, and Infrastructure) and SDG 3 (Good Health and Well-Being), mapped to 54 and 48 projects,
respectively. SDG 14 (Life below Water) appears the least, mapping to two projects, followed by SDG 7(Affordable and Clean Energy) mapped to 10 projects.

Figure 5: Number of Projects Mapped per SDG

Source: An elaboration by SEI.
Note: SDG = Sustainable Development Goal. This graph shows the total number of projects
mapped under each SDG at the goal level for all 100 projects.

30

Of the projects mapped, some crosscut many SDGs, so a holistic view is recommended. The SDGs have aninterconnected nature since they were designed to be an “integrated and indivisible” set, where every goal
must be met to truly achieve sustainable development (UN 2016), so crosscutting by projects is to be
expected. For instance, based on SEI’s expertise and perspective on the 2030 Agenda, it can be argued thatclimate change and the management of ecosystems and biodiversity are predominant topics in SDGs 2 (Zero
Hunger), 6 (Clean Water and Sanitation), 13 (Climate Action), 14 (Life below Water) and 15 (Life on Land).
Similarly, according to UN Women (2018), the following SDGs are gender-sensitive: 1 (No Poverty), 3 (Good
Health and Well-Being), 4 (Quality Education), 5 (Gender Equality), 8 (Economic Growth), and 16 (Peace,
Justice and Strong Institutions). If these groupings are applied to the mapping results, 66 percent of the
projects are connected with environmental and climate change SDGs, 79 percent are related to gendersensitive SDGs, and 49 percent are connected to both groups of SDGs. Only 4 percent of projects are notconnected to either environmental or gender-related SDGs (figure 6).

Figure 6: Network Graph of the Projects Mapped toEnvironmental and Gender-Related SDGs

31

Source: An elaboration by SEI.
Note: SDG = Sustainable Development Goal. Environmental SDGs are in green, gender-sensitive
SDGs are in purple, and other SDGs are shown in grey. The lines show how individual projects
are connected to the SDGs at the goal level, with the line color corresponding to the type of SDG.
The thickness of the line represents the number of targets that are mapped from a project tospecific goals, as shown in table 3, so the thicker the line, the more connections there are withindividual targets from one goal. Moreover, the size of the circles show the number of projects
mapped under each SDG—larger circles depict agreater number of projects.

When examining the mapping to targets, target 17.9 (Enhancing capacity for the SDGs) appears the most (50projects). Within that same SDG, target 17.5 (Investment promotion regimes for least developed countries) is
mapped to 25 projects, and target 17.3 (Additional financial resources for developing countries) is mapped to
23. The frequent mapping of projects to SDG 17 targets may be explained by the World Bank’s focus on
capacity building and economic development in member countries. SDG 17 was designed within the 2030
Agenda to provide means of implementation to reach the other SDGs, such as financing, assistance, andcapacity building (UN DESA n.d.).
It is interesting to note that targets 17.3, 17.5, and 17.9, while being representative of the World Bank
objectives of ending extreme poverty and promoting shared prosperity, do not contain topics that may be
more indicative of the type of action in the analyzed projects (such as health, food production, and climatechange). In this regard, the prominence of SDG 17 does not reflect the thematic diversity of the analyzed dataset, which contains actions referring to various sustainable development sectors. Accordingly, it makes sense
to provide additional analysis of the mapping by controlling for SDG 17 targets in an attempt to better reveal
the thematic inclination of the examined subset. This was achieved by removing all the connections to SDG
17 targets and reaggregating the data.
When controlling for the prevalence of SDG 17 in the mapping results, all targets of four SDGs are mapped toat least one project: SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities
and Communities), and SDG 13 (Climate Action). Moreover, five SDGs have all but one of their targets
mapped to at least one project: SDG 1 (No Poverty), SDG 3 (Good Health and Well-Being), SDG 4 (Quality
Education), SDG 7 (Affordable and Clean Energy), and 9 (Industry, Innovation, and Infrastructure).

32

Figure 7: Project Frequency by SDG Target
Goal
SDG 1
SDG 2
SDG 3
SDG 4
SDG 5
SDG 6
SDG 7
SDG 8
SDG 9
SDG 10
SDG 11
SDG 12
SDG 13

SDG 14

SDG 15
SDG 16

Target.1.2.3.4 .5 .a .b

.1

.2

.3

.4 .5 .a .b

.1

.2

.3

.4 .5 .6 .7

.8

.9

.a

.b .c

.1

.2

.3

.4 .5 .6 .7

.a

.b

.c

.1

.2

.3

.4 .5 .6 .a

.b

.c

.1

.2

.3

.4 .5 .6 .a

.b
.1

.2

.3

.a .b

10

.1

.2

.3

.4 .5 .6 .7

15

.8

.9

.10

.a .b

20

.1

.2

.3

.4 .5 .a .b

30.cLegend0
15Number of projects
mapped per target

35

.c
.1

.2

.3

.4 .5 .6 .7

.a

.b

.c

.1

.2

.3

.a

.b

.c

.1

.2

.3

.4 .5 .6 .7

.a
.1

.b
.2

.c
.3

.a .b

.1

.2

.3

.4 .5 .6 .7

.a

.b

.c

.1

.2

.3

.4 .5 .6 .7

.8

.9

.a

.b .c

.1

.2

.3

.4 .5 .6 .7

.8

.9

.10

.a .b

.4 .5 .6 .7

SDG

The respective SDG
has at least one
project mappedto each of its targets

Source: An elaboration by SEI.
Note: SDG = Sustainable Development Goal. This visualization shows the number of projects
mapped per target. In the goal column, the SDGs with all their targets mapped to at least one
project are highlighted in blue. In the target column, the cell color shows the number of projects
mapped to each target. The intensity of the blue colors shows the number of projects mapped pertarget (with darker colors representing higher values), while cells in white mean that there are no
mapped projects to the target. The numbers and letters under the Target column correspond to
the related target to each SDG. The results from SDG 17 were subtracted to produce this data.

33

On the other hand, by controlling for SDG 17, at the target level (figure 8), targets 3.8 (Achieve universal
health coverage), 3.c (Increase health financing), and 2.b (Prevent agricultural market restrictions) are theones that appear the most, with 35, 26, and 23 mapped projects, respectively. Then, targets 2.c (Ensure
proper functioning of food commodity markets) and 6.2 (Access to sanitation) have 21 mapped projects each,followed by targets 3.3 (End communicable diseases), 6.1 (Access to safe drinking water), 9.1 (Develop
resilient infrastructure), and 9.5 (Enhance technical and industrial innovation), with 20 projects each.

Figure 8: Tree map of the 100 Projects Mapped

34

Source: An elaboration by SEI.
Note: SDG = Sustainable Development Goal. The squares represent the SDGs, with the largest
squares per color showing the goals and smaller squares and font size showing the targets. The
size of each square is proportional to the number of projects mapped under the corresponding
goal or target. The results mapped to SDG 17 were removed (controlled for) to produce this data.

35

SECTION 4:
Discussion

36

Section 4. Discussion
As we approach 2030, it is essential to know how different development activities are aligned and contributing
to the SDGs. This is essential to foster private sector financing toward sustainable activities, and the SDGs
are a widely recognized framework that can be used to increase transparency around the impact
of investments.
As section 1 shows, a growing family of methodologies is providing an alternative means of linking activities
or investments to the SDGs. For example, some approaches conceptually map indicators or categories to theSDGs, assigning any activity that uses such an indicator or category to the corresponding SDG or target. This
process is efficient in terms of time and technical capacity. However, since the original indicators andcategories used to group the non-SDG activities are different from the ones in the SDGs, these categories do
not always map one to one with the SDGs and may lead to imprecise mapping; in fact, some categories are
difficult to map to the SDGs because of their thematic differences.
This paper favors approaches that allow for closer inspection of the analyzed activities, mapping them directly
to the SDGs without intermediate categories. However, this can be time and resource-intensive if done
manually, so methods that automatically perform this process are needed. Therefore, the methodology
proposed in this paper employed an automatic detection method based on text mining to map projects to the
SDGs. Boolean queries based on a controlled vocabulary from World Bank projects and taxonomy were used
to mitigate false results from polysemy (see Bordignon 2021).
The results show indicative linkages of a subset of World Bank–financed projects to the SDGs, and a strong
connection to SDG 17 in particular, which may be explained by the World Bank’s focus on capacity building
and economic development in member countries. However, it is important to note that there are limitations
found in the boundaries of the mapping exercise that should be considered when interpreting the results.
The first limitation is that the study analyzes only 100 projects, a subset of the roughly 600 active projects inthe fiscal 2020 World Bank project portfolio. Therefore, the results cannot provide a complete picture of therange, results, and contribution of World Bank development projects and activities to the SDGs. It is worth
mentioning that a broader mapping to include the entire World Bank project portfolio was published in theWorld Bank SDBs and Green Bonds Impact Report covering fiscal 2021 (World Bank 2022), with the Boolean
queries tested against the whole set of World Bank projects active in fiscal 2021 (approximately 700 projects).
The results were manually validated using the process described in section 2. Scaling up the methodology
included adjusting the controlled vocabulary and text mining protocol and provides more accurate results anda more comprehensive picture of the IBRD portfolio of development activities.

37

Moreover, the adjustment of the Boolean queries allowed controlling for SDG 17 (Partnerships for the goals).
Because many of the SDG 17 targets are about financial assistance and capacity building in developing
countries, and therefore relate to most World Bank projects, the following targets were removed from theBoolean queries in the fiscal 2021 analysis: 17.2 (Assistance for least developing countries), 17.3 (Additional
financial resources for developing countries), and 17.9 (Enhancing capacity for the SDGs). The remaining
SDG 17 targets were revised against the vocabulary in the new subset of fiscal 2021 projects to design
Boolean queries that capture specific themes such as investment promotion regimes (17.5), scientific andtechnological development (targets 17.6, 17.7, and 17.8), market regulations (targets 17.10, 17.11, and17.12), policy coherence (targets 17.13, 17.14 and 17.15), public and private partnerships (targets 17.16 and17.17), and monitoring (targets 17.18 and 17.19).
The second limitation of the mapping exercise is that the developed methodology represents one of the manysolutions available for indicative mapping of investments to the SDGs. The constructed text mining protocol issubjective, and the assigning of meaning to the keywords and phrases used in KH Coder was based on theparticular understanding of SDGs and World Bank themes by the team that applied the methodology.
Because the SDGs relate to politically sensitive topics, their meaning and the ways to achieve them can vary(see Mair et al. 2018), and polysemy (see Bordignon 2021) affects understanding, various text mining
protocols corresponding to different understandings of the SDGs are possible. Different mapping
methodologies are likely to result in distinct, and perhaps divergent, mapping results.
The third limitation is the most significant of the proposed SDG mapping approach: the approach does notaccount for the differential and weighted impact that individual projects can have on the SDGs. For instance,
the results show that there are 54 projects mapped to SDG 9 (Industry, Innovation, and Infrastructure), while
only two projects fall under SDG 14 (Life below Water), but no further indicators or information are provided tounderstand the relative impact or contribution of the 54 projects under SDG 9 versus the two projects mapped
under SDG 14. The above limitation is also true for every mapping methodology examined in the literature
review, except for KfW’s approach (Dangelmaier 2019), which shows SDG contributions of KfW’s activities
using financial resources as a weighting factor. However, like all the methodologies, this one also has
limitations, as it does not account for the efficiency of the use of the financial resources deployed. Further,with this approach, the use of financial resources for weighting impact does not account for spillover effects
on different sectors.
This last limitation points toward an interesting research area: the blending of SDG mapping and SDG
interaction methodologies. It is worth mentioning that the latter type of methodologies conforms to a growing
area of research attempting to calculate how SDGs impact one another through synergies and trade-offs in a
specific geography (Bennich, Weitz, and Carlsen 2020). In this regard, the French investment bank Natixis(2018) has already called for the use of causation-based and benchmarking methodologies to model how
development and investment activities impact the SDGs. Natixis also argues that SDG mapping
methodologies must consider location-dependent SDG interlinkages to assess which activities can be themost synergistic in a region.

38This paper argues for another possibility: connecting SDG mapping methodologies with the emerging fieldof SDG interactions. This would facilitate the understanding of how individual development policies
interconnect with the SDGs, showing different levels of impact for different actions. For instance, the degreeof how impactful one project is to the SDGs could be measured based on how many synergistic SDGs the
project is mapped to. On the contrary, if the project is mapped to SDGs producing trade-offs, it could have anegative impact.
Nonetheless, previously generated interaction values must exist to calculate such SDG interactions index forany project. Therefore, the calculation of SDG interaction values in different countries and regions also
requires research. However, the use of mapping approaches in SDG interaction studies would allow for amore general application of SDG interaction methodologies, as it would permit the quick conversion of actions
and policies to the SDGs. Furthermore, linking SDG mapping and SDG interaction methodologies would also
allow the modeling of interlinkages using financial investments, among other indicators. Some authors
already suggest this (for instance, Forouli et al. 2020; Guerrero and Castañeda 2020). The relationshipbetween SDG mapping and interaction methodologies is a topic for further research and discussion.

Conclusion
With the SDGs providing a common reference framework to measure the impact of development activities
toward sustainability, the mapping of investments to the SDGs is critical with less than a decade remaining toachieve the SDGs. Future investments must be made strategically. In this regard, this paper aims tocontribute to the ongoing SDG mapping efforts through the development of a methodology that uses WorldBank projects to observe their indicative linkages to the SDGs and contributes to the World Bank’s efforts topromote sustainable capital markets with a focus on transparency and disclosure. This work involved thecreation of a text mining methodology that was applied to a subset of IBRD-funded projects that were added
to the World Bank SDB project portfolio in fiscal 2020.
To obtain more accurate results, the approach uses Boolean queries based on vocabulary specific to theWorld Bank. As the mapping shows, the use of a controlled vocabulary for the World Bank activities allowed
us to obtain results with a PPV of 75 percent, validated through a random selection of 20 projects that weremanually mapped. In addition, in analyzing the results, the reader should recognize the deep interconnections
between the SDGs and would benefit from using a holistic approach to interpreting results to potentially avoid
any one-sided analysis.
The authors recognize three main limitations of the experiment and their methodology. Firstly, the mapping
exercise analyzes only 100 projects, a small subset of the roughly 600 projects in the World Bank’s fiscal
2020 project portfolio. Therefore, the results cannot provide a complete picture of the linkages of World Bank
development projects and activities to the SDGs. Yet, to scale up the approach described in this paper, the39methodology has been applied to map the entire World Bank project portfolio active in fiscal 2021, offering amore comprehensive picture of World Bank development activities. The new analysis also allows foradjustments in the controlled vocabulary and the resulting text mining protocol as well as control for theprevalence of SDG 17, due to the Bank’s focus on capacity building and economic development, producing
more accurate results. Secondly, the developed methodology is subjective, and different mapping
methodologies could produce divergent results. And thirdly, and most importantly, the methodology does not
calculate the different weighted impacts that individual development actions can have on the SDGs. This is an
important caveat to this paper’s approach that is also present in the other mapping methodologies identified in
the literature review.
In this regard, the weighting of the impact of individual actions is perhaps the most important research
opportunity in the continuing development of SDG mapping methodologies. This paper proposes connecting
SDG mapping approaches with SDG interaction methodologies to address this gap. The complementation
between SDG mapping and SDG interaction methodologies could allow for the calculation of SDG interaction
indexes for individual projects while facilitating the conversion of non-SDG activities into SDGs, which would
simplify the application of SDG interaction methodologies. This endeavor could facilitate the implementationof the SDGs globally and accelerate their achievement through the strategic allocation of investments in theremaining years of the 2030 Agenda.
Finally, the proposed methodology may be adapted in the broader sustainable finance space if scaled up andfurther developed, through artificial intelligence (such as, machine learning, NLP, and sentiment analysis), forexample, to support the growing requirements for disclosures of investments and risks related to sustainablefinance and climate. As new regulations and taxonomies emerge at the national and regional levels for setting
definitions and standards around sustainable activities, capital markets participants will be increasingly
subject to reporting on the alignment of their investments to these emerging frameworks. To comply withthese requirements, investors will need to undertake extensive analysis of information and large data sets thatwill benefit from tools that can automate the process. Text mining approaches such as the one presented inthis paper, which allows for the connection of activities and investments to specific topics (keywords), may bescaled up and adapted to assess the alignment of investments to sustainable finance targets to support netzero trajectories and activities that contribute to social good.

40

Appendixes
Appendix 1. Full Results of the Mapping Exercise, Project to Target
The following table shows the full mapping results of the 100 projects from fiscal 2020 at the SDG target level.
Each row corresponds to the World Bank project number in the far-left column. The SDG columns show theconnections between projects and SDG targets: a value of “0” indicates no mapping, whereas a value of “1”indicates that the project is mapped to the corresponding target. The final column shows the total number ofmapped targets for each project.

41

42

43

44

Appendix 2. Full Results of the Mapping Exercise, Project to Goal
The following table shows the full mapping results of the 100 projects from fiscal 2020 at the goal level. Eachrow corresponds to the World Bank project number in the far-left column. The SDG columns represent the
number of times each project referred to the goal. The darkness of the color corresponds to the number oftargets mapped under that goal, so the darker the tone, the greater number of SDG targets mapped tothe project.

45

Appendix 3.1. Validation Exercise
The following table shows the results from the 20 fiscal 2020 projects chosen for the validation exercise. Eachrow corresponds to the World Bank project number in the far-left column, while the SDG columns display thecorrespondence between the automatic and manual mappings according to these values: true positives (TP)(the SDG targets that were mapped in both the manual and automatic mappings); true negatives (TN) (whenno mapping was obtained both in the automatic and manual exercises); false positives (FP) (the mapping
results that were obtained in the automatic mapping but not in the manual mapping); and false negatives (FN)(the mapping results that were obtained in the manual mapping but not in the automatic mapping). The colors
help differentiate these values: TP are shown in light blue; TN, in dark blue; FP, in light red; FN, in dark red.

46

Appendix 3.2. Validation Exercise
The following table shows the World Bank project number in the far-left column, and the columns show thetotal sum of true positives (TP) (the SDG targets that were mapped in both the manual and automatic
mappings); true negatives (TN) (when no mapping was obtained both in the automatic and manual
exercises); false positives (FP) (the mapping results that were obtained in the automatic mapping but not in
the manual mapping); and false negatives (FN) (the mapping results that were obtained in the manual
mapping but not in the automatic mapping), and PPV values for each project. The PPV is obtained by dividing
the number of TP by the TP plus FP (see Chu 1999; Saito and Rehmsmeier 2015). The bottom row depicts
the corresponding values for the 20 projects used in the validation exercise.

47

Appendix 4. Results Graph Network: Project to Goal, Full Results
This network graph shows the connection between the projects and the SDGs visually. The thickness of theconnections shows how many targets from a specific SDG are connected to one project. The thicker the line,
the more targets connected to a project. The size of the circle represents how many projects are mapped toeach SDG: the larger the circle, the greater the number of projects mapped to the SDG. The graph shows thefull results, including SDG 17.

48

Appendix 5. Results Graph Network: Project to Goal, Results without SDG 17This network graph shows the connection between the projects and the SDGs visually. The thickness of theconnections shows how many targets from a specific SDG are connected to one project. The thicker the line,
the more targets connected to a project. The size of the circle represents how many projects are mapped toeach SDG: the larger the circle, the greater the number of projects mapped to the SDG. The graph shows theresults excluding SDG 17.

49

Endnotes
1 This means asking only whether a keyword occurs or does not occur in the document. The number of times

a word occurs is not part of the model, and neither is any measure of what words are adjacent to the keyword.
2 To be added to the project portfolio, a project must have begun disbursement of funds in fiscal 2020.
3 Henceforth, “themes” refers exclusively to the World Bank taxonomy.
4 This paper uses “categories” to refer to the many taxonomies and subdivisions employed by institutions toclassify their activities by sustainable development sectors and topics, such as the World Bank taxonomy(World Bank 2016).

50

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Using Automated Text Mining
to Align Investments with the
Sustainable Development Goals:
A Case Study Analyzing
World Bank Projects
Authors: Efraim Hernández-Orozco (SEI),
Mario Cárdenas-Vélez (SEI), Colleen Keenan(World Bank), Zoe Russo (World Bank)