Innovation for improving climate-related data—Lessons learned from setting up a data hub

Abstract

In this article, we present a framework to assess the challenges in the climate-related data landscape. From our perspective, we describe challenges and opportunities for innovation to close existing gaps in selected data quality dimensions for central banks and statistical offices. We further examine the role of networks in promoting innovation and international collaboration, highlighting practical experiences with a case study of the Sustainable Finance Data Hub at the Deutsche Bundesbank. Finally, we discuss how these lessons can provide avenues to enhance data quality in central banks and official statistics and outline directions for future research.

Full text

ORIGINALVERÖFFENTLICHUNG
https://doi.org/10.1007/s11943-023-00326-w
AStA Wirtschafts- und Sozialstatistisches Archiv
Innovation for improving climate-related data—Lessons
learned from setting up a data hub
Hendrik Christian Doll · Gabriela Alves Werb
Received: 31 March 2023 / Accepted: 16 August 2023
© The Author(s) 2023
Abstract In this article, we present a framework to assess the challenges in the
climate-related data landscape. From our perspective, we describe challenges and
opportunities for innovation to close existing gaps in selected data quality dimen-
sions for central banks and statistical offices. We further examine the role of networks
in promoting innovation and international collaboration, highlighting practical ex-
periences with a case study of the Sustainable Finance Data Hub at the Deutsche
Bundesbank. Finally, we discuss how these lessons can provide avenues to enhance
data quality in central banks and official statistics and outline directions for future
research.
Keywords Climate risks · Climate-related data · Data gaps · Hub and spoke ·
Sustainable finance · Data integration
JEL-Classification C81, E59, Q38
1 Introduction
As climate change grows into one of the most urgent and complex challenges of
our time, central banks increasingly rely on climate-related data to support decision-
All views expressed in this work are personal views of the authors and do not necessarily reflect the
views of the Deutsche Bundesbank or the Eurosystem.
Hendrik Christian Doll
Sustainable Finance Data Hub, Deutsche Bundesbank, Frankfurt, Germany
E-Mail: hendrik.doll@bundesbank.de
Gabriela Alves Werb
Data Service Centre, Deutsche Bundesbank, Frankfurt, Germany
Frankfurt University of Applied Sciences, Frankfurt, Germany
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H. C. Doll, G. Alves Werb
making across their core functions (Campiglio et al. 2018). Central banks use data
on these risks to support climate stress tests and disclosures, analyses for banking
supervision, financial stability, monetary policy, and statistical purposes (Deutsche
Bundesbank 2022b; ECB 2022a, b, 2023;FED2021).
Similarly, statistical offices face the growing need to provide climate-related data
and indicators. More than 60 heads of statistical offices recently defined a set of
climate-related indicators to be established at the national level (UNECE 2021a).
The United Nations has also promoted similar initiatives to support national statisti-
cal offices in this undertaking (United Nations Economic and Social Council 2022).
These data are instrumental in producing a comprehensive and internationally com-
parable set of climate-related indicators and statistics to support policy decisions,
foster public discourse, and monitor progress toward fulfilling the Sustainable De-
velopment Goals (SDG) or the goals of the Paris Agreement (Destatis 2022; Eurostat
2021).
Consequently, central banks and statistical offices typically require granular data
on physical and transition risks to support climate-related analyses and to develop
indicators. Transition risks relate to the uncertainty in the shift to a less carbon-
intensive economy, which requires societies to reshape their production methods
and reduce their reliance on carbon-based assets. Physical risks arise from adverse
climate events and extreme weather, such as floods, storms, wildfires, or droughts,
as well as disruptions in ecosystems related to climate change.
Despite the undisputed importance of data for assessing climate risks, there
are still substantial gaps in their availability, reliability, and comparability (NGFS
2022b). In the absence of unified sources for high-quality data, the current climate-
related data landscape is characterized by several fragmented sources, including
administrative, commercial, and publicly available data in different frequencies, for-
mats, and units of observation. Each source alone provides a specific subset of
measurements and observations with somewhat limited coverage. Therefore, har-
monizing these data to leverage them jointly is one of the crucial challenges ahead.
The path forward requires improvements in data collection efforts, which initia-
tives and legislation have supported in several jurisdictions. In Europe, the Corporate
Sustainability Reporting Directive (CSRD) aims to expand the universe of reporting
firms and increase the comparability of climate-related information disclosed at the
firm level (CSRD 2021). However, the CSRD should only take full effect in 2027,
when small and medium enterprises must also report relative to the previous financial
year. Furthermore, the Regulation (EU) 2019/2088, also known as the Sustainable
Finance Disclosure Regulation (SFDR), and the revision of Pillar 3 disclosures to
include climate-related risks (EBA 2022) are expected to improve the quality and
structure of disclosed climate-related information in the financial services sector.
There is also an increasing number of joint initiatives from several international
institutions. One example is the effort towards a new Data Gaps Initiative (DGI)
developed by the Inter-Agency Group on Economic and Financial Statistics (IAG)
in joint work with the Financial Stability Board (FSB). In this high-level work plan,
climate-related data are one of the four priority areas (IMF 2022). Nevertheless,
the recommendations are expected to be implemented within five years of the work
plan’s launch, so observing their first effects will take a few years. In the meantime,
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
given the urgency with which climate risks may materialize, there is a growing need
for alternative pathways to address these gaps (IPCC, 2022).
In this paper, we outline a framework to assess the current challenges to incor-
porating climate-related data into central banks’ and statistical offices’ decision-
making and discuss how these create opportunities for innovation. We describe how
to improve the quality of these data by leveraging novel data sources and methods to
enhance their coverage, timeliness, accessibility, reliability, and integration. Then,
we examine the role of collaboration networks in advancing the climate-related data
landscape, considering different cooperation models and highlighting the experi-
ences of the Sustainable Finance Data Hub at the Deutsche Bundesbank. Finally,
we discuss how these lessons generalize, show avenues to enhance data quality in
official statistics, and outline directions for future research.
2 Current challenges in the climate-related data landscape
The current gaps in the climate-related data landscape present several challenges for
central banks and statistical offices to base their analyses, disclosures, and decisions
on reproducible and reliable information (NGFS 2022b). Following a user-centric
approach (Wang and Strong 1996), we present a framework to classify and assess
these challenges across several dimensions. By doing so, we aim to capture the main
aspects of data quality from the perspective of consumers of climate-related data
in central banks and statistical offices. We link current challenges to the FAIR data
principles, i.e., findability, accessibility, inter-operability, and re-usability (Wilkinson
et al. 2016).
We start by discussing the typical shortcomings of relying solely on administra-
tive and commercial data. While leveraging alternative sources for structured and
unstructured data can contribute to addressing them, doing so brings another set
of challenges, which we examine subsequently. Figure 1provides an integrated
overview of the current challenges associated with the climate-related data land-
scape.
Challenges in the
Climate-Related
Data Landscape
TIMELINESS
Available me series are
short, available with delays
and low granularity
RELIABILITY
Data may be inaccurate,
incomplete, or non-
auditable
ACCESSIBILITY
Data have usage
and sharing
restricons
INTEGRATION
Data are dispersed
across several
sources
VARIETY
Lack of unified data
structures and
methodologies
COVERAGE
Available universe
oen covers only
the largest players
Fig. 1 Proposed Framework to Classify and Assess Challenges in the Current Climate-Related Data
Lanscape
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H. C. Doll, G. Alves Werb
2.1 Data coverage and timeliness of information remain a challenge
Data coverage limitations are one of the most salient barriers to comprehensive cli-
mate-related analyses. Administrative data only cover limited sets of indicators and
firms, as policymakers must often make compromises when attempting to reduce the
reporting burden for the affected stakeholders. Similarly, other policy-related data
sources, such as the European Union Emissions Trading System (EU ETS), only
cover firms in specific sectors, such as aviation and selected energy-intensive man-
ufacturing sectors. Novel reporting frameworks such as the CSRD in Europe should
substantially expand the number of reporting firms in selected jurisdictions. How-
ever, the resulting universe will still mainly comprise the largest firms, as measured
by balance sheet totals, net sales, and the number of employees.
Against this scenario, it is not uncommon for central banks to source proprietary
data from commercial providers. For example, the European System of Central
Banks (ESCB) recently announced the joint procurement of climate-related data
from multiple providers, equipping member central banks with broader indicators
for assessing physical and transition risks (Deutsche Bundesbank 2022a). In official
statistics, a similar need is increasingly recognized for incorporating proprietary data
(e.g., Eurostat 2022;UN2017). Nevertheless, proprietary data from commercial
providers also have substantial coverage drawbacks, as they typically only focus on
large corporations in Europe and North America. Smaller firms that are regionally
strong or even internationally highly successful and might therefore be systemically
relevant for climate-related analyses are typically not represented in the universe
provided by these data.
Furthermore, additional issues characterize the market for proprietary data, such
as long-run dependencies, the potential of increasing prices due to monopolies, and
a lack of published methodological and quality frameworks (Eurostat 2022). When
relying on climate-related data from proprietary sources, these issues are to an
extent mitigated by competition among data providers with similar offers regarding
transition and physical risk data. Also the multi-source approach is an attempt to
reduce over-reliance on any one source (Deutsche Bundesbank 2022a).
As the reporting frameworks for climate-related data are relatively new, only
a few firms disclosed a narrow set of indicators on an infrequent basis in the past
decades before being legally required to do so. The essential balance between the
data needs and the reporting burden for affected firms also culminated in reduced
data granularity and reporting frequency in administrative data. Consequently, the
available time series are still relatively sparse and short for most firms, sectors,
and indicators. When aiming for FAIR data, these issues represent a lack of (re-
)usability, especially taken together with changing disclosure formats.
Furthermore, firms typically only report climate-related data yearly for the pre-
vious financial year, in a process that may take several months after the respective
financial year-end. Consequently, data timeliness is also a significant concern, as
there may be a substantial lag between when firms or policymakers take a given
action and the time in which the most current information about the respective
indicators is available. In practice, this temporal mismatch prevents the timely in-
corporation of recent developments into decision-making. As the effects of interest
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
might also have complex dynamics, aggregating the data at the year level potentially
further diminishes their analytical potential.
Because commercial data providers also rely to some extent on voluntary disclo-
sures from firms, they also suffer from this shortcoming. As these providers also
require time to process the information contained in the disclosures before incor-
porating it into their proprietary data products, in their case, timeliness is typically
an even more prominent issue. It is, therefore, not uncommon for the most current
proprietary data to have a gap of two years or more.
2.2 Climate data come in a variety of formats and underlie restricted access
Another barrier central banks and statistical offices face when handling adminis-
trative data is that these often underlie a strict access control regime within the
institution. Depending on the legal basis for obtaining the data from the report-
ing parties, data access might be restricted to the department responsible for the
assessment or supervision, which precludes, for example, data reuse for statisti-
cal purposes. Furthermore, indicators of interest or their proxies may be dispersed
across multiple reporting frameworks and institutions. In this case, institutions face
even more significant legal, organizational, and technical obstacles to accessing and
sharing these data.
While commercial data typically do not underlie these same constraints, the
licensing agreement terms with the commercial data provider set the boundaries for
using and sharing them. The license may refer to a user, department, or institution
and typically does not allow sharing of the data partly or in its entirety with third
parties, even if in a summarized or aggregated manner. These access restrictions exist
despite the often publicly available nature of climate-related data at their original
source and can potentially hinder data accessibility, which is also a relevant aspect
in the FAIR data framework.
For example, the Research Data and Service Centre of Deutsche Bundesbank
provides access to Bundesbank microdata for internal and external researchers. Cur-
rently, administrative microdata is available for banks, securities, firms, and house-
holds. Linking these data with the commercial climate-related data that is internally
available would provide a great analytical potential. However, this possibility cannot
currently be extended to external researchers due to the aforementioned licensing
restrictions.
Furthermore, the long-standing lack of unified standards aligned with the method-
ological and conceptual differences in recent reporting frameworksleads to relatively
low comparability of climate-related data across different sources. One notable ex-
ample is the case of Scope 3 greenhouse gas emissions, which denote the emissions
measured throughout the firm’s value chain except those originating from the firm’s
assets or energy consumption. They include upstream and downstream activities,
such as those associated with the product’s supply chain, usage, and end-of-life
treatment (Greenhouse Gas Protocol 2011).
As Ducoulombier (2021) notes, existing reporting practices do not support cross-
firm comparisons. First, each firm may rely on a different mixture of primary and
secondary data to estimate the emissions in its value chain. Second, the allocation
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H. C. Doll, G. Alves Werb
of the emissions across the different suppliers, inputs, and outputs will invariably
follow different assumptions and methodologies, rendering the resulting estimates
hardly comparable. Third, firms may change their data sources, assumptions, or
methodologies across reporting periods, impairing comparisons for the same firm
over time. Taken together, these shortcomings represent the often prevalent lack of
inter-operability in the climate-related data domain, an aspect the FAIR framework
strives for.
This issue becomes more pronounced in the case of commercial data because
commercial data providers often rely on proprietary algorithms to estimate missing
data points and calculate aggregated scores. However, they typically do not disclose
the exact variables and weights driving these calculations. Empirical data exhibit
substantial variation in the climate-related indicators from different providers for
the same firm-year pairs, suggesting that each data provider relies on a different,
undisclosed set of assumptions and methodologies. These characteristics obstruct
the usage of these data for reproducible analyses and research. Finally, the variety
of formats and sources and the prevalence of restrict access regimes often render
the FAIR dimension of findability difficult to apply for climate-related data.
2.3 In the absence of unified data, integration and reliability are essential
There is a demand for promoting mutual understanding and transparency in the
assumptions and methodological decisions in the current variety of reported and
estimated data. Despite the international efforts in different jurisdictions, such as
the European Taxonomy Regulation (Regulation (EU) 2020/852) or the steps from
the International Sustainability Standards Board towards establishing global stan-
dards (IFRS 2022), more dialogue is required to break silos across countries and
disciplines.
However, it is noteworthy that such processes are typically lengthy. One example
is the international discussion about general data quality standards, which started
decades ago with several supporting countries. Despite all efforts, the ISO 8000
standard was only published more than 20 years later and remains a work in progress
with many remaining disputes (Cai and Zhu 2015).
Luckily, we may tackle some of the challenges currently associated with ad-
ministrative and commercial climate-related data by relying on the vast amount of
information available from alternative data sources. These may include documents
and reports from firms, press releases, spreadsheets with crowdsourced data, user
search behavior, user-generated content, or satellite images, among many others.
Nevertheless, due to the wide variety of these data, which often include several data
types and complex data structures, leveraging them brings additional obstacles.
For example, unstructured data require advanced data extraction, processing, and
modeling methods. Furthermore, handling the complexity of such varied data in-
volves orchestrating a portfolio of structured, semi-structured, and unstructured data
products from different sources. This paradigm requires institutions to move away
from data silos and establish appropriate data lakes, supporting a richer constellation
of data types and structures. However, central banks and statistical offices must use
a proper data governance framework to accompany this process. Otherwise, data
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
lakes can quickly become “data swamps” (Hai et al. 2016), and data will not be
usable.
Furthermore, tackling the difficulties in integrating data from a potentially large
number of sources requires a suitable metadata management concept, which provides
the necessary context and semantic information that allows users to establish con-
nections between the different data sources, types, and units of observation. These
aspects are especially crucial in this context due to the lack of unique identifiers to
integrate the data across all sources.
Finally, while leveraging several data sources should deliver complementary in-
formation and foster a more comprehensive perspective of climate-related risks,
doing so also presents the risk of facing multiple instances of inaccurate or incom-
plete data. Central banks and statistical offices must therefore develop methodologies
to handle situations of conflicting information, which may rely on algorithms that
assess the reliability of individual sources and establish a consensus mechanism.
Such a consensus-driven approach is especially relevant in this context, given that
the data, in many cases, might be non-verifiable or non-auditable by third parties.
Furthermore, this approach fosters the FAIR dimensions of data inter-operability
and re-usability.
These numerous obstacles in extracting, integrating, and sharing climate-related
data represent an unparalleled chance to foster innovative approaches. In the next
section, we provide an overview of how these challenges present opportunities for
interdisciplinary innovation. Furthermore, we discuss the potential of alternative data
sources and novel methods to close gaps in selected dimensions of climate-related
data quality.
3 A roadmap for innovation in climate-related data
The outlined challenges for climate-related data map out areas with crucial need for
innovation. In this section, we describe promising avenues to increase data coverage
and improve data access and integration by relying on unstructured data, alternative
data sources, and machine learning methods. Doing so supports FAIR climate-related
data, particularly in the dimensions accessibility, inter-operability and (re-)usability.
3.1 Increasing data coverage and timeliness by leveraging unstructured data
One promising avenue to improve the coverage of climate-related data is to draw on
publicly available data, which may come from several sources, such as firms, (in-
ter)governmental agencies, civil society organizations, or individual citizens. These
data have the advantage of being easily accessible and not being subject to usage
restrictions, which enables interested stakeholders to acquire, merge, and share them.
For example, many firms disclose information about the exposure of their assets to
climate risks, the environmental impact of their activities, and their efforts to reduce
their environmental footprint. Nevertheless, this information is typically unstructured
and dispersed across multiple sources, such as annual, integrated, or sustainability
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H. C. Doll, G. Alves Werb
reports, investor presentations, dedicated websites, press releases, or social media
(Alves Werb and Doll 2023).
Often, these sources contain data in multiple modalities. For example, sustain-
ability reports typically combine the information in text, tables, and images con-
taining charts in a single document. Because they potentially carry additional or
complementary information, it would be beneficial to acquire all of them. However,
automatically identifying and extracting these different modalities in the reports is
challenging because they are typically not disclosed in a machine-readable format.
Instead, most firm disclosures are in PDF format, following different layouts (e.g.,
single, double, or multi-column).
Furthermore, contrary to other types of annual firm disclosures, sustainability
reports have no standard document sections. Consequently, there is substantial vari-
ation in the information presented and its sequence, both within sustainability re-
ports of a single firm and across reports of different firms. These aspects represent
obstacles to adopting a standard approach to extract each data format from an ex-
tensive collection of sustainability reports. When handling similar use cases, we
have observed that open-source or proprietary solutions that deliver good results for
standardized documents typically do not perform well or do so only for a limited
subset of reports.
Possible solutions include training a new supervised model to identify and extract
text, tables, and charts from sustainability reports. However, this solution requires
large volumes of annotated data, which is, in practice, a resource-intensive undertak-
ing. Another alternative is to leverage the power of transfer learning by relying on
pre-trained models. In the domain of deep learning, transformer-based architectures
offer state-of-the-art models to extract one or more modalities and fine-tune them
to the particular use case of sustainability reports. One example is to fine-tune pre-
trained transformer models to identify topics in the text contained in sustainability
reports.
However, instead of separately addressing each modality, we foresee a promising
avenue in jointly extracting and leveraging the multiple data modalities embedded in
sustainability reports, in a process known as multimodal deep learning. Such models
can harness the potential of several modalities and are at the core of the current
developments in deep learning. Consequently, we argue that there is a high, yet
unexplored potential, to apply them in the context of climate-related data, particularly
with sustainability reports.
3.2 Improving data accessibility with alternative data
In recent years, an increasing body of literature in economics has drawn on al-
ternative data to improve inflation, economic activity, labor market outcomes, or
financial risk models (Ber˛esewicz 2017; Froidevaux et al. 2022; Kapetanios and
Papailias 2018). In this context, alternative data refers to data originally collected
by institutions or individuals for other purposes than that of the analysis, such as
credit card spending, search engine queries, or webscraped price data from online
shops. Several studies rely on combining traditional statistics with alternative data
to fill the gaps in series with a lower frequency, such as yearly or quarterly.
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
Central banks and statistical offices also followed this movement and have pro-
gressively incorporated alternative data in their research and analyses (Chen et al.
2022). As doing so results in a challenge for traditional statistical methods, these
applications typically rely on non-parametric machine learning methods as a robust
alternative to jointly leveraging these data.
Nevertheless, central banks often rely on aggregated risk indicators from commer-
cial providers in their analyses involving physical and transition risks. One possible
reason is that such indicators consolidate several dimensions and abstract from a few
methodological challenges while typically providing identifiers that facilitate the in-
tegration with administrative data. However, these data often preclude a comprehen-
sive analysis of the different climate-related aspects and their possible interplay with
the economic variables or units of observation of interest. Furthermore, due to their
proprietary nature, they are typically subject to strict usage and sharing restrictions.
In the context of climate-related data, alternative data for central banks and sta-
tistical offices may include structured ocean, weather, and atmospheric data that are
frequently publicly available from environmental, space, and other local and inter-
governmental agencies at a jurisdictional or geographic level. Meteorologists and
climate scientists use these data extensively for weather forecasting and complex
climate modeling. Given their substantial analytical value, we expect that leveraging
them will enable more robust models and provide novel insights. Potential use cases
include estimates of asset or property insurance coverage and their potential gaps,
estimates of the impact of extreme weather on labor supply and productivity, and
forecasts of government spending for disaster response, among several others.
Furthermore, numerous international efforts, such as those spearheaded by the
European Space Agency (ESA) or the National Aeronautics and Space Adminis-
tration (NASA), produce an increasing volume of geospatial data, such as satellite
imagery and remote sensing data from specialized instruments. These data are avail-
able in an open-source format and represent a substantial breakthrough in computing
indicators for changes in vegetation, water bodies, erosion, or desertification, as well
as in detecting expansion and potential vulnerabilities in agricultural or urban areas.
In addition, they enable assessments of the potential or actual impact of natural
hazards, such as wildfires, floods, or storms.
The solutions from international collaborations keep growing with new data prod-
ucts, such as the Multi-Mission Algorithm and Analysis Platform (MAAP) for
biomass, which integrates data from ESA and NASA missions (Albinet et al. 2019).
In contrast to administrative or commercial data, these platforms offer invaluable
resources that central banks and statistical offices can leverage, integrate, and share
with little to no confidentiality restrictions.
Furthermore, anonymized location-based data, such as mobile phone data, can
provide real-time and more accurate information about mobility choices in each
administrative regionand provide a valuable resource for studying commuting effects
(Daas et al. 2011). It is also possible to use it to enrich the existing data to estimate
the transition risk due to urban car dependency. These data have a much higher
frequency than vehicle registrations and are less costly to obtain than data about
individual mobility preferences, which typically come from surveys.
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H. C. Doll, G. Alves Werb
Similarly, we identify a unique window of opportunity to reuse administrative,
proprietary, and crowdsourced data in novel ways to increase our understanding
of climate-related risks. There are many use cases for improving their usability to
enrich existing data sources with additional information on the exposure of assets,
firms, or administrative regions to transition and physical risks.
However, we must address the methodological and integration challenges to ex-
plore this avenue. Given the multiple data sources, remote sensing parameters, in-
struments, and resolutions in which the data are available, it is essential to under-
stand how these aspects and their interplay impact the subsequent inferences (e.g.,
Rashkovetsky et al. 2021; Tavakkoli Piralilou et al. 2022). There has also been an
active discussion in the economics community on how to deal with methodological
challenges. One example is the advance in accounting for the role of extreme events
when incorporating granular ocean, weather, and atmospheric data into economic
models (Auffhammer 2018;BIS2021; Pindyck 2017).
Alternative data also require a solid understanding of the phenomena in their
generating process. While efficient metadata supports users in understanding a few
data properties, these schemas cannot represent many essential considerations and
specific domain knowledge. For example, the data may follow non-traditional sam-
pling or be influenced by other non-trivial factors. Addressing these challenges is
crucial to improve alternative data’s usability and unlock their promising analytical
potential.
To do so, we require close collaboration with the disciplines that study these
phenomena to understand the data’s representativeness and what inferences they
allow. In addition, there are increasing efforts to leverage machine-learning methods
for data integration and knowledge representation with encouraging results. We
discuss these aspects in detail in the next section.
3.3 Enhancing data integration and reliability with machine learning
In the previous sections, we built upon the vast potential of unstructured data and
alternative data sources to discuss avenues to improve selected quality dimensions
in climate-related data. However, as the number of explored data sources, formats,
units of observation, and frequencies grows, it becomes increasingly difficult to
integrate and jointly leverage these data.
One of the first steps is to conceptualize and implement a software solution to
orchestrate the data, document its lineage, and make it available for subsequent anal-
yses. To do so, we also require a methodology to integrate the data models, which
may include establishing relationships among the multiple measurement units, fre-
quencies, and units of observation present in all data sources. For example, data may
refer to a project, an industrial facility, a firm’s activities in a particular geographic
area, or a geographic coordinate. Consequently, if we are to use these different as-
pects to measure a firm’s exposure to climate-related risks, it is crucial to define
how to represent them jointly.
The next challenge is record linkage, that is, to identify the same entity or a group
of related entities (e.g., ownership structures, branches, or subsidiaries) across mul-
tiple data sources. In the field of information retrieval, researchers also refer to this
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
problem as entity resolution. The absence of unique common identifiers to do so
is a frequent problem faced by central banks and statistical offices when handling
multiple sources of survey and administrative data. Historically, they have tackled
this problem with a mixture of deterministic and probabilistic approaches (Bakker
et al. 2014; Gessendorfer et al. 2018; Schnell 2010). However, there has been in-
creased interest in machine learning methods to address data quality issues in official
statistics (UNECE 2021b).
Existing literature suggests that supervised learning methods applied to record
linkage provide superior results than those from unsupervised methods, such as
K-means (e.g., Christen 2012). Recent advances include combining graph convolu-
tional networks and siamese networks to leverage the relationships and contextual
information in knowledge graphs (Krivosheev et al. 2021). Nevertheless, supervised
approaches require a substantial volume of high-quality training data, which might
be difficult and too expensive to obtain in practice, as the data distribution is highly
unbalanced towards negative (non-matches) pairs.
In the past years, there have been many attempts to circumvent this issue, most of
which relying on neural networks (Barlaug and Gulla 2021). For example, Ebraheem
et al. (2018) use pre-trained word embeddings to train recurrent neural networks that
require less training data, whereas Kasai et al. (2019) propose a mixture of transfer
learning and active learning. In their approach, the neural network learns from
similar domains with large volumes of labeled data. Then, it relies on user feedback
for a limited number of informative samples to fine-tune the transferred model to
the field of interest.
On the other hand, Wu et al. (2020) propose an unsupervised method based
on Gaussian Mixture Models that reaches performance levels comparable to some
supervised approaches. We sustain that applications for climate-related data should
build on these recent developments, as labeled training data is typically unavailable
for applications in this domain. Another advantage is that their implementations are
typically easily accessible with publicly available source codes and documentation
in dedicated libraries (e.g., Hosseini et al. 2020).
Furthermore, we argue that novel machine learning applications in this context
should increasingly focus on the fusion of several data sources and modalities (e.g.,
sensor, satellite images, textual, or tabular) to deliver more robust inferences and
increase data reliability. This paradigm shift should enable us to take full advantage
of the extensive landscape of climate-related data available. One example would
be to jointly leverage historical tabular data on local flood incidence and severity,
current weather data, textual descriptions of a firm’s reforestation efforts, and satellite
images to monitor the risk of floods in a firm’s primary production location.
In the context of multimodal learning, neural networks became popular for their
performance and suitability for multiple modeling purposes (Baltrušaitis et al. 2018).
For example, it is possible to train the network to perform a specific task, such as
predicting the risk of floods. Alternatively, we may rely on an unsupervised approach,
such as with an autoencoder architecture, to provide an abstract data representation.
The resulting model can later be used to find similar observations using a siamese
architecture or as a first step to fine-tune the result for a specific prediction task.
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H. C. Doll, G. Alves Werb
More recent research has also shed light on the effect of linkage errors on the
results of subsequent statistical analyses based on the matched data (Chambers and
DinizdaSilva2020; Han and Lahiri 2019). Particularly relevant are scenarios in
which the probability of obtaining a match correlates with an unobserved variable
that is potentially associated with the variables of interest.
Given the typically unknown sampling and complexity of many alternative data
sources (Ber˛esewicz 2017) and potential biases in the representativeness of the ad-
ministrative and commercial data (Meyer and Mittag 2021), the interplay between
machine learning and data quality remains an active field for further research in
several disciplines (Ilyas and Rekatsinas 2022). Consequently, we argue that major
developments in this research frontier will require intensive collaboration across in-
terdisciplinary research communities and institutions. In the next section, we present
a framework for developing a collaborative climate-related data network.
4 Collaborating within and across organizations
A large body of literature investigates innovation in organizations, which has re-
mained a topic of high interest to management and strategy researchers over the
decades. Previous research indicates that corporate culture, technology, and the ex-
ternal market environment are important determinants of organizations’ innovation
paths and success (e.g., Kitchell 1995; Tellis et al. 2009). However, Damanpour
(1996) finds in a meta-analysis with more than 20 studies that their effect depends
significantly on how we define and measure these constructs.
One topic of particular interest is how collaboration networks can foster inno-
vation within and across organizations. While the importance of networks is in-
disputable, there is much debate about their optimal configuration. Baum et al.
(2010) find that heterogeneity and complementarity in the knowledge background
of the network members promote a more productive network. Other studies suggest
that networks should avoid geographic concentration in a single country and foster
“structural holes” that avoid static ego networks and allow for exchange with novel
partners (e.g., Kumar and Zaheer 2018).
In our domain of interest, central banks have a track record of decades of in-
ternational collaboration with well-established global networks. Furthermore, given
their dual role in generating and consuming climate-related data, they have a great
incentive to drive innovation to improve its quality and availability (Artman et al.
2023; Rosolia et al. 2021). However, because of this endeavor’s novel challenges,
it is necessary to intensify global collaboration among central banks and extend it
to other stakeholders involved in climate-related data, such as academic institutions,
non-governmental organizations, and commercial data providers (IFC 2021; NGFS
2022b).
Because tackling the challenges with climate-related data requires joining efforts
from several disciplines and perspectives, there is a promising potential for co-
innovation among central banks, academic, and industry partners (NGFS 2020). This
scenario yields a complex landscape where many central banks strive to align with
each other and other partners. Nevertheless, similar cases of successful international
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
collaborations between the industry and academic institutions in other disciplines
can provide valuable insights and best practices for central banks’ efforts (Owen-
Smith et al. 2002; Rybnicek and Königsgruber 2019). Many central banks also
keep close academic ties for joint research, which they can further extend for other
projects.
In a similar context, a recent survey from the World Bank about experiences
of international innovation activities in banking supervision examined different or-
ganizational setups for combining the expertise of supervisors and data scientists
(World Bank 2021). As banking supervision standards are often developed glob-
ally—e.g., with the Basel Committee on Banking Supervision and the Financial
Stability Board—and national banking supervisors tend to be closely interweaved,
particularly within Europe’s Single Supervisory Mechanism (SSM), this domain
offers incentives to collaborate internationally. The survey identified three main
prevalent approaches to organizing such innovation units.
One approach is a centralized data science or innovation unit, where all data
scientists work together in the same department to innovate and improve analytics
across the organization. This model is also known as a “center of excellence”.
While this configuration enables dedicated teams to focus on selected innovation
initiatives, the independence from business units may limit the contact with users
and their demands.
An alternative is decentralizing data science teams and placing them across dif-
ferent business areas. The goal is to promote collaboration between business experts
and data scientists for a strong user focus. However, this setup poses challenges for
innovation flows, as it may foster silos between departments, reducing the opportu-
nity for knowledge sharing.
Finally, another possibility is the hub and spoke model, which combines elements
of centralized and decentralized teams. A centralized business unit acts as a hub
coordinating with experts in all business areas in this setup. The goal is to enable
information flows across the different teams. The hub and spoke model can mediate
complex interactions across stakeholders, increasing value in larger organizations.
The hub and spoke network model first became popular in logistics organiza-
tions, such as package delivery and airline transportation, and originally referred to
a system in which goods flow between two points via a central node. This setup
contrasts with point-to-point networks, where goods are sent directly between two
points (Alderighi et al. 2007). Because of the fewer connections, this model reduces
complexity while ensuring an efficient network. When nis the number of dots in
a network, linking all dots requires n– 1 connections. In contrast, a point-to-point
network requires (n– 1) / 2 connections to achieve the same result.
The hub and spoke model gained traction in recent years to describe the flow of
information in networks and orient organizational units’ setup as hubs. Longo et al.
(2013) describe this model’s application for virtual communities, where firm-based
hubs connect to spokes in external web-based communities.
Several organizations report applying a hub and spoke setup in the context of
banking supervision to facilitate innovation with a mixture of centralized teams and
interdisciplinary teams (World Bank 2021). The SSM in Europe also offers exam-
ples of best practice for joint innovation with several countries and stakeholders. In
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H. C. Doll, G. Alves Werb
the complex context of the European banking supervision, a hub connects all stake-
holders working on innovation projects in the SSM, identifying synergies and needs
across them. The hub structures the work in agile teams composed of members of
the European Central Bank (ECB) and staff from national central banks (McCaul
2022).
In the climate-related data domain with this organizational pattern the national
climate-related data centers are the hubs and the various in-house and external
climate-related data stakeholders are the spokes. Furthermore, there is a second
layer of hub and spoke collaboration in international cooperation, as outlined in
Fig. 2. In this layer, dedicated organizations represent the international hubs and the
national central banks are the spokes.
This organizational hub and spoke setup seems to be gaining traction. For exam-
ple, the ECB founded the Climate Change Center in 2021, combining expertise from
diverse business areas in one hub (Lagarde 2021). Similarly, the Banca d’Italia’s Cli-
mate Change and Sustainability Hub is mandated to enable analyses and facilitate
information flows among participants in national and international groups (Banca
d’Italia 2022).
In an example of collaboration with academia, the Monetary Authority of Singa-
pore has three hubs that foster collaboration with academic institutions on sustainable
finance—one of which aims to acquire and translate granular climate-related data to
measure firms’ sustainability performance (MAS 2022).
Internationally, central banks launched a series of projects and working groups to
jointly explore innovation, such as the Network for Greening the Financial System
(NGFS). In the NGFS, 121 central banks and financial supervisors currently col-
laborate in several working groups to accelerate and spread best practices related to
measuring and monitoring climate risks. It has quickly become an important forum
Fig. 2 The Two-Layer Hub and Spoke Model of Collaboration on Climate-Related Innovation Activities
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
since its launch in 2017, raising attention to data gaps (NGFS 2022b) and working
towards improvements within its expert networks.
This multi-layered setup is reflected in the NGFS’ self-description as a global
focal point for central banks and supervisors to address climate-related and environ-
mental risks with a strong focus on climate-related data (NGFS 2022a). Similarly,
the Bank for International Settlements’ (BIS) Innovation Hub aspires to be a global
network of central bank experts on innovation. It recently stated green finance as
one of four focus areas (BIS 2022a). In this existing network, the BIS increasingly
launches projects focused on climate-related issues. The BIS Innovation Hub cur-
rently has strong a pipeline of several projects that aim to improve climate-related
analyses and data availability, such as Project Viridis (BIS 2022c), and to develop
digital solutions for trading green bonds, such as Project Genesis (BIS 2022b).
These networks trigger collaborations from which the entire ecosystem benefits,
as many developed frameworks and methods are transferrable to other substan-
tive problems. Statistical offices with strong international networks and established
collaborations have similar opportunities to share and develop methods and best
practices with regards to climate-related data (UNECE 2018).
This organizational structure efficiently integrates domain and data science know-
ledge, providing an exchange forum to disseminate best practices and drive innova-
tion in climate-related data with internal and external partners. In the next section,
we present a detailed discussion with a case study of one such data hub, namely
the setup and experiences of the Sustainable Finance Data Hub at the Deutsche
Bundesbank.
5 Case study of the Sustainable Finance Data Hub
To support climate-related analysis andfoster innovation toward closing existing data
gaps, the Deutsche Bundesbank set up the Sustainable Finance Data Hub (SFDH)
in 2020 to serve user needs for the whole bank (Fehr et al. 2022). The SFDH is
a specialized unit within the Data Service Centre of the Directorate General (DG)
Statistics that provides a data central access point and serves as a center of com-
petence for methodological questions. It aims to promote in-house data availability,
enable transparency to the public, engage in central bank cooperation, and close data
gaps through novel data generation in innovation projects.
As outlined in Fig. 3, the SFDH focuses on the three core pillars to achieve
these goals: data, innovation, and exchange. By leveraging innovative methods and
novel data sources and joining forces through an increased national and international
exchange, it focuses on improving the availability and usability of climate-related
data. Fehr et al. (2021) provide a detailed description of this approach. Subsequently,
we outline the activities in its three pillars.
5.1 Providing high-quality climate-related data
The SFDH acts as a focal point to provide climate-related data for all business areas
and related networks. To ensure user-centric data provision and generation, it reaches
K

H. C. Doll, G. Alves Werb
Exchange
Sustainable
Finance
Data Hub
Climate-related
micro data are the
basis of the SFDH’s
work
Enhancing
communicaon and
coordinaon improves
data usability for all
Data need to be
transformed and
generated in
innovave ways
- Proprietary firm-level
data on transion and
physical risks
-Textual datafrom
Sustainability reports
- Geo-spaal data
- Internal community
building
- ESCB exchange on
climate data
- NGFS expert network
on climate-related
data
- Collaboraon with
academia
- Central bank
collaboraon, e.g.
BIS Innovaon Hub
Fig. 3 Core Tasks of the Bundesbank’s Sustainable Finance Data Hub
out to users bi-yearly in user group meetings to assess their needs. A regular survey
further complements this assessment. Thereby, the SFDH obtains information about
data usage and current demands—e.g., which data packages have a higher demand
and which information might still be missing. The SFDH further engages with
multiple stakeholders to procure data and curate innovative solutions to address the
priority needs.
Recently, the Bundesbank led the joint Eurosystem procurement for two climate-
related data providers that are now available for all ESCB Central Banks under
the same commercial conditions (Deutsche Bundesbank 2022a). As a result, all
participating central banks can easily leverage the same granular climate-related
data, improving the comparability of their analyses and intensifying collaboration
and exchange.
When making these data available, the SFDH is committed to the FAIR data
principles. Therefore, after concluding the procurement phase, it is essential to ensure
inter-operability by integrating the commercial data with other administrative data,
as most analyses rely on merging climate-related data to instrument-, firm-, or bank-
level data. Consequently, the SFDH provides integration solutions to almost all
internal administrative data by applying record linkage using supervised machine
learning (Doll et al. 2021). Applying high-quality string-based record linkage with
machine learning algorithms delivers immediate user value by improving the data
usability in several contexts. The resulting data are available to all internal users in
traditional file structures and in the Bundesbank internal Hadoop cluster.
Furthermore, the established methods and pipelines provide a solid basis for col-
lecting and using novel data from unstructured sources. To this end, innovation
activities provide a comprehensive set of published textual corporate sustainability
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
Tab l e 1 Available and Expected Data Sets for Internal Users in the SFDH (as of 2023)
Name Description Status
Firm-level propri-
etary data
Proprietary data from two providers on tran-
sition risks and physical risk at the asset- and
firm-level in multiple packages
Available
Textual data Corporate sustainability reports in textual
form of large global corporations for several
years
Available
Geo-spatial propri-
etary data
Proprietary data on physical risks at the geo-
spatial level
Ongoing provisioning
New administra-
tive data
Firm-level data that will be available through
regulation, such as the CSRD (phased avail-
ability from 2025 onwards)
Scheduled changes in regula-
tion
Enhanced admin-
istrative data
Enhanced quality checks of available data
through data validation using image data,
such as from satellites and street view
Foreseen through academic
collaboration projects
New structured
data from unstruc-
tured sources
Indicators extracted from available textual
data
Foreseen through innovation
in international central bank
collaboration projects
reports over multiple years and firms. This data set is available to users interested in
exploring analyses based on unstructured data. As the data needs continue evolving,
the SFDH has an ongoing pipeline, including the procurement of additional climate-
related data packages scheduled for 2023. Furthermore, it aims to continuously pro-
vide novel data generated by innovation activities—a list of available and expected
data for internal users is provided Table 1.
Finally, to provide easily findable and inter-operable data sets for research and
analyses, the SFDH harmonizes the available data in a content-wise manner and
documents the data comprehensively following a structured metadata schema from
the Data Documentation Initiative (Vardigan et al. 2008). The easy onboarding
and data provision processes foster internal data accessibility, whereas novel data
generation is expected to provide additional value and enhance data accessibility for
external users.
5.2 Enhancing data usability through coordination
To promote public transparency and facilitate the comprehension of ongoing de-
velopments, the SFDH makes information available to a broader audience through
dashboards and selected data sets on the Bundesbank’s statistics website. This is an
area in which statistical offices and central banks increasingly provide immediate
value to society (IMF 2023;Townsend2021). Furthermore, the procured granular
climate-related data support the Bundesbank’s public climate reporting, both at a na-
tional and European level, in collaboration with the ECB (e.g., Deutsche Bundesbank
2022b; ECB 2023).
The SFDH also actively reaches out to internal and external stakeholders to foster
collaboration within its network. For building internal community within the Bun-
desbank, the SFDH acts as a central hub providing various fora to connect interested
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H. C. Doll, G. Alves Werb
stakeholders and spread best practices in handling climate-related data. Activities
include the “Climate Data Consultation Hours”, organizing the “Sustainable Finance
Community”, engaging in the “Sustainable Finance Expert Group” and discussing
user needs in the user group, among others. This approach represents the internal
hub and spoke layer within the Bundesbank’s network, where the SFDH is the hub
and the business areas are the spokes.
The “Climate Data Consultation Hours” are a bi-weekly platform hosted by the
SFDH where stakeholders from all business areas can obtain information about
climate-related data access. It is a low-threshold format to raise data-related ques-
tions, connect with other users that may have faced similar questions, and provide
feedback. This format fosters ongoing user engagement and enables the SFDH to
tailor its activities in a user-centric manner. Furthermore, it catalyzes concrete data
quality improvements, such as correcting inconsistencies and providing novel ideas
to improve existing data products.
This forum is complemented by the “Sustainable Finance Community”, a monthly
forum organized by the SFDH in which climate data experts from the DG Statistics
connect, present their work and discuss new developments. Similarly, the SFDH
organizes “Sustainable Finance Expert Group” to connect stakeholders across all
DGs and ensure efficient information flows among data providers and users.
These internal community-building activities aim to ensure a user-centric focus
in all SFDH’s developments (feedback from users to the hub), to share information
about new developments (hub to users), and among users about ongoing projects
(users to users). In an area with abundant data gaps and fast momentum, such
as climate-related data, this format streamlines information flows and efficiently
allocates scarce expert resources.
Externally, the SFDH contributes to and organizes exchange fora at the European
and international levels to share and benefit from best practices. For example, it
acts as the current secretariat of an exchange network for ESCB central banks
to collaborate and enhance the analytical value of the jointly procured climate-
related data. Other examples are international networks such as the NGFS or the
BIS Innovation Hub.
The SFDH actively contributes to NGFS working groups and currently chairs the
NGFS expert’s network on climate-related data. The NGFS expert’s network on cli-
mate-related data aims to consolidate the NGFS community of statisticians and data
scientists to foster a regular dialogue on climate data-related topics. The work plan
also envisions exchanging experiences and best practices for setting up climate-re-
lated data hubs. This organizational engagement is complemented by a Bundesbank
board member who is the current vice chair of the entire NGFS and is scheduled to
be the chair in 2024 (Deutsche Bundesbank 2022c).
5.3 Leveraging innovation to enhance climate-related data
In the BIS Innovation Hub, central banks collaborate to develop joint initiatives. In
joint work with the Banco de España and the ECB, the SFDH successfully pitched
the Project Gaia to the new Eurosystem Centre of the BIS Innovation Hub (BIS
2023). This project aims to increase the transparency of climate-related disclosures
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
by exploring natural language processing, optical character recognition, and machine
learning to extract relevant metrics from publicly available sustainability reports.
The project aspires to provide an open-source database of firm sustainability reports
with a full-text search engine to allow analysts to identify sustainability-related
disclosures in an intuitive user interface to facilitate climate risk assessments.
The project establishes a sandbox to apply novel methods for improving the qual-
ity of climate-related data by building an integrated pipeline to web scrape reports,
extracting relevant indicators from text, tables, and charts, and providing a user
interface for analysis. Consequently, Project Gaia represents a concrete innovation
step toward our call for increasing data coverage by leveraging unstructured data
and improving their usability.
The internal and external networks and projects build the basis to foster col-
laborative innovation, thereby supporting the SFDH’s third pillar, innovation. This
setup represents the external hub and spoke layer, where the international fora are
the hubs and the SFDH participates as the spoke. This additional layer enables the
SFDH to share the Bundesbank’s best practices across the network and to act as
a disseminator, circulating international best practices to internal stakeholders.
University collaborations and internal coding challenges further complement the
innovation activities at the SFDH. These joint projects aim to explore advanced
methods to extract relevant information from unstructured data sources. In a planned
project with the Technical University of Darmstadt, the SFDH seeks to validate firm-
level structured master data with image data from satellites and street view. The goal
is to apply multimodal learning methods to validate contextual information in admin-
istrative data, such as sector classification or addresses. While this is a more general
use case, there is a close collaboration with the SFDH to transfer the knowledge and
experiences with multimodal learning to improve climate-related data.
Furthermore, in a project with the Frankfurt University of Applied Sciences, the
SFDH explores novel methods to extract information about firms’ environmental,
social, and governance (ESG) performance from text and tables in corporate sustain-
ability reports. To foster transparency and promote data democratization to a broader
group of interested stakeholders, this project also includes a searchable repository of
reports in PDF format and a publicly available dashboard to visualize the extracted
indicators.
Overall, the call from central bank users for high-quality, easy-to-use climate
data shapes the SFDH’s role in promoting innovation within the different layers
of its network. The following section discusses how these experiences and best
practices may transfer to other organizations that leverage climate-related and other
administrative data.
6 Lessons learned for central banks and official statistics
The setup of the Bundesbank’s SFDH provided several lessons learned. First, it
underlined climate-related data’s potential to serve as a sandbox for using alter-
native data sources and machine learning methods, showcasing the possibilities to
leverage these innovations to improve several dimensions of data quality. Second, it
K

H. C. Doll, G. Alves Werb
outlined the potential to transfer novel methods and practices to other administrative
data collections. Third, it demonstrated that combining data domain knowledge and
innovation in the institutional setup is vital to curate high-quality data and enable
knowledge sharing. In this section, we outline these lessons in greater detail.
Climate-related data is an excellent catalyst for collaboration because many in-
stitutions strive to acquire the same global data, given its global public good nature.
To counter the challenges the current climate-related data landscape presents, the
SFDH has made substantial progress by combining existing administrative data
sources with commercial data and fostering innovation projects that leverage alter-
native data sources and novel methods. Nevertheless, we see this undertaking as
a continuous process that involves assimilating best practices and lessons learned to
tackle current and future challenges.
Its often relatively low confidentiality facilitates knowledge sharing for climate-
related data at the granular level, which is not always possible with other types
of administrative data. However, the collaboration experience and trust developed
within the networks established to promote climate-related data provide a solid
foundation for future collaboration in other use cases.
Despite the different constraints imposed by other settings, transferring these
lessons learned and further innovations to other types of administrative data is cru-
cial, as their quality can highly benefit from incorporating alternative data sources.
This opportunity is especially present when one or more of the following conditions
hold: (i) alternative data sources exist to improve data coverage, (ii) alternative data
sources exist to validate existing indicators, or (iii) alternative data sources exist to
obtain higher frequency or more granular data.
This potential has also been recognized by the central banks’ statistics community
(e.g., Rosolia et al. 2021) and by statistical offices (Salgado and Oancea 2020;
UNECE 2021b). Furthermore, there is also an excellent, partly unexplored potential
to transfer the application of machine learning to improve the quality of many
statistical data collections.
In the context of official statistics, the United Nations Economic Commission for
Europe (UNECE) provides an illustrative example of collaboration within statistical
offices to leverage alternative data sources for migration statistics, given the limita-
tions of available surveys and existing administrative data. They provide examples
of projects integrating mobile phone, credit card, and social network data to enhance
the existing data and reduce issues of coverage and accuracy (UNECE 2022).
Furthermore, while setting up the SFDH, we learned that combining data and
innovation knowledge in a central hub is vital for efficient knowledge sharing.
Consequently, the choice of organizational setup is an important success factor. This
organizational setup is conceptionally known as a hub and spoke model. In its inner
layer, the hub is the SFDH and the spokes are the internal business areas working
with climate-related data. In the outer layer, international fora and working groups
are the hubs and the SFDH acts as the spoke.
This configuration also fosters innovation in other domains, especially in more
extensive networks, when the number of involved institutions and stakeholders be-
comes large. In the context of central banks, the European SSM, with its many
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
national members, provides a natural setting to apply the hub and spoke setup for
other innovation initiatives.
Statistical offices have long established similarly large multi-layered networks
for close collaboration, where efficient information flows are paramount. In the case
of Germany, its 14 state statistical offices with one additional federal statistical
office represent one collaboration layer. This layer is complemented by a layer of
international collaboration, such as within the European Statistical System (ESS),
the United Nations Statistics Division (UNSD), or the UNECE.
As part of this hub and spoke approach, user-centric innovation remains an on-
going priority. Based on the SFDH’s experiences, it is paramount to reach out and
leverage users’ domain knowledge and ideas in all business areas. To this end,
the SFDH runs regular surveys and user workshops to collect data needs, usage
profiles, and novel ideas for further innovation. All innovation projects start with
a design thinking workshop to collect user pain points and suggestions. This ap-
proach stimulates stakeholder engagement and generalizes to innovation activities
in other contexts.
These lessons apply to other innovation activities across central banks and statis-
tical offices. A broad stakeholder buy-in fosters idea generation, user-centric devel-
opment, and timely incorporation of the results into production. Ensuring efficient
information flows enhances the speed at which participants disseminate best prac-
tices throughout the network, within and across organizations. In our experience,
a hub and spoke setup provides a fruitful and successful setting to do so.
Moreover, internal management support is a crucial factor in the setup of the
SFDH that arguably extends to other settings. Broad support is critical to rely on
resources from a wide range of stakeholders and to promote collaboration with
various business areas. This support is also important in setting up international
cooperation layers. In the case of Bundesbank, the close involvement of a board
member in networks as the NGFS and its president pointing to the need to devote
more attention to climate-related matters at his inaugural speech paved the way for
the related initiatives (Deutsche Bundesbank 2022d). The urgency to increase the
availability of climate-related data has been a crucial driver for a fast setup of hubs,
innovation projects, and collaborations over the past years.
7Conclusion
This paper provides a comprehensive review of the challenges the current climate-
related data landscape brings to central banks and statistical offices. We describe
potential avenues to leverage alternative data sources and novel methods to close
gaps in selected dimensions of climate-related data quality. We describe a framework
for developing collaborative climate-related data networks and proceed with a case
study of the setup of the Sustainable Finance Data Hub at the Deutsche Bundesbank.
Finally, we outline lessons learned and best practices to enable innovation and foster
quality for climate-related and other types of administrative data.
By doing so, we aim to provide a foundation for central banks and statistical
offices to respond by forming robust collaboration networks, apply suitable orga-
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H. C. Doll, G. Alves Werb
nizational changes, and optimally leverage innovation initiatives to close existing
data gaps. This step is crucial to enable multiple stakeholders to capture the range
of climate risks and estimate their consequences throughout the economy, fostering
informed decisions.
Given the pressing need for high-quality data to foster informed analyses and the
existing challenges in the climate-related data landscape, there is significant momen-
tum in innovation and collaboration activities. Several institutions and stakeholders
are joining efforts to leverage alternative data sources and novel methods to close
the gaps in climate-related data, especially data coverage, timeliness, accessibility,
reliability, and integration. Central banks have a unique perspective on user needs
since they produce and consume climate-related data.
From our perspective, several key organizational factors are crucial for success in
this context. Internal collaboration across business areas and external reach across
peer organizations, institutional backing, and efficient information flow are essential
in fostering innovation and disseminating best practices.
Given their influence and potential to bridge multiple stakeholders’ needs and
contributions, we believe that central banks can substantively influence the climate-
related data landscape and innovation in data extraction, integration, and dissemi-
nation. We hope that the discussion in this article can contribute to fostering the
exchange between researchers and practitioners on avenues to promote access to
high-quality climate-related data and can inform the setup of dedicated organiza-
tional units. Furthermore, we foresee a great potential to apply the methods and
lessons learned in the context of climate-related data to other types of administrative
data.
Acknowledgements We thank Stefan Bender and Christine Schlitzer for valuable comments on an early
version of this paper.
Funding Open Access funding enabled and organized by Projekt DEAL.
Conflict of interest H.C. Doll and G. Alves Werb declare that they have no known competing financial
interests or personal relationships that could have appeared to influence the work reported in this paper.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.
0/.
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