Time for structural change in central bank statistics?
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How to support the transition to a climate-friendly economy
Claudia M. Buch
(Vice-President
of the Deutsche Bundesbank)
We are in the midst of a global process of structural change. Geopolitical risks have
increased, addressing climate change has become all the more urgent, demographic
trends and digitalisation affect the real economy and our societies.
Central bank statistics are certainly not the key actors, but they are nevertheless
important. They provide crucial information on the financial system and the real
economy, information which is relevant not only for monetary policy and financial
stability but also for researchers and the broader public. Therefore, central bank
statistics have to change as well in order to support and reflect structural changes.
What role can statistics play in providing solutions to societal problems? Take the
case of mitigating climate change. Current decisions by firms, consumers, and
investors do not sufficiently take adverse effects on the climate into account. We
over-use greenhouse gases. Internalising climate-related externalities requires
adjustments in relative prices. Implicit subsidies to the economy arising from the
underpricing of climate externalities, but also from the underpricing of energy
security, need to be withdrawn. Ultimately, policy decisions are needed, above all
the pricing of carbon emissions that change the behaviour of emitters of carbon.
Such policy decisions need to be taken by elected politicians. But irrespective of the
exact policy instrument that is chosen - be it CO2 prices, emissions trading systems,
or other regulations - information on greenhouse gas emissions is needed. This is
where good statistics play an important role. Better measurement and better statistics
are certainly no substitute for the right policies, but they are needed to complement
such policies and to make informed choices.
Moreover, central banks themselves have a genuine interest in understanding
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climate-related risks: exposure to transition and physical risks can have severe
implications for the functioning of the financial system and financial stability. Banking
supervisors need to assess the vulnerability of financial institutions to climate risks.
Increases in energy prices affect inflation. Not least, central bank statistics are an
integral element of the data infrastructure related to the climate transition.
So what are the challenges - and are the current statistical systems fit for purpose? I
see three main priorities:
First, activities that pollute the atmosphere and contribute to climate change
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need to be measured, priced, and reduced.
Second, the necessary information on emissions is needed sufficiently quickly
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- and to everybody who needs it. Good management of risks requires good
measurement.
Third, climate change is a global problem, eventually requiring a global
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standard for measuring greenhouse gas emissions.
These objectives are hard to achieve, so we need to take practical steps to get there:
Good data infrastructures are needed. Infrastructures are public goods. They
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provide the largest benefits when having harmonized standards and providing
broad access to relevant information.
In terms of data, we need to be pragmatic and start with what is available.
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Many relevant data are generated by the private sector. This requires striking
the right balance between private incentives to generate information and
broad usability of such data.
Standardization of reporting can start from a minimum set of information,
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allowing for regional and country-specific diversity, while reporting can be
improved and refined over time.
Practical solutions should allow for experimentation and learning from
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experiences of the past: If a new problem has the same structure as one we
have already solved, we might be able to leverage a solution we are familiar
with.
Statistical systems, including central bank statistics, have made significant progress
over the past decades. Granular, high frequency data has become much more easily
available; new information is being collected, and new data sources have been
tapped. This is a good starting point.
However, as statistics providers, we also have to adapt to the new environment.
Providing relevant information supporting the climate transition requires new ways of
gathering, compiling, and disseminating data. This calls for more agility so that we
can make progress sufficiently fast.
In the following, I will provide some historical examples of how structural change has
paved the way for new data and information systems. I will then draw implications for
sustainable finance data, including learning from experiences, incentivizing the
private sector, regulating the market for information, and providing infrastructures for
data sharing.
1 Structural change and measurement: historical examples
Addressing climate change would, ideally, require a global standard to measure and
attribute greenhouse gas emissions. History can provide examples of how this can be
achieved. The invention of double-entry bookkeeping, the invention of national
accounts, and the measurement of time share similarities with today's challenges.
These examples provide three main lessons:
Structural changes such as globalization and industrialization created the need
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for innovations in terms of measurement and data.
Solutions found in the past involved experimentation in the private sector and
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coordination through the public sector. Governments have played an active
role in incentivizing innovation and providing the necessary infrastructures.
Developing new measurement systems has taken time, and it involved
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continuous improvements.
1.1 The evolution of double-entry bookkeeping
Accounting for greenhouse gas emissions at the firm-level shares similarities with
standard accounting objectives:
Corporate cost accounting links the costs of resources such as labour and
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capital to price setting and production decisions.
Climate accounting and taxonomies aim at measuring the use of carbon and -
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indirectly - the carbon contained in goods and services.
Double-entry bookkeeping is a comprehensive information system for commerce that
looks at every transaction from two angles: credit (the uses of funds) and debit (the
sources of funds). It developed in parallel to the emergence of modern financial
systems. During the Renaissance era, rich Italian merchant cities raised funds and
provided finance for overseas manufactories. By the late 15th century, bookkeeping
systems had evolved sufficiently far to be codified. Luca Pacioli, a friar,
mathematician and close collaborator of Leonardo da Vinci described the system for
the first time in a textbook on mathematics. By that time, financial accounting was
indeed considered to be highly scientific, and Pacioli's contributions enabled others
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to study and use it.
Later, the Industrial Revolution saw cost accounting emerge as a second navigation
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system. Strategic decisions on whether to produce and launch new products, how to
price products and how much to produce required an understanding of the
productlevel costs involved. Overhead costs, wages, and the costs of capital goods needed
to be correctly assigned to individual products.
It has taken centuries until we have arrived at the current accounting systems, and
we have not yet settled at a fully consistent global standard. This, however, has not
constrained firms from using and benefiting from accounting tools. These tools are
also not without criticism. The contribution of fair value accounting to the global
financial crisis has, for example, been discussed, and existing rules have been
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subjected to academic scrutiny and impact evaluations.
1.2 The invention of national accounts
Understanding the sources and uses of resources at the firm-level is one thing, but
policymaking also requires an assessment of aggregate developments. John
Maynard Keynes promoted an active role of governments in managing aggregate
supply and demand - a response to the severe shocks to the global economy during
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the Great Depression.
In order to manage the macro-economy, policymakers need empirical information.
National accounts were developed as a basis for informed decision making in times
of structural change. Four Nobel prizes were awarded to scholars that contributed to
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a comprehensive national accounts system. Irving Fisher made important early
contributions, advocating the use of a double-entry accounting system at the
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countrylevel.
Specifically, Wassily Leontief developed input-output (IO) analysis to study the
interactions of economic sectors, interactions in the production process, and supply
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chains. Input-output analysis became an important instrument for planning in the
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post-war area.
At the current juncture, input-output models are standard tools for assessing the
implications of energy shortages and supply-chain disruptions. Our environmental
statistics rest on these foundations. The System of Environmental-Economic
Accounting (SEEA) is a consistent, harmonised and international platform for the
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reporting of environmentally relevant stocks and flows. Environmental input-output
models (EEIO) compute the direct and indirect carbon content and energy use for
firms, sectors, or countries. In fact, Wassily Leontief (1970) pioneered the use of
input-output methodology to quantify the link between output and pollution - simply
by re-interpreting the underlying system of equations describing production
interactions.
6
See Kenessey (1994a, 1994b).
7
These were Richard Stone (in 1984), Simon Kuznets (1971), Wassily Leontief (1973), and Jan Tinbergen
(1969).
8
See Allen (1993) and Kenessey (1994b, 1997).
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Leontief (1986) covers much of his work. For IO analysis in general, see Miller and Blair (2022), and specifically
Chapter 10 for environmental IO analysis.
10
Initially, Ludwig Erhard was opposed to establishing Input-Output analysis in Germany, as he considered it an
instrument for planification. Only in the late 1960s, when the analytical and scientific value became apparent,
Input-Output tables were published on a regular basis in Germany. See Destatis (2010).
11
See United Nations (2014).
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Deutsche Bundesbank, Directorate General Communications
Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Germany, tel.: +49 (0)69 9566 33511, fax: +49 (0)69 709097 9000
presse@bundesbank.de, www.bundesbank.de
Reproduction permitted only if source is stated.
1.3 Measuring time
The measurement of time is another example illustrating the interaction between the
private and the public sector. With today's digital tools, measuring time has become
so instantaneous that we almost do not pay attention to it anymore. As we disembark
from planes in remote parts of the world, our electronic devices automatically switch
to the new time zone.
In earlier eras of globalisation, measuring the exact time was an almost
insurmountable challenge. On the high sea, the most important piece of information
is knowing your exact position. This is defined by latitude and longitude. Latitude is
easily determined by the sun and the stars, with the help of a sextant. Longitude is
much harder to measure. The exact time at a location with a known longitude, such
as Greenwich, is needed to infer the longitude of your own position. Without a
standardised and exact time, navigators could not afford to take the shortest route,
they had to sail along known coasts.
This inefficiency was addressed through a combination of public-sector incentives
and private-sector innovation. Governments of sea-faring nations offered prizes for a
workable solution. In 1717, the British Parliament offered a reward of up to £20,000
(about £3,350,000 in today's prices) for a seagoing clock that was sufficiently
accurate to measure longitude. John Harrison, a British carpenter and clockmaker,
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solved the problem and revolutionised navigation and international trade.
In this example, improved measurement was driven by structural change and then
itself became a major driver of economic development. It also generated rents:
clockmaking was a highly lucrative business at the time when international travel took
off.
2 Implications for climate statistics
The above examples show that an active public sector is needed to support and
develop measurement systems. Measurement systems help to solve coordination
problems, but they themselves require coordination.
Standardised systems of measurement such as the metric system have huge
benefits. They save us from discussing how to express the length of screws or the
voltage of power lines. Nevertheless, there is no market mechanism that could lead
us to the metre or the litre or the kilogram. By establishing measurement systems
and statistics, the public sector plays an important and beneficial role.
2.1 How carbon accounting can solve the basic allocation problem
To support decisions by policymakers, firms, and consumers, we need to understand
how the emission of greenhouse gases relates to the production of goods and
services. A large number of allocation questions need to be answered: How to
allocate emissions from overhead activities such as heating, transportation, or the
uses of capital goods to the product of a firm? How to deal with these allocation
issues for multiproduct firms? How to measure the carbon content of imports and
exports that run through complex global value chains?
At first sight, the task seems daunting, and it seems to require an entirely new type of
information system.
The good news is that these allocation issues have been solved conceptually. And
the solution is well understood - financial accounting and cost accounting, the
information systems that firms have been using since the Renaissance era and the
Industrial Revolution.
Keeping track of carbon is conceptually similar to keeping track of the sources and
uses of external funding. When it comes to allocating the use of carbon to products
(and other activities), the cost accounting routines developed to determine financial
costs for products can be used - in principle, we can even use the same software
tools.
The solution to the allocation problem is a part of what is called "carbon
accounting".13 Carbon accounting can provide a system of product-level metrics for
carbon content. It starts with a record of direct emissions. To compute indirect
emissions, information on the carbon content of inputs is needed as well. With this
information at hand, it is possible to calculate the carbon content of inputs in just the
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same way as input prices are used to determine product costs.
At first glance, this might look circular: the carbon contents of inputs need to be
combined in order to compute the carbon contents of outputs. This is where good
measurement kicks in. Essentially, carbon accounting can follow an iterative process:
At the outset, no product-specific data on input carbon contents is available.
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Instead, producers can use proxies supplied by existing statistics based on
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sectoral data. Producers can then calculate the carbon content of their
products using their private information on direct emissions and production
technology.
In a next step, the carbon contents of output is disclosed to buyers.
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This information can feed into the next stages of the value chain, just like
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product prices. At this point, product-specific indicators are available, though
imprecise.
This "price" system will converge to the true product-specific carbon contents
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fairly rapidly. It is a system of indicators that yields the information needed to
make informed decisions on climate-related allocation issues.
Essentially, this iterative process describes a market-based learning mechanism:
Producers start using proxies, and by adding their own private information to the pool
of knowledge, publicly available information becomes more accurate.
The role of the public sector is two-fold: First, the starting values with sectoral
information on carbon contents need to be provided. Standards are needed for
auditing carbon accounting jointly with financial accounting. Auditing needs to make
sure that the carbon content of input matches the carbon content of output on the
firm-level. What inputs to consider and how to evaluate them needs to be
determined. Such initiatives can build on the existing Greenhouse Gas Protocol
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standards for direct emissions and indirect emissions.
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With a carbon accounting system in place, the sectoral statistics can and should be based on firm level carbon
content data. Currently, they are produced on a sectoral level based on firm-level surveys on input structure and
carbon emissions.
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Von Kalckreuth (2022a) studies this process and shows how it can converge. Market learning is simulated
based on data for Germany.
17
See WRI and WBCSD (2004) on direct emissions, and WRI and WBCSD (2011a) on indirect emissions, as well
as WRI and WBCSD (2011b) for measurement at the product level. Regarding indirect emissions, the GHG
Protocol has been criticised for its optionality and the impracticality of measurement it measurement
suggestions. Firms are supposed to collect their own information on their respective value chain. The
suggestions by Kaplan and Ramanna (2021a) and von Kalckreuth (2022a) can simplify measurement, but there
need to be standards on what inputs to consider.
Page 8 of 18
Deutsche Bundesbank, Directorate General Communications
Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Germany, tel.: +49 (0)69 9566 33511, fax: +49 (0)69 709097 9000
presse@bundesbank.de, www.bundesbank.de
Reproduction permitted only if source is stated.
Second, statistical institutions can provide a platform for information on carbon
contents disclosed by firms. Firms can enter information on the carbon content of
their products.
In all this, the private and the public sector have a role to play. The private sector
needs to report relevant information and be incentivized to do so; the public sector
needs to provide the infrastructure needed for this information to be used efficiently.
Incentivizing firms to collect and disclose information that is potentially considered
sensitive is obviously an important caveat of a system of climate accounting. In
principle, competitive forces can be used to incentivize a critical mass of firms to
disclose. If buyers care about products with a low carbon content, producers of such
goods should have incentives to disclose these indicators to the public. Producers
not disclosing that information would fear to be stigmatized - which creates a signal
value for disclosing. The power of this signal can be enhanced if firm-level
information on carbon contents is made available on a public platform. The signal can
be reinforced further by computing average carbon contents for non-disclosing
producers and obliging firms to use these as proxies for input carbon contents in
case their provider does not disclose. Ideally, this may lead to voluntary disclosure by
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(almost) all producers. Note that mandatory disclosure is not required for this
mechanism to work. However, without a sufficiently large number of firms who
disclose, the mechanism would not operate either. It may be necessary to make
disclosure obligatory for a subset of participants, such as large producers in highly
GHG-intensive industries.
2.2 Implications for sustainable finance data
Carbon accounting with an exchange of information between market participants is a
conceptually attractive way to solve the allocation problem inherent in the attribution
of greenhouse gas emissions, but it has not been broadly tested in practice.
Implementation costs and reporting burden are issues at the firm-level. Proposals for
taxonomies already generate concern about implementation costs without actually
delivering firm-level information on carbon contents. So is carbon accounting by
means of a price-like metric only a nice theoretical construct without much practical
relevance? Can it enhance current ESG initiatives and taxonomies to actually deliver
what is needed?
Generally, moving ahead with the sustainable finance data agenda requires a
threestep approach:
Step 1: What information is needed?
In terms of taking stock of what is needed, significant progress has already been
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made. We have the FSB climate roadmap, work by the Network for Greening the
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Financial System (NGFS) on bridging data gaps, and reports from the IFC Working
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Group on Sustainable Finance Issues. Hence, there is probably little value added in
conducting additional broad based surveys on data needs.
Instead, it would pay off to focus on data needs for specific projects and
measurement approaches. Take the system of carbon accounting as an example. To
prepare the ground for such a system, detailed proxies for carbon contents from
international input-output models are needed to compute the carbon contents of
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imports. This relates to a suggestion for the new Data Gaps Initiative (DGI). In
addition, markets need to be provided with standards regarding what kind of inputs to
record. The Central Product Classification (CPC) managed by the United Nations
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Statistics Division developed by the UN Statistics Division may be used as a basis
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to this effect, also for recording information on carbon contents.
Step 2: What information is available?
Repositories of sources for sustainability data, such as the directory of the Network
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for Greening the Financial System, provide information on which data is available.
Going forward, it will be important to maintain and update these tools on a permanent
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basis.
What existing surveys have shown is that a lot of information is available - but often
difficult to access, costly, and thus underused. Hence, attention should focus on
identifying and removing frictions in the process of using data.
19
See Financial Stability Board (2022).
20
See Network for Greening the Financial System (2022).
21
See IFC (2022).
22
See A New G20 Data Gaps Initiative - a statistical response to urgent policy needs
23
The latest official version, CPC (2015), is currently under revision. More detailed regional classifications derive
from CPC, such as the European CPA.
24
See United Nations Statistics Division (2015).
25
See NGFS Directory: The NGFS Directory (masdkp.io).
26
See Network for Greening the Financial System (2022) and The NGFS Directory (masdkp.io).
Page 10 of 18
Deutsche Bundesbank, Directorate General Communications
Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Germany, tel.: +49 (0)69 9566 33511, fax: +49 (0)69 709097 9000
presse@bundesbank.de, www.bundesbank.de
Reproduction permitted only if source is stated.
Let us start with the easiest case - relevant data that are available and can be used
already. Even for these data, better infrastructures for data-sharing are needed. For
instance, the European Union is working on a single access point providing
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centralised access to publicly available information relevant for sustainable finance.
As regards to data sharing, international standard-setters have an important role to
play. In macroeconomic statistics, the IMF Special Data Dissemination Standards are
good examples. These standards are promoted by international organisations,
signed by member countries, and the IMF monitors the implementation.
An international statistical microdata standard could enhance consistency in
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crossborder statistics, improve the usability of microdata, and improve data sharing. Yet,
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we currently have different standards for datasets and use cases. Tracking, for
example, multinational enterprise groups is hardly feasible with a purely national
statistical approach - it is indispensable though to track global GHG emissions.
In terms of practical steps, a roadmap should be developed by the Inter-Agency
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Group on Economic and Financial Statistics (IAG) that member countries can
implement according to their level of development. This roadmap should include
principles to be followed when handling microdata, uniform standards for the
description of micro-datasets, and finally exchange of microdata. Here, the
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envisaged European data spaces might create new momentum.31
In 2021, a consensus concerning the development of an international microdata
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standard was reached by the G20. The G20 Finance Ministers and Central Bank
Governors mandated the IMF, the FSB and the (IAG) to develop a workplan for the
27
See European Single Access Point (ESAP) | European Economic and Social Committee (europa.eu).
28
See Buch (2021).
29
One example is the Multilateral Memorandum of Understanding Concerning Consultation and Cooperation and
the Exchange of Information (MMoU) of the International Organisation of Securities Commission, which sets an
international benchmark for cross-border co-operation. Established in 2002, it has provided securities regulators
with the tools for combating cross-border fraud and misconduct that can weaken global markets and undermine
investor confidence (see IOSCO (2002) and IOSCO (2016)).
30
The IAG members are the Bank for International Settlements (BIS), the European Central Bank (ECB),
Eurostat, the IMF (Chair), the Organisation for Economic Co-operation and Development (OECD), the United
Nations (UN), and the World Bank (WB). The FSB is invited to participate in topics in which they have a direct
involvement. See IMF | About the Data Gaps Initiative
31
For example, Digital object identifier (DOI) registration of datasets could be used. In Germany, DOIs are
assigned by the registration agency for social science and economic data (da|ra). See
https://www.da-ra.de/geta-doi
32
See European Commission (2022).
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The workshop concluded to support follow-up work on developing an international microdata standard, to
investigate what is feasible, including how undertakings by member countries could be established and
enforced.
new DGI. In 2022, these institutions were asked to fine-tune the workplan. Hence,
all the necessary ingredients are in place to start work on a global microdata
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standard.
Step 3: How to close data gaps?
Even under the most optimistic scenario in terms of enhancing the usability of
existing data, gaps will remain. International investors are increasingly calling for
improved reporting by companies not only on climate, but also on social and
governance (ESG) issues. In 2021, the IFRS Foundation Trustees thus announced
the creation of a new standard-setting board - the International Sustainability
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Standards Board (ISSB) - to help meet this demand.
Closing data gaps sufficiently fast requires costs and benefits to be well-balanced
and practical approaches to be taken. New reporting requirements require strong
justifications and well-justified data needs. The EU proposal for a Corporate
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Sustainability Reporting Directive (CSRD) is a good example in this regard.
Under the new EU taxonomy and through the efforts of the ISSB, firms will disclose
new and relevant data. Overall, it is important to improve the cost-benefit balance of
this reporting. The G7 under this year's German presidency thus called for
international organisations to take concrete steps to improve accessibility of
sustainability data.
At the same time, ESG criteria and taxonomies are not without criticism. A recent
issue of the Economist argues that ESG standards are flawed for many reasons - for
being too imprecise, for not having clear objectives, and for addressing too many
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different aspects of firms' behaviour. Many of these points are quite valid. Yet, they
should not lead us to abandon efforts. Rather, describing the potential pitfalls of ESG
standards and taxonomies also helps to improve them - just as, in the past,
accounting and measurement systems have been improved upon.
Currently, the taxonomy does not make use of information on the carbon or energy
content of the production process. Rather, the taxonomy follows a binary approach,
characterising certain activities as "aligned" based on a summary assessment of the
predominant technology.
In the longer term, it is not useful to classify entire industries as being
"nonsustainable". The ideal basis would be a carbon accounting system. If designed well,
the cost-benefit balance of such a system can be quite favourable. Carbon
accounting can be an enabler for research, development and ecologically efficient
production technology and inform the next stage in the development of the EU
taxonomy.
3 Priorities for central bank statistics
Every crisis requires the revision of current practices. In this sense, the statistics
community has responded quite well to the global financial crisis. We now have much
better and more granular information about vulnerabilities in the financial and
nonfinancial system.
However, we also have to admit that closing relevant data gaps has taken a long
time. New data needs emerge as new shocks hit the real economy and as the
financial system evolves. Even though more than a decade has passed since the
global financial crisis, we are still discussing data gaps and necessary adjustments to
our financial and monetary statistics.
Addressing climate challenges and providing sustainable finance data cannot wait
that long. If global emissions continue at the current pace over the next decade,
global warming will accelerate even more quickly.
The standard way in which statistical data are defined, developed, collected, and
compiled can be quite cumbersome and time-consuming. The final data point shared
with external users at the end of the statistical value chain might be of very high
quality, but it is reported with a delay. Hence, we need to find ways to provide
relevant information quickly, albeit without sacrificing too much in terms of quality.
This has two implications for climate statistics:
First, as regards sustainable finance data, we need to start with using the information
that is available already, enhance usability, and close data gaps in a targeted way.
Second, we need to streamline our operations and become more agile in order to
deliver solutions in a timely manner. As relevant information comes from private data
sources, bottom-up approaches ensuring the best use of such data needs to be
combined with top-down approaches that regulate the market for information.
Digitalization can be helpful in this regard. Existing accounting systems were
invented long before the digital tools we use nowadays came into being. Leveraging
digitalisation might be an important way of bridging existing information gaps. To give
an example: The BIS Eurosystem Centre project on green finance is building an
open-source database of corporate reports, coupled with a full-text search engine to
identify sustainability-related disclosures. Machine learning and natural language
processing tools will be used to organise and structure these data.
Going forward, I see four main priorities for our work to which the Irving Fisher
Committee can contribute:
Learn: Much of the relevant climate-related data comes from private, non-traditional,
and not very standardized sources. Central banks need to learn how to deal with the
characteristics of such data in comparison with more established data sources.
Furthermore, central banks have to learn how to use new technology to open up to
new data, such as machine learning algorithms. The IFC can promote cooperation
among central banks to discuss the need for data standards and new methodologies
in sustainable finance data through meetings and other platforms for information
sharing. Doing so, the IFC should cooperate as closely as possible with other bodies
in order to avoid overlaps and use synergies. Concrete deliverables of meetings
should be defined and fed into existing work programmes.
Incentivize: Ultimately, incentives to measure, price, and reduce activities that harm
the climate need to be set by climate policies, but statistics play a complementary
role. This includes incentivizing the private sector to develop, create and deliver novel
(technical) solutions for the provision of data. Public institutions, in turn, require a
good understanding of the market for information and the incentive structure of
private data providers. Well-designed regulations and standard-setting of those
markets is required as well. Historical examples show that innovations in the field of
measurement and data often come about as a result of fruitful cooperation between
the private and the public sector.
Provide infrastructures: Public sector involvement is needed for establishing data
infrastructures. Repositories, dashboards, and information pla