Product carbon content

At the heart of environmental problems is a situation in which the effect that producing and using goods has on scarce resources is not properly reflected in the price system. In the case of GHGs, the scarce resource is the capacity of the environment to absorb carbon emissions.

For a massive reduction of GHG emissions, it is vital that consumers, investors and policymakers be able to properly evaluate the environmental consequences of production activities so that they can make the right choices.

What is it one would ideally expect from an indicator system designed for climate mitigation and specifically for financial sustainability purposes? We need exact quantitative information on the relevant emissions at the level of both firms and products. All emissions, direct and indirect, need to be covered, the latter not as loose estimates, but based on realised material flows and micro-level production interdependencies. Granular information is notably scarce, especially on indirect emissions. But it is indeed granular information that is required to make meaningful distinctions that go beyond favouring products and firms in sectors with a low carbon intensity or selecting stocks that happen to be in high-tech sectors.

A metric that summarises the relevant information needed to make decisions on the production, use and consumption of goods and services is the product carbon content, defined here as the total amount of carbon equivalents emitted in the course of production of a good or service, either directly or indirectly through the use of intermediate input products. The definition of indirect emissions is recursive, recurring to the product carbon content of earlier production stages. The concept has two additional important complements: a process of information exchange between providers and users of intermediate inputs, as described by Kaplan and Ramanna (2021a, 2021b), and micro-level standards for the measuring of direct emissions, such as the one provided by the GHG Protocol.

The system is lean and informative. It condenses the relevant product and enterprise-specific information into a single number: the GHG value. Like prices, product carbon contents are easy to understand, manage and communicate. The envisaged scenario is one in which, at all levels of production, goods and services have two tags – the financial price to pay and the GHG value.

At each stage of production, the metric captures and carries forward the environmental resources that have been used up to that point. In a peer group of close substitutes, product carbon contents allow for the identification of inefficient producers and production technologies. Regarding unrelated goods, consumers and policymakers can compare and weigh their respective usefulness against their consequences for the climate. Product carbon contents are like real rates of exchange between products and their consequences for the environment. It is a quantity structure that helps to trace the price effects of carbon reduction policies at all levels – an important input for monetary policy in the transition to a low carbon economy. It may also be used to derive targets for allocation purposes.

This is all that measurement can give. My research discusses the methodology that will enable implementation. The key is the recursive nature of the metric, enabling Input-Output (IO) analytics, and decentralised data generation from an exchange of information between buyers and sellers of inputs. The iterative process can be started based on existing statistics!

More information: 

von Kalckreuth, Ulf, Product level greenhouse gas contents — how to get there? SUERF Policy Note 288, September 2022.

von Kalckreuth, Ulf: Pulling ourselves up by our bootstraps: the greenhouse gas value of products, enterprises and industries, Deutsche Bundesbank Discussion Paper 23/2022.

Fintech and statistics

Since the beginning of the last decade, they are with us. Fintech companies are innovative and technology oriented and they provide financial services: either old services in new ways or altogether new services.

For central bank statistics, fintech companies are a challenge. What they do is relevant, no doubt, but typically there is no reporting obligation, quite often there is not even a fitting statistical description of their activity. The system of industrial classification is the backbone of statistical reporting, but it is not able to distinguish innovative financial activity from traditional finance. You may find the problem characterized here.

Fintech is a global phenomenon, and the Irving Fisher Committee on Central Bank Statistics (IFC) of the Bank for International Settlements (BIS) has organized a Working Group on Fintech data issues to better understand the nature of this statistical challenge and to make suggestions for improvements. I was involved in this work, which is forward-looking and pioneering in many respects. In August 2019, a seminar in Kuala Lumpur collected first results. In July 2020, the final report of the Working Group was published by the BIS, laying out a roadmap towards setting up statistical Fintech monitoring systems.

A massive problem for statistics is finding and tracking fintech firms. For many important fintech-activities, there are no reporting or registration obligations, and the statistical classification systems for economic activities have not yet been adapted to fintech. There are three ways to proceed. One is working on the advancement of classification systems, eg NACE and its derivatives. In a group of colleagues in the ESCB, we work on making fintech activities recognizable in the classification of activities and products. Second, AI methods can be used to seep through large masses of structured and unstructured data in order to identify fintech activities, by startups and mature companies. Here is a conference paper with first results on this track. Third, developing a fintech segmentation, one may collect the existing information and make it accessible in a data hub. We are exploring this route by building up an experimental data set.

Ultimately, surveys are a classical statistical instrument, which can be especially useful in situations where the field to be described is open and subject to change. In collaboration with colleges from the Research Centre of the Deutsche Bundesbank, I helped to design a survey on fintech use by German households, as a module of the Panel of Household Finance (PHF), see below. Here are first results of this fascinating study.

IMIDIAS – A hub for micro data at the Deutsche Bundesbank

In 2013, the Statistics Department of the Deutsche Bundesbank was mandated:to establish an integrated interdepartmental information system for analytical and research purposes; to define governance and roles; to develop a Research Data and Service Centre (RDSC), to develop a statistical microdata warehouse; and to step up active support for research projects.

The “integrated microdata-based information and analysis system” (IMIDIAS) initiative aimed at a coherent solution to these requirements. IMIDIAS is a pragmatic approach that leaves the core production system untouched and keeps data management decentralised. 

Briefly, IMIDIAS is meant to perform data integration ex post, using the finalised and quality-controlled process data as a sources layer for a statistical data warehouse, the House of Micro Data (HoM). In the HoM, data are integrated on the basis of joint reference data and made accessible by means of an SDMX superstructure. On this basis, a newly created Research Data and Service Centre (RDSC) offers data and analysis services, for analysts and researchers. The ultimate aim of IMIDIAS is to make available what is already there, in a consistent, effective, cost-efficient and user-friendly way, for designated purposes and complying with strict confidentiality rules. 

More information: 

Irving Fisher Committee on Central Bank Statistics, Data-sharing: issues and good practices, Report to BIS Governors prepared by the Task Force on Data Sharing, January 2015, pp 39.

Ulf von Kalckreuth, A Research Data and Service Centre (RDSC) at the Deutsche Bundesbank a draft concept. IFC-Bulletin No 37, Irving-Fisher Committee on Central Bank Statistics, 2014.

The PHF – a survey on household finances and wealth in Germany

The Panel on Household Finances (PHF) is an encompassing panel survey on household finances and wealth in Germany conducted by the Deutsche Bundesbank. It covers the balance sheets, pension claims, savings, incomes and work histories of households, together with some information on consumption patterns, attitudes, expectations and standard demographic characteristics. 

A representative sample comprising 3,565 households provide data for the first survey wave between September 2010 and July 2011. Wealthy households were oversampled on the basis of micro-geographic indicators in order to shed light on the distribution and the composition of wealth across households. 

The PHF data data provide a comprehensive view of households’ assets and debts and their determinants, thus allowing a better understanding of issues such as saving and consumption behaviour, the distribution of wealth or insolvency risks. The anonymised micro data are available for scientific use. Because the PHF is part of the HFCS, a harmonised survey coordinated by the ECB being carried out in all euro-area countries, it is relatively easy to place the German results in a European context. The high data quality makes it a fruitful resource for researchers and monetary policymakers alike.

The results of the first wave of the PHF and the HFCS at large were published in March 2013, revealing a surprising degree of heterogeneity within Germany and in Europe. The data for the second wave were collected in 2014. As many households as possible from the first wave were recontacted, thereby creating a full panel. In 2020, the PHF is preparing the fourth wave.

More information:

PHF survey website