The Fab City approach means that an urban economy is gradually developing into a fully circular economy until 2054, where local demand is met by local production. However, it is unclear at which point a Fab City currently is in this transition. The concept of the Fab City Index is a measure that could somehow quantify its state. It was first developed in France in 2018. The Fab City Index toolkit aims to capture how different economic sectors are performing with regards to the Fab City goal of a fully circular economy and to make this self-sufficiency comparable among existing Fab Cities. However, the French approach is closed source. Thus, we describe an alternative approach based on publicly available data taking Hamburg as an example, and we identify 16 macro-sectors whose development could inform a Fab City strategy. Preliminary results show that Hamburg still has a long way to go, though there is potential for self-sufficiency in some sectors. Measuring consumption and recycling thoroughly should be a high priority. A visual tool like the Fab City Dashboard could document the progress being made. The insights can inform both city administrations in deciding which sectors should be strengthened and statistical offices in expanding their future data collection.
9.1 Introduction
Fab Cities have set themselves an ambitious goal: by 2054, they want to have completed their transformation to a full circular economy according to the DIDO-Paradigm (Diez, 2018, p. 13). That means that they have to produce everything that is consumed within their borders by themselves, they neither import raw materials nor export any waste, nor do they emit greenhouse gasses above a sustainable level. Only data is imported or exported. Thus, material input to production has to come from inside the city by means of urban mining, recycling, upcycling or re-use. So, the circular economy is even more ambitious than just a (re)localized economy that could still import raw materials and would only focus on manufacturing within city limits. Obviously, no city in Europe, and certainly no big city with a population over half a million inhabitants, is anywhere near such a fully circular economy today. Quite on the contrary, since five decades of de-industrialization have left many European cities with a dwindling manufacturing capacity (Rowthorn & Ramaswamy, 1997; Kollmeyer, 2009; Škuflić & Družić, 2016). A great deal of goods must be imported. Manufacturing has moved offshore, and so has production in general if we include food and energy resources. And yet, the production base has to be rebuilt if Fab Cities are to accomplish their 2054 goal.
However, it is not self-evident what production capacity has to be rebuilt exactly. So, in addition to spurring innovation in digitization, production machinery and recycling capacity, a Fab City needs to evaluate where it stands on its way to a fully circular economy. Which production sectors are strong and economically sound, which are underdeveloped or even missing? To assess the state of a Fab City on its way towards the 2054 goal, a measure – or a toolkit of measures – would be needed.
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The first Fab City to address the need for such measures has been Grand Paris, together with consulting firm Utopies. In 2018, they introduced the concept of the Fab City Index (Florentin et al., 2018). It comprises three sets of measures: the priority of an economic sector in a Fab City’s strategic agenda, its self-sufficiency with regards to the 2054 goal and the index number itself, which can attain a value between 0 and 100. 0 means: nothing is produced within the city boundaries; 100 means: the urban economy is fully circular, nothing has to be imported (and no waste to be exported). This index number aims to make progress in the comparability of different Fab Cities and incentivize them to step up efforts in building a circular economy.
Unfortunately, the Fab City Index as conceived by Fab City Grand Paris and Utopies is closed-source. There is no documentation yet on how the index value is calculated nor which data are being used. That means that other Fab Cities cannot replicate the analysis on their own. Given the centrality of the open-source paradigm for the Fab City concept, the sub-project WP 3.3 of the INTERFACER project thus aims to develop a framework for an “open-source Fab City Index” that could be applied independently by other Fab Cities, at least in Europe. In the following, we propose such a framework by taking the city of Hamburg as an example.
9.1.1 Theoretical Motivation
Indexes have long been in use to capture the change of state of an economy or a society over time and make it comparable to other entities. One well-known example is the consumer price index. It collects price data for numerous goods, from a representative market basket in a given year in a standardized way and records their price increases against the previous year. Goods are grouped in categories, the price increase of which are given individual weights that reflect their demand and everyday consumption, thus their current relevance. The weighted price increases of the categories are summed up and yield the index number, i.e., the aggregated price increase over all categories against the previous year (or the base year).1 The increase marks the inflation rate. If, for instance, the index value for the current year is 1.09, the market basket is 9% more expensive than in the previous year. The weighting is important because different categories of goods experience different price increases that are individually insignificant but matter in aggregation.
Another example for an index number is the Gini Coefficient, which is an index for the degree of inequality in the distribution of a country’s income or wealth. Mathematically it is not a sum but the ratio of two geometrical areas that represent distributions of wealth in the population. The index number can attain values between 0 and 1, wherein 0 marks perfect equality, 1 stands for maximum inequality, meaning one individual owns the country’s whole wealth. The Gini Coefficient is used to compare the degree of equality between different countries and can thus, given equality is a societal value, inform policy-makers to take measures against rising inequality if the index value rises over a longer period.
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In a similar way, a Fab City Index could be developed that aggregates the development of local production and recycling capacities of a city’s economic sectors into a single number. With it, a Fab City’s progress over time can be quantified and made comparable to other cities. At the same time, it can provide a more detailed look into a city’s development towards a Fab City because, analogous to the consumer price index, it requires a thorough analysis of individual economic sectors. Here, a typical measure for local production capacity in a certain sector could be the share of a product in local demand that could theoretically be manufactured in a Fab City. If, for instance, a Fab City can produce half the number of new cars that are bought throughout a given year, the share and thus the measure of the respective sector is 50%. To make results comparable across different Fab Cities the measures should follow standardized classifications of economic sectors or categories of goods: for the production side, statistics are ordered according to the NACE classification scheme that was introduced for national economic statistics in the EU in 2006 (European Commission, 2008)2; for the consumption side, statistics follow the COICOP classification (United Nations, 2018).3 With these measures, institutions and agencies that advance the Fab City concept would be able to make informed decisions for which economic sectors and categories of products they would have to step up efforts in order to reach self-sufficiency.
9.2 The French Concept
To calculate an ideal Fab City Index and assess the state of different economic sectors, it would require complete and exact data on production and consumption within the urban territory. For instance, how many sneakers, washing machines or other goods have been bought and how many of them have been manufactured inside the Fab City limits? These data do not exist yet. There is no count of individual items manufactured and bought over the course of a year. Thus, the Fab City Index concept as conceived by Fab City Grand Paris and Utopies works with an indirect modeling approach which draws on several databases about, among others, economic data of companies, household income, imports and exports. The LOCAL SHIFT model developed by Utopies analyzes 257 sectors which are aggregated to the following 12 macro-sectors in Table 9.1.
Table 9.1
Macro-sectors
- Agriculture, fishing industry
- Extractive industries
- Forestry, woodwork
- Mineral construction materials
- Metal industry
- Machines/equipment
- Other manufactured goods
- Food and beverages
- Fashion, textile
- Paper, cardboard, printing
- Chemistry
- Plastic, rubber
Each macro-sector is assigned a value between 0 and 10 for two dimensions: level of self-sufficiency and level of priority. Self-sufficiency means “the territorial entity’s capacity to cover local demand for a given sector”, while priority indicates a “sector’s strategic importance with regard to local demand”. The results for all sectors are finally aggregated into a final score between 0 and 100 which is the index number. A score of 100 means that a Fab City has reached the state of a fully circular economy and can produce its entire demand by itself. For Paris, a Fab City Index of 37.58 in 2018 and a level of self-sufficiency of 8.7% has been calculated (Florentin et al., 2018).
While this approach is truly pioneering work, it has three shortcomings: firstly, the LOCAL SHIFT model is closed source. How and under which assumptions the data is processed is not revealed, thus other Fab Cities cannot replicate the analysis themselves. Secondly, the 12 aggregated macro-sectors represent the divisions of the NACE classification too closely. On the urban level, extractive industries – like oil production – and mineral construction materials are either not that relevant or simply nonexistent. On the other hand, NACE division C26, Manufacture of computer, electronic and optical products, is probably subsumed under Machines/Equipment, which does not match its importance for an urban economy in the early twenty-first century. Also, “Repair” and “Recycling” are absent in the 12 macro-sectors, though both would be of utmost importance for a circular economy. Thirdly, it is unclear whether and how consumption enters into the model.
9.3 The Hamburg Approach
According to the Fab City Global Alliance’s emphasis on an open-source approach towards production, documentation and operations, it would be important that these measures be based on openly accessible data. Thus, we suggest an alternative approach that draws on data publicly available for Hamburg. Though these data are far from being complete, they enable us to make a first assessment of where Hamburg stands with regards to becoming a Fab City.
However, the NACE classification has inherited a Fordist perspective on the economy, which is heavily extractive in its production of resources and gives much weight to classical industrial sectors like the construction of machinery including vehicles of all sorts. However, the rationale of a Fab City’s circular economy should not be to just replicate the current system of manufacturing and consumption with local means. It has to take into account that the current system is inherently unsustainable because it relies too much on extraction and growth of output. At the same time, the shift of major European cities towards an economy centered around services which use digital technologies today in one way or the other needs considering. Thus, we suggest 16 macro-sectors that partly differ from the aggregation in the French approach. These sectors are shown in Table 9.2. For each sector corresponding metrics and/or relevant NACE divisions or groups are given.
Table 9.2
16 macro-sectors which have to be monitored for measuring the progress towards a fully circular economy
Macro-sector
Metric
NACE
1
Energy
MWh/TJ renewable
(D35)
2
Water
Groundwater supply in Mio. m3
(E36)
3
Agriculture and fishing
Global hectares
A01, A03
4
Food and beverages
Production value vs. consumption expenses in €
C10, C11
5
Forestry and products of wood
[Estimate]
A02, C16, C18.
6
Textiles and clothing
Production value vs. consumption expenses in €
C13, C14, C15
7
Chemical products
Production value vs. consumption expenses in €
C19, C20, C21, C22
8
Extractive industries, mining and quarrying
Biomass production (no fossil or mineral deposits)
B5, B6, B7, B8
9
Metal industry
Production value vs. consumption expenses in €
C24, C25
10
Machinery and equipment
Production value vs. consumption expenses in €
C27, C28
11
Vehicles and transport equipment
Production value vs. consumption expenses in €
C29, C30
12
IT and communication
Production value vs. consumption expenses in €
C26, C18.2
13
Other manufactured goods
Production value vs. consumption expenses in €
C23, C32
14
Construction
Number of companies
C41, C42, C43
15
Repair
Production value vs. consumption expenses in €
C33, G45, S95
16
Waste and recycling
Amounts in t
(E37, E38, E39)
9.4 Data Sources
Concerning energy, data are available from the Statistical Office for Hamburg and Schleswig-Holstein,4 concerning water, from the local provider Hamburg Wasser.5 Concerning productive sectors, we draw on data for agriculture and for manufacturing sectors as they are being annually collected by the Statistical Office.6 Data concerning waste collection comes from the Hamburg Department of the Environment, Climate and Energy.7 Concerning recycling data, we rely on numbers given by Hamburg’s Department of Sanitation8 as well as the Statistical Office.9 Data in greenhouse gas emissions come from the Statistical Office.10
Concerning private consumption, data are available from the 2018 Survey of Consumption Expenses of Private Households conducted every five years by the Statistical Office for Hamburg and Schleswig-Holstein.11 This is the only direct data collection on consumption that is publicly available. Concerning company consumption, we use data about investments in the manufacturing sectors.12
In addition, to give a rough breakdown of material flows for Hamburg, we use the Raw Material Consumption numbers for Germany from Eurostat which were calculated by the Institut für Energie und Umweltforschung ifeu (institute for energy and environmental research) in Heidelberg (Schoer et al., 2021). Concerning ecological footprints in global hectares, we use numbers from the respective study of the Zukunftsrat Hamburg (future council) conducted in 2012 (Zukunftsrat Hamburg, 2012).
While the data for energy, water, waste and recycling can be directly analyzed, data for production and consumption have to be matched whenever possible. Production data is grouped according to NACE codes, private consumption data according to COICOP codes. For this purpose, we have built a concordance table between NACE codes and COICOP codes because we want to a) estimate what fraction of demand a sector could theoretically produce, and b) weight different sectors according to the weighting scheme for the consumer price index calculations. As Ganglmair and colleagues correctly noted, concerning consumers, “not all industries are equally relevant”, and they have built a similar concordance table for the calculation of price markups (2020), which can be found in the annex.
The weighting scheme for the consumer price index is a well-founded tool that has long been in use for measuring inflation (Statistisches Bundesamt, 2019). It reflects the relevance of groups of goods and services for consumers, i.e., citizens. All weights are given in per mille and add up to 1000. After matching production data with consumption data via the concordance table, only weights are used where a matching of data is possible. These weights can be adjusted so that they add up to 1000 again, and then be used to calculate the index number. Hence, we could get a first estimate of a “consumption-based” Fab City Index.
The Hamburg Chamber of Commerce has provided a geographical breakdown of companies classified according to NACE categories for the seven districts of Hamburg. It allows for an identification of where certain (sub)sectors in the city are clustered. If aligned with the groups of goods for the consumer price index by use of the concordance table, this makes the depiction of the number of manufacturers for a certain group of goods possible, if there are any at all.
9.5 Preliminary Results
Data are evaluated for the year 2019 because this was the last year before the Sars-CoV-2 pandemic distorted the economy. It is important to understand that matching production value and consumption expenses through the concordance table can only reveal what potential production capacity there is compared to its demand. The numbers do not indicate that a certain sector is manufacturing the very goods that are actually consumed in Hamburg. It indicates that there would probably be enough machinery and equipment (and expertise) to shift production to goods that would meet the local demand. This matching is tenable because production data are given in production value (that is annual production capacity in market prices minus several taxes like alcohol tax and customs – not revenue), whereas the consumption expenses are given in household money spent (that is market prices, from which VAT has to be deducted). So, both data sets are market price data.
Though the project is incomplete as of now, some insights are already available. Some macro-sectors are at a good starting point for the requirements of a circular economy because they already show a significant capacity to match local demand: Macro-sector 4, food and beverages, can be assigned a manufacturing capacity of roughly 50% of the local demand. The construction sector is quite strong, accounting for more than half of the companies registered with the chamber of commerce. This is no surprise given the amount of construction – and demolition – projects in Hamburg.
Macro-sector 13, other goods, could meet over 60% of local demand if we match production value for NACE class C32 with private consumption expenses in COICOP groups 05.1, 05.4, 05.5, 05.6, 09.2, 09.3 and 09.5 (furniture, recreational goods plus groups of semi-durable and durable goods for households).
Macro-sector 10, machinery and equipment, has a strong base with an annual production value of more than 2.6 billion Euro which is considerably higher than the local investments in machinery and equipment by Hamburg manufacturers in 2019.
Macro-sector 7 is another strong one, chemical products. The manufacturing capacity especially for pharmaceutical products is very high based on production value data. Actually, it produces more than the local demand inferred from consumption data.
However, some of the macro-sectors are quite weak: Though there are some strong players in macro-sector 12, IT and communication, these are specialized semiconductor manufacturers that do not produce consumer devices like personal computers or smartphones and could not easily switch production to these devices high in demand.
Naturally, lacking deposits inside the city limits, macro-sector 6, fossil fuels and mining and quarrying, is nearly completely dependent on imports. The high production value for NACE division C19, manufacture of coke and refined petroleum products, of more than 1.8 billion Euro in 2019 is only possible because Hamburg as Germany’s biggest port is a highly important trade center for crude petroleum.
Macro-sector 11, vehicles and transport equipment, comprises some 190 companies, none of which is one of the big car manufacturers. The one exemption in the vehicle sector is the Airbus aircraft plant that is responsible for roughly a quarter of Hamburg’s export turnover.
Macro-sectors 1 and 3, energy and agriculture/fishing, are evidently weak because the share of renewables in energy production is still low, while agricultural land is in short supply. Compared to the area of 9.1 million global hectares needed to feed a population of 1.84 million (Zukunftsrat Hamburg, 2012), the land area used by agriculture is less than 15,000 hectares. Figure 9.1 shows a summary of the preliminary results.
Fig. 9.1
Each macro-sector has a bar that runs from 0 to 100% local production capacity. The blurring of the actual preliminary value indicates the uncertainty of that current value
×
Now that we have preliminary self-sufficiency levels for each macro-sector, we have to assign weighting factors to each of them. For some macro-sectors, we can use the weighting factors from the consumer price index where consumption expenses have been recorded. For others we rely on an informed guess as of yet. Table 9.3 shows the weighting factors that could be used for a first index number calculation.
Table 9.3
Weightings in the fourth column come from weightings of consumption expense categories that were applicable to the calculation. Weightings in the fifth column are estimates for sectors where no consumption data are available as of yet. Weights in both columns are given in per mille and add up to 1000
From these weighting estimates, we would get a Fab City Index value for Hamburg of 37 on a scale between 0 and 100. However, the weighting factors require further discussions as we will show in the next section.
9.6 Discussion and Outlook
Though these insights already indicate which point a strategy for a circular economy should focus on, they only give a rough picture. Much more hard data is needed. The publicly available data suffer from several limitations: for one, legal restrictions. Production values are collected for companies with 20 or more employees. Data for 2019 is shown for 1246 companies in the series E I 5, while there are more than 17,500 companies classified by the chamber of commerce in the NACE groups in manufacturing, albeit most of them are small-scale enterprises. Unfortunately, production value is unavailable for all companies considered in the series. If a NACE group features only a low single digit number of companies and one of them has a huge market share in comparison to the others, the production value is not given due to the protection of competition. Otherwise, production values of the small companies could be inferred from the approximately known value of the big player. This regulation distorts the data.
Import and export data could principally help to clarify which sectors, and that is: which areas of consumption, are primarily dependent on imports. Unfortunately, transshipments in Hamburg’s port distort this data. Imports are given for general trade,13 i.e., goods that are not consumed in the city but go in stock and can stay there for an unknown amount of time, are not excluded from the import value. On the other hand, exports are counted as special trade,14 goods manufactured or finished in Hamburg. So, the transit of goods is not recorded, thus we cannot infer the real balance of goods from the balance of import and export values. According to the Statistical Office, this makes Hamburg an exception compared to other federate states of Germany to date.
A third limitation is the aforementioned lack of comprehensive consumption data. In the regular consumer survey, expenses are inquired only for a limited number of goods, not the entirety of goods as listed in the COICOP classification. Thus, the numbers mostly apply to aggregated classes of goods like clothing and footwear, whereas the expenses, for instance, for household appliances are not collected. Given that household appliances constitute an important equipment for households – though not being replaced very often –, an average number would be helpful for a circular economy strategy that takes the local manufacturing of household appliances into account.
In general, the data being collected has a bias towards a traditional industrial policy which emphasizes output and growth rates, and which does not explicitly take sustainability issues into account, let alone the imperative of a circular economy. With respect to the requirements of the Fab City concept, the extent and reliability of environmental data meanwhile outmatches that of economic data, which is remarkable given that economic statistics have a much longer history.
Whether the perspective of traditional industrial policy is taken or the perspective of a Fab City circular economy affects the priority of the macro-sectors which is reflected in the weighting scheme of the consumer price index. For instance, macro-sector 11, vehicles and transport equipment, currently has a high priority for the German economy in general because it is associated with jobs and exports as well as the aspiration of limitless mobility. On the consumption side, its weighting factor is quite high with a value of 129.05 per mille. In the future, its priority certainly has to decrease. Hamburg for instance has had a fleet of more than 950,000 vehicles – including 813,847 passenger cars – in 2021. A Fab City would not seek to constantly renew this fleet by adding thousands of cars each year. That means: The priority of a macro-sector and hence its weighting factor in the index calculation is not simply a question of statistics but an eminently political question. It has to reflect thresholds of sustainability. In the case of macro-sector 11: what is the sustainability threshold for urban mobility – concerning the number of private cars and the frequency of public transport services? Should a Fab City eventually be a bicycle city where car mobility becomes a rarity such that the manufacturing of cars is of minor importance? The answers to these questions will strongly affect the priority of macro-sector 11. The same holds for other sectors.
That said, the Fab City Index concept as introduced here is only a starting point. It can serve as a framework with which first assessments of a Fab City’s development are possible in the next few years, but parts of the framework can and will change over time. The priorities, that is the weighting factors of the macro-sectors, have to be constantly reviewed. Even the suggested classification of the 16 macro-sectors is not fixed once and for all. Yet, without a structured framework for assessing the efforts towards a circular economy the road to the Fab City 2054 goal cannot be taken.
There is another caveat: the Fab City concept can probably not adhere to a city’s territory in a stricter sense. Hamburg, being a federal city state in Germany, has the advantage of having data available on the city level. However, in the long run, the metropolitan region will probably be the more practical reference frame. It is not only the agricultural production of the surrounding regions that Hamburg relies on and cannot substitute easily. Macro-sector 2, water, gives another example: all drinking water in Hamburg is extracted from groundwater, but the groundwater supply inside city limits covers only 82.7% of its consumption. The rest is drawn from the surroundings, with secured water rights. Thus, Hamburg would have to reduce its water consumption. Regarding the increasing risk of droughts even in the rainy North of Germany, a reduction alone could perhaps not be enough to keep the city’s water demand and the groundwater supply in balance. How the macro-sectors will develop and if the metropolitan region has to be included will require political decisions.
That does not change the fact that the advance of the Fab City concept should be accompanied by a refined data collection strategy. This will certainly not be implemented at short notice. However, more accurate and more relevant data would make the steps towards a fully circular economy more transparent. It could also help to spur innovation and make progress comparable across the Fab City network. Finally, it could coalesce with current efforts of some cities to implement dashboards that visualize environmental and/or smart city metrics. Fab City Hamburg e. V. plans to implement a “Fab City Dashboard” that would show key metrics as indicated above. This would not only help policymakers but companies and the general public alike to comprehend what is needed for a realization of the Fab City potential. Fortunately, a unified data framework at least for European cities is already at hand with NACE and COICOP classifications. More comprehensive data could substantially support the next steps.
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Regulation (EC) No. 1893/2006. NACE stands for the French term “nomenclature statistique des activités économiques dans la Communauté européenne”. The German equivalent is WZ 2008 (WZ for “Wirtschaftszweige”).
These are mainly the statistical reports series C I 3 Der Anbau von Gemüse und Erdbeeren in Hamburg, i.e., production of fruits and vegetables, and C III Die Viehwirtschaft in Hamburg, i.e., livestock production, for agriculture, and E I 5 Die Produktion des Verarbeitenden Gewerbes in Hamburg, i.e., manufacturing.
The classification of consumer expenses in this survey still follows SEA-CPI 2013. In 2021, SEA-CPI was made congruent with COICOP (Statistisches Bundesamt, 2021).
