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Study to examine the socio-
economic impact of
Copernicus in the EU
Report on The socio-economic impact of the
Copernicus programme
Written by PwC October - 2016
European Commission
EUROPEAN COMMISSION Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs
DG GROW — Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs
I.3 Unit — Space Data for Societal Challenges and Growth
Contact: Thibaud Delourme
E-mail: [email protected]
European Commission
B-1049 Brussels
EUROPEAN COMMISSION
Study to examine the socio-
economic impact of Copernicus in the EU
Report on The socio-economic impact of the
Copernicus programme
Final report – The socio-economic impacts of the Copernicus programme
i
LEGAL NOTICE This document has been prepared for the European Commission however it reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. More information on the European Union is available on the Internet (http://www.europa.eu). Luxembourg: Publications Office of the European Union, 2016 ISBN number 978-92-79-59010-8 doi:number 10.2873/733800 © European Union, 2016 Data provided in this report are the property of their rightful owners. Further data reuse is not permitted without the express authorisation of the data owner.
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Report Socio-economic impacts of space activities in the EU in 2015 and beyond
ii
Executive summary
This report presents an assessment of the overall economic impact derived from public spending on
the Copernicus programme. The report compiles the results from two other reports:
Strategy&, 2015. Study to examine the GDP impact of space activities in the EU. Final
Report Prepared by Strategy& for the European Commission. September, 2015.
This study focused on the GDP impact of EC & ESA spending on the Copernicus Space
component over the period 2008 – 2013.
PwC – Strategy&, 2016. Study to examine the socio-economic impact of Copernicus in
the EU. Final Report Prepared by PwC – Strategy& for the European Commission. July,
2016.
This study focused on the socio-economic impacts of freely available and open Copernicus
data & products in Europe. The project analysed the use of Copernicus data along eight
sector value chains: agriculture, ocean monitoring, insurance, O&G, renewable energies,
urban monitoring, air quality monitoring and forestry.
The economic assessment was performed using two different approaches. The economic benefits
derived from public investment in the Space & Services components of the Copernicus programme
were assessed using a GDP impact methodology. The GDP impact – or transactional economic impact
– is the first impact to fully materialize after the initial investment. GDP impact mostly includes the
impact of space manufacturing activities on the local or regional industrial sectors. However, this
assessment does not include the actual use of infrastructure, i.e. the usage of Copernicus data &
products available for free. The complexity of the economic linkages and imapcts arising from the
use of Copernicus data & products justifies the use of a micro-diffusion model to understand at the
level of the firm how the data & products are used and how they are creating specific knowledge
that leads to sales increases or cost reductions. Wider methodologies such as the GDP impact
assessment methodology are not able to capture the complexity of this type of phenomenon and
assess accurately such economic impact.
The overall economic impact of the Copernicus programme was estimated using two different
scenarios: a conservative scenario and an optimistic one.
The conservative scenario is more prudent and robust than the optimistic one; it relies only on
the results of the eight value chain case studies and uses lower-bound estimates of impacts.
The scenario offers a very conservative result on the catalytic impact derived from the availability of
free and open Copernicus data & products. The assessment has led to the estimation of ex-post
enabled revenues of EUR 38.3 M (2015) and ex-ante cumulative enabled revenues of EUR 445.1 M
(2016 – 2020).
The optimistic scenario is derived from the overall size of the markets under scrutiny, optimistic
growth rates (CAGR), and optimistic adoption rates for end-users, together with GIS revenues
for Europe. The assessment has led to the estimation of ex-post enabled revenues of EUR 342.7 M
(2015) and ex-ante cumulative enabled revenues of EUR 2 792.9 M (2016 – 2020).
Figure 1 summarises together all the monetary impacts – transactional and catalytic impacts – derived
from public investment (EC & ESA) in the Copernicus programme. In some cases, the initial
investment in the space programme varies considerably between years, and so to, therefore, do some
estimated impacts. Other impacts materialise at a date later than the initial investment. Consequently,
it is more informative to look at results for the period as a whole rather than for individual years,
distinguishing only between 2014 and 2021 and the period before it.
Report Socio-economic impacts of space activities in the EU in 2015 and beyond
iii
Figure 1 – Overall monetary impact1 of EC & ESA investment in the Copernicus programme,
based on eight selected value chains (Source: Strategy& - PwC analysis)
As stated earlier, the enabled revenues derived from the use of Copernicus products are only based
on eight very specific value-chains and do not include the overall enabled revenues for all the
potential sectorial use of Copernicus in Europe. Furthermore, all the non-monetary benefits that are
generated by the Copernicus programme are not included in this assessment. In the case of the
enabled revenues for the downstream market and end-users, two scenarios were derived to represent
the variance in the use and the acceptance of Copernicus products among the EO downstream and
GIS markets, and among end-users.
The overall cumulative GVA and enabled revenues generated by the Copernicus programme (Space
& Services components and downstream usage of Copernicus products & data) from its beginning
and 2021 have been estimated on the basis of the following two scenarios:
Conservative scenario: EUR 10 766.0 M;
Optimistic scenario: EUR 13 418.2 M.
In both cases, EUR 4 405.04 M was generated before 2014.
1 The category “Before 2013” illustrates the cumulative benefits from the beginning of the Copernicus programme up to 2013. The category “2014-2021” illustrates the cumulative benefits from 2014 to 2021 but it does not include the
benefits from the previous category.
Final report – EEE Non-dependence
Table of contents
EXECUTIVE SUMMARY ................................................................................................... II
INTRODUCTION ........................................................................................................ - 1 -
Objectives and scope of the study ....................................................................... - 1 -
THE COPERNICUS PROGRAMME .................................................................................. - 2 -
Programme description ...................................................................................... - 2 -
Programme structure ..................................................................................... - 3 -
Programme funding and financial overview ........................................................... - 4 -
SOCIO-ECONOMIC ASSESSMENT METHODOLOGY ......................................................... - 6 -
Introduction ...................................................................................................... - 6 -
GDP Impact Assessment Methodology .................................................................. - 8 -
What is GDP Impact? ................................................................................... - 8 -
GDP Impact Assessment Methodology ...................................................... - 10 -
Baseline “Single Tier” modelling approach description ............................ - 12 -
Available data for test case modelling ........................................................ - 14 -
Space Supply Chain Modelling - Black box model evolution ................... - 16 -
Micro-diffusion model ....................................................................................... - 18 -
The original BETA methodology ............................................................... - 18 -
The adaptation of BETA methodology to the context of the enabled
revenues by the availability of Copernicus data & products ........ - 21 -
SUMMARY OF THE PREVIOUS STUDIES ...................................................................... - 24 -
The GDP impact of the public investment made in the Copernicus programme ........ - 24 -
Financial overview ..................................................................................... - 24 -
Results and analysis .................................................................................... - 25 -
Conclusion .................................................................................................. - 29 -
The enabled revenues derived from the use of the Copernicus products ................. - 31 -
Agriculture .................................................................................................. - 31 -
Forestry…. .................................................................................................. - 32 -
Urban monitoring ....................................................................................... - 34 -
Insurance ..................................................................................................... - 35 -
Ocean monitoring ....................................................................................... - 37 -
Oil and Gas ................................................................................................. - 37 -
Renewable energies .................................................................................... - 38 -
Air quality ................................................................................................... - 40 -
UPDATE OF THE GDP IMPACT OF COPERNICUS ........................................................... - 42 -
Copernicus upstream investment over the period 2014 – 2015 and the forecast for the period 2016 – 2021 ........................................................................... - 42 -
Copernicus core services: GDP impact of the EC spending made in the Copernicus core services’ entrusted entities ............................................................... - 44 -
OVERALL ECONOMIC ASSESSMENT OF EUROPEAN PUBLIC INVESTMENT MADE IN THE
COPERNICUS PROGRAMME ............................................................................... - 46 -
The Copernicus Space Component impact assessment ......................................... - 46 -
The Copernicus Services Component impact assessment ...................................... - 48 -
The Copernicus downstream and end-user impact assessment .............................. - 51 -
Copernicus infrastructure and utilisation: a comparison of costs and benefits .......... - 53 -
Final report – EEE Non-dependence
5
Conclusion on the overall economic impact of the Copernicus programme ................... 54
Final report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 1 -
Introduction
Objectives and scope of the study
This study aims to provide the European Commission with an overall socio-economic assessment of
the GDP impact of the Copernicus programme in Europe and the enabled revenues derived from the
programme. The report combines the results of two past studies performed by Strategy& - PwC:
Study to examine the GDP impact of space activities in the EU. The Copernicus
Programme case study. Final Report Prepared by Strategy& for the European Commission.
September 2015.
Study to examine the socio-economic impact of Copernicus in the EU. Report on the
Copernicus downstream sector and user benefits. Final Report Prepared by Strategy& - PwC
for the European Commission. June 2016.
The report also contains an update on the GDP impact of the public spending (EC & ESA) on the
Copernicus Space and Copernicus Services Components.
The objective of the study is to address two evidence gaps in the literature. The first gap is a lack of
analysis of the Copernicus programme at the downstream and end-user level. No overall assessment
of the end-user benefits, both public and private, derived from the Copernicus programme has been
performed in the past.
The second gap in the literature is related to the absence of an overall assessment of the socio-
economic impact of the Copernicus programme, which combines the upstream and downstream
analyses. The recent study conducted by Strategy& - PwC to assess the socio-economic impact of
the Copernicus downstream sector and end-users fills this gap in the literature, in conjunction with
the study focusing on the upstream GDP impact of the Copernicus programme, to offer a full picture
of the programme and its impacts.
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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The Copernicus Programme
Programme description
The Copernicus European Earth observation programme (formerly called Global Monitoring for
Environment and Security - GMES) was initiated in 1998. The programme is a joint initiative between
the European Commission (EC), its Members States, the European Space Agency (ESA), and the
European Environment Agency (EEA).
The rationale and motivation for Copernicus came from the need to have autonomous and
independent access to information in relation to the environment and security on a global level.
Copernicus represents the European contribution to the international observation systems focusing
on environment monitoring (land, marine, atmosphere and climate change): the Global Earth
Observation System of Systems (GEOSS), the Global Climate Observing System (GCOS), and the
Global Ocean Observing System (GOOS).
From its inception, the programme gradually acquired relevance and strategic importance within the
wider European space policy and was ultimately adopted by the EU as the second flagship space
programme, next to Galileo, which is the European global navigation satellite system (GNSS)
programme. In 2012, the European Commission renamed the GMES Programme as Copernicus.
The main objective of the Copernicus programme is to provide Earth Observation (EO) services to
European society to support economic development and increase the well-being of European
citizens. It relies on an open and free-of-charge data policy, and aims to increase European capacity
and demand for EO. The general objectives for the Copernicus programme, as specified by the
European Parliament2 are:
Monitor the Earth to support the protection of the environment and the efforts of civil
protection and civil security;
Maximise the socio-economic benefits, thereby supporting the Europe 2020 strategy and its
objectives of smart, sustainable and inclusive growth by promoting the use of Earth
observation in applications and services;
Foster the development of a competitive European space and services industry and maximise
opportunities for European enterprises to develop and provide innovative Earth observation
systems and services;
Ensure autonomous access to environmental knowledge and key technologies for Earth
observation and geo-information services, thereby enabling Europe to achieve independent
decision-making and action.
The European EO programme will rely on a constellation of seven EO satellites called Sentinels which,
together with other satellites that form “contributing missions” and ground infrastructures for unified
data processing, represent the space segment of the programme. The space segment enables
Copernicus core services, which are described in the following subsections.
2 Sources: Regulation (EU) No 377/2014 of the European Parliament and of the Council of 3 April 2014 establishing the
Copernicus Programme and repealing Regulation (EU) No 911/2010)
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 3 -
The successful orbit insertion of Sentinel 1A in April 2014 and Sentinel 2A in June 2015 initiated the
operational phase of Copernicus services, which enabled the commencement of the land and ocean
monitoring services.
Programme structure
Copernicus is managed by the European Commission (EC) and the European Space Agency (ESA): the
space segment procurement and deployment is coordinated by ESA, while the services side is
managed by the EC.
The Copernicus Space segment comprises two types of satellite missions, the dedicated Copernicus
Sentinels and “Contributing Missions”, which are Earth Observation missions originating from
national space agency programmes within Europe. Data from all missions are streamed and made
freely available for Copernicus services from a unified ground segment which completes the Space
Component. A mechanism to integrate, harmonise and coordinate access to all the relevant data
from different missions is being developed by ESA in cooperation with other national space agencies,
EUMETSAT and, where applicable, with non-European contributors to the Copernicus objectives. The
set-up and deployment of the space segment engages the upstream space industry in satellite and
ground segment manufacturing and also in launch services.
The Copernicus Services segment includes the six core services enabled by the Copernicus
programme: land monitoring, emergency management, atmosphere monitoring, maritime
environment monitoring, climate change and security. The service part also includes an “In-situ
component”, which is coordinated by the EEA and supports both main areas of the Copernicus
programme, “Copernicus Services” and “Copernicus Space Segment” with data. The subsection
“Copernicus Services” provides more information about each of the six core services and the
contribution of the EEA.
The high level view of the organisation of the Copernicus Programme is illustrated in the figure below.
Figure 2 – Organisation of Copernicus programme
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 4 -
Programme funding and financial overview
The figures used in this sub-section are based on the GDP impact report of Strategy& (2015) 3. The
figures up to 2013 are based on payments actually made by EC & ESA, while all the investments made
over the period 2014 – 2021 are based on commitments by EC & ESA (spending that is planned but
not yet made). No more recent figures were available to update the commitments over 2014-21.
The Copernicus programme is co-funded by EC & ESA. Global funding for the Copernicus programme
over 2000-13 was about EUR 3.2 B. A large part of the Copernicus budget (76%) was dedicated the
Space component, and mainly for the development of the Sentinels. The Space component budget
was EUR 2.43 B, out of which ESA provided EUR 1650 M (67,90%) with the remaining EUR 780 M
(32,01%) coming from the EC.
For the Services component, including in-situ component, the EC provided funding to the tune of
EUR 520 M (68,42%), completed by EUR 240 M (31,57%) from ESA. The Space/Services Components
budget breakdown, with further breakdown into EC & ESA spending, is shown in the figure below.
Figure 3 – Copernicus programme funding by EC & ESA over the period 2000-2013 (Source:
Strategy&, 2015)4
The total funding (for both segments, incl. in situ) by ESA is approx. EUR 1.9 B, while that for EC is
estimated to be EUR 1.3 B5.
The EU Horizon 2020 programme includes the provision of about EUR 4.3 B for the environment
monitoring programme6. EC and ESA have signed a common agreement to commit over EUR 3 B to
manage and implement the Copernicus Space Component between 2014 and 2021. More specifically,
the planned investments over the period 2014 – 2021 for the Service and Space components amount
to EUR 897 M and EUR 3394 M respectively.7
3 Sources: publicly available EC documentation;
EC-ESA Agreement on the implementation of the Space Component of GMES (2013). ESA Annual financial report. 4 Sources: Strategy&, 2015. Study GDP Impact. Final Report Prepared by Strategy& for the European Commission.
September, 2015. 5 Sources: EC publicly available documentations;
EC-ESA Agreement on the implementation of the Space Component of GMES (2013). ESA Annual financial
report. 6 Sources: EC publicly available documentations;
EC-ESA Agreement on the implementation of the Space Component of GMES (2013). ESA Annual financial
report. 7 Sources: Regulation (EU) No 377/2014 of the European Parliament and of the Council of 3 April 2014
establishing the Copernicus Programme and repealing Regulation (EU) No 911/2010)
EC: 32%
ESA: 68%
Space Component(EUR 2 430)
EC: 68%
ESA: 32%
Services Component(EUR 760)
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 5 -
Figure 4 – Planned public spending (EC & ESA) on the Copernicus programme over the
period 2014 - 2021 (Source: EC; Strategy&, 2015)4
As mentioned earlier, the team does not have access to most recent figures to perform a more up-
to-date analysis.
Services: 897
Space Components: 3 394
Copernicus Services & Space Components over the period
2014 – 2021(EUR 4 291 M)
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 6 -
Socio-economic Assessment
Methodology
Introduction
Socio-economic impact assessments are important for public institutions since they demonstrate the
broad benefits and value for money of public spending, and help provide transparency vis-à-vis
stakeholders. Investments made in a given programme have different types of impacts, ranging from
monetary to non-monetary impacts. Figure 5 gives an overview of the monetary and non-monetary
impacts derived from a given investment in space activities.
Figure 5 – Socio-economic impacts related to investments in a space programme (Source:
Strategy&, 2015)4
The transactional economic impact – or GDP impact – is the first impact to fully materialize after the
initial investment. The GDP impact includes three main economic assessments (direct, indirect and
induced impacts) and an employment impact; it is one of the two main monetary impacts, together
with the catalytic impact, related to an investment in space activities. The GDP impact mostly includes
the impact of space manufacturing activities on the local or regional industrial sectors. More detail
on to these GDP impacts is available in the next section. The transactional economic impact
materializes over the short-term compared to the other socio-economic impacts and various
standardized methodologies exist to assess these impacts, which facilitate comparison over time and
with other industrial sectors.
Catalytic impacts are the second monetary impact materializing from investments in space activities.
In most cases, this type of impact requires more time to materialize compared to the GDP impact.
Catalytic impacts can be split into two main categories: utilization of an infrastructure funded by the
initial investment under scrutiny and spin-off/spillover effects. The first one includes all the enabled
revenues by the use of a given infrastructure and this is particularly of interest in the case of the
Copernicus programme. The enabled revenues of the Copernicus programme, for example, include
all the revenues enabled by the availability of free Copernicus products, such as an increase in sales
of existing products or the creation of new streams of revenues. Enabled revenues from the utilization
Transactional Economic Impact (GDP)
Direct Impact
(spending of the upstream)
Indirect
(spending
of tier 2
suppliers)
Induced
(spending
of
employees)
Catalytic
Impacts
Employment
Impact
Other
Quantitative
Impacts
Qualitative
Impacts
(Revenues
enabled by
space
systems –
satellite
industry
and
services
and non-
space
industries
and
services;
Spin-offs)
(Employment
supported
by GDP
impacts)
(Patents,
publications
etc.)
(Non
quantifiable
R&D spillovers,
social and
environmental
impacts,
impacts on
security,
prestige,
outreach etc,)
Monetary impacts
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 7 -
of infrastructure materialize mainly in the downstream industry. More details on enabled revenues
are available in the section on the Micro-diffusion model. The second category of catalytic impacts,
the spin-off effect – also called spillover effect – aims at assessing the expertise and knowledge
developed by an organization related to the initial investment. The knowledge and expertise
developed and re-used to develop new products, new services, improvements in quality or efficiency,
cost reduction, etc. are classified as spin-off effects and are quantified in terms of enabled revenues
and cost reduction. Spin-off impacts are mostly technological and industrial, and most of these
effects will be realized in the upstream part of the industry; this report does not assess the spin-off
effect of the Copernicus programme on the upstream industry.
The non-monetary impacts gather the “other quantitative impacts” and the “qualitative impacts”. The
first category includes all the metrics that cannot be turned into, or not typically available as monetary
values. The range of metrics vary from the number of patents or publications related to the
programme under scrutiny to the number of lives saved thanks to the use of a type of satellite, for
example. The “qualitative impacts” gather all the non-quantifiable information such as social and
environmental impact, education & science or the prestige and strategic impacts related to a given
space programme. This report does not assess the non-monetary impacts of the Copernicus
programme.
The following sections present the two type of methodologies used in the previous studies to assess
the GDP impact in one hand and the enabled revenues on another hand of the Copernicus
programme.
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 8 -
GDP Impact Assessment Methodology
What is GDP Impact?
The GDP impact represents the straight economic impact derived from an injection of spending into
the economy. The concept is the basis of expansive policies to boost economies in periods of
recession: institutional spending stimulates further spending in the wider economy, leading to a
multiplying effect.
In the space sector specifically, spending that is injected in the upstream industry (usually) leads to a
cascade of spending and economic activity through the supply chain, as shown in Figure 6. The space
industry spends a portion of the funding on purchases from suppliers (e.g. for components or
subsystems). These suppliers, in turn, spend a part of those funds further down the supply chain (e.g.
for raw materials). Meanwhile, all companies within the supply chain pay their employees’ salaries.
These salaries, in turn, support consumer spending in the wider economy. The compound effect of
all the spending that results from the initial upstream spending constitutes the GDP impact of the
initial investment.
Figure 6 – Schematic of the relationships among GDP impact components (Source: FAA)
According to generally accepted best practices, the GDP impact is calculated by combining these
three components:
Direct GDP Impact: The GDP impact of direct spending into the space industry, which is
typically initiated in upstream manufacturing, is the economic activity stimulated in the space
industry itself; it represents the Gross Value Added (GVA, equal to labour plus profit) realised
in the industrial sector receiving the initial spending
Indirect GDP Impact: The indirect impact is the economic activity (i.e. GVA) supported by
the expenditures on suppliers of equipment and supplies to support the space industry
orders, and their own suppliers further along in the value chain. These secondary or “2nd
round” impacts would not occur if the upstream space industry operations had not required
the support to fulfil their orders
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 9 -
Induced GDP Impact: The induced impact is represented by the economic activity (i.e. GVA)
supported by those people directly or indirectly employed (i.e. in space suppliers) via the
space industry when they spend their incomes on goods and services in the wider European
economy. This induced spending supports the industries that supply these non-space
purchases, and includes housing, retail outlets, companies producing consumer goods and
a variety of service industries.
Figure 7 - GDP Impact components (Source: Strategy&, 2015)
An assessment of these three components theoretically implies tracing the flow of spending through
the entire extended supply chain in the local/regional economy of interest, with all imports subtracted
as cash outflows. In practice, spending effects are statistically modelled for a variety of industrial
sectors in order to enable assessments of the GDP impact.
To understand the actual GDP impact relative to “some kind of spending,” economists define GDP
multiplier(s), which typically relate the final resulting GDP impact to the direct GDP impact. Two
types of GDP multipliers are commonly used in socio-economic impact studies8:
𝑇𝑦𝑝𝑒 𝐼 𝐺𝐷𝑃 𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 = (𝐷𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡 + 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡)
𝐷𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡
𝑇𝑦𝑝𝑒 𝐼𝐼 𝐺𝐷𝑃 𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 = (𝐷𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡 + 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡 + 𝐼𝑛𝑑𝑢𝑐𝑒𝑑 𝐼𝑚𝑝𝑎𝑐𝑡)
𝐷𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡
Type I multiplier does not include the induced impact: such a multiplier is calculated when statistical
data on consumer spending is not available. The Type II multiplier is on the other hand representative
of the overall GDP impact as it captures also the induced impact, i.e. the space industry’s and
suppliers’ employee spending effects (household spending): as such, it is more commonly used.
These multipliers provide an indication of how the initial spending quantity creates economic activity
(GVA) in industrial sectors other than the recipient of the institutional investments, i.e. how wide is
the reach of the investment and how extensive is the industry’s supply chain.
In some cases, the Type II GDP multiplier is referred to the initial spending rather than the Direct
Impact:
𝑇𝑦𝑝𝑒 𝐼𝐼 𝐺𝐷𝑃 (𝑅𝑒𝑡𝑢𝑟𝑛)𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 = (𝐷𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡 + 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡 + 𝐼𝑛𝑑𝑢𝑐𝑒𝑑 𝐼𝑚𝑝𝑎𝑐𝑡)
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔
8 Richardson, Harry, W. 1985. “Input-output and economic base multipliers: Looking backward and forward,” Journal of
Regional Science, Vol. 25, No. 4, pp. 607-662
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
- 10 -
When expressed in this way, the multiplier provides an indication of the “return” in terms of
incremental GVA, obtained as a result of the initial spending.
A GDP impact analysis provides a rules-based and transparent measure of the economic
importance of investment (in space) for the European economy. It communicates the importance
of such investments using standard measures of economic activity (GDP). Additionally, such an
impact assessment allows the derivation of further impacts on jobs, wages and tax revenues; those
being a direct consequence of the increased economic activity, as shown in the figure below.
Figure 8 – Economic activity metrics connected to GDP Impact Analysis
Of all the metrics above, Government Tax Revenues is of particular interest and the impact of the
Copernicus programme on Government Tax Revenues is estimated as the difference from the
baseline (represented by the economy with no institutional spending in the specific space
programme) of the following four components:
Income direct tax
Indirect tax (Value Added Tax)
Employers social security contribution
Employees' social security contribution
It is important to stress here that government revenues are not a part of the total value added: they
represent the total spending that goes back to the government, i.e. which goes out the
macroeconomic flow. Government revenue calculations provide an extremely powerful argument to
decision makers, since they can show whether the increased economic activity stimulated by the
original investment returns tax revenues, and scale of these revenues.
Practically, GDP impact assessments are usually realised using linear input-output models based on
the use of statistical spending data per each industrial sector. In the case of the space sector, no
statistical sectorial spending data exists for Europe as a whole: input-output models group space
within the wider transportation sector (i.e. together with aviation, shipbuilding, land transportation
etc.), thus losing all details pertaining to the peculiarities of the space supply chain. This means that
GDP impact modelling for space requires complementary data retrieval from the space industry
and dedicated modelling of the space sector’s upstream activity.
To date, the GDP impact from space activities has been assessed for specific topics in the US (via FAA
and NASA contracted studies) and for a few European countries (UK, France). No structured GDP
impact assessment has been conducted at EU level.
GDP Impact Assessment Methodology
The study has the objective to produce a simple and actionable GDP impact assessment
methodology, which produces results that are comparable with similar studies carried out for non-
space sectors, and which can be applied to EU-funded space programmes in a repeatable way.
The approach chosen uses the E3ME model for the modelling of the wider knock-on effects on the
European economy, and carries out dedicated modelling of the space supply chain in order to provide
the E3M3 model with spending inputs associated with non-space industrial sectors.
1. First step – modelling of the space upstream: This step takes as input the institutional
spending in space, and produces as output the space Gross Value Added (equal to labour
GDP EmploymentSalaries and
Wages
Government Tax
Revenues
Standard Measures of Economic Activities
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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plus profit) and the spending into a number of non-space industrial sectors that are modelled
in the E3ME.
2. Second step: The spending into non-space industrial sector is fed into the E3ME model,
producing as output the overall GDP impact (total GVA), employment impact and
government revenues.
The key to the methodology definition is the approach taken to model the space upstream, i.e. the
part that is complementary to E3ME model.
Different approaches can be chosen depending on the data available. An analysis of the input
tracking data of EU space programmes showed that input spending is usually tracked in the form of
payments per company per country, rather than by contract information. This implies that most
subcontractors are tracked together with the associated payments.
The most common type of available input data led to the choice of an upstream modelling approach
as a “single tier”, one without the need to rebuild the contractual structure within the space supply
chain (Figure 9).
Figure 9 – GDP impact assessment approach at a glance
Another reason the single tier approach was chosen was the potential for a future adaptation into a
“plug-and-play” approach: with subsequent application of the “single tier” approach on more EU
space programmes, and the continual retrieval of data from the European space industry, an
opportunity is there to build a model that is precise enough to be used as a black-box. This is
elaborated in the following sub-sections.
Upstream industry model
Modelling of the upstream space industry
per country
Spending allocation per sector and per
country
E3ME Model
EU Programme
spending
A
B
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Cambridge Econometrics’ E3ME Model
E3ME is a computer-based model of the world’s economic and energy systems and the
environment. It was originally developed through the European Commission’s research framework
programmes and is now widely used in Europe and beyond for policy assessment, for forecasting
and for research purposes. Although the model is usually applied economic impacts analysis of
energy and climate policies, E3ME can also be applied, as in this study, for investigating supply
change, multipliers and labour market impacts.
The figure below summarises the model’s key features. For more details on the model, see the
appendix.
Baseline “Single Tier” modelling approach
description
Consultation with the EC about available input spending data related to the major EU programmes
allowed the conclusion that a reference format of data tracking for EU programmes does exist:
programme data are generally tracked in the form of payments retained by each recipient company
in each EU country. Most payments to subcontractors down the supply chain are tracked, with a
resolution of one thousand Euros.
The approach is based on the following steps:
1. Review of the companies receiving payments within the programme in each country:
The input data in the form of payment per company per country provide a repository of
companies involved in the specific programme, providing an exhaustive de facto mapping
of the space supply chain for that specific programme.
2. Subtraction of imports from input data: Payments given to companies from outside the
EU do not contribute to EU GDP, and are therefore excluded. In fact, some GDP impact to
Europe may still come from payments given to companies outside the EU in the form of
knock-back effects (non-EU companies still buying supplies from EU), but such effects are
very difficult to assess and are usually insignificant.
3. Classification of the companies as Space and Non-Space: Each company in the space
supply chain is classified as (aero)space and non-(aero)space on the basis of their main
source of revenues; indeed, the wider supply chain of any major space programme includes
companies that do not consider space as their main sector of business.
4. Classification of non-space companies into an E3ME industrial sector, and direct
allocation of the related spending to E3ME: Non-space companies are, when applicable
Detailed Coverage
• 33 European regions
+ 20 world regions
• 69/43 economic
sectors and 43/28
consumption
categories
• 22 fuel users of 12
fuels
Consistent
• Based on system of
national accounting
• Input-output tables
• Bilateral trade
Comprehensive
• Whole energy,
environment
(including material)
and economy system
• Two ways feedbacks
between each module
• Many policy
instruments
Forward Looking
• Annual projections to
2050
• Behavioural equations
with effects from
previous outcomes
• Ex-ante scenario
analysis (ex-post also
feasible)
• 1970-2010 database
• 33 stochastic
equations
• No prior assumptions
• Econometrics
specification allows for
short-medium and
long term analysis
• E3: Energy,
Environment (Inc.
material) and
economy modules
• Power generation
sub-module
• Research can be
decentralised
Highly Empirical
Modular
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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and possible, classified by E3ME as one of the non-space industrial sectors presented in
section 0, and the payments given to those companies are directly allocated to those sectors
as input to the E3ME model.
5. Classification of space companies in sub-categories: Companies that were classified as
space are further aggregated into categories like system integrators/primes, power control,
structures etc., on the basis of their analysed main role within the programme.
6. Retrieval of spending data per each space company sub-category, via offline survey:
The data retrieval is aimed at understanding, for each space category, how much of the
retained EC payment goes into labour, how much is profit (labour and profit together
constitute the space GVA), how much is spent on imports from outside the EU (taken out of
the assessment) and how much goes into untracked spending in other industrial sectors.
This is required to further allocate spending to E3ME non-space sectors.
7. Selection of major companies in each space sub-category for face-to-face meetings
and data retrieval: The major stakeholders and primes are selected for dedicated direct
consultation.
8. Processing of the upstream modelling results and input into the E3ME for the final
wider European economy GDP impact modelling.
The approach relies on an appropriate company classification, via a detailed understanding of
the space programme activities and of the role that major companies play in it. The company
classification effort was checked with major industry players (prime/integrators).
Figure 10 – Schematic of the detailed space upstream modelling approach
The chosen approach characterizes the actual supply chain structure in a non-deterministic manner
(a feature that makes it applicable to different space programmes), and is based on an industry data
retrieval effort that:
Uses aggregated industry data per company category, thus eliminating confidentiality and
sensitivity concerns in the industry data collection process: as source data is never used in
raw form in the final modelling, the data collection can happen under Non-Disclosure
Agreements, making the retrieval of sensitive industry data possible;
Can be easily standardized and implemented at institutional level on a regular basis: the
institution of a dedicated process to collect this data on the industry, under clauses of non-
disclosure of raw data, can be implemented at country and European level.
EC
spending
Space supply chain
Space companies Non-space
GVA:
• Labour spending
• Profit
Further spending:
• Other suppliers untracked in the input data
Sub-categories E3ME non-space
industrial sectors
E3ME
• Labour spending
• Profit
• Specific geographical spending
Step 1
Step 2
Step 3 and 4
Step 5 and 6
Step 7
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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A standardized procedure for data collection from the industry in aggregated form could make the
chosen methodology easily applicable to different EU programmes. Recommendations on regular
data retrieval processes from the industry are provided as part of the output of this study.
Available data for test case modelling
As explained in more detail in the next section dedicated to the Copernicus case study, the input
spending data set retrieved from EC was comprised of:
Item Type Granularity
Time
period
1 Payments per year, per programme, consolidated per
country
2008-2013
2 Payments per year, per programme, per company, per
country
2008-2013
3 Supply chain data list of involved companies per programme, per
country, with, in some cases, details on the
contract activity
2008-2013
Table 1 – Available data set for the GDP Impact Assessment
The available data covers the entire supply chain, including space and non-space companies involved
in the programme per each country. As mentioned earlier, after a first step involving a review of the
company list, the second step in the modelling process involves the classification of companies into
Aerospace or Non-Aerospace.
Non-Aerospace companies are directly associated to one or more of the selected industrial sectors
included within the E3ME model.
Once this classification is achieved, the single tier model is built as a custom tailored interface to the
E3ME model. This model provides input data to the E3ME model to assess the GDP impact of the EC
and ESA funding per year and per country in the form and format for which E3ME is optimized.
For Aerospace companies, this single tier approach basically derives the aggregated portion of
spending in labour, and untracked raw materials spending, as well as profits expressed in average %
of revenues. Additionally, the classification of non-space companies, in accordance to the E3ME
industrial sector list presented in Table 2, allows provision of aggregated spending in those E3ME
sectors.
The E3ME model then assesses the spending in the wider economy, employees spending, as well as
profits and government taxes.
E3ME
Industry
No. Applicable E3ME sectors
NACE Rev.2
classification
1 Coke & ref petroleum C19
2 Other chemicals C20
3 Rubber & plastic products C22
4 Non-metallic mineral prods C23
5 Basic metals C24
6 Fabricated metal prods C25
7 Computer, optical & electronic C26
8 Electrical equipment C27
9 Other machinery & equipment C28
10 Motor vehicles C29
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E3ME
Industry
No. Applicable E3ME sectors
NACE Rev.2
classification
11 Other transport equipment C30
12 Repair & installation machinery C33
13 Electricity Part of D
14 Gas, steam & air conditioning Part of D
15 Water, treatment &supply E36
16 Sewerage & waste management E37-39
17 Construction F
18 Land transport, pipelines H49
19 Water transport H50
20 Air transport H51
21 Accommodation & food services (in Guiana) I
22 Telecommunications J61
23 Computer programming, info services J62_J63
24 Legal, account, & consulting services M69_M70
Table 2 – E3ME sectors identified as relevant in the modelling of the EU space supply chain
The result of the single tier modelling of the space supply chain is a detailed cost breakdown of all
economic activities carried out by companies involved into the programme, as well as the cost
breakdown of non-aerospace activities into sectorial buckets, whether directly performed under EC
contract by non-aerospace companies, or procured by aerospace companies from other sectors. This
results in a detailed allocation by country by sector by aerospace activity type by year, which is used
to feed the E3ME input/output modelling. The results included:
Spending on labour in aerospace, used (as part of the input) to calculate the GDP direct and
induced impact
Cumulative spending in other industrial sectors (E3ME sectors), used to calculate in the E3ME
model the GDP indirect impact, as well as the induced impact
The single-tier model output feeds into the E3ME model. Critical inputs to the E3ME model are
described in the following table.
Item Description Granularity
1 Employment figures (‘000) per year, per country
2 Total compensation of employees
(EUR)
per year, per country
3 Average wage (EUR/person) per year, per country
4 Gross output (EUR) per year, per country
5 Value added (EUR) per year, per country
6 Spending in other sectors per year, per country, for each of the sectors
identified in Table 2
Table 3 – E3ME Model Required Inputs
The E3ME model produces the outputs for the GDP impact assessment, as described in Table 4 below.
Item Description Granularity
1 Value added per year, per country
2 Government revenues / taxes per year, per country
4 Employment per year, per country
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5 Multiplier type II - value added per year, per country
6 Multiplier type II – employment per year, per country
Table 4 – E3ME Outputs
The Figure 11 below shows a schematic of the actual space upstream modelling, and the way it
connects to the E3ME model. The figure also shows the logical flow behind the E3ME model itself,
and the outputs it produces.
Figure 11 – Space supply chain modelling and E3ME wider economy model
The GDP impact assessment is carried out by the E3ME model on an annualized basis for the defined
programme periods, and per each participating country. However, since spending effects materialize
over multiple years (and not just in the year of the initial spending) and given the timespan of the
chosen test case, final results in terms of GDP multiplier are presented in the case study as an average
across the entire time-period.
Space Supply Chain Modelling - Black box
model evolution
The chosen modelling approach is based on the retrieval of company spending data per categories.
As a result of the modelling, it is possible to derive an aggregated spending per each country in a
specific space industrial area.
Once a certain number of programmes in the launcher, satellite and service categories have been
modelled, a simpler model could be built and tested. Such a model would work as a black box:
inserting as input spending data per each country would lead to an automatic calculation of space
GVA and spending into E3ME sectors:
In each country, spending allocation within a programme would have to be carried out within
three main categories: space launches, satellite manufacturing, and non-hardware related
space activities (IT, software, etc.).
Within the model, the spending allocation in each of those macro-categories would then
automatically produce a decomposition of the spending into space GVA (i.e. space labour +
profit), imports, and spending into other E3ME sectors.
Spending into the EU space programme
Wages
Suppliers to households
Imports
Savings
Imports
Other suppliers
(not detailed)
Taxes on
purchases
Social Security/
Public Welfare
Income taxes
VAT
Upstream
Modelling
Labour
ProfitProfit
Retained
Earnings
E3ME
Modelling
Capital flow in
Capital flow out
Prime and ALL suppliers (by payment value)
Labour
(by labour ratios)
Single
TIER
…
Imports Spending in other sectors (as per E3ME
categories)
S1
S2
Sn
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The results of the black box modelling could then be easily entered into the E3ME model for
a calculation of the overall GDP impact.
Basically, the building and maintaining of such a black box-model allows quick GDP impact
assessments (from a run of the black box model plus a run of the E3ME model). The figure below
shows a schematic of such black-box approach.
Figure 12 – Schematic of a prospective Black-Box approach to space supply chain modelling
for GDP impact assessment
The maintenance of the model would, of course, require continual spending data retrieval and update
from the industry in order to keep model assumptions up to date.
Country i
Launchers Satellites Other
Space GVA
Non space sectors
…
…
%
EU Programme
spending
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Micro-diffusion model
Micro-diffusion models are used to understand and assess complex economic phenomena, such as
knowledge and technological spin-offs, and their impacts. The most famous and commonly accepted
micro-diffusion model among economists is the BETA methodology that has been used since the
1980s to assess the spillovers derived from a public investment in a given space
project/grant/contract. It was used successfully for Europe, Canada and the United States of America.
The team has adapted a new methodology derived from the BETA one to assess the revenues enabled
as a result of the Copernicus data and products being free and open; such phenomena require a
micro-economic approach. The following sections present the original BETA methodology and how
it was adapted to assess the spillovers from the Copernicus programme.
The original BETA methodology
The economic benefits enabled by public investments in space activities are mostly intangible and
complex to understand. The changing nature of spillover – spillover and spin-off are both used to
describe such phenomenon – requires a micro-economic approach, rather than a macro-economic
one, in order to understand a complexity of the relationships and impacts at the level of the firm. The
BETA methodology was developed by the Bureau d’Économie Théorique Appliquée at the Université
Louis Pasteur in Strasbourg for this purpose. The main goal was to assess the impact of the knowledge
and expertise developed by an organisation during an ESA contract. The method was used afterwards
to assess the spillover derived from Canadian Space Agency (CSA) and NASA contracts. The
methodology relies on direct face-to-face interviews with executives from a large number of firms
that were contracted by a national space agency. The rationale behind this micro-economic approach
is to understand and capture the value-added of the expertise developed by the company being
employed by a space agency, contract by contract, company per company. The use of this
methodology is commonly accepted among both economists and space agencies to measure the
impact of spin-offs derived from public investments in space activities. It is a robust methodology
that has already been used several times to assess the economic benefits derived from public
investments in space.
The data collection is organised in a three-step process. First, it aims to understand, at the micro-
economic level, if a company has developed a particular knowledge on a contract funded by public
money. Then, if the first answer is positive, the interviewer has to understand if this particular
knowledge has led to significant improvements in the company. If there are significant
improvements, the next stage aims to estimate if this improvement has led to an increase in value-
added captured by the firm, in terms of sales increases, cost reductions and/or the retention or
creation of a critical mass of experts within the company. Finally, the last step aims to estimate the
paternity9 of the impact regarding the government funding, asking for a range of percentage values.
From the given range, the lower figure is always chosen for the analysis to provide a conservative
analysis. The aim here is to make a minimal estimation of a complex phenomenon to
demonstrate its existence rather than giving an exact estimation of the economic benefits
enabled by public investment, here through national space agency contracts.
The quantification method is based on the information gathered during direct interviews, and
secondary data such as internal documents from the companies, studies from expert groups, financial
reports or official reports from national space agencies. The aim of these direct interviews is to
understand and assess the effect over time of the contracts awarded to each company. These effects
9 Paternity means in this context the origin of the phenomena.
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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have to be unintended to be considered as spillover effects and are measured by quantifying the
added value associated with all investments (Guinet, 2011), estimated as the unintended sales
increase and cost reductions that result as a consequence of the expertise developed under each one
of these contracts. On the other hand, the secondary documents are necessary to better understand
the context and the particularity of a given technology or product, to assess properly the value-added
created and the real contribution of public funding.
The quantification method relies on the assessment of the four following sub-effects (Amesse et al.,
2002) presented below:
Technological effect: an improvement of existing technology and/or the development of a
new technology due to a contract with a national space agency, leading to an increase in
sales or cost reductions (in terms of value-added);
Commercial effect: a reputation effect, where having worked for a national space agency
leads to the development of an actual market or the creation of a new one, leading to an
increase in sales or cost reductions (in terms of value-added);
Organisation effect: the improvement or development of new organisational methods,
and better project management of quality control methods, leading to significant cost
reductions (in terms of value-added)
Critical mass: the retention or creation of a critical mass of experts (also called Highly
Qualified People (HQP)) who contribute to the competitive advantage of the firm.
The three first sub-effects are quantified through value-added at the firm level, represented by
unintended sales increases and cost reductions due to the expertise/technology developed while
working for a national space agency. This added-value is often supported or complemented by the
development and/or maintenance of a critical mass of experts within the firm. This highly skilled core
of people contributes to the competitive advantage of the company. This sub-effect can also be
quantified and expressed in terms of value-added for the company, but it is normally quantified
separately in order to capture the impact of a given national space agency contract on the creation
and/or retention of critical knowledge within an organisation. More details are available in the table
below.
Table 5: Economic effects related to public investments in space (Source: Mosaic, 2014)
The following quantification methodology is based on the work of Cohendet (1997).
Description of sub-effect
Technological effect Sales of technological products not included in the original contract with a national space agency
Sales of new products using a technology acquired or
developed during a contract with a national space
agency
Increased sales of current products enhanced by the
experience of working with a national space agency
Sales by a new department or a new company
created thanks to the technology developed working
with a national space agency
Commercial effect Reputation effect of working with a national space agency
New network and partnerships developed thanks to
the national space agency’s contract
International collaborations
Organizational effect Quality management Project management
Production methods
Critical mass Retention of a critical mass of expertise within the firm
Improvement of existing critical mass
Development of new competences
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The final unit of measurement used to express spillover effects on a firm is the added value, together
with the estimated value that results from setting up and maintaining highly skilled design and
production teams (defined above as the “critical mass”). The quantification exercise thus involves
determining how the work carried out by national space agency projects affects these two
parameters. The process is illustrated in the diagram below (Figure 13). The contracts that firms obtain
from the national space agency, like all their other activities, affect the four basic factors
corresponding to the four types of effects described earlier (technological, commercial,
organisational, and critical mass). These, in turn, contribute to increases in sales and reductions in
costs, and thus, under some circumstances, help to increase the firm’s added value.
To quantify the sales increase, the interviewees were asked to estimate, as a percentage, two sets
of coefficients:
The Q1 coefficients accounting for the estimated influence of each effect, whether
technological (Q1t), commercial (Q1c) or organizational (Q1o), on the effective sales increase.
These coefficients represent the proportion of economic value related to each one of the
various effect; the sum of Q1t, Q1c and Q1o must therefore be equal to 100%.
The Q2 coefficients accounting for the (minimal) estimated influence or paternity of national
space agency contracts (or projects, in this case) on each one of the three effects mentioned
above. These coefficients characterize how much of these effects is due to national space
agency investments, with regards to the firms’ other activities; while they must be between
0% and 100%, their sum is not equal to 100%. They are very often based on objective data
such as the share of national space agency’s funding in the development of the product or
service in question.
The increase in added value (related to sales increases) is obtained by multiplying the effective
increase in sales related to national space agency activities by these two sets of coefficients.
To quantify cost reduction, the interviewees were asked to estimate savings on inputs, lower reject
rates, savings in production time and miniaturization. This is calculated:
Directly, by adding up the savings made thanks to methods acquired under national space
agency contracts;
Indirectly, by multiplying the value of savings made over the period under scrutiny with the
percentage of influence of national space agency contracts (provided by the Q2 coefficients).
The increase in added value (related to cost reduction) is obtained by multiplying the effective cost
reduction related to national space agency activities by the set of Q2 coefficients, whenever required.
The total increase in added value is obtained by adding together the increase in sales and the cost
reduction related to national space agency investments. This sum represents the total spillover effect
or non-anticipated revenues generated by the industry over the period under scrutiny in addition to
the “direct” economic effects derived from national space agency investments. The “multiplier” for
each company’s national space agency projects is obtained by dividing the increase in added value
by the total value of the project (or sum of related contracts). The global multiplier is the average of
all company multipliers, or the average value created from the spillover effect for one dollar invested
in space.
This seemingly complex procedure, which remained well understood by all respondents, was
designed to meet two essential requirements. First, it enabled us to isolate the specific contribution
made by national space agency contracts from the firm's other activities, so as to allow for the fact
that technologies or production methods often stem from developments made in a number of
different projects over a period of time. Secondly, it enabled us to provide a minimum estimate of
the volume of spillover effects rather than a precise value attached to them (given, for instance, that
some items may be overlooked). Consequently, the corporate managers taking part in the survey
were asked to assess the influence of national space agency contracts in terms of an estimated range,
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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of which only the lower figure was used for the final calculation. The focus of this study, in that
respect, is more on the existence of the phenomenon, rather than on its exact quantification.
Figure 13 – Quantification method of economic spillovers (inspired by Mosaic, 2014)10
Once the results have been obtained by type of actors on the sample under scrutiny, the results can
be extrapolated from the sample to the overall population – in most of the cases the overall
population is considered to be the overall contracts granted by a national space agency on a given
period of time. The extrapolation is straightforward and commonly accepted among economists:
once multipliers have been derived from the sample for each type of actors per type of activity, the
same multipliers are applied directly to the overall population per type of activity. The overall results
give a sense of a minimal estimation of the spin-off enabled by the original national space agency
grants/contracts/investments.
The adaptation of BETA methodology to the
context of the revenues enabled by the
availability of Copernicus data & products
The economic impact derived from the availability of Copernicus data and products for free is similar
to the context of a spin-off in terms of economic quantification. As explained in the previous section,
the BETA methodology aims to understand and assess the knowledge created thanks to national
space contract and re-used in another context as either an increase in sales or a reduction in costs.
The free availability of Copernicus data and products for intermediates and end users can be assessed
by understanding and assessing the type of knowledge created by these users thanks to the existence
of a programme such as Copernicus. However, some calculations related to linking cost and revenues,
through the provision of multipliers, cannot be performed in the case of Copernicus. The
quantification of the critical mass of experts also cannot be directly performed because accessing
specific information in each value chain related to the average salary of specific position/jobs within
companies is too sensitive.
10 Source: adapted from Mosaic, 2014. Spillovers effects from CSA contracts for the period from 2005 to 2014. Report
prepared for the Canadian Space Agency. Montréal, Canada.
Non-aerospace activities
Space activities funded by public spending
Other space activities
Technological
effect
Commercial
effect
Organisational
effect
Critical mass
Cost reduction
Sales increase
Estimated value of critical mass
Increase in added
value
Δ Q2 coefficients
(Estimated influence of
national space agency
contract on four effects)
Δ Q1 coefficients
(Estimated influence of
four effects on economic
variables)
Draft report –Socio-economic impacts of space activities in the EU in 2015 and beyond
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The methodology used to assess the revenues enabled by the Copernicus programme is a micro
diffusion model adapted from the original BETA methodology to fit to the context of the use of EO
data, in this particular case, the use of Copernicus data & products. The following paragraphs present
the approach used to assess the revenues enabled by the availability of free Copernicus data &
products.
The first step relies on the characterisation of the value chain under scrutiny. The goal of this
phase is to understand the value-chain and supply-chain structure in order to identify the scope
of analysis. Using a mix of secondary documents and direct consultation with experts, the structure
of each value chain is identified, highlighting the main actors in each value chain. Another goal of
this first expert consultation is to perform an initial market sizing in order to understand the main
components and trends on these markets (market size, growth rate, potential substitution products,
relative importance of EO, etc.).
The second step aims to identify and map the principal actors of a given value chain in order to
understand the role of each one and to gather information on each type of organisations. This step
also aims to develop a prior understanding on how EO data are used in general, and potentially
Copernicus data & products, in the value chain under scrutiny. This step should lead to the creation
of a data base gathering all the relevant information per actor, such as nature & size of actor,
geographic repartition, etc.
The third step is to pick a representative sample of specific end-users for each value chain. This
step varies from the original BETA methodology since no prior data facilitating analysis are available
on the population under scrutiny, such as national space agency’s contracts/grants. In the case of the
Copernicus programme, the team has decided to derive a specific case study within each value chain
considered as representative in terms of geographic repartition and type of actors. Each case study
should include organisations from all type of actors identified in the value-chain structure
(step one) and organisations coming from all over Europe. In statistics, a sample gathering at
least 30% of the observations can be considered representative if these observations chart the
diversity of the overall population in terms of type of actors, geographical repartition and size of the
companies. Given the size of the value chain under scrutiny (European O&G upstream, insurance
for disaster, agriculture, renewable energies, etc.) and the intangible character of the market for
Copernicus data & products within each value chain –no prior economic assessment was
performed on the revenues enabled by an open data EO programme on the value chains under
scrutiny – the team has decided to pick the more representative case studies, without being
able to respect exactly this statistical rule; this element is one of the main limits of the
methodology used.
The fourth step is based on the actual quantification of benefits for the intermediate and end
users of each value chain. It relies on the direct consultation of the organisation included in the
case study pinpointed during the previous step. Two types of data have to be collected during
interviews in order to measure benefits enabled by the availability of free Copernicus data &
products. Qualitative data enables better understanding the specific context of the
organisation and their business model. Understanding how the Copernicus data & products are
used (alone or coupled with other sources of data) and the importance of Copernicus data & products
are useful information to support and complement the quantitative analysis, especially for the
paternity assessment. Quantification of the benefits enabled by Copernicus data is the second
phase of the interview. The aim is to assess the intermediate and end-users’ benefits based on
revenues, investment made to support business development and the role play by Copernicus data
to enable revenues. The quantification process relies on the assessment of the increase in
revenues and reductions in costs, compared to the activities performed by the organisation
before the availability of free Copernicus data & products. The effects are inspired by the original
BETA methodology but they were adapted to better fit the Copernicus programme context. Three
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types of effects have to be assessed in order to understand the impacts of Copernicus on the private
end-users. These three effects can be:
Market effect: the availability of open Copernicus data enables an innovative offer for the
private end-users by:
- Increasing sales of existing products;
- Increasing sales of new products on existing markets.
- Creating a new market.
Commercial effect: the availability of open and affordable Copernicus data enables the
development of a new or/and better commercial network for the private end-users by:
- Developing new networks and partnerships thanks to the Copernicus programme;
- Developing international collaborations.
Organisational effect: the availability of open and affordable Copernicus data enables
organisational improvements within the organisation by:
- Improving production methods;
- Improving efficiency/productivity.
Once the users’ benefits are assessed, a range of paternity coming from the Copernicus data &
products is estimated by the team in conjunction with the organisation interviewed. In most of the
cases, once the overall revenues of the company derived from the use of EO data in general have
been assessed, the interviewer can isolate a range for the contribution of Copernicus data & products
to the overall revenues of the firm. This process is strongly related to the qualitative and quantitative
data collected during all the process (context of the firm, substitution products available, etc.). On
this range, the team would automatically take the smallest figure in order to provide a minimal
estimation of the paternity coming from the use of Copernicus data. The aim of this micro-diffusion
approach is to show the existence of the phenomenon rather than providing an exact figure,
in order to prevent any overestimation of benefits. The complexity of the economic
phenomenon in the case of the use of Copernicus data & products justifies the use of a micro-
diffusion model to understand at the level of the firm how the data & products are used and
how they are creating specific knowledge that leads to sales increases or cost reductions. Wider
methodologies such as the GDP impact assessment methodology are not able to capture the
complexity of this type of phenomenon and assess accurately such economic impact.
The fifth and final step is to extrapolate the results obtained in the case study to the overall
population. The aim of the extrapolation is to assess the overall size of the potential market for each
type of actors identified in the first step of the approach. The overall market is assessed using a mix
of public (Annual financial statement, industry reports and surveys, etc.) and private (Bloomberg,
Technavio, Rystad Energy, etc.) sources of data. Once the market of each type of actor has been
identified, the percentage of the overall revenues attributed to the Copernicus data & products per
type of actor is extracted from the case study. The next phase aims to apply these percentages to the
overall size of the market for each type of actor; this step enables the potential size of the market for
Copernicus data & products in the value chain under scrutiny to be assessed.
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Summary of the previous
studies
The GDP impact of the public investment made
in the Copernicus programme
The GDP impact of the public investment made in the Copernicus programme is based on the study
prepared by Strategy& in September 2015 for the European Commission. It aims at assessing the
GDP impact related to the joint-investment of EC and ESA in the Copernicus infrastructure
Financial overview
During the period under scrutiny, more than 1.6 billion euros were spent by EC and ESA in the
Copernicus programme. Of the total programme spending in the 2008-2013 period, about 30 %,
representing over EUR 480 M, was funded by EC. In addition to these EUR 480 M, EUR 106 M were
spent by EC as a pre-payment for the future launches of Sentinels satellites. The chart below shows
the split in the five main categories of spending over the period.
Figure 14 – Repartition of EC spending in Copernicus programme over the period 2008-2013
(Source: Strategy&, 2015)4
The geographical repartition of the companies receiving major EC spending enables us to point out
the main regional repartition of spending.
Figure 15 shows the European regional clusters of high economic activity related to Copernicus on
the period 2008-2013.
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Figure 15 - Clusters of high economic activity connected to EC spending for the Copernicus
programme in 2008-2013 (Source: Strategy& 2015)
The figure reports a widely-spread clusters activity in Europe enabled by EC investment in Copernicus
programme. As expected, countries like Germany, France or Italy and also to a lesser extent UK and
Spain show clusters of high economic activity connected to the programme. The remaining 70% of
the total programme spending in the 2008-2013 is provided by ESA and represents around EUR 1.2
B over the period 2008-2013. The detailed input data as payment per company per country over the
period for ESA spending in Copernicus was not been made available, so no information regarding
the granularity of the ESA part of spending can be presented here.
Results and analysis Upstream Modelling
Figure 16 represents the repartition of EC-only and overall spending captured by the aerospace
industry but also the impact on other industrial sectors in Europe enabled by such investments. The
countries receiving the biggest share of spending are respectively Germany, France and Italy in both
samples. In both samples, around 3/5 of the spending remains in the aerospace organisations in the
EC-only and overall spending sample with respectively a space GVA of 56% and 60%. However,
spending in non-aerospace sectors are unexpectedly high compared to other public spending in
high-tech industry in Europe with around 40% of the spending in both samples.
Figure 16 – Spending in space GVA and non-aerospace industrial sectors in Europe for the
Copernicus programme over the period 2008-2013 (Source: Strategy&, 2015)
Overall spending in Copernicus programme (2008 – 2013)EC-only spending in Copernicus programme (2008 – 2013)
3%
11%
8%
56%
7%
4%
15%
3%
3%60% 0% 8%
12%
13%
Space GVA
Legal, account, & consulting services
Computer programming, info services
Telecommunications
Computer, optical & electronic
Electrical equipment
Other
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These industrial sectors were then regrouped to larger categories to facilitate the spending’s analysis:
Raw materials, including “Coke & ref petroleum”, “Other chemicals”, “Rubber & plastic
products”, “Non-metallic mineral prods”, “Basic metals” and “Fabricated metal prods”;
Basic equipment & machinery, including “Electrical equipment”, “Other machinery &
equipment”, “Motor vehicles”, “Other transport equipment” and “Repair & installation
equipment”;
Value-added equipment, including “Computer, optical & electronic”;
Transport (freight and human), including “Land transport, pipelines”, Water transport” and
“Air transport”;
Facilities (management of existing facilities and new infrastructures), including “Electricity”,
“Gas, stream & air conditioning”, “Water, treatment & supply”, “Sewerage & waste
management”, “Construction” and “Accommodation & food services”;
Services, including “Telecommunications”, “Computer programming, info services” and
“Legal, account & consulting services”.
Figure 17 – Spending in non-aerospace organisations in Europe for the Copernicus
programme over the period 2008-2013 (Source: Strategy&, 2015)4
Even if some difference in terms of percentage can be noticed between both samples, the biggest
share of spending in non-aerospace sectors, more than 65%, is supporting the space manufacturing
industry (raw materials, basic equipment & machinery and value-added equipment). Facilities and
transport spending are rather high and correlated with the space manufacturing industry in order to
support this activity. Finally, investments in the services have 2 components. The first one is funding
technical experts to support the manufacturing part of activities (mostly engineering consultants).
The second one is supporting the development of applications and services and ground activities to
enable the Copernicus core services.
The difference between both samples can be explained by the composition of the overall spending
sample using only Top10 companies which gathers mainly satellites and ground equipment
manufacturing companies. This statement explains the strong importance of basic equipment &
machinery in the overall sample and the decreasing importance of raw materials. Since raw materials
are usually less expensive than value-added equipment and basic equipment & machinery, in a large
sample such as the overall one (compared to the EC-only sample), raw materials spending are diluted
and less important in terms of percentage.
GDP Impact Assessment (E3ME) results
The logic underpinning the calculation of the Type II multiplier using the E3ME model is as follows:
16%
6%
11%
29%
31%
6%