Introducing Space Debris as a Systemic Risk

Often, space debris risk is defined as an emerging risk, since it is difficult to quantify and its potential impact on business is not sufficiently taken into account. This is based on the following promises: the rapid change of space debris environment, the growing inter-connectivity between space and ground-based infrastructures, the development of new technologies for problem assessing and mitigation, though “systemic risk”

Resilience matrix showing the direct temporal

Figure 8.8: Resilience matrix showing the direct temporal (plan and prepare for, absorb, recover from, and adapt) and spatial (social, information, physical) domains of resilience for space debris related publications of UNOOSA (COPUOS, STS, LSC), European Code of Conduct for Space Debris Mitigation (tagged EU), Inter-Agency Space Debris Coordination Committee (IADC)’s Space Debris Mitigation Guidelines, International Telecommunication Union (ITU)’s Recommendations, International Academy of Astronautics (IAA)’s Study on Space Traffic Management

approach is a more appropriate concept for space debris risk assessment. Systemic risk is also sometimes called network risk, since it emerges from complex non-linear cause-effect interactions among individual elements or agents with different and often conflicting interests. In addition, each element or agent is characterized by its own risk portfolio. The examples of systemic risks are the financial crisis of 2008, pandemics, cybersecurity, global climate change. Systemic risks can trigger unexpected large- scale changes to a system or imply uncontrollable large-scale threats to it [82]. In other words, they tend to be fat-tailed.

The lack of knowledge about interdependencies requires advanced approaches to problem solving through risk thinking. The traditional linear methods have limited application. The concept of “femtorisks” stresses the importance of challenging standard approaches for risk assessment [83]. Because in a systems-approach there may be many competing solutions with no clear best, the challenge for their governance is to assure transparency, accountability, and inclusiveness of the risk management process, and effectiveness, stationarity, equity, and sustainability of the outcome [84]. One of the solutions is the principle of collective responsibility proposed by Helbing, D. [85].

Compared to critical risks, systemic risks do not attract the same attention and tend to be underestimated. The OECD defines critical risks as a rapid-onset event that pose the strategically significant risks as a result of their probability and likelihood. Another spaceborne hazard, space weather, was defined as a future global shock by the OECD [86]. Therefore, the concept of critical infrastructure resilience with respect to space threats is mainly focused on ground-based critical infrastructures, especially on power grids, which is the current backbone of modern critical infrastructures [87,88]. Governments established the programs for assessing space weather risk, and this risk is included in the national risk portfolio of several countries (USA, Canada, Finland. Sweden, Norway, United Kingdom. Germany, the Netherlands, Hungary). They specified the tasks that will lead to improvements in policies, practices and procedures for decreasing vulnerability.

Recently, the OECD has developed a framework for good governance of critical infrastructure resilience that considers the system and interconnected risks to critical infrastructures [89]. This framework can be a good starting point for creating practices of space debris risk assessment. However, two points should be noted. The first point is stated by Bresch, D. N. [90]:

[a] critical element of robust foresight includes innovation and experimentation.

The second point is that enhancing resilience to systemic risk is possible within systems thinking. Systems thinking is an inherent assumption within the complexity theory [911.

Towards Risk Transfer. Conclusions

Dealing with the space debris risk has become a day-to-day business for space asset owners and operators. Moreover, there is an overarching societal interest in avoiding catastrophic impacts on the society well-being due to the collisions. Experience shows that the time after a disastrous event is the window of opportunities for implementing actions. The relevant stakeholders - in order to avoid the same loss in the future - are eager to implement actions with higher cost in order to boost a long-term resilience. However, the experience of catastrophic event mitigation (e.g. eruption of Eyjafjallajokull volcano in 2010, Japan tsunami 2011, hurricane Sandy in 2012. and others) proved that prevention is more beneficial than mitigation. Overall, the risk strategies can be classified with respect to the financial impact and likelihood of the event’s occurrence as follows [92]:

  • Accept the risk - activities responding to a practice of monitoring the risk if the cost-benefit analysis determines that the cost to mitigate the risk is higher than the cost to bear the risk. The best response in this case is to accept the risk.
  • Transfer the risk - activities with low probability of occurring, but with a large financial impact. The best response is to transfer a portion or all of the risk to a third party by purchasing insurance, hedging, outsourcing, or entering into partnerships.
  • Mitigate the risk - activities with a likelihood of occurring, but a small financial impact. The best response is to use management control systems to reduce the risk of potential loss.

• Avoid the risk - activities with a high likelihood of loss and large financial impact. The best response is to avoid the activity.

Risk transfer from policyholders to insurance, and later to reinsurance companies is an effective mechanism for ensuring financial stability. In other words, pooling risks reduces the uncertainty of expected loss over a given period of time. It helps to preserve the continuity of business especially in the presence of extensive events. Babylonian King Hammarubi’s code (1750 B.C.) was the first record of insurance. Across the centuries, merchants were forming a pool to spread the loss. Enhancing maritime information exchange in Lloyd’s Coffee House led to underwriting development. The first record is dated 1757. Large fires across Europe drove the development of the reinsurance sector: Cologne Re after the Hamburg Fire of 1842; and Swiss Reinsurance Company Ltd. (Swiss Re) after the Glarus Fire of 1861. These severe events demonstrated the need for reinsurance, although many reinsurance companies were in fact founded to prevent the outflow of reinsurance premiums from local economies to foreign ones [93].

Event scenario is the basis of the catastrophe models, which are used for specifying the amount of risk to be transferred and the price for it. A catastrophe model is a computerized system that generates a robust set of simulated events and estimates the magnitude, intensity and location of the event to evaluate the amount of damage and calculate the insured loss as a result of a catastrophic event [94]. The identification of key catastrophe models development milestones is given by Grossi, R [95]. Catastrophe models are used by all three parties: insurance companies - who underwrite the original policy, reinsurance brokers - who works on behalf of the insurer to transfer risk to one or more reinsurances via reinsurance policies, and reinsurance companies. The graphical representation of a catastrophe model structure is given in Fig. 8.9.

Catastrophe model structure

Figure 8.9: Catastrophe model structure

Any catastrophe model consists of four components:

  • (1) Hazard - is a process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation. Two types of hazard models are used: deterministic (scenario models) - which are determined by assumed initial modelling conditions like historical hazard events or what-if scenarios, or probabilistic models - which estimate the probability of an event of a given severity. Contrary to deterministic models, probabilistic models can correlate spatial and temporal risks for a credibly scaled event. They combine historical data with theoretical and statistical models. The space debris peril is characterized by limited data points compared to other natural and technical hazards. However, even for more frequent hazards, insurance companies have only 10- or 20-years time-series of claims. It is due to the fact that the underlying trends - such as exposure landscape, infrastructure reliability standards, construc- tion/maintenance costs - change.
  • (2) Exposure data - is the primary input to catastrophe model. The exposure can be evaluated using a combination of geospatial mapping and probabilistic modelling. The quality of exposure data varies greatly in various parts of the world and the industry lines. Along with object location, sum insured, primary and secondary modifiers should be considered. The S-curve approach is normally used for dealing with the change of the exposure amount through the life-cycle. Catastrophe models can assess the economic impact by associating an event- based model with an economic exposure database [96].
  • (3) Vulnerability component - links hazard and exposure components. Most vulnerability models are arranged as a series of damage functions, which enable look-up between hazard intensity and estimated damage as a ratio of total value [97]. Damage ratio DR (i,j) for a i object from a j peril can be represented as shown in Eq. (8.7).

(4) Loss module - is an output of a vulnerability module. It translates infrastructure damage into costs covered by insurance. It is usually expressed as ground up loss which is the entire amount of an insurance loss, including deductibles, before application of any retention or reinsurance; retained (client) loss, which is the loss to be insured; gross loss, which is the amount of a ceding company’s loss irrespective of any reinsurance recoveries due. It is essential that the loss reflects the impact of insurance products and mechanisms. The main functions of a loss module are: reflecting any insurance and reinsurance policy conditions; aggregating the location-coverage losses to higher levels (e.g. policy or country levels); back allocating the impact of higher-level policy structure to lower levels so that impact of higher-level structures can be understood and summarized at a more detailed level; calculating summary metrics such as Average Annual Loss (AAL), Occurrence Exceedance Probability (OEP) and Aggregate Exceedance Probability (AEP) curves [97]. Afterwards, the catastrophe model should pass the iteration validation process. At the end, there should be a clear understanding of:

  • • Which spatial and temporal resolutions were achieved? Is this high enough?
  • • How are footprints calibrated?
  • • Which historical events are considered? For model creation? For its validation?
  • • Which hazard metrics are used?
  • • How does loss data vary among territories and industries?
  • • What is the exposure data quality?
  • • What are the sources and the range of uncertainties?

Historically, insurers provide a proven and tested insurance product for property insurance against the failure of that satellite during launch or operation and will typically recoup only the cost of the satellite, not the loss of future revenue [98]. The evolving market and environment conditions dictate the need of new insurance products. One of the leaders in the space insurance industry, Swiss Re, states that the main challenge is to find a way to reconcile the carefully crafted insurance product that responds to the bespoke requirements of the constellation operators with the heightened risk that the deployment of such a large number of new satellites clustered in an already densely populated LEO increasingly poses [99]. Orbital space is considered to be very large, and the relative size of space assets is in contrast very small. Besides the fast increase of the number of satellites, the collision possibility is still relatively low, though the severity of the consequences is high. The tools for assessing space debris risk are getting continuously developed including the improvement of the Meteoroid and Space debris Terrestrial Environment Reference (MASTER) [100]. According to the risk strategies descriptions given above, one of the best solutions is a risk transfer. However, the insurance is not helpful until relevant stakeholders clearly answer: “what are we trying to avoid?” and “what problem are we trying to solve?”.

The space debris problem is inseparable from the problem of improving critical infrastructure resilience. The on-going dialogues among industry, scientists, policy makers and economists should give more information on: “how extreme can future events be?”, “what is the expected frequency of such events?”, and “what damage can be expected?”. Realistic and adequate answers to these questions are the silver bullets for defining appropriate resilience enhancement strategy including governance options. It should be noted that any steps taken to reduce long-term risks also minimize the potential increase of short-term risks.


AAL Average Annual Loss

AEP Aggregate Exceedance Probability

COMM Communication Satellite

COPUOS Committee on the Peaceful Use of Outer Space

ESA European Space Agency

EU European Union

GDP Gross Domestic Product

GEO Geostationary Earth Orbit

GMD Geomagnetic disturbance

GNSS Global Navigation Satellite System

GVA Gross Value-Added

IAA International Academy of Astronautics

IAC International Astronautical Congress

IADC Inter-Agency Space Debris Coordination Committee

ITU International Telecommunication Union

LEO Low Earth Orbit Region

LSC Legal Subcommittee of COPUOS

MASTER Meteoroid and Space Debris Terrestrial Environment Reference MEO Medium Earth Orbit Region

NACE Nomenclature des Activites Economiques dans la Communaute Europeenne

NASA National Aeronautics and Space Administration

NEO Near-Earth Object

NIPP National Infrastructure Protection Plan

NOAA National Oceanic and Atmospheric Administration

OECD Organization for Economic Cooperation and Development

OEP Occurrence Exceedance Probability

STS Scientific and Technical Subcommittee of COPUOS

UNDRR United Nations Office for Disaster Risk Reduction

UNOOSA United Nations Office for Outer Space Affairs

WIOD World Input-Output Database


Aggregate Exceedance Probability (AEP): The probability of the sum of event losses in a year exceeding a certain level.

Average Annual Loss (AAL): The expected loss cost over a one-year time period.

Cost/risk criteria: A criteria of the loss and required cost for system reinforcement.

Critical infrastructure: An infrastructure that provides an essential support for economic and social well-being, public safety and the functioning key government responsibilities, such that disruption or destruction of the infrastructure would result in catastrophic and far-reaching damage.

Critical risk: A rapid-onset event that pose the strategically significant risk as a result of its probability and likelihood.

Damage ratio: The estimated repair cost of an asset at risk divided by the replacement cost of an asset.

Emerging risk: An issue that is perceived to be potentially significant but which may not be fully understood.

Exposure data: The data representing the assets to be modelled.

Governance: The processes, controls and oversight put in place for ensuring that catastrophe risk is properly managed.

Hazard: A process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation. Geomagnetic disturbance (GMD) is considered as a hazard and a blackout caused by the GMD is a disaster.

Liability Convention: Convention on International Liability for Damage Caused by Space Objects (1972).

Mitigation: Actions taken to reduce the impact of a hazard.

Moon Agreement: Agreement Governing the Activities of States on the Moon and Other Celestial Bodies (1979).

Realistic disaster scenario: Catastrophe scenario used for exposure management.

Resilience: The ability of households, communities and nations to absorb and recover from shocks, whilst positively adapting and transforming their structures and means for living in the face of long-term stresses, change and uncertainty.

Risk: An expected loss due to a particular hazard for a given area and a reference period.

Registration Convention: Convention on Registration of Objects Launched into Outer Space (1975).

Rescue Agreement: Agreement on the Rescue of Astronauts, the Return of Astronauts and the Return of Objects Launched into Outer Space (1968).

Scenario: A representation of a possible event based on scientific analysis or expert knowledge.

Systemic risk: A risk that emerges from complex interactions among individual elements or agents, which are attributed with their own risk portfolio.


Johnson-Freese, J. (2007). Space as a Strategic Asset. Columbia University Press.

Burch, R. (2019). Resilient Space Systems Design: An Introduction. CRC Press.

Linkov, I„ & Trump. B. D. (2019). Resilience and Governance. In The Science and Practice of Resilience (pp. 59-79). Springer, Cham.

Lowe, C. J., & Macdonald, M. (2020). Space mission resilience with inter-satellite networking. Reliability Engineering & System Safety, 193, 106608.

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