The benefits of investing into improved carbon flux monitoring

Abstract

Operationalizing a Global Carbon Observing and Analysis System (www.geocarbon.net) would provide a sound basis for monitoring actual carbon fluxes and thus getting quantities right when pricing carbon – be it in a cap-and-trade scheme or under a tax regime. However, such monitoring systems are expensive and—especially in times of economic weakness—budgets for science and environmental policy are under particular scrutiny. In this study, we attempt to demonstrate the magnitude of benefits of improved information about actual carbon fluxes. Such information enables better-informed policy-making and thus paves the way for a more secure investment environment when decarbonizing the energy sector. The numerical results provide a robust indication of a positive social value of improving carbon monitoring systems when compared to their cost, especially for the more ambitious climate policies.

Keywords:

• carbon flux monitoring
• carbon pricing
• uncertainty
• investment
• decarbonization

Public Interest Statement

With the Paris Agreement, the remaining emissions quota that can still be emitted to the atmosphere before substantially reducing chances to keep the degree of global warming below levels deemed to be “dangerous” by the UNFCCC, has been further decreased. Moving to implementation of these ambitious goals will require the penalization of fossil fuels and the support of clean technologies. However, pricing carbon is subject to uncertainty, i.a. because revisions in carbon measurements will be associated with revisions of caps. Frequent revisions of policy, however, will deter socially desirable investments and imply extra costs, which could be kept minimal if better observations of carbon fluxes were available. In times of tight public resources, an evaluation of the potential cost savings is essential for decision-makers investing in better observing systems. We find a positive social value of improving monitoring systems (compared to their cost), especially for ambitious climate policies.

Notes

1. The total expected cost for operating the whole network are determined by summing the cost for each year and discounting back to the first period until infinity, as we are looking at the long run.

2. This is in accordance with rising shadow prices for carbon under any stabilization target. Carbon prices rise exponentially over time if emission reductions are allocated optimally over time, or if banking and borrowing of emission permits is allowed. These assumptions are standard in first best analysis of climate stabilization policies.

3. We assume both the investment and operational cost to be constant. This simplification is important for the model complexity, since it will enable us to derive the results analytically thus to provide the results necessary for the second layer.

4. There is no learning-by-doing, which could in this setup only be integrated in the form of special learning functions and would be beyond the scope of our analysis.

5. For example in an EU ETS type of scheme or in the form of taxation.

6. The assumption of risk neutrality is standard in economic analysis of such investment problems and incorporating risk aversion in this specific setup is mathematically intricate and beyond the scope of our analysis. Intuitively, the impact on the results should depend on how the risk aversion would be modeled, but should not reduce the benefits from improved monitoring, as volatility would be valued negatively by investors.

7. See Knopf et al. (2013) for a model comparison focusing on five countries from the EU-27, in particular Germany.

8. Note that in some countries CCS is not an option currently due to legal barriers (Austria) and low public acceptance (Germany, see Von Hirschhausen, Herold, & Oei, 2012), which would need to be overcome for full realization of the potentials calculated here. However, we are presenting only one possible pathway of transformation of the energy system and others which are conceivable and comparable in cost structure would similarly benefit from a more stable pricing signal, which is independent of the technology chosen for the analysis.

9. An exponential decrease has been tested as well. The results remain qualitatively the same; the difference in quantitative results is only minor.

10. This is a simplifying assumption. In order to determine the actual decrease more precisely, we would need to evaluate further (smaller) networks and their efficiency would depend very much on the specific sites excluded, the baseline network for monitoring, the timing of improving the baseline network. However, we think it is fair to assume that beyond a certain level of observation, marginal additions will not lead to major improvements in carbon price stability when it has already been stabilized substantially.

11. All $ values are converted into EUR values at an exchange rate of 1.3 US$/EUR.

Additional information

Funding

The work presented in this article has been carried out under the EU-funded Seventh Framework Programme GEOCARBON project [grant number 283080].

Notes on contributors

S. Fuss

The authors (JS, SF, TK, MS, MG, MT, MH) are from different institutes (International Institute for Systems Aanlysis, Mercator Research Institute on Global Commons and Climate Change, The Inversion Lab, Lund University, Fondazione Eni Enrico Mattei, Comenius University, Lviv Polytechnic National University, Euro-Mediterranean Center on Climate Change (CMCC Foundation) and Max-Planck-Institute for Biogeochemistry) and represent a variety of disciplines (economics, mathematics, remote sensing, forestry, physics, biogeochemistry and integrated assessment of climate change and climate change mitigation). Under the EU-funded GEOCARBON project (www.geocarbon.net), they collaborated to evaluate the economic benefits of an improved carbon monitoring system.