Executive summary - Imperial College London

Name: Cohen Julien
University: Imperial College of London
Msc: Environmental Technology (Centre of Environmental Policy)
Thesis title: “Carbon Capture and Storage market’s CAPEX and OPEX stochastic frontier
analysis: A tool for shareholders and politics”
Supervisors: Tim Cockerill, Colin Thirtle
Academic year: 2009
Executive summary
One of the primary objectives of the UN Framework Convention on Climate Change is to
mitigate CO2 emissions. Carbon Capture and Storage (CCS) is seen as one of the potential
solutions to achieve this goal. Nevertheless, this market is very unstable and full of
uncertainties. This thesis investigates non-technical and technical risks to CCS. The non
technical issues discussed include different aspects of the market. The legal and regulatory
issues at national and international levels are reviewed. International marine treaties give
rise to problems for storage; domestic laws have showed CCS liability and property rights
issues. On the other hand, regulatory issues, with for instance the unclear process of CCS
inclusion in CDM (Clean Development Mechanism), slow down the development of the
market. Besides, on a macroeconomic point of view one can mention the risks and cost
variability of CCS through different examples. That is why, the thesis is focused on assessing
the potential risk variability through quantitative tools like risk analysis or using Monte Carlo
simulation; Moreover it normalizes CAPEX (Capital Expenditure) and OPEX (Operational
Expenditure) from different studies available in the literature to have the same base of
comparison for different technologies with different set of assumptions. Nevertheless, these
models are run for all the technical aspects of CCS because it is easier to quantify. The
second part of the thesis is then focused on outlining the CCS technologies throughout
power plants endowed with capture technology. The techniques to capture, transport and
store CO2 are discussed and are facilitating the understanding of the quantitative models.
The underlying idea is to find cost functions’ efficiencies for different set of CCS technologies
to help shareholders finding the optimal solution. Indeed, knowing cost efficiencies and
sources of CCS uncertainty allow reducing the overall risk for investment. The theory of the
econometric models is explained to understand all the concepts linked with the ideas

developed above. The cost efficiencies’ findings are found through a stochastic frontier
analysis along with the findings of CAPEX and OPEX production functions. For a specific type
of power plant endowed with capture technology, the econometric models provide the
associated cost efficiency (i.e. are investors minimizing their costs?) and uncertainties (the
objective is to know which parameters can vary a lot in terms of costs from one power plant
to another); the latter is given to help investors to improve the CCS plant’s efficiency. Once
this is done, the “learning by doing” principle (using learning curve theory) allows in a
different way to know which investment is going to be the more successful on a long term
basis. Nevertheless, non-technical aspects, once quantified, can give a different insight on
the market because of its impact compared to the models run with only the technical
parameters. This other kind of work is also performed in order to issue policy implications
and is a method to clarify and improve the development of the CCS market. A numerous
numbers of assumptions were necessary to develop and find relevant results. Nevertheless,
it has also introduced a bias which is considered all along the thesis and lead to some model
limitations. This is also through this entire set of models that risks can be reduced. In other
words, all the models work hand in hand and are not independent to each other. The main
conclusions of these models can be summarized as follows and are discussed at the same
time:
As a matter of efficiencies, it seems that through the literature (46 studies in that case) IGCC
(Integrated Gas Combined Cycle) plants even with different set of technologies (different
capture methods, different gasifiers, retrofitted or not on an existing power plant) are the
most cost efficient ones. It means that in terms of CAPEX and OPEX it better minimizes their
cost than any other plant. Then, there are NGCC (Natural Gas Combined Cycle), PC
(Pulverized Coal) and the less efficient and more risky one, OXYFUEL (pure oxygen
combustion) plants. Indeed, even if it is the less effective one, it also has the highest
standard deviation relative to its peer. This means that OXYFUEL cost efficiencies vary a lot
from one study to another and on average remain low. The risk analysis and the Monte
Carlo simulation show the same results that for the stochastic frontier analysis (cost
efficiency analysis) which confirms the previous conclusion. Another interesting finding is
that the bigger is the power plant the bigger is the uncertainty with an important rise of risk
for investors. It again proves that the market is unstable and needs small CCS plants for now,
bigger one are not mastered in terms of technologies. Interestingly enough, on a long term

asis (based on the “learning by doing” principle), IGCC seems one more time to be the
better option followed by PC and then NGCC and OXYFUEL. One can say that the general
trends found are supported by the different models run. Besides, policy implications
showed that quantifying qualitative issues and introduce it into the models could entirely
change the results found in this study and then draw different trends and conclusions.
Nevertheless, it has been demonstrated throughout few examples that if non technical
issues were solved in order to facilitate CCS’ development, trends should be quite the same
with less risk and better cost minimization; but, for now, a lack of transparency and hurdles
at different levels prevent the full development of this market (non technical issues
developed earlier). At least, it is slowed down and deters stakeholders and/or investors to
be part of the market. Nevertheless, the results presented above shows that no general
trends are easy to draw especially because of the number of assumptions made. A different
strategy should be used. Each technology having its strengths and weaknesses, investors
should then carry a step by step analysis and be focused on the weaknesses of each
technology to make it stronger without removing the established strengths. Some trends
associated with sometimes strong assumptions have been drawn but the market is still too
uncertain for only one technology to be trusted. The recommendation here is for example
to take IGCC plant with business plan’s set of assumptions and to conduct all the different
analysis carried in this study and draw at each step conclusions. Finally, every investor with
a specific case (a certain power plant with a certain set of technologies) can rank its cost
strategy among competitors (represented here by the 46 studies) and minimize its risk if
each one of them accepts the models’ assumptions.