„Structuring the risks of
based on Monte Carlo simulations“
Juan Carlos Mejia
Prof. Dr. Georg Erdmann
Dipl.- Ing. Johannes Henkel
– Problem Statement and Goal
– Research Questions
– Analytical Approach and expected Results
– System of the Clean Development Mechanism
– Risk Management
Identifying the risks of CDM Projects
– Identification and Allocation of Risk Factors
– Classification and Definition of Risks
– Risk Correlations
– Model specification
– Model Set Up
– Model Input Values and Probability Distributions of the Risks
– Simulation Results of the Risk Effects Quantification
The loss of CER issuance relative to respective PDD estimation is ranging
from 26% * to 72% **
– During its qualification and operation the CDM project faces several risks which
can influence the delivery time and volume of CERs
The CER price of the project is influenced by risks on the demand and
supply for CERs as well as by qualification and project specific risks
Since decisions regarding the CDM project finance and the CER purchasing
are generally made on the basis of the assessment of the net CDM revenue,
risks directly influence investor decisions
This study aims to structure the risks of CDM projects
according to their source of origin within the CDM process
cycle and their impact on the net CDM revenue
– To assess CER issuance of the project
• How much is issued?
– To give an referential adjusted CER volume and CER price
– To estimate a risk adjusted CDM revenue
Which risks have to be considered during a CDM project
cycle in order to quantify the risk impact on the CER volume,
price and net CDM revenue?
What is the uncertainty of the achievable net CDM revenue
CDM Brazilian hydropower projects
, the individual risk
contribution to the CDM revenue variance and what are the
risks with the major contribution?
Analytical Approach and expected Results
Risk identification modelling
Risk quantification modelling
– The influence of the risks on the CER volume, CER price and finally on
the CER revenue will be quantified by means of a
This study is expected to provide an indicator for the CDM
investment decision regarding CDM direct investment or
The System of the Clean Development Mechanism I
The risks within the subsystems influence indirectly the
CDM revenue. The CER volume and CER price variables
External Influences Technical set up and
operation CDM market CER Volume CER Price
RisksCDM Revenue CER Transactio-nal Costs CDM Governance
Interactionss with local conditions Modalities/ Procedures Execution of the CDM governance CDM Revenue risk
The CDM System Governance
The CDM-system governance can directly influence the CER volume of
The CDM Project Technical set up and operation
The CDM-project technical set up and operation can directly influence the
CER volume since a good project performance leads to real emissions
reductions which potentially can become issued
Development of the CDM market I
The development of the CDM market regarding CER primary and secondary
prices and CER volume will be influenced by the processes sourcing from the
CDM governance and the technical set up/operation of the project
The development of the CDM market can directly influence the added-value
of CDM projects0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 30 40 50 60
Price Range in $/t CO2e
Pr ob a bil it yof Hi g he r Pr ice s
Empirical Survivor of the CER Price Forward Curve Study
0.0 0.2 0.4 0.6 0.8 1.0 0 10 20 30 40 50 60
Price Range in $/t CO2e
Pr ob a bil it yof Hi g he r Pr ice s
Definition of the object and focus of the risk management
Identification and allocation of risk factors
Risk classification and definition
Identification of the risk variables and their effects on the object of the risks management
Mathematical modelling of the effects of the risk factors and variables on the object of the
Input of probability distributions of the identified risks considering risks correlations
Quantification of the risk effects
Mitigation of the risks
The Monte Carlo simulation, as quantification methodology for
risk effects which enables one to forecast uncertain variables
The forecast provides a most likely value (modal value) and a
probability distribution of possible outcomes of the forecast.
Monte Carlo Simulation assesses the total risk effect of all possible
combinations of interrelated uncertain variables set at the same
time to the probability distribution of all possible values
Enable to inspect the entire distribution of the outcomes of the CER
volume, primary CER price and the CDM revenue of a CDM
Model Specification – Assumptions I
Assumptions that impact the model´s approach
On the short and middle term, historic pattern represent future patterns
Assumptions that impact the applicability of the model and
the results of the calculations
Risk mitigation possibilities will be not considered
Only technical feasible projects which have been built already can be assessed
with the model
The additionality examination risk directly influences the CDM project
Delays do not have an effect on the CER volume
The future CERs of the project “primary CERs” can be sold at a
risk-adjusted price any time of the project development phases.
The CER price reference is the EU-A price
Model Specification – Assumptions II
The stage the project is at in the development cycle when the ERPA is
signed is also a key determinant of the price the buyer can achieve for
future delivery of CERs, since obviously the risk of non-delivery is higher
earlier in the project cycle
Model Set Up – Model Algorithm Description I
Net CDM Revenue, risk-adjusted
= CER Volume, risk-adjusted *
CER Price, risk-adjusted – CER Transactional Costs
CER volume risk adjustment
CER Volume, risk-adjusted
= CER Volume expected binary risk-adjusted – CER
CER Volume expected, binary risk-adjusted
= binomial (1, %) * CER Volume expected
CER Volume discount
= triangular (minimal discount %, average discount %, maximal
discount %) * CER Volume expected
CER Volume, risk-adjusted =
binomial (1, %) * CER Volume expected –
Model Set Up – Model Algorithm Description II
CER price risk adjustment
CER Price, risk-adjusted
) * EU-A price – Risk Premium
risk premium * (1-
) EU-A price
= 100% - % CER Volume future issued risk-adjusted (relative to
the originally CER volume requested at certain project development phase)
Primary CER Price
0% 45% 90%
CERVolume future issued risk-adjusted (relative to the originally CER
volume expected at certain project development phase)
Model Set Up – Model Algorithm Description III
Model Set Up – Model Algorithm Description IV
Transactional cost input variables
TACs Total = TACs fix + TACs variable
TACs fix = TACs upfront fix + TACs yearly fix
TACs variable = TACs yearly variable
Simulation of the net CDM revenue and individual risk
• The outcome of the simulation is the probability distribution of the
future issued risk- adjusted CER volume, of the risk-adjusted primary
CER price and of the risk-adjusted net CDM revenues at the following
four representative project development phases:
Before registration (non-registered project)
After registration (registered project)
In operation (operational project)
After verification (verified project)
% risk type I + % risk type II + % risk type III ……. + % risk type III
= 1 = 100% of variance of net CDM Revenue, distribution
Model Input Values and Probability Distributions of the Risks I1 Approved methodology applicability criteria risk or (new methodology approval risk) Baseline methodology application risk Additionality examination risk Stakeholder consultation risk Validation public comments risk Host Country DNA approval risk Request for corrections risk Regulatory project performance risk Opportunity cost risk Project output market risk Technical project performance risk Managerial project performance risk Monitoring proceedings compliance risk Binary CER Volume adjustment Risk impact probability % 100% 1 (33%) 100,00% 100,00% 100,00% 100,00% 85,00% 85,00% 85,00% 95,00% 80,00% 100,00% 100,00% 100,00% Min. CER Volume discount % 1 (5.5%) 0,00% 7,50% 2,50% Most likest Volume discount (highest probability) % 1 (10.5%) 0,00 10,00 5,00 Max. Volume discount % 1(15.5%) 0,00% 12,50% 7,50% Continuous CER Volume adjustment
Risk main class Qualification risk Operation and verification risk
Risk probability distributions and discount factors CER volume risk adjustment
Expected Volume 60.000
CER price risk adjustment
T CO2 Expected Volume 60.000 €/t CO2 % Reference Price:EU-A α 20 15%
Model Input Values and Probability Distributions of the Risks II
Average deviation of the registered and issued CER volume
0.00 200,000.00 400,000.00 600,000.00 800,000.00 1,000,000.00 1,200,000.00 1,400,000.00 1,600,000.00 1,800,000.00 2,000,000.00 C E R s is s ued hy dr o ov e ral l C E R s is s ued l a rg e s c a le ov er al l C E R s is s ued s m al l s c a le ov er al l C E R s is s ued B raz il C E R s is s ued B raz il la rg e s c a le C E R s is s ued B raz il s m a ll sca le
Regional and project scale categories
To n C O 2 eq . -10.00% -5.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% A v er age devi a ti o n
Registered CER vo lume Issued CER vo lume A verage deviatio n o f the CER vo lume
Simulation Results of the Risk Effects Quantification I
Frequency Compar ison
.000 .250 .500 .750 1.000 0 15,000 30,000 45,000 60,000
R-ad is sued C ER v ol. of v erified projec t
R-ad. iss ued CER vol. of non-regis tered
R-ad. iss ued CER vol. of regis tered proj
R-ad is sued C ER v ol. of oper ational pr oj Ov erlay Cha rt Frequency Chart Mean = 26,554 .000 .120 .240 .359 .479 0 599 2396 0 13, 750 27,500 41, 250 55,000 5,000 Trials 0 Outliers
Forecast: R-ad. issued CER vol. of non-registered
Frequency Chart Mean = 32,476 .000 .091 .182 .272 .363 0 454 908 1816 0 13,750 27,500 41, 250 55,000 5,000 Trials 0 Outliers
Forecast: R-ad. issued CER vol. of registered proj
Frequency Chart .052 .105 .157 .209 261.2 522.5 783.7 1045 5,000 Trials 200 Outliers
Forecast: R-ad issued CER vol. of operational proj
Frequency Chart .006 .013 .019 .025 31. 5 63 94. 5 126 5,000 Trials 11 Outliers
Simulation Results of the Risk Effects Quantification IIFrequency Chart .000 .250 .500 .750 1.000 0 5000 17.00 17.00 17.00 17.00 17.00 5,000 Trials 0 Outliers Forecast: R-ad primary CER price of verified proje
Frequency Chart Mean = 10.40 .000 .120 .240 .359 .479 0 599 2396 6.00 8.50 11.00 13.50 16.00 5,000 Trials 0 Outliers Forecast: R-ad. primary CER price of non-registere
Frequency Chart Mean = 11.38 .000 .091 .182 .272 .363 0 454 908 1816 6.00 8.50 11.00 13.50 16.00 5,000 Trials 0 Outliers Forecast: R-ad. primary CER price of registered pr
Frequency Chart Mean = 16.56 .000 .240 .480 .720 .960 0 4800 10.00 11.75 13.50 15.25 17.00 5,000 Trials 200 Outliers Forecast: R-ad primary CER price of operational pr
Simulation Results of the Risk Effects Quantification IIIFrequency Chart .123 .184 .245 613.5 920.2 1227 5,000 Trials 200 Outliers Forecast: R-ad net CDM revenue of operational proj
Frequency Chart Mean = 327,186. 32 .000 .091 .182 .272 .363 0 454 908 1816 -200,000.00 25, 000.00 250, 000.00 475, 000.00 700,000.00 5,000 Trials 0 Outliers Forecast: R-ad net CDM revenue of registered proje
Frequency Chart Mean = 241,516.83 .000 .120 .240 .359 .479 0 599 2396 -200,000.00 25,000.00 250,000.00 475, 000.00 700,000.00 5,000 Trials 0 Outliers Forecast: R-ad. net CDM revenue of non-registered
Frequency Chart .012 .018 .024 60. 5 90. 75 121 5,000 Trials 18 Outliers Forecast: R-ad net CDM revenue of verified project
Frequenc y Comparison .000 .250 .500 .750 1.000 -200,000.00 75,000.00 350,000.00 625,000.00 900,000.00
R-ad. net CDM rev enue of non-regis ter ed
R-ad net CD M rev enue of r egistered pr oje
R-ad net CD M rev enue of operational proj
R-ad net CD M rev enue of v erified pr oject Ov erlay Cha rt
Target Forecast: R-ad. net CDM revenue of non-registered Project output market risk 24.6%
Request for corrections risk 16.8% *
Host country DNA approval risk 15.7% *
Regulatory project performance risk 14.1% Continuous monitoring proceedings compli 13.9% Continuous technical project performance 13.5% Opportunity cost risk 1.3% Continuous baseline methodology applicat 0.0% Monitoring proceedings compliance risk 0.0% *
Baseline methodology application risk 0.0% *
Stakeholder consultation risk 0.0% *
Approved methodology applicability crite 0.0% Technical project performance risk 0.0% *
Additionality examination risk 0.0% *
Managerial project performance risk 0.0% *
Validation public comments risk 0.0% *
0% 25% 50% 75% 100% Measured by Contribution to Variance
* - Correlated assumption
Addressing the main Questions I
Which risks have to be considered during a CDM project cycle in order to
quantify the risk impact on the CER volume, price and net CDM revenue?
– During the qualification, operation and verification processes, CDM projects
must face several common project and CDM-specific risks that could lead to a
reduction of the expected i.e. projected CER volume
– The risk identification analysis concludes that a CDM project is exposed to a
number of risks classified in methodology, validation, host country approval
and registration risks within the qualification process and in operational project
performance compliance and monitoring proceedings compliance risks within
the operation and verification process
– The results of the risk correlation analysis concludes that the CDM related risks:
additionality examination, the baseline methodology application risk and the
common project risks: technical project performance risk and the managerial
project performance risk have to be specially considered within the CDM
development cycle since they correlate mostly with relevant CDM related risks
and therefore can be considered as main risk drivers
Addressing the main Questions II
What is the uncertainty of the achievable net CDM revenue of CDM Brazilian
hydropower projects, the individual risk contribution to the CDM revenue
variance and what are the risks with the major contribution?
– The simulation results for the Brazilian hydropower CDM projects shows that
risk-adjusted forecasts of the net CDM revenue for the non-registered project compared
to the verified project forecasts, there is a significant difference of 636,208 €. That
means that only 27% of the expected CDM revenue can be achieved from a
– On the contrary, projects in operation can achieve a mean value of 787,871 € during
the first year of generation representing 89% of the expected CDM revenue.
– The risk impact of the project performance compliance risks i.e. output market risk,
the regulatory project performance risk, the continuous technical performance and
the opportunity costs risks contribute mainly to the variance of the risk-adjusted
CDM revenue forecast, which together account for 53.4%.
– The qualification risks which are the request for corrections risk and the host country
DNA approval risks, affect the registration of the project and play a medium role on
the risk-adjusted net CDM revenue since their contributions account for 32.5%.
– The continuous monitoring proceedings compliance risk affects the verification of
CERs and contributes 13.9% to the variance, playing a minor role. Within the project
performance compliance risks, the impact of the project output market risk at 24.5%
is larger than the impact of the others risks within this group of risks.
The study results
that the whole risk impact decreases
according to the increase in project maturity. At an early
development phase, the project faces all the risks that occur during
the qualification, operation and verification. Therefore, in terms of
CER volume discounts, non-registered projects are more risky than
a project in operation
However, regarding volume deviation, the strongest impact on the
volume deviation between projected and issued CERs is
determined by the operation of the project and its associated risks
Therefore, CDM investors have to be very careful in assessing
operational CDM and non-CDM risks in their forecasts
¾ 3E. 2005. EcoSecurities, EcoInvest and Econergy calculation of the emissions factors for grid connected CDM projects.
¾ ANNEL. 2005. Agência Nacional de Energia Elétrica (Brazilian power regulatory agency). Brazil. http://www.aneel.gov.br/
¾ Brealey R., MyersS. 2002. Principles of Corporate Finance. Mcgraw-Hill, UK.
¾ Castro, J. 2006. Expert coordinator of validation and verification of CDM projects at TÜV-Süd. Personal Communications on 27th May 2007.
¾ Chapman, C. 1997. Project risk analysis and management – PRAM the generic process. International Journal of Project Management 15, UK.
¾ Chapman, C., Ward, S. 1996. Project Risk Management: Processes, Techniques and Insights. John Wiley & Sons Ltd, UK.
¾ Clarke, C. 2000. Cross-Check Survey: Annexes and Final Report, A WCD Survey prepared as an input to the World Commission on Dams, Cape Town. http:\\www.dams.org.
¾ DNA Brazil. 2007. Comissão Interministerial de Mudança Global do Clima, Brazil.
¾ EcoInvest. 2006. PDD documents of CDM hydropower projects in Brazil: Project design document of Mascarenhas CDM hydropower project, Brazil. http://cdm.unfccc.int/Projects/Validation/index.html
¾ EcoSecurities. 2007. Personal Communications via consultancy department on 10th January 2007.
¾ EnergyBrainpool. 2006. A scenario analysis of the CER price development between 2008 and 2012, Berlin.
¾ European Commission. 2007. European Emission trading system. http://ec.europa.eu/environment/climat/emission.htm
¾ Fernandez, P. 2006. President of Montalban Methane Power Corporation MMPC and CDM expert. Personal Communications on 15th January 2007.
¾ Financial dictionary, 2007. http://www.anz.com/edna/dictionary.asp?action=content&content=correlation
¾ Harmsen, H. 2006. Research expert at Energy Research Center Netherlands (ECN). Personal Communications on 18 th November 2006.
¾ IEA, 2000. Hydropower and the environment: Present context and guidelines for future actions. Annex III of report: Implementing Agreement for Hydropower Technologies and Programmes. Paris.
¾ IETA. 2005. IETA Roundtable on the Clean Development Mechanism: Moderator’s personal impressions (12-13 July 2005). London.
¾ IETA. 2006. State and trends of the carbon market. Washington. http://www.ieta.org/ieta/www/pages/download.php?docID=2281
¾ Klunne, W. 2006. Principal renewable energy expert coordinator of the ADB FINESSE program. Personal Communications on 18 th November 2006.
¾ Matsuhashi, R., et al. 2004. Clean development mechanism projects and portfolio risks. Energy Journal Vol. 29. Elsevier, Amsterdam.
¾ Miller, R., Lessard, R.L. 2000. The Strategic Management of Large Engineering Projects: Shaping Institutions, Risks, and Governance. Massachusetts Institute of Technology, USA.
¾ Nicholas, J.M. 2001. Project management for business and technology, principles and practice (2nd. Edition). Prentice Hall, USA.
¾ ONS-ADO. 2004. Acompanhamento Diário da Operação do Sistema Iterligado Nacional. Daily reports on the whole interconnected electricity system from Jan. 1.2001 to Dec. 31.2003. Centro Nacional de Operação do Sistema (ONS-CNOS), Brazil. http://www.ons.gov.br/.
¾ Oud, E. 2002. The evolving context of hydropower development, Management of Technological Complex Projects. Energy Policy Vol. 30, the Netherlands.
¾ Petrobras. 2006. www.petrobras.com.br
¾ Point Carbon. 2006. CDM/ JI Monitor and Project management database. Oslo. www.pointcarbon.com
¾ Schaeffer, R., J. Logan, A. S. Szklo, W. Chandler and J. C. de Souza. 2000. Electric Power Option in Brazil. Pew Center on Global Climate Change. USA.
¾ UNEP FI Climate Change Working Group. 2005. Finance for Carbon Solutions: The Clean Development Mechanism, the Financial Sector Perspective. SEFI/UNEP FI, CEO Briefing, France.
¾ UNEP Risø. 2006. Legal issues guidebook to the clean development mechanism. Denmark. http://www.cd4cdm.org/Publications/CDM%20Legal%20Issues%20Guidebook.pdf
¾ UNEP Risø. 2007. CDM pipeline database (October 2006 - Mai 2007). http:\\www.cd4cdm.org.
¾ UNFCCC homepage. 2007. www.unfccc.int
¾ UNFCCC I. 2007. Small scale methodologies. http://cdm.unfccc.int/methodologies/SSCmethodologies/approved.html
¾ UNFCCC II. 2007. Additional Guidance related to registration fee for proposed clean development mechanism project activities. http://cdm.unfccc.int/EB/023/eb23_repan35.pdf
¾ UNFCCC III. 2007. Aapproved baseline methodology for grid connected hydropower projects ACM002
http://cdm.unfccc.int/methodologies/PAmethodologies/approved.html ¾ UNFCCC IV. 2007. Additionality Tool
¾ UNFCCC. 1997. Kyoto Protocol to the United Nations Framework Convention on Climate Change (Kyoto, 11 December 1997). http:\\unfccc.int\cdm.
¾ UNFCCC. 2001. Modalities and procedures for a clean development mechanism, Annex to the decision 17/CP.7. http:\\unfccc.int\cdm.
¾ UNFCCC. 2004. Meetings of the Meth-Panel and terms of references for the Methodology Panel (version 2). http://cdm.unfccc.int/Panels/meth/index.html