Structuring the risks of CDM projects based on Monte Carlo simulations

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(1)

„Structuring the risks of

CDM projects

based on Monte Carlo simulations“

by

Juan Carlos Mejia

Supervision:

Prof. Dr. Georg Erdmann

Dipl.- Ing. Johannes Henkel

(2)

Agenda

¾

Introduction

– Problem Statement and Goal

– Research Questions

– Analytical Approach and expected Results

¾

Theoretical Background

– System of the Clean Development Mechanism

– Risk Management

¾

Identifying the risks of CDM Projects

– Identification and Allocation of Risk Factors

– Classification and Definition of Risks

– Risk Correlations

¾

Risk Assessment

– Model specification

– Model Set Up

– Model Input Values and Probability Distributions of the Risks

– Simulation Results of the Risk Effects Quantification

(3)

Problem Statement

¾

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

¾

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

¾

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

(4)

Goal

¾

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

(5)

Research questions

¾

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?

¾

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?

(6)

Analytical Approach and expected Results

¾

Risk identification modelling

¾

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

Monte Carlo

simulation exercise

¾

This study is expected to provide an indicator for the CDM

investment decision regarding CDM direct investment or

CER purchasing

(7)

The System of the Clean Development Mechanism I

¾

The risks within the subsystems influence indirectly the

CDM revenue. The CER volume and CER price variables

become uncertain

External Influences Technical set up and

operation CDM market CER Volume CER Price

Risks

CDM Revenue CER Transactio-nal Costs CDM Governance

Interactionss with local conditions Modalities/ Procedures Execution of the CDM governance CDM Revenue risk

(8)

The CDM System Governance

¾

The CDM-system governance can directly influence the CER volume of

(9)

The CDM Project Technical set up and operation

¾

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

(10)

Development of the CDM market I

¾

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

¾

The development of the CDM market can directly influence the added-value

of CDM projects

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

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

(11)
(12)

Risk Management

¾

Risk identification

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

¾

Risk assessment

Mathematical modelling of the effects of the risk factors and variables on the object of the

risks management

Input of probability distributions of the identified risks considering risks correlations

matrix

Quantification of the risk effects

¾

Risk control

Mitigation of the risks

¾

Risks evaluation

(13)

Risk Identification

(14)

Risk Assessment

¾

The Monte Carlo simulation, as quantification methodology for

risk effects which enables one to forecast uncertain variables

¾

The forecast provides a most likely value (modal value) and a

probability distribution of possible outcomes of the forecast.

¾

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

¾

Enable to inspect the entire distribution of the outcomes of the CER

volume, primary CER price and the CDM revenue of a CDM

(15)
(16)
(17)
(18)
(19)

Model Specification – Assumptions I

¾

Assumptions that impact the model´s approach

On the short and middle term, historic pattern represent future patterns

¾

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

eligibility

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

(20)

Model Specification – Assumptions II

¾

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

(21)

Model Set Up – Model Algorithm Description I

¾

Initialization

Net CDM Revenue, risk-adjusted

= CER Volume, risk-adjusted *

CER Price, risk-adjusted – CER Transactional Costs

¾

CER volume risk adjustment

CER Volume, risk-adjusted

= CER Volume expected binary risk-adjusted – CER

Volume discount

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 –

(22)

Model Set Up – Model Algorithm Description II

¾

CER price risk adjustment

CER Price, risk-adjusted

= (1-

α

) * EU-A price – Risk Premium

Risk Premium

=

β

risk premium * (1-

α

) EU-A price

β

risk premium

= 100% - % CER Volume future issued risk-adjusted (relative to

the originally CER volume requested at certain project development phase)

Primary CER Price

(1-

α

)

EU-A

CER PriceFloor

CER Price

= 0

0% 45% 90%

%

CER

Volume future issued risk-adjusted (relative to the originally CER

volume expected at certain project development phase)

(23)

Model Set Up – Model Algorithm Description III

(24)

Model Set Up – Model Algorithm Description IV

¾

Transactional cost input variables

TACs Total = TACs fix + TACs variable

TACs fix = TACs upfront fix + TACs yearly fix

TACs variable = TACs yearly variable

¾

Simulation of the net CDM revenue and individual risk

contributions

• 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

(25)

Model Input Values and Probability Distributions of the Risks I

1 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 types

Risk probability distributions and discount factors CER volume risk adjustment

T CO2

Expected Volume 60.000

CER price risk adjustment

T CO2 Expected Volume 60.000 €/t CO2 % Reference Price:EU-A α 20 15%

(26)

Model Input Values and Probability Distributions of the Risks II

¾

Example

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

(27)

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

(28)

Simulation Results of the Risk Effects Quantification II

Frequency 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

(29)

Simulation Results of the Risk Effects Quantification III

Frequency 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

(30)

Risk Contribution

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

(31)

Addressing the main Questions I

¾

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

(32)

Addressing the main Questions II

¾

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

non-registered project.

– 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.

(33)

Final Statements

¾

The study results

confirm

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

¾

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

¾

Therefore, CDM investors have to be very careful in assessing

operational CDM and non-CDM risks in their forecasts

(34)

References

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.

http://www.mct.gov.br/clima/ingles/comunic/cimgc.htm(15.06.07)

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.

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References

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

http://cdm.unfccc.int/methodologies/PAmethodologies/AdditionalityTools/Additionality_tool.pdf

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

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