Structuring the risks of CDM projects based on Monte Carlo simulations

Transcription

1 In cooperation with: 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 Introduction Problem Statement and Goal Research Questions Analytical Approach and expected Results Theoretical Background 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 Risk Assessment Model specification Model Set Up Model Input Values and Probability Distributions of the Risks Simulation Results of the Risk Effects Quantification Final Conclusions and Outlook 2

3 Problem Statement 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 3

4 Goal 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 4

5 Research questions 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 of CDM Brazilian hydropower projects, the individual risk contribution to the CDM revenue variance and what are the risks with the major contribution? 5

6 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 Monte Carlo simulation exercise This study is expected to provide an indicator for the CDM investment decision regarding CDM direct investment or CER purchasing 6

7 The System of the Clean Development Mechanism I Interactionss with local conditions Technical set up and operation CER Volume Modalities/ Procedures Execution of the CDM governance CDM Governance Risks CER Price CDM Revenue CDM Revenue risk CDM market CER Transactio-nal Costs External Influences The risks within the subsystems influence indirectly the CDM revenue. The CER volume and CER price variables become uncertain 7

8 The CDM System Governance The CDM-system governance can directly influence the CER volume of emission reductions the project finally receives issued 8

9 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 9

10 Development of the CDM market I Empirical Survivor of the CER Price Forward Curve Study 1.0 Probability of Higher Prices Price Range in $/t CO2e 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 projects 10

11 11 Development of the CDM market II

12 Risk Management 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 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 Risk control Mitigation of the risks Risks evaluation Continuous evaluation of the risks in view of new conditions 12

13 Risk Identification Causal Analysis 13

14 Risk Assessment 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 project 14

15 15 Identification and Allocation of Risk Factors I

16 16 Identification and Allocation of Risk Factors II

17 17 Classification and Definition of Risks

18 18 Risk Correlations

19 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 eligibility Delays do not have an effect on the CER volume The future CERs of the project primary CERs can be sold at a riskadjusted price any time of the project development phases. The CER price reference is the EU-A price Assumptions that are related to the data sources 19

20 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 20

21 Model Set Up Model Algorithm Description I Initialization 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 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 triangular (minimal discount %, average discount %, maximal discount %) * CER Volume expected 21

22 Model Set Up Model Algorithm Description II 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 Price Floor CER Price = 0 0% 45% 90% Note: α<1 is described in the next paragraphs % CER Volume future issued riskadjusted (relative to the originally CER volume expected at certain project development phase) 22

23 Model Set Up Model Algorithm Description III CER price risk adjustment 23

24 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 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 24

25 Model Input Values and Probability Distributions of the Risks I CER volume risk adjustment Expected Volume T CO2 Risk probability distributions and discount factors Risk main class Qualification risk Operation and verification risk Risk types 1 Approved methodology applicability criteria risk or (new methodology approval risk) Baseline methodology application risk Additionality Stakeholder examination risk consultation risk Validation public comments risk Host Country DNA approval risk Request for corrections risk Regulatory project performance risk Opportunity cost risk Project output Technical project Managerial project market risk performance risk 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% Continuous CER Volume adjustment Min. CER Volume discount Most likest Volume discount (highest probability) Max. Volume discount % 1 (5.5%) 0,00% 7,50% 2,50% % 1 (10.5%) 0,00 10,00 5,00 % 1 (15.5%) 0,00% 12,50% 7,50% CER price risk adjustment Expected Volume T CO2 Reference Price:EU-A α α EU-A CER price Floor 20 15% 17,00 6,00 /t CO2 % /t CO2 /t CO2 25

26 Model Input Values and Probability Distributions of the Risks II Example Average deviation of the registered and issued CER volume 2,000, % 1,800, ,600, % 1,400, % Ton CO2 eq. 1,200, ,000, , % 5.00% Average deviation 600, % 400, , % 0.00 CERs issued hydro overall CERs issued large scale overall CERs issued small scale overall CERs issued Brazil CERs issued Brazil large scale Regional and project scale categories CERs issued Brazil small scale % Registered CER volume Issued CER volume Average deviation of the CER volume 26

27 Simulation Results of the Risk Effects Quantification I Ov erlay Chart Frequency Comparison R-ad issued CER vol. of verified project.750 R-ad. issued CER vol. of non-registered.500 R-ad. issued CER vol. of registered proj ,000 30,000 45,000 60,000 R-ad issued CER vol. of operational proj Forecast: R-ad. issued CER vol. of non-registered 5,000 Trials Frequency Chart 0 Outliers Forecast: R-ad. issued CER vol. of registered proj 5,000 Trials Frequency Chart 0 Outliers Mean = 26, Mean = 32, ,750 27,500 41,250 55, ,750 27,500 41,250 55,000 Forecast: R-ad issued CER vol. of operational proj 5,000 Trials Frequency Chart 200 Outliers Forecast: R-ad issued CER vol. of verified project 5,000 Trials Frequency Chart 11 Outliers Mean = 54, Mean = 59, ,000 33,750 42,500 51,250 60,000 59,995 59,996 59,997 59,997 59,998 27

28 Simulation Results of the Risk Effects Quantification II Forecast: R-ad. primarycer price of non-registere 5,000 Trials Frequency Chart 0 Outliers Forecast: R-ad. primarycer price of registered pr 5,000 Trials Frequency Chart 0 Outliers Mean = Mean = Forecast: R-ad primarycer price of operational pr Forecast: R-ad primarycer price of verified proje 5,000 Trials Frequency Chart 200 Outliers 5,000 Trials Frequency Chart 0 Outliers Mean =

29 Simulation Results of the Risk Effects Quantification III Ov erlay Chart Frequency Comparison R-ad. net CDM revenue of non-registered.750 R-ad net CDM revenue of registered proje.500 R-ad net CDM revenue of operational proj , , , , , R-ad net CDM revenue of verified project Forecast: R-ad. net CDM revenue of non-registered 5,000 Trials Frequency Chart 0 Outliers Forecast: R-ad net CDM revenue of registered proje 5,000 Trials Frequency Chart 0 Outliers Mean = 241, Mean = 327, , , , , , , , , , , Forecast: R-ad net CDM revenue of operational proj 5,000 Trials Frequency Chart 200 Outliers Forecast: R-ad net CDM revenue of verified project 5,000 Trials Frequency Chart 18 Outliers Mean = 787, Mean = 877, , , , , , , , , , ,

30 Risk Contribution Target Forecast: R-ad. net CDM revenue of non-registered Project output market risk 24.6% Sensitivity Chart * * 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% * - Correlated assumption 0% 25% 50% 75% 100% Measured by Contribution to Variance 30

31 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 31

32 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 riskadjusted 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 nonregistered 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. 32

33 Final Statements 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 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 33

34 References 3E EcoSecurities, EcoInvest and Econergy calculation of the emissions factors for grid connected CDM projects. ANNEL Agência Nacional de Energia Elétrica (Brazilian power regulatory agency). Brazil. Brealey R., Myers S Principles of Corporate Finance. Mcgraw-Hill, UK. Castro, J Expert coordinator of validation and verification of CDM projects at TÜV-Süd. Personal Communications on 27th May Chapman, C Project risk analysis and management PRAM the generic process. International Journal of Project Management 15, UK. Chapman, C., Ward, S Project Risk Management: Processes, Techniques and Insights. John Wiley & Sons Ltd, UK. Clarke, C Cross-Check Survey: Annexes and Final Report, A WCD Survey prepared as an input to the World Commission on Dams, Cape Town. DNA Brazil Comissão Interministerial de Mudança Global do Clima, Brazil. ( ) EcoInvest PDD documents of CDM hydropower projects in Brazil: Project design document of Mascarenhas CDM hydropower project, Brazil. EcoSecurities Personal Communications via consultancy department on 10th January EnergyBrainpool A scenario analysis of the CER price development between 2008 and 2012, Berlin. European Commission European Emission trading system. Fernandez, P President of Montalban Methane Power Corporation MMPC and CDM expert. Personal Communications on 15th January Financial dictionary, Harmsen, H Research expert at Energy Research Center Netherlands (ECN). Personal Communications on 18 th November IEA, Hydropower and the environment: Present context and guidelines for future actions. Annex III of report: Implementing Agreement for Hydropower Technologies and Programmes. Paris. IETA IETA Roundtable on the Clean Development Mechanism: Moderator s personal impressions (12-13 July 2005). London. IETA State and trends of the carbon market. Washington. Klunne, W Principal renewable energy expert coordinator of the ADB FINESSE program. Personal Communications on 18 th November Matsuhashi, R., et al Clean development mechanism projects and portfolio risks. Energy Journal Vol. 29. Elsevier, Amsterdam. Miller, R., Lessard, R.L The Strategic Management of Large Engineering Projects: Shaping Institutions, Risks, and Governance. Massachusetts Institute of Technology, USA. Nicholas, J.M Project management for business and technology, principles and practice (2nd. Edition). Prentice Hall, USA. 34

35 References ONS-ADO Acompanhamento Diário da Operação do Sistema Iterligado Nacional. Daily reports on the whole interconnected electricity system from Jan to Dec Centro Nacional de Operação do Sistema (ONS- CNOS), Brazil. Oud, E The evolving context of hydropower development, Management of Technological Complex Projects. Energy Policy Vol. 30, the Netherlands. Petrobras Point Carbon CDM/ JI Monitor and Project management database. Oslo. Schaeffer, R., J. Logan, A. S. Szklo, W. Chandler and J. C. de Souza Electric Power Option in Brazil. Pew Center on Global Climate Change. USA. UNEP FI Climate Change Working Group Finance for Carbon Solutions: The Clean Development Mechanism, the Financial Sector Perspective. SEFI/UNEP FI, CEO Briefing, France. UNEP Risø Legal issues guidebook to the clean development mechanism. Denmark. UNEP Risø CDM pipeline database (October Mai 2007). UNFCCC homepage UNFCCC I Small scale methodologies. UNFCCC II Additional Guidance related to registration fee for proposed clean development mechanism project activities. UNFCCC III Aapproved baseline methodology for grid connected hydropower projects ACM002 UNFCCC IV Additionality Tool UNFCCC Kyoto Protocol to the United Nations Framework Convention on Climate Change (Kyoto, 11 December 1997). UNFCCC Modalities and procedures for a clean development mechanism, Annex to the decision 17/CP.7. UNFCCC Meetings of the Meth-Panel and terms of references for the Methodology Panel (version 2). UNFCCC Procedures for the submission and consideration of a proposed new methodology (version 10). 35