Folie 1 - UNFCCC

download report

Transcript Folie 1 - UNFCCC

CDM baseline standardization – key policy questions

Axel Michaelowa Center for Comparative and International Studies (CIS), University of Zurich and ETH Zurich; Perspectives

[email protected]

, [email protected]

Joint Workshop, Bonn, March 13, 2011

Harnessing emissions reduction potential

CDM

CDM

CDM

CDM

CDM

CDM

CDM

 Source: IPCC (2007)

Potential 2030, bottom-up studies

Preventing emissions take-off

0.95

0.9

Critical

0.85

level

0.8

of HDI

0.75

HDI 0.7

0.65

0.6

0 2 Source: Michaelowa and Michaelowa (2009) 4 6 8 10 t CO2/capita China Korea Singapore Malaysia Hong Kong Ireland Israel Portugal Spain

What can be standardized?

Use of pre-defined applicable to values / many projects at once

• • •

Baseline setting Additionality determination parameters Criteria, emission factors, calculation methods, equations, models feeding into baseline methodologies

Across

project types E.g. all electricity related projects

Within individual project types

E.g. benchmark for N 2 O from adipic acid

Why standardization?

Administrative improvements

to the CDM: Increased efficiency of registration process

• • •

Greater objectivity , consistency and predictability Reduced transaction costs Increased project flow

Broader systemic

Guaranteeing integrity improvements: and improving

Improved distribution project types environmental across host countries and

Trade-offs between these goals??

Careful implementation and regulatory oversight !

Potential risks

Subjectivity is not really eliminated, but shifted from project registration process to the baseline setting stage

• •

One off decision , difficult to reverse Gaming with standard setting can lock in too lenient baselines / non-conservative parameters

High costs for public especially if frequent updating

Aggregation level

is crucial Too high: risk for environmental integrity, and of reaching all mitigation potential

administrations Too low: data confidentiality issues ,

Types of standards

Emissions intensity

• •

benchmarks (add. /bl.) X t CO 2 / amount of product or service Homogeneous products, large number of entities, normal performance distribution

Technology /

• • •

practice standards Average of top X % performance Reference technology that is Project technology that is (add./bl.) common practice highly innovative

Market penetration

• •

rates (add.) X percentage of installed capacity Economies of scale and learning are important

Model (add/bl)

Types of standards II

Deemed savings

• •

defaults (emission reduction) X t CO 2 reduced per installation and year Requires good understanding of usage patterns

Utilization

• •

defaults (add.) X % plant load factor / x hours average daily use Limited variability of parameters load factor influencing plant

Positive lists

• •

Technology (add.) Applicable if no other revenues than CERs or if technology clearly faces a cost gap to alternative technologies providing the same service

Key issues for benchmarks

Type of benchmark e.g tCO 2 output / t Aggregation level Stringency level Updating frequency Process?

Product or service?

Vintage?

Geographic area?

Average?

Best 20%?

Best used?

Best available?

Fixed improvement factor?

According to data?

Decision on stringency

Emission intensity (tCO 2 / t output) A Plants B C D CERs Baseline benchmark Additionality benchmark

   

Greenfield vs brownfield

Share New plants Existing plants Efficiency

Share

Vintages count!

40 year-old plants 5 year-old plants Efficiency

Technology shifts

Benchmark development

Initial feasibility study for the CDM benchmarking:  How large is the expected emission reduction potential for a benchmarking-based CDM?   What is the level of complexity expected? Which efforts are needed regarding the data collection? Decision on whether to develop a benchmarking based CDM for the sector/product Development of the benchmarking approach (1) Definition of the system boundary (2) Identification of key performance indicator (3) Selection of peers for comparison (Choice on the aggregation level) Data collection Choice of MRV procedures Data collection (Monitoring, Reporting, Verification) Selection of the stringency level (1) Preliminary choice on stringency level (2) Evaluation of the impact (3) Decision on stringency levels    Approval of the CDM benchmarking: Approval of benchmarking approach Approval of the data adequacy Approval of selected stringency level

Initial feasibility study for the CDM benchmarking:  How large is the expected emission reduction potential for a benchmarking-based CDM?   What is the level of complexity expected? Which efforts are needed regarding the data collection? Decision on whether to develop a benchmarking based CDM for the sector/product Development of the benchmarking approach (1) Definition of the system boundary Data collection Choice of MRV procedures (2) Identification of key performance indicator (3) Selection of peers for comparison (Choice on the aggregation level)

Benchmark development II

Data collection (Monitoring, Reporting, Verification) Selection of the stringency level (1) Preliminary choice on stringency level (2) Evaluation of the impact (3) Decision on stringency levels    Approval of the CDM benchmarking: Approval of benchmarking approach Approval of the data adequacy Approval of selected stringency level

Policy questions

Which sectors and project types should be prioritized for standardization?

• •

Highly homogeneous , large-scale industries?

Small, dispersed emissions sources?

How stringent should standardized approaches be to guarantee a sufficiently high environmental integrity?

More stringent than project-based approaches?

• •

What lessons can be drawn from of standardization existing use in offset programmes?

Role of experts?

US programmes (CAR, RGGI, CCX)

Policy questions

Who should administer standardized methodologies?

• • •

CDM EB?

Project developers?

and develop Should there be a Baseline Standard rulebook ?

How can we

• •

prioritize countries and regions?

Underrepresented regions?

Regions with highest potential?

How can DNAs be enabled to decide whether to apply standardized baselines?

• •

Capacity building required Can distortions be prevented?