1. International financial transfers in climate policy:
blessing or curse?
Brigitte Knopf, Michael Jakob, Jan Steckel, Christian Flachsland

2. Research Questions
What financial flows can be expected from emission trading due to climate change mitigation?
How does this relate to tranfers from FDI or Foreign Aid?
What are appropriate mechanisms and governance structures to avoid (or at least minimize) outcomes that slow down economic development

3. Results from Integrated Assessment Models
Data for financial flows from emission trading available from 2 model comparisons (with 3 models each):
RECIPE (Edenhofer et al. 2009, Luderer et al. 2011)
Different technology options
Different allocation schemes for emission trading systems
ADAM (Knopf et al. 2009, Edenhofer et al. 2010)
Different mitigation targets
Always assumed: fully implemented cap&trade system
Relatively coarse regionalization (CHN, IND, Rest of non-Annex-I (RNAI), AFR)

4. Results from Integrated Assessment Models (IAM)
RECIPE:
Different technology options:
nuclear fixed to baseline (fixNUC);
renewbles fixed to baseline (fixREN);
biomass use fixed to baseline (fixBIO);
CCS not available (noCCS)
Different allocation schemes for emission trading systems:
Contraction and Convergence (C&C);
Common but differentiated convergence (CDC);
Per GDP shares
ADAM:
Different climate mitigation targets, i.e. different probabilities for 2°C
550ppm
450ppm
400ppm

5. Financial flow rel. to GDP [%] Financial flow [bln US$]
Financial Flow in IAMs
India
Financial flow rel. to GDP [%]
Financial flow [bln US$]
Large spread between different models
Steep increase in financial flow

6. Financial flows in IAMs
India
India
Financial flow [bln $]
China
China
Financial flow rel. to GDP [%]
For IND always positive flow, for CHN partly negative

7. Resource Revenues, Foreign Aid, and Foreign Direct Investment
Most recent data on Resource Exports and FDI are for 2009, on foreign aid for Source: UNCTAD (2010), WDI (2010)
Relevant order of magnitude of financial flows from emissions trading

8. Financial Flows – Example for China
2030
Models
Technology
Allocation
C&C / 550
Climate target

9. Financial Flows in DC – absolute numbers
2030
2050
Financial flow [bln $]
RNAI and IND: net exporter, CHN: net importer of emission allowances
Largest uncertainty is related to the different models

10. Influence of the CO2 Price in the models
Financial flow = volume of emission permit exports * CO2 price

11. CO2 Price
2030
2050
Large spread due to different model-dependent CO2 prices

12. Financial Flows in DC – numbers rel. to GDP
2030
2050
Financial flow rel. to GDP [%]
Large influence of allocation scheme, especially for „GDP shares“
Influence of technological availability and climate target is limited

13. Financial flows for AFR
2030
2050
Financial flow rel. to GDP [%]
Largest financial transfers are reported for AFR

14. Types of financial transfers in climate policy related to known problems of resource revenues
Dutch
Disease
Rent-seeking
/ corruption
Volatility
Full binding cap&trade
Sectoral (non-binding) cap&trade
Sectoral CDM
Policy CDM
Fund based approaches NAMAs

15. Conclusions
Climate finance can be expected to amount to large sums, roughly comparable to foreign aid.
Problems discussed in relation with aid and resource revenues (Dutch Disease, corruption, volatility) have to be taken into account with respect to climate finance.
The size of financial flows – and hence the optimal policy regime – depends on the model, allocation scheme, technology and the climate target. The range of uncertainty is extremely large.
Fund-based systems might be more appropriate than allowance trading when it comes to dealing with volatility.

16. Backup

17. Large uncertainties of financial transfers from emissions trading
Conclusion
Large uncertainties of financial transfers from emissions trading
Tranfers depend on
The model used (M)
The allocation scheme (A)
The climate target (C)
The available technologies (T)
Transfers are in the order of magnitude of FDI and larger
Transfer (relative to GDP) are in the order of magnitude where negative growth effects can be expected (for >20%, Feeny and McGillivray (2011)

18. Financial flows in IAMs
USA
EUR
Financial flow [bln $]
Financial flow [bln $]
Financial flow rel. to GDP [%]
Financial flow rel. to GDP [%]

19. 2030