Determinants of CO 2 emissions-GDP Decoupling in Brazil for the period 2000-2009.

1. Determinants of CO2 emissions-GDP Decoupling in Brazil for the period 2000-2009. Luciano Charlita de Freitas Shinji Kaneko Graduate School for International Development and Cooperation Hiroshima University 34th IAEE Conference Stockholm, 21st June 2011

2. Outline of today’s presentation 1. Background on Brazilian energy mix and sources of CO2 emissions. 2. Research objectives. 3. Methodology. 4. Results. 5. Conclusions.

3. Timeline of Brazilian efforts towards emission mitigation 1990s • United Nations Conference on Environment and Development (Rio 92) in Rio de Janeiro. • Brazil became the first signatory country of the Establishment of the United Nations Framework Convention on Climate Change (UNFCCC). • 1997- Kyoto Protocol at COP3. • Brazil joined the Developing countries group; • Actively participated in the development of the Clean Development Mechanism (CDM), afterwards included in the Article 12 of the Kyoto Protocol.

4. Timeline of Brazilian efforts towards emission mitigation Earlier 2000s • 2003 – Launch of Flex-Fuel technology that allows simultaneous usage of ethanol and blended gasoline in a same car. • 2004- Release of first Emission Inventory at COP 10 in Buenos Aires; • 2006, a survey with 1000 adults from different regions of Brazil concluded that 2% of the respondents believed the climate change was the main environmental problem in Brazil, the last option in a list of 10 items(IBOPE/WWF, 2006). • 2006 – Brazil suggest the creation of a mechanism to effectively promote the reduction of emissions form deforestation in developing countries. The proposal gave origin to the REED project (Reducing Emissions from Deforestation and Degradation) at COP 12 in Nairobi;

5. Timeline of Brazilian efforts towards emission mitigation Later 2000s • 2008 - Brazil recognizes the commitment to reduce emissions in a voluntary basis at the COP24 in Poznan; • 2010 - Release of Second National Communication, GHG Emissions Inventory; • Later in 2010, a survey with 2002 adults from different regions of the country revealed that 26% of respondents believe that Climate change is the main environmental concern in Brazil, the third after deforestation and water supply and pollution (IBOPE/CNI, 2010). • 2010 – Brazil commits to reduce GHG emissions in 36.1 to 38.9% (in 2009 bau; ~19% reduction on 2005 level) by 2020.

6. CO2 Emissions in Brazil and Boundaries of this study (values in Gg CO2) Source: MCT, 2009

7. Energy consumption by source in Brazil Hydropower Source: EPE, 2010

8. Share of Renewable and Non-renewable sources Source: EPE, 2010

9. Objectives of this Study • to identify the events of decoupling between CO2emission from energy consumption and GDP growth in Brazil. • to identify the determinants of decoupling events in Brazil.

10. Decoupling • Decouplingrefers to the linkage between economic activity and environmental pressure, or under the terminology of the OECD, the link between “economic goods” (e.g. economic growth) and “environmental bads” (e.g. CO2 emissions). • Is used as an indicator for environmental policy performance. • Absolute decoupling occurs when the CO2 emission displays a stable or descending trend over time contrasting to the growth of economic activity, in opposite direction. • Relative decoupling occurs when the growth rate of CO2 emissions is lower than the growth rate of economic activity.

11. General Methodology - Index Decomposition • Features: • Allows to evaluate historical changes in aggregate indicators according to selected determinants of CO2 emission changes; • Extensive usage in emission studies; • Evaluation can be derivate from time-series data on energy demand and/or CO2 emissions. • The Log-Mean Divisia Index Method I (LMDI) (Ang and Liu, 2001) is the main approach in index decomposition method used by researchers and preferred method by policy makers(Ang, 2004); • Desirable Properties of LMDI: • Time reversal property (from t0 to t1 = from t1 to t0); • Factor reversal property (decomposition is perfect, i.e. no residual); • Easy formulation allowing the usage of multiple factors; • Easy conversion between multiplicative (percentage change) and additive forms (quantity change);

12. General Methodology - Log-Mean Divisia Index I (LMDI) Index Identity (example)  Multiplicative Additive Inter temporal approach results in residuals LMDI allows the distribution of residuals between factors by taking a lag of average residuals between 2 intervals Weight in Multiplicative LMDI decomposition Weight in Additive LMDI decomposition i= sector

13. Decomposition notation and structure X X P X X X Carbon Intensity Energy Mix Energy Intensity Economic Structure Economic Activity Population Emission Change i1:Energy i2:Industry i3:Public & Services i4:Agriculture & Feedstock i5:Transport

14. Methodology - LMDI Formulae Multiplicative decomposition Where, t – t-1 = 1 year (also calculated for the intervals 1980-1994; 2004-2009) i= productive sectors (5); j= energy sources (19);

15. Data Source: General Structure of the Brazilian Energy Balance Final Primary Energy Consumption Secondary Energy Input Secondary Imports Secondary Exports Primary Imports Primary Exports Energy Products Final Consumption Primary Gross Supply (a) Primary Net Supply (b) Secondary Gross Supply (c) Final Total Consumption Primary Energy Input Secondary Net Supply (d) Final Energy Secondary Production Conversion Facilities Primary Production Secondary Stock Variation Secondary Losses Primary Stock Variation Primary Losses Non-energy Final Consumption Conversion Losses Not used and Reinjection of Energy Not used and Reinjection of Energy Energy Conversion Secondary Energy Final Consumption • All energy data use in this study refers to Final energy consumption; • CO2 is estimated based on the final energy consumption data by employing IPCC guidelines for emission inventory (IPCC, 2006); Source: EPE, 2010

16. Results: Decoupling events in Brazil 2009: 1st event of absolute decoupling Events of relative decoupling

17. Results: Decoupling events in Brazil if the decoupling factor is zero or negative there is no evidence of decoupling, while values close to 1 indicate lower environmental pressure 1980-1994 2004-2009

18. Results: Determinants of decoupling events Multiplicative Decomposition

19. Results: Determinants of decoupling events Additive Decomposition CI: Carbon Intensity; EI: Energy Intensity; EM: Energy Mix; ES: Economy Structure; G: Economy Activity; P: Population; CO2: Emission Change in the interval

20. Results: Summary • Carbon intensity reduction, energy mix diversification and economy structure change were the driving forces of emissions mitigation in periods of relative and absolute decoupling. • Results for Brazil differ from others transition economies that experienced relative or absolute decoupling in the last 40 years. Improvement in emissions levels in most countries are mostly associated to Technology intensive alternatives (ex. reduction of energy intensity). • Finds for Energy Mix and Carbon Intensity in Brazil are associate to the usage of natural resources – Energy Mix Diversification. • In general, the driving factors for emission mitigation in Brazil are the same for the periods of decoupling in the 1980s and 2000s.

21. Conclusions remarks • Historically, Energy Mix and Carbon Intensity are the main drivers for emission mitigation in Brazil; • In the last 40 years Brazil experienced mainly a coupled relations between CO2 emissions and GDP. Eventually, it was observed episodes of relative decoupling. In 2009 it was observed the first event of absolute decouplingin Brazil; • Determinants of emission reductions in Brazil differs from other developed and transition countries where Technology intensive routes associated to energy intensity systems are the main driving factors for emission mitigation. • Driving factors underlying decoupling events are the same for both the 1970s and the 2000s what creates some suspicious on new efforts developed within the current discussion on emission mitigation. (New challenges, old solutions)

22. Thank you lcfreitas@yahoo.com.br

23. Energy Intensity Growth Sample of selected countries (1971=1)

24. Methodology – CO2 emission estimation ǂOtherPetroleum Secondary Sources includes Refinery Gas, Petroleum Coke and others oil derivates (EPE, 2009). *Refers to vegetal and industrial residues used to heat and vapor generation. Source: IPCC (2006)