Your SlideShare is downloading. ×
Co2 emission prospect
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Co2 emission prospect

1,302
views

Published on

Using the data from the Energy Information Administration to evaluate the prospect for reduction in CO2 emission.

Using the data from the Energy Information Administration to evaluate the prospect for reduction in CO2 emission.

Published in: Education, Technology

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,302
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
21
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Using EIA data to evaluate prospect of CO2 emission reduction Gaetan “Guy” Lion June 2010
  • 2. CO2 emission depends on four causal factors
    • Population (P);
    • Output per capita (O) : $GDP per capita;
    • Energy intensity (E) : energy consumed per $ of GDP;
    • Carbon intensity (C) : the amount of CO2 emitted per unit of energy produced;
    • The EIA calls them Kaya factors as they are multiplicative (on an indexed basis):
    • P x O x E x C = CO2 emission
  • 3. History & forecast of CO2 emission and four Kaya factors This interesting graph from the EIA shows the history of CO2 emissions and the four underlying Kaya factors indexed to their 2007 value (2007 = 1). Between 1990 and 2007, CO2 emissions have steadily increased because output per capita and population have risen faster than energy intensity and carbon intensity have declined. The EIA expects those trends to continue through 2035.
  • 4. What the EIA data means Those are the indexed value of the 4 Kaya factors and CO2 emissions in 1990, 2007, and 2035 as shown on graph on previous slide. Those are the derived annual % change over the historical period (1990 – 2007) and the forecast (2007 – 2035). As shown, the resulting CO2 emission annual growth is slowing down from 1.92% during the historical period vs 1.28% during the forecast. In 2035, CO2 emissions are expected to be 42.8% higher than in 2007 even though Energy intensity is expected to decline by 38.7%
  • 5. Exploring different scenarios We generated 21 scenarios, keeping Population growth and Output per capita unchanged vs EIA forecast. We used 3 different Carbon intensity % change (same as EIA, 2 x EIA, and 3 x EIA) and 7 different Energy intensity levels. The 7 scenarios in green represent the ones corresponding to a decrease in CO2 emission by 2035.
  • 6. What changes by 2035 do those scenarios entail? Over the 17 year period from 1990 to 2007, we achieved an annual reduction in Energy Intensity of –1.37%. The EIA forecasts a reduction of -1.73% p.a. over a 28 year period. The “green” scenarios entail reaching a reduction level of at least -2.75% p.a. This corresponds to a -54.2% reduction by 2035 vs the -38.7% in the EIA forecasts. Over the 17 year period from 1990 to 2007, we achieved an annual reduction in Carbon Intensity of –0.05%. The EIA forecasts a reduction of -0.16% p.a. over a 28 year period. Assuming we can achieve a -2.75% p.a. reduction in Energy intensity, the “green” scenario entails reaching a reduction Carbon Intensity of -0.48%. This is 3 x the EIA forecast.
  • 7. How about Population and Output per capita? Although the EIA forecast is quite different from the historical period in terms of both Population and Output per capita growth, those large changes are in opposite directions. The resulting World GDP growth rate changes much less. And, it is somewhat slower.
  • 8. Streamlining 4 Kaya factors into just 2
    • We can easily combine the 4 Kaya factors into 2:
    • First, lets combine Population and Output per capita into World GDP (as shown on previous slide).
    • Second, lets combine Energy Intensity and Carbon Intensity into C02 emission per $ of GDP . This makes sense since it is equal to:
    • Energy Intensity times Carbon Intensity.
    • This streamlining into just two Kaya factors allows us to explore CO2 emission target change scenarios.
  • 9. CO2 emission target change scenarios This model has 3 inputs: 1) Target change in CO2 emission; 2) Years to achieve that change; and 3) World GDP growth p.a. Given those three inputs (yellow cells), the model generates the resulting annual change in CO2 emission per $ of GPD (green cells). For each target change in CO2 emission, we consider three different terms: 20, 30, and 50 years.
  • 10. How challenging are those CO2 emission change targets? This is the same output shown on previous slide except we show actual results for the 1990 – 2007 period and the EIA forecast for the 2007 – 2035 period. As shown in the model, the reduction in CO2 emission per $ of GDP are far greater than what we have achieved over the past couple of decades and what the EIA forecasts through 2035.
  • 11. Necessary cumulative reduction in CO2 emission per $ of GDP The graph shows the cumulative reduction in CO2 emission per $ of GDP to maintain 0% growth in CO2 emission over the next 50 years. It contemplates four scenarios associated with World GDP growth ranging from 2% to 4%. It also shows the curve using actual rate achieved over the 1990 – 2007 period.
  • 12. No growth in CO2 emission looks tough Over next 50 years, if real World GDP grows within a range between 3% to 4% p.a. we would have to reduce CO2 emission per $ of GDP by close to 80% or more. Even if real World GDP grew by only 2% p.a., we would have to reduce CO2 emission by over 60%. This rate is already much faster than actual rate over the 17 years from 1990 to 2007.
  • 13. EIA Forecast This is a replication of the EIA graph on slide 3 using just 2 Kaya factors instead of 4. It shows that despite reducing CO2 emission per $ of GDP by more than – 40%, CO2 emission would still rise by over 40% over the next 28years .
  • 14. Modifying the EIA forecast For reduction in CO2 emission per $ of GDP we used the actual rate achieved over the 1990 – 2007 period instead of the EIA forecast. We still used the EIA GDP forecast. Now, CO2 emission per $ of GDP declines by around 30% and the resulting increase of CO2 emission rises above 60% .
  • 15. Challenge: Rise of China = Rise of Coal The EIA forecasts that coal will hold up a steady share of electricity generation. This is because of China’s rising electricity capacity relying on coal-fired power plants. The EIA forecasts that coal’s share of World energy use will rise and partly make up for the relative decline from liquids (mainly petroleum products).
  • 16. Growth in CO2 emissions comes mainly from non-OECD countries The EIA forecast reflects that non-OECD countries are associated with more rapid economic growth associated with an electricity generation using mainly coal. Thus, non-OECD countries account for almost the entire growth in CO2 emissions.
  • 17. Prospect for reducing CO2 emission
    • The prospect is unlikely. The basic rule is that your reduction in CO2 emission per $ of GDP (in % change in absolute term) has to exceed GDP growth.
    • The EIA forecasts that the OECD countries will stabilize their CO2 emission levels through 2035; Meanwhile, the non-OECD countries with more rapid economic growth and reliance on coal for generating electricity are forecasted to experience rapid growth in CO2 emission.
  • 18. Appendix: Data Source
    • U.S. Energy Information Administration/Internal Energy Outlook 2010.