1. The Impact of Climatic Shocks on Alberta’s Economy: A Vector Autoregression Analysis by Wes Lu Supervisors: Vic Adamowicz and Sandeep Mohapatra Department of Resource Economics and Environmental Sociology University of Alberta 1

2. Climate Change: How Do We Know ? (Credit: Vostok ice core data/J.R. Petit et al.; NOAA Mauna Loa CO2 record.)

3. Other Evidence of Climate Change ◦Changed precipitation pattern ◦Rise in global sea levels ◦Retreating glaciers ◦Extreme weather events (e.g., floods, droughts and storms)

4. The Economic Impact of Climate Change ◦Nordhaus (2008) found that the present value of climatic change damage may be $22.6 trillion (in 2005 U.S. dollars) ◦ It is estimated that if no specific actions are taken, the overall cost due to climate change will be at least 5% of global GDP each year (Stern et al. 2006). ◦Moreover the impacts of climate change are not homogeneous across regions and hence region specific studies are required to inform policymakers. ◦Adapting to climate change is essential to mitigate the negative impacts and maintain prosperity for our society

5. Why This is Important In Canada? ◦Climate change is considered to be more intense for northern regions including Alberta, Canada. ◦Most Canadian studies are dated and only a few of them are economy-wide and have analyzed the impacts of climate change at the regional level. ◦Among the large Canadian literature on national and regional economic growth and its drivers, not a single study considers climate change as an explanatory factor ◦This is the first Canadian study using a time series model to investigate the dynamic relationship between climate change and economic growth

6. GDP Level vs GDP Growth Rate Effects of Climate Change -- How are they different?!!

7. 7 (Credit: Delavane Diaz 2015)

8. Main Research Objective To analyze the quarterly response of Alberta’s agricultural, non- agricultural, and total GDP growth to temperature and precipitation shocks, as well as oil price shock. 8

9. Different Methods of Estimating Climate Impacts Agricultural Sector Production function: does not capture adaptations to climate change (Deschenes and Greenstone 2007) the variables are usually jointly determined and the potential of omitted variable Profit function: the role of storage may not be captured (Fisher et al. 2012) Hedonic method may confound climate with other unmeasured land characteristics, which lead to unknown bias in the sign and magnitude of the resulting omitted variables (Deschenes and Greenstone 2007) 9

10. Different Methods of Estimating Climate Impacts Economy-wide Analysis: CGE (computable general equilibrium) models usually do not capture the simultaneous impacts of climate change on the agricultural sector and other sectors IAM (Integrated assessment model) models Limited knowledge about climate sensitivity and damage functions (e.g., the link between increasing temperature and GDP growth) Different IAMs tend to have different assumptions in terms of functional forms, parameter values and discount rates. Therefore, the estimates are wide-ranging. For example, using the DICE model, Nordhaus (2011) reported a SCC of $11 per ton. In contrast, Stern (2007) reported a SCC of $200 per ton using the PAGE model. These above approaches are restricted by strong underlying assumptions! 10

11. VAR Model vs VARX Model vs VARX_WL Model The VAR (i.e., vector autocorrelation regression) model ◦based on statistical regularities rather than relatively strong assumptions ◦all variables in a VAR assumed to be endogenous The VARX (i.e., vector autocorrelation regression with exogenous variables) model ◦able to differentiate between endogenous variables and exogenous variables ◦The standard VARX model, like the standard VAR model, only allows calculation of impulse responses to shocks in endogenous variables. The VARX_WL model We follow Fomby et al. (2013) and develop a new econometric approach that allows for calculation of mean responses of exogenous shocks in a VARX model 11

12. Data and Methods 12

13. Data and Methods 13

14. Results 14

15. Results (Oil Price Shock) 15 Total GDP GrowthAgr. GDP Growth

16. 16 Results (Climatic Shocks) Total GDP Growth: Agr. GDP Growth: (3) (2) (4) (1)

17. Cumulative Effect for GDP Growth Cumulative effect: a summation of mean responses of each period for a period of four years (16 quarters) starting from the climatic shocks Temperature shocks: o Agr. GDP growth: -0.49 o Total GDP growth: -0.07 Precipitation shocks: o Agr. GDP growth: +0.09 o Total GDP growth: +0.004 Both climatic shocks tend to induce stronger effects on agricultural GDP growth than on total GDP growth 17

18. Static and Dynamic Forecasts of GDP Growth 18 Total GDP Agr. GDP

19. In Sample Forecast Scenario 1: ◦a 30% increase in temperatures for each quarter within the forecast period. Scenario 2: ◦a 30% increase in precipitation. Scenario 3: ◦both temperatures and precipitation go up by 30%. Scenario 4: ◦a 30% increase in temperatures and a 30% decrease in precipitation. 19

20. Forecasts of GDP Growth under Different Scenarios 20 Total GDP Growth Rate

21. Average Economic Impact on GDP growth Scenario 1: a 30% increase in temperatures for each quarter within the forecast period. Scenario 2: a 30% increase in precipitation. Scenario 3: both temperatures and precipitation go up by 30%. Scenario 4: a 30% increase in temperatures and a 30% decrease in precipitation. Total GDP Growth: S-1: 0.6%S-2: 0.2%S-3: 0.4%S-4: 0.9 Agr. GDP Growth: S-1: 0.01%S-2: 2%S-3: 2%S-4: 2% 21

22. Static and Dynamic Forecasts of Total GDP Growth 22 Total GDP Agr. GDP

23. Average Economic Impact on GDP growth Scenario 1: a 30% increase in temperatures for each quarter within the forecast period. Scenario 2: a 30% increase in precipitation. Scenario 3: both temperatures and precipitation go up by 30%. Scenario 4: a 30% increase in temperatures and a 30% decrease in precipitation. Total GDP Growth: S-1: 0.6%S-2: 0.2%S-3: 0.4%S-4: 0.9% 23

24. Average Economic Impact on GDP growth Scenario 1: a 30% increase in temperatures for each quarter within the forecast period. Scenario 2: a 30% increase in precipitation. Scenario 3: both temperatures and precipitation go up by 30%. Scenario 4: a 30% increase in temperatures and a 30% decrease in precipitation. Total GDP Growth: S-1: 0.6%S-2: 0.2%S-3: 0.4%S-4: 0.9% 24

25. Concluding Remarks 1.Temperature shocks tend to have significant and negative impacts on GDP growth rate 2.Precipitation shocks tend to result in overall positive impacts on GDP growth but are not significant 3.Increased precipitation tends to partially alleviate negative impacts on economic growth due to increased extreme high temperatures, while decreased precipitation tends to exacerbate on such negative effects 25

26. Concluding Remarks 4.In theory, the VARX model incorporates adaptation. However, the results still suggest significant and negative climate impacts of temperature shocks on GDP growth. This indicates the potential climate impacts may go beyond our historical adaptation. Autonomous adaptation or Planned adaptation? 5.It is important for Albertans to be prepared to mitigate the negative impacts. 6.Environment or Oil production?! 26

27. Acknowledgements I would like to thank my supervisors, Dr. Vic Adamowicz and Dr. Sandeep Mohapatra. Without their invaluable contributions, this thesis would have never been finished. I would like to thank Dr. Monireh Faramarzi for preparing and providing the climate data for this study.

28. Thank you! Wes Lu Email: wlu2@ualberta.cawlu2@ualberta.ca Phone: 5873340860