The Relation of Energy to the Macroeconomy

The composition of our energy has changed over the years, but fossil fuels remain easily 80% of our source for per capita energy consumption.

Sample of 36 Countries Energy per cap and PPP GDP per cap Algeria India Poland Australia Indonesia Portugal Belgium Italy Romania Brazil Japan Saudi Arabia Canada Kazakhstan South Africa Chile Korea Spain China Malaysia Sweden Colombia Mexico Thailand Czech Netherlands Turkey Egypt NZ UAE France Nigeria UK Germany Norway USA

These results indicate that countries that use more energy relative to GDP have made technical transitions to greater conservation – i.e. the poor cannot afford to be conservative in resource usage

Oil Prices and Inflation – A Very Simple Simulation Study by the St. Louis Fed Three Scenarios

Elasticity = 0.46

What about the evidence that CO2 emissions are being driven by economic activities?

Tests on the Full Sample indicate there is no stable long run relation and no short run relation

Cointegration Tests on Reduced Sample 1990-2009 Step 1: testing for a unit root in l_CO2 Augmented Dickey-Fuller test for l_CO2 including one lag of (1-L)l_CO2 sample size 18 unit-root null hypothesis: a = 1 test with constant model: (1-L)y = b0 + (a-1)*y(-1) + ... + e estimated value of (a - 1): 0.0177903 test statistic: tau_c(1) = 1.14622 asymptotic p-value 0.9979 1st-order autocorrelation coeff. for e: -0.027 Step 2: testing for a unit root in l_WorldGDP Augmented Dickey-Fuller test for l_WorldGDP including one lag of (1-L)l_WorldGDP sample size 18 unit-root null hypothesis: a = 1 test with constant model: (1-L)y = b0 + (a-1)*y(-1) + ... + e estimated value of (a - 1): -0.0269365 test statistic: tau_c(1) = -1.23768 asymptotic p-value 0.6603 1st-order autocorrelation coeff. for e: 0.150

Cointegration Exists between CO2 and Real World PPP GDP Step 3: cointegrating regression Cointegrating regression - OLS, using observations 1990-2009 (T = 20) Dependent variable: l_CO2 coefficient std. error t-ratio p-value ----------------------------------------------------------- const 1.48957 0.0704207 21.15 3.65e-014 *** l_WorldGDP 0.139230 0.00221713 62.80 1.53e-022 *** Mean dependent var 5.911725 S.D. dependent var 0.029016 Sum squared resid 0.000073 S.E. of regression 0.002010 R-squared 0.995456 Adjusted R-squared 0.995204 Log-likelihood 96.87222 Akaike criterion -189.7444 Schwarz criterion -187.7530 Hannan-Quinn -189.3557 rho 0.310138 Durbin-Watson 1.297370 Step 4: testing for a unit root in uhat Augmented Dickey-Fuller test for uhat including one lag of (1-L)uhat sample size 18 unit-root null hypothesis: a = 1 model: (1-L)y = (a-1)*y(-1) + ... + e estimated value of (a - 1): -0.907261 test statistic: tau_c(2) = -3.2874 asymptotic p-value 0.05653 1st-order autocorrelation coeff. for e: 0.057 Cointegration Exists between CO2 and Real World PPP GDP

No Strong Evidence for Error Correction Model 3: OLS, using observations 1991-2009 (T = 19) Dependent variable: d_l_CO2 coefficient std. error t-ratio p-value ----------------------------------------------------------- const 0.00346611 0.00107632 3.220 0.0053 *** d_l_WorldGDP 0.0374457 0.0312015 1.200 0.2476 uhat2_1 -0.190643 0.238153 -0.8005 0.4351 Mean dependent var 0.004689 S.D. dependent var 0.001493 Sum squared resid 0.000037 S.E. of regression 0.001517 R-squared 0.082699 Adjusted R-squared -0.031964 F(2, 16) 0.721238 P-value(F) 0.501297 Log-likelihood 98.00291 Akaike criterion -190.0058 Schwarz criterion -187.1725 Hannan-Quinn -189.5263 rho 0.075107 Durbin-Watson 1.816704 No Strong Evidence for Error Correction

The Small Amount of Available Data Does NOT Show CO2 is Caused by GDP Growth -- Only Episodic Evidence -- Not Statistical Evidence.

corr (d_l_WorldGDP, d_l_CO2) = 0.23791670 Under the null hypothesis of no correlation: t(23) = 1.17474, with two-tailed p-value 0.2521 ---- which shows that we cannot reject the null

Rising atmospheric concentrations of CO2 appear to be caused by something else – not mainly GDP growth – probably at best 25% of CO2 growth is caused by rising GDP