An Assessment of Climate Change

But there remain issues about the data. The key problem is one of data and whether the data is reliable. This is because everyone uses the data and all judgements of the computer models must rest on how well they predict the data. But there remain issues about the data.

There have been many critics of the massaging of the temperature data. Long Run Data -- ice cores, tree rings, principal components, etc. Short Run Data – urban heat islands, bias corrections, choice of mean, etc. There have been many critics of the massaging of the temperature data.

Global Temperature Anomalies The global mean surface air temperature for that period was estimated to be 14°C (57°F), with an uncertainty of several tenths of a degree.

CO2 Atmospheric Concentration in Parts Per Million (Million)

Note that this time series model (ARMAX) fits the data well and predicts that in about 33 years the temperature of the earth will rise about 1o C or 3o C in 100 years, assuming that CO2 concentration continues to grow at the 2000 – 2016 rate. There is no physical modeling or computer simulation. This is a straightforward time series forecast using monthly CO2 and temperature data from 1965 to 2016.

Do Economic Activities Significantly Cause Growth in CO2 Concentrations?

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 24 unit-root null hypothesis: a = 1 test with constant model: (1-L)y = b0 + (a-1)*y(-1) + ... + e estimated value of (a - 1): 0.0164246 test statistic: tau_c(1) = 1.86446 asymptotic p-value 0.9998 1st-order autocorrelation coeff. for e: -0.039 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 24 unit-root null hypothesis: a = 1 test with constant model: (1-L)y = b0 + (a-1)*y(-1) + ... + e estimated value of (a - 1): 0.00363817 test statistic: tau_c(1) = 0.320148 asymptotic p-value 0.9794 1st-order autocorrelation coeff. for e: -0.014

Step 3: cointegrating regression OLS, using observations 1990-2015 (T = 26) Dependent variable: l_CO2 coefficient std. error t-ratio p-value ----------------------------------------------------------- const 1.37110 0.0504209 27.19 1.52e-019 *** l_WorldGDP 0.142969 0.00158209 90.37 6.47e-032 *** Mean dependent var 5.927330 S.D. dependent var 0.038816 Sum squared resid 0.000110 S.E. of regression 0.002145 R-squared 0.997070 Adjusted R-squared 0.996948 Log-likelihood 123.9137 Akaike criterion −243.8274 Schwarz criterion −241.3112 Hannan-Quinn −243.1029 rho 0.507919 Durbin-Watson 0.970465 Step 4: testing for a unit root in uhat Augmented Dickey-Fuller test for uhat including one lag of (1-L)uhat sample size 24 unit-root null hypothesis: a = 1 model: (1-L)y = (a-1)*y(-1) + ... + e estimated value of (a - 1): -0.635726 test statistic: tau_c(2) = -2.77986 asymptotic p-value 0.1722 1st-order autocorrelation coeff. for e: 0.057 There is evidence for a cointegrating relationship if: The unit-root hypothesis is not rejected for the individual variables, and (b) the unit-root hypothesis is rejected for the residuals (uhat) from the cointegrating regression.

Model 3: Error Correction OLS, using observations 1991-2015 (T = 25) Dependent variable: d_l_CO2 coefficient std. error t-ratio p-value ----------------------------------------------------------------------------------------- const 0.00372601 0.000842408 4.423 0.0002 *** d_l_WorldGDP 0.0356621 0.0236823 1.506 0.1463 uhat_1 −0.161309 0.159346 −1.012 0.3224 Mean dependent var 0.004930 S.D. dependent var 0.001410 Sum squared resid 0.000043 S.E. of regression 0.001398 R-squared 0.098593 Adjusted R-squared 0.016647 F(2, 22) 1.203146 P-value(F) 0.319249 Log-likelihood 130.4357 Akaike criterion −254.8714 Schwarz criterion −251.2147 Hannan-Quinn −253.8572 rho 0.104015 Durbin-Watson 1.747711 About ¼ of the CO2 growth is due to GDP growth, if we accept the estimates to the left. But, statistically speaking the null of 0% cannot be rejected at the standard 1%, 5% , or 10% significance levels. To stabilize CO2 levels we would need a 10% fall in world GDP, ceteris paribus. This seems extreme and unacceptable.

Temporal Ordering is from Black (Temp) to Light Gray (CO2)