The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part IV)

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for global temperatures (Temp5). This simulation is similar to the one done in Part III except this time the temperature variable being evaluated contains the running five year average of global temperatures.

R2	0.99
Adjusted R2	0.97
SE	0.030
Term	Coefficient	95% CI	SE	t statistic	DF	p
Intercept	-23.22	-47.24	to 0.79	10.615	-2.19	9	0.0564
Temp	-0.09777	-0.32014	to 0.12460	0.098301	-0.99	9	0.3459
CO2	0.01546	-0.01863	to 0.04956	0.015072	1.03	9	0.3317
Energy Cost	-0.102	-0.219	to 0.015	0.0516	-1.98	9	0.0794
USP	-2.602	-8.271	to 3.067	2.5060	-1.04	9	0.3261
Energy Cons	2.7961E-08	1.9196E-09	to 5.4003E-08	1.1512E-008	2.43	9	0.0381
CO2 GDP	-0.000432	-0.004280	to 0.003416	0.0017010	-0.25	9	0.8052
CO2 GDP1	0.0002631	-0.0030553	to 0.0035816	0.00146694	0.18	9	0.8616
Coal KWH1	3.359	-0.876	to 7.594	1.8722	1.79	9	0.1064
NG KWH1	1.007	-0.460	to 2.474	0.6485	1.55	9	0.1549
Oil KWH1	1.93	-0.02	to 3.88	0.863	2.24	9	0.0521
NP KWH1	8.649	-4.094	to 21.393	5.6334	1.54	9	0.1591
BIO KWH1	1.463	-2.617	to 5.543	1.8036	0.81	9	0.4382
Hydro KWH1	1.534	-0.890	to 3.957	1.0713	1.43	9	0.1860
Geo KWH1	0.1998	-4.0556	to 4.4552	1.88113	0.11	9	0.9177
Solar KWH1	3.759	-4.558	to 12.075	3.6762	1.02	9	0.3333
Wind KWH1	2.843	-1.707	to 7.394	2.0116	1.41	9	0.1912

Source of variation	Sum squares	DF	Mean square	F statistic	p
Model	0.737	16	0.046	52.60	<0.0001
Residual	0.008	9	0.001
Total	0.745	25

Coefficients	Coefficient Value	Value	Temperature		Ave	Temperature
Intercept	-23.22	1.00E+00	-2.32E+01		1.00E+00	-23.22
Coal KWH1	3.359	1.48E+00	4.97E+00		9.00E-01	3.0231
NG KWH1	1.007	4.71E+00	4.74E+00		3.10E+00	3.1217
Oil KWH1	1.93	1.85E+00	3.57E+00		1.15E+00	2.2195
NP KWH1	8.649	2.30E-01	1.99E+00		2.30E-01	1.98927
Hydro KWH1	1.534	3.67E-01	5.63E-01		3.67E-01	0.562978
Geo KWH1	0.1988	9.20E-02	1.83E-02		9.20E-02	0.0182896
Solar KWh1	3.739	4.17E-02	1.56E-01		5.00E-01	1.8695
Wind KWH1	2.843	1.43E-01	4.07E-01		1.40E+00	3.9802
CO2 GDP	-0.000432	5.10E+02	-2.20E-01		4.80E+02	-0.20736
CO2 GDP1	0.0002631	4.16E+02	1.09E-01		3.90E+02	0.102609
USP	-2.602	1.80E-01	-4.68E-01		1.60E-01	-0.41632
Energy Cost	-0.102	1.00E+01	-1.02E+00		1.00E+01	-1.02
Biomass KWH1	1.463	3.21E-01	4.70E-01		3.21E-01	0.469623
Energy ConsP	2.80E-08	9.46E+07	2.65E+00		5.50E+07	1.537855
CO2	0.01546	3.90E+02	6.03E+00		4.30E+02	6.6478
Temp	-0.09777	7.00E-01	-6.84E-02		7.00E-01	-0.068439
Result			6.74E-01			0.6103056

The model has excellent correlation as shown by the R² variable equal to 0.99. The results indicate that the 2009 value for the 5 year average of global temperature can decrease from 0.7 degrees to .6 degrees by decreasing the United States reliance on coal, natural gas, and oil by 25% and increasing the United States reliance on Solar and Wind energy by 25% (highlighted in red). Nuclear power, hydro power, biomass, and geothermal variables were held constant. Other 2009 variables were adjusted to keep up with the trends of using renewable energies such as increased energy costs, higher global temperatures (Temp – even with lower CO2 emissions the global temperature trend is upward), lower energy consumption (including the USP variable), and lower CO2 to GDP numbers.

Global warming alarmist may look at this result as proof that moving to renewable energy sources will eliminate CO2 and therefore, reduce global temperatures. This, however, is not true and this result can be misleading as will be revealed in future blog posts evaluating this model further.

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