Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We revisit statistical estimates for the relation between weather and energy consumption in Massachusetts using times series for heating degree hours that are calculated from hourly data with different set points and set backs. Using hourly values to calculate heating degree hours supports models that generate more accurate out-of-sample forecasts than models estimated from time series for heating degree-days calculated the traditional way. Furthermore, the set point and set back used to calculate heating degree hours generates statistically measurable differences in the accuracy of out-of-sample forecasts. These results indicate that assuming a set point of 65°F biases statistical estimates for the effect of a warming climate on energy use. We also quantify a new mechanism by which climate change will affect energy use—the temperature of tap water. As climate warms, the temperature of tap water that supplies hot water tanks rises, which reduces the amount of energy consumed to provide hot water. Finally, we use the statistical models to generate a spatial (1km×1km) and temporal (hourly) downscaling of carbon emissions that will be used to simulate a model for atmospheric transport and validate our understanding of the sources and sinks of carbon for the urban atmosphere.