If agents learn-by-doing and are myopic, less advanced firms might adopt new technologies while more advanced ¿rms stick with the old technology. This kind of overtaking can also occur if agents are forward looking and have high discount rates. However, overtaking never occurs if agents are sufficiently patient. A finite discount rate increases the set of states at which agents adopt new technologies, so more patient agents tend to upgrade their technology more frequently.

In many cases the optimal open-loop policy to influence agents who solve dynamic problems is time-inconsistent. We show how to construct a time-consistent open-loop policy rule. We also consider an additional restriction under which the time-consistent open-loop policy is stationary. We use examples to illustrate the properties of these tax rules.

This study examines the spatial pattern of older and newer vehicle ownership rates by using aggregated census and non-census data for small geographic units. Although there is considerable research on how overall ownership varies among neighborhoods, there is a paucity of research on how the age composition of personal vehicles varies across space. While we expect disadvantaged neighborhoods to have fewer and older vehicles, it is important to quantify these phenomena. The spatial pattern and its determinants are critical to answering basic policy questions such as the impact of transportation on accessing economic opportunities, and to estimating large-scale transportation models for urban planning. Moreover, the age of car also has broadersocietal consequences because of externalities. An older vehicle produces much more pollutants per mile, therefore contributes disproportionately to the mobile emissions (Dill, 2001). For example, a 1985 vehicle tends to generate nearly 38 times more carbon monoxide per mile than a 2001 model (Beydoun and Guldmann, 2006). For individuals and households, vintage is important because older vehicles are less reliable, thus offering less service to their owners.

Clothing affects people’s perception of the thermal environment. In this research two predictive models of clothing insulation have been developed based on 6,333 selected observations taken from ASHRAE RP-884 and RP-921 databases. The database has been used to statistically analyze the influence of 20 variables on clothing insulation.

The results show that the median clothing insulation is 0.59 clo (0.50 clo (n=2,760) in summer and 0.66 clo (n=3,580) in winter). Clothing insulation is correlated with outdoor air temperature (r=0.45), operative temperature (r=0.3), relative humidity (r=0.26), air velocity (r=0.14) and metabolic activity (r=0.12).

Two mixed regression models were developed. In the first one clothing insulation is a function of outdoor air temperature measured at 6 o’clock in the morning and in the second one the influence of indoor operative temperature is also taken into account. The models were able to predict only 19 and 22% of the total variance, respectively. These low predicting powers are better than the assumption of constant clothing insulation for the heating (1 clo) and cooling (0.5 clo) seasons.

Clothing affects people’s perception of the thermal environment. Two dynamic predictive models of clothing insulation were developed based on 6,333 selected observations of the 23,475 available in ASHRAE RP-884 and RP-921 databases. The observations were used to statistically analyze the influence of 20 variables on clothing insulation.

The results show that the median clothing insulation is 0.59 clo (0.50 clo (n=3,384) in summer and 0.69 clo (n=2,949) in winter). The median winter clothing insulation value is significantly smaller than the value suggested in the international standards (1.0 clo). The California data (n= 2,950) shows that occupants dress equally in naturally and mechanically conditioned buildings and all the data has female and male dressing with quite similar clothing insulation levels. Clothing insulation is correlated with outdoor air (r = 0.45) and indoor operative (r=0.3) temperatures, and relative humidity (r=0.26) An index to predict the presence of a dress code is developed.

Two multivariable linear mixed models were developed. In the first one clothing is a function of outdoor air temperature measured at 6 o’clock, and the second one adds the influence of indoor operative temperature. The models were able to predict 19 and 22% of the total variance, respectively. Climate variables explain only a small part of human clothing behavior; nonetheless, the predictive models allow more precise thermal comfort calculation, energy simulation, HVAC sizing and building operation than previous practice of keeping the clothing insulation values equal to 0.5 in the cooling season and 1 in the heating season.