The Influence of Urbanization on Cooling Energy Consumption in Xi＇an

Based on data of daily air temperature during 1951-2013,long-term variation characteristics of cooling degree days（ CDD） in Xi＇an and Chang＇an in summer were analyzed by using CDD to evaluate cooling energy consumption and 26 ℃ as the basic temperature of CDD. The results indicated that the changing trends of CDD in Xi＇an and Chang＇an were basically identical within a year,and the demand for cooling refrigeration was large mainly from June to August,especially in July. The maximum of urban-rural difference of CDD between Xi＇an and Chang＇an appeared in June.In order to achieve the same temperature,energy needed by the urban area was 5-7 ℃·d more than the suburb from June to August. Temperature and the cooling energy consumption were closely related,and the correlation degree increased with the rise of temperature. The effects of temperature increase of 1 ℃ on cooling energy consumption rate in Xi＇an were more obvious than that in Chang＇an. In both Xi＇an and Chang＇an,the effects of temperature increase of 1 ℃ on cooling energy consumption rate in July and August were greater than that in May,June and September.Evaluation models of cooling energy consumption in summer in Xi＇an and Chang＇an were built using temperature anomaly and CDD variability and can be applied to business systems.

IMPACT OF CLIMATE ON SUMMER RESIDENTIAL ELECTRICITY CONSUMPTION IN HONG KONG

The influence of climatic variables and cooling degree days (CDD) on summer residential electricity consumption for the period 1980 through 1994 in Hong Kong was investigated. The association between Clo, a measure of amount of Clothing insulation to maintain comfort, and residential electricity consumption was also examined. Utilizing monthly data and multiple regression analyses, it is discovered vapor pressure was not significantly related to electricity consumption while Cloud cover was negatively associated with electricity use. Climatic variables, CDD and Clo provided highly comparable results in modeling summer residential electricity consumption. Mean temperature and Cloud gave the best result. Clo yielded a slightly higher R2 value (0.867) than that of CDD (0.865) in the models. These results indicated that Clo could replace the weather variables and CDD to model electricity consumption.

An Approach to Quantify the Heat Wave Strength and Price a Heat Derivative for Risk Hedging

Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature （SAT）. Cooling degree days （CDD） are used as the weather index for trade. In this study, a call-option model was used as an example for calculating the price of the index. Two heat stress indices were developed to describe the severity and physical impact of heat waves. The daily Global Historical Climatology Network （GHCN-D） SAT data from 1901 to 2007 from the southern California, USA, were used. A major California heat wave that occurred 20-25 October 1965 was studied. The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1~ x0.1~ latitude-longitude grid. The resulting comparison indicates that （a） the interpolated data can be used as reliable proxy to price the CDD and （b） a normal distribution model cannot always be used to reliably calculate the CDD price. In conclusion, the data, models, and procedures described in this study have potential application in hedging agricultural and other risks.