Results 1 - 10 of 93
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[en] Despite the worldwide popularity of CDD- and HDD-type weather derivatives based on temperature, a different class of weather derivatives, so-called summer day options, is more popular in Japan; the payoffs are determined by the number of summer days (i.e., the days whose average temperature is above 25 C) during the contract period. In this paper, we price such summer day options by the good-deal bounds of Cochrane and Saa-Requejo [Cochrane, J.H., and J. Saa-Requejo, 2000, Beyond Arbitrage: Good-Deal Asset Price Bounds in Incomplete Markets, Journal of Political Economy 108, 79-119.], using temperature data for Tokyo. (author)
[en] Farmland values have traditionally been valued using seasonal temperature and precipitation but degree days over the growing season offer a more compact alternative. We find that degree days and daily temperature are interchangeable over the growing season. However, the impact of degree days in spring and summer is quite different. Climate effects outside the growing season are also significant. Cross sectional evidence suggests seasonal temperature and precipitation are very important whereas temperature extremes have relatively small effects. - Highlights: • The use of degree days in Ricardian models is reviewed. • Daily temperature and degree days are interchangeable over the growing season. • Seasonal climate effects are significantly different. • The four season average temperature model is superior to the degree days model.
[en] Thermal summation, growing degree-day (GDD) accumulation was measured with a commercial degree-day computer in Wisconsin. Upright cranberry shoot length, berry length, and shoot and berry dry weight accumulations were positively correlated with GDD accumulations. Fruit ethylene evolution and anthocyanin production were not correlated with GDD. Increased ethylene evolution preceded initiation of anthocyanin production. A preliminary schematic characterizing seasonal development of 'Searles' cranberry in relation to GDD was developed
[en] Highlights: • A model of evaluating effects of one cold wave on heating energy consumption is constructed. • Cold wave has a greater effect on the developed regions than relatively undeveloped ones. • Heating energy consumption per area has a positive correlation with HDD in developed areas. • Heating energy consumption per area has a spurious relation with HDD in undeveloped areas. • Heating period should be determined by temperature variation instead of being fixed. The heating energy consumption per floor area (HECPA) and heating degree days (HDD) are effective indicators in quantifying the energy demand for heating with climate change. Using the heating energy consumption and meteorological data, an attempt has been made to analyse the relationship between the HECPA and HDD in different regions of northern China by the linear regression model. Based on the constructed model, the effects of one cold wave on heating energy consumption in different regions are evaluated. The results show that the HECPA and HDD in Beijing have a positive correlation with a correlation coefficient of 0.68. During the cold wave in 2016, the heating energy consumption in Beijing approximately increases 2.37% compared with 2014. However, no correlation has been found between the HECPA and HDD in the relatively undeveloped regions. It seems that the cold wave has a greater effect on the developed regions than relatively undeveloped ones. It is considered that the reasons for the little effect of one cold wave on heating energy consumption in the undeveloped regions are outdated heating systems, insufficient energy supply for heating and low living standards.
[en] Highlights: •A novel integrated approach is developed for residential energy factor analysis. •Severe homogeneity of U.S. residential energy factors is revealed (mean VIF > 15). •A total of 32 key heterogeneous energy factors (Mean VIF = 1.21) are identified. •Core factor subset sufficiently represents raw space (mean correlation = 0.86). •Top three variables can interpret more than 33% residential energy variations. -- Abstract: Numerous energy computing frameworks were created with the aim of sustaining energy efficiency strategy to achieve residential sustainability in the U.S. While beneficial, without generic information on factor structure within building energy systems, most extant instruments are inclined to scope explanatory factor variables subjectively and diversely. Consequently, their intended utility often decreases with potential unstableness and limited generalizability among complicated energy system interactions. To overcome these issues, this paper develops a novel systematic homogeneity decomposition approach combining variable clustering and principal component analysis to identify key energy factor structure of residential buildings at the U.S. nation level. This study quantitatively results that, 32 key inter-heterogeneous energy variables (mean variance inflation factor = 1.21) appear sufficient to robustly profile the U.S. residential systems with an average Pearson correlation of 0.86 while reducing data burden by 68%. Top three significant variables relate to heating degree days, indoor environment and building vintage, respectively explaining 13%, 11% and 9% of energy variations. Thus, two major contributions are expected as follows. (1) These above obtained quantitative results can provide objective information for decision makers to sensibly select critical variables for robust energy computing with improved interpretability and generalizability by commonly using the above simplified 32-factor space (extracted from a 99-factor space) while saving data cost. (2) The developed novel approach can be useful in other countries for energy factor structure decomposing purpose since it has no geographical restrictions.
[en] Due to periodic changes in gas prices, many households are faced with three different gas prices within a single consumption year. Energy utilities do not read the meter after each price change. They convert the various prices to a single mean applying the weighted degree days method. In this method, the outside temperature is the major factor, besides temperature-independent factors such as natural gas consumption, thermal insolation and wind. A study has been carried out to investigate to what extent the natural gas consumption measured using the degree days method deviates from the actual natural gas consumption. Although the method can be somewhat refined, it is advised to retain the current method
[en] A method (which is supposed to be used first time in Pakistan) Degree-Days for the prediction of seasonal energy requirements for cooling is briefly discussed. This method requires the simulation of the pattern of external temperature variations in buildings, over seasons, in response, to exposure to the weather conditions. The cooling degree-days of capital cities of four provinces and the capital of Pakistan, Karachi, Lahore, Peshawar, Quetta, and Islamabad from 1987-1996, are calculated from the available meteorological data by using a computer program. The seasonal cooling energy requirement of a sample dwelling in different regions of Pakistan is also compared. This study shows that the average cooling degree-days in Lahore are about seven times more than the degree-days in Quetta. In Pakistan cooling requirement starts from April to October. (author)
[en] It has been conjectured that global warming will increase the prevalence of insect pests in many agro-ecosystems. In this paper, we quantitatively assess four of the key pests of maize, one of the most important systems in North American grain production. Using empirically generated estimates of pest overwintering thresholds and degree-day requirements, along with climate change projections from a high-resolution climate model, we project potential future ranges for each of these pests in the United States. Our analysis suggests the possibility of increased winter survival and greater degree-day accumulations for each of the pests surveyed. We find that relaxed cold limitation could expand the range of all four pest taxa, including a substantial range expansion in the case of corn earworm (H. zea), a migratory, cold-intolerant pest. Because the corn earworm is a cosmopolitan pest that has shown resistance to insecticides, our results suggest that this expansion could also threaten other crops, including those in high-value areas of the western United States. Because managing significant additional pressure from this suite of established pests would require additional pest management inputs, the projected decreases in cold limitation and increases in heat accumulation have the potential to significantly alter the pest management landscape for North American maize production. Further, these range expansions could have substantial economic impacts through increased seed and insecticide costs, decreased yields, and the downstream effects of changes in crop yield variability.
[en] Field experiments on mustard were conducted for 4 consecutive years to quantify crop growth and development in relation to thermal time under arid conditions. The crop was maintained under (a) three irrigations (control), each of 60 mm depth, (b) at 50% potential evapo-transpiration (PET) irrigation level, and (c) at 100% PET irrigation level. Growth of mustard in relation to accumulated growing degree days under all treatments was closely represented by the Hoerl function. The correlation coefficients ranged between 0.95 and 0.99 and were significant at p = 0.01. The crop irrigated at 100% PET level required less thermal time (40 degrees C d) compared to control crop (45 degrees C d) for appearance of each leaf tip on the main shoot. Heat and energy use efficiencies were higher for the crop irrigated at 100% PET. However, water-use efficiency was higher for the crop maintained at 50% PET rates. Measurements of light distribution within canopy revealed that red to infrared ratio at the bottom of the crop canopy was the lowest at peak flowering stage as compared to other stages. (author)
[en] The spatiotemporal pattern of the dynamics of surface air temperature and precipitation and those bioclimatic indices that are based upon factors which control vegetation cover are investigated. Surface air temperature and precipitation data are retrieved from the ECMWF ERA Interim reanalysis and APHRODITE JMA datasets, respectively, which were found to be the closest to the observational data. We created an archive of bioclimatic indices for further detailed studies of interrelations between local climate and vegetation cover changes, which include carbon uptake changes related to changes of vegetation types and amount, as well as with spatial shifts of vegetation zones. Meanwhile, analysis reveals significant positive trends of the growing season length accompanied by a statistically significant increase of the sums of the growing degree days and precipitation over the south of West Siberia. The trends hint at a tendency for an increase of vegetation ecosystems' productivity across the south of West Siberia (55°–60°N, 59°–84°E) in the past several decades and (if sustained) may lead to a future increase of vegetation productivity in this region.