An Investigation of the Greenhouse Gas Emissions in European Countries Buildings According to the Life-Cycle

Cooling and heating degree days in the US: The role of macroeconomic variables and its impact on environmental sustainability

This study investigates the urban heat island characteristics of four major areas of Hong Kong. The areas of study include a densely populated and well-developed commercial area (i.e., Tsim Sha Tsui) and three suburban areas (i.e., Cheung Chau, Lau Fau Shan and Sha Tin) with differing degrees of development. The weather station data of respective areas were acquired from the Hong Kong Observatory. The urban heat island intensity, determined as the air-temperature difference between the selected urban/suburban area and the reference rural area (i.e., Ta Kuw Ling) with thin population and lush vegetation, was used for the analysis. Results showed stronger heat island effect during winter and nighttime than during summer and daytime. An investigation of the cooling and heating degree days indicate that all areas have observed higher number of cooling degree days. However, the cooling degree days were the maximum while heating degree days were the minimum in the urban area (i.e., Tsim Sha Tsui). Clearly, the minimum heating degree days and the maximum cooling degree days in the urban area were a direct consequence of urban heat island. The 10-year (i.e., from 1995 to 2005) average shows that Cheung Chau experienced the least number of cooling degree days while Lau Fau Shan experienced the highest number of heating degree days. Seemingly, the area of Cheung Chau offers better thermal comfort conditions with the minimum number of cooling and heating degree days.

One simple way to estimate the relationship between air temperature and the energy needed for heating and cooling is to use the concept of degree day. Cooling degree days (CDD) and heating degree days (HDD) are indicators of the energy required to reach comfort levels and are related directly to energy demands. Therefore, using a novel approach, we examine the current conditions and future projections in degree days over Mexico using observations (Livneh and CPC), ERA5 reanalysis, and simulations from the Regional Climate Model (RegCM4). The RegCM4 experiments were driven by different General Circulation Models for two Representative Concentration Pathways scenarios. We consider three 20-year periods as “present conditions” (1995–2014), “near-future conditions” (2041–2060), and “far-future conditions” (2080–2099). The results suggest that in the future, under the lowest radiative forcing scenario there will be a smaller increase (decrease) in CDD (HDD) for the far-future, as compared to the near-future. This could represent the model’s response to the peak of radiative forcing at mid-century and its subsequent decline. For the highest radiative forcing scenario, we found a greater increase (decrease) in CDD (HDD) for the far-future, which could be explained by the response of the RegCM4 to the warming increase projected for 2100.

Estimating the characteristics of the Urban Heat Island Effect in Nicosia, Cyprus, using multiyear urban and rural climatic data and analysis

New villages: Case studies: No. 2, south woodham ferrers: Carl Bray 1981, 34 pp.

Összefoglalás. Az elmúlt évtizedekben a globális felmelegedés hatásai Európában, azon belül a Kárpát-medencében is nyilvánvalóvá váltak, s ez a hatás jelentős mértékű az energiaszektorra is. A kizárólag az időjárás által befolyásolt függő fűtési és hűtési energiaigényt leíró technikai klímaindexek alakulását mutatjuk be Magyarországon, nevezetesen a fűtési foknap (Heating Degree Days) és a hűtési foknap (Cooling Degree Days) paramétereket vizsgáljuk. A jelen klímát leginkább jellemző éves és havi normálértékek mellett a változásokat is elemezzük. A múlt század elejétől a fűtési foknapok egyértelmű csökkenése, míg a hűtési foknapok egyértelmű emelkedése figyelhető meg. Országos átlagban 314,6 foknappal [°C nap] csökkent a fűtési foknapok éves összege a lineáris trendmodell szerint 1901-től, míg ugyanerre az időszakra 79,4 ℃ nappal nőtt a hűtési foknap értéke. A legutóbbi három évtizedben, a legintenzívebb melegedés időszakában igen markánsak a változások, s ez a folyamat minden bizonnyal folytatódik a század végéig és azon túl is. Summary. In recent decades, the effects of global warming have become evident in Europe, including the Carpathian Basin, and this impact is significant for the energy sector. The most obvious impact of climate change in Hungary is the increase in extremes associated with high temperatures. Not only summer, but also winter and transition seasons show a warming trend, affecting all productive and service sectors. Rising temperatures could lead to shorter heating seasons and milder cold months, potentially reducing heating energy demand. However the heat waves have become more frequent and intense due to warming, leading to an increase in cooling energy demand. In the first part of this article, we present the evolution of heating degree-days from the beginning of the last century to the present day for the whole area of Hungary. The heating degree day is an indicator of the energy consumption for heating of buildings, which depends only on the weather. That is a temperature value, expressed in °Cday, which is proportional to the amount of energy required to heat the indoor environment to a given temperature on a given day, taking into account the daily minimum, maximum and mean temperature for a specific base temperature (15.5°Cday). Importantly, its value does not depend on the insulation of the buildings, economic indicators or the type of energy sources. Essentially, the colder the weather, the more the air temperature deviates from the base temperature of 15.5 degrees Celsius, the more energy is needed to heat the indoor environment and the higher the heating degree day value will be. In addition, we present the analysis of cooling degree days. The cooling degree day is derived in a similar way, following the logic of the heating degree day derivation. Therefore, the warmer the weather and the higher the air temperature is above the base temperature of 22°C, the more energy is needed to cool the indoor environment. Based on the results presented in the article, we can conclude that the annual as well as the monthly amounts of heating degree-days have decreased since the beginning of the last century, with the greatest decrease in mountainous areas and in Western Hungary. One of the few benefits of climate change is that less energy is needed to heat interiors, and this is particularly true for the last decades, when annual heating degree-day amounts were usually lower than normal. At the same time, as heating degree-days have decreased, as the heat waves have become more frequent and intense with warming, resulting in an increase in cooling degree-days. In Hungary, on average, the last 30 years have been characterised by the highest cooling degree-day values over the last century’s climate normal periods. On a national average, the annual amount of heating degree-days has decreased by 314.6°Cday according to the linear trend model since 1901, while the cooling degree-day value has increased by 79.4°Cday over the same period. During the period of most intense warming, the August weather-dependent cooling energy demand increased the most. Due to the urban heat island effect in the inner-city environment, cooling of buildings requires additional energy. The recent trends is likely to continue until the end of the century and beyond. Therefore important to monitor changes in climate parameters affecting energy security and to develop an effective strategy and action plans to address the climate risks for the energy sector.

The change in heating and cooling needs of Greece in the near future due to the climate change is assessed in the present study. Global and regional climate models and two different representative concentration pathways (RCPs) are used to simulate the expected change in temperature. A widely used methodology of computation of heating degree days (HDDs) and cooling degree days (CDDs) is employed with a base temperature of 18 °C. In agreement with the expected temperature rise in the near future, an HDD decrease and CDD increase under both RCPs is also expected. The changes under RCP8.5 are stronger compared to those under RCP4.5. Differences related to topography are noted. The HDD decrease is stronger than CDD increase but the relative increase in CDDs is higher than the relative increase in HDDs. The highest absolute decreases in HDDs are expected for February and March while the highest absolute increases in CDDs are expected during the three summer months.

Daily electricity demand forecasting for peak load is an important indicator for domestic electricity power plant operation. In this paper, we tried to improve the predictive accuracy by using weather information which is widely used for electricity demand forecasting. We analyzed the effect of daily-based temperature, wind speed, and humidity and especially compared the effect of temperature with the national representative temperature, which was calculated by weighted mean of the five major cities in Korea, Cooling Degree Day (CDD), Heating Degree Day (HDD), and Heating and Cooling Degree Day (HCDD), which was derived from the combination of HDD and CDD. After fitting the training data from 2010 to 2016 and testing the several models for the year 2017 in order to evaluate the predictive accuracy of each model, the model including HCDD and holiday effect showed the best performance based on Mean Absolute Percentage Error.

In addition to the adverse effect of extreme weather and weather variation across the globe, the ecological deficit accounting associated with the USA is perceived to have further worsen the country's environmental quality. Considering the aforementioned motivation, this study examined the effects of cooling degree days, heating degree days and ecological footprint on environmental degradation in the USA over the period of 1960 to 2016. While employing the Autoregressive Distributed Lag (ARDL) and Bounds testing to cointegration approaches, the gross domestic product (GDP) per capita is further incorporated in the estimation model to avoid estimation bias thus enhancing a robust estimate. The result overwhelmingly found that the cooling degree days, the heating degree days and the ecological footprint accounting aggravates the country's environmental degradation. Worse still, the study further presents that there is short-run adverse impacts of the heating and cooling degree days, and the short-run and long-run ecological footprint on the country's environmental sustainability. Moreover, there is statistical evidence that the income growth in the USA especially in the long run will not also improve the environmental quality. Irrespective of the income-environmental degradation long-run relationship, the relieving impact of income growth on environmental degradation is observed in the short run. In general, the study presents relevant policy pathway for implementation.

We investigate the future changes in the climate zone and six extreme temperature indices in Korea, using the 20-km high-resolution atmospheric general circulation model (MRI-AGCM3.1S). The Trewartha and Koppen climate classification schemes are applied, and four summer-based extreme temperature indices (i.e., summer days, tropical nights, growing degree days, and cooling degree days (CDD) and two winter-based indices (frost days and heating degree days (HDD) are analyzed. To represent significantly the change in threshold indices, the monthly mean bias is corrected in model. The model result reasonably captures the temporal and spatial distribution of the present-day extreme temperatures associated with topography. It was found that in the future climate, the area of the subtropical climate zone in Korea expands northward and increases by 21% under the Trewartha classification scheme and by 35% under the Koppen classification scheme. The spatial change in extreme climate indices is significantly modulated by geographical characteristics in relation to land-ocean thermal inertia and topographical effects. The change is manifested more in coastal regions than in inland regions, except for that in summer days and HDD. Regions with higher indices in the present climate tend to reveal a larger increase in the future climate. The summer-based indices display an increasing trend, while the winter-based indices show a decreasing trend. The most significant increase is in tropical nights (+452%), whereas the most significant decrease is in HDD (−25%). As an important indicator of energy-saving applications, the changes in HDD and CDD are compared in terms of the frequency and intensity. The future changes in CDD reveal a higher frequency but a lower temperature than those in HDD. The more frequent changes in CDD may be due to a higher and less dispersed occurrence probability of extreme temperatures during the warm season. The greater increase in extreme temperature events during the summer season remains an important implication of projecting future changes in extreme climate events.

Energy analysis plays a vital role in developing an optimum and cost-effective design for the heating, ventilating and air conditioning system of a building. The values of heating degree days (HDD) and cooling degree days (CDD) are used to analyze the demand for energy for buildings. In this study, the HDD and CDD values for Turkey were calculated using estimated temperature values. An artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) were applied for the estimation of temperature values. The Levenberg–Marquardt training algorithm was used for the feedforward backpropagation of the ANN, whereas the Sugeno-type fuzzy inference system was used for the ANFIS. Meteorological data from the last 11 years were used for the proposed approaches, and these were applied to different stations (n = 50) of the Turkish State Meteorological Service. In relation to the insulated building design, base temperatures have been considered as 16 °C and 18 °C for the HDD and 22 °C and 24 °C for the CDD based on a literature review. Two novel maps were prepared for the CDD and HDD. The calculated degree days and the maps are consistent with those from the Turkish State Meteorological Service.

Modifications in energy demand in urban areas as a result of climate changes: an assessment for the southeast Mediterranean region

An examination of electricity generation by utility organizations in the Southeast United States

Manufacturing facilities consumed about 32% of total domestic energy in the United States in 2016. To evaluate the energy savings achieved through the implementation of energy conservation projects and to establish powerful arguments for future projects, accurate energy performance tracking methods are necessary. The classic energy intensity method (i.e., the ratio of annual total energy over annual total production) is the means most widely used to measure savings because it can be understood and calculated easily. This method considers the variation in production rates to some extent; however, it fundamentally assumes facilities' base energy consumption (energy consumption at zero production) to be zero, which rarely holds true. Furthermore, this method does not consider variations in other relevant parameters, such as weather conditions. Therefore, the regression models approach is commonly recommended to track energy performance improvements. However, because it requires more data and statistical expertise, the regression models approach has been adopted by only a few facilities, and many others suspect it is not worth the effort for their specific cases. For this reason, the improved accuracy this approach offers needs to be demonstrated. By analyzing 477 monthly energy (electricity and natural gas) data sets, this study quantitatively compared the accuracy of classic energy intensity, one-variable (production only) linear regression models, and two-variable (i.e., production and weather) linear regression models for two manufacturing sectors (primary metal and transportation equipment manufacturing). Results showed that significant improvements in accuracy were achieved with one-variable regression models when compared with the classic energy intensity method and with two-variable regression models when compared with one-variable regression models. These improvements were demonstrated using the p values of intercept, cooling degree days, and heating degree days. Based on these results, to achieve a good balance between accuracy improvements and resources requirements, one-variable (production only) linear regression models for electricity consumption and two-variable (production and heating degree days) linear regression models for natural gas consumption are recommended for both sectors in the case of limited resources. Facilities can also use the results to decide which approaches fit better in their cases.

Heating degree days and cooling degree days are overall measures of the demand for heating and cooling services. These measures serve as indices for financial products known as temperature derivatives (and more generally, weather derivatives), which allow utility companies and other businesses to hedge against weather risks. Weather derivatives are traded on the Chicago Mercantile Exchange, and as of 2011, the size of the weather risk market was estimated to be $11.8bn. Questions linking climate change to weather derivatives naturally arise. In this paper, we estimate trends in the annual projected number of heating degree days and cooling degree days over North America using regional climate model output for years 2038–2068. The regional climate model data are obtained from the North American Regional Climate Change Assessment Program. Two methods are demonstrated, one empirical and data driven, and the other taking advantage of spatial statistical models. Both methods incorporate regional climate model ensemble variability and produce trend estimates across North America. Copyright © 2014 John Wiley & Sons, Ltd.