\r\n\t• Role of technological innovation and corporate risk management \r\n\t• Challenges for corporate governance while launching corporate environmental management among emerging economies \r\n\t• Demonstrating the relationship between environmental risk management and sustainable management \r\n\t• Contemplating strategic corporate environmental responsibility under the influence of cultural barriers \r\n\t• Risk management in different countries – the international management dimension \r\n\t• Global Standardization vs local adaptation of corporate environmental risk management in multinational corporations. \r\n\t• Is there a transnational approach to environmental risk management? \r\n\t• Approaches towards Risk management strategies in the short-term and long-term.
",isbn:"978-1-83968-906-2",printIsbn:"978-1-83968-905-5",pdfIsbn:"978-1-83968-907-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"9b65afaff43ec930bc6ee52c4aa1f78f",bookSignature:"Dr. Muddassar Sarfraz and Prof. Larisa Ivascu",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10226.jpg",keywords:"Global Risk Management, Risk Assessment, Climate Risk, Environmental Management, International Business, Business Sustainability, Corporate Governance, Financial Market, Financial Risks, Sustainable Economic Environment, Business Valuation, Organizational Behavior",numberOfDownloads:131,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 24th 2020",dateEndSecondStepPublish:"October 22nd 2020",dateEndThirdStepPublish:"December 21st 2020",dateEndFourthStepPublish:"March 11th 2021",dateEndFifthStepPublish:"May 10th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Muddassar Sarfraz focuses on corporate social responsibility, human resource management, strategic management, and business management. He is a member of the British Academy of Management (UK), Chinese Economists Society (USA), World Economic Association (UK), American Economic Association (USA), and an Ambassador of the International MBA program of Chongqing University, PR China, for Pakistan.",coeditorOneBiosketch:"Dr. Larisa Ivascu's area of research includes sustainability, management, and strategic management. She has published over 190 papers in international journals. She is vice-president of the Society for Ergonomics and Work Environment Management, Timisoara, and a member of the World Economics Association (WEA), International Economics Development and Research Center (IEDRC), Engineering, and Management Research Center (CCIM).",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"260655",title:"Dr.",name:"Muddassar",middleName:null,surname:"Sarfraz",slug:"muddassar-sarfraz",fullName:"Muddassar Sarfraz",profilePictureURL:"https://mts.intechopen.com/storage/users/260655/images/system/260655.jpeg",biography:"Dr Muddassar Sarfraz is working at the Binjiang College, Nanjing University of Information Science and Technology, Wuxi, Jiangsu, China. He has obtained his PhD in Management Sciences and Engineering from the Business School of Hohai University. He holds an International Master of Business Administration (IMBA) from Chongqing University (China) and Master of Business Administration (HR) from The University of Lahore. He has published tens of papers in foreign authoritative journals and academic conferences both at home and abroad.\nHe is the Book Editor of Sustainable Management Practices, Analyzing the Relationship between Corporate Governance, CSR, Sustainability, and Cogitating the Interconnection between Corporate Social Responsibility and Sustainability. He is the Associate and Guest Editor of Frontiers in Psychology, International Journal of Humanities and Social Development Research and the Journal of Science and Innovative Technologies. He is an Editorial Board Member of the International Journal of Human Resource as well as a member of the British Academy of Management (UK), Chinese Economists Society (USA), World Economic Association (UK), American Economic Association (USA), and an Ambassador of the International MBA program of Chongqing University, PR China, for Pakistan. \nHis research focuses on corporate social responsibility, human resource management, strategic management, and business management.",institutionString:"Binjiang College, Nanjing University of Information Science &Technology, Wuxi, Jiangsu",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:{id:"288698",title:"Prof.",name:"Larisa",middleName:null,surname:"Ivascu",slug:"larisa-ivascu",fullName:"Larisa Ivascu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRfMOQA0/Profile_Picture_1594716735521",biography:"Dr Larisa IVAȘCU is currently an associate professor at the Politehnica University of Timisoara. 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She is vice-president of the Society for Ergonomics and Work Environment Management, Timisoara, and a member of World Economics Association (WEA), International Economics Development and Research Center (IEDRC), Engineering and Management Research Center (CCIM), and Member of Academic Management Society of Romania (SAMRO, http://samro.ro/).",institutionString:"Politehnica University of Timisoara",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"7",title:"Business, Management and Economics",slug:"business-management-and-economics"}],chapters:[{id:"75308",title:"Insurance Business and Sustainable Development",slug:"insurance-business-and-sustainable-development",totalDownloads:14,totalCrossrefCites:0,authors:[null]},{id:"75456",title:"Risk Communication in the Age of COVID-19",slug:"risk-communication-in-the-age-of-covid-19",totalDownloads:11,totalCrossrefCites:0,authors:[null]},{id:"74554",title:"Fuzzy Approach Model to Portfolio Risk Response Strategies",slug:"fuzzy-approach-model-to-portfolio-risk-response-strategies",totalDownloads:106,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"184402",firstName:"Romina",lastName:"Rovan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/184402/images/4747_n.jpg",email:"romina.r@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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\n\t\t\t
1. Introduction
\n\t\t\t
In ecological studies, the use of different sampling methods for the same purpose influence data quality and thus the resulting conclusions (Coddington et al. 1996; Fisher 1999). For example, to collect arthropods from soil and litter samples a soil corer or a shovel may be used. Soil corers compact the soil (Meyer 1996) making difficult for organisms to leave the sample while shovels create a large disturbance (Longino et al. 2002) promoting mobile organisms to leave and reducing their apparent abundance in the sample. As a consequence the diversity of collected arthropods will vary between these two procedures, resulting in either an under- or overestimate of the diversity of the collected fauna (André et al. 2002 ). These different results will lead the researcher to infer different conclusions. Therefore it is essential to assess how different procedures affect the abundance, richness and species composition of the retrieved arthropods.
\n\t\t\t
Arthropods are usually retrieved from soil/litter samples with Berlese-Tullgren funnels (Walter et al. 1987; Rohitha 1992; MacFadyen 1961; Bremner 1990; Lakly & Crossley 2000; MacFadyen 1953; Haarlov 1947). In these funnels, a source of heat (i.e. a light bulb) is placed above the sample, and a collecting vial filled with a killing solution (e.g. 70% ethanol) is placed below the sample. Light from the bulb has a double effect because light per se forces photophobic organisms to move away from the source, and light heats the sample. As the sample dries, a temperature and humidity gradient is created between the upper and lower surfaces of the sample (Haarlov 1947; Block 1966). As this gradient moves downwards, animals are forced down into the collecting liquid (Coleman et al. 2004). By increasing the temperature within the funnel, heat speeds drying (Coleman et al. 2004) but may also burn organisms before their collection and thus decreases estimates of their abundance (Walter et al. 1987). Alternatively, in remote field conditions, extractions without light are logistically more affordable and feasible, in which case the establishment of the gradient and the drying out of the sample depends on the room temperature in which the extractions are performed (Krell et al. 2005). Both, extractions with and without light, create different conditions within the sample, as a consequence, the use, or no use, of light during extractions, can result in different groups of arthropods being extracted, and thus a different set of data (Agosti et al. 2000).
\n\t\t\t
The duration of arthropod extraction can also affect diversity estimates. Extraction periods reported in the literature vary from 2 d (Burgess et al. 1999), 3 d (Hasegawa 1997), to 4 d (Oliver & Beattie 1996; Bestelmeyer et al. 2000) and up to 7 d (Chen & Wise 1999; Walter et al. 1987). Long extraction periods are generally assumed to result in more complete extractions and higher abundance of the extracted fauna (Oliver & Beattie 1996) as organisms with low mobility require more time to exit the sample, but longer extraction periods may expose the samples to potential contamination with foreign organisms. On the other hand, to establish an adequate period of extraction, the environment of origin and the developmental stage should be taken into account (André et al. 2002). For example, organisms adapted to extreme environments, such as areas devoid of vegetation cover that have large temperature fluctuations, may require longer extraction periods than organisms adapted to less extreme environments. Furthermore, organisms from the same habitat but occurring in the dry or wet seasons (Oliver & Beattie 1996) or different developmental stages (Søvik & Leinaas 2002) may differ in the extraction period required to retrieve them. As a consequence, in order to collect reliable data, it is necessary to assess how an adequate duration of the extraction varies among environments of origin and developmental stages of the focal organism.
\n\t\t\t
The present study was carried out in the Caribbean island of Puerto Rico, specifically in tropical dry and wet forests with contrasting environmental conditions (Ewel & Whitmore 1973). The objective of this study was to assess how the diversity of extracted arthropods was affected by variations in the collection and extraction methodologies, and by variations in the duration of the extraction. We present abundance, richness and composition of the collected fauna. The information presented here will provide researchers with data to simplify the logistics of arthropod sampling and extraction, and to better choose a specific procedure for a given focal organism in a given habitat.
\n\t\t
\n\t\t
\n\t\t\t
2. Materials and methods
\n\t\t\t
\n\t\t\t\t
2.1. Study site
\n\t\t\t\t
This study was carried out in north-eastern Puerto Rico in two forests of contrasting conditions. Samples from the litter and soil horizon (0-5 cm) were obtained in March 2003 from a wet forest site at the El Verde Field Station (Luquillo Experimental Forest, 18.33080, -65.82320, WGS 84), and from a dry forest site in the former Roosevelt Roads Military Base (Ceiba, 18.24800, -65.63290, WGS 84).
\n\t\t\t\t
The wet forest site is located in the Luquillo Experimental Forest, where mean monthly temperature ranges from 23.5ºC in January to 27ºC in September (http://www.lternet.edu/sites/luq/fulldescription.php?site=LUQ), and total annual precipitation is 3524 mm yr-1 (García-Martinó et al. 1996) with a mild dry season from January to April (Schowalter & Ganio 1999). Soils are highly weathered; soil nutrients are 0.49% S, 0.35% N, 4.92% C, 0.30 P mg/g soil, and the C/N ratio is 14.2 (Gould et al. 2006); humus accumulation is low because there is rapid decomposition. Vegetation at the site is described as closed evergreen broad leaf forest that lays within the subtropical wet forest Holdridge life zone (Gould et al. 2006). The forest is dominated by Dacryodes excelsa and Manilkara bidentata (Schowalter & Ganio 1999; Gould et al. 2006).
\n\t\t\t\t
The dry forest site is located within the former Roosevelt Roads Military Base, where mean monthly temperature is 27.5ºC and annual precipitation is 1,262 mm yr-1 (Gould et al. 2006). It has a pronounced dry season that runs from November to April, and a wet season that usually runs from May to October (http://www.ceducapr.com/ceiba.htm). The soils are sandy or clayey with a developed organic matter (http://www.ceducapr.com/ceiba.htm). Soil nutrients are 0.06% S, 0.61% N, 6.34% C, 0.48 P mg/g soil, and the C/N ratio is 10.4 (Gould et al. 2006). This is a closed, mixed-evergreen deciduous, broad leaf forest that lays within the subtropical dry forest Holdridge life zone (Gould et al. 2006). This forest is dominated by Bucida buceras and Guapira fragrans (Gould et al. 2006).
\n\t\t\t\t
In summary, these forests present contrasting conditions because, the wet forest has lower temperature and higher precipitation than the dry forest. In addition, the dry forest has a pronounced dry season while in the wet forest; the dry season is measured as number of days with no effective rain. The organic horizon is thin in the wet forest, and thick in the dry forest. As a consequence, the wet forest is warm and humid with thin litter and almost no humus, while the dry forest is hot and dry with deep litter and humus.
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
2.2. Data collection
\n\t\t\t\t
In each forest, a 50 m x 50 m area was located, and within this area 40 litter samples were collected. Each sample was 100-cm2 (10 cm x 10 cm), and was collected down to mineral soil. Litter depth was measured three times inside each of the 100-cm2 areas. Inside the same 100-cm2 area and after collecting the litter samples, two soil samples were collected: one using a soil corer (4.3 cm diameter and 5 cm height) and another one using shovels. For the shovel sampling, soil was collected with a shovel and served into a corer to assure that the soil volume in the shovelled sample was similar to that obtained with the soil corer. This sampling design resulted in 40 litter samples, 40 soil shovelled samples and 40 soil cored samples from each forest, giving a total of 120 samples in each forest type.
\n\t\t\t\t
In the laboratory, the litter and soil shovelled samples were each placed in small Berlese-Tullgren funnels (Bioquip 2845) (10 cm height and 11 cm diameter) (Fig. 1A& 1B). The soil corer samples were placed in hand made funnels. For this, a wooden skeleton was built with basal holes covered with a mesh (Fig. 1C). Over each hole, a corer was placed and covered with a metallic funnel (11 cm x 5 cm) dia. The metallic funnels were then covered with a wooden ceiling (Fig. 1D). All funnels had the ceiling with an opening.
\n\t\t\t\t
Samples were randomly assigned to two treatments in which extraction was done with or without light. For this, the 40 litter samples were split into two groups: 20 samples were extracted with light and 20 samples were extracted without light. The 40 soil shovelled samples and the 40 soil cored samples were similarly randomly assigned to one of these extraction treatments. When extraction was with light, a 20V-bulb was hanging through the funnel’s opening, and was kept at maximum intensity during all the extraction period to control for the effect of changing light intensity during extractions. When extraction was without light, no bulb was placed over the funnel. All samples were located simultaneously in the same room where temperature and humidity were controlled.
\n\t\t\t\t
Vials containing the killing solution (ethanol 70%) and collected arthropods were retrieved at 24, 48, 72, 144 and 168 hours after placement in the funnels. Thus each of the 120 samples
\n\t\t\t\t
Figure 1.
A and B: Berlese-Tullgren funnels (Bioquip 2845) used for the litter and shovelled soil samples. (Photos A and B provided by M. F. Barberena-Arias, Universidad del Turabo). C and D: wooden skeleton used for the cored soil samples. E through H: examples of collected arthropods. E: Collembola Sminthurida, F: Coleoptera Corylophidae, G: Acari Oribatida, H: Psocoptera. (Photos C through H provided by G. González, Soil Ecology Program, IITF-US Forest Service).
\n\t\t\t\t
per forest was retrieved at five sequential times giving a total of 600 samples per forest that were processed separately. For each sample, arthropod abundance was recorded, and arthropods were identified to the lowest category possible such as class, subclass, order or suborder, and classified as adult or immature (MacAlpine 1989; Triplehorn & Johnson 2004; Krantz 1978) (Fig. 1E through 1H). Collembola were not separated as adults or immature because it is difficult to differentiate among developmental stages.
\n\t\t\t\t
All litter and soil samples were weighed before extraction, and after extraction all samples were oven dried at 65ºC for a week and then weighed again. During the experiment, sample temperature and humidity were not measured but from reviewing literature, we assumed that the use of a light bulb during extractions increased sample temperature and dried the sample resulting in a gradient of temperature and humidity within the sample, while in the extractions without light, the establishment of the gradient would depend on the temperature and humidity of the room where extractions were performed (MacFadyen 1953; MacFadyen 1961; Haarlov 1947; Søvik & Leinaas 2002; Block 1966).
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
2.3. Data analyses
\n\t\t\t\t
Data analyses were done using SigmaStat 3.0 (Systat Software Inc., www.sigmaplot.com) and PCORD 4.0 (PC ORD - Multivariate Analysis of Ecological Data, home.centurytel.net/ ~mjm/pcordwin.htm). Litter depth, and litter and soil dry weights were compared between forests using a Mann Whitney Rank Sum test (Sokal & Rohlf 1994). Arthropod abundance was standardized to individuals per square meter. Two way ANOVAs were used to establish the effect of light and duration of the extraction (time) on litter arthropod abundance and on the abundance of developmental stages. Three-way ANOVAs were used to establish the effect of corer vs shovel, light and duration of the extraction (time) on soil arthropod and developmental stage abundance. Non Metric Multidimensional Scaling (NMS) and a Multi Response Permutation Procedure (MRPP) were used to establish the identity and sequence in which arthropods were extracted. NMS was run with the Sorensen dissimilarity index based on presence/absence and a maximum of three axes were allowed. Non Metric Multidimensional Scaling places sampling units in space based on similarity such that close points have a similar composition, and MRPP establishes significant differences based on the dissimilarity matrix calculated with the Sorensen index (McCune & Grace 2002). The MRPP was used to establish statistical differences in composition due to collection, extraction and duration of the extraction (time), and the significant value was set at 0.05. Throughout the text, results are expressed as mean ± standard error.
\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
3. Results
\n\t\t\t
There were significant effects of forest type (dry vs wet) on litter depth (Mann Whitney, n=120, T= 9843.5, p<0.001), litter dry weight (Mann Whitney, n=40, T= 2410.0, p<0.001) and soil bulk density (Mann Whitney, n=80, T= 7206.0, p=0.009). Mean litter depth was higher in the dry forest, 3.7 cm (±0.1), than in the wet forest, 2.2 cm (±0.1), and mean litter dry weight was 5.7 kg m-2 (±458.8) in the dry forest and 0.8 kg m-2 (±54.5) in the wet forest. Mean soil bulk density was higher in the wet forest, 0.6 g cm-3 (±0.02), than in the dry forest, 0.5 g cm-3 (±0.02).
\n\t\t\t
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3.1. Litter arthropods
\n\t\t\t\t
Acari and Collembola were dominant in both forests. Acari were significantly more abundant in the dry forest while, Collembola were more abundant in the wet forest (Table 1). In the dry forest, there were, on average, 655 Acari m-2 and 583 Collembola m-2, while in the wet forest, there were 623 and 635, respectively.
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\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
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Table 1.
Mean abundance m-2 (±standard error) and percent dominance of litter arthropods in dry and wet forests. Alphabets indicate significant difference between forests for a particular group (Mann-Whitney Rank Sum Test, α=0.05, n=40 for each forest).
\n\t\t\t\t
In both forests, there was a significant effect of light on the abundance of litter arthropods, being higher when extractions were done with light. In the dry forest, there were significant effects of light and time on the abundance of the extracted arthropods while in the wet forest only time had a highly significant effect (Table 2). In the dry forest, >20,000 ind m-2 were extracted with light and in the wet forest <5,000 ind m-2 were extracted with light (Fig. 2). Through time, in both the dry and the wet forests, a high abundance was obtained in the first 24 h, with a slight increase at 48 h (Fig. 3). In the dry forest, some individuals were still recovered after 168 h, while in the wet forest all individuals were collected in the first 48 h. In general, in the dry forest, >90% of total extracted individuals was obtained after 144 h (6 d) of extraction with light, while in the wet forest >90% of total individuals was obtained after 48 h (2 d).
\n\t\t\t\t
Figure 2.
Mean abundance of litter arthropods (ind m-2) extracted with and without light. Bars represent standard error.
\n\t\t\t\t
Figure 3.
Relative extraction efficiency (below) in dry and wet forests. Bars represent standard error.
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In both forests, duration of the extraction (time) and light significantly influenced the identity and sequence in which arthropods were extracted (NMS, MRPP) (Fig. 4). In the dry forest and during the first 24 h, Blattodea and Protura were extracted mainly without light, while Acari (Fig. 1G), Collembola (Fig. 1E) and Pseudoscorpiones were extracted mainly with light. These groups represent organisms that are mostly considered detrivitivores, omnivores or predators, but microbivores (mainly Acari, Oribatida) were still recovered after 144 h of extraction. In the wet forest, all groups were extracted in the first 24 h, Chilopoda, Hymenoptera and Symphyla were extracted without light after the first 24 h, and Acari, Diplopoda and Diptera were extracted with light in the same extraction period.
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Table 2.
Effect of use of light during extraction (with and without light) and duration of the extraction (time) (24 h, 48 h, 72 h, 144 h and 168 h) on litter arthropods in dry and wet forests (two 2-way AOV, α = 0.005, n=199 for the dry forest and n=200 for the wet forest). The effects were evaluated for total abundance (ind m-2) and abundance per developmental stage (ind m-2).
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Developmental stages. There were significant effects of light and time on the abundance of both immature and adults in the litter (Table 2). In the dry forest, both immature and adults were more abundant when extraction was done with light, and in the wet forest immature were more abundant when extraction was done without light, but adults were abundant in both extraction treatments (Table 3). Through time, both immature and adults followed the same pattern as established before: in the dry forest, >90% was obtained after 144h (6 d), while in the wet forest, >90% was obtained after 48h (2 d).
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Figure 4.
Identity and sequence of extraction of litter arthropods extracted with and without light in dry and wet forests. The sequence of extraction within each type (with and without light) through time is connected by a line.
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Table 3.
Mean abundance per square meter (+/- standard error) of litter and soil arthropods in dry and wet forests. Alphabets indicate significant differences between extraction methods (with and without light) for a particular developmental stage in a specific forest (Mann-Whitney Rank Sum Test, α=0.05, n=40 for litter arthropods in each forest, and n=80 for soil arthropods in each forest).
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3.2. Soil arthropods
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Overall, soil arthropod abundance was higher in the dry forest than in the wet forest (Table 4). Acari and Collembola were dominant in both forests, but both orders were significantly more abundant in the dry forest. There were, on average, 14,021 Acari m-2 and 3,904 Collembola m-2 in the dry forest, while in the wet forest, there were 2,511 and 2,786 ind m-2 respectively.
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In both forests, there were significant effects of sampling technique, and of light and time, on the abundance of the extracted arthropods (Table 5). In both forests, extraction without light rendered higher abundance of soil microarthropods than extraction with light (Fig. 5). In the dry forest, collection with a corer and extraction without light rendered 5,015 ind m-2 while corer with light rendered 3,343, and shovel without light and shovel with light rendered 6,026 ind m-2 and 4,780, respectively. In the wet forest, corer without light had 4,076 ind m-2, corer with light had 1,129, shovel without light had 2,331.4 and shovel with had 1,319. As established before, in the dry forest, arthropods continued to be collected after 144 h of extraction, and samples collected with shovel and extracted without light rendered the highest abundance. In the wet forest, arthropods were collected within the first 48 h except for the corer without light where arthropods were collected even after 144 h and this collection method rendered the highest abundance (Fig. 6).
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In both forests, time significantly influenced the identity and sequence in which soil arthropods were extracted (NMS, MRPP) (Fig. 7). In the dry forest, Isoptera was extracted with corer with light in the first 24 h, Pseudoscorpiones with corer without light in the same time period 24 h, and Protura and Hemiptera with corer without light after 48 h. Also, Hymenoptera, Isopoda, Diplopoda and Chilopoda were extracted with shovel after 24 h (Fig. 7). In this forest, organisms representing several trophic groups (such as predators and omnivores) were extracted in the first 48 h, but those representing microbivores (mainly Oribatida) were still collected after 144 h. In the wet forest, Isoptera was extracted with corer with light after 24 h, Diplopoda and Chilopoda were extracted with shovel with light after 24 h, and Hymenoptera and Collembola were extracted with shovel without light after 24 h.
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Table 4.
Mean abundance per square meter (+/-standard error) and percent dominance of soil arthropods in dry and wet forests. Alphabets indicate significant difference between forests for a particular group (Mann-Whitney Ran Sum Test, α=0.05, n=80 for each forest).
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Developmental stages. For immature arthropod abundance, there were significant effects of light and duration of the extraction (time) in both forests (Table 5). For adult arthropod abundance, there were significant effects of collection and duration of extraction (time) in the dry forest and, of collection, light and duration of the extraction (time) in the wet forest (Table 5). In the dry forest, immature were significantly more abundant when the sample was collected with shovels and extracted without light (Table 3). The abundance of adults depended on the collection method: for samples collected with a corer, a higher abundance was obtained when extracted without light; but for samples collected with shovel a higher abundance was obtained when extracted with light. In the wet forest, all soil samples, both corer and shovel that were extracted without light rendered a higher abundance than their counterparts extracted with light (Table 3).
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Table 5.
Effect of collection (corer and shovel), extraction (with and without light) and duration of the extraction (time) (24 h, 48 h, 72 h, 144 h and 168 h) on the abundance of soil arthropods (two 3-way AOV, α = 0.005, n=400 for each forest ). The effects were evaluated for total abundance (ind m-2) and abundance per developmental stage (ind m-2).
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Figure 5.
Mean abundance of soil arthropods (ind m-2) collected by corer or shovel, and extracted with and without light. Bars represent standard error.
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Figure 6.
Extraction efficiency in dry and wet forests. Bars represent standard error.
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Figure 7.
Identity and sequence of soil arthropods collected by corer or shovel, and extracted with and without light in dry and wet forests. The sequence of extraction within each collecting technique with extraction type through time is connected by a line.
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4. Discussion
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The objective of this study was to determine how the diversity of retrieved arthropods was affected by collection technique, use of light during extractions in Berlese-Tullgren funnels and duration of the extraction. For use of light during extractions, we found that litter arthropod abundance was highest when extraction was done with light but soil arthropods were highest when extraction was done without light. We found that forest type (tropical wet vs dry forest) influenced the sampling technique that was best suited because, in the wet forest, soil arthropod abundance was highest when collection was done with soil corers, while in the dry forest soil arthropod abundance was highest when collection was done with shovels. Finally, we found that forest type also influenced duration of the extraction because in the wet forest, 90% of arthropods were recovered within the first 24 h while, in the dry forest the same percent was obtained after 144 h of extraction.
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Litter and soil arthropods responded differently to the use of light during extractions. One explanation is that the use of light may have speeded the desiccation of the sample forcing more litter animals to exit the sample than in the extractions without light. On the contrary, soil arthropods are more sensitive to increasing temperature or decreasing humidity than litter arthropods, in which case the use of light during extractions would made arthropods inactive before leaving the sample and thus their apparent abundance would decrease. Furthermore, litter arthropods inhabit a clear and warm habitat (litter) (Eviner & Chapin 2003) and thus may require an increase in temperature and in light incidence to exit the samples. But soil arthropods inhabit a comparatively cooler and darker habitat (soil) (Eviner & Chapin 2003) and thus may be sensitive to increasing temperature and light incidence to the point that the use of light during extraction may have resulted in an underestimation of soil arthropod abundance. In both forests, we retrieved more adults than immature, possibly because the soft cuticle of immature makes them more susceptible to the decreasing humidity within the extraction funnel, and because immature organisms, such as mites (majority of the immatures retrieved in this study), undergo inactivity when moulting and thus cannot leave the sample (Søvik & Leinaas 2002). As a consequence another extraction methodology, such as flotation, should be more suitable for immature forms (Hale 1964; Walter et al. 1987; Lakly & Crossley 2000; Søvik & Leinaas 2002).
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In both forests, soil arthropods left the shovelled samples faster than the cored samples. Also, abundance was highest from shovelled samples in the dry forest, and in the wet forest abundance was highest from the cored samples although the pace of retrieval was slow. One explanation is that the loose structure of shovelled samples retained less humidity and dried out faster allowing the temperature/humidity gradient to be established sooner than in the compact cored samples where more humidity could be retained (MacFadyen 1953). This would have a dual effect, in wet forest samples, the gradient resulting from drying out the cored sample at room temperature moved slowly downwards forcing arthropods to leave the sample but not being large enough to kill them (as would occur in the shovelled samples) resulting in higher arthropod abundance in cored than in shovelled samples. In the dry forest samples, the gradient resulting from drying the shovelled sample at room temperature reached higher critical levels and became larger than in cored samples, forcing arthropods outwards, resulting in higher estimates of arthropod abundance in the shovelled samples.
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Arthropods from the wet forest were recovered faster than arthropods from the dry forest. Macfadyen (1961) proposed that humidity-loving animals require high levels of humidity to be active, as a consequence they respond quicker to changes in humidity than humidity-resistant animals. On the contrary, humidity-resistant animals require high temperatures and low humidity to be forced to exit the sample. During extractions, temperature of the sample begins to increase immediately with a significant increase after 16 – 24 h, and humidity within the sample drops simultaneously with the significant increase in temperature (Haarlov 1947; Block 1966). Following MacFadyen (1953), we can explain why arthropods from the wet forest exited the sample faster than those from the dry forest. Arthropods from the wet forest left the sample within the first 24 h in response to the increase in temperature and the decrease in humidity that occurred in the sample when extraction begins. As the sample became hotter and drier, any animal remaining in the sample could have become inactive (or killed) by low humidity and high temperature. On the other hand, arthropods from the dry forest required longer extraction times because for these humidity-resistant animals, the critical levels of humidity required for them to leave the sample, took longer to be established.
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The environmental characteristics of the two forests studied here were different and contribute another explanation to our results. Dry periods in the wet forest and the concomitant response of the biota to these periods are based on the number of dry days, because monthly rainfall is always above 100mm (Cuevas et al. 1991; Cuevas & Lugo 1998). On the contrary, dry days in the dry forest are the common condition, an average of 200 dry days per year, with pulses of heavy rainfall occurring during the wet season (http://cirrus.dnr.state.sc.us/cgi-bin/sercc/cliMAIN.pl?pr8412). As a consequence, arthropods from the dry forest come from a habitat with higher temperatures and longer periods of drier conditions than do arthropods from wet forest. The more extreme conditions in the dry forest may have make arthropods less responsive to higher temperature and drier air within the extraction funnel, resulting in longer extraction times.
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We found that arthropod abundance was significantly higher in the dry forest than in the wet forest. Litter depth and dry weight were higher in the dry forest, litter was 40% deeper and 86% heavier in the dry forest than in the wet forest, suggesting that habitat and resource availability significantly influenced arthropod abundance (Mulder et al. 1999). Several researchers have found that Berlese-Tullgren extractions underestimate arthropod abundance. Nevertheless, in this study we found that total abundances fall within similar ranges to those reported in the literature, such as in Mexico and Perú, where abundances are reported to be 4,303 – 6,409 ind/m2 (Lavelle et al. 1992) respectively. In addition, Berlese-Tullgren funnels are the predominant methodology to collect arthropods from litter/soil samples, but care should be taken because some groups are sensitive to light and are not effectively recovered with funnel extraction, such as collembolans in the family Onychiuridae (Coleman et al. 2004), or in our case, Proturans that were much more abundant in extractions without light and almost absent when light was used.
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The structure of the retrieved community was affected by duration of extraction. Other authors have also found that during extractions, different taxonomic groups leave the sample at different times (Krell et al. 2005; Block 1966). In an extraction that lasted three days (Block 1966), Mesostigmata mites left the sample during the first day, while the majority of Collembola and Cryptostigmata mites left the sample mainly during the second day, and Prostigmata began to leave the sample at the third day. Also, Krell et al. (2005) using an alternative extraction method, Winkler bags, also found that duration of the extraction affected the structure of the retrieved community, for example 70% of adult beetles and ants were retrieved within three days of extraction but Chilopoda required 3 to 4 wk. By using Berlese-Tullgren funnel, we found that both Collembola and Acari began to leave the sample during the first 24 h, also we retrieved few adult Coleoptera (the majority of Coleoptera were larvae), and the majority of ants and centipedes left the samples within the first 24 h of extraction.
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5. Conclusion
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\n\t\t\t\tKrell et al. (2005) proposed that if the aim of the study is to rapidly assess the litter/soil fauna, short extraction times should be enough. On the contrary, if the aim of the study is to exhaustively assess this fauna, then the methodology should be standardized, such as assessing optimum extraction times and biases due to collection methods. Our results also suggest that methodology standardization is necessary because (1) to reach similar percents (90%), extraction periods were longer for samples from dry forests than from wet forests, (2) the use of light promoted litter arthropods to leave the sample producing high abundances, but for immature and soil arthropods the use of light resulted in low abundances, and (3) cored samples rendered higher abundances in wet forests than in dry forests where shovel samples rendered higher abundances. In addition, our data suggest that samples from dry environments should be extracted for longer periods than those coming from wet environments. Also, if the focal organisms are soil arthropods, then extraction without light should result in high abundances. Finally, the collection method best suited depends on the environment to be sampled: in this study for wet habitats cored soil samples resulted in higher abundances than shovelled samples which resulted in highest abundances when sampling dry habitats.
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Acknowledgments
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This research received partial support from Crest-Center for Applied Tropical Ecology and Conservation (Crest-Catec) of the University of Puerto Rico at Rio Piedras Campus, grant NSF-HRD-0206200 through a fellowship to MFBA, and from grant DEB-0218039 from the National Science Foundation to the Institute of Tropical Ecosystem Studies, University of Puerto Rico, and the USDA Forest Service, International Institute of Tropical Forestry as part of the Long Term Ecological Research Program in the Luquillo Experimental Forest. Logistic support was received from the United States Department of Agriculture Forest Service International Institute of Tropical Forestry (USDA FS IITF). We would like to thank anonymous reviewers for their comments on previous versions of this manuscript. We would like to thank Maria Rivera for her help during sample collection, and Elvin Rodriguez, Xiomara Cruz and Paola Santiago for their help during sample processing.
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\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/31962.pdf",chapterXML:"https://mts.intechopen.com/source/xml/31962.xml",downloadPdfUrl:"/chapter/pdf-download/31962",previewPdfUrl:"/chapter/pdf-preview/31962",totalDownloads:3645,totalViews:284,totalCrossrefCites:2,totalDimensionsCites:6,hasAltmetrics:0,dateSubmitted:"March 17th 2011",dateReviewed:"September 2nd 2011",datePrePublished:null,datePublished:"March 14th 2012",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/31962",risUrl:"/chapter/ris/31962",book:{slug:"tropical-forests"},signatures:"María Fernanda Barberena-Arias, Grizelle González and Elvira Cuevas",authors:[{id:"82355",title:"Dr.",name:"Grizelle",middleName:null,surname:"Gonzalez",fullName:"Grizelle Gonzalez",slug:"grizelle-gonzalez",email:"ggonzalez@fs.fed.us",position:null,institution:{name:"US Forest Service",institutionURL:null,country:{name:"United States of America"}}},{id:"82366",title:"Dr.",name:"M.F.",middleName:"Fernanda",surname:"Barberena-Arias",fullName:"M.F. Barberena-Arias",slug:"m.f.-barberena-arias",email:"barberena.mf@gmail.com",position:null,institution:{name:"Universidad del Turabo",institutionURL:null,country:{name:"United States of America"}}},{id:"82379",title:"Dr.",name:"Elvira",middleName:null,surname:"Cuevas",fullName:"Elvira Cuevas",slug:"elvira-cuevas",email:"ecuevas@uprrp.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1. Study site",level:"2"},{id:"sec_3_2",title:"2.2. Data collection",level:"2"},{id:"sec_4_2",title:"2.3. Data analyses",level:"2"},{id:"sec_6",title:"3. Results",level:"1"},{id:"sec_6_2",title:"3.1. Litter arthropods",level:"2"},{id:"sec_7_2",title:"3.2. Soil arthropods",level:"2"},{id:"sec_9",title:"4. Discussion",level:"1"},{id:"sec_10",title:"5. Conclusion",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAgosti\n\t\t\t\t\t\t\tD.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAlonso\n\t\t\t\t\t\t\tL. E.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000 The ALL protocol: a standard protocol for the collection of ground-dwelling ants. Ants: Standard methods for measuring and monitoring biodiversity (eds D. Agosti, J.D. Majer, L.E. Alonso & T.R. Schultz), 204\n\t\t\t\t\t206 . Smithsonian Institution, Princeton Editorial Associates, Washington.\n\t\t\t'},{id:"B2",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAndre\n\t\t\t\t\t\t\tH. M.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDuarme\n\t\t\t\t\t\t\tX.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLebrum\n\t\t\t\t\t\t\tP.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002 Soil biodiversity: myth, reality or conning? 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M.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1999 Invertebrate communities in a tropical rain forest canopy in Puerto Rico following Hurricane Hugo. Ecological Entomology, 24\n\t\t\t\t\t191\n\t\t\t\t\t201 .\n\t\t\t'},{id:"B34",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSokal\n\t\t\t\t\t\t\tR. R.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRohlf\n\t\t\t\t\t\t\tF. J.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1994\n\t\t\t\t\tBiometry the principles and practices of statistics in biological research. WH Freeman, New Jersey.\n\t\t\t'},{id:"B35",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSøvik\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLeinaas\n\t\t\t\t\t\t\tH. P.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002 Variation in extraction efficiency between juvenile and adult oribatid mites: Ameronothrus lineatus (Oribatida, Acari) in a Macfadyen high-gradient canister extractor. Pedobiologia, 46\n\t\t\t\t\t34\n\t\t\t\t\t41 .\n\t\t\t'},{id:"B36",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTriplehorn\n\t\t\t\t\t\t\tC. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tJohnson\n\t\t\t\t\t\t\tN. F.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2004\n\t\t\t\t\tBorror and Delong’s introduction to the study of insects. Thomson Brooks/Cole, Florence, Kentucky.\n\t\t\t'},{id:"B37",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWalter\n\t\t\t\t\t\t\tD. E.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKethley\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMoore\n\t\t\t\t\t\t\tJ. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1987 A heptane flotation method for microarthropods from semiarid soils, with comparison to the Merchant-Crossley high-gradient extraction method and estimates of microarthropod biomass. Pedobiologia, 30\n\t\t\t\t\t221\n\t\t\t\t\t232 .\n\t\t\t'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"María Fernanda Barberena-Arias",address:"",affiliation:'
Universidad del Turabo, School of Sciences and Technology, Gurabo,, Puerto Rico
University of Puerto Rico, Department of Biology, San Juan,, Puerto Rico
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Aragao",authors:[{id:"113882",title:"Dr.",name:"Celso",middleName:null,surname:"Von Randow",fullName:"Celso Von Randow",slug:"celso-von-randow"},{id:"182397",title:"Dr.",name:"Gabriel",middleName:null,surname:"De Oliveira",fullName:"Gabriel De Oliveira",slug:"gabriel-de-oliveira"},{id:"190567",title:"Dr.",name:"Elisabete",middleName:null,surname:"Caria Moraes",fullName:"Elisabete Caria Moraes",slug:"elisabete-caria-moraes"},{id:"190568",title:"Dr.",name:"Nathaniel",middleName:null,surname:"Brunsell",fullName:"Nathaniel Brunsell",slug:"nathaniel-brunsell"},{id:"190574",title:"M.Sc.",name:"Guilherme",middleName:"Augusto Verola",surname:"Mataveli",fullName:"Guilherme Mataveli",slug:"guilherme-mataveli"},{id:"222162",title:"Dr.",name:"Yosio",middleName:null,surname:"Shimabukuro",fullName:"Yosio Shimabukuro",slug:"yosio-shimabukuro"},{id:"222163",title:"Dr.",name:"Thiago",middleName:null,surname:"V. Dos Santos",fullName:"Thiago V. Dos Santos",slug:"thiago-v.-dos-santos"},{id:"222165",title:"Dr.",name:"Luiz E. O. C.",middleName:null,surname:"Aragão",fullName:"Luiz E. O. C. Aragão",slug:"luiz-e.-o.-c.-aragao"}]},{id:"59882",title:"People’s Perceptions of Ecosystem Services Provided by Tropical Dry Forests: A Comparative Case Study in Southern Ecuador",slug:"people-s-perceptions-of-ecosystem-services-provided-by-tropical-dry-forests-a-comparative-case-study",signatures:"Veronica Iniguez-Gallardo, Zeina Halasa and Johanna Briceño",authors:[{id:"216972",title:"Dr.",name:"Veronica",middleName:null,surname:"Iniguez-Gallardo",fullName:"Veronica Iniguez-Gallardo",slug:"veronica-iniguez-gallardo"},{id:"219647",title:"Dr.",name:"Zeina",middleName:null,surname:"Halasa",fullName:"Zeina Halasa",slug:"zeina-halasa"},{id:"219648",title:"MSc.",name:"Johanna",middleName:null,surname:"Briceño",fullName:"Johanna Briceño",slug:"johanna-briceno"}]},{id:"58927",title:"South China Tropical Forest Changes in Response to Economic Development and Protection Policies",slug:"south-china-tropical-forest-changes-in-response-to-economic-development-and-protection-policies",signatures:"Shudong Wang and Taixia Wu",authors:[{id:"217154",title:"Dr.",name:"Shudong",middleName:null,surname:"Wang",fullName:"Shudong Wang",slug:"shudong-wang"},{id:"218273",title:"Prof.",name:"Taixia",middleName:null,surname:"Wu",fullName:"Taixia Wu",slug:"taixia-wu"}]},{id:"61072",title:"A Re-Examination of the Validity of the “Separative and Exclusive Conservation Model”: Insights from an Ethnobiological Study in Maluku, East Indonesia",slug:"a-re-examination-of-the-validity-of-the-separative-and-exclusive-conservation-model-insights-from-an",signatures:"Masatoshi Sasaoka",authors:[{id:"198898",title:"Dr.",name:"Masatoshi",middleName:null,surname:"Sasaoka",fullName:"Masatoshi Sasaoka",slug:"masatoshi-sasaoka"}]},{id:"61747",title:"Physicochemistry and Utilization of Wood Vinegar from Carbonization of Tropical Biomass Waste",slug:"physicochemistry-and-utilization-of-wood-vinegar-from-carbonization-of-tropical-biomass-waste",signatures:"Yongyuth Theapparat, Ausa Chandumpai and Damrongsak\nFaroongsarng",authors:[{id:"219997",title:"Dr.",name:"Yongyuth",middleName:null,surname:"Theapparat",fullName:"Yongyuth Theapparat",slug:"yongyuth-theapparat"},{id:"226821",title:"Dr.",name:"Ausa",middleName:null,surname:"Chandumpai",fullName:"Ausa Chandumpai",slug:"ausa-chandumpai"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"66151",title:"Primary Energy Factor for Electricity Mix: The Case of Slovenia",doi:"10.5772/intechopen.84570",slug:"primary-energy-factor-for-electricity-mix-the-case-of-slovenia",body:'
1. Introduction
The building sector in Europe is responsible for 40% of energy consumption and 36% of CO2 emissions. Due to the high estimated energy saving potential of the building sector, the European Union (EU) set up a policy framework focused on reducing the energy of buildings which consists of policy actions, i.e., Energy Performance of Buildings Directive (EPBD) [1], Energy Efficiency Directive (EED) [2], EcoDesign Directive [3], Energy Labelling Regulation [4], and the Renewable Energy Directive (RED) [5]. The EED was prepared with the goal to achieve a 20% energy consumption reduction target across the EU. It establishes a number of important provisions to be implemented by the EU Member States, including the requirement to establish obligatory national energy efficiency targets, national building energy efficiency strategies, a requirement to renovate 3% of public sector buildings annually, the need to establish energy efficiency obligation schemes, and provisions for auditing and metering.
The evaluation of energy consumption, reduction, or efficiency on the building level is somehow problematic since different technical systems use various forms of energy to operate. Therefore, energy consumption and efficiency should be evaluated on a common basis. A single metric for combining different sources or types of energy is primary energy (PE). As the name indicates, PE evaluates different forms of energy based on the conversion of primary energy to useful energy. However, the concept does not differentiate between different energy forms. Therefore, exergy could be incorporated into the concept as it reflects the energy “quality” in terms of its capacity to do work. Although there are currently no requests, for such an approach, from energy practitioners, exergy analysis could gain significantly on importance in light of future resource scarcity to, for example, penalize the use of exergy-rich energy vectors for low-temperature applications.
The task of measuring energy efficiency may seem straightforward, contingent only on the choice of indicators for the input and output. In reality, however, both can be measured in numerous ways, and choosing one approach over another always leads to trade-offs [6, 7, 8, 9, 10, 11]. Based on the input and output characteristics, three main indicator groups can be distinguished:
Thermodynamic indicators—inputs and outputs represented in terms of thermodynamic quantities (e.g., the thermal efficiency of a heating system)
Physical-thermodynamic indicators—energy inputs represented by thermodynamic quantities, outputs represented with physical units (e.g., building energy use intensity)
Economic-thermodynamic indicators—products or services represented by market prices, energy represented by means of thermodynamic quantities (e.g., GDP energy intensity)
Each of these approaches has its advantages and disadvantages and should, thus, be defined with regard to the area of application, while considering environmental, social, economic, or other aspects of energy efficiency.
PE has become an important policy metric in the EU. Namely, the EPBD prescribes that the energy performance of a building shall also include a numeric indicator of PE, based on primary energy factors (PEF) per energy carrier, which may be based on national or regional annual weighted averages or a specific value for onsite production. A PEF connects primary and final energy. It indicates how much primary energy is used to generate a unit of electricity or a unit of useable thermal energy. The PEF describes the efficiency of converting energy from primary sources (e.g., coal, crude oil) to a secondary energy carrier (e.g., electricity, natural gas) that provides energy services delivered to end users. In the EU, the Member States can freely define its value. Consequently, this has become a political decision, with a direct impact on the actual energy consumption of a building.
Similar concept of analysis of the impact of building and appliance energy consumption is used in the USA. Compared to the more legislative-constrained EU approach the US approach is more market oriented. Full-fuel-cycle (FFC) metrics are used in building codes and appliance standards to evaluate the energy and environmental impact of consumer fuels and appliances [12].
To translate PE into final energy use, the PEF is applied in several EU legislative documents. In the EED and EPBD, the PEF is used to convert final energy consumption into PE consumption to monitor progress against targets. The EPB Directive aims at reducing the PE demand for buildings. Since technologies applied in the building and improvements in the building envelope lead to savings in final energy, the PEF is applied to convert these savings into primary energy.
The latest version of the EPB Directive [13] claims that “the energy performance of a building shall be expressed by a numeric indicator of PE use for the purpose of both energy performance certification and compliance with minimum energy performance requirements.” In addition, Member States may define additional numeric indicators of total nonrenewable and renewable primary energy use and of greenhouse gas emission. Member States have some flexibility in defining these metrics.
EED requires energy targets expressed in both primary and final energy form. PEFs are applied for conversion of final energy savings into primary energy savings. EPBD and EED both allow the Member States the option of choosing their own PEF values. Within the EcoDesign Directive and Energy Labelling Directive, the PEF value of 2.5 for electricity is prescribed to allow a comparison.
From the foregoing, it is evident that the PEF is defined on two different boundary conditions within the EU legislation. For instance, the boundary condition for energy-consuming appliances is defined at the appliance level. The next level of boundary is the building (or part of it), defined as a sum of all energy used by different appliances considering different energy sources. This boundary condition is important when on-site-produced renewable energy is used by building appliances.
The method for calculating the PE for fossil fuels is quite straightforward and consistent, while the calculation of PEFs for electricity or heat generated from renewable energies or grid-supplied electricity is more complex. First of all, the PEF for fossil fuels (also for combustible renewable fuels) does not change significantly over time. For electricity, especially grid supplied, the calculation of PEF involves different energy sources as well as different electricity generation technologies. The combination of various PE sources forms a so-called power generation mix, which is the share of different energy sources used to generate electricity. The share of energy sources changes over time depending on the availability of energy sources and the level of demand. However, evaluating this is a challenge especially in renewable energy sources and nuclear energy.
2. Methodology
PE sources are usually defined as inputs into energy systems (or conversion processes) which convert them into secondary energy carriers such as electricity, oil products, heat, or mechanical work. The EPBD [13] defines primary energy as the energy that has not been subjected to any (human induced) conversion or transformation process.
As mentioned before, PEF connects primary and final energy. It indicates how much primary energy is used to generate a unit of electricity or a unit of useable thermal energy, according to Eq. (1):
PEF=primaryenergyfinalenergyE1
PE is divided into renewable and nonrenewable energy [14]. The sum of renewable and nonrenewable energy is total energy. Energy extracted from sources that are naturally replenished on a human timescale is called renewable energy. The definition of renewable energy also includes some forms of energy carrier such as biomass and energy recovered from waste. For nonrenewable energy sources, the extraction rate is higher than refill rate. Energy obtained from nonrenewable energy sources is called nonrenewable energy. This approach enables the determination of three primary energy factors for each energy carrier [14, 15]:
Energy sources can be further divided into combustible and noncombustible. Where primary energy is used to characterize fossil fuels, the embodied energy of the fuel is available as thermal energy, and typically around 70% is lost in conversion to electrical or mechanical energy.
In accordance with the laws of thermodynamics, the renewable PEF can be derived from the relevant energy conversion efficiency. For example, the electricity from a PV system with an overall efficiency of 20% can be considered to have a renewable PEF of 5. There is a similar 60–80% conversion loss when wind energy is converted to electricity. This also applies to nuclear energy, where only around 10% of the fuel’s energy content is converted to electricity.
Although primary energy factors are thermodynamically universal, many different calculation methods exist. Moreover, there are also national variations. In order to calculate the PEFs, two approaches are mainly used, namely the partial substitution method and the physical energy method. They differ in the way how to calculate the PEFs from nuclear power plants and renewable energy sources such as hydroelectric power plants, solar energy, geothermal energy, etc.
The partial substitution method solves the aforementioned problem by concentrating on the theoretical energy content in traditional fossil fuels (coal, oil, and gas). The PEF for a mixture of electricity is calculated from these sources by dividing the energy content of the fuel as the input energy with the generated electricity. In the case of renewable energy and nuclear energy, this means calculating how much primary energy would be needed for such an amount of electricity if it were produced from fossil fuels.
The physical energy method differs from the partial substitution method in that it uses a different approach for the evaluation of primary energy in the production of electricity from hydro, wind, and nuclear power plants. The calculation of the PEF for the production of electricity from nuclear and geothermal energy is based on the thermal energy of the steam boiler that drives the turbine of the power plant. The efficiency of nuclear power plants is estimated at 33 and 10% for geothermal. For other renewable energy sources, such as hydro, wind, and solar energy, this is equal to gross electricity production.
The calculation of the PEF can also be made using the method described in the standard SIST EN 15603:2008 [15]. The standard describes two alternative approaches for calculating the factor, namely, the total and nonrenewable PEF. The difference between these factors is that the latter does not include the use of renewable energy. In addition, the national PEF for the electricity mix is based either on the average electricity mix or on the marginal electricity production. The standard defines the default PEFs for different energy sources, including electricity. The values of the factors are given in Table 1.
We made a calculation of the PEF for the electricity mix in Slovenia, based on the three previously described methods, and conducted a temporal comparison. Statistical data on the generation of electricity from individual sources were obtained from the Statistical Office of Slovenia [16]. Table 2 shows the produced electricity by years from various sources of energy.
The electricity mix in Slovenia is mainly composed of five sources of primary energy, namely nuclear, fossil, hydro, wind, and solar energy. Since Slovenia is a member of the EU, the directives stipulate that, by 2020, as much as 20% of the energy used is to be recovered from renewable energy sources as far as electricity is concerned. Therefore, in addition to calculating the factor for previous years, we have also tried to predict the generation of energy from individual sources, using linear regression, and then determine the resulting PEF for the electricity mix and the share of renewable sources. Figure 1 presents the sources of energy, the share of energy sources in the production of electricity, and the share of energy from renewable sources.
Figure 1.
Electricity mix in Slovenia.
Figure 1 shows that electricity generation from fossil fuels is somewhat lower, while production from solar energy and hydro resources is increasing. Generally speaking, the share of renewable resources is increasing. Wind energy represents a very small share; therefore, increasing the share is not noticeable from the figure, but if we look at Table 1, we see that production is slowly increasing from 2013 onward.
2.1 Calculation of primary energy factor by partial substitution method
In this method, the PE equivalent of the sources of electricity generation represents the amount of energy that would be necessary to generate an identical amount of electricity with conventional thermal power plants [17]. The PE equivalent is calculated using an average generating efficiency of these plants. This method has several shortcomings including the difficulty of choosing an appropriate energy conversion efficiency to determine the energy value of renewable energy and nuclear energy. For example, it may not be possible to quantify the energy content in the wind or the sun that serves as a fuel for wind and solar power plants. In conventional nuclear power plants, only 10% of the theoretical energy content in the fuel is converted to electricity. The partial substitution solves this challenge by focusing on the theoretical energy content of traditional fossil fuels (coal, gas, and oil). PEF for electricity produced from these sources is calculated by dividing the energy content of the fuel with the electricity production. For renewable and nuclear power, the partial substitution method calculates how much PE would be required if the electricity was generated from fossil fuels. Therefore, a conversion efficiency of 40% is assumed for these types of energy [18]. Also the efficiency of fossil fuel production is 40%. By means of these set values, we obtained for 2017 the results shown in Table 3.
PEF
Nonrenewable
Total
Fuel oil
1.35
1.35
Gas
1.36
1.36
Biomass
0.07
1.07
Hydro power plant (electricity)
0.5
1.5
Nuclear power plant (electricity)
2.8
2.8
Coal power plant (electricity)
4.05
4.05
Table 1.
Primary energy factors according to the Standard EN 15603:2008.
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Nuclear
5528
5207
5459
5884
5548
5695
6273
5739
5657
6215
5528
5300
6370
5648
5715
6285
Fossil
5759
5657
5718
5772
5975
6082
6107
5945
6067
6073
5958
5661
4440
5081
5718
5610
Hydro
3313
2957
4095
3461
3591
3266
4018
4715
4703
3706
4087
4923
6366
4091
4782
4141
Wind
0
0
0
0
0
0
0
0
0
0
0
4
4
6
6
6
Solar
0
0
0
0
0
0
1
4
13
66
163
215
257
274
267
283
Table 2.
Yearly historical data on the electricity production in Slovenia (values in GWh) [16].
Production [GWh]
Efficiency
Primary energy [GWh]
Nuclear
6285
40%
14,288
Fossil
5610
40%
14,295
Hydro
4141
40%
11,955
Wind
6
40%
15
Solar
283
40%
668
Total
16,325
40,813
Table 3.
Calculation of PE by partial substitution method for the production of electricity in Slovenia in 2017.
As mentioned above, PE was obtained by dividing the energy produced by the production efficiency. This gave us the amount of PE needed to produce a certain amount of electricity. PE does not take into account the network losses; therefore, we calculated how much the losses are and what is our consumption. From this data we could then directly calculate the PEF for the electricity mix. We assumed that the amount of losses was 10% of the energy produced [18]. If by this method the factors are calculated for all the years, we can see that the factors do not change, which is because we have assumed that the efficiency is always the same, so the ratio between the energy used and the electricity produced is constant.
2.2 Calculation of primary energy factor by physical energy content method
The energy content method distinguishes itself in the approach for evaluating renewable sources and nuclear power plants production [19, 20]. PE in this method is considered as the first practically utilizable energy flow. In the case of directly combustible energy carriers (e.g., coal, natural gas, oil, biogas, bio liquids, solid biomass, combustible municipal/industrial waste), PE is defined as the heat generated in the combustion process. For non-directly combustible energy sources, PE can be expressed with the produced heat (e.g., nuclear, geothermal and solar thermal) or produced electricity (e.g., solar photovoltaic, wind, hydro, tide, wave, and ocean).
A PEF value of 1 is assumed for fuels. For noncombustible renewables a conversion efficiency of 100% is assumed. In contrast, a conversion efficiency of 33% is assumed for nuclear power stations. For combustible renewables such as biomass, the conversion efficiency is calculated from [15]. The resulting PEF for electricity from the various sources are 1 for hydro, wind, and solar PV; 3–4 for biomass; and 3 for solar thermal and nuclear. The results for 2017 are shown in Table 4.
Production [GWh]
Efficiency
Primary energy [GWh]
Nuclear
6285
33%
19,045
Fossil
5610
40%
14,025
Hydro
4141
100%
4141
Wind
6
100%
6
Solar (PV)
283
100%
283
Total
16,325
37,500
Table 4.
Calculation of PE by physical energy content method for the electricity production in Slovenia in 2017.
Just like at the partial substitution method, we took into account 10% losses in the network to obtain the PE shown in Table 5.
Production [GWh]
Network loss [GWh]
Useful energy [GWh]
Primary energy [GWh]
PEF
Total
16,325
1632.5
14,692
37,500
2.55
Table 5.
Calculation of PEF by physical energy content method for the electricity production in Slovenia in 2017.
The calculated PEF for the electricity mix using the physical energy method for 2017 is 2.55. For this year, this value is similar to the value assumed for Slovenia, i.e., 2.5. In order to observe the temporal variation of PEF, the same calculations were also carried out for previous years, based on statistical data for Slovenia. The results are illustrated in Figure 2.
Figure 2.
PEFs for the electricity mix in Slovenia using the physical energy method for the years 2000–2018.
Figure 2 shows that the factor is constantly changing, but we can notice that from 2011 onward the factor has fallen slightly. The likely reason for this is that the share of renewable resources began to increase markedly in the meantime. Since this method assumes 100% conversion efficiency for electricity produced from renewable sources, the primary energy for production is the same as production itself.
2.3 Calculation of the primary energy factor according to the Standard EN 15603:2008
The last calculation was carried out by using the default PEFs prescribed by the standard SIST EN 15603 [15]. This methodology evaluates separately the nonrenewable part and the total part of PE. Solar energy (PV) was evaluated in the same way as water and wind energy. Therefore, the default factors are the same in this case. In this method, we used the fractions of individual energies which comprise the mixture of electricity from Table 1. The full calculation for 2017 is shown in Table 6.
2017
PEF [/]
Nonrenewable
Total
Slovenia (average)
Energy share [%]
Nonrenewable
Total
Nuclear
2.8
2.8
38.5
1.05
1.08
Fossil
4.05
4.05
34.36
1.39
1.39
Hydro
0.5
1.5
25.37
0.13
0.38
Wind
0.5
1.5
0.04
0.00
0.00
Solar
0.5
1.5
1.73
0.01
0.03
Sum
2.61
2.88
Table 6.
Calculation of the PEF of electricity mix for Slovenia for 2017, using the reference values from the standard SIST EN 15603.
In Table 6, two PEFs for the electric mixture are calculated through the fractions of individual energies composing the electricity mix in Slovenia for 2017. We can see that the average PEF for nonrenewable is less than the total factor. The reason for this is that the default primary factors that take into account only the nonrenewable part of primary energy are lower than the total or total factor. The difference between the two average factors is almost 0.3, which is not negligible. As with previous methods, here again, the calculation was also performed for previous years, with the same default factors. The results are shown in Figure 3.
Figure 3.
Average PEFs for nonrenewable and total PE calculated in accordance with SIST EN 15603.
3. Results and discussion
By comparing the methods, we can find that the calculation after partial substitution yields the same results for each year. This is due to the default efficiency, which is based on certain default values. Since we get the same PEF for the electricity mixture in all years, we cannot see changes in individual years. It is also impossible to predict what will happen to the factor in the coming years. We can see that the factor is 2.78, which represents a higher value than the predicted factor for Slovenia, which is 2.5 [21].
In the case of the physical energy method, we can better categorize individual years, and from the calculations, we see the PEF fluctuation. Physical energy method assumes energy conversion efficiency of 100% for renewable sources (produced electricity equals primary energy). The highest value of the factor occurred in 2003, while the lowest value amounted to 2.23 in 2016. The reason for such a change in the last year is in the increased production of electricity from renewable sources.
In the last method proposed by the standard SIST EN 15603, which computes two factors, we can see that in the case of the total factor, the value is higher than the average PEF, which takes into account only the nonrenewable part of energy. This is the case for renewable energy sources where PEF values are lower by threefold in comparison to nonrenewable energy sources. What is logical is that we do not consume any energy for the generation of hydro, wind, and solar energy. Likewise, we can also notice here that both factors are the highest in 2003, while they are the lowest in 2014. The reason for this is that the share of produced electricity from fossil fuels is the lowest, and the share of water energy is the highest, which means that due to the low share of energy from fossil fuels and high energy from renewable energy, the factor of PE has decreased.
3.1 Forecast of electricity generation and impact on PEF
By analyzing statistical data and calculating the PEF, we can predict the change of PEF for the electricity mix of Slovenia. The total production of electricity for the coming years and the annual growth of production were calculated by adding the individual quantities of electricity that were calculated by linear regression for each source separately. This means that we added the predicted production of electricity from nuclear power, fossil fuels, hydroelectric power, wind energy, and solar energy. With this simple linear regression, we predicted the amount of energy produced from different sources and how it affects the PEF. The predictions were made for 2020, 2030, and 2040 (Table 7). The share of individual sources and the total share of renewables are shown in Table 8.
Year
2017
2020
2030
2040
Nuclear
6285
6147
6574
7001
Fossil
5610
5592
5524
5455
Hydro
4141
5350
6384
7418
Wind
6
9
14
25
Solar
283
475
894
1312
Total
16,325
17,574
19,392
21,211
Table 7.
Forecast of total electricity production [GWh].
Year
2017
2020
2030
2040
Nuclear
38.5
35.0
33.9
33.0
Fossil
34.36
31.8
28.5
25.7
Hydro
25.37
30.4
32.9
35.0
Wind
0.04
0.1
0.1
0.1
Solar
1.73
2.7
4.6
6.2
Total share of renewables
27.1
33.2
37.6
41.3
Table 8.
Prediction of energy shares in the production of electricity.
In Table 8, we see that the nuclear energy share will decrease over time as well as for fossil fuels, whose share will decrease by more than 5% by 2040. In the case of hydro energy, the share will increase by just over 7%. Wind energy already represents a very small share in electricity, so in the future it is not expected to grow significantly. The share of solar energy will also increase; by 2040, we can expect an almost 5% increase. As we can see, Slovenia already generates a large share of electricity from renewable sources; by 2040, we can expect that this share will grow by almost 15%.
3.2 Forecast of the primary energy factor for Slovenia
For the partial substitution method, we used the same production efficiency as given in Table 3. The only difference is that in this case we carry out the calculation for 2020, 2030, and 2040. In Table 9 we see an example of the calculation for 2020, where we used the previously predicted quantity of produced electricity.
Production [GWh]
Network loss [GWh]
Useful energy [GWh]
Primary energy [GWh]
PEF
Total
17,574
1757.4
15,816
43,934
2.78
Table 9.
Calculation of predicted PEF by partial substitution method for the production of electricity in Slovenia in 2020.
The PEF calculated according to the method of partial substitution method does not change over the years. The reason why the factor remains the same is that the method assumes the same production efficiency for all energy sources.
For the physical energy method, we used the same production efficiency as in Chapter 2.2. The predictions for 2020, 2030, and 2040 have been recalculated, taking into account the energy production predicted by linear regression. In this method we also considered 10% network losses in the network. The forecasts of the PEF are listed in Table 10.
Year
Production [GWh]
Primary energy [GWh]
PEF
2020
17,574
38,442
2.43
2030
19,392
41,024
2.35
2040
21,211
43,607
2.23
Table 10.
Forecast of the PEF for the electricity mix in Slovenia using the physical energy method.
We can see that the PEF will decrease over time. This result is logical, since the share of renewable energy sources will increase substantially over time. Hence, the PEF is expected to decrease. For better transparency, the PEF calculated by the physical energy method is depicted along its forecast in Figure 4.
Figure 4.
PEF of electricity calculated according to the physical energy method.
Calculation of PEF according to the standard SIST EN 15603 was carried out as described in Chapter 2.3. In this method we use the proportions of individual sources determined by linear regression. Two PEFs are proposed, namely, the average PEF-nonrenewable and average PEF-total. The PEFs for 2020 are given in Table 11. The average PEF for the electricity mix with predicted values is illustrated in Figure 5. It can be noticed that by 2040, the average PEF for nonrenewable energy will decrease to a value of 2.17, while the average PEF-total will be 2.58.
2020
PEF
Nonrenewable
Total
Slovenia (average)
Energy Share [%]
Nonrenewable
Total
Nuclear
2.8
2.8
34.98
0.98
0.98
Fossil
4.05
4.05
31.82
1.29
1.29
Hydro
0.5
1.5
30.45
0.15
0.46
Wind
0.5
1.5
0.05
0.00
0.00
Solar
0.5
1.5
2.70
0.01
0.04
Sum
2.43
2.77
Table 11.
Forecast of the PEF for the electricity mix in Slovenia for 2020, using the reference values from the standard SIST EN 15603.
Figure 5.
Average PEF for electricity mix according to the SIST EN 15603 method with predicted values.
According to the conversion factors of PE, discrepancy between nonrenewable and total PEFs for the electricity mix can be significant. From Figure 6, we can see the annual progress of all the PEFs, calculated with all three evaluated methods, for electricity in Slovenia.
Figure 6.
Comparison of the methods of calculating the PEF for the electricity mix in Slovenia.
With the partial substitution method, we can see that the PEF for electricity does not change over the years, i.e., it remains 2.78. The reason for this lies in the assumption about the efficiency of production from renewable energy sources and nuclear energy, where 40% efficiency is taken into account. Furthermore, the same efficiency is also used for fossil fuels. Therefore, the efficiency of production from all primary sources is 40%. This is why we get the same PEF for all years. This means that according to this method, we do not get the correct representation of the PEF for the electricity mix, or the assumptions are not applicable for the case of Slovenia. In the event that Slovenia produced part of the electricity from biomass, whose production efficiency is estimated with 30% in this method, the PEF would be more volatile. However, Slovenia does not use biomass for the production of electricity; therefore, this method does not give us the useful values of the factor. We also notice that the factor 2.78 is quite high in terms of other methods.
The other method used to determine the PEF for electricity is the physical energy method. With this method we evaluate the efficiency of production from renewable energy sources as 100%, while the default efficiency of nuclear power generation and fossil fuel is 33 and 40%, respectively. The PEF calculated according to this method is very low, as shown in Figure 4. The reason is in the assumption that the efficiency of production from renewable sources is 100% and Slovenia has a large share of renewable sources in its electricity production, mainly from hydropower sources. In the previous analyses of individual years and forecasts, we also noticed that the share of renewable resources is increasing over time. For this reason, from Figure 4 decreasing trend for the future is clear. This means that a PEF determined by this method will slowly decrease with respect to the increase in renewable energy sources in electricity generation.
With calculation according to the standard SIST EN 15603, we calculated two different primary energy factors: the average PEF for nonrenewables, which takes into account only the nonrenewable part of the energy of individual primary sources, and the PEF, which takes into account the total share of primary energies. We used the default values of the individual factors determined by the method for each primary source separately. We can see that the average PEF for nonrenewable energy is much lower than the total. The reason for this is that the default values of the factors that we use to calculate the nonrenewable and total factor are different. The greatest differences occur in renewable energy sources. This is because renewable energy sources have a very small share of nonrenewable energy. Therefore, the factors for calculating the individual PE sources are low in the case of hydropower, wind, and solar energy. When calculating the total factor, the factor value for these types of energy is 1.5. Moreover, a different calculation approach is used in this method, i.e., the PEF is calculated through the shares of individual energy sources in the total electricity.
4. Conclusions
PEFs are used to describe the conversion efficiency from primary energy sources to secondary energy sources, which are supplied to end consumers. PEFs are, therefore, used for comparing necessary quantities of primary energy to the final energy demands. At EU level as well as national levels, PEFs are used for converting final energy to primary energy consumption, for comparing efficiency of devices with different energy sources as well as to benchmark building energy performance. As it stands, the EU Member States can autonomously determine national PEFs, which in turn can skew the evaluation process of primary energy use in buildings.
We analyzed the three most commonly used methods used to determine the PEF for the electricity mix. We examined what are the assumptions of the individual methods and the individual default values that the method assumes. Then, using these methods, the value of the PEF for electricity in Slovenia was determined. We also recalculated with all the methods how the PEF changed over time at an annual level. All calculations were made using statistical data about produced electricity from various primary energy sources and individual assumptions determined by the methods. In addition, a statistical analysis using linear regression was carried out in order to predict the future PEF values for all three considered methods.
We have found that the methods differ in the evaluation of individual primary sources, which has a significant impact on the PEF value. In addition, we observed that the factor is also changing in terms of the electricity production from different sources, which means that the factor depends on the amount of energy that is produced either from nonrenewable sources of energy or from renewable energy sources. If the annual production of electricity from renewable energy sources is higher, we can expect a lower PEF and vice versa. We also noted that the share of renewable resources increases over time, which is also noticeable in the predicted values of production from renewable energy sources.
We also found that with the partial substitution method, we do not get representative results about the PEF, since it remains constant over the years. This means that this method does not provide a proper representation of the PEFs and, hence, is not applicable for the case in Slovenia. The method of physical energy gives the efficiency of production from renewable energy sources as 100%. Here, too, the question arises as to whether the evaluation is completely correct and if we can truly assume that the use of PE is equal to the actual production of electricity. In the third method, defined in the standard SIST EN 15603, which provides two PEFs, a certain measure of criticality of the assumed factors for the different sources of energy is used.
\n',keywords:"primary energy, primary energy factor, electricity mix, renewable energy sources",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66151.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66151.xml",downloadPdfUrl:"/chapter/pdf-download/66151",previewPdfUrl:"/chapter/pdf-preview/66151",totalDownloads:379,totalViews:0,totalCrossrefCites:1,dateSubmitted:"September 13th 2018",dateReviewed:"January 20th 2019",datePrePublished:"March 14th 2019",datePublished:"July 22nd 2020",dateFinished:null,readingETA:"0",abstract:"According to the European energy policy, the energy use of technical systems in buildings is given at the level of primary energy. This calculation requires knowledge of the primary energy conversion factors according to their source; however, there is currently no single European-wide recognized method for their determination. The aim of this study is to present and compare three methods for determining primary energy factors, namely the method of partial substitution, the physical energy method, and calculation according to EN 15603 standard. For the case study, the electricity factor for Slovenia was calculated according to the aforementioned methods. The results of this study showed that the methods differ in the evaluation of individual primary sources, which has a significant impact on the PEF value. We found that with the partial substitution method, we do not get representative results about the PEF. The method of physical energy defines the efficiency of production from renewable energy sources as 100%. The question arises if we can truly assume that the use of PE is equal to the actual production of electricity. In the third method, defined in the EN 15603 standard, which provides two PEFs, a certain measure of criticality of the assumed factors for the different sources of energy is used.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66151",risUrl:"/chapter/ris/66151",signatures:"Matjaž Prek",book:{id:"7633",title:"Energy Policy",subtitle:null,fullTitle:"Energy Policy",slug:"energy-policy",publishedDate:"July 22nd 2020",bookSignature:"Tolga Taner",coverURL:"https://cdn.intechopen.com/books/images_new/7633.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"144302",title:"Dr.",name:"Matjaz",middleName:null,surname:"Prek",fullName:"Matjaz Prek",slug:"matjaz-prek",email:"matjaz.prek@fs.uni-lj.si",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methodology",level:"1"},{id:"sec_2_2",title:"2.1 Calculation of primary energy factor by partial substitution method",level:"2"},{id:"sec_3_2",title:"2.2 Calculation of primary energy factor by physical energy content method",level:"2"},{id:"sec_4_2",title:"2.3 Calculation of the primary energy factor according to the Standard EN 15603:2008",level:"2"},{id:"sec_6",title:"3. Results and discussion",level:"1"},{id:"sec_6_2",title:"3.1 Forecast of electricity generation and impact on PEF",level:"2"},{id:"sec_7_2",title:"3.2 Forecast of the primary energy factor for Slovenia",level:"2"},{id:"sec_9",title:"4. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Directive 2010/31/EU of European Parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings (Recast). 2010. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2010:153:0013:0035:en:PDF'},{id:"B2",body:'Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 On Energy Efficiency, Amending Directives 2009/125/EC and 2010/30/EU and Repealing Directives 2004/8/EC and 2006/32/EC. 2012. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2012:315:0001:0056:en:PDF'},{id:"B3",body:'Directive 2009/125/EC of the European Parliament and of the Council of 21 October 2009 Establishing a Framework for the Setting of Eco-Design Requirements for Energy-Related Products. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:285:0010:0035:en:PDF'},{id:"B4",body:'Regulation EU 2017/1369 of the European Parliament and of the Council of 4 July 2017 Setting a Framework for Energy Labelling and Repealing Directive 2010/30/EU. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R1369&rid=7'},{id:"B5",body:'Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing Directives 2001/77/EC and 2003/30/EC. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009L0028&rid=8'},{id:"B6",body:'Taner T, Sivrioglu M. Data on energy, exergy analysis and optimisation for a sugar factory. Data in Brief. 2015;5:408-410. DOI: 10.1016/j.dib.2015.09.028'},{id:"B7",body:'Taner T, Sivrioglu M. Energy-exergy analysis and optimisation of a model sugar factory in Turkey. Energy. 2015;93:641-654. DOI: 10.1016/j.energy.2015.09.007'},{id:"B8",body:'Taner T. Energy and exergy analyze of PEM fuel cell: A case study of modeling and simulations. Energy. 2018;143:284-294. DOI: 10.1016/j.energy.2017.10.102'},{id:"B9",body:'Taner T. Exergy analysis of a circulating fluidized bed power plant co-firing with olive pits: A case study of power plant in Turkey. Energy. 2017;140:40-46. DOI: 10.1016/j.energy.2017.08.042'},{id:"B10",body:'Taner T. Optimisation processes of energy efficiency for a drying plant: A case of study for Turkey. Applied Thermal Engineering. 2015;80:247-260. DOI: 10.1016/j.applthermaleng.2015.01.076'},{id:"B11",body:'Taner T, Sivrioglu M. A techno-economic & cost analysis of a turbine power plant: A case study for sugar plant. Renewable and Sustainable Energy Reviews. 2017;78:722-730. DOI: 10.1016/j.rser.2017.04.104'},{id:"B12",body:'ANSI/ASHRAE Standard 105-2014. Standard Method of Determining, Expressing and Comparing Building Energy Performance and Greenhouse Gas Emission. Atlanta, USA: ASHRAE; 2014'},{id:"B13",body:'Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 Amending Directive 2010/31/EU on the Energy Performance of Buildings and Directive 2012/27/EU on Energy Efficiency. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32018L0844&from=IT'},{id:"B14",body:'ISO 52000-1:2017. Energy performance of buildings—Overarching EPB assessment—Part 1: General framework and procedures. Geneva: International Organization for Standardization; 2017'},{id:"B15",body:'SIST EN 15603:2008. Energy performance of buildings—Overall energy use and definitions of energy ratings. Brussels: European Committee for Standardization; 2008'},{id:"B16",body:'Statistični Urad Republike Slovenije: Električna Energija (GWh). Slovenija. Available from: https://pxweb.stat.si/pxweb/Dialog/varval.asp?ma=1817602S&ti=&path=../Database/Okolje/18_energetika/03_18176_elektricna_energija/&lang=2'},{id:"B17",body:'Segers R. Three options to calculate the percentage renewable energy: An example for a EU policy debate. Energy Policy. 2008;36:3243-3248. DOI: 10.1016/j.enpol.2008.05.014'},{id:"B18",body:'Conversion Factors for Electricity in Energy Policy. A Review of Regulatory Application of Conversion Factors for Electricity and an Assessment of their Impact on EU Energy and Climate Goals. Norway: ADAPT Consulting a.s.; 2013'},{id:"B19",body:'IEA (International Energy Agency). Energy Statistics Manual. France: IED Publications; 2005'},{id:"B20",body:'Eurostat: Definition of the Primary Energy Content of Fuels. Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php/Calculation_methodologies_for_the_share_of_renewables_in_energy_consumption#Definition_of_the_primary_energy_content_of_fuels'},{id:"B21",body:'Tehnična Smernica TSG-1-004:2010. Učinkovita Raba Energije. Slovenia: Ministrstvo za Okolje in Prostor; 2010. Available from: http://www.mop.gov.si/fileadmin/mop.gov.si/pageuploads/zakonodaja/graditev_objektov/TSG_01_004_2010_ure.pdf'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Matjaž Prek",address:"matjaz.prek@fs.uni-lj.si",affiliation:'
Faculty of Mechanical Engineering, University of Ljubljana, Ljubljana, Slovenia
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