Input variables to calculate production costs of coal thermal electrical energy. The reference values are considered to be the most probable values. Also estimated 10 % and 90 % uncertainty ranges are given. (IEA, 2005; IEA, 2010b; EPIA, 2010)
\r\n\tThis book aims to present an overview of the current status of nanofibers, fabrication and recent trends in the fabrication of nanofibers, and functional nanofibers and applications of nanofibers in various fields including environmental, bio-sensing, drug delivery, catalysis, and medical. The book hopes to provide a piece of up-to-date information about the mentioned topics and fundamental knowledge necessary for the advanced study in the field of nanofibers and their applications, making it interesting to research students, scientists, engineers, and material scientists.
",isbn:"978-1-80356-387-9",printIsbn:"978-1-80356-386-2",pdfIsbn:"978-1-80356-388-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"a255898117275990dffe83c75a9f815d",bookSignature:"Dr. Maaz Khan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11462.jpg",keywords:"Nanofiber, Nanofiber Fabrication, Functional Nanofiber, Nanofiber Application, Fiber Technology, Electrospinning, Drug Delivery, Fabrication Strategy, Commercialization, Polymer, Tissue Engineering, Catalysis",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2022",dateEndSecondStepPublish:"April 26th 2022",dateEndThirdStepPublish:"June 25th 2022",dateEndFourthStepPublish:"September 13th 2022",dateEndFifthStepPublish:"November 12th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Maaz Khan is an expert in the field of Nanoscience and Nanotechnology with over 100 articles and 3,300 citations to his name.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"107765",title:"Dr.",name:"Maaz",middleName:null,surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan",profilePictureURL:"https://mts.intechopen.com/storage/users/107765/images/system/107765.png",biography:"Dr. Maaz Khan is working as Deputy Chief Scientist (Professor) at PINSTECH, Pakistan. He has done Ph.D. and post doctorate in the field of Material Science (Nanoscience). His research interests include fabrication of nanomaterials and their structural, optical, magnetic, and electrical characterizations. He has authored more than 100 research articles and published 10 books. Presently, he is the Editor-in-Chief of ‘Journal of Materials, Processing and Design\\' and \\'The Nucleus\\'. He is also the Executive Editor of \\'International Journal of Nano Studies and Technology\\'. 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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. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5404",title:"Raman Spectroscopy and Applications",subtitle:null,isOpenForSubmission:!1,hash:"7d447d2811c5d3fc696761bb12fe3166",slug:"raman-spectroscopy-and-applications",bookSignature:"Khan Maaz",coverURL:"https://cdn.intechopen.com/books/images_new/5404.jpg",editedByType:"Edited by",editors:[{id:"107765",title:"Dr.",name:"Maaz",surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4644",title:"The Transmission Electron Microscope",subtitle:"Theory and Applications",isOpenForSubmission:!1,hash:"6ef878a14961b97ec0bc5c1762a46aa0",slug:"the-transmission-electron-microscope-theory-and-applications",bookSignature:"Khan 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by"}},{type:"book",id:"5884",title:"Unraveling the Safety Profile of Nanoscale Particles and Materials",subtitle:"From Biomedical to Environmental Applications",isOpenForSubmission:!1,hash:"5e5811aa0f15ab9d8b6a235e8408875d",slug:"unraveling-the-safety-profile-of-nanoscale-particles-and-materials-from-biomedical-to-environmental-applications",bookSignature:"Andreia C. Gomes and Marisa P. Sarria",coverURL:"https://cdn.intechopen.com/books/images_new/5884.jpg",editedByType:"Edited by",editors:[{id:"146466",title:"Prof.",name:"Andreia",surname:"Ferreira de Castro Gomes",slug:"andreia-ferreira-de-castro-gomes",fullName:"Andreia Ferreira de Castro Gomes"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"19648",title:"Forecasting the Future of Renewables",doi:"10.5772/24270",slug:"forecasting-the-future-of-renewables",body:'\n\t\tThe global electric power consumption has increased for decades and the growth seems to continue in the foreseen future. At the same time the known conventional fossil energy resources will be decreasing and, evidently, we will be short of them. On top of that we are facing serious problems of climate change due to CO2 emission. These kind of giant problems ahead of us have finally raised a genuine interest on the utilization of alternative energy sources. First we will face the shortage of oil and gas, which can be substituted for some time with energy sources like coal and beat, if environment and climatic issues are ignored. However, climatic change is a major driver in energy sector at the moment, and only renewable energy sources provide a sustainable outlook.
\n\t\t\tRenewable energy sources cover only a fraction of the present energy consumption, because most of them are not competitive on the market. Only hydropower is clearly competitive. In special cases also other renewable energy sources can be competitive, for example, biomass as a source of combined heat and power. Major technology development is still needed for most of the renewable energy sources to achieve market penetration. Both on public finance and private business decision point of view it is essential to know, when these evolving electric power production technologies are competitive on the market and what is their foreseen market volume, i.e., electric energy production potential. Market penetration depends on the development of the evolving energy production technology in question and on the forthcoming cost of electric energy production using conventional primary energy sources.
\n\t\t\tTo forecast the market diffusion of new evolving electric energy production technologies, the dependence of the market diffusion on technology and energy market variables need to be analysed. Market diffusion can be analysed with various qualitative or quantitative methods, which all involve a considerable amount of uncertainty. If a reasonable amount of historical data of the evolution of the technology is available, quantitative methods can be applied to forecast the market penetration. Due to numerous uncertainties related to the input parameters it has turned out that a simple and straightforward method is usually as good as a more sophisticated method. At the end on long term market mechanics will rule out the development and the analyses can be based on the production cost of electrical energy. Production cost depends basically on environmental variables, such as the availability of the energy source used, and on variables related to the production technology in question, like the investment cost.
\n\t\t\tProduction costs of electrical energy for most renewable energy sources are mainly due to investment costs and to some extend on production and maintenance costs, but the energy source is free of costs. In these cases production costs can be estimated based on technology learning, i.e., concepts like learning factor and progress ratio. In addition to investment, production and maintenance costs power production costs for conventional non-renewable energy sources are based on fuel prices. Conventional technologies are already quite mature with slow learning rate. Therefore, changes in power production costs are mainly due to changes in fuel prices.
\n\t\t\tIn this chapter it will be demonstrated how technology development and market penetration of evolving energy production technologies can be forecasted by quantitative methods. In particular, we look on electric power production, because its role in the energy sector increases both absolutely and relatively. We have analyzed the relevant variables for power production costs, their dependencies on each other and their future development on long term. These issues have been combined with feasible technology evolution and market diffusion models (Armstrong, 2001; Junginger, 2005; Martino, 1993; Meredith & Mantel, 1995; Rogers, 2003). We will show that a plausible model can be developed to forecast, when an evolving new power production technology becomes competitive on the market. We also analyse the market penetration of photovoltaic power production with this model. This simple model can be applied quite easily also to other evolving power production technologies to obtain useful forecasts.
\n\t\tWorld net electricity generation has globally increased until 2010 with an average annual rate of 3.0 % outpacing the growth of total energy consumption by 1.3 %. World net electricity generation will increase by an average of 2.3 % per year until 2035 according to the IEO2010 Reference Case continuing to outpace the growth in total energy usage throughout the projection period. This will end up to an increase by 87 % to 35 000 TWh until 2035. High fossil fuel prices recorded between 2003 and 2008, combined with concerns about the environmental consequences of greenhouse gas emissions, have renewed interest in the development of alternative energy sources with respect to fossil fuels—specifically, nuclear power and renewable energy sources. In the IEO2010 Reference Case, long-term prospects continue to improve for generation from both nuclear and renewable energy sources. Coal is foreseen to cover most of the growth of electricity generation with 7000 TWh and renewable energy sources are the second sources covering 4500 TWh of the growth. Renewable energy sources have the largest growth rate in electricity generation in the IEO2010 Reference Case with an average annual growth of 3.0 %. This will end up to an increase by almost 130 % until 2035. Accordingly, the renewable share of world electricity generation would grow from 18 % in 2007 to 23 % in 2035. Majority of this growth will be due to the increase in wind and solar power production. (EIA, 2010b)
\n\t\t\tThe era of change from the utilisation of fossil energy to renewable energy seems to be inevitable when we run out of fossil fuel resources. The transition will be ruled out by “market forces” on demand and supply basis. It is quite realistic to anticipate that the prices of conventional energy resources will increase while their stock decreases. This means that alternative energy production technologies will become more competitive. At the same time the cost of new evolving energy technologies can be estimated to decrease following typical technology development trends. In the following basic outlines are given how to forecast this technology transition. First the basic outlines to evaluate production costs of electricity generation from conventional energy sources are presented and then for evolving electricity production technologies from renewable energy sources.
\n\t\t\tOil, natural gas, coal and beat covered 81.2 % of the global primary energy consumption in 2008 and 87.0 % of the total primary energy usage was covered by non-renewable fuels. 5.5 % of the total electrical energy was produced by oil, 21.3 % by natural gas, 40.0 % by coal and beat and 13.5% by nuclear energy. Hydropower covered 15.9 % leaving only a share of 2.8 % for other renewable energy sources. (IEA, 2010a) The average global growth rate of electric power usage has been estimated to be larger than the growth rate of total energy usage in the future and new renewable energy sources will have the highest relative growth. However, the biggest total grow in power production will be in the use of coal. (EIA, 2010b)
\n\t\t\t\tConventional power production technologies have been utilized for several decades and they are quite mature. The main technologies have also very high volumes in power production, which means that their relative capacity does not increase rabidly. Therefore, the technological development can be expected to be quite modest in the future and no major changes in investment, production and maintenance costs are expected. For simplicity, these variables can be supposed to stay constant as a function of time for conventional power production technologies.
\n\t\t\t\tProduction cost of electrical energy
where
Globally the main energy sources in power production are coal, natural gas, hydropower, nuclear energy and oil. Hydropower and nuclear power are the low cost basic sources of electrical energy. In most parts of the world the marginal price of electrical energy follows the cost of natural gas or coal thermal electric power production, and changes in the production costs are primarily due to changes in fuel prices. Oil has a quite global price and, therefore, it would be an ideal reference price for analysing global energy cost issues. However, oil is not a basic source of electrical energy in all parts of the world due to its limited resources and high price. Therefore, it is not suitable for defining global marginal price of electrical energy.
\n\t\t\t\tNatural gas has quite global price, which follows the oil price closely. Coal price is more local, but it also follows the oil price to some extent. As an example of this, the oil, natural gas and coal prices in the European Union are presented in Figure 1 from 1985 to 2005. Market prices of natural gas and coal follow the oil price closely having correlation coefficients of 0.88 and 0.76, respectively. Furthermore, there seems to be a direct pricing mechanism between oil and gas, because the gas price and the oil price shifted one year ahead have a correlation of 0.94. Due to these strong correlations oil, natural gas and coal prices can be expected to have roughly the same increase rate in the future.
\n\t\t\t\tOil, natural gas and coal prices in EU in the period from 1985 to 2005 normalized to 100 in 1992. The black line is the oil price of the previous year. (
Coal has the largest resources of conventional energy in the world and its resources are distributed quite evenly around the world. It also has the lowest market price. For example, in 2008 in electric power sector the price of natural gas was over 4 times higher than the price of coal in term of energy content and oil was over 7 times more expensive (EIA, 2010a). Therefore, it is plausible to assume that in most parts of the world the marginal price of electrical energy is determined by the coal price. This situation can be anticipated to stay also in the future, because the fossil fuel prices are strongly connected to each other and no major relative changes are expected. For these reasons, coal thermal electric power production has been selected to be the reference fossil source of electrical energy in analysing market diffusion of evolving power production technologies based on renewable energy sources.
\n\t\t\t\tAll variable values used in this work correspond to the state of the art modern technology. For all variables we have defined a reference value, which is in many cases a mean value of a representative sample of technologies or a value from a most probable scenario given for the future. In 2008 the average investment costs of modern thermal coal power stations around the world have been analysed to be close to 1400 €/kW (IEA, 2010b). Based on Valkealahti and Nevaranta (Valkealahti & Nevaranta, 2007) investment costs for about 80 % of the coal power stations can be estimated to be between 1250 and 1550 €/kW. These values have been used as the minimum and maximum values to describe the variation of the investment costs in the analyses later on (Table 1).
\n\t\t\t\tAnnual production and maintenance costs for coal thermal electric power production are typically around 4 % of the investment costs (IEA, 2010b). Annual production time of a thermal power plant varies considerably depending on a large number of variables. Typically electric loads in the network have seasonal and also shorter term variations causing breaks in production, regular maintenance is needed etc. If a power plant would operate the whole year with nominal power without breaks, the capacity factor
\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
Investment cost (€/kW) | \n\t\t\t\t\t\t\t1250 | \n\t\t\t\t\t\t\t1400 | \n\t\t\t\t\t\t\t1550 | \n\t\t\t\t\t\t
Annual production and maintenance cost (€/kW) | \n\t\t\t\t\t\t\t50 | \n\t\t\t\t\t\t\t60 | \n\t\t\t\t\t\t\t70 | \n\t\t\t\t\t\t
Economic lifetime (y) | \n\t\t\t\t\t\t\t25 | \n\t\t\t\t\t\t\t30 | \n\t\t\t\t\t\t\t35 | \n\t\t\t\t\t\t
Capacity factor (h) | \n\t\t\t\t\t\t\t4000 | \n\t\t\t\t\t\t\t6000 | \n\t\t\t\t\t\t\t8000 | \n\t\t\t\t\t\t
Efficiency | \n\t\t\t\t\t\t\t0.35 | \n\t\t\t\t\t\t\t0.40 | \n\t\t\t\t\t\t\t0.45 | \n\t\t\t\t\t\t
Interest rate (%) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 5 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t |
Coal price (€/MWh) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 8 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t |
Annual coal cost growth rate by IEA (%) | \n\t\t\t\t\t\t\t-0.8 | \n\t\t\t\t\t\t\t1.9 | \n\t\t\t\t\t\t\t3.3 | \n\t\t\t\t\t\t
Annual coal cost growth rate by EPIA (%) | \n\t\t\t\t\t\t\t0.4 | \n\t\t\t\t\t\t\t2.0 | \n\t\t\t\t\t\t\t3.6 | \n\t\t\t\t\t\t
Input variables to calculate production costs of coal thermal electrical energy. The reference values are considered to be the most probable values. Also estimated 10 % and 90 % uncertainty ranges are given. (IEA, 2005; IEA, 2010b; EPIA, 2010)
The known fossil energy resources of coal, oil, natural gas and U235 have been estimated to last about 220, 40, 60 and 70 years, respectively, with the present consumption and roughly the same amount of resources are estimated to be found more (British Petroleum, 2006; British Petroleum, 2004; EIA, 2002). Based on this information it has been estimated that somewhere between 2020 and 2050, the primary energy consumption of non-renewable energy sources will start to decrease. This transition will be preceded by a considerable increase of primary energy prices. Therefore, the long term primary energy price development of fossil fuels has to be taken into account in technology diffusion analyzes.
\n\t\t\t\tCoal prices have typically changed marginally mostly due to the large resources compared to other fossil fuels and to the current usage of coal. During the last decade coal price has increased considerably following the rapid growth of oil price, but during the last years it has returned close to the long term price level. To have a realistic starting point for analysing the future coal thermal electric power production costs, an average of the last 10 year has been used as a current coal price. By this way the most dramatic effects of short term price fluctuations were reduced.
\n\t\t\t\tIn Table 1 there has been presented also scenarios for the future coal price by International Energy Agency and European Photovoltaic Industry Association until 2035 and 2030, respectively (IEA, 2010a; EPIA, 2010). Both scenarios actually predict fluctuating growth rates for the projection period from which we have calculated average growth rates to be used in our analysis. In both scenarios low and high coal costs estimates are given in addition to the most probable reference estimates for price development. The reference cost growth rates in the two scenarios are almost the same and also the high cost growth rates are close to each. Only the low cost estimates differ considerably from each other, IEA providing even a negative annual growth rate estimate of -0.8 %. This does not seem to be feasible and, therefore, the coal cost growth rates predicted by EPIA have been used as our reference in the analysis.
\n\t\t\tThe cost of electrical energy produced by evolving renewable power production technologies can be calculated with equation (1) basically in the same way as for conventional technologies. The main difference is that the investment, production and maintenance costs of evolving technologies are still decreasing strongly due to technical, manufacturing, process etc. development. This kind of technology cost development can be described by a learning curve (Junginger, 2005), where a variable, like the investment cost
where
Technology development is commonly known to follow learning curves, where product cost or some other market related quantity decreases exponentially as a function of cumulative production. Also the development of solar photovoltaic electricity generation technology, which we use as an example of evolving renewable electric power production technologies to demonstrate the method of forecasting market penetration of new evolving renewable electric energy production technologies, follows the exponential learning curve development. Several studies have been done on the development of solar photovoltaic power technology providing progress rations for investment costs in the range from 0.75 to 0.82. For example, a progress ratio 0.75 has been obtained for the period from 1976 to 2002 (Poponi, 2003) and 0.77 for the period from 1981 to 2000 (Parente et al., 2002). A resent analysis for the period from 1980 until 2015 provides a progress ratio 0.80 (Beneking, 2007). Progress ratio tends to increase with increasing cumulative production volume meaning that the learning rate
We have used the progress ratio 0.80 for solar photovoltaic power technology in the analysis corresponding to a 20 % decrease of the investment cost, when the cumulative manufacturing volume doubles. This is in line with historic development and resent estimates (Beneking, 2007). Investment cost is a major cost factor in generating electricity with solar photovoltaic power plants. Energy source for solar photovoltaic power is totally free, it is free of annual production costs and also maintenance costs are minimal. Therefore, solar photovoltaic electricity costs are predominantly based on the investment cost
To be able to calculate the future investment or production cost of an evolving electricity generation technology based on technology learning rate we need to know the development of the manufacturing volume. International Energy Agency (IEA) and European Renewable Energy Council (EREC) have both estimated the future increase of installed capacity for different power production technologies, which use renewable energy resources (IEA, 2003; EREC, 2004). Most resent estimates for the cumulative growth of installed production capacity of solar photovoltaic power have been done by European Photovoltaic Industry Association (EPIA), EREC and IEA (EPIA, 2008; EREC, 2008; IEA, 2010). All these estimates have decreasing trends for the cumulative capacity growth rate starting from the current level of about 33 % per year decreasing down to around 5 % after 2040 as shown in Figure 2. Almost identical cumulative capacity, investment cost and production cost estimates for solar photovoltaic electricity generation were achieved by using these growth rate estimates, because they differ so little from each other. Therefore, we have used the estimated growth rate by EREC in our detailed analysis later on, which extends to 2050.
\n\t\t\t\tAnnual increase of the cumulative installed production capacity of solar photovoltaic electricity generation estimated by EPIA, EREC and IEA for the period from 2005 to 2050. The estimate by EPIA extends only to 2030. (
To introduce range of variation for the estimated annual increase of the cumulative installed production capacity, it has been multiplied by a capacity growth factor (Table 2). A growth factor of 1.0 gives the original EREC estimate and factors 0.50 and 1.50 give low and high extremes for the growth. By this way it was possible to analyse the sensitivity of our forecasting model and the technology development on uncertainties in input variables. The same was done also with progress ration by introducing low and high limits of 0.75 and 0.85, respectively.
\n\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
Electricity production cost (€/kWh) | \n\t\t\t\t\t\t\t220 | \n\t\t\t\t\t\t\t330 | \n\t\t\t\t\t\t\t440 | \n\t\t\t\t\t\t
Capacity growth factor | \n\t\t\t\t\t\t\t0.5 | \n\t\t\t\t\t\t\t1.0 | \n\t\t\t\t\t\t\t1.5 | \n\t\t\t\t\t\t
Progress ratio | \n\t\t\t\t\t\t\t0.75 | \n\t\t\t\t\t\t\t0.80 | \n\t\t\t\t\t\t\t0.85 | \n\t\t\t\t\t\t
Input variables to calculate production cost for solar photovoltaic electrical energy. The reference values are considered to be the most probable values. Also estimated 10 % and 90 % uncertainty ranges are given. (EREC, 2008; Poponi, 2003; Parente et al., 2002; Beneking, 2007)
Market penetration of solar photovoltaic electricity generation has been analysed with respect to thermal coal based power generation to demonstrate how diffusion of evolving renewable electricity generation technologies and their market penetration can be plausibly estimate by using a simple and straightforward method. Development and market penetration of evolving renewable power production technologies involve so many uncertainties and variables that it is, in practice, waste of time for doing very sophisticated analyses. It is actually more fruitful to figure out causes and effects of the most important factors for the development by a simple method.
\n\t\t\tThere are several rational ways to define market penetration and none of those is the best without objection. Our choice for the reference is the production cost of coal thermal power, which in most parts of the world defines the marginal cost of electrical energy production. Coal is the cheapest source of fossil energy due to its largest recourses. Also coal price can be expected to incline least of all the fossil energy sources. Furthermore, solar photovoltaic electricity is available only when Sun is shining. For economical reasons it should be produced and used always when the energy source is available. Therefore, it can be considered as a basic source of energy in the same way as coal thermal electricity.
\n\t\t\tFor each input variable of solar photovoltaic or coal thermal electricity generation a medium or most probable value has been used as a reference. These reference values have been used to calculate the reference forecast of market penetration, i.e., the point of time when solar photovoltaic achieves grid parity with coal thermal electricity generation. In Figure 3 the reference forecasts for the costs of solar photovoltaic and coal thermal electricity have been presented for the period from 2010 to 2050. The point of grid parity in the reference case takes place in 2036 and has been marked by “Ref” in Figure 3.
\n\t\t\tIn addition to the reference values feasible minimum and maximum values have been introduced for each variable to be able to evaluate the sensitivity of the market penetration on different factors. When statistical data on a certain variable was available 10 % and 90 % uncertainty values have been used as minimum and maximum values. For many variables value range or extreme forecasts from reliable reference sources have been used as minimum and maximum values. Sensitivity of the market penetration on different input variables has been tested by changing the value of one variable at a time while other variables have their reference values.
\n\t\t\tEstimated electrical energy production costs for the period from 2010 to 2050 by solar photovoltaic and coal thermal power stations. Electricity production costs using coal are shown with estimated minimum, reference and maximum annual coal price growth rates of 0.4, 2.0 and 3.6 %, respectively. (
One of the main uncertainties to forecast market penetration of solar photovoltaic technology is related to the uncertainty of the forthcoming cost of coal. To foresee the price of coal in the future is very difficult. Therefore, the use of a conservative forecast is justified, like the one provided by Europian Photovoltaic Industry Association with an annual coal price growth rate of 2.0 % as a reference scenario and 0.4 and 3.6 % as low and high growth rate scenarios, respective (EPIA, 2010). Calculated electricity generation costs with growth rates of 0.4 and 3.6 % are shown in Figure 3. Grid parities are achieved in 2048 and 2030, accordingly. Market penetration of solar photovoltaic takes place only 6 year earlier, if the coal price growth rate increases from 2.0 to 3.6 %, but will be delayed by 12 years, if the growth rate is decreased to 0.4 %. This demonstrates that coal price, and more commonly fossil fuel prices, has a major effect on the market penetration of solar photovoltaic technology.
\n\t\t\tCapacity factor of coal thermal electricity generation has also a major effect on solar photovoltaic electricity market penetration. The coal thermal electricity generation costs until 2050 for capacity factors 4000, 6000 and 8000 hours are shown in Figure 4. The cost curve shifts to higher costs with decreasing capacity factor as expected. With the capacity factor of 4000 h the grid parity will be achieved already in 2030, but with a capacity factor of 8000 h only in 2039. This demonstrates that market penetration of solar PV is strongly a local issue. If coal thermal power is used to generate electricity for basic loads with a capacity factor of 8000 h, market penetration will take place 9 years later than in the case of seasonal usage of coal thermal power with a capacity factor of 4000 h.
\n\t\t\tEstimated electrical energy production costs for the period from 2010 to 2050 by solar photovoltaic and coal thermal power stations. Electricity production costs using coal are shown with estimated minimum, reference and maximum annual coal thermal power capacity factors of 4000, 6000 and 8000 hours, respectively.
As already discussed in section 2.2, solar photovoltaic power production costs are between 220 and 440 €/MWh in realistic areas on the Earth for photovoltaic power production (EREC, 2008). Electricity production costs by solar photovoltaic power plants for the period from 2010 to 2050 are shown for current production costs of 220, 330 and 440 €/MWh in Figure 5. Solar photovoltaic electricity cost curve shifts upwards with increasing initial production cost so that the grid parity moves from 2026 for the lowest cost of 220 €/MWh up to 2045 for the highest cost of 440 €/MWh. Solar photovoltaic electricity production costs have been reported to be 440 €/MWh in Berlin, 390 €/MWh in Paris, 330 €/MWh in Washington, 300 €/MWh in Hong Kong, 280 €/MWh in Sydney and Madrid, 250 €/MWh in Bangkok and 220 €/MWh in Los Angeles and Dubai in 2007 (EPIA, 2008). This means that in places like Los Angeles and Dubai solar photovoltaic technology would penetrate fully to the market already in 2026, but in Central and North Europe the market penetration would take place only in 2045.
\n\t\t\tThe uncertainties on market penetration of solar photovoltaic electricity due to uncertainties in input variables of the model have been presented in Figure 6. The reference market penetration in the year 2036 has been calculated using the reference values of variables for solar photovoltaic power and coal thermal power (Tables 1 and 2). Minimum and maximum variable values have been used to evaluate the sensitivity of the market penetration on the uncertainty of each input variable. Minimum and maximum market penetration times have been calculated by changing the value of one input variable at a time while the other input variables have their reference values.
\n\t\t\tThe biggest uncertainty seems to come from the production capacity growth (capacity growth factor) of solar PV power. If the cumulative solar photovoltaic electricity production capacity growth rate is only half from the forecasted growth rate (Figure 2), the market
\n\t\t\tEstimated electrical energy production costs for the period from 2010 to 2050 by solar photovoltaic and coal thermal power stations. Electricity production costs by solar photovoltaic are shown with estimated current minimum, reference and maximum production costs of 220, 330 and 440 €/MWh, respectively. (
penetration will be out of the scope of the study (later than in 2060). On the other hand, if there would be a 50 % increase in the growth rate, the market penetration would take place already in 2023. This shown clearly that technological development depends strongly on the manufacturing volume, when learning rate is high. Electricity production capacity by solar photovoltaic power has been growing during the last decade with the growth rates presented in Figure 2 and there are no reasons to anticipate that the growth rate would be lower than the forecasted ones (EPIA, 2008; EREC, 2008; IEA, 2010). The growth rate forecasts can be actually considered to be slightly pessimistic and even somewhat higher growth rates can be expected. The strong dependence between technology development and production volume of evolving renewable electricity production technologies with high learning rates is also a clear signal for political decision makers. Market penetration of evolving renewable electricity production technologies can be can facilitates by supporting the market growth.
\n\t\t\tUncertainties due to other solar photovoltaic electricity generation related variables are also considerable. The second largest uncertainty is related to the learning rate causing a market penetration range from 2026 to 2055. It is not plausible to anticipate that learning rate would increase meaning that the historical long term progress ratio trend would decrease, for example from 0.80 to 0.75. Progress ratio of a technology does not usually decrease but increases as a function of time, because technology becomes gradually more mature. On the other hand, technological development has turned out to follow learning rates quite consistently. The used reference progress ration of 0.80 for solar photovoltaic was a slightly conservative choice from the set of published progress ratios and, therefore, there are no reasons to anticipate a larger ratio for the near future. Perhaps the main message in here is that one should select the progress ratio carefully to get plausible results.
\n\t\t\tThe third input parameter related to the development of solar photovoltaic was production cost of electricity. On the contrary to earlier variables, the wide range in production costs actually exists on the market and does not originate from uncertainties of predictions. Phenomena related to this variable have been already discussed in connection of Figure 5.
\n\t\t\tIt is not a surprise that uncertainty or range of variation of most variables affecting the coal thermal electricity generation have minimal effects on market penetration of solar photovoltaic electricity. Plausible changes in investment costs or production and maintenance costs can change the time of market penetration only by a year or two (Figure 6). Also the power plant efficiency can change the time of market penetration by five years in maximum. A more important input variable on forecasting point of view is the capacity factor of coal thermal electricity generation introducing a range of 9 years for market penetration. Future development of the coal prize has the biggest effect and uncertainty of 18 years to the forecasted market penetration. The effects and phenomena related to these input variables have been already discussed in more detail in connection of Figures 3 and 4.
\n\t\t\tTime when electrical energy produced by solar photovoltaic power plant has the same production cost than by thermal coal power stations. The reference market penetration time is shown by a solid line. The bars show the uncertainty of the market penetration with respect to the range of variation of each variable (maximum and minimum values in
There are also other ways to evaluate the sensitivity of market penetration on uncertainties and range of variation of input variables than to compare their effects one by one to the reference case. For example their combined effect could provide interesting information. However, the uncertainties due combined effect of several variables can be deduced quite reliably from the effects of single variables. Furthermore, the combined effects of several variables are not usually much bigger than the largest effects due to single variables, because the variable with the largest effect dominates the combined effect. To demonstrate this, the market penetration of solar photovoltaic electricity generation was calculated using the minimum and maximum input variable values of coal thermal electricity generation, which led to an earlier market penetration than in the reference case. The outcome was that market penetration would take place in 2025, just five years earlier than in the case of having only the capacity factor changed to its minimum value of 4000 h. One must also point out that it is highly unlikely that all variables would have the extreme values at the same time.
\n\t\t\tAnnual electricity generation with evolving renewable power production technologies for the period from 2007 to 2035 according to the IEO2010 Reference Case (
Cumulative electricity generation estimates according to the IEO2010 Reference Case (EIA, 2010b) for evolving renewable energy sources for the period from 2005 to 2040 are shown in Figure 7. Also the cumulative solar photovoltaic electricity production corresponding to the estimated capacity growth rate by EREC (Figure 2) is provided using the EIA solar electricity generation in 2007 (EIA, 2010b) as a reference value. The overall renewable energy production growth forecasted by EIA seems to be in balance except for solar power, which is in contradiction with other estimates (Figure 2). There are no reasons to believe that solar photovoltaic as a source of electrical energy would stay constant for decades, while it has had the highest capacity growth rate of all forms of producing electrical energy for years. On the other hand the solid line for solar photovoltaic electricity production is consistent with existing information. Around 2020 it will be the second largest renewable source of electrical energy after wind power when hydropower is excluded. Production capacity of wind power will grow fastest during the next few decades, but somewhere around 2050 solar photovoltaic will outpace it being then the largest source of renewable energy in producing electric power.
\n\t\tThe aim of this study was to demonstrate how the market diffusion of new evolving electrical energy production technologies can be forecasted. The dependence of the market diffusion on technology and energy market variables has been analysed. A simple forecasting method has been presented together with uncertainty analyses on the studied variables. The results confirm that it is possible to quantitatively forecast the market penetration of evolving energy technologies. These kinds of analyses can support decision making both on National level and in companies developing energy technology.
\n\t\t\tThe market penetration of solar photovoltaic electricity generation has been studied in detail, because it will be in a major role in generating electrical energy on long term in the future. Coal thermal electricity generation was used as a reference technology for market penetration. The analyses showed that in the reference case solar photovoltaic electricity generation should be competitive without subventions somewhere around 2036. In areas with favourable solar radiation conditions, such as in Los Angeles and Dubai, solar photovoltaic power will achieve grip parity with thermal coal power already in 2026. In less favourable areas, like in Central and North Europe, grid parity will be achieved just around 2045. Also periodicity of the electricity demand affects the market penetration of photovoltaic electricity. If the capacity factor of coal thermal electricity production is only 4000 h instead of 6000 h used as a reference value, market penetration will happen in 2030 5 years earlier than in the reference case. If the capacity factor is 8000 h, market penetration will be delayed until 2039. As a summary, the market penetration is strongly a local matter.
\n\t\t\tMarket penetration of solar photovoltaic electricity depends also on the forthcoming production capacity growth and the learning rate of the technology. If the learning rate of the solar photovoltaic technology would improve by 5 % from the current trend so that the progress ration would decrease from 0.80 to 0.75, market penetration would happen 10 years earlier that in the reference case. However, this kind on increased technology learning does not usually take place. The annual increase of the solar photovoltaic electricity production capacity is huge, around 33 %/a. In principle, the growth rate could still increase by half leading to a market penetration already in 2023. Market penetration depends also on the development in other areas of energy technology. One important parameter turned out to be the coal price. If, for example, the high coal price scenario by EPIA (EPIA, 2010) comes true, market penetration of solar photovoltaic will take place 6 years earlier than in the reference case. As a summary there are many technology related uncertainties having major effects to the market penetration of solar photovoltaic electricity generation.
\n\t\tThe coronavirus disease, which emerged with respiratory symptoms (high fever, cough, shortness of breath) in Wuhan, China’s Hubei Province in December 2019, has turned into a worldwide pandemic. The novel coronavirus disease (COVID-19), defined by the World Health Organization (WHO) on January 13, 2020, spread rapidly in 6 continents and hundreds of countries after China, causing many deaths [1]. At the time of preparation for this study (August 23, 2021), approximately 211,730,035 people worldwide were infected and 4,430,697 people died due to COVID-19 [2]. The COVID-19 outbreak, defined as the first pandemic caused by coronaviruses, has caused global concern. The COVID-19 pandemic has brought many challenges. A few of these difficulties are stigma and xenophobia. The stigma associated with COVID-19 has serious implications for the lives of healthcare professionals, patients, and those who have had the disease. This makes a difficult situation, such as the fight against the epidemic, even more difficult. Health workers, infected individuals, and their families are seen as possible sources of infection and are exposed to various discrimination and stigmatization. Not allowing healthcare workers to use public transportation, being asked to vacate their rented houses, being exposed to verbal and physical violence, abandoning the woman with COVID-19 who gave birth by her family, calling the street where the house of a person with COVID-19 is located as a ‘corona road’ and people avoiding this street are examples of stigma [3]. Epidemics that contain many uncertainties, such as the COVID-19 epidemic, can cause serious social stigma. For example, Russian Jewish immigrants were stigmatized because of the typhus and cholera epidemics in 1892, and Native Americans in the region were stigmatized because of the 1993 hantavirus epidemic in the United States. Also, an epidemic of bubonic plague, the so-called “black death” attributed to rats transported by ship from Hong Kong in the spring of 1900, resulted in discrimination and stigmatization in the San Francisco Chinatown community [4]. Stigmatized persons may give up seeking treatment, people may fear and avoid stigmatized persons, society may be prejudiced against stigmatized persons, and this may turn into verbal or physical violence against stigmatized persons or groups. For fear of being stigmatized and labeled as someone with an infectious disease, many at-risk people may not seek help until symptoms become very severe. In fact, many people may not seek help for treatment at all [5]. The COVID-19 pandemic has created an environment with too many risks for stigma. Xenophobia is a word that means fear and hatred of foreigners [6]. Xenophobia is spreading in many countries during the pandemic and is mostly directed against Asians. Such xenophobic incidents have been reported in countries such as Belgium, Croatia, Finland, France, Germany, Hungary, Italy, the Netherlands, Russia, Ukraine, and the United Kingdom. Cases of xenophobia include verbal attacks and accusations of spreading the virus to the public [7]. In this section, it is aimed to discuss the history of the concepts of stigma and xenophobia, its psychosocial dimension, its relationship with the COVID-19 pandemic, methods of combating risk factors in the light of literature information, and to raise awareness about this situation.
Stigma refers to the situation of being exposed to an accusatory, exclusionary approach and attitude due to the existence of a situation that should be ashamed for a person or group, being different from normal or different from other individuals with any feature. Stigma as a word means a scar, a stain, a sign of shame and humiliation that marks the person [8]. Etymologically, the concept of stigmatization was used for the first time in Ancient Greek with the meanings of hole, puncture, wound, scar, and today it is used in the sense of humiliation and loss of reputation, similar to this definition. Sociologist E. Goffman defined stigma as social rejection resulting from negatively perceived characteristics [9]. According to Goffman, the term stamp goes back to the Greeks who cut or burned the skins of criminals, slaves, and traitors to describe them as tainted or immoral people to be avoided [9]. Stigma is a term that refers to social disapproval, not just a physical marking. Link and Phelan extended Goffman’s conceptualization by identifying four attributes of stigma; (1) individual differences are noticed, (2) these differences are perceived negatively by society, (3) the stigmatized group is viewed as an outgroup, (4) the end result is loss of opportunity, power, or status [10].
While explaining the reasons for stigma, 4 terms were used. These are as follows:
Throughout history, human beings have been exposed to dangerous diseases that force them to change their behavior to adapt to new conditions. WHO has defined a pandemic as “the worldwide spread of a new disease”. From smallpox of the 19th century to COVID-19 of the 21st century, epidemics and pandemics have always been associated with stigma and serious social consequences [12]. Apart from natural disasters or wars affecting a certain geographical region, infectious diseases affect the whole world and humanity without borders, as we witnessed in the COVID-19 epidemic. To date, there have been 21 pandemics affecting humanity. The most well-known and most severe of these is the plague epidemic that emerged in the fourteenth century. In previous studies, it was reported that the population of the whole world decreased by 1/4 in the plague pandemic and the population of many important cities was completely destroyed [13, 14]. Other major pandemics are Spanish Flu (1918–1920), HIV epidemic, Smallpox in the former Yugoslavia (1972), severe acute respiratory syndrome (SARS) (2003), “Swine Flu” or H1N1/09 (2009), Middle East Respiratory Syndrome (MERS), Ebola (2014–2016), and ZIKA (2015–2016) pandemics. These pandemics have caused many casualties [15]. There are differences between the first known pandemics and more recent pandemics because during the first pandemics the population was independent of each other, that is, isolated. However, in the present times when human mobility has increased, the significant increase in interregional and even intercontinental communication and interaction has changed the course of today’s pandemics. On the other hand, the development of transportation and communication in the global arena and increased contact with a different human, animal, and ecosystem populations facilitated the spread of the pandemic [16]. Medical stigma is seen in almost every period of history. Diseases such as leprosy, plague, syphilis, tuberculosis, cancer, AIDS, which affect societies, are the diseases that cause stigmatization [17, 18]. As diseases such as tuberculosis and syphilis became treatable, stigmatizing attitudes towards these diseases decreased over time [19]. Leprosy, which has a history as old as human history and is one of the first stigmatized diseases, was described as an “evil” given to man by God [20]. Those who contracted syphilis in the fifteenth century were condemned by society. Tuberculosis, named in the eighteenth century, was seen as a disease belonging to the lower class. Since the 1900s, with the recognition of cancer types and the increasing number of people receiving this diagnosis, prejudiced behaviors have been made against cancer patients, and discriminatory approaches against cancer patients have continued until the last 20 years. There were comments about AIDS, which emerged in the 1980s and was formerly known as a homosexual disease, as “a punishment given by God to sinners”. Along with cancer, tuberculosis, leprosy, syphilis, and epilepsy, AIDS has also become one of the diseases that create stigma [20].
During the pandemic process, people’s relationships with each other have changed. Newborn baby visits could not be made, university graduates could not share their graduation moments with their loved ones. Many people were not able to physically meet with their elderly family members. Many people were unable to attend the funerals of their closest relatives and friends, and could not adequately mourn their loss [21]. As a result of all these changes, stigma has been an important problem during the COVID-19 pandemic period. It has been modified through human interactions, social distancing, and other restrictions to limit the spread of the virus. Much more emphasis has been put on health systems, which are often under heavy load, and they have become inadequate. Inadequacies in health systems have led to inequalities among people in accessing health. As a result of health inequalities, the COVID-19 pandemic has rekindled or strengthened sensitive social issues such as stigma, discrimination, and racism [22]. In a qualitative study conducted in Pakistan, participants described their neighbors’ hostile attitudes when COVID-19 was detected in their family members. Neighbors asked some people to leave the neighborhood. A person whose spouse had COVID-19 reported that other family members were reluctant to meet with him, even though his spouse’s test result was negative. This stigma has even been reported among doctors. A 55-year-old doctor whose wife contracted the coronavirus explained that despite a negative test result and taking protective measures at work, he was treated as if he was a carrier of the infection. People who have been found to have COVID-19 in their relatives have also been exposed to discrimination in the workplace. Some participants felt that the stigma and discrimination they faced in their region were so great. They reported that they had a problem and that they were planning to change their houses [23]. We can say that this group is at high risk of stigmatization since the disease especially affects the population over the age of 65. At the same time, returnees from abroad face stigma [24]. Stigma has become a serious problem for healthcare professionals, especially during the COVID-19 pandemic. During such epidemics of widespread infectious disease, healthcare workers are often stigmatized by people in their own community [3]. During the COVID-19 pandemic, healthcare workers are hailed as “heroes” in the media. However, this does not eliminate the possibility of discriminatory attitudes towards healthcare professionals based on the fear that healthcare professionals are carriers of COVID-19. In a study conducted in the United States and Canada during the COVID-19 pandemic, an online questionnaire about stigmatizing healthcare professionals was administered to 3551 non-healthcare workers. More than a quarter of respondents reported that health workers should be kept separate from their communities and families. More than one-third of respondents reported avoiding healthcare workers for fear of infection. People who stigmatize healthcare workers also tend to avoid other people, avoid pharmacies and supermarkets, and stay at home all the time [25]. In a study conducted in Egypt with 509 physicians, 138 of whom directly care for COVID-19 patients, 159 (31.2%) physicians reported severe COVID-19-related stigma. The overall COVID-19-related stigma score was higher in those working in the quarantine hospital. A significant number of physicians have experienced the stigma associated with COVID-19 [26]. In another study conducted with 529 physicians during the COVID-19 process, approximately one-third (31%) of the participants reported that they were concerned about stigma due to their profession as a healthcare workers. About 13.8% reported that they were worried about avoiding family members due to stigma [27]. Stigma adds an unnecessary burden to healthcare workers’ lives and can contribute to healthcare worker burnout [28]. For this reason, protective measures should be taken against stigmatization, especially among healthcare professionals, and more support should be provided to healthcare professionals in this regard. In cases where stigma is high, people may tend to hide the disease as a coping strategy to avoid discrimination. Hiding infections and avoiding testing is a serious problem and can contribute to an increased risk of infection and a decrease in protective behaviors [29]. Additionally, the stigma associated with COVID-19 can become a barrier to control and prevent COVID-19. It is because people with high levels of stigma are less likely to explain their health status and seek treatment [13]. WHO Director-General Dr. As Tedros Adhanom Ghebreyesus stated that “Honestly, stigma is more dangerous than the coronavirus itself” [30]. The stigma associated with infectious diseases is associated with the clinical features of such diseases and socio-cultural factors [31, 32]. For example, hepatitis A survivors rarely suffer from stigma, but hepatitis B and C survivors often experience high levels of stigma due to more serious long-term effects [33]. Because COVID-19 disease is caused by a new virus, it is not clear how widespread or severe stigma is among survivors. The effect of stigma in the future is a matter of curiosity.
Xenophobia is literally a compound word formed by two Latin words. It found its semantic equivalent with the combination of the words xénos, which indicates the difference, and phóbos, which means fear and/or horror, and entered the literature in the sense of the individual or society’s fear of the foreign and different from themselves. The state of being afraid of strangers, which is mentioned within the scope of the definition of xenophobia, also includes a discriminating, hateful, humiliating, and standardizing hostile attitude towards a different person. In this context, the target audience includes people who are not those from the relevant region, who are not citizens, or who differ in this context [34]. Xenophobia is defined as “attitudes, prejudices, and behaviors that reject, exclude, and often disparage people who are foreign to the society or national identity or who are perceived as a foreign” [35]. In order to understand the concept of xenophobia, it is useful to distinguish it from the concept of racism, which is often confused with each other. While xenophobia expresses a behavior based on the idea that the other is alien to the group; racism explains discrimination based on differences in physical characteristics such as skin color, hair type, and face shape. Racism is also considered the most extreme level of xenophobia [35]. In recent years, migration movements have increased and continue to increase in the world with the expectation of regional conflicts, climate change, security, employment, education, health, and generally higher welfare [36]. While the concept of alien can be considered a universal concept, this is not the case for xenophobia. Xenophobia is an emerging concept: it was first used by Anatole France in 1901 in Monsieur Bergeret a Paris. Situations such as the term anti-Semitism of the Dreyfus affair that shook domestic politics in France at the turn of the twentieth century, and the violent form of nationalism that emerged at the time provided a social and political background for xenophobia [37]. In 1906—the full year of Dreyfus’ rehabilitation - xenophobia was first listed in a French dictionary: Nou eau Larousse illustre. In the following years, it was included in the dictionary in many languages, especially in English [38]. The concept of xenophobia came to the fore again in the COVID-19 epidemic and became the subject of research.
The effects of the pandemic, which has heavily influenced all countries in the world, are felt in many areas socially, economically, politically, and spiritually [39, 40]. Pandemics have historically been linked to political and economic relations, foreign interventions, conflict, and concerns about maintaining social control in society [41]. The COVID-19 pandemic, on the one hand, contributed to the mutual solidarity and support of states, societies, and individuals from different socioeconomic statuses, on the other hand, it also caused widespread fears and concerns that triggered the current culture of distrust and discrimination, especially against immigrants. Due to the pandemic, social and economic inequalities towards immigrants have increased, and immigrants have been discriminated against and stigmatized [42]. Immigrants have been one of the vulnerable groups that have suffered the most during the pandemic [43]. Xenophobia spread like the virus itself, affecting not only those of Chinese descent but also those of any East Asian descent or nationality [44]. The increase in the prevalence of xenophobia causes stigmatization and targeting of various groups in the society and therefore the inability to provide health services appropriately [45]. In order to evaluate xenophobia comprehensively, it is necessary to acknowledge how certain diseases and social conditions fuel fear and discrimination, and that the stigmatization of various groups in society due to the disease is an important challenge for global development [46]. Throughout history, viral diseases have often been associated with the place or regions where outbreaks first occurred. In 2015, WHO issued guidelines to stop this practice and thereby reduce prejudice and xenophobia towards these regions or people of these regions [47]. Despite these guidelines for naming diseases in order to avoid stigmatizing communication, stigmatizing expressions such as “Spanish flu” and “Mexican swine flu” are frequently used. Such stigmatizing statements suggest that there is a relationship between strangers and a particular epidemic, leading to increased fear of strangers [48]. Efforts are ongoing to prevent the use of false and stigmatizing statements. However, as the number of COVID-19 cases increased in the US and around the world in early 2020, terms such as Chinese virus, Kung-Flu, Chinese coronavirus, and Wuhan virus were used for the COVID-19 virus by leaders in the US and some sections of society [49]. Such naming of the virus has led to the legitimization of the negativities towards Asian communities, the disruption of treatment services, and the stigma of these communities. With the study of Reny and Barreto, it has been experimentally proven that linking the pandemic to a social group in this way will activate negative attitudes towards Asian communities [49]. Even the smallest changes in expression styles and communication are reflected in the way socio-cultural structures are presented and perceived. The style and language used in the transmission of information about the virus can contribute greatly to the spread of xenophobia as well as to its prevention. In the current situation, the widespread stigmatization of immigrants with negative expressions causes an increase in xenophobia. It is seen that racist acts against Asians and Asian-Americans have increased in connection with COVID-19 cases in the USA, and Asian societies have become vulnerable to verbal and physical abuse due to widespread prejudices [45]. Verbal and physical abuse of Asian Americans has been reported at subway stations in Los Angeles and New York [45]. It was reported that 1135 verbal abuse, embarrassment, and physical attacks against Asian Americans took place in the USA in 2 weeks [42]. In another study conducted in Malaysia, it was determined that xenophobia and racism towards Rohingyas increased due to the COVID-19 pandemic. This is supported by reports of increased hate speech against the Rohingya, both on social media and in government discourse [50]. In a study conducted with Asian university students in Poland, it was determined that the COVID-19 pandemic triggered xenophobic attitudes towards students. 61.2% of the students stated that they were exposed to prejudice, and 47.1% stated that these prejudiced attitudes occurred on public transport and on the street. Reactions towards Asian students are keeping away from them, changing seats on the bus, maintaining a safe distance, covering mouth and nose, showing judgmental facial expressions, pointing fingers and speaking in a whisper, spitting, throwing beer bottles, and using offensive language [51]. Another study found that 90% of respondents in China displayed discriminatory attitudes towards people from Hubei province, such as reporting their presence to local authorities, avoiding them, and actively removing them from their communities [52]. There are concerns that verbal and physical attacks on Asians may continue to increase during the pandemic. In hospitals, some patients have been observed verbally abusing Asian service providers and refusing care. Some Asians and Asian-Americans stated that they may not seek help for fear of discrimination, both in public spaces and within the healthcare system. This puts communities at risk. The increase in racist acts against Asian communities, especially due to fear and misinformation, puts these communities at risk in terms of not only their physical health but also their mental health [42].
There are several risk factors for stigma and xenophobia. One of them is isolation and quarantine practices. Social distancing in the COVID-19 pandemic is an effective way to reduce morbidity and mortality. However, it should be noted that social distancing can increase stigma for affected populations [5]. It has been reported that quarantine, hopelessness, financial losses are associated with social stigma during the COVID-19 process. Quarantined individuals are more likely to be stigmatized and socially rejected. Stigma is a very important issue, especially for involving people who are quarantined. Media reporting is a powerful tool for influencing public opinion and contributed to stigma in previous outbreaks [53]. It is reported in the literature that there may be people who carry the virus asymptomatically in the COVID-19 pandemic. This is another risk factor for stigma and xenophobia. People can see and stigmatize other people as a constant carrier [54]. Those with COVID-19 may be accused of not following stay-at-home directives or not taking appropriate precautions when going out. People can be stigmatized even when they do not have the virus. For example, someone with allergies, congestion, and sneezing may be stigmatized for leaving their home while sick [55]. Another risk factor for stigma is social media. When the COVID-19 outbreak broke out, hate speech about China and Chinese people on social media provoked social stigma. In a study, a search for “china and coronavirus” on Twitter found 3,457,402 tweets about China-related to COVID-19. Hate speech was detected in 25,467 tweets [56]. Another risk factor for stigma is the language used. Researchers are careful when naming the COVID-19 virus to avoid any stigma. Tedros Adhanom Ghebreyesus, the director of the World Health Organization, said he needed to come up with a name that does not refer to a geographical place, an animal, a person, or a group of people, but is also pronounced and related to the disease [57]. This sensitivity suggests that pointing to a certain group will increase stigma even when naming the virus. For example, being able to say a patient with a diagnosis of COVID-19 instead of a patient with COVID can reduce stigma. Words can create a stigma against geographic regions and certain populations and cause prejudice and panic [5]. Pandemics can cause intense stigma in certain populations. For example, during the 2003 SARS epidemic, discrimination against people of Asian descent was widely reported worldwide, affecting the care-seeking behavior and mental health of many people of Asian descent [4]. The African-Americans Policy Forum (AAPF), an advocacy group for Asian Americans in the United States, reported 1500 incidents of discrimination in 45 states between March 19, 2020, and April 24, 2020. These complaints ranged from verbal abuse to physical attacks in public. Most of the complaints were reported as verbal abuse, including children and the elderly [18]. A Chinese group also living in the UK received intense stigmatizing and accusatory Facebook posts [58]. It is necessary to be very careful when giving news about health workers who are at risk of intense stigma. Exposure to dramatic news images of sick and deceased healthcare professionals can cause the viewing public to exaggerate the risk of personal infection and stigmatize healthcare professionals [59]. In the pandemic process, the influence of the media and politicians comes to the fore as two important factors that trigger xenophobic tendencies towards immigrants. Discourses in the media and the way politicians plan and conduct the pandemic process and the expressions they use in this process shape attitudes and perceptions towards immigrants in society [42].
With the onset of the COVID-19 pandemic, psychosocial and economic problems began to be experienced all over the world. Some of these problems were related to stigmatizing and xenophobic attitudes. Stigma can lead to disadvantages in many areas of life, including interpersonal relationships, education, and work-life. Such discriminatory attitudes can limit life opportunities, for example, through loss of income, unemployment, reduced access to housing or health care [60]. Individuals who perceive that they are stigmatized may report guilt, self-blame behavior, self-devaluation, self-isolation, low self-esteem, and being ostracized or ignored by others. Stigma is closely related to mental health problems, especially depression. Studies have found strong positive associations between stigma and depression; depression and stigma also have some common symptoms such as guilt, self-blame, and low self-esteem [61]. Reports from various countries show that discriminatory and xenophobic behavior causes food insecurity and the return of immigrants to their home countries [42]. With the rise of xenophobic tendencies in the USA, the demands for Asian Americans to return to their countries have also increased, and this has led to the greater exclusion of Asian Americans in society [62]. Asian Americans face serious problems such as physical assault, verbal abuse, coughing on themselves, being fired from shops and restaurants, discrimination, and vandalism in the workplace [63].
People over the age of 65 suffer from the disease more severely due to the effect of other existing medical diseases. With the spread of the coronavirus and growing fear and anxiety, especially among older adults, the issue of coronavirus stigma in older people has become a major social concern. Elderly people are accused of ignoring their own health and public health because they do not comply with protective measures sufficiently [64]. Disregarding their own will, they are the target of more prohibitive measures, and ageism has been exacerbated during this pandemic. The hypothesis that older people are more likely to have COVID-19 leads to the fact that people in the community are less likely to contact with older people. This led to the isolation of the elderly. This stigma can weaken social cohesion in the elderly and lead to social isolation among older adults [65]. In the first days of the epidemic, people living in China, even from all Asian countries, foreigners or foreigners were perceived as a possible threat. Refugees were included in the current risk group. Many names that affect the public, from heads of states to officials, made speeches accusing refugees of spreading the virus; discrimination and hate crimes against Mexicans in the USA, Africans in Italy, and refugee groups in Bosnia, Jordan, Singapore, and Greece took place in the press [66]. Health workers who are in close contact with patients may be excluded, and when people around them see them in shopping, in an apartment, or at home, they may exhibit discriminatory behaviors even when necessary physical distance and adequate precautions are taken. In May 2020, 13 humanitarian organizations such as the World Medical Association, Red Crescent, Red Cross and medical associations made a statement to draw attention to and prevent attacks against healthcare workers during the pandemic. In many different countries, from Mexico to India, healthcare workers are being attacked for fear of transmitting COVID [1]. Burnout is more common in healthcare workers who are faced with stigmatization [67].
During the pandemic period, people mostly stayed in touch via social media as part of social isolation measures. However, there is an increase in the number of false information and fake news that can negatively affect the health and life of individuals on social media [68]. WHO director Tedros made a statement as “we are not only fighting a pandemic, we are also fighting an infodemic” when the COVID-19 pandemic started [18]. Infodemia is a word derived from the English words “information” and “pandemic”. Infodemia can be defined as the excessive circulation of misinformation. WHO reported that the spread of unrealistic or erroneous information about COVID-19 can cause panic and fear in societies, make it difficult to fight the disease, and increase stigma [69]. The spread of false or false information about COVID-19 can cause panic and fear in communities. It can complicate the fight against the disease as well as increase stigma and xenophobia [70]. One of the most negative consequences of the epidemic is the rise of xenophobia. Media reports can have this effect. The lives of individuals exposed to xenophobia may be adversely affected by this situation [49]. Infodemia is a facilitating factor of the stigma associated with COVID-19 [71]. Infodemia is also a serious problem for vaccination studies. Misinformation about COVID-19 vaccines is a serious threat to both public health and national economic security [72]. The infodemic brought by each epidemic has become one of the most compelling factors at the center of the COVID-19 struggle, with the spread of social media communication networks in the recent period.
Stigma and xenophobia are serious problems. Countries that are successful in infection control thanks to methods such as strict screening, patient isolation, contact tracing, and quarantine should also address the risk of stigma and the negative effects that may arise. Disease-related education and provision of quarantine and public health information to the general public can reduce stigma [5]. Applying strategies to reduce stigma in other diseases for COVID-19 may be important to combat stigma. People affected by COVID-19 should be actively involved in the development and implementation of stigma mitigation strategies and interventions. Lack of correct information and misinformation are the main causes of xenophobia and stigma. This should be taken into account in stigma reduction strategies. Information about COVID-19 should be conveyed concisely and in a culturally appropriate manner to the wider population in a variety of local languages, with particular attention to stigmatized communities. Considered a major force in the fight against COVID 19, the media can play a crucial role by not spreading unconfirmed and exaggerated claims that can promote stigma and xenophobia. The media should spread the right information in order to convey hope, unity, and solidarity to large masses. Finally, it is important to involve those who affect society in the fight against stigma, to create public awareness, and to pay attention to cultural characteristics, to combat stigma during the epidemic [73]. In studies, it is recommended that such studies be carried out and educational interventions should be made with professional staff who have leadership characteristics in the society, such as health workers, police and school children, clergy, headmen, journalists, celebrities. Accurately informing the leading professional groups in society is seen as an effective method in alleviating the burden of stigmatization [74].
In the current pandemic process, we are going through times that we have never experienced before as the whole world. COVID-19 will have devastating consequences on humanity in the short and long term, causing significant sociological, economic, and psychological problems. Stigma and xenophobia is a barrier to medical evaluation, communication, delivering, and receiving necessary care due to fear and is associated with both physical and mental health complications. Healthcare workers who heroically fight the epidemic during this process are especially at risk of stigmatization. Also, various ethnic groups are at risk of xenophobia in epidemics. COVID-19 related stigma needs to be addressed rigorously by professionals and health care providers as well as authorities. Fighting stigma and xenophobia is a vital issue as much as fighting the epidemic. The pandemic will end one day, but the effects of stigma and xenophobia and the effects of the pandemic may continue for many years and cause devastating results. In addition to developing national strategies to prevent stigma and xenophobia, international cooperation is needed. It is necessary to recognize stigma and xenophobia in epidemic periods and to create training and policies to combat these problems. This will lead to a stronger sense of unity, more effective scientific communication, greater adherence to the rules and guidelines set for combating the pandemic, more efficient use of medical means, and ultimately better management of the pandemic as a whole.
I gratefully commemorate all healthcare professionals who lost their lives due to COVID-19.
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The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence"},{id:"23",title:"Computational Neuroscience",scope:"Computational neuroscience focuses on biologically realistic abstractions and models validated and solved through computational simulations to understand principles for the development, structure, physiology, and ability of the nervous system. This topic is dedicated to biologically plausible descriptions and computational models - at various abstraction levels - of neurons and neural systems. This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. Particularly interesting are models of various types of more compound functions and abilities, various and more general fundamental principles (e.g., regarding architecture, organization, learning, development, etc.) found at various spatial and temporal levels.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",keywords:"Single-Neuron Modeling, Sensory Processing, Motor Control, Memory and Synaptic Pasticity, Attention, Identification, Categorization, Discrimination, Learning, Development, Axonal Patterning and Guidance, Neural Architecture, Behaviours and Dynamics of Networks, Cognition and the Neuroscientific Basis of Consciousness"},{id:"24",title:"Computer Vision",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR"},{id:"25",title:"Evolutionary Computation",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization"},{id:"26",title:"Machine Learning and Data Mining",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence"},{id:"27",title:"Multi-Agent Systems",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. 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We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems.
\r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
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