Descriptive statistics
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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\r\n\tOrganic synthesis has always been one of the central topics of research for the scientific community in the academic laboratories and industrial world. Many striking journal articles and remarkable reviews and books have been published in the past year describing the practicability and applications of the subject demonstrating the importance of organic synthesis. In the present book, we will be putting together the topics in organic synthesis which may include but not limited to, (1) the basic terms and concepts, (2) various organic reactions including reduction, oxidation, addition, elimination, rearrangements, and cycloadditions, (3) Total Synthesis of Natural products, (4) transition metal catalysts, organocatalysts, enzymes and biotransformations, (5) applications in medicinal chemistry and drug design and development, (6) purification methods and characterization techniques, etc. To set a limit and to increase the scope of the book, author(s) are encouraged to send the chapters that include selected examples with practical applications and good yielding reactions reported within the past decade. Older topics with significant findings or their essence to prepare the foundation may be included in the chapter are welcomed as well.
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[1]. One of the main features of DS includes cognitive impairments. Specifically, adolescents with DS have been shown to have lower levels on executive functions including working memory, inhibition, planning and set switching than typically developing children matched for mental age [2]. These cognitive deficits can limit their abilities to perform activities of daily living. Thus, interventions to improve their capacity to perform activities independently would help to improve quality of life and reduce the costs associated with providing care for them. We are investigating the effects of physical activity interventions on the cognitive skills in persons with DS.
Executive functions are a set of higher-order control processes that take place primarily in the frontal lobe of the brain [2], which deal with the decisions to make actions, and planning how to accomplish tasks [3]. Executive function includes concept formation, task switching, inhibition, volition, planning, purposeful action, and effective performance [3]. These are necessary in order for a person to engage in tasks independently. People with deficits in executive function are often called lazy due to this lack of initiative, but executive function is necessary for a person to initiate self-care routines or work independently. Many researches have documented that people with DS have shown deficits in executive functioning (e.g., [4, 5]). Improving executive functions could in turn improve many other independent living skills. Below we have highlighted a few executive functions that we measured in response to an exercise intervention in persons with DS.
Working memory is information that people actively keep in their mind and manipulate [3, 6]. If human memory were a computer, the working memory would have been an active window where a person would have manipulated the informational contents. Working memory is limited in size, yet it is important for many other tasks from remembering words to learning new motor skills [6, 7]. Several studies have found that people with DS have significant deficits in working memory [8, 9].
Set switching is the ability to change a course of thought or action based on changing requirements [3]. In clinical settings, this is typically done with a card sorting test where children are first asked to sort the cards by shape and then by color. Children with typical development are unable to switch to the second sorting rule at three years old. By four years of age, a child can change rules with some struggle, and by five years old, a child can shift to the new rule with ease [10]. On a practical level, set switching is demonstrated while children are working on something when a parent tells them that they need to get ready to leave the house. The ease at which the children are able to transit between the two tasks reflects their capacity for set switching. Set switching also requires working memory to process the change in tasks and the ability to inhibit the first behavior pattern [11]. Set switching activates a network of cells in the frontoparietal region of the brain, including the inferior frontal gyrus, anterior cingulate cortex, and supramarginal gyrus [12]. When someone has difficulty with set switching, it can result in inflexibility and perseverative behaviors. People with DS have significant deficits in set switching in comparison to people with typical development [2, 8].
People with DS in general show deficits with language, especially with expressive vocabulary [13–15]. Typically this is tested by asking people to recall words related to a particular category or words that start with a certain letter. Neuroimaging studies have shown that letter-based verbal fluency is mediated by the frontal cortex and category-based verbal fluency by the temporal cortex; parietal lobe mediates both tasks [16]. Nash and Snowling [17] found that people with DS showed deficits in verbal fluency in comparison with peers of typical development.
As previously described, there is a vast amount of research that documents cognitive deficits in persons with DS. We believe that it is time to focus on interventions aimed at improving cognitive functions in persons with DS. Our innovative exercise intervention and results will be explained next.
Exercise is a logical intervention for effective treatment of cognitive impairments in persons with DS because the positive influence of voluntary exercise on cognition has been demonstrated in other typical populations [18, 19], including children [20, 21] and older adults [22, 23]. Furthermore, voluntary exercise has been shown to improve memory in mice models (Ts65Dn) of DS [24]. However, a recent review of the therapeutic benefits of exercise in persons with DS found that exercise was nonsignificant in improving physical and mental health outcomes in persons with DS [25]. Because persons with DS move slowly [26] due to slower reaction times [27], deficits in muscular strength [28], and reduced cardiorespiratory capacity [29], adolescents with DS typically ‘do’ not exercise at a relatively high rate, ‘thus, they miss out on the opportunity to gain’ cognitive improvements through neuroplasticity in the brain. Furthermore, approximately 61% of persons with DS have been shown to have low exercise tolerance [30] which reduces their exercise time and intensity and which seems to limit the cognitive benefits of exercise for persons with DS [25]. The voluntary aspect of the exercise imposes major limitations in the quantity and quality of exercise in special populations such as those with DS.
There is an emerging body of literature in healthy older adults and individuals with Alzheimer’s disease indicating that exercise results in structural and functional changes in the brain [31]. These alterations in brain structure and function suggest that CNS function can be altered via voluntary exercise in individuals with relatively normal and abnormal patterns of activation within the motor cortex. However, because persons with DS have limited motor output due to physiological and psychosocial factors, their ability to induce changes in CNS function may be compromised when engaging in voluntary exercise performed at their preferred (i.e., low) rates. They may need to have exercise augmented through mechanical assistance as proposed in our assisted exercise paradigm, coined Assissted Cycling Therapy (ACT) in 2013 [32]. Assisted exercise is an approach initially used with animals which were exercised on a motorized treadmill at a rate greater than their voluntary exercise rate. Assisted exercise has demonstrated improvements in cognitive functioning in animals [33] and most recently in patients with Parkinson’s disease [34, 35]. ACT has been suggested to improve motor and cognitive function through its neuroprotective properties as demonstrated in Figure 1, a model proposed by Alberts and colleagues [34].
Proposed model for ACT (forced exercise) (Alberts et al., 2011).
ACT is an emerging exercise paradigm especially suited for clinical populations who have limited voluntary movement output, exercise capacity, or exercise motivation. During ACT the electric motor of the bicycle is engaged which helps to increase pedaling cadence to a predetermined rate. We have used absolute cadences of approximately 80 rpm or relative cadences of 35% greater than the initial voluntary pedaling rate. The initial pedaling rates, however, may need to be increased gradually for comfort and familiarization. The ACT condition often leads to reduced power compared to voluntary pedaling as indicated in Table 1 by the lower average power contribution of our participants in the ACT condition than the voluntary cycling condition. As can be seen in Figure 2, special procedures were utilized to ensure that the feet were not positioned too far forward and that they did not slip forward, side-to-side, or backward to ensure a high degree of safety at the high pedaling rates.
Pedal-foot interface.
The length of our intervention period was eight weeks with three cycling sessions per week. Before each cycling session, the resting heart rate (HR) was obtained while the participant was sitting on the bike. A five-minute warm up at a voluntary rate was completed before the 30-minute cycling session regardless of the condition (ACT or voluntary cycling (VC)). On the first day, the average cadence from the warm-up period was multiplied by 1.35 to determine the initial ACT cadence. This step was omitted in the voluntary cycling condition. Thus, the cadence on the first day of the ACT intervention was set at a rate which was 35% faster than the voluntary cadence. A three- to five-minute cool-down at the end of the ACT or voluntary cycling session was optional. During the cool-down, the motor was not engaged. The average HR (bpm), cadence (rpm), and power (Watts) was recorded every five minutes during the cycling session (refer to Table 1 for mean values). These averages did not include the warm-up period.
To monitor Rate of Perceived Exertion (RPE), we used a modified -point RPE scale. A rating of 2 or 3 on the -point RPE scale (1, easy/not-tired; 2, a little hard/a little tired; 3, hard/tired; 4, very hard/very tired) was desired to keep the exercise intensity at a moderate level. The goal was for participants to cycle between 64 and 76% of their age-predicted maximal HR (HRmax = 210–0.56×age–31, [36]) which corresponds with a moderate exercise intensity as dictated by the American College of Sports Medicine [37]. Thus, for most participants in the ACT condition, we increased cadence from session to session by 3–5 rpm, based on tolerance, up to the maximum cadence of the motor (e.g., 95 rpm) or until 64% of age-predicted HRmax or a personal tolerance limit was reached. Participants in the ACT group took on average 13.2 cycling sessions to reach this point. Participants in the voluntary cycling group were not encouraged to pedal faster as the goal was to have them exercise at their preferred voluntary rate (refer to Table 1 for cadence values).
For this randomized control trial, participants were randomly allocated to eight weeks of ACT, eight weeks of VC, or eight weeks of no cycling (NC). The ACT and VC conditions were described in the previous section. Participants in the NC group completed only the pre- and posttesting sessions separated by eight weeks and they were instructed not to change their usual physical activity habits and therapy regiments for the eight weeks. Inclusion criteria consisted of trisomy-21 and a chronological age of 9–26 years. Exclusion criteria consisted of other genetic conditions and neurological disorders (e.g., ADHD and autism), medical contraindications to exercise, and sensory or physical impairments which preclude completion of the cycling intervention. During the pretesting sessions (first visit to the laboratory), the participants’ height, weight, vision, hearing, and mental age were recorded or assessed. Mental age was determined with the Peabody Picture Vocabulary Test (4th ed.; [38]) (refer to Table 1 for chronological andmental age values). In addition, all participants had functional hearing and vision for the purpose of the testing procedures. Then, three executive function tests were administered in random order.
\n\t\t\t | \n\t\t\t\tACT (n = 18)\n\t\t\t | \n\t\t\t\n\t\t\t\tVC (n = 16)\n\t\t\t | \n\t\t\t\n\t\t\t\tNC (n = 14)\n\t\t\t | \n\t\t\t\n\t\t\t\tOne-way ANOVA p-value\n\t\t\t | \n\t\t||||
\n\t\t\t\tMean\n\t\t\t | \n\t\t\t\n\t\t\t\tSD\n\t\t\t | \n\t\t\t\n\t\t\t\tMean\n\t\t\t | \n\t\t\t\n\t\t\t\tSD\n\t\t\t | \n\t\t\t\n\t\t\t\tMean\n\t\t\t | \n\t\t\t\n\t\t\t\tSD\n\t\t\t | \n\t\t|||
Chronological age (years) | \n\t\t\t19.4 | \n\t\t\t4.9 | \n\t\t\t18.4 | \n\t\t\t3.4 | \n\t\t\t17.0 | \n\t\t\t4.0 | \n\t\t\t0.304 | \n\t\t|
Mental age (years) | \n\t\t\t\n\t\t\t | 6.1 | \n\t\t\t3.3 | \n\t\t\t5.2 | \n\t\t\t2.1 | \n\t\t\t6.0 | \n\t\t\t1.8 | \n\t\t\t0.687 | \n\t\t
BMI (kg/m2) | \n\t\t\t\n\t\t\t | 27.7 | \n\t\t\t7.0 | \n\t\t\t27.3 | \n\t\t\t4.2 | \n\t\t\t27.5 | \n\t\t\t9.5 | \n\t\t\t0.889 | \n\t\t
Cadence (rpm) | \n\t\t\t\n\t\t\t | 77.2 | \n\t\t\t2.2 | \n\t\t\t43.1 | \n\t\t\t8.9 | \n\t\t\t\n\t\t\t | \n\t\t\t | <0.001\'1\n\t\t\t | \n\t\t
Power (Watts) | \n\t\t\t\n\t\t\t | 22.1 | \n\t\t\t12.1 | \n\t\t\t26.6 | \n\t\t\t21.7 | \n\t\t\t\n\t\t\t | \n\t\t\t | 0.396\'1\n\t\t\t | \n\t\t
Heart rate (bpm) | \n\t\t\t\n\t\t\t | 98.7 | \n\t\t\t8.0 | \n\t\t\t100.7 | \n\t\t\t7.7 | \n\t\t\t\n\t\t\t | \n\t\t\t | 0.642\'1\n\t\t\t | \n\t\t
Descriptive statistics
1 Independent samples t-tests were used to test group differences.
The verbal fluency test consisted of four categories: animals, food and drinks, words that start with an S, and words that start with an F. The participants were given one minute per category and had to name as many words in the category as possible. The verbal fluency test was a test of verbal long term and working memory, attention, and inhibition [39, 40]. As mentioned, verbal fluency and other speech and language deficits are well documented in persons with DS [41–43]. Verbal fluency tests have been used as behavioral measures of hippocampal and prefrontal cortex function [40, 43].
A backward digit span test was administered as a behavioral measure of working memory, which requires the simultaneous storage and processing of information [6, 44]. It is considered a prefrontal function [6]. During the backward digit span test, participants had to reverse a sequence of numbers given by the investigator. The investigator was providing progressively longer sequences of numbers until the participant could no longer accurately articulate the given sequence in reverse order.
The Wisconsin Card Sorting test (modified for DS) measures set switching ability and working memory which are functions of the frontal cortex and parts of the parietal lobe [45, 46]. In this task, the participants are asked to match either shapes or colors with rule changes taking place during testing. Adolescents with DS have been found to have reduced capacity for set switching compared to typically developing adolescents [2]. These three executive function tests were repeated during posttesting.
Cohen’s d effect sizes are considered small to medium if they range from 0.2 to 0.5, medium to large if they range from 0.5 to 0.8, and large if they are greater than 0.8 [47]. The effect sizes for the verbal fluency composite score were d = 0.15 for ACT, d = 0.21 for VC, and d = 0.06 for NC. The verbal fluency of the ACT and VC groups improved more than the verbal fluency of the NC group. The effect sizes for the backward digit span test were d = 0.31 for the ACT group, d = 0.17 for the VC group, and d = 0.00 for the NC group. The ACT and VC groups both showed improved working memory while the improvement was greatest in the ACT group. The effect sizes for the Wisconsin Card Sorting test were d = -0.21 for the ACT group, d = 0.87 for the VC group, and d = 0.00 for the NC group. Only the VC group improved in their set-switching ability. See Figure 3 for a visual representation of the results.
Pre- to postchange scores (postscore–prescore) in the verbal fluency test (VF), backward digit memory span test, and the Wisconsin Card Sorting test (WISC) by group. Larger, more positive change scores reflect an improvement in cognitive function.
It is clear that cycling exercise, whether it is assisted or voluntary, is more beneficial to executive function than no exercise. However, ACT seems to be more effective in improving working memory, whereas VC seems to be more effective in improving verbal long-term memory, and set switching than ACT.
Based on our results, a moderate exercise intensity, of between 64 and 76% HRmax, may not be necessary for benefits as the average HR during ACT or VC cycling sessions was just below 64% of the average age-predicted HRmax. The average chronological age of our participants in the ACT and VC group was 19.4 years and 18.4 years, respectively. This translates to minimum average target HRs (64%) of 107.6 bpm and 108.0 bpm in the ACT and VC group, respectively. In addition, their average exercising target HR of 98.7 bpm and 100.7 bpm were below the target HR range. In fact, on the first day of cycling, only 11% of ACT and 31% VC participants reached 64% of their age-predicted maximal HR. The only difference between the ACT and VC groups was the cadence at which they were cycling. We can thus conclude that the specific adaptations in terms of executive function are due to the different rates of movement.
The greater movement frequency during ACT would presumably lead to more frequent stimulation of the Golgi tendon organs and muscle spindle fibers in the lower extremity musculature and associated tendons, which in turn translates to greater afferent input to the frontal motor cortex [34]. This greater stimulus frequency in turn seems to be necessary to maximize benefits to working memory but does not seem necessary to improve long-term memory recall, attention, or set-switching ability. As can be seen in Table 1, heart rate s, and therefore cardiovascular workloads, were similar between ACT and VC, the only plausible explanation that remains for these group differences is the voluntary movement output during VC. The voluntary activation of certain areas of the motor cortex may thus be unique to voluntary exercise or greater in magnitude than the afferent stimulation resulting from ACT and thus benefit the frontal cortex in specific ways.
Differences in performance among executive function tasks, as observed in this study, have been documented [48]. Our results also suggest that different executive functions (e.g., working memory, attention, inhibition, and set-switching), though all mediated by the frontal cortex, may differentially benefit from different modes of exercise.
Our future research is to investigate whether exercise can prevent or slow the progression of Alzheimer’s disease (AD) in persons with DS. AD is a serious dysfunction of global cognitive control, and adults with DS are at three to five times the risk of early onset of AD compared to the general population [5, 49]. AD is a neurocognitive degenerative disease that causes a loss of memory, thinking, and functioning abilities. It is known that as the lifespan of adults with DS increases, the prevalence of AD will rise concomitantly. However, there is not much research in this area [50]. Research in this field can be aimed toward the development of an intervention, as well as prevention of AD in patients with DS. In order to reach that status, there must be more involvement of the DS population into clinical trials so that sufficient data can be collected and analyzed.
In addition, brain imaging is an important tool and is used in both clinical diagnosis and in the research of AD in persons with DS. In this research, many different types of imaging techniques are used to determine participant eligibility as well as to test if the imaging can accurately predict the participant’s risk of developing AD. These imaging techniques include, but are not limited to, amyloid PET scans, functional MRIs, structural MRIs, and CTs [51]. Each type of imaging will provide a view into the different mechanisms leading to the development of AD. Amyloid PET scans are a type of positron emission tomography in which amyloid plaque is targeted with radioactive tracers to be seen on the image. It is thought that an abnormal amount of these plaques predisposes the participant to developing AD. While this type of imaging is futile in the clinical setting, researchers can choose to look for participants who have this abnormal amount of amyloid plaque but show no symptoms, so that an intervention can be created for those individuals. MRIs and CTs have more impact in the clinical setting because they focus on the structure of the brain. Research has shown that abnormalities in the hippocampus as well as general atrophy of the brain can lead to dementia. Physicians are able to use these imaging tools in order to help determine the etiology of dementia. These types of imaging techniques can also be used in patients with DS in a similar manner. They can help to predict which patients are at a higher risk than others so that an intervention can be put in place earlier.
Physical activity of any kind will most likely improve cognitive functioning in persons with DS. Often physical activity that the person enjoys (e.g., dancing, gardening, and walking dog) are the types of programs that are sustained. Only 10–12% of people with DS learn to ride a bicycle and very few learn before 10 years old [52]. However, cycling does allow for fast rates of lower limb movement which is crucial because the increased movement rate may trigger the endogenous release of neurotrophic factors that facilitate the motoric and neural changes that underlie improved motor and cognitive function. To date, our stationary research bicycle is not for sale. One clinical research trial with Parkinson’s patients used a tandem bicycle in which the front rider is typical and the back rider had Parkinson’s disease. Because the pedals were yoked, the rear rider was pedaling at the same rate as the front rider. However, this may take a lot of practice to perform safely. An alternative recommendation is to be conscious of when you are physically active to try to increase the rate of movement whenever possible. For practical purposes, measure the rate of movement by counting the number of revolutions during a set time period (e.g., 10 s.) and extrapolating to one minute. Heart rate could be palpated or measured by a simple Polar HR monitor. In addition, Rate of Perceived Exertion should be monitored every five minutes especially in special populations such as DS where heart rate responses are different from those of the general population due to chronotropic incompetence [53]. The main goal is to have fun and be safe, and remember if you move faster, you may think faster!
Climate change is the disruption in the long-term seasonal weather patterns caused by global warming. How will long-term climate change affect businesses and the financial system, and how should impacts be managed over the course of the twenty-first century? These are some of the questions that have gained unprecedented attention in public discourse as global warming projections for the coming decades get worse.
\nClimate change exacerbates existing risks and creates new risks for natural and human systems [1]. The World Economic Forum’s Global Risk Report specifies that three of the five topmost likely global risks are related to climate change. Specifically, it ranks failure of climate change mitigation and adaptation as the one most likely to impact on global risk [2]. The adverse effects of climate change are pervasive and systemic, affecting all asset classes, industries, and economies, and in turn, the financial system.
\nThe bankruptcy of California’s largest electric utility, Pacific Gas and Electric (PG and E), dubbed the first climate change bankruptcy [3], demonstrates the possible disruptions of production and consumption, and reduction in future asset values from impacts of climate change [4]. Notably, Mark Carney, the former governor of the Bank of England, has linked climate-related risks to financial stability. He noted that the combination of the weight of scientific evidence and the dynamics of the financial system suggest that in the fullness of time, climate change will threaten financial stability and longer-term prosperity [5].
\nFrom the foregoing that climate change has developed to one of the greatest global challenges, it is imperative to examine the climate change science and uncertainties associated with climate change, while identifying and explaining climate-related risks, the financial aspect of climate change, credit implications of climate change, integration of climate-related risks into credit risk assessment, and climate risk management.
\nThe main aim of the chapter is to enumerate the channels through which climate change can cause credit risks and affect the stability of the financial system. Approaches to incorporate climate change into corporate risk management are also discussed. The chapter employs a systematic literature review approach to explore the relationship between the three notions of climate-related risks, credit risk, and financial stability toward achieving its objectives.
\nThe rest of the chapter is divided into seven parts. Section 2 discusses the science and uncertainties involved in climate change. While various forms of climate-related risks are presented in Section 3, Section 4 enumerates their credit risk implications. How to integrate climate-related risks into credit risk assessment is the focus of Section 5. Sections 6 and 7 explore how climate change can negatively impact financial stability and how organizations could manage climate-related risks, respectively. Section 8 presents the findings and makes suggestions for further research.
\nSince Arrhenius [6] established and quantified the contribution of carbon dioxide (CO2) to climate change, the consensus among publishing scientists, international agencies, and leading scientific societies in climate science is that the increase in the earth’s temperature we are currently witnessing is anthropogenic, that is man-made [1, 7, 8] caused by the release of greenhouse gases (GHGs) into the atmosphere. The most prevalent of these GHGs is carbon dioxide (CO2), associated with burning fossil fuels, industrial processes, forestry, and other land uses, but other gases—such as methane (CH4) and nitrous oxide (N2O)—are also contributing [9].
\nThe decay rate of GHG in the atmosphere alters as the average temperature level increases. There has been a striking rise in temperatures over the last decade as the level of CO2 in the atmosphere has skyrocketed. Global temperatures have been far higher in the past decade compared with their 100-year average, in tandem with an unprecedented rise in CO2 in the atmosphere as shown in Figure 1.
\nAtmospheric carbon dioxide and Earth’s surface temperature (1880–2019) [10]. Yearly temperature compared to the twentieth-century average (red and blue bars) from 1880 to 2019, based on the data from National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NOAA NCEI), plus atmospheric carbon dioxide concentrations (gray line): 1880–1958 from Institute for Atmospheric and Climate Science (IAC), 1959–2019 from NOAA Earth System Research Laboratories. Original graph by Dr. Howard diamond NOAA Air Resources Laboratory, and adapted by NOAA Climate.gov.
Scientific advances that allow long-dated horizons suggest that irrevocable temperature increases have already been locked in (see Figure 2). Moreover, the current trends are on track to lead to systemic disruptions to ecosystems, societies, and economies [12] and may be catastrophic and irreversible for human populations, according to more than 11,000 scientists [13].
\nClimate risk scenarios: Projections of carbon emissions and global warming (emissions of CO2 in gigatons per year) [11].
While the future is always unknown, we speak of risk if the probability distribution of possible future outcomes is known and of uncertainty if it is not. Human-induced climate change, its impacts, mitigation, and adaptation are fraught with uncertainty. The future pathways for GHG emissions and temperatures set out by climate scientists embody both risk and uncertainty.
\nThe uncertainties involved in climate change preclude prediction of the precise nature, timing, frequency, intensity, and location of climate change impacts. These uncertainties also depend on a multitude of demographic and socioeconomic factors, such as technology, values and preferences, and policies, which are also deeply uncertain [14]. Added to these demographic and socioeconomic sources of uncertainty is scientific uncertainty which arises from our incomplete knowledge of the climate system [15].
\nDue to these interacting sources of uncertainty, studies of climate change and its impacts rarely yield consensus on the distribution of exposure, vulnerability, or possible outcomes. Thus, in contrast to risk situations where the probability distributions are known, there are no well-defined probability density functions (which are among the most common tools for characterizing uncertainty) for climate change [16].
\nClimate uncertainty leads to imprecision in estimating climate and economic outcomes. This implies not only imperfect understanding of the ability of mitigation pathways to deliver temperature outcomes but also suggests that there is a significant possibility that the tails of the distribution are considerably fatter than currently estimated. Fat-tailed climate events could not only significantly damage growth and welfare, but economic mechanisms may also be ineffective in responding appropriately. This could result in structural economic changes, and banks may find themselves facing abrupt adjustment which could be severely financially disruptive [17].
\nClimate-related risks are mainly divided into two basic sets: physical risks from more frequent and severe meteorological and hydrological events, and transition risks from the process of decarbonization that is aimed at mitigating global warming.
\nThe physical climate-related risks represent the economic costs and financial losses due to increasing frequency and severity of climate-related weather events (e.g., storms, floods, or heat waves) and the effects of long-term changes in climate patterns (e.g., ocean acidification, rising sea levels, or changes in precipitation), resulting from continuously growing GHG emissions [18, 19].
\nPhysical risks can affect both the supply and demand sides of the economy. On the supply side, natural disasters can disrupt business activity and trade and destroy infrastructure, diverting capital from technology and innovation to reconstruction and replacement [20]. It affects output levels and output growth by impacting labor productivity, speeding up the depreciation of capital stock, increasing cost of repair and replacement, and reducing funds allocated to research and innovation [21]. On the demand side, increasing expenditures for repair and replacement will, ceteris paribus, reduce investment on and consumption demand for other goods. Business investment could also be dampened by uncertainty about future demand and growth prospects and substantial price impacts [22]. Households confronted with more frequent extreme weather events might increase precautionary saving, which would depress private consumption in general [21].
\nTransition risks arise as a result of the shift to a low-carbon economy (such as changes in public regulation, technology, or in households’ or investors’ preferences) triggering changes in demand-related factors. This adjustment process is likely to have a significant impact on the economy and, in particular, on some financial asset values [11].
\nTransition risks are characterized by a radical uncertainty on the nature of the low-carbon pathway (i.e., the pathway for reducing greenhouse gas emissions, which restructures the economy) and a more usual uncertainty on the methods for implementing this pathway in economic and social terms [23].
\nOver the last few years, the topic of stranded assets, caused by risk factors like physical climate change impacts, as well as societal and regulatory responses to climate change, has loomed larger [24]. Stranded assets are defined as assets that have suffered from unanticipated or premature write-downs, devaluations, or conversion to liabilities [25]. With transition toward a lower-carbon economy, carbon assets are expected to suffer from unanticipated or premature write-offs, downward revaluations, or get converted to liabilities [26].
\nEstimation by McGlade and Ekins [27] shows that approximately one third of the current oil reserves, half the gas reserves, and almost 90% of the coal reserves would become stranded assets if global temperature target of the Paris Agreement is attained. While an early and smooth transition results in much fewer risks, too rapid an adjustment of asset prices due to a late transition might eventually bring about a climate Minsky moment—a sudden drop in assets prices [21].
\nPhysical risk and transition risks are correlated, because the more transition policies enter into force, the fewer physical risks are likely to materialize. On the other hand, the harder the economy is hit by physical risks, the stronger the demand will be for effective transition measures [21].
\nMaterializing physical and potentially also transition risks will drive up liability risks [21]. Liability risks materialize when organizations are directly or indirectly adjudged legally responsible for climate-related losses and must financially compensate other parties [28, 29]. Organizations are also prone to increasing liability risk if they do not manage transition risks well as enshrined in the polluter pays principle. Organizations whose activities are negatively affected by unmitigated climate change could seek compensation from those who had caused or allowed the damage and thereby at least partially internalize the negative externalities [21].
\nCredit risk is the risk of a financial loss resulting from a borrower’s failure to repay part of or all the interests and the principal of a loan. Climate-related risks affect all three dimensions of credit risk—a borrower’s capacity to generate enough income to service and repay its debt as well as the capital and collateral that back the loan [30].
\nFor financial institutions, credit risks can materialize directly, through their exposures to corporations, households, and countries that experience climate shocks, or indirectly, through the effects of climate change on the wider economy and feedback effects within the financial system. Exposures manifest themselves through increased default risk of loan portfolios or lower values of assets [31].
\nCorporate credit portfolios are also at risk, as highlighted by the PG&E’s bankruptcy. Increase in extreme and severe weather events could have second-round effects on the price of corporate bonds, and the rise in debt defaults would induce climate-related financial instability which would adversely affect credit expansion and magnify the negative impact of climate change on financial activity [19].
\nTransition risks materialize on the asset side of financial institutions, which could incur losses on exposure to firms with business models not built around the economics of low-carbon emissions [31]. Climate change mitigation policies to reduce GHG emissions can create costs for carbon-intensive sectors and companies, thereby influencing the credit quality of GHG-intensive borrowers and also credit risks to banks [32]. Ongoing developments in the international climate policy arena show there will be more rigorous future global climate policy regime. Noncompliance with mitigation policies might become reputational risks and therefore credit risks. Hence, both compliance and noncompliance with the mitigation policies will have implications for loan providers, equity investors, and project financiers [32].
\nLiterature establishing the link between climate change and credit risk is growing. Kleimeier and Viehs [33] show a significant and negative relation between CO2 emission levels and the cost of bank loans. Delis, De Greif, and Ongena [34] observe that banks appeared to start pricing climate policy risk after the Paris Climate Agreement, while Ginglinger and Quentin [35] find that greater climate risk leads to lower leverage in the post-2015 period.
\nCapasso et al. [36] investigated the relationship between exposure to climate change and firm credit risk and found that the exposure to climate risks affects the creditworthiness of loans and bonds issued by corporates. Similarly, Delis et al. [34] demonstrated that climate policy risk is priced in syndicated loans, especially in sectors related to fossil fuel. Jung et al. [37] provided evidence of the existence of a positive association between the cost of debt and carbon-related risks for firms. Rajhi and Albuquerque [38] submitted that natural disasters are predictive of higher nonperforming loans and higher likelihood of default in developing countries. Battiston et al. [39] found that while direct exposures to the fossil fuel sector are small, the combined exposures to climate policy-relevant sectors are large, heterogeneous, and amplified by large indirect exposures via financial counterparties. Ilhan et al. [40] showed for a sample of S&P 500 companies that higher emissions increase downside risk—the potential losses that may occur if a particular investment position is taken. Monasterolo and De Angelis [41] indicated that investors require higher risk premia for carbon-intensive industries’ equity.
\nThere are two approaches for integrating climate-related risks into credit risk assessments. On the one hand, there is a risk approach whose objective is to integrate a new source of risk in order to accurately measure credit risk and assumes that a risk differential between green and brown assets exists; on the other hand, there is an economic policy approach, aiming to foster the transition to a low-carbon economy by shifting credit from brown to green activities [42].
\nUnder the risk approach, the risk-weight factor is recalibrated for all categories of assets to identify the differential due to climate-related risks. The differential should be taken into consideration when determining pricing and capital requirements. When the objective is to adjust capital requirements as an economic policy tool to allocate credit to specific sectors, the accurate level of climate-related risks is not a central concern anymore. This approach rather focuses on channeling credits to facilitate the transition toward a low-carbon economy. The objective is to foster transition by introducing a financial incentive through the capital adequacy regulation without following a risk reasoning [42].
\nClimate-related risks are expected to be included in all relevant stages of the credit-granting process and credit processing. Specifically, institutions are expected to form an opinion on how climate-related risks affect the borrower’s default risk. The climate factors that are material to the borrower’s default risk of the exposure are expected to be identified and assessed. As part of this assessment, institutions may take into consideration the quality of the clients’ own management of climate-related risks. They are also to give appropriate consideration to changes in the risk profile of sectors and geographies driven by climate-related risks [28].
\nIn quantifying, evaluating, and factoring climate-related risks into credit risk assessment, institutions require risk indicators or ratings for their counterparties that take into account climate-related and environmental risks. This is achieved by identifying borrowers that may be exposed, directly or indirectly, to increased climate-related risks. Critical exposures to such risks should be highlighted and, where applicable, considered under various scenarios with the aim of ensuring the ability to assess and introduce in a timely manner any appropriate risk mitigation measures including pricing [28].
\nCounterparty credit scoring requires detailed sectoral and geographic metrics to interpret climate-related risks as a view of financial vulnerability, taking into account mitigation measures. The resulting risk score can be used to inform credit decisions and to create a portfolio overview. The score can also be embedded in internal and external climate-related risk reporting (Table 1) [43].
\n1. Defining climate scenarios | \n2. Estimating economic and financial impacts | \n3. Translating financial impacts into credit risk measures | \n
---|---|---|
“The estimation of the impact of climate change and of the transition to a low-carbon economy on credit risk relies first on the definition of physical scenarios for climate change and for the transition. These scenarios define how climate change will impact the variables that are relevant for economic activities, how a transition will mitigate these impacts and which measures are taken to steer the transition.” | \n“Once the impact of climate change on the variables relevant for economic activities has been estimated, its consequences must be translated into economic terms though macro and microeconomic simulations. This step basically assesses the direct and indirect repercussions of climate change and the transition to a low-carbon economy in economic terms and identifies which actors are affected by them and by how much. Once the economic effects on actors have been identified, the next step is to estimate the impact of these effects on both their cash flows and their balance sheets.” | \n“Based on this assessment of financial impacts on firms and households, the next step is to compute how changes in cash flows and balance sheets will affect their credit worthiness in terms of probability of default and loss given default – and thus also in their credit ratings.” | \n
Steps for integrating physical climate risk into credit risk assessment processes [44].
For better integration of climate-related risks into credit risk assessment, Monnin [30] advocates addressing the limitations of historical data; expanding the horizon of credit risk models; finding the right level of data granularity; identifying the relevant climate-related risk exposure metrics; and translating economic impact into financial risk metrics.
\nEstimates of the aggregate economic impacts of climate change and the costs of mitigation both vary widely and are highly dependent on factors such as core assumptions, model design, sectoral coverage, and scenario selection [45]. On the one hand, available estimates suggest that physical damage from climate change could reach one tenth, or even one fifth, of global GDP by the end of this century, with considerable uncertainties around amplifying dynamics. In terms of current global output, this would amount to USD 8–17 trillion. On the other hand, some estimates suggest the transition to a low-carbon economy will require investment of between USD 1 trillion and USD 4 trillion in constant terms when considering the energy sector alone, or up to USD 20 trillion when looking at the economy more broadly [11].
\nDietz et al. [46] employed standard integrated assessment model (IAM) and the climate value-at-risk (VAR) framework to quantitatively investigate the physical impact of climate change on the financial system. They found that without mitigation efforts, physical risks related to climate change could lead to a loss of USD 2.5–24.2 trillion of the value of global financial assets. For the transition risks, estimate by Mercure et al. [47] put the discounted global wealth losses from stranded fossil fuel assets may amount from USD 1–4 trillion.
\nPhysical impacts of climate change as well as the transition toward a resilient low-carbon economy pose significant challenges for macro-financial management, as they can damage the balance sheets of governments, households, firms, and financial institutions due to the adverse and possibly abrupt impacts on investment and economic growth, fiscal revenue and expenditure, debt sustainability, and the valuation of financial assets. In turn, macro-financial risks translate into weakened resilience to physical climate risks and constrained capacity for climate adaptation and mitigation efforts. Transition risks are particularly high for countries that generate a significant share of public revenue from carbon-intensive industries. Lower-income and conflict-affected oil and gas exporters (mostly in Africa and the Middle East) are more vulnerable and less able to manage a low-carbon transition. They have not yet converted hydrocarbon rents into other sources of export revenues needed to grow and diversify [48].
\nFor banks, climate-related risk factors manifest as increasing credit, market, and operational risks [49]. Climate-related financial risks may weaken financial sector balance sheets and induce or amplify macro-financial risks, particularly in the case of shocks. Such shocks could stem from disasters or sudden changes in policy, technology, or consumer preferences. The resulting financial sector losses and volatility in financial and commodity markets can adversely impact funding, liquidity, and lending conditions and weaken financial sector balance sheets, giving rise to negative feedback loops with macro-fiscal implications. Emerging markets and developing economies may be particularly affected, given that their financial markets are less resilient to such shocks [48]. By destroying the capital of firms and reducing their profitability and liquidity, climate change is likely to increase the rate of default of corporate loans that could harm the stability of the banking system [19]. A recent survey by the Bank of England on the preparedness of UK banks for climate change found their planning horizons averaged 4 years, likely too short even to account for likely physical and liability risks [50].
\nClimate change also has implications for insurance companies on both sides of the balance sheet: as investors and as underwriters. As institutional investors, insurance companies face largely similar transition and physical risks as other asset managers. They are disproportionally affected due to the long-term nature of their equity and infrastructure investments. As underwriters, pricing risks may arise from changing risk profiles to insured assets [48]. According to Lloyd’s of London, damages from weather-related losses around the world have increased from an annual average of USD 50 billion in the 1980s to close to USD 200 billion in the past 10 years [51].
\nInstitutional investors will be disproportionately affected by climate change, given their much longer-term investment horizons. In addition to the climate-related risks that affect financial stability, second-tier impacts from climate change (such as food security, social, and political unrest, and biodiversity loss) are likely to be nonlinear, characterized by tipping points, and material over the long term [48]. The Economist Intelligence Unit (EIU) [52] put the value of global stock of manageable assets at risk from climate change till the end of the century at USD 4.2–43 trillion (in 2015 value terms).
\nClimate change qualifies as a systemic event. A systemic event is defined in economics by three essential elements: a shock, which can be a broad shock simultaneously affecting a wide range of institutions, or a limited shock followed by an important domino effect; contagion effects through a web of interrelations; and the endogenous nature of this shock, meaning that it is caused by cumulated disequilibria over time [53].
\nThree fundamental reasons at least can justify this transposition of the financial concept of systemic risk to climate change. First, climate change impacts are systemic in nature. They affect the whole planet, in most of its dimensions. They have the ability to profoundly change the earth system as we currently know it. The second is the radical uncertainty. Historical data provide no useful guidance to future climate events or/and outcomes. Finally, the notion of a climate systemic risk at world scale provides a powerful new rationale in the debate on international monetary reform [53].
\nClimate and financial fragilities reinforce each other. They are intertwined into positive feedback loops so that climate systemic risks also incur financial systemic risks. Financial fragility to external risks may increase climate fragility through negative externality effects. Conversely, climate fragility incurs new risks that may reinforce financial fragility, as Figure 3 illustrates. The realization of a climate systemic risk translates into potential financial turmoil and this in turn can increase around the provision of the ultimate liquidity [53].
\nRelationship between climate and financial fragilities [53].
Physical and transition risk drivers impact economic activities, which in turn impact the financial system. This impact can occur directly through, for example, lower corporate profitability or the devaluation of assets, or indirectly, through macro-financial changes [28]. Climate-related risks—in particular, transition risks—are actually closer to being in a state of uncertainty. Further uncertainty is created by the highly interconnected nature of the modern financial system. Interlinkages among financial institutions—both banks and nonbanks—can amplify both positive and negative shocks and significantly decrease the accuracy of default probabilities [54].
\nPhysical risks can cause economic costs and financial losses across different financial portfolios (e.g., loans, equities, and bonds) and also affect the expectation of future losses, which can threaten the solvency of households, businesses, and governments and therefore financial institutions [18]. The exposure of financial institutions to physical risks can trigger contagion and asset devaluations propagating throughout the financial system [18]. Rapid and ambitious transition to low-carbon economy will lead to transition risks with large fractions of proven reserves of fossil fuel becoming stranded assets, with potentially systemic consequences for the financial system. Other fossil fuel-dependent sectors will probably be impacted indirectly as a consequence [55]. The size of the impact depends on the assumptions made about when and how the transition happens and which sectors it affects. The risk is that a sharp reassessment of climate change risks could lead to a financial market reassessment, leading to a spiral of persistent tightening of financial conditions as losses ensue [18].
\nA wholesale reassessment of prospects, as climate-related risks are reevaluated, could destabilize markets, spark a pro-cyclical crystallization of losses, and lead to a persistent tightening of financial conditions: a climate Minsky moment—involving a rapid, system-wide (downward) repricing of carbon assets which would threaten financial stability [56].
\nThe economic effects of bank losses and asset price deflation can exacerbate climate-induced financial instability [57]. This calls for a collective prudential approach—monetary policies and banking regulations, which intend to act on eliminating possible future outcomes more than on internalizing externality, because of radical uncertainty [53].
\nRisk management has increasingly become a well-established tool for climate change adaptation, given the significant uncertainty about future impacts and the inability to rely on historic data as a basis for current action [58, 59]. Risk management is part of a comprehensive suite of tools for climate change adaptation, with international and national standards being developed to assist governments, businesses, and communities [59].
\nISO 14090:2019: Adaptation to climate change—Principles, requirements and guidelines specifies the principles, requirements, and guidelines for adaptation to climate change. These include the integration of adaptation within or across organizations, understanding impacts and uncertainties, and how these can be used to inform decisions. ISO 14091:2019 provides guidance for assessing the risks related to the potential impacts of climate change. It describes how to understand vulnerability and how to develop and implement a sound risk assessment in the context of climate change. It can be used for assessing both present and future climate change risks.
\nAs part of their overall internal control framework, organizations should have an institution-wide risk management framework that extends across all business lines and internal units, including internal control functions [28]. The risk management framework should encompass financial and nonfinancial risks, on-balance-sheet risks, and off-balance-sheet risks, including risks that the institution is currently exposed to and for risks that the institution may be exposed to going forward [28].
\nEceiza et al. [43] enumerate five principles of climate-related risk management to include formulation of climate-related risk governance, to ensure the board focuses on the risks and for climate-risk management, and to cascade throughout the organization; tailor strategic plans and business models toward embedding climate-related risks in risk frameworks and capital allocation processes; inject climate-related risk considerations into all risk-management processes to align climate-related risk exposure with risk appetite; periodic scenario analysis and stress tests to assess the organization’s resilience; and focus on enablers and build capacity, including technology, data, and talent to manage climate-related risks.
\nA strategic approach to managing the financial risks from climate change developed by the Prudential Regulation Authority (PRA) (Bank of England) entails governance, risk management, scenario analysis, and disclosure. A firm’s board should understand and assess the financial risks from climate change that affect the firm and should be able to address and oversee these risks within the firm’s overall business strategy and risk appetite. Firms are expected to employ their existing business strategy and risk management frameworks to address financial risks from climate change, in line with their board-approved risk appetite. Firms should identify, measure, monitor, manage, and report on their exposure to these risks to their stakeholders. Material exposures should be included in their Internal Capital Adequacy Assessment Process (ICAAP) or Own Risk and Solvency Assessment (ORSA). Where appropriate, the PRA expects firms to consider a range of quantitative and qualitative tools and metrics to monitor their exposure to financial risks from climate change. Firms should provide evidence of how material risks will be mitigated and have credible plans or policies to manage these exposures. The management information should enable the board to discuss, challenge, and take decisions relating to the firm’s management of the financial risks from climate change. Scenario analysis should also be used to explore the resilience and vulnerabilities of a firm’s business model to a range of outcomes, based on different transition paths to a low-carbon economy, as well as a path where no transition occurs. Disclosures should be as insightful as possible, reflecting the firm’s evolving understanding of the financial risks from climate change (Figure 4) [60].
\nElements of climate-related risk management [60].
Similar good practices of governance and climate-related risk management recommended by the French Prudential Supervision and Resolution Authority (Banque de France) [61] include the following: integration of climate-related risks, including their implementation and monitoring, into the strategy of institutions; the internal organization of institutions, both in terms of the allocation of responsibility over all business lines, and in terms of the structuring of climate risk control, should align with the institutions’ strategic orientations; full integration of the material risks induced by climate change into the risk appetite framework of the institution, and also mobilization of appropriate tools to allow for a thorough assessment of these risks; and disclosure of the institution’s strategy and its risk management mechanisms with regard to climate change to ensure both a better understanding and a better integration of climate-related risks.
\nClimate change is rapidly proceeding, and climate-related risks are being exacerbated. While the mechanisms of physical climate change and the possible impacts are scientifically well understood, the specific estimates of these impacts are associated with uncertainty.
\nClimate change will affect all sectors of the economy, and it is relevant to investors and financial institutions, posing an unprecedented challenge to the governance of global socioeconomic and financial systems. Climate-related risks touch on the interests of a broad range of stakeholders across the private and public sectors, impact all the key dimensions of credit risk, and are the main channels through which climate change can affect financial stability.
\nThis chapter establishes the need for organizations to have a holistic, well-documented, and institution-wide risk management framework that extends across all business lines and internal units to manage their climate-related risks.
\nIn all, the chapter provides a preliminary view on how climate change can cause credit risk and financial instability. As such, the chapter does not comprehensively address the complex tasks of managing climate-related risks in organizations. A more comprehensive study is required on what strategies and approaches are needed to manage uncertainties and risks that are an integral part of climate change in organizations.
\nIntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
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\\n\\n1. RETRACTIONS
\\n\\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\\n\\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\\n\\nPublishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n1.2. REMOVALS AND CANCELLATIONS
\\n\\n2. STATEMENTS OF CONCERN
\\n\\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n3. CORRECTIONS
\\n\\nA Correction will be issued by the Academic Editor when:
\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\\n\\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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