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Hypothetical risk attribution framework. The relative contribution of the different risk terms are shown over time, resulting in an overall risk trend. The width and fuzziness of the lines indicate the degree of uncertainty for the respective risk term. Blue indicates climatic risk drivers (probability of occurrence and intensity of an extreme weather event), whereas red represents social and economic factors. Intensity is not shown in a separate graph as it is related to probability of occurrence. Yellow shading shows the integration of blue- and red-coloured drivers, with risk being a function of all terms multiplied (as indicated by 'X').
Source publication
If research on attribution of extreme weather events is to inform
emerging climate change policies, it needs to diagnose all of the
components of risk.
Contexts in source publication
Context 1
... move forwards we propose framing the attribution problem with a more integrated risk concept. Risk is defined as a function of the probability of occurrence of an extreme weather event and the associated consequences, with consequences being a function of the intensity of the physical weather event, the exposed assets and their vulnerabilities (Fig. 1). The intensity of the event can be expressed, for instance, as wind velocity or flooding height. Exposed asset values are typically monetary values of buildings or infrastructure, but can also include assets not valued in monetary terms, such as loss of lives. Vulnerability may reflect the physical resistance of structures to flooding, ...
Context 2
... may reflect the physical resistance of structures to flooding, but also the degree of preparedness of people or the capacity to recover from disasters. Figure 1 illustrates that all drivers of risk -and thus risk itself -are essentially dynamic. The analysis of the relative contribution of drivers requires high-quality records through time, but limited data availability and quality adds uncertainty to the detection of change and the attribution process. ...
Citations
... Freight transport systems are facing ever-growing demand and increasingly stringent requirements as a billion of tons of cargo are transported globally. To mitigate air emissions from freight transportation, policymakers should frame the policies considering the worldwide demand of energy [4][5][6].Concern has been raised in international debates under the United Nations Framework Convention on Climate Change (UNFCCC) regarding who is responsible for loss and damage caused by climate change-related issues, as well as who is eligible to receive compensation [7]. Greenhouse gas (GHG) emissions from this sector primarily involve fossil fuels emitted by road, rail, air, and marine transportation. ...
Greenhouse gas (GHG) emissions through freight transportation have received growing concerns and are one of the critical issues for the United Nation's Sustainability Development Goals (SDGs). The literature survey reveals that freight transportation makes up global GHG emissions and its carbon emissions may double by 2050. In European Union (EU) carbon taxes are a promising way to reduce CO2Carbon di oxide and other GHG emissions. The European Energy Exchange (EEX) recently launched its new Zero Carbon Freight Index (ZCFI). EEX-ZCFI provides the first insight into how much the price of carbon will add to freight costs. This work developed the analysis of a ZCFI using a statistical model (i.e. GARCH and FBM) with the intention of offering a reference for understating the wide-range ramifications of such indices. Preprocessing methods (descriptive statistics, unit root test, and an ARCH effect test) are performed to verify the validity of the GARCH (1,1) model for forecasting volatility. This work employs the EEX-ZCFI time series for January 2020 to August 2022. To further examine the carbon freight indices, a Ljung-Box test method based on the GARCH model was applied. The bootstrapped returns are forming a linear relation with the forecast data; therefore, it concluded that the model designed strongly fits with the time series. With GARCH optimal model parameters we have forecasted the carbon freight index time series data and hypothetically examined the influence on the carbon emission with C5TC time series, which can also be applied in the Asia-pacific region.
... Urban flood risk is a growing concern (Addison- Atkinson et al., 2022;Chen et al., 2015;Doocy et al., 2013) given the high urbanization rate (Birkmann et al., 2016;Chen et al., 2022;Gross, 2016) and the intense anticipated rainfall events due to climate change (Hettiarachchi et al., 2018;Pfahl et al., 2017;Sanderson et al., 2019). The flood risk mapping of an urban area remains a challenging task due to the variability in the direct and indirect flood impacts (Kreibich et al., 2014) and in the flood vulnerability (Chen et al., 2019;Huggel et al., 2013;Lv et al., 2022) associated with various socioeconomic contexts in different parts of a city, as well as due to intricate urban layouts that induce complex flow patterns influencing the flood hazard (Leandro et al., 2016;Li et al., 2021a;Lin et al., 2021). ...
The multiple flow paths existing in urban environments lead to complex flow fields during urban flooding. Modelling these flow processes with three-dimensional numerical models may be scientifically sound; however, such numerical models are computationally demanding. To ascertain whether urban floods can be modelled with faster tools, this study investigated for the first time the capacity of the 2D shallow water equations (SWE) in modelling the flow patterns within and around urban blocks with openings, i.e., involving flow exchanges between the flows in the streets and within the urban blocks (e.g., through alleys leading to courtyards or through broken windows or doors). Laboratory experiments of idealized urban floods were simulated with two academic 2D SWE models, with their most notable difference being the parameterization of the eddy viscosity. Specifically, the first model had a zero-order turbulence closure while the second model had a second-order depth-averaged k-ε turbulence closure. Thirteen urban layouts were considered with steady flow and five with unsteady flow. Both models simulated the flow depths accurately for the steady cases. The discharge distribution in the streets and the flow velocities were predicted with lower accuracy, particularly in layouts with large open spaces. The average deviation of the modelled discharge distribution at the outlets was 2.5% and 7.3% for the first and second model, respectively. For the unsteady cases, only the first model was tested. It predicted well the velocity pattern during the falling limb of a flood wave, while it did not reproduce all recirculation zones in the rising limb. The peak flow depths in the streets and the peak discharges at the outlets were predicted with an average deviation of 6.7% and 8.6%, respectively. Even though some aspects of the flow in an urban setup are 3D, the findings of this study support the modelling of such processes with 2D SWE models.
... There are measures for the reallocation of adaptation resources to develop adaptive capacity and Carlson, 2021). In terms of climate change impacts, this is often linked to the question of attribution, i.e., whether a particular event can be attributed to climate change (Huggel et al., 2013). This level of discussion is rarely seen in adaptation strategy and planning documents (Juhola, 2019) but may become more prevalent in the future (Thompson and Otto, 2015). ...
Considerations of justice with regards to climate change adaptation are increasingly called for in the academic literature, but little attention has been paid to the dimensions of justice regarding the development of adaptation policy and instruments used. Thus, there is a gap when it comes to connecting the dimensions of justice to different types of adaptation strategies and plans. Here, we synthesise the findings of previous studies to create an adaptation justice index for the four dimensions of climate justice in the context of adaptation: recognitional, distributive, procedural and restorative justice. This index can be used ex ante to analyse and compare climate adaptation strategies and plans in different societal contexts as well as at different levels of governance, and we illustrate this by analysing four national and four city-level strategies. As adaptation planning is still a relatively new area of climate governance, the results offer potential for justice informed evaluation of adaptation plans and strategies.
... An increase in weather and climate extremes has also been observed since about 1950 due to anthropogenic climate change (IPCC 2012(IPCC , 2021. This is often equated with the growing impact of climatic disasters (Huggel et al. 2013;Bouwer 2019;IPCCB, 2021). However, the detection and attribution of the spatial and temporal trend of climatic disaster impacts remain elusive. ...
... Loss normalization is the commonly used approach in the literature to re-express the impacts in terms of vulnerability through normalization by the exposure and to investigate if there is a residual trend in normalized impacts that could be attributed to climate change (Huggel et al. 2013;Estrada et al. 2015;Bouwer 2019). However, the usefulness of the normalization approach to establish whether there is a remaining trend that could be attributed to climate change is limited, because the underlying assumptions may not hold, such as the relevance of the normalization variables to detrend the impacts due to socioeconomic changes (Estrada et al. 2015). ...
... However, the usefulness of the normalization approach to establish whether there is a remaining trend that could be attributed to climate change is limited, because the underlying assumptions may not hold, such as the relevance of the normalization variables to detrend the impacts due to socioeconomic changes (Estrada et al. 2015). Similarly, its current inability to appropriately account for the change in vulnerability does not allow it to detect the role of climatic hazards in the observed impacts (Huggel et al. 2013). Therefore, we employed a regressionbased approach to study the attribution of disaster mortality to indicators of climatic hazards, exposure, and vulnerability. ...
The impacts of climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts relative to income are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. Here we assess the spatiotemporal trends of climatic disaster impacts and vulnerability and their attribution to climatic and socioeconomic factors at the subnational scale in a low-income country, using Nepal as a case study. Loss of life is the most extreme consequence of disasters. Therefore, we employed human mortality as a measure of disaster impacts, and mortality normalized by exposed population as a measure of human vulnerability. We found that climatic disaster frequency and mortality increased in Nepal from 1992 to 2021. However, vulnerability decreased, most likely due to economic growth and progress in disaster risk reduction and climate change adaptation. Disaster mortality is positively correlated with disaster frequency and negatively correlated with per capita income but is not correlated with the exposed population. Hence, population growth may not have caused the rise in disaster mortality in Nepal. The strong rise in disaster incidence, potentially due to climate change, has overcome the effect of decreasing vulnerability and caused the rise in disaster mortality.
... In fact, as noted in ( [16] p. 21) a major challenge to the modeling of the human systems impacts of extreme events is the assessment of specific characteristics of each hazard. Consequently, improved assessment of hazard strongly impacts the quantification and attribution of its associated damages [17] and enables accurate and fine-scale investigations of adaptation processes. ...
Human adaptation to climate change is the outcome of long-term decisions continuously made and revised by local communities. Adaptation choices can be represented by economic investment models in which the often large upfront cost of adaptation is offset by the future benefits of avoiding losses due to future natural hazards. In this context, we investigate the role that expectations of future natural hazards have on adaptation in the Colorado River basin of the USA. We apply an innovative approach that quantifies the impacts of changes in concurrent climate extremes, with a focus on flooding events. By including the expectation of future natural hazards in adaptation models, we examine how public policies can focus on this component to support local community adaptation efforts. Findings indicate that considering the concurrent distribution of several variables makes quantification and prediction of extremes easier, more realistic, and consequently improves our capability to model human systems adaptation. Hazard expectation is a leading force in adaptation. Even without assuming increases in exposure, the Colorado River basin is expected to face harsh increases in damage from flooding events unless local communities are able to incorporate climate change and expected increases in extremes in their adaptation planning and decision making.
... There are measures for the reallocation of adaptation resources to develop adaptive capacity and Carlson, 2021). In terms of climate change impacts, this is often linked to the question of attribution, i.e., whether a particular event can be attributed to climate change (Huggel et al., 2013). This level of discussion is rarely seen in adaptation strategy and planning documents (Juhola, 2019) but may become more prevalent in the future (Thompson and Otto, 2015). ...
... An increase in extreme weather events has also been observed since about 1950 due to anthropogenic climate change (IPCC 2012). This is often equated with the growing impact of climatic disasters (Huggel et al. 2013;Bouwer 2019). However, the detection and attribution of the spatial and temporal trend of climatic disaster impacts remain elusive. ...
... Loss normalization is the commonly used approach in the literature to re-express the impacts in terms of vulnerability through normalization by the exposure, and to investigate if there is a residual trend in normalized impacts that could be attributed to climate change (Huggel et al. 2013;Estrada et al. 2015;Bouwer 2019). However, the usefulness of the normalization approach to establish whether there is a remaining trend that could be attributed to climate change is limited, because the underlying assumptions may not hold, such as the relevance of the normalization variables to detrend the impacts due to socioeconomic changes (Estrada et al. 2015). ...
... However, the usefulness of the normalization approach to establish whether there is a remaining trend that could be attributed to climate change is limited, because the underlying assumptions may not hold, such as the relevance of the normalization variables to detrend the impacts due to socioeconomic changes (Estrada et al. 2015). Similarly, its current inability to appropriately account for the change in vulnerability does not allow it to detect the role of climatic hazards in the observed impacts (Huggel et al. 2013). Therefore, we employed a regression-based approach to study the attribution of disaster mortality to indicators of climatic hazards, exposure, and vulnerability. ...
The impacts of climatic disasters have been rising globally. Several studies argue that this upward trend is due to rapid growth in the population and wealth exposed to disasters. Others argue that rising extreme weather events due to anthropogenic climate change are responsible for the increase. Hence, the causes of the increase in disaster impacts remain elusive. Disaster impacts are higher in low-income countries, but existing studies are mostly from developed countries or at the cross-country level. Here we assess the spatiotemporal trends of climatic disaster impacts and vulnerability and their attribution to climatic and socioeconomic factors at the subnational scale in a low-income country, using Nepal as a case study. Loss of life is the most extreme consequence of disasters. Therefore, we employed human mortality as a measure of disaster impacts, and mortality normalized by exposed population as a measure of human vulnerability. We found that climatic disaster frequency and mortality increased in Nepal from 1991 to 2020. However, vulnerability decreased, most likely due to economic growth and progress in disaster risk reduction and climate change adaptation. Disaster mortality is positively correlated with disaster frequency and negatively correlated with per capita income but is not correlated with exposed population. Hence, population growth may not have caused the rise in disaster mortality in Nepal. The strong rise in disaster incidence, potentially due to climate change, has overcome the effect of decreasing vulnerability and caused the rise in disaster mortality.
... The complexity of loss attribution makes it difficult to explore the interrelationship among disaster losses and risk factors by concentrating on hazards or human activity-related exposure and vulnerability in relative isolation (Huggel et al 2013). Therefore, we incorporate socioeconomic factors and physical hazards and develop an integrated model to quantify how the TC-induced DELs (L) respond to changes in TC-related hazards (H), economic exposure (E) and vulnerability (V), thereby providing a foundation for understanding TC loss attribution. ...
Tropical cyclones (TCs) have devastating impacts and are responsible for significant damage. Consequently, for TC-induced direct economic loss (DEL) attribution all factors associated with risk (i.e. hazard, exposure and vulnerability) must be examined. This research quantifies the relationship between TC-induced DELs and maximum wind speed, asset value and Gross Domestic Product (GDP) per capita using a regression model with TC records from 2000 to 2015 for China's mainland area. The coefficient of the maximum wind speed term indicates that a doubling of the maximum wind speed increases DELs by 225% [97%, 435%] when the other two variables are held constant. The coefficient of the asset value term indicates that a doubling of asset value exposed to TCs increases DELs by 79% [58%, 103%]; thus, if hazard and vulnerability are assumed to be constant in the future, then a dramatic escalation in TC-induced DELs will occur given the increase in asset value, suggesting that TC-prone areas with rapid urbanization and wealth accumulation will inevitably be subject to higher risk. Reducing the asset value exposure via land-use planning, for example, is important for decreasing TC risk. The coefficient of GDP per capita term indicates that a doubling in GDP per capita could decrease DELs by 54% [39%, 66%]. Because accumulated assets constantly increase people's demand for improved security, stakeholders must invest in risk identification, early warning systems, emergency management and other effective prevention measures with increasing income to reduce vulnerability. This research aims to quantitatively connect TC risk (expected DELs, specifically) to physical and socioeconomic drivers and emphasizes how human dimensions could contribute to TC risk. Moreover, the model can be used to estimate TC risk under climate change and future socioeconomic development in the context of China.
... Following the current global urbanization trend, the economic and population growth is expected to continue to grow at a faster rate in coastal areas than inland [11,12], which would lead to an increase in the losses caused by typhoons [13,14]. Over the past few years, researchers in various fields, such as meteorology or physical geography have begun to investigate the causes for this typhoon-induced increase in losses, and for mitigation measures [15,16]. Recent research works concluded that this trend will not slow down unless effective adaptation measures or policies are implemented to counteract or reduce the risk inherent to these extreme events [17]. ...
... They cannot be addressed by appeal to compensatory justice. This is why, in practice, compensatory claims for some specific (risk of) L&D demand the detection of anthropogenic cascades demonstrating why this L&D can be attributed to anthropogenic climate change (Huggel et al. 2013). Hence, the worry for the advocate of compensatory justice is that some victims of climate L&D might not be harmed in a normatively relevant sense, whereby considerations of compensation become unsuitable. ...
... So, on the one hand, what could be looked for are ways of differentiating responsibilities without relying on the wrongfulness of emissions, liability and compensation. However, on the other hand, as attribution research matures and international climate policy develops, it may become more feasible to rely on causal explanations to help determine the differentiation of responsibilities in line with a compensatory approach (Boran and Heath 2016; Thompson and Otto 2015; see chapter by James et al. 2018), although doing so may be ambitious at this point (Huggel et al. 2013;James et al. 2014;Huggel et al. 2016). 6 ...
This chapter lays out what we take to be the main types of justice and ethical challenges concerning those adverse effects of climate change leading to climate-related Loss and Damage (L&D). We argue that it is essential to clearly differentiate between the challenges concerning mitigation and adaptation and those ethical issues exclusively relevant for L&D in order to address the ethical aspects pertaining to L&D in international climate policy. First, we show that depending on how mitigation and adaptation are distinguished from L&D, the primary focus of policy measures and their ethical implications will vary. Second, we distinguish between a distributive justice framework and a compensatory justice scheme for delivering L&D measures. Third, in order to understand the differentiated remedial responsibilities concerning L&D, we categorise the measures and policy approaches available. Fourth, depending on the kind of L&D and which remedies are possible, we explain the difference between remedial and outcome responsibilities of different actors.
