Special Issue "Climate Change, Sustainable Development and Disaster Risks"

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (31 December 2021).

Special Issue Editor

Prof. Dr. Rajib Shaw
E-Mail Website
Guest Editor
Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan
Interests: disaster governance; emerging technology; urban resilience; climate change adaptation; risk communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The year 2021 is supposed to be a super year for development, climate change, and disaster risk reduction. This year marks the completion of five years from three landmark global frameworks: the Sustainable Development Goals (SDGs), Sendai Framework for Disaster Risk Reduction (SFDRR), and Paris Agreement (PA). Sendai Framework has broadened the scope of hazards, by including biological hazards, NATECH (natural hazard induced technological disasters), and cascading hazards. Coronavirus, in the form COVID-19, has created a major pandemic, which not only affects people’s lives but has deep-rooted impacts on several development goals and climate change targets. Several climate-related hazards like typhoons/ cyclones/ hurricanes, flooding, and heatwaves have added complexities to the current pandemic situation, causing cascading risks. Thus, it is high time to focus on new research areas that link sustainable development, climate change, and disaster risks. In this context, this Special Issue addresses (but is not restricted to) several questions, such as the following: 1) What are the key policy convergence among these three global frameworks? 2) What are the key implementation examples? 3) How can the current pandemic impact global targets? 4) What innovations can help to achieve SDGs, Sendai, and PA targets? 5) Which types of stakeholder partnerships accelerate the process? 6) How can citizen science and responsible citizenship play a role in the adaptation and reduction of disaster risks? This Special Issue welcomes review articles, new concepts, policy analysis, new methodology and innovation, applications of technology, case studies, etc.

Prof. Dr. Rajib Shaw
Guest Editor

Manuscript Submission Information

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Keywords

  • climate change adaptation
  • disaster risk reduction
  • sustainable development goals
  • policy regime
  • emerging technologies
  • citizen science

Published Papers (4 papers)

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Research

Article
On the Use of Ensemble Predictions for Parametric Typhoon Insurance
Climate 2021, 9(12), 174; https://doi.org/10.3390/cli9120174 - 01 Dec 2021
Viewed by 475
Abstract
Parametric typhoon insurances are an increasingly used financial tool to mitigate the enormous impact of tropical cyclones, as they can quickly distribute much-needed resources, e.g., for post-disaster recovery. In order to optimise the reliability and efficiency of parametric insurance, it is essential to [...] Read more.
Parametric typhoon insurances are an increasingly used financial tool to mitigate the enormous impact of tropical cyclones, as they can quickly distribute much-needed resources, e.g., for post-disaster recovery. In order to optimise the reliability and efficiency of parametric insurance, it is essential to have well-defined trigger points for any post-disaster payout. This requires a robust localised hazard assessment for a given region. However, due to the rarity of severe, landfalling tropical cyclones, it is difficult to obtain a robust hazard assessment based on historical observations. A recent approach makes use of unrealised, high impact tropical cyclones from state-of-the-art ensemble prediction systems to build a physically consistent event set, which would be equivalent to about 10,000 years of observations. In this study, we demonstrate that (1) alternative trigger points of parametric typhoon insurance can be constructed from a local perspective and the added value of such trigger points can be analysed by comparing with an experimental set-up informed by current practice; (2) the estimation of the occurrence of tropical cyclone-related losses on the provincial level can be improved. We further discuss the potential future development of a general tropical cyclone compound parametric insurance. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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Article
Identification of Weather Influences on Flight Punctuality Using Machine Learning Approach
Climate 2021, 9(8), 127; https://doi.org/10.3390/cli9080127 - 06 Aug 2021
Viewed by 866
Abstract
One of the top long-term threats to airport resilience is extreme climate-induced conditions, which negatively affect the airport and flight operations. Recent examples, including hurricanes, storms, extreme temperatures (cold/hot), and heavy rains, have damaged airport facilities, interrupted air traffic, and caused higher operational [...] Read more.
One of the top long-term threats to airport resilience is extreme climate-induced conditions, which negatively affect the airport and flight operations. Recent examples, including hurricanes, storms, extreme temperatures (cold/hot), and heavy rains, have damaged airport facilities, interrupted air traffic, and caused higher operational costs. With the development of civil aviation and the pre-COVID-19 surging demand for flights, the passengers’ complaints of flight delay increased, according to FoxBusiness. This study aims to discover the weather factors affecting flight punctuality and determine a high-dimensional scale of consequences stemming from weather conditions and flight operational aspects. Machine learning has been developed in correlation with the weather and statistical data for operations at Birmingham Airport as a case study. The cross-correlated datasets have been kindly provided by Birmingham Airport and the Meteorological Office. The scope and emphasis of this study is placed on the machine learning application to practical flight punctuality prediction in relation to climate conditions. Random forest, artificial neural network, support vector machine, and linear regression are used to develop predictive models. Grid-search and cross-validation are used to select the best parameters. The model can grasp the trend of flight punctuality rates well where R2 is 0.80 and the root mean square error (RMSE) is less than 15% using the model developed by random forest technique. The insights derived from this study will help Airport Authorities and the Insurance industry in predicting the scale of consequences in order to promptly enact and enable adaptative airport climate resilience plans, including air traffic rescheduling, financial resilience to climate variances and extreme weather conditions. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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Article
Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management
Climate 2021, 9(4), 66; https://doi.org/10.3390/cli9040066 - 16 Apr 2021
Viewed by 1146
Abstract
Radiation management (RM) has been proposed as a conceivable climate engineering (CE) intervention to mitigate global warming. In this study, we used a coupled climate model (MPI-ESM) with a very idealized setup to investigate the efficacy and risks of CE at a local [...] Read more.
Radiation management (RM) has been proposed as a conceivable climate engineering (CE) intervention to mitigate global warming. In this study, we used a coupled climate model (MPI-ESM) with a very idealized setup to investigate the efficacy and risks of CE at a local scale in space and time (regional radiation management, RRM) assuming that cloud modification is technically possible. RM is implemented in the climate model by the brightening of low-level clouds (solar radiation management, SRM) and thinning of cirrus (terrestrial radiation management, TRM). The region chosen is North America, and we simulated a period of 30 years. The implemented sustained RM resulted in a net local radiative forcing of −9.8 Wm2 and a local cooling of −0.8 K. Surface temperature (SAT) extremes (90th and 10th percentiles) show negative anomalies in the target region. However, substantial climate impacts were also simulated outside the target area, with warming in the Arctic and pronounced precipitation change in the eastern Pacific. As a variant of RRM, a targeted intervention to suppress heat waves (HW) was investigated in further simulations by implementing intermittent cloud modification locally, prior to the simulated HW situations. In most cases, the intermittent RRM results in a successful reduction of temperatures locally, with substantially smaller impacts outside the target area compared to the sustained RRM. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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Article
Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery
Climate 2021, 9(4), 58; https://doi.org/10.3390/cli9040058 - 06 Apr 2021
Cited by 2 | Viewed by 1470
Abstract
Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support [...] Read more.
Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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