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Climate Change, Sustainable Development and Disaster Risks
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Climate Change, Sustainable Development and Disaster Risks

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 61904

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

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Keywords

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

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Published Papers (7 papers)

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Research

Jump to: Review

18 pages, 5420 KiB  
Article
Applying Machine Learning for Threshold Selection in Drought Early Warning System
by Hui Luo, Jessica Bhardwaj, Suelynn Choy and Yuriy Kuleshov
Climate 2022, 10(7), 97; https://doi.org/10.3390/cli10070097 - 30 Jun 2022
Cited by 3 | Viewed by 2743
Abstract
This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time [...] Read more.
This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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21 pages, 1606 KiB  
Article
Implications of Flood Risk Reduction Interventions on Community Resilience: An Assessment of Community Perception in Bangladesh
by Md. Sazzad Ansari, Jeroen Warner, Vibhas Sukhwani and Rajib Shaw
Climate 2022, 10(2), 20; https://doi.org/10.3390/cli10020020 - 6 Feb 2022
Cited by 10 | Viewed by 5540
Abstract
Bangladesh, a flat densely populated country in a dynamic delta, is vulnerable to recurring flood disasters. Various types of structural and non-structural flood risk reduction interventions have been implemented over the years to safeguard the people and assets. In that context, the present [...] Read more.
Bangladesh, a flat densely populated country in a dynamic delta, is vulnerable to recurring flood disasters. Various types of structural and non-structural flood risk reduction interventions have been implemented over the years to safeguard the people and assets. In that context, the present study assesses the community perception about the implications of such diverse interventions on community resilience, in three reasonably proximate settlements, with varying characteristics: the Type 1 settlement has a flood protection embankment; the Type 2 settlement has no flood risk reduction intervention, and the Type 3 settlement has non-structural interventions. Through a mixed-method assessment in selected settlements, the study results reveal both positive and negative implications of these interventions on local communities. While the embankment has contributed towards enhancing infrastructural resilience in the Type 1 settlement, it still reportedly does not provide complete flood safety. On the other hand, the non-structural measures are reported to have increased community competencies in the Type 3 settlement, but the long-term sustainability of these traits is uncertain. Furthermore, the study results uncover “connectedness among local communities” as an inherent characteristic in all three locations, whereas flood risk reduction interventions are stated to be partly associated with social tension and the marginalization of certain socio-economic groups. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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25 pages, 4442 KiB  
Article
On the Use of Ensemble Predictions for Parametric Typhoon Insurance
by Kelvin S. Ng, Gregor C. Leckebusch, Qian Ye, Wenwen Ying and Haoran Zhao
Climate 2021, 9(12), 174; https://doi.org/10.3390/cli9120174 - 1 Dec 2021
Cited by 2 | Viewed by 3103
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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12 pages, 2659 KiB  
Article
Identification of Weather Influences on Flight Punctuality Using Machine Learning Approach
by Sakdirat Kaewunruen, Jessada Sresakoolchai and Yue Xiang
Climate 2021, 9(8), 127; https://doi.org/10.3390/cli9080127 - 6 Aug 2021
Cited by 7 | Viewed by 4632
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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22 pages, 25011 KiB  
Article
Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management
by Sudhakar Dipu, Johannes Quaas, Martin Quaas, Wilfried Rickels, Johannes Mülmenstädt and Olivier Boucher
Climate 2021, 9(4), 66; https://doi.org/10.3390/cli9040066 - 16 Apr 2021
Cited by 4 | Viewed by 4379
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 Wm−2 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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11 pages, 7460 KiB  
Article
Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery
by Saman Ghaffarian and Sobhan Emtehani
Climate 2021, 9(4), 58; https://doi.org/10.3390/cli9040058 - 6 Apr 2021
Cited by 17 | Viewed by 5033
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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Review

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32 pages, 1841 KiB  
Review
The Climate Change Challenge: A Review of the Barriers and Solutions to Deliver a Paris Solution
by Filipe Duarte Santos, Paulo Lopes Ferreira and Jiesper Strandsbjerg Tristan Pedersen
Climate 2022, 10(5), 75; https://doi.org/10.3390/cli10050075 - 20 May 2022
Cited by 66 | Viewed by 34599
Abstract
Global greenhouse gas (GHG) emissions have continued to grow persistently since 1750. The United Nations Framework Convention on Climate Change (UNFCCC) entered into force in 1994 to stabilize GHG emissions. Since then, the increasingly harmful impacts of global climate change and repeated scientific [...] Read more.
Global greenhouse gas (GHG) emissions have continued to grow persistently since 1750. The United Nations Framework Convention on Climate Change (UNFCCC) entered into force in 1994 to stabilize GHG emissions. Since then, the increasingly harmful impacts of global climate change and repeated scientific warnings about future risks have not been enough to change the emissions trend and enforce policy actions. This paper synthesizes the climate change challenges and the insofar insufficient mitigation responses via an integrated literature review. The fossil industry, mainstream economic thinking, national rather than international interests, and political strive for short-term interests present key barriers to climate mitigation. A continuation of such trends is reflected in the Dice model, leading to a 3.5 °C temperature increase by 2100. Despite receiving the Nobel Prize for integrating climate change into long-run macroeconomic analysis via the Dice model, increases in global mean temperatures overshooting the 1.5 °C to 2 °C Paris targets imply an intensified disruption in the human–climate system. Past and present policy delays and climate disruption pave the way for solar radiation management (SRM) geoengineering solutions with largely unknown and potentially dangerous side effects. This paper argues against SRM geoengineering and evaluates critical mitigation solutions leading to a decrease in global temperatures without overshooting the Paris targets. The essential drivers and barriers are discussed through a unified approach to tipping points in the human–climate system. The scientific literature presents many economically and technologically viable solutions and the policy and measures required to implement them. The present paper identifies the main barriers to integrating them in a globally cooperative way, presenting an efficient, long-term, and ethical policy approach to climate change. Full article
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
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