The frequency of intense natural disasters increased notably from the 1970s to the 2000s. Around half of these events happen in Asia and Pacific Area. Intense hydrometeorological disasters and climatological disasters accounted for most of the worldwide increase in natural disasters.The Springer.com Open Access Science and Media website publish a new paper about disaster prevention and climate action. This pubblication is an indipendent evalutaion at the Asian Development Bank.
A Comparative Study of the Mortality Risk of Extreme Temperature in Urban and...Global Risk Forum GRFDavos
6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland
Economic Impacts of Tipping Points in the Climate SystemOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Simon Dietz, London School of Economics
Incorporating Climate Tipping Points Into Policy AnalysisOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Elizabeth Kopits, US Environmental Protection Agency
Saving the world from climate threats vs. dispelling climate myths and fearsFabius Maximus
A presentation at an invited seminar by Demetris Koutsoyiannis at WasserCluster Lunz on 20 April 2017. He is a Professor of Hydrology & Hydrosystems at the Dept. of Water Resources, School of Civil Engineering, National Technical University of Athens (NTUA).
Economic Implications of Multiple Interacting Tipping PointsOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Yonyang Cai, The Ohio State University
State of Scientific Knowledge on Climate Tipping PointsOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Timothy M. Lenton, University of Exeter
Developing social vulnerability index for newcastle extreme temperature riskAlex Nwoko
This vocational dissertation was undertaken in collaboration with Newcastle City Council. This study was aimed at developing a quantitative social vulnerability indices for assessing extreme temperature vulnerability in Newcastle. This report is expected to help in identifying localized community-level social vulnerability determinants for emergency planning and response. The first objective of this study was to determine the social indicators which could contribute to increased losses on well-being. First, drawing theoretical justification from the literature and consultation with experts at Newcastle City Council, an initial set of indicators was collected from census data for 910 Output Areas (OAs) in Newcastle. These datasets were used to quantify to what extent their availability or lack can contribute to an overall increase or decrease in vulnerability in different parts of Newcastle. The summary of social vulnerability proxies developed in this study is presented in Chapter 3.
The second part of the analysis combines statistics and GIS to compare the relationship between sensitivity, adaptive capacity and enhanced exposure sub-indices and their components. The result of this investigation indicates that there is a significant statistical relationship between sensitivity and adaptive capacity, and also between sensitivity and enhanced exposure. The spatial relationship was tested using Getis Ord Gi* hotspot analysis and Ripley's K statistic, which found a significant clustering of vulnerability driven by both “sensitivity”, “adaptive capacity” and “enhanced exposure”. This study has identified the most vulnerable output areas in Newcastle in these wards; Walker, Elswick, Jesmond, Newburn, and Gosforth. From these observations, this report advocates the inclusion of social indicators in vulnerability analysis to reveal the marginalized population otherwise not acknowledged.
Finally, a proximity assessment of health and emergency services was carried out to reveal the southern cluster of emergency facilities and inefficient coverage of ambulance services. The identified accessibility-deprived output areas are located in the wards on the Northern parts including; Woolsington, Parkland, Fawdon, East and West Gosforth, and Castle.
This report summarizes by noting that the new framework is only intended to inform the periodic review of emergency planning and response strategies in Newcastle, suggesting an adoption of spatially detailed data to improve quantitative understanding of the spatial distribution of extreme temperature-related social vulnerability. It finally recommends an improvement in institutional adaptive capacity to handle emergencies in Newcastle.
Impact of Future Climate Change on water availability in Kupang CityWillem Sidharno
Observed climate change could affect water availability in the future. Changes also
occurred Kupang city in recent decades, an increase in the magnitude of the damage caused
by drought due to climate change. In an attempt to explore the effects of drought can be
aggravated by climate change. in this paper, the author will be analyze impact of changes in
the water balance in Kupang city. To achieve that, the author will use the procedure consists
of two procedures: Temperature and precipitation are modeled under two typical emission
A1FI and B1 scenarios evaluated in this study for future projections in Kupang, discharge
simulations using rainfall Mock generated daily rainfall and water balance monthly Data
analysis WEAP (water Evaluation and Planning System) based simulation Mock. Due to the
significant uncertainty involved in forecasting future water consumption and water yield, the
author will use the three scenarios assumed water consumption and water three outcome
scenarios. Three scenarios of water consumption, ie, "Low", "Medium" and "High" in
accordance with the expected number of water consumption. Disposal obtained from mock
simulations during the simulation period. Finally, the water balance analysis conducted by
WEAP based on a combination of the three scenarios of water consumption. With this
procedure, it is possible to explore different scenarios of water consumption and water
results and the results of this study can be used to establish the proper planning to minimize
the impact of drought on water availability to support water requirement due to climate
change in Kupang city.
Spatio-Temporal Dynamics of Climatic Parameters in TogoPremier Publishers
The detection of the variability and trends of weather variables is a necessary step in the explanation of the impacts of climate change on ecosystems and agricultural production activities. To this end, this study analyses the spatio-temporal trends of precipitation, temperature, evapotranspiration, relative humidity, wind speed (weather stations in Lomé, Atakpamé, Sokodé, Kara and Dapaong) and insolation (stations in Lomé, Atakpamé, Sokodé, Kara and Mango). The data analyzed are from the meteorological directorate-general. Trends over time were calculated over monthly, annual and seasonal intervals using analysis of variability (coefficient of variation, precipitation concentration indices) and trend analysis techniques using Mann-Kendall and Sen slope methods, allowing non-parametric statistical analysis. The results show a general upward trend in the inter-seasonally, intra and inter-yearly in precipitation, temperature (maximal and minimal) and evapotranspiration. On the other hand, a general downward trend is detected for relative humidity (maximal and minimal) and insolation. The results raise concerns for food security in Togo inasmuch as increases in rainfall, temperature and evapotranspiration coupled with decreases in relative humidity and insolation negatively affect agricultural production. It urgently needs that the public authorities take adaptation and mitigation measures.
LAVORO: Bando di selezione per operatori professionali infermieristici catego...Mario Robusti
L'Ente per i Servizi Tecnico-Amministrativi di Area Vasta Sud Est ha pubblicato il 5 novembre un avviso che da 20 giorni ai potenziali candidati per entrare nelle selezioni.
UNOCHA Global Humanitarian Overview. Status Report of august 2014Mario Robusti
2014 has seen a major surge in humanitarian crises around the world. Inter-agency strategic response and regional response plans now target over 76 million people in thirty-one countries compared to 52 million in December 2013. 102 million people are estimated to be in need of humanitarian assistance compared to 81 million in December 2013. Global financial requirements to cover humanitarian needs rose from US$12.9 billion in 2013 to $17.3 billion now. More and more crises are having a regional impact with a spill-over effect on countries which are already fragile.
A Comparative Study of the Mortality Risk of Extreme Temperature in Urban and...Global Risk Forum GRFDavos
6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland
Economic Impacts of Tipping Points in the Climate SystemOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Simon Dietz, London School of Economics
Incorporating Climate Tipping Points Into Policy AnalysisOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Elizabeth Kopits, US Environmental Protection Agency
Saving the world from climate threats vs. dispelling climate myths and fearsFabius Maximus
A presentation at an invited seminar by Demetris Koutsoyiannis at WasserCluster Lunz on 20 April 2017. He is a Professor of Hydrology & Hydrosystems at the Dept. of Water Resources, School of Civil Engineering, National Technical University of Athens (NTUA).
Economic Implications of Multiple Interacting Tipping PointsOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Yonyang Cai, The Ohio State University
State of Scientific Knowledge on Climate Tipping PointsOECD Environment
Presentation given during the OECD Expert workshop on Economic Modelling of Climate and Related Tipping Points by Timothy M. Lenton, University of Exeter
Developing social vulnerability index for newcastle extreme temperature riskAlex Nwoko
This vocational dissertation was undertaken in collaboration with Newcastle City Council. This study was aimed at developing a quantitative social vulnerability indices for assessing extreme temperature vulnerability in Newcastle. This report is expected to help in identifying localized community-level social vulnerability determinants for emergency planning and response. The first objective of this study was to determine the social indicators which could contribute to increased losses on well-being. First, drawing theoretical justification from the literature and consultation with experts at Newcastle City Council, an initial set of indicators was collected from census data for 910 Output Areas (OAs) in Newcastle. These datasets were used to quantify to what extent their availability or lack can contribute to an overall increase or decrease in vulnerability in different parts of Newcastle. The summary of social vulnerability proxies developed in this study is presented in Chapter 3.
The second part of the analysis combines statistics and GIS to compare the relationship between sensitivity, adaptive capacity and enhanced exposure sub-indices and their components. The result of this investigation indicates that there is a significant statistical relationship between sensitivity and adaptive capacity, and also between sensitivity and enhanced exposure. The spatial relationship was tested using Getis Ord Gi* hotspot analysis and Ripley's K statistic, which found a significant clustering of vulnerability driven by both “sensitivity”, “adaptive capacity” and “enhanced exposure”. This study has identified the most vulnerable output areas in Newcastle in these wards; Walker, Elswick, Jesmond, Newburn, and Gosforth. From these observations, this report advocates the inclusion of social indicators in vulnerability analysis to reveal the marginalized population otherwise not acknowledged.
Finally, a proximity assessment of health and emergency services was carried out to reveal the southern cluster of emergency facilities and inefficient coverage of ambulance services. The identified accessibility-deprived output areas are located in the wards on the Northern parts including; Woolsington, Parkland, Fawdon, East and West Gosforth, and Castle.
This report summarizes by noting that the new framework is only intended to inform the periodic review of emergency planning and response strategies in Newcastle, suggesting an adoption of spatially detailed data to improve quantitative understanding of the spatial distribution of extreme temperature-related social vulnerability. It finally recommends an improvement in institutional adaptive capacity to handle emergencies in Newcastle.
Impact of Future Climate Change on water availability in Kupang CityWillem Sidharno
Observed climate change could affect water availability in the future. Changes also
occurred Kupang city in recent decades, an increase in the magnitude of the damage caused
by drought due to climate change. In an attempt to explore the effects of drought can be
aggravated by climate change. in this paper, the author will be analyze impact of changes in
the water balance in Kupang city. To achieve that, the author will use the procedure consists
of two procedures: Temperature and precipitation are modeled under two typical emission
A1FI and B1 scenarios evaluated in this study for future projections in Kupang, discharge
simulations using rainfall Mock generated daily rainfall and water balance monthly Data
analysis WEAP (water Evaluation and Planning System) based simulation Mock. Due to the
significant uncertainty involved in forecasting future water consumption and water yield, the
author will use the three scenarios assumed water consumption and water three outcome
scenarios. Three scenarios of water consumption, ie, "Low", "Medium" and "High" in
accordance with the expected number of water consumption. Disposal obtained from mock
simulations during the simulation period. Finally, the water balance analysis conducted by
WEAP based on a combination of the three scenarios of water consumption. With this
procedure, it is possible to explore different scenarios of water consumption and water
results and the results of this study can be used to establish the proper planning to minimize
the impact of drought on water availability to support water requirement due to climate
change in Kupang city.
Spatio-Temporal Dynamics of Climatic Parameters in TogoPremier Publishers
The detection of the variability and trends of weather variables is a necessary step in the explanation of the impacts of climate change on ecosystems and agricultural production activities. To this end, this study analyses the spatio-temporal trends of precipitation, temperature, evapotranspiration, relative humidity, wind speed (weather stations in Lomé, Atakpamé, Sokodé, Kara and Dapaong) and insolation (stations in Lomé, Atakpamé, Sokodé, Kara and Mango). The data analyzed are from the meteorological directorate-general. Trends over time were calculated over monthly, annual and seasonal intervals using analysis of variability (coefficient of variation, precipitation concentration indices) and trend analysis techniques using Mann-Kendall and Sen slope methods, allowing non-parametric statistical analysis. The results show a general upward trend in the inter-seasonally, intra and inter-yearly in precipitation, temperature (maximal and minimal) and evapotranspiration. On the other hand, a general downward trend is detected for relative humidity (maximal and minimal) and insolation. The results raise concerns for food security in Togo inasmuch as increases in rainfall, temperature and evapotranspiration coupled with decreases in relative humidity and insolation negatively affect agricultural production. It urgently needs that the public authorities take adaptation and mitigation measures.
LAVORO: Bando di selezione per operatori professionali infermieristici catego...Mario Robusti
L'Ente per i Servizi Tecnico-Amministrativi di Area Vasta Sud Est ha pubblicato il 5 novembre un avviso che da 20 giorni ai potenziali candidati per entrare nelle selezioni.
UNOCHA Global Humanitarian Overview. Status Report of august 2014Mario Robusti
2014 has seen a major surge in humanitarian crises around the world. Inter-agency strategic response and regional response plans now target over 76 million people in thirty-one countries compared to 52 million in December 2013. 102 million people are estimated to be in need of humanitarian assistance compared to 81 million in December 2013. Global financial requirements to cover humanitarian needs rose from US$12.9 billion in 2013 to $17.3 billion now. More and more crises are having a regional impact with a spill-over effect on countries which are already fragile.
Civil Protection Forum 2015: Draft programMario Robusti
The European Civil Protection Forum is organised by the European Commission, Directorate General Humanitarian Aid and Civil Protection (DG ECHO) every two years.
Avviso pubblico per selezione autisti ambulanza ASUR Marche zona 5Mario Robusti
Il 25 novembre scade il termine per l'iscrizione al concorso di selezione per autisti soccorritori di ambulanza organizzato dall'Asur Marche, zona vasta numero 5, che serve a stilare una graduatoria di operatori tecnici specializzati con cui effettuare assunzioni a tempo determinato perl'area di Ascoli Piceno e San Benedetto del Tronto.
"Per non morire d'Europa" è una analisi approfondita della politica europea del dopo Trattato di Lisbona, che prende spunto dalle problematiche di alimentazione, agricoltura e produzione di energia dopo la crisi del petrolio e di fronte alle nuove sfide della produzione sostenibile.
global disaster trends- emerging risks of disaster- climate changeNitin Vadhel
Disaster risk trends are a measure of the sustainability of development.
Trend analysis helps us to understand patterns of disaster risk and, consequently, whether disaster risk reduction is being effective.
Using disaster trends to inform policy and practice requires a good understanding of the limits of these trends.
The pattern the trend displays (rising, falling or fluctuating) is only as real as the amount, quality and reliability of the data used. For instance, patterns of disaster losses may actually reflect a number of factors unrelated to disaster risk, including the time period over which they are measured and improvements in disaster risk reporting.
In order to account for these problems, analysts determine the statistical significance of the trend.
How a hazard event may turn into a disaster in the societyTarmin Akther
This document describes about hazard and disaster. Besides how hazard becomes a disaster and negatively affect in the society. Hazard is an incident which turns into a disaster in the long run.
Climate Change - Impacts and Humanitarian ImplicationsCharles Ehrhart
Climate change: impacts and humanitarian implications. Presentation at the Dubai International Humanitarian Aid & Development Conference (DIHAD), April 2009.
health can be affected by many factors.These may be in terms of environment and also internal body changes depending on climate.It is discussed in details on these slides the main factors that attribute to the health problems.Countries vary differently in terms of number of people contracting diseases due to different physical,social and psychological effects.
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uilding Human Resilience
he Role of Public Health Preparedness and Response As an
daptation to Climate Change
ark E. Keim, MD
bstract: Global climate change will increase the probability of extreme weather events, including
heatwaves, drought, wildfire, cyclones, and heavy precipitation that could cause floods and
landslides. Such events create significant public health needs that can exceed local capacity
to respond, resulting in excess morbidity or mortality and in the declaration of disasters.
Human vulnerability to any disaster is a complex phenomenon with social, economic,
health, and cultural dimensions. Vulnerability to natural disasters has two sides: the degree
of exposure to dangerous hazards (susceptibility) and the capacity to cope with or recover
from disaster consequences (resilience). Vulnerability reduction programs reduce suscep-
tibility and increase resilience. Susceptibility to disasters is reduced largely by prevention
and mitigation of emergencies. Emergency preparedness and response and recovery
activities—including those that address climate change—increase disaster resilience.
Because adaptation must occur at the community level, local public health agencies are
uniquely placed to build human resilience to climate-related disasters. This article discusses
the role of public health in reducing human vulnerability to climate change within the
context of select examples for emergency preparedness and response.
(Am J Prev Med 2008;35(5):508 –516) Published by Elsevier Inc. on behalf of American Journal of
Preventive Medicine.
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limate Change and Extreme Weather Events
lobal climate change will increase the probabil-
ity of extreme weather events1 (Table 1), which
may be associated either with high precipita-
ion (i.e., storms, floods, and landslides) or with low
recipitation (i.e., heat, drought, wildfire).1 These
vents often overwhelm the capacity of communities
nd local governments to respond, requiring outside
ssistance. Such mismatches between needs and re-
ources often result in declarations of disaster.
High-precipitation events, which are likely to in-
rease in frequency, will compound the risk of flood
nd landslide disasters. According to the UN Intergov-
rnmental Panel on Climate Change (IPCC): “Many
illions more people are projected to be flooded every
ear due to sea-level rise by the 2080s. In some areas
eatwaves are expected to increase in severity and
requency, expanding drought affected areas.”1 In low-
atitude regions, crop productivity is expected to de-
rease, thus increasing the risk for hunger, particularly
n Africa and small island developing States. “By 2020,
etween 75 and 250 million people are projected to be
xposed to an increase in water stress.”1
rom the National Center for Environmental Health, Agency for
oxic Substances and Disease Registry, CDC, Atlanta, Georgia
Address ...
Social Protection and Climate Resilience: A Review Of Sub-Saharan African Cas...Agriculture Journal IJOEAR
— Social protection is mainly used for assisting the most vulnerable in the area of poverty reduction. However, international development scholars are arguing that social protection aside impacting the poor can also help in climate resilience. This study examines selected case studies in the social protection and climate resilient debate in Sub-Saharan Africa. Using qualitative and quantitative approaches in data collection, the study finds that social protection through cash transfers have been able to build climate resilience among participants of the scheme. Though findings from the study were minimal, a wide range of research needs to be carried out to determine the impact of social protection on climate shocks on a broader scale.
First appearing on the blog of Donna LaFramboise, this draft was confirmed as authentic by an IPCC spokesman, according to Justin Gills of The New York Times. Here's the blog post: http://nofrakkingconsensus.com/2013/11/01/new-ipcc-leak-working-group-2s-summary-for-policymakers/
Here's Gillis's news story, which focuses on the draft's conclusions about agriculture: Climate Change Seen Posing Risk to Food Supplies http://nyti.ms/1iBa1tR
Strategies for managing climate risk: a case study of smallholder farmers in ...Premier Publishers
This study analyses the factors affecting Ethiopian farmers’ choice of ex-ante adaptation and ex-post coping strategies for climate risk. We use multivariate probit models to explain the choice of various adaptation and coping strategies. We find that plot characteristics such as slope, depth, soil type and soil fertility, and farm size are important factors affecting the choice of adaptation strategy. These plot characteristics also significantly affect the choice of particular coping strategies such as selling livestock, reducing meals and borrowing. The results also show that plot management practices such as leaving crop residues, intercropping and use of non-recycled hybrid maize are associated with a reduced likelihood of choosing coping measures such as selling livestock. We advocate increased farmer education on improved farm management practices to reduce household vulnerability to climate change and variability.
Running head accident and casual factor events1accident and ca.docxSUBHI7
Running head: accident and casual factor events 1
accident and casual factor events 7Accident and Casual Factor Events
Wesley D. Herron
Waldorf University
Accident And Causal Factor Events
The causal factor and events chart describe a graphical and written description for the time sequence of events contributing the various events related to an accident. The construction of the causal factor and events factor chart helps investigators to conduct an in-depth research and identifying the root cause of accidents. The following are charts showing the various elements involved in event charting:
· Condition- This refers to the distinct state that facilitates event occurrence. a condition may be weather, equipment status, the health status of an employee as well as any other factor affecting an event
· Event-Refers to the point in time described by the occurrence of specific actions.
· Accident- describesthe action, state and even condition where a system is not meeting one or more of its design objectives (Oakley, 2012). This includes real accidents and also near misses. This is the main focus of the analysis or even the evaluation.
· Primary event line- These are the main sequences of occurrence that resulted in the accidents. The primary event line gives the basic nature of an occurrence in a logical progression. However, it does not provide the contributing causes, and it often contains the accident but does not essentially end with an accident event. the primary event line comprises of both conditions and events
· Primary events and conditions- the conditions and events described make up the primary event line.
· Secondary event lines. These are a series of occurrences that lead to primary conditions and events. Secondary event lines enhance the development of the primary event line to show all the facilitating elements of an event. Causal factors are often found in secondary event lines, and they have both the events and conditions.
· Secondary events and conditions- these are conditions and events that make up the secondary event line.
Causal factors
These are key conditions and events that when eliminated could have prevented or reduced an accident and its effects (Oakley, 2012). Causal factors include equipment failure, human error and they include activities such as initiating event for an accident, failed safeguards, and having reasonable safeguards that were not provided.
Items of note
These are undesirable events, and conditions that have been identified in an analysis and they have to be addressed or corrected. However, the events did not contribute to the focus of the accident, and they are shown as separate boxes specifically outside the event chain.
Events and accidents are examined to identify the causes of their event and to decide the moves that must be made to avoid a repeat. It is basic that the accident investigators profoundly investigate into both the events and the conditions ...
Similar to Contributors to the frequency of intense climate disasters in asia pacific countries (20)
L’immobilizzazione in caso di trauma pediatricoMario Robusti
Come utilizzare al meglio i presidi pediatrici?
Soccorrere un bambino è una delle attività più complesse e delicate fra per un soccorritore. Fortunatamente i casi in cui l’attività di soccorso traumatico riguardano un paziente in età pediatrica sono rari.
La rarità dei casi in passato ha fatto trascurare lo sviluppo di dotazioni medicali dedicate in modo specifico all’intervento sul lattante, il neonato o sul bambino.
Oggi è chiaro che il bambino non è un piccolo adulto.
Il supporto aereo nell’evacuazione medica di emergenza Degli operatori specia...Mario Robusti
Come si fornisce un valido supporto aereo nell’evacuazione medica di emergenza per gli operatori di forze speciali? Ne ha parlato il dirigente medico della Polizia di Stato il dottor Fabio Ciciliano, esperto di medicina delle catastrofi e parte del dipartimento di Protezione Civile
Il modulo sanitario nella Protezione civileMario Robusti
Benvenuti in questo episodio podcast di Rescue Press, dedicato ad approfondimenti scientifici nel mondo dell’emergenza e del soccorso. Oggi, grazie alla collaborazione con il dottor Alberto Baratta del 118 di Massa, direttore della base di Elisoccorso Pegaso 3, e alla collaborazione della HEMS Association, possiamo presentarvi un estratto dell’HEMS Congress 2019.
Dalla sessione dedicata alle attività di Protezione Civile ascolterete l’intervento integrale della dottoressa Isabella Bartoli Referente Sanitario della Regione Sicilia per le Grandi Emergenze, e direttore della SUES 118 di Catania.
Durante HEMS Congress, Bartoli ha presentato la struttura dei moduli sanitari per le maxi emergenze allestite presso i vari centri di soccorso, per fornire aiuto rapidamente in caso di calamità.
Godetevi questo intervento e ricordatevi che questo podcast è disponibile sul sito web academy.rescue.press insieme a tutte le novità del mondo del soccorso pre-ospedaliero saranno online su www.rescue.press. Vi basterà cliccare sul link e registrarvi per commentare, discutere e inviare i vostri contenuti scientifici per migliorare il mondo del soccorso, a qualsiasi livello voi ne facciate parte.
Il funzionamento della C.R.O.S.S. e il sistema di aiuto alla Regione colpita ...Mario Robusti
Benvenuti a questo episodio podcast di Rescue Press, dedicato ad approfondimenti scientifici nel mondo dell’emergenza e del soccorso. Oggi, grazie alla collaborazione con il dottor Alberto Baratta del 118 di Massa, direttore della base di Elisoccorso Pegaso 3, e alla collaborazione della HEMS Association, possiamo presentarvi un estratto dell’HEMS Congress 2019. Dalla sessione dedicata alle attività di Protezione Civile ascolterete l’intervento integrale della dottoressa Rita Rossi, del Dipartimento Interaziendale per l’emergenza sanitaria territoriale, e direttore del 118 della città metropolitana di Torino. Come direttore della CROSS di Torino, la dottoressa Rossi ha presentato nell’occasione proprio il funzionamento della C.R.O.S.S. e il sistema di aiuto alla Regione colpita da calamità.
Ascoltiamo quindi l’intervento, e vi ricordiamo che questo podcast è disponibile sul sito web academy.rescue.press e che tutte le novità del mondo del soccorso pre-ospedaliero saranno online su www.rescue.press. Vi basterà cliccare sul link e registrarvi per commentare, discutere e inviare i vostri contenuti scientifici per migliorare il mondo del soccorso, a qualsiasi livello voi ne facciate parte.
Grazie e buon ascolto.
Tubi flessibili per il settore farmaceutico e medicaleMario Robusti
Universalflex commercializza tubi tusil view per aspirazione di prodotti alimentari, cosmetici e farmaceutici.
Queso tubo supera i test di migrazione in accordo alla normativa BfR Recommendation XV & XXI Cat. 2.
Non è adatto ad essere utilizzato come materiale da innesto ed impianto in esseri viventi. Non è adatto per sangue o per altri fluidi umani.
The ability to provide safe, urgent and integrated care is fundamental to the future
delivery of the health and social care system. We need information to follow the
patient along their pathway, so clinicians and patients can have access to the right
information at the right time. In addition, commissioners need to be able to link patient information across multiple settings to improve the services provided to their population. This needs an underpinning primary identifier across the system - the NHS Number (NHSN)
EBOLA - Trasporto in E.R. solo di competenza ASLMario Robusti
Esclusi i servizi convenzionati e le associazioni per ragioni di sicurezza e attrezzature. Il documento integrale emesso dalla Regione Emilia-Romagna
www.emergency-live.com
The Fire and Rescue Service Books is a guidance for organize a safe system of...Mario Robusti
Operational guidance for incidents involving hazardous material.
In everyday language "hazardous materials" means solids, liquids, or gases that can wound people, other living organisms, or damage property, or the environment. They not only include materials that are toxic, radioactive, flammable, explosive, corrosive, oxidizers, asphyxiates, biohazards, pathogen or allergen substances and organisms, but also materials with physical conditions or other characteristics that render them hazardous in specific circumstances, such as compressed gases and liquids, or hot/cold materials.
The Department for Communities & local Government, with collaboration of CFRA (Chief Fire & Rescue Adviser) publish in 2012 an interesting guideline for a bettere organization of the incident ground, following safe and correct procedure. The target of this operational guidance, about the "incident involving hazardous materials" is to provide an unvaried approach for common operational practices. This simple rules could give a better explanation about the interoperability between fire and rescue services, other emergency responders, industry experts and other relevant groups. These common principles, practices and procedures are intended to support the development of safe systems of work on the incident ground and to enhance national resilience.
This book promotes and enhance good practice within the Fire and Rescue Service and is offered as a current industry standard. It is envisaged that this will help establish high standards of efficiency and safety in the interests of employers, employees and the general public.
The Guidance, which is compiled using the best sources of information known at the date of issue, is intended for use by competent persons. The application of the guidance does not remove the need for appropriate technical and managerial judgement in practical situations with due regard to local circumstances, nor does it confer any immunity or exemption from relevant legal requirements, including by-laws.Those investigating compliance with the law may refer to this guidance as illustrating an industry standard.
This book could contain interesting suggestion that could interest also Firefighters, first responder and EMS from different country, wich not follow law, guidelines or practices from United Kingdom or Commonwealth Countries.
Major incidents involving hazardous materials in the United Kingdom are rare. Such incidents place significant demands on local fire and rescue services and often require resources and support from other fire and rescue services and emergency responders.
However smaller scale incidents involving hazardous materials are more prevalent and these may require a response from any fire and rescue service in England.
The Fire and Rescue Service Operational Guidance – Incidents involving hazardous materials provid
Time is money, but how much? The Monetary Value of Response Time for AmbulanceMario Robusti
The monetary values for ambulance emergency services were calculated for two different time factors, response time, which is the time from when a call is received by the emergency medical service call-taking center until the response team arrives at the emergency scene, and operational time, which includes the time to the hospital. The study was performed in two steps. First, marginal effects of reduced fatalities and injuries for a 1-minute change in the time factors were calculated. Second, the marginal effects and the monetary values were put together to find a value per minute.
NHS review: transforming urgent and emergency care services in EnglandMario Robusti
NHS England published an update on the Urgent and Emergency Care Review, which builds on NHS England’s future vision for urgent and emergency care in Transforming urgent and emergency care services in England.
READ THE ARTICLE OF EMERGENCY-LIVE HERE:
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Contributors to the frequency of intense climate disasters in asia pacific countries
1. Climatic Change
DOI 10.1007/s10584-014-1232-y
Contributors to the frequency of intense climate disasters
in Asia-Pacific countries
Vinod Thomas & Jose Ramon G. Albert &
Cameron Hepburn
Received: 13 September 2013 /Accepted: 17 August 2014
# The Author(s) 2014. This article is published with open access at Springerlink.com
1 Introduction
The frequency of intense natural disasters (defined here as events triggered by hazards of
nature and causing at least 100 deaths or affecting the survival needs of at least 1,000 people)1
increased notably from the 1970s to the 2000s. Intense hydrometeorological disasters (related
to floods and storms), and climatological disasters (related to droughts and heat waves) to a
lesser extent, accounted for most of the worldwide increase in natural disasters (Fig. 1).
Around half of these occurred in Asia and the Pacific (“Asia-Pacific” from here on); nearly
three-fourths of these were intense hydro-meteorological disasters.
While improved reporting is sometimes credited with some of such rising frequencies, our
focus on intense events (which are less likely to be under-reported in the past) reduces that
possibility (Peduzzi et al. 2009; Thomas et al. 2013). Furthermore, while we note a sizable
increase in the frequency of intense climate-related2 disasters, the frequency of intense
geophysical disasters (related to earthquakes and volcanoes) has only slightly increased.
1Reported events causing at least 10 deaths, affecting at least 100 people, or prompting a declaration of a state of
emergency or a call for international assistance are recorded in the International Disaster Database (EM-DAT). Of
these recorded disasters, this paper considers intense events, defined here as those causing at least 100 deaths or
affecting the survival needs of at least 1,000 people.
2Most climate-related disasters are rapid onset disasters (e.g. disasters related to cyclones, hurricanes and
typhoons). In the case of floods, while some arrive very fast, others arrive within specific season or with a few
days warning and are slow onset. Droughts are relatively slow onset disasters that can occur over a long period of
time.
V. Thomas (*)
Asian Development Bank, 6 ADB Avenue, Mandaluyong 1550, Philippines
e-mail: vthomas@adb.org
J. R. G. Albert
Philippine Institute for Development Studies, 106 Amorsolo Street, Legaspi Village, 1229 Makati,
Philippines
e-mail: jalbert@mail.pids.gov.ph
C. Hepburn
Smith School of Enterprise and the Environment, Oxford University Centre for the Environment, South
Parks Road, Oxford OX1 3QY, United Kingdom
e-mail: cameron.hepburn@smithschool.ox.ac.uk
2. Fig. 1 Global frequency of intense natural disasters by type (1971–2012)
Figure 2 sets out three linkages involving climate-related disasters. First, greenhouse gas
(GHG) emissions alter atmospheric GHG concentrations and thus affect climate variables,
specifically temperature and precipitation (IPCC 2007). Second, changes in the climate
variables affect the frequency of climate-related hazards (IPCC 2012). Third, the frequency
of climate-related hazards affects the risk of natural disasters (IPCC 2012; Stott et al. 2012).
We define disaster risk as the likelihood of losses (deaths, injuries, destruction and damage to
property, and others) that a hazard might cause. Disaster risk is influenced by three elements:
(i) the hazard itself; (ii) the population exposed to the hazard (exposure); and (iii) the
community’s ability to withstand its impact (vulnerability) (Peduzzi et al. 2009; Thomas
et al. 2013). Population exposure and vulnerability together can turn a specific natural hazard
into a disaster.
There is a great deal of research on the science of climate change (IPCC 2013; IPCC 2012;
Hansen and Sato 2012; Huber and Knutti 2012; Knutson et al. 2010; and Trenberth 2011), as
well as a growing and somewhat controversial literature on natural disasters and climate
change (IPCC 2012 and 2013; Mendelsohn et al. 2012; Bouwer 2011; Kunkel et al. 2013;
Pielke et al. 2008; Li et al. 2013; Hallegatte 2012; Coumou and Rahmsdorf 2012).3 Different
studies bring out the link between GHG emissions and climate change as well as climate
related hazards. Some of the literature, on the other hand, suggests that anthropogenic climate
change does not have an impact on the losses from natural disasters. This paper contributes to
the literature through an econometric analysis, a set of statistical methods commonly used in
analyzing socioeconomic variables to test a hypothesis. This allows us to examine the
importance of three principal factors, exposure, vulnerability and climate change, taken
together, in the rising threat of natural disasters in Asia-Pacific.
Kellenberg and Mobarak (2008) and Kahn (2005) examined the role of exposure and
vulnerability as determinants of the frequency of death toll from natural disasters, employing
negative binomial regressions over annual country observations (panel data) worldwide for the
periods 1975–2002 and 1980–2002. Other econometric analyses (Rentschler 2013; Bakkensen
2013; Noy 2009; Skidmore and Toya 2002) considered the determinants of disaster cost. Our
study builds on existing econometric models that analyze hypothesized contributors to a
phenomenon, in this case, natural disasters. As in many of these models, it assesses the
significance of the key factors contributing to the frequency of intense natural disasters over
3 For a more detailed discussion of related literature, see Thomas et al. (2013).
Climatic Change
3. Climatic Change
time and across countries. One main difference from previous work is our use of climate
anomalies as independent variables, in addition to exposure and vulnerability. We employ the
frequency of intense natural disasters as the dependent variable because it is less likely to have
a measurement bias compared to the alternative of using value of losses or damages due to
disasters.
2 Methods and Data
It is very difficult to establish causal relations among the variables in our analysis. But
following Kellenberg and Mobarak (2008) and Kahn (2005), we use econometrics to test
the association between the frequencies of intense disasters due to floods and storms, as well as
those due to droughts and heat waves, and the three identified factors of disaster risk.
The dependent variables in the analysis are the annual frequencies of hydromete-orological4
(Hit) and climatological5 disasters (Cit) that killed 100 or more people or
affected 1,000 or more in country i in year t. Data for these variables are obtained
from EM-DAT (2011).6 Other research defines disaster risk as the probable level of
damages in US dollars, based on historical data. However, measuring the impact of
natural disaster in monetary terms is constrained by the lack of standards for compa-rable
estimation across countries.
As both dependent variables are count variables, two econometric count models are
used. The Poisson model, which assumes that the count dependent variable has a
Poisson distribution, is employed for climatological disasters because it satisfies the
necessary equidispersion assumption (i.e., the mean of the frequency of climatological
disasters is equal to its variance). Meanwhile the negative binomial regression model7
(Allison and Waterman 2002) is employed for hydrometeorological disasters as like-lihood
ratio tests indicate over-dispersion (i.e., the variance of the frequency of
hydrometeorological disasters is greater than its mean). Specifically, we estimate the
following regression equations using annual observations for each of the 53 Asia-
Pacific countries8 (panel data) over 1971–2010, both with country fixed and random
effects.9 The term "panel data" refers to observations of the same entities such as
people, firms or countries over different points in time. Regression equations based on
such data, or panel regressions, allow for the estimation of a relationship between
4 EM-DAT defines hydrological disasters as “events which are caused by deviations in the normal water cycle
and/or overflow of bodies of water caused by wind set-up,” and meteorological disasters are those “caused by
short-lived/small to mesoscale atmospheric processes (in the spectrum from minutes to days).”
5 Climatological disasters in EM-DAT refer to “events caused by longlived/meso- to macro-scale processes.”
6 A summary of the descriptive statistics for the variables used in the regressions is provided in Appendix
Table 5.
7 The negative binomial regression model is an econometric model for count variables with over-dispersion. It is
derived from the Poisson model by adding an independently distributed error term (ε). It allows the mean to differ
from the variance of the count variable such that variability of Y is assumed to be equal to the mean: VarðYjXÞ
¼ EðYjXÞ ½1 þ αEðYjXÞ where EðYjXÞ ¼ expðβ0
þ β1 X1
þ β2X2 þ⋯þ βkXk Þ and α is a measure of
dispersion. When α is not significantly greater from zero (i.e., no overdispersion), the negative binomial model
reduces to the Poisson model. The negative binomial regression model is more efficient when α is significantly
different from zero (i.e., the count outcome data are over dispersed).
8 See Appendix Table 6 for a list of countries in our sample.
9 In panel data analysis, while the random effects model assumes that individual (e.g. country) specific factors are
uncorrelated with the independent variables, the fixed effects model recognizes that the individual specific factors
are correlated with the independent variables.
4. Fig. 2 Greenhouse gases, climate, and disaster risk
Climatic Change
dependent and independent variables while controlling for time-invariant country
specific factors and/or variables that change over time but not across countries.10
E Hit
Uit ; Vit ;Wit ; Xit h i¼ exp β0 þ β1Uit þ β2Vit þ β3Wit þ β4Xit ð Þexp εit ð Þ: ð1Þ
E Cit
Uit; Vit;Wit; Xit; Zit h i¼ exp β0 þ β1Uit þ β2Vit þ β3Wit þ β4Xit þ β5Zit ð Þð2Þ
Population exposure (Uit ) is measured here by the logarithm of population density (people
per square kilometer) in the country, as a proxy for the actual population exposed to a hazard
(Birkmann 2007; Taubenböck et al. 2008). We use national-level data for all variables given
their availability for all (socio-economic and climatic) variables, and given the advantages of
consistency across all variables.We recognize that the use of national-level data can hide intra-country
information, for example, on the distribution of the population within the country such
as the rise or presence of densely populated coastal areas. Further research could seek to collect
and analyze data on relevant variables at an intra-country level.
The regression also includes total population per country Xit ð Þ as a control variable to
reflect the possibility that the frequency of natural disasters surpassing the intense reporting
threshold would be higher when the overall population increases.
Socioeconomic vulnerability (Vit ), or the population’s capability to cope with a hazard, is
represented here by the natural logarithm of real income per capita. Vulnerability, a multi-dimensional
concept,11 is a function of wealth and other demographic and socioeconomic
10 See Baltagi (2008)
11 Aside from economic, social, cultural, environmental, and psychological dimensions, some studies also
consider physical and ecological dimensions of vulnerability (e.g. McDowell et al. 2013; Hutanuwatr 2012;
Hinkel 2011; Luers 2005). Here, these are captured by the exposure variable in this paper. While the various
dimensions of vulnerability can be contextual, there are also common elements across time, space, and scale (see
Kelly and Adger 2000, Turner II et al. 2003, Thomalla et al. 2006, Füssel and Klein 2006, Smith and Wandel
2006, O’Brien et al. 2007, and Internal Disaster Monitoring Centre 2009). For a detailed literature on social
vulnerability, see Cutter et al. (2009).
5. Climatic Change
factors (Füssel and Klein 2006). In this paper, we focus on socioeconomic vulnerability which
is often proxied by income levels. Previous research has shown vulnerability to be positively
associated with disaster risk (Kahn 2005; Noy 2009; Toya and Skidmore 2007). The square of
the natural logarithm of real income per capita is included to capture a hypothesized nonlinear,
inverted-U relationship between natural disasters and average income which is posited to
emerge as the result of two different effects (Kahn 2005; Kellenberg and Mobarak 2008). First,
as income increases from very low levels, individuals may rationally choose to take advantage
of new economic opportunities that involve bearing more disaster risk. However, as income
grows further and the marginal utility of further consumption falls, the risk-return trade-off
moves to favor reduction of disaster risk.
For the water-related and temperature-related climate hazards, we employ annual data on
average precipitation anomaly (Wit ) and average surface temperature anomaly (Zit ). These
are based on quality-controlled monthly data from the Global Precipitation Climatology Center
(GPCC 2011) and theMet Office Hadley Centre and the Climate Research Unit12 (CRU) at the
University of East Anglia (Morice et al. 2012).13 Both the GPCC’s precipitation anomaly and
CRU’s temperature anomaly are computed as departures from the average for its 30-year base
climatology period—1961–1990 (the period with the best data coverage).14 Climate anomalies
more accurately describe how climate varies over larger areas, as well as how it trends over
time, and allow more meaningful comparisons between locations (with varying elevations)
than absolute climate indicators.
Average climate anomaly by country and year are computed as mean anomalies of all grid
squares lying within a bounding box containing the country defined by the maximum and
minimum latitude and longitude.15 Grid level climate anomaly data are available from the
Food and Agriculture Office of the United Nations.
In representing climate as an independent variable, equation (1) relies on precipitation
anomaly, which is, in turn, influenced by the temperature anomaly. Equation (2) includes
temperature anomaly as an additional independent variable reflecting the observation that
temperature is more directly related to climatological disasters than hydrometeorological
disasters.
We also consider whether the climate anomaly coefficients vary over five subregions of
Asia-Pacific, specifically Southeast Asia, East Asia, South Asia, Central Asia and the Pacific
(see Fig. 3).16 We consider this because precipitation is even more spatially variable than
temperature (Rudolf et al. 2010). According to the 2012 Intergovernmental Panel on Climate
Change (IPCC) Report “it was likely that there had been increases in the frequency of heavy
precipitation events (e.g., 95th percentile) over the second half of the 20th century within many
12 CRU’s HADCRUT3 temperature anomaly dataset is available on a spatial resolution of 2.5°×2.5° (latitude by
longitude) grid across land and ocean surfaces.
13 Based on quality-controlled data from 67,200 stations world-wide, GPCC’s full precipitation dataset is
available on a high spatial resolution (latitude by longitude: 2.5°×2.5° global grid). Other longitudinal global
precipitation datasets are based on data from less number of stations and available on a lower spatial resolution
only. For instance, the Global Historical Climatology Network (GHCN) precipitation dataset which has recent
global data up to 2012 is based only on 20,590 stations worldwide and is available on a 5°×5°resolution only.
14 Different climate anomaly datasets use different base climatology periods. For instance, GHCN uses 1971–
2000 as its base climatology period for its precipitation anomaly datasets. It is important to note that overall,
adjustment of data to different reference periods does not change the shape of climate anomaly time-series or
affect the trends within it.
15 The average climate anomaly in country i and year t is computed as follows: Xit =Σm
maxð j;kÞ
Σ
minð j;kÞ
xim=ð# of gridsÞ
where xim is the average climate anomaly for country i and month m, (j,k) represents (j x k) spatial grid.
16 This is implemented empirically by the use of an interaction variable, which is the product of average climate
anomaly and a dummy variable that takes a value 1 if the country belongs to a subregion and 0 otherwise.
6. Fig. 3 Map of Asia and the Pacific and its subregions
Climatic Change
land regions… There is low to medium confidence in trends in heavy precipitation in Asia…
However, statistically significant positive and negative trends were observed at subregional
scales within these regions…”
This paper uses an unconditional fixed effects negative binomial panel regression model
(all parameters estimated) rather than a conditional fixed effects panel regression model (or
conditional on the value of a sufficient statistic for one or more parameters). The latter would
not be a true fixed effects model, because it does not control for unchanging covariates or the
variables that are possibly predictive of the outcome under study (Allison and Waterman
2002).
Since changes in climate typically persist for decades or longer (IPCC 2012), we also look
at disaster frequencies on a decadal basis. To capture the effect of the frequency of climate
anomalies, we estimate equations (1) and (2) using decadal observations for each Asia-Pacific
country in the sample over four decades: 1971–1980, 1981–1990, 1991–2000, 2001–2010.
We also consider the number of years with positive average precipitation (temperature)
anomaly as an additional explanatory variable.
The panel regressions employed here have limitations. The panel data used in the regres-sions
are somewhat unbalanced, because data for climate and vulnerability indicators are not
available for some years in some countries for the period under study (see Appendix Table 6
for number of sample observations and period by country). Also, population exposure and
vulnerability may not be fully exogenous. If the regressors are not strongly exogenous, then
the coefficient estimates will be inconsistent and thus cannot be relied upon to compute
marginal effects. Nonetheless, while the frequency of disasters may affect the population
exposed to disasters and their income, its direct effect on the exposure and vulnerability
indicators in this paper is expected to be limited.
The period covered in the analysis was limited by the availability of quality-controlled
global precipitation data.17 The choice of variables used in the model is constrained by data
availability. For example, variables such as land use (Brath et al. 2006), the actual number of
people living in the hazard-prone areas, and the share of these figures to the total population
would have been of keen interest. However, these data are neither regularly generated nor
17 GPCC’s full precipitation dataset is updated up to 2010 only. Other alternative longitudinal global precipitation
datasets are based on data from fewer stations and available on a lower spatial resolution only.
7. Climatic Change
readily available. Furthermore, our analysis is based on the annual picture because data for
some of the control variables, such as population density and population, are only available
annually.
3 Results
Table 1 shows the results of our negative binomial model with random (columns 1 and 2) and
country-fixed effects (columns 3 and 4), unpacking the drivers of disasters due to floods and
storms in Asia-Pacific in the past four decades, using annual country-level data. Results of
Table 1 Intense hydrometeorological disasters in Asia and the Pacific
Dependent variable: Intense hydrometeorological disasters (1971–2010)
Explanatory variables Random effects Country-fixed effects
(1) (2) (3) (4)
Exposure
Ln(population density) 0.2366 0.2409 1.1928*** 1.2429***
[0.148] [0.149] [0.255] [0.258]
Vulnerability
Ln(GDP per capita, constant 2000 $) 0.9213 0.9525 1.9222 1.9601
[1.460] [1.462] [1.242] [1.242]
Square of Ln(GDP per capita, constant 2000 $) −0.0758 −0.0776 −0.1101 −0.1121
[0.100] [0.100] [0.086] [0.086]
Climate hazard
Average precipitation deviation 0.0111*** 0.0114*** 0.0062*** 0.0037***
[0.002] [0.002] [0.002] [0.001]
Average precipitation deviation x East Asia −0.0086** 0.0074***
[0.004] [0.002]
Average precipitation deviation x South Asia 0.0054 0.0127***
[0.004] [0.002]
Average precipitation deviation x Central Asia 0.0497*** 0.0995***
[0.016] [0.026]
Average precipitation deviation x Pacific −0.0021 −0.0014
[0.005] [0.005]
Population (in million) 0.0023*** 0.0023*** −0.0002 −0.0003
[0.001] [0.001] [0.001] [0.001]
Observations 1,685 1,685 1,685 1,685
Number of economies 53 53 53 53
Pseudo R-squared 0.099 0.100 0.369 0.372
Alpha 1.631 1.622 0.049 0.046
LR test of alpha=0 (Chi-bar, χ) 691.9 679.7 9.032 8.180
*** significant at 1 %. Figures are negative binomial panel regression results. An intercept term and country
dummies are included but not reported here. The coefficient of ‘Average Precipitation Deviation‘ in specifications
(2) and (4) represents the coefficient for Southeast Asia― the reference subregion (omitted dummy). Robust
country-cluster z statistics in brackets
8. Climatic Change
generalized Hausman specification tests (Hausman and Taylor 1981), as well as the strongly
significant estimates for most country indicator variables, support the use of country fixed-effects
(equation 1; also see results in columns 3 and 4 in Table 1), instead of random effects
(equation 2; columns 1 and 2 in Table 1). These results confirm that country-specific factors—
possibly ones such as governance effectiveness and quality of institutions (Tol and Yohe 2007;
Brooks et al. 2005; Barr et al. 2010)—may contribute to the frequency of intense climate-related
disasters in Asia-Pacific. Their effect might also be viewed as interaction effects of
exposure and vulnerability.
We find a statistically significant relation between the annual frequency of these disasters and two
of the three drivers—population exposure and climate hazards. The relationship between their
annual frequencies and the vulnerability variable in this model does not show statistical significance.
Exposure, proxied by population density, is a strong significant driver, both statistically and
economically. Specifically, a 1%increase in the number of people per square kilometer is associated
with a 1.2 % increase in the average annual frequency of intense hydrometeorological disasters in a
country (see columns 3 and 4 in Table 1). All else equal, countries that are more densely populated
are significantly more likely to suffer an intense disaster when a storm or flood hits.
When vulnerability is represented by average income level, the coefficients on the level and
square of income suggest first a positive association between income and annual frequency of
the disaster at lower levels of income and then a negative association at higher levels, but they
are not statistically significant. When vulnerability is represented by a poverty indicator, (see
Thomas et al. 2013), the coefficient estimates are not robust to changes in sample time periods
(such as 1980–2010 or 1990–2010), or to the use of different threshold levels for poverty head
count ratio (such as 25 % or 35 %).
Average precipitation anomaly is a consistently significant factor in explaining the annual
frequency of intense climate disasters. Holding other variables constant, a unit increase in the
annual average precipitation anomaly from the 30-year average baseline rainfall (1960–1990)
is associated with around a 0.6 % increase in the annual frequency of disasters due to floods
and storms. Temperature anomaly might also be considered a potential climate hazard variable
with regard to meteorological disasters. Reflective of observations in IPCC (2012),18 however,
we find a relationship between average temperature anomaly and the frequency of intense
meteorological disasters that is very sensitive to model specification—random effects and
country-fixed effects. The relationship between average precipitation anomaly and frequency
of hydrometeorological disasters (as well as hydrological ones) remains positive and signifi-cant
with and without the inclusion of the average temperature anomaly variable. It also
remains positive and strongly significant when climate subregion interaction terms are used
(columns 2 and 4 in Table 1).
The coefficient for average precipitation anomaly varies across subregions. For example, results
in column 4 suggest that a unit increase in the annual average precipitation anomaly is associated
with a 0.4 % average increase in frequency of disasters due to floods and storms in Southeast Asia;
1.1%in East Asia (i.e., the sum of 0.0037 and 0.0074); 1.6%in SouthAsia; 10.3%in CentralAsia;
and 0.2%in the Pacific.19 Except for the Pacific, all the coefficient estimates for the interaction terms
are significantly different from zero.
18 IPCC (2012) finds lower confidence with respect to increases for storms than floods, and a demonstrable link
to climate more tenuous.
19 Note that Central Asia has relatively few intense floods and storms, so a given increase in precipitation
deviation in this otherwise relatively dry subregion implies a large percentage change in disasters, especially in
view of its lack of preparedness.
9. Climatic Change
The coefficient estimate for average precipitation anomaly remains positive and strongly
significant when a time trend variable is included to control for other factors that affect the
frequency of intense disasters and that are not directly observable but are highly correlated
with time (e.g., location of population). The coefficient of the time trend variable is significant
and positive across specifications (not shown).
Table 2 illustrates what this might mean for some of the countries in question.
Using the coefficient estimates for average precipitation in the specification with the
highest pseudo R-squared (column 4 in Table 1), we estimate the increase in the
frequency of these disasters from the 2001–2010 levels for Indonesia, the Philippines,
and Thailand under three scenarios. We use the observed increase in average precip-itation
anomaly from 1991–2000 to 2001–2010 (8.33 mm [mm] per month) among
countries in Southeast Asia, including Brunei Darussalam, Indonesia, the Lao People’s
Democratic Republic, Malaysia, the Philippines, Singapore, Thailand, and Viet Nam,
for setting the possible increase in the average precipitation anomaly under three
scenarios: Scenario 1 (low increase) represents an increase in the average precipitation
anomaly by 4 mm per month; Scenario 2 (moderate increase) by 8 mm per month;
and Scenario 3 (high increase) by 12 mm per month.
The estimates suggest that, holding other things constant, an increase in average precipi-tation
anomaly by 8 mm per month (moderate scenario), as experienced in Southeast Asia in
the last decade, could be associated with an increase in the average frequency of disasters due
to floods and storms in Indonesia by an average of around 0.18 per year from an average of 6 a
year in 2001–2010. This is equivalent to an additional event in Indonesia every 5–6 years. In
the Philippines the moderate scenario is associated with an increase of around 0.35 disaster a
year, or an additional disaster every 3 years; in Thailand the increase is 0.11 disaster a year, or
an additional disaster every 9 years. If the increase in average precipitation hits 12 mm per
month (high scenario), greater increases in the frequency of these disasters can be expected:
one additional event every 4 years in Indonesia, every 2 years in the Philippines, and every
6 years in Thailand. These assumptions were based on the observed increases in precipitation
anomalies between the last two decades (1991–2000 and 2001–2010). This does not neces-sarily
mean that precipitation levels would increase in the future. IPCC 2014 notes that
precipitation trends vary across countries with both increasing and decreasing trends observed
in different parts of Asia.
Table 2 Increase in intense hydrometeorological disasters associated with an increase in precipitation deviation
Country Annual frequency of
hydro-meteorological di-sasters
(2001–2010)
Precipitation deviation
(millimeters per
month, 2001–2010)
Scenario 1:
Low increase
(dW=4)
Scenario 2:
Moderate
increase (dW=
8)
Scenario 3:
High increase
(dW=12)
Increase in the annual frequency of intense
hydrometeorological disasters associated with
an increase in precipitation deviation ðdH ¼
β dW Ho )
3
Indonesia 6.00 9.92 0.09 0.18 0.27
Philippines 11.80 10.84 0.18 0.35 0.53
Thailand 3.60 1.14 0.05 0.11 0.16
Estimates are computed using the estimated coefficient of ‘Average precipitation deviation’ for the reference
subregion (Southeast Asia) in Table 1 column (4), that is, β3 = 0.0037
10. Climatic Change
Table 3 shows a negative relationship between average precipitation anomaly and intense
climatological disasters. It also shows a positive relationship between temperature anomaly
and intense climatological disasters: disasters due to droughts and heat waves tend to
occur when it is unusually dry and hot. IPCC 2012 finds “medium confidence” that
droughts have become more intense and frequent, which together with the rising
trends in precipitation and temperature anomalies, provides the context for the present
findings.
A unit increase in average precipitation anomaly is associated with a 3 % reduction
in the annual frequency of disasters due to droughts and heat waves, while a unit
increase in temperature anomaly is associated with a 72 % increase in the annual
frequency of disasters due to droughts and heat waves (see column 3 in Table 3). The
coefficients of these two climate variables are significant with both random and
country fixed-effects. However, the coefficients for the climate variables are not
statistically significant when including climate subregion interaction terms. Except
for Central Asia and the Pacific, there is no significant variation in the effect of
average precipitation anomaly on climatological disasters across subregions. The
coefficients of exposure and vulnerability variables also are not significant.
Table 4 confirms that population exposure and vulnerability, as well as of the
frequency of climate anomalies, are significant in explaining decadal changes in the
average annual frequency of intense disasters due to floods and storms. Specifically,
changes in average annual income per capita are significant in explaining decadal
changes in the average annual frequency of these disasters. Both the average precipita-tion
anomaly and the number of years that the average precipitation anomaly was
positive are significant. Similarly, average temperature anomaly and the frequency of
positive temperature anomaly are both significant for explaining decadal increases in the
annual average frequency of intense climatological disasters but not population density
nor income. While Hausman specification tests justify the use of country-fixed effects,
since the number of decadal observations per country is at most only four, we rely in this
case on the results with random effects.
This econometric analysis gives us a basis to explore further the role of the climate
variables, in addition to socioeconomic variables in assessing disaster trends. The association
between precipitation anomaly and disasters related to floods and storms features with a high
degree of statistical significance.
Climatic data are annualized to have a common frequency and hence balance across
variables: all other variables used in our regression analysis, such as the number of disasters,
GDP, population are available annually. We use climate anomalies rather than absolute climate
values since the former would seem to allow for more meaningful comparisons across
countries. For example, even if two countries have the same temperature in a month it is
possible that for one country it is greater than the long term average and the opposite for the
other. The IPCC also principally uses annual mean anomalies in their presentations. Alterna-tive
measures such as extremes of precipitation and temperature anomalies in a year may
present some problems. For example, extreme precipitation anomaly in a dry season may not
result in flooding. Aggregation of anomalies to the annual and country level, however, could
smoothen some of the seasonal and geographical fluctuations. Further research on more
granular temporal periods and spatial scales would be of interest conditional upon the
necessary data being available.
It would also be useful to test our model using alternative exposure, vulnerability and
climate variables, as well as continue extending the data series as far as possible. The work can
also be extended to include other regions of world.
11. Climatic Change
4 Discussion
The main purpose of this study is to discuss the major factors associated with the frequency of
climate-related disasters observed in Asia-Pacific over recent decades. Taken together, the
Table 3 Intense climatological disasters in Asia and the Pacific
Dependent variable: Frequency of intense climatological disasters (1971–2010)
Explanatory variables Random effects Country-fixed effects
(1) (2) (3) (4)
Exposure
Ln(population density) −0.0263 −0.0901 1.1802 1.4895
[0.222] [0.212] [0.861] [1.128]
Vulnerability
Ln(GDP per capita, constant 2000 $) −1.1159 −1.3585 0.0223 −0.4207
[1.322] [1.347] [1.890] [2.430]
Square of Ln(GDP per capita, constant 2000 $) 0.0599 0.0780 0.0138 0.0440
[0.095] [0.095] [0.126] [0.167]
Climate hazard
Average precipitation deviation −0.0163*** −0.0146 −0.0267*** −0.0157*
[0.006] [0.013] [0.009] [0.009]
Average precipitation deviation x East Asia 0.0080 0.0052
[0.017] [0.020]
Average precipitation deviation x South Asia −0.0208 −0.0131
[0.024] [0.021]
Average precipitation deviation x Central Asia −0.0562* −0.1804*
[0.033] [0.094]
Average precipitation deviation x Pacific 0.0033 −0.0348*
[0.018] [0.020]
Average temperature anomalies 0.5505*** 1.2078*** 0.7241** 0.1850
[0.204] [0.459] [0.313] [0.608]
Average temperature anomalies x East Asia −0.6663 0.6453
[0.610] [0.717]
Average temperature anomalies x South Asia 0.0303 0.3148
[0.521] [0.547]
Average temperature anomalies x Central Asia −1.2266** 1.0074
[0.501] [0.624]
Average temperature anomalies x Pacific −0.7344 −0.7539
[0.736] [1.405]
Population (million) 0.0022*** 0.0022*** −0.0010 −0.0010
[0.000] [0.000] [0.001] [0.001]
Observations 1,685 1,685 1,685 1,685
Pseudo R-squared 0.1760 0.1940 0.340 0.351
* significant at 10 %; ** significant at 5 %; *** significant at 1 %. Figures are Poisson panel regression results.
An intercept term and country dummies are included, but not reported here. The coefficient of average
precipitation in specifications (2) and (4) represents the coefficient for Southeast Asia― the reference subregion
(omitted dummy). Robust country-cluster z statistics in brackets
12. Table 4 Intense hydrometeorological and climatological disasters in Asia and the Pacific
Climatic Change
Dependent variable: Frequency of intense hydrometeorological/climatological disasters, by decade (1971–2010)
Explanatory variables (1) (2) (3) (4)
Hydrometeorological Climatological
Exposure
Ln(population density) 0.1781* 0.2183** −0.0060 −0.0076
[0.096] [0.108] [0.013] [0.013]
Vulnerability
Ln(GDP per capita, constant 2000 $) 5.6830*** 5.5177*** −0.0293 −0.0321
[2.134] [2.091] [0.135] [0.137]
Square of Ln(GDP per capita, constant 2000 $) −0.3530*** −0.3422*** 0.0015 0.0016
[0.131] [0.128] [0.009] [0.009]
Climate
Average precipitation deviation 0.0480** −0.0005
[0.024] [0.002]
Average temperature anomalies 0.0718*
[0.039]
No. of years per decade that average precipitation deviation
exceeds 1961–1990 average
0.1280* 0.0028
[0.069] [0.009]
No. of years per decade with average temperature deviation
exceeds 1961–1990 average
0.0121***
[0.005]
Population (in million) 0.0091*** 0.0090*** 0.0008*** 0.0008***
[0.002] [0.002] [0.000] [0.000]
Observations 156 156 156 156
R-squared 0.5288 0.5297 0.5111 0.5137
No. of countries 52 52 52 52
* significant at 10 %; ** significant at 5 %; *** significant at 1 %. Figures are results of negative binomial panel
regression with random effects. An intercept term is included but not reported here. Robust statistics in brackets.
Only 52economies with complete observations by decade were included in this decadal analysis. The Occupied
Palestinian Territory was excluded in the analysis since it has incomplete observations across the 4 decades in
1971–2010
results of the econometric analysis suggest that three principal factors—rising population
exposure, population vulnerability, and climate anomalies —play a part, even if notably
differentiated amongst them and across subregions. While climatological disasters are strongly
associated with changing temperature, hydrometeorological disasters are most clearly associ-ated
with rising exposure of people as well as precipitation anomalies.
Results from this study suggest that precipitation and temperature anomalies are
empirically relevant in explaining the threats from hazards of nature. Many policy
makers are aware that atmospheric concentrations of CO2, the primary anthropogenic
GHG, have recently surpassed 400 ppm and are set to exceed 450 ppm in less than a
quarter of a century, given current increases of over 2 ppm a year. IPCC’s 5th
Assessment Report affirms the human influence in the warming of the climate system,
mostly through the increase in the atmospheric concentration of CO2, while this
paper’s econometric results suggest an association between climate change and greater
frequency of intense disasters due to floods, storms, droughts and heat waves. These
13. two results put together suggest an association between the frequency of intense
climate-related disasters in Asia-Pacific and GHG emissions.
Two sets of policy implications follow. First, reducing exposure of the population and their
vulnerability should feature centrally in policies to confront the growing threat from floods,
storms, droughts and heat waves. Second, climate mitigation should be added as a crucial
dimension in disaster prevention.
To sum up, this study finds that anthropogenic climate change is associated with the
frequency of intense natural disasters in Asia-Pacific countries. A major implication of this
is that, in addition to dealing with exposure and vulnerability, disaster prevention would
benefit from addressing climate change through reducing man made greenhouse gases in the
atmosphere. Prevention of natural disasters, as compared to response, does not receive
adequate policy attention because these are generally considered one off acts of nature rather
than being influenced by climatic factors caused by human activities. By giving an empirical
basis to the relationship between climate anomalies and frequency of disasters, this study
draws attention to prevention, including climate mitigation.
Acknowledgments The basic structure of this paper is built on a recent ADB Economics Working Paper by
Vinod Thomas, Jose Ramon G. Albert and Rosa T, Perez. The authors are grateful to the Centre for Research on
the Epidemiology of Disasters and to the Manila Observatory for collaboration and data support; Ramon Lopez,
Debarati Guha-Sapir, Geoffrey Ducanes, and P.V. Srinivasan for their inputs; Robert Brakenridge, Hina Choksy,
Sam Fankhauser, Thomas Kansy, Philip Keefer, Kelly Levin, Tom McDermott, Eric Neumayer, Swenja
Surminksi and John Ward for their suggestions; and Ken Chomitz, Walter Kolkma, Valerie Reppelin-Hill, Alan
Rodger, Vankina B. Tulasidhar, and Tomoo Ueda for comments; and Ruth Francisco for valuable assistance.
Appendix
Table 5 Descriptive statistics (1971–2010)
Variable Obs. Mean Std.
Dev.
Min. Max.
ti
Frequency of intense hydrometeorological disasters 1,685 1.11 2.48 0.00 25.00
Frequency of intense climatological disasters 1,685 0.11 0.42 0.00 6.00
Population density (people per square km) 1,685 622.62 2,171.06 1.12 19,416.29
Population (million) 1,685 71.44 217.56 0.05 1,337.83
GDP per capita (constant 2000 $) 1,685 6,579.37 9,707.36 122.09 61,374.75
Average precipitation deviation (mm/month) W¼ Σn
1 pt
s−sp
=n : where ps t
represents the average precipitation in station
s in year t ps is the average precipitation for 1960–1990, and
n is the total number of stations in country i
1,685 −0.77 14.88 −95.44 74.88
ti
East Asia 1,685 0.09 5.67 −39.44 46.96
South Asia 1,685 −0.04 3.85 −29.48 26.23
Southeast Asia 1,685 0.18 8.88 −55.78 60.65
Central Asia 1,685 −0.13 2.55 −14.05 12.96
Pacific 1,685 −0.86 9.43 −95.44 74.88
Average temperature anomaliesW¼ Σn
1 at
s−as =n : where as t
represents the average temperature in station s in year t, as is
the average temperature for 1960–1990, and n is the total
number of stations in country i
1,685 0.32 0.50 −1.42 2.41
Climatic Change
14. Table 5 (continued)
Variable Obs. Mean Std.
Dev.
Climatic Change
Min. Max.
East Asia 1,685 0.04 0.22 −1.02 2.41
South Asia 1,685 0.05 0.23 −1.17 1.74
Southeast Asia 1,685 0.04 0.16 −0.50 1.14
Central Asia 1,685 0.16 0.43 −1.42 2.22
Pacific 1,685 0.03 0.12 −0.60 1.19
GDP=gross domestic product, Max. = maximum value,Min. = minimum value, Obs. = number of observations,
PPP=purchasing power parity, Std. Dev.=standard deviation
Sources: Data on number of intense climate-related disasters based on natural disaster observations reported on
Emergency Event Database (EM-DAT) Centre for Research on the Epidemiology of Disasters accessed 6
December 2011 (http://www.emdat.be); Population, Population Density, and GDP per capita are from the
World Bank’s World Development Indicators Online accessed 14 January 2012 (http://databank.worldbank.
org); Average Precipitation Deviation is from Global Precipitation Climatology Center (GPCC 2011) accessed 28
December 2011 (http://www.gpcc.dwd.de); Average Temperature Anomalies is from Met Office Hadley Centre
and the Climate Research Unit at the University of East Anglia accessed 28 December 2011 (http://www.cru.uea.
ac.uk/cru/data/temperature)
Table 6 Number of sample observations by economy and subregion*
Economy Number of Observations Period
1971-80 1981-90 1991-2000 2000-10 Total
East Asia
China, People’s Rep. of 10 10 10 10 40 1971–2010
Hong Kong, China 10 10 10 10 40 1971–2010
Japan 10 10 10 10 40 1971–2010
Korea, Rep. of 10 10 10 10 40 1971–2010
Macao, China – 9 10 10 29 1982–2010
Mongolia – 10 10 10 30 1981–2010
South Asia
Bangladesh 10 10 10 10 40 1971–2010
Bhutan 1 10 10 10 31 1980–2010
India 10 10 10 10 40 1971–2010
Iran 10 10 10 9 39 1971–2009
Nepal 10 10 10 10 40 1971–2010
Pakistan 10 10 10 10 40 1971–2010
Sri Lanka 10 10 10 10 40 1971–2010
Southeast Asia
Brunei Darussalam 7 10 10 10 37 1974–2010
Cambodia – – 8 10 18 1993–2010
Indonesia 10 10 10 10 40 1971–2010
Lao PDR – 7 10 10 27 1984–2010
15. Climatic Change
Table 6 (continued)
Economy Number of Observations Period
1971-80 1981-90 1991-2000 2000-10 Total
Malaysia 10 10 10 10 40 1971–2010
Philippines 10 10 10 10 40 1971–2010
Singapore 10 10 10 10 40 1971–2010
Thailand 10 10 10 10 40 1971–2010
Timor-Leste – – 2 10 12 1999–2010
Viet Nam – 7 10 10 27 1984–2010
Central Asia
Armenia – 1 10 10 21 1990–2010
Azerbaijan – 1 10 10 21 1990–2010
Bahrain 1 10 10 10 31 1980–2010
Cyprus 6 10 10 10 36 1975–2010
Georgia 10 10 10 10 40 1971–2010
Iraq – – 4 10 14 1997–2010
Israel 10 10 10 10 40 1971–2010
Jordan 6 10 10 10 36 1975–2010
Kazakhstan – 1 10 10 21 1990–2010
Kuwait – – 6 10 16 1986–2010
Kyrgyzstan – 5 10 10 25 1986–2010
Lebanon – 3 10 10 23 1988–2010
State of Palestine – – 7 5 12 1994–2005
Oman 10 10 10 10 40 1971–2010
Qatar – – 1 10 11 2000–2010
Saudi Arabia 10 10 10 10 40 1971–2010
Syrian Arab Republic 10 10 10 10 40 1971–2010
Tajikistan – 6 10 10 26 1986–2010
Turkmenistan – 4 10 10 24 1987–2010
United Arab Emirates 6 10 10 10 36 1975–2010
Uzbekistan – 4 10 10 24 1987–2010
Yemen – 1 10 10 21 1990–2010
Pacific
Australia 10 10 10 10 40 1971–2010
Fiji 10 10 10 10 40 1971–2010
Kiribati 10 10 10 10 40 1971–2010
New Caledonia 10 10 10 – 30 1971–2000
New Zealand 4 10 10 10 34 1977–2010
Papua New Guinea 10 10 10 10 40 1971–2010
Solomon Islands – 1 10 10 21 1990–2010
Vanuatu 2 10 10 10 32 1979–2010
*This follows the United Nation’s (UN) geographical subregional groupings since most of the data used (except
for the climate anomalies and frequency of disasters) are from UN agencies. ‘–’denotes missing observations
16. Climatic Change
Open Access This article is distributed under the terms of the Creative Commons Attribution License which
permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are
credited.
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