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Trend Analysis and Spatial Distribution of Meteorological Disaster Losses in China, 2004–2015
Meteorological disasters caused a lot of losses. We involved six categories (all disasters, floods, hail, typhoon, snow and heatwave) to observe their death and economic losses’ spatial-time distribution. The time trend of mortality was analyzed using a chi-square test for linear trends. Economic lo...
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Published in: | Atmosphere 2022-02, Vol.13 (2), p.208 |
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description | Meteorological disasters caused a lot of losses. We involved six categories (all disasters, floods, hail, typhoon, snow and heatwave) to observe their death and economic losses’ spatial-time distribution. The time trend of mortality was analyzed using a chi-square test for linear trends. Economic loss was described by direct economic loss and loss rate of GDP, whose trends were described by a trend line. Using annual percent change (APC) estimated by fitting weighted linear regression model, the change degree of mortality was assessed. On a national level, there was a statistically significant decreasing trend in mortality of all disasters (Z = −39.82, p < 0.05), floods (Z = −18.79, p < 0.05), hail (Z = −20.43, p < 0.05), typhoon (Z = −37.47, p < 0.05), snow (Z = −9.02, p < 0.05) and heatwave (Z = −8.76, p < 0.05) from 2004 to 2015 in China. The time trend of the loss rate of GDP was decreasing while the trend of direct economic losses was increasing. Western China was the most seriously hit area. APCs remained in downward trends (APCs < 0) in most of the provinces, while central provinces were with upward trends (APCs > 0). Areas with increasing mortality (APCs > 0) for different disasters included the southwest areas and Zhejiang (for floods), the northwest and south areas (for hail), Sichuan, Guangxi and Hainan (for typhoon), the west and northeast areas (for snow) and Hebei, Henan and Shanghai (for heatwave). As for economic losses, eastern areas were hit with a high amount of economic losses, but central areas were hit with a high GDP loss rate. Generally, nationwide death and economic losses caused by meteorological disasters have decreased. However, there were some relatively serious effects in the central and western areas for which urgent attention from policymakers is required. |
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We involved six categories (all disasters, floods, hail, typhoon, snow and heatwave) to observe their death and economic losses’ spatial-time distribution. The time trend of mortality was analyzed using a chi-square test for linear trends. Economic loss was described by direct economic loss and loss rate of GDP, whose trends were described by a trend line. Using annual percent change (APC) estimated by fitting weighted linear regression model, the change degree of mortality was assessed. On a national level, there was a statistically significant decreasing trend in mortality of all disasters (Z = −39.82, p < 0.05), floods (Z = −18.79, p < 0.05), hail (Z = −20.43, p < 0.05), typhoon (Z = −37.47, p < 0.05), snow (Z = −9.02, p < 0.05) and heatwave (Z = −8.76, p < 0.05) from 2004 to 2015 in China. The time trend of the loss rate of GDP was decreasing while the trend of direct economic losses was increasing. Western China was the most seriously hit area. APCs remained in downward trends (APCs < 0) in most of the provinces, while central provinces were with upward trends (APCs > 0). Areas with increasing mortality (APCs > 0) for different disasters included the southwest areas and Zhejiang (for floods), the northwest and south areas (for hail), Sichuan, Guangxi and Hainan (for typhoon), the west and northeast areas (for snow) and Hebei, Henan and Shanghai (for heatwave). As for economic losses, eastern areas were hit with a high amount of economic losses, but central areas were hit with a high GDP loss rate. Generally, nationwide death and economic losses caused by meteorological disasters have decreased. However, there were some relatively serious effects in the central and western areas for which urgent attention from policymakers is required.]]></description><identifier>ISSN: 2073-4433</identifier><identifier>EISSN: 2073-4433</identifier><identifier>DOI: 10.3390/atmos13020208</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Chi-square test ; Climate change ; direct economic losses ; Disasters ; Distribution ; Economic analysis ; Economic impact ; Economics ; Floods ; GDP ; Gross Domestic Product ; Hail ; Heat waves ; Hurricanes ; meteorological disasters ; Mortality ; Rain ; Regression models ; Snow ; Spatial analysis ; Spatial distribution ; Statistical analysis ; Statistical tests ; time trend ; Trend analysis ; Trends ; Typhoons</subject><ispartof>Atmosphere, 2022-02, Vol.13 (2), p.208</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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We involved six categories (all disasters, floods, hail, typhoon, snow and heatwave) to observe their death and economic losses’ spatial-time distribution. The time trend of mortality was analyzed using a chi-square test for linear trends. Economic loss was described by direct economic loss and loss rate of GDP, whose trends were described by a trend line. Using annual percent change (APC) estimated by fitting weighted linear regression model, the change degree of mortality was assessed. On a national level, there was a statistically significant decreasing trend in mortality of all disasters (Z = −39.82, p < 0.05), floods (Z = −18.79, p < 0.05), hail (Z = −20.43, p < 0.05), typhoon (Z = −37.47, p < 0.05), snow (Z = −9.02, p < 0.05) and heatwave (Z = −8.76, p < 0.05) from 2004 to 2015 in China. The time trend of the loss rate of GDP was decreasing while the trend of direct economic losses was increasing. Western China was the most seriously hit area. APCs remained in downward trends (APCs < 0) in most of the provinces, while central provinces were with upward trends (APCs > 0). Areas with increasing mortality (APCs > 0) for different disasters included the southwest areas and Zhejiang (for floods), the northwest and south areas (for hail), Sichuan, Guangxi and Hainan (for typhoon), the west and northeast areas (for snow) and Hebei, Henan and Shanghai (for heatwave). As for economic losses, eastern areas were hit with a high amount of economic losses, but central areas were hit with a high GDP loss rate. Generally, nationwide death and economic losses caused by meteorological disasters have decreased. However, there were some relatively serious effects in the central and western areas for which urgent attention from policymakers is required.]]></description><subject>Chi-square test</subject><subject>Climate change</subject><subject>direct economic losses</subject><subject>Disasters</subject><subject>Distribution</subject><subject>Economic analysis</subject><subject>Economic impact</subject><subject>Economics</subject><subject>Floods</subject><subject>GDP</subject><subject>Gross Domestic Product</subject><subject>Hail</subject><subject>Heat waves</subject><subject>Hurricanes</subject><subject>meteorological disasters</subject><subject>Mortality</subject><subject>Rain</subject><subject>Regression models</subject><subject>Snow</subject><subject>Spatial analysis</subject><subject>Spatial distribution</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>time trend</subject><subject>Trend analysis</subject><subject>Trends</subject><subject>Typhoons</subject><issn>2073-4433</issn><issn>2073-4433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpVUUtLBDEMHkRBUY_eC14d7TSdTuco6xNWPKjn0kdGu4zTte0evPkf_If-Eqsrokkgr48vJKmqg4YeA_T0ROfnkBqgrKjcqHYY7aDmHGDzT7xd7ae0oEV4Dwz4TqXuI06OnE56fE0-EV2Su6XOXo_kzKccvVllHyYSBnKDGUMMY3j0dt3WKWMk85ASJuInMnvykz4irPB_vL0z2rR71dagx4T7P363erg4v59d1fPby-vZ6by20NFcI6WmBdk6jgKd6BztGEqpOZreUsfBDFIMekCUYJ1rnRZgDTjaIm2F4LBbXa95XdALtYz-WcdXFbRX34UQH5WO2dsRlRHOOgZGGt5x2zeayQ6bYr3gvZG2cB2uuZYxvKwwZbUIq1gulBQTwBjnooeCqtcoG8v-EYffqQ1VXy9R_14Cn4rjfv0</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Qi, Qian</creator><creator>Jiang, Baofa</creator><creator>Ma, Wei</creator><creator>Marley, Gifty</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8092-3280</orcidid><orcidid>https://orcid.org/0000-0002-6647-1740</orcidid></search><sort><creationdate>20220201</creationdate><title>Trend Analysis and Spatial Distribution of Meteorological Disaster Losses in China, 2004–2015</title><author>Qi, Qian ; Jiang, Baofa ; Ma, Wei ; Marley, Gifty</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-e00b5385d4e6ed67d072e88a4eb9c0d43bf86fafee83cdd5da63cb3d05e056643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Chi-square test</topic><topic>Climate change</topic><topic>direct economic losses</topic><topic>Disasters</topic><topic>Distribution</topic><topic>Economic analysis</topic><topic>Economic impact</topic><topic>Economics</topic><topic>Floods</topic><topic>GDP</topic><topic>Gross Domestic Product</topic><topic>Hail</topic><topic>Heat waves</topic><topic>Hurricanes</topic><topic>meteorological disasters</topic><topic>Mortality</topic><topic>Rain</topic><topic>Regression models</topic><topic>Snow</topic><topic>Spatial analysis</topic><topic>Spatial distribution</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>time trend</topic><topic>Trend analysis</topic><topic>Trends</topic><topic>Typhoons</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qi, Qian</creatorcontrib><creatorcontrib>Jiang, Baofa</creatorcontrib><creatorcontrib>Ma, Wei</creatorcontrib><creatorcontrib>Marley, Gifty</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Atmosphere</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qi, Qian</au><au>Jiang, Baofa</au><au>Ma, Wei</au><au>Marley, Gifty</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Trend Analysis and Spatial Distribution of Meteorological Disaster Losses in China, 2004–2015</atitle><jtitle>Atmosphere</jtitle><date>2022-02-01</date><risdate>2022</risdate><volume>13</volume><issue>2</issue><spage>208</spage><pages>208-</pages><issn>2073-4433</issn><eissn>2073-4433</eissn><abstract><![CDATA[Meteorological disasters caused a lot of losses. We involved six categories (all disasters, floods, hail, typhoon, snow and heatwave) to observe their death and economic losses’ spatial-time distribution. The time trend of mortality was analyzed using a chi-square test for linear trends. Economic loss was described by direct economic loss and loss rate of GDP, whose trends were described by a trend line. Using annual percent change (APC) estimated by fitting weighted linear regression model, the change degree of mortality was assessed. On a national level, there was a statistically significant decreasing trend in mortality of all disasters (Z = −39.82, p < 0.05), floods (Z = −18.79, p < 0.05), hail (Z = −20.43, p < 0.05), typhoon (Z = −37.47, p < 0.05), snow (Z = −9.02, p < 0.05) and heatwave (Z = −8.76, p < 0.05) from 2004 to 2015 in China. The time trend of the loss rate of GDP was decreasing while the trend of direct economic losses was increasing. Western China was the most seriously hit area. APCs remained in downward trends (APCs < 0) in most of the provinces, while central provinces were with upward trends (APCs > 0). Areas with increasing mortality (APCs > 0) for different disasters included the southwest areas and Zhejiang (for floods), the northwest and south areas (for hail), Sichuan, Guangxi and Hainan (for typhoon), the west and northeast areas (for snow) and Hebei, Henan and Shanghai (for heatwave). As for economic losses, eastern areas were hit with a high amount of economic losses, but central areas were hit with a high GDP loss rate. Generally, nationwide death and economic losses caused by meteorological disasters have decreased. However, there were some relatively serious effects in the central and western areas for which urgent attention from policymakers is required.]]></abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/atmos13020208</doi><orcidid>https://orcid.org/0000-0002-8092-3280</orcidid><orcidid>https://orcid.org/0000-0002-6647-1740</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Chi-square test Climate change direct economic losses Disasters Distribution Economic analysis Economic impact Economics Floods GDP Gross Domestic Product Hail Heat waves Hurricanes meteorological disasters Mortality Rain Regression models Snow Spatial analysis Spatial distribution Statistical analysis Statistical tests time trend Trend analysis Trends Typhoons |
title | Trend Analysis and Spatial Distribution of Meteorological Disaster Losses in China, 2004–2015 |
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