{"title":"工作场所的热浪:韩国受热浪影响职业的类型和空间分布","authors":"Sangyun Jeong , Hanna Kang , Minjin Cho , Up Lim","doi":"10.1016/j.scs.2024.105921","DOIUrl":null,"url":null,"abstract":"<div><div>Adapting to heatwaves and other climate change impacts requires identifying vulnerable demographic segments within regions. However, investigations into the spatial distribution of heatwave-vulnerable workers and its implications for local economies have been limited. This study categorizes occupations exposed to heatwaves into five subgroups and analyzes temporal changes in their spatial distributions via a spatial Markov chain model. The results indicate significant heterogeneity in vulnerability among heatwave-exposed occupations, with variations in income, foreign worker proportions, and job instability. The analysis reveals that heatwave-exposed workers are primarily concentrated outside the capital region. Group 3 (manufacturing) exhibited notable industrial clustering, whereas Group 5 (agriculture and fishery) presented high and stable concentrations in rural areas. Conversely, Group 4 (low-skilled and market-sensitive) demonstrates substantial spatial variability. Spatial Markov chain analysis highlights Group 3′s strong agglomeration tendencies influenced by neighboring cities, whereas Group 5 shows minimal spatial effects. Groups 2 and 4 experience considerable shifts in spatial distribution, with Group 2 showing only a 68.7 % probability of sustaining high concentration and Group 4 showing a 62.7 % probability. Recommendations for adaptation strategies and future research related to the economic impacts of climate change are provided on the basis of these findings.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105921"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Heatwaves at work: Typology and spatial distributions of occupations exposed to heatwaves in Korea\",\"authors\":\"Sangyun Jeong , Hanna Kang , Minjin Cho , Up Lim\",\"doi\":\"10.1016/j.scs.2024.105921\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Adapting to heatwaves and other climate change impacts requires identifying vulnerable demographic segments within regions. However, investigations into the spatial distribution of heatwave-vulnerable workers and its implications for local economies have been limited. This study categorizes occupations exposed to heatwaves into five subgroups and analyzes temporal changes in their spatial distributions via a spatial Markov chain model. The results indicate significant heterogeneity in vulnerability among heatwave-exposed occupations, with variations in income, foreign worker proportions, and job instability. The analysis reveals that heatwave-exposed workers are primarily concentrated outside the capital region. Group 3 (manufacturing) exhibited notable industrial clustering, whereas Group 5 (agriculture and fishery) presented high and stable concentrations in rural areas. Conversely, Group 4 (low-skilled and market-sensitive) demonstrates substantial spatial variability. Spatial Markov chain analysis highlights Group 3′s strong agglomeration tendencies influenced by neighboring cities, whereas Group 5 shows minimal spatial effects. Groups 2 and 4 experience considerable shifts in spatial distribution, with Group 2 showing only a 68.7 % probability of sustaining high concentration and Group 4 showing a 62.7 % probability. Recommendations for adaptation strategies and future research related to the economic impacts of climate change are provided on the basis of these findings.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"116 \",\"pages\":\"Article 105921\"},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670724007455\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007455","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Heatwaves at work: Typology and spatial distributions of occupations exposed to heatwaves in Korea
Adapting to heatwaves and other climate change impacts requires identifying vulnerable demographic segments within regions. However, investigations into the spatial distribution of heatwave-vulnerable workers and its implications for local economies have been limited. This study categorizes occupations exposed to heatwaves into five subgroups and analyzes temporal changes in their spatial distributions via a spatial Markov chain model. The results indicate significant heterogeneity in vulnerability among heatwave-exposed occupations, with variations in income, foreign worker proportions, and job instability. The analysis reveals that heatwave-exposed workers are primarily concentrated outside the capital region. Group 3 (manufacturing) exhibited notable industrial clustering, whereas Group 5 (agriculture and fishery) presented high and stable concentrations in rural areas. Conversely, Group 4 (low-skilled and market-sensitive) demonstrates substantial spatial variability. Spatial Markov chain analysis highlights Group 3′s strong agglomeration tendencies influenced by neighboring cities, whereas Group 5 shows minimal spatial effects. Groups 2 and 4 experience considerable shifts in spatial distribution, with Group 2 showing only a 68.7 % probability of sustaining high concentration and Group 4 showing a 62.7 % probability. Recommendations for adaptation strategies and future research related to the economic impacts of climate change are provided on the basis of these findings.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;