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Classification and Evaluation of Current Climate Vulnerability Assessment Methods

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Abstract

Vulnerability to climate change is a complex, multi-dimensional construct influenced by multiple interacting factors. Several methods and approaches have been developed over the past three decades, yet there are no standard methods for assessing vulnerability (Connelly et al. in State of the art report (4) vulnerability assessment: definitions, indicators and existing assessment methods (issue 4), 2015). The vulnerability assessment studies differ in conceptualization, methodology, sectors affected, exposure to specific hazards, regional factors, and the scale of impact. Assessment of climate vulnerability and identification of indicators to measure it are significant problems. This paper provides a comprehensive and systematic review of indicator-based vulnerability assessment studies from 1990 to 2020. We analyse 84 studies to understand various aspects of vulnerability assessment—concept and approach, dimensions and indicators, and assessment methods. Though multi-dimensional assessments represent the overall vulnerability of an area, only 29.8% of the studies assessed more than one dimension. Analysis shows that 68.8% (75 of 109) of the identified indicators belong to the socioeconomic dimension. Socioeconomic vulnerability is the most assessed, and environmental vulnerability is the least assessed dimension, possibly attributed to ease of data availability. Due to the lack of methodological differences, there has been confusion associated with index-based and indicator-based studies in the literature (Ramieri et al. in Methods for assessing coastal vulnerability to climate change. ETC CCA Tech Paper 1/2011 (issue January), 2011. 10.13140/RG.2.1.1906.9840). Therefore, we develop a taxonomy of the existing vulnerability assessment methods based on their methodological approach. To avoid ambiguity, we denote all methods that employ indicators as indicator-based vulnerability assessment methods and classify them into index-based, clustering-based, and GIS-based methods. Finally, we discuss the advantages and disadvantages of each vulnerability assessment method and the open challenges in this research area.

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Funding

This work is supported by the Department of Science and Technology (DST), Government of India [Grant number: DST/CCP/CoE/140/2018(G)].

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Authors and Affiliations

  1. Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India

    Sindhuja Kasthala

  2. Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Mumbai, India

    D. Parthasarathy & K. Narayanan

  3. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India

    Arun B. Inamdar

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  1. Sindhuja Kasthala
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  2. D. Parthasarathy
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  3. K. Narayanan
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Correspondence to Sindhuja Kasthala.

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Appendices

Appendix-1 Literature summary table

No

Reference

Spatial extent

Spatial granularity

Dimensions assessed

No. of indicators

Vulnerability assessment method

Weightage technique

1

V. Gornitz et al. (1991)

East Coast, USA

Continuous Coastline

PV

7

Unweighted aggregation

 ~ 

2

Gornitz et al. (1994)

East Coast, USA

Continuous Coastline

PV

13

Weighted aggregation (Implicit)

Function

3

Briguglio (1995)

Small Island Developing States (SIDS)

Country

EV + PV

3

Weighted aggregation (explicit)

Subjective

4

Clark et al. (1998)

Revere City, USA

Census Block

SEV

34

Weighted aggregation (Explicit)

DEA

5

Thieler and Hammar-Klose (1999)

Atlantic Coast, USA

Continuous Coastline

PV

6

Unweighted aggregation

 ~ 

6

Kaly et al. (1999)

Australia, Fiji and Tuvalu

Country

PV + SEV + EV

57

Weighted aggregation (explicit)

Subjective

7

Cutter et al. (2000)

South Carolina, USA

Census Block

SEV

20

Unweighted aggregation

 ~ 

8

Bryan et al. (2001)

Northern Spencer Gulf study

Continuous Coastline

PV

4

GIS-based

 ~ 

9

Davidson et al. (2001)

USA

County

SEV

27

Weighted aggregation (explicit)

AHP

10

Wu et al. (2002)

New Jersey, USA

Census Block

SEV

9

Unweighted aggregation

 ~ 

11

Villa and McLeod (2002)

South Pacific SIDS

Country

EV

47

Weighted aggregation (explicit)

Expert group

12

Cutter et al. (2003)

USA

County

SEV

42

Weighted aggregation (Implicit)

 ~ 

13

Vincent (2004)

Africa

Country

SEV

9

Weighted aggregation (explicit)

Subjective

14

NATCOM, p. 113 (2004)

East and West Coast, India

District

SEV

9

Clustering-based

 ~ 

15

Boruff et al. (2005)

US coast

County

PV + SEV

45

Weighted aggregation (Implicit)

 ~ 

16

Pendleton et al. (2005)

North Carolina, USA

Continuous Coastline

PV

6

Unweighted aggregation

 ~ 

17

Brooks et al. (2005)

Various countries

Country

SEV

11

Weighted aggregation (explicit)

Subjective

18

Doukakis. (2005)

Greece

Continuous Coastline

PV

6

Weighted aggregation (explicit)

Subjective

19

Kumpulainen (2006)

EU

Country

EV + SEV

4

Weighted aggregation (explicit)

Subjective

20

Kumar and Tholkappian (2006)

East and West Coast, India

District

PV + SEV

12

Unweighted aggregation

 ~ 

21

Rygel et al. (2006)

Hampton Roads, USA

Census Block

SEV

57

Weighted aggregation (Explicit)

Pareto ranking

22

Kleinosky et al. (2007)

Hampton Roads, USA

Census Block

SEV

57

Weighted aggregation (Explicit)

Pareto ranking

23

Diez et al. (2007)

Buenos Aires, Argentina

Continuous Coastline

PV

7

Weighted aggregation (explicit)

Subjective

24

Borden et al. (2007)

132 U.S. cities

City

SEV

78

Weighted aggregation (Implicit)

 ~ 

25

Szlafsztein and Sterr (2007)

Pará, Brazil

Municipal Districts

PV + SEV

16

GIS-based

Subjective

26

Boruff and Cutter (2007)

Caribbean island nations (Saint Vincent and Barbados)

Country

SEV

15

Weighted aggregation (Implicit)

 ~ 

27

Hegde and Reju (2007)

Mangalore, India

Continuous Coastline

PV + SEV

4

Unweighted aggregation

 ~ 

28

Cutter and Finch (2008)

USA

County

SEV

42

Weighted aggregation (Implicit)

 ~ 

29

Schmidtlein et al. (2008)

Charleston, South Carolina; Los Angeles, California; and New Orleans, Louisiana

County, Intermediate and Tract

level

SEV

33

Weighted aggregation (Implicit)

 ~ 

30

Shahid and Behrawan (2008)

Bangladesh

District

SEV

7

Unweighted aggregation

 ~ 

31

Sharma and Patwardhan (2008)

East and West Coast, India

District

PV + SEV

5

Clustering-based, Unweighted aggregation

 ~ 

32

Rao et al. (2008)

Andhra Pradesh, India

Continuous Coastline

PV

5

Weighted aggregation (explicit)

Subjective

33

Balica et al. (2009)

Various case studies

River basin, Sub-catchment and Urban area

PV + SEV + EV

28

Component-based aggregation

 ~ 

34

Fekete (2009)

Germany

County

SEV

41

Weighted aggregation (Implicit)

 ~ 

35

Dwarakish et al. (2009)

Udipi, Karnataka, India

Continuous Coastline

PV

6

Unweighted aggregation

 ~ 

36

Narayanan and Patnaik (2009)

East Coast, India

District

SEV

14

Unweighted aggregation

 ~ 

37

Hahn et al. (2009)

Mabote and Moma Districts, Mozambique

District

SEV

28

Unweighted aggregation

 ~ 

38

Burton (2010)

Mississippi’s Gulf Coast

County

SEV

32

Weighted aggregation (Implicit)

 ~ 

39

Balica and Wright (2010)

Various case studies

River basin, Sub-catchment and Urban area

PV + SEV + EV

71

Implicit Weighting

 ~ 

40

Gbetibouo et al. (2010)

South Africa

Provinces

EV + SEV

19

Weighted aggregation (Explicit)

PCA

41

Vincent and Cull (2010)

Maangani, South Africa

Household

SEV

7

Weighted aggregation (explicit)

Subjective

42

Kumar et al. (2010)

Orissa, India

Continuous Coastline

PV

8

Unweighted aggregation

 ~ 

43

Palmer et al. (2011)

KwaZulu-Natal, South Africa

Continuous Coastline

PV

7

Unweighted aggregation

 ~ 

44

Müller et al. (2011)

Chile

Municipality

SEV

12

GIS-based

Surveys

45

Sheik Mujabar and Chandrasekar (2013)

Tamilnadu, India

Continuous Coastline

PV

6

Unweighted aggregation

 ~ 

46

Holand et al. (2011)

Norway

Municipality

SEV

33

Weighted aggregation (Implicit)

 ~ 

47

Mahendra et al. (2011)

Cuddalore-villupuram, India

Continuous Coastline

PV

4

GIS-based

 ~ 

48

Orencio and Fujii (2013)

Baler, Aurora, Philippines

Community

EV + SEV

21

Unweighted aggregation

 

49

Balica et al. (2012)

BuenosAires, Calcutta & 7 other cities

City

PV + SEV

19

Component-based aggregation

 ~ 

50

Farhan and Lim (2012)

Seribu Islands, Indonesia

Islands

EV

5

GIS-based

Subjective

51

Yin et al. (2012)

China

Continuous Coastline

PV

8

Weighted aggregation (explicit)

AHP

52

Arun Kumar and Kunte (2012)

Chennai, India

Continuous Coastline

PV

8

Unweighted aggregation

 ~ 

53

INCOIS (2012)

Coastal India

Continuous Coastline

PV

7

Unweighted aggregation

 ~ 

54

Yin et al. (2013)

Coastal China

Community

PV + SEV

14

Weighted aggregation (explicit)

AHP

55

Joevivek et al. (2013)

Southern Tamilnadu, India

District

PV

6

Unweighted aggregation

 ~ 

56

Addo (2013)

Accra, Ghana

Continuous Coastline

PV

7

Unweighted aggregation

 ~ 

57

Ge et al. (2013)

Yangtze River Delta, China

Continuous Coastline

SEV

9

Weighted aggregation (Implicit)

PPC

58

Saxena et al. (2013)

Cuddalore, Tamilnadu, India

Continuous Coastline

PV + SEV

75

Weighted aggregation (explicit)

AHP

59

Murali et al. (2013)

Puducherry, India

County

PV + SEV

11

Weighted aggregation (explicit)

AHP

60

Armaș and Gavriș (2013)

Bucharest, Romania

Settlement/Community

SEV

18

Weighted aggregation (Implicit)

subjective

61

Felsenstein and Lichter (2014)

Israel

Continuous Coastline

SEV

6

Weighted aggregation (explicit)

Subjective

62

Siagian et al. (2014)

Indonesia

District

SEV

10

Weighted aggregation (Implicit)

PCA

63

Islam et al. (2014)

Bangladesh Coast

Community

PV + SEV

25

Component-based aggregation

 ~ 

64

Ahsan and Warner (2014)

southwest Bangladesh

Community

SEV

27

Weighted aggregation (explicit)

Subjective

65

Lixin et al. (2014)

323 cities in China

City

SEV

12

Weighted aggregation (explicit)

Subjective

66

González-Riancho et al. (2014)

El Salvador coast

Municipality

EV + SEV

24

Weighted aggregation (explicit)

Subjective

67

Lee (2014)

Taiwan

Township

SEV

13

Unweighted aggregation

 ~ 

68

Yoo et al. (2014)

Jakarta, Indonesia

Municipal District

EV + SEV

10

Component-based aggregation

Subjective

69

Kunte et al. (2014)

Goa, India

Continuous Coastline

PV + SEV

9

Unweighted aggregation

 ~ 

70

Bahinipati (2014)

Odisha, India

District

PV + SEV

36

Weighted aggregation (Implicit)

 ~ 

71

Yang et al. (2015)

China

Provinces

SEV

115

Weighted aggregation (Implicit)

 ~ 

72

Mahapatra et al. (2015)

South Gujarat, India

Continuous Coastline

PV + SEV

9

Weighted aggregation (explicit)

AHP

73

Bergstrand et al. (2015)

USA

County

SEV

30

Weighted aggregation (Implicit)

 ~ 

74

Salik et al. (2015)

Pakistan

Household

EV + SEV

32

Component-based aggregation

 ~ 

75

Su et al. (2015)

Chinese coastal cities

City

SEV

17

Component-based aggregation

 ~ 

76

Sherly et al. (2015)

Mumbai, India

Ward

SEV

33

Weighted aggregation (Explicit)

DEA

77

Maiti et al. (2015)

East Coast, India

District

PV + SEV

43

Component-based aggregation

PCA

78

Fernandez et al. (2016)

Vila Nova de Gaia, Portugal

Municipality

SEV

26

GIS-based

AHP

79

Colburn et al. (2016)

East & Gulf Coast, US

Community

SEV

25

Weighted aggregation (Implicit)

 ~ 

80

Mansur et al. (2016)

Amazon Delta & Estuary

Urban sector

SEV

13

Weighted aggregation (explicit)

AHP

81

Yadav and Barve (2017)

Odisha, India

Household

SEV

29

Component-based aggregation

 ~ 

82

Stafford and Abramowitz (2017)

Hampton Roads, USA

Census tract/ Sub-county

SEV

41

Clustering-based, Weighted aggregation (Implicit)

PCA

83

Mazumdar and Paul (2018)

Odisha, India

Census Block

SEV

32

Weighted aggregation (Implicit)

 ~ 

84

Sahoo and Bhaskaran (2018)

Odisha, India

District

PV + SEV + EV

16

Weighted aggregation (Implicit)

 ~ 

Appendix-II List of indicators and frequency of occurrence (among 84 studies):

2.1 Physical Vulnerability

  1. 1.

    Shoreline change—29.76% (25 among 84 studies)

  2. 2.

    Geomorphology—27.38% (23)

  3. 3.

    Significant wave height—26.19% (22)

  4. 4.

    Sea-level rise—26.19% (22)

  5. 5.

    Tidal range—25% (21)

  6. 6.

    Coastal slope—23.80% (20)

  7. 7.

    Coastal elevation—16.67% (14)

  8. 8.

    Coast length—8.33% (7)

  9. 9.

    Geology—5.95% (5)

  10. 10.

    Topography—4.76% (4)

  11. 11.

    Distance from sea/river—3.57% (3)

  12. 12.

    Coast length—2.38% (2)

  13. 13.

    Insularity—2.38% (2)

  14. 14.

    Drainage—2.38% (2)

2.2 Socio-Economic Vulnerability

  1. 1.

    Population, population density—42.88% (36)

  2. 2.

    Age, gender (vulnerable population)—42.88% (36)

  3. 3.

    Income, poverty—35.71% (30)

  4. 4.

    Housing infrastructure (quality, price, rented/own, old/new, disaster-resistant, etc.)—34.52% (29)

  5. 5.

    Literacy—29.76% (25)

  6. 6.

    Employment rate, total employed, labour force—28.57% (24)

  7. 7.

    Critical infrastructure (hospitals, etc.)—21.42% (18)

  8. 8.

    Family structure (size)—20.23% (17)

  9. 9.

    GDP—19.04% (16)

  10. 10.

    Disabled population—16.67% (14)

  11. 11.

    Roads and other transport—15.47% (13)

  12. 12.

    Urban or rural—14.28% (12)

  13. 13.

    Education level—14.28% (12)

  14. 14.

    Marginalized population (socioeconomic status)—14.28% (12)

  15. 15.

    Agriculture dependency (area under cultivation, etc.)—14.28% (12)

  16. 16.

    Population growth—13.09% (11)

  17. 17.

    Race and ethnicity—13.09% (11)

  18. 18.

    Sanitation, drainage—13.09% (11)

  19. 19.

    Female employment—11.90% (10)

  20. 20.

    Occupational structure—11.90% (10)

  21. 21.

    Energy, electricity—11.90% (10)

  22. 22.

    Emergency infrastructure (cyclone shelters, etc.)—11.90% (10)

  23. 23.

    Water supply—10.71% (9)

  24. 24.

    Information, awareness & internet facilities—10.71% (9)

  25. 25.

    Vehicle ownership—9.52% (8)

  26. 26.

    Warning & evacuation system—9.52% (8)

  27. 27.

    Cultural heritage, tourist density—8.33% (7)

  28. 28.

    Infrastructure management—8.33% (7)

  29. 29.

    Built density—8.33% (7)

  30. 30.

    Infant mortality rate—7.14% (6)

  31. 31.

    Birth rate, death rate—7.14% (6)

  32. 32.

    Migration—7.14% (6)

  33. 33.

    Past experience/ recovery time—7.14% (6)

  34. 34.

    Institutional infrastructure—7.14% (6)

  35. 35.

    Communication (radio, television, phones, etc.)—7.14% (6)

  36. 36.

    Female literacy—5.95% (5)

  37. 37.

    Deaths/ mortality—5.95% (5)

  38. 38.

    Life expectancy—5.95% (5)

  39. 39.

    Human Development Index—5.95% (5)

  40. 40.

    Non-workers—5.95% (5)

  41. 41.

    Availability of capital (human, social, natural, financial & physical)—5.95% (5)

  42. 42.

    Damaged houses, houseless population—4.76% (4)

  43. 43.

    Agricultural labourers—4.76% (4)

  44. 44.

    Livestock population—4.76% (4)

  45. 45.

    Irrigation—4.76% (4)

  46. 46.

    Food production (rice, milk, eggs, etc.)—4.76% (4)

  47. 47.

    Insurance, security—4.76% (4)

  48. 48.

    Educational infrastructure—4.76% (4)

  49. 49.

    Household industry workers—3.57% (3)

  50. 50.

    Marginal workers—3.57% (3)

  51. 51.

    Spread of institutional setup—3.57% (3)

  52. 52.

    Agriculture productivity, crop diversification—3.57% (3)

  53. 53.

    Fishing vessels and methods—3.57% (3)

  54. 54.

    Food security—3.57% (3)

  55. 55.

    Community participation—3.57% (3)

  56. 56.

    Banking facilities—3.57% (3)

  57. 57.

    Assets—3.57% (3)

  58. 58.

    Hazardous waste infrastructure, toxic waste—3.57% (3)

  59. 59.

    Storage capacity (dams)—3.57% (3)

  60. 60.

    Commercial and industrial development—2.38% (2)

  61. 61.

    Consumption of fertilizers—2.38% (2)

  62. 62.

    Cropping intensity—2.38% (2)

  63. 63.

    Damage to croplands—2.38% (2)

  64. 64.

    Gini coefficient—2.38% (2)

  65. 65.

    Investments in research and development—2.38% (2)

  66. 66.

    Commercial infrastructure—2.38% (2)

  67. 67.

    Industries—2.38% (2)

  68. 68.

    Isolated areas—1.19% (1)

  69. 69.

    Duration in current occupation—1.19% (1)

  70. 70.

    Government Effectiveness—1.19% (1)

  71. 71.

    Manufacturing (infrastructure)—1.19% (1)

  72. 72.

    Small-scale industries—1.19% (1)

  73. 73.

    Access to basic services—1.19% (1)

  74. 74.

    Cooperative societies—1.19% (1)

  75. 75.

    Nuclear facilities—1.19% (1)

2.3 Environmental Vulnerability

  1. 1.

    Landuse Landcover changes—16.67% (14)

  2. 2.

    Protected areas, reserves—5.95% (5)

  3. 3.

    Coral extraction, sand/gravel/other extraction—4.76% (4)

  4. 4.

    Ecosystem changes—3.57% (3)

  5. 5.

    Biodiversity—3.57% (3)

  6. 6.

    Wetland valuation—2.38% (2)

  7. 7.

    Environmental development projects—2.38% (2)

  8. 8.

    Deforestation/ forest change rate—2.38% (2)

  9. 9.

    Mangroves (health, depletion, etc.)—2.38% (2)

  10. 10.

    Threatened/degraded ecosystems—2.38% (2)

  11. 11.

    Unpopulated area—2.38% (2)

  12. 12.

    Evaporation rate—2.38% (2)

  13. 13.

    Degraded growth/ area—2.38% (2)

  14. 14.

    Pollution, pollutant area—2.38% (2)

  15. 15.

    Hazardous waste—1.19% (1)

  16. 16.

    Access to ecosystem services—1.19% (1)

  17. 17.

    Overfishing—1.19% (1)

  18. 18.

    Endemic species—1.19% (1)

  19. 19.

    Pathogen outbreaks—1.19% (1)

  20. 20.

    War or civil strife—1.19% (1)

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Kasthala, S., Parthasarathy, D., Narayanan, K. et al. Classification and Evaluation of Current Climate Vulnerability Assessment Methods. Soc Indic Res 171, 605–639 (2024). https://doi.org/10.1007/s11205-023-03271-x

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