Abstract
Research on barriers to climate change adaptation has, hitherto, disproportionately focused on institutional barriers. Despite the critical importance of personal barriers in shaping the adaptive response of humanity to climate change and variability, the literature on the subject is rather nascent. This study is premised on the hypothesis that place-specific characteristics (where you live) and compositional (both biosocial and sociocultural) factors may be salient to differentials in adaptation to climate change in coastal areas of developing countries. This is because adaptation to climate change is inherently local. Using cross-sectional survey data on 1,253 individuals (606 males and 647 females), barriers to adaptation to climate change were observed to vary with place, indicating that there is inequality in barriers to adaptation. In the multivariate models, the place-specific differences in barriers to adaptation were robust and remained statistically significant even when socio-demographic (compositional) variables were controlled. Observed differences in barriers to adaptation to climate change in coastal Tanzania mainly reflect strong place-specific disparities among groups indicating the need for adaptation policies that are responsive to processes of socio-institutional learning in a specific context, involving multiple people that have a stake in the present and the future of that place. These people are making complex, multifaceted choices about managing and adapting to climate-related risks and opportunities, often in the face of resource constraints and competing agendas.
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References
Adaptation Fund [AF] (2011). Project/Program Proposal: Implementation of concrete adaptation measures to reduce vulnerability of livelihood and economy of coastal and lakeshore communities in Tanzania. Submitted by UNEP and the Vice President’s Office, Division of Environment. http://www.adaptation-fund.org/node/1087
Adger, W. N. (2010). Social capital, collective action, and adaptation to climate change. In Der Klimawandel (pp. 327–345). VS Verlag für Sozialwissenschaften.
Adger, W. N., Dessai, S., Goulden, M., Hulme, M., Lorenzoni, I., Nelson, D. R., et al. (2009). Are there social limits to adaptation to climate change? Climatic Change, 93(3–4), 335–354.
Agrawal, A. (2010). Local institutions and adaptation to climate change. Social dimensions of climate change equity and vulnerability in a warming world (pp. 173–198). Washington DC: World Bank.
Ahmed, S. A., Diffenbaugh, N. S., Hertel, T. W., Lobell, D. B., Ramankutty, N., Rios, A. R., & Rowhani, P. (2011). Climate volatility and poverty vulnerability in Tanzania. Global Environmental Change, 21(1), 46–55.
Amundsen, H., Berglund, F., & Westskogô, H. (2010). Overcoming barriers to climate change adaptation—a question of multilevel governance? Environment and Planning C: Government and Policy, 28, 276–289.
Arndt, C., Farmer, W., Strzepek, K., & Thurlow, J. (2012). Climate change, agriculture and food security in Tanzania. Review of Development Economics, 16(3), 378–393.
Arora-Jonsson, S. (2011). Virtue and vulnerability: Discourses on women, gender and climate change. Global Environmental Change, 21(2), 744–751.
Artur, L., & Hilhorst, D. (2012). Everyday realities of climate change adaptation in Mozambique. Global Environment Change, 22(2), 529–536.
Baron, J. (2006). Thinking about global warming. Climatic Change, 77(1–2), 137–150.
Biesbroek, G. R., Klostermann, J. E., Termeer, C. J., & Kabat, P. (2013). On the nature of barriers to climate change adaptation. Regional Environmental Change, 1–11.
Blake, J. (1999). Overcoming the ‘value-action gap’ in environmental policy: Tensions between national policy and local experience. Local Environment, 4(3), 257–278.
Brooks, N., Neil Adger, W., & Mick Kelly, P. (2005). The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Global Environmental Change, 15(2), 151–163.
Bryan, E., Deressa, T. T., Gbetibouo, G. A., & Ringler, C. (2009). Adaptation to climate change in Ethiopia and South Africa: Options and constraints. Environmental Science & Policy, 12(4), 413–426.
Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, C., & Herrero, M. (2013). Adapting agriculture to climate change in Kenya: Household strategies and determinants. Journal of Environmental Management, 114, 26–35.
Burch, S. (2010). Transforming barriers into enablers of action on climate change: Insights from three municipal case studies in British Columbia, Canada. Global Environmental Change, 20(2), 287–297.
Cash, D. W., Adger, W. N., Berkes, F., Garden, P., Lebel, L., Olsson, P., et al. (2006). Scale and cross-scale dynamics: governance and information in a multilevel world. Ecology and Society, 11(2), 8.
Demetriades, J., & Esplen, E. (2008). The gender dimensions of poverty and climate change adaptation. IDS Bulletin, 39(4), 24–31.
Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248–255.
Eriksen, S. E., Klein, R. J., Ulsrud, K., Næss, L. O., & O’Brien, K. (2007). Climate change adaptation and poverty reduction: Key interactions and critical measures. Report prepared for the Norwegian Agency for Development Cooperation (Norad). GECHS Report, 1.
Ford, J. D., & Smit, B. (2004). A framework for assessing the vulnerability of communities in the Canadian Arctic to risks associated with climate change. Arctic, 389–400.
Francis, J., & Bryceson, I. (2001). Tanzanian coastal and marine resources: Some examples illustrating questions of sustainable use. Chapter, 4, 76–102.
Grothmann, T., & Patt, A. (2005). Adaptive capacity and human cognition: the process of individual adaptation to climate change. Global Environmental Change, 15(3), 199–213.
Halsnæs, K., & Verhagen, J. (2007). Development based climate change adaptation and mitigation—conceptual issues and lessons learned in studies in developing countries. Mitigation and Adaptation Strategies for Global Change, 12(5), 665–684.
Hepworth, N. (2010). Climate Change Vulnerability and Adaptation Preparedness in Tanzania. Nairobi: Heinrich Boll Foundation. Retrieved on 3rd March 2014 from http://www.boell.de/downloads/worldwide/Tanzania_Climate_Change_Adaptation_Preparedness.pdf.
Hove, H., Echeverria, D., Parry, J., & International Institute for Sustainable Development. (2011, November). Review of Current and Planned Adaptation Action: East Africa. Adaptation Partnership. Retrieved on 15th May 2014 from CAKE: http://www.cakex.org/virtual-library/review-current-and-planned-adaptation-action-east-africa.
Howden, S. M., Soussana, J. F., Tubiello, F. N., Chhetri, N., Dunlop, M., & Meinke, H. (2007). Adapting agriculture to climate change. Proceedings of the National Academy of Sciences, 104, 19691–19696.
http://www.nbs.go.tz/takwimu/references/Tanzania_in_figures2012.pdf
International Institute for Environment and Development [IIED]. (2009). “Cultivating Success: the need to climate-proof Tanzanian agriculture.” Retrieved on 15th May 2014 from http://www.iied.org/pubs/pdfs/17073IIED.pdf
Irwin, A., & Wynne, B. (Eds.). (1996). Misunderstanding science?: The public reconstruction of science and technology. UK: Cambridge University Press. 240 pp.
Jack, C. (2010). Climate Projections for United Republic of Tanzania. University of Cape Town Climate Systems Analysis Group. Retrieved on 15th May 2014 from http://economics-of-cc-in-tanzania.org/images/Climate_projections_CCE_report_ver1_1__1__1_v2.pdf
Jones, L. (2010). Overcoming social barriers to climate change adaptation. Overseas Development Institute: Background Note July 2010. Retrieved on 13/01/2014 from http://dspace.cigilibrary.org/jspui/bitstream/123456789/29328/1/Overcoming%20social%20barriers%20to%20climate%20change%20adaptation.pdf?1
Kithiia, J. (2011). Climate change risk responses in East African cities: need, barriers and opportunities. Current Opinion in Environmental Sustainability, 3(3), 176–180.
Laukkonen, J., Blanco, P. K., Lenhart, J., Keiner, M., Cavric, B., & Kinuthia-Njenga, C. (2009). Combining climate change adaptation and mitigation measures at the local level. Habitat International, 33(3), 287–292.
Lorenzoni, I., Pidgeon, N. F., & O’Connor, R. E. (2005). Dangerous climate change: The role for risk research. Risk Analysis, 25(6), 1387–1398.
Lowe, T., Brown, K., Dessai, S., de França Doria, M., Haynes, K., & Vincent, K. (2006). Does tomorrow ever come? Disaster narrative and public perceptions of climate change. Public Understanding of Science, 15(4), 435–457.
Maddison, D. (2006). The perception of and adaptation to climate change in Africa. CEEPA. Discussion Paper No. 10. Centre for Environmental Economics and Policy in Africa. University of Pretoria, Pretoria, South Africa.
Matasci, C., Kruse, S., Barawid, N., & Thalmann, P. (2013). Exploring barriers to climate change adaptation in the Swiss tourism sector. Mitigation and Adaptation Strategies for Global Change, 1–16.
McCarthy, J. J. (Ed.). (2001). Climate change 2001: Impacts, adaptation, and vulnerability: Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press.
McGranahan, G., Balk, D., & Anderson, B. (2007). The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and Urbanization, 19(1), 17–37.
McSweeney, C.,New, M. & Lizcano, G. (2010). UNDP Climate Change Country Profiles: Tanzania. Available: http://country-profiles.geog.ox.ac.uk/ [Accessed 10 July 2014].
McSweeney, C., New, M., Lizcano, G., & Lu, X. (2010b). The UNDP climate change country profiles improving the accessibility of observed and projected climate information for studies of climate change in developing countries. Bulletin of the American Meteorological Society, 91, 157–166.
Measham, T. G., Preston, B. L., Smith, T. F., Brooke, C., Gorddard, R., Withycombe, G., & Morrison, C. (2011). Adapting to climate change through local municipal planning: barriers and challenges. Mitigation and Adaptation Strategies for Global Change, 16(8), 889–909.
Mertz, O., Mbow, C., Reenberg, A., Genesio, L., Lambin, E. F., D’haen, S., et al. (2011). Adaptation strategies and climate vulnerability in the Sudano-Sahelian region of West Africa. Atmospheric Science Letters, 12(1), 104–108.
Mngulwi, B. S. (2003). Country review: United Republic of Tanzania. Review of the state of world marine capture fisheries management: Indian Ocean, 447.
Morton, J. F. (2007). The impact of climate change on smallholder and subsistence agriculture. Proceedings of the National Academy of Sciences, 104(50), 19680–19685.
Moser, S. C. (2005). Impact assessments and policy responses to sea-level rise in three US states: An exploration of human-dimension uncertainties. Global Environmental Change, 15(4), 353–369.
Moser, S. C., & Ekstrom, J. A. (2010). A framework to diagnose barriers to climate change adaptation. Proceedings of the National Academy of Sciences, 107(51), 22026–22031.
Mukheibir, P., Kuruppu, N., Gero, A., & Herriman, J. (2013). Cross-scale barriers to climate change adaptation in local government. Gold Coast: National Climate Change Adaptation Research Facility. 95 pp.
Naess, L. O., Bang, G., Eriksen, S., & Vevatne, J. (2005). Institutional adaptation to climate change: Flood responses at the municipal level in Norway. Global Environmental Change, 15(2), 125–138.
National Bureau of Statistics. (2013). Tanzania in figures 2012. Ministry of Finance, June 2013, page 23. Retrieved in July 2014 from.
Nellemann, C., Verma, R., & Hislop, L. (2011). Women at the frontline of climate change: Gender risks and hopes. A rapid response assessment. Norway: United Nations Environment Programme, GRID-Arendal.
Nielsen, J. Ø., & Reenberg, A. (2010). Cultural barriers to climate change adaptation: A case study from Northern Burkina Faso. Global Environmental Change, 20(1), 142–152.
Niemeyer, S., Petts, J., & Hobson, K. (2005). Rapid climate change and society: Assessing responses and thresholds. Risk Analysis, 25(6), 1443–1456.
O’Brien, K., Eriksen, S., Sygna, L., & Naess, L. O. (2006). Questioning complacency: Climate change impacts, vulnerability and adaptation in Norway. Ambio, 35, 50–56.
Paavola, J. (2008). Livelihoods, vulnerability and adaptation to climate change in Morogoro, Tanzania. Environmental Science & Policy, 11(7), 642–654.
Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J., & Hanson, C. E. (Eds.). (2007). Climate Change 2007: Impacts, adaptation and vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. 976 pp.
Patt, A., & Gwata, C. (2002). Effective seasonal climate forecast applications: Examining constraints for subsistence farmers in Zimbabwe. Global Environmental Change, 12(3), 185–195.
Pol, L. G., & Thomas, R. K. (2013). Population Composition. In The Demography of Health and Healthcare (pp. 65–89). Springer Netherlands.
Reser, J. P., Bradley, G., & Ellul, M. (2012). Coping with climate change: Bringing psychological adaptation in from the cold. In B. Molinelli & V. Grimaldo (Eds.), Handbook of the psychology of coping: New research: Nova Publishers.
Reser, J. P., & Swim, J. K. (2011). Adapting to and coping with the threat and impacts of climate change. American Psychologist, 66(4), 277–289.
Smit, B., Pilifosova, O. (2001). Adaptation to climate change in the context of sustainable development and equity. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001: impacts, adaptation and vulnerability. Contribution of working group II to the third assessment report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge
Sturgis, P., & Allum, N. (2004). Science in society: Re-evaluating the deficit model of public attitudes. Public Understanding of Science, 13(1), 55–74.
Swim, J., Clayton, S., Doherty, T., Gifford, R., Howard, G., Reser, J., Stern, P. & Weber, E. (2009). Psychology and global climate change: Addressing a multi-faceted phenomenon and set of challenges. A report by the American Psychological Association’s task force on the interface between psychology and global climate change. American Psychological Association, Washington. Retrieved on 15th May 2014 from http://www.apa.org/science/about/publications/climate-change.pdf
Terry, G. (2009). No climate justice without gender justice: an overview of the issues. Gender & Development, 17(1), 5–18.
Tol, R. S., Klein, R. J., & Nicholls, R. J. (2008). Towards successful adaptation to sea-level rise along Europe’s coasts. Journal of Coastal Research, 432–442.
United Republic of Tanzania (2013). Population Distribution by Administrative Units. Page 1. Retrieved on 15th June 2014 from http://ihi.eprints.org/2169/1/Age_Sex_Distribution.pdf
Unsworth, K. L., Russell, S. V., Lewandowsky, S., Lawrence, C., Fielding, K. S., Heath, J., et al. (2013). What about me? Factors affecting individual adaptive coping capacity across different populations. Gold Coast: National Climate Change Adaptation Research Facility. 149 pp.
Watkiss, P., Downing, T., Dyszynski, J., Pye, S., Savage, M., Goodwin, J., Longanecker, M. & Lynn, S. (2011). The economics of climate change in the United Republic of Tanzania. Global Climate Adaptation Partnership (GCAP).
Weber, E. U. (2006). Experience-based and description-based perceptions of long-term risk: Why global warming does not scare us (Yet). Climatic Change, 77(1–2), 103–120.
Wilby, R. L., & Dessai, S. (2010). Robust adaptation to climate change. Weather, 65(7), 180–185.
Wiseman, J., Biggs, C., Rickards, L., & Edwards, T. (2011). Scenarios for climate adaptation. Melbourne: Victorian Centre for Climate Change Adaptation Research.
Acknowledgments
We acknowledge research funding from ‘the Indian Ocean World: The Making of the First Global Economy in the Context of Human Environment Interaction’ project within the framework of Major Collaborative Research Initiative (MCRI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Many thanks to Karen Van Kerkoerle, of the Cartographic Unit, Department of Geography, University of Western Ontario, for drawing the map of the study areas.
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Appendix
Appendix
Contingency tables show the distribution of the barriers to adaptation to climate change by Place-specific and compositional (biosocial and sociocultural) variables
Distribution of self-reported barriers to adaptation to climate change: don’t know what steps to take to protect myself (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 9.9300, Pr = 0.002, Cramer’s V = 0.09 | ||
Male | 45.3 | 54.7 | |
Female | 54.7 | 45.3 | |
Age | χ² (3) = 0.5389, Pr = 0.900, Cramer’s V = 0.02 | ||
18–35 | 49.8 | 50.2 | |
36–50 | 50.9 | 49.1 | |
51–65 | 48.9 | 51.1 | |
More than 65 | 53.2 | 46.8 | |
Marital status | χ² (1) = 0.7202, Pr = 0.396, Cramer’s V = −0.02 | ||
Unmarried | 51.9 | 48.1 | |
Married | 49.2 | 50.8 | |
Ethnicity | χ² (2) = 11.2682, Pr = 0.004, Cramer’s V = 0.10 | ||
Zaramo | 59.8 | 40.2 | |
Sambaa | 45.4 | 54.6 | |
Others | 48.0 | 52.0 | |
Religion | χ² (2) = 32.4003, Pr = 0.000, Cramer’s V = 0.16 | ||
Christian | 39.5 | 60.5 | |
Muslim | 55.8 | 44.2 | |
Traditional | 0.0 | 100.0 | |
Employment | χ² (1) = 3.3447, Pr = 0.067, Cramer’s V = −0.05 | ||
Unemployed | 60.0 | 40.0 | |
Employed | 49.4 | 50.6 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 50.0298, Pr = 0.000, Cramer’s V = 0.21 | ||
No education | 64.4 | 35.6 | |
Primary | 57.8 | 42.2 | |
Secondary | 44.9 | 55.1 | |
Tertiary | 31.4 | 68.6 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 0.6692, Pr = 0.413, Cramer’s V = 0.02 | ||
Yes | 54.2 | 45.8 | |
No | 49.8 | 50.2 | |
Region | χ² (2) = 60.5685, Pr = 0.000, Cramer’s V = 0.23 | ||
Dar es Salaam and Zanzibar | 38.3 | 61.7 | |
Pwani | 63.9 | 36.1 | |
Tanga | 58.6 | 41.4 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 13.3316, Pr = 0.000, Cramer’s V = 0.11 | ||
Not easy | 43.3 | 56.7 | |
Easy | 54.4 | 45.6 | |
Residential locality | χ² (1) = 41.7254, Pr = 0.000, Cramer’s V = 0.19 | ||
Rural | 61.7 | 38.3 | |
Urban | 42.2 | 57.8 | |
Distribution of self-reported barriers to adaptation to climate change: lack the skill needed (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 8.0164, Pr = 0.005, Cramer’s V = 0.08 | ||
Male | 66.2 | 33.8 | |
Female | 73.9 | 26.1 | |
Age | χ² (3) = 4.2596, Pr = 0.235, Cramer’s V = 0.06 | ||
18–35 | 69.0 | 31.0 | |
36–50 | 68.7 | 31.3 | |
51–65 | 71.8 | 28.2 | |
More than 65 | 79.2 | 20.8 | |
Marital status | χ² (1) = 3.9051, Pr = 0.048, Cramer’s V = 0.06 | ||
Unmarried | 66.6 | 33.4 | |
Married | 72.2 | 27.8 | |
Ethnicity | χ² (2) = 4.7400, Pr = 0.093, Cramer’s V = 0.06 | ||
Zaramo | 75.9 | 24.1 | |
Sambaa | 67.2 | 32.8 | |
Others | 69.1 | 30.9 | |
Religion | χ² (2) = 5.4271, Pr = 0.06, Cramer’s V = 0.07 | ||
Christian | 66.1 | 33.9 | |
Muslim | 72.4 | 27.6 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 12.0575, Pr = 0.001, Cramer’s V = −0.09 | ||
Unemployed | 86.2 | 13.8 | |
Employed | 69.1 | 30.9 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 59.7848, Pr = 0.000, Cramer’s V = 0.23 | ||
No education | 83.9 | 16.1 | |
Primary | 77.9 | 22.1 | |
Secondary | 65.3 | 34.7 | |
Tertiary | 51.0 | 49.0 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 0.5051, Pr = 0.477, Cramer’s V = 0.02 | ||
Yes | 67.5 | 32.5 | |
No | 70.6 | 29.4 | |
Region | χ² (2) = 16.9107, Pr = 0.000, Cramer’s V = 0.12 | ||
Dar es Salaam and Zanzibar | 64.5 | 35.5 | |
Pwani | 75.7 | 24.3 | |
Tanga | 75.6 | 24.4 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 4.5675, Pr = 0.033, Cramer’s V = −0.06 | ||
Not easy | 73.9 | 26.1 | |
Easy | 68.0 | 32.0 | |
Residential locality | χ² (1) = 33.9572, Pr = 0.000, Cramer’s V = 0.17 | ||
Rural | 63.8 | 36.2 | |
Urban | 79.6 | 20.4 | |
Distribution of self-reported barriers to adaptation to climate change: lack of personal energy or motivation (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 3.4068, Pr = 0.065, Cramer’s V = 0.05 | ||
Male | 51.2 | 48.8 | |
Female | 56.7 | 43.3 | |
Age | χ² (3) = 7.9184, Pr = 0.048, Cramer’s V = 0.08 | ||
18–35 | 58.9 | 41.1 | |
36–50 | 52.2 | 47.8 | |
51–65 | 48.5 | 51.5 | |
More than 65 | 57.1 | 42.9 | |
Marital status | χ² (1) = 22.1577, Pr = 0.000, Cramer’s V = −0.14 | ||
Unmarried | 63.5 | 36.5 | |
Married | 49.0 | 51.0 | |
Ethnicity | χ² (2) = 0.8937, Pr = 0.640, Cramer’s V = 0.03 | ||
Zaramo | 56.8 | 43.2 | |
Sambaa | 53.8 | 46.2 | |
Others | 53.3 | 46.7 | |
Religion | χ² (2) = 0.0800, Pr = 0.961, Cramer’s V = 0.0084 | ||
Christian | 53.6 | 46.4 | |
Muslim | 54.3 | 45.7 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 0.7637, Pr = 0.382, Cramer’s V = −0.03 | ||
Unemployed | 58.8 | 41.2 | |
Employed | 53.7 | 46.3 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 12.5329, Pr = 0.006, Cramer’s V = 0.10 | ||
No education | 71.3 | 28.7 | |
Primary | 52.0 | 48.0 | |
Secondary | 51.9 | 48.1 | |
Tertiary | 55.7 | 44.3 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 3.3476, Pr = 0.07, Cramer’s V = −0.05 | ||
Yes | 53.1 | 46.9 | |
No | 61.8 | 38.2 | |
Region | χ² (2) = 13.7881, Pr = 0.001, Cramer’s V = 0.11 | ||
Dar es Salaam and Zanzibar | 58.4 | 41.6 | |
Pwani | 55.4 | 44.6 | |
Tanga | 45.3 | 54.7 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 38.6438, Pr = 0.000, Cramer’s V = 0.18 | ||
Not easy | 42.3 | 57.7 | |
Easy | 61.3 | 38.7 | |
Residential locality | χ² (1) = 13.9949, Pr = 0.000, Cramer’s V = −0.11 | ||
Rural | |||
Urban | |||
Distribution of self-reported barriers to adaptation to climate change: lack of time (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 1.9559, Pr = 0.162, Cramer’s V = −0.04 | ||
Male | 16.1 | 83.6 | |
Female | 13.2 | 86.8 | |
Age | χ² (3) = 2.8717, Pr = 0.538, Cramer’s V = 0.05 | ||
18–35 | 14.5 | 85.5 | |
36–50 | 12.7 | 87.3 | |
51–65 | 16.3 | 83.7 | |
More than 65 | 18.2 | 81.8 | |
Marital status | χ² (1) = 0.8748, Pr = 0.350, Cramer’s V = −0.03 | ||
Unmarried | 15.9 | 84.1 | |
Married | 13.9 | 86.1 | |
Ethnicity | χ² (2) = 18.8154, Pr = 0.000, Cramer’s V = 0.12 | ||
Zaramo | 10.6 | 89.4 | |
Sambaa | 5.0 | 95.0 | |
Others | 17.3 | 82.7 | |
Religion | χ² (2) = 5.8896, Pr = 0.053, Cramer’s V = 0.07 | ||
Christian | 18.1 | 81.9 | |
Muslim | 12.8 | 87.2 | |
Traditional | 25.0 | 75.0 | |
Employment | χ² (1) = 1.291, Pr = 0.000, Cramer’s V = −0.03 | ||
Unemployed | 18.8 | 81.2 | |
Employed | 14.3 | 85.7 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 9.6827, Pr = 0.021, Cramer’s V = 0.09 | ||
No education | 14.9 | 85.1 | |
Primary | 11.6 | 88.4 | |
Secondary | 15.9 | 84.1 | |
Tertiary | 20.6 | 79.4 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 3. 9668, Pr = 0.046, Cramer’s V = 0.06 | ||
Yes | 8.9 | 91.1 | |
No | 15.3 | 84.7 | |
Region | χ² (2) = 39.2638, Pr = 0.000, Cramer’s V = 0.18 | ||
Dar es Salaam and Zanzibar | 21.2 | 78.8 | |
Pwani | 10.4 | 89.7 | |
Tanga | 6.8 | 93.2 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 16.5106, Pr = 0.000, Cramer’s V = 0.11 | ||
Not easy | 9.3 | 90.7 | |
Easy | 17.9 | 82.1 | |
Residential locality | χ² (1) = 37.7121, Pr = 0.000, Cramer’s V = 0.17 | ||
Rural | 7.4 | 92.6 | |
Urban | 19.6 | 80.4 | |
Distribution of self-reported barriers to adaptation to climate change: lack of money or resources needed (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 4.5769, Pr = 0.032, Cramer’s V = 0.06 | ||
Male | 61.6 | 38.4 | |
Female | 67.7 | 32.3 | |
Age | χ² (3) = 6.6403, Pr = 0.084, Cramer’s V = 0.07 | ||
18–35 | 63.3 | 36.7 | |
36–50 | 66.8 | 33.2 | |
51–65 | 61.1 | 38.9 | |
More than 65 | 75.3 | 24.7 | |
Marital status | χ² (1) = 3.9364, Pr = 0.047, Cramer’s V = −0.06 | ||
Unmarried | 68.6 | 31.4 | |
Married | 62.7 | 37.3 | |
Ethnicity | |||
Zaramo | 65.2 | 34.8 | χ² (2) = 0.6799, Pr = 0.712, Cramer’s V = 0.02 |
Sambaa | 61.3 | 38.7 | |
Others | 65.1 | 34.8 | |
Religion | χ² (2) = 6.7887, Pr = 0.034, Cramer’s V = 0.08 | ||
Christian | 59.7 | 40.3 | |
Muslim | 67.4 | 32.6 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 6.5310, Pr = 0.011, Cramer’s V = −0.07 | ||
Unemployed | 77.5 | 22.5 | |
Employed | 63.8 | 36.2 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 34.1185, Pr = 0.000, Cramer’s V = 0.17 | ||
No education | 86.2 | 13.8 | |
Primary | 68.0 | 32.0 | |
Secondary | 60.5 | 39.5 | |
Tertiary | 53.1 | 46.9 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 49.8885, Pr = 0.000, Cramer’s V = −0.21 | ||
Yes | 95.0 | 5.0 | |
No | 61.3 | 38.7 | |
Region | χ² (2) = 15.2163, Pr = 0.000, Cramer’s V = 0.12 | ||
Dar es Salaam and Zanzibar | 59.5 | 40.5 | |
Pwani | 72.9 | 27.1 | |
Tanga | 66.8 | 33.2 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 21. 7830, Pr = 0. 000, Cramer’s V = 0.14 | ||
Not easy | 56.3 | 43.7 | |
Easy | 70.0 | 30.0 | |
Residential locality | χ² (1) = 1.5169, Pr = 0.218, Cramer’s V = 0.04 | ||
Rural | 66.9 | 33.1 | |
Urban | 63.3 | 36.7 | |
Distribution of self-reported barriers to adaptation to climate change: lack of help from others (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 2.3778, Pr = 0.123, Cramer’s V = −0.05 | ||
Male | 65.7 | 34.3 | |
Female | 61.3 | 38.7 | |
Age | χ² (3) = 8.1575, Pr = 0.04, Cramer’s V = 0.08 | ||
18–35 | 63.8 | 36.2 | |
36–50 | 59.7 | 40.3 | |
51–65 | 69.6 | 30.4 | |
More than 65 | 57.1 | 42.9 | |
Marital status | χ² (1) = 1.1464, Pr = 0.284, Cramer’s V = 0.03 | ||
Unmarried | 61.2 | 38.8 | |
Married | 64.5 | 35.5 | |
Ethnicity | |||
Zaramo | 64.8 | 35.2 | χ² (2) = 3.7892, Pr = 0.150, Cramer’s V = 0.06 |
Sambaa | 70.6 | 29.4 | |
Others | 61.8 | 38.2 | |
Religion | χ² (2) = 2.7308, Pr = 0.255, Cramer’s V = 0.05 | ||
Christian | 64.5 | 35.5 | |
Muslim | 62.6 | 37.4 | |
Traditional | 100.0 | 0.0 | |
Employment | χ² (1) = 1.2519, Pr = 0.263, Cramer’s V = 0.03 | ||
Unemployed | 57.5 | 42.5 | |
Employed | 63.8 | 36.2 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 40.0745, Pr = 0.000, Cramer’s V = 0.19 | ||
No education | 37.9 | 62.1 | |
Primary | 60.2 | 39.8 | |
Secondary | 72.3 | 27.7 | |
Tertiary | 69.1 | 30.9 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 15.6198, Pr = 0.000, Cramer’s V = 0.12 | ||
Yes | 46.7 | 53.3 | |
No | 65.3 | 34.7 | |
Region | χ² (2) = 52.2237, Pr = 0.000, Cramer’s V = 0.21 | ||
Dar es Salaam and Zanzibar | 72.9 | 27.1 | |
Pwani | 61.4 | 38.6 | |
Tanga | 48.2 | 51.8 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 16.3284, Pr = 0.000, Cramer’s V = −0.12 | ||
Not easy | 70.7 | 29.3 | |
Easy | 58.9 | 41.1 | |
Residential locality | χ² (1) = 2.9761, Pr = 0.085, Cramer’s V = −0.05 | ||
Rural | 60.4 | 39.6 | |
Urban | 65.4 | 34.6 | |
Distribution of self-reported barriers to adaptation to climate change: feel I don’t make a difference (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 0.0229, Pr = 0.880, Cramer’s V = −0.004 | ||
Male | 55.1 | 44.9 | |
Female | 54.7 | 45.3 | |
Age | |||
18–35 | 53.4 | 46.6 | χ² (3) = 3.2677, Pr = 0.352, Cramer’s V = 0.05 |
36–50 | 52.8 | 47.2 | |
51–65 | 59.3 | 40.7 | |
More than 65 | 57.1 | 42.9 | |
Marital status | χ² (1) = 20.0352, Pr = 0.000, Cramer’s V = 0.13 | ||
Unmarried | 45.8 | 54.2 | |
Married | 59.7 | 40.3 | |
Ethnicity | |||
Zaramo | 53.8 | 46.2 | χ² (2) = 0.6465, Pr = 0.724, Cramer’s V = 0.02 |
Sambaa | 52.1 | 47.9 | |
Others | 55.6 | 44.4 | |
Religion | χ² (2) = 2.7983, Pr = 0.247, Cramer’s V = 0.05 | ||
Christian | 57.9 | 42.1 | |
Muslim | 53.3 | 46.7 | |
Traditional | 75.0 | 25.0 | |
Employment | χ² (1) = 8.2399, Pr = 0.004, Cramer’s V = −0.08 | ||
Unemployed | 70.0 | 30.0 | |
Employed | 53.7 | 46.3 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 3.9816, Pr = 0.263, Cramer’s V = 0.06 | ||
No education | 44.8 | 55.2 | |
Primary | 55.7 | 44.3 | |
Secondary | 56.4 | 43.6 | |
Tertiary | 54.6 | 45.4 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 22.0902, Pr = 0.000, Cramer’s V = 0.14 | ||
Yes | 35.0 | 65.0 | |
No | 57.3 | 42.7 | |
Region | χ² (2) = 5.2663, Pr = 0.072, Cramer’s V = 0.07 | ||
Dar es Salaam and Zanzibar | 58.4 | 41.6 | |
Pwani | 52.1 | 47.9 | |
Tanga | 51.1 | 48.9 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 9.5747, Pr = 0.002, Cramer’s V = −0.09 | ||
Not easy | 60.7 | 39.3 | |
Easy | 51.3 | 48.7 | |
Residential locality | χ² (1) = 0.0039, Pr = 0.950, Cramer’s V = 0.001 | ||
Rural | 55.0 | 45.0 | |
Urban | 54.8 | 45.2 | |
Distribution of self-reported barriers to adaptation to climate change: I don’t believe in climate change (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 1.3781, Pr = 0.240, Cramer’s V = 0.03 | ||
Male | 6.3 | 93.7 | |
Female | 8.1 | 91.9 | |
Age | χ² (3) = 4.9499, Pr = 0.176, Cramer’s V = 0.07 | ||
18–35 | 8.9 | 91.1 | |
36–50 | 5.3 | 94.7 | |
51–65 | 6.7 | 93.3 | |
More than 65 | 10.4 | 89.6 | |
Marital status | χ² (1) = 0.0256, Pr = 0.873, Cramer’s V = 0.005 | ||
Unmarried | 7.1 | 92.9 | |
Married | 7.3 | 92.7 | |
Ethnicity | χ² (2) = 12.9632, Pr = 0.002, Cramer’s V = 0.09 | ||
Zaramo | 6.8 | 93.2 | |
Sambaa | 0.8 | 99.2 | |
Others | 8.4 | 91.6 | |
Religion | χ² (2) = 10.4669, Pr = 0.005, Cramer’s V = 0.09 | ||
Christian | 10.4 | 89.6 | |
Muslim | 5.6 | 94.4 | |
Traditional | 25.0 | 75.0 | |
Employment | χ² (1) = 2.9819, Pr = 0.084, Cramer’s V = −0.05 | ||
Unemployed | 12.5 | 87.5 | |
Employed | 6.9 | 93.1 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 8.8704, Pr = 0.031, Cramer’s V = 0.09 | ||
No education | 3.4 | 96.6 | |
Primary | 5.8 | 94.2 | |
Secondary | 8.3 | 91.7 | |
Tertiary | 11.3 | 88.7 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 8.5152, Pr = 0.004, Cramer’s V = 0.09 | ||
Yes | 0.8 | 99.2 | |
No | 8.0 | 92.0 | |
Region | χ² (2) = 64.8263, Pr = 0.000, Cramer’s V = 0.24 | ||
Dar es Salaam and Zanzibar | 13.6 | 86.4 | |
Pwani | 2.9 | 97.1 | |
Tanga | 0.0 | 100.0 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 6.1195, Pr = 0.013, Cramer’s V = 0.07 | ||
Not easy | 4.9 | 95.1 | |
Easy | 8.7 | 91.3 | |
Residential locality | χ² (1) = 38.2802, Pr = 0.000, Cramer’s V = −0.18 | ||
Rural | 1.5 | 98.5 | |
Urban | 11.2 | 88.8 | |
Distribution of self-reported barriers to adaptation to climate change: believe God will protect me (n = 1,130)
Variables | Yes (%) | No (%) | Pearson’s χ² (df) |
|---|---|---|---|
Compositional factors | |||
Sex | χ² (1) = 0.1709, Pr = 0.679, Cramer’s V = −0.01 | ||
Male | 40.6 | 59.4 | |
Female | 39.4 | 60.6 | |
Age | χ² (3) = 8.1728, Pr = 0.043, Cramer’s V = 0.08 | ||
18–35 | 34.7 | 65.3 | |
36–50 | 41.6 | 58.4 | |
51–65 | 43.7 | 56.3 | |
More than 65 | 46.8 | 53.2 | |
Marital status | χ² (1) = 1.6277, Pr = 0.202, Cramer’s V = 0.04 | ||
Unmarried | |||
Married | |||
Ethnicity | χ² (2) = 5.7772, Pr = 0.056, Cramer’s V = 0.07 | ||
Zaramo | 42.8 | 57.2 | |
Sambaa | 30.2 | 69.8 | |
Others | 40.7 | 59.4 | |
Religion | χ² (2) = 0.5618, Pr = 0.755, Cramer’s V = 0.02 | ||
Christian | 38.7 | 61.3 | |
Muslim | 40.6 | 59.4 | |
Traditional | 50.0 | 50.0 | |
Employment | χ² (1) = 2.6996, Pr = 0.100, Cramer’s V = −0.05 | ||
Unemployed | 48.8 | 51.2 | |
Employed | 39.3 | 60.7 | |
Income* | – | – | – |
Educational attainment | χ² (3) = 2.2766, Pr = 0.517, Cramer’s V = 0.24 | ||
No education | 34.5 | 65.5 | |
Primary | 39.1 | 60.9 | |
Secondary | 42.7 | 57.3 | |
Tertiary | 40.7 | 59.3 | |
Place-specific factors | |||
Availability of health facility in the neighbourhood | χ² (1) = 3.9549, Pr = 0.047, Cramer’s V = 0.06 | ||
Yes | 31.7 | 68.3 | |
No | 41.0 | 59.0 | |
Region | χ² (2) = 24.4885, Pr = 0.000, Cramer’s V = 0.15 | ||
Dar es Salaam and Zanzibar | 45.3 | 54.7 | |
Pwani | 42.5 | 57.5 | |
Tanga | 28.3 | 71.7 | |
Distance to nearest health facility* | – | – | – |
Accessibility of health facility in the neighbourhood | χ² (1) = 14.0137, Pr = 0.000, Cramer’s V = −0.11 | ||
Not easy | 47.0 | 53.0 | |
Easy | 35.7 | 64.3 | |
Residential locality | χ² (1) = 9.3832, Pr = 0.002, Cramer’s V = −0.09 | ||
Rural | 34.6 | 65.4 | |
Urban | 43.7 | 56.3 | |
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Armah, F.A., Luginaah, I., Hambati, H. et al. Assessing barriers to adaptation to climate change in coastal Tanzania: Does where you live matter?. Popul Environ 37, 231–263 (2015). https://doi.org/10.1007/s11111-015-0232-9
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DOI: https://doi.org/10.1007/s11111-015-0232-9