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In the current decade, the fact that climate change is a continuous process and that humans have been contributing to this change is indisputable. Therefore, the whole society and the decision-makers who guide the process of application of regional policy principles, are facing a challenge as to which measures need to be taken to minimise the consequences of this change. Although we live in a global world, it can be observed that interactions occur in each unit on an individual basis. As we have more and more knowledge and information on the space, we can indicate which units, regions and spaces have the greatest predisposition to be subject to climate change. Moreover, being aware of the level of risk, we can attempt to implement tools that will help society to accept climate change and properly adapt to it. A space’s predisposition to climate change is not only determined by the weather, environmental or geographical conditions. The literature on the subject indicates three basic determinants of the predisposition, i.e. the exposure, vulnerability and the adaptive capacity. Only all of these elements grouped together can provide an answer to the question about a unit’s predisposition. The article focuses on the indices which represent all three determinants of the predisposition. It should be noted that depending on the availability of data and their aggregation, there is no possibility of using the same indices for all countries. This, however, does not prevent the performance of a uniform analysis for spaces included in the same statistics. The article presents a case study for agricultural land in the province of Warmia and Mazury. Using Ward’s method, four subregions with similar determinants of the predisposition to climate change were distinguished. Three subregions stand out, as two of them have a significant impact of exposure (S.1) and vulnerability (S.2), while the third subregion dominates in terms of adaptive capacity (S.4).
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
198--206
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
autor
- University of Warmia and Mazury in Olsztyn, Faculty of Geodesy, Geospatial and Civil Engineering, Institute of Geoinformation and Cartography, ul. Oczapowskiego 2, 10-719 Olsztyn, Poland
Bibliografia
- 1. Błażejczyk K., Żmudzka E., 2013. Global climate change – overview after 25 years of IPCC. Kosmos 62/ 1, 1-11 (in Polish).
- 2. Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., Herrero, M., 2013. Adapting agriculture to climate change in Kenya: household strategies and determinants. J. Environ. Manag. 114, 26–35.
- 3. Burck J., Hagen U., Marten F., Höhne N., Bals Ch., 2018. CCPI Results 2019. Germanwatch, NewClimate Institute & Climate Action Network, ISBN 978-3-943704-68-6.
- 4. Ciołek D., Brodzicki T., Szlachta J., 2018. Estimates of GDP in poviats of the Warmian-Masurian Voivodeship as well as conclusions and recommendations for planning the future of regional policy of the voivodship in the time horizon up to 2030. Meeting of the Monitoring Committee for the implementation of the Social and Economic Development Strategy of the Warmian-Masurian Voivodship by 2025, Olsztyn, 11-07-2018 r, [Accessed 11-03-2019] (in Polish).
- 5. Coumou, D. and S. Rahmstorf. 2012. A decade of weather extremes. Nature Climate Change 2: 491–496.
- 6. Cardona O.D., Van Aalst M.K., Birkmann J., Fordham M., Mcgregor G., Perez R., Pulwarty R.S., Schipper E.L.F., Sinh B.T., 2012: Determinants of Risk: Exposure And Vulnerability. In: Managing the Risks Of Extreme Events And Disasters To Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, And P.M. Midgley (Eds.)]. A Special Report of Working Groups I And II of The Intergovernmental Panel on Climate Change (Ipcc). Cambridge University Press, Cambridge, Uk, And New York, Ny, Usa, pp. 65-108.
- 7. Eckstein D., Hutfils M-L, Winges M., 2018. Global climate risk index 2019. Who Suffers Most from Extreme Weather Events? Weather-related Loss Events in 2017 and 1998 to 2017. Think Tank & Research, GermanWatch, https://germanwatch.org [accessed 14-03-2019].
- 8. EEA 2017. Report no 1/2017 climate change, impacts and vulnerability in Europe 2016 an indicator-based report. Publications office of the European Union, Luxembourg.
- 9. Granados, A. 2012. Estimate Social Vulnerability Index to climate change in Mexico. Population Association of America 2012 annual meeting. San Francisco, CA, 3–5 may, 2012.
- 10. Hahn M, Reiderer A., Foster S., 2009. The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique, Global Environmental Change 19 (2009), 74–88.
- 11. Hiremath D., B., Shiyani R.L., 2013. Analysis of Vulnerability Indices in Various Agro-Climatic Zones of Gujarat. Ind. Jn. of Agri. Econ. Vol. 68, No. 1, Jan.-March 2013.
- 12. IPCC 2018. The Intergovernmental Panel on Climate Change (IPCC) report. https://www.ipcc.ch/ [Accessed 13-03-2019].
- 13. Kocur-Bera K., 2016. Convergence of spatial characteristics areas exposed to effects of extreme events. Administratio Locorum 15/4, DOI: https://doi.org/10.31648/aspal.659 (in Polish).
- 14. Kocur-Bera K., 2018. A safe space of rural areas in the context of the occurrence of extreme weather events – a case study covering a part of the Euroregion Baltic. Land Use Policy 71, 518-529; https://doi.org/10.1016/j.landusepol.2017.11.013.
- 15. Kocur-Bera K., 2015. Identification subpopulations with similar characteristics in terms of extreme weather events. Acta Scientarum Polonorum, Administratio Locorum 14/3, 75-89 (in Polish)
- 16. Kundzewicz Z.W., 2011. Climate changes, their reasons and effects – observations and projections. Landform Analysis 15, 39-49 (in Polish);
- 17. Ludena C.E., Won Yoon S., 2015. Local Vulnerability Indicators and Adaptation to Climate Change. A Survey. Climate Change and Sustainability Division, Technical Note N. 857.
- 18. ODR, 2016. Yields of main crops harvest in 2016. Collective harvesting of crops from 19 poviats in the province Warmia-Mazury. www.w-modr.pl [Accessed 11-03-2019] (in Polish).
- 19. ODR, 2018. List of financial losses due to natural disasters. Unpublished materials (in Polish).
- 20. Pawlewicz K., 2014. Relationships between social capital and socio-economic development based on rural communes in the warmińskomazurskie voivodeship. Journal of Agrobuissnes and Development 3(41), 373-381, DOI: 10.17306/JARD.2016.65.
- 21. Polsky, C., Neff, R., Yarnal, B., 2007. Building comparable global change vulnerability assessments: the vulnerability scoping diagram. Glob. Environ. Change 17 (3e4), 472e485.
- 22. Ritchie H., Roser M., 2019. Natural Disasters. Source: https://Ourworldindata.Org/Natural-Disasters’ [Accessed 06-03-2019].
- 23. Rudnicki R., Kluba M., 2014. Integrated rural development in the light of EU policy. T.1, Nicolaus Copernicus University in Toruń (in Polish).
- 24. Stec M., Janas A., Kuliński A., 2005. Grouping the Countries of European Union with Regard to the Human Capital Resources. Nierówności Społeczne a Wzrost Gospodarczy vol. 6, pp. 135-146, YADDA: bwmeta1.element.ekon-element-000171212151, (in Polish);
- 25. Sridevi G., Jyotishi A., Mahapatra S., Jagadeesh G., Bedamatta S., 2014. Climate Change Vulnerability in Agriculture Sector: Indexing and Mapping of Four Southern Indian States. Quaderni Working Paper DSE No. 966, p. 32, Italy.
- 26. Salamon J., 2010. Methodology for assessment of environmental and socio-economic conditionings of multifunctional rural development. University of Agriculture in Kraków, (in Polish).
- 27. Žurovec O., Cadro S., Sitaula B.K., 2017. Quantitative Assessment of Vulnerability to Climate Change in Rural Municipalities of Bosnia and Herzegovina. Sustainability 9, 1208; doi:10.3390/su9071208;
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0c6e9dd9-e981-49eb-b439-f836c6e99d75