Household vulnerability to climate change : Assessment for Rwanda

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1. www.akademiya2063.org Household vulnerability to climate change : Assessment for Rwanda CACCI Knowledge Seminar By Sambane Yade AKADEMIYA2063 Research Analyst, Department of Operational Analysis AKADEMIYA2063 By Getaw Tadesse AKADEMIYA2063 Director, Department of Operational Analysis AKADEMIYA2063
2. www.akademiya2063.org OUTLINE I. INTRODUCTION II. METHODOLOGY III. DATA SOURCE AND PROCESSING IV. RESULTS AND DISCUSSION V. CONCLUSION
3. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION
4. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION ü Background: National Adaptation Plans (NAPs) Reduce vulnerability to the impacts of climate change by building adaptive capacity and resilience Mainstreaming adaptation in development policies and strategies; Nationally Determined Contribution s (NDCs) Need of vulnerability assessment; Paris Agreement in 2015
5. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION q Goal : The mainly goal of the exercice is to estimate household vulnerability to climate change at national and district levels using the 2021 NISR survey q The specifics objectives : § Identify the factors that contribute to vulnerability § Compute and compare the level of vulnerability between regions or districts. § Analyse the potential impacts of climate change and the adaptation capacity of the households.
6. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION Why we make vulnerability assessment? q Track progress made in climate actions and building resilience § Countries have made several commitments under their NDCs and NAPs § Regular monitoring and tracking is needed to evaluate progress and performances q Target interventions across locations and interventions § Vulnerability is household and location specific § It varies across households and geographic locations § Therefore, it is important to identify social groups and locations which are more vulnerable than the other for better targeting interventions
7. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION Type of vulnerabilities ü It is important to note the type of vulnerably that is being assessed Based the type of damage q Environmental vulnerability : the amount of physical damage caused by a shock § Land degradation § Biodiversity loss q Economic vulnerability: the amount of economic damage due tot a shock § Income loss § Asset depletion/ crisis § Productivity decline q Social vulnerability: the social damage caused by a shock ü Displacement ü Hunger ü Health and death
8. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION Based on the type of shock q Climate shock § Exposure is uniform to all in a given environment/community § Adaptation capacity varies across households q Market shock § Exposure also varies across households depending on income source, a reduction in wheat price may affect only wheat farmers q Health sock § Exposure also varies across individuals Ø The parameters and indicators used to assess vulnerability are different for different shocks Type of vulnerabilities
9. www.akademiya2063.org I. INTRODUCTION AND JUSTIFICATION Based on the type of subject q Economy-wide vulnerability: § How susceptible is the macro-economy for a shock § Asses at macro-economic level § Needs economy-wide interventions q Community vulnerability ü The impact a shock on a community in general ü Measured at community level q Household vulnerability ü The impact of as a shock at household level Type of vulnerabilities
10. www.akademiya2063.org 1-Who are vulnerable? 2-To what? 3-Where? Introduction Therefore , when we talk about vulnerability assessment, we need to answer these three questions Q1=Identify Assessment Unity (Region, houshold, …) Q2 Help to define the vulnerability Q3 Help policies to make intervention
11. www.akademiya2063.org II. METHODOLOGY
12. www.akademiya2063.org 12 IPCC defines vulnerability as "the extent to which a natural or social system is likely to be damaged by the impacts of climate change, and is a function of exposure, sensitivity and adaptive capacity”. ü Definition: ü The Dimentions : II. METHODOLOGY
13. www.akademiya2063.org 13 VCC index Exposure index 1 n! # "#$ %! V",' !()*+,-. Sensitivity index 1 n/ # "#$ %" V",' /.%+'0'1'02 Adaptation capacity index 1 n34 # "#$ %#$ V",' 3%56'7'02 0* 495)0 1/3 1/3 1/3 II. METHODOLOGY Household vulnerability is determined by calculating the three dimensions (exposure, sensitivity and adaptation capacity). Subsequently, the dimension scores are summed to obtain the overall vulnerability to climate change index (VCC index).
14. www.akademiya2063.org THE INDICATORS CONSIDERED FOR THE EXPOSURE INDEX 14 Indicators Correlation with VCC Exposure to drought/ Irregular rainfall Positive Exposure to flooding Positive Exposure to fires Positive Exposure to heavy rainfall Positive Computing the exposure dimension Exposure: refers to the magnitude of climatic variations which households in a district are expected to face .
15. www.akademiya2063.org Computing the exposure dimension Districts % Household experiencing drought/irregular rainfall % Household having suffered Flooding % Household having suffered Fires Bugesera 37.73 11.68 0.0 Burera 19.77 2.59 0.0 Gakenke 36.38 8.08 0.0 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Rusizi 20.13 14.81 0.0 Rutsiro 19.87 1.76 0.0 Rwamagana 32.64 5.7 0.0 Districts Drought/ Irregular rainfall Flooding Fires Bugesera 0.030 0.013 0.000 Burera 0.000 0.000 0.000 Gakenke 0.040 0.000 0.000 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Rusizi 1.000 0.446 0.000 Rutsiro 0.528 0.656 0.000 Rwamagana 0.667 0.434 0.000 Districts Exposure Nyarugenge 0.02 Gasabo 0.00 -- -- -- -- Kirehe 0.72 Ngoma 0.59 Bugesera 0.55 Normalisation Correlation 𝑀𝑎𝑥, 𝑀𝑖𝑛 -1, +1 Average (I1, I2, I3) q How to compute exposure dimension n? 𝑽𝒏𝒐𝒓 =𝑽𝒎𝒂𝒙%𝒗𝒂𝒍𝒖𝒆 𝑽𝒎𝒂𝒙%𝑽𝒎𝒊𝒏 𝑽𝒏𝒐𝒓 =𝒗𝒂𝒍𝒖𝒆%𝑽𝒎𝒊𝒏 𝑽𝒎𝒂𝒙%𝑽𝒎𝒊𝒏
16. www.akademiya2063.org The indicators considered for the sensitivity index 16 INDICATORS CORRELATION SENS WITH VCC % of household suffered of High rate of crop disease positive % of household suffered of High rate of animal diseases positive % of household suffered Significant drop in prices of agricultural products positive % of household suffered of High prices for agricultural inputs positive % of household suffered of High food prices positive % of household suffered of Significant loss of off-farm income positive % of household suffered of Locust attacks or other crop pests positive % of households with at least one member suffered Skin problems positive % of households with at least one member suffered Eye disease positive % of households eat less than you should have? positive % of households ate a poor variety of food positive % of households couldn't eat healthy, nutritious food positive % of households worried about not being able to eat positive % of households with fertility plot (good-medium-low) negative Computing the sensitivity dimension Sensitivity in this case refers to the degree to which households may be affected by exposure to climate change risks
17. www.akademiya2063.org Computing the sensitivity dimension Normalisation Correlation 𝑀𝑎𝑥, 𝑀𝑖𝑛 -1, +1 Average (I1, I2, … , I12) Districts % of household suffered of High rate of crop disease Food insecure index Wealth index (Poor) Access to water (yes) Nyarugenge 0 5.39 21.2 94.07 Gasabo 0 5.33 7.95 96.43 Kicukiro 0 3.62 4.95 89.64 -- -- -- -- -- -- -- -- -- -- Ngoma 5.31 14.12 55.81 59.18 Bugesera 1.23 18.26 33.22 59.57 Districts % of household suffered of High rate of crop disease Food insecure index Wealth index (Poor) Access to water (yes) Nyarugenge 0.000 0.036 0.204 0.058 Gasabo 0.000 0.035 0.038 0.000 Kicukiro 0.000 0.000 0.000 0.167 -- -- -- -- -- -- -- -- -- -- Ngoma 0.327 0.215 0.640 0.916 Bugesera 0.076 0.299 0.356 0.907 Districts Sentivity Nyarugenge 0.32 Gasabo 0.12 -- -- -- -- Kirehe 0.45 Ngoma 0.42 Bugesera 0.33 q How to compute sensitivity dimension? 𝑽𝒏𝒐𝒓 =𝑽𝒎𝒂𝒙%𝒗𝒂𝒍𝒖𝒆 𝑽𝒎𝒂𝒙%𝑽𝒎𝒊𝒏 𝑽𝒏𝒐𝒓 =𝒗𝒂𝒍𝒖𝒆%𝑽𝒎𝒊𝒏 𝑽𝒎𝒂𝒙%𝑽𝒎𝒊𝒏
18. www.akademiya2063.org The indicators considered for constructing the adaptive capacity index. 18 Indicators Correlation sens with VCC % household received Government / NGO cash transfers negative Households with access to credit (%) negative % household with Improved seed variety negative % household used animal waste negative % household used household waste negative % household Use of inorganic fertilizer negative % household Use of phytosanitary products negative % household praticed multiple cropping negative % household with irigue plot negative % household with water access negative % household with electricity access negative Female entrepreneurship index negative Proportion of household heads using agricultural information negative Households having developed shock adaptation strategies (%) negative Households having processed agricultural products (%) negative Households members of an association or cooperative (%) negative Proportion of women with access to agricultural land by region (%) negative Proportion of households with access to private veterinary services negative Proportion of households with vaccinated animals negative Computing the adaptation capacity dimension Adaptive capacity: The ability of households to adjust to climate change
19. www.akademiya2063.org Computing the adaptation capacity dimension Normalisation Correlation 𝑀𝑎𝑥, 𝑀𝑖𝑛 -1, +1 Average (I1, I2, … , I20) Districts Member of any association or cooperative weather and climate information Training and technical assistance in improved agricultural Nyarugenge 16.02 52.08 0.00 Gasabo 15.97 24.14 0.00 -- -- -- -- -- -- -- -- Ngoma 34.58 37.26 0.00 Bugesera 57.52 24.44 0.00 Districts Member of any association or cooperative weather and climate information Training and technical assistance in improved agricultural Nyarugenge 0.941 0.393 0.252 Gasabo 0.942 0.002 0.668 -- -- -- -- -- -- -- -- Ngoma 0.520 0.883 0.473 Bugesera 0.000 0.934 0.664 Districts Adaptation inability Nyarugenge 0.65 Gasabo 0.50 -- -- -- -- Kirehe 0.62 Ngoma 0.68 Bugesera 0.58 q How to compute adaptation dimension? 𝑽𝒏𝒐𝒓 =𝑽𝒎𝒂𝒙%𝒗𝒂𝒍𝒖𝒆 𝑽𝒎𝒂𝒙%𝑽𝒎𝒊𝒏 𝑽𝒏𝒐𝒓 =𝒗𝒂𝒍𝒖𝒆%𝑽𝒎𝒊𝒏 𝑽𝒎𝒂𝒙%𝑽𝒎𝒊𝒏
20. www.akademiya2063.org Computing the overall VCC index Sentivity 0.32 0.12 -- -- 0.45 0.42 0.33 Adaptation inability 0.65 0.50 -- -- 0.62 0.68 0.58 Districts Nyarugenge Gasabo -- -- Kirehe Ngoma Bugesera Exposure 0.02 0.00 -- -- 0.72 0.59 0.55 Districts VCC index Nyarugenge 0.33 Gasabo 0.21 -- -- -- -- Kirehe 0.54 Ngoma 0.55 Bugesera 0.45 1/3 Districts Ponderation Nyarugenge P1 Gasabo P2 -- -- -- -- Kirehe P28 Ngoma P29 Bugesera P30 National Exposure 0.38 Sentivity 0.29 Adaptation inability 0.61 1/3 VCC index 0.33 q How to compute the VCC index ?
21. www.akademiya2063.org The absolute categorization helps to compare the state of VCC across countries Classification of vulnerability level Relative” categorization’’ Absolute” categorization’’ The relative categorization helps to compare and prioritize the districts of the country. Legend Legend Very high level of vulnerability High level of vulnerability Moderate level of vulnerability Low level of vulnerability Very low level of vulnerability Much more vulnerable More vulnerable Low vulnerable Much low vulnerable Data classification is a technique used in data analysis and machine learning to organize data into categories according to agreed criteria (indicators)
22. www.akademiya2063.org III. DATA SOURCE AND PROCESSING
23. www.akademiya2063.org 23 III. DATA SOURCE AND PROCESSING
24. www.akademiya2063.org INDICATORS USED FOR EXPOSURE INDEX 24 Indicators Correlation with VCC Exposure to drought/ Irregular rainfall Positive Exposure to flooding Positive Exposure to fires Positive Exposure to heavy rainfall Positive III. DATA SOURCE AND PROCESSING
25. www.akademiya2063.org Indicators used for sensitivity index 25 INDICATORS CORRELATION SENS WITH VCC % of household suffered of High rate of crop disease positive % of household suffered of High rate of animal diseases positive % of household suffered Significant drop in prices of agricultural products positive % of household suffered of High prices for agricultural inputs positive % of household suffered of High food prices positive % of household suffered of Significant loss of off-farm income positive % of household suffered of Locust attacks or other crop pests positive % of households eat less than you should have? positive % of households skipped a meal not enough money positive % of households ate a poor variety of food positive % of households couldn't eat healthy, nutritious food positive % of households worried about not being able to eat positive % of households with fertility plot (good-medium-low) negative Wealth (Yes) negative Access to water (Yes) negative III. DATA SOURCE AND PROCESSING
26. www.akademiya2063.org Indicators considered for constructing the adaptive capacity index. 26 Indicators Correlation sens with VCC Member of any association or cooperative negative % household having sent transfer positive % household received Government / NGO cash transfers negative Households with access to credit (%) negative % household with Improved seed variety negative % household used animal waste negative % household used household waste negative % household Use of inorganic fertilizer negative % household Use of phytosanitary products negative % household praticed multiple cropping negative % household with irigue plot negative % household with water access negative % household with electricity access negative Female entrepreneurship index negative Proportion of household heads using agricultural information negative Households having developed shock adaptation strategies (%) negative Households having processed agricultural products (%) negative Households members of an association or cooperative (%) negative Proportion of women with access to agricultural land by region (%) negative Proportion of households with access to private veterinary services negative Proportion of households with vaccinated animals negative III. DATA SOURCE AND PROCESSING
27. www.akademiya2063.org IV. RESULTS AND DISCUSSION
28. www.akademiya2063.org IV. RESULTS AND DISCUSSION Source: Authors’ calculations using the 2021 CFSVA data (NISR 2023) 1- The results reveal that the overall level of household vulnerability in Rwanda is 0.44 on a scale of 0 to 1. 3-These results indicate that although the level of sensitivity (0.29) is somewhat low, households in Rwanda have a high exposure (0.38) to climate change risks and have limited capacity to adapt (0.61) to adverse effects. 4-Karongi, Nyaruguru and Gisagara were the three districts with the largest score of vulnerable in 2021. The two districts with the least vulnerable households were Gasabo and Kicukiro . 0.29 0.38 0.61 0.43 SENSITIVITY EXPOSURE INABILITY TO ADAPT VCC_INDEX CLIMATE CHANGE VULNERABILITY BY COMPONENT
29. www.akademiya2063.org IV. RESULTS AND DISCUSSION Interpretation: On average, the VCC index has declined from 0.49 to 0.43, indicating that five percent of the vulnerable households have become less vulnerable and more adaptive to climate change within the last three years. Although the indicators used in the two years are slightly different, this decline may indicate that households are generally recording strong improvements in terms of reducing vulnerability to climate change. District/province VCC_2018 VCC_2021 Variation Level Kicukiro 0.478 0.238 -0.240 |||||||||||||||||||||||| Gasabo 0.444 0.208 -0.236 ||||||||||||||||||||||| Huye 0.566 0.422 -0.144 |||||||||||||| Nyarugenge 0.475 0.331 -0.144 |||||||||||||| Nyabihu 0.502 0.379 -0.123 |||||||||||| Ruhango 0.536 0.418 -0.118 ||||||||||| Muhanga 0.472 0.356 -0.116 ||||||||||| Rutsiro 0.498 0.383 -0.115 ||||||||||| Nyamasheke 0.514 0.401 -0.113 ||||||||||| Rubavu 0.500 0.392 -0.108 |||||||||| Nyamagabe 0.520 0.427 -0.093 ||||||||| Kamonyi 0.502 0.420 -0.082 |||||||| Nyagatare 0.513 0.434 -0.079 ||||||| Burera 0.471 0.398 -0.073 ||||||| Kayonza 0.500 0.429 -0.071 ||||||| Rulindo 0.451 0.382 -0.069 |||||| Rwamagana 0.484 0.436 -0.048 |||| Ngororero 0.503 0.458 -0.045 |||| Rusizi 0.487 0.446 -0.041 |||| Musanze 0.454 0.413 -0.041 |||| Bugesera 0.494 0.455 -0.039 ||| Nyanza 0.525 0.510 -0.015 | Gicumbi 0.472 0.457 -0.015 | Ngoma 0.498 0.499 0.001 Gisagara 0.525 0.535 0.010 Kirehe 0.487 0.511 0.024 || Nyaruguru 0.527 0.556 0.029 || Karongi 0.543 0.574 0.031 ||| Gatsibo 0.459 0.492 0.033 ||| Gakenke 0.452 0.503 0.051 ||||| 0.49 0.43 2018 2021 Level of vulnerability to climate change
30. www.akademiya2063.org IV. RESULTS AND DISCUSSION 0.48 0.28 0.03 Drought/ Irregular rainfall Flooding Fires Exposure to Climate change vulnerability Source: Authors’ calculations using the 2021 CFSVA data (NISR 2023) 1-The major source of exposure among households in Rwanda is the frequency and intensity of Irregular rainfall. 2-The households in the districts of Nyaruguru, Kirehe, Gicumbi, face high exposure to climate change risks. 3-Households in the districts of Gasabo, Kicukiro, Nyarugenge, Rubavu, Rutsiro, Burera, Nyamagabe and to a lesser extent, Nyagatare and Muhanga, have the lowest exposure to climate change risks
31. www.akademiya2063.org IV. RESULTS AND DISCUSSION Interpretation: High rate of animal diseases High prices for agricultural inputs High food prices Loss or reduced Income Disability of household head Food insecure index Landslides/mudsl ides Death a working household member Wealth index experience of a shock Access to water Sensitivity to Climate change 1-The two major sources of sensitivity among households in Rwanda are the water access and the low level of household wealth . 2-The households in Gakenke and Karongi districts are the most sensitive to climate change risks. 3-The districts of Gasabo, Gicumbi, Kicukiro, Kamonyi, Nyamasheke, Ruhango, Kayonza, have the lowest scores.
32. www.akademiya2063.org IV. RESULTS AND DISCUSSION Human capital Resilience Agricultural infrastructural Economic Social protection program Breeding Adaptation issues of Climate change vulnerability 1-The deux major sources of adaptation inability among households in Rwanda are the le low level of human capital through by the following variables (Member of any association or cooperative, education, weather and climate information) and the extend of social protection program. 2-The two districts which have greater capacity to adapt to climate change shocks are Kicukiro and Gakenke. 3-The districts which have greater incapacity to adapt to climate change shocks are Rwamagana, Rubavu, Nyagatare , Burera, Gatsibo.
33. www.akademiya2063.org Relative classification Absolute classification 2021 2021
34. www.akademiya2063.org Absolute classification Absolute classification Burera Gakenke Gicumbi Musanze Rulindo Gisagara Huye Kamonyi Muhanga Nyamagabe Nyanza Nyaruguru Ruhango Bugesera Gatsibo Kayonza Kirehe Ngoma Nyagatare Rwamagana Karongi Ngororero Nyabihu Nyamasheke Rubavu Rusizi Rutsiro Gasabo Kicukiro Nyarugenge Very high level of vulnerability High level of vulnerability Moderate level of vulnerability Low level of vulnerability Very low level of vulnerability Source: Authors' calculations using the 2018 CFSVA data Households vulnarability on climate change by district in Rwanda 2018 2018 2021
35. www.akademiya2063.org Relative classification Relative classification 2018 2021 Burera Gakenke Gicumbi Musanze Rulindo Gisagara Huye Kamonyi Muhanga Nyamagabe Nyanza Nyaruguru Ruhango Bugesera Gatsibo Kayonza Kirehe Ngoma Nyagatare Rwamagana Karongi Ngororero Nyabihu Nyamasheke Rubavu Rusizi Rutsiro Gasabo Kicukiro Nyarugenge Legend Very much vulnerable Much vulnerable Less vulnerable Much less vulnerable Source: Authors' calculations using the 2018 CFSVA data Households vulnarability on climate change by district in Rwanda 2018
36. www.akademiya2063.org V. CONCLUSION
37. www.akademiya2063.org V. CONCLUSION To this end we have developed a composite household vulnerability indicator drawn from Integrated Result Framework (IRF) for climate action of Senegal and Rwanda and guided by the conceptual framework of climate-related risks in the Fifth Assessment Report (AR5) of Working Group of the Intergovernmental Panel on Climate Change (IPCC). In summary, the objective of this exercise was to carry out an assessment of household vulnerability to climate change in Rwanda by using the 2021 CFSVA data (NISR 2023). The results obtained allowed us to make comparisons of the level of vulnerability of households between districts in Rwanda and to identify the factors. These results can contribute to better identifying priority targets in the context of implementation of the national adaptation plan for climate change.
38. www.akademiya2063.org THANK YOU MERCI CACCI Knowledge Seminar