RSUSSH 2020
IN20-079 Local Communities Vulnerability to climate change at Indawgyi Biosphere Reserve in Myanmar
Presenter: Khun Set Thar
Mahidol University, Thailand
Abstract
The purpose of this study was to explore the vulnerability to climate change of local communities and to assess factors affecting the vulnerability of local communities to climate change in the Indawgyi Biosphere Reserve, Myanmar. Semi-structured questionnaires were distributed to 218 household heads from two different communities’ villages in Indawgyi Biosphere Reserve, Myanmar. Households vulnerability index (HVI), descriptive statistics and chi-square tests were used in the analysis. The results showed that the majority of participants had low vulnerability (47.3 %), moderate vulnerable (46.3 %), and only 6.4 % of participants had a high vulnerability. Moreover, gender, education level, and employment status were found to be statistically associated with the vulnerability level. Even though most of the local communities’ vulnerabilities to climate change was low and moderate level, some of them were found to be highly vulnerable. Therefore, enhancing the climate change resilience of rural livelihoods through community-based restoration should be conducted in the biosphere reserve in order to improve the local community’s climate change adaptation programs.
Citation format:
Thar, K., Teartisup, P., & Kerdsueb, P.. (2020). Local Communities Vulnerability to climate change at Indawgyi Biosphere Reserve in Myanmar. Proceeding in RSU International Research Conference, May 1, 2020. Pathum Thani, Thailand.QUESTIONS & ANSWERS
My suggestions are as follows:
The objective of the study and the approach for descriptive statistics are OK. showing results in Table 1 and Table 2. From Table 2., I suggest the to show some characteristics of those vulnerable groups by descriptive statistics (i.e, percentage, mean, etc) to show who are the most vulnerable and medium vulnerable groups. You can disaggregate all data into different groups to explore in details.
For Table 3, I am not sure which method or statistical programs you use; for example, you do not mentioned that you run by OLS or order Logit, probit, etc. and I am not sure how you put marital statuses and employment status into the estimation. As those statuses cannot be put in order scales like other variables, they are only be put in dummies. If you put them in rankings as other variables (i.e., income, education, age), that result may be some misinterpretation. My suggestion is you have to explain more about this Table and check the correction.
With some further works, the paper can also conclude that who seems to be the vulnerable and targets for policy intervention as well as what are appropriate policies.
Dear DR. SAUWALAK KITTIPRAPAS (CHAIRPERSON),
Thank you so much for your comments on my research. Yes I agree with you for my table 3 because I did misinterpretation on that part. Like i explainded in data anlysis part , I only used inferential statistics _ chisquare test to find the assoication between independent variables and dependent variable which is HVI score/ Vulnerablity level. Now I had already fixed it. Could you please check my revised manuscripts as per your comments which was uploaded in my account on 14/04/2020, time 14:02 ?
Best regards,
Khun