Person:

Aung, Thiri Shwesin

The impacts of armed conflict on the environment are extremely complex and difficult to investigate, given the impossibility of accessing the affected area and reliable data limitation. Very-high-resolution satellite imageries and highly reliable machine learning algorithms become very useful in studying direct and indirect impacts of war on the ecosystem, in addition to connected effects on human lives. The Rohingya conflict is described as one of the worst humanitarian crises and human-made disasters of the 21st Century. Quantification of damage due to the conflict and the suitability of human resettlement has been lacking despite the ongoing agreements to repatriate refugees and the importance of ecosystem services for the communities' survival. In this work, the investigation of environmental conditions pre-, during, and post-conflict in the conflict zone was carried out using satellite data. The Google Earth Engine (GEE) cloud-based computing platform with a widely applied algorithm, the Random Forest (RF) classifier was implemented and experienced. Striking near-complete demolition of inhabited regions, dramatic and highly significant increase in burning areas, and substantial deforestation were found. The study discusses the reasons behind such findings from the Rakhine case and debates some general conservation lessons applicable to other countries undergoing post-conflict transitions.

Covid-19 is a global health emergency and a systemic human development crisis. Marginalized populations’ ability to respond tends to be low and associated impacts can be serious for already vulnerable communities. The virus impacts vulnerable populations unequally. Pandemic containment measures can render vulnerable populations to multiple interacting stressors. In this paper, we report on an investigation into health and social vulnerability of Rohingya refugees in Malaysia. Results suggest that the Rohingya refugees are highly vulnerable. Factors contributing to their vulnerability are Covid-19 response, resilience, susceptibility, basic need conditions, anxiety, social stigma, awareness of Covid-19 prevention and isolation and fear.

Purpose: Rivers control biophysical processes that underpin essential ecosystem services. Myanmar’s rivers provide great opportunities for increasing energy supply at low costs from hydropower plants and make important contributions to the national economy. However, associated environmental impacts, as well as input and output flows of hydropower developments, remain less well understood. In this paper, we report on an investigation of the overall environmental effects of five hydropower plants in Myanmar, using a life-cycle impact assessment (LCIA) approach. The primary objective of the paper is to generate detailed lifecycle inventory data and quantify the environmental impacts of the existing five hydropower plants in Myanmar.

Material and method: This paper reports on a “cradle to grave” LCIA for five hydropower plants in which environmental impacts associated with construction, operation and maintenance, transportation, and decommissioning of large-scale hydropower plants in Myanmar were systematically assessed.

Results: Construction, transportation, operation and maintenance phases are most sensitive to global warming, mineral resource depletion, acidification, freshwater aquatic ecotoxicity, human toxicity and photochemical ozone creation. There is heterogeneity in hydropower plants’ effects on the environment, based on the size of the power plant.

Conclusion: Strategic selection of hydropower projects is suggested to enhance resilience in environmentally sensitive areas. It is concluded that more comprehensive and rigorous environmental and social impact assessment (ESIA) is needed, not only for mega-dams but also for the smaller-scale hydropower plants.

This paper studies socioeconomic and environmental changes in the neighboring areas Bangladesh- Myanmar border from 2012 to 2019, thus covering the period before and after the 2017 Rakhine conflict in Myanmar and outflux of refugees across the border to Bangladesh. Given the scarcity and costliness of traditional data collection methods in such conflict areas, the paper uses a novel methodological model based on very-high-resolution satellite imagery, nighttime satellite imagery, and machine-learning algorithms to generate reliable and reusable data for comparative assessment of the impacts of the Rakhine conflict. Assessments of welfare and environmental risks using this approach can be accurate and scalable across different regions and times when other data are unavailable. Key findings are: the general livelihood situation has worsened and income sources shrunk in Rakhine; forced migration damaged the ecologically fragile regions in the two countries; the destruction of aquaculture wetland ecosystems is observed in Rakhine; the deforestation rate reached 20% in Rakhine and 13% on the Bangladeshi side of the border. The results can provide guidance to policymakers and international actors as they work to repatriate the victims of the conflict in Rakhine and minimize the conflict’s security and environmental consequences. The methodology can be applied to other data-poor conflict and refugee areas in the world.