The COVID-19 pandemic is a global problem that confronted the economy and household food security of many countries. This study aimed to analyze the determinants of a household's food insecurity status in the era of the COVID-19 pandemic in Ethiopia. A panel data of 2,410 households in a six-round High-Frequency Phone Survey were retrieved from the World Bank database. The product of the corresponding pairwise severity weight and household responses to each coping strategy was summed up to get the individual's Coping Strategy Index. The Random Effect Model (REM) for panel data analysis was used to identify factors associated with household-related food insecurity during the COVID-19 pandemic. The descriptive statistics result shows that 18.63% and 11.08% of rural households and 56.55% and 38.13% of urban residents were food secure in the first and sixth rounds, respectively. On the contrary, 3.65% and 3.2% of rural households and 6.8% and 7.18% of urban households experienced severe food insecurity from the first to the sixth round, respectively. Most households have maintained their food security in urban areas than rural residents. However, the number of food secure households was gradually reduced from Round-1 to Round-6. Besides, REM output indicates that access to financial services, farm income, wage employment, income from property, investment, and savings, and NGO assistance negatively affected household's food insecurity. Whereas government support showed a positive association with households' food insecurity. Based on the findings, we recommend that households should adopt the behavior of enhancing and diversifying their sources of income, and the government also emphasize the establishment of national social security services by taking experience from NGOs’ emergency response mechanisms.

Ethiopia's Green Legacy Initiative (GLI), launched in 2019, aims to combat environmental degradation through large-scale afforestation. This study quantitatively evaluates GLI's impact on land use/cover (LU/C) dynamics (2013–2024) in Gubalafto District, North-eastern Ethiopia, using Landsat-8 and Sentinel-2 imagery processed via Google Earth Engine (GEE) employing the Random Forest (RF) algorithm and Normalized Difference Vegetation Index (NDVI) analysis. The RF classifier achieved high accuracy (OA = 99% for 2016–2024 and OA = 73% in 2013) in mapping six LU/C classes. Key findings reveal significant ecological shifts: woodland increased by 41.85 km² (net gain), largely through conversion from agricultural land (76.93 km²) and bareland (16.06 km²). Forest cover fluctuated, declining from 16.43% (165.96 km²) in 2013 to 14.60% (147.47 km²) in 2024; this might be influenced by armed conflict post-2020 that disrupted restoration efforts. The NDVI analysis showed high-density vegetation rising from 0.98% (9.89 km²) in 2013 to 9.38% (94.71 km²) in 2024, peaking in 2020 (NDVI max: 0.95). Despite the nationwide planting of 38.6 billion seedlings, district-level survival rates averaged only 39.80% (range: 7.59–76.50%), indicating a critical gap between planting efforts and sustainable outcomes. The study demonstrates GLI's effectiveness in woodland recovery and greening but highlights vulnerabilities from socio-political instability and inadequate seedling maintenance. Prioritizing survival rates through improved species selection, irrigation, community engagement, and integrating GLI within broader peace-building frameworks are indeed to ensure long-term ecological resilience.

In the coming three decades, world waste will be expected to increase by 70%. Coupled with the absence of waste management technologies, unregulated dumping remained a threat to sustainable development in developing countries. To this end, this study emphasized to trace socially acceptable and environmentally friendly solid waste dumpsite for the northeastern arid cities of Ethiopia. The tuneful integration of GIS and MCDA were used for the end-to-end processing of 17 variables. At the earlier, to increase alternatives dumpsites and seeking for safe zone outside the cities, 20km buffer at Semera-logia city was used as a spatial scope of the study. Fifteen key informants (KIs) from related departments of the city and Samara University were selected to validate the model result. In the end, Ordinal Regression (OR) analysis was used to test the significance of the criteria’s on suitable dumpsite selection. Thus, variables such as distance from surface water (11.32%), distance from the settlement (9.88%), distance from boreholes (8.87%), and aquifer productivity (8.51%) were the top weighed criteria for the study area. The weighted overlay result shows that 10.75% and 38.64% of the study area is found to be convenient and prohibited for dumping, respectively. Indeed, the KIs were agreed on 77% of the validation points and the agreement decreases from points of not-suitable to highly suitable. Except for aquifer productivity, all the 16 variables are significantly and negatively affect dumpsites suitability. This result can be further used as a preliminary database for experts to design landfill structures and also management operations for decision-makers.

Flooding remains a major environmental hazard in the Lower Awash River Basin (ARB), disproportionately affecting agropastoral households. This study integrates GIS‑based flood hazard mapping with the IPCC‑aligned Livelihood Vulnerability Index (LVI‑IPCC) to identify exposure hotspots and socioeconomic susceptibility in Afambo, Aysaita, and Dubti districts. Flood hazard was mapped using multi‑criteria decision analysis (MCDA‑AHP) with seven biophysical variables, while 294 households were selected using probability proportional to size and systematic sampling. Model validation using the 2021 Global Flood Monitoring event showed that 52% of observed flooded areas overlapped with very‑high hazard zones and 26% with high zones. About 57% of the basin faces high (34%) or very high (23%) flood susceptibility, concentrated along the Awash River floodplain. Notably, this hazard prediction found valid terms of observed flood events of 2020, where 78% of actual flood extents were aligned with the high to very high level of susceptibility areas. LVI‑IPCC results showed Dubti as the most vulnerable district (IPCC‑VI = − 0.083), driven by high exposure and sensitivity and comparatively lower adaptive capacity. Aysaita exhibited severe physical flood exposure, while Afambo’s vulnerability stemmed from limited adaptive resources. Key drivers include proximity to rivers, low elevation, and gentle slopes (0–0.5°). Socioeconomically, financial inclusion, irrigation access, and diversified incomes significantly reduced vulnerability. We recommend district-specific flood management: Dubti requires financial empowerment, Aysaita needs expanded irrigation infrastructure, and Afambo warrants enhanced early-warning systems. The study concludes that integrating spatial hazard information with livelihood dimensions provides a robust basis for building resilience in flood‑prone dryland ecosystems.