Downloads provided by UsageCounts
Kenya is currently experiencing several challenges owing to its growing population, including outstretched resources, environmental degradation, and urban pollution. To achieve sustainable, smart and green growth which comprises a secure water-energy-food (WEF) nexus, efficient and renewable energy uptake, and low emissions per capita, Kenya could adopt a Climate, Land, Energy and Water systems (CLEWs) model that examines the interconnections and interactions between these key critical resources. This study uses OSeMOSYS software to explore three (3) scenarios: business as usual (BAU), green energy transition (GET), and increasing forest cover (IFC). The analysis shows that capital investment in renewables is key to achieving a green energy transition. We recommend adopting a data-driven integrated decision support model (CLEWs) to inform policy development based on these scenarios. Our policy insights suggest adopting renewable and decentralized energy solutions, promoting sustainable agriculture, afforestation, and agroforestry, and greening transport and infrastructure development to minimize emissions. The poster summarizes the project developed as part of EMP-A 2023.
Smart and green growth, Climate change, NDCs, OSeMOSYS, CLEWs modeling
Smart and green growth, Climate change, NDCs, OSeMOSYS, CLEWs modeling
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 28 | |
| downloads | 23 |

Views provided by UsageCounts
Downloads provided by UsageCounts