Role of Household’s Tree Population, Socio-economic and Behavioural Determinants on Carbon Footprint Mitigation and Carbon Credit Balance in East Ugenya Ward, Kenya

Abstract

Scope 1 harmful emissions are directly linked to high levels of industrialization; Scope 2 and 3 carbon footprints are locally oriented and indirectly associated with household activities and behavioural alignment. East Ugenya Ward is perceived as the leader in firewood consumption, with the socioeconomically marginalized population in Siaya County resorting to this mode of fuel usage. Conversely, how the mentioned factors relate to both carbon footprints and credits is concluded with no concrete local and global resolution. The effort to reverse households’ carbon emissions through green energy campaigns has proved less operative due to little understanding of carbon-related working concepts and socio-economic hardships. This study analyses the role of household Tree population. It assesses the role of socio-economic and behavioural determinants in relation to carbon footprints and potential credits that can arise through sound environmental management within local community initiatives. Three hundred eighty-four household heads were interrogated. A descriptive cross-sectional research design and simple random sampling were found to be functional. Databases were Questionnaires, field research, measurement, photography, Focused Group Discussions, observation, key informants, and enumeration. Carbon Footprint Calculator (C.F.C.) and (V.C.S.)-Verra were used to assess the household’s emissions and potential credits. The spatial scale for tree population count was 20 m x 20 m quadrat. The tree-based biomass was translated using a conventional carbon sink conversion (Tons of Co2 Equivalent- tCo2eq). Data analysis involved the use of SPSS. The potential net carbon offset was (M = 0.334, SD = 0.006) tCo2eq per household. The Multinomial Logistic Regression model X2 (8, N= 384) = 24.69, Nagelkerke R2=.56, p <. 001, Strongly proved that the belief that Carbon Credit is profitable had a significant statistical association with Carbon Footprint Mitigation. The multiple linear coefficients of determination proved that 67.6%, F (381) = 69.51, p = .031, R2 = .676 of change in Carbon Footprints and 72.1%, F (381) = 72.58, p = .026, R2 =.721 of the variation in Net Carbon Credits, was attributable to combined variation in Tree population, Mean household age, and mean average monthly income. Both the Carbon Footprint and Carbon credit are affected. Therefore, local sensitization is needed to achieve knowledge and understanding of favourable emission budgets and profitable carbon trade