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

  • David Ochieng Oduor Maseno University
  • Peter Otieno Opeyo Beijing University of Technology
  • Dorice Anyango Oduor Multilateral Environmental Agreement
Keywords: Carbon Emission, Carbon Credits Formula, GHG, Climate Change Adaptation and Mitigation, Environment, Socio-economic Behaviour, Species Diversity and Plant Population
Share Article:


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


Download data is not yet available.


Abbas, H. S. M., Xu, X., & Sun, C. (2021). Role of foreign direct investment interaction to energy consumption and institutional governance in sustainable GHG emission reduction. Environmental Science and Pollution Research, 28(40), 56808-56821.

Basu, P. (2009). A green investment: If growing forests in India can generate lucrative carbon credits, then why isn’t everyone planting trees? Nature, 457(7226), 144-147.

Billings, J., Zeitel, L., Lukomnik, J., Carey, T. S., Blank, A. E., & Newman, L. (1993). Impact of socio-economic status on hospital use in New York City. Health Affairs, 12(1), 162-173.

Cacho, O., Hean, R., Ginoga, K., Wise, R., Djaenudin, D., Lugina, M., ... & Khasanah, N. (2008). Economic potential of land-use change and forestry for carbon sequestration and poverty reduction. ACIAR.

Coelho Junior, L. M., de Lourdes da Costa Martins, K., & Carvalho, M. (2019). Carbon footprint associated with firewood consumption in northeast Brazil: an analysis by the IPCC 2013 GWP 100y Criterion. Waste and Biomass Valorization, 10, 2985-2993.

Eini-Zinab, H., Shoaibinobarian, N., Ranjbar, G., Ostad, A. N., & Sobhani, S. R. (2021). Association between the socio-economic status of households and a more sustainable diet. Public Health Nutrition, 24(18), 6566-6574.

Eshton, B., & Katima, J. H. (2015). Carbon footprints of production and use of liquid biofuels in Tanzania. Renewable and Sustainable Energy Reviews, 42, 672-680.

Hailemariam, A., Dzhumashev, R., & Shahbaz, M. (2020). Carbon emissions, income inequality and economic development. Empirical Economics, 59(3), 1139-1159.

Halkos, G. E., & Tsirivis, A. S. (2023). Electricity production and sustainable development: The role of renewable energy sources and specific socio-economic factors. Energies, 16(2), 721.

Hu, H., Qi, S., & Chen, Y. (2023). Using green technology for a better tomorrow: How enterprises and governments utilize the carbon trading system and incentive policies. China Economic Review, 78, 101933.

Jone, C. M., & Kammen, D. M. (2011). Quantifying carbon footprint reduction opportunities for US households and communities. Environmental science & technology, 45(9), 4088-4095.

Kassouri, Y., & Altıntaş, H. (2020). Human well-being versus ecological footprint in MENA countries: a trade-off? Journal f Environmental Management, 263, 110405.

Kragt, M. E., Gibson, F. L., Maseyk, F., & Wilson, K. A. (2016). Public willingness to pay for carbon farming and its co-benefits. Ecological Economics, 126, 125-131.

Li, R., Wang, Q., Liu, Y., & Jiang, R. (2021). Per-capita carbon emissions in 147 countries: the effect of economic, energy, social, and trade structural changes. Sustainable Production and Consumption, 27, 1149-1164.

Li, W., Long, R., Chen, H., Yang, M., Chen, F., Zheng, X., & Li, C. (2019). Would personal carbon trading enhance individuals adopting the intention of battery electric vehicles more effectively than a carbon tax? Resources, Conservation and Recycling, 149, 638-645.

Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country an analyses. Population and environment, 35, 286-304.

Lin, S. M. (2017). Identify predictors of university students’ continuance intention to use online carbon footprint calculators. Behaviour & Information Technology, 36(3), 294-311.

Mensah, J. T. (2014). Carbon emissions, energy consumption and output: a threshold analysis on the causal dynamics in emerging African economies. Energy Policy, 70, 172-182.

Mogaka, B. O., Bett, H. K., & Karanja Ng'ang'a, S. (2021). Socio-economic factors influencing the choice of climate-smart soil practices among farmers in western Kenya. Journal of Agriculture and Food Research, 5, 100168.

Mohammed Albiman, M., Nassor Suleiman, N., & Omar Baka, H. (2015). The relationship between energy consumption, CO2 emissions and economic growth in T Tanzania. International Journal of Energy Sector Management, 9(3), 361-375.

Moser, S., & Kleinhückelkotten, S. (2018). Good intentions, but low impacts: diverging importance of motivational and socio-economic determinants explaining pro-environmental behavior, energy use, and carbon footprint. Environment and behavior, 50(6), 626-656.

Mostafa, M. M. (2016). Egyptian consumers’ willingness to pay for carbon-labeled products: A contingent valuation analysis of socio-economic factors. Journal of cleaner production, 135, 821-828.

Musafiri, C. M., Kiboi, M., Macharia, J., Ng'etich, O. K., Kosgei, D. K., Mulianga, B., ... & Nam, E., & Jin, T. (2021). Mitigating carbon emissions by energy transition, energy efficiency, and electrification: Difference between regulation indicators and empirical data. Journal of Cleaner Production, 300, 126962.

Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T., & Stern, P. C. (2021). The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions. Nature Energy, 6(11), 1011-1016.

Oduor, D. O., Mutavi, I. N., & Long’ora, A. E. (2022). Effects of Socio-cultural Attributes on Dominant Tree Species Diversity in Ugenya Sub-County Siaya County, Kenya.

Okoko, A., Reinhard, J., von Dach, S. W., Zah, R., Kiteme, B., Owuor, S., & Ehrensperger, A. (2017). The carbon footprints of alternative value chains for biomass energy for cooking in Kenya and Tanzania. Sustainable Energy Technologies and Assessments, 22, 124-133.

Oloo, J. O., Makenzi, P. M., Mwangi, J. G., & Abdulrazack, A. S. (2013). Dominant Tree Species for Increasing Ground Cover and their Distribution in Siaya County, Kenya. International Journal of Agriculture Innovations and Research, 2(3), 373-378.

Oluoch, F., Ayodo, G., Owino, F., & Okuto, E. (2020). Modeling the Impact of Traveling Time on the Utilization of Maternity Services Using Routine Health Facility Data in Siaya County, Western Kenya.

Ondiek, R. A., Vuolo, F., Kipkemboi, J., Kitaka, N., Lautsch, E., Hein, T., & Schmid, E. (2020). Socio-economic determinants of land use/cover change in wetlands in East Africa: a case study analysis of the Anyiko Wetland, Kenya. Frontiers in Environmental Science, 7, 207.

Opeyo, P. O. (2018). Conditional Maximum Likelihood Estimation for Logistic Panel Data Models with Non-Responses (Doctoral dissertation, JKUAT-PAUSTI).

Ottelin, J., Heinonen, J., Nässén, J., & Junnila, S. (2019). Household carbon footprint patterns by the degree of urbanization in Europe. Environmental Research Letters, 14(11), 114016.

Owino, C. N., Kitaka, N., Kipkemboi, J., & Ondiek, R. A. (2020). Assessment of greenhouse gasses emission in smallholder rice paddies converted from Anyiko Wetland, Kenya. Frontiers in Environmental Science, 8, 80.

Patel, R., Marvuglia, A., Baustert, P., Huang, Y., Shivakumar, A., Nikolic, I., & Verma, T. (2022). Quantifying households’ carbon footprint in cities using socio-economic attributes: A case study for The Hague (Netherlands). Sustainable Cities and Society, 86, 104087.

Shao, L., Zhang, H., & Irfan, M. (2022). How public expenditure in recreational and cultural industry and socio-economic status caused environmental sustainability in OECD countries? Economic research-Ekonomska istraživanja, 35(1), 4625-4642.

Shen, L., Lin, F., & Cheng, T. C. E. (2022). Low-Carbon transition models of high carbon supply chains under the mixed carbon cap-and-trade and carbon tax policy in the carbon neutrality era. International Journal of Environmental Research and Public Health, 19(18), 11150.

Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce and management, 2(11), 1-22.

Sobrino, N., & Monzon, A. (2014). The impact of the economic crisis and policy actions on GHG emissions from road transport in Spain. Energy Policy, 74, 486-498.

Song, Y. J., Ma, F. W., & Qu, J. Y. (2020). Impacts of cultural diversity on carbon emission effects: from the perspective of environmental regulations. International Journal of Environmental Research and Public Health, 17(17), 6109.

Spilker, G., & Nugent, N. (2022). Voluntary Carbon Market Derivatives: Growth, Innovation, & Usage. Borsa Istanbul Review.

Wang, S., Sun, P., Sun, H., Liu, Q., Liu, S., & Lu, D. (2022). Spatiotemporal variations of carbon emissions and their driving factors in the Yellow River Basin. International Journal of Environmental Research and Public Health, 19(19), 12884.

Wei, H., Zuo, T., Liu, H., & Yang, Y. J. (2017). Integrating land use and socio-economic factors into scenario-based travel demand and carbon emission impact study. Urban Rail Transit, 3, 3-14.

Wiedenhofer, D., Smetschka, B., Akenji, L., Jalas, M., & Haberl, H. (2018). Household time use, carbon footprints, and urban form: a review of the potential contributions of everyday living to the 1.5 C climate target. Current opinion in environmental sustainability, 30, 7-17.

Yan, D., Lei, Y., & Li, L. (2017). Driving factor analysis of carbon emissions in China’s power sector for low-carbon economy. Mathematical Problems in Engineering, 2017.

Yan, Q., Qin, G., Zhang, M., & Xiao, B. (2019). Research on real purchasing behavior analysis of electric cars in Beijing based on structural equation modeling and multinomial logit model. Sustainability, 11(20), 5870.

Yang, B., & Usman, M. (2021). Do industrialization, economic growth and globalization processes influence the ecological footprint and healthcare expenditures? Fresh insights based on the STIRPAT model for countries with the highest healthcare expenditures. Sustainable Production and Consumption, 28, 893-910.

Yang, Y., Li, Y., & Guo, Y. (2022). Impact of the differences in carbon footprint driving factors on carbon emission reduction of urban agglomerations given S.D.G.s: A case study of the Guanzhong in China. Sustainable Cities and Society, 85, 104024.

Zhang, J., Zheng, Z., Zhang, L., Qin, Y., Wang, J., & Cui, P. (2021). Digital consumption innovation, socio-economic factors and low-carbon consumption: Empirical analysis based on China. Technology in Society, 67, 101730.

5 November, 2023
How to Cite
Oduor, D., Opeyo, P., & Oduor, D. (2023). Role of Household’s Tree Population, Socio-economic and Behavioural Determinants on Carbon Footprint Mitigation and Carbon Credit Balance in East Ugenya Ward, Kenya. East African Journal of Environment and Natural Resources, 6(1), 447-458.