Determinants of Households’ Agricultural and Energy Associated Greenhouse Gases Emissions among Smallholders in Western Kenya
Bibliographic record
Abstract
Global efforts in reducing greenhouse gases (GHG) emissions and mitigating the impacts of climate change necessitates prioritization of developing effective strategies for estimating per capita carbon footprint, forecasting and addressing the major drivers. A survey was administered among 380 agricultural households in western Kenya with specific objectives of i) to utilize various emissions indices to establish total households emissions, ii) to establish households production and consumption related GHGs’ emissions and iii) determine socio-economic factors influencing per adult equivalent GHGs’ emissions at the households. Four cluster Sub-counties including Mt. Elgon, Bumula, Bungoma North and Sabatia were purposively sampled as influenced by agro-ecological, socio-economic, agricultural production and biomass energy sourcing characteristics for the study. A pre-set questionnaire was used to collect demographic, agricultural production, and energy sourcing and utilization information. Using the survey, households various agricultural activities and levels of utilization of agricultural inputs and energy sources were quantified. The quantified values were multiplied by respective emission’s factor derived from global statistics to estimate total emission. Enteric emissions accounted for 98 percent of livestock management associated GHGs. Every household emitted 2922kg CO2 Equivalent (Eq) from livestock management per annum. Maize associated GHGs emission in 2017 was 12817kg CO2 Eq with 81, 13 and 6 percent linked to residue decomposition, organic soil management and soil nutrient replenishment respectively. Maize production, biomass cooking energy, livestock management and lighting accounted for 47, 37, 13 and 3 percent respectively of total household emissions. Factors that significantly influenced adult equivalent GHGs emissions were consumption expenditure (P<0.01), household size (P<0.01), maize yield (P<0.01) and geographical locations. Efforts to reduce households GHGs emissions need to address adoption of clearner cooking and lighting energy, efficiency in livestock production and use of inorganic farming inputs for crop production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".