IPCC standard emission factors for different energy [21].

Abstract

These data are not the result of a survey and are therefore not structured as such. It includes details of the parameters and calculation results of the life-cycle analysis carried out on the COVID tests.

Geographic coverage

no geographic coverage (simulation data only)

Analysis unit

Two functional units are used in this study: (a) the performance of a test, and (b) the detection of a positive case

Kind of data

Process-produced data [pro]

Sampling procedure

no sampling, simulation data only

Mode of data collection

Other [oth]

Research instrument

no questionnaires were used

Cleaning operations

A proprietary Excel tool developed by the authors was used to perform the LCA analysis. The tool's calculation flows were based on the ISO 14040 standard, and the emission factors were taken from the Excel version of the Ecoinvent database : https://ecoinvent.org/database/

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.

Tea sector is a major contributor to Kenya’s economy through foreign exchange via export. However, extensive amount of energy is required to produce one kilogram of tea, making tea processing energy-intensive. Comparing greenhouse gas emissions from different types of energy consumed in tea factories is imperative to enable policymakers make informed intervention in emission reduction. Reducing greenhouse gas emissions in tea factories is one of the pathways to meeting Kenya’s nationally determined 32% reduction of carbon emissions by 2030 and commitment to the Paris Agreement. This paper assesses greenhouse gas emissions from different sources of energy used in four tea factories in Kenya. The Intergovernmental Panel on Climate Change emission factor is used to calculate the total emissions of each type of energy used for 5 years. Life cycle assessment using SimaPro 8 software, Eco-indicator 99 method and Eco invent database was used to assess the specific compound causing the emission. The findings reveal that the 5-year greenhouse gas emissions by biogas, solar, wood, briquettes, and electricity are 336.111, 7.108, 3057.729, and 1,338.28 kg CO2/MWh, respectively. Firewood has the highest concentration of carbon dioxide, while solar energy has the least. Analysis of variance confirms significant difference (0.05>p = 0.0272) in greenhouse gas emissions from the different energy sources. Post-hoc analyses shows a significant difference in emissions between solar and firewood (p

Tea sector is a major contributor to Kenya’s economy through foreign exchange via export. However, extensive amount of energy is required to produce one kilogram of tea, making tea processing energy-intensive. Comparing greenhouse gas emissions from different types of energy consumed in tea factories is imperative to enable policymakers make informed intervention in emission reduction. Reducing greenhouse gas emissions in tea factories is one of the pathways to meeting Kenya’s nationally determined 32% reduction of carbon emissions by 2030 and commitment to the Paris Agreement. This paper assesses greenhouse gas emissions from different sources of energy used in four tea factories in Kenya. The Intergovernmental Panel on Climate Change emission factor is used to calculate the total emissions of each type of energy used for 5 years. Life cycle assessment using SimaPro 8 software, Eco-indicator 99 method and Eco invent database was used to assess the specific compound causing the emission. The findings reveal that the 5-year greenhouse gas emissions by biogas, solar, wood, briquettes, and electricity are 336.111, 7.108, 3057.729, and 1,338.28 kg CO2/MWh, respectively. Firewood has the highest concentration of carbon dioxide, while solar energy has the least. Analysis of variance confirms significant difference (0.05>p = 0.0272) in greenhouse gas emissions from the different energy sources. Post-hoc analyses shows a significant difference in emissions between solar and firewood (p