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Greenhouse Gases

Greenhouse Gases: A comprehensive overview of the various gaseous compounds that contribute to the greenhouse effect and global warming.
These gases, such as carbon dioxide, methane, and nitrous oxide, trap heat in the Earth's atmosphere, leading to climate change and its far-reaching impacts.
Researchers can leverage PubCompare.ai's AI-driven platform to effortlessly identify the most effective protocols and products for their Greenhouse Gases research needs, streamlining their workflow and optimizing their results.
The platform can help locate the best protocols from published literature, pre-prints, and patents, enabling researchers to make informed decisions and advance their work in this critical field.

Most cited protocols related to «Greenhouse Gases»

1
Estimating Climate Change Mortality Impacts
Cited 13 times
A detailed description of the data, analytical framework, and statistical methods, partly described in previous work,16 (link) is provided in the appendix.
We estimated location-specific associations using observed data on outdoor temperature and mortality. For this purpose, we obtained information from a dataset created through the Multi-Country Multi-City (MCC) Collaborative Research Network. The dataset is composed of observed daily time series of mean temperature and mortality counts for all causes or non-external causes only (International Classification of Diseases [ICD] codes 0–799 in ICD-9 and codes A00-R99 in ICD-10) in largely overlapping periods ranging from Jan 1, 1984, to Dec 31, 2015, in addition to location-specific meta-variables (appendix).
We computed future effects under alternative climate change scenarios using modelled climate and mortality projections. First, we obtained daily mean temperature series under scenarios of climate change consistent with the four representative concentration pathways (RCPs) defined in the 2014 IPCC report.2 These four scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) correspond to increasing greenhouse gas concentration trajectories, and describe a range of changes in climate and related global warming, from mild (RCP2.6) to extreme (RCP8.5). We generated the temperature series under each RCP by general circulation models (GCMs), which offer a representation of past, current, and future climate dependent on greenhouse gas emissions. Specifically, projections for five GCMs, representative of the range of available climate models, were developed and made available by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP).17 (link) The ISI-MIP database provides daily mean temperature for historical (1960–2005) and projected (2006–99) periods, bias-corrected and downscaled at a 0·5° × 0·5° spatial resolution, as single runs of each GCM under each RCP. We extracted the modelled daily temperature series for each of the studied locations in the period 1990–2099 by linking the coordinates with the corresponding cell of the grid, and recalibrated the modelled series using the observed series.18 We computed projected daily series of all-cause mortality as the average observed counts for each day of the year, repeated along the same projection period (1990–2099).
Gasparrini A., Guo Y., Sera F., Vicedo-Cabrera A.M., Huber V., Tong S., de Sousa Zanotti Stagliorio Coelho M., Nascimento Saldiva P.H., Lavigne E., Matus Correa P., Valdes Ortega N., Kan H., Osorio S., Kyselý J., Urban A., Jaakkola J.J., Ryti N.R., Pascal M., Goodman P.G., Zeka A., Michelozzi P., Scortichini M., Hashizume M., Honda Y., Hurtado-Diaz M., Cesar Cruz J., Seposo X., Kim H., Tobias A., Iñiguez C., Forsberg B., Åström D.O., Ragettli M.S., Guo Y.L., Wu C.F., Zanobetti A., Schwartz J., Bell M.L., Dang T.N., Van D.D., Heaviside C., Vardoulakis S., Hajat S., Haines A, & Armstrong B. (2017). Projections of temperature-related excess mortality under climate change scenarios. The Lancet. Planetary Health, 1(9), e360-e367.
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Publication 2017
Climate Climate Change Greenhouse Gases Grid Cells
2
Modeling Ixodes ricinus Distribution
Cited 7 times
Primary occurrence records for I. ricinus were obtained from diverse sources. Data were drawn from the Global Biodiversity Information Facility (GBIF; www.gbif.org; ~2110 occurrence points), VectorMap (www.vectormap.org; ~1801 occurrence points), and the scientific literature [20 (link)] (~1195 points; S1 File). Sampling was concentrated in Great Britain and Germany thanks to surveillance by the European Vector Map Program of the European Center for Disease Prevention and Control (ECDC; http://ecdc.europa.eu/en/healthtopics/vector/vector-maps/). The initial set of occurrence records was subjected to several data cleaning steps to reduce possible biases in calibrating ecological niche models (ENMs) [21 (link)]. (1) We discarded all records with unknown geographic references, and removed all duplicate records. (2) The data were further filtered by distance, so that all redundant records occurring in a single 10’ cell (~20 km) were omitted. (3) Finally, we accounted for marked differences in sampling density across countries: data records were filtered by balancing the density of occurrences on a country-by-country basis. We chose Spain as a reasonable intermediate-density reference point (6 occurrence records /100,000 km2) to overcome problems associated with oversampling or undersampling observed in some countries. Although we discarded large numbers of data points, this step removes large-scale spatial biases, and allows a better estimation of niche characteristics [22 (link)].
The final balanced dataset of I. ricinus included 416 occurrence points, which we separated five times randomly into equal-sized subsets of 208 points, one subset was for model calibration and the other for model evaluation (Fig 1). These 5 random subgroups provide replicate views of model results and give a better idea of the variation resulting from the availability of occurrence data.
We obtained data on 19 “bioclimatic” variables from the WorldClim climate data version 1.4 [23 ] available via www.worldclim.org. These variables were derived from interpolation of average monthly temperature and rainfall data obtained from weather stations during 1950–2000. We removed variables 8–9 and 18–19 because of known spatial artefacts. We used the data layers at 10’ spatial resolution because of the continental extent of our models. We obtained parallel data layers for 17 general circulation models (GCMs; Table 1) for each representative concentration pathway (RCP) for each time period. We chose two representative concentration pathways, RCP 4.5 and RCP 8.5 (corresponding to lower and higher greenhouse gas emissions, respectively) for 2050 and 2070 to account for possible climate change influences in both scenarios and in two different times. We used diverse GCMs available from the WorldClim archive to estimate both the future distributional potential of I. ricinus based on each individual GCM, which was a key element in assessing uncertainty in predictions deriving from GCM choice.
Alkishe A.A., Peterson A.T, & Samy A.M. (2017). Climate change influences on the potential geographic distribution of the disease vector tick Ixodes ricinus. PLoS ONE, 12(12), e0189092.
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Publication 2017
Cells Climate Climate Change Cloning Vectors DNA Replication Europeans Gas Chromatography-Mass Spectrometry Greenhouse Gases Microtubule-Associated Proteins Ricinus, lice
3
Modeling Climate and Health Impacts of Air Pollution
Cited 6 times
We applied an atmospheric chemistry–general circulation model to calculate the impacts of air pollution on climate and public health (SI Appendix, SI Methods). The model comprehensively accounts for emissions, multiphase chemistry, and other processes that control atmospheric composition. Model results include concentrations of ozone (O3) and particulate matter, including PM2.5 (particulates with a diameter <2.5 µm), being the main cause of morbidity and mortality (2 (link), 9 (link)). The results for PM2.5 and O3 served as input to the health impact calculations, based on the Global Burden of Disease methodology (2 (link)). We applied a Global Exposure Mortality Model (GEMM) for PM2.5 that is based on an unmatched large number of cohort studies in many countries, and accounts for additional causes of death than considered previously (10 (link)). The GEMM calculations were complemented with those for O3, accounting for about 3% of the total excess mortality rate. The atmospheric chemistry model was initially run for 20 y (excluding 5-y spin-up) with prescribed ocean temperatures to analyze health impacts and climate forcings, following IPCC recommendations (11 ), including changes in cloud reflectivity through the effects of aerosols on cloud condensation nuclei (CCN) (12 ). Uniquely, we included the increase in CCN activity of aeolian (wind-blown) dust particles is due to interaction with air pollution (chemical “aging”), which generally increases their ability to take up water. SI Appendix, Fig. S1 shows a comparison between modeled and satellite observed aerosol optical depth, SI Appendix, Fig. S2 for rainfall, SI Appendix, Fig. S3 for PM2.5 and dust aerosol optical depth, and SI Appendix, Figs. S4 and S5 present the calculated aerosol radiative forcing of climate, which match the IPCC ensemble model estimates (11 ). Subsequently, the same model was rerun for 30-y periods (excluding 5-y spin-up) with an interactive ocean to compute equilibrium climate responses. We accounted for air pollution and greenhouse gases in idealized scenario calculations to characterize the public health and climate impacts of a hypothetical phaseout from fossil-fuel-related and other anthropogenic emissions, a distinction that could be essential for policy-making.
Lelieveld J., Klingmüller K., Pozzer A., Burnett R.T., Haines A, & Ramanathan V. (2019). Effects of fossil fuel and total anthropogenic emission removal on public health and climate. Proceedings of the National Academy of Sciences of the United States of America, 116(15), 7192-7197.
Publication 2019
Air Pollution Cell Nucleus Climate Cocaine Figs Greenhouse Gases Ozone Radiation Reflex Vision Wind
4
Estimating Household Cooking Emissions
Cited 6 times
Because most emission inventories report total residential emissions (Bond et al. 2004 (link); Lamarque et al. 2010 ; Shen et al. 2012 (link); Streets et al. 2003 (link)) with no distinction between cooking and heating, our general approach was to calculate a) the proportion of PM2.5-hh emissions attributable to cooking (rather than heating), and then b) the proportion of APM2.5 attributable to PM2.5-hh. To focus specifically on the residential sector, we used GAINS and Equation 1 to determine the fraction of PPM2.5-hh from cooking with solid fuels such as hard coal, agricultural residues, fuelwood, and dung, for each country or subnational jurisdiction (IIASA 2012 ):
(PIT + STOVE)/ΣDOM = PPM2.5-hh from cooking, [1]
where PIT indicates emissions from open fire cooking with solid fuels (teragrams of PPM2.5 per country), STOVE represents emissions from combusting solid fuels in residential cooking stoves (teragrams of PPM2.5 per country), and DOM indicates total emissions from all residential sources, including boilers and heating stoves (teragrams of PPM2.5 per country). Non-fuel emissions associated with cooking (such as volatile organic compounds created by frying) are not included.
Within GAINS, we used a scenario that draws on data from the International Energy Agency (IEA 2011 ). GAINS estimates current and future PPM2.5 emissions using activity data, fuel-specific uncontrolled emission factors, the removal efficiency of emission control measures, and the extent to which such measures are applied (Amann et al. 2011 ; Kupiainen and Klimont 2007 ). For household cooking with solid fuels from 1990 through 2010, no technical control measures were applied in the model.
We multiplied the fraction of residential PPM2.5 attributable to household cooking by the proportion of total ambient population-weighted PM2.5 attributable to household combustion (PM2.5-hh) (Equation 2). The latter proportion (%PM2.5-hh) was generated using TM5-FASST.
%PPM2.5-hh from cooking × %PM2.5-hh = %PM2.5-cook, [2]
where all analysis in this equation is at the country level, %PPM2.5-hh from cooking is the quantity derived in Equation 1, and %PM2.5-hh = μg/m3 PM2.5-hh/μg/m3 PM2.5.
Equation 3 shows the method by which country-level results were combined to produce regional population-weighted estimates.
We used global estimates of annual average ambient population-weighted PM2.5 concentrations, which were developed for the GBD 2010 study (Brauer et al. 2012 (link)) as well as the Global Energy Assessment (Riahi et al. 2012 ), to estimate the proportions and absolute concentrations of PM2.5-cook, on a regional basis. The underlying methodology for deriving PM2.5 concentrations is described in Rao et al. (2012) and combines the global integrated assessment model MESSAGE (Rao and Riahi 2006 ; Strubegger et al. 2004 ) with TM5 (see Supplemental Material, “Model Methodologies”). MESSAGE covers all greenhouse gas–emitting sectors; in the residential sector, MESSAGE includes an explicit representation of the energy use of rural and urban households with different income levels. Fuel choices at the household level consider the full portfolio of commercial fuels as well as traditional biomass for cooking, heating, and specific use of electricity of household appliances (Ekholm et al. 2011 ). TM5-FASST was used to determine PM2.5-hh. Secondary organic aerosol formation was included in TM5-FASST estimates of annual average population-weighted PM2.5 concentrations (see Supplemental Material, Figure S1, for more information on the emission and source categories included in this analysis). Dust and sea salt increments were estimated by comparing concentrations generated by TM5-FASST with those developed with TM5-FASST, satellite data, and ground measurements for GBD 2010 and published by Brauer et al. (2012) (link). Positive differences between GBD 2010 and TM5-FASST were assumed to be representative of dust and sea salt increments and were included in estimates of APM2.5 to better approximate the proportional role of household solid fuel use for cooking in creating APM2.5.
Following GBD 2010 (IHME 2010 ), this analysis considers PM2.5 emissions for three time points: 1990, 2005, and 2010. The data cover 170 countries (see Supplemental Material, Table S1) in 20 of the 21 GBD 2010 regions; the majority of missing countries are small (population < 1 million each) and together they account for 34 million people in 2010, that is, < 1% of the world population.
Data sources and models used in our analysis are summarized in Table 1. Regional population and household emissions estimates are shown in Supplemental Material, Table S4.
We estimated the burden of disease associated with exposure to outdoor PM2.5 air pollution that can be attributed to household cooking by applying the derived proportions of APM2.5 due to household cooking with solid fuels to the GBD 2010 burden of disease estimates for ambient air pollution (Lim et al. 2012 (link)). We scaled results—that is, we applied percentages of ambient air pollution due to household cooking with solid fuels (the risk factor) to the burden estimates while preserving the exposure–response relationships used to determine the overall burden of disease attributable to ambient air pollution.
Chafe Z.A., Brauer M., Klimont Z., Van Dingenen R., Mehta S., Rao S., Riahi K., Dentener F, & Smith K.R. (2014). Household Cooking with Solid Fuels Contributes to Ambient PM2.5 Air Pollution and the Burden of Disease. Environmental Health Perspectives, 122(12), 1314-1320.
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Publication 2014
Air Pollution Coal Electricity Feces Greenhouse Gases Households Personality Inventories Sodium Chloride Volatile Organic Compounds
5
Paleoclimate Modeling Experiments Across Eras
Cited 5 times
We used output from the following: (i) the PMIP2 and PMIP3 mid-Holocene (6 ka) and preindustrial (0 ka) experiments, publicly available online at the Earth System Grid (http://pcmdi9.llnl.gov/); (ii) the TraCE-21ka, a fully coupled, transient simulation conducted with the National Center for Atmospheric Research Community Climate System Model version 3 (CCSM3) (43 , 44 (link)); and (iii) the prescribed vegetation and dust experiments conducted with the EC-Earth model (27 ). The PMIP simulations and EC-Earth mid-Holocene control experiments were forced with the same changes in boundary conditions, which include orbital forcing and greenhouse gases (59 ). The vegetation and the dust concentrations were assumed identical to the preindustrial climate. Two additional idealized experiments were performed with EC-Earth, in which Saharan land cover is set to shrub (“Green Sahara” experiment) and, additionally, dust concentrations (“Green Sahara–Reduced Dust” experiment) were reduced by as much as 80% on the basis of recent estimates of Saharan dust flux reduction during the mid-Holocene (9 , 10 ). The TraCE simulation uses a complete suite of changing boundary conditions for the last 21,000 years, including changes in orbital, greenhouse gas, ice sheet, and freshwater forcings. See the Supplementary Materials for further details on the model simulations and analyses, including a list and description of the models used.
Tierney J.E., Pausata F.S, & deMenocal P.B. (2017). Rainfall regimes of the Green Sahara. Science Advances, 3(1), e1601503.
Publication 2017
Climate Greenhouse Gases Ice Cover Transients

Most recents protocols related to «Greenhouse Gases»

1
Evaluating UVA's Sustainability Action Plans
The default values in the IEFT are based on UVA's 2016 operations. To evaluate the impacts of the Nitrogen and Greenhouse Gas Action Plans, UVA's activity plan was projected to the goal year of 2025 based on a business-as-usual (BAU) 2016 scenario. These estimates were made based on population, gross square footage, and food purchasing trends (see Supplementary Table S8) and were entered into the IEFT. We calculated UVA's 2025 baseline activities to reflect a business-as-usual (BAU) scenario based on the projected increase in gross square footage, population, and food demand from 2016 to 2025. These changes resulted in increases across all four footprint indicators from 2016 to 2025. The GHG footprint increased by 19%, N footprint by 18%, P footprint by 18%, and W footprint by 4% from 2016 to 2025, predominantly due to the food and energy sectors (Table 2). For each scenario, the BAU calucated within the IEFT was altered by sector, depending on the specific strategy (see Supplementary Table S1, Table S2, Table S3, Table S4, and Table S5).
Dukes E., Cheng S., Mogen S., Galloway J., Leach A., Trimble A.R., Pettit A., Compton J, & Pennino M. (2023). Footprints in Action: How UVA Is Managing Its Sustainability Stewardship. Sustainability and Climate Change, 16(1), 48-63.
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Publication 2023
Food Greenhouse Gases Nitrogen
2
Calculating Environmental Damage Costs of GHG and N
Damage costs associated with both GHG and N were calculated for different usage categories (electricity, food, commuting, etc.). For N only, damage costs were also calculated for social-environmental sectors (human health, agriculture, ecosystems, climate), various media (land, water, air), and different N types (NO3, NH3, N2O, dissolved N). Damage costs associated with specific N fluxes were measured in terms of dollar per kg N released to the environment. The specific damage costs were largely obtained from a regional study of the Chesapeake Bay watershed (Birch et al., 2011), in which UVA is located, supplemented with other values from the literature (Compton et al., 2017 ). The values represent incremental or marginal increases in cost associated with the impacts of N use on a per unit of N basis and assume a linear response function (see Supplementary Table S9).
For the social costs of GHG (CO2-C and N2O-N), there is significant variance in the estimates depending on scope (global vs. national impacts) and discount rates. In a literature review, Tol (2005 ) found modal estimates of only $2/t GHG and concludes that mean values are likely less than $50/t GHG. The Interagency Working Group on the Social Cost of Greenhouse Gases (2016 ) estimated a global-scale social cost of GHG of from $14 to $74 per metric ton of carbon dioxide equivalent (MTCDE) for a range of discount rates, while current estimates from the EPA for national costs are from $1 to $7 per MTCDE. Values of $30 per ton of CO2 equivalent were used as an intermediate value. All dollar values were adjusted for inflation and pegged to the US dollar in 2016 using the consumer price index (US Bureau of Labor Statistics, n.d. ), to coincide with the 2016 IEFT data.
Marginal damage costs associated with GHG and N released by UVA were calculated using the IEFT based on GHG and N release by chemical compound and mode of impact. The N release from NOx-N, NH3-N, and N2O-N were combined with the damage costs associated with those forms. Virtual N from food production was apportioned into dissolved N, NH3, NOx, and N2O (Houlton et al., 2013 ). The hydrologic N released to water was used to estimate the damages associated with release to surface water, groundwater, and coastal systems.
Currently, there are no damage cost evaluations completed for P and W. If these data become available in the future, they can be included damage cost estimations.
Dukes E., Cheng S., Mogen S., Galloway J., Leach A., Trimble A.R., Pettit A., Compton J, & Pennino M. (2023). Footprints in Action: How UVA Is Managing Its Sustainability Stewardship. Sustainability and Climate Change, 16(1), 48-63.
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Publication 2023
Betula Carbon dioxide Climate Conclude Resin Ecosystem Electricity Food Greenhouse Gases Homo sapiens Obstetric Labor Social Group
3
Strategies for Sustainability at UVA
All data used in the IEFT were gathered by either UVA Facilities Management, Dining, or Health System operations. The data were entered into the IEFT for calendar year 2016 to calculate the GHG, N, P, and W footprints simultaneously (see Supplementary Table S1).
In order to achieve these reduction goals, UVA has written two relevant action plans outlining strategies to reduce these footprints: the Greenhouse Gas Action Plan (2017) and the Nitrogen Action Plan (2019) (Table 2). The aim of these action plans is to outline strategies the university will implement by 2025 in order to meet environmental sustainability goals. All strategies evaluated in this study were derived from these action plans. The column on the left describe the strategies listed while the two columns on the right indicate whether or not the strategy is included in each plan: Y (yes, it is included) or N (no, it is not included). More information on individual scenario improvements can be found in Supplementary Table S2.
Dukes E., Cheng S., Mogen S., Galloway J., Leach A., Trimble A.R., Pettit A., Compton J, & Pennino M. (2023). Footprints in Action: How UVA Is Managing Its Sustainability Stewardship. Sustainability and Climate Change, 16(1), 48-63.
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Publication 2023
Greenhouse Gases Nitrogen
4
Simultaneous Greenhouse Gas Flux Measurement
The simultaneous determination of methane, carbon dioxide and nitrous oxide fluxes was calculated by using static black chamber method. We used closed box-gas chromatography (Zhang et al., 2015 (link)). Before transplanting rice to the plot, a PVC flux loop was permanently embedded in each plot to continuously monitor GHG emissions during the experiment period. A 5cm deep groove on the edge of each base was used to inject water and seal the gas chamber during gas production, in order to prevent gas exchange. The cross-sectional area of the gas tank was 0.25m2 (50cm*50cm) and the height was 50cm. Once the rice grew taller, the height was increased to 1m.
Sponge and aluminum foil were wrapped around the exterior of the gas box to prevent drastic changes in temperature inside the chamber. The gas chamber was also equipped with a small fan to ensure that the gas in the box was fully mixed. Sampling was conducted 9:00 a.m. and 11:00 a.m. every time, using a 60ml medical syringe to collect gas from the top of the chamber, once every 5 minutes, for a total of four times (Hao et al., 2001 (link)).
Before gas chromatography analysis, the gas was transferred to a vacuum airbag for less than a day to ensure that the gas was not mixed with the outside environment. A gas chromatograph was equipped with an electron capture detector (ECD) and a flame ionization detector (Agilent 7890A network gas chromatograph, Gow Mac instruments, Bethlehem, PA, USA). It was used for the simultaneous analysis of methane, carbon dioxide, and nitrous oxide gas concentrations (Liu S. et al., 2016 (link)). The linear regression slope for the greenhouse gas concentration of the continuous samples was calculated, and the data with the linear regression value r2<0.9 was removed from the dataset for gas flux calculation (Liu et al., 2013 (link)). The gas flux calculation formula is as follows:
Where F is the gas emission flux (mg m-2 h-1), H is the height of the sampling chamber, ρ is the gas density in the standard state, and dC/dt is the slope of the concentration growth of the gas concentration fitted by a linear equation (mg m-3 h-1). T is the temperature in the sampling chamber at the time of sampling(°C). During the test, cumulative methane, carbon dioxide, and nitrous oxide emissions were sequentially accumulated from the flux of each two adjacent intervals. For N2O cumulative emission, the final value was multiplied by atomic mass of N2 divided by atomic mass of N2O (Liu et al., 2013 (link); Liu S. et al., 2016 (link); Iqbal et al., 2021 (link)). The gas flux calculation formula and the total cumulative GHG emission during one growing season was also calculated according to protocol given in (Iqbal et al., 2021 (link)).
Iqbal M.F., Zhang Y., Kong P., Wang Y., Cao K., Zhao L., Xiao X, & Fan X. (2023). High-yielding nitrate transporter cultivars also mitigate methane and nitrous oxide emissions in paddy. Frontiers in Plant Science, 14, 1133643.
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Publication 2023
Aluminum Body Temperature Changes Carbon dioxide Electrons Flame Ionization Gas Chromatography Greenhouse Gases Methane Oryza sativa Phocidae Porifera Syringes Vacuum
5
Greenhouse Gas Emissions of Foods and Meals
GHGE values for individual foods and ready meals expressed as gCO2 equivalents (gCO2e) were obtained from a range of open-access sources, including academic studies, retailers and producers published between 2008 and 2016(20 ,21 ), added to the NDNS nutrient databank(21 ,22 ). GHGE values were based on the emissions of six greenhouse gases which were converted into an equivalent amount of carbon dioxide (CO2 equivalent or CO2e), based on the relative global warming impact of each gas, and the final carbon footprint was expressed as the weight of carbon dioxide(20 ). The climate metric used to aggregate the GHGE measurements into CO2e were those reported by Department for Environment Food and Rural Affairs, UK(23 ). GHGE values from studies using complete cradle-to-grave life cycle analysis (LCA)(20 ), obtained following the international PAS 2050 standard(24 ), were selected where possible. We identified CO2e for 153 food and drink items in the NDNS nutrient databank, and where a GHGE value for a specific item was not available, reasonable substitute data were discussed and imputed by a team of three nutrition scientists, based on the food type, food group and compositional similarity of the products.
To estimate the GHGE for home-cooked meals, we estimated GHGE of the raw ingredients, establishing the weight of each ingredient and the weight of the whole cooked meal using Nutritics, which is nutrition management software for recipe and menu management, food labels, diet and activity analysis, and meal planning (Nutritics Ltd). Based on BBC Good Food(25 ) and Sainsbury’s recipes(26 ), we established cooking methods and times. For home-cooked meals requiring more than one cooking method, GHGE data for each cooking method were added together. In addition, we recorded the longest cooking time suggested for the frozen versions of ready meals. If there was more than one suggested cooking method (e.g. oven and microwave), data for both methods were recorded separately.
To estimate the full GHGE until serving the meal, we combined the GHGE from the recipes’ ingredients or ready meals (value up to the supermarket shelf), which include emissions due to land use change, farm-related emissions, animal feed, processing, transport, retail and packaging) with GHGE produced by the different cooking methods. For the latter, GHGE of cooking appliances were based on manufacturer information(27 ) and adjusted to the conversion factors provided by the UK government in 2021(28 ) and cooking time (Equation 1):
where a is the cooking time, b is the GHGE of cooking appliances based on manufacturer information and adjusted to the conversion factors given by the UK government 2021, and c is the weight of the recipe or ready meal product.
Aceves-Martins M., Denton P, & de Roos B. (2023). Ready meals, especially those that are animal-based and cooked in an oven, have lower nutritional quality and higher greenhouse gas emissions and are more expensive than equivalent home-cooked meals. Public Health Nutrition, 26(3), 531-539.
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Publication 2023
Carbon dioxide Carbon Footprint Climate Diet Food Food Labeling Freezing Greenhouse Gases Microwaves Nutrients

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More about "Greenhouse Gases"

Greenhouse gases are a diverse group of atmospheric compounds that contribute to the greenhouse effect and global warming.
These gases, such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), trap heat within the Earth's atmosphere, leading to climate change and its far-reaching impacts.
Researchers studying greenhouse gases can leverage advanced analytical tools and instrumentation to better understand these complex systems.
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The LI-8100 and GC-2010 Plus are specialized instruments that can be used to measure and quantify greenhouse gas concentrations in various environmental samples.
Beyond data analysis, researchers may also utilize equipment like the Synchron LX20, UniCel DxC 800 Synchron, and Orion 3 Star Plus pH meter to conduct precise measurements and characterizations relevant to greenhouse gas research.
Tert-butanol and T-butanol are chemical compounds that may be involved in greenhouse gas-related processes or serve as reference standards.
The D5300 camera can be employed to capture visual data, such as aerial imagery, that can provide valuable insights into greenhouse gas sources, sinks, and transport mechanisms.
By leveraging this diverse array of tools and technologies, researchers can gain a deeper understanding of greenhouse gases and develop more effective strategies to mitigate their impact on the global climate.
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