Modeling American Household Fluid Milk Consumption and their Resulting Greenhouse Gas Emissions

U.S. consumers are the largest contributors to food waste generation (FWG), but few models have explained how households waste food. This study examines how discrete-event simulation (DES) can identify areas for reducing FWG through packaging and consumer milk consumption behavioral changes. Household model parameters included: amount and type of consumption, type and number of containers bought, buying behavior, and shelf life of milk. Simulations comparing the purchase of quart, half gallon, and gallon milk containers were run for 10,000 days to identify which package type reduced waste for 50 1, 2 and 4-person households. Based on consumption averages from the U.S. National Dairy Council, results suggest that if 1 and 4-person households change their purchasing behavior from 1 half-gallon to 1 quart and 2 gallons to 3 half-gallons, they can reduce their greenhouse gas (GHG) emissions from milk consumption by 33% and 12%, respectively, without reducing their total milk consumption. Purchasing enough smaller containers to be equivalent to a larger size decreased spoilage, but not enough to reduce a consumer’s total milk consumption GHG emissions. Results showed that packaging accounts for 5% of the total milk consumption GHG emissions; most of a consumer’s impact comes from milk spoilage and consumption.

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Down scaling to regional assessment of greenhouse gas emissions to enable consistency in accounting for emissions reduction projects and national inventory accounts for northern beef production in Australia

The Rangeland Journal ◽

10.1071/rj15061 ◽

2016 ◽

Vol 38 (3) ◽

pp. 219 ◽

Cited By ~ 2

Author(s):

Sandra J. Eady ◽

Guillaume Havard ◽

Steven G. Bray ◽

William Holmes ◽

Javi Navarro

Keyword(s):

Greenhouse Gas ◽

Production Efficiency ◽

Ghg Emissions ◽

Emissions Reduction ◽

Model Parameters ◽

Reproduction Rate ◽

National Accounts ◽

Beef Production ◽

National Inventory ◽

State Territory

This paper explores the effect of using regional data for livestock attributes on estimation of greenhouse gas (GHG) emissions for the northern beef industry in Australia, compared with using state/territory-wide values, as currently used in Australia’s national GHG inventory report. Regional GHG emissions associated with beef production are reported for 21 defined agricultural statistical regions within state/territory jurisdictions. A management scenario for reduced emissions that could qualify as an Emissions Reduction Fund (ERF) project was used to illustrate the effect of regional level model parameters on estimated abatement levels. Using regional parameters, instead of state level parameters, for liveweight (LW), LW gain and proportion of cows lactating and an expanded number of livestock classes, gives a 5.2% reduction in estimated emissions (range +12% to –34% across regions). Estimated GHG emissions intensity (emissions per kilogram of LW sold) varied across the regions by up to 2.5-fold, ranging from 10.5 kg CO2-e kg–1 LW sold for Darling Downs, Queensland, through to 25.8 kg CO2-e kg–1 LW sold for the Pindan and North Kimberley, Western Australia. This range was driven by differences in production efficiency, reproduction rate, growth rate and survival. This suggests that some regions in northern Australia are likely to have substantial opportunities for GHG abatement and higher livestock income. However, this must be coupled with the availability of management activities that can be implemented to improve production efficiency; wet season phosphorus (P) supplementation being one such practice. An ERF case study comparison showed that P supplementation of a typical-sized herd produced an estimated reduction of 622 t CO2-e year–1, or 7%, compared with a non-P supplemented herd. However, the different model parameters used by the National Inventory Report and ERF project means that there was an anomaly between the herd emissions for project cattle excised from the national accounts (13 479 t CO2-e year–1) and the baseline herd emissions estimated for the ERF project (8 896 t CO2-e year–1) before P supplementation was implemented. Regionalising livestock model parameters in both ERF projects and the national accounts offers the attraction of being able to more easily and accurately reflect emissions savings from this type of emissions reduction project in Australia’s national GHG accounts.

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Calibrating a Discrete-Event Simulation for Quantification of Sex-Specific Colorectal Neoplasia Development

10.21203/rs.3.rs-733405/v1 ◽

2021 ◽

Author(s):

Carolina Vivas-Valencia ◽

Nan Kong ◽

Aditya Sai ◽

Thomas F Imperiale

Keyword(s):

Local Search ◽

Colorectal Neoplasia ◽

Discrete Event ◽

Calibration Procedure ◽

Disease Models ◽

Model Parameters ◽

Two Phase ◽

Event Simulation ◽

Male Cohort ◽

Phase Calibration

Abstract Background: Medical evidence collected from new observational studies can sometimes significantly alter our understanding of disease incidence and progression. This requires efficient and accurate calibration of disease models to help quantify the differences between observed cohorts. However, in model calibration, it is common to encounter overfitting with many model parameters but few observational outcomes. Additionally, the difficulty in evaluating fitting performance is significant due to a large degree of outcome variation and expensive computations for even a single simulation run. Methods: We developed a two-phase calibration procedure to address the above challenges. As a proof-of-the-concept study, we verified the procedure with a discrete-event-simulation-based study on sex-specific colorectal neoplasia development. For the study, we estimated eight disease model parameters that govern colorectal adenoma incidence risk and growth rates at three distinct states: non-advanced, advanced adenoma, and adenoma becoming cancerous. For the calibration, we defined the likelihood measure by a relative weighted sum-of-squares difference between the three actual prevalence values reported in a recent publication and those predicted by a discrete-event colorectal cancer simulation. In phase I of the calibration procedure, we performed a series of low-dimensional sampling-based grid searches to identify reasonably good candidate parameter designs. In phase II, we performed a local search-based approach to further improve the model fit.Results: Overall, our two-phase procedure showed better goodness of fit than a straightforward implementation of the Nelder-Mead algorithm, yielding a 10-fold reduction in calibration error (0.0025 vs. 0.0251 for an all-white mixed-family-history male cohort on the likelihood measure defined above). Further, the two-phase procedure was more effective in calibrating a validated simulation model for a female cohort than a male cohort. Finally, in phase II, performing local search on each of the parameters sequentially is more effective than searching the entire parameter space simultaneously. Conclusions: The proposed two-phase calibration procedure is effective for estimating computationally expensive stochastic dynamic disease models. In addition, initial parameter search range truncation and sensitivity analysis on various parameters can be computationally cost-effective.

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