scholarly journals Cost Benefit and Risk Analysis of Low iLUC Bioenergy Production in Europe Using Monte Carlo Simulation

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1650
Author(s):  
Traverso L. ◽  
Mazzoli E. ◽  
Miller C. ◽  
Pulighe G. ◽  
Perelli C. ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ghg Emissions ◽  
Cost Benefit ◽  
Economic Feasibility ◽  
Biofuel Production ◽  
Giant Reed ◽  
Bioenergy Crops ◽  
Indirect Land Use Change ◽  
Negative Results

Extensive surfaces of land are currently under-utilized, marginal and/or contaminated (MUC) in many EU and neighbouring countries. In the past few years, scientific research has demonstrated that bioenergy crops can potentially render this land profitable, generating income for the local populations and, at the same time, reaching the goals of the new Renewable Energy Directive (REDII) without interfering with food production. The main purpose of this paper is to measure net economic returns by computing benefits and costs of low indirect Land Use Change (iLUC) biofuel production on MUC land from the perspective of both the private investors and social welfare. A standard cost-benefit technique was applied to analyse and compare net returns of different advanced bioenergy value-chains in monetary terms. Productivity, economic feasibility and green-house gas (GHG) emissions impact were assessed and considered for the economic analysis. The considered pathways were cellulosic or second generation (2G) ethanol from Giant reed (Arundo donax) in Italy, electricity from miscanthus, biochemicals from spontaneous grass and cultivated Lucerne (Alpha-alfae) with sorghum for biomethane in Germany, and 2G ethanol from Willow (Salix viminalis) in Ukraine. For the risk assessment, Monte Carlo simulation was applied. The results indicated that in Italy and Ukraine, although the production of 2G ethanol would allow positive net yearly margins, the investments will not be profitable compared to the baseline scenarios. In Germany, the work showed good profitability for combined heat and power (CHP) and biochemicals. On the other hand, investments in biomethane showed negative results compared with the baseline scenarios. Finally, the Monte Carlo simulation enabled us to identify the range of possible economic results that could be attained once volatility is factored in. While for Italy the likelihood of yielding positive results remains lower than 20 percent, case studies in Ukraine and Germany showed higher certainty levels, ranging from 49 to 91 percent.

scholarly journals Biofuel production and soil GHG emissions after land-use change to switchgrass and giant reed in the U.S. Southeast

2017 ◽  
Vol 7 (1) ◽  
pp. e00125 ◽  
Author(s):  
Andrea Nocentini ◽  
John Field ◽  
Andrea Monti ◽  
Keith Paustian
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Ghg Emissions ◽  
Biofuel Production ◽  
Giant Reed ◽  
The U.S

scholarly journals Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4965
Author(s):  
Kun Mo Lee ◽  
Min Hyeok Lee ◽  
Jong Seok Lee ◽  
Joo Young Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Parametric Bootstrap ◽  
Random Variable ◽  
Ghg Emission ◽  
Nonparametric Bootstrap ◽  
Mcs Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

scholarly journals An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach

2020 ◽  
Vol 12 (16) ◽  
pp. 6528
Author(s):  
Ibrahim L. Kadigi ◽  
Khamaldin D. Mutabazi ◽  
Damas Philip ◽  
James W. Richardson ◽  
Jean-Claude Bizimana ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Climate Variability ◽  
Farming Systems ◽  
Economic Feasibility ◽  
High Price ◽  
System Of Rice Intensification ◽  
Cash Income ◽  
Rice Farming ◽  
Simulation Approach

Tanzania is the second-largest producer of rice (Oryza sativa) in Eastern, Central, and Southern Africa after Madagascar. Unfortunately, the sector has been performing poorly due to many constraints, including poor agricultural practices and climate variability. In addressing the challenge, the government is making substantial investments to speed the agriculture transformation into a more modernized, commercial, and highly productive and profitable sector. Our objective was to apply a Monte Carlo simulation approach to assess the economic feasibility of alternative rice farming systems operating in Tanzania while considering risk analysis for decision-makers with different risk preferences to make better management decisions. The rice farming systems in this study comprise rice farms using traditional practices and those using some or all of the recommended system of rice intensification (SRI) practices. The overall results show 2% and zero probability of net cash income (NCI) being negative for partial and full SRI adopters, respectively. Meanwhile, farmers using local and improved seeds have 66% and 60% probability of NCI being negative, correspondingly. Rice farms which applied fertilizers in addition to improved seeds have a 21% probability of negative returns. Additionally, net income for rice farms using local seeds was slightly worthwhile when the transaction made during the harvesting period compared to farms applied improved varieties due to a relatively high price for local seeds. These results help to inform policymakers and agencies promoting food security and eradication of poverty on the benefits of encouraging improved rice farming practices in the country. Despite climate variability, in Tanzania, it is still possible for rice farmers to increase food production and income through the application of improved technologies, particularly SRI management practices, which have shown a promising future.

Probabilistic private cost-benefit analysis for green roof installation: A Monte Carlo simulation approach

2016 ◽  
Vol 20 ◽  
pp. 317-327 ◽  
Author(s):  
Amir Mahdiyar ◽  
Sanaz Tabatabaee ◽  
Aidin Nobahar Sadeghifam ◽  
Saeed Reza Mohandes ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cost Benefit Analysis ◽  
Green Roof ◽  
Cost Benefit ◽  
Simulation Approach ◽  
Benefit Analysis ◽  
Private Cost

scholarly journals Investigations on Biogas Recovery from Anaerobic Digestion of Raw Sludge and Its Mixture with Agri-Food Wastes: Application to the Largest Industrial Estate in Oman

2021 ◽  
Vol 13 (7) ◽  
pp. 3698
Author(s):  
Salah Jellali ◽  
Yassine Charabi ◽  
Muhammad Usman ◽  
Abdullah Al-Badi ◽  
Mejdi Jeguirim
Keyword(s):  
Anaerobic Digestion ◽  
Net Present Value ◽  
Cost Benefit Analysis ◽  
Ghg Emissions ◽  
Treatment Plant ◽  
Cost Benefit ◽  
Electrical Energy ◽  
Economic Feasibility ◽  
Food Wastes ◽  
Industrial Estates

This work is intended to evaluate the technical, environmental, and economic feasibility of converting the sludge produced at an industrial estate’s wastewater treatment plant (WWTP) in Oman into energy through anaerobic digestion (AD). In this study, three different scenarios were analyzed. They concerned the digestion of the total amount of the produced sludge alone (240 m3 day−1) (scenario 1), and its co-digestion with wet agri-food wastes (AFW) at rates of two tonnes day−1 (scenario 2) and ten tonnes day−1 (scenario 3). Based on the analyses of sludge samples, an intensive literature review regarding sludge and AFW Physico-chemical and energetic characteristics and the use of the cost–benefit analysis (CBA) approach, it was found that, for the overall duration of the project (20 years), the AD of the sludge alone (scenario 1) permitted the production of 43.9 GWh of electricity, the reduction of greenhouse gas (GHG) emissions (more than 37,000 tonnes equivalent CO2 (TCO2)) and exhibited positive net present value (NPV: $393,483) and an internal return rate (IRR) of 19.4%. Co-digesting sludge with AFW significantly increased all of these key performance indicators. For instance, scenario 3 results in the recovery of electrical energy of 82.2 GWh and avoids the emission of 70,602 tCO2. Moreover, a higher NPV and IRR of $851,876 and 21.8%, respectively, and a payback period (PBP) of only seven years were calculated. The sensitivity analysis revealed that a decrease in total expenses by 15% results in a significant increase of the NPV and the IRR to $1,418,704 and 33.9%, respectively, for scenario 3. Considering a pessimistic assumption (an increase of the total expenses by 15%), all studied scenarios remain attractive. For instance, for scenario 3, the NPV, IRR, and PBP were evaluated to $285,047, 13.5%, and 9 years, respectively. Therefore, the co-digestion of sludge with agri-food wastes for energy recovery purposes could be considered a promising, eco-friendly, and economically viable approach in the Omani industrial estates.

scholarly journals Economic viability of implementing an infrastructure for recycling bedding sand from a free-stall facility for dairy cows

2021 ◽  
pp. 361-374
Author(s):  
Marcos Aurélio Lopes ◽  
◽  
Fabiana Alves Demeu ◽  
Eduardo Mitke Brandão Reis ◽  
André Luis Ribeiro Lima ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Operating Cost ◽  
Estimation Method ◽  
Cost Benefit ◽  
Minas Gerais ◽  
Point Estimation ◽  
Economic Viability ◽  
Total Production ◽  
Point Estimation Method

This study proposes to examine the economic viability of implementing the necessary infrastructure for the recycling of bedding sand from a free-stall facility in a milk production system in southern Minas Gerais, Brazil. In specific terms, the total production cost (TC), total operating cost (TOC) and effective operating cost (EOC) of a cubic meter of recycled sand were estimated in order to estimate the total sand consumption for the free-stall system and per bed year-1 as well as the equilibrium point of the amount of recycled sand, in cubic meters. The experiment was carried out on a farm located in the south of Minas Gerais from January 2016 to December 2017. Three scenarios were analyzed by the tree-point estimation method (MOP - most likely, optimistic, and pessimistic). Utilization of 85%, 95% and 75% of the recycled sand was considered for scenarios 1, 2 and 3, respectively. In all of them, the value charged per cubic meter of sand by a supplier close to the farm was considered. Monte Carlo simulation was also carried out with hurdle rates (HR) of up to 90%. Under the studied conditions, sand recycling showed to be economically viable in all scenarios, with positive net present values (NPV), internal rates of return above the HR, simple and discounted payback below the 10-year horizon, and satisfactory cost benefit-1 ratios (greater than 1). The EOC of one cubic meter of recycled sand was estimated at R$5.04, R$4.51 and R$5.72 for scenarios 1, 2 and 3, respectively, whereas the average TC, considering all scenarios, was R$6.84 (+0.81), which is less than the acquisition price of R$28.57 at the sand extraction site. The TC was R$37,219.51 and R$34,637.74 for the scenarios with HR of 8.50 and 6.99%, respectively, whereas TOC was R$22,572.08 in all analyzed scenarios. The estimated total annual sand consumption by the free-stall system was 526.44 m³, with an estimated average of 1.23 m³ (+0.28) bed-1 year-1. All Monte Carlo simulation models showed positive NPV as well as HR of up to 90%, which reflect a high probability of positive NPV.

Viabilidade econômica na produção de calçados por meio da simulação de monte carlo / Economic feasibility in footwear production through monte carlo simulation

2021 ◽  
Vol 3 (4) ◽  
pp. 3061-3074
Author(s):  
Ilana Renata Lizi Panzenhagen ◽  
Matheus Binotto Francescatto ◽  
Cristiano Roos
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Rio Grande ◽  
Economic Feasibility ◽  
Rio Grande Do Sul

O setor calçadista do Brasil apresenta-se no ranking mundial como um dos maiores produtores e consumidores de calçados. Neste sentido, o objetivo desta pesquisa é identificar qual cenário entre os estados do Rio Grande do Sul e do Ceará se mostra mais promissor para a instalação de uma fábrica de calçados para uma determinada empresa. A classificação metodológica deste trabalho é de natureza aplicada, de abordagem quantitativa e com objetivos descritivos. Ainda, tem como procedimento técnico inicial a revisão bibliográfica, seguido da modelagem e simulação. A partir da revisão de literatura foi possível estruturar os cenários. Após, foi realizada a coleta de dados e os fluxos de caixa e cálculos de VPL, TIR, Payback Simples e Payback Descontado foram feitos. Assim, foram realizadas Simulações de Monte Carlo. Para complemento do estudo e comparação com os resultados obtidos na simulação, foram estruturados cenários a partir de alterações manuais em planilha eletrônica. Ambas as formas de análise demonstraram a garantia de lucratividade na instalação de uma fábrica em qualquer um dos cenários. Entretanto, o Ceará apresenta maior atratividade por promover um retorno do investimento de forma mais rápida e gerar um lucro maior ao final dos períodos analisados.

scholarly journals Economic feasibility analysis for co-generation of power by diesel generator at peak time: Monte Carlo simulation approach in a hospital

2021 ◽  
Vol 43 ◽  
pp. e50965
Author(s):  
Flávio Fraga Vilela ◽  
João Victor Soares do Amaral ◽  
Gustavo dos Santos Leal ◽  
Gabriel Fernandes de Oliveira ◽  
José Arnaldo Barra Montevechi ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Net Present Value ◽  
Local Population ◽  
Electricity Consumption ◽  
Economic Feasibility ◽  
Peak Time ◽  
Simulation Approach ◽  
Diesel Generator

The cost of electricity in hospitals represents a significant portion in its context of operating expenses. Therefore, it is important to constantly think about ways to reduce this cost without losing the quality and reliability required for hospital care activity. It is well known, that reducing electricity consumption has a direct impact on the effective management of hospital cash flow, so it is imperative to rationalize this resource. In this context, the objective of this paper focuses on analyzing the economic feasibility of purchasing and using a diesel generator to find the peak hour demand and verifying financial uncertainty by applying a Monte Carlo simulation approach to risk analysis. The target hospital of this research is located in southeastern of Brazil and it is part of a foundation that covers educational and assistance activities, serving the local population and thousands of patients during the year. Finally, the economic risk analysis applied through the Monte Carlo simulation found that the acquisition of the aforementioned diesel generator has a very high probability of viability. Therefore, it is verified that the investment is viable and attractive from the hospital's economic and operational point of view, while the Net Present Value remains positive, with the expected value of R$ 868,358.84, considering the risk and uncertainty analysis having an attractive internal returning rate of 78.76% per year.

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