Allocation of Economic Parameters Between Heat and Electricity for a CHP

A field survey was conducted during 2016 to estimate the profitability of normal season and off-season muskmelon cultivation in district Sialkot, Pakistan. The primary data was collected from forty farmers with convenience sampling method. Economic parameters like net return and BCR were employed. Off-season muskmelon cultivation was found economically feasible due to additions of yield (17%), gross income (122%), profit (161%) and market price (90%) in comparison to normal season crop. Therefore, it is recommended to cultivate the off-season muskmelon (BCR 3.26) to obtain more profit and fulfill the customer demand in less supply period instead of normal season cultivation (BCR 2.44).

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THE EFFECT OF SOCIO-ECONOMIC PARAMETERS ON WASTE GENERATION AT COUNTY LEVEL

Environmental Engineering and Management Journal ◽

10.30638/eemj.2017.210 ◽

2017 ◽

Vol 16 (9) ◽

pp. 2025-2033

Author(s):

Izabela Anna Talalaj

Keyword(s):

County Level ◽

Waste Generation ◽

Economic Parameters

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Socio-Economic Position, Cancer Incidence and Stage at Diagnosis: A Nationwide Cohort Study in Belgium

Cancers ◽

10.3390/cancers13050933 ◽

2021 ◽

Vol 13 (5) ◽

pp. 933

Author(s):

Michael Rosskamp ◽

Julie Verbeeck ◽

Sylvie Gadeyne ◽

Freija Verdoodt ◽

Harlinde De Schutter

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Cohort Study ◽

Cancer Incidence ◽

Regression Models ◽

Female Breast Cancer ◽

Stage At Diagnosis ◽

Female Breast ◽

Economic Parameters ◽

Economic Position

Background: Socio-economic position is associated with cancer incidence, but the direction and magnitude of this relationship differs across cancer types, geographical regions, and socio-economic parameters. In this nationwide cohort study, we evaluated the association between different individual-level socio-economic and -demographic factors, cancer incidence, and stage at diagnosis in Belgium. Methods: The 2001 census was linked to the nationwide Belgian Cancer Registry for cancer diagnoses between 2004 and 2013. Socio-economic parameters included education level, household composition, and housing conditions. Incidence rate ratios were assessed through Poisson regression models. Stage-specific analyses were conducted through logistic regression models. Results: Deprived groups showed higher risks for lung cancer and head and neck cancers, whereas an inverse relation was observed for malignant melanoma and female breast cancer. Typically, associations were more pronounced in men than in women. A lower socio-economic position was associated with reduced chances of being diagnosed with known or early stage at diagnosis; the strongest disparities were found for male lung cancer and female breast cancer. Conclusions: This study identified population groups at increased risk of cancer and unknown or advanced stage at diagnosis in Belgium. Further investigation is needed to build a comprehensive picture of socio-economic inequality in cancer incidence.

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Life Cycle Cost Analysis of a Single-Family House in Sweden

Buildings ◽

10.3390/buildings11050215 ◽

2021 ◽

Vol 11 (5) ◽

pp. 215

Author(s):

Bojana Petrović ◽

Xingxing Zhang ◽

Ola Eriksson ◽

Marita Wallhagen

Keyword(s):

Life Cycle ◽

Life Cycle Cost ◽

Single Family ◽

Labor Costs ◽

Construction Costs ◽

Economic Parameters ◽

A Minor ◽

Family House ◽

Replacement Operation

The objective of this paper was to explore long-term costs for a single-family house in Sweden during its entire lifetime. In order to estimate the total costs, considering construction, replacement, operation, and end-of-life costs over the long term, the life cycle cost (LCC) method was applied. Different cost solutions were analysed including various economic parameters in a sensitivity analysis. Economic parameters used in the analysis include various nominal discount rates (7%, 5%, and 3%), an inflation rate of 2%, and energy escalation rates (2–6%). The study includes two lifespans (100 and 50 years). The discounting scheme was used in the calculations. Additionally, carbon-dioxide equivalent (CO2e) emissions were considered and systematically analysed with costs. Findings show that when the discount rate is decreased from 7% to 3%, the total costs are increased significantly, by 44% for a 100-year lifespan, while for a 50 years lifespan the total costs show a minor increase by 18%. The construction costs represent a major part of total LCC, with labor costs making up half of them. Considering costs and emissions together, a full correlation was not found, while a partial relationship was investigated. Results can be useful for decision-makers in the building sector.

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Impact of electron accelerator technical and economic parameters on unit cost of radiation processing

Radiation Physics and Chemistry ◽

10.1016/j.radphyschem.2021.109713 ◽

2021 ◽

pp. 109713

Author(s):

Z. Zimek

Keyword(s):

Electron Accelerator ◽

Unit Cost ◽

Radiation Processing ◽

Economic Parameters

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Thermo-Economic Analysis of Combined Cycles

2003 International Joint Power Generation Conference ◽

10.1115/ijpgc2003-40119 ◽

2003 ◽

Author(s):

R. Yadav ◽

Priyesh Srivastava ◽

Samir Saraswati

Keyword(s):

Economic Analysis ◽

Combined Cycle ◽

Rate Of Return ◽

Fuel Prices ◽

Discount Cash Flow ◽

Large Size ◽

Maximum Value ◽

Medium Range ◽

Economic Parameters ◽

Combined Cycles

The paper presents a thermo-economic analysis of gas/steam combined cycle. The stated objective is achieved by optimizing thermo-economic parameters for simple combined cycle (large and medium range) and to apply this to economic model of these cycles. The economic parameters evaluated in the present study include discount cash flow rate of return (DCRR) and gross payout period (GPO), two terms commonly employed in engineering economic analysis. DCRR and GPO are calculated for various electric sale and fuel prices. It has been found that maximum value of DCRR and minimum value of GPO are found with large size plant.

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Hydrocarbon Field Re-Development as Markov Decision Process

10.2118/206041-ms ◽

2021 ◽

Author(s):

Martin Sieberer ◽

Torsten Clemens

Keyword(s):

Markov Decision Process ◽

Decision Process ◽

Risk Attitude ◽

Transition Function ◽

Monetary Value ◽

Economic Parameters ◽

Markov Decision ◽

Model Ensembles ◽

Hydrocarbon Field ◽

Expected Monetary Value

Abstract Hydrocarbon field (re-)development requires that a multitude of decisions are made under uncertainty. These decisions include the type and size of surface facilities, location, configuration and number of wells but also which data to acquire. Both types of decisions, which development to choose and which data to acquire, are strongly coupled. The aim of appraisal is to maximize value while minimizing data acquisition costs. These decisions have to be done under uncertainty owing to the inherent uncertainty of the subsurface but also of other costs and economic parameters. Conventional Value Of Information (VOI) evaluations can be used to determine how much can be spend to acquire data. However, VOI is very challenging to calculate for complex sequences of decisions with various costs and including the risk attitude of the decision maker. We are using a fully observable Markov-Decision-Process (MDP) to determine the policy for the sequence and type of measurements and decisions to do. A fully observable MDP is characterised by the states (here: description of the system at a certain point in time), actions (here: measurements and development scenario), transition function (probabilities of transitioning from one state to the next), and rewards (costs for measurements, Expected Monetary Value (EMV) of development options). Solving the MDP gives the optimum policy, sequence of the decisions, the Probability Of Maturation (POM) of a project, the Expected Monetary Value (EMV), the expected loss, the expected appraisal costs, and the Probability of Economic Success (PES). These key performance indicators can then be used to select in a portfolio of projects the ones generating the highest expected reward for the company. Combining the production forecasts from numerical model ensembles with probabilistic capital and operating expenditures and economic parameters allows for quantitative decision making under uncertainty.

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