This paper focuses on the relation between a CO2 emission cost and CO2 emission reductions in the power sector.
Nonlinear fuel and carbon emission cost functions, chance constraints and binary aircraft assignment decisions make the problem significantly more difficult.
The study found that CO2 emission cost was the highest for brick walls, followed by concrete brick walls.
We develop a decision support system using a large-scale linear program with the goal of maximizing profit with and without the emission cost.
In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem.
Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost.
We propose a nonlinear mixed integer programming model due to the nonlinear fuel consumption and CO2 emission cost terms in the objective function, which is handled by second order conic reformulation.
Computation experiments are performed to illustrate the effect of the stage duration on the two objective values, and the results show that longer stage duration leads to higher total unmet demand and total fuel and CO2 emission cost.
This paper introduces a new dynamic green bike repositioning problem (DGBRP) that simultaneously minimizes the total unmet demand of the bike-sharing system and the fuel and CO2 emission cost of the repositioning vehicle over an operational period.
It is concluded that CO2 emission, cost, and space utilization of the column in the real building can be reduced by 21.05%, 14.97%, and 20.18%, respectively, with the optimum section type and material strength.
This paper mainly focused on designing a building's energy system under demand, costs (like carbon emission cost, primary energy saving…) and prices (like fuel tariff and electricity prices…) uncertainties concerning data insufficiency by means of robust optimization.
