A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction

Government-industry interactions for emissions control can be modelled as Stackelberg or leader-follower games. Government acts as the leader by setting regulations and economic incentives, while industry as the follower reacts to these policies by selecting cost-optimal emissions reduction techniqu...

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Main Authors: Raymond R. Tan, Kathleen B. Aviso
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2022-12-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/12994
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author Raymond R. Tan
Kathleen B. Aviso
author_facet Raymond R. Tan
Kathleen B. Aviso
author_sort Raymond R. Tan
collection DOAJ
description Government-industry interactions for emissions control can be modelled as Stackelberg or leader-follower games. Government acts as the leader by setting regulations and economic incentives, while industry as the follower reacts to these policies by selecting cost-optimal emissions reduction techniques. The problem for the leader is to calibrate policies in anticipation of the follower’s rational reaction. In this work, a bilevel mixed integer linear programming (BMILP) model is developed for the deployment of a finite set of emissions reduction techniques. Government controls the emissions reduction target and subsidy rate for each emissions reduction technique, while industry selects which techniques to implement. The latter also has to pay a penalty if actual emissions exceed the regulatory target. An interactive fuzzy optimization algorithm is also developed for finding an approximate satisficing solution. The model and solution algorithm are illustrated using a case study.
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spelling doaj.art-191d24712888492d990a70e860ec91392022-12-22T04:22:35ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162022-12-019710.3303/CET2297060A Bilevel Mixed-Integer Linear Programming Model for Emissions ReductionRaymond R. TanKathleen B. AvisoGovernment-industry interactions for emissions control can be modelled as Stackelberg or leader-follower games. Government acts as the leader by setting regulations and economic incentives, while industry as the follower reacts to these policies by selecting cost-optimal emissions reduction techniques. The problem for the leader is to calibrate policies in anticipation of the follower’s rational reaction. In this work, a bilevel mixed integer linear programming (BMILP) model is developed for the deployment of a finite set of emissions reduction techniques. Government controls the emissions reduction target and subsidy rate for each emissions reduction technique, while industry selects which techniques to implement. The latter also has to pay a penalty if actual emissions exceed the regulatory target. An interactive fuzzy optimization algorithm is also developed for finding an approximate satisficing solution. The model and solution algorithm are illustrated using a case study.https://www.cetjournal.it/index.php/cet/article/view/12994
spellingShingle Raymond R. Tan
Kathleen B. Aviso
A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction
Chemical Engineering Transactions
title A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction
title_full A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction
title_fullStr A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction
title_full_unstemmed A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction
title_short A Bilevel Mixed-Integer Linear Programming Model for Emissions Reduction
title_sort bilevel mixed integer linear programming model for emissions reduction
url https://www.cetjournal.it/index.php/cet/article/view/12994
work_keys_str_mv AT raymondrtan abilevelmixedintegerlinearprogrammingmodelforemissionsreduction
AT kathleenbaviso abilevelmixedintegerlinearprogrammingmodelforemissionsreduction
AT raymondrtan bilevelmixedintegerlinearprogrammingmodelforemissionsreduction
AT kathleenbaviso bilevelmixedintegerlinearprogrammingmodelforemissionsreduction