Dynamic Bilevel Optimisation for Carbon Emission Reduction: Integrating Nonlinear Policy Functions and Adaptive Genetic Algorithms.
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| Title: | Dynamic Bilevel Optimisation for Carbon Emission Reduction: Integrating Nonlinear Policy Functions and Adaptive Genetic Algorithms. |
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| Authors: | Haroon, Ali M. A.1 alialiuon@gmail.com, Ngnotchouye, Jean Medard T.2 Ngnotchouye@ukzn.ac.za, Tilahun, Surafel Luleseged3 surafel42@gmail.com |
| Source: | Engineering Letters. Jul2026, Vol. 34 Issue 7, p2797-2807. 11p. |
| Subjects: | Bilevel programming, Carbon dioxide mitigation, Genetic algorithms, Tax incentives, Policy analysis, Energy industries |
| Geographic Terms: | South Africa |
| Abstract: | This paper presents a dynamic bilevel optimisation framework for designing and evaluating carbon emission reduction policies. It models the strategic interaction between a policymaker (the leader), who seeks to minimize emissions, and profit-maximizing energy producers (the followers). We introduce nonlinear tax and subsidy functions that evolve over time, enabling a more realistic simulation of escalating policy interventions. Energy production technologies are classified into three categories: high-carbon, medium-carbon, and low-carbon, based on their carbon intensity and unit production costs. Our theoretical analysis establishes the existence of a solution and, most importantly, formally proves that these policy instruments can guarantee a finite-horizon phase-out of high-carbon sources. To solve the model, we propose an adaptive genetic algorithm with time-decaying crossover and mutation parameters. We validate the framework in a case study calibrated with data from South Africa's energy sector. The results show that the optimal dynamic policy substantially outperforms static benchmarks, achieving deep decarbonization by engineering a predictable coal phase-out while satisfying system constraints. Our framework offers a prescriptive and robust decision support tool for policymakers to design and calibrate adaptive, data-driven climate policies. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088783 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic Bilevel Optimisation for Carbon Emission Reduction: Integrating Nonlinear Policy Functions and Adaptive Genetic Algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Haroon%2C+Ali+M%2E+A%2E%22">Haroon, Ali M. A.</searchLink><relatesTo>1</relatesTo><i> alialiuon@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ngnotchouye%2C+Jean+Medard+T%2E%22">Ngnotchouye, Jean Medard T.</searchLink><relatesTo>2</relatesTo><i> Ngnotchouye@ukzn.ac.za</i><br /><searchLink fieldCode="AR" term="%22Tilahun%2C+Surafel+Luleseged%22">Tilahun, Surafel Luleseged</searchLink><relatesTo>3</relatesTo><i> surafel42@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jul2026, Vol. 34 Issue 7, p2797-2807. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bilevel+programming%22">Bilevel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+dioxide+mitigation%22">Carbon dioxide mitigation</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Tax+incentives%22">Tax incentives</searchLink><br /><searchLink fieldCode="DE" term="%22Policy+analysis%22">Policy analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+industries%22">Energy industries</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22South+Africa%22">South Africa</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a dynamic bilevel optimisation framework for designing and evaluating carbon emission reduction policies. It models the strategic interaction between a policymaker (the leader), who seeks to minimize emissions, and profit-maximizing energy producers (the followers). We introduce nonlinear tax and subsidy functions that evolve over time, enabling a more realistic simulation of escalating policy interventions. Energy production technologies are classified into three categories: high-carbon, medium-carbon, and low-carbon, based on their carbon intensity and unit production costs. Our theoretical analysis establishes the existence of a solution and, most importantly, formally proves that these policy instruments can guarantee a finite-horizon phase-out of high-carbon sources. To solve the model, we propose an adaptive genetic algorithm with time-decaying crossover and mutation parameters. We validate the framework in a case study calibrated with data from South Africa's energy sector. The results show that the optimal dynamic policy substantially outperforms static benchmarks, achieving deep decarbonization by engineering a predictable coal phase-out while satisfying system constraints. Our framework offers a prescriptive and robust decision support tool for policymakers to design and calibrate adaptive, data-driven climate policies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 2797 Subjects: – SubjectFull: Bilevel programming Type: general – SubjectFull: Carbon dioxide mitigation Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Tax incentives Type: general – SubjectFull: Policy analysis Type: general – SubjectFull: Energy industries Type: general – SubjectFull: South Africa Type: general Titles: – TitleFull: Dynamic Bilevel Optimisation for Carbon Emission Reduction: Integrating Nonlinear Policy Functions and Adaptive Genetic Algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Haroon, Ali M. A. – PersonEntity: Name: NameFull: Ngnotchouye, Jean Medard T. – PersonEntity: Name: NameFull: Tilahun, Surafel Luleseged IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 7 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
