Dynamic Bilevel Optimisation for Carbon Emission Reduction: Integrating Nonlinear Policy Functions and Adaptive Genetic Algorithms.

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Bibliographic Details
Title:	Dynamic Bilevel Optimisation for Carbon Emission Reduction: Integrating Nonlinear Policy Functions and Adaptive Genetic Algorithms.
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]
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Database:	Engineering Source

Description
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]
ISSN:	1816093X