EURO 2025 Leeds
Abstract Submission

3144. Decarbonization under Cap-and-Trade Regulation: An Integrated Slack-Based DDF Model with a Shapley Value Approach

Invited abstract in session MD-60: DEA methodological developments I, stream Data Envelopment Analysis and its applications.

Monday, 14:30-16:00
Room: Western LT

Authors (first author is the speaker)

1. Elnaz Azadi
Business School, Surrey University
2. Ali Emrouznejad
Surrey Business School, University of Surrey
3. Xun Zhou
Surrey Business School, University of Surrey

Abstract

In many industries, carbon emissions are an unavoidable byproduct of producing final goods, reflecting an intrinsic trade-off embedded within production processes. Recognizing this inherent relationship, the directional distance function (DDF) model has emerged as a prominent tool within the data envelopment analysis (DEA) framework to measure efficiency improvements. This model enables decision-makers to simultaneously pursue undesirable output reductions and desirable output expansions in chosen directions. Building on this model, we propose a novel Slack-Based Measure Directional Distance Function (SBM-DDF) model within a cap-and-trade system to reallocate emission allowances across multiple stages of a networked supply chain. The model facilitates an iterative, market-oriented mechanism for controlling and reducing carbon emissions, while enabling flexibility and adaptability through carbon permit trading. The model reallocates emission allowances across different stages within a network supply chain structure, ensuring that each stage adheres to a predefined emission cap. This reallocation identifies whether emission permits should be curtailed or made available for trade. Individual stages within decision-making units (DMUs) can then sell surplus emission permits to their peer stages across different DMUs seeking additional allowances. The study further examines whether the coalition of DMUs in the carbon permit trading market facilitates a gradual reduction in emission

Keywords

Status: accepted

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