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ORIGINAL RESEARCH ARTICLE

Big data-driven carbon footprint tracking and game-theoretic incentive design in green logistics supply chains

Lin Sun1* ,  Jun Zeng2
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1 School of Accounting and Finance, Shandong Vocational and Technical University of International Studies, Rizhao, Shandong, China
2 Research Office, Shandong Vocational and Technical University of International Studies, Rizhao, Shandong, China
AJWEP, 026040017 https://doi.org/10.36922/AJWEP026040017
Received: 23 January 2026 | Revised: 7 March 2026 | Accepted: 12 March 2026 | Published online: 29 April 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

This study examines big data-driven carbon footprint tracking in green supply chains and the design of game-theoretic incentive mechanisms, with a focus on China’s logistics industry. We develop a comprehensive analytical framework integrating machine learning prediction, network topology analysis, and multi-agent evolutionary game theory. Using road freight statistics and operational data from 2018 to 2023, we model and forecast logistics carbon emission intensity through multiple regression and machine learning models. Complex network metrics were applied to characterize the interregional carbon emission correlation structures and identify high-emission nodes. Building on this, the study introduces three key actors—government regulators, logistics enterprises, and suppliers—and establishes functions for emission reduction subsidies and excess emission penalties. An evolutionary game model is formulated, and numerical simulations examine how subsidy intensity and penalty severity influence the system’s evolutionary equilibrium. Results indicate that China’s logistics carbon emissions exhibit a temporal pattern of initial growth followed by stabilization. Spatially, emissions cluster prominently in transportation- and industry-dense regions such as North China, the Yangtze River Delta, and the Pearl River Delta, with high-emission nodes exhibiting high centrality within the network. When incentive parameters are weak, enterprises tend to adopt passive emission reduction strategies. However, under scenarios with higher subsidies and moderate penalties, the proportion of enterprises adopting emission reduction strategies increases in an S-shaped manner over time, ultimately converging to a low-carbon stable equilibrium. The findings indicate that combining big data-driven carbon footprint tracking with differentiated subsidy–penalty policies helps identify regional priority emission reduction targets and critical links, providing quantitative support for designing collaborative emission reduction policies and incentive mechanisms tailored to logistics supply chains.

Graphical abstract
Keywords
Green supply chain
Carbon footprint
Logistics industry
Big data analysis
Evolutionary game theory
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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