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Data-driven low-carbon transformation management for manufacturing enterprises: an eco-efficiency perspective
Cuixia Zhang1, Fan Liu2,3, Conghu Liu1
1College of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, China.
Environmental Science and Pollution Research International
|September 5, 2023
Summary
This study introduces a data-driven strategy for manufacturing enterprises to achieve low-carbon transformation. The eco-efficiency model improves ecological efficiency by 3.6% and reduces waste by 12%.
Area of Science:
- Environmental Science
- Industrial Engineering
- Sustainability Studies
Background:
- Achieving carbon neutrality necessitates the low-carbon transformation of manufacturing enterprises.
- Eco-efficiency is a critical perspective for evaluating manufacturing sustainability.
- Existing methods lack a quantitative, data-driven approach for low-carbon transformation.
Purpose of the Study:
- To propose a data-driven strategy for the low-carbon transformation of manufacturing enterprises.
- To develop a quantitative model for evaluating the eco-efficiency of manufacturing production systems.
- To establish a measurable, evaluable, and optimized method for low-carbon upgrading.
Main Methods:
- Collected input (energy, materials, equipment, R&D, services) and output (waste, products) data from manufacturing production systems.
- Constructed an ecological efficiency model focused on carbon emissions for quantitative evaluation.
- Developed and validated a "measurable, evaluable, and optimized" transformation method using enterprise data (2017-2021).
Main Results:
- The proposed method effectively improved enterprise ecological efficiency by 3.6%.
- Waste emissions were reduced by 12% through the implemented strategy.
- The data-driven approach demonstrated feasibility and effectiveness in practice.
Conclusions:
- The study provides novel tools for enhancing the eco-efficiency of manufacturing systems.
- Offers theoretical and methodological support for manufacturing enterprises pursuing low-carbon transformation.
- Highlights the importance of a data-driven, eco-efficiency perspective for sustainable manufacturing.
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