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Centralized resource allocation with technological progress under a meta-frontier framework: towards China's
1XUTELI School, Beijing Institute of Technology, Beijing, 100081, China.
Carbon Balance and Management
|June 28, 2026
Summary
Optimizing China's resource allocation can boost economic growth and cut carbon emissions. A new data envelopment analysis (DEA) method enhances efficiency and target setting for sustainable development.
Area of Science:
- Environmental Economics
- Operations Research
- Econometrics
Background:
- Efficient resource allocation is vital for China's economic growth and carbon emission reduction goals.
- Existing models often overlook technological heterogeneity and forward-looking efficiency targets.
Purpose of the Study:
- To develop a novel centralized resource allocation approach integrating technological progress and heterogeneity.
- To optimize China's energy-economy-emissions system for enhanced economic output and reduced environmental impact.
Main Methods:
- A new data envelopment analysis (DEA) model is proposed, incorporating technological progress into the environmental production possibility set.
- The model extends to a meta-frontier framework to address technological heterogeneity among decision-making units.
- The approach enhances forward-looking decision-making and incentive compatibility for target setting.
Main Results:
- Optimal interprovincial allocation of energy and labor can increase aggregate economic output by 6.95% and reduce carbon emissions by 5,681.17 million tons.
- Further optimization with incentives could boost 2024 economic output by 1.8% and cut emissions by 47.19%.
- Significant labor misallocation exists between China's eastern and central regions.
Conclusions:
- The proposed DEA method effectively optimizes resource allocation for economic and environmental goals in China.
- Policy adjustments should balance economic development with emission reduction, considering regional labor misallocation.
- The study provides managerial insights for improving resource allocation efficiency and sustainability.
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