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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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Intertemporal environmental efficiency assessment in China: A new network-based dynamic super-efficiency measure.

Ruiyue Lin1, Zongxin Li2

  • 1College of Mathematics and Physics, Wenzhou University, Wenzhou, Zhejiang, PR China.

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Summary

This study introduces a dynamic data envelopment analysis (DEA) model to rank intertemporal environmental efficiency. The findings reveal significant regional disparities in China

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Area of Science:

  • Environmental Economics
  • Operations Research
  • Regional Science

Background:

  • Assessing intertemporal environmental efficiency is crucial for sustainable development.
  • Existing dynamic data envelopment analysis (DEA) models have limitations in comprehensive ranking and identifying inefficiency factors.
  • Understanding regional variations in environmental performance is key for targeted policy interventions.

Purpose of the Study:

  • To develop a novel dynamic DEA model for a complete, intertemporal ranking of environmental efficiency.
  • To identify specific factors and time periods contributing to decision-making units' (DMUs) efficiency or inefficiency.
  • To analyze the environmental efficiency of Chinese provinces from 2008 to 2017.

Main Methods:

  • Combining network-based dynamic DEA with super-efficiency and unified efficiency under natural and managerial disposability.
  • Designing a dynamic DEA model and a corresponding dynamic super-efficiency DEA model.
  • Applying slack analysis to pinpoint sources of inefficiency.

Main Results:

  • Significant regional differences in environmental efficiency were observed across China's provinces.
  • Eastern efficient provinces showed no clear advantage in energy, labor, or wastewater, while central/western efficient provinces lacked advantages in SO2 emissions and GDP.
  • Inefficient eastern provinces need to reduce energy, SO2, and labor, while increasing capital investment; central/western provinces should focus on reducing SO2 and labor.

Conclusions:

  • The proposed dynamic DEA models provide a comprehensive framework for ranking environmental efficiency over time.
  • Targeted strategies are needed to address regional inefficiencies, focusing on specific inputs like energy, labor, and SO2 emissions.
  • Increasing gross domestic capital formation is recommended for most provinces to enhance overall environmental efficiency.