4.7 Article

Assessing the eco-efficiency of complex forestry enterprises using LCA/time-series DEA methodology

Journal

ECOLOGICAL INDICATORS
Volume 142, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ecolind.2022.109166

Keywords

Eco-efficiency; Complex forestry enterprise; LCA; Time-series DEA; Case study

Funding

  1. National Natural Science Foundation of China [72001190]
  2. China Ministry of Education [20YJC630173]
  3. Ministry of Educations Humanities and Social Science project via the China Ministry of Education

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This research proposes a hybrid approach of life cycle assessment (LCA) and time-series data envelopment analysis (time-series DEA) to evaluate the eco-efficiency of complex forestry enterprises. The study finds that in efficient years, the environmental impact is only about 45.25% of that in inefficient years, and the percentage decreases even further for fiber consumption and energy consumption. The findings are important for guiding complex forestry enterprises in improving their eco-efficiency and environmental impact.
Complex forestry enterprises have the potential to lead the current industrial upgrade under the strategy of carbon neutrality and carbon peaking. This paper aims to investigate the inputs and outputs influencing the eco-efficiency of complex forestry enterprises through a case study. A hybrid ensemble approach of life cycle assessment (LCA) and time-series data envelopment analysis (time-series DEA) was proposed to evaluate the case enterprise's eco-efficiency. It is found that, from the perspective of the average intensity of key input and output factors, environmental impact in efficient years is 45.25% of that in inefficient ones, and when it comes to the fiber consumption and the energy consumption, this percentage turns into 65.53% and 77.66%. With the findings, we provide guidelines for complex forestry enterprises to enhance the eco-efficiency in improving environmental impact and in giving full play to the advantages of forestry industrial chain resources. Our findings also contribute to the existent literature by providing a systematic study based on a corporate time-series dataset.

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