The lifecycle of wood from tropical forests in Costa Rica

Alice, Federico E.

doi:10.18174/501873

Cited by 1 publication

(1 citation statement)

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“…It is noteworthy that the combined use of life cycle assessment and data envelopment analysis, known as the LCA + DEA method, is an emerging and growing research field [25]. The LCA method can estimate the impact of the wood processing industry on the environment at all stages [18,[26][27][28][29][30][31], and the DEA method can be responsible for a comprehensive assessment of ecological efficiency [1,32]. At present, the popular models used for efficiency evaluation in the DEA method mainly include the super slacks-based model (super-SBM), the network slacks-based model (NSBM), the epsilon-based measure model (EBM), and so on, but there are the following problems in effectiveness research [32]: (1) Most of them use the traditional DEA analysis method and cannot calculate the input-output efficiency of each stage; (2) Although some scholars have considered each stage of the product, they have not considered the connection mechanism between stages and the impact between stages, so they cannot accurately measure the overall efficiency of the product.…”

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confidence: 99%

Carbon Footprint Assessment and Efficiency Measurement of Wood Processing Industry Based on Life Cycle Assessment

Zhang

Yang

2023

Sustainability

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The contradiction between economic development and the ecological barrier function of the wood processing sector is increasingly deepening. However, China has not yet established a life cycle database for the wood processing industry, which makes it difficult to scientifically evaluate its carbon emission efficiency. Therefore, based on the panel data of 31 provinces (autonomous regions and municipalities) in China from 2014 to 2020 published in the China Statistical Yearbook, this paper combined a life cycle assessment and dynamic network slacks-based model, and the carbon emission efficiency of different stages was measured from the multidimensional perspective of labor, energy, capital input, and economic and ecological benefits output. The results show that: (1) The overall carbon emission efficiency is 0.6161, and the coastal and northwest regions have higher efficiency; (2) The efficiency levels of Inner Mongolia, Shanghai, and another four provinces have been increasing while other regions have significant differences; (3) Beijing, Jiangsu, and the other four provinces are at an effective level overall. The most serious efficiency loss is at the processing stage. These findings provide an important reference for promoting the low-carbon cycle development of China’s wood processing industry and the formulation of policies in various regions.

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