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

Zhang, Xu-Yao; Xu, Dayu

doi:10.1016/j.ecolind.2022.109166

Cited by 22 publications

(13 citation statements)

References 89 publications

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“…Sedivka (2009) used a set of variables close to those used in the current research but didn't use the DEA model. Some authors used labour and capital invested (Hua et al, 2007;Yang et al, 2016;Tsolas, 2011;Zhang and Xu, 2022;Ning et al, 2018). This practice follows the main production inputs (Woodwell, 1998) in economics.…”

Section: Methodsmentioning

confidence: 99%

Performance measuring of wood-processing microenterprises through Data Envelopment Analysis: A case study of Slovakia, Poland, and Bulgaria

Sedliačiková,

Neykov,

Dobrovič

et al. 2024

JESI

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In the context of the role the wood-processing industry has in Slovakia, Poland, and Bulgaria, as an income provider in rural regions and contemporary challenges like inflation and intensive competition, there is a need to assess the performance of microenterprises using Data Envelopment Analysis (DEA), considering the crucial role these entities play in the regional economies. The aim is the creation of a more universally applicable DEA model for assessing the efficiency and performance of wood-processing microenterprises, taking into account the unique challenges and opportunities in Slovakia, Poland, and Bulgaria, defining the profile of optimal enterprise according to methodology in the current research, and indicating the leading problems in their performance. In their management, wood-processing enterprises respond to changes in the external environment, pursuing profit extraction in the competitive struggle. Comparisons with similar companies provide data on the economic efficiency of the sector and the gaps the enterprise needs to correct. Data from the Eurostat Structural Business Statistics were involved for 2011-2020. The current study used Data Envelopment Analysis (DEA), a nonparametric technique that allows enterprises to compare their efficiency frontiers and, from there, reveal their competitiveness. Thus, they can be arranged and measured, and the differences between the inputs and outputs of enterprises can be measured, as well as the efficiency. The results revealed that all the surveyed countries have a problem with the gross value added by a wood-processing micro-enterprise. Polish and Bulgarian enterprises have a problem with pure technical efficiency. Slovakian enterprises have excellent performance and can be used as a benchmark in optimizing the activities of Polish and Bulgarian enterprises.

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Section: Methodsmentioning

confidence: 99%

Performance measuring of wood-processing microenterprises through Data Envelopment Analysis: A case study of Slovakia, Poland, and Bulgaria

Sedliačiková,

Neykov,

Dobrovič

et al. 2024

JESI

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“…Due to its advantages in efficiency evaluation, the DEA model has become a mainstream method for eco-efficiency evaluation in recent years [14][15][16][17][18][19]. The traditional BCC-DEA model based on the production theory is considered from the perspective of a homogeneous increase in output or a homogeneous decrease in input.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China

Liu,

Zhang,

Yuan

et al. 2024

PLoS ONE

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This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

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“…Secondly, in terms of research content, scholars have conducted more studies on the ecoefficiency of specific industries, mainly including tourism [2,23], industry [3,4], agriculture [24], forestry [12], and energy [13], while fewer studies have been conducted on the eco-efficiency of the overall industry. Meanwhile, as the research continues to deepen, scholars have expanded their research from a single region to different regional scales, covering research on the whole country [25,26], geographical regions [27], provincial (city) [28,29], economic zones [30,31], and urban clusters [32,33].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this context, most of the studies on eco-efficiency in the existing literature have been conducted at the micro-level and municipal scale, such as tourism [1,2], industry [3,4], agriculture [5], and city cluster [6,7]. Fewer studies and measurements have been conducted on industrialization and provincial eco-efficiency; in terms of eco-efficiency measurement methods such as the indicator method [8,9], 3D ecological footprint model [10], DEA model [5,[11][12][13][14], SFA model [15], and EBM model [16], the research methods have become increasingly diversified. There is little literature on the calculation of eco-efficiency values from an industry perspective using the SBM model considering non-desired outputs.…”

Section: Introductionmentioning

confidence: 99%

Study on the measurement of industrial eco-efficiency, spatial distribution and influencing factors in Yangtze River Economic Belt

et al. 2023

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Nowadays, China insists on the strategy of ecological priority and green development, and the Yangtze River Economic Belt is a pioneering demonstration belt for the construction of ecological civilization in China. Promoting industrial ecological efficiency is of great significance to Chinese sustainable development and high-quality economic development. Based on the provincial panel data of 11 provinces and cities in the Yangtze River Economic Belt region from 2011 to 2020, we use the super-efficient slacks-based measure of efficiency SBM model to measure the industrial eco-efficiency of the Yangtze River Economic Belt, reveal the spatial differences in industrial eco-efficiency of different provincial units, and explore the industrial eco-efficiency. The results show that: (1) from the perspective of the Yangtze River Economic Belt as a whole, industrial eco-efficiency is on a stable upward trend, but the efficiency level is not high, and there is more room for improvement; (2) in terms of the spatial distribution of industrial eco-efficiency in the Yangtze River Economic Belt, there are significant differences, with industrial eco-efficiency in the downstream region being higher than that in the midstream region and the upstream region, and the lowest in the midstream region, while in terms of the spatial distribution pattern, the industrial eco-efficiency of 11 provinces and cities in the Yangtze River Economic Belt eco-efficiency has a significant positive spatial autocorrelation; (3) in terms of influencing factors, economic development level, factor endowment, and foreign investment can significantly influence industrial eco-efficiency in the Yangtze River Economic Belt. The study’s result helps provide theoretical guidance and practical reference for promoting the green and ecological development of industries in the Yangtze River Economic Belt.

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

Cited by 22 publications

References 89 publications

Performance measuring of wood-processing microenterprises through Data Envelopment Analysis: A case study of Slovakia, Poland, and Bulgaria

Performance measuring of wood-processing microenterprises through Data Envelopment Analysis: A case study of Slovakia, Poland, and Bulgaria

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China

Study on the measurement of industrial eco-efficiency, spatial distribution and influencing factors in Yangtze River Economic Belt

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