Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors

Fujii, Hidemichi; Managi, Shunsuke

doi:10.1016/j.gloenvcha.2015.06.005

Cited by 39 publications

(20 citation statements)

References 29 publications

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“…Furthermore, the capital equipment and labor requirements for reducing CO2 emissions vary across industries because the types of fuel consumed as intermediate materials also vary [17]. Therefore, industrial characteristics provide important information for creating effective energy saving and CO2 reduction policies in each industry.…”

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confidence: 99%

Firm-level environmentally sensitive productivity and innovation in China

2016

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This study analyzes productive efficiency in relation to CO2 emissions using a unique dataset of 562 Chinese manufacturing firms for the period from 2005 to 2009. We develop a directional distance function approach to identify technical innovators in the area of CO2 emissions. The results indicate that a large number of technical innovators are observed in the textile, paper, steel, and computer industries. Furthermore, there are clearly different trends in productivity change and corporate performance across industries and provinces. This result implies that policy makers need to consider industrial and regional characteristics to develop effective policies that conserve energy and reduce CO2 emissions.

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confidence: 99%

Firm-level environmentally sensitive productivity and innovation in China

2016

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“…By using data envelopment analysis, Yagi et al (2015) indicated that the environmental, social, and governance activities of firms do not considerably affect environmental efficiency [27]. proposed optimal production resources reallocation to reduce CO 2 emission based on data envelopment analysis [28]. While the sustainability of oil refining enterprises consists of the economic, ecological, and social sustainability.…”

Section: Sustainability Index System Establishmentmentioning

confidence: 99%

Sustainability Assessment of Refining Enterprises Using a DEA-Based Model

Dong

Sun

et al. 2017

Sustainability

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Abstract:As one of the basic industries supporting the national economy development and energy demand, the refining industry is expected to provide combustion energy, reduce pollution emission, and improve utilization efficiency. With more stringent requirement for environmental protection, refining enterprises have to insist on the sustainable development to achieve industrial optimization. Evaluation of the sustainability of enterprises can help them understand their situation more objectively and guide them to establish modes for sustainable development. In this study, the evaluation system is firstly built from perspectives of economic, ecological, and social sustainability, including six second-grade indexes and seventeen third-grade indexes, which can accurately reflect the entire sustainability contents of refining enterprises. Then, a DEA-based model is constructed, which selects seven input indexes (e.g., the asset-liability ratio and comprehensive energy consumption per unit of output) and nine output indexes (e.g., return on assets, asset turnover, and science and technology investment strength). The DEA-based model can not only objectively evaluate the sustainability level, but also find out the restriction factors for further optimization. Third, to demonstrate the validity of the model, 15 enterprises are selected for case studies, among which only four are identified as having strong sustainability. For the other 11 enterprises, projection analyses are implemented, and the DMU values of three enterprises characterized by low efficiency are adjusted to find out the restriction factors, which reflect the model's efficiency and its potentially wide application in the future. Finally, specific suggestions are proposed for the enhancement of sustainability of refining enterprises.

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“…This study selects average annual number of employees (AANE), completed investment in fixed assets (CIFA), and comprehensive energy consumption (CEC) of each city, industrial waste water discharge (IWWD), industrial soot emission (ISE), industrial sulphur dioxide emission (ISD) as the input indicators and comprehensive utilization of waste(CUW), and manufacturing gross output value (GOV) as the output indicators [74][75][76][77].…”

Section: Indicator Elaborationmentioning

confidence: 99%

Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry

Gong

Chen

2017

Sustainability

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This study utilizes the Data Envelopment Efficiency (DEA) model to assess input-output efficiency from two perspectives. First, not considering the distribution of interval data, we introduce an adjusted parameter to transform interval data to determination data. Second, by contrast, we take into account the distribution characteristics of interval data and test the DEA model with interval data based on linear uniform distribution and normal distribution with uncertainty. Based on the normal distribution DEA evaluation model, this paper aims to evaluate the input-output performance of the manufacturing industry with the constraint of environmental pollution in the Yangtze River Delta (YRD) region, China. Research has shown that the optimal solution of the normal distribution model is better than that of linear distribution. Therefore, it is imperative to adopt an appropriate method to evaluate the energy and environmental efficiency of this region.

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Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors

Cited by 39 publications

References 29 publications

Firm-level environmentally sensitive productivity and innovation in China

Firm-level environmentally sensitive productivity and innovation in China

Sustainability Assessment of Refining Enterprises Using a DEA-Based Model

Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry

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