Does low-carbon supply chain management reduce greenhouse gas emissions more effectively than existing environmental initiatives? An empirical analysis of Japanese manufacturing firms

2015 ◽  
Vol 27 (1) ◽  
pp. 33-60 ◽  
Author(s):  
Kimitaka Nishitani ◽  
Katsuhiko Kokubu ◽  
Takehisa Kajiwara
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Empirical Analysis ◽  
Manufacturing Firms ◽  
Low Carbon ◽  
Chain Management ◽  
Environmental Initiatives ◽  
Carbon Supply

Measuring Low Carbon Supply Chain

2019 ◽  
pp. 1105-1116
Author(s):  
Muhammad Shabir Shaharudin ◽  
Yudi Fernando
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Greenhouse Gas Emission ◽  
Review Literature ◽  
Extended Model ◽  
Low Carbon ◽  
Chain Management ◽  
Carbon Supply ◽  
Or Practice ◽  
Reduce Carbon Emission

The threat of climate change is due to increasing carbon emissions of manufacturing production and transportation. Currently, government is encouraging manufacturing to reduce carbon emission and conduct low carbon supply chain management (LCSCM). In order to solve the greenhouse gas emission dilemma, LCSCM is essential for manufacturing firms' stakeholders. Supply chain partners are expected to know the proper measurement of emissions to solve this problem. This chapter's aim is to review literature on how to measure LCSCM. In the past, the concept of green supply chain management (GSCM) was practiced to promote and reduce environmental risks. However, GSCM is a driver or practice to achieve environmental outcomes. The extended model of GSCM currently practices LCSCM through carbon footprint (CF) concept. In other words, LCSCM is an outcome that both interests researchers and persuades practitioners.

Measuring Low Carbon Supply Chain

2018 ◽  
pp. 5446-5455
Author(s):  
Muhammad Shabir Shaharudin ◽  
Yudi Fernando
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Greenhouse Gas Emission ◽  
Review Literature ◽  
Extended Model ◽  
Low Carbon ◽  
Chain Management ◽  
Carbon Supply ◽  
Or Practice ◽  
Reduce Carbon Emission

The threat of climate change is due to increasing carbon emissions of manufacturing production and transportation. Currently, Government is encouraging manufacturing to reduce carbon emission and conducting Low Carbon Supply Chain Management (LCSCM). In order to solve greenhouse gas emission dilemma, LCSCM is essential for manufacturing firm's stakeholders. Supply chain partners are expected to know the proper measurement of emissions to solve this problem. This study aim is to review literature on how to measure LCSCM. In the past, the concept of Green Supply Chain Management (GSCM) is practiced to promote and reduce environmental risks. However, GSCM is a driver or practice to achieve environmental outcome. The extended model of GSCM has currently practices in LCSCM through Carbon Footprint (CF) concept. In other word, LCSCM is an outcome that both researcher interests and practitioner persuades.

The use of an optimisation model to design a green supply chain

2016 ◽  
Vol 27 (2) ◽  
pp. 595-618 ◽  
Author(s):  
Janya Chanchaichujit ◽  
Jose Saavedra-Rosas ◽  
Mohammed Quaddus ◽  
Martin West
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Green Supply Chain ◽  
Gas Emissions ◽  
Chain Management ◽  
Content Type ◽  
Rubber Industry ◽  
Optimisation Model

Purpose – The purpose of this paper is to take the first step in solving environmental supply chain management issues. It proposes a green supply chain management (GSCM) model which would provide environmental benefits to the Thai rubber industry. To this end, a GSCM optimisation model was formulated, whereby the manufacturing processes of rubber products, along with their distribution and transportation, could be improved. The expected result is that total greenhouse gas emissions would be minimised and environmental performance maximised. Design/methodology/approach – Linear programming was chosen as the mathematical programming for investigation into the problem of finding the association of quantity of rubber product flow between the supply chain entities (farmer, trader group, and factory) and the transportation mode and route, with a view to minimise total greenhouse gas emissions. Findings – The results indicate that by using the proposed model, GHG emissions could be minimised to 1.08 tons of GHGs per ton of product. Practical implications – A GSCM model developed in this research can be used as a decision support tool for Thai rubber policy makers. This would allow them to better manage the Thai rubber industry to achieve environmental benefit. Originality/value – This research is among the first attempts to develop a GSCM model for the Thai rubber industry. It can contribute to providing a basis for a GSCM modelling framework, along with a formulation for research development in this area.

scholarly journals A Fuzzy-Grey Multicriteria Decision Making Approach for Green Supplier Selection in Low-Carbon Supply Chain

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qinghua Pang ◽  
Tiantian Yang ◽  
Mingzhen Li ◽  
Yi Shen
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supplier Selection ◽  
Multicriteria Decision Making ◽  
Low Carbon ◽  
Chain Management ◽  
Multicriteria Decision ◽  
Green Supplier Selection ◽  
Carbon Supply

Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach.

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