Measuring carbon performance for sustainable green supply chain practices: a developing country scenario. (English) Zbl 07311718
Summary: Carbon emissions due to economic activities are recognized to be global problem. Governments of all countries need to evolve environmental policies and practices for large-scale collective actions to regulate green house gas emission. Fuel quality standards for vehicles, stricter codes for construction, emission limits for industrial units and power plants are some of measures advocated to speed up emission control. This study investigates how far different sectors of a developing economy are able to manage green supply chain with respect to 4 aspects of environmental practices viz. Green procurement, green logistics, green products and process designs and regulatory framework. Globally, corporate social responsibility (CSR) assumes significance in recent years not only with respect to societal issues but also for environmental protection. Research suggests that CSR department creates culture for implementation of CSR activities. We investigate whether CSR departments in the sample organizations have made any difference in achieving emission control objectives. Data are from manufacturing organizations in a congested industrial region of India. We apply non-parametric Kruskal-Wallis and Mann-Whitney tests; then regression analysis is carried out to ascertain predictability of carbon reduction performance with respect to 4 environmental constructs. Results highlight positive roles of inclusion of green enablers – green procurement, green logistics, green product and process design as contributory factors for improvement in carbon performance and reveal that green logistics in the given scenario need major improvement in carbon performance. Our model also considers the impact of size of the organization on carbon performance in terms of workforce.
MSC:
| 90Bxx | Operations research and management science |
Keywords:
green supply chain; Green enablers; corporate social responsibility; carbon emissions; organizational performance; multiple linear regressionReferences:
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