Abstract
The concept of green supply chains is now accepted in many corporate organizations in Asia. Since the early nineties, when industry became aware of the increasing relevance of sustainable development, many business enterprises in Asia have adopted environmental initiatives as an integral part of their business practices. In time these organizations came to realize that the environmental initiatives needed to encompass not only the organization's own business practices but also the entire stretch of operations across the supply chain. In other words, they felt the need to include the employees, suppliers, customers, waste handlers, and other business partners in the greening process (Bacallan, 2000). Thus, an integrated supply chain approach was called for. Such an approach should be able to identify the environmental aspects at every stage, assess the environmental impacts associated with these aspects, prioritize them, and design action plans to mitigate their adverse effects on the environment if any. An integrated green supply chain approach would take into consideration the inbound logistics phase of the supply chain, the production or internal supply chain, the outbound logistics phase, and the reverse logistics phase (Rao & Holt, 2005; Sarkis, 1999; Seuring & Muller, 2007). For many organizations in Asia, the green supply chain approach has also emerged as a way to demonstrate their commitment to sustainability (Seuring et al., 2008).
However, there is a continuous need to measure and monitor the extent to which environmental performance is actually achieved. To assess this performance, a system of indicators across the supply chain is proposed, which is computationally easy to implement at the industry level. To demonstrate that the system of environmental indicators does measure performance, an empirical approach is adopted to test whether the system correlates with the four constructs of environmental sustainability: resource conservation, energy efficiency, reduction of hazardous waste, and reduction of greenhouse gas emissions (Vachon & Mao, 2008).
In order to check these linkages of environmental indicators to the constituents of environmental performance, four multiple regression models were run. In the first model, the dependent variable was resource conservation, the independent predictor variables being the 20 environmental indicators grouped under the four constructs: Inbound logistics, production or internal logistics, outbound logistics, and reverse logistics. In the second, third, and fourth models, the dependent variables were energy efficiency, reduction of hazardous waste, and minimization of emission of greenhouse gases, respectively.
Upon running the regression models, the models for resource conservation, reduction of hazardous waste, and reduction of emission of greenhouse gases were found to be significant at 5 percent significance level while the model for energy efficiency was significant at 10 percent level.