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Measuring Supply Chain Efficiency from a Green Perspective Report


Logistics has attracted growing interests from numerous scholars and organizations, due to its contribution in changing human lives. However, little focus has been directed to its impact on national and global environmental sustainability. Researchers aim at developing green concepts in logistics management with an aim of developing an efficient green supply chain. Kim and Min (2011) conducted a research about the impacts of economic growth and activities of logistics.

They aimed it at increasing National Income on the environment, using Logistics Performance Index (LPI) and Environment Performance Index (EPI). This paper provides an analysis and critique of the article, written by Kim and Min, titled, “Measuring supply chain efficiency from a green perspective”. In addition, it provides other perspectives that can be integrated as viable measures in the green supply chain.

Ilsuk Kim and Hokey Min Article Analysis

The authors provide the first technique of measuring supply chain efficiency by modeling a Green Logistics Performance Index (GLPI). They use the GLPI to assess the green supply chain performance of several countries. Kim and Min (2011) argue that nations that focus attention on rapidly increasing economic development do it at the expense of environmental protection.

They learn that the highly industrialized and export-oriented countries aim at developing infrastructure of logistics for the purpose of rapid economic growth. In the end, these activities lead to environmental degradation. In contrast, agricultural-based countries that rely on natural resources are less likely to impact on their environment than export oriented nations.


Kim and Min utilize secondary data in their research. Majorly, they rely on data compiled by the World Bank and World Economic Forum. These researchers utilize data on Gross National Income per capita (GNI per capita) of different countries, which include Asia, Europe, Africa, and the United States. They run multiple regressions on countries’ GNI with the LPI data, which they collect through an online compilation survey.

The researchers rely on interviews of over one thousand multinational corporation logistics professional officers in 130 countries in Asia, Europe, Africa, and North America. They develop five evaluation scales, which they use to convert the survey scores of each country in determining logistics efficiency. Through multiple regressions, correlation coefficients are drawn from the LPI, National Income, and the EPI.

They include international LPI categories such as custom efficiency, infrastructure, international shipments, logistics, quality and competence, consignment tracking, and shipment timeliness as their indicators. Kim and Min consider EPI in evaluating the environmental performance of different countries. They also conduct an analysis of individual level of emission, air and water pollution, biodiversity, greenhouse gas emissions, agriculture, and forestry in their study.

Furthermore, Kim and Min (2011) opine that countries with relatively high incomes tend to have national environmental policies with green logistics supply systems. The strict environmental rules and regulations compel industries within the developed nations to control their pollution footprints. These industries develop innovative and efficient production technologies to minimize environmental degradation.

According to Kim and Min (2011), the geographic size of a country determines the effect that its logistics activities have on the environment. Geographically, large countries with concentrated economic activities in specific regions are less likely to experience devastating environmental degradations, compared to nations with even distributions. In addition, large countries with relatively small populations and slow economic growth rates have efficient green logistics policies.

Article critique

From the article provided by Kim and Min (2011), several criticisms can be drawn. They were heavily dependent on economic growth and the level of government intervention, which regulates industrial production activities as a major determinant of green logistics performance of a country.

According to Rao and Holt (2005), and Klyza and Sousa (2008), pressure from communities and green sensitive consumers, force governments and industries to put in place environment conscious policies, in order to prevent environmental degradation (Rao & Holt, 2005).

Kim and Min (2011) acknowledge that governments in Nordic countries and other developed nations have long-standing policies that discourage environmental degradation. According to Paulraj (2009), they make a grave assumption that the national conservation policy is a reserve for governments (Paulraj, 2009). However, the state may establish these regulations under pressure from the community and environment conscious consumers.

The quality and quantity of renewal resources may buffer countries from the effects of industrial activities on the environment (Nikbakhsh, 2009). This implies that nations with vast resources of renewable energy can accelerate economic growth with minimal impacts on the environment.

This argument contradicts the ideas propounded by Kim and Min. The authors do not take into account that the firm’s sustainability strategy can utilize greener energy without strict government controls and policies. In reference to Nikbakhsh (2009) and Dinda (2004), efficient environmental protection measures come as nations continue to industrialize (Nikbakhsh, 2009) and (Dinda, 2004). Nations view it necessary that environmental protection can sustain economic growth in the long-run, and hence develop appropriate policies.

They construct GLPI due to the interdependence of LPI and EPI. Specific metrics utilized in Kim’s and Min’s GLPI include EPI (outdoor air pollution, sulfur dioxide emissions, nitrogen oxides emissions, non-methane volatile organic compound emission, and industrial greenhouse gas emissions) and LPI (Customs, infrastructure, international shipment, logistics, quality, and competence, tracking and tracing, and timeliness).

EPI indicators were included in the GLPI because they indicate conditions of environmental performance, while the LPI were included due to their direct or indirect relations to the environment. A combination of the LPI and EPI was used to develop GLPI. The two logistics and environmental performance are essential for eco-efficiency. GLPI examines the logistics performance of a country and its impacts on the environment (Subramanian & Economist’s, 2012).

GLPI is mathematically expressed, thereby generating the environmental performance in an individual country. The analysis of the GLPI affirms the relationship between LPI and EPI. GLPI finds that countries, which focus on high economic growth, put in place supporting infrastructure to facilitate faster movement of producing goods for distribution across the globe (Zanjirani, Asgari & Davarzani, 2009).

This logistical infrastructure development negatively impacts on the environment. This is depicted from the graph regressing GLPI and the national income. Through these Kim and Min are able to establish a relationship between LPI and EPI.

There exist a number of gaps and missing things in Kim and Min research. First, Kim and Min miss out on technological standardization problems facing supply chain firms and nations, which should be implemented by addressing each element of the supply chain. In addition to this, the research also misses to explain how inter-organizational performance management increases logistics efficiency so as to have a positive focus on the environment.

It limits itself on government policy and financial resources as a means of acquiring logistics efficiency and reduction of its impacts on the environment. The article also fails to provide analysis and design of environment that reflect supply chain linkages to the environmental degradation.

The researchers have not provided an integrative framework between LPI and GLPI. They have not clearly explained the importance of supply chain managements adopting a green strategy. A green design, with or without national policies and huge resources encourages environmental awareness, and hence contributes little impact of logistics infrastructure on the environment (Nikbakhsh 2009).

In the construction of the GLPI model, Kim and Min (2011) utilized the LPI model. This design makes use of a number of dimensions in measuring the international and domestic logistics. They argue that tariffs and trade barriers are capable of distorting the efficiency of logistics, and hence contribute to an inferior green supply chain at the global level.

According to the Subramanian & Economist’s (2012), Anderson and Marcouiller (2002), and Wu and Dunn (1995), apart from the infrastructural challenges, trade restrictions and un-harmonized cross-border standards greatly hamper logistics systems.

This results in freight delivery delays, especially in the receiving-end countries (Anderson & Marcouiller, 2002) and (Wu & Dunn, 1995). Therefore, institutional quality logistics and trade policy across borders significantly impact on efficiency in logistics systems of organizations across the world.

Kim and Min (2011) utilize the LPI in the construction of the GNPI model. The LPI approach focuses on logistics components and procedures that enhance logistic efficiency at a micro-level (Kim & Min, 2011). In addition, the EPI measures the impacts of industrial and logistics activities, and their implications on the environment (Subramanian & Economist, 2012). However, these indices do not address some issues exclusively.

The LPI is based on perceptions, hence suffers from weaknesses. Secondly, it fails to account for the industry’s performance. It ignores the cost, time, and reliability of the sector in the analysis of logistics efficiency (Wu & Dunn, 1995). The EPI design focuses on the macro impact of economic activities on the environment, without regard to the cross-border spill-over of the activities.

However, an efficient index should assess performance at the micro-level by considering time, cost, and reliability of a logistics system. This will enhance generation of sub-chain scores, which in turn affect the overall logistics efficiency score. In addition, it will help determine improvements of the supply chain by reflecting on how sustainable logistics systems of individual countries are improving (Klyza, CMG & Sousa, 2008).

From a macro perspective, cross-border pollution causes serious environmental problems to other neighboring nations (Hatzipanayotou, Lahiri & Michael, 2008). For instance, Japan and Korea are suffering from air pollution from China’s increased economic and logistic activities.

On this note, it is vital to be informed that a country can have little in terms of environmental protection because of aggregate cross-border pollution impacts. This is not caused by lack of financial resources or strong national policy as Kim and Min suggest in their article.

Summary and conclusion

Kim and Min (2011) attempt to incorporate the LPI and EPI models in analyzing how countries strive to accelerate economic growth by going green. They realize that export oriented and logistics intensive nations are more likely to be vulnerable to environmental degradation. They argue that developed countries have greener logistics, due to the availability of resources and strong national policies focused on environmental protection.

However, several weaknesses can be drawn from their analysis. They ignore the cross-border pollution effects on regional environmental pollution. They also admit that LPI is distorted to some extent, and hence influences trade restrictions. Logistics ignore and disregard the role of community and consumers, on environmental protection.

Furthermore, little is highlighted concerning the use of renewable and greener sources of energy for sustainability of individual firms. Kim and Min argue that they had limited literature on the use of the LPI and EPI model in measuring the efficiency of logistics, which have an impact on the green supply chain. Therefore, further research is suggested on the most appropriate and efficient means of measuring the supply chain efficiency from a green perspective.

List of References

Anderson, JE & Marcouiller, D 2002, “Insecurity and the Pattern of Trade: An Empirical Investigation,” The Review of Economics and Statistic, vol. 84, no. 2, pp. 342–52.

Dinda, S 2004, “Environmental Kuznets curve hypothesis: a survey,” Ecological economics, vol. 49, no. 4, pp. 431-455.

Hatzipanayotou, P, Lahiri, S & Michael, MS 2008, “Cross-border pollution, terms of trade, and welfare,” Environmental and Resource Economics, vol. 41, no. 3, vol. 327-345.

Kim, I & Min, H 2011, “Measuring supply chain efficiency from a green perspective,” Management Research Review, vol. 34, no. 11, pp. 1169-1189.

Klyza, CMG & Sousa, DJ 2008, American environmental policy, 1990-2006: Beyond gridlock, MIT Press, Cambridge, Mass.

Nikbakhsh, E 2009, “Green supply chain management”. Supply Chain and Logistics in National, International and Governmental Environment, vol. 1, no. 1, pp. 195-220).

Paulraj, A 2009, “Environmental motivations: a classification scheme and its impact on environmental strategies and practices,” Business Strategy and the Environment, vol. 18, no. 7, pp. 453–468.

Rao, P & Holt, D 2005, “Do green supply chains lead to competitiveness and economic performance?” International Journal of Operations & Production Management, vol. 25, no. 9, pp. 898– 916.

Subramanian, U & Economist’s, FC 2012, Trade Logistics Reforms, <>.

Wu, H and Dunn, S 1995, “Environmentally responsible logistics systems”, International Journal of Physical Distribution & Logistics Management, Vol. 25, No. 2, pp. 20-38.

Zanjirani, FR, Asgari, N, & Davarzani, H. 2009, Supply chain and logistics in national, international and governmental environment: Concepts and models, Physica, Heidelberg.

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