Yong Shin Park

Integrating expert weighting and multi-criteria decision making into eco-efficiency analysis: the case of US manufacturing

In this paper, the effect of weighting strategies on sustainability performance assessment is addressed. Eco-efficiency is used as the main metric for sustainability performance evaluation. An integrated input-output life cycle assessment (LCA) and multi criteria decision making (MCDM) approach is employed. The US manufacturing sectors’ LCA results are used in conjunction with the proposed MCDM framework to perform the eco-efficiency evaluation of 276 US manufacturing sectors. Five environmental impact categories are considered as the negative factors, namely: greenhouse gas emissions, energy use, water withdrawal, hazardous waste generation and toxic releases into air and the economic output of each manufacturing sector is considered to be the positive output. To study the overall impact of different weighting strategies; twenty weighting scenarios are designed. Five pairs of weights considered for the overall economic versus environmental impacts along with four specific weighting strategies based on Harvard, SAB, EPP and Equal weighting for each pair. According to the results of the statistical analysis, it is concluded that the weighing strategies applied to the overall environmental impacts and economic outputs cause statistically significant differences in the eco-efficiency scores.

Integrated Multimodal Transportation Model for a Switchgrass-Based Bioethanol Supply Chain: Case Study in North Dakota

In this study, a mixed integer linear programming model that integrates multimodal transport—truck and rail—into the switchgrass-based bioethanol supply chain was formulated. The objective of this study was to minimize the total cost for cultivation and harvesting, infrastructure, the storage process, bioethanol production, and transportation. Strategic decisions, including the number and location of intermodal facilities and biorefineries, and tactical decisions, such as the amount of biomass shipped, processed, and converted into bioethanol, were validated by using North Dakota as a case study. It was found that the multimodal transport scenario was more cost effective than a single mode of transport (truck) and resulted in a lower cost for bioethanol. A sensitivity analysis was conducted to demonstrate the impact of key factors in the decision to use multimodal transport in a switchgrass-based bioethanol supply chain and on the cost of bioethanol.

Carbon footprint stock analysis of US manufacturing: a time series input-output LCA

Purpose The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries. Design/methodology/approach Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO2-equivalent, whereas the economic outputs were measured in million dollar economic activity. Findings The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO2 intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.” Originality/value This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

Interactive Spatiotemporal Analysis of Oil Spills Using Comap in North Dakota

The aim of the study is to analyze the oil spill pattern from various types of incidents and contaminants to determine the extent that incident data can be used as a baseline to prevent hazardous material releases and improve response activities at a state level. This study addresses the importance of collecting and sharing oil spill incidents as well as analytics using the data. Temporal, spatial and spatiotemporal analysis techniques are employed for the oil-spill related environmental incidents observed in the state of North Dakota, United States of America, from 2000 to 2014, as a result of the oil boom. Specifically, spatiotemporal methods are used to examine how the patterns of environmental incidents in North Dakota, which vary with the time of day, the day, the month, and the season. Results indicate that there were critical spatial and time variations in the distribution of environmental incidents. Application of spatiotemporal interaction visualization techniques, called comap has the potential to help planners and decision makers formulate policy to mitigate the risks associated with environmental incidents, improve safety, and allocate resources.