Sustainability assessment of universities as small-scale urban systems: A comparative analysis using Fisher Information and Data Envelopment Analysis
Introduction
While urban sustainability has gained increasing attention, most planning studies are indicator-based (e.g., Maclaren, 1996, Huang et al., 1998, Hemphill et al., 2004). An integrated set of sustainability indicators is needed to understand the interactions among sub-systems and to represent local characteristics for planning analysis (Turcu, 2013). However, heterogeneities among sustainability indicators, such as varied units of data variables and complex relationships among variables over time, present great challenges for an integrated analysis.
A promising approach for an integrated system analysis is “urban metabolism” (UM), which originated from an analogy to the metabolic process in ecological systems and has evolved to incorporate socioeconomic systems. Fundamentally, UM is an accounting methodology that records and connects the inputs, outputs, and stocks of elements of a system. The metabolic view of urban systems facilitates the identification of operational inefficiencies for long-term planning processes. The concept of UM gained popularity after Abel Wolman (1965) published an audit of basic inputs and outputs of a hypothetical U.S. city to better understand urban environmental challenges, including adequate water supply, effective sewage disposal, and air pollution control. With an initial focus on material and energy flows, recent research using UM framework has been expanded to include both social and economic components in cities (Kennedy et al., 2011a, Kennedy et al., 2011b, 2014; Gonzalez-Mejía et al., 2012; Conke and Ferreira, 2015).
Despite its significant value in system assessment and planning, UM only has limited implementation in quantitative ways (Decker et al., 2000, Golubiewski, 2012, Pincetl et al., 2012, Rosado et al., 2014). Data limitations have been consistently identified as primary barriers for implementing empirical analysis, validating quantitative methods, and revealing policy references (Niza et al., 2009, Wiegand et al., 2010). While researchers (Codoban and Kennedy, 2008) have documented the compelling value of neighborhood-level UM analysis, the refined scale of urban systems presents particularly significant data challenges.
This study argues that there are potential opportunities for data-driven research of small-scale urban systems. For example, urban universities share the same characteristics as cities, given their large stocks and flows of people, materials, and finance. New students enroll and senior students graduate every year; faculty, staff, and students living off-campus need to commute between campus and their residence every day; new financial resources may be obtained and existing funding is expended; materials and energy are consumed; wastewater, air emissions, and solid waste are generated from office buildings, research labs, classrooms, student dorms, restaurants, medical centers, and recreational centers. Such information, especially for public universities, is often accessible.
Accordingly, this study implemented quantitative methods of sustainability assessment and developed a proof of concept of UM by examining urban universities as small-scale urban systems. Campus metabolism studies have been emerging, such as the Campus Metabolism Mapping Project at the University of South Florida and the interactive web tool Campus Metabolism™ at Arizona State University (Aden-Buie et al., 2012, Arizona State University, 2018, Barlett and Chase, 2013, Silva et al., 2015). However, these projects solely focus on environmental factors that are represented by energy and material flows. This study expands the scope of environmental metabolism by integrating financial, institutional, and physical factors as well. Essentially, this study discusses strategies to fill in the gaps in two ways: (1) applicable methods for multivariate UM analysis, with special attention to data integration and variable selection; and (2) empirical data that can characterize multiple sub-components of an urban system over time. The next section (Section 2) reviews two quantitative methods, Fisher Information (FI) and Data Envelopment Analysis (DEA), respectively, and discusses their applicability in urban sustainability assessment. Section 3 elaborates on the “metabolic” indicators that characterize urban universities as small-scale urban systems in a case illustration. Section 4 presents the data implementation results and compares the results from FI and DEA. Section 5 concludes the paper by discussing policy implications and future research needs.
Section snippets
Quantitative assessment methods review
One well-known definition of sustainable development is from the United Nation's “Our common future” report (Brundtland, G.H., 1987), where sustainable development was defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” It has also been suggested that sustainability should include three components: the natural environment, society, and economic performance (Elkington, 1994). Nevertheless, a generally
Case illustration: sustainability assessment of an urban university
To implement quantitative methods for urban sustainability assessment, this study has identified urban universities as a testbed for small-scale urban systems. In many ways, urban universities share similar characteristics of cities in terms of their mixed land uses (e.g., offices, cafeterias, dorms/residences, hospitals/labs, power plants, roads, landscape, community centers), multi-functionalities, and large stocks and flows of people, materials, and finance on a regular basis. Since the
Implementation of Fisher Information and Data Envelopment Analysis
In this section, we present the results from FI and DEA, including variable selection and sensitivity analysis, then discuss the results and compare the two quantitative methods.
Summary and future research
This study contributes to the literature on urban sustainability assessment in terms of both quantitative techniques and data references. Theoretically, it demonstrates the potential applicability of multidisciplinary methods, such as FI (from Statistics) and DEA (from Operations Research), that may be particularly useful to integrate multiple variables in various units for urban system analysis. Practically, it demonstrates a promising opportunity for data-driven studies of neighborhood-level
Acknowledgement
The authors appreciate the support they received from the US National Science Foundation (NSF) under Grant CyberSEES Type II: Data Integration for Urban Metabolism (No. CISE-1331800). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. The authors would also like to acknowledge the constructive comments from Dr. Heriberto Cabezas and the research assistance from Sydney Blankers and
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