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Seven Approaches to Manage Complex Coupled Human and Natural Systems: A Sustainability Toolbox Zhongming Lu, Osvaldo Broesicke, Michael E. Chang, Junchen Yan, Ming Xu, Sybil Derrible, James R. Mihelcic, Benedict R. Schwegler, and John C. Crittenden Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b01982 • Publication Date (Web): 25 Jul 2019 Downloaded from pubs.acs.org on July 27, 2019

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Environmental Science & Technology

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Seven Approaches to Manage Complex Coupled Human and Natural Systems: A Sustainability Toolbox

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Zhongming Lu*,†, Osvaldo A. Broesicke*, ‡, Michael E. Chang‡, Junchen Yan‡, Ming Xu¶, §, Sybil Derrible||, James R. Mihelcic#, Ben Schwegler††, John C. Crittenden‡

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†

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‡

Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China Brook Byers Institute for Sustainable Systems (BBISS), School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

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¶ School

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#

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†† Center

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*Main Corresponding Author ([email protected], T: 001-404-894-7895, Address: 828 West Peachtree Street, Suite 320, Atlanta, Georgia 30332, USA)

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*Secondary Corresponding Author ([email protected], T: (852) 3469 2398, Address: Division of Environment and Sustainability, HKUST, Clear Water Bay, Kowloon, Hong Kong.

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Abstract

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Since the publication of the Report of the World Commission on Environment and Development

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in 1987, there have been numerous studies on sustainability. These studies created new

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knowledge and tools for understanding and managing complex coupled human and natural

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systems. In this article, we used a topic modeling technique to analyze 12,526 peer-reviewed

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research articles and identify the research questions and the approaches that were used or

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developed in each of the studies. These approaches were then classified by function. The analysis

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revealed twenty-three categories of research questions and seven functional approach classes –

of Environment and Sustainability, University of Michigan, Ann Arbor, MI 481091041, USA Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, USA Complex and Sustainable Urban Networks (CSUN) Laboratory, 2095 Engineering Research, Facility, University of Illinois at Chicago, Chicago, IL 60607-7023, USA Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA for Integrated Facility Engineering, Stanford University, Stanford, CA 94305, USA

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design for sustainability, modeling of complexity, sustainability indicators, life cycle

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sustainability assessment, decision making support, sustainability governance, and engagement –

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each of which is described here as an individual approach or tool within a larger sustainability

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toolbox. The article concludes with a discussion about using the sustainability toolbox as an

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integrated knowledge system to support transdisciplinary study and decision-making.

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Introduction

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Sustainable development is a worldwide challenge in the pursuit of a more environmentally

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benign, economically prosperous, and socially just future for humans1. The United Nations (UN)

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Sustainable Development Goals (SDGs) clearly indicate the global consensus on humanity’s

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shared desire for sustainability2. In one sense, many argue that humanity must recreate the

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anthroposphere to exist within the means of nature. This requires society to generate new

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knowledge, technologies, processes, programs, and policies to continue improving human well-

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being by using renewable natural resources and producing no more waste than what nature can

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assimilate. Addressing the challenge of sustainability requires the integration of both natural and

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social sciences into a meta-discipline that operates at local, regional, and global scales3.

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Past sustainability research spans multiple scales, from discovering new materials at the

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nano-scale to mitigating climate impacts at the global scale. Embedded within these studies are

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tools used to analyze the complexity behind sustainability issues and to create innovative

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solutions. These tools facilitate sustainable design and provide guidance towards more

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sustainable decision-making. To generate awareness among designers, operators, and decision-

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makers on sustainability, some organizations have developed blueprints for sustainable

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development4. Others have compiled frameworks and methods to assess sustainability, engage

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stakeholders, and manage risk5. Rating systems (e.g., LEED® 6, ENVISIONTM 7, and Level(s)8) 2 ACS Paragon Plus Environment

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embed some of these tools into their ”sustainability” calculations to provide criteria (e.g., higher

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energy efficiency, healthier occupant space, or renewable material use) required to attain a

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specific sustainability standard. The growing popularity of these tools coupled with government

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mandates (e.g., LEED® Gold standards for all new U.S. federally-owned facilities9) has

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encouraged organizations to incorporate the indicators used in these rating systems into their

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design processes. The variety of available sustainability tools represents the complexity of

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sustainability challenges. Coincidentally, the sheer number of available tools may overwhelm

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researchers and practitioners because they lack guidelines to help them navigate through the

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complex landscape of sustainability tools. Accordingly, sustainability researchers, practitioners,

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and educators need a comprehensive catalog that contains details on the available approaches

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applied to sustainability problems.

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Recognizing this need, previous papers have performed bibliometric analyses or

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extensive literature reviews on sustainability topics. A majority of these reviews on sustainability

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have focused on specific assessment approaches (e.g., building or community rating systems10)

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or a specific discipline (e.g., industrial ecology11 or urban metabolism12), and are thus

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constrained in their breadth. As an alternative to traditional literature review, some studies have

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incorporated co-citation analysis11 or topic modeling to extract information from a journal

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database.

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Co-citation analysis generates a network where each node represents a journal paper. The

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links between nodes represent the relationship between each journal paper and the citations

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contained within. Subsequently, this network can assist users to identify the interrelationships

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between nodes and locate emerging node communities11. Co-citation analysis can thus assist

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users in understanding the breadth of the research, and how other papers within or between



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communities may influence certain trends. Topic modeling, on the other hand, identifies

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recurring strings of texts within a dataset. Topic modeling has gained traction over the last

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twenty years to aid users to automate and quickly review the current state of research, identify

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existing bias within a field, and discover “hidden” topics. It has been applied to identify research

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trends in staple crops13 and educational leadership14, identify academic concerns for dam

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construction15, and to quickly analyze and organize patents16. Typically, topic modeling also

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requires the interpretation by experts to assign topics to the linear combinations of words or

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strings extracted from the literature16.

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In this meta-study, we employ a topic modeling technique to identify and classify the

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approaches used in more than 12,000 peer-reviewed studies published on sustainability over

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twenty-seven years. These approaches are components of a larger dynamic and growing

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sustainability toolbox that can provide frameworks for those in decision-making positions for

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better informed, science-based decisions as lead the world to a more sustainable future17.

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Methods & Materials

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Data. We searched for the keywords “sustainability” or “sustainable” on the Web of Science

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Core Collection and refined the search using “tool”. We identified 12,526 research article

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abstracts, which span a wide variety of research from 1990 to 2017 (Figure S1). These papers all

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refer to the broad concept of sustainability and the development or application of a “tool.” The

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actual number of sustainability research articles available in the literature is much larger

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including those focusing on other or more specific topics that do not include the specified

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keywords. Because the Web of Science Core Collection is not all-inclusive, some journals,

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scientific papers, and books may be excluded from the database, thereby impacting the scope and

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breadth of our study. The Web of Science Core Collection in-depth guide can be used to help 4 ACS Paragon Plus Environment

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understand the limitations of our method because it provides a deeper understanding of the

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collection’s content18.

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Topic Modeling. We used topic modeling to classify the tools within the 12,526 abstracts into

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several representative topics. This approach generates an overview that summarizes each cluster

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of abstracts and eliminates the necessity to read and process them individually or to subjectively

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describe the resultant classifications19. Four articles we reviewed previously that apply topic

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modeling13–16 combine Latent Dirichlet Allocation (LDA) with Gibbs sampling. LDA analyzes a

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text and generates statistical distributions of latent topics20. Gibbs sampling is a Markov chain

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algorithm that incorporates Monte Carlo simulation to predict, in our case, the structure of the

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topics or linear combinations of words20. Combined, the LDA-Gibbs method generates a list of

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topics that are prevalent within the analyzed text.

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The technique used to perform the document clustering and topic modeling is

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Nonnegative Matrix Factorization (NMF)21. NMF, a deterministic algorithm, is similar to LDA

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in how it represents topics and is an appropriate substitution for LDA20. NMF is formulated as: min ‖𝑋 ― 𝑊𝐻‖2𝐹

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(1)

𝑊,𝐻 ≥ 0

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where the text is encoded within column vectors of the matrices 𝑋 ∈ ℝ𝑚+ × 𝑛, 𝑊 ∈ ℝ𝑚+ × 𝑘, and 𝐻 ∈

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ℝ𝑘+× 𝑛, and ‖⋯‖𝐹 represents the Frobenius norm21. The matrix 𝑋 is a term-document matrix

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(composed of n terms, or data-points, in an m-dimensional space), of which each column is a term-

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frequency vector that represents each document22. In solving, the columns of 𝑊 are vectors that

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represent the generated clusters (i.e., k topics extracted from the text-body) and the values in each

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column of 𝐻 are the cluster indicators. The optimization described by Eq. 1 approximates 𝑋’s

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columns (which represents the documents’ body text) with nonnegative linear combinations of

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matrices 𝑊 and H. 5 ACS Paragon Plus Environment


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Results & Discussion

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We identified 23 categories of research questions and 7 approaches implemented to address these

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sustainability questions using the topic modeling technique (refer to Tables S1 and S2 for topic

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keywords, and Figure S2 for the correlations among research themes). Interestingly, there is no

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single research question that requires only one or a few approaches. Instead, the seven identified

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approaches are all indispensable for addressing each category of the research questions. For

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example, in the collection of investigations focused on the development of “sustainable

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products” (2,089 out of 12,526 abstracts), all seven approaches were employed to some extent,

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with “sustainability governance” occurring the most frequently. Figure 1 shows the linkages

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between the identified research questions and the employed tools. Overall, we assume that the

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occurrence frequency of a tool category within each research theme reflects the utility of that

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tool in addressing the corresponding challenges.

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In comparison to similar studies within different fields, three of the articles we reviewed

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also discuss the emergence of research questions and methods as separate topics through their

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analysis to some extent. One study generated research topics but did not explicitly detail the tools

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that were employed in each topic13. The other two differentiated between research questions and

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methods that emerged as separate topics types; however, neither maps the methodology

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specifically to the questions11,14. Instead, these two studies discuss the prevalence of each

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methodology within their respective dataset11,14.

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Among the seven approaches identified in Figure 1, “engagement/ stakeholder

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engagement” is the most frequently cited across all research questions. The second most

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common was “governance,” which may reflect the tight coupling between these two toolsets.

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This combined frequency is perhaps an indicator rooted in the fact that design for sustainability

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is a social challenge at its core. Another interpretation of the dominant frequency of 6 ACS Paragon Plus Environment

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“governance” and “stakeholder engagement” is that they are not part of typical STEM curricula

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nor professional education, and thus serve as a catch-all for problems that engineering

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professionals are typically inexperienced in resolving.

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Figure 1. Sankey diagram linking the identified categories of sustainability approaches and the research questions that were identified from the Topic Modeling analysis of 12,526 published research abstracts with keywords “sustainability” (or sustainable) and “tool.” The color of each line corresponds to each sustainability approach category. The thickness of each line that links the approaches and research questions is proportional to the frequency that each approach was utilized within each research question category. See Table S3 for the values of each link. The diagram was created using SankeyMATIC.

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Design for sustainability

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Design offers an opportunity to improve sustainability. The object of design can be more

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sustainable products, processes, policies, buildings, infrastructures, or any other goods or

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services, which can range from the molecular to the global scales. Tools in this category include



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the need for inspiration in design solutions, design decision support, and stakeholder-engaged

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iterative design (Figure 2).

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Figure 2. The types of decision-support design tools (x-axis) and the design scale (y-axis). Adapted from Cucuzzella (2015)23.

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In regards to design inspiration, biomimicry24 and ergonomics25 have been powerful drivers in

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inspiring creative design. Some questions include: how has nature addressed these issues through

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millennia of evolution, how do individuals and communities interact with the design, and what

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frameworks exist that may provide clues that lead to more sustainable and robust design?

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Biomimicry takes cues from nature to find more effective, resource efficient, and prolonged

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sustainable solutions for human and technological problems — e.g., passive building cooling

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replicating termite mounds26, agent-based modeling from ants’ rule of pursuit27, polymer

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matrices that mimic the tunichrome in tunicate’s blood cells for selective metal sequestration28,

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and robust network design inspired by ecological network analysis29. The Biomimicry Institute

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provides a biomimicry tool database30.


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Centered on human functionality or human-system interactions, ergonomics is widely

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used in new production procedures and tool development that consider human factors to improve

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the working environment (e.g., improve labor productivity and workplace health and safety) and

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reduce environmental burdens25. Examples of ergonomic design include augmented reality31, the

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strength-enhancing Roboglove32, and the social inclusion of workers to promote sustainable

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development33. In conjunction with design support tools and stakeholder participation for design

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assessment and parameter refinement, bio-inspired and ergonomic solutions can be very robust

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regarding sustainable performance.

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Sustainability frameworks, like the 12 Principles of Green Design34 or the 12 Principles

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of Infrastructure Ecology35, encourage designers to consider life cycle and systems-level

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impacts, exploit synergies and interdependencies, and develop solutions that span across social,

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economic, and environmental dimensions. Nonetheless, the authors of each set of frameworks

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acknowledge that a sustainable (or green) design does not necessarily need to meet all 12

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principles, nor do all apply to every situation. Accordingly, these frameworks promote holistic

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rather than reductionist (i.e., end-of-pipe) design approaches to limit impacts on human and

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environmental health. As one example, in a holistic approach, the City of New York uses

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watershed protection strategies to provide high-quality unfiltered water to 9 million residents,

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saving $8-10 billion in capital expenses and approximately $1 million/day in operational costs

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compared to the alternative reductionist approach (i.e., water treatment)36. Other holistic

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approaches in design include the green infrastructure design for stormwater management37, from

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the design of small rain gardens to the restoration of Seoul’s Cheonggyecheon river38.

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Tools within this category focus on the need to create sustainable goods, specifically on

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the supply and production side39. The IPAT equation, 𝐼𝑚𝑝𝑎𝑐𝑡 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 × 𝐴𝑓𝑓𝑙𝑢𝑒𝑛𝑐𝑒 ×



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𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 (where Affluence is a proxy for the demand of technology40), suggests that

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technological improvement alone is insufficient to offset population and affluence growth. This

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line of thought implies that resource demand is proportional to the level of technology

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manufactured in the economic marketplace. This requires rigorous testing to validate41. In any

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case, designs that explicitly include a consumption-demand balance should be encouraged

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because they can be evaluated. For example, a consumer who installs energy-efficient lighting

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(e.g., LEDs) may pay less for energy; however, the net savings from energy-efficient

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improvements may be smaller due to the rebound effect42. Similarly, a growing population in an

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automobile-dependent city instills longer commute distances and higher carbon emissions, which

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offsets the technological benefits of more fuel-efficient vehicles. In other words, an improvement

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to a sub-system (e.g., fuel-efficient vehicles) does not necessarily improve the whole system

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(e.g., transportation system) if human behavior or choices (e.g., driving longer distances) counter

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those technological improvements. To avoid rebound effects and stimulate more sustainable

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lifestyles, an option is to invest in more tools that enable the design of infrastructures or other

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systems at larger scales (e.g., city or economic) and allow practitioners to go beyond incremental

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improvements to enable functional and system innovations. Past efforts include urban parametric

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design optimization43, the use of the crowd as a user-centered design tool44, and a cloud-based

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virtual reality technology for urban design and consensus building45.

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Modeling of Complexity

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Modeling is essential to gain a better understanding of complex systems and their impacts. A

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variety of models exist, such as agent-based modeling, that generally allow users to explore

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alternative system behaviors and scenarios, and to develop effective technological and policy

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solutions that improve sustainability outcomes. The scale of modeling can vary from an 10 ACS Paragon Plus Environment

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individual process to a larger-scale socioeconomic and environmental system. Moreover, the

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coupling of two or more complex dynamic systems is also key to the development of accurate

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and realistic models. Via computable general equilibrium modeling, for instance, researchers

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estimated the regional rebound effect through the behaviors of electricity producers, consumers,

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actors, and markets42. Another common technique used to model complexity is system-dynamic

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modeling46, and it is often a primary approach used to describe the interactions that govern a

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large socio-ecological system’s behavior. However, system-dynamic modeling in itself does not

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incorporate spatial analysis and it does not support spatially-explicit solution development. As an

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alternative, agent-based modeling is often used since it can capture the interactions between

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individuals and the environment to help identify the emergence of spatial patterns (e.g., land use,

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traffic congestion, and diffusion of innovations). Arguably, in the future, an even more

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comprehensive complexity management framework can be created by combining multi-scale

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process modeling of the physical environment with agent-based modeling of human-environment

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interactions and system-dynamic modeling of systems interactions, enabling a framework that

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considers human factors and natural processes to investigate and evaluate sustainable designs.

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The reliability of complex systems models depends, in part, on the availability and

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quality of data input into the models. Moreover, many complex systems tend to produce large

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amounts of data. In addressing this limitation, machine learning can be utilized as an alternative

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to predict and forecast system behaviors47. Its primary benefit is that it generally does not require

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much knowledge about the internal mechanisms that drive the functions and adaptation of the

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system. Machine learning models have also been employed to model complex hydrological

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phenomena in nature using hydro-meteorological variables such as streamflow, rainfall, and

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temperature as model inputs to predict a certain phenomenon (e.g., daily suspended sediment



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load)48. The accuracy of machine learning models increases as more real-time data becomes

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available for model training. A machine learning model is not universally applicable, however,

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because the training data is subject to the situation (e.g., timescale and/or location). New research

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also focuses on integrating machine learning models with physics-based models49.

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Another challenge of modeling complex systems is model validation, especially for large-

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scale complex systems (e.g., social systems, cities, economies, and ecosystems). Comparing

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historic observations with simulations is one way to validate a model, but it does not guarantee

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the credibility of model structures and components because only a few observations are generally

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available from thousands of possibilities that arise from system complexity. Participatory

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modeling was developed to overcome this shortfall. This method takes advantage of

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stakeholders’ knowledge and experience to develop a consensus model and apply it to explore

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actionable strategies. Cases of participatory modeling include mobile assessment of organic

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xenobiotics in rivers50, management of mountain summer pastures 51, sustainable shrimp

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production systems52, and rainwater harvesting to supplement water needs53.

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While these studies highlight applications of participatory modeling, the approach is not

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without flaws. One weakness of participatory modeling is asymmetric and incomplete

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information. Asymmetric information skews the influence that inexperienced stakeholders have

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on an outcome in favor of experts in the field54, especially if they come across as credible and

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legitimate (discussed later in the Engagement/Stakeholder Engagement section)55. Similarly, lack

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of knowledge or expertise among stakeholders, especially on topics on which knowledge is

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limited (e.g., land-use change, climate change, emerging contaminants, loss of biodiversity)56,

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will reduce the efficacy of gathered data for the participatory model. Accordingly, participatory

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modeling is a powerful tool for validation in the presence of complexity but its effectiveness is


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highly dependent on the knowledge of stakeholders. More information on participatory

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modeling is available elsewhere54 and discussion on mitigating the problem of asymmetric and

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incomplete information is discussed later in the Engagement/Stakeholder Engagement section of

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this paper.

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Sustainability Indicators

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Many published sustainability indicators represent economic, social, and environmental

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components (e.g., human development index57, genuine progress indicator58, and environmental

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sustainability index59). Sustainability indicators simplify the communication, comparison, and

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discussion of complex systems. Sustainability tools that are relevant to indicators tend to address

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how to select, calculate, and interpret these indicators for sustainability assessment and decision

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support. These tools usually start with a theoretical framework, which may describe the system

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performance (e.g., a “pressure-state-response” model of ecosystems) or standardized evaluation

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criteria (e.g., meet the objectives, deliver useful information, and guide the actions for

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improvement). A primary challenge for all indicators is the lag-time between when data is

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collected and interpreted to when it can be used for assessing and validating decisions and

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system responses.

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The Internet of Things (IoT) could help overcome the data lag and enable real-time

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monitoring and assessment. The smart city concept, e.g., incorporates IoT to frequently monitor

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the performance and activity of infrastructures and inform the operators on the dynamic changes

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occurring within cities60. Many cities have adopted this initiative to monitor energy, water,

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waste, and transportation to understand the interdependencies that exist between these

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infrastructures61. IoT also propelled the digitization and automation of the manufacturing sector,



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often referred to Industry 4.0, by allowing multiple systems, machines, and processes exchange,

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to analyze and process data to enhance manufacturing62.

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Interpreting indicators is important for decision-making but can be difficult given their

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variety, differences in methodologies, and partitioning of impacts, all of which may overload the

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interpreter with information. A common strategy to avoid information overload is to combine

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multiple indicators into fewer indices. This involves normalizing, weighting, and aggregating

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multiple indicators into one composite index. Indicators are normalized by dividing the obtained

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value by a “normal” value (e.g., an average or another measure of central tendency). Weighting

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is generally subject to stakeholder preferences, which makes it difficult to develop a common

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data basis for comparison across studies and cases. Other approaches to calculating a composite

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index include principal component analysis to reduce data dimensions and mining techniques

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inspired by complexity theory (e.g., an Entangled Economy model in evolutionary ecology to

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measure the cooperation and competition of indicators as fortitude)63. Finally, interpreting

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indicators is also subject to the means by which the indicator data is visualized, which enables

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effective communication and discussions on sustainability strengths and weaknesses.

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While indicators are an important tool for sustainability, research has yet to resolve

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several challenges. The first challenge is for an indicator to be commensurable across different

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media (e.g., energy, water, materials, or waste) and scales (e.g., spatial scales of planet,

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countries, cities, industries, or households or temporal scales of hours and days to years and

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centuries). One set of indicators, however, cannot account for the vast heterogeneity in the

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system environment, elements, and dynamics64. For instance, carbon intensities vary across cities

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due to factors such as population density, urban form and topologies, climate, affluence, and

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economic structure. Statistical analysis may explain how these factors influence carbon


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intensities; however, it is questionable to benchmark the carbon intensity associated with each

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factor for references by decomposing the contribution of the carbon intensities due to data

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uncertainty, incompleteness, and timeliness.

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A second unresolved challenge is a difficulty in using indicators to determine future

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pathways toward sustainability. Any potential decision is subject to indicator selection. Further,

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indicators often provide only vague and uncertain evidence of a more sustainable future. Because

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of this lack of stronger signals, communities are usually wary of using indicators to justify large

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investments of limited financial resources. The final unresolved challenge is the limited capacity

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of indicators to inform about “emergent properties.” When components combine to produce

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larger functional wholes, new properties can emerge that were not present or evident at the

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organizational level below. For example, ecosystem services like the regional capacity to retain

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nutrients for growing food or store carbon can vary widely in response to land use policies65. It is

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unknown what underlying factors, processes, or interactions are responsible for the change in

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these emergent properties, and the use of indicators will require additional investigation to

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inform sustainable solution development.

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Life Cycle Sustainability Assessment (LCSA)

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Life Cycle Sustainability Assessment (LCSA) is an analytic toolset that includes life cycle

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assessment (LCA), life cycle costing evaluation (LCC) and social life cycle assessment (SLCA)

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for integrated assessment of environmental, economic, and social impacts of human activities66.

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LCC and SLCA are developed primarily based on LCA, which is a mature tool for evaluating the

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environmental impacts of products, services, and infrastructure assets67. Traditionally, LCA

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analyzes the impacts on three areas of protection: human health, ecosystem health, and depletion

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of natural resources68. In addition, LCA can quantify the impacts on global or local sustainability 15 ACS Paragon Plus Environment


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(e.g., global warming potential, human or ecosystem toxicity, and nutrient enrichment) so that

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users can link their activities to the planetary boundaries69. Because some regions are more

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vulnerable to human activities (e.g., oil exploration in the Arctic), LCA is increasingly applied to

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define impact categories and characterization factors locally, especially for the threats that apply

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to local sustainability. For some products such as nano-enabled applications, impact pathways

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and estimates remain uncertain in terms of toxicity. These should be further investigated outside

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of any LCA application to evaluate the sustainability of emerging materials.

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LCC can evaluate the life cycle cost of different stages of competing activities, and to

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determine the trade-offs between environmental benefits and economic costs when combined

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with LCA. SLCA is the least developed approach in the LCSA family70. The UN Environment

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Programme (UNEP) published the first international guideline for SLCA and defined the social

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impacts mainly in terms of employees’ working environment (e.g., hazard exposure, safety, and

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capital productivity) and quality of life (e.g., salary, working hours, and insurance)71. However,

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not all measures of social impacts are quantitative and the tradeoffs among social impacts depend

350

on stakeholders’ preferences. That is not to say that tools that measure social activity are

351

nonexistent. The genuine progress indicator (GPI), for example, monetizes the costs and benefits

352

of social and environmental issues (i.e., consumption, wealth distribution, volunteering, crime,

353

family breakdown) and aggregates them into one metric72. However, standardized methods that

354

aggregate social impacts at various scales and scopes are not yet available for LCSA

355

frameworks. Without a proper scheme for aggregating social impacts, users of LCSA rely more

356

on quantitative LCA and LCC results to make decisions.

357

A Life Cycle Inventory (LCI) is the main database of LCSA. Numerous studies aim to

358

address data insufficiency and timelines in determining the LCI. The primary data sources for


Page 17 of 31


359

LCI include direct reports from operations (e.g., meter readings, operation logs/journals),

360

publications, and government statistics73. Increasingly, data-driven, computational approaches

361

are used to estimate LCI data without relying on primary data from the traditional sources74. In

362

addition, most LCI databases represent environmental impacts of a certain process or system at

363

the sectoral level. The use of sectoral average data cannot distinguish an individual

364

organization’s technological advancement. Some studies of hybrid LCI construction

365

methodologies combine process-based data, economic input-output analysis, and simulation

366

(e.g., parameterized process model) to create a more robust LCI. The introduction of hybrid

367

techniques also expands the traditional static LCSA and present time-varying impacts and cost

368

along the life cycle from a dynamic perspective. Parametric LCA, for example, incorporates

369

spatiotemporal variance into the LCA outputs depending on the operating system’s variables75.

370

Besides the LCSA technique development, a number of tools exist that facilitate the

371

translation of sustainability assessment into business value creation. Hotspot screening highlights

372

the critical phases and activities to facilitate stakeholder involvement. Uncertainty and sensitivity

373

analysis address the LCI limits and account for future uncertainty during the life cycle. Multi-

374

objective optimization allows users to vary system configurations to improve performance across

375

environmental, social, and economic objectives. Multi-objective optimization is an example of a

376

joint application of LCSA and a design support tool (e.g., building information modeling

377

(BIM)76, INSIGHT77, data envelopment analysis, and computer-aided product development).

378

These applications – referred to as decision support systems – allow users to improve the

379

sustainable performance of products, processes, and systems. This and other decision support

380

systems are discussed in the following section.



381

Page 18 of 31

Decision Support

382

Sustainable decision-making (e.g., selection of materials, project locations, and

383

technologies) is essentially a multi-criteria decision-making (MCDM) problem. The decision-

384

making procedure is complex and involves choosing sustainability indicators and evaluating

385

different options to find a solution that satisfied one or more sustainability objectives. The

386

procedure depends on stakeholders developing a set of criteria (e.g., risk, cost, and benefit) and

387

determining the importance of these criteria in design (i.e., how much weight should be applied

388

to each criterion).

389

Over recent years, many MCDM techniques have been developed and can simplify

390

complex decision-making. Three prevalent techniques include (1) analytic hierarchy process

391

(AHP) 78, (2) technique for order of preference by similarity to ideal solution (TOPSIS) 79, and

392

(3) elimination and choice expressing reality (ELECTRE) 80. To be more specific, AHP is a

393

pairwise comparison of alternatives against each criterion78, TOPSIS ranks alternatives based on

394

the geometric distance between the "ideal" and "worst" solutions79, and ELECTRE prioritizes

395

choice selection according to an outranking relation on a set of alternatives80. These techniques

396

differ with respect to criterion aggregation schemes to rank alternatives. For a user of these

397

techniques, it is common to combine several MCDM techniques to increase the transparency of

398

the evaluation process and improve the credibility of the final decision for stakeholders.

399

Multidisciplinary design optimization (MDO) is a computational MCDM technique that

400

combines multiobjective optimization (MOO) and parametric design (see Lin and Gerber81 and

401

Best et al43). An advantage of MDO – as a derivative of MOO – is the identification Pareto

402

optimal solutions – solutions in which no objective can be improved without in turn repressing

403

another43,81. Accordingly, MDO allows decision makers to evaluate the trade-offs between


Page 19 of 31


404

objectives of a large design space and enables designers to narrow down the range of design

405

options or parameters that will meet their desired performance characteristics.

406

Decision-making can be subjective because some criteria are qualitative, especially social

407

impact measures with values such as “high”, “average”, and “low.” It is hard for individuals to

408

distinguish the various levels and to select one as representative without hesitation or

409

uncertainty. Fuzzy logic is one approach for addressing human reasoning problems in which

410

choices are neither exact nor very inexact82. Unlike Boolean logic, which possesses only two

411

values − 1 or 0 − fuzzy logic can have values between 0 and 1, which indicate the degree of

412

truth. The integration of fuzzy logic and MCDM techniques provides more flexibility for

413

combining qualitative and quantitative evaluations to rank alternatives83. Sensitivity analysis

414

provides additional information about the impact of fuzzy inference on the ranking and the need

415

to reduce the fuzziness. Although fuzzy logic can help address the uncertainties that stem from

416

asymmetric information and the stakeholders’ lack of knowledge on a specific topic, fuzzy logic

417

models are limited to linear relationships between concepts and a specific point in time54.

418

Therefore, the outputs of a fuzzy logic model are not transferable across systems where its

419

dimensions (e.g., stakeholders or their set of options) change, especially over a dynamic

420

timeframe54. Other methods for reducing uncertainty for decision-making and participatory

421

models are discussed in the Engagement section.

422

Sustainability Governance/Management

423

One purpose of sustainability governance tools in the business sector is to develop management

424

solutions for reducing the impact of human development on the environment. In these kinds of

425

applications, sustainable governance usually begins with an accounting of environmental impacts

426

(e.g., ecological footprint analysis, emergy analysis, or environmental impact cost-benefit 19 ACS Paragon Plus Environment


427

analysis), and then identifies the actions for impact mitigation. For instance, banks use

428

environmental impact assessment to measure a project’s environmental risk and then use this

429

information as an input for bank lending decisions84. Businesses use these tools to protect the

430

environment and to develop business strategies that will ensure their survival85.

431

Page 20 of 31

For national and local governments and nongovernmental organizations, governance

432

tools appear as policy, management, and legislation instruments whose objectives are to increase

433

sustainable behaviors of individuals and organizations. Examples include environmental taxes,

434

public interest litigation, and governmental accounting efforts (e.g., making the promotion of

435

Chinese governors, mayors, and state-owned enterprise leaders contingent on the achievement of

436

quantifiable environmental performance objectives). One institutional challenge that can affect

437

the effectiveness of these governance instruments is weak implementation and enforcement (e.g.,

438

inadequate authority or the inability of local officials, limited public participation, inadequate

439

disclosure of information, or weak monitoring)86. Additional challenges include the lack of

440

available design alternatives that can be implemented with locally available technology.

441

At the global scale, sustainability governance efforts mainly rely on international and

442

regional agreements for environmental cooperation – e.g., the Paris Agreement to reduce

443

greenhouse gas (GHG) emissions. Reaching an agreement is a complex and arduous process but

444

it can be the most effective instrument to address interest conflicts, conserve national resources,

445

and achieve the SDGs. One excellent example is the Montreal Protocol, where international

446

cooperation between 197 parties led to the elimination of more than 98% of controlled ozone-

447

depleting substances, many of which are also GHGs, between 1987 and 201487.


Page 21 of 31


448

Engagement / Stakeholder Engagement

449

Beyond technology transfer and finance, sustainability is also a social challenge that requires

450

collaborative actions by individual communities and societies88. Engagement is indispensable for

451

developing collaborative strategies, which recognize the inherent complexity of sustainability

452

challenges and the need for integrated knowledge and expertise from diverse social actors. There

453

are four expectations rising from public engagement55. The first expectation, impartiality

454

(legitimacy), is that any underlying science or technical analysis conducted by scientists and used

455

to justify a decision or action should be fair and free of bias. The second expectation, inclusion

456

(salience), is that all stakeholders are invited to participate, observe, or have the opportunity to

457

ask questions about any information that is used to formulate the relevant questions, develop

458

solutions, or assess the potential consequences. The third expectation, credibility, is that

459

stakeholders perceive the information as trustworthy and meeting plausibility standards. The

460

final expectation, open-access, is that any newly created knowledge is shared openly with all

461

stakeholders and that efforts are made to increase their capacity to understand and utilize the

462

information. When the frontiers between knowledge and action simultaneously enhance these

463

four criteria, efforts to mobilize technical expertise for sustainability are more likely to be

464

effective, even if it is impossible to optimize all three89. Based on three modes of stakeholder

465

participation (i.e., non-participation, tokenism, and citizen power)90, Stewart91 suggested an

466

example of the hierarchy of engagement (Table 1).

467

Table 1. Types of stakeholder engagement. Adapted from Stewart91. High ↑

Delegative

Level of Engagement

Consultative

↓ Low

Informative

Citizen panels, voting in membership organizations, jointly managed community research projects, representation on the stakeholder advisory board Focus groups, surveys, feedback forms, advisory users groups, electronic forums Public meetings, media stories, advertising, publicity material, exhibitions, lobbying

468 21 ACS Paragon Plus Environment


469

Page 22 of 31

To fulfill these expectations, participatory tools are an essential prerequisite for proactive

470

engagement. Traditional engagement forms include one-on-one meetings, phone calls, formal

471

meetings, advisory boards, and emails92, which can be costly and sometimes ineffective,

472

especially when interacting with disadvantaged communities. Crowdsourcing uses citizens as

473

distributed sensors to feed researchers or government with data and establish sustained

474

relationships between researchers and the broader society93. For example, Amazon’s Mechanical

475

Turk was used to collect responses from ~800 people on their preference for integrated low-

476

impact and transit-oriented development in Atlanta (Georgia) for choice modeling94. This and

477

other crowdsourcing platforms can help decision-makers gain insight into residents’ preferences

478

quickly and inexpensively. Participatory online geospatial technologies, such as digital mapping,

479

social media, and smartphone apps, provide individuals with access to upload place-based

480

information that can help inform decisions (e.g., avoid congestion or purchase products that meet

481

environmentally just criteria).

482

Besides the participatory approaches and technologies that enable connections between

483

researchers, practitioners, and stakeholders, other methods and tools also foster brainstorming

484

and coproduction of knowledge. System mapping is one such approach for diverse groups to

485

describe the elements, connections, and dynamics of a complex system, to define the problem

486

that should be addressed, and to collaborate on solutions. Using this tool, participants can move

487

beyond their own limited knowledge domain and develop a system’s perspective. Semantic

488

analysis and topic modeling are other engagement tools that can help participants and organizers

489

quickly analyze a large number of documents or prior studies to synthesize that information into

490

actionable knowledge.


Page 23 of 31


491

Engagement is not only a process to improve understanding and to develop solutions

492

among individuals, but it should also enhance citizens’ capacity towards understanding other

493

perspectives (i.e., empathy) and enrich their ability to work collaboratively. Such building of

494

social capital is a crucial component for building citizen capacity and trust in both scientific and

495

decision-making institutions, and for more actively engaging society in sustainable

496

development95. The social variables that are enhanced (and can be measured) include shared

497

norms and values of trust, reciprocity and solidarity, and the formation and maintenance of social

498

networks96. The formation of Public-Private Partnerships is one popular strategy for creating

499

successful engagements.

500

The Future of the Sustainability Toolbox

501

The seven categories of tools identified via this meta-analysis should be considered the core

502

toolset that every sustainability practitioner should have a basic familiarity with, understanding

503

of, and experience using. Any or all of these are common skills that might be adapted to and

504

included in any curriculum that has sustainability as a primary learning outcome. In the past,

505

mastery and application of any one tool was sufficient to advance sustainability in some

506

meaningful way. The problem sets on which researchers and practitioners are asked to study and

507

apply sustainability principles, however, are becoming larger and more complex. A subset of

508

these problems can be classified as the Gigaton Problems97, which refer to the massive global-

509

scale use of non-renewable resources and the overwhelming of natural cycles to the detriment of

510

the ecosystem health and services. For instance, the projected global population increase, current

511

and future energy demands98, water consumption, resource extraction, automobile use, and GHG

512

emissions99 and plastic generation and waste100 are all in the Giga-scale (i.e., billions). In turn,

513

the tools must be used together to provide more useful diagnostic and prognostic analyses, more 23 ACS Paragon Plus Environment


Page 24 of 31

514

creative design innovations, and more engaging problem formulation and solving (See Figure 3).

515

Importantly, engagement must include a diverse set of stakeholders that includes the most

516

disadvantaged community members to ensure sustainable solutions are also equitable.

517 518 519 520

Figure 3. Integrated tool functions of sustainability toolbox and the interconnections arising from the literature in recognition of the complexity of sustainable challenges. The interconnections represent the inputs and outputs of each of the seven sustainability tool categories and.

521 522

It is widely recognized that the study of sustainability problems is a transdisciplinary task

523

and one needs to go beyond a given knowledge domain to study causes and solutions. However,

524

it is impossible for one to grasp all relevant disciplines and set up an inclusive system before

525

studying sustainability problems. Accordingly, we observed that most literature only studied one

526

particular aspect of one specific sustainability problem of interest. To support transdisciplinary

527

studies, the sustainability toolbox cannot remain a list of stand-alone tools but instead must

528

become diverse clusters of interconnected knowledge modules about design, modeling,


Page 25 of 31


529

evaluation, and management of complex systems of interest (see Table S4 for a list of

530

sustainability tools). Researchers have already started coupling tools and methods to construct

531

systems-based approaches on a case-by-case basis. These case-by-case efforts are valuable but

532

not universally applicable. Mechanisms to code and assemble multidisciplinary knowledge

533

modules in the form of one tool docking with others is needed to understand the impact of

534

human activities on local and global sustainability. This task of exploring how these tools can be

535

integrated is only just beginning, and more research is needed that shows how they can

536

effectively be used together to address larger and more complex problems in sustainability.

537

Associated Content

538

Supporting Information

539

The Supporting Information is available free of charge on the ASC Publications website at DOI:

540

.

541

The SI contains further information regarding the data clusters, topic word frequencies,

542

and the code used in this review.

543

Data availability.

544

The source data for our analysis (the analyzed abstracts) is available from the Web of Science

545

(https://apps.webofknowledge.com/).

546

Acknowledgments

547

This research was supported by the Brook Byers Institute for Sustainable Systems, Georgia

548

Institute of Technology and the Hightower Chair of the Georgia Institute of Technology. This

549

work was also supported by the Georgia Research Alliance and the grant for “Resilient and

550

Sustainable Infrastructure” (#0836046) from the National Science Foundation, Division of



Page 26 of 31

551

Emerging Frontiers in Research and Innovations (EFRI). The startup support from the Hong

552

Kong University of Science and Technology is also acknowledged. Finally, Mr. Broesicke would

553

like to acknowledge funding from the ARCS Foundation and the Alfred P. Sloan Foundation's

554

Minority Ph.D. (MPHD) program. The views and ideas expressed herein are solely those of the

555

authors and do not represent the ideas of the funding agencies in any form.

556

Author Information

557 558

*Phone: 915-269-4307; e-mail: [email protected]

559

**Phone: (852) 3469 2398; email: [email protected]

560 561

ORCID Zhongming Lu: 000-0002-4151-5065

562

Osvaldo Broesicke: 0000-0002-5587-0383

563

John C. Crittenden: 000-0002-9048-7208

564

Competing Interests

565

The authors declare no competing financial interests

566

Contributions

567

Z. L. performed the topic choice modeling and analyzed the data. J. C. directed and conceived

568

the project. Z. L., O. B., and M. C. wrote a majority of paper with guidance, input, edits, and

569

additional writing from J. Y., M. X., S. D., J. M., and B. S.

570

Affiliations

571 572

Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China

573 574

Brook Byers Institute for Sustainable Systems (BBISS), School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

575 576

School of Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109-1041, USA

Corresponding Authors


Page 27 of 31


577 578

Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, USA

579 580

Complex and Sustainable Urban Networks (CSUN) Laboratory, 2095 Engineering Research, Facility, University of Illinois at Chicago, Chicago, IL 60607-7023, USA

581 582

Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA

583

Center for Integrated Facility Engineering, Stanford University, Stanford, CA 94305, USA



584

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