Predicting tipping points in mutualistic networks through dimension reduction.

Complex networked systems ranging from ecosystems and the climate to economic, social, and infrastructure systems can exhibit a tipping point (a "point of no return") at which a total collapse of the system occurs. To understand the dynamical mechanism of a tipping point and to predict its occurrence as a system parameter varies are of uttermost importance, tasks that are hindered by the often extremely high dimensionality of the underlying system. Using complex mutualistic networks in ecology as a prototype class of systems, we carry out a dimension reduction process to arrive at an effective 2D system with the two dynamical variables corresponding to the average pollinator and plant abundances. We show, using 59 empirical mutualistic networks extracted from real data, that our 2D model can accurately predict the occurrence of a tipping point, even in the presence of stochastic disturbances. We also find that, because of the lack of sufficient randomness in the structure of the real networks, weighted averaging is necessary in the dimension reduction process. Our reduced model can serve as a paradigm for understanding and predicting the tipping point dynamics in real world mutualistic networks for safeguarding pollinators, and the general principle can be extended to a broad range of disciplines to address the issues of resilience and sustainability.

Rethinking Resilience Analytics.

The concept of "resilience analytics" has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data-driven analytic techniques, as well as the prevalence of high-profile natural and man-made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber-physical-social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.

Novel Method of Sensitivity Analysis Improves the Prioritization of Research in Anticipatory Life Cycle Assessment of Emerging Technologies.

It is now common practice in environmental life cycle assessment (LCA) to conduct sensitivity analyses to identify critical parameters and prioritize further research. Typical approaches include variation of input parameters one at a time to determine the corresponding variation in characterized midpoints or normalized and weighted end points. Generally, those input parameters that cause the greatest variations in output criteria are accepted as the most important subjects of further investigation. However, in comparative LCA of emerging technologies, the typical approach to sensitivity analysis may misdirect research and development (R&D) toward addressing uncertainties that are inconsequential or counterproductive. This paper presents a novel method of sensitivity analysis for a decision-driven, anticipatory LCA of three emerging photovoltaic (PV) technologies: amorphous-Si (a-Si), CdTe and ribbon-Si. Although traditional approaches identify metal depletion as critical, a hypothetical reduction of uncertainty in metal depletion fails to improve confidence in the environmental comparison. By contrast, the novel approach directs attention toward marine eutrophication, where uncertainty reduction significantly improves decision confidence in the choice between a-Si and CdTe. The implication is that the novel method will result in better recommendations on the choice of the environmentally preferable emerging technology alternative for commercialization.

Intergroup Cooperation in Common Pool Resource Dilemmas.

Fundamental problems of environmental sustainability, including climate change and fisheries management, require collective action on a scale that transcends the political and cultural boundaries of the nation-state. Rational, self-interested neoclassical economic theories of human behavior predict tragedy in the absence of third party enforcement of agreements and practical difficulties that prevent privatization. Evolutionary biology offers a theory of cooperation, but more often than not in a context of discrimination against other groups. That is, in-group boundaries are necessarily defined by those excluded as members of out-groups. However, in some settings human's exhibit behavior that is inconsistent with both rational economic and group driven cooperation of evolutionary biological theory. This paper reports the results of a non-cooperative game-theoretic exercise that models a tragedy of the commons problem in which groups of players may advance their own positions only at the expense of other groups. Students enrolled from multiple universities and assigned to different multi-university identity groups participated in experiments that repeatedly resulted in cooperative outcomes despite intergroup conflicts and expressions of group identity. We offer three possible explanations: (1) students were cooperative because they were in an academic setting; (2) students may have viewed their instructors as the out-group; or (3) the emergence of a small number of influential, ethical leaders is sufficient to ensure cooperation amongst the larger groups. From our data and analysis, we draw out lessons that may help to inform approaches for institutional design and policy negotiations, particularly in climate change management.

Intertemporal cumulative radiative forcing effects of photovoltaic deployments.

Current policies accelerating photovoltaics (PV) deployments are motivated by environmental goals, including reducing greenhouse gas (GHG) emissions by displacing electricity generated from fossil-fuels. Existing practice assesses environmental benefits on a net life-cycle basis, where displaced GHG emissions offset those generated during PV production. However, this approach does not consider that the environmental costs of GHG release during production are incurred early, while environmental benefits accrue later. Thus, where policy targets suggest meeting GHG reduction goals established by a certain date, rapid PV deployment may have counterintuitive, albeit temporary, undesired consequences. On a cumulative radiative forcing (CRF) basis, the environmental improvements attributable to PV might be realized much later than is currently understood, particularly when PV manufacturing utilizes GHG-intensive energy sources (e.g., coal), but deployment occurs in areas with less GHG-intensive electricity sources (e.g., hydroelectric). This paper details a dynamic CRF model to examine the intertemporal warming impacts of PV deployments in California and Wyoming. CRF payback times are longer than GHG payback times by 6-12 years in California and 6-11 years in Wyoming depending on the PV technology mix and deployment strategy. For the same PV capacity being deployed, early installations yield greater CRF benefits (calculated over 10 and 25 years) than installations occurring later in time. Further, CRF benefits are maximized when PV technologies with the lowest manufacturing GHG footprint (cadmium telluride) are deployed in locations with the most GHG-intensive grids (i.e., Wyoming).

Illustrating anticipatory life cycle assessment for emerging photovoltaic technologies.

Current research policy and strategy documents recommend applying life cycle assessment (LCA) early in research and development (R&D) to guide emerging technologies toward decreased environmental burden. However, existing LCA practices are ill-suited to support these recommendations. Barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. Overcoming these challenges requires methodological advances that help identify environmental opportunities prior to large R&D investments. Such an anticipatory approach to LCA requires synthesis of social, environmental, and technical knowledge beyond the capabilities of current practices. This paper introduces a novel framework for anticipatory LCA that incorporates technology forecasting, risk research, social engagement, and comparative impact assessment, then applies this framework to photovoltaic (PV) technologies. These examples illustrate the potential for anticipatory LCA to prioritize research questions and help guide environmentally responsible innovation of emerging technologies.

An experiential, game-theoretic pedagogy for sustainability ethics.

The wicked problems that constitute sustainability require students to learn a different set of ethical skills than is ordinarily required by professional ethics. The focus for sustainability ethics must be redirected towards: (1) reasoning rather than rules, and (2) groups rather than individuals. This need for a different skill set presents several pedagogical challenges to traditional programs of ethics education that emphasize abstraction and reflection at the expense of experimentation and experience. This paper describes a novel pedagogy of sustainability ethics that is based on noncooperative, game-theoretic problems that cause students to confront two salient questions: "What are my obligations to others?" and "What am I willing to risk in my own well-being to meet those obligations?" In comparison to traditional professional ethics education, the game-based pedagogy moves the learning experience from: passive to active, apathetic to emotionally invested, narratively closed to experimentally open, and from predictable to surprising. In the context of game play, where players must make decisions that can adversely impact classmates, students typically discover a significant gap between their moral aspirations and their moral actions. When the games are delivered sequentially as part of a full course in Sustainability Ethics, students may experience a moral identity crisis as they reflect upon the incongruity of their self-understanding and their behavior. Repeated play allows students to reconcile this discrepancy through group deliberation that coordinates individual decisions to achieve collective outcomes. It is our experience that students gradually progress through increased levels of group tacit knowledge as they encounter increasingly complex game situations.

Coupling multi-criteria decision analysis, life-cycle assessment, and risk assessment for emerging threats.

Emerging environmental threats such as novel chemical compounds, biological agents, and nanomaterials present serious challenges to traditional models of risk analysis and regulatory risk management processes. Even a massive expansion of risk and life-cycle assessment research efforts is unlikely to keep pace with rapid technological change resulting in new and modified materials with changing properties. Therefore, it is essential to have a framework for interpreting available information in the context of high uncertainty and a strategy for prioritizing research efforts to reduce those uncertainties that are most critical. We discuss how integrating the three analytic approaches of risk assessment, life-cycle assessment, and multicriteria decision analysis into a framework permits understanding uncertainty and prioritizes needs for scientific research. Our approach is illustrated with two separate cases: nanomaterials and contaminated sediment remediation.

Application of stochastic multiattribute analysis to assessment of single walled carbon nanotube synthesis processes.

The unprecedented uncertainty associated with engineered nanomaterials greatly expands the need for research regarding their potential environmental consequences. However, decision-makers such as regulatory agencies, product developers, or other nanotechnology stakeholders may not find the results of such research directly informative of decisions intended to mitigate environmental risks. To help interpret research findings and prioritize new research needs, there is an acute need for structured decision-analytic aids that are operable in a context of extraordinary uncertainty. Whereas existing stochastic decision-analytic techniques explore uncertainty only in decision-maker preference information, this paper extends model uncertainty to technology performance. As an illustrative example, the framework is applied to the case of single-wall carbon nanotubes. Four different synthesis processes (arc, high pressure carbon monoxide, chemical vapor deposition, and laser) are compared based on five salient performance criteria. A probabilistic rank ordering of preferred processes is determined using outranking normalization and a linear-weighted sum for different weighting scenarios including completely unknown weights and four fixed-weight sets representing hypothetical stakeholder views. No single process pathway dominates under all weight scenarios, but it is likely that some inferior process technologies could be identified as low priorities for further research.

Comparative life cycle assessment of lignocellulosic ethanol production: biochemical versus thermochemical conversion.

Lignocellulosic biomass can be converted into ethanol through either biochemical or thermochemical conversion processes. Biochemical conversion involves hydrolysis and fermentation while thermochemical conversion involves gasification and catalytic synthesis. Even though these routes produce comparable amounts of ethanol and have similar energy efficiency at the plant level, little is known about their relative environmental performance from a life cycle perspective. Especially, the indirect impacts, i.e. emissions and resource consumption associated with the production of various process inputs, are largely neglected in previous studies. This article compiles material and energy flow data from process simulation models to develop life cycle inventory and compares the fossil fuel consumption, greenhouse gas emissions, and water consumption of both biomass-to-ethanol production processes. The results are presented in terms of contributions from feedstock, direct, indirect, and co-product credits for four representative biomass feedstocks i.e., wood chips, corn stover, waste paper, and wheat straw. To explore the potentials of the two conversion pathways, different technological scenarios are modeled, including current, 2012 and 2020 technology targets, as well as different production/co-production configurations. The modeling results suggest that biochemical conversion has slightly better performance on greenhouse gas emission and fossil fuel consumption, but that thermochemical conversion has significantly less direct, indirect, and life cycle water consumption. Also, if the thermochemical plant operates as a biorefinery with mixed alcohol co-products separated for chemicals, it has the potential to achieve better performance than biochemical pathway across all environmental impact categories considered due to higher co-product credits associated with chemicals being displaced. The results from this work serve as a starting point for developing full life cycle assessment model that facilitates effective decision-making regarding lignocellulosic ethanol production.

The "weak" interdependence of infrastructure systems produces mixed percolation transitions in multilayer networks.

Previous studies of multilayer network robustness model cascading failures via a node-to-node percolation process that assumes "strong" interdependence across layers-once a node in any layer fails, its neighbors in other layers fail immediately and completely with all links removed. This assumption is not true of real interdependent infrastructures that have emergency procedures to buffer against cascades. In this work, we consider a node-to-link failure propagation mechanism and establish "weak" interdependence across layers via a tolerance parameter α which quantifies the likelihood that a node survives when one of its interdependent neighbors fails. Analytical and numerical results show that weak interdependence produces a striking phenomenon: layers at different positions within the multilayer system experience distinct percolation transitions. Especially, layers with high super degree values percolate in an abrupt manner, while those with low super degree values exhibit both continuous and discontinuous transitions. This novel phenomenon we call mixed percolation transitions has significant implications for network robustness. Previous results that do not consider cascade tolerance and layer super degree may be under- or over-estimating the vulnerability of real systems. Moreover, our model reveals how nodal protection activities influence failure dynamics in interdependent, multilayer systems.

Comparative, collaborative, and integrative risk governance for emerging technologies.

Various emerging technologies challenge existing governance processes to identify, assess, and manage risk. Though the existing risk-based paradigm has been essential for assessment of many chemical, biological, radiological, and nuclear technologies, a complementary approach may be warranted for the early-stage assessment and management challenges of high uncertainty technologies ranging from nanotechnology to synthetic biology to artificial intelligence, among many others. This paper argues for a risk governance approach that integrates quantitative experimental information alongside qualitative expert insight to characterize and balance the risks, benefits, costs, and societal implications of emerging technologies. Various articles in scholarly literature have highlighted differing points of how to address technological uncertainty, and this article builds upon such knowledge to explain how an emerging technology risk governance process should be driven by a multi-stakeholder effort, incorporate various disparate sources of information, review various endpoints and outcomes, and comparatively assess emerging technology performance against existing conventional products in a given application area. At least in the early stages of development when quantitative data for risk assessment remain incomplete or limited, such an approach can be valuable for policymakers and decision makers to evaluate the impact that such technologies may have upon human and environmental health.

Leaching behavior of estuarine sediments and cement-stabilized sediments in upland management environments.

Contaminated surficial sediments in major ports and harbors remain a significant economic liability during routine dredging operations. Numerous beneficial uses have been suggested in recent years that promise reduced sediment management costs concomitant with a productive material use. This manuscript describes the leaching of metals and metalloids from surficial sediments and from controlled low strength material (flowable fill) produced using the estuarine sediments as replacement for sand. Sediments from two locations within the New York/New Jersey Harbor area (Gowanus Canal and Newtown Creek) (USA), were subjected to pH-dependent leaching tests and liquid-to-solid ratio dependent leaching tests. Results indicate that, in general, maximum contaminant levels for drinking water, used here as a benchmark for metals concentrations in leachate, were exceeded only at pH values less than 5 or greater than 9. Leaching as a function of increasing liquid-to-solid ratio demonstrated that pH controlled the observed behavior: unamended sediment leached lower concentrations of all elements except for the oxyanion arsenate. The flowable fill material, despite dilution of the sediment and incorporation into a cementitious matrix, leached higher element concentrations except for arsenic due to the high pH of the material. It was also shown that a much more grossly contaminated material (Newtown Creek) had a very similar leaching behavior to the less contaminated Gowanus Canal material. Speciation calculations demonstrated that dissolved organic carbon plays a significant role in the leaching observed from these estuarine sediments and the flowable fill made with the high organic matter content sediments.

Integrate life-cycle assessment and risk analysis results, not methods.

Two analytic perspectives on environmental assessment dominate environmental policy and decision-making: risk analysis (RA) and life-cycle assessment (LCA). RA focuses on management of a toxicological hazard in a specific exposure scenario, while LCA seeks a holistic estimation of impacts of thousands of substances across multiple media, including non-toxicological and non-chemically deleterious effects. While recommendations to integrate the two approaches have remained a consistent feature of environmental scholarship for at least 15 years, the current perception is that progress is slow largely because of practical obstacles, such as a lack of data, rather than insurmountable theoretical difficulties. Nonetheless, the emergence of nanotechnology presents a serious challenge to both perspectives. Because the pace of nanomaterial innovation far outstrips acquisition of environmentally relevant data, it is now clear that a further integration of RA and LCA based on dataset completion will remain futile. In fact, the two approaches are suited for different purposes and answer different questions. A more pragmatic approach to providing better guidance to decision-makers is to apply the two methods in parallel, integrating only after obtaining separate results.

Resilience and efficiency in transportation networks.

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.

Advancing Alternative Analysis: Integration of Decision Science.

BACKGROUND: Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals. OBJECTIVES: We assessed whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics. METHODS: A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and were prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings. RESULTS: We concluded that the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients and would also advance the science of decision analysis. CONCLUSIONS: We advance four recommendations: a) engaging the systematic development and evaluation of decision approaches and tools; b) using case studies to advance the integration of decision analysis into alternatives analysis; c) supporting transdisciplinary research; and d) supporting education and outreach efforts. https://doi.org/10.1289/EHP483.

Extreme events in multilayer, interdependent complex networks and control.

We investigate the emergence of extreme events in interdependent networks. We introduce an inter-layer traffic resource competing mechanism to account for the limited capacity associated with distinct network layers. A striking finding is that, when the number of network layers and/or the overlap among the layers are increased, extreme events can emerge in a cascading manner on a global scale. Asymptotically, there are two stable absorption states: a state free of extreme events and a state of full of extreme events, and the transition between them is abrupt. Our results indicate that internal interactions in the multiplex system can yield qualitatively distinct phenomena associated with extreme events that do not occur for independent network layers. An implication is that, e.g., public resource competitions among different service providers can lead to a higher resource requirement than naively expected. We derive an analytical theory to understand the emergence of global-scale extreme events based on the concept of effective betweenness. We also articulate a cost-effective control scheme through increasing the capacity of very few hubs to suppress the cascading process of extreme events so as to protect the entire multi-layer infrastructure against global-scale breakdown.