The 'High-with-Low' Scenario Narrative: Key Themes, Cross-Cutting Linkages, and Implications for Modelling

We define a global ‘High-with-Low’ scenario that delivers high wellbeing with low energy and material resource consumption while limiting global warming in line with Paris Agreement targets. The High-with-Low scenario comprises a rich thematic narrative and a quantitative framework for interpreting the narrative using systems and sectoral modelling tools at different scales. The three central themes of the High-with-Low scenario are decent living standards for all, innovation and granularity, and digitalization. Inter-linkages between these themes emphasize drivers of change towards a High-with-Low future that include decentralization, adaptability to local needs, accelerated diffusion through peer and network effects, and the management of complexity on shared infrastructures. However, the direction of change is not deterministic. The High-with-Low scenario envisages a set of specific and strong governance institutions for coordinating a highly distributed global sustainability transition. To help develop and enrich these narrative aspects, we also set out some guidelines and parameterisations for quantitative model interpretations of the High-with-Low scenario. These guidelines are not universally prescriptive but rather define a set of quantitative reference points against which model inputs, processes, and outputs can be iteratively tested for consistency. In particular, we emphasize the overall development pattern of the High-with-Low scenario as one of conditional convergence in which energy services for well-being increase substantially in the Global South catching up to levels maintained in the Global North, while associated resource use tends to converge, combining a contraction in the Global North with relatively modest increases in the Global South

Lifting industrial ecology modeling to a new level of quality and transparency: a call for more transparent publications and a collaborative open source software framework

Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.FWN – Publicaties zonder aanstelling Universiteit Leide

Material Stock Demographics: Cars in Great Britain.

Recent literature on material flow analysis has been focused on quantitative characterization of past material flows. Fewer analyses exist on past and prospective quantification of stocks of materials in-use. Some of these analyses explore the composition of products' stocks, but a focus on the characterization of material stocks and its relation with service delivery is often neglected. We propose the use of the methods of human demography to characterize material stocks, defined herein as stock demographics, exploring the insights that this approach could provide for the sustainable management of materials. We exemplify an application of stock demographics by characterizing the composition and service delivery of iron, steel, and aluminum stocks of cars in Great Britain, 2002-2012. The results show that in this period the stock has become heavier, it is traveling less, and it is idle for more time. The visualization of material stocks' dynamics demonstrates the pace of product replacement as a function of its usefulness and enables the formulation of policy interventions and the exploration of future trends.This work was supported by EPSRC, grant reference EP/N02351X/1.This is the final version of the article. It first appeared from the American Chemical Society via https://doi.org/10.1021/acs.est.5b0501

Lessons, narratives, and research directions for a sustainable circular economy

The current enthusiasm for the circular economy (CE) offers a unique opportunity to advance the impact of research on sustainability transitions. Diverse interpretations of CE by scholars, however, produce partly opposing assessments of its potential benefits, which can hinder progress. Here, we synthesize policy-relevant lessons and research directions for a sustainable CE and identify three narratives—optimist, reformist, and skeptical—that underpin the ambiguity in CE assessments. Based on 54 key CE scholars’ insights, we identify three research needs: the articulation and discussion of ontologically distinct CE narratives; bridging of technical, managerial, socio-economic, environmental, and political CE perspectives; and critical assessment of opportunities and limits of CE science–policy interactions. Our findings offer practical guidance for scholars to engage reflexively with the rapid expansion of CE knowledge, identify and pursue high-impact research directions, and communicate more effectively with practitioners and policymakers

Life Cycle Management of Infrastructures

By definition, life cycle management (LCM) is a framework “of concepts, techniques, and procedures to address environmental, economic, technological, and social aspects of products and organizations in order to achieve continuous ‘sustainable’ improvement from a life cycle perspective” (Hunkeler et al.\ua02001). Thus, LCM theoretically integrates all sustainability dimensions, and strives to provide a holistic perspective. It also assists in the efficient and effective use of constrained natural and financial resources to reduce negative impacts on society (Sonnemann and Leeuw\ua02006; Adibi et al.\ua02015). The LCM of infrastructures is the adaptation of product life cycle management (PLM) as techniques to the design, construction, and management of infrastructures. Infrastructure life cycle management requires accurate and extensive information that might be generated through different kinds of intelligent and connected information workflows, such as building information modeling (BIM)

Circular economy inspired imaginaries for sustainable innovations

In this chapter, Narayan and Tidström draw on the concept of imaginaries to show how Circular Economy (CE) can facilitate values that enable sustainable innovation. Innovation is key for sustainability, however, understanding and implementing sustainable innovation is challenging, and identifying the kind of actions that could direct sustainable innovations is important. The findings of this study indicate that CE-inspired imaginaries enable collaboration and by relating such imaginaries to common and shared social and cultural values, intermediaries could motivate actors into taking actions that contribute to sustainable innovation.fi=vertaisarvioitu|en=peerReviewed

Material Cycles, Industry and Service Provisioning: A Review of Low Energy and Material Demand Modelling and Scenarios

Developing transformative pathways for industry’s compliance with international climate targets requires model-based insights on how supply- and demand-side measures affect industry, material cycles, global supply chains, socio-economic activities and service provisioning supporting societal wellbeing. Herein, we review the recent literature modelling the industrial system for Low Energy and Materials Demand (LEMD) futures, resulting in lowered environmental pressures without relying on negative emissions. We identify 77 innovative studies drawing on nine distinct industry modelling traditions and critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provision. We find large potentials of combined supply- and demand-side measures to reduce current economy-wide material use by -56%, energy use by -40 to -60%, and GHG emissions by -70% to net-zero. We call for strengthening interdisciplinary collaborations between industry modelling traditions and demand-side research, to produce more insightful scenarios and discuss research challenges and recommendations

Low carbon technology performance vs infrastructure vulnerability: Analysis through the local and global properties space

Renewable energy technologies, necessary for low-carbon infrastructure networks, are being adopted to help reduce fossil fuel dependence and meet carbon mitigation targets. The evolution of these technologies has progressed based on the enhancement of technology-specific performance criteria, without explicitly considering the wider system (global) impacts. This paper presents a methodology for simultaneously assessing local (technology) and global (infrastructure) performance, allowing key technological interventions to be evaluated with respect to their effect on the vulnerability of wider infrastructure systems. We use exposure of low carbon infrastructure to critical material supply disruption (criticality) to demonstrate the methodology. A series of local performance changes are analyzed; and by extension of this approach, a method for assessing the combined criticality of multiple materials for one specific technology is proposed. Via a case study of wind turbines at both the material (magnets) and technology (turbine generators) levels, we demonstrate that analysis of a given intervention at different levels can lead to differing conclusions regarding the effect on vulnerability. Infrastructure design decisions should take a systemic approach; without these multilevel considerations, strategic goals aimed to help meet low-carbon targets, that is, through long-term infrastructure transitions, could be significantly jeopardized

The steel scrap age.

Steel production accounts for 25% of industrial carbon emissions. Long-term forecasts of steel demand and scrap supply are needed to develop strategies for how the steel industry could respond to industrialization and urbanization in the developing world while simultaneously reducing its environmental impact, and in particular, its carbon footprint. We developed a dynamic stock model to estimate future final demand for steel and the available scrap for 10 world regions. Based on evidence from developed countries, we assumed that per capita in-use stocks will saturate eventually. We determined the response of the entire steel cycle to stock saturation, in particular the future split between primary and secondary steel production. During the 21st century, steel demand may peak in the developed world, China, the Middle East, Latin America, and India. As China completes its industrialization, global primary steel production may peak between 2020 and 2030 and decline thereafter. We developed a capacity model to show how extensive trade of finished steel could prolong the lifetime of the Chinese steelmaking assets. Secondary steel production will more than double by 2050, and it may surpass primary production between 2050 and 2060: the late 21st century can become the steel scrap age