Quantifying waste prevention with LCA to motivate holistic resource

With the Waste Framework Directive, waste prevention was promoted first priority for all EU member states in 2008 because it is superior to preparing for re-use, recycling and other waste management options in terms of resource efficiency. However, the actual implementation of waste prevention activities has so far been hesitant and the focus on end-of-pipe waste management instead of a holistic resource management prevails. Reasons include the limited measurability of waste prevention effects and the consequential lack of awareness, motivation and incentives. Our research aims to quantify waste prevention and its environmental impacts using Life Cycle Assessments and, thereby, to induce the efficient implementation of waste prevention concepts into municipalities’ strategies. Within a two-year-project, we developed a framework to support municipalities in establishing holistic resource management. The framework comprises three phases to set up waste prevention concepts and includes a detailed guideline as well as a methodology for the evaluation of potentials and measures. Since the impacts of waste prevention can only be identified with a holistic approach, LCAs were used to calculate the potentials for quantitative as well as qualitative waste prevention. The analyzed measures were selected based on an empirical study covering 111 municipalities and include 5 waste streams and several areas of responsibility. To better motivate communities to take action and to implement waste prevention concepts, tangible impact categories and their midpoint-values were used. The impact categories for this study emerge directly from the European Waste Framework Directive and cover Waste Generation (Waste), Global Warming Potential (GWP), Water Scarcity (WSI), Mineral Resource Depletion (MRD) and Human Toxicity (H.-Toxicity). Please click Additional Files below to see the full abstract

Environmental benefits of large‐scale second‐generation bioethanol production in the EU: an integrated supply chain network optimization and life cycle assessment approach

The use of agricultural residues for the generation of bioethanol has the potential to substitute fuels such as petrol or first‐generation bioethanol and thereby generate environmental benefits. Scientific research in this field typically confines the environmental dimension to global warming, disregarding other environmental impact and damage categories. By multi‐criteria mixed‐integer linear programming, this work examines environmental benefits and economic viability of optimal second‐generation bioethanol production network configurations to substitute petrol and/or first‐generation bioethanol in the EU. The results comprise environmentally optimal decisions for 18 impact and 3 damage categories, as well as economically optimal solutions for different excise and carbon tax scenarios. The impact categories global warming potential, particulate matter, and land use are affected the most. Optimal network decisions for different environmental objectives can be clustered into three groups of mutual congruencies, but opportunity costs between the different groups can be very high, indicating conflicting decisions. The decision to substitute petrol or first‐generation ethanol has the greatest influence. The results of the multi‐dimensional analysis suggest that the damage categories human health and ecosystem quality are suitable to unveil tradeoffs between conflicting environmental impacts, for example, global warming and land use. Taking human health and ecosystem quality as environmental decision criteria, second‐generation bioethanol should be used to concurrently substitute first‐generation bioethanol and petrol (100% and 18% of today's demand in the EU, respectively). However, economic optimization shows that with current taxation, bioethanol is hardly competitive with petrol, and that excise tax abatement or carbon taxes are needed to achieve these volumes. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.Horizon 2020 Framework Programme http://dx.doi.org/10.13039/10001066

A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem

Serial-batch scheduling problems are widespread in several industries (e.g., the metal processing industry or industrial 3D printing) and consist of two subproblems that must be solved simultaneously: the grouping of jobs into batches and the sequencing of the created batches. This problem’s NP-hard nature prevents optimally solving large-scale problems; therefore, heuristic solution methods are a common choice to effectively tackle the problem. One of the best-performing heuristics in the literature is the ATCS–BATCS(β) heuristic which has three control parameters. To achieve a good solution quality, most appropriate parameters must be determined a priori or within a multi-start approach. As multi-start approaches performing (full) grid searches on the parameters lack efficiency, we propose a machine learning enhanced grid search. To that, Artificial Neural Networks are used to predict the performance of the heuristic given a specific problem instance and specific heuristic parameters. Based on these predictions, we perform a grid search on a smaller set of most promising heuristic parameters. The comparison to the ATCS–BATCS(β) heuristics shows that our approach reaches a very competitive mean solution quality that is only 2.5% lower and that it is computationally much more efficient: computation times can be reduced by 89.2% on average

Island and Indigenous systems of circularity: how Hawaiʻi can inform the development of universal circular economy policy goals

Given the dire consequences of the present global climate crisis, the need for alternative ecologically based economic models could not be more urgent. The economic and environmental concerns of the circular economy are well-developed in the literature. However, there remains a gap in research concerning the circular economy’s impact on culture and social equity. The underdeveloped social and cultural pillars of the circular economy, along with universal policy goals calling for a context- and need-based framework, makes it necessary to bridge natural and social science objectives in the circular economy. Islands can serve as model systems for studying the circular economy. We examine how Hawaiʻi, through the philosophy of aloha ʻāina, the Hawaiian ancestral circular economy, and contemporary community approaches toward advancing Indigenous economic justice can be one model system for understanding principles of circularity and policy advocacy. We introduce the concept of the ancestral circular economy and how aspects of this Indigenous institution can inform the development of universal circular economy policy goals. Furthermore, we present aloha ʻāina as a framework for reciprocal care between human–environment relations while addressing the social and cultural pillars that aid in the development of these dimensions of the circular economy