Top-down Approach Research Articles

Effectiveness of exercise-based sports injury prevention programmes in reducing injury rates in adolescents and their implementation in the community: a mixed-methods systematic review

ObjectiveDespite evidence supporting the efficacy of sport injury prevention programmes (SIPPs) in adolescents, implementation of SIPPs in community settings is low. This review aims to synthesise and integrate evidence on...

Inverting social innovation to transform health system responses to climate change adaptation and mitigation in the global south

Systems thinking is aimed at understanding and solving complex problems that cut across sectors, an approach that requires accurate, timely, and multisectoral data. Citizen-driven big data can advance systems thinking, considering the widespread use of digital devices. Using digital platforms, data from these devices can transform health systems to predict and prevent global health crises and respond rapidly to emerging crises by providing citizens with real-time support. For example, citizens can obtain real-time support to help with public health risks via a digital app, which can predict evolving risks. These big data can be aggregated and visualized on digital dashboards, which can provide decision-makers with advanced data analytics to facilitate jurisdiction-level rapid responses to evolving climate change impacts (e.g., direct public health crisis communication). In the context of climate change, digital platforms can strengthen rapid responses by integrating information across systems (e.g., food, health, and social services) via citizen big data. More importantly, these big data can be used for rapid decision-making,a paradigm-changing approach that can invert social innovation, which we define as co-conceptualizing societal solutions with vulnerable communities to improve economic development with a focus on community wellbeing. However, to foster equitable and inclusive digital partnerships that invert social innovation, it is critical to avoid top-down approaches that sometimes result when researchers in the Global North and South collaborate. Equitable Global South–North partnerships can be built by combining digital citizen science and community-based participatory research to ethically leverage citizen-driven big data for rapid responses across international jurisdictions.

The trends in contemporary authoritarian leadership studies: A bibliometric data analysis

The study of authoritarian leadership has undergone significant development, with researchers exploring its different dimensions and consequences. This leadership style, characterized by a top-down approach and centralized decision-making authority, has been extensively examined in psychology, organizational behavior, and management literature. Scholars have delved into the effects of authoritarian leadership on various aspects of organizations such as employee satisfaction, motivation levels, productivity rates, turnover rates, and team dynamics. The research landscape surrounding authoritarian leadership has witnessed a recent surge in interest as scholars strive to understand its intricate connections with different variables. The primary objective of this study is to conduct a comprehensive bibliometric analysis on authoritarian leadership, aiming to identify the key research areas, influential authors, prominent journals in the field, and citation patterns. To our knowledge, no bibliometric analysis on authoritarian leadership can be found in the Scopus database, highlighting the novelty of our research. Through a source-based examination of scholarly articles and their citations pertaining to authoritarian leadership, this analysis offers valuable insights into the current state of research in this domain. By focusing on publications from the past decade onwards, we aim to uncover trends and potential gaps within existing literature while also providing guidance for future research endeavors. Our research findings will provide valuable insights into the phenomenon of authoritarian leadership, contributing to a deeper understanding of its implications. By delving into this topic, we hope to pave the way for future studies and investigations in this field that can build upon our findings and expand knowledge even further.

Novel Fabrication Techniques for Ultra-thin Silicon Based Flexible Electronics

Abstract Flexible electronics offer a multitude of advantages, such as flexibility, lightweight properties, portability, and high durability. These unique properties allow for seamless applications to curved and soft surfaces, leading to extensive utilization across a wide range of fields in consumer electronics. These applications, for example, span integrated circuits, solar cells, batteries, wearable devices, bio-implants, soft robotics, and biomimetic applications. Recently, flexible electronic devices have been developed using a variety of materials such as organic, carbon-based, and inorganic semiconducting materials. Silicon (Si) owing to its mature fabrication process, excellent electrical, optical, thermal properties, and cost-efficiency, remains a compelling material choice for flexible electronics. Consequently, the research on ultra-thin Si in the context of flexible electronics is studied rigorously nowadays. The thinning of Si is crucially important for flexible electronics as it reduces its bending stiffness and the resultant bending strain, thereby enhancing flexibility while preserving its exceptional properties. This review provides a comprehensive overview of the recent efforts in the fabrication techniques for forming ultra-thin Si using top-down and bottom-up approaches and explores their utilization in flexible electronics and their applications.

There’s power in the community: a sociomaterial analysis of university learning spaces

AbstractInternationally, the significance of the relationship between the university environment and the student experience is well-documented. In response, UK university leaders have driven forward policies that focus on estates’ expansion and regeneration. The restrictions necessitated by the COVID-19 pandemic presented an opportunity to explore questions around the importance of the materiality of campus and its impact on the student experience. This case study examines students’ experiences over time within a post-1992 UK university during the 2020/2021 academic year and makes a tri-fold contribution. First, it explores how restrictions placed on learning spaces can foreground the relationship(s) between space and learning practice. Second, through adopting a sociomateriality perspective, the paper examines students’ reactions to the top-down approach taken to Higher Education (HE) policymaking, and the potential for exposing manifestations of power within the student experience. Third, the paper illustrates how photovoice methodology can encourage reflections on the impact of materiality on the student experience. The findings reveal two principal themes: power dynamics and community participation. The authors make recommendations for university leaders to adopt a community-first, co-creation approach towards future policymaking that enables meaningful dialogue with students and educators and drives forward sustainable, inclusive change.

Top-Down Design Method of a Time Domain Accelerometer with Adjustable Resolution

A top-down design methodology and implementation of a time domain sensor is presented in this paper. The acceleration resolution of the time domain sensor is equal to the time-measurement accuracy divided by the sensor sensitivity. Combined with the sensitivity formula, the acceleration resolution is proportional to the vibration amplitude, the time-measurement accuracy, and the third power of the resonant frequency. According to the available time-measurement accuracy and the desired acceleration resolution, the parameters including the vibration amplitude and the resonant frequency were theoretically calculated. The geometrical configuration of the time domain sensor device was designed based on the calculated parameters. Then, the designed device was fabricated based on a standard silicon-on-insulator process and a matched interface circuit was developed for the fabricated device. Experimental results demonstrated that the design methodology is effective and feasible. Moreover, the implemented sensor works well. In addition, the acceleration resolution can be tuned by adjusting the time-measurement accuracy and the vibration amplitude. All the reported results of this work can be expanded to other time domain inertial sensors, e.g., a gyroscope or tilt sensor.

Entrepreneurial universities and integrated sustainability for the knowledge-based economy: self-perception and some structural challenges in the Gulf region

Innovation and the promotion of entrepreneurship are requirements of the transition towards knowledge-based economies. Universities are assigned a key role in advancing entrepreneurship through the restructuring of teaching, research, and governance with an emphasis on innovation and sustainability. In the Gulf region, universities are increasingly adopting reforms aimed at redefining their missions towards entrepreneurial education. This study examines the impact of university-based innovation and entrepreneurial models on progressing towards knowledge-based economies, with Qatar University serving as a case study. It qualitatively assesses recent university reforms and contextualizes these reforms within limitations in the national context and the political economy of the rentier states in the Gulf. The findings suggest that for Gulf region universities to effectively become entrepreneurial and support sustainable development, they must cultivate an entrepreneurial culture and mindset. This requires structural and cultural shifts that resonate with national context, institutional values, and the global sustainability goals. The study indicates that a top-down approach to developing entrepreneurial universities is less effective. It advocates for policies and incentives that motivate universities to become entrepreneurial. Ultimately, the study’s insights aim to guide policymaking to develop knowledge economy frameworks in Qatar towards a sustainable future.

A top-down character segmentation approach for Assamese and Telugu handwritten documents

A top-down character segmentation approach for Assamese and Telugu handwritten documents

Synthesis, characterization, and optical studies of lead-free perovskite, Cs3M2Br9 (M = Bi, Sb) nanocrystals

In recent years, there has been a surge in research interest surrounding all inorganic lead perovskite nanocrystals (NCs) owing to their exceptional performance in various optoelectronic devices. Nevertheless, the presence of lead and concerns regarding its toxicity and stability have cast a shadow over their potential wide-ranging applications. In this study, we present the synthesis of two lead-free all-inorganic perovskite materials, Cs3M2Br9 (where M = Bi or Sb), in bulk form, using an aqueous halide acid medium. Additionally, we describe a top-down approach for the fabrication of two lead-free all-inorganic perovskite, Cs3M2Br9 (M = Bi or Sb) NCs. The Cs3Sb2Br9 NCs display a sheet-like morphology, while Cs3Bi2Br9 NCs exhibit a cubic structure. The PL spectra of Cs3Sb2Br9 and Cs3Bi2Br9 NCs exhibit distinct emission peaks at 476 nm and 472 nm, respectively, with relatively narrow full width at half maximum (FWHM) of 44 nm and 38 nm, respectively. Cs3Sb2Br9 and Cs3Bi2Br9 NCs demonstrate excellent thermal stability up to 320 °C and 470 °C, respectively. Notably, the high photoluminescence quantum yield (PLQY) of these NCs, Cs3Sb2Br9 NCs (31 %) and Cs3Bi2Br9 NCs (36 %) are highly beneficial for optoelectronic applications is observed.

Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches

Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

Streamlining Protein Fractional Synthesis Rates Using SP3 Beads and Stable Isotope Mass Spectrometry: A Case Study on the Plant Ribosome.

Ribosomes are an archetypal ribonucleoprotein assembly. Due to ribosomal evolution and function, r-proteins share specific physicochemical similarities, making the riboproteome particularly suited for tailored proteome profiling methods. Moreover, the structural proteome of ribonucleoprotein assemblies reflects context-dependent functional features. Thus, characterizing the state of riboproteomes provides insights to uncover the context-dependent functionality of r-protein rearrangements, as they relate to what has been termed the ribosomal code, a concept that parallels that of the histone code, in which chromatin rearrangements influence gene expression. Compared to high-resolution ribosomal structures, omics methods lag when it comes to offering customized solutions to close the knowledge gap between structure and function that currently exists in riboproteomes. Purifying the riboproteome and subsequent shot-gun proteomics typically involves protein denaturation and digestion with proteases. The results are relative abundances of r-proteins at the ribosome population level. We have previously shown that, to gain insight into the stoichiometry of individual proteins, it is necessary to measure by proteomics bound r-proteins and normalize their intensities by the sum of r-protein abundances per ribosomal complex, i.e., 40S or 60S subunits. These calculations ensure that individual r-protein stoichiometries represent the fraction of each family/paralog relative to the complex, effectively revealing which r-proteins become substoichiometric in specific physiological scenarios. Here, we present an optimized method to profile the riboproteome of any organism as well as the synthesis rates of r-proteins determined by stable isotope-assisted mass spectrometry. Our method purifies the r-proteins in a reversibly denatured state, which offers the possibility for combined top-down and bottom-up proteomics. Our method offers a milder native denaturation of the r-proteome via a chaotropic GuHCl solution as compared with previous studies that use irreversible denaturation under highly acidic conditions to dissociate rRNA and r-proteins. As such, our method is better suited to conserve post-translational modifications (PTMs). Subsequently, our method carefully considers the amino acid composition of r-proteins to select an appropriate protease for digestion. We avoid non-specific protease cleavage by increasing the pH of our standardized r-proteome dilutions that enter the digestion pipeline and by using a digestion buffer that ensures an optimal pH for a reliable protease digestion process. Finally, we provide the R package ProtSynthesis to study the fractional synthesis rates of r-proteins. The package uses physiological parameters as input to determine peptide or protein fractional synthesis rates. Once the physiological parameters are measured, our equations allow a fair comparison between treatments that alter the biological equilibrium state of the system under study. Our equations correct peptide enrichment using enrichments in soluble amino acids, growth rates, and total protein accumulation. As a means of validation, our pipeline fails to find "false" enrichments in non-labeled samples while also filtering out proteins with multiple unique peptides that have different enrichment values, which are rare in our datasets. These two aspects reflect the accuracy of our tool. Our method offers the possibility of elucidating individual r-protein family/paralog abundances, PTM status, fractional synthesis rates, and dynamic assembly into ribosomal complexes if top-down and bottom-up proteomic approaches are used concomitantly, taking one step further into mapping the native and dynamic status of the r-proteome onto high-resolution ribosome structures. In addition, our method can be used to study the proteomes of all macromolecular assemblies that can be purified, although purification is the limiting step, and the efficacy and accuracy of the proteases may be limited depending on the digestion requirements. Key features • Efficient purification of the ribosomal proteome: streamlined procedure for the specific purification of the ribosomal proteome or complex Ome. • Accurate calculation of fractional synthesis rates: robust method for calculating fractional protein synthesis rates in macromolecular complexes under different physiological steady states. • Holistic ribosome methodology focused on plants: comprehensive approach that provides insights into the ribosomes and translational control of plants, demonstrated using cold acclimation [1]. • Tailored strategies for stable isotope labeling in plants: methodology focusing on materials and labeling considerations specific to free and proteinogenic amino acid analysis [2].

Rational construction of genome-minimized Streptomyces host for the expression of secondary metabolite gene clusters

Streptomyces offer a wealth of naturally occurring compounds with diverse structures, many of which possess significant pharmaceutical values. However, new product exploration and increased yield of specific compounds in Streptomyces have been technically challenging due to their slow growth rate, complex culture conditions and intricate genetic backgrounds. In this study, we screened dozens of Streptomyces strains inhabiting in a plant rhizosphere for fast-growing candidates, and further employed CRISPR/Cas-based engineering techniques for stepwise refinement of a particular strain, Streptomyces sp. A-14 that harbors a 7.47 Mb genome. After strategic removal of nonessential genomic regions and most gene clusters, we reduced its genome size to 6.13 Mb, while preserving its growth rate to the greatest extent. We further demonstrated that cleaner metabolic background of this engineered strain was well suited for the expression and characterization of heterologous gene clusters, including the biosynthetic pathways of actinorhodin and polycyclic tetramate macrolactams. Moreover, this streamlined genome is anticipated to facilitate directing the metabolic flux towards the production of desired compounds and increasing their yields.

Applying metabolic modeling and multi-omics to elucidate the biotransformation mechanisms of marine algal toxin domoic acid (DA) in sediments

Domoic acid (DA)-producing algal blooms are a global marine environmental issue. However, there has been no previous research addressing the question regarding the fate of DA in marine benthic environments. In this work, we investigated the DA fate in the water-sediment microcosm via the integrative analysis of a top-down metabolic model, metagenome, and metabolome. Results demonstrated that biodegradation is the leading mechanism for the nonconservative attenuation of DA. Specifically, DA degradation was prominently completed by the sediment aerobic community, with a degradation rate of 0.0681 ± 0.00954 d−1. The DA degradation pathway included hydration, dehydrogenation, hydrolysis, decarboxylation, automatic ring opening of hydration, and β oxidation reactions. Moreover, the reverse ecological analysis demonstrated that the microbial community transitioned from nutrient competition to metabolic cross-feeding during DA degradation, further enhancing the cooperation between DA degraders and other taxa. Finally, we reconstructed the metabolic process of microbial communities during DA degradation and confirmed that the metabolism of amino acid and organic acid drove the degradation of DA. Overall, our work not only elucidated the fate of DA in marine environments but also provided crucial insights for applying metabolic models and multi-omics to investigate the biotransformation of other contaminants.

Co-operative forest owner associations - harmonized values for sustainable development?

Sustainable development is an important concept for competitive advantage for all types of organisations and is often implemented by a top-down strategic approach. Forest owner associations are based on the co-operative idea and are therefore expected to include key stakeholders such as members in strategic decisions. A challenge is to incorporate strategic sustainability management in an organisation. It refers to considering organisational objectives, maintaining relations with key stakeholders and keeping the organisational identity. This study aimed to explore how forest owner associations position themselves for sustainable development. A qualitative case study based on semi-structured interviews, (including five employees and eleven members) and sustainability reports suggests that sustainable development is integrated to different extents in forest owner associations in Sweden. A strategic approach is deemed necessary for a dynamic incorporation of sustainable development that is structured and leads to the improvement of responsible practice in the organisation. This study highlights the importance of considering sustainability strategies and co-operative identity in the process of implementing sustainability management in forest co-operatives. Further research could focus on corporate strategies for sustainable development with an extended longitudinal perspective.

Designing function-specific minimal microbiomes from large microbial communities

Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome.

CenterNet++ for Object Detection.

There are two mainstream approaches for object detection: top-down and bottom-up. The state-of-the-art approaches are mainly top-down methods. In this paper, we demonstrate that bottom-up approaches show competitive performance compared with top-down approaches and have higher recall rates. Our approach, named CenterNet, detects each object as a triplet of keypoints (top-left and bottom-right corners and the center keypoint). We first group the corners according to some designed cues and confirm the object locations based on the center keypoints. The corner keypoints allow the approach to detect objects of various scales and shapes and the center keypoint reduces the confusion introduced by a large number of false-positive proposals. Our approach is an anchor-free detector because it does not need to define explicit anchor boxes. We adapt our approach to backbones with different structures, including 'hourglass'-like networks and 'pyramid'-like networks, which detect objects in single-resolution and multi-resolution feature maps, respectively. On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieve average precisions (APs) of 53.7% and 57.1%, respectively, outperforming all existing bottom-up detectors and achieving state-of-the-art performance. We also design a real-time CenterNet model, which achieves a good trade-off between accuracy and speed, with an AP of 43.6% at 30.5 frames per second (FPS).

Transport policies and their everyday impact on mobilities of older adults in Bengaluru

Background and objectives: Cities in the Global South are simultaneously witnessing wide-scale population ageing and rapid urbanisation. There is overwhelming pressure on policy makers to rapidly envision and provision age-friendly transport environments. In Indian cities, later-age mobilities face unique risks due to traffic congestion and inequities. However, the policy and transport research has treated congestion, equity and age-friendly mobilities as unrelated issues. This article goes a step ahead to understand how the policies to mitigate congestion can impact the provisioning of age-friendly infrastructure. Research design and methods: Bengaluru, once termed the ‘pensioners’ paradise’, is known for its ‘alarming’ levels of traffic congestion (Pucher et al, 2005; Pojani and Stead, 2017). We review Bengaluru’s transport policies and peer-reviewed articles concerning these policies to understand the positioning of age-friendly transport within this context. Results: Our findings recognise a shift from sufficientarianism to utilitarian orientations in Bengaluru’s policy making on easing congestion, which narrows the options for later-age transport users in terms of access and affordability. Some of the features not unique to Bengaluru – such as top-down planning, institutional fragmentation and distancing of users from policy making – have resulted in buses being inaccessible for passengers with mobility issues, metro rail being unaffordable for those on low incomes, lack of safe modes of transport and safe access to streets. Discussion and implications: The article is an initial foray into the ambiguity in the philosophical underpinning of transport policy making, which prioritises solving congestion ahead of providing age-friendly transport. We highlight specific challenges the cities of the Global South experience as they envision age-friendly transport policies amid the ascendance of congestion-solving policies.

Discrete forecast reconciliation

This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The proposed framework produces coherent joint probabilistic forecasts for count hierarchical time series. Two discrete reconciliation algorithms are also proposed and compared against generalisations of the top-down and bottom-up approaches for count data. Two simulation experiments and two empirical examples are conducted to validate that the proposed reconciliation algorithms improve forecast accuracy. The empirical applications are forecasting criminal offences in Washington D.C. and product unit sales in the M5 dataset. Compared to benchmarks, the proposed framework shows superior performance in both simulations and empirical studies.

Spatial modelling of street-level carbon emissions with multi-source open data: A case study of Guangzhou

A refined spatial understanding of carbon emissions is crucial for advancing low-carbon development. This study aims to develop a comprehensive, open data-based approach for spatial modelling of carbon emissions at street level, covering five sectors: industry, transportation, residential & public service, commerce, and agriculture in Guangzhou. Two sets of open data, including statistical yearbook data and urban morphology data, were analyzed using a comprehensive methodology that integrates both bottom-up and top-down approaches to map the spatial distribution of carbon emissions. The findings delineate the carbon emission hierarchy across five distinct sectors as follows: Industrial (37.9%), Transportation (31.3%), Residential (18.6%), Commercial and Public Services (11.6%), and Agriculture (0.6%). The industrial sector emerges as the largest contributor, emitting 61.59 million tons, chiefly situated in suburban industrial zones like Huangpu and Panyu. Following closely is transportation, emitting 50.87 million tons, concentrated around Baiyun International Airport, ports, and urban areas with heavy traffic. Commercial and residential sectors emit 18.94 million tons, primarily within densely populated areas such as Tianhe and Haizhu. Agricultural emissions total 1.02 million tons, predominantly located on the city's outskirts, notably in Nansha. The findings of this study could provide information support for identifying carbon emission hotspots and developing sector-specific low-carbon urban planning strategies.

Sustainability risk assessment in manufacturing: A Life Cycle Assessment-based Failure Mode and Effects Analysis approach

This study introduces a methodology integrating Life Cycle Assessment (LCA) with Failure Mode and Effects Analysis (FMEA) to identify and assess environmental sustainability risks in manufacturing processes in alignment with the Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS). In contrast to existing top-down approaches at sector and company level, this methodology specifically addresses the challenge of transitory sustainability risks in manufacturing, driven by rapid changes in regulatory frameworks and market expectations, by providing a robust and adaptable framework for comprehensive, bottom-up risk assessment directly on the manufacturing shop floor. Through a detailed literature review, the paper identifies gaps in current risk assessment methods and proposes a novel approach that enhances traditional FMEA by incorporating LCA results, thereby facilitating a comprehensive, quantitative analysis of sustainability risks. The practical application of this methodology is demonstrated through a case study, underscoring its effectiveness in real-world settings and potential for broader applications in sustainable manufacturing. The results presented in the paper also include quantitative sustainability performance benchmarks for the EU28 industrial sector, offering a strategic tool for manufacturers to improve sustainability practices, align operations with environmental standards, and mitigate transitory sustainability risks. This research contributes to the sustainable evolution of manufacturing, providing a methodological foundation for understanding and managing sustainability risks.