20253 research outputs found
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Defining ‘abated’ fossil fuel and industrial process emissions
Full text linkThere is scientific consensus that limiting warming in line with the Paris Agreement goals requires reaching net zero CO2 emissions by mid-century and net negative emissions thereafter. Because of the entrenchment of current fossil fuel energy and feedstock demand estimated in almost all global modelled scenarios, 'abated' fossil fuel and industrial process and product use (IPPU) CO2 emissions, using carbon capture and storage (CCS) technologies to perform carbon management, are likely to be part of any transition. In addition to fossil fuel combustion, this will be primarily in cement & lime kilns, chemical production, and possibly waste incineration and iron and steel making, in processes producing maximally concentrated CO2 waste streams. Abated fossil fuel and IPPU CO2 emissions in the context of recent commitments, however, requires consideration of capture rates for fuel processing and end-use, permanence of storage, reduction of upstream production and end-use fugitive methane, and sufficient means to sequester residual emissions. Based on an assessment of evolving CCS technologies in existing sectors and jurisdictions, criteria are proposed for defining a benchmark for 'abated' fossil fuel and IPPU emissions as where near 100 % GHG abatement is to be eventually achieved, with N2O and fluorinated gases considered separately. This can be accomplished through: 1) CO2 capture rates of more than or equal to 95 % of CO2 emitted; 2) permanent storage of captured emissions; 3) reducing upstream and end-use fugitive methane emissions to <0.5 % and towards 0.2 % of gas production & an equivalent for coal; and 4) counterbalancing remaining emissions using permanent carbon dioxide removal. Application of these criteria to just steel and cement yields estimates of more than or equal to 1.37 Gt CO2 per year reductions after all other reasonable and lower cost actions are taken. At the same time, we acknowledge the value of capture rates below 95 %, so as long they are designed to enable eventual full abatement through process learning. We also discuss commercialisation and deployment policy for CCS, highlighting the need to integrate these criteria into international climate agreements
Beyond the Floodplain: Integrating Probabilities and Storylines to Explore Regional Uncertain Direct and Cascading Climate Risks in Multi‐Sectoral Systems
Full text linkEffective management of future regional climate risks in interconnected multi-sectoral systems is complicated by uncertainties in risk drivers within both human and natural systems. Comprehensive yet comprehensible targeted climate risk information exploring these uncertainties is essential for the strategic allocation of limited resources to vulnerable areas and sectors in the region. Yet conventional approaches struggle to provide it. This study addresses this gap by introducing an interdisciplinary framework incorporating meteorological, hydrological, and socio-economic perspectives. A “plausibilistic” flood risk assessment approach is presented which combines both climate and socio-economic storylines. Plausible climate scenario storylines are sampled based on their relevance for local impacts, allowing the assessment of conditional changes in high-impact probabilistic discharges. Plausible socio-economic storylines are integrated to asses future urban area and economic sectoral development. This information allows the projection of the impact potential in the region and its cascading socio-economic effects. An example application to the flood-prone, transboundary Lielupe basin shared by Latvia and Lithuania highlights sub-catchments and sectors consistently vulnerable across diverse, relevant, and credible set of future storylines. The framework thus equips regional risk managers with targeted and robust risk information, providing a strong knowledge base for prioritizing adaptation planning
Weaving through time: Stocks and flows of textile fibers in China (1978–2022)
No full textTextiles are fabrics of daily life, relying on fibers as their core components. Despite growing research on fast fashion's environmental impacts, systematic quantification of textile fiber flows and stocks remains limited. This study applies dynamic material flow analysis (dMFA) to track flows and stocks of nine types of fibers in China (1978-2022), the global leader in fiber and textile production. The findings indicate a marked transformation in textile production and consumption structure in China through time. China recorded a cumulative production of 1.09 Gt fibers and net export of 437 Mt fibers and textiles over the past 45 years. By 2022, 347 Mt fibers remained as stocks. Current textile waste recovery rate stands at 17 %, predominantly downcycling. Achieving closed-loop fiber-to-fiber recycling demands cross-value-chain collaboration. This quantitative assessment provides the foundation for understanding the complexity of the textile fiber system and paves the way for creating a circular textile fiber economy
Ten Years of (De)industrialisation in Central and Eastern Europe: A Comparative Analysis
Full text linkIn today’s global economy, discussions on industrialisation and deindustrialisation are central. While prominent cases like Germany’s manufacturing exodus to countries such as China and the USA grab the headlines, understanding the dynamics in Central and Eastern Europe (CEE) is crucial. This region, with its intricate historical, geopolitical and economic legacies, offers a unique perspective to examine industrial sector trajectories amidst global economic shifts. In CEE, the development of manufacturing is influenced by multifaceted processes such as backshoring.
The concept of backshoring gained momentum amid the Covid-19 pandemic, prompting nations to re-evaluate their dependency on distant manufacturing hubs and thus turn to nurturing domestic production resilience. Nearshoring has also emerged as a prevailing trend in Western Europe, focusing on reducing logistical complexities by relocating production closer to end markets. Additionally, the notion of friendshoring has emerged as a response to geopolitics and shared economic interests, leading to the relocation of manufacturing operations to allied nations. However, these industrialisation drivers are counterbalanced by certain challenges. Escalating energy costs have diminished manufacturing competitiveness, eroding profit margins. Sanctions limiting trade with Russia have disrupted supply chains, and global value chain shifts have potentially marginalised the manufacturing capacity of the CEE region. This study comprehensively analyses manufacturing indicators over a decade to determine whether Central and Eastern European countries experienced industrialisation or deindustrialisation during that period. Considering its the People’s Republic of China serves as a benchmark. While there are no definitive signs of significant deindustrialisation, vigilance is needed as market sentiment and supply chain optimisation strategies evolve within the manufacturing sector
A Reduced-Complexity Model of Process-Based IAMs
Full text linkThis working document presents the development and calibration of abatement cost functions for a reduced-complexity integrated assessment model (IAM). A total of ten cost functions, partially linked to one another, are developed and calibrated based on complex process-based IAMs. This design allows the reduced-complexity model to replicate scenarios produced by more detailed IAMs while running significantly faster. The improved computational efficiency enables the exploration scenarios based on multiple parameter sets, each representing a complex IAM, to provide a robust representation of technological uncertainty.
The final model is versatile, functioning either as an optimization tool (e.g., for cost minimization under
a temperature target or welfare maximization when linked to a Ramsey growth model like DICE) or as a
simulation tool that takes a carbon price path as input. It is important to note that this document focuses
solely on the cost functions, which form the model’s core, as well as some exploratory model extensions.
Other components, such as climate or economic modules, can be easily linked using existing models
Green jobs and just transition: Employment implications of Europe's Net Zero pathway
Full text linkThe European Green Deal promises a “just and inclusive transition” to net-zero emissions by 2050, but employment implications remain poorly quantified. We address how Europe's net-zero transition affects energy sector employment and whether current policies ensure a just transition for affected workers. While the net-zero transition creates substantial net employment gains, we argue that significant mismatches in skills, geography, and timing require more targeted policy interventions than currently provided. Using the WITCH integrated assessment model coupled with global employment factors, we estimate changes across five job categories and eleven energy technologies for EU member states under current policies and the Net Zero emission target by 2050. Results show Europe's energy jobs increase substantially by 2050: from 1.3 million today to over 2 million under current policies and 2.5–3 million under Net Zero. Renewable energy accounts for 80 % of total energy jobs by 2050 under Net Zero, with solar PV representing three-quarters of job growth due to high labor intensity, while wind contributes 15 %. However, 300,000 jobs are lost in the coal and oil sectors under Net Zero (versus 100,000 under current policies), concentrated in Poland, Germany, and the Czech Republic. We also analyze the EU Just Transition Fund allocations to assess policy alignment and find a policy emphasis on addressing fossil fuel phase-out impacts rather than facilitating workforce transition to renewable energy. While coal-dependent countries receive substantial funding, critical gaps exist in skills development programs necessary for renewable energy expansion
The Discussions of Monkeypox Misinformation on Social Media
Full text linkThe global outbreak of the monkeypox virus was declared a health emergency by the World Health Organization (WHO). During such emergencies, misinformation about health suggestions can spread rapidly, leading to serious consequences. This study investigates the relationships between tweet readability, user engagement, and susceptibility to misinformation. Our conceptual model posits that tweet readability influences user engagement, which in turn affects the spread of misinformation. Specifically, we hypothesize that tweets with higher readability and grammatical correctness garner more user engagement and that misinformation tweets tend to be less readable than accurate information tweets. To test these hypotheses, we collected over 1.4 million tweets related to monkeypox discussions on X (formerly Twitter) and trained a semi-supervised learning classifier to categorize them as misinformation or not-misinformation. We analyzed the readability and grammar levels of these tweets using established metrics. Our findings indicate that readability and grammatical correctness significantly boost user engagement with accurate information, thereby enhancing its dissemination. Conversely, misinformation tweets are generally less readable, which reduces their spread. This study contributes to the advancement of knowledge by elucidating the role of readability in combating misinformation. Practically, it suggests that improving the readability and grammatical correctness of accurate information can enhance user engagement and consequently mitigate the spread of misinformation during health emergencies. These insights offer valuable strategies for public health communication and social media platforms to more effectively address misinformation
Efficient derivation of allometric models using laser scanning for improved AGB estimations
Full text linkAbove Ground Biomass (AGB), the total dry biomass found above the ground, plays a vital role in understanding the global carbon cycle and biodiversity. Recognized by international organizations as an Essential Climate Variable, AGB is a key component for carbon accounting and climate modeling. Despite its importance, accurately estimating AGB remains a challenge.
Allometric models have long been a central focus of research due to their critical importance in estimating variables such as AGB based on the relatively easy-to-measure single-tree parameters such as Diameter at Breast Height (DBH) and Tree Height (TH). This led to the development of numerous species- and biome-specific allometries. Many of these models are accessible through dedicated online platforms or published scientific studies. However, their derivation is resource-intensive, and they exhibit significant variability across different species and ecosystems, both limiting their broader applicability.
Terrestrial Laser Scanning (TLS), provides a non-destructive and highly accurate method for estimating AGB through volume calculation. TLS-generated point clouds can be processed into Quantitative Structure Models (QSMs) by fitting a hierarchy of cylinders to the 3D data, enabling precise AGB estimation. Additionally, these QSM-derived tree volumes can be used to optimize parameters for allometric models.
In this contribution, we explore the application of a novel toolbox to derive allometric models for diverse forest environments and species. The toolbox was employed to generate highly accurate single-tree volume measurements, which were combined with traditional measurements of DBH and TH to develop finely tuned allometric models. A key focus of the research is the investigation of an integrated workflow for enhancing traditional forest inventory practices. This workflow combines TLS-derived QSMs with in-situ measurements of DBH and TH, which, as demonstrated in various studies, can also be increasingly reliable obtained using smartphones. This approach introduces new possibilities for studying and monitoring AGB in forests with greater efficiency and broader accessibility
Land Cover and Change Map Accuracy Assessment and Area Estimation Good Practices Protocol. Version 0.1
Full text linkQuantifying minimum mobility and transport needs: The who, the where and the why
Full text linkThe concept of “sustainable consumption corridors” bridges two topics critical to assessing energy and transport systems: human wellbeing and planetary boundaries. However, large disagreements remain regarding how to define minimum, essential and decent levels of demand, which form the floor of such corridors. Aggregate approaches based upon distance travelled (e.g. passenger-kilometres) are insufficient, as they omit why people move. To address this gap, we build upon established theories of fundamental human needs and needs-oriented mobility research to define “decent mobility” as the condition when an individual can enact a set of trips that allow satisfaction of their needs, within their resources and capabilities. We explain how this definition unifies (i) individual capabilities and resources (time, money), (ii) available physical infrastructure and services, and (iii) socio-political contexts that shape personal freedom. We then operationalise and quantify decent mobility with a “persona” approach. We model two case studies with very distinct mobility systems – Switzerland and Mauritius – to illustrate the flexibility of the framework. They show which methods and data sources are required to consistently assess decent mobility of individuals, as well as travel time, distance, energy use, and emissions. Overall, the framework offers a method for evaluating present and future transport systems by putting human needs and their heterogeneity at the centre