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Cost-effective climate benefits through fluorocarbon lifecycle management in China
Full text linkAchieving global climate goals requires heightened ambition and innovative measures. Banks of hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs), potent non-CO2 greenhouse gases, represent a significant yet untapped mitigation opportunity. Globally, fluorocarbon refrigerant banks are estimated at 24 Gt CO2-eq and continue to grow, forming a massive and expanding reservoir of greenhouse gases that will eventually be released into the atmosphere if left unaddressed. While the Montreal Protocol and its Kigali Amendment regulate the production and consumption of fluorocarbons, emissions from existing stocks remain largely unregulated. Fluorocarbon lifecycle management (FLM) – encompassing leakage prevention, recovery, recycling, reclamation and destruction – presents a viable solution to mitigate these emissions. In China, the world’s largest producer and consumer of HCFCs and HFCs, implementing FLM could unlock substantial mitigation potential beyond current climate action, serving as a critical step toward net-zero goals. This study provides the necessary systematic evaluation to harness this opportunity.
To comprehensively assess the emission profiles of banked fluorocarbons with or without FLM, we developed the Extended Lifecycle Emissions Framework (ELEF), a refined emission modeling approach rooted in IPCC methodologies. ELEF expands conventional frameworks to cover both direct and indirect emissions across the entire lifecycle of fluorocarbons in equipment/product. A bottom-up cost analysis, adapted from the widely applied Greenhouse gas and Air pollution Interactions and Synergies (GAINS) framework to capture sector- and substance-specific treatment nuances, was conducted to assess the cost-effectiveness of FLM in China. Leveraging detailed activity data and localized emission factors, we reconstructed the country’s fluorocarbon banks and emissions from 2000 and projected them through 2060. Mitigation potential was then quantified across varying ambition levels defined by abatement cost cap, with climate impacts assessed using impulse response functions (IRFs) that incorporate climate-carbon feedback.
Our results reveal that China currently holds 3.6 ± 0.1 Gt CO2-eq of fluorocarbon banks, which are projected to peak at 4.5 ± 0.1 Gt CO2-eq by 2034. If unmanaged, emissions from these banks could contribute an additional 0.014℃ to global warming by mid-century. FLM, however, could prevent up to 8.0 Gt CO2-eq of cumulative emissions by 2060, reducing the peak temperature increase contribution by 62.4%. Notably, 57 out of 76 mitigation options analyzed exhibit average abatement costs below 10 USD/t CO2-eq, enabling 93.2% of the maximum mitigation potential at a total cost of 18.9 billion USD. These cost-effective measures could deliver additional mitigation equivalent to over 50% of the 13 Gt CO2-eq reductions pledged under the Kigali Amendment in China, or reduce the surface warming contribution of global HFC emissions in 2050 by more than 10%.
This study introduces a robust framework for assessing the costs and benefits of FLM. By applying it to China, we demonstrate the significant mitigation scale and feasibility of national-level implementation. Our findings highlight the substantial and cost-effective climate benefits achievable through FLM, offering policymakers an actionable pathway to bridge the emission gap and echoing recent international calls for immediate action
Measuring the experts’ perception about the suitability of natural disaster risk mitigation solutions using minimal risk assessment information, a Multi-Criteria Decision Analysis approach
Full text linkFinding a sustainable solution to disaster risk mitigation needs to consider different aspects of the disaster’s impact along with social, economic, and physical characteristics of the region. In this regard, a desirable solution for disaster risk mitigation for a region is the one tailored to the local characteristics. These local characteristics not only help measure the different aspects of a disaster impact but also portray existing pressing issues as priorities. While the former can be modeled using risk and resilience assessment models, the latter can be measured from experts’ points of view. Ultimately, the combination of the expert’s perception on important issues and the output of risk and resilience assessment models can be used to evaluate the optimality of each disaster risk mitigation solution.
In this research, a Multi-Criteria Decision Analysis (MCDA) framework is developed to provide an evaluation of each disaster risk mitigation. The developed framework is designed to be able to run on the action-outcome results from risk and resilience assessment models and the cardinal ranking of the decision criteria, representing decision-makers’ expert opinion on the priorities in mitigating and managing disaster risk. The developed MCDA framework is very practical as it can run on action-outcome results, and these results are accessible from a large variety of risk and resilience assessment models. Furthermore, the developed MCDA framework takes into account the uncertainty in the risk and resilience assessment models. In compatibility with running on minimal available information, the MCDA’s decision model is simplified to one layer with a single layer of the decision criteria.
Additionally, as the number of competing mitigation solutions might increase rapidly in practice, the MCDA framework is developed to handle a huge number of alternatives more efficiently and with relatively limited computational resources. The MCDA framework is developed based on the CAR method of eliciting the preferences among mitigation alternatives. The final results evaluate the competing disaster risk mitigation solution based on available data (as processed by risk and resilience assessment models) and the expert’s opinion on important issues and their preferences on the important aspects of disaster impact. As such, the final results provide an estimation of the expert’s belief on the desirability of each of the disaster risk mitigation solutions.
This MCDA framework is developed as part of the Horizon Europe project MEDiate (Multi-hazard and risk-informed system for Enhanced local and regional Disaster risk management). This project is dedicated to creating a decision-support system (DSS) for disaster risk management that not only takes into account the complexities of multiple interacting natural hazards but also tailors the final solution to the characteristics, priorities, and concerns of the local communities and decision-makers. The MEDiate framework is implemented on four different testbeds (Oslo (Norway), Nice (France), Essex (UK), and Múlaþing (Iceland)), each of which has a different multi-hazard pair and different socio-economic characteristics. The deployment of the developed MCDA framework on different natural hazards and socio-economic characteristics shows its flexible practicality
Co-Creating a Safe Operating Space Framework for Water Resources: Insights from the Danube Basin case study
Full text linkSignificant increases in water withdrawals over the past century have driven severe environmental challenges worldwide, including water scarcity, declining water quality, and the loss of freshwater biodiversity. These challenges are projected to intensify due to climate and societal changes in the coming decades. To address these issues, it is critical to define a Safe Operating Space (SOS) for water resources that ensures a sustainable and adequate water supply, meeting quality standards for both human needs and natural ecosystems.
Building on the Planetary Boundaries framework, the concept of Safe Operating Space (SOS) has emerged in the last decades to assess sustainable resource use within the Earth’s carrying capacity while maintaining human well-being. Within the Horizon Europe SOS-Water project, we are working to define the SOS for the entire water resources using in an integrated approach incorporating modelling, monitoring, development of advanced indicators and inclusive stakeholder engagement based on true collaboration. SOS-Water works with stakeholders in four case studies in Europe and overseas (Danube, Rhine, Jucar and Mekong basins) to co-create future scenarios and management pathways.
The results of SOS-Water will improve knowledge of water resource availability and improve water planning and management at local, regional and global levels. This will ensure equitable water distribution across societies, economies, and ecosystems, fostering resilience, social equity, and economic efficiency.
This proposed talk will showcase the application of the SOS-Water framework to the Danube Basin, with a focus on its inclusive and iterative participatory approach which actively engages stakeholders in co-defining visions, water values, and management options. We will present insights from the first stakeholder workshop, showcasing how these contributions shaped the preliminary SOS framework for the basin. Additionally, we will outline how this co-creation process will continue to define adaptation pathways and guide sustainable water management practices to address critical water challenges in the Danube Basin
Projecting Forest Fire Probability in South Korea under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net)
Full text linkClimate change-induced heat waves and densely forested areas near urban centers in South Korea create complex challenges for wildfire response systems. Various forest fire models have been developed to address this, each with unique strengths and weaknesses. Process-based models offer high interpretability through human domain knowledge but require extensive optimization, while machine learning models automatically identify important features but have limited interpretability. To leverage the strengths of both models, this study aimed to integrate human domain knowledge into a machine learning framework. IIASA's wildfire cLimate impacts and Adaptation Model (FLAM)—a process-based model incorporating biophysical and human impacts—was developed as a neural network called FLAM-Net. Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. FLAM-Net was applied at multiple scales and integrated through U-Net-based architecture, named Deep Neural FLAM (DN-FLAM), to produce downscaled predictions. The optimization revealed spatial concentration of fires near metropolitan areas and the east coast, with temporal concentration in spring due to agricultural burning. Integration of multi-scale features through DN-FLAM achieved optimal performance with Pearson's r values of 0.943, 0.840, and 0.641 for temporal, spatial, and spatio-temporal validations. Future projections based on Shared Socioeconomic Pathways (SSP) indicated increasing fire frequencies until 2050, followed by a decrease due to increased precipitation. This study demonstrates the benefits of the hybrid approach, providing interpretability, accuracy, and efficient optimization. These hybrid models offer scientific evidence to guide locally tailored decision-making for climate change-induced forest fires and lay the groundwork for global application through their optimization capabilities
An oversampling-undersampling strategy for large-scale data linkage
Full text linkEffective record linkage in big data, particularly in imbalanced datasets, is a critical yet highly challenging task due to the inherent complexity involved. This article utilizes an oversampling-undersampling strategy to address linkage imbalances, enabling more accurate and efficient record linkage within large-scale datasets. It tries to increase the instances of the minority class and decrease the dominance of the majority classes to try to reach a more balanced dataset that can be used for training and testing. Sensitivity testing was carried out by varying the training-test ratio and degree of imbalance
A geospatial perspective on electrification strategy in urbanizing Africa
Full text linkEfforts to achieve Sustainable Development Goal (SDG) 7, to ensure modern energy for all, have largely followed models of rural electrification premised on extending the provision of electricity to remote, low-income populations. Yet, urbanization in Africa has produced complex and densifying human settlement patterns with diverse economic and energetic needs. Much of the body of work supporting SDG 7 relies on a binary rural-urban categorization and has yet to engage critically with the increasing spatial, demographic, and economic heterogeneity of these spaces. This analysis uses geospatial techniques to evaluate the distribution of the unelectrified in sub-Saharan Africa along a 30-category spatial framework which describes space along a rural-urban continuum. Our results highlight large concentrations of unelectrified people in the peripheries of small to medium cities, which themselves are often poorly electrified. More sophisticated ways of understanding the spatiality of electrification can provide strategic insights on how we assess the needs and barriers to access for diverse communities, select and innovate appropriate technologies and solutions, and define effective jurisdictions for government institutions
Modeling supports the implementation of urban greening as a response to the challenges of sustainable spatial planning
No full textUrban greening is critical for sustainable urban development, climate change mitigation, and biodiversity conservation. However, the effectiveness of urban greening varies depending on the specific goals (e.g., enhancing biodiversity, reducing urban heat, or both) and their spatial implementation. To address the spatial variability in the effectiveness of greening, we propose a spatial decision support model based on the non-dominated sorting genetic algorithm-II (NSGA-II). This model aims to optimize urban greening locations to maximize biomass density, mitigate urban heat stress, and improve landscape connectivity. Applied to Suwon City, South Korea, the model's effectiveness was evaluated against a business-as-usual (BAU) scenario across four scenarios: connectivity-based, biomass density-based, heat stress-based, and an integrated-based scenario. The integrated approach, balancing trade-offs between ecological benefits and implementation costs, outperformed the BAU scenario by 8.84 %. Despite highlighting a weaker correlation with heat stress mitigation, this outcome indicates significant improvements in biomass density and landscape connectivity. Our findings underscore the necessity of an integrated planning approach to urban greening, as it can contribute toward attaining urban development goals. Additionally, by proposing an app-based model for policymakers, our outputs should enable the reconciliation of multiple environmental objectives in urban landscapes
Assessing progress in monitoring and implementing the EU biodiversity strategy for 2030
Full text linkThe aim of this science for policy report is twofold. First, it presents the state of play and the next steps in developing a monitoring framework for the EU Biodiversity Strategy for 2030 (EU BDS). Second, it provides an overview of progress made in implementing the EU BDS to date, as well as an assessment of the likelihood of reaching its targets by 2030. It mobilises various data sources – the official EU BDS and other policy-relevant progress monitoring tools, scientific literature and expert opinions – to provide a state of play of key achievements and remaining gaps in both monitoring and implementing the EU BDS as we approach its mid-term mark. Almost half of the actions are completed; the remaining half are mostly in progress, and a few are delayed. Indicators are published to track progress towards more than 40 % of the EU BDS targets and, with the notable exception of those on the state of biodiversity, the EU is showing progress in the right direction towards most of the evaluated targets; however, the pace of progress needs to accelerate massively to reach the 2030 targets. Further effort and engagement with the scientific community is needed to fill the remaining monitoring gaps, while a better implementation of the environmental policies would be necessary to meet the maximum of targets by 2030
Evolution of innovation and production supply chains: the case of microalgae-based β-carotene
Full text linkAbstract
Establishing new bio-based sectors requires effective implementation of innovation and production supply chains, often competing with established synthetic technologies. Our analytical model conceptualizes the competition between an incumbent industry and a competitive fringe, each producing differentiated products. Although motivated by the β-carotene case, the model is versatile and applicable to other contexts involving novel products entering markets dominated by established technologies. Developed by university researchers and commercialized by start-ups, natural β-carotene was eventually integrated into major synthetic corporations. Initially niche and costly, it gained market competitiveness through innovation and expanded applications, driving technological advancements and significantly benefiting the broader algae-based industry
