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Impact pathways:geo-operations for turning plastic waste into carbon capture
No full textPurpose – We explore how an operations and supply chain approach to geo-engineering can enhance circular economy approaches and mitigate climate change. We illustrate how such geo-operations – specifically the combination of plastics and biowaste processing – can be systematically leveraged for carbon capture. Design/methodology/approach – The study applies production theory and operations management perspectives to develop a carbon transfer model. It traces carbon flows through the extended plastics supply chain and interconnected natural systems, from raw material inputs, through production and reuse cycles, to the ultimate disposal. By mapping carbon transfers between natural systems and artificial systems, the framework highlights the systemic impact pathways for operations and supply chain management. Findings – Single interventions such as bio-based materials, chemical recycling or policy instruments have limited impact in isolation. However, when combined systemically, these individual solutions can form geo-engineering operational pathways that draw out atmospheric carbon and refossilize it, thus transforming the plastics technosphere from a source of emissions to a means for carbon capture. Research limitations/implications – The study is conceptual and develops theoretical propositions on systemic impact, rather than presenting empirical findings. Future research should empirically investigate the feasibility, scale and trade-offs of the proposed geo-operations pathways. Practical implications – The carbon transfer model and impact pathways guide policymakers, producers and waste managers on integrating the circular economy and geo-operations for climate change mitigation and carbon capture. Social implications – By reframing plastics not only as a source of problematic waste but also as a possible vehicle for climate mitigation, the paper suggests new opportunities and responsibilities for industry and society. Originality/value – This paper proposes the development of geo-operations as a systemic pathway for integrating circular economy and carbon sequestration interventions. It also presents a framework to assess the impact of combinations of interventions on carbon flows.</p
Deep spatio-temporal learning for multi-hazard events: A ConvGRU multi-label classification approach
No full textThe forecasting of multi-hazards is a vital, though underinvestigated, area of disaster risk management. The traditional studies have mainly focused on single-hazard forecasting, thus leaving its utility in real-world and realistic scenarios. This study, in turn, presents a spatio-temporal multi-label classification model, a framework designed expressly to capture the complex interrelationships between a range of hazards. The methodological framework used disaster occurrence data from the Open Federal Emergency Management Agency (OpenFEMA) database and converted the raw records of disasters into a multi-label dataset. Pressure-level reanalysis data is extracted from Climate Data Store (CDS) based on the multi-hazard event. Spatial data is extracted in 25 59 grid format in different temporal dependencies (12 h, 8 h, 6 h) at the 850 hPa pressure level. The model architecture combines convolutional neural networks (CNNs) with spatial attention mechanisms and gated recurrent units (GRUs) that model the temporal sequences. This combination enables multi-hazard predictions by utilizing the spatial and temporal data. Experimental analysis reveals that the proposed model outperformed the baseline variants, i.e., 2D CNN, Convolutional Long Short-Term Memory (ConvLSTM), and Convolutional Gated Recurrent Unit (ConvGRU) without attention. The proposed model achieved per-class accuracy up to 0.8868, the subset accuracy is 0.55, and the Hamming loss up to 0.127, which are 3.88%, 13.59% and 21.12% performance improvements over the baseline models respectively. In addition, the use of various lead times and the fusion of multiple lead times (12 h+8 h+6 h) significantly improves the predictive capability. The proposed framework has high potential for disaster preparedness and early warning systems in the real world. It proposes a flexible and efficient method of dealing with the growing complexity of multi-hazard environments
