
Climate change, driven by escalating atmospheric carbon dioxide (CO₂) concentrations now exceeding 420 ppm, represents the most profound challenge to global stability. This interdisciplinary paper introduces a novel philosophical-numerical framework—AquaNumerica—for understanding and advancing carbon sequestration and emission reduction technologies. Drawing on recent advancements in Carbon Capture, Utilization, and Storage (CCUS) including direct air capture (DAC), bioenergy with carbon capture and storage (BECCS), biochar production, and mineral carbonation, we demonstrate how the foundational codes 3, 7, and 12 provide a unified language for designing resilient carbon management systems. The framework integrates empirical data from emerging technologies with philosophical reflection on humanity's relationship with carbon, proposing that effective climate action requires not only technological innovation but also a fundamental shift in how we conceptualize carbon—from waste to asset, from problem to opportunity. By examining synergies between material science, artificial intelligence, and numerical philosophy, this paper offers a holistic pathway toward achieving net-zero emissions and long-term climate resilience.
Carbon Capture, Carbon Sequestration, CCUS, Climate Change, AquaNumerica, Codes 3 7 12, Emission Reduction, Direct Air Capture, Biochar, Sustainable Development, SDG 13, Net Zero, CO2 Removal, Carbon Engineering, BECCS, Carbon Mineralization, Climate Resilience, Paris Agreement, Greenhouse Gas, Carbon Cycle
Carbon Capture, Carbon Sequestration, CCUS, Climate Change, AquaNumerica, Codes 3 7 12, Emission Reduction, Direct Air Capture, Biochar, Sustainable Development, SDG 13, Net Zero, CO2 Removal, Carbon Engineering, BECCS, Carbon Mineralization, Climate Resilience, Paris Agreement, Greenhouse Gas, Carbon Cycle
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
