Scholarlink

The climate crisis requires new ways to combine technological progress with ecologically responsible practices. This chapter presents a novel framework for carbon management as a water system and artificial intelligence (AI) as its “hydrological engineer” to optimize the flow, storage,  and purification of carbon emissions to the atmosphere. This framework by way of AI, environmental science, and economics correlates with three pillars: (1) real-time data lakes and IoT networks enabling dynamic carbon accounting, (2) predictive algorithms optimizing adaptive emissions trading systems, and (3) geospatial AI and blockchain transparency facilitating precision-driven carbon offset projects. The approach is system-oriented and regards carbon as a circulative resource—it carries emissions from sources to natural and engineered sinks. Although the framework provides scalable approaches to net-zero transitions—like AI-optimized reforestation and fraud-resistant carbon markets—it also poses ethical dilemmas like algorithmic bias, data sovereignty, and equity in climate governance. This work highlights the importance of interdisciplinary collaboration, inclusion in design,  and green AI innovations to create an intersection between technological potential and planetary boundaries. In the end, this hydrological roadmap is a provocation to stakeholders to deconstruct carbon from waste to resource, a thing to be depleted from the Earth goddess’s regenerative cycles.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Data Lakes to Carbon Sinks: AI’s Hydrological Approach to Emissions

  • 章节

  • 发表日期：

    2025年

查看全文
  • Lê Nguyên Bảo,
  • Subhankar Das,
  • Subhra R. Mondal

摘要

The climate crisis requires new ways to combine technological progress with ecologically responsible practices. This chapter presents a novel framework for carbon management as a water system and artificial intelligence (AI) as its “hydrological engineer” to optimize the flow, storage,  and purification of carbon emissions to the atmosphere. This framework by way of AI, environmental science, and economics correlates with three pillars: (1) real-time data lakes and IoT networks enabling dynamic carbon accounting, (2) predictive algorithms optimizing adaptive emissions trading systems, and (3) geospatial AI and blockchain transparency facilitating precision-driven carbon offset projects. The approach is system-oriented and regards carbon as a circulative resource—it carries emissions from sources to natural and engineered sinks. Although the framework provides scalable approaches to net-zero transitions—like AI-optimized reforestation and fraud-resistant carbon markets—it also poses ethical dilemmas like algorithmic bias, data sovereignty, and equity in climate governance. This work highlights the importance of interdisciplinary collaboration, inclusion in design,  and green AI innovations to create an intersection between technological potential and planetary boundaries. In the end, this hydrological roadmap is a provocation to stakeholders to deconstruct carbon from waste to resource, a thing to be depleted from the Earth goddess’s regenerative cycles.