Big Earth Data in Support of the Sustainable Development Goals (2022) - China : Utilizing Technology for Global Impact / Huadong Guo.

1. Introduction -- References -- 2. SDG 2 Zero Hunger -- 2.1. Background -- 2.2. Main Contributions -- 2.3. Case Studies -- 2.3.1. Monitoring and Assessing Saline-Alkali Land Improvement and Utilization in Western Jilin Province -- 2.3.1.1. Background -- 2.3.1.2. Data -- 2.3.1.3. Methods -- 2.3.1.4. Results and Analysis -- Temporal Variation in the Saline-Alkali Land Area in Western Jilin Province -- Analysis of the Change Rate of Saline-Alkali Land -- Analysis of Saline-Alkali Land and Land Use Conversion -- Influencing Factors of Saline-Alkali Land Area -- 2.3.1.5. Discussion and Outlook -- 2.3.2. Coupling Driving Relationship Between Spatiotemporal Change in Cultivated Soil Organic Carbon and Crop Yield in the Beijing-Tianjin-Hebei Urban Agglomeration -- 2.3.2.1. Background -- 2.3.2.2. Data -- 2.3.2.3. Methods -- 2.3.2.4. Results and Analysis -- Temporal and Spatial Changes in the Organic Carbon Content in Cultivated Soil Surfaces -- Temporal and Spatial Changes in the Grain Output in the Beijing-Tianjin-Hebei Urban Agglomeration -- Coupling Analysis of Topsoil Organic Carbon and Crop Yield in the Beijing-Tianjin-Hebei Urban Agglomeration -- Analysis of the Strategy of the Synergistic Promotion of the Soil Organic Carbon Content and Grain Yield—Taking Quzhou as an Example -- 2.3.2.5. Discussion and Outlook -- 2.3.3. Assessment of Potential for Cropland Carbon Sequestration in China Under Global Change -- 2.3.3.1. Background -- 2.3.3.2. Data -- 2.3.3.3. Methods -- 2.3.3.4. Results and Analysis -- Simulation of Current Changes in Cropland Soil Organic Carbon Density -- Future Cropland Carbon Sink Projections Under the Baseline Management Scenario -- 2.3.3.5. Discussion and Outlook -- 2.3.4. Temporal and Spatial Variations in Carbon Emissions for Cropping Systems in China -- 2.3.4.1. Background -- 2.3.4.2. Data -- 2.3.4.3. Methods -- 2.3.4.4. Results and Analysis -- 2.3.4.5. Discussion and Outlook -- 2.3.5. Food Loss and Waste and Its Reduction Pathways in China -- 2.3.5.1. Background -- 2.3.5.2. Data -- 2.3.5.3. Methods -- 2.3.5.4. Results and Analysis -- 2.3.5.5. Discussion and Outlook -- 2.4. Summary -- References -- 3. SDG 6 Clean Water and Sanitation -- 3.1. Background -- 3.2. Main Contributions -- 3.3. Case Studies -- 3.3.1. Water Quality Monitoring and Assessment of Drinking Water Sources in China -- 3.3.1.1. Background -- 3.3.1.2. Data -- 3.3.1.3. Methods -- 3.3.1.4. Results and Analysis -- Distribution of Online Monitoring Stations for Water Quality at Drinking Water Sources -- Spatial Clustering Analysis of Water Quality in Drinking Water Sources -- Analysis of Changes in Water Quality Monitoring Indicators for Drinking Water Sources -- Water Quality and Safety Evaluation Results of Drinking Water Sources -- 3.3.1.5. Discussion and Outlook -- 3.3.2. Proportion of Water Bodies with Good Ambient Water Quality in China’s Provinces and Change Assessment in 2015 and 2020 -- 3.3.2.1. Background -- 3.3.2.2. Data -- 3.3.2.3. Methods -- Surface Water Quality Evaluation Method -- Assessment Methods of Groundwater Quality -- 3.3.2.4. Results and Analysis -- Inter-Provincial Comparison of Surface Water Bodies with Good Quality -- Spatial Distribution of Surface Water Bodies with Good Quality in All Provinces of China -- Changes in the Proportion of Surface Water Bodies with Good Quality in All Provinces of China from 2015 to 2020 -- General Groundwater Quality of China from 2019 to 2021 -- General Groundwater Quality of All Provinces from 2019 to 2021 -- Policy Recommendations -- 3.3.2.5. Discussion and Outlook -- 3.3.3. Assessment of Change in Water Use Efficiency of Three Major Grain Crops in China -- 3.3.3.1. Background -- 3.3.3.2. Data -- 3.3.3.3. Methods -- 3.3.3.4. Results and Analysis -- 3.3.3.5. Discussion and Outlook -- 3.3.4. Changes and Drivers of Water Stress in China from 2010 to 2030 -- 3.3.4.1. Background -- 3.3.4.2. Data -- 3.3.4.3. Methods -- 3.3.4.4. Results and Analysis -- Changes in Water Stress Levels and Climate Drivers in China from 2010 to 2020 -- Seasonal Variations in Water Stress in China and the Impact of Droughts -- Drivers of Water Use Changes in China -- Simulation of China’s Water Stress Levels from 2020 to 2030 Under Multiple Scenarios -- 3.3.4.5. Discussion and Outlook -- 3.3.5. Assessment of Data Supporting Capacity for Provincial Integrated Water Resources Management in China -- 3.3.5.1. Background -- 3.3.5.2. Data -- 3.3.5.3. Methods -- 3.3.5.4. Results and Analysis -- Data Supporting Capacity for IWRM in China -- Data Supporting Capacity for Provincial IWRM -- 3.3.5.5. Discussion and Outlook -- 3.3.6. Comprehensive Assessment of China’s SDG 6 Progress from 2015 to 2020 -- 3.3.6.1. Background -- 3.3.6.2. Data -- 3.3.6.3. Methods -- 3.3.6.4. Results and Analysis -- 3.3.6.5. Discussion and Outlook -- 3.4. Summary -- References -- 4. SDG 7 Affordable and Clean Energy -- 4.1. Background -- 4.2. Main Contributions -- 4.3. Case Studies -- 4.3.1. Construction and Spatiotemporal Distribution Analysis of PV Power Stations in China -- 4.3.1.1. Background -- 4.3.1.2. Data -- 4.3.1.3. Methods -- 4.3.1.4. Results and Analysis -- Distribution of China’s PV Power Plants -- Spatiotemporal Distribution Changes of China’s PV Power Plants -- 4.3.1.5. Discussion and Outlook -- 4.3.2. Spatiotemporal Monitoring and Analysis of China’s High Energy-Consuming Industries -- 4.3.2.1. Background -- 4.3.2.2. Data -- 4.3.2.3. Methods -- 4.3.2.4. Results and Analysis -- Spatial Distribution Pattern of China’s High Energy-Consuming Industries -- Spatiotemporal Distribution Characteristics of China’s High Energy-Consuming Industries -- In 2021, the Energy Consumption Per Unit of GDP in China Decreased by One-Fifth Compared to 2014, Making an Important Contribution to Global Energy Efficiency -- 4.3.2.5. Discussion and Outlook -- 4.4. Summary -- References -- 5. SDG 11 Sustainable Cities and Communities -- 5.1. Background -- 5.2. Main Contributions -- 5.3. Case Studies -- 5.3.1. Changes in Urban Land Use Efficiency in China -- 5.3.1.1. Background -- 5.3.1.2. Data -- 5.3.1.3. Methods -- 5.3.1.4. Results and Analysis -- High-Precision Mapping of Urban Built-Up Areas -- LCRPGR -- BPC -- 5.3.1.5. Discussion and

This open access book showcases the innovative practices of Big Earth Data methods through a collection of comprehensive case studies from China to monitor and evaluate indicators for seven SDGs, i.e., zero hunger (SDG 2), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), climate action (SDG 13), life below water (SDG 14), life on land (SDG 15), and to analyze the interactions among multiple SDGs indicators.The emphasis on Big Earth Data is highly relevant within the context of growing global challenges. Disaster risk mitigation, climate change, global food security, resource management, and environmental challenges all are interlinked through earth systems and processes that are independent of human constructs. Therefore, these case studies highlight methods and practices of spatial information mining and integrated SDG evaluation, which include evaluating the synergy and trade-off relationships among the SDGs in the context of their correlations; simulating multiple indicators' interactions in future environmental, economic and social scenarios in the context of their temporal variations; designing integrated evaluations of regional SDGs in the context of experience with the study of multiple indicators. Big Earth Data therefore has the potential to support informed policy and decision support at global, regional, and local scales.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.