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010   $a981-9742-31-5
024 7 $a10.1007/978-981-97-4231-8
035   $a(CKB)36328512800041
035   $a(MiAaPQ)EBC31727508
035   $a(Au-PeEL)EBL31727508
035   $a(DE-He213)978-981-97-4231-8
035   $a(EXLCZ)9936328512800041
100   $a20241005d2024      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBig Earth Data in Support of the Sustainable Development Goals (2022) - China /$fby Huadong Guo
205   $a1st ed. 2024.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024.
215   $a1 online resource (314 pages)
225 1 $aSustainable Development Goals Series,$x2523-3092
311 08$a981-9742-30-7 
327   $a -- Introduction.  -- SDG 2 Zero Hunger.  -- SDG 6 Clean Water and Sanitation.  -- SDG 7 Affordable and Clean Energy.  -- SDG 11 Sustainable Cities and Communities.  -- SDG 13 Climate Action.  -- SDG 14 Life Below Water.  -- SDG 15 Life on Land.  -- Interactions Among the SDGs and Integrated Evaluations.  -- Summary and Prospects.
330   $aThis open access book showcases the innovative practices of Big Earth Data methods through a collection of comprehensive case studies from China to monitor and evaluate indicators for seven SDGs, i.e., zero hunger (SDG 2), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), climate action (SDG 13), life below water (SDG 14), life on land (SDG 15), and to analyze the interactions among multiple SDGs indicators. The emphasis on Big Earth Data is highly relevant within the context of growing global challenges. Disaster risk mitigation, climate change, global food security, resource management, and environmental challenges all are interlinked through earth systems and processes that are independent of human constructs. Therefore, these case studies highlight methods and practices of spatial information mining and integrated SDG evaluation, which include evaluating the synergy and trade-off relationships among the SDGs in the context of their correlations; simulating multiple indicators? interactions in future environmental, economic and social scenarios in the context of their temporal variations; designing integrated evaluations of regional SDGs in the context of experience with the study of multiple indicators. Big Earth Data therefore has the potential to support informed policy and decision support at global, regional, and local scales.
410  0$aSustainable Development Goals Series,$x2523-3092
606   $aSustainability
606   $aEnergy policy
606   $aEnergy policy
606   $aPhysical geography
606   $aGeographic information systems
606   $aSustainability
606   $aEnergy Policy, Economics and Management
606   $aEarth System Sciences
606   $aGeographical Information System
615  0$aSustainability.
615  0$aEnergy policy.
615  0$aEnergy policy.
615  0$aPhysical geography.
615  0$aGeographic information systems.
615 14$aSustainability.
615 24$aEnergy Policy, Economics and Management.
615 24$aEarth System Sciences.
615 24$aGeographical Information System.
676   $a304.2
700   $aGuo$b Huadong$0993478
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910896188703321
996   $aBig Earth Data in Support of the Sustainable Development Goals (2022) - China$94383333
997   $aUNINA