Enabling conditions for an equitable and sustainable blue economy
收藏DataONE2021-11-29 更新2024-06-08 收录
下载链接:
https://search.dataone.org/view/sha256:f9e82883e4230fb763985d32319af3c4806e4b6a387dd4c60d278e1b9e020a28
下载链接
链接失效反馈官方服务:
资源简介:
AbstractThe future of the global ocean economy is currently envisioned as an advancement towards a ‘Blue Economy’—socially equitable, environmentally sustainable, and economically viable ocean industries. However, there are current tensions between development discourses from perspectives of natural capital versus social equity and environmental justice. Here we show there are stark differences in Blue Economy outlooks when social conditions and governance capacity beyond resource availability are considered, and highlight limits to establishing multiple overlapping industries. The key differences in regional capacities to achieve a Blue Economy are not due to available natural resources, but include factors such as national stability, corruption, and infrastructure, that can be improved through targeted investments and cross-scale cooperation. Knowledge gaps can be addressed by integrating historical natural and social science information on the drivers and outcomes of resource use and management, thus identifying equitable pathways to establishing or transforming ocean sectors. Policy-makers must engage researchers and stakeholders to promote evidence-based, collaborative planning that ensures that sectors are chosen carefully, local benefits are prioritized, and the Blue Economy delivers on its social, environmental, and economic goals. , MethodsThis dataset presents all results necessary to reproduce the figures and analysis in the corresponding peer-reviewed article. All input data are also included, but any use must give credit to their original authors and sources; we strongly urge users to personally contact corresponding authors. These are specifically noted in the Supplementary Information 3 file of our peer-reviewed article, and include: Hutchison J, Manica A, Swetnam R, Balmford A, Spalding M (2014) Predicting global patterns in mangrove forest biomass. Conservation Letters 7(3): 233–240. http://data.unep-wcmc.org/datasets/39 McOwen C, Weatherdon LV, Bochove J, Sullivan E, Blyth S, Zockler C, Stanwell- Smith D, Kingston N, Martin CS, Spalding M, Fletcher S (2017). A global map of saltmarshes. Biodiversity Data Journal 5: e11764. http://data.unep-wcmc.org/datasets/43 UNEP-WCMC, Short FT (2016). Global distribution of seagrasses (version 4.0). Fourth update to the data layer used in Green and Short (2003). Cambridge (UK): UNEP World Conservation Monitoring Centre. http://data.unep-wcmc.org/datasets/7 Zheng, C.-W., and Pan, J. 2014. Assessment of the global ocean wind energy resource. Renewable and Sustainable Energy Reviews 33: 382–391. doi:10.1016/j.rser.2014.01.065. Bonjean F. and G.S.E. Lagerloef, 2002 , Diagnostic model and analysis of the surface currents in the tropical Pacific ocean, J. Phys. Oceanogr., 32, 2,938-2,954 https://podaac.jpl.nasa.gov/dataset/OSCAR_L4_OC_third-deg General Bathymetric Chart of the Oceans (GEBCO). https://www.bodc.ac.uk/data/documents/nodb/301801/ Wessel, P., and W. H. F. Smith. 1996. A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. World Tourism Organization (UNWTO). 2018. Yearbook of tourism statistics. Data 2012-2016. UNWTO, Madrid. DOI: https://doi.org/10.18111/9789284419531 Gagné, T. O., Reygondeau, G., Jenkins, C. N., Sexton, J. O., Bograd, S. J., Hazen, E. L., & Van Houtan, K. S. 2020. Towards a global understanding of the drivers of marine and terrestrial biodiversity. PloS one, 15(2), e0228065. Reygondeau, G. 2019. Current and future biogeography of exploited marine exploited groups under climate change. In: Predicting Future Oceans (pp. 87-101). Elsevier. Cheung, William W. L., Vicky W. Y. Lam, Jorge L. Sarmiento, Kelly Kearney, Reg Watson, Dirk Zeller, and Daniel Pauly. 2010. “Large-Scale Redistribution of Maximum Fisheries Catch Potential in the Global Ocean under Climate Change.” Global Change Biology 16 (1): 24–35. doi:10.1111/j.1365-2486.2009.01995.x Oyinlola, M.A., Reygondeau, G., Wabnitz, C.C., Troell, M., and Cheung, W.W. 2018. Global estimation of areas with suitable environmental conditions for mariculture species. PLoS One 13(1): e0191086. The Fund For Peace (FFP). 2018. Fragile States Index. https://fragilestatesindex.org/2018/04/24/fragile-states-index-2018-annual-report/ United Nations Development Programme (UNDP). 2017. Gender Inequality Index. http://hdr.undp.org/en/content/gender-inequality-index-gii Daniel Kaufmann, Aart Kraay and Massimo Mastruzzi. 2010. The Worldwide Governance Indicators: A Summary of Methodology, Data and Analytical Issues. World Bank Policy Research Working Paper No. 5430. www.govindicators.org World Bank. 2018. DataBank. https://databank.worldbank.org/data/home Halpern et al. 2012. An index to assess the health and benefits of the global ocean. Nature 488(7413): 615–620. doi:10.1038/nature11397. , Usage notesThese data are results of an analysis at the global and regional level for an academic paper, and should not be used for other geographic scales or purposes. Assumptions, indicators, data sources, and weighting of indicators must be specifically discussed and selected in context for results to be meaningful. Please contact the corresponding author (a.cisneros@oceans.ubc.ca) if you have any questions.
摘要 当前全球海洋经济的未来愿景被定位为向“蓝色经济(Blue Economy)”的进阶路径——即兼具社会公平性、环境可持续性与经济可行性的海洋产业体系。然而当前自然资本视角下的发展话语,与社会公平、环境正义视角下的发展话语之间存在显著张力。本研究表明,当考虑资源可得性之外的社会条件与治理能力时,蓝色经济的发展前景存在显著差异,并凸显了多重重叠产业布局的局限性。各区域实现蓝色经济的核心能力差异并非源于可获取的自然资源,而是取决于国家稳定性、腐败状况、基础设施建设等因素,此类因素可通过针对性投资与跨尺度合作得到改善。填补知识缺口可通过整合有关资源利用与管理的驱动因素及结果的历史自然科学与社会科学信息实现,以此识别建立或转型海洋产业部门的公平路径。政策制定者应联合研究人员与利益相关方,推动循证协作式规划,确保审慎选择产业部门、优先保障本地收益,并让蓝色经济切实达成其社会、环境与经济目标。
方法 本数据集包含复现该同行评议论文中所有图表与分析所需的全部结果,同时附带所有输入数据。任何使用行为均需注明原始作者与数据来源;我们强烈建议用户直接联系通讯作者。相关要求已在本论文的补充材料3文件中明确说明,涉及的数据源如下:
1. 哈奇森 J、马尼卡 A、斯威特南 R、巴姆福德 A、斯波尔丁 M(2014)《预测全球红树林生物量分布格局》,《保护通讯》7(3): 233–240。http://data.unep-wcmc.org/datasets/39
2. 麦克欧文 C、韦瑟登 LV、博霍夫 J、沙利文 E、布莱斯 S、佐克勒 C、斯坦韦尔-史密斯 D、金斯顿 N、马丁 CS、斯波尔丁 M、弗莱彻 S(2017)《全球盐沼分布图》,《生物多样性数据期刊》5: e11764。http://data.unep-wcmc.org/datasets/43
3. 联合国环境规划署世界保护监测中心(UNEP-WCMC)、肖特 FT(2016)《全球海草分布(第4.0版)》,为格林与肖特(2003)所用数据图层的第四次更新,英国剑桥:联合国环境规划署世界保护监测中心。http://data.unep-wcmc.org/datasets/7
4. 郑 C-W、潘 J(2014)《全球海洋风能资源评估》,《可再生与可持续能源评论》33: 382–391。DOI:10.1016/j.rser.2014.01.065
5. 邦让 F、拉格洛夫 GSE(2002)《热带太平洋表层海流诊断模型与分析》,《物理海洋学杂志》32: 2938–2954。https://podaac.jpl.nasa.gov/dataset/OSCAR_L4_OC_third-deg
6. 全球海洋水深测量图(GEBCO)。https://www.bodc.ac.uk/data/documents/nodb/301801/
7. 韦塞尔 P、史密斯 WHF(1996)《全球一致性、分层高分辨率海岸线数据库》,《地球物理学研究杂志》101(B4): 8741–8743。DOI:10.1029/96JB00104
8. 世界旅游组织(UNWTO)(2018)《旅游统计年鉴:2012-2016年数据》,西班牙马德里:世界旅游组织。DOI: https://doi.org/10.18111/9789284419531
9. 加涅 TO、雷贡多 G、詹金斯 CN、塞克斯顿 JO、博格拉德 SJ、黑曾 EL、范胡坦 KS(2020)《迈向全球认知海洋与陆地生物多样性驱动因素》,《公共科学图书馆·综合》15(2): e0228065
10. 雷贡多 G(2019)《气候变化下开发利用海洋类群的当前与未来生物地理学》,载于《预测未来海洋》(第87-101页),爱思唯尔出版社
11. 周 WWL、林 VWY、萨米恩托 JL、基尼 K、沃森 R、策勒 D、保利 D(2010)《气候变化下全球海洋最大渔业捕捞潜力的大规模再分布》,《全球变化生物学》16(1):24–35。DOI:10.1111/j.1365-2486.2009.01995.x
12. 奥因洛拉 MA、雷贡多 G、瓦布尼茨 CC、特罗埃尔 M、周 WW(2018)《全球海水养殖物种适宜环境区域评估》,《公共科学图书馆·综合》13(1): e0191086
13. 和平基金会(FFP)(2018)《脆弱国家指数》。https://fragilestatesindex.org/2018/04/24/fragile-states-index-2018-annual-report/
14. 联合国开发计划署(UNDP)(2017)《性别不平等指数》。http://hdr.undp.org/en/content/gender-inequality-index-gii
15. 考夫曼 D、克莱 A、马斯特鲁齐 M(2010)《全球治理指标:方法论、数据与分析问题综述》,世界银行政策研究工作论文第5430号。www.govindicators.org
16. 世界银行(2018)《数据银行》。https://databank.worldbank.org/data/home
17. 哈尔彭等(2012)《评估全球海洋健康与效益的指数》,《自然》488(7413):615–620。DOI:10.1038/nature11397.
使用说明 本数据集为一篇学术论文中全球与区域尺度分析的结果,不得用于其他地理尺度或用途。若要使分析结果具备实际意义,需结合具体场景专门讨论并选取假设条件、指标、数据源及指标权重。如有任何疑问,请联系通讯作者(a.cisneros@oceans.ubc.ca)。
创建时间:
2024-03-16



