Prioritising Sustainable Development Goals, characterising interactions, and identifying solutions for local sustainability

This data set contains evidence and literature on interactions among the 29 selected targets of priority Sustainable Development Goals (SDGs) for the Goulburn-Murray. This table (i.e., Table C.3.xlsx) summarises: (i) the amount of evidence for each interaction, (ii) robustness of the evidence for each interaction (summary terms: ‘limited’, ‘medium’ or ‘robust’), (iii) scores assigned with the Nilsson scoring methodology (Nilsson et al. 2016), (iv) level of confidence in assigned scores derived from authors’ judgement (summary terms: ‘low’, ‘medium’ or ‘high’). As an example, target 2.3 pertains to doubling agricultural productivity and target 6.3 relates to improving water quality, hence pollution resulting from unsustainable agriculture can counteract (−2 scoring) reductions in water pollution and the protection of water and related ecosystems. Because multiple documents discussed this interaction, confidence in the score was high. Achieving target 6.3 (water quality) and target 6.6 (protecting and restoring water-related ecosystems) was expected to lead to co-benefits (+2, reinforcing). Due to the number of documents mentioning this interaction, our confidence in the score was high. Also, in this table for some targets that are conceptually overlapping and may have the same evidence, therefore the first two columns can refer to multiple targets. Also, this data set contains coded statements too (Coded statements of all SDGs.pdf) . We prioritised SDGs and targets for the region by performing a computer-aided review of the literature. We assessed relevant literature with the software package NVivo Pro 12. We identified statements related to each SDG, then coded that content manually by assigning statements to relevant SDGs. As a first stage, we searched abstracts to find statements relevant to each of the 17 SDGs. Some statements were only associated with one SDG, while others were related to multiple SDGs. These coded statements were extracted from NVivo Pro 12.

本数据集包含针对古尔本-默里（Goulburn-Murray）地区经遴选的29个优先可持续发展目标（SDGs, Sustainable Development Goals）相关交互作用的证据与文献。表格Table C.3.xlsx汇总了以下四项内容：(i) 各交互作用的证据体量；(ii) 各交互作用证据的稳健性（分级术语为「有限」「中等」或「强」）；(iii) 采用尼尔森评分法（Nilsson scoring methodology，Nilsson et al. 2016）所得的评分；(iv) 由研究者主观判断得出的评分置信水平（分级术语为「低」「中等」或「高」）。

以具体目标2.3为例，其核心内容为实现农业生产率翻倍，而目标6.3则关乎改善水质；因此不可持续农业生产产生的污染会抵消（评分为-2）水污染削减及水与相关生态系统保护工作的成效。鉴于多篇文献均讨论了该交互作用，本次评分的置信水平较高。

实现目标6.3（水质改善）与目标6.6（保护及修复水相关生态系统）预计可产生协同效益（评分为+2，即强化效应）。因提及该交互作用的文献数量较多，本次评分的置信水平同样较高。此外，本表格中部分概念重叠且可能共享同一证据的目标，因此前两列可对应多个具体目标。

本数据集还包含编码后的表述文档《所有可持续发展目标编码表述.pdf》（Coded statements of all SDGs.pdf）。我们通过计算机辅助文献检索流程为该区域筛选优先可持续发展目标与具体目标：首先借助NVivo Pro 12软件包开展相关文献分析，先检索文献摘要以获取与17项可持续发展目标相关的表述，随后手动将这些表述归类至对应可持续发展目标；部分表述仅关联单一可持续发展目标，其余则涉及多个可持续发展目标，最终从NVivo Pro 12中导出所有编码后的表述。