Macura Biljana, Ran Ylva, Persson U Martin, Abu Hatab Assem, Jonell Malin, Lindahl Therese, Röös Elin
Stockholm Environment Institute, P.O. Box 24218, 104 51, Stockholm, Sweden.
Department of Energy and Technology, Swedish University of Agricultural Sciences, 750 07, Uppsala, Sweden.
Environ Evid. 2022 Apr 25;11(1):17. doi: 10.1186/s13750-022-00271-1.
The global food system is causing considerable environmental harm. A transition towards more sustainable consumption is needed. Targeted public policy interventions are crucial for stimulating such transition. While there is extensive research about the promotion of more environmentally sustainable food consumption, this knowledge is scattered across different sources. This systematic map aims to collate and describe the available evidence on public policy interventions such as laws, directives, taxes and information campaigns, for achieving sustainable food consumption patterns.
We will search bibliographic databases, specialist websites, Google Scholar and bibliographies of relevant reviews. Searches for academic literature will be performed in English, while searches for grey literature will be performed in English, Swedish, Danish and Norwegian. Screening, including consistency checking exercises, will be done at two levels: title and abstract, and full text. We will use machine learning algorithms to support screening at the title and abstract level. Coding and meta-data extraction will include bibliographic information, policy details and context, and measured environmental outcome(s). The evidence base will be summarised narratively using tables and graphs and presented as an online interactive searchable database and a website that will allow for visualisation, filtering and exploring systematic map findings, knowledge gaps and clusters.
全球粮食系统正在对环境造成相当大的危害。需要向更可持续的消费模式转变。有针对性的公共政策干预对于推动这种转变至关重要。虽然关于促进更具环境可持续性的粮食消费已有广泛研究，但这些知识分散在不同来源。本系统综述旨在整理和描述有关法律、指令、税收和宣传活动等公共政策干预措施的现有证据，以实现可持续的粮食消费模式。
我们将检索书目数据库、专业网站、谷歌学术以及相关综述的参考文献。学术文献检索将用英文进行，灰色文献检索将用英文、瑞典文、丹麦文和挪威文进行。筛选，包括一致性检查，将在两个层面进行：标题和摘要以及全文。我们将使用机器学习算法来支持标题和摘要层面的筛选。编码和元数据提取将包括书目信息、政策细节和背景以及衡量的环境结果。证据库将通过表格和图表进行叙述性总结，并呈现为一个在线交互式可搜索数据库和一个网站，该网站将允许对系统综述的结果、知识空白和聚类进行可视化、筛选和探索。
