[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118319-en":3,"doc-seo-118319-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118319,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Forest owners’ perceptions of machine learning - Insights from Swedish forestry","Machine learning is becoming increasingly important in environmental decision-making, especially in forestry. Building on the role of forest-owner typologies while addressing their limited attention to owners’ relationships with technology, the study uses Swedish forestry policy as context and applies Q-methodology. Eleven qualitative interviews generated 33 statements, later ranked by 26 participants. Inverted factor analysis identifies four ideal-type perceptions of machine learning, interpreted via self-determination theory.","Environmental Science and Policy 162 (2024) 103945  \nContents lists available at ScienceDirect  \nEnvironmental Science and Policy  \njournal [homepage: www.elsevier.com/locate/envsci](homepage: www.elsevier.com/locate/envsci)  \n| Forest owners’ perceptions of machine learning: Insights from swedish forestry |  |  |  |\n| --- | --- | --- | --- |\n| Joakim Wising a,*, Camilla Sandstr¨om a, William Lidbergb\u003Cbr>a Dept. of Political Science, Umeå University, Umeå 901 87, Sweden\u003Cbr>b Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå 901 83, Sweden |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Private forest owners Decision-making Machine learning Environmental policy Q-methodology Factor analysis |  | Machine learning is becoming increasingly important in environmental decision-making, particularly in forestry. While forest-owner typologies help in understanding private forest management strategies, they often overlook owners’ relationships with technology. This is crucial for ensuring that data-driven advancements in forestry benefit society. Using Swedish forestry policy as a case, we applied Q-methodology to explore forest owners’perceptions of machine learning. We conducted 11 qualitative interviews to generate 33 statements, which were then ranked by 26 participants. Inverted factor analysis identified four ideal-type perceptions of machine learning, interpreted through self-determination theory. The first perception views machine learning as unhelpful and socially disruptive. The second sees it as a complement to forest governance. The third expresses no strong opinions reflecting a relative disengagement from forestry. The fourth considers it essential for decisionmaking, particularly for absentee forest owners. The extracted perceptions align with existing forest owner typologies when it comes to reliance on others and willingness to take advice. The discussion includes concrete policy recommendations, focusing on privacy concerns, educational initiatives, and strategies for communicating uncertainty. |  |\n\n1. Introduction  \nTechnology is playing an increasingly vital role in environmental decision-making, with machine learning (ML) emerging as a key tool due to advances in computing power. In forestry, ML techniques are being applied to improve decision-making in areas such as road network planning, risk management, and water-ditch management (Mohtashami et al., 2023; Busarello et al., 2023). These technologies are intended tobe integrated across various decision-making levels, including policymaking, policy implementation, and support for individual forest owners, potentially transforming forestry practices and policies (Skogsstyrelsen, 2023). This transformation is significant, especially in Europe, where a large portion of forests is privately owned, and the actions of these owners are crucial to achieving political and environmental goals. However, private forest owners are not a homogenous group, and little is known about their relationship with new technologies, particularly ML. Although there is optimism about ML’s potential in environmental decision-making, there is a gap in understanding how individual forest owners perceive and adopt these technologies (Galaz et al., 2021; Wreford et al., 2021).  \nResearch by Bergdahl et al. (2023) suggests that self-determination theory (SDT), with its focus on the psychological needs of competence, autonomy, and relatedness, may help explore individuals’ perceptions toward new technologies. In this context, it is crucial to consider private forest owners in the development and implementation of ML in forestry (Li et al., 2021). Sweden, with its significant share of Europe’s forests and diverse group of forest owners, serves as a valuable case study. The Swedish state has invested in ML as part of its environmental goals for forestry (N¨aringsdepartementet, 2022; Skogforsk, 2021), making it an ideal context t","cbCaijI4Z91s24xG","https://ap.wps.com/l/cbCaijI4Z91s24xG","pdf",1571842,1,10,"English","en",105,"# Introduction\n## Research questions and study aim","[{\"question\":\"What does the study investigate about machine learning in forestry?\",\"answer\":\"It explores how Swedish private forest owners perceive machine learning in forestry and how those perceptions can be interpreted through self-determination theory.\"},{\"question\":\"How were perceptions of machine learning measured in the study?\",\"answer\":\"The study used Q-methodology: 11 qualitative interviews produced 33 statements, which 26 participants ranked for analysis.\"},{\"question\":\"What are the four ideal-type perceptions identified?\",\"answer\":\"The findings include: (1) machine learning as unhelpful and socially disruptive; (2) machine learning as a complement to forest governance; (3) limited engagement with no strong views; and (4) machine learning as essential for decision-making, especially for absentee forest owners.\"}]","Forest owners’ perceptions of machine learning - Insights from Swedish forestry | PDF",1785683037,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forest-owners-perceptions-of-machine-learning-insights-from-swedish-forestry","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forest-owners-perceptions-of-machine-learning-insights-from-swedish-forestry/118319/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the study investigate about machine learning in forestry?","Question",{"text":75,"@type":76},"It explores how Swedish private forest owners perceive machine learning in forestry and how those perceptions can be interpreted through self-determination theory.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were perceptions of machine learning measured in the study?",{"text":80,"@type":76},"The study used Q-methodology: 11 qualitative interviews produced 33 statements, which 26 participants ranked for analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the four ideal-type perceptions identified?",{"text":84,"@type":76},"The findings include: (1) machine learning as unhelpful and socially disruptive; 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