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Purpose: This pathway discusses the need for further research into how focal companies measure and manage indirect Scope 3 emissions in their value chains. Design/methodology/approach: This pathway relies on the authors' qualitative research on European companies' decarbonisation strategies. We analysed self-reported and tertiary data from 33 manufacturing European companies considered leaders in climate action. Additionally, interviews were conducted with four experts who work to elaborate decarbonisation strategies in large companies that are part of global value chains. Findings: The limited visibility of focal firms over their value chains and data collection capacity raises questions on the reliability of indirect emissions inventories and companies' ability to manage indirect emissions. Research limitations/implications: Investigation of the emerging empirical phenomena of indirect emissions may lead to valuable insights for the measurement and management of sustainability issues. Practical implications: Our piece explores the process of constructing indirect emissions inventories, assists in ensuring accurate interpretation of the data and provokes discussion on focal companies' role in managing Scope 3 emissions. Social implications: Many stakeholders refer to companies' Scope 3 inventories. This piece exposes inconsistencies in these inventories and what companies' responsibilities are in managing emissions. Originality/value: Research on managing sustainability issues in supply and value chains is increasing, and indirect emissions are central in this space. This impact pathway seeks to stimulate research in underdeveloped areas by discussing the limitations of this data and highlighting practical limitations present in its management. [ABSTRACT FROM AUTHOR]

Multi-professional service teams might isolate from the organisation and inhibit knowledge sharing with others. Professional organisations are thus stimulating their teams to bond more closely with other units, hoping this would facilitate knowledge sharing. Yet, studies on social bonding suggest this could actually deter from knowledge sharing. Our study investigates this further asking: do employees display greater intention to share knowledge when their teams possess greater structural, relational and cognitive social capital? Our cross-level study, grounded on a sample of 226 employees (39 teams in four Hospice & Palliative Care Organisations), shows that individuals embedded in teams with greater social capital are indeed more motivated to share knowledge; and that each form of social bonding—increasing frequency of interactions, trust and mutual support, and similarity of goals/meanings—stimulates different mechanisms of knowledge sharing (attitude, subjective norm and perceived behavioural control). [ABSTRACT FROM AUTHOR]

Accurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps. Plain Language Summary: This study enhances the accuracy with which the aboveground biomass (AGB) of high‐AGB forests is estimated, and the results should be applicable to other subtropical forest regions. Using a machine learning approach to fuse multisource data, we propose to integrate spatial statistical analyses to identify critical cofactors, such as dominant tree species, canopy closure, and herbaceous plant cover. These factors are then incorporated into the analysis to further refine high‐AGB estimates. Applying these methods substantially improves the accuracy of high‐AGB estimates and is crucial in supporting forest management, conservation, and related policy development in subtropical regions. Key Points: Spatial statistical analysis identifies key auxiliary factors and refines estimates of high aboveground biomass (AGB)Incorporating the dominant auxiliary factors increases the accuracy of high‐AGB estimates in tropical forestsSpatial statistics analysis combined with the use of multisource data and machine learning allows for accurate estimates of high‐AGB regions [ABSTRACT FROM AUTHOR]

International oil companies play a central role in the transition towards a low‐carbon economy. These companies have the leadership and influence to advance technological alternatives or sustain the current dependence on fossil fuels. This article aims to analyse the decarbonisation strategies that European oil companies are performing in the wake of climate change. A document analysis was integrated with carbon emission data from 10 European international oil companies and uncovered the four main strategies adopted by companies: sustained carbon dependence, carbon emissions compensation, carbon emissions mitigation and carbon independence. The results indicate that companies have adopted variable levels of action, despite their overlapping discourse on climate mitigation, with only one of the analysed firms performing a transition away from fossil fuels. [ABSTRACT FROM AUTHOR]