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This manuscript presents a systematic meta-narrative review of peer-reviewed publications considering community acceptance and social impacts of site-specific Carbon Capture Utilization and Storage (CCUS) projects to inform the design and implementation of CCUS projects who seek to engage with communities during this process, as well as similar climate mitigation and adaptation initiatives. A meta-narrative approach to systematic review was utilized to understand literature from a range of site specific CCUS studies. 53 peer-reviewed papers were assessed reporting empirical evidence from studies on community impacts and social acceptance of CCUS projects published between 2009 and 2021. Three separate areas of contestation were identified. The first contestation was on acceptance, including how acceptance was conceptualized, how the different CCUS projects engaged with communities, and the role of acceptance in social learning. The second contestation related to communities: how communities were represented, where the communities were located in relation to the CCUS projects, and how the communities were defined. The third contestation was around CCUS impacts and the factors influencing individuals’ perceptions of impacts, the role of uncertainty, and how impacts were challenged by local communities, politicians and scientists involved in the projects. The next step was to explore how these contestations were conceptualised, the aspects of commonality and difference, as well as the notable omissions. This facilitated a synthesis of the key dimensions of each contestation to inform our discussion regarding community awareness and acceptance of CCUS projects. This review concludes that each CCUS project is complex thus it is not advisable to provide best practice guidelines that will ensure particular outcomes. This systematic review shared recommendations in the literature as to how best to facilitate community engagement in relation to CCUS projects and similar place-based industrial innovation projects. These recommendations focus on the importance of providing transparency, acknowledging uncertainty and encouraging collaboration.

Recreational boating is increasingly popular, and noise from small boats may have strong impacts on the health of cetaceans. Although animals’ reactions to boat noise have been studied, this has not previously been done in controlled settings. Here, we investigated how noise from a small boat influenced harbor porpoise (Phocoena phocoena) behavior. Observing porpoises with a drone, we collected their responses when passing animals at 10 and 20 knots. Porpoises started changing behavior when the boat was

Recreational boating is increasingly popular, and noise from small boats may have a strong impact on the health of cetaceans. Although the animals’ reactions to boat noise have been studied, this has not previously been done in controlled settings. Here, we investigated how noise from a small boat influenced harbor porpoise (Phocoena phocoena) behavior using a drone. We collected porpoise responses when passing the animals at 10 and 20 knots. The animals already started changing behavior when the boat was

Study Aim We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns. Data Description Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables. Nuup Kangerlua (Godthåbsfjord) _Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness Kangerluarsunnguaq (Kobbefjord) Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height Methods Field survey in Nuup Kangerlua We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022). Field survey in Kangerluarsunnguaq We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022). Biotic variables We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height. Climate variables We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021). Topography and terrain wetness indicator variables We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness. Computer code Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim) Third-party data Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).