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Concurrently implemented green initiatives to combat global environmental crises may be curtailed or even sacrificed given the ongoing global economic contraction. We collected empirical data and information about green initiatives from 15 sites or countries worldwide. We systematically explored how specific policy, intended behaviors, and gains of given green initiative may interact with those of other green initiatives concurrently implemented in the same geographic area or involving the same recipients. Surprisingly, we found that spillover effects were very divergent: one initiative could reduce the gain of another by 22 % ~ 100 %, representing alarming losses, while in other instances, substantial co-benefits could arise as one initiative can increase the gain of another by 9 % ~ 310 %. Leveraging these effects will help countries keep green initiatives with significant co-benefits but stop initiatives with substantial spillover losses in the face of widespread budget cuts, better meeting the United Nations’ sustainable development goals. 

Abstract. The warming of the Arctic is affecting the carbon cycle of tundraecosystems. Most research on carbon fluxes from Arctic tundra ecosystems hasfocused on abiotic environmental controls (e.g., temperature, rainfall, orradiation). However, Arctic tundra vegetation, and therefore the carbonbalance of these ecosystems, can be substantially impacted by herbivory. Inthis study we tested how vegetation consumption by brown lemmings (Lemmus trimucronatus) canimpact carbon exchange of a wet-sedge tundra ecosystem near Utqiaġvik,Alaska during the summer and the recovery of vegetation during the followingsummer. We placed brown lemmings in individual enclosure plots and testedthe impact of lemmings' herbivory on carbon dioxide (CO2) fluxes, methane(CH4) fluxes, and the normalized difference vegetation index (NDVI)immediately after lemming removal and during the following growing season.During the first summer of the experiment, lemmings' herbivory reduced plantbiomass (as shown by the decrease in the NDVI) and decreased net CO2uptake while not significantly impacting CH4 emissions. CH4emissions were likely not significantly affected due to CH4 beingproduced deeper in the soil and escaping from the stem bases of the vascularplants. The summer following the lemming treatments, NDVI and net CO2fluxes returned to magnitudes similar to those observed before the start ofthe experiment, suggesting a complete recovery of the vegetation and atransitory nature of the impact of lemming herbivory. Overall, lemmingherbivory has short-term but substantial effects on carbon sequestration byvegetation and might contribute to the considerable interannual variabilityin CO2 fluxes from tundra ecosystems. 

Detecting small objects (e.g., manhole covers, license plates, and roadside milestones) in urban images is a long-standing challenge mainly due to the scale of small object and background clutter. Although convolution neural network (CNN)-based methods have made significant progress and achieved impressive results in generic object detection, the problem of small object detection remains unsolved. To address this challenge, in this study we developed an end-to-end network architecture that has three significant characteristics compared to previous works. First, we designed a backbone network module, namely Reduced Downsampling Network (RD-Net), to extract informative feature representations with high spatial resolutions and preserve local information for small objects. Second, we introduced an Adjustable Sample Selection (ADSS) module which frees the Intersection-over-Union (IoU) threshold hyperparameters and defines positive and negative training samples based on statistical characteristics between generated anchors and ground reference bounding boxes. Third, we incorporated the generalized Intersection-over-Union (GIoU) loss for bounding box regression, which efficiently bridges the gap between distance-based optimization loss and area-based evaluation metrics. We demonstrated the effectiveness of our method by performing extensive experiments on the public Urban Element Detection (UED) dataset acquired by Mobile Mapping Systems (MMS). The Average Precision (AP) of the proposed method was 81.71%, representing an improvement of 1.2% compared with the popular detection framework Faster R-CNN. 

Community forests have been established worldwide to sustainably manage forest ecosystem services while maintaining the livelihoods of local residents. The Chitwan National Park in Nepal is a world-renowned biodiversity hotspot, where community forests were consolidated in the park’s buffer zone after 1993. These western Chitwan community forests stand as the frontiers of human–environment interactions, nurturing endangered large mammal species while providing significant natural resources for local residents. Nevertheless, no systematic forest cover assessment has been conducted for these forests since their establishment. In this study, we examined the green vegetation dynamics of these community forests for the years 1988–2018 using Landsat surface reflectance products. Combining an automatic water extraction index, spectral mixture analysis and the normalized difference fraction index (NDFI), we developed water masks and quantified the water-adjusted green vegetation fractions and NDFI values in the forests. Results showed that all forests have been continuously greening up since their establishment, and the average green vegetation cover of all forests increased from approximately 30% in 1988 to above 70% in 2018. With possible contributions from the invasion of exotic understory plant species, we credit community forestry programs for some of the green-up signals. Monitoring of forest vegetation dynamics is critical for evaluating the effectiveness of community forestry as well as developing sustainable forest management policies. Our research will provide positive feedbacks to local community forest committees and users.