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The spatial prediction of growing stock volume is one of the most frequent application of remote sensing for supporting the sustainable management of forest ecosystems. For such a purpose data from active or passive sensors are used as predictor variables in combination with measures taken in the field in sampling plots. The Sentinel-2 (S2) satellites are equipped with a Multi Spectral Instrument (MSI) capable of acquiring 13 bands in the visible and infrared domains with a spatial resolution varying between 10 and 60 m. The present study aimed at evaluating the performance of the S2-MSI imagery for estimating the growing stock volume of forest ecosystems. To do so we used 240 plots measured in two study areas in Italy. The imputation was carried out with eight k-Nearest Neighbours (k-NN) methods available in the open source YaImpute R package. In order to evaluate the S2-MSI performance we repeated the experimental protocol also with two other sets of images acquired by two well-known satellites equipped with multi spectral instruments: Landsat 8 OLI and RapidEye scanner. We found that S2 worked better than Landsat in 37.5% of the cases and in 62.5% of the cases better than RapidEye. In one study area the best performance was obtained with Landsat OLI (RMSD = 6.84%) and in the other with S2 (RMSD = 22.94%), both with the k-NN system based on a distance matrix calculated with the Random Forest algorithm. The results confirmed that S2 images are suitable for predicting growing stock volume obtaining good performances (average RMSD for both the test areas of less than 19%). [ABSTRACT FROM AUTHOR]

A great debate around development scenarios has come to define conversations around the economy and the environment, two dimensions that struggle to find a proper balance. In this paper we apply unconditional growth model analyses to a new and unique dataset of European regions between 2008 and 2016 and identify four development scenarios – green growth, green de-growth, black growth and black de-growth – characterized by different relationships between CO 2 emissions and economic growth. We then map European regions across these four scenarios and describe the differences that occurred among regions in terms of socio-economic externalities, mainly competences, investments and well-being. Drawing on our analyses, we contribute to the debate on development scenarios and ecological macro-economics, as well as discuss implications for sustainability policy and research. • Industrial transition is characterised by environmental and socio-economic factors. • EU regions are clustered based on CO2 and Gross Value Added over a 9-year period. • Decoupling environmental and economic components defines four development scenarios. • Socio-economic effects of transition are tested by unconditional growth modelling. • Competences result a key feature for developing absolute decoupling scenarios. [ABSTRACT FROM AUTHOR]

Forest structural diversity plays a major role for forest management, conservation and restoration and is recognized as a fundamental aspect of forest biodiversity. The assessment, maintenance and restoration of a diversified forest structure have become major foci in the effort to preserve forest ecosystems from loss of biological diversity. However, the assessment of forest biodiversity is difficult because it involves multiple components and is characterized using multiple variables. The objective of the study was to develop a methodological approach for predicting, mapping, and constructing a statistical inference for a multiple-variable index of forest structural diversity. The method included three key components: (i) use of the k-Nearest Neighbors (k-NN) technique, field plot data, and airborne laser scanning metrics to predict multiple forest structural diversity variables simultaneously, (ii) incorporation of multiple diversity variable predictions into a single index, and (iii) construction of a statistically rigorous inference for the population mean of the index. Three structural diversity variables were selected to illustrate the method: growing stock volume and the standard deviations of tree diameter at breast-height and tree height. Optimization of the k-NN technique produced mean relative deviations less in absolute value than 0.04 for predictions for each of the three structural diversity variables, R 2 values between 0.50 and 0.66 which were in the range of values reported in the literature, and a confidence interval for the population mean of the index whose half-width was approximately 5% of the mean. Finally, the spatial pattern depicted in the resulting map of forest structural diversity for the study area contributed to validating the proposed method. [ABSTRACT FROM AUTHOR]

The k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta-analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN. [ABSTRACT FROM AUTHOR]

Among the wide array of terrestrial habitats, forest and wooded lands are the richest from both biological and genetic points of view because of their inherent structural and compositional complexity and diversity. Although species composition is an important biodiversity feature, forest structure may be even more relevant for biodiversity assessments because a diversified structure is likely to have more niches, which in turn, host more species and contribute to a more efficient use of available resources. Structure plays a major role as a diversity indicator for management purposes where maps of forest structural diversity are of great utility when planning conservation strategies. Airborne laser scanning (ALS) data have been demonstrated to be a reliable and valid source of information for describing the three-dimensional structure of forests. Using ALS metrics as predictor variables, we developed regression models for predicting indices of forest structural diversity for a study area in Molise, Italy. The study had two primary objectives: (i) to estimate indices of structural diversity for the entire study area, and (ii) to construct maps depicting the spatial pattern of the structural diversity indices. Our results demonstrate the utility of simple linear models using ALS data for improving areal estimates of mean structural diversity, and the resulting maps capture the patterns of structural diversity in the study area. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to enter the debate on the antecedents of knowledge exploration and exploitation by investigating the role of social capital and environmental dynamisms. The contribution is grounded on existing insights that no empirical evidence exists on the link between social capital and exploration/exploitation behaviors. Design/methodology/approach – The model considers three social capital dimensions – structural, relational and cognitive. It also considers the moderation effect of environmental dynamism on the link between social capital and knowledge exploration and exploitation. Head physicians from Italian hospitals were surveyed using a structured questionnaire. The data set consists of 174 observations, analyzed using seemingly unrelated regression techniques. Findings – This research provides evidence of the positive effect of structural, relational and cognitive social capital on knowledge exploration and exploitation – thus adding to a literature which has thus far concentrated on contextual and structural properties. Cohesive and strong ties are instrumental to gain access to external knowledge assets and stimuli, and to recombine the knowledge available within the unit. Practical implications – The results can support hospital managers in designing initiatives that recognize the centrality of network ties for strategies of continuous improvement. Social networks represent the locus in which hospital units can identify and acquire knowledge from outside and the locus in which knowledge can be shared and recombined. Managers should encourage initiatives that support systematic connections among units. Originality/value – This research provides evidence of the positive effect of structural, relational and cognitive social capital on knowledge exploration and exploitation – thus adding to a literature which has thus far concentrated on contextual and structural properties. Cohesive and strong ties are instrumental to gain access to external knowledge assets and stimuli, and to recombine the knowledge available within the unit. The results adds to conflicting evidence on environmental dynamism –shown here to exert a direct positive impact on exploitation and exploration, as well as to moderate the link between relational and structural social capital and exploration. [ABSTRACT FROM AUTHOR]

This paper aims to develop a model for assessing and valuing the intellectual capital of an organization by gathering data from individual employees. Also, the paper empirically investigates the relationship between intellectual capital and a specific dimension of employees' performance. Two surveys were conducted on employees of a leading company in the Italian food product market. The sample analysed comprises 1117 observations. A structural equation modelling ( SEM) methodology was used to define the measurement model and to determine the relations between intellectual capital and employees' performance. The model for intellectual capital assessment developed in this paper may integrate already existing intellectual capital frameworks. The three dimensions of intellectual capital identified mutually affect and have a positive effect on employees' performance. Our findings contribute to the discussion in the promising research stream of intellectual capital management. As limitations, this study focuses on a single company, and it exclusively adopts individual-level perceptual indicators. These elements make it difficult for any inter-company comparison. [ABSTRACT FROM AUTHOR]

Abstract: Instagram has grown in popularity among young adults and adolescents and is currently the second-favorite social network in the world. Research on its relationship to mental well-being is still relatively small and has yielded contradictory results. This study explores the relationship between time spent on Instagram and depressive symptoms, self-esteem, and disordered eating attitudes in a nonclinical sample of female Instagram users aged 18-35 years. In addition, it explores the mediating role of social comparison. A total of 1172 subjects completed a one-time-only online survey. Three different mediation analyses were performed to test the hypotheses that social comparison on Instagram mediates the association time spent on Instagram with depressive symptoms (model 1), self-esteem (model 2), and disordered eating attitudes (model 3). All three models showed that the relationship between intensity of Instagram use and the respective mental health indicator is completely mediated by the tendency for social comparison on Instagram. [ABSTRACT FROM AUTHOR]

The journey towards sustainability has become paramount to industry, government and research. To date, the main contributions have proposed valuable theoretical models to study the transitions to sustainability. However, a quantitative examination of the phenomenon is still limited. In this paper, we exploit a multilevel-growth model to empirically explore the relevance of different geographical scales in studying sustainability transitions. By analysing a novel, 9-year longitudinal dataset that covers European carbon emission intensity data on four different scales (from specific districts to whole states), we show whether and how multiple geographical scales support the study of sustainability transition pathways. Drawing on our analysis, we contribute to the debate on economic geography, sustainability transition and carbon emission intensity, as well as discuss implications for sustainability policy, strategy and research. • We apply a multilevel-growth model to a novel nine-year EU carbon intensity dataset. • EU regions show different patterns of carbon intensity between 2008 and 2016. • High-emitting areas show greater declines in carbon intensity compared to low-emitting. • NUTS 2 and 3 represent the most significant geographical scales for analysing carbon intensity. • Different geographical scales do matter in explaining carbon intensity pathways in different areas. [ABSTRACT FROM AUTHOR]

The extensive use of fossil fuels in recent decades is a well-known cause of the climate crisis. Climate action inevitably requires the strategic reorientation of industries that are part of the fossil fuel regime. International oil companies are central to this regime and possess the incumbent's characteristic power and influence to steer this process. However, European international oil companies continue to invest in fossil fuels, even as they acknowledge the climate crisis imperative. Socio-political and economic dynamics can either reinforce the oil regime or pressure firms to take climate action. We aim to comprehensively analyse the dynamics between external socio-political and economic actors' pressures to climate action on the one side, and the industry response on the other, from 2005 to 2019, using Geels Triple Embeddedness Framework. Drawing on a wide range of qualitative and quantitative longitudinal data (e.g. regulations, oil and renewables market trends, companies' investments in oil, renewables, and low-carbon technologies), we characterised the pressures and responses of the different analytical categories and established three phases. The results indicate that policy pressure on the oil regime in this period lacked constancy and comprehensiveness. The Kyoto Protocol ratification produced an initial momentum that prompted some companies to invest in alternative fuels and renewables, but efforts faded after 2010. Societal calls for a transition from fossil fuels and divestments from oil companies have risen since 2017. Recent socio-economic pressures combined with policy pledges for net-zero emissions have prompted all companies to invest beyond fossil fuels. However, efforts are still marginal and additional advancements in climate policy are necessary to foster the renewables market and to promote the phase-out of oil. Key policy insights Europe lacks a comprehensive policy framework to support the fast deployment of available alternative fuels. Policies need to foster the development and adoption of alternatives to petrochemical products, alongside their reduction and recycling. Apart from supporting renewable energy and other alternatives to oil, policies also need to target the phase-out of oil and send a clear signal to companies. [ABSTRACT FROM AUTHOR]

Climate change, population growth, and current rate of consumption at global scale have prompted academic and business communities to challenge the current models of production towards more circular approaches. This study aims at understanding what actions small and medium-sized enterprises (SMEs) are taking to meet the challenges and opportunities of the circular economy (CE), analysing actions, barriers, enablers and the connection between CE, business strategy and performance. This research involved 254 Italian SMEs through a multi-method approach, including interviews, surveys, and focus groups. Twenty different CE practices related to waste management, packaging, supply chain and product/process design have been explored. The results show that several CE practices are simultaneously implemented by SMEs, thus supporting the notion that CE implies a systemic approach to company's value creation. In particular, waste management was widely applied (e.g. separated waste collection was carried out by 84% of the companies surveyed), while resource saving practices were implemented by only 14% of the sample. Higher costs are the main barrier to CE for early adopters (5.13 on a 7-point Likert-type scale). However, companies implementing CE practices perceive them as a business opportunity rather than a cost, thus showing that CE may represent a source of value creation for companies, particularly SMEs. • The study involved 254 Italian SMEs through interviews, surveys, and focus groups. • Actions taken by SMEs to meet challenges and opportunities of CE are investigated. • CE Practices focused on waste management, packaging, supply chain and design. • Sustainability results an opportunity rather than a cost for CE-oriented firms. • Business opportunities emerge from the development of new value chains within CE. [ABSTRACT FROM AUTHOR]

Air quality management represents a reason for concern in indoor environments, especially now that the COVID-19 pandemic has shown how microbial aerosols pose a threat to human health, requiring proper monitoring. This is particularly true in public and working environments, where the turnover of occupants is high. The hospitality sector, in particular, has been severely affected by limitations related to emergency containment, and it needs to redefine its operations in the perspective of a "new normal" in the post-COVID-19 era. Considering the necessity to provide consistent information about indoor air quality, promote adequate management and increase safety, we developed a case study in cooperation with a major hotel in Turin. A sensing network has been implemented based on corporate-grade monitoring devices, compliant with the RESET standards, recently proposed and applied here to the hospitality sector for the first time. The network is able to detect the concentration of gaseous contaminants and fine particulate matter in semi-continuous mode. The study involved areas of the hotel with different purposes, such as guestrooms, hall, kitchen, restaurant and fitness center. Several valuable insights emerged in support of air quality management and pathways for future research can be outlined, based on the innovative dataset developed. [ABSTRACT FROM AUTHOR]

The extraction of forest information from SAR images is particularly complex in Mediterranean areas, since they are characterized by high spatial fragmentation and heterogeneity. We have investigated the use of multi-frequency SAR data from different sensors (ALOS/PALSAR and ENVISAT/ASAR) for estimating forest biomass in two test areas in Central Italy (San Rossore and Molise), where detailed in-situ measurements and Airborne Laser Scanning (ALS) data were available. The study focused on the estimation of growing stock volume (GS, in m 3 /ha) by using an inversion algorithm based on artificial neural networks (ANN). The ANN algorithm was first appropriately trained using the available GS estimates obtained from ALS data. The potential of this algorithm was then improved through the innovative use of a simulated dataset, generated by a forward electromagnetic model based on the Radiative Transfer Theory (RTT). The algorithm is able to merge SAR data at L and C bands for predicting GS in diversified Mediterranean environments. The performed analyses indicated that GS was correctly estimated by integrating information from L and C bands on both test areas, with the following statistics: R > 0.97 and RMSE = 28.5 m 3 /ha for the independent test, and R = 0.86 and RMSE ≈ 77 m 3 /ha for the final independent validation, the latter performed on the forest stands of both areas not included in the ALS acquisitions and where conventional measurements were available. The research then illustrates the potential of using the obtained GS estimates from SAR data to drive the simulations of forest net primary production (NPP). This experiment produced spatially explicit estimates of GS current annual increments that are slightly less accurate than those obtained from ground observations ( R = 0.75 and RMSE ≈ 1.5 m 3 /ha/year). [ABSTRACT FROM AUTHOR]

The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters. Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using the echo-based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable results. Accuracies were greatest for dominant height (Hd) (R 2 = 0.91; RMSE% = 8.2%) and mean height weighted by basal area (R 2 = 0.83; RMSE% = 10.5%) when using the echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standard error of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m. [ABSTRACT FROM AUTHOR]

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO. [ABSTRACT FROM AUTHOR]

Among the forestry-related applications for which airborne laser scanning (ALS) data have been shown to be beneficial, forest inventory has been investigated as much if not more than other applications. Metrics extracted from ALS data for spatial units such as plots and grid cells are typically of two forms: echo-based metrics derived directly from the three-dimensional distribution of the point cloud data and metrics derived from a canopy height model (CHM). For both cases, a large number of metrics can be calculated and used to construct parametric and non-parametric models to predict forest variables. We compared model-assisted estimates of total forest aboveground biomass (AGB) obtained using echo-based and CHM-based height metrics with two prediction methods: (i) a parametric linear model, and (ii) the non-parametric k-Nearest Neighbors (k-NN) technique. Model-assisted (MA) estimators were used with sample data obtained using a two-phase, tessellation stratified sampling (TSS) framework to estimate population parameters. The study was conducted in Molise Region in central Italy. For the four combinations of metrics and prediction techniques, estimates of total biomass were similar, in the range 1.96–2.1 million t, with standard error estimates that were also similar, in the range 0.20–0.21 t. Thus, the CHM-based metrics produced AGB estimates that were similar to and as accurate as those for the echo-based metrics, regardless of whether the parametric or the non-parametric prediction method was used. Additionally, the proposed MA estimator was more accurate than the estimator that did not use auxiliary data. [ABSTRACT FROM AUTHOR]