Managing urban socio-technical change? Comparing energy technology controversies in three European contexts

A {\em local graph partitioning algorithm} finds a set of vertices with small conductance (i.e. a sparse cut) by adaptively exploring part of a large graph $G$, starting from a specified vertex. For the algorithm to be local, its complexity must be bounded in terms of the size of the set that it outputs, with at most a weak dependence on the number $n$ of vertices in $G$. Previous local partitioning algorithms find sparse cuts using random walks and personalized PageRank. In this paper, we introduce a randomized local partitioning algorithm that finds a sparse cut by simulating the {\em volume-biased evolving set process}, which is a Markov chain on sets of vertices. We prove that for any set of vertices $A$ that has conductance at most $\phi$, for at least half of the starting vertices in $A$ our algorithm will output (with probability at least half), a set of conductance $O(\phi^{1/2} \log^{1/2} n)$. We prove that for a given run of the algorithm, the expected ratio between its computational complexity and the volume of the set that it outputs is $O(\phi^{-1/2} polylog(n))$. In comparison, the best previous local partitioning algorithm, due to Andersen, Chung, and Lang, has the same approximation guarantee, but a larger ratio of $O(\phi^{-1} polylog(n))$ between the complexity and output volume. Using our local partitioning algorithm as a subroutine, we construct a fast algorithm for finding balanced cuts. Given a fixed value of $\phi$, the resulting algorithm has complexity $O((m+n\phi^{-1/2}) polylog(n))$ and returns a cut with conductance $O(\phi^{1/2} \log^{1/2} n)$ and volume at least $v_{\phi}/2$, where $v_{\phi}$ is the largest volume of any set with conductance at most $\phi$.Comment: 20 pages, no figure

Finnish Energy Policy in Transition

This open access book examines the role of citizens in sustainable energy transitions across Europe. It explores energy problem framing, policy approaches and practical responses to the challenge of securing clean, affordable and sustainable energy for all citizens, focusing on households as the main unit of analysis. The book revolves around ten contributions that each summarise national trends, socio-material characteristics, and policy responses to contemporary energy issues affecting householders in different countries, and provides good practice examples for designing and implementing sustainable energy initiatives. Prominent concerns include reducing carbon emissions, energy poverty, sustainable consumption, governance, practices, innovations and sustainable lifestyles. The opening and closing contributions consider European level energy policy, dominant and alternative problem framings and similarities and differences between European countries in relation to reducing household energy use. Overall, the book is a valuable resource for researchers, policy-makers, practitioners and others interested in sustainable energy perspectives. In Finland, energy policy is in transition towards integrating energy projects in broader sustainability, liveability and innovation contexts. While energy saving has been pursued for decades, it is now part of a broader tendency in urban planning to promote sustainable lifestyles. Transition manifests in local actors’ redistribution of power, challenging conventional ways of infrastructure development, forging new networks, and seeking novel solutions. The experimental case presented in the chapter, Smart Kalasatama, shows that local governments are close to citizens and, therefore, can infuence the conditions for sustainable consumption and quality of life. Although they have an important role in energy policy, they still might lack the resources, expertise and the power to innovate, to evaluate projects, and in particular, to scale up innovative practices.Non peer reviewe

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Sleep quality, duration and behavioral symptoms among 5–6-year-old children

The objective of the present study was to examine whether parent-reported short sleep duration and sleeping difficulties are related to behavioral symptoms among pre-school aged children. The study is a cross-sectional survey of 297 families with 5–6-year-old children. The Sleep Disturbance Scale for children was used to measure sleep duration and sleeping difficulties, and the Child Behavior Checklist and Teacher’s Report Form were used to measure attention problems, and internalizing and externalizing symptoms. In multivariate logistic regression models, short sleep duration was according to parental reports related to inattention (adjusted odds ratio 4.70, 95% CI 1.58–14.00), internalizing (adjusted odds ratio 3.84, 95% CI 1.32–11.21), and total psychiatric symptoms (adjusted odds ratio 3.53, 95% CI 1.23–10.17) while according to teacher's reports it was almost significantly related to internalizing symptoms (adjusted odds ratio 4.20, 95% CI 0.86–20.51). Sleeping difficulties were strongly related to all subtypes of psychiatric symptoms according to parental reports (adjusted odds ratios ranging from 6.47 to 11.71) and to externalizing symptoms according to teachers’ reports (adjusted odds ratio 7.35, 95% CI 1.69–32.08). Both short sleep duration and sleeping difficulties are associated with children's behavioral symptoms. Intervention studies are needed to study whether childrens behavioral symptoms can be reduced by lengthening sleep duration or improving sleep quality

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications

Developing LCA-based benchmarks for sustainable consumption - for and with users

This article presents the development process of a consumer-oriented, illustrative benchmarking tool enabling consumers to use the results of environmental life cycle assessment (LCA) to make informed decisions. Active and environmentally conscious consumers and environmental communicators were identified as key target groups for this type of information. A brochure presenting the benchmarking tool was developed as an participatory, iterative process involving consumer focus groups, stakeholder workshops and questionnaire-based feedback. In addition to learning what works and what does not, detailed suggestions on improved wording and figures were obtained, as well as a wealth of ideas for future applications