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• Seaweed protein content determination by means of machine learning is proposed. • Protein content can be determined un-distractively in-situ via spectroscopy. • Spectral absorption across 560-674 nm was found to be highly informative. • The accuracy of the model was validated in an external validation trial. • Analytical and technological foundations for a generic model were established. Determining seaweed protein concentration and the associated phenotype is critical for food industries that require precise tools to moderate concentration fluctuations and attenuate risks. Algal protein extraction and profiling have been widely investigated, but content determination involves a costly, time-consuming and high-energy, laboratory-based fractionation technique. The present study examines the potential of a field spectroscopy technology as a precise, non-destructive tool for on-site detection of red seaweed protein concentration. By using information from a large dataset of 144 Gracilaria sp. specimens, studied in a land-based cultivation set-up, under six treatment regimes during two cultivation seasons, and an artificial neural network, machine learning algorithm and diffuse visible–near infrared reflectance spectroscopy, predicted protein concentrations in the algae were obtained. The prediction results were highly accurate (R2 = 0.95; RMSE = 0.84), exhibiting a high correlation with the analytically determined values. External validation of the model derived from a separate trial, exhibited even better results (R2 = 0.99; RMSE = 0.45). This model, trained to convert phenotypic spectral measurements and pigment intensity into accurate protein content predictions, can be adapted to include diversified algae species and usages. [ABSTRACT FROM AUTHOR]

Protected areas offer unique opportunities for recreation, but the non-market nature of these benefits presents a significant challenge when trying to represent value in the decision-making processes. The most common techniques to value recreation are based on resource-intensive primary surveys which are difficult to perform at a large scale or in remote locations. This is true in the case of Italy, where a large and diverse network of protected areas suffers from lack of data. Here, we offer an alternative data source for the valuation of recreation by integrating the metadata of geotagged photographs from social media into single-site, individual travel cost models for 67 Italian protected areas. Count data model results are generally consistent with standard economic and consumer demand theory for ordinary goods, with a zero-truncated Poisson model returning down sloping demand curves for 50 of 67 sites. A significant travel cost coefficient was returned for 33 sites (p -value <0.05) for which consumer surplus estimates were found in the range between €6.33 and €87.16, with a mean value per trip of €32.82. Although not without their own challenges, the results presented highlight the possibilities of new forms of spatial big data as a novel data source for environmental economists. [ABSTRACT FROM AUTHOR]

Geolocated social media data counts are increasingly used as proxy for number of visits in natural areas, including their spatial and temporal distribution. This paper synthesizes the empirical evidence concerning the correlation of social media data counts and visits through multi-level meta-analytical models. Analysis of 355 correlations from 41 studies reveals a strong correlation for annual number of visits over multiple sites (pooled Pearson's r = 0.73) and for monthly visits in a single site (pooled Pearson's r = 0.84). Using data from multiple social media sources improves the correlation. Mixed results are obtained with regard to the effect of social media penetration rate and designation as national park on the correlation. Future studies should focus on broadening the scope of investigation to middle and low-income countries, developing a systematic approach toward the use of covariates, and comparing the results from social media data to those from other emerging monitoring techniques. • 355 correlations between social media data counts and visits are analyzed. • Strong correlation for multi-site annual visits and monthly visits is found. • Using data from multiple social media sources improves correlation. • Covariates may increase correlation but a systematic approach is missing. • A broader empirical basis is needed to fully appreciate the technique's potential. [ABSTRACT FROM AUTHOR]

Environmental governance is highly sensitive to temporal dynamics, due to the ever-accelerating rate of technological changes, the cumulative nature of environmental impacts and the complexity of multi-level environmental policy processes. Yet, temporality is generally only implicitly included in frameworks used for describing or assessing policy response in the broad context of social-ecological systems, such as the widely used Driver-Pressure-State-Impact-Response (DPSIR) framework. As a result, the application of such frameworks often does not give due attention to questions of temporality, with potential negative impacts on attaining environmental goals. The current work proposes to modify the DPSIR framework to explicitly incorporate temporal aspects. We suggest two extensions of the common framework to account for time lags and allow for early response through a "response shift-left" mechanism. The potential of the modified framework—temporal DPSIR (tDPSIR)—to shed light on these temporal aspects is demonstrated through analysis of the European Union's response to pollution of the marine environment by plastic bottle waste. The analysis emphasizes the pronounced time lags between the initiation of this anthropogenic pressure and effective governance capable of curbing emissions. We discuss how tDPSIR can be applied to a range of environmental issues to populate databases of time lags in environmental governance, which, in turn, can be analysed for systemic patterns and chains of causality. [ABSTRACT FROM AUTHOR]

Data-as-a-Service (DaaS) can help facilitate the successful adoption of innovative digital solutions by water utilities. However, little is known about the processes used to adopt this model, including the initial challenges and required utility maturity factors. This study engaged diverse stakeholders through a service design approach to support water utilities in evaluating their suitability to adopt DaaS. The findings demonstrate an innovative method by which both DaaS providers and utilities can better analyze their needs and strategic interests, and those of the people they serve. In so doing, they can co-create mutual trust needed to tackle complex water policy challenges. • Data-as-a-Service adoption is limited by procurement processes and water utility culture. • A utility decision support tool should be tailored, interactive, and prove the business case. • The service design method can effectively engage diverse, water industry stakeholders. [ABSTRACT FROM AUTHOR]

While technologies in the water sector have been advancing over the past few decades, complementary innovation in business models is needed to support the adoption of these technologies. One emerging opportunity is an outsourced approach to data collection, delivery, and analysis known as "Data-as-a-Service." This study is the first to explore the drivers, barriers, and implementation trends for water and wastewater utilities to adopt this model. The findings provide valuable insights for utility managers looking for new ways to adopt innovative technologies and regulators and policymakers seeking to encourage utilities to make data-driven decisions. • Data-as-a-Service can facilitate the uptake of innovative solutions within the water sector. • The main utility motivations to adopt Data-as-a-Service were ease of operation and reduced risk. • Besides having internal solutions, the main barriers were data ownership and security concerns. • Wastewater utilities appear to have more complex data management needs than water utilities. [ABSTRACT FROM AUTHOR]

Emerging forms of mobile phone data generated from the use of mobile phone applications have the potential to advance scientific research across a range of disciplines. However, there are risks regarding uncertainties in the socio-demographic representativeness of these data, which may introduce bias and mislead policy recommendations. This paper addresses the issue directly by developing a novel approach to assessing socio-demographic representativeness, demonstrating this with two large independent mobile phone application datasets, Huq and Tamoco, each with three years data for a large and diverse city-region (Glasgow, Scotland) home to over 1.8 million people. We advance methods for detecting home location by including high-resolution land use data in the process and test representativeness across multiple dimensions. Our findings offer greater confidence in using mobile phone app data for research and planning. Both datasets show good representativeness compared to the known population distribution. Indeed, they achieve better population coverage than the 'gold standard' random sample survey which is the alternative source of data on population mobility in this region. More importantly, our approach provides an improved benchmark for assessing the quality of similar data sources in the future. • Data from the use of mobile phone apps offer new potential for social scientists. • This potential is limited by questions of bias and data representativeness. • Applying a novel home detection approach we improve home location estimates. • We show a very high level of representativeness across two independent datasets. • These findings provide a foundation for the use of app data in social research. [ABSTRACT FROM AUTHOR]