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Online social media represent an extensive, opportunistic source of behavioral data and revealed preferences for ecosystem services (ES) analysis. Such data may allow to advance the approach, scale and timespan to which ES are assessed, mapping and valued. This is especially relevant in the context of developing regions whose decision support tools are often limited by a lack of resources and funding. This research presents an economic valuation tool for recreational ES, suitable at wide spatial scales, relying on crowdsourced metadata from social media with a proof of concept tested on an Indian tropical Ramsar wetland. We demonstrate how geotagged photographs from Flickr can be used in the context of a developing country to (i) map nature-based recreation patterns, (ii) value recreational ecosystem services, and (iii) investigate how recreational benefits are affected by changes in ecosystem quality. The case-study application is the Vembanad Lake in Kerala, India, and the adjacent backwaters. Geographic Information Systems are implemented to extract 4328 Flickr photographs that are used to map hot spots of recreation and infer the home location of wetland visitors from within Kerala state with good accuracy. An individual, single-site travel cost demand function is generated and estimated using both Poisson and Negative Binomial regressions, which results in mean consumer surplus estimates between Rs. 2227–3953 ($34–$62) per visit and annual domestic recreation benefits of Rs. 7.53–13.37 billion ($115.5–$205 million) in the investigated wetlands. Improvement in water quality to a level that supports wildlife and fisheries is projected to result in a Rs. 260 million ($4 million) annual increase in recreational benefits, while restoring previously encroached lake area would result in almost Rs. 50 million ($760,000) in yearly value increase. [ABSTRACT FROM AUTHOR]

Patterns of public use in 273 natural treatment systems worldwide are investigated by means of geotagged data from two popular photo-sharing websites, using spatial analysis and regression techniques. Standardized Major Axis (SMA) regression is found to perform better than other univariate calibration models in terms of goodness of fit with reported visitation frequencies and predictive accuracy, and is used to predict visitation rates in 139 systems that are associated with at least one geotagged photograph. High visitation rates are found in free-water surface (FWS) constructed wetlands and mixed pond-constructed wetlands systems, as well as systems treating surface water or stormwater runoff. Geographic Information System (GIS) techniques are used to map hot and cold spots of public use in two highly visited systems. Binomial logit regression reveals that the probability to be associated with at least one geotagged photograph is a function of system size, system type, and influent water quality. The findings are discussed in terms of their implications for the evaluation of public use in multifunctional ecologically engineered systems as well as the applicability of the proposed methodology to other natural and man-made ecosystems. [ABSTRACT FROM AUTHOR]

• 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]

Natural water treatment systems have long been recognized as sources of ancillary benefits in the form of cultural ecosystem services. To date, there is a lack of quantitative understanding of the extent and welfare impact of such benefits. This paper investigates 166 natural treatment systems worldwide and provides the first quantitative assessment of their recreational and educational benefits. The public use is highly influenced by the type of recreational activities, presence of recreational and educational facilities, and accessibility of the systems. Using value transfer techniques, we estimate the mean and median monetary values of recreational benefits in 8397 and 530 €/ha/year, respectively. We compare such value flows with operation and management costs and other ecosystem services provided by these systems. [ABSTRACT FROM AUTHOR]

This paper examines the welfare dimension of the recreational services of coastal ecosystems. First, we construct a global database of primary valuation studies that focus on recreational benefits of coastal ecosystems. Second, the profile of each of the 253 individual observations is enriched with characteristics of the built coastal environment (accessibility, anthropogenic pressure, human development level), natural coastal environment (presence of protected area, ecosystem type, marine biodiversity), geo-climatic factors (temperature, precipitation), and sociopolitical context. We then propose a meta-analytical framework that is built upon a Geographic Information System (GIS) and allow for the exploration of the spatial dimension of the valued ecosystems, including the role of spatial heterogeneity of the selected meta-regression variables as well as the spatial profile of the transferred values. The empirical outcome results in the first global map of the values of coastal recreation, which may play a crucial role in identifying and ranking coastal area conservation priorities from a socio-economic perspective. [Copyright &y& Elsevier]

Abstract: Shading by riparian vegetation affects incident solar radiation and water temperature in small- and moderate-size streams, and is thus an important component in the influence of forested riparian buffers on streams. The water quality effects of riparian shading are largely unknown. A simulation study was carried out to evaluate the effect of shading on six water quality variables in a moderate-size Belgian river stretch. A dynamic modelling approach making use of the River Water Quality Model No. 1 was chosen to represent the system. The scenarios developed indicate that shading may be an effective tool in controlling stream eutrophication (44% reduction in phytoplankton productivity in the simulated stretch) but has a limited effect on dissolved oxygen, chemical oxygen demand, nitrates, ammonium nitrogen, and phosphates. Results suggest that shading can effectively be implemented as a direct management strategy to improve water quality conditions in small and moderate-size watercourses that are exposed to excessive algal growth during summer periods. [Copyright &y& Elsevier]

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]