Diverse values of nature for sustainability.

Twenty-five years since foundational publications on valuing ecosystem services for human well-being1,2, addressing the global biodiversity crisis3 still implies confronting barriers to incorporating nature's diverse values into decision-making. These barriers include powerful interests supported by current norms and legal rules such as property rights, which determine whose values and which values of nature are acted on. A better understanding of how and why nature is (under)valued is more urgent than ever4. Notwithstanding agreements to incorporate nature's values into actions, including the Kunming-Montreal Global Biodiversity Framework (GBF)5 and the UN Sustainable Development Goals6, predominant environmental and development policies still prioritize a subset of values, particularly those linked to markets, and ignore other ways people relate to and benefit from nature7. Arguably, a 'values crisis' underpins the intertwined crises of biodiversity loss and climate change8, pandemic emergence9 and socio-environmental injustices10. On the basis of more than 50,000 scientific publications, policy documents and Indigenous and local knowledge sources, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) assessed knowledge on nature's diverse values and valuation methods to gain insights into their role in policymaking and fuller integration into decisions7,11. Applying this evidence, combinations of values-centred approaches are proposed to improve valuation and address barriers to uptake, ultimately leveraging transformative changes towards more just (that is, fair treatment of people and nature, including inter- and intragenerational equity) and sustainable futures.

Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis.

Mobility restrictions during the COVID-19 pandemic ostensibly prevented the public from transmitting the disease in public places, but they also hampered outdoor recreation, despite the importance of blue-green spaces (e.g., parks and natural areas) for physical and mental health. We assess whether restrictions on human movement, particularly in blue-green spaces, affected the transmission of COVID-19. Our assessment uses a spatially resolved dataset of COVID-19 case numbers for 848 administrative units across 153 countries during the first year of the pandemic (February 2020 to February 2021). We measure mobility in blue-green spaces with planetary-scale aggregate and anonymized mobility flows derived from mobile phone tracking data. We then use machine learning forecast models and linear mixed-effects models to explore predictors of COVID-19 growth rates. After controlling for a number of environmental factors, we find no evidence that increased visits to blue-green space increase COVID-19 transmission. By contrast, increases in the total mobility and relaxation of other non-pharmaceutical interventions such as containment and closure policies predict greater transmission. Ultraviolet radiation stands out as the strongest environmental mitigant of COVID-19 spread, while temperature, humidity, wind speed, and ambient air pollution have little to no effect. Taken together, our analyses produce little evidence to support public health policies that restrict citizens from outdoor mobility in blue-green spaces, which corroborates experimental studies showing low risk of outdoor COVID-19 transmission. However, we acknowledge and discuss some of the challenges of big data approaches to ecological regression analyses such as this, and outline promising directions and opportunities for future research.

Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway.

The predicted extreme temperatures of global warming are magnified in cities due to the urban heat island effect. Even if the target for average temperature increase in the Paris Climate Agreement is met, temperatures during the hottest month in a northern city like Oslo are predicted to rise by over 5 °C by 2050. We hypothesised that heat-related diagnoses for heat-sensitive citizens (75+) in Oslo are correlated to monthly air temperatures, and that green infrastructure such as tree canopy cover reduces extreme land surface temperatures and thus reduces health risk from heat exposure. Monthly air temperatures were significantly correlated to the number of skin-related diagnoses at the city level, but were unrelated to diagnoses under circulatory, nervous system, or general categories. Satellite-derived spatially-explicit measures revealed that on one of the hottest days during the summer of 2018, landscape units composed of paved, midrise or lowrise buildings gave off the most heat (39 °C), whereas units composed of complete tree canopy cover, or mixed (i.e. tree and grass) vegetation maintained temperatures of between 29 and 32 °C. Land surface temperatures were negatively correlated to tree canopy cover (R2 = 0.45) and vegetation greenness (R2 = 0.41). In a scenario in which each city tree was replaced by the most common non-tree cover in its neighbourhood, the area of Oslo exceeding a 30 °C health risk threshold during the summer would increase from 23 to 29%. Combining modelling results with population at risk at census tract level, we estimated that each tree in the city currently mitigates additional heat exposure of one heat-sensitive person by one day. Our results indicate that maintaining and restoring tree cover provides an ecosystem service of urban heat reduction. Our findings have particular relevance for health benefit estimation in urban ecosystem accounting and municipal policy decisions regarding ecosystem-based climate adaptation.

Creating urban green infrastructure where it is needed - A spatial ecosystem service-based decision analysis of green roofs in Barcelona.

As cities face increasing pressure from densification trends, green roofs represent a valuable source of ecosystem services for residents of compact metropolises where available green space is scarce. However, to date little research has been conducted regarding the holistic benefits of green roofs at a citywide scale, with local policymakers lacking practical guidance to inform expansion of green roofs coverage. The study addresses this issue by developing a spatial multi-criteria screening tool applied in Barcelona, Spain to determine: 1) where green roofs should be prioritized in Barcelona based on expert elicited demand for a wide range of ecosystem services and 2) what type of design of potential green roofs would optimize the ecosystem service provision. As inputs to the model, fifteen spatial indicators were selected as proxies for ecosystem service deficits and demands (thermal regulation, runoff control, habitat and pollination, food production, recreation, and social cohesion) along with five decision alternatives for green roof design (extensive, semi-intensive, intensive, naturalized, and allotment). These indicators and alternatives were analyzed probabilistically and spatially, then weighted according to feedback from local experts. Results of the assessment indicate that there is high demand across Barcelona for the ecosystem services that green roofs potentially might provide, particularly in dense residential neighborhoods and the industrial south. Experts identified habitat, pollination and thermal regulation as the most needed ES with runoff control and food production as the least demanded. Naturalized roofs generated the highest potential ecosystem service provision levels for 87.5% of rooftop area, apart from smaller areas of central Barcelona where intensive rooftops were identified as the preferable green roof design. Overall, the spatial model developed in this study offers a flexible screening based on spatial multi-criteria decision analysis that can be easily adjusted to guide municipal policy in other cities considering the effectiveness of green infrastructure as source of ecosystem services.

Enabling Green and Blue Infrastructure to Improve Contributions to Human Well-Being and Equity in Urban Systems.

The circumstances under which different ecosystem service benefits can be realized differ. The benefits tend to be coproduced and to be enabled by multiple interacting social, ecological, and technological factors, which is particularly evident in cities. As many cities are undergoing rapid change, these factors need to be better understood and accounted for, especially for those most in need of benefits. We propose a framework of three systemic filters that affect the flow of ecosystem service benefits: the interactions among green, blue, and built infrastructures; the regulatory power and governance of institutions; and people's individual and shared perceptions and values. We argue that more fully connecting green and blue infrastructure to its urban systems context and highlighting dynamic interactions among the three filters are key to understanding how and why ecosystem services have variable distribution, continuing inequities in who benefits, and the long-term resilience of the flows of benefits.

Practical application of spatial ecosystem service models to aid decision support.

Ecosystem service (ES) spatial modelling is a key component of the integrated assessments designed to support policies and management practices aiming at environmental sustainability. ESTIMAP ("Ecosystem Service Mapping Tool") is a collection of spatially explicit models, originally developed to support policies at a European scale. We based our analysis on 10 case studies, and 3 ES models. Each case study applied at least one model at a local scale. We analyzed the applications with respect to: the adaptation process; the "precision differential" which we define as the variation generated in the model between the degree of spatial variation within the spatial distribution of ES and what the model captures; the stakeholders' opinions on the usefulness of models. We propose a protocol for adapting ESTIMAP to the local conditions. We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs. We then present the opinion of stakeholders; that in general considered the approach useful for stimulating discussion and supporting communication. Major constraints identified were the lack of spatial data with sufficient level of detail, and the level of expertise needed to set up and compute the models.

A social Bouba/Kiki effect: A bias for people whose names match their faces.

The "bouba/kiki effect" is the robust tendency to associate rounded objects (vs. angular objects) with names that require rounding of the mouth to pronounce, and may reflect synesthesia-like mapping across perceptual modalities. Here we show for the first time a "social" bouba/kiki effect, such that experimental participants associate round names ("Bob," "Lou") with round-faced (vs. angular-faced) individuals. Moreover, consistent with a bias for expectancy-consistent information, we find that participants like targets with "matching" names, both when name-face fit is measured and when it is experimentally manipulated. Finally, we show that such bias could have important practical consequences: An analysis of voting data reveals that Senatorial candidates earn 10% more votes when their names fit their faces very well, versus very poorly. These and similar cross-modal congruencies suggest that social judgment involves not only amodal application of stored information (e.g., stereotypes) to new stimuli, but also integration of perceptual and bodily input.

Constraining Forest Certificate's Market to Improve Cost-Effectiveness of Biodiversity Conservation in São Paulo State, Brazil.

The recently launched Brazilian "forest certificates" market is expected to reduce environmental compliance costs for landowners through an offset mechanism, after a long history of conservation laws based in command-and-control and strict rules. In this paper we assessed potential costs and evaluated the cost-effectiveness of the instrument when introducing to this market constraints that aim to address conservation objectives more specifically. Using the conservation planning software Marxan with Zones we simulated different scopes for the "forest certificates" market, and compared their cost-effectiveness with that of existing command-and-control (C&C), i.e. compliance to the Legal Reserve on own property, in the state of São Paulo. The simulations showed a clear potential of the constrained "forest certificates" market to improve conservation effectiveness and increase cost-effectiveness on allocation of Legal Reserves. Although the inclusion of an additional constraint of targeting the BIOTA Conservation Priority Areas doubled the cost (+95%) compared with a "free trade" scenario constrained only by biome, this option was still 50% less costly than the baseline scenario of compliance with Legal Reserve at the property.

Evaluating interactions of forest conservation policies on avoided deforestation.

We estimate the effects on deforestation that have resulted from policy interactions between parks and payments and between park buffers and payments in Costa Rica between 2000 and 2005. We show that the characteristics of the areas where protected and unprotected lands are located differ significantly. Additionally, we find that land characteristics of each of the policies and of the places where they interact also differ significantly. To adequately estimate the effects of the policies and their interactions, we use matching methods. Matching is implemented not only to define adequate control groups, as in previous research, but also to define those groups of locations under the influence of policies that are comparable to each other. We find that it is more effective to locate parks and payments away from each other, rather than in the same location or near each other. The high levels of enforcement inside both parks and lands with payments, and the presence of conservation spillovers that reduce deforestation near parks, significantly reduce the potential impact of combining these two policies.

Spatial overlap between environmental policy instruments and areas of high conservation value in forest.

In order to safeguard biodiversity in forest we need to know how forest policy instruments work. Here we use a nationwide network of 9400 plots in productive forest to analyze to what extent large-scale policy instruments, individually and together, target forest of high conservation value in Norway. We studied both instruments working through direct regulation; Strict Protection and Landscape Protection, and instruments working through management planning and voluntary schemes of forest certification; Wilderness Area and Mountain Forest. As forest of high conservation value (HCV-forest) we considered the extent of 12 Biodiversity Habitats and the extent of Old-Age Forest. We found that 22% of productive forest area contained Biodiversity Habitats. More than 70% of this area was not covered by any large-scale instruments. Mountain Forest covered 23%, while Strict Protection and Wilderness both covered 5% of the Biodiversity Habitat area. A total of 9% of productive forest area contained Old-Age Forest, and the relative coverage of the four instruments was similar as for Biodiversity Habitats. For all instruments, except Landscape Protection, the targeted areas contained significantly higher proportions of HCV-forest than areas not targeted by these instruments. Areas targeted by Strict Protection had higher proportions of HCV-forest than areas targeted by other instruments, except for areas targeted by Wilderness Area which showed similar proportions of Biodiversity Habitats. There was a substantial amount of spatial overlap between the policy tools, but no incremental conservation effect of overlapping instruments in terms of contributing to higher percentages of targeted HCV-forest. Our results reveal that although the current policy mix has an above average representation of forest of high conservation value, the targeting efficiency in terms of area overlap is limited. There is a need to improve forest conservation and a potential to cover this need by better targeting high conservation value areas.

Ecosystem services and opportunity costs shift spatial priorities for conserving forest biodiversity.

Inclusion of spatially explicit information on ecosystem services in conservation planning is a fairly new practice. This study analyses how the incorporation of ecosystem services as conservation features can affect conservation of forest biodiversity and how different opportunity cost constraints can change spatial priorities for conservation. We created spatially explicit cost-effective conservation scenarios for 59 forest biodiversity features and five ecosystem services in the county of Telemark (Norway) with the help of the heuristic optimisation planning software, Marxan with Zones. We combined a mix of conservation instruments where forestry is either completely (non-use zone) or partially restricted (partial use zone). Opportunity costs were measured in terms of foregone timber harvest, an important provisioning service in Telemark. Including a number of ecosystem services shifted priority conservation sites compared to a case where only biodiversity was considered, and increased the area of both the partial (+36.2%) and the non-use zone (+3.2%). Furthermore, opportunity costs increased (+6.6%), which suggests that ecosystem services may not be a side-benefit of biodiversity conservation in this area. Opportunity cost levels were systematically changed to analyse their effect on spatial conservation priorities. Conservation of biodiversity and ecosystem services trades off against timber harvest. Currently designated nature reserves and landscape protection areas achieve a very low proportion (9.1%) of the conservation targets we set in our scenario, which illustrates the high importance given to timber production at present. A trade-off curve indicated that large marginal increases in conservation target achievement are possible when the budget for conservation is increased. Forty percent of the maximum hypothetical opportunity costs would yield an average conservation target achievement of 79%.

Demonstrating a new framework for the comparison of environmental impacts from small- and large-scale hydropower and wind power projects.

Climate change and the needed reductions in the use of fossil fuels call for the development of renewable energy sources. However, renewable energy production, such as hydropower (both small- and large-scale) and wind power have adverse impacts on the local environment by causing reductions in biodiversity and loss of habitats and species. This paper compares the environmental impacts of many small-scale hydropower plants with a few large-scale hydropower projects and one wind power farm, based on the same set of environmental parameters; land occupation, reduction in wilderness areas (INON), visibility and impacts on red-listed species. Our basis for comparison was similar energy volumes produced, without considering the quality of the energy services provided. The results show that small-scale hydropower performs less favourably in all parameters except land occupation. The land occupation of large hydropower and wind power is in the range of 45-50 m(2)/MWh, which is more than two times larger than the small-scale hydropower, where the large land occupation for large hydropower is explained by the extent of the reservoirs. On all the three other parameters small-scale hydropower performs more than two times worse than both large hydropower and wind power. Wind power compares similarly to large-scale hydropower regarding land occupation, much better on the reduction in INON areas, and in the same range regarding red-listed species. Our results demonstrate that the selected four parameters provide a basis for further development of a fair and consistent comparison of impacts between the analysed renewable technologies.

Bayesian networks in environmental and resource management.

This overview article for the special series, "Bayesian Networks in Environmental and Resource Management," reviews 7 case study articles with the aim to compare Bayesian network (BN) applications to different environmental and resource management problems from around the world. The article discusses advances in the last decade in the use of BNs as applied to environmental and resource management. We highlight progress in computational methods, best-practices for model design and model communication. We review several research challenges to the use of BNs in environmental and resource management that we think may find a solution in the near future with further research attention.

Use of stochastic multi-criteria decision analysis to support sustainable management of contaminated sediments.

Sustainable management of contaminated sediments requires careful prioritization of available resources and focuses on efforts to optimize decisions that consider environmental, economic, and societal aspects simultaneously. This may be achieved by combining different analytical approaches such as risk analysis (RA), life cycle analysis (LCA), multicriteria decision analysis (MCDA), and economic valuation methods. We propose the use of stochastic MCDA based on outranking algorithms to implement integrative sustainability strategies for sediment management. In this paper we use the method to select the best sediment management alternatives for the dibenzo-p-dioxin and -furan (PCDD/F) contaminated Grenland fjord in Norway. In the analysis, the benefits of health risk reductions and socio-economic benefits from removing seafood health advisories are evaluated against the detriments of remedial costs and life cycle environmental impacts. A value-plural based weighing of criteria is compared to criteria weights mimicking traditional cost-effectiveness (CEA) and cost-benefit (CBA) analyses. Capping highly contaminated areas in the inner or outer fjord is identified as the most preferable remediation alternative under all criteria schemes and the results are confirmed by a probabilistic sensitivity analysis. The proposed methodology can serve as a flexible framework for future decision support and can be a step toward more sustainable decision making for contaminated sediment management. It may be applicable to the broader field of ecosystem restoration for trade-off analysis between ecosystem services and restoration costs.

Use of multicriteria involvement processes to enhance transparency and stakeholder participation at Bergen Harbor, Norway.

Use of participatory stakeholder engagement processes could be important to reduce the risk of potential conflicts in managing contaminated sites. Most stakeholder engagement techniques are qualitative in nature and require experienced facilitators. This study proposes a multicriteria involvement process to enhance transparency and stakeholder participation and applies it to a contaminated sediment management case study for Bergen Harbor, Norway. The suggested multicriteria involvement process builds on the quantitative principles of multicriteria decision analysis and also incorporates group interaction and learning through qualitative participatory methods. Three different advisory groups consisting of local residents, local stakeholders, and nonresident sediment experts were invited to participate in a stakeholder engagement process to provide consensual comparative advice on sediment remediation alternatives. In order for stakeholders or residents to be able to embrace a complex decision such as selection of remediation alternatives, the involvement process with lateral learning, combined with multicriteria decision analysis providing structure, robustness and transparent documentation was preferable. In addition, a multicriteria involvement process resulted in consistent ranking of remediation alternatives across residents, stakeholder, and experts, relative to individual intuitive ranking without the multicriteria involvement process.

Modeling the effects and uncertainties of contaminated sediment remediation scenarios in a Norwegian fjord by Markov chain Monte Carlo simulation.

Multimedia environmental fate models are useful tools to investigate the long-term impacts of remediation measures designed to alleviate potential ecological and human health concerns in contaminated areas. Estimating and communicating the uncertainties associated with the model simulations is a critical task for demonstrating the transparency and reliability of the results. The Extended Fourier Amplitude Sensitivity Test(Extended FAST) method for sensitivity analysis and Bayesian Markov chain Monte Carlo (MCMC) method for uncertainty analysis and model calibration have several advantages over methods typically applied for multimedia environmental fate models. Most importantly, the simulation results and their uncertainties can be anchored to the available observations and their uncertainties. We apply these techniques for simulating the historical fate of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the Grenland fjords, Norway, and for predicting the effects of different contaminated sediment remediation (capping) scenarios on the future levels of PCDD/Fs in cod and crab therein. The remediation scenario simulations show that a significant remediation effect can first be seen when significant portions of the contaminated sediment areas are cleaned up, and that increase in capping area leads to both earlier achievement of good fjord status and narrower uncertainty in the predicted timing for this.