Conference Agenda

The global biodiversity crisis demands innovative financing mechanisms to support nature conservation and restoration. Nature-based Solutions (NbS) present an innovative and cost-effective approach to conserving ecosystems while providing multiple benefits. However, funding constraints and the challenges of quantifying ecological benefits often limit their widespread implementation. This study introduces an approach to biodiversity financing through water tariffs focusing on the middle River Brenta area in North-Eastern Italy. This represents a pioneering example in the country of integrating ecosystem services into the financing of water resource management. The study area is part of a Natura 2000 site that supplies water to 1.5 million people annually. Using a multi-step approach, we compare current land use practices with five alternative NbS scenarios identified through stakeholder focus groups. We model the impact of these scenarios on water infiltration, carbon sequestration, and habitat conservation while conducting a cost-effective analysis to assess the development and maintenance costs of NbS relative to the ES they provide. Our results indicate that forest infiltration areas are the most effective for groundwater recharge and carbon sequestration, while constructed wetlands perform best in habitat conservation. Blended solutions emerge as the most efficient option, offering significant opportunities for biodiversity finance by delivering economic returns and conservation benefits. This research contributes to the ongoing discourse on biodiversity finance, aligning with multiple initiatives under the EU Green Deal umbrella and the Sustainable Finance Taxonomy (SFT), which seeks to redirect financial flows to slow down and reverse biodiversity loss.

Coastal and marine ecosystems provide critical environmental and socio-economic benefits but face increasing threats. Effective management strategies, including ecosystem conservation, restoration, and creation, are essential to safeguarding these ecosystems. However, public preferences and willingness to pay (WTP) for these management types remain underexplored. This study employs Discrete Choice Experiments (DCEs) to simultaneously assess public support and economic valuation for conservation, restoration, and creation interventions in the Great Barrier Reef (GBR), Australia. Using a sample of 882 Queensland adults, we compare preferences across bundled and unbundled choice formats. Our findings reveal that when assessed independently, conservation is the preferred management type, with respondents prioritizing the protection of pristine areas and expressing concerns over ecosystem degradation. However, in a bundled format, support for all management types increases, and WTP estimates rise substantially, suggesting that bundling enhances perceived value and willingness to pay. These results have key policy implications, advocating for the integrated implementation of conservation, restoration, and creation efforts in environmental management frameworks. Our findings contribute to the Nature-based Solutions literature by empirically validating the relationship between intervention intensity and marginal value. Additionally, this study highlights the impact of bundling in discrete choice experiments, emphasizing the need for further research into the cognitive mechanisms underlying bundling effects and refinements in valuation methodologies to better inform ecosystem management decisions.

We present an economic experiment where two groups interact in a linear public good game. We study two questions: First, is group behavior different when their action is framed as “taking from a public good” versus “giving to a public good”?

Second, how do groups behave when one of them takes from the public good while the other one gives? Analyzing the effect of framing is important because it sug- gests a way to encourage cooperation. Analyzing this effect for groups is important because groups (teams) are fundamental decision-making units in the economy. Fi- nally, analyzing opposing action spaces (one group gives while the other takes) is important because they characterize many real-world social dilemmas. We find that framing also affects groups: take-groups cooperate significantly less than give-groups in symmetric settings (with either two take- or two give-groups). In contrast, we do not find that take-groups behave differently from give-groups in opposing (or asym- metric) settings with one take- and one give-group; both groups are as cooperative as give-groups in a standard symmetric setting.

For over 4 decades, the IUCN has assessed the conservation status of thousands of species, categorising them in order of concern from Least Concern to Critically Endangered and Extinct. We assess the likelihood for a species to change from a given conservation status to another, estimating a yearly Markov switching model. This allows us to calculate the likelihood of extinction by 2100 for species in each category. We also investigate how these likelihoods of extinction differ for different subgroups of species (mammals, birds, reptiles, amphibians plants) and different periods.

Next, we assess the relationship between habitat and the likelihood of

belonging to an IUCN status. Combining this with our Markov switching model, we express the risk of extinction as a function of current and natural habitat.

Third, having established an extinction likelihood function, we can estimate the effect of developing a plot of land on the number of extinctions before 2100 by calculating the change of extinction risk for all species that are (potentially) present on that plot of land.

Forth, we develop the concept of the value of a statistical species (or cost of an expected life-year) as the ratio of the cost of protecting land and its effect on the likelihood of a species going extinct. This value allows us to rank plots of lands according to their priority for conservation.

Finally, we compare our empirical species extinction function with other approaches in the literature, such as the Species Area Relationship and the STAR biodiversity index.

Migratory bird species make major contributions to ecosystem services and

play an important role in human recreation and culture. Yet many migrant

bird species have declined over the past three decades. Habitat degradation

constitutes one of the most important threats for these species. Habitat

protection measures are usually designated at the national scale but, because

migrants move across international borders, intergovernmental coordination

is crucial to safeguard migratory species. Recently several authors have

developed ecological{economic procedures to design ecologically effective and

economically cost-efficient conservation plans for migratory birds. A common

underlying assumption in these proposals is that conservation planning is

implemented by collaborative international partnerships. Unfortunately, this

is not a credible assumption for many species. In fact, most migratory bird

species have inadequate protected area coverage for at least one part of their

annual cycle. In this paper we set up a simple bioeconomic model where we

explore optimal habitat protection and stock levels for the migratory bird

species with and without transboundary conservation eorts. In order to

further explore the results of the bioeconomic model, a case study involving

the White Stork (Ciconia ciconia) is considered.

Stated preference research often includes respondents’ past experiences with environmental events to assess their willingness-to-pay (WTP) for mitigating adverse outcomes. However, the quality of these self-reported experiences has received limited attention. This study employs a choice experiment (CE) model to assess the economic value of an improved red tide (RT) air quality forecasting system in five southwest Florida counties. Unlike traditional studies that focus on nonmarket goods like water or air quality, this study is the first to value better public information on the location and severity of RT—a significant environmental issue that has not been previously quantified. By integrating survey-elicited experiences with objective scientific data, such as satellite-derived chlorophyll-a (Chl-a) concentrations and respiratory irritation (RI) levels from citizen scientists, we validate and enhance the accuracy of respondents’ WTP estimates. We find that respondents prefer a new forecasting system under higher Chl-a and RI levels, and that incorporating scientific data helps improve the reliability of WTP estimates. This research contributes to the nonmarket valuation literature by quantifying the value of enhanced forecasting information, and serves as an important step toward linking scientific data with nonmarket economic outcomes for policy development.