Estimating Benefits of Nature-based Solutions: Diverging Values From Choice Experiments With Time or Money Payments

Abstract

Nature-based solutions (NBS) provide a promising means to a climate resilient future. To guide investments in NBS, stated preference studies have become a common tool to evaluate the benefits of NBS in developing countries. Due to subsistence lifestyles and generally lower incomes, SP studies in developing countries increasingly use time payments as an alternative to the traditionally implemented money payments. It remains unclear, however, how time values should be converted into money values, how the payment affects willingness to pay (WTP) estimates, and how this influence varies across settings with different levels of market integration. We compare the results of choice experiments that use either time or money payments and that are implemented in urban and rural Ghana. The choice experiments target to value different NBS aimed at erosion prevention and other ecosystem service benefits along the highly erosion prone Ghanaian coastline. Time payments are converted into monetary units using two generic wage-based conversion rates and one novel individual-specific non-wage-based conversion rate. We find higher WTP estimates for the time payments. Moreover, we find that the underlying implicit assumptions related to the currently commonly applied generic wage-based conversion rates do not hold. Finally, we find higher levels of market integration and smaller WTP disparities in the urban site, providing evidence that market integration allows for convergence of WTP estimates. These results provide guidance on the accurate estimation of NBS benefits through the implementation of stated preference studies with time payments.

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

“…As far as the authors are aware, there are no applications in scientific literature for the monetarization of land cover and usage or AI geospatial deep learning at a national, regional, or local contribution scale for services provided by the public administration, therefore a comparison of the results obtained to date is extremely difficult. At the same time, fewer are scientific works on monetary attribution to the components of the coverage for the estimation of ecosystem services and nature based solutions [90,91], but with the intent of estimates of mere research and not of actual transfer of technological application to the public sector. Furthermore, the works themselves are on a large and non-municipal scale and involve geographical areas different from those under study.…”

Combining spatial planning and ecosystem services value to assist ecological compensation decision-making—A case study of Yangtze River Delta ecological barrier, China

“…It is motivated by multiple political goals that are "bundled" (Lv et al, 2015), such as alleviating poverty through EC (Fan et al, 2020). In China, EC entails not only the monetized expression of ESs, but also includes a range of policies and institutions (Li et al, 2016;Hagedoorn et al, 2021). The implementation of EC is based on China's main functional area planning, which takes the development, protection and utilization of natural resources into account (Zhang and Zong, 2010).…”