This article presents a green roof cost-benefit analysis (CBA). Green roofs are roofs which are partially or completely covered by vegetation. We discuss the benefits and costs of light self-sustaining vegetated roofs. The benefits of the ecosystem services (ES) provided by green roofs can be classified into private and public benefits. We apply the selected valuation methods first in Helsinki, Finland and subsequently explain how results can be transferred to other urban locations. Past research and this study show that private benefits are usually not high enough to justify the expensive investment for a private decision maker. However, when the public benefits are added to the private benefits, social benefits are higher than the costs of green roofs in most cases.Past research quantified most types of the benefits, excluding scenic and biodiversity benefits. Scenic benefits denote the intangible benefits that people derive from the presence of green space, including at least aesthetic and psychological ones. In this article, special emphasis is placed on the valuation of the scenic benefits; these are among the most challenging benefits to valuate in monetary terms. We employ hedonic pricing theory, implemented via spatial regression models, and green roof implementation scenarios in order to estimate the aggregate willingness to pay for a “unit” of green roof. The results show that the scenic benefits can be a significant attribute in cost-benefit calculations. Yet, the amount of benefits strongly depends on the green roof design.
Road traffic safety is the result of a complex interaction of factors, and causes behind road vehicle crashes require different measures to reduce their impacts. This study assesses how strongly the variation in daily winter crash rates associates with weather conditions in Finland. This is done by illustrating trends and spatiotemporal variation in the crash rates, by showing how a GIS application can evidence the association between temporary rises in regional crash rates and the occurrence of bad weather, and with a regression model on crash rate sensitivity to adverse weather conditions. The analysis indicates that a base rate of crashes depending on non-weather factors exists, and some combinations of extreme weather conditions are able to substantially push up crash rates on days with bad weather. Some spatial causation factors, such as variation of geophysical characteristics causing systematic differences in the distributions of weather variables, exist. Yet, even in winter, non-spatial factors are normally more significant. GIS data can support optimal deployment of rescue services and enhance in-depth quantitative analysis by helping to identify the most appropriate spatial and temporal resolutions. However, the supportive role of GIS should not be inferred as existence of highly significant spatial causation.
Atmospheric predictability has improved by approximately 1 day per decade during the last 20 years based on verification results of ECMWF forecast output. In Finland, locally applied accuracy measures indicate marked improvements in the quality of forecasts for the general public since the late 1980s. It is assumed that similar trends will continue to the foreseeable future. Use of weather information will allow for better options in the decision-making of various stake holders in the transport sector, such as commuters or tourists, transport infrastructure owners and transport service and maintenance operators. This paper discusses the economic impacts and value of weather forecasts on different transport modes (road, rail, air) highlighting the effects of potential improvements in forecast quality in the expected future climates in Europe. It is not only the improved quality of available weather forecasts that will define the value of information. The way in which the information is communicated and how it is being utilized by decision-makers are highly relevant steps in a weather service value chain. Rather than applying the traditional Cost-Loss model, which would relate improved forecast accuracy to increased expected utility, an alternative approach is being applied. This 'Weather Service Chain Analysis' (WSCA) accounts for imperfect features in the communication chain and in the use of weather information by analysing the decay of the total potential benefits via decomposing the information flow from original forecast generation to final benefit realization. Concrete estimates are provided for the road transport modes both in Finland and in Europe.
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