28 research outputs found

    Protecting Electricity Networks from Natural Hazards

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    This handbook supports OSCE participating States in better protecting critical electrical energy infrastructure from natural hazards. By providing risk management options, tools and case studies, it is designed as a guide for policy-makers, state authorities, transmission networks operators and regulators in charge of protecting energy networks. In recent years the risk of supra-national power blackouts in the OSCE area causing significant economic losses has increased. One contributing factor is that extreme weather conditions occur more frequently. Another is an increased connectivity of power and telecommunication infrastructures and a higher technical complexity of the grid due to a changing energy mix, leaving industrial and commercial companies, the public and the private sector at risk

    Spatial dimensions of stated preference valuation in environmental and resource economics: methods, trends and challenges

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    Meta-analysis of nature conservation values in Asia & Oceania: Data heterogeneity and benefit transfer issues

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    We conduct a meta-analysis (MA) of around 100 studies valuing nature conservation in Asia and Oceania. Dividing our dataset into two levels of heterogeneity in terms of good characteristics (endangered species vs. nature conservation more generally) and valuation methods, we show that the degree of regularity and conformity with theory and empirical expectations is higher for the more homogenous dataset of contingent valuation of endangered species. For example, we find that willingness to pay (WTP) for preservation of mammals tends to be higher than other species and that WTP for species preservation increases with income. Increasing the degree of heterogeneity in the valuation data, however, preserves much of the regularity, and the explanatory power of some of our models is in the range of other MA studies of goods typically assumed to be more homogenous (such as water quality). Subjecting our best MA models to a simple test forecasting values for out-of-sample observations, shows median (mean) forecasting errors of 24 (46) percent for endangered species and 46 (89) percent for nature conservation more generally, approaching levels that may be acceptable in benefit transfer for policy use. We recommend that the most prudent MA practice is to control for heterogeneity in regressions and sensitivity analysis, rather than to limit datasets by non-transparent criteria to a level of heterogeneity deemed acceptable to the individual analyst. However, the trade-off will always be present and the issue of acceptable level of heterogeneity in MA is far from settle

    Voluntary environmental action and export destinations: the case of forest certification

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    There is an increasing tendency for forest product firms worldwide to adopt sound environmental management practices by voluntarily agreeing to have their forest practices certified by third parties. Using a simple model of profit maximization, we illustrate that the puzzling emergence of this non-state, self-imposed governance structure is compatible with firms' profit motives. An empirical model using firm data from three countries shows firm location and export destinations play a key role in firms' decisions to seek certification, while the nature of forestland ownership has no significant impact on certification decisions

    Wildfire Smoke and Health Impacts: A Closer Look at Fire Attributes and their Marginal Effects

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    Existing studies on the economic impact of wildfire smoke have focused on single fire events or entire seasons without considering the marginal effect of daily fire progression on downwind communities. Neither approach allows for an examination of the impact of even the most basic fire attributes, such as distance and fuel type, on air quality and health outcomes. Improved knowledge of these effects can provide important guidance for efficient wildfire management strategies. This study aims to bridge this gap using detailed information on 24 large-scale wildfires that sent smoke plumes to the Reno/Sparks area of Northern Nevada over a 4-year period. We relate the daily acreage burned by these fires to daily data on air pollutants and local hospital admissions. Using information on medical expenses, we compute the per-acre health cost of wildfires of different attributes. We find that patient counts can be causally linked to fires as far as 200–300 miles from the impact area. As expected, the marginal impact per acre burned generally diminishes with distance and for fires with lighter fuel loads. Our results also highlight the importance of allowing for temporal lags between fire occurrence and pollutant levels

    Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images

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    We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms
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