Methods and Data. PREPA reserves the right to review and approve all methods and data, which the Contractor develops hereunder. Such review or approval shall in no way relieve the Contractor from its responsibilities, obligations or liabilities under this Contract. The Contractor shall obtain such reviews or approval in writing from PREPA. The Contractor shall keep at the working area a copy of the Contract and its supplementary documents at all times, give the Engineer access thereto.
Methods and Data. Among other things, the GLWQA calls on the two nations to define “the threat to human health from critical pollutants” found in the Great Lakes region. In its December 2001 request, the IJC asked ATSDR to review those health assessments it conducted on hazardous waste sites within AOCs on the United States side of the Great Lakes region. The IJC stated further that It would be most helpful if ATSDR could identify evaluated sites within each AOC, the Hazard Category assigned to each site, any relevant demographic information available to ATSDR concerning the populations at risk, completed exposure pathways identified, and the priority substances following these pathways. This request was more complex than it appeared. AOCs do not correlate well geographically with waste sites that ATSDR has evaluated. Some waste sites occupy small parts of an AOC while others may be only partly contained within the AOC. Sources of contamination may lie close to but not within an AOC while still contributing to environmental pollution within the AOC. Many sources of contamination exist that are not ATSDR-evaluated sites. Finally, many sites that have been evaluated by ATSDR have since been remediated and thus do not provide current information environmental contamination in the AOCs. Therefore, in assembling this report, ATSDR scientists considered whether additional data sources might be useful in answering the request. We surveyed many sources of data on environmental exposures and human health (see Appendix 3 for the environmental and health data that were considered). At the core of the final report are the ATSDR site assessment/public health assessment data from hazardous waste sites in the AOCs or in counties that are in close proximity to AOCs. This report compiles previously published public health assessment documents for the 26 U.S. AOCs and for 54 counties in geographic proximity to those AOCs. We have updated those assessments with additional information from the U.S. EPA and other sources to reflect remediation efforts since the time of the initial assessment. In addition, to provide a more complete and holistic picture of ongoing chemical inputs into the Great Lakes AOCs and add value to the final report, ATSDR provided examples of data from three other major U.S. EPA data sources, including U.S. EPA 2001 chemical release data from its Toxic Release Inventory (TRI), U.S. EPA 2004 data on pollutant discharges into water, from its National Pollutant Discharge Eli...
Methods and Data. Fodder is a market good and therefore can be calculated using market-based methods and exchange values. The market-based methods - rent prices, resource rent, market price approaches and hybrid method of market price and resource rent were tested in order to calculate fodder production for all Estonian grasslands. Data from agricultural statistics, national accounts, Material Flow Account (MFA) and some others were used.
Methods and Data. Name of the dataset Data type Source 1251 - Consumption of fuels and energy 2017 Statistics Statistics Estonia Ecosystem unit map Spatial data Statistics Estonia 34 Poollooduslike koosluste tegevuskava aastateks 2014-2020 xxxxx://xxx.xxxxx.xx/sites/default/files/plk_tegevuskava2016.pdf 35 Keskkonnaamet. Kuidas hooldada ja taastada poollooduslikke kooslusi. xxxxx://xxx.xxxxxxxxxxxxxx.xx/n/node/3404 36 Lepasaar, Xxxxx & Xxxxxxx, Xxxxx. (2015). Non-market value of Estonian semi-natural grasslands: a contingent valuation study. Eesti poolloodusliku rohumaa turuväline väärtus: tingliku hindamise uuring. Discussions on Estonian Economic Policy. 23. 10.15157/tpep.v23i2.12494. We found that the market price of harvested hay/grass with the purpose to be used as fuel is the best estimation of the value of the ecosystem service. For valuing the service the data about the quantity and purchase prices of fuels recorded in energy statistics were used. The companies which used hay/grass as a fuel were determined and the purchase prices they had paid for the fuel were added. Prices without VAT were used in calculations to decrease the amount of human input. No further deductions of other human inputs were made. Another possible method is to look at gross (or net) value added generated by economic activities that depend upon natural capital. In this case it is the heat production plants that require grass as fuel. Gross value added (GVA) is the value of economic output minus the costs of intermediate inputs. Net value added (NVA) is gross value added minus the consumption of fixed capital (depreciation). NVA and GVA both reflect the combined return on capital, labour and natural capital, for instance in a country or in a sector. The value of GVA is already incorporated in GDP in NA. It describes how important the ecosystem service is and the contribution of the ecosystem to the economy.
Methods and Data. People in Estonia go hunting to obtain game for their own use or sell it to meat processing companies who sell the products made out of it. As game is traded in the functioning market, it gives the reason to use the market prices to value the service. Only some of the big game: elk, red deer, xxx deer, wild boar, and brown bear have commercial importance in that approach. Skins of elk and red deer are traded in small quantities. Because of the lack of demand, skins of small game are not traded in the market. Name of the dataset Data type Source Hunted game 2018/19 Statistics Estonian Environment Agency Weight of game’s cold body Literature, expert consultation Xxxxxxxx, T. (2003)39 - Purchase prices in 2019 Meat processing companies Ecosystem unit map Spatial data Statistics Estonia We considered using the sum of the quantity of hunted big game multiplied by the average quantity of meat obtained from the game species (weight of game carcass) and purchase price of game meat (without value-added tax (VAT)) a good approximation for the value of the ecosystem service of providing game. = ∑ ∗ ∗ + ∑ ∗ =1 =1 where ai - quantity of hunted game species; bi – average weight of the cold body of game species (kg); ci - average price of the meat of game species (without VAT) (€/kg); di – price of skin of game species (without VAT) (€/kg); The statistics for hunted game is available for the hunting year 2018/2019 (from March 2018 to February 2019), we considered it as an input for the year 2018. The statistics include hunted game for each hunting district, the area of hunting district and the number of users (hunters) of hunting districts. Purchase prices of big game for the current year are available on web sites of meat processing companies. Additionally, meat processing companies were contacted to get the average weight of the cold body of the game. The value of skins was also added. The purchase prices of skins of elk and red deer were obtained from the webpage of Estonian Hunters’ Society40 which purchases the skins for further reprocessing. 39 Tiit Randveer. Jahiraamat. 2003 40 xxxx://xxx.xxx.xx/
Methods and Data. Name of the dataset Data type Source Herbs that were marketed over 100 kg in 2015 Table Sepp. J., Raal, A. (2017) Ravimtaimede turustamisest Eestis aastal 2015 - Market prices in 2019 xxxxxxxxx.xx Ecosystem unit map Spatial data Statistics Estonia Products made from medicinal herbs are market goods and therefore we considered using the sum of the amount of marketed medicinal herb that grow in the wild on grasslands multiplied by the average 41 Pihlik, U. 1999. Ravimtaimed. Eesti bioloogilise mitmekesisuse ülevaate materjale. Tallinn-Tartu price of the products made from the herb (without value-added tax (VAT)) a good approximation for the value of the ecosystem service that is providing medicinal herbs. ℎ = ∑ ∗ =1 where ai - quantity of marketed medicinal herb that grow in the wild in the grasslands (kg); bi - average price of the products made from the herb (without VAT) (€/kg); The assessment is based on the survey carried out on the quantities of herbs that were marketed in apothecaries. The data was collected directly from the companies situated in Estonia that own a permit to handle drugs according to the register of activity licences of Agency of Medicines of the Republic of Estonia. But using this data has its shortfalls: the survey was carried out in 2015 so the data is somewhat outdated. As the survey only investigated companies that market their products in apothecaries, other smaller companies that grow and collect herbs but sell them in stores or markets are excluded from the assessment. In future, developments of assess the service of providing medicinal herbs the idea is to use the data from wholesale companies and then also smaller producing companies are included in the assessment. Also the consumption of herbs by households is not examined in this assessment and the only way to count for them is to collect the data by carrying out a separate survey. Data of marketed herbs from all the ecosystems was analysed in the survey. As a first step, it was necessary to determine the plant species that grow wildly on the grasslands to include them in the service assessment. This was achieved based on the habitat preference of the species. Additionally it was needed to determine if the herb that indeed grows on the grasslands was collected from the wild as some of the herbs listed in the survey are only collected from the wild, some are cultivated and collected from fields and for some both origins are possible. Herbs grown and collected from fields w...
Methods and Data. For the net carbon sequestration in grasslands, two methods were discussed: the social cost of carbon and payment for ecosystem service. The latter was considered relevant. Name of the dataset Data type Source - Reference values (Emission Factors) Greenhouse gas reporting of the LULUCF sector European Union Allowance Price Spot price xxxxx://xxxxxxxxxxxxxxxx.xxx/en/ets-prices Ecosystem unit map Spatial data Statistics Estonia
Methods and Data. The benefit transfer method is not a valuation method as such, but it is a method where benefits calculated for one place and time are transferred to another place and time or to the same place but another time.56 In general, this is an acceptable method in environmental economics, under certain conditions (e.g resource constraints). Main steps of benefit transfer are:
Methods and Data. Hunting is an activity that requires very specific equipment and licences. Therefore we can consider that the expenditure a hunter makes with the purpose to engage in the activity is the expenditure made to use the ecosystem service recreational hunting and we can consider the consumer expenditures as a marginal value of the ecosystem service. Name of the dataset Data type Source Hunted game 2018/19 Statistics Estonian Environment Agency Value and cost of hunting, 2016 Literature Xxxxx Xxxxx (FACE). The economic value of hunting in the EU. Presentation. 2016 Map of hunting districts in Estonia Spatial Data Estonian Environment Agency Ecosystem unit map Spatial data Statistics Estonia Hunting in Estonia is regulated so that every hunter who wishes to xxxx needs to have a valid hunting licence and pay a yearly fee for hunting rights. Expenditures to obtain a hunting licence include specific schooling and taking exams, but this is a one-time process and statistics about these are difficult to acquire. A hunter needs to pay a yearly fee for hunting rights which is 10 € per year. To widen the scope, we included other expenditures a hunter makes. No suitable data was found in Estonia but using benefit transfer method we adapted the data about the average yearly expenditures of German hunters in 201677 for Estonian context in 2018 by applying purchasing power standard (Table 32). According to expert opinion, there is no need to consider lease of a hunting ground which is the biggest contributor for the overall expenditure for a hunter in Germany as an expenditure for a hunter in Estonia due to differences in hunting systems so we excluded the expenditures made for leasing from our calculations.
Methods and Data. To assess the usage of MySpace among artists in the music industry, the current work embarked on a comprehensive content analysis of a sample of MySpace Music profile pages. In order to facilitate data collection and input, initial research efforts were shared among six individuals: Emory University Associate Professor Xx. Xxxxxxx Xxxx, Xxxxx University Ph D. candidates Xxxxx Xxxxxx, Xxx Xxx, and Jin Won Xxxxx, and Emory College BA Honors candidates Xxx Xxxx and myself. Subsequent data collection and data analysis were my individual effort. Sampling MySpace As noted by Fields et al. (2010) in their analysis of online social networks, the MySpace social network presents a variety of challenges for gathering a representative sample of musician pages. Since each MySpace profile is uniquely assigned a numeric identifier upon its creation, Hinduja and Xxxxxxx (2008) utilized a random number generator in their content analysis of adolescent MySpace profile pages. Unfortunately, the numeric identifiers assigned to MySpace profiles do not distinguish between MySpace Music profiles and general user profiles. Hinduja and Xxxxxxx examined the population of general user profiles, which comprise the overwhelming majority of profiles in the MySpace network. Moreover, at the time of their sampling efforts – less than one year following the inception of MySpace, there were substantially fewer orphaned/deleted accounts. Hinduja and Xxxxxxx’x sampling method – executed in the summer of 2006 – rendered only 5.9 percent error due to deleted accounts. Today, a sampling method similar to that used by Hinduja and Xxxxxxx would derive an exponentially greater error because “[the MySpace network is] plagued by spammers and orphaned accounts… and noisy data” (Fields et al., 2010, p. 2). Thus, gathering a random sample of MySpace artist profiles via a random number generator was not a feasible sampling method for this study. The difficulty in gathering a representative sample of artists on MySpace mirrors sampling difficulty in broader studies of the music industry. Historically, those who research the music industry have struggled to collect a representative sample of musicians because “no source of any kind… offers an exhaustive listing of every [musical] act… in the U.S. mainstream market” (Xxxx, 2004b, p. 1448). These researchers have traditionally relied on popularity charts to track performing acts (Xxxxxxx, 2010; Xxxxx, 1992; Xxxx, 2004b). Since there is no exhaustive listing...
