The Annual Energy Outlook presents longterm annual projections of energy supply, demand, and prices focused on the U.S. through 2050, based on results from EIA's National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internally-consistent sets of assumptions, the results of which are presented as cases. The analysis in AEO2014 focuses on five primary cases: a Reference case, Low and High Economic Growth cases, and Low and High Oil Price cases. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data for 1969/1970 is not available for Cork, Limerick, Galway, Waterford and Other areas The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. National and Other Areas figure changed for 2015 on 27/6/15 as revised data received from Local Authorities Prices includes houses and apartments measured in €

This dataset is restricted, for more information please contact the author. Data were collected from multiple sources:The Electricity & Co-Generation Regulatory AuthoritySaudi Electricity companyWeb news article (2015, December 28). Increase of Fuel, Electricity and Water prices. Retrieved from https://akhbaar24.argaam.com/article/detail/255091accessed on March 22, 2018.In October 1984, the government adopted a Tariff that increased with increasing consumption. The changes of Tariffs started in November 1984.Tariff approved by Council of Ministries 170 and become effective in October 2000. This Tariff remained effective for approximately ten years The residential, agricultural, mosques, and charitable societies remained unchanged till 2018In 2010, a new tariff for government, commercial, and industrial consumption came into force, this was adopted by a decision of ECRA's board, to set tariffs for non-residential consumption with an upper limit of SR0.26/kWh.In 2015, the total value of electricity consumed by the residential sector was worth about 38 billion U.S. dollars.In 2018, the Council of Ministers has approved gradual revision of energy prices in the Kingdom including changes to electricity tariffs effective from Jan. 1. 2018, the Electricity and Cogeneration Regulatory Authority (ECRA) announced that new prices will take effect on January 1st, 2018.source: ECRACitation: Alghamdi, Abeer. 2018. “Changes in Saudi Arabia Electricity Prices.” [dataset]. https://datasource.kapsarc.org/explore/dataset/electricity-prices-in-saudi-arabia/information/. 

Note: Updates to this data product are discontinued. The China agricultural and economic database is a collection of agricultural-related data from official statistical publications of the People's Republic of China. Analysts and policy professionals around the world need information about the rapidly changing Chinese economy, but statistics are often published only in China and sometimes only in Chinese-language publications. This product assembles a wide variety of data items covering agricultural production, inputs, prices, food consumption, output of industrial products relevant to the agricultural sector, and macroeconomic data.

Annual data on electricity generating capacity, electricity generation and useful thermal output, fuel receipts, fuel stocks, sales, consumption, and emissions in the United States. Based on Form EIA-861 and Form EIA-860 data. Annual time series extend back to 1994.

This dataset contains Electricity Tariff by Authority for- 2007-2019. Data from Federal Competitiveness and Statistics Authority. Follow datasource.kapsarc.org for timely data to advance energy economics research.

The Feed Grains Database contains statistics on four feed grains (corn, grain sorghum, barley, and oats), foreign coarse grains (feed grains plus rye, millet, and mixed grains), hay, and related items.

Fruit and Tree Nut Data provide users with comprehensive statistics on fresh and processed fruits, melons, and tree nuts in the United States, as well as some global data for these sectors. It harmonizes and integrates data from the ERS market outlook program with data collected by different Federal and international statistical agencies to facilitate analyses of economic performance over time and across domestic and foreign markets.

This data set provides monthly average price values, and the differences among those values, at the farm, wholesale, and retail stages of the production and marketing chain for selected cuts of beef, pork, and broilers. In addition, retail prices are provided for beef and pork cuts, turkey, whole chickens, eggs, and dairy products. Price spreads are reported for last 6 years, 12 quarters, and 24 months. The retail price file provides monthly estimates for the last 6 months. The historical file provides data since 1970.

This dataset contains Henry hub natural gas spot prices from 1997. Data from US Energy information administration. Notes:- Referring "Natural gas spot and future prices (NYMEX)"- Prices are based on delivery at the Henry Hub in Louisiana. Official daily closing prices at 2:30 p.m.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €

ERS annually calculates "normalized prices," which smooth out the effects of shortrun seasonal or cyclical variation, for key agricultural inputs and outputs. They are used to evaluate the benefits of projects affecting agriculture.

On-Farm Residue Removal Study for Resilient Economic Agricultural Practices in Morris, Minnesota Interest in harvesting crop residues for energy has waxed and waned since the oil embargo of 1973. Since the at least the late 1990’s interest has been renewed due to concern of peak oil, highly volatile natural gas prices, replacing fossil fuel with renewable sources and a push for energy independence. The studies conducted on harvesting crop residues during the 1970’s and1980’s focused primarily on erosion risk and nutrient removal as a result early estimates of residue availability focused on erosion control (Perlack et al., 2005). More recently, the focus has expanded to also address harvest impacts on soil organic matter and other constraints (Wilhelm et al., 2007; Wilhelm et al., 2010). In West Central Minnesota, crop residues have been proposed a replacement for natural gas (Archer and Johnson, 2012) while nationally residues are also be considered for cellulosic ethanol production (US DOE, 2011). The objective of the on-farm study was to assess the impact of residue harvest on working farms with different management systems and soils. Indicators of erosion risk, soil organic matter, and crop productivity is response to grain plus cob, or grain plus stover compared to grain only harvest.

Explore the Penn World Table dataset featuring key economic indicators like real GDP, population, human capital index, and more. Access detailed information and analysis for various countries. Expenditure, GDP, PPP, output, Population, working hours, Index, Household, Consumption, Capital , IRR, prices Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Venezuela, Yemen, Zambia, Zimbabwe, World Follow data.kapsarc.org for timely data to advance energy economics research. When using these data, please refer to the following paper:Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt

About the Project The KAPSARC Energy Model of China (KEM China) project began in 2014 to study energy and environmental issues in China. KEM China has been developed to understand China’s energy economy and fuel mix, how they are impacted by government intervention, as well as their interaction with global markets. It is a modular integrated mixed-complementarity problem model that optimizes supply decisions, minimizing fuel and technology costs, while taking into account the effect of government regulation on prices and the environment.Key PointsWhen energy sectors transition from government-controlled to market-driven systems, the legacy regulatory instruments can create unintended market distortions and lead to higher costs. In China, the most notable regulatory throwback is ceilings on electricity prices that generators can charge utilities, which are specified by plant type and region. We built a mixed complementarity model calibrated to 2012 data to examine the impact of these price caps on the electricity and coal sectors. Our study highlights the following major findings: Capped on-grid tariffs incentivize market concentration and vertical integration so that generators can cross-subsidize power plants, ensure an uninterrupted supply of fuel and reduce the impact of volatility in fuel prices. Tight price caps can cause the system to deviate from the least-cost capacity and fuel mix. In 2012, this resulted in an additional annual cost of at least 45 billion RMB, or 4 percent of China’s total power system cost. The government also had to subsidize some of the losses, which indicates that this regulatory design is not responsive to market realities. Price constraints can impact the outcomes of other policy initiatives causing them to veer from intended goals. In the case of China, according to our modeling, greater installed wind capacity does not have a significant impact on the amount of coal consumed. Also, abolishing restrictive tariff caps on coal-fired generation does not increase coal use because the utilization rate of peak-shaving coal plants drops. We also estimate, using the model, subsidies required for a range of wind capacity additions to China’s power generation mix and find that the feed-in tariff could have been less generous.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data marked with n/a over the period 1969 and 1973 are not available. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Figure changed on the 27/6/16 as revised data received from the Local authority Includes houses and apartments, measured in €

This data product provides users with comprehensive statistics on fresh and processed vegetables and pulses in the United States, as well as global production and trade data for these sectors. It harmonizes and integrates data from the ERS market outlook program with data collected by different Federal and international statistical agencies to facilitate analyses of economic performance over time, and across domestic and foreign markets.

The Weekly Petroleum Status Report contains information on petroleum, gasoline, crude oil, etc. The report is updated weekly on Wednesdays at 1:00 p.m. (Eastern Time)

This data product contains statistics on wheat-including the five classes of wheat: hard red winter, hard red spring, soft red winter, white, and durum-and rye. Includes data published in the monthly Wheat Outlook and previously annual Wheat Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series. Most data are on a marketing year basis, but some are calendar year.