Coal fell to 108.35 USD/T on December 1, 2025, down 1.86% from the previous day. Over the past month, Coal's price has fallen 1.14%, and is down 20.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coal - values, historical data, forecasts and news - updated on December of 2025.

Coking Coal rose to 201.25 USD/T on November 10, 2025, up 0.12% from the previous day. Over the past month, Coking Coal's price has risen 3.60%, but it is still 4.39% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Coking Coal.

China's residential sector has experienced rapid electrification and gasification. Among rural households, however, coal still accounts for a large share of energy use, especially in the north. Use of coal for cooking and heating brings large health and pollution risks. From a theoretical viewpoint, economic tools such as taxes and subsidies have the potential to play a crucial role in addressing this issue. In this paper, a provincial-level dataset is used to estimate the price and income elasticities of aggregate coal demand by rural households. We find that coal is a non-Giffen inferior good for the rural household sector. This means that future income growth may help to induce switching from coal. Demand is becoming more price elastic as rural incomes grow. We also find that rural residential coal demand is more price- and income-responsive in the south than the north, perhaps because of fewer substitution options in the north. Our results provide benchmarks and parameters for policy simulation research. Provincial panel data for 1998–2012. Variables: Ln Coal consumption Ln Real coal price index Ln Real coal price level Ln GDP Secondary share of economy (%) State-owned share of total revenue from industrial enterprises (%) Five-year energy conservation assignments to industry (%) Post-2005 retired thermal power capacity (%) Ln Real gasoline price

Monthly and long-term coking coal price data (US$/mt): historical series and analyst forecasts curated by FocusEconomics.

Coal is a combustible black or brownish-black sedimentary rock, formed as rock strata called coal seams. Coal is mostly carbon with variable amounts of other elements, chiefly hydrogen, sulfur, oxygen, and nitrogen. Coal is formed when dead plant matter decays into peat and is converted into coal by the heat and pressure of deep burial over millions of years. Vast deposits of coal originate in former wetlands called coal forests that covered much of the Earth's tropical land areas during the late Carboniferous (Pennsylvanian) and Permian times. However, many significant coal deposits are younger than this and originate from the Mesozoic and Cenozoic eras.

Coal is used primarily as a fuel. While coal has been known and used for thousands of years, its usage was limited until the Industrial Revolution. With the invention of the steam engine, coal consumption increased. In 2020, coal supplied about a quarter of the world's primary energy and over a third of its electricity. Some iron and steel-making and other industrial processes burn coal.

Source: https://en.wikipedia.org/wiki/Coal

About this dataset

Context

The dataset tabulates the Coal City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Coal City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Coal City was 5,784, a 0.63% increase year-by-year from 2022. Previously, in 2022, Coal City population was 5,748, an increase of 0.05% compared to a population of 5,745 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Coal City increased by 923. In this period, the peak population was 5,805 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Coal City is shown in this column.
• Year on Year Change: This column displays the change in Coal City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Coal City Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Coal Center population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Coal Center across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Coal Center was 110, a 0.92% increase year-by-year from 2022. Previously, in 2022, Coal Center population was 109, a decline of 0.91% compared to a population of 110 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Coal Center decreased by 19. In this period, the peak population was 139 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Coal Center is shown in this column.
• Year on Year Change: This column displays the change in Coal Center population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Coal Center Population by Year. You can refer the same here

In the Great Britain market, you can also trade without an asset such as a power plant, renewables or batteries. Therefore, it can be differentiated between physical trades – backed by an asset – and solely financial non-physical trades on the markets without actually providing or receiving energy.

This dataset can be used in building forecast models, optimization models and in developing first trading strategies for both physical and non-physical energy trading.

A primer: Using this dataset, one can have a closer look into two auctions of the day-ahead electricity market in Great Britain and develop as well as back test a trading strategy maximizing profits between both auctions.

Content

 a csv-file (“auction_data.csv”) containing actual prices and traded volumes of both auctions as well as a price forecast for the first auction.  a csv-file (“forecast_inputs.csv”) with input variables that can be used to forecast the prices of the second auction (you can find a more detailed description of the input variables in a separate txt-file – “description_input_variables.txt”)  a csv-file (“system_prices.csv”) with the forecasted price range of the system prices as well as the actual prices

Demand + System Margin - The availability of the system, using the daily forecast availability data (UOU data) except in the case of wind farms where a wind forecast is used from GFS weather data.

Demand - An adjustment of the demand forecast to add back on embedded wind and solar to get a truer demand shape. For values beyond the end of the half hourly demand data from National Grid, the data is shaped from the published peak demand values using typical demand curves.

Within Day Availability - An adjusted availability figure for the system that is reduced based upon rules around likely plant issues and potential non-delivery of potential availability.

Margin - The difference between Availability and Demand forecasted.

Within Day Margin - The difference between the Within Day Availability and Demand forecasted.

Long-Term Wind - A wind forecast based upon GFS weather data.

Long-Term Solar - National Grid solar forecast.

Long-Term Wind Over Demand - The Long-Term Wind values divided by Demand values.

Long-Term Wind Over Margin - The Long-Term Wind values divided by Margin values.

Long-Term Solar Over Demand - The Long-Term Solar values divided by Demand values.

Long-Term Solar Over Margin - The Long-Term Solar values divided by Margin values.

Margin Over Demand - The Margin values divided by Demand values.

SNSP Forecast - forecasts system non-synchronous penetration, which is the percentage of how much generation or imports that will be on the system that are not synchronized with frequency.

Stack Price - The breakeven cost of generation as reported by a stack model. This stack model uses as inputs Spectron daily carbon, coal and gas prices (based upon closing prices) and uses UOU 2–14-day availability forecast data by unit. Where margin levels are tight an uplift is applied to reflect the increase reluctance to generate given the risk of high imbalance prices.

Within Day Stack Price - As with the Stack Price values but using reduced levels of availability via the same reductions carried out for the Within Day Availability data set.

Previous Day-Ahead Price - Gets the last day ahead price value (last published before the auction).

Previous Continuous Half-Hour Volume-Weighted Average Price (VWAP) - Gets the volume weighted average price of all trades on half-hourly contracts in the continuous intraday market from 7 days before, i.e. on a Monday it will be for the previous Monday.

Inertia Forecast - a forecast for pre-balancing Inertia based upon the fundamentals-based generation forecast data.

India Coal: Representative Price: Non-Coking: Grade: G12 data was reported at 1,686.000 INR/Ton in Oct 2025. This records an increase from the previous number of 1,607.000 INR/Ton for Sep 2025. India Coal: Representative Price: Non-Coking: Grade: G12 data is updated monthly, averaging 2,185.000 INR/Ton from Mar 2020 (Median) to Oct 2025, with 68 observations. The data reached an all-time high of 4,068.000 INR/Ton in May 2022 and a record low of 1,205.000 INR/Ton in Oct 2020. India Coal: Representative Price: Non-Coking: Grade: G12 data remains active status in CEIC and is reported by Ministry of Coal. The data is categorized under India Premium Database’s Energy Sector – Table IN.RBU: Coal: Representative Price.

The 'Anthracite Coal Price Information' dataset from the Korea Mining and Energy Corporation is provided to ease the burden of fuel costs for ordinary citizens and to promote price stability. This data includes the price of anthracite coal by grade and price fluctuations by period of increase, which is useful for understanding the difference in support for anthracite manufacturers and transporters. This allows for analysis of the government's price support policy and price trends in the anthracite industry. The data is provided free of charge on the public data portal and can be downloaded and used in CSV format. This information is used as important basic data for policymakers and researchers to establish anthracite price policies and respond to market fluctuations.

About the Project The KAPSARC Energy Model of China (KEM China) project began in 2014 to study energy and environmental issues in China, focusing initially on the coal supply industry. 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 optimizes supply decisions, minimizing fuel and technology costs, while taking into account the effect of government regulation on prices and the environment.Key Points The extraordinary pace of development of China’s coal industry created transportation bottlenecks, which increased the price of delivered domestic coal and impacted global seaborne coal prices. Congestion costs added extra costs of energy supply to the Chinese economy, calculated to be RMB 228 billion in 2011. Debottlenecking has reduced the price of Chinese domestic coal delivered to the coastal regions and contributed to the reduction in global seaborne prices since 2011. Our analysis suggests that the existing tariff structure retains most of the economic efficiency of marginal cost pricing. Though many of the infrastructure expansions delivered strongly positive rates of return, some may represent pre-investment in future needs.

Iron Ore rose to 106.94 USD/T on December 1, 2025, up 2.00% from the previous day. Over the past month, Iron Ore's price has risen 1.04%, and is up 1.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Iron Ore - values, historical data, forecasts and news - updated on December of 2025.

About this dataset

Context

The dataset tabulates the Coal Creek population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Coal Creek across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Coal Creek was 367, a 0% decrease year-by-year from 2022. Previously, in 2022, Coal Creek population was 367, an increase of 0.27% compared to a population of 366 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Coal Creek increased by 72. In this period, the peak population was 367 in the year 2022. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Coal Creek is shown in this column.
• Year on Year Change: This column displays the change in Coal Creek population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Coal Creek Population by Year. You can refer the same here

Electric power generation, fuel consumed, and cost of fuel. Fuels include coal, petroleum products, uranium and others. Data presented at the national and provincial levels, however not all combinations are available.

LME Index rose to 4,700 Index Points on October 29, 2025, up 0.79% from the previous day. Over the past month, LME Index's price has risen 7.33%, and is up 13.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. LME Index - values, historical data, forecasts and news - updated on December of 2025.

Clean hydrogen has the potential to serve as an energy carrier and feedstock in decarbonizing energy systems, especially in “hard-to-abate” sectors. Although many countries have implemented policies to promote electrolytic hydrogen development, the impact of these measures on costs of production and greenhouse gas emissions remains unclear. Our study conducts an integrated analysis of provincial levelized costs and life cycle greenhouse gas emissions for all hydrogen production types in China. We find that subsidies are critical to accelerate low carbon electrolytic hydrogen development. Subsidies on renewable-based hydrogen provide cost-effective carbon dioxide equivalent (CO2e) emission reductions. However, subsidies on grid-based hydrogen increase CO2e emissions even compared with coal-based hydrogen because grid electricity in China still relies heavily on coal power and likely will beyond 2030. In fact, CO2e emissions from grid-based hydrogen may increase further if China continues to approve new coal power plants. The levelized costs of renewable energy-based electrolytic hydrogen vary among provinces. Transporting renewable-based hydrogen through pipelines from low- to high-cost production regions reduces the national average levelized cost of renewables-based hydrogen but may increase the risk of hydrogen leakage and the resulting indirect warming effects. Our findings emphasize that policy and economic support for nonfossil electrolytic hydrogen is critical to avoid an increase in CO2e emissions as hydrogen use rises during a clean energy transition.

A rapid coal phase-out is needed to meet the goals of the Paris Agreement, but is hindered by serious challenges ranging from vested interests to the risks of social disruption. To understand how to organize a global coal phase-out, it is crucial to go beyond cost-effective climate mitigation scenarios and learn from the experience of previous coal transitions. Despite the relevance of the topic, evidence remains fragmented throughout different research fields, and not easily accessible. To address this gap, this paper provides a systematic map and comprehensive review of the literature on historical coal transitions. We use computer-assisted systematic mapping and review methods to chart and evaluate the available evidence on historical declines in coal production and consumption. We extracted a dataset of 278 case studies from 194 publications, covering coal transitions in 44 countries and ranging from the end of the 19th century until 2021. We find a relatively recent and rapidly expanding body of literature reflecting the growing importance of an early coal phase-out in scientific and political debates. Previous evidence has primarily focused on the United Kingdom, the United States, and Germany, while other countries that experienced large coal declines, like those in Eastern Europe, are strongly underrepresented. An increasing number of studies, mostly published in the last 5 years, has been focusing on China. Most of the countries successfully reducing coal dependency have undergone both demand-side and supply-side transitions. This supports the use of policy approaches targeting both demand and supply to achieve a complete coal phase-out. From a political economy perspective, our dataset highlights that most transitions are driven by rising production costs for coal, falling prices for alternative energies, or local environmental concerns, especially regarding air pollution. The main challenges for coal-dependent regions are structural change transformations, in particular for industry and labor. Rising unemployment is the most largely documented outcome in the sample. Policymakers at multiple levels are instrumental in facilitating coal transitions. They rely mainly on regulatory instruments to foster the transitions and compensation schemes or investment plans to deal with their transformative processes. Even though many models suggest that coal phase-outs are among the low-hanging fruits on the way to climate neutrality and meeting the international climate goals, our case studies analysis highlights the intricate political economy at work that needs to be addressed through well-designed and just policies.

About this dataset

Context

The dataset tabulates the Coal Valley population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Coal Valley across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Coal Valley was 3,816, a 0.08% decrease year-by-year from 2022. Previously, in 2022, Coal Valley population was 3,819, a decline of 0.57% compared to a population of 3,841 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Coal Valley increased by 217. In this period, the peak population was 4,025 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Coal Valley is shown in this column.
• Year on Year Change: This column displays the change in Coal Valley population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Coal Valley Population by Year. You can refer the same here

The parameters of impact test.