Since 2011 Indonesia has become the world’s largest exporter of steam coal. Two supporting factors of Indonesia to be the largest exporter are its enormous production and low operating cost. This paper foresees the production and extraction cost of Indonesian coal in the coming period to evaluate marketing policies and estimate the cost of Indonesian coal supply in domestic market as well as in export market. The production forecasting is carried out by Gompertz curve. Peak production of Indonesian coal is expected to take place in 2026. Moreover, the extraction cost in coal basins which produce high calorific value of coal, in accordance to the operating cost forecasting, is higher than the one with low calorific value of coal due to its higher stripping ratio. Three main basins of Central Sumatra, Tarakan, and Barito basins play major rule in supplying coal for domestic use in short term. And other coal basins such as South Sumatra, Kutai, Bengkulu, and Ombilin basins provide long term supply in the domestic and export markets.
Indonesia is the country’s largest steam coal exporter in the world. Its steam coal supply continues to increase approximately 17% per year and accommodates approximately 20% of the total volume of coal trading in the international market every year [
The future of Indonesian coal production is captivating to discuss considering its position as the largest steam coal supplier. Some researchers have carried out prominent studies on the future of Indonesian coal production. They are the Indonesian Coal Mining Association (ICMA) (2010), Mohr and Evans (2009), Paztek and Croft (2010), and Hook et al. (2010) [
With respect to the production and supply of Indonesian coal in the export market, this research will focus on the production and cost forecasting of coal extraction. Extraction cost in which its value is equivalent to the operating costs is drawn an attention as, for non-renewable resource, it is a function of cumulative production. This condition may affect the comparative advantages of Indonesian coal in the export market. The coal production forecasting will be carried out using the Gompertz curve method to the respective coal basins. Operating cost will be predicted based on trend analysis of the profitability value of coal exploitation in every coal basin. Coal basin approach is chosen as it accommodates coal quality aspect which influences the depletion rate of the coal and the profitability value of the coal exploitation.
Coal in Indonesia is mainly mined in 7 economic coal basins located in Sumatra and Kalimantan (
Observing its formation time, Indonesian coal is classified into the tertiary coal. Coal quality can be classified either based on the settings of the tectonic plates or its relationship to the geothermal gradient and tectonic stress [
Basin | Typical Deposit | Calorific Value (KCal/Kg) | Moisture Contentb | Sulfur Contentc | Ash Contentd |
---|---|---|---|---|---|
Ombilin | Lenticilar and small coverage area | 7000 | Low | Low | Low |
Barito-Tanjung | Thin and continuous in lateral direction | 6000 | Low | High | Low |
Bengkulu | Thick and wide coverage area | <5000 | High | Low | High |
South Sumatra, Central Sumatra | <5000 | High | Low | High | |
Barito-Warukin | <5000 | High | Low | High | |
Kutai and Tarakan | <5000 | High | Low | High |
Note: aGeneral quality represent in as received (AR) basis; bLow moisture content is Total Moisture (TM) <30% and High moisture content means TM >30%; cLow sulfur content means Sulfur (S) <1% and High sulfur content means S >1%; dLow ash content means Ash <10% and High ash content means Ash >10%.
Production and Depletion of Indonesian CoalCoal production in Indonesia has started since the late 1800s in Ombilin and Mahakam coal fields. From the late 1800s until 1980s the coal production weren’t much, mainly due to the lack competitiveness of coal in compare to oil as energy source. But the situation changed after the boom of the oil price in 1973. Many countries then started to look for an alternative energy source [
The success of such coal exploration activities triggered coal production. The coal production was started since early 1990s when the mining companies of CCOW 1st generation started its production activity. The success of the first CCOW stimulates the establishment of the second and third CCOW in 1994 and 1997, respectively. Although they are not as successful as the first CCOW generation as not all the mining companies in second and third generations reached their production phase yet, all the three CCOWs provide significant impact on the Indonesia’s coal production. In between 1990-2013, the coal production increased around 17% per year (
When the coal production is classified based on its quality (
Depletion rate is formulated as the inverse of R/P (Equation (1)). There are 2 types of depletion rate of non-renewable resource; depletion rate of the ultimate recoverable reserves (URR) and depletion rate of the remaining reserves. Depletion rate of URR analyzes part of which is produced annually. Meanwhile, the depletion rate of remaining reserve analyzes how fast the rest of the reserves will be depleted. The latter method is more widely used.
Note
・ dRRR,t = depletion of the remaining reserves
・ qt = production in year
・ Rr = remaining reserves
・ URRt = ultimate recoverable reserves in year t
・ Qt = cumulative production in year t
The depletion rate is influenced by physical, technical, and economical factors. In the case of petroleum, its depletion rate is influenced by fluid flow. Of many determinant factors of fluid flow, i.e. pressure, rock compressibility, dissolution, formation slope, capillarity, and more, the reservoir pressure and water influx are the two most dominant factors which influence the depletion rate of petroleum. The investigation of the depletion rate of productions of some nonrenewable resources shows that the maximum depletion rate generally occurs when the production reaches its peak point or the end of the plateau, after which the depletion rate will be stable or else lower [
In the case of coal in Indonesia, the depletion rate is influenced by the quality of the coal in each coal basin (
Production of coal, like other non-renewable resources, is influenced by many factors. Factors affecting the production capacity of coal are the availability of reserves, the demand/market, and the development of technology on exploration, mining, and pro- cessing. In addition to the aforementioned physical factors, there are also considerations with respect to economic, social, and environmental aspects which influence the coal production.
Basin | Remaining Reserve (Million Tonnes) | Proportion of Reserves | Production (Million Tonnes) | Depletion Rate | ||
---|---|---|---|---|---|---|
Low CV | Med CV | > High CV | ||||
Ombilin | 158 | 0% | 2% | 98% | 0.5 | 0.7% |
Bengkulu | 19 | 0% | 20% | 80% | 6.8 | 6.8% |
Central Sumatra | 689 | 90% | 4% | 6% | 2.3 | 0.3% |
South Sumatra | 12,428 | 54% | 45% | 1% | 25.8 | 0.2% |
Kutai and Tarakan | 14,015 | 9% | 86% | 5% | 178.8 | 1.3% |
Barito | 4772 | 17% | 66% | 17% | 207.8 | 4.4% |
The influencing factors of production, be it all or some, need to be considered to determine the production forecasting method. The most commonly used method in forecasting the coal production is Growth curve. The growth curve, be it Logistic, Gompertz, or Richard curve, assumes that the influencing factors of production are accumulated into the production behavior of such non-renewable resources. Thus, the extrapolation of the growth curve for the coming period will also accommodate the influencing factor of production [
In this study, the forecasting of Indonesian coal production will be carried out using Gompertz curve. This method assumes that the influencing factors have no significant change throughout the duration of the forecasting period. Thus, in the model such factors remain constant with respect to time. This method is generally used for short term forecasting.
Note
・ y(t): Cumulative production in time function t
・ URR: Ultimate Recoverable Reserves
・ k: Constant
・ t − t0: Duration of forecast
Gompertz curve predicts the production based on previous time series data of the cumulative production. The data will be smoothed with respect to Equation 2. Iteration process is required to determine k, constant of the equation. Production forecasting is carried out by extrapolating the graph by assuming constant URR and single-peak curve. The curve will reach its maximum point (peak production) when the coal reserve is depleted of around 37% of the total URR [
Equation (2) of Gompertz curve indicates that the cumulative production value of y(t) will only depend on URR value. Thus, the accuracy of URR will determine the accuracy of the coal production forecasting. The URR can be determined through two approaches, i.e. quoted reserves and production history based in reserves. Quoted reserves are estimated based on official data issued by an official institution. Meanwhile URR based on production history is analyzed and determined based on historical data of production such as Hubbert linearization Process [
Debate often arises in deciding which type of URR is more realistic as an input of production forecasting. URR of quoted reserves refers to the total reserves, both the economical reserves in current time and the reserves that will be mined in future. In other words, URR of the quoted reserves refers to the maximum reserve. URR of production history, in other hand, estimates the reserve based on current condition. It refers only to the economical reserves in current time.
In this study, the URR is determined based on the quoted reserves acquired from the Geological Agency, Indonesian Ministry of Energy and Mineral Resources. Quoted reserves approach is chosen to study the long-term effects of the availability of reserves and coal production with respect to the extraction cost. URR values of the Indonesian coal basins are listed in
By using historical data of coal production from1990 to 2013, URR quoted reserves (
Coal Basin | Remaining Reserve in 2013 (Million Tonnes) | Cumulative Production (Million Tonnes) | Total URR (Million Tonnes) |
---|---|---|---|
Ombilin | 158.4 | 23.2 | 181.6 |
Bengkulu | 18.9 | 26.4 | 45.4 |
Central Sumatra | 689.2 | 16.7 | 705.9 |
South Sumatra | 12,428 | 285.7 | 12,713 |
Tarakan | 1261 | 233.8 | 1495 |
Kutai | 12,029 | 1270 | 13,299 |
Barito | 4771 | 1585 | 6353 |
Total | 31,357 | 3441 | 34,798 |
Basin | Gompertz curve Equation | Peak year | Peak Production (million tonnes) | Depletion rate at peak |
---|---|---|---|---|
Ombilin | 2048 | 1.6 | 1.40% | |
Bengkulu | 2011 | 1.2 | 4.33% | |
Central Sumatra | 2040 | 12.7 | 2.88% | |
South Sumatra | 2069 | 111.6 | 1.39% | |
Tarakan | 2023 | 38.5 | 4.24% | |
Kutai | 2031 | 241.7 | 2.92% | |
Barito | 2018 | 173.4 | 4.35% | |
Indonesia | 2026 | 485.4 | 1.89% |
2065. Indonesia’s coal production relies heavily on these three coal basins of Kalimantan Island. The decline in production from these Kalimantan basins from 2024 cannot be replaced by the production from the Sumatran basins due to its superiority. Kalimantan coal is preferred by consumers due to its high calorific value, closer location to the consumer and the ease of transportation (by the river and its infrastructure that supports large quantity production).
Depletion rate analysis is performed to examine the reasonability of the forecasting results. Data of the maximum depletion rate of other non-renewable resources is gathered. It includes the US copper production of 4.3%, South African gold production of 4.1%, and US coal production of about 3% [
One of the main components in production of coal and mineral resources is the extraction cost whose value is equivalent to the operating cost. The operating cost tends to increase as the reserves decrease. This phenomenon, according to Hotelling (1931), occurs as people/community will mine/extract a resource of low operating cost before switching to other resources with higher operating cost [
Herein, the analysis of the correlation between the cumulative production and operating cost in Indonesian coal mining will be conducted for three coal mining companies, i.e. PT Adaro Indonesia (Adaro), PT Arutmin Indonesia (Arutmin), and PT Kaltim Prima Coal (KPC). They are sufficiently representative as their productions are approximately 30% of total coal production in Indonesia. The cumulative production data and operating cost at the three companies are plotted in
Cumulative production and operating cost in Indonesian coal mining, like other non-renewable resources, shows similar trend in which the production cost will increase along with the increase of the cumulative production. The cumulative data from the three mining companies shows that within 10 years the operating cost increased by an average of 10% per year while production increased by 9.3% per year.
The operating cost in Indonesian coal mining increases primarily due to the increase in the stripping ratio (SR) (
In terms of the operating cost per stripping ratio, the three coal mining companies have varying costs. Adaro’s cost per stripping ratio is the largest among the three, of USD 6.15 per ton, followed by Arutmin 3.39 USD per ton, and t KPC 3.17 USD per ton. The difference in the operating cost of these companies is due to different supply chain production. Adaro requires greater cost as it needs to transport coal from the mine to the selling point as far as 200 Km using barges. KPC experiences the lowest cost as its mine site is adjacent to the beach. Thus, it only requires an overland conveyor to load the coal onto barges or ships before being transported to the consumer.
In 2012 there were more than 900 coal mining licenses in Indonesia [
Model of coal mine operating cost is developed with the assumption that the cost will increase as the cumulative production increases. The increase in the operating cost with respect to the cumulative production forms particularly similar trend as long as there is no significant change in the condition of mineable reserves, such like the change in the mining method from the open pit mining into underground mining.
In the case of Indonesia, the operating cost model is developed in every economic coal basin. The steps to formulate operating cost model are as follows; 1) determine the operating cost based on the gross profit margin, 2) plotting the historical data of the average operating cost and the cumulative production, 3) trend analysis to smoothing the cumulative availability curve.
Gross profit margin approach is chosen in estimating the operating cost as it enables operating cost data collection of all the Indonesian coal mines. Gross profit margin is the profit value after the deduction of cost of goods sold (mining, processing, and transportation costs) and royalty. The average values of the gross profit margin with respect to the coal basin of period 1998 to 2013 are listed in
By knowing the profit portion from the gross profit margin, the operating cost is determined as the difference between the revenue and the profit. Revenue earned from
Basin | Component | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Price referencea | 20.7 | 19.1 | 23.0 | 28.2 | 21.7 | 27.8 | 63.7 | 53.1 | 47.7 | 75.8 | 139.3 | 70.1 | 96.7 | 117.0 | 96.8 | 82.2 | |
Ombilin | Average production CVb | 6612 | 6497 | 6418 | 6448 | 6512 | 6705 | 6859 | 6663 | 6593 | 6961 | 6332 | 6300 | 6304 | 6500 | 6500 | |
Price estimated c | 29.5 | 27.4 | 30.9 | 36.0 | 30.0 | 37.1 | 75.3 | 62.4 | 56.4 | 89.2 | 142.0 | 75.2 | 100.7 | 123.8 | 103.9 | ||
Average gross profit margin | 39% | 39% | 39% | 39% | 39% | 39% | 34% | 28% | 23% | 28% | 36% | 39% | 37% | 42% | 33% | ||
Average operating costd | 18.1 | 16.8 | 18.9 | 22.1 | 18.4 | 22.7 | 49.7 | 44.9 | 43.5 | 64.6 | 91.5 | 45.6 | 63.7 | 71.4 | 70.1 | ||
Bengkulu | Average production CV | 6234 | 6193 | 6190 | 6154 | 6222 | 6356 | 6385 | 6442 | 6509 | 6351 | 6300 | 6320 | 6358 | 6248 | 6249 | 6300 |
Price estimated | 19.6 | 17.9 | 21.6 | 26.3 | 20.4 | 26.8 | 61.6 | 51.8 | 47.1 | 73.0 | 133.0 | 67.1 | 93.1 | 110.8 | 91.6 | 78.4 | |
Average gross profit margin | 39% | 39% | 39% | 39% | 39% | 39% | 34% | 28% | 23% | 28% | 36% | 39% | 37% | 42% | 33% | 26% | |
Average operating cost | 12.0 | 11.0 | 13.2 | 16.1 | 12.5 | 16.4 | 40.7 | 37.2 | 36.3 | 52.9 | 85.7 | 40.7 | 58.9 | 63.9 | 61.8 | 58.4 | |
Central Sumatra | Average production CV | 5594 | 5537 | 5581 | 5349 | 5304 | 5779 | 5917 | 5941 | 5653 | 5663 | ||||||
Price estimated | 61.4 | 51.9 | 47.8 | 68.5 | 119.0 | 69.0 | 94.5 | 113.2 | 90.4 | 78.0 | |||||||
Average gross profit margin | 34% | 28% | 23% | 28% | 36% | 39% | 37% | 42% | 33% | 26% | |||||||
Average operating cost | 40.6 | 37.3 | 36.8 | 49.7 | 76.7 | 41.8 | 59.8 | 65.3 | 60.9 | 58.1 | |||||||
South Sumatra | Average production CV | 5900 | 5900 | 5900 | 5926 | 5931 | 5928 | 5933 | 5931 | 5932 | 5926 | 5923 | 5885 | 5914 | 5767 | 5756 | 5768 |
Price estimated | 19.6 | 18.1 | 21.8 | 26.7 | 20.5 | 26.3 | 60.3 | 50.3 | 45.2 | 71.9 | 130.7 | 65.0 | 90.6 | 104.9 | 86.7 | 74.1 | |
Average gross profit margin | 39% | 39% | 39% | 39% | 39% | 39% | 40% | 39% | 38% | 40% | 49% | 54% | 46% | 50% | 44% | 31% | |
Average operating cost | 12.0 | 11.1 | 13.4 | 16.4 | 12.6 | 16.1 | 36.3 | 30.9 | 28.1 | 43.1 | 66.8 | 29.8 | 48.7 | 52.5 | 48.6 | 51.1 | |
Tarakan | Average production CV | 5544 | 5537 | 5518 | 5534 | 5548 | 5534 | 5469 | 5453 | 5480 | 5503 | 5495 | 5519 | 5518 | 5523 | 5465 | 5378 |
Price estimated | 17.4 | 16.0 | 19.3 | 23.6 | 18.2 | 23.3 | 52.8 | 43.9 | 39.6 | 63.2 | 116.0 | 58.6 | 80.9 | 97.9 | 80.1 | 67.0 | |
Average gross profit margin | 39% | 39% | 39% | 39% | 39% | 39% | 32% | 26% | 21% | 26% | 34% | 41% | 35% | 42% | 33% | 24% | |
Average operating cost | 10.7 | 9.8 | 11.8 | 14.5 | 11.2 | 14.3 | 35.9 | 32.4 | 31.4 | 46.9 | 76.9 | 34.7 | 52.6 | 56.8 | 53.7 | 50.9 | |
Kutai | Average production CV | 6214 | 6228 | 6225 | 6222 | 6218 | 6218 | 6254 | 6270 | 6256 | 6212 | 6166 | 6164 | 6101 | 6039 | 6012 | 6071 |
Price estimated | 19.5 | 18.0 | 21.7 | 26.5 | 20.4 | 26.2 | 60.4 | 50.5 | 45.3 | 71.4 | 130.2 | 65.4 | 89.4 | 107.1 | 88.2 | 75.6 | |
Average gross profit margin | 39% | 39% | 39% | 39% | 39% | 39% | 33% | 33% | 23% | 26% | 31% | 33% | 33% | 37% | 27% | 21% | |
Average operating cost | 12.0 | 11.0 | 13.3 | 16.3 | 12.5 | 16.1 | 40.2 | 34.0 | 35.0 | 52.6 | 89.3 | 43.8 | 59.7 | 67.5 | 64.5 | 59.7 | |
Barito | Average production CV | 5533 | 5530 | 5545 | 5535 | 5544 | 5525 | 5543 | 5566 | 5576 | 5592 | 5597 | 5593 | 5626 | 5650 | 5640 | 5635 |
Price estimated | 17.4 | 16.0 | 19.3 | 23.6 | 18.2 | 23.3 | 53.5 | 44.8 | 40.3 | 64.3 | 118.2 | 59.4 | 82.4 | 100.2 | 82.7 | 70.2 | |
Average gross profit margin | 39% | 39% | 39% | 39% | 39% | 39% | 29% | 22% | 18% | 25% | 37% | 42% | 38% | 45% | 35% | 29% | |
Average operating cost | 10.7 | 9.8 | 11.9 | 14.5 | 11.2 | 14.3 | 38.0 | 35.1 | 33.0 | 48.1 | 74.8 | 34.7 | 50.8 | 55.0 | 53.9 | 49.8 |
Note: a,cUnit for price reference and price estimate are in USD/ton; bUnit for average production CV is in Kcal/Kg; dUnit for average operating cost is in USD/ton.
the coal sale is a function of coal quality and benchmark price. The benchmark price herein used as the basis of estimation is the Asian spot price index which is applicable for coal with calorific value of 6600 kcal/kg (adb).
After plotting the data of the cumulative production and the estimated operating cost, trend analysis is then performed to determine the correlation between the two variables.
The operating cost model is obtained as a function of the cumulative production. Such a model can therefore be used to predict the operating cost of the foreseeable future. Results obtained from forecasting the coal production of each basin using Gompertz curve as described in Section 3 will hereinafter be input on the operating cost model to obtain the estimated operating cost. Results of the estimated operating cost from 2014 to 2040 are plotted in
The results of production and operating cost forecasting may be used for greater purposes like coal policy analysis and prospect evaluation of the Indonesian coal sales in export market. Herein Domestic Market Obligation (DMO) policy is analyzed. Meanwhile, with respect to the prospect evaluation, herein the ability of Indonesian coal to compete in the export market will be observed in terms of the operating costs.
DMO is the Indonesian government’s policy which aims to ensure the continuity of coal supply in the domestic market. The policy is implemented by Decree of the Ministry of Energy and Mineral Resources (MEMR) No. 342009 on the priority of mineral and coal supply for domestic needs by setting up a minimum percentage of coal sales in the domestic market based on the estimated domestic consumption and future production. The quantity of domestic sales for each coal mining company will be determined based on the minimum percentage stated. Implementation of the DMO has faced many problems. One of the problems is consumer preference to choose producer who offers lowest price. Unfortunately, those producers may not be one of the mining companies listed in Ministerial Decree. The mining companies listed in Ministerial Decree may then be difficult to get domestic consumers due to their mining technique and mining location which will generate higher selling price.
Meanwhile, the evaluation of the coal sales prospect in the export market is carried out by estimating the supply cost and comparing it with the supply cost from other producing countries. The coal production is prioritized for the domestic supply. Once domestic needs are met, the rest of the production will be sold in the export market. As a result, the domestic consumers will get a lower price than the overseas consumers.
The main factor to decide the DMO policy analysis and the evaluation of sales prospect in the export market is the domestic coal demand. Currently, only about 20% of Indonesia’s coal production is consumed domestically. Much of the coal is consumed for the purpose of electricity generation and cement industry. In the future the increase in domestic coal consumption is led by the power plant sector. This increase is strongly affected by government policy to substitute oil-based power plants with coal-based power plants. Starting from 2006, PLN, the Indonesian state electric company, announced a 10,000 MW fast-track program to build 33 coal-fired power plants with total capacity of 9.48 GW.
PLN and BPPT, the State Electricity Company and the Agency for Assessment and Application of Technology of Indonesia, have been forecasting the coal demand in the domestic market. PLN, in its electricity supply plan of 2015 to 2024, estimated in 2024 Indonesian coal demand for power plant coal supply of around 171 million tonnes [
With respect to DMO and policy planning of the coal marketing, the supply fulfilment of domestic coal demand in 2020 and 2030 will be evaluated. According to BPPT, the Indonesian coal demand is forecasted about 156 million tons in 2020 and 354 million tons in 2030. When the total coal demand is plotted in the supply curve developed based on coal production and operating cost forecasting (
results are obtained:
・ In 2020, coal for domestic demand is cheaper when is supplied by Central Sumatra, Tarakan, and Barito basins with an average supply cost of USD 64.8 per ton.
・ In 2030, as the coal demand increases, all the economic coal basins except the Ombilin basin can be used to meet the domestic coal demand with an average supply cost of USD 84.6 per ton.
As the domestic coal demand is fulfilled by the aforementioned coal basins, there is significant impact on the coal supply cost in the export market. In 2020, the average supply cost of Indonesian coal in the export market is USD 78.0 per ton. Such cost is expected to increase to USD 100.6 per ton in 2030.
Forecasting the coal production, operating cost as well as the reduction of coal reserves may be very beneficial in planning the national coal policies such as domestic coal marketing policy and the export market policy.
Indonesian coal production forecasting using Gompertz curve with the economic coal basin approach obtains the peak production of 485 million tonnes and peak year in 2026. Kalimantan coal basins (Kutai, Barito, and Tarakan basins) play a greater role than Sumatra coal basins (Ombilin, Bengkulu, Central Sumatra and South Sumatra basins) in supporting the total national production. The decline in production of Kalimantan basins after year 2024 cannot be replaced by coal production from Sumatra basins.
The operating cost forecasting was carried out using trend analysis of historical data of the operating cost based on the cumulative availability curve whereby the operating cost/price will increase as the cumulative production increases. Logarithm trend was chosen assuming no significant changes in mining methods and equipment during the extraction period. Thus, future operating cost will merely depend on the quality of the coal produced in each basin. High CV coal production in Ombilin, Kutai, Bengkulu and South Sumatra basins will cost higher than the production of low CV coal in Barito, Tarakan and Central Sumatra basins. Production of high CV coal provides greater opportunities to the producer to increase the stripping ratio in order to obtain larger production than the production of low CV coal. As a result, the operating cost of high CV coal production is relatively higher than that of low CV coal.
Coal production and operating cost forecasting can be used as defining factors to establish better coal marketing policy. In 2020 the coal supply for domestic use will be cheaper if supplied from Central Sumatra, Tarakan, and Barito basins with an average supply cost of USD 64.8 per ton. With the increase of domestic coal demand in 2030, all the coal basins except Ombilin basin need to supply the domestic coal demand with an average supply cost of USD 84.6 per ton. In the export market supply, the minimum coal price from Indonesia is expected to increase from originally USD 78.0 per ton in 2020 to USD 100.6 per ton in 2030.
Rosyid, F.A. and Adachi, T. (2016) Forecasting on Indonesian Coal Production and Future Extraction Cost: A Tool for Formulating Policy on Coal Marketing. Natural Resources, 7, 677-696. http://dx.doi.org/10.4236/nr.2016.712054