Billion Tons Of Standard Coal Research Articles

Geothermal resource evaluation of the Middle Permian Qixia-Maokou Formation in the southern Sichuan Basin, China

The southern Sichuan Basin is an abnormal area of high present geothermal fields in the Sichuan Basin. It is a favorable area for the development of geothermal resources in the Sichuan Basin. But its evaluation of geothermal resources is lagging, which has severely affected the development of geothermal resources in the southern Sichuan Basin. In the paper, firstly, based on the temperature data of wells, the geothermal gradients and heat flows of typical wells in the study area were calculated, and the temperatures at the top and bottom of the reservoir in the Qixia-Maokou Formation were calculated using a one-dimensional steady-state heat conduction equation. Then, geothermal resource intensities and reserves of the Middle Permian Qixia-Maokou Formation in the southern Sichuan Basin were evaluated by using a method for rapidly calculating geothermal resources in sedimentary basins by multi-data fusion. Finally, suggestions for geothermal development were proposed. The results show that the geothermal gradient and heat flow values in the southern Sichuan Basin range from 15.6 to 33.70 °C/km and 49 to 85 mW/m2, respectively, and the temperature of the reservoir in the Qixia-Maokou Formation ranges from 50 to 120 °C. The geothermal resources are 3.09 × 1021 J, equivalent to 105 billion tons of standard coal, and the recoverable resources are 6.18 × 1020 J, equivalent to 21 billion tons of standard coal. The development of demonstration projects will focus on the mid- to low-temperature geothermal power generation, geothermal grain drying and geothermal agriculture in the southwestern region of the southern Sichuan Basin. The research results can provide a resource basis for the development of geothermal resources in the southern Sichuan Basin.

Study on China’s New Energy Development Trend through Achieving the Consumption Proportion Target for Non-fossil Energy

Recently, the Statistical Communiqué of the People’s Republic of China on the 2022 National Economic and Social Development (hereinafter referred to as "Communiqués") was published by the National Bureau of Statistics. According to the Communiqués, China’s total primary energy consumption in 2022 amounted to 5.41 billion tons of standard coal. It is projected that by 2025, the total energy consumption will reach approximately 5.84-6.04 billion tons of standard coal. In order to meet the target of non-fossil energy consumption accounting for 20% of total energy consumption by 2025, the installed new energy capacity within operating areas of State Grid Corporation of China is anticipated to exceed the previous estimate by 100-140 million kilowatts. From a risk assessment standpoint, the research team has constructed an evaluation model to gauge China’s new energy development trend, taking into account the accomplishment of the consumption proportion target for non-fossil energy. They delve into the issues brought about by the unexpected development of new energy and put forth pertinent measures to tackle them.

Total-Factor Energy Efficiency and Its Driving Factors in China’s Agricultural Sector: An Empirical Analysis of the Regional Differences

Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc.

Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)

Forecasting China’s clean energy consumption has great significance for China in making sustainably economic development strategies. Because the main factors affecting China’s clean energy consumption are economic scale and population size, and there are three variables in total, this paper tries to simulate and forecast China’s clean energy consumption using the grey model GM (1, 3). However, the conventional grey GM (1, N) model has great simulation and forecasting errors, the main reason for which is the structural inconsistency between the grey differential equation for parameter estimation and the whitening equation for forecasting. In this case, this paper improves the conventional model and provides an improved model GM (1, N). The modeling results show that the improved grey model GM (1, N) built with the method proposed improves simulation and forecasting precision greatly compared with conventional models. To compare the model with other forecasting models, this paper builds a grey GM (1, 1) model, a regression model and a difference equation model. The comparison results show that the improved grey model GM (1, N) built with the method proposed shows simulation and forecasting precision superior to that of other models as a whole. In the final section, the paper forecasts China’s clean energy consumption from 2019 to 2025 using the improved grey model GM (1, N). The forecasting results show that, by 2025, China’s clean energy consumption shall reach the equivalent of 1.504976082 billion tons of standard coal. From 2019 to 2025, clean energy consumption shall increase by 11.32% annually on average, far above the economic growth rate, indicating China’s economic growth shall have a great demand for clean energy in the future. Studies have shown that China’s clean energy consumption shall increase rapidly with economic growth and population increase in the next few years.

Technological gap, scale economy, and China's industrial energy demand

Abstract The industrial sector in China is energy-intensive and accounts for more than 60% of the nation's total energy consumption (demand). Narrating the story behind the growth of China's industrial energy consumption could help us understand the past and prepare for the future. This paper aims to investigate the factors affecting China's industrial energy consumption in the 21st century by using an extended production-theoretical decomposition approach (PDA) with considering the technological gap and scale of the economy effects. The new proposed decomposition framework includes four new factors, namely, the energy technological gap, the output technological gap, energy scale efficiency, and output scale efficiency. The results show that in the 21st century, the energy consumption of China's industry increased by more than one billion tons of standard coal. The expansion of the industrial economy is a dominant driver of China's industrial energy consumption, while potential energy intensity, energy technology, and output technology are the three largest energy-saving factors. In addition to the conventional factors, the newly proposed factors also have important effects on China's industrial energy consumption. Specifically, output scale efficiency is an energy-saving factor, while the energy technological gap, output technological gap, and energy scale efficiency are drivers of energy consumption. This suggests that China's industry is at the stage of returns to scale that is beneficial to output performance but harmful to energy performance. Meanwhile, the widening of the industrial technological gaps has negative impacts on China's industrial energy conservation. The proposed decomposition approach can provide information that cannot be obtained from traditional approaches and it can also be applied to many other cases for investigating feasible paths of energy-saving and emissions reduction.

Energy relations between China and the countries along the Belt and Road: An analysis of the distribution of energy resources and interdependence relationships

The Belt and Road Initiative (BRI) forms the core of China's foreign policy and future foreign cooperation, and energy cooperation is an important component of the BRI. Energy security is closely related to the interdependent relations between countries. The present paper investigates energy interdependent relations between China and the countries along the Belt and Road using data collected in the BP Statistical Review of World Energy, between 2000 and 2015. The main results are as follows: (1) Abundant proven reserves of energy resources exist in these countries, which were equivalent to 758.73 billion tons of standard coal in 2015, accounting for 52.27% of the world. The distribution of these resources presents a pattern with two centers—one in Russia, and one in West Asia and the Middle East. (2) Established interdependence relations exist between China and the countries along the Belt and Road in terms of energy cooperation. China has imported 437.21 million tons of standard coal from these countries and their guarantee degree to China's energy security is 58.42%, of which 13.56% refers to coal, 73.37% to oil, and 13.06% to gas. Meanwhile, their dependence degree in energy exports to China was 13.56%, or 11.15% for coal, 14.92% for oil, and 10.55% for gas. An interdependence relation thus exists between China and these countries, and the degree of mutual dependence between them was 0.23 (0.22 for coal, 0.13 for oil, and 0.38 for gas), locating China in a relatively passive position with respect to the countries along the Belt and Road as a whole, but a relatively active position in relation to individual countries (with the exception of Russia). Our findings are useful to the tasks of identifying the spatial distribution pattern of energy in countries along the Belt and Road, assisting in the promotion of energy cooperation between China and these countries, and the future construction of the BRI.

Exploratory research into the enhanced geothermal system power generation project: The Qiabuqia geothermal field, Northwest China

Geothermal energy from hot dry rock is considered to be a source of clean renewable energy. This energy resource in the Gonghe basin of Northwest China is equivalent to 630.3 billion tons of standard coal and represents a massive potential source of geothermal energy. In particular, the Qiabuqia geothermal field located in the eastern part of the basin holds an average geothermal gradient in a granite stratum of 7.1 °C/100 m and reaches a temperature of 236 °C at 3705 m depth in its GR1 well. This major breakthrough in geothermal exploration technology is promising for accelerating the construction of the enhanced geothermal system demonstration power plant in China. Firstly, we initially used actual geological, field, and laboratory data pertaining to the Qiabuqia site to investigate the temperature, rock properties, and in situ stress state of the geothermal reservoir through laboratory analysis and numerical calculation methods. Secondly, a probable reservoir stimulation scheme for the GR1 well in Qiabuqia site on the basis of laboratory experiments and numerical simulation was explored. Finally, we designed a possible enhanced geothermal system and numerically investigated the effects of injection rate, well spacing, and injection temperature on the system performance, providing useful insight into hydraulic pressure, thermal production, and power generation processes for the Qiabuqia geothermal field. Results indicate that the Qiabuqia site is suitable for the development of a demonstration power plant with an installed capacity of 4.02–4.74 MW if a three-vertical-well system is used.

Forecasting the total energy consumption in China using a new-structure grey system model

PurposeChina is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC.Design/methodology/approachTwo matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years.FindingsTherefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency.Originality/valueA new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

Dynamic Game Analysis of Coal Electricity Market Involving Multi-Interests

The coal consumption of China reached 2.75 billion tons of standard coal in 2013, which accounted for 67.5% of total energy consumption and more than 50% of global coal consumption. Therefore, the impact of coal price is huge on coal market and even energy market in China. As a large consumer of coal, thermal power enterprise has a strong sensitivity to coal price. In order to balance the rising cost of enterprises due to coal price, we need to analyze the interests of multiple stakeholders. Firstly, this paper combined the Nash equilibrium and cobweb model and proposed the characteristics in different cobweb model. Then, for coal, power, and energy companies, the dynamic game analysis model is constructed. This model gives a game analysis in four scenarios and quantifies the decision of each stakeholder in different coal prices. Finally, the impact figure of different coal prices on each stakeholder has been drawn. The impacts of different coal or thermal power prices on different markets have been put forward, so relevant policy recommendations have been proposed combined with the cobweb model.

Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China’s Energy Consumption

To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country. This study applies individual models (BP, GM (1, 1), triple exponential smoothing model, and polynomial trend extrapolation model) and combination forecasting models to predict China’s energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation and fitting error of forecasting model, it is more effective to evaluate the performance of forecasting model. Firstly, the forecasting model’s performances rank in line with ACD. Then ACD is firstly proposed to choose individual models for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individual models. The new method based on ACD shows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China’s energy consumption will be up to 5.7988 billion tons of standard coal in 2018.

Microalgal biodiesel in China: Opportunities and challenges

With rapid economic development, energy consumption in China has tripled in the past 20 years, exceeding 2.8 billion tons of standard coal in 2008. The search for new green energy as substitutes for nonrenewable energy resources has become an urgent task. Biodiesel is one of the most important bioenergy sources. According to the Mid- and Long-term Development Plan for Renewable Energy in China, the consumption of biodiesel in China will reach 0.2 million tons in 2010 and 2.0 million tons in 2020. However, large-scale production of biodiesel is restricted by the limited sources of raw materials. Microalgal oil is a prospective raw material for biodiesel production. Development of technology for the production and commercialization of biodiesel from microalgae has become a hot topic in the field of bioenergy and CO 2 emission mitigation. Biodiesel from microalgae can be produced at laboratory-scale, but the cost is too high. Few studies on the commercialization of the technology of producing biodiesel from microalgae have been reported. In this review, recent progress on the research and development of biodiesel from microalgae that have resulted in scientific breakthroughs and innovation in engineering in China are introduced. The existing challenges are also discussed. Based on a detailed analysis, several novel strategies on commercial biodiesel production from microalgae are proposed.

Research on the prospects of low-carbon economic development in China based on LEAP model

Abstract Scenarios prediction provides an important reference for the Chinese government to develop low-carbon economy. Four critical factors, the per capita GDP, energy consumption, energy structure, and CO2 emissions,are mainly considered as the indicators to measure the level of low-carbon economic development. Meanwhile, based on LEAP model,the base scenario, low-carbon scenario,and frustrated low-carbon scenario are formulated to simulate China's low-carbon economic development level in 2050. The results show that the total terminal energy demands in the three kinds of scenarios are respectively 6.095 billion tons of standard coal, 5.236 billion tons of standard coal, and 6.239 billion tons of standard coal in 2050. The study indicates that China has achieved a considerable decrease in its CO 2 emissions mainly due to improved energy intensity.In addition, fuel switching and renewable energy penetration also exhibit positive effect to the CO2 decrease.It is a more effective guarantee for achieving the goal of low-carbon economy to adjust the industrial structure, fully develop clean coal and coal technology, and improve energy efficiency.

The Application of Combination Forecasting Model in Energy Consumption System

Abstract In view of the complexity and nonlinear characteristics of Chinese energy consumption system, this paper respectively makes use of neural network, grey forecast and time series to construct models depending on historical data of Chinese energy consumption. According to standard deviation method, suited weight is distributed to the prediction of each individual model, and a combination forecasting model is established. Finally, the combination model is applied to predict Chinese energy consumption in the following six years. The results show that Chinese energy consumption will be 4.21 billion tons of standard coal in the year of 2015.