Energy Demand In China Research Articles

Climate policy uncertainty and its impact on energy demand: An empirical evidence using the Fourier augmented ARDL model

Global climate change and its subsequent impact on energy demand present pressing issues for policymakers. Existing literature presents various determinants of energy demand, but the intricate relationship between energy demand and climate policy uncertainty (CPU) remains underexplored. Utilizing the Fourier-augmented ARDL (FA-ARDL) model and drawing from monthly data spanning March 1995 to August 2022, we investigate the impact of CPU on energy demand in China. Our study finds a significant long-run co-integration between climate policy uncertainty (CPU) and renewable energy demand. The FA-ARDL analysis shows that CPU negatively impacts renewable energy demand in both the short and long term, as it leads to higher renewable energy prices. These increased prices deter stakeholders from investing in or adopting renewable technologies, making renewables less competitive compared to traditional energy sources. Our findings are helpful for policymakers to communicate the climate objectives since mitigating climate-related uncertainties would substantially drive renewable energy consumption.

Analysis of the Evolution Law of China's Economic Growth and Carbon Emissions

Global CO2 emissions are on the rise, thus governments are increasingly concerned about carbon emissions. As the trend of China's carbon emissions is also changing, previous studies have looked for factors affecting carbon emissions, including energy and economy, etc. This paper uses the Environmental Kuznets Curve (EKC) theory to have a descriptive statistical analysis of carbon emissions and related economic indicators in mainland China. In the period that follows, China's energy demand potential is huge, but it also faces a severe CO₂ emission reduction challenge. Improving energy efficiency and optimizing energy mix are effective solutions to China's carbon emission problem. China still has a long way to go in industrializing its economy and strengthening the structural transformation of the service sector, characterized by rapid development and huge energy demand. This study will help us to understand the evolution of carbon emissions in China and provide guidance for future carbon emission management and mitigation. Further research and actions to improve energy efficiency, adjust industrial structure and promote clean energy development are important for reducing China's carbon emissions.

Exploring the impacts of tourism development and exchange rate on renewable energy demand in China: A nonlinear perspective

The demand for carbon-free or renewable energy sources is on the rise due to their role in boosting environmental performance and controlling emissions. The majority of previous research is based on the symmetry assumption while analyzing the association between tourism demand (TD), exchange rate volatility (ERV), and renewable energy consumption (REC). To plug this hole, this research scrutinizes the impact of TD and ERV on REC in China. To investigate the short and long-run implications of TD and ERV on REC, this analysis employs the autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) techniques. The study's innovative approach enhances its scientific value through a novel and insightful examination of the variables of interest. The linear model results indicate that TD has a positive and significant long-run impact on REC. Conversely, ERV has a negative and significant long-run impact on REC. The NARDL model finds that a positive TD shock significantly and positively affects REC in the long run, while a negative TD shock does not exert a noticeable impact on REC. A positive ERV shock significantly and negatively affects REC in the long term, whereas a negative ERV shock has an insignificant impact on REC. Conversely, only a positive ERV shock has a significant positive impact on REC in the short run. These findings imply that policymakers must adopt stable and reliable foreign exchange market policies and promote sustainable tourism practices at tourist hotspots.

Research on voltage comprehensive compensation system and control strategy of high-speed railway traction network

High-speed railway, as one of the most important forms of transportation, have been developed rapidly throughout the world. As the people’s living condition improves, the energy demand in China’s transport sector has grown dramatically and will keep increasing in the short term. In order to support the realization of ’ energy conservation ’ vision, it is urgent to adopt energy technology to change the structure of traditional high-speed railway system and realize the green, high efficiency and high elasticity transformation of high-speed railway system. High-speed railway will cause voltage fluctuation of the traction network during driving, which may affect the safety and reliability of high-speed railway in severe cases. In order to improve the voltage fluctuation of traction network caused by high-speed railway, this paper proposes a comprehensive voltage compensation system (VCCS) and its control strategy for high-speed railway traction network. Firstly, the causes of voltage fluctuation in traction network are analyzed, and the existing compensation methods at home and abroad are introduced. Then, the topology and compensation principle of high-speed railway traction network voltage compensation system are introduced, and the corresponding control strategy is proposed. Finally, the correctness of the theoretical analysis is verified by simulation experiments. The experimental results show that the VCCS proposed in this paper can ensure that the high-speed train can maintain full power operation under various typical working conditions, and effectively improve the voltage fluctuation of the traction network.

Pollution exacerbates interregional flows of virtual scarce water driven by energy demand in China

Existing studies on the virtual scarce water flows within the water-energy context focus on water quantity while largely ignoring water quality. This study improves the quantification method of scarce water uses by considering both blue water (representing water quantity) and grey water (indicating water quality). Based on a scarce-water extended multi-regional input–output model, we investigate the virtual scarce water flows driven by energy demand across 31 Chinese regions in 2012 and 2017. The results show that considering water quality provides new insights into the patterns of interregional flows of virtual scarce water driven by energy demand. The virtual integrated scarce water (VISW) flows, which consider both water quantity and quality, are 5 times the volume of virtual quantity-based scarce water (VQSW) flows. Moreover, certain regions (e.g., Hebei) are recognized as net VISW exporters, but are net importers in terms of VQSW. There are significant differences in the critical interregional pairs identified based on net VISW flows (e.g., Shandong–Zhejiang, and Shandong-Guangdong) and net VQSW flows (e.g., Heilongjiang-Guangdong, and Liaoning–Shaanxi). To reduce water scarcity based on the combined effect of both quantity and quality, the critical VISW interregional pairs should enhance cooperation through compensation payments and interregional technology transfer. This study highlights the importance of water quality in the assessment of virtual scarce water uses. Employing virtual scarce water as a policy tool to mitigate water scarcity might fail without the consideration of water quality.

Forecasting China's energy demand post-COVID-19 pandemic: Insights from energy type differences and regional differences

As the first country to restart the economy after the COVID-19 pandemic, China's fast-growing energy consumption has brought huge challenges to the energy system. In this context, ensuring a stable energy supply requires accurate estimates of energy consumption for China's post-Covid-19 pandemic economic recovery. To this end, this study uses multiple panel regression model to explore the relationship between energy consumption and economic growth from the perspective of energy sources (total energy, coal, oil, natural gas) and regional difference. The data from 30 provinces in China from 2000 to 2017 were selected. Our findings indicate that China economic growth has led to the largest increase for oil consumption, followed by natural gas consumption, and finally coal consumption. That is, China economic growth has led to the largest increase for oil consumption, followed by natural gas consumption, and finally coal consumption. In addition, the coefficients of regional energy consumption equations are heterogeneous. Among them, energy consumption growth in provinces with high energy consumption is most affected by economic growth, followed by provinces with low energy consumption, and finally provinces with middle energy consumption.

Research on energy saving potential and countermeasures in China’s transport sector

As the people’s living condition improves, the energy demand in China’s transport sector has grown dramatically and will keep increasing in the short term. In this research, we adopted a bottom-up approach to conduct the energy conservation analysis in China’s transport sector during the 14th Five-Year Plan Period based on the LEAP model. This study aims to estimate the energy saving potential of the transport sector in China under three scenarios: the Business as Usual (BAU) scenario, the Energy Conservation (EC) scenario, and the Enhanced Energy Conservation (ECC) scenario. The results indicated that the total energy demand in China’s transport sector would continue to grow over the 14th Five-Year Plan Period, and it would come up to 665.29 million tons of coal equivalent (Mtce) by 2025 in the BAU scenario. The EC scenario and the EEC scenario would save 27.80 and 40.99 Mtce by 2025 compared to the total energy demand in the BAU scenario. Besides, the energy demand of freight transport, intercity passenger transport and urban passenger transport would reach 250.45, 60.75 and 353.89 Mtce by 2025 in the BAU scenario. The corresponding energy saving potential of freight transport, intercity passenger transport and urban passenger transport would be 20.45, 2.22 and 5.12 Mtce by 2025 in the EC scenario, and would be 27.64, 3.34 and 10.01 Mtce by 2025 in the EEC scenario. Considering the significant energy saving potential of the transport sector in China, several major tasks and policy recommendations were then proposed to further reduce the transportation energy consumption.

China’s Energy Demand Forecasting Based on the Hybrid PSO-LSSVR Model

Forecasting energy demand accurately is the basis for the formulation and implementation of energy planning. In this paper, energy demand influencing factors are mainly decomposed into scale economy effect, population size effect, energy structure effect, and residential consumption effect based on the Logarithmic Mean Divisia Index (LMDI). Then, the Cointegration and Granger Causality tests are used to discover the influencing factors of energy demand in China. On this basis, a hybrid optimization algorithm, the least-squares support-vector regression optimized by particle swarm optimization (PSO-LSSVR), is proposed to forecast the energy demand of China. Then, three scenarios are set up to analyze the further development of drive factors of energy demand. Finally, in accordance with the forecasting results, some suggestions related to China’s energy development policy are given. The main results are as follows. First, gross domestic product (GDP), the total population at the end of the year (POP), the coal consumption ratio in energy (CCR), and residential consumption levels (RCLs) are dominant indicators of energy demand in China. Second, the improved PSO-LSSVR model has significant superiority than other models in energy demand forecasting, a complex and nonlinear system with small samples. Third, China’s energy demand will peak in 2022, which is 4.9 million tce in all scenarios.

Fiscal decentralization as new determinant of renewable energy demand in China: The role of income inequality and urbanization

This paper examines the nexus between fiscal decentralization and renewable energy demand by including income inequality, economic growth, urbanization, and economic globalization as the main determinants in the energy demand function for the case of China. Quarterly frequency data for the period 1980–2018 are used for empirical analysis. Our empirical results show fiscal decentralization positively impacts the demand for renewable energy; in other words, fiscal decentralization can help increase renewable energy demand. In addition, income inequality is negatively linked to renewable energy consumption, while economic growth is positively associated with renewable energy demand. Urbanization and economic globalization add to renewable energy consumption. This study presents Chinese authorities with new guidelines for using fiscal decentralization as an economic tool to formulate effective energy policies for long-term development of the use of renewable energy sources.

Optimal design and thermodynamic analysis on the hydrogen oxidation reactor in a combined hydrogen production and power generation system based on coal gasification in supercritical water

The novel power generation system based on coal gasification in supercritical water (SCW) holds great promise to meet the increasing energy demands in China, with less emissions in comparison to the traditional coal-fired power generation. The stable and complete oxidation of hydrogen occurring in the supercritical water oxidation (SCWO) reactor is paramount to the safety and overall energy efficiency of the system. In order to better understand and improve the performance of this reactor, optimal design and thermodynamic analysis were conducted using Aspen Plus. The outlet hydrogen concentration and the internal temperature of the reactor were taken as the two key indicators of the optimal design. The optimized results for the reactor were found to be the reactor diameter of 90 mm, axial length of 10.75 m, oxygen inlet number of 3 with the oxygen ratio of 11.0%/39.9%/49.1%, under the given inlet working fluid components and flow rates. The three-stage reactor design was confirmed to have good robustness when different types of coals are adopted for generating syngas. In addition, by analyzing the enthalpy and exergy flow distributions in the reactor, it was revealed that the total exergy destruction for the whole three-stage reactor is only about 6.29%, and that the exergy destruction is consistent with the oxidation process. These findings could be used as guidelines for the design and implementation of the SCWO reactor towards higher safety margin and energy efficiency for this new coal-boiled power generation technique.

The sectorally heterogeneous and time-varying price elasticities of energy demand in China

The price elasticities of energy demand are intrinsically sectorally heterogeneous and time-varying, which is crucial for policy assessment, yet little studied. Therefore, in this study, we apply a semiparametric model that allows the coefficients to vary with continuous and categorical attributes to explore the sector-specific and year-specific price elasticities. We take China as an example, covering 32 industrial sectors from 1999 to 2015. Two main results have been found: (a) yearly average elasticities increased during the sample period and experienced faster growth since the 11th Five-Year-Plan period; and (b) the price elasticities showed significant sectoral heterogeneity in their magnitude, range, and trend. Nonmetal products (e.g., cement), metals, and chemicals have the largest elasticities. The mining and equipment manufacturing sectors are close to the sample average value. The light industry sector has the largest heterogeneity. The weighted mean of our results is consistent with those in the existing literature but provides more sector-specific information.

Energy demand and supply planning of China through 2060

At the General Debate of the 75th session of the United Nations General Assembly, a new objective that China will stop adding to the global warming problem by 2060 was proposed. However, the energy demand of China is increasing with the fast-growing economy and China relies heavily for its electricity on coal, which will make it difficult to achieve carbon neutrality. Therefore, the energy demand and supply planning in the future for China should be optimized. In this work, the Long-range Energy Alternatives Planning System was used to forecast the end-use energy demand of China. A new mixed-integer linear programming model was developed to optimize the energy structure, infrastructure projects and exploitation schemes under the constraint of greenhouse gas emissions. Furthermore, an economic, feasible and sustainable energy planning was also obtained. Results show that the electrification rate, green hydrogen demand and other energy demand of China will increase substantially. The coal production capacity will be concentrated, the integration of oil production and refining will be gradually realized, and the production of non-fossil power will increase considerably. The total greenhouse gas emissions of the energy consumption in China between 2017 and 2060 will be approximately 262,783 million tons of CO2 equivalents.

Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting

Accurate estimations of nuclear energy consumption are an essential process for formulating appropriate policies and plans in the energy sector and associated companies. This paper presents a novel structure-adaptive grey model with an adjustable time power based on the nonlinear and complicated characteristics of nuclear energy consumption, in which three core innovations are summarized below. Initially, the generalized time response function for projections is theoretically deduced, which overcomes the fundamental flaws in the conventional grey model. Subsequently, the Cultural Algorithm is employed to determine the optimum values of the time power item to improve the adaptability and flexibility to confront diverse forecasting issues. Further, Monte-Carlo Simulation and Probability Density Analysis (PDA) are originally introduced to enhance the robustness of the proposed model. For illustration and verification purposes, experiments on predicting nuclear energy consumption in China and America are conducted in comparison with a range of benchmark models, including other prevalent grey models, conventional econometric technology, and artificial intelligences. The performance of the novel technique is evaluated from two different perspectives of PDA and level accuracy, confirming that this model is a very promising and powerful tool for predicting nuclear energy demands in China and America from 2019 to 2023.

Estimating economic impacts from future energy demand changes due to climate change and economic development in China

The energy demand is significantly affected by climate change and economic factors, and the distribution of the demand among different regions in China is unequal because of the large regional differences in climate and economic development. Moreover, demand changes will directly affect the energy sector as the supply side amplifies the economic impact through the industrial linkages among regions and sectors. Therefore, we developed an integrated assessment model combining an energy demand-driven model (EDD) and adaptive multiregional input-output model (AMRIO). The assumptions were used in the model based on China's future climate, economy and energy development. In addition, we evaluated the century-scale energy demand change and its possible economic impact in 5 future scenarios (SSPx-y of CMIP6). The results show that the following: (i) The energy demand in China will continue to increase by approximately 1.89–1.94 times by 2100, resulting in a negative economic impact on GDP ranging from 8.19% to 12.05%. (ii) The energy sector will cumulatively benefit by 26% because of the increase in demand; however, other sectors will suffer negative economic ripple impacts due to the imbalance of supply and demand, especially manufacturing (719%) and agriculture (577%). The results provide quantitative information for policymakers to fully consider the positive and negative impacts of future energy change when making climate mitigation strategies.

Research on the Influence of Reservoir Physical Property Time Change on Reservoir Water Drive Development

With the continuous development of China’s social economy, the total demand for energy in China’s industrial production is constantly increasing, among which petroleum energy is an important energy type needed in industrial production. China’s petroleum energy mainly depends on imports, which is very unfavorable to China’s economic development and energy security strategy. Therefore, it is necessary to improve China’s oil exploitation efficiency, increase oil production and alleviate China’s energy shortage. In this paper, the influence of time variation of reservoir physical properties on reservoir water flooding development is deeply studied and analyzed, and some reasonable suggestions and measures are put forward to further promote the development of China’s petroleum development engineering industry. In the oil field developed by water flooding, the long-term scouring force of water will lead to a series of complex physical and chemical changes in the microscopic space of oil reservoir, which will lead to certain changes in the physical parameters of oil reservoir. This phenomenon is called time-varying physical properties. Time-varying physical properties are the main factors affecting reservoir exploration and development, so it is necessary for staff to grasp the relevant technical key points, so as to make clear the changes and the changed reservoir physical parameters. According to the changes, the subsequent work will be adjusted accordingly. Time-varying reservoir will affect the movement law of oil and water in the reservoir, thus affecting the actual development effect of the reservoir, and also affecting the distribution of remaining reservoirs after reservoir development.

Influencing Factors, Energy Consumption, and Carbon Emission of Central Heating in China: A Supply Chain Perspective

The rapid growth of energy demand in China’s central heating sector and the large differences in regions have posed challenges to its energy supply safety, which affected the progress of China’s energy transformation. From a supply chain perspective, this study uses the feasible generalized least squares method to conduct empirical research on the central heating data of 17 provinces in China from 2006 to 2017. The results shows that the main factors of central heating includes energy consumption structure, heat generation method, heat transport carrier, heating degree days and heating area; The main factor that increases the energy consumption of central heating in each province is the same, namely Heating area (HA). However, the main factors that reduce energy consumption in each province are different; using gas instead of coal for clean heating can reduce some greenhouse gas emissions while bringing huge gas supply pressure. According to the results, this study provides some policy suggestions.

Health risks of occupational exposure to toxic chemicals in coal mine workplaces based on risk assessment mathematical model based on deep learning

Abstract With the increasing demand for energy in China, more and more coal mines are mined. At the same time, along with the safety and health risks brought by occupational exposure in mine operation, it is very important to assess the risk of toxic substances in the working environment. The purpose of this study is to evaluate and analyze the health risk of occupational exposure to toxic chemicals in coal mines based on deep learning algorithm and mathematical model. In this study, the working environment and employees of four working areas in a mine in Shanxi Province were selected as the research objects, and the gas in the working environment was sampled. Through the methods of mathematical evaluation model and single factor evaluation grade, the lung condition of employees and the content of harmful gas components were calculated, and the risk index of the area was obtained. The experimental results show that in the range of 0.56 kg/s–3.06 kg/s, the induced airflow velocity of chute increases by 54%, and the induced airflow velocity of guide chute increases by 53%. It shows that the increase of induced airflow velocity increases significantly with the increase of coal cutting amount, and the content of harmful substances in the working environment is higher. It is concluded that the deep learning mathematical model in this study is very effective and accurate in the health risk assessment caused by toxic chemicals in coal mines, and contributes to the risk assessment. It is very important for the safety management of the workers in the mine.

Analysis of Guangdong Energy Demand Forecast Based on DIP-REM Model

Computer data mining technology has a positive impact on the accurate prediction of China's energy demand. In order to effectively alleviate the energy crisis that China is currently facing, and further forecast the energy demand, it will provide an active driving force for the sustainable economic development of our country in the future. In this study, the authors studied the current energy use trends in China, and then the authors used the DIP-REM model to predict their future development needs based on a study of the use of energy resources in Guangdong Province. This model can accurately predict the future energy expansion value of Guangdong Province. The research aims to provide a reference for related research on China's energy demand forecast.

The effect of climate change on building heating and cooling energy demand in China

Climate has a key impact on building energy efficiency. The impact of climate change on heating and cooling degree-days of China during the past 60 years was studied in this paper. The meteorological data of 613 cities published by National Climate Center of China was applied in this research. The study results showed that the impact of climate change on the spatial distribution characteristics of heating and cooling degree-days is obvious. The area with HDD18 °C over 2000 d·°C has dramatic shrunk during recent 30 years compared with the period from 1964 to 1983, while the area with CDD26 °C over 90 d·°C has expanded during recent 30 years. The impact of climate change on the HDD18 °C and CDD26 °C of each city is inhomogeneity. The decrease of HDD18 °C mainly occurred in the north and northwest of China, and the increase of CDD26 mainly occurred in the southeast of China. The outcomes of this paper may provide a theoretical basis for building energy efficiency design in future.

ÇİNİN MÜASİR ENERJİ SİYASƏTİ: PROBLEMLƏR VƏ PERSPEKTİVLƏR

China is the world's rapidly growing economy after the United States. Economic growth has increased China's energy demand. Ensuring energy security is important for China. In order to study China's modern energy policy, the article first examines the nature of the country's fuel and energy complex. The future prospects and problems of this field were discussed. The article also examines the main threats to China’s modern energy security. Key words: energy, energy security, energy policy, China’s development, fuel and energy complex