Natural Gas Consumption In China Research Articles

What makes the provincial inequality of natural gas consumption in China? Evidence from Gini-coefficient approach

Many researchers have focused on inequalities in income and energy consumption, but research on the regional disparities in natural gas consumption (NGC) is insufficient, which presents a gap about the development of informed policies for sustainable consumption. Increasing provincial NGC is a substantial measure to improve environmental protection, clean production, and the living standards of local citizens. It is necessary to determine the leading causes of inequality in gas consumption to improve the NGC in China’s provinces. To fill this research gap, we use the Gini coefficient and Lorenz curve to measure NGC inequality and apply hierarchical cluster analysis and a regression model with Partial R2 to estimate the factors of provincial NGC. The empirical results show a consistently increasing trend in China’s NGC over 1997–2019, which has not been accompanied by a steady decrease in inequality, and low inequality is mainly caused by several major natural gas-consuming provinces. Further, the NGC in coastal provinces is more significantly affected by GDP, foreign direct investment, and financial liberation, and the impact of urbanization level and population on provincial NGC has different patterns.

A robust stacking model for predicting oil and natural gas consumption in China

ABSTRACT Accurate prediction of oil and natural gas consumption (ONGC) is crucial for energy security and greenhouse gas emission control. This study uses machine learning to improve forecast accuracy by transforming time series predictions into supervised learning models. A novel stacking learning method, with added cross-validation, enhances model diversity and robustness. The key findings are: (1) The stacking model outperforms base models in predicting China’s ONGC. It achieves R2 scores of 94.44% for oil and 98.33% for natural gas, with corresponding RMSE scores of 0.5325 and 0.2919. (2) When comparing the scores of the models in the validation set using cross-validation, it can be observed that the stacking model exhibits the most consistent performance. (3) Through the diversification of models, the stacking approach enhances robustness and achieves better generalization on new datasets. The study provides fresh insights into model stacking for energy consumption prediction.

Carbon emissions from various natural gas end-use sectors for 31 Chinese provinces between 2017 and 2021

Natural gas (NG) is a low-carbon fuel that is becoming a crucial transitional energy in China for reducing carbon emissions. In this study, a life-cycle assessment was performed to correlate carbon emissions and NG consumption for different end uses in China. A bottom-up life-cycle assessment framework was combined with carbon emission coefficients to quantify NG consumption in 31 Chinese provinces between 2017 and 2021, as well as the carbon emissions (in carbon dioxide (CO2) equivalents, including CO2 and methane) released during NG production, transportation, and consumption. The carbon emission factors for different types of end-use consumption were considered. The assessment results indicate that both NG consumption and life-cycle carbon emissions from NG use have increased since 2017. Between 2017 and 2021, NG consumption in China increased from 260 to 370 billion cubic meters and life-cycle carbon emissions from NG increased by 39% (from 930 to 1292 Mt CO2). The carbon emissions released during NG production and transportation accounted for approximately 31% of NG life-cycle emissions. Considerable variations in NG life-cycle carbon emissions were identified across different provinces and sectors, highlighting the need for targeted efforts to reduce carbon emissions. The objective of this study was to provide useful insights into sustainability development of the NG industry in China for optimizing NG allocations to different end uses and maximizing the environmental and economic benefits of NG.

Spatial correlation network pattern and evolution mechanism of natural gas consumption in China—Complex network-based ERGM model

Based on network analysis and exponential random graph analysis methods (ERGM), the spatial network pattern of gas consumption and its evolution mechanism are investigated using Chinese inter-provincial panel data from 2008 to 2021. This paper shows that the spatial association strength level of natural gas consumption shows an enhanced spatiotemporal evolution. Network density is enhanced. Hierarchy decreases in a stepped way, the network efficiency increases in a fluctuating way, the redundant relation decreases, and the number of its bidirectional spillover relation increases year by year. The eastern seaboard is an economically developed region which is in the “net benefit” position. The provinces of the Tianjin, Hebei, and Shandong are the “two-way spillover” segments. Giving full play to the advantage of the balance between local markets and foreign markets gas consumption. Fujian, Chongqing, Inner Mongolia, Shanxi and Hubei are in the “broker” segment, which tends to concentrate on natural gas consumption outside the segment by their resource endowments. The block of “net spillover” involves the risk of carbon transfer and it should be more responsible for energy saving and carbon reduction; The convergence of per capita economic development, the size of population, industrial structure and urbanization rate leads to selective preferences for inter-provincial natural gas consumption. The large population sizes of provinces have “economies of scale” and “local market effects”, which prefer to deploy natural gas. Carbon emission linkage network is the most important for the formation of spatial network patterns of natural gas consumption, followed by the influence of economic linkage networks and land proximity. Less stringent environmental regulations and an open regional environment are conducive to the formation of a network of spatial correlations in natural gas consumption. Accordingly, new ideas and policy recommendations for inter-provincial emission reduction are finally proposed.

Role of fossil fuels resources on high-quality economic development: Evidence from China

This study examines how the efficiency of coal, crude oil, and natural gas consumption in China between 1995 and 2021 influences its sustainable development index using time series analysis. Results provided by the ARDL (Autoregressive Distributed Lag) indicate that a 1% increase in coal consumption efficiency raises the sustainable development index by 0.86% in the short term and 0.63% in the long term. Similarly, a 1% improvement in natural gas efficiency corresponds to a 0.32% short-term and 0.15% long-term index increase. However, hindrances to sustainable development include carbon emissions, healthcare costs, and energy poverty. Effective fossil fuel management, especially for high carbon-emitting sources, is crucial. The Chinese government should foster technological progress, support green patents, implement fintech and big data solutions for green finance, and introduce emission taxation policies to enhance fossil fuel efficiency and overall sustainable development.

An outlook analysis on China's natural gas consumption forecast by 2035: Applying a seasonal forecasting method

The proposed targets to achieve peak carbon before 2030 and carbon neutrality before 2060 would require China to accelerate the development of natural gas infrastructure. The forecasted future of natural gas consumption in China will not only help learn about the future usage of natural gas but also guide government departments to conduct more scientific planning and management of natural gas production and pipeline channels. Therefore, this paper fully considers the characteristics of natural gas consumption affected by seasonal factors and uses the 2018–2021 quarterly natural gas consumption time-series data to analyze whether natural gas consumption contains trends and seasonality. It is noticeable that seasons impact China's natural gas consumption, so the moving average method is applied to calculate the seasonal index. Based on this, China's natural gas consumption from 2022 to 2035 is forecasted. As results show, China's gas consumption will continue to grow over the next 14 years but at a slower rate. By 2035, it will double from the 2021 level, reaching 765.4 billion cubic meters, but the growth rate will drop to below 4%, entering a period of low growth. Finally, the paper illustrates current situation of natural gas development combined with rationalized suggestions for future natural gas production and energy security in China.

Forecast of natural gas supply and demand in China under the background of “Dual Carbon Targets”

As a kind of clean energy which creates little carbon dioxide, natural gas will play a key role in the process of achieving “Peak Carbon Dioxide Emission” and “Carbon Neutrality”. The Long-range Energy Alternatives Planning System (LEAP) model was improved by using new parameters including comprehensive energy efficiency and terminal effective energy consumption. The Back Propagation (BP) Neural Network—LEAP model was proposed to predict key data such as total primary energy consumption, energy mix, carbon emissions from energy consumption, and natural gas consumption in China. Moreover, natural gas production in China was forecasted by the production composition method. Finally, based on the forecast results of natural gas supply and demand, suggestions were put forward on the development of China's natural gas industry under the background of “Dual Carbon Targets”. The research results indicate that under the background of carbon peak and carbon neutrality, China's primary energy consumption will peak (59.4×108 tce) around 2035, carbon emissions from energy consumption will peak (103.4×108 t) by 2025, and natural gas consumption will peak (6100×108 m3) around 2040, of which the largest increase will be contributed by the power sector and industrial sector. China's peak natural gas production is about (2800–3400)×108 m3, including (2100–2300)×108 m3 conventional gas (including tight gas), (600–1050)×108 m3 shale gas, and (150–220)×108 m3 coalbed methane. Under the background of carbon peak and carbon neutrality, the natural gas consumption and production of China will further increase, showing a great potential of the natural gas industry.

Urban natural gas consumption forecasting by novel wavelet-kernelized grey system model

Natural gas is playing a key role in the Carbon Neutral path, which is clean and abundant. However it is difficult to collect sufficient data of urban natural gas consumption in China, and such data sets often present high nonlinearity and complex features, making it difficult to make accurate forecasts for the mid-small cities based on small samples. In this work, a novel wavelet kernel-based grey system model is proposed by using the wavelet kernel-based machine learning and the grey system modelling, taking advantage of the features of nonlinearity and periodicity of the wavelet kernel. A complete computational algorithm is presented by utilizing a hold-out cross validation-based grid-search scheme for selecting the optimal hyperparameters. Three case studies are carried out based on the real-world data sets of urban natural gas consumption in Kunming China, in which the proposed model outperforms other 15 time series forecasting models (including kernel-based models, grey system models and deep learning models), illustrating its priority in such forecasting tasks and high potential in similar applications.

Is China’s Natural Gas Consumption Converging? Empirical Research Based on Spatial Econometrics

Excessive regional differences in energy consumption have led to inequality and energy poverty. It is essential to clarify the factors of energy consumption convergence to solve this problem. We use the spatial convergence model to analyze the convergence characteristics and conditions of China’s natural gas consumption from 2005 to 2017. The results of spatial absolute convergence show that there is absolute convergence of natural gas consumption in China, and the economic competition among provinces slightly hinders the convergence. Furthermore, based on the spatial Durbin model and the spatial conditional convergence model, we found that insufficient pipe network construction and the price difference caused by provincial borders are the main factors hindering the flow of natural gas, which also restricts the spatial convergence of natural gas consumption. The development of the tertiary industry and the improvement of purchasing power will help accelerate the convergence of natural gas consumption. This research not only evaluates the spatial convergence of China’s natural gas consumption for the first time, but also provides an analytical idea for formulating policies to eliminate poverty in energy consumption.

What influences natural gas consumption in China: A closer look at their marginal and lag effects

Natural gas is a crucial transitional energy for China to achieve the emission reduction goal for the sustainable development of the economy. In order to more accurately analyze the relationship between natural gas consumption and economic development, this paper fully considers the lag effect of natural gas consumption driving factors, including resource scarcity, environmental regulation, and technological development. Firstly, stepwise regression method is used to select driving factors to ensure the validity of the marginal effect model, considering of the possibility of multiple collinearities in marginal model. Secondly, quantile regression model and impulse response functions in vector autoregressive model are applied to estimate the marginal and lag effects of factors on natural gas consumption. The results show that gross domestic product is the most critical driving factor of natural gas consumption, and its coefficient is the highest, far higher than technological development. It has the largest lag effect on natural gas consumption, and has a great influence on itself in the first stage of lag. Resource scarcity and environmental regulation have negative effects on natural gas consumption, but only exist in the first lag stage. • Fully considering the lag effect of natural gas consumption driving factors, including resource scarcity, environmental regulation, and technological development. • Quantile regression model and vector autoregressive model are applied to estimate the marginal and lag effects of factors on natural gas consumption. • Lag effects of natural gas consumption on itself are the greatest at the first lag phase, followed by gross domestic product. • Resource scarcity and environmental regulation have negative effects on natural gas consumption, but only exist in the first lag stage.

Research on the monthly fluctuation of natural gas demand in China

AbstractWith the rapid growth of natural gas consumption in China, the monthly peak shaving and security issues of the gas supply have become increasingly prominent. In view of this situation, the monthly fluctuation law of natural gas consumption in China must be studied to guide gas storage peak shaving and gas supply planning. In this study, the concepts of the gas year (statistical method of breaking through the calendar year to gas year and reflecting the complete gas consumption cycle), typical year (the year that had the representative load curve of China), and some fluctuation characteristics parameters were applied to study the monthly fluctuation law of natural gas consumption in China. Furthermore, according to the monthly statistical data of China, including natural gas demand, power generation, crude steel output, refined copper output, the monthly average temperature in the typical city, the relationship between influencing factors and natural gas demand was analyzed by the gray relative correlation method. Based on the stepwise regression Cobb–Douglas (C–D) production function, the natural gas demand of China in the gas year of 2030/31 was predicted. The research results can be used for energy planning, statistics, peak shaving of gas storage, liquefied natural gas trade, and can also be used as a reference for energy big data analysis and refined management.

Exploring provincial eco-efficiency and its coupling relationship with natural gas consumption in China

With rapid economic development and massive greenhouse gas emission, eco-efficiency (EE) has gained significant attention. Natural gas is a crucial clean energy, and has significant influence on EE. Based on the panel data of 30 provinces in China from 2000 to 2017, this research studied the potential improvements in environmental regulation, industrial structure, capital investment, and technological investment of ineffective provinces. In addition, the coupling relationships between natural gas consumption (NGC) and EE are researched, and examined the parameters marginal effect by using quantile regression. The results indicate that, first, there are large inter-provincial differences in the improvement potential. Investment in technology is effective in China’s current development stage, and its improvement can largely promote EE. Second, the dynamic evolution relationship between NGC and EE among provinces has no same patterns. Finally, the coupling degree of EE and NGC shows an upward trend, and the provincial average coupling degree increased by 62.6% from 0.564 in 2000 to 0.917 in 2017, indicates a harmonious coordinate relationship trend between the EE and NGC level. As a result, a series of measures are put forward to better promote the development of provincial NGC and EE.

Medium- and long-term development path of natural gas consumption in China: Based on a multi-model comparison framework

As a kind of relatively clean fossil energy, natural gas will face severe uncertainties in the background of carbon neutrality. Reviewing the medium- and long-term development trend of natural gas consumption is the prerequisite to better understanding the status of natural gas and ensure the sustainable development of natural gas industry. In this paper, a multi-model comparison framework is established on the basis of 8 representative integrated assessment models (CE3METL, DNE21+, IPAC, AIM/CGE, IMAGE, REMIND, WITCH and POLES). Then, the medium- and long-term evolution trend of China's total primary energy consumption and natural gas consumption in different scenarios of climate policy is discussed. In addition, comparative analysis is conducted on key time nodes such as 2030, 2045 and 2060. And the following research results are obtained. Firstly, the prediction results of most models indicate that China's total primary energy consumption in 2060 will be lower than the level in 2019 except for the scenario of national determined contribution (NDC). Secondly, by 2060, the carbon neutrality goal year, the cross-model average level of natural gas consumption under the NDC, 2.0 °C, and 1.5 °C scenarios will be 6943 × 108 m3, 4342 × 108 m3 and 2502 × 108 m3, respectively. Thirdly, under the 1.5 °C scenario, the decrease of total primary energy consumption shall begin in 2020, which means that faster technological progress and more effective combination of “nudge” policies are needed. Fourthly, under the temperature control goal of 2.0 °C, natural gas consumption will account for about 13.6% of China's primary energy consumption in 2060, and this proportion will drop to 9% under the stricter 1.5 °C scenario. In conclusion, the multi-model comparison results can present different development paths of China's total primary energy consumption and natural gas consumption and its proportion during 2020–2060 more comprehensively, so as to provide support for formulating the medium- and long-term policies and strategies of natural gas industry under the challenge of climate change.

Forecasting natural gas consumption of China by using a novel fractional grey model with time power term

Natural gas, an important low-carbon and clean energy, is increasingly replacing high-pollution sources such as coal and gasoline. Accurate natural gas consumption forecasts are important to policy makers in making plans, saving costs, and improving efficiency. This study developed a discrete fractional grey model with a time power term (denoted as DFGM(1,1,tα)) with reference to the discretization technique. The new model is simplified, generalized, and overcomes existing model drawbacks. Moreover, the quantum genetic algorithm (QGA) is used to determine the new coefficients, namely, the fractional order and time-power coefficient. Based on observations from 2001 to 2018, the novel model predicted the natural gas consumption in China from 2019 to 2025 better than other benchmarks. Specifically, natural gas consumption was predicted to maintain a steady upward trend, reaching 315.30 billion cubic metres (hereinafter referred to as Bcm) in 2020 and 439.14 Bcm in 2025. Reasonable suggestions are put forward for associated sectors based on the forecasts.

Forecasting the short-term urban gas daily demand in winter based on the XGBoost algorithm

The share of natural gas in the primary energy consumption is continuously increasing. The urban gas plays a major part in the natural gas consumption in China, and thus the accurate prediction of the short-term urban gas daily demand in winter facilitates the healthy development of the natural gas consumption and helps to maintain the secure and appropriate urban gas supply. This paper thoroughly analyzed the factors affecting the urban gas demand in winter, introduced the XGBoost algorithm for the forecast, and made accurate predictions regarding the violently-fluctuating short-term urban gas daily demand in winter. In the simulation tests, the Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the forecast made in the proposed model are 8.269 and 1.515%, respectively. Compared with other statistical algorithms and machine learning/artificial intelligence algorithms such as the multivariate regression, random forest and support vector machine, the proposed model has shown better fitting performance and higher prediction accuracy. Therefore, the XGBoost-based prediction model can provide references for the short-term urban gas daily demand forecasting.

Forecasting annual natural gas consumption via the application of a novel hybrid model.

Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

Analysis of Driving Factors of Natural Gas Consumption in China: Based on Regional Integration and LMDI Method

In recent years, China has developed rapidly in terms of natural gas. Driven by regional economic integration, a regional natural gas market composed of neighboring provinces and cities has taken shape gradually, and it tends to grow toward the same direction in policy formulation, resource coordination, facility construction and market expansion. Based on the factors such as geographical location, economic conditions and natural gas consumption, this thesis tends to conduct a horizontal comparative analysis on the economic development conditions, natural gas consumption characteristics and natural gas pipeline network density of the three regions with obvious regional integration feature in China. Then according to LMDI, four core indicators, namely economic growth effect, energy intensity effect, energy structure effect, and substitution effect are selected. The thesis is expected to explore the contribution of different indicators to the growth of natural gas consumption in these three different regions. According to the results, it can be concluded that the driving factors of natural gas consumption differ greatly among regions. Specifically speaking, the more developed the economy is, the greater the contribution of the energy substitution effect to natural gas consumption will be, and the contribution of the economic growth effect to natural gas consumption will be smaller. Despite that the contribution of energy intensity in different regions shows slight difference, the energy structure contributes the least effect, which further explains the current differences in China’s natural gas consumption characteristics and the reasons behind.

Income inequality and natural gas consumption in China: Do heterogeneous and threshold effects exist?

AbstractTo test whether income inequality affects natural gas consumption in China, this study investigates the causal nexus between income inequality and natural gas consumption based on panel data of China's 30 provinces from 2005 to 2017. Fully considering the potential cross‐sectional dependence and endogeneity issues, the system generalised method of moments (SYS‐GMM) is selected as the benchmark estimation approach. Furthermore, the threshold effect and regional heterogeneity are further analysed. The results indicate that the impact of income inequality on natural gas consumption depends on the level of economic development in China; specifically, a significant inverted U‐shaped relationship is found between income inequality and natural gas consumption. Notably, at the current stage of economic development, income inequality has a significant and negative influence on natural gas consumption. Furthermore, the causality analysis for the three major regions in the country confirms the existence of significant regional differences, revealing that, only for the central region, unidirectional causality runs from natural gas consumption to income inequality.

Is Natural Gas Consumption Mitigating Air Pollution? Fresh Evidence from National and Regional Analysis in China

As China has called for the mitigation of fine particulate matter such as PM 2.5 emissions, developing the natural gas industry is considered as an effective way to reach this mitigation goal. The positive impact of natural gas on inhibiting PM 2.5 emissions is well documented, but the regional difference in this field is scant. Therefore, this study assesses the impact of natural gas consumption on PM 2.5 emissions and examines the environmental Kuznets curve (EKC) relationship between economic growth and PM 2.5 emissions, based on the panel data of 30 provinces in China from 1998 to 2016. Furthermore, the regional difference is analyzed from the perspective of economic development and natural gas development. The estimation results report two inverse relationships between PM 2.5 emissions and natural gas consumption in the case of China. Specifically, at the national level, PM 2.5 emissions has risen by 0.023% with natural gas consumption demonstrating 1% growth, which indicates that natural gas consumption in China will exacerbate PM 2.5 emissions. However, the use of natural gas has an additional effect on PM 2.5 emissions in the regions with high levels of natural gas consumption. For the high consumption region of natural gas, a 1% increment of natural gas usage inhibits the release of PM 2.5 by 0.017%. The result implies that the mitigation effects on PM 2.5 emissions only arise in the areas where natural gas is largely used. Furthermore, the validity of EKC pattens between economic growth and PM 2.5 emissions is confirmed across China, and the EKC has reached its peak. Varied causality links among PM 2.5 emissions, economic development, and natural gas consumption have been distinguished according to the causality flow. Finally, two efficient policy implications are proposed to address PM 2.5 emissions and accelerate the growth of the natural gas industry in China.

Approach for simulating natural gas consumption factor based on grey prediction method

The natural gas consumption has changed significantly in last ten years in China. This paper conducted a series of evaluations to analyze the China natural gas consumption. By analyzing the natural gas consumption sector, three important sectors, Industrial, Residential and Transport, are chosen as the study objectives. The main application of these three factors were discussed and different determinants for each sector were chosen. Correlation factor of each determinant was then calculated through grey coefficients analysis to investigate the relationship between determinants and natural gas consumption. We hope this evaluation can help to provide better policy plan of natural gas consumption in China.