Energy Structure Adjustment Research Articles

A novel multivariate grey model of energy consumption under carbon trading background and its application

Climate warming and the energy crisis have been high-profile issues today, and carbon trading is considered one of the essential means to solve the problem. Carbon trading cannot ignore the impact on energy consumption and carbon emissions. Therefore, it is beneficial to develop a low-carbon economy to scientifically predict the trend of energy consumption and make timely investment decisions and policies in carbon trading. Based on the modelling mechanism of energy economy and the grey forecasting model, this paper studies the dynamic relationship among energy consumption, carbon emissions, economic growth, and carbon trading price and then constructs a grey prediction model of energy consumption driven by carbon trading. The least-square method and recursive mathematical method were used to obtain the model’s parameter estimation and corresponding time formula, respectively. In the end, we apply the model to the energy consumption in China, and three experimental results reveal that the prediction performance of the novel model is significantly better than the existing four grey models. And it is predicted that the energy consumption of coal in China will increase by 15.3118% in 2025, which can provide an essential reference for the energy structure adjustment and environmental policymaking in China.

Measurement of carbon emissions from marine fisheries and system dynamics simulation analysis: China’s northern marine economic zone case

The economic growth of marine fishery in China is overall stable, with output of mariculture, marine fishing, and carbon emission of marine fishery is increasing. To integrate the concept of low-carbon and green development into the whole process of marine fishery development, it is necessary to comprehend the status quo and development trend of marine fishery carbon emission. This study measures the carbon emissions of marine fisheries in the Northern Marine Economic Circle, a development region representing China’s marine fisheries, from 2006 to 2019. We employed a system dynamics approach to establish a marine fisheries carbon emission dynamic model. In our model, five scenarios are set up to dynamically simulate the development and evolution trend of future marine fisheries carbon emissions, under the impact of rapid economic development, energy structure adjustment, and industrial structure adjustment. The results are as follows. (i) Total carbon emissions from marine fisheries in the northern marine economic zone show a year-on-year increase from 2006 to 2019. (ii) Simulations from the five scenarios indicate that rapid economic development significantly impact the increase of carbon emissions from marine fisheries, while adjustment of the energy and industrial structures helped control carbon emissions from marine fisheries. This study provides relevant information to control carbon emissions at the marine level, and predicts the development trend of carbon emissions from marine fisheries, which can provide a reference for fisheries to seek an effective path of energy conservation and emission reduction, and improve their high-quality development level.

Study of the mechanism of digitalization boosting urban low-carbon transformation

The climate problem is severe and many urban climates are poor. To deal with a series of climate problems, low-carbon measures are indispensable. Digital development is becoming a new driving force for social development. Therefore, the development of urban low-carbon transformation should be based on digital construction. This paper explores the significance and theoretical mechanism of the research of the mechanism of digitalization boosting urban low-carbon transition. Theoretically, digitalization can reduce carbon emissions through mechanisms such as optimizing industrial structure, adjusting energy structure, improving government governance efficiency, and changing people’s way of production and life. Finally, from the perspective of carbon neutrality, this paper puts forward relevant countermeasures, from the following six aspects: building a carbon emission measurement and monitoring system, promoting the digital transformation of enterprises, increasing the research and development of new energy technologies, improving the carbon emission market trading system, optimizing the carbon emission administrative management system, and practicing the low-carbon concept.

A Statistical Review of Considerations on the Implementation Path of China’s “Double Carbon” Goal

The goal of “carbon peak” and “carbon neutrality” is China’s long-term development strategy for low greenhouse gas emissions in the 21st century (hereinafter referred to as the “double carbon” goal), with the goal that carbon dioxide emissions strive to reach an emissions peak by 2030 and strive to achieve carbon neutrality by 2060. Achieving the goal of “double carbon” is an inherent requirement for promoting high-quality development. Conforming to the development trend of the times, the “double carbon” goal is the necessary path of industrial transformation. The current situation of China’s energy production, consumption, and utilization in recent years shows that China’s energy has a simple structure, heavy external dependence, and low utilization efficiency which severely restricts the realization of China’s “double carbon” goal. Commencing the analysis of the current situation, this paper summarizes the implementation path from a “carbon emission reduction” and “carbon-negative technology” to accelerate the realization of the “double carbon” goal. First, statistical methods are used to analyze the implementation of a “carbon emission reduction” based on industrial technology reform, an energy structure adjustment, market mechanisms, public participation, and international cooperation. Second, the “carbon negative technology” path is explored in terms of afforestation, carbon capture, utilization and sequestration (CCUS), ecological management, and other technologies. In addition, feasible suggestions are made for four relationships, namely, the short and long-term, overall and local, development and emissions reduction, and government and market. This paper contributes to the study of energy development in the world and serves as a reference for follow-up studies.

Optimal Strategy for Low-Carbon Development of Power Industry in Northeast China Considering the ‘Dual Carbon’ Goal

With China’s carbon peak and target proposed and the power system as an important source of carbon emissions, its carbon reduction issues are of great concern. However, the mismatch between power demand, resources, and generation facilities in different regions of China creates enormous carbon reduction pressures, especially in the region covered by China’s Northeast Power Grid (NE Grid). Therefore, in this study, a two-stage power optimization model considering Carbon Capture, Utilization, and Storage (CCUS) was established to optimize the national power generation and transmission in China from 2020 to 2060 first and then to optimize the regional road map of energy structure adjustment for China’s NE grid and its CO2 storage strategy. The first stage optimization results show that the national power interaction in China can make full use of the energy resource advantages of the NE grid, especially Jilin and Mengdong, delivering clean electricity to the North China grid as it reaches its “dual carbon” goal. Additionally, the second-stage optimization results show that the NE grid can make good use of CCUS for its thermal power transition. Annual CO2 storage will peak at 204 Mt in 2050 as thermal power withdraws in the NE grid region, which provides a reference for other regions to realize carbon neutrality of their power system by using CCUS.

Carbon Emission and Structure Analysis of Transport Industry Based on Input-output Method: China as an Example

Due to its enormous carbon dioxide emissions, China's transport industry has become a key area for energy conservation and emission reduction. In this study, we employ the input-output method to examine the carbon dioxide emissions caused by the transport industry's energy consumption. In addition, the structure decomposition method is used to analyze the transport industry's carbon dioxide emissions in depth. The results of the research work deserve the attention of policy makers. First, the direct and indirect carbon dioxide emissions of road transport account for 53 % and 47 % of the transport industry. Second, the direct and indirect carbon dioxide emissions caused by consumption expenditure account for 55 % and 56 % of the final use structure respectively. Third, the carbon dioxide emissions produced by the five heavy industries when meeting the final use of the transport industry account for 75 % of all industries. Fourth, the adjustment of energy structure and energy intensity is expected to reduce the carbon dioxide emissions of the transport industry by 101,963 and 128,313kt in 2035 compared with the baseline scenario. The results indicate that, to control carbon dioxide emissions in the transport industry, special attention should be paid to road transport activity, consumption expenditure in the transport industry, the influence of other heavy industries, the adjustment of energy structure and energy intensity. Consequently, informatization of road transportation, development of public transportation, investment in high-tech industries, and aggressive development of clean energy become essential strategies.

The efforts of China to combat air pollution during the period of 2015–2018: A case study assessing the environmental, health and economic benefits in the Beijing-Tianjin-Hebei and surrounding “2 + 26” regions

During the period of 2015–2018, Chinese government had made great efforts to mitigate air pollutants, such as air quality monitoring, energy structure adjustment, and pollutant emission reduction from industry, transportation and household sectors. With the special investment of 152 billion Chinese Yuan (CNY) in the Beijing-Tianjin-Hebei (BTH) and surrounding “2 + 26” regions, the annual local concentrations of PM2.5, PM10, SO2 and NO2 decreased from 77, 132, 38 and 46 μg/m3 to 60, 109, 20 and 43 μg/m3. It was estimated that the improvement in air quality avoided 27,021 (95 % CIs 12,548–39,738) premature deaths attributed to air pollution exposure based on an exposure-response function, including 45 %, 17 % and 15 % of cardiopulmonary, lung cancer and respiratory morality cases. Air pollution reduction was also effective in reducing work time loss, which reduced the total working time loss by 3.8 × 107 (95 % CIs 1.8 × 107–5.6 × 107) h, and the per capita working time loss by 0.28 (95 % CIs 0.13–0.41) h/capita by 2018. From the economic aspect, air pollution control actions in those regions saved 95.6 (95 % CIs 44.2–141) billion CNY economic loss by using the value of statistical life (VSL). The total benefit-cost ratio was 63.7 % (95 % CIs 29.4 %–93.7 %). The cost-effectiveness in Beijing and Tianjin were relatively low due to the regional contribution from other cities of the air pollution transmission channel. Despite the uncertainties, the results clearly show the significance of the environmental, health and economic benefits of actions in the BTH and surrounding “2 + 26” regions for combating air pollution.

Dynamic changes in provincial exhaust emissions in China in the carbon peak and neutrality setting: based on the effects of energy consumption and economic growth.

Reducing exhaust emissions, promoting economic development, and realizing clean energy utilization have always been concerns in China. To measure the relationship between them, this study selects the data related to energy consumption, economic growth, and exhaust emissions from 2000 to 2019 in 30 Chinese provinces. By constructing a panel vector autoregressive (PVAR) model, the dynamic relationship between them in China is quantitatively analyzed. The results show that there is a long-term interaction between energy consumption, economic growth, and exhaust emissions. Among them, economic growth is highly dependent on energy consumption, but it can promote the reduction of exhaust emissions. However, energy consumption will produce a large amount of industrial waste, such as sulfur dioxide and carbon dioxide emissions. Specifically, the industrial structure and energy structure have the most obvious impact on reducing industrial sulfur dioxide emissions and carbon emissions, with the proportion between 0.071-0.090 and 0.031-0.032, respectively. Therefore, the adjustment of industrial structure and energy structure is the key to exhaust emission reduction.

A novel structure adaptive fractional discrete grey forecasting model and its application in China’s crude oil production prediction

Crude oil resources are related to all aspects of people's life, and play a vital role in the development of the national economy. Using nonlinear discrete data to reasonably predict crude oil production can help the government adjust energy structure and formulate energy development strategy, which has great practical significance. In this paper, a novel r-order accumulation operation with a parameter is proposed, and a novel structure adaptive fractional discrete grey forecasting model is established. Several classical optimization algorithms are compared, and the Grey Wolf Optimizer (GWO) is selected to calculate the parameters. For testifying the effectiveness of the model, a prediction model is constructed based on the total crude oil production in Qinghai, Liaoning and Shaanxi provinces of China, and a performance comparison experiment is designed with the existing six grey models. In addition, Monte Carlo simulation and probability density analysis provide a new perspective to further illustrate the robustness and accuracy of the proposed model. The experimental results show that this model is superior to the other six models in terms of fitting accuracy, prediction accuracy and model stability.

How does new-type urbanization affect air pollution? Empirical evidence based on spatial spillover effect and spatial Durbin model.

To alleviate the ecological and environmental problems caused by rapid urban development, China has formulated and implemented the new-type urbanization strategy. However, there is insufficient empirical research on the specific relationship between new-type urbanization and air pollution. Therefore, based on the panel data of 30 provinces in China from 2005 to 2018, this paper constructs a comprehensive evaluation index system of new-type urbanization using the five dimensions of population, economy, space, society, and green. The spatial Durbin model and the spatial mediating model are used to discuss the spatial effect, transmission mechanism, and regional heterogeneity of new-type urbanization on air pollution. The results show that China's air pollution mainly presents a spatial pattern of high-high agglomeration and low-low agglomeration, and there are spatial fluctuations. The construction of new-type urbanization significantly reduces local air pollution, and the industrial structure optimization, technological innovation, and energy structure adjustment are considered as important transmission mechanisms. However, under the fiscal decentralization and political tournament system in China, the policy implementation deviation may weaken the emission reduction effect of new-type urbanization, which is not conducive to regional environmental governance. From the sub-regional level, the impact of new-type urbanization on air pollution has regional heterogeneity. A robustness test confirms the reliability of our research conclusions. This study also proposes some policy suggestions that the government can utilize in grasping the policy focus of new-type urbanization construction to discover effective ways of controlling air pollution.

Hierarchical multi-objective design of regional integrated energy system under heterogeneous low-carbon policies

Fossil-to-renewable energy system transition is an inevitable future trend considering carbon dioxide emissions mitigation burdens. However, huge investment costs and insufficient policy support are still the two main barriers to further expand renewable energy penetration. To transcend these barriers, from the perspective of both policy makers and energy suppliers, this study simultaneously integrates low-carbon policy formulations and regional integrated energy system (IES) reshaping to achieve an affordable decarbonization pathways. Mixed Integer Quadratically-Constrained Programs (MIQCP) models are constructed, and hierarchical multi-objective optimization is applied to quantify local government's decision-making costs and energy suppliers' system transition costs. A new rural community in Dalian (China) is taken as a case study to capture the relationship among energy structure adjustment, energy system reshaping, and renewable subsidy and carbon tax adjustment under different CO2 emission reduction regulations. The result shows that under the same CO2 emission reduction rate, portfolio policies can benefit both the policy maker and the energy supplier by balancing the local government revenue and expenditure, and reducing the annual total costs of IES. In addition, via IESs optimization, a higher renewable energy penetration rate could be obtained and the energy supplier could achieve a competitive cost within 40% CO2 emission reduction targets.

Accident type analysis and consequence simulation of LNG terminals equipment and facilities

A new round of energy change, energy clean and low carbon development become an inevitable trend. In the adjustment of energy structure, natural gas becomes the strongest growth carrier and LNG reserve peak regulation is of great significance. Building liquefied natural gas receiving center to secure domestic natural gas supply has become the main target, task and key work. Through the simulation of various equipment and facilities in the LNG terminal under different leakage scenarios, the accident scenarios are selected according to the protection distance criteria, the simulation of steam diffusion, pool fire, jet fire and explosion is carried out, and the influence range of consequences is obtained. The thesis provides a theoretical basis for the quantitative risk assessment for LNG terminal accident and the prediction of emergencies, having positive guiding significance for risk management and emergency disposal of new or operating LNG terminals.

Waste biomass derived carbon supported Mo2C/C composite materials for heavy metal adsorption and hydrogen evolution reaction

Application of waste biomass resources in heavy metal adsorption and new energy development is of great significance to improving environmental pollution and the adjustment of energy structure. For this purpose, we designed a simple method for preparing porous Mo2C/C composite materials based on resource-rich biomass carbon (derived from waste pine wood) and ammonium molybdate, and successfully applied it to the above field. The effects of holding time and sintering temperature on the phase compositions and morphological structures of samples were investigated, and the heavy adsorption capacity (Cr(Ⅲ)) and hydrogen evolution reaction of composite materials were also studied. The results showed that porous biomass Mo2C/C composite materials had significantly higher oxidation resistance, and more prominent pore structure as compared to the pretreated biomass carbon. In addition, porous biomass Mo2C/C composite materials showed excellent adsorption performance for heavy metal Cr(Ⅲ) and catalytic hydrogen evolution reaction performance, its adsorption capacity and Tafel slope were 43.2 mg/g and 123.9 mV/dec, respectively.

Identifying the key drivers in retrieving blue sky during rapid urbanization in Shenzhen, China

Determining the relationship between air quality and economic development can help develop effective strategies for environmental sustainability. However, such analyses have scarcely been performed due to a lack of reliable methods, especially at the urban scale. Shenzhen, an epitome of China's reform and opening-up, has experienced rapid urbanization from a fishing village to a megacity in only 40 years. Here, we observed great historical changes in air quality that deteriorated first and then improved in Shenzhen and successfully quantified key socioeconomic drivers of the reduction in PM2.5 in the last ten years using a macrolevel economic decomposition model, which was strongly supported by microlevel evidence from source apportionment modelling based on the long-term PM2.5 component observation. We found that the benefits from energy and industrial structure adjustments were not necessarily enough to offset the adverse effects of rapid urban economic growth, while proactive and powerful emission control of extensive pollution sources, especially those for gaseous precursors of secondary PM2.5 components (e.g., SO2 and VOCs), was indispensable to drive the continuous decline in PM2.5. The success in retrieving blue sky in Shenzhen provides unique insights for the sustainable development of global megacities.

Short-term wind speed prediction based on feature extraction with Multi-task Lasso and Multilayer Perceptron

The vigorous development of wind energy will help to promoting the adjustment of energy structure, the control of atmospheric smog, and the transformation of economic development mode. However, the randomness and volatility of wind speed make it difficult to predict with high accuracy. High-precision short-term wind speed prediction is inseparable from effective variable selection. In order to select highly relevant variables for short-term wind speed prediction, Multi-Task Lasso is applied to extract features, so the change trend of wind speed can be expressed exactly, and the Multilayer Perceptron (MLP) is applied to predict based on the extracted features. Firstly, Multi-Task Lasso is used to establish a linear regression model, the leading periods and the original input variables are determined from the perspective of prediction accuracy; Secondly, according to the optimization results of Multi-Task Lasso, the optimal features that can best express the wind speed fluctuation is extracted from different influencing factors and different leading periods based on the weight; Finally, the extracted optimal feature set is used as the input variable of MLP to establish prediction model. The results show that the effect of feature selection is obvious, the prediction accuracy is improved by more than 17%, and the short-term wind speed prediction model has good prediction accuracy and good generalization ability.

Towards single‐atom photocatalysts for future carbon‐neutral application

AbstractThe implementation of carbon neutrality to achieve the goal of controlling global warming in the Paris Climate Agreement is currently the most important international climate issue. Efficient utilization of solar energy through photocatalysts is of great significance to the adjustment of energy structure. Single‐atom photocatalysts have excellent catalytic activity and selectivity in a variety of applications, including sustainable energy conversion, chemical synthesis, CO2 reduction, environmental remediation, and other areas, owing to their unique electronic structure and high atom usage. Here, we elaborated on the content and implementation direction of the carbon neutrality policy and demonstrated the design principles of single‐atom photocatalysts. Recent single‐atom synthesis strategies are summarized, and representative characterization methods have been shown to further reveal the structure–performance relationship. Then, we focus on the application of single‐atom photocatalysts in CO2 reduction, sustainable energy conversion, and environmental remediation. Finally, the opportunities and challenges in the application of single‐atom photocatalysts were discussed based on its current development.

Pollution Characteristics and Influencing Factors of PM2.5 in Shanxi Province Based on Wavelet Transform

Based on the daily average concentration of PM2.5, social influencing factor data, and meteorological data of 11 cities in Shanxi Province from 2015 to 2019, the concentration period of PM2.5 was determined using wavelet transform. The correlation between PM2.5 and social influencing factors and meteorological factors was explored respectively through Spearman correlation and the wavelet coherence spectrum, and the main influencing factors of long-term and short-term management and control of PM2.5 were determined. The results showed that the concentration of PM2.5 in Shanxi Province showed an upward trend from 2015 to 2017, with an average annual increase rate of 4.3% and a downward trend from 2018 to 2019, with an average annual decrease rate of 4.2%. The average concentration of PM2.5 showed a "U" distribution, with the highest value in January (95 μg·m-3) and the lowest in August (34 μg·m-3); the average value in winter was approximately twice that in summer. The ρ(PM2.5) in southern cities such as Linfen was 62 μg·m-3, and the average value in Datong and other northern cities was 45 μg·m-3, which was high in the south and low in the north. There were significant periodic changes in PM2.5 concentration in the 11 cities, including a long period of approximately 293 d and a short period of approximately 27 d. Among them, the energy consumption level and industrial structure were the strong driving factors affecting the PM2.5 concentration in the long period of Shanxi Province. In the short period, it was greatly affected by the change in atmospheric circulation, and different cities were affected by typical meteorological factors. Linfen, Yuncheng, Datong, Shuozhou, and Xinzhou were vulnerable to wind speed; Jinzhong and Luliang were vulnerable to temperature; and Taiyuan, Jincheng, Yangquan, and Changzhi were uniquely and significantly affected by relative humidity. Therefore, industrial structure adjustment and energy structure adjustment are key to the long-term control of atmospheric PM2.5 and the long-term improvement of air quality in Shanxi Province. The differential impact of different urban meteorological factors on PM2.5 should be considered when carrying out short-term regional joint prevention and control.

A two-phase factorial input-output model for analyzing CO2-emission reduction pathway and strategy from multiple perspectives – A case study of Fujian province

In this study, a two-phase factorial input-output model (TFIOM) is developed for carbon dioxide (CO2) emission mitigation, through integrating input-output model (IOM) and two-phase factorial analysis (TFA) into a general framework. TFIOM can identify the main sectors of CO2 emission, quantify the individual and interactive effects of the main factors (i.e. various CO2 management strategies implemented on different sectors) on reduction target, as well as seek CO2-emission reduction pathways and strategies from multiple perspectives (i.e. energy structure adjustment strategy (ESAS), technology upgrade strategy (TUS), and industrial structure adjustment strategy (ISAS)). The developed TFIOM is applied to Fujian province (in China), and results reveal that industries of chemical, non-metallic, metal smelting (MSI), and electric heating (EGW) are the main CO2-emission sectors (i.e. occupying 57.6% of direct emission and 56.4% of indirect emission). The joint application of ESAS (applied to EGW) and TUS (applied to MSI) has the best effect in reducing carbon emission intensity (decreasing by 24.5%). Among all scenarios, Scenario 115 shows the most desirable emission-reduction effect (i.e. carbon emission intensity, energy intensity, total direct emission, and total indirect emission would respectively decrease by 22.3%, 7.6%, 21.3% and 5.1%, and the cost of reducing CO2 per ton would be 182 RMB¥).

A novel fractional time-delayed grey Bernoulli forecasting model and its application for the energy production and consumption prediction

Energy affects the stable and sustainable development of social economy. Energy prediction plays an important role in the process of China’s energy market transformation. Scientific and reasonable energy predicting method can help government to make decisions effectively, and then adjust energy structure and industrial layout. The energy field is full of fractional order phenomenon and nonlinear disturbance. Aiming at the energy data sets with the characteristics of scarcity, complexity and nonlinear, a mathematical model including time delay term and Bernoulli equation can be used to fit this trend. A new fractional time-delayed grey Bernoulli model is proposed, and the new model has a wider application in the nonlinear field. The model is discretized by integral, and the least square estimation of the linear parameters and the approximate time response equation are obtained. The Grey Wolf Optimizer (GWO) is used to search the optimal parameters of the model. In addition, the energy prediction model is established from the perspective of renewable energy and fossil energy, and the effectiveness of the model is verified by three actual cases of renewable energy, crude oil and fossil fuel. Compared with the other seven grey models, the results show that the new model has higher prediction performance. Finally, the energy development trend in the next few years is predicted by using the proposed model, and relevant conclusions are drawn according to the prediction results.

A Dynamic and Cooperative Control Strategy for Multi-Hybrid Energy Storage System of DC Microgrid Based on SOC

With the increasingly serious crisis of fossil energy and environmental pollution, clean renewable energy becomes the inevitable choice of energy structure adjustment. The instability of output power of distributed renewable energy system greatly affects the operation of DC microgrid. The hybrid energy storage system (HESS) composed of High-Energy Battery (HEB) and High-Power Battery (HPB) can solve the above problems. Thus, this paper proposes a dynamic and cooperative control strategy for multi-HESS based on state of charge (SOC). Based on the traditional LPF method and droop control, this paper proposes a control strategy that requires no communication among multiple hybrid energy storage (HES) modules. This method can realize the stable control of HEB current, reduce the change times of HEB charging-discharging mode, prevent HEB from overcharging and overdischarging, prolong the service life of HEB and balance different energy storage SOC, so as to improve the operation stability and economy of DC microgrid. In addition, the method has certain robustness against sudden failures. Simulation and experiment results show the effectiveness of the proposed method.