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The industrial sector is China's major element of energy consumption and carbon emissions, and carbon emissions reduction in industrial parks has become an important foothold in carbon reduction work. Predicting the future evolution trend of carbon emissions in industrial parks under different policy orientations is of great significance for China to achieve the carbon emissions peaking and carbon neutrality goals. Based on the current status of electricity consumption and carbon emissions in industrial parks, this paper takes a light industrial park as the research object, analyzes the source of carbon emissions in this park, combines the characteristics of resource endowment and industrial structure, and considers the factors influencing society, load curve, energy supply and carbon sink, thus constructing a system dynamics model of carbon emission evolution for this park. Different influencing factors are selected to set multiple target scenarios, and the GDP, carbon emissions, carbon emissions reduction and near‐zero carbon evaluation coefficient of light industrial parks are used as characterization indicators for simulation analysis. The analysis results revealed that the comprehensive scenario set by increasing the maximum installed capacity of renewable energy units and raising the proportions of environmental and science and technology inputs while adjusting the industrial growth rate of the park performed best. In this scenario, the economic development of the park and the process of carbon emissions reduction are considered. [ABSTRACT FROM AUTHOR]

Energy consumption is closely related to industrial structure, economic prosperity, and population. Because of the Granger causal relationship between GDP and energy consumption, many researchers consider the regression method to predict energy consumption using economic variables. However, some other researchers regard the time series method (forecast) to estimate for energy consumption. To address the advantages and disadvantages of two methods in energy consumption prediction, we performed the deep learning (DL) based on the case of China. The experiment results show that the accuracy of time series forecast is much higher than regression methods. In addition, we also explored the relationship between industry, economy, population, and energy consumption. We found that GDP, population, secondary sector, and tertiary sector were significant to energy consumption. A tertiary sector can help to reduce energy consumption to some extent. This could give suggestions about changing industry structure. More and more attention has been paid to energy intensity. The strategic approach to sustainable development requires reducing energy intensity while developing the economy. Since China is committed to achieving carbon emission peak by 2030, this paper predicts the energy intensity of China in the next decade based on a DL algorithm. The prediction results show that energy intensity would slightly decrease in the next decade. Different types of energy represent the industrial structure behind economic growth. Therefore, this paper was conducted using ridge regression (RR) to explore the relationship between different types of energy and energy intensity. In addition, the effect (positive or negative) of different energy sources in reducing energy intensity has also been explored. We found that the best way of developing the economy and reducing energy consumption is to use natural gas and hydroelectric instead of coal. This can guide policy development about changing energy source structure. [ABSTRACT FROM AUTHOR]

In coastal cities, a large amount of cold energy is released into the nearby seawater during the process of regasification of liquefied natural gas (LNG) at the receiving terminal, and meanwhile, a large amount of exported and/or imported foods need to be frozen by bulk cold energy near the sea. In this paper, a new multistage cold energy recovery/utilization system is investigated to link the LNG cold energy directly to supply the coastal cold store. This design uses a realistic case in Ningbo LNG receiving station in China, and for the very first time considers how to utilize the LNG cold energy in an efficient manner regarding the technical, economic, and environmental benefits. A three‐stage LNG cold energy utilization system is designed and evaluated by considering different temperatures, pressures, and heat exchange parameters. Compared to conventional electric‐driven cold stores, the proposed multistage cold energy recovery/utilization system has a much cheaper capital cost during the initial investment stage, and also avoids the utility power consumption during the daily operation period. In the case study, from Ningbo LNG receiving station to a 10 MW class cold store, the proposed multienergy system can save the energy of 20.05 kilotons of standard coal, and the carbon emission reduction is up to 1.49 × 108 kg per year. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

Regional hybrid energy system (RHES) is an effective way to accommodate clean energy and reduce its uncertainty. It is also an important development direction for energy market reform, playing an important role in the field of energy supply and demand. Based on electricity price, thermal price, and the relationship between load supply and demand, the paper proposes an optimal scheduling model for regional energy systems to promote clean energy consumption. Firstly, the typical structure and system modules of the integrated regional energy system are introduced in detail, and the two subsystem operating models of electricity and heating are given. Secondly, a multi‐objective function that combines the economics and stability of the system is constructed. The model maximizes the economic benefits and minimizes the net load fluctuations on the basis of maximizing the consumption of clean energy. The multi‐objective singularity is realized by fuzzy membership function. The decision‐making risk caused by the uncertainty of clean energy output is analyzed by introducing robust optimization theory. Finally, the effectiveness of the proposed model is verified based on a region in northwestern China. The results show that the proposed multi‐objective model can combine different optimization demands together, maximize the economic characteristics of clean energy while properly controlling risks, and provide reasonable support for decision makers to develop optimal operation plans. [ABSTRACT FROM AUTHOR]

Energy supply policy quality is an important factor that would impact national energy security. While existing research conducted policy evaluation from the lens of performance assessment, less study has been devoted to evaluating energy supply policy from the perspective of policy making. This study takes the energy supply policy documents issued by China's central government during the '13th Five‐Year Plan' period (2016–2020) as the research sample, and pioneers the use of the extended policy modelling consistency (PMC) index model combined with the text mining methodology to construct a policy evaluation index system with the characteristics of the energy supply policy which conducts a more in‐depth quantitative evaluation of the energy supply policy documents. The results show that the average PMC index value of China's energy supply policies is 7.26, and the overall quality is high. The concerns of existing policies are mainly focused on reform and development planning, safety production management and project engineering construction, but there is still room for improvement in policy predictability, coordination of policy areas, clarity of policy basis and goals and comprehensiveness of policy tool combinations. Based on this, China should improve the comprehensiveness of its energy supply policy tool combinations in terms of policy design norm; strengthen policy predictability and coordination of policy areas in terms of policy implementation guarantee; and clarify the basis and goals of policies in terms of policy orientation enhancement, to provide a reference basis for the formulation and improvement of future energy supply policies and to realise the sustainable development of energy supply. [ABSTRACT FROM AUTHOR]

With the promulgation and implementation of a large number of renewable energy policies in China, the rapid development of renewable energy is accompanied by the prominent phenomenon of renewable energy abandoning wind and light. Energy storage technology is the key technology to promote the consumption of renewable energy. The government can promote the energy storage technology through the incentive policy of energy storage industry. Firstly, content analysis method is used to analyze China's energy storage policy, and five incentive policies for promoting energy storage technology are obtained. Secondly, built a game model of energy storage technology promotion based on the evolutionary game theory. Finally, use MATLAB software for numerical simulation. Numerical simulation results show that: (a) When the local government chooses to promote less, energy enterprises will eventually adopt the nonconfigure strategy; (b) when the local government chooses to promote more vigorously, energy enterprises will finally adopt the configure strategy; (c) increase in the total electricity sold by energy enterprises with energy storage devices, the sales price of energy stored per unit, the compensation price of energy stored per unit, tax relief standards, and incentive costs of local governments can promote energy enterprises to choose configure strategies; and (d) the reduction of unit energy storage cost, configure cost, and comprehensive tax rate can promote energy enterprises to choose configure strategy. [ABSTRACT FROM AUTHOR]

Many power plants have been transformed and operated using low‐pressure cylinder near‐zero output (LP‐ZO) technology in China to make way for or provide necessary guarantees for renewable energy power generation. This research focuses on utilizing numerical simulation tools to improve the operational economy of units with LP‐ZO. The LP‐ZO thermodynamic and energy‐consumption model of a reference plant is established, and the momentum particle swarm optimization method is used to optimize the unit operations. Moreover, a model to determine the economic operational range of the combined heat and power plant with LP‐ZO is proposed. The results show that the LP‐ZO mode operations of the unit can reduce the minimum peak shaving rate and improve the flexibility of operations. The energy efficiency of the LP‐ZO mode is increased due to the recovery of a significant portion of the cold source losses. However, the irreversible exergy loss caused by large temperature difference heat exchange reduces the exergy efficiency of the unit. After operational optimization, the reference plant can save approximately 68.15 t d−1 of coal and reduce carbon dioxide emissions by about 27 t tce−1 d−1. This study provides an operating approach to the CHP plant with LP‐ZO unit in enhancing flexibility and economy. [ABSTRACT FROM AUTHOR]

The green total factor productivity (GTFP) has become an important index of balancing the energy consumptions, economic development, and environmental protections. This study extends the slacks‐based directional distance function (SDDF) of Fare and Grosskopf into the framework of the Malmquist–Luenberger productivity index to estimate the slack‐based GTFP. This SDDF can allow decision‐making units to explore the optimal direction and measure a more precise productivity index. For the empirical study, we employ the new model to measure the slack‐based GTFP for Chinese 30 provinces over the period of 2005–2017. Furthermore, we investigate the effect of the regional differences on the GTFP and the convergence of the Chinese GTFP with β‐converge and σ‐convergence. Our results show that there is a rapid improvement in the GTFP in China, which is attributed to the technical change. However, there exists a significant difference in GTFP for three regions of China. The eastern region performs significantly better than the others. Finally, we find there is a strong convergence in China, and the eastern region has a faster convergence than the other regions. [ABSTRACT FROM AUTHOR]

With the improvement of environmental protection awareness and transformation of industrial structure, China's energy consumption structure has changed constantly. Although the proportion of coal consumption is gradually decreasing, the consumption has not seen a significant decline; it even increased in 2018. Many researchers have focused on changes in one specific type of energy such as coal or solar. However, the overall energy structure was ignored. In this study, an integrated convolutional neural network (CNN) with Long Short‐term Memory (LSTM) was used to predict the energy structure of China. Historical data were considered as pixels for convolution by layers of the CNN and then encoded by LSTM layers. To verify the effectiveness of the method, CNN‐LSTM was compared with six previous methods using data from 1965 to 2018. The results showed that CNN‐LSTM was superior in predicting energy structure. Finally, the energy structure of China in the next decade was predicted. The prediction results demonstrated that although the share of clean energy consumption will continue to increase, coal consumption will not decline. [ABSTRACT FROM AUTHOR]

Based on exergoeconomic theory, the energy consumption situation for a typical oil transfer station in eastern China is evaluated. According to the analysis of the distribution for logistics and exergy flow, the crude oil gathering and transportation system is divided into two subsystems: pipe network and oil transfer station. The unit exergoeconomic costs for each component exergy flow are calculated by computing method in matrix pattern, of which the pressure exergy and thermal exergy at the end of the process are 812.58 yuan/GJ and 1575.24 yuan/GJ, respectively. Simultaneously, exergoeconomic evaluation indicators such as exergy cost growth coefficient, relative cost difference, and exergoeconomic coefficient are calculated, which can conclude the weak link of technical system is heating furnace equipment. The growth value of its exergy cost growth coefficient is 14.988, the relative cost difference is 94.503, and the exergoeconomic coefficient is 9.046, so it has great potential for energy saving. Exergoeconomic analysis method can provide a scientific and effective method or basis during the energy‐saving reconstruction for oil gathering and transportation system. [ABSTRACT FROM AUTHOR]

Mankind is constantly pursuing economic growth and social development, and while these bring convenience to people, they involve large amounts of greenhouse gas emissions under the heavy use of energy, which produces quite serious air pollution, which not only affects people's health but also leads to ecological environmental damage. Nowadays, the impact of the environment and the negative impacts of the world's economies while promoting economic growth are the most important issues for economies to achieve balanced development. This study uses the dynamic slack‐based data envelopment analysis (DEA) model to assess the environmental energy efficiency of high‐income economies (including China) and explore the negative impacts on the environment, to obtain a basis for energy‐saving emission reduction methods or configurations by using 48 high‐income economies (including China) from 2010 to 2014. The interperiod (carryover) variable is gross domestic product (GDP). The empirical results show that economies with high energy efficiency have a large consumption of energy and are unable to reduce carbon dioxide emissions. We also find that there exists a difference in the results depending on whether GDP is considered as the carryover variable. European economies are more efficient in energy consumption than others, and Asian economies are the most inefficient. In order to pursue GDP growth, economies need to consider reducing their energy consumption and CO2 emissions and improve their energy usage efficiency, further implementing sustainability. [ABSTRACT FROM AUTHOR]