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In this paper, we forecast the self-sufficiency rate of China's total energy and energy compositions by two-hybrid gray models: the improved non-homogeneous gray model and nash non-linear gray multivariable model, and provide some significant data for energy policy. At first, we propose newly the improved one variable gray model (INHGM-Markov) which combines the non-homogeneous gray model (NHGM(1,1)) with Markov chain model and forecast the production and consumption amount of China's total energy. The results show that the production and consumption amount increase slowly after 2012 and the values are 3660.10 and 4584.04 million tons of SCE in 2022. Next, the production and consumption percents of the energy compositions are simulated with the improved nash nonlinear gray multivariable model (INNGM(1, N)). In 2022, the production percents estimated to be 51.56, 7.55, 8.54, and 32.35%, respectively, for coal, crude oil, natural gas and others energy as the energy compositions, while the consumption percents 47.01, 21.82, 8.72, and 22,45%. The production and consumption amount of coal energy have a maximum values around 2013 and 2014, and then decrease until 1887.19 and 2154.88 million tons of SCE in 2022. Finally, the forecasting values of the self-sufficiency rates of the total energy and the energy compositions are determined to be 0.79, 0.88, 0.27, 0.78, and 1.15, which means that China government should focus on securing energy resource, especially crude oil and natural gas energy, for the sustainable development of the economy. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

Technological progress is frequently prioritized to resolve the contradiction between large resource consumption and rapid economic growth. However, in previous energy literature, it is frequently discussed as a whole without considering the heterogeneity. This paper divides the technology into production-oriented and energy-conservation types. Subsequently, a provincial dataset in China between 2000 and 2018 is applied to explore the influence of technology on energy demand. Data on the stock of different patents are employed to identify various technology types. When discussing technology in entirety, empirical regressions indicate that technology does not serve to be a major contributor to the evolution of energy demand. Conversely, when dividing technology into production-oriented and energy-conservation types, both were considered as major forces influencing energy demand but with different effects. The channel analysis suggests that production-oriented technology drives energy demand mainly by increasing individuals' income and expanding the industrial production scale. The energy-conservation technology is beneficial in controlling the energy demand. The spatial analysis indicates that compared to local technology, technology spillovers from neighbouring regions are essential in influencing energy demand. • Technology has not exerted a significant role in the evolution of energy demand. • Energy-conservation type of technology curbs the energy demand. • Production-type of technology drives the energy demand through more economic activities. • Technology spillovers are important to explain the evolution of energy demand. [ABSTRACT FROM AUTHOR]

The proposed policy targets of "2030 carbon peaking" and "2060 carbon neutrality" further accelerate the substitution of natural gas for oil and coal. In this paper, we systematically considered the growth trend of natural gas demand from both the total amount and the structure of energy.On the basis of 60-year sample data, we built the HMM model innovatively to simulate the complementary and alternative structural effects between different energy. In addition, according to the different policy intensity, we simulated the history of energy demand and forecast the future trend in three scenarios. The empirical results show that the share of natural gas in primary energy consumption in China in 2035 is 16.5%, 17.3%, and 13.6%, respectively, translating into natural gas demand of 6975, 7316, and 5748 bcm. Therefore, policymakers should promote energy transformation in a steady and orderly manner based on national conditions and strengthen gas demand-side management on the premise of ensuring reasonable demand. Our prediction of natural gas demand from a new perspective can assist policymakers to formulate the natural gas demand-side policy and import plan, and also provide a reference for studies of achieving the goal of energy transition and "2060 carbon neutrality". • The overall and structural effects of gas demand growth. • The evolution trend of energy demand structure is predicted through three ideas. • Structure model is used to simulate the substitution of energy sources. • The prediction results of hidden Markov model are more reasonable. [ABSTRACT FROM AUTHOR]

Analyzing the drivers of energy demand and predicting energy consumption can help to shape national policies on energy transformation and energy security. This paper estimates the demand for the various forms of energy consumed in China. A least absolute shrinkage and selection operator-random forest (Lasso-RF) two-stage model is first proposed to identify the drivers of the demand in energy. A support vector regression-compositional data second exponential smoothing (SVR-CDSES) model is then established to forecast the demand for coal, oil, natural gas, and primary electricity. The empirical analysis results inform that China's appetite for energy in the next decade will grow at an annualised rate of 2.68%. The demand for coal will rise initially and then fall at a rate of 0.79% per annum. The annual growth rate in the demand for oil will be relatively stable at 3.52%. However, the demand for natural gas and primary electricity will increase rapidly, with the highest annual growth rate at 8.05%, pointing to greater awareness of clean energy. • Study the drivers of China's energy demand and the trend of energy demand. • A new two-stage model is presented to identify the drivers in energy demand. • A new compound Support Vector Regression model is proposed to predict energy demand. • Some managerial insights and policy guidelines for energy demand are provided. [ABSTRACT FROM AUTHOR]

The prioritized purpose of this dissertation is to put forward a scientific and plausible interval of carbon price benchmark on the unified carbon trading market, of which will be scheduled to be established in the year of 2016. A model named EMD–GARCH that integrated Empirical Mode Decomposition (EMD) with Generalized Autoregressive Conditionally Heteroskedastic (GARCH) together is presented here in order to forecast the carbon price of the five pilots (Shenzhen, Shanghai, Beijing, Guangdong, and Tianjin) in 2016 so as to provide exogenous data for later analysis. For further study, a recursive dynamic Computable General Equilibrium (CGE) model will be structured to explore the impacts on China’s macroeconomics, environmental quality, and energy demand respectively under 8 different simulation scenarios, which are being designed in terms of the aforementioned calculated carbon price outcome. The simulation results illustrate that the unified carbon price policy will exert a comprehensive effect on China in various perspectives that we discussed in this paper. Moreover, regarding the carbon price scenarios, the price level by itself generates a different degree of impacts in improving the environmental quality, decreasing the energy demand, and increasing the macroeconomic growth. Consequently, based on the analysis of those three approaches mix in this study, we suggest the alternative interval of carbon price benchmark in the national unified carbon trading market with a lower bound of 30 yuan/tCO 2 to an upper bound of 50 yuan/tCO 2 . [ABSTRACT FROM AUTHOR]

Abstract: China has been experiencing industrialization and urbanization since reform and opening of its economy in 1978. Energy consumption in the country has featured issues such as a coal-dominated energy mix, low energy efficiency and high emissions. Thus, it is of great importance to explore the factors driving the increase in energy consumption in the past two decades and estimate the potential for decreasing energy demands in the future. In this paper a hybrid energy input–output model is used to decompose driving factors to identify how these factors impact changes in energy intensity. A modified RAS approach is applied to project energy requirements in a BAU scenario and an alternative scenario. The results show that energy input mix, industry structure and technology improvements have major influences on energy demand. Energy demand in China will continue to increase at a rapid rate if the economy develops as in the past decades, and is projected to reach 4.7 billion tce in 2020. However, the huge potential for a decrease cannot be neglected, since growth could be better by adjusting the energy mix and industrial structure and enhancing technology improvements. The total energy demand could be less than 4.0 billion tce in 2020. [Copyright &y& Elsevier]

Abstract: Forecasts of energy demand, the fuel mix meeting that demand and the associated emissions are a key requirement for informed energy planning and policy decisions to ensure energy security and address climate change. While there have been many studies on China focusing on the short and medium term (to 2020 and 2050) there is little in the literature focusing on the long term (to 2100). This paper seeks to address those gaps on sectoral energy demands and emissions on long term by following a two-stage approach. It develops key energy indicators on useful energy demand, transport mobility and end use fuel demand for various sectors. The main drivers of these indicators are socio-economic parameters. The indicators are used to project energy service demands and emissions forward for China in TIMES G5 model at least cost approach. The results from this reference scenario suggest that China will require approximately 4 Gtoe of primary energy, by the end of the 21st century to deliver 3 Gtoe final energy consumption, 10 PWh of electricity generation, 1.3 Gtoe of energy imports, which will results in 10 Gt CO2 emissions. [ABSTRACT FROM AUTHOR]

Abstract: The design and operation of combined cooling, heating and power (CCHP) systems are greatly dependent upon the seasonal atmospheric conditions, which determine thermal and power demands of buildings. This paper presents a mathematical analysis of CCHP system in comparison to separate system. The corresponding primary energy consumption in thermal demand management (TDM) and electrical demand management (EDM) operation modes are deduced. Three relative criteria, primary energy saving (PES), CO2 emission reduction (CO2ER), and annual total cost saving (ATCS) are employed to evaluate the respective performances of CCHP systems for a hypothetical building in five different climate zones from the technical, environmental and economic aspects. The results indicate that CCHP system in TDM mode in the cold area, where the building requires more heating during the year, achieves more benefit over separate system while CCHP system in EDM mode suits the building having stable thermal demand in mild climate zone. [Copyright &y& Elsevier]

This paper summarises the methodology and results of work involved in the investigation of energy demand in six Chinese villages included in the SUCCESS Project. The procedures used to collect data associated with local energy demand are explained and the approach to data analysis is explained. Results are provided in terms for delivered energy consumption, as an indicator of energy demand; primary energy consumption, as an indicator of energy resource depletion; and carbon dioxide emissions, as an indicator of global climate change. Similarities and differences between results for this sample of villages are considered. The important causes of differences in results are investigated. [Copyright &y& Elsevier]

It is vital for human's sustainable development to control global warming well below 2 °C compared to pre-industrial levels. In this paper, four scenarios, namely baseline (BAS) scenario, carbon capture and storage (CCS) scenario, high electricity consumption (HEC) scenario and renewable energy (REN) scenario, are developed to estimate the energy demand and carbon emission during 2020–2050 in transitional China. Furthermore, the health co-benefits of different pollutants reduction are further estimated. Our results show that the final energy demand of China will increase steadily in the BAS scenario, reaching approximately 5200 million tons of coal equivalents (tce) in 2050. While it is likely to peak around 3500 million tce in 2035 if industry and energy structure reform is actively implemented. The CO 2 emissions will stabilize at 10 billion tons in the BAS scenario. The CCS, HIG and REN scenarios can all achieve the CO 2 emission target in China with the constraints of temperature rising no more than 2 °C in 2030. Only the REN scenario, however, can achieve the 2 °C target in the medium term. Regarding health co-benefits, in 2050,102.67, 187.11 and 222.84 billion USD are gained in China under the CCS, HIG and REN scenarios, respectively, compared to the BAS scenario. Another important finding is that industry, electricity conversion and transportation sectors have the greatest health co-benefits in China, and NO x reduction plays a dominant role, which will reduce 291,000 mortality cases in the REN scenario in 2050. [ABSTRACT FROM AUTHOR]

Xiao, Z., 2020. Structure and fatigue analysis of semisubmersible offshore communication platform. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 506–509. Coconut Creek (Florida), ISSN 0749-0208. As an important tool and means of marine oil and gas resources, a marine communication platform not only plays an important role in the development of marine high technology in China, but also plays an important role in marine environment monitoring. Therefore, it has become the current research hot spot. This paper first introduces the fatigue damage analysis method of a semisubmersible offshore communication platform, then calculates its structural design and hydrodynamic load, and finally analyzes the structural fatigue and maintenance of the semisubmersible offshore communication platform and gives maintenance advice. [ABSTRACT FROM AUTHOR]

Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend. [ABSTRACT FROM AUTHOR]